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aimhubio__aim-2669
`TensorboardFolderTracker` missing Image import ## 🐛 Bug Currently the `TensorboardFolderTracker` will crash if there are any images that need to be parsed. This is because currently `aim.Image` is only imported during type checking, however `_process_tb_event` attempts to create an `Image` instance, without access ### To reproduce - Create a tensorboard events file that contains an image - Create a run using the `aim.ext.tensorboard_tracker.tracker.Run` runner. - Observe the console ### Expected behavior Images are converted and added to the RocksDB database. ### Environment - Aim Version 3.17.3 - Python version 3.10.5 - pip version 22.0.4 - OS Ubuntu - Any other relevant information
[ { "content": "from tensorboard.backend.event_processing.directory_watcher import DirectoryWatcher\nfrom tensorboard.backend.event_processing import event_file_loader\nimport tensorflow as tf\nfrom tensorboard.util import tensor_util\nimport time\nimport threading\nfrom pathlib import Path\nimport logging\nimport os\nimport weakref\nimport queue\n\nfrom typing import TYPE_CHECKING, Any\n\nif TYPE_CHECKING:\n from aim import Audio, Image\n\n\nclass TensorboardTracker:\n def __init__(self, tracker, sync_tensorboard_log_dir: str) -> None:\n self.tracker = tracker\n self.sync_tensorboard_log_dir = sync_tensorboard_log_dir\n self.tensorboard_folder_watchers = []\n self._thread = threading.Thread(target=self._monitor_eventfiles, daemon=True)\n self.directories_track_status = {}\n self._shutdown = False\n self._started = False\n self._watcher_queue = queue.Queue()\n\n def _monitor_eventfiles(self):\n while True:\n if self._shutdown:\n break\n for event_file in set(Path(self.sync_tensorboard_log_dir).rglob(\"*.tfevents*\")):\n dir = str(event_file.parent.absolute())\n if dir not in self.directories_track_status:\n self.directories_track_status[dir] = \"NOT_STARTED\"\n for dir, status in self.directories_track_status.items():\n if status == \"NOT_STARTED\":\n tensorboard_folder_watcher = TensorboardFolderTracker(dir, self._watcher_queue)\n tensorboard_folder_watcher.start()\n self.tensorboard_folder_watchers.append(tensorboard_folder_watcher)\n self.directories_track_status[dir] = \"STARTED\"\n time.sleep(5)\n\n def start(self):\n if self._started:\n return\n self._started = True\n self._thread.start()\n self._consumer = TensorboardEventConsumer(\n self._watcher_queue, self.tracker\n )\n self._consumer.start()\n\n def stop(self):\n if not self._started:\n return\n self._shutdown = True\n self._thread.join()\n for tensorboard_folder_watcher in self.tensorboard_folder_watchers:\n tensorboard_folder_watcher.stop()\n self._consumer.stop()\n\n def close(self):\n \"\"\"Interface to make compatible with Resource AutoClean\"\"\"\n self.stop()\n\n\nclass TensorboardFolderTracker:\n def __init__(self, tensorboard_event_folder: str, queue: queue.Queue) -> None:\n self.queue = queue\n self.supported_plugins = (\"images\", \"scalars\")\n self.unsupported_plugin_noticed = False\n self.folder_name = os.path.basename(tensorboard_event_folder)\n self._thread = threading.Thread(target=self._process_event)\n self._generator = DirectoryWatcher(tensorboard_event_folder, event_file_loader.EventFileLoader)\n self._shutdown = False\n self._started = False\n\n def start(self):\n if self._started:\n return\n self._started = True\n self._thread.start()\n\n def stop(self):\n if not self._started:\n return\n self._shutdown = True\n self._thread.join()\n\n def _process_event(self):\n while True:\n if self._shutdown:\n break\n for event in self._generator.Load():\n self._process_tb_event(event)\n time.sleep(1)\n\n def _process_tb_event(self, event):\n def create_ndarray(tensor):\n res = tensor_util.make_ndarray(tensor)\n if res.dtype == \"object\":\n return None\n else:\n return res\n\n step = event.step\n fail_count = 0\n _err_info = None\n\n for value in event.summary.value:\n tag = value.tag\n plugin_name = value.metadata.plugin_data.plugin_name\n if len(plugin_name) > 0 and plugin_name not in self.supported_plugins:\n if not self.unsupported_plugin_noticed:\n logging.warning(\n \"Found unsupported plugin type({}) in the log file. \"\n \"Data for these wont be processed. \"\n \"Supported plugin types are: {}\".format(plugin_name, \", \".join(self.supported_plugins)),\n )\n self.unsupported_plugin_noticed = True\n continue\n track_val = None\n try:\n if value.HasField(\"tensor\"):\n # TODO: [MV] check the case when audios are passed via tensor\n if plugin_name == \"images\":\n tensor = value.tensor.string_val[2:]\n track_val = [Image(tf.image.decode_image(t).numpy()) for t in tensor]\n if len(track_val) == 1:\n track_val = track_val[0]\n elif plugin_name == \"scalars\" or plugin_name == \"\":\n track_val = create_ndarray(value.tensor)\n else:\n track_val = value.tensor.float_val[0]\n elif value.HasField(\"simple_value\"):\n track_val = value.simple_value\n elif value.HasField(\"image\"):\n track_val = Image(tf.image.decode_image(value.image.encoded_image_string).numpy())\n elif value.HasField(\"audio\"):\n tf_audio, sample_rate = tf.audio.decode_wav(value.audio.encoded_audio_string)\n track_val = Audio(tf_audio.numpy(), rate=sample_rate)\n\n except RuntimeError as exc:\n # catch all the nasty failures\n fail_count += 1\n if not _err_info:\n _err_info = str(exc)\n continue\n\n if track_val is not None:\n self.queue.put(TensorboardEvent(track_val, tag, step, context={'entry': self.folder_name}))\n if fail_count:\n logging.warning(f\"Failed to process {fail_count} entries. First exception: {_err_info}\")\n\n\nclass TensorboardEvent:\n\n def __init__(self, value: Any, name: str, step: int, context: dict) -> None:\n self.value = value\n self.name = name\n self.step = step\n self.context = context\n\n\nclass TensorboardEventConsumer:\n\n def __init__(self, queue: queue.Queue, tracker) -> None:\n self._tracker = weakref.ref(tracker)\n self._queue = queue\n self._thread = threading.Thread(target=self._process_events, daemon=True)\n self._shutdown = False\n self._started = False\n\n def start(self):\n if self._started:\n return\n self._started = True\n self._thread.start()\n\n def _process_events(self):\n while True:\n try:\n event = self._queue.get(True, 1)\n if event:\n self._tracker()(event.value, event.name, event.step, context=event.context)\n except queue.Empty:\n event = None\n if self._shutdown:\n break\n\n def stop(self):\n if not self._started:\n return\n self._shutdown = True\n self._thread.join()\n", "path": "aim/ext/tensorboard_tracker/tracker.py" } ]
[ { "content": "from tensorboard.backend.event_processing.directory_watcher import DirectoryWatcher\nfrom tensorboard.backend.event_processing import event_file_loader\nimport tensorflow as tf\nfrom tensorboard.util import tensor_util\nimport time\nimport threading\nfrom pathlib import Path\nimport logging\nimport os\nimport weakref\nimport queue\n\nfrom typing import Any\nfrom aim import Audio, Image\n\n\nclass TensorboardTracker:\n def __init__(self, tracker, sync_tensorboard_log_dir: str) -> None:\n self.tracker = tracker\n self.sync_tensorboard_log_dir = sync_tensorboard_log_dir\n self.tensorboard_folder_watchers = []\n self._thread = threading.Thread(target=self._monitor_eventfiles, daemon=True)\n self.directories_track_status = {}\n self._shutdown = False\n self._started = False\n self._watcher_queue = queue.Queue()\n\n def _monitor_eventfiles(self):\n while True:\n if self._shutdown:\n break\n for event_file in set(Path(self.sync_tensorboard_log_dir).rglob(\"*.tfevents*\")):\n dir = str(event_file.parent.absolute())\n if dir not in self.directories_track_status:\n self.directories_track_status[dir] = \"NOT_STARTED\"\n for dir, status in self.directories_track_status.items():\n if status == \"NOT_STARTED\":\n tensorboard_folder_watcher = TensorboardFolderTracker(dir, self._watcher_queue)\n tensorboard_folder_watcher.start()\n self.tensorboard_folder_watchers.append(tensorboard_folder_watcher)\n self.directories_track_status[dir] = \"STARTED\"\n time.sleep(5)\n\n def start(self):\n if self._started:\n return\n self._started = True\n self._thread.start()\n self._consumer = TensorboardEventConsumer(\n self._watcher_queue, self.tracker\n )\n self._consumer.start()\n\n def stop(self):\n if not self._started:\n return\n self._shutdown = True\n self._thread.join()\n for tensorboard_folder_watcher in self.tensorboard_folder_watchers:\n tensorboard_folder_watcher.stop()\n self._consumer.stop()\n\n def close(self):\n \"\"\"Interface to make compatible with Resource AutoClean\"\"\"\n self.stop()\n\n\nclass TensorboardFolderTracker:\n def __init__(self, tensorboard_event_folder: str, queue: queue.Queue) -> None:\n self.queue = queue\n self.supported_plugins = (\"images\", \"scalars\")\n self.unsupported_plugin_noticed = False\n self.folder_name = os.path.basename(tensorboard_event_folder)\n self._thread = threading.Thread(target=self._process_event)\n self._generator = DirectoryWatcher(tensorboard_event_folder, event_file_loader.EventFileLoader)\n self._shutdown = False\n self._started = False\n\n def start(self):\n if self._started:\n return\n self._started = True\n self._thread.start()\n\n def stop(self):\n if not self._started:\n return\n self._shutdown = True\n self._thread.join()\n\n def _process_event(self):\n while True:\n if self._shutdown:\n break\n for event in self._generator.Load():\n self._process_tb_event(event)\n time.sleep(1)\n\n def _process_tb_event(self, event):\n def create_ndarray(tensor):\n res = tensor_util.make_ndarray(tensor)\n if res.dtype == \"object\":\n return None\n else:\n return res\n\n step = event.step\n fail_count = 0\n _err_info = None\n\n for value in event.summary.value:\n tag = value.tag\n plugin_name = value.metadata.plugin_data.plugin_name\n if len(plugin_name) > 0 and plugin_name not in self.supported_plugins:\n if not self.unsupported_plugin_noticed:\n logging.warning(\n \"Found unsupported plugin type({}) in the log file. \"\n \"Data for these wont be processed. \"\n \"Supported plugin types are: {}\".format(plugin_name, \", \".join(self.supported_plugins)),\n )\n self.unsupported_plugin_noticed = True\n continue\n track_val = None\n try:\n if value.HasField(\"tensor\"):\n # TODO: [MV] check the case when audios are passed via tensor\n if plugin_name == \"images\":\n tensor = value.tensor.string_val[2:]\n track_val = [Image(tf.image.decode_image(t).numpy()) for t in tensor]\n if len(track_val) == 1:\n track_val = track_val[0]\n elif plugin_name == \"scalars\" or plugin_name == \"\":\n track_val = create_ndarray(value.tensor)\n else:\n track_val = value.tensor.float_val[0]\n elif value.HasField(\"simple_value\"):\n track_val = value.simple_value\n elif value.HasField(\"image\"):\n track_val = Image(tf.image.decode_image(value.image.encoded_image_string).numpy())\n elif value.HasField(\"audio\"):\n tf_audio, sample_rate = tf.audio.decode_wav(value.audio.encoded_audio_string)\n track_val = Audio(tf_audio.numpy(), rate=sample_rate)\n\n except RuntimeError as exc:\n # catch all the nasty failures\n fail_count += 1\n if not _err_info:\n _err_info = str(exc)\n continue\n\n if track_val is not None:\n self.queue.put(TensorboardEvent(track_val, tag, step, context={'entry': self.folder_name}))\n if fail_count:\n logging.warning(f\"Failed to process {fail_count} entries. First exception: {_err_info}\")\n\n\nclass TensorboardEvent:\n\n def __init__(self, value: Any, name: str, step: int, context: dict) -> None:\n self.value = value\n self.name = name\n self.step = step\n self.context = context\n\n\nclass TensorboardEventConsumer:\n\n def __init__(self, queue: queue.Queue, tracker) -> None:\n self._tracker = weakref.ref(tracker)\n self._queue = queue\n self._thread = threading.Thread(target=self._process_events, daemon=True)\n self._shutdown = False\n self._started = False\n\n def start(self):\n if self._started:\n return\n self._started = True\n self._thread.start()\n\n def _process_events(self):\n while True:\n try:\n event = self._queue.get(True, 1)\n if event:\n self._tracker()(event.value, event.name, event.step, context=event.context)\n except queue.Empty:\n event = None\n if self._shutdown:\n break\n\n def stop(self):\n if not self._started:\n return\n self._shutdown = True\n self._thread.join()\n", "path": "aim/ext/tensorboard_tracker/tracker.py" } ]
diff --git a/CHANGELOG.md b/CHANGELOG.md index 57c867207b..2017eb2edd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,7 +13,8 @@ ### Fixes -- Convert NaNs and Infs in responses to strings (n-gao) +- Convert NaNs and Infs in responses to strings (n-gao) +- Import `Image` and `Audio` for `TensorboardFolderTracker` (alansaul) ## 3.17.4 May 4, 2023 diff --git a/aim/ext/tensorboard_tracker/tracker.py b/aim/ext/tensorboard_tracker/tracker.py index ce00b5e1e0..2d86d3c92a 100644 --- a/aim/ext/tensorboard_tracker/tracker.py +++ b/aim/ext/tensorboard_tracker/tracker.py @@ -10,10 +10,8 @@ import weakref import queue -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from aim import Audio, Image +from typing import Any +from aim import Audio, Image class TensorboardTracker: diff --git a/tests/README.md b/tests/README.md index 3b7f693039..8dbe0c6f2e 100644 --- a/tests/README.md +++ b/tests/README.md @@ -5,6 +5,7 @@ Be able to test the correctness of the - `aim engine` - `aim sdk` - `aim ql` + - `extensions` ### Folder Structure @@ -16,6 +17,8 @@ tests test_*.py ql test_*.py + ext + test_*.py ``` ## Run diff --git a/tests/ext/__init__.py b/tests/ext/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/ext/test_tensorboard_tracker.py b/tests/ext/test_tensorboard_tracker.py new file mode 100644 index 0000000000..545af772e0 --- /dev/null +++ b/tests/ext/test_tensorboard_tracker.py @@ -0,0 +1,113 @@ +from queue import Queue +import numpy as np +from PIL import ImageChops as PILImageChops +import tensorflow as tf +from tensorboard.compat.proto.summary_pb2 import SummaryMetadata, Summary +from tensorboard.compat.proto.tensor_pb2 import TensorProto +from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto +from tensorboard.compat.proto.event_pb2 import Event +from torch.utils.tensorboard.summary import image, scalar + +from aim import Image +from aim.ext.tensorboard_tracker.tracker import TensorboardFolderTracker + +from tests.base import TestBase + + +def images_same_data(image1: Image, image2: Image) -> bool: + """ + Compare two Aim images to see if they contain the same values + """ + image_diff = PILImageChops.difference( + image1.to_pil_image(), image2.to_pil_image() + ) + return image_diff.getbbox() is None + + +class TestTensorboardTracker(TestBase): + + def test__process_tb_image_event(self): + # Given + queue = Queue() + tracker = TensorboardFolderTracker(tensorboard_event_folder='dummy', queue=queue) + height, width, channels = 5, 4, 3 + # Note channels is last + image_np = np.random.randint(0, 16, (height, width, channels)).astype(dtype=np.uint8) + # Create image summary in standard format + image_summary = image(tag='test_image', tensor=image_np, dataformats='HWC') + event = Event(summary=image_summary) + + # When + tracker._process_tb_event(event) + + # Then + tracked_image = queue.get().value + original_image = Image(image_np) + self.assertTrue(isinstance(tracked_image, Image)) + self.assertTrue(tracked_image.size == original_image.size) + self.assertTrue(images_same_data(tracked_image, original_image)) + + def test__process_tb_image_plugin_event(self): + # Given + queue = Queue() + tracker = TensorboardFolderTracker(tensorboard_event_folder='dummy', queue=queue) + height, width, channels = 5, 4, 3 + # Note channels is last + image_np = np.random.randint(0, 16, (height, width, channels)).astype(dtype=np.uint8) + # Create image summary in format of plugin + plugin_data = SummaryMetadata.PluginData(plugin_name='images') + smd = SummaryMetadata(plugin_data=plugin_data, ) + tensor = TensorProto(dtype='DT_STRING', + string_val=[ + f"{height}".encode(encoding='utf_8'), + f"{width}".encode(encoding='utf_8'), + tf.image.encode_png(image_np).numpy(), + ], + tensor_shape=TensorShapeProto(dim=[TensorShapeProto.Dim(size=3)])) + + image_summary = Summary( + value=[Summary.Value(tag='test_image', metadata=smd, tensor=tensor)] + ) + event = Event(summary=image_summary) + + # When + tracker._process_tb_event(event) + + # Then + tracked_image = queue.get().value + original_image = Image(image_np) + self.assertTrue(isinstance(tracked_image, Image)) + self.assertTrue(tracked_image.size == original_image.size) + self.assertTrue(images_same_data(tracked_image, original_image)) + + def test__process_tb_scalar_simple_value_event(self): + # Given + queue = Queue() + tracker = TensorboardFolderTracker(tensorboard_event_folder='dummy', queue=queue) + scalar_np = np.array(0.32, dtype=np.float32) + scalar_summary = scalar('test_scalar', scalar_np, new_style=False) + event = Event(summary=scalar_summary) + + # When + tracker._process_tb_event(event) + + # Then + tracked_scalar = queue.get().value + self.assertTrue(isinstance(tracked_scalar, float)) + self.assertTrue(np.allclose(tracked_scalar, scalar_np)) + + def test__process_tb_scalar_plugin_event(self): + # Given + queue = Queue() + tracker = TensorboardFolderTracker(tensorboard_event_folder='dummy', queue=queue) + scalar_np = np.array(0.32, dtype=np.float32) + scalar_summary = scalar('test_scalar', scalar_np, new_style=True) + event = Event(summary=scalar_summary) + + # When + tracker._process_tb_event(event) + + # Then + tracked_scalar = queue.get().value + self.assertTrue(isinstance(tracked_scalar, np.ndarray)) + self.assertTrue(np.allclose(tracked_scalar, scalar_np))
pex-tool__pex-2123
Release 2.1.133 On the docket: + [x] python<=3.8 symlink with a suffix (eg 3.7m) can create a venv without a pythonX.Y symlink which breaks pex assumptions that pythonX.Y is always available #2119
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.132\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.133\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.rst b/CHANGES.rst index dd0d64575..73c5d8e43 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,12 +1,21 @@ Release Notes ============= +2.1.133 +------- + +This release fixes ``--venv`` mode PEX venv script shebangs for some +scenarios using Python ``<=3.7`` interpreters. + +* Fix venv script shebangs. #2122 + `PR #2122 <https://github.com/pantsbuild/pex/pull/2122>`_ + 2.1.132 ------- This release brings support for the latest Pip release with -`--pip-version 23.1` or by using new support for pinning to the latest -version of Pip supported by Pex with `--pip-version latest`. +``--pip-version 23.1`` or by using new support for pinning to the latest +version of Pip supported by Pex with ``--pip-version latest``. * Add support for Pip 23.1 (#2114) `PR #2114 <https://github.com/pantsbuild/pex/pull/2114>`_ diff --git a/pex/version.py b/pex/version.py index ddfe692f1..600f0ec89 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.132" +__version__ = "2.1.133"
pex-tool__pex-2104
Release 2.1.130 On the docket: + [x] Pex fails to lock - missing artifact #2098
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.129\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.130\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.rst b/CHANGES.rst index 46c800c11..7db19a69e 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,15 @@ Release Notes ============= +2.1.130 +------- + +This release fixes a regression locking certain complex cases of direct +and transitive requirement interactions as exemplified in #2098. + +* Guard lock analysis against Pip-cached artifacts. (#2103) + `PR #2103 <https://github.com/pantsbuild/pex/pull/2103>`_ + 2.1.129 ------- diff --git a/pex/version.py b/pex/version.py index 2553debad..2be2a4246 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.129" +__version__ = "2.1.130"
pex-tool__pex-2081
Release 2.1.126 On the docket: + [x] Resolve sdist builds can race and fail. #2078
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.125\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.126\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.rst b/CHANGES.rst index ce3114e28..112d072a0 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,16 @@ Release Notes ============= +2.1.126 +------- + +This release fixes a long standing (> 4 years old!) concurrency bug +when building the same sdist for the 1st time and racing another Pex +process doing the same sdist build. + +* Guard against racing sdist builds. (#2080) + `PR #2080 <https://github.com/pantsbuild/pex/pull/2080>`_ + 2.1.125 ------- diff --git a/pex/version.py b/pex/version.py index cafa484de..014827637 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.125" +__version__ = "2.1.126"
pex-tool__pex-1987
Release 2.1.114 On the docket: + [ ] Only insert "" to head of sys.path if a venv PEX runs in interpreter mode #1984 + [x] venv_dir calculation doesn't correctly handle PEX_PYTHON_PATH with symlinks. #1885
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.113\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.114\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.rst b/CHANGES.rst index c2f86b7b9..097046b8f 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,17 @@ Release Notes ============= +2.1.114 +------- + +This release brings two fixes for ``--venv`` mode PEXes. + +* Only insert "" to head of sys.path if a venv PEX runs in interpreter mode (#1984) + `PR #1984 <https://github.com/pantsbuild/pex/pull/1984>`_ + +* Map pex python path interpreter to realpath when creating venv dir hash. (#1972) + `PR #1972 <https://github.com/pantsbuild/pex/pull/1972>`_ + 2.1.113 ------- diff --git a/pex/version.py b/pex/version.py index 5a26142a6..1417a9b98 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.113" +__version__ = "2.1.114"
pex-tool__pex-2258
Release 2.1.148 On the docket: + [x] The Pex CLI should warn when it creates a PEX zip that requires zip64. #2247
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.147\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.148\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.md b/CHANGES.md index 70201b9bb..84cd183cd 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,5 +1,16 @@ # Release Notes +## 2.1.148 + +Add support to the Pex for checking if built PEXes are valid Python +zipapps. Currently, Python zipapps must reside in 32 bit zip files due +to limitations of the stdlib `zipimport` module's `zipimporter`; so this +check amounts to a check that the built PEX zip does not use ZIP64 +extensions. The check is controlled with a new +`--check {none,warn,error}` option, defaulting to warn. + +* Add --check support for zipapps. (#2253) + ## 2.1.147 Add support for `--use-pip-config` to allow the Pip Pex calls to read diff --git a/pex/version.py b/pex/version.py index 34e32d6eb..ae2ef1582 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.147" +__version__ = "2.1.148"
pex-tool__pex-2214
Release 2.1.142 On the docket: + [x] KeyError when locking awscli on Python 3.11 #2211
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.141\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.142\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.md b/CHANGES.md index bb214e656..5bbc1c157 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,5 +1,12 @@ # Release Notes +## 2.1.142 + +This release fixes Pex to handle Pip backtracking due to sdist build +errors when attempting to extract metadata. + +* Handle backtracking due to sdist build errors. (#2213) + ## 2.1.141 This release fixes the Pex CLI to work when run from a read-only diff --git a/pex/version.py b/pex/version.py index eb2be6ed5..f385f96a2 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.141" +__version__ = "2.1.142"
pex-tool__pex-2153
Release 2.1.137 On the docket: + [x] A locked requirement with mixed artifact types fails to lock. #2150
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.136\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.137\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.rst b/CHANGES.rst index e2b40876e..86b69c27c 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,16 @@ Release Notes ============= +2.1.137 +------- + +This release fixes a long standing bug in lock file creation for exotic +locking scenarios pulling the same project from multiple artifact +sources (any mix of URLs, VCS and local project directories). + +* Fix inter-artifact comparisons. (#2152) + `PR #2152 <https://github.com/pantsbuild/pex/pull/2152>`_ + 2.1.136 ------- diff --git a/pex/version.py b/pex/version.py index 2f3f7bef4..5f72c8fd6 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.136" +__version__ = "2.1.137"
pex-tool__pex-2055
Release 2.1.122 On the docket: + [x] Support the latest Pip releases: 22.3.1 & 23.0 #2056 + [x] Lock sdists with prepare-metadata-for-build-wheel. #2053 + [x] Fix `execute_parallel` "leaking" a thread. #2052
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.121\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.122\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.rst b/CHANGES.rst index 052808652..869877089 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,24 @@ Release Notes ============= +2.1.122 +------- + +This release fixes posix file locks used by Pex internally and enhances +lock creation to support locking sdist-only C extension projects that +do not build on the current platform. Pex is also updated to support +`--pip-version 22.3.1` and `--pip-version 23.0`, bringing it up to date +with the latest Pip's available. + +* Support the latest Pip releases: 22.3.1 & 23.0 (#2056) + `PR #2053 <https://github.com/pantsbuild/pex/pull/2056>`_ + +* Lock sdists with ``prepare-metadata-for-build-wheel``. (#2053) + `PR #2053 <https://github.com/pantsbuild/pex/pull/2053>`_ + +* Fix ``execute_parallel`` "leaking" a thread. (#2052) + `PR #2052 <https://github.com/pantsbuild/pex/pull/2052>`_ + 2.1.121 ------- diff --git a/pex/version.py b/pex/version.py index 2513fd6e8..c30e2a6bb 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.121" +__version__ = "2.1.122"
pex-tool__pex-1976
Release 2.1.113 On the docket: + [x] Restore AtomicDirectory non-locked good behavior. #1974
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.112\"\n", "path": "pex/version.py" } ]
[ { "content": "# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\n__version__ = \"2.1.113\"\n", "path": "pex/version.py" } ]
diff --git a/CHANGES.rst b/CHANGES.rst index 5765b0113..c2f86b7b9 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,16 @@ Release Notes ============= +2.1.113 +------- + +This is a hotfix release that fixes errors installing wheels when there +is high parallelism in execution of Pex processes. These issues were a +regression introduced by #1961 included in the 2.1.112 release. + +* Restore AtomicDirectory non-locked good behavior. (#1974) + `PR #1974 <https://github.com/pantsbuild/pex/pull/1974>`_ + 2.1.112 ------- diff --git a/pex/version.py b/pex/version.py index ff657f9d8..5a26142a6 100644 --- a/pex/version.py +++ b/pex/version.py @@ -1,4 +1,4 @@ # Copyright 2015 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). -__version__ = "2.1.112" +__version__ = "2.1.113"
wemake-services__wemake-python-styleguide-1588
Add "wrapper" to NESTED_FUNCTIONS_WHITELIST? # Rule request ## Thesis and reasoning Decorators are often created inside functions. These decorators are then supposed to be used on top of other functions, which means they must accept one, and also that they will often create and return a new function by wrapping the original one. It's a level-3 nesting of functions. Maybe WPS430 should be relaxed, or `NESTED_FUNCTIONS_WHITELIST` could include another name like `wrapper`? Code example: ```python from functools import wraps def python(versions): """Run through multiple Python versions.""" def decorator(func): @wraps(func) def wrapper(context, *args, **kwargs): for version in versions: # do things with version and context func(context, *args, **kwargs) return wrapper return decorator @python(["3.6", "3.7", "3.8"]) def task(context): ... ``` What do you think 🙂 ?
[ { "content": "\"\"\"\nThis module contains list of white- and black-listed ``python`` members.\n\nWe add values here when we want to make them public.\nOr when a value is reused in several places.\nThen, we automatically have to add it here and document it.\n\nOther constants that are not used across modules\nand does not require to be documented can be defined where they are used.\n\nAll values here must be documented with ``#:`` comments.\n\"\"\"\n\nimport math\nimport re\n\nfrom typing_extensions import Final\n\n#: List of functions we forbid to use.\nFUNCTIONS_BLACKLIST: Final = frozenset((\n # Code generation:\n 'eval',\n 'exec',\n 'compile',\n\n # Termination:\n 'exit',\n 'quit',\n\n # Magic:\n 'globals',\n 'locals',\n 'vars',\n 'dir',\n\n # IO:\n 'print',\n 'pprint',\n 'input',\n 'breakpoint',\n\n # Attribute access:\n 'hasattr',\n 'delattr',\n\n # Gratis:\n 'copyright',\n 'help',\n 'credits',\n\n # Dynamic imports:\n '__import__',\n\n # OOP:\n 'staticmethod',\n\n # Mypy:\n 'reveal_type',\n))\n\n#: List of module metadata we forbid to use.\nMODULE_METADATA_VARIABLES_BLACKLIST: Final = frozenset((\n '__author__',\n '__all__',\n '__version__',\n '__about__',\n))\n\n#: List of variable names we forbid to use.\nVARIABLE_NAMES_BLACKLIST: Final = frozenset((\n # Meaningless words:\n 'data',\n 'result',\n 'results',\n 'item',\n 'items',\n 'value',\n 'values',\n 'val',\n 'vals',\n 'var',\n 'vars',\n 'variable',\n 'content',\n 'contents',\n 'info',\n 'handle',\n 'handler',\n 'file',\n 'obj',\n 'objects',\n 'objs',\n 'some',\n 'do',\n 'param',\n 'params',\n 'parameters',\n\n # Confuseables:\n 'no',\n 'true',\n 'false',\n\n # Names from examples:\n 'foo',\n 'bar',\n 'baz',\n))\n\n#: List of characters sequences that are hard to read.\nUNREADABLE_CHARACTER_COMBINATIONS: Final = frozenset((\n '1l',\n '1I',\n '0O',\n 'O0',\n # Not included: 'lI', 'l1', 'Il'\n # Because these names are quite common in real words.\n))\n\n#: List of special names that are used only as first argument in methods.\nSPECIAL_ARGUMENT_NAMES_WHITELIST: Final = frozenset((\n 'self',\n 'cls',\n 'mcs',\n))\n\n#: List of all magic methods from the python docs.\nALL_MAGIC_METHODS: Final = frozenset((\n '__new__',\n '__init__',\n '__del__',\n\n '__repr__',\n '__str__',\n '__bytes__',\n '__format__',\n\n '__lt__',\n '__le__',\n '__eq__',\n '__ne__',\n '__gt__',\n '__ge__',\n\n '__hash__',\n '__bool__',\n\n '__getattr__',\n '__getattribute__',\n '__setattr__',\n '__delattr__',\n '__dir__',\n\n '__get__',\n '__set__',\n '__delete__',\n '__set_name__',\n\n '__init_subclass__',\n '__instancecheck__',\n '__subclasscheck__',\n '__class_getitem__',\n\n '__call__',\n '__len__',\n '__length_hint__',\n '__getitem__',\n '__setitem__',\n '__delitem__',\n '__missing__',\n '__iter__',\n '__reversed__',\n '__contains__',\n\n '__add__',\n '__sub__',\n '__mul__',\n '__matmul__',\n '__truediv__',\n '__floordiv__',\n '__mod__',\n '__divmod__',\n '__pow__',\n '__lshift__',\n '__rshift__',\n '__and__',\n '__xor__',\n '__or__',\n '__radd__',\n '__rsub__',\n '__rmul__',\n '__rmatmul__',\n '__rtruediv__',\n '__rfloordiv__',\n '__rmod__',\n '__rdivmod__',\n '__rpow__',\n '__rlshift__',\n '__rrshift__',\n '__rand__',\n '__rxor__',\n '__ror__',\n '__iadd__',\n '__isub__',\n '__imul__',\n '__imatmul__',\n '__itruediv__',\n '__ifloordiv__',\n '__imod__',\n '__ipow__',\n '__ilshift__',\n '__irshift__',\n '__iand__',\n '__ixor__',\n '__ior__',\n '__neg__',\n '__pos__',\n '__abs__',\n '__invert__',\n '__complex__',\n '__int__',\n '__float__',\n '__index__',\n '__round__',\n '__trunc__',\n '__floor__',\n '__ceil__',\n\n '__enter__',\n '__exit__',\n\n '__await__',\n '__aiter__',\n '__anext__',\n '__aenter__',\n '__aexit__',\n))\n\n#: List of magic methods that are forbidden to use.\nMAGIC_METHODS_BLACKLIST: Final = frozenset((\n # Since we don't use `del`:\n '__del__',\n '__delitem__',\n '__delete__',\n\n # Since we don't use `pickle`:\n '__reduce__',\n '__reduce_ex__',\n\n '__dir__', # since we don't use `dir()`\n '__delattr__', # since we don't use `delattr()`\n))\n\n#: List of magic methods that are not allowed to be generators.\nYIELD_MAGIC_METHODS_BLACKLIST: Final = ALL_MAGIC_METHODS.difference({\n # Allowed to be used with ``yield`` keyword:\n '__call__', # Fixes Issue:146\n '__iter__',\n})\n\n#: List of magic methods that are not allowed to be async.\nASYNC_MAGIC_METHODS_BLACKLIST: Final = ALL_MAGIC_METHODS.difference({\n # In order of appearance on\n # https://docs.python.org/3/reference/datamodel.html#basic-customization\n # Allowed magic methods are:\n '__anext__',\n '__aenter__',\n '__aexit__',\n})\n\n#: List of builtin classes that are allowed to subclass.\nALLOWED_BUILTIN_CLASSES: Final = frozenset((\n 'type',\n 'object',\n))\n\n#: List of nested functions' names we allow to use.\nNESTED_FUNCTIONS_WHITELIST: Final = frozenset((\n 'decorator',\n 'factory',\n))\n\n#: List of allowed ``__future__`` imports.\nFUTURE_IMPORTS_WHITELIST: Final = frozenset((\n 'annotations',\n 'generator_stop',\n))\n\n#: List of blacklisted module names.\nMODULE_NAMES_BLACKLIST: Final = frozenset((\n 'util',\n 'utils',\n 'utilities',\n 'helpers',\n))\n\n#: List of allowed module magic names.\nMAGIC_MODULE_NAMES_WHITELIST: Final = frozenset((\n '__init__',\n '__main__',\n))\n\n#: List of bad magic module functions.\nMAGIC_MODULE_NAMES_BLACKLIST: Final = frozenset((\n '__getattr__',\n '__dir__',\n))\n\n#: Regex pattern to name modules.\nMODULE_NAME_PATTERN: Final = re.compile(r'^_?_?[a-z][a-z\\d_]*[a-z\\d](__)?$')\n\n#: Common numbers that are allowed to be used without being called \"magic\".\nMAGIC_NUMBERS_WHITELIST: Final = frozenset((\n 0, # both int and float\n 0.1,\n 0.5,\n 1.0,\n 100,\n 1000,\n 1024, # bytes\n 24, # hours\n 60, # seconds, minutes\n\n 1j, # imaginary part of a complex number\n))\n\n#: Maximum amount of ``pragma`` no-cover comments per module.\nMAX_NO_COVER_COMMENTS: Final = 5\n\n#: Maximum length of ``yield`` ``tuple`` expressions.\nMAX_LEN_YIELD_TUPLE: Final = 5\n\n#: Maximum number of compare nodes in a single expression.\nMAX_COMPARES: Final = 2\n\n#: Maximum number of conditions in a single ``if`` or ``while`` statement.\nMAX_CONDITIONS: Final = 4\n\n#: Maximum number of `elif` blocks in a single `if` condition:\nMAX_ELIFS: Final = 3\n\n#: Maximum number of ``except`` cases in a single ``try`` clause.\nMAX_EXCEPT_CASES: Final = 3\n\n#: Approximate constants which real values should be imported from math module.\nMATH_APPROXIMATE_CONSTANTS: Final = frozenset((\n math.pi,\n math.e,\n math.tau,\n))\n\n#: List of vague method names that may cause confusion if imported as is:\nVAGUE_IMPORTS_BLACKLIST: Final = frozenset((\n 'read',\n 'write',\n 'load',\n 'loads',\n 'dump',\n 'dumps',\n 'parse',\n 'safe_load',\n 'safe_dump',\n 'load_all',\n 'dump_all',\n 'safe_load_all',\n 'safe_dump_all',\n))\n\n#: List of literals without arguments we forbid to use.\nLITERALS_BLACKLIST: Final = frozenset((\n 'int',\n 'float',\n 'str',\n 'bytes',\n 'bool',\n 'complex',\n))\n\n#: List of functions in which arguments must be tuples.\nTUPLE_ARGUMENTS_METHODS: Final = frozenset((\n 'frozenset',\n))\n\n#: Conditions that can appear in the ``if`` statement to allow nested imports.\nALLOWED_NESTED_IMPORTS_CONDITIONS: Final = frozenset((\n 'TYPE_CHECKING',\n))\n\n#: List of commonly used aliases\nALIAS_NAMES_WHITELIST: Final = frozenset((\n 'np',\n 'pd',\n 'df',\n 'plt',\n 'sns',\n 'tf',\n 'cv',\n))\n\n# Internal variables\n# ==================\n\n# Please, do not touch values beyond this line!\n# ---------------------------------------------\n\n# They are not publicly documented since they are not used by the end user.\n# But, we still need them to be defined here.\n\n# Used as a default filename, when it is not passed by flake8:\nSTDIN: Final = 'stdin'\n\n# Used to specify as a placeholder for `__init__`:\nINIT: Final = '__init__'\n\n# Used to determine when we are running on Windows:\nWINDOWS_OS: Final = 'nt'\n\n# Used as a placeholder for special `_` variable:\nUNUSED_PLACEHOLDER: Final = '_'\n", "path": "wemake_python_styleguide/constants.py" } ]
[ { "content": "\"\"\"\nThis module contains list of white- and black-listed ``python`` members.\n\nWe add values here when we want to make them public.\nOr when a value is reused in several places.\nThen, we automatically have to add it here and document it.\n\nOther constants that are not used across modules\nand does not require to be documented can be defined where they are used.\n\nAll values here must be documented with ``#:`` comments.\n\"\"\"\n\nimport math\nimport re\n\nfrom typing_extensions import Final\n\n#: List of functions we forbid to use.\nFUNCTIONS_BLACKLIST: Final = frozenset((\n # Code generation:\n 'eval',\n 'exec',\n 'compile',\n\n # Termination:\n 'exit',\n 'quit',\n\n # Magic:\n 'globals',\n 'locals',\n 'vars',\n 'dir',\n\n # IO:\n 'print',\n 'pprint',\n 'input',\n 'breakpoint',\n\n # Attribute access:\n 'hasattr',\n 'delattr',\n\n # Gratis:\n 'copyright',\n 'help',\n 'credits',\n\n # Dynamic imports:\n '__import__',\n\n # OOP:\n 'staticmethod',\n\n # Mypy:\n 'reveal_type',\n))\n\n#: List of module metadata we forbid to use.\nMODULE_METADATA_VARIABLES_BLACKLIST: Final = frozenset((\n '__author__',\n '__all__',\n '__version__',\n '__about__',\n))\n\n#: List of variable names we forbid to use.\nVARIABLE_NAMES_BLACKLIST: Final = frozenset((\n # Meaningless words:\n 'data',\n 'result',\n 'results',\n 'item',\n 'items',\n 'value',\n 'values',\n 'val',\n 'vals',\n 'var',\n 'vars',\n 'variable',\n 'content',\n 'contents',\n 'info',\n 'handle',\n 'handler',\n 'file',\n 'obj',\n 'objects',\n 'objs',\n 'some',\n 'do',\n 'param',\n 'params',\n 'parameters',\n\n # Confuseables:\n 'no',\n 'true',\n 'false',\n\n # Names from examples:\n 'foo',\n 'bar',\n 'baz',\n))\n\n#: List of characters sequences that are hard to read.\nUNREADABLE_CHARACTER_COMBINATIONS: Final = frozenset((\n '1l',\n '1I',\n '0O',\n 'O0',\n # Not included: 'lI', 'l1', 'Il'\n # Because these names are quite common in real words.\n))\n\n#: List of special names that are used only as first argument in methods.\nSPECIAL_ARGUMENT_NAMES_WHITELIST: Final = frozenset((\n 'self',\n 'cls',\n 'mcs',\n))\n\n#: List of all magic methods from the python docs.\nALL_MAGIC_METHODS: Final = frozenset((\n '__new__',\n '__init__',\n '__del__',\n\n '__repr__',\n '__str__',\n '__bytes__',\n '__format__',\n\n '__lt__',\n '__le__',\n '__eq__',\n '__ne__',\n '__gt__',\n '__ge__',\n\n '__hash__',\n '__bool__',\n\n '__getattr__',\n '__getattribute__',\n '__setattr__',\n '__delattr__',\n '__dir__',\n\n '__get__',\n '__set__',\n '__delete__',\n '__set_name__',\n\n '__init_subclass__',\n '__instancecheck__',\n '__subclasscheck__',\n '__class_getitem__',\n\n '__call__',\n '__len__',\n '__length_hint__',\n '__getitem__',\n '__setitem__',\n '__delitem__',\n '__missing__',\n '__iter__',\n '__reversed__',\n '__contains__',\n\n '__add__',\n '__sub__',\n '__mul__',\n '__matmul__',\n '__truediv__',\n '__floordiv__',\n '__mod__',\n '__divmod__',\n '__pow__',\n '__lshift__',\n '__rshift__',\n '__and__',\n '__xor__',\n '__or__',\n '__radd__',\n '__rsub__',\n '__rmul__',\n '__rmatmul__',\n '__rtruediv__',\n '__rfloordiv__',\n '__rmod__',\n '__rdivmod__',\n '__rpow__',\n '__rlshift__',\n '__rrshift__',\n '__rand__',\n '__rxor__',\n '__ror__',\n '__iadd__',\n '__isub__',\n '__imul__',\n '__imatmul__',\n '__itruediv__',\n '__ifloordiv__',\n '__imod__',\n '__ipow__',\n '__ilshift__',\n '__irshift__',\n '__iand__',\n '__ixor__',\n '__ior__',\n '__neg__',\n '__pos__',\n '__abs__',\n '__invert__',\n '__complex__',\n '__int__',\n '__float__',\n '__index__',\n '__round__',\n '__trunc__',\n '__floor__',\n '__ceil__',\n\n '__enter__',\n '__exit__',\n\n '__await__',\n '__aiter__',\n '__anext__',\n '__aenter__',\n '__aexit__',\n))\n\n#: List of magic methods that are forbidden to use.\nMAGIC_METHODS_BLACKLIST: Final = frozenset((\n # Since we don't use `del`:\n '__del__',\n '__delitem__',\n '__delete__',\n\n # Since we don't use `pickle`:\n '__reduce__',\n '__reduce_ex__',\n\n '__dir__', # since we don't use `dir()`\n '__delattr__', # since we don't use `delattr()`\n))\n\n#: List of magic methods that are not allowed to be generators.\nYIELD_MAGIC_METHODS_BLACKLIST: Final = ALL_MAGIC_METHODS.difference({\n # Allowed to be used with ``yield`` keyword:\n '__call__', # Fixes Issue:146\n '__iter__',\n})\n\n#: List of magic methods that are not allowed to be async.\nASYNC_MAGIC_METHODS_BLACKLIST: Final = ALL_MAGIC_METHODS.difference({\n # In order of appearance on\n # https://docs.python.org/3/reference/datamodel.html#basic-customization\n # Allowed magic methods are:\n '__anext__',\n '__aenter__',\n '__aexit__',\n})\n\n#: List of builtin classes that are allowed to subclass.\nALLOWED_BUILTIN_CLASSES: Final = frozenset((\n 'type',\n 'object',\n))\n\n#: List of nested functions' names we allow to use.\nNESTED_FUNCTIONS_WHITELIST: Final = frozenset((\n 'decorator',\n 'factory',\n 'wrapper',\n))\n\n#: List of allowed ``__future__`` imports.\nFUTURE_IMPORTS_WHITELIST: Final = frozenset((\n 'annotations',\n 'generator_stop',\n))\n\n#: List of blacklisted module names.\nMODULE_NAMES_BLACKLIST: Final = frozenset((\n 'util',\n 'utils',\n 'utilities',\n 'helpers',\n))\n\n#: List of allowed module magic names.\nMAGIC_MODULE_NAMES_WHITELIST: Final = frozenset((\n '__init__',\n '__main__',\n))\n\n#: List of bad magic module functions.\nMAGIC_MODULE_NAMES_BLACKLIST: Final = frozenset((\n '__getattr__',\n '__dir__',\n))\n\n#: Regex pattern to name modules.\nMODULE_NAME_PATTERN: Final = re.compile(r'^_?_?[a-z][a-z\\d_]*[a-z\\d](__)?$')\n\n#: Common numbers that are allowed to be used without being called \"magic\".\nMAGIC_NUMBERS_WHITELIST: Final = frozenset((\n 0, # both int and float\n 0.1,\n 0.5,\n 1.0,\n 100,\n 1000,\n 1024, # bytes\n 24, # hours\n 60, # seconds, minutes\n\n 1j, # imaginary part of a complex number\n))\n\n#: Maximum amount of ``pragma`` no-cover comments per module.\nMAX_NO_COVER_COMMENTS: Final = 5\n\n#: Maximum length of ``yield`` ``tuple`` expressions.\nMAX_LEN_YIELD_TUPLE: Final = 5\n\n#: Maximum number of compare nodes in a single expression.\nMAX_COMPARES: Final = 2\n\n#: Maximum number of conditions in a single ``if`` or ``while`` statement.\nMAX_CONDITIONS: Final = 4\n\n#: Maximum number of `elif` blocks in a single `if` condition:\nMAX_ELIFS: Final = 3\n\n#: Maximum number of ``except`` cases in a single ``try`` clause.\nMAX_EXCEPT_CASES: Final = 3\n\n#: Approximate constants which real values should be imported from math module.\nMATH_APPROXIMATE_CONSTANTS: Final = frozenset((\n math.pi,\n math.e,\n math.tau,\n))\n\n#: List of vague method names that may cause confusion if imported as is:\nVAGUE_IMPORTS_BLACKLIST: Final = frozenset((\n 'read',\n 'write',\n 'load',\n 'loads',\n 'dump',\n 'dumps',\n 'parse',\n 'safe_load',\n 'safe_dump',\n 'load_all',\n 'dump_all',\n 'safe_load_all',\n 'safe_dump_all',\n))\n\n#: List of literals without arguments we forbid to use.\nLITERALS_BLACKLIST: Final = frozenset((\n 'int',\n 'float',\n 'str',\n 'bytes',\n 'bool',\n 'complex',\n))\n\n#: List of functions in which arguments must be tuples.\nTUPLE_ARGUMENTS_METHODS: Final = frozenset((\n 'frozenset',\n))\n\n#: Conditions that can appear in the ``if`` statement to allow nested imports.\nALLOWED_NESTED_IMPORTS_CONDITIONS: Final = frozenset((\n 'TYPE_CHECKING',\n))\n\n#: List of commonly used aliases\nALIAS_NAMES_WHITELIST: Final = frozenset((\n 'np',\n 'pd',\n 'df',\n 'plt',\n 'sns',\n 'tf',\n 'cv',\n))\n\n# Internal variables\n# ==================\n\n# Please, do not touch values beyond this line!\n# ---------------------------------------------\n\n# They are not publicly documented since they are not used by the end user.\n# But, we still need them to be defined here.\n\n# Used as a default filename, when it is not passed by flake8:\nSTDIN: Final = 'stdin'\n\n# Used to specify as a placeholder for `__init__`:\nINIT: Final = '__init__'\n\n# Used to determine when we are running on Windows:\nWINDOWS_OS: Final = 'nt'\n\n# Used as a placeholder for special `_` variable:\nUNUSED_PLACEHOLDER: Final = '_'\n", "path": "wemake_python_styleguide/constants.py" } ]
diff --git a/wemake_python_styleguide/constants.py b/wemake_python_styleguide/constants.py index 25e04b158..c570a0f1f 100644 --- a/wemake_python_styleguide/constants.py +++ b/wemake_python_styleguide/constants.py @@ -278,6 +278,7 @@ NESTED_FUNCTIONS_WHITELIST: Final = frozenset(( 'decorator', 'factory', + 'wrapper', )) #: List of allowed ``__future__`` imports.
fedora-infra__bodhi-507
setup.py test doesn't include extra_requires from fedmsg deps ``` ====================================================================== ERROR: Failure: ImportError (No module named psutil) ---------------------------------------------------------------------- Traceback (most recent call last): File "/home/decause/.virtualenvs/bodhi-python2.7/lib/python2.7/site-packages/nose/loader.py", line 418, in loadTestsFromName addr.filename, addr.module) File "/home/decause/.virtualenvs/bodhi-python2.7/lib/python2.7/site-packages/nose/importer.py", line 47, in importFromPath return self.importFromDir(dir_path, fqname) File "/home/decause/.virtualenvs/bodhi-python2.7/lib/python2.7/site-packages/nose/importer.py", line 94, in importFromDir mod = load_module(part_fqname, fh, filename, desc) File "/home/decause/code/bodhi/bodhi/tests/test_masher.py", line 27, in <module> from bodhi.consumers.masher import Masher, MasherThread File "/home/decause/code/bodhi/bodhi/consumers/masher.py", line 30, in <module> import fedmsg.consumers File "/home/decause/code/bodhi/.eggs/fedmsg-0.16.0-py2.7.egg/fedmsg/consumers/__init__.py", line 25, in <module> import psutil ImportError: No module named psutil ---------------------------------------------------------------------- Ran 335 tests in 138.787s FAILED (errors=1) ```
[ { "content": "import __main__\n__requires__ = __main__.__requires__ = 'WebOb>=1.4.1'\nimport pkg_resources\n\n# The following two imports are required to shut up an\n# atexit error when running tests with python 2.7\nimport logging\nimport multiprocessing\n\nimport os\nimport sys\n\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(here, 'README.rst')).read()\nCHANGES = open(os.path.join(here, 'CHANGES.txt')).read()\n\nrequires = [\n 'pyramid',\n 'pyramid_mako',\n 'pyramid_debugtoolbar',\n 'pyramid_tm',\n 'waitress',\n 'colander',\n 'cornice',\n\n 'python-openid',\n 'pyramid_fas_openid',\n 'packagedb-cli',\n\n 'sqlalchemy',\n 'zope.sqlalchemy',\n\n 'webhelpers',\n 'progressbar',\n\n 'bunch',\n\n # for captchas\n 'cryptography',\n 'Pillow',\n\n # Useful tools\n 'kitchen',\n 'python-fedora',\n 'pylibravatar',\n 'pyDNS',\n 'dogpile.cache',\n 'arrow',\n 'markdown',\n\n # i18n, that we're not actually doing yet.\n #'Babel',\n #'lingua',\n\n # External resources\n 'python-bugzilla',\n 'simplemediawiki',\n 'fedmsg',\n\n 'Sphinx',\n\n # For the bodhi-client\n 'click',\n\n 'WebOb>=1.4.1',\n ]\n\nif sys.version_info[:3] < (2,7,0):\n requires.append('importlib')\n\nif sys.version_info[:3] < (2,5,0):\n requires.append('pysqlite')\n\nsetup(name='bodhi',\n version='2.0',\n description='bodhi',\n long_description=README + '\\n\\n' + CHANGES,\n classifiers=[\n \"Programming Language :: Python\",\n \"Framework :: Pyramid\",\n \"Topic :: Internet :: WWW/HTTP\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI :: Application\",\n ],\n author='',\n author_email='',\n url='',\n keywords='web fedora pyramid',\n packages=find_packages(),\n include_package_data=True,\n zip_safe=False,\n install_requires = requires,\n tests_require = [\n 'nose',\n 'nose-cov',\n 'webtest',\n 'mock'\n ],\n test_suite=\"nose.collector\",\n message_extractors = { '.': [\n #('**.py', 'lingua_python', None),\n #('**.mak', 'lingua_xml', None),\n ]},\n entry_points = \"\"\"\\\n [paste.app_factory]\n main = bodhi:main\n [console_scripts]\n initialize_bodhi_db = bodhi.scripts.initializedb:main\n bodhi = bodhi.cli:cli\n bodhi-push = bodhi.push:push\n bodhi-expire-overrides = bodhi.scripts.expire_overrides:main\n [moksha.consumer]\n masher = bodhi.consumers.masher:Masher\n updates = bodhi.consumers.updates:UpdatesHandler\n \"\"\",\n paster_plugins=['pyramid'],\n )\n\n", "path": "setup.py" } ]
[ { "content": "import __main__\n__requires__ = __main__.__requires__ = 'WebOb>=1.4.1'\nimport pkg_resources\n\n# The following two imports are required to shut up an\n# atexit error when running tests with python 2.7\nimport logging\nimport multiprocessing\n\nimport os\nimport sys\n\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(here, 'README.rst')).read()\nCHANGES = open(os.path.join(here, 'CHANGES.txt')).read()\n\nrequires = [\n 'pyramid',\n 'pyramid_mako',\n 'pyramid_debugtoolbar',\n 'pyramid_tm',\n 'waitress',\n 'colander',\n 'cornice',\n\n 'python-openid',\n 'pyramid_fas_openid',\n 'packagedb-cli',\n\n 'sqlalchemy',\n 'zope.sqlalchemy',\n\n 'webhelpers',\n 'progressbar',\n\n 'bunch',\n\n # for captchas\n 'cryptography',\n 'Pillow',\n\n # Useful tools\n 'kitchen',\n 'python-fedora',\n 'pylibravatar',\n 'pyDNS',\n 'dogpile.cache',\n 'arrow',\n 'markdown',\n\n # i18n, that we're not actually doing yet.\n #'Babel',\n #'lingua',\n\n # External resources\n 'python-bugzilla',\n 'simplemediawiki',\n\n # \"python setup.py test\" needs one of fedmsg's setup.py extra_requires\n 'fedmsg[consumers]',\n\n 'Sphinx',\n\n # For the bodhi-client\n 'click',\n\n 'WebOb>=1.4.1',\n ]\n\nif sys.version_info[:3] < (2,7,0):\n requires.append('importlib')\n\nif sys.version_info[:3] < (2,5,0):\n requires.append('pysqlite')\n\nsetup(name='bodhi',\n version='2.0',\n description='bodhi',\n long_description=README + '\\n\\n' + CHANGES,\n classifiers=[\n \"Programming Language :: Python\",\n \"Framework :: Pyramid\",\n \"Topic :: Internet :: WWW/HTTP\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI :: Application\",\n ],\n author='',\n author_email='',\n url='',\n keywords='web fedora pyramid',\n packages=find_packages(),\n include_package_data=True,\n zip_safe=False,\n install_requires = requires,\n tests_require = [\n 'nose',\n 'nose-cov',\n 'webtest',\n 'mock'\n ],\n test_suite=\"nose.collector\",\n message_extractors = { '.': [\n #('**.py', 'lingua_python', None),\n #('**.mak', 'lingua_xml', None),\n ]},\n entry_points = \"\"\"\\\n [paste.app_factory]\n main = bodhi:main\n [console_scripts]\n initialize_bodhi_db = bodhi.scripts.initializedb:main\n bodhi = bodhi.cli:cli\n bodhi-push = bodhi.push:push\n bodhi-expire-overrides = bodhi.scripts.expire_overrides:main\n [moksha.consumer]\n masher = bodhi.consumers.masher:Masher\n updates = bodhi.consumers.updates:UpdatesHandler\n \"\"\",\n paster_plugins=['pyramid'],\n )\n\n", "path": "setup.py" } ]
diff --git a/setup.py b/setup.py index 9f78b56417..46fa402cb1 100644 --- a/setup.py +++ b/setup.py @@ -57,7 +57,9 @@ # External resources 'python-bugzilla', 'simplemediawiki', - 'fedmsg', + + # "python setup.py test" needs one of fedmsg's setup.py extra_requires + 'fedmsg[consumers]', 'Sphinx',
scikit-image__scikit-image-1741
peak_local_max Incorrect output type This [function](http://scikit-image.org/docs/dev/api/skimage.feature.html#peak-local-max) is returning a `list` instead of an `ndarray` if no peaks are detected. I traced the problem till this [line](https://github.com/scikit-image/scikit-image/blob/master/skimage/feature/peak.py#L122). However, I have to check if there is other case (beyond this line) that produces an incorrect output. I will work on it this weekend and submit a pull-request or a code snippet here
[ { "content": "import numpy as np\nimport scipy.ndimage as ndi\nfrom ..filters import rank_order\n\n\ndef peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,\n exclude_border=True, indices=True, num_peaks=np.inf,\n footprint=None, labels=None):\n \"\"\"\n Find peaks in an image, and return them as coordinates or a boolean array.\n\n Peaks are the local maxima in a region of `2 * min_distance + 1`\n (i.e. peaks are separated by at least `min_distance`).\n\n NOTE: If peaks are flat (i.e. multiple adjacent pixels have identical\n intensities), the coordinates of all such pixels are returned.\n\n Parameters\n ----------\n image : ndarray of floats\n Input image.\n min_distance : int\n Minimum number of pixels separating peaks in a region of `2 *\n min_distance + 1` (i.e. peaks are separated by at least\n `min_distance`). If `exclude_border` is True, this value also excludes\n a border `min_distance` from the image boundary.\n To find the maximum number of peaks, use `min_distance=1`.\n threshold_abs : float\n Minimum intensity of peaks.\n threshold_rel : float\n Minimum intensity of peaks calculated as `max(image) * threshold_rel`.\n exclude_border : bool\n If True, `min_distance` excludes peaks from the border of the image as\n well as from each other.\n indices : bool\n If True, the output will be an array representing peak coordinates.\n If False, the output will be a boolean array shaped as `image.shape`\n with peaks present at True elements.\n num_peaks : int\n Maximum number of peaks. When the number of peaks exceeds `num_peaks`,\n return `num_peaks` peaks based on highest peak intensity.\n footprint : ndarray of bools, optional\n If provided, `footprint == 1` represents the local region within which\n to search for peaks at every point in `image`. Overrides\n `min_distance`, except for border exclusion if `exclude_border=True`.\n labels : ndarray of ints, optional\n If provided, each unique region `labels == value` represents a unique\n region to search for peaks. Zero is reserved for background.\n\n Returns\n -------\n output : ndarray or ndarray of bools\n\n * If `indices = True` : (row, column, ...) coordinates of peaks.\n * If `indices = False` : Boolean array shaped like `image`, with peaks\n represented by True values.\n\n Notes\n -----\n The peak local maximum function returns the coordinates of local peaks\n (maxima) in a image. A maximum filter is used for finding local maxima.\n This operation dilates the original image. After comparison between\n dilated and original image, peak_local_max function returns the\n coordinates of peaks where dilated image = original.\n\n Examples\n --------\n >>> img1 = np.zeros((7, 7))\n >>> img1[3, 4] = 1\n >>> img1[3, 2] = 1.5\n >>> img1\n array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 1.5, 0. , 1. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])\n\n >>> peak_local_max(img1, min_distance=1)\n array([[3, 2],\n [3, 4]])\n\n >>> peak_local_max(img1, min_distance=2)\n array([[3, 2]])\n\n >>> img2 = np.zeros((20, 20, 20))\n >>> img2[10, 10, 10] = 1\n >>> peak_local_max(img2, exclude_border=False)\n array([[10, 10, 10]])\n\n \"\"\"\n out = np.zeros_like(image, dtype=np.bool)\n # In the case of labels, recursively build and return an output\n # operating on each label separately\n if labels is not None:\n label_values = np.unique(labels)\n # Reorder label values to have consecutive integers (no gaps)\n if np.any(np.diff(label_values) != 1):\n mask = labels >= 1\n labels[mask] = 1 + rank_order(labels[mask])[0].astype(labels.dtype)\n labels = labels.astype(np.int32)\n\n # New values for new ordering\n label_values = np.unique(labels)\n for label in label_values[label_values != 0]:\n maskim = (labels == label)\n out += peak_local_max(image * maskim, min_distance=min_distance,\n threshold_abs=threshold_abs,\n threshold_rel=threshold_rel,\n exclude_border=exclude_border,\n indices=False, num_peaks=np.inf,\n footprint=footprint, labels=None)\n\n if indices is True:\n return np.transpose(out.nonzero())\n else:\n return out.astype(np.bool)\n\n if np.all(image == image.flat[0]):\n if indices is True:\n return []\n else:\n return out\n\n image = image.copy()\n # Non maximum filter\n if footprint is not None:\n image_max = ndi.maximum_filter(image, footprint=footprint,\n mode='constant')\n else:\n size = 2 * min_distance + 1\n image_max = ndi.maximum_filter(image, size=size, mode='constant')\n mask = (image == image_max)\n image *= mask\n\n if exclude_border:\n # zero out the image borders\n for i in range(image.ndim):\n image = image.swapaxes(0, i)\n image[:min_distance] = 0\n image[-min_distance:] = 0\n image = image.swapaxes(0, i)\n\n # find top peak candidates above a threshold\n peak_threshold = max(np.max(image.ravel()) * threshold_rel, threshold_abs)\n\n # get coordinates of peaks\n coordinates = np.argwhere(image > peak_threshold)\n\n if coordinates.shape[0] > num_peaks:\n intensities = image.flat[np.ravel_multi_index(coordinates.transpose(),image.shape)]\n idx_maxsort = np.argsort(intensities)[::-1]\n coordinates = coordinates[idx_maxsort][:num_peaks]\n\n if indices is True:\n return coordinates\n else:\n nd_indices = tuple(coordinates.T)\n out[nd_indices] = True\n return out\n", "path": "skimage/feature/peak.py" } ]
[ { "content": "import numpy as np\nimport scipy.ndimage as ndi\nfrom ..filters import rank_order\n\n\ndef peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,\n exclude_border=True, indices=True, num_peaks=np.inf,\n footprint=None, labels=None):\n \"\"\"\n Find peaks in an image, and return them as coordinates or a boolean array.\n\n Peaks are the local maxima in a region of `2 * min_distance + 1`\n (i.e. peaks are separated by at least `min_distance`).\n\n NOTE: If peaks are flat (i.e. multiple adjacent pixels have identical\n intensities), the coordinates of all such pixels are returned.\n\n Parameters\n ----------\n image : ndarray of floats\n Input image.\n min_distance : int\n Minimum number of pixels separating peaks in a region of `2 *\n min_distance + 1` (i.e. peaks are separated by at least\n `min_distance`). If `exclude_border` is True, this value also excludes\n a border `min_distance` from the image boundary.\n To find the maximum number of peaks, use `min_distance=1`.\n threshold_abs : float\n Minimum intensity of peaks.\n threshold_rel : float\n Minimum intensity of peaks calculated as `max(image) * threshold_rel`.\n exclude_border : bool\n If True, `min_distance` excludes peaks from the border of the image as\n well as from each other.\n indices : bool\n If True, the output will be an array representing peak coordinates.\n If False, the output will be a boolean array shaped as `image.shape`\n with peaks present at True elements.\n num_peaks : int\n Maximum number of peaks. When the number of peaks exceeds `num_peaks`,\n return `num_peaks` peaks based on highest peak intensity.\n footprint : ndarray of bools, optional\n If provided, `footprint == 1` represents the local region within which\n to search for peaks at every point in `image`. Overrides\n `min_distance`, except for border exclusion if `exclude_border=True`.\n labels : ndarray of ints, optional\n If provided, each unique region `labels == value` represents a unique\n region to search for peaks. Zero is reserved for background.\n\n Returns\n -------\n output : ndarray or ndarray of bools\n\n * If `indices = True` : (row, column, ...) coordinates of peaks.\n * If `indices = False` : Boolean array shaped like `image`, with peaks\n represented by True values.\n\n Notes\n -----\n The peak local maximum function returns the coordinates of local peaks\n (maxima) in a image. A maximum filter is used for finding local maxima.\n This operation dilates the original image. After comparison between\n dilated and original image, peak_local_max function returns the\n coordinates of peaks where dilated image = original.\n\n Examples\n --------\n >>> img1 = np.zeros((7, 7))\n >>> img1[3, 4] = 1\n >>> img1[3, 2] = 1.5\n >>> img1\n array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 1.5, 0. , 1. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n [ 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])\n\n >>> peak_local_max(img1, min_distance=1)\n array([[3, 2],\n [3, 4]])\n\n >>> peak_local_max(img1, min_distance=2)\n array([[3, 2]])\n\n >>> img2 = np.zeros((20, 20, 20))\n >>> img2[10, 10, 10] = 1\n >>> peak_local_max(img2, exclude_border=False)\n array([[10, 10, 10]])\n\n \"\"\"\n out = np.zeros_like(image, dtype=np.bool)\n # In the case of labels, recursively build and return an output\n # operating on each label separately\n if labels is not None:\n label_values = np.unique(labels)\n # Reorder label values to have consecutive integers (no gaps)\n if np.any(np.diff(label_values) != 1):\n mask = labels >= 1\n labels[mask] = 1 + rank_order(labels[mask])[0].astype(labels.dtype)\n labels = labels.astype(np.int32)\n\n # New values for new ordering\n label_values = np.unique(labels)\n for label in label_values[label_values != 0]:\n maskim = (labels == label)\n out += peak_local_max(image * maskim, min_distance=min_distance,\n threshold_abs=threshold_abs,\n threshold_rel=threshold_rel,\n exclude_border=exclude_border,\n indices=False, num_peaks=np.inf,\n footprint=footprint, labels=None)\n\n if indices is True:\n return np.transpose(out.nonzero())\n else:\n return out.astype(np.bool)\n\n if np.all(image == image.flat[0]):\n if indices is True:\n return np.empty((0, 2), np.int)\n else:\n return out\n\n image = image.copy()\n # Non maximum filter\n if footprint is not None:\n image_max = ndi.maximum_filter(image, footprint=footprint,\n mode='constant')\n else:\n size = 2 * min_distance + 1\n image_max = ndi.maximum_filter(image, size=size, mode='constant')\n mask = (image == image_max)\n image *= mask\n\n if exclude_border:\n # zero out the image borders\n for i in range(image.ndim):\n image = image.swapaxes(0, i)\n image[:min_distance] = 0\n image[-min_distance:] = 0\n image = image.swapaxes(0, i)\n\n # find top peak candidates above a threshold\n peak_threshold = max(np.max(image.ravel()) * threshold_rel, threshold_abs)\n\n # get coordinates of peaks\n coordinates = np.argwhere(image > peak_threshold)\n\n if coordinates.shape[0] > num_peaks:\n intensities = image.flat[np.ravel_multi_index(coordinates.transpose(),image.shape)]\n idx_maxsort = np.argsort(intensities)[::-1]\n coordinates = coordinates[idx_maxsort][:num_peaks]\n\n if indices is True:\n return coordinates\n else:\n nd_indices = tuple(coordinates.T)\n out[nd_indices] = True\n return out\n", "path": "skimage/feature/peak.py" } ]
diff --git a/skimage/feature/peak.py b/skimage/feature/peak.py index 599a6e581cd..421abeec363 100644 --- a/skimage/feature/peak.py +++ b/skimage/feature/peak.py @@ -119,7 +119,7 @@ def peak_local_max(image, min_distance=10, threshold_abs=0, threshold_rel=0.1, if np.all(image == image.flat[0]): if indices is True: - return [] + return np.empty((0, 2), np.int) else: return out diff --git a/skimage/feature/tests/test_peak.py b/skimage/feature/tests/test_peak.py index 90fe9b627cb..e62562083a6 100644 --- a/skimage/feature/tests/test_peak.py +++ b/skimage/feature/tests/test_peak.py @@ -11,6 +11,7 @@ def test_trivial_case(): trivial = np.zeros((25, 25)) peak_indices = peak.peak_local_max(trivial, min_distance=1, indices=True) + assert type(peak_indices) is np.ndarray assert not peak_indices # inherent boolean-ness of empty list peaks = peak.peak_local_max(trivial, min_distance=1, indices=False) assert (peaks.astype(np.bool) == trivial).all() @@ -89,7 +90,7 @@ def test_num_peaks3D(): image[5,5,::5] = np.arange(20) peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2) assert len(peaks_limited) == 2 - + def test_reorder_labels(): image = np.random.uniform(size=(40, 60))
digitalfabrik__integreat-cms-470
Do not commit trivial changes to documentation ### Motivation <!-- A clear and concise description of what the motivation for the new feature is, and what problem it is solving. --> At the moment, our CircleCI jobs `build-documentation` and `deploy-documentation` automatically build our documentation and commit/push it to the `gh-pages` branch. Most of the times, this commit includes only trivial changes (see e.g. 6d6c89beda44ebd2448526a18636c464e1111355). This makes the "last changed" date very unreliable, because it is always the date of the last commit, not the date when the documentation actually changed the last time. ### Proposed Solution <!-- A clear and concise description of the feature you would like to add, and how it solves the motivating problem. --> Do not commit the changes to the documentation if only the "Last updated on ..." changes ### Alternatives <!-- A clear and concise description of any alternative solutions or features you've considered, and why you're proposed solution is better. --> Remove the "last changed" date Do not commit trivial changes to documentation ### Motivation <!-- A clear and concise description of what the motivation for the new feature is, and what problem it is solving. --> At the moment, our CircleCI jobs `build-documentation` and `deploy-documentation` automatically build our documentation and commit/push it to the `gh-pages` branch. Most of the times, this commit includes only trivial changes (see e.g. 6d6c89beda44ebd2448526a18636c464e1111355). This makes the "last changed" date very unreliable, because it is always the date of the last commit, not the date when the documentation actually changed the last time. ### Proposed Solution <!-- A clear and concise description of the feature you would like to add, and how it solves the motivating problem. --> Do not commit the changes to the documentation if only the "Last updated on ..." changes ### Alternatives <!-- A clear and concise description of any alternative solutions or features you've considered, and why you're proposed solution is better. --> Remove the "last changed" date
[ { "content": "\"\"\"\nConfiguration file for the Sphinx documentation builder.\n\nThis file only contains a selection of the most common options. For a full\nlist see the documentation:\nhttps://www.sphinx-doc.org/en/master/usage/configuration.html\n\"\"\"\n\n# -- Path setup --------------------------------------------------------------\n\nimport os\nimport sys\nimport inspect\nimport importlib\nimport django\n\nfrom backend.settings import VERSION\n\n# Append project source directory to path environment variable\nsys.path.append(os.path.abspath(\"../src/\"))\nos.environ[\"DJANGO_SETTINGS_MODULE\"] = \"backend.settings\"\n\n\n# Setup Django\ndjango.setup()\n\n\ndef setup(app):\n \"\"\"\n Registeration and setup.\n\n This method does the initial setup for the docs generation.\n \"\"\"\n # Register the docstring processor with sphinx to improve the appearance of Django models\n app.connect(\"autodoc-process-docstring\", process_django_models)\n\n\n# -- Project information -----------------------------------------------------\n\n\nproject = \"integreat-cms\"\n# pylint: disable=redefined-builtin\ncopyright = \"2020, Integreat\"\nauthor = \"Integreat\"\n\n# The full version, including alpha/beta/rc tags\nrelease = VERSION\n\n# -- General configuration ---------------------------------------------------\n\n# All enabled sphinx extensions\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.githubpages\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.linkcode\",\n \"sphinxcontrib_django\",\n \"sphinx_rtd_theme\",\n]\n\n# Enable cross-references to other documentations\nintersphinx_mapping = {\n \"python\": (\"https://docs.python.org/3.7\", None),\n \"pipenv\": (\"https://pipenv.pypa.io/en/latest/\", None),\n \"sphinx\": (\"https://www.sphinx-doc.org/en/master/\", None),\n \"sphinx-rtd-tutorial\": (\n \"https://sphinx-rtd-tutorial.readthedocs.io/en/latest/\",\n None,\n ),\n \"django\": (\n \"https://docs.djangoproject.com/en/2.2/\",\n \"https://docs.djangoproject.com/en/2.2/_objects/\",\n ),\n \"django-mptt\": (\"https://django-mptt.readthedocs.io/en/latest/\", None),\n \"wsgi\": (\"https://wsgi.readthedocs.io/en/latest/\", None),\n}\n\n# The path for patched template files\ntemplates_path = [\"templates\"]\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.\nhtml_theme = \"sphinx_rtd_theme\"\n# Do not show the project name, only the logo\nhtml_theme_options = {\n \"logo_only\": False,\n \"collapse_navigation\": False,\n}\n# The logo shown in the menu bar\nhtml_logo = \"../src/cms/static/images/integreat-logo-white.png\"\n# The facivon of the html doc files\nhtml_favicon = \"../src/cms/static/images/favicon.ico\"\n# The url where the docs should be published (via gh-pages)\nhtml_baseurl = \"https://Integreat.github.io/cms-django/\"\n# Do not include links to the documentation source (.rst files) in build\nhtml_show_sourcelink = False\n# Do not include a link to sphinx\nhtml_show_sphinx = False\n# Include last updated timestamp\nhtml_last_updated_fmt = \"%b %d, %Y\"\n\n# -- Modify default Django model parameter types------------------------------\n\n\n# pylint: disable=unused-argument, too-many-locals, too-many-branches\ndef process_django_models(app, what, name, obj, options, lines):\n \"\"\"Append correct param types from fields to model documentation.\"\"\"\n if inspect.isclass(obj) and issubclass(obj, django.db.models.Model):\n # Intersphinx mapping to django.contrib.postgres documentation does not work, so here the manual link\n postgres_docu = (\n intersphinx_mapping.get(\"django\")[1][0] + \"ref/contrib/postgres/fields/\"\n )\n # include_hidden to get also ManyToManyFields\n for field in obj._meta.get_fields(include_hidden=True):\n field_type = type(field).__name__\n field_module = type(field).__module__\n if field_module == \"django.contrib.postgres.fields.array\":\n # Fix intersphinx mappings for django.contrib.postgres fields\n type_line = (\n f\":type {field.name}: `ArrayField <{postgres_docu}#arrayfield>`_\"\n )\n elif field_module == \"django.contrib.postgres.fields.jsonb\":\n # Fix intersphinx mappings for django.contrib.postgres fields\n type_line = (\n f\":type {field.name}: `JSONField <{postgres_docu}#jsonfield>`_\"\n )\n elif field_module in [\"django.db.models.fields.related\", \"mptt.fields\"]:\n # Fix intersphinx mappings for related fields (ForeignKey, OneToOneField, ManyToManyField, ...)\n # Also includes related MPTT fields (TreeForeignKey, TreeOneToOneField, TreeManyToManyField, ...)\n remote_model = field.remote_field.get_related_field().model\n type_line = f\":type {field.name}: {field_type} to :class:`~{remote_model.__module__}.{remote_model.__name__}`\"\n elif field_module == \"django.db.models.fields.reverse_related\":\n # Fix intersphinx mappings for reverse related fields (ManyToOneRel, OneToOneRel, ManyToManyRel, ...)\n remote_model = field.remote_field.model\n type_line = f\":type {field.name}: Reverse {field_type[:-3]} Relation from :class:`~{remote_model.__module__}.{remote_model.__name__}`\"\n else:\n if \"django.db.models\" in field_module:\n # Scope with django.db.models * imports (remove all sub-module-paths)\n field_module = \"django.db.models\"\n # Fix type hint to enable correct intersphinx mappings to other documentations\n type_line = f\":type {field.name}: ~{field_module}.{field_type}\"\n # This loop gets the indexes which are needed to update the type hints of the model parameters.\n # It makes it possible to split the parameter section into multiple parts, e.g. params inherited from a base\n # model and params of a sub model (otherwise the type hints would not be recognized when separated from\n # the parameter description).\n param_index = None\n next_param_index = None\n type_index = None\n for index, line in enumerate(lines):\n if param_index is None and f\":param {field.name}:\" in line:\n # The index of the field param is only used to determine the next param line\n param_index = index\n elif (\n param_index is not None\n and next_param_index is None\n and (\":param \" in line or line == \"\")\n ):\n # The line of the next param after the field, this is the index where we will insert the type.\n # Sometimes the param descriptions extend over multiple lines, so we cannot just do param_index + 1.\n # If the line is empty, the param description is finished, even if it extends over multiple lines.\n next_param_index = index\n elif type_index is None and f\":type {field.name}:\" in line:\n # The index of the old type hint, we will either move this line or replace it\n type_index = index\n break\n if next_param_index is None:\n # In case the current field is the last param, we just append the type at the very end of lines\n next_param_index = len(lines)\n # For some params, the type line is not automatically generated and thus the type_index might be `None`\n if type_index is not None:\n # We delete the old type index, because we will replace it with the new type line\n del lines[type_index]\n # Insert the new type line just before the next param\n lines.insert(next_param_index, type_line)\n return lines\n\n\n# -- Source Code links to GitHub ---------------------------------------------\n\n\ndef linkcode_resolve(domain, info):\n \"\"\"Link source code to GitHub.\"\"\"\n if domain != \"py\" or not info[\"module\"]:\n return None\n filename = info[\"module\"].replace(\".\", \"/\")\n module = importlib.import_module(info[\"module\"])\n basename = os.path.splitext(module.__file__)[0]\n if basename.endswith(\"__init__\"):\n filename += \"/__init__\"\n item = module\n line_number_reference = \"\"\n for piece in info[\"fullname\"].split(\".\"):\n item = getattr(item, piece)\n try:\n line_number_reference = f\"#L{inspect.getsourcelines(item)[1]}\"\n except (TypeError, IOError):\n pass\n return f\"https://github.com/Integreat/cms-django/blob/develop/src/{filename}.py{line_number_reference}\"\n", "path": "sphinx/conf.py" } ]
[ { "content": "\"\"\"\nConfiguration file for the Sphinx documentation builder.\n\nThis file only contains a selection of the most common options. For a full\nlist see the documentation:\nhttps://www.sphinx-doc.org/en/master/usage/configuration.html\n\"\"\"\n\n# -- Path setup --------------------------------------------------------------\n\nimport os\nimport sys\nimport inspect\nimport importlib\nimport django\n\nfrom backend.settings import VERSION\n\n# Append project source directory to path environment variable\nsys.path.append(os.path.abspath(\"../src/\"))\nos.environ[\"DJANGO_SETTINGS_MODULE\"] = \"backend.settings\"\n\n\n# Setup Django\ndjango.setup()\n\n\ndef setup(app):\n \"\"\"\n Registeration and setup.\n\n This method does the initial setup for the docs generation.\n \"\"\"\n # Register the docstring processor with sphinx to improve the appearance of Django models\n app.connect(\"autodoc-process-docstring\", process_django_models)\n\n\n# -- Project information -----------------------------------------------------\n\n\nproject = \"integreat-cms\"\n# pylint: disable=redefined-builtin\ncopyright = \"2020, Integreat\"\nauthor = \"Integreat\"\n\n# The full version, including alpha/beta/rc tags\nrelease = VERSION\n\n# -- General configuration ---------------------------------------------------\n\n# All enabled sphinx extensions\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.githubpages\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.linkcode\",\n \"sphinxcontrib_django\",\n \"sphinx_rtd_theme\",\n \"sphinx_last_updated_by_git\",\n]\n\n# Enable cross-references to other documentations\nintersphinx_mapping = {\n \"python\": (\"https://docs.python.org/3.7\", None),\n \"pipenv\": (\"https://pipenv.pypa.io/en/latest/\", None),\n \"sphinx\": (\"https://www.sphinx-doc.org/en/master/\", None),\n \"sphinx-rtd-tutorial\": (\n \"https://sphinx-rtd-tutorial.readthedocs.io/en/latest/\",\n None,\n ),\n \"django\": (\n \"https://docs.djangoproject.com/en/2.2/\",\n \"https://docs.djangoproject.com/en/2.2/_objects/\",\n ),\n \"django-mptt\": (\"https://django-mptt.readthedocs.io/en/latest/\", None),\n \"wsgi\": (\"https://wsgi.readthedocs.io/en/latest/\", None),\n}\n\n# The path for patched template files\ntemplates_path = [\"templates\"]\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.\nhtml_theme = \"sphinx_rtd_theme\"\n# Do not show the project name, only the logo\nhtml_theme_options = {\n \"logo_only\": False,\n \"collapse_navigation\": False,\n}\n# The logo shown in the menu bar\nhtml_logo = \"../src/cms/static/images/integreat-logo-white.png\"\n# The facivon of the html doc files\nhtml_favicon = \"../src/cms/static/images/favicon.ico\"\n# The url where the docs should be published (via gh-pages)\nhtml_baseurl = \"https://Integreat.github.io/cms-django/\"\n# Do not include links to the documentation source (.rst files) in build\nhtml_show_sourcelink = False\n# Do not include a link to sphinx\nhtml_show_sphinx = False\n# Include last updated timestamp\nhtml_last_updated_fmt = \"%b %d, %Y\"\n\n# -- Modify default Django model parameter types------------------------------\n\n\n# pylint: disable=unused-argument, too-many-locals, too-many-branches\ndef process_django_models(app, what, name, obj, options, lines):\n \"\"\"Append correct param types from fields to model documentation.\"\"\"\n if inspect.isclass(obj) and issubclass(obj, django.db.models.Model):\n # Intersphinx mapping to django.contrib.postgres documentation does not work, so here the manual link\n postgres_docu = (\n intersphinx_mapping.get(\"django\")[1][0] + \"ref/contrib/postgres/fields/\"\n )\n # include_hidden to get also ManyToManyFields\n for field in obj._meta.get_fields(include_hidden=True):\n field_type = type(field).__name__\n field_module = type(field).__module__\n if field_module == \"django.contrib.postgres.fields.array\":\n # Fix intersphinx mappings for django.contrib.postgres fields\n type_line = (\n f\":type {field.name}: `ArrayField <{postgres_docu}#arrayfield>`_\"\n )\n elif field_module == \"django.contrib.postgres.fields.jsonb\":\n # Fix intersphinx mappings for django.contrib.postgres fields\n type_line = (\n f\":type {field.name}: `JSONField <{postgres_docu}#jsonfield>`_\"\n )\n elif field_module in [\"django.db.models.fields.related\", \"mptt.fields\"]:\n # Fix intersphinx mappings for related fields (ForeignKey, OneToOneField, ManyToManyField, ...)\n # Also includes related MPTT fields (TreeForeignKey, TreeOneToOneField, TreeManyToManyField, ...)\n remote_model = field.remote_field.get_related_field().model\n type_line = f\":type {field.name}: {field_type} to :class:`~{remote_model.__module__}.{remote_model.__name__}`\"\n elif field_module == \"django.db.models.fields.reverse_related\":\n # Fix intersphinx mappings for reverse related fields (ManyToOneRel, OneToOneRel, ManyToManyRel, ...)\n remote_model = field.remote_field.model\n type_line = f\":type {field.name}: Reverse {field_type[:-3]} Relation from :class:`~{remote_model.__module__}.{remote_model.__name__}`\"\n else:\n if \"django.db.models\" in field_module:\n # Scope with django.db.models * imports (remove all sub-module-paths)\n field_module = \"django.db.models\"\n # Fix type hint to enable correct intersphinx mappings to other documentations\n type_line = f\":type {field.name}: ~{field_module}.{field_type}\"\n # This loop gets the indexes which are needed to update the type hints of the model parameters.\n # It makes it possible to split the parameter section into multiple parts, e.g. params inherited from a base\n # model and params of a sub model (otherwise the type hints would not be recognized when separated from\n # the parameter description).\n param_index = None\n next_param_index = None\n type_index = None\n for index, line in enumerate(lines):\n if param_index is None and f\":param {field.name}:\" in line:\n # The index of the field param is only used to determine the next param line\n param_index = index\n elif (\n param_index is not None\n and next_param_index is None\n and (\":param \" in line or line == \"\")\n ):\n # The line of the next param after the field, this is the index where we will insert the type.\n # Sometimes the param descriptions extend over multiple lines, so we cannot just do param_index + 1.\n # If the line is empty, the param description is finished, even if it extends over multiple lines.\n next_param_index = index\n elif type_index is None and f\":type {field.name}:\" in line:\n # The index of the old type hint, we will either move this line or replace it\n type_index = index\n break\n if next_param_index is None:\n # In case the current field is the last param, we just append the type at the very end of lines\n next_param_index = len(lines)\n # For some params, the type line is not automatically generated and thus the type_index might be `None`\n if type_index is not None:\n # We delete the old type index, because we will replace it with the new type line\n del lines[type_index]\n # Insert the new type line just before the next param\n lines.insert(next_param_index, type_line)\n return lines\n\n\n# -- Source Code links to GitHub ---------------------------------------------\n\n\ndef linkcode_resolve(domain, info):\n \"\"\"Link source code to GitHub.\"\"\"\n if domain != \"py\" or not info[\"module\"]:\n return None\n filename = info[\"module\"].replace(\".\", \"/\")\n module = importlib.import_module(info[\"module\"])\n basename = os.path.splitext(module.__file__)[0]\n if basename.endswith(\"__init__\"):\n filename += \"/__init__\"\n item = module\n line_number_reference = \"\"\n for piece in info[\"fullname\"].split(\".\"):\n item = getattr(item, piece)\n try:\n line_number_reference = f\"#L{inspect.getsourcelines(item)[1]}\"\n except (TypeError, IOError):\n pass\n return f\"https://github.com/Integreat/cms-django/blob/develop/src/{filename}.py{line_number_reference}\"\n", "path": "sphinx/conf.py" } ]
diff --git a/Pipfile b/Pipfile index c8c609700d..a9bcd6b4c0 100644 --- a/Pipfile +++ b/Pipfile @@ -16,6 +16,7 @@ pylint-django = "*" pylint-runner = "*" sphinx = "*" sphinxcontrib-django = "*" +sphinx-last-updated-by-git = "*" sphinx-rtd-theme = "*" [packages] diff --git a/Pipfile.lock b/Pipfile.lock index 1ccafe893e..a30d979b26 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -1,7 +1,7 @@ { "_meta": { "hash": { - "sha256": "7f2b410d75f2b5983cc773585a3738d7a058acf8baf2417c7e5e76156c02d8e9" + "sha256": "f4294c69d8adfb109254979367353e64b3511216cf5bb13a51c7ad32143adebd" }, "pipfile-spec": 6, "requires": { @@ -852,6 +852,14 @@ "index": "pypi", "version": "==3.1.2" }, + "sphinx-last-updated-by-git": { + "hashes": [ + "sha256:7017ba80de387cddbdf403201e950b8667e37c5773796874a7750098edd33e70", + "sha256:f103776539bb19fdf16714a653b6ccb5a4e31483fed9b18717e1797859e73cab" + ], + "index": "pypi", + "version": "==0.2.2" + }, "sphinx-rtd-theme": { "hashes": [ "sha256:22c795ba2832a169ca301cd0a083f7a434e09c538c70beb42782c073651b707d", diff --git a/sphinx/conf.py b/sphinx/conf.py index 33ba9269e9..10428bf826 100644 --- a/sphinx/conf.py +++ b/sphinx/conf.py @@ -56,6 +56,7 @@ def setup(app): "sphinx.ext.linkcode", "sphinxcontrib_django", "sphinx_rtd_theme", + "sphinx_last_updated_by_git", ] # Enable cross-references to other documentations
meltano__meltano-6901
ci: PyPi publish job fails in "Build distribution" step with error `module 'sqlalchemy' has no attribute 'orm'` https://github.com/meltano/meltano/actions/runs/3267990463/jobs/5373871668
[ { "content": "\"\"\"add resource type to embed token\n\nRevision ID: 23ea52e6d784\nRevises: ceb00d7ff3bd\nCreate Date: 2020-02-12 09:29:31.592426\n\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\nfrom meltano.migrations.utils.dialect_typing import (\n get_dialect_name,\n max_string_length_for_dialect,\n)\n\n# revision identifiers, used by Alembic.\nrevision = \"23ea52e6d784\"\ndown_revision = \"ceb00d7ff3bd\"\nbranch_labels = None\ndepends_on = None\n\nSession = sa.orm.sessionmaker()\n\n\ndef upgrade():\n dialect_name = get_dialect_name()\n max_string_length = max_string_length_for_dialect(dialect_name)\n\n op.add_column(\n \"embed_tokens\", sa.Column(\"resource_type\", sa.String(max_string_length))\n )\n\n metadata = sa.MetaData(bind=op.get_bind())\n Embed_Tokens = sa.Table(\"embed_tokens\", metadata, autoload=True)\n op.execute(Embed_Tokens.update().values({\"resource_type\": \"report\"}))\n\n\ndef downgrade():\n op.drop_column(\"embed_tokens\", \"resource_type\")\n", "path": "src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py" } ]
[ { "content": "\"\"\"add resource type to embed token\n\nRevision ID: 23ea52e6d784\nRevises: ceb00d7ff3bd\nCreate Date: 2020-02-12 09:29:31.592426\n\n\"\"\"\nimport sqlalchemy as sa\nimport sqlalchemy.orm\nfrom alembic import op\n\nfrom meltano.migrations.utils.dialect_typing import (\n get_dialect_name,\n max_string_length_for_dialect,\n)\n\n# revision identifiers, used by Alembic.\nrevision = \"23ea52e6d784\"\ndown_revision = \"ceb00d7ff3bd\"\nbranch_labels = None\ndepends_on = None\n\nSession = sa.orm.sessionmaker()\n\n\ndef upgrade():\n dialect_name = get_dialect_name()\n max_string_length = max_string_length_for_dialect(dialect_name)\n\n op.add_column(\n \"embed_tokens\", sa.Column(\"resource_type\", sa.String(max_string_length))\n )\n\n metadata = sa.MetaData(bind=op.get_bind())\n Embed_Tokens = sa.Table(\"embed_tokens\", metadata, autoload=True)\n op.execute(Embed_Tokens.update().values({\"resource_type\": \"report\"}))\n\n\ndef downgrade():\n op.drop_column(\"embed_tokens\", \"resource_type\")\n", "path": "src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py" } ]
diff --git a/src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py b/src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py index 70e6d097cf..fc4060a65e 100644 --- a/src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py +++ b/src/meltano/migrations/versions/23ea52e6d784_add_resource_type_to_embed_token.py @@ -6,6 +6,7 @@ """ import sqlalchemy as sa +import sqlalchemy.orm from alembic import op from meltano.migrations.utils.dialect_typing import (
liqd__a4-opin-388
timeline wrong way? the phases in the timeline seem to be sorted in the wrong direction: ![bildschirmfoto 2016-10-21 um 13 33 06](https://cloud.githubusercontent.com/assets/2905743/19597159/1eee7114-9793-11e6-97f9-5acd3d610f46.png) ![bildschirmfoto 2016-10-21 um 13 33 18](https://cloud.githubusercontent.com/assets/2905743/19597161/22f8492e-9793-11e6-97a6-8b7e4e2b4388.png)
[ { "content": "from django.core.exceptions import ValidationError\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext as _\n\nfrom euth.modules import models as modules_models\n\nfrom . import content\nfrom .validators import validate_content\n\n\nclass PhasesQuerySet(models.QuerySet):\n\n def active_phases(self):\n now = timezone.now()\n return self.filter(start_date__lte=now, end_date__gt=now)\n\n\nclass Phase(models.Model):\n name = models.CharField(max_length=80)\n description = models.TextField(max_length=300)\n type = models.CharField(max_length=128, validators=[validate_content])\n module = models.ForeignKey(modules_models.Module, on_delete=models.CASCADE)\n start_date = models.DateTimeField(blank=True, null=True)\n end_date = models.DateTimeField(blank=True, null=True)\n\n objects = PhasesQuerySet.as_manager()\n\n def __str__(self):\n return '{} ({})'.format(self.name, self.type)\n\n def content(self):\n return content[self.type]\n\n def clean(self):\n if self.end_date and self.start_date:\n if self.end_date < self.start_date:\n raise ValidationError({\n 'end_date': _('End date can not be smaller'\n 'than the start date.')\n })\n super().clean()\n\n @property\n def view(self):\n return content[self.type].view\n\n def has_feature(self, feature, model):\n return content[self.type].has_feature(feature, model)\n", "path": "euth/phases/models.py" } ]
[ { "content": "from django.core.exceptions import ValidationError\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext as _\n\nfrom euth.modules import models as modules_models\n\nfrom . import content\nfrom .validators import validate_content\n\n\nclass PhasesQuerySet(models.QuerySet):\n\n def active_phases(self):\n now = timezone.now()\n return self.filter(start_date__lte=now, end_date__gt=now)\n\n\nclass Phase(models.Model):\n name = models.CharField(max_length=80)\n description = models.TextField(max_length=300)\n type = models.CharField(max_length=128, validators=[validate_content])\n module = models.ForeignKey(modules_models.Module, on_delete=models.CASCADE)\n start_date = models.DateTimeField(blank=True, null=True)\n end_date = models.DateTimeField(blank=True, null=True)\n\n objects = PhasesQuerySet.as_manager()\n\n class Meta:\n ordering = ['type']\n\n def __str__(self):\n return '{} ({})'.format(self.name, self.type)\n\n def content(self):\n return content[self.type]\n\n def clean(self):\n if self.end_date and self.start_date:\n if self.end_date < self.start_date:\n raise ValidationError({\n 'end_date': _('End date can not be smaller'\n 'than the start date.')\n })\n super().clean()\n\n @property\n def view(self):\n return content[self.type].view\n\n def has_feature(self, feature, model):\n return content[self.type].has_feature(feature, model)\n", "path": "euth/phases/models.py" } ]
diff --git a/euth/phases/models.py b/euth/phases/models.py index 0a2e799e7..bee6de8b5 100644 --- a/euth/phases/models.py +++ b/euth/phases/models.py @@ -26,6 +26,9 @@ class Phase(models.Model): objects = PhasesQuerySet.as_manager() + class Meta: + ordering = ['type'] + def __str__(self): return '{} ({})'.format(self.name, self.type) diff --git a/tests/projects/test_project_models.py b/tests/projects/test_project_models.py index 1e2e46fa6..6d6538977 100644 --- a/tests/projects/test_project_models.py +++ b/tests/projects/test_project_models.py @@ -93,3 +93,12 @@ def test_image_deleted_after_update(project_factory, ImagePNG): assert not os.path.isfile(image_path) assert not os.path.isfile(thumbnail_path) + + [email protected]_db +def test_phases_property(module, phase_factory): + project = module.project + phase1 = phase_factory(module=module, type='fake:30:type') + phase2 = phase_factory(module=module, type='fake:20:type') + + assert list(project.phases) == [phase2, phase1]
vacanza__python-holidays-1699
Update branch names Rename: - `master` -> `main` - `beta` -> `dev`
[ { "content": "#!/usr/bin/env python3\n\n# python-holidays\n# ---------------\n# A fast, efficient Python library for generating country, province and state\n# specific sets of holidays on the fly. It aims to make determining whether a\n# specific date is a holiday as fast and flexible as possible.\n#\n# Authors: dr-prodigy <[email protected]> (c) 2017-2023\n# ryanss <[email protected]> (c) 2014-2017\n# Website: https://github.com/dr-prodigy/python-holidays\n# License: MIT (see LICENSE file)\n\nimport argparse\nimport re\nimport sys\nfrom datetime import date\nfrom pathlib import Path\nfrom typing import Dict, Set\n\nfrom git import Repo\nfrom github import Github\nfrom github.GithubException import UnknownObjectException\n\nsys.path.append(f\"{Path.cwd()}\")\nimport holidays # noqa: E402\n\nBRANCH_NAME = \"beta\"\nHEADER_TEMPLATE = \"\"\"\nVersion {version}\n============\n\nReleased {month} {day}, {year}\n\"\"\"\nIGNORED_CONTRIBUTORS = {\"dependabot[bot]\", \"github-actions[bot]\"}\nREPOSITORY_NAME = \"vacanza/python-holidays\"\n\n\nclass ReleaseNotesGenerator:\n \"\"\"\n Generates release notes based on local git commits and GitHub PRs metadata.\n\n Usage example: scripts/generate_release_notes.py\n \"\"\"\n\n def __init__(self) -> None:\n arg_parser = argparse.ArgumentParser()\n arg_parser.add_argument(\n \"-a\",\n \"--author-only\",\n action=\"extend\",\n default=[],\n help=\"Add only author as a contributor for this PR\",\n nargs=\"+\",\n type=int,\n )\n arg_parser.add_argument(\n \"-c\",\n \"--cut-off-at\",\n help=\"Cut off at PR\",\n required=False,\n type=int,\n )\n arg_parser.add_argument(\n \"-e\",\n \"--exclude\",\n action=\"extend\",\n default=[],\n help=\"Exclude this PR from the release notes\",\n nargs=\"+\",\n type=int,\n )\n arg_parser.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"store_true\",\n default=False,\n help=\"Verbose output\",\n )\n self.args = arg_parser.parse_args()\n\n self.local_repo = Repo(Path.cwd())\n self.remote_repo = Github(self.github_token).get_repo(REPOSITORY_NAME)\n\n self.previous_commits: Set[str] = set()\n self.pull_requests: Dict[int, str] = {}\n\n self.tag = holidays.__version__\n\n try:\n latest_tag = self.remote_repo.get_tags()[0]\n self.latest_tag_name = latest_tag.name\n self.previous_commits.add(latest_tag.commit.sha)\n except IndexError:\n self.latest_tag_name = None\n\n @property\n def github_token(self, path=Path(\".github_token\")):\n \"\"\"Return GitHub access token.\"\"\"\n return path.read_text(encoding=\"UTF-8\").strip()\n\n @property\n def is_ready(self):\n \"\"\"Perform environment checks and input validation.\"\"\"\n current_branch = str(self.local_repo.active_branch)\n if current_branch != BRANCH_NAME:\n exit(\n f\"Switch to '{BRANCH_NAME}' first (currently in \"\n f\"'{current_branch}'). Use 'git switch {BRANCH_NAME}'.\"\n )\n\n return True\n\n @property\n def sorted_pull_requests(self):\n def custom_order(pr):\n pr = re.findall(r\"^(.*) \\(#\\d+ .*\\)$\", pr)[0]\n\n if re.findall(r\"^(Introduce|Refactor)\", pr) or re.findall(r\"Add .* support\", pr):\n weight = 10\n elif re.findall(r\"^Add .* holidays$\", pr):\n weight = 20\n elif re.findall(r\"(^Localize|localization$)\", pr):\n weight = 30\n elif re.findall(r\"^Fix\", pr):\n weight = 40\n elif re.findall(r\"^(Change|Improve|Optimize|Update|Upgrade)\", pr):\n weight = 50\n else:\n weight = 100\n\n return (weight, pr)\n\n return sorted(self.pull_requests.values(), key=custom_order)\n\n def add_pull_request(self, pull_request):\n \"\"\"Add pull request information to the release notes dict.\"\"\"\n author = pull_request.user.login if pull_request.user else None\n if author in IGNORED_CONTRIBUTORS:\n print((f\"Skipping #{pull_request.number} {pull_request.title}\" f\" by {author}\"))\n return None\n\n # Skip failed release attempt PRs, version upgrades.\n pr_title = pull_request.title\n skip_titles = (f\"v.{self.tag}\", \"Bump\", \"Revert\")\n for skip_title in skip_titles:\n if pr_title.startswith(skip_title):\n return None\n\n # Get contributors (expand from commits by default).\n contributors = set()\n if pull_request.number not in self.args.author_only:\n for commit in pull_request.get_commits():\n if commit.author:\n contributors.add(commit.author.login)\n\n if author in contributors:\n contributors.remove(author)\n contributors = (f\"@{c}\" for c in [author] + sorted(contributors, key=str.lower))\n self.pull_requests[pull_request.number] = (\n f\"{pull_request.title} (#{pull_request.number} by \" f\"{', '.join(contributors)})\"\n )\n\n def generate_release_notes(self):\n \"\"\"Generate release notes contents.\"\"\"\n print(\"Processing pull requests...\")\n self.get_new_pull_requests()\n self.get_old_pull_requests()\n print(\"Done!\")\n\n def get_new_pull_requests(self):\n \"\"\"Get PRs created after the latest release.\n\n This operation also populates a set of previous release commits.\n \"\"\"\n cut_off_at = self.args.cut_off_at\n excluded_pr_numbers = set(self.args.exclude)\n for pull_request in self.remote_repo.get_pulls(state=\"closed\"):\n # Stop getting pull requests after previous release tag or specific PR number.\n cut_off = cut_off_at and pull_request.number == cut_off_at\n if cut_off or pull_request.title == self.latest_tag_name:\n # Get previous release commits SHAs.\n for commit in pull_request.get_commits():\n self.previous_commits.add(commit.sha)\n break\n\n # Skip closed unmerged PRs.\n if not pull_request.merged:\n continue\n\n if pull_request.number in excluded_pr_numbers:\n if self.args.verbose:\n print(f\"Excluding PR #{pull_request.number} as requested\")\n continue\n\n if self.args.verbose:\n messages = [f\"Fetching PR #{pull_request.number}\"]\n if pull_request.number in self.args.author_only:\n messages.append(\"(keeping PR author as a sole contributor)\")\n print(\" \".join(messages))\n\n self.add_pull_request(pull_request)\n\n def get_old_pull_requests(self):\n \"\"\"Get PRs created before the latest release.\"\"\"\n pull_request_numbers = set()\n for commit in self.local_repo.iter_commits():\n if commit.hexsha in self.previous_commits:\n break\n\n try:\n pull_request_number = re.findall(\n r\"#(\\d{3,})\",\n commit.message,\n )[0]\n pull_request_numbers.add(int(pull_request_number))\n except IndexError:\n continue\n\n # Fetch old PRs metadata only. Skip all known PRs.\n pull_request_numbers -= set(self.pull_requests.keys())\n pull_request_numbers -= set(self.args.exclude)\n for pull_request_number in pull_request_numbers:\n if self.args.verbose:\n messages = [f\"Fetching PR #{pull_request_number}\"]\n if pull_request_number in self.args.author_only:\n messages.append(\"(keeping PR author as a sole contributor)\")\n print(\" \".join(messages))\n\n try:\n self.add_pull_request(self.remote_repo.get_pull(pull_request_number))\n # 3rd party contributions to forks.\n except UnknownObjectException:\n pass\n\n def print_release_notes(self):\n \"\"\"Print generated release notes.\"\"\"\n print(\"\")\n if self.pull_requests:\n today = date.today()\n print(\n HEADER_TEMPLATE.format(\n day=today.day,\n month=today.strftime(\"%B\"),\n version=self.tag,\n year=today.year,\n )\n )\n print(\"\\n\".join((f\"- {pr}\" for pr in self.sorted_pull_requests)))\n\n else:\n print(f\"No changes since {self.latest_tag_name} release.\")\n\n\nif __name__ == \"__main__\":\n rng = ReleaseNotesGenerator()\n if rng.is_ready:\n rng.generate_release_notes()\n rng.print_release_notes()\n", "path": "scripts/generate_release_notes.py" } ]
[ { "content": "#!/usr/bin/env python3\n\n# python-holidays\n# ---------------\n# A fast, efficient Python library for generating country, province and state\n# specific sets of holidays on the fly. It aims to make determining whether a\n# specific date is a holiday as fast and flexible as possible.\n#\n# Authors: dr-prodigy <[email protected]> (c) 2017-2023\n# ryanss <[email protected]> (c) 2014-2017\n# Website: https://github.com/dr-prodigy/python-holidays\n# License: MIT (see LICENSE file)\n\nimport argparse\nimport re\nimport sys\nfrom datetime import date\nfrom pathlib import Path\nfrom typing import Dict, Set\n\nfrom git import Repo\nfrom github import Github\nfrom github.GithubException import UnknownObjectException\n\nsys.path.append(f\"{Path.cwd()}\")\nimport holidays # noqa: E402\n\nBRANCH_NAME = \"dev\"\nHEADER_TEMPLATE = \"\"\"\nVersion {version}\n============\n\nReleased {month} {day}, {year}\n\"\"\"\nIGNORED_CONTRIBUTORS = {\"dependabot[bot]\", \"github-actions[bot]\"}\nREPOSITORY_NAME = \"vacanza/python-holidays\"\n\n\nclass ReleaseNotesGenerator:\n \"\"\"\n Generates release notes based on local git commits and GitHub PRs metadata.\n\n Usage example: scripts/generate_release_notes.py\n \"\"\"\n\n def __init__(self) -> None:\n arg_parser = argparse.ArgumentParser()\n arg_parser.add_argument(\n \"-a\",\n \"--author-only\",\n action=\"extend\",\n default=[],\n help=\"Add only author as a contributor for this PR\",\n nargs=\"+\",\n type=int,\n )\n arg_parser.add_argument(\n \"-c\",\n \"--cut-off-at\",\n help=\"Cut off at PR\",\n required=False,\n type=int,\n )\n arg_parser.add_argument(\n \"-e\",\n \"--exclude\",\n action=\"extend\",\n default=[],\n help=\"Exclude this PR from the release notes\",\n nargs=\"+\",\n type=int,\n )\n arg_parser.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"store_true\",\n default=False,\n help=\"Verbose output\",\n )\n self.args = arg_parser.parse_args()\n\n self.local_repo = Repo(Path.cwd())\n self.remote_repo = Github(self.github_token).get_repo(REPOSITORY_NAME)\n\n self.previous_commits: Set[str] = set()\n self.pull_requests: Dict[int, str] = {}\n\n self.tag = holidays.__version__\n\n try:\n latest_tag = self.remote_repo.get_tags()[0]\n self.latest_tag_name = latest_tag.name\n self.previous_commits.add(latest_tag.commit.sha)\n except IndexError:\n self.latest_tag_name = None\n\n @property\n def github_token(self, path=Path(\".github_token\")):\n \"\"\"Return GitHub access token.\"\"\"\n return path.read_text(encoding=\"UTF-8\").strip()\n\n @property\n def is_ready(self):\n \"\"\"Perform environment checks and input validation.\"\"\"\n current_branch = str(self.local_repo.active_branch)\n if current_branch != BRANCH_NAME:\n exit(\n f\"Switch to '{BRANCH_NAME}' first (currently in \"\n f\"'{current_branch}'). Use 'git switch {BRANCH_NAME}'.\"\n )\n\n return True\n\n @property\n def sorted_pull_requests(self):\n def custom_order(pr):\n pr = re.findall(r\"^(.*) \\(#\\d+ .*\\)$\", pr)[0]\n\n if re.findall(r\"^(Introduce|Refactor)\", pr) or re.findall(r\"Add .* support\", pr):\n weight = 10\n elif re.findall(r\"^Add .* holidays$\", pr):\n weight = 20\n elif re.findall(r\"(^Localize|localization$)\", pr):\n weight = 30\n elif re.findall(r\"^Fix\", pr):\n weight = 40\n elif re.findall(r\"^(Change|Improve|Optimize|Update|Upgrade)\", pr):\n weight = 50\n else:\n weight = 100\n\n return (weight, pr)\n\n return sorted(self.pull_requests.values(), key=custom_order)\n\n def add_pull_request(self, pull_request):\n \"\"\"Add pull request information to the release notes dict.\"\"\"\n author = pull_request.user.login if pull_request.user else None\n if author in IGNORED_CONTRIBUTORS:\n print((f\"Skipping #{pull_request.number} {pull_request.title}\" f\" by {author}\"))\n return None\n\n # Skip failed release attempt PRs, version upgrades.\n pr_title = pull_request.title\n skip_titles = (f\"v.{self.tag}\", \"Bump\", \"Revert\")\n for skip_title in skip_titles:\n if pr_title.startswith(skip_title):\n return None\n\n # Get contributors (expand from commits by default).\n contributors = set()\n if pull_request.number not in self.args.author_only:\n for commit in pull_request.get_commits():\n if commit.author:\n contributors.add(commit.author.login)\n\n if author in contributors:\n contributors.remove(author)\n contributors = (f\"@{c}\" for c in [author] + sorted(contributors, key=str.lower))\n self.pull_requests[pull_request.number] = (\n f\"{pull_request.title} (#{pull_request.number} by \" f\"{', '.join(contributors)})\"\n )\n\n def generate_release_notes(self):\n \"\"\"Generate release notes contents.\"\"\"\n print(\"Processing pull requests...\")\n self.get_new_pull_requests()\n self.get_old_pull_requests()\n print(\"Done!\")\n\n def get_new_pull_requests(self):\n \"\"\"Get PRs created after the latest release.\n\n This operation also populates a set of previous release commits.\n \"\"\"\n cut_off_at = self.args.cut_off_at\n excluded_pr_numbers = set(self.args.exclude)\n for pull_request in self.remote_repo.get_pulls(state=\"closed\"):\n # Stop getting pull requests after previous release tag or specific PR number.\n cut_off = cut_off_at and pull_request.number == cut_off_at\n if cut_off or pull_request.title == self.latest_tag_name:\n # Get previous release commits SHAs.\n for commit in pull_request.get_commits():\n self.previous_commits.add(commit.sha)\n break\n\n # Skip closed unmerged PRs.\n if not pull_request.merged:\n continue\n\n if pull_request.number in excluded_pr_numbers:\n if self.args.verbose:\n print(f\"Excluding PR #{pull_request.number} as requested\")\n continue\n\n if self.args.verbose:\n messages = [f\"Fetching PR #{pull_request.number}\"]\n if pull_request.number in self.args.author_only:\n messages.append(\"(keeping PR author as a sole contributor)\")\n print(\" \".join(messages))\n\n self.add_pull_request(pull_request)\n\n def get_old_pull_requests(self):\n \"\"\"Get PRs created before the latest release.\"\"\"\n pull_request_numbers = set()\n for commit in self.local_repo.iter_commits():\n if commit.hexsha in self.previous_commits:\n break\n\n try:\n pull_request_number = re.findall(\n r\"#(\\d{3,})\",\n commit.message,\n )[0]\n pull_request_numbers.add(int(pull_request_number))\n except IndexError:\n continue\n\n # Fetch old PRs metadata only. Skip all known PRs.\n pull_request_numbers -= set(self.pull_requests.keys())\n pull_request_numbers -= set(self.args.exclude)\n for pull_request_number in pull_request_numbers:\n if self.args.verbose:\n messages = [f\"Fetching PR #{pull_request_number}\"]\n if pull_request_number in self.args.author_only:\n messages.append(\"(keeping PR author as a sole contributor)\")\n print(\" \".join(messages))\n\n try:\n self.add_pull_request(self.remote_repo.get_pull(pull_request_number))\n # 3rd party contributions to forks.\n except UnknownObjectException:\n pass\n\n def print_release_notes(self):\n \"\"\"Print generated release notes.\"\"\"\n print(\"\")\n if self.pull_requests:\n today = date.today()\n print(\n HEADER_TEMPLATE.format(\n day=today.day,\n month=today.strftime(\"%B\"),\n version=self.tag,\n year=today.year,\n )\n )\n print(\"\\n\".join((f\"- {pr}\" for pr in self.sorted_pull_requests)))\n\n else:\n print(f\"No changes since {self.latest_tag_name} release.\")\n\n\nif __name__ == \"__main__\":\n rng = ReleaseNotesGenerator()\n if rng.is_ready:\n rng.generate_release_notes()\n rng.print_release_notes()\n", "path": "scripts/generate_release_notes.py" } ]
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index d81d30823..159b16f62 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -41,5 +41,5 @@ Your PR description goes here. Thanks again for your contribution! --> -[contributing-guidelines]: https://github.com/vacanza/python-holidays/blob/beta/CONTRIBUTING.rst -[docs]: https://github.com/vacanza/python-holidays/tree/beta/docs/source +[contributing-guidelines]: https://github.com/vacanza/python-holidays/blob/dev/CONTRIBUTING.rst +[docs]: https://github.com/vacanza/python-holidays/tree/dev/docs/source diff --git a/.github/dependabot.yml b/.github/dependabot.yml index b26f34be8..204845091 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -5,11 +5,11 @@ updates: schedule: interval: weekly day: wednesday - target-branch: beta + target-branch: dev - package-ecosystem: github-actions directory: / schedule: interval: weekly day: wednesday - target-branch: beta + target-branch: dev diff --git a/.github/workflows/pre-commit-autoupdate.yml b/.github/workflows/pre-commit-autoupdate.yml index e347fcb4f..ab1cd089d 100644 --- a/.github/workflows/pre-commit-autoupdate.yml +++ b/.github/workflows/pre-commit-autoupdate.yml @@ -24,7 +24,7 @@ jobs: - uses: peter-evans/[email protected] with: - base: beta + base: dev body: Update pre-commit hooks to their latest versions. branch: update-pre-commit-hooks commit-message: 'chore: Update pre-commit hooks' diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index ec10a35d5..2935704e6 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -3,7 +3,7 @@ Contributing ============ .. _prs: https://github.com/vacanza/python-holidays/pulls -.. _`beta branch`: https://github.com/vacanza/python-holidays/tree/beta +.. _`dev branch`: https://github.com/vacanza/python-holidays/tree/dev .. |contributors| image:: https://img.shields.io/github/contributors/vacanza/python-holidays :target: https://github.com/vacanza/python-holidays/graphs/contributors :alt: contributors @@ -15,7 +15,7 @@ Basics ------ When contributing with fixes and new features, please start forking/branching -from the `beta branch`_ to work on the latest code and reduce merging issues. +from the `dev branch`_ to work on the latest code and reduce merging issues. If you add/change holiday official dates or names you must include references to all sources (government sites, archived web pages, wiki pages, etc) you've used while working on this PR. Contributed PRs_ are required to include valid test diff --git a/README.rst b/README.rst index e87184a4b..28b5a7c09 100644 --- a/README.rst +++ b/README.rst @@ -42,7 +42,7 @@ flexible as possible. :target: https://github.com/psf/black :alt: Code style - .. image:: https://img.shields.io/coverallsCoverage/github/vacanza/python-holidays?branch=master&color=%2341B5BE&style=flat + .. image:: https://img.shields.io/coverallsCoverage/github/vacanza/python-holidays?branch=main&color=%2341B5BE&style=flat :target: https://coveralls.io/r/vacanza/python-holidays :alt: Code coverage @@ -60,8 +60,8 @@ flexible as possible. :target: https://github.com/vacanza/python-holidays/graphs/contributors :alt: GitHub contributors - .. image:: https://img.shields.io/github/last-commit/vacanza/python-holidays/beta?color=%2341BE4A&style=flat - :target: https://github.com/vacanza/python-holidays/commits/beta + .. image:: https://img.shields.io/github/last-commit/vacanza/python-holidays/dev?color=%2341BE4A&style=flat + :target: https://github.com/vacanza/python-holidays/commits/dev :alt: GitHub last commit @@ -74,14 +74,14 @@ The latest stable version can always be installed or updated via pip: $ pip install --upgrade holidays -The latest development (beta) version can be installed directly from GitHub: +The latest development (dev) version can be installed directly from GitHub: .. code-block:: shell - $ pip install --upgrade https://github.com/vacanza/python-holidays/tarball/beta + $ pip install --upgrade https://github.com/vacanza/python-holidays/tarball/dev -All new features are always first pushed to beta branch, then released on -master branch upon official version upgrades. +All new features are always first pushed to dev branch, then released on +main branch upon official version upgrades. Documentation ------------- diff --git a/RELEASE.rst b/RELEASE.rst index f327210d2..c710266eb 100644 --- a/RELEASE.rst +++ b/RELEASE.rst @@ -3,24 +3,24 @@ How to release a new version of Python Holidays - Finalize the current development version - - switch to ``beta`` branch and pull the most recent changes - from https://github.com/vacanza/python-holidays remote ``beta`` branch. + - switch to ``dev`` branch and pull the most recent changes + from https://github.com/vacanza/python-holidays remote ``dev`` branch. - generate release notes by running the following script ``scripts/generate_release_notes.py`` - insert the script's output into the top of ``CHANGES`` file (see previous release notes for consistent formatting) - - commit the updated ``CHANGES`` file to ``beta`` branch with the following + - commit the updated ``CHANGES`` file to ``dev`` branch with the following commit message 'Finalize v<version>', e.g. 'Finalize v0.39' - - push changes to https://github.com/vacanza/python-holidays ``beta`` branch + - push changes to https://github.com/vacanza/python-holidays ``dev`` branch - make sure the push related CI/CD job(s) have been completed successfully -- Merge the finalized changes into ``master`` branch: +- Merge the finalized changes into ``main`` branch: - - create a new PR for the recent changes from ``beta`` to ``master`` branch + - create a new PR for the recent changes from ``dev`` to ``main`` branch using 'v<version>' as a PR title and the previously generated release notes as a PR description - get the PR reviewed by at least one of the code owners - - merge the PR into ``master`` branch with 'Create a merge commit' action + - merge the PR into ``main`` branch with 'Create a merge commit' action (**do not use 'Squash and merge'**) - make sure the PR related CI/CD job(s) have been completed successfully - make sure readthedocs.org documentation build jobs at @@ -33,7 +33,7 @@ How to release a new version of Python Holidays on the 'Draft a new release' button - click on 'Choose a tag', enter 'v<version>' into the input field (you should see something like 'Create a new tag: v0.39' on publish' - - select **master** - instead of default ``beta`` in 'Target' dropdown + - select **main** - instead of default ``dev`` in 'Target' dropdown - put 'v<version>' into 'Release title' field, e.g. 'v0.39' - click on 'Generate release notes' button to collect new contributors and full changelog link information (we normally keep it at the bottom with @@ -57,12 +57,12 @@ How to release a new version of Python Holidays - send "Python Holidays 'v<version>' has been released!" (or similar) message to Vacanza Team Slack #release channel - - pull the recent changes from ``master`` branch into ``beta`` + - pull the recent changes from ``main`` branch into ``dev`` - bump the Python Holidays version at ``holidays/__init__.py`` file - create a commit with 'Initialize v<version>' message, e.g. - 'Initialize v0.40' and push it to ``beta`` branch (this may require + 'Initialize v0.40' and push it to ``dev`` branch (this may require running ``make package`` to pass the tests locally) - - make sure ``beta`` branch **is not behind** the master branch (there + - make sure ``dev`` branch **is not behind** the ``main`` branch (there will be a message on top of the - https://github.com/vacanza/python-holidays/tree/beta page in case it is) + https://github.com/vacanza/python-holidays/tree/dev page in case it is) - make sure the push related CI/CD job(s) have been completed successfully diff --git a/scripts/generate_release_notes.py b/scripts/generate_release_notes.py index 8e21b6e1c..cfcf08139 100755 --- a/scripts/generate_release_notes.py +++ b/scripts/generate_release_notes.py @@ -25,7 +25,7 @@ sys.path.append(f"{Path.cwd()}") import holidays # noqa: E402 -BRANCH_NAME = "beta" +BRANCH_NAME = "dev" HEADER_TEMPLATE = """ Version {version} ============
pytorch__examples-182
Division error Training a model for `fast-neural-style` raises a RuntimeError from variable division during input normalization. - python2.7 - torch==0.1.12.post2 - torchvision==0.1.8 ```` Traceback (most recent call last): File "neural_style/neural_style.py", line 226, in <module> main() File "neural_style/neural_style.py", line 220, in main train(args) File "neural_style/neural_style.py", line 65, in train style_v = utils.normalize_batch(style_v) File "/home/paperspace/embro/neural_style/utils.py", line 42, in normalize_batch batch /= Variable(std) File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 793, in __idiv__ return self.div_(other) File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 323, in div_ raise RuntimeError("div_ only supports scalar multiplication") ````
[ { "content": "import torch\nfrom PIL import Image\nfrom torch.autograd import Variable\n\n\ndef load_image(filename, size=None, scale=None):\n img = Image.open(filename)\n if size is not None:\n img = img.resize((size, size), Image.ANTIALIAS)\n elif scale is not None:\n img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)\n return img\n\n\ndef save_image(filename, data):\n img = data.clone().clamp(0, 255).numpy()\n img = img.transpose(1, 2, 0).astype(\"uint8\")\n img = Image.fromarray(img)\n img.save(filename)\n\n\ndef gram_matrix(y):\n (b, ch, h, w) = y.size()\n features = y.view(b, ch, w * h)\n features_t = features.transpose(1, 2)\n gram = features.bmm(features_t) / (ch * h * w)\n return gram\n\n\ndef normalize_batch(batch):\n # normalize using imagenet mean and std\n mean = batch.data.new(batch.data.size())\n std = batch.data.new(batch.data.size())\n mean[:, 0, :, :] = 0.485\n mean[:, 1, :, :] = 0.456\n mean[:, 2, :, :] = 0.406\n std[:, 0, :, :] = 0.229\n std[:, 1, :, :] = 0.224\n std[:, 2, :, :] = 0.225\n batch = torch.div(batch, 255.0)\n batch -= Variable(mean)\n batch /= Variable(std)\n return batch\n", "path": "fast_neural_style/neural_style/utils.py" } ]
[ { "content": "import torch\nfrom PIL import Image\nfrom torch.autograd import Variable\n\n\ndef load_image(filename, size=None, scale=None):\n img = Image.open(filename)\n if size is not None:\n img = img.resize((size, size), Image.ANTIALIAS)\n elif scale is not None:\n img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)\n return img\n\n\ndef save_image(filename, data):\n img = data.clone().clamp(0, 255).numpy()\n img = img.transpose(1, 2, 0).astype(\"uint8\")\n img = Image.fromarray(img)\n img.save(filename)\n\n\ndef gram_matrix(y):\n (b, ch, h, w) = y.size()\n features = y.view(b, ch, w * h)\n features_t = features.transpose(1, 2)\n gram = features.bmm(features_t) / (ch * h * w)\n return gram\n\n\ndef normalize_batch(batch):\n # normalize using imagenet mean and std\n mean = batch.data.new(batch.data.size())\n std = batch.data.new(batch.data.size())\n mean[:, 0, :, :] = 0.485\n mean[:, 1, :, :] = 0.456\n mean[:, 2, :, :] = 0.406\n std[:, 0, :, :] = 0.229\n std[:, 1, :, :] = 0.224\n std[:, 2, :, :] = 0.225\n batch = torch.div(batch, 255.0)\n batch -= Variable(mean)\n batch = batch / Variable(std)\n return batch\n", "path": "fast_neural_style/neural_style/utils.py" } ]
diff --git a/fast_neural_style/neural_style/utils.py b/fast_neural_style/neural_style/utils.py index d86b243440..525c25148c 100644 --- a/fast_neural_style/neural_style/utils.py +++ b/fast_neural_style/neural_style/utils.py @@ -39,5 +39,5 @@ def normalize_batch(batch): std[:, 2, :, :] = 0.225 batch = torch.div(batch, 255.0) batch -= Variable(mean) - batch /= Variable(std) + batch = batch / Variable(std) return batch
freqtrade__freqtrade-4302
Invalid JSON returned by rest_client.py ## Describe your environment * Operating system: Linux (Docker) * Python Version: 3.8.6 * CCXT version: 1.40.99 * Freqtrade Version: 2021.1 ## Describe the problem: The JSON output from `rest_client.py` is not a valid JSON. The reason of the problem is that the output uses single quotes instead of double quotes, and also some strings should be surrounded by quotes (At least `True, False and None`, and possibly some other strings). ### Steps to reproduce: 1. Start the bot 2. Make sure that there are some open trades 3. Run the `rest_client.py status` command to show the open trades, and pass the result to JQ to verify the validity of the JSON ouput ### Observed Results: * What happened? : The JSON output is not a valid JSON * What did you expect to happen? The JSON output to be a valid JSON. ### Relevant code exceptions or logs Example of the error : ``` root@3fda54e2f926:/freqtrade# python3 scripts/rest_client.py --config config_binance.json status | jq . parse error: Invalid numeric literal at line 1, column 13 ``` To fix the JSON output, we need to : - convert the single quotes `'` to double quotes `"` - add double quotes around the words `True`, `False` and `None`, as JSON only allow numbers to not be quoted. Example of the fix : ``` root@3fda54e2f926:/freqtrade# python3 scripts/rest_client.py --config config_binance.json status | tr "'" "\"" | sed -e "s|True|\"True\"|g" | sed -e "s|False|\"False\"|g" | sed -e "s|None|\"None\"|g" | jq -c . [{"trade_id":2,"pair":"XRP/USDT","is_open":"True","exchange":"binance","amount":205.5,"amount_requested":205.5,"stake_amount":99,"strategy":"ichis","timeframe":60,"fee_open":0.001,"fee_open_cost":0.09897907500000001,"fee_open_currency":"USDT","fee_close":0.001,"fee_close_cost":"None","fee_close_currency":"None","open_date_hum":"7 minutes ago","open_date":"2021-02-01 01:44:14","open_timestamp":1612143854504,"open_rate":0.48165,"open_rate_requested":0.48165,"open_trade_value":99.07805408,"close_date_hum":"None","close_date":"None","close_timestamp":"None","close_rate":"None","close_rate_requested":"None","close_profit":"None","close_profit_pct":"None","close_profit_abs":"None","profit_ratio":-0.00935377,"profit_pct":-0.94,"profit_abs":-0.92675363,"sell_reason":"None","sell_order_status":"None","stop_loss_abs":0.385864,"stop_loss_ratio":-0.2,"stop_loss_pct":-20,"stoploss_order_id":"None","stoploss_last_update":"2021-02-01 01:44:51","stoploss_last_update_timestamp":1612143891511,"initial_stop_loss_abs":0.38532000000000005,"initial_stop_loss_ratio":-0.2,"initial_stop_loss_pct":-20,"min_rate":0.47578,"max_rate":0.48233,"open_order_id":"None","stoploss_current_dist":-0.09223600000000004,"stoploss_current_dist_pct":-19.29,"stoploss_current_dist_ratio":-0.19292198,"stoploss_entry_dist":-19.86229713,"stoploss_entry_dist_ratio":-0.20047121,"base_currency":"USDT","current_profit":-0.00935377,"current_profit_abs":-0.92675363,"current_profit_pct":-0.94,"current_rate":0.4781,"open_order":"None"}] ``` Another example with the command `stats` : ``` root@3fda54e2f926:/freqtrade# python3 scripts/rest_client.py --config config_binance.json stats | jq . parse error: Invalid numeric literal at line 1, column 16 ``` With the fix : ``` root@3fda54e2f926:/freqtrade# python3 scripts/rest_client.py --config config_binance.json stats | tr "'" "\"" | jq -c . {"sell_reasons":{"trailing_stop_loss":{"wins":1,"losses":0,"draws":0}},"durations":{"wins":"1307.344079","draws":"N/A","losses":"N/A"}} ```
[ { "content": "#!/usr/bin/env python3\n\"\"\"\nSimple command line client into RPC commands\nCan be used as an alternate to Telegram\n\nShould not import anything from freqtrade,\nso it can be used as a standalone script.\n\"\"\"\n\nimport argparse\nimport inspect\nimport json\nimport logging\nimport re\nimport sys\nfrom pathlib import Path\nfrom urllib.parse import urlencode, urlparse, urlunparse\n\nimport rapidjson\nimport requests\nfrom requests.exceptions import ConnectionError\n\n\nlogging.basicConfig(\n level=logging.INFO,\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n)\nlogger = logging.getLogger(\"ft_rest_client\")\n\n\nclass FtRestClient():\n\n def __init__(self, serverurl, username=None, password=None):\n\n self._serverurl = serverurl\n self._session = requests.Session()\n self._session.auth = (username, password)\n\n def _call(self, method, apipath, params: dict = None, data=None, files=None):\n\n if str(method).upper() not in ('GET', 'POST', 'PUT', 'DELETE'):\n raise ValueError('invalid method <{0}>'.format(method))\n basepath = f\"{self._serverurl}/api/v1/{apipath}\"\n\n hd = {\"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\"\n }\n\n # Split url\n schema, netloc, path, par, query, fragment = urlparse(basepath)\n # URLEncode query string\n query = urlencode(params) if params else \"\"\n # recombine url\n url = urlunparse((schema, netloc, path, par, query, fragment))\n\n try:\n resp = self._session.request(method, url, headers=hd, data=json.dumps(data))\n # return resp.text\n return resp.json()\n except ConnectionError:\n logger.warning(\"Connection error\")\n\n def _get(self, apipath, params: dict = None):\n return self._call(\"GET\", apipath, params=params)\n\n def _delete(self, apipath, params: dict = None):\n return self._call(\"DELETE\", apipath, params=params)\n\n def _post(self, apipath, params: dict = None, data: dict = None):\n return self._call(\"POST\", apipath, params=params, data=data)\n\n def start(self):\n \"\"\"Start the bot if it's in the stopped state.\n\n :return: json object\n \"\"\"\n return self._post(\"start\")\n\n def stop(self):\n \"\"\"Stop the bot. Use `start` to restart.\n\n :return: json object\n \"\"\"\n return self._post(\"stop\")\n\n def stopbuy(self):\n \"\"\"Stop buying (but handle sells gracefully). Use `reload_config` to reset.\n\n :return: json object\n \"\"\"\n return self._post(\"stopbuy\")\n\n def reload_config(self):\n \"\"\"Reload configuration.\n\n :return: json object\n \"\"\"\n return self._post(\"reload_config\")\n\n def balance(self):\n \"\"\"Get the account balance.\n\n :return: json object\n \"\"\"\n return self._get(\"balance\")\n\n def count(self):\n \"\"\"Return the amount of open trades.\n\n :return: json object\n \"\"\"\n return self._get(\"count\")\n\n def locks(self):\n \"\"\"Return current locks\n\n :return: json object\n \"\"\"\n return self._get(\"locks\")\n\n def daily(self, days=None):\n \"\"\"Return the amount of open trades.\n\n :return: json object\n \"\"\"\n return self._get(\"daily\", params={\"timescale\": days} if days else None)\n\n def edge(self):\n \"\"\"Return information about edge.\n\n :return: json object\n \"\"\"\n return self._get(\"edge\")\n\n def profit(self):\n \"\"\"Return the profit summary.\n\n :return: json object\n \"\"\"\n return self._get(\"profit\")\n\n def stats(self):\n \"\"\"Return the stats report (durations, sell-reasons).\n\n :return: json object\n \"\"\"\n return self._get(\"stats\")\n\n def performance(self):\n \"\"\"Return the performance of the different coins.\n\n :return: json object\n \"\"\"\n return self._get(\"performance\")\n\n def status(self):\n \"\"\"Get the status of open trades.\n\n :return: json object\n \"\"\"\n return self._get(\"status\")\n\n def version(self):\n \"\"\"Return the version of the bot.\n\n :return: json object containing the version\n \"\"\"\n return self._get(\"version\")\n\n def show_config(self):\n \"\"\"\n Returns part of the configuration, relevant for trading operations.\n :return: json object containing the version\n \"\"\"\n return self._get(\"show_config\")\n\n def logs(self, limit=None):\n \"\"\"Show latest logs.\n\n :param limit: Limits log messages to the last <limit> logs. No limit to get all the trades.\n :return: json object\n \"\"\"\n return self._get(\"logs\", params={\"limit\": limit} if limit else 0)\n\n def trades(self, limit=None):\n \"\"\"Return trades history.\n\n :param limit: Limits trades to the X last trades. No limit to get all the trades.\n :return: json object\n \"\"\"\n return self._get(\"trades\", params={\"limit\": limit} if limit else 0)\n\n def delete_trade(self, trade_id):\n \"\"\"Delete trade from the database.\n Tries to close open orders. Requires manual handling of this asset on the exchange.\n\n :param trade_id: Deletes the trade with this ID from the database.\n :return: json object\n \"\"\"\n return self._delete(\"trades/{}\".format(trade_id))\n\n def whitelist(self):\n \"\"\"Show the current whitelist.\n\n :return: json object\n \"\"\"\n return self._get(\"whitelist\")\n\n def blacklist(self, *args):\n \"\"\"Show the current blacklist.\n\n :param add: List of coins to add (example: \"BNB/BTC\")\n :return: json object\n \"\"\"\n if not args:\n return self._get(\"blacklist\")\n else:\n return self._post(\"blacklist\", data={\"blacklist\": args})\n\n def forcebuy(self, pair, price=None):\n \"\"\"Buy an asset.\n\n :param pair: Pair to buy (ETH/BTC)\n :param price: Optional - price to buy\n :return: json object of the trade\n \"\"\"\n data = {\"pair\": pair,\n \"price\": price\n }\n return self._post(\"forcebuy\", data=data)\n\n def forcesell(self, tradeid):\n \"\"\"Force-sell a trade.\n\n :param tradeid: Id of the trade (can be received via status command)\n :return: json object\n \"\"\"\n\n return self._post(\"forcesell\", data={\"tradeid\": tradeid})\n\n def strategies(self):\n \"\"\"Lists available strategies\n\n :return: json object\n \"\"\"\n return self._get(\"strategies\")\n\n def strategy(self, strategy):\n \"\"\"Get strategy details\n\n :param strategy: Strategy class name\n :return: json object\n \"\"\"\n return self._get(f\"strategy/{strategy}\")\n\n def plot_config(self):\n \"\"\"Return plot configuration if the strategy defines one.\n\n :return: json object\n \"\"\"\n return self._get(\"plot_config\")\n\n def available_pairs(self, timeframe=None, stake_currency=None):\n \"\"\"Return available pair (backtest data) based on timeframe / stake_currency selection\n\n :param timeframe: Only pairs with this timeframe available.\n :param stake_currency: Only pairs that include this timeframe\n :return: json object\n \"\"\"\n return self._get(\"available_pairs\", params={\n \"stake_currency\": stake_currency if timeframe else '',\n \"timeframe\": timeframe if timeframe else '',\n })\n\n def pair_candles(self, pair, timeframe, limit=None):\n \"\"\"Return live dataframe for <pair><timeframe>.\n\n :param pair: Pair to get data for\n :param timeframe: Only pairs with this timeframe available.\n :param limit: Limit result to the last n candles.\n :return: json object\n \"\"\"\n return self._get(\"available_pairs\", params={\n \"pair\": pair,\n \"timeframe\": timeframe,\n \"limit\": limit,\n })\n\n def pair_history(self, pair, timeframe, strategy, timerange=None):\n \"\"\"Return historic, analyzed dataframe\n\n :param pair: Pair to get data for\n :param timeframe: Only pairs with this timeframe available.\n :param strategy: Strategy to analyze and get values for\n :param timerange: Timerange to get data for (same format than --timerange endpoints)\n :return: json object\n \"\"\"\n return self._get(\"pair_history\", params={\n \"pair\": pair,\n \"timeframe\": timeframe,\n \"strategy\": strategy,\n \"timerange\": timerange if timerange else '',\n })\n\n\ndef add_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"command\",\n help=\"Positional argument defining the command to execute.\",\n nargs=\"?\"\n )\n\n parser.add_argument('--show',\n help='Show possible methods with this client',\n dest='show',\n action='store_true',\n default=False\n )\n\n parser.add_argument('-c', '--config',\n help='Specify configuration file (default: %(default)s). ',\n dest='config',\n type=str,\n metavar='PATH',\n default='config.json'\n )\n\n parser.add_argument(\"command_arguments\",\n help=\"Positional arguments for the parameters for [command]\",\n nargs=\"*\",\n default=[]\n )\n\n args = parser.parse_args()\n return vars(args)\n\n\ndef load_config(configfile):\n file = Path(configfile)\n if file.is_file():\n with file.open(\"r\") as f:\n config = rapidjson.load(f, parse_mode=rapidjson.PM_COMMENTS |\n rapidjson.PM_TRAILING_COMMAS)\n return config\n else:\n logger.warning(f\"Could not load config file {file}.\")\n sys.exit(1)\n\n\ndef print_commands():\n # Print dynamic help for the different commands using the commands doc-strings\n client = FtRestClient(None)\n print(\"Possible commands:\\n\")\n for x, y in inspect.getmembers(client):\n if not x.startswith('_'):\n doc = re.sub(':return:.*', '', getattr(client, x).__doc__, flags=re.MULTILINE).rstrip()\n print(f\"{x}\\n\\t{doc}\\n\")\n\n\ndef main(args):\n\n if args.get(\"show\"):\n print_commands()\n sys.exit()\n\n config = load_config(args['config'])\n url = config.get('api_server', {}).get('server_url', '127.0.0.1')\n port = config.get('api_server', {}).get('listen_port', '8080')\n username = config.get('api_server', {}).get('username')\n password = config.get('api_server', {}).get('password')\n\n server_url = f\"http://{url}:{port}\"\n client = FtRestClient(server_url, username, password)\n\n m = [x for x, y in inspect.getmembers(client) if not x.startswith('_')]\n command = args[\"command\"]\n if command not in m:\n logger.error(f\"Command {command} not defined\")\n print_commands()\n return\n\n print(getattr(client, command)(*args[\"command_arguments\"]))\n\n\nif __name__ == \"__main__\":\n args = add_arguments()\n main(args)\n", "path": "scripts/rest_client.py" } ]
[ { "content": "#!/usr/bin/env python3\n\"\"\"\nSimple command line client into RPC commands\nCan be used as an alternate to Telegram\n\nShould not import anything from freqtrade,\nso it can be used as a standalone script.\n\"\"\"\n\nimport argparse\nimport inspect\nimport json\nimport logging\nimport re\nimport sys\nfrom pathlib import Path\nfrom urllib.parse import urlencode, urlparse, urlunparse\n\nimport rapidjson\nimport requests\nfrom requests.exceptions import ConnectionError\n\n\nlogging.basicConfig(\n level=logging.INFO,\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n)\nlogger = logging.getLogger(\"ft_rest_client\")\n\n\nclass FtRestClient():\n\n def __init__(self, serverurl, username=None, password=None):\n\n self._serverurl = serverurl\n self._session = requests.Session()\n self._session.auth = (username, password)\n\n def _call(self, method, apipath, params: dict = None, data=None, files=None):\n\n if str(method).upper() not in ('GET', 'POST', 'PUT', 'DELETE'):\n raise ValueError('invalid method <{0}>'.format(method))\n basepath = f\"{self._serverurl}/api/v1/{apipath}\"\n\n hd = {\"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\"\n }\n\n # Split url\n schema, netloc, path, par, query, fragment = urlparse(basepath)\n # URLEncode query string\n query = urlencode(params) if params else \"\"\n # recombine url\n url = urlunparse((schema, netloc, path, par, query, fragment))\n\n try:\n resp = self._session.request(method, url, headers=hd, data=json.dumps(data))\n # return resp.text\n return resp.json()\n except ConnectionError:\n logger.warning(\"Connection error\")\n\n def _get(self, apipath, params: dict = None):\n return self._call(\"GET\", apipath, params=params)\n\n def _delete(self, apipath, params: dict = None):\n return self._call(\"DELETE\", apipath, params=params)\n\n def _post(self, apipath, params: dict = None, data: dict = None):\n return self._call(\"POST\", apipath, params=params, data=data)\n\n def start(self):\n \"\"\"Start the bot if it's in the stopped state.\n\n :return: json object\n \"\"\"\n return self._post(\"start\")\n\n def stop(self):\n \"\"\"Stop the bot. Use `start` to restart.\n\n :return: json object\n \"\"\"\n return self._post(\"stop\")\n\n def stopbuy(self):\n \"\"\"Stop buying (but handle sells gracefully). Use `reload_config` to reset.\n\n :return: json object\n \"\"\"\n return self._post(\"stopbuy\")\n\n def reload_config(self):\n \"\"\"Reload configuration.\n\n :return: json object\n \"\"\"\n return self._post(\"reload_config\")\n\n def balance(self):\n \"\"\"Get the account balance.\n\n :return: json object\n \"\"\"\n return self._get(\"balance\")\n\n def count(self):\n \"\"\"Return the amount of open trades.\n\n :return: json object\n \"\"\"\n return self._get(\"count\")\n\n def locks(self):\n \"\"\"Return current locks\n\n :return: json object\n \"\"\"\n return self._get(\"locks\")\n\n def daily(self, days=None):\n \"\"\"Return the amount of open trades.\n\n :return: json object\n \"\"\"\n return self._get(\"daily\", params={\"timescale\": days} if days else None)\n\n def edge(self):\n \"\"\"Return information about edge.\n\n :return: json object\n \"\"\"\n return self._get(\"edge\")\n\n def profit(self):\n \"\"\"Return the profit summary.\n\n :return: json object\n \"\"\"\n return self._get(\"profit\")\n\n def stats(self):\n \"\"\"Return the stats report (durations, sell-reasons).\n\n :return: json object\n \"\"\"\n return self._get(\"stats\")\n\n def performance(self):\n \"\"\"Return the performance of the different coins.\n\n :return: json object\n \"\"\"\n return self._get(\"performance\")\n\n def status(self):\n \"\"\"Get the status of open trades.\n\n :return: json object\n \"\"\"\n return self._get(\"status\")\n\n def version(self):\n \"\"\"Return the version of the bot.\n\n :return: json object containing the version\n \"\"\"\n return self._get(\"version\")\n\n def show_config(self):\n \"\"\"\n Returns part of the configuration, relevant for trading operations.\n :return: json object containing the version\n \"\"\"\n return self._get(\"show_config\")\n\n def logs(self, limit=None):\n \"\"\"Show latest logs.\n\n :param limit: Limits log messages to the last <limit> logs. No limit to get all the trades.\n :return: json object\n \"\"\"\n return self._get(\"logs\", params={\"limit\": limit} if limit else 0)\n\n def trades(self, limit=None):\n \"\"\"Return trades history.\n\n :param limit: Limits trades to the X last trades. No limit to get all the trades.\n :return: json object\n \"\"\"\n return self._get(\"trades\", params={\"limit\": limit} if limit else 0)\n\n def delete_trade(self, trade_id):\n \"\"\"Delete trade from the database.\n Tries to close open orders. Requires manual handling of this asset on the exchange.\n\n :param trade_id: Deletes the trade with this ID from the database.\n :return: json object\n \"\"\"\n return self._delete(\"trades/{}\".format(trade_id))\n\n def whitelist(self):\n \"\"\"Show the current whitelist.\n\n :return: json object\n \"\"\"\n return self._get(\"whitelist\")\n\n def blacklist(self, *args):\n \"\"\"Show the current blacklist.\n\n :param add: List of coins to add (example: \"BNB/BTC\")\n :return: json object\n \"\"\"\n if not args:\n return self._get(\"blacklist\")\n else:\n return self._post(\"blacklist\", data={\"blacklist\": args})\n\n def forcebuy(self, pair, price=None):\n \"\"\"Buy an asset.\n\n :param pair: Pair to buy (ETH/BTC)\n :param price: Optional - price to buy\n :return: json object of the trade\n \"\"\"\n data = {\"pair\": pair,\n \"price\": price\n }\n return self._post(\"forcebuy\", data=data)\n\n def forcesell(self, tradeid):\n \"\"\"Force-sell a trade.\n\n :param tradeid: Id of the trade (can be received via status command)\n :return: json object\n \"\"\"\n\n return self._post(\"forcesell\", data={\"tradeid\": tradeid})\n\n def strategies(self):\n \"\"\"Lists available strategies\n\n :return: json object\n \"\"\"\n return self._get(\"strategies\")\n\n def strategy(self, strategy):\n \"\"\"Get strategy details\n\n :param strategy: Strategy class name\n :return: json object\n \"\"\"\n return self._get(f\"strategy/{strategy}\")\n\n def plot_config(self):\n \"\"\"Return plot configuration if the strategy defines one.\n\n :return: json object\n \"\"\"\n return self._get(\"plot_config\")\n\n def available_pairs(self, timeframe=None, stake_currency=None):\n \"\"\"Return available pair (backtest data) based on timeframe / stake_currency selection\n\n :param timeframe: Only pairs with this timeframe available.\n :param stake_currency: Only pairs that include this timeframe\n :return: json object\n \"\"\"\n return self._get(\"available_pairs\", params={\n \"stake_currency\": stake_currency if timeframe else '',\n \"timeframe\": timeframe if timeframe else '',\n })\n\n def pair_candles(self, pair, timeframe, limit=None):\n \"\"\"Return live dataframe for <pair><timeframe>.\n\n :param pair: Pair to get data for\n :param timeframe: Only pairs with this timeframe available.\n :param limit: Limit result to the last n candles.\n :return: json object\n \"\"\"\n return self._get(\"available_pairs\", params={\n \"pair\": pair,\n \"timeframe\": timeframe,\n \"limit\": limit,\n })\n\n def pair_history(self, pair, timeframe, strategy, timerange=None):\n \"\"\"Return historic, analyzed dataframe\n\n :param pair: Pair to get data for\n :param timeframe: Only pairs with this timeframe available.\n :param strategy: Strategy to analyze and get values for\n :param timerange: Timerange to get data for (same format than --timerange endpoints)\n :return: json object\n \"\"\"\n return self._get(\"pair_history\", params={\n \"pair\": pair,\n \"timeframe\": timeframe,\n \"strategy\": strategy,\n \"timerange\": timerange if timerange else '',\n })\n\n\ndef add_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"command\",\n help=\"Positional argument defining the command to execute.\",\n nargs=\"?\"\n )\n\n parser.add_argument('--show',\n help='Show possible methods with this client',\n dest='show',\n action='store_true',\n default=False\n )\n\n parser.add_argument('-c', '--config',\n help='Specify configuration file (default: %(default)s). ',\n dest='config',\n type=str,\n metavar='PATH',\n default='config.json'\n )\n\n parser.add_argument(\"command_arguments\",\n help=\"Positional arguments for the parameters for [command]\",\n nargs=\"*\",\n default=[]\n )\n\n args = parser.parse_args()\n return vars(args)\n\n\ndef load_config(configfile):\n file = Path(configfile)\n if file.is_file():\n with file.open(\"r\") as f:\n config = rapidjson.load(f, parse_mode=rapidjson.PM_COMMENTS |\n rapidjson.PM_TRAILING_COMMAS)\n return config\n else:\n logger.warning(f\"Could not load config file {file}.\")\n sys.exit(1)\n\n\ndef print_commands():\n # Print dynamic help for the different commands using the commands doc-strings\n client = FtRestClient(None)\n print(\"Possible commands:\\n\")\n for x, y in inspect.getmembers(client):\n if not x.startswith('_'):\n doc = re.sub(':return:.*', '', getattr(client, x).__doc__, flags=re.MULTILINE).rstrip()\n print(f\"{x}\\n\\t{doc}\\n\")\n\n\ndef main(args):\n\n if args.get(\"show\"):\n print_commands()\n sys.exit()\n\n config = load_config(args['config'])\n url = config.get('api_server', {}).get('server_url', '127.0.0.1')\n port = config.get('api_server', {}).get('listen_port', '8080')\n username = config.get('api_server', {}).get('username')\n password = config.get('api_server', {}).get('password')\n\n server_url = f\"http://{url}:{port}\"\n client = FtRestClient(server_url, username, password)\n\n m = [x for x, y in inspect.getmembers(client) if not x.startswith('_')]\n command = args[\"command\"]\n if command not in m:\n logger.error(f\"Command {command} not defined\")\n print_commands()\n return\n\n print(json.dumps(getattr(client, command)(*args[\"command_arguments\"])))\n\n\nif __name__ == \"__main__\":\n args = add_arguments()\n main(args)\n", "path": "scripts/rest_client.py" } ]
diff --git a/scripts/rest_client.py b/scripts/rest_client.py index 2232b842122..b6e66cfa4a2 100755 --- a/scripts/rest_client.py +++ b/scripts/rest_client.py @@ -379,7 +379,7 @@ def main(args): print_commands() return - print(getattr(client, command)(*args["command_arguments"])) + print(json.dumps(getattr(client, command)(*args["command_arguments"]))) if __name__ == "__main__":
pydantic__pydantic-1204
Mypy does not allow using the root validator with arguments other than pre or _func # Bug Output of `python -c "import pydantic.utils; print(pydantic.utils.version_info())"`: ``` pydantic version: 1.4 pydantic compiled: True install path: /home/[username]/Desktop/[project]/env/lib/python3.6/site-packages/pydantic python version: 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] platform: Linux-5.3.0-26-generic-x86_64-with-Ubuntu-18.04-bionic optional deps. installed: ['typing-extensions'] ``` Code: ```py @root_validator(pre=False, skip_on_failure=True) def test_validator(cls, values): return values ``` Output: ``` test.py:5: error: No overload variant of "root_validator" matches argument types "bool", "bool" test.py:5: note: Possible overload variants: test.py:5: note: def root_validator(_func: Callable[..., Any]) -> classmethod test.py:5: note: def root_validator(*, pre: bool = ...) -> Callable[[Callable[..., Any]], classmethod] ```
[ { "content": "import warnings\nfrom collections import ChainMap\nfrom functools import wraps\nfrom itertools import chain\nfrom types import FunctionType\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, overload\n\nfrom .errors import ConfigError\nfrom .typing import AnyCallable\nfrom .utils import in_ipython\n\n\nclass Validator:\n __slots__ = 'func', 'pre', 'each_item', 'always', 'check_fields', 'skip_on_failure'\n\n def __init__(\n self,\n func: AnyCallable,\n pre: bool = False,\n each_item: bool = False,\n always: bool = False,\n check_fields: bool = False,\n skip_on_failure: bool = False,\n ):\n self.func = func\n self.pre = pre\n self.each_item = each_item\n self.always = always\n self.check_fields = check_fields\n self.skip_on_failure = skip_on_failure\n\n\nif TYPE_CHECKING:\n from inspect import Signature\n\n from .main import BaseConfig\n from .fields import ModelField\n from .types import ModelOrDc\n\n ValidatorCallable = Callable[[Optional[ModelOrDc], Any, Dict[str, Any], ModelField, Type[BaseConfig]], Any]\n ValidatorsList = List[ValidatorCallable]\n ValidatorListDict = Dict[str, List[Validator]]\n\n_FUNCS: Set[str] = set()\nROOT_KEY = '__root__'\nVALIDATOR_CONFIG_KEY = '__validator_config__'\nROOT_VALIDATOR_CONFIG_KEY = '__root_validator_config__'\n\n\ndef validator(\n *fields: str,\n pre: bool = False,\n each_item: bool = False,\n always: bool = False,\n check_fields: bool = True,\n whole: bool = None,\n allow_reuse: bool = False,\n) -> Callable[[AnyCallable], classmethod]:\n \"\"\"\n Decorate methods on the class indicating that they should be used to validate fields\n :param fields: which field(s) the method should be called on\n :param pre: whether or not this validator should be called before the standard validators (else after)\n :param each_item: for complex objects (sets, lists etc.) whether to validate individual elements rather than the\n whole object\n :param always: whether this method and other validators should be called even if the value is missing\n :param check_fields: whether to check that the fields actually exist on the model\n :param allow_reuse: whether to track and raise an error if another validator refers to the decorated function\n \"\"\"\n if not fields:\n raise ConfigError('validator with no fields specified')\n elif isinstance(fields[0], FunctionType):\n raise ConfigError(\n \"validators should be used with fields and keyword arguments, not bare. \" # noqa: Q000\n \"E.g. usage should be `@validator('<field_name>', ...)`\"\n )\n\n if whole is not None:\n warnings.warn(\n 'The \"whole\" keyword argument is deprecated, use \"each_item\" (inverse meaning, default False) instead',\n DeprecationWarning,\n )\n assert each_item is False, '\"each_item\" and \"whole\" conflict, remove \"whole\"'\n each_item = not whole\n\n def dec(f: AnyCallable) -> classmethod:\n f_cls = _prepare_validator(f, allow_reuse)\n setattr(\n f_cls,\n VALIDATOR_CONFIG_KEY,\n (\n fields,\n Validator(func=f_cls.__func__, pre=pre, each_item=each_item, always=always, check_fields=check_fields),\n ),\n )\n return f_cls\n\n return dec\n\n\n@overload\ndef root_validator(_func: AnyCallable) -> classmethod:\n ...\n\n\n@overload\ndef root_validator(*, pre: bool = False) -> Callable[[AnyCallable], classmethod]:\n ...\n\n\ndef root_validator(\n _func: Optional[AnyCallable] = None, *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False\n) -> Union[classmethod, Callable[[AnyCallable], classmethod]]:\n \"\"\"\n Decorate methods on a model indicating that they should be used to validate (and perhaps modify) data either\n before or after standard model parsing/validation is performed.\n \"\"\"\n if _func:\n f_cls = _prepare_validator(_func, allow_reuse)\n setattr(\n f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)\n )\n return f_cls\n\n def dec(f: AnyCallable) -> classmethod:\n f_cls = _prepare_validator(f, allow_reuse)\n setattr(\n f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)\n )\n return f_cls\n\n return dec\n\n\ndef _prepare_validator(function: AnyCallable, allow_reuse: bool) -> classmethod:\n \"\"\"\n Avoid validators with duplicated names since without this, validators can be overwritten silently\n which generally isn't the intended behaviour, don't run in ipython (see #312) or if allow_reuse is False.\n \"\"\"\n f_cls = function if isinstance(function, classmethod) else classmethod(function)\n if not in_ipython() and not allow_reuse:\n ref = f_cls.__func__.__module__ + '.' + f_cls.__func__.__qualname__\n if ref in _FUNCS:\n raise ConfigError(f'duplicate validator function \"{ref}\"; if this is intended, set `allow_reuse=True`')\n _FUNCS.add(ref)\n return f_cls\n\n\nclass ValidatorGroup:\n def __init__(self, validators: 'ValidatorListDict') -> None:\n self.validators = validators\n self.used_validators = {'*'}\n\n def get_validators(self, name: str) -> Optional[Dict[str, Validator]]:\n self.used_validators.add(name)\n validators = self.validators.get(name, [])\n if name != ROOT_KEY:\n validators += self.validators.get('*', [])\n if validators:\n return {v.func.__name__: v for v in validators}\n else:\n return None\n\n def check_for_unused(self) -> None:\n unused_validators = set(\n chain(\n *[\n (v.func.__name__ for v in self.validators[f] if v.check_fields)\n for f in (self.validators.keys() - self.used_validators)\n ]\n )\n )\n if unused_validators:\n fn = ', '.join(unused_validators)\n raise ConfigError(\n f\"Validators defined with incorrect fields: {fn} \" # noqa: Q000\n f\"(use check_fields=False if you're inheriting from the model and intended this)\"\n )\n\n\ndef extract_validators(namespace: Dict[str, Any]) -> Dict[str, List[Validator]]:\n validators: Dict[str, List[Validator]] = {}\n for var_name, value in namespace.items():\n validator_config = getattr(value, VALIDATOR_CONFIG_KEY, None)\n if validator_config:\n fields, v = validator_config\n for field in fields:\n if field in validators:\n validators[field].append(v)\n else:\n validators[field] = [v]\n return validators\n\n\ndef extract_root_validators(namespace: Dict[str, Any]) -> Tuple[List[AnyCallable], List[Tuple[bool, AnyCallable]]]:\n from inspect import signature\n\n pre_validators: List[AnyCallable] = []\n post_validators: List[Tuple[bool, AnyCallable]] = []\n for name, value in namespace.items():\n validator_config: Optional[Validator] = getattr(value, ROOT_VALIDATOR_CONFIG_KEY, None)\n if validator_config:\n sig = signature(validator_config.func)\n args = list(sig.parameters.keys())\n if args[0] == 'self':\n raise ConfigError(\n f'Invalid signature for root validator {name}: {sig}, \"self\" not permitted as first argument, '\n f'should be: (cls, values).'\n )\n if len(args) != 2:\n raise ConfigError(f'Invalid signature for root validator {name}: {sig}, should be: (cls, values).')\n # check function signature\n if validator_config.pre:\n pre_validators.append(validator_config.func)\n else:\n post_validators.append((validator_config.skip_on_failure, validator_config.func))\n return pre_validators, post_validators\n\n\ndef inherit_validators(base_validators: 'ValidatorListDict', validators: 'ValidatorListDict') -> 'ValidatorListDict':\n for field, field_validators in base_validators.items():\n if field not in validators:\n validators[field] = []\n validators[field] += field_validators\n return validators\n\n\ndef make_generic_validator(validator: AnyCallable) -> 'ValidatorCallable':\n \"\"\"\n Make a generic function which calls a validator with the right arguments.\n\n Unfortunately other approaches (eg. return a partial of a function that builds the arguments) is slow,\n hence this laborious way of doing things.\n\n It's done like this so validators don't all need **kwargs in their signature, eg. any combination of\n the arguments \"values\", \"fields\" and/or \"config\" are permitted.\n \"\"\"\n from inspect import signature\n\n sig = signature(validator)\n args = list(sig.parameters.keys())\n first_arg = args.pop(0)\n if first_arg == 'self':\n raise ConfigError(\n f'Invalid signature for validator {validator}: {sig}, \"self\" not permitted as first argument, '\n f'should be: (cls, value, values, config, field), \"values\", \"config\" and \"field\" are all optional.'\n )\n elif first_arg == 'cls':\n # assume the second argument is value\n return wraps(validator)(_generic_validator_cls(validator, sig, set(args[1:])))\n else:\n # assume the first argument was value which has already been removed\n return wraps(validator)(_generic_validator_basic(validator, sig, set(args)))\n\n\ndef prep_validators(v_funcs: Iterable[AnyCallable]) -> 'ValidatorsList':\n return [make_generic_validator(f) for f in v_funcs if f]\n\n\nall_kwargs = {'values', 'field', 'config'}\n\n\ndef _generic_validator_cls(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable':\n # assume the first argument is value\n has_kwargs = False\n if 'kwargs' in args:\n has_kwargs = True\n args -= {'kwargs'}\n\n if not args.issubset(all_kwargs):\n raise ConfigError(\n f'Invalid signature for validator {validator}: {sig}, should be: '\n f'(cls, value, values, config, field), \"values\", \"config\" and \"field\" are all optional.'\n )\n\n if has_kwargs:\n return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)\n elif args == set():\n return lambda cls, v, values, field, config: validator(cls, v)\n elif args == {'values'}:\n return lambda cls, v, values, field, config: validator(cls, v, values=values)\n elif args == {'field'}:\n return lambda cls, v, values, field, config: validator(cls, v, field=field)\n elif args == {'config'}:\n return lambda cls, v, values, field, config: validator(cls, v, config=config)\n elif args == {'values', 'field'}:\n return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field)\n elif args == {'values', 'config'}:\n return lambda cls, v, values, field, config: validator(cls, v, values=values, config=config)\n elif args == {'field', 'config'}:\n return lambda cls, v, values, field, config: validator(cls, v, field=field, config=config)\n else:\n # args == {'values', 'field', 'config'}\n return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)\n\n\ndef _generic_validator_basic(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable':\n has_kwargs = False\n if 'kwargs' in args:\n has_kwargs = True\n args -= {'kwargs'}\n\n if not args.issubset(all_kwargs):\n raise ConfigError(\n f'Invalid signature for validator {validator}: {sig}, should be: '\n f'(value, values, config, field), \"values\", \"config\" and \"field\" are all optional.'\n )\n\n if has_kwargs:\n return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)\n elif args == set():\n return lambda cls, v, values, field, config: validator(v)\n elif args == {'values'}:\n return lambda cls, v, values, field, config: validator(v, values=values)\n elif args == {'field'}:\n return lambda cls, v, values, field, config: validator(v, field=field)\n elif args == {'config'}:\n return lambda cls, v, values, field, config: validator(v, config=config)\n elif args == {'values', 'field'}:\n return lambda cls, v, values, field, config: validator(v, values=values, field=field)\n elif args == {'values', 'config'}:\n return lambda cls, v, values, field, config: validator(v, values=values, config=config)\n elif args == {'field', 'config'}:\n return lambda cls, v, values, field, config: validator(v, field=field, config=config)\n else:\n # args == {'values', 'field', 'config'}\n return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)\n\n\ndef gather_all_validators(type_: 'ModelOrDc') -> Dict[str, classmethod]:\n all_attributes = ChainMap(*[cls.__dict__ for cls in type_.__mro__])\n return {\n k: v\n for k, v in all_attributes.items()\n if hasattr(v, VALIDATOR_CONFIG_KEY) or hasattr(v, ROOT_VALIDATOR_CONFIG_KEY)\n }\n", "path": "pydantic/class_validators.py" } ]
[ { "content": "import warnings\nfrom collections import ChainMap\nfrom functools import wraps\nfrom itertools import chain\nfrom types import FunctionType\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, overload\n\nfrom .errors import ConfigError\nfrom .typing import AnyCallable\nfrom .utils import in_ipython\n\n\nclass Validator:\n __slots__ = 'func', 'pre', 'each_item', 'always', 'check_fields', 'skip_on_failure'\n\n def __init__(\n self,\n func: AnyCallable,\n pre: bool = False,\n each_item: bool = False,\n always: bool = False,\n check_fields: bool = False,\n skip_on_failure: bool = False,\n ):\n self.func = func\n self.pre = pre\n self.each_item = each_item\n self.always = always\n self.check_fields = check_fields\n self.skip_on_failure = skip_on_failure\n\n\nif TYPE_CHECKING:\n from inspect import Signature\n\n from .main import BaseConfig\n from .fields import ModelField\n from .types import ModelOrDc\n\n ValidatorCallable = Callable[[Optional[ModelOrDc], Any, Dict[str, Any], ModelField, Type[BaseConfig]], Any]\n ValidatorsList = List[ValidatorCallable]\n ValidatorListDict = Dict[str, List[Validator]]\n\n_FUNCS: Set[str] = set()\nROOT_KEY = '__root__'\nVALIDATOR_CONFIG_KEY = '__validator_config__'\nROOT_VALIDATOR_CONFIG_KEY = '__root_validator_config__'\n\n\ndef validator(\n *fields: str,\n pre: bool = False,\n each_item: bool = False,\n always: bool = False,\n check_fields: bool = True,\n whole: bool = None,\n allow_reuse: bool = False,\n) -> Callable[[AnyCallable], classmethod]:\n \"\"\"\n Decorate methods on the class indicating that they should be used to validate fields\n :param fields: which field(s) the method should be called on\n :param pre: whether or not this validator should be called before the standard validators (else after)\n :param each_item: for complex objects (sets, lists etc.) whether to validate individual elements rather than the\n whole object\n :param always: whether this method and other validators should be called even if the value is missing\n :param check_fields: whether to check that the fields actually exist on the model\n :param allow_reuse: whether to track and raise an error if another validator refers to the decorated function\n \"\"\"\n if not fields:\n raise ConfigError('validator with no fields specified')\n elif isinstance(fields[0], FunctionType):\n raise ConfigError(\n \"validators should be used with fields and keyword arguments, not bare. \" # noqa: Q000\n \"E.g. usage should be `@validator('<field_name>', ...)`\"\n )\n\n if whole is not None:\n warnings.warn(\n 'The \"whole\" keyword argument is deprecated, use \"each_item\" (inverse meaning, default False) instead',\n DeprecationWarning,\n )\n assert each_item is False, '\"each_item\" and \"whole\" conflict, remove \"whole\"'\n each_item = not whole\n\n def dec(f: AnyCallable) -> classmethod:\n f_cls = _prepare_validator(f, allow_reuse)\n setattr(\n f_cls,\n VALIDATOR_CONFIG_KEY,\n (\n fields,\n Validator(func=f_cls.__func__, pre=pre, each_item=each_item, always=always, check_fields=check_fields),\n ),\n )\n return f_cls\n\n return dec\n\n\n@overload\ndef root_validator(_func: AnyCallable) -> classmethod:\n ...\n\n\n@overload\ndef root_validator(\n *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False\n) -> Callable[[AnyCallable], classmethod]:\n ...\n\n\ndef root_validator(\n _func: Optional[AnyCallable] = None, *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False\n) -> Union[classmethod, Callable[[AnyCallable], classmethod]]:\n \"\"\"\n Decorate methods on a model indicating that they should be used to validate (and perhaps modify) data either\n before or after standard model parsing/validation is performed.\n \"\"\"\n if _func:\n f_cls = _prepare_validator(_func, allow_reuse)\n setattr(\n f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)\n )\n return f_cls\n\n def dec(f: AnyCallable) -> classmethod:\n f_cls = _prepare_validator(f, allow_reuse)\n setattr(\n f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure)\n )\n return f_cls\n\n return dec\n\n\ndef _prepare_validator(function: AnyCallable, allow_reuse: bool) -> classmethod:\n \"\"\"\n Avoid validators with duplicated names since without this, validators can be overwritten silently\n which generally isn't the intended behaviour, don't run in ipython (see #312) or if allow_reuse is False.\n \"\"\"\n f_cls = function if isinstance(function, classmethod) else classmethod(function)\n if not in_ipython() and not allow_reuse:\n ref = f_cls.__func__.__module__ + '.' + f_cls.__func__.__qualname__\n if ref in _FUNCS:\n raise ConfigError(f'duplicate validator function \"{ref}\"; if this is intended, set `allow_reuse=True`')\n _FUNCS.add(ref)\n return f_cls\n\n\nclass ValidatorGroup:\n def __init__(self, validators: 'ValidatorListDict') -> None:\n self.validators = validators\n self.used_validators = {'*'}\n\n def get_validators(self, name: str) -> Optional[Dict[str, Validator]]:\n self.used_validators.add(name)\n validators = self.validators.get(name, [])\n if name != ROOT_KEY:\n validators += self.validators.get('*', [])\n if validators:\n return {v.func.__name__: v for v in validators}\n else:\n return None\n\n def check_for_unused(self) -> None:\n unused_validators = set(\n chain(\n *[\n (v.func.__name__ for v in self.validators[f] if v.check_fields)\n for f in (self.validators.keys() - self.used_validators)\n ]\n )\n )\n if unused_validators:\n fn = ', '.join(unused_validators)\n raise ConfigError(\n f\"Validators defined with incorrect fields: {fn} \" # noqa: Q000\n f\"(use check_fields=False if you're inheriting from the model and intended this)\"\n )\n\n\ndef extract_validators(namespace: Dict[str, Any]) -> Dict[str, List[Validator]]:\n validators: Dict[str, List[Validator]] = {}\n for var_name, value in namespace.items():\n validator_config = getattr(value, VALIDATOR_CONFIG_KEY, None)\n if validator_config:\n fields, v = validator_config\n for field in fields:\n if field in validators:\n validators[field].append(v)\n else:\n validators[field] = [v]\n return validators\n\n\ndef extract_root_validators(namespace: Dict[str, Any]) -> Tuple[List[AnyCallable], List[Tuple[bool, AnyCallable]]]:\n from inspect import signature\n\n pre_validators: List[AnyCallable] = []\n post_validators: List[Tuple[bool, AnyCallable]] = []\n for name, value in namespace.items():\n validator_config: Optional[Validator] = getattr(value, ROOT_VALIDATOR_CONFIG_KEY, None)\n if validator_config:\n sig = signature(validator_config.func)\n args = list(sig.parameters.keys())\n if args[0] == 'self':\n raise ConfigError(\n f'Invalid signature for root validator {name}: {sig}, \"self\" not permitted as first argument, '\n f'should be: (cls, values).'\n )\n if len(args) != 2:\n raise ConfigError(f'Invalid signature for root validator {name}: {sig}, should be: (cls, values).')\n # check function signature\n if validator_config.pre:\n pre_validators.append(validator_config.func)\n else:\n post_validators.append((validator_config.skip_on_failure, validator_config.func))\n return pre_validators, post_validators\n\n\ndef inherit_validators(base_validators: 'ValidatorListDict', validators: 'ValidatorListDict') -> 'ValidatorListDict':\n for field, field_validators in base_validators.items():\n if field not in validators:\n validators[field] = []\n validators[field] += field_validators\n return validators\n\n\ndef make_generic_validator(validator: AnyCallable) -> 'ValidatorCallable':\n \"\"\"\n Make a generic function which calls a validator with the right arguments.\n\n Unfortunately other approaches (eg. return a partial of a function that builds the arguments) is slow,\n hence this laborious way of doing things.\n\n It's done like this so validators don't all need **kwargs in their signature, eg. any combination of\n the arguments \"values\", \"fields\" and/or \"config\" are permitted.\n \"\"\"\n from inspect import signature\n\n sig = signature(validator)\n args = list(sig.parameters.keys())\n first_arg = args.pop(0)\n if first_arg == 'self':\n raise ConfigError(\n f'Invalid signature for validator {validator}: {sig}, \"self\" not permitted as first argument, '\n f'should be: (cls, value, values, config, field), \"values\", \"config\" and \"field\" are all optional.'\n )\n elif first_arg == 'cls':\n # assume the second argument is value\n return wraps(validator)(_generic_validator_cls(validator, sig, set(args[1:])))\n else:\n # assume the first argument was value which has already been removed\n return wraps(validator)(_generic_validator_basic(validator, sig, set(args)))\n\n\ndef prep_validators(v_funcs: Iterable[AnyCallable]) -> 'ValidatorsList':\n return [make_generic_validator(f) for f in v_funcs if f]\n\n\nall_kwargs = {'values', 'field', 'config'}\n\n\ndef _generic_validator_cls(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable':\n # assume the first argument is value\n has_kwargs = False\n if 'kwargs' in args:\n has_kwargs = True\n args -= {'kwargs'}\n\n if not args.issubset(all_kwargs):\n raise ConfigError(\n f'Invalid signature for validator {validator}: {sig}, should be: '\n f'(cls, value, values, config, field), \"values\", \"config\" and \"field\" are all optional.'\n )\n\n if has_kwargs:\n return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)\n elif args == set():\n return lambda cls, v, values, field, config: validator(cls, v)\n elif args == {'values'}:\n return lambda cls, v, values, field, config: validator(cls, v, values=values)\n elif args == {'field'}:\n return lambda cls, v, values, field, config: validator(cls, v, field=field)\n elif args == {'config'}:\n return lambda cls, v, values, field, config: validator(cls, v, config=config)\n elif args == {'values', 'field'}:\n return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field)\n elif args == {'values', 'config'}:\n return lambda cls, v, values, field, config: validator(cls, v, values=values, config=config)\n elif args == {'field', 'config'}:\n return lambda cls, v, values, field, config: validator(cls, v, field=field, config=config)\n else:\n # args == {'values', 'field', 'config'}\n return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config)\n\n\ndef _generic_validator_basic(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable':\n has_kwargs = False\n if 'kwargs' in args:\n has_kwargs = True\n args -= {'kwargs'}\n\n if not args.issubset(all_kwargs):\n raise ConfigError(\n f'Invalid signature for validator {validator}: {sig}, should be: '\n f'(value, values, config, field), \"values\", \"config\" and \"field\" are all optional.'\n )\n\n if has_kwargs:\n return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)\n elif args == set():\n return lambda cls, v, values, field, config: validator(v)\n elif args == {'values'}:\n return lambda cls, v, values, field, config: validator(v, values=values)\n elif args == {'field'}:\n return lambda cls, v, values, field, config: validator(v, field=field)\n elif args == {'config'}:\n return lambda cls, v, values, field, config: validator(v, config=config)\n elif args == {'values', 'field'}:\n return lambda cls, v, values, field, config: validator(v, values=values, field=field)\n elif args == {'values', 'config'}:\n return lambda cls, v, values, field, config: validator(v, values=values, config=config)\n elif args == {'field', 'config'}:\n return lambda cls, v, values, field, config: validator(v, field=field, config=config)\n else:\n # args == {'values', 'field', 'config'}\n return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config)\n\n\ndef gather_all_validators(type_: 'ModelOrDc') -> Dict[str, classmethod]:\n all_attributes = ChainMap(*[cls.__dict__ for cls in type_.__mro__])\n return {\n k: v\n for k, v in all_attributes.items()\n if hasattr(v, VALIDATOR_CONFIG_KEY) or hasattr(v, ROOT_VALIDATOR_CONFIG_KEY)\n }\n", "path": "pydantic/class_validators.py" } ]
diff --git a/changes/1192-samuelcolvin.md b/changes/1192-samuelcolvin.md new file mode 100644 index 00000000000..da7a14ea329 --- /dev/null +++ b/changes/1192-samuelcolvin.md @@ -0,0 +1 @@ +fix mypy signature for `root_validator` diff --git a/pydantic/class_validators.py b/pydantic/class_validators.py index 19f2924ccfa..3d1032e789e 100644 --- a/pydantic/class_validators.py +++ b/pydantic/class_validators.py @@ -103,7 +103,9 @@ def root_validator(_func: AnyCallable) -> classmethod: @overload -def root_validator(*, pre: bool = False) -> Callable[[AnyCallable], classmethod]: +def root_validator( + *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False +) -> Callable[[AnyCallable], classmethod]: ... diff --git a/tests/mypy/modules/success.py b/tests/mypy/modules/success.py index f1aa5eab9fd..b80d77e4723 100644 --- a/tests/mypy/modules/success.py +++ b/tests/mypy/modules/success.py @@ -33,7 +33,7 @@ def check_age(cls, value: int) -> int: def root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]: return values - @root_validator(pre=True) + @root_validator(pre=True, allow_reuse=False, skip_on_failure=False) def pre_root_check(cls, values: Dict[str, Any]) -> Dict[str, Any]: return values
Pycord-Development__pycord-2345
AttributeError: 'Interaction' object has no attribute 'entitlements' ### Summary The newest verision (2.4.1.dev241+g9683629e) crashes when executing slash commands ### Reproduction Steps Start the bot code below and run the `hello` command ### Minimal Reproducible Code ```python import discord bot = discord.Bot() @bot.slash_command() async def hello(ctx): await ctx.respond("Hello!") bot.run("") ``` ### Expected Results Bot responds with `Hello!` ### Actual Results ``` Traceback (most recent call last): File "C:\Users\Timo\Desktop\pythonProject\main.py", line 11, in <module> bot.run("...") File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\client.py", line 766, in run return future.result() ^^^^^^^^^^^^^^^ File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\client.py", line 745, in runner await self.start(*args, **kwargs) File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\client.py", line 709, in start await self.connect(reconnect=reconnect) File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\client.py", line 601, in connect await self.ws.poll_event() File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\gateway.py", line 604, in poll_event await self.received_message(msg.data) File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\gateway.py", line 554, in received_message func(data) File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\state.py", line 816, in parse_interaction_create interaction = Interaction(data=data, state=self) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\interactions.py", line 170, in __init__ self._from_data(data) File "C:\Users\Timo\AppData\Local\Programs\Python\Python312\Lib\site-packages\discord\interactions.py", line 187, in _from_data self.entitlements: list[Entitlement] = [ ^^^^^^^^^^^^^^^^^ AttributeError: 'Interaction' object has no attribute 'entitlements' ``` ### Intents - ### System Information - Python v3.10.9-final - py-cord v2.4.1-final - aiohttp v3.9.1 - system info: Windows 10 10.0.22631 ### Checklist - [X] I have searched the open issues for duplicates. - [X] I have shown the entire traceback, if possible. - [X] I have removed my token from display, if visible. ### Additional Context _No response_
[ { "content": "\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2015-2021 Rapptz\nCopyright (c) 2021-present Pycord Development\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the \"Software\"),\nto deal in the Software without restriction, including without limitation\nthe rights to use, copy, modify, merge, publish, distribute, sublicense,\nand/or sell copies of the Software, and to permit persons to whom the\nSoftware is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\nOR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\nFROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\nDEALINGS IN THE SOFTWARE.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nfrom typing import TYPE_CHECKING, Any, Coroutine, Union\n\nfrom . import utils\nfrom .channel import ChannelType, PartialMessageable, _threaded_channel_factory\nfrom .enums import InteractionResponseType, InteractionType, try_enum\nfrom .errors import ClientException, InteractionResponded, InvalidArgument\nfrom .file import File\nfrom .flags import MessageFlags\nfrom .member import Member\nfrom .message import Attachment, Message\nfrom .monetization import Entitlement\nfrom .object import Object\nfrom .permissions import Permissions\nfrom .user import User\nfrom .webhook.async_ import (\n Webhook,\n WebhookMessage,\n async_context,\n handle_message_parameters,\n)\n\n__all__ = (\n \"Interaction\",\n \"InteractionMessage\",\n \"InteractionResponse\",\n \"MessageInteraction\",\n)\n\nif TYPE_CHECKING:\n from aiohttp import ClientSession\n\n from .channel import (\n CategoryChannel,\n DMChannel,\n ForumChannel,\n GroupChannel,\n StageChannel,\n TextChannel,\n VoiceChannel,\n )\n from .client import Client\n from .commands import OptionChoice\n from .embeds import Embed\n from .guild import Guild\n from .mentions import AllowedMentions\n from .state import ConnectionState\n from .threads import Thread\n from .types.interactions import Interaction as InteractionPayload\n from .types.interactions import InteractionData\n from .types.interactions import MessageInteraction as MessageInteractionPayload\n from .ui.modal import Modal\n from .ui.view import View\n\n InteractionChannel = Union[\n VoiceChannel,\n StageChannel,\n TextChannel,\n ForumChannel,\n CategoryChannel,\n Thread,\n DMChannel,\n GroupChannel,\n PartialMessageable,\n ]\n\nMISSING: Any = utils.MISSING\n\n\nclass Interaction:\n \"\"\"Represents a Discord interaction.\n\n An interaction happens when a user does an action that needs to\n be notified. Current examples are slash commands and components.\n\n .. versionadded:: 2.0\n\n Attributes\n ----------\n id: :class:`int`\n The interaction's ID.\n type: :class:`InteractionType`\n The interaction type.\n guild_id: Optional[:class:`int`]\n The guild ID the interaction was sent from.\n channel: Optional[Union[:class:`abc.GuildChannel`, :class:`abc.PrivateChannel`, :class:`Thread`]]\n The channel the interaction was sent from.\n channel_id: Optional[:class:`int`]\n The ID of the channel the interaction was sent from.\n application_id: :class:`int`\n The application ID that the interaction was for.\n user: Optional[Union[:class:`User`, :class:`Member`]]\n The user or member that sent the interaction. Will be `None` in PING interactions.\n message: Optional[:class:`Message`]\n The message that sent this interaction.\n token: :class:`str`\n The token to continue the interaction. These are valid\n for 15 minutes.\n data: :class:`dict`\n The raw interaction data.\n locale: :class:`str`\n The user's locale.\n guild_locale: :class:`str`\n The guilds preferred locale, if invoked in a guild.\n custom_id: Optional[:class:`str`]\n The custom ID for the interaction.\n \"\"\"\n\n __slots__: tuple[str, ...] = (\n \"id\",\n \"type\",\n \"guild_id\",\n \"channel\",\n \"channel_id\",\n \"data\",\n \"application_id\",\n \"message\",\n \"user\",\n \"locale\",\n \"guild_locale\",\n \"token\",\n \"version\",\n \"custom_id\",\n \"_channel_data\",\n \"_message_data\",\n \"_guild_data\",\n \"_guild\",\n \"_permissions\",\n \"_app_permissions\",\n \"_state\",\n \"_session\",\n \"_original_response\",\n \"_cs_app_permissions\",\n \"_cs_response\",\n \"_cs_followup\",\n \"_cs_channel\",\n )\n\n def __init__(self, *, data: InteractionPayload, state: ConnectionState):\n self._state: ConnectionState = state\n self._session: ClientSession = state.http._HTTPClient__session\n self._original_response: InteractionMessage | None = None\n self._from_data(data)\n\n def _from_data(self, data: InteractionPayload):\n self.id: int = int(data[\"id\"])\n self.type: InteractionType = try_enum(InteractionType, data[\"type\"])\n self.data: InteractionData | None = data.get(\"data\")\n self.token: str = data[\"token\"]\n self.version: int = data[\"version\"]\n self.channel_id: int | None = utils._get_as_snowflake(data, \"channel_id\")\n self.guild_id: int | None = utils._get_as_snowflake(data, \"guild_id\")\n self.application_id: int = int(data[\"application_id\"])\n self.locale: str | None = data.get(\"locale\")\n self.guild_locale: str | None = data.get(\"guild_locale\")\n self.custom_id: str | None = (\n self.data.get(\"custom_id\") if self.data is not None else None\n )\n self._app_permissions: int = int(data.get(\"app_permissions\", 0))\n self.entitlements: list[Entitlement] = [\n Entitlement(data=e, state=self._state) for e in data.get(\"entitlements\", [])\n ]\n\n self.message: Message | None = None\n self.channel = None\n\n self.user: User | Member | None = None\n self._permissions: int = 0\n\n self._guild: Guild | None = None\n self._guild_data = data.get(\"guild\")\n if self.guild is None and self._guild_data:\n self._guild = Guild(data=self._guild_data, state=self)\n\n # TODO: there's a potential data loss here\n if self.guild_id:\n guild = (\n self.guild\n or self._state._get_guild(self.guild_id)\n or Object(id=self.guild_id)\n )\n try:\n member = data[\"member\"] # type: ignore\n except KeyError:\n pass\n else:\n self._permissions = int(member.get(\"permissions\", 0))\n if not isinstance(guild, Object):\n cache_flag = self._state.member_cache_flags.interaction\n self.user = guild._get_and_update_member(\n member, int(member[\"user\"][\"id\"]), cache_flag\n )\n else:\n self.user = Member(state=self._state, data=member, guild=guild)\n else:\n try:\n self.user = User(state=self._state, data=data[\"user\"])\n except KeyError:\n pass\n\n if channel := data.get(\"channel\"):\n if (ch_type := channel.get(\"type\")) is not None:\n factory, ch_type = _threaded_channel_factory(ch_type)\n\n if ch_type in (ChannelType.group, ChannelType.private):\n self.channel = factory(\n me=self.user, data=channel, state=self._state\n )\n elif self.guild:\n self.channel = factory(\n guild=self.guild, state=self._state, data=channel\n )\n else:\n self.channel = self.cached_channel\n\n self._channel_data = channel\n\n if message_data := data.get(\"message\"):\n self.message = Message(\n state=self._state, channel=self.channel, data=message_data\n )\n\n self._message_data = message_data\n\n @property\n def client(self) -> Client:\n \"\"\"Returns the client that sent the interaction.\"\"\"\n return self._state._get_client()\n\n @property\n def guild(self) -> Guild | None:\n \"\"\"The guild the interaction was sent from.\"\"\"\n if self._guild:\n return self._guild\n return self._state and self._state._get_guild(self.guild_id)\n\n def is_command(self) -> bool:\n \"\"\"Indicates whether the interaction is an application command.\"\"\"\n return self.type == InteractionType.application_command\n\n def is_component(self) -> bool:\n \"\"\"Indicates whether the interaction is a message component.\"\"\"\n return self.type == InteractionType.component\n\n @utils.cached_slot_property(\"_cs_channel\")\n def cached_channel(self) -> InteractionChannel | None:\n \"\"\"The channel the\n interaction was sent from.\n\n Note that due to a Discord limitation, DM channels are not resolved since there is\n no data to complete them. These are :class:`PartialMessageable` instead.\n \"\"\"\n guild = self.guild\n channel = guild and guild._resolve_channel(self.channel_id)\n if channel is None:\n if self.channel_id is not None:\n type = (\n ChannelType.text\n if self.guild_id is not None\n else ChannelType.private\n )\n return PartialMessageable(\n state=self._state, id=self.channel_id, type=type\n )\n return None\n return channel\n\n @property\n def permissions(self) -> Permissions:\n \"\"\"The resolved permissions of the member in the channel, including overwrites.\n\n In a non-guild context where this doesn't apply, an empty permissions object is returned.\n \"\"\"\n return Permissions(self._permissions)\n\n @utils.cached_slot_property(\"_cs_app_permissions\")\n def app_permissions(self) -> Permissions:\n \"\"\"The resolved permissions of the application in the channel, including overwrites.\"\"\"\n return Permissions(self._app_permissions)\n\n @utils.cached_slot_property(\"_cs_response\")\n def response(self) -> InteractionResponse:\n \"\"\"Returns an object responsible for handling responding to the interaction.\n\n A response can only be done once. If secondary messages need to be sent, consider using :attr:`followup`\n instead.\n \"\"\"\n return InteractionResponse(self)\n\n @utils.cached_slot_property(\"_cs_followup\")\n def followup(self) -> Webhook:\n \"\"\"Returns the followup webhook for followup interactions.\"\"\"\n payload = {\n \"id\": self.application_id,\n \"type\": 3,\n \"token\": self.token,\n }\n return Webhook.from_state(data=payload, state=self._state)\n\n async def original_response(self) -> InteractionMessage:\n \"\"\"|coro|\n\n Fetches the original interaction response message associated with the interaction.\n\n If the interaction response was :meth:`InteractionResponse.send_message` then this would\n return the message that was sent using that response. Otherwise, this would return\n the message that triggered the interaction.\n\n Repeated calls to this will return a cached value.\n\n Returns\n -------\n InteractionMessage\n The original interaction response message.\n\n Raises\n ------\n HTTPException\n Fetching the original response message failed.\n ClientException\n The channel for the message could not be resolved.\n \"\"\"\n\n if self._original_response is not None:\n return self._original_response\n\n # TODO: fix later to not raise?\n channel = self.channel\n if channel is None:\n raise ClientException(\"Channel for message could not be resolved\")\n\n adapter = async_context.get()\n http = self._state.http\n data = await adapter.get_original_interaction_response(\n application_id=self.application_id,\n token=self.token,\n session=self._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n )\n state = _InteractionMessageState(self, self._state)\n message = InteractionMessage(state=state, channel=channel, data=data) # type: ignore\n self._original_response = message\n return message\n\n @utils.deprecated(\"Interaction.original_response\", \"2.2\")\n async def original_message(self):\n \"\"\"An alias for :meth:`original_response`.\n\n Returns\n -------\n InteractionMessage\n The original interaction response message.\n\n Raises\n ------\n HTTPException\n Fetching the original response message failed.\n ClientException\n The channel for the message could not be resolved.\n \"\"\"\n return await self.original_response()\n\n async def edit_original_response(\n self,\n *,\n content: str | None = MISSING,\n embeds: list[Embed] = MISSING,\n embed: Embed | None = MISSING,\n file: File = MISSING,\n files: list[File] = MISSING,\n attachments: list[Attachment] = MISSING,\n view: View | None = MISSING,\n allowed_mentions: AllowedMentions | None = None,\n delete_after: float | None = None,\n suppress: bool = False,\n ) -> InteractionMessage:\n \"\"\"|coro|\n\n Edits the original interaction response message.\n\n This is a lower level interface to :meth:`InteractionMessage.edit` in case\n you do not want to fetch the message and save an HTTP request.\n\n This method is also the only way to edit the original message if\n the message sent was ephemeral.\n\n Parameters\n ----------\n content: Optional[:class:`str`]\n The content to edit the message with or ``None`` to clear it.\n embeds: List[:class:`Embed`]\n A list of embeds to edit the message with.\n embed: Optional[:class:`Embed`]\n The embed to edit the message with. ``None`` suppresses the embeds.\n This should not be mixed with the ``embeds`` parameter.\n file: :class:`File`\n The file to upload. This cannot be mixed with ``files`` parameter.\n files: List[:class:`File`]\n A list of files to send with the content. This cannot be mixed with the\n ``file`` parameter.\n attachments: List[:class:`Attachment`]\n A list of attachments to keep in the message. If ``[]`` is passed\n then all attachments are removed.\n allowed_mentions: :class:`AllowedMentions`\n Controls the mentions being processed in this message.\n See :meth:`.abc.Messageable.send` for more information.\n view: Optional[:class:`~discord.ui.View`]\n The updated view to update this message with. If ``None`` is passed then\n the view is removed.\n delete_after: Optional[:class:`float`]\n If provided, the number of seconds to wait in the background\n before deleting the message we just edited. If the deletion fails,\n then it is silently ignored.\n suppress: :class:`bool`\n Whether to suppress embeds for the message.\n\n Returns\n -------\n :class:`InteractionMessage`\n The newly edited message.\n\n Raises\n ------\n HTTPException\n Editing the message failed.\n Forbidden\n Edited a message that is not yours.\n TypeError\n You specified both ``embed`` and ``embeds`` or ``file`` and ``files``\n ValueError\n The length of ``embeds`` was invalid.\n \"\"\"\n\n previous_mentions: AllowedMentions | None = self._state.allowed_mentions\n params = handle_message_parameters(\n content=content,\n file=file,\n files=files,\n attachments=attachments,\n embed=embed,\n embeds=embeds,\n view=view,\n allowed_mentions=allowed_mentions,\n previous_allowed_mentions=previous_mentions,\n suppress=suppress,\n )\n adapter = async_context.get()\n http = self._state.http\n data = await adapter.edit_original_interaction_response(\n self.application_id,\n self.token,\n session=self._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n payload=params.payload,\n multipart=params.multipart,\n files=params.files,\n )\n\n # The message channel types should always match\n state = _InteractionMessageState(self, self._state)\n message = InteractionMessage(state=state, channel=self.channel, data=data) # type: ignore\n if view and not view.is_finished():\n view.message = message\n self._state.store_view(view, message.id)\n\n if delete_after is not None:\n await self.delete_original_response(delay=delete_after)\n\n return message\n\n @utils.deprecated(\"Interaction.edit_original_response\", \"2.2\")\n async def edit_original_message(self, **kwargs):\n \"\"\"An alias for :meth:`edit_original_response`.\n\n Returns\n -------\n :class:`InteractionMessage`\n The newly edited message.\n\n Raises\n ------\n HTTPException\n Editing the message failed.\n Forbidden\n Edited a message that is not yours.\n TypeError\n You specified both ``embed`` and ``embeds`` or ``file`` and ``files``\n ValueError\n The length of ``embeds`` was invalid.\n \"\"\"\n return await self.edit_original_response(**kwargs)\n\n async def delete_original_response(self, *, delay: float | None = None) -> None:\n \"\"\"|coro|\n\n Deletes the original interaction response message.\n\n This is a lower level interface to :meth:`InteractionMessage.delete` in case\n you do not want to fetch the message and save an HTTP request.\n\n Parameters\n ----------\n delay: Optional[:class:`float`]\n If provided, the number of seconds to wait before deleting the message.\n The waiting is done in the background and deletion failures are ignored.\n\n Raises\n ------\n HTTPException\n Deleting the message failed.\n Forbidden\n Deleted a message that is not yours.\n \"\"\"\n adapter = async_context.get()\n http = self._state.http\n func = adapter.delete_original_interaction_response(\n self.application_id,\n self.token,\n session=self._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n )\n\n if delay is not None:\n utils.delay_task(delay, func)\n else:\n await func\n\n @utils.deprecated(\"Interaction.delete_original_response\", \"2.2\")\n async def delete_original_message(self, **kwargs):\n \"\"\"An alias for :meth:`delete_original_response`.\n\n Raises\n ------\n HTTPException\n Deleting the message failed.\n Forbidden\n Deleted a message that is not yours.\n \"\"\"\n return await self.delete_original_response(**kwargs)\n\n async def respond(self, *args, **kwargs) -> Interaction | WebhookMessage:\n \"\"\"|coro|\n\n Sends either a response or a message using the followup webhook determined by whether the interaction\n has been responded to or not.\n\n Returns\n -------\n Union[:class:`discord.Interaction`, :class:`discord.WebhookMessage`]:\n The response, its type depending on whether it's an interaction response or a followup.\n \"\"\"\n try:\n if not self.response.is_done():\n return await self.response.send_message(*args, **kwargs)\n else:\n return await self.followup.send(*args, **kwargs)\n except InteractionResponded:\n return await self.followup.send(*args, **kwargs)\n\n async def edit(self, *args, **kwargs) -> InteractionMessage | None:\n \"\"\"|coro|\n\n Either respond to the interaction with an edit_message or edits the existing response, determined by\n whether the interaction has been responded to or not.\n\n Returns\n -------\n Union[:class:`discord.InteractionMessage`, :class:`discord.WebhookMessage`]:\n The response, its type depending on whether it's an interaction response or a followup.\n \"\"\"\n try:\n if not self.response.is_done():\n return await self.response.edit_message(*args, **kwargs)\n else:\n return await self.edit_original_response(*args, **kwargs)\n except InteractionResponded:\n return await self.edit_original_response(*args, **kwargs)\n\n def to_dict(self) -> dict[str, Any]:\n \"\"\"\n Converts this interaction object into a dict.\n\n Returns\n -------\n Dict[:class:`str`, Any]\n A dictionary of :class:`str` interaction keys bound to the respective value.\n \"\"\"\n\n data = {\n \"id\": self.id,\n \"application_id\": self.application_id,\n \"type\": self.type.value,\n \"token\": self.token,\n \"version\": self.version,\n }\n\n if self.data is not None:\n data[\"data\"] = self.data\n if (resolved := self.data.get(\"resolved\")) and self.user is not None:\n if (users := resolved.get(\"users\")) and (\n user := users.get(self.user.id)\n ):\n data[\"user\"] = user\n if (members := resolved.get(\"members\")) and (\n member := members.get(self.user.id)\n ):\n data[\"member\"] = member\n\n if self.guild_id is not None:\n data[\"guild_id\"] = self.guild_id\n\n if self.channel_id is not None:\n data[\"channel_id\"] = self.channel_id\n\n if self.locale:\n data[\"locale\"] = self.locale\n\n if self.guild_locale:\n data[\"guild_locale\"] = self.guild_locale\n\n if self._message_data:\n data[\"message\"] = self._message_data\n\n return data\n\n\nclass InteractionResponse:\n \"\"\"Represents a Discord interaction response.\n\n This type can be accessed through :attr:`Interaction.response`.\n\n .. versionadded:: 2.0\n \"\"\"\n\n __slots__: tuple[str, ...] = (\n \"_responded\",\n \"_parent\",\n \"_response_lock\",\n )\n\n def __init__(self, parent: Interaction):\n self._parent: Interaction = parent\n self._responded: bool = False\n self._response_lock = asyncio.Lock()\n\n def is_done(self) -> bool:\n \"\"\"Indicates whether an interaction response has been done before.\n\n An interaction can only be responded to once.\n \"\"\"\n return self._responded\n\n async def defer(self, *, ephemeral: bool = False, invisible: bool = True) -> None:\n \"\"\"|coro|\n\n Defers the interaction response.\n\n This is typically used when the interaction is acknowledged\n and a secondary action will be done later.\n\n This can only be used with the following interaction types:\n\n - :attr:`InteractionType.application_command`\n - :attr:`InteractionType.component`\n - :attr:`InteractionType.modal_submit`\n\n .. note::\n The follow-up response will also be non-ephemeral if the `ephemeral`\n argument is ``False``, and ephemeral if ``True``.\n\n Parameters\n ----------\n ephemeral: :class:`bool`\n Indicates whether the deferred message will eventually be ephemeral.\n This only applies to :attr:`InteractionType.application_command` interactions,\n or if ``invisible`` is ``False``.\n invisible: :class:`bool`\n Indicates whether the deferred type should be 'invisible'\n (:attr:`InteractionResponseType.deferred_message_update`)\n instead of 'thinking' (:attr:`InteractionResponseType.deferred_channel_message`).\n In the Discord UI, this is represented as the bot thinking of a response. You must\n eventually send a followup message via :attr:`Interaction.followup` to make this thinking state go away.\n This parameter does not apply to interactions of type :attr:`InteractionType.application_command`.\n\n Raises\n ------\n HTTPException\n Deferring the interaction failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n defer_type: int = 0\n data: dict[str, Any] | None = None\n parent = self._parent\n if (\n parent.type is InteractionType.component\n or parent.type is InteractionType.modal_submit\n ):\n defer_type = (\n InteractionResponseType.deferred_message_update.value\n if invisible\n else InteractionResponseType.deferred_channel_message.value\n )\n if not invisible and ephemeral:\n data = {\"flags\": 64}\n elif parent.type is InteractionType.application_command:\n defer_type = InteractionResponseType.deferred_channel_message.value\n if ephemeral:\n data = {\"flags\": 64}\n\n if defer_type:\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n type=defer_type,\n data=data,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n )\n )\n self._responded = True\n\n async def pong(self) -> None:\n \"\"\"|coro|\n\n Pongs the ping interaction.\n\n This should rarely be used.\n\n Raises\n ------\n HTTPException\n Ponging the interaction failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n if parent.type is InteractionType.ping:\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n type=InteractionResponseType.pong.value,\n )\n )\n self._responded = True\n\n async def send_message(\n self,\n content: Any | None = None,\n *,\n embed: Embed = None,\n embeds: list[Embed] = None,\n view: View = None,\n tts: bool = False,\n ephemeral: bool = False,\n allowed_mentions: AllowedMentions = None,\n file: File = None,\n files: list[File] = None,\n delete_after: float = None,\n ) -> Interaction:\n \"\"\"|coro|\n\n Responds to this interaction by sending a message.\n\n Parameters\n ----------\n content: Optional[:class:`str`]\n The content of the message to send.\n embeds: List[:class:`Embed`]\n A list of embeds to send with the content. Maximum of 10. This cannot\n be mixed with the ``embed`` parameter.\n embed: :class:`Embed`\n The rich embed for the content to send. This cannot be mixed with\n ``embeds`` parameter.\n tts: :class:`bool`\n Indicates if the message should be sent using text-to-speech.\n view: :class:`discord.ui.View`\n The view to send with the message.\n ephemeral: :class:`bool`\n Indicates if the message should only be visible to the user who started the interaction.\n If a view is sent with an ephemeral message, and it has no timeout set then the timeout\n is set to 15 minutes.\n allowed_mentions: :class:`AllowedMentions`\n Controls the mentions being processed in this message.\n See :meth:`.abc.Messageable.send` for more information.\n delete_after: :class:`float`\n If provided, the number of seconds to wait in the background\n before deleting the message we just sent.\n file: :class:`File`\n The file to upload.\n files: List[:class:`File`]\n A list of files to upload. Must be a maximum of 10.\n\n Returns\n -------\n :class:`.Interaction`\n The interaction object associated with the sent message.\n\n Raises\n ------\n HTTPException\n Sending the message failed.\n TypeError\n You specified both ``embed`` and ``embeds``.\n ValueError\n The length of ``embeds`` was invalid.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n payload: dict[str, Any] = {\n \"tts\": tts,\n }\n\n if embed is not None and embeds is not None:\n raise TypeError(\"cannot mix embed and embeds keyword arguments\")\n\n if embed is not None:\n embeds = [embed]\n\n if embeds:\n if len(embeds) > 10:\n raise ValueError(\"embeds cannot exceed maximum of 10 elements\")\n payload[\"embeds\"] = [e.to_dict() for e in embeds]\n\n if content is not None:\n payload[\"content\"] = str(content)\n\n if ephemeral:\n payload[\"flags\"] = 64\n\n if view is not None:\n payload[\"components\"] = view.to_components()\n\n state = self._parent._state\n\n if allowed_mentions is None:\n payload[\"allowed_mentions\"] = (\n state.allowed_mentions and state.allowed_mentions.to_dict()\n )\n\n elif state.allowed_mentions is not None:\n payload[\"allowed_mentions\"] = state.allowed_mentions.merge(\n allowed_mentions\n ).to_dict()\n else:\n payload[\"allowed_mentions\"] = allowed_mentions.to_dict()\n if file is not None and files is not None:\n raise InvalidArgument(\"cannot pass both file and files parameter to send()\")\n\n if file is not None:\n if not isinstance(file, File):\n raise InvalidArgument(\"file parameter must be File\")\n else:\n files = [file]\n\n if files is not None:\n if len(files) > 10:\n raise InvalidArgument(\n \"files parameter must be a list of up to 10 elements\"\n )\n elif not all(isinstance(file, File) for file in files):\n raise InvalidArgument(\"files parameter must be a list of File\")\n\n parent = self._parent\n adapter = async_context.get()\n http = parent._state.http\n try:\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n type=InteractionResponseType.channel_message.value,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n data=payload,\n files=files,\n )\n )\n finally:\n if files:\n for file in files:\n file.close()\n\n if view is not None:\n if ephemeral and view.timeout is None:\n view.timeout = 15 * 60.0\n\n view.parent = self._parent\n self._parent._state.store_view(view)\n\n self._responded = True\n if delete_after is not None:\n await self._parent.delete_original_response(delay=delete_after)\n return self._parent\n\n async def edit_message(\n self,\n *,\n content: Any | None = MISSING,\n embed: Embed | None = MISSING,\n embeds: list[Embed] = MISSING,\n file: File = MISSING,\n files: list[File] = MISSING,\n attachments: list[Attachment] = MISSING,\n view: View | None = MISSING,\n delete_after: float | None = None,\n suppress: bool | None = MISSING,\n allowed_mentions: AllowedMentions | None = None,\n ) -> None:\n \"\"\"|coro|\n\n Responds to this interaction by editing the original message of\n a component or modal interaction.\n\n Parameters\n ----------\n content: Optional[:class:`str`]\n The new content to replace the message with. ``None`` removes the content.\n embeds: List[:class:`Embed`]\n A list of embeds to edit the message with.\n embed: Optional[:class:`Embed`]\n The embed to edit the message with. ``None`` suppresses the embeds.\n This should not be mixed with the ``embeds`` parameter.\n file: :class:`File`\n A new file to add to the message. This cannot be mixed with ``files`` parameter.\n files: List[:class:`File`]\n A list of new files to add to the message. Must be a maximum of 10. This\n cannot be mixed with the ``file`` parameter.\n attachments: List[:class:`Attachment`]\n A list of attachments to keep in the message. If ``[]`` is passed\n then all attachments are removed.\n view: Optional[:class:`~discord.ui.View`]\n The updated view to update this message with. If ``None`` is passed then\n the view is removed.\n delete_after: Optional[:class:`float`]\n If provided, the number of seconds to wait in the background\n before deleting the message we just edited. If the deletion fails,\n then it is silently ignored.\n suppress: Optional[:class:`bool`]\n Whether to suppress embeds for the message.\n allowed_mentions: Optional[:class:`~discord.AllowedMentions`]\n Controls the mentions being processed in this message. If this is\n passed, then the object is merged with :attr:`~discord.Client.allowed_mentions`.\n The merging behaviour only overrides attributes that have been explicitly passed\n to the object, otherwise it uses the attributes set in :attr:`~discord.Client.allowed_mentions`.\n If no object is passed at all then the defaults given by :attr:`~discord.Client.allowed_mentions`\n are used instead.\n\n Raises\n ------\n HTTPException\n Editing the message failed.\n TypeError\n You specified both ``embed`` and ``embeds``.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n msg = parent.message\n state = parent._state\n message_id = msg.id if msg else None\n if parent.type not in (InteractionType.component, InteractionType.modal_submit):\n return\n\n payload = {}\n if content is not MISSING:\n payload[\"content\"] = None if content is None else str(content)\n if embed is not MISSING and embeds is not MISSING:\n raise TypeError(\"cannot mix both embed and embeds keyword arguments\")\n\n if embed is not MISSING:\n embeds = [] if embed is None else [embed]\n if embeds is not MISSING:\n payload[\"embeds\"] = [e.to_dict() for e in embeds]\n\n if attachments is not MISSING:\n payload[\"attachments\"] = [a.to_dict() for a in attachments]\n\n if view is not MISSING:\n state.prevent_view_updates_for(message_id)\n payload[\"components\"] = [] if view is None else view.to_components()\n\n if file is not MISSING and files is not MISSING:\n raise InvalidArgument(\n \"cannot pass both file and files parameter to edit_message()\"\n )\n\n if file is not MISSING:\n if not isinstance(file, File):\n raise InvalidArgument(\"file parameter must be a File\")\n else:\n files = [file]\n if \"attachments\" not in payload:\n # we keep previous attachments when adding a new file\n payload[\"attachments\"] = [a.to_dict() for a in msg.attachments]\n\n if files is not MISSING:\n if len(files) > 10:\n raise InvalidArgument(\n \"files parameter must be a list of up to 10 elements\"\n )\n elif not all(isinstance(file, File) for file in files):\n raise InvalidArgument(\"files parameter must be a list of File\")\n if \"attachments\" not in payload:\n # we keep previous attachments when adding new files\n payload[\"attachments\"] = [a.to_dict() for a in msg.attachments]\n\n if suppress is not MISSING:\n flags = MessageFlags._from_value(self._parent.message.flags.value)\n flags.suppress_embeds = suppress\n payload[\"flags\"] = flags.value\n\n if allowed_mentions is None:\n payload[\"allowed_mentions\"] = (\n state.allowed_mentions and state.allowed_mentions.to_dict()\n )\n\n elif state.allowed_mentions is not None:\n payload[\"allowed_mentions\"] = state.allowed_mentions.merge(\n allowed_mentions\n ).to_dict()\n else:\n payload[\"allowed_mentions\"] = allowed_mentions.to_dict()\n\n adapter = async_context.get()\n http = parent._state.http\n try:\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n type=InteractionResponseType.message_update.value,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n data=payload,\n files=files,\n )\n )\n finally:\n if files:\n for file in files:\n file.close()\n\n if view and not view.is_finished():\n view.message = msg\n state.store_view(view, message_id)\n\n self._responded = True\n if delete_after is not None:\n await self._parent.delete_original_response(delay=delete_after)\n\n async def send_autocomplete_result(\n self,\n *,\n choices: list[OptionChoice],\n ) -> None:\n \"\"\"|coro|\n Responds to this interaction by sending the autocomplete choices.\n\n Parameters\n ----------\n choices: List[:class:`OptionChoice`]\n A list of choices.\n\n Raises\n ------\n HTTPException\n Sending the result failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n\n if parent.type is not InteractionType.auto_complete:\n return\n\n payload = {\"choices\": [c.to_dict() for c in choices]}\n\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n type=InteractionResponseType.auto_complete_result.value,\n data=payload,\n )\n )\n\n self._responded = True\n\n async def send_modal(self, modal: Modal) -> Interaction:\n \"\"\"|coro|\n Responds to this interaction by sending a modal dialog.\n This cannot be used to respond to another modal dialog submission.\n\n Parameters\n ----------\n modal: :class:`discord.ui.Modal`\n The modal dialog to display to the user.\n\n Raises\n ------\n HTTPException\n Sending the modal failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n\n payload = modal.to_dict()\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n type=InteractionResponseType.modal.value,\n data=payload,\n )\n )\n self._responded = True\n self._parent._state.store_modal(modal, self._parent.user.id)\n return self._parent\n\n async def premium_required(self) -> Interaction:\n \"\"\"|coro|\n Responds to this interaction by sending a premium required message.\n\n Raises\n ------\n HTTPException\n Sending the message failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n type=InteractionResponseType.premium_required.value,\n )\n )\n self._responded = True\n return self._parent\n\n async def _locked_response(self, coro: Coroutine[Any]):\n \"\"\"|coro|\n\n Wraps a response and makes sure that it's locked while executing.\n\n Parameters\n ----------\n coro: Coroutine[Any]\n The coroutine to wrap.\n\n Raises\n ------\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n async with self._response_lock:\n if self.is_done():\n coro.close() # cleanup un-awaited coroutine\n raise InteractionResponded(self._parent)\n await coro\n\n\nclass _InteractionMessageState:\n __slots__ = (\"_parent\", \"_interaction\")\n\n def __init__(self, interaction: Interaction, parent: ConnectionState):\n self._interaction: Interaction = interaction\n self._parent: ConnectionState = parent\n\n def _get_guild(self, guild_id):\n return self._parent._get_guild(guild_id)\n\n def store_user(self, data):\n return self._parent.store_user(data)\n\n def create_user(self, data):\n return self._parent.create_user(data)\n\n @property\n def http(self):\n return self._parent.http\n\n def __getattr__(self, attr):\n return getattr(self._parent, attr)\n\n\nclass InteractionMessage(Message):\n \"\"\"Represents the original interaction response message.\n\n This allows you to edit or delete the message associated with\n the interaction response. To retrieve this object see :meth:`Interaction.original_response`.\n\n This inherits from :class:`discord.Message` with changes to\n :meth:`edit` and :meth:`delete` to work.\n\n .. versionadded:: 2.0\n \"\"\"\n\n __slots__ = ()\n _state: _InteractionMessageState\n\n async def edit(\n self,\n content: str | None = MISSING,\n embeds: list[Embed] = MISSING,\n embed: Embed | None = MISSING,\n file: File = MISSING,\n files: list[File] = MISSING,\n attachments: list[Attachment] = MISSING,\n view: View | None = MISSING,\n allowed_mentions: AllowedMentions | None = None,\n delete_after: float | None = None,\n suppress: bool | None = MISSING,\n ) -> InteractionMessage:\n \"\"\"|coro|\n\n Edits the message.\n\n Parameters\n ----------\n content: Optional[:class:`str`]\n The content to edit the message with or ``None`` to clear it.\n embeds: List[:class:`Embed`]\n A list of embeds to edit the message with.\n embed: Optional[:class:`Embed`]\n The embed to edit the message with. ``None`` suppresses the embeds.\n This should not be mixed with the ``embeds`` parameter.\n file: :class:`File`\n The file to upload. This cannot be mixed with ``files`` parameter.\n files: List[:class:`File`]\n A list of files to send with the content. This cannot be mixed with the\n ``file`` parameter.\n attachments: List[:class:`Attachment`]\n A list of attachments to keep in the message. If ``[]`` is passed\n then all attachments are removed.\n allowed_mentions: :class:`AllowedMentions`\n Controls the mentions being processed in this message.\n See :meth:`.abc.Messageable.send` for more information.\n view: Optional[:class:`~discord.ui.View`]\n The updated view to update this message with. If ``None`` is passed then\n the view is removed.\n delete_after: Optional[:class:`float`]\n If provided, the number of seconds to wait in the background\n before deleting the message we just edited. If the deletion fails,\n then it is silently ignored.\n suppress: Optional[:class:`bool`]\n Whether to suppress embeds for the message.\n\n Returns\n -------\n :class:`InteractionMessage`\n The newly edited message.\n\n Raises\n ------\n HTTPException\n Editing the message failed.\n Forbidden\n Edited a message that is not yours.\n TypeError\n You specified both ``embed`` and ``embeds`` or ``file`` and ``files``\n ValueError\n The length of ``embeds`` was invalid.\n \"\"\"\n if attachments is MISSING:\n attachments = self.attachments or MISSING\n if suppress is MISSING:\n suppress = self.flags.suppress_embeds\n return await self._state._interaction.edit_original_response(\n content=content,\n embeds=embeds,\n embed=embed,\n file=file,\n files=files,\n attachments=attachments,\n view=view,\n allowed_mentions=allowed_mentions,\n delete_after=delete_after,\n suppress=suppress,\n )\n\n async def delete(self, *, delay: float | None = None) -> None:\n \"\"\"|coro|\n\n Deletes the message.\n\n Parameters\n ----------\n delay: Optional[:class:`float`]\n If provided, the number of seconds to wait before deleting the message.\n The waiting is done in the background and deletion failures are ignored.\n\n Raises\n ------\n Forbidden\n You do not have proper permissions to delete the message.\n NotFound\n The message was deleted already.\n HTTPException\n Deleting the message failed.\n \"\"\"\n await self._state._interaction.delete_original_response(delay=delay)\n\n\nclass MessageInteraction:\n \"\"\"Represents a Discord message interaction.\n\n This is sent on the message object when the message is a response\n to an interaction without an existing message e.g. application command.\n\n .. versionadded:: 2.0\n\n .. note::\n Responses to message components do not include this property.\n\n Attributes\n ----------\n id: :class:`int`\n The interaction's ID.\n type: :class:`InteractionType`\n The interaction type.\n name: :class:`str`\n The name of the invoked application command.\n user: :class:`User`\n The user that sent the interaction.\n data: :class:`dict`\n The raw interaction data.\n \"\"\"\n\n __slots__: tuple[str, ...] = (\"id\", \"type\", \"name\", \"user\", \"data\", \"_state\")\n\n def __init__(self, *, data: MessageInteractionPayload, state: ConnectionState):\n self._state = state\n self.data = data\n self.id: int = int(data[\"id\"])\n self.type: InteractionType = data[\"type\"]\n self.name: str = data[\"name\"]\n self.user: User = self._state.store_user(data[\"user\"])\n", "path": "discord/interactions.py" } ]
[ { "content": "\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2015-2021 Rapptz\nCopyright (c) 2021-present Pycord Development\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the \"Software\"),\nto deal in the Software without restriction, including without limitation\nthe rights to use, copy, modify, merge, publish, distribute, sublicense,\nand/or sell copies of the Software, and to permit persons to whom the\nSoftware is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\nOR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\nFROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\nDEALINGS IN THE SOFTWARE.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport asyncio\nfrom typing import TYPE_CHECKING, Any, Coroutine, Union\n\nfrom . import utils\nfrom .channel import ChannelType, PartialMessageable, _threaded_channel_factory\nfrom .enums import InteractionResponseType, InteractionType, try_enum\nfrom .errors import ClientException, InteractionResponded, InvalidArgument\nfrom .file import File\nfrom .flags import MessageFlags\nfrom .member import Member\nfrom .message import Attachment, Message\nfrom .monetization import Entitlement\nfrom .object import Object\nfrom .permissions import Permissions\nfrom .user import User\nfrom .webhook.async_ import (\n Webhook,\n WebhookMessage,\n async_context,\n handle_message_parameters,\n)\n\n__all__ = (\n \"Interaction\",\n \"InteractionMessage\",\n \"InteractionResponse\",\n \"MessageInteraction\",\n)\n\nif TYPE_CHECKING:\n from aiohttp import ClientSession\n\n from .channel import (\n CategoryChannel,\n DMChannel,\n ForumChannel,\n GroupChannel,\n StageChannel,\n TextChannel,\n VoiceChannel,\n )\n from .client import Client\n from .commands import OptionChoice\n from .embeds import Embed\n from .guild import Guild\n from .mentions import AllowedMentions\n from .state import ConnectionState\n from .threads import Thread\n from .types.interactions import Interaction as InteractionPayload\n from .types.interactions import InteractionData\n from .types.interactions import MessageInteraction as MessageInteractionPayload\n from .ui.modal import Modal\n from .ui.view import View\n\n InteractionChannel = Union[\n VoiceChannel,\n StageChannel,\n TextChannel,\n ForumChannel,\n CategoryChannel,\n Thread,\n DMChannel,\n GroupChannel,\n PartialMessageable,\n ]\n\nMISSING: Any = utils.MISSING\n\n\nclass Interaction:\n \"\"\"Represents a Discord interaction.\n\n An interaction happens when a user does an action that needs to\n be notified. Current examples are slash commands and components.\n\n .. versionadded:: 2.0\n\n Attributes\n ----------\n id: :class:`int`\n The interaction's ID.\n type: :class:`InteractionType`\n The interaction type.\n guild_id: Optional[:class:`int`]\n The guild ID the interaction was sent from.\n channel: Optional[Union[:class:`abc.GuildChannel`, :class:`abc.PrivateChannel`, :class:`Thread`]]\n The channel the interaction was sent from.\n channel_id: Optional[:class:`int`]\n The ID of the channel the interaction was sent from.\n application_id: :class:`int`\n The application ID that the interaction was for.\n user: Optional[Union[:class:`User`, :class:`Member`]]\n The user or member that sent the interaction. Will be `None` in PING interactions.\n message: Optional[:class:`Message`]\n The message that sent this interaction.\n token: :class:`str`\n The token to continue the interaction. These are valid\n for 15 minutes.\n data: :class:`dict`\n The raw interaction data.\n locale: :class:`str`\n The user's locale.\n guild_locale: :class:`str`\n The guilds preferred locale, if invoked in a guild.\n custom_id: Optional[:class:`str`]\n The custom ID for the interaction.\n \"\"\"\n\n __slots__: tuple[str, ...] = (\n \"id\",\n \"type\",\n \"guild_id\",\n \"channel\",\n \"channel_id\",\n \"data\",\n \"application_id\",\n \"message\",\n \"user\",\n \"locale\",\n \"guild_locale\",\n \"token\",\n \"version\",\n \"custom_id\",\n \"entitlements\",\n \"_channel_data\",\n \"_message_data\",\n \"_guild_data\",\n \"_guild\",\n \"_permissions\",\n \"_app_permissions\",\n \"_state\",\n \"_session\",\n \"_original_response\",\n \"_cs_app_permissions\",\n \"_cs_response\",\n \"_cs_followup\",\n \"_cs_channel\",\n )\n\n def __init__(self, *, data: InteractionPayload, state: ConnectionState):\n self._state: ConnectionState = state\n self._session: ClientSession = state.http._HTTPClient__session\n self._original_response: InteractionMessage | None = None\n self._from_data(data)\n\n def _from_data(self, data: InteractionPayload):\n self.id: int = int(data[\"id\"])\n self.type: InteractionType = try_enum(InteractionType, data[\"type\"])\n self.data: InteractionData | None = data.get(\"data\")\n self.token: str = data[\"token\"]\n self.version: int = data[\"version\"]\n self.channel_id: int | None = utils._get_as_snowflake(data, \"channel_id\")\n self.guild_id: int | None = utils._get_as_snowflake(data, \"guild_id\")\n self.application_id: int = int(data[\"application_id\"])\n self.locale: str | None = data.get(\"locale\")\n self.guild_locale: str | None = data.get(\"guild_locale\")\n self.custom_id: str | None = (\n self.data.get(\"custom_id\") if self.data is not None else None\n )\n self._app_permissions: int = int(data.get(\"app_permissions\", 0))\n self.entitlements: list[Entitlement] = [\n Entitlement(data=e, state=self._state) for e in data.get(\"entitlements\", [])\n ]\n\n self.message: Message | None = None\n self.channel = None\n\n self.user: User | Member | None = None\n self._permissions: int = 0\n\n self._guild: Guild | None = None\n self._guild_data = data.get(\"guild\")\n if self.guild is None and self._guild_data:\n self._guild = Guild(data=self._guild_data, state=self)\n\n # TODO: there's a potential data loss here\n if self.guild_id:\n guild = (\n self.guild\n or self._state._get_guild(self.guild_id)\n or Object(id=self.guild_id)\n )\n try:\n member = data[\"member\"] # type: ignore\n except KeyError:\n pass\n else:\n self._permissions = int(member.get(\"permissions\", 0))\n if not isinstance(guild, Object):\n cache_flag = self._state.member_cache_flags.interaction\n self.user = guild._get_and_update_member(\n member, int(member[\"user\"][\"id\"]), cache_flag\n )\n else:\n self.user = Member(state=self._state, data=member, guild=guild)\n else:\n try:\n self.user = User(state=self._state, data=data[\"user\"])\n except KeyError:\n pass\n\n if channel := data.get(\"channel\"):\n if (ch_type := channel.get(\"type\")) is not None:\n factory, ch_type = _threaded_channel_factory(ch_type)\n\n if ch_type in (ChannelType.group, ChannelType.private):\n self.channel = factory(\n me=self.user, data=channel, state=self._state\n )\n elif self.guild:\n self.channel = factory(\n guild=self.guild, state=self._state, data=channel\n )\n else:\n self.channel = self.cached_channel\n\n self._channel_data = channel\n\n if message_data := data.get(\"message\"):\n self.message = Message(\n state=self._state, channel=self.channel, data=message_data\n )\n\n self._message_data = message_data\n\n @property\n def client(self) -> Client:\n \"\"\"Returns the client that sent the interaction.\"\"\"\n return self._state._get_client()\n\n @property\n def guild(self) -> Guild | None:\n \"\"\"The guild the interaction was sent from.\"\"\"\n if self._guild:\n return self._guild\n return self._state and self._state._get_guild(self.guild_id)\n\n def is_command(self) -> bool:\n \"\"\"Indicates whether the interaction is an application command.\"\"\"\n return self.type == InteractionType.application_command\n\n def is_component(self) -> bool:\n \"\"\"Indicates whether the interaction is a message component.\"\"\"\n return self.type == InteractionType.component\n\n @utils.cached_slot_property(\"_cs_channel\")\n def cached_channel(self) -> InteractionChannel | None:\n \"\"\"The channel the\n interaction was sent from.\n\n Note that due to a Discord limitation, DM channels are not resolved since there is\n no data to complete them. These are :class:`PartialMessageable` instead.\n \"\"\"\n guild = self.guild\n channel = guild and guild._resolve_channel(self.channel_id)\n if channel is None:\n if self.channel_id is not None:\n type = (\n ChannelType.text\n if self.guild_id is not None\n else ChannelType.private\n )\n return PartialMessageable(\n state=self._state, id=self.channel_id, type=type\n )\n return None\n return channel\n\n @property\n def permissions(self) -> Permissions:\n \"\"\"The resolved permissions of the member in the channel, including overwrites.\n\n In a non-guild context where this doesn't apply, an empty permissions object is returned.\n \"\"\"\n return Permissions(self._permissions)\n\n @utils.cached_slot_property(\"_cs_app_permissions\")\n def app_permissions(self) -> Permissions:\n \"\"\"The resolved permissions of the application in the channel, including overwrites.\"\"\"\n return Permissions(self._app_permissions)\n\n @utils.cached_slot_property(\"_cs_response\")\n def response(self) -> InteractionResponse:\n \"\"\"Returns an object responsible for handling responding to the interaction.\n\n A response can only be done once. If secondary messages need to be sent, consider using :attr:`followup`\n instead.\n \"\"\"\n return InteractionResponse(self)\n\n @utils.cached_slot_property(\"_cs_followup\")\n def followup(self) -> Webhook:\n \"\"\"Returns the followup webhook for followup interactions.\"\"\"\n payload = {\n \"id\": self.application_id,\n \"type\": 3,\n \"token\": self.token,\n }\n return Webhook.from_state(data=payload, state=self._state)\n\n async def original_response(self) -> InteractionMessage:\n \"\"\"|coro|\n\n Fetches the original interaction response message associated with the interaction.\n\n If the interaction response was :meth:`InteractionResponse.send_message` then this would\n return the message that was sent using that response. Otherwise, this would return\n the message that triggered the interaction.\n\n Repeated calls to this will return a cached value.\n\n Returns\n -------\n InteractionMessage\n The original interaction response message.\n\n Raises\n ------\n HTTPException\n Fetching the original response message failed.\n ClientException\n The channel for the message could not be resolved.\n \"\"\"\n\n if self._original_response is not None:\n return self._original_response\n\n # TODO: fix later to not raise?\n channel = self.channel\n if channel is None:\n raise ClientException(\"Channel for message could not be resolved\")\n\n adapter = async_context.get()\n http = self._state.http\n data = await adapter.get_original_interaction_response(\n application_id=self.application_id,\n token=self.token,\n session=self._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n )\n state = _InteractionMessageState(self, self._state)\n message = InteractionMessage(state=state, channel=channel, data=data) # type: ignore\n self._original_response = message\n return message\n\n @utils.deprecated(\"Interaction.original_response\", \"2.2\")\n async def original_message(self):\n \"\"\"An alias for :meth:`original_response`.\n\n Returns\n -------\n InteractionMessage\n The original interaction response message.\n\n Raises\n ------\n HTTPException\n Fetching the original response message failed.\n ClientException\n The channel for the message could not be resolved.\n \"\"\"\n return await self.original_response()\n\n async def edit_original_response(\n self,\n *,\n content: str | None = MISSING,\n embeds: list[Embed] = MISSING,\n embed: Embed | None = MISSING,\n file: File = MISSING,\n files: list[File] = MISSING,\n attachments: list[Attachment] = MISSING,\n view: View | None = MISSING,\n allowed_mentions: AllowedMentions | None = None,\n delete_after: float | None = None,\n suppress: bool = False,\n ) -> InteractionMessage:\n \"\"\"|coro|\n\n Edits the original interaction response message.\n\n This is a lower level interface to :meth:`InteractionMessage.edit` in case\n you do not want to fetch the message and save an HTTP request.\n\n This method is also the only way to edit the original message if\n the message sent was ephemeral.\n\n Parameters\n ----------\n content: Optional[:class:`str`]\n The content to edit the message with or ``None`` to clear it.\n embeds: List[:class:`Embed`]\n A list of embeds to edit the message with.\n embed: Optional[:class:`Embed`]\n The embed to edit the message with. ``None`` suppresses the embeds.\n This should not be mixed with the ``embeds`` parameter.\n file: :class:`File`\n The file to upload. This cannot be mixed with ``files`` parameter.\n files: List[:class:`File`]\n A list of files to send with the content. This cannot be mixed with the\n ``file`` parameter.\n attachments: List[:class:`Attachment`]\n A list of attachments to keep in the message. If ``[]`` is passed\n then all attachments are removed.\n allowed_mentions: :class:`AllowedMentions`\n Controls the mentions being processed in this message.\n See :meth:`.abc.Messageable.send` for more information.\n view: Optional[:class:`~discord.ui.View`]\n The updated view to update this message with. If ``None`` is passed then\n the view is removed.\n delete_after: Optional[:class:`float`]\n If provided, the number of seconds to wait in the background\n before deleting the message we just edited. If the deletion fails,\n then it is silently ignored.\n suppress: :class:`bool`\n Whether to suppress embeds for the message.\n\n Returns\n -------\n :class:`InteractionMessage`\n The newly edited message.\n\n Raises\n ------\n HTTPException\n Editing the message failed.\n Forbidden\n Edited a message that is not yours.\n TypeError\n You specified both ``embed`` and ``embeds`` or ``file`` and ``files``\n ValueError\n The length of ``embeds`` was invalid.\n \"\"\"\n\n previous_mentions: AllowedMentions | None = self._state.allowed_mentions\n params = handle_message_parameters(\n content=content,\n file=file,\n files=files,\n attachments=attachments,\n embed=embed,\n embeds=embeds,\n view=view,\n allowed_mentions=allowed_mentions,\n previous_allowed_mentions=previous_mentions,\n suppress=suppress,\n )\n adapter = async_context.get()\n http = self._state.http\n data = await adapter.edit_original_interaction_response(\n self.application_id,\n self.token,\n session=self._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n payload=params.payload,\n multipart=params.multipart,\n files=params.files,\n )\n\n # The message channel types should always match\n state = _InteractionMessageState(self, self._state)\n message = InteractionMessage(state=state, channel=self.channel, data=data) # type: ignore\n if view and not view.is_finished():\n view.message = message\n self._state.store_view(view, message.id)\n\n if delete_after is not None:\n await self.delete_original_response(delay=delete_after)\n\n return message\n\n @utils.deprecated(\"Interaction.edit_original_response\", \"2.2\")\n async def edit_original_message(self, **kwargs):\n \"\"\"An alias for :meth:`edit_original_response`.\n\n Returns\n -------\n :class:`InteractionMessage`\n The newly edited message.\n\n Raises\n ------\n HTTPException\n Editing the message failed.\n Forbidden\n Edited a message that is not yours.\n TypeError\n You specified both ``embed`` and ``embeds`` or ``file`` and ``files``\n ValueError\n The length of ``embeds`` was invalid.\n \"\"\"\n return await self.edit_original_response(**kwargs)\n\n async def delete_original_response(self, *, delay: float | None = None) -> None:\n \"\"\"|coro|\n\n Deletes the original interaction response message.\n\n This is a lower level interface to :meth:`InteractionMessage.delete` in case\n you do not want to fetch the message and save an HTTP request.\n\n Parameters\n ----------\n delay: Optional[:class:`float`]\n If provided, the number of seconds to wait before deleting the message.\n The waiting is done in the background and deletion failures are ignored.\n\n Raises\n ------\n HTTPException\n Deleting the message failed.\n Forbidden\n Deleted a message that is not yours.\n \"\"\"\n adapter = async_context.get()\n http = self._state.http\n func = adapter.delete_original_interaction_response(\n self.application_id,\n self.token,\n session=self._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n )\n\n if delay is not None:\n utils.delay_task(delay, func)\n else:\n await func\n\n @utils.deprecated(\"Interaction.delete_original_response\", \"2.2\")\n async def delete_original_message(self, **kwargs):\n \"\"\"An alias for :meth:`delete_original_response`.\n\n Raises\n ------\n HTTPException\n Deleting the message failed.\n Forbidden\n Deleted a message that is not yours.\n \"\"\"\n return await self.delete_original_response(**kwargs)\n\n async def respond(self, *args, **kwargs) -> Interaction | WebhookMessage:\n \"\"\"|coro|\n\n Sends either a response or a message using the followup webhook determined by whether the interaction\n has been responded to or not.\n\n Returns\n -------\n Union[:class:`discord.Interaction`, :class:`discord.WebhookMessage`]:\n The response, its type depending on whether it's an interaction response or a followup.\n \"\"\"\n try:\n if not self.response.is_done():\n return await self.response.send_message(*args, **kwargs)\n else:\n return await self.followup.send(*args, **kwargs)\n except InteractionResponded:\n return await self.followup.send(*args, **kwargs)\n\n async def edit(self, *args, **kwargs) -> InteractionMessage | None:\n \"\"\"|coro|\n\n Either respond to the interaction with an edit_message or edits the existing response, determined by\n whether the interaction has been responded to or not.\n\n Returns\n -------\n Union[:class:`discord.InteractionMessage`, :class:`discord.WebhookMessage`]:\n The response, its type depending on whether it's an interaction response or a followup.\n \"\"\"\n try:\n if not self.response.is_done():\n return await self.response.edit_message(*args, **kwargs)\n else:\n return await self.edit_original_response(*args, **kwargs)\n except InteractionResponded:\n return await self.edit_original_response(*args, **kwargs)\n\n def to_dict(self) -> dict[str, Any]:\n \"\"\"\n Converts this interaction object into a dict.\n\n Returns\n -------\n Dict[:class:`str`, Any]\n A dictionary of :class:`str` interaction keys bound to the respective value.\n \"\"\"\n\n data = {\n \"id\": self.id,\n \"application_id\": self.application_id,\n \"type\": self.type.value,\n \"token\": self.token,\n \"version\": self.version,\n }\n\n if self.data is not None:\n data[\"data\"] = self.data\n if (resolved := self.data.get(\"resolved\")) and self.user is not None:\n if (users := resolved.get(\"users\")) and (\n user := users.get(self.user.id)\n ):\n data[\"user\"] = user\n if (members := resolved.get(\"members\")) and (\n member := members.get(self.user.id)\n ):\n data[\"member\"] = member\n\n if self.guild_id is not None:\n data[\"guild_id\"] = self.guild_id\n\n if self.channel_id is not None:\n data[\"channel_id\"] = self.channel_id\n\n if self.locale:\n data[\"locale\"] = self.locale\n\n if self.guild_locale:\n data[\"guild_locale\"] = self.guild_locale\n\n if self._message_data:\n data[\"message\"] = self._message_data\n\n return data\n\n\nclass InteractionResponse:\n \"\"\"Represents a Discord interaction response.\n\n This type can be accessed through :attr:`Interaction.response`.\n\n .. versionadded:: 2.0\n \"\"\"\n\n __slots__: tuple[str, ...] = (\n \"_responded\",\n \"_parent\",\n \"_response_lock\",\n )\n\n def __init__(self, parent: Interaction):\n self._parent: Interaction = parent\n self._responded: bool = False\n self._response_lock = asyncio.Lock()\n\n def is_done(self) -> bool:\n \"\"\"Indicates whether an interaction response has been done before.\n\n An interaction can only be responded to once.\n \"\"\"\n return self._responded\n\n async def defer(self, *, ephemeral: bool = False, invisible: bool = True) -> None:\n \"\"\"|coro|\n\n Defers the interaction response.\n\n This is typically used when the interaction is acknowledged\n and a secondary action will be done later.\n\n This can only be used with the following interaction types:\n\n - :attr:`InteractionType.application_command`\n - :attr:`InteractionType.component`\n - :attr:`InteractionType.modal_submit`\n\n .. note::\n The follow-up response will also be non-ephemeral if the `ephemeral`\n argument is ``False``, and ephemeral if ``True``.\n\n Parameters\n ----------\n ephemeral: :class:`bool`\n Indicates whether the deferred message will eventually be ephemeral.\n This only applies to :attr:`InteractionType.application_command` interactions,\n or if ``invisible`` is ``False``.\n invisible: :class:`bool`\n Indicates whether the deferred type should be 'invisible'\n (:attr:`InteractionResponseType.deferred_message_update`)\n instead of 'thinking' (:attr:`InteractionResponseType.deferred_channel_message`).\n In the Discord UI, this is represented as the bot thinking of a response. You must\n eventually send a followup message via :attr:`Interaction.followup` to make this thinking state go away.\n This parameter does not apply to interactions of type :attr:`InteractionType.application_command`.\n\n Raises\n ------\n HTTPException\n Deferring the interaction failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n defer_type: int = 0\n data: dict[str, Any] | None = None\n parent = self._parent\n if (\n parent.type is InteractionType.component\n or parent.type is InteractionType.modal_submit\n ):\n defer_type = (\n InteractionResponseType.deferred_message_update.value\n if invisible\n else InteractionResponseType.deferred_channel_message.value\n )\n if not invisible and ephemeral:\n data = {\"flags\": 64}\n elif parent.type is InteractionType.application_command:\n defer_type = InteractionResponseType.deferred_channel_message.value\n if ephemeral:\n data = {\"flags\": 64}\n\n if defer_type:\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n type=defer_type,\n data=data,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n )\n )\n self._responded = True\n\n async def pong(self) -> None:\n \"\"\"|coro|\n\n Pongs the ping interaction.\n\n This should rarely be used.\n\n Raises\n ------\n HTTPException\n Ponging the interaction failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n if parent.type is InteractionType.ping:\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n type=InteractionResponseType.pong.value,\n )\n )\n self._responded = True\n\n async def send_message(\n self,\n content: Any | None = None,\n *,\n embed: Embed = None,\n embeds: list[Embed] = None,\n view: View = None,\n tts: bool = False,\n ephemeral: bool = False,\n allowed_mentions: AllowedMentions = None,\n file: File = None,\n files: list[File] = None,\n delete_after: float = None,\n ) -> Interaction:\n \"\"\"|coro|\n\n Responds to this interaction by sending a message.\n\n Parameters\n ----------\n content: Optional[:class:`str`]\n The content of the message to send.\n embeds: List[:class:`Embed`]\n A list of embeds to send with the content. Maximum of 10. This cannot\n be mixed with the ``embed`` parameter.\n embed: :class:`Embed`\n The rich embed for the content to send. This cannot be mixed with\n ``embeds`` parameter.\n tts: :class:`bool`\n Indicates if the message should be sent using text-to-speech.\n view: :class:`discord.ui.View`\n The view to send with the message.\n ephemeral: :class:`bool`\n Indicates if the message should only be visible to the user who started the interaction.\n If a view is sent with an ephemeral message, and it has no timeout set then the timeout\n is set to 15 minutes.\n allowed_mentions: :class:`AllowedMentions`\n Controls the mentions being processed in this message.\n See :meth:`.abc.Messageable.send` for more information.\n delete_after: :class:`float`\n If provided, the number of seconds to wait in the background\n before deleting the message we just sent.\n file: :class:`File`\n The file to upload.\n files: List[:class:`File`]\n A list of files to upload. Must be a maximum of 10.\n\n Returns\n -------\n :class:`.Interaction`\n The interaction object associated with the sent message.\n\n Raises\n ------\n HTTPException\n Sending the message failed.\n TypeError\n You specified both ``embed`` and ``embeds``.\n ValueError\n The length of ``embeds`` was invalid.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n payload: dict[str, Any] = {\n \"tts\": tts,\n }\n\n if embed is not None and embeds is not None:\n raise TypeError(\"cannot mix embed and embeds keyword arguments\")\n\n if embed is not None:\n embeds = [embed]\n\n if embeds:\n if len(embeds) > 10:\n raise ValueError(\"embeds cannot exceed maximum of 10 elements\")\n payload[\"embeds\"] = [e.to_dict() for e in embeds]\n\n if content is not None:\n payload[\"content\"] = str(content)\n\n if ephemeral:\n payload[\"flags\"] = 64\n\n if view is not None:\n payload[\"components\"] = view.to_components()\n\n state = self._parent._state\n\n if allowed_mentions is None:\n payload[\"allowed_mentions\"] = (\n state.allowed_mentions and state.allowed_mentions.to_dict()\n )\n\n elif state.allowed_mentions is not None:\n payload[\"allowed_mentions\"] = state.allowed_mentions.merge(\n allowed_mentions\n ).to_dict()\n else:\n payload[\"allowed_mentions\"] = allowed_mentions.to_dict()\n if file is not None and files is not None:\n raise InvalidArgument(\"cannot pass both file and files parameter to send()\")\n\n if file is not None:\n if not isinstance(file, File):\n raise InvalidArgument(\"file parameter must be File\")\n else:\n files = [file]\n\n if files is not None:\n if len(files) > 10:\n raise InvalidArgument(\n \"files parameter must be a list of up to 10 elements\"\n )\n elif not all(isinstance(file, File) for file in files):\n raise InvalidArgument(\"files parameter must be a list of File\")\n\n parent = self._parent\n adapter = async_context.get()\n http = parent._state.http\n try:\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n type=InteractionResponseType.channel_message.value,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n data=payload,\n files=files,\n )\n )\n finally:\n if files:\n for file in files:\n file.close()\n\n if view is not None:\n if ephemeral and view.timeout is None:\n view.timeout = 15 * 60.0\n\n view.parent = self._parent\n self._parent._state.store_view(view)\n\n self._responded = True\n if delete_after is not None:\n await self._parent.delete_original_response(delay=delete_after)\n return self._parent\n\n async def edit_message(\n self,\n *,\n content: Any | None = MISSING,\n embed: Embed | None = MISSING,\n embeds: list[Embed] = MISSING,\n file: File = MISSING,\n files: list[File] = MISSING,\n attachments: list[Attachment] = MISSING,\n view: View | None = MISSING,\n delete_after: float | None = None,\n suppress: bool | None = MISSING,\n allowed_mentions: AllowedMentions | None = None,\n ) -> None:\n \"\"\"|coro|\n\n Responds to this interaction by editing the original message of\n a component or modal interaction.\n\n Parameters\n ----------\n content: Optional[:class:`str`]\n The new content to replace the message with. ``None`` removes the content.\n embeds: List[:class:`Embed`]\n A list of embeds to edit the message with.\n embed: Optional[:class:`Embed`]\n The embed to edit the message with. ``None`` suppresses the embeds.\n This should not be mixed with the ``embeds`` parameter.\n file: :class:`File`\n A new file to add to the message. This cannot be mixed with ``files`` parameter.\n files: List[:class:`File`]\n A list of new files to add to the message. Must be a maximum of 10. This\n cannot be mixed with the ``file`` parameter.\n attachments: List[:class:`Attachment`]\n A list of attachments to keep in the message. If ``[]`` is passed\n then all attachments are removed.\n view: Optional[:class:`~discord.ui.View`]\n The updated view to update this message with. If ``None`` is passed then\n the view is removed.\n delete_after: Optional[:class:`float`]\n If provided, the number of seconds to wait in the background\n before deleting the message we just edited. If the deletion fails,\n then it is silently ignored.\n suppress: Optional[:class:`bool`]\n Whether to suppress embeds for the message.\n allowed_mentions: Optional[:class:`~discord.AllowedMentions`]\n Controls the mentions being processed in this message. If this is\n passed, then the object is merged with :attr:`~discord.Client.allowed_mentions`.\n The merging behaviour only overrides attributes that have been explicitly passed\n to the object, otherwise it uses the attributes set in :attr:`~discord.Client.allowed_mentions`.\n If no object is passed at all then the defaults given by :attr:`~discord.Client.allowed_mentions`\n are used instead.\n\n Raises\n ------\n HTTPException\n Editing the message failed.\n TypeError\n You specified both ``embed`` and ``embeds``.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n msg = parent.message\n state = parent._state\n message_id = msg.id if msg else None\n if parent.type not in (InteractionType.component, InteractionType.modal_submit):\n return\n\n payload = {}\n if content is not MISSING:\n payload[\"content\"] = None if content is None else str(content)\n if embed is not MISSING and embeds is not MISSING:\n raise TypeError(\"cannot mix both embed and embeds keyword arguments\")\n\n if embed is not MISSING:\n embeds = [] if embed is None else [embed]\n if embeds is not MISSING:\n payload[\"embeds\"] = [e.to_dict() for e in embeds]\n\n if attachments is not MISSING:\n payload[\"attachments\"] = [a.to_dict() for a in attachments]\n\n if view is not MISSING:\n state.prevent_view_updates_for(message_id)\n payload[\"components\"] = [] if view is None else view.to_components()\n\n if file is not MISSING and files is not MISSING:\n raise InvalidArgument(\n \"cannot pass both file and files parameter to edit_message()\"\n )\n\n if file is not MISSING:\n if not isinstance(file, File):\n raise InvalidArgument(\"file parameter must be a File\")\n else:\n files = [file]\n if \"attachments\" not in payload:\n # we keep previous attachments when adding a new file\n payload[\"attachments\"] = [a.to_dict() for a in msg.attachments]\n\n if files is not MISSING:\n if len(files) > 10:\n raise InvalidArgument(\n \"files parameter must be a list of up to 10 elements\"\n )\n elif not all(isinstance(file, File) for file in files):\n raise InvalidArgument(\"files parameter must be a list of File\")\n if \"attachments\" not in payload:\n # we keep previous attachments when adding new files\n payload[\"attachments\"] = [a.to_dict() for a in msg.attachments]\n\n if suppress is not MISSING:\n flags = MessageFlags._from_value(self._parent.message.flags.value)\n flags.suppress_embeds = suppress\n payload[\"flags\"] = flags.value\n\n if allowed_mentions is None:\n payload[\"allowed_mentions\"] = (\n state.allowed_mentions and state.allowed_mentions.to_dict()\n )\n\n elif state.allowed_mentions is not None:\n payload[\"allowed_mentions\"] = state.allowed_mentions.merge(\n allowed_mentions\n ).to_dict()\n else:\n payload[\"allowed_mentions\"] = allowed_mentions.to_dict()\n\n adapter = async_context.get()\n http = parent._state.http\n try:\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n type=InteractionResponseType.message_update.value,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n data=payload,\n files=files,\n )\n )\n finally:\n if files:\n for file in files:\n file.close()\n\n if view and not view.is_finished():\n view.message = msg\n state.store_view(view, message_id)\n\n self._responded = True\n if delete_after is not None:\n await self._parent.delete_original_response(delay=delete_after)\n\n async def send_autocomplete_result(\n self,\n *,\n choices: list[OptionChoice],\n ) -> None:\n \"\"\"|coro|\n Responds to this interaction by sending the autocomplete choices.\n\n Parameters\n ----------\n choices: List[:class:`OptionChoice`]\n A list of choices.\n\n Raises\n ------\n HTTPException\n Sending the result failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n\n if parent.type is not InteractionType.auto_complete:\n return\n\n payload = {\"choices\": [c.to_dict() for c in choices]}\n\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n type=InteractionResponseType.auto_complete_result.value,\n data=payload,\n )\n )\n\n self._responded = True\n\n async def send_modal(self, modal: Modal) -> Interaction:\n \"\"\"|coro|\n Responds to this interaction by sending a modal dialog.\n This cannot be used to respond to another modal dialog submission.\n\n Parameters\n ----------\n modal: :class:`discord.ui.Modal`\n The modal dialog to display to the user.\n\n Raises\n ------\n HTTPException\n Sending the modal failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n\n payload = modal.to_dict()\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n type=InteractionResponseType.modal.value,\n data=payload,\n )\n )\n self._responded = True\n self._parent._state.store_modal(modal, self._parent.user.id)\n return self._parent\n\n async def premium_required(self) -> Interaction:\n \"\"\"|coro|\n Responds to this interaction by sending a premium required message.\n\n Raises\n ------\n HTTPException\n Sending the message failed.\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n if self._responded:\n raise InteractionResponded(self._parent)\n\n parent = self._parent\n\n adapter = async_context.get()\n http = parent._state.http\n await self._locked_response(\n adapter.create_interaction_response(\n parent.id,\n parent.token,\n session=parent._session,\n proxy=http.proxy,\n proxy_auth=http.proxy_auth,\n type=InteractionResponseType.premium_required.value,\n )\n )\n self._responded = True\n return self._parent\n\n async def _locked_response(self, coro: Coroutine[Any]):\n \"\"\"|coro|\n\n Wraps a response and makes sure that it's locked while executing.\n\n Parameters\n ----------\n coro: Coroutine[Any]\n The coroutine to wrap.\n\n Raises\n ------\n InteractionResponded\n This interaction has already been responded to before.\n \"\"\"\n async with self._response_lock:\n if self.is_done():\n coro.close() # cleanup un-awaited coroutine\n raise InteractionResponded(self._parent)\n await coro\n\n\nclass _InteractionMessageState:\n __slots__ = (\"_parent\", \"_interaction\")\n\n def __init__(self, interaction: Interaction, parent: ConnectionState):\n self._interaction: Interaction = interaction\n self._parent: ConnectionState = parent\n\n def _get_guild(self, guild_id):\n return self._parent._get_guild(guild_id)\n\n def store_user(self, data):\n return self._parent.store_user(data)\n\n def create_user(self, data):\n return self._parent.create_user(data)\n\n @property\n def http(self):\n return self._parent.http\n\n def __getattr__(self, attr):\n return getattr(self._parent, attr)\n\n\nclass InteractionMessage(Message):\n \"\"\"Represents the original interaction response message.\n\n This allows you to edit or delete the message associated with\n the interaction response. To retrieve this object see :meth:`Interaction.original_response`.\n\n This inherits from :class:`discord.Message` with changes to\n :meth:`edit` and :meth:`delete` to work.\n\n .. versionadded:: 2.0\n \"\"\"\n\n __slots__ = ()\n _state: _InteractionMessageState\n\n async def edit(\n self,\n content: str | None = MISSING,\n embeds: list[Embed] = MISSING,\n embed: Embed | None = MISSING,\n file: File = MISSING,\n files: list[File] = MISSING,\n attachments: list[Attachment] = MISSING,\n view: View | None = MISSING,\n allowed_mentions: AllowedMentions | None = None,\n delete_after: float | None = None,\n suppress: bool | None = MISSING,\n ) -> InteractionMessage:\n \"\"\"|coro|\n\n Edits the message.\n\n Parameters\n ----------\n content: Optional[:class:`str`]\n The content to edit the message with or ``None`` to clear it.\n embeds: List[:class:`Embed`]\n A list of embeds to edit the message with.\n embed: Optional[:class:`Embed`]\n The embed to edit the message with. ``None`` suppresses the embeds.\n This should not be mixed with the ``embeds`` parameter.\n file: :class:`File`\n The file to upload. This cannot be mixed with ``files`` parameter.\n files: List[:class:`File`]\n A list of files to send with the content. This cannot be mixed with the\n ``file`` parameter.\n attachments: List[:class:`Attachment`]\n A list of attachments to keep in the message. If ``[]`` is passed\n then all attachments are removed.\n allowed_mentions: :class:`AllowedMentions`\n Controls the mentions being processed in this message.\n See :meth:`.abc.Messageable.send` for more information.\n view: Optional[:class:`~discord.ui.View`]\n The updated view to update this message with. If ``None`` is passed then\n the view is removed.\n delete_after: Optional[:class:`float`]\n If provided, the number of seconds to wait in the background\n before deleting the message we just edited. If the deletion fails,\n then it is silently ignored.\n suppress: Optional[:class:`bool`]\n Whether to suppress embeds for the message.\n\n Returns\n -------\n :class:`InteractionMessage`\n The newly edited message.\n\n Raises\n ------\n HTTPException\n Editing the message failed.\n Forbidden\n Edited a message that is not yours.\n TypeError\n You specified both ``embed`` and ``embeds`` or ``file`` and ``files``\n ValueError\n The length of ``embeds`` was invalid.\n \"\"\"\n if attachments is MISSING:\n attachments = self.attachments or MISSING\n if suppress is MISSING:\n suppress = self.flags.suppress_embeds\n return await self._state._interaction.edit_original_response(\n content=content,\n embeds=embeds,\n embed=embed,\n file=file,\n files=files,\n attachments=attachments,\n view=view,\n allowed_mentions=allowed_mentions,\n delete_after=delete_after,\n suppress=suppress,\n )\n\n async def delete(self, *, delay: float | None = None) -> None:\n \"\"\"|coro|\n\n Deletes the message.\n\n Parameters\n ----------\n delay: Optional[:class:`float`]\n If provided, the number of seconds to wait before deleting the message.\n The waiting is done in the background and deletion failures are ignored.\n\n Raises\n ------\n Forbidden\n You do not have proper permissions to delete the message.\n NotFound\n The message was deleted already.\n HTTPException\n Deleting the message failed.\n \"\"\"\n await self._state._interaction.delete_original_response(delay=delay)\n\n\nclass MessageInteraction:\n \"\"\"Represents a Discord message interaction.\n\n This is sent on the message object when the message is a response\n to an interaction without an existing message e.g. application command.\n\n .. versionadded:: 2.0\n\n .. note::\n Responses to message components do not include this property.\n\n Attributes\n ----------\n id: :class:`int`\n The interaction's ID.\n type: :class:`InteractionType`\n The interaction type.\n name: :class:`str`\n The name of the invoked application command.\n user: :class:`User`\n The user that sent the interaction.\n data: :class:`dict`\n The raw interaction data.\n \"\"\"\n\n __slots__: tuple[str, ...] = (\"id\", \"type\", \"name\", \"user\", \"data\", \"_state\")\n\n def __init__(self, *, data: MessageInteractionPayload, state: ConnectionState):\n self._state = state\n self.data = data\n self.id: int = int(data[\"id\"])\n self.type: InteractionType = data[\"type\"]\n self.name: str = data[\"name\"]\n self.user: User = self._state.store_user(data[\"user\"])\n", "path": "discord/interactions.py" } ]
diff --git a/CHANGELOG.md b/CHANGELOG.md index 9210527012..ced04899ae 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -232,6 +232,9 @@ These changes are available on the `master` branch, but have not yet been releas ([#2337](https://github.com/Pycord-Development/pycord/pull/2337)) - Fixed `TypeError` due to `(Sync)WebhookMessage._thread_id` being set to `None`. ([#2343](https://github.com/Pycord-Development/pycord/pull/2343)) +- Fixed `AttributeError` due to `entitlements` not being included in + `Interaction.__slots__`. + ([#2345](https://github.com/Pycord-Development/pycord/pull/2345)) ## [2.4.1] - 2023-03-20 diff --git a/discord/interactions.py b/discord/interactions.py index 6443af1c04..0e254d514a 100644 --- a/discord/interactions.py +++ b/discord/interactions.py @@ -148,6 +148,7 @@ class Interaction: "token", "version", "custom_id", + "entitlements", "_channel_data", "_message_data", "_guild_data",
pyodide__pyodide-3562
Error about `--user` and `--target` flag when installing xbuildenv I sometimes get following error while installing xbuild environment: ```bash $ pyodide build . Downloading xbuild environment Installing xbuild environment stderr: ERROR: Can not combine '--user' and '--target' [notice] A new release of pip available: 22.3.1 -> 23.0 [notice] To update, run: /home/gitpod/.pyenv/versions/3.10.2/bin/python -m pip install --upgrade pip ``` It happens here, which installs host site packages: https://github.com/pyodide/pyodide/blob/7cc1058358242a5a9012edbb8163d86a860a1a28/pyodide-build/pyodide_build/install_xbuildenv.py#L50-L57 I think we need to add `--no-user` flag explicitly to prevent this error.
[ { "content": "import argparse\nimport json\nimport shutil\nimport subprocess\nfrom pathlib import Path\nfrom urllib.request import urlopen, urlretrieve\n\nfrom .common import exit_with_stdio, get_make_flag, get_pyodide_root\nfrom .create_pypa_index import create_pypa_index\nfrom .logger import logger\n\n\ndef make_parser(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:\n parser.description = (\n \"Install xbuild env.\\n\\n\"\n \"The installed environment is the same as the one that would result from\\n\"\n \"`PYODIDE_PACKAGES='scipy' make` except that it is much faster.\\n\"\n \"The goal is to enable out-of-tree builds for binary packages that depend\\n\"\n \"on numpy or scipy.\\n\"\n \"Note: this is a private endpoint that should not be used outside of the Pyodide Makefile.\"\n )\n parser.add_argument(\"--download\", action=\"store_true\", help=\"Download xbuild env\")\n parser.add_argument(\"xbuildenv\", type=str, nargs=1)\n return parser\n\n\ndef download_xbuildenv(version: str, xbuildenv_path: Path) -> None:\n from shutil import rmtree, unpack_archive\n from tempfile import NamedTemporaryFile\n\n logger.info(\"Downloading xbuild environment\")\n rmtree(xbuildenv_path, ignore_errors=True)\n with NamedTemporaryFile(suffix=\".tar\") as f:\n urlretrieve(\n f\"https://github.com/pyodide/pyodide/releases/download/{version}/xbuildenv-{version}.tar.bz2\",\n f.name,\n )\n unpack_archive(f.name, xbuildenv_path)\n\n\ndef install_xbuildenv(version: str, xbuildenv_path: Path) -> None:\n logger.info(\"Installing xbuild environment\")\n xbuildenv_path = xbuildenv_path / \"xbuildenv\"\n pyodide_root = get_pyodide_root()\n xbuildenv_root = xbuildenv_path / \"pyodide-root\"\n host_site_packages = xbuildenv_root / Path(\n get_make_flag(\"HOSTSITEPACKAGES\")\n ).relative_to(pyodide_root)\n host_site_packages.mkdir(exist_ok=True, parents=True)\n result = subprocess.run(\n [\n \"pip\",\n \"install\",\n \"-t\",\n host_site_packages,\n \"-r\",\n xbuildenv_path / \"requirements.txt\",\n ],\n capture_output=True,\n encoding=\"utf8\",\n )\n if result.returncode != 0:\n exit_with_stdio(result)\n # Copy the site-packages-extras (coming from the cross-build-files meta.yaml\n # key) over the site-packages directory with the newly installed packages.\n shutil.copytree(\n xbuildenv_path / \"site-packages-extras\", host_site_packages, dirs_exist_ok=True\n )\n cdn_base = f\"https://cdn.jsdelivr.net/pyodide/v{version}/full/\"\n if (xbuildenv_root / \"repodata.json\").exists():\n repodata_bytes = (xbuildenv_root / \"repodata.json\").read_bytes()\n else:\n repodata_url = cdn_base + \"repodata.json\"\n with urlopen(repodata_url) as response:\n repodata_bytes = response.read()\n repodata = json.loads(repodata_bytes)\n version = repodata[\"info\"][\"version\"]\n create_pypa_index(repodata[\"packages\"], xbuildenv_root, cdn_base)\n\n\ndef main(args: argparse.Namespace) -> None:\n from . import __version__\n\n xbuildenv_path = Path(args.xbuildenv[0])\n version = __version__\n if args.download:\n download_xbuildenv(version, xbuildenv_path)\n install_xbuildenv(version, xbuildenv_path)\n", "path": "pyodide-build/pyodide_build/install_xbuildenv.py" } ]
[ { "content": "import argparse\nimport json\nimport shutil\nimport subprocess\nfrom pathlib import Path\nfrom urllib.request import urlopen, urlretrieve\n\nfrom .common import exit_with_stdio, get_make_flag, get_pyodide_root\nfrom .create_pypa_index import create_pypa_index\nfrom .logger import logger\n\n\ndef make_parser(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:\n parser.description = (\n \"Install xbuild env.\\n\\n\"\n \"The installed environment is the same as the one that would result from\\n\"\n \"`PYODIDE_PACKAGES='scipy' make` except that it is much faster.\\n\"\n \"The goal is to enable out-of-tree builds for binary packages that depend\\n\"\n \"on numpy or scipy.\\n\"\n \"Note: this is a private endpoint that should not be used outside of the Pyodide Makefile.\"\n )\n parser.add_argument(\"--download\", action=\"store_true\", help=\"Download xbuild env\")\n parser.add_argument(\"xbuildenv\", type=str, nargs=1)\n return parser\n\n\ndef download_xbuildenv(version: str, xbuildenv_path: Path) -> None:\n from shutil import rmtree, unpack_archive\n from tempfile import NamedTemporaryFile\n\n logger.info(\"Downloading xbuild environment\")\n rmtree(xbuildenv_path, ignore_errors=True)\n with NamedTemporaryFile(suffix=\".tar\") as f:\n urlretrieve(\n f\"https://github.com/pyodide/pyodide/releases/download/{version}/xbuildenv-{version}.tar.bz2\",\n f.name,\n )\n unpack_archive(f.name, xbuildenv_path)\n\n\ndef install_xbuildenv(version: str, xbuildenv_path: Path) -> None:\n logger.info(\"Installing xbuild environment\")\n xbuildenv_path = xbuildenv_path / \"xbuildenv\"\n pyodide_root = get_pyodide_root()\n xbuildenv_root = xbuildenv_path / \"pyodide-root\"\n host_site_packages = xbuildenv_root / Path(\n get_make_flag(\"HOSTSITEPACKAGES\")\n ).relative_to(pyodide_root)\n host_site_packages.mkdir(exist_ok=True, parents=True)\n result = subprocess.run(\n [\n \"pip\",\n \"install\",\n \"--no-user\",\n \"-t\",\n host_site_packages,\n \"-r\",\n xbuildenv_path / \"requirements.txt\",\n ],\n capture_output=True,\n encoding=\"utf8\",\n )\n if result.returncode != 0:\n exit_with_stdio(result)\n # Copy the site-packages-extras (coming from the cross-build-files meta.yaml\n # key) over the site-packages directory with the newly installed packages.\n shutil.copytree(\n xbuildenv_path / \"site-packages-extras\", host_site_packages, dirs_exist_ok=True\n )\n cdn_base = f\"https://cdn.jsdelivr.net/pyodide/v{version}/full/\"\n if (xbuildenv_root / \"repodata.json\").exists():\n repodata_bytes = (xbuildenv_root / \"repodata.json\").read_bytes()\n else:\n repodata_url = cdn_base + \"repodata.json\"\n with urlopen(repodata_url) as response:\n repodata_bytes = response.read()\n repodata = json.loads(repodata_bytes)\n version = repodata[\"info\"][\"version\"]\n create_pypa_index(repodata[\"packages\"], xbuildenv_root, cdn_base)\n\n\ndef main(args: argparse.Namespace) -> None:\n from . import __version__\n\n xbuildenv_path = Path(args.xbuildenv[0])\n version = __version__\n if args.download:\n download_xbuildenv(version, xbuildenv_path)\n install_xbuildenv(version, xbuildenv_path)\n", "path": "pyodide-build/pyodide_build/install_xbuildenv.py" } ]
diff --git a/docs/project/changelog.md b/docs/project/changelog.md index 7790fef37c2..5e447fee1ba 100644 --- a/docs/project/changelog.md +++ b/docs/project/changelog.md @@ -108,6 +108,9 @@ myst: CPU cores in the system and uses them for parallel builds. {pr}`3559` +- {{ Fix }} Fixed pip install error when installing cross build environment. + {pr}`3562` + ### Pyodide CLI - Added `pyodide py-compile` CLI command that py compiles a wheel, converting diff --git a/pyodide-build/pyodide_build/install_xbuildenv.py b/pyodide-build/pyodide_build/install_xbuildenv.py index e5a40358312..23e6a68d91f 100644 --- a/pyodide-build/pyodide_build/install_xbuildenv.py +++ b/pyodide-build/pyodide_build/install_xbuildenv.py @@ -51,6 +51,7 @@ def install_xbuildenv(version: str, xbuildenv_path: Path) -> None: [ "pip", "install", + "--no-user", "-t", host_site_packages, "-r",
joke2k__faker-1423
Faker adds path objects to sys.path_importer_cache, breaking other packages * Faker version: 6.6.3 * OS: Gentoo Linux After importing `faker`, entries with `PosixPath` objects are added as keys to `sys.path_importer_cache`. However, the keys are supposed to be regular `str`s there, and the wrong type breaks software relying on `str` methods being available, e.g. astroid: ``` ___________________________________________ ClassNodeTest.test_slots_added_dynamically_still_inferred ____________________________________________ self = <tests.unittest_scoped_nodes.ClassNodeTest testMethod=test_slots_added_dynamically_still_inferred> def tearDown(self): del sys.path[0] datadir = find("") for key in list(sys.path_importer_cache): > if key.startswith(datadir): E AttributeError: 'PosixPath' object has no attribute 'startswith' tests/resources.py:41: AttributeError ``` Note that since Faker installs a pytest plugin, it is autoloaded by default in all programs' test suites. ### Steps to reproduce ``` import sys import faker print(sys.path_importer_cache) ``` ### Expected behavior The printed dict should only contain `str` keys. ### Actual behavior ``` [...] PosixPath('/usr/lib/python3.9/site-packages/faker/providers/address'): FileFinder(PosixPath('/usr/lib/python3.9/site-packages/faker/providers/address')), PosixPath('/usr/lib/python3.9/site-packages/faker/providers/automotive'): FileFinder(PosixPath('/usr/lib/python3.9/site-packages/faker/providers/automotive')), [...] ```
[ { "content": "import pkgutil\nimport sys\n\nfrom importlib import import_module\nfrom pathlib import Path\nfrom types import ModuleType\nfrom typing import List, Set\n\n\ndef get_path(module: ModuleType) -> str:\n if getattr(sys, 'frozen', False):\n # frozen\n\n if getattr(sys, '_MEIPASS', False):\n # PyInstaller\n lib_dir = Path(getattr(sys, '_MEIPASS'))\n else:\n # others\n lib_dir = Path(sys.executable).parent / 'lib'\n\n path = lib_dir.joinpath(*module.__package__.split(\".\"))\n else:\n # unfrozen\n path = Path(module.__file__).parent\n return path\n\n\ndef list_module(module: ModuleType) -> List[str]:\n path = get_path(module)\n\n if getattr(sys, '_MEIPASS', False):\n # PyInstaller\n return [file.parent.name for file in Path(path).glob('*/__init__.py')]\n else:\n return [name for _, name, is_pkg in pkgutil.iter_modules([path]) if is_pkg]\n\n\ndef find_available_locales(providers: List[str]) -> List[str]:\n available_locales: Set[str] = set()\n\n for provider_path in providers:\n\n provider_module = import_module(provider_path)\n if getattr(provider_module, 'localized', False):\n langs = list_module(provider_module)\n available_locales.update(langs)\n available_locales: List[str] = sorted(available_locales)\n return available_locales\n\n\ndef find_available_providers(modules: List[ModuleType]) -> List[str]:\n available_providers = set()\n for providers_mod in modules:\n if providers_mod.__package__:\n providers = [\n '.'.join([providers_mod.__package__, mod])\n for mod in list_module(providers_mod) if mod != '__pycache__'\n ]\n available_providers.update(providers)\n return sorted(available_providers)\n", "path": "faker/utils/loading.py" } ]
[ { "content": "import pkgutil\nimport sys\n\nfrom importlib import import_module\nfrom pathlib import Path\nfrom types import ModuleType\nfrom typing import List, Set\n\n\ndef get_path(module: ModuleType) -> str:\n if getattr(sys, 'frozen', False):\n # frozen\n\n if getattr(sys, '_MEIPASS', False):\n # PyInstaller\n lib_dir = Path(getattr(sys, '_MEIPASS'))\n else:\n # others\n lib_dir = Path(sys.executable).parent / 'lib'\n\n path = lib_dir.joinpath(*module.__package__.split(\".\"))\n else:\n # unfrozen\n path = Path(module.__file__).parent\n return str(path)\n\n\ndef list_module(module: ModuleType) -> List[str]:\n path = get_path(module)\n\n if getattr(sys, '_MEIPASS', False):\n # PyInstaller\n return [file.parent.name for file in Path(path).glob('*/__init__.py')]\n else:\n return [name for _, name, is_pkg in pkgutil.iter_modules([path]) if is_pkg]\n\n\ndef find_available_locales(providers: List[str]) -> List[str]:\n available_locales: Set[str] = set()\n\n for provider_path in providers:\n\n provider_module = import_module(provider_path)\n if getattr(provider_module, 'localized', False):\n langs = list_module(provider_module)\n available_locales.update(langs)\n available_locales: List[str] = sorted(available_locales)\n return available_locales\n\n\ndef find_available_providers(modules: List[ModuleType]) -> List[str]:\n available_providers = set()\n for providers_mod in modules:\n if providers_mod.__package__:\n providers = [\n '.'.join([providers_mod.__package__, mod])\n for mod in list_module(providers_mod) if mod != '__pycache__'\n ]\n available_providers.update(providers)\n return sorted(available_providers)\n", "path": "faker/utils/loading.py" } ]
diff --git a/faker/utils/loading.py b/faker/utils/loading.py index 18ffb6482c..757a012565 100644 --- a/faker/utils/loading.py +++ b/faker/utils/loading.py @@ -22,7 +22,7 @@ def get_path(module: ModuleType) -> str: else: # unfrozen path = Path(module.__file__).parent - return path + return str(path) def list_module(module: ModuleType) -> List[str]:
microsoft__DeepSpeed-2267
[BUG] ImportError: cannot import name 'OrderedDict' python3.6, master version ``` Traceback (most recent call last): File "/opt/conda/bin/ds_report", line 3, in <module> from deepspeed.env_report import cli_main File "/opt/conda/lib/python3.6/site-packages/deepspeed/__init__.py", line 16, in <module> from .runtime.engine import DeepSpeedEngine, DeepSpeedOptimizerCallable, DeepSpeedSchedulerCallable File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/engine.py", line 30, in <module> from deepspeed.runtime.bf16_optimizer import BF16_Optimizer File "/opt/conda/lib/python3.6/site-packages/deepspeed/runtime/bf16_optimizer.py", line 5, in <module> from typing import OrderedDict ImportError: cannot import name 'OrderedDict' ```
[ { "content": "\"\"\"\nCopyright 2022 The Microsoft DeepSpeed Team\n\"\"\"\n\nfrom typing import OrderedDict\nimport torch\nimport os\nfrom deepspeed import comm as dist\nfrom deepspeed.runtime.constants import PIPE_REPLICATED\nfrom deepspeed.ops.op_builder import UtilsBuilder\nfrom deepspeed.runtime import ZeROOptimizer\nfrom packaging import version as pkg_version\n\nfrom deepspeed.git_version_info import version\nfrom deepspeed.runtime.utils import (get_global_norm_of_tensors,\n clip_tensors_by_global_norm,\n DummyOptim,\n align_dense_tensors,\n all_gather_dp_groups,\n bwc_tensor_model_parallel_rank,\n is_model_parallel_parameter,\n see_memory_usage)\n\nfrom deepspeed.checkpoint.constants import (DS_VERSION,\n PARTITION_COUNT,\n BASE_OPTIMIZER_STATE,\n SINGLE_PARTITION_OF_FP32_GROUPS,\n CLIP_GRAD,\n GROUP_PADDINGS,\n PARAM_SLICE_MAPPINGS,\n FP32_WEIGHT_KEY)\n\nimport types\n\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass fragment_address:\n numel: int\n start: int\n\n\n@dataclass\nclass tensor_fragment:\n lp_fragment: torch.Tensor\n lp_fragment_address: fragment_address\n hp_fragment: torch.Tensor\n hp_fragment_address: fragment_address\n optim_fragment: {}\n\n def update_hp(self):\n self.hp_fragment.data.copy_(self.lp_fragment.data)\n\n def update_lp(self):\n self.lp_fragment.data.copy_(self.hp_fragment.data)\n\n def get_optim_state_fragment(self, key):\n if key in self.optim_fragment:\n return self.optim_fragment[key]\n else:\n raise ValueError(f'{key} not found in optimizer state fragment')\n\n def get_hp_fragment_address(self):\n return self.hp_fragment_address\n\n def get_optim_state_keys(self):\n return list(self.optim_fragment.keys())\n\n\ndef get_full_hp_param(self, optim_state_key=None):\n reduce_buffer = torch.zeros_like(self, dtype=torch.float32).flatten()\n if self._hp_mapping is not None:\n lp_frag_address = self._hp_mapping.lp_fragment_address\n reduce_fragment = torch.narrow(reduce_buffer,\n 0,\n lp_frag_address.start,\n lp_frag_address.numel)\n if optim_state_key is None:\n hp_fragment = self._hp_mapping.hp_fragment\n else:\n hp_fragment = self._hp_mapping.get_optim_state_fragment(optim_state_key)\n\n reduce_fragment.data.copy_(hp_fragment.data)\n dist.all_reduce(reduce_buffer, group=self._dp_group)\n return reduce_buffer.reshape_as(self)\n\n\ndef load_hp_checkpoint_state(self, folder, tp_rank, tp_world_size):\n hp_mapping = self._hp_mapping\n optim_state_keys = hp_mapping.get_optim_state_keys()\n hp_keys = [FP32_WEIGHT_KEY] + optim_state_keys\n checkpoint_files = {key: os.path.join(folder, f\"{key}.pt\") for key in hp_keys}\n\n for file in checkpoint_files.values():\n assert os.path.isfile(file), f'{file} is not a valid file'\n\n for key in hp_keys:\n ckpt_file = checkpoint_files[key]\n ckpt_dict = torch.load(ckpt_file)\n full_hp_param = ckpt_dict['param']\n\n # need to deal with slices that were averaged.\n # the opposite of averaging here becomes an exact copy of the first slice\n # I thought of 2 ways:\n # implementation a. find a way for a client to pass a dict with patterns\n # if any(re.search(pattern, folder) for pattern in WEIGHTS_TO_AVERAGE_PATTERNS):\n # tp_rank = 0\n # tp_world_size = 1\n # the other approach is to assume that the saved data is correct and if full_hp_param.shape ==\n # self.shape that means we automatically copy?\n # implementation b.\n # this version requires no additional data passed from the client\n # if the shapes already match it must be slices that were averaged - so we just hack around those\n if full_hp_param.shape == self.shape:\n tp_rank = 0\n tp_world_size = 1\n\n # special case for word_embeddings weights which get padded differently depending on TP degree.\n # the converter to universal currently strips the original padding completely so the saved\n # weight is padding-free and we just need to add new padding depending on the target TP\n # degree\n vocab_divisibility_padding_tensor = ckpt_dict.get(\n 'vocab_divisibility_padding_tensor',\n None)\n if vocab_divisibility_padding_tensor is not None:\n # In the absence of data passed from the user wrt new padded vocab specific to tp degree\n # we can again derive that data by reverse engineering the target shapes like so:\n padded_target_vocab_size = self.shape[0] * tp_world_size\n if padded_target_vocab_size > full_hp_param.shape[0]:\n # Need to expand\n padding_tensor = vocab_divisibility_padding_tensor.expand(\n padded_target_vocab_size - full_hp_param.shape[0])\n # Implement the following concat in efficient way using pad\n #full_hp_param = torch.cat((full_hp_param, padding_tensor), 0)\n full_hp_param = torch.nn.functional.pad(full_hp_param,\n (0,\n 0,\n 0,\n padding_tensor.shape[0]),\n \"constant\",\n 0)\n full_hp_param[:-padding_tensor.shape[0], :] = padding_tensor\n else:\n # Need to shrink or keep the same\n full_hp_param = full_hp_param[:padded_target_vocab_size, :]\n\n full_param_numel = full_hp_param.numel()\n tp_slice_numel = self.numel()\n # if key == FP32_WEIGHT_KEY and 'word_embeddings.weight' in folder:\n # print_rank_0(f'{full_hp_param[:10]=}', force=True)\n\n\n assert full_param_numel == tp_world_size * tp_slice_numel, \\\n f'Loading {ckpt_file} full param numel {full_param_numel} != tensor slice numel {tp_slice_numel} * tp_world_size {tp_world_size}'\n dst_tensor = hp_mapping.hp_fragment if key == FP32_WEIGHT_KEY else hp_mapping.get_optim_state_fragment(\n key)\n\n # print(f\"{full_hp_param.shape=} {full_param_numel=} {folder=}\")\n # print(f\"{dst_tensor.shape=} {dst_tensor.numel()=}{folder=}\")\n\n # since when we do many to 1 on tp we cat sometimes on dim=0 and other times on dim=1 we have to do exactly the same in reverse\n chunk_dim = ckpt_dict.get('cat_dim', 0)\n\n # this performs the opposite of cat when merging TP slices\n tp_hp_slice = full_hp_param.chunk(tp_world_size, chunk_dim)[tp_rank]\n tp_hp_slice = tp_hp_slice.flatten()\n\n lp_frag_address = hp_mapping.lp_fragment_address\n tp_hp_fragment = tp_hp_slice.narrow(0,\n lp_frag_address.start,\n lp_frag_address.numel)\n assert dst_tensor.numel() == lp_frag_address.numel, \\\n f'Load checkpoint {key} dst_tensor numel {dst_tensor.numel()} != src numel {lp_frag_address.numel}'\n\n # print(f\"{key} SHAPE: {tp_hp_slice.shape=}\")\n # print(f\"{key} SHAPE: {dst_tensor.shape=}\")\n # print(f\"{key} SHAPE: {tp_hp_fragment.shape=}\")\n dst_tensor.data.copy_(tp_hp_fragment.data)\n\n\nclass BF16_Optimizer(ZeROOptimizer):\n def __init__(self,\n init_optimizer,\n param_names,\n mpu=None,\n clip_grad=0.0,\n norm_type=2,\n allgather_bucket_size=5000000000,\n dp_process_group=None,\n timers=None):\n super().__init__()\n see_memory_usage('begin bf16_optimizer', force=True)\n self.timers = timers\n self.optimizer = init_optimizer\n self.param_names = param_names\n self.using_real_optimizer = not isinstance(self.optimizer, DummyOptim)\n\n self.clip_grad = clip_grad\n self.norm_type = norm_type\n self.mpu = mpu\n self.allgather_bucket_size = int(allgather_bucket_size)\n self.dp_process_group = dp_process_group\n self.dp_rank = dist.get_rank(group=self.dp_process_group)\n self.real_dp_process_group = [\n dp_process_group for i in range(len(self.optimizer.param_groups))\n ]\n\n # Load pre-built or JIT compile (un)flatten ops\n util_ops = UtilsBuilder().load()\n self.flatten = util_ops.flatten\n self.unflatten = util_ops.unflatten\n\n #align nccl all-gather send buffers to 4-bye boundary\n self.nccl_start_alignment_factor = 2 # 4-byte alignment/sizeof(fp16) = 2\n\n # Build BF16/FP32 groups\n self.bf16_groups = []\n self.bf16_groups_flat = []\n self.bf16_partitioned_groups = []\n\n self.fp32_groups_flat_partition = []\n\n # Maintain different fp32 gradients views for convenience\n self.fp32_groups_gradients = []\n self.fp32_groups_gradients_flat = []\n self.fp32_groups_actual_gradients_flat = []\n self.fp32_groups_gradient_flat_partition = []\n self.fp32_groups_has_gradients = []\n\n self.step_count = 0\n self.group_paddings = []\n\n if self.using_real_optimizer:\n self._setup_for_real_optimizer()\n\n see_memory_usage('end bf16_optimizer', force=True)\n\n def _setup_for_real_optimizer(self):\n dp_world_size = dist.get_world_size(group=self.dp_process_group)\n self.partition_count = [\n dp_world_size for i in range(len(self.optimizer.param_groups))\n ]\n\n for i, param_group in enumerate(self.optimizer.param_groups):\n see_memory_usage(f'before initializing group {i}', force=True)\n\n partition_id = dist.get_rank(group=self.real_dp_process_group[i])\n\n # grab the original list\n self.bf16_groups.append(param_group['params'])\n\n # create flat bf16 params\n self.bf16_groups_flat.append(\n self._flatten_dense_tensors_aligned(\n self.bf16_groups[i],\n self.nccl_start_alignment_factor * dp_world_size))\n\n # Make bf16 params point to flat tensor storage\n self._update_storage_to_flattened_tensor(\n tensor_list=self.bf16_groups[i],\n flat_tensor=self.bf16_groups_flat[i])\n\n # divide flat weights into equal sized partitions\n partition_size = self.bf16_groups_flat[i].numel() // dp_world_size\n bf16_dp_partitions = [\n self.bf16_groups_flat[i].narrow(0,\n dp_index * partition_size,\n partition_size)\n for dp_index in range(dp_world_size)\n ]\n self.bf16_partitioned_groups.append(bf16_dp_partitions)\n\n # create fp32 params partition\n self.fp32_groups_flat_partition.append(\n bf16_dp_partitions[partition_id].clone().float().detach())\n self.fp32_groups_flat_partition[i].requires_grad = True\n\n num_elem_list = [t.numel() for t in self.bf16_groups[i]]\n\n # create fp32 gradients\n self.fp32_groups_gradients_flat.append(\n torch.zeros_like(self.bf16_groups_flat[i],\n dtype=torch.float32))\n\n # track individual fp32 gradients for entire model\n fp32_gradients = self._split_flat_tensor(\n flat_tensor=self.fp32_groups_gradients_flat[i],\n num_elem_list=num_elem_list)\n self.fp32_groups_gradients.append(fp32_gradients)\n\n # flat tensor corresponding to actual fp32 gradients (i.e., minus alignment padding)\n length_without_padding = sum(num_elem_list)\n self.fp32_groups_actual_gradients_flat.append(\n torch.narrow(self.fp32_groups_gradients_flat[i],\n 0,\n 0,\n length_without_padding))\n\n # flat tensor corresponding to gradient partition\n self.fp32_groups_gradient_flat_partition.append(\n torch.narrow(self.fp32_groups_gradients_flat[i],\n 0,\n partition_id * partition_size,\n partition_size))\n\n # track fp32 gradient updates\n self.fp32_groups_has_gradients.append([False] * len(self.bf16_groups[i]))\n\n # Record padding required for alignment\n if partition_id == dist.get_world_size(\n group=self.real_dp_process_group[i]) - 1:\n padding = self.bf16_groups_flat[i].numel() - length_without_padding\n else:\n padding = 0\n\n self.group_paddings.append(padding)\n\n # update optimizer param groups to reference fp32 params partition\n param_group['params'] = [self.fp32_groups_flat_partition[i]]\n\n see_memory_usage(f'after initializing group {i}', force=True)\n\n see_memory_usage('before initialize_optimizer', force=True)\n self.initialize_optimizer_states()\n see_memory_usage('end initialize_optimizer', force=True)\n\n # Need optimizer states initialized before linking lp to optimizer state\n self._link_all_hp_params()\n self._param_slice_mappings = self._create_param_mapping()\n\n def _create_param_mapping(self):\n param_mapping = []\n for i, _ in enumerate(self.optimizer.param_groups):\n param_mapping_per_group = OrderedDict()\n for lp in self.bf16_groups[i]:\n if lp._hp_mapping is not None:\n lp_name = self.param_names[lp]\n param_mapping_per_group[\n lp_name] = lp._hp_mapping.get_hp_fragment_address()\n param_mapping.append(param_mapping_per_group)\n\n return param_mapping\n\n def _link_all_hp_params(self):\n dp_world_size = dist.get_world_size(group=self.dp_process_group)\n for i, param_group in enumerate(self.optimizer.param_groups):\n # Link bf16 and fp32 params in partition\n partition_id = dist.get_rank(group=self.real_dp_process_group[i])\n partition_size = self.bf16_groups_flat[i].numel() // dp_world_size\n self._link_hp_params(self.bf16_groups[i],\n self.fp32_groups_flat_partition[i],\n partition_id * partition_size,\n partition_size,\n self.real_dp_process_group[i])\n\n def _init_lp_to_hp_mapping(self,\n lp_param_list,\n partition_start,\n partition_size,\n dp_group):\n current_offset = 0\n param_and_offset_list = []\n partition_end = partition_start + partition_size\n for lp_param in lp_param_list:\n lp_param._hp_mapping = None\n lp_param._dp_group = dp_group\n lp_param.get_full_hp_param = types.MethodType(get_full_hp_param, lp_param)\n lp_param.load_hp_checkpoint_state = types.MethodType(\n load_hp_checkpoint_state,\n lp_param)\n # lp_param overlaps with partition if both are true\n # 1) current_offset < partition_end,\n # 2) current_offset + lp_param.numel() >= partition_start\n lp_param_end = current_offset + lp_param.numel()\n if current_offset < partition_end and lp_param_end > partition_start:\n param_and_offset_list.append((lp_param, current_offset))\n current_offset += lp_param.numel()\n\n return param_and_offset_list\n\n def _link_hp_params(self,\n lp_param_list,\n flat_hp_partition,\n partition_start,\n partition_size,\n dp_group):\n local_lp_param_and_offset = self._init_lp_to_hp_mapping(\n lp_param_list,\n partition_start,\n partition_size,\n dp_group)\n\n hp_end = partition_start + partition_size\n for lp_param, lp_start in local_lp_param_and_offset:\n lp_end = lp_param.numel() + lp_start\n hp_start = partition_start\n\n fragment_start = max(lp_start, hp_start)\n fragment_end = min(lp_end, hp_end)\n # print(\n # f'{self.dp_rank=} {lp_start=} {lp_end-lp_start=} {hp_start=} {hp_end-hp_start=} {fragment_start=} {fragment_end-fragment_start=}'\n # )\n assert fragment_start < fragment_end, \\\n f'fragment start {fragment_start} should be < fragment_end {fragment_end}'\n\n fragment_numel = fragment_end - fragment_start\n hp_frag_address = fragment_address(start=fragment_start - hp_start,\n numel=fragment_numel)\n hp_fragment_tensor = flat_hp_partition.narrow(0,\n hp_frag_address.start,\n hp_frag_address.numel)\n\n optim_fragment = {\n key: value.narrow(0,\n hp_frag_address.start,\n hp_frag_address.numel)\n for key,\n value in self.optimizer.state[flat_hp_partition].items()\n if torch.is_tensor(value) and value.dim() > 0\n }\n\n lp_frag_address = fragment_address(start=fragment_start - lp_start,\n numel=fragment_numel)\n lp_fragment_tensor = lp_param.flatten().narrow(0,\n lp_frag_address.start,\n lp_frag_address.numel)\n\n lp_param._hp_mapping = tensor_fragment(lp_fragment=lp_fragment_tensor,\n lp_fragment_address=lp_frag_address,\n hp_fragment=hp_fragment_tensor,\n hp_fragment_address=hp_frag_address,\n optim_fragment=optim_fragment)\n\n def initialize_optimizer_states(self):\n \"\"\"Take an optimizer step with zero-valued gradients to allocate internal\n optimizer state.\n\n This helps prevent memory fragmentation by allocating optimizer state at the\n beginning of training instead of after activations have been allocated.\n \"\"\"\n for param_partition, grad_partition in zip(self.fp32_groups_flat_partition, self.fp32_groups_gradient_flat_partition):\n param_partition.grad = grad_partition\n\n self.optimizer.step()\n\n self.clear_hp_grads()\n\n def _split_flat_tensor(self, flat_tensor, num_elem_list):\n assert sum(num_elem_list) <= flat_tensor.numel()\n tensor_list = []\n offset = 0\n for num_elem in num_elem_list:\n dense_tensor = torch.narrow(flat_tensor, 0, offset, num_elem)\n tensor_list.append(dense_tensor)\n offset += num_elem\n\n return tensor_list\n\n def _update_storage_to_flattened_tensor(self, tensor_list, flat_tensor):\n updated_params = self.unflatten(flat_tensor, tensor_list)\n for p, q in zip(tensor_list, updated_params):\n p.data = q.data\n\n def _flatten_dense_tensors_aligned(self, tensor_list, alignment):\n return self.flatten(align_dense_tensors(tensor_list, alignment))\n\n @torch.no_grad()\n def step(self, closure=None):\n if closure is not None:\n raise NotImplementedError(f'{self.__class__} does not support closure.')\n\n all_groups_norm = get_global_norm_of_tensors(\n input_tensors=self.get_grads_for_norm(),\n mpu=self.mpu,\n norm_type=self.norm_type)\n self._global_grad_norm = all_groups_norm\n\n assert all_groups_norm > 0.\n if self.clip_grad > 0.:\n clip_tensors_by_global_norm(\n input_tensors=self.get_grads_for_norm(for_clipping=True),\n max_norm=self.clip_grad,\n global_norm=all_groups_norm,\n mpu=self.mpu)\n\n self.optimizer.step()\n\n self.update_lp_params()\n\n self.clear_hp_grads()\n self.step_count += 1\n\n def backward(self, loss, update_hp_grads=True, clear_lp_grads=False, **bwd_kwargs):\n \"\"\"Perform a backward pass and copy the low-precision gradients to the\n high-precision copy.\n\n We copy/accumulate to the high-precision grads now to prevent accumulating in the\n bf16 grads after successive backward() calls (i.e., grad accumulation steps > 1)\n\n The low-precision grads are deallocated during this procedure.\n \"\"\"\n self.clear_lp_grads()\n loss.backward(**bwd_kwargs)\n\n if update_hp_grads:\n self.update_hp_grads(clear_lp_grads=clear_lp_grads)\n\n @torch.no_grad()\n def update_hp_grads(self, clear_lp_grads=False):\n for i, group in enumerate(self.bf16_groups):\n for j, lp in enumerate(group):\n if lp.grad is None:\n continue\n\n hp_grad = self.fp32_groups_gradients[i][j]\n assert hp_grad is not None, \\\n f'high precision param has no gradient, lp param_id = {id(lp)} group_info = [{i}][{j}]'\n\n hp_grad.data.add_(lp.grad.data.to(hp_grad.dtype).view(hp_grad.shape))\n lp._hp_grad = hp_grad\n self.fp32_groups_has_gradients[i][j] = True\n\n # clear gradients\n if clear_lp_grads:\n lp.grad = None\n\n @torch.no_grad()\n def get_grads_for_reduction(self):\n return self.fp32_groups_gradients_flat\n\n @torch.no_grad()\n def get_grads_for_norm(self, for_clipping=False):\n grads = []\n tensor_mp_rank = bwc_tensor_model_parallel_rank(mpu=self.mpu)\n for i, group in enumerate(self.bf16_groups):\n for j, lp in enumerate(group):\n if not for_clipping:\n if hasattr(lp, PIPE_REPLICATED) and lp.ds_pipe_replicated:\n continue\n\n if not (tensor_mp_rank == 0 or is_model_parallel_parameter(lp)):\n continue\n\n if not self.fp32_groups_has_gradients[i][j]:\n continue\n\n grads.append(self.fp32_groups_gradients[i][j])\n\n return grads\n\n @torch.no_grad()\n def update_lp_params(self):\n for i, (bf16_partitions, fp32_partition) in enumerate(zip(self.bf16_partitioned_groups, self.fp32_groups_flat_partition)):\n partition_id = dist.get_rank(group=self.real_dp_process_group[i])\n bf16_partitions[partition_id].data.copy_(fp32_partition.data)\n # print_rank_0(f'update_lp_params {i=} {partition_id=}', force=True)\n # if i == 0:\n # print_rank_0(f'{fp32_partition[:10]=}', force=True)\n\n all_gather_dp_groups(partitioned_param_groups=self.bf16_partitioned_groups,\n dp_process_group=self.real_dp_process_group,\n start_alignment_factor=self.nccl_start_alignment_factor,\n allgather_bucket_size=self.allgather_bucket_size)\n\n def clear_hp_grads(self):\n for flat_gradients in self.fp32_groups_gradients_flat:\n flat_gradients.zero_()\n\n for i, group in enumerate(self.fp32_groups_gradients):\n self.fp32_groups_has_gradients[i] = [False] * len(group)\n\n def clear_lp_grads(self):\n for group in self.bf16_groups:\n for param in group:\n param.grad = None\n\n def state_dict(self):\n state_dict = {}\n state_dict[CLIP_GRAD] = self.clip_grad\n state_dict[BASE_OPTIMIZER_STATE] = self.optimizer.state_dict()\n state_dict[SINGLE_PARTITION_OF_FP32_GROUPS] = self.fp32_groups_flat_partition\n state_dict[GROUP_PADDINGS] = self.group_paddings\n state_dict[PARTITION_COUNT] = self.partition_count\n state_dict[DS_VERSION] = version\n state_dict[PARAM_SLICE_MAPPINGS] = self._param_slice_mappings\n\n return state_dict\n\n # Restore base optimizer fp32 weights bfloat16 weights\n def _restore_from_bit16_weights(self):\n for i, group in enumerate(self.bf16_groups):\n partition_id = dist.get_rank(group=self.real_dp_process_group[i])\n for bf16_partitions, fp32_partition in zip(self.bf16_partitioned_groups, self.fp32_groups_flat_partition):\n fp32_partition.data.copy_(bf16_partitions[partition_id].data)\n\n def refresh_fp32_params(self):\n self._restore_from_bit16_weights()\n\n def load_state_dict(self,\n state_dict_list,\n checkpoint_folder,\n load_optimizer_states=True,\n load_from_fp32_weights=False):\n if checkpoint_folder:\n self._load_universal_checkpoint(checkpoint_folder,\n load_optimizer_states,\n load_from_fp32_weights)\n else:\n self._load_legacy_checkpoint(state_dict_list,\n load_optimizer_states,\n load_from_fp32_weights)\n\n def _load_legacy_checkpoint(self,\n state_dict_list,\n load_optimizer_states=True,\n load_from_fp32_weights=False):\n\n dp_rank = dist.get_rank(group=self.dp_process_group)\n current_rank_sd = state_dict_list[dp_rank]\n\n ckpt_version = current_rank_sd.get(DS_VERSION, False)\n assert ckpt_version, f\"Empty ds_version in checkpoint, not clear how to proceed\"\n ckpt_version = pkg_version.parse(ckpt_version)\n\n self.clip_grad = current_rank_sd.get(CLIP_GRAD, self.clip_grad)\n\n if load_optimizer_states:\n self.optimizer.load_state_dict(current_rank_sd[BASE_OPTIMIZER_STATE])\n\n if load_from_fp32_weights:\n for current, saved in zip(self.fp32_groups_flat_partition, current_rank_sd[SINGLE_PARTITION_OF_FP32_GROUPS]):\n src_tensor = _get_padded_tensor(saved, current.numel())\n current.data.copy_(src_tensor.data)\n\n if load_optimizer_states:\n self._link_all_hp_params()\n\n def _load_universal_checkpoint(self,\n checkpoint_folder,\n load_optimizer_states,\n load_from_fp32_weights):\n self._load_hp_checkpoint_state(checkpoint_folder)\n\n @property\n def param_groups(self):\n \"\"\"Forward the wrapped optimizer's parameters.\"\"\"\n return self.optimizer.param_groups\n\n def _load_hp_checkpoint_state(self, checkpoint_dir):\n checkpoint_dir = os.path.join(checkpoint_dir, \"zero\")\n tp_rank = bwc_tensor_model_parallel_rank(mpu=self.mpu)\n tp_world_size = self.mpu.get_slice_parallel_world_size()\n\n for i, _ in enumerate(self.optimizer.param_groups):\n for lp in self.bf16_groups[i]:\n if lp._hp_mapping is not None:\n #print(f\"Loading {self.param_names[lp]} {tp_rank=} {tp_world_size=}\")\n lp.load_hp_checkpoint_state(\n os.path.join(checkpoint_dir,\n self.param_names[lp]),\n tp_rank,\n tp_world_size)\n\n\ndef _get_padded_tensor(src_tensor, size):\n if src_tensor.numel() >= size:\n return src_tensor\n padded_tensor = torch.zeros(size, dtype=src_tensor.dtype, device=src_tensor.device)\n slice_tensor = torch.narrow(padded_tensor, 0, 0, src_tensor.numel())\n slice_tensor.data.copy_(src_tensor.data)\n return padded_tensor\n\n\n'''\nLogic for lp_param to hp_param mapping\n\nlp lp0 lp1 lp2 lp3 lp4 <------- indices/names\nlp [ ][ ][ ][ ][ ] <-------- tensors\nflat_lp [ ] <-------- flat lp params\nflat_hp [ ] <------------------ flat hp partition on current rank\nfull_hp [ ] <------- full flat hp params\n\n\nlp2\n full numel = 16\n lp_frag\n numel = 12\n frag_start = 3\n frag_end = 15\n hp_frag\n numel = 12\n frag_start = 0\n frag_end = 11\n\n hp_frag.copy_(lp_frag)\n\n\nlp3:\n full numel = 4\n lp_frag\n numel = 4\n start = 0\n end = 3\n hp_frag\n numel = 4\n start = 12\n end = 15\n\n\nlp4:\n full numel = 12\n lp_frag\n numel = 4\n start = 0\n end = 3\n hp_frag\n numel = 4\n start = 16\n end = 19\n\n\n\nVisual depiction of above\nlp { }\nflat_lp [ ]\nflat_hp ( )\n\n\nflat_lp [ { ( } ) ]\n lx hx ly hy\n ly-hx\n\n\nlp { }\nflat_lp [ ]\nflat_hp ( )\n\n\nflat_lp [ ( { ) } ]\n hx lx hy ly\n hy-lx\n\nlp { }\nflat_lp [ ]\nflat_hp ( )\n\n\nflat_lp [ ( { } ) ]\n hx lx ly hy\n ly-lx\n\nlp -> (lx, hy)\nflat_hp -> (hx, hy)\n'''\n", "path": "deepspeed/runtime/bf16_optimizer.py" } ]
[ { "content": "\"\"\"\nCopyright 2022 The Microsoft DeepSpeed Team\n\"\"\"\n\nfrom collections import OrderedDict\nimport torch\nimport os\nfrom deepspeed import comm as dist\nfrom deepspeed.runtime.constants import PIPE_REPLICATED\nfrom deepspeed.ops.op_builder import UtilsBuilder\nfrom deepspeed.runtime import ZeROOptimizer\nfrom packaging import version as pkg_version\n\nfrom deepspeed.git_version_info import version\nfrom deepspeed.runtime.utils import (get_global_norm_of_tensors,\n clip_tensors_by_global_norm,\n DummyOptim,\n align_dense_tensors,\n all_gather_dp_groups,\n bwc_tensor_model_parallel_rank,\n is_model_parallel_parameter,\n see_memory_usage)\n\nfrom deepspeed.checkpoint.constants import (DS_VERSION,\n PARTITION_COUNT,\n BASE_OPTIMIZER_STATE,\n SINGLE_PARTITION_OF_FP32_GROUPS,\n CLIP_GRAD,\n GROUP_PADDINGS,\n PARAM_SLICE_MAPPINGS,\n FP32_WEIGHT_KEY)\n\nimport types\n\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass fragment_address:\n numel: int\n start: int\n\n\n@dataclass\nclass tensor_fragment:\n lp_fragment: torch.Tensor\n lp_fragment_address: fragment_address\n hp_fragment: torch.Tensor\n hp_fragment_address: fragment_address\n optim_fragment: {}\n\n def update_hp(self):\n self.hp_fragment.data.copy_(self.lp_fragment.data)\n\n def update_lp(self):\n self.lp_fragment.data.copy_(self.hp_fragment.data)\n\n def get_optim_state_fragment(self, key):\n if key in self.optim_fragment:\n return self.optim_fragment[key]\n else:\n raise ValueError(f'{key} not found in optimizer state fragment')\n\n def get_hp_fragment_address(self):\n return self.hp_fragment_address\n\n def get_optim_state_keys(self):\n return list(self.optim_fragment.keys())\n\n\ndef get_full_hp_param(self, optim_state_key=None):\n reduce_buffer = torch.zeros_like(self, dtype=torch.float32).flatten()\n if self._hp_mapping is not None:\n lp_frag_address = self._hp_mapping.lp_fragment_address\n reduce_fragment = torch.narrow(reduce_buffer,\n 0,\n lp_frag_address.start,\n lp_frag_address.numel)\n if optim_state_key is None:\n hp_fragment = self._hp_mapping.hp_fragment\n else:\n hp_fragment = self._hp_mapping.get_optim_state_fragment(optim_state_key)\n\n reduce_fragment.data.copy_(hp_fragment.data)\n dist.all_reduce(reduce_buffer, group=self._dp_group)\n return reduce_buffer.reshape_as(self)\n\n\ndef load_hp_checkpoint_state(self, folder, tp_rank, tp_world_size):\n hp_mapping = self._hp_mapping\n optim_state_keys = hp_mapping.get_optim_state_keys()\n hp_keys = [FP32_WEIGHT_KEY] + optim_state_keys\n checkpoint_files = {key: os.path.join(folder, f\"{key}.pt\") for key in hp_keys}\n\n for file in checkpoint_files.values():\n assert os.path.isfile(file), f'{file} is not a valid file'\n\n for key in hp_keys:\n ckpt_file = checkpoint_files[key]\n ckpt_dict = torch.load(ckpt_file)\n full_hp_param = ckpt_dict['param']\n\n # need to deal with slices that were averaged.\n # the opposite of averaging here becomes an exact copy of the first slice\n # I thought of 2 ways:\n # implementation a. find a way for a client to pass a dict with patterns\n # if any(re.search(pattern, folder) for pattern in WEIGHTS_TO_AVERAGE_PATTERNS):\n # tp_rank = 0\n # tp_world_size = 1\n # the other approach is to assume that the saved data is correct and if full_hp_param.shape ==\n # self.shape that means we automatically copy?\n # implementation b.\n # this version requires no additional data passed from the client\n # if the shapes already match it must be slices that were averaged - so we just hack around those\n if full_hp_param.shape == self.shape:\n tp_rank = 0\n tp_world_size = 1\n\n # special case for word_embeddings weights which get padded differently depending on TP degree.\n # the converter to universal currently strips the original padding completely so the saved\n # weight is padding-free and we just need to add new padding depending on the target TP\n # degree\n vocab_divisibility_padding_tensor = ckpt_dict.get(\n 'vocab_divisibility_padding_tensor',\n None)\n if vocab_divisibility_padding_tensor is not None:\n # In the absence of data passed from the user wrt new padded vocab specific to tp degree\n # we can again derive that data by reverse engineering the target shapes like so:\n padded_target_vocab_size = self.shape[0] * tp_world_size\n if padded_target_vocab_size > full_hp_param.shape[0]:\n # Need to expand\n padding_tensor = vocab_divisibility_padding_tensor.expand(\n padded_target_vocab_size - full_hp_param.shape[0])\n # Implement the following concat in efficient way using pad\n #full_hp_param = torch.cat((full_hp_param, padding_tensor), 0)\n full_hp_param = torch.nn.functional.pad(full_hp_param,\n (0,\n 0,\n 0,\n padding_tensor.shape[0]),\n \"constant\",\n 0)\n full_hp_param[:-padding_tensor.shape[0], :] = padding_tensor\n else:\n # Need to shrink or keep the same\n full_hp_param = full_hp_param[:padded_target_vocab_size, :]\n\n full_param_numel = full_hp_param.numel()\n tp_slice_numel = self.numel()\n # if key == FP32_WEIGHT_KEY and 'word_embeddings.weight' in folder:\n # print_rank_0(f'{full_hp_param[:10]=}', force=True)\n\n\n assert full_param_numel == tp_world_size * tp_slice_numel, \\\n f'Loading {ckpt_file} full param numel {full_param_numel} != tensor slice numel {tp_slice_numel} * tp_world_size {tp_world_size}'\n dst_tensor = hp_mapping.hp_fragment if key == FP32_WEIGHT_KEY else hp_mapping.get_optim_state_fragment(\n key)\n\n # print(f\"{full_hp_param.shape=} {full_param_numel=} {folder=}\")\n # print(f\"{dst_tensor.shape=} {dst_tensor.numel()=}{folder=}\")\n\n # since when we do many to 1 on tp we cat sometimes on dim=0 and other times on dim=1 we have to do exactly the same in reverse\n chunk_dim = ckpt_dict.get('cat_dim', 0)\n\n # this performs the opposite of cat when merging TP slices\n tp_hp_slice = full_hp_param.chunk(tp_world_size, chunk_dim)[tp_rank]\n tp_hp_slice = tp_hp_slice.flatten()\n\n lp_frag_address = hp_mapping.lp_fragment_address\n tp_hp_fragment = tp_hp_slice.narrow(0,\n lp_frag_address.start,\n lp_frag_address.numel)\n assert dst_tensor.numel() == lp_frag_address.numel, \\\n f'Load checkpoint {key} dst_tensor numel {dst_tensor.numel()} != src numel {lp_frag_address.numel}'\n\n # print(f\"{key} SHAPE: {tp_hp_slice.shape=}\")\n # print(f\"{key} SHAPE: {dst_tensor.shape=}\")\n # print(f\"{key} SHAPE: {tp_hp_fragment.shape=}\")\n dst_tensor.data.copy_(tp_hp_fragment.data)\n\n\nclass BF16_Optimizer(ZeROOptimizer):\n def __init__(self,\n init_optimizer,\n param_names,\n mpu=None,\n clip_grad=0.0,\n norm_type=2,\n allgather_bucket_size=5000000000,\n dp_process_group=None,\n timers=None):\n super().__init__()\n see_memory_usage('begin bf16_optimizer', force=True)\n self.timers = timers\n self.optimizer = init_optimizer\n self.param_names = param_names\n self.using_real_optimizer = not isinstance(self.optimizer, DummyOptim)\n\n self.clip_grad = clip_grad\n self.norm_type = norm_type\n self.mpu = mpu\n self.allgather_bucket_size = int(allgather_bucket_size)\n self.dp_process_group = dp_process_group\n self.dp_rank = dist.get_rank(group=self.dp_process_group)\n self.real_dp_process_group = [\n dp_process_group for i in range(len(self.optimizer.param_groups))\n ]\n\n # Load pre-built or JIT compile (un)flatten ops\n util_ops = UtilsBuilder().load()\n self.flatten = util_ops.flatten\n self.unflatten = util_ops.unflatten\n\n #align nccl all-gather send buffers to 4-bye boundary\n self.nccl_start_alignment_factor = 2 # 4-byte alignment/sizeof(fp16) = 2\n\n # Build BF16/FP32 groups\n self.bf16_groups = []\n self.bf16_groups_flat = []\n self.bf16_partitioned_groups = []\n\n self.fp32_groups_flat_partition = []\n\n # Maintain different fp32 gradients views for convenience\n self.fp32_groups_gradients = []\n self.fp32_groups_gradients_flat = []\n self.fp32_groups_actual_gradients_flat = []\n self.fp32_groups_gradient_flat_partition = []\n self.fp32_groups_has_gradients = []\n\n self.step_count = 0\n self.group_paddings = []\n\n if self.using_real_optimizer:\n self._setup_for_real_optimizer()\n\n see_memory_usage('end bf16_optimizer', force=True)\n\n def _setup_for_real_optimizer(self):\n dp_world_size = dist.get_world_size(group=self.dp_process_group)\n self.partition_count = [\n dp_world_size for i in range(len(self.optimizer.param_groups))\n ]\n\n for i, param_group in enumerate(self.optimizer.param_groups):\n see_memory_usage(f'before initializing group {i}', force=True)\n\n partition_id = dist.get_rank(group=self.real_dp_process_group[i])\n\n # grab the original list\n self.bf16_groups.append(param_group['params'])\n\n # create flat bf16 params\n self.bf16_groups_flat.append(\n self._flatten_dense_tensors_aligned(\n self.bf16_groups[i],\n self.nccl_start_alignment_factor * dp_world_size))\n\n # Make bf16 params point to flat tensor storage\n self._update_storage_to_flattened_tensor(\n tensor_list=self.bf16_groups[i],\n flat_tensor=self.bf16_groups_flat[i])\n\n # divide flat weights into equal sized partitions\n partition_size = self.bf16_groups_flat[i].numel() // dp_world_size\n bf16_dp_partitions = [\n self.bf16_groups_flat[i].narrow(0,\n dp_index * partition_size,\n partition_size)\n for dp_index in range(dp_world_size)\n ]\n self.bf16_partitioned_groups.append(bf16_dp_partitions)\n\n # create fp32 params partition\n self.fp32_groups_flat_partition.append(\n bf16_dp_partitions[partition_id].clone().float().detach())\n self.fp32_groups_flat_partition[i].requires_grad = True\n\n num_elem_list = [t.numel() for t in self.bf16_groups[i]]\n\n # create fp32 gradients\n self.fp32_groups_gradients_flat.append(\n torch.zeros_like(self.bf16_groups_flat[i],\n dtype=torch.float32))\n\n # track individual fp32 gradients for entire model\n fp32_gradients = self._split_flat_tensor(\n flat_tensor=self.fp32_groups_gradients_flat[i],\n num_elem_list=num_elem_list)\n self.fp32_groups_gradients.append(fp32_gradients)\n\n # flat tensor corresponding to actual fp32 gradients (i.e., minus alignment padding)\n length_without_padding = sum(num_elem_list)\n self.fp32_groups_actual_gradients_flat.append(\n torch.narrow(self.fp32_groups_gradients_flat[i],\n 0,\n 0,\n length_without_padding))\n\n # flat tensor corresponding to gradient partition\n self.fp32_groups_gradient_flat_partition.append(\n torch.narrow(self.fp32_groups_gradients_flat[i],\n 0,\n partition_id * partition_size,\n partition_size))\n\n # track fp32 gradient updates\n self.fp32_groups_has_gradients.append([False] * len(self.bf16_groups[i]))\n\n # Record padding required for alignment\n if partition_id == dist.get_world_size(\n group=self.real_dp_process_group[i]) - 1:\n padding = self.bf16_groups_flat[i].numel() - length_without_padding\n else:\n padding = 0\n\n self.group_paddings.append(padding)\n\n # update optimizer param groups to reference fp32 params partition\n param_group['params'] = [self.fp32_groups_flat_partition[i]]\n\n see_memory_usage(f'after initializing group {i}', force=True)\n\n see_memory_usage('before initialize_optimizer', force=True)\n self.initialize_optimizer_states()\n see_memory_usage('end initialize_optimizer', force=True)\n\n # Need optimizer states initialized before linking lp to optimizer state\n self._link_all_hp_params()\n self._param_slice_mappings = self._create_param_mapping()\n\n def _create_param_mapping(self):\n param_mapping = []\n for i, _ in enumerate(self.optimizer.param_groups):\n param_mapping_per_group = OrderedDict()\n for lp in self.bf16_groups[i]:\n if lp._hp_mapping is not None:\n lp_name = self.param_names[lp]\n param_mapping_per_group[\n lp_name] = lp._hp_mapping.get_hp_fragment_address()\n param_mapping.append(param_mapping_per_group)\n\n return param_mapping\n\n def _link_all_hp_params(self):\n dp_world_size = dist.get_world_size(group=self.dp_process_group)\n for i, param_group in enumerate(self.optimizer.param_groups):\n # Link bf16 and fp32 params in partition\n partition_id = dist.get_rank(group=self.real_dp_process_group[i])\n partition_size = self.bf16_groups_flat[i].numel() // dp_world_size\n self._link_hp_params(self.bf16_groups[i],\n self.fp32_groups_flat_partition[i],\n partition_id * partition_size,\n partition_size,\n self.real_dp_process_group[i])\n\n def _init_lp_to_hp_mapping(self,\n lp_param_list,\n partition_start,\n partition_size,\n dp_group):\n current_offset = 0\n param_and_offset_list = []\n partition_end = partition_start + partition_size\n for lp_param in lp_param_list:\n lp_param._hp_mapping = None\n lp_param._dp_group = dp_group\n lp_param.get_full_hp_param = types.MethodType(get_full_hp_param, lp_param)\n lp_param.load_hp_checkpoint_state = types.MethodType(\n load_hp_checkpoint_state,\n lp_param)\n # lp_param overlaps with partition if both are true\n # 1) current_offset < partition_end,\n # 2) current_offset + lp_param.numel() >= partition_start\n lp_param_end = current_offset + lp_param.numel()\n if current_offset < partition_end and lp_param_end > partition_start:\n param_and_offset_list.append((lp_param, current_offset))\n current_offset += lp_param.numel()\n\n return param_and_offset_list\n\n def _link_hp_params(self,\n lp_param_list,\n flat_hp_partition,\n partition_start,\n partition_size,\n dp_group):\n local_lp_param_and_offset = self._init_lp_to_hp_mapping(\n lp_param_list,\n partition_start,\n partition_size,\n dp_group)\n\n hp_end = partition_start + partition_size\n for lp_param, lp_start in local_lp_param_and_offset:\n lp_end = lp_param.numel() + lp_start\n hp_start = partition_start\n\n fragment_start = max(lp_start, hp_start)\n fragment_end = min(lp_end, hp_end)\n # print(\n # f'{self.dp_rank=} {lp_start=} {lp_end-lp_start=} {hp_start=} {hp_end-hp_start=} {fragment_start=} {fragment_end-fragment_start=}'\n # )\n assert fragment_start < fragment_end, \\\n f'fragment start {fragment_start} should be < fragment_end {fragment_end}'\n\n fragment_numel = fragment_end - fragment_start\n hp_frag_address = fragment_address(start=fragment_start - hp_start,\n numel=fragment_numel)\n hp_fragment_tensor = flat_hp_partition.narrow(0,\n hp_frag_address.start,\n hp_frag_address.numel)\n\n optim_fragment = {\n key: value.narrow(0,\n hp_frag_address.start,\n hp_frag_address.numel)\n for key,\n value in self.optimizer.state[flat_hp_partition].items()\n if torch.is_tensor(value) and value.dim() > 0\n }\n\n lp_frag_address = fragment_address(start=fragment_start - lp_start,\n numel=fragment_numel)\n lp_fragment_tensor = lp_param.flatten().narrow(0,\n lp_frag_address.start,\n lp_frag_address.numel)\n\n lp_param._hp_mapping = tensor_fragment(lp_fragment=lp_fragment_tensor,\n lp_fragment_address=lp_frag_address,\n hp_fragment=hp_fragment_tensor,\n hp_fragment_address=hp_frag_address,\n optim_fragment=optim_fragment)\n\n def initialize_optimizer_states(self):\n \"\"\"Take an optimizer step with zero-valued gradients to allocate internal\n optimizer state.\n\n This helps prevent memory fragmentation by allocating optimizer state at the\n beginning of training instead of after activations have been allocated.\n \"\"\"\n for param_partition, grad_partition in zip(self.fp32_groups_flat_partition, self.fp32_groups_gradient_flat_partition):\n param_partition.grad = grad_partition\n\n self.optimizer.step()\n\n self.clear_hp_grads()\n\n def _split_flat_tensor(self, flat_tensor, num_elem_list):\n assert sum(num_elem_list) <= flat_tensor.numel()\n tensor_list = []\n offset = 0\n for num_elem in num_elem_list:\n dense_tensor = torch.narrow(flat_tensor, 0, offset, num_elem)\n tensor_list.append(dense_tensor)\n offset += num_elem\n\n return tensor_list\n\n def _update_storage_to_flattened_tensor(self, tensor_list, flat_tensor):\n updated_params = self.unflatten(flat_tensor, tensor_list)\n for p, q in zip(tensor_list, updated_params):\n p.data = q.data\n\n def _flatten_dense_tensors_aligned(self, tensor_list, alignment):\n return self.flatten(align_dense_tensors(tensor_list, alignment))\n\n @torch.no_grad()\n def step(self, closure=None):\n if closure is not None:\n raise NotImplementedError(f'{self.__class__} does not support closure.')\n\n all_groups_norm = get_global_norm_of_tensors(\n input_tensors=self.get_grads_for_norm(),\n mpu=self.mpu,\n norm_type=self.norm_type)\n self._global_grad_norm = all_groups_norm\n\n assert all_groups_norm > 0.\n if self.clip_grad > 0.:\n clip_tensors_by_global_norm(\n input_tensors=self.get_grads_for_norm(for_clipping=True),\n max_norm=self.clip_grad,\n global_norm=all_groups_norm,\n mpu=self.mpu)\n\n self.optimizer.step()\n\n self.update_lp_params()\n\n self.clear_hp_grads()\n self.step_count += 1\n\n def backward(self, loss, update_hp_grads=True, clear_lp_grads=False, **bwd_kwargs):\n \"\"\"Perform a backward pass and copy the low-precision gradients to the\n high-precision copy.\n\n We copy/accumulate to the high-precision grads now to prevent accumulating in the\n bf16 grads after successive backward() calls (i.e., grad accumulation steps > 1)\n\n The low-precision grads are deallocated during this procedure.\n \"\"\"\n self.clear_lp_grads()\n loss.backward(**bwd_kwargs)\n\n if update_hp_grads:\n self.update_hp_grads(clear_lp_grads=clear_lp_grads)\n\n @torch.no_grad()\n def update_hp_grads(self, clear_lp_grads=False):\n for i, group in enumerate(self.bf16_groups):\n for j, lp in enumerate(group):\n if lp.grad is None:\n continue\n\n hp_grad = self.fp32_groups_gradients[i][j]\n assert hp_grad is not None, \\\n f'high precision param has no gradient, lp param_id = {id(lp)} group_info = [{i}][{j}]'\n\n hp_grad.data.add_(lp.grad.data.to(hp_grad.dtype).view(hp_grad.shape))\n lp._hp_grad = hp_grad\n self.fp32_groups_has_gradients[i][j] = True\n\n # clear gradients\n if clear_lp_grads:\n lp.grad = None\n\n @torch.no_grad()\n def get_grads_for_reduction(self):\n return self.fp32_groups_gradients_flat\n\n @torch.no_grad()\n def get_grads_for_norm(self, for_clipping=False):\n grads = []\n tensor_mp_rank = bwc_tensor_model_parallel_rank(mpu=self.mpu)\n for i, group in enumerate(self.bf16_groups):\n for j, lp in enumerate(group):\n if not for_clipping:\n if hasattr(lp, PIPE_REPLICATED) and lp.ds_pipe_replicated:\n continue\n\n if not (tensor_mp_rank == 0 or is_model_parallel_parameter(lp)):\n continue\n\n if not self.fp32_groups_has_gradients[i][j]:\n continue\n\n grads.append(self.fp32_groups_gradients[i][j])\n\n return grads\n\n @torch.no_grad()\n def update_lp_params(self):\n for i, (bf16_partitions, fp32_partition) in enumerate(zip(self.bf16_partitioned_groups, self.fp32_groups_flat_partition)):\n partition_id = dist.get_rank(group=self.real_dp_process_group[i])\n bf16_partitions[partition_id].data.copy_(fp32_partition.data)\n # print_rank_0(f'update_lp_params {i=} {partition_id=}', force=True)\n # if i == 0:\n # print_rank_0(f'{fp32_partition[:10]=}', force=True)\n\n all_gather_dp_groups(partitioned_param_groups=self.bf16_partitioned_groups,\n dp_process_group=self.real_dp_process_group,\n start_alignment_factor=self.nccl_start_alignment_factor,\n allgather_bucket_size=self.allgather_bucket_size)\n\n def clear_hp_grads(self):\n for flat_gradients in self.fp32_groups_gradients_flat:\n flat_gradients.zero_()\n\n for i, group in enumerate(self.fp32_groups_gradients):\n self.fp32_groups_has_gradients[i] = [False] * len(group)\n\n def clear_lp_grads(self):\n for group in self.bf16_groups:\n for param in group:\n param.grad = None\n\n def state_dict(self):\n state_dict = {}\n state_dict[CLIP_GRAD] = self.clip_grad\n state_dict[BASE_OPTIMIZER_STATE] = self.optimizer.state_dict()\n state_dict[SINGLE_PARTITION_OF_FP32_GROUPS] = self.fp32_groups_flat_partition\n state_dict[GROUP_PADDINGS] = self.group_paddings\n state_dict[PARTITION_COUNT] = self.partition_count\n state_dict[DS_VERSION] = version\n state_dict[PARAM_SLICE_MAPPINGS] = self._param_slice_mappings\n\n return state_dict\n\n # Restore base optimizer fp32 weights bfloat16 weights\n def _restore_from_bit16_weights(self):\n for i, group in enumerate(self.bf16_groups):\n partition_id = dist.get_rank(group=self.real_dp_process_group[i])\n for bf16_partitions, fp32_partition in zip(self.bf16_partitioned_groups, self.fp32_groups_flat_partition):\n fp32_partition.data.copy_(bf16_partitions[partition_id].data)\n\n def refresh_fp32_params(self):\n self._restore_from_bit16_weights()\n\n def load_state_dict(self,\n state_dict_list,\n checkpoint_folder,\n load_optimizer_states=True,\n load_from_fp32_weights=False):\n if checkpoint_folder:\n self._load_universal_checkpoint(checkpoint_folder,\n load_optimizer_states,\n load_from_fp32_weights)\n else:\n self._load_legacy_checkpoint(state_dict_list,\n load_optimizer_states,\n load_from_fp32_weights)\n\n def _load_legacy_checkpoint(self,\n state_dict_list,\n load_optimizer_states=True,\n load_from_fp32_weights=False):\n\n dp_rank = dist.get_rank(group=self.dp_process_group)\n current_rank_sd = state_dict_list[dp_rank]\n\n ckpt_version = current_rank_sd.get(DS_VERSION, False)\n assert ckpt_version, f\"Empty ds_version in checkpoint, not clear how to proceed\"\n ckpt_version = pkg_version.parse(ckpt_version)\n\n self.clip_grad = current_rank_sd.get(CLIP_GRAD, self.clip_grad)\n\n if load_optimizer_states:\n self.optimizer.load_state_dict(current_rank_sd[BASE_OPTIMIZER_STATE])\n\n if load_from_fp32_weights:\n for current, saved in zip(self.fp32_groups_flat_partition, current_rank_sd[SINGLE_PARTITION_OF_FP32_GROUPS]):\n src_tensor = _get_padded_tensor(saved, current.numel())\n current.data.copy_(src_tensor.data)\n\n if load_optimizer_states:\n self._link_all_hp_params()\n\n def _load_universal_checkpoint(self,\n checkpoint_folder,\n load_optimizer_states,\n load_from_fp32_weights):\n self._load_hp_checkpoint_state(checkpoint_folder)\n\n @property\n def param_groups(self):\n \"\"\"Forward the wrapped optimizer's parameters.\"\"\"\n return self.optimizer.param_groups\n\n def _load_hp_checkpoint_state(self, checkpoint_dir):\n checkpoint_dir = os.path.join(checkpoint_dir, \"zero\")\n tp_rank = bwc_tensor_model_parallel_rank(mpu=self.mpu)\n tp_world_size = self.mpu.get_slice_parallel_world_size()\n\n for i, _ in enumerate(self.optimizer.param_groups):\n for lp in self.bf16_groups[i]:\n if lp._hp_mapping is not None:\n #print(f\"Loading {self.param_names[lp]} {tp_rank=} {tp_world_size=}\")\n lp.load_hp_checkpoint_state(\n os.path.join(checkpoint_dir,\n self.param_names[lp]),\n tp_rank,\n tp_world_size)\n\n\ndef _get_padded_tensor(src_tensor, size):\n if src_tensor.numel() >= size:\n return src_tensor\n padded_tensor = torch.zeros(size, dtype=src_tensor.dtype, device=src_tensor.device)\n slice_tensor = torch.narrow(padded_tensor, 0, 0, src_tensor.numel())\n slice_tensor.data.copy_(src_tensor.data)\n return padded_tensor\n\n\n'''\nLogic for lp_param to hp_param mapping\n\nlp lp0 lp1 lp2 lp3 lp4 <------- indices/names\nlp [ ][ ][ ][ ][ ] <-------- tensors\nflat_lp [ ] <-------- flat lp params\nflat_hp [ ] <------------------ flat hp partition on current rank\nfull_hp [ ] <------- full flat hp params\n\n\nlp2\n full numel = 16\n lp_frag\n numel = 12\n frag_start = 3\n frag_end = 15\n hp_frag\n numel = 12\n frag_start = 0\n frag_end = 11\n\n hp_frag.copy_(lp_frag)\n\n\nlp3:\n full numel = 4\n lp_frag\n numel = 4\n start = 0\n end = 3\n hp_frag\n numel = 4\n start = 12\n end = 15\n\n\nlp4:\n full numel = 12\n lp_frag\n numel = 4\n start = 0\n end = 3\n hp_frag\n numel = 4\n start = 16\n end = 19\n\n\n\nVisual depiction of above\nlp { }\nflat_lp [ ]\nflat_hp ( )\n\n\nflat_lp [ { ( } ) ]\n lx hx ly hy\n ly-hx\n\n\nlp { }\nflat_lp [ ]\nflat_hp ( )\n\n\nflat_lp [ ( { ) } ]\n hx lx hy ly\n hy-lx\n\nlp { }\nflat_lp [ ]\nflat_hp ( )\n\n\nflat_lp [ ( { } ) ]\n hx lx ly hy\n ly-lx\n\nlp -> (lx, hy)\nflat_hp -> (hx, hy)\n'''\n", "path": "deepspeed/runtime/bf16_optimizer.py" } ]
diff --git a/deepspeed/runtime/bf16_optimizer.py b/deepspeed/runtime/bf16_optimizer.py index 303267f0494d..a9988e2c498f 100644 --- a/deepspeed/runtime/bf16_optimizer.py +++ b/deepspeed/runtime/bf16_optimizer.py @@ -2,7 +2,7 @@ Copyright 2022 The Microsoft DeepSpeed Team """ -from typing import OrderedDict +from collections import OrderedDict import torch import os from deepspeed import comm as dist
borgbackup__borg-540
stats no longer shows day of week borg 0.29.0 When using create --stats the output no longer shows the day of the week. 0.28.2 output: Keeping archive: panda-121415_1835 Mon Dec 14 18:35:50 2015 Keeping archive: panda-121415_0925 Mon Dec 14 09:26:27 2015 Keeping archive: panda-121315_1835 Sun Dec 13 18:36:03 2015 .... 0.29.0 output: Keeping archive: panda-121515_1537 2015-12-15 15:37:50 Keeping archive: panda-121515_0654 2015-12-15 06:55:36 Keeping archive: panda-121415_1835 2015-12-14 18:35:50 Keeping archive: panda-121315_1835 2015-12-13 18:36:03 ....
[ { "content": "from .support import argparse # see support/__init__.py docstring, DEPRECATED - remove after requiring py 3.4\n\nimport binascii\nfrom collections import namedtuple\nfrom functools import wraps\nimport grp\nimport os\nimport pwd\nimport re\ntry:\n from shutil import get_terminal_size\nexcept ImportError:\n def get_terminal_size(fallback=(80, 24)):\n TerminalSize = namedtuple('TerminalSize', ['columns', 'lines'])\n return TerminalSize(int(os.environ.get('COLUMNS', fallback[0])), int(os.environ.get('LINES', fallback[1])))\nimport sys\nimport platform\nimport time\nimport unicodedata\n\nfrom datetime import datetime, timezone, timedelta\nfrom fnmatch import translate\nfrom operator import attrgetter\n\n\nfrom . import hashindex\nfrom . import chunker\nfrom . import crypto\nimport msgpack\nimport msgpack.fallback\n\n\n# return codes returned by borg command\n# when borg is killed by signal N, rc = 128 + N\nEXIT_SUCCESS = 0 # everything done, no problems\nEXIT_WARNING = 1 # reached normal end of operation, but there were issues\nEXIT_ERROR = 2 # terminated abruptly, did not reach end of operation\n\n\nclass Error(Exception):\n \"\"\"Error base class\"\"\"\n\n # if we raise such an Error and it is only catched by the uppermost\n # exception handler (that exits short after with the given exit_code),\n # it is always a (fatal and abrupt) EXIT_ERROR, never just a warning.\n exit_code = EXIT_ERROR\n # show a traceback?\n traceback = False\n\n def get_message(self):\n return type(self).__doc__.format(*self.args)\n\n\nclass ErrorWithTraceback(Error):\n \"\"\"like Error, but show a traceback also\"\"\"\n traceback = True\n\n\nclass IntegrityError(ErrorWithTraceback):\n \"\"\"Data integrity error\"\"\"\n\n\nclass ExtensionModuleError(Error):\n \"\"\"The Borg binary extension modules do not seem to be properly installed\"\"\"\n\n\ndef check_extension_modules():\n from . import platform\n if hashindex.API_VERSION != 2:\n raise ExtensionModuleError\n if chunker.API_VERSION != 2:\n raise ExtensionModuleError\n if crypto.API_VERSION != 2:\n raise ExtensionModuleError\n if platform.API_VERSION != 2:\n raise ExtensionModuleError\n\n\nclass Manifest:\n\n MANIFEST_ID = b'\\0' * 32\n\n def __init__(self, key, repository):\n self.archives = {}\n self.config = {}\n self.key = key\n self.repository = repository\n\n @classmethod\n def load(cls, repository, key=None):\n from .key import key_factory\n cdata = repository.get(cls.MANIFEST_ID)\n if not key:\n key = key_factory(repository, cdata)\n manifest = cls(key, repository)\n data = key.decrypt(None, cdata)\n manifest.id = key.id_hash(data)\n m = msgpack.unpackb(data)\n if not m.get(b'version') == 1:\n raise ValueError('Invalid manifest version')\n manifest.archives = dict((k.decode('utf-8'), v) for k, v in m[b'archives'].items())\n manifest.timestamp = m.get(b'timestamp')\n if manifest.timestamp:\n manifest.timestamp = manifest.timestamp.decode('ascii')\n manifest.config = m[b'config']\n return manifest, key\n\n def write(self):\n self.timestamp = datetime.utcnow().isoformat()\n data = msgpack.packb(StableDict({\n 'version': 1,\n 'archives': self.archives,\n 'timestamp': self.timestamp,\n 'config': self.config,\n }))\n self.id = self.key.id_hash(data)\n self.repository.put(self.MANIFEST_ID, self.key.encrypt(data))\n\n def list_archive_infos(self, sort_by=None, reverse=False):\n # inexpensive Archive.list_archives replacement if we just need .name, .id, .ts\n ArchiveInfo = namedtuple('ArchiveInfo', 'name id ts')\n archives = []\n for name, values in self.archives.items():\n ts = parse_timestamp(values[b'time'].decode('utf-8'))\n id = values[b'id']\n archives.append(ArchiveInfo(name=name, id=id, ts=ts))\n if sort_by is not None:\n archives = sorted(archives, key=attrgetter(sort_by), reverse=reverse)\n return archives\n\n\ndef prune_within(archives, within):\n multiplier = {'H': 1, 'd': 24, 'w': 24*7, 'm': 24*31, 'y': 24*365}\n try:\n hours = int(within[:-1]) * multiplier[within[-1]]\n except (KeyError, ValueError):\n # I don't like how this displays the original exception too:\n raise argparse.ArgumentTypeError('Unable to parse --within option: \"%s\"' % within)\n if hours <= 0:\n raise argparse.ArgumentTypeError('Number specified using --within option must be positive')\n target = datetime.now(timezone.utc) - timedelta(seconds=hours*60*60)\n return [a for a in archives if a.ts > target]\n\n\ndef prune_split(archives, pattern, n, skip=[]):\n last = None\n keep = []\n if n == 0:\n return keep\n for a in sorted(archives, key=attrgetter('ts'), reverse=True):\n period = to_localtime(a.ts).strftime(pattern)\n if period != last:\n last = period\n if a not in skip:\n keep.append(a)\n if len(keep) == n:\n break\n return keep\n\n\nclass Statistics:\n\n def __init__(self):\n self.osize = self.csize = self.usize = self.nfiles = 0\n self.last_progress = 0 # timestamp when last progress was shown\n\n def update(self, size, csize, unique):\n self.osize += size\n self.csize += csize\n if unique:\n self.usize += csize\n\n summary = \"\"\"\\\n Original size Compressed size Deduplicated size\n{label:15} {stats.osize_fmt:>20s} {stats.csize_fmt:>20s} {stats.usize_fmt:>20s}\"\"\"\n\n def __str__(self):\n return self.summary.format(stats=self, label='This archive:')\n\n def __repr__(self):\n return \"<{cls} object at {hash:#x} ({self.osize}, {self.csize}, {self.usize})>\".format(cls=type(self).__name__, hash=id(self), self=self)\n\n @property\n def osize_fmt(self):\n return format_file_size(self.osize)\n\n @property\n def usize_fmt(self):\n return format_file_size(self.usize)\n\n @property\n def csize_fmt(self):\n return format_file_size(self.csize)\n\n def show_progress(self, item=None, final=False, stream=None, dt=None):\n now = time.time()\n if dt is None or now - self.last_progress > dt:\n self.last_progress = now\n columns, lines = get_terminal_size()\n if not final:\n msg = '{0.osize_fmt} O {0.csize_fmt} C {0.usize_fmt} D {0.nfiles} N '.format(self)\n path = remove_surrogates(item[b'path']) if item else ''\n space = columns - len(msg)\n if space < len('...') + len(path):\n path = '%s...%s' % (path[:(space//2)-len('...')], path[-space//2:])\n msg += \"{0:<{space}}\".format(path, space=space)\n else:\n msg = ' ' * columns\n print(msg, file=stream or sys.stderr, end=\"\\r\")\n (stream or sys.stderr).flush()\n\n\ndef get_keys_dir():\n \"\"\"Determine where to repository keys and cache\"\"\"\n return os.environ.get('BORG_KEYS_DIR',\n os.path.join(os.path.expanduser('~'), '.borg', 'keys'))\n\n\ndef get_cache_dir():\n \"\"\"Determine where to repository keys and cache\"\"\"\n xdg_cache = os.environ.get('XDG_CACHE_HOME', os.path.join(os.path.expanduser('~'), '.cache'))\n return os.environ.get('BORG_CACHE_DIR', os.path.join(xdg_cache, 'borg'))\n\n\ndef to_localtime(ts):\n \"\"\"Convert datetime object from UTC to local time zone\"\"\"\n return datetime(*time.localtime((ts - datetime(1970, 1, 1, tzinfo=timezone.utc)).total_seconds())[:6])\n\n\ndef parse_timestamp(timestamp):\n \"\"\"Parse a ISO 8601 timestamp string\"\"\"\n if '.' in timestamp: # microseconds might not be present\n return datetime.strptime(timestamp, '%Y-%m-%dT%H:%M:%S.%f').replace(tzinfo=timezone.utc)\n else:\n return datetime.strptime(timestamp, '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc)\n\n\ndef load_excludes(fh):\n \"\"\"Load and parse exclude patterns from file object. Empty lines and lines starting with '#' are ignored, but\n whitespace is not stripped.\n \"\"\"\n patterns = (line.rstrip('\\r\\n') for line in fh if not line.startswith('#'))\n return [ExcludePattern(pattern) for pattern in patterns if pattern]\n\n\ndef update_excludes(args):\n \"\"\"Merge exclude patterns from files with those on command line.\"\"\"\n if hasattr(args, 'exclude_files') and args.exclude_files:\n if not hasattr(args, 'excludes') or args.excludes is None:\n args.excludes = []\n for file in args.exclude_files:\n args.excludes += load_excludes(file)\n file.close()\n\n\ndef adjust_patterns(paths, excludes):\n if paths:\n return (excludes or []) + [IncludePattern(path) for path in paths] + [ExcludePattern('*')]\n else:\n return excludes\n\n\ndef exclude_path(path, patterns):\n \"\"\"Used by create and extract sub-commands to determine\n whether or not an item should be processed.\n \"\"\"\n for pattern in (patterns or []):\n if pattern.match(path):\n return isinstance(pattern, ExcludePattern)\n return False\n\n\n# For both IncludePattern and ExcludePattern, we require that\n# the pattern either match the whole path or an initial segment\n# of the path up to but not including a path separator. To\n# unify the two cases, we add a path separator to the end of\n# the path before matching.\n\ndef normalized(func):\n \"\"\" Decorator for the Pattern match methods, returning a wrapper that\n normalizes OSX paths to match the normalized pattern on OSX, and\n returning the original method on other platforms\"\"\"\n @wraps(func)\n def normalize_wrapper(self, path):\n return func(self, unicodedata.normalize(\"NFD\", path))\n\n if sys.platform in ('darwin',):\n # HFS+ converts paths to a canonical form, so users shouldn't be\n # required to enter an exact match\n return normalize_wrapper\n else:\n # Windows and Unix filesystems allow different forms, so users\n # always have to enter an exact match\n return func\n\n\nclass IncludePattern:\n \"\"\"Literal files or directories listed on the command line\n for some operations (e.g. extract, but not create).\n If a directory is specified, all paths that start with that\n path match as well. A trailing slash makes no difference.\n \"\"\"\n def __init__(self, pattern):\n self.pattern_orig = pattern\n self.match_count = 0\n\n if sys.platform in ('darwin',):\n pattern = unicodedata.normalize(\"NFD\", pattern)\n\n self.pattern = os.path.normpath(pattern).rstrip(os.path.sep)+os.path.sep\n\n @normalized\n def match(self, path):\n matches = (path+os.path.sep).startswith(self.pattern)\n if matches:\n self.match_count += 1\n return matches\n\n def __repr__(self):\n return '%s(%s)' % (type(self), self.pattern)\n\n def __str__(self):\n return self.pattern_orig\n\n\nclass ExcludePattern(IncludePattern):\n \"\"\"Shell glob patterns to exclude. A trailing slash means to\n exclude the contents of a directory, but not the directory itself.\n \"\"\"\n def __init__(self, pattern):\n self.pattern_orig = pattern\n self.match_count = 0\n\n if pattern.endswith(os.path.sep):\n self.pattern = os.path.normpath(pattern).rstrip(os.path.sep)+os.path.sep+'*'+os.path.sep\n else:\n self.pattern = os.path.normpath(pattern)+os.path.sep+'*'\n\n if sys.platform in ('darwin',):\n self.pattern = unicodedata.normalize(\"NFD\", self.pattern)\n\n # fnmatch and re.match both cache compiled regular expressions.\n # Nevertheless, this is about 10 times faster.\n self.regex = re.compile(translate(self.pattern))\n\n @normalized\n def match(self, path):\n matches = self.regex.match(path+os.path.sep) is not None\n if matches:\n self.match_count += 1\n return matches\n\n def __repr__(self):\n return '%s(%s)' % (type(self), self.pattern)\n\n def __str__(self):\n return self.pattern_orig\n\n\ndef timestamp(s):\n \"\"\"Convert a --timestamp=s argument to a datetime object\"\"\"\n try:\n # is it pointing to a file / directory?\n ts = os.stat(s).st_mtime\n return datetime.utcfromtimestamp(ts)\n except OSError:\n # didn't work, try parsing as timestamp. UTC, no TZ, no microsecs support.\n for format in ('%Y-%m-%dT%H:%M:%SZ', '%Y-%m-%dT%H:%M:%S+00:00',\n '%Y-%m-%dT%H:%M:%S', '%Y-%m-%d %H:%M:%S',\n '%Y-%m-%dT%H:%M', '%Y-%m-%d %H:%M',\n '%Y-%m-%d', '%Y-%j',\n ):\n try:\n return datetime.strptime(s, format)\n except ValueError:\n continue\n raise ValueError\n\n\ndef ChunkerParams(s):\n chunk_min, chunk_max, chunk_mask, window_size = s.split(',')\n if int(chunk_max) > 23:\n # do not go beyond 2**23 (8MB) chunk size now,\n # COMPR_BUFFER can only cope with up to this size\n raise ValueError('max. chunk size exponent must not be more than 23 (2^23 = 8MiB max. chunk size)')\n return int(chunk_min), int(chunk_max), int(chunk_mask), int(window_size)\n\n\ndef CompressionSpec(s):\n values = s.split(',')\n count = len(values)\n if count < 1:\n raise ValueError\n compression = values[0]\n try:\n compression = int(compression)\n if count > 1:\n raise ValueError\n # DEPRECATED: it is just --compression N\n if 0 <= compression <= 9:\n print('Warning: --compression %d is deprecated, please use --compression zlib,%d.' % (compression, compression))\n if compression == 0:\n print('Hint: instead of --compression zlib,0 you could also use --compression none for better performance.')\n print('Hint: archives generated using --compression none are not compatible with borg < 0.25.0.')\n return dict(name='zlib', level=compression)\n raise ValueError\n except ValueError:\n # --compression algo[,...]\n name = compression\n if name in ('none', 'lz4', ):\n return dict(name=name)\n if name in ('zlib', 'lzma', ):\n if count < 2:\n level = 6 # default compression level in py stdlib\n elif count == 2:\n level = int(values[1])\n if not 0 <= level <= 9:\n raise ValueError\n else:\n raise ValueError\n return dict(name=name, level=level)\n raise ValueError\n\n\ndef dir_is_cachedir(path):\n \"\"\"Determines whether the specified path is a cache directory (and\n therefore should potentially be excluded from the backup) according to\n the CACHEDIR.TAG protocol\n (http://www.brynosaurus.com/cachedir/spec.html).\n \"\"\"\n\n tag_contents = b'Signature: 8a477f597d28d172789f06886806bc55'\n tag_path = os.path.join(path, 'CACHEDIR.TAG')\n try:\n if os.path.exists(tag_path):\n with open(tag_path, 'rb') as tag_file:\n tag_data = tag_file.read(len(tag_contents))\n if tag_data == tag_contents:\n return True\n except OSError:\n pass\n return False\n\n\ndef dir_is_tagged(path, exclude_caches, exclude_if_present):\n \"\"\"Determines whether the specified path is excluded by being a cache\n directory or containing user-specified tag files. Returns a list of the\n paths of the tag files (either CACHEDIR.TAG or the matching\n user-specified files).\n \"\"\"\n tag_paths = []\n if exclude_caches and dir_is_cachedir(path):\n tag_paths.append(os.path.join(path, 'CACHEDIR.TAG'))\n if exclude_if_present is not None:\n for tag in exclude_if_present:\n tag_path = os.path.join(path, tag)\n if os.path.isfile(tag_path):\n tag_paths.append(tag_path)\n return tag_paths\n\n\ndef format_time(t):\n \"\"\"use ISO-8601 date and time format\n \"\"\"\n return t.strftime('%Y-%m-%d %H:%M:%S')\n\n\ndef format_timedelta(td):\n \"\"\"Format timedelta in a human friendly format\n \"\"\"\n # Since td.total_seconds() requires python 2.7\n ts = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10 ** 6) / float(10 ** 6)\n s = ts % 60\n m = int(ts / 60) % 60\n h = int(ts / 3600) % 24\n txt = '%.2f seconds' % s\n if m:\n txt = '%d minutes %s' % (m, txt)\n if h:\n txt = '%d hours %s' % (h, txt)\n if td.days:\n txt = '%d days %s' % (td.days, txt)\n return txt\n\n\ndef format_file_mode(mod):\n \"\"\"Format file mode bits for list output\n \"\"\"\n def x(v):\n return ''.join(v & m and s or '-'\n for m, s in ((4, 'r'), (2, 'w'), (1, 'x')))\n return '%s%s%s' % (x(mod // 64), x(mod // 8), x(mod))\n\n\ndef format_file_size(v, precision=2):\n \"\"\"Format file size into a human friendly format\n \"\"\"\n return sizeof_fmt_decimal(v, suffix='B', sep=' ', precision=precision)\n\n\ndef sizeof_fmt(num, suffix='B', units=None, power=None, sep='', precision=2):\n for unit in units[:-1]:\n if abs(round(num, precision)) < power:\n if isinstance(num, int):\n return \"{}{}{}{}\".format(num, sep, unit, suffix)\n else:\n return \"{:3.{}f}{}{}{}\".format(num, precision, sep, unit, suffix)\n num /= float(power)\n return \"{:.{}f}{}{}{}\".format(num, precision, sep, units[-1], suffix)\n\n\ndef sizeof_fmt_iec(num, suffix='B', sep='', precision=2):\n return sizeof_fmt(num, suffix=suffix, sep=sep, precision=precision, units=['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi', 'Yi'], power=1024)\n\n\ndef sizeof_fmt_decimal(num, suffix='B', sep='', precision=2):\n return sizeof_fmt(num, suffix=suffix, sep=sep, precision=precision, units=['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y'], power=1000)\n\n\ndef format_archive(archive):\n return '%-36s %s' % (archive.name, format_time(to_localtime(archive.ts)))\n\n\ndef memoize(function):\n cache = {}\n\n def decorated_function(*args):\n try:\n return cache[args]\n except KeyError:\n val = function(*args)\n cache[args] = val\n return val\n return decorated_function\n\n\n@memoize\ndef uid2user(uid, default=None):\n try:\n return pwd.getpwuid(uid).pw_name\n except KeyError:\n return default\n\n\n@memoize\ndef user2uid(user, default=None):\n try:\n return user and pwd.getpwnam(user).pw_uid\n except KeyError:\n return default\n\n\n@memoize\ndef gid2group(gid, default=None):\n try:\n return grp.getgrgid(gid).gr_name\n except KeyError:\n return default\n\n\n@memoize\ndef group2gid(group, default=None):\n try:\n return group and grp.getgrnam(group).gr_gid\n except KeyError:\n return default\n\n\ndef posix_acl_use_stored_uid_gid(acl):\n \"\"\"Replace the user/group field with the stored uid/gid\n \"\"\"\n entries = []\n for entry in safe_decode(acl).split('\\n'):\n if entry:\n fields = entry.split(':')\n if len(fields) == 4:\n entries.append(':'.join([fields[0], fields[3], fields[2]]))\n else:\n entries.append(entry)\n return safe_encode('\\n'.join(entries))\n\n\ndef safe_decode(s, coding='utf-8', errors='surrogateescape'):\n \"\"\"decode bytes to str, with round-tripping \"invalid\" bytes\"\"\"\n return s.decode(coding, errors)\n\n\ndef safe_encode(s, coding='utf-8', errors='surrogateescape'):\n \"\"\"encode str to bytes, with round-tripping \"invalid\" bytes\"\"\"\n return s.encode(coding, errors)\n\n\nclass Location:\n \"\"\"Object representing a repository / archive location\n \"\"\"\n proto = user = host = port = path = archive = None\n # borg mount's FUSE filesystem creates one level of directories from\n # the archive names. Thus, we must not accept \"/\" in archive names.\n ssh_re = re.compile(r'(?P<proto>ssh)://(?:(?P<user>[^@]+)@)?'\n r'(?P<host>[^:/#]+)(?::(?P<port>\\d+))?'\n r'(?P<path>[^:]+)(?:::(?P<archive>[^/]+))?$')\n file_re = re.compile(r'(?P<proto>file)://'\n r'(?P<path>[^:]+)(?:::(?P<archive>[^/]+))?$')\n scp_re = re.compile(r'((?:(?P<user>[^@]+)@)?(?P<host>[^:/]+):)?'\n r'(?P<path>[^:]+)(?:::(?P<archive>[^/]+))?$')\n # get the repo from BORG_RE env and the optional archive from param.\n # if the syntax requires giving REPOSITORY (see \"borg mount\"),\n # use \"::\" to let it use the env var.\n # if REPOSITORY argument is optional, it'll automatically use the env.\n env_re = re.compile(r'(?:::(?P<archive>[^/]+)?)?$')\n\n def __init__(self, text=''):\n self.orig = text\n if not self.parse(self.orig):\n raise ValueError\n\n def parse(self, text):\n valid = self._parse(text)\n if valid:\n return True\n m = self.env_re.match(text)\n if not m:\n return False\n repo = os.environ.get('BORG_REPO')\n if repo is None:\n return False\n valid = self._parse(repo)\n if not valid:\n return False\n self.archive = m.group('archive')\n return True\n\n def _parse(self, text):\n m = self.ssh_re.match(text)\n if m:\n self.proto = m.group('proto')\n self.user = m.group('user')\n self.host = m.group('host')\n self.port = m.group('port') and int(m.group('port')) or None\n self.path = m.group('path')\n self.archive = m.group('archive')\n return True\n m = self.file_re.match(text)\n if m:\n self.proto = m.group('proto')\n self.path = m.group('path')\n self.archive = m.group('archive')\n return True\n m = self.scp_re.match(text)\n if m:\n self.user = m.group('user')\n self.host = m.group('host')\n self.path = m.group('path')\n self.archive = m.group('archive')\n self.proto = self.host and 'ssh' or 'file'\n return True\n return False\n\n def __str__(self):\n items = [\n 'proto=%r' % self.proto,\n 'user=%r' % self.user,\n 'host=%r' % self.host,\n 'port=%r' % self.port,\n 'path=%r' % self.path,\n 'archive=%r' % self.archive,\n ]\n return ', '.join(items)\n\n def to_key_filename(self):\n name = re.sub('[^\\w]', '_', self.path).strip('_')\n if self.proto != 'file':\n name = self.host + '__' + name\n return os.path.join(get_keys_dir(), name)\n\n def __repr__(self):\n return \"Location(%s)\" % self\n\n def canonical_path(self):\n if self.proto == 'file':\n return self.path\n else:\n if self.path and self.path.startswith('~'):\n path = '/' + self.path\n elif self.path and not self.path.startswith('/'):\n path = '/~/' + self.path\n else:\n path = self.path\n return 'ssh://{}{}{}{}'.format('{}@'.format(self.user) if self.user else '',\n self.host,\n ':{}'.format(self.port) if self.port else '',\n path)\n\n\ndef location_validator(archive=None):\n def validator(text):\n try:\n loc = Location(text)\n except ValueError:\n raise argparse.ArgumentTypeError('Invalid location format: \"%s\"' % text)\n if archive is True and not loc.archive:\n raise argparse.ArgumentTypeError('\"%s\": No archive specified' % text)\n elif archive is False and loc.archive:\n raise argparse.ArgumentTypeError('\"%s\" No archive can be specified' % text)\n return loc\n return validator\n\n\ndef decode_dict(d, keys, encoding='utf-8', errors='surrogateescape'):\n for key in keys:\n if isinstance(d.get(key), bytes):\n d[key] = d[key].decode(encoding, errors)\n return d\n\n\ndef remove_surrogates(s, errors='replace'):\n \"\"\"Replace surrogates generated by fsdecode with '?'\n \"\"\"\n return s.encode('utf-8', errors).decode('utf-8')\n\n\n_safe_re = re.compile(r'^((\\.\\.)?/+)+')\n\n\ndef make_path_safe(path):\n \"\"\"Make path safe by making it relative and local\n \"\"\"\n return _safe_re.sub('', path) or '.'\n\n\ndef daemonize():\n \"\"\"Detach process from controlling terminal and run in background\n \"\"\"\n pid = os.fork()\n if pid:\n os._exit(0)\n os.setsid()\n pid = os.fork()\n if pid:\n os._exit(0)\n os.chdir('/')\n os.close(0)\n os.close(1)\n os.close(2)\n fd = os.open('/dev/null', os.O_RDWR)\n os.dup2(fd, 0)\n os.dup2(fd, 1)\n os.dup2(fd, 2)\n\n\nclass StableDict(dict):\n \"\"\"A dict subclass with stable items() ordering\"\"\"\n def items(self):\n return sorted(super().items())\n\n\nif sys.version < '3.3':\n # st_xtime_ns attributes only available in 3.3+\n def st_atime_ns(st):\n return int(st.st_atime * 1e9)\n\n def st_ctime_ns(st):\n return int(st.st_ctime * 1e9)\n\n def st_mtime_ns(st):\n return int(st.st_mtime * 1e9)\n\n # unhexlify in < 3.3 incorrectly only accepts bytes input\n def unhexlify(data):\n if isinstance(data, str):\n data = data.encode('ascii')\n return binascii.unhexlify(data)\nelse:\n def st_atime_ns(st):\n return st.st_atime_ns\n\n def st_ctime_ns(st):\n return st.st_ctime_ns\n\n def st_mtime_ns(st):\n return st.st_mtime_ns\n\n unhexlify = binascii.unhexlify\n\n\ndef bigint_to_int(mtime):\n \"\"\"Convert bytearray to int\n \"\"\"\n if isinstance(mtime, bytes):\n return int.from_bytes(mtime, 'little', signed=True)\n return mtime\n\n\ndef int_to_bigint(value):\n \"\"\"Convert integers larger than 64 bits to bytearray\n\n Smaller integers are left alone\n \"\"\"\n if value.bit_length() > 63:\n return value.to_bytes((value.bit_length() + 9) // 8, 'little', signed=True)\n return value\n\n\ndef is_slow_msgpack():\n return msgpack.Packer is msgpack.fallback.Packer\n\n\ndef yes(msg=None, retry_msg=None, false_msg=None, true_msg=None,\n default=False, default_notty=None, default_eof=None,\n falsish=('No', 'no', 'N', 'n'), truish=('Yes', 'yes', 'Y', 'y'),\n env_var_override=None, ifile=None, ofile=None, input=input):\n \"\"\"\n Output <msg> (usually a question) and let user input an answer.\n Qualifies the answer according to falsish and truish as True or False.\n If it didn't qualify and retry_msg is None (no retries wanted),\n return the default [which defaults to False]. Otherwise let user retry\n answering until answer is qualified.\n\n If env_var_override is given and it is non-empty, counts as truish answer\n and won't ask user for an answer.\n If we don't have a tty as input and default_notty is not None, return its value.\n Otherwise read input from non-tty and proceed as normal.\n If EOF is received instead an input, return default_eof [or default, if not given].\n\n :param msg: introducing message to output on ofile, no \\n is added [None]\n :param retry_msg: retry message to output on ofile, no \\n is added [None]\n (also enforces retries instead of returning default)\n :param false_msg: message to output before returning False [None]\n :param true_msg: message to output before returning True [None]\n :param default: default return value (empty answer is given) [False]\n :param default_notty: if not None, return its value if no tty is connected [None]\n :param default_eof: return value if EOF was read as answer [same as default]\n :param falsish: sequence of answers qualifying as False\n :param truish: sequence of answers qualifying as True\n :param env_var_override: environment variable name [None]\n :param ifile: input stream [sys.stdin] (only for testing!)\n :param ofile: output stream [sys.stderr]\n :param input: input function [input from builtins]\n :return: boolean answer value, True or False\n \"\"\"\n # note: we do not assign sys.stdin/stderr as defaults above, so they are\n # really evaluated NOW, not at function definition time.\n if ifile is None:\n ifile = sys.stdin\n if ofile is None:\n ofile = sys.stderr\n if default not in (True, False):\n raise ValueError(\"invalid default value, must be True or False\")\n if default_notty not in (None, True, False):\n raise ValueError(\"invalid default_notty value, must be None, True or False\")\n if default_eof not in (None, True, False):\n raise ValueError(\"invalid default_eof value, must be None, True or False\")\n if msg:\n print(msg, file=ofile, end='')\n ofile.flush()\n if env_var_override:\n value = os.environ.get(env_var_override)\n # currently, any non-empty value counts as truish\n # TODO: change this so one can give y/n there?\n if value:\n value = bool(value)\n value_str = truish[0] if value else falsish[0]\n print(\"{} (from {})\".format(value_str, env_var_override), file=ofile)\n return value\n if default_notty is not None and not ifile.isatty():\n # looks like ifile is not a terminal (but e.g. a pipe)\n return default_notty\n while True:\n try:\n answer = input() # XXX how can we use ifile?\n except EOFError:\n return default_eof if default_eof is not None else default\n if answer in truish:\n if true_msg:\n print(true_msg, file=ofile)\n return True\n if answer in falsish:\n if false_msg:\n print(false_msg, file=ofile)\n return False\n if retry_msg is None:\n # no retries wanted, we just return the default\n return default\n if retry_msg:\n print(retry_msg, file=ofile, end='')\n ofile.flush()\n\n\nclass ProgressIndicatorPercent:\n def __init__(self, total, step=5, start=0, same_line=False, msg=\"%3.0f%%\", file=sys.stderr):\n \"\"\"\n Percentage-based progress indicator\n\n :param total: total amount of items\n :param step: step size in percent\n :param start: at which percent value to start\n :param same_line: if True, emit output always on same line\n :param msg: output message, must contain one %f placeholder for the percentage\n :param file: output file, default: sys.stderr\n \"\"\"\n self.counter = 0 # 0 .. (total-1)\n self.total = total\n self.trigger_at = start # output next percentage value when reaching (at least) this\n self.step = step\n self.file = file\n self.msg = msg\n self.same_line = same_line\n\n def progress(self, current=None):\n if current is not None:\n self.counter = current\n pct = self.counter * 100 / self.total\n self.counter += 1\n if pct >= self.trigger_at:\n self.trigger_at += self.step\n return pct\n\n def show(self, current=None):\n pct = self.progress(current)\n if pct is not None:\n return self.output(pct)\n\n def output(self, percent):\n print(self.msg % percent, file=self.file, end='\\r' if self.same_line else '\\n') # python 3.3 gives us flush=True\n self.file.flush()\n\n def finish(self):\n if self.same_line:\n print(\" \" * len(self.msg % 100.0), file=self.file, end='\\r')\n\n\n\nclass ProgressIndicatorEndless:\n def __init__(self, step=10, file=sys.stderr):\n \"\"\"\n Progress indicator (long row of dots)\n\n :param step: every Nth call, call the func\n :param file: output file, default: sys.stderr\n \"\"\"\n self.counter = 0 # call counter\n self.triggered = 0 # increases 1 per trigger event\n self.step = step # trigger every <step> calls\n self.file = file\n\n def progress(self):\n self.counter += 1\n trigger = self.counter % self.step == 0\n if trigger:\n self.triggered += 1\n return trigger\n\n def show(self):\n trigger = self.progress()\n if trigger:\n return self.output(self.triggered)\n\n def output(self, triggered):\n print('.', end='', file=self.file) # python 3.3 gives us flush=True\n self.file.flush()\n\n def finish(self):\n print(file=self.file)\n\n\ndef sysinfo():\n info = []\n info.append('Platform: %s' % (' '.join(platform.uname()), ))\n if sys.platform.startswith('linux'):\n info.append('Linux: %s %s %s LibC: %s %s' % (platform.linux_distribution() + platform.libc_ver()))\n info.append('Python: %s %s' % (platform.python_implementation(), platform.python_version()))\n info.append('')\n return '\\n'.join(info)\n", "path": "borg/helpers.py" } ]
[ { "content": "from .support import argparse # see support/__init__.py docstring, DEPRECATED - remove after requiring py 3.4\n\nimport binascii\nfrom collections import namedtuple\nfrom functools import wraps\nimport grp\nimport os\nimport pwd\nimport re\ntry:\n from shutil import get_terminal_size\nexcept ImportError:\n def get_terminal_size(fallback=(80, 24)):\n TerminalSize = namedtuple('TerminalSize', ['columns', 'lines'])\n return TerminalSize(int(os.environ.get('COLUMNS', fallback[0])), int(os.environ.get('LINES', fallback[1])))\nimport sys\nimport platform\nimport time\nimport unicodedata\n\nfrom datetime import datetime, timezone, timedelta\nfrom fnmatch import translate\nfrom operator import attrgetter\n\n\nfrom . import hashindex\nfrom . import chunker\nfrom . import crypto\nimport msgpack\nimport msgpack.fallback\n\n\n# return codes returned by borg command\n# when borg is killed by signal N, rc = 128 + N\nEXIT_SUCCESS = 0 # everything done, no problems\nEXIT_WARNING = 1 # reached normal end of operation, but there were issues\nEXIT_ERROR = 2 # terminated abruptly, did not reach end of operation\n\n\nclass Error(Exception):\n \"\"\"Error base class\"\"\"\n\n # if we raise such an Error and it is only catched by the uppermost\n # exception handler (that exits short after with the given exit_code),\n # it is always a (fatal and abrupt) EXIT_ERROR, never just a warning.\n exit_code = EXIT_ERROR\n # show a traceback?\n traceback = False\n\n def get_message(self):\n return type(self).__doc__.format(*self.args)\n\n\nclass ErrorWithTraceback(Error):\n \"\"\"like Error, but show a traceback also\"\"\"\n traceback = True\n\n\nclass IntegrityError(ErrorWithTraceback):\n \"\"\"Data integrity error\"\"\"\n\n\nclass ExtensionModuleError(Error):\n \"\"\"The Borg binary extension modules do not seem to be properly installed\"\"\"\n\n\ndef check_extension_modules():\n from . import platform\n if hashindex.API_VERSION != 2:\n raise ExtensionModuleError\n if chunker.API_VERSION != 2:\n raise ExtensionModuleError\n if crypto.API_VERSION != 2:\n raise ExtensionModuleError\n if platform.API_VERSION != 2:\n raise ExtensionModuleError\n\n\nclass Manifest:\n\n MANIFEST_ID = b'\\0' * 32\n\n def __init__(self, key, repository):\n self.archives = {}\n self.config = {}\n self.key = key\n self.repository = repository\n\n @classmethod\n def load(cls, repository, key=None):\n from .key import key_factory\n cdata = repository.get(cls.MANIFEST_ID)\n if not key:\n key = key_factory(repository, cdata)\n manifest = cls(key, repository)\n data = key.decrypt(None, cdata)\n manifest.id = key.id_hash(data)\n m = msgpack.unpackb(data)\n if not m.get(b'version') == 1:\n raise ValueError('Invalid manifest version')\n manifest.archives = dict((k.decode('utf-8'), v) for k, v in m[b'archives'].items())\n manifest.timestamp = m.get(b'timestamp')\n if manifest.timestamp:\n manifest.timestamp = manifest.timestamp.decode('ascii')\n manifest.config = m[b'config']\n return manifest, key\n\n def write(self):\n self.timestamp = datetime.utcnow().isoformat()\n data = msgpack.packb(StableDict({\n 'version': 1,\n 'archives': self.archives,\n 'timestamp': self.timestamp,\n 'config': self.config,\n }))\n self.id = self.key.id_hash(data)\n self.repository.put(self.MANIFEST_ID, self.key.encrypt(data))\n\n def list_archive_infos(self, sort_by=None, reverse=False):\n # inexpensive Archive.list_archives replacement if we just need .name, .id, .ts\n ArchiveInfo = namedtuple('ArchiveInfo', 'name id ts')\n archives = []\n for name, values in self.archives.items():\n ts = parse_timestamp(values[b'time'].decode('utf-8'))\n id = values[b'id']\n archives.append(ArchiveInfo(name=name, id=id, ts=ts))\n if sort_by is not None:\n archives = sorted(archives, key=attrgetter(sort_by), reverse=reverse)\n return archives\n\n\ndef prune_within(archives, within):\n multiplier = {'H': 1, 'd': 24, 'w': 24*7, 'm': 24*31, 'y': 24*365}\n try:\n hours = int(within[:-1]) * multiplier[within[-1]]\n except (KeyError, ValueError):\n # I don't like how this displays the original exception too:\n raise argparse.ArgumentTypeError('Unable to parse --within option: \"%s\"' % within)\n if hours <= 0:\n raise argparse.ArgumentTypeError('Number specified using --within option must be positive')\n target = datetime.now(timezone.utc) - timedelta(seconds=hours*60*60)\n return [a for a in archives if a.ts > target]\n\n\ndef prune_split(archives, pattern, n, skip=[]):\n last = None\n keep = []\n if n == 0:\n return keep\n for a in sorted(archives, key=attrgetter('ts'), reverse=True):\n period = to_localtime(a.ts).strftime(pattern)\n if period != last:\n last = period\n if a not in skip:\n keep.append(a)\n if len(keep) == n:\n break\n return keep\n\n\nclass Statistics:\n\n def __init__(self):\n self.osize = self.csize = self.usize = self.nfiles = 0\n self.last_progress = 0 # timestamp when last progress was shown\n\n def update(self, size, csize, unique):\n self.osize += size\n self.csize += csize\n if unique:\n self.usize += csize\n\n summary = \"\"\"\\\n Original size Compressed size Deduplicated size\n{label:15} {stats.osize_fmt:>20s} {stats.csize_fmt:>20s} {stats.usize_fmt:>20s}\"\"\"\n\n def __str__(self):\n return self.summary.format(stats=self, label='This archive:')\n\n def __repr__(self):\n return \"<{cls} object at {hash:#x} ({self.osize}, {self.csize}, {self.usize})>\".format(cls=type(self).__name__, hash=id(self), self=self)\n\n @property\n def osize_fmt(self):\n return format_file_size(self.osize)\n\n @property\n def usize_fmt(self):\n return format_file_size(self.usize)\n\n @property\n def csize_fmt(self):\n return format_file_size(self.csize)\n\n def show_progress(self, item=None, final=False, stream=None, dt=None):\n now = time.time()\n if dt is None or now - self.last_progress > dt:\n self.last_progress = now\n columns, lines = get_terminal_size()\n if not final:\n msg = '{0.osize_fmt} O {0.csize_fmt} C {0.usize_fmt} D {0.nfiles} N '.format(self)\n path = remove_surrogates(item[b'path']) if item else ''\n space = columns - len(msg)\n if space < len('...') + len(path):\n path = '%s...%s' % (path[:(space//2)-len('...')], path[-space//2:])\n msg += \"{0:<{space}}\".format(path, space=space)\n else:\n msg = ' ' * columns\n print(msg, file=stream or sys.stderr, end=\"\\r\")\n (stream or sys.stderr).flush()\n\n\ndef get_keys_dir():\n \"\"\"Determine where to repository keys and cache\"\"\"\n return os.environ.get('BORG_KEYS_DIR',\n os.path.join(os.path.expanduser('~'), '.borg', 'keys'))\n\n\ndef get_cache_dir():\n \"\"\"Determine where to repository keys and cache\"\"\"\n xdg_cache = os.environ.get('XDG_CACHE_HOME', os.path.join(os.path.expanduser('~'), '.cache'))\n return os.environ.get('BORG_CACHE_DIR', os.path.join(xdg_cache, 'borg'))\n\n\ndef to_localtime(ts):\n \"\"\"Convert datetime object from UTC to local time zone\"\"\"\n return datetime(*time.localtime((ts - datetime(1970, 1, 1, tzinfo=timezone.utc)).total_seconds())[:6])\n\n\ndef parse_timestamp(timestamp):\n \"\"\"Parse a ISO 8601 timestamp string\"\"\"\n if '.' in timestamp: # microseconds might not be present\n return datetime.strptime(timestamp, '%Y-%m-%dT%H:%M:%S.%f').replace(tzinfo=timezone.utc)\n else:\n return datetime.strptime(timestamp, '%Y-%m-%dT%H:%M:%S').replace(tzinfo=timezone.utc)\n\n\ndef load_excludes(fh):\n \"\"\"Load and parse exclude patterns from file object. Empty lines and lines starting with '#' are ignored, but\n whitespace is not stripped.\n \"\"\"\n patterns = (line.rstrip('\\r\\n') for line in fh if not line.startswith('#'))\n return [ExcludePattern(pattern) for pattern in patterns if pattern]\n\n\ndef update_excludes(args):\n \"\"\"Merge exclude patterns from files with those on command line.\"\"\"\n if hasattr(args, 'exclude_files') and args.exclude_files:\n if not hasattr(args, 'excludes') or args.excludes is None:\n args.excludes = []\n for file in args.exclude_files:\n args.excludes += load_excludes(file)\n file.close()\n\n\ndef adjust_patterns(paths, excludes):\n if paths:\n return (excludes or []) + [IncludePattern(path) for path in paths] + [ExcludePattern('*')]\n else:\n return excludes\n\n\ndef exclude_path(path, patterns):\n \"\"\"Used by create and extract sub-commands to determine\n whether or not an item should be processed.\n \"\"\"\n for pattern in (patterns or []):\n if pattern.match(path):\n return isinstance(pattern, ExcludePattern)\n return False\n\n\n# For both IncludePattern and ExcludePattern, we require that\n# the pattern either match the whole path or an initial segment\n# of the path up to but not including a path separator. To\n# unify the two cases, we add a path separator to the end of\n# the path before matching.\n\ndef normalized(func):\n \"\"\" Decorator for the Pattern match methods, returning a wrapper that\n normalizes OSX paths to match the normalized pattern on OSX, and\n returning the original method on other platforms\"\"\"\n @wraps(func)\n def normalize_wrapper(self, path):\n return func(self, unicodedata.normalize(\"NFD\", path))\n\n if sys.platform in ('darwin',):\n # HFS+ converts paths to a canonical form, so users shouldn't be\n # required to enter an exact match\n return normalize_wrapper\n else:\n # Windows and Unix filesystems allow different forms, so users\n # always have to enter an exact match\n return func\n\n\nclass IncludePattern:\n \"\"\"Literal files or directories listed on the command line\n for some operations (e.g. extract, but not create).\n If a directory is specified, all paths that start with that\n path match as well. A trailing slash makes no difference.\n \"\"\"\n def __init__(self, pattern):\n self.pattern_orig = pattern\n self.match_count = 0\n\n if sys.platform in ('darwin',):\n pattern = unicodedata.normalize(\"NFD\", pattern)\n\n self.pattern = os.path.normpath(pattern).rstrip(os.path.sep)+os.path.sep\n\n @normalized\n def match(self, path):\n matches = (path+os.path.sep).startswith(self.pattern)\n if matches:\n self.match_count += 1\n return matches\n\n def __repr__(self):\n return '%s(%s)' % (type(self), self.pattern)\n\n def __str__(self):\n return self.pattern_orig\n\n\nclass ExcludePattern(IncludePattern):\n \"\"\"Shell glob patterns to exclude. A trailing slash means to\n exclude the contents of a directory, but not the directory itself.\n \"\"\"\n def __init__(self, pattern):\n self.pattern_orig = pattern\n self.match_count = 0\n\n if pattern.endswith(os.path.sep):\n self.pattern = os.path.normpath(pattern).rstrip(os.path.sep)+os.path.sep+'*'+os.path.sep\n else:\n self.pattern = os.path.normpath(pattern)+os.path.sep+'*'\n\n if sys.platform in ('darwin',):\n self.pattern = unicodedata.normalize(\"NFD\", self.pattern)\n\n # fnmatch and re.match both cache compiled regular expressions.\n # Nevertheless, this is about 10 times faster.\n self.regex = re.compile(translate(self.pattern))\n\n @normalized\n def match(self, path):\n matches = self.regex.match(path+os.path.sep) is not None\n if matches:\n self.match_count += 1\n return matches\n\n def __repr__(self):\n return '%s(%s)' % (type(self), self.pattern)\n\n def __str__(self):\n return self.pattern_orig\n\n\ndef timestamp(s):\n \"\"\"Convert a --timestamp=s argument to a datetime object\"\"\"\n try:\n # is it pointing to a file / directory?\n ts = os.stat(s).st_mtime\n return datetime.utcfromtimestamp(ts)\n except OSError:\n # didn't work, try parsing as timestamp. UTC, no TZ, no microsecs support.\n for format in ('%Y-%m-%dT%H:%M:%SZ', '%Y-%m-%dT%H:%M:%S+00:00',\n '%Y-%m-%dT%H:%M:%S', '%Y-%m-%d %H:%M:%S',\n '%Y-%m-%dT%H:%M', '%Y-%m-%d %H:%M',\n '%Y-%m-%d', '%Y-%j',\n ):\n try:\n return datetime.strptime(s, format)\n except ValueError:\n continue\n raise ValueError\n\n\ndef ChunkerParams(s):\n chunk_min, chunk_max, chunk_mask, window_size = s.split(',')\n if int(chunk_max) > 23:\n # do not go beyond 2**23 (8MB) chunk size now,\n # COMPR_BUFFER can only cope with up to this size\n raise ValueError('max. chunk size exponent must not be more than 23 (2^23 = 8MiB max. chunk size)')\n return int(chunk_min), int(chunk_max), int(chunk_mask), int(window_size)\n\n\ndef CompressionSpec(s):\n values = s.split(',')\n count = len(values)\n if count < 1:\n raise ValueError\n compression = values[0]\n try:\n compression = int(compression)\n if count > 1:\n raise ValueError\n # DEPRECATED: it is just --compression N\n if 0 <= compression <= 9:\n print('Warning: --compression %d is deprecated, please use --compression zlib,%d.' % (compression, compression))\n if compression == 0:\n print('Hint: instead of --compression zlib,0 you could also use --compression none for better performance.')\n print('Hint: archives generated using --compression none are not compatible with borg < 0.25.0.')\n return dict(name='zlib', level=compression)\n raise ValueError\n except ValueError:\n # --compression algo[,...]\n name = compression\n if name in ('none', 'lz4', ):\n return dict(name=name)\n if name in ('zlib', 'lzma', ):\n if count < 2:\n level = 6 # default compression level in py stdlib\n elif count == 2:\n level = int(values[1])\n if not 0 <= level <= 9:\n raise ValueError\n else:\n raise ValueError\n return dict(name=name, level=level)\n raise ValueError\n\n\ndef dir_is_cachedir(path):\n \"\"\"Determines whether the specified path is a cache directory (and\n therefore should potentially be excluded from the backup) according to\n the CACHEDIR.TAG protocol\n (http://www.brynosaurus.com/cachedir/spec.html).\n \"\"\"\n\n tag_contents = b'Signature: 8a477f597d28d172789f06886806bc55'\n tag_path = os.path.join(path, 'CACHEDIR.TAG')\n try:\n if os.path.exists(tag_path):\n with open(tag_path, 'rb') as tag_file:\n tag_data = tag_file.read(len(tag_contents))\n if tag_data == tag_contents:\n return True\n except OSError:\n pass\n return False\n\n\ndef dir_is_tagged(path, exclude_caches, exclude_if_present):\n \"\"\"Determines whether the specified path is excluded by being a cache\n directory or containing user-specified tag files. Returns a list of the\n paths of the tag files (either CACHEDIR.TAG or the matching\n user-specified files).\n \"\"\"\n tag_paths = []\n if exclude_caches and dir_is_cachedir(path):\n tag_paths.append(os.path.join(path, 'CACHEDIR.TAG'))\n if exclude_if_present is not None:\n for tag in exclude_if_present:\n tag_path = os.path.join(path, tag)\n if os.path.isfile(tag_path):\n tag_paths.append(tag_path)\n return tag_paths\n\n\ndef format_time(t):\n \"\"\"use ISO-8601 date and time format\n \"\"\"\n return t.strftime('%a, %Y-%m-%d %H:%M:%S')\n\n\ndef format_timedelta(td):\n \"\"\"Format timedelta in a human friendly format\n \"\"\"\n # Since td.total_seconds() requires python 2.7\n ts = (td.microseconds + (td.seconds + td.days * 24 * 3600) * 10 ** 6) / float(10 ** 6)\n s = ts % 60\n m = int(ts / 60) % 60\n h = int(ts / 3600) % 24\n txt = '%.2f seconds' % s\n if m:\n txt = '%d minutes %s' % (m, txt)\n if h:\n txt = '%d hours %s' % (h, txt)\n if td.days:\n txt = '%d days %s' % (td.days, txt)\n return txt\n\n\ndef format_file_mode(mod):\n \"\"\"Format file mode bits for list output\n \"\"\"\n def x(v):\n return ''.join(v & m and s or '-'\n for m, s in ((4, 'r'), (2, 'w'), (1, 'x')))\n return '%s%s%s' % (x(mod // 64), x(mod // 8), x(mod))\n\n\ndef format_file_size(v, precision=2):\n \"\"\"Format file size into a human friendly format\n \"\"\"\n return sizeof_fmt_decimal(v, suffix='B', sep=' ', precision=precision)\n\n\ndef sizeof_fmt(num, suffix='B', units=None, power=None, sep='', precision=2):\n for unit in units[:-1]:\n if abs(round(num, precision)) < power:\n if isinstance(num, int):\n return \"{}{}{}{}\".format(num, sep, unit, suffix)\n else:\n return \"{:3.{}f}{}{}{}\".format(num, precision, sep, unit, suffix)\n num /= float(power)\n return \"{:.{}f}{}{}{}\".format(num, precision, sep, units[-1], suffix)\n\n\ndef sizeof_fmt_iec(num, suffix='B', sep='', precision=2):\n return sizeof_fmt(num, suffix=suffix, sep=sep, precision=precision, units=['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi', 'Yi'], power=1024)\n\n\ndef sizeof_fmt_decimal(num, suffix='B', sep='', precision=2):\n return sizeof_fmt(num, suffix=suffix, sep=sep, precision=precision, units=['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y'], power=1000)\n\n\ndef format_archive(archive):\n return '%-36s %s' % (archive.name, format_time(to_localtime(archive.ts)))\n\n\ndef memoize(function):\n cache = {}\n\n def decorated_function(*args):\n try:\n return cache[args]\n except KeyError:\n val = function(*args)\n cache[args] = val\n return val\n return decorated_function\n\n\n@memoize\ndef uid2user(uid, default=None):\n try:\n return pwd.getpwuid(uid).pw_name\n except KeyError:\n return default\n\n\n@memoize\ndef user2uid(user, default=None):\n try:\n return user and pwd.getpwnam(user).pw_uid\n except KeyError:\n return default\n\n\n@memoize\ndef gid2group(gid, default=None):\n try:\n return grp.getgrgid(gid).gr_name\n except KeyError:\n return default\n\n\n@memoize\ndef group2gid(group, default=None):\n try:\n return group and grp.getgrnam(group).gr_gid\n except KeyError:\n return default\n\n\ndef posix_acl_use_stored_uid_gid(acl):\n \"\"\"Replace the user/group field with the stored uid/gid\n \"\"\"\n entries = []\n for entry in safe_decode(acl).split('\\n'):\n if entry:\n fields = entry.split(':')\n if len(fields) == 4:\n entries.append(':'.join([fields[0], fields[3], fields[2]]))\n else:\n entries.append(entry)\n return safe_encode('\\n'.join(entries))\n\n\ndef safe_decode(s, coding='utf-8', errors='surrogateescape'):\n \"\"\"decode bytes to str, with round-tripping \"invalid\" bytes\"\"\"\n return s.decode(coding, errors)\n\n\ndef safe_encode(s, coding='utf-8', errors='surrogateescape'):\n \"\"\"encode str to bytes, with round-tripping \"invalid\" bytes\"\"\"\n return s.encode(coding, errors)\n\n\nclass Location:\n \"\"\"Object representing a repository / archive location\n \"\"\"\n proto = user = host = port = path = archive = None\n # borg mount's FUSE filesystem creates one level of directories from\n # the archive names. Thus, we must not accept \"/\" in archive names.\n ssh_re = re.compile(r'(?P<proto>ssh)://(?:(?P<user>[^@]+)@)?'\n r'(?P<host>[^:/#]+)(?::(?P<port>\\d+))?'\n r'(?P<path>[^:]+)(?:::(?P<archive>[^/]+))?$')\n file_re = re.compile(r'(?P<proto>file)://'\n r'(?P<path>[^:]+)(?:::(?P<archive>[^/]+))?$')\n scp_re = re.compile(r'((?:(?P<user>[^@]+)@)?(?P<host>[^:/]+):)?'\n r'(?P<path>[^:]+)(?:::(?P<archive>[^/]+))?$')\n # get the repo from BORG_RE env and the optional archive from param.\n # if the syntax requires giving REPOSITORY (see \"borg mount\"),\n # use \"::\" to let it use the env var.\n # if REPOSITORY argument is optional, it'll automatically use the env.\n env_re = re.compile(r'(?:::(?P<archive>[^/]+)?)?$')\n\n def __init__(self, text=''):\n self.orig = text\n if not self.parse(self.orig):\n raise ValueError\n\n def parse(self, text):\n valid = self._parse(text)\n if valid:\n return True\n m = self.env_re.match(text)\n if not m:\n return False\n repo = os.environ.get('BORG_REPO')\n if repo is None:\n return False\n valid = self._parse(repo)\n if not valid:\n return False\n self.archive = m.group('archive')\n return True\n\n def _parse(self, text):\n m = self.ssh_re.match(text)\n if m:\n self.proto = m.group('proto')\n self.user = m.group('user')\n self.host = m.group('host')\n self.port = m.group('port') and int(m.group('port')) or None\n self.path = m.group('path')\n self.archive = m.group('archive')\n return True\n m = self.file_re.match(text)\n if m:\n self.proto = m.group('proto')\n self.path = m.group('path')\n self.archive = m.group('archive')\n return True\n m = self.scp_re.match(text)\n if m:\n self.user = m.group('user')\n self.host = m.group('host')\n self.path = m.group('path')\n self.archive = m.group('archive')\n self.proto = self.host and 'ssh' or 'file'\n return True\n return False\n\n def __str__(self):\n items = [\n 'proto=%r' % self.proto,\n 'user=%r' % self.user,\n 'host=%r' % self.host,\n 'port=%r' % self.port,\n 'path=%r' % self.path,\n 'archive=%r' % self.archive,\n ]\n return ', '.join(items)\n\n def to_key_filename(self):\n name = re.sub('[^\\w]', '_', self.path).strip('_')\n if self.proto != 'file':\n name = self.host + '__' + name\n return os.path.join(get_keys_dir(), name)\n\n def __repr__(self):\n return \"Location(%s)\" % self\n\n def canonical_path(self):\n if self.proto == 'file':\n return self.path\n else:\n if self.path and self.path.startswith('~'):\n path = '/' + self.path\n elif self.path and not self.path.startswith('/'):\n path = '/~/' + self.path\n else:\n path = self.path\n return 'ssh://{}{}{}{}'.format('{}@'.format(self.user) if self.user else '',\n self.host,\n ':{}'.format(self.port) if self.port else '',\n path)\n\n\ndef location_validator(archive=None):\n def validator(text):\n try:\n loc = Location(text)\n except ValueError:\n raise argparse.ArgumentTypeError('Invalid location format: \"%s\"' % text)\n if archive is True and not loc.archive:\n raise argparse.ArgumentTypeError('\"%s\": No archive specified' % text)\n elif archive is False and loc.archive:\n raise argparse.ArgumentTypeError('\"%s\" No archive can be specified' % text)\n return loc\n return validator\n\n\ndef decode_dict(d, keys, encoding='utf-8', errors='surrogateescape'):\n for key in keys:\n if isinstance(d.get(key), bytes):\n d[key] = d[key].decode(encoding, errors)\n return d\n\n\ndef remove_surrogates(s, errors='replace'):\n \"\"\"Replace surrogates generated by fsdecode with '?'\n \"\"\"\n return s.encode('utf-8', errors).decode('utf-8')\n\n\n_safe_re = re.compile(r'^((\\.\\.)?/+)+')\n\n\ndef make_path_safe(path):\n \"\"\"Make path safe by making it relative and local\n \"\"\"\n return _safe_re.sub('', path) or '.'\n\n\ndef daemonize():\n \"\"\"Detach process from controlling terminal and run in background\n \"\"\"\n pid = os.fork()\n if pid:\n os._exit(0)\n os.setsid()\n pid = os.fork()\n if pid:\n os._exit(0)\n os.chdir('/')\n os.close(0)\n os.close(1)\n os.close(2)\n fd = os.open('/dev/null', os.O_RDWR)\n os.dup2(fd, 0)\n os.dup2(fd, 1)\n os.dup2(fd, 2)\n\n\nclass StableDict(dict):\n \"\"\"A dict subclass with stable items() ordering\"\"\"\n def items(self):\n return sorted(super().items())\n\n\nif sys.version < '3.3':\n # st_xtime_ns attributes only available in 3.3+\n def st_atime_ns(st):\n return int(st.st_atime * 1e9)\n\n def st_ctime_ns(st):\n return int(st.st_ctime * 1e9)\n\n def st_mtime_ns(st):\n return int(st.st_mtime * 1e9)\n\n # unhexlify in < 3.3 incorrectly only accepts bytes input\n def unhexlify(data):\n if isinstance(data, str):\n data = data.encode('ascii')\n return binascii.unhexlify(data)\nelse:\n def st_atime_ns(st):\n return st.st_atime_ns\n\n def st_ctime_ns(st):\n return st.st_ctime_ns\n\n def st_mtime_ns(st):\n return st.st_mtime_ns\n\n unhexlify = binascii.unhexlify\n\n\ndef bigint_to_int(mtime):\n \"\"\"Convert bytearray to int\n \"\"\"\n if isinstance(mtime, bytes):\n return int.from_bytes(mtime, 'little', signed=True)\n return mtime\n\n\ndef int_to_bigint(value):\n \"\"\"Convert integers larger than 64 bits to bytearray\n\n Smaller integers are left alone\n \"\"\"\n if value.bit_length() > 63:\n return value.to_bytes((value.bit_length() + 9) // 8, 'little', signed=True)\n return value\n\n\ndef is_slow_msgpack():\n return msgpack.Packer is msgpack.fallback.Packer\n\n\ndef yes(msg=None, retry_msg=None, false_msg=None, true_msg=None,\n default=False, default_notty=None, default_eof=None,\n falsish=('No', 'no', 'N', 'n'), truish=('Yes', 'yes', 'Y', 'y'),\n env_var_override=None, ifile=None, ofile=None, input=input):\n \"\"\"\n Output <msg> (usually a question) and let user input an answer.\n Qualifies the answer according to falsish and truish as True or False.\n If it didn't qualify and retry_msg is None (no retries wanted),\n return the default [which defaults to False]. Otherwise let user retry\n answering until answer is qualified.\n\n If env_var_override is given and it is non-empty, counts as truish answer\n and won't ask user for an answer.\n If we don't have a tty as input and default_notty is not None, return its value.\n Otherwise read input from non-tty and proceed as normal.\n If EOF is received instead an input, return default_eof [or default, if not given].\n\n :param msg: introducing message to output on ofile, no \\n is added [None]\n :param retry_msg: retry message to output on ofile, no \\n is added [None]\n (also enforces retries instead of returning default)\n :param false_msg: message to output before returning False [None]\n :param true_msg: message to output before returning True [None]\n :param default: default return value (empty answer is given) [False]\n :param default_notty: if not None, return its value if no tty is connected [None]\n :param default_eof: return value if EOF was read as answer [same as default]\n :param falsish: sequence of answers qualifying as False\n :param truish: sequence of answers qualifying as True\n :param env_var_override: environment variable name [None]\n :param ifile: input stream [sys.stdin] (only for testing!)\n :param ofile: output stream [sys.stderr]\n :param input: input function [input from builtins]\n :return: boolean answer value, True or False\n \"\"\"\n # note: we do not assign sys.stdin/stderr as defaults above, so they are\n # really evaluated NOW, not at function definition time.\n if ifile is None:\n ifile = sys.stdin\n if ofile is None:\n ofile = sys.stderr\n if default not in (True, False):\n raise ValueError(\"invalid default value, must be True or False\")\n if default_notty not in (None, True, False):\n raise ValueError(\"invalid default_notty value, must be None, True or False\")\n if default_eof not in (None, True, False):\n raise ValueError(\"invalid default_eof value, must be None, True or False\")\n if msg:\n print(msg, file=ofile, end='')\n ofile.flush()\n if env_var_override:\n value = os.environ.get(env_var_override)\n # currently, any non-empty value counts as truish\n # TODO: change this so one can give y/n there?\n if value:\n value = bool(value)\n value_str = truish[0] if value else falsish[0]\n print(\"{} (from {})\".format(value_str, env_var_override), file=ofile)\n return value\n if default_notty is not None and not ifile.isatty():\n # looks like ifile is not a terminal (but e.g. a pipe)\n return default_notty\n while True:\n try:\n answer = input() # XXX how can we use ifile?\n except EOFError:\n return default_eof if default_eof is not None else default\n if answer in truish:\n if true_msg:\n print(true_msg, file=ofile)\n return True\n if answer in falsish:\n if false_msg:\n print(false_msg, file=ofile)\n return False\n if retry_msg is None:\n # no retries wanted, we just return the default\n return default\n if retry_msg:\n print(retry_msg, file=ofile, end='')\n ofile.flush()\n\n\nclass ProgressIndicatorPercent:\n def __init__(self, total, step=5, start=0, same_line=False, msg=\"%3.0f%%\", file=sys.stderr):\n \"\"\"\n Percentage-based progress indicator\n\n :param total: total amount of items\n :param step: step size in percent\n :param start: at which percent value to start\n :param same_line: if True, emit output always on same line\n :param msg: output message, must contain one %f placeholder for the percentage\n :param file: output file, default: sys.stderr\n \"\"\"\n self.counter = 0 # 0 .. (total-1)\n self.total = total\n self.trigger_at = start # output next percentage value when reaching (at least) this\n self.step = step\n self.file = file\n self.msg = msg\n self.same_line = same_line\n\n def progress(self, current=None):\n if current is not None:\n self.counter = current\n pct = self.counter * 100 / self.total\n self.counter += 1\n if pct >= self.trigger_at:\n self.trigger_at += self.step\n return pct\n\n def show(self, current=None):\n pct = self.progress(current)\n if pct is not None:\n return self.output(pct)\n\n def output(self, percent):\n print(self.msg % percent, file=self.file, end='\\r' if self.same_line else '\\n') # python 3.3 gives us flush=True\n self.file.flush()\n\n def finish(self):\n if self.same_line:\n print(\" \" * len(self.msg % 100.0), file=self.file, end='\\r')\n\n\n\nclass ProgressIndicatorEndless:\n def __init__(self, step=10, file=sys.stderr):\n \"\"\"\n Progress indicator (long row of dots)\n\n :param step: every Nth call, call the func\n :param file: output file, default: sys.stderr\n \"\"\"\n self.counter = 0 # call counter\n self.triggered = 0 # increases 1 per trigger event\n self.step = step # trigger every <step> calls\n self.file = file\n\n def progress(self):\n self.counter += 1\n trigger = self.counter % self.step == 0\n if trigger:\n self.triggered += 1\n return trigger\n\n def show(self):\n trigger = self.progress()\n if trigger:\n return self.output(self.triggered)\n\n def output(self, triggered):\n print('.', end='', file=self.file) # python 3.3 gives us flush=True\n self.file.flush()\n\n def finish(self):\n print(file=self.file)\n\n\ndef sysinfo():\n info = []\n info.append('Platform: %s' % (' '.join(platform.uname()), ))\n if sys.platform.startswith('linux'):\n info.append('Linux: %s %s %s LibC: %s %s' % (platform.linux_distribution() + platform.libc_ver()))\n info.append('Python: %s %s' % (platform.python_implementation(), platform.python_version()))\n info.append('')\n return '\\n'.join(info)\n", "path": "borg/helpers.py" } ]
diff --git a/borg/helpers.py b/borg/helpers.py index 62b3278163..57f0a70226 100644 --- a/borg/helpers.py +++ b/borg/helpers.py @@ -462,7 +462,7 @@ def dir_is_tagged(path, exclude_caches, exclude_if_present): def format_time(t): """use ISO-8601 date and time format """ - return t.strftime('%Y-%m-%d %H:%M:%S') + return t.strftime('%a, %Y-%m-%d %H:%M:%S') def format_timedelta(td):
TheAlgorithms__Python-8746
Revert "Create guess_the_number_search.py" Reverts TheAlgorithms/Python#7937 @ChrisO345 the algorithm you merged failed tests, you shouldn't have merged it > https://github.com/TheAlgorithms/Python/actions/runs/4997927546/jobs/8952811360 > https://results.pre-commit.ci/run/github/63476337/1684282951.oykZY7Z4R3qR94KO0YZS2Q
[ { "content": "\"\"\"\nguess the number using lower,higher and the value to find or guess\n\nsolution works by dividing lower and higher of number guessed\n\nsuppose lower is 0, higher is 1000 and the number to guess is 355\n\n>>> guess_the_number(10, 1000, 17)\nstarted...\nguess the number : 17\ndetails : [505, 257, 133, 71, 40, 25, 17]\n\n\"\"\"\n\n\ndef temp_input_value(\n min_val: int = 10, max_val: int = 1000, option: bool = True\n) -> int:\n \"\"\"\n Temporary input values for tests\n\n >>> temp_input_value(option=True)\n 10\n\n >>> temp_input_value(option=False)\n 1000\n\n >>> temp_input_value(min_val=100, option=True)\n 100\n\n >>> temp_input_value(min_val=100, max_val=50)\n Traceback (most recent call last):\n ...\n ValueError: Invalid value for min_val or max_val (min_value < max_value)\n\n >>> temp_input_value(\"ten\",\"fifty\",1)\n Traceback (most recent call last):\n ...\n AssertionError: Invalid type of value(s) specified to function!\n\n >>> temp_input_value(min_val=-100, max_val=500)\n -100\n\n >>> temp_input_value(min_val=-5100, max_val=-100)\n -5100\n \"\"\"\n assert (\n isinstance(min_val, int)\n and isinstance(max_val, int)\n and isinstance(option, bool)\n ), \"Invalid type of value(s) specified to function!\"\n\n if min_val > max_val:\n raise ValueError(\"Invalid value for min_val or max_val (min_value < max_value)\")\n return min_val if option else max_val\n\n\ndef get_avg(number_1: int, number_2: int) -> int:\n \"\"\"\n Return the mid-number(whole) of two integers a and b\n\n >>> get_avg(10, 15)\n 12\n\n >>> get_avg(20, 300)\n 160\n\n >>> get_avg(\"abcd\", 300)\n Traceback (most recent call last):\n ...\n TypeError: can only concatenate str (not \"int\") to str\n\n >>> get_avg(10.5,50.25)\n 30\n \"\"\"\n return int((number_1 + number_2) / 2)\n\n\ndef guess_the_number(lower: int, higher: int, to_guess: int) -> None:\n \"\"\"\n The `guess_the_number` function that guess the number by some operations\n and using inner functions\n\n >>> guess_the_number(10, 1000, 17)\n started...\n guess the number : 17\n details : [505, 257, 133, 71, 40, 25, 17]\n\n >>> guess_the_number(-10000, 10000, 7)\n started...\n guess the number : 7\n details : [0, 5000, 2500, 1250, 625, 312, 156, 78, 39, 19, 9, 4, 6, 7]\n\n >>> guess_the_number(10, 1000, \"a\")\n Traceback (most recent call last):\n ...\n AssertionError: argument values must be type of \"int\"\n\n >>> guess_the_number(10, 1000, 5)\n Traceback (most recent call last):\n ...\n ValueError: guess value must be within the range of lower and higher value\n\n >>> guess_the_number(10000, 100, 5)\n Traceback (most recent call last):\n ...\n ValueError: argument value for lower and higher must be(lower > higher)\n \"\"\"\n assert (\n isinstance(lower, int) and isinstance(higher, int) and isinstance(to_guess, int)\n ), 'argument values must be type of \"int\"'\n\n if lower > higher:\n raise ValueError(\"argument value for lower and higher must be(lower > higher)\")\n\n if not lower < to_guess < higher:\n raise ValueError(\n \"guess value must be within the range of lower and higher value\"\n )\n\n def answer(number: int) -> str:\n \"\"\"\n Returns value by comparing with entered `to_guess` number\n \"\"\"\n if number > to_guess:\n return \"high\"\n elif number < to_guess:\n return \"low\"\n else:\n return \"same\"\n\n print(\"started...\")\n\n last_lowest = lower\n last_highest = higher\n\n last_numbers = []\n\n while True:\n number = get_avg(last_lowest, last_highest)\n last_numbers.append(number)\n\n if answer(number) == \"low\":\n last_lowest = number\n elif answer(number) == \"high\":\n last_highest = number\n else:\n break\n\n print(f\"guess the number : {last_numbers[-1]}\")\n print(f\"details : {str(last_numbers)}\")\n\n\ndef main() -> None:\n \"\"\"\n starting point or function of script\n \"\"\"\n lower = int(input(\"Enter lower value : \").strip())\n higher = int(input(\"Enter high value : \").strip())\n guess = int(input(\"Enter value to guess : \").strip())\n guess_the_number(lower, higher, guess)\n\n\nif __name__ == \"__main__\":\n main()\n", "path": "other/guess_the_number_search.py" } ]
[ { "content": "\"\"\"\nguess the number using lower,higher and the value to find or guess\n\nsolution works by dividing lower and higher of number guessed\n\nsuppose lower is 0, higher is 1000 and the number to guess is 355\n\n>>> guess_the_number(10, 1000, 17)\nstarted...\nguess the number : 17\ndetails : [505, 257, 133, 71, 40, 25, 17]\n\n\"\"\"\n\n\ndef temp_input_value(\n min_val: int = 10, max_val: int = 1000, option: bool = True\n) -> int:\n \"\"\"\n Temporary input values for tests\n\n >>> temp_input_value(option=True)\n 10\n\n >>> temp_input_value(option=False)\n 1000\n\n >>> temp_input_value(min_val=100, option=True)\n 100\n\n >>> temp_input_value(min_val=100, max_val=50)\n Traceback (most recent call last):\n ...\n ValueError: Invalid value for min_val or max_val (min_value < max_value)\n\n >>> temp_input_value(\"ten\",\"fifty\",1)\n Traceback (most recent call last):\n ...\n AssertionError: Invalid type of value(s) specified to function!\n\n >>> temp_input_value(min_val=-100, max_val=500)\n -100\n\n >>> temp_input_value(min_val=-5100, max_val=-100)\n -5100\n \"\"\"\n assert (\n isinstance(min_val, int)\n and isinstance(max_val, int)\n and isinstance(option, bool)\n ), \"Invalid type of value(s) specified to function!\"\n\n if min_val > max_val:\n raise ValueError(\"Invalid value for min_val or max_val (min_value < max_value)\")\n return min_val if option else max_val\n\n\ndef get_avg(number_1: int, number_2: int) -> int:\n \"\"\"\n Return the mid-number(whole) of two integers a and b\n\n >>> get_avg(10, 15)\n 12\n\n >>> get_avg(20, 300)\n 160\n\n >>> get_avg(\"abcd\", 300)\n Traceback (most recent call last):\n ...\n TypeError: can only concatenate str (not \"int\") to str\n\n >>> get_avg(10.5,50.25)\n 30\n \"\"\"\n return int((number_1 + number_2) / 2)\n\n\ndef guess_the_number(lower: int, higher: int, to_guess: int) -> None:\n \"\"\"\n The `guess_the_number` function that guess the number by some operations\n and using inner functions\n\n >>> guess_the_number(10, 1000, 17)\n started...\n guess the number : 17\n details : [505, 257, 133, 71, 40, 25, 17]\n\n >>> guess_the_number(-10000, 10000, 7)\n started...\n guess the number : 7\n details : [0, 5000, 2500, 1250, 625, 312, 156, 78, 39, 19, 9, 4, 6, 7]\n\n >>> guess_the_number(10, 1000, \"a\")\n Traceback (most recent call last):\n ...\n AssertionError: argument values must be type of \"int\"\n\n >>> guess_the_number(10, 1000, 5)\n Traceback (most recent call last):\n ...\n ValueError: guess value must be within the range of lower and higher value\n\n >>> guess_the_number(10000, 100, 5)\n Traceback (most recent call last):\n ...\n ValueError: argument value for lower and higher must be(lower > higher)\n \"\"\"\n assert (\n isinstance(lower, int) and isinstance(higher, int) and isinstance(to_guess, int)\n ), 'argument values must be type of \"int\"'\n\n if lower > higher:\n raise ValueError(\"argument value for lower and higher must be(lower > higher)\")\n\n if not lower < to_guess < higher:\n raise ValueError(\n \"guess value must be within the range of lower and higher value\"\n )\n\n def answer(number: int) -> str:\n \"\"\"\n Returns value by comparing with entered `to_guess` number\n \"\"\"\n if number > to_guess:\n return \"high\"\n elif number < to_guess:\n return \"low\"\n else:\n return \"same\"\n\n print(\"started...\")\n\n last_lowest = lower\n last_highest = higher\n\n last_numbers = []\n\n while True:\n number = get_avg(last_lowest, last_highest)\n last_numbers.append(number)\n\n if answer(number) == \"low\":\n last_lowest = number\n elif answer(number) == \"high\":\n last_highest = number\n else:\n break\n\n print(f\"guess the number : {last_numbers[-1]}\")\n print(f\"details : {last_numbers!s}\")\n\n\ndef main() -> None:\n \"\"\"\n starting point or function of script\n \"\"\"\n lower = int(input(\"Enter lower value : \").strip())\n higher = int(input(\"Enter high value : \").strip())\n guess = int(input(\"Enter value to guess : \").strip())\n guess_the_number(lower, higher, guess)\n\n\nif __name__ == \"__main__\":\n main()\n", "path": "other/guess_the_number_search.py" } ]
diff --git a/DIRECTORY.md b/DIRECTORY.md index 46bd51ce91ea..82791cde183d 100644 --- a/DIRECTORY.md +++ b/DIRECTORY.md @@ -605,6 +605,7 @@ * [Newton Raphson](maths/newton_raphson.py) * [Number Of Digits](maths/number_of_digits.py) * [Numerical Integration](maths/numerical_integration.py) + * [Odd Sieve](maths/odd_sieve.py) * [Perfect Cube](maths/perfect_cube.py) * [Perfect Number](maths/perfect_number.py) * [Perfect Square](maths/perfect_square.py) @@ -712,6 +713,7 @@ * [Gauss Easter](other/gauss_easter.py) * [Graham Scan](other/graham_scan.py) * [Greedy](other/greedy.py) + * [Guess The Number Search](other/guess_the_number_search.py) * [H Index](other/h_index.py) * [Least Recently Used](other/least_recently_used.py) * [Lfu Cache](other/lfu_cache.py) diff --git a/other/guess_the_number_search.py b/other/guess_the_number_search.py index 0439223f2ec9..01e8898bbb8a 100644 --- a/other/guess_the_number_search.py +++ b/other/guess_the_number_search.py @@ -148,7 +148,7 @@ def answer(number: int) -> str: break print(f"guess the number : {last_numbers[-1]}") - print(f"details : {str(last_numbers)}") + print(f"details : {last_numbers!s}") def main() -> None:
encode__httpx-2125
httpx cli --proxy and --proxies option problem The `httpx --help` shows `--proxy` option, but actually it accepts `--proxies` option. - `--proxy` https://github.com/encode/httpx/blob/master/httpx/_main.py#L72 - `--proxies` https://github.com/encode/httpx/blob/master/httpx/_main.py#L379 Which is correct? Is there an undocumented change from`--proxy` to `--proxies`?
[ { "content": "import functools\nimport json\nimport sys\nimport typing\n\nimport click\nimport httpcore\nimport pygments.lexers\nimport pygments.util\nimport rich.console\nimport rich.markup\nimport rich.progress\nimport rich.syntax\nimport rich.table\n\nfrom ._client import Client\nfrom ._exceptions import RequestError\nfrom ._models import Response\nfrom ._status_codes import codes\n\n\ndef print_help() -> None:\n console = rich.console.Console()\n\n console.print(\"[bold]HTTPX :butterfly:\", justify=\"center\")\n console.print()\n console.print(\"A next generation HTTP client.\", justify=\"center\")\n console.print()\n console.print(\n \"Usage: [bold]httpx[/bold] [cyan]<URL> [OPTIONS][/cyan] \", justify=\"left\"\n )\n console.print()\n\n table = rich.table.Table.grid(padding=1, pad_edge=True)\n table.add_column(\"Parameter\", no_wrap=True, justify=\"left\", style=\"bold\")\n table.add_column(\"Description\")\n table.add_row(\n \"-m, --method [cyan]METHOD\",\n \"Request method, such as GET, POST, PUT, PATCH, DELETE, OPTIONS, HEAD.\\n\"\n \"[Default: GET, or POST if a request body is included]\",\n )\n table.add_row(\n \"-p, --params [cyan]<NAME VALUE> ...\",\n \"Query parameters to include in the request URL.\",\n )\n table.add_row(\n \"-c, --content [cyan]TEXT\", \"Byte content to include in the request body.\"\n )\n table.add_row(\n \"-d, --data [cyan]<NAME VALUE> ...\", \"Form data to include in the request body.\"\n )\n table.add_row(\n \"-f, --files [cyan]<NAME FILENAME> ...\",\n \"Form files to include in the request body.\",\n )\n table.add_row(\"-j, --json [cyan]TEXT\", \"JSON data to include in the request body.\")\n table.add_row(\n \"-h, --headers [cyan]<NAME VALUE> ...\",\n \"Include additional HTTP headers in the request.\",\n )\n table.add_row(\n \"--cookies [cyan]<NAME VALUE> ...\", \"Cookies to include in the request.\"\n )\n table.add_row(\n \"--auth [cyan]<USER PASS>\",\n \"Username and password to include in the request. Specify '-' for the password to use \"\n \"a password prompt. Note that using --verbose/-v will expose the Authorization \"\n \"header, including the password encoding in a trivially reversible format.\",\n )\n\n table.add_row(\n \"--proxy [cyan]URL\",\n \"Send the request via a proxy. Should be the URL giving the proxy address.\",\n )\n\n table.add_row(\n \"--timeout [cyan]FLOAT\",\n \"Timeout value to use for network operations, such as establishing the connection, \"\n \"reading some data, etc... [Default: 5.0]\",\n )\n\n table.add_row(\"--follow-redirects\", \"Automatically follow redirects.\")\n table.add_row(\"--no-verify\", \"Disable SSL verification.\")\n table.add_row(\n \"--http2\", \"Send the request using HTTP/2, if the remote server supports it.\"\n )\n\n table.add_row(\n \"--download [cyan]FILE\",\n \"Save the response content as a file, rather than displaying it.\",\n )\n\n table.add_row(\"-v, --verbose\", \"Verbose output. Show request as well as response.\")\n table.add_row(\"--help\", \"Show this message and exit.\")\n console.print(table)\n\n\ndef get_lexer_for_response(response: Response) -> str:\n content_type = response.headers.get(\"Content-Type\")\n if content_type is not None:\n mime_type, _, _ = content_type.partition(\";\")\n try:\n return pygments.lexers.get_lexer_for_mimetype(mime_type.strip()).name\n except pygments.util.ClassNotFound: # pragma: nocover\n pass\n return \"\" # pragma: nocover\n\n\ndef format_request_headers(request: httpcore.Request, http2: bool = False) -> str:\n version = \"HTTP/2\" if http2 else \"HTTP/1.1\"\n headers = [\n (name.lower() if http2 else name, value) for name, value in request.headers\n ]\n method = request.method.decode(\"ascii\")\n target = request.url.target.decode(\"ascii\")\n lines = [f\"{method} {target} {version}\"] + [\n f\"{name.decode('ascii')}: {value.decode('ascii')}\" for name, value in headers\n ]\n return \"\\n\".join(lines)\n\n\ndef format_response_headers(\n http_version: bytes,\n status: int,\n reason_phrase: typing.Optional[bytes],\n headers: typing.List[typing.Tuple[bytes, bytes]],\n) -> str:\n version = http_version.decode(\"ascii\")\n reason = (\n codes.get_reason_phrase(status)\n if reason_phrase is None\n else reason_phrase.decode(\"ascii\")\n )\n lines = [f\"{version} {status} {reason}\"] + [\n f\"{name.decode('ascii')}: {value.decode('ascii')}\" for name, value in headers\n ]\n return \"\\n\".join(lines)\n\n\ndef print_request_headers(request: httpcore.Request, http2: bool = False) -> None:\n console = rich.console.Console()\n http_text = format_request_headers(request, http2=http2)\n syntax = rich.syntax.Syntax(http_text, \"http\", theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n syntax = rich.syntax.Syntax(\"\", \"http\", theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n\n\ndef print_response_headers(\n http_version: bytes,\n status: int,\n reason_phrase: typing.Optional[bytes],\n headers: typing.List[typing.Tuple[bytes, bytes]],\n) -> None:\n console = rich.console.Console()\n http_text = format_response_headers(http_version, status, reason_phrase, headers)\n syntax = rich.syntax.Syntax(http_text, \"http\", theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n syntax = rich.syntax.Syntax(\"\", \"http\", theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n\n\ndef print_response(response: Response) -> None:\n console = rich.console.Console()\n lexer_name = get_lexer_for_response(response)\n if lexer_name:\n if lexer_name.lower() == \"json\":\n try:\n data = response.json()\n text = json.dumps(data, indent=4)\n except ValueError: # pragma: nocover\n text = response.text\n else:\n text = response.text\n\n syntax = rich.syntax.Syntax(text, lexer_name, theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n else:\n console.print(f\"<{len(response.content)} bytes of binary data>\")\n\n\ndef format_certificate(cert: dict) -> str: # pragma: nocover\n lines = []\n for key, value in cert.items():\n if isinstance(value, (list, tuple)):\n lines.append(f\"* {key}:\")\n for item in value:\n if key in (\"subject\", \"issuer\"):\n for sub_item in item:\n lines.append(f\"* {sub_item[0]}: {sub_item[1]!r}\")\n elif isinstance(item, tuple) and len(item) == 2:\n lines.append(f\"* {item[0]}: {item[1]!r}\")\n else:\n lines.append(f\"* {item!r}\")\n else:\n lines.append(f\"* {key}: {value!r}\")\n return \"\\n\".join(lines)\n\n\ndef trace(name: str, info: dict, verbose: bool = False) -> None:\n console = rich.console.Console()\n if name == \"connection.connect_tcp.started\" and verbose:\n host = info[\"host\"]\n console.print(f\"* Connecting to {host!r}\")\n elif name == \"connection.connect_tcp.complete\" and verbose:\n stream = info[\"return_value\"]\n server_addr = stream.get_extra_info(\"server_addr\")\n console.print(f\"* Connected to {server_addr[0]!r} on port {server_addr[1]}\")\n elif name == \"connection.start_tls.complete\" and verbose: # pragma: nocover\n stream = info[\"return_value\"]\n ssl_object = stream.get_extra_info(\"ssl_object\")\n version = ssl_object.version()\n cipher = ssl_object.cipher()\n server_cert = ssl_object.getpeercert()\n alpn = ssl_object.selected_alpn_protocol()\n console.print(f\"* SSL established using {version!r} / {cipher[0]!r}\")\n console.print(f\"* Selected ALPN protocol: {alpn!r}\")\n if server_cert:\n console.print(\"* Server certificate:\")\n console.print(format_certificate(server_cert))\n elif name == \"http11.send_request_headers.started\" and verbose:\n request = info[\"request\"]\n print_request_headers(request, http2=False)\n elif name == \"http2.send_request_headers.started\" and verbose: # pragma: nocover\n request = info[\"request\"]\n print_request_headers(request, http2=True)\n elif name == \"http11.receive_response_headers.complete\":\n http_version, status, reason_phrase, headers = info[\"return_value\"]\n print_response_headers(http_version, status, reason_phrase, headers)\n elif name == \"http2.receive_response_headers.complete\": # pragma: nocover\n status, headers = info[\"return_value\"]\n http_version = b\"HTTP/2\"\n reason_phrase = None\n print_response_headers(http_version, status, reason_phrase, headers)\n\n\ndef download_response(response: Response, download: typing.BinaryIO) -> None:\n console = rich.console.Console()\n console.print()\n content_length = response.headers.get(\"Content-Length\")\n with rich.progress.Progress(\n \"[progress.description]{task.description}\",\n \"[progress.percentage]{task.percentage:>3.0f}%\",\n rich.progress.BarColumn(bar_width=None),\n rich.progress.DownloadColumn(),\n rich.progress.TransferSpeedColumn(),\n ) as progress:\n description = f\"Downloading [bold]{rich.markup.escape(download.name)}\"\n download_task = progress.add_task(\n description,\n total=int(content_length or 0),\n start=content_length is not None,\n )\n for chunk in response.iter_bytes():\n download.write(chunk)\n progress.update(download_task, completed=response.num_bytes_downloaded)\n\n\ndef validate_json(\n ctx: click.Context,\n param: typing.Union[click.Option, click.Parameter],\n value: typing.Any,\n) -> typing.Any:\n if value is None:\n return None\n\n try:\n return json.loads(value)\n except json.JSONDecodeError: # pragma: nocover\n raise click.BadParameter(\"Not valid JSON\")\n\n\ndef validate_auth(\n ctx: click.Context,\n param: typing.Union[click.Option, click.Parameter],\n value: typing.Any,\n) -> typing.Any:\n if value == (None, None):\n return None\n\n username, password = value\n if password == \"-\": # pragma: nocover\n password = click.prompt(\"Password\", hide_input=True)\n return (username, password)\n\n\ndef handle_help(\n ctx: click.Context,\n param: typing.Union[click.Option, click.Parameter],\n value: typing.Any,\n) -> None:\n if not value or ctx.resilient_parsing:\n return\n\n print_help()\n ctx.exit()\n\n\[email protected](add_help_option=False)\[email protected](\"url\", type=str)\[email protected](\n \"--method\",\n \"-m\",\n \"method\",\n type=str,\n help=(\n \"Request method, such as GET, POST, PUT, PATCH, DELETE, OPTIONS, HEAD. \"\n \"[Default: GET, or POST if a request body is included]\"\n ),\n)\[email protected](\n \"--params\",\n \"-p\",\n \"params\",\n type=(str, str),\n multiple=True,\n help=\"Query parameters to include in the request URL.\",\n)\[email protected](\n \"--content\",\n \"-c\",\n \"content\",\n type=str,\n help=\"Byte content to include in the request body.\",\n)\[email protected](\n \"--data\",\n \"-d\",\n \"data\",\n type=(str, str),\n multiple=True,\n help=\"Form data to include in the request body.\",\n)\[email protected](\n \"--files\",\n \"-f\",\n \"files\",\n type=(str, click.File(mode=\"rb\")),\n multiple=True,\n help=\"Form files to include in the request body.\",\n)\[email protected](\n \"--json\",\n \"-j\",\n \"json\",\n type=str,\n callback=validate_json,\n help=\"JSON data to include in the request body.\",\n)\[email protected](\n \"--headers\",\n \"-h\",\n \"headers\",\n type=(str, str),\n multiple=True,\n help=\"Include additional HTTP headers in the request.\",\n)\[email protected](\n \"--cookies\",\n \"cookies\",\n type=(str, str),\n multiple=True,\n help=\"Cookies to include in the request.\",\n)\[email protected](\n \"--auth\",\n \"auth\",\n type=(str, str),\n default=(None, None),\n callback=validate_auth,\n help=(\n \"Username and password to include in the request. \"\n \"Specify '-' for the password to use a password prompt. \"\n \"Note that using --verbose/-v will expose the Authorization header, \"\n \"including the password encoding in a trivially reversible format.\"\n ),\n)\[email protected](\n \"--proxies\",\n \"proxies\",\n type=str,\n default=None,\n help=\"Send the request via a proxy. Should be the URL giving the proxy address.\",\n)\[email protected](\n \"--timeout\",\n \"timeout\",\n type=float,\n default=5.0,\n help=(\n \"Timeout value to use for network operations, such as establishing the \"\n \"connection, reading some data, etc... [Default: 5.0]\"\n ),\n)\[email protected](\n \"--follow-redirects\",\n \"follow_redirects\",\n is_flag=True,\n default=False,\n help=\"Automatically follow redirects.\",\n)\[email protected](\n \"--no-verify\",\n \"verify\",\n is_flag=True,\n default=True,\n help=\"Disable SSL verification.\",\n)\[email protected](\n \"--http2\",\n \"http2\",\n type=bool,\n is_flag=True,\n default=False,\n help=\"Send the request using HTTP/2, if the remote server supports it.\",\n)\[email protected](\n \"--download\",\n type=click.File(\"wb\"),\n help=\"Save the response content as a file, rather than displaying it.\",\n)\[email protected](\n \"--verbose\",\n \"-v\",\n type=bool,\n is_flag=True,\n default=False,\n help=\"Verbose. Show request as well as response.\",\n)\[email protected](\n \"--help\",\n is_flag=True,\n is_eager=True,\n expose_value=False,\n callback=handle_help,\n help=\"Show this message and exit.\",\n)\ndef main(\n url: str,\n method: str,\n params: typing.List[typing.Tuple[str, str]],\n content: str,\n data: typing.List[typing.Tuple[str, str]],\n files: typing.List[typing.Tuple[str, click.File]],\n json: str,\n headers: typing.List[typing.Tuple[str, str]],\n cookies: typing.List[typing.Tuple[str, str]],\n auth: typing.Optional[typing.Tuple[str, str]],\n proxies: str,\n timeout: float,\n follow_redirects: bool,\n verify: bool,\n http2: bool,\n download: typing.Optional[typing.BinaryIO],\n verbose: bool,\n) -> None:\n \"\"\"\n An HTTP command line client.\n Sends a request and displays the response.\n \"\"\"\n if not method:\n method = \"POST\" if content or data or files or json else \"GET\"\n\n try:\n with Client(\n proxies=proxies,\n timeout=timeout,\n verify=verify,\n http2=http2,\n ) as client:\n with client.stream(\n method,\n url,\n params=list(params),\n content=content,\n data=dict(data),\n files=files, # type: ignore\n json=json,\n headers=headers,\n cookies=dict(cookies),\n auth=auth,\n follow_redirects=follow_redirects,\n extensions={\"trace\": functools.partial(trace, verbose=verbose)},\n ) as response:\n if download is not None:\n download_response(response, download)\n else:\n response.read()\n if response.content:\n print_response(response)\n\n except RequestError as exc:\n console = rich.console.Console()\n console.print(f\"[red]{type(exc).__name__}[/red]: {exc}\")\n sys.exit(1)\n\n sys.exit(0 if response.is_success else 1)\n", "path": "httpx/_main.py" } ]
[ { "content": "import functools\nimport json\nimport sys\nimport typing\n\nimport click\nimport httpcore\nimport pygments.lexers\nimport pygments.util\nimport rich.console\nimport rich.markup\nimport rich.progress\nimport rich.syntax\nimport rich.table\n\nfrom ._client import Client\nfrom ._exceptions import RequestError\nfrom ._models import Response\nfrom ._status_codes import codes\n\n\ndef print_help() -> None:\n console = rich.console.Console()\n\n console.print(\"[bold]HTTPX :butterfly:\", justify=\"center\")\n console.print()\n console.print(\"A next generation HTTP client.\", justify=\"center\")\n console.print()\n console.print(\n \"Usage: [bold]httpx[/bold] [cyan]<URL> [OPTIONS][/cyan] \", justify=\"left\"\n )\n console.print()\n\n table = rich.table.Table.grid(padding=1, pad_edge=True)\n table.add_column(\"Parameter\", no_wrap=True, justify=\"left\", style=\"bold\")\n table.add_column(\"Description\")\n table.add_row(\n \"-m, --method [cyan]METHOD\",\n \"Request method, such as GET, POST, PUT, PATCH, DELETE, OPTIONS, HEAD.\\n\"\n \"[Default: GET, or POST if a request body is included]\",\n )\n table.add_row(\n \"-p, --params [cyan]<NAME VALUE> ...\",\n \"Query parameters to include in the request URL.\",\n )\n table.add_row(\n \"-c, --content [cyan]TEXT\", \"Byte content to include in the request body.\"\n )\n table.add_row(\n \"-d, --data [cyan]<NAME VALUE> ...\", \"Form data to include in the request body.\"\n )\n table.add_row(\n \"-f, --files [cyan]<NAME FILENAME> ...\",\n \"Form files to include in the request body.\",\n )\n table.add_row(\"-j, --json [cyan]TEXT\", \"JSON data to include in the request body.\")\n table.add_row(\n \"-h, --headers [cyan]<NAME VALUE> ...\",\n \"Include additional HTTP headers in the request.\",\n )\n table.add_row(\n \"--cookies [cyan]<NAME VALUE> ...\", \"Cookies to include in the request.\"\n )\n table.add_row(\n \"--auth [cyan]<USER PASS>\",\n \"Username and password to include in the request. Specify '-' for the password to use \"\n \"a password prompt. Note that using --verbose/-v will expose the Authorization \"\n \"header, including the password encoding in a trivially reversible format.\",\n )\n\n table.add_row(\n \"--proxies [cyan]URL\",\n \"Send the request via a proxy. Should be the URL giving the proxy address.\",\n )\n\n table.add_row(\n \"--timeout [cyan]FLOAT\",\n \"Timeout value to use for network operations, such as establishing the connection, \"\n \"reading some data, etc... [Default: 5.0]\",\n )\n\n table.add_row(\"--follow-redirects\", \"Automatically follow redirects.\")\n table.add_row(\"--no-verify\", \"Disable SSL verification.\")\n table.add_row(\n \"--http2\", \"Send the request using HTTP/2, if the remote server supports it.\"\n )\n\n table.add_row(\n \"--download [cyan]FILE\",\n \"Save the response content as a file, rather than displaying it.\",\n )\n\n table.add_row(\"-v, --verbose\", \"Verbose output. Show request as well as response.\")\n table.add_row(\"--help\", \"Show this message and exit.\")\n console.print(table)\n\n\ndef get_lexer_for_response(response: Response) -> str:\n content_type = response.headers.get(\"Content-Type\")\n if content_type is not None:\n mime_type, _, _ = content_type.partition(\";\")\n try:\n return pygments.lexers.get_lexer_for_mimetype(mime_type.strip()).name\n except pygments.util.ClassNotFound: # pragma: nocover\n pass\n return \"\" # pragma: nocover\n\n\ndef format_request_headers(request: httpcore.Request, http2: bool = False) -> str:\n version = \"HTTP/2\" if http2 else \"HTTP/1.1\"\n headers = [\n (name.lower() if http2 else name, value) for name, value in request.headers\n ]\n method = request.method.decode(\"ascii\")\n target = request.url.target.decode(\"ascii\")\n lines = [f\"{method} {target} {version}\"] + [\n f\"{name.decode('ascii')}: {value.decode('ascii')}\" for name, value in headers\n ]\n return \"\\n\".join(lines)\n\n\ndef format_response_headers(\n http_version: bytes,\n status: int,\n reason_phrase: typing.Optional[bytes],\n headers: typing.List[typing.Tuple[bytes, bytes]],\n) -> str:\n version = http_version.decode(\"ascii\")\n reason = (\n codes.get_reason_phrase(status)\n if reason_phrase is None\n else reason_phrase.decode(\"ascii\")\n )\n lines = [f\"{version} {status} {reason}\"] + [\n f\"{name.decode('ascii')}: {value.decode('ascii')}\" for name, value in headers\n ]\n return \"\\n\".join(lines)\n\n\ndef print_request_headers(request: httpcore.Request, http2: bool = False) -> None:\n console = rich.console.Console()\n http_text = format_request_headers(request, http2=http2)\n syntax = rich.syntax.Syntax(http_text, \"http\", theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n syntax = rich.syntax.Syntax(\"\", \"http\", theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n\n\ndef print_response_headers(\n http_version: bytes,\n status: int,\n reason_phrase: typing.Optional[bytes],\n headers: typing.List[typing.Tuple[bytes, bytes]],\n) -> None:\n console = rich.console.Console()\n http_text = format_response_headers(http_version, status, reason_phrase, headers)\n syntax = rich.syntax.Syntax(http_text, \"http\", theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n syntax = rich.syntax.Syntax(\"\", \"http\", theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n\n\ndef print_response(response: Response) -> None:\n console = rich.console.Console()\n lexer_name = get_lexer_for_response(response)\n if lexer_name:\n if lexer_name.lower() == \"json\":\n try:\n data = response.json()\n text = json.dumps(data, indent=4)\n except ValueError: # pragma: nocover\n text = response.text\n else:\n text = response.text\n\n syntax = rich.syntax.Syntax(text, lexer_name, theme=\"ansi_dark\", word_wrap=True)\n console.print(syntax)\n else:\n console.print(f\"<{len(response.content)} bytes of binary data>\")\n\n\ndef format_certificate(cert: dict) -> str: # pragma: nocover\n lines = []\n for key, value in cert.items():\n if isinstance(value, (list, tuple)):\n lines.append(f\"* {key}:\")\n for item in value:\n if key in (\"subject\", \"issuer\"):\n for sub_item in item:\n lines.append(f\"* {sub_item[0]}: {sub_item[1]!r}\")\n elif isinstance(item, tuple) and len(item) == 2:\n lines.append(f\"* {item[0]}: {item[1]!r}\")\n else:\n lines.append(f\"* {item!r}\")\n else:\n lines.append(f\"* {key}: {value!r}\")\n return \"\\n\".join(lines)\n\n\ndef trace(name: str, info: dict, verbose: bool = False) -> None:\n console = rich.console.Console()\n if name == \"connection.connect_tcp.started\" and verbose:\n host = info[\"host\"]\n console.print(f\"* Connecting to {host!r}\")\n elif name == \"connection.connect_tcp.complete\" and verbose:\n stream = info[\"return_value\"]\n server_addr = stream.get_extra_info(\"server_addr\")\n console.print(f\"* Connected to {server_addr[0]!r} on port {server_addr[1]}\")\n elif name == \"connection.start_tls.complete\" and verbose: # pragma: nocover\n stream = info[\"return_value\"]\n ssl_object = stream.get_extra_info(\"ssl_object\")\n version = ssl_object.version()\n cipher = ssl_object.cipher()\n server_cert = ssl_object.getpeercert()\n alpn = ssl_object.selected_alpn_protocol()\n console.print(f\"* SSL established using {version!r} / {cipher[0]!r}\")\n console.print(f\"* Selected ALPN protocol: {alpn!r}\")\n if server_cert:\n console.print(\"* Server certificate:\")\n console.print(format_certificate(server_cert))\n elif name == \"http11.send_request_headers.started\" and verbose:\n request = info[\"request\"]\n print_request_headers(request, http2=False)\n elif name == \"http2.send_request_headers.started\" and verbose: # pragma: nocover\n request = info[\"request\"]\n print_request_headers(request, http2=True)\n elif name == \"http11.receive_response_headers.complete\":\n http_version, status, reason_phrase, headers = info[\"return_value\"]\n print_response_headers(http_version, status, reason_phrase, headers)\n elif name == \"http2.receive_response_headers.complete\": # pragma: nocover\n status, headers = info[\"return_value\"]\n http_version = b\"HTTP/2\"\n reason_phrase = None\n print_response_headers(http_version, status, reason_phrase, headers)\n\n\ndef download_response(response: Response, download: typing.BinaryIO) -> None:\n console = rich.console.Console()\n console.print()\n content_length = response.headers.get(\"Content-Length\")\n with rich.progress.Progress(\n \"[progress.description]{task.description}\",\n \"[progress.percentage]{task.percentage:>3.0f}%\",\n rich.progress.BarColumn(bar_width=None),\n rich.progress.DownloadColumn(),\n rich.progress.TransferSpeedColumn(),\n ) as progress:\n description = f\"Downloading [bold]{rich.markup.escape(download.name)}\"\n download_task = progress.add_task(\n description,\n total=int(content_length or 0),\n start=content_length is not None,\n )\n for chunk in response.iter_bytes():\n download.write(chunk)\n progress.update(download_task, completed=response.num_bytes_downloaded)\n\n\ndef validate_json(\n ctx: click.Context,\n param: typing.Union[click.Option, click.Parameter],\n value: typing.Any,\n) -> typing.Any:\n if value is None:\n return None\n\n try:\n return json.loads(value)\n except json.JSONDecodeError: # pragma: nocover\n raise click.BadParameter(\"Not valid JSON\")\n\n\ndef validate_auth(\n ctx: click.Context,\n param: typing.Union[click.Option, click.Parameter],\n value: typing.Any,\n) -> typing.Any:\n if value == (None, None):\n return None\n\n username, password = value\n if password == \"-\": # pragma: nocover\n password = click.prompt(\"Password\", hide_input=True)\n return (username, password)\n\n\ndef handle_help(\n ctx: click.Context,\n param: typing.Union[click.Option, click.Parameter],\n value: typing.Any,\n) -> None:\n if not value or ctx.resilient_parsing:\n return\n\n print_help()\n ctx.exit()\n\n\[email protected](add_help_option=False)\[email protected](\"url\", type=str)\[email protected](\n \"--method\",\n \"-m\",\n \"method\",\n type=str,\n help=(\n \"Request method, such as GET, POST, PUT, PATCH, DELETE, OPTIONS, HEAD. \"\n \"[Default: GET, or POST if a request body is included]\"\n ),\n)\[email protected](\n \"--params\",\n \"-p\",\n \"params\",\n type=(str, str),\n multiple=True,\n help=\"Query parameters to include in the request URL.\",\n)\[email protected](\n \"--content\",\n \"-c\",\n \"content\",\n type=str,\n help=\"Byte content to include in the request body.\",\n)\[email protected](\n \"--data\",\n \"-d\",\n \"data\",\n type=(str, str),\n multiple=True,\n help=\"Form data to include in the request body.\",\n)\[email protected](\n \"--files\",\n \"-f\",\n \"files\",\n type=(str, click.File(mode=\"rb\")),\n multiple=True,\n help=\"Form files to include in the request body.\",\n)\[email protected](\n \"--json\",\n \"-j\",\n \"json\",\n type=str,\n callback=validate_json,\n help=\"JSON data to include in the request body.\",\n)\[email protected](\n \"--headers\",\n \"-h\",\n \"headers\",\n type=(str, str),\n multiple=True,\n help=\"Include additional HTTP headers in the request.\",\n)\[email protected](\n \"--cookies\",\n \"cookies\",\n type=(str, str),\n multiple=True,\n help=\"Cookies to include in the request.\",\n)\[email protected](\n \"--auth\",\n \"auth\",\n type=(str, str),\n default=(None, None),\n callback=validate_auth,\n help=(\n \"Username and password to include in the request. \"\n \"Specify '-' for the password to use a password prompt. \"\n \"Note that using --verbose/-v will expose the Authorization header, \"\n \"including the password encoding in a trivially reversible format.\"\n ),\n)\[email protected](\n \"--proxies\",\n \"proxies\",\n type=str,\n default=None,\n help=\"Send the request via a proxy. Should be the URL giving the proxy address.\",\n)\[email protected](\n \"--timeout\",\n \"timeout\",\n type=float,\n default=5.0,\n help=(\n \"Timeout value to use for network operations, such as establishing the \"\n \"connection, reading some data, etc... [Default: 5.0]\"\n ),\n)\[email protected](\n \"--follow-redirects\",\n \"follow_redirects\",\n is_flag=True,\n default=False,\n help=\"Automatically follow redirects.\",\n)\[email protected](\n \"--no-verify\",\n \"verify\",\n is_flag=True,\n default=True,\n help=\"Disable SSL verification.\",\n)\[email protected](\n \"--http2\",\n \"http2\",\n type=bool,\n is_flag=True,\n default=False,\n help=\"Send the request using HTTP/2, if the remote server supports it.\",\n)\[email protected](\n \"--download\",\n type=click.File(\"wb\"),\n help=\"Save the response content as a file, rather than displaying it.\",\n)\[email protected](\n \"--verbose\",\n \"-v\",\n type=bool,\n is_flag=True,\n default=False,\n help=\"Verbose. Show request as well as response.\",\n)\[email protected](\n \"--help\",\n is_flag=True,\n is_eager=True,\n expose_value=False,\n callback=handle_help,\n help=\"Show this message and exit.\",\n)\ndef main(\n url: str,\n method: str,\n params: typing.List[typing.Tuple[str, str]],\n content: str,\n data: typing.List[typing.Tuple[str, str]],\n files: typing.List[typing.Tuple[str, click.File]],\n json: str,\n headers: typing.List[typing.Tuple[str, str]],\n cookies: typing.List[typing.Tuple[str, str]],\n auth: typing.Optional[typing.Tuple[str, str]],\n proxies: str,\n timeout: float,\n follow_redirects: bool,\n verify: bool,\n http2: bool,\n download: typing.Optional[typing.BinaryIO],\n verbose: bool,\n) -> None:\n \"\"\"\n An HTTP command line client.\n Sends a request and displays the response.\n \"\"\"\n if not method:\n method = \"POST\" if content or data or files or json else \"GET\"\n\n try:\n with Client(\n proxies=proxies,\n timeout=timeout,\n verify=verify,\n http2=http2,\n ) as client:\n with client.stream(\n method,\n url,\n params=list(params),\n content=content,\n data=dict(data),\n files=files, # type: ignore\n json=json,\n headers=headers,\n cookies=dict(cookies),\n auth=auth,\n follow_redirects=follow_redirects,\n extensions={\"trace\": functools.partial(trace, verbose=verbose)},\n ) as response:\n if download is not None:\n download_response(response, download)\n else:\n response.read()\n if response.content:\n print_response(response)\n\n except RequestError as exc:\n console = rich.console.Console()\n console.print(f\"[red]{type(exc).__name__}[/red]: {exc}\")\n sys.exit(1)\n\n sys.exit(0 if response.is_success else 1)\n", "path": "httpx/_main.py" } ]
diff --git a/httpx/_main.py b/httpx/_main.py index 7bd6b90846..ebcb65214f 100644 --- a/httpx/_main.py +++ b/httpx/_main.py @@ -69,7 +69,7 @@ def print_help() -> None: ) table.add_row( - "--proxy [cyan]URL", + "--proxies [cyan]URL", "Send the request via a proxy. Should be the URL giving the proxy address.", )
MongoEngine__mongoengine-2043
Missuse of write_concern in Document.save It is possible to define write_concern on the connection. However, while calling save method on a document, the following code (line 229 in document.py) tells you that if it's not define on save call, it is erased, whatever is your settings on the connection: ``` if write_concern is None: write_concern = {"w": 1} ``` The idea is to delete those two lines to fallback on connection settings.
[ { "content": "import re\nimport warnings\n\nfrom bson.dbref import DBRef\nimport pymongo\nfrom pymongo.read_preferences import ReadPreference\nimport six\nfrom six import iteritems\n\nfrom mongoengine import signals\nfrom mongoengine.base import (BaseDict, BaseDocument, BaseList,\n DocumentMetaclass, EmbeddedDocumentList,\n TopLevelDocumentMetaclass, get_document)\nfrom mongoengine.common import _import_class\nfrom mongoengine.connection import DEFAULT_CONNECTION_NAME, get_db\nfrom mongoengine.context_managers import (set_write_concern,\n switch_collection,\n switch_db)\nfrom mongoengine.errors import (InvalidDocumentError, InvalidQueryError,\n SaveConditionError)\nfrom mongoengine.pymongo_support import IS_PYMONGO_3, list_collection_names\nfrom mongoengine.queryset import (NotUniqueError, OperationError,\n QuerySet, transform)\n\n__all__ = ('Document', 'EmbeddedDocument', 'DynamicDocument',\n 'DynamicEmbeddedDocument', 'OperationError',\n 'InvalidCollectionError', 'NotUniqueError', 'MapReduceDocument')\n\n\ndef includes_cls(fields):\n \"\"\"Helper function used for ensuring and comparing indexes.\"\"\"\n first_field = None\n if len(fields):\n if isinstance(fields[0], six.string_types):\n first_field = fields[0]\n elif isinstance(fields[0], (list, tuple)) and len(fields[0]):\n first_field = fields[0][0]\n return first_field == '_cls'\n\n\nclass InvalidCollectionError(Exception):\n pass\n\n\nclass EmbeddedDocument(six.with_metaclass(DocumentMetaclass, BaseDocument)):\n \"\"\"A :class:`~mongoengine.Document` that isn't stored in its own\n collection. :class:`~mongoengine.EmbeddedDocument`\\ s should be used as\n fields on :class:`~mongoengine.Document`\\ s through the\n :class:`~mongoengine.EmbeddedDocumentField` field type.\n\n A :class:`~mongoengine.EmbeddedDocument` subclass may be itself subclassed,\n to create a specialised version of the embedded document that will be\n stored in the same collection. To facilitate this behaviour a `_cls`\n field is added to documents (hidden though the MongoEngine interface).\n To enable this behaviour set :attr:`allow_inheritance` to ``True`` in the\n :attr:`meta` dictionary.\n \"\"\"\n\n __slots__ = ('_instance', )\n\n # The __metaclass__ attribute is removed by 2to3 when running with Python3\n # my_metaclass is defined so that metaclass can be queried in Python 2 & 3\n my_metaclass = DocumentMetaclass\n\n # A generic embedded document doesn't have any immutable properties\n # that describe it uniquely, hence it shouldn't be hashable. You can\n # define your own __hash__ method on a subclass if you need your\n # embedded documents to be hashable.\n __hash__ = None\n\n def __init__(self, *args, **kwargs):\n super(EmbeddedDocument, self).__init__(*args, **kwargs)\n self._instance = None\n self._changed_fields = []\n\n def __eq__(self, other):\n if isinstance(other, self.__class__):\n return self._data == other._data\n return False\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def to_mongo(self, *args, **kwargs):\n data = super(EmbeddedDocument, self).to_mongo(*args, **kwargs)\n\n # remove _id from the SON if it's in it and it's None\n if '_id' in data and data['_id'] is None:\n del data['_id']\n\n return data\n\n def save(self, *args, **kwargs):\n warnings.warn(\"EmbeddedDocument.save is deprecated and will be removed in a next version of mongoengine.\"\n \"Use the parent document's .save() or ._instance.save()\",\n DeprecationWarning, stacklevel=2)\n self._instance.save(*args, **kwargs)\n\n def reload(self, *args, **kwargs):\n warnings.warn(\"EmbeddedDocument.reload is deprecated and will be removed in a next version of mongoengine.\"\n \"Use the parent document's .reload() or ._instance.reload()\",\n DeprecationWarning, stacklevel=2)\n self._instance.reload(*args, **kwargs)\n\n\nclass Document(six.with_metaclass(TopLevelDocumentMetaclass, BaseDocument)):\n \"\"\"The base class used for defining the structure and properties of\n collections of documents stored in MongoDB. Inherit from this class, and\n add fields as class attributes to define a document's structure.\n Individual documents may then be created by making instances of the\n :class:`~mongoengine.Document` subclass.\n\n By default, the MongoDB collection used to store documents created using a\n :class:`~mongoengine.Document` subclass will be the name of the subclass\n converted to lowercase. A different collection may be specified by\n providing :attr:`collection` to the :attr:`meta` dictionary in the class\n definition.\n\n A :class:`~mongoengine.Document` subclass may be itself subclassed, to\n create a specialised version of the document that will be stored in the\n same collection. To facilitate this behaviour a `_cls`\n field is added to documents (hidden though the MongoEngine interface).\n To enable this behaviourset :attr:`allow_inheritance` to ``True`` in the\n :attr:`meta` dictionary.\n\n A :class:`~mongoengine.Document` may use a **Capped Collection** by\n specifying :attr:`max_documents` and :attr:`max_size` in the :attr:`meta`\n dictionary. :attr:`max_documents` is the maximum number of documents that\n is allowed to be stored in the collection, and :attr:`max_size` is the\n maximum size of the collection in bytes. :attr:`max_size` is rounded up\n to the next multiple of 256 by MongoDB internally and mongoengine before.\n Use also a multiple of 256 to avoid confusions. If :attr:`max_size` is not\n specified and :attr:`max_documents` is, :attr:`max_size` defaults to\n 10485760 bytes (10MB).\n\n Indexes may be created by specifying :attr:`indexes` in the :attr:`meta`\n dictionary. The value should be a list of field names or tuples of field\n names. Index direction may be specified by prefixing the field names with\n a **+** or **-** sign.\n\n Automatic index creation can be disabled by specifying\n :attr:`auto_create_index` in the :attr:`meta` dictionary. If this is set to\n False then indexes will not be created by MongoEngine. This is useful in\n production systems where index creation is performed as part of a\n deployment system.\n\n By default, _cls will be added to the start of every index (that\n doesn't contain a list) if allow_inheritance is True. This can be\n disabled by either setting cls to False on the specific index or\n by setting index_cls to False on the meta dictionary for the document.\n\n By default, any extra attribute existing in stored data but not declared\n in your model will raise a :class:`~mongoengine.FieldDoesNotExist` error.\n This can be disabled by setting :attr:`strict` to ``False``\n in the :attr:`meta` dictionary.\n \"\"\"\n\n # The __metaclass__ attribute is removed by 2to3 when running with Python3\n # my_metaclass is defined so that metaclass can be queried in Python 2 & 3\n my_metaclass = TopLevelDocumentMetaclass\n\n __slots__ = ('__objects',)\n\n @property\n def pk(self):\n \"\"\"Get the primary key.\"\"\"\n if 'id_field' not in self._meta:\n return None\n return getattr(self, self._meta['id_field'])\n\n @pk.setter\n def pk(self, value):\n \"\"\"Set the primary key.\"\"\"\n return setattr(self, self._meta['id_field'], value)\n\n def __hash__(self):\n \"\"\"Return the hash based on the PK of this document. If it's new\n and doesn't have a PK yet, return the default object hash instead.\n \"\"\"\n if self.pk is None:\n return super(BaseDocument, self).__hash__()\n\n return hash(self.pk)\n\n @classmethod\n def _get_db(cls):\n \"\"\"Some Model using other db_alias\"\"\"\n return get_db(cls._meta.get('db_alias', DEFAULT_CONNECTION_NAME))\n\n @classmethod\n def _disconnect(cls):\n \"\"\"Detach the Document class from the (cached) database collection\"\"\"\n cls._collection = None\n\n @classmethod\n def _get_collection(cls):\n \"\"\"Return the corresponding PyMongo collection of this document.\n Upon the first call, it will ensure that indexes gets created. The returned collection then gets cached\n \"\"\"\n if not hasattr(cls, '_collection') or cls._collection is None:\n # Get the collection, either capped or regular.\n if cls._meta.get('max_size') or cls._meta.get('max_documents'):\n cls._collection = cls._get_capped_collection()\n else:\n db = cls._get_db()\n collection_name = cls._get_collection_name()\n cls._collection = db[collection_name]\n\n # Ensure indexes on the collection unless auto_create_index was\n # set to False.\n # Also there is no need to ensure indexes on slave.\n db = cls._get_db()\n if cls._meta.get('auto_create_index', True) and\\\n db.client.is_primary:\n cls.ensure_indexes()\n\n return cls._collection\n\n @classmethod\n def _get_capped_collection(cls):\n \"\"\"Create a new or get an existing capped PyMongo collection.\"\"\"\n db = cls._get_db()\n collection_name = cls._get_collection_name()\n\n # Get max document limit and max byte size from meta.\n max_size = cls._meta.get('max_size') or 10 * 2 ** 20 # 10MB default\n max_documents = cls._meta.get('max_documents')\n\n # MongoDB will automatically raise the size to make it a multiple of\n # 256 bytes. We raise it here ourselves to be able to reliably compare\n # the options below.\n if max_size % 256:\n max_size = (max_size // 256 + 1) * 256\n\n # If the collection already exists and has different options\n # (i.e. isn't capped or has different max/size), raise an error.\n if collection_name in list_collection_names(db, include_system_collections=True):\n collection = db[collection_name]\n options = collection.options()\n if (\n options.get('max') != max_documents or\n options.get('size') != max_size\n ):\n raise InvalidCollectionError(\n 'Cannot create collection \"{}\" as a capped '\n 'collection as it already exists'.format(cls._collection)\n )\n\n return collection\n\n # Create a new capped collection.\n opts = {'capped': True, 'size': max_size}\n if max_documents:\n opts['max'] = max_documents\n\n return db.create_collection(collection_name, **opts)\n\n def to_mongo(self, *args, **kwargs):\n data = super(Document, self).to_mongo(*args, **kwargs)\n\n # If '_id' is None, try and set it from self._data. If that\n # doesn't exist either, remote '_id' from the SON completely.\n if data['_id'] is None:\n if self._data.get('id') is None:\n del data['_id']\n else:\n data['_id'] = self._data['id']\n\n return data\n\n def modify(self, query=None, **update):\n \"\"\"Perform an atomic update of the document in the database and reload\n the document object using updated version.\n\n Returns True if the document has been updated or False if the document\n in the database doesn't match the query.\n\n .. note:: All unsaved changes that have been made to the document are\n rejected if the method returns True.\n\n :param query: the update will be performed only if the document in the\n database matches the query\n :param update: Django-style update keyword arguments\n \"\"\"\n if query is None:\n query = {}\n\n if self.pk is None:\n raise InvalidDocumentError('The document does not have a primary key.')\n\n id_field = self._meta['id_field']\n query = query.copy() if isinstance(query, dict) else query.to_query(self)\n\n if id_field not in query:\n query[id_field] = self.pk\n elif query[id_field] != self.pk:\n raise InvalidQueryError('Invalid document modify query: it must modify only this document.')\n\n # Need to add shard key to query, or you get an error\n query.update(self._object_key)\n\n updated = self._qs(**query).modify(new=True, **update)\n if updated is None:\n return False\n\n for field in self._fields_ordered:\n setattr(self, field, self._reload(field, updated[field]))\n\n self._changed_fields = updated._changed_fields\n self._created = False\n\n return True\n\n def save(self, force_insert=False, validate=True, clean=True,\n write_concern=None, cascade=None, cascade_kwargs=None,\n _refs=None, save_condition=None, signal_kwargs=None, **kwargs):\n \"\"\"Save the :class:`~mongoengine.Document` to the database. If the\n document already exists, it will be updated, otherwise it will be\n created.\n\n :param force_insert: only try to create a new document, don't allow\n updates of existing documents.\n :param validate: validates the document; set to ``False`` to skip.\n :param clean: call the document clean method, requires `validate` to be\n True.\n :param write_concern: Extra keyword arguments are passed down to\n :meth:`~pymongo.collection.Collection.save` OR\n :meth:`~pymongo.collection.Collection.insert`\n which will be used as options for the resultant\n ``getLastError`` command. For example,\n ``save(..., write_concern={w: 2, fsync: True}, ...)`` will\n wait until at least two servers have recorded the write and\n will force an fsync on the primary server.\n :param cascade: Sets the flag for cascading saves. You can set a\n default by setting \"cascade\" in the document __meta__\n :param cascade_kwargs: (optional) kwargs dictionary to be passed throw\n to cascading saves. Implies ``cascade=True``.\n :param _refs: A list of processed references used in cascading saves\n :param save_condition: only perform save if matching record in db\n satisfies condition(s) (e.g. version number).\n Raises :class:`OperationError` if the conditions are not satisfied\n :param signal_kwargs: (optional) kwargs dictionary to be passed to\n the signal calls.\n\n .. versionchanged:: 0.5\n In existing documents it only saves changed fields using\n set / unset. Saves are cascaded and any\n :class:`~bson.dbref.DBRef` objects that have changes are\n saved as well.\n .. versionchanged:: 0.6\n Added cascading saves\n .. versionchanged:: 0.8\n Cascade saves are optional and default to False. If you want\n fine grain control then you can turn off using document\n meta['cascade'] = True. Also you can pass different kwargs to\n the cascade save using cascade_kwargs which overwrites the\n existing kwargs with custom values.\n .. versionchanged:: 0.8.5\n Optional save_condition that only overwrites existing documents\n if the condition is satisfied in the current db record.\n .. versionchanged:: 0.10\n :class:`OperationError` exception raised if save_condition fails.\n .. versionchanged:: 0.10.1\n :class: save_condition failure now raises a `SaveConditionError`\n .. versionchanged:: 0.10.7\n Add signal_kwargs argument\n \"\"\"\n if self._meta.get('abstract'):\n raise InvalidDocumentError('Cannot save an abstract document.')\n\n signal_kwargs = signal_kwargs or {}\n signals.pre_save.send(self.__class__, document=self, **signal_kwargs)\n\n if validate:\n self.validate(clean=clean)\n\n if write_concern is None:\n write_concern = {'w': 1}\n\n doc = self.to_mongo()\n\n created = ('_id' not in doc or self._created or force_insert)\n\n signals.pre_save_post_validation.send(self.__class__, document=self,\n created=created, **signal_kwargs)\n # it might be refreshed by the pre_save_post_validation hook, e.g., for etag generation\n doc = self.to_mongo()\n\n if self._meta.get('auto_create_index', True):\n self.ensure_indexes()\n\n try:\n # Save a new document or update an existing one\n if created:\n object_id = self._save_create(doc, force_insert, write_concern)\n else:\n object_id, created = self._save_update(doc, save_condition,\n write_concern)\n\n if cascade is None:\n cascade = (self._meta.get('cascade', False) or\n cascade_kwargs is not None)\n\n if cascade:\n kwargs = {\n 'force_insert': force_insert,\n 'validate': validate,\n 'write_concern': write_concern,\n 'cascade': cascade\n }\n if cascade_kwargs: # Allow granular control over cascades\n kwargs.update(cascade_kwargs)\n kwargs['_refs'] = _refs\n self.cascade_save(**kwargs)\n\n except pymongo.errors.DuplicateKeyError as err:\n message = u'Tried to save duplicate unique keys (%s)'\n raise NotUniqueError(message % six.text_type(err))\n except pymongo.errors.OperationFailure as err:\n message = 'Could not save document (%s)'\n if re.match('^E1100[01] duplicate key', six.text_type(err)):\n # E11000 - duplicate key error index\n # E11001 - duplicate key on update\n message = u'Tried to save duplicate unique keys (%s)'\n raise NotUniqueError(message % six.text_type(err))\n raise OperationError(message % six.text_type(err))\n\n # Make sure we store the PK on this document now that it's saved\n id_field = self._meta['id_field']\n if created or id_field not in self._meta.get('shard_key', []):\n self[id_field] = self._fields[id_field].to_python(object_id)\n\n signals.post_save.send(self.__class__, document=self,\n created=created, **signal_kwargs)\n\n self._clear_changed_fields()\n self._created = False\n\n return self\n\n def _save_create(self, doc, force_insert, write_concern):\n \"\"\"Save a new document.\n\n Helper method, should only be used inside save().\n \"\"\"\n collection = self._get_collection()\n with set_write_concern(collection, write_concern) as wc_collection:\n if force_insert:\n return wc_collection.insert_one(doc).inserted_id\n # insert_one will provoke UniqueError alongside save does not\n # therefore, it need to catch and call replace_one.\n if '_id' in doc:\n raw_object = wc_collection.find_one_and_replace(\n {'_id': doc['_id']}, doc)\n if raw_object:\n return doc['_id']\n\n object_id = wc_collection.insert_one(doc).inserted_id\n\n return object_id\n\n def _get_update_doc(self):\n \"\"\"Return a dict containing all the $set and $unset operations\n that should be sent to MongoDB based on the changes made to this\n Document.\n \"\"\"\n updates, removals = self._delta()\n\n update_doc = {}\n if updates:\n update_doc['$set'] = updates\n if removals:\n update_doc['$unset'] = removals\n\n return update_doc\n\n def _save_update(self, doc, save_condition, write_concern):\n \"\"\"Update an existing document.\n\n Helper method, should only be used inside save().\n \"\"\"\n collection = self._get_collection()\n object_id = doc['_id']\n created = False\n\n select_dict = {}\n if save_condition is not None:\n select_dict = transform.query(self.__class__, **save_condition)\n\n select_dict['_id'] = object_id\n\n # Need to add shard key to query, or you get an error\n shard_key = self._meta.get('shard_key', tuple())\n for k in shard_key:\n path = self._lookup_field(k.split('.'))\n actual_key = [p.db_field for p in path]\n val = doc\n for ak in actual_key:\n val = val[ak]\n select_dict['.'.join(actual_key)] = val\n\n update_doc = self._get_update_doc()\n if update_doc:\n upsert = save_condition is None\n last_error = collection.update(select_dict, update_doc,\n upsert=upsert, **write_concern)\n if not upsert and last_error['n'] == 0:\n raise SaveConditionError('Race condition preventing'\n ' document update detected')\n if last_error is not None:\n updated_existing = last_error.get('updatedExisting')\n if updated_existing is False:\n created = True\n # !!! This is bad, means we accidentally created a new,\n # potentially corrupted document. See\n # https://github.com/MongoEngine/mongoengine/issues/564\n\n return object_id, created\n\n def cascade_save(self, **kwargs):\n \"\"\"Recursively save any references and generic references on the\n document.\n \"\"\"\n _refs = kwargs.get('_refs') or []\n\n ReferenceField = _import_class('ReferenceField')\n GenericReferenceField = _import_class('GenericReferenceField')\n\n for name, cls in self._fields.items():\n if not isinstance(cls, (ReferenceField,\n GenericReferenceField)):\n continue\n\n ref = self._data.get(name)\n if not ref or isinstance(ref, DBRef):\n continue\n\n if not getattr(ref, '_changed_fields', True):\n continue\n\n ref_id = \"%s,%s\" % (ref.__class__.__name__, str(ref._data))\n if ref and ref_id not in _refs:\n _refs.append(ref_id)\n kwargs[\"_refs\"] = _refs\n ref.save(**kwargs)\n ref._changed_fields = []\n\n @property\n def _qs(self):\n \"\"\"Return the queryset to use for updating / reloading / deletions.\"\"\"\n if not hasattr(self, '__objects'):\n self.__objects = QuerySet(self, self._get_collection())\n return self.__objects\n\n @property\n def _object_key(self):\n \"\"\"Get the query dict that can be used to fetch this object from\n the database. Most of the time it's a simple PK lookup, but in\n case of a sharded collection with a compound shard key, it can\n contain a more complex query.\n \"\"\"\n select_dict = {'pk': self.pk}\n shard_key = self.__class__._meta.get('shard_key', tuple())\n for k in shard_key:\n path = self._lookup_field(k.split('.'))\n actual_key = [p.db_field for p in path]\n val = self\n for ak in actual_key:\n val = getattr(val, ak)\n select_dict['__'.join(actual_key)] = val\n return select_dict\n\n def update(self, **kwargs):\n \"\"\"Performs an update on the :class:`~mongoengine.Document`\n A convenience wrapper to :meth:`~mongoengine.QuerySet.update`.\n\n Raises :class:`OperationError` if called on an object that has not yet\n been saved.\n \"\"\"\n if self.pk is None:\n if kwargs.get('upsert', False):\n query = self.to_mongo()\n if '_cls' in query:\n del query['_cls']\n return self._qs.filter(**query).update_one(**kwargs)\n else:\n raise OperationError(\n 'attempt to update a document not yet saved')\n\n # Need to add shard key to query, or you get an error\n return self._qs.filter(**self._object_key).update_one(**kwargs)\n\n def delete(self, signal_kwargs=None, **write_concern):\n \"\"\"Delete the :class:`~mongoengine.Document` from the database. This\n will only take effect if the document has been previously saved.\n\n :param signal_kwargs: (optional) kwargs dictionary to be passed to\n the signal calls.\n :param write_concern: Extra keyword arguments are passed down which\n will be used as options for the resultant ``getLastError`` command.\n For example, ``save(..., w: 2, fsync: True)`` will\n wait until at least two servers have recorded the write and\n will force an fsync on the primary server.\n\n .. versionchanged:: 0.10.7\n Add signal_kwargs argument\n \"\"\"\n signal_kwargs = signal_kwargs or {}\n signals.pre_delete.send(self.__class__, document=self, **signal_kwargs)\n\n # Delete FileFields separately\n FileField = _import_class('FileField')\n for name, field in iteritems(self._fields):\n if isinstance(field, FileField):\n getattr(self, name).delete()\n\n try:\n self._qs.filter(\n **self._object_key).delete(write_concern=write_concern, _from_doc_delete=True)\n except pymongo.errors.OperationFailure as err:\n message = u'Could not delete document (%s)' % err.message\n raise OperationError(message)\n signals.post_delete.send(self.__class__, document=self, **signal_kwargs)\n\n def switch_db(self, db_alias, keep_created=True):\n \"\"\"\n Temporarily switch the database for a document instance.\n\n Only really useful for archiving off data and calling `save()`::\n\n user = User.objects.get(id=user_id)\n user.switch_db('archive-db')\n user.save()\n\n :param str db_alias: The database alias to use for saving the document\n\n :param bool keep_created: keep self._created value after switching db, else is reset to True\n\n\n .. seealso::\n Use :class:`~mongoengine.context_managers.switch_collection`\n if you need to read from another collection\n \"\"\"\n with switch_db(self.__class__, db_alias) as cls:\n collection = cls._get_collection()\n db = cls._get_db()\n self._get_collection = lambda: collection\n self._get_db = lambda: db\n self._collection = collection\n self._created = True if not keep_created else self._created\n self.__objects = self._qs\n self.__objects._collection_obj = collection\n return self\n\n def switch_collection(self, collection_name, keep_created=True):\n \"\"\"\n Temporarily switch the collection for a document instance.\n\n Only really useful for archiving off data and calling `save()`::\n\n user = User.objects.get(id=user_id)\n user.switch_collection('old-users')\n user.save()\n\n :param str collection_name: The database alias to use for saving the\n document\n\n :param bool keep_created: keep self._created value after switching collection, else is reset to True\n\n\n .. seealso::\n Use :class:`~mongoengine.context_managers.switch_db`\n if you need to read from another database\n \"\"\"\n with switch_collection(self.__class__, collection_name) as cls:\n collection = cls._get_collection()\n self._get_collection = lambda: collection\n self._collection = collection\n self._created = True if not keep_created else self._created\n self.__objects = self._qs\n self.__objects._collection_obj = collection\n return self\n\n def select_related(self, max_depth=1):\n \"\"\"Handles dereferencing of :class:`~bson.dbref.DBRef` objects to\n a maximum depth in order to cut down the number queries to mongodb.\n\n .. versionadded:: 0.5\n \"\"\"\n DeReference = _import_class('DeReference')\n DeReference()([self], max_depth + 1)\n return self\n\n def reload(self, *fields, **kwargs):\n \"\"\"Reloads all attributes from the database.\n\n :param fields: (optional) args list of fields to reload\n :param max_depth: (optional) depth of dereferencing to follow\n\n .. versionadded:: 0.1.2\n .. versionchanged:: 0.6 Now chainable\n .. versionchanged:: 0.9 Can provide specific fields to reload\n \"\"\"\n max_depth = 1\n if fields and isinstance(fields[0], int):\n max_depth = fields[0]\n fields = fields[1:]\n elif 'max_depth' in kwargs:\n max_depth = kwargs['max_depth']\n\n if self.pk is None:\n raise self.DoesNotExist('Document does not exist')\n\n obj = self._qs.read_preference(ReadPreference.PRIMARY).filter(\n **self._object_key).only(*fields).limit(\n 1).select_related(max_depth=max_depth)\n\n if obj:\n obj = obj[0]\n else:\n raise self.DoesNotExist('Document does not exist')\n for field in obj._data:\n if not fields or field in fields:\n try:\n setattr(self, field, self._reload(field, obj[field]))\n except (KeyError, AttributeError):\n try:\n # If field is a special field, e.g. items is stored as _reserved_items,\n # a KeyError is thrown. So try to retrieve the field from _data\n setattr(self, field, self._reload(field, obj._data.get(field)))\n except KeyError:\n # If field is removed from the database while the object\n # is in memory, a reload would cause a KeyError\n # i.e. obj.update(unset__field=1) followed by obj.reload()\n delattr(self, field)\n\n self._changed_fields = list(\n set(self._changed_fields) - set(fields)\n ) if fields else obj._changed_fields\n self._created = False\n return self\n\n def _reload(self, key, value):\n \"\"\"Used by :meth:`~mongoengine.Document.reload` to ensure the\n correct instance is linked to self.\n \"\"\"\n if isinstance(value, BaseDict):\n value = [(k, self._reload(k, v)) for k, v in value.items()]\n value = BaseDict(value, self, key)\n elif isinstance(value, EmbeddedDocumentList):\n value = [self._reload(key, v) for v in value]\n value = EmbeddedDocumentList(value, self, key)\n elif isinstance(value, BaseList):\n value = [self._reload(key, v) for v in value]\n value = BaseList(value, self, key)\n elif isinstance(value, (EmbeddedDocument, DynamicEmbeddedDocument)):\n value._instance = None\n value._changed_fields = []\n return value\n\n def to_dbref(self):\n \"\"\"Returns an instance of :class:`~bson.dbref.DBRef` useful in\n `__raw__` queries.\"\"\"\n if self.pk is None:\n msg = 'Only saved documents can have a valid dbref'\n raise OperationError(msg)\n return DBRef(self.__class__._get_collection_name(), self.pk)\n\n @classmethod\n def register_delete_rule(cls, document_cls, field_name, rule):\n \"\"\"This method registers the delete rules to apply when removing this\n object.\n \"\"\"\n classes = [get_document(class_name)\n for class_name in cls._subclasses\n if class_name != cls.__name__] + [cls]\n documents = [get_document(class_name)\n for class_name in document_cls._subclasses\n if class_name != document_cls.__name__] + [document_cls]\n\n for klass in classes:\n for document_cls in documents:\n delete_rules = klass._meta.get('delete_rules') or {}\n delete_rules[(document_cls, field_name)] = rule\n klass._meta['delete_rules'] = delete_rules\n\n @classmethod\n def drop_collection(cls):\n \"\"\"Drops the entire collection associated with this\n :class:`~mongoengine.Document` type from the database.\n\n Raises :class:`OperationError` if the document has no collection set\n (i.g. if it is `abstract`)\n\n .. versionchanged:: 0.10.7\n :class:`OperationError` exception raised if no collection available\n \"\"\"\n coll_name = cls._get_collection_name()\n if not coll_name:\n raise OperationError('Document %s has no collection defined '\n '(is it abstract ?)' % cls)\n cls._collection = None\n db = cls._get_db()\n db.drop_collection(coll_name)\n\n @classmethod\n def create_index(cls, keys, background=False, **kwargs):\n \"\"\"Creates the given indexes if required.\n\n :param keys: a single index key or a list of index keys (to\n construct a multi-field index); keys may be prefixed with a **+**\n or a **-** to determine the index ordering\n :param background: Allows index creation in the background\n \"\"\"\n index_spec = cls._build_index_spec(keys)\n index_spec = index_spec.copy()\n fields = index_spec.pop('fields')\n drop_dups = kwargs.get('drop_dups', False)\n if IS_PYMONGO_3 and drop_dups:\n msg = 'drop_dups is deprecated and is removed when using PyMongo 3+.'\n warnings.warn(msg, DeprecationWarning)\n elif not IS_PYMONGO_3:\n index_spec['drop_dups'] = drop_dups\n index_spec['background'] = background\n index_spec.update(kwargs)\n\n if IS_PYMONGO_3:\n return cls._get_collection().create_index(fields, **index_spec)\n else:\n return cls._get_collection().ensure_index(fields, **index_spec)\n\n @classmethod\n def ensure_index(cls, key_or_list, drop_dups=False, background=False,\n **kwargs):\n \"\"\"Ensure that the given indexes are in place. Deprecated in favour\n of create_index.\n\n :param key_or_list: a single index key or a list of index keys (to\n construct a multi-field index); keys may be prefixed with a **+**\n or a **-** to determine the index ordering\n :param background: Allows index creation in the background\n :param drop_dups: Was removed/ignored with MongoDB >2.7.5. The value\n will be removed if PyMongo3+ is used\n \"\"\"\n if IS_PYMONGO_3 and drop_dups:\n msg = 'drop_dups is deprecated and is removed when using PyMongo 3+.'\n warnings.warn(msg, DeprecationWarning)\n elif not IS_PYMONGO_3:\n kwargs.update({'drop_dups': drop_dups})\n return cls.create_index(key_or_list, background=background, **kwargs)\n\n @classmethod\n def ensure_indexes(cls):\n \"\"\"Checks the document meta data and ensures all the indexes exist.\n\n Global defaults can be set in the meta - see :doc:`guide/defining-documents`\n\n .. note:: You can disable automatic index creation by setting\n `auto_create_index` to False in the documents meta data\n \"\"\"\n background = cls._meta.get('index_background', False)\n drop_dups = cls._meta.get('index_drop_dups', False)\n index_opts = cls._meta.get('index_opts') or {}\n index_cls = cls._meta.get('index_cls', True)\n if IS_PYMONGO_3 and drop_dups:\n msg = 'drop_dups is deprecated and is removed when using PyMongo 3+.'\n warnings.warn(msg, DeprecationWarning)\n\n collection = cls._get_collection()\n # 746: when connection is via mongos, the read preference is not necessarily an indication that\n # this code runs on a secondary\n if not collection.is_mongos and collection.read_preference > 1:\n return\n\n # determine if an index which we are creating includes\n # _cls as its first field; if so, we can avoid creating\n # an extra index on _cls, as mongodb will use the existing\n # index to service queries against _cls\n cls_indexed = False\n\n # Ensure document-defined indexes are created\n if cls._meta['index_specs']:\n index_spec = cls._meta['index_specs']\n for spec in index_spec:\n spec = spec.copy()\n fields = spec.pop('fields')\n cls_indexed = cls_indexed or includes_cls(fields)\n opts = index_opts.copy()\n opts.update(spec)\n\n # we shouldn't pass 'cls' to the collection.ensureIndex options\n # because of https://jira.mongodb.org/browse/SERVER-769\n if 'cls' in opts:\n del opts['cls']\n\n if IS_PYMONGO_3:\n collection.create_index(fields, background=background, **opts)\n else:\n collection.ensure_index(fields, background=background,\n drop_dups=drop_dups, **opts)\n\n # If _cls is being used (for polymorphism), it needs an index,\n # only if another index doesn't begin with _cls\n if index_cls and not cls_indexed and cls._meta.get('allow_inheritance'):\n\n # we shouldn't pass 'cls' to the collection.ensureIndex options\n # because of https://jira.mongodb.org/browse/SERVER-769\n if 'cls' in index_opts:\n del index_opts['cls']\n\n if IS_PYMONGO_3:\n collection.create_index('_cls', background=background,\n **index_opts)\n else:\n collection.ensure_index('_cls', background=background,\n **index_opts)\n\n @classmethod\n def list_indexes(cls):\n \"\"\" Lists all of the indexes that should be created for given\n collection. It includes all the indexes from super- and sub-classes.\n \"\"\"\n if cls._meta.get('abstract'):\n return []\n\n # get all the base classes, subclasses and siblings\n classes = []\n\n def get_classes(cls):\n\n if (cls not in classes and\n isinstance(cls, TopLevelDocumentMetaclass)):\n classes.append(cls)\n\n for base_cls in cls.__bases__:\n if (isinstance(base_cls, TopLevelDocumentMetaclass) and\n base_cls != Document and\n not base_cls._meta.get('abstract') and\n base_cls._get_collection().full_name == cls._get_collection().full_name and\n base_cls not in classes):\n classes.append(base_cls)\n get_classes(base_cls)\n for subclass in cls.__subclasses__():\n if (isinstance(base_cls, TopLevelDocumentMetaclass) and\n subclass._get_collection().full_name == cls._get_collection().full_name and\n subclass not in classes):\n classes.append(subclass)\n get_classes(subclass)\n\n get_classes(cls)\n\n # get the indexes spec for all of the gathered classes\n def get_indexes_spec(cls):\n indexes = []\n\n if cls._meta['index_specs']:\n index_spec = cls._meta['index_specs']\n for spec in index_spec:\n spec = spec.copy()\n fields = spec.pop('fields')\n indexes.append(fields)\n return indexes\n\n indexes = []\n for klass in classes:\n for index in get_indexes_spec(klass):\n if index not in indexes:\n indexes.append(index)\n\n # finish up by appending { '_id': 1 } and { '_cls': 1 }, if needed\n if [(u'_id', 1)] not in indexes:\n indexes.append([(u'_id', 1)])\n if cls._meta.get('index_cls', True) and cls._meta.get('allow_inheritance'):\n indexes.append([(u'_cls', 1)])\n\n return indexes\n\n @classmethod\n def compare_indexes(cls):\n \"\"\" Compares the indexes defined in MongoEngine with the ones\n existing in the database. Returns any missing/extra indexes.\n \"\"\"\n\n required = cls.list_indexes()\n\n existing = []\n for info in cls._get_collection().index_information().values():\n if '_fts' in info['key'][0]:\n index_type = info['key'][0][1]\n text_index_fields = info.get('weights').keys()\n existing.append(\n [(key, index_type) for key in text_index_fields])\n else:\n existing.append(info['key'])\n missing = [index for index in required if index not in existing]\n extra = [index for index in existing if index not in required]\n\n # if { _cls: 1 } is missing, make sure it's *really* necessary\n if [(u'_cls', 1)] in missing:\n cls_obsolete = False\n for index in existing:\n if includes_cls(index) and index not in extra:\n cls_obsolete = True\n break\n if cls_obsolete:\n missing.remove([(u'_cls', 1)])\n\n return {'missing': missing, 'extra': extra}\n\n\nclass DynamicDocument(six.with_metaclass(TopLevelDocumentMetaclass, Document)):\n \"\"\"A Dynamic Document class allowing flexible, expandable and uncontrolled\n schemas. As a :class:`~mongoengine.Document` subclass, acts in the same\n way as an ordinary document but has expanded style properties. Any data\n passed or set against the :class:`~mongoengine.DynamicDocument` that is\n not a field is automatically converted into a\n :class:`~mongoengine.fields.DynamicField` and data can be attributed to that\n field.\n\n .. note::\n\n There is one caveat on Dynamic Documents: undeclared fields cannot start with `_`\n \"\"\"\n\n # The __metaclass__ attribute is removed by 2to3 when running with Python3\n # my_metaclass is defined so that metaclass can be queried in Python 2 & 3\n my_metaclass = TopLevelDocumentMetaclass\n\n _dynamic = True\n\n def __delattr__(self, *args, **kwargs):\n \"\"\"Delete the attribute by setting to None and allowing _delta\n to unset it.\n \"\"\"\n field_name = args[0]\n if field_name in self._dynamic_fields:\n setattr(self, field_name, None)\n self._dynamic_fields[field_name].null = False\n else:\n super(DynamicDocument, self).__delattr__(*args, **kwargs)\n\n\nclass DynamicEmbeddedDocument(six.with_metaclass(DocumentMetaclass, EmbeddedDocument)):\n \"\"\"A Dynamic Embedded Document class allowing flexible, expandable and\n uncontrolled schemas. See :class:`~mongoengine.DynamicDocument` for more\n information about dynamic documents.\n \"\"\"\n\n # The __metaclass__ attribute is removed by 2to3 when running with Python3\n # my_metaclass is defined so that metaclass can be queried in Python 2 & 3\n my_metaclass = DocumentMetaclass\n\n _dynamic = True\n\n def __delattr__(self, *args, **kwargs):\n \"\"\"Delete the attribute by setting to None and allowing _delta\n to unset it.\n \"\"\"\n field_name = args[0]\n if field_name in self._fields:\n default = self._fields[field_name].default\n if callable(default):\n default = default()\n setattr(self, field_name, default)\n else:\n setattr(self, field_name, None)\n\n\nclass MapReduceDocument(object):\n \"\"\"A document returned from a map/reduce query.\n\n :param collection: An instance of :class:`~pymongo.Collection`\n :param key: Document/result key, often an instance of\n :class:`~bson.objectid.ObjectId`. If supplied as\n an ``ObjectId`` found in the given ``collection``,\n the object can be accessed via the ``object`` property.\n :param value: The result(s) for this key.\n\n .. versionadded:: 0.3\n \"\"\"\n\n def __init__(self, document, collection, key, value):\n self._document = document\n self._collection = collection\n self.key = key\n self.value = value\n\n @property\n def object(self):\n \"\"\"Lazy-load the object referenced by ``self.key``. ``self.key``\n should be the ``primary_key``.\n \"\"\"\n id_field = self._document()._meta['id_field']\n id_field_type = type(id_field)\n\n if not isinstance(self.key, id_field_type):\n try:\n self.key = id_field_type(self.key)\n except Exception:\n raise Exception('Could not cast key as %s' %\n id_field_type.__name__)\n\n if not hasattr(self, '_key_object'):\n self._key_object = self._document.objects.with_id(self.key)\n return self._key_object\n return self._key_object\n", "path": "mongoengine/document.py" } ]
[ { "content": "import re\nimport warnings\n\nfrom bson.dbref import DBRef\nimport pymongo\nfrom pymongo.read_preferences import ReadPreference\nimport six\nfrom six import iteritems\n\nfrom mongoengine import signals\nfrom mongoengine.base import (BaseDict, BaseDocument, BaseList,\n DocumentMetaclass, EmbeddedDocumentList,\n TopLevelDocumentMetaclass, get_document)\nfrom mongoengine.common import _import_class\nfrom mongoengine.connection import DEFAULT_CONNECTION_NAME, get_db\nfrom mongoengine.context_managers import (set_write_concern,\n switch_collection,\n switch_db)\nfrom mongoengine.errors import (InvalidDocumentError, InvalidQueryError,\n SaveConditionError)\nfrom mongoengine.pymongo_support import IS_PYMONGO_3, list_collection_names\nfrom mongoengine.queryset import (NotUniqueError, OperationError,\n QuerySet, transform)\n\n__all__ = ('Document', 'EmbeddedDocument', 'DynamicDocument',\n 'DynamicEmbeddedDocument', 'OperationError',\n 'InvalidCollectionError', 'NotUniqueError', 'MapReduceDocument')\n\n\ndef includes_cls(fields):\n \"\"\"Helper function used for ensuring and comparing indexes.\"\"\"\n first_field = None\n if len(fields):\n if isinstance(fields[0], six.string_types):\n first_field = fields[0]\n elif isinstance(fields[0], (list, tuple)) and len(fields[0]):\n first_field = fields[0][0]\n return first_field == '_cls'\n\n\nclass InvalidCollectionError(Exception):\n pass\n\n\nclass EmbeddedDocument(six.with_metaclass(DocumentMetaclass, BaseDocument)):\n \"\"\"A :class:`~mongoengine.Document` that isn't stored in its own\n collection. :class:`~mongoengine.EmbeddedDocument`\\ s should be used as\n fields on :class:`~mongoengine.Document`\\ s through the\n :class:`~mongoengine.EmbeddedDocumentField` field type.\n\n A :class:`~mongoengine.EmbeddedDocument` subclass may be itself subclassed,\n to create a specialised version of the embedded document that will be\n stored in the same collection. To facilitate this behaviour a `_cls`\n field is added to documents (hidden though the MongoEngine interface).\n To enable this behaviour set :attr:`allow_inheritance` to ``True`` in the\n :attr:`meta` dictionary.\n \"\"\"\n\n __slots__ = ('_instance', )\n\n # The __metaclass__ attribute is removed by 2to3 when running with Python3\n # my_metaclass is defined so that metaclass can be queried in Python 2 & 3\n my_metaclass = DocumentMetaclass\n\n # A generic embedded document doesn't have any immutable properties\n # that describe it uniquely, hence it shouldn't be hashable. You can\n # define your own __hash__ method on a subclass if you need your\n # embedded documents to be hashable.\n __hash__ = None\n\n def __init__(self, *args, **kwargs):\n super(EmbeddedDocument, self).__init__(*args, **kwargs)\n self._instance = None\n self._changed_fields = []\n\n def __eq__(self, other):\n if isinstance(other, self.__class__):\n return self._data == other._data\n return False\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def to_mongo(self, *args, **kwargs):\n data = super(EmbeddedDocument, self).to_mongo(*args, **kwargs)\n\n # remove _id from the SON if it's in it and it's None\n if '_id' in data and data['_id'] is None:\n del data['_id']\n\n return data\n\n def save(self, *args, **kwargs):\n warnings.warn(\"EmbeddedDocument.save is deprecated and will be removed in a next version of mongoengine.\"\n \"Use the parent document's .save() or ._instance.save()\",\n DeprecationWarning, stacklevel=2)\n self._instance.save(*args, **kwargs)\n\n def reload(self, *args, **kwargs):\n warnings.warn(\"EmbeddedDocument.reload is deprecated and will be removed in a next version of mongoengine.\"\n \"Use the parent document's .reload() or ._instance.reload()\",\n DeprecationWarning, stacklevel=2)\n self._instance.reload(*args, **kwargs)\n\n\nclass Document(six.with_metaclass(TopLevelDocumentMetaclass, BaseDocument)):\n \"\"\"The base class used for defining the structure and properties of\n collections of documents stored in MongoDB. Inherit from this class, and\n add fields as class attributes to define a document's structure.\n Individual documents may then be created by making instances of the\n :class:`~mongoengine.Document` subclass.\n\n By default, the MongoDB collection used to store documents created using a\n :class:`~mongoengine.Document` subclass will be the name of the subclass\n converted to lowercase. A different collection may be specified by\n providing :attr:`collection` to the :attr:`meta` dictionary in the class\n definition.\n\n A :class:`~mongoengine.Document` subclass may be itself subclassed, to\n create a specialised version of the document that will be stored in the\n same collection. To facilitate this behaviour a `_cls`\n field is added to documents (hidden though the MongoEngine interface).\n To enable this behaviourset :attr:`allow_inheritance` to ``True`` in the\n :attr:`meta` dictionary.\n\n A :class:`~mongoengine.Document` may use a **Capped Collection** by\n specifying :attr:`max_documents` and :attr:`max_size` in the :attr:`meta`\n dictionary. :attr:`max_documents` is the maximum number of documents that\n is allowed to be stored in the collection, and :attr:`max_size` is the\n maximum size of the collection in bytes. :attr:`max_size` is rounded up\n to the next multiple of 256 by MongoDB internally and mongoengine before.\n Use also a multiple of 256 to avoid confusions. If :attr:`max_size` is not\n specified and :attr:`max_documents` is, :attr:`max_size` defaults to\n 10485760 bytes (10MB).\n\n Indexes may be created by specifying :attr:`indexes` in the :attr:`meta`\n dictionary. The value should be a list of field names or tuples of field\n names. Index direction may be specified by prefixing the field names with\n a **+** or **-** sign.\n\n Automatic index creation can be disabled by specifying\n :attr:`auto_create_index` in the :attr:`meta` dictionary. If this is set to\n False then indexes will not be created by MongoEngine. This is useful in\n production systems where index creation is performed as part of a\n deployment system.\n\n By default, _cls will be added to the start of every index (that\n doesn't contain a list) if allow_inheritance is True. This can be\n disabled by either setting cls to False on the specific index or\n by setting index_cls to False on the meta dictionary for the document.\n\n By default, any extra attribute existing in stored data but not declared\n in your model will raise a :class:`~mongoengine.FieldDoesNotExist` error.\n This can be disabled by setting :attr:`strict` to ``False``\n in the :attr:`meta` dictionary.\n \"\"\"\n\n # The __metaclass__ attribute is removed by 2to3 when running with Python3\n # my_metaclass is defined so that metaclass can be queried in Python 2 & 3\n my_metaclass = TopLevelDocumentMetaclass\n\n __slots__ = ('__objects',)\n\n @property\n def pk(self):\n \"\"\"Get the primary key.\"\"\"\n if 'id_field' not in self._meta:\n return None\n return getattr(self, self._meta['id_field'])\n\n @pk.setter\n def pk(self, value):\n \"\"\"Set the primary key.\"\"\"\n return setattr(self, self._meta['id_field'], value)\n\n def __hash__(self):\n \"\"\"Return the hash based on the PK of this document. If it's new\n and doesn't have a PK yet, return the default object hash instead.\n \"\"\"\n if self.pk is None:\n return super(BaseDocument, self).__hash__()\n\n return hash(self.pk)\n\n @classmethod\n def _get_db(cls):\n \"\"\"Some Model using other db_alias\"\"\"\n return get_db(cls._meta.get('db_alias', DEFAULT_CONNECTION_NAME))\n\n @classmethod\n def _disconnect(cls):\n \"\"\"Detach the Document class from the (cached) database collection\"\"\"\n cls._collection = None\n\n @classmethod\n def _get_collection(cls):\n \"\"\"Return the corresponding PyMongo collection of this document.\n Upon the first call, it will ensure that indexes gets created. The returned collection then gets cached\n \"\"\"\n if not hasattr(cls, '_collection') or cls._collection is None:\n # Get the collection, either capped or regular.\n if cls._meta.get('max_size') or cls._meta.get('max_documents'):\n cls._collection = cls._get_capped_collection()\n else:\n db = cls._get_db()\n collection_name = cls._get_collection_name()\n cls._collection = db[collection_name]\n\n # Ensure indexes on the collection unless auto_create_index was\n # set to False.\n # Also there is no need to ensure indexes on slave.\n db = cls._get_db()\n if cls._meta.get('auto_create_index', True) and\\\n db.client.is_primary:\n cls.ensure_indexes()\n\n return cls._collection\n\n @classmethod\n def _get_capped_collection(cls):\n \"\"\"Create a new or get an existing capped PyMongo collection.\"\"\"\n db = cls._get_db()\n collection_name = cls._get_collection_name()\n\n # Get max document limit and max byte size from meta.\n max_size = cls._meta.get('max_size') or 10 * 2 ** 20 # 10MB default\n max_documents = cls._meta.get('max_documents')\n\n # MongoDB will automatically raise the size to make it a multiple of\n # 256 bytes. We raise it here ourselves to be able to reliably compare\n # the options below.\n if max_size % 256:\n max_size = (max_size // 256 + 1) * 256\n\n # If the collection already exists and has different options\n # (i.e. isn't capped or has different max/size), raise an error.\n if collection_name in list_collection_names(db, include_system_collections=True):\n collection = db[collection_name]\n options = collection.options()\n if (\n options.get('max') != max_documents or\n options.get('size') != max_size\n ):\n raise InvalidCollectionError(\n 'Cannot create collection \"{}\" as a capped '\n 'collection as it already exists'.format(cls._collection)\n )\n\n return collection\n\n # Create a new capped collection.\n opts = {'capped': True, 'size': max_size}\n if max_documents:\n opts['max'] = max_documents\n\n return db.create_collection(collection_name, **opts)\n\n def to_mongo(self, *args, **kwargs):\n data = super(Document, self).to_mongo(*args, **kwargs)\n\n # If '_id' is None, try and set it from self._data. If that\n # doesn't exist either, remote '_id' from the SON completely.\n if data['_id'] is None:\n if self._data.get('id') is None:\n del data['_id']\n else:\n data['_id'] = self._data['id']\n\n return data\n\n def modify(self, query=None, **update):\n \"\"\"Perform an atomic update of the document in the database and reload\n the document object using updated version.\n\n Returns True if the document has been updated or False if the document\n in the database doesn't match the query.\n\n .. note:: All unsaved changes that have been made to the document are\n rejected if the method returns True.\n\n :param query: the update will be performed only if the document in the\n database matches the query\n :param update: Django-style update keyword arguments\n \"\"\"\n if query is None:\n query = {}\n\n if self.pk is None:\n raise InvalidDocumentError('The document does not have a primary key.')\n\n id_field = self._meta['id_field']\n query = query.copy() if isinstance(query, dict) else query.to_query(self)\n\n if id_field not in query:\n query[id_field] = self.pk\n elif query[id_field] != self.pk:\n raise InvalidQueryError('Invalid document modify query: it must modify only this document.')\n\n # Need to add shard key to query, or you get an error\n query.update(self._object_key)\n\n updated = self._qs(**query).modify(new=True, **update)\n if updated is None:\n return False\n\n for field in self._fields_ordered:\n setattr(self, field, self._reload(field, updated[field]))\n\n self._changed_fields = updated._changed_fields\n self._created = False\n\n return True\n\n def save(self, force_insert=False, validate=True, clean=True,\n write_concern=None, cascade=None, cascade_kwargs=None,\n _refs=None, save_condition=None, signal_kwargs=None, **kwargs):\n \"\"\"Save the :class:`~mongoengine.Document` to the database. If the\n document already exists, it will be updated, otherwise it will be\n created.\n\n :param force_insert: only try to create a new document, don't allow\n updates of existing documents.\n :param validate: validates the document; set to ``False`` to skip.\n :param clean: call the document clean method, requires `validate` to be\n True.\n :param write_concern: Extra keyword arguments are passed down to\n :meth:`~pymongo.collection.Collection.save` OR\n :meth:`~pymongo.collection.Collection.insert`\n which will be used as options for the resultant\n ``getLastError`` command. For example,\n ``save(..., write_concern={w: 2, fsync: True}, ...)`` will\n wait until at least two servers have recorded the write and\n will force an fsync on the primary server.\n :param cascade: Sets the flag for cascading saves. You can set a\n default by setting \"cascade\" in the document __meta__\n :param cascade_kwargs: (optional) kwargs dictionary to be passed throw\n to cascading saves. Implies ``cascade=True``.\n :param _refs: A list of processed references used in cascading saves\n :param save_condition: only perform save if matching record in db\n satisfies condition(s) (e.g. version number).\n Raises :class:`OperationError` if the conditions are not satisfied\n :param signal_kwargs: (optional) kwargs dictionary to be passed to\n the signal calls.\n\n .. versionchanged:: 0.5\n In existing documents it only saves changed fields using\n set / unset. Saves are cascaded and any\n :class:`~bson.dbref.DBRef` objects that have changes are\n saved as well.\n .. versionchanged:: 0.6\n Added cascading saves\n .. versionchanged:: 0.8\n Cascade saves are optional and default to False. If you want\n fine grain control then you can turn off using document\n meta['cascade'] = True. Also you can pass different kwargs to\n the cascade save using cascade_kwargs which overwrites the\n existing kwargs with custom values.\n .. versionchanged:: 0.8.5\n Optional save_condition that only overwrites existing documents\n if the condition is satisfied in the current db record.\n .. versionchanged:: 0.10\n :class:`OperationError` exception raised if save_condition fails.\n .. versionchanged:: 0.10.1\n :class: save_condition failure now raises a `SaveConditionError`\n .. versionchanged:: 0.10.7\n Add signal_kwargs argument\n \"\"\"\n if self._meta.get('abstract'):\n raise InvalidDocumentError('Cannot save an abstract document.')\n\n signal_kwargs = signal_kwargs or {}\n signals.pre_save.send(self.__class__, document=self, **signal_kwargs)\n\n if validate:\n self.validate(clean=clean)\n\n if write_concern is None:\n write_concern = {}\n\n doc = self.to_mongo()\n\n created = ('_id' not in doc or self._created or force_insert)\n\n signals.pre_save_post_validation.send(self.__class__, document=self,\n created=created, **signal_kwargs)\n # it might be refreshed by the pre_save_post_validation hook, e.g., for etag generation\n doc = self.to_mongo()\n\n if self._meta.get('auto_create_index', True):\n self.ensure_indexes()\n\n try:\n # Save a new document or update an existing one\n if created:\n object_id = self._save_create(doc, force_insert, write_concern)\n else:\n object_id, created = self._save_update(doc, save_condition,\n write_concern)\n\n if cascade is None:\n cascade = (self._meta.get('cascade', False) or\n cascade_kwargs is not None)\n\n if cascade:\n kwargs = {\n 'force_insert': force_insert,\n 'validate': validate,\n 'write_concern': write_concern,\n 'cascade': cascade\n }\n if cascade_kwargs: # Allow granular control over cascades\n kwargs.update(cascade_kwargs)\n kwargs['_refs'] = _refs\n self.cascade_save(**kwargs)\n\n except pymongo.errors.DuplicateKeyError as err:\n message = u'Tried to save duplicate unique keys (%s)'\n raise NotUniqueError(message % six.text_type(err))\n except pymongo.errors.OperationFailure as err:\n message = 'Could not save document (%s)'\n if re.match('^E1100[01] duplicate key', six.text_type(err)):\n # E11000 - duplicate key error index\n # E11001 - duplicate key on update\n message = u'Tried to save duplicate unique keys (%s)'\n raise NotUniqueError(message % six.text_type(err))\n raise OperationError(message % six.text_type(err))\n\n # Make sure we store the PK on this document now that it's saved\n id_field = self._meta['id_field']\n if created or id_field not in self._meta.get('shard_key', []):\n self[id_field] = self._fields[id_field].to_python(object_id)\n\n signals.post_save.send(self.__class__, document=self,\n created=created, **signal_kwargs)\n\n self._clear_changed_fields()\n self._created = False\n\n return self\n\n def _save_create(self, doc, force_insert, write_concern):\n \"\"\"Save a new document.\n\n Helper method, should only be used inside save().\n \"\"\"\n collection = self._get_collection()\n with set_write_concern(collection, write_concern) as wc_collection:\n if force_insert:\n return wc_collection.insert_one(doc).inserted_id\n # insert_one will provoke UniqueError alongside save does not\n # therefore, it need to catch and call replace_one.\n if '_id' in doc:\n raw_object = wc_collection.find_one_and_replace(\n {'_id': doc['_id']}, doc)\n if raw_object:\n return doc['_id']\n\n object_id = wc_collection.insert_one(doc).inserted_id\n\n return object_id\n\n def _get_update_doc(self):\n \"\"\"Return a dict containing all the $set and $unset operations\n that should be sent to MongoDB based on the changes made to this\n Document.\n \"\"\"\n updates, removals = self._delta()\n\n update_doc = {}\n if updates:\n update_doc['$set'] = updates\n if removals:\n update_doc['$unset'] = removals\n\n return update_doc\n\n def _save_update(self, doc, save_condition, write_concern):\n \"\"\"Update an existing document.\n\n Helper method, should only be used inside save().\n \"\"\"\n collection = self._get_collection()\n object_id = doc['_id']\n created = False\n\n select_dict = {}\n if save_condition is not None:\n select_dict = transform.query(self.__class__, **save_condition)\n\n select_dict['_id'] = object_id\n\n # Need to add shard key to query, or you get an error\n shard_key = self._meta.get('shard_key', tuple())\n for k in shard_key:\n path = self._lookup_field(k.split('.'))\n actual_key = [p.db_field for p in path]\n val = doc\n for ak in actual_key:\n val = val[ak]\n select_dict['.'.join(actual_key)] = val\n\n update_doc = self._get_update_doc()\n if update_doc:\n upsert = save_condition is None\n last_error = collection.update(select_dict, update_doc,\n upsert=upsert, **write_concern)\n if not upsert and last_error['n'] == 0:\n raise SaveConditionError('Race condition preventing'\n ' document update detected')\n if last_error is not None:\n updated_existing = last_error.get('updatedExisting')\n if updated_existing is False:\n created = True\n # !!! This is bad, means we accidentally created a new,\n # potentially corrupted document. See\n # https://github.com/MongoEngine/mongoengine/issues/564\n\n return object_id, created\n\n def cascade_save(self, **kwargs):\n \"\"\"Recursively save any references and generic references on the\n document.\n \"\"\"\n _refs = kwargs.get('_refs') or []\n\n ReferenceField = _import_class('ReferenceField')\n GenericReferenceField = _import_class('GenericReferenceField')\n\n for name, cls in self._fields.items():\n if not isinstance(cls, (ReferenceField,\n GenericReferenceField)):\n continue\n\n ref = self._data.get(name)\n if not ref or isinstance(ref, DBRef):\n continue\n\n if not getattr(ref, '_changed_fields', True):\n continue\n\n ref_id = \"%s,%s\" % (ref.__class__.__name__, str(ref._data))\n if ref and ref_id not in _refs:\n _refs.append(ref_id)\n kwargs[\"_refs\"] = _refs\n ref.save(**kwargs)\n ref._changed_fields = []\n\n @property\n def _qs(self):\n \"\"\"Return the queryset to use for updating / reloading / deletions.\"\"\"\n if not hasattr(self, '__objects'):\n self.__objects = QuerySet(self, self._get_collection())\n return self.__objects\n\n @property\n def _object_key(self):\n \"\"\"Get the query dict that can be used to fetch this object from\n the database. Most of the time it's a simple PK lookup, but in\n case of a sharded collection with a compound shard key, it can\n contain a more complex query.\n \"\"\"\n select_dict = {'pk': self.pk}\n shard_key = self.__class__._meta.get('shard_key', tuple())\n for k in shard_key:\n path = self._lookup_field(k.split('.'))\n actual_key = [p.db_field for p in path]\n val = self\n for ak in actual_key:\n val = getattr(val, ak)\n select_dict['__'.join(actual_key)] = val\n return select_dict\n\n def update(self, **kwargs):\n \"\"\"Performs an update on the :class:`~mongoengine.Document`\n A convenience wrapper to :meth:`~mongoengine.QuerySet.update`.\n\n Raises :class:`OperationError` if called on an object that has not yet\n been saved.\n \"\"\"\n if self.pk is None:\n if kwargs.get('upsert', False):\n query = self.to_mongo()\n if '_cls' in query:\n del query['_cls']\n return self._qs.filter(**query).update_one(**kwargs)\n else:\n raise OperationError(\n 'attempt to update a document not yet saved')\n\n # Need to add shard key to query, or you get an error\n return self._qs.filter(**self._object_key).update_one(**kwargs)\n\n def delete(self, signal_kwargs=None, **write_concern):\n \"\"\"Delete the :class:`~mongoengine.Document` from the database. This\n will only take effect if the document has been previously saved.\n\n :param signal_kwargs: (optional) kwargs dictionary to be passed to\n the signal calls.\n :param write_concern: Extra keyword arguments are passed down which\n will be used as options for the resultant ``getLastError`` command.\n For example, ``save(..., w: 2, fsync: True)`` will\n wait until at least two servers have recorded the write and\n will force an fsync on the primary server.\n\n .. versionchanged:: 0.10.7\n Add signal_kwargs argument\n \"\"\"\n signal_kwargs = signal_kwargs or {}\n signals.pre_delete.send(self.__class__, document=self, **signal_kwargs)\n\n # Delete FileFields separately\n FileField = _import_class('FileField')\n for name, field in iteritems(self._fields):\n if isinstance(field, FileField):\n getattr(self, name).delete()\n\n try:\n self._qs.filter(\n **self._object_key).delete(write_concern=write_concern, _from_doc_delete=True)\n except pymongo.errors.OperationFailure as err:\n message = u'Could not delete document (%s)' % err.message\n raise OperationError(message)\n signals.post_delete.send(self.__class__, document=self, **signal_kwargs)\n\n def switch_db(self, db_alias, keep_created=True):\n \"\"\"\n Temporarily switch the database for a document instance.\n\n Only really useful for archiving off data and calling `save()`::\n\n user = User.objects.get(id=user_id)\n user.switch_db('archive-db')\n user.save()\n\n :param str db_alias: The database alias to use for saving the document\n\n :param bool keep_created: keep self._created value after switching db, else is reset to True\n\n\n .. seealso::\n Use :class:`~mongoengine.context_managers.switch_collection`\n if you need to read from another collection\n \"\"\"\n with switch_db(self.__class__, db_alias) as cls:\n collection = cls._get_collection()\n db = cls._get_db()\n self._get_collection = lambda: collection\n self._get_db = lambda: db\n self._collection = collection\n self._created = True if not keep_created else self._created\n self.__objects = self._qs\n self.__objects._collection_obj = collection\n return self\n\n def switch_collection(self, collection_name, keep_created=True):\n \"\"\"\n Temporarily switch the collection for a document instance.\n\n Only really useful for archiving off data and calling `save()`::\n\n user = User.objects.get(id=user_id)\n user.switch_collection('old-users')\n user.save()\n\n :param str collection_name: The database alias to use for saving the\n document\n\n :param bool keep_created: keep self._created value after switching collection, else is reset to True\n\n\n .. seealso::\n Use :class:`~mongoengine.context_managers.switch_db`\n if you need to read from another database\n \"\"\"\n with switch_collection(self.__class__, collection_name) as cls:\n collection = cls._get_collection()\n self._get_collection = lambda: collection\n self._collection = collection\n self._created = True if not keep_created else self._created\n self.__objects = self._qs\n self.__objects._collection_obj = collection\n return self\n\n def select_related(self, max_depth=1):\n \"\"\"Handles dereferencing of :class:`~bson.dbref.DBRef` objects to\n a maximum depth in order to cut down the number queries to mongodb.\n\n .. versionadded:: 0.5\n \"\"\"\n DeReference = _import_class('DeReference')\n DeReference()([self], max_depth + 1)\n return self\n\n def reload(self, *fields, **kwargs):\n \"\"\"Reloads all attributes from the database.\n\n :param fields: (optional) args list of fields to reload\n :param max_depth: (optional) depth of dereferencing to follow\n\n .. versionadded:: 0.1.2\n .. versionchanged:: 0.6 Now chainable\n .. versionchanged:: 0.9 Can provide specific fields to reload\n \"\"\"\n max_depth = 1\n if fields and isinstance(fields[0], int):\n max_depth = fields[0]\n fields = fields[1:]\n elif 'max_depth' in kwargs:\n max_depth = kwargs['max_depth']\n\n if self.pk is None:\n raise self.DoesNotExist('Document does not exist')\n\n obj = self._qs.read_preference(ReadPreference.PRIMARY).filter(\n **self._object_key).only(*fields).limit(\n 1).select_related(max_depth=max_depth)\n\n if obj:\n obj = obj[0]\n else:\n raise self.DoesNotExist('Document does not exist')\n for field in obj._data:\n if not fields or field in fields:\n try:\n setattr(self, field, self._reload(field, obj[field]))\n except (KeyError, AttributeError):\n try:\n # If field is a special field, e.g. items is stored as _reserved_items,\n # a KeyError is thrown. So try to retrieve the field from _data\n setattr(self, field, self._reload(field, obj._data.get(field)))\n except KeyError:\n # If field is removed from the database while the object\n # is in memory, a reload would cause a KeyError\n # i.e. obj.update(unset__field=1) followed by obj.reload()\n delattr(self, field)\n\n self._changed_fields = list(\n set(self._changed_fields) - set(fields)\n ) if fields else obj._changed_fields\n self._created = False\n return self\n\n def _reload(self, key, value):\n \"\"\"Used by :meth:`~mongoengine.Document.reload` to ensure the\n correct instance is linked to self.\n \"\"\"\n if isinstance(value, BaseDict):\n value = [(k, self._reload(k, v)) for k, v in value.items()]\n value = BaseDict(value, self, key)\n elif isinstance(value, EmbeddedDocumentList):\n value = [self._reload(key, v) for v in value]\n value = EmbeddedDocumentList(value, self, key)\n elif isinstance(value, BaseList):\n value = [self._reload(key, v) for v in value]\n value = BaseList(value, self, key)\n elif isinstance(value, (EmbeddedDocument, DynamicEmbeddedDocument)):\n value._instance = None\n value._changed_fields = []\n return value\n\n def to_dbref(self):\n \"\"\"Returns an instance of :class:`~bson.dbref.DBRef` useful in\n `__raw__` queries.\"\"\"\n if self.pk is None:\n msg = 'Only saved documents can have a valid dbref'\n raise OperationError(msg)\n return DBRef(self.__class__._get_collection_name(), self.pk)\n\n @classmethod\n def register_delete_rule(cls, document_cls, field_name, rule):\n \"\"\"This method registers the delete rules to apply when removing this\n object.\n \"\"\"\n classes = [get_document(class_name)\n for class_name in cls._subclasses\n if class_name != cls.__name__] + [cls]\n documents = [get_document(class_name)\n for class_name in document_cls._subclasses\n if class_name != document_cls.__name__] + [document_cls]\n\n for klass in classes:\n for document_cls in documents:\n delete_rules = klass._meta.get('delete_rules') or {}\n delete_rules[(document_cls, field_name)] = rule\n klass._meta['delete_rules'] = delete_rules\n\n @classmethod\n def drop_collection(cls):\n \"\"\"Drops the entire collection associated with this\n :class:`~mongoengine.Document` type from the database.\n\n Raises :class:`OperationError` if the document has no collection set\n (i.g. if it is `abstract`)\n\n .. versionchanged:: 0.10.7\n :class:`OperationError` exception raised if no collection available\n \"\"\"\n coll_name = cls._get_collection_name()\n if not coll_name:\n raise OperationError('Document %s has no collection defined '\n '(is it abstract ?)' % cls)\n cls._collection = None\n db = cls._get_db()\n db.drop_collection(coll_name)\n\n @classmethod\n def create_index(cls, keys, background=False, **kwargs):\n \"\"\"Creates the given indexes if required.\n\n :param keys: a single index key or a list of index keys (to\n construct a multi-field index); keys may be prefixed with a **+**\n or a **-** to determine the index ordering\n :param background: Allows index creation in the background\n \"\"\"\n index_spec = cls._build_index_spec(keys)\n index_spec = index_spec.copy()\n fields = index_spec.pop('fields')\n drop_dups = kwargs.get('drop_dups', False)\n if IS_PYMONGO_3 and drop_dups:\n msg = 'drop_dups is deprecated and is removed when using PyMongo 3+.'\n warnings.warn(msg, DeprecationWarning)\n elif not IS_PYMONGO_3:\n index_spec['drop_dups'] = drop_dups\n index_spec['background'] = background\n index_spec.update(kwargs)\n\n if IS_PYMONGO_3:\n return cls._get_collection().create_index(fields, **index_spec)\n else:\n return cls._get_collection().ensure_index(fields, **index_spec)\n\n @classmethod\n def ensure_index(cls, key_or_list, drop_dups=False, background=False,\n **kwargs):\n \"\"\"Ensure that the given indexes are in place. Deprecated in favour\n of create_index.\n\n :param key_or_list: a single index key or a list of index keys (to\n construct a multi-field index); keys may be prefixed with a **+**\n or a **-** to determine the index ordering\n :param background: Allows index creation in the background\n :param drop_dups: Was removed/ignored with MongoDB >2.7.5. The value\n will be removed if PyMongo3+ is used\n \"\"\"\n if IS_PYMONGO_3 and drop_dups:\n msg = 'drop_dups is deprecated and is removed when using PyMongo 3+.'\n warnings.warn(msg, DeprecationWarning)\n elif not IS_PYMONGO_3:\n kwargs.update({'drop_dups': drop_dups})\n return cls.create_index(key_or_list, background=background, **kwargs)\n\n @classmethod\n def ensure_indexes(cls):\n \"\"\"Checks the document meta data and ensures all the indexes exist.\n\n Global defaults can be set in the meta - see :doc:`guide/defining-documents`\n\n .. note:: You can disable automatic index creation by setting\n `auto_create_index` to False in the documents meta data\n \"\"\"\n background = cls._meta.get('index_background', False)\n drop_dups = cls._meta.get('index_drop_dups', False)\n index_opts = cls._meta.get('index_opts') or {}\n index_cls = cls._meta.get('index_cls', True)\n if IS_PYMONGO_3 and drop_dups:\n msg = 'drop_dups is deprecated and is removed when using PyMongo 3+.'\n warnings.warn(msg, DeprecationWarning)\n\n collection = cls._get_collection()\n # 746: when connection is via mongos, the read preference is not necessarily an indication that\n # this code runs on a secondary\n if not collection.is_mongos and collection.read_preference > 1:\n return\n\n # determine if an index which we are creating includes\n # _cls as its first field; if so, we can avoid creating\n # an extra index on _cls, as mongodb will use the existing\n # index to service queries against _cls\n cls_indexed = False\n\n # Ensure document-defined indexes are created\n if cls._meta['index_specs']:\n index_spec = cls._meta['index_specs']\n for spec in index_spec:\n spec = spec.copy()\n fields = spec.pop('fields')\n cls_indexed = cls_indexed or includes_cls(fields)\n opts = index_opts.copy()\n opts.update(spec)\n\n # we shouldn't pass 'cls' to the collection.ensureIndex options\n # because of https://jira.mongodb.org/browse/SERVER-769\n if 'cls' in opts:\n del opts['cls']\n\n if IS_PYMONGO_3:\n collection.create_index(fields, background=background, **opts)\n else:\n collection.ensure_index(fields, background=background,\n drop_dups=drop_dups, **opts)\n\n # If _cls is being used (for polymorphism), it needs an index,\n # only if another index doesn't begin with _cls\n if index_cls and not cls_indexed and cls._meta.get('allow_inheritance'):\n\n # we shouldn't pass 'cls' to the collection.ensureIndex options\n # because of https://jira.mongodb.org/browse/SERVER-769\n if 'cls' in index_opts:\n del index_opts['cls']\n\n if IS_PYMONGO_3:\n collection.create_index('_cls', background=background,\n **index_opts)\n else:\n collection.ensure_index('_cls', background=background,\n **index_opts)\n\n @classmethod\n def list_indexes(cls):\n \"\"\" Lists all of the indexes that should be created for given\n collection. It includes all the indexes from super- and sub-classes.\n \"\"\"\n if cls._meta.get('abstract'):\n return []\n\n # get all the base classes, subclasses and siblings\n classes = []\n\n def get_classes(cls):\n\n if (cls not in classes and\n isinstance(cls, TopLevelDocumentMetaclass)):\n classes.append(cls)\n\n for base_cls in cls.__bases__:\n if (isinstance(base_cls, TopLevelDocumentMetaclass) and\n base_cls != Document and\n not base_cls._meta.get('abstract') and\n base_cls._get_collection().full_name == cls._get_collection().full_name and\n base_cls not in classes):\n classes.append(base_cls)\n get_classes(base_cls)\n for subclass in cls.__subclasses__():\n if (isinstance(base_cls, TopLevelDocumentMetaclass) and\n subclass._get_collection().full_name == cls._get_collection().full_name and\n subclass not in classes):\n classes.append(subclass)\n get_classes(subclass)\n\n get_classes(cls)\n\n # get the indexes spec for all of the gathered classes\n def get_indexes_spec(cls):\n indexes = []\n\n if cls._meta['index_specs']:\n index_spec = cls._meta['index_specs']\n for spec in index_spec:\n spec = spec.copy()\n fields = spec.pop('fields')\n indexes.append(fields)\n return indexes\n\n indexes = []\n for klass in classes:\n for index in get_indexes_spec(klass):\n if index not in indexes:\n indexes.append(index)\n\n # finish up by appending { '_id': 1 } and { '_cls': 1 }, if needed\n if [(u'_id', 1)] not in indexes:\n indexes.append([(u'_id', 1)])\n if cls._meta.get('index_cls', True) and cls._meta.get('allow_inheritance'):\n indexes.append([(u'_cls', 1)])\n\n return indexes\n\n @classmethod\n def compare_indexes(cls):\n \"\"\" Compares the indexes defined in MongoEngine with the ones\n existing in the database. Returns any missing/extra indexes.\n \"\"\"\n\n required = cls.list_indexes()\n\n existing = []\n for info in cls._get_collection().index_information().values():\n if '_fts' in info['key'][0]:\n index_type = info['key'][0][1]\n text_index_fields = info.get('weights').keys()\n existing.append(\n [(key, index_type) for key in text_index_fields])\n else:\n existing.append(info['key'])\n missing = [index for index in required if index not in existing]\n extra = [index for index in existing if index not in required]\n\n # if { _cls: 1 } is missing, make sure it's *really* necessary\n if [(u'_cls', 1)] in missing:\n cls_obsolete = False\n for index in existing:\n if includes_cls(index) and index not in extra:\n cls_obsolete = True\n break\n if cls_obsolete:\n missing.remove([(u'_cls', 1)])\n\n return {'missing': missing, 'extra': extra}\n\n\nclass DynamicDocument(six.with_metaclass(TopLevelDocumentMetaclass, Document)):\n \"\"\"A Dynamic Document class allowing flexible, expandable and uncontrolled\n schemas. As a :class:`~mongoengine.Document` subclass, acts in the same\n way as an ordinary document but has expanded style properties. Any data\n passed or set against the :class:`~mongoengine.DynamicDocument` that is\n not a field is automatically converted into a\n :class:`~mongoengine.fields.DynamicField` and data can be attributed to that\n field.\n\n .. note::\n\n There is one caveat on Dynamic Documents: undeclared fields cannot start with `_`\n \"\"\"\n\n # The __metaclass__ attribute is removed by 2to3 when running with Python3\n # my_metaclass is defined so that metaclass can be queried in Python 2 & 3\n my_metaclass = TopLevelDocumentMetaclass\n\n _dynamic = True\n\n def __delattr__(self, *args, **kwargs):\n \"\"\"Delete the attribute by setting to None and allowing _delta\n to unset it.\n \"\"\"\n field_name = args[0]\n if field_name in self._dynamic_fields:\n setattr(self, field_name, None)\n self._dynamic_fields[field_name].null = False\n else:\n super(DynamicDocument, self).__delattr__(*args, **kwargs)\n\n\nclass DynamicEmbeddedDocument(six.with_metaclass(DocumentMetaclass, EmbeddedDocument)):\n \"\"\"A Dynamic Embedded Document class allowing flexible, expandable and\n uncontrolled schemas. See :class:`~mongoengine.DynamicDocument` for more\n information about dynamic documents.\n \"\"\"\n\n # The __metaclass__ attribute is removed by 2to3 when running with Python3\n # my_metaclass is defined so that metaclass can be queried in Python 2 & 3\n my_metaclass = DocumentMetaclass\n\n _dynamic = True\n\n def __delattr__(self, *args, **kwargs):\n \"\"\"Delete the attribute by setting to None and allowing _delta\n to unset it.\n \"\"\"\n field_name = args[0]\n if field_name in self._fields:\n default = self._fields[field_name].default\n if callable(default):\n default = default()\n setattr(self, field_name, default)\n else:\n setattr(self, field_name, None)\n\n\nclass MapReduceDocument(object):\n \"\"\"A document returned from a map/reduce query.\n\n :param collection: An instance of :class:`~pymongo.Collection`\n :param key: Document/result key, often an instance of\n :class:`~bson.objectid.ObjectId`. If supplied as\n an ``ObjectId`` found in the given ``collection``,\n the object can be accessed via the ``object`` property.\n :param value: The result(s) for this key.\n\n .. versionadded:: 0.3\n \"\"\"\n\n def __init__(self, document, collection, key, value):\n self._document = document\n self._collection = collection\n self.key = key\n self.value = value\n\n @property\n def object(self):\n \"\"\"Lazy-load the object referenced by ``self.key``. ``self.key``\n should be the ``primary_key``.\n \"\"\"\n id_field = self._document()._meta['id_field']\n id_field_type = type(id_field)\n\n if not isinstance(self.key, id_field_type):\n try:\n self.key = id_field_type(self.key)\n except Exception:\n raise Exception('Could not cast key as %s' %\n id_field_type.__name__)\n\n if not hasattr(self, '_key_object'):\n self._key_object = self._document.objects.with_id(self.key)\n return self._key_object\n return self._key_object\n", "path": "mongoengine/document.py" } ]
diff --git a/docs/changelog.rst b/docs/changelog.rst index 356e2b65b..80b92b814 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -12,6 +12,7 @@ Development - disconnect now clears `mongoengine.connection._connection_settings` - disconnect now clears the cached attribute `Document._collection` - POTENTIAL BREAKING CHANGE: Aggregate gives wrong results when used with a queryset having limit and skip #2029 +- Fix the default write concern of .save that was overwriting the connection write concern #568 - mongoengine now requires pymongo>=3.5 #2017 - Generate Unique Indices for SortedListField and EmbeddedDocumentListFields #2020 - connect() fails immediately when db name contains invalid characters #2031 #1718 diff --git a/mongoengine/document.py b/mongoengine/document.py index 753520c7e..5ccedbfab 100644 --- a/mongoengine/document.py +++ b/mongoengine/document.py @@ -375,7 +375,7 @@ def save(self, force_insert=False, validate=True, clean=True, self.validate(clean=clean) if write_concern is None: - write_concern = {'w': 1} + write_concern = {} doc = self.to_mongo()
redis__redis-py-2316
RediSearch: search command doesn't support asyncio Pipeline The RediSearch search command returns an instance of the `Result` class except when the Redis client is a `Pipeline` because `Pipeline` returns itself instead of a result when you execute a command. There's code that checks for this in both the `SearchCommands` and `AsyncSearchCommands` classes: https://github.com/redis/redis-py/blob/4b0543d567aef36ac467ce495d831a24575d8d5b/redis/commands/search/commands.py#L414 https://github.com/redis/redis-py/blob/4b0543d567aef36ac467ce495d831a24575d8d5b/redis/commands/search/commands.py#L883 However, this check doesn't work if the `Pipeline` is from the `redis.asyncio.client` module. The following modification should fix the issue: ```python from redis.client import Pipeline from redis.asyncio.client import Pipeline as AsyncPipeline ... if isinstance(res, Pipeline) or isinstance(res, AsyncPipeline): return res ... ``` I'm not sure if it makes sense to check for both `Pipeline` types or if the `SearchCommands` class should check for just `Pipeline` and the `AsyncSearchCommands` class should check for just `AsyncPipeline`. Let me know and I can make a PR. Or feel free to make the changes yourself if that's easier. Thanks!
[ { "content": "import redis\n\nfrom ...asyncio.client import Pipeline as AsyncioPipeline\nfrom .commands import AsyncSearchCommands, SearchCommands\n\n\nclass Search(SearchCommands):\n \"\"\"\n Create a client for talking to search.\n It abstracts the API of the module and lets you just use the engine.\n \"\"\"\n\n class BatchIndexer:\n \"\"\"\n A batch indexer allows you to automatically batch\n document indexing in pipelines, flushing it every N documents.\n \"\"\"\n\n def __init__(self, client, chunk_size=1000):\n\n self.client = client\n self.execute_command = client.execute_command\n self._pipeline = client.pipeline(transaction=False, shard_hint=None)\n self.total = 0\n self.chunk_size = chunk_size\n self.current_chunk = 0\n\n def __del__(self):\n if self.current_chunk:\n self.commit()\n\n def add_document(\n self,\n doc_id,\n nosave=False,\n score=1.0,\n payload=None,\n replace=False,\n partial=False,\n no_create=False,\n **fields,\n ):\n \"\"\"\n Add a document to the batch query\n \"\"\"\n self.client._add_document(\n doc_id,\n conn=self._pipeline,\n nosave=nosave,\n score=score,\n payload=payload,\n replace=replace,\n partial=partial,\n no_create=no_create,\n **fields,\n )\n self.current_chunk += 1\n self.total += 1\n if self.current_chunk >= self.chunk_size:\n self.commit()\n\n def add_document_hash(self, doc_id, score=1.0, replace=False):\n \"\"\"\n Add a hash to the batch query\n \"\"\"\n self.client._add_document_hash(\n doc_id, conn=self._pipeline, score=score, replace=replace\n )\n self.current_chunk += 1\n self.total += 1\n if self.current_chunk >= self.chunk_size:\n self.commit()\n\n def commit(self):\n \"\"\"\n Manually commit and flush the batch indexing query\n \"\"\"\n self._pipeline.execute()\n self.current_chunk = 0\n\n def __init__(self, client, index_name=\"idx\"):\n \"\"\"\n Create a new Client for the given index_name.\n The default name is `idx`\n\n If conn is not None, we employ an already existing redis connection\n \"\"\"\n self.MODULE_CALLBACKS = {}\n self.client = client\n self.index_name = index_name\n self.execute_command = client.execute_command\n self._pipeline = client.pipeline\n\n def pipeline(self, transaction=True, shard_hint=None):\n \"\"\"Creates a pipeline for the SEARCH module, that can be used for executing\n SEARCH commands, as well as classic core commands.\n \"\"\"\n p = Pipeline(\n connection_pool=self.client.connection_pool,\n response_callbacks=self.MODULE_CALLBACKS,\n transaction=transaction,\n shard_hint=shard_hint,\n )\n p.index_name = self.index_name\n return p\n\n\nclass AsyncSearch(Search, AsyncSearchCommands):\n class BatchIndexer(Search.BatchIndexer):\n \"\"\"\n A batch indexer allows you to automatically batch\n document indexing in pipelines, flushing it every N documents.\n \"\"\"\n\n async def add_document(\n self,\n doc_id,\n nosave=False,\n score=1.0,\n payload=None,\n replace=False,\n partial=False,\n no_create=False,\n **fields,\n ):\n \"\"\"\n Add a document to the batch query\n \"\"\"\n self.client._add_document(\n doc_id,\n conn=self._pipeline,\n nosave=nosave,\n score=score,\n payload=payload,\n replace=replace,\n partial=partial,\n no_create=no_create,\n **fields,\n )\n self.current_chunk += 1\n self.total += 1\n if self.current_chunk >= self.chunk_size:\n await self.commit()\n\n async def commit(self):\n \"\"\"\n Manually commit and flush the batch indexing query\n \"\"\"\n await self._pipeline.execute()\n self.current_chunk = 0\n\n def pipeline(self, transaction=True, shard_hint=None):\n \"\"\"Creates a pipeline for the SEARCH module, that can be used for executing\n SEARCH commands, as well as classic core commands.\n \"\"\"\n p = AsyncPipeline(\n connection_pool=self.client.connection_pool,\n response_callbacks=self.MODULE_CALLBACKS,\n transaction=transaction,\n shard_hint=shard_hint,\n )\n p.index_name = self.index_name\n return p\n\n\nclass Pipeline(SearchCommands, redis.client.Pipeline):\n \"\"\"Pipeline for the module.\"\"\"\n\n\nclass AsyncPipeline(AsyncSearchCommands, AsyncioPipeline):\n \"\"\"AsyncPipeline for the module.\"\"\"\n", "path": "redis/commands/search/__init__.py" } ]
[ { "content": "import redis\n\nfrom ...asyncio.client import Pipeline as AsyncioPipeline\nfrom .commands import AsyncSearchCommands, SearchCommands\n\n\nclass Search(SearchCommands):\n \"\"\"\n Create a client for talking to search.\n It abstracts the API of the module and lets you just use the engine.\n \"\"\"\n\n class BatchIndexer:\n \"\"\"\n A batch indexer allows you to automatically batch\n document indexing in pipelines, flushing it every N documents.\n \"\"\"\n\n def __init__(self, client, chunk_size=1000):\n\n self.client = client\n self.execute_command = client.execute_command\n self._pipeline = client.pipeline(transaction=False, shard_hint=None)\n self.total = 0\n self.chunk_size = chunk_size\n self.current_chunk = 0\n\n def __del__(self):\n if self.current_chunk:\n self.commit()\n\n def add_document(\n self,\n doc_id,\n nosave=False,\n score=1.0,\n payload=None,\n replace=False,\n partial=False,\n no_create=False,\n **fields,\n ):\n \"\"\"\n Add a document to the batch query\n \"\"\"\n self.client._add_document(\n doc_id,\n conn=self._pipeline,\n nosave=nosave,\n score=score,\n payload=payload,\n replace=replace,\n partial=partial,\n no_create=no_create,\n **fields,\n )\n self.current_chunk += 1\n self.total += 1\n if self.current_chunk >= self.chunk_size:\n self.commit()\n\n def add_document_hash(self, doc_id, score=1.0, replace=False):\n \"\"\"\n Add a hash to the batch query\n \"\"\"\n self.client._add_document_hash(\n doc_id, conn=self._pipeline, score=score, replace=replace\n )\n self.current_chunk += 1\n self.total += 1\n if self.current_chunk >= self.chunk_size:\n self.commit()\n\n def commit(self):\n \"\"\"\n Manually commit and flush the batch indexing query\n \"\"\"\n self._pipeline.execute()\n self.current_chunk = 0\n\n def __init__(self, client, index_name=\"idx\"):\n \"\"\"\n Create a new Client for the given index_name.\n The default name is `idx`\n\n If conn is not None, we employ an already existing redis connection\n \"\"\"\n self.MODULE_CALLBACKS = {}\n self.client = client\n self.index_name = index_name\n self.execute_command = client.execute_command\n self._pipeline = client.pipeline\n\n def pipeline(self, transaction=True, shard_hint=None):\n \"\"\"Creates a pipeline for the SEARCH module, that can be used for executing\n SEARCH commands, as well as classic core commands.\n \"\"\"\n p = Pipeline(\n connection_pool=self.client.connection_pool,\n response_callbacks=self.MODULE_CALLBACKS,\n transaction=transaction,\n shard_hint=shard_hint,\n )\n p.index_name = self.index_name\n return p\n\n\nclass AsyncSearch(Search, AsyncSearchCommands):\n class BatchIndexer(Search.BatchIndexer):\n \"\"\"\n A batch indexer allows you to automatically batch\n document indexing in pipelines, flushing it every N documents.\n \"\"\"\n\n async def add_document(\n self,\n doc_id,\n nosave=False,\n score=1.0,\n payload=None,\n replace=False,\n partial=False,\n no_create=False,\n **fields,\n ):\n \"\"\"\n Add a document to the batch query\n \"\"\"\n self.client._add_document(\n doc_id,\n conn=self._pipeline,\n nosave=nosave,\n score=score,\n payload=payload,\n replace=replace,\n partial=partial,\n no_create=no_create,\n **fields,\n )\n self.current_chunk += 1\n self.total += 1\n if self.current_chunk >= self.chunk_size:\n await self.commit()\n\n async def commit(self):\n \"\"\"\n Manually commit and flush the batch indexing query\n \"\"\"\n await self._pipeline.execute()\n self.current_chunk = 0\n\n def pipeline(self, transaction=True, shard_hint=None):\n \"\"\"Creates a pipeline for the SEARCH module, that can be used for executing\n SEARCH commands, as well as classic core commands.\n \"\"\"\n p = AsyncPipeline(\n connection_pool=self.client.connection_pool,\n response_callbacks=self.MODULE_CALLBACKS,\n transaction=transaction,\n shard_hint=shard_hint,\n )\n p.index_name = self.index_name\n return p\n\n\nclass Pipeline(SearchCommands, redis.client.Pipeline):\n \"\"\"Pipeline for the module.\"\"\"\n\n\nclass AsyncPipeline(AsyncSearchCommands, AsyncioPipeline, Pipeline):\n \"\"\"AsyncPipeline for the module.\"\"\"\n", "path": "redis/commands/search/__init__.py" } ]
diff --git a/redis/commands/search/__init__.py b/redis/commands/search/__init__.py index 923711b8c4..70e9c279e5 100644 --- a/redis/commands/search/__init__.py +++ b/redis/commands/search/__init__.py @@ -167,5 +167,5 @@ class Pipeline(SearchCommands, redis.client.Pipeline): """Pipeline for the module.""" -class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline): +class AsyncPipeline(AsyncSearchCommands, AsyncioPipeline, Pipeline): """AsyncPipeline for the module.""" diff --git a/tests/test_asyncio/test_search.py b/tests/test_asyncio/test_search.py index aa52602588..ec83e565e5 100644 --- a/tests/test_asyncio/test_search.py +++ b/tests/test_asyncio/test_search.py @@ -16,7 +16,7 @@ from redis.commands.search.query import GeoFilter, NumericFilter, Query from redis.commands.search.result import Result from redis.commands.search.suggestion import Suggestion -from tests.conftest import skip_ifmodversion_lt +from tests.conftest import skip_if_redis_enterprise, skip_ifmodversion_lt WILL_PLAY_TEXT = os.path.abspath( os.path.join(os.path.dirname(__file__), "testdata", "will_play_text.csv.bz2") @@ -1043,3 +1043,22 @@ async def test_aggregations_sort_by_and_limit(modclient: redis.Redis): res = await modclient.ft().aggregate(req) assert len(res.rows) == 1 assert res.rows[0] == ["t1", "b"] + + [email protected] +@skip_if_redis_enterprise() +async def test_search_commands_in_pipeline(modclient: redis.Redis): + p = await modclient.ft().pipeline() + p.create_index((TextField("txt"),)) + p.add_document("doc1", payload="foo baz", txt="foo bar") + p.add_document("doc2", txt="foo bar") + q = Query("foo bar").with_payloads() + await p.search(q) + res = await p.execute() + assert res[:3] == ["OK", "OK", "OK"] + assert 2 == res[3][0] + assert "doc1" == res[3][1] + assert "doc2" == res[3][4] + assert "foo baz" == res[3][2] + assert res[3][5] is None + assert res[3][3] == res[3][6] == ["txt", "foo bar"]
mars-project__mars-1302
[BUG] mt.linalg.norm failed and raises TypeError <!-- Thank you for your contribution! Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue. --> **Describe the bug** `mt.linalg.norm` failed and raises `TypeError: copy() got an unexpected keyword argument 'order' ` **To Reproduce** To help us reproducing this bug, please provide information below: 1. Your Python version 2. The version of Mars you use 3. Versions of crucial packages, such as numpy, scipy and protobuf 4. Full stack of the error. 5. Minimized code to reproduce the error. ``` In [2]: import mars.tensor as mt In [3]: t = mt.random.rand(10, 10, chunk_size=5) In [4]: mt.linalg.norm(t).execute() --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-4-900a2d2bec75> in <module> ----> 1 mt.linalg.norm(t).execute() ~/Workspace/mars/mars/core.py in execute(self, session, **kw) 559 560 def execute(self, session=None, **kw): --> 561 self._data.execute(session, **kw) 562 return self 563 ~/Workspace/mars/mars/core.py in execute(self, session, **kw) 368 369 # no more fetch, thus just fire run --> 370 session.run(self, **kw) 371 # return Tileable or ExecutableTuple itself 372 return self ~/Workspace/mars/mars/session.py in run(self, *tileables, **kw) 404 tileables = tuple(mt.tensor(t) if not isinstance(t, (Entity, Base)) else t 405 for t in tileables) --> 406 result = self._sess.run(*tileables, **kw) 407 408 for t in tileables: ~/Workspace/mars/mars/session.py in run(self, *tileables, **kw) 102 # set number of running cores 103 self.context.set_ncores(kw['n_parallel']) --> 104 res = self._executor.execute_tileables(tileables, **kw) 105 return res 106 ~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs) 387 _kernel_mode.eager = False 388 _kernel_mode.eager_count = enter_eager_count + 1 --> 389 return func(*args, **kwargs) 390 finally: 391 _kernel_mode.eager_count -= 1 ~/Workspace/mars/mars/utils.py in inner(*args, **kwargs) 481 def inner(*args, **kwargs): 482 with build_mode(): --> 483 return func(*args, **kwargs) 484 return inner 485 ~/Workspace/mars/mars/executor.py in execute_tileables(self, tileables, fetch, n_parallel, n_thread, print_progress, mock, compose) 825 # build chunk graph, tile will be done during building 826 chunk_graph = chunk_graph_builder.build( --> 827 tileables, tileable_graph=tileable_graph) 828 tileable_graph = chunk_graph_builder.prev_tileable_graph 829 temp_result_keys = set(result_keys) ~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs) 387 _kernel_mode.eager = False 388 _kernel_mode.eager_count = enter_eager_count + 1 --> 389 return func(*args, **kwargs) 390 finally: 391 _kernel_mode.eager_count -= 1 ~/Workspace/mars/mars/utils.py in inner(*args, **kwargs) 481 def inner(*args, **kwargs): 482 with build_mode(): --> 483 return func(*args, **kwargs) 484 return inner 485 ~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph) 340 341 chunk_graph = super().build( --> 342 tileables, tileable_graph=tileable_graph) 343 self._iterative_chunk_graphs.append(chunk_graph) 344 if len(self._interrupted_ops) == 0: ~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs) 387 _kernel_mode.eager = False 388 _kernel_mode.eager_count = enter_eager_count + 1 --> 389 return func(*args, **kwargs) 390 finally: 391 _kernel_mode.eager_count -= 1 ~/Workspace/mars/mars/utils.py in inner(*args, **kwargs) 481 def inner(*args, **kwargs): 482 with build_mode(): --> 483 return func(*args, **kwargs) 484 return inner 485 ~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph) 253 # for further execution 254 partial_tiled_chunks = \ --> 255 self._on_tile_failure(tileable_data.op, exc_info) 256 if partial_tiled_chunks is not None and \ 257 len(partial_tiled_chunks) > 0: ~/Workspace/mars/mars/tiles.py in inner(op, exc_info) 292 on_tile_failure(op, exc_info) 293 else: --> 294 raise exc_info[1].with_traceback(exc_info[2]) from None 295 return inner 296 ~/Workspace/mars/mars/tiles.py in build(self, tileables, tileable_graph) 233 continue 234 try: --> 235 tiled = self._tile(tileable_data, tileable_graph) 236 tiled_op.add(tileable_data.op) 237 for t, td in zip(tileable_data.op.outputs, tiled): ~/Workspace/mars/mars/tiles.py in _tile(self, tileable_data, tileable_graph) 328 if any(inp.op in self._interrupted_ops for inp in tileable_data.inputs): 329 raise TilesError('Tile fail due to failure of inputs') --> 330 return super()._tile(tileable_data, tileable_graph) 331 332 @kernel_mode ~/Workspace/mars/mars/tiles.py in _tile(self, tileable_data, tileable_graph) 191 t._nsplits = o.nsplits 192 elif on_tile is None: --> 193 tds[0]._inplace_tile() 194 else: 195 tds = on_tile(tileable_data.op.outputs, tds) ~/Workspace/mars/mars/core.py in _inplace_tile(self) 160 161 def _inplace_tile(self): --> 162 return handler.inplace_tile(self) 163 164 def __getattr__(self, attr): ~/Workspace/mars/mars/tiles.py in inplace_tile(self, to_tile) 126 if not to_tile.is_coarse(): 127 return to_tile --> 128 dispatched = self.dispatch(to_tile.op) 129 self._assign_to([d.data for d in dispatched], to_tile.op.outputs) 130 return to_tile ~/Workspace/mars/mars/utils.py in _wrapped(*args, **kwargs) 387 _kernel_mode.eager = False 388 _kernel_mode.eager_count = enter_eager_count + 1 --> 389 return func(*args, **kwargs) 390 finally: 391 _kernel_mode.eager_count -= 1 ~/Workspace/mars/mars/tiles.py in dispatch(self, op) 113 return self._handlers[op_cls](op) 114 try: --> 115 return op_cls.tile(op) 116 except NotImplementedError as ex: 117 cause = ex ~/Workspace/mars/mars/tensor/linalg/norm.py in tile(cls, op) 96 return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=out_chunks, nsplits=nsplits) 97 ---> 98 r = cls._norm(x.astype(op.outputs[0].dtype), ord, axis, keepdims) 99 recursive_tile(r) 100 new_op = op.copy() ~/Workspace/mars/mars/tensor/base/astype.py in _astype(tensor, dtype, order, casting, copy) 154 155 if tensor.dtype == dtype and tensor.order == tensor_order: --> 156 return tensor if not copy else tensor.copy(order=order) 157 elif not np.can_cast(tensor.dtype, dtype, casting=casting): 158 raise TypeError('Cannot cast array from {0!r} to {1!r} ' TypeError: copy() got an unexpected keyword argument 'order' ```
[ { "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2020 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport itertools\nfrom collections.abc import Iterable\n\nimport numpy as np\n\nfrom ... import opcodes as OperandDef\nfrom ...serialize import ValueType, KeyField, AnyField, TupleField, BoolField\nfrom ...utils import recursive_tile\nfrom ..array_utils import device, as_same_device\nfrom ..operands import TensorHasInput, TensorOperandMixin\nfrom ..arithmetic import sqrt\nfrom ..datasource import tensor as astensor\nfrom .svd import svd\n\n\nclass TensorNorm(TensorHasInput, TensorOperandMixin):\n _op_type_ = OperandDef.NORM\n\n _input = KeyField('input')\n _ord = AnyField('ord')\n _axis = TupleField('axis', ValueType.int32)\n _keepdims = BoolField('keepdims')\n\n def __init__(self, ord=None, axis=None, keepdims=None, dtype=None, sparse=False, **kw):\n super().__init__(_ord=ord, _axis=axis, _keepdims=keepdims, _dtype=dtype,\n _sparse=sparse, **kw)\n\n @property\n def ord(self):\n return getattr(self, '_ord', None)\n\n @property\n def axis(self):\n return self._axis\n\n @property\n def keepdims(self):\n return self._keepdims\n\n def _set_inputs(self, inputs):\n super()._set_inputs(inputs)\n self._input = self._inputs[0]\n\n def __call__(self, x):\n r = x.astype(self.dtype)\n shape = self._norm(r, self._ord, self._axis, self._keepdims).shape\n return self.new_tensor([x], shape)\n\n @classmethod\n def tile(cls, op):\n x = op.input\n axis = op.axis\n ord = op.ord\n keepdims = op.keepdims\n\n axis_chunk_shapes = tuple(x.chunk_shape[i] for i in axis)\n can_apply_norm = all(s == 1 for s in axis_chunk_shapes)\n\n if can_apply_norm:\n axis_set = set(axis)\n get_shape = lambda shape: tuple(s if i not in axis_set else 1 for i, s in enumerate(shape)\n if i not in axis_set or keepdims)\n\n out_chunk_shape = get_shape(x.chunk_shape)\n out_chunks = []\n for idx in itertools.product(*[range(s) for s in out_chunk_shape]):\n idx_iter = iter(idx)\n in_idx = tuple(0 if i in axis_set and not keepdims else next(idx_iter)\n for i in range(x.ndim))\n\n c = x.cix[in_idx]\n chunk_op = op.copy().reset_key()\n out_chunk = chunk_op.new_chunk([c], shape=get_shape(c.shape), index=idx)\n out_chunks.append(out_chunk)\n\n nsplits = [tuple(c.shape[i] for c in out_chunks\n if all(idx == 0 for j, idx in enumerate(c.index) if j != i))\n for i in range(len(out_chunks[0].shape))]\n new_op = op.copy()\n return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=out_chunks, nsplits=nsplits)\n\n r = cls._norm(x.astype(op.outputs[0].dtype), ord, axis, keepdims)\n recursive_tile(r)\n new_op = op.copy()\n return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=r.chunks, nsplits=r.nsplits)\n\n @staticmethod\n def _norm(r, ord, axis, keepdims):\n if ord is None:\n return sqrt((abs(r) ** 2).sum(axis=axis, keepdims=keepdims))\n elif ord == 'nuc':\n if len(axis) == 1:\n raise ValueError('Invalid norm order for vectors.')\n return svd(r)[1][np.newaxis].sum(keepdims=keepdims)\n elif ord == np.inf:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n r = abs(r)\n if len(axis) == 1:\n return r.max(axis=axis, keepdims=keepdims)\n else:\n return r.sum(axis=axis[1], keepdims=keepdims).max(keepdims=keepdims)\n elif ord == -np.inf:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n r = abs(r)\n if len(axis) == 1:\n return r.min(axis=axis, keepdims=keepdims)\n else:\n return r.sum(axis=axis[1], keepdims=keepdims).min(keepdims=keepdims)\n elif ord == 0:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n if len(axis) == 2:\n raise ValueError('Invalid norm order for matrices.')\n return (r != 0).astype(r.dtype).sum(axis=axis, keepdims=keepdims)\n elif ord == 1:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n r = abs(r)\n if len(axis) == 1:\n return r.sum(axis=axis, keepdims=keepdims)\n else:\n return r.sum(axis=axis[0], keepdims=keepdims).max(keepdims=keepdims)\n elif ord == -1 and len(axis) == 2:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n return abs(r).sum(axis=axis[0], keepdims=keepdims).min(keepdims=keepdims)\n elif ord == 2 and len(axis) == 2:\n return svd(r)[1][np.newaxis].max(keepdims=keepdims)\n elif ord == -2 and len(axis) == 2:\n return svd(r)[1][np.newaxis].min(keepdims=keepdims)\n else:\n if len(axis) == 2:\n raise ValueError('Invalid norm order for matrices.')\n\n return (abs(r) ** ord).sum(axis=axis, keepdims=keepdims) ** (1.0 / ord)\n\n @classmethod\n def execute(cls, ctx, op):\n (x,), device_id, xp = as_same_device(\n [ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True)\n\n with device(device_id):\n ctx[op.outputs[0].key] = xp.linalg.norm(x, ord=op.ord, axis=op.axis,\n keepdims=op.keepdims)\n\n\ndef norm(x, ord=None, axis=None, keepdims=False):\n r\"\"\"\n Matrix or vector norm.\n\n This function is able to return one of eight different matrix norms,\n or one of an infinite number of vector norms (described below), depending\n on the value of the ``ord`` parameter.\n\n Parameters\n ----------\n x : array_like\n Input tensor. If `axis` is None, `x` must be 1-D or 2-D.\n ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional\n Order of the norm (see table under ``Notes``). inf means mars tensor's\n `inf` object.\n axis : {int, 2-tuple of ints, None}, optional\n If `axis` is an integer, it specifies the axis of `x` along which to\n compute the vector norms. If `axis` is a 2-tuple, it specifies the\n axes that hold 2-D matrices, and the matrix norms of these matrices\n are computed. If `axis` is None then either a vector norm (when `x`\n is 1-D) or a matrix norm (when `x` is 2-D) is returned.\n keepdims : bool, optional\n If this is set to True, the axes which are normed over are left in the\n result as dimensions with size one. With this option the result will\n broadcast correctly against the original `x`.\n\n Returns\n -------\n n : float or Tensor\n Norm of the matrix or vector(s).\n\n Notes\n -----\n For values of ``ord <= 0``, the result is, strictly speaking, not a\n mathematical 'norm', but it may still be useful for various numerical\n purposes.\n\n The following norms can be calculated:\n\n ===== ============================ ==========================\n ord norm for matrices norm for vectors\n ===== ============================ ==========================\n None Frobenius norm 2-norm\n 'fro' Frobenius norm --\n 'nuc' nuclear norm --\n inf max(sum(abs(x), axis=1)) max(abs(x))\n -inf min(sum(abs(x), axis=1)) min(abs(x))\n 0 -- sum(x != 0)\n 1 max(sum(abs(x), axis=0)) as below\n -1 min(sum(abs(x), axis=0)) as below\n 2 2-norm (largest sing. value) as below\n -2 smallest singular value as below\n other -- sum(abs(x)**ord)**(1./ord)\n ===== ============================ ==========================\n\n The Frobenius norm is given by [1]_:\n\n :math:`||A||_F = [\\\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`\n\n The nuclear norm is the sum of the singular values.\n\n References\n ----------\n .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,\n Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15\n\n Examples\n --------\n >>> from mars.tensor import linalg as LA\n >>> import mars.tensor as mt\n >>> a = mt.arange(9) - 4\n >>> a.execute()\n array([-4, -3, -2, -1, 0, 1, 2, 3, 4])\n >>> b = a.reshape((3, 3))\n >>> b.execute()\n array([[-4, -3, -2],\n [-1, 0, 1],\n [ 2, 3, 4]])\n\n >>> LA.norm(a).execute()\n 7.745966692414834\n >>> LA.norm(b).execute()\n 7.745966692414834\n >>> LA.norm(b, 'fro').execute()\n 7.745966692414834\n >>> LA.norm(a, mt.inf).execute()\n 4.0\n >>> LA.norm(b, mt.inf).execute()\n 9.0\n >>> LA.norm(a, -mt.inf).execute()\n 0.0\n >>> LA.norm(b, -mt.inf).execute()\n 2.0\n\n >>> LA.norm(a, 1).execute()\n 20.0\n >>> LA.norm(b, 1).execute()\n 7.0\n >>> LA.norm(a, -1).execute()\n 0.0\n >>> LA.norm(b, -1).execute()\n 6.0\n >>> LA.norm(a, 2).execute()\n 7.745966692414834\n >>> LA.norm(b, 2).execute()\n 7.3484692283495345\n\n >>> LA.norm(a, -2).execute()\n 0.0\n >>> LA.norm(b, -2).execute()\n 4.351066026358965e-18\n >>> LA.norm(a, 3).execute()\n 5.8480354764257312\n >>> LA.norm(a, -3).execute()\n 0.0\n\n Using the `axis` argument to compute vector norms:\n\n >>> c = mt.array([[ 1, 2, 3],\n ... [-1, 1, 4]])\n >>> LA.norm(c, axis=0).execute()\n array([ 1.41421356, 2.23606798, 5. ])\n >>> LA.norm(c, axis=1).execute()\n array([ 3.74165739, 4.24264069])\n >>> LA.norm(c, ord=1, axis=1).execute()\n array([ 6., 6.])\n\n Using the `axis` argument to compute matrix norms:\n\n >>> m = mt.arange(8).reshape(2,2,2)\n >>> LA.norm(m, axis=(1,2)).execute()\n array([ 3.74165739, 11.22497216])\n >>> LA.norm(m[0, :, :]).execute(), LA.norm(m[1, :, :]).execute()\n (3.7416573867739413, 11.224972160321824)\n\n \"\"\"\n x = astensor(x)\n\n if ord == 'fro':\n ord = None\n if axis is not None:\n if isinstance(axis, Iterable):\n axis = tuple(axis)\n else:\n axis = (axis,)\n else:\n axis = tuple(range(x.ndim))\n\n op = TensorNorm(ord=ord, axis=axis, keepdims=keepdims,\n dtype=np.result_type(x.dtype, np.float_), sparse=x.issparse())\n return op(x)\n", "path": "mars/tensor/linalg/norm.py" } ]
[ { "content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2020 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport itertools\nfrom collections.abc import Iterable\n\nimport numpy as np\n\nfrom ... import opcodes as OperandDef\nfrom ...serialize import ValueType, KeyField, AnyField, TupleField, BoolField\nfrom ...utils import recursive_tile\nfrom ..array_utils import device, as_same_device\nfrom ..operands import TensorHasInput, TensorOperandMixin\nfrom ..arithmetic import sqrt\nfrom ..datasource import tensor as astensor\nfrom .svd import svd\n\n\nclass TensorNorm(TensorHasInput, TensorOperandMixin):\n _op_type_ = OperandDef.NORM\n\n _input = KeyField('input')\n _ord = AnyField('ord')\n _axis = TupleField('axis', ValueType.int32)\n _keepdims = BoolField('keepdims')\n\n def __init__(self, ord=None, axis=None, keepdims=None, dtype=None, sparse=False, **kw):\n super().__init__(_ord=ord, _axis=axis, _keepdims=keepdims, _dtype=dtype,\n _sparse=sparse, **kw)\n\n @property\n def ord(self):\n return getattr(self, '_ord', None)\n\n @property\n def axis(self):\n return self._axis\n\n @property\n def keepdims(self):\n return self._keepdims\n\n def _set_inputs(self, inputs):\n super()._set_inputs(inputs)\n self._input = self._inputs[0]\n\n def __call__(self, x):\n r = x.astype(self.dtype)\n shape = self._norm(r, self._ord, self._axis, self._keepdims).shape\n return self.new_tensor([x], shape)\n\n @classmethod\n def tile(cls, op):\n x = astensor(op.input)\n axis = op.axis\n ord = op.ord\n keepdims = op.keepdims\n\n axis_chunk_shapes = tuple(x.chunk_shape[i] for i in axis)\n can_apply_norm = all(s == 1 for s in axis_chunk_shapes)\n\n if can_apply_norm:\n axis_set = set(axis)\n get_shape = lambda shape: tuple(s if i not in axis_set else 1 for i, s in enumerate(shape)\n if i not in axis_set or keepdims)\n\n out_chunk_shape = get_shape(x.chunk_shape)\n out_chunks = []\n for idx in itertools.product(*[range(s) for s in out_chunk_shape]):\n idx_iter = iter(idx)\n in_idx = tuple(0 if i in axis_set and not keepdims else next(idx_iter)\n for i in range(x.ndim))\n\n c = x.cix[in_idx]\n chunk_op = op.copy().reset_key()\n out_chunk = chunk_op.new_chunk([c], shape=get_shape(c.shape), index=idx)\n out_chunks.append(out_chunk)\n\n nsplits = [tuple(c.shape[i] for c in out_chunks\n if all(idx == 0 for j, idx in enumerate(c.index) if j != i))\n for i in range(len(out_chunks[0].shape))]\n new_op = op.copy()\n return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=out_chunks, nsplits=nsplits)\n\n r = cls._norm(x.astype(op.outputs[0].dtype), ord, axis, keepdims)\n recursive_tile(r)\n new_op = op.copy()\n return new_op.new_tensors(op.inputs, op.outputs[0].shape, chunks=r.chunks, nsplits=r.nsplits)\n\n @staticmethod\n def _norm(r, ord, axis, keepdims):\n if ord is None:\n return sqrt((abs(r) ** 2).sum(axis=axis, keepdims=keepdims))\n elif ord == 'nuc':\n if len(axis) == 1:\n raise ValueError('Invalid norm order for vectors.')\n return svd(r)[1][np.newaxis].sum(keepdims=keepdims)\n elif ord == np.inf:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n r = abs(r)\n if len(axis) == 1:\n return r.max(axis=axis, keepdims=keepdims)\n else:\n return r.sum(axis=axis[1], keepdims=keepdims).max(keepdims=keepdims)\n elif ord == -np.inf:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n r = abs(r)\n if len(axis) == 1:\n return r.min(axis=axis, keepdims=keepdims)\n else:\n return r.sum(axis=axis[1], keepdims=keepdims).min(keepdims=keepdims)\n elif ord == 0:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n if len(axis) == 2:\n raise ValueError('Invalid norm order for matrices.')\n return (r != 0).astype(r.dtype).sum(axis=axis, keepdims=keepdims)\n elif ord == 1:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n r = abs(r)\n if len(axis) == 1:\n return r.sum(axis=axis, keepdims=keepdims)\n else:\n return r.sum(axis=axis[0], keepdims=keepdims).max(keepdims=keepdims)\n elif ord == -1 and len(axis) == 2:\n if r.ndim > 2:\n raise ValueError('Improper number of dimensions to norm.')\n return abs(r).sum(axis=axis[0], keepdims=keepdims).min(keepdims=keepdims)\n elif ord == 2 and len(axis) == 2:\n return svd(r)[1][np.newaxis].max(keepdims=keepdims)\n elif ord == -2 and len(axis) == 2:\n return svd(r)[1][np.newaxis].min(keepdims=keepdims)\n else:\n if len(axis) == 2:\n raise ValueError('Invalid norm order for matrices.')\n\n return (abs(r) ** ord).sum(axis=axis, keepdims=keepdims) ** (1.0 / ord)\n\n @classmethod\n def execute(cls, ctx, op):\n (x,), device_id, xp = as_same_device(\n [ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True)\n\n with device(device_id):\n ctx[op.outputs[0].key] = xp.linalg.norm(x, ord=op.ord, axis=op.axis,\n keepdims=op.keepdims)\n\n\ndef norm(x, ord=None, axis=None, keepdims=False):\n r\"\"\"\n Matrix or vector norm.\n\n This function is able to return one of eight different matrix norms,\n or one of an infinite number of vector norms (described below), depending\n on the value of the ``ord`` parameter.\n\n Parameters\n ----------\n x : array_like\n Input tensor. If `axis` is None, `x` must be 1-D or 2-D.\n ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional\n Order of the norm (see table under ``Notes``). inf means mars tensor's\n `inf` object.\n axis : {int, 2-tuple of ints, None}, optional\n If `axis` is an integer, it specifies the axis of `x` along which to\n compute the vector norms. If `axis` is a 2-tuple, it specifies the\n axes that hold 2-D matrices, and the matrix norms of these matrices\n are computed. If `axis` is None then either a vector norm (when `x`\n is 1-D) or a matrix norm (when `x` is 2-D) is returned.\n keepdims : bool, optional\n If this is set to True, the axes which are normed over are left in the\n result as dimensions with size one. With this option the result will\n broadcast correctly against the original `x`.\n\n Returns\n -------\n n : float or Tensor\n Norm of the matrix or vector(s).\n\n Notes\n -----\n For values of ``ord <= 0``, the result is, strictly speaking, not a\n mathematical 'norm', but it may still be useful for various numerical\n purposes.\n\n The following norms can be calculated:\n\n ===== ============================ ==========================\n ord norm for matrices norm for vectors\n ===== ============================ ==========================\n None Frobenius norm 2-norm\n 'fro' Frobenius norm --\n 'nuc' nuclear norm --\n inf max(sum(abs(x), axis=1)) max(abs(x))\n -inf min(sum(abs(x), axis=1)) min(abs(x))\n 0 -- sum(x != 0)\n 1 max(sum(abs(x), axis=0)) as below\n -1 min(sum(abs(x), axis=0)) as below\n 2 2-norm (largest sing. value) as below\n -2 smallest singular value as below\n other -- sum(abs(x)**ord)**(1./ord)\n ===== ============================ ==========================\n\n The Frobenius norm is given by [1]_:\n\n :math:`||A||_F = [\\\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`\n\n The nuclear norm is the sum of the singular values.\n\n References\n ----------\n .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,\n Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15\n\n Examples\n --------\n >>> from mars.tensor import linalg as LA\n >>> import mars.tensor as mt\n >>> a = mt.arange(9) - 4\n >>> a.execute()\n array([-4, -3, -2, -1, 0, 1, 2, 3, 4])\n >>> b = a.reshape((3, 3))\n >>> b.execute()\n array([[-4, -3, -2],\n [-1, 0, 1],\n [ 2, 3, 4]])\n\n >>> LA.norm(a).execute()\n 7.745966692414834\n >>> LA.norm(b).execute()\n 7.745966692414834\n >>> LA.norm(b, 'fro').execute()\n 7.745966692414834\n >>> LA.norm(a, mt.inf).execute()\n 4.0\n >>> LA.norm(b, mt.inf).execute()\n 9.0\n >>> LA.norm(a, -mt.inf).execute()\n 0.0\n >>> LA.norm(b, -mt.inf).execute()\n 2.0\n\n >>> LA.norm(a, 1).execute()\n 20.0\n >>> LA.norm(b, 1).execute()\n 7.0\n >>> LA.norm(a, -1).execute()\n 0.0\n >>> LA.norm(b, -1).execute()\n 6.0\n >>> LA.norm(a, 2).execute()\n 7.745966692414834\n >>> LA.norm(b, 2).execute()\n 7.3484692283495345\n\n >>> LA.norm(a, -2).execute()\n 0.0\n >>> LA.norm(b, -2).execute()\n 4.351066026358965e-18\n >>> LA.norm(a, 3).execute()\n 5.8480354764257312\n >>> LA.norm(a, -3).execute()\n 0.0\n\n Using the `axis` argument to compute vector norms:\n\n >>> c = mt.array([[ 1, 2, 3],\n ... [-1, 1, 4]])\n >>> LA.norm(c, axis=0).execute()\n array([ 1.41421356, 2.23606798, 5. ])\n >>> LA.norm(c, axis=1).execute()\n array([ 3.74165739, 4.24264069])\n >>> LA.norm(c, ord=1, axis=1).execute()\n array([ 6., 6.])\n\n Using the `axis` argument to compute matrix norms:\n\n >>> m = mt.arange(8).reshape(2,2,2)\n >>> LA.norm(m, axis=(1,2)).execute()\n array([ 3.74165739, 11.22497216])\n >>> LA.norm(m[0, :, :]).execute(), LA.norm(m[1, :, :]).execute()\n (3.7416573867739413, 11.224972160321824)\n\n \"\"\"\n x = astensor(x)\n\n if ord == 'fro':\n ord = None\n if axis is not None:\n if isinstance(axis, Iterable):\n axis = tuple(axis)\n else:\n axis = (axis,)\n else:\n axis = tuple(range(x.ndim))\n\n op = TensorNorm(ord=ord, axis=axis, keepdims=keepdims,\n dtype=np.result_type(x.dtype, np.float_), sparse=x.issparse())\n return op(x)\n", "path": "mars/tensor/linalg/norm.py" } ]
diff --git a/mars/tensor/linalg/norm.py b/mars/tensor/linalg/norm.py index c057c26728..29871d6599 100644 --- a/mars/tensor/linalg/norm.py +++ b/mars/tensor/linalg/norm.py @@ -64,7 +64,7 @@ def __call__(self, x): @classmethod def tile(cls, op): - x = op.input + x = astensor(op.input) axis = op.axis ord = op.ord keepdims = op.keepdims diff --git a/mars/tensor/linalg/tests/test_linalg_execute.py b/mars/tensor/linalg/tests/test_linalg_execute.py index 599d8e6a39..739ff6d394 100644 --- a/mars/tensor/linalg/tests/test_linalg_execute.py +++ b/mars/tensor/linalg/tests/test_linalg_execute.py @@ -811,6 +811,12 @@ def testNormExecution(self): res = self.executor.execute_tensor(t)[0] self.assertAlmostEqual(res, 3.14, delta=1) + raw = np.random.RandomState(0).rand(10, 10) + d = norm(tensor(raw, chunk_size=5)) + expected = self.executor.execute_tensor(d, concat=True)[0] + result = np.linalg.norm(raw) + np.testing.assert_allclose(expected, result) + def testTensordotExecution(self): size_executor = ExecutorForTest(sync_provider_type=ExecutorForTest.SyncProviderType.MOCK)
pallets__werkzeug-1726
Pytest fails due to missing dependency Reproduction: Activate virtualenv and execute `pytest` Expected result: Tests are run Actual result: ```(env) :~/git/werkzeug[master ?]🙂 pytest ========================= test session starts ========================== platform darwin -- Python 3.6.8, pytest-5.3.2, py-1.8.0, pluggy-0.13.0 rootdir: /Users/latham/git/werkzeug, inifile: setup.cfg, testpaths: tests plugins: mock-1.11.2, cov-2.8.1 collected 563 items / 1 error / 562 selected ================================ ERRORS ================================ _________________ ERROR collecting tests/test_debug.py _________________ tests/test_debug.py:372: in <module> @pytest.mark.timeout(2) ../../Library/Python/3.6/lib/python/site-packages/_pytest/mark/structures.py:327: in __getattr__ PytestUnknownMarkWarning, E pytest.PytestUnknownMarkWarning: Unknown pytest.mark.timeout - is this a typo? You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/latest/mark.html !!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!! =========================== 1 error in 1.60s ===========================```
[ { "content": "import io\nimport re\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nwith io.open(\"README.rst\", \"rt\", encoding=\"utf8\") as f:\n readme = f.read()\n\nwith io.open(\"src/werkzeug/__init__.py\", \"rt\", encoding=\"utf8\") as f:\n version = re.search(r'__version__ = \"(.*?)\"', f.read(), re.M).group(1)\n\nsetup(\n name=\"Werkzeug\",\n version=version,\n url=\"https://palletsprojects.com/p/werkzeug/\",\n project_urls={\n \"Documentation\": \"https://werkzeug.palletsprojects.com/\",\n \"Code\": \"https://github.com/pallets/werkzeug\",\n \"Issue tracker\": \"https://github.com/pallets/werkzeug/issues\",\n },\n license=\"BSD-3-Clause\",\n author=\"Armin Ronacher\",\n author_email=\"[email protected]\",\n maintainer=\"Pallets\",\n maintainer_email=\"[email protected]\",\n description=\"The comprehensive WSGI web application library.\",\n long_description=readme,\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Environment :: Web Environment\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: BSD License\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2\",\n \"Programming Language :: Python :: 2.7\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: Implementation :: CPython\",\n \"Programming Language :: Python :: Implementation :: PyPy\",\n \"Topic :: Internet :: WWW/HTTP :: Dynamic Content\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI :: Application\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI :: Middleware\",\n \"Topic :: Software Development :: Libraries :: Application Frameworks\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n ],\n packages=find_packages(\"src\"),\n package_dir={\"\": \"src\"},\n include_package_data=True,\n python_requires=\">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*\",\n extras_require={\n \"watchdog\": [\"watchdog\"],\n \"dev\": [\n \"pytest\",\n \"coverage\",\n \"tox\",\n \"sphinx\",\n \"pallets-sphinx-themes\",\n \"sphinx-issues\",\n ],\n },\n)\n", "path": "setup.py" } ]
[ { "content": "import io\nimport re\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nwith io.open(\"README.rst\", \"rt\", encoding=\"utf8\") as f:\n readme = f.read()\n\nwith io.open(\"src/werkzeug/__init__.py\", \"rt\", encoding=\"utf8\") as f:\n version = re.search(r'__version__ = \"(.*?)\"', f.read(), re.M).group(1)\n\nsetup(\n name=\"Werkzeug\",\n version=version,\n url=\"https://palletsprojects.com/p/werkzeug/\",\n project_urls={\n \"Documentation\": \"https://werkzeug.palletsprojects.com/\",\n \"Code\": \"https://github.com/pallets/werkzeug\",\n \"Issue tracker\": \"https://github.com/pallets/werkzeug/issues\",\n },\n license=\"BSD-3-Clause\",\n author=\"Armin Ronacher\",\n author_email=\"[email protected]\",\n maintainer=\"Pallets\",\n maintainer_email=\"[email protected]\",\n description=\"The comprehensive WSGI web application library.\",\n long_description=readme,\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Environment :: Web Environment\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: BSD License\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2\",\n \"Programming Language :: Python :: 2.7\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: Implementation :: CPython\",\n \"Programming Language :: Python :: Implementation :: PyPy\",\n \"Topic :: Internet :: WWW/HTTP :: Dynamic Content\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI :: Application\",\n \"Topic :: Internet :: WWW/HTTP :: WSGI :: Middleware\",\n \"Topic :: Software Development :: Libraries :: Application Frameworks\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n ],\n packages=find_packages(\"src\"),\n package_dir={\"\": \"src\"},\n include_package_data=True,\n python_requires=\">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*\",\n extras_require={\n \"watchdog\": [\"watchdog\"],\n \"dev\": [\n \"pytest\",\n \"pytest-timeout\",\n \"coverage\",\n \"tox\",\n \"sphinx\",\n \"pallets-sphinx-themes\",\n \"sphinx-issues\",\n ],\n },\n)\n", "path": "setup.py" } ]
diff --git a/setup.py b/setup.py index 157d884ab..de09ac3c7 100644 --- a/setup.py +++ b/setup.py @@ -57,6 +57,7 @@ "watchdog": ["watchdog"], "dev": [ "pytest", + "pytest-timeout", "coverage", "tox", "sphinx",
cisagov__manage.get.gov-1181
Update "User management" title to read "Domain managers" ### Issue description and context During user testing, we consistently saw that participants didn't know where to add another domain manager. The section title "User management" didn't resonate with anyone we talked to. Therefore, we need to update that title to read: "Domain managers." ![Image](https://github.com/cisagov/getgov/assets/119689946/d947fce8-0b94-4f74-b531-12708e2bdabd) ### Acceptance criteria - Navigation title is changed from "User management" to "Domain managers" - Page title is changed from "User management" to "Domain managers" ### Links to other issues _No response_
[ { "content": "\"\"\"Views for a single Domain.\n\nAuthorization is handled by the `DomainPermissionView`. To ensure that only\nauthorized users can see information on a domain, every view here should\ninherit from `DomainPermissionView` (or DomainInvitationPermissionDeleteView).\n\"\"\"\n\nimport logging\n\nfrom django.contrib import messages\nfrom django.contrib.messages.views import SuccessMessageMixin\nfrom django.db import IntegrityError\nfrom django.shortcuts import redirect\nfrom django.template import RequestContext\nfrom django.urls import reverse\nfrom django.views.generic.edit import FormMixin\n\nfrom registrar.models import (\n Domain,\n DomainInformation,\n DomainInvitation,\n User,\n UserDomainRole,\n)\nfrom registrar.models.public_contact import PublicContact\nfrom registrar.models.utility.contact_error import ContactError\n\nfrom ..forms import (\n ContactForm,\n DomainOrgNameAddressForm,\n DomainAddUserForm,\n DomainSecurityEmailForm,\n NameserverFormset,\n DomainDnssecForm,\n DomainDsdataFormset,\n DomainDsdataForm,\n DomainKeydataFormset,\n DomainKeydataForm,\n)\n\nfrom epplibwrapper import (\n common,\n extensions,\n RegistryError,\n CANNOT_CONTACT_REGISTRY,\n GENERIC_ERROR,\n)\n\nfrom ..utility.email import send_templated_email, EmailSendingError\nfrom .utility import DomainPermissionView, DomainInvitationPermissionDeleteView\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass DomainBaseView(DomainPermissionView):\n \"\"\"\n Base View for the Domain. Handles getting and setting the domain\n in session cache on GETs. Also provides methods for getting\n and setting the domain in cache\n \"\"\"\n\n def get(self, request, *args, **kwargs):\n self._get_domain(request)\n context = self.get_context_data(object=self.object)\n return self.render_to_response(context)\n\n def _get_domain(self, request):\n \"\"\"\n get domain from session cache or from db and set\n to self.object\n set session to self for downstream functions to\n update session cache\n \"\"\"\n self.session = request.session\n # domain:private_key is the session key to use for\n # caching the domain in the session\n domain_pk = \"domain:\" + str(self.kwargs.get(\"pk\"))\n cached_domain = self.session.get(domain_pk)\n\n if cached_domain:\n self.object = cached_domain\n else:\n self.object = self.get_object()\n self._update_session_with_domain()\n\n def _update_session_with_domain(self):\n \"\"\"\n update domain in the session cache\n \"\"\"\n domain_pk = \"domain:\" + str(self.kwargs.get(\"pk\"))\n self.session[domain_pk] = self.object\n\n\nclass DomainFormBaseView(DomainBaseView, FormMixin):\n \"\"\"\n Form Base View for the Domain. Handles getting and setting\n domain in cache when dealing with domain forms. Provides\n implementations of post, form_valid and form_invalid.\n \"\"\"\n\n def post(self, request, *args, **kwargs):\n \"\"\"Form submission posts to this view.\n\n This post method harmonizes using DomainBaseView and FormMixin\n \"\"\"\n self._get_domain(request)\n form = self.get_form()\n if form.is_valid():\n return self.form_valid(form)\n else:\n return self.form_invalid(form)\n\n def form_valid(self, form):\n # updates session cache with domain\n self._update_session_with_domain()\n\n # superclass has the redirect\n return super().form_valid(form)\n\n def form_invalid(self, form):\n # updates session cache with domain\n self._update_session_with_domain()\n\n # superclass has the redirect\n return super().form_invalid(form)\n\n\nclass DomainView(DomainBaseView):\n\n \"\"\"Domain detail overview page.\"\"\"\n\n template_name = \"domain_detail.html\"\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n default_email = self.object.get_default_security_contact().email\n context[\"default_security_email\"] = default_email\n\n security_email = self.object.get_security_email()\n if security_email is None or security_email == default_email:\n context[\"security_email\"] = None\n return context\n context[\"security_email\"] = security_email\n return context\n\n\nclass DomainOrgNameAddressView(DomainFormBaseView):\n \"\"\"Organization name and mailing address view\"\"\"\n\n model = Domain\n template_name = \"domain_org_name_address.html\"\n context_object_name = \"domain\"\n form_class = DomainOrgNameAddressForm\n\n def get_form_kwargs(self, *args, **kwargs):\n \"\"\"Add domain_info.organization_name instance to make a bound form.\"\"\"\n form_kwargs = super().get_form_kwargs(*args, **kwargs)\n form_kwargs[\"instance\"] = self.object.domain_info\n return form_kwargs\n\n def get_success_url(self):\n \"\"\"Redirect to the overview page for the domain.\"\"\"\n return reverse(\"domain-org-name-address\", kwargs={\"pk\": self.object.pk})\n\n def form_valid(self, form):\n \"\"\"The form is valid, save the organization name and mailing address.\"\"\"\n form.save()\n\n messages.success(\n self.request, \"The organization name and mailing address has been updated.\"\n )\n\n # superclass has the redirect\n return super().form_valid(form)\n\n\nclass DomainAuthorizingOfficialView(DomainFormBaseView):\n \"\"\"Domain authorizing official editing view.\"\"\"\n\n model = Domain\n template_name = \"domain_authorizing_official.html\"\n context_object_name = \"domain\"\n form_class = ContactForm\n\n def get_form_kwargs(self, *args, **kwargs):\n \"\"\"Add domain_info.authorizing_official instance to make a bound form.\"\"\"\n form_kwargs = super().get_form_kwargs(*args, **kwargs)\n form_kwargs[\"instance\"] = self.object.domain_info.authorizing_official\n return form_kwargs\n\n def get_success_url(self):\n \"\"\"Redirect to the overview page for the domain.\"\"\"\n return reverse(\"domain-authorizing-official\", kwargs={\"pk\": self.object.pk})\n\n def form_valid(self, form):\n \"\"\"The form is valid, save the authorizing official.\"\"\"\n form.save()\n\n messages.success(\n self.request, \"The authorizing official for this domain has been updated.\"\n )\n\n # superclass has the redirect\n return super().form_valid(form)\n\n\nclass DomainDNSView(DomainBaseView):\n \"\"\"DNS Information View.\"\"\"\n\n template_name = \"domain_dns.html\"\n\n\nclass DomainNameserversView(DomainFormBaseView):\n \"\"\"Domain nameserver editing view.\"\"\"\n\n template_name = \"domain_nameservers.html\"\n form_class = NameserverFormset\n\n def get_initial(self):\n \"\"\"The initial value for the form (which is a formset here).\"\"\"\n nameservers = self.object.nameservers\n initial_data = []\n\n if nameservers is not None:\n # Add existing nameservers as initial data\n initial_data.extend({\"server\": name} for name, *ip in nameservers)\n\n # Ensure at least 3 fields, filled or empty\n while len(initial_data) < 2:\n initial_data.append({})\n\n return initial_data\n\n def get_success_url(self):\n \"\"\"Redirect to the nameservers page for the domain.\"\"\"\n return reverse(\"domain-dns-nameservers\", kwargs={\"pk\": self.object.pk})\n\n def get_context_data(self, **kwargs):\n \"\"\"Adjust context from FormMixin for formsets.\"\"\"\n context = super().get_context_data(**kwargs)\n # use \"formset\" instead of \"form\" for the key\n context[\"formset\"] = context.pop(\"form\")\n return context\n\n def get_form(self, **kwargs):\n \"\"\"Override the labels and required fields every time we get a formset.\"\"\"\n formset = super().get_form(**kwargs)\n\n for i, form in enumerate(formset):\n form.fields[\"server\"].label += f\" {i+1}\"\n if i < 2:\n form.fields[\"server\"].required = True\n else:\n form.fields[\"server\"].required = False\n return formset\n\n def form_valid(self, formset):\n \"\"\"The formset is valid, perform something with it.\"\"\"\n\n # Set the nameservers from the formset\n nameservers = []\n for form in formset:\n try:\n as_tuple = (form.cleaned_data[\"server\"],)\n nameservers.append(as_tuple)\n except KeyError:\n # no server information in this field, skip it\n pass\n self.object.nameservers = nameservers\n\n messages.success(\n self.request, \"The name servers for this domain have been updated.\"\n )\n\n # superclass has the redirect\n return super().form_valid(formset)\n\n\nclass DomainDNSSECView(DomainFormBaseView):\n \"\"\"Domain DNSSEC editing view.\"\"\"\n\n template_name = \"domain_dnssec.html\"\n form_class = DomainDnssecForm\n\n def get_context_data(self, **kwargs):\n \"\"\"The initial value for the form (which is a formset here).\"\"\"\n context = super().get_context_data(**kwargs)\n\n has_dnssec_records = self.object.dnssecdata is not None\n\n # Create HTML for the modal button\n modal_button = (\n '<button type=\"submit\" '\n 'class=\"usa-button\" '\n 'name=\"disable_dnssec\">Disable DNSSEC</button>'\n )\n\n context[\"modal_button\"] = modal_button\n context[\"has_dnssec_records\"] = has_dnssec_records\n context[\"dnssec_enabled\"] = self.request.session.pop(\"dnssec_enabled\", False)\n\n return context\n\n def get_success_url(self):\n \"\"\"Redirect to the DNSSEC page for the domain.\"\"\"\n return reverse(\"domain-dns-dnssec\", kwargs={\"pk\": self.object.pk})\n\n def post(self, request, *args, **kwargs):\n \"\"\"Form submission posts to this view.\"\"\"\n self._get_domain(request)\n form = self.get_form()\n if form.is_valid():\n if \"disable_dnssec\" in request.POST:\n try:\n self.object.dnssecdata = {}\n except RegistryError as err:\n errmsg = \"Error removing existing DNSSEC record(s).\"\n logger.error(errmsg + \": \" + err)\n messages.error(self.request, errmsg)\n request.session[\"dnssec_ds_confirmed\"] = False\n request.session[\"dnssec_key_confirmed\"] = False\n elif \"enable_dnssec\" in request.POST:\n request.session[\"dnssec_enabled\"] = True\n request.session[\"dnssec_ds_confirmed\"] = False\n request.session[\"dnssec_key_confirmed\"] = False\n\n return self.form_valid(form)\n\n\nclass DomainDsDataView(DomainFormBaseView):\n \"\"\"Domain DNSSEC ds data editing view.\"\"\"\n\n template_name = \"domain_dsdata.html\"\n form_class = DomainDsdataFormset\n form = DomainDsdataForm\n\n def get_initial(self):\n \"\"\"The initial value for the form (which is a formset here).\"\"\"\n dnssecdata: extensions.DNSSECExtension = self.object.dnssecdata\n initial_data = []\n\n if dnssecdata is not None:\n if dnssecdata.keyData is not None:\n # TODO: Throw an error\n # Note: This is moot if we're\n # removing key data\n pass\n\n if dnssecdata.dsData is not None:\n # Add existing nameservers as initial data\n initial_data.extend(\n {\n \"key_tag\": record.keyTag,\n \"algorithm\": record.alg,\n \"digest_type\": record.digestType,\n \"digest\": record.digest,\n }\n for record in dnssecdata.dsData\n )\n\n # Ensure at least 1 record, filled or empty\n while len(initial_data) == 0:\n initial_data.append({})\n\n return initial_data\n\n def get_success_url(self):\n \"\"\"Redirect to the DS Data page for the domain.\"\"\"\n return reverse(\"domain-dns-dnssec-dsdata\", kwargs={\"pk\": self.object.pk})\n\n def get_context_data(self, **kwargs):\n \"\"\"Adjust context from FormMixin for formsets.\"\"\"\n context = super().get_context_data(**kwargs)\n # use \"formset\" instead of \"form\" for the key\n context[\"formset\"] = context.pop(\"form\")\n\n # set the dnssec_ds_confirmed flag in the context for this view\n # based either on the existence of DS Data in the domain,\n # or on the flag stored in the session\n dnssecdata: extensions.DNSSECExtension = self.object.dnssecdata\n\n if dnssecdata is not None and dnssecdata.dsData is not None:\n self.request.session[\"dnssec_ds_confirmed\"] = True\n\n context[\"dnssec_ds_confirmed\"] = self.request.session.get(\n \"dnssec_ds_confirmed\", False\n )\n return context\n\n def post(self, request, *args, **kwargs):\n \"\"\"Formset submission posts to this view.\"\"\"\n self._get_domain(request)\n formset = self.get_form()\n\n if \"confirm-ds\" in request.POST:\n request.session[\"dnssec_ds_confirmed\"] = True\n request.session[\"dnssec_key_confirmed\"] = False\n return super().form_valid(formset)\n\n if \"btn-cancel-click\" in request.POST:\n return redirect(\"/\", {\"formset\": formset}, RequestContext(request))\n\n if formset.is_valid():\n return self.form_valid(formset)\n else:\n return self.form_invalid(formset)\n\n def form_valid(self, formset):\n \"\"\"The formset is valid, perform something with it.\"\"\"\n\n # Set the dnssecdata from the formset\n dnssecdata = extensions.DNSSECExtension()\n\n for form in formset:\n try:\n # if 'delete' not in form.cleaned_data\n # or form.cleaned_data['delete'] == False:\n dsrecord = {\n \"keyTag\": form.cleaned_data[\"key_tag\"],\n \"alg\": int(form.cleaned_data[\"algorithm\"]),\n \"digestType\": int(form.cleaned_data[\"digest_type\"]),\n \"digest\": form.cleaned_data[\"digest\"],\n }\n if dnssecdata.dsData is None:\n dnssecdata.dsData = []\n dnssecdata.dsData.append(common.DSData(**dsrecord))\n except KeyError:\n # no cleaned_data provided for this form, but passed\n # as valid; this can happen if form has been added but\n # not been interacted with; in that case, want to ignore\n pass\n try:\n self.object.dnssecdata = dnssecdata\n except RegistryError as err:\n errmsg = \"Error updating DNSSEC data in the registry.\"\n logger.error(errmsg)\n logger.error(err)\n messages.error(self.request, errmsg)\n return self.form_invalid(formset)\n else:\n messages.success(\n self.request, \"The DS Data records for this domain have been updated.\"\n )\n # superclass has the redirect\n return super().form_valid(formset)\n\n\nclass DomainKeyDataView(DomainFormBaseView):\n \"\"\"Domain DNSSEC key data editing view.\"\"\"\n\n template_name = \"domain_keydata.html\"\n form_class = DomainKeydataFormset\n form = DomainKeydataForm\n\n def get_initial(self):\n \"\"\"The initial value for the form (which is a formset here).\"\"\"\n dnssecdata: extensions.DNSSECExtension = self.object.dnssecdata\n initial_data = []\n\n if dnssecdata is not None:\n if dnssecdata.dsData is not None:\n # TODO: Throw an error?\n # Note: this is moot if we're\n # removing Key data\n pass\n\n if dnssecdata.keyData is not None:\n # Add existing keydata as initial data\n initial_data.extend(\n {\n \"flag\": record.flags,\n \"protocol\": record.protocol,\n \"algorithm\": record.alg,\n \"pub_key\": record.pubKey,\n }\n for record in dnssecdata.keyData\n )\n\n # Ensure at least 1 record, filled or empty\n while len(initial_data) == 0:\n initial_data.append({})\n\n return initial_data\n\n def get_success_url(self):\n \"\"\"Redirect to the Key Data page for the domain.\"\"\"\n return reverse(\"domain-dns-dnssec-keydata\", kwargs={\"pk\": self.object.pk})\n\n def get_context_data(self, **kwargs):\n \"\"\"Adjust context from FormMixin for formsets.\"\"\"\n context = super().get_context_data(**kwargs)\n # use \"formset\" instead of \"form\" for the key\n context[\"formset\"] = context.pop(\"form\")\n\n # set the dnssec_key_confirmed flag in the context for this view\n # based either on the existence of Key Data in the domain,\n # or on the flag stored in the session\n dnssecdata: extensions.DNSSECExtension = self.object.dnssecdata\n\n if dnssecdata is not None and dnssecdata.keyData is not None:\n self.request.session[\"dnssec_key_confirmed\"] = True\n\n context[\"dnssec_key_confirmed\"] = self.request.session.get(\n \"dnssec_key_confirmed\", False\n )\n return context\n\n def post(self, request, *args, **kwargs):\n \"\"\"Formset submission posts to this view.\"\"\"\n self._get_domain(request)\n formset = self.get_form()\n\n if \"confirm-key\" in request.POST:\n request.session[\"dnssec_key_confirmed\"] = True\n request.session[\"dnssec_ds_confirmed\"] = False\n self.object.save()\n return super().form_valid(formset)\n\n if \"btn-cancel-click\" in request.POST:\n return redirect(\"/\", {\"formset\": formset}, RequestContext(request))\n\n if formset.is_valid():\n return self.form_valid(formset)\n else:\n return self.form_invalid(formset)\n\n def form_valid(self, formset):\n \"\"\"The formset is valid, perform something with it.\"\"\"\n\n # Set the nameservers from the formset\n dnssecdata = extensions.DNSSECExtension()\n\n for form in formset:\n try:\n # if 'delete' not in form.cleaned_data\n # or form.cleaned_data['delete'] == False:\n keyrecord = {\n \"flags\": int(form.cleaned_data[\"flag\"]),\n \"protocol\": int(form.cleaned_data[\"protocol\"]),\n \"alg\": int(form.cleaned_data[\"algorithm\"]),\n \"pubKey\": form.cleaned_data[\"pub_key\"],\n }\n if dnssecdata.keyData is None:\n dnssecdata.keyData = []\n dnssecdata.keyData.append(common.DNSSECKeyData(**keyrecord))\n except KeyError:\n # no server information in this field, skip it\n pass\n try:\n self.object.dnssecdata = dnssecdata\n except RegistryError as err:\n errmsg = \"Error updating DNSSEC data in the registry.\"\n logger.error(errmsg)\n logger.error(err)\n messages.error(self.request, errmsg)\n return self.form_invalid(formset)\n else:\n messages.success(\n self.request, \"The Key Data records for this domain have been updated.\"\n )\n # superclass has the redirect\n return super().form_valid(formset)\n\n\nclass DomainYourContactInformationView(DomainFormBaseView):\n \"\"\"Domain your contact information editing view.\"\"\"\n\n template_name = \"domain_your_contact_information.html\"\n form_class = ContactForm\n\n def get_form_kwargs(self, *args, **kwargs):\n \"\"\"Add domain_info.submitter instance to make a bound form.\"\"\"\n form_kwargs = super().get_form_kwargs(*args, **kwargs)\n form_kwargs[\"instance\"] = self.request.user.contact\n return form_kwargs\n\n def get_success_url(self):\n \"\"\"Redirect to the your contact information for the domain.\"\"\"\n return reverse(\"domain-your-contact-information\", kwargs={\"pk\": self.object.pk})\n\n def form_valid(self, form):\n \"\"\"The form is valid, call setter in model.\"\"\"\n\n # Post to DB using values from the form\n form.save()\n\n messages.success(\n self.request, \"Your contact information for this domain has been updated.\"\n )\n\n # superclass has the redirect\n return super().form_valid(form)\n\n\nclass DomainSecurityEmailView(DomainFormBaseView):\n \"\"\"Domain security email editing view.\"\"\"\n\n template_name = \"domain_security_email.html\"\n form_class = DomainSecurityEmailForm\n\n def get_initial(self):\n \"\"\"The initial value for the form.\"\"\"\n initial = super().get_initial()\n security_contact = self.object.security_contact\n if security_contact is None or security_contact.email == \"[email protected]\":\n initial[\"security_email\"] = None\n return initial\n initial[\"security_email\"] = security_contact.email\n return initial\n\n def get_success_url(self):\n \"\"\"Redirect to the security email page for the domain.\"\"\"\n return reverse(\"domain-security-email\", kwargs={\"pk\": self.object.pk})\n\n def form_valid(self, form):\n \"\"\"The form is valid, call setter in model.\"\"\"\n\n # Set the security email from the form\n new_email: str = form.cleaned_data.get(\"security_email\", \"\")\n\n # If we pass nothing for the sec email, set to the default\n if new_email is None or new_email.strip() == \"\":\n new_email = PublicContact.get_default_security().email\n\n contact = self.object.security_contact\n\n # If no default is created for security_contact,\n # then we cannot connect to the registry.\n if contact is None:\n messages.error(self.request, CANNOT_CONTACT_REGISTRY)\n return redirect(self.get_success_url())\n\n contact.email = new_email\n\n try:\n contact.save()\n except RegistryError as Err:\n if Err.is_connection_error():\n messages.error(self.request, CANNOT_CONTACT_REGISTRY)\n logger.error(f\"Registry connection error: {Err}\")\n else:\n messages.error(self.request, GENERIC_ERROR)\n logger.error(f\"Registry error: {Err}\")\n except ContactError as Err:\n messages.error(self.request, GENERIC_ERROR)\n logger.error(f\"Generic registry error: {Err}\")\n else:\n messages.success(\n self.request, \"The security email for this domain has been updated.\"\n )\n\n # superclass has the redirect\n return redirect(self.get_success_url())\n\n\nclass DomainUsersView(DomainBaseView):\n \"\"\"User management page in the domain details.\"\"\"\n\n template_name = \"domain_users.html\"\n\n\nclass DomainAddUserView(DomainFormBaseView):\n \"\"\"Inside of a domain's user management, a form for adding users.\n\n Multiple inheritance is used here for permissions, form handling, and\n details of the individual domain.\n \"\"\"\n\n template_name = \"domain_add_user.html\"\n form_class = DomainAddUserForm\n\n def get_success_url(self):\n return reverse(\"domain-users\", kwargs={\"pk\": self.object.pk})\n\n def _domain_abs_url(self):\n \"\"\"Get an absolute URL for this domain.\"\"\"\n return self.request.build_absolute_uri(\n reverse(\"domain\", kwargs={\"pk\": self.object.id})\n )\n\n def _make_invitation(self, email_address):\n \"\"\"Make a Domain invitation for this email and redirect with a message.\"\"\"\n invitation, created = DomainInvitation.objects.get_or_create(\n email=email_address, domain=self.object\n )\n if not created:\n # that invitation already existed\n messages.warning(\n self.request,\n f\"{email_address} has already been invited to this domain.\",\n )\n else:\n # created a new invitation in the database, so send an email\n domaininfo = DomainInformation.objects.filter(domain=self.object)\n first = domaininfo.first().creator.first_name\n last = domaininfo.first().creator.last_name\n full_name = f\"{first} {last}\"\n\n try:\n send_templated_email(\n \"emails/domain_invitation.txt\",\n \"emails/domain_invitation_subject.txt\",\n to_address=email_address,\n context={\n \"domain_url\": self._domain_abs_url(),\n \"domain\": self.object,\n \"full_name\": full_name,\n },\n )\n except EmailSendingError:\n messages.warning(self.request, \"Could not send email invitation.\")\n logger.warn(\n \"Could not sent email invitation to %s for domain %s\",\n email_address,\n self.object,\n exc_info=True,\n )\n else:\n messages.success(\n self.request, f\"Invited {email_address} to this domain.\"\n )\n\n return redirect(self.get_success_url())\n\n def form_valid(self, form):\n \"\"\"Add the specified user on this domain.\"\"\"\n requested_email = form.cleaned_data[\"email\"]\n # look up a user with that email\n try:\n requested_user = User.objects.get(email=requested_email)\n except User.DoesNotExist:\n # no matching user, go make an invitation\n return self._make_invitation(requested_email)\n\n try:\n UserDomainRole.objects.create(\n user=requested_user,\n domain=self.object,\n role=UserDomainRole.Roles.MANAGER,\n )\n except IntegrityError:\n # User already has the desired role! Do nothing??\n pass\n\n messages.success(self.request, f\"Added user {requested_email}.\")\n\n return redirect(self.get_success_url())\n\n\nclass DomainInvitationDeleteView(\n DomainInvitationPermissionDeleteView, SuccessMessageMixin\n):\n object: DomainInvitation # workaround for type mismatch in DeleteView\n\n def get_success_url(self):\n return reverse(\"domain-users\", kwargs={\"pk\": self.object.domain.id})\n\n def get_success_message(self, cleaned_data):\n return f\"Successfully canceled invitation for {self.object.email}.\"\n", "path": "src/registrar/views/domain.py" } ]
[ { "content": "\"\"\"Views for a single Domain.\n\nAuthorization is handled by the `DomainPermissionView`. To ensure that only\nauthorized users can see information on a domain, every view here should\ninherit from `DomainPermissionView` (or DomainInvitationPermissionDeleteView).\n\"\"\"\n\nimport logging\n\nfrom django.contrib import messages\nfrom django.contrib.messages.views import SuccessMessageMixin\nfrom django.db import IntegrityError\nfrom django.shortcuts import redirect\nfrom django.template import RequestContext\nfrom django.urls import reverse\nfrom django.views.generic.edit import FormMixin\n\nfrom registrar.models import (\n Domain,\n DomainInformation,\n DomainInvitation,\n User,\n UserDomainRole,\n)\nfrom registrar.models.public_contact import PublicContact\nfrom registrar.models.utility.contact_error import ContactError\n\nfrom ..forms import (\n ContactForm,\n DomainOrgNameAddressForm,\n DomainAddUserForm,\n DomainSecurityEmailForm,\n NameserverFormset,\n DomainDnssecForm,\n DomainDsdataFormset,\n DomainDsdataForm,\n DomainKeydataFormset,\n DomainKeydataForm,\n)\n\nfrom epplibwrapper import (\n common,\n extensions,\n RegistryError,\n CANNOT_CONTACT_REGISTRY,\n GENERIC_ERROR,\n)\n\nfrom ..utility.email import send_templated_email, EmailSendingError\nfrom .utility import DomainPermissionView, DomainInvitationPermissionDeleteView\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass DomainBaseView(DomainPermissionView):\n \"\"\"\n Base View for the Domain. Handles getting and setting the domain\n in session cache on GETs. Also provides methods for getting\n and setting the domain in cache\n \"\"\"\n\n def get(self, request, *args, **kwargs):\n self._get_domain(request)\n context = self.get_context_data(object=self.object)\n return self.render_to_response(context)\n\n def _get_domain(self, request):\n \"\"\"\n get domain from session cache or from db and set\n to self.object\n set session to self for downstream functions to\n update session cache\n \"\"\"\n self.session = request.session\n # domain:private_key is the session key to use for\n # caching the domain in the session\n domain_pk = \"domain:\" + str(self.kwargs.get(\"pk\"))\n cached_domain = self.session.get(domain_pk)\n\n if cached_domain:\n self.object = cached_domain\n else:\n self.object = self.get_object()\n self._update_session_with_domain()\n\n def _update_session_with_domain(self):\n \"\"\"\n update domain in the session cache\n \"\"\"\n domain_pk = \"domain:\" + str(self.kwargs.get(\"pk\"))\n self.session[domain_pk] = self.object\n\n\nclass DomainFormBaseView(DomainBaseView, FormMixin):\n \"\"\"\n Form Base View for the Domain. Handles getting and setting\n domain in cache when dealing with domain forms. Provides\n implementations of post, form_valid and form_invalid.\n \"\"\"\n\n def post(self, request, *args, **kwargs):\n \"\"\"Form submission posts to this view.\n\n This post method harmonizes using DomainBaseView and FormMixin\n \"\"\"\n self._get_domain(request)\n form = self.get_form()\n if form.is_valid():\n return self.form_valid(form)\n else:\n return self.form_invalid(form)\n\n def form_valid(self, form):\n # updates session cache with domain\n self._update_session_with_domain()\n\n # superclass has the redirect\n return super().form_valid(form)\n\n def form_invalid(self, form):\n # updates session cache with domain\n self._update_session_with_domain()\n\n # superclass has the redirect\n return super().form_invalid(form)\n\n\nclass DomainView(DomainBaseView):\n\n \"\"\"Domain detail overview page.\"\"\"\n\n template_name = \"domain_detail.html\"\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n default_email = self.object.get_default_security_contact().email\n context[\"default_security_email\"] = default_email\n\n security_email = self.object.get_security_email()\n if security_email is None or security_email == default_email:\n context[\"security_email\"] = None\n return context\n context[\"security_email\"] = security_email\n return context\n\n\nclass DomainOrgNameAddressView(DomainFormBaseView):\n \"\"\"Organization name and mailing address view\"\"\"\n\n model = Domain\n template_name = \"domain_org_name_address.html\"\n context_object_name = \"domain\"\n form_class = DomainOrgNameAddressForm\n\n def get_form_kwargs(self, *args, **kwargs):\n \"\"\"Add domain_info.organization_name instance to make a bound form.\"\"\"\n form_kwargs = super().get_form_kwargs(*args, **kwargs)\n form_kwargs[\"instance\"] = self.object.domain_info\n return form_kwargs\n\n def get_success_url(self):\n \"\"\"Redirect to the overview page for the domain.\"\"\"\n return reverse(\"domain-org-name-address\", kwargs={\"pk\": self.object.pk})\n\n def form_valid(self, form):\n \"\"\"The form is valid, save the organization name and mailing address.\"\"\"\n form.save()\n\n messages.success(\n self.request, \"The organization name and mailing address has been updated.\"\n )\n\n # superclass has the redirect\n return super().form_valid(form)\n\n\nclass DomainAuthorizingOfficialView(DomainFormBaseView):\n \"\"\"Domain authorizing official editing view.\"\"\"\n\n model = Domain\n template_name = \"domain_authorizing_official.html\"\n context_object_name = \"domain\"\n form_class = ContactForm\n\n def get_form_kwargs(self, *args, **kwargs):\n \"\"\"Add domain_info.authorizing_official instance to make a bound form.\"\"\"\n form_kwargs = super().get_form_kwargs(*args, **kwargs)\n form_kwargs[\"instance\"] = self.object.domain_info.authorizing_official\n return form_kwargs\n\n def get_success_url(self):\n \"\"\"Redirect to the overview page for the domain.\"\"\"\n return reverse(\"domain-authorizing-official\", kwargs={\"pk\": self.object.pk})\n\n def form_valid(self, form):\n \"\"\"The form is valid, save the authorizing official.\"\"\"\n form.save()\n\n messages.success(\n self.request, \"The authorizing official for this domain has been updated.\"\n )\n\n # superclass has the redirect\n return super().form_valid(form)\n\n\nclass DomainDNSView(DomainBaseView):\n \"\"\"DNS Information View.\"\"\"\n\n template_name = \"domain_dns.html\"\n\n\nclass DomainNameserversView(DomainFormBaseView):\n \"\"\"Domain nameserver editing view.\"\"\"\n\n template_name = \"domain_nameservers.html\"\n form_class = NameserverFormset\n\n def get_initial(self):\n \"\"\"The initial value for the form (which is a formset here).\"\"\"\n nameservers = self.object.nameservers\n initial_data = []\n\n if nameservers is not None:\n # Add existing nameservers as initial data\n initial_data.extend({\"server\": name} for name, *ip in nameservers)\n\n # Ensure at least 3 fields, filled or empty\n while len(initial_data) < 2:\n initial_data.append({})\n\n return initial_data\n\n def get_success_url(self):\n \"\"\"Redirect to the nameservers page for the domain.\"\"\"\n return reverse(\"domain-dns-nameservers\", kwargs={\"pk\": self.object.pk})\n\n def get_context_data(self, **kwargs):\n \"\"\"Adjust context from FormMixin for formsets.\"\"\"\n context = super().get_context_data(**kwargs)\n # use \"formset\" instead of \"form\" for the key\n context[\"formset\"] = context.pop(\"form\")\n return context\n\n def get_form(self, **kwargs):\n \"\"\"Override the labels and required fields every time we get a formset.\"\"\"\n formset = super().get_form(**kwargs)\n\n for i, form in enumerate(formset):\n form.fields[\"server\"].label += f\" {i+1}\"\n if i < 2:\n form.fields[\"server\"].required = True\n else:\n form.fields[\"server\"].required = False\n return formset\n\n def form_valid(self, formset):\n \"\"\"The formset is valid, perform something with it.\"\"\"\n\n # Set the nameservers from the formset\n nameservers = []\n for form in formset:\n try:\n as_tuple = (form.cleaned_data[\"server\"],)\n nameservers.append(as_tuple)\n except KeyError:\n # no server information in this field, skip it\n pass\n self.object.nameservers = nameservers\n\n messages.success(\n self.request, \"The name servers for this domain have been updated.\"\n )\n\n # superclass has the redirect\n return super().form_valid(formset)\n\n\nclass DomainDNSSECView(DomainFormBaseView):\n \"\"\"Domain DNSSEC editing view.\"\"\"\n\n template_name = \"domain_dnssec.html\"\n form_class = DomainDnssecForm\n\n def get_context_data(self, **kwargs):\n \"\"\"The initial value for the form (which is a formset here).\"\"\"\n context = super().get_context_data(**kwargs)\n\n has_dnssec_records = self.object.dnssecdata is not None\n\n # Create HTML for the modal button\n modal_button = (\n '<button type=\"submit\" '\n 'class=\"usa-button\" '\n 'name=\"disable_dnssec\">Disable DNSSEC</button>'\n )\n\n context[\"modal_button\"] = modal_button\n context[\"has_dnssec_records\"] = has_dnssec_records\n context[\"dnssec_enabled\"] = self.request.session.pop(\"dnssec_enabled\", False)\n\n return context\n\n def get_success_url(self):\n \"\"\"Redirect to the DNSSEC page for the domain.\"\"\"\n return reverse(\"domain-dns-dnssec\", kwargs={\"pk\": self.object.pk})\n\n def post(self, request, *args, **kwargs):\n \"\"\"Form submission posts to this view.\"\"\"\n self._get_domain(request)\n form = self.get_form()\n if form.is_valid():\n if \"disable_dnssec\" in request.POST:\n try:\n self.object.dnssecdata = {}\n except RegistryError as err:\n errmsg = \"Error removing existing DNSSEC record(s).\"\n logger.error(errmsg + \": \" + err)\n messages.error(self.request, errmsg)\n request.session[\"dnssec_ds_confirmed\"] = False\n request.session[\"dnssec_key_confirmed\"] = False\n elif \"enable_dnssec\" in request.POST:\n request.session[\"dnssec_enabled\"] = True\n request.session[\"dnssec_ds_confirmed\"] = False\n request.session[\"dnssec_key_confirmed\"] = False\n\n return self.form_valid(form)\n\n\nclass DomainDsDataView(DomainFormBaseView):\n \"\"\"Domain DNSSEC ds data editing view.\"\"\"\n\n template_name = \"domain_dsdata.html\"\n form_class = DomainDsdataFormset\n form = DomainDsdataForm\n\n def get_initial(self):\n \"\"\"The initial value for the form (which is a formset here).\"\"\"\n dnssecdata: extensions.DNSSECExtension = self.object.dnssecdata\n initial_data = []\n\n if dnssecdata is not None:\n if dnssecdata.keyData is not None:\n # TODO: Throw an error\n # Note: This is moot if we're\n # removing key data\n pass\n\n if dnssecdata.dsData is not None:\n # Add existing nameservers as initial data\n initial_data.extend(\n {\n \"key_tag\": record.keyTag,\n \"algorithm\": record.alg,\n \"digest_type\": record.digestType,\n \"digest\": record.digest,\n }\n for record in dnssecdata.dsData\n )\n\n # Ensure at least 1 record, filled or empty\n while len(initial_data) == 0:\n initial_data.append({})\n\n return initial_data\n\n def get_success_url(self):\n \"\"\"Redirect to the DS Data page for the domain.\"\"\"\n return reverse(\"domain-dns-dnssec-dsdata\", kwargs={\"pk\": self.object.pk})\n\n def get_context_data(self, **kwargs):\n \"\"\"Adjust context from FormMixin for formsets.\"\"\"\n context = super().get_context_data(**kwargs)\n # use \"formset\" instead of \"form\" for the key\n context[\"formset\"] = context.pop(\"form\")\n\n # set the dnssec_ds_confirmed flag in the context for this view\n # based either on the existence of DS Data in the domain,\n # or on the flag stored in the session\n dnssecdata: extensions.DNSSECExtension = self.object.dnssecdata\n\n if dnssecdata is not None and dnssecdata.dsData is not None:\n self.request.session[\"dnssec_ds_confirmed\"] = True\n\n context[\"dnssec_ds_confirmed\"] = self.request.session.get(\n \"dnssec_ds_confirmed\", False\n )\n return context\n\n def post(self, request, *args, **kwargs):\n \"\"\"Formset submission posts to this view.\"\"\"\n self._get_domain(request)\n formset = self.get_form()\n\n if \"confirm-ds\" in request.POST:\n request.session[\"dnssec_ds_confirmed\"] = True\n request.session[\"dnssec_key_confirmed\"] = False\n return super().form_valid(formset)\n\n if \"btn-cancel-click\" in request.POST:\n return redirect(\"/\", {\"formset\": formset}, RequestContext(request))\n\n if formset.is_valid():\n return self.form_valid(formset)\n else:\n return self.form_invalid(formset)\n\n def form_valid(self, formset):\n \"\"\"The formset is valid, perform something with it.\"\"\"\n\n # Set the dnssecdata from the formset\n dnssecdata = extensions.DNSSECExtension()\n\n for form in formset:\n try:\n # if 'delete' not in form.cleaned_data\n # or form.cleaned_data['delete'] == False:\n dsrecord = {\n \"keyTag\": form.cleaned_data[\"key_tag\"],\n \"alg\": int(form.cleaned_data[\"algorithm\"]),\n \"digestType\": int(form.cleaned_data[\"digest_type\"]),\n \"digest\": form.cleaned_data[\"digest\"],\n }\n if dnssecdata.dsData is None:\n dnssecdata.dsData = []\n dnssecdata.dsData.append(common.DSData(**dsrecord))\n except KeyError:\n # no cleaned_data provided for this form, but passed\n # as valid; this can happen if form has been added but\n # not been interacted with; in that case, want to ignore\n pass\n try:\n self.object.dnssecdata = dnssecdata\n except RegistryError as err:\n errmsg = \"Error updating DNSSEC data in the registry.\"\n logger.error(errmsg)\n logger.error(err)\n messages.error(self.request, errmsg)\n return self.form_invalid(formset)\n else:\n messages.success(\n self.request, \"The DS Data records for this domain have been updated.\"\n )\n # superclass has the redirect\n return super().form_valid(formset)\n\n\nclass DomainKeyDataView(DomainFormBaseView):\n \"\"\"Domain DNSSEC key data editing view.\"\"\"\n\n template_name = \"domain_keydata.html\"\n form_class = DomainKeydataFormset\n form = DomainKeydataForm\n\n def get_initial(self):\n \"\"\"The initial value for the form (which is a formset here).\"\"\"\n dnssecdata: extensions.DNSSECExtension = self.object.dnssecdata\n initial_data = []\n\n if dnssecdata is not None:\n if dnssecdata.dsData is not None:\n # TODO: Throw an error?\n # Note: this is moot if we're\n # removing Key data\n pass\n\n if dnssecdata.keyData is not None:\n # Add existing keydata as initial data\n initial_data.extend(\n {\n \"flag\": record.flags,\n \"protocol\": record.protocol,\n \"algorithm\": record.alg,\n \"pub_key\": record.pubKey,\n }\n for record in dnssecdata.keyData\n )\n\n # Ensure at least 1 record, filled or empty\n while len(initial_data) == 0:\n initial_data.append({})\n\n return initial_data\n\n def get_success_url(self):\n \"\"\"Redirect to the Key Data page for the domain.\"\"\"\n return reverse(\"domain-dns-dnssec-keydata\", kwargs={\"pk\": self.object.pk})\n\n def get_context_data(self, **kwargs):\n \"\"\"Adjust context from FormMixin for formsets.\"\"\"\n context = super().get_context_data(**kwargs)\n # use \"formset\" instead of \"form\" for the key\n context[\"formset\"] = context.pop(\"form\")\n\n # set the dnssec_key_confirmed flag in the context for this view\n # based either on the existence of Key Data in the domain,\n # or on the flag stored in the session\n dnssecdata: extensions.DNSSECExtension = self.object.dnssecdata\n\n if dnssecdata is not None and dnssecdata.keyData is not None:\n self.request.session[\"dnssec_key_confirmed\"] = True\n\n context[\"dnssec_key_confirmed\"] = self.request.session.get(\n \"dnssec_key_confirmed\", False\n )\n return context\n\n def post(self, request, *args, **kwargs):\n \"\"\"Formset submission posts to this view.\"\"\"\n self._get_domain(request)\n formset = self.get_form()\n\n if \"confirm-key\" in request.POST:\n request.session[\"dnssec_key_confirmed\"] = True\n request.session[\"dnssec_ds_confirmed\"] = False\n self.object.save()\n return super().form_valid(formset)\n\n if \"btn-cancel-click\" in request.POST:\n return redirect(\"/\", {\"formset\": formset}, RequestContext(request))\n\n if formset.is_valid():\n return self.form_valid(formset)\n else:\n return self.form_invalid(formset)\n\n def form_valid(self, formset):\n \"\"\"The formset is valid, perform something with it.\"\"\"\n\n # Set the nameservers from the formset\n dnssecdata = extensions.DNSSECExtension()\n\n for form in formset:\n try:\n # if 'delete' not in form.cleaned_data\n # or form.cleaned_data['delete'] == False:\n keyrecord = {\n \"flags\": int(form.cleaned_data[\"flag\"]),\n \"protocol\": int(form.cleaned_data[\"protocol\"]),\n \"alg\": int(form.cleaned_data[\"algorithm\"]),\n \"pubKey\": form.cleaned_data[\"pub_key\"],\n }\n if dnssecdata.keyData is None:\n dnssecdata.keyData = []\n dnssecdata.keyData.append(common.DNSSECKeyData(**keyrecord))\n except KeyError:\n # no server information in this field, skip it\n pass\n try:\n self.object.dnssecdata = dnssecdata\n except RegistryError as err:\n errmsg = \"Error updating DNSSEC data in the registry.\"\n logger.error(errmsg)\n logger.error(err)\n messages.error(self.request, errmsg)\n return self.form_invalid(formset)\n else:\n messages.success(\n self.request, \"The Key Data records for this domain have been updated.\"\n )\n # superclass has the redirect\n return super().form_valid(formset)\n\n\nclass DomainYourContactInformationView(DomainFormBaseView):\n \"\"\"Domain your contact information editing view.\"\"\"\n\n template_name = \"domain_your_contact_information.html\"\n form_class = ContactForm\n\n def get_form_kwargs(self, *args, **kwargs):\n \"\"\"Add domain_info.submitter instance to make a bound form.\"\"\"\n form_kwargs = super().get_form_kwargs(*args, **kwargs)\n form_kwargs[\"instance\"] = self.request.user.contact\n return form_kwargs\n\n def get_success_url(self):\n \"\"\"Redirect to the your contact information for the domain.\"\"\"\n return reverse(\"domain-your-contact-information\", kwargs={\"pk\": self.object.pk})\n\n def form_valid(self, form):\n \"\"\"The form is valid, call setter in model.\"\"\"\n\n # Post to DB using values from the form\n form.save()\n\n messages.success(\n self.request, \"Your contact information for this domain has been updated.\"\n )\n\n # superclass has the redirect\n return super().form_valid(form)\n\n\nclass DomainSecurityEmailView(DomainFormBaseView):\n \"\"\"Domain security email editing view.\"\"\"\n\n template_name = \"domain_security_email.html\"\n form_class = DomainSecurityEmailForm\n\n def get_initial(self):\n \"\"\"The initial value for the form.\"\"\"\n initial = super().get_initial()\n security_contact = self.object.security_contact\n if security_contact is None or security_contact.email == \"[email protected]\":\n initial[\"security_email\"] = None\n return initial\n initial[\"security_email\"] = security_contact.email\n return initial\n\n def get_success_url(self):\n \"\"\"Redirect to the security email page for the domain.\"\"\"\n return reverse(\"domain-security-email\", kwargs={\"pk\": self.object.pk})\n\n def form_valid(self, form):\n \"\"\"The form is valid, call setter in model.\"\"\"\n\n # Set the security email from the form\n new_email: str = form.cleaned_data.get(\"security_email\", \"\")\n\n # If we pass nothing for the sec email, set to the default\n if new_email is None or new_email.strip() == \"\":\n new_email = PublicContact.get_default_security().email\n\n contact = self.object.security_contact\n\n # If no default is created for security_contact,\n # then we cannot connect to the registry.\n if contact is None:\n messages.error(self.request, CANNOT_CONTACT_REGISTRY)\n return redirect(self.get_success_url())\n\n contact.email = new_email\n\n try:\n contact.save()\n except RegistryError as Err:\n if Err.is_connection_error():\n messages.error(self.request, CANNOT_CONTACT_REGISTRY)\n logger.error(f\"Registry connection error: {Err}\")\n else:\n messages.error(self.request, GENERIC_ERROR)\n logger.error(f\"Registry error: {Err}\")\n except ContactError as Err:\n messages.error(self.request, GENERIC_ERROR)\n logger.error(f\"Generic registry error: {Err}\")\n else:\n messages.success(\n self.request, \"The security email for this domain has been updated.\"\n )\n\n # superclass has the redirect\n return redirect(self.get_success_url())\n\n\nclass DomainUsersView(DomainBaseView):\n \"\"\"Domain managers page in the domain details.\"\"\"\n\n template_name = \"domain_users.html\"\n\n\nclass DomainAddUserView(DomainFormBaseView):\n \"\"\"Inside of a domain's user management, a form for adding users.\n\n Multiple inheritance is used here for permissions, form handling, and\n details of the individual domain.\n \"\"\"\n\n template_name = \"domain_add_user.html\"\n form_class = DomainAddUserForm\n\n def get_success_url(self):\n return reverse(\"domain-users\", kwargs={\"pk\": self.object.pk})\n\n def _domain_abs_url(self):\n \"\"\"Get an absolute URL for this domain.\"\"\"\n return self.request.build_absolute_uri(\n reverse(\"domain\", kwargs={\"pk\": self.object.id})\n )\n\n def _make_invitation(self, email_address):\n \"\"\"Make a Domain invitation for this email and redirect with a message.\"\"\"\n invitation, created = DomainInvitation.objects.get_or_create(\n email=email_address, domain=self.object\n )\n if not created:\n # that invitation already existed\n messages.warning(\n self.request,\n f\"{email_address} has already been invited to this domain.\",\n )\n else:\n # created a new invitation in the database, so send an email\n domaininfo = DomainInformation.objects.filter(domain=self.object)\n first = domaininfo.first().creator.first_name\n last = domaininfo.first().creator.last_name\n full_name = f\"{first} {last}\"\n\n try:\n send_templated_email(\n \"emails/domain_invitation.txt\",\n \"emails/domain_invitation_subject.txt\",\n to_address=email_address,\n context={\n \"domain_url\": self._domain_abs_url(),\n \"domain\": self.object,\n \"full_name\": full_name,\n },\n )\n except EmailSendingError:\n messages.warning(self.request, \"Could not send email invitation.\")\n logger.warn(\n \"Could not sent email invitation to %s for domain %s\",\n email_address,\n self.object,\n exc_info=True,\n )\n else:\n messages.success(\n self.request, f\"Invited {email_address} to this domain.\"\n )\n\n return redirect(self.get_success_url())\n\n def form_valid(self, form):\n \"\"\"Add the specified user on this domain.\"\"\"\n requested_email = form.cleaned_data[\"email\"]\n # look up a user with that email\n try:\n requested_user = User.objects.get(email=requested_email)\n except User.DoesNotExist:\n # no matching user, go make an invitation\n return self._make_invitation(requested_email)\n\n try:\n UserDomainRole.objects.create(\n user=requested_user, domain=self.object, role=UserDomainRole.Roles.ADMIN\n )\n except IntegrityError:\n # User already has the desired role! Do nothing??\n pass\n\n messages.success(self.request, f\"Added user {requested_email}.\")\n\n return redirect(self.get_success_url())\n\n\nclass DomainInvitationDeleteView(\n DomainInvitationPermissionDeleteView, SuccessMessageMixin\n):\n object: DomainInvitation # workaround for type mismatch in DeleteView\n\n def get_success_url(self):\n return reverse(\"domain-users\", kwargs={\"pk\": self.object.domain.id})\n\n def get_success_message(self, cleaned_data):\n return f\"Successfully canceled invitation for {self.object.email}.\"\n", "path": "src/registrar/views/domain.py" } ]
diff --git a/src/registrar/templates/domain_detail.html b/src/registrar/templates/domain_detail.html index e0d672093..4ddbd673a 100644 --- a/src/registrar/templates/domain_detail.html +++ b/src/registrar/templates/domain_detail.html @@ -52,7 +52,7 @@ <h2 class="margin-top-neg-1"> DNS name servers </h2> {% include "includes/summary_item.html" with title='Security email' value='None provided' edit_link=url %} {% endif %} {% url 'domain-users' pk=domain.id as url %} - {% include "includes/summary_item.html" with title='User management' users='true' list=True value=domain.permissions.all edit_link=url %} + {% include "includes/summary_item.html" with title='Domain managers' users='true' list=True value=domain.permissions.all edit_link=url %} </div> {% endblock %} {# domain_content #} diff --git a/src/registrar/templates/domain_sidebar.html b/src/registrar/templates/domain_sidebar.html index 1acd87eeb..ac45ad04c 100644 --- a/src/registrar/templates/domain_sidebar.html +++ b/src/registrar/templates/domain_sidebar.html @@ -100,7 +100,7 @@ <a href="{{ url }}" {% if request.path|startswith:url %}class="usa-current"{% endif %} > - User management + Domain managers </a> </li> </ul> diff --git a/src/registrar/templates/domain_users.html b/src/registrar/templates/domain_users.html index 22b9d18d1..f66eef5a6 100644 --- a/src/registrar/templates/domain_users.html +++ b/src/registrar/templates/domain_users.html @@ -1,10 +1,23 @@ {% extends "domain_base.html" %} -{% load static %} +{% load static url_helpers %} -{% block title %}User management | {{ domain.name }} | {% endblock %} +{% block title %}Domain managers | {{ domain.name }} | {% endblock %} {% block domain_content %} - <h1>User management</h1> + <h1>Domain managers</h1> + + <p> + Domain managers can update all information related to a domain within the + .gov registrar, including contact details, authorizing official, security + email, and DNS name servers. + </p> + + <ul> + <li>There is no limit to the number of domain managers you can add.</li> + <li>After adding a domain manager, an email invitation will be sent to that user with + instructions on how to set up an account.</li> + <li>To remove a domain manager, <a href="{% public_site_url 'contact/' %}" class="usa-link">contact us</a> for assistance. + </ul> {% if domain.permissions %} <section class="section--outlined"> diff --git a/src/registrar/tests/test_views.py b/src/registrar/tests/test_views.py index 0e8f895af..1262347a1 100644 --- a/src/registrar/tests/test_views.py +++ b/src/registrar/tests/test_views.py @@ -1199,14 +1199,14 @@ def test_domain_overview_blocked_for_ineligible_user(self): self.assertEqual(response.status_code, 403) -class TestDomainUserManagement(TestDomainOverview): - def test_domain_user_management(self): +class TestDomainManagers(TestDomainOverview): + def test_domain_managers(self): response = self.client.get( reverse("domain-users", kwargs={"pk": self.domain.id}) ) - self.assertContains(response, "User management") + self.assertContains(response, "Domain managers") - def test_domain_user_management_add_link(self): + def test_domain_managers_add_link(self): """Button to get to user add page works.""" management_page = self.app.get( reverse("domain-users", kwargs={"pk": self.domain.id}) diff --git a/src/registrar/views/domain.py b/src/registrar/views/domain.py index aa71a7551..d9b671a65 100644 --- a/src/registrar/views/domain.py +++ b/src/registrar/views/domain.py @@ -656,7 +656,7 @@ def form_valid(self, form): class DomainUsersView(DomainBaseView): - """User management page in the domain details.""" + """Domain managers page in the domain details.""" template_name = "domain_users.html"
pytorch__TensorRT-1849
Add Test Suite for `torch.compile` backend Partitioning/Lowering Phases - Add robust test suite for `torch.compile` backend, ensuring each phase functions correctly - Add general-purpose utilities for test expansion as the backend evolves
[ { "content": "import torch\n\nfrom typing import Any, Union, Sequence, Dict\nfrom torch_tensorrt import _Input, Device\n\n\ndef prepare_inputs(\n inputs: Union[_Input.Input, torch.Tensor, Sequence, Dict],\n device: torch.device = torch.device(\"cuda\"),\n) -> Any:\n if isinstance(inputs, _Input.Input):\n if isinstance(inputs.shape, dict):\n return inputs.example_tensor(optimization_profile_field=\"opt_shape\").to(\n device\n )\n else:\n return inputs.example_tensor().to(device)\n\n elif isinstance(inputs, torch.Tensor):\n return inputs\n\n elif isinstance(inputs, list):\n prepared_input = list()\n\n for input_obj in inputs:\n prepared_input.append(prepare_inputs(input_obj))\n\n return prepared_input\n\n elif isinstance(inputs, tuple):\n prepared_input = list()\n\n for input_obj in inputs:\n prepared_input.append(prepare_inputs(input_obj))\n\n return tuple(prepared_input)\n\n elif isinstance(inputs, dict):\n prepared_input = dict()\n\n for key, input_obj in inputs.items():\n prepared_input[key] = prepare_inputs(input_obj)\n\n return prepared_input\n\n else:\n raise ValueError(\n f\"Invalid input type {type(inputs)} encountered in the torch_compile input parsing. \"\n + \"Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}\"\n )\n\n\ndef prepare_device(device: Union[Device, torch.device]) -> torch.device:\n if isinstance(device, Device):\n if device.gpu_id != -1:\n device = torch.device(device.gpu_id)\n else:\n raise ValueError(\"Invalid GPU ID provided for the CUDA device provided\")\n\n elif isinstance(device, torch.device):\n device = device\n\n else:\n raise ValueError(\n \"Invalid device provided. Supported options: torch.device | torch_tensorrt.Device\"\n )\n", "path": "py/torch_tensorrt/dynamo/torch_compile/utils.py" } ]
[ { "content": "import torch\n\nfrom typing import Any, Union, Sequence, Dict\nfrom torch_tensorrt import _Input, Device\n\n\ndef prepare_inputs(\n inputs: Union[_Input.Input, torch.Tensor, Sequence, Dict],\n device: torch.device = torch.device(\"cuda\"),\n) -> Any:\n if isinstance(inputs, _Input.Input):\n if isinstance(inputs.shape, dict):\n return inputs.example_tensor(optimization_profile_field=\"opt_shape\").to(\n device\n )\n else:\n return inputs.example_tensor().to(device)\n\n elif isinstance(inputs, torch.Tensor):\n return inputs\n\n elif isinstance(inputs, list):\n prepared_input = list()\n\n for input_obj in inputs:\n prepared_input.append(prepare_inputs(input_obj))\n\n return prepared_input\n\n elif isinstance(inputs, tuple):\n prepared_input = list()\n\n for input_obj in inputs:\n prepared_input.append(prepare_inputs(input_obj))\n\n return tuple(prepared_input)\n\n elif isinstance(inputs, dict):\n prepared_input = dict()\n\n for key, input_obj in inputs.items():\n prepared_input[key] = prepare_inputs(input_obj)\n\n return prepared_input\n\n else:\n raise ValueError(\n f\"Invalid input type {type(inputs)} encountered in the torch_compile input parsing. \"\n + \"Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}\"\n )\n\n\ndef prepare_device(device: Union[Device, torch.device]) -> torch.device:\n if isinstance(device, Device):\n if device.gpu_id != -1:\n device = torch.device(device.gpu_id)\n else:\n raise ValueError(\"Invalid GPU ID provided for the CUDA device provided\")\n\n elif isinstance(device, torch.device):\n device = device\n\n else:\n raise ValueError(\n \"Invalid device provided. Supported options: torch.device | torch_tensorrt.Device\"\n )\n\n return device\n", "path": "py/torch_tensorrt/dynamo/torch_compile/utils.py" } ]
diff --git a/.circleci/config.yml b/.circleci/config.yml index 88b547729e..1604bea3df 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -727,6 +727,22 @@ commands: - store_artifacts: path: /tmp/testlogs + test-dynamo-torch_compile-core: + description: "Test the Dynamo torch_compile path" + steps: + - run: + name: Run Dynamo torch_compile core tests + command: | + cd py/torch_tensorrt/dynamo/torch_compile + pushd test/ + pytest --junitxml=/tmp/artifacts/test_results/dynamo/torch_compile/test_results.xml + popd + + - store_test_results: + path: /tmp/artifacts + - store_artifacts: + path: /tmp/testlogs + test-dynamo-torch_compile: description: "Test the Dynamo torch_compile path" steps: @@ -953,6 +969,7 @@ jobs: # We install torch after torch-trt because pip automatically enforces the version constraint otherwise - dump-test-env - test-dynamo-torch_compile + - test-dynamo-torch_compile-core - test-dynamo-fx_ts package-x86_64-linux: diff --git a/py/torch_tensorrt/dynamo/test/utils.py b/py/torch_tensorrt/dynamo/test/utils.py index ff6bc39158..b1e6632ec3 100644 --- a/py/torch_tensorrt/dynamo/test/utils.py +++ b/py/torch_tensorrt/dynamo/test/utils.py @@ -13,42 +13,3 @@ def cosine_similarity(gt_tensor, pred_tensor): res = res.cpu().detach().item() return res - - -def same_output_format(trt_output, torch_output): - # For each encountered collection type, ensure the torch and trt outputs agree - # on type and size, checking recursively through all member elements. - if isinstance(trt_output, tuple): - return ( - isinstance(torch_output, tuple) - and (len(trt_output) == len(torch_output)) - and all( - same_output_format(trt_entry, torch_entry) - for trt_entry, torch_entry in zip(trt_output, torch_output) - ) - ) - elif isinstance(trt_output, list): - return ( - isinstance(torch_output, list) - and (len(trt_output) == len(torch_output)) - and all( - same_output_format(trt_entry, torch_entry) - for trt_entry, torch_entry in zip(trt_output, torch_output) - ) - ) - elif isinstance(trt_output, dict): - return ( - isinstance(torch_output, dict) - and (len(trt_output) == len(torch_output)) - and (trt_output.keys() == torch_output.keys()) - and all( - same_output_format(trt_output[key], torch_output[key]) - for key in trt_output.keys() - ) - ) - elif isinstance(trt_output, set) or isinstance(trt_output, frozenset): - raise AssertionError( - "Unsupported output type 'set' encountered in output format check." - ) - else: - return type(trt_output) is type(torch_output) diff --git a/py/torch_tensorrt/dynamo/torch_compile/test/test_compiler_utils.py b/py/torch_tensorrt/dynamo/torch_compile/test/test_compiler_utils.py new file mode 100644 index 0000000000..da7157c3e5 --- /dev/null +++ b/py/torch_tensorrt/dynamo/torch_compile/test/test_compiler_utils.py @@ -0,0 +1,57 @@ +from torch_tensorrt.dynamo.torch_compile.utils import prepare_device, prepare_inputs +from utils import same_output_format +import torch_tensorrt +import unittest +import torch + + +class TestPrepareDevice(unittest.TestCase): + def test_prepare_cuda_device(self): + gpu_id = 0 + device = torch.device(f"cuda:{gpu_id}") + prepared_device = prepare_device(device) + self.assertTrue(isinstance(prepared_device, torch.device)) + self.assertTrue(prepared_device.index == gpu_id) + + def test_prepare_trt_device(self): + gpu_id = 4 + device = torch_tensorrt.Device(gpu_id=gpu_id) + prepared_device = prepare_device(device) + self.assertTrue(isinstance(prepared_device, torch.device)) + self.assertTrue(prepared_device.index == gpu_id) + + +class TestPrepareInputs(unittest.TestCase): + def test_prepare_single_tensor_input(self): + inputs = [torch.ones((4, 4))] + prepared_inputs = prepare_inputs(inputs) + self.assertTrue( + same_output_format(inputs, prepared_inputs, enforce_tensor_type=False) + ) + + def test_prepare_trt_input(self): + inputs = [torch_tensorrt.Input(shape=(4, 3), dtype=torch.float)] + prepared_inputs = prepare_inputs(inputs) + self.assertTrue( + same_output_format(inputs, prepared_inputs, enforce_tensor_type=False) + ) + + def test_prepare_mixed_type_compound_tensor_input(self): + inputs = { + "first": [ + torch.ones((4, 4)), + torch_tensorrt.Input(shape=(4, 3), dtype=torch.float), + ], + "second": ( + torch.rand((5, 1)), + (torch.rand((5, 1)), torch_tensorrt.Input(shape=(2, 3))), + ), + } + prepared_inputs = prepare_inputs(inputs) + self.assertTrue( + same_output_format(inputs, prepared_inputs, enforce_tensor_type=False) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/py/torch_tensorrt/dynamo/torch_compile/test/test_lowering.py b/py/torch_tensorrt/dynamo/torch_compile/test/test_lowering.py new file mode 100644 index 0000000000..f8f181827f --- /dev/null +++ b/py/torch_tensorrt/dynamo/torch_compile/test/test_lowering.py @@ -0,0 +1,54 @@ +from functools import partial +from utils import fx_dynamo_testing_backend +from torch.testing._internal.common_utils import run_tests, TestCase +import torch + + +class TestTRTModule(TestCase): + def test_lowering_inplace_op(self): + class FullySupported(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + def forward(self, x, y): + x = torch.ops.aten.add_.Tensor(x, y) + x = torch.ops.aten.relu_.default(x) + return x + + # Operations expected to be included in the traced graph after decompositions + expected_ops = {torch.ops.aten.add.Tensor, torch.ops.aten.relu.default} + + # Trace module and set up custom backend to track intermediate graphs + fx_graph = torch.fx.symbolic_trace(FullySupported()) + partitioned_graphs = [] + custom_backend = partial( + fx_dynamo_testing_backend, + store_intermediate_graphs=partitioned_graphs, + ) + + # Invoke compilation + compiled_graph = torch.compile(fx_graph, backend=custom_backend) + compiled_graph( + torch.rand( + 5, + ).cuda(), + torch.rand( + 5, + ).cuda(), + ) + + # Iterate over intermediate graphs, attempt to match nodes + for fx_module in partitioned_graphs: + for _, submodule in fx_module.named_children(): + for node in submodule.graph.nodes: + + if node.op == "call_function" and node.target in expected_ops: + expected_ops.remove(node.target) + + self.assertEqual( + len(expected_ops), 0, "All operators should have been decomposed" + ) + + +if __name__ == "__main__": + run_tests() diff --git a/py/torch_tensorrt/dynamo/torch_compile/test/test_partitioning.py b/py/torch_tensorrt/dynamo/torch_compile/test/test_partitioning.py new file mode 100644 index 0000000000..b068f9c413 --- /dev/null +++ b/py/torch_tensorrt/dynamo/torch_compile/test/test_partitioning.py @@ -0,0 +1,68 @@ +from torch_tensorrt.dynamo.torch_compile.lowering import partition +from torch.testing._internal.common_utils import run_tests, TestCase +import torch +from copy import deepcopy +import numpy as np + + +class TestPartitioning(TestCase): + def test_partition_fully_supported_one_op(self): + class FullySupportedOneOp(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + def forward(self, x, y): + return torch.ops.aten.add.Tensor(x, y) + + fx_graph = torch.fx.symbolic_trace(FullySupportedOneOp()) + partitioned_graph = partition(deepcopy(fx_graph)) + self.assertEqual( + len(list(partitioned_graph.named_children())), + 0, + "Single operators should not be segmented", + ) + + def test_partition_fully_supported_multi_op(self): + class FullySupportedMultiOp(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + def forward(self, x, y): + sum_ = torch.ops.aten.sub.Tensor(x, y) + concat_ = torch.ops.aten.cat.default(x, sum_) + relu_ = torch.ops.aten.relu.default(concat_) + pow_ = torch.ops.aten.pow.Tensor_Scalar(relu_, 2) + return pow_ + + fx_graph = torch.fx.symbolic_trace(FullySupportedMultiOp()) + partitioned_graph = partition(deepcopy(fx_graph)) + self.assertEqual( + len(list(partitioned_graph.named_children())), + 1, + "All operators are supported, there should be one segment", + ) + + def test_partition_partially_supported_multi_op(self): + class PartiallySupportedMultiOp(torch.nn.Module): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + def forward(self, x, y): + sum_1 = torch.ops.aten.add.Tensor(x, y) + sum_2 = torch.ops.aten.add.Tensor(x, sum_1) + sum_ = np.sum(sum_1) + np.sum(sum_2) + relu_ = torch.ops.aten.relu.default(sum_) + pow_ = torch.ops.aten.pow.Tensor_Scalar(relu_, 2) + return pow_ + + fx_graph = torch.fx.symbolic_trace(PartiallySupportedMultiOp()) + partitioned_graph = partition(deepcopy(fx_graph)) + self.assertEqual( + len(list(partitioned_graph.named_children())), + 2, + "Unsupported operators interleave supported ones, expected 2 segments", + ) + + +if __name__ == "__main__": + run_tests() diff --git a/py/torch_tensorrt/dynamo/torch_compile/test/utils.py b/py/torch_tensorrt/dynamo/torch_compile/test/utils.py new file mode 100644 index 0000000000..bdcbbfcc4a --- /dev/null +++ b/py/torch_tensorrt/dynamo/torch_compile/test/utils.py @@ -0,0 +1,94 @@ +from copy import deepcopy +from functools import partial +from typing import List, Sequence +import torch +from torch_tensorrt.dynamo.torch_compile.lowering._decompositions import ( + get_decompositions, +) +from torch_tensorrt.dynamo.torch_compile.lowering._partition import ( + partition, +) + +from torch._dynamo.backends.common import fake_tensor_unsupported + +from torch._functorch.aot_autograd import aot_module_simplified, make_boxed_compiler + + +@fake_tensor_unsupported +def fx_dynamo_testing_backend( + gm: torch.fx.GraphModule, + sample_inputs: Sequence[torch.Tensor], + *, + store_intermediate_graphs: List, +): + """Helper Dynamo backend exclusively for testing""" + custom_backend = partial( + compile_module_testing, + store_intermediate_graphs=store_intermediate_graphs, + ) + + # Invoke AOTAutograd to translate operators to aten + return aot_module_simplified( + gm, + sample_inputs, + fw_compiler=make_boxed_compiler(custom_backend), + decompositions=get_decompositions(), + ) + + +def compile_module_testing( + gm: torch.fx.GraphModule, + example_inputs: Sequence[torch.Tensor], + *, + store_intermediate_graphs: List, +) -> torch.fx.GraphModule: + """Helper compiler exclusively for testing""" + partitioned_module = partition(gm) + + # Store intermediate graph from partitioned module + store_intermediate_graphs.append(deepcopy(partitioned_module)) + + return partitioned_module + + +def same_output_format(trt_output, torch_output, enforce_tensor_type=True): + # For each encountered collection type, ensure the torch and trt outputs agree + # on type and size, checking recursively through all member elements. + if isinstance(trt_output, tuple): + return ( + isinstance(torch_output, tuple) + and (len(trt_output) == len(torch_output)) + and all( + same_output_format(trt_entry, torch_entry, enforce_tensor_type) + for trt_entry, torch_entry in zip(trt_output, torch_output) + ) + ) + elif isinstance(trt_output, list): + return ( + isinstance(torch_output, list) + and (len(trt_output) == len(torch_output)) + and all( + same_output_format(trt_entry, torch_entry, enforce_tensor_type) + for trt_entry, torch_entry in zip(trt_output, torch_output) + ) + ) + elif isinstance(trt_output, dict): + return ( + isinstance(torch_output, dict) + and (len(trt_output) == len(torch_output)) + and (trt_output.keys() == torch_output.keys()) + and all( + same_output_format( + trt_output[key], torch_output[key], enforce_tensor_type + ) + for key in trt_output.keys() + ) + ) + elif isinstance(trt_output, set) or isinstance(trt_output, frozenset): + raise AssertionError( + "Unsupported output type 'set' encountered in output format check." + ) + elif enforce_tensor_type: + return type(trt_output) is type(torch_output) + else: + return True diff --git a/py/torch_tensorrt/dynamo/torch_compile/utils.py b/py/torch_tensorrt/dynamo/torch_compile/utils.py index c096eb9397..ba76536338 100644 --- a/py/torch_tensorrt/dynamo/torch_compile/utils.py +++ b/py/torch_tensorrt/dynamo/torch_compile/utils.py @@ -64,3 +64,5 @@ def prepare_device(device: Union[Device, torch.device]) -> torch.device: raise ValueError( "Invalid device provided. Supported options: torch.device | torch_tensorrt.Device" ) + + return device
projectmesa__mesa-398
error launching Flocker I've Anaconda with python 3.6 & Mesa 0.8.1 I launch Flocker's run.py and I get this error: ``` Flockers e$ python run.py Traceback (most recent call last): File "run.py", line 1, in <module> from flockers.server import server File "/Users/e/Dropbox/devlib/notebooks/mesa-master/examples/Flockers/flockers/server.py", line 20, in <module> server = ModularServer(BoidModel, [boid_canvas], "Boids", model_params) File "/Users/e/anaconda3/lib/python3.6/site-packages/mesa/visualization/ModularVisualization.py", line 287, in __init__ self.reset_model() File "/Users/e/anaconda3/lib/python3.6/site-packages/mesa/visualization/ModularVisualization.py", line 313, in reset_model self.model = self.model_cls(**model_params) TypeError: __init__() got an unexpected keyword argument 'N' ```
[ { "content": "from mesa.visualization.ModularVisualization import ModularServer\n\nfrom .model import BoidModel\nfrom .SimpleContinuousModule import SimpleCanvas\n\n\ndef boid_draw(agent):\n return {\"Shape\": \"circle\", \"r\": 2, \"Filled\": \"true\", \"Color\": \"Red\"}\n\nboid_canvas = SimpleCanvas(boid_draw, 500, 500)\nmodel_params = {\n \"N\": 100,\n \"width\": 100,\n \"height\": 100,\n \"speed\": 5,\n \"vision\": 10,\n \"separation\": 2\n}\n\nserver = ModularServer(BoidModel, [boid_canvas], \"Boids\", model_params)\n", "path": "examples/Flockers/flockers/server.py" } ]
[ { "content": "from mesa.visualization.ModularVisualization import ModularServer\n\nfrom .model import BoidModel\nfrom .SimpleContinuousModule import SimpleCanvas\n\n\ndef boid_draw(agent):\n return {\"Shape\": \"circle\", \"r\": 2, \"Filled\": \"true\", \"Color\": \"Red\"}\n\nboid_canvas = SimpleCanvas(boid_draw, 500, 500)\nmodel_params = {\n \"population\": 100,\n \"width\": 100,\n \"height\": 100,\n \"speed\": 5,\n \"vision\": 10,\n \"separation\": 2\n}\n\nserver = ModularServer(BoidModel, [boid_canvas], \"Boids\", model_params)\n", "path": "examples/Flockers/flockers/server.py" } ]
diff --git a/.travis.yml b/.travis.yml index 22c39694fff..c7a0e5fd874 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,5 +16,6 @@ script: # * E123 - indentation on data structures - flake8 . --ignore=F403,E501,E123,E128 --exclude=docs,build - nosetests --with-coverage --cover-package=mesa + - ./tests/test_end_to_end_viz.sh after_success: - coveralls diff --git a/examples/Flockers/flockers/server.py b/examples/Flockers/flockers/server.py index 73d665cb214..72cc2455a19 100644 --- a/examples/Flockers/flockers/server.py +++ b/examples/Flockers/flockers/server.py @@ -9,7 +9,7 @@ def boid_draw(agent): boid_canvas = SimpleCanvas(boid_draw, 500, 500) model_params = { - "N": 100, + "population": 100, "width": 100, "height": 100, "speed": 5, diff --git a/tests/test_end_to_end_viz.sh b/tests/test_end_to_end_viz.sh new file mode 100755 index 00000000000..04405b8aaba --- /dev/null +++ b/tests/test_end_to_end_viz.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +cd examples/Flockers +python run.py & +PID=$! +sleep 3 +curl localhost:8521 | grep Boids +kill $PID
hylang__hy-2220
Add header notice to "stable" line documentation to point users to the alpha cycle documentation I was reading documentation and noticed that hy.contrib.walk is mentioned there: https://docs.hylang.org/en/stable/contrib/walk.html however it appears that hy.contrib.walk file is no longer on the master branch. https://github.com/hylang/hy/blob/6ba90fd3f853b2ddc391aa3358f9386c41d831c4/hy/contrib/walk.hy is it a bug in documentation or I'm missing something?
[ { "content": "# This file is execfile()d with the current directory set to its containing dir.\n\nimport re, os, sys, time, html\n\nsys.path.insert(0, os.path.abspath('..'))\n\nextensions = [\n 'sphinx.ext.napoleon',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.autodoc',\n 'sphinx.ext.viewcode',\n 'sphinxcontrib.hydomain',\n]\n\nfrom get_version import __version__ as hy_version\n\n# Read the Docs might dirty its checkout, so strip the dirty flag.\nhy_version = re.sub(r'[+.]dirty\\Z', '', hy_version)\n\ntemplates_path = ['_templates']\nsource_suffix = '.rst'\n\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'hy'\ncopyright = '%s the authors' % time.strftime('%Y')\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = \".\".join(hy_version.split(\".\")[:-1])\n# The full version, including alpha/beta/rc tags.\nrelease = hy_version\nhy_descriptive_version = html.escape(hy_version)\nif \"+\" in hy_version:\n hy_descriptive_version += \" <strong style='color: red;'>(unstable)</strong>\"\n\nexclude_patterns = ['_build', 'coreteam.rst']\nadd_module_names = True\n\npygments_style = 'sphinx'\n\nimport sphinx_rtd_theme\nhtml_theme = 'sphinx_rtd_theme'\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\nhtml_use_smartypants = False\nhtml_show_sphinx = False\n\nhtml_context = dict(\n hy_descriptive_version = hy_descriptive_version)\n\nhighlight_language = 'clojure'\n\nintersphinx_mapping = dict(\n py = ('https://docs.python.org/3/', None),\n py3_10 = ('https://docs.python.org/3.10/', None),\n hyrule = ('https://hyrule.readthedocs.io/en/master/', None))\n# ** Generate Cheatsheet\nimport json\nfrom pathlib import Path\nfrom itertools import zip_longest\n\ndef refize(spec):\n role = ':hy:func:'\n if isinstance(spec, dict):\n _name = spec['name']\n uri = spec['uri']\n if spec.get('internal'):\n role = ':ref:'\n else:\n uri = spec\n _name = str.split(uri, '.')[-1]\n return '{}`{} <{}>`'.format(role, _name, uri)\n\n\ndef format_refs(refs, indent):\n args = [iter(map(refize, refs))]\n ref_groups = zip_longest(*args, fillvalue=\"\")\n return str.join(\n ' \\\\\\n' + ' ' * (indent + 3),\n [str.join(' ', ref_group) for ref_group in ref_groups],\n )\n\n\ndef format_row(category, divider_loc):\n return '{title: <{width}} | {methods}'.format(\n width=divider_loc,\n title=category['name'],\n methods=format_refs(category['methods'], divider_loc)\n )\n\n\ndef format_table(table_spec):\n table_name = table_spec['name']\n categories = table_spec['categories']\n longest_cat_name = max(len(category['name']) for category in categories)\n table = [\n table_name,\n '-' * len(table_name),\n '',\n '=' * longest_cat_name + ' ' + '=' * 25,\n *(format_row(category, longest_cat_name) for category in categories),\n '=' * longest_cat_name + ' ' + '=' * 25,\n ''\n ]\n return '\\n'.join(table)\n\n\n# Modifications to the cheatsheet should be added in `cheatsheet.json`\ncheatsheet_spec = json.loads(Path('./docs/cheatsheet.json').read_text())\ncheatsheet = [\n '..',\n ' DO NOT MODIFY THIS FILE. IT IS AUTO GENERATED BY ``conf.py``',\n ' If you need to change or add methods, modify ``cheatsheet_spec`` in ``conf.py``',\n '',\n '.. _cheatsheet:',\n '',\n 'Cheatsheet',\n '==========',\n '',\n *map(format_table, cheatsheet_spec),\n]\nPath('./docs/cheatsheet.rst').write_text('\\n'.join(cheatsheet))\n\n\n# ** Sphinx App Setup\n\n\ndef setup(app):\n app.add_css_file('overrides.css')\n", "path": "docs/conf.py" } ]
[ { "content": "# This file is execfile()d with the current directory set to its containing dir.\n\nimport re, os, sys, time, html\n\nsys.path.insert(0, os.path.abspath('..'))\n\nextensions = [\n 'sphinx.ext.napoleon',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.autodoc',\n 'sphinx.ext.viewcode',\n 'sphinxcontrib.hydomain',\n]\n\nfrom get_version import __version__ as hy_version\n\n# Read the Docs might dirty its checkout, so strip the dirty flag.\nhy_version = re.sub(r'[+.]dirty\\Z', '', hy_version)\n\ntemplates_path = ['_templates']\nsource_suffix = '.rst'\n\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'hy'\ncopyright = '%s the authors' % time.strftime('%Y')\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = \".\".join(hy_version.split(\".\")[:-1])\n# The full version, including alpha/beta/rc tags.\nrelease = hy_version\nhy_descriptive_version = html.escape(hy_version)\nif \"+\" in hy_version:\n hy_descriptive_version += \" <strong style='color: red;'>(unstable)</strong>\"\n\nexclude_patterns = ['_build', 'coreteam.rst']\nadd_module_names = True\n\npygments_style = 'sphinx'\n\nimport sphinx_rtd_theme\nhtml_theme = 'sphinx_rtd_theme'\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\nhtml_use_smartypants = False\nhtml_show_sphinx = False\n\nhtml_context = dict(\n hy_descriptive_version = hy_descriptive_version,\n has_active_alpha = True,\n)\n\nhighlight_language = 'clojure'\n\nintersphinx_mapping = dict(\n py = ('https://docs.python.org/3/', None),\n py3_10 = ('https://docs.python.org/3.10/', None),\n hyrule = ('https://hyrule.readthedocs.io/en/master/', None))\n# ** Generate Cheatsheet\nimport json\nfrom pathlib import Path\nfrom itertools import zip_longest\n\ndef refize(spec):\n role = ':hy:func:'\n if isinstance(spec, dict):\n _name = spec['name']\n uri = spec['uri']\n if spec.get('internal'):\n role = ':ref:'\n else:\n uri = spec\n _name = str.split(uri, '.')[-1]\n return '{}`{} <{}>`'.format(role, _name, uri)\n\n\ndef format_refs(refs, indent):\n args = [iter(map(refize, refs))]\n ref_groups = zip_longest(*args, fillvalue=\"\")\n return str.join(\n ' \\\\\\n' + ' ' * (indent + 3),\n [str.join(' ', ref_group) for ref_group in ref_groups],\n )\n\n\ndef format_row(category, divider_loc):\n return '{title: <{width}} | {methods}'.format(\n width=divider_loc,\n title=category['name'],\n methods=format_refs(category['methods'], divider_loc)\n )\n\n\ndef format_table(table_spec):\n table_name = table_spec['name']\n categories = table_spec['categories']\n longest_cat_name = max(len(category['name']) for category in categories)\n table = [\n table_name,\n '-' * len(table_name),\n '',\n '=' * longest_cat_name + ' ' + '=' * 25,\n *(format_row(category, longest_cat_name) for category in categories),\n '=' * longest_cat_name + ' ' + '=' * 25,\n ''\n ]\n return '\\n'.join(table)\n\n\n# Modifications to the cheatsheet should be added in `cheatsheet.json`\ncheatsheet_spec = json.loads(Path('./docs/cheatsheet.json').read_text())\ncheatsheet = [\n '..',\n ' DO NOT MODIFY THIS FILE. IT IS AUTO GENERATED BY ``conf.py``',\n ' If you need to change or add methods, modify ``cheatsheet_spec`` in ``conf.py``',\n '',\n '.. _cheatsheet:',\n '',\n 'Cheatsheet',\n '==========',\n '',\n *map(format_table, cheatsheet_spec),\n]\nPath('./docs/cheatsheet.rst').write_text('\\n'.join(cheatsheet))\n\n\n# ** Sphinx App Setup\n\n\ndef setup(app):\n app.add_css_file('overrides.css')\n", "path": "docs/conf.py" } ]
diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html new file mode 100644 index 000000000..5a39ae454 --- /dev/null +++ b/docs/_templates/layout.html @@ -0,0 +1,15 @@ +{% extends "!layout.html" %} + +{% block extrabody %} +{% if has_active_alpha %} +<div class="wy-nav-content-wrap"> + <div id="dev-warning" class="wy-nav-content" style="overflow-wrap: breakword; padding: 1em 1em 0.1em 1em; background: #ffe761;"> + <p> + Hy is currently in an active alpha cycle. Make sure you're looking + at the correct version of the documentation for your install by + using the version selector in the bottom-left corner of this page. + </p> + </div> +</div> +{% endif %} +{% endblock %} diff --git a/docs/conf.py b/docs/conf.py index 9f184c6e1..a384609de 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -56,7 +56,9 @@ html_show_sphinx = False html_context = dict( - hy_descriptive_version = hy_descriptive_version) + hy_descriptive_version = hy_descriptive_version, + has_active_alpha = True, +) highlight_language = 'clojure'
pallets__werkzeug-2001
Update docs: werkzeug escape utility also translates single quotes This is a bit nitpicky. The escape utility now uses python's built-in html library for escaping. This will also escape single quotes (') in addition to double quotes ("). It would be helpful if someone could update the docs as escaping single quotes can have implications for XSS vulnerabilities in html. Environment: - Python version: >=3.5 - Werkzeug version: latest
[ { "content": "import codecs\nimport io\nimport mimetypes\nimport os\nimport pathlib\nimport pkgutil\nimport re\nimport sys\nimport typing as t\nimport unicodedata\nimport warnings\nfrom datetime import datetime\nfrom html.entities import name2codepoint\nfrom time import struct_time\nfrom time import time\nfrom zlib import adler32\n\nfrom ._internal import _DictAccessorProperty\nfrom ._internal import _missing\nfrom ._internal import _parse_signature\nfrom ._internal import _TAccessorValue\nfrom .datastructures import Headers\nfrom .exceptions import NotFound\nfrom .exceptions import RequestedRangeNotSatisfiable\nfrom .security import safe_join\nfrom .urls import url_quote\nfrom .wsgi import wrap_file\n\nif t.TYPE_CHECKING:\n from wsgiref.types import WSGIEnvironment\n from .wrappers import Response\n\n_entity_re = re.compile(r\"&([^;]+);\")\n_filename_ascii_strip_re = re.compile(r\"[^A-Za-z0-9_.-]\")\n_windows_device_files = (\n \"CON\",\n \"AUX\",\n \"COM1\",\n \"COM2\",\n \"COM3\",\n \"COM4\",\n \"LPT1\",\n \"LPT2\",\n \"LPT3\",\n \"PRN\",\n \"NUL\",\n)\n\n\nclass cached_property(property):\n \"\"\"A decorator that converts a function into a lazy property. The\n function wrapped is called the first time to retrieve the result\n and then that calculated result is used the next time you access\n the value::\n\n class Foo(object):\n\n @cached_property\n def foo(self):\n # calculate something important here\n return 42\n\n The class has to have a `__dict__` in order for this property to\n work.\n \"\"\"\n\n def __init__(\n self,\n fget: t.Callable[[t.Any], t.Any],\n name: t.Optional[str] = None,\n doc: t.Optional[str] = None,\n ) -> None:\n super().__init__(fget, doc=doc)\n self.__name__ = name or fget.__name__\n self.__module__ = fget.__module__\n\n def __set__(self, obj: object, value: t.Any) -> None:\n obj.__dict__[self.__name__] = value\n\n def __get__(self, obj: object, type: type = None) -> t.Any: # type: ignore\n if obj is None:\n return self\n value = obj.__dict__.get(self.__name__, _missing)\n if value is _missing:\n value = self.fget(obj) # type: ignore\n obj.__dict__[self.__name__] = value\n return value\n\n\ndef invalidate_cached_property(obj: object, name: str) -> None:\n \"\"\"Invalidates the cache for a :class:`cached_property`:\n\n >>> class Test(object):\n ... @cached_property\n ... def magic_number(self):\n ... print(\"recalculating...\")\n ... return 42\n ...\n >>> var = Test()\n >>> var.magic_number\n recalculating...\n 42\n >>> var.magic_number\n 42\n >>> invalidate_cached_property(var, \"magic_number\")\n >>> var.magic_number\n recalculating...\n 42\n\n You must pass the name of the cached property as the second argument.\n \"\"\"\n if not isinstance(getattr(obj.__class__, name, None), cached_property):\n raise TypeError(\n f\"Attribute {name!r} of object {obj} is not a\"\n \" cached_property, cannot be invalidated.\"\n )\n del obj.__dict__[name]\n\n\nclass environ_property(_DictAccessorProperty[_TAccessorValue]):\n \"\"\"Maps request attributes to environment variables. This works not only\n for the Werkzeug request object, but also any other class with an\n environ attribute:\n\n >>> class Test(object):\n ... environ = {'key': 'value'}\n ... test = environ_property('key')\n >>> var = Test()\n >>> var.test\n 'value'\n\n If you pass it a second value it's used as default if the key does not\n exist, the third one can be a converter that takes a value and converts\n it. If it raises :exc:`ValueError` or :exc:`TypeError` the default value\n is used. If no default value is provided `None` is used.\n\n Per default the property is read only. You have to explicitly enable it\n by passing ``read_only=False`` to the constructor.\n \"\"\"\n\n read_only = True\n\n def lookup(self, obj: t.Any) -> \"WSGIEnvironment\":\n return obj.environ\n\n\nclass header_property(_DictAccessorProperty[_TAccessorValue]):\n \"\"\"Like `environ_property` but for headers.\"\"\"\n\n def lookup(self, obj: t.Any) -> Headers:\n return obj.headers\n\n\nclass HTMLBuilder:\n \"\"\"Helper object for HTML generation.\n\n Per default there are two instances of that class. The `html` one, and\n the `xhtml` one for those two dialects. The class uses keyword parameters\n and positional parameters to generate small snippets of HTML.\n\n Keyword parameters are converted to XML/SGML attributes, positional\n arguments are used as children. Because Python accepts positional\n arguments before keyword arguments it's a good idea to use a list with the\n star-syntax for some children:\n\n >>> html.p(class_='foo', *[html.a('foo', href='foo.html'), ' ',\n ... html.a('bar', href='bar.html')])\n '<p class=\"foo\"><a href=\"foo.html\">foo</a> <a href=\"bar.html\">bar</a></p>'\n\n This class works around some browser limitations and can not be used for\n arbitrary SGML/XML generation. For that purpose lxml and similar\n libraries exist.\n\n Calling the builder escapes the string passed:\n\n >>> html.p(html(\"<foo>\"))\n '<p>&lt;foo&gt;</p>'\n\n .. deprecated:: 2.0\n Will be removed in 2.1.\n \"\"\"\n\n _entity_re = re.compile(r\"&([^;]+);\")\n _entities = name2codepoint.copy()\n _entities[\"apos\"] = 39\n _empty_elements = {\n \"area\",\n \"base\",\n \"basefont\",\n \"br\",\n \"col\",\n \"command\",\n \"embed\",\n \"frame\",\n \"hr\",\n \"img\",\n \"input\",\n \"keygen\",\n \"isindex\",\n \"link\",\n \"meta\",\n \"param\",\n \"source\",\n \"wbr\",\n }\n _boolean_attributes = {\n \"selected\",\n \"checked\",\n \"compact\",\n \"declare\",\n \"defer\",\n \"disabled\",\n \"ismap\",\n \"multiple\",\n \"nohref\",\n \"noresize\",\n \"noshade\",\n \"nowrap\",\n }\n _plaintext_elements = {\"textarea\"}\n _c_like_cdata = {\"script\", \"style\"}\n\n def __init__(self, dialect):\n self._dialect = dialect\n\n def __call__(self, s):\n import html\n\n warnings.warn(\n \"'utils.HTMLBuilder' is deprecated and will be removed in 2.1.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return html.escape(s)\n\n def __getattr__(self, tag):\n import html\n\n warnings.warn(\n \"'utils.HTMLBuilder' is deprecated and will be removed in 2.1.\",\n DeprecationWarning,\n stacklevel=2,\n )\n if tag[:2] == \"__\":\n raise AttributeError(tag)\n\n def proxy(*children, **arguments):\n buffer = f\"<{tag}\"\n for key, value in arguments.items():\n if value is None:\n continue\n if key[-1] == \"_\":\n key = key[:-1]\n if key in self._boolean_attributes:\n if not value:\n continue\n if self._dialect == \"xhtml\":\n value = f'=\"{key}\"'\n else:\n value = \"\"\n else:\n value = f'=\"{html.escape(value)}\"'\n buffer += f\" {key}{value}\"\n if not children and tag in self._empty_elements:\n if self._dialect == \"xhtml\":\n buffer += \" />\"\n else:\n buffer += \">\"\n return buffer\n buffer += \">\"\n\n children_as_string = \"\".join([str(x) for x in children if x is not None])\n\n if children_as_string:\n if tag in self._plaintext_elements:\n children_as_string = html.escape(children_as_string)\n elif tag in self._c_like_cdata and self._dialect == \"xhtml\":\n children_as_string = f\"/*<![CDATA[*/{children_as_string}/*]]>*/\"\n buffer += children_as_string + f\"</{tag}>\"\n return buffer\n\n return proxy\n\n def __repr__(self):\n return f\"<{type(self).__name__} for {self._dialect!r}>\"\n\n\nhtml = HTMLBuilder(\"html\")\nxhtml = HTMLBuilder(\"xhtml\")\n\n# https://cgit.freedesktop.org/xdg/shared-mime-info/tree/freedesktop.org.xml.in\n# https://www.iana.org/assignments/media-types/media-types.xhtml\n# Types listed in the XDG mime info that have a charset in the IANA registration.\n_charset_mimetypes = {\n \"application/ecmascript\",\n \"application/javascript\",\n \"application/sql\",\n \"application/xml\",\n \"application/xml-dtd\",\n \"application/xml-external-parsed-entity\",\n}\n\n\ndef get_content_type(mimetype: str, charset: str) -> str:\n \"\"\"Returns the full content type string with charset for a mimetype.\n\n If the mimetype represents text, the charset parameter will be\n appended, otherwise the mimetype is returned unchanged.\n\n :param mimetype: The mimetype to be used as content type.\n :param charset: The charset to be appended for text mimetypes.\n :return: The content type.\n\n .. versionchanged:: 0.15\n Any type that ends with ``+xml`` gets a charset, not just those\n that start with ``application/``. Known text types such as\n ``application/javascript`` are also given charsets.\n \"\"\"\n if (\n mimetype.startswith(\"text/\")\n or mimetype in _charset_mimetypes\n or mimetype.endswith(\"+xml\")\n ):\n mimetype += f\"; charset={charset}\"\n\n return mimetype\n\n\ndef detect_utf_encoding(data: bytes) -> str:\n \"\"\"Detect which UTF encoding was used to encode the given bytes.\n\n The latest JSON standard (:rfc:`8259`) suggests that only UTF-8 is\n accepted. Older documents allowed 8, 16, or 32. 16 and 32 can be big\n or little endian. Some editors or libraries may prepend a BOM.\n\n :internal:\n\n :param data: Bytes in unknown UTF encoding.\n :return: UTF encoding name\n\n .. versionadded:: 0.15\n \"\"\"\n head = data[:4]\n\n if head[:3] == codecs.BOM_UTF8:\n return \"utf-8-sig\"\n\n if b\"\\x00\" not in head:\n return \"utf-8\"\n\n if head in (codecs.BOM_UTF32_BE, codecs.BOM_UTF32_LE):\n return \"utf-32\"\n\n if head[:2] in (codecs.BOM_UTF16_BE, codecs.BOM_UTF16_LE):\n return \"utf-16\"\n\n if len(head) == 4:\n if head[:3] == b\"\\x00\\x00\\x00\":\n return \"utf-32-be\"\n\n if head[::2] == b\"\\x00\\x00\":\n return \"utf-16-be\"\n\n if head[1:] == b\"\\x00\\x00\\x00\":\n return \"utf-32-le\"\n\n if head[1::2] == b\"\\x00\\x00\":\n return \"utf-16-le\"\n\n if len(head) == 2:\n return \"utf-16-be\" if head.startswith(b\"\\x00\") else \"utf-16-le\"\n\n return \"utf-8\"\n\n\ndef format_string(string, context):\n \"\"\"String-template format a string:\n\n >>> format_string('$foo and ${foo}s', dict(foo=42))\n '42 and 42s'\n\n This does not do any attribute lookup.\n\n :param string: the format string.\n :param context: a dict with the variables to insert.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use :class:`string.Template` instead.\n \"\"\"\n from string import Template\n\n warnings.warn(\n \"'utils.format_string' is deprecated and will be removed in\"\n \" 2.1. Use 'string.Template' instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return Template(string).substitute(context)\n\n\ndef secure_filename(filename: str) -> str:\n r\"\"\"Pass it a filename and it will return a secure version of it. This\n filename can then safely be stored on a regular file system and passed\n to :func:`os.path.join`. The filename returned is an ASCII only string\n for maximum portability.\n\n On windows systems the function also makes sure that the file is not\n named after one of the special device files.\n\n >>> secure_filename(\"My cool movie.mov\")\n 'My_cool_movie.mov'\n >>> secure_filename(\"../../../etc/passwd\")\n 'etc_passwd'\n >>> secure_filename('i contain cool \\xfcml\\xe4uts.txt')\n 'i_contain_cool_umlauts.txt'\n\n The function might return an empty filename. It's your responsibility\n to ensure that the filename is unique and that you abort or\n generate a random filename if the function returned an empty one.\n\n .. versionadded:: 0.5\n\n :param filename: the filename to secure\n \"\"\"\n filename = unicodedata.normalize(\"NFKD\", filename)\n filename = filename.encode(\"ascii\", \"ignore\").decode(\"ascii\")\n\n for sep in os.path.sep, os.path.altsep:\n if sep:\n filename = filename.replace(sep, \" \")\n filename = str(_filename_ascii_strip_re.sub(\"\", \"_\".join(filename.split()))).strip(\n \"._\"\n )\n\n # on nt a couple of special files are present in each folder. We\n # have to ensure that the target file is not such a filename. In\n # this case we prepend an underline\n if (\n os.name == \"nt\"\n and filename\n and filename.split(\".\")[0].upper() in _windows_device_files\n ):\n filename = f\"_{filename}\"\n\n return filename\n\n\ndef escape(s):\n \"\"\"Replace ``&``, ``<``, ``>``, and ``\"`` with HTML-safe sequences.\n\n ``None`` is escaped to an empty string.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use MarkupSafe instead.\n \"\"\"\n import html\n\n warnings.warn(\n \"'utils.escape' is deprecated and will be removed in 2.1. Use\"\n \" MarkupSafe instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n\n if s is None:\n return \"\"\n\n if hasattr(s, \"__html__\"):\n return s.__html__()\n\n if not isinstance(s, str):\n s = str(s)\n\n return html.escape(s, quote=True)\n\n\ndef unescape(s):\n \"\"\"The reverse of :func:`escape`. This unescapes all the HTML\n entities, not only those inserted by ``escape``.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use MarkupSafe instead.\n \"\"\"\n import html\n\n warnings.warn(\n \"'utils.unescape' is deprecated and will be removed in 2.1. Use\"\n \" MarkupSafe instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return html.unescape(s)\n\n\ndef redirect(\n location: str, code: int = 302, Response: t.Optional[t.Type[\"Response\"]] = None\n) -> \"Response\":\n \"\"\"Returns a response object (a WSGI application) that, if called,\n redirects the client to the target location. Supported codes are\n 301, 302, 303, 305, 307, and 308. 300 is not supported because\n it's not a real redirect and 304 because it's the answer for a\n request with a request with defined If-Modified-Since headers.\n\n .. versionadded:: 0.6\n The location can now be a unicode string that is encoded using\n the :func:`iri_to_uri` function.\n\n .. versionadded:: 0.10\n The class used for the Response object can now be passed in.\n\n :param location: the location the response should redirect to.\n :param code: the redirect status code. defaults to 302.\n :param class Response: a Response class to use when instantiating a\n response. The default is :class:`werkzeug.wrappers.Response` if\n unspecified.\n \"\"\"\n import html\n\n if Response is None:\n from .wrappers import Response # type: ignore\n\n display_location = html.escape(location)\n if isinstance(location, str):\n # Safe conversion is necessary here as we might redirect\n # to a broken URI scheme (for instance itms-services).\n from .urls import iri_to_uri\n\n location = iri_to_uri(location, safe_conversion=True)\n response = Response( # type: ignore\n '<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 3.2 Final//EN\">\\n'\n \"<title>Redirecting...</title>\\n\"\n \"<h1>Redirecting...</h1>\\n\"\n \"<p>You should be redirected automatically to target URL: \"\n f'<a href=\"{html.escape(location)}\">{display_location}</a>. If'\n \" not click the link.\",\n code,\n mimetype=\"text/html\",\n )\n response.headers[\"Location\"] = location\n return response\n\n\ndef append_slash_redirect(environ: \"WSGIEnvironment\", code: int = 301) -> \"Response\":\n \"\"\"Redirects to the same URL but with a slash appended. The behavior\n of this function is undefined if the path ends with a slash already.\n\n :param environ: the WSGI environment for the request that triggers\n the redirect.\n :param code: the status code for the redirect.\n \"\"\"\n new_path = environ[\"PATH_INFO\"].strip(\"/\") + \"/\"\n query_string = environ.get(\"QUERY_STRING\")\n if query_string:\n new_path += f\"?{query_string}\"\n return redirect(new_path, code)\n\n\ndef send_file(\n path_or_file: t.Union[os.PathLike, str, t.BinaryIO],\n environ: \"WSGIEnvironment\",\n mimetype: t.Optional[str] = None,\n as_attachment: bool = False,\n download_name: t.Optional[str] = None,\n conditional: bool = True,\n add_etags: bool = True,\n last_modified: t.Optional[t.Union[datetime, int, float, struct_time]] = None,\n max_age: t.Optional[\n t.Union[int, t.Callable[[t.Optional[t.Union[os.PathLike, str]]], int]]\n ] = None,\n use_x_sendfile: bool = False,\n response_class: t.Optional[t.Type[\"Response\"]] = None,\n _root_path: t.Optional[t.Union[os.PathLike, str]] = None,\n):\n \"\"\"Send the contents of a file to the client.\n\n The first argument can be a file path or a file-like object. Paths\n are preferred in most cases because Werkzeug can manage the file and\n get extra information from the path. Passing a file-like object\n requires that the file is opened in binary mode, and is mostly\n useful when building a file in memory with :class:`io.BytesIO`.\n\n Never pass file paths provided by a user. The path is assumed to be\n trusted, so a user could craft a path to access a file you didn't\n intend.\n\n If the WSGI server sets a ``file_wrapper`` in ``environ``, it is\n used, otherwise Werkzeug's built-in wrapper is used. Alternatively,\n if the HTTP server supports ``X-Sendfile``, ``use_x_sendfile=True``\n will tell the server to send the given path, which is much more\n efficient than reading it in Python.\n\n :param path_or_file: The path to the file to send, relative to the\n current working directory if a relative path is given.\n Alternatively, a file-like object opened in binary mode. Make\n sure the file pointer is seeked to the start of the data.\n :param environ: The WSGI environ for the current request.\n :param mimetype: The MIME type to send for the file. If not\n provided, it will try to detect it from the file name.\n :param as_attachment: Indicate to a browser that it should offer to\n save the file instead of displaying it.\n :param download_name: The default name browsers will use when saving\n the file. Defaults to the passed file name.\n :param conditional: Enable conditional and range responses based on\n request headers. Requires passing a file path and ``environ``.\n :param add_etags: Calculate an ETag for the file. Requires passing a\n file path.\n :param last_modified: The last modified time to send for the file,\n in seconds. If not provided, it will try to detect it from the\n file path.\n :param max_age: How long the client should cache the file, in\n seconds. If set, ``Cache-Control`` will be ``public``, otherwise\n it will be ``no-cache`` to prefer conditional caching.\n :param use_x_sendfile: Set the ``X-Sendfile`` header to let the\n server to efficiently send the file. Requires support from the\n HTTP server. Requires passing a file path.\n :param response_class: Build the response using this class. Defaults\n to :class:`~werkzeug.wrappers.Response`.\n :param _root_path: Do not use. For internal use only. Use\n :func:`send_from_directory` to safely send files under a path.\n\n .. versionadded:: 2.0.0\n Adapted from Flask's implementation.\n\n .. versionchanged:: 2.0.0\n ``download_name`` replaces Flask's ``attachment_filename``\n parameter. If ``as_attachment=False``, it is passed with\n ``Content-Disposition: inline`` instead.\n\n .. versionchanged:: 2.0.0\n ``max_age`` replaces Flask's ``cache_timeout`` parameter.\n ``conditional`` is enabled and ``max_age`` is not set by\n default.\n \"\"\"\n if response_class is None:\n from .wrappers import Response\n\n response_class = Response\n\n path: t.Optional[pathlib.Path] = None\n file: t.Optional[t.BinaryIO] = None\n size: t.Optional[int] = None\n mtime: t.Optional[float] = None\n\n if isinstance(path_or_file, (os.PathLike, str)) or hasattr( # type: ignore\n path_or_file, \"__fspath__\"\n ):\n path_or_file = t.cast(t.Union[os.PathLike, str], path_or_file)\n\n # Flask will pass app.root_path, allowing its send_file wrapper\n # to not have to deal with paths.\n if _root_path is not None:\n path = pathlib.Path(_root_path, path_or_file)\n else:\n path = pathlib.Path(path_or_file).absolute()\n\n stat = path.stat()\n size = stat.st_size\n mtime = stat.st_mtime\n else:\n file = path_or_file\n\n if download_name is None and path is not None:\n download_name = path.name\n\n if mimetype is None:\n if download_name is None:\n raise TypeError(\n \"Unable to detect the MIME type because a file name is\"\n \" not available. Either set 'download_name', pass a\"\n \" path instead of a file, or set 'mimetype'.\"\n )\n\n mimetype = mimetypes.guess_type(download_name)[0] or \"application/octet-stream\"\n\n headers = Headers()\n\n if download_name is not None:\n try:\n download_name.encode(\"ascii\")\n except UnicodeEncodeError:\n simple = unicodedata.normalize(\"NFKD\", download_name)\n simple = simple.encode(\"ascii\", \"ignore\").decode(\"ascii\")\n quoted = url_quote(download_name, safe=\"\")\n names = {\"filename\": simple, \"filename*\": f\"UTF-8''{quoted}\"}\n else:\n names = {\"filename\": download_name}\n\n value = \"attachment\" if as_attachment else \"inline\"\n headers.set(\"Content-Disposition\", value, **names)\n elif as_attachment:\n raise TypeError(\n \"No name provided for attachment. Either set\"\n \" 'download_name' or pass a path instead of a file.\"\n )\n\n if use_x_sendfile and path:\n headers[\"X-Sendfile\"] = str(path)\n data = None\n else:\n if file is None:\n file = path.open(\"rb\") # type: ignore\n elif isinstance(file, io.BytesIO):\n size = file.getbuffer().nbytes\n elif isinstance(file, io.TextIOBase):\n raise ValueError(\"Files must be opened in binary mode or use BytesIO.\")\n\n data = wrap_file(environ, file)\n\n rv = response_class(\n data, mimetype=mimetype, headers=headers, direct_passthrough=True\n )\n\n if size is not None:\n rv.content_length = size\n\n if last_modified is not None:\n rv.last_modified = last_modified # type: ignore\n elif mtime is not None:\n rv.last_modified = mtime # type: ignore\n\n rv.cache_control.no_cache = True\n\n # Flask will pass app.get_send_file_max_age, allowing its send_file\n # wrapper to not have to deal with paths.\n if callable(max_age):\n max_age = max_age(path)\n\n if max_age is not None:\n if max_age > 0:\n rv.cache_control.no_cache = None\n rv.cache_control.public = True\n\n rv.cache_control.max_age = max_age\n rv.expires = int(time() + max_age) # type: ignore\n\n if add_etags and path is not None:\n check = adler32(str(path).encode(\"utf-8\")) & 0xFFFFFFFF\n rv.set_etag(f\"{mtime}-{size}-{check}\")\n\n if conditional:\n try:\n rv = rv.make_conditional(environ, accept_ranges=True, complete_length=size)\n except RequestedRangeNotSatisfiable:\n if file is not None:\n file.close()\n\n raise\n\n # Some x-sendfile implementations incorrectly ignore the 304\n # status code and send the file anyway.\n if rv.status_code == 304:\n rv.headers.pop(\"x-sendfile\", None)\n\n return rv\n\n\ndef send_from_directory(\n directory: t.Union[os.PathLike, str],\n path: t.Union[os.PathLike, str],\n environ: \"WSGIEnvironment\",\n **kwargs,\n) -> \"Response\":\n \"\"\"Send a file from within a directory using :func:`send_file`.\n\n This is a secure way to serve files from a folder, such as static\n files or uploads. Uses :func:`~werkzeug.security.safe_join` to\n ensure the path coming from the client is not maliciously crafted to\n point outside the specified directory.\n\n If the final path does not point to an existing regular file,\n returns a 404 :exc:`~werkzeug.exceptions.NotFound` error.\n\n :param directory: The directory that ``path`` must be located under.\n :param path: The path to the file to send, relative to\n ``directory``.\n :param environ: The WSGI environ for the current request.\n :param kwargs: Arguments to pass to :func:`send_file`.\n\n .. versionadded:: 2.0.0\n Adapted from Flask's implementation.\n \"\"\"\n path = safe_join(os.fspath(directory), os.fspath(path))\n\n if path is None:\n raise NotFound()\n\n # Flask will pass app.root_path, allowing its send_from_directory\n # wrapper to not have to deal with paths.\n if \"_root_path\" in kwargs:\n path = os.path.join(kwargs[\"_root_path\"], path)\n\n try:\n if not os.path.isfile(path):\n raise NotFound()\n except ValueError:\n # path contains null byte on Python < 3.8\n raise NotFound()\n\n return send_file(path, environ, **kwargs)\n\n\ndef import_string(import_name: str, silent: bool = False) -> t.Any:\n \"\"\"Imports an object based on a string. This is useful if you want to\n use import paths as endpoints or something similar. An import path can\n be specified either in dotted notation (``xml.sax.saxutils.escape``)\n or with a colon as object delimiter (``xml.sax.saxutils:escape``).\n\n If `silent` is True the return value will be `None` if the import fails.\n\n :param import_name: the dotted name for the object to import.\n :param silent: if set to `True` import errors are ignored and\n `None` is returned instead.\n :return: imported object\n \"\"\"\n import_name = import_name.replace(\":\", \".\")\n try:\n try:\n __import__(import_name)\n except ImportError:\n if \".\" not in import_name:\n raise\n else:\n return sys.modules[import_name]\n\n module_name, obj_name = import_name.rsplit(\".\", 1)\n module = __import__(module_name, globals(), locals(), [obj_name])\n try:\n return getattr(module, obj_name)\n except AttributeError as e:\n raise ImportError(e)\n\n except ImportError as e:\n if not silent:\n raise ImportStringError(import_name, e).with_traceback(sys.exc_info()[2])\n\n return None\n\n\ndef find_modules(\n import_path: str, include_packages: bool = False, recursive: bool = False\n) -> t.Iterator[str]:\n \"\"\"Finds all the modules below a package. This can be useful to\n automatically import all views / controllers so that their metaclasses /\n function decorators have a chance to register themselves on the\n application.\n\n Packages are not returned unless `include_packages` is `True`. This can\n also recursively list modules but in that case it will import all the\n packages to get the correct load path of that module.\n\n :param import_path: the dotted name for the package to find child modules.\n :param include_packages: set to `True` if packages should be returned, too.\n :param recursive: set to `True` if recursion should happen.\n :return: generator\n \"\"\"\n module = import_string(import_path)\n path = getattr(module, \"__path__\", None)\n if path is None:\n raise ValueError(f\"{import_path!r} is not a package\")\n basename = f\"{module.__name__}.\"\n for _importer, modname, ispkg in pkgutil.iter_modules(path):\n modname = basename + modname\n if ispkg:\n if include_packages:\n yield modname\n if recursive:\n yield from find_modules(modname, include_packages, True)\n else:\n yield modname\n\n\ndef validate_arguments(func, args, kwargs, drop_extra=True):\n \"\"\"Checks if the function accepts the arguments and keyword arguments.\n Returns a new ``(args, kwargs)`` tuple that can safely be passed to\n the function without causing a `TypeError` because the function signature\n is incompatible. If `drop_extra` is set to `True` (which is the default)\n any extra positional or keyword arguments are dropped automatically.\n\n The exception raised provides three attributes:\n\n `missing`\n A set of argument names that the function expected but where\n missing.\n\n `extra`\n A dict of keyword arguments that the function can not handle but\n where provided.\n\n `extra_positional`\n A list of values that where given by positional argument but the\n function cannot accept.\n\n This can be useful for decorators that forward user submitted data to\n a view function::\n\n from werkzeug.utils import ArgumentValidationError, validate_arguments\n\n def sanitize(f):\n def proxy(request):\n data = request.values.to_dict()\n try:\n args, kwargs = validate_arguments(f, (request,), data)\n except ArgumentValidationError:\n raise BadRequest('The browser failed to transmit all '\n 'the data expected.')\n return f(*args, **kwargs)\n return proxy\n\n :param func: the function the validation is performed against.\n :param args: a tuple of positional arguments.\n :param kwargs: a dict of keyword arguments.\n :param drop_extra: set to `False` if you don't want extra arguments\n to be silently dropped.\n :return: tuple in the form ``(args, kwargs)``.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use :func:`inspect.signature` instead.\n \"\"\"\n warnings.warn(\n \"'utils.validate_arguments' is deprecated and will be removed\"\n \" in 2.1. Use 'inspect.signature' instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n parser = _parse_signature(func)\n args, kwargs, missing, extra, extra_positional = parser(args, kwargs)[:5]\n if missing:\n raise ArgumentValidationError(tuple(missing))\n elif (extra or extra_positional) and not drop_extra:\n raise ArgumentValidationError(None, extra, extra_positional)\n return tuple(args), kwargs\n\n\ndef bind_arguments(func, args, kwargs):\n \"\"\"Bind the arguments provided into a dict. When passed a function,\n a tuple of arguments and a dict of keyword arguments `bind_arguments`\n returns a dict of names as the function would see it. This can be useful\n to implement a cache decorator that uses the function arguments to build\n the cache key based on the values of the arguments.\n\n :param func: the function the arguments should be bound for.\n :param args: tuple of positional arguments.\n :param kwargs: a dict of keyword arguments.\n :return: a :class:`dict` of bound keyword arguments.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use :meth:`Signature.bind` instead.\n \"\"\"\n warnings.warn(\n \"'utils.bind_arguments' is deprecated and will be removed in\"\n \" 2.1. Use 'Signature.bind' instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n (\n args,\n kwargs,\n missing,\n extra,\n extra_positional,\n arg_spec,\n vararg_var,\n kwarg_var,\n ) = _parse_signature(func)(args, kwargs)\n values = {}\n for (name, _has_default, _default), value in zip(arg_spec, args):\n values[name] = value\n if vararg_var is not None:\n values[vararg_var] = tuple(extra_positional)\n elif extra_positional:\n raise TypeError(\"too many positional arguments\")\n if kwarg_var is not None:\n multikw = set(extra) & {x[0] for x in arg_spec}\n if multikw:\n raise TypeError(\n f\"got multiple values for keyword argument {next(iter(multikw))!r}\"\n )\n values[kwarg_var] = extra\n elif extra:\n raise TypeError(f\"got unexpected keyword argument {next(iter(extra))!r}\")\n return values\n\n\nclass ArgumentValidationError(ValueError):\n \"\"\"Raised if :func:`validate_arguments` fails to validate\n\n .. deprecated:: 2.0\n Will be removed in 2.1 along with utils.bind/validate_arguments.\n \"\"\"\n\n def __init__(self, missing=None, extra=None, extra_positional=None):\n self.missing = set(missing or ())\n self.extra = extra or {}\n self.extra_positional = extra_positional or []\n super().__init__(\n \"function arguments invalid.\"\n f\" ({len(self.missing)} missing,\"\n f\" {len(self.extra) + len(self.extra_positional)} additional)\"\n )\n\n\nclass ImportStringError(ImportError):\n \"\"\"Provides information about a failed :func:`import_string` attempt.\"\"\"\n\n #: String in dotted notation that failed to be imported.\n import_name: str\n #: Wrapped exception.\n exception: BaseException\n\n def __init__(self, import_name, exception):\n self.import_name = import_name\n self.exception = exception\n msg = import_name\n name = \"\"\n tracked = []\n for part in import_name.replace(\":\", \".\").split(\".\"):\n name = f\"{name}.{part}\" if name else part\n imported = import_string(name, silent=True)\n if imported:\n tracked.append((name, getattr(imported, \"__file__\", None)))\n else:\n track = [f\"- {n!r} found in {i!r}.\" for n, i in tracked]\n track.append(f\"- {name!r} not found.\")\n track_str = \"\\n\".join(track)\n msg = (\n f\"import_string() failed for {import_name!r}. Possible reasons\"\n f\" are:\\n\\n\"\n \"- missing __init__.py in a package;\\n\"\n \"- package or module path not included in sys.path;\\n\"\n \"- duplicated package or module name taking precedence in\"\n \" sys.path;\\n\"\n \"- missing module, class, function or variable;\\n\\n\"\n f\"Debugged import:\\n\\n{track_str}\\n\\n\"\n f\"Original exception:\\n\\n{type(exception).__name__}: {exception}\"\n )\n break\n\n super().__init__(msg)\n\n def __repr__(self):\n return f\"<{type(self).__name__}({self.import_name!r}, {self.exception!r})>\"\n", "path": "src/werkzeug/utils.py" } ]
[ { "content": "import codecs\nimport io\nimport mimetypes\nimport os\nimport pathlib\nimport pkgutil\nimport re\nimport sys\nimport typing as t\nimport unicodedata\nimport warnings\nfrom datetime import datetime\nfrom html.entities import name2codepoint\nfrom time import struct_time\nfrom time import time\nfrom zlib import adler32\n\nfrom ._internal import _DictAccessorProperty\nfrom ._internal import _missing\nfrom ._internal import _parse_signature\nfrom ._internal import _TAccessorValue\nfrom .datastructures import Headers\nfrom .exceptions import NotFound\nfrom .exceptions import RequestedRangeNotSatisfiable\nfrom .security import safe_join\nfrom .urls import url_quote\nfrom .wsgi import wrap_file\n\nif t.TYPE_CHECKING:\n from wsgiref.types import WSGIEnvironment\n from .wrappers import Response\n\n_entity_re = re.compile(r\"&([^;]+);\")\n_filename_ascii_strip_re = re.compile(r\"[^A-Za-z0-9_.-]\")\n_windows_device_files = (\n \"CON\",\n \"AUX\",\n \"COM1\",\n \"COM2\",\n \"COM3\",\n \"COM4\",\n \"LPT1\",\n \"LPT2\",\n \"LPT3\",\n \"PRN\",\n \"NUL\",\n)\n\n\nclass cached_property(property):\n \"\"\"A decorator that converts a function into a lazy property. The\n function wrapped is called the first time to retrieve the result\n and then that calculated result is used the next time you access\n the value::\n\n class Foo(object):\n\n @cached_property\n def foo(self):\n # calculate something important here\n return 42\n\n The class has to have a `__dict__` in order for this property to\n work.\n \"\"\"\n\n def __init__(\n self,\n fget: t.Callable[[t.Any], t.Any],\n name: t.Optional[str] = None,\n doc: t.Optional[str] = None,\n ) -> None:\n super().__init__(fget, doc=doc)\n self.__name__ = name or fget.__name__\n self.__module__ = fget.__module__\n\n def __set__(self, obj: object, value: t.Any) -> None:\n obj.__dict__[self.__name__] = value\n\n def __get__(self, obj: object, type: type = None) -> t.Any: # type: ignore\n if obj is None:\n return self\n value = obj.__dict__.get(self.__name__, _missing)\n if value is _missing:\n value = self.fget(obj) # type: ignore\n obj.__dict__[self.__name__] = value\n return value\n\n\ndef invalidate_cached_property(obj: object, name: str) -> None:\n \"\"\"Invalidates the cache for a :class:`cached_property`:\n\n >>> class Test(object):\n ... @cached_property\n ... def magic_number(self):\n ... print(\"recalculating...\")\n ... return 42\n ...\n >>> var = Test()\n >>> var.magic_number\n recalculating...\n 42\n >>> var.magic_number\n 42\n >>> invalidate_cached_property(var, \"magic_number\")\n >>> var.magic_number\n recalculating...\n 42\n\n You must pass the name of the cached property as the second argument.\n \"\"\"\n if not isinstance(getattr(obj.__class__, name, None), cached_property):\n raise TypeError(\n f\"Attribute {name!r} of object {obj} is not a\"\n \" cached_property, cannot be invalidated.\"\n )\n del obj.__dict__[name]\n\n\nclass environ_property(_DictAccessorProperty[_TAccessorValue]):\n \"\"\"Maps request attributes to environment variables. This works not only\n for the Werkzeug request object, but also any other class with an\n environ attribute:\n\n >>> class Test(object):\n ... environ = {'key': 'value'}\n ... test = environ_property('key')\n >>> var = Test()\n >>> var.test\n 'value'\n\n If you pass it a second value it's used as default if the key does not\n exist, the third one can be a converter that takes a value and converts\n it. If it raises :exc:`ValueError` or :exc:`TypeError` the default value\n is used. If no default value is provided `None` is used.\n\n Per default the property is read only. You have to explicitly enable it\n by passing ``read_only=False`` to the constructor.\n \"\"\"\n\n read_only = True\n\n def lookup(self, obj: t.Any) -> \"WSGIEnvironment\":\n return obj.environ\n\n\nclass header_property(_DictAccessorProperty[_TAccessorValue]):\n \"\"\"Like `environ_property` but for headers.\"\"\"\n\n def lookup(self, obj: t.Any) -> Headers:\n return obj.headers\n\n\nclass HTMLBuilder:\n \"\"\"Helper object for HTML generation.\n\n Per default there are two instances of that class. The `html` one, and\n the `xhtml` one for those two dialects. The class uses keyword parameters\n and positional parameters to generate small snippets of HTML.\n\n Keyword parameters are converted to XML/SGML attributes, positional\n arguments are used as children. Because Python accepts positional\n arguments before keyword arguments it's a good idea to use a list with the\n star-syntax for some children:\n\n >>> html.p(class_='foo', *[html.a('foo', href='foo.html'), ' ',\n ... html.a('bar', href='bar.html')])\n '<p class=\"foo\"><a href=\"foo.html\">foo</a> <a href=\"bar.html\">bar</a></p>'\n\n This class works around some browser limitations and can not be used for\n arbitrary SGML/XML generation. For that purpose lxml and similar\n libraries exist.\n\n Calling the builder escapes the string passed:\n\n >>> html.p(html(\"<foo>\"))\n '<p>&lt;foo&gt;</p>'\n\n .. deprecated:: 2.0\n Will be removed in 2.1.\n \"\"\"\n\n _entity_re = re.compile(r\"&([^;]+);\")\n _entities = name2codepoint.copy()\n _entities[\"apos\"] = 39\n _empty_elements = {\n \"area\",\n \"base\",\n \"basefont\",\n \"br\",\n \"col\",\n \"command\",\n \"embed\",\n \"frame\",\n \"hr\",\n \"img\",\n \"input\",\n \"keygen\",\n \"isindex\",\n \"link\",\n \"meta\",\n \"param\",\n \"source\",\n \"wbr\",\n }\n _boolean_attributes = {\n \"selected\",\n \"checked\",\n \"compact\",\n \"declare\",\n \"defer\",\n \"disabled\",\n \"ismap\",\n \"multiple\",\n \"nohref\",\n \"noresize\",\n \"noshade\",\n \"nowrap\",\n }\n _plaintext_elements = {\"textarea\"}\n _c_like_cdata = {\"script\", \"style\"}\n\n def __init__(self, dialect):\n self._dialect = dialect\n\n def __call__(self, s):\n import html\n\n warnings.warn(\n \"'utils.HTMLBuilder' is deprecated and will be removed in 2.1.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return html.escape(s)\n\n def __getattr__(self, tag):\n import html\n\n warnings.warn(\n \"'utils.HTMLBuilder' is deprecated and will be removed in 2.1.\",\n DeprecationWarning,\n stacklevel=2,\n )\n if tag[:2] == \"__\":\n raise AttributeError(tag)\n\n def proxy(*children, **arguments):\n buffer = f\"<{tag}\"\n for key, value in arguments.items():\n if value is None:\n continue\n if key[-1] == \"_\":\n key = key[:-1]\n if key in self._boolean_attributes:\n if not value:\n continue\n if self._dialect == \"xhtml\":\n value = f'=\"{key}\"'\n else:\n value = \"\"\n else:\n value = f'=\"{html.escape(value)}\"'\n buffer += f\" {key}{value}\"\n if not children and tag in self._empty_elements:\n if self._dialect == \"xhtml\":\n buffer += \" />\"\n else:\n buffer += \">\"\n return buffer\n buffer += \">\"\n\n children_as_string = \"\".join([str(x) for x in children if x is not None])\n\n if children_as_string:\n if tag in self._plaintext_elements:\n children_as_string = html.escape(children_as_string)\n elif tag in self._c_like_cdata and self._dialect == \"xhtml\":\n children_as_string = f\"/*<![CDATA[*/{children_as_string}/*]]>*/\"\n buffer += children_as_string + f\"</{tag}>\"\n return buffer\n\n return proxy\n\n def __repr__(self):\n return f\"<{type(self).__name__} for {self._dialect!r}>\"\n\n\nhtml = HTMLBuilder(\"html\")\nxhtml = HTMLBuilder(\"xhtml\")\n\n# https://cgit.freedesktop.org/xdg/shared-mime-info/tree/freedesktop.org.xml.in\n# https://www.iana.org/assignments/media-types/media-types.xhtml\n# Types listed in the XDG mime info that have a charset in the IANA registration.\n_charset_mimetypes = {\n \"application/ecmascript\",\n \"application/javascript\",\n \"application/sql\",\n \"application/xml\",\n \"application/xml-dtd\",\n \"application/xml-external-parsed-entity\",\n}\n\n\ndef get_content_type(mimetype: str, charset: str) -> str:\n \"\"\"Returns the full content type string with charset for a mimetype.\n\n If the mimetype represents text, the charset parameter will be\n appended, otherwise the mimetype is returned unchanged.\n\n :param mimetype: The mimetype to be used as content type.\n :param charset: The charset to be appended for text mimetypes.\n :return: The content type.\n\n .. versionchanged:: 0.15\n Any type that ends with ``+xml`` gets a charset, not just those\n that start with ``application/``. Known text types such as\n ``application/javascript`` are also given charsets.\n \"\"\"\n if (\n mimetype.startswith(\"text/\")\n or mimetype in _charset_mimetypes\n or mimetype.endswith(\"+xml\")\n ):\n mimetype += f\"; charset={charset}\"\n\n return mimetype\n\n\ndef detect_utf_encoding(data: bytes) -> str:\n \"\"\"Detect which UTF encoding was used to encode the given bytes.\n\n The latest JSON standard (:rfc:`8259`) suggests that only UTF-8 is\n accepted. Older documents allowed 8, 16, or 32. 16 and 32 can be big\n or little endian. Some editors or libraries may prepend a BOM.\n\n :internal:\n\n :param data: Bytes in unknown UTF encoding.\n :return: UTF encoding name\n\n .. versionadded:: 0.15\n \"\"\"\n head = data[:4]\n\n if head[:3] == codecs.BOM_UTF8:\n return \"utf-8-sig\"\n\n if b\"\\x00\" not in head:\n return \"utf-8\"\n\n if head in (codecs.BOM_UTF32_BE, codecs.BOM_UTF32_LE):\n return \"utf-32\"\n\n if head[:2] in (codecs.BOM_UTF16_BE, codecs.BOM_UTF16_LE):\n return \"utf-16\"\n\n if len(head) == 4:\n if head[:3] == b\"\\x00\\x00\\x00\":\n return \"utf-32-be\"\n\n if head[::2] == b\"\\x00\\x00\":\n return \"utf-16-be\"\n\n if head[1:] == b\"\\x00\\x00\\x00\":\n return \"utf-32-le\"\n\n if head[1::2] == b\"\\x00\\x00\":\n return \"utf-16-le\"\n\n if len(head) == 2:\n return \"utf-16-be\" if head.startswith(b\"\\x00\") else \"utf-16-le\"\n\n return \"utf-8\"\n\n\ndef format_string(string, context):\n \"\"\"String-template format a string:\n\n >>> format_string('$foo and ${foo}s', dict(foo=42))\n '42 and 42s'\n\n This does not do any attribute lookup.\n\n :param string: the format string.\n :param context: a dict with the variables to insert.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use :class:`string.Template` instead.\n \"\"\"\n from string import Template\n\n warnings.warn(\n \"'utils.format_string' is deprecated and will be removed in\"\n \" 2.1. Use 'string.Template' instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return Template(string).substitute(context)\n\n\ndef secure_filename(filename: str) -> str:\n r\"\"\"Pass it a filename and it will return a secure version of it. This\n filename can then safely be stored on a regular file system and passed\n to :func:`os.path.join`. The filename returned is an ASCII only string\n for maximum portability.\n\n On windows systems the function also makes sure that the file is not\n named after one of the special device files.\n\n >>> secure_filename(\"My cool movie.mov\")\n 'My_cool_movie.mov'\n >>> secure_filename(\"../../../etc/passwd\")\n 'etc_passwd'\n >>> secure_filename('i contain cool \\xfcml\\xe4uts.txt')\n 'i_contain_cool_umlauts.txt'\n\n The function might return an empty filename. It's your responsibility\n to ensure that the filename is unique and that you abort or\n generate a random filename if the function returned an empty one.\n\n .. versionadded:: 0.5\n\n :param filename: the filename to secure\n \"\"\"\n filename = unicodedata.normalize(\"NFKD\", filename)\n filename = filename.encode(\"ascii\", \"ignore\").decode(\"ascii\")\n\n for sep in os.path.sep, os.path.altsep:\n if sep:\n filename = filename.replace(sep, \" \")\n filename = str(_filename_ascii_strip_re.sub(\"\", \"_\".join(filename.split()))).strip(\n \"._\"\n )\n\n # on nt a couple of special files are present in each folder. We\n # have to ensure that the target file is not such a filename. In\n # this case we prepend an underline\n if (\n os.name == \"nt\"\n and filename\n and filename.split(\".\")[0].upper() in _windows_device_files\n ):\n filename = f\"_{filename}\"\n\n return filename\n\n\ndef escape(s):\n \"\"\"Replace ``&``, ``<``, ``>``, ``\"``, and ``'`` with HTML-safe\n sequences.\n\n ``None`` is escaped to an empty string.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use MarkupSafe instead.\n \"\"\"\n import html\n\n warnings.warn(\n \"'utils.escape' is deprecated and will be removed in 2.1. Use\"\n \" MarkupSafe instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n\n if s is None:\n return \"\"\n\n if hasattr(s, \"__html__\"):\n return s.__html__()\n\n if not isinstance(s, str):\n s = str(s)\n\n return html.escape(s, quote=True)\n\n\ndef unescape(s):\n \"\"\"The reverse of :func:`escape`. This unescapes all the HTML\n entities, not only those inserted by ``escape``.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use MarkupSafe instead.\n \"\"\"\n import html\n\n warnings.warn(\n \"'utils.unescape' is deprecated and will be removed in 2.1. Use\"\n \" MarkupSafe instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return html.unescape(s)\n\n\ndef redirect(\n location: str, code: int = 302, Response: t.Optional[t.Type[\"Response\"]] = None\n) -> \"Response\":\n \"\"\"Returns a response object (a WSGI application) that, if called,\n redirects the client to the target location. Supported codes are\n 301, 302, 303, 305, 307, and 308. 300 is not supported because\n it's not a real redirect and 304 because it's the answer for a\n request with a request with defined If-Modified-Since headers.\n\n .. versionadded:: 0.6\n The location can now be a unicode string that is encoded using\n the :func:`iri_to_uri` function.\n\n .. versionadded:: 0.10\n The class used for the Response object can now be passed in.\n\n :param location: the location the response should redirect to.\n :param code: the redirect status code. defaults to 302.\n :param class Response: a Response class to use when instantiating a\n response. The default is :class:`werkzeug.wrappers.Response` if\n unspecified.\n \"\"\"\n import html\n\n if Response is None:\n from .wrappers import Response # type: ignore\n\n display_location = html.escape(location)\n if isinstance(location, str):\n # Safe conversion is necessary here as we might redirect\n # to a broken URI scheme (for instance itms-services).\n from .urls import iri_to_uri\n\n location = iri_to_uri(location, safe_conversion=True)\n response = Response( # type: ignore\n '<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 3.2 Final//EN\">\\n'\n \"<title>Redirecting...</title>\\n\"\n \"<h1>Redirecting...</h1>\\n\"\n \"<p>You should be redirected automatically to target URL: \"\n f'<a href=\"{html.escape(location)}\">{display_location}</a>. If'\n \" not click the link.\",\n code,\n mimetype=\"text/html\",\n )\n response.headers[\"Location\"] = location\n return response\n\n\ndef append_slash_redirect(environ: \"WSGIEnvironment\", code: int = 301) -> \"Response\":\n \"\"\"Redirects to the same URL but with a slash appended. The behavior\n of this function is undefined if the path ends with a slash already.\n\n :param environ: the WSGI environment for the request that triggers\n the redirect.\n :param code: the status code for the redirect.\n \"\"\"\n new_path = environ[\"PATH_INFO\"].strip(\"/\") + \"/\"\n query_string = environ.get(\"QUERY_STRING\")\n if query_string:\n new_path += f\"?{query_string}\"\n return redirect(new_path, code)\n\n\ndef send_file(\n path_or_file: t.Union[os.PathLike, str, t.BinaryIO],\n environ: \"WSGIEnvironment\",\n mimetype: t.Optional[str] = None,\n as_attachment: bool = False,\n download_name: t.Optional[str] = None,\n conditional: bool = True,\n add_etags: bool = True,\n last_modified: t.Optional[t.Union[datetime, int, float, struct_time]] = None,\n max_age: t.Optional[\n t.Union[int, t.Callable[[t.Optional[t.Union[os.PathLike, str]]], int]]\n ] = None,\n use_x_sendfile: bool = False,\n response_class: t.Optional[t.Type[\"Response\"]] = None,\n _root_path: t.Optional[t.Union[os.PathLike, str]] = None,\n):\n \"\"\"Send the contents of a file to the client.\n\n The first argument can be a file path or a file-like object. Paths\n are preferred in most cases because Werkzeug can manage the file and\n get extra information from the path. Passing a file-like object\n requires that the file is opened in binary mode, and is mostly\n useful when building a file in memory with :class:`io.BytesIO`.\n\n Never pass file paths provided by a user. The path is assumed to be\n trusted, so a user could craft a path to access a file you didn't\n intend.\n\n If the WSGI server sets a ``file_wrapper`` in ``environ``, it is\n used, otherwise Werkzeug's built-in wrapper is used. Alternatively,\n if the HTTP server supports ``X-Sendfile``, ``use_x_sendfile=True``\n will tell the server to send the given path, which is much more\n efficient than reading it in Python.\n\n :param path_or_file: The path to the file to send, relative to the\n current working directory if a relative path is given.\n Alternatively, a file-like object opened in binary mode. Make\n sure the file pointer is seeked to the start of the data.\n :param environ: The WSGI environ for the current request.\n :param mimetype: The MIME type to send for the file. If not\n provided, it will try to detect it from the file name.\n :param as_attachment: Indicate to a browser that it should offer to\n save the file instead of displaying it.\n :param download_name: The default name browsers will use when saving\n the file. Defaults to the passed file name.\n :param conditional: Enable conditional and range responses based on\n request headers. Requires passing a file path and ``environ``.\n :param add_etags: Calculate an ETag for the file. Requires passing a\n file path.\n :param last_modified: The last modified time to send for the file,\n in seconds. If not provided, it will try to detect it from the\n file path.\n :param max_age: How long the client should cache the file, in\n seconds. If set, ``Cache-Control`` will be ``public``, otherwise\n it will be ``no-cache`` to prefer conditional caching.\n :param use_x_sendfile: Set the ``X-Sendfile`` header to let the\n server to efficiently send the file. Requires support from the\n HTTP server. Requires passing a file path.\n :param response_class: Build the response using this class. Defaults\n to :class:`~werkzeug.wrappers.Response`.\n :param _root_path: Do not use. For internal use only. Use\n :func:`send_from_directory` to safely send files under a path.\n\n .. versionadded:: 2.0.0\n Adapted from Flask's implementation.\n\n .. versionchanged:: 2.0.0\n ``download_name`` replaces Flask's ``attachment_filename``\n parameter. If ``as_attachment=False``, it is passed with\n ``Content-Disposition: inline`` instead.\n\n .. versionchanged:: 2.0.0\n ``max_age`` replaces Flask's ``cache_timeout`` parameter.\n ``conditional`` is enabled and ``max_age`` is not set by\n default.\n \"\"\"\n if response_class is None:\n from .wrappers import Response\n\n response_class = Response\n\n path: t.Optional[pathlib.Path] = None\n file: t.Optional[t.BinaryIO] = None\n size: t.Optional[int] = None\n mtime: t.Optional[float] = None\n\n if isinstance(path_or_file, (os.PathLike, str)) or hasattr( # type: ignore\n path_or_file, \"__fspath__\"\n ):\n path_or_file = t.cast(t.Union[os.PathLike, str], path_or_file)\n\n # Flask will pass app.root_path, allowing its send_file wrapper\n # to not have to deal with paths.\n if _root_path is not None:\n path = pathlib.Path(_root_path, path_or_file)\n else:\n path = pathlib.Path(path_or_file).absolute()\n\n stat = path.stat()\n size = stat.st_size\n mtime = stat.st_mtime\n else:\n file = path_or_file\n\n if download_name is None and path is not None:\n download_name = path.name\n\n if mimetype is None:\n if download_name is None:\n raise TypeError(\n \"Unable to detect the MIME type because a file name is\"\n \" not available. Either set 'download_name', pass a\"\n \" path instead of a file, or set 'mimetype'.\"\n )\n\n mimetype = mimetypes.guess_type(download_name)[0] or \"application/octet-stream\"\n\n headers = Headers()\n\n if download_name is not None:\n try:\n download_name.encode(\"ascii\")\n except UnicodeEncodeError:\n simple = unicodedata.normalize(\"NFKD\", download_name)\n simple = simple.encode(\"ascii\", \"ignore\").decode(\"ascii\")\n quoted = url_quote(download_name, safe=\"\")\n names = {\"filename\": simple, \"filename*\": f\"UTF-8''{quoted}\"}\n else:\n names = {\"filename\": download_name}\n\n value = \"attachment\" if as_attachment else \"inline\"\n headers.set(\"Content-Disposition\", value, **names)\n elif as_attachment:\n raise TypeError(\n \"No name provided for attachment. Either set\"\n \" 'download_name' or pass a path instead of a file.\"\n )\n\n if use_x_sendfile and path:\n headers[\"X-Sendfile\"] = str(path)\n data = None\n else:\n if file is None:\n file = path.open(\"rb\") # type: ignore\n elif isinstance(file, io.BytesIO):\n size = file.getbuffer().nbytes\n elif isinstance(file, io.TextIOBase):\n raise ValueError(\"Files must be opened in binary mode or use BytesIO.\")\n\n data = wrap_file(environ, file)\n\n rv = response_class(\n data, mimetype=mimetype, headers=headers, direct_passthrough=True\n )\n\n if size is not None:\n rv.content_length = size\n\n if last_modified is not None:\n rv.last_modified = last_modified # type: ignore\n elif mtime is not None:\n rv.last_modified = mtime # type: ignore\n\n rv.cache_control.no_cache = True\n\n # Flask will pass app.get_send_file_max_age, allowing its send_file\n # wrapper to not have to deal with paths.\n if callable(max_age):\n max_age = max_age(path)\n\n if max_age is not None:\n if max_age > 0:\n rv.cache_control.no_cache = None\n rv.cache_control.public = True\n\n rv.cache_control.max_age = max_age\n rv.expires = int(time() + max_age) # type: ignore\n\n if add_etags and path is not None:\n check = adler32(str(path).encode(\"utf-8\")) & 0xFFFFFFFF\n rv.set_etag(f\"{mtime}-{size}-{check}\")\n\n if conditional:\n try:\n rv = rv.make_conditional(environ, accept_ranges=True, complete_length=size)\n except RequestedRangeNotSatisfiable:\n if file is not None:\n file.close()\n\n raise\n\n # Some x-sendfile implementations incorrectly ignore the 304\n # status code and send the file anyway.\n if rv.status_code == 304:\n rv.headers.pop(\"x-sendfile\", None)\n\n return rv\n\n\ndef send_from_directory(\n directory: t.Union[os.PathLike, str],\n path: t.Union[os.PathLike, str],\n environ: \"WSGIEnvironment\",\n **kwargs,\n) -> \"Response\":\n \"\"\"Send a file from within a directory using :func:`send_file`.\n\n This is a secure way to serve files from a folder, such as static\n files or uploads. Uses :func:`~werkzeug.security.safe_join` to\n ensure the path coming from the client is not maliciously crafted to\n point outside the specified directory.\n\n If the final path does not point to an existing regular file,\n returns a 404 :exc:`~werkzeug.exceptions.NotFound` error.\n\n :param directory: The directory that ``path`` must be located under.\n :param path: The path to the file to send, relative to\n ``directory``.\n :param environ: The WSGI environ for the current request.\n :param kwargs: Arguments to pass to :func:`send_file`.\n\n .. versionadded:: 2.0.0\n Adapted from Flask's implementation.\n \"\"\"\n path = safe_join(os.fspath(directory), os.fspath(path))\n\n if path is None:\n raise NotFound()\n\n # Flask will pass app.root_path, allowing its send_from_directory\n # wrapper to not have to deal with paths.\n if \"_root_path\" in kwargs:\n path = os.path.join(kwargs[\"_root_path\"], path)\n\n try:\n if not os.path.isfile(path):\n raise NotFound()\n except ValueError:\n # path contains null byte on Python < 3.8\n raise NotFound()\n\n return send_file(path, environ, **kwargs)\n\n\ndef import_string(import_name: str, silent: bool = False) -> t.Any:\n \"\"\"Imports an object based on a string. This is useful if you want to\n use import paths as endpoints or something similar. An import path can\n be specified either in dotted notation (``xml.sax.saxutils.escape``)\n or with a colon as object delimiter (``xml.sax.saxutils:escape``).\n\n If `silent` is True the return value will be `None` if the import fails.\n\n :param import_name: the dotted name for the object to import.\n :param silent: if set to `True` import errors are ignored and\n `None` is returned instead.\n :return: imported object\n \"\"\"\n import_name = import_name.replace(\":\", \".\")\n try:\n try:\n __import__(import_name)\n except ImportError:\n if \".\" not in import_name:\n raise\n else:\n return sys.modules[import_name]\n\n module_name, obj_name = import_name.rsplit(\".\", 1)\n module = __import__(module_name, globals(), locals(), [obj_name])\n try:\n return getattr(module, obj_name)\n except AttributeError as e:\n raise ImportError(e)\n\n except ImportError as e:\n if not silent:\n raise ImportStringError(import_name, e).with_traceback(sys.exc_info()[2])\n\n return None\n\n\ndef find_modules(\n import_path: str, include_packages: bool = False, recursive: bool = False\n) -> t.Iterator[str]:\n \"\"\"Finds all the modules below a package. This can be useful to\n automatically import all views / controllers so that their metaclasses /\n function decorators have a chance to register themselves on the\n application.\n\n Packages are not returned unless `include_packages` is `True`. This can\n also recursively list modules but in that case it will import all the\n packages to get the correct load path of that module.\n\n :param import_path: the dotted name for the package to find child modules.\n :param include_packages: set to `True` if packages should be returned, too.\n :param recursive: set to `True` if recursion should happen.\n :return: generator\n \"\"\"\n module = import_string(import_path)\n path = getattr(module, \"__path__\", None)\n if path is None:\n raise ValueError(f\"{import_path!r} is not a package\")\n basename = f\"{module.__name__}.\"\n for _importer, modname, ispkg in pkgutil.iter_modules(path):\n modname = basename + modname\n if ispkg:\n if include_packages:\n yield modname\n if recursive:\n yield from find_modules(modname, include_packages, True)\n else:\n yield modname\n\n\ndef validate_arguments(func, args, kwargs, drop_extra=True):\n \"\"\"Checks if the function accepts the arguments and keyword arguments.\n Returns a new ``(args, kwargs)`` tuple that can safely be passed to\n the function without causing a `TypeError` because the function signature\n is incompatible. If `drop_extra` is set to `True` (which is the default)\n any extra positional or keyword arguments are dropped automatically.\n\n The exception raised provides three attributes:\n\n `missing`\n A set of argument names that the function expected but where\n missing.\n\n `extra`\n A dict of keyword arguments that the function can not handle but\n where provided.\n\n `extra_positional`\n A list of values that where given by positional argument but the\n function cannot accept.\n\n This can be useful for decorators that forward user submitted data to\n a view function::\n\n from werkzeug.utils import ArgumentValidationError, validate_arguments\n\n def sanitize(f):\n def proxy(request):\n data = request.values.to_dict()\n try:\n args, kwargs = validate_arguments(f, (request,), data)\n except ArgumentValidationError:\n raise BadRequest('The browser failed to transmit all '\n 'the data expected.')\n return f(*args, **kwargs)\n return proxy\n\n :param func: the function the validation is performed against.\n :param args: a tuple of positional arguments.\n :param kwargs: a dict of keyword arguments.\n :param drop_extra: set to `False` if you don't want extra arguments\n to be silently dropped.\n :return: tuple in the form ``(args, kwargs)``.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use :func:`inspect.signature` instead.\n \"\"\"\n warnings.warn(\n \"'utils.validate_arguments' is deprecated and will be removed\"\n \" in 2.1. Use 'inspect.signature' instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n parser = _parse_signature(func)\n args, kwargs, missing, extra, extra_positional = parser(args, kwargs)[:5]\n if missing:\n raise ArgumentValidationError(tuple(missing))\n elif (extra or extra_positional) and not drop_extra:\n raise ArgumentValidationError(None, extra, extra_positional)\n return tuple(args), kwargs\n\n\ndef bind_arguments(func, args, kwargs):\n \"\"\"Bind the arguments provided into a dict. When passed a function,\n a tuple of arguments and a dict of keyword arguments `bind_arguments`\n returns a dict of names as the function would see it. This can be useful\n to implement a cache decorator that uses the function arguments to build\n the cache key based on the values of the arguments.\n\n :param func: the function the arguments should be bound for.\n :param args: tuple of positional arguments.\n :param kwargs: a dict of keyword arguments.\n :return: a :class:`dict` of bound keyword arguments.\n\n .. deprecated:: 2.0\n Will be removed in 2.1. Use :meth:`Signature.bind` instead.\n \"\"\"\n warnings.warn(\n \"'utils.bind_arguments' is deprecated and will be removed in\"\n \" 2.1. Use 'Signature.bind' instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n (\n args,\n kwargs,\n missing,\n extra,\n extra_positional,\n arg_spec,\n vararg_var,\n kwarg_var,\n ) = _parse_signature(func)(args, kwargs)\n values = {}\n for (name, _has_default, _default), value in zip(arg_spec, args):\n values[name] = value\n if vararg_var is not None:\n values[vararg_var] = tuple(extra_positional)\n elif extra_positional:\n raise TypeError(\"too many positional arguments\")\n if kwarg_var is not None:\n multikw = set(extra) & {x[0] for x in arg_spec}\n if multikw:\n raise TypeError(\n f\"got multiple values for keyword argument {next(iter(multikw))!r}\"\n )\n values[kwarg_var] = extra\n elif extra:\n raise TypeError(f\"got unexpected keyword argument {next(iter(extra))!r}\")\n return values\n\n\nclass ArgumentValidationError(ValueError):\n \"\"\"Raised if :func:`validate_arguments` fails to validate\n\n .. deprecated:: 2.0\n Will be removed in 2.1 along with utils.bind/validate_arguments.\n \"\"\"\n\n def __init__(self, missing=None, extra=None, extra_positional=None):\n self.missing = set(missing or ())\n self.extra = extra or {}\n self.extra_positional = extra_positional or []\n super().__init__(\n \"function arguments invalid.\"\n f\" ({len(self.missing)} missing,\"\n f\" {len(self.extra) + len(self.extra_positional)} additional)\"\n )\n\n\nclass ImportStringError(ImportError):\n \"\"\"Provides information about a failed :func:`import_string` attempt.\"\"\"\n\n #: String in dotted notation that failed to be imported.\n import_name: str\n #: Wrapped exception.\n exception: BaseException\n\n def __init__(self, import_name, exception):\n self.import_name = import_name\n self.exception = exception\n msg = import_name\n name = \"\"\n tracked = []\n for part in import_name.replace(\":\", \".\").split(\".\"):\n name = f\"{name}.{part}\" if name else part\n imported = import_string(name, silent=True)\n if imported:\n tracked.append((name, getattr(imported, \"__file__\", None)))\n else:\n track = [f\"- {n!r} found in {i!r}.\" for n, i in tracked]\n track.append(f\"- {name!r} not found.\")\n track_str = \"\\n\".join(track)\n msg = (\n f\"import_string() failed for {import_name!r}. Possible reasons\"\n f\" are:\\n\\n\"\n \"- missing __init__.py in a package;\\n\"\n \"- package or module path not included in sys.path;\\n\"\n \"- duplicated package or module name taking precedence in\"\n \" sys.path;\\n\"\n \"- missing module, class, function or variable;\\n\\n\"\n f\"Debugged import:\\n\\n{track_str}\\n\\n\"\n f\"Original exception:\\n\\n{type(exception).__name__}: {exception}\"\n )\n break\n\n super().__init__(msg)\n\n def __repr__(self):\n return f\"<{type(self).__name__}({self.import_name!r}, {self.exception!r})>\"\n", "path": "src/werkzeug/utils.py" } ]
diff --git a/src/werkzeug/utils.py b/src/werkzeug/utils.py index f5dbfad1f..2713413ba 100644 --- a/src/werkzeug/utils.py +++ b/src/werkzeug/utils.py @@ -446,7 +446,8 @@ def secure_filename(filename: str) -> str: def escape(s): - """Replace ``&``, ``<``, ``>``, and ``"`` with HTML-safe sequences. + """Replace ``&``, ``<``, ``>``, ``"``, and ``'`` with HTML-safe + sequences. ``None`` is escaped to an empty string.
great-expectations__great_expectations-1500
Use cleaner solution for non-truncating division in python 2 Prefer `from __future__ import division` to `1.*x/y`
[ { "content": "# -*- coding: utf-8 -*-\nfrom great_expectations import DataContext\n\nGREETING = \"\"\"<cyan>\\\n ___ _ ___ _ _ _\n / __|_ _ ___ __ _| |_ | __|_ ___ __ ___ __| |_ __ _| |_(_)___ _ _ ___\n| (_ | '_/ -_) _` | _| | _|\\ \\ / '_ \\/ -_) _| _/ _` | _| / _ \\ ' \\(_-<\n \\___|_| \\___\\__,_|\\__| |___/_\\_\\ .__/\\___\\__|\\__\\__,_|\\__|_\\___/_||_/__/\n |_|\n ~ Always know what to expect from your data ~\n</cyan>\"\"\"\n\nLETS_BEGIN_PROMPT = \"\"\"Let's configure a new Data Context.\n\nFirst, Great Expectations will create a new directory:\n\n great_expectations\n |-- great_expectations.yml\n |-- expectations\n |-- notebooks\n |-- plugins\n |-- .gitignore\n |-- uncommitted\n |-- config_variables.yml\n |-- documentation\n |-- validations\n\nOK to proceed?\"\"\"\n\nPROJECT_IS_COMPLETE = \"This looks like an existing project that <green>appears complete!</green> You are <green>ready to roll.</green>\\n\"\n\nRUN_INIT_AGAIN = (\n \"OK. You must run <green>great_expectations init</green> to fix the missing files!\"\n)\n\nCOMPLETE_ONBOARDING_PROMPT = \"\"\"To run locally, we need some files that are not in source control.\n - Anything existing will not be modified.\n - Would you like to fix this automatically?\"\"\"\n\nSLACK_SETUP_INTRO = \"\"\"\n<cyan>========== Slack Notifications ==========</cyan>\n\"\"\"\n\nSLACK_SETUP_PROMPT = \"Would you like to set up Slack data quality notifications?\"\n\nSLACK_DOC_LINK = \"\"\"http://docs.greatexpectations.io/en/latest/getting_started/cli_init.html#configuring-slack-notifications\n\"\"\"\n\nSLACK_WEBHOOK_PROMPT = \"\"\"Please add your Slack webhook below. Getting one is easy!\n\"\"\"\n\nSLACK_LATER = \"\\nTo setup Slack later please see the the slack section in the CLI init getting started guide.\"\n\nSLACK_SETUP_COMPLETE = \"\"\"\nOK. <green>Slack is set up.</green> To modify this in the future please see the slack section in the CLI init getting started guide.\"\"\"\n\nONBOARDING_COMPLETE = \"\"\"\nGreat Expectations added some missing files required to run.\n - You may see new files in `<yellow>great_expectations/uncommitted</yellow>`.\n - You may need to add secrets to `<yellow>great_expectations/uncommitted/config_variables.yml</yellow>` to finish onboarding.\n\"\"\"\n\nBUILD_DOCS_PROMPT = \"Would you like to build & view this project's Data Docs!?\"\n\nNO_DATASOURCES_FOUND = \"\"\"<red>Error: No datasources were found.</red> Please add one by:\n - running `<green>great_expectations datasource new</green>` or\n - by editing the {} file\n\"\"\".format(\n DataContext.GE_YML\n)\n\nSETUP_SUCCESS = \"\\n<cyan>Congratulations! Great Expectations is now set up.</cyan>\"\n\nSECTION_SEPARATOR = \"\\n================================================================================\\n\"\n\nDONE = \"Done\"\n", "path": "great_expectations/cli/cli_messages.py" } ]
[ { "content": "# -*- coding: utf-8 -*-\nfrom great_expectations import DataContext\n\nGREETING = \"\"\"<cyan>\\\n ___ _ ___ _ _ _\n / __|_ _ ___ __ _| |_ | __|_ ___ __ ___ __| |_ __ _| |_(_)___ _ _ ___\n| (_ | '_/ -_) _` | _| | _|\\ \\ / '_ \\/ -_) _| _/ _` | _| / _ \\ ' \\(_-<\n \\___|_| \\___\\__,_|\\__| |___/_\\_\\ .__/\\___\\__|\\__\\__,_|\\__|_\\___/_||_/__/\n |_|\n ~ Always know what to expect from your data ~\n</cyan>\"\"\"\n\nLETS_BEGIN_PROMPT = \"\"\"Let's configure a new Data Context.\n\nFirst, Great Expectations will create a new directory:\n\n great_expectations\n |-- great_expectations.yml\n |-- expectations\n |-- checkpoints \n |-- notebooks\n |-- plugins\n |-- .gitignore\n |-- uncommitted\n |-- config_variables.yml\n |-- documentation\n |-- validations\n\nOK to proceed?\"\"\"\n\nPROJECT_IS_COMPLETE = \"This looks like an existing project that <green>appears complete!</green> You are <green>ready to roll.</green>\\n\"\n\nRUN_INIT_AGAIN = (\n \"OK. You must run <green>great_expectations init</green> to fix the missing files!\"\n)\n\nCOMPLETE_ONBOARDING_PROMPT = \"\"\"To run locally, we need some files that are not in source control.\n - Anything existing will not be modified.\n - Would you like to fix this automatically?\"\"\"\n\nSLACK_SETUP_INTRO = \"\"\"\n<cyan>========== Slack Notifications ==========</cyan>\n\"\"\"\n\nSLACK_SETUP_PROMPT = \"Would you like to set up Slack data quality notifications?\"\n\nSLACK_DOC_LINK = \"\"\"http://docs.greatexpectations.io/en/latest/getting_started/cli_init.html#configuring-slack-notifications\n\"\"\"\n\nSLACK_WEBHOOK_PROMPT = \"\"\"Please add your Slack webhook below. Getting one is easy!\n\"\"\"\n\nSLACK_LATER = \"\\nTo setup Slack later please see the the slack section in the CLI init getting started guide.\"\n\nSLACK_SETUP_COMPLETE = \"\"\"\nOK. <green>Slack is set up.</green> To modify this in the future please see the slack section in the CLI init getting started guide.\"\"\"\n\nONBOARDING_COMPLETE = \"\"\"\nGreat Expectations added some missing files required to run.\n - You may see new files in `<yellow>great_expectations/uncommitted</yellow>`.\n - You may need to add secrets to `<yellow>great_expectations/uncommitted/config_variables.yml</yellow>` to finish onboarding.\n\"\"\"\n\nBUILD_DOCS_PROMPT = \"Would you like to build & view this project's Data Docs!?\"\n\nNO_DATASOURCES_FOUND = \"\"\"<red>Error: No datasources were found.</red> Please add one by:\n - running `<green>great_expectations datasource new</green>` or\n - by editing the {} file\n\"\"\".format(\n DataContext.GE_YML\n)\n\nSETUP_SUCCESS = \"\\n<cyan>Congratulations! Great Expectations is now set up.</cyan>\"\n\nSECTION_SEPARATOR = \"\\n================================================================================\\n\"\n\nDONE = \"Done\"\n", "path": "great_expectations/cli/cli_messages.py" } ]
diff --git a/great_expectations/cli/cli_messages.py b/great_expectations/cli/cli_messages.py index fbc9de052a84..57f0a8a585d5 100644 --- a/great_expectations/cli/cli_messages.py +++ b/great_expectations/cli/cli_messages.py @@ -17,6 +17,7 @@ great_expectations |-- great_expectations.yml |-- expectations + |-- checkpoints |-- notebooks |-- plugins |-- .gitignore
zulip__zulip-28952
Add instructions to download .zuliprc file https://zulip.com/api/configuring-python-bindings describes .zuliprc files, but does not give instructions for where download them. We should fix this. - [ ] Add instructions for downloading a bot's .zuliprc file and your .zuliprc file to https://zulip.com/api/configuring-python-bindings. We'll might want to add some section headings to this page as part of this change. The instructions should have tabs for downloading the file for a bot vs. for yourself. - [ ] Your own .zuliprc file is downloaded via the "Show/change your API key" on SETTINGS / ACCOUNT & PRIVACY. While we're here, let's rename that button to "Manage your API key".
[ { "content": "import re\nfrom typing import Any, Dict, List, Mapping, Optional\n\nimport markdown\nfrom markdown.extensions import Extension\nfrom markdown.preprocessors import Preprocessor\nfrom typing_extensions import override\n\nfrom zerver.lib.markdown.priorities import PREPROCESSOR_PRIORITES\n\nSTART_TABBED_SECTION_REGEX = re.compile(r\"^\\{start_tabs\\}$\")\nEND_TABBED_SECTION_REGEX = re.compile(r\"^\\{end_tabs\\}$\")\nTAB_CONTENT_REGEX = re.compile(r\"^\\{tab\\|([^}]+)\\}$\")\n\nTABBED_SECTION_TEMPLATE = \"\"\"\n<div class=\"tabbed-section {tab_class}\" markdown=\"1\">\n{nav_bar}\n<div class=\"blocks\">\n{blocks}\n</div>\n</div>\n\"\"\".strip()\n\nNAV_BAR_TEMPLATE = \"\"\"\n<ul class=\"nav\">\n{tabs}\n</ul>\n\"\"\".strip()\n\nNAV_LIST_ITEM_TEMPLATE = \"\"\"\n<li data-tab-key=\"{data_tab_key}\" tabindex=\"0\">{label}</li>\n\"\"\".strip()\n\nDIV_TAB_CONTENT_TEMPLATE = \"\"\"\n<div data-tab-key=\"{data_tab_key}\" markdown=\"1\">\n{content}\n</div>\n\"\"\".strip()\n\n# If adding new entries here, also check if you need to update\n# tabbed-instructions.js\nTAB_SECTION_LABELS = {\n \"desktop-web\": \"Desktop/Web\",\n \"ios\": \"iOS\",\n \"android\": \"Android\",\n \"mac\": \"macOS\",\n \"windows\": \"Windows\",\n \"linux\": \"Linux\",\n \"python\": \"Python\",\n \"js\": \"JavaScript\",\n \"curl\": \"curl\",\n \"zulip-send\": \"zulip-send\",\n \"web\": \"Web\",\n \"desktop\": \"Desktop\",\n \"mobile\": \"Mobile\",\n \"mm-default\": \"Default installation\",\n \"mm-cloud\": \"Cloud instance\",\n \"mm-docker\": \"Docker\",\n \"mm-gitlab-omnibus\": \"GitLab Omnibus\",\n \"mm-self-hosting-cloud-export\": \"Self hosting (cloud export)\",\n \"require-invitations\": \"Require invitations\",\n \"allow-anyone-to-join\": \"Allow anyone to join\",\n \"restrict-by-email-domain\": \"Restrict by email domain\",\n \"zoom\": \"Zoom\",\n \"jitsi-meet\": \"Jitsi Meet\",\n \"bigbluebutton\": \"BigBlueButton\",\n \"disable\": \"Disabled\",\n \"chrome\": \"Chrome\",\n \"firefox\": \"Firefox\",\n \"desktop-app\": \"Desktop app\",\n \"system-proxy-settings\": \"System proxy settings\",\n \"custom-proxy-settings\": \"Custom proxy settings\",\n \"stream\": \"From a stream view\",\n \"not-stream\": \"From other views\",\n \"via-recent-conversations\": \"Via recent conversations\",\n \"via-inbox-view\": \"Via inbox view\",\n \"via-left-sidebar\": \"Via left sidebar\",\n \"instructions-for-all-platforms\": \"Instructions for all platforms\",\n \"public-streams\": \"Public streams\",\n \"private-streams\": \"Private streams\",\n \"web-public-streams\": \"Web-public streams\",\n \"via-user-card\": \"Via user card\",\n \"via-user-profile\": \"Via user profile\",\n \"via-organization-settings\": \"Via organization settings\",\n \"via-personal-settings\": \"Via personal settings\",\n \"via-stream-settings\": \"Via stream settings\",\n \"default-subdomain\": \"Default subdomain\",\n \"custom-subdomain\": \"Custom subdomain\",\n \"zulip-cloud-standard\": \"Zulip Cloud Standard\",\n \"zulip-cloud-plus\": \"Zulip Cloud Plus\",\n \"request-sponsorship\": \"Request sponsorship\",\n \"request-education-pricing\": \"Request education pricing\",\n \"zulip-cloud\": \"Zulip Cloud\",\n \"self-hosting\": \"Self hosting\",\n \"okta\": \"Okta\",\n \"onelogin\": \"OneLogin\",\n \"azuread\": \"AzureAD\",\n \"keycloak\": \"Keycloak\",\n \"auth0\": \"Auth0\",\n \"logged-in\": \"If you are logged in\",\n \"logged-out\": \"If you are logged out\",\n \"user\": \"User\",\n \"bot\": \"Bot\",\n \"on-sign-up\": \"On sign-up\",\n \"via-paste\": \"Via paste\",\n \"via-drag-and-drop\": \"Via drag-and-drop\",\n \"via-markdown\": \"Via Markdown\",\n \"via-compose-box-buttons\": \"Via compose box buttons\",\n \"stream-compose\": \"Compose to a stream\",\n \"dm-compose\": \"Compose a DM\",\n \"v8\": \"Zulip Server 8.0+\",\n \"v6\": \"Zulip Server 6.0+\",\n \"v4\": \"Zulip Server 4.0+\",\n \"all-versions\": \"All versions\",\n}\n\n\nclass TabbedSectionsGenerator(Extension):\n @override\n def extendMarkdown(self, md: markdown.Markdown) -> None:\n md.preprocessors.register(\n TabbedSectionsPreprocessor(md, self.getConfigs()),\n \"tabbed_sections\",\n PREPROCESSOR_PRIORITES[\"tabbed_sections\"],\n )\n\n\nclass TabbedSectionsPreprocessor(Preprocessor):\n def __init__(self, md: markdown.Markdown, config: Mapping[str, Any]) -> None:\n super().__init__(md)\n\n @override\n def run(self, lines: List[str]) -> List[str]:\n tab_section = self.parse_tabs(lines)\n while tab_section:\n if \"tabs\" in tab_section:\n tab_class = \"has-tabs\"\n else:\n tab_class = \"no-tabs\"\n tab_section[\"tabs\"] = [\n {\n \"tab_key\": \"instructions-for-all-platforms\",\n \"start\": tab_section[\"start_tabs_index\"],\n }\n ]\n nav_bar = self.generate_nav_bar(tab_section)\n content_blocks = self.generate_content_blocks(tab_section, lines)\n rendered_tabs = TABBED_SECTION_TEMPLATE.format(\n tab_class=tab_class, nav_bar=nav_bar, blocks=content_blocks\n )\n\n start = tab_section[\"start_tabs_index\"]\n end = tab_section[\"end_tabs_index\"] + 1\n lines = [*lines[:start], rendered_tabs, *lines[end:]]\n tab_section = self.parse_tabs(lines)\n return lines\n\n def generate_content_blocks(self, tab_section: Dict[str, Any], lines: List[str]) -> str:\n tab_content_blocks = []\n for index, tab in enumerate(tab_section[\"tabs\"]):\n start_index = tab[\"start\"] + 1\n try:\n # If there are more tabs, we can use the starting index\n # of the next tab as the ending index of the previous one\n end_index = tab_section[\"tabs\"][index + 1][\"start\"]\n except IndexError:\n # Otherwise, just use the end of the entire section\n end_index = tab_section[\"end_tabs_index\"]\n\n content = \"\\n\".join(lines[start_index:end_index]).strip()\n tab_content_block = DIV_TAB_CONTENT_TEMPLATE.format(\n data_tab_key=tab[\"tab_key\"],\n # Wrapping the content in two newlines is necessary here.\n # If we don't do this, the inner Markdown does not get\n # rendered properly.\n content=f\"\\n{content}\\n\",\n )\n tab_content_blocks.append(tab_content_block)\n return \"\\n\".join(tab_content_blocks)\n\n def generate_nav_bar(self, tab_section: Dict[str, Any]) -> str:\n li_elements = []\n for tab in tab_section[\"tabs\"]:\n tab_key = tab.get(\"tab_key\")\n tab_label = TAB_SECTION_LABELS.get(tab_key)\n if tab_label is None:\n raise ValueError(\n f\"Tab '{tab_key}' is not present in TAB_SECTION_LABELS in zerver/lib/markdown/tabbed_sections.py\"\n )\n\n li = NAV_LIST_ITEM_TEMPLATE.format(data_tab_key=tab_key, label=tab_label)\n li_elements.append(li)\n\n return NAV_BAR_TEMPLATE.format(tabs=\"\\n\".join(li_elements))\n\n def parse_tabs(self, lines: List[str]) -> Optional[Dict[str, Any]]:\n block: Dict[str, Any] = {}\n for index, line in enumerate(lines):\n start_match = START_TABBED_SECTION_REGEX.search(line)\n if start_match:\n block[\"start_tabs_index\"] = index\n\n tab_content_match = TAB_CONTENT_REGEX.search(line)\n if tab_content_match:\n block.setdefault(\"tabs\", [])\n tab = {\"start\": index, \"tab_key\": tab_content_match.group(1)}\n block[\"tabs\"].append(tab)\n\n end_match = END_TABBED_SECTION_REGEX.search(line)\n if end_match:\n block[\"end_tabs_index\"] = index\n break\n return block\n\n\ndef makeExtension(*args: Any, **kwargs: str) -> TabbedSectionsGenerator:\n return TabbedSectionsGenerator(**kwargs)\n", "path": "zerver/lib/markdown/tabbed_sections.py" } ]
[ { "content": "import re\nfrom typing import Any, Dict, List, Mapping, Optional\n\nimport markdown\nfrom markdown.extensions import Extension\nfrom markdown.preprocessors import Preprocessor\nfrom typing_extensions import override\n\nfrom zerver.lib.markdown.priorities import PREPROCESSOR_PRIORITES\n\nSTART_TABBED_SECTION_REGEX = re.compile(r\"^\\{start_tabs\\}$\")\nEND_TABBED_SECTION_REGEX = re.compile(r\"^\\{end_tabs\\}$\")\nTAB_CONTENT_REGEX = re.compile(r\"^\\{tab\\|([^}]+)\\}$\")\n\nTABBED_SECTION_TEMPLATE = \"\"\"\n<div class=\"tabbed-section {tab_class}\" markdown=\"1\">\n{nav_bar}\n<div class=\"blocks\">\n{blocks}\n</div>\n</div>\n\"\"\".strip()\n\nNAV_BAR_TEMPLATE = \"\"\"\n<ul class=\"nav\">\n{tabs}\n</ul>\n\"\"\".strip()\n\nNAV_LIST_ITEM_TEMPLATE = \"\"\"\n<li data-tab-key=\"{data_tab_key}\" tabindex=\"0\">{label}</li>\n\"\"\".strip()\n\nDIV_TAB_CONTENT_TEMPLATE = \"\"\"\n<div data-tab-key=\"{data_tab_key}\" markdown=\"1\">\n{content}\n</div>\n\"\"\".strip()\n\n# If adding new entries here, also check if you need to update\n# tabbed-instructions.js\nTAB_SECTION_LABELS = {\n \"desktop-web\": \"Desktop/Web\",\n \"ios\": \"iOS\",\n \"android\": \"Android\",\n \"mac\": \"macOS\",\n \"windows\": \"Windows\",\n \"linux\": \"Linux\",\n \"python\": \"Python\",\n \"js\": \"JavaScript\",\n \"curl\": \"curl\",\n \"zulip-send\": \"zulip-send\",\n \"web\": \"Web\",\n \"desktop\": \"Desktop\",\n \"mobile\": \"Mobile\",\n \"mm-default\": \"Default installation\",\n \"mm-cloud\": \"Cloud instance\",\n \"mm-docker\": \"Docker\",\n \"mm-gitlab-omnibus\": \"GitLab Omnibus\",\n \"mm-self-hosting-cloud-export\": \"Self hosting (cloud export)\",\n \"require-invitations\": \"Require invitations\",\n \"allow-anyone-to-join\": \"Allow anyone to join\",\n \"restrict-by-email-domain\": \"Restrict by email domain\",\n \"zoom\": \"Zoom\",\n \"jitsi-meet\": \"Jitsi Meet\",\n \"bigbluebutton\": \"BigBlueButton\",\n \"disable\": \"Disabled\",\n \"chrome\": \"Chrome\",\n \"firefox\": \"Firefox\",\n \"desktop-app\": \"Desktop app\",\n \"system-proxy-settings\": \"System proxy settings\",\n \"custom-proxy-settings\": \"Custom proxy settings\",\n \"stream\": \"From a stream view\",\n \"not-stream\": \"From other views\",\n \"via-recent-conversations\": \"Via recent conversations\",\n \"via-inbox-view\": \"Via inbox view\",\n \"via-left-sidebar\": \"Via left sidebar\",\n \"instructions-for-all-platforms\": \"Instructions for all platforms\",\n \"public-streams\": \"Public streams\",\n \"private-streams\": \"Private streams\",\n \"web-public-streams\": \"Web-public streams\",\n \"via-user-card\": \"Via user card\",\n \"via-user-profile\": \"Via user profile\",\n \"via-organization-settings\": \"Via organization settings\",\n \"via-personal-settings\": \"Via personal settings\",\n \"via-stream-settings\": \"Via stream settings\",\n \"default-subdomain\": \"Default subdomain\",\n \"custom-subdomain\": \"Custom subdomain\",\n \"zulip-cloud-standard\": \"Zulip Cloud Standard\",\n \"zulip-cloud-plus\": \"Zulip Cloud Plus\",\n \"request-sponsorship\": \"Request sponsorship\",\n \"request-education-pricing\": \"Request education pricing\",\n \"zulip-cloud\": \"Zulip Cloud\",\n \"self-hosting\": \"Self hosting\",\n \"okta\": \"Okta\",\n \"onelogin\": \"OneLogin\",\n \"azuread\": \"AzureAD\",\n \"keycloak\": \"Keycloak\",\n \"auth0\": \"Auth0\",\n \"logged-in\": \"If you are logged in\",\n \"logged-out\": \"If you are logged out\",\n \"user\": \"User\",\n \"bot\": \"Bot\",\n \"on-sign-up\": \"On sign-up\",\n \"via-paste\": \"Via paste\",\n \"via-drag-and-drop\": \"Via drag-and-drop\",\n \"via-markdown\": \"Via Markdown\",\n \"via-compose-box-buttons\": \"Via compose box buttons\",\n \"stream-compose\": \"Compose to a stream\",\n \"dm-compose\": \"Compose a DM\",\n \"v8\": \"Zulip Server 8.0+\",\n \"v6\": \"Zulip Server 6.0+\",\n \"v4\": \"Zulip Server 4.0+\",\n \"all-versions\": \"All versions\",\n \"for-a-bot\": \"For a bot\",\n \"for-yourself\": \"For yourself\",\n}\n\n\nclass TabbedSectionsGenerator(Extension):\n @override\n def extendMarkdown(self, md: markdown.Markdown) -> None:\n md.preprocessors.register(\n TabbedSectionsPreprocessor(md, self.getConfigs()),\n \"tabbed_sections\",\n PREPROCESSOR_PRIORITES[\"tabbed_sections\"],\n )\n\n\nclass TabbedSectionsPreprocessor(Preprocessor):\n def __init__(self, md: markdown.Markdown, config: Mapping[str, Any]) -> None:\n super().__init__(md)\n\n @override\n def run(self, lines: List[str]) -> List[str]:\n tab_section = self.parse_tabs(lines)\n while tab_section:\n if \"tabs\" in tab_section:\n tab_class = \"has-tabs\"\n else:\n tab_class = \"no-tabs\"\n tab_section[\"tabs\"] = [\n {\n \"tab_key\": \"instructions-for-all-platforms\",\n \"start\": tab_section[\"start_tabs_index\"],\n }\n ]\n nav_bar = self.generate_nav_bar(tab_section)\n content_blocks = self.generate_content_blocks(tab_section, lines)\n rendered_tabs = TABBED_SECTION_TEMPLATE.format(\n tab_class=tab_class, nav_bar=nav_bar, blocks=content_blocks\n )\n\n start = tab_section[\"start_tabs_index\"]\n end = tab_section[\"end_tabs_index\"] + 1\n lines = [*lines[:start], rendered_tabs, *lines[end:]]\n tab_section = self.parse_tabs(lines)\n return lines\n\n def generate_content_blocks(self, tab_section: Dict[str, Any], lines: List[str]) -> str:\n tab_content_blocks = []\n for index, tab in enumerate(tab_section[\"tabs\"]):\n start_index = tab[\"start\"] + 1\n try:\n # If there are more tabs, we can use the starting index\n # of the next tab as the ending index of the previous one\n end_index = tab_section[\"tabs\"][index + 1][\"start\"]\n except IndexError:\n # Otherwise, just use the end of the entire section\n end_index = tab_section[\"end_tabs_index\"]\n\n content = \"\\n\".join(lines[start_index:end_index]).strip()\n tab_content_block = DIV_TAB_CONTENT_TEMPLATE.format(\n data_tab_key=tab[\"tab_key\"],\n # Wrapping the content in two newlines is necessary here.\n # If we don't do this, the inner Markdown does not get\n # rendered properly.\n content=f\"\\n{content}\\n\",\n )\n tab_content_blocks.append(tab_content_block)\n return \"\\n\".join(tab_content_blocks)\n\n def generate_nav_bar(self, tab_section: Dict[str, Any]) -> str:\n li_elements = []\n for tab in tab_section[\"tabs\"]:\n tab_key = tab.get(\"tab_key\")\n tab_label = TAB_SECTION_LABELS.get(tab_key)\n if tab_label is None:\n raise ValueError(\n f\"Tab '{tab_key}' is not present in TAB_SECTION_LABELS in zerver/lib/markdown/tabbed_sections.py\"\n )\n\n li = NAV_LIST_ITEM_TEMPLATE.format(data_tab_key=tab_key, label=tab_label)\n li_elements.append(li)\n\n return NAV_BAR_TEMPLATE.format(tabs=\"\\n\".join(li_elements))\n\n def parse_tabs(self, lines: List[str]) -> Optional[Dict[str, Any]]:\n block: Dict[str, Any] = {}\n for index, line in enumerate(lines):\n start_match = START_TABBED_SECTION_REGEX.search(line)\n if start_match:\n block[\"start_tabs_index\"] = index\n\n tab_content_match = TAB_CONTENT_REGEX.search(line)\n if tab_content_match:\n block.setdefault(\"tabs\", [])\n tab = {\"start\": index, \"tab_key\": tab_content_match.group(1)}\n block[\"tabs\"].append(tab)\n\n end_match = END_TABBED_SECTION_REGEX.search(line)\n if end_match:\n block[\"end_tabs_index\"] = index\n break\n return block\n\n\ndef makeExtension(*args: Any, **kwargs: str) -> TabbedSectionsGenerator:\n return TabbedSectionsGenerator(**kwargs)\n", "path": "zerver/lib/markdown/tabbed_sections.py" } ]
diff --git a/api_docs/api-keys.md b/api_docs/api-keys.md index 2c6dc2b1de332..1f902fa9e88d4 100644 --- a/api_docs/api-keys.md +++ b/api_docs/api-keys.md @@ -17,7 +17,7 @@ Anyone with your API key can impersonate you, so be doubly careful with it. {settings_tab|account-and-privacy} -1. Under **API key**, click **Show/change your API key**. +1. Under **API key**, click **Manage your API key**. 1. Enter your password, and click **Get API key**. If you don't know your password, click **reset it** and follow the diff --git a/api_docs/configuring-python-bindings.md b/api_docs/configuring-python-bindings.md index d5b51b43c145b..25720f98f4019 100644 --- a/api_docs/configuring-python-bindings.md +++ b/api_docs/configuring-python-bindings.md @@ -5,15 +5,65 @@ easily, called the [Python bindings](https://pypi.python.org/pypi/zulip/). One of the most notable use cases for these bindings are bots developed using Zulip's [bot framework](/api/writing-bots). -In order to use them, you need to configure them with your API key and other -settings. There are two ways to achieve that: +In order to use them, you need to configure them with your identity +(account, API key, and Zulip server URL). There are a few ways to +achieve that: - - With a file called `.zuliprc`, located in your home directory. - - With - [environment variables](https://en.wikipedia.org/wiki/Environment_variable) - set up in your host machine. +- Using a `zuliprc` file, referenced via the `--config-file` option or + the `config_file` option to the `zulip.Client` constructor + (recommended for bots). +- Using a `zuliprc` file in your home directory at `~/.zuliprc` + (recommended for your own API key). +- Using the [environment + variables](https://en.wikipedia.org/wiki/Environment_variable) + documented below. +- Using the `--api-key`, `--email`, and `--site` variables as command + line parameters. +- Using the `api_key`, `email`, and `site` parameters to the + `zulip.Client` constructor. -A `.zuliprc` file is a plain text document that looks like this: +## Download a `zuliprc` file + +{start_tabs} + +{tab|for-a-bot} + +{settings_tab|your-bots} + +1. Click the **download** (<i class="fa fa-download"></i>) icon on the profile + card of the desired bot to download the bot's `zuliprc` file. + +!!! warn "" + + Anyone with a bot's API key can impersonate the bot, so be careful with it! + +{tab|for-yourself} + +{settings_tab|account-and-privacy} + +1. Under **API key**, click **Manage your API key**. + +1. Enter your password, and click **Get API key**. If you don't know your + password, click **reset it** and follow the + instructions from there. + +1. Click **Download zuliprc** to download your `zuliprc` file. + +1. (optional) If you'd like your credentials to be used by default + when using the Zulip API on your computer, move the `zuliprc` file + to `~/.zuliprc` in your home directory. + +!!! warn "" + + Anyone with your API key can impersonate you, so be doubly careful with it. + +{end_tabs} + +## Configuration keys and environment variables + +`zuliprc` is a configuration file written in the +[INI file format](https://en.wikipedia.org/wiki/INI_file), +which contains key-value pairs as shown in the following example: ``` [api] @@ -29,7 +79,7 @@ can be found in the following table: <table class="table"> <thead> <tr> - <th><code>.zuliprc</code> key</th> + <th><code>zuliprc</code> key</th> <th>Environment variable</th> <th>Required</th> <th>Description</th> @@ -102,3 +152,10 @@ can be found in the following table: </td> </tr> </table> + +## Related articles + +* [Installation instructions](/api/installation-instructions) +* [API keys](/api/api-keys) +* [Running bots](/api/running-bots) +* [Deploying bots](/api/deploying-bots) diff --git a/help/add-a-bot-or-integration.md b/help/add-a-bot-or-integration.md index f1ac70b91539c..f65ac9427ee88 100644 --- a/help/add-a-bot-or-integration.md +++ b/help/add-a-bot-or-integration.md @@ -38,7 +38,7 @@ is visible and available for anyone to use. as the **bot type**. Depending on the type of bot you're creating, you may need to download its -`.zuliprc` configuration file. For that, click the **download** +`zuliprc` configuration file. For that, click the **download** (<i class="fa fa-download"></i>) icon under the bot's name. ## Related articles diff --git a/templates/zerver/integrations/google-calendar.md b/templates/zerver/integrations/google-calendar.md index b700ba54ba5c6..e9a21b3afbb35 100644 --- a/templates/zerver/integrations/google-calendar.md +++ b/templates/zerver/integrations/google-calendar.md @@ -20,7 +20,7 @@ your reminders directly in your Zulip feed. then clicking on **Personal settings**. 1. Click on the tab that’s labeled **Account & privacy** and click on - **Show/change your API key**. Enter your password if prompted, and + **Manage your API key**. Enter your password if prompted, and download the `zuliprc` file. Save this file as `.zuliprc` to your `~/` directory. diff --git a/web/templates/settings/account_settings.hbs b/web/templates/settings/account_settings.hbs index 057a1b4738c40..5bb89c528768f 100644 --- a/web/templates/settings/account_settings.hbs +++ b/web/templates/settings/account_settings.hbs @@ -127,7 +127,7 @@ </p> <div id="api_key_button_container" class="inline-block {{#unless user_has_email_set}}disabled_setting_tooltip{{/unless}}"> <button class="button rounded" id="api_key_button" {{#unless user_has_email_set}}disabled="disabled"{{/unless}}> - {{t "Show/change your API key" }} + {{t "Manage your API key" }} </button> </div> </div> diff --git a/web/templates/settings/api_key_modal.hbs b/web/templates/settings/api_key_modal.hbs index c4c94d75e638e..90821f9d7558e 100644 --- a/web/templates/settings/api_key_modal.hbs +++ b/web/templates/settings/api_key_modal.hbs @@ -36,7 +36,7 @@ </button> <div id="api_key_buttons"> <button class="modal__btn dialog_submit_button" id="regenerate_api_key" aria-label="{{t 'Generate new API key' }}">{{t "Generate new API key" }}</button> - <a class="modal__btn dialog_submit_button" id="download_zuliprc" download="zuliprc" tabindex="0">{{t "Download .zuliprc" }}</a> + <a class="modal__btn dialog_submit_button" id="download_zuliprc" download="zuliprc" tabindex="0">{{t "Download zuliprc" }}</a> </div> </footer> </div> diff --git a/zerver/lib/markdown/tabbed_sections.py b/zerver/lib/markdown/tabbed_sections.py index 52d0945fd0152..c707eeb2be698 100644 --- a/zerver/lib/markdown/tabbed_sections.py +++ b/zerver/lib/markdown/tabbed_sections.py @@ -112,6 +112,8 @@ "v6": "Zulip Server 6.0+", "v4": "Zulip Server 4.0+", "all-versions": "All versions", + "for-a-bot": "For a bot", + "for-yourself": "For yourself", }
ivy-llc__ivy-15263
eigh
[ { "content": "# local\nimport ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n to_ivy_arrays_and_back,\n from_zero_dim_arrays_to_scalar,\n)\n\n\n@to_ivy_arrays_and_back\n@from_zero_dim_arrays_to_scalar\ndef eigvalsh(a, /, UPLO=\"L\"):\n return ivy.eigvalsh(a, UPLO=UPLO)\n\n\n@to_ivy_arrays_and_back\ndef eig(a):\n return ivy.eig(a)\n\n\n@from_zero_dim_arrays_to_scalar\ndef eigh(a, /, UPLO=\"L\"):\n return ivy.eigh(a, UPLO=UPLO)\n", "path": "ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py" } ]
[ { "content": "# local\nimport ivy\nfrom ivy.functional.frontends.numpy.func_wrapper import (\n to_ivy_arrays_and_back,\n from_zero_dim_arrays_to_scalar,\n)\n\n\n@to_ivy_arrays_and_back\n@from_zero_dim_arrays_to_scalar\ndef eigvalsh(a, /, UPLO=\"L\"):\n return ivy.eigvalsh(a, UPLO=UPLO)\n\n\n@to_ivy_arrays_and_back\ndef eig(a):\n return ivy.eig(a)\n\n\n@to_ivy_arrays_and_back\n@from_zero_dim_arrays_to_scalar\ndef eigh(a, /, UPLO=\"L\"):\n return ivy.eigh(a, UPLO=UPLO)\n", "path": "ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py" } ]
diff --git a/ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py b/ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py index d974f7e7ff21f..33412f60265d2 100644 --- a/ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py +++ b/ivy/functional/frontends/numpy/linalg/matrix_eigenvalues.py @@ -17,6 +17,7 @@ def eig(a): return ivy.eig(a) +@to_ivy_arrays_and_back @from_zero_dim_arrays_to_scalar def eigh(a, /, UPLO="L"): return ivy.eigh(a, UPLO=UPLO)
hylang__hy-2312
New release It's time for a new release soon. Here are the things I'd like to get done, or at least try to get done, first. If you think you'll make a PR soon that you'd also like to get in for this release, mention that, too. Volunteers to take these tasks on are also welcome. - ~#2291~; ~#2292~ - These are more difficult than I thought. I don't think I'm going to make the release wait for them. - Install bytecode (for Hy and for Hyrule): hylang/hyrule#42; at least partly addresses #1747
[ { "content": "# This file is execfile()d with the current directory set to its containing dir.\n\nimport html\nimport os\nimport re\nimport sys\nimport time\n\nsys.path.insert(0, os.path.abspath(\"..\"))\n\nextensions = [\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.viewcode\",\n \"sphinxcontrib.hydomain\",\n]\n\nfrom get_version import __version__ as hy_version\n\n# Read the Docs might dirty its checkout, so strip the dirty flag.\nhy_version = re.sub(r\"[+.]dirty\\Z\", \"\", hy_version)\n\ntemplates_path = [\"_templates\"]\nsource_suffix = \".rst\"\n\nmaster_doc = \"index\"\n\n# General information about the project.\nproject = \"hy\"\ncopyright = \"%s the authors\" % time.strftime(\"%Y\")\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = \".\".join(hy_version.split(\".\")[:-1])\n# The full version, including alpha/beta/rc tags.\nrelease = hy_version\nhy_descriptive_version = html.escape(hy_version)\nif \"+\" in hy_version:\n hy_descriptive_version += \" <strong style='color: red;'>(unstable)</strong>\"\n\nexclude_patterns = [\"_build\", \"coreteam.rst\"]\nadd_module_names = True\n\npygments_style = \"sphinx\"\n\nimport sphinx_rtd_theme\n\nhtml_theme = \"sphinx_rtd_theme\"\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\nhtml_use_smartypants = False\nhtml_show_sphinx = False\n\nhtml_context = dict(\n hy_descriptive_version=hy_descriptive_version,\n has_active_alpha=True,\n)\n\nhighlight_language = \"clojure\"\n\nintersphinx_mapping = dict(\n py=(\"https://docs.python.org/3/\", None),\n py3_10=(\"https://docs.python.org/3.10/\", None),\n hyrule=(\"https://hyrule.readthedocs.io/en/master/\", None),\n)\n# ** Generate Cheatsheet\nimport json\nfrom itertools import zip_longest\nfrom pathlib import Path\n\n\ndef refize(spec):\n role = \":hy:func:\"\n if isinstance(spec, dict):\n _name = spec[\"name\"]\n uri = spec[\"uri\"]\n if spec.get(\"internal\"):\n role = \":ref:\"\n else:\n uri = spec\n _name = str.split(uri, \".\")[-1]\n return \"{}`{} <{}>`\".format(role, _name, uri)\n\n\ndef format_refs(refs, indent):\n args = [iter(map(refize, refs))]\n ref_groups = zip_longest(*args, fillvalue=\"\")\n return str.join(\n \" \\\\\\n\" + \" \" * (indent + 3),\n [str.join(\" \", ref_group) for ref_group in ref_groups],\n )\n\n\ndef format_row(category, divider_loc):\n return \"{title: <{width}} | {methods}\".format(\n width=divider_loc,\n title=category[\"name\"],\n methods=format_refs(category[\"methods\"], divider_loc),\n )\n\n\ndef format_table(table_spec):\n table_name = table_spec[\"name\"]\n categories = table_spec[\"categories\"]\n longest_cat_name = max(len(category[\"name\"]) for category in categories)\n table = [\n table_name,\n \"-\" * len(table_name),\n \"\",\n \"=\" * longest_cat_name + \" \" + \"=\" * 25,\n *(format_row(category, longest_cat_name) for category in categories),\n \"=\" * longest_cat_name + \" \" + \"=\" * 25,\n \"\",\n ]\n return \"\\n\".join(table)\n\n\n# Modifications to the cheatsheet should be added in `cheatsheet.json`\ncheatsheet_spec = json.loads(Path(\"./docs/cheatsheet.json\").read_text())\ncheatsheet = [\n \"..\",\n \" DO NOT MODIFY THIS FILE. IT IS AUTO GENERATED BY ``conf.py``\",\n \" If you need to change or add methods, modify ``cheatsheet_spec`` in ``conf.py``\",\n \"\",\n \".. _cheatsheet:\",\n \"\",\n \"Cheatsheet\",\n \"==========\",\n \"\",\n *map(format_table, cheatsheet_spec),\n]\nPath(\"./docs/cheatsheet.rst\").write_text(\"\\n\".join(cheatsheet))\n\n\n# ** Sphinx App Setup\n\n\ndef setup(app):\n app.add_css_file(\"overrides.css\")\n", "path": "docs/conf.py" } ]
[ { "content": "# This file is execfile()d with the current directory set to its containing dir.\n\nimport html\nimport os\nimport re\nimport sys\nimport time\n\nsys.path.insert(0, os.path.abspath(\"..\"))\n\nextensions = [\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.viewcode\",\n \"sphinxcontrib.hydomain\",\n]\n\nfrom get_version import __version__ as hy_version\n\n# Read the Docs might dirty its checkout, so strip the dirty flag.\nhy_version = re.sub(r\"[+.]dirty\\Z\", \"\", hy_version)\n\ntemplates_path = [\"_templates\"]\nsource_suffix = \".rst\"\n\nmaster_doc = \"index\"\n\n# General information about the project.\nproject = \"hy\"\ncopyright = \"%s the authors\" % time.strftime(\"%Y\")\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = \".\".join(hy_version.split(\".\")[:-1])\n# The full version, including alpha/beta/rc tags.\nrelease = hy_version\nhy_descriptive_version = html.escape(hy_version)\nif \"+\" in hy_version:\n hy_descriptive_version += \" <strong style='color: red;'>(unstable)</strong>\"\n\nexclude_patterns = [\"_build\", \"coreteam.rst\"]\nadd_module_names = True\n\npygments_style = \"sphinx\"\n\nimport sphinx_rtd_theme\n\nhtml_theme = \"sphinx_rtd_theme\"\nhtml_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\nhtml_use_smartypants = False\nhtml_show_sphinx = False\n\nhtml_context = dict(\n hy_descriptive_version=hy_descriptive_version)\n\nhighlight_language = \"clojure\"\n\nintersphinx_mapping = dict(\n py=(\"https://docs.python.org/3/\", None),\n py3_10=(\"https://docs.python.org/3.10/\", None),\n hyrule=(\"https://hyrule.readthedocs.io/en/master/\", None),\n)\n# ** Generate Cheatsheet\nimport json\nfrom itertools import zip_longest\nfrom pathlib import Path\n\n\ndef refize(spec):\n role = \":hy:func:\"\n if isinstance(spec, dict):\n _name = spec[\"name\"]\n uri = spec[\"uri\"]\n if spec.get(\"internal\"):\n role = \":ref:\"\n else:\n uri = spec\n _name = str.split(uri, \".\")[-1]\n return \"{}`{} <{}>`\".format(role, _name, uri)\n\n\ndef format_refs(refs, indent):\n args = [iter(map(refize, refs))]\n ref_groups = zip_longest(*args, fillvalue=\"\")\n return str.join(\n \" \\\\\\n\" + \" \" * (indent + 3),\n [str.join(\" \", ref_group) for ref_group in ref_groups],\n )\n\n\ndef format_row(category, divider_loc):\n return \"{title: <{width}} | {methods}\".format(\n width=divider_loc,\n title=category[\"name\"],\n methods=format_refs(category[\"methods\"], divider_loc),\n )\n\n\ndef format_table(table_spec):\n table_name = table_spec[\"name\"]\n categories = table_spec[\"categories\"]\n longest_cat_name = max(len(category[\"name\"]) for category in categories)\n table = [\n table_name,\n \"-\" * len(table_name),\n \"\",\n \"=\" * longest_cat_name + \" \" + \"=\" * 25,\n *(format_row(category, longest_cat_name) for category in categories),\n \"=\" * longest_cat_name + \" \" + \"=\" * 25,\n \"\",\n ]\n return \"\\n\".join(table)\n\n\n# Modifications to the cheatsheet should be added in `cheatsheet.json`\ncheatsheet_spec = json.loads(Path(\"./docs/cheatsheet.json\").read_text())\ncheatsheet = [\n \"..\",\n \" DO NOT MODIFY THIS FILE. IT IS AUTO GENERATED BY ``conf.py``\",\n \" If you need to change or add methods, modify ``cheatsheet_spec`` in ``conf.py``\",\n \"\",\n \".. _cheatsheet:\",\n \"\",\n \"Cheatsheet\",\n \"==========\",\n \"\",\n *map(format_table, cheatsheet_spec),\n]\nPath(\"./docs/cheatsheet.rst\").write_text(\"\\n\".join(cheatsheet))\n\n\n# ** Sphinx App Setup\n\n\ndef setup(app):\n app.add_css_file(\"overrides.css\")\n", "path": "docs/conf.py" } ]
diff --git a/NEWS.rst b/NEWS.rst index 9d548edaf..02fd8bba7 100644 --- a/NEWS.rst +++ b/NEWS.rst @@ -1,24 +1,40 @@ .. default-role:: code -Unreleased +0.24.0 (released 2022-06-23) ============================== +This release is a direct successor to 1.0a4. We've returned to 0.* +version numbers to work around the inflexibility of PyPI and pip +regarding the default version to install. (We skipped some version +numbers because this release is several major releases since 0.20.0.) +Sorry for the mess. + Removals ------------------------------ * Tag macros have been removed. Use reader macros instead, rewriting `(defmacro "#foo" [arg] …)` as `(defreader foo (setv arg (.parse-one-form &reader)) …)`. -* `hy.read-str` has been removed. Use `hy.read`, which now accepts - strings, instead. * `with-decorator` and `#@` have been removed in favor of decorator lists (see below). -* `hy.cmdline.run_repl` has been replaced with `hy.cmdline.HyREPL.run`. +* Fraction literals have been removed. Use `fractions.Fraction` + instead. +* Unrecognized backslash escapes in string and byte literals are + no longer allowed. (They've been `deprecated in Python since 3.6 + <https://docs.python.org/3.6/reference/lexical_analysis.html#index-23>`_.) +* A bare `#` is no longer a legal symbol. +* `u` is no longer allowed as a string prefix. (It had no effect, + anyway.) +* `hy.read-str` has been removed. Use `hy.read`, which now accepts + strings, instead. -Breaking Changes +Other Breaking Changes ------------------------------ -* `if` now requires all three arguments. For cases with less than - three arguments (i.e., with no else-clause), `when` is a drop-in - replacement. +* Tuples are now indicated with `#( … )`, as in `#(1 2 3)`, instead of + `(, … )`, as in `(, 1 2 3)`. +* Tuples have their own model type, `hy.models.Tuple`, instead of + being represented as `Expression`\s. +* `if` now requires all three arguments. For the two-argument case + (i.e., with no else-clause), `when` is a drop-in replacement. * `cond` has a new unbracketed syntax:: (cond [a b] [x y z]) ; Old @@ -26,68 +42,61 @@ Breaking Changes * `defmacro` once again requires the macro name as a symbol, not a string literal. -* The parser has been completely rewritten. It is mostly - backwards-compatible, with a few exceptions: - - - Unescaped double quotes are now allowed inside replacement - fields of f-strings. - - Unrecognized backslash escapes in string and byte literals are - now syntax errors. (They've been `deprecated in Python since 3.6 - <https://docs.python.org/3.6/reference/lexical_analysis.html#index-23>`_.) - - ``u`` is no longer allowed as a string prefix. (It had no effect, - anyway.) - - A bare `#` is no longer a legal symbol. - +* Annotations are now indicated by `#^` instead of `^`. +* `annotate` (but not `#^`) now takes the target first and the type + second, as in `(annotate x int)`. +* The way f-strings are parsed has changed, such that unescaped double + quotes are now allowed inside replacement fields. +* Non-ASCII whitespace is no longer ignored during tokenization like + ASCII whitespace. * The mangling rules have been refined to account for Python's treatment of distinct names as referring to the same variable if they're NFKC-equivalent. Very little real code should be affected. -* Non-ASCII whitespace is no longer ignored by the lexer like ASCII - whitespace. -* Tuples are now defined by the form `#(1 2 3)` and compile to the model - `hy.models.Tuple` -* Tuples now wrap to `hy.models.Tuple` during model promotion -* Fraction literals have been removed -* Annotations now use `#^` instead of `^` -* ``annotate`` takes the target first and the type second. i.e. ``(annotate x int)`` +* `hy.cmdline.run_repl` has been replaced with + `hy.cmdline.HyREPL.run`. Bug Fixes ------------------------------ -* Readline is now imported only when necessary to avoid triggering a - CPython bug regarding the standard module `curses` (`bpo-2675`_). -* Elements of `builtins` such as `help` are no longer overridden until - the Hy REPL actually starts. -* Crash when using Keywords as `match` value. +* Fixed a crash when using keyword objects in `match`. * Fixed a scoping bug in comprehensions in `let` bodies. -* Tab completion in the Hy REPL now properly unmangles symbol names. -* `!=` with model objects is now consistent with `=`. -* Module names supplied to `hy -m` are now mangled. * Literal newlines (of all three styles) are now recognized properly in string and bytes literals. -* ``defmacro`` no longer allows arguments after ``#* args`` -* Tracebacks from code parsed with `hy.read` now show source positions. -* Hy now pre-compiles .hy files during setup/installation. +* `defmacro` no longer allows further arguments after `#* args`. +* `!=` with model objects is now consistent with `=`. +* Tracebacks from code parsed with `hy.read` now show source + positions. +* Elements of `builtins` such as `help` are no longer overridden until + the REPL actually starts. +* Readline is now imported only when necessary, to avoid triggering a + CPython bug regarding the standard module `curses` + (`cpython#46927`_). +* Module names supplied to `hy -m` are now mangled. +* Hy now precompiles its own Hy code during installation. New Features ------------------------------ * Added user-defined reader macros, defined with `defreader`. -* Python reserved words are allowed once more as parameter names and - keyword arguments. Hy includes a workaround for a CPython bug that - prevents the generation of legal Python code for these cases - (`bpo-46520`_). * `defn` and `defclass` now allow a decorator list as their first argument. -* You can now set the variable `_hy_export_macros` to control what macros are - collected by `(require module *)` -* New macro `export` * `...` is now understood to refer to `Ellipsis`, as in Python. +* Python reserved words are allowed once more as parameter names and + keyword arguments. Hy includes a workaround for a CPython bug that + prevents the generation of legal Python code for these cases + (`cpython#90678`_). +* New macro `export`. + + - Or you can set the variable `_hy_export_macros` to control what + macros are collected by `(require module *)`. + +* New macro `delmacro`. * New function `hy.read_many`. -* new function `hy.model_patterns.parse_if` +* New function `hy.model_patterns.parse_if`. +* New function `hy.model_patterns.in_tuple`. * Added a command-line option `-u` (or `--unbuffered`) per CPython. -* new function `hy.model_patterns.in_tuple` -* new core macro `delmacro` to delete user defined or require'd macros. +* Tab-completion in the REPL now attempts to unmangle names. -.. _bpo-2675: https://bugs.python.org/issue2675#msg265564 -.. _bpo-46520: https://bugs.python.org/issue46520 +.. _cpython#46927: https://github.com/python/cpython/issues/46927#issuecomment-1093418916 +.. _cpython#90678: https://github.com/python/cpython/issues/90678 1.0a4 (released 2022-01-09) ============================== diff --git a/README.md b/README.md index 62b85ccbe..8cc4d9270 100644 --- a/README.md +++ b/README.md @@ -11,13 +11,13 @@ Hy is a Lisp dialect that's embedded in Python. Since Hy transforms its Lisp code into Python abstract syntax tree (AST) objects, you have the whole beautiful world of Python at your fingertips, in Lisp form. -To install the latest alpha of Hy, just use the command `pip3 install --pre +To install the latest release of Hy, just use the command `pip3 install --user hy`. Then you can start an interactive read-eval-print loop (REPL) with the command `hy`, or run a Hy program with `hy myprogram.hy`. * [Try Hy with a web console](https://hylang.github.io/hy-interpreter) -* [Why Hy?](http://docs.hylang.org/en/alpha/whyhy.html) -* [Tutorial](http://docs.hylang.org/en/alpha/tutorial.html) +* [Why Hy?](http://docs.hylang.org/en/stable/whyhy.html) +* [Tutorial](http://docs.hylang.org/en/stable/tutorial.html) Project ------- @@ -25,8 +25,7 @@ Project * Code: https://github.com/hylang/hy * Documentation: * master, for use with the latest revision on GitHub: http://docs.hylang.org/en/master - * alpha, for use with the latest alpha release: http://hylang.org/en/alpha - * stable, for use with version 0.20.0: http://hylang.org/en/stable + * stable, for use with the latest release on PyPI: http://hylang.org/en/stable * Bug reports: We have no bugs! Your bugs are your own! (https://github.com/hylang/hy/issues) * License: MIT (Expat) * [Hacking on Hy](http://docs.hylang.org/en/master/hacking.html) diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html deleted file mode 100644 index 5a39ae454..000000000 --- a/docs/_templates/layout.html +++ /dev/null @@ -1,15 +0,0 @@ -{% extends "!layout.html" %} - -{% block extrabody %} -{% if has_active_alpha %} -<div class="wy-nav-content-wrap"> - <div id="dev-warning" class="wy-nav-content" style="overflow-wrap: breakword; padding: 1em 1em 0.1em 1em; background: #ffe761;"> - <p> - Hy is currently in an active alpha cycle. Make sure you're looking - at the correct version of the documentation for your install by - using the version selector in the bottom-left corner of this page. - </p> - </div> -</div> -{% endif %} -{% endblock %} diff --git a/docs/conf.py b/docs/conf.py index 986817839..8c00214b3 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -61,9 +61,7 @@ html_show_sphinx = False html_context = dict( - hy_descriptive_version=hy_descriptive_version, - has_active_alpha=True, -) + hy_descriptive_version=hy_descriptive_version) highlight_language = "clojure" diff --git a/docs/index.rst b/docs/index.rst index 7d7cc8de4..2b77e6199 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -14,7 +14,7 @@ Hy is a Lisp dialect that's embedded in Python. Since Hy transforms its Lisp code into Python abstract syntax tree (AST) objects, you have the whole beautiful world of Python at your fingertips, in Lisp form. -To install the latest alpha of Hy, just use the command ``pip3 install --pre +To install the latest release of Hy, just use the command ``pip3 install --user hy``. Then you can start an interactive read-eval-print loop (REPL) with the command ``hy``, or run a Hy program with ``hy myprogram.hy``.
Azure__azure-cli-extensions-4911
`az webpubsub client start` errors with `TypeError: As of 3.10, the *loop* parameter was removed from Lock() since it is no longer necessary` - If the issue is to do with Azure CLI 2.0 in-particular, create an issue here at [Azure/azure-cli](https://github.com/Azure/azure-cli/issues) ### Related command ```console $ az webpubsub client start --name twitch-pubsub --resource-group twitchRG --user user1 --hub-name hub1 The command failed with an unexpected error. Here is the traceback: As of 3.10, the *loop* parameter was removed from Lock() since it is no longer necessary Traceback (most recent call last): File "/opt/az/lib/python3.10/site-packages/knack/cli.py", line 231, in invoke cmd_result = self.invocation.execute(args) File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/__init__.py", line 663, in execute raise ex File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/__init__.py", line 726, in _run_jobs_serially results.append(self._run_job(expanded_arg, cmd_copy)) File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/__init__.py", line 697, in _run_job result = cmd_copy(params) File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/__init__.py", line 333, in __call__ return self.handler(*args, **kwargs) File "/opt/az/lib/python3.10/site-packages/azure/cli/core/commands/command_operation.py", line 121, in handler return op(**command_args) File "/home/anthony/.azure/cliextensions/webpubsub/azext_webpubsub/client.py", line 58, in start_client asyncio.get_event_loop().run_until_complete(connect(token['url'])) File "/opt/az/lib/python3.10/asyncio/base_events.py", line 646, in run_until_complete return future.result() File "/home/anthony/.azure/cliextensions/webpubsub/azext_webpubsub/client.py", line 43, in connect async with websockets.connect(url, subprotocols=['json.webpubsub.azure.v1']) as ws: File "/home/anthony/.azure/cliextensions/webpubsub/websockets/client.py", line 517, in __aenter__ return await self File "/home/anthony/.azure/cliextensions/webpubsub/websockets/client.py", line 535, in __await_impl__ transport, protocol = await self._create_connection() File "/opt/az/lib/python3.10/asyncio/base_events.py", line 1089, in create_connection transport, protocol = await self._create_connection_transport( File "/opt/az/lib/python3.10/asyncio/base_events.py", line 1107, in _create_connection_transport protocol = protocol_factory() File "/home/anthony/.azure/cliextensions/webpubsub/websockets/client.py", line 69, in __init__ super().__init__(**kwargs) File "/home/anthony/.azure/cliextensions/webpubsub/websockets/protocol.py", line 235, in __init__ self._drain_lock = asyncio.Lock( File "/opt/az/lib/python3.10/asyncio/locks.py", line 78, in __init__ super().__init__(loop=loop) File "/opt/az/lib/python3.10/asyncio/mixins.py", line 17, in __init__ raise TypeError( TypeError: As of 3.10, the *loop* parameter was removed from Lock() since it is no longer necessary ``` ### Extension name (the extension in question) webpubsub ### Description of issue (in as much detail as possible) appears this just needs an upgrade I was able to work around by running (I'm in azure cloud shell): ```bash /opt/az/bin/python3.10 -m pip install websockets --upgrade --target ~/.azure/cliextensions/webpubsub/ ```
[ { "content": "#!/usr/bin/env python\n\n# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n\n\nfrom codecs import open\nfrom setuptools import setup, find_packages\ntry:\n from azure_bdist_wheel import cmdclass\nexcept ImportError:\n from distutils import log as logger\n logger.warn(\"Wheel is not available, disabling bdist_wheel hook\")\n\n# TODO: Confirm this is the right version number you want and it matches your\n# HISTORY.rst entry.\nVERSION = '1.1.0'\n\n# The full list of classifiers is available at\n# https://pypi.python.org/pypi?%3Aaction=list_classifiers\nCLASSIFIERS = [\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Developers',\n 'Intended Audience :: System Administrators',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'License :: OSI Approved :: MIT License',\n]\n\n# TODO: Add any additional SDK dependencies here\nDEPENDENCIES = [\n 'websockets~=8.1'\n]\n\nwith open('README.rst', 'r', encoding='utf-8') as f:\n README = f.read()\nwith open('HISTORY.rst', 'r', encoding='utf-8') as f:\n HISTORY = f.read()\n\nsetup(\n name='webpubsub',\n version=VERSION,\n description='Microsoft Azure Command-Line Tools Webpubsub Extension',\n # TODO: Update author and email, if applicable\n author='Microsoft Corporation',\n author_email='[email protected]',\n # TODO: change to your extension source code repo if the code will not be put in azure-cli-extensions repo\n url='https://github.com/Azure/azure-cli-extensions/tree/main/src/webpubsub',\n long_description=README + '\\n\\n' + HISTORY,\n license='MIT',\n classifiers=CLASSIFIERS,\n packages=find_packages(),\n install_requires=DEPENDENCIES,\n package_data={'azext_webpubsub': ['azext_metadata.json']},\n)\n", "path": "src/webpubsub/setup.py" } ]
[ { "content": "#!/usr/bin/env python\n\n# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n\n\nfrom codecs import open\nfrom setuptools import setup, find_packages\ntry:\n from azure_bdist_wheel import cmdclass\nexcept ImportError:\n from distutils import log as logger\n logger.warn(\"Wheel is not available, disabling bdist_wheel hook\")\n\n# TODO: Confirm this is the right version number you want and it matches your\n# HISTORY.rst entry.\nVERSION = '1.1.0'\n\n# The full list of classifiers is available at\n# https://pypi.python.org/pypi?%3Aaction=list_classifiers\nCLASSIFIERS = [\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Developers',\n 'Intended Audience :: System Administrators',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'License :: OSI Approved :: MIT License',\n]\n\n# TODO: Add any additional SDK dependencies here\nDEPENDENCIES = [\n 'websockets>=8.1'\n]\n\nwith open('README.rst', 'r', encoding='utf-8') as f:\n README = f.read()\nwith open('HISTORY.rst', 'r', encoding='utf-8') as f:\n HISTORY = f.read()\n\nsetup(\n name='webpubsub',\n version=VERSION,\n description='Microsoft Azure Command-Line Tools Webpubsub Extension',\n # TODO: Update author and email, if applicable\n author='Microsoft Corporation',\n author_email='[email protected]',\n # TODO: change to your extension source code repo if the code will not be put in azure-cli-extensions repo\n url='https://github.com/Azure/azure-cli-extensions/tree/main/src/webpubsub',\n long_description=README + '\\n\\n' + HISTORY,\n license='MIT',\n classifiers=CLASSIFIERS,\n packages=find_packages(),\n install_requires=DEPENDENCIES,\n package_data={'azext_webpubsub': ['azext_metadata.json']},\n)\n", "path": "src/webpubsub/setup.py" } ]
diff --git a/src/webpubsub/setup.py b/src/webpubsub/setup.py index 7d0b6786e17..0429389e714 100644 --- a/src/webpubsub/setup.py +++ b/src/webpubsub/setup.py @@ -34,7 +34,7 @@ # TODO: Add any additional SDK dependencies here DEPENDENCIES = [ - 'websockets~=8.1' + 'websockets>=8.1' ] with open('README.rst', 'r', encoding='utf-8') as f:
benoitc__gunicorn-1699
"Connection refused" when using keep-alive with gthread Gunicorn version: 19.7.1 (also tried master branch) This bug could be reproduced with attached files: - test_http_gunicorn_raw_keep-alive-requests.py (test runner) - myapp.py (gunicorn app that should be in the same directory) Test case starts command: ``` gunicorn --worker-class gthread --workers 1 --threads 20 --keep-alive 9999 \ --log-level DEBUG --bind localhost:7777 myapp:app ``` with logs redirected to `/tmp/_test_gunicorn.out` and then create separate threads to open http session and send 1000 requests within it. In my case gunicorn server received request `/request/1096` and then reset connection (see `tcp.stream eq 10` from attached `gunicorn_reset_keep_alived_connection.pcapng` NOTE: this is race condition so it may happen that all requests finish with success (see myapp.py -> without `time.sleep` it almost never fails) but with that `time.sleep` it usually fails and then script should output line similar to: `[1] Failed request id=1096 with ('Connection aborted.', error(104, 'Connection reset by peer'))` it mean that thread sent request `GET /request/1096` and received `Connection reset by peer` (so the last one request from that session that succeed is `GET /request/1095`).
[ { "content": "# -*- coding: utf-8 -\n#\n# This file is part of gunicorn released under the MIT license.\n# See the NOTICE for more information.\n\n# design:\n# a threaded worker accepts connections in the main loop, accepted\n# connections are are added to the thread pool as a connection job. On\n# keepalive connections are put back in the loop waiting for an event.\n# If no event happen after the keep alive timeout, the connectoin is\n# closed.\n\nfrom collections import deque\nfrom datetime import datetime\nimport errno\nfrom functools import partial\nimport os\nimport socket\nimport ssl\nimport sys\nfrom threading import RLock\nimport time\n\nfrom .. import http\nfrom ..http import wsgi\nfrom .. import util\nfrom . import base\nfrom .. import six\n\n\ntry:\n import concurrent.futures as futures\nexcept ImportError:\n raise RuntimeError(\"\"\"\n You need to install the 'futures' package to use this worker with this\n Python version.\n \"\"\")\n\ntry:\n from asyncio import selectors\nexcept ImportError:\n from gunicorn import selectors\n\n\nclass TConn(object):\n\n def __init__(self, cfg, sock, client, server):\n self.cfg = cfg\n self.sock = sock\n self.client = client\n self.server = server\n\n self.timeout = None\n self.parser = None\n\n # set the socket to non blocking\n self.sock.setblocking(False)\n\n def init(self):\n self.sock.setblocking(True)\n if self.parser is None:\n # wrap the socket if needed\n if self.cfg.is_ssl:\n self.sock = ssl.wrap_socket(self.sock, server_side=True,\n **self.cfg.ssl_options)\n\n # initialize the parser\n self.parser = http.RequestParser(self.cfg, self.sock)\n\n def set_timeout(self):\n # set the timeout\n self.timeout = time.time() + self.cfg.keepalive\n\n def close(self):\n util.close(self.sock)\n\n def __lt__(self, other):\n return self.timeout < other.timeout\n\n __cmp__ = __lt__\n\n\nclass ThreadWorker(base.Worker):\n\n def __init__(self, *args, **kwargs):\n super(ThreadWorker, self).__init__(*args, **kwargs)\n self.worker_connections = self.cfg.worker_connections\n self.max_keepalived = self.cfg.worker_connections - self.cfg.threads\n # initialise the pool\n self.tpool = None\n self.poller = None\n self._lock = None\n self.futures = deque()\n self._keep = deque()\n self.nr_conns = 0\n\n @classmethod\n def check_config(cls, cfg, log):\n max_keepalived = cfg.worker_connections - cfg.threads\n\n if max_keepalived <= 0 and cfg.keepalive:\n log.warning(\"No keepalived connections can be handled. \" +\n \"Check the number of worker connections and threads.\")\n\n def init_process(self):\n self.tpool = futures.ThreadPoolExecutor(max_workers=self.cfg.threads)\n self.poller = selectors.DefaultSelector()\n self._lock = RLock()\n super(ThreadWorker, self).init_process()\n\n def handle_quit(self, sig, frame):\n self.alive = False\n # worker_int callback\n self.cfg.worker_int(self)\n self.tpool.shutdown(False)\n time.sleep(0.1)\n sys.exit(0)\n\n def _wrap_future(self, fs, conn):\n fs.conn = conn\n self.futures.append(fs)\n fs.add_done_callback(self.finish_request)\n\n def enqueue_req(self, conn):\n conn.init()\n # submit the connection to a worker\n fs = self.tpool.submit(self.handle, conn)\n self._wrap_future(fs, conn)\n\n def accept(self, server, listener):\n try:\n sock, client = listener.accept()\n # initialize the connection object\n conn = TConn(self.cfg, sock, client, server)\n self.nr_conns += 1\n # enqueue the job\n self.enqueue_req(conn)\n except EnvironmentError as e:\n if e.errno not in (errno.EAGAIN,\n errno.ECONNABORTED, errno.EWOULDBLOCK):\n raise\n\n def reuse_connection(self, conn, client):\n with self._lock:\n # unregister the client from the poller\n self.poller.unregister(client)\n # remove the connection from keepalive\n try:\n self._keep.remove(conn)\n except ValueError:\n # race condition\n return\n\n # submit the connection to a worker\n self.enqueue_req(conn)\n\n def murder_keepalived(self):\n now = time.time()\n while True:\n with self._lock:\n try:\n # remove the connection from the queue\n conn = self._keep.popleft()\n except IndexError:\n break\n\n delta = conn.timeout - now\n if delta > 0:\n # add the connection back to the queue\n with self._lock:\n self._keep.appendleft(conn)\n break\n else:\n self.nr_conns -= 1\n # remove the socket from the poller\n with self._lock:\n try:\n self.poller.unregister(conn.sock)\n except EnvironmentError as e:\n if e.errno != errno.EBADF:\n raise\n except KeyError:\n # already removed by the system, continue\n pass\n\n # close the socket\n conn.close()\n\n def is_parent_alive(self):\n # If our parent changed then we shut down.\n if self.ppid != os.getppid():\n self.log.info(\"Parent changed, shutting down: %s\", self)\n return False\n return True\n\n def run(self):\n # init listeners, add them to the event loop\n for sock in self.sockets:\n sock.setblocking(False)\n # a race condition during graceful shutdown may make the listener\n # name unavailable in the request handler so capture it once here\n server = sock.getsockname()\n acceptor = partial(self.accept, server)\n self.poller.register(sock, selectors.EVENT_READ, acceptor)\n\n while self.alive:\n # notify the arbiter we are alive\n self.notify()\n\n # can we accept more connections?\n if self.nr_conns < self.worker_connections:\n # wait for an event\n events = self.poller.select(1.0)\n for key, _ in events:\n callback = key.data\n callback(key.fileobj)\n\n # check (but do not wait) for finished requests\n result = futures.wait(self.futures, timeout=0,\n return_when=futures.FIRST_COMPLETED)\n else:\n # wait for a request to finish\n result = futures.wait(self.futures, timeout=1.0,\n return_when=futures.FIRST_COMPLETED)\n\n # clean up finished requests\n for fut in result.done:\n self.futures.remove(fut)\n\n if not self.is_parent_alive():\n break\n\n # hanle keepalive timeouts\n self.murder_keepalived()\n\n self.tpool.shutdown(False)\n self.poller.close()\n\n for s in self.sockets:\n s.close()\n\n futures.wait(self.futures, timeout=self.cfg.graceful_timeout)\n\n def finish_request(self, fs):\n if fs.cancelled():\n self.nr_conns -= 1\n fs.conn.close()\n return\n\n try:\n (keepalive, conn) = fs.result()\n # if the connection should be kept alived add it\n # to the eventloop and record it\n if keepalive:\n # flag the socket as non blocked\n conn.sock.setblocking(False)\n\n # register the connection\n conn.set_timeout()\n with self._lock:\n self._keep.append(conn)\n\n # add the socket to the event loop\n self.poller.register(conn.sock, selectors.EVENT_READ,\n partial(self.reuse_connection, conn))\n else:\n self.nr_conns -= 1\n conn.close()\n except:\n # an exception happened, make sure to close the\n # socket.\n self.nr_conns -= 1\n fs.conn.close()\n\n def handle(self, conn):\n keepalive = False\n req = None\n try:\n req = six.next(conn.parser)\n if not req:\n return (False, conn)\n\n # handle the request\n keepalive = self.handle_request(req, conn)\n if keepalive:\n return (keepalive, conn)\n except http.errors.NoMoreData as e:\n self.log.debug(\"Ignored premature client disconnection. %s\", e)\n\n except StopIteration as e:\n self.log.debug(\"Closing connection. %s\", e)\n except ssl.SSLError as e:\n if e.args[0] == ssl.SSL_ERROR_EOF:\n self.log.debug(\"ssl connection closed\")\n conn.sock.close()\n else:\n self.log.debug(\"Error processing SSL request.\")\n self.handle_error(req, conn.sock, conn.client, e)\n\n except EnvironmentError as e:\n if e.errno not in (errno.EPIPE, errno.ECONNRESET):\n self.log.exception(\"Socket error processing request.\")\n else:\n if e.errno == errno.ECONNRESET:\n self.log.debug(\"Ignoring connection reset\")\n else:\n self.log.debug(\"Ignoring connection epipe\")\n except Exception as e:\n self.handle_error(req, conn.sock, conn.client, e)\n\n return (False, conn)\n\n def handle_request(self, req, conn):\n environ = {}\n resp = None\n try:\n self.cfg.pre_request(self, req)\n request_start = datetime.now()\n resp, environ = wsgi.create(req, conn.sock, conn.client,\n conn.server, self.cfg)\n environ[\"wsgi.multithread\"] = True\n self.nr += 1\n if self.alive and self.nr >= self.max_requests:\n self.log.info(\"Autorestarting worker after current request.\")\n resp.force_close()\n self.alive = False\n\n if not self.cfg.keepalive:\n resp.force_close()\n elif len(self._keep) >= self.max_keepalived:\n resp.force_close()\n\n respiter = self.wsgi(environ, resp.start_response)\n try:\n if isinstance(respiter, environ['wsgi.file_wrapper']):\n resp.write_file(respiter)\n else:\n for item in respiter:\n resp.write(item)\n\n resp.close()\n request_time = datetime.now() - request_start\n self.log.access(resp, req, environ, request_time)\n finally:\n if hasattr(respiter, \"close\"):\n respiter.close()\n\n if resp.should_close():\n self.log.debug(\"Closing connection.\")\n return False\n except EnvironmentError:\n # pass to next try-except level\n six.reraise(*sys.exc_info())\n except Exception:\n if resp and resp.headers_sent:\n # If the requests have already been sent, we should close the\n # connection to indicate the error.\n self.log.exception(\"Error handling request\")\n try:\n conn.sock.shutdown(socket.SHUT_RDWR)\n conn.sock.close()\n except EnvironmentError:\n pass\n raise StopIteration()\n raise\n finally:\n try:\n self.cfg.post_request(self, req, environ, resp)\n except Exception:\n self.log.exception(\"Exception in post_request hook\")\n\n return True\n", "path": "gunicorn/workers/gthread.py" } ]
[ { "content": "# -*- coding: utf-8 -\n#\n# This file is part of gunicorn released under the MIT license.\n# See the NOTICE for more information.\n\n# design:\n# a threaded worker accepts connections in the main loop, accepted\n# connections are are added to the thread pool as a connection job. On\n# keepalive connections are put back in the loop waiting for an event.\n# If no event happen after the keep alive timeout, the connectoin is\n# closed.\n\nfrom collections import deque\nfrom datetime import datetime\nimport errno\nfrom functools import partial\nimport os\nimport socket\nimport ssl\nimport sys\nfrom threading import RLock\nimport time\n\nfrom .. import http\nfrom ..http import wsgi\nfrom .. import util\nfrom . import base\nfrom .. import six\n\n\ntry:\n import concurrent.futures as futures\nexcept ImportError:\n raise RuntimeError(\"\"\"\n You need to install the 'futures' package to use this worker with this\n Python version.\n \"\"\")\n\ntry:\n from asyncio import selectors\nexcept ImportError:\n from gunicorn import selectors\n\n\nclass TConn(object):\n\n def __init__(self, cfg, sock, client, server):\n self.cfg = cfg\n self.sock = sock\n self.client = client\n self.server = server\n\n self.timeout = None\n self.parser = None\n\n # set the socket to non blocking\n self.sock.setblocking(False)\n\n def init(self):\n self.sock.setblocking(True)\n if self.parser is None:\n # wrap the socket if needed\n if self.cfg.is_ssl:\n self.sock = ssl.wrap_socket(self.sock, server_side=True,\n **self.cfg.ssl_options)\n\n # initialize the parser\n self.parser = http.RequestParser(self.cfg, self.sock)\n\n def set_timeout(self):\n # set the timeout\n self.timeout = time.time() + self.cfg.keepalive\n\n def close(self):\n util.close(self.sock)\n\n\nclass ThreadWorker(base.Worker):\n\n def __init__(self, *args, **kwargs):\n super(ThreadWorker, self).__init__(*args, **kwargs)\n self.worker_connections = self.cfg.worker_connections\n self.max_keepalived = self.cfg.worker_connections - self.cfg.threads\n # initialise the pool\n self.tpool = None\n self.poller = None\n self._lock = None\n self.futures = deque()\n self._keep = deque()\n self.nr_conns = 0\n\n @classmethod\n def check_config(cls, cfg, log):\n max_keepalived = cfg.worker_connections - cfg.threads\n\n if max_keepalived <= 0 and cfg.keepalive:\n log.warning(\"No keepalived connections can be handled. \" +\n \"Check the number of worker connections and threads.\")\n\n def init_process(self):\n self.tpool = futures.ThreadPoolExecutor(max_workers=self.cfg.threads)\n self.poller = selectors.DefaultSelector()\n self._lock = RLock()\n super(ThreadWorker, self).init_process()\n\n def handle_quit(self, sig, frame):\n self.alive = False\n # worker_int callback\n self.cfg.worker_int(self)\n self.tpool.shutdown(False)\n time.sleep(0.1)\n sys.exit(0)\n\n def _wrap_future(self, fs, conn):\n fs.conn = conn\n self.futures.append(fs)\n fs.add_done_callback(self.finish_request)\n\n def enqueue_req(self, conn):\n conn.init()\n # submit the connection to a worker\n fs = self.tpool.submit(self.handle, conn)\n self._wrap_future(fs, conn)\n\n def accept(self, server, listener):\n try:\n sock, client = listener.accept()\n # initialize the connection object\n conn = TConn(self.cfg, sock, client, server)\n self.nr_conns += 1\n # enqueue the job\n self.enqueue_req(conn)\n except EnvironmentError as e:\n if e.errno not in (errno.EAGAIN,\n errno.ECONNABORTED, errno.EWOULDBLOCK):\n raise\n\n def reuse_connection(self, conn, client):\n with self._lock:\n # unregister the client from the poller\n self.poller.unregister(client)\n # remove the connection from keepalive\n try:\n self._keep.remove(conn)\n except ValueError:\n # race condition\n return\n\n # submit the connection to a worker\n self.enqueue_req(conn)\n\n def murder_keepalived(self):\n now = time.time()\n while True:\n with self._lock:\n try:\n # remove the connection from the queue\n conn = self._keep.popleft()\n except IndexError:\n break\n\n delta = conn.timeout - now\n if delta > 0:\n # add the connection back to the queue\n with self._lock:\n self._keep.appendleft(conn)\n break\n else:\n self.nr_conns -= 1\n # remove the socket from the poller\n with self._lock:\n try:\n self.poller.unregister(conn.sock)\n except EnvironmentError as e:\n if e.errno != errno.EBADF:\n raise\n except KeyError:\n # already removed by the system, continue\n pass\n\n # close the socket\n conn.close()\n\n def is_parent_alive(self):\n # If our parent changed then we shut down.\n if self.ppid != os.getppid():\n self.log.info(\"Parent changed, shutting down: %s\", self)\n return False\n return True\n\n def run(self):\n # init listeners, add them to the event loop\n for sock in self.sockets:\n sock.setblocking(False)\n # a race condition during graceful shutdown may make the listener\n # name unavailable in the request handler so capture it once here\n server = sock.getsockname()\n acceptor = partial(self.accept, server)\n self.poller.register(sock, selectors.EVENT_READ, acceptor)\n\n while self.alive:\n # notify the arbiter we are alive\n self.notify()\n\n # can we accept more connections?\n if self.nr_conns < self.worker_connections:\n # wait for an event\n events = self.poller.select(1.0)\n for key, _ in events:\n callback = key.data\n callback(key.fileobj)\n\n # check (but do not wait) for finished requests\n result = futures.wait(self.futures, timeout=0,\n return_when=futures.FIRST_COMPLETED)\n else:\n # wait for a request to finish\n result = futures.wait(self.futures, timeout=1.0,\n return_when=futures.FIRST_COMPLETED)\n\n # clean up finished requests\n for fut in result.done:\n self.futures.remove(fut)\n\n if not self.is_parent_alive():\n break\n\n # hanle keepalive timeouts\n self.murder_keepalived()\n\n self.tpool.shutdown(False)\n self.poller.close()\n\n for s in self.sockets:\n s.close()\n\n futures.wait(self.futures, timeout=self.cfg.graceful_timeout)\n\n def finish_request(self, fs):\n if fs.cancelled():\n self.nr_conns -= 1\n fs.conn.close()\n return\n\n try:\n (keepalive, conn) = fs.result()\n # if the connection should be kept alived add it\n # to the eventloop and record it\n if keepalive:\n # flag the socket as non blocked\n conn.sock.setblocking(False)\n\n # register the connection\n conn.set_timeout()\n with self._lock:\n self._keep.append(conn)\n\n # add the socket to the event loop\n self.poller.register(conn.sock, selectors.EVENT_READ,\n partial(self.reuse_connection, conn))\n else:\n self.nr_conns -= 1\n conn.close()\n except:\n # an exception happened, make sure to close the\n # socket.\n self.nr_conns -= 1\n fs.conn.close()\n\n def handle(self, conn):\n keepalive = False\n req = None\n try:\n req = six.next(conn.parser)\n if not req:\n return (False, conn)\n\n # handle the request\n keepalive = self.handle_request(req, conn)\n if keepalive:\n return (keepalive, conn)\n except http.errors.NoMoreData as e:\n self.log.debug(\"Ignored premature client disconnection. %s\", e)\n\n except StopIteration as e:\n self.log.debug(\"Closing connection. %s\", e)\n except ssl.SSLError as e:\n if e.args[0] == ssl.SSL_ERROR_EOF:\n self.log.debug(\"ssl connection closed\")\n conn.sock.close()\n else:\n self.log.debug(\"Error processing SSL request.\")\n self.handle_error(req, conn.sock, conn.client, e)\n\n except EnvironmentError as e:\n if e.errno not in (errno.EPIPE, errno.ECONNRESET):\n self.log.exception(\"Socket error processing request.\")\n else:\n if e.errno == errno.ECONNRESET:\n self.log.debug(\"Ignoring connection reset\")\n else:\n self.log.debug(\"Ignoring connection epipe\")\n except Exception as e:\n self.handle_error(req, conn.sock, conn.client, e)\n\n return (False, conn)\n\n def handle_request(self, req, conn):\n environ = {}\n resp = None\n try:\n self.cfg.pre_request(self, req)\n request_start = datetime.now()\n resp, environ = wsgi.create(req, conn.sock, conn.client,\n conn.server, self.cfg)\n environ[\"wsgi.multithread\"] = True\n self.nr += 1\n if self.alive and self.nr >= self.max_requests:\n self.log.info(\"Autorestarting worker after current request.\")\n resp.force_close()\n self.alive = False\n\n if not self.cfg.keepalive:\n resp.force_close()\n elif len(self._keep) >= self.max_keepalived:\n resp.force_close()\n\n respiter = self.wsgi(environ, resp.start_response)\n try:\n if isinstance(respiter, environ['wsgi.file_wrapper']):\n resp.write_file(respiter)\n else:\n for item in respiter:\n resp.write(item)\n\n resp.close()\n request_time = datetime.now() - request_start\n self.log.access(resp, req, environ, request_time)\n finally:\n if hasattr(respiter, \"close\"):\n respiter.close()\n\n if resp.should_close():\n self.log.debug(\"Closing connection.\")\n return False\n except EnvironmentError:\n # pass to next try-except level\n six.reraise(*sys.exc_info())\n except Exception:\n if resp and resp.headers_sent:\n # If the requests have already been sent, we should close the\n # connection to indicate the error.\n self.log.exception(\"Error handling request\")\n try:\n conn.sock.shutdown(socket.SHUT_RDWR)\n conn.sock.close()\n except EnvironmentError:\n pass\n raise StopIteration()\n raise\n finally:\n try:\n self.cfg.post_request(self, req, environ, resp)\n except Exception:\n self.log.exception(\"Exception in post_request hook\")\n\n return True\n", "path": "gunicorn/workers/gthread.py" } ]
diff --git a/gunicorn/workers/gthread.py b/gunicorn/workers/gthread.py index 5f918e2e7..862f873d8 100644 --- a/gunicorn/workers/gthread.py +++ b/gunicorn/workers/gthread.py @@ -74,11 +74,6 @@ def set_timeout(self): def close(self): util.close(self.sock) - def __lt__(self, other): - return self.timeout < other.timeout - - __cmp__ = __lt__ - class ThreadWorker(base.Worker):
ManimCommunity__manim-2740
Documentation Bug: Cylinder.get_direction() The documentation `get_direction` method for `Cylinder` mentions a function called `shoelace_direction` which returns a string, either "CW" or "CCW". However, the implementation of `get_direction` returns a 3d vector. This is the correct behavior in this context, but the documentation is incorrect.
[ { "content": "\"\"\"Three-dimensional mobjects.\"\"\"\n\nfrom __future__ import annotations\n\n__all__ = [\n \"ThreeDVMobject\",\n \"Surface\",\n \"Sphere\",\n \"Dot3D\",\n \"Cube\",\n \"Prism\",\n \"Cone\",\n \"Arrow3D\",\n \"Cylinder\",\n \"Line3D\",\n \"Torus\",\n]\n\n\nfrom typing import *\n\nimport numpy as np\nfrom colour import Color\n\nfrom manim import config\nfrom manim.constants import *\nfrom manim.mobject.geometry.arc import Circle\nfrom manim.mobject.geometry.polygram import Square\nfrom manim.mobject.mobject import *\nfrom manim.mobject.opengl.opengl_compatibility import ConvertToOpenGL\nfrom manim.mobject.opengl.opengl_mobject import OpenGLMobject\nfrom manim.mobject.types.vectorized_mobject import VGroup, VMobject\nfrom manim.utils.color import *\nfrom manim.utils.iterables import tuplify\nfrom manim.utils.space_ops import normalize, perpendicular_bisector, z_to_vector\n\n\nclass ThreeDVMobject(VMobject, metaclass=ConvertToOpenGL):\n def __init__(self, shade_in_3d=True, **kwargs):\n super().__init__(shade_in_3d=shade_in_3d, **kwargs)\n\n\nclass Surface(VGroup, metaclass=ConvertToOpenGL):\n \"\"\"Creates a Parametric Surface using a checkerboard pattern.\n\n Parameters\n ----------\n func :\n The function that defines the surface.\n u_range :\n The range of the ``u`` variable: ``(u_min, u_max)``.\n v_range :\n The range of the ``v`` variable: ``(v_min, v_max)``.\n resolution :\n The number of samples taken of the surface. A tuple\n can be used to define different resolutions for ``u`` and\n ``v`` respectively.\n\n Examples\n --------\n .. manim:: ParaSurface\n :save_last_frame:\n\n class ParaSurface(ThreeDScene):\n def func(self, u, v):\n return np.array([np.cos(u) * np.cos(v), np.cos(u) * np.sin(v), u])\n\n def construct(self):\n axes = ThreeDAxes(x_range=[-4,4], x_length=8)\n surface = Surface(\n lambda u, v: axes.c2p(*self.func(u, v)),\n u_range=[-PI, PI],\n v_range=[0, TAU]\n )\n self.set_camera_orientation(theta=70 * DEGREES, phi=75 * DEGREES)\n self.add(axes, surface)\n \"\"\"\n\n def __init__(\n self,\n func: Callable[[float, float], np.ndarray],\n u_range: Sequence[float] = [0, 1],\n v_range: Sequence[float] = [0, 1],\n resolution: Sequence[int] = 32,\n surface_piece_config: dict = {},\n fill_color: Color = BLUE_D,\n fill_opacity: float = 1.0,\n checkerboard_colors: Sequence[Color] = [BLUE_D, BLUE_E],\n stroke_color: Color = LIGHT_GREY,\n stroke_width: float = 0.5,\n should_make_jagged: bool = False,\n pre_function_handle_to_anchor_scale_factor: float = 0.00001,\n **kwargs,\n ) -> None:\n self.u_range = u_range\n self.v_range = v_range\n super().__init__(**kwargs)\n self.resolution = resolution\n self.surface_piece_config = surface_piece_config\n self.fill_color = fill_color\n self.fill_opacity = fill_opacity\n self.checkerboard_colors = checkerboard_colors\n self.stroke_color = stroke_color\n self.stroke_width = stroke_width\n self.should_make_jagged = should_make_jagged\n self.pre_function_handle_to_anchor_scale_factor = (\n pre_function_handle_to_anchor_scale_factor\n )\n self.func = func\n self._setup_in_uv_space()\n self.apply_function(lambda p: func(p[0], p[1]))\n if self.should_make_jagged:\n self.make_jagged()\n\n def _get_u_values_and_v_values(self):\n res = tuplify(self.resolution)\n if len(res) == 1:\n u_res = v_res = res[0]\n else:\n u_res, v_res = res\n\n u_values = np.linspace(*self.u_range, u_res + 1)\n v_values = np.linspace(*self.v_range, v_res + 1)\n\n return u_values, v_values\n\n def _setup_in_uv_space(self):\n u_values, v_values = self._get_u_values_and_v_values()\n faces = VGroup()\n for i in range(len(u_values) - 1):\n for j in range(len(v_values) - 1):\n u1, u2 = u_values[i : i + 2]\n v1, v2 = v_values[j : j + 2]\n face = ThreeDVMobject()\n face.set_points_as_corners(\n [\n [u1, v1, 0],\n [u2, v1, 0],\n [u2, v2, 0],\n [u1, v2, 0],\n [u1, v1, 0],\n ],\n )\n faces.add(face)\n face.u_index = i\n face.v_index = j\n face.u1 = u1\n face.u2 = u2\n face.v1 = v1\n face.v2 = v2\n faces.set_fill(color=self.fill_color, opacity=self.fill_opacity)\n faces.set_stroke(\n color=self.stroke_color,\n width=self.stroke_width,\n opacity=self.stroke_opacity,\n )\n self.add(*faces)\n if self.checkerboard_colors:\n self.set_fill_by_checkerboard(*self.checkerboard_colors)\n\n def set_fill_by_checkerboard(self, *colors, opacity=None):\n n_colors = len(colors)\n for face in self:\n c_index = (face.u_index + face.v_index) % n_colors\n face.set_fill(colors[c_index], opacity=opacity)\n return self\n\n def set_fill_by_value(\n self,\n axes: Mobject,\n colors: Union[Iterable[Color], Color],\n axis: int = 2,\n ):\n \"\"\"Sets the color of each mobject of a parametric surface to a color relative to its axis-value\n\n Parameters\n ----------\n axes :\n The axes for the parametric surface, which will be used to map axis-values to colors.\n colors :\n A list of colors, ordered from lower axis-values to higher axis-values. If a list of tuples is passed\n containing colors paired with numbers, then those numbers will be used as the pivots.\n axis :\n The chosen axis to use for the color mapping. (0 = x, 1 = y, 2 = z)\n\n Returns\n -------\n :class:`~.Surface`\n The parametric surface with a gradient applied by value. For chaining.\n\n Examples\n --------\n .. manim:: FillByValueExample\n :save_last_frame:\n\n class FillByValueExample(ThreeDScene):\n def construct(self):\n resolution_fa = 42\n self.set_camera_orientation(phi=75 * DEGREES, theta=-160 * DEGREES)\n axes = ThreeDAxes(x_range=(0, 5, 1), y_range=(0, 5, 1), z_range=(-1, 1, 0.5))\n def param_surface(u, v):\n x = u\n y = v\n z = np.sin(x) * np.cos(y)\n return z\n surface_plane = Surface(\n lambda u, v: axes.c2p(u, v, param_surface(u, v)),\n resolution=(resolution_fa, resolution_fa),\n v_range=[0, 5],\n u_range=[0, 5],\n )\n surface_plane.set_style(fill_opacity=1)\n surface_plane.set_fill_by_value(axes=axes, colors=[(RED, -0.5), (YELLOW, 0), (GREEN, 0.5)], axis=2)\n self.add(axes, surface_plane)\n \"\"\"\n\n ranges = [axes.x_range, axes.y_range, axes.z_range]\n\n if type(colors[0]) is tuple:\n new_colors, pivots = [[i for i, j in colors], [j for i, j in colors]]\n else:\n new_colors = colors\n\n pivot_min = ranges[axis][0]\n pivot_max = ranges[axis][1]\n pivot_frequency = (pivot_max - pivot_min) / (len(new_colors) - 1)\n pivots = np.arange(\n start=pivot_min,\n stop=pivot_max + pivot_frequency,\n step=pivot_frequency,\n )\n\n for mob in self.family_members_with_points():\n axis_value = axes.point_to_coords(mob.get_midpoint())[axis]\n if axis_value <= pivots[0]:\n mob.set_color(new_colors[0])\n elif axis_value >= pivots[-1]:\n mob.set_color(new_colors[-1])\n else:\n for i, pivot in enumerate(pivots):\n if pivot > axis_value:\n color_index = (axis_value - pivots[i - 1]) / (\n pivots[i] - pivots[i - 1]\n )\n color_index = min(color_index, 1)\n mob_color = interpolate_color(\n new_colors[i - 1],\n new_colors[i],\n color_index,\n )\n if config.renderer == \"opengl\":\n mob.set_color(mob_color, recurse=False)\n else:\n mob.set_color(mob_color, family=False)\n break\n\n return self\n\n\n# Specific shapes\n\n\nclass Sphere(Surface):\n \"\"\"A mobject representing a three-dimensional sphere.\n\n Examples\n ---------\n\n .. manim:: ExampleSphere\n :save_last_frame:\n\n class ExampleSphere(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(phi=PI / 6, theta=PI / 6)\n sphere1 = Sphere(\n center=(3, 0, 0),\n radius=1,\n resolution=(20, 20),\n u_range=[0.001, PI - 0.001],\n v_range=[0, TAU]\n )\n sphere1.set_color(RED)\n self.add(sphere1)\n sphere2 = Sphere(center=(-1, -3, 0), radius=2, resolution=(18, 18))\n sphere2.set_color(GREEN)\n self.add(sphere2)\n sphere3 = Sphere(center=(-1, 2, 0), radius=2, resolution=(16, 16))\n sphere3.set_color(BLUE)\n self.add(sphere3)\n \"\"\"\n\n def __init__(\n self,\n center=ORIGIN,\n radius=1,\n resolution=None,\n u_range=(0, TAU),\n v_range=(0, PI),\n **kwargs,\n ):\n if config.renderer == \"opengl\":\n res_value = (101, 51)\n else:\n res_value = (24, 12)\n\n resolution = resolution if resolution is not None else res_value\n\n self.radius = radius\n\n super().__init__(\n self.func,\n resolution=resolution,\n u_range=u_range,\n v_range=v_range,\n **kwargs,\n )\n\n self.shift(center)\n\n def func(self, u, v):\n return self.radius * np.array(\n [np.cos(u) * np.sin(v), np.sin(u) * np.sin(v), -np.cos(v)],\n )\n\n\nclass Dot3D(Sphere):\n \"\"\"A spherical dot.\n\n Parameters\n --------\n point : Union[:class:`list`, :class:`numpy.ndarray`], optional\n The location of the dot.\n radius : :class:`float`, optional\n The radius of the dot.\n color : :class:`~.Colors`, optional\n The color of the :class:`Dot3D`\n\n Examples\n --------\n\n .. manim:: Dot3DExample\n :save_last_frame:\n\n class Dot3DExample(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(phi=75*DEGREES, theta=-45*DEGREES)\n\n axes = ThreeDAxes()\n dot_1 = Dot3D(point=axes.coords_to_point(0, 0, 1), color=RED)\n dot_2 = Dot3D(point=axes.coords_to_point(2, 0, 0), radius=0.1, color=BLUE)\n dot_3 = Dot3D(point=[0, 0, 0], radius=0.1, color=ORANGE)\n self.add(axes, dot_1, dot_2,dot_3)\n \"\"\"\n\n def __init__(\n self,\n point=ORIGIN,\n radius=DEFAULT_DOT_RADIUS,\n color=WHITE,\n resolution=(8, 8),\n **kwargs,\n ):\n super().__init__(center=point, radius=radius, resolution=resolution, **kwargs)\n self.set_color(color)\n\n\nclass Cube(VGroup):\n def __init__(\n self,\n side_length=2,\n fill_opacity=0.75,\n fill_color=BLUE,\n stroke_width=0,\n **kwargs,\n ):\n self.side_length = side_length\n super().__init__(\n fill_color=fill_color,\n fill_opacity=fill_opacity,\n stroke_width=stroke_width,\n **kwargs,\n )\n\n def generate_points(self):\n for vect in IN, OUT, LEFT, RIGHT, UP, DOWN:\n face = Square(\n side_length=self.side_length,\n shade_in_3d=True,\n )\n face.flip()\n face.shift(self.side_length * OUT / 2.0)\n face.apply_matrix(z_to_vector(vect))\n\n self.add(face)\n\n init_points = generate_points\n\n\nclass Prism(Cube):\n \"\"\"A cuboid.\n\n Examples\n --------\n\n .. manim:: ExamplePrism\n :save_last_frame:\n\n class ExamplePrism(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(phi=60 * DEGREES, theta=150 * DEGREES)\n prismSmall = Prism(dimensions=[1, 2, 3]).rotate(PI / 2)\n prismLarge = Prism(dimensions=[1.5, 3, 4.5]).move_to([2, 0, 0])\n self.add(prismSmall, prismLarge)\n \"\"\"\n\n def __init__(self, dimensions=[3, 2, 1], **kwargs):\n self.dimensions = dimensions\n super().__init__(**kwargs)\n\n def generate_points(self):\n super().generate_points()\n for dim, value in enumerate(self.dimensions):\n self.rescale_to_fit(value, dim, stretch=True)\n\n\nclass Cone(Surface):\n \"\"\"A circular cone.\n Can be defined using 2 parameters: its height, and its base radius.\n The polar angle, theta, can be calculated using arctan(base_radius /\n height) The spherical radius, r, is calculated using the pythagorean\n theorem.\n\n Examples\n --------\n .. manim:: ExampleCone\n :save_last_frame:\n\n class ExampleCone(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n cone = Cone(direction=X_AXIS+Y_AXIS+2*Z_AXIS)\n self.set_camera_orientation(phi=5*PI/11, theta=PI/9)\n self.add(axes, cone)\n\n Parameters\n --------\n base_radius : :class:`float`\n The base radius from which the cone tapers.\n height : :class:`float`\n The height measured from the plane formed by the base_radius to the apex of the cone.\n direction : :class:`numpy.array`\n The direction of the apex.\n show_base : :class:`bool`\n Whether to show the base plane or not.\n v_range : :class:`Sequence[float]`\n The azimuthal angle to start and end at.\n u_min : :class:`float`\n The radius at the apex.\n checkerboard_colors : :class:`bool`\n Show checkerboard grid texture on the cone.\n \"\"\"\n\n def __init__(\n self,\n base_radius=1,\n height=1,\n direction=Z_AXIS,\n show_base=False,\n v_range=[0, TAU],\n u_min=0,\n checkerboard_colors=False,\n **kwargs,\n ):\n self.direction = direction\n self.theta = PI - np.arctan(base_radius / height)\n\n super().__init__(\n self.func,\n v_range=v_range,\n u_range=[u_min, np.sqrt(base_radius**2 + height**2)],\n checkerboard_colors=checkerboard_colors,\n **kwargs,\n )\n # used for rotations\n self._current_theta = 0\n self._current_phi = 0\n\n if show_base:\n self.base_circle = Circle(\n radius=base_radius,\n color=self.fill_color,\n fill_opacity=self.fill_opacity,\n stroke_width=0,\n )\n self.base_circle.shift(height * IN)\n self.add(self.base_circle)\n\n self._rotate_to_direction()\n\n def func(self, u, v):\n \"\"\"Converts from spherical coordinates to cartesian.\n Parameters\n ---------\n u : :class:`float`\n The radius.\n v : :class:`float`\n The azimuthal angle.\n \"\"\"\n r = u\n phi = v\n return np.array(\n [\n r * np.sin(self.theta) * np.cos(phi),\n r * np.sin(self.theta) * np.sin(phi),\n r * np.cos(self.theta),\n ],\n )\n\n def _rotate_to_direction(self):\n x, y, z = self.direction\n\n r = np.sqrt(x**2 + y**2 + z**2)\n if r > 0:\n theta = np.arccos(z / r)\n else:\n theta = 0\n\n if x == 0:\n if y == 0: # along the z axis\n phi = 0\n else:\n phi = np.arctan(np.inf)\n if y < 0:\n phi += PI\n else:\n phi = np.arctan(y / x)\n if x < 0:\n phi += PI\n\n # Undo old rotation (in reverse order)\n self.rotate(-self._current_phi, Z_AXIS, about_point=ORIGIN)\n self.rotate(-self._current_theta, Y_AXIS, about_point=ORIGIN)\n\n # Do new rotation\n self.rotate(theta, Y_AXIS, about_point=ORIGIN)\n self.rotate(phi, Z_AXIS, about_point=ORIGIN)\n\n # Store values\n self._current_theta = theta\n self._current_phi = phi\n\n def set_direction(self, direction):\n self.direction = direction\n self._rotate_to_direction()\n\n def get_direction(self):\n return self.direction\n\n\nclass Cylinder(Surface):\n \"\"\"A cylinder, defined by its height, radius and direction,\n\n Examples\n ---------\n .. manim:: ExampleCylinder\n :save_last_frame:\n\n class ExampleCylinder(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n cylinder = Cylinder(radius=2, height=3)\n self.set_camera_orientation(phi=75 * DEGREES, theta=30 * DEGREES)\n self.add(axes, cylinder)\n\n Parameters\n ---------\n radius : :class:`float`\n The radius of the cylinder.\n height : :class:`float`\n The height of the cylinder.\n direction : :class:`numpy.array`\n The direction of the central axis of the cylinder.\n v_range : :class:`Sequence[float]`\n The height along the height axis (given by direction) to start and end on.\n show_ends : :class:`bool`\n Whether to show the end caps or not.\n \"\"\"\n\n def __init__(\n self,\n radius=1,\n height=2,\n direction=Z_AXIS,\n v_range=[0, TAU],\n show_ends=True,\n resolution=(24, 24),\n **kwargs,\n ):\n self._height = height\n self.radius = radius\n super().__init__(\n self.func,\n resolution=resolution,\n u_range=[-self._height / 2, self._height / 2],\n v_range=v_range,\n **kwargs,\n )\n if show_ends:\n self.add_bases()\n self._current_phi = 0\n self._current_theta = 0\n self.set_direction(direction)\n\n def func(self, u, v):\n \"\"\"Converts from cylindrical coordinates to cartesian.\n Parameters\n ---------\n u : :class:`float`\n The height.\n v : :class:`float`\n The azimuthal angle.\n \"\"\"\n height = u\n phi = v\n r = self.radius\n return np.array([r * np.cos(phi), r * np.sin(phi), height])\n\n def add_bases(self):\n \"\"\"Adds the end caps of the cylinder.\"\"\"\n color = self.color if config[\"renderer\"] == \"opengl\" else self.fill_color\n opacity = self.opacity if config[\"renderer\"] == \"opengl\" else self.fill_opacity\n self.base_top = Circle(\n radius=self.radius,\n color=color,\n fill_opacity=opacity,\n shade_in_3d=True,\n stroke_width=0,\n )\n self.base_top.shift(self.u_range[1] * IN)\n self.base_bottom = Circle(\n radius=self.radius,\n color=color,\n fill_opacity=opacity,\n shade_in_3d=True,\n stroke_width=0,\n )\n self.base_bottom.shift(self.u_range[0] * IN)\n self.add(self.base_top, self.base_bottom)\n\n def _rotate_to_direction(self):\n x, y, z = self.direction\n\n r = np.sqrt(x**2 + y**2 + z**2)\n if r > 0:\n theta = np.arccos(z / r)\n else:\n theta = 0\n\n if x == 0:\n if y == 0: # along the z axis\n phi = 0\n else: # along the x axis\n phi = np.arctan(np.inf)\n if y < 0:\n phi += PI\n else:\n phi = np.arctan(y / x)\n if x < 0:\n phi += PI\n\n # undo old rotation (in reverse direction)\n self.rotate(-self._current_phi, Z_AXIS, about_point=ORIGIN)\n self.rotate(-self._current_theta, Y_AXIS, about_point=ORIGIN)\n\n # do new rotation\n self.rotate(theta, Y_AXIS, about_point=ORIGIN)\n self.rotate(phi, Z_AXIS, about_point=ORIGIN)\n\n # store new values\n self._current_theta = theta\n self._current_phi = phi\n\n def set_direction(self, direction):\n # if get_norm(direction) is get_norm(self.direction):\n # pass\n self.direction = direction\n self._rotate_to_direction()\n\n def get_direction(self):\n return self.direction\n\n\nclass Line3D(Cylinder):\n \"\"\"A cylindrical line, for use in ThreeDScene.\n\n Examples\n ---------\n .. manim:: ExampleLine3D\n :save_last_frame:\n\n class ExampleLine3D(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n line = Line3D(start=np.array([0, 0, 0]), end=np.array([2, 2, 2]))\n self.set_camera_orientation(phi=75 * DEGREES, theta=30 * DEGREES)\n self.add(axes, line)\n\n Parameters\n ---------\n start : :class:`numpy.array`\n The start position of the line.\n end : :class:`numpy.array`\n The end position of the line.\n thickness : :class:`float`\n The thickness of the line.\n \"\"\"\n\n def __init__(self, start=LEFT, end=RIGHT, thickness=0.02, color=None, **kwargs):\n self.thickness = thickness\n self.set_start_and_end_attrs(start, end, **kwargs)\n if color is not None:\n self.set_color(color)\n\n def set_start_and_end_attrs(self, start, end, **kwargs):\n \"\"\"Sets the start and end points of the line.\n\n If either ``start`` or ``end`` are :class:`Mobjects <.Mobject>`, this gives their centers.\n \"\"\"\n rough_start = self.pointify(start)\n rough_end = self.pointify(end)\n self.vect = rough_end - rough_start\n self.length = np.linalg.norm(self.vect)\n self.direction = normalize(self.vect)\n # Now that we know the direction between them,\n # we can the appropriate boundary point from\n # start and end, if they're mobjects\n self.start = self.pointify(start, self.direction)\n self.end = self.pointify(end, -self.direction)\n super().__init__(\n height=np.linalg.norm(self.vect),\n radius=self.thickness,\n direction=self.direction,\n **kwargs,\n )\n self.shift((self.start + self.end) / 2)\n\n def pointify(self, mob_or_point, direction=None):\n if isinstance(mob_or_point, (Mobject, OpenGLMobject)):\n mob = mob_or_point\n if direction is None:\n return mob.get_center()\n else:\n return mob.get_boundary_point(direction)\n return np.array(mob_or_point)\n\n def get_start(self):\n return self.start\n\n def get_end(self):\n return self.end\n\n @classmethod\n def parallel_to(\n cls, line: Line3D, point: Sequence[float] = ORIGIN, length: float = 5, **kwargs\n ):\n \"\"\"Returns a line parallel to another line going through\n a given point.\n\n Parameters\n ----------\n line\n The line to be parallel to.\n point\n The point to pass through.\n kwargs\n Additional parameters to be passed to the class.\n\n Examples\n --------\n .. manim:: ParallelLineExample\n :save_last_frame:\n\n class ParallelLineExample(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(PI / 3, -PI / 4)\n ax = ThreeDAxes((-5, 5), (-5, 5), (-5, 5), 10, 10, 10)\n line1 = Line3D(RIGHT * 2, UP + OUT, color=RED)\n line2 = Line3D.parallel_to(line1, color=YELLOW)\n self.add(ax, line1, line2)\n \"\"\"\n point = np.array(point)\n vect = normalize(line.vect)\n return cls(\n point + vect * length / 2,\n point - vect * length / 2,\n **kwargs,\n )\n\n @classmethod\n def perpendicular_to(\n cls, line: Line3D, point: Sequence[float] = ORIGIN, length: float = 5, **kwargs\n ):\n \"\"\"Returns a line perpendicular to another line going through\n a given point.\n\n Parameters\n ----------\n line\n The line to be perpendicular to.\n point\n The point to pass through.\n kwargs\n Additional parameters to be passed to the class.\n\n Examples\n --------\n .. manim:: PerpLineExample\n :save_last_frame:\n\n class PerpLineExample(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(PI / 3, -PI / 4)\n ax = ThreeDAxes((-5, 5), (-5, 5), (-5, 5), 10, 10, 10)\n line1 = Line3D(RIGHT * 2, UP + OUT, color=RED)\n line2 = Line3D.perpendicular_to(line1, color=BLUE)\n self.add(ax, line1, line2)\n \"\"\"\n point = np.array(point)\n\n norm = np.cross(line.vect, point - line.start)\n if all(np.linalg.norm(norm) == np.zeros(3)):\n raise ValueError(\"Could not find the perpendicular.\")\n\n start, end = perpendicular_bisector([line.start, line.end], norm)\n vect = normalize(end - start)\n return cls(\n point + vect * length / 2,\n point - vect * length / 2,\n **kwargs,\n )\n\n\nclass Arrow3D(Line3D):\n \"\"\"An arrow made out of a cylindrical line and a conical tip.\n\n Examples\n ---------\n .. manim:: ExampleArrow3D\n :save_last_frame:\n\n class ExampleArrow3D(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n arrow = Arrow3D(start=np.array([0, 0, 0]), end=np.array([2, 2, 2]))\n self.set_camera_orientation(phi=75 * DEGREES, theta=30 * DEGREES)\n self.add(axes, arrow)\n\n Parameters\n ---------\n start : :class:`numpy.array`\n The start position of the arrow.\n end : :class:`numpy.array`\n The end position of the arrow.\n thickness : :class:`float`\n The thickness of the arrow.\n height : :class:`float`\n The height of the conical tip.\n base_radius: :class:`float`\n The base radius of the conical tip.\n \"\"\"\n\n def __init__(\n self,\n start=LEFT,\n end=RIGHT,\n thickness=0.02,\n height=0.3,\n base_radius=0.08,\n color=WHITE,\n **kwargs,\n ):\n super().__init__(\n start=start, end=end, thickness=thickness, color=color, **kwargs\n )\n\n self.length = np.linalg.norm(self.vect)\n self.set_start_and_end_attrs(\n self.start,\n self.end - height * self.direction,\n **kwargs,\n )\n\n self.cone = Cone(\n direction=self.direction, base_radius=base_radius, height=height, **kwargs\n )\n self.cone.shift(end)\n self.add(self.cone)\n self.set_color(color)\n\n\nclass Torus(Surface):\n \"\"\"A torus.\n\n Examples\n ---------\n .. manim :: ExampleTorus\n :save_last_frame:\n\n class ExampleTorus(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n torus = Torus()\n self.set_camera_orientation(phi=75 * DEGREES, theta=30 * DEGREES)\n self.add(axes, torus)\n\n Parameters\n ---------\n major_radius : :class:`float`\n Distance from the center of the tube to the center of the torus.\n minor_radius : :class:`float`\n Radius of the tube.\n \"\"\"\n\n def __init__(\n self,\n major_radius=3,\n minor_radius=1,\n u_range=(0, TAU),\n v_range=(0, TAU),\n resolution=None,\n **kwargs,\n ):\n if config.renderer == \"opengl\":\n res_value = (101, 101)\n else:\n res_value = (24, 24)\n\n resolution = resolution if resolution is not None else res_value\n\n self.R = major_radius\n self.r = minor_radius\n super().__init__(\n self.func,\n u_range=u_range,\n v_range=v_range,\n resolution=resolution,\n **kwargs,\n )\n\n def func(self, u, v):\n P = np.array([np.cos(u), np.sin(u), 0])\n return (self.R - self.r * np.cos(v)) * P - self.r * np.sin(v) * OUT\n", "path": "manim/mobject/three_d/three_dimensions.py" } ]
[ { "content": "\"\"\"Three-dimensional mobjects.\"\"\"\n\nfrom __future__ import annotations\n\n__all__ = [\n \"ThreeDVMobject\",\n \"Surface\",\n \"Sphere\",\n \"Dot3D\",\n \"Cube\",\n \"Prism\",\n \"Cone\",\n \"Arrow3D\",\n \"Cylinder\",\n \"Line3D\",\n \"Torus\",\n]\n\n\nfrom typing import *\n\nimport numpy as np\nfrom colour import Color\n\nfrom manim import config\nfrom manim.constants import *\nfrom manim.mobject.geometry.arc import Circle\nfrom manim.mobject.geometry.polygram import Square\nfrom manim.mobject.mobject import *\nfrom manim.mobject.opengl.opengl_compatibility import ConvertToOpenGL\nfrom manim.mobject.opengl.opengl_mobject import OpenGLMobject\nfrom manim.mobject.types.vectorized_mobject import VGroup, VMobject\nfrom manim.utils.color import *\nfrom manim.utils.iterables import tuplify\nfrom manim.utils.space_ops import normalize, perpendicular_bisector, z_to_vector\n\n\nclass ThreeDVMobject(VMobject, metaclass=ConvertToOpenGL):\n def __init__(self, shade_in_3d=True, **kwargs):\n super().__init__(shade_in_3d=shade_in_3d, **kwargs)\n\n\nclass Surface(VGroup, metaclass=ConvertToOpenGL):\n \"\"\"Creates a Parametric Surface using a checkerboard pattern.\n\n Parameters\n ----------\n func :\n The function that defines the surface.\n u_range :\n The range of the ``u`` variable: ``(u_min, u_max)``.\n v_range :\n The range of the ``v`` variable: ``(v_min, v_max)``.\n resolution :\n The number of samples taken of the surface. A tuple\n can be used to define different resolutions for ``u`` and\n ``v`` respectively.\n\n Examples\n --------\n .. manim:: ParaSurface\n :save_last_frame:\n\n class ParaSurface(ThreeDScene):\n def func(self, u, v):\n return np.array([np.cos(u) * np.cos(v), np.cos(u) * np.sin(v), u])\n\n def construct(self):\n axes = ThreeDAxes(x_range=[-4,4], x_length=8)\n surface = Surface(\n lambda u, v: axes.c2p(*self.func(u, v)),\n u_range=[-PI, PI],\n v_range=[0, TAU]\n )\n self.set_camera_orientation(theta=70 * DEGREES, phi=75 * DEGREES)\n self.add(axes, surface)\n \"\"\"\n\n def __init__(\n self,\n func: Callable[[float, float], np.ndarray],\n u_range: Sequence[float] = [0, 1],\n v_range: Sequence[float] = [0, 1],\n resolution: Sequence[int] = 32,\n surface_piece_config: dict = {},\n fill_color: Color = BLUE_D,\n fill_opacity: float = 1.0,\n checkerboard_colors: Sequence[Color] = [BLUE_D, BLUE_E],\n stroke_color: Color = LIGHT_GREY,\n stroke_width: float = 0.5,\n should_make_jagged: bool = False,\n pre_function_handle_to_anchor_scale_factor: float = 0.00001,\n **kwargs,\n ) -> None:\n self.u_range = u_range\n self.v_range = v_range\n super().__init__(**kwargs)\n self.resolution = resolution\n self.surface_piece_config = surface_piece_config\n self.fill_color = fill_color\n self.fill_opacity = fill_opacity\n self.checkerboard_colors = checkerboard_colors\n self.stroke_color = stroke_color\n self.stroke_width = stroke_width\n self.should_make_jagged = should_make_jagged\n self.pre_function_handle_to_anchor_scale_factor = (\n pre_function_handle_to_anchor_scale_factor\n )\n self.func = func\n self._setup_in_uv_space()\n self.apply_function(lambda p: func(p[0], p[1]))\n if self.should_make_jagged:\n self.make_jagged()\n\n def _get_u_values_and_v_values(self):\n res = tuplify(self.resolution)\n if len(res) == 1:\n u_res = v_res = res[0]\n else:\n u_res, v_res = res\n\n u_values = np.linspace(*self.u_range, u_res + 1)\n v_values = np.linspace(*self.v_range, v_res + 1)\n\n return u_values, v_values\n\n def _setup_in_uv_space(self):\n u_values, v_values = self._get_u_values_and_v_values()\n faces = VGroup()\n for i in range(len(u_values) - 1):\n for j in range(len(v_values) - 1):\n u1, u2 = u_values[i : i + 2]\n v1, v2 = v_values[j : j + 2]\n face = ThreeDVMobject()\n face.set_points_as_corners(\n [\n [u1, v1, 0],\n [u2, v1, 0],\n [u2, v2, 0],\n [u1, v2, 0],\n [u1, v1, 0],\n ],\n )\n faces.add(face)\n face.u_index = i\n face.v_index = j\n face.u1 = u1\n face.u2 = u2\n face.v1 = v1\n face.v2 = v2\n faces.set_fill(color=self.fill_color, opacity=self.fill_opacity)\n faces.set_stroke(\n color=self.stroke_color,\n width=self.stroke_width,\n opacity=self.stroke_opacity,\n )\n self.add(*faces)\n if self.checkerboard_colors:\n self.set_fill_by_checkerboard(*self.checkerboard_colors)\n\n def set_fill_by_checkerboard(self, *colors, opacity=None):\n n_colors = len(colors)\n for face in self:\n c_index = (face.u_index + face.v_index) % n_colors\n face.set_fill(colors[c_index], opacity=opacity)\n return self\n\n def set_fill_by_value(\n self,\n axes: Mobject,\n colors: Union[Iterable[Color], Color],\n axis: int = 2,\n ):\n \"\"\"Sets the color of each mobject of a parametric surface to a color relative to its axis-value\n\n Parameters\n ----------\n axes :\n The axes for the parametric surface, which will be used to map axis-values to colors.\n colors :\n A list of colors, ordered from lower axis-values to higher axis-values. If a list of tuples is passed\n containing colors paired with numbers, then those numbers will be used as the pivots.\n axis :\n The chosen axis to use for the color mapping. (0 = x, 1 = y, 2 = z)\n\n Returns\n -------\n :class:`~.Surface`\n The parametric surface with a gradient applied by value. For chaining.\n\n Examples\n --------\n .. manim:: FillByValueExample\n :save_last_frame:\n\n class FillByValueExample(ThreeDScene):\n def construct(self):\n resolution_fa = 42\n self.set_camera_orientation(phi=75 * DEGREES, theta=-160 * DEGREES)\n axes = ThreeDAxes(x_range=(0, 5, 1), y_range=(0, 5, 1), z_range=(-1, 1, 0.5))\n def param_surface(u, v):\n x = u\n y = v\n z = np.sin(x) * np.cos(y)\n return z\n surface_plane = Surface(\n lambda u, v: axes.c2p(u, v, param_surface(u, v)),\n resolution=(resolution_fa, resolution_fa),\n v_range=[0, 5],\n u_range=[0, 5],\n )\n surface_plane.set_style(fill_opacity=1)\n surface_plane.set_fill_by_value(axes=axes, colors=[(RED, -0.5), (YELLOW, 0), (GREEN, 0.5)], axis=2)\n self.add(axes, surface_plane)\n \"\"\"\n\n ranges = [axes.x_range, axes.y_range, axes.z_range]\n\n if type(colors[0]) is tuple:\n new_colors, pivots = [[i for i, j in colors], [j for i, j in colors]]\n else:\n new_colors = colors\n\n pivot_min = ranges[axis][0]\n pivot_max = ranges[axis][1]\n pivot_frequency = (pivot_max - pivot_min) / (len(new_colors) - 1)\n pivots = np.arange(\n start=pivot_min,\n stop=pivot_max + pivot_frequency,\n step=pivot_frequency,\n )\n\n for mob in self.family_members_with_points():\n axis_value = axes.point_to_coords(mob.get_midpoint())[axis]\n if axis_value <= pivots[0]:\n mob.set_color(new_colors[0])\n elif axis_value >= pivots[-1]:\n mob.set_color(new_colors[-1])\n else:\n for i, pivot in enumerate(pivots):\n if pivot > axis_value:\n color_index = (axis_value - pivots[i - 1]) / (\n pivots[i] - pivots[i - 1]\n )\n color_index = min(color_index, 1)\n mob_color = interpolate_color(\n new_colors[i - 1],\n new_colors[i],\n color_index,\n )\n if config.renderer == \"opengl\":\n mob.set_color(mob_color, recurse=False)\n else:\n mob.set_color(mob_color, family=False)\n break\n\n return self\n\n\n# Specific shapes\n\n\nclass Sphere(Surface):\n \"\"\"A mobject representing a three-dimensional sphere.\n\n Examples\n ---------\n\n .. manim:: ExampleSphere\n :save_last_frame:\n\n class ExampleSphere(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(phi=PI / 6, theta=PI / 6)\n sphere1 = Sphere(\n center=(3, 0, 0),\n radius=1,\n resolution=(20, 20),\n u_range=[0.001, PI - 0.001],\n v_range=[0, TAU]\n )\n sphere1.set_color(RED)\n self.add(sphere1)\n sphere2 = Sphere(center=(-1, -3, 0), radius=2, resolution=(18, 18))\n sphere2.set_color(GREEN)\n self.add(sphere2)\n sphere3 = Sphere(center=(-1, 2, 0), radius=2, resolution=(16, 16))\n sphere3.set_color(BLUE)\n self.add(sphere3)\n \"\"\"\n\n def __init__(\n self,\n center=ORIGIN,\n radius=1,\n resolution=None,\n u_range=(0, TAU),\n v_range=(0, PI),\n **kwargs,\n ):\n if config.renderer == \"opengl\":\n res_value = (101, 51)\n else:\n res_value = (24, 12)\n\n resolution = resolution if resolution is not None else res_value\n\n self.radius = radius\n\n super().__init__(\n self.func,\n resolution=resolution,\n u_range=u_range,\n v_range=v_range,\n **kwargs,\n )\n\n self.shift(center)\n\n def func(self, u, v):\n return self.radius * np.array(\n [np.cos(u) * np.sin(v), np.sin(u) * np.sin(v), -np.cos(v)],\n )\n\n\nclass Dot3D(Sphere):\n \"\"\"A spherical dot.\n\n Parameters\n --------\n point : Union[:class:`list`, :class:`numpy.ndarray`], optional\n The location of the dot.\n radius : :class:`float`, optional\n The radius of the dot.\n color : :class:`~.Colors`, optional\n The color of the :class:`Dot3D`\n\n Examples\n --------\n\n .. manim:: Dot3DExample\n :save_last_frame:\n\n class Dot3DExample(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(phi=75*DEGREES, theta=-45*DEGREES)\n\n axes = ThreeDAxes()\n dot_1 = Dot3D(point=axes.coords_to_point(0, 0, 1), color=RED)\n dot_2 = Dot3D(point=axes.coords_to_point(2, 0, 0), radius=0.1, color=BLUE)\n dot_3 = Dot3D(point=[0, 0, 0], radius=0.1, color=ORANGE)\n self.add(axes, dot_1, dot_2,dot_3)\n \"\"\"\n\n def __init__(\n self,\n point=ORIGIN,\n radius=DEFAULT_DOT_RADIUS,\n color=WHITE,\n resolution=(8, 8),\n **kwargs,\n ):\n super().__init__(center=point, radius=radius, resolution=resolution, **kwargs)\n self.set_color(color)\n\n\nclass Cube(VGroup):\n def __init__(\n self,\n side_length=2,\n fill_opacity=0.75,\n fill_color=BLUE,\n stroke_width=0,\n **kwargs,\n ):\n self.side_length = side_length\n super().__init__(\n fill_color=fill_color,\n fill_opacity=fill_opacity,\n stroke_width=stroke_width,\n **kwargs,\n )\n\n def generate_points(self):\n for vect in IN, OUT, LEFT, RIGHT, UP, DOWN:\n face = Square(\n side_length=self.side_length,\n shade_in_3d=True,\n )\n face.flip()\n face.shift(self.side_length * OUT / 2.0)\n face.apply_matrix(z_to_vector(vect))\n\n self.add(face)\n\n init_points = generate_points\n\n\nclass Prism(Cube):\n \"\"\"A cuboid.\n\n Examples\n --------\n\n .. manim:: ExamplePrism\n :save_last_frame:\n\n class ExamplePrism(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(phi=60 * DEGREES, theta=150 * DEGREES)\n prismSmall = Prism(dimensions=[1, 2, 3]).rotate(PI / 2)\n prismLarge = Prism(dimensions=[1.5, 3, 4.5]).move_to([2, 0, 0])\n self.add(prismSmall, prismLarge)\n \"\"\"\n\n def __init__(self, dimensions=[3, 2, 1], **kwargs):\n self.dimensions = dimensions\n super().__init__(**kwargs)\n\n def generate_points(self):\n super().generate_points()\n for dim, value in enumerate(self.dimensions):\n self.rescale_to_fit(value, dim, stretch=True)\n\n\nclass Cone(Surface):\n \"\"\"A circular cone.\n Can be defined using 2 parameters: its height, and its base radius.\n The polar angle, theta, can be calculated using arctan(base_radius /\n height) The spherical radius, r, is calculated using the pythagorean\n theorem.\n\n Examples\n --------\n .. manim:: ExampleCone\n :save_last_frame:\n\n class ExampleCone(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n cone = Cone(direction=X_AXIS+Y_AXIS+2*Z_AXIS)\n self.set_camera_orientation(phi=5*PI/11, theta=PI/9)\n self.add(axes, cone)\n\n Parameters\n --------\n base_radius : :class:`float`\n The base radius from which the cone tapers.\n height : :class:`float`\n The height measured from the plane formed by the base_radius to the apex of the cone.\n direction : :class:`numpy.array`\n The direction of the apex.\n show_base : :class:`bool`\n Whether to show the base plane or not.\n v_range : :class:`Sequence[float]`\n The azimuthal angle to start and end at.\n u_min : :class:`float`\n The radius at the apex.\n checkerboard_colors : :class:`bool`\n Show checkerboard grid texture on the cone.\n \"\"\"\n\n def __init__(\n self,\n base_radius=1,\n height=1,\n direction=Z_AXIS,\n show_base=False,\n v_range=[0, TAU],\n u_min=0,\n checkerboard_colors=False,\n **kwargs,\n ):\n self.direction = direction\n self.theta = PI - np.arctan(base_radius / height)\n\n super().__init__(\n self.func,\n v_range=v_range,\n u_range=[u_min, np.sqrt(base_radius**2 + height**2)],\n checkerboard_colors=checkerboard_colors,\n **kwargs,\n )\n # used for rotations\n self._current_theta = 0\n self._current_phi = 0\n\n if show_base:\n self.base_circle = Circle(\n radius=base_radius,\n color=self.fill_color,\n fill_opacity=self.fill_opacity,\n stroke_width=0,\n )\n self.base_circle.shift(height * IN)\n self.add(self.base_circle)\n\n self._rotate_to_direction()\n\n def func(self, u, v):\n \"\"\"Converts from spherical coordinates to cartesian.\n Parameters\n ---------\n u : :class:`float`\n The radius.\n v : :class:`float`\n The azimuthal angle.\n \"\"\"\n r = u\n phi = v\n return np.array(\n [\n r * np.sin(self.theta) * np.cos(phi),\n r * np.sin(self.theta) * np.sin(phi),\n r * np.cos(self.theta),\n ],\n )\n\n def _rotate_to_direction(self):\n x, y, z = self.direction\n\n r = np.sqrt(x**2 + y**2 + z**2)\n if r > 0:\n theta = np.arccos(z / r)\n else:\n theta = 0\n\n if x == 0:\n if y == 0: # along the z axis\n phi = 0\n else:\n phi = np.arctan(np.inf)\n if y < 0:\n phi += PI\n else:\n phi = np.arctan(y / x)\n if x < 0:\n phi += PI\n\n # Undo old rotation (in reverse order)\n self.rotate(-self._current_phi, Z_AXIS, about_point=ORIGIN)\n self.rotate(-self._current_theta, Y_AXIS, about_point=ORIGIN)\n\n # Do new rotation\n self.rotate(theta, Y_AXIS, about_point=ORIGIN)\n self.rotate(phi, Z_AXIS, about_point=ORIGIN)\n\n # Store values\n self._current_theta = theta\n self._current_phi = phi\n\n def set_direction(self, direction):\n self.direction = direction\n self._rotate_to_direction()\n\n def get_direction(self):\n return self.direction\n\n\nclass Cylinder(Surface):\n \"\"\"A cylinder, defined by its height, radius and direction,\n\n Examples\n ---------\n .. manim:: ExampleCylinder\n :save_last_frame:\n\n class ExampleCylinder(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n cylinder = Cylinder(radius=2, height=3)\n self.set_camera_orientation(phi=75 * DEGREES, theta=30 * DEGREES)\n self.add(axes, cylinder)\n\n Parameters\n ---------\n radius : :class:`float`\n The radius of the cylinder.\n height : :class:`float`\n The height of the cylinder.\n direction : :class:`numpy.array`\n The direction of the central axis of the cylinder.\n v_range : :class:`Sequence[float]`\n The height along the height axis (given by direction) to start and end on.\n show_ends : :class:`bool`\n Whether to show the end caps or not.\n \"\"\"\n\n def __init__(\n self,\n radius=1,\n height=2,\n direction=Z_AXIS,\n v_range=[0, TAU],\n show_ends=True,\n resolution=(24, 24),\n **kwargs,\n ):\n self._height = height\n self.radius = radius\n super().__init__(\n self.func,\n resolution=resolution,\n u_range=[-self._height / 2, self._height / 2],\n v_range=v_range,\n **kwargs,\n )\n if show_ends:\n self.add_bases()\n self._current_phi = 0\n self._current_theta = 0\n self.set_direction(direction)\n\n def func(self, u, v):\n \"\"\"Converts from cylindrical coordinates to cartesian.\n Parameters\n ---------\n u : :class:`float`\n The height.\n v : :class:`float`\n The azimuthal angle.\n \"\"\"\n height = u\n phi = v\n r = self.radius\n return np.array([r * np.cos(phi), r * np.sin(phi), height])\n\n def add_bases(self):\n \"\"\"Adds the end caps of the cylinder.\"\"\"\n color = self.color if config[\"renderer\"] == \"opengl\" else self.fill_color\n opacity = self.opacity if config[\"renderer\"] == \"opengl\" else self.fill_opacity\n self.base_top = Circle(\n radius=self.radius,\n color=color,\n fill_opacity=opacity,\n shade_in_3d=True,\n stroke_width=0,\n )\n self.base_top.shift(self.u_range[1] * IN)\n self.base_bottom = Circle(\n radius=self.radius,\n color=color,\n fill_opacity=opacity,\n shade_in_3d=True,\n stroke_width=0,\n )\n self.base_bottom.shift(self.u_range[0] * IN)\n self.add(self.base_top, self.base_bottom)\n\n def _rotate_to_direction(self):\n x, y, z = self.direction\n\n r = np.sqrt(x**2 + y**2 + z**2)\n if r > 0:\n theta = np.arccos(z / r)\n else:\n theta = 0\n\n if x == 0:\n if y == 0: # along the z axis\n phi = 0\n else: # along the x axis\n phi = np.arctan(np.inf)\n if y < 0:\n phi += PI\n else:\n phi = np.arctan(y / x)\n if x < 0:\n phi += PI\n\n # undo old rotation (in reverse direction)\n self.rotate(-self._current_phi, Z_AXIS, about_point=ORIGIN)\n self.rotate(-self._current_theta, Y_AXIS, about_point=ORIGIN)\n\n # do new rotation\n self.rotate(theta, Y_AXIS, about_point=ORIGIN)\n self.rotate(phi, Z_AXIS, about_point=ORIGIN)\n\n # store new values\n self._current_theta = theta\n self._current_phi = phi\n\n def set_direction(self, direction):\n # if get_norm(direction) is get_norm(self.direction):\n # pass\n self.direction = direction\n self._rotate_to_direction()\n\n def get_direction(self):\n \"\"\"Returns the direction of the central axis of the cylinder.\"\"\"\n return self.direction\n\n\nclass Line3D(Cylinder):\n \"\"\"A cylindrical line, for use in ThreeDScene.\n\n Examples\n ---------\n .. manim:: ExampleLine3D\n :save_last_frame:\n\n class ExampleLine3D(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n line = Line3D(start=np.array([0, 0, 0]), end=np.array([2, 2, 2]))\n self.set_camera_orientation(phi=75 * DEGREES, theta=30 * DEGREES)\n self.add(axes, line)\n\n Parameters\n ---------\n start : :class:`numpy.array`\n The start position of the line.\n end : :class:`numpy.array`\n The end position of the line.\n thickness : :class:`float`\n The thickness of the line.\n \"\"\"\n\n def __init__(self, start=LEFT, end=RIGHT, thickness=0.02, color=None, **kwargs):\n self.thickness = thickness\n self.set_start_and_end_attrs(start, end, **kwargs)\n if color is not None:\n self.set_color(color)\n\n def set_start_and_end_attrs(self, start, end, **kwargs):\n \"\"\"Sets the start and end points of the line.\n\n If either ``start`` or ``end`` are :class:`Mobjects <.Mobject>`, this gives their centers.\n \"\"\"\n rough_start = self.pointify(start)\n rough_end = self.pointify(end)\n self.vect = rough_end - rough_start\n self.length = np.linalg.norm(self.vect)\n self.direction = normalize(self.vect)\n # Now that we know the direction between them,\n # we can the appropriate boundary point from\n # start and end, if they're mobjects\n self.start = self.pointify(start, self.direction)\n self.end = self.pointify(end, -self.direction)\n super().__init__(\n height=np.linalg.norm(self.vect),\n radius=self.thickness,\n direction=self.direction,\n **kwargs,\n )\n self.shift((self.start + self.end) / 2)\n\n def pointify(self, mob_or_point, direction=None):\n if isinstance(mob_or_point, (Mobject, OpenGLMobject)):\n mob = mob_or_point\n if direction is None:\n return mob.get_center()\n else:\n return mob.get_boundary_point(direction)\n return np.array(mob_or_point)\n\n def get_start(self):\n return self.start\n\n def get_end(self):\n return self.end\n\n @classmethod\n def parallel_to(\n cls, line: Line3D, point: Sequence[float] = ORIGIN, length: float = 5, **kwargs\n ):\n \"\"\"Returns a line parallel to another line going through\n a given point.\n\n Parameters\n ----------\n line\n The line to be parallel to.\n point\n The point to pass through.\n kwargs\n Additional parameters to be passed to the class.\n\n Examples\n --------\n .. manim:: ParallelLineExample\n :save_last_frame:\n\n class ParallelLineExample(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(PI / 3, -PI / 4)\n ax = ThreeDAxes((-5, 5), (-5, 5), (-5, 5), 10, 10, 10)\n line1 = Line3D(RIGHT * 2, UP + OUT, color=RED)\n line2 = Line3D.parallel_to(line1, color=YELLOW)\n self.add(ax, line1, line2)\n \"\"\"\n point = np.array(point)\n vect = normalize(line.vect)\n return cls(\n point + vect * length / 2,\n point - vect * length / 2,\n **kwargs,\n )\n\n @classmethod\n def perpendicular_to(\n cls, line: Line3D, point: Sequence[float] = ORIGIN, length: float = 5, **kwargs\n ):\n \"\"\"Returns a line perpendicular to another line going through\n a given point.\n\n Parameters\n ----------\n line\n The line to be perpendicular to.\n point\n The point to pass through.\n kwargs\n Additional parameters to be passed to the class.\n\n Examples\n --------\n .. manim:: PerpLineExample\n :save_last_frame:\n\n class PerpLineExample(ThreeDScene):\n def construct(self):\n self.set_camera_orientation(PI / 3, -PI / 4)\n ax = ThreeDAxes((-5, 5), (-5, 5), (-5, 5), 10, 10, 10)\n line1 = Line3D(RIGHT * 2, UP + OUT, color=RED)\n line2 = Line3D.perpendicular_to(line1, color=BLUE)\n self.add(ax, line1, line2)\n \"\"\"\n point = np.array(point)\n\n norm = np.cross(line.vect, point - line.start)\n if all(np.linalg.norm(norm) == np.zeros(3)):\n raise ValueError(\"Could not find the perpendicular.\")\n\n start, end = perpendicular_bisector([line.start, line.end], norm)\n vect = normalize(end - start)\n return cls(\n point + vect * length / 2,\n point - vect * length / 2,\n **kwargs,\n )\n\n\nclass Arrow3D(Line3D):\n \"\"\"An arrow made out of a cylindrical line and a conical tip.\n\n Examples\n ---------\n .. manim:: ExampleArrow3D\n :save_last_frame:\n\n class ExampleArrow3D(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n arrow = Arrow3D(start=np.array([0, 0, 0]), end=np.array([2, 2, 2]))\n self.set_camera_orientation(phi=75 * DEGREES, theta=30 * DEGREES)\n self.add(axes, arrow)\n\n Parameters\n ---------\n start : :class:`numpy.array`\n The start position of the arrow.\n end : :class:`numpy.array`\n The end position of the arrow.\n thickness : :class:`float`\n The thickness of the arrow.\n height : :class:`float`\n The height of the conical tip.\n base_radius: :class:`float`\n The base radius of the conical tip.\n \"\"\"\n\n def __init__(\n self,\n start=LEFT,\n end=RIGHT,\n thickness=0.02,\n height=0.3,\n base_radius=0.08,\n color=WHITE,\n **kwargs,\n ):\n super().__init__(\n start=start, end=end, thickness=thickness, color=color, **kwargs\n )\n\n self.length = np.linalg.norm(self.vect)\n self.set_start_and_end_attrs(\n self.start,\n self.end - height * self.direction,\n **kwargs,\n )\n\n self.cone = Cone(\n direction=self.direction, base_radius=base_radius, height=height, **kwargs\n )\n self.cone.shift(end)\n self.add(self.cone)\n self.set_color(color)\n\n\nclass Torus(Surface):\n \"\"\"A torus.\n\n Examples\n ---------\n .. manim :: ExampleTorus\n :save_last_frame:\n\n class ExampleTorus(ThreeDScene):\n def construct(self):\n axes = ThreeDAxes()\n torus = Torus()\n self.set_camera_orientation(phi=75 * DEGREES, theta=30 * DEGREES)\n self.add(axes, torus)\n\n Parameters\n ---------\n major_radius : :class:`float`\n Distance from the center of the tube to the center of the torus.\n minor_radius : :class:`float`\n Radius of the tube.\n \"\"\"\n\n def __init__(\n self,\n major_radius=3,\n minor_radius=1,\n u_range=(0, TAU),\n v_range=(0, TAU),\n resolution=None,\n **kwargs,\n ):\n if config.renderer == \"opengl\":\n res_value = (101, 101)\n else:\n res_value = (24, 24)\n\n resolution = resolution if resolution is not None else res_value\n\n self.R = major_radius\n self.r = minor_radius\n super().__init__(\n self.func,\n u_range=u_range,\n v_range=v_range,\n resolution=resolution,\n **kwargs,\n )\n\n def func(self, u, v):\n P = np.array([np.cos(u), np.sin(u), 0])\n return (self.R - self.r * np.cos(v)) * P - self.r * np.sin(v) * OUT\n", "path": "manim/mobject/three_d/three_dimensions.py" } ]
diff --git a/manim/mobject/three_d/three_dimensions.py b/manim/mobject/three_d/three_dimensions.py index 80aa100788..6f04b7ccf6 100644 --- a/manim/mobject/three_d/three_dimensions.py +++ b/manim/mobject/three_d/three_dimensions.py @@ -687,6 +687,7 @@ def set_direction(self, direction): self._rotate_to_direction() def get_direction(self): + """Returns the direction of the central axis of the cylinder.""" return self.direction
boto__botocore-658
Pin jmespatch dependency version Can this library pin its jmespath dependency to a specific version? Currently, it depends on the development branch of the jmespath GitHub repo - which is not stable nor deterministic. Currently, this project's setup.py requires version 0.7.1 but the upstream GitHub repo/branch does not deliver that version - so this project's dependency graph is disconnected. This can result in runtime errors for downstream consumers - like my organization did today.
[ { "content": "#!/usr/bin/env python\nimport botocore\nimport sys\n\nfrom setuptools import setup, find_packages\n\n\nrequires = ['jmespath==0.7.1',\n 'python-dateutil>=2.1,<3.0.0',\n 'docutils>=0.10']\n\n\nif sys.version_info[:2] == (2, 6):\n # For python2.6 we have a few other dependencies.\n # First we need an ordered dictionary so we use the\n # 2.6 backport.\n requires.append('ordereddict==1.1')\n # Then we need simplejson. This is because we need\n # a json version that allows us to specify we want to\n # use an ordereddict instead of a normal dict for the\n # JSON objects. The 2.7 json module has this. For 2.6\n # we need simplejson.\n requires.append('simplejson==3.3.0')\n\n\nsetup(\n name='botocore',\n version=botocore.__version__,\n description='Low-level, data-driven core of boto 3.',\n long_description=open('README.rst').read(),\n author='Amazon Web Services',\n url='https://github.com/boto/botocore',\n scripts=[],\n packages=find_packages(exclude=['tests*']),\n package_data={'botocore': ['data/*.json', 'data/*/*.json'],\n 'botocore.vendored.requests': ['*.pem']},\n include_package_data=True,\n install_requires=requires,\n extras_require={\n ':python_version==\"2.6\"': [\n 'ordereddict==1.1',\n 'simplejson==3.3.0',\n ]\n },\n license=\"Apache License 2.0\",\n classifiers=(\n 'Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Developers',\n 'Intended Audience :: System Administrators',\n 'Natural Language :: English',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.6',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n ),\n)\n", "path": "setup.py" } ]
[ { "content": "#!/usr/bin/env python\nimport botocore\nimport sys\n\nfrom setuptools import setup, find_packages\n\n\nrequires = ['jmespath>=0.7.1,<1.0.0',\n 'python-dateutil>=2.1,<3.0.0',\n 'docutils>=0.10']\n\n\nif sys.version_info[:2] == (2, 6):\n # For python2.6 we have a few other dependencies.\n # First we need an ordered dictionary so we use the\n # 2.6 backport.\n requires.append('ordereddict==1.1')\n # Then we need simplejson. This is because we need\n # a json version that allows us to specify we want to\n # use an ordereddict instead of a normal dict for the\n # JSON objects. The 2.7 json module has this. For 2.6\n # we need simplejson.\n requires.append('simplejson==3.3.0')\n\n\nsetup(\n name='botocore',\n version=botocore.__version__,\n description='Low-level, data-driven core of boto 3.',\n long_description=open('README.rst').read(),\n author='Amazon Web Services',\n url='https://github.com/boto/botocore',\n scripts=[],\n packages=find_packages(exclude=['tests*']),\n package_data={'botocore': ['data/*.json', 'data/*/*.json'],\n 'botocore.vendored.requests': ['*.pem']},\n include_package_data=True,\n install_requires=requires,\n extras_require={\n ':python_version==\"2.6\"': [\n 'ordereddict==1.1',\n 'simplejson==3.3.0',\n ]\n },\n license=\"Apache License 2.0\",\n classifiers=(\n 'Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Developers',\n 'Intended Audience :: System Administrators',\n 'Natural Language :: English',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.6',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n ),\n)\n", "path": "setup.py" } ]
diff --git a/setup.py b/setup.py index ea3260e993..b1ed6c109d 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ from setuptools import setup, find_packages -requires = ['jmespath==0.7.1', +requires = ['jmespath>=0.7.1,<1.0.0', 'python-dateutil>=2.1,<3.0.0', 'docutils>=0.10']
nltk__nltk-2595
Move from nose to pytest or nose2 https://nose.readthedocs.io/en/latest/ -- nose is on life support. I personally prefer pytest, but nose2 may also be considered. Has this been discussed very much yet?
[ { "content": "# Natural Language Toolkit: Corpus Readers\n#\n# Copyright (C) 2001-2020 NLTK Project\n# Author: Edward Loper <[email protected]>\n# URL: <http://nltk.org/>\n# For license information, see LICENSE.TXT\n\n# TODO this docstring isn't up-to-date!\n\"\"\"\nNLTK corpus readers. The modules in this package provide functions\nthat can be used to read corpus files in a variety of formats. These\nfunctions can be used to read both the corpus files that are\ndistributed in the NLTK corpus package, and corpus files that are part\nof external corpora.\n\nAvailable Corpora\n=================\n\nPlease see http://www.nltk.org/nltk_data/ for a complete list.\nInstall corpora using nltk.download().\n\nCorpus Reader Functions\n=======================\nEach corpus module defines one or more \"corpus reader functions\",\nwhich can be used to read documents from that corpus. These functions\ntake an argument, ``item``, which is used to indicate which document\nshould be read from the corpus:\n\n- If ``item`` is one of the unique identifiers listed in the corpus\n module's ``items`` variable, then the corresponding document will\n be loaded from the NLTK corpus package.\n- If ``item`` is a filename, then that file will be read.\n\nAdditionally, corpus reader functions can be given lists of item\nnames; in which case, they will return a concatenation of the\ncorresponding documents.\n\nCorpus reader functions are named based on the type of information\nthey return. Some common examples, and their return types, are:\n\n- words(): list of str\n- sents(): list of (list of str)\n- paras(): list of (list of (list of str))\n- tagged_words(): list of (str,str) tuple\n- tagged_sents(): list of (list of (str,str))\n- tagged_paras(): list of (list of (list of (str,str)))\n- chunked_sents(): list of (Tree w/ (str,str) leaves)\n- parsed_sents(): list of (Tree with str leaves)\n- parsed_paras(): list of (list of (Tree with str leaves))\n- xml(): A single xml ElementTree\n- raw(): unprocessed corpus contents\n\nFor example, to read a list of the words in the Brown Corpus, use\n``nltk.corpus.brown.words()``:\n\n >>> from nltk.corpus import brown\n >>> print(\", \".join(brown.words()))\n The, Fulton, County, Grand, Jury, said, ...\n\n\"\"\"\n\nimport re\n\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.corpus.util import LazyCorpusLoader\nfrom nltk.corpus.reader import *\n\nabc = LazyCorpusLoader(\n \"abc\",\n PlaintextCorpusReader,\n r\"(?!\\.).*\\.txt\",\n encoding=[(\"science\", \"latin_1\"), (\"rural\", \"utf8\")],\n)\nalpino = LazyCorpusLoader(\"alpino\", AlpinoCorpusReader, tagset=\"alpino\")\nbrown = LazyCorpusLoader(\n \"brown\",\n CategorizedTaggedCorpusReader,\n r\"c[a-z]\\d\\d\",\n cat_file=\"cats.txt\",\n tagset=\"brown\",\n encoding=\"ascii\",\n)\ncess_cat = LazyCorpusLoader(\n \"cess_cat\",\n BracketParseCorpusReader,\n r\"(?!\\.).*\\.tbf\",\n tagset=\"unknown\",\n encoding=\"ISO-8859-15\",\n)\ncess_esp = LazyCorpusLoader(\n \"cess_esp\",\n BracketParseCorpusReader,\n r\"(?!\\.).*\\.tbf\",\n tagset=\"unknown\",\n encoding=\"ISO-8859-15\",\n)\ncmudict = LazyCorpusLoader(\"cmudict\", CMUDictCorpusReader, [\"cmudict\"])\ncomtrans = LazyCorpusLoader(\"comtrans\", AlignedCorpusReader, r\"(?!\\.).*\\.txt\")\ncomparative_sentences = LazyCorpusLoader(\n \"comparative_sentences\",\n ComparativeSentencesCorpusReader,\n r\"labeledSentences\\.txt\",\n encoding=\"latin-1\",\n)\nconll2000 = LazyCorpusLoader(\n \"conll2000\",\n ConllChunkCorpusReader,\n [\"train.txt\", \"test.txt\"],\n (\"NP\", \"VP\", \"PP\"),\n tagset=\"wsj\",\n encoding=\"ascii\",\n)\nconll2002 = LazyCorpusLoader(\n \"conll2002\",\n ConllChunkCorpusReader,\n r\".*\\.(test|train).*\",\n (\"LOC\", \"PER\", \"ORG\", \"MISC\"),\n encoding=\"utf-8\",\n)\nconll2007 = LazyCorpusLoader(\n \"conll2007\",\n DependencyCorpusReader,\n r\".*\\.(test|train).*\",\n encoding=[(\"eus\", \"ISO-8859-2\"), (\"esp\", \"utf8\")],\n)\ncrubadan = LazyCorpusLoader(\"crubadan\", CrubadanCorpusReader, r\".*\\.txt\")\ndependency_treebank = LazyCorpusLoader(\n \"dependency_treebank\", DependencyCorpusReader, r\".*\\.dp\", encoding=\"ascii\"\n)\nfloresta = LazyCorpusLoader(\n \"floresta\",\n BracketParseCorpusReader,\n r\"(?!\\.).*\\.ptb\",\n \"#\",\n tagset=\"unknown\",\n encoding=\"ISO-8859-15\",\n)\nframenet15 = LazyCorpusLoader(\n \"framenet_v15\",\n FramenetCorpusReader,\n [\n \"frRelation.xml\",\n \"frameIndex.xml\",\n \"fulltextIndex.xml\",\n \"luIndex.xml\",\n \"semTypes.xml\",\n ],\n)\nframenet = LazyCorpusLoader(\n \"framenet_v17\",\n FramenetCorpusReader,\n [\n \"frRelation.xml\",\n \"frameIndex.xml\",\n \"fulltextIndex.xml\",\n \"luIndex.xml\",\n \"semTypes.xml\",\n ],\n)\ngazetteers = LazyCorpusLoader(\n \"gazetteers\", WordListCorpusReader, r\"(?!LICENSE|\\.).*\\.txt\", encoding=\"ISO-8859-2\"\n)\ngenesis = LazyCorpusLoader(\n \"genesis\",\n PlaintextCorpusReader,\n r\"(?!\\.).*\\.txt\",\n encoding=[\n (\"finnish|french|german\", \"latin_1\"),\n (\"swedish\", \"cp865\"),\n (\".*\", \"utf_8\"),\n ],\n)\ngutenberg = LazyCorpusLoader(\n \"gutenberg\", PlaintextCorpusReader, r\"(?!\\.).*\\.txt\", encoding=\"latin1\"\n)\nieer = LazyCorpusLoader(\"ieer\", IEERCorpusReader, r\"(?!README|\\.).*\")\ninaugural = LazyCorpusLoader(\n \"inaugural\", PlaintextCorpusReader, r\"(?!\\.).*\\.txt\", encoding=\"latin1\"\n)\n# [XX] This should probably just use TaggedCorpusReader:\nindian = LazyCorpusLoader(\n \"indian\", IndianCorpusReader, r\"(?!\\.).*\\.pos\", tagset=\"unknown\", encoding=\"utf8\"\n)\n\njeita = LazyCorpusLoader(\"jeita\", ChasenCorpusReader, r\".*\\.chasen\", encoding=\"utf-8\")\nknbc = LazyCorpusLoader(\"knbc/corpus1\", KNBCorpusReader, r\".*/KN.*\", encoding=\"euc-jp\")\nlin_thesaurus = LazyCorpusLoader(\"lin_thesaurus\", LinThesaurusCorpusReader, r\".*\\.lsp\")\nmac_morpho = LazyCorpusLoader(\n \"mac_morpho\",\n MacMorphoCorpusReader,\n r\"(?!\\.).*\\.txt\",\n tagset=\"unknown\",\n encoding=\"latin-1\",\n)\nmachado = LazyCorpusLoader(\n \"machado\",\n PortugueseCategorizedPlaintextCorpusReader,\n r\"(?!\\.).*\\.txt\",\n cat_pattern=r\"([a-z]*)/.*\",\n encoding=\"latin-1\",\n)\nmasc_tagged = LazyCorpusLoader(\n \"masc_tagged\",\n CategorizedTaggedCorpusReader,\n r\"(spoken|written)/.*\\.txt\",\n cat_file=\"categories.txt\",\n tagset=\"wsj\",\n encoding=\"utf-8\",\n sep=\"_\",\n)\nmovie_reviews = LazyCorpusLoader(\n \"movie_reviews\",\n CategorizedPlaintextCorpusReader,\n r\"(?!\\.).*\\.txt\",\n cat_pattern=r\"(neg|pos)/.*\",\n encoding=\"ascii\",\n)\nmultext_east = LazyCorpusLoader(\n \"mte_teip5\", MTECorpusReader, r\"(oana).*\\.xml\", encoding=\"utf-8\"\n)\nnames = LazyCorpusLoader(\n \"names\", WordListCorpusReader, r\"(?!\\.).*\\.txt\", encoding=\"ascii\"\n)\nnps_chat = LazyCorpusLoader(\n \"nps_chat\", NPSChatCorpusReader, r\"(?!README|\\.).*\\.xml\", tagset=\"wsj\"\n)\nopinion_lexicon = LazyCorpusLoader(\n \"opinion_lexicon\",\n OpinionLexiconCorpusReader,\n r\"(\\w+)\\-words\\.txt\",\n encoding=\"ISO-8859-2\",\n)\nppattach = LazyCorpusLoader(\n \"ppattach\", PPAttachmentCorpusReader, [\"training\", \"test\", \"devset\"]\n)\nproduct_reviews_1 = LazyCorpusLoader(\n \"product_reviews_1\", ReviewsCorpusReader, r\"^(?!Readme).*\\.txt\", encoding=\"utf8\"\n)\nproduct_reviews_2 = LazyCorpusLoader(\n \"product_reviews_2\", ReviewsCorpusReader, r\"^(?!Readme).*\\.txt\", encoding=\"utf8\"\n)\npros_cons = LazyCorpusLoader(\n \"pros_cons\",\n ProsConsCorpusReader,\n r\"Integrated(Cons|Pros)\\.txt\",\n cat_pattern=r\"Integrated(Cons|Pros)\\.txt\",\n encoding=\"ISO-8859-2\",\n)\nptb = LazyCorpusLoader( # Penn Treebank v3: WSJ and Brown portions\n \"ptb\",\n CategorizedBracketParseCorpusReader,\n r\"(WSJ/\\d\\d/WSJ_\\d\\d|BROWN/C[A-Z]/C[A-Z])\\d\\d.MRG\",\n cat_file=\"allcats.txt\",\n tagset=\"wsj\",\n)\nqc = LazyCorpusLoader(\n \"qc\", StringCategoryCorpusReader, [\"train.txt\", \"test.txt\"], encoding=\"ISO-8859-2\"\n)\nreuters = LazyCorpusLoader(\n \"reuters\",\n CategorizedPlaintextCorpusReader,\n \"(training|test).*\",\n cat_file=\"cats.txt\",\n encoding=\"ISO-8859-2\",\n)\nrte = LazyCorpusLoader(\"rte\", RTECorpusReader, r\"(?!\\.).*\\.xml\")\nsenseval = LazyCorpusLoader(\"senseval\", SensevalCorpusReader, r\"(?!\\.).*\\.pos\")\nsentence_polarity = LazyCorpusLoader(\n \"sentence_polarity\",\n CategorizedSentencesCorpusReader,\n r\"rt-polarity\\.(neg|pos)\",\n cat_pattern=r\"rt-polarity\\.(neg|pos)\",\n encoding=\"utf-8\",\n)\nsentiwordnet = LazyCorpusLoader(\n \"sentiwordnet\", SentiWordNetCorpusReader, \"SentiWordNet_3.0.0.txt\", encoding=\"utf-8\"\n)\nshakespeare = LazyCorpusLoader(\"shakespeare\", XMLCorpusReader, r\"(?!\\.).*\\.xml\")\nsinica_treebank = LazyCorpusLoader(\n \"sinica_treebank\",\n SinicaTreebankCorpusReader,\n [\"parsed\"],\n tagset=\"unknown\",\n encoding=\"utf-8\",\n)\nstate_union = LazyCorpusLoader(\n \"state_union\", PlaintextCorpusReader, r\"(?!\\.).*\\.txt\", encoding=\"ISO-8859-2\"\n)\nstopwords = LazyCorpusLoader(\n \"stopwords\", WordListCorpusReader, r\"(?!README|\\.).*\", encoding=\"utf8\"\n)\nsubjectivity = LazyCorpusLoader(\n \"subjectivity\",\n CategorizedSentencesCorpusReader,\n r\"(quote.tok.gt9|plot.tok.gt9)\\.5000\",\n cat_map={\"quote.tok.gt9.5000\": [\"subj\"], \"plot.tok.gt9.5000\": [\"obj\"]},\n encoding=\"latin-1\",\n)\nswadesh = LazyCorpusLoader(\n \"swadesh\", SwadeshCorpusReader, r\"(?!README|\\.).*\", encoding=\"utf8\"\n)\nswadesh110 = LazyCorpusLoader(\n 'panlex_swadesh', PanlexSwadeshCorpusReader, r'swadesh110/.*\\.txt', encoding='utf8'\n)\nswadesh207 = LazyCorpusLoader(\n 'panlex_swadesh', PanlexSwadeshCorpusReader, r'swadesh207/.*\\.txt', encoding='utf8'\n)\nswitchboard = LazyCorpusLoader(\"switchboard\", SwitchboardCorpusReader, tagset=\"wsj\")\ntimit = LazyCorpusLoader(\"timit\", TimitCorpusReader)\ntimit_tagged = LazyCorpusLoader(\n \"timit\", TimitTaggedCorpusReader, r\".+\\.tags\", tagset=\"wsj\", encoding=\"ascii\"\n)\ntoolbox = LazyCorpusLoader(\n \"toolbox\", ToolboxCorpusReader, r\"(?!.*(README|\\.)).*\\.(dic|txt)\"\n)\ntreebank = LazyCorpusLoader(\n \"treebank/combined\",\n BracketParseCorpusReader,\n r\"wsj_.*\\.mrg\",\n tagset=\"wsj\",\n encoding=\"ascii\",\n)\ntreebank_chunk = LazyCorpusLoader(\n \"treebank/tagged\",\n ChunkedCorpusReader,\n r\"wsj_.*\\.pos\",\n sent_tokenizer=RegexpTokenizer(r\"(?<=/\\.)\\s*(?![^\\[]*\\])\", gaps=True),\n para_block_reader=tagged_treebank_para_block_reader,\n tagset=\"wsj\",\n encoding=\"ascii\",\n)\ntreebank_raw = LazyCorpusLoader(\n \"treebank/raw\", PlaintextCorpusReader, r\"wsj_.*\", encoding=\"ISO-8859-2\"\n)\ntwitter_samples = LazyCorpusLoader(\"twitter_samples\", TwitterCorpusReader, r\".*\\.json\")\nudhr = LazyCorpusLoader(\"udhr\", UdhrCorpusReader)\nudhr2 = LazyCorpusLoader(\"udhr2\", PlaintextCorpusReader, r\".*\\.txt\", encoding=\"utf8\")\nuniversal_treebanks = LazyCorpusLoader(\n \"universal_treebanks_v20\",\n ConllCorpusReader,\n r\".*\\.conll\",\n columntypes=(\n \"ignore\",\n \"words\",\n \"ignore\",\n \"ignore\",\n \"pos\",\n \"ignore\",\n \"ignore\",\n \"ignore\",\n \"ignore\",\n \"ignore\",\n ),\n)\nverbnet = LazyCorpusLoader(\"verbnet\", VerbnetCorpusReader, r\"(?!\\.).*\\.xml\")\nwebtext = LazyCorpusLoader(\n \"webtext\", PlaintextCorpusReader, r\"(?!README|\\.).*\\.txt\", encoding=\"ISO-8859-2\"\n)\nwordnet = LazyCorpusLoader(\n \"wordnet\",\n WordNetCorpusReader,\n LazyCorpusLoader(\"omw\", CorpusReader, r\".*/wn-data-.*\\.tab\", encoding=\"utf8\"),\n)\nwordnet_ic = LazyCorpusLoader(\"wordnet_ic\", WordNetICCorpusReader, r\".*\\.dat\")\nwords = LazyCorpusLoader(\n \"words\", WordListCorpusReader, r\"(?!README|\\.).*\", encoding=\"ascii\"\n)\n\n# defined after treebank\npropbank = LazyCorpusLoader(\n \"propbank\",\n PropbankCorpusReader,\n \"prop.txt\",\n r\"frames/.*\\.xml\",\n \"verbs.txt\",\n lambda filename: re.sub(r\"^wsj/\\d\\d/\", \"\", filename),\n treebank,\n) # Must be defined *after* treebank corpus.\nnombank = LazyCorpusLoader(\n \"nombank.1.0\",\n NombankCorpusReader,\n \"nombank.1.0\",\n r\"frames/.*\\.xml\",\n \"nombank.1.0.words\",\n lambda filename: re.sub(r\"^wsj/\\d\\d/\", \"\", filename),\n treebank,\n) # Must be defined *after* treebank corpus.\npropbank_ptb = LazyCorpusLoader(\n \"propbank\",\n PropbankCorpusReader,\n \"prop.txt\",\n r\"frames/.*\\.xml\",\n \"verbs.txt\",\n lambda filename: filename.upper(),\n ptb,\n) # Must be defined *after* ptb corpus.\nnombank_ptb = LazyCorpusLoader(\n \"nombank.1.0\",\n NombankCorpusReader,\n \"nombank.1.0\",\n r\"frames/.*\\.xml\",\n \"nombank.1.0.words\",\n lambda filename: filename.upper(),\n ptb,\n) # Must be defined *after* ptb corpus.\nsemcor = LazyCorpusLoader(\n \"semcor\", SemcorCorpusReader, r\"brown./tagfiles/br-.*\\.xml\", wordnet\n) # Must be defined *after* wordnet corpus.\n\nnonbreaking_prefixes = LazyCorpusLoader(\n \"nonbreaking_prefixes\",\n NonbreakingPrefixesCorpusReader,\n r\"(?!README|\\.).*\",\n encoding=\"utf8\",\n)\nperluniprops = LazyCorpusLoader(\n \"perluniprops\",\n UnicharsCorpusReader,\n r\"(?!README|\\.).*\",\n nltk_data_subdir=\"misc\",\n encoding=\"utf8\",\n)\n\n# mwa_ppdb = LazyCorpusLoader(\n# 'mwa_ppdb', MWAPPDBCorpusReader, r'(?!README|\\.).*', nltk_data_subdir='misc', encoding='utf8')\n\n# See https://github.com/nltk/nltk/issues/1579\n# and https://github.com/nltk/nltk/issues/1716\n#\n# pl196x = LazyCorpusLoader(\n# 'pl196x', Pl196xCorpusReader, r'[a-z]-.*\\.xml',\n# cat_file='cats.txt', textid_file='textids.txt', encoding='utf8')\n#\n# ipipan = LazyCorpusLoader(\n# 'ipipan', IPIPANCorpusReader, r'(?!\\.).*morph\\.xml')\n#\n# nkjp = LazyCorpusLoader(\n# 'nkjp', NKJPCorpusReader, r'', encoding='utf8')\n#\n# panlex_lite = LazyCorpusLoader(\n# 'panlex_lite', PanLexLiteCorpusReader)\n#\n# ycoe = LazyCorpusLoader(\n# 'ycoe', YCOECorpusReader)\n#\n# corpus not available with NLTK; these lines caused help(nltk.corpus) to break\n# hebrew_treebank = LazyCorpusLoader(\n# 'hebrew_treebank', BracketParseCorpusReader, r'.*\\.txt')\n\n# FIXME: override any imported demo from various corpora, see https://github.com/nltk/nltk/issues/2116\ndef demo():\n # This is out-of-date:\n abc.demo()\n brown.demo()\n # chat80.demo()\n cmudict.demo()\n conll2000.demo()\n conll2002.demo()\n genesis.demo()\n gutenberg.demo()\n ieer.demo()\n inaugural.demo()\n indian.demo()\n names.demo()\n ppattach.demo()\n senseval.demo()\n shakespeare.demo()\n sinica_treebank.demo()\n state_union.demo()\n stopwords.demo()\n timit.demo()\n toolbox.demo()\n treebank.demo()\n udhr.demo()\n webtext.demo()\n words.demo()\n\n\n# ycoe.demo()\n\nif __name__ == \"__main__\":\n # demo()\n pass\n\n# ** this is for nose **\n# unload all corpus after tests\ndef teardown_module(module=None):\n import nltk.corpus\n\n for name in dir(nltk.corpus):\n obj = getattr(nltk.corpus, name, None)\n if isinstance(obj, CorpusReader) and hasattr(obj, \"_unload\"):\n obj._unload()\n", "path": "nltk/corpus/__init__.py" } ]
[ { "content": "# Natural Language Toolkit: Corpus Readers\n#\n# Copyright (C) 2001-2020 NLTK Project\n# Author: Edward Loper <[email protected]>\n# URL: <http://nltk.org/>\n# For license information, see LICENSE.TXT\n\n# TODO this docstring isn't up-to-date!\n\"\"\"\nNLTK corpus readers. The modules in this package provide functions\nthat can be used to read corpus files in a variety of formats. These\nfunctions can be used to read both the corpus files that are\ndistributed in the NLTK corpus package, and corpus files that are part\nof external corpora.\n\nAvailable Corpora\n=================\n\nPlease see http://www.nltk.org/nltk_data/ for a complete list.\nInstall corpora using nltk.download().\n\nCorpus Reader Functions\n=======================\nEach corpus module defines one or more \"corpus reader functions\",\nwhich can be used to read documents from that corpus. These functions\ntake an argument, ``item``, which is used to indicate which document\nshould be read from the corpus:\n\n- If ``item`` is one of the unique identifiers listed in the corpus\n module's ``items`` variable, then the corresponding document will\n be loaded from the NLTK corpus package.\n- If ``item`` is a filename, then that file will be read.\n\nAdditionally, corpus reader functions can be given lists of item\nnames; in which case, they will return a concatenation of the\ncorresponding documents.\n\nCorpus reader functions are named based on the type of information\nthey return. Some common examples, and their return types, are:\n\n- words(): list of str\n- sents(): list of (list of str)\n- paras(): list of (list of (list of str))\n- tagged_words(): list of (str,str) tuple\n- tagged_sents(): list of (list of (str,str))\n- tagged_paras(): list of (list of (list of (str,str)))\n- chunked_sents(): list of (Tree w/ (str,str) leaves)\n- parsed_sents(): list of (Tree with str leaves)\n- parsed_paras(): list of (list of (Tree with str leaves))\n- xml(): A single xml ElementTree\n- raw(): unprocessed corpus contents\n\nFor example, to read a list of the words in the Brown Corpus, use\n``nltk.corpus.brown.words()``:\n\n >>> from nltk.corpus import brown\n >>> print(\", \".join(brown.words()))\n The, Fulton, County, Grand, Jury, said, ...\n\n\"\"\"\n\nimport re\n\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.corpus.util import LazyCorpusLoader\nfrom nltk.corpus.reader import *\n\nabc = LazyCorpusLoader(\n \"abc\",\n PlaintextCorpusReader,\n r\"(?!\\.).*\\.txt\",\n encoding=[(\"science\", \"latin_1\"), (\"rural\", \"utf8\")],\n)\nalpino = LazyCorpusLoader(\"alpino\", AlpinoCorpusReader, tagset=\"alpino\")\nbrown = LazyCorpusLoader(\n \"brown\",\n CategorizedTaggedCorpusReader,\n r\"c[a-z]\\d\\d\",\n cat_file=\"cats.txt\",\n tagset=\"brown\",\n encoding=\"ascii\",\n)\ncess_cat = LazyCorpusLoader(\n \"cess_cat\",\n BracketParseCorpusReader,\n r\"(?!\\.).*\\.tbf\",\n tagset=\"unknown\",\n encoding=\"ISO-8859-15\",\n)\ncess_esp = LazyCorpusLoader(\n \"cess_esp\",\n BracketParseCorpusReader,\n r\"(?!\\.).*\\.tbf\",\n tagset=\"unknown\",\n encoding=\"ISO-8859-15\",\n)\ncmudict = LazyCorpusLoader(\"cmudict\", CMUDictCorpusReader, [\"cmudict\"])\ncomtrans = LazyCorpusLoader(\"comtrans\", AlignedCorpusReader, r\"(?!\\.).*\\.txt\")\ncomparative_sentences = LazyCorpusLoader(\n \"comparative_sentences\",\n ComparativeSentencesCorpusReader,\n r\"labeledSentences\\.txt\",\n encoding=\"latin-1\",\n)\nconll2000 = LazyCorpusLoader(\n \"conll2000\",\n ConllChunkCorpusReader,\n [\"train.txt\", \"test.txt\"],\n (\"NP\", \"VP\", \"PP\"),\n tagset=\"wsj\",\n encoding=\"ascii\",\n)\nconll2002 = LazyCorpusLoader(\n \"conll2002\",\n ConllChunkCorpusReader,\n r\".*\\.(test|train).*\",\n (\"LOC\", \"PER\", \"ORG\", \"MISC\"),\n encoding=\"utf-8\",\n)\nconll2007 = LazyCorpusLoader(\n \"conll2007\",\n DependencyCorpusReader,\n r\".*\\.(test|train).*\",\n encoding=[(\"eus\", \"ISO-8859-2\"), (\"esp\", \"utf8\")],\n)\ncrubadan = LazyCorpusLoader(\"crubadan\", CrubadanCorpusReader, r\".*\\.txt\")\ndependency_treebank = LazyCorpusLoader(\n \"dependency_treebank\", DependencyCorpusReader, r\".*\\.dp\", encoding=\"ascii\"\n)\nfloresta = LazyCorpusLoader(\n \"floresta\",\n BracketParseCorpusReader,\n r\"(?!\\.).*\\.ptb\",\n \"#\",\n tagset=\"unknown\",\n encoding=\"ISO-8859-15\",\n)\nframenet15 = LazyCorpusLoader(\n \"framenet_v15\",\n FramenetCorpusReader,\n [\n \"frRelation.xml\",\n \"frameIndex.xml\",\n \"fulltextIndex.xml\",\n \"luIndex.xml\",\n \"semTypes.xml\",\n ],\n)\nframenet = LazyCorpusLoader(\n \"framenet_v17\",\n FramenetCorpusReader,\n [\n \"frRelation.xml\",\n \"frameIndex.xml\",\n \"fulltextIndex.xml\",\n \"luIndex.xml\",\n \"semTypes.xml\",\n ],\n)\ngazetteers = LazyCorpusLoader(\n \"gazetteers\", WordListCorpusReader, r\"(?!LICENSE|\\.).*\\.txt\", encoding=\"ISO-8859-2\"\n)\ngenesis = LazyCorpusLoader(\n \"genesis\",\n PlaintextCorpusReader,\n r\"(?!\\.).*\\.txt\",\n encoding=[\n (\"finnish|french|german\", \"latin_1\"),\n (\"swedish\", \"cp865\"),\n (\".*\", \"utf_8\"),\n ],\n)\ngutenberg = LazyCorpusLoader(\n \"gutenberg\", PlaintextCorpusReader, r\"(?!\\.).*\\.txt\", encoding=\"latin1\"\n)\nieer = LazyCorpusLoader(\"ieer\", IEERCorpusReader, r\"(?!README|\\.).*\")\ninaugural = LazyCorpusLoader(\n \"inaugural\", PlaintextCorpusReader, r\"(?!\\.).*\\.txt\", encoding=\"latin1\"\n)\n# [XX] This should probably just use TaggedCorpusReader:\nindian = LazyCorpusLoader(\n \"indian\", IndianCorpusReader, r\"(?!\\.).*\\.pos\", tagset=\"unknown\", encoding=\"utf8\"\n)\n\njeita = LazyCorpusLoader(\"jeita\", ChasenCorpusReader, r\".*\\.chasen\", encoding=\"utf-8\")\nknbc = LazyCorpusLoader(\"knbc/corpus1\", KNBCorpusReader, r\".*/KN.*\", encoding=\"euc-jp\")\nlin_thesaurus = LazyCorpusLoader(\"lin_thesaurus\", LinThesaurusCorpusReader, r\".*\\.lsp\")\nmac_morpho = LazyCorpusLoader(\n \"mac_morpho\",\n MacMorphoCorpusReader,\n r\"(?!\\.).*\\.txt\",\n tagset=\"unknown\",\n encoding=\"latin-1\",\n)\nmachado = LazyCorpusLoader(\n \"machado\",\n PortugueseCategorizedPlaintextCorpusReader,\n r\"(?!\\.).*\\.txt\",\n cat_pattern=r\"([a-z]*)/.*\",\n encoding=\"latin-1\",\n)\nmasc_tagged = LazyCorpusLoader(\n \"masc_tagged\",\n CategorizedTaggedCorpusReader,\n r\"(spoken|written)/.*\\.txt\",\n cat_file=\"categories.txt\",\n tagset=\"wsj\",\n encoding=\"utf-8\",\n sep=\"_\",\n)\nmovie_reviews = LazyCorpusLoader(\n \"movie_reviews\",\n CategorizedPlaintextCorpusReader,\n r\"(?!\\.).*\\.txt\",\n cat_pattern=r\"(neg|pos)/.*\",\n encoding=\"ascii\",\n)\nmultext_east = LazyCorpusLoader(\n \"mte_teip5\", MTECorpusReader, r\"(oana).*\\.xml\", encoding=\"utf-8\"\n)\nnames = LazyCorpusLoader(\n \"names\", WordListCorpusReader, r\"(?!\\.).*\\.txt\", encoding=\"ascii\"\n)\nnps_chat = LazyCorpusLoader(\n \"nps_chat\", NPSChatCorpusReader, r\"(?!README|\\.).*\\.xml\", tagset=\"wsj\"\n)\nopinion_lexicon = LazyCorpusLoader(\n \"opinion_lexicon\",\n OpinionLexiconCorpusReader,\n r\"(\\w+)\\-words\\.txt\",\n encoding=\"ISO-8859-2\",\n)\nppattach = LazyCorpusLoader(\n \"ppattach\", PPAttachmentCorpusReader, [\"training\", \"test\", \"devset\"]\n)\nproduct_reviews_1 = LazyCorpusLoader(\n \"product_reviews_1\", ReviewsCorpusReader, r\"^(?!Readme).*\\.txt\", encoding=\"utf8\"\n)\nproduct_reviews_2 = LazyCorpusLoader(\n \"product_reviews_2\", ReviewsCorpusReader, r\"^(?!Readme).*\\.txt\", encoding=\"utf8\"\n)\npros_cons = LazyCorpusLoader(\n \"pros_cons\",\n ProsConsCorpusReader,\n r\"Integrated(Cons|Pros)\\.txt\",\n cat_pattern=r\"Integrated(Cons|Pros)\\.txt\",\n encoding=\"ISO-8859-2\",\n)\nptb = LazyCorpusLoader( # Penn Treebank v3: WSJ and Brown portions\n \"ptb\",\n CategorizedBracketParseCorpusReader,\n r\"(WSJ/\\d\\d/WSJ_\\d\\d|BROWN/C[A-Z]/C[A-Z])\\d\\d.MRG\",\n cat_file=\"allcats.txt\",\n tagset=\"wsj\",\n)\nqc = LazyCorpusLoader(\n \"qc\", StringCategoryCorpusReader, [\"train.txt\", \"test.txt\"], encoding=\"ISO-8859-2\"\n)\nreuters = LazyCorpusLoader(\n \"reuters\",\n CategorizedPlaintextCorpusReader,\n \"(training|test).*\",\n cat_file=\"cats.txt\",\n encoding=\"ISO-8859-2\",\n)\nrte = LazyCorpusLoader(\"rte\", RTECorpusReader, r\"(?!\\.).*\\.xml\")\nsenseval = LazyCorpusLoader(\"senseval\", SensevalCorpusReader, r\"(?!\\.).*\\.pos\")\nsentence_polarity = LazyCorpusLoader(\n \"sentence_polarity\",\n CategorizedSentencesCorpusReader,\n r\"rt-polarity\\.(neg|pos)\",\n cat_pattern=r\"rt-polarity\\.(neg|pos)\",\n encoding=\"utf-8\",\n)\nsentiwordnet = LazyCorpusLoader(\n \"sentiwordnet\", SentiWordNetCorpusReader, \"SentiWordNet_3.0.0.txt\", encoding=\"utf-8\"\n)\nshakespeare = LazyCorpusLoader(\"shakespeare\", XMLCorpusReader, r\"(?!\\.).*\\.xml\")\nsinica_treebank = LazyCorpusLoader(\n \"sinica_treebank\",\n SinicaTreebankCorpusReader,\n [\"parsed\"],\n tagset=\"unknown\",\n encoding=\"utf-8\",\n)\nstate_union = LazyCorpusLoader(\n \"state_union\", PlaintextCorpusReader, r\"(?!\\.).*\\.txt\", encoding=\"ISO-8859-2\"\n)\nstopwords = LazyCorpusLoader(\n \"stopwords\", WordListCorpusReader, r\"(?!README|\\.).*\", encoding=\"utf8\"\n)\nsubjectivity = LazyCorpusLoader(\n \"subjectivity\",\n CategorizedSentencesCorpusReader,\n r\"(quote.tok.gt9|plot.tok.gt9)\\.5000\",\n cat_map={\"quote.tok.gt9.5000\": [\"subj\"], \"plot.tok.gt9.5000\": [\"obj\"]},\n encoding=\"latin-1\",\n)\nswadesh = LazyCorpusLoader(\n \"swadesh\", SwadeshCorpusReader, r\"(?!README|\\.).*\", encoding=\"utf8\"\n)\nswadesh110 = LazyCorpusLoader(\n 'panlex_swadesh', PanlexSwadeshCorpusReader, r'swadesh110/.*\\.txt', encoding='utf8'\n)\nswadesh207 = LazyCorpusLoader(\n 'panlex_swadesh', PanlexSwadeshCorpusReader, r'swadesh207/.*\\.txt', encoding='utf8'\n)\nswitchboard = LazyCorpusLoader(\"switchboard\", SwitchboardCorpusReader, tagset=\"wsj\")\ntimit = LazyCorpusLoader(\"timit\", TimitCorpusReader)\ntimit_tagged = LazyCorpusLoader(\n \"timit\", TimitTaggedCorpusReader, r\".+\\.tags\", tagset=\"wsj\", encoding=\"ascii\"\n)\ntoolbox = LazyCorpusLoader(\n \"toolbox\", ToolboxCorpusReader, r\"(?!.*(README|\\.)).*\\.(dic|txt)\"\n)\ntreebank = LazyCorpusLoader(\n \"treebank/combined\",\n BracketParseCorpusReader,\n r\"wsj_.*\\.mrg\",\n tagset=\"wsj\",\n encoding=\"ascii\",\n)\ntreebank_chunk = LazyCorpusLoader(\n \"treebank/tagged\",\n ChunkedCorpusReader,\n r\"wsj_.*\\.pos\",\n sent_tokenizer=RegexpTokenizer(r\"(?<=/\\.)\\s*(?![^\\[]*\\])\", gaps=True),\n para_block_reader=tagged_treebank_para_block_reader,\n tagset=\"wsj\",\n encoding=\"ascii\",\n)\ntreebank_raw = LazyCorpusLoader(\n \"treebank/raw\", PlaintextCorpusReader, r\"wsj_.*\", encoding=\"ISO-8859-2\"\n)\ntwitter_samples = LazyCorpusLoader(\"twitter_samples\", TwitterCorpusReader, r\".*\\.json\")\nudhr = LazyCorpusLoader(\"udhr\", UdhrCorpusReader)\nudhr2 = LazyCorpusLoader(\"udhr2\", PlaintextCorpusReader, r\".*\\.txt\", encoding=\"utf8\")\nuniversal_treebanks = LazyCorpusLoader(\n \"universal_treebanks_v20\",\n ConllCorpusReader,\n r\".*\\.conll\",\n columntypes=(\n \"ignore\",\n \"words\",\n \"ignore\",\n \"ignore\",\n \"pos\",\n \"ignore\",\n \"ignore\",\n \"ignore\",\n \"ignore\",\n \"ignore\",\n ),\n)\nverbnet = LazyCorpusLoader(\"verbnet\", VerbnetCorpusReader, r\"(?!\\.).*\\.xml\")\nwebtext = LazyCorpusLoader(\n \"webtext\", PlaintextCorpusReader, r\"(?!README|\\.).*\\.txt\", encoding=\"ISO-8859-2\"\n)\nwordnet = LazyCorpusLoader(\n \"wordnet\",\n WordNetCorpusReader,\n LazyCorpusLoader(\"omw\", CorpusReader, r\".*/wn-data-.*\\.tab\", encoding=\"utf8\"),\n)\nwordnet_ic = LazyCorpusLoader(\"wordnet_ic\", WordNetICCorpusReader, r\".*\\.dat\")\nwords = LazyCorpusLoader(\n \"words\", WordListCorpusReader, r\"(?!README|\\.).*\", encoding=\"ascii\"\n)\n\n# defined after treebank\npropbank = LazyCorpusLoader(\n \"propbank\",\n PropbankCorpusReader,\n \"prop.txt\",\n r\"frames/.*\\.xml\",\n \"verbs.txt\",\n lambda filename: re.sub(r\"^wsj/\\d\\d/\", \"\", filename),\n treebank,\n) # Must be defined *after* treebank corpus.\nnombank = LazyCorpusLoader(\n \"nombank.1.0\",\n NombankCorpusReader,\n \"nombank.1.0\",\n r\"frames/.*\\.xml\",\n \"nombank.1.0.words\",\n lambda filename: re.sub(r\"^wsj/\\d\\d/\", \"\", filename),\n treebank,\n) # Must be defined *after* treebank corpus.\npropbank_ptb = LazyCorpusLoader(\n \"propbank\",\n PropbankCorpusReader,\n \"prop.txt\",\n r\"frames/.*\\.xml\",\n \"verbs.txt\",\n lambda filename: filename.upper(),\n ptb,\n) # Must be defined *after* ptb corpus.\nnombank_ptb = LazyCorpusLoader(\n \"nombank.1.0\",\n NombankCorpusReader,\n \"nombank.1.0\",\n r\"frames/.*\\.xml\",\n \"nombank.1.0.words\",\n lambda filename: filename.upper(),\n ptb,\n) # Must be defined *after* ptb corpus.\nsemcor = LazyCorpusLoader(\n \"semcor\", SemcorCorpusReader, r\"brown./tagfiles/br-.*\\.xml\", wordnet\n) # Must be defined *after* wordnet corpus.\n\nnonbreaking_prefixes = LazyCorpusLoader(\n \"nonbreaking_prefixes\",\n NonbreakingPrefixesCorpusReader,\n r\"(?!README|\\.).*\",\n encoding=\"utf8\",\n)\nperluniprops = LazyCorpusLoader(\n \"perluniprops\",\n UnicharsCorpusReader,\n r\"(?!README|\\.).*\",\n nltk_data_subdir=\"misc\",\n encoding=\"utf8\",\n)\n\n# mwa_ppdb = LazyCorpusLoader(\n# 'mwa_ppdb', MWAPPDBCorpusReader, r'(?!README|\\.).*', nltk_data_subdir='misc', encoding='utf8')\n\n# See https://github.com/nltk/nltk/issues/1579\n# and https://github.com/nltk/nltk/issues/1716\n#\n# pl196x = LazyCorpusLoader(\n# 'pl196x', Pl196xCorpusReader, r'[a-z]-.*\\.xml',\n# cat_file='cats.txt', textid_file='textids.txt', encoding='utf8')\n#\n# ipipan = LazyCorpusLoader(\n# 'ipipan', IPIPANCorpusReader, r'(?!\\.).*morph\\.xml')\n#\n# nkjp = LazyCorpusLoader(\n# 'nkjp', NKJPCorpusReader, r'', encoding='utf8')\n#\n# panlex_lite = LazyCorpusLoader(\n# 'panlex_lite', PanLexLiteCorpusReader)\n#\n# ycoe = LazyCorpusLoader(\n# 'ycoe', YCOECorpusReader)\n#\n# corpus not available with NLTK; these lines caused help(nltk.corpus) to break\n# hebrew_treebank = LazyCorpusLoader(\n# 'hebrew_treebank', BracketParseCorpusReader, r'.*\\.txt')\n\n# FIXME: override any imported demo from various corpora, see https://github.com/nltk/nltk/issues/2116\ndef demo():\n # This is out-of-date:\n abc.demo()\n brown.demo()\n # chat80.demo()\n cmudict.demo()\n conll2000.demo()\n conll2002.demo()\n genesis.demo()\n gutenberg.demo()\n ieer.demo()\n inaugural.demo()\n indian.demo()\n names.demo()\n ppattach.demo()\n senseval.demo()\n shakespeare.demo()\n sinica_treebank.demo()\n state_union.demo()\n stopwords.demo()\n timit.demo()\n toolbox.demo()\n treebank.demo()\n udhr.demo()\n webtext.demo()\n words.demo()\n\n\n# ycoe.demo()\n\nif __name__ == \"__main__\":\n # demo()\n pass\n\n# ** this is for unit testing **\n# unload all corpus after tests\ndef teardown_module(module=None):\n import nltk.corpus\n\n for name in dir(nltk.corpus):\n obj = getattr(nltk.corpus, name, None)\n if isinstance(obj, CorpusReader) and hasattr(obj, \"_unload\"):\n obj._unload()\n", "path": "nltk/corpus/__init__.py" } ]
diff --git a/.gitignore b/.gitignore index 0a9e9afadc..c9f8051b17 100644 --- a/.gitignore +++ b/.gitignore @@ -14,7 +14,7 @@ web/_build *.tox *.errs .noseids -.coverage +.coverage* nltk/test/*.html nltk/test/tweets* model.crf.tagger diff --git a/AUTHORS.md b/AUTHORS.md index 1bb005ec94..46d1e22d41 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -270,6 +270,7 @@ - Jacob Moorman <https://github.com/jdmoorman> - Cory Nezin <https://github.com/corynezin> - Matt Chaput +- Danny Sepler <https://github.com/dannysepler> - Akshita Bhagia <https://github.com/AkshitaB> ## Others whose work we've taken and included in NLTK, but who didn't directly contribute it: diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index d48909b3e8..92ba3f4e32 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -136,6 +136,15 @@ For a better design of your code, we recommend using a technique called where you write your tests **before** writing the actual code that implements the desired feature. +You can use `pytest` to run your tests, no matter which type of test it is: + +``` +cd nltk/test +pytest util.doctest # doctest +pytest unit/translate/test_nist.py # unittest +pytest # all tests +``` + ## Continuous Integration @@ -162,7 +171,7 @@ The [`.travis.yml`](https://github.com/nltk/nltk/blob/travis/.travis.yml) file c - `py-travis` tox test environment generally - the `extras = all` dependencies in needed to emulate `pip install nltk[all]`, see https://tox.readthedocs.io/en/latest/config.html#confval-extras=MULTI-LINE-LIST - for the `py-travis-third-party` build, it will run `tools/travis/third-party.sh` to install third-party tools (Stanford NLP tools and CoreNLP and SENNA) - - calls `tools/travis/coverage-pylint.sh` shell script that calls the `nltk/nltk/test/runtests.py` with [`coverage`](https://pypi.org/project/coverage/) and + - calls `tools/travis/coverage-pylint.sh` shell script that calls `pytest` with [`pytest-cov`](https://pytest-cov.readthedocs.io/) and - calls `pylint` # Currently, disabled because there's lots to clean... - before returning a `true` to state that the build is successful diff --git a/Makefile b/Makefile index 7c8bf8cabc..537be973f4 100644 --- a/Makefile +++ b/Makefile @@ -17,16 +17,14 @@ all: dist ######################################################################## # TESTING ######################################################################## - -DOCTEST_DRIVER = nltk/test/runtests.py DOCTEST_FILES = nltk/test/*.doctest DOCTEST_CODE_FILES = nltk/*.py nltk/*/*.py doctest: - $(PYTHON) $(DOCTEST_DRIVER) $(DOCTEST_FILES) + pytest $(DOCTEST_FILES) doctest_code: - $(PYTHON) $(DOCTEST_DRIVER) $(DOCTEST_CODE_FILES) + pytest $(DOCTEST_CODE_FILES) demotest: find nltk -name "*.py"\ diff --git a/jenkins-job-config.xml b/jenkins-job-config.xml index d9d411917d..957a90d427 100644 --- a/jenkins-job-config.xml +++ b/jenkins-job-config.xml @@ -133,7 +133,7 @@ <sourceEncoding>ASCII</sourceEncoding> </hudson.plugins.cobertura.CoberturaPublisher> <hudson.tasks.junit.JUnitResultArchiver> - <testResults>**/nosetests_scrubbed.xml</testResults> + <testResults>**/coverage_scrubbed.xml</testResults> <keepLongStdio>false</keepLongStdio> <testDataPublishers/> </hudson.tasks.junit.JUnitResultArchiver> diff --git a/jenkins.sh b/jenkins.sh index 58becb130c..5be27c07db 100755 --- a/jenkins.sh +++ b/jenkins.sh @@ -2,15 +2,15 @@ cd `dirname $0` -#download nltk python dependencies +# Download nltk python dependencies pip install --upgrade -r pip-req.txt pip install --upgrade matplotlib pip install --upgrade https://github.com/PyCQA/pylint/archive/master.zip -#download nltk data packages +# Download nltk data packages python -c "import nltk; nltk.download('all')" || echo "NLTK data download failed: $?" -#download external dependencies +# Download external dependencies pushd ${HOME} [[ ! -d 'third' ]] && mkdir 'third' pushd 'third' @@ -76,17 +76,16 @@ echo "---- CLASSPATH: ----" echo $CLASSPATH echo "---- MODELS: ----" echo $STANFORD_MODELS -echo "---- NLTK runtests.py ----" +echo "---- Running tests ----" -#coverage -coverage erase -coverage run --source=nltk nltk/test/runtests.py -v --with-xunit -coverage xml --omit=nltk/test/* -iconv -c -f utf-8 -t utf-8 nosetests.xml > nosetests_scrubbed.xml +# Coverage +rm -f coverage_scrubbed.xml +pytest --cov=nltk --cov-report xml nltk/test/ +iconv -c -f utf-8 -t utf-8 coverage.xml > coverage_scrubbed.xml # Create a default pylint configuration file. touch ~/.pylintrc pylint -f parseable nltk > pylintoutput -#script always succeeds +# Script always succeeds true diff --git a/nltk/corpus/__init__.py b/nltk/corpus/__init__.py index 3821907dad..3d7ba1e77a 100644 --- a/nltk/corpus/__init__.py +++ b/nltk/corpus/__init__.py @@ -482,7 +482,7 @@ def demo(): # demo() pass -# ** this is for nose ** +# ** this is for unit testing ** # unload all corpus after tests def teardown_module(module=None): import nltk.corpus diff --git a/nltk/test/Makefile b/nltk/test/Makefile index f2e4f7a25e..984182ed44 100644 --- a/nltk/test/Makefile +++ b/nltk/test/Makefile @@ -9,7 +9,7 @@ HTML = $(TESTS:.doctest=.html) # $(IPYNB:.ipynb=.html) IPYNB = $(wildcard *.ipynb) .doctest.errs: - python ./runtests.py $< > $@ + pytest $< > $@ .doctest.html: rst2html.py $< > $@ diff --git a/nltk/test/childes.doctest b/nltk/test/childes.doctest index 7900c541b6..6c7455f497 100644 --- a/nltk/test/childes.doctest +++ b/nltk/test/childes.doctest @@ -4,6 +4,12 @@ Read the XML version of the CHILDES corpus. +Setup +===== + + >>> from nltk.test.childes_fixt import setup_module + >>> setup_module() + How to use CHILDESCorpusReader ============================== diff --git a/nltk/test/childes_fixt.py b/nltk/test/childes_fixt.py index daac182ba7..ccda7c2930 100644 --- a/nltk/test/childes_fixt.py +++ b/nltk/test/childes_fixt.py @@ -1,16 +1,14 @@ # -*- coding: utf-8 -*- - -def setup_module(module): - from nose import SkipTest +def setup_module(): + import pytest import nltk.data try: nltk.data.find("corpora/childes/data-xml/Eng-USA-MOR/") except LookupError as e: - print(e) - raise SkipTest( + pytest.skip( "The CHILDES corpus is not found. " "It should be manually downloaded and saved/unpacked " "to [NLTK_Data_Dir]/corpora/childes/" - ) from e + ) diff --git a/nltk/test/classify.doctest b/nltk/test/classify.doctest index 26d14e6c69..d3ab385b9d 100644 --- a/nltk/test/classify.doctest +++ b/nltk/test/classify.doctest @@ -5,6 +5,9 @@ Classifiers ============= + >>> from nltk.test.classify_fixt import setup_module + >>> setup_module() + Classifiers label tokens with category labels (or *class labels*). Typically, labels are represented with strings (such as ``"health"`` or ``"sports"``. In NLTK, classifiers are defined using classes that diff --git a/nltk/test/classify_fixt.py b/nltk/test/classify_fixt.py index cf1f0b66b6..0d0d0c7ac5 100644 --- a/nltk/test/classify_fixt.py +++ b/nltk/test/classify_fixt.py @@ -2,10 +2,10 @@ # most of classify.doctest requires numpy -def setup_module(module): - from nose import SkipTest +def setup_module(): + import pytest try: import numpy - except ImportError as e: - raise SkipTest("classify.doctest requires numpy") from e + except ImportError: + pytest.skip("classify.doctest requires numpy") diff --git a/nltk/test/corpus.doctest b/nltk/test/corpus.doctest index 73b8fd792f..42671b509d 100644 --- a/nltk/test/corpus.doctest +++ b/nltk/test/corpus.doctest @@ -2196,4 +2196,8 @@ access to its tuples() method [('NUM:dist', 'How far is it from Denver to Aspen ?'), ('LOC:city', 'What county is Modesto , California in ?'), ...] +Teardown +======== + >>> from nltk.corpus import teardown_module + >>> teardown_module() diff --git a/nltk/test/corpus_fixt.py b/nltk/test/corpus_fixt.py deleted file mode 100644 index 17b011bd6f..0000000000 --- a/nltk/test/corpus_fixt.py +++ /dev/null @@ -1,3 +0,0 @@ -# -*- coding: utf-8 -*- - -from nltk.corpus import teardown_module diff --git a/nltk/test/discourse.doctest b/nltk/test/discourse.doctest index a5dabe8ae1..3ea674ef23 100644 --- a/nltk/test/discourse.doctest +++ b/nltk/test/discourse.doctest @@ -9,6 +9,12 @@ Discourse Checking >>> from nltk.sem import logic >>> logic._counter._value = 0 +Setup +===== + + >>> from nltk.test.childes_fixt import setup_module + >>> setup_module() + Introduction ============ diff --git a/nltk/test/discourse_fixt.py b/nltk/test/discourse_fixt.py index c213ad90ea..6f97c85cf2 100644 --- a/nltk/test/discourse_fixt.py +++ b/nltk/test/discourse_fixt.py @@ -3,14 +3,14 @@ # FIXME: the entire discourse.doctest is skipped if Prover9/Mace4 is # not installed, but there are pure-python parts that don't need Prover9. -def setup_module(module): - from nose import SkipTest +def setup_module(): + import pytest from nltk.inference.mace import Mace try: m = Mace() m._find_binary("mace4") except LookupError as e: - raise SkipTest( + pytest.skip( "Mace4/Prover9 is not available so discourse.doctest is skipped" - ) from e + ) diff --git a/nltk/test/gensim.doctest b/nltk/test/gensim.doctest index 386e3e0dad..c2ea04733e 100644 --- a/nltk/test/gensim.doctest +++ b/nltk/test/gensim.doctest @@ -4,6 +4,9 @@ ======================================= Demonstrate word embedding using Gensim ======================================= + + >>> from nltk.test.gensim_fixt import setup_module + >>> setup_module() We demonstrate three functions: - Train the word embeddings using brown corpus; diff --git a/nltk/test/gensim_fixt.py b/nltk/test/gensim_fixt.py index 0b11d13988..517385b3f4 100644 --- a/nltk/test/gensim_fixt.py +++ b/nltk/test/gensim_fixt.py @@ -1,10 +1,10 @@ # -*- coding: utf-8 -*- -def setup_module(module): - from nose import SkipTest +def setup_module(): + import pytest try: import gensim - except ImportError as e: - raise SkipTest("Gensim doctest requires gensim") from e + except ImportError: + pytest.skip("Gensim doctest requires gensim") diff --git a/nltk/test/gluesemantics_malt.doctest b/nltk/test/gluesemantics_malt.doctest index a76e96f355..20e0fabb0b 100644 --- a/nltk/test/gluesemantics_malt.doctest +++ b/nltk/test/gluesemantics_malt.doctest @@ -7,6 +7,9 @@ Glue Semantics ============================================================================== + >>> from nltk.test.gluesemantics_malt_fixt import setup_module + >>> setup_module() + >>> from nltk.sem.glue import * >>> nltk.sem.logic._counter._value = 0 diff --git a/nltk/test/gluesemantics_malt_fixt.py b/nltk/test/gluesemantics_malt_fixt.py index a566c259cf..4583a633cf 100644 --- a/nltk/test/gluesemantics_malt_fixt.py +++ b/nltk/test/gluesemantics_malt_fixt.py @@ -1,11 +1,11 @@ # -*- coding: utf-8 -*- -def setup_module(module): - from nose import SkipTest +def setup_module(): + import pytest from nltk.parse.malt import MaltParser try: depparser = MaltParser("maltparser-1.7.2") except LookupError as e: - raise SkipTest("MaltParser is not available") from e + pytest.skip("MaltParser is not available") diff --git a/nltk/test/inference.doctest b/nltk/test/inference.doctest index 5bf15015a0..006a3206e2 100644 --- a/nltk/test/inference.doctest +++ b/nltk/test/inference.doctest @@ -5,6 +5,9 @@ Logical Inference and Model Building ==================================== + >>> from nltk.test.inference_fixt import setup_module + >>> setup_module() + >>> from nltk import * >>> from nltk.sem.drt import DrtParser >>> from nltk.sem import logic diff --git a/nltk/test/inference_fixt.py b/nltk/test/inference_fixt.py index 1c2df992be..754aa5dd4e 100644 --- a/nltk/test/inference_fixt.py +++ b/nltk/test/inference_fixt.py @@ -1,14 +1,14 @@ # -*- coding: utf-8 -*- -def setup_module(module): - from nose import SkipTest +def setup_module(): + import pytest from nltk.inference.mace import Mace try: m = Mace() m._find_binary("mace4") - except LookupError as e: - raise SkipTest( + except LookupError: + pytest.skip( "Mace4/Prover9 is not available so inference.doctest was skipped" - ) from e + ) diff --git a/nltk/test/nonmonotonic.doctest b/nltk/test/nonmonotonic.doctest index ea17c60cde..45b5fd1095 100644 --- a/nltk/test/nonmonotonic.doctest +++ b/nltk/test/nonmonotonic.doctest @@ -5,6 +5,9 @@ Nonmonotonic Reasoning ====================== + >>> from nltk.test.nonmonotonic_fixt import setup_module + >>> setup_module() + >>> from nltk import * >>> from nltk.inference.nonmonotonic import * >>> from nltk.sem import logic diff --git a/nltk/test/nonmonotonic_fixt.py b/nltk/test/nonmonotonic_fixt.py index eff6ae9f37..f03be1aff3 100644 --- a/nltk/test/nonmonotonic_fixt.py +++ b/nltk/test/nonmonotonic_fixt.py @@ -1,14 +1,14 @@ # -*- coding: utf-8 -*- -def setup_module(module): - from nose import SkipTest +def setup_module(): + import pytest from nltk.inference.mace import Mace try: m = Mace() m._find_binary("mace4") except LookupError as e: - raise SkipTest( + pytest.skip( "Mace4/Prover9 is not available so nonmonotonic.doctest was skipped" - ) from e + ) diff --git a/nltk/test/portuguese_en.doctest b/nltk/test/portuguese_en.doctest index 84cee4a60f..63dc00294d 100644 --- a/nltk/test/portuguese_en.doctest +++ b/nltk/test/portuguese_en.doctest @@ -18,6 +18,9 @@ Chapter 1 of the NLTK book contains many elementary programming examples, all with English texts. In this section, we'll see some corresponding examples using Portuguese. Please refer to the chapter for full discussion. *Vamos!* + >>> from nltk.test.portuguese_en_fixt import setup_module + >>> setup_module() + >>> from nltk.examples.pt import * *** Introductory Examples for the NLTK Book *** Loading ptext1, ... and psent1, ... @@ -563,3 +566,9 @@ and print them in descending order of frequency: ter 249 dois 231 + +Teardown +--------- + + >>> from nltk.corpus import teardown_module + >>> teardown_module() diff --git a/nltk/test/portuguese_en_fixt.py b/nltk/test/portuguese_en_fixt.py index f417bc6a48..548d745ffb 100644 --- a/nltk/test/portuguese_en_fixt.py +++ b/nltk/test/portuguese_en_fixt.py @@ -1,10 +1,8 @@ # -*- coding: utf-8 -*- -from nltk.corpus import teardown_module +def setup_module(): + import pytest -def setup_module(module): - from nose import SkipTest - - raise SkipTest( + pytest.skip( "portuguese_en.doctest imports nltk.examples.pt which doesn't exist!" ) diff --git a/nltk/test/probability_fixt.py b/nltk/test/probability_fixt.py index a3d092906d..4388f430b4 100644 --- a/nltk/test/probability_fixt.py +++ b/nltk/test/probability_fixt.py @@ -6,9 +6,9 @@ def setup_module(module): - from nose import SkipTest + import pytest try: import numpy - except ImportError as e: - raise SkipTest("probability.doctest requires numpy") from e + except ImportError: + pytest.skip("probability.doctest requires numpy") diff --git a/nltk/test/pytest.ini b/nltk/test/pytest.ini new file mode 100644 index 0000000000..f9973618ca --- /dev/null +++ b/nltk/test/pytest.ini @@ -0,0 +1,13 @@ +[pytest] +doctest_optionflags=ELLIPSIS NORMALIZE_WHITESPACE IGNORE_EXCEPTION_DETAIL + +# --doctest-continue-on-failure allows the test to always +# get to the teardown, if it exists +addopts = --doctest-glob *.doctest --doctest-continue-on-failure + +# Other options for creating valid teardowns for doctests: +# 1. Turn doctests into unittest tests +# - https://docs.python.org/3/library/doctest.html#unittest-api +# 2. Use sphinx doctests +# - https://www.sphinx-doc.org/en/master/usage/extensions/doctest.html +# 3. Convert the tests that require setup/teardown into pytest tests with fixtures diff --git a/nltk/test/runtests.py b/nltk/test/runtests.py deleted file mode 100755 index 9dc06ec9be..0000000000 --- a/nltk/test/runtests.py +++ /dev/null @@ -1,75 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -import sys -import os -import nose -from nose.plugins.manager import PluginManager -from nose.plugins.doctests import Doctest -from nose.plugins import builtin - -NLTK_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")) -sys.path.insert(0, NLTK_ROOT) - -NLTK_TEST_DIR = os.path.join(NLTK_ROOT, "nltk") - -if __name__ == "__main__": - # there shouldn't be import from NLTK for coverage to work properly - try: - # Import RedNose plugin for colored test output - from rednose import RedNose - - rednose_available = True - except ImportError: - rednose_available = False - - class NltkPluginManager(PluginManager): - """ - Nose plugin manager that replaces standard doctest plugin - with a patched version and adds RedNose plugin for colored test output. - """ - - def loadPlugins(self): - for plug in builtin.plugins: - self.addPlugin(plug()) - if rednose_available: - self.addPlugin(RedNose()) - - super(NltkPluginManager, self).loadPlugins() - - manager = NltkPluginManager() - manager.loadPlugins() - - # allow passing extra options and running individual tests - # Examples: - # - # python runtests.py semantics.doctest - # python runtests.py --with-id -v - # python runtests.py --with-id -v nltk.featstruct - - args = sys.argv[1:] - if not args: - args = [NLTK_TEST_DIR] - - if all(arg.startswith("-") for arg in args): - # only extra options were passed - args += [NLTK_TEST_DIR] - - # Activate RedNose and hide skipped test messages from output - if rednose_available: - args += ["--rednose", "--hide-skips"] - - arguments = [ - "--exclude=", # why is this needed? - # '--with-xunit', - # '--xunit-file=$WORKSPACE/nosetests.xml', - # '--nocapture', - "--with-doctest", - # '--doctest-tests', - # '--debug=nose,nose.importer,nose.inspector,nose.plugins,nose.result,nose.selector', - "--doctest-extension=.doctest", - "--doctest-fixtures=_fixt", - "--doctest-options=+ELLIPSIS,+NORMALIZE_WHITESPACE,+IGNORE_EXCEPTION_DETAIL", - # '--verbosity=3', - ] + args - - nose.main(argv=arguments, plugins=manager.plugins) diff --git a/nltk/test/segmentation_fixt.py b/nltk/test/segmentation_fixt.py deleted file mode 100644 index 09ddc68fdc..0000000000 --- a/nltk/test/segmentation_fixt.py +++ /dev/null @@ -1,11 +0,0 @@ -# -*- coding: utf-8 -*- - - -# skip segmentation.doctest if numpy is not available -def setup_module(module): - from nose import SkipTest - - try: - import numpy - except ImportError as e: - raise SkipTest("segmentation.doctest requires numpy") from e diff --git a/nltk/test/semantics.doctest b/nltk/test/semantics.doctest index 32c0f84025..5565ff6221 100644 --- a/nltk/test/semantics.doctest +++ b/nltk/test/semantics.doctest @@ -5,6 +5,10 @@ Semantics ========= + >>> # Setup tests by setting the counter to 0 + >>> from nltk.sem import logic + >>> logic._counter._value = 0 + >>> import nltk >>> from nltk.sem import Valuation, Model >>> v = [('adam', 'b1'), ('betty', 'g1'), ('fido', 'd1'), diff --git a/nltk/test/semantics_fixt.py b/nltk/test/semantics_fixt.py deleted file mode 100644 index 8d67144d6a..0000000000 --- a/nltk/test/semantics_fixt.py +++ /dev/null @@ -1,7 +0,0 @@ -# -*- coding: utf-8 -*- - -# reset the variables counter before running tests -def setup_module(module): - from nltk.sem import logic - - logic._counter._value = 0 diff --git a/nltk/test/translate.doctest b/nltk/test/translate.doctest index 87966fb4c0..e93f9eec27 100644 --- a/nltk/test/translate.doctest +++ b/nltk/test/translate.doctest @@ -240,3 +240,8 @@ Here are some examples: *Statistical Machine Translation*, Cambridge University Press +Teardown +======== + + >>> from nltk.corpus import teardown_module + >>> teardown_module() diff --git a/nltk/test/translate_fixt.py b/nltk/test/translate_fixt.py deleted file mode 100644 index 17b011bd6f..0000000000 --- a/nltk/test/translate_fixt.py +++ /dev/null @@ -1,3 +0,0 @@ -# -*- coding: utf-8 -*- - -from nltk.corpus import teardown_module diff --git a/nltk/test/unit/conftest.py b/nltk/test/unit/conftest.py new file mode 100644 index 0000000000..5ff8204bd4 --- /dev/null +++ b/nltk/test/unit/conftest.py @@ -0,0 +1,15 @@ +import pytest + + [email protected](autouse=True) +def mock_plot(mocker): + """ Disable matplotlib plotting in test code """ + + try: + import matplotlib.pyplot as plt + + mocker.patch.object(plt, 'show') + except ImportError: + pytest.skip( + "mock_plot imports matplotlib which doesn't exist!" + ) diff --git a/nltk/test/unit/test_seekable_unicode_stream_reader.py b/nltk/test/unit/test_seekable_unicode_stream_reader.py index c5d15831ba..d59d84becc 100644 --- a/nltk/test/unit/test_seekable_unicode_stream_reader.py +++ b/nltk/test/unit/test_seekable_unicode_stream_reader.py @@ -91,7 +91,7 @@ def test_reader(): try: # skip strings that can't be encoded with the current encoding string.encode(encoding) - yield check_reader, string, encoding + check_reader(string, encoding) except UnicodeEncodeError: pass @@ -120,7 +120,7 @@ def test_reader_on_large_string(): def _check(encoding, n=1000): check_reader(LARGE_STRING, encoding, n) - yield _check, encoding + _check(encoding) except UnicodeEncodeError: pass diff --git a/nltk/test/wordnet.doctest b/nltk/test/wordnet.doctest index 54c597511c..b5bbbb7303 100644 --- a/nltk/test/wordnet.doctest +++ b/nltk/test/wordnet.doctest @@ -602,3 +602,11 @@ Patch-1 https://github.com/nltk/nltk/pull/2065 Adding 3 functions (relations) t Synset('french.n.01') >>> wn.synsets("slang")[1].in_usage_domains()[18] Synset('can-do.s.01') + + +------------- +Teardown test +------------- + + >>> from nltk.corpus import wordnet + >>> wordnet._unload() diff --git a/nltk/test/wordnet_fixt.py b/nltk/test/wordnet_fixt.py deleted file mode 100644 index 09ba27cc71..0000000000 --- a/nltk/test/wordnet_fixt.py +++ /dev/null @@ -1,7 +0,0 @@ -# -*- coding: utf-8 -*- - - -def teardown_module(module=None): - from nltk.corpus import wordnet - - wordnet._unload() diff --git a/pip-req.txt b/pip-req.txt index 85a1ecf581..2f7d5ee4e6 100644 --- a/pip-req.txt +++ b/pip-req.txt @@ -1,6 +1,5 @@ nose>=1.3.0 tox>=1.6.1 -coverage>=3.7.1 pylint>=1.1.0 numpy>=1.8.0 scipy>=0.13.2 @@ -11,4 +10,3 @@ gensim>=0.11.1 pyparsing>=2.0.3 twython>=3.2.0 regex>=2019.08.19 - diff --git a/requirements-test.txt b/requirements-test.txt index ffb8d8782f..290c3ec8d0 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -1,4 +1,6 @@ tox nose -coverage pylint +pytest>=6.0.1 +pytest-mock +pytest-cov>=2.10.1 diff --git a/tools/travis/coverage-pylint.sh b/tools/travis/coverage-pylint.sh index 7383a94e49..ad70587ba5 100644 --- a/tools/travis/coverage-pylint.sh +++ b/tools/travis/coverage-pylint.sh @@ -16,10 +16,9 @@ pip -V echo "$(pwd)" # Know which directory tox is running this shell from. #coverage -coverage erase -coverage run --source=nltk $(pwd)/runtests.py -v --with-xunit -coverage xml --omit=$(pwd)/* -iconv -c -f utf-8 -t utf-8 nosetests.xml > nosetests_scrubbed.xml +rm -f coverage_scrubbed.xml +pytest --cov=nltk --cov-report xml +iconv -c -f utf-8 -t utf-8 coverage.xml > coverage_scrubbed.xml # Create a default pylint configuration file. ##touch $HOME/.pylintrc diff --git a/tox.ini b/tox.ini index 488a929d31..f803c80c88 100644 --- a/tox.ini +++ b/tox.ini @@ -19,12 +19,13 @@ passenv = * deps = numpy nose >= 1.2.1 - coverage text-unidecode twython pyparsing + pytest + pytest-cov + pytest-mock python-crfsuite - rednose regex changedir = nltk/test @@ -33,39 +34,44 @@ commands = ; they can't be installed in one command pip install scipy scikit-learn - ; python runtests.py --with-coverage --cover-inclusive --cover-package=nltk --cover-html --cover-html-dir={envdir}/docs [] - python runtests.py [] + ; pytest --cov=nltk --cov-report html:{envdir}/docs nltk/test/ + pytest [testenv:pypy] ; numpy is bundled with pypy; coverage is extra slow and ; the coverage results are not that different from CPython. deps = nose >= 1.2.1 + pytest + pytest-mock twython commands = - python runtests.py [] + pytest [testenv:py35-nodeps] basepython = python3.5 deps = nose >= 1.2.1 - rednose -commands = python runtests.py [] + pytest + pytest-mock +commands = pytest [testenv:py36-nodeps] basepython = python3.6 deps = nose >= 1.2.1 - rednose -commands = python runtests.py [] + pytest + pytest-mock +commands = pytest [testenv:py37-nodeps] basepython = python3.7 deps = nose >= 1.2.1 - rednose -commands = python runtests.py [] + pytest + pytest-mock +commands = pytest # Use minor version agnostic basepython, but specify testenv # control Python2/3 versions using jenkins' user-defined matrix instead. diff --git a/web/dev/jenkins.rst b/web/dev/jenkins.rst index e5b3025b82..0a210a046f 100644 --- a/web/dev/jenkins.rst +++ b/web/dev/jenkins.rst @@ -68,25 +68,21 @@ installed beforehand, and to make them run a series of extra environment variables are initialized. These dependencies will not be detailed until the last section. -The test suite itself consists of doctests. These are found in each module as -docstrings, and in all the .doctest files under the test folder in the nltk -repo. We run these tests using nose_, find code coverage using `coverage.py`_ -and check for `PEP-8`_ etc. standard violations using `pylint`_. +The test suite itself consists of doctests and unittests. Doctests are found in +each module as docstrings, and in all the .doctest files under the test folder in +the nltk repo. We run these tests using pytest_, find code coverage using +`pytest-cov`_ and check for `PEP-8`_ etc. standard violations using `pylint`_. All these tools are easily installable through pip your favourite OS' software -packaging system. For testing, only nose_ is really needed. This is also the -only software that does not work properly out of the box. To use the options -+ELLIPSIS and +NORMALIZE_WHITESPACE in our doctests, we have installed nose -from source with `a patch that allows this`_ applied. +packaging system. For testing, you can install the requirements with ``pip install -r requirements-test.txt`` The results of these programs are parsed and published by the jenkins instance, giving us pretty graphs :) -.. _nose: http://readthedocs.org/docs/nose/ -.. _`coverage.py`: http://nedbatchelder.com/code/coverage/ +.. _pytest: https://docs.pytest.org/ +.. _`pytest-cov`: http://pytest-cov.readthedocs.io/ .. _`PEP-8`: http://www.python.org/dev/peps/pep-0008/ .. _`pylint`: http://www.logilab.org/project/pylint -.. _`a patch that allows this`: https://github.com/nose-devs/nose/issues/7 The builds diff --git a/web/dev/local_testing.rst b/web/dev/local_testing.rst index 110987855d..d72d67ed32 100644 --- a/web/dev/local_testing.rst +++ b/web/dev/local_testing.rst @@ -14,8 +14,8 @@ NLTK testing 4. Make sure all NLTK data is downloaded (see ``nltk.download()``); 5. run 'tox' command from the root nltk folder. It will install dependencies - and run ``nltk/test/runtests.py`` script for all available interpreters. - You may pass any options to runtests.py script separating them by '--'. + and run ``pytest`` for all available interpreters. + You may also pass any pytest options here (for example, `-v` for verbose). It may take a long time at first run, but the subsequent runs will be much faster. @@ -30,7 +30,6 @@ that failed in the last test run:: tox -e py36 -- -v --failed - Run tree doctests for all available interpreters:: tox -- tree.doctest @@ -48,9 +47,9 @@ In order to skip numpy & friends, use ``..-nodeps`` environments:: It is also possible to run tests without tox. This way NLTK would be tested only under single interpreter, but it may be easier to have numpy and other libraries installed this way. In order to run tests without tox, make sure -``nose >= 1.2.1`` is installed and execute runtests.py script:: +to ``pip install -r test-requirements.txt`` and run ``pytest``:: - nltk/test/runtests.py + pytest nltk/test/ Writing tests @@ -71,7 +70,7 @@ unittests. Test should be written as unittest if some of the following apply: * test deals with non-ascii unicode and Python 2.x support is required; * test is a regression test that is not necessary for documentational purposes. -Unittests currently reside in ``nltk/test/unit/test_*.py`` files; nose +Unittests currently reside in ``nltk/test/unit/test_*.py`` files; pytest is used for test running. If a test should be written as unittest but also has a documentational value @@ -85,13 +84,13 @@ There are some gotchas with NLTK doctests (and with doctests in general): * Don't write ``+ELLIPSIS``, ``+NORMALIZE_WHITESPACE``, ``+IGNORE_EXCEPTION_DETAIL`` flags (they are already ON by default in NLTK). -* Do not write doctests that has non-ascii output (they are not supported in +* Do not write doctests that have non-ascii output (they are not supported in Python 2.x). Incorrect:: >>> greeting u'Привет' - The proper way is to rewrite such doctest as unittest. + The proper way is to rewrite such a doctest as a unittest. * In order to conditionally skip a doctest in a separate ``nltk/test/foo.doctest`` file, create ``nltk.test/foo_fixt.py`` @@ -100,10 +99,10 @@ There are some gotchas with NLTK doctests (and with doctests in general): # <a comment describing why should the test be skipped> def setup_module(module): - from nose import SkipTest + import pytest if some_condition: - raise SkipTest("foo.doctest is skipped because <...>") + pytest.skip("foo.doctest is skipped because <...>") * In order to conditionally skip all doctests from the module/class/function docstrings, put the following function in a top-level module namespace:: @@ -111,10 +110,10 @@ There are some gotchas with NLTK doctests (and with doctests in general): # <a comment describing why should the tests from this module be skipped> def setup_module(module): - from nose import SkipTest + import pytest if some_condition: - raise SkipTest("doctests from nltk.<foo>.<bar> are skipped because <...>") + pytest.skip("doctests from nltk.<foo>.<bar> are skipped because <...>") A good idea is to define ``__all__`` in such module and omit ``setup_module`` from ``__all__``. @@ -150,7 +149,7 @@ If the code requires some external dependencies, then * tests for this code should be skipped if the dependencies are not available: use ``setup_module`` for doctests (as described above) and - ``nltk.test.unit.utils.skip / skipIf`` decorators or ``nose.SkipTest`` + ``@pytest.mark.skipif / @pytest.mark.skip`` decorators or ``pytest.skip`` exception for unittests; * if the dependency is a Python package, it should be added to tox.ini (but not to ..-nodeps environments).
open-mmlab__mmdetection-6034
Missing '**kwargs' parameters passing to imshow_bboxes() in show_result() of rpn.py https://github.com/open-mmlab/mmdetection/blob/bde7b4b7eea9dd6ee91a486c6996b2d68662366d/mmdet/models/detectors/rpn.py#L155 '**kwargs' parameters haven't passed to mmcv.imshow_bboxes() in show_result() of mmdetection/mmdet/models/detectors/rpn.py
[ { "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\n\nimport mmcv\nimport torch\nfrom mmcv.image import tensor2imgs\n\nfrom mmdet.core import bbox_mapping\nfrom ..builder import DETECTORS, build_backbone, build_head, build_neck\nfrom .base import BaseDetector\n\n\[email protected]_module()\nclass RPN(BaseDetector):\n \"\"\"Implementation of Region Proposal Network.\"\"\"\n\n def __init__(self,\n backbone,\n neck,\n rpn_head,\n train_cfg,\n test_cfg,\n pretrained=None,\n init_cfg=None):\n super(RPN, self).__init__(init_cfg)\n if pretrained:\n warnings.warn('DeprecationWarning: pretrained is deprecated, '\n 'please use \"init_cfg\" instead')\n backbone.pretrained = pretrained\n self.backbone = build_backbone(backbone)\n self.neck = build_neck(neck) if neck is not None else None\n rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None\n rpn_head.update(train_cfg=rpn_train_cfg)\n rpn_head.update(test_cfg=test_cfg.rpn)\n self.rpn_head = build_head(rpn_head)\n self.train_cfg = train_cfg\n self.test_cfg = test_cfg\n\n def extract_feat(self, img):\n \"\"\"Extract features.\n\n Args:\n img (torch.Tensor): Image tensor with shape (n, c, h ,w).\n\n Returns:\n list[torch.Tensor]: Multi-level features that may have\n different resolutions.\n \"\"\"\n x = self.backbone(img)\n if self.with_neck:\n x = self.neck(x)\n return x\n\n def forward_dummy(self, img):\n \"\"\"Dummy forward function.\"\"\"\n x = self.extract_feat(img)\n rpn_outs = self.rpn_head(x)\n return rpn_outs\n\n def forward_train(self,\n img,\n img_metas,\n gt_bboxes=None,\n gt_bboxes_ignore=None):\n \"\"\"\n Args:\n img (Tensor): Input images of shape (N, C, H, W).\n Typically these should be mean centered and std scaled.\n img_metas (list[dict]): A List of image info dict where each dict\n has: 'img_shape', 'scale_factor', 'flip', and may also contain\n 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.\n For details on the values of these keys see\n :class:`mmdet.datasets.pipelines.Collect`.\n gt_bboxes (list[Tensor]): Each item are the truth boxes for each\n image in [tl_x, tl_y, br_x, br_y] format.\n gt_bboxes_ignore (None | list[Tensor]): Specify which bounding\n boxes can be ignored when computing the loss.\n\n Returns:\n dict[str, Tensor]: A dictionary of loss components.\n \"\"\"\n if (isinstance(self.train_cfg.rpn, dict)\n and self.train_cfg.rpn.get('debug', False)):\n self.rpn_head.debug_imgs = tensor2imgs(img)\n\n x = self.extract_feat(img)\n losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None,\n gt_bboxes_ignore)\n return losses\n\n def simple_test(self, img, img_metas, rescale=False):\n \"\"\"Test function without test time augmentation.\n\n Args:\n imgs (list[torch.Tensor]): List of multiple images\n img_metas (list[dict]): List of image information.\n rescale (bool, optional): Whether to rescale the results.\n Defaults to False.\n\n Returns:\n list[np.ndarray]: proposals\n \"\"\"\n x = self.extract_feat(img)\n # get origin input shape to onnx dynamic input shape\n if torch.onnx.is_in_onnx_export():\n img_shape = torch._shape_as_tensor(img)[2:]\n img_metas[0]['img_shape_for_onnx'] = img_shape\n proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)\n if rescale:\n for proposals, meta in zip(proposal_list, img_metas):\n proposals[:, :4] /= proposals.new_tensor(meta['scale_factor'])\n if torch.onnx.is_in_onnx_export():\n return proposal_list\n\n return [proposal.cpu().numpy() for proposal in proposal_list]\n\n def aug_test(self, imgs, img_metas, rescale=False):\n \"\"\"Test function with test time augmentation.\n\n Args:\n imgs (list[torch.Tensor]): List of multiple images\n img_metas (list[dict]): List of image information.\n rescale (bool, optional): Whether to rescale the results.\n Defaults to False.\n\n Returns:\n list[np.ndarray]: proposals\n \"\"\"\n proposal_list = self.rpn_head.aug_test_rpn(\n self.extract_feats(imgs), img_metas)\n if not rescale:\n for proposals, img_meta in zip(proposal_list, img_metas[0]):\n img_shape = img_meta['img_shape']\n scale_factor = img_meta['scale_factor']\n flip = img_meta['flip']\n flip_direction = img_meta['flip_direction']\n proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape,\n scale_factor, flip,\n flip_direction)\n return [proposal.cpu().numpy() for proposal in proposal_list]\n\n def show_result(self, data, result, top_k=20, **kwargs):\n \"\"\"Show RPN proposals on the image.\n\n Args:\n data (str or np.ndarray): Image filename or loaded image.\n result (Tensor or tuple): The results to draw over `img`\n bbox_result or (bbox_result, segm_result).\n top_k (int): Plot the first k bboxes only\n if set positive. Default: 20\n\n Returns:\n np.ndarray: The image with bboxes drawn on it.\n \"\"\"\n mmcv.imshow_bboxes(data, result, top_k=top_k)\n", "path": "mmdet/models/detectors/rpn.py" } ]
[ { "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport warnings\n\nimport mmcv\nimport torch\nfrom mmcv.image import tensor2imgs\n\nfrom mmdet.core import bbox_mapping\nfrom ..builder import DETECTORS, build_backbone, build_head, build_neck\nfrom .base import BaseDetector\n\n\[email protected]_module()\nclass RPN(BaseDetector):\n \"\"\"Implementation of Region Proposal Network.\"\"\"\n\n def __init__(self,\n backbone,\n neck,\n rpn_head,\n train_cfg,\n test_cfg,\n pretrained=None,\n init_cfg=None):\n super(RPN, self).__init__(init_cfg)\n if pretrained:\n warnings.warn('DeprecationWarning: pretrained is deprecated, '\n 'please use \"init_cfg\" instead')\n backbone.pretrained = pretrained\n self.backbone = build_backbone(backbone)\n self.neck = build_neck(neck) if neck is not None else None\n rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None\n rpn_head.update(train_cfg=rpn_train_cfg)\n rpn_head.update(test_cfg=test_cfg.rpn)\n self.rpn_head = build_head(rpn_head)\n self.train_cfg = train_cfg\n self.test_cfg = test_cfg\n\n def extract_feat(self, img):\n \"\"\"Extract features.\n\n Args:\n img (torch.Tensor): Image tensor with shape (n, c, h ,w).\n\n Returns:\n list[torch.Tensor]: Multi-level features that may have\n different resolutions.\n \"\"\"\n x = self.backbone(img)\n if self.with_neck:\n x = self.neck(x)\n return x\n\n def forward_dummy(self, img):\n \"\"\"Dummy forward function.\"\"\"\n x = self.extract_feat(img)\n rpn_outs = self.rpn_head(x)\n return rpn_outs\n\n def forward_train(self,\n img,\n img_metas,\n gt_bboxes=None,\n gt_bboxes_ignore=None):\n \"\"\"\n Args:\n img (Tensor): Input images of shape (N, C, H, W).\n Typically these should be mean centered and std scaled.\n img_metas (list[dict]): A List of image info dict where each dict\n has: 'img_shape', 'scale_factor', 'flip', and may also contain\n 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.\n For details on the values of these keys see\n :class:`mmdet.datasets.pipelines.Collect`.\n gt_bboxes (list[Tensor]): Each item are the truth boxes for each\n image in [tl_x, tl_y, br_x, br_y] format.\n gt_bboxes_ignore (None | list[Tensor]): Specify which bounding\n boxes can be ignored when computing the loss.\n\n Returns:\n dict[str, Tensor]: A dictionary of loss components.\n \"\"\"\n if (isinstance(self.train_cfg.rpn, dict)\n and self.train_cfg.rpn.get('debug', False)):\n self.rpn_head.debug_imgs = tensor2imgs(img)\n\n x = self.extract_feat(img)\n losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None,\n gt_bboxes_ignore)\n return losses\n\n def simple_test(self, img, img_metas, rescale=False):\n \"\"\"Test function without test time augmentation.\n\n Args:\n imgs (list[torch.Tensor]): List of multiple images\n img_metas (list[dict]): List of image information.\n rescale (bool, optional): Whether to rescale the results.\n Defaults to False.\n\n Returns:\n list[np.ndarray]: proposals\n \"\"\"\n x = self.extract_feat(img)\n # get origin input shape to onnx dynamic input shape\n if torch.onnx.is_in_onnx_export():\n img_shape = torch._shape_as_tensor(img)[2:]\n img_metas[0]['img_shape_for_onnx'] = img_shape\n proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)\n if rescale:\n for proposals, meta in zip(proposal_list, img_metas):\n proposals[:, :4] /= proposals.new_tensor(meta['scale_factor'])\n if torch.onnx.is_in_onnx_export():\n return proposal_list\n\n return [proposal.cpu().numpy() for proposal in proposal_list]\n\n def aug_test(self, imgs, img_metas, rescale=False):\n \"\"\"Test function with test time augmentation.\n\n Args:\n imgs (list[torch.Tensor]): List of multiple images\n img_metas (list[dict]): List of image information.\n rescale (bool, optional): Whether to rescale the results.\n Defaults to False.\n\n Returns:\n list[np.ndarray]: proposals\n \"\"\"\n proposal_list = self.rpn_head.aug_test_rpn(\n self.extract_feats(imgs), img_metas)\n if not rescale:\n for proposals, img_meta in zip(proposal_list, img_metas[0]):\n img_shape = img_meta['img_shape']\n scale_factor = img_meta['scale_factor']\n flip = img_meta['flip']\n flip_direction = img_meta['flip_direction']\n proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape,\n scale_factor, flip,\n flip_direction)\n return [proposal.cpu().numpy() for proposal in proposal_list]\n\n def show_result(self, data, result, top_k=20, **kwargs):\n \"\"\"Show RPN proposals on the image.\n\n Args:\n data (str or np.ndarray): Image filename or loaded image.\n result (Tensor or tuple): The results to draw over `img`\n bbox_result or (bbox_result, segm_result).\n top_k (int): Plot the first k bboxes only\n if set positive. Default: 20\n\n Returns:\n np.ndarray: The image with bboxes drawn on it.\n \"\"\"\n mmcv.imshow_bboxes(data, result, top_k=top_k, **kwargs)\n", "path": "mmdet/models/detectors/rpn.py" } ]
diff --git a/mmdet/models/detectors/rpn.py b/mmdet/models/detectors/rpn.py index c829c26233a..c70ede2ba37 100644 --- a/mmdet/models/detectors/rpn.py +++ b/mmdet/models/detectors/rpn.py @@ -152,4 +152,4 @@ def show_result(self, data, result, top_k=20, **kwargs): Returns: np.ndarray: The image with bboxes drawn on it. """ - mmcv.imshow_bboxes(data, result, top_k=top_k) + mmcv.imshow_bboxes(data, result, top_k=top_k, **kwargs)
gratipay__gratipay.com-1953
CRLFInjection reports in Sentry We keep getting CRLFInjection exception reports in sentry that @whit537 keeps marking 'ok' :smile: If they are ok, we should be catching them. One of the later ones is a GET for ``` /Allan ns:netsparker056650=vuln/ ```
[ { "content": "\"\"\"Wireup\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport os\nimport sys\n\nimport aspen\nimport balanced\nimport gittip\nimport raven\nimport psycopg2\nimport stripe\nfrom gittip.models.community import Community\nfrom gittip.models.participant import Participant\nfrom gittip.models import GittipDB\n\n\ndef canonical():\n gittip.canonical_scheme = os.environ['CANONICAL_SCHEME']\n gittip.canonical_host = os.environ['CANONICAL_HOST']\n\n\ndef db():\n dburl = os.environ['DATABASE_URL']\n maxconn = int(os.environ['DATABASE_MAXCONN'])\n db = GittipDB(dburl, maxconn=maxconn)\n\n # register hstore type\n with db.get_cursor() as cursor:\n psycopg2.extras.register_hstore(cursor, globally=True, unicode=True)\n\n db.register_model(Community)\n db.register_model(Participant)\n\n return db\n\n\ndef billing():\n stripe.api_key= os.environ['STRIPE_SECRET_API_KEY']\n stripe.publishable_api_key= os.environ['STRIPE_PUBLISHABLE_API_KEY']\n balanced.configure(os.environ['BALANCED_API_SECRET'])\n\n\ndef username_restrictions(website):\n if not hasattr(gittip, 'RESTRICTED_USERNAMES'):\n gittip.RESTRICTED_USERNAMES = os.listdir(website.www_root)\n\n\ndef make_sentry_teller(website):\n if not website.sentry_dsn:\n aspen.log_dammit(\"Won't log to Sentry (SENTRY_DSN is empty).\")\n def noop(exception, request=None):\n pass\n return noop\n\n sentry = raven.Client(website.sentry_dsn)\n\n def tell_sentry(exception, request=None):\n\n # Decide if we care.\n # ==================\n\n if exception.__class__ is aspen.Response:\n\n if exception.code < 500:\n\n # Only log server errors to Sentry. For responses < 500 we use\n # stream-/line-based access logging. See discussion on:\n\n # https://github.com/gittip/www.gittip.com/pull/1560.\n\n return\n\n\n # Find a user.\n # ============\n # | is disallowed in usernames, so we can use it here to indicate\n # situations in which we can't get a username.\n\n request_context = getattr(request, 'context', None)\n user = {}\n user_id = 'n/a'\n if request_context is None:\n username = '| no context'\n else:\n user = request.context.get('user', None)\n if user is None:\n username = '| no user'\n else:\n is_anon = getattr(user, 'ANON', None)\n if is_anon is None:\n username = '| no ANON'\n elif is_anon:\n username = '| anonymous'\n else:\n participant = getattr(user, 'participant', None)\n if participant is None:\n username = '| no participant'\n else:\n username = getattr(user.participant, 'username', None)\n if username is None:\n username = '| no username'\n else:\n user_id = user.participant.id\n username = username.encode('utf8')\n user = { 'id': user_id\n , 'is_admin': user.participant.is_admin\n , 'is_suspicious': user.participant.is_suspicious\n , 'claimed_time': user.participant.claimed_time.isoformat()\n , 'url': 'https://www.gittip.com/{}/'.format(username)\n }\n\n\n # Fire off a Sentry call.\n # =======================\n\n tags = { 'username': username\n , 'user_id': user_id\n }\n extra = { 'filepath': getattr(request, 'fs', None)\n , 'request': str(request).splitlines()\n , 'user': user\n }\n result = sentry.captureException(tags=tags, extra=extra)\n\n\n # Emit a reference string to stdout.\n # ==================================\n\n ident = sentry.get_ident(result)\n aspen.log_dammit('Exception reference: ' + ident)\n\n return tell_sentry\n\n\ndef nanswers():\n from gittip.models import participant\n participant.NANSWERS_THRESHOLD = int(os.environ['NANSWERS_THRESHOLD'])\n\n\nclass BadEnvironment(SystemExit):\n pass\n\ndef envvars(website):\n\n missing_keys = []\n malformed_values = []\n\n def envvar(key, cast=None):\n if key not in os.environ:\n missing_keys.append(key)\n return \"\"\n value = os.environ[key].decode('ASCII')\n if cast is not None:\n try:\n value = cast(value)\n except:\n err = str(sys.exc_info()[1])\n malformed_values.append((key, err))\n return \"\"\n return value\n\n def is_yesish(val):\n return val.lower() in ('1', 'true', 'yes')\n\n website.bitbucket_consumer_key = envvar('BITBUCKET_CONSUMER_KEY')\n website.bitbucket_consumer_secret = envvar('BITBUCKET_CONSUMER_SECRET')\n website.bitbucket_callback = envvar('BITBUCKET_CALLBACK')\n\n website.github_client_id = envvar('GITHUB_CLIENT_ID')\n website.github_client_secret = envvar('GITHUB_CLIENT_SECRET')\n website.github_callback = envvar('GITHUB_CALLBACK')\n\n website.twitter_consumer_key = envvar('TWITTER_CONSUMER_KEY')\n website.twitter_consumer_secret = envvar('TWITTER_CONSUMER_SECRET')\n website.twitter_access_token = envvar('TWITTER_ACCESS_TOKEN')\n website.twitter_access_token_secret = envvar('TWITTER_ACCESS_TOKEN_SECRET')\n website.twitter_callback = envvar('TWITTER_CALLBACK')\n\n website.bountysource_www_host = envvar('BOUNTYSOURCE_WWW_HOST')\n website.bountysource_api_host = envvar('BOUNTYSOURCE_API_HOST')\n website.bountysource_api_secret = envvar('BOUNTYSOURCE_API_SECRET')\n website.bountysource_callback = envvar('BOUNTYSOURCE_CALLBACK')\n\n website.venmo_client_id = envvar('VENMO_CLIENT_ID')\n website.venmo_client_secret = envvar('VENMO_CLIENT_SECRET')\n website.venmo_callback = envvar('VENMO_CALLBACK')\n\n website.openstreetmap_api = envvar('OPENSTREETMAP_API')\n website.openstreetmap_consumer_key = envvar('OPENSTREETMAP_CONSUMER_KEY')\n website.openstreetmap_consumer_secret = envvar('OPENSTREETMAP_CONSUMER_SECRET')\n website.openstreetmap_callback = envvar('OPENSTREETMAP_CALLBACK')\n\n website.asset_version_url = envvar('GITTIP_ASSET_VERSION_URL') \\\n .replace('%version', website.version)\n website.asset_url = envvar('GITTIP_ASSET_URL')\n website.cache_static = is_yesish(envvar('GITTIP_CACHE_STATIC'))\n website.compress_assets = is_yesish(envvar('GITTIP_COMPRESS_ASSETS'))\n\n website.google_analytics_id = envvar('GOOGLE_ANALYTICS_ID')\n website.sentry_dsn = envvar('SENTRY_DSN')\n\n website.min_threads = envvar('MIN_THREADS', int)\n website.log_busy_threads_every = envvar('LOG_BUSY_THREADS_EVERY', int)\n website.log_metrics = is_yesish(envvar('LOG_METRICS'))\n\n if malformed_values:\n malformed_values.sort()\n these = len(malformed_values) != 1 and 'these' or 'this'\n plural = len(malformed_values) != 1 and 's' or ''\n aspen.log_dammit(\"=\" * 42)\n aspen.log_dammit( \"Oh no! Gittip.com couldn't understand %s \" % these\n , \"environment variable%s:\" % plural\n )\n aspen.log_dammit(\" \")\n for key, err in malformed_values:\n aspen.log_dammit(\" {} ({})\".format(key, err))\n aspen.log_dammit(\" \")\n aspen.log_dammit(\"See ./default_local.env for hints.\")\n\n aspen.log_dammit(\"=\" * 42)\n keys = ', '.join([key for key in malformed_values])\n raise BadEnvironment(\"Malformed envvar{}: {}.\".format(plural, keys))\n\n if missing_keys:\n missing_keys.sort()\n these = len(missing_keys) != 1 and 'these' or 'this'\n plural = len(missing_keys) != 1 and 's' or ''\n aspen.log_dammit(\"=\" * 42)\n aspen.log_dammit( \"Oh no! Gittip.com needs %s missing \" % these\n , \"environment variable%s:\" % plural\n )\n aspen.log_dammit(\" \")\n for key in missing_keys:\n aspen.log_dammit(\" \" + key)\n aspen.log_dammit(\" \")\n aspen.log_dammit( \"(Sorry, we must've started looking for \"\n , \"%s since you last updated Gittip!)\" % these\n )\n aspen.log_dammit(\" \")\n aspen.log_dammit(\"Running Gittip locally? Edit ./local.env.\")\n aspen.log_dammit(\"Running the test suite? Edit ./tests/env.\")\n aspen.log_dammit(\" \")\n aspen.log_dammit(\"See ./default_local.env for hints.\")\n\n aspen.log_dammit(\"=\" * 42)\n keys = ', '.join([key for key in missing_keys])\n raise BadEnvironment(\"Missing envvar{}: {}.\".format(plural, keys))\n", "path": "gittip/wireup.py" } ]
[ { "content": "\"\"\"Wireup\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\nimport os\nimport sys\n\nimport aspen\nimport balanced\nimport gittip\nimport raven\nimport psycopg2\nimport stripe\nfrom gittip.models.community import Community\nfrom gittip.models.participant import Participant\nfrom gittip.models import GittipDB\n\n\ndef canonical():\n gittip.canonical_scheme = os.environ['CANONICAL_SCHEME']\n gittip.canonical_host = os.environ['CANONICAL_HOST']\n\n\ndef db():\n dburl = os.environ['DATABASE_URL']\n maxconn = int(os.environ['DATABASE_MAXCONN'])\n db = GittipDB(dburl, maxconn=maxconn)\n\n # register hstore type\n with db.get_cursor() as cursor:\n psycopg2.extras.register_hstore(cursor, globally=True, unicode=True)\n\n db.register_model(Community)\n db.register_model(Participant)\n\n return db\n\n\ndef billing():\n stripe.api_key= os.environ['STRIPE_SECRET_API_KEY']\n stripe.publishable_api_key= os.environ['STRIPE_PUBLISHABLE_API_KEY']\n balanced.configure(os.environ['BALANCED_API_SECRET'])\n\n\ndef username_restrictions(website):\n if not hasattr(gittip, 'RESTRICTED_USERNAMES'):\n gittip.RESTRICTED_USERNAMES = os.listdir(website.www_root)\n\n\ndef make_sentry_teller(website):\n if not website.sentry_dsn:\n aspen.log_dammit(\"Won't log to Sentry (SENTRY_DSN is empty).\")\n def noop(exception, request=None):\n pass\n return noop\n\n sentry = raven.Client(website.sentry_dsn)\n\n def tell_sentry(exception, request=None):\n\n # Decide if we care.\n # ==================\n\n if isinstance(exception, aspen.Response):\n\n if exception.code < 500:\n\n # Only log server errors to Sentry. For responses < 500 we use\n # stream-/line-based access logging. See discussion on:\n\n # https://github.com/gittip/www.gittip.com/pull/1560.\n\n return\n\n\n # Find a user.\n # ============\n # | is disallowed in usernames, so we can use it here to indicate\n # situations in which we can't get a username.\n\n request_context = getattr(request, 'context', None)\n user = {}\n user_id = 'n/a'\n if request_context is None:\n username = '| no context'\n else:\n user = request.context.get('user', None)\n if user is None:\n username = '| no user'\n else:\n is_anon = getattr(user, 'ANON', None)\n if is_anon is None:\n username = '| no ANON'\n elif is_anon:\n username = '| anonymous'\n else:\n participant = getattr(user, 'participant', None)\n if participant is None:\n username = '| no participant'\n else:\n username = getattr(user.participant, 'username', None)\n if username is None:\n username = '| no username'\n else:\n user_id = user.participant.id\n username = username.encode('utf8')\n user = { 'id': user_id\n , 'is_admin': user.participant.is_admin\n , 'is_suspicious': user.participant.is_suspicious\n , 'claimed_time': user.participant.claimed_time.isoformat()\n , 'url': 'https://www.gittip.com/{}/'.format(username)\n }\n\n\n # Fire off a Sentry call.\n # =======================\n\n tags = { 'username': username\n , 'user_id': user_id\n }\n extra = { 'filepath': getattr(request, 'fs', None)\n , 'request': str(request).splitlines()\n , 'user': user\n }\n result = sentry.captureException(tags=tags, extra=extra)\n\n\n # Emit a reference string to stdout.\n # ==================================\n\n ident = sentry.get_ident(result)\n aspen.log_dammit('Exception reference: ' + ident)\n\n return tell_sentry\n\n\ndef nanswers():\n from gittip.models import participant\n participant.NANSWERS_THRESHOLD = int(os.environ['NANSWERS_THRESHOLD'])\n\n\nclass BadEnvironment(SystemExit):\n pass\n\ndef envvars(website):\n\n missing_keys = []\n malformed_values = []\n\n def envvar(key, cast=None):\n if key not in os.environ:\n missing_keys.append(key)\n return \"\"\n value = os.environ[key].decode('ASCII')\n if cast is not None:\n try:\n value = cast(value)\n except:\n err = str(sys.exc_info()[1])\n malformed_values.append((key, err))\n return \"\"\n return value\n\n def is_yesish(val):\n return val.lower() in ('1', 'true', 'yes')\n\n website.bitbucket_consumer_key = envvar('BITBUCKET_CONSUMER_KEY')\n website.bitbucket_consumer_secret = envvar('BITBUCKET_CONSUMER_SECRET')\n website.bitbucket_callback = envvar('BITBUCKET_CALLBACK')\n\n website.github_client_id = envvar('GITHUB_CLIENT_ID')\n website.github_client_secret = envvar('GITHUB_CLIENT_SECRET')\n website.github_callback = envvar('GITHUB_CALLBACK')\n\n website.twitter_consumer_key = envvar('TWITTER_CONSUMER_KEY')\n website.twitter_consumer_secret = envvar('TWITTER_CONSUMER_SECRET')\n website.twitter_access_token = envvar('TWITTER_ACCESS_TOKEN')\n website.twitter_access_token_secret = envvar('TWITTER_ACCESS_TOKEN_SECRET')\n website.twitter_callback = envvar('TWITTER_CALLBACK')\n\n website.bountysource_www_host = envvar('BOUNTYSOURCE_WWW_HOST')\n website.bountysource_api_host = envvar('BOUNTYSOURCE_API_HOST')\n website.bountysource_api_secret = envvar('BOUNTYSOURCE_API_SECRET')\n website.bountysource_callback = envvar('BOUNTYSOURCE_CALLBACK')\n\n website.venmo_client_id = envvar('VENMO_CLIENT_ID')\n website.venmo_client_secret = envvar('VENMO_CLIENT_SECRET')\n website.venmo_callback = envvar('VENMO_CALLBACK')\n\n website.openstreetmap_api = envvar('OPENSTREETMAP_API')\n website.openstreetmap_consumer_key = envvar('OPENSTREETMAP_CONSUMER_KEY')\n website.openstreetmap_consumer_secret = envvar('OPENSTREETMAP_CONSUMER_SECRET')\n website.openstreetmap_callback = envvar('OPENSTREETMAP_CALLBACK')\n\n website.asset_version_url = envvar('GITTIP_ASSET_VERSION_URL') \\\n .replace('%version', website.version)\n website.asset_url = envvar('GITTIP_ASSET_URL')\n website.cache_static = is_yesish(envvar('GITTIP_CACHE_STATIC'))\n website.compress_assets = is_yesish(envvar('GITTIP_COMPRESS_ASSETS'))\n\n website.google_analytics_id = envvar('GOOGLE_ANALYTICS_ID')\n website.sentry_dsn = envvar('SENTRY_DSN')\n\n website.min_threads = envvar('MIN_THREADS', int)\n website.log_busy_threads_every = envvar('LOG_BUSY_THREADS_EVERY', int)\n website.log_metrics = is_yesish(envvar('LOG_METRICS'))\n\n if malformed_values:\n malformed_values.sort()\n these = len(malformed_values) != 1 and 'these' or 'this'\n plural = len(malformed_values) != 1 and 's' or ''\n aspen.log_dammit(\"=\" * 42)\n aspen.log_dammit( \"Oh no! Gittip.com couldn't understand %s \" % these\n , \"environment variable%s:\" % plural\n )\n aspen.log_dammit(\" \")\n for key, err in malformed_values:\n aspen.log_dammit(\" {} ({})\".format(key, err))\n aspen.log_dammit(\" \")\n aspen.log_dammit(\"See ./default_local.env for hints.\")\n\n aspen.log_dammit(\"=\" * 42)\n keys = ', '.join([key for key in malformed_values])\n raise BadEnvironment(\"Malformed envvar{}: {}.\".format(plural, keys))\n\n if missing_keys:\n missing_keys.sort()\n these = len(missing_keys) != 1 and 'these' or 'this'\n plural = len(missing_keys) != 1 and 's' or ''\n aspen.log_dammit(\"=\" * 42)\n aspen.log_dammit( \"Oh no! Gittip.com needs %s missing \" % these\n , \"environment variable%s:\" % plural\n )\n aspen.log_dammit(\" \")\n for key in missing_keys:\n aspen.log_dammit(\" \" + key)\n aspen.log_dammit(\" \")\n aspen.log_dammit( \"(Sorry, we must've started looking for \"\n , \"%s since you last updated Gittip!)\" % these\n )\n aspen.log_dammit(\" \")\n aspen.log_dammit(\"Running Gittip locally? Edit ./local.env.\")\n aspen.log_dammit(\"Running the test suite? Edit ./tests/env.\")\n aspen.log_dammit(\" \")\n aspen.log_dammit(\"See ./default_local.env for hints.\")\n\n aspen.log_dammit(\"=\" * 42)\n keys = ', '.join([key for key in missing_keys])\n raise BadEnvironment(\"Missing envvar{}: {}.\".format(plural, keys))\n", "path": "gittip/wireup.py" } ]
diff --git a/gittip/wireup.py b/gittip/wireup.py index 3c3fbbdd28..5cd99c4ce7 100644 --- a/gittip/wireup.py +++ b/gittip/wireup.py @@ -60,7 +60,7 @@ def tell_sentry(exception, request=None): # Decide if we care. # ================== - if exception.__class__ is aspen.Response: + if isinstance(exception, aspen.Response): if exception.code < 500:
imAsparky__django-cookiecutter-14
[FEAT]: Add Tox, Pytest and test config **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context or screenshots about the feature request here.
[ { "content": "\"\"\"Django Cookiecutter Sphinx build configuration file.\"\"\"\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath('.'))\n\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'Django Cookiecutter'\ncopyright = '2021, Mark Sevelj'\nauthor = 'Mark Sevelj'\n\n# The full version, including alpha/beta/rc tags\nrelease = __version__\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"myst_parser\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"sphinx_copybutton\",\n \"sphinx_inline_tabs\",\n \"sphinx.ext.todo\",\n\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\"_build\", \"build\", \"Thumbs.db\", \".DS_Store\"]\n\npygments_style = \"monokai\"\npygments_dark_style = \"monokai\"\n\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'furo'\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# sphinx-copybutton is a lightweight code-block copy button\n# config options are here https://sphinx-copybutton.readthedocs.io/en/latest/\n# This config removes Python Repl + continuation, Bash line prefixes,\n# ipython and qtconsole + continuation, jupyter-console + continuation and preceding line numbers\ncopybutton_prompt_text = (\n r\"^\\d|^.\\d|^\\d\\d|^\\d\\d\\d|>>> |\\.\\.\\. |\\$ |In \\[\\d*\\]: | {2,5}\\.\\.\\.: | {5,8}: \"\n)\ncopybutton_prompt_is_regexp = True\n\n# datalad download-url http://www.tldp.org/LDP/Bash-Beginners-Guide/Bash-Beginners-Guide.pdf \\\n# --dataset . \\\n# -m \"add beginners guide on bash\" \\\n# -O books/bash_guide.pdf\n# is correctly pasted with the following setting\ncopybutton_line_continuation_character = \"\\\\\"\n", "path": "docs/source/conf.py" } ]
[ { "content": "\"\"\"Django Cookiecutter Sphinx build configuration file.\"\"\"\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath('.'))\n\n__version__ = \"0.4.0\"\n\n# -- Project information -----------------------------------------------------\n\nproject = 'Django Cookiecutter'\ncopyright = '2021, Mark Sevelj'\nauthor = 'Mark Sevelj'\n\n# The full version, including alpha/beta/rc tags\nrelease = __version__\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"myst_parser\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"sphinx_copybutton\",\n \"sphinx_inline_tabs\",\n \"sphinx.ext.todo\",\n\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\"_build\", \"build\", \"Thumbs.db\", \".DS_Store\"]\n\npygments_style = \"monokai\"\npygments_dark_style = \"monokai\"\n\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'furo'\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# sphinx-copybutton is a lightweight code-block copy button\n# config options are here https://sphinx-copybutton.readthedocs.io/en/latest/\n# This config removes Python Repl + continuation, Bash line prefixes,\n# ipython and qtconsole + continuation, jupyter-console + continuation and preceding line numbers\ncopybutton_prompt_text = (\n r\"^\\d|^.\\d|^\\d\\d|^\\d\\d\\d|>>> |\\.\\.\\. |\\$ |In \\[\\d*\\]: | {2,5}\\.\\.\\.: | {5,8}: \"\n)\ncopybutton_prompt_is_regexp = True\n\n# datalad download-url http://www.tldp.org/LDP/Bash-Beginners-Guide/Bash-Beginners-Guide.pdf \\\n# --dataset . \\\n# -m \"add beginners guide on bash\" \\\n# -O books/bash_guide.pdf\n# is correctly pasted with the following setting\ncopybutton_line_continuation_character = \"\\\\\"\n", "path": "docs/source/conf.py" } ]
diff --git a/.github/workflows/test_contribution.yaml b/.github/workflows/test_contribution.yaml new file mode 100644 index 00000000..34c9e868 --- /dev/null +++ b/.github/workflows/test_contribution.yaml @@ -0,0 +1,36 @@ +name: Test Contributions + +on: + pull_request: + branches: [main] + + # push: + # branches: [main] + + workflow_dispatch: + +jobs: + test_contribs: + strategy: + matrix: + python-version: ["3.6", "3.7", "3.8", "3.9"] + os: [macos-latest, ubuntu-latest, windows-latest] + + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install tox tox-gh-actions + + - name: Test with tox + run: tox diff --git a/docs/source/requirements.txt b/docs/requirements.txt similarity index 100% rename from docs/source/requirements.txt rename to docs/requirements.txt diff --git a/docs/source/conf.py b/docs/source/conf.py index 7ad9709a..5a30952f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -16,7 +16,7 @@ import sys sys.path.insert(0, os.path.abspath('.')) - +__version__ = "0.4.0" # -- Project information ----------------------------------------------------- diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 00000000..39baf331 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,2 @@ +[pytest] +testpaths = tests/ diff --git a/requirements_dev.txt b/requirements_dev.txt index 2ecd9682..92218998 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,2 +1,5 @@ Django==3.2.7 pre-commit==2.15.0 +pytest==6.2.5 +pytest-cookies==0.6.1 +tox==3.24.4 diff --git a/tests/test_fake.py b/tests/test_fake.py new file mode 100644 index 00000000..6078684d --- /dev/null +++ b/tests/test_fake.py @@ -0,0 +1,4 @@ +def test_fake_test(): + """A Fake test""" + + assert 3 == 3 diff --git a/tox.ini b/tox.ini new file mode 100644 index 00000000..fcdaabe9 --- /dev/null +++ b/tox.ini @@ -0,0 +1,39 @@ +[tox] +skipsdist = true +skip_missing_interpreters = true +envlist = + py36 + py37 + py38 + py39 + py38 pypy + py39-docs + +[testenv:docs] +basepython=python +changedir=docs/source +deps= -r{toxinidir}/docs/requirements.txt +commands= + sphinx-build -b html -d {envtmpdir}/doctrees . {envtmpdir}/html + +[gh-actions] +python = + pypy-3.8: pypy3 + 3.9: py39 + 3.8: py38 + 3.7: py37 + 3.6: py36 + +[gh-actions:env] +PLATFORM = + ubuntu-latest: linux + macos-latest: macos + windows-latest: windows + +[testenv] +setenv = + PYTHONPATH = {toxinidir} +deps = + -r{toxinidir}/requirements_dev.txt +commands = + pytest -v {posargs:tests}
saulpw__visidata-1304
[undo develop] undoing a reload blanks the entire sheet Since v2.5 undo for reload has been removed, and replaced with quitguard+confirm! However, in that case an undo should not be set. Current behaviour is that it blanks the sheet.
[ { "content": "import itertools\nfrom copy import copy\n\nfrom visidata import vd, options, VisiData, BaseSheet, UNLOADED\n\nBaseSheet.init('undone', list) # list of CommandLogRow for redo after undo\n\nvd.option('undo', True, 'enable undo/redo')\n\nnonUndo = '''commit open-file'''.split()\n\ndef isUndoableCommand(longname):\n for n in nonUndo:\n if longname.startswith(n):\n return False\n return True\n\[email protected]\ndef addUndo(vd, undofunc, *args, **kwargs):\n 'On undo of latest command, call ``undofunc(*args, **kwargs)``.'\n if options.undo:\n # occurs when VisiData is just starting up\n if getattr(vd, 'activeCommand', UNLOADED) is UNLOADED:\n return\n r = vd.modifyCommand\n # some special commands, like open-file, do not have an undofuncs set\n if not r or not isUndoableCommand(r.longname):\n return\n if not r.undofuncs:\n r.undofuncs = []\n r.undofuncs.append((undofunc, args, kwargs))\n\n\[email protected]\ndef undo(vd, sheet):\n if not options.undo:\n vd.fail(\"options.undo not enabled\")\n\n # don't allow undo of first command on a sheet, which is always the command that created the sheet.\n for cmdlogrow in sheet.cmdlog_sheet.rows[:0:-1]:\n if cmdlogrow.undofuncs:\n for undofunc, args, kwargs, in cmdlogrow.undofuncs[::-1]:\n undofunc(*args, **kwargs)\n sheet.undone.append(cmdlogrow)\n sheet.cmdlog_sheet.rows.remove(cmdlogrow)\n\n vd.clearCaches() # undofunc can invalidate the drawcache\n\n vd.moveToReplayContext(cmdlogrow, sheet)\n vd.status(\"%s undone\" % cmdlogrow.longname)\n return\n\n vd.fail(\"nothing to undo on current sheet\")\n\n\[email protected]\ndef redo(vd, sheet):\n sheet.undone or vd.fail(\"nothing to redo\")\n cmdlogrow = sheet.undone.pop()\n vd.replayOne(cmdlogrow)\n vd.status(\"%s redone\" % cmdlogrow.longname)\n\n# undoers\ndef undoAttrFunc(objs, attrname):\n 'Return closure that sets attrname on each obj to its former value.'\n oldvals = [(o, getattr(o, attrname)) for o in objs]\n def _undofunc():\n for o, v in oldvals:\n setattr(o, attrname, v)\n return _undofunc\n\n\nclass Fanout(list):\n 'Fan out attribute changes to every element in a list.'\n def __getattr__(self, k):\n return Fanout([getattr(o, k) for o in self])\n\n def __setattr__(self, k, v):\n vd.addUndo(undoAttrFunc(self, k))\n for o in self:\n setattr(o, k, v)\n\n def __call__(self, *args, **kwargs):\n return Fanout([o(*args, **kwargs) for o in self])\n\n\ndef undoAttrCopyFunc(objs, attrname):\n 'Return closure that sets attrname on each obj to its former value.'\n oldvals = [(o, copy(getattr(o, attrname))) for o in objs]\n def _undofunc():\n for o, v in oldvals:\n setattr(o, attrname, v)\n return _undofunc\n\n\[email protected]\ndef addUndoSetValues(vd, cols, rows):\n 'Add undo function to reset values for *rows* in *cols*.'\n oldvals = [(c, r, c.getValue(r)) for c,r in itertools.product(cols, vd.Progress(rows, gerund='doing'))]\n def _undo():\n for c, r, v in oldvals:\n c.setValue(r, v)\n vd.addUndo(_undo)\n\[email protected]\ndef addUndoColNames(vd, cols):\n oldnames = [(c, c.name) for c in cols]\n def _undo():\n for c, name in oldnames:\n c.name = name\n vd.addUndo(_undo)\n\n\nBaseSheet.addCommand('U', 'undo-last', 'vd.undo(sheet)', 'Undo the most recent change (options.undo must be enabled)')\nBaseSheet.addCommand('R', 'redo-last', 'vd.redo(sheet)', 'Redo the most recent undo (options.undo must be enabled)')\n", "path": "visidata/undo.py" } ]
[ { "content": "import itertools\nfrom copy import copy\n\nfrom visidata import vd, options, VisiData, BaseSheet, UNLOADED\n\nBaseSheet.init('undone', list) # list of CommandLogRow for redo after undo\n\nvd.option('undo', True, 'enable undo/redo')\n\nnonUndo = '''commit open-file reload-sheet'''.split()\n\ndef isUndoableCommand(longname):\n for n in nonUndo:\n if longname.startswith(n):\n return False\n return True\n\[email protected]\ndef addUndo(vd, undofunc, *args, **kwargs):\n 'On undo of latest command, call ``undofunc(*args, **kwargs)``.'\n if options.undo:\n # occurs when VisiData is just starting up\n if getattr(vd, 'activeCommand', UNLOADED) is UNLOADED:\n return\n r = vd.modifyCommand\n # some special commands, like open-file, do not have an undofuncs set\n if not r or not isUndoableCommand(r.longname):\n return\n if not r.undofuncs:\n r.undofuncs = []\n r.undofuncs.append((undofunc, args, kwargs))\n\n\[email protected]\ndef undo(vd, sheet):\n if not options.undo:\n vd.fail(\"options.undo not enabled\")\n\n # don't allow undo of first command on a sheet, which is always the command that created the sheet.\n for cmdlogrow in sheet.cmdlog_sheet.rows[:0:-1]:\n if cmdlogrow.undofuncs:\n for undofunc, args, kwargs, in cmdlogrow.undofuncs[::-1]:\n undofunc(*args, **kwargs)\n sheet.undone.append(cmdlogrow)\n sheet.cmdlog_sheet.rows.remove(cmdlogrow)\n\n vd.clearCaches() # undofunc can invalidate the drawcache\n\n vd.moveToReplayContext(cmdlogrow, sheet)\n vd.status(\"%s undone\" % cmdlogrow.longname)\n return\n\n vd.fail(\"nothing to undo on current sheet\")\n\n\[email protected]\ndef redo(vd, sheet):\n sheet.undone or vd.fail(\"nothing to redo\")\n cmdlogrow = sheet.undone.pop()\n vd.replayOne(cmdlogrow)\n vd.status(\"%s redone\" % cmdlogrow.longname)\n\n# undoers\ndef undoAttrFunc(objs, attrname):\n 'Return closure that sets attrname on each obj to its former value.'\n oldvals = [(o, getattr(o, attrname)) for o in objs]\n def _undofunc():\n for o, v in oldvals:\n setattr(o, attrname, v)\n return _undofunc\n\n\nclass Fanout(list):\n 'Fan out attribute changes to every element in a list.'\n def __getattr__(self, k):\n return Fanout([getattr(o, k) for o in self])\n\n def __setattr__(self, k, v):\n vd.addUndo(undoAttrFunc(self, k))\n for o in self:\n setattr(o, k, v)\n\n def __call__(self, *args, **kwargs):\n return Fanout([o(*args, **kwargs) for o in self])\n\n\ndef undoAttrCopyFunc(objs, attrname):\n 'Return closure that sets attrname on each obj to its former value.'\n oldvals = [(o, copy(getattr(o, attrname))) for o in objs]\n def _undofunc():\n for o, v in oldvals:\n setattr(o, attrname, v)\n return _undofunc\n\n\[email protected]\ndef addUndoSetValues(vd, cols, rows):\n 'Add undo function to reset values for *rows* in *cols*.'\n oldvals = [(c, r, c.getValue(r)) for c,r in itertools.product(cols, vd.Progress(rows, gerund='doing'))]\n def _undo():\n for c, r, v in oldvals:\n c.setValue(r, v)\n vd.addUndo(_undo)\n\[email protected]\ndef addUndoColNames(vd, cols):\n oldnames = [(c, c.name) for c in cols]\n def _undo():\n for c, name in oldnames:\n c.name = name\n vd.addUndo(_undo)\n\n\nBaseSheet.addCommand('U', 'undo-last', 'vd.undo(sheet)', 'Undo the most recent change (options.undo must be enabled)')\nBaseSheet.addCommand('R', 'redo-last', 'vd.redo(sheet)', 'Redo the most recent undo (options.undo must be enabled)')\n", "path": "visidata/undo.py" } ]
diff --git a/visidata/undo.py b/visidata/undo.py index 175512acf..8a4b20dd5 100644 --- a/visidata/undo.py +++ b/visidata/undo.py @@ -7,7 +7,7 @@ vd.option('undo', True, 'enable undo/redo') -nonUndo = '''commit open-file'''.split() +nonUndo = '''commit open-file reload-sheet'''.split() def isUndoableCommand(longname): for n in nonUndo:
scipy__scipy-12486
scipy.stats.poisson docs for rate = 0 I noticed that the docs for [scipy.stats.poisson](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.poisson.html) are not clear about the intended behaviour when `\lambda = 0 `. Strictly speaking, the pmf for the Poisson is ill-defined at `\lambda = 0`; however, the `\lambda \to ` limit of a Poisson seems to me well-defined and intuitive, P(n | \lambda = 0) = { 1 if n = 0 { 0 otherwise The mean and variance are `$E(n) = Var(n) = \lambda = 0$` as expected. The Poisson is implemented this way (requring only non-negative rate paramater) in [R](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/Poisson.html), which should give some confidence that it isn't an obviously silly thing to do. Indeed, the current behaviour appears to be just this, ``` >>> from scipy.stats import poisson >>> poisson.pmf(1, 0.) 0.0 >>> poisson.pmf(0, 0.) 1.0 ``` so, can we add a note to the docs that this is the intended treatment? See https://github.com/scikit-hep/pyhf/issues/293#issuecomment-627207254 where this issue arose indirectly.
[ { "content": "#\n# Author: Travis Oliphant 2002-2011 with contributions from\n# SciPy Developers 2004-2011\n#\nfrom functools import partial\nfrom scipy import special\nfrom scipy.special import entr, logsumexp, betaln, gammaln as gamln\nfrom scipy._lib._util import _lazywhere, rng_integers\n\nfrom numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh\n\nimport numpy as np\n\nfrom ._distn_infrastructure import (\n rv_discrete, _ncx2_pdf, _ncx2_cdf, get_distribution_names)\n\n\nclass binom_gen(rv_discrete):\n r\"\"\"A binomial discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `binom` is:\n\n .. math::\n\n f(k) = \\binom{n}{k} p^k (1-p)^{n-k}\n\n for ``k`` in ``{0, 1,..., n}``.\n\n `binom` takes ``n`` and ``p`` as shape parameters.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, n, p, size=None, random_state=None):\n return random_state.binomial(n, p, size)\n\n def _argcheck(self, n, p):\n return (n >= 0) & (p >= 0) & (p <= 1)\n\n def _get_support(self, n, p):\n return self.a, n\n\n def _logpmf(self, x, n, p):\n k = floor(x)\n combiln = (gamln(n+1) - (gamln(k+1) + gamln(n-k+1)))\n return combiln + special.xlogy(k, p) + special.xlog1py(n-k, -p)\n\n def _pmf(self, x, n, p):\n # binom.pmf(k) = choose(n, k) * p**k * (1-p)**(n-k)\n return exp(self._logpmf(x, n, p))\n\n def _cdf(self, x, n, p):\n k = floor(x)\n vals = special.bdtr(k, n, p)\n return vals\n\n def _sf(self, x, n, p):\n k = floor(x)\n return special.bdtrc(k, n, p)\n\n def _ppf(self, q, n, p):\n vals = ceil(special.bdtrik(q, n, p))\n vals1 = np.maximum(vals - 1, 0)\n temp = special.bdtr(vals1, n, p)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, n, p, moments='mv'):\n q = 1.0 - p\n mu = n * p\n var = n * p * q\n g1, g2 = None, None\n if 's' in moments:\n g1 = (q - p) / sqrt(var)\n if 'k' in moments:\n g2 = (1.0 - 6*p*q) / var\n return mu, var, g1, g2\n\n def _entropy(self, n, p):\n k = np.r_[0:n + 1]\n vals = self._pmf(k, n, p)\n return np.sum(entr(vals), axis=0)\n\n\nbinom = binom_gen(name='binom')\n\n\nclass bernoulli_gen(binom_gen):\n r\"\"\"A Bernoulli discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `bernoulli` is:\n\n .. math::\n\n f(k) = \\begin{cases}1-p &\\text{if } k = 0\\\\\n p &\\text{if } k = 1\\end{cases}\n\n for :math:`k` in :math:`\\{0, 1\\}`.\n\n `bernoulli` takes :math:`p` as shape parameter.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, p, size=None, random_state=None):\n return binom_gen._rvs(self, 1, p, size=size, random_state=random_state)\n\n def _argcheck(self, p):\n return (p >= 0) & (p <= 1)\n\n def _get_support(self, p):\n # Overrides binom_gen._get_support!x\n return self.a, self.b\n\n def _logpmf(self, x, p):\n return binom._logpmf(x, 1, p)\n\n def _pmf(self, x, p):\n # bernoulli.pmf(k) = 1-p if k = 0\n # = p if k = 1\n return binom._pmf(x, 1, p)\n\n def _cdf(self, x, p):\n return binom._cdf(x, 1, p)\n\n def _sf(self, x, p):\n return binom._sf(x, 1, p)\n\n def _ppf(self, q, p):\n return binom._ppf(q, 1, p)\n\n def _stats(self, p):\n return binom._stats(1, p)\n\n def _entropy(self, p):\n return entr(p) + entr(1-p)\n\n\nbernoulli = bernoulli_gen(b=1, name='bernoulli')\n\n\nclass betabinom_gen(rv_discrete):\n r\"\"\"A beta-binomial discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The beta-binomial distribution is a binomial distribution with a\n probability of success `p` that follows a beta distribution.\n\n The probability mass function for `betabinom` is:\n\n .. math::\n\n f(k) = \\binom{n}{k} \\frac{B(k + a, n - k + b)}{B(a, b)}\n\n for ``k`` in ``{0, 1,..., n}``, :math:`n \\geq 0`, :math:`a > 0`,\n :math:`b > 0`, where :math:`B(a, b)` is the beta function.\n\n `betabinom` takes :math:`n`, :math:`a`, and :math:`b` as shape parameters.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Beta-binomial_distribution\n\n %(after_notes)s\n\n .. versionadded:: 1.4.0\n\n See Also\n --------\n beta, binom\n\n %(example)s\n\n \"\"\"\n\n def _rvs(self, n, a, b, size=None, random_state=None):\n p = random_state.beta(a, b, size)\n return random_state.binomial(n, p, size)\n\n def _get_support(self, n, a, b):\n return 0, n\n\n def _argcheck(self, n, a, b):\n return (n >= 0) & (a > 0) & (b > 0)\n\n def _logpmf(self, x, n, a, b):\n k = floor(x)\n combiln = -log(n + 1) - betaln(n - k + 1, k + 1)\n return combiln + betaln(k + a, n - k + b) - betaln(a, b)\n\n def _pmf(self, x, n, a, b):\n return exp(self._logpmf(x, n, a, b))\n\n def _stats(self, n, a, b, moments='mv'):\n e_p = a / (a + b)\n e_q = 1 - e_p\n mu = n * e_p\n var = n * (a + b + n) * e_p * e_q / (a + b + 1)\n g1, g2 = None, None\n if 's' in moments:\n g1 = 1.0 / sqrt(var)\n g1 *= (a + b + 2 * n) * (b - a)\n g1 /= (a + b + 2) * (a + b)\n if 'k' in moments:\n g2 = a + b\n g2 *= (a + b - 1 + 6 * n)\n g2 += 3 * a * b * (n - 2)\n g2 += 6 * n ** 2\n g2 -= 3 * e_p * b * n * (6 - n)\n g2 -= 18 * e_p * e_q * n ** 2\n g2 *= (a + b) ** 2 * (1 + a + b)\n g2 /= (n * a * b * (a + b + 2) * (a + b + 3) * (a + b + n))\n g2 -= 3\n return mu, var, g1, g2\n\n\nbetabinom = betabinom_gen(name='betabinom')\n\n\nclass nbinom_gen(rv_discrete):\n r\"\"\"A negative binomial discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n Negative binomial distribution describes a sequence of i.i.d. Bernoulli\n trials, repeated until a predefined, non-random number of successes occurs.\n\n The probability mass function of the number of failures for `nbinom` is:\n\n .. math::\n\n f(k) = \\binom{k+n-1}{n-1} p^n (1-p)^k\n\n for :math:`k \\ge 0`.\n\n `nbinom` takes :math:`n` and :math:`p` as shape parameters where n is the\n number of successes, whereas p is the probability of a single success.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, n, p, size=None, random_state=None):\n return random_state.negative_binomial(n, p, size)\n\n def _argcheck(self, n, p):\n return (n > 0) & (p >= 0) & (p <= 1)\n\n def _pmf(self, x, n, p):\n # nbinom.pmf(k) = choose(k+n-1, n-1) * p**n * (1-p)**k\n return exp(self._logpmf(x, n, p))\n\n def _logpmf(self, x, n, p):\n coeff = gamln(n+x) - gamln(x+1) - gamln(n)\n return coeff + n*log(p) + special.xlog1py(x, -p)\n\n def _cdf(self, x, n, p):\n k = floor(x)\n return special.betainc(n, k+1, p)\n\n def _sf_skip(self, x, n, p):\n # skip because special.nbdtrc doesn't work for 0<n<1\n k = floor(x)\n return special.nbdtrc(k, n, p)\n\n def _ppf(self, q, n, p):\n vals = ceil(special.nbdtrik(q, n, p))\n vals1 = (vals-1).clip(0.0, np.inf)\n temp = self._cdf(vals1, n, p)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, n, p):\n Q = 1.0 / p\n P = Q - 1.0\n mu = n*P\n var = n*P*Q\n g1 = (Q+P)/sqrt(n*P*Q)\n g2 = (1.0 + 6*P*Q) / (n*P*Q)\n return mu, var, g1, g2\n\n\nnbinom = nbinom_gen(name='nbinom')\n\n\nclass geom_gen(rv_discrete):\n r\"\"\"A geometric discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `geom` is:\n\n .. math::\n\n f(k) = (1-p)^{k-1} p\n\n for :math:`k \\ge 1`.\n\n `geom` takes :math:`p` as shape parameter.\n\n %(after_notes)s\n\n See Also\n --------\n planck\n\n %(example)s\n\n \"\"\"\n def _rvs(self, p, size=None, random_state=None):\n return random_state.geometric(p, size=size)\n\n def _argcheck(self, p):\n return (p <= 1) & (p >= 0)\n\n def _pmf(self, k, p):\n return np.power(1-p, k-1) * p\n\n def _logpmf(self, k, p):\n return special.xlog1py(k - 1, -p) + log(p)\n\n def _cdf(self, x, p):\n k = floor(x)\n return -expm1(log1p(-p)*k)\n\n def _sf(self, x, p):\n return np.exp(self._logsf(x, p))\n\n def _logsf(self, x, p):\n k = floor(x)\n return k*log1p(-p)\n\n def _ppf(self, q, p):\n vals = ceil(log1p(-q) / log1p(-p))\n temp = self._cdf(vals-1, p)\n return np.where((temp >= q) & (vals > 0), vals-1, vals)\n\n def _stats(self, p):\n mu = 1.0/p\n qr = 1.0-p\n var = qr / p / p\n g1 = (2.0-p) / sqrt(qr)\n g2 = np.polyval([1, -6, 6], p)/(1.0-p)\n return mu, var, g1, g2\n\n\ngeom = geom_gen(a=1, name='geom', longname=\"A geometric\")\n\n\nclass hypergeom_gen(rv_discrete):\n r\"\"\"A hypergeometric discrete random variable.\n\n The hypergeometric distribution models drawing objects from a bin.\n `M` is the total number of objects, `n` is total number of Type I objects.\n The random variate represents the number of Type I objects in `N` drawn\n without replacement from the total population.\n\n %(before_notes)s\n\n Notes\n -----\n The symbols used to denote the shape parameters (`M`, `n`, and `N`) are not\n universally accepted. See the Examples for a clarification of the\n definitions used here.\n\n The probability mass function is defined as,\n\n .. math:: p(k, M, n, N) = \\frac{\\binom{n}{k} \\binom{M - n}{N - k}}\n {\\binom{M}{N}}\n\n for :math:`k \\in [\\max(0, N - M + n), \\min(n, N)]`, where the binomial\n coefficients are defined as,\n\n .. math:: \\binom{n}{k} \\equiv \\frac{n!}{k! (n - k)!}.\n\n %(after_notes)s\n\n Examples\n --------\n >>> from scipy.stats import hypergeom\n >>> import matplotlib.pyplot as plt\n\n Suppose we have a collection of 20 animals, of which 7 are dogs. Then if\n we want to know the probability of finding a given number of dogs if we\n choose at random 12 of the 20 animals, we can initialize a frozen\n distribution and plot the probability mass function:\n\n >>> [M, n, N] = [20, 7, 12]\n >>> rv = hypergeom(M, n, N)\n >>> x = np.arange(0, n+1)\n >>> pmf_dogs = rv.pmf(x)\n\n >>> fig = plt.figure()\n >>> ax = fig.add_subplot(111)\n >>> ax.plot(x, pmf_dogs, 'bo')\n >>> ax.vlines(x, 0, pmf_dogs, lw=2)\n >>> ax.set_xlabel('# of dogs in our group of chosen animals')\n >>> ax.set_ylabel('hypergeom PMF')\n >>> plt.show()\n\n Instead of using a frozen distribution we can also use `hypergeom`\n methods directly. To for example obtain the cumulative distribution\n function, use:\n\n >>> prb = hypergeom.cdf(x, M, n, N)\n\n And to generate random numbers:\n\n >>> R = hypergeom.rvs(M, n, N, size=10)\n\n \"\"\"\n def _rvs(self, M, n, N, size=None, random_state=None):\n return random_state.hypergeometric(n, M-n, N, size=size)\n\n def _get_support(self, M, n, N):\n return np.maximum(N-(M-n), 0), np.minimum(n, N)\n\n def _argcheck(self, M, n, N):\n cond = (M > 0) & (n >= 0) & (N >= 0)\n cond &= (n <= M) & (N <= M)\n return cond\n\n def _logpmf(self, k, M, n, N):\n tot, good = M, n\n bad = tot - good\n result = (betaln(good+1, 1) + betaln(bad+1, 1) + betaln(tot-N+1, N+1) -\n betaln(k+1, good-k+1) - betaln(N-k+1, bad-N+k+1) -\n betaln(tot+1, 1))\n return result\n\n def _pmf(self, k, M, n, N):\n # same as the following but numerically more precise\n # return comb(good, k) * comb(bad, N-k) / comb(tot, N)\n return exp(self._logpmf(k, M, n, N))\n\n def _stats(self, M, n, N):\n # tot, good, sample_size = M, n, N\n # \"wikipedia\".replace('N', 'M').replace('n', 'N').replace('K', 'n')\n M, n, N = 1.*M, 1.*n, 1.*N\n m = M - n\n p = n/M\n mu = N*p\n\n var = m*n*N*(M - N)*1.0/(M*M*(M-1))\n g1 = (m - n)*(M-2*N) / (M-2.0) * sqrt((M-1.0) / (m*n*N*(M-N)))\n\n g2 = M*(M+1) - 6.*N*(M-N) - 6.*n*m\n g2 *= (M-1)*M*M\n g2 += 6.*n*N*(M-N)*m*(5.*M-6)\n g2 /= n * N * (M-N) * m * (M-2.) * (M-3.)\n return mu, var, g1, g2\n\n def _entropy(self, M, n, N):\n k = np.r_[N - (M - n):min(n, N) + 1]\n vals = self.pmf(k, M, n, N)\n return np.sum(entr(vals), axis=0)\n\n def _sf(self, k, M, n, N):\n # This for loop is needed because `k` can be an array. If that's the\n # case, the sf() method makes M, n and N arrays of the same shape. We\n # therefore unpack all inputs args, so we can do the manual\n # integration.\n res = []\n for quant, tot, good, draw in zip(k, M, n, N):\n # Manual integration over probability mass function. More accurate\n # than integrate.quad.\n k2 = np.arange(quant + 1, draw + 1)\n res.append(np.sum(self._pmf(k2, tot, good, draw)))\n return np.asarray(res)\n\n def _logsf(self, k, M, n, N):\n res = []\n for quant, tot, good, draw in zip(k, M, n, N):\n if (quant + 0.5) * (tot + 0.5) < (good - 0.5) * (draw - 0.5):\n # Less terms to sum if we calculate log(1-cdf)\n res.append(log1p(-exp(self.logcdf(quant, tot, good, draw))))\n else:\n # Integration over probability mass function using logsumexp\n k2 = np.arange(quant + 1, draw + 1)\n res.append(logsumexp(self._logpmf(k2, tot, good, draw)))\n return np.asarray(res)\n\n def _logcdf(self, k, M, n, N):\n res = []\n for quant, tot, good, draw in zip(k, M, n, N):\n if (quant + 0.5) * (tot + 0.5) > (good - 0.5) * (draw - 0.5):\n # Less terms to sum if we calculate log(1-sf)\n res.append(log1p(-exp(self.logsf(quant, tot, good, draw))))\n else:\n # Integration over probability mass function using logsumexp\n k2 = np.arange(0, quant + 1)\n res.append(logsumexp(self._logpmf(k2, tot, good, draw)))\n return np.asarray(res)\n\n\nhypergeom = hypergeom_gen(name='hypergeom')\n\n\n# FIXME: Fails _cdfvec\nclass logser_gen(rv_discrete):\n r\"\"\"A Logarithmic (Log-Series, Series) discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `logser` is:\n\n .. math::\n\n f(k) = - \\frac{p^k}{k \\log(1-p)}\n\n for :math:`k \\ge 1`.\n\n `logser` takes :math:`p` as shape parameter.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, p, size=None, random_state=None):\n # looks wrong for p>0.5, too few k=1\n # trying to use generic is worse, no k=1 at all\n return random_state.logseries(p, size=size)\n\n def _argcheck(self, p):\n return (p > 0) & (p < 1)\n\n def _pmf(self, k, p):\n # logser.pmf(k) = - p**k / (k*log(1-p))\n return -np.power(p, k) * 1.0 / k / special.log1p(-p)\n\n def _stats(self, p):\n r = special.log1p(-p)\n mu = p / (p - 1.0) / r\n mu2p = -p / r / (p - 1.0)**2\n var = mu2p - mu*mu\n mu3p = -p / r * (1.0+p) / (1.0 - p)**3\n mu3 = mu3p - 3*mu*mu2p + 2*mu**3\n g1 = mu3 / np.power(var, 1.5)\n\n mu4p = -p / r * (\n 1.0 / (p-1)**2 - 6*p / (p - 1)**3 + 6*p*p / (p-1)**4)\n mu4 = mu4p - 4*mu3p*mu + 6*mu2p*mu*mu - 3*mu**4\n g2 = mu4 / var**2 - 3.0\n return mu, var, g1, g2\n\n\nlogser = logser_gen(a=1, name='logser', longname='A logarithmic')\n\n\nclass poisson_gen(rv_discrete):\n r\"\"\"A Poisson discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `poisson` is:\n\n .. math::\n\n f(k) = \\exp(-\\mu) \\frac{\\mu^k}{k!}\n\n for :math:`k \\ge 0`.\n\n `poisson` takes :math:`\\mu` as shape parameter.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n\n # Override rv_discrete._argcheck to allow mu=0.\n def _argcheck(self, mu):\n return mu >= 0\n\n def _rvs(self, mu, size=None, random_state=None):\n return random_state.poisson(mu, size)\n\n def _logpmf(self, k, mu):\n Pk = special.xlogy(k, mu) - gamln(k + 1) - mu\n return Pk\n\n def _pmf(self, k, mu):\n # poisson.pmf(k) = exp(-mu) * mu**k / k!\n return exp(self._logpmf(k, mu))\n\n def _cdf(self, x, mu):\n k = floor(x)\n return special.pdtr(k, mu)\n\n def _sf(self, x, mu):\n k = floor(x)\n return special.pdtrc(k, mu)\n\n def _ppf(self, q, mu):\n vals = ceil(special.pdtrik(q, mu))\n vals1 = np.maximum(vals - 1, 0)\n temp = special.pdtr(vals1, mu)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, mu):\n var = mu\n tmp = np.asarray(mu)\n mu_nonzero = tmp > 0\n g1 = _lazywhere(mu_nonzero, (tmp,), lambda x: sqrt(1.0/x), np.inf)\n g2 = _lazywhere(mu_nonzero, (tmp,), lambda x: 1.0/x, np.inf)\n return mu, var, g1, g2\n\n\npoisson = poisson_gen(name=\"poisson\", longname='A Poisson')\n\n\nclass planck_gen(rv_discrete):\n r\"\"\"A Planck discrete exponential random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `planck` is:\n\n .. math::\n\n f(k) = (1-\\exp(-\\lambda)) \\exp(-\\lambda k)\n\n for :math:`k \\ge 0` and :math:`\\lambda > 0`.\n\n `planck` takes :math:`\\lambda` as shape parameter. The Planck distribution\n can be written as a geometric distribution (`geom`) with\n :math:`p = 1 - \\exp(-\\lambda)` shifted by `loc = -1`.\n\n %(after_notes)s\n\n See Also\n --------\n geom\n\n %(example)s\n\n \"\"\"\n def _argcheck(self, lambda_):\n return lambda_ > 0\n\n def _pmf(self, k, lambda_):\n return -expm1(-lambda_)*exp(-lambda_*k)\n\n def _cdf(self, x, lambda_):\n k = floor(x)\n return -expm1(-lambda_*(k+1))\n\n def _sf(self, x, lambda_):\n return exp(self._logsf(x, lambda_))\n\n def _logsf(self, x, lambda_):\n k = floor(x)\n return -lambda_*(k+1)\n\n def _ppf(self, q, lambda_):\n vals = ceil(-1.0/lambda_ * log1p(-q)-1)\n vals1 = (vals-1).clip(*(self._get_support(lambda_)))\n temp = self._cdf(vals1, lambda_)\n return np.where(temp >= q, vals1, vals)\n\n def _rvs(self, lambda_, size=None, random_state=None):\n # use relation to geometric distribution for sampling\n p = -expm1(-lambda_)\n return random_state.geometric(p, size=size) - 1.0\n\n def _stats(self, lambda_):\n mu = 1/expm1(lambda_)\n var = exp(-lambda_)/(expm1(-lambda_))**2\n g1 = 2*cosh(lambda_/2.0)\n g2 = 4+2*cosh(lambda_)\n return mu, var, g1, g2\n\n def _entropy(self, lambda_):\n C = -expm1(-lambda_)\n return lambda_*exp(-lambda_)/C - log(C)\n\n\nplanck = planck_gen(a=0, name='planck', longname='A discrete exponential ')\n\n\nclass boltzmann_gen(rv_discrete):\n r\"\"\"A Boltzmann (Truncated Discrete Exponential) random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `boltzmann` is:\n\n .. math::\n\n f(k) = (1-\\exp(-\\lambda)) \\exp(-\\lambda k) / (1-\\exp(-\\lambda N))\n\n for :math:`k = 0,..., N-1`.\n\n `boltzmann` takes :math:`\\lambda > 0` and :math:`N > 0` as shape parameters.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _argcheck(self, lambda_, N):\n return (lambda_ > 0) & (N > 0)\n\n def _get_support(self, lambda_, N):\n return self.a, N - 1\n\n def _pmf(self, k, lambda_, N):\n # boltzmann.pmf(k) =\n # (1-exp(-lambda_)*exp(-lambda_*k)/(1-exp(-lambda_*N))\n fact = (1-exp(-lambda_))/(1-exp(-lambda_*N))\n return fact*exp(-lambda_*k)\n\n def _cdf(self, x, lambda_, N):\n k = floor(x)\n return (1-exp(-lambda_*(k+1)))/(1-exp(-lambda_*N))\n\n def _ppf(self, q, lambda_, N):\n qnew = q*(1-exp(-lambda_*N))\n vals = ceil(-1.0/lambda_ * log(1-qnew)-1)\n vals1 = (vals-1).clip(0.0, np.inf)\n temp = self._cdf(vals1, lambda_, N)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, lambda_, N):\n z = exp(-lambda_)\n zN = exp(-lambda_*N)\n mu = z/(1.0-z)-N*zN/(1-zN)\n var = z/(1.0-z)**2 - N*N*zN/(1-zN)**2\n trm = (1-zN)/(1-z)\n trm2 = (z*trm**2 - N*N*zN)\n g1 = z*(1+z)*trm**3 - N**3*zN*(1+zN)\n g1 = g1 / trm2**(1.5)\n g2 = z*(1+4*z+z*z)*trm**4 - N**4 * zN*(1+4*zN+zN*zN)\n g2 = g2 / trm2 / trm2\n return mu, var, g1, g2\n\n\nboltzmann = boltzmann_gen(name='boltzmann', a=0,\n longname='A truncated discrete exponential ')\n\n\nclass randint_gen(rv_discrete):\n r\"\"\"A uniform discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `randint` is:\n\n .. math::\n\n f(k) = \\frac{1}{high - low}\n\n for ``k = low, ..., high - 1``.\n\n `randint` takes ``low`` and ``high`` as shape parameters.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _argcheck(self, low, high):\n return (high > low)\n\n def _get_support(self, low, high):\n return low, high-1\n\n def _pmf(self, k, low, high):\n # randint.pmf(k) = 1./(high - low)\n p = np.ones_like(k) / (high - low)\n return np.where((k >= low) & (k < high), p, 0.)\n\n def _cdf(self, x, low, high):\n k = floor(x)\n return (k - low + 1.) / (high - low)\n\n def _ppf(self, q, low, high):\n vals = ceil(q * (high - low) + low) - 1\n vals1 = (vals - 1).clip(low, high)\n temp = self._cdf(vals1, low, high)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, low, high):\n m2, m1 = np.asarray(high), np.asarray(low)\n mu = (m2 + m1 - 1.0) / 2\n d = m2 - m1\n var = (d*d - 1) / 12.0\n g1 = 0.0\n g2 = -6.0/5.0 * (d*d + 1.0) / (d*d - 1.0)\n return mu, var, g1, g2\n\n def _rvs(self, low, high, size=None, random_state=None):\n \"\"\"An array of *size* random integers >= ``low`` and < ``high``.\"\"\"\n if np.asarray(low).size == 1 and np.asarray(high).size == 1:\n # no need to vectorize in that case\n return rng_integers(random_state, low, high, size=size)\n\n if size is not None:\n # NumPy's RandomState.randint() doesn't broadcast its arguments.\n # Use `broadcast_to()` to extend the shapes of low and high\n # up to size. Then we can use the numpy.vectorize'd\n # randint without needing to pass it a `size` argument.\n low = np.broadcast_to(low, size)\n high = np.broadcast_to(high, size)\n randint = np.vectorize(partial(rng_integers, random_state),\n otypes=[np.int_])\n return randint(low, high)\n\n def _entropy(self, low, high):\n return log(high - low)\n\n\nrandint = randint_gen(name='randint', longname='A discrete uniform '\n '(random integer)')\n\n\n# FIXME: problems sampling.\nclass zipf_gen(rv_discrete):\n r\"\"\"A Zipf discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `zipf` is:\n\n .. math::\n\n f(k, a) = \\frac{1}{\\zeta(a) k^a}\n\n for :math:`k \\ge 1`.\n\n `zipf` takes :math:`a` as shape parameter. :math:`\\zeta` is the\n Riemann zeta function (`scipy.special.zeta`)\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, a, size=None, random_state=None):\n return random_state.zipf(a, size=size)\n\n def _argcheck(self, a):\n return a > 1\n\n def _pmf(self, k, a):\n # zipf.pmf(k, a) = 1/(zeta(a) * k**a)\n Pk = 1.0 / special.zeta(a, 1) / k**a\n return Pk\n\n def _munp(self, n, a):\n return _lazywhere(\n a > n + 1, (a, n),\n lambda a, n: special.zeta(a - n, 1) / special.zeta(a, 1),\n np.inf)\n\n\nzipf = zipf_gen(a=1, name='zipf', longname='A Zipf')\n\n\nclass dlaplace_gen(rv_discrete):\n r\"\"\"A Laplacian discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `dlaplace` is:\n\n .. math::\n\n f(k) = \\tanh(a/2) \\exp(-a |k|)\n\n for integers :math:`k` and :math:`a > 0`.\n\n `dlaplace` takes :math:`a` as shape parameter.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _pmf(self, k, a):\n # dlaplace.pmf(k) = tanh(a/2) * exp(-a*abs(k))\n return tanh(a/2.0) * exp(-a * abs(k))\n\n def _cdf(self, x, a):\n k = floor(x)\n f = lambda k, a: 1.0 - exp(-a * k) / (exp(a) + 1)\n f2 = lambda k, a: exp(a * (k+1)) / (exp(a) + 1)\n return _lazywhere(k >= 0, (k, a), f=f, f2=f2)\n\n def _ppf(self, q, a):\n const = 1 + exp(a)\n vals = ceil(np.where(q < 1.0 / (1 + exp(-a)),\n log(q*const) / a - 1,\n -log((1-q) * const) / a))\n vals1 = vals - 1\n return np.where(self._cdf(vals1, a) >= q, vals1, vals)\n\n def _stats(self, a):\n ea = exp(a)\n mu2 = 2.*ea/(ea-1.)**2\n mu4 = 2.*ea*(ea**2+10.*ea+1.) / (ea-1.)**4\n return 0., mu2, 0., mu4/mu2**2 - 3.\n\n def _entropy(self, a):\n return a / sinh(a) - log(tanh(a/2.0))\n\n def _rvs(self, a, size=None, random_state=None):\n # The discrete Laplace is equivalent to the two-sided geometric\n # distribution with PMF:\n # f(k) = (1 - alpha)/(1 + alpha) * alpha^abs(k)\n # Reference:\n # https://www.sciencedirect.com/science/\n # article/abs/pii/S0378375804003519\n # Furthermore, the two-sided geometric distribution is\n # equivalent to the difference between two iid geometric \n # distributions.\n # Reference (page 179):\n # https://pdfs.semanticscholar.org/61b3/\n # b99f466815808fd0d03f5d2791eea8b541a1.pdf\n # Thus, we can leverage the following:\n # 1) alpha = e^-a\n # 2) probability_of_success = 1 - alpha (Bernoulli trial)\n probOfSuccess = -np.expm1(-np.asarray(a))\n x = random_state.geometric(probOfSuccess, size=size)\n y = random_state.geometric(probOfSuccess, size=size)\n return x - y\n\n\ndlaplace = dlaplace_gen(a=-np.inf,\n name='dlaplace', longname='A discrete Laplacian')\n\n\nclass skellam_gen(rv_discrete):\n r\"\"\"A Skellam discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n Probability distribution of the difference of two correlated or\n uncorrelated Poisson random variables.\n\n Let :math:`k_1` and :math:`k_2` be two Poisson-distributed r.v. with\n expected values :math:`\\lambda_1` and :math:`\\lambda_2`. Then,\n :math:`k_1 - k_2` follows a Skellam distribution with parameters\n :math:`\\mu_1 = \\lambda_1 - \\rho \\sqrt{\\lambda_1 \\lambda_2}` and\n :math:`\\mu_2 = \\lambda_2 - \\rho \\sqrt{\\lambda_1 \\lambda_2}`, where\n :math:`\\rho` is the correlation coefficient between :math:`k_1` and\n :math:`k_2`. If the two Poisson-distributed r.v. are independent then\n :math:`\\rho = 0`.\n\n Parameters :math:`\\mu_1` and :math:`\\mu_2` must be strictly positive.\n\n For details see: https://en.wikipedia.org/wiki/Skellam_distribution\n\n `skellam` takes :math:`\\mu_1` and :math:`\\mu_2` as shape parameters.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, mu1, mu2, size=None, random_state=None):\n n = size\n return (random_state.poisson(mu1, n) -\n random_state.poisson(mu2, n))\n\n def _pmf(self, x, mu1, mu2):\n px = np.where(x < 0,\n _ncx2_pdf(2*mu2, 2*(1-x), 2*mu1)*2,\n _ncx2_pdf(2*mu1, 2*(1+x), 2*mu2)*2)\n # ncx2.pdf() returns nan's for extremely low probabilities\n return px\n\n def _cdf(self, x, mu1, mu2):\n x = floor(x)\n px = np.where(x < 0,\n _ncx2_cdf(2*mu2, -2*x, 2*mu1),\n 1 - _ncx2_cdf(2*mu1, 2*(x+1), 2*mu2))\n return px\n\n def _stats(self, mu1, mu2):\n mean = mu1 - mu2\n var = mu1 + mu2\n g1 = mean / sqrt((var)**3)\n g2 = 1 / var\n return mean, var, g1, g2\n\n\nskellam = skellam_gen(a=-np.inf, name=\"skellam\", longname='A Skellam')\n\n\nclass yulesimon_gen(rv_discrete):\n r\"\"\"A Yule-Simon discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n\n The probability mass function for the `yulesimon` is:\n\n .. math::\n\n f(k) = \\alpha B(k, \\alpha+1)\n\n for :math:`k=1,2,3,...`, where :math:`\\alpha>0`.\n Here :math:`B` refers to the `scipy.special.beta` function.\n\n The sampling of random variates is based on pg 553, Section 6.3 of [1]_.\n Our notation maps to the referenced logic via :math:`\\alpha=a-1`.\n\n For details see the wikipedia entry [2]_.\n\n References\n ----------\n .. [1] Devroye, Luc. \"Non-uniform Random Variate Generation\",\n (1986) Springer, New York.\n\n .. [2] https://en.wikipedia.org/wiki/Yule-Simon_distribution\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, alpha, size=None, random_state=None):\n E1 = random_state.standard_exponential(size)\n E2 = random_state.standard_exponential(size)\n ans = ceil(-E1 / log1p(-exp(-E2 / alpha)))\n return ans\n\n def _pmf(self, x, alpha):\n return alpha * special.beta(x, alpha + 1)\n\n def _argcheck(self, alpha):\n return (alpha > 0)\n\n def _logpmf(self, x, alpha):\n return log(alpha) + special.betaln(x, alpha + 1)\n\n def _cdf(self, x, alpha):\n return 1 - x * special.beta(x, alpha + 1)\n\n def _sf(self, x, alpha):\n return x * special.beta(x, alpha + 1)\n\n def _logsf(self, x, alpha):\n return log(x) + special.betaln(x, alpha + 1)\n\n def _stats(self, alpha):\n mu = np.where(alpha <= 1, np.inf, alpha / (alpha - 1))\n mu2 = np.where(alpha > 2,\n alpha**2 / ((alpha - 2.0) * (alpha - 1)**2),\n np.inf)\n mu2 = np.where(alpha <= 1, np.nan, mu2)\n g1 = np.where(alpha > 3,\n sqrt(alpha - 2) * (alpha + 1)**2 / (alpha * (alpha - 3)),\n np.inf)\n g1 = np.where(alpha <= 2, np.nan, g1)\n g2 = np.where(alpha > 4,\n (alpha + 3) + (alpha**3 - 49 * alpha - 22) / (alpha *\n (alpha - 4) * (alpha - 3)), np.inf)\n g2 = np.where(alpha <= 2, np.nan, g2)\n return mu, mu2, g1, g2\n\n\nyulesimon = yulesimon_gen(name='yulesimon', a=1)\n\n\n# Collect names of classes and objects in this module.\npairs = list(globals().items())\n_distn_names, _distn_gen_names = get_distribution_names(pairs, rv_discrete)\n\n__all__ = _distn_names + _distn_gen_names\n", "path": "scipy/stats/_discrete_distns.py" } ]
[ { "content": "#\n# Author: Travis Oliphant 2002-2011 with contributions from\n# SciPy Developers 2004-2011\n#\nfrom functools import partial\nfrom scipy import special\nfrom scipy.special import entr, logsumexp, betaln, gammaln as gamln\nfrom scipy._lib._util import _lazywhere, rng_integers\n\nfrom numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh\n\nimport numpy as np\n\nfrom ._distn_infrastructure import (\n rv_discrete, _ncx2_pdf, _ncx2_cdf, get_distribution_names)\n\n\nclass binom_gen(rv_discrete):\n r\"\"\"A binomial discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `binom` is:\n\n .. math::\n\n f(k) = \\binom{n}{k} p^k (1-p)^{n-k}\n\n for ``k`` in ``{0, 1,..., n}``.\n\n `binom` takes ``n`` and ``p`` as shape parameters.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, n, p, size=None, random_state=None):\n return random_state.binomial(n, p, size)\n\n def _argcheck(self, n, p):\n return (n >= 0) & (p >= 0) & (p <= 1)\n\n def _get_support(self, n, p):\n return self.a, n\n\n def _logpmf(self, x, n, p):\n k = floor(x)\n combiln = (gamln(n+1) - (gamln(k+1) + gamln(n-k+1)))\n return combiln + special.xlogy(k, p) + special.xlog1py(n-k, -p)\n\n def _pmf(self, x, n, p):\n # binom.pmf(k) = choose(n, k) * p**k * (1-p)**(n-k)\n return exp(self._logpmf(x, n, p))\n\n def _cdf(self, x, n, p):\n k = floor(x)\n vals = special.bdtr(k, n, p)\n return vals\n\n def _sf(self, x, n, p):\n k = floor(x)\n return special.bdtrc(k, n, p)\n\n def _ppf(self, q, n, p):\n vals = ceil(special.bdtrik(q, n, p))\n vals1 = np.maximum(vals - 1, 0)\n temp = special.bdtr(vals1, n, p)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, n, p, moments='mv'):\n q = 1.0 - p\n mu = n * p\n var = n * p * q\n g1, g2 = None, None\n if 's' in moments:\n g1 = (q - p) / sqrt(var)\n if 'k' in moments:\n g2 = (1.0 - 6*p*q) / var\n return mu, var, g1, g2\n\n def _entropy(self, n, p):\n k = np.r_[0:n + 1]\n vals = self._pmf(k, n, p)\n return np.sum(entr(vals), axis=0)\n\n\nbinom = binom_gen(name='binom')\n\n\nclass bernoulli_gen(binom_gen):\n r\"\"\"A Bernoulli discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `bernoulli` is:\n\n .. math::\n\n f(k) = \\begin{cases}1-p &\\text{if } k = 0\\\\\n p &\\text{if } k = 1\\end{cases}\n\n for :math:`k` in :math:`\\{0, 1\\}`.\n\n `bernoulli` takes :math:`p` as shape parameter.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, p, size=None, random_state=None):\n return binom_gen._rvs(self, 1, p, size=size, random_state=random_state)\n\n def _argcheck(self, p):\n return (p >= 0) & (p <= 1)\n\n def _get_support(self, p):\n # Overrides binom_gen._get_support!x\n return self.a, self.b\n\n def _logpmf(self, x, p):\n return binom._logpmf(x, 1, p)\n\n def _pmf(self, x, p):\n # bernoulli.pmf(k) = 1-p if k = 0\n # = p if k = 1\n return binom._pmf(x, 1, p)\n\n def _cdf(self, x, p):\n return binom._cdf(x, 1, p)\n\n def _sf(self, x, p):\n return binom._sf(x, 1, p)\n\n def _ppf(self, q, p):\n return binom._ppf(q, 1, p)\n\n def _stats(self, p):\n return binom._stats(1, p)\n\n def _entropy(self, p):\n return entr(p) + entr(1-p)\n\n\nbernoulli = bernoulli_gen(b=1, name='bernoulli')\n\n\nclass betabinom_gen(rv_discrete):\n r\"\"\"A beta-binomial discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The beta-binomial distribution is a binomial distribution with a\n probability of success `p` that follows a beta distribution.\n\n The probability mass function for `betabinom` is:\n\n .. math::\n\n f(k) = \\binom{n}{k} \\frac{B(k + a, n - k + b)}{B(a, b)}\n\n for ``k`` in ``{0, 1,..., n}``, :math:`n \\geq 0`, :math:`a > 0`,\n :math:`b > 0`, where :math:`B(a, b)` is the beta function.\n\n `betabinom` takes :math:`n`, :math:`a`, and :math:`b` as shape parameters.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Beta-binomial_distribution\n\n %(after_notes)s\n\n .. versionadded:: 1.4.0\n\n See Also\n --------\n beta, binom\n\n %(example)s\n\n \"\"\"\n\n def _rvs(self, n, a, b, size=None, random_state=None):\n p = random_state.beta(a, b, size)\n return random_state.binomial(n, p, size)\n\n def _get_support(self, n, a, b):\n return 0, n\n\n def _argcheck(self, n, a, b):\n return (n >= 0) & (a > 0) & (b > 0)\n\n def _logpmf(self, x, n, a, b):\n k = floor(x)\n combiln = -log(n + 1) - betaln(n - k + 1, k + 1)\n return combiln + betaln(k + a, n - k + b) - betaln(a, b)\n\n def _pmf(self, x, n, a, b):\n return exp(self._logpmf(x, n, a, b))\n\n def _stats(self, n, a, b, moments='mv'):\n e_p = a / (a + b)\n e_q = 1 - e_p\n mu = n * e_p\n var = n * (a + b + n) * e_p * e_q / (a + b + 1)\n g1, g2 = None, None\n if 's' in moments:\n g1 = 1.0 / sqrt(var)\n g1 *= (a + b + 2 * n) * (b - a)\n g1 /= (a + b + 2) * (a + b)\n if 'k' in moments:\n g2 = a + b\n g2 *= (a + b - 1 + 6 * n)\n g2 += 3 * a * b * (n - 2)\n g2 += 6 * n ** 2\n g2 -= 3 * e_p * b * n * (6 - n)\n g2 -= 18 * e_p * e_q * n ** 2\n g2 *= (a + b) ** 2 * (1 + a + b)\n g2 /= (n * a * b * (a + b + 2) * (a + b + 3) * (a + b + n))\n g2 -= 3\n return mu, var, g1, g2\n\n\nbetabinom = betabinom_gen(name='betabinom')\n\n\nclass nbinom_gen(rv_discrete):\n r\"\"\"A negative binomial discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n Negative binomial distribution describes a sequence of i.i.d. Bernoulli\n trials, repeated until a predefined, non-random number of successes occurs.\n\n The probability mass function of the number of failures for `nbinom` is:\n\n .. math::\n\n f(k) = \\binom{k+n-1}{n-1} p^n (1-p)^k\n\n for :math:`k \\ge 0`.\n\n `nbinom` takes :math:`n` and :math:`p` as shape parameters where n is the\n number of successes, whereas p is the probability of a single success.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, n, p, size=None, random_state=None):\n return random_state.negative_binomial(n, p, size)\n\n def _argcheck(self, n, p):\n return (n > 0) & (p >= 0) & (p <= 1)\n\n def _pmf(self, x, n, p):\n # nbinom.pmf(k) = choose(k+n-1, n-1) * p**n * (1-p)**k\n return exp(self._logpmf(x, n, p))\n\n def _logpmf(self, x, n, p):\n coeff = gamln(n+x) - gamln(x+1) - gamln(n)\n return coeff + n*log(p) + special.xlog1py(x, -p)\n\n def _cdf(self, x, n, p):\n k = floor(x)\n return special.betainc(n, k+1, p)\n\n def _sf_skip(self, x, n, p):\n # skip because special.nbdtrc doesn't work for 0<n<1\n k = floor(x)\n return special.nbdtrc(k, n, p)\n\n def _ppf(self, q, n, p):\n vals = ceil(special.nbdtrik(q, n, p))\n vals1 = (vals-1).clip(0.0, np.inf)\n temp = self._cdf(vals1, n, p)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, n, p):\n Q = 1.0 / p\n P = Q - 1.0\n mu = n*P\n var = n*P*Q\n g1 = (Q+P)/sqrt(n*P*Q)\n g2 = (1.0 + 6*P*Q) / (n*P*Q)\n return mu, var, g1, g2\n\n\nnbinom = nbinom_gen(name='nbinom')\n\n\nclass geom_gen(rv_discrete):\n r\"\"\"A geometric discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `geom` is:\n\n .. math::\n\n f(k) = (1-p)^{k-1} p\n\n for :math:`k \\ge 1`.\n\n `geom` takes :math:`p` as shape parameter.\n\n %(after_notes)s\n\n See Also\n --------\n planck\n\n %(example)s\n\n \"\"\"\n def _rvs(self, p, size=None, random_state=None):\n return random_state.geometric(p, size=size)\n\n def _argcheck(self, p):\n return (p <= 1) & (p >= 0)\n\n def _pmf(self, k, p):\n return np.power(1-p, k-1) * p\n\n def _logpmf(self, k, p):\n return special.xlog1py(k - 1, -p) + log(p)\n\n def _cdf(self, x, p):\n k = floor(x)\n return -expm1(log1p(-p)*k)\n\n def _sf(self, x, p):\n return np.exp(self._logsf(x, p))\n\n def _logsf(self, x, p):\n k = floor(x)\n return k*log1p(-p)\n\n def _ppf(self, q, p):\n vals = ceil(log1p(-q) / log1p(-p))\n temp = self._cdf(vals-1, p)\n return np.where((temp >= q) & (vals > 0), vals-1, vals)\n\n def _stats(self, p):\n mu = 1.0/p\n qr = 1.0-p\n var = qr / p / p\n g1 = (2.0-p) / sqrt(qr)\n g2 = np.polyval([1, -6, 6], p)/(1.0-p)\n return mu, var, g1, g2\n\n\ngeom = geom_gen(a=1, name='geom', longname=\"A geometric\")\n\n\nclass hypergeom_gen(rv_discrete):\n r\"\"\"A hypergeometric discrete random variable.\n\n The hypergeometric distribution models drawing objects from a bin.\n `M` is the total number of objects, `n` is total number of Type I objects.\n The random variate represents the number of Type I objects in `N` drawn\n without replacement from the total population.\n\n %(before_notes)s\n\n Notes\n -----\n The symbols used to denote the shape parameters (`M`, `n`, and `N`) are not\n universally accepted. See the Examples for a clarification of the\n definitions used here.\n\n The probability mass function is defined as,\n\n .. math:: p(k, M, n, N) = \\frac{\\binom{n}{k} \\binom{M - n}{N - k}}\n {\\binom{M}{N}}\n\n for :math:`k \\in [\\max(0, N - M + n), \\min(n, N)]`, where the binomial\n coefficients are defined as,\n\n .. math:: \\binom{n}{k} \\equiv \\frac{n!}{k! (n - k)!}.\n\n %(after_notes)s\n\n Examples\n --------\n >>> from scipy.stats import hypergeom\n >>> import matplotlib.pyplot as plt\n\n Suppose we have a collection of 20 animals, of which 7 are dogs. Then if\n we want to know the probability of finding a given number of dogs if we\n choose at random 12 of the 20 animals, we can initialize a frozen\n distribution and plot the probability mass function:\n\n >>> [M, n, N] = [20, 7, 12]\n >>> rv = hypergeom(M, n, N)\n >>> x = np.arange(0, n+1)\n >>> pmf_dogs = rv.pmf(x)\n\n >>> fig = plt.figure()\n >>> ax = fig.add_subplot(111)\n >>> ax.plot(x, pmf_dogs, 'bo')\n >>> ax.vlines(x, 0, pmf_dogs, lw=2)\n >>> ax.set_xlabel('# of dogs in our group of chosen animals')\n >>> ax.set_ylabel('hypergeom PMF')\n >>> plt.show()\n\n Instead of using a frozen distribution we can also use `hypergeom`\n methods directly. To for example obtain the cumulative distribution\n function, use:\n\n >>> prb = hypergeom.cdf(x, M, n, N)\n\n And to generate random numbers:\n\n >>> R = hypergeom.rvs(M, n, N, size=10)\n\n \"\"\"\n def _rvs(self, M, n, N, size=None, random_state=None):\n return random_state.hypergeometric(n, M-n, N, size=size)\n\n def _get_support(self, M, n, N):\n return np.maximum(N-(M-n), 0), np.minimum(n, N)\n\n def _argcheck(self, M, n, N):\n cond = (M > 0) & (n >= 0) & (N >= 0)\n cond &= (n <= M) & (N <= M)\n return cond\n\n def _logpmf(self, k, M, n, N):\n tot, good = M, n\n bad = tot - good\n result = (betaln(good+1, 1) + betaln(bad+1, 1) + betaln(tot-N+1, N+1) -\n betaln(k+1, good-k+1) - betaln(N-k+1, bad-N+k+1) -\n betaln(tot+1, 1))\n return result\n\n def _pmf(self, k, M, n, N):\n # same as the following but numerically more precise\n # return comb(good, k) * comb(bad, N-k) / comb(tot, N)\n return exp(self._logpmf(k, M, n, N))\n\n def _stats(self, M, n, N):\n # tot, good, sample_size = M, n, N\n # \"wikipedia\".replace('N', 'M').replace('n', 'N').replace('K', 'n')\n M, n, N = 1.*M, 1.*n, 1.*N\n m = M - n\n p = n/M\n mu = N*p\n\n var = m*n*N*(M - N)*1.0/(M*M*(M-1))\n g1 = (m - n)*(M-2*N) / (M-2.0) * sqrt((M-1.0) / (m*n*N*(M-N)))\n\n g2 = M*(M+1) - 6.*N*(M-N) - 6.*n*m\n g2 *= (M-1)*M*M\n g2 += 6.*n*N*(M-N)*m*(5.*M-6)\n g2 /= n * N * (M-N) * m * (M-2.) * (M-3.)\n return mu, var, g1, g2\n\n def _entropy(self, M, n, N):\n k = np.r_[N - (M - n):min(n, N) + 1]\n vals = self.pmf(k, M, n, N)\n return np.sum(entr(vals), axis=0)\n\n def _sf(self, k, M, n, N):\n # This for loop is needed because `k` can be an array. If that's the\n # case, the sf() method makes M, n and N arrays of the same shape. We\n # therefore unpack all inputs args, so we can do the manual\n # integration.\n res = []\n for quant, tot, good, draw in zip(k, M, n, N):\n # Manual integration over probability mass function. More accurate\n # than integrate.quad.\n k2 = np.arange(quant + 1, draw + 1)\n res.append(np.sum(self._pmf(k2, tot, good, draw)))\n return np.asarray(res)\n\n def _logsf(self, k, M, n, N):\n res = []\n for quant, tot, good, draw in zip(k, M, n, N):\n if (quant + 0.5) * (tot + 0.5) < (good - 0.5) * (draw - 0.5):\n # Less terms to sum if we calculate log(1-cdf)\n res.append(log1p(-exp(self.logcdf(quant, tot, good, draw))))\n else:\n # Integration over probability mass function using logsumexp\n k2 = np.arange(quant + 1, draw + 1)\n res.append(logsumexp(self._logpmf(k2, tot, good, draw)))\n return np.asarray(res)\n\n def _logcdf(self, k, M, n, N):\n res = []\n for quant, tot, good, draw in zip(k, M, n, N):\n if (quant + 0.5) * (tot + 0.5) > (good - 0.5) * (draw - 0.5):\n # Less terms to sum if we calculate log(1-sf)\n res.append(log1p(-exp(self.logsf(quant, tot, good, draw))))\n else:\n # Integration over probability mass function using logsumexp\n k2 = np.arange(0, quant + 1)\n res.append(logsumexp(self._logpmf(k2, tot, good, draw)))\n return np.asarray(res)\n\n\nhypergeom = hypergeom_gen(name='hypergeom')\n\n\n# FIXME: Fails _cdfvec\nclass logser_gen(rv_discrete):\n r\"\"\"A Logarithmic (Log-Series, Series) discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `logser` is:\n\n .. math::\n\n f(k) = - \\frac{p^k}{k \\log(1-p)}\n\n for :math:`k \\ge 1`.\n\n `logser` takes :math:`p` as shape parameter.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, p, size=None, random_state=None):\n # looks wrong for p>0.5, too few k=1\n # trying to use generic is worse, no k=1 at all\n return random_state.logseries(p, size=size)\n\n def _argcheck(self, p):\n return (p > 0) & (p < 1)\n\n def _pmf(self, k, p):\n # logser.pmf(k) = - p**k / (k*log(1-p))\n return -np.power(p, k) * 1.0 / k / special.log1p(-p)\n\n def _stats(self, p):\n r = special.log1p(-p)\n mu = p / (p - 1.0) / r\n mu2p = -p / r / (p - 1.0)**2\n var = mu2p - mu*mu\n mu3p = -p / r * (1.0+p) / (1.0 - p)**3\n mu3 = mu3p - 3*mu*mu2p + 2*mu**3\n g1 = mu3 / np.power(var, 1.5)\n\n mu4p = -p / r * (\n 1.0 / (p-1)**2 - 6*p / (p - 1)**3 + 6*p*p / (p-1)**4)\n mu4 = mu4p - 4*mu3p*mu + 6*mu2p*mu*mu - 3*mu**4\n g2 = mu4 / var**2 - 3.0\n return mu, var, g1, g2\n\n\nlogser = logser_gen(a=1, name='logser', longname='A logarithmic')\n\n\nclass poisson_gen(rv_discrete):\n r\"\"\"A Poisson discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `poisson` is:\n\n .. math::\n\n f(k) = \\exp(-\\mu) \\frac{\\mu^k}{k!}\n\n for :math:`k \\ge 0`.\n\n `poisson` takes :math:`\\mu` as shape parameter.\n When mu = 0 then at quantile k = 0, ``pmf`` method\n returns `1.0`.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n\n # Override rv_discrete._argcheck to allow mu=0.\n def _argcheck(self, mu):\n return mu >= 0\n\n def _rvs(self, mu, size=None, random_state=None):\n return random_state.poisson(mu, size)\n\n def _logpmf(self, k, mu):\n Pk = special.xlogy(k, mu) - gamln(k + 1) - mu\n return Pk\n\n def _pmf(self, k, mu):\n # poisson.pmf(k) = exp(-mu) * mu**k / k!\n return exp(self._logpmf(k, mu))\n\n def _cdf(self, x, mu):\n k = floor(x)\n return special.pdtr(k, mu)\n\n def _sf(self, x, mu):\n k = floor(x)\n return special.pdtrc(k, mu)\n\n def _ppf(self, q, mu):\n vals = ceil(special.pdtrik(q, mu))\n vals1 = np.maximum(vals - 1, 0)\n temp = special.pdtr(vals1, mu)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, mu):\n var = mu\n tmp = np.asarray(mu)\n mu_nonzero = tmp > 0\n g1 = _lazywhere(mu_nonzero, (tmp,), lambda x: sqrt(1.0/x), np.inf)\n g2 = _lazywhere(mu_nonzero, (tmp,), lambda x: 1.0/x, np.inf)\n return mu, var, g1, g2\n\n\npoisson = poisson_gen(name=\"poisson\", longname='A Poisson')\n\n\nclass planck_gen(rv_discrete):\n r\"\"\"A Planck discrete exponential random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `planck` is:\n\n .. math::\n\n f(k) = (1-\\exp(-\\lambda)) \\exp(-\\lambda k)\n\n for :math:`k \\ge 0` and :math:`\\lambda > 0`.\n\n `planck` takes :math:`\\lambda` as shape parameter. The Planck distribution\n can be written as a geometric distribution (`geom`) with\n :math:`p = 1 - \\exp(-\\lambda)` shifted by `loc = -1`.\n\n %(after_notes)s\n\n See Also\n --------\n geom\n\n %(example)s\n\n \"\"\"\n def _argcheck(self, lambda_):\n return lambda_ > 0\n\n def _pmf(self, k, lambda_):\n return -expm1(-lambda_)*exp(-lambda_*k)\n\n def _cdf(self, x, lambda_):\n k = floor(x)\n return -expm1(-lambda_*(k+1))\n\n def _sf(self, x, lambda_):\n return exp(self._logsf(x, lambda_))\n\n def _logsf(self, x, lambda_):\n k = floor(x)\n return -lambda_*(k+1)\n\n def _ppf(self, q, lambda_):\n vals = ceil(-1.0/lambda_ * log1p(-q)-1)\n vals1 = (vals-1).clip(*(self._get_support(lambda_)))\n temp = self._cdf(vals1, lambda_)\n return np.where(temp >= q, vals1, vals)\n\n def _rvs(self, lambda_, size=None, random_state=None):\n # use relation to geometric distribution for sampling\n p = -expm1(-lambda_)\n return random_state.geometric(p, size=size) - 1.0\n\n def _stats(self, lambda_):\n mu = 1/expm1(lambda_)\n var = exp(-lambda_)/(expm1(-lambda_))**2\n g1 = 2*cosh(lambda_/2.0)\n g2 = 4+2*cosh(lambda_)\n return mu, var, g1, g2\n\n def _entropy(self, lambda_):\n C = -expm1(-lambda_)\n return lambda_*exp(-lambda_)/C - log(C)\n\n\nplanck = planck_gen(a=0, name='planck', longname='A discrete exponential ')\n\n\nclass boltzmann_gen(rv_discrete):\n r\"\"\"A Boltzmann (Truncated Discrete Exponential) random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `boltzmann` is:\n\n .. math::\n\n f(k) = (1-\\exp(-\\lambda)) \\exp(-\\lambda k) / (1-\\exp(-\\lambda N))\n\n for :math:`k = 0,..., N-1`.\n\n `boltzmann` takes :math:`\\lambda > 0` and :math:`N > 0` as shape parameters.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _argcheck(self, lambda_, N):\n return (lambda_ > 0) & (N > 0)\n\n def _get_support(self, lambda_, N):\n return self.a, N - 1\n\n def _pmf(self, k, lambda_, N):\n # boltzmann.pmf(k) =\n # (1-exp(-lambda_)*exp(-lambda_*k)/(1-exp(-lambda_*N))\n fact = (1-exp(-lambda_))/(1-exp(-lambda_*N))\n return fact*exp(-lambda_*k)\n\n def _cdf(self, x, lambda_, N):\n k = floor(x)\n return (1-exp(-lambda_*(k+1)))/(1-exp(-lambda_*N))\n\n def _ppf(self, q, lambda_, N):\n qnew = q*(1-exp(-lambda_*N))\n vals = ceil(-1.0/lambda_ * log(1-qnew)-1)\n vals1 = (vals-1).clip(0.0, np.inf)\n temp = self._cdf(vals1, lambda_, N)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, lambda_, N):\n z = exp(-lambda_)\n zN = exp(-lambda_*N)\n mu = z/(1.0-z)-N*zN/(1-zN)\n var = z/(1.0-z)**2 - N*N*zN/(1-zN)**2\n trm = (1-zN)/(1-z)\n trm2 = (z*trm**2 - N*N*zN)\n g1 = z*(1+z)*trm**3 - N**3*zN*(1+zN)\n g1 = g1 / trm2**(1.5)\n g2 = z*(1+4*z+z*z)*trm**4 - N**4 * zN*(1+4*zN+zN*zN)\n g2 = g2 / trm2 / trm2\n return mu, var, g1, g2\n\n\nboltzmann = boltzmann_gen(name='boltzmann', a=0,\n longname='A truncated discrete exponential ')\n\n\nclass randint_gen(rv_discrete):\n r\"\"\"A uniform discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `randint` is:\n\n .. math::\n\n f(k) = \\frac{1}{high - low}\n\n for ``k = low, ..., high - 1``.\n\n `randint` takes ``low`` and ``high`` as shape parameters.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _argcheck(self, low, high):\n return (high > low)\n\n def _get_support(self, low, high):\n return low, high-1\n\n def _pmf(self, k, low, high):\n # randint.pmf(k) = 1./(high - low)\n p = np.ones_like(k) / (high - low)\n return np.where((k >= low) & (k < high), p, 0.)\n\n def _cdf(self, x, low, high):\n k = floor(x)\n return (k - low + 1.) / (high - low)\n\n def _ppf(self, q, low, high):\n vals = ceil(q * (high - low) + low) - 1\n vals1 = (vals - 1).clip(low, high)\n temp = self._cdf(vals1, low, high)\n return np.where(temp >= q, vals1, vals)\n\n def _stats(self, low, high):\n m2, m1 = np.asarray(high), np.asarray(low)\n mu = (m2 + m1 - 1.0) / 2\n d = m2 - m1\n var = (d*d - 1) / 12.0\n g1 = 0.0\n g2 = -6.0/5.0 * (d*d + 1.0) / (d*d - 1.0)\n return mu, var, g1, g2\n\n def _rvs(self, low, high, size=None, random_state=None):\n \"\"\"An array of *size* random integers >= ``low`` and < ``high``.\"\"\"\n if np.asarray(low).size == 1 and np.asarray(high).size == 1:\n # no need to vectorize in that case\n return rng_integers(random_state, low, high, size=size)\n\n if size is not None:\n # NumPy's RandomState.randint() doesn't broadcast its arguments.\n # Use `broadcast_to()` to extend the shapes of low and high\n # up to size. Then we can use the numpy.vectorize'd\n # randint without needing to pass it a `size` argument.\n low = np.broadcast_to(low, size)\n high = np.broadcast_to(high, size)\n randint = np.vectorize(partial(rng_integers, random_state),\n otypes=[np.int_])\n return randint(low, high)\n\n def _entropy(self, low, high):\n return log(high - low)\n\n\nrandint = randint_gen(name='randint', longname='A discrete uniform '\n '(random integer)')\n\n\n# FIXME: problems sampling.\nclass zipf_gen(rv_discrete):\n r\"\"\"A Zipf discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `zipf` is:\n\n .. math::\n\n f(k, a) = \\frac{1}{\\zeta(a) k^a}\n\n for :math:`k \\ge 1`.\n\n `zipf` takes :math:`a` as shape parameter. :math:`\\zeta` is the\n Riemann zeta function (`scipy.special.zeta`)\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, a, size=None, random_state=None):\n return random_state.zipf(a, size=size)\n\n def _argcheck(self, a):\n return a > 1\n\n def _pmf(self, k, a):\n # zipf.pmf(k, a) = 1/(zeta(a) * k**a)\n Pk = 1.0 / special.zeta(a, 1) / k**a\n return Pk\n\n def _munp(self, n, a):\n return _lazywhere(\n a > n + 1, (a, n),\n lambda a, n: special.zeta(a - n, 1) / special.zeta(a, 1),\n np.inf)\n\n\nzipf = zipf_gen(a=1, name='zipf', longname='A Zipf')\n\n\nclass dlaplace_gen(rv_discrete):\n r\"\"\"A Laplacian discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n The probability mass function for `dlaplace` is:\n\n .. math::\n\n f(k) = \\tanh(a/2) \\exp(-a |k|)\n\n for integers :math:`k` and :math:`a > 0`.\n\n `dlaplace` takes :math:`a` as shape parameter.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _pmf(self, k, a):\n # dlaplace.pmf(k) = tanh(a/2) * exp(-a*abs(k))\n return tanh(a/2.0) * exp(-a * abs(k))\n\n def _cdf(self, x, a):\n k = floor(x)\n f = lambda k, a: 1.0 - exp(-a * k) / (exp(a) + 1)\n f2 = lambda k, a: exp(a * (k+1)) / (exp(a) + 1)\n return _lazywhere(k >= 0, (k, a), f=f, f2=f2)\n\n def _ppf(self, q, a):\n const = 1 + exp(a)\n vals = ceil(np.where(q < 1.0 / (1 + exp(-a)),\n log(q*const) / a - 1,\n -log((1-q) * const) / a))\n vals1 = vals - 1\n return np.where(self._cdf(vals1, a) >= q, vals1, vals)\n\n def _stats(self, a):\n ea = exp(a)\n mu2 = 2.*ea/(ea-1.)**2\n mu4 = 2.*ea*(ea**2+10.*ea+1.) / (ea-1.)**4\n return 0., mu2, 0., mu4/mu2**2 - 3.\n\n def _entropy(self, a):\n return a / sinh(a) - log(tanh(a/2.0))\n\n def _rvs(self, a, size=None, random_state=None):\n # The discrete Laplace is equivalent to the two-sided geometric\n # distribution with PMF:\n # f(k) = (1 - alpha)/(1 + alpha) * alpha^abs(k)\n # Reference:\n # https://www.sciencedirect.com/science/\n # article/abs/pii/S0378375804003519\n # Furthermore, the two-sided geometric distribution is\n # equivalent to the difference between two iid geometric \n # distributions.\n # Reference (page 179):\n # https://pdfs.semanticscholar.org/61b3/\n # b99f466815808fd0d03f5d2791eea8b541a1.pdf\n # Thus, we can leverage the following:\n # 1) alpha = e^-a\n # 2) probability_of_success = 1 - alpha (Bernoulli trial)\n probOfSuccess = -np.expm1(-np.asarray(a))\n x = random_state.geometric(probOfSuccess, size=size)\n y = random_state.geometric(probOfSuccess, size=size)\n return x - y\n\n\ndlaplace = dlaplace_gen(a=-np.inf,\n name='dlaplace', longname='A discrete Laplacian')\n\n\nclass skellam_gen(rv_discrete):\n r\"\"\"A Skellam discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n Probability distribution of the difference of two correlated or\n uncorrelated Poisson random variables.\n\n Let :math:`k_1` and :math:`k_2` be two Poisson-distributed r.v. with\n expected values :math:`\\lambda_1` and :math:`\\lambda_2`. Then,\n :math:`k_1 - k_2` follows a Skellam distribution with parameters\n :math:`\\mu_1 = \\lambda_1 - \\rho \\sqrt{\\lambda_1 \\lambda_2}` and\n :math:`\\mu_2 = \\lambda_2 - \\rho \\sqrt{\\lambda_1 \\lambda_2}`, where\n :math:`\\rho` is the correlation coefficient between :math:`k_1` and\n :math:`k_2`. If the two Poisson-distributed r.v. are independent then\n :math:`\\rho = 0`.\n\n Parameters :math:`\\mu_1` and :math:`\\mu_2` must be strictly positive.\n\n For details see: https://en.wikipedia.org/wiki/Skellam_distribution\n\n `skellam` takes :math:`\\mu_1` and :math:`\\mu_2` as shape parameters.\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, mu1, mu2, size=None, random_state=None):\n n = size\n return (random_state.poisson(mu1, n) -\n random_state.poisson(mu2, n))\n\n def _pmf(self, x, mu1, mu2):\n px = np.where(x < 0,\n _ncx2_pdf(2*mu2, 2*(1-x), 2*mu1)*2,\n _ncx2_pdf(2*mu1, 2*(1+x), 2*mu2)*2)\n # ncx2.pdf() returns nan's for extremely low probabilities\n return px\n\n def _cdf(self, x, mu1, mu2):\n x = floor(x)\n px = np.where(x < 0,\n _ncx2_cdf(2*mu2, -2*x, 2*mu1),\n 1 - _ncx2_cdf(2*mu1, 2*(x+1), 2*mu2))\n return px\n\n def _stats(self, mu1, mu2):\n mean = mu1 - mu2\n var = mu1 + mu2\n g1 = mean / sqrt((var)**3)\n g2 = 1 / var\n return mean, var, g1, g2\n\n\nskellam = skellam_gen(a=-np.inf, name=\"skellam\", longname='A Skellam')\n\n\nclass yulesimon_gen(rv_discrete):\n r\"\"\"A Yule-Simon discrete random variable.\n\n %(before_notes)s\n\n Notes\n -----\n\n The probability mass function for the `yulesimon` is:\n\n .. math::\n\n f(k) = \\alpha B(k, \\alpha+1)\n\n for :math:`k=1,2,3,...`, where :math:`\\alpha>0`.\n Here :math:`B` refers to the `scipy.special.beta` function.\n\n The sampling of random variates is based on pg 553, Section 6.3 of [1]_.\n Our notation maps to the referenced logic via :math:`\\alpha=a-1`.\n\n For details see the wikipedia entry [2]_.\n\n References\n ----------\n .. [1] Devroye, Luc. \"Non-uniform Random Variate Generation\",\n (1986) Springer, New York.\n\n .. [2] https://en.wikipedia.org/wiki/Yule-Simon_distribution\n\n %(after_notes)s\n\n %(example)s\n\n \"\"\"\n def _rvs(self, alpha, size=None, random_state=None):\n E1 = random_state.standard_exponential(size)\n E2 = random_state.standard_exponential(size)\n ans = ceil(-E1 / log1p(-exp(-E2 / alpha)))\n return ans\n\n def _pmf(self, x, alpha):\n return alpha * special.beta(x, alpha + 1)\n\n def _argcheck(self, alpha):\n return (alpha > 0)\n\n def _logpmf(self, x, alpha):\n return log(alpha) + special.betaln(x, alpha + 1)\n\n def _cdf(self, x, alpha):\n return 1 - x * special.beta(x, alpha + 1)\n\n def _sf(self, x, alpha):\n return x * special.beta(x, alpha + 1)\n\n def _logsf(self, x, alpha):\n return log(x) + special.betaln(x, alpha + 1)\n\n def _stats(self, alpha):\n mu = np.where(alpha <= 1, np.inf, alpha / (alpha - 1))\n mu2 = np.where(alpha > 2,\n alpha**2 / ((alpha - 2.0) * (alpha - 1)**2),\n np.inf)\n mu2 = np.where(alpha <= 1, np.nan, mu2)\n g1 = np.where(alpha > 3,\n sqrt(alpha - 2) * (alpha + 1)**2 / (alpha * (alpha - 3)),\n np.inf)\n g1 = np.where(alpha <= 2, np.nan, g1)\n g2 = np.where(alpha > 4,\n (alpha + 3) + (alpha**3 - 49 * alpha - 22) / (alpha *\n (alpha - 4) * (alpha - 3)), np.inf)\n g2 = np.where(alpha <= 2, np.nan, g2)\n return mu, mu2, g1, g2\n\n\nyulesimon = yulesimon_gen(name='yulesimon', a=1)\n\n\n# Collect names of classes and objects in this module.\npairs = list(globals().items())\n_distn_names, _distn_gen_names = get_distribution_names(pairs, rv_discrete)\n\n__all__ = _distn_names + _distn_gen_names\n", "path": "scipy/stats/_discrete_distns.py" } ]
diff --git a/scipy/stats/_discrete_distns.py b/scipy/stats/_discrete_distns.py index 88b06a6ebc6d..1db68980ce56 100644 --- a/scipy/stats/_discrete_distns.py +++ b/scipy/stats/_discrete_distns.py @@ -584,6 +584,8 @@ class poisson_gen(rv_discrete): for :math:`k \ge 0`. `poisson` takes :math:`\mu` as shape parameter. + When mu = 0 then at quantile k = 0, ``pmf`` method + returns `1.0`. %(after_notes)s
StackStorm__st2-4064
Packs are not listed in WEBUI for users with observer role Hi Team, In stackstorm WebUI , page **does not render** on clicking packs tab by logged in user having **observer role**. It highlighted the below error when checked in debug logs of browser `{ **"faultstring": "User \"arul\" doesn't have required permission \"pack_search\""** }` How can the permission `pack_search` be appended to observer role? Is it possible to provide `pack_search` permission for all the packs as `--- name: "pack_search_role" description: "Role which grants pack_search permission to all packs" permission_grants: - resource_uid: "packs" permission_types: - "pack_search" ` Is this a know issue? **Please look into this and advice. Also find screenshot for the same**. ![pack_search_error](https://user-images.githubusercontent.com/9215832/38351375-c0a8e8f0-38cc-11e8-866c-581e6a23d2f8.png) Thanks, Suraj S
[ { "content": "# Licensed to the StackStorm, Inc ('StackStorm') under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nModule containing resolver classes which contain permission resolving logic for different resource\ntypes.\n\"\"\"\n\nfrom __future__ import absolute_import\nimport sys\nimport logging as stdlib_logging\n\nfrom st2common import log as logging\nfrom st2common.models.db.pack import PackDB\nfrom st2common.models.db.webhook import WebhookDB\nfrom st2common.models.system.common import ResourceReference\nfrom st2common.constants.triggers import WEBHOOK_TRIGGER_TYPE\nfrom st2common.persistence.execution import ActionExecution\nfrom st2common.rbac.types import PermissionType\nfrom st2common.rbac.types import ResourceType\nfrom st2common.rbac.types import SystemRole\nfrom st2common.rbac.types import GLOBAL_PACK_PERMISSION_TYPES\nfrom st2common.services.rbac import get_roles_for_user\nfrom st2common.services.rbac import get_all_permission_grants_for_user\n\nLOG = logging.getLogger(__name__)\n\n__all__ = [\n 'RunnerPermissionsResolver',\n 'PackPermissionsResolver',\n 'SensorPermissionsResolver',\n 'ActionPermissionsResolver',\n 'ActionAliasPermissionsResolver',\n 'RulePermissionsResolver',\n 'RuleEnforcementPermissionsResolver',\n 'KeyValuePermissionsResolver',\n 'ExecutionPermissionsResolver',\n 'WebhookPermissionsResolver',\n 'TracePermissionsResolver',\n 'TriggerPermissionsResolver',\n 'StreamPermissionsResolver',\n 'InquiryPermissionsResolver',\n\n 'get_resolver_for_resource_type',\n 'get_resolver_for_permission_type'\n]\n\n# \"Read\" permission names which are granted to observer role by default\nREAD_PERMISSION_NAMES = [\n 'view',\n 'list'\n]\n\n\nclass PermissionsResolver(object):\n \"\"\"\n Base Permissions Resolver class.\n\n Permission resolver classes implement permission resolving / checking logic for a particular\n resource type.\n \"\"\"\n\n resource_type = None # Constant for the resource type this resolver refers to\n\n def user_has_permission(self, user_db, permission_type):\n \"\"\"\n Method for checking user permissions which are not tied to a particular resource.\n \"\"\"\n raise NotImplementedError()\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n \"\"\"\n Method for checking user permissions on a resource which is to be created (e.g.\n create operation).\n \"\"\"\n raise NotImplementedError()\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n \"\"\"\n Method for checking user permissions on an existing resource (e.g. get one, edit, delete\n operations).\n \"\"\"\n raise NotImplementedError()\n\n def _user_has_list_permission(self, user_db, permission_type):\n \"\"\"\n Common method for checking if a user has specific \"list\" resource permission (e.g.\n rules_list, action_list, etc.).\n \"\"\"\n assert PermissionType.get_permission_name(permission_type) == 'list'\n return self._user_has_global_permission(user_db=user_db, permission_type=permission_type)\n\n def _user_has_global_permission(self, user_db, permission_type):\n \"\"\"\n Custom method for checking if user has a particular global permission which doesn't apply\n to a specific resource but it's system-wide aka global permission.\n \"\"\"\n log_context = {\n 'user_db': user_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n permission_types = [permission_type]\n\n # Check direct grants\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n permission_types=permission_types)\n if len(permission_grants) >= 1:\n self._log('Found a direct grant', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n def _user_has_system_role_permission(self, user_db, permission_type):\n \"\"\"\n Check the user system roles and return True if user has the required permission.\n\n :rtype: ``bool``\n \"\"\"\n permission_name = PermissionType.get_permission_name(permission_type)\n\n user_role_dbs = get_roles_for_user(user_db=user_db)\n user_role_names = [role_db.name for role_db in user_role_dbs]\n\n if SystemRole.SYSTEM_ADMIN in user_role_names:\n # System admin has all the permissions\n return True\n elif SystemRole.ADMIN in user_role_names:\n # Admin has all the permissions\n return True\n elif SystemRole.OBSERVER in user_role_names and permission_name in READ_PERMISSION_NAMES:\n # Observer role has \"view\" permission on all the resources\n return True\n\n return False\n\n def _matches_permission_grant(self, resource_db, permission_grant, permission_type,\n all_permission_type):\n \"\"\"\n :rtype: ``bool``\n \"\"\"\n if permission_type in permission_grant.permission_types:\n # Direct permission grant\n return True\n elif all_permission_type in permission_grant.permission_types:\n # \"ALL\" permission grant\n return True\n\n return False\n\n def _get_all_permission_type_for_resource(self, resource_db):\n \"\"\"\n Retrieve \"ALL\" permission type for the provided resource.\n \"\"\"\n resource_type = resource_db.get_resource_type()\n permission_type = PermissionType.get_permission_type(resource_type=resource_type,\n permission_name='all')\n return permission_type\n\n def _log(self, message, extra, level=stdlib_logging.DEBUG, **kwargs):\n \"\"\"\n Custom logger method which prefix message with the class and caller method name.\n \"\"\"\n class_name = self.__class__.__name__\n method_name = sys._getframe().f_back.f_code.co_name\n message_prefix = '%s.%s: ' % (class_name, method_name)\n message = message_prefix + message\n\n LOG.log(level, message, extra=extra, **kwargs)\n\n\nclass ContentPackResourcePermissionsResolver(PermissionsResolver):\n \"\"\"\n Base permissions resolver class which contains common functionality for resources which belong\n to a pack (sensors, actions, action aliases, rules, ...).\n \"\"\"\n\n resource_type = None\n\n # A list of resource-specific permission types which grant / imply \"view\" permission type\n view_grant_permission_types = []\n\n def _user_has_resource_permission(self, user_db, pack_uid, resource_uid, permission_type):\n log_context = {\n 'user_db': user_db,\n 'pack_uid': pack_uid,\n 'resource_uid': resource_uid,\n 'resource_type': self.resource_type,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n self._log('Checking grants via system role permissions', extra=log_context)\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n view_permission_type = PermissionType.get_permission_type(resource_type=self.resource_type,\n permission_name='view')\n all_permission_type = PermissionType.get_permission_type(resource_type=self.resource_type,\n permission_name='all')\n\n if permission_type == view_permission_type:\n # Note: Some permissions such as \"create\", \"modify\", \"delete\" and \"execute\" also\n # grant / imply \"view\" permission\n permission_types = self.view_grant_permission_types[:] + [permission_type]\n elif permission_type not in all_permission_type:\n permission_types = [all_permission_type, permission_type]\n else:\n permission_types = [permission_type]\n\n # Check direct grants on the specified resource\n self._log('Checking direct grants on the specified resource', extra=log_context)\n resource_types = [self.resource_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=resource_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n if len(permission_grants) >= 1:\n self._log('Found a direct grant on the action', extra=log_context)\n return True\n\n # Check grants on the parent pack\n self._log('Checking grants on the parent resource', extra=log_context)\n resource_types = [ResourceType.PACK]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=pack_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the action parent pack', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass RunnerPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"runner_type\" resource type.\n \"\"\"\n resource_type = ResourceType.RUNNER\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.RUNNER_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n resource_uid = resource_db.get_uid()\n resource_types = [ResourceType.RUNNER]\n permission_types = [permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=resource_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a direct grant on the runner type', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass PackPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"pack\" resource type.\n \"\"\"\n\n resource_type = ResourceType.PACK\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in GLOBAL_PACK_PERMISSION_TYPES\n\n if permission_type == PermissionType.PACK_LIST:\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n else:\n return self._user_has_global_permission(user_db=user_db,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n resource_uid = resource_db.get_uid()\n resource_types = [ResourceType.PACK]\n permission_types = [permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=resource_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a direct grant on the pack', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass SensorPermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"sensor\" resource type.\n \"\"\"\n\n resource_type = ResourceType.SENSOR\n view_grant_permission_types = [\n PermissionType.SENSOR_ALL,\n PermissionType.SENSOR_MODIFY\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.SENSOR_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n sensor_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=sensor_uid,\n permission_type=permission_type)\n\n\nclass ActionPermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"action\" resource type.\n \"\"\"\n\n resource_type = ResourceType.ACTION\n view_grant_permission_types = [\n PermissionType.ACTION_ALL,\n PermissionType.ACTION_CREATE,\n PermissionType.ACTION_MODIFY,\n PermissionType.ACTION_DELETE,\n PermissionType.ACTION_EXECUTE,\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.ACTION_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.ACTION_CREATE]\n\n action_uid = resource_api.get_uid()\n pack_uid = resource_api.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=action_uid,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n action_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=action_uid,\n permission_type=permission_type)\n\n\nclass ActionAliasPermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"action_alias\" resource type.\n \"\"\"\n\n resource_type = ResourceType.ACTION_ALIAS\n view_grant_permission_types = [\n PermissionType.ACTION_ALIAS_ALL,\n PermissionType.ACTION_ALIAS_CREATE,\n PermissionType.ACTION_ALIAS_MODIFY,\n PermissionType.ACTION_ALIAS_DELETE\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.ACTION_ALIAS_LIST,\n PermissionType.ACTION_ALIAS_MATCH,\n PermissionType.ACTION_ALIAS_HELP]\n\n if permission_type == PermissionType.ACTION_ALIAS_LIST:\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n elif permission_type in [PermissionType.ACTION_ALIAS_MATCH,\n PermissionType.ACTION_ALIAS_HELP]:\n return self._user_has_global_permission(user_db=user_db,\n permission_type=permission_type)\n else:\n raise ValueError('Unsupported permission type: %s' % (permission_type))\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.ACTION_ALIAS_CREATE]\n\n action_alias_uid = resource_api.get_uid()\n pack_uid = resource_api.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=action_alias_uid,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n action_alias_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=action_alias_uid,\n permission_type=permission_type)\n\n\nclass RulePermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"rule\" resource type.\n \"\"\"\n\n resource_type = ResourceType.RULE\n view_grant_permission_types = [\n PermissionType.RULE_ALL,\n PermissionType.RULE_CREATE,\n PermissionType.RULE_MODIFY,\n PermissionType.RULE_DELETE\n ]\n\n def user_has_trigger_permission(self, user_db, trigger):\n \"\"\"\n Check if the user has access to the provided trigger.\n\n This method is to be used during rule create and update where we check if the user has the\n necessary trigger permissions.\n\n Note: Right now we only support webhook triggers.\n\n :param trigger: \"trigger\" attribute of the RuleAPI object.\n :type trigger: ``dict``\n \"\"\"\n log_context = {\n 'user_db': user_db,\n 'trigger': trigger,\n 'resolver': self.__class__.__name__\n }\n\n trigger_type = trigger['type']\n trigger_parameters = trigger.get('parameters', {})\n\n if trigger_type != WEBHOOK_TRIGGER_TYPE:\n self._log('Not a webhook trigger type, ignoring trigger permission checking',\n extra=log_context)\n return True\n\n resolver = get_resolver_for_resource_type(ResourceType.WEBHOOK)\n webhook_db = WebhookDB(name=trigger_parameters['url'])\n permission_type = PermissionType.WEBHOOK_CREATE\n result = resolver.user_has_resource_db_permission(user_db=user_db,\n resource_db=webhook_db,\n permission_type=permission_type)\n\n if result is True:\n self._log('Found a matching trigger grant', extra=log_context)\n return True\n\n self._log('No matching trigger grants found', extra=log_context)\n return False\n\n def user_has_action_permission(self, user_db, action_ref):\n \"\"\"\n Check if the user has \"execute\" permission on the provided action.\n \"\"\"\n pass\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.RULE_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.RULE_CREATE]\n\n rule_uid = resource_api.get_uid()\n pack_uid = resource_api.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=rule_uid,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n rule_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=rule_uid,\n permission_type=permission_type)\n\n\nclass RuleEnforcementPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"rule enforcement\" resource type.\n \"\"\"\n resource_type = ResourceType.RULE_ENFORCEMENT\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.RULE_ENFORCEMENT_LIST]\n permission_type = PermissionType.RULE_LIST\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n rule_spec = getattr(resource_db, 'rule', None)\n rule_uid = rule_spec.uid\n rule_id = rule_spec.id\n rule_pack = ResourceReference.get_pack(rule_spec.ref)\n\n if not rule_uid or not rule_id or not rule_pack:\n LOG.error('Rule UID or ID or PACK not present in enforcement object. ' +\n ('UID = %s, ID = %s, PACK = %s' % (rule_uid, rule_id, rule_pack)) +\n 'Cannot assess access permissions without it. Defaulting to DENY.')\n return False\n\n # TODO: Add utility methods for constructing uids from parts\n pack_db = PackDB(ref=rule_pack)\n rule_pack_uid = pack_db.get_uid()\n\n rule_permission_type = None\n if permission_type == PermissionType.RULE_ENFORCEMENT_VIEW:\n rule_permission_type = PermissionType.RULE_VIEW\n elif permission_type == PermissionType.RULE_ENFORCEMENT_LIST:\n rule_permission_type = PermissionType.RULE_LIST\n else:\n raise ValueError('Invalid permission type: %s' % (permission_type))\n\n permission_types = [PermissionType.RULE_ALL, rule_permission_type]\n\n view_permission_type = PermissionType.get_permission_type(resource_type=ResourceType.RULE,\n permission_name='view')\n\n if rule_permission_type == view_permission_type:\n permission_types = (RulePermissionsResolver.view_grant_permission_types[:] +\n [rule_permission_type])\n\n # Check grants on the pack of the rule to which enforcement belongs to\n resource_types = [ResourceType.PACK]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=rule_pack_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the enforcement rule parent pack', extra=log_context)\n return True\n\n # Check grants on the rule the enforcement belongs to\n resource_types = [ResourceType.RULE]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=rule_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the enforcement\\'s rule.', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass KeyValuePermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"key value pair\" resource type.\n \"\"\"\n\n resource_type = ResourceType.KEY_VALUE_PAIR\n\n def user_has_permission(self, user_db, permission_type):\n # TODO: We don't support assigning permissions on key value pairs yet\n return True\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n # TODO: We don't support assigning permissions on key value pairs yet\n return True\n\n\nclass ExecutionPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"execution\" resource type.\n \"\"\"\n\n resource_type = ResourceType.EXECUTION\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.EXECUTION_LIST,\n PermissionType.EXECUTION_VIEWS_FILTERS_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n action = resource_db['action']\n\n # TODO: Add utility methods for constructing uids from parts\n pack_db = PackDB(ref=action['pack'])\n\n action_uid = action['uid']\n action_pack_uid = pack_db.get_uid()\n\n # Note: \"action_execute\" also grants / implies \"execution_re_run\" and \"execution_stop\"\n if permission_type == PermissionType.EXECUTION_VIEW:\n action_permission_type = PermissionType.ACTION_VIEW\n elif permission_type in [PermissionType.EXECUTION_RE_RUN,\n PermissionType.EXECUTION_STOP]:\n action_permission_type = PermissionType.ACTION_EXECUTE\n elif permission_type == PermissionType.EXECUTION_ALL:\n action_permission_type = PermissionType.ACTION_ALL\n elif permission_type == PermissionType.EXECUTION_VIEWS_FILTERS_LIST:\n action_permission_type = PermissionType.EXECUTION_VIEWS_FILTERS_LIST\n else:\n raise ValueError('Invalid permission type: %s' % (permission_type))\n\n # Check grants on the pack of the action to which execution belongs to\n resource_types = [ResourceType.PACK]\n permission_types = [PermissionType.ACTION_ALL, action_permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=action_pack_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the execution action parent pack', extra=log_context)\n return True\n\n # Check grants on the action the execution belongs to\n resource_types = [ResourceType.ACTION]\n permission_types = [PermissionType.ACTION_ALL, action_permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=action_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the execution action', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass WebhookPermissionsResolver(PermissionsResolver):\n\n resource_type = ResourceType.WEBHOOK\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.WEBHOOK_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n webhook_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.WEBHOOK]\n permission_types = [PermissionType.WEBHOOK_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=webhook_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the webhook', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass TimerPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for timers (timers are just a special type of triggers).\n \"\"\"\n\n resource_type = ResourceType.TIMER\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.TIMER_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n timer_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.TIMER]\n permission_types = [PermissionType.TIMER_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=timer_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the timer', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass ApiKeyPermissionResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"api key\" resource type.\n \"\"\"\n\n resource_type = ResourceType.API_KEY\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.API_KEY_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.API_KEY_CREATE]\n return self._user_has_global_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n api_key_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.API_KEY]\n permission_types = [PermissionType.API_KEY_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=api_key_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the api key', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass TracePermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"trace\" resource type.\n \"\"\"\n\n resource_type = ResourceType.TRACE\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.TRACE_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n trace_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.TRACE]\n permission_types = [PermissionType.TRACE_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=trace_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the trace', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass TriggerPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for trigger and timers (timers are just a special type of triggers).\n \"\"\"\n\n resource_type = ResourceType.TRIGGER\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.TRIGGER_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n timer_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.TRIGGER]\n permission_types = [PermissionType.TRIGGER_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=timer_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the timer', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass PolicyTypePermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"policy type\" resource.\n \"\"\"\n\n resource_type = ResourceType.POLICY_TYPE\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.POLICY_TYPE_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n policy_type_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.POLICY_TYPE]\n permission_types = [PermissionType.POLICY_TYPE_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=policy_type_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the policy type', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass PolicyPermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"policy\" resource type.\n \"\"\"\n\n resource_type = ResourceType.POLICY\n view_grant_permission_types = [\n PermissionType.POLICY_ALL,\n PermissionType.POLICY_CREATE,\n PermissionType.POLICY_MODIFY,\n PermissionType.POLICY_DELETE\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.POLICY_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.POLICY_CREATE]\n\n policy_uid = resource_api.get_uid()\n pack_uid = resource_api.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=policy_uid,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n policy_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=policy_uid,\n permission_type=permission_type)\n\n\nclass StreamPermissionsResolver(PermissionsResolver):\n resource_type = ResourceType.STREAM\n view_grant_permission_types = []\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.STREAM_VIEW]\n return self._user_has_global_permission(user_db=user_db, permission_type=permission_type)\n\n\nclass InquiryPermissionsResolver(PermissionsResolver):\n resource_type = ResourceType.INQUIRY\n view_grant_permission_types = [\n PermissionType.INQUIRY_LIST,\n PermissionType.INQUIRY_VIEW,\n PermissionType.INQUIRY_RESPOND,\n PermissionType.INQUIRY_ALL\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.INQUIRY_LIST, PermissionType.INQUIRY_ALL]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n \"\"\"\n Method for checking user permissions on an existing resource (e.g. get one, edit, delete\n operations).\n\n NOTE:\n Because we're borrowing the ActionExecutionDB model, the resource_db parameter is\n effectively ignored. All other filters are passed to get_all_permission_grants_for_user.\n Since all Inquiry permission types are global, this will still correctly return a list of\n grants.\n \"\"\"\n\n permission_types = [\n PermissionType.INQUIRY_VIEW,\n PermissionType.INQUIRY_RESPOND,\n PermissionType.INQUIRY_ALL\n ]\n\n assert permission_type in permission_types\n\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check for explicit Inquiry grants first\n resource_types = [ResourceType.INQUIRY]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the inquiry', extra=log_context)\n return True\n\n # If the inquiry has a parent (is in a workflow) we want to\n # check permissions of the parent action and pack, and inherit\n # if applicable\n if resource_db.parent:\n\n # Retrieve objects for parent workflow action and pack\n wf_exc = ActionExecution.get(id=resource_db.parent)\n wf_action = wf_exc['action']\n # TODO: Add utility methods for constructing uids from parts\n wf_pack_db = PackDB(ref=wf_action['pack'])\n wf_action_uid = wf_action['uid']\n wf_action_pack_uid = wf_pack_db.get_uid()\n\n # Check grants on the pack of the workflow that the Inquiry was generated from\n resource_types = [ResourceType.PACK]\n permission_types = [PermissionType.ACTION_ALL, PermissionType.ACTION_EXECUTE]\n permission_grants = get_all_permission_grants_for_user(\n user_db=user_db,\n resource_uid=wf_action_pack_uid,\n resource_types=resource_types,\n permission_types=permission_types\n )\n\n if len(permission_grants) >= 1:\n log_context['wf_action_pack_uid'] = wf_action_pack_uid\n self._log(\n 'Found a grant on the parent pack for an inquiry workflow',\n extra=log_context\n )\n return True\n\n # Check grants on the workflow that the Inquiry was generated from\n resource_types = [ResourceType.ACTION]\n permission_types = [PermissionType.ACTION_ALL, PermissionType.ACTION_EXECUTE]\n permission_grants = get_all_permission_grants_for_user(\n user_db=user_db,\n resource_uid=wf_action_uid,\n resource_types=resource_types,\n permission_types=permission_types\n )\n\n if len(permission_grants) >= 1:\n log_context['wf_action_uid'] = wf_action_uid\n self._log('Found a grant on the inquiry workflow', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\ndef get_resolver_for_resource_type(resource_type):\n \"\"\"\n Return resolver instance for the provided resource type.\n\n :rtype: Instance of :class:`PermissionsResolver`\n \"\"\"\n if resource_type == ResourceType.RUNNER:\n resolver_cls = RunnerPermissionsResolver\n elif resource_type == ResourceType.PACK:\n resolver_cls = PackPermissionsResolver\n elif resource_type == ResourceType.SENSOR:\n resolver_cls = SensorPermissionsResolver\n elif resource_type == ResourceType.ACTION:\n resolver_cls = ActionPermissionsResolver\n elif resource_type == ResourceType.ACTION_ALIAS:\n resolver_cls = ActionAliasPermissionsResolver\n elif resource_type == ResourceType.RULE:\n resolver_cls = RulePermissionsResolver\n elif resource_type == ResourceType.EXECUTION:\n resolver_cls = ExecutionPermissionsResolver\n elif resource_type == ResourceType.KEY_VALUE_PAIR:\n resolver_cls = KeyValuePermissionsResolver\n elif resource_type == ResourceType.WEBHOOK:\n resolver_cls = WebhookPermissionsResolver\n elif resource_type == ResourceType.TIMER:\n resolver_cls = TimerPermissionsResolver\n elif resource_type == ResourceType.API_KEY:\n resolver_cls = ApiKeyPermissionResolver\n elif resource_type == ResourceType.RULE_ENFORCEMENT:\n resolver_cls = RuleEnforcementPermissionsResolver\n elif resource_type == ResourceType.TRACE:\n resolver_cls = TracePermissionsResolver\n elif resource_type == ResourceType.TRIGGER:\n resolver_cls = TriggerPermissionsResolver\n elif resource_type == ResourceType.POLICY_TYPE:\n resolver_cls = PolicyTypePermissionsResolver\n elif resource_type == ResourceType.POLICY:\n resolver_cls = PolicyPermissionsResolver\n elif resource_type == ResourceType.STREAM:\n resolver_cls = StreamPermissionsResolver\n elif resource_type == ResourceType.INQUIRY:\n resolver_cls = InquiryPermissionsResolver\n else:\n raise ValueError('Unsupported resource: %s' % (resource_type))\n\n resolver_instance = resolver_cls()\n return resolver_instance\n\n\ndef get_resolver_for_permission_type(permission_type):\n \"\"\"\n Return resolver instance for the provided permission type.\n\n :rtype: Instance of :class:`PermissionsResolver`\n \"\"\"\n resource_type = PermissionType.get_resource_type(permission_type=permission_type)\n resolver_instance = get_resolver_for_resource_type(resource_type=resource_type)\n return resolver_instance\n", "path": "st2common/st2common/rbac/resolvers.py" } ]
[ { "content": "# Licensed to the StackStorm, Inc ('StackStorm') under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nModule containing resolver classes which contain permission resolving logic for different resource\ntypes.\n\"\"\"\n\nfrom __future__ import absolute_import\nimport sys\nimport logging as stdlib_logging\n\nfrom st2common import log as logging\nfrom st2common.models.db.pack import PackDB\nfrom st2common.models.db.webhook import WebhookDB\nfrom st2common.models.system.common import ResourceReference\nfrom st2common.constants.triggers import WEBHOOK_TRIGGER_TYPE\nfrom st2common.persistence.execution import ActionExecution\nfrom st2common.rbac.types import PermissionType\nfrom st2common.rbac.types import ResourceType\nfrom st2common.rbac.types import SystemRole\nfrom st2common.rbac.types import GLOBAL_PACK_PERMISSION_TYPES\nfrom st2common.services.rbac import get_roles_for_user\nfrom st2common.services.rbac import get_all_permission_grants_for_user\n\nLOG = logging.getLogger(__name__)\n\n__all__ = [\n 'RunnerPermissionsResolver',\n 'PackPermissionsResolver',\n 'SensorPermissionsResolver',\n 'ActionPermissionsResolver',\n 'ActionAliasPermissionsResolver',\n 'RulePermissionsResolver',\n 'RuleEnforcementPermissionsResolver',\n 'KeyValuePermissionsResolver',\n 'ExecutionPermissionsResolver',\n 'WebhookPermissionsResolver',\n 'TracePermissionsResolver',\n 'TriggerPermissionsResolver',\n 'StreamPermissionsResolver',\n 'InquiryPermissionsResolver',\n\n 'get_resolver_for_resource_type',\n 'get_resolver_for_permission_type'\n]\n\n# \"Read\" permission names which are granted to observer role by default\nREAD_PERMISSION_NAMES = [\n 'view',\n 'list',\n 'search'\n]\n\n\nclass PermissionsResolver(object):\n \"\"\"\n Base Permissions Resolver class.\n\n Permission resolver classes implement permission resolving / checking logic for a particular\n resource type.\n \"\"\"\n\n resource_type = None # Constant for the resource type this resolver refers to\n\n def user_has_permission(self, user_db, permission_type):\n \"\"\"\n Method for checking user permissions which are not tied to a particular resource.\n \"\"\"\n raise NotImplementedError()\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n \"\"\"\n Method for checking user permissions on a resource which is to be created (e.g.\n create operation).\n \"\"\"\n raise NotImplementedError()\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n \"\"\"\n Method for checking user permissions on an existing resource (e.g. get one, edit, delete\n operations).\n \"\"\"\n raise NotImplementedError()\n\n def _user_has_list_permission(self, user_db, permission_type):\n \"\"\"\n Common method for checking if a user has specific \"list\" resource permission (e.g.\n rules_list, action_list, etc.).\n \"\"\"\n assert PermissionType.get_permission_name(permission_type) == 'list'\n return self._user_has_global_permission(user_db=user_db, permission_type=permission_type)\n\n def _user_has_global_permission(self, user_db, permission_type):\n \"\"\"\n Custom method for checking if user has a particular global permission which doesn't apply\n to a specific resource but it's system-wide aka global permission.\n \"\"\"\n log_context = {\n 'user_db': user_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n permission_types = [permission_type]\n\n # Check direct grants\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n permission_types=permission_types)\n if len(permission_grants) >= 1:\n self._log('Found a direct grant', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n def _user_has_system_role_permission(self, user_db, permission_type):\n \"\"\"\n Check the user system roles and return True if user has the required permission.\n\n :rtype: ``bool``\n \"\"\"\n permission_name = PermissionType.get_permission_name(permission_type)\n\n user_role_dbs = get_roles_for_user(user_db=user_db)\n user_role_names = [role_db.name for role_db in user_role_dbs]\n\n if SystemRole.SYSTEM_ADMIN in user_role_names:\n # System admin has all the permissions\n return True\n elif SystemRole.ADMIN in user_role_names:\n # Admin has all the permissions\n return True\n elif SystemRole.OBSERVER in user_role_names and permission_name in READ_PERMISSION_NAMES:\n # Observer role has \"view\" permission on all the resources\n return True\n\n return False\n\n def _matches_permission_grant(self, resource_db, permission_grant, permission_type,\n all_permission_type):\n \"\"\"\n :rtype: ``bool``\n \"\"\"\n if permission_type in permission_grant.permission_types:\n # Direct permission grant\n return True\n elif all_permission_type in permission_grant.permission_types:\n # \"ALL\" permission grant\n return True\n\n return False\n\n def _get_all_permission_type_for_resource(self, resource_db):\n \"\"\"\n Retrieve \"ALL\" permission type for the provided resource.\n \"\"\"\n resource_type = resource_db.get_resource_type()\n permission_type = PermissionType.get_permission_type(resource_type=resource_type,\n permission_name='all')\n return permission_type\n\n def _log(self, message, extra, level=stdlib_logging.DEBUG, **kwargs):\n \"\"\"\n Custom logger method which prefix message with the class and caller method name.\n \"\"\"\n class_name = self.__class__.__name__\n method_name = sys._getframe().f_back.f_code.co_name\n message_prefix = '%s.%s: ' % (class_name, method_name)\n message = message_prefix + message\n\n LOG.log(level, message, extra=extra, **kwargs)\n\n\nclass ContentPackResourcePermissionsResolver(PermissionsResolver):\n \"\"\"\n Base permissions resolver class which contains common functionality for resources which belong\n to a pack (sensors, actions, action aliases, rules, ...).\n \"\"\"\n\n resource_type = None\n\n # A list of resource-specific permission types which grant / imply \"view\" permission type\n view_grant_permission_types = []\n\n def _user_has_resource_permission(self, user_db, pack_uid, resource_uid, permission_type):\n log_context = {\n 'user_db': user_db,\n 'pack_uid': pack_uid,\n 'resource_uid': resource_uid,\n 'resource_type': self.resource_type,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n self._log('Checking grants via system role permissions', extra=log_context)\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n view_permission_type = PermissionType.get_permission_type(resource_type=self.resource_type,\n permission_name='view')\n all_permission_type = PermissionType.get_permission_type(resource_type=self.resource_type,\n permission_name='all')\n\n if permission_type == view_permission_type:\n # Note: Some permissions such as \"create\", \"modify\", \"delete\" and \"execute\" also\n # grant / imply \"view\" permission\n permission_types = self.view_grant_permission_types[:] + [permission_type]\n elif permission_type not in all_permission_type:\n permission_types = [all_permission_type, permission_type]\n else:\n permission_types = [permission_type]\n\n # Check direct grants on the specified resource\n self._log('Checking direct grants on the specified resource', extra=log_context)\n resource_types = [self.resource_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=resource_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n if len(permission_grants) >= 1:\n self._log('Found a direct grant on the action', extra=log_context)\n return True\n\n # Check grants on the parent pack\n self._log('Checking grants on the parent resource', extra=log_context)\n resource_types = [ResourceType.PACK]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=pack_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the action parent pack', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass RunnerPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"runner_type\" resource type.\n \"\"\"\n resource_type = ResourceType.RUNNER\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.RUNNER_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n resource_uid = resource_db.get_uid()\n resource_types = [ResourceType.RUNNER]\n permission_types = [permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=resource_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a direct grant on the runner type', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass PackPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"pack\" resource type.\n \"\"\"\n\n resource_type = ResourceType.PACK\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in GLOBAL_PACK_PERMISSION_TYPES\n\n if permission_type == PermissionType.PACK_LIST:\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n else:\n return self._user_has_global_permission(user_db=user_db,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n resource_uid = resource_db.get_uid()\n resource_types = [ResourceType.PACK]\n permission_types = [permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=resource_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a direct grant on the pack', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass SensorPermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"sensor\" resource type.\n \"\"\"\n\n resource_type = ResourceType.SENSOR\n view_grant_permission_types = [\n PermissionType.SENSOR_ALL,\n PermissionType.SENSOR_MODIFY\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.SENSOR_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n sensor_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=sensor_uid,\n permission_type=permission_type)\n\n\nclass ActionPermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"action\" resource type.\n \"\"\"\n\n resource_type = ResourceType.ACTION\n view_grant_permission_types = [\n PermissionType.ACTION_ALL,\n PermissionType.ACTION_CREATE,\n PermissionType.ACTION_MODIFY,\n PermissionType.ACTION_DELETE,\n PermissionType.ACTION_EXECUTE,\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.ACTION_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.ACTION_CREATE]\n\n action_uid = resource_api.get_uid()\n pack_uid = resource_api.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=action_uid,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n action_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=action_uid,\n permission_type=permission_type)\n\n\nclass ActionAliasPermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"action_alias\" resource type.\n \"\"\"\n\n resource_type = ResourceType.ACTION_ALIAS\n view_grant_permission_types = [\n PermissionType.ACTION_ALIAS_ALL,\n PermissionType.ACTION_ALIAS_CREATE,\n PermissionType.ACTION_ALIAS_MODIFY,\n PermissionType.ACTION_ALIAS_DELETE\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.ACTION_ALIAS_LIST,\n PermissionType.ACTION_ALIAS_MATCH,\n PermissionType.ACTION_ALIAS_HELP]\n\n if permission_type == PermissionType.ACTION_ALIAS_LIST:\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n elif permission_type in [PermissionType.ACTION_ALIAS_MATCH,\n PermissionType.ACTION_ALIAS_HELP]:\n return self._user_has_global_permission(user_db=user_db,\n permission_type=permission_type)\n else:\n raise ValueError('Unsupported permission type: %s' % (permission_type))\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.ACTION_ALIAS_CREATE]\n\n action_alias_uid = resource_api.get_uid()\n pack_uid = resource_api.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=action_alias_uid,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n action_alias_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=action_alias_uid,\n permission_type=permission_type)\n\n\nclass RulePermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"rule\" resource type.\n \"\"\"\n\n resource_type = ResourceType.RULE\n view_grant_permission_types = [\n PermissionType.RULE_ALL,\n PermissionType.RULE_CREATE,\n PermissionType.RULE_MODIFY,\n PermissionType.RULE_DELETE\n ]\n\n def user_has_trigger_permission(self, user_db, trigger):\n \"\"\"\n Check if the user has access to the provided trigger.\n\n This method is to be used during rule create and update where we check if the user has the\n necessary trigger permissions.\n\n Note: Right now we only support webhook triggers.\n\n :param trigger: \"trigger\" attribute of the RuleAPI object.\n :type trigger: ``dict``\n \"\"\"\n log_context = {\n 'user_db': user_db,\n 'trigger': trigger,\n 'resolver': self.__class__.__name__\n }\n\n trigger_type = trigger['type']\n trigger_parameters = trigger.get('parameters', {})\n\n if trigger_type != WEBHOOK_TRIGGER_TYPE:\n self._log('Not a webhook trigger type, ignoring trigger permission checking',\n extra=log_context)\n return True\n\n resolver = get_resolver_for_resource_type(ResourceType.WEBHOOK)\n webhook_db = WebhookDB(name=trigger_parameters['url'])\n permission_type = PermissionType.WEBHOOK_CREATE\n result = resolver.user_has_resource_db_permission(user_db=user_db,\n resource_db=webhook_db,\n permission_type=permission_type)\n\n if result is True:\n self._log('Found a matching trigger grant', extra=log_context)\n return True\n\n self._log('No matching trigger grants found', extra=log_context)\n return False\n\n def user_has_action_permission(self, user_db, action_ref):\n \"\"\"\n Check if the user has \"execute\" permission on the provided action.\n \"\"\"\n pass\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.RULE_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.RULE_CREATE]\n\n rule_uid = resource_api.get_uid()\n pack_uid = resource_api.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=rule_uid,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n rule_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=rule_uid,\n permission_type=permission_type)\n\n\nclass RuleEnforcementPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"rule enforcement\" resource type.\n \"\"\"\n resource_type = ResourceType.RULE_ENFORCEMENT\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.RULE_ENFORCEMENT_LIST]\n permission_type = PermissionType.RULE_LIST\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n rule_spec = getattr(resource_db, 'rule', None)\n rule_uid = rule_spec.uid\n rule_id = rule_spec.id\n rule_pack = ResourceReference.get_pack(rule_spec.ref)\n\n if not rule_uid or not rule_id or not rule_pack:\n LOG.error('Rule UID or ID or PACK not present in enforcement object. ' +\n ('UID = %s, ID = %s, PACK = %s' % (rule_uid, rule_id, rule_pack)) +\n 'Cannot assess access permissions without it. Defaulting to DENY.')\n return False\n\n # TODO: Add utility methods for constructing uids from parts\n pack_db = PackDB(ref=rule_pack)\n rule_pack_uid = pack_db.get_uid()\n\n rule_permission_type = None\n if permission_type == PermissionType.RULE_ENFORCEMENT_VIEW:\n rule_permission_type = PermissionType.RULE_VIEW\n elif permission_type == PermissionType.RULE_ENFORCEMENT_LIST:\n rule_permission_type = PermissionType.RULE_LIST\n else:\n raise ValueError('Invalid permission type: %s' % (permission_type))\n\n permission_types = [PermissionType.RULE_ALL, rule_permission_type]\n\n view_permission_type = PermissionType.get_permission_type(resource_type=ResourceType.RULE,\n permission_name='view')\n\n if rule_permission_type == view_permission_type:\n permission_types = (RulePermissionsResolver.view_grant_permission_types[:] +\n [rule_permission_type])\n\n # Check grants on the pack of the rule to which enforcement belongs to\n resource_types = [ResourceType.PACK]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=rule_pack_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the enforcement rule parent pack', extra=log_context)\n return True\n\n # Check grants on the rule the enforcement belongs to\n resource_types = [ResourceType.RULE]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=rule_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the enforcement\\'s rule.', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass KeyValuePermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"key value pair\" resource type.\n \"\"\"\n\n resource_type = ResourceType.KEY_VALUE_PAIR\n\n def user_has_permission(self, user_db, permission_type):\n # TODO: We don't support assigning permissions on key value pairs yet\n return True\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n # TODO: We don't support assigning permissions on key value pairs yet\n return True\n\n\nclass ExecutionPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"execution\" resource type.\n \"\"\"\n\n resource_type = ResourceType.EXECUTION\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.EXECUTION_LIST,\n PermissionType.EXECUTION_VIEWS_FILTERS_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n action = resource_db['action']\n\n # TODO: Add utility methods for constructing uids from parts\n pack_db = PackDB(ref=action['pack'])\n\n action_uid = action['uid']\n action_pack_uid = pack_db.get_uid()\n\n # Note: \"action_execute\" also grants / implies \"execution_re_run\" and \"execution_stop\"\n if permission_type == PermissionType.EXECUTION_VIEW:\n action_permission_type = PermissionType.ACTION_VIEW\n elif permission_type in [PermissionType.EXECUTION_RE_RUN,\n PermissionType.EXECUTION_STOP]:\n action_permission_type = PermissionType.ACTION_EXECUTE\n elif permission_type == PermissionType.EXECUTION_ALL:\n action_permission_type = PermissionType.ACTION_ALL\n elif permission_type == PermissionType.EXECUTION_VIEWS_FILTERS_LIST:\n action_permission_type = PermissionType.EXECUTION_VIEWS_FILTERS_LIST\n else:\n raise ValueError('Invalid permission type: %s' % (permission_type))\n\n # Check grants on the pack of the action to which execution belongs to\n resource_types = [ResourceType.PACK]\n permission_types = [PermissionType.ACTION_ALL, action_permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=action_pack_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the execution action parent pack', extra=log_context)\n return True\n\n # Check grants on the action the execution belongs to\n resource_types = [ResourceType.ACTION]\n permission_types = [PermissionType.ACTION_ALL, action_permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=action_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the execution action', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass WebhookPermissionsResolver(PermissionsResolver):\n\n resource_type = ResourceType.WEBHOOK\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.WEBHOOK_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n webhook_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.WEBHOOK]\n permission_types = [PermissionType.WEBHOOK_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=webhook_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the webhook', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass TimerPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for timers (timers are just a special type of triggers).\n \"\"\"\n\n resource_type = ResourceType.TIMER\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.TIMER_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n timer_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.TIMER]\n permission_types = [PermissionType.TIMER_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=timer_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the timer', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass ApiKeyPermissionResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"api key\" resource type.\n \"\"\"\n\n resource_type = ResourceType.API_KEY\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.API_KEY_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.API_KEY_CREATE]\n return self._user_has_global_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n api_key_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.API_KEY]\n permission_types = [PermissionType.API_KEY_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=api_key_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the api key', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass TracePermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"trace\" resource type.\n \"\"\"\n\n resource_type = ResourceType.TRACE\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.TRACE_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n trace_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.TRACE]\n permission_types = [PermissionType.TRACE_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=trace_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the trace', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass TriggerPermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for trigger and timers (timers are just a special type of triggers).\n \"\"\"\n\n resource_type = ResourceType.TRIGGER\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.TRIGGER_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n timer_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.TRIGGER]\n permission_types = [PermissionType.TRIGGER_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=timer_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the timer', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass PolicyTypePermissionsResolver(PermissionsResolver):\n \"\"\"\n Permission resolver for \"policy type\" resource.\n \"\"\"\n\n resource_type = ResourceType.POLICY_TYPE\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.POLICY_TYPE_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check custom roles\n policy_type_uid = resource_db.get_uid()\n\n # Check direct grants on the webhook\n resource_types = [ResourceType.POLICY_TYPE]\n permission_types = [PermissionType.POLICY_TYPE_ALL, permission_type]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_uid=policy_type_uid,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the policy type', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\nclass PolicyPermissionsResolver(ContentPackResourcePermissionsResolver):\n \"\"\"\n Permission resolver for \"policy\" resource type.\n \"\"\"\n\n resource_type = ResourceType.POLICY\n view_grant_permission_types = [\n PermissionType.POLICY_ALL,\n PermissionType.POLICY_CREATE,\n PermissionType.POLICY_MODIFY,\n PermissionType.POLICY_DELETE\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.POLICY_LIST]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_api_permission(self, user_db, resource_api, permission_type):\n assert permission_type in [PermissionType.POLICY_CREATE]\n\n policy_uid = resource_api.get_uid()\n pack_uid = resource_api.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=policy_uid,\n permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n policy_uid = resource_db.get_uid()\n pack_uid = resource_db.get_pack_uid()\n return self._user_has_resource_permission(user_db=user_db, pack_uid=pack_uid,\n resource_uid=policy_uid,\n permission_type=permission_type)\n\n\nclass StreamPermissionsResolver(PermissionsResolver):\n resource_type = ResourceType.STREAM\n view_grant_permission_types = []\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.STREAM_VIEW]\n return self._user_has_global_permission(user_db=user_db, permission_type=permission_type)\n\n\nclass InquiryPermissionsResolver(PermissionsResolver):\n resource_type = ResourceType.INQUIRY\n view_grant_permission_types = [\n PermissionType.INQUIRY_LIST,\n PermissionType.INQUIRY_VIEW,\n PermissionType.INQUIRY_RESPOND,\n PermissionType.INQUIRY_ALL\n ]\n\n def user_has_permission(self, user_db, permission_type):\n assert permission_type in [PermissionType.INQUIRY_LIST, PermissionType.INQUIRY_ALL]\n return self._user_has_list_permission(user_db=user_db, permission_type=permission_type)\n\n def user_has_resource_db_permission(self, user_db, resource_db, permission_type):\n \"\"\"\n Method for checking user permissions on an existing resource (e.g. get one, edit, delete\n operations).\n\n NOTE:\n Because we're borrowing the ActionExecutionDB model, the resource_db parameter is\n effectively ignored. All other filters are passed to get_all_permission_grants_for_user.\n Since all Inquiry permission types are global, this will still correctly return a list of\n grants.\n \"\"\"\n\n permission_types = [\n PermissionType.INQUIRY_VIEW,\n PermissionType.INQUIRY_RESPOND,\n PermissionType.INQUIRY_ALL\n ]\n\n assert permission_type in permission_types\n\n log_context = {\n 'user_db': user_db,\n 'resource_db': resource_db,\n 'permission_type': permission_type,\n 'resolver': self.__class__.__name__\n }\n self._log('Checking user resource permissions', extra=log_context)\n\n # First check the system role permissions\n has_system_role_permission = self._user_has_system_role_permission(\n user_db=user_db, permission_type=permission_type)\n\n if has_system_role_permission:\n self._log('Found a matching grant via system role', extra=log_context)\n return True\n\n # Check for explicit Inquiry grants first\n resource_types = [ResourceType.INQUIRY]\n permission_grants = get_all_permission_grants_for_user(user_db=user_db,\n resource_types=resource_types,\n permission_types=permission_types)\n\n if len(permission_grants) >= 1:\n self._log('Found a grant on the inquiry', extra=log_context)\n return True\n\n # If the inquiry has a parent (is in a workflow) we want to\n # check permissions of the parent action and pack, and inherit\n # if applicable\n if resource_db.parent:\n\n # Retrieve objects for parent workflow action and pack\n wf_exc = ActionExecution.get(id=resource_db.parent)\n wf_action = wf_exc['action']\n # TODO: Add utility methods for constructing uids from parts\n wf_pack_db = PackDB(ref=wf_action['pack'])\n wf_action_uid = wf_action['uid']\n wf_action_pack_uid = wf_pack_db.get_uid()\n\n # Check grants on the pack of the workflow that the Inquiry was generated from\n resource_types = [ResourceType.PACK]\n permission_types = [PermissionType.ACTION_ALL, PermissionType.ACTION_EXECUTE]\n permission_grants = get_all_permission_grants_for_user(\n user_db=user_db,\n resource_uid=wf_action_pack_uid,\n resource_types=resource_types,\n permission_types=permission_types\n )\n\n if len(permission_grants) >= 1:\n log_context['wf_action_pack_uid'] = wf_action_pack_uid\n self._log(\n 'Found a grant on the parent pack for an inquiry workflow',\n extra=log_context\n )\n return True\n\n # Check grants on the workflow that the Inquiry was generated from\n resource_types = [ResourceType.ACTION]\n permission_types = [PermissionType.ACTION_ALL, PermissionType.ACTION_EXECUTE]\n permission_grants = get_all_permission_grants_for_user(\n user_db=user_db,\n resource_uid=wf_action_uid,\n resource_types=resource_types,\n permission_types=permission_types\n )\n\n if len(permission_grants) >= 1:\n log_context['wf_action_uid'] = wf_action_uid\n self._log('Found a grant on the inquiry workflow', extra=log_context)\n return True\n\n self._log('No matching grants found', extra=log_context)\n return False\n\n\ndef get_resolver_for_resource_type(resource_type):\n \"\"\"\n Return resolver instance for the provided resource type.\n\n :rtype: Instance of :class:`PermissionsResolver`\n \"\"\"\n if resource_type == ResourceType.RUNNER:\n resolver_cls = RunnerPermissionsResolver\n elif resource_type == ResourceType.PACK:\n resolver_cls = PackPermissionsResolver\n elif resource_type == ResourceType.SENSOR:\n resolver_cls = SensorPermissionsResolver\n elif resource_type == ResourceType.ACTION:\n resolver_cls = ActionPermissionsResolver\n elif resource_type == ResourceType.ACTION_ALIAS:\n resolver_cls = ActionAliasPermissionsResolver\n elif resource_type == ResourceType.RULE:\n resolver_cls = RulePermissionsResolver\n elif resource_type == ResourceType.EXECUTION:\n resolver_cls = ExecutionPermissionsResolver\n elif resource_type == ResourceType.KEY_VALUE_PAIR:\n resolver_cls = KeyValuePermissionsResolver\n elif resource_type == ResourceType.WEBHOOK:\n resolver_cls = WebhookPermissionsResolver\n elif resource_type == ResourceType.TIMER:\n resolver_cls = TimerPermissionsResolver\n elif resource_type == ResourceType.API_KEY:\n resolver_cls = ApiKeyPermissionResolver\n elif resource_type == ResourceType.RULE_ENFORCEMENT:\n resolver_cls = RuleEnforcementPermissionsResolver\n elif resource_type == ResourceType.TRACE:\n resolver_cls = TracePermissionsResolver\n elif resource_type == ResourceType.TRIGGER:\n resolver_cls = TriggerPermissionsResolver\n elif resource_type == ResourceType.POLICY_TYPE:\n resolver_cls = PolicyTypePermissionsResolver\n elif resource_type == ResourceType.POLICY:\n resolver_cls = PolicyPermissionsResolver\n elif resource_type == ResourceType.STREAM:\n resolver_cls = StreamPermissionsResolver\n elif resource_type == ResourceType.INQUIRY:\n resolver_cls = InquiryPermissionsResolver\n else:\n raise ValueError('Unsupported resource: %s' % (resource_type))\n\n resolver_instance = resolver_cls()\n return resolver_instance\n\n\ndef get_resolver_for_permission_type(permission_type):\n \"\"\"\n Return resolver instance for the provided permission type.\n\n :rtype: Instance of :class:`PermissionsResolver`\n \"\"\"\n resource_type = PermissionType.get_resource_type(permission_type=permission_type)\n resolver_instance = get_resolver_for_resource_type(resource_type=resource_type)\n return resolver_instance\n", "path": "st2common/st2common/rbac/resolvers.py" } ]
diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 5f83d9ee74..6e351a4f36 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -103,6 +103,9 @@ Fixed expressions and default values. (bug fix) #4050 #4050 Reported by @rakeshrm. +* Make sure ``observer`` system role also grants ``pack_search`` permission. (bug fix) #4063 #4064 + + Reported by @SURAJTHEGREAT. 2.6.0 - January 19, 2018 ------------------------ diff --git a/st2api/tests/unit/controllers/v1/test_packs_rbac.py b/st2api/tests/unit/controllers/v1/test_packs_rbac.py index 08805fa125..3227337266 100644 --- a/st2api/tests/unit/controllers/v1/test_packs_rbac.py +++ b/st2api/tests/unit/controllers/v1/test_packs_rbac.py @@ -17,8 +17,10 @@ import six from st2common.router import Response +from st2common.services import packs as pack_service from st2api.controllers.v1.actionexecutions import ActionExecutionsControllerMixin from tests.base import APIControllerWithRBACTestCase +from tests.unit.controllers.v1.test_packs import PACK_INDEX http_client = six.moves.http_client @@ -90,3 +92,24 @@ def test_get_all_limit_minus_one(self): resp = self.app.get('/v1/packs?limit=-1') self.assertEqual(resp.status_code, http_client.OK) + + @mock.patch.object(pack_service, 'fetch_pack_index', + mock.MagicMock(return_value=(PACK_INDEX, {}))) + def test_pack_search(self): + user_db = self.users['no_permissions'] + self.use_user(user_db) + + data = {'query': 'test'} + resp = self.app.post_json('/v1/packs/index/search', data, expect_errors=True) + + expected_msg = 'User \"no_permissions\" doesn\'t have required permission \"pack_search\"' + self.assertEqual(resp.status_code, http_client.FORBIDDEN) + self.assertEqual(resp.json['faultstring'], expected_msg) + + # Observer role also grants pack_search permission + user_db = self.users['observer'] + self.use_user(user_db) + + data = {'query': 'test'} + resp = self.app.post_json('/v1/packs/index/search', data) + self.assertEqual(resp.status_code, http_client.OK) diff --git a/st2common/st2common/rbac/resolvers.py b/st2common/st2common/rbac/resolvers.py index 6e76935791..f5fafd8da1 100644 --- a/st2common/st2common/rbac/resolvers.py +++ b/st2common/st2common/rbac/resolvers.py @@ -60,7 +60,8 @@ # "Read" permission names which are granted to observer role by default READ_PERMISSION_NAMES = [ 'view', - 'list' + 'list', + 'search' ]
imAsparky__django-cookiecutter-16
[FEAT]: Add Tox, Pytest and test config **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context or screenshots about the feature request here.
[ { "content": "\"\"\"Django Cookiecutter Sphinx build configuration file.\"\"\"\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath('.'))\n\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'Django Cookiecutter'\ncopyright = '2021, Mark Sevelj'\nauthor = 'Mark Sevelj'\n\n# The full version, including alpha/beta/rc tags\nrelease = __version__\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"myst_parser\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"sphinx_copybutton\",\n \"sphinx_inline_tabs\",\n \"sphinx.ext.todo\",\n\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\"_build\", \"build\", \"Thumbs.db\", \".DS_Store\"]\n\npygments_style = \"monokai\"\npygments_dark_style = \"monokai\"\n\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'furo'\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# sphinx-copybutton is a lightweight code-block copy button\n# config options are here https://sphinx-copybutton.readthedocs.io/en/latest/\n# This config removes Python Repl + continuation, Bash line prefixes,\n# ipython and qtconsole + continuation, jupyter-console + continuation and preceding line numbers\ncopybutton_prompt_text = (\n r\"^\\d|^.\\d|^\\d\\d|^\\d\\d\\d|>>> |\\.\\.\\. |\\$ |In \\[\\d*\\]: | {2,5}\\.\\.\\.: | {5,8}: \"\n)\ncopybutton_prompt_is_regexp = True\n\n# datalad download-url http://www.tldp.org/LDP/Bash-Beginners-Guide/Bash-Beginners-Guide.pdf \\\n# --dataset . \\\n# -m \"add beginners guide on bash\" \\\n# -O books/bash_guide.pdf\n# is correctly pasted with the following setting\ncopybutton_line_continuation_character = \"\\\\\"\n", "path": "docs/source/conf.py" } ]
[ { "content": "\"\"\"Django Cookiecutter Sphinx build configuration file.\"\"\"\n\n# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath('.'))\n\n__version__ = \"0.4.0\"\n\n# -- Project information -----------------------------------------------------\n\nproject = 'Django Cookiecutter'\ncopyright = '2021, Mark Sevelj'\nauthor = 'Mark Sevelj'\n\n# The full version, including alpha/beta/rc tags\nrelease = __version__\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"myst_parser\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"sphinx_copybutton\",\n \"sphinx_inline_tabs\",\n \"sphinx.ext.todo\",\n\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\"_build\", \"build\", \"Thumbs.db\", \".DS_Store\"]\n\npygments_style = \"monokai\"\npygments_dark_style = \"monokai\"\n\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'furo'\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# sphinx-copybutton is a lightweight code-block copy button\n# config options are here https://sphinx-copybutton.readthedocs.io/en/latest/\n# This config removes Python Repl + continuation, Bash line prefixes,\n# ipython and qtconsole + continuation, jupyter-console + continuation and preceding line numbers\ncopybutton_prompt_text = (\n r\"^\\d|^.\\d|^\\d\\d|^\\d\\d\\d|>>> |\\.\\.\\. |\\$ |In \\[\\d*\\]: | {2,5}\\.\\.\\.: | {5,8}: \"\n)\ncopybutton_prompt_is_regexp = True\n\n# datalad download-url http://www.tldp.org/LDP/Bash-Beginners-Guide/Bash-Beginners-Guide.pdf \\\n# --dataset . \\\n# -m \"add beginners guide on bash\" \\\n# -O books/bash_guide.pdf\n# is correctly pasted with the following setting\ncopybutton_line_continuation_character = \"\\\\\"\n", "path": "docs/source/conf.py" } ]
diff --git a/.github/workflows/test_contribution.yaml b/.github/workflows/test_contribution.yaml new file mode 100644 index 00000000..34c9e868 --- /dev/null +++ b/.github/workflows/test_contribution.yaml @@ -0,0 +1,36 @@ +name: Test Contributions + +on: + pull_request: + branches: [main] + + # push: + # branches: [main] + + workflow_dispatch: + +jobs: + test_contribs: + strategy: + matrix: + python-version: ["3.6", "3.7", "3.8", "3.9"] + os: [macos-latest, ubuntu-latest, windows-latest] + + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install tox tox-gh-actions + + - name: Test with tox + run: tox diff --git a/docs/source/requirements.txt b/docs/requirements.txt similarity index 100% rename from docs/source/requirements.txt rename to docs/requirements.txt diff --git a/docs/source/conf.py b/docs/source/conf.py index 7ad9709a..5a30952f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -16,7 +16,7 @@ import sys sys.path.insert(0, os.path.abspath('.')) - +__version__ = "0.4.0" # -- Project information ----------------------------------------------------- diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 00000000..39baf331 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,2 @@ +[pytest] +testpaths = tests/ diff --git a/requirements_dev.txt b/requirements_dev.txt index 2ecd9682..92218998 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,2 +1,5 @@ Django==3.2.7 pre-commit==2.15.0 +pytest==6.2.5 +pytest-cookies==0.6.1 +tox==3.24.4 diff --git a/tests/test_fake.py b/tests/test_fake.py new file mode 100644 index 00000000..6078684d --- /dev/null +++ b/tests/test_fake.py @@ -0,0 +1,4 @@ +def test_fake_test(): + """A Fake test""" + + assert 3 == 3 diff --git a/tox.ini b/tox.ini new file mode 100644 index 00000000..fcdaabe9 --- /dev/null +++ b/tox.ini @@ -0,0 +1,39 @@ +[tox] +skipsdist = true +skip_missing_interpreters = true +envlist = + py36 + py37 + py38 + py39 + py38 pypy + py39-docs + +[testenv:docs] +basepython=python +changedir=docs/source +deps= -r{toxinidir}/docs/requirements.txt +commands= + sphinx-build -b html -d {envtmpdir}/doctrees . {envtmpdir}/html + +[gh-actions] +python = + pypy-3.8: pypy3 + 3.9: py39 + 3.8: py38 + 3.7: py37 + 3.6: py36 + +[gh-actions:env] +PLATFORM = + ubuntu-latest: linux + macos-latest: macos + windows-latest: windows + +[testenv] +setenv = + PYTHONPATH = {toxinidir} +deps = + -r{toxinidir}/requirements_dev.txt +commands = + pytest -v {posargs:tests}
pyca__cryptography-1244
RSAPublicNumbers should have a nicer repr Instead of: ``` <cryptography.hazmat.primitives.asymmetric.rsa.RSAPublicNumbers object at 0x106547290> ``` Something like: ``` <RSAPublicNumbers(e=65537, n=<some big product of primes>)> ``` would be great
[ { "content": "# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import absolute_import, division, print_function\n\nimport warnings\n\nimport six\n\nfrom cryptography import utils\nfrom cryptography.exceptions import UnsupportedAlgorithm, _Reasons\nfrom cryptography.hazmat.backends.interfaces import RSABackend\n\n\ndef generate_private_key(public_exponent, key_size, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n _verify_rsa_parameters(public_exponent, key_size)\n return backend.generate_rsa_private_key(public_exponent, key_size)\n\n\ndef _verify_rsa_parameters(public_exponent, key_size):\n if public_exponent < 3:\n raise ValueError(\"public_exponent must be >= 3.\")\n\n if public_exponent & 1 == 0:\n raise ValueError(\"public_exponent must be odd.\")\n\n if key_size < 512:\n raise ValueError(\"key_size must be at least 512-bits.\")\n\n\ndef _check_private_key_components(p, q, private_exponent, dmp1, dmq1, iqmp,\n public_exponent, modulus):\n if modulus < 3:\n raise ValueError(\"modulus must be >= 3.\")\n\n if p >= modulus:\n raise ValueError(\"p must be < modulus.\")\n\n if q >= modulus:\n raise ValueError(\"q must be < modulus.\")\n\n if dmp1 >= modulus:\n raise ValueError(\"dmp1 must be < modulus.\")\n\n if dmq1 >= modulus:\n raise ValueError(\"dmq1 must be < modulus.\")\n\n if iqmp >= modulus:\n raise ValueError(\"iqmp must be < modulus.\")\n\n if private_exponent >= modulus:\n raise ValueError(\"private_exponent must be < modulus.\")\n\n if public_exponent < 3 or public_exponent >= modulus:\n raise ValueError(\"public_exponent must be >= 3 and < modulus.\")\n\n if public_exponent & 1 == 0:\n raise ValueError(\"public_exponent must be odd.\")\n\n if dmp1 & 1 == 0:\n raise ValueError(\"dmp1 must be odd.\")\n\n if dmq1 & 1 == 0:\n raise ValueError(\"dmq1 must be odd.\")\n\n if p * q != modulus:\n raise ValueError(\"p*q must equal modulus.\")\n\n\ndef _check_public_key_components(e, n):\n if n < 3:\n raise ValueError(\"n must be >= 3.\")\n\n if e < 3 or e >= n:\n raise ValueError(\"e must be >= 3 and < n.\")\n\n if e & 1 == 0:\n raise ValueError(\"e must be odd.\")\n\n\nclass RSAPublicKey(object):\n def __init__(self, public_exponent, modulus):\n warnings.warn(\n \"The RSAPublicKey class is deprecated and will be removed in a \"\n \"future version.\",\n utils.DeprecatedIn05,\n stacklevel=2\n )\n if (\n not isinstance(public_exponent, six.integer_types) or\n not isinstance(modulus, six.integer_types)\n ):\n raise TypeError(\"RSAPublicKey arguments must be integers.\")\n\n _check_public_key_components(public_exponent, modulus)\n\n self._public_exponent = public_exponent\n self._modulus = modulus\n\n def verifier(self, signature, padding, algorithm, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n return backend.create_rsa_verification_ctx(self, signature, padding,\n algorithm)\n\n def encrypt(self, plaintext, padding, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n return backend.encrypt_rsa(self, plaintext, padding)\n\n @property\n def key_size(self):\n return utils.bit_length(self.modulus)\n\n @property\n def public_exponent(self):\n return self._public_exponent\n\n @property\n def modulus(self):\n return self._modulus\n\n @property\n def e(self):\n return self.public_exponent\n\n @property\n def n(self):\n return self.modulus\n\n\ndef _modinv(e, m):\n \"\"\"\n Modular Multiplicative Inverse. Returns x such that: (x*e) mod m == 1\n \"\"\"\n x1, y1, x2, y2 = 1, 0, 0, 1\n a, b = e, m\n while b > 0:\n q, r = divmod(a, b)\n xn, yn = x1 - q * x2, y1 - q * y2\n a, b, x1, y1, x2, y2 = b, r, x2, y2, xn, yn\n return x1 % m\n\n\ndef rsa_crt_iqmp(p, q):\n \"\"\"\n Compute the CRT (q ** -1) % p value from RSA primes p and q.\n \"\"\"\n return _modinv(q, p)\n\n\ndef rsa_crt_dmp1(private_exponent, p):\n \"\"\"\n Compute the CRT private_exponent % (p - 1) value from the RSA\n private_exponent and p.\n \"\"\"\n return private_exponent % (p - 1)\n\n\ndef rsa_crt_dmq1(private_exponent, q):\n \"\"\"\n Compute the CRT private_exponent % (q - 1) value from the RSA\n private_exponent and q.\n \"\"\"\n return private_exponent % (q - 1)\n\n\nclass RSAPrivateKey(object):\n def __init__(self, p, q, private_exponent, dmp1, dmq1, iqmp,\n public_exponent, modulus):\n warnings.warn(\n \"The RSAPrivateKey class is deprecated and will be removed in a \"\n \"future version.\",\n utils.DeprecatedIn05,\n stacklevel=2\n )\n if (\n not isinstance(p, six.integer_types) or\n not isinstance(q, six.integer_types) or\n not isinstance(dmp1, six.integer_types) or\n not isinstance(dmq1, six.integer_types) or\n not isinstance(iqmp, six.integer_types) or\n not isinstance(private_exponent, six.integer_types) or\n not isinstance(public_exponent, six.integer_types) or\n not isinstance(modulus, six.integer_types)\n ):\n raise TypeError(\"RSAPrivateKey arguments must be integers.\")\n\n _check_private_key_components(p, q, private_exponent, dmp1, dmq1, iqmp,\n public_exponent, modulus)\n\n self._p = p\n self._q = q\n self._dmp1 = dmp1\n self._dmq1 = dmq1\n self._iqmp = iqmp\n self._private_exponent = private_exponent\n self._public_exponent = public_exponent\n self._modulus = modulus\n\n @classmethod\n def generate(cls, public_exponent, key_size, backend):\n warnings.warn(\n \"generate is deprecated and will be removed in a future version.\",\n utils.DeprecatedIn05,\n stacklevel=2\n )\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n _verify_rsa_parameters(public_exponent, key_size)\n key = backend.generate_rsa_private_key(public_exponent, key_size)\n private_numbers = key.private_numbers()\n return RSAPrivateKey(\n p=private_numbers.p,\n q=private_numbers.q,\n dmp1=private_numbers.dmp1,\n dmq1=private_numbers.dmq1,\n iqmp=private_numbers.iqmp,\n private_exponent=private_numbers.d,\n public_exponent=private_numbers.public_numbers.e,\n modulus=private_numbers.public_numbers.n\n )\n\n def signer(self, padding, algorithm, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n return backend.create_rsa_signature_ctx(self, padding, algorithm)\n\n def decrypt(self, ciphertext, padding, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n return backend.decrypt_rsa(self, ciphertext, padding)\n\n @property\n def key_size(self):\n return utils.bit_length(self.modulus)\n\n def public_key(self):\n return RSAPublicKey(self.public_exponent, self.modulus)\n\n @property\n def p(self):\n return self._p\n\n @property\n def q(self):\n return self._q\n\n @property\n def private_exponent(self):\n return self._private_exponent\n\n @property\n def public_exponent(self):\n return self._public_exponent\n\n @property\n def modulus(self):\n return self._modulus\n\n @property\n def d(self):\n return self.private_exponent\n\n @property\n def dmp1(self):\n return self._dmp1\n\n @property\n def dmq1(self):\n return self._dmq1\n\n @property\n def iqmp(self):\n return self._iqmp\n\n @property\n def e(self):\n return self.public_exponent\n\n @property\n def n(self):\n return self.modulus\n\n\nclass RSAPrivateNumbers(object):\n def __init__(self, p, q, d, dmp1, dmq1, iqmp,\n public_numbers):\n if (\n not isinstance(p, six.integer_types) or\n not isinstance(q, six.integer_types) or\n not isinstance(d, six.integer_types) or\n not isinstance(dmp1, six.integer_types) or\n not isinstance(dmq1, six.integer_types) or\n not isinstance(iqmp, six.integer_types)\n ):\n raise TypeError(\n \"RSAPrivateNumbers p, q, d, dmp1, dmq1, iqmp arguments must\"\n \" all be an integers.\"\n )\n\n if not isinstance(public_numbers, RSAPublicNumbers):\n raise TypeError(\n \"RSAPrivateNumbers public_numbers must be an RSAPublicNumbers\"\n \" instance.\"\n )\n\n self._p = p\n self._q = q\n self._d = d\n self._dmp1 = dmp1\n self._dmq1 = dmq1\n self._iqmp = iqmp\n self._public_numbers = public_numbers\n\n @property\n def p(self):\n return self._p\n\n @property\n def q(self):\n return self._q\n\n @property\n def d(self):\n return self._d\n\n @property\n def dmp1(self):\n return self._dmp1\n\n @property\n def dmq1(self):\n return self._dmq1\n\n @property\n def iqmp(self):\n return self._iqmp\n\n @property\n def public_numbers(self):\n return self._public_numbers\n\n def private_key(self, backend):\n return backend.load_rsa_private_numbers(self)\n\n\nclass RSAPublicNumbers(object):\n def __init__(self, e, n):\n if (\n not isinstance(e, six.integer_types) or\n not isinstance(n, six.integer_types)\n ):\n raise TypeError(\"RSAPublicNumbers arguments must be integers.\")\n\n self._e = e\n self._n = n\n\n @property\n def e(self):\n return self._e\n\n @property\n def n(self):\n return self._n\n\n def public_key(self, backend):\n return backend.load_rsa_public_numbers(self)\n", "path": "cryptography/hazmat/primitives/asymmetric/rsa.py" } ]
[ { "content": "# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import absolute_import, division, print_function\n\nimport warnings\n\nimport six\n\nfrom cryptography import utils\nfrom cryptography.exceptions import UnsupportedAlgorithm, _Reasons\nfrom cryptography.hazmat.backends.interfaces import RSABackend\n\n\ndef generate_private_key(public_exponent, key_size, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n _verify_rsa_parameters(public_exponent, key_size)\n return backend.generate_rsa_private_key(public_exponent, key_size)\n\n\ndef _verify_rsa_parameters(public_exponent, key_size):\n if public_exponent < 3:\n raise ValueError(\"public_exponent must be >= 3.\")\n\n if public_exponent & 1 == 0:\n raise ValueError(\"public_exponent must be odd.\")\n\n if key_size < 512:\n raise ValueError(\"key_size must be at least 512-bits.\")\n\n\ndef _check_private_key_components(p, q, private_exponent, dmp1, dmq1, iqmp,\n public_exponent, modulus):\n if modulus < 3:\n raise ValueError(\"modulus must be >= 3.\")\n\n if p >= modulus:\n raise ValueError(\"p must be < modulus.\")\n\n if q >= modulus:\n raise ValueError(\"q must be < modulus.\")\n\n if dmp1 >= modulus:\n raise ValueError(\"dmp1 must be < modulus.\")\n\n if dmq1 >= modulus:\n raise ValueError(\"dmq1 must be < modulus.\")\n\n if iqmp >= modulus:\n raise ValueError(\"iqmp must be < modulus.\")\n\n if private_exponent >= modulus:\n raise ValueError(\"private_exponent must be < modulus.\")\n\n if public_exponent < 3 or public_exponent >= modulus:\n raise ValueError(\"public_exponent must be >= 3 and < modulus.\")\n\n if public_exponent & 1 == 0:\n raise ValueError(\"public_exponent must be odd.\")\n\n if dmp1 & 1 == 0:\n raise ValueError(\"dmp1 must be odd.\")\n\n if dmq1 & 1 == 0:\n raise ValueError(\"dmq1 must be odd.\")\n\n if p * q != modulus:\n raise ValueError(\"p*q must equal modulus.\")\n\n\ndef _check_public_key_components(e, n):\n if n < 3:\n raise ValueError(\"n must be >= 3.\")\n\n if e < 3 or e >= n:\n raise ValueError(\"e must be >= 3 and < n.\")\n\n if e & 1 == 0:\n raise ValueError(\"e must be odd.\")\n\n\nclass RSAPublicKey(object):\n def __init__(self, public_exponent, modulus):\n warnings.warn(\n \"The RSAPublicKey class is deprecated and will be removed in a \"\n \"future version.\",\n utils.DeprecatedIn05,\n stacklevel=2\n )\n if (\n not isinstance(public_exponent, six.integer_types) or\n not isinstance(modulus, six.integer_types)\n ):\n raise TypeError(\"RSAPublicKey arguments must be integers.\")\n\n _check_public_key_components(public_exponent, modulus)\n\n self._public_exponent = public_exponent\n self._modulus = modulus\n\n def verifier(self, signature, padding, algorithm, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n return backend.create_rsa_verification_ctx(self, signature, padding,\n algorithm)\n\n def encrypt(self, plaintext, padding, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n return backend.encrypt_rsa(self, plaintext, padding)\n\n @property\n def key_size(self):\n return utils.bit_length(self.modulus)\n\n @property\n def public_exponent(self):\n return self._public_exponent\n\n @property\n def modulus(self):\n return self._modulus\n\n @property\n def e(self):\n return self.public_exponent\n\n @property\n def n(self):\n return self.modulus\n\n\ndef _modinv(e, m):\n \"\"\"\n Modular Multiplicative Inverse. Returns x such that: (x*e) mod m == 1\n \"\"\"\n x1, y1, x2, y2 = 1, 0, 0, 1\n a, b = e, m\n while b > 0:\n q, r = divmod(a, b)\n xn, yn = x1 - q * x2, y1 - q * y2\n a, b, x1, y1, x2, y2 = b, r, x2, y2, xn, yn\n return x1 % m\n\n\ndef rsa_crt_iqmp(p, q):\n \"\"\"\n Compute the CRT (q ** -1) % p value from RSA primes p and q.\n \"\"\"\n return _modinv(q, p)\n\n\ndef rsa_crt_dmp1(private_exponent, p):\n \"\"\"\n Compute the CRT private_exponent % (p - 1) value from the RSA\n private_exponent and p.\n \"\"\"\n return private_exponent % (p - 1)\n\n\ndef rsa_crt_dmq1(private_exponent, q):\n \"\"\"\n Compute the CRT private_exponent % (q - 1) value from the RSA\n private_exponent and q.\n \"\"\"\n return private_exponent % (q - 1)\n\n\nclass RSAPrivateKey(object):\n def __init__(self, p, q, private_exponent, dmp1, dmq1, iqmp,\n public_exponent, modulus):\n warnings.warn(\n \"The RSAPrivateKey class is deprecated and will be removed in a \"\n \"future version.\",\n utils.DeprecatedIn05,\n stacklevel=2\n )\n if (\n not isinstance(p, six.integer_types) or\n not isinstance(q, six.integer_types) or\n not isinstance(dmp1, six.integer_types) or\n not isinstance(dmq1, six.integer_types) or\n not isinstance(iqmp, six.integer_types) or\n not isinstance(private_exponent, six.integer_types) or\n not isinstance(public_exponent, six.integer_types) or\n not isinstance(modulus, six.integer_types)\n ):\n raise TypeError(\"RSAPrivateKey arguments must be integers.\")\n\n _check_private_key_components(p, q, private_exponent, dmp1, dmq1, iqmp,\n public_exponent, modulus)\n\n self._p = p\n self._q = q\n self._dmp1 = dmp1\n self._dmq1 = dmq1\n self._iqmp = iqmp\n self._private_exponent = private_exponent\n self._public_exponent = public_exponent\n self._modulus = modulus\n\n @classmethod\n def generate(cls, public_exponent, key_size, backend):\n warnings.warn(\n \"generate is deprecated and will be removed in a future version.\",\n utils.DeprecatedIn05,\n stacklevel=2\n )\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n _verify_rsa_parameters(public_exponent, key_size)\n key = backend.generate_rsa_private_key(public_exponent, key_size)\n private_numbers = key.private_numbers()\n return RSAPrivateKey(\n p=private_numbers.p,\n q=private_numbers.q,\n dmp1=private_numbers.dmp1,\n dmq1=private_numbers.dmq1,\n iqmp=private_numbers.iqmp,\n private_exponent=private_numbers.d,\n public_exponent=private_numbers.public_numbers.e,\n modulus=private_numbers.public_numbers.n\n )\n\n def signer(self, padding, algorithm, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n return backend.create_rsa_signature_ctx(self, padding, algorithm)\n\n def decrypt(self, ciphertext, padding, backend):\n if not isinstance(backend, RSABackend):\n raise UnsupportedAlgorithm(\n \"Backend object does not implement RSABackend.\",\n _Reasons.BACKEND_MISSING_INTERFACE\n )\n\n return backend.decrypt_rsa(self, ciphertext, padding)\n\n @property\n def key_size(self):\n return utils.bit_length(self.modulus)\n\n def public_key(self):\n return RSAPublicKey(self.public_exponent, self.modulus)\n\n @property\n def p(self):\n return self._p\n\n @property\n def q(self):\n return self._q\n\n @property\n def private_exponent(self):\n return self._private_exponent\n\n @property\n def public_exponent(self):\n return self._public_exponent\n\n @property\n def modulus(self):\n return self._modulus\n\n @property\n def d(self):\n return self.private_exponent\n\n @property\n def dmp1(self):\n return self._dmp1\n\n @property\n def dmq1(self):\n return self._dmq1\n\n @property\n def iqmp(self):\n return self._iqmp\n\n @property\n def e(self):\n return self.public_exponent\n\n @property\n def n(self):\n return self.modulus\n\n\nclass RSAPrivateNumbers(object):\n def __init__(self, p, q, d, dmp1, dmq1, iqmp,\n public_numbers):\n if (\n not isinstance(p, six.integer_types) or\n not isinstance(q, six.integer_types) or\n not isinstance(d, six.integer_types) or\n not isinstance(dmp1, six.integer_types) or\n not isinstance(dmq1, six.integer_types) or\n not isinstance(iqmp, six.integer_types)\n ):\n raise TypeError(\n \"RSAPrivateNumbers p, q, d, dmp1, dmq1, iqmp arguments must\"\n \" all be an integers.\"\n )\n\n if not isinstance(public_numbers, RSAPublicNumbers):\n raise TypeError(\n \"RSAPrivateNumbers public_numbers must be an RSAPublicNumbers\"\n \" instance.\"\n )\n\n self._p = p\n self._q = q\n self._d = d\n self._dmp1 = dmp1\n self._dmq1 = dmq1\n self._iqmp = iqmp\n self._public_numbers = public_numbers\n\n @property\n def p(self):\n return self._p\n\n @property\n def q(self):\n return self._q\n\n @property\n def d(self):\n return self._d\n\n @property\n def dmp1(self):\n return self._dmp1\n\n @property\n def dmq1(self):\n return self._dmq1\n\n @property\n def iqmp(self):\n return self._iqmp\n\n @property\n def public_numbers(self):\n return self._public_numbers\n\n def private_key(self, backend):\n return backend.load_rsa_private_numbers(self)\n\n\nclass RSAPublicNumbers(object):\n def __init__(self, e, n):\n if (\n not isinstance(e, six.integer_types) or\n not isinstance(n, six.integer_types)\n ):\n raise TypeError(\"RSAPublicNumbers arguments must be integers.\")\n\n self._e = e\n self._n = n\n\n @property\n def e(self):\n return self._e\n\n @property\n def n(self):\n return self._n\n\n def public_key(self, backend):\n return backend.load_rsa_public_numbers(self)\n\n def __repr__(self):\n return \"<RSAPublicNumbers(e={0}, n={1})>\".format(self._e, self._n)\n", "path": "cryptography/hazmat/primitives/asymmetric/rsa.py" } ]
diff --git a/cryptography/hazmat/primitives/asymmetric/rsa.py b/cryptography/hazmat/primitives/asymmetric/rsa.py index 15ec52ac4d02..398b3763b1a6 100644 --- a/cryptography/hazmat/primitives/asymmetric/rsa.py +++ b/cryptography/hazmat/primitives/asymmetric/rsa.py @@ -402,3 +402,6 @@ def n(self): def public_key(self, backend): return backend.load_rsa_public_numbers(self) + + def __repr__(self): + return "<RSAPublicNumbers(e={0}, n={1})>".format(self._e, self._n) diff --git a/tests/hazmat/primitives/test_rsa.py b/tests/hazmat/primitives/test_rsa.py index 8e850737b5db..e53ff06bac64 100644 --- a/tests/hazmat/primitives/test_rsa.py +++ b/tests/hazmat/primitives/test_rsa.py @@ -27,6 +27,7 @@ ) from cryptography.hazmat.primitives import hashes, interfaces from cryptography.hazmat.primitives.asymmetric import padding, rsa +from cryptography.hazmat.primitives.asymmetric.rsa import RSAPublicNumbers from .fixtures_rsa import ( RSA_KEY_1024, RSA_KEY_1025, RSA_KEY_1026, RSA_KEY_1027, RSA_KEY_1028, @@ -1973,3 +1974,7 @@ def test_invalid_private_numbers_argument_values(self, backend): n=33 ) ).private_key(backend) + + def test_public_number_repr(self): + num = RSAPublicNumbers(1, 1) + assert repr(num) == "<RSAPublicNumbers(e=1, n=1)>"
openstates__openstates-scrapers-2707
MO failing since at least 2018-12-09 MO has been failing since 2018-12-09 Based on automated runs it appears that MO has not run successfully in 2 days (2018-12-09). ``` 06:11:33 INFO pupa: SB158 06:11:33 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=20875 06:11:34 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=20875 06:11:35 INFO pupa: save bill SB 158 in 2019 as bill_716103c4-fc42-11e8-b9f8-02f1fb7ee550.json 06:11:35 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=39332 06:11:36 INFO pupa: SB159 06:11:36 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=39332 06:11:37 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=39332 06:11:38 INFO pupa: save bill SB 159 in 2019 as bill_732da4be-fc42-11e8-b9f8-02f1fb7ee550.json 06:11:38 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=39145 06:11:39 INFO pupa: SB160 06:11:39 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=39145 06:11:40 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=39145 06:11:41 INFO pupa: save bill SB 160 in 2019 as bill_74f5473e-fc42-11e8-b9f8-02f1fb7ee550.json 06:11:41 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=51423 06:11:42 INFO pupa: SB161 06:11:42 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=51423 06:11:43 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=51423 06:11:44 INFO pupa: save bill SB 161 in 2019 as bill_76bf4ed4-fc42-11e8-b9f8-02f1fb7ee550.json 06:11:44 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=53657 06:11:45 INFO pupa: SB162 06:11:45 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/Actions.aspx?SessionType=R&BillID=53657 06:11:46 INFO scrapelib: GET - https://www.senate.mo.gov/19info/BTS_Web/BillText.aspx?SessionType=R&BillID=53657 06:11:47 INFO pupa: save bill SB 162 in 2019 as bill_788977b2-fc42-11e8-b9f8-02f1fb7ee550.json 06:11:47 INFO scrapelib: GET - http://www.senate.mo.gov/19info/BTS_Web/Bill.aspx?SessionType=R&BillID=53656 06:11:48 INFO pupa: XXXXXX loaded Open States pupa settings... mo (scrape, import) bills: {} Traceback (most recent call last): File "/opt/openstates/venv-pupa//bin/pupa", line 11, in <module> load_entry_point('pupa', 'console_scripts', 'pupa')() File "/opt/openstates/venv-pupa/src/pupa/pupa/cli/__main__.py", line 68, in main subcommands[args.subcommand].handle(args, other) File "/opt/openstates/venv-pupa/src/pupa/pupa/cli/commands/update.py", line 274, in handle return self.do_handle(args, other, juris) File "/opt/openstates/venv-pupa/src/pupa/pupa/cli/commands/update.py", line 320, in do_handle report['scrape'] = self.do_scrape(juris, args, scrapers) File "/opt/openstates/venv-pupa/src/pupa/pupa/cli/commands/update.py", line 175, in do_scrape report[scraper_name] = scraper.do_scrape(**scrape_args) File "/opt/openstates/venv-pupa/src/pupa/pupa/scrape/base.py", line 112, in do_scrape for obj in self.scrape(**kwargs) or []: File "/opt/openstates/openstates/openstates/mo/bills.py", line 627, in scrape yield from self._scrape_upper_chamber(session) File "/opt/openstates/openstates/openstates/mo/bills.py", line 600, in _scrape_upper_chamber session, File "/opt/openstates/openstates/openstates/mo/bills.py", line 193, in _parse_senate_billpage action_url = action_url[0].attrib['href'] File "src/lxml/etree.pyx", line 2457, in lxml.etree._Attrib.__getitem__ KeyError: 'href' ``` Visit http://bobsled.openstates.org for more info.
[ { "content": "import re\nimport pytz\nimport datetime as dt\nfrom collections import defaultdict\n\nimport lxml.html\nfrom pupa.scrape import Scraper, Bill, VoteEvent\n\nfrom openstates.utils import LXMLMixin\n\nfrom .utils import (clean_text, house_get_actor_from_action,\n senate_get_actor_from_action)\n\nbill_types = {\n 'HB ': 'bill',\n 'HJR': 'joint resolution',\n 'HCR': 'concurrent resolution',\n 'SB ': 'bill',\n 'SJR': 'joint resolution',\n 'SCR': 'concurrent resolution'\n}\n\nTIMEZONE = pytz.timezone('America/Chicago')\n\n\nclass MOBillScraper(Scraper, LXMLMixin):\n _house_base_url = 'http://www.house.mo.gov'\n # List of URLS that aren't working when we try to visit them (but\n # probably should work):\n _bad_urls = []\n _subjects = defaultdict(list)\n _session_id = ''\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._scrape_subjects(self.latest_session())\n\n def _get_action(self, actor, action):\n # Alright. This covers both chambers and everyting else.\n flags = [\n ('Introduced', 'introduction'),\n ('Offered', 'introduction'),\n ('First Read', 'reading-1'),\n ('Read Second Time', 'reading-2'),\n ('Second Read', 'reading-2'),\n # make sure passage is checked before reading-3\n ('Third Read and Passed', 'passage'),\n ('Reported Do Pass', 'committee-passage'),\n ('Voted Do Pass', 'committee-passage'),\n ('Third Read', 'reading-3'),\n ('Referred', 'referral-committee'),\n ('Withdrawn', 'withdrawal'),\n ('S adopted', 'passage'),\n ('Truly Agreed To and Finally Passed', 'passage'),\n ('Signed by Governor', 'executive-signature'),\n ('Approved by Governor', 'executive-signature'),\n ('Vetoed by Governor', 'executive-veto'),\n ('Legislature voted to override Governor\\'s veto', 'veto-override-passage'),\n ]\n categories = []\n for flag, acat in flags:\n if flag in action:\n categories.append(acat)\n\n return categories or None\n\n def _get_votes(self, date, actor, action, bill, url):\n vre = r'(?P<leader>.*)(AYES|YEAS):\\s+(?P<yeas>\\d+)\\s+(NOES|NAYS):\\s+(?P<nays>\\d+).*'\n if 'YEAS' in action.upper() or 'AYES' in action.upper():\n match = re.match(vre, action)\n if match:\n v = match.groupdict()\n yes, no = int(v['yeas']), int(v['nays'])\n vote = VoteEvent(\n chamber=actor,\n motion_text=v['leader'],\n result='pass' if yes > no else 'fail',\n classification='passage',\n start_date=TIMEZONE.localize(date),\n bill=bill,\n )\n vote.add_source(url)\n yield vote\n\n def _parse_cosponsors_from_bill(self, bill, url):\n bill_page = self.get(url).text\n bill_page = lxml.html.fromstring(bill_page)\n table = bill_page.xpath('//table[@id=\"CoSponsorTable\"]')\n assert len(table) == 1\n for row in table[0].xpath('./tr'):\n name = row[0].text_content()\n if re.search(r'no co-sponsors', name, re.IGNORECASE):\n continue\n bill.add_sponsorship(\n row[0].text_content(),\n entity_type='person',\n classification='cosponsor',\n primary=False,\n )\n\n def _scrape_subjects(self, session):\n self._scrape_senate_subjects(session)\n if 'S' in session:\n self.warning('skipping house subjects for special session')\n else:\n self._scrape_house_subjects(session)\n\n def session_type(self, session):\n # R or S1\n return 'R' if len(session) == 4 else session[4:]\n\n def _scrape_senate_subjects(self, session):\n self.info('Collecting subject tags from upper house.')\n\n subject_list_url = 'http://www.senate.mo.gov/{}info/BTS_Web/'\\\n 'Keywords.aspx?SessionType=%s'.format(session[2:4], self.session_type(session))\n subject_page = self.lxmlize(subject_list_url)\n\n # Create a list of all possible bill subjects.\n subjects = self.get_nodes(subject_page, '//h3')\n\n for subject in subjects:\n subject_text = self.get_node(\n subject,\n './a[string-length(text()) > 0]/text()[normalize-space()]')\n subject_text = re.sub(r'([\\s]*\\([0-9]+\\)$)', '', subject_text)\n\n # Bills are in hidden spans after the subject labels.\n bill_ids = subject.getnext().xpath(\n './b/a/text()[normalize-space()]')\n\n for bill_id in bill_ids:\n self.info('Found {}.'.format(bill_id))\n self._subjects[bill_id].append(subject_text)\n\n def _parse_senate_billpage(self, bill_url, year):\n bill_page = self.lxmlize(bill_url)\n\n # get all the info needed to record the bill\n # TODO probably still needs to be fixed\n bill_id = bill_page.xpath('//*[@id=\"lblBillNum\"]')[0].text_content()\n bill_title = bill_page.xpath('//*[@id=\"lblBillTitle\"]')[0].text_content()\n bill_desc = bill_page.xpath('//*[@id=\"lblBriefDesc\"]')[0].text_content()\n # bill_lr = bill_page.xpath('//*[@id=\"lblLRNum\"]')[0].text_content()\n\n bill_type = \"bill\"\n triplet = bill_id[:3]\n if triplet in bill_types:\n bill_type = bill_types[triplet]\n\n subs = []\n bid = bill_id.replace(\" \", \"\")\n\n if bid in self._subjects:\n subs = self._subjects[bid]\n self.info(\"With subjects for this bill\")\n\n self.info(bid)\n\n bill = Bill(\n bill_id,\n title=bill_desc,\n chamber='upper',\n legislative_session=self._session_id,\n classification=bill_type,\n )\n bill.subject = subs\n bill.add_abstract(bill_desc, note='abstract')\n bill.add_source(bill_url)\n\n if bill_title:\n bill.add_title(bill_title)\n\n # Get the primary sponsor\n sponsor = bill_page.xpath('//a[@id=\"hlSponsor\"]')[0]\n bill_sponsor = sponsor.text_content()\n # bill_sponsor_link = sponsor.attrib.get('href')\n bill.add_sponsorship(\n bill_sponsor,\n entity_type='person',\n classification='primary',\n primary=True,\n )\n\n # cosponsors show up on their own page, if they exist\n cosponsor_tag = bill_page.xpath('//a[@id=\"hlCoSponsors\"]')\n if len(cosponsor_tag) > 0 and cosponsor_tag[0].attrib.get('href'):\n self._parse_senate_cosponsors(bill, cosponsor_tag[0].attrib['href'])\n\n # get the actions\n action_url = bill_page.xpath('//a[@id=\"hlAllActions\"]')\n if len(action_url) > 0:\n action_url = action_url[0].attrib['href']\n self._parse_senate_actions(bill, action_url)\n\n # stored on a separate page\n versions_url = bill_page.xpath('//a[@id=\"hlFullBillText\"]')\n if len(versions_url) > 0 and versions_url[0].attrib.get('href'):\n self._parse_senate_bill_versions(bill, versions_url[0].attrib['href'])\n\n yield bill\n\n def _parse_senate_bill_versions(self, bill, url):\n bill.add_source(url)\n versions_page = self.get(url).text\n versions_page = lxml.html.fromstring(versions_page)\n version_tags = versions_page.xpath('//li/font/a')\n\n # some pages are updated and use different structure\n if not version_tags:\n version_tags = versions_page.xpath('//tr/td/a[contains(@href, \".pdf\")]')\n\n for version_tag in version_tags:\n description = version_tag.text_content()\n pdf_url = version_tag.attrib['href']\n if pdf_url.endswith('pdf'):\n mimetype = 'application/pdf'\n else:\n mimetype = None\n bill.add_version_link(description, pdf_url, media_type=mimetype,\n on_duplicate='ignore')\n\n def _parse_senate_actions(self, bill, url):\n bill.add_source(url)\n actions_page = self.get(url).text\n actions_page = lxml.html.fromstring(actions_page)\n bigtable = actions_page.xpath('/html/body/font/form/table/tr[3]/td/div/table/tr')\n\n for row in bigtable:\n date = row[0].text_content()\n date = dt.datetime.strptime(date, '%m/%d/%Y')\n action = row[1].text_content()\n actor = senate_get_actor_from_action(action)\n type_class = self._get_action(actor, action)\n bill.add_action(\n action, TIMEZONE.localize(date), chamber=actor, classification=type_class)\n\n def _parse_senate_cosponsors(self, bill, url):\n bill.add_source(url)\n cosponsors_page = self.get(url).text\n cosponsors_page = lxml.html.fromstring(cosponsors_page)\n # cosponsors are all in a table\n cosponsors = cosponsors_page.xpath('//table[@id=\"dgCoSponsors\"]/tr/td/a')\n\n for cosponsor_row in cosponsors:\n # cosponsors include district, so parse that out\n cosponsor_string = cosponsor_row.text_content()\n cosponsor = clean_text(cosponsor_string)\n cosponsor = cosponsor.split(',')[0]\n\n # they give us a link to the congressperson, so we might\n # as well keep it.\n if cosponsor_row.attrib.get('href'):\n # cosponsor_url = cosponsor_row.attrib['href']\n bill.add_sponsorship(\n cosponsor,\n entity_type='person',\n classification='cosponsor',\n primary=False,\n )\n else:\n bill.add_sponsorship(\n cosponsor,\n entity_type='person',\n classification='cosponsor',\n primary=False,\n )\n\n def _scrape_house_subjects(self, session):\n self.info('Collecting subject tags from lower house.')\n\n subject_list_url = \\\n 'http://house.mo.gov/LegislationSP.aspx?code=R&category=subjectindex&year={}'\\\n .format(session)\n subject_page = self.lxmlize(subject_list_url)\n\n # Create a list of all the possible bill subjects.\n subjects = self.get_nodes(\n subject_page,\n \"//div[@id='ContentPlaceHolder1_panelParentDIV']\" # ...\n \"/div[@id='panelDIV']//div[@id='ExpandedPanel']//a\")\n\n # Find the list of bills within each subject.\n for subject in subjects:\n\n subject_text = re.sub(r\"\\([0-9]+\\).*\", '', subject.text, re.IGNORECASE).strip()\n self.info('Searching for bills in {}.'.format(subject_text))\n\n subject_page = self.lxmlize(subject.attrib['href'])\n\n bill_nodes = self.get_nodes(\n subject_page,\n '//table[@id=\"reportgrid\"]/tbody/tr[@class=\"reportbillinfo\"]')\n\n # Move onto the next subject if no bills were found.\n if bill_nodes is None or not (len(bill_nodes) > 0):\n continue\n\n for bill_node in bill_nodes:\n bill_id = self.get_node(\n bill_node,\n '(./td)[1]/a/text()[normalize-space()]')\n\n # Skip to the next bill if no ID could be found.\n if bill_id is None or not (len(bill_id) > 0):\n continue\n\n self.info('Found {}.'.format(bill_id))\n self._subjects[bill_id].append(subject_text)\n\n def _parse_house_actions(self, bill, url):\n bill.add_source(url)\n actions_page = self.get(url).text\n actions_page = lxml.html.fromstring(actions_page)\n rows = actions_page.xpath('//table/tr')\n\n for row in rows:\n # new actions are represented by having dates in the first td\n # otherwise, it's a continuation of the description from the\n # previous action\n if len(row) > 0 and row[0].tag == 'td':\n if len(row[0].text_content().strip()) > 0:\n date = row[0].text_content().strip()\n date = dt.datetime.strptime(date, '%m/%d/%Y')\n action = row[2].text_content().strip()\n else:\n action += ('\\n' + row[2].text_content())\n action = action.rstrip()\n actor = house_get_actor_from_action(action)\n type_class = self._get_action(actor, action)\n\n yield from self._get_votes(date, actor, action, bill, url)\n\n bill.add_action(\n action, TIMEZONE.localize(date), chamber=actor, classification=type_class)\n\n def _parse_house_billpage(self, url, year):\n bill_list_page = self.get(url).text\n bill_list_page = lxml.html.fromstring(bill_list_page)\n # find the first center tag, take the text after\n # 'House of Representatives' and before 'Bills' as\n # the session name\n # header_tag = bill_list_page.xpath(\n # '//*[@id=\"ContentPlaceHolder1_lblAssemblyInfo\"]'\n # )[0].text_content()\n # if header_tag.find('1st Extraordinary Session') != -1:\n # session = year + ' 1st Extraordinary Session'\n # elif header_tag.find('2nd Extraordinary Session') != -1:\n # session = year + ' 2nd Extraordinary Session'\n # else:\n session = year\n\n bills = bill_list_page.xpath('//table[@id=\"reportgrid\"]//tr')\n\n isEven = False\n count = 0\n bills = bills[2:]\n for bill in bills:\n\n if not isEven:\n # the non even rows contain bill links, the other rows contain brief\n # descriptions of the bill.\n count = count + 1\n yield from self._parse_house_bill(bill[0][0].attrib['href'], session)\n isEven = not isEven\n\n def _parse_house_bill(self, url, session):\n # using the print page makes the page simpler, and also *drastically* smaller\n # (8k rather than 100k)\n url = re.sub(\"billsummary\", \"billsummaryprn\", url)\n url = '%s/%s' % (self._house_base_url, url)\n\n # the URL is an iframed version now, so swap in for the actual bill page\n\n url = url.replace('Bill.aspx', 'BillContent.aspx')\n url = url.replace('&code=R', '&code=R&style=new')\n\n # http://www.house.mo.gov/Bill.aspx?bill=HB26&year=2017&code=R\n # http://www.house.mo.gov/BillContent.aspx?bill=HB26&year=2017&code=R&style=new\n\n bill_page = self.get(url).text\n bill_page = lxml.html.fromstring(bill_page)\n bill_page.make_links_absolute(url)\n\n bill_id = bill_page.xpath('//*[@class=\"entry-title\"]/div')\n if len(bill_id) == 0:\n self.info(\"WARNING: bill summary page is blank! (%s)\" % url)\n self._bad_urls.append(url)\n return\n bill_id = bill_id[0].text_content()\n bill_id = clean_text(bill_id)\n\n bill_desc = bill_page.xpath('//*[@class=\"BillDescription\"]')[0].text_content()\n bill_desc = clean_text(bill_desc)\n\n table_rows = bill_page.xpath('//table/tr')\n # if there is a cosponsor all the rows are pushed down one for the extra row\n # for the cosponsor:\n cosponsorOffset = 0\n if table_rows[2][0].text_content().strip() == 'Co-Sponsor:':\n cosponsorOffset = 1\n\n lr_label_tag = table_rows[3 + cosponsorOffset]\n assert lr_label_tag[0].text_content().strip() == 'LR Number:'\n # bill_lr = lr_label_tag[1].text_content()\n\n lastActionOffset = 0\n if table_rows[4 + cosponsorOffset][0].text_content().strip() == 'Governor Action:':\n lastActionOffset = 1\n official_title_tag = table_rows[5 + cosponsorOffset + lastActionOffset]\n assert official_title_tag[0].text_content().strip() == 'Bill String:'\n official_title = official_title_tag[1].text_content()\n\n # could substitute the description for the name,\n # but keeping it separate for now.\n\n bill_type = \"bill\"\n triplet = bill_id[:3]\n\n if triplet in bill_types:\n bill_type = bill_types[triplet]\n bill_number = int(bill_id[3:].strip())\n else:\n bill_number = int(bill_id[3:])\n\n subs = []\n bid = bill_id.replace(\" \", \"\")\n\n if bid in self._subjects:\n subs = self._subjects[bid]\n self.info(\"With subjects for this bill\")\n\n self.info(bid)\n\n if bill_desc == \"\":\n if bill_number <= 20:\n # blank bill titles early in session are approp. bills\n bill_desc = 'Appropriations Bill'\n else:\n self.error(\"Blank title. Skipping. {} / {} / {}\".format(\n bill_id, bill_desc, official_title\n ))\n return\n\n bill = Bill(\n bill_id,\n chamber='lower',\n title=bill_desc,\n legislative_session=self._session_id,\n classification=bill_type,\n )\n bill.subject = subs\n bill.add_title(official_title, note='official')\n\n bill.add_source(url)\n\n bill_sponsor = clean_text(table_rows[0][1].text_content())\n # try:\n # bill_sponsor_link = table_rows[0][1][0].attrib['href']\n # except IndexError:\n # return\n bill.add_sponsorship(\n bill_sponsor,\n entity_type='person',\n classification='primary',\n primary=True,\n )\n\n # check for cosponsors\n sponsors_url, = bill_page.xpath(\n \"//a[contains(@href, 'CoSponsors.aspx')]/@href\")\n self._parse_cosponsors_from_bill(bill, sponsors_url)\n\n # actions_link_tag = bill_page.xpath('//div[@class=\"Sections\"]/a')[0]\n # actions_link = '%s/%s' % (self._house_base_url,actions_link_tag.attrib['href'])\n # actions_link = re.sub(\"content\", \"print\", actions_link)\n\n actions_link, = bill_page.xpath(\n \"//a[contains(@href, 'BillActions.aspx')]/@href\")\n yield from self._parse_house_actions(bill, actions_link)\n\n # get bill versions\n doc_tags = bill_page.xpath('//div[@class=\"BillDocuments\"][1]/span')\n for doc_tag in reversed(doc_tags):\n doc = clean_text(doc_tag.text_content())\n text_url = '%s%s' % (\n self._house_base_url,\n doc_tag[0].attrib['href']\n )\n bill.add_document_link(doc, text_url, media_type='text/html')\n\n # get bill versions\n version_tags = bill_page.xpath('//div[@class=\"BillDocuments\"][2]/span')\n for version_tag in reversed(version_tags):\n version = clean_text(version_tag.text_content())\n for vurl in version_tag.xpath(\".//a\"):\n if vurl.text == 'PDF':\n mimetype = 'application/pdf'\n else:\n mimetype = 'text/html'\n bill.add_version_link(version, vurl.attrib['href'], media_type=mimetype,\n on_duplicate='ignore')\n\n # house bill versions\n # everything between the row containing \"Bill Text\"\" and the next div.DocHeaderRow\n version_rows = bill_page.xpath(\n '//div[contains(text(),\"Bill Text\")]/'\n 'following-sibling::div[contains(@class,\"DocRow\") '\n 'and count(preceding-sibling::div[contains(@class,\"DocHeaderRow\")])=1]')\n for row in version_rows:\n # some rows are just broken links, not real versions\n if row.xpath('.//div[contains(@class,\"textType\")]/a/@href'):\n version = row.xpath('.//div[contains(@class,\"textType\")]/a/text()')[0].strip()\n path = row.xpath('.//div[contains(@class,\"textType\")]/a/@href')[0].strip()\n if '.pdf' in path:\n mimetype = 'application/pdf'\n else:\n mimetype = 'text/html'\n bill.add_version_link(version, path, media_type=mimetype,\n on_duplicate='ignore')\n\n # house bill summaries\n # everything between the row containing \"Bill Summary\"\" and the next div.DocHeaderRow\n summary_rows = bill_page.xpath(\n '//div[contains(text(),\"Bill Summary\")]/'\n 'following-sibling::div[contains(@class,\"DocRow\") '\n 'and count(following-sibling::div[contains(@class,\"DocHeaderRow\")])=1]')\n\n # if there are no amedments, we need a different xpath for summaries\n if not summary_rows:\n summary_rows = bill_page.xpath(\n '//div[contains(text(),\"Bill Summary\")]/'\n 'following-sibling::div[contains(@class,\"DocRow\")]')\n\n for row in reversed(summary_rows):\n version = row.xpath('.//div[contains(@class,\"textType\")]/a/text()')[0].strip()\n if version:\n path = row.xpath('.//div[contains(@class,\"textType\")]/a/@href')[0].strip()\n summary_name = 'Bill Summary ({})'.format(version)\n if '.pdf' in path:\n mimetype = 'application/pdf'\n else:\n mimetype = 'text/html'\n bill.add_document_link(summary_name, path, media_type=mimetype,\n on_duplicate='ignore')\n\n # house bill amendments\n amendment_rows = bill_page.xpath('//div[contains(text(),\"Amendment\")]/'\n 'following-sibling::div[contains(@class,\"DocRow\")]')\n\n for row in reversed(amendment_rows):\n version = row.xpath('.//div[contains(@class,\"DocInfoCell\")]/a[1]/text()')[0].strip()\n path = row.xpath('.//div[contains(@class,\"DocInfoCell\")]/a[1]/@href')[0].strip()\n summary_name = 'Amendment {}'.format(version)\n\n defeated_icon = row.xpath('.//img[contains(@title,\"Defeated\")]')\n if defeated_icon:\n summary_name = '{} (Defeated)'.format(summary_name)\n\n adopted_icon = row.xpath('.//img[contains(@title,\"Adopted\")]')\n if adopted_icon:\n summary_name = '{} (Adopted)'.format(summary_name)\n\n distributed_icon = row.xpath('.//img[contains(@title,\"Distributed\")]')\n if distributed_icon:\n summary_name = '{} (Distributed)'.format(summary_name)\n\n if '.pdf' in path:\n mimetype = 'application/pdf'\n else:\n mimetype = 'text/html'\n bill.add_version_link(summary_name, path, media_type=mimetype,\n on_duplicate='ignore')\n\n yield bill\n\n def _scrape_upper_chamber(self, session):\n self.info('Scraping bills from upper chamber.')\n\n year2 = \"%02d\" % (int(session[:4]) % 100)\n\n # Save the root URL, since we'll use it later.\n bill_root = 'http://www.senate.mo.gov/{}info/BTS_Web/'.format(year2)\n index_url = bill_root + 'BillList.aspx?SessionType=' + self.session_type(session)\n\n index_page = self.get(index_url).text\n index_page = lxml.html.fromstring(index_page)\n # Each bill is in it's own table (nested within a larger table).\n bill_tables = index_page.xpath('//a[@id]')\n\n if not bill_tables:\n return\n\n for bill_table in bill_tables:\n # Here we just search the whole table string to get the BillID that\n # the MO senate site uses.\n if re.search(r'dgBillList.*hlBillNum', bill_table.attrib['id']):\n yield from self._parse_senate_billpage(\n bill_root + bill_table.attrib.get('href'),\n session,\n )\n\n def _scrape_lower_chamber(self, session):\n self.info('Scraping bills from lower chamber.')\n\n if 'S' in session:\n year = session[:4]\n code = session[4:]\n else:\n year = session\n code = 'R'\n\n bill_page_url = '{}/BillList.aspx?year={}&code={}'.format(\n self._house_base_url, year, code)\n yield from self._parse_house_billpage(bill_page_url, year)\n\n def scrape(self, chamber=None, session=None):\n if not session:\n session = self.latest_session()\n self.info('no session specified, using %s', session)\n\n # special sessions and other year manipulation messes up the session variable\n # but we need it for correct output\n self._session_id = session\n\n if chamber in ['upper', None]:\n yield from self._scrape_upper_chamber(session)\n if chamber in ['lower', None]:\n yield from self._scrape_lower_chamber(session)\n\n if len(self._bad_urls) > 0:\n self.warning('WARNINGS:')\n for url in self._bad_urls:\n self.warning('{}'.format(url))\n", "path": "openstates/mo/bills.py" } ]
[ { "content": "import re\nimport pytz\nimport datetime as dt\nfrom collections import defaultdict\n\nimport lxml.html\nfrom pupa.scrape import Scraper, Bill, VoteEvent\n\nfrom openstates.utils import LXMLMixin\n\nfrom .utils import (clean_text, house_get_actor_from_action,\n senate_get_actor_from_action)\n\nbill_types = {\n 'HB ': 'bill',\n 'HJR': 'joint resolution',\n 'HCR': 'concurrent resolution',\n 'SB ': 'bill',\n 'SJR': 'joint resolution',\n 'SCR': 'concurrent resolution'\n}\n\nTIMEZONE = pytz.timezone('America/Chicago')\n\n\nclass MOBillScraper(Scraper, LXMLMixin):\n _house_base_url = 'http://www.house.mo.gov'\n # List of URLS that aren't working when we try to visit them (but\n # probably should work):\n _bad_urls = []\n _subjects = defaultdict(list)\n _session_id = ''\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._scrape_subjects(self.latest_session())\n\n def _get_action(self, actor, action):\n # Alright. This covers both chambers and everyting else.\n flags = [\n ('Introduced', 'introduction'),\n ('Offered', 'introduction'),\n ('First Read', 'reading-1'),\n ('Read Second Time', 'reading-2'),\n ('Second Read', 'reading-2'),\n # make sure passage is checked before reading-3\n ('Third Read and Passed', 'passage'),\n ('Reported Do Pass', 'committee-passage'),\n ('Voted Do Pass', 'committee-passage'),\n ('Third Read', 'reading-3'),\n ('Referred', 'referral-committee'),\n ('Withdrawn', 'withdrawal'),\n ('S adopted', 'passage'),\n ('Truly Agreed To and Finally Passed', 'passage'),\n ('Signed by Governor', 'executive-signature'),\n ('Approved by Governor', 'executive-signature'),\n ('Vetoed by Governor', 'executive-veto'),\n ('Legislature voted to override Governor\\'s veto', 'veto-override-passage'),\n ]\n categories = []\n for flag, acat in flags:\n if flag in action:\n categories.append(acat)\n\n return categories or None\n\n def _get_votes(self, date, actor, action, bill, url):\n vre = r'(?P<leader>.*)(AYES|YEAS):\\s+(?P<yeas>\\d+)\\s+(NOES|NAYS):\\s+(?P<nays>\\d+).*'\n if 'YEAS' in action.upper() or 'AYES' in action.upper():\n match = re.match(vre, action)\n if match:\n v = match.groupdict()\n yes, no = int(v['yeas']), int(v['nays'])\n vote = VoteEvent(\n chamber=actor,\n motion_text=v['leader'],\n result='pass' if yes > no else 'fail',\n classification='passage',\n start_date=TIMEZONE.localize(date),\n bill=bill,\n )\n vote.add_source(url)\n yield vote\n\n def _parse_cosponsors_from_bill(self, bill, url):\n bill_page = self.get(url).text\n bill_page = lxml.html.fromstring(bill_page)\n table = bill_page.xpath('//table[@id=\"CoSponsorTable\"]')\n assert len(table) == 1\n for row in table[0].xpath('./tr'):\n name = row[0].text_content()\n if re.search(r'no co-sponsors', name, re.IGNORECASE):\n continue\n bill.add_sponsorship(\n row[0].text_content(),\n entity_type='person',\n classification='cosponsor',\n primary=False,\n )\n\n def _scrape_subjects(self, session):\n self._scrape_senate_subjects(session)\n if 'S' in session:\n self.warning('skipping house subjects for special session')\n else:\n self._scrape_house_subjects(session)\n\n def session_type(self, session):\n # R or S1\n return 'R' if len(session) == 4 else session[4:]\n\n def _scrape_senate_subjects(self, session):\n self.info('Collecting subject tags from upper house.')\n\n subject_list_url = 'http://www.senate.mo.gov/{}info/BTS_Web/'\\\n 'Keywords.aspx?SessionType=%s'.format(session[2:4], self.session_type(session))\n subject_page = self.lxmlize(subject_list_url)\n\n # Create a list of all possible bill subjects.\n subjects = self.get_nodes(subject_page, '//h3')\n\n for subject in subjects:\n subject_text = self.get_node(\n subject,\n './a[string-length(text()) > 0]/text()[normalize-space()]')\n subject_text = re.sub(r'([\\s]*\\([0-9]+\\)$)', '', subject_text)\n\n # Bills are in hidden spans after the subject labels.\n bill_ids = subject.getnext().xpath(\n './b/a/text()[normalize-space()]')\n\n for bill_id in bill_ids:\n self.info('Found {}.'.format(bill_id))\n self._subjects[bill_id].append(subject_text)\n\n def _parse_senate_billpage(self, bill_url, year):\n bill_page = self.lxmlize(bill_url)\n\n # get all the info needed to record the bill\n # TODO probably still needs to be fixed\n bill_id = bill_page.xpath('//*[@id=\"lblBillNum\"]')[0].text_content()\n bill_title = bill_page.xpath('//*[@id=\"lblBillTitle\"]')[0].text_content()\n bill_desc = bill_page.xpath('//*[@id=\"lblBriefDesc\"]')[0].text_content()\n # bill_lr = bill_page.xpath('//*[@id=\"lblLRNum\"]')[0].text_content()\n\n bill_type = \"bill\"\n triplet = bill_id[:3]\n if triplet in bill_types:\n bill_type = bill_types[triplet]\n\n subs = []\n bid = bill_id.replace(\" \", \"\")\n\n if bid in self._subjects:\n subs = self._subjects[bid]\n self.info(\"With subjects for this bill\")\n\n self.info(bid)\n\n if bid == 'XXXXXX':\n self.info(\"Skipping Junk Bill\")\n return\n\n bill = Bill(\n bill_id,\n title=bill_desc,\n chamber='upper',\n legislative_session=self._session_id,\n classification=bill_type,\n )\n bill.subject = subs\n bill.add_abstract(bill_desc, note='abstract')\n bill.add_source(bill_url)\n\n if bill_title:\n bill.add_title(bill_title)\n\n # Get the primary sponsor\n sponsor = bill_page.xpath('//a[@id=\"hlSponsor\"]')[0]\n bill_sponsor = sponsor.text_content()\n # bill_sponsor_link = sponsor.attrib.get('href')\n bill.add_sponsorship(\n bill_sponsor,\n entity_type='person',\n classification='primary',\n primary=True,\n )\n\n # cosponsors show up on their own page, if they exist\n cosponsor_tag = bill_page.xpath('//a[@id=\"hlCoSponsors\"]')\n if len(cosponsor_tag) > 0 and cosponsor_tag[0].attrib.get('href'):\n self._parse_senate_cosponsors(bill, cosponsor_tag[0].attrib['href'])\n\n # get the actions\n action_url = bill_page.xpath('//a[@id=\"hlAllActions\"]')\n if len(action_url) > 0:\n action_url = action_url[0].attrib['href']\n self._parse_senate_actions(bill, action_url)\n\n # stored on a separate page\n versions_url = bill_page.xpath('//a[@id=\"hlFullBillText\"]')\n if len(versions_url) > 0 and versions_url[0].attrib.get('href'):\n self._parse_senate_bill_versions(bill, versions_url[0].attrib['href'])\n\n yield bill\n\n def _parse_senate_bill_versions(self, bill, url):\n bill.add_source(url)\n versions_page = self.get(url).text\n versions_page = lxml.html.fromstring(versions_page)\n version_tags = versions_page.xpath('//li/font/a')\n\n # some pages are updated and use different structure\n if not version_tags:\n version_tags = versions_page.xpath('//tr/td/a[contains(@href, \".pdf\")]')\n\n for version_tag in version_tags:\n description = version_tag.text_content()\n pdf_url = version_tag.attrib['href']\n if pdf_url.endswith('pdf'):\n mimetype = 'application/pdf'\n else:\n mimetype = None\n bill.add_version_link(description, pdf_url, media_type=mimetype,\n on_duplicate='ignore')\n\n def _parse_senate_actions(self, bill, url):\n bill.add_source(url)\n actions_page = self.get(url).text\n actions_page = lxml.html.fromstring(actions_page)\n bigtable = actions_page.xpath('/html/body/font/form/table/tr[3]/td/div/table/tr')\n\n for row in bigtable:\n date = row[0].text_content()\n date = dt.datetime.strptime(date, '%m/%d/%Y')\n action = row[1].text_content()\n actor = senate_get_actor_from_action(action)\n type_class = self._get_action(actor, action)\n bill.add_action(\n action, TIMEZONE.localize(date), chamber=actor, classification=type_class)\n\n def _parse_senate_cosponsors(self, bill, url):\n bill.add_source(url)\n cosponsors_page = self.get(url).text\n cosponsors_page = lxml.html.fromstring(cosponsors_page)\n # cosponsors are all in a table\n cosponsors = cosponsors_page.xpath('//table[@id=\"dgCoSponsors\"]/tr/td/a')\n\n for cosponsor_row in cosponsors:\n # cosponsors include district, so parse that out\n cosponsor_string = cosponsor_row.text_content()\n cosponsor = clean_text(cosponsor_string)\n cosponsor = cosponsor.split(',')[0]\n\n # they give us a link to the congressperson, so we might\n # as well keep it.\n if cosponsor_row.attrib.get('href'):\n # cosponsor_url = cosponsor_row.attrib['href']\n bill.add_sponsorship(\n cosponsor,\n entity_type='person',\n classification='cosponsor',\n primary=False,\n )\n else:\n bill.add_sponsorship(\n cosponsor,\n entity_type='person',\n classification='cosponsor',\n primary=False,\n )\n\n def _scrape_house_subjects(self, session):\n self.info('Collecting subject tags from lower house.')\n\n subject_list_url = \\\n 'http://house.mo.gov/LegislationSP.aspx?code=R&category=subjectindex&year={}'\\\n .format(session)\n subject_page = self.lxmlize(subject_list_url)\n\n # Create a list of all the possible bill subjects.\n subjects = self.get_nodes(\n subject_page,\n \"//div[@id='ContentPlaceHolder1_panelParentDIV']\" # ...\n \"/div[@id='panelDIV']//div[@id='ExpandedPanel']//a\")\n\n # Find the list of bills within each subject.\n for subject in subjects:\n\n subject_text = re.sub(r\"\\([0-9]+\\).*\", '', subject.text, re.IGNORECASE).strip()\n self.info('Searching for bills in {}.'.format(subject_text))\n\n subject_page = self.lxmlize(subject.attrib['href'])\n\n bill_nodes = self.get_nodes(\n subject_page,\n '//table[@id=\"reportgrid\"]/tbody/tr[@class=\"reportbillinfo\"]')\n\n # Move onto the next subject if no bills were found.\n if bill_nodes is None or not (len(bill_nodes) > 0):\n continue\n\n for bill_node in bill_nodes:\n bill_id = self.get_node(\n bill_node,\n '(./td)[1]/a/text()[normalize-space()]')\n\n # Skip to the next bill if no ID could be found.\n if bill_id is None or not (len(bill_id) > 0):\n continue\n\n self.info('Found {}.'.format(bill_id))\n self._subjects[bill_id].append(subject_text)\n\n def _parse_house_actions(self, bill, url):\n bill.add_source(url)\n actions_page = self.get(url).text\n actions_page = lxml.html.fromstring(actions_page)\n rows = actions_page.xpath('//table/tr')\n\n for row in rows:\n # new actions are represented by having dates in the first td\n # otherwise, it's a continuation of the description from the\n # previous action\n if len(row) > 0 and row[0].tag == 'td':\n if len(row[0].text_content().strip()) > 0:\n date = row[0].text_content().strip()\n date = dt.datetime.strptime(date, '%m/%d/%Y')\n action = row[2].text_content().strip()\n else:\n action += ('\\n' + row[2].text_content())\n action = action.rstrip()\n actor = house_get_actor_from_action(action)\n type_class = self._get_action(actor, action)\n\n yield from self._get_votes(date, actor, action, bill, url)\n\n bill.add_action(\n action, TIMEZONE.localize(date), chamber=actor, classification=type_class)\n\n def _parse_house_billpage(self, url, year):\n bill_list_page = self.get(url).text\n bill_list_page = lxml.html.fromstring(bill_list_page)\n # find the first center tag, take the text after\n # 'House of Representatives' and before 'Bills' as\n # the session name\n # header_tag = bill_list_page.xpath(\n # '//*[@id=\"ContentPlaceHolder1_lblAssemblyInfo\"]'\n # )[0].text_content()\n # if header_tag.find('1st Extraordinary Session') != -1:\n # session = year + ' 1st Extraordinary Session'\n # elif header_tag.find('2nd Extraordinary Session') != -1:\n # session = year + ' 2nd Extraordinary Session'\n # else:\n session = year\n\n bills = bill_list_page.xpath('//table[@id=\"reportgrid\"]//tr')\n\n isEven = False\n count = 0\n bills = bills[2:]\n for bill in bills:\n\n if not isEven:\n # the non even rows contain bill links, the other rows contain brief\n # descriptions of the bill.\n count = count + 1\n yield from self._parse_house_bill(bill[0][0].attrib['href'], session)\n isEven = not isEven\n\n def _parse_house_bill(self, url, session):\n # using the print page makes the page simpler, and also *drastically* smaller\n # (8k rather than 100k)\n url = re.sub(\"billsummary\", \"billsummaryprn\", url)\n url = '%s/%s' % (self._house_base_url, url)\n\n # the URL is an iframed version now, so swap in for the actual bill page\n\n url = url.replace('Bill.aspx', 'BillContent.aspx')\n url = url.replace('&code=R', '&code=R&style=new')\n\n # http://www.house.mo.gov/Bill.aspx?bill=HB26&year=2017&code=R\n # http://www.house.mo.gov/BillContent.aspx?bill=HB26&year=2017&code=R&style=new\n\n bill_page = self.get(url).text\n bill_page = lxml.html.fromstring(bill_page)\n bill_page.make_links_absolute(url)\n\n bill_id = bill_page.xpath('//*[@class=\"entry-title\"]/div')\n if len(bill_id) == 0:\n self.info(\"WARNING: bill summary page is blank! (%s)\" % url)\n self._bad_urls.append(url)\n return\n bill_id = bill_id[0].text_content()\n bill_id = clean_text(bill_id)\n\n bill_desc = bill_page.xpath('//*[@class=\"BillDescription\"]')[0].text_content()\n bill_desc = clean_text(bill_desc)\n\n table_rows = bill_page.xpath('//table/tr')\n # if there is a cosponsor all the rows are pushed down one for the extra row\n # for the cosponsor:\n cosponsorOffset = 0\n if table_rows[2][0].text_content().strip() == 'Co-Sponsor:':\n cosponsorOffset = 1\n\n lr_label_tag = table_rows[3 + cosponsorOffset]\n assert lr_label_tag[0].text_content().strip() == 'LR Number:'\n # bill_lr = lr_label_tag[1].text_content()\n\n lastActionOffset = 0\n if table_rows[4 + cosponsorOffset][0].text_content().strip() == 'Governor Action:':\n lastActionOffset = 1\n official_title_tag = table_rows[5 + cosponsorOffset + lastActionOffset]\n assert official_title_tag[0].text_content().strip() == 'Bill String:'\n official_title = official_title_tag[1].text_content()\n\n # could substitute the description for the name,\n # but keeping it separate for now.\n\n bill_type = \"bill\"\n triplet = bill_id[:3]\n\n if triplet in bill_types:\n bill_type = bill_types[triplet]\n bill_number = int(bill_id[3:].strip())\n else:\n bill_number = int(bill_id[3:])\n\n subs = []\n bid = bill_id.replace(\" \", \"\")\n\n if bid in self._subjects:\n subs = self._subjects[bid]\n self.info(\"With subjects for this bill\")\n\n self.info(bid)\n\n if bill_desc == \"\":\n if bill_number <= 20:\n # blank bill titles early in session are approp. bills\n bill_desc = 'Appropriations Bill'\n else:\n self.error(\"Blank title. Skipping. {} / {} / {}\".format(\n bill_id, bill_desc, official_title\n ))\n return\n\n bill = Bill(\n bill_id,\n chamber='lower',\n title=bill_desc,\n legislative_session=self._session_id,\n classification=bill_type,\n )\n bill.subject = subs\n bill.add_title(official_title, note='official')\n\n bill.add_source(url)\n\n bill_sponsor = clean_text(table_rows[0][1].text_content())\n # try:\n # bill_sponsor_link = table_rows[0][1][0].attrib['href']\n # except IndexError:\n # return\n bill.add_sponsorship(\n bill_sponsor,\n entity_type='person',\n classification='primary',\n primary=True,\n )\n\n # check for cosponsors\n sponsors_url, = bill_page.xpath(\n \"//a[contains(@href, 'CoSponsors.aspx')]/@href\")\n self._parse_cosponsors_from_bill(bill, sponsors_url)\n\n # actions_link_tag = bill_page.xpath('//div[@class=\"Sections\"]/a')[0]\n # actions_link = '%s/%s' % (self._house_base_url,actions_link_tag.attrib['href'])\n # actions_link = re.sub(\"content\", \"print\", actions_link)\n\n actions_link, = bill_page.xpath(\n \"//a[contains(@href, 'BillActions.aspx')]/@href\")\n yield from self._parse_house_actions(bill, actions_link)\n\n # get bill versions\n doc_tags = bill_page.xpath('//div[@class=\"BillDocuments\"][1]/span')\n for doc_tag in reversed(doc_tags):\n doc = clean_text(doc_tag.text_content())\n text_url = '%s%s' % (\n self._house_base_url,\n doc_tag[0].attrib['href']\n )\n bill.add_document_link(doc, text_url, media_type='text/html')\n\n # get bill versions\n version_tags = bill_page.xpath('//div[@class=\"BillDocuments\"][2]/span')\n for version_tag in reversed(version_tags):\n version = clean_text(version_tag.text_content())\n for vurl in version_tag.xpath(\".//a\"):\n if vurl.text == 'PDF':\n mimetype = 'application/pdf'\n else:\n mimetype = 'text/html'\n bill.add_version_link(version, vurl.attrib['href'], media_type=mimetype,\n on_duplicate='ignore')\n\n # house bill versions\n # everything between the row containing \"Bill Text\"\" and the next div.DocHeaderRow\n version_rows = bill_page.xpath(\n '//div[contains(text(),\"Bill Text\")]/'\n 'following-sibling::div[contains(@class,\"DocRow\") '\n 'and count(preceding-sibling::div[contains(@class,\"DocHeaderRow\")])=1]')\n for row in version_rows:\n # some rows are just broken links, not real versions\n if row.xpath('.//div[contains(@class,\"textType\")]/a/@href'):\n version = row.xpath('.//div[contains(@class,\"textType\")]/a/text()')[0].strip()\n path = row.xpath('.//div[contains(@class,\"textType\")]/a/@href')[0].strip()\n if '.pdf' in path:\n mimetype = 'application/pdf'\n else:\n mimetype = 'text/html'\n bill.add_version_link(version, path, media_type=mimetype,\n on_duplicate='ignore')\n\n # house bill summaries\n # everything between the row containing \"Bill Summary\"\" and the next div.DocHeaderRow\n summary_rows = bill_page.xpath(\n '//div[contains(text(),\"Bill Summary\")]/'\n 'following-sibling::div[contains(@class,\"DocRow\") '\n 'and count(following-sibling::div[contains(@class,\"DocHeaderRow\")])=1]')\n\n # if there are no amedments, we need a different xpath for summaries\n if not summary_rows:\n summary_rows = bill_page.xpath(\n '//div[contains(text(),\"Bill Summary\")]/'\n 'following-sibling::div[contains(@class,\"DocRow\")]')\n\n for row in reversed(summary_rows):\n version = row.xpath('.//div[contains(@class,\"textType\")]/a/text()')[0].strip()\n if version:\n path = row.xpath('.//div[contains(@class,\"textType\")]/a/@href')[0].strip()\n summary_name = 'Bill Summary ({})'.format(version)\n if '.pdf' in path:\n mimetype = 'application/pdf'\n else:\n mimetype = 'text/html'\n bill.add_document_link(summary_name, path, media_type=mimetype,\n on_duplicate='ignore')\n\n # house bill amendments\n amendment_rows = bill_page.xpath('//div[contains(text(),\"Amendment\")]/'\n 'following-sibling::div[contains(@class,\"DocRow\")]')\n\n for row in reversed(amendment_rows):\n version = row.xpath('.//div[contains(@class,\"DocInfoCell\")]/a[1]/text()')[0].strip()\n path = row.xpath('.//div[contains(@class,\"DocInfoCell\")]/a[1]/@href')[0].strip()\n summary_name = 'Amendment {}'.format(version)\n\n defeated_icon = row.xpath('.//img[contains(@title,\"Defeated\")]')\n if defeated_icon:\n summary_name = '{} (Defeated)'.format(summary_name)\n\n adopted_icon = row.xpath('.//img[contains(@title,\"Adopted\")]')\n if adopted_icon:\n summary_name = '{} (Adopted)'.format(summary_name)\n\n distributed_icon = row.xpath('.//img[contains(@title,\"Distributed\")]')\n if distributed_icon:\n summary_name = '{} (Distributed)'.format(summary_name)\n\n if '.pdf' in path:\n mimetype = 'application/pdf'\n else:\n mimetype = 'text/html'\n bill.add_version_link(summary_name, path, media_type=mimetype,\n on_duplicate='ignore')\n\n yield bill\n\n def _scrape_upper_chamber(self, session):\n self.info('Scraping bills from upper chamber.')\n\n year2 = \"%02d\" % (int(session[:4]) % 100)\n\n # Save the root URL, since we'll use it later.\n bill_root = 'http://www.senate.mo.gov/{}info/BTS_Web/'.format(year2)\n index_url = bill_root + 'BillList.aspx?SessionType=' + self.session_type(session)\n\n index_page = self.get(index_url).text\n index_page = lxml.html.fromstring(index_page)\n # Each bill is in it's own table (nested within a larger table).\n bill_tables = index_page.xpath('//a[@id]')\n\n if not bill_tables:\n return\n\n for bill_table in bill_tables:\n # Here we just search the whole table string to get the BillID that\n # the MO senate site uses.\n if re.search(r'dgBillList.*hlBillNum', bill_table.attrib['id']):\n yield from self._parse_senate_billpage(\n bill_root + bill_table.attrib.get('href'),\n session,\n )\n\n def _scrape_lower_chamber(self, session):\n self.info('Scraping bills from lower chamber.')\n\n if 'S' in session:\n year = session[:4]\n code = session[4:]\n else:\n year = session\n code = 'R'\n\n bill_page_url = '{}/BillList.aspx?year={}&code={}'.format(\n self._house_base_url, year, code)\n yield from self._parse_house_billpage(bill_page_url, year)\n\n def scrape(self, chamber=None, session=None):\n if not session:\n session = self.latest_session()\n self.info('no session specified, using %s', session)\n\n # special sessions and other year manipulation messes up the session variable\n # but we need it for correct output\n self._session_id = session\n\n if chamber in ['upper', None]:\n yield from self._scrape_upper_chamber(session)\n if chamber in ['lower', None]:\n yield from self._scrape_lower_chamber(session)\n\n if len(self._bad_urls) > 0:\n self.warning('WARNINGS:')\n for url in self._bad_urls:\n self.warning('{}'.format(url))\n", "path": "openstates/mo/bills.py" } ]
diff --git a/openstates/mo/bills.py b/openstates/mo/bills.py index ce8fcea385..ca00cec489 100644 --- a/openstates/mo/bills.py +++ b/openstates/mo/bills.py @@ -157,6 +157,10 @@ def _parse_senate_billpage(self, bill_url, year): self.info(bid) + if bid == 'XXXXXX': + self.info("Skipping Junk Bill") + return + bill = Bill( bill_id, title=bill_desc,
hylang__hy-1898
`lfor` can't see an imported name in `hy -c` This one is deeply bizarre. ``` $ CODE='(import math) (print (lfor j [1] (math.sqrt 5)))' $ hy -c "$CODE" Traceback (most recent call last): File "/usr/local/bin/hy", line 8, in <module> sys.exit(hy_main()) File "<string>", line 1, in <module> File "<string>", line 1, in <listcomp> NameError: name 'math' is not defined $ echo "$CODE" | hy hy 0.18.0 using CPython(default) 3.7.5 on Linux => [2.23606797749979] => now exiting HyREPL... $ echo "$CODE" > /tmp/foo.hy && hy /tmp/foo.hy [2.23606797749979] ```
[ { "content": "# Copyright 2020 the authors.\n# This file is part of Hy, which is free software licensed under the Expat\n# license. See the LICENSE.\n\nfrom __future__ import print_function\n\nimport colorama\ncolorama.init()\n\nimport argparse\nimport code\nimport ast\nimport sys\nimport os\nimport io\nimport importlib\nimport py_compile\nimport traceback\nimport runpy\nimport types\nimport time\nimport linecache\nimport hashlib\nimport codeop\nimport builtins\n\nimport astor.code_gen\n\nimport hy\n\nfrom hy.lex import hy_parse, mangle\nfrom contextlib import contextmanager\nfrom hy.lex.exceptions import PrematureEndOfInput\nfrom hy.compiler import (HyASTCompiler, hy_eval, hy_compile,\n hy_ast_compile_flags)\nfrom hy.errors import (HyLanguageError, HyRequireError, HyMacroExpansionError,\n filtered_hy_exceptions, hy_exc_handler)\nfrom hy.importer import runhy\nfrom hy.completer import completion, Completer\nfrom hy.macros import macro, require\nfrom hy.models import HyExpression, HyString, HySymbol\n\n\nsys.last_type = None\nsys.last_value = None\nsys.last_traceback = None\n\n\nclass HyQuitter(object):\n def __init__(self, name):\n self.name = name\n\n def __repr__(self):\n return \"Use (%s) or Ctrl-D (i.e. EOF) to exit\" % (self.name)\n\n __str__ = __repr__\n\n def __call__(self, code=None):\n try:\n sys.stdin.close()\n except:\n pass\n raise SystemExit(code)\n\nclass HyHelper(object):\n def __repr__(self):\n return (\"Use (help) for interactive help, or (help object) for help \"\n \"about object.\")\n\n def __call__(self, *args, **kwds):\n import pydoc\n return pydoc.help(*args, **kwds)\n\n\nbuiltins.quit = HyQuitter('quit')\nbuiltins.exit = HyQuitter('exit')\nbuiltins.help = HyHelper()\n\n@contextmanager\ndef extend_linecache(add_cmdline_cache):\n _linecache_checkcache = linecache.checkcache\n\n def _cmdline_checkcache(*args):\n _linecache_checkcache(*args)\n linecache.cache.update(add_cmdline_cache)\n\n linecache.checkcache = _cmdline_checkcache\n yield\n linecache.checkcache = _linecache_checkcache\n\n\n_codeop_maybe_compile = codeop._maybe_compile\n\n\ndef _hy_maybe_compile(compiler, source, filename, symbol):\n \"\"\"The `codeop` version of this will compile the same source multiple\n times, and, since we have macros and things like `eval-and-compile`, we\n can't allow that.\n \"\"\"\n if not isinstance(compiler, HyCompile):\n return _codeop_maybe_compile(compiler, source, filename, symbol)\n\n for line in source.split(\"\\n\"):\n line = line.strip()\n if line and line[0] != ';':\n # Leave it alone (could do more with Hy syntax)\n break\n else:\n if symbol != \"eval\":\n # Replace it with a 'pass' statement (i.e. tell the compiler to do\n # nothing)\n source = \"pass\"\n\n return compiler(source, filename, symbol)\n\n\ncodeop._maybe_compile = _hy_maybe_compile\n\n\nclass HyCompile(codeop.Compile, object):\n \"\"\"This compiler uses `linecache` like\n `IPython.core.compilerop.CachingCompiler`.\n \"\"\"\n\n def __init__(self, module, locals, ast_callback=None,\n hy_compiler=None, cmdline_cache={}):\n self.module = module\n self.locals = locals\n self.ast_callback = ast_callback\n self.hy_compiler = hy_compiler\n\n super(HyCompile, self).__init__()\n\n self.flags |= hy_ast_compile_flags\n\n self.cmdline_cache = cmdline_cache\n\n def _cache(self, source, name):\n entry = (len(source),\n time.time(),\n [line + '\\n' for line in source.splitlines()],\n name)\n\n linecache.cache[name] = entry\n self.cmdline_cache[name] = entry\n\n def _update_exc_info(self):\n self.locals['_hy_last_type'] = sys.last_type\n self.locals['_hy_last_value'] = sys.last_value\n # Skip our frame.\n sys.last_traceback = getattr(sys.last_traceback, 'tb_next',\n sys.last_traceback)\n self.locals['_hy_last_traceback'] = sys.last_traceback\n\n def __call__(self, source, filename=\"<input>\", symbol=\"single\"):\n\n if source == 'pass':\n # We need to return a no-op to signal that no more input is needed.\n return (compile(source, filename, symbol),) * 2\n\n hash_digest = hashlib.sha1(source.encode(\"utf-8\").strip()).hexdigest()\n name = '{}-{}'.format(filename.strip('<>'), hash_digest)\n\n try:\n hy_ast = hy_parse(source, filename=name)\n except Exception:\n # Capture a traceback without the compiler/REPL frames.\n sys.last_type, sys.last_value, sys.last_traceback = sys.exc_info()\n self._update_exc_info()\n raise\n\n self._cache(source, name)\n\n try:\n hy_ast = hy_parse(source, filename=filename)\n root_ast = ast.Interactive if symbol == 'single' else ast.Module\n\n # Our compiler doesn't correspond to a real, fixed source file, so\n # we need to [re]set these.\n self.hy_compiler.filename = filename\n self.hy_compiler.source = source\n exec_ast, eval_ast = hy_compile(hy_ast, self.module, root=root_ast,\n get_expr=True,\n compiler=self.hy_compiler,\n filename=filename, source=source)\n\n if self.ast_callback:\n self.ast_callback(exec_ast, eval_ast)\n\n exec_code = super(HyCompile, self).__call__(exec_ast, name, symbol)\n eval_code = super(HyCompile, self).__call__(eval_ast, name, 'eval')\n\n except HyLanguageError:\n # Hy will raise exceptions during compile-time that Python would\n # raise during run-time (e.g. import errors for `require`). In\n # order to work gracefully with the Python world, we convert such\n # Hy errors to code that purposefully reraises those exceptions in\n # the places where Python code expects them.\n sys.last_type, sys.last_value, sys.last_traceback = sys.exc_info()\n self._update_exc_info()\n exec_code = super(HyCompile, self).__call__(\n 'import hy._compat; hy._compat.reraise('\n '_hy_last_type, _hy_last_value, _hy_last_traceback)',\n name, symbol)\n eval_code = super(HyCompile, self).__call__('None', name, 'eval')\n\n return exec_code, eval_code\n\n\nclass HyCommandCompiler(codeop.CommandCompiler, object):\n def __init__(self, *args, **kwargs):\n self.compiler = HyCompile(*args, **kwargs)\n\n def __call__(self, *args, **kwargs):\n try:\n return super(HyCommandCompiler, self).__call__(*args, **kwargs)\n except PrematureEndOfInput:\n # We have to do this here, because `codeop._maybe_compile` won't\n # take `None` for a return value (at least not in Python 2.7) and\n # this exception type is also a `SyntaxError`, so it will be caught\n # by `code.InteractiveConsole` base methods before it reaches our\n # `runsource`.\n return None\n\n\nclass HyREPL(code.InteractiveConsole, object):\n def __init__(self, spy=False, output_fn=None, locals=None,\n filename=\"<stdin>\"):\n\n # Create a proper module for this REPL so that we can obtain it easily\n # (e.g. using `importlib.import_module`).\n # We let `InteractiveConsole` initialize `self.locals` when it's\n # `None`.\n super(HyREPL, self).__init__(locals=locals,\n filename=filename)\n\n module_name = self.locals.get('__name__', '__console__')\n # Make sure our newly created module is properly introduced to\n # `sys.modules`, and consistently use its namespace as `self.locals`\n # from here on.\n self.module = sys.modules.setdefault(module_name,\n types.ModuleType(module_name))\n self.module.__dict__.update(self.locals)\n self.locals = self.module.__dict__\n\n # Load cmdline-specific macros.\n require('hy.cmdline', self.module, assignments='ALL')\n\n self.hy_compiler = HyASTCompiler(self.module)\n\n self.cmdline_cache = {}\n self.compile = HyCommandCompiler(self.module,\n self.locals,\n ast_callback=self.ast_callback,\n hy_compiler=self.hy_compiler,\n cmdline_cache=self.cmdline_cache)\n\n self.spy = spy\n self.last_value = None\n self.print_last_value = True\n\n if output_fn is None:\n self.output_fn = repr\n elif callable(output_fn):\n self.output_fn = output_fn\n else:\n if \".\" in output_fn:\n parts = [mangle(x) for x in output_fn.split(\".\")]\n module, f = '.'.join(parts[:-1]), parts[-1]\n self.output_fn = getattr(importlib.import_module(module), f)\n else:\n self.output_fn = getattr(builtins, mangle(output_fn))\n\n # Pre-mangle symbols for repl recent results: *1, *2, *3\n self._repl_results_symbols = [mangle(\"*{}\".format(i + 1)) for i in range(3)]\n self.locals.update({sym: None for sym in self._repl_results_symbols})\n\n # Allow access to the running REPL instance\n self.locals['_hy_repl'] = self\n\n def ast_callback(self, exec_ast, eval_ast):\n if self.spy:\n try:\n # Mush the two AST chunks into a single module for\n # conversion into Python.\n new_ast = ast.Module(\n exec_ast.body + [ast.Expr(eval_ast.body)],\n type_ignores=[])\n print(astor.to_source(new_ast))\n except Exception:\n msg = 'Exception in AST callback:\\n{}\\n'.format(\n traceback.format_exc())\n self.write(msg)\n\n def _error_wrap(self, error_fn, exc_info_override=False, *args, **kwargs):\n sys.last_type, sys.last_value, sys.last_traceback = sys.exc_info()\n\n if exc_info_override:\n # Use a traceback that doesn't have the REPL frames.\n sys.last_type = self.locals.get('_hy_last_type', sys.last_type)\n sys.last_value = self.locals.get('_hy_last_value', sys.last_value)\n sys.last_traceback = self.locals.get('_hy_last_traceback',\n sys.last_traceback)\n\n # Sadly, this method in Python 2.7 ignores an overridden `sys.excepthook`.\n if sys.excepthook is sys.__excepthook__:\n error_fn(*args, **kwargs)\n else:\n sys.excepthook(sys.last_type, sys.last_value, sys.last_traceback)\n\n self.locals[mangle(\"*e\")] = sys.last_value\n\n def showsyntaxerror(self, filename=None):\n if filename is None:\n filename = self.filename\n\n self._error_wrap(super(HyREPL, self).showsyntaxerror,\n exc_info_override=True,\n filename=filename)\n\n def showtraceback(self):\n self._error_wrap(super(HyREPL, self).showtraceback)\n\n def runcode(self, code):\n try:\n eval(code[0], self.locals)\n self.last_value = eval(code[1], self.locals)\n # Don't print `None` values.\n self.print_last_value = self.last_value is not None\n except SystemExit:\n raise\n except Exception as e:\n # Set this to avoid a print-out of the last value on errors.\n self.print_last_value = False\n self.showtraceback()\n\n def runsource(self, source, filename='<stdin>', symbol='exec'):\n try:\n res = super(HyREPL, self).runsource(source, filename, symbol)\n except (HyMacroExpansionError, HyRequireError):\n # We need to handle these exceptions ourselves, because the base\n # method only handles `OverflowError`, `SyntaxError` and\n # `ValueError`.\n self.showsyntaxerror(filename)\n return False\n except (HyLanguageError):\n # Our compiler will also raise `TypeError`s\n self.showtraceback()\n return False\n\n # Shift exisitng REPL results\n if not res:\n next_result = self.last_value\n for sym in self._repl_results_symbols:\n self.locals[sym], next_result = next_result, self.locals[sym]\n\n # Print the value.\n if self.print_last_value:\n try:\n output = self.output_fn(self.last_value)\n except Exception:\n self.showtraceback()\n return False\n\n print(output)\n\n return res\n\n\n@macro(\"koan\")\ndef koan_macro(ETname):\n return HyExpression([HySymbol('print'),\n HyString(\"\"\"\n Ummon asked the head monk, \"What sutra are you lecturing on?\"\n \"The Nirvana Sutra.\"\n \"The Nirvana Sutra has the Four Virtues, hasn't it?\"\n \"It has.\"\n Ummon asked, picking up a cup, \"How many virtues has this?\"\n \"None at all,\" said the monk.\n \"But ancient people said it had, didn't they?\" said Ummon.\n \"What do you think of what they said?\"\n Ummon struck the cup and asked, \"You understand?\"\n \"No,\" said the monk.\n \"Then,\" said Ummon, \"You'd better go on with your lectures on the sutra.\"\n\"\"\")])\n\n\n@macro(\"ideas\")\ndef ideas_macro(ETname):\n return HyExpression([HySymbol('print'),\n HyString(r\"\"\"\n\n => (import [sh [figlet]])\n => (figlet \"Hi, Hy!\")\n _ _ _ _ _ _\n | | | (_) | | | |_ _| |\n | |_| | | | |_| | | | | |\n | _ | |_ | _ | |_| |_|\n |_| |_|_( ) |_| |_|\\__, (_)\n |/ |___/\n\n\n;;; string things\n(.join \", \" [\"what\" \"the\" \"heck\"])\n\n\n;;; this one plays with command line bits\n(import [sh [cat grep]])\n(-> (cat \"/usr/share/dict/words\") (grep \"-E\" \"bro$\"))\n\n\n;;; filtering a list w/ a lambda\n(filter (fn [x] (= (% x 2) 0)) (range 0 10))\n\n\n;;; swaggin' functional bits (Python rulez)\n(max (map (fn [x] (len x)) [\"hi\" \"my\" \"name\" \"is\" \"paul\"]))\n\n\"\"\")])\n\n\ndef run_command(source, filename=None):\n __main__ = importlib.import_module('__main__')\n require(\"hy.cmdline\", __main__, assignments=\"ALL\")\n try:\n tree = hy_parse(source, filename=filename)\n except HyLanguageError:\n hy_exc_handler(*sys.exc_info())\n return 1\n\n with filtered_hy_exceptions():\n hy_eval(tree, None, __main__, filename=filename, source=source)\n return 0\n\n\ndef run_repl(hr=None, **kwargs):\n import platform\n sys.ps1 = \"=> \"\n sys.ps2 = \"... \"\n\n if not hr:\n hr = HyREPL(**kwargs)\n\n namespace = hr.locals\n with filtered_hy_exceptions(), \\\n extend_linecache(hr.cmdline_cache), \\\n completion(Completer(namespace)):\n hr.interact(\"{appname} {version} using \"\n \"{py}({build}) {pyversion} on {os}\".format(\n appname=hy.__appname__,\n version=hy.__version__,\n py=platform.python_implementation(),\n build=platform.python_build()[0],\n pyversion=platform.python_version(),\n os=platform.system()\n ))\n\n return 0\n\n\ndef run_icommand(source, **kwargs):\n if os.path.exists(source):\n # Emulate Python cmdline behavior by setting `sys.path` relative\n # to the executed file's location.\n if sys.path[0] == '':\n sys.path[0] = os.path.realpath(os.path.split(source)[0])\n else:\n sys.path.insert(0, os.path.split(source)[0])\n\n with io.open(source, \"r\", encoding='utf-8') as f:\n source = f.read()\n filename = source\n else:\n filename = '<string>'\n\n hr = HyREPL(**kwargs)\n with filtered_hy_exceptions():\n res = hr.runsource(source, filename=filename)\n\n # If the command was prematurely ended, show an error (just like Python\n # does).\n if res:\n hy_exc_handler(sys.last_type, sys.last_value, sys.last_traceback)\n\n return run_repl(hr)\n\n\nUSAGE = \"%(prog)s [-h | -i cmd | -c cmd | -m module | file | -] [arg] ...\"\nVERSION = \"%(prog)s \" + hy.__version__\nEPILOG = \"\"\"\n file program read from script\n module module to execute as main\n - program read from stdin\n [arg] ... arguments passed to program in sys.argv[1:]\n\"\"\"\n\n\ndef cmdline_handler(scriptname, argv):\n parser = argparse.ArgumentParser(\n prog=\"hy\",\n usage=USAGE,\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=EPILOG)\n parser.add_argument(\"-c\", dest=\"command\",\n help=\"program passed in as a string\")\n parser.add_argument(\"-m\", dest=\"mod\",\n help=\"module to run, passed in as a string\")\n parser.add_argument(\"-E\", action='store_true',\n help=\"ignore PYTHON* environment variables\")\n parser.add_argument(\"-B\", action='store_true',\n help=\"don't write .py[co] files on import; also PYTHONDONTWRITEBYTECODE=x\")\n parser.add_argument(\"-i\", dest=\"icommand\",\n help=\"program passed in as a string, then stay in REPL\")\n parser.add_argument(\"--spy\", action=\"store_true\",\n help=\"print equivalent Python code before executing\")\n parser.add_argument(\"--repl-output-fn\",\n help=\"function for printing REPL output \"\n \"(e.g., hy.contrib.hy-repr.hy-repr)\")\n parser.add_argument(\"-v\", \"--version\", action=\"version\", version=VERSION)\n\n # this will contain the script/program name and any arguments for it.\n parser.add_argument('args', nargs=argparse.REMAINDER,\n help=argparse.SUPPRESS)\n\n # Get the path of the Hy cmdline executable and swap it with\n # `sys.executable` (saving the original, just in case).\n # XXX: The `__main__` module will also have `__file__` set to the\n # entry-point script. Currently, I don't see an immediate problem, but\n # that's not how the Python cmdline works.\n hy.executable = argv[0]\n hy.sys_executable = sys.executable\n sys.executable = hy.executable\n\n # Need to split the args. If using \"-m\" all args after the MOD are sent to\n # the module in sys.argv.\n module_args = []\n if \"-m\" in argv:\n mloc = argv.index(\"-m\")\n if len(argv) > mloc+2:\n module_args = argv[mloc+2:]\n argv = argv[:mloc+2]\n\n options = parser.parse_args(argv[1:])\n\n if options.E:\n # User did \"hy -E ...\"\n _remove_python_envs()\n\n if options.B:\n sys.dont_write_bytecode = True\n\n if options.command:\n # User did \"hy -c ...\"\n return run_command(options.command, filename='<string>')\n\n if options.mod:\n # User did \"hy -m ...\"\n sys.argv = [sys.argv[0]] + options.args + module_args\n runpy.run_module(options.mod, run_name='__main__', alter_sys=True)\n return 0\n\n if options.icommand:\n # User did \"hy -i ...\"\n return run_icommand(options.icommand, spy=options.spy,\n output_fn=options.repl_output_fn)\n\n if options.args:\n if options.args[0] == \"-\":\n # Read the program from stdin\n return run_command(sys.stdin.read(), filename='<stdin>')\n\n else:\n # User did \"hy <filename>\"\n filename = options.args[0]\n\n # Emulate Python cmdline behavior by setting `sys.path` relative\n # to the executed file's location.\n if sys.path[0] == '':\n sys.path[0] = os.path.realpath(os.path.split(filename)[0])\n else:\n sys.path.insert(0, os.path.split(filename)[0])\n\n try:\n sys.argv = options.args\n with filtered_hy_exceptions():\n runhy.run_path(filename, run_name='__main__')\n return 0\n except FileNotFoundError as e:\n print(\"hy: Can't open file '{0}': [Errno {1}] {2}\".format(\n e.filename, e.errno, e.strerror), file=sys.stderr)\n sys.exit(e.errno)\n except HyLanguageError:\n hy_exc_handler(*sys.exc_info())\n sys.exit(1)\n\n # User did NOTHING!\n return run_repl(spy=options.spy, output_fn=options.repl_output_fn)\n\n\n# entry point for cmd line script \"hy\"\ndef hy_main():\n sys.path.insert(0, \"\")\n sys.exit(cmdline_handler(\"hy\", sys.argv))\n\n\ndef hyc_main():\n parser = argparse.ArgumentParser(prog=\"hyc\")\n parser.add_argument(\"files\", metavar=\"FILE\", nargs='*',\n help=('File(s) to compile (use STDIN if only'\n ' \"-\" or nothing is provided)'))\n parser.add_argument(\"-v\", action=\"version\", version=VERSION)\n\n options = parser.parse_args(sys.argv[1:])\n\n rv = 0\n if len(options.files) == 0 or (\n len(options.files) == 1 and options.files[0] == '-'):\n while True:\n filename = sys.stdin.readline()\n if not filename:\n break\n filename = filename.rstrip('\\n')\n try:\n py_compile.compile(filename, doraise=True)\n except py_compile.PyCompileError as error:\n rv = 1\n sys.stderr.write(\"%s\\n\" % error.msg)\n except OSError as error:\n rv = 1\n sys.stderr.write(\"%s\\n\" % error)\n else:\n for filename in options.files:\n try:\n print(\"Compiling %s\" % filename)\n py_compile.compile(filename, doraise=True)\n except py_compile.PyCompileError as error:\n # return value to indicate at least one failure\n rv = 1\n sys.stderr.write(\"%s\\n\" % error.msg)\n return rv\n\n\n# entry point for cmd line script \"hy2py\"\ndef hy2py_main():\n import platform\n\n options = dict(prog=\"hy2py\", usage=\"%(prog)s [options] [FILE]\",\n formatter_class=argparse.RawDescriptionHelpFormatter)\n parser = argparse.ArgumentParser(**options)\n parser.add_argument(\"FILE\", type=str, nargs='?',\n help=\"Input Hy code (use STDIN if \\\"-\\\" or \"\n \"not provided)\")\n parser.add_argument(\"--with-source\", \"-s\", action=\"store_true\",\n help=\"Show the parsed source structure\")\n parser.add_argument(\"--with-ast\", \"-a\", action=\"store_true\",\n help=\"Show the generated AST\")\n parser.add_argument(\"--without-python\", \"-np\", action=\"store_true\",\n help=(\"Do not show the Python code generated \"\n \"from the AST\"))\n\n options = parser.parse_args(sys.argv[1:])\n\n if options.FILE is None or options.FILE == '-':\n filename = '<stdin>'\n source = sys.stdin.read()\n else:\n filename = options.FILE\n with io.open(options.FILE, 'r', encoding='utf-8') as source_file:\n source = source_file.read()\n\n with filtered_hy_exceptions():\n hst = hy_parse(source, filename=filename)\n\n if options.with_source:\n # need special printing on Windows in case the\n # codepage doesn't support utf-8 characters\n if platform.system() == \"Windows\":\n for h in hst:\n try:\n print(h)\n except:\n print(str(h).encode('utf-8'))\n else:\n print(hst)\n print()\n print()\n\n with filtered_hy_exceptions():\n _ast = hy_compile(hst, '__main__', filename=filename, source=source)\n\n if options.with_ast:\n if platform.system() == \"Windows\":\n _print_for_windows(astor.dump_tree(_ast))\n else:\n print(astor.dump_tree(_ast))\n print()\n print()\n\n if not options.without_python:\n if platform.system() == \"Windows\":\n _print_for_windows(astor.code_gen.to_source(_ast))\n else:\n print(astor.code_gen.to_source(_ast))\n\n parser.exit(0)\n\n\n# need special printing on Windows in case the\n# codepage doesn't support utf-8 characters\ndef _print_for_windows(src):\n for line in src.split(\"\\n\"):\n try:\n print(line)\n except:\n print(line.encode('utf-8'))\n\n# remove PYTHON* environment variables,\n# such as \"PYTHONPATH\"\ndef _remove_python_envs():\n for key in list(os.environ.keys()):\n if key.startswith(\"PYTHON\"):\n os.environ.pop(key)\n", "path": "hy/cmdline.py" } ]
[ { "content": "# Copyright 2020 the authors.\n# This file is part of Hy, which is free software licensed under the Expat\n# license. See the LICENSE.\n\nfrom __future__ import print_function\n\nimport colorama\ncolorama.init()\n\nimport argparse\nimport code\nimport ast\nimport sys\nimport os\nimport io\nimport importlib\nimport py_compile\nimport traceback\nimport runpy\nimport types\nimport time\nimport linecache\nimport hashlib\nimport codeop\nimport builtins\n\nimport astor.code_gen\n\nimport hy\n\nfrom hy.lex import hy_parse, mangle\nfrom contextlib import contextmanager\nfrom hy.lex.exceptions import PrematureEndOfInput\nfrom hy.compiler import (HyASTCompiler, hy_eval, hy_compile,\n hy_ast_compile_flags)\nfrom hy.errors import (HyLanguageError, HyRequireError, HyMacroExpansionError,\n filtered_hy_exceptions, hy_exc_handler)\nfrom hy.importer import runhy\nfrom hy.completer import completion, Completer\nfrom hy.macros import macro, require\nfrom hy.models import HyExpression, HyString, HySymbol\n\n\nsys.last_type = None\nsys.last_value = None\nsys.last_traceback = None\n\n\nclass HyQuitter(object):\n def __init__(self, name):\n self.name = name\n\n def __repr__(self):\n return \"Use (%s) or Ctrl-D (i.e. EOF) to exit\" % (self.name)\n\n __str__ = __repr__\n\n def __call__(self, code=None):\n try:\n sys.stdin.close()\n except:\n pass\n raise SystemExit(code)\n\nclass HyHelper(object):\n def __repr__(self):\n return (\"Use (help) for interactive help, or (help object) for help \"\n \"about object.\")\n\n def __call__(self, *args, **kwds):\n import pydoc\n return pydoc.help(*args, **kwds)\n\n\nbuiltins.quit = HyQuitter('quit')\nbuiltins.exit = HyQuitter('exit')\nbuiltins.help = HyHelper()\n\n@contextmanager\ndef extend_linecache(add_cmdline_cache):\n _linecache_checkcache = linecache.checkcache\n\n def _cmdline_checkcache(*args):\n _linecache_checkcache(*args)\n linecache.cache.update(add_cmdline_cache)\n\n linecache.checkcache = _cmdline_checkcache\n yield\n linecache.checkcache = _linecache_checkcache\n\n\n_codeop_maybe_compile = codeop._maybe_compile\n\n\ndef _hy_maybe_compile(compiler, source, filename, symbol):\n \"\"\"The `codeop` version of this will compile the same source multiple\n times, and, since we have macros and things like `eval-and-compile`, we\n can't allow that.\n \"\"\"\n if not isinstance(compiler, HyCompile):\n return _codeop_maybe_compile(compiler, source, filename, symbol)\n\n for line in source.split(\"\\n\"):\n line = line.strip()\n if line and line[0] != ';':\n # Leave it alone (could do more with Hy syntax)\n break\n else:\n if symbol != \"eval\":\n # Replace it with a 'pass' statement (i.e. tell the compiler to do\n # nothing)\n source = \"pass\"\n\n return compiler(source, filename, symbol)\n\n\ncodeop._maybe_compile = _hy_maybe_compile\n\n\nclass HyCompile(codeop.Compile, object):\n \"\"\"This compiler uses `linecache` like\n `IPython.core.compilerop.CachingCompiler`.\n \"\"\"\n\n def __init__(self, module, locals, ast_callback=None,\n hy_compiler=None, cmdline_cache={}):\n self.module = module\n self.locals = locals\n self.ast_callback = ast_callback\n self.hy_compiler = hy_compiler\n\n super(HyCompile, self).__init__()\n\n self.flags |= hy_ast_compile_flags\n\n self.cmdline_cache = cmdline_cache\n\n def _cache(self, source, name):\n entry = (len(source),\n time.time(),\n [line + '\\n' for line in source.splitlines()],\n name)\n\n linecache.cache[name] = entry\n self.cmdline_cache[name] = entry\n\n def _update_exc_info(self):\n self.locals['_hy_last_type'] = sys.last_type\n self.locals['_hy_last_value'] = sys.last_value\n # Skip our frame.\n sys.last_traceback = getattr(sys.last_traceback, 'tb_next',\n sys.last_traceback)\n self.locals['_hy_last_traceback'] = sys.last_traceback\n\n def __call__(self, source, filename=\"<input>\", symbol=\"single\"):\n\n if source == 'pass':\n # We need to return a no-op to signal that no more input is needed.\n return (compile(source, filename, symbol),) * 2\n\n hash_digest = hashlib.sha1(source.encode(\"utf-8\").strip()).hexdigest()\n name = '{}-{}'.format(filename.strip('<>'), hash_digest)\n\n try:\n hy_ast = hy_parse(source, filename=name)\n except Exception:\n # Capture a traceback without the compiler/REPL frames.\n sys.last_type, sys.last_value, sys.last_traceback = sys.exc_info()\n self._update_exc_info()\n raise\n\n self._cache(source, name)\n\n try:\n hy_ast = hy_parse(source, filename=filename)\n root_ast = ast.Interactive if symbol == 'single' else ast.Module\n\n # Our compiler doesn't correspond to a real, fixed source file, so\n # we need to [re]set these.\n self.hy_compiler.filename = filename\n self.hy_compiler.source = source\n exec_ast, eval_ast = hy_compile(hy_ast, self.module, root=root_ast,\n get_expr=True,\n compiler=self.hy_compiler,\n filename=filename, source=source)\n\n if self.ast_callback:\n self.ast_callback(exec_ast, eval_ast)\n\n exec_code = super(HyCompile, self).__call__(exec_ast, name, symbol)\n eval_code = super(HyCompile, self).__call__(eval_ast, name, 'eval')\n\n except HyLanguageError:\n # Hy will raise exceptions during compile-time that Python would\n # raise during run-time (e.g. import errors for `require`). In\n # order to work gracefully with the Python world, we convert such\n # Hy errors to code that purposefully reraises those exceptions in\n # the places where Python code expects them.\n sys.last_type, sys.last_value, sys.last_traceback = sys.exc_info()\n self._update_exc_info()\n exec_code = super(HyCompile, self).__call__(\n 'import hy._compat; hy._compat.reraise('\n '_hy_last_type, _hy_last_value, _hy_last_traceback)',\n name, symbol)\n eval_code = super(HyCompile, self).__call__('None', name, 'eval')\n\n return exec_code, eval_code\n\n\nclass HyCommandCompiler(codeop.CommandCompiler, object):\n def __init__(self, *args, **kwargs):\n self.compiler = HyCompile(*args, **kwargs)\n\n def __call__(self, *args, **kwargs):\n try:\n return super(HyCommandCompiler, self).__call__(*args, **kwargs)\n except PrematureEndOfInput:\n # We have to do this here, because `codeop._maybe_compile` won't\n # take `None` for a return value (at least not in Python 2.7) and\n # this exception type is also a `SyntaxError`, so it will be caught\n # by `code.InteractiveConsole` base methods before it reaches our\n # `runsource`.\n return None\n\n\nclass HyREPL(code.InteractiveConsole, object):\n def __init__(self, spy=False, output_fn=None, locals=None,\n filename=\"<stdin>\"):\n\n # Create a proper module for this REPL so that we can obtain it easily\n # (e.g. using `importlib.import_module`).\n # We let `InteractiveConsole` initialize `self.locals` when it's\n # `None`.\n super(HyREPL, self).__init__(locals=locals,\n filename=filename)\n\n module_name = self.locals.get('__name__', '__console__')\n # Make sure our newly created module is properly introduced to\n # `sys.modules`, and consistently use its namespace as `self.locals`\n # from here on.\n self.module = sys.modules.setdefault(module_name,\n types.ModuleType(module_name))\n self.module.__dict__.update(self.locals)\n self.locals = self.module.__dict__\n\n # Load cmdline-specific macros.\n require('hy.cmdline', self.module, assignments='ALL')\n\n self.hy_compiler = HyASTCompiler(self.module)\n\n self.cmdline_cache = {}\n self.compile = HyCommandCompiler(self.module,\n self.locals,\n ast_callback=self.ast_callback,\n hy_compiler=self.hy_compiler,\n cmdline_cache=self.cmdline_cache)\n\n self.spy = spy\n self.last_value = None\n self.print_last_value = True\n\n if output_fn is None:\n self.output_fn = repr\n elif callable(output_fn):\n self.output_fn = output_fn\n else:\n if \".\" in output_fn:\n parts = [mangle(x) for x in output_fn.split(\".\")]\n module, f = '.'.join(parts[:-1]), parts[-1]\n self.output_fn = getattr(importlib.import_module(module), f)\n else:\n self.output_fn = getattr(builtins, mangle(output_fn))\n\n # Pre-mangle symbols for repl recent results: *1, *2, *3\n self._repl_results_symbols = [mangle(\"*{}\".format(i + 1)) for i in range(3)]\n self.locals.update({sym: None for sym in self._repl_results_symbols})\n\n # Allow access to the running REPL instance\n self.locals['_hy_repl'] = self\n\n def ast_callback(self, exec_ast, eval_ast):\n if self.spy:\n try:\n # Mush the two AST chunks into a single module for\n # conversion into Python.\n new_ast = ast.Module(\n exec_ast.body + [ast.Expr(eval_ast.body)],\n type_ignores=[])\n print(astor.to_source(new_ast))\n except Exception:\n msg = 'Exception in AST callback:\\n{}\\n'.format(\n traceback.format_exc())\n self.write(msg)\n\n def _error_wrap(self, error_fn, exc_info_override=False, *args, **kwargs):\n sys.last_type, sys.last_value, sys.last_traceback = sys.exc_info()\n\n if exc_info_override:\n # Use a traceback that doesn't have the REPL frames.\n sys.last_type = self.locals.get('_hy_last_type', sys.last_type)\n sys.last_value = self.locals.get('_hy_last_value', sys.last_value)\n sys.last_traceback = self.locals.get('_hy_last_traceback',\n sys.last_traceback)\n\n # Sadly, this method in Python 2.7 ignores an overridden `sys.excepthook`.\n if sys.excepthook is sys.__excepthook__:\n error_fn(*args, **kwargs)\n else:\n sys.excepthook(sys.last_type, sys.last_value, sys.last_traceback)\n\n self.locals[mangle(\"*e\")] = sys.last_value\n\n def showsyntaxerror(self, filename=None):\n if filename is None:\n filename = self.filename\n\n self._error_wrap(super(HyREPL, self).showsyntaxerror,\n exc_info_override=True,\n filename=filename)\n\n def showtraceback(self):\n self._error_wrap(super(HyREPL, self).showtraceback)\n\n def runcode(self, code):\n try:\n eval(code[0], self.locals)\n self.last_value = eval(code[1], self.locals)\n # Don't print `None` values.\n self.print_last_value = self.last_value is not None\n except SystemExit:\n raise\n except Exception as e:\n # Set this to avoid a print-out of the last value on errors.\n self.print_last_value = False\n self.showtraceback()\n\n def runsource(self, source, filename='<stdin>', symbol='exec'):\n try:\n res = super(HyREPL, self).runsource(source, filename, symbol)\n except (HyMacroExpansionError, HyRequireError):\n # We need to handle these exceptions ourselves, because the base\n # method only handles `OverflowError`, `SyntaxError` and\n # `ValueError`.\n self.showsyntaxerror(filename)\n return False\n except (HyLanguageError):\n # Our compiler will also raise `TypeError`s\n self.showtraceback()\n return False\n\n # Shift exisitng REPL results\n if not res:\n next_result = self.last_value\n for sym in self._repl_results_symbols:\n self.locals[sym], next_result = next_result, self.locals[sym]\n\n # Print the value.\n if self.print_last_value:\n try:\n output = self.output_fn(self.last_value)\n except Exception:\n self.showtraceback()\n return False\n\n print(output)\n\n return res\n\n\n@macro(\"koan\")\ndef koan_macro(ETname):\n return HyExpression([HySymbol('print'),\n HyString(\"\"\"\n Ummon asked the head monk, \"What sutra are you lecturing on?\"\n \"The Nirvana Sutra.\"\n \"The Nirvana Sutra has the Four Virtues, hasn't it?\"\n \"It has.\"\n Ummon asked, picking up a cup, \"How many virtues has this?\"\n \"None at all,\" said the monk.\n \"But ancient people said it had, didn't they?\" said Ummon.\n \"What do you think of what they said?\"\n Ummon struck the cup and asked, \"You understand?\"\n \"No,\" said the monk.\n \"Then,\" said Ummon, \"You'd better go on with your lectures on the sutra.\"\n\"\"\")])\n\n\n@macro(\"ideas\")\ndef ideas_macro(ETname):\n return HyExpression([HySymbol('print'),\n HyString(r\"\"\"\n\n => (import [sh [figlet]])\n => (figlet \"Hi, Hy!\")\n _ _ _ _ _ _\n | | | (_) | | | |_ _| |\n | |_| | | | |_| | | | | |\n | _ | |_ | _ | |_| |_|\n |_| |_|_( ) |_| |_|\\__, (_)\n |/ |___/\n\n\n;;; string things\n(.join \", \" [\"what\" \"the\" \"heck\"])\n\n\n;;; this one plays with command line bits\n(import [sh [cat grep]])\n(-> (cat \"/usr/share/dict/words\") (grep \"-E\" \"bro$\"))\n\n\n;;; filtering a list w/ a lambda\n(filter (fn [x] (= (% x 2) 0)) (range 0 10))\n\n\n;;; swaggin' functional bits (Python rulez)\n(max (map (fn [x] (len x)) [\"hi\" \"my\" \"name\" \"is\" \"paul\"]))\n\n\"\"\")])\n\n\ndef run_command(source, filename=None):\n __main__ = importlib.import_module('__main__')\n require(\"hy.cmdline\", __main__, assignments=\"ALL\")\n try:\n tree = hy_parse(source, filename=filename)\n except HyLanguageError:\n hy_exc_handler(*sys.exc_info())\n return 1\n\n with filtered_hy_exceptions():\n hy_eval(tree, __main__.__dict__, __main__, filename=filename, source=source)\n return 0\n\n\ndef run_repl(hr=None, **kwargs):\n import platform\n sys.ps1 = \"=> \"\n sys.ps2 = \"... \"\n\n if not hr:\n hr = HyREPL(**kwargs)\n\n namespace = hr.locals\n with filtered_hy_exceptions(), \\\n extend_linecache(hr.cmdline_cache), \\\n completion(Completer(namespace)):\n hr.interact(\"{appname} {version} using \"\n \"{py}({build}) {pyversion} on {os}\".format(\n appname=hy.__appname__,\n version=hy.__version__,\n py=platform.python_implementation(),\n build=platform.python_build()[0],\n pyversion=platform.python_version(),\n os=platform.system()\n ))\n\n return 0\n\n\ndef run_icommand(source, **kwargs):\n if os.path.exists(source):\n # Emulate Python cmdline behavior by setting `sys.path` relative\n # to the executed file's location.\n if sys.path[0] == '':\n sys.path[0] = os.path.realpath(os.path.split(source)[0])\n else:\n sys.path.insert(0, os.path.split(source)[0])\n\n with io.open(source, \"r\", encoding='utf-8') as f:\n source = f.read()\n filename = source\n else:\n filename = '<string>'\n\n hr = HyREPL(**kwargs)\n with filtered_hy_exceptions():\n res = hr.runsource(source, filename=filename)\n\n # If the command was prematurely ended, show an error (just like Python\n # does).\n if res:\n hy_exc_handler(sys.last_type, sys.last_value, sys.last_traceback)\n\n return run_repl(hr)\n\n\nUSAGE = \"%(prog)s [-h | -i cmd | -c cmd | -m module | file | -] [arg] ...\"\nVERSION = \"%(prog)s \" + hy.__version__\nEPILOG = \"\"\"\n file program read from script\n module module to execute as main\n - program read from stdin\n [arg] ... arguments passed to program in sys.argv[1:]\n\"\"\"\n\n\ndef cmdline_handler(scriptname, argv):\n parser = argparse.ArgumentParser(\n prog=\"hy\",\n usage=USAGE,\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=EPILOG)\n parser.add_argument(\"-c\", dest=\"command\",\n help=\"program passed in as a string\")\n parser.add_argument(\"-m\", dest=\"mod\",\n help=\"module to run, passed in as a string\")\n parser.add_argument(\"-E\", action='store_true',\n help=\"ignore PYTHON* environment variables\")\n parser.add_argument(\"-B\", action='store_true',\n help=\"don't write .py[co] files on import; also PYTHONDONTWRITEBYTECODE=x\")\n parser.add_argument(\"-i\", dest=\"icommand\",\n help=\"program passed in as a string, then stay in REPL\")\n parser.add_argument(\"--spy\", action=\"store_true\",\n help=\"print equivalent Python code before executing\")\n parser.add_argument(\"--repl-output-fn\",\n help=\"function for printing REPL output \"\n \"(e.g., hy.contrib.hy-repr.hy-repr)\")\n parser.add_argument(\"-v\", \"--version\", action=\"version\", version=VERSION)\n\n # this will contain the script/program name and any arguments for it.\n parser.add_argument('args', nargs=argparse.REMAINDER,\n help=argparse.SUPPRESS)\n\n # Get the path of the Hy cmdline executable and swap it with\n # `sys.executable` (saving the original, just in case).\n # XXX: The `__main__` module will also have `__file__` set to the\n # entry-point script. Currently, I don't see an immediate problem, but\n # that's not how the Python cmdline works.\n hy.executable = argv[0]\n hy.sys_executable = sys.executable\n sys.executable = hy.executable\n\n # Need to split the args. If using \"-m\" all args after the MOD are sent to\n # the module in sys.argv.\n module_args = []\n if \"-m\" in argv:\n mloc = argv.index(\"-m\")\n if len(argv) > mloc+2:\n module_args = argv[mloc+2:]\n argv = argv[:mloc+2]\n\n options = parser.parse_args(argv[1:])\n\n if options.E:\n # User did \"hy -E ...\"\n _remove_python_envs()\n\n if options.B:\n sys.dont_write_bytecode = True\n\n if options.command:\n # User did \"hy -c ...\"\n return run_command(options.command, filename='<string>')\n\n if options.mod:\n # User did \"hy -m ...\"\n sys.argv = [sys.argv[0]] + options.args + module_args\n runpy.run_module(options.mod, run_name='__main__', alter_sys=True)\n return 0\n\n if options.icommand:\n # User did \"hy -i ...\"\n return run_icommand(options.icommand, spy=options.spy,\n output_fn=options.repl_output_fn)\n\n if options.args:\n if options.args[0] == \"-\":\n # Read the program from stdin\n return run_command(sys.stdin.read(), filename='<stdin>')\n\n else:\n # User did \"hy <filename>\"\n filename = options.args[0]\n\n # Emulate Python cmdline behavior by setting `sys.path` relative\n # to the executed file's location.\n if sys.path[0] == '':\n sys.path[0] = os.path.realpath(os.path.split(filename)[0])\n else:\n sys.path.insert(0, os.path.split(filename)[0])\n\n try:\n sys.argv = options.args\n with filtered_hy_exceptions():\n runhy.run_path(filename, run_name='__main__')\n return 0\n except FileNotFoundError as e:\n print(\"hy: Can't open file '{0}': [Errno {1}] {2}\".format(\n e.filename, e.errno, e.strerror), file=sys.stderr)\n sys.exit(e.errno)\n except HyLanguageError:\n hy_exc_handler(*sys.exc_info())\n sys.exit(1)\n\n # User did NOTHING!\n return run_repl(spy=options.spy, output_fn=options.repl_output_fn)\n\n\n# entry point for cmd line script \"hy\"\ndef hy_main():\n sys.path.insert(0, \"\")\n sys.exit(cmdline_handler(\"hy\", sys.argv))\n\n\ndef hyc_main():\n parser = argparse.ArgumentParser(prog=\"hyc\")\n parser.add_argument(\"files\", metavar=\"FILE\", nargs='*',\n help=('File(s) to compile (use STDIN if only'\n ' \"-\" or nothing is provided)'))\n parser.add_argument(\"-v\", action=\"version\", version=VERSION)\n\n options = parser.parse_args(sys.argv[1:])\n\n rv = 0\n if len(options.files) == 0 or (\n len(options.files) == 1 and options.files[0] == '-'):\n while True:\n filename = sys.stdin.readline()\n if not filename:\n break\n filename = filename.rstrip('\\n')\n try:\n py_compile.compile(filename, doraise=True)\n except py_compile.PyCompileError as error:\n rv = 1\n sys.stderr.write(\"%s\\n\" % error.msg)\n except OSError as error:\n rv = 1\n sys.stderr.write(\"%s\\n\" % error)\n else:\n for filename in options.files:\n try:\n print(\"Compiling %s\" % filename)\n py_compile.compile(filename, doraise=True)\n except py_compile.PyCompileError as error:\n # return value to indicate at least one failure\n rv = 1\n sys.stderr.write(\"%s\\n\" % error.msg)\n return rv\n\n\n# entry point for cmd line script \"hy2py\"\ndef hy2py_main():\n import platform\n\n options = dict(prog=\"hy2py\", usage=\"%(prog)s [options] [FILE]\",\n formatter_class=argparse.RawDescriptionHelpFormatter)\n parser = argparse.ArgumentParser(**options)\n parser.add_argument(\"FILE\", type=str, nargs='?',\n help=\"Input Hy code (use STDIN if \\\"-\\\" or \"\n \"not provided)\")\n parser.add_argument(\"--with-source\", \"-s\", action=\"store_true\",\n help=\"Show the parsed source structure\")\n parser.add_argument(\"--with-ast\", \"-a\", action=\"store_true\",\n help=\"Show the generated AST\")\n parser.add_argument(\"--without-python\", \"-np\", action=\"store_true\",\n help=(\"Do not show the Python code generated \"\n \"from the AST\"))\n\n options = parser.parse_args(sys.argv[1:])\n\n if options.FILE is None or options.FILE == '-':\n filename = '<stdin>'\n source = sys.stdin.read()\n else:\n filename = options.FILE\n with io.open(options.FILE, 'r', encoding='utf-8') as source_file:\n source = source_file.read()\n\n with filtered_hy_exceptions():\n hst = hy_parse(source, filename=filename)\n\n if options.with_source:\n # need special printing on Windows in case the\n # codepage doesn't support utf-8 characters\n if platform.system() == \"Windows\":\n for h in hst:\n try:\n print(h)\n except:\n print(str(h).encode('utf-8'))\n else:\n print(hst)\n print()\n print()\n\n with filtered_hy_exceptions():\n _ast = hy_compile(hst, '__main__', filename=filename, source=source)\n\n if options.with_ast:\n if platform.system() == \"Windows\":\n _print_for_windows(astor.dump_tree(_ast))\n else:\n print(astor.dump_tree(_ast))\n print()\n print()\n\n if not options.without_python:\n if platform.system() == \"Windows\":\n _print_for_windows(astor.code_gen.to_source(_ast))\n else:\n print(astor.code_gen.to_source(_ast))\n\n parser.exit(0)\n\n\n# need special printing on Windows in case the\n# codepage doesn't support utf-8 characters\ndef _print_for_windows(src):\n for line in src.split(\"\\n\"):\n try:\n print(line)\n except:\n print(line.encode('utf-8'))\n\n# remove PYTHON* environment variables,\n# such as \"PYTHONPATH\"\ndef _remove_python_envs():\n for key in list(os.environ.keys()):\n if key.startswith(\"PYTHON\"):\n os.environ.pop(key)\n", "path": "hy/cmdline.py" } ]
diff --git a/NEWS.rst b/NEWS.rst index 220b9f764..8c97e0f16 100644 --- a/NEWS.rst +++ b/NEWS.rst @@ -19,6 +19,7 @@ Bug Fixes * Quoted f-strings are no longer evaluated prematurely. * Fixed a regression in the production of error messages for empty expressions. +* Fixed a scoping bug for code executed with `hy -c`. 0.18.0 ============================== diff --git a/hy/cmdline.py b/hy/cmdline.py index b9ffcdb55..6277d3d7f 100644 --- a/hy/cmdline.py +++ b/hy/cmdline.py @@ -429,7 +429,7 @@ def run_command(source, filename=None): return 1 with filtered_hy_exceptions(): - hy_eval(tree, None, __main__, filename=filename, source=source) + hy_eval(tree, __main__.__dict__, __main__, filename=filename, source=source) return 0 diff --git a/tests/test_bin.py b/tests/test_bin.py index 6c8e51c75..9bb63c8f4 100644 --- a/tests/test_bin.py +++ b/tests/test_bin.py @@ -266,6 +266,10 @@ def test_bin_hy_cmd(): _, err = run_cmd("hy -c \"(koan\"", expect=1) assert "Premature end of input" in err + # https://github.com/hylang/hy/issues/1879 + output, _ = run_cmd( + """hy -c '(setv x "bing") (defn f [] (+ "fiz" x)) (print (f))'""") + assert 'fizbing' in output def test_bin_hy_icmd(): output, _ = run_cmd("hy -i \"(koan)\"", "(ideas)")
deepset-ai__haystack-3705
Bad Semaphore initialization in RequestLimiter **Describe the bug** RequestLimiter takes a number as parameter and use it to set up a Semaphore. The issue is that the environment variable indicates the concurrent allowed requests per worker. When the semaphore is created (https://github.com/deepset-ai/haystack/blob/6790eaf7d8be05c5674d97a75cc5783e00a66875/rest_api/rest_api/controller/utils.py#L13), this value is set down by 1. This is clearly not what the project tried to achieve (at least per naming). **Error message** REST API will always return it's busy, error 503 when CONCURRENT_REQUEST_PER_WORKER is equal to CONCURRENT_REQUEST_PER_WORKER -1. When user set the concurrency to 1, it will never be able to call the API, since the Semaphore declaration will be Semaphore(0) **Expected behavior** Being able to set the request limits using the env variable CONCURRENT_REQUEST_PER_WORKER **Additional context** **To Reproduce** **FAQ Check** - [x] Have you had a look at [our new FAQ page](https://haystack.deepset.ai/overview/faq)? **System:** - OS: Ubuntu - GPU/CPU: i7/ Nvidia - Haystack version (commit or version number): 1.9 - DocumentStore: - Reader: - Retriever:
[ { "content": "from typing import Type, NewType\n\nimport inspect\nfrom contextlib import contextmanager\nfrom threading import Semaphore\n\nfrom fastapi import Form, HTTPException\nfrom pydantic import BaseModel\n\n\nclass RequestLimiter:\n def __init__(self, limit):\n self.semaphore = Semaphore(limit - 1)\n\n @contextmanager\n def run(self):\n acquired = self.semaphore.acquire(blocking=False)\n if not acquired:\n raise HTTPException(status_code=503, detail=\"The server is busy processing requests.\")\n try:\n yield acquired\n finally:\n self.semaphore.release()\n\n\nStringId = NewType(\"StringId\", str)\n\n\ndef as_form(cls: Type[BaseModel]):\n \"\"\"\n Adds an as_form class method to decorated models. The as_form class method\n can be used with FastAPI endpoints\n \"\"\"\n new_params = [\n inspect.Parameter(\n field.alias,\n inspect.Parameter.POSITIONAL_ONLY,\n default=(Form(field.default) if not field.required else Form(...)),\n )\n for field in cls.__fields__.values()\n ]\n\n async def _as_form(**data):\n return cls(**data)\n\n sig = inspect.signature(_as_form)\n sig = sig.replace(parameters=new_params)\n _as_form.__signature__ = sig # type: ignore\n setattr(cls, \"as_form\", _as_form)\n return cls\n", "path": "rest_api/rest_api/controller/utils.py" } ]
[ { "content": "from typing import Type, NewType\n\nimport inspect\nfrom contextlib import contextmanager\nfrom threading import Semaphore\n\nfrom fastapi import Form, HTTPException\nfrom pydantic import BaseModel\n\n\nclass RequestLimiter:\n def __init__(self, limit):\n self.semaphore = Semaphore(limit)\n\n @contextmanager\n def run(self):\n acquired = self.semaphore.acquire(blocking=False)\n if not acquired:\n raise HTTPException(status_code=503, detail=\"The server is busy processing requests.\")\n try:\n yield acquired\n finally:\n self.semaphore.release()\n\n\nStringId = NewType(\"StringId\", str)\n\n\ndef as_form(cls: Type[BaseModel]):\n \"\"\"\n Adds an as_form class method to decorated models. The as_form class method\n can be used with FastAPI endpoints\n \"\"\"\n new_params = [\n inspect.Parameter(\n field.alias,\n inspect.Parameter.POSITIONAL_ONLY,\n default=(Form(field.default) if not field.required else Form(...)),\n )\n for field in cls.__fields__.values()\n ]\n\n async def _as_form(**data):\n return cls(**data)\n\n sig = inspect.signature(_as_form)\n sig = sig.replace(parameters=new_params)\n _as_form.__signature__ = sig # type: ignore\n setattr(cls, \"as_form\", _as_form)\n return cls\n", "path": "rest_api/rest_api/controller/utils.py" } ]
diff --git a/rest_api/rest_api/controller/utils.py b/rest_api/rest_api/controller/utils.py index 5579d3f0b5..49587968ed 100644 --- a/rest_api/rest_api/controller/utils.py +++ b/rest_api/rest_api/controller/utils.py @@ -10,7 +10,7 @@ class RequestLimiter: def __init__(self, limit): - self.semaphore = Semaphore(limit - 1) + self.semaphore = Semaphore(limit) @contextmanager def run(self):
saulpw__visidata-2269
save as csv actually saves tsv when file existing file extension is CSV, i.e. uppercase - PR available #2269 **Small description** Slightly hperbolic ;-) Corruption of file format, opening a csv but saving as csv results in TSV data in a file named csv **Expected result** CSV, not TSV **Actual result with screenshot** If you get an unexpected error, please include the full stack trace that you get with `Ctrl-E`. No error, contents: header1 header2 1 one 2 two **Steps to reproduce with sample data and a .vd** Datafile, called bug.CSV header1,header2 1,one 2,two 1. open data file, MUST have uppercase CSV on end (works fine for lower). E.g., `visidata bug.CSV` 2. save (ctrl-s) 3. hit enter to accept current filename 4. hit `y` to overwrite 5. Display will say saving TSV 6. sanity check file contents First try reproducing without any user configuration by using the flag `-N`. e.g. `echo "abc" | vd -f txt -N` Please attach the commandlog (saved with `Ctrl-D`) to show the steps that led to the issue. See [here](http://visidata.org/docs/save-restore/) for more details. **Additional context** Please include the version of VisiData and Python. Windows: (py311csv) C:\code\py>python Python 3.11.3 (tags/v3.11.3:f3909b8, Apr 4 2023, 23:49:59) [MSC v.1934 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> ^Z (py311csv) C:\code\py>visidata --version saul.pw/VisiData v3.0.2
[ { "content": "import collections\nimport os\nfrom copy import copy\n\nfrom visidata import vd\nfrom visidata import Sheet, BaseSheet, VisiData, IndexSheet, Path, Progress, TypedExceptionWrapper\n\nvd.option('safe_error', '#ERR', 'error string to use while saving', replay=True)\nvd.option('save_encoding', 'utf-8', 'encoding passed to codecs.open when saving a file', replay=True, help=vd.help_encoding)\n\[email protected]\ndef safe_trdict(vs):\n 'returns string.translate dictionary for replacing tabs and newlines'\n if vs.options.safety_first:\n delim = vs.options.delimiter\n return {\n 0: '', # strip NUL completely\n ord(delim): vs.options.tsv_safe_tab, # \\t\n 10: vs.options.tsv_safe_newline, # \\n\n 13: vs.options.tsv_safe_newline, # \\r\n }\n return {}\n\n\[email protected]\ndef iterdispvals(sheet, *cols, format=False):\n 'For each row in sheet, yield OrderedDict of values for given cols. Values are typed if format=False, or a formatted display string if format=True.'\n if not cols:\n cols = sheet.visibleCols\n\n transformers = collections.OrderedDict() # list of transformers for each column in order\n for col in cols:\n transformers[col] = [ col.type ]\n if format:\n formatMaker = getattr(col, 'formatter_'+(col.formatter or sheet.options.disp_formatter))\n transformers[col].append(formatMaker(col._formatdict))\n trdict = sheet.safe_trdict()\n if trdict:\n transformers[col].append(lambda v,trdict=trdict: v.translate(trdict))\n\n options_safe_error = sheet.options.safe_error\n for r in Progress(sheet.rows):\n dispvals = collections.OrderedDict() # [col] -> value\n for col, transforms in transformers.items():\n try:\n dispval = col.getValue(r)\n\n except Exception as e:\n vd.exceptionCaught(e)\n dispval = options_safe_error or str(e)\n\n try:\n for t in transforms:\n if dispval is None:\n break\n elif isinstance(dispval, TypedExceptionWrapper):\n dispval = options_safe_error or str(dispval)\n break\n else:\n dispval = t(dispval)\n\n if dispval is None and format:\n dispval = ''\n except Exception as e:\n dispval = str(dispval)\n\n dispvals[col] = dispval\n\n yield dispvals\n\n\[email protected]\ndef itervals(sheet, *cols, format=False):\n for row in sheet.iterdispvals(*cols, format=format):\n yield [row[c] for c in cols]\n\[email protected]\ndef getDefaultSaveName(sheet):\n src = getattr(sheet, 'source', None)\n if hasattr(src, 'scheme') and src.scheme:\n return src.name + src.suffix\n if isinstance(src, Path):\n if sheet.options.is_set('save_filetype', sheet):\n # if save_filetype is over-ridden from default, use it as the extension\n return str(src.with_suffix('')) + '.' + sheet.options.save_filetype\n return str(src)\n else:\n return sheet.name+'.'+getattr(sheet, 'filetype', sheet.options.save_filetype)\n\n\[email protected]\ndef save_cols(vd, cols):\n sheet = cols[0].sheet\n vs = copy(sheet)\n vs.columns = list(cols)\n vs.rows = sheet.rows\n if len(cols) == 1:\n savedcoltxt = cols[0].name + ' column'\n else:\n savedcoltxt = '%s columns' % len(cols)\n path = vd.inputPath('save %s to: ' % savedcoltxt, value=vs.getDefaultSaveName())\n vd.saveSheets(path, vs)\n\n\[email protected]\ndef saveSheets(vd, givenpath, *vsheets, confirm_overwrite=True):\n 'Save all *vsheets* to *givenpath*.'\n\n if not vsheets: # blank tuple\n vd.warning('no sheets to save')\n return\n\n filetypes = [givenpath.ext, vd.options.save_filetype]\n\n vd.clearCaches()\n\n for ft in filetypes:\n savefunc = getattr(vsheets[0], 'save_' + ft, None) or getattr(vd, 'save_' + ft, None)\n if savefunc:\n filetype = ft\n break\n\n if savefunc is None:\n vd.fail(f'no function to save as {filetype}')\n\n if confirm_overwrite:\n vd.confirmOverwrite(givenpath)\n\n vd.status('saving %s sheets to %s as %s' % (len(vsheets), givenpath.given, filetype))\n\n if not givenpath.given.endswith('/'): # forcibly specify save individual files into directory by ending path with /\n for vs in vsheets:\n vs.hasBeenModified = False\n # savefuncs(vd, p, *vsheets) will have 2 argcount (*vsheets does not get counted as an arg)\n # savefuncs(vd, p, vs) will have 3 argcount (vs counts as an arg, along with vd, path)\n if savefunc.__code__.co_argcount == 3 and len(vsheets) > 1:\n vd.fail(f'cannot save multiple {filetype} sheets to non-dir')\n return vd.execAsync(savefunc, givenpath, *vsheets)\n\n # path is a dir\n\n # save as individual files in the givenpath directory\n try:\n os.makedirs(givenpath, exist_ok=True)\n except FileExistsError:\n pass\n\n if not givenpath.is_dir():\n vd.fail(f'cannot save multiple {filetype} sheets to non-dir')\n\n def _savefiles(vsheets, givenpath, savefunc, filetype):\n for vs in vsheets:\n p = Path((givenpath / vs.name).with_suffix('.'+filetype))\n savefunc(p, vs)\n vs.hasBeenModified = False\n\n vd.status(f'{givenpath} save finished') #2157\n\n return vd.execAsync(_savefiles, vsheets, givenpath, savefunc, filetype)\n\n\[email protected]\ndef save_zip(vd, p, *vsheets):\n vd.clearCaches()\n\n import tempfile\n import zipfile\n with tempfile.TemporaryDirectory() as tmpdir:\n with zipfile.ZipFile(str(p), 'w', zipfile.ZIP_DEFLATED, allowZip64=True, compresslevel=9) as zfp:\n for vs in Progress(vsheets):\n filetype = vs.options.save_filetype\n tmpp = Path(f'{tmpdir}{vs.name}.{filetype}')\n savefunc = getattr(vs, 'save_' + filetype, None) or getattr(vd, 'save_' + filetype, None)\n savefunc(tmpp, vs)\n zfp.write(tmpp, f'{vs.name}.{vs.options.save_filetype}')\n\n\[email protected]\ndef save_txt(vd, p, *vsheets):\n if len(vsheets) == 1 and vsheets[0].nVisibleCols > 1: #2173\n return vd.save_tsv(p, vsheets[0])\n\n with p.open(mode='w', encoding=vsheets[0].options.save_encoding) as fp:\n for vs in vsheets:\n unitsep = vs.options.delimiter\n rowsep = vs.options.row_delimiter\n for dispvals in vs.iterdispvals(*vs.visibleCols, format=True):\n fp.write(unitsep.join(dispvals.values()))\n fp.write(rowsep)\n\n\[email protected]\ndef rootSheet(sheet):\n r = sheet\n while isinstance(r.source, BaseSheet):\n r = r.source\n\n return r\n\n\nBaseSheet.addCommand('^S', 'save-sheet', 'vd.saveSheets(inputPath(\"save to: \", value=getDefaultSaveName()), sheet)', 'save current sheet to filename in format determined by extension (default .tsv)')\nBaseSheet.addCommand('', 'save-sheet-really', 'vd.saveSheets(Path(getDefaultSaveName()), sheet, confirm_overwrite=False)', 'save current sheet without asking for filename or confirmation')\nBaseSheet.addCommand('', 'save-source', 'vd.saveSheets(rootSheet().source, rootSheet())', 'save root sheet to its source')\nBaseSheet.addCommand('g^S', 'save-all', 'vd.saveSheets(inputPath(\"save all sheets to: \"), *vd.stackedSheets)', 'save all sheets to given file or directory)')\nIndexSheet.addCommand('g^S', 'save-selected', 'vd.saveSheets(inputPath(\"save %d sheets to: \" % nSelectedRows, value=\"_\".join(getattr(vs, \"name\", None) or \"blank\" for vs in selectedRows)), *selectedRows)', 'save all selected sheets to given file or directory')\nSheet.addCommand('', 'save-col', 'save_cols([cursorCol])', 'save current column only to filename in format determined by extension (default .tsv)')\nSheet.addCommand('', 'save-col-keys', 'save_cols(keyCols + [cursorCol])', 'save key columns and current column to filename in format determined by extension (default .tsv)')\n\nvd.addMenuItems('''\n File > Save > current sheet > save-sheet\n File > Save > all sheets > save-all\n File > Save > current column > save-col\n File > Save > keys and current column > save-col-keys\n''')\n", "path": "visidata/save.py" } ]
[ { "content": "import collections\nimport os\nfrom copy import copy\n\nfrom visidata import vd\nfrom visidata import Sheet, BaseSheet, VisiData, IndexSheet, Path, Progress, TypedExceptionWrapper\n\nvd.option('safe_error', '#ERR', 'error string to use while saving', replay=True)\nvd.option('save_encoding', 'utf-8', 'encoding passed to codecs.open when saving a file', replay=True, help=vd.help_encoding)\n\[email protected]\ndef safe_trdict(vs):\n 'returns string.translate dictionary for replacing tabs and newlines'\n if vs.options.safety_first:\n delim = vs.options.delimiter\n return {\n 0: '', # strip NUL completely\n ord(delim): vs.options.tsv_safe_tab, # \\t\n 10: vs.options.tsv_safe_newline, # \\n\n 13: vs.options.tsv_safe_newline, # \\r\n }\n return {}\n\n\[email protected]\ndef iterdispvals(sheet, *cols, format=False):\n 'For each row in sheet, yield OrderedDict of values for given cols. Values are typed if format=False, or a formatted display string if format=True.'\n if not cols:\n cols = sheet.visibleCols\n\n transformers = collections.OrderedDict() # list of transformers for each column in order\n for col in cols:\n transformers[col] = [ col.type ]\n if format:\n formatMaker = getattr(col, 'formatter_'+(col.formatter or sheet.options.disp_formatter))\n transformers[col].append(formatMaker(col._formatdict))\n trdict = sheet.safe_trdict()\n if trdict:\n transformers[col].append(lambda v,trdict=trdict: v.translate(trdict))\n\n options_safe_error = sheet.options.safe_error\n for r in Progress(sheet.rows):\n dispvals = collections.OrderedDict() # [col] -> value\n for col, transforms in transformers.items():\n try:\n dispval = col.getValue(r)\n\n except Exception as e:\n vd.exceptionCaught(e)\n dispval = options_safe_error or str(e)\n\n try:\n for t in transforms:\n if dispval is None:\n break\n elif isinstance(dispval, TypedExceptionWrapper):\n dispval = options_safe_error or str(dispval)\n break\n else:\n dispval = t(dispval)\n\n if dispval is None and format:\n dispval = ''\n except Exception as e:\n dispval = str(dispval)\n\n dispvals[col] = dispval\n\n yield dispvals\n\n\[email protected]\ndef itervals(sheet, *cols, format=False):\n for row in sheet.iterdispvals(*cols, format=format):\n yield [row[c] for c in cols]\n\[email protected]\ndef getDefaultSaveName(sheet):\n src = getattr(sheet, 'source', None)\n if hasattr(src, 'scheme') and src.scheme:\n return src.name + src.suffix\n if isinstance(src, Path):\n if sheet.options.is_set('save_filetype', sheet):\n # if save_filetype is over-ridden from default, use it as the extension\n return str(src.with_suffix('')) + '.' + sheet.options.save_filetype\n return str(src)\n else:\n return sheet.name+'.'+getattr(sheet, 'filetype', sheet.options.save_filetype)\n\n\[email protected]\ndef save_cols(vd, cols):\n sheet = cols[0].sheet\n vs = copy(sheet)\n vs.columns = list(cols)\n vs.rows = sheet.rows\n if len(cols) == 1:\n savedcoltxt = cols[0].name + ' column'\n else:\n savedcoltxt = '%s columns' % len(cols)\n path = vd.inputPath('save %s to: ' % savedcoltxt, value=vs.getDefaultSaveName())\n vd.saveSheets(path, vs)\n\n\[email protected]\ndef saveSheets(vd, givenpath, *vsheets, confirm_overwrite=True):\n 'Save all *vsheets* to *givenpath*.'\n\n if not vsheets: # blank tuple\n vd.warning('no sheets to save')\n return\n\n filetypes = [givenpath.ext.lower(), vd.options.save_filetype.lower()]\n\n vd.clearCaches()\n\n for ft in filetypes:\n savefunc = getattr(vsheets[0], 'save_' + ft, None) or getattr(vd, 'save_' + ft, None)\n if savefunc:\n filetype = ft\n break\n\n if savefunc is None:\n vd.fail(f'no function to save as {filetype}')\n\n if confirm_overwrite:\n vd.confirmOverwrite(givenpath)\n\n vd.status('saving %s sheets to %s as %s' % (len(vsheets), givenpath.given, filetype))\n\n if not givenpath.given.endswith('/'): # forcibly specify save individual files into directory by ending path with /\n for vs in vsheets:\n vs.hasBeenModified = False\n # savefuncs(vd, p, *vsheets) will have 2 argcount (*vsheets does not get counted as an arg)\n # savefuncs(vd, p, vs) will have 3 argcount (vs counts as an arg, along with vd, path)\n if savefunc.__code__.co_argcount == 3 and len(vsheets) > 1:\n vd.fail(f'cannot save multiple {filetype} sheets to non-dir')\n return vd.execAsync(savefunc, givenpath, *vsheets)\n\n # path is a dir\n\n # save as individual files in the givenpath directory\n try:\n os.makedirs(givenpath, exist_ok=True)\n except FileExistsError:\n pass\n\n if not givenpath.is_dir():\n vd.fail(f'cannot save multiple {filetype} sheets to non-dir')\n\n def _savefiles(vsheets, givenpath, savefunc, filetype):\n for vs in vsheets:\n p = Path((givenpath / vs.name).with_suffix('.'+filetype))\n savefunc(p, vs)\n vs.hasBeenModified = False\n\n vd.status(f'{givenpath} save finished') #2157\n\n return vd.execAsync(_savefiles, vsheets, givenpath, savefunc, filetype)\n\n\[email protected]\ndef save_zip(vd, p, *vsheets):\n vd.clearCaches()\n\n import tempfile\n import zipfile\n with tempfile.TemporaryDirectory() as tmpdir:\n with zipfile.ZipFile(str(p), 'w', zipfile.ZIP_DEFLATED, allowZip64=True, compresslevel=9) as zfp:\n for vs in Progress(vsheets):\n filetype = vs.options.save_filetype\n tmpp = Path(f'{tmpdir}{vs.name}.{filetype}')\n savefunc = getattr(vs, 'save_' + filetype, None) or getattr(vd, 'save_' + filetype, None)\n savefunc(tmpp, vs)\n zfp.write(tmpp, f'{vs.name}.{vs.options.save_filetype}')\n\n\[email protected]\ndef save_txt(vd, p, *vsheets):\n if len(vsheets) == 1 and vsheets[0].nVisibleCols > 1: #2173\n return vd.save_tsv(p, vsheets[0])\n\n with p.open(mode='w', encoding=vsheets[0].options.save_encoding) as fp:\n for vs in vsheets:\n unitsep = vs.options.delimiter\n rowsep = vs.options.row_delimiter\n for dispvals in vs.iterdispvals(*vs.visibleCols, format=True):\n fp.write(unitsep.join(dispvals.values()))\n fp.write(rowsep)\n\n\[email protected]\ndef rootSheet(sheet):\n r = sheet\n while isinstance(r.source, BaseSheet):\n r = r.source\n\n return r\n\n\nBaseSheet.addCommand('^S', 'save-sheet', 'vd.saveSheets(inputPath(\"save to: \", value=getDefaultSaveName()), sheet)', 'save current sheet to filename in format determined by extension (default .tsv)')\nBaseSheet.addCommand('', 'save-sheet-really', 'vd.saveSheets(Path(getDefaultSaveName()), sheet, confirm_overwrite=False)', 'save current sheet without asking for filename or confirmation')\nBaseSheet.addCommand('', 'save-source', 'vd.saveSheets(rootSheet().source, rootSheet())', 'save root sheet to its source')\nBaseSheet.addCommand('g^S', 'save-all', 'vd.saveSheets(inputPath(\"save all sheets to: \"), *vd.stackedSheets)', 'save all sheets to given file or directory)')\nIndexSheet.addCommand('g^S', 'save-selected', 'vd.saveSheets(inputPath(\"save %d sheets to: \" % nSelectedRows, value=\"_\".join(getattr(vs, \"name\", None) or \"blank\" for vs in selectedRows)), *selectedRows)', 'save all selected sheets to given file or directory')\nSheet.addCommand('', 'save-col', 'save_cols([cursorCol])', 'save current column only to filename in format determined by extension (default .tsv)')\nSheet.addCommand('', 'save-col-keys', 'save_cols(keyCols + [cursorCol])', 'save key columns and current column to filename in format determined by extension (default .tsv)')\n\nvd.addMenuItems('''\n File > Save > current sheet > save-sheet\n File > Save > all sheets > save-all\n File > Save > current column > save-col\n File > Save > keys and current column > save-col-keys\n''')\n", "path": "visidata/save.py" } ]
diff --git a/visidata/save.py b/visidata/save.py index e2e9e0320..a4574209e 100644 --- a/visidata/save.py +++ b/visidata/save.py @@ -110,7 +110,7 @@ def saveSheets(vd, givenpath, *vsheets, confirm_overwrite=True): vd.warning('no sheets to save') return - filetypes = [givenpath.ext, vd.options.save_filetype] + filetypes = [givenpath.ext.lower(), vd.options.save_filetype.lower()] vd.clearCaches()
RedHatInsights__insights-core-2879
'TypeError' object has no attribute 'tb_frame' While fetching object details from insight inspect, getting kicked out from the ipython console with the following error. 'TypeError' object has no attribute 'tb_frame' (gss-rules) ⌊gss-rules⌋»$ insights inspect insights.parsers.installed_rpms.InstalledRpms ~/scripts/rhel7_sosreport/ IPython Console Usage Info: Enter 'InstalledRpms.' and tab to get a list of properties Example: In [1]: InstalledRpms.<property_name> Out[1]: <property value> To exit ipython enter 'exit' and hit enter or use 'CTL D' Starting IPython Interpreter Now In [1]: InstalledRpms 'TypeError' object has no attribute 'tb_frame'
[ { "content": "import os\nimport sys\nfrom setuptools import setup, find_packages\n\n__here__ = os.path.dirname(os.path.abspath(__file__))\n\npackage_info = dict.fromkeys([\"RELEASE\", \"COMMIT\", \"VERSION\", \"NAME\"])\n\nfor name in package_info:\n with open(os.path.join(__here__, \"insights\", name)) as f:\n package_info[name] = f.read().strip()\n\nentry_points = {\n 'console_scripts': [\n 'insights-collect = insights.collect:main',\n 'insights-run = insights:main',\n 'insights = insights.command_parser:main',\n 'insights-cat = insights.tools.cat:main',\n 'insights-dupkeycheck = insights.tools.dupkeycheck:main',\n 'insights-inspect = insights.tools.insights_inspect:main',\n 'insights-info = insights.tools.query:main',\n 'insights-ocpshell= insights.ocpshell:main',\n 'client = insights.client:run',\n 'mangle = insights.util.mangle:main'\n ]\n}\n\nruntime = set([\n 'six',\n 'requests',\n 'redis',\n 'cachecontrol',\n 'cachecontrol[redis]',\n 'cachecontrol[filecache]',\n 'defusedxml',\n 'lockfile',\n 'jinja2',\n])\n\nif (sys.version_info < (2, 7)):\n runtime.add('pyyaml>=3.10,<=3.13')\nelse:\n runtime.add('pyyaml')\n\n\ndef maybe_require(pkg):\n try:\n __import__(pkg)\n except ImportError:\n runtime.add(pkg)\n\n\nmaybe_require(\"importlib\")\nmaybe_require(\"argparse\")\n\n\nclient = set([\n 'requests'\n])\n\ndevelop = set([\n 'futures==3.0.5',\n 'wheel',\n])\n\ndocs = set([\n 'Sphinx<=3.0.2',\n 'nbsphinx',\n 'sphinx_rtd_theme',\n 'ipython',\n 'colorama',\n 'jinja2',\n 'Pygments'\n])\n\ntesting = set([\n 'coverage==4.3.4',\n 'pytest==3.0.6',\n 'pytest-cov==2.4.0',\n 'mock==2.0.0',\n])\n\ncluster = set([\n 'ansible',\n 'pandas',\n 'colorama',\n])\n\nopenshift = set([\n 'openshift'\n])\n\nlinting = set([\n 'flake8==2.6.2',\n])\n\noptional = set([\n 'python-cjson',\n 'python-logstash',\n 'python-statsd',\n 'watchdog',\n])\n\nif __name__ == \"__main__\":\n # allows for runtime modification of rpm name\n name = os.environ.get(\"INSIGHTS_CORE_NAME\", package_info[\"NAME\"])\n\n setup(\n name=name,\n version=package_info[\"VERSION\"],\n description=\"Insights Core is a data collection and analysis framework\",\n long_description=open(\"README.rst\").read(),\n url=\"https://github.com/redhatinsights/insights-core\",\n author=\"Red Hat, Inc.\",\n author_email=\"[email protected]\",\n packages=find_packages(),\n install_requires=list(runtime),\n package_data={'': ['LICENSE']},\n license='Apache 2.0',\n extras_require={\n 'develop': list(runtime | develop | client | docs | linting | testing | cluster),\n 'develop26': list(runtime | develop | client | linting | testing | cluster),\n 'client': list(runtime | client),\n 'client-develop': list(runtime | develop | client | linting | testing),\n 'cluster': list(runtime | cluster),\n 'openshift': list(runtime | openshift),\n 'optional': list(optional),\n 'docs': list(docs),\n 'linting': list(linting | client),\n 'testing': list(testing | client)\n },\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Developers',\n 'Natural Language :: English',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.6',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6'\n ],\n entry_points=entry_points,\n include_package_data=True\n )\n", "path": "setup.py" } ]
[ { "content": "import os\nimport sys\nfrom setuptools import setup, find_packages\n\n__here__ = os.path.dirname(os.path.abspath(__file__))\n\npackage_info = dict.fromkeys([\"RELEASE\", \"COMMIT\", \"VERSION\", \"NAME\"])\n\nfor name in package_info:\n with open(os.path.join(__here__, \"insights\", name)) as f:\n package_info[name] = f.read().strip()\n\nentry_points = {\n 'console_scripts': [\n 'insights-collect = insights.collect:main',\n 'insights-run = insights:main',\n 'insights = insights.command_parser:main',\n 'insights-cat = insights.tools.cat:main',\n 'insights-dupkeycheck = insights.tools.dupkeycheck:main',\n 'insights-inspect = insights.tools.insights_inspect:main',\n 'insights-info = insights.tools.query:main',\n 'insights-ocpshell= insights.ocpshell:main',\n 'client = insights.client:run',\n 'mangle = insights.util.mangle:main'\n ]\n}\n\nruntime = set([\n 'six',\n 'requests',\n 'redis',\n 'cachecontrol',\n 'cachecontrol[redis]',\n 'cachecontrol[filecache]',\n 'defusedxml',\n 'lockfile',\n 'jinja2',\n])\n\nif (sys.version_info < (2, 7)):\n runtime.add('pyyaml>=3.10,<=3.13')\nelse:\n runtime.add('pyyaml')\n\n\ndef maybe_require(pkg):\n try:\n __import__(pkg)\n except ImportError:\n runtime.add(pkg)\n\n\nmaybe_require(\"importlib\")\nmaybe_require(\"argparse\")\n\n\nclient = set([\n 'requests'\n])\n\ndevelop = set([\n 'futures==3.0.5',\n 'wheel',\n])\n\ndocs = set([\n 'Sphinx<=3.0.2',\n 'nbsphinx',\n 'sphinx_rtd_theme',\n 'ipython',\n 'colorama',\n 'jinja2',\n 'Pygments',\n 'jedi<0.18.0' # Open issue with jedi 0.18.0 and iPython <= 7.19\n # https://github.com/davidhalter/jedi/issues/1714\n])\n\ntesting = set([\n 'coverage==4.3.4',\n 'pytest==3.0.6',\n 'pytest-cov==2.4.0',\n 'mock==2.0.0',\n])\n\ncluster = set([\n 'ansible',\n 'pandas',\n 'colorama',\n])\n\nopenshift = set([\n 'openshift'\n])\n\nlinting = set([\n 'flake8==2.6.2',\n])\n\noptional = set([\n 'python-cjson',\n 'python-logstash',\n 'python-statsd',\n 'watchdog',\n])\n\nif __name__ == \"__main__\":\n # allows for runtime modification of rpm name\n name = os.environ.get(\"INSIGHTS_CORE_NAME\", package_info[\"NAME\"])\n\n setup(\n name=name,\n version=package_info[\"VERSION\"],\n description=\"Insights Core is a data collection and analysis framework\",\n long_description=open(\"README.rst\").read(),\n url=\"https://github.com/redhatinsights/insights-core\",\n author=\"Red Hat, Inc.\",\n author_email=\"[email protected]\",\n packages=find_packages(),\n install_requires=list(runtime),\n package_data={'': ['LICENSE']},\n license='Apache 2.0',\n extras_require={\n 'develop': list(runtime | develop | client | docs | linting | testing | cluster),\n 'develop26': list(runtime | develop | client | linting | testing | cluster),\n 'client': list(runtime | client),\n 'client-develop': list(runtime | develop | client | linting | testing),\n 'cluster': list(runtime | cluster),\n 'openshift': list(runtime | openshift),\n 'optional': list(optional),\n 'docs': list(docs),\n 'linting': list(linting | client),\n 'testing': list(testing | client)\n },\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Developers',\n 'Natural Language :: English',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.6',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6'\n ],\n entry_points=entry_points,\n include_package_data=True\n )\n", "path": "setup.py" } ]
diff --git a/setup.py b/setup.py index 43283af7b2..d3aadc939e 100644 --- a/setup.py +++ b/setup.py @@ -70,7 +70,9 @@ def maybe_require(pkg): 'ipython', 'colorama', 'jinja2', - 'Pygments' + 'Pygments', + 'jedi<0.18.0' # Open issue with jedi 0.18.0 and iPython <= 7.19 + # https://github.com/davidhalter/jedi/issues/1714 ]) testing = set([
GeotrekCE__Geotrek-admin-4021
Problème de thumbnail avec les SVG Bug détecté à partir de la version 2.101.4 de Geotrek Admin. Celui est déclenché par l'ajout d'un SVG comme pictogramme sur un lieu de renseignement. Explication : la dernière version de easy_thumbnail n'accepte pas de faire le thumbnail d'un SVG. -> l'api V2 plante
[ { "content": "#!/usr/bin/python3\nimport os\nimport distutils.command.build\nfrom pathlib import Path\nfrom setuptools import setup, find_packages\nfrom shutil import copy\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\n\nclass BuildCommand(distutils.command.build.build):\n def run(self):\n distutils.command.build.build.run(self)\n from django.core.management import call_command\n curdir = os.getcwd()\n for subdir in ('geotrek', ):\n os.chdir(subdir)\n call_command('compilemessages')\n for path in Path('.').rglob('*.mo'):\n copy(path, os.path.join(curdir, self.build_lib, subdir, path))\n os.chdir(curdir)\n\n\nsetup(\n name='geotrek',\n version=open(os.path.join(here, 'VERSION')).read().strip(),\n author='Makina Corpus',\n author_email='[email protected]',\n url='https://makina-corpus.com',\n description=\"Geotrek\",\n scripts=['manage.py'],\n install_requires=[\n 'Django==3.2.*',\n 'mapentity',\n 'chardet',\n 'cairosvg',\n 'cairocffi',\n 'env_file',\n # pinned by requirements.txt\n 'pymemcache',\n 'coreschema',\n 'coreapi',\n 'psycopg2',\n 'pdfimpose',\n 'docutils',\n 'Pillow',\n 'simplekml',\n 'pygal',\n 'paperclip',\n 'django-extended-choices',\n 'django-modelcluster',\n 'django-mptt',\n 'geojson',\n 'tif2geojson',\n 'drf-dynamic-fields',\n 'drf-yasg',\n 'xlrd',\n 'landez',\n 'large-image-source-vips',\n 'django-large-image',\n 'celery',\n 'redis',\n 'django-celery-results',\n 'drf-extensions',\n 'django-colorfield',\n 'Fiona',\n 'markdown',\n \"weasyprint==52.5\", # newer version required libpango (not available in bionic)\n 'django-weasyprint<2.0.0', # 2.10 require weasyprint > 53\n \"django-clearcache\",\n \"pyopenair\",\n # prod,\n 'gunicorn',\n 'sentry-sdk',\n ],\n cmdclass={\"build\": BuildCommand},\n include_package_data=True,\n license='BSD, see LICENSE file.',\n packages=find_packages(),\n classifiers=['Natural Language :: English',\n 'Environment :: Web Environment',\n 'Framework :: Django',\n 'Development Status :: 5 - Production/Stable',\n 'Programming Language :: Python :: 3'],\n)\n", "path": "setup.py" } ]
[ { "content": "#!/usr/bin/python3\nimport os\nimport distutils.command.build\nfrom pathlib import Path\nfrom setuptools import setup, find_packages\nfrom shutil import copy\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\n\nclass BuildCommand(distutils.command.build.build):\n def run(self):\n distutils.command.build.build.run(self)\n from django.core.management import call_command\n curdir = os.getcwd()\n for subdir in ('geotrek', ):\n os.chdir(subdir)\n call_command('compilemessages')\n for path in Path('.').rglob('*.mo'):\n copy(path, os.path.join(curdir, self.build_lib, subdir, path))\n os.chdir(curdir)\n\n\nsetup(\n name='geotrek',\n version=open(os.path.join(here, 'VERSION')).read().strip(),\n author='Makina Corpus',\n author_email='[email protected]',\n url='https://makina-corpus.com',\n description=\"Geotrek\",\n scripts=['manage.py'],\n install_requires=[\n 'Django==3.2.*',\n 'mapentity',\n 'chardet',\n 'cairosvg',\n 'cairocffi',\n 'env_file',\n # pinned by requirements.txt\n 'pymemcache',\n 'coreschema',\n 'coreapi',\n 'psycopg2',\n 'pdfimpose',\n 'docutils',\n 'Pillow',\n 'simplekml',\n 'pygal',\n 'paperclip',\n 'django-extended-choices',\n 'django-modelcluster',\n 'django-mptt',\n 'geojson',\n 'tif2geojson',\n 'drf-dynamic-fields',\n 'drf-yasg',\n 'xlrd',\n 'landez',\n 'large-image-source-vips',\n 'django-large-image',\n 'celery',\n 'redis',\n 'django-celery-results',\n 'drf-extensions',\n 'django-colorfield',\n 'Fiona',\n 'markdown',\n \"weasyprint==52.5\", # newer version required libpango (not available in bionic)\n 'django-weasyprint<2.0.0', # 2.10 require weasyprint > 53\n \"django-clearcache\",\n \"pyopenair\",\n # prod,\n 'gunicorn',\n 'sentry-sdk',\n 'easy-thumbnails[svg]',\n ],\n cmdclass={\"build\": BuildCommand},\n include_package_data=True,\n license='BSD, see LICENSE file.',\n packages=find_packages(),\n classifiers=['Natural Language :: English',\n 'Environment :: Web Environment',\n 'Framework :: Django',\n 'Development Status :: 5 - Production/Stable',\n 'Programming Language :: Python :: 3'],\n)\n", "path": "setup.py" } ]
diff --git a/debian/rules b/debian/rules index 4f314f4e5b..6394839dd1 100755 --- a/debian/rules +++ b/debian/rules @@ -10,7 +10,7 @@ override_dh_virtualenv: --python /usr/bin/python3.8 \ --upgrade-pip \ --preinstall wheel \ - --preinstall setuptools \ + --preinstall setuptools==69.2.0 \ --builtin-venv \ --extra-pip-arg --no-cache-dir \ --extra-pip-arg --quiet diff --git a/docs/changelog.rst b/docs/changelog.rst index 936c514cd5..2e74944dbb 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -5,6 +5,10 @@ CHANGELOG 2.103.2+dev (XXXX-XX-XX) ------------------------ +**Bug fixes** + +- Fix api crash when using an svg file for information desks (fixes #3860) + **Breaking changes** - Geotrek-rando v2 support is deprecated, `sync_rando` command and Sync rando menu view are removed. diff --git a/geotrek/api/tests/test_v2.py b/geotrek/api/tests/test_v2.py index 2802e0003e..9db3d252e0 100644 --- a/geotrek/api/tests/test_v2.py +++ b/geotrek/api/tests/test_v2.py @@ -2156,12 +2156,32 @@ def test_informationdesk_list(self): tourism_models.InformationDesk ) + def test_informationdesk_list_with_svg(self): + info_desk = tourism_factory.InformationDeskFactory( + photo=get_dummy_uploaded_image_svg(), name='test!') + info_desk.save() + self.treks[0].information_desks.add(info_desk) + self.check_number_elems_response( + self.get_informationdesk_list(), + tourism_models.InformationDesk + ) + def test_informationdesk_detail(self): self.check_structure_response( self.get_informationdesk_detail(self.info_desk.pk), INFORMATION_DESK_PROPERTIES_JSON_STRUCTURE ) + def test_informationdesk_detail_with_svg(self): + info_desk = tourism_factory.InformationDeskFactory( + photo=get_dummy_uploaded_image_svg()) + info_desk.save() + self.treks[0].information_desks.add(info_desk) + self.check_structure_response( + self.get_informationdesk_detail(info_desk.pk), + INFORMATION_DESK_PROPERTIES_JSON_STRUCTURE + ) + def test_informationdesk_filter_trek(self): response = self.get_informationdesk_list({'trek': self.treks[0].pk}) self.assertEqual(response.json()["count"], 1) diff --git a/requirements.txt b/requirements.txt index 03d999bcc8..3ec14c5ac3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -52,7 +52,9 @@ cffi==1.16.0 # pyvips # weasyprint chardet==5.2.0 - # via geotrek (setup.py) + # via + # geotrek (setup.py) + # reportlab charset-normalizer==3.2.0 # via requests click==8.1.3 @@ -85,6 +87,7 @@ coreschema==0.0.4 cssselect2==0.7.0 # via # cairosvg + # svglib # weasyprint datetime==5.2 # via appy @@ -172,8 +175,9 @@ drf-yasg==1.21.5 # via # django-large-image # geotrek (setup.py) -easy-thumbnails==2.8.5 +easy-thumbnails[svg]==2.8.5 # via + # geotrek (setup.py) # mapentity # paperclip env-file==2020.12.3 @@ -217,7 +221,9 @@ large-image==1.20.3 large-image-source-vips==1.17.2 # via geotrek (setup.py) lxml==4.9.3 - # via mapentity + # via + # mapentity + # svglib mapentity==8.7.2 # via geotrek (setup.py) markdown==3.4.4 @@ -258,6 +264,7 @@ pillow==10.2.0 # geotrek (setup.py) # large-image # paperclip + # reportlab # weasyprint prompt-toolkit==3.0.39 # via click-repl @@ -297,6 +304,10 @@ rcssmin==1.1.1 # via django-compressor redis==4.5.4 # via geotrek (setup.py) +reportlab==4.1.0 + # via + # easy-thumbnails + # svglib requests==2.31.0 # via # coreapi @@ -326,12 +337,15 @@ soupsieve==2.5 # via beautifulsoup4 sqlparse==0.4.4 # via django +svglib==1.5.1 + # via easy-thumbnails tif2geojson==0.1.3 # via geotrek (setup.py) tinycss2==1.2.1 # via # cairosvg # cssselect2 + # svglib # weasyprint transaction==3.1.0 # via zodb diff --git a/setup.py b/setup.py index 7be60e8fc2..558ba1f1a0 100644 --- a/setup.py +++ b/setup.py @@ -72,6 +72,7 @@ def run(self): # prod, 'gunicorn', 'sentry-sdk', + 'easy-thumbnails[svg]', ], cmdclass={"build": BuildCommand}, include_package_data=True,
PaddlePaddle__models-3377
如果eos发生变化无法做infer的操作 [attention_model.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/ocr_recognition/attention_model.py)这个文件中如果eos发生变化无法做infer的操作（比如因为要识别有数字所以eos改成了其他值） attention_infer的方法中 ```xml selected_ids, selected_scores = fluid.layers.beam_search( pre_ids, pre_score, topk_indices, accu_scores, beam_size, eos, # 这里应该从1改成eos，否则eos如果发生变化会导致无法做infer操作 #level=0 ) ```
[ { "content": "# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport paddle.fluid as fluid\nimport six\n\ndecoder_size = 128\nword_vector_dim = 128\nmax_length = 100\nsos = 0\neos = 1\nbeam_size = 1\n\ndef conv_bn_pool(input,\n group,\n out_ch,\n act=\"relu\",\n is_test=False,\n pool=True,\n use_cudnn=True):\n tmp = input\n for i in six.moves.xrange(group):\n filter_size = 3\n conv_std = (2.0 / (filter_size**2 * tmp.shape[1]))**0.5\n conv_param = fluid.ParamAttr(\n initializer=fluid.initializer.Normal(0.0, conv_std))\n tmp = fluid.layers.conv2d(\n input=tmp,\n num_filters=out_ch[i],\n filter_size=3,\n padding=1,\n bias_attr=False,\n param_attr=conv_param,\n act=None, # LinearActivation\n use_cudnn=use_cudnn)\n\n tmp = fluid.layers.batch_norm(input=tmp, act=act, is_test=is_test)\n if pool == True:\n tmp = fluid.layers.pool2d(\n input=tmp,\n pool_size=2,\n pool_type='max',\n pool_stride=2,\n use_cudnn=use_cudnn,\n ceil_mode=True)\n\n return tmp\n\n\ndef ocr_convs(input, is_test=False, use_cudnn=True):\n tmp = input\n tmp = conv_bn_pool(tmp, 2, [16, 16], is_test=is_test, use_cudnn=use_cudnn)\n tmp = conv_bn_pool(tmp, 2, [32, 32], is_test=is_test, use_cudnn=use_cudnn)\n tmp = conv_bn_pool(tmp, 2, [64, 64], is_test=is_test, use_cudnn=use_cudnn)\n tmp = conv_bn_pool(\n tmp, 2, [128, 128], is_test=is_test, pool=False, use_cudnn=use_cudnn)\n return tmp\n\n\ndef encoder_net(images, rnn_hidden_size=200, is_test=False, use_cudnn=True):\n\n conv_features = ocr_convs(images, is_test=is_test, use_cudnn=use_cudnn)\n\n sliced_feature = fluid.layers.im2sequence(\n input=conv_features,\n stride=[1, 1],\n filter_size=[conv_features.shape[2], 1])\n\n para_attr = fluid.ParamAttr(initializer=fluid.initializer.Normal(0.0, 0.02))\n bias_attr = fluid.ParamAttr(\n initializer=fluid.initializer.Normal(0.0, 0.02), learning_rate=2.0)\n\n fc_1 = fluid.layers.fc(input=sliced_feature,\n size=rnn_hidden_size * 3,\n param_attr=para_attr,\n bias_attr=False)\n fc_2 = fluid.layers.fc(input=sliced_feature,\n size=rnn_hidden_size * 3,\n param_attr=para_attr,\n bias_attr=False)\n\n gru_forward = fluid.layers.dynamic_gru(\n input=fc_1,\n size=rnn_hidden_size,\n param_attr=para_attr,\n bias_attr=bias_attr,\n candidate_activation='relu')\n gru_backward = fluid.layers.dynamic_gru(\n input=fc_2,\n size=rnn_hidden_size,\n is_reverse=True,\n param_attr=para_attr,\n bias_attr=bias_attr,\n candidate_activation='relu')\n\n encoded_vector = fluid.layers.concat(\n input=[gru_forward, gru_backward], axis=1)\n encoded_proj = fluid.layers.fc(input=encoded_vector,\n size=decoder_size,\n bias_attr=False)\n\n return gru_backward, encoded_vector, encoded_proj\n\n\ndef gru_decoder_with_attention(target_embedding, encoder_vec, encoder_proj,\n decoder_boot, decoder_size, num_classes):\n def simple_attention(encoder_vec, encoder_proj, decoder_state):\n decoder_state_proj = fluid.layers.fc(input=decoder_state,\n size=decoder_size,\n bias_attr=False)\n decoder_state_expand = fluid.layers.sequence_expand(\n x=decoder_state_proj, y=encoder_proj)\n concated = encoder_proj + decoder_state_expand\n concated = fluid.layers.tanh(x=concated)\n attention_weights = fluid.layers.fc(input=concated,\n size=1,\n act=None,\n bias_attr=False)\n attention_weights = fluid.layers.sequence_softmax(\n input=attention_weights)\n weigths_reshape = fluid.layers.reshape(x=attention_weights, shape=[-1])\n scaled = fluid.layers.elementwise_mul(\n x=encoder_vec, y=weigths_reshape, axis=0)\n context = fluid.layers.sequence_pool(input=scaled, pool_type='sum')\n return context\n\n rnn = fluid.layers.DynamicRNN()\n\n with rnn.block():\n current_word = rnn.step_input(target_embedding)\n encoder_vec = rnn.static_input(encoder_vec)\n encoder_proj = rnn.static_input(encoder_proj)\n hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True)\n context = simple_attention(encoder_vec, encoder_proj, hidden_mem)\n fc_1 = fluid.layers.fc(input=context,\n size=decoder_size * 3,\n bias_attr=False)\n fc_2 = fluid.layers.fc(input=current_word,\n size=decoder_size * 3,\n bias_attr=False)\n decoder_inputs = fc_1 + fc_2\n h, _, _ = fluid.layers.gru_unit(\n input=decoder_inputs, hidden=hidden_mem, size=decoder_size * 3)\n rnn.update_memory(hidden_mem, h)\n out = fluid.layers.fc(input=h,\n size=num_classes + 2,\n bias_attr=True,\n act='softmax')\n rnn.output(out)\n return rnn()\n\n\ndef attention_train_net(args, data_shape, num_classes):\n\n images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')\n label_in = fluid.layers.data(\n name='label_in', shape=[1], dtype='int32', lod_level=1)\n label_out = fluid.layers.data(\n name='label_out', shape=[1], dtype='int32', lod_level=1)\n\n gru_backward, encoded_vector, encoded_proj = encoder_net(images)\n\n backward_first = fluid.layers.sequence_pool(\n input=gru_backward, pool_type='first')\n decoder_boot = fluid.layers.fc(input=backward_first,\n size=decoder_size,\n bias_attr=False,\n act=\"relu\")\n\n label_in = fluid.layers.cast(x=label_in, dtype='int64')\n trg_embedding = fluid.layers.embedding(\n input=label_in,\n size=[num_classes + 2, word_vector_dim],\n dtype='float32')\n prediction = gru_decoder_with_attention(trg_embedding, encoded_vector,\n encoded_proj, decoder_boot,\n decoder_size, num_classes)\n fluid.clip.set_gradient_clip(fluid.clip.GradientClipByValue(args.gradient_clip))\n label_out = fluid.layers.cast(x=label_out, dtype='int64')\n\n _, maxid = fluid.layers.topk(input=prediction, k=1)\n error_evaluator = fluid.evaluator.EditDistance(\n input=maxid, label=label_out, ignored_tokens=[sos, eos])\n\n inference_program = fluid.default_main_program().clone(for_test=True)\n\n cost = fluid.layers.cross_entropy(input=prediction, label=label_out)\n sum_cost = fluid.layers.reduce_sum(cost)\n LR = args.lr\n if args.lr_decay_strategy == \"piecewise_decay\":\n learning_rate = fluid.layers.piecewise_decay([50000], [LR, LR * 0.01])\n else:\n learning_rate = LR\n\n optimizer = fluid.optimizer.Adadelta(\n learning_rate=learning_rate, epsilon=1.0e-6, rho=0.9)\n optimizer.minimize(sum_cost)\n\n model_average = None\n if args.average_window > 0:\n model_average = fluid.optimizer.ModelAverage(\n args.average_window,\n min_average_window=args.min_average_window,\n max_average_window=args.max_average_window)\n\n return sum_cost, error_evaluator, inference_program, model_average\n\n\ndef simple_attention(encoder_vec, encoder_proj, decoder_state, decoder_size):\n decoder_state_proj = fluid.layers.fc(input=decoder_state,\n size=decoder_size,\n bias_attr=False)\n decoder_state_expand = fluid.layers.sequence_expand(\n x=decoder_state_proj, y=encoder_proj)\n concated = fluid.layers.elementwise_add(encoder_proj, decoder_state_expand)\n concated = fluid.layers.tanh(x=concated)\n attention_weights = fluid.layers.fc(input=concated,\n size=1,\n act=None,\n bias_attr=False)\n attention_weights = fluid.layers.sequence_softmax(input=attention_weights)\n weigths_reshape = fluid.layers.reshape(x=attention_weights, shape=[-1])\n scaled = fluid.layers.elementwise_mul(\n x=encoder_vec, y=weigths_reshape, axis=0)\n context = fluid.layers.sequence_pool(input=scaled, pool_type='sum')\n return context\n\n\ndef attention_infer(images, num_classes, use_cudnn=True):\n\n max_length = 20\n gru_backward, encoded_vector, encoded_proj = encoder_net(\n images, is_test=True, use_cudnn=use_cudnn)\n\n backward_first = fluid.layers.sequence_pool(\n input=gru_backward, pool_type='first')\n decoder_boot = fluid.layers.fc(input=backward_first,\n size=decoder_size,\n bias_attr=False,\n act=\"relu\")\n init_state = decoder_boot\n array_len = fluid.layers.fill_constant(\n shape=[1], dtype='int64', value=max_length)\n counter = fluid.layers.zeros(shape=[1], dtype='int64', force_cpu=True)\n\n # fill the first element with init_state\n state_array = fluid.layers.create_array('float32')\n fluid.layers.array_write(init_state, array=state_array, i=counter)\n\n # ids, scores as memory\n ids_array = fluid.layers.create_array('int64')\n scores_array = fluid.layers.create_array('float32')\n\n init_ids = fluid.layers.data(\n name=\"init_ids\", shape=[1], dtype=\"int64\", lod_level=2)\n init_scores = fluid.layers.data(\n name=\"init_scores\", shape=[1], dtype=\"float32\", lod_level=2)\n\n fluid.layers.array_write(init_ids, array=ids_array, i=counter)\n fluid.layers.array_write(init_scores, array=scores_array, i=counter)\n\n cond = fluid.layers.less_than(x=counter, y=array_len)\n while_op = fluid.layers.While(cond=cond)\n with while_op.block():\n pre_ids = fluid.layers.array_read(array=ids_array, i=counter)\n pre_state = fluid.layers.array_read(array=state_array, i=counter)\n pre_score = fluid.layers.array_read(array=scores_array, i=counter)\n\n pre_ids_emb = fluid.layers.embedding(\n input=pre_ids,\n size=[num_classes + 2, word_vector_dim],\n dtype='float32')\n\n context = simple_attention(encoded_vector, encoded_proj, pre_state,\n decoder_size)\n\n # expand the recursive_sequence_lengths of pre_state to be the same with pre_score\n pre_state_expanded = fluid.layers.sequence_expand(pre_state, pre_score)\n context_expanded = fluid.layers.sequence_expand(context, pre_score)\n fc_1 = fluid.layers.fc(input=context_expanded,\n size=decoder_size * 3,\n bias_attr=False)\n fc_2 = fluid.layers.fc(input=pre_ids_emb,\n size=decoder_size * 3,\n bias_attr=False)\n\n decoder_inputs = fc_1 + fc_2\n current_state, _, _ = fluid.layers.gru_unit(\n input=decoder_inputs,\n hidden=pre_state_expanded,\n size=decoder_size * 3)\n\n current_state_with_lod = fluid.layers.lod_reset(\n x=current_state, y=pre_score)\n # use score to do beam search\n current_score = fluid.layers.fc(input=current_state_with_lod,\n size=num_classes + 2,\n bias_attr=True,\n act='softmax')\n topk_scores, topk_indices = fluid.layers.topk(\n current_score, k=beam_size)\n\n # calculate accumulated scores after topk to reduce computation cost\n accu_scores = fluid.layers.elementwise_add(\n x=fluid.layers.log(topk_scores),\n y=fluid.layers.reshape(\n pre_score, shape=[-1]),\n axis=0)\n selected_ids, selected_scores = fluid.layers.beam_search(\n pre_ids,\n pre_score,\n topk_indices,\n accu_scores,\n beam_size,\n 1, # end_id\n #level=0\n )\n\n fluid.layers.increment(x=counter, value=1, in_place=True)\n\n # update the memories\n fluid.layers.array_write(current_state, array=state_array, i=counter)\n fluid.layers.array_write(selected_ids, array=ids_array, i=counter)\n fluid.layers.array_write(selected_scores, array=scores_array, i=counter)\n\n # update the break condition: up to the max length or all candidates of\n # source sentences have ended.\n length_cond = fluid.layers.less_than(x=counter, y=array_len)\n finish_cond = fluid.layers.logical_not(\n fluid.layers.is_empty(x=selected_ids))\n fluid.layers.logical_and(x=length_cond, y=finish_cond, out=cond)\n\n ids, scores = fluid.layers.beam_search_decode(ids_array, scores_array,\n beam_size, eos)\n return ids\n\n\ndef attention_eval(data_shape, num_classes, use_cudnn=True):\n images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')\n label_in = fluid.layers.data(\n name='label_in', shape=[1], dtype='int32', lod_level=1)\n label_out = fluid.layers.data(\n name='label_out', shape=[1], dtype='int32', lod_level=1)\n label_out = fluid.layers.cast(x=label_out, dtype='int64')\n label_in = fluid.layers.cast(x=label_in, dtype='int64')\n\n gru_backward, encoded_vector, encoded_proj = encoder_net(\n images, is_test=True, use_cudnn=use_cudnn)\n\n backward_first = fluid.layers.sequence_pool(\n input=gru_backward, pool_type='first')\n decoder_boot = fluid.layers.fc(input=backward_first,\n size=decoder_size,\n bias_attr=False,\n act=\"relu\")\n trg_embedding = fluid.layers.embedding(\n input=label_in,\n size=[num_classes + 2, word_vector_dim],\n dtype='float32')\n prediction = gru_decoder_with_attention(trg_embedding, encoded_vector,\n encoded_proj, decoder_boot,\n decoder_size, num_classes)\n _, maxid = fluid.layers.topk(input=prediction, k=1)\n error_evaluator = fluid.evaluator.EditDistance(\n input=maxid, label=label_out, ignored_tokens=[sos, eos])\n cost = fluid.layers.cross_entropy(input=prediction, label=label_out)\n sum_cost = fluid.layers.reduce_sum(cost)\n return error_evaluator, sum_cost\n", "path": "PaddleCV/ocr_recognition/attention_model.py" } ]
[ { "content": "# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport paddle.fluid as fluid\nimport six\n\ndecoder_size = 128\nword_vector_dim = 128\nmax_length = 100\nsos = 0\neos = 1\nbeam_size = 1\n\ndef conv_bn_pool(input,\n group,\n out_ch,\n act=\"relu\",\n is_test=False,\n pool=True,\n use_cudnn=True):\n tmp = input\n for i in six.moves.xrange(group):\n filter_size = 3\n conv_std = (2.0 / (filter_size**2 * tmp.shape[1]))**0.5\n conv_param = fluid.ParamAttr(\n initializer=fluid.initializer.Normal(0.0, conv_std))\n tmp = fluid.layers.conv2d(\n input=tmp,\n num_filters=out_ch[i],\n filter_size=3,\n padding=1,\n bias_attr=False,\n param_attr=conv_param,\n act=None, # LinearActivation\n use_cudnn=use_cudnn)\n\n tmp = fluid.layers.batch_norm(input=tmp, act=act, is_test=is_test)\n if pool == True:\n tmp = fluid.layers.pool2d(\n input=tmp,\n pool_size=2,\n pool_type='max',\n pool_stride=2,\n use_cudnn=use_cudnn,\n ceil_mode=True)\n\n return tmp\n\n\ndef ocr_convs(input, is_test=False, use_cudnn=True):\n tmp = input\n tmp = conv_bn_pool(tmp, 2, [16, 16], is_test=is_test, use_cudnn=use_cudnn)\n tmp = conv_bn_pool(tmp, 2, [32, 32], is_test=is_test, use_cudnn=use_cudnn)\n tmp = conv_bn_pool(tmp, 2, [64, 64], is_test=is_test, use_cudnn=use_cudnn)\n tmp = conv_bn_pool(\n tmp, 2, [128, 128], is_test=is_test, pool=False, use_cudnn=use_cudnn)\n return tmp\n\n\ndef encoder_net(images, rnn_hidden_size=200, is_test=False, use_cudnn=True):\n\n conv_features = ocr_convs(images, is_test=is_test, use_cudnn=use_cudnn)\n\n sliced_feature = fluid.layers.im2sequence(\n input=conv_features,\n stride=[1, 1],\n filter_size=[conv_features.shape[2], 1])\n\n para_attr = fluid.ParamAttr(initializer=fluid.initializer.Normal(0.0, 0.02))\n bias_attr = fluid.ParamAttr(\n initializer=fluid.initializer.Normal(0.0, 0.02), learning_rate=2.0)\n\n fc_1 = fluid.layers.fc(input=sliced_feature,\n size=rnn_hidden_size * 3,\n param_attr=para_attr,\n bias_attr=False)\n fc_2 = fluid.layers.fc(input=sliced_feature,\n size=rnn_hidden_size * 3,\n param_attr=para_attr,\n bias_attr=False)\n\n gru_forward = fluid.layers.dynamic_gru(\n input=fc_1,\n size=rnn_hidden_size,\n param_attr=para_attr,\n bias_attr=bias_attr,\n candidate_activation='relu')\n gru_backward = fluid.layers.dynamic_gru(\n input=fc_2,\n size=rnn_hidden_size,\n is_reverse=True,\n param_attr=para_attr,\n bias_attr=bias_attr,\n candidate_activation='relu')\n\n encoded_vector = fluid.layers.concat(\n input=[gru_forward, gru_backward], axis=1)\n encoded_proj = fluid.layers.fc(input=encoded_vector,\n size=decoder_size,\n bias_attr=False)\n\n return gru_backward, encoded_vector, encoded_proj\n\n\ndef gru_decoder_with_attention(target_embedding, encoder_vec, encoder_proj,\n decoder_boot, decoder_size, num_classes):\n def simple_attention(encoder_vec, encoder_proj, decoder_state):\n decoder_state_proj = fluid.layers.fc(input=decoder_state,\n size=decoder_size,\n bias_attr=False)\n decoder_state_expand = fluid.layers.sequence_expand(\n x=decoder_state_proj, y=encoder_proj)\n concated = encoder_proj + decoder_state_expand\n concated = fluid.layers.tanh(x=concated)\n attention_weights = fluid.layers.fc(input=concated,\n size=1,\n act=None,\n bias_attr=False)\n attention_weights = fluid.layers.sequence_softmax(\n input=attention_weights)\n weigths_reshape = fluid.layers.reshape(x=attention_weights, shape=[-1])\n scaled = fluid.layers.elementwise_mul(\n x=encoder_vec, y=weigths_reshape, axis=0)\n context = fluid.layers.sequence_pool(input=scaled, pool_type='sum')\n return context\n\n rnn = fluid.layers.DynamicRNN()\n\n with rnn.block():\n current_word = rnn.step_input(target_embedding)\n encoder_vec = rnn.static_input(encoder_vec)\n encoder_proj = rnn.static_input(encoder_proj)\n hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True)\n context = simple_attention(encoder_vec, encoder_proj, hidden_mem)\n fc_1 = fluid.layers.fc(input=context,\n size=decoder_size * 3,\n bias_attr=False)\n fc_2 = fluid.layers.fc(input=current_word,\n size=decoder_size * 3,\n bias_attr=False)\n decoder_inputs = fc_1 + fc_2\n h, _, _ = fluid.layers.gru_unit(\n input=decoder_inputs, hidden=hidden_mem, size=decoder_size * 3)\n rnn.update_memory(hidden_mem, h)\n out = fluid.layers.fc(input=h,\n size=num_classes + 2,\n bias_attr=True,\n act='softmax')\n rnn.output(out)\n return rnn()\n\n\ndef attention_train_net(args, data_shape, num_classes):\n\n images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')\n label_in = fluid.layers.data(\n name='label_in', shape=[1], dtype='int32', lod_level=1)\n label_out = fluid.layers.data(\n name='label_out', shape=[1], dtype='int32', lod_level=1)\n\n gru_backward, encoded_vector, encoded_proj = encoder_net(images)\n\n backward_first = fluid.layers.sequence_pool(\n input=gru_backward, pool_type='first')\n decoder_boot = fluid.layers.fc(input=backward_first,\n size=decoder_size,\n bias_attr=False,\n act=\"relu\")\n\n label_in = fluid.layers.cast(x=label_in, dtype='int64')\n trg_embedding = fluid.layers.embedding(\n input=label_in,\n size=[num_classes + 2, word_vector_dim],\n dtype='float32')\n prediction = gru_decoder_with_attention(trg_embedding, encoded_vector,\n encoded_proj, decoder_boot,\n decoder_size, num_classes)\n fluid.clip.set_gradient_clip(fluid.clip.GradientClipByValue(args.gradient_clip))\n label_out = fluid.layers.cast(x=label_out, dtype='int64')\n\n _, maxid = fluid.layers.topk(input=prediction, k=1)\n error_evaluator = fluid.evaluator.EditDistance(\n input=maxid, label=label_out, ignored_tokens=[sos, eos])\n\n inference_program = fluid.default_main_program().clone(for_test=True)\n\n cost = fluid.layers.cross_entropy(input=prediction, label=label_out)\n sum_cost = fluid.layers.reduce_sum(cost)\n LR = args.lr\n if args.lr_decay_strategy == \"piecewise_decay\":\n learning_rate = fluid.layers.piecewise_decay([50000], [LR, LR * 0.01])\n else:\n learning_rate = LR\n\n optimizer = fluid.optimizer.Adadelta(\n learning_rate=learning_rate, epsilon=1.0e-6, rho=0.9)\n optimizer.minimize(sum_cost)\n\n model_average = None\n if args.average_window > 0:\n model_average = fluid.optimizer.ModelAverage(\n args.average_window,\n min_average_window=args.min_average_window,\n max_average_window=args.max_average_window)\n\n return sum_cost, error_evaluator, inference_program, model_average\n\n\ndef simple_attention(encoder_vec, encoder_proj, decoder_state, decoder_size):\n decoder_state_proj = fluid.layers.fc(input=decoder_state,\n size=decoder_size,\n bias_attr=False)\n decoder_state_expand = fluid.layers.sequence_expand(\n x=decoder_state_proj, y=encoder_proj)\n concated = fluid.layers.elementwise_add(encoder_proj, decoder_state_expand)\n concated = fluid.layers.tanh(x=concated)\n attention_weights = fluid.layers.fc(input=concated,\n size=1,\n act=None,\n bias_attr=False)\n attention_weights = fluid.layers.sequence_softmax(input=attention_weights)\n weigths_reshape = fluid.layers.reshape(x=attention_weights, shape=[-1])\n scaled = fluid.layers.elementwise_mul(\n x=encoder_vec, y=weigths_reshape, axis=0)\n context = fluid.layers.sequence_pool(input=scaled, pool_type='sum')\n return context\n\n\ndef attention_infer(images, num_classes, use_cudnn=True):\n\n max_length = 20\n gru_backward, encoded_vector, encoded_proj = encoder_net(\n images, is_test=True, use_cudnn=use_cudnn)\n\n backward_first = fluid.layers.sequence_pool(\n input=gru_backward, pool_type='first')\n decoder_boot = fluid.layers.fc(input=backward_first,\n size=decoder_size,\n bias_attr=False,\n act=\"relu\")\n init_state = decoder_boot\n array_len = fluid.layers.fill_constant(\n shape=[1], dtype='int64', value=max_length)\n counter = fluid.layers.zeros(shape=[1], dtype='int64', force_cpu=True)\n\n # fill the first element with init_state\n state_array = fluid.layers.create_array('float32')\n fluid.layers.array_write(init_state, array=state_array, i=counter)\n\n # ids, scores as memory\n ids_array = fluid.layers.create_array('int64')\n scores_array = fluid.layers.create_array('float32')\n\n init_ids = fluid.layers.data(\n name=\"init_ids\", shape=[1], dtype=\"int64\", lod_level=2)\n init_scores = fluid.layers.data(\n name=\"init_scores\", shape=[1], dtype=\"float32\", lod_level=2)\n\n fluid.layers.array_write(init_ids, array=ids_array, i=counter)\n fluid.layers.array_write(init_scores, array=scores_array, i=counter)\n\n cond = fluid.layers.less_than(x=counter, y=array_len)\n while_op = fluid.layers.While(cond=cond)\n with while_op.block():\n pre_ids = fluid.layers.array_read(array=ids_array, i=counter)\n pre_state = fluid.layers.array_read(array=state_array, i=counter)\n pre_score = fluid.layers.array_read(array=scores_array, i=counter)\n\n pre_ids_emb = fluid.layers.embedding(\n input=pre_ids,\n size=[num_classes + 2, word_vector_dim],\n dtype='float32')\n\n context = simple_attention(encoded_vector, encoded_proj, pre_state,\n decoder_size)\n\n # expand the recursive_sequence_lengths of pre_state to be the same with pre_score\n pre_state_expanded = fluid.layers.sequence_expand(pre_state, pre_score)\n context_expanded = fluid.layers.sequence_expand(context, pre_score)\n fc_1 = fluid.layers.fc(input=context_expanded,\n size=decoder_size * 3,\n bias_attr=False)\n fc_2 = fluid.layers.fc(input=pre_ids_emb,\n size=decoder_size * 3,\n bias_attr=False)\n\n decoder_inputs = fc_1 + fc_2\n current_state, _, _ = fluid.layers.gru_unit(\n input=decoder_inputs,\n hidden=pre_state_expanded,\n size=decoder_size * 3)\n\n current_state_with_lod = fluid.layers.lod_reset(\n x=current_state, y=pre_score)\n # use score to do beam search\n current_score = fluid.layers.fc(input=current_state_with_lod,\n size=num_classes + 2,\n bias_attr=True,\n act='softmax')\n topk_scores, topk_indices = fluid.layers.topk(\n current_score, k=beam_size)\n\n # calculate accumulated scores after topk to reduce computation cost\n accu_scores = fluid.layers.elementwise_add(\n x=fluid.layers.log(topk_scores),\n y=fluid.layers.reshape(\n pre_score, shape=[-1]),\n axis=0)\n selected_ids, selected_scores = fluid.layers.beam_search(\n pre_ids,\n pre_score,\n topk_indices,\n accu_scores,\n beam_size,\n eos, # end_id\n #level=0\n )\n\n fluid.layers.increment(x=counter, value=1, in_place=True)\n\n # update the memories\n fluid.layers.array_write(current_state, array=state_array, i=counter)\n fluid.layers.array_write(selected_ids, array=ids_array, i=counter)\n fluid.layers.array_write(selected_scores, array=scores_array, i=counter)\n\n # update the break condition: up to the max length or all candidates of\n # source sentences have ended.\n length_cond = fluid.layers.less_than(x=counter, y=array_len)\n finish_cond = fluid.layers.logical_not(\n fluid.layers.is_empty(x=selected_ids))\n fluid.layers.logical_and(x=length_cond, y=finish_cond, out=cond)\n\n ids, scores = fluid.layers.beam_search_decode(ids_array, scores_array,\n beam_size, eos)\n return ids\n\n\ndef attention_eval(data_shape, num_classes, use_cudnn=True):\n images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')\n label_in = fluid.layers.data(\n name='label_in', shape=[1], dtype='int32', lod_level=1)\n label_out = fluid.layers.data(\n name='label_out', shape=[1], dtype='int32', lod_level=1)\n label_out = fluid.layers.cast(x=label_out, dtype='int64')\n label_in = fluid.layers.cast(x=label_in, dtype='int64')\n\n gru_backward, encoded_vector, encoded_proj = encoder_net(\n images, is_test=True, use_cudnn=use_cudnn)\n\n backward_first = fluid.layers.sequence_pool(\n input=gru_backward, pool_type='first')\n decoder_boot = fluid.layers.fc(input=backward_first,\n size=decoder_size,\n bias_attr=False,\n act=\"relu\")\n trg_embedding = fluid.layers.embedding(\n input=label_in,\n size=[num_classes + 2, word_vector_dim],\n dtype='float32')\n prediction = gru_decoder_with_attention(trg_embedding, encoded_vector,\n encoded_proj, decoder_boot,\n decoder_size, num_classes)\n _, maxid = fluid.layers.topk(input=prediction, k=1)\n error_evaluator = fluid.evaluator.EditDistance(\n input=maxid, label=label_out, ignored_tokens=[sos, eos])\n cost = fluid.layers.cross_entropy(input=prediction, label=label_out)\n sum_cost = fluid.layers.reduce_sum(cost)\n return error_evaluator, sum_cost\n", "path": "PaddleCV/ocr_recognition/attention_model.py" } ]
diff --git a/PaddleCV/ocr_recognition/attention_model.py b/PaddleCV/ocr_recognition/attention_model.py index 4d3b4da50a..963d2168fd 100755 --- a/PaddleCV/ocr_recognition/attention_model.py +++ b/PaddleCV/ocr_recognition/attention_model.py @@ -325,7 +325,7 @@ def attention_infer(images, num_classes, use_cudnn=True): topk_indices, accu_scores, beam_size, - 1, # end_id + eos, # end_id #level=0 )
dmlc__gluon-nlp-184
API doc examples are currently not easy to copy/paste users may want to use a snippet from example directly, so making the notebooks copy-friendly is important currently the code blocks have python shell prefix ">>>" in them. see http://gluon-nlp.mxnet.io/api/notes/data_api.html
[ { "content": "# -*- coding: utf-8 -*-\n#\n# documentation build configuration file, created by\n# sphinx-quickstart on Thu Jul 23 19:40:08 2015.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\nimport sys\nimport os, subprocess\nimport shlex\nimport recommonmark\nimport sphinx_gallery\nfrom recommonmark.parser import CommonMarkParser\nfrom recommonmark.transform import AutoStructify\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\ncurr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))\nsys.path.insert(0, os.path.join(curr_path, '..'))\n\n# -- General configuration ------------------------------------------------\n\n# Version information.\nimport gluonnlp as nlp\nversion = nlp.__version__\nrelease = nlp.__version__\n\n# General information about the project.\nproject = u'gluonnlp'\nauthor = u'%s developers' % project\ncopyright = u'2018, %s' % author\ngithub_doc_root = 'http://gluon-nlp.mxnet.io/{}/'.format(str(version))\n\n# add markdown parser\nCommonMarkParser.github_doc_root = github_doc_root\nsource_parsers = {\n '.md': CommonMarkParser\n}\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.autosummary',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.viewcode',\n 'sphinx.ext.napoleon',\n 'sphinx.ext.mathjax',\n 'sphinx_gallery.gen_gallery',\n 'nbsphinx',\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\nnbsphinx_kernel_name = 'python3'\nnbsphinx_allow_errors = True\nnbsphinx_timeout = 1200\nhtml_sourcelink_suffix = ''\n\nnbsphinx_prolog = \"\"\"\n{% set paths = env.docname.split('/') %}\n\n.. only:: html\n\n :download:`[Download] <{{ \"../%s.zip\"|format(paths[1]) }}>`\n\"\"\"\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n# source_suffix = ['.rst', '.md']\nsource_suffix = ['.rst', '.ipynb', '.md']\n\n# The encoding of source files.\n#source_encoding = 'utf-8-sig'\n\n# generate autosummary even if no references\nautosummary_generate = True\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n#today = ''\n# Else, today_fmt is used as the format for a strftime call.\n#today_fmt = '%B %d, %Y'\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\nhtml_logo = '_static/gluon_white.png'\n\n# The name of an image file (relative to this directory) to use as a favicon of\n# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\nhtml_favicon = '_static/gluon_s2.png'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = ['_build', '**.ipynb_checkpoints']\n\n# The reST default role (used for this markup: `text`) to use for all\n# documents.\n#default_role = None\n\n# If true, '()' will be appended to :func: etc. cross-reference text.\n#add_function_parentheses = True\n\n# If true, the current module name will be prepended to all description\n# unit titles (such as .. function::).\n#add_module_names = True\n\n# If true, sectionauthor and moduleauthor directives will be shown in the\n# output. They are ignored by default.\n#show_authors = False\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# A list of ignored prefixes for module index sorting.\n#modindex_common_prefix = []\n\n# If true, keep warnings as \"system message\" paragraphs in the built documents.\n#keep_warnings = False\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme is set by the make target\nhtml_theme = os.environ.get('NNVM_THEME', 'rtd')\n\non_rtd = os.environ.get('READTHEDOCS', None) == 'True'\n# only import rtd theme and set it if want to build docs locally\nif not on_rtd and html_theme == 'rtd':\n import sphinx_rtd_theme\n html_theme = 'sphinx_rtd_theme'\n html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = project + 'doc'\n\n# -- Options for LaTeX output ---------------------------------------------\nlatex_elements = {\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, '%s.tex' % project, project,\n author, 'manual'),\n]\n\n# hook for doxygen\ndef run_doxygen(folder):\n \"\"\"Run the doxygen make command in the designated folder.\"\"\"\n try:\n #retcode = subprocess.call(\"cd %s; make doc\" % folder, shell=True)\n retcode = subprocess.call(\"rm -rf _build/html/doxygen\", shell=True)\n retcode = subprocess.call(\"mkdir -p _build/html\", shell=True)\n retcode = subprocess.call(\"cp -rf doxygen/html _build/html/doxygen\", shell=True)\n if retcode < 0:\n sys.stderr.write(\"doxygen terminated by signal %s\" % (-retcode))\n except OSError as e:\n sys.stderr.write(\"doxygen execution failed: %s\" % e)\n\nintersphinx_mapping = {\n 'python': ('https://docs.python.org/{.major}'.format(sys.version_info), None),\n 'mxnet': ('https://mxnet.apache.org/', None),\n 'numpy': ('http://docs.scipy.org/doc/numpy/', None),\n 'scipy': ('http://docs.scipy.org/doc/scipy/reference', None),\n 'matplotlib': ('http://matplotlib.org/', None),\n 'nltk': ('http://www.nltk.org/', None),\n}\n\n\nfrom sphinx_gallery.sorting import ExplicitOrder\n\nexamples_dirs = []\ngallery_dirs = []\n\nsubsection_order = ExplicitOrder([])\n\ndef generate_doxygen_xml(app):\n \"\"\"Run the doxygen make commands if we're on the ReadTheDocs server\"\"\"\n run_doxygen('..')\n\ndef setup(app):\n # Add hook for building doxygen xml when needed\n # no c++ API for now\n app.connect(\"builder-inited\", generate_doxygen_xml)\n app.add_config_value('recommonmark_config', {\n 'url_resolver': lambda url: github_doc_root + url,\n 'auto_doc_ref': True\n }, True)\n app.add_transform(AutoStructify)\n app.add_javascript('google_analytics.js')\n\n\nsphinx_gallery_conf = {\n 'backreferences_dir': 'gen_modules/backreferences',\n 'doc_module': ('gluonnlp', 'mxnet', 'numpy'),\n'reference_url': {\n 'gluonnlp': None,\n 'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1'},\n 'examples_dirs': examples_dirs,\n 'gallery_dirs': gallery_dirs,\n 'subsection_order': subsection_order,\n 'find_mayavi_figures': False,\n 'filename_pattern': '.py',\n 'expected_failing_examples': []\n}\n\n# Napoleon settings\nnapoleon_use_ivar = True\n\n", "path": "docs/conf.py" } ]
[ { "content": "# -*- coding: utf-8 -*-\n#\n# documentation build configuration file, created by\n# sphinx-quickstart on Thu Jul 23 19:40:08 2015.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\nimport sys\nimport os, subprocess\nimport shlex\nimport recommonmark\nimport sphinx_gallery\nfrom recommonmark.parser import CommonMarkParser\nfrom recommonmark.transform import AutoStructify\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\ncurr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))\nsys.path.insert(0, os.path.join(curr_path, '..'))\n\n# -- General configuration ------------------------------------------------\n\n# Version information.\nimport gluonnlp as nlp\nversion = nlp.__version__\nrelease = nlp.__version__\n\n# General information about the project.\nproject = u'gluonnlp'\nauthor = u'%s developers' % project\ncopyright = u'2018, %s' % author\ngithub_doc_root = 'http://gluon-nlp.mxnet.io/{}/'.format(str(version))\n\n# add markdown parser\nCommonMarkParser.github_doc_root = github_doc_root\nsource_parsers = {\n '.md': CommonMarkParser\n}\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.autosummary',\n 'sphinx.ext.intersphinx',\n 'sphinx.ext.viewcode',\n 'sphinx.ext.napoleon',\n 'sphinx.ext.mathjax',\n 'sphinx_gallery.gen_gallery',\n 'nbsphinx',\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\nnbsphinx_kernel_name = 'python3'\nnbsphinx_allow_errors = True\nnbsphinx_timeout = 1200\nhtml_sourcelink_suffix = ''\n\nnbsphinx_prolog = \"\"\"\n{% set paths = env.docname.split('/') %}\n\n.. only:: html\n\n :download:`[Download] <{{ \"../%s.zip\"|format(paths[1]) }}>`\n\"\"\"\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n# source_suffix = ['.rst', '.md']\nsource_suffix = ['.rst', '.ipynb', '.md']\n\n# The encoding of source files.\n#source_encoding = 'utf-8-sig'\n\n# generate autosummary even if no references\nautosummary_generate = True\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n#today = ''\n# Else, today_fmt is used as the format for a strftime call.\n#today_fmt = '%B %d, %Y'\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\nhtml_logo = '_static/gluon_white.png'\n\n# The name of an image file (relative to this directory) to use as a favicon of\n# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\nhtml_favicon = '_static/gluon_s2.png'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = ['_build', '**.ipynb_checkpoints']\n\n# The reST default role (used for this markup: `text`) to use for all\n# documents.\n#default_role = None\n\n# If true, '()' will be appended to :func: etc. cross-reference text.\n#add_function_parentheses = True\n\n# If true, the current module name will be prepended to all description\n# unit titles (such as .. function::).\n#add_module_names = True\n\n# If true, sectionauthor and moduleauthor directives will be shown in the\n# output. They are ignored by default.\n#show_authors = False\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# A list of ignored prefixes for module index sorting.\n#modindex_common_prefix = []\n\n# If true, keep warnings as \"system message\" paragraphs in the built documents.\n#keep_warnings = False\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme is set by the make target\nhtml_theme = os.environ.get('NNVM_THEME', 'rtd')\n\non_rtd = os.environ.get('READTHEDOCS', None) == 'True'\n# only import rtd theme and set it if want to build docs locally\nif not on_rtd and html_theme == 'rtd':\n import sphinx_rtd_theme\n html_theme = 'sphinx_rtd_theme'\n html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = project + 'doc'\n\n# -- Options for LaTeX output ---------------------------------------------\nlatex_elements = {\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, '%s.tex' % project, project,\n author, 'manual'),\n]\n\n# hook for doxygen\ndef run_doxygen(folder):\n \"\"\"Run the doxygen make command in the designated folder.\"\"\"\n try:\n #retcode = subprocess.call(\"cd %s; make doc\" % folder, shell=True)\n retcode = subprocess.call(\"rm -rf _build/html/doxygen\", shell=True)\n retcode = subprocess.call(\"mkdir -p _build/html\", shell=True)\n retcode = subprocess.call(\"cp -rf doxygen/html _build/html/doxygen\", shell=True)\n if retcode < 0:\n sys.stderr.write(\"doxygen terminated by signal %s\" % (-retcode))\n except OSError as e:\n sys.stderr.write(\"doxygen execution failed: %s\" % e)\n\nintersphinx_mapping = {\n 'python': ('https://docs.python.org/{.major}'.format(sys.version_info), None),\n 'mxnet': ('https://mxnet.apache.org/', None),\n 'numpy': ('http://docs.scipy.org/doc/numpy/', None),\n 'scipy': ('http://docs.scipy.org/doc/scipy/reference', None),\n 'matplotlib': ('http://matplotlib.org/', None),\n 'nltk': ('http://www.nltk.org/', None),\n}\n\n\nfrom sphinx_gallery.sorting import ExplicitOrder\n\nexamples_dirs = []\ngallery_dirs = []\n\nsubsection_order = ExplicitOrder([])\n\ndef generate_doxygen_xml(app):\n \"\"\"Run the doxygen make commands if we're on the ReadTheDocs server\"\"\"\n run_doxygen('..')\n\ndef setup(app):\n # Add hook for building doxygen xml when needed\n # no c++ API for now\n app.connect(\"builder-inited\", generate_doxygen_xml)\n app.add_config_value('recommonmark_config', {\n 'url_resolver': lambda url: github_doc_root + url,\n 'auto_doc_ref': True\n }, True)\n app.add_transform(AutoStructify)\n app.add_javascript('google_analytics.js')\n app.add_javascript('copybutton.js')\n\n\nsphinx_gallery_conf = {\n 'backreferences_dir': 'gen_modules/backreferences',\n 'doc_module': ('gluonnlp', 'mxnet', 'numpy'),\n'reference_url': {\n 'gluonnlp': None,\n 'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1'},\n 'examples_dirs': examples_dirs,\n 'gallery_dirs': gallery_dirs,\n 'subsection_order': subsection_order,\n 'find_mayavi_figures': False,\n 'filename_pattern': '.py',\n 'expected_failing_examples': []\n}\n\n# Napoleon settings\nnapoleon_use_ivar = True\n\n", "path": "docs/conf.py" } ]
diff --git a/docs/_static/copybutton.js b/docs/_static/copybutton.js new file mode 100644 index 0000000000..a8e45151ef --- /dev/null +++ b/docs/_static/copybutton.js @@ -0,0 +1,65 @@ +// Copyright 2014 PSF. Licensed under the PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 +// File originates from the cpython source found in Doc/tools/sphinxext/static/copybutton.js + +$(document).ready(function() { + /* Add a [>>>] button on the top-right corner of code samples to hide + * the >>> and ... prompts and the output and thus make the code + * copyable. */ + var div = $('.highlight-python .highlight,' + + '.highlight-default .highlight,' + + '.highlight-python3 .highlight') + var pre = div.find('pre'); + + // get the styles from the current theme + pre.parent().parent().css('position', 'relative'); + var hide_text = 'Hide the prompts and output'; + var show_text = 'Show the prompts and output'; + var border_width = pre.css('border-top-width'); + var border_style = pre.css('border-top-style'); + var border_color = pre.css('border-top-color'); + var button_styles = { + 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', + 'border-color': border_color, 'border-style': border_style, + 'border-width': border_width, 'color': border_color, 'text-size': '75%', + 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em', + 'border-radius': '0 3px 0 0' + } + + // create and add the button to all the code blocks that contain >>> + div.each(function(index) { + var jthis = $(this); + if (jthis.find('.gp').length > 0) { + var button = $('<span class="copybutton">&gt;&gt;&gt;</span>'); + button.css(button_styles) + button.attr('title', hide_text); + button.data('hidden', 'false'); + jthis.prepend(button); + } + // tracebacks (.gt) contain bare text elements that need to be + // wrapped in a span to work with .nextUntil() (see later) + jthis.find('pre:has(.gt)').contents().filter(function() { + return ((this.nodeType == 3) && (this.data.trim().length > 0)); + }).wrap('<span>'); + }); + + // define the behavior of the button when it's clicked + $('.copybutton').click(function(e){ + e.preventDefault(); + var button = $(this); + if (button.data('hidden') === 'false') { + // hide the code output + button.parent().find('.go, .gp, .gt').hide(); + button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden'); + button.css('text-decoration', 'line-through'); + button.attr('title', show_text); + button.data('hidden', 'true'); + } else { + // show the code output + button.parent().find('.go, .gp, .gt').show(); + button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible'); + button.css('text-decoration', 'none'); + button.attr('title', hide_text); + button.data('hidden', 'false'); + } + }); +}); diff --git a/docs/conf.py b/docs/conf.py index dc8c203b58..93de5392d4 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -216,6 +216,7 @@ def setup(app): }, True) app.add_transform(AutoStructify) app.add_javascript('google_analytics.js') + app.add_javascript('copybutton.js') sphinx_gallery_conf = {
scrapy__scrapy-6347
Set METAREFRESH_IGNORE_TAGS to ["noscript"] by default I was wrong in https://github.com/scrapy/scrapy/issues/3844. The default value should be `["noscript"]`, to deal with [antibot behaviors](https://github.com/scrapy/scrapy/commit/ec1ef0235f9deee0c263c9b31652d3e74a754acc). Found by @mukthy. Set METAREFRESH_IGNORE_TAGS to ["noscript"] by default I was wrong in https://github.com/scrapy/scrapy/issues/3844. The default value should be `["noscript"]`, to deal with [antibot behaviors](https://github.com/scrapy/scrapy/commit/ec1ef0235f9deee0c263c9b31652d3e74a754acc). Found by @mukthy.
[ { "content": "\"\"\"\nThis module contains the default values for all settings used by Scrapy.\n\nFor more information about these settings you can read the settings\ndocumentation in docs/topics/settings.rst\n\nScrapy developers, if you add a setting here remember to:\n\n* add it in alphabetical order\n* group similar settings without leaving blank lines\n* add its documentation to the available settings documentation\n (docs/topics/settings.rst)\n\n\"\"\"\n\nimport sys\nfrom importlib import import_module\nfrom pathlib import Path\n\nADDONS = {}\n\nAJAXCRAWL_ENABLED = False\n\nASYNCIO_EVENT_LOOP = None\n\nAUTOTHROTTLE_ENABLED = False\nAUTOTHROTTLE_DEBUG = False\nAUTOTHROTTLE_MAX_DELAY = 60.0\nAUTOTHROTTLE_START_DELAY = 5.0\nAUTOTHROTTLE_TARGET_CONCURRENCY = 1.0\n\nBOT_NAME = \"scrapybot\"\n\nCLOSESPIDER_TIMEOUT = 0\nCLOSESPIDER_PAGECOUNT = 0\nCLOSESPIDER_ITEMCOUNT = 0\nCLOSESPIDER_ERRORCOUNT = 0\n\nCOMMANDS_MODULE = \"\"\n\nCOMPRESSION_ENABLED = True\n\nCONCURRENT_ITEMS = 100\n\nCONCURRENT_REQUESTS = 16\nCONCURRENT_REQUESTS_PER_DOMAIN = 8\nCONCURRENT_REQUESTS_PER_IP = 0\n\nCOOKIES_ENABLED = True\nCOOKIES_DEBUG = False\n\nDEFAULT_ITEM_CLASS = \"scrapy.item.Item\"\n\nDEFAULT_REQUEST_HEADERS = {\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n \"Accept-Language\": \"en\",\n}\n\nDEPTH_LIMIT = 0\nDEPTH_STATS_VERBOSE = False\nDEPTH_PRIORITY = 0\n\nDNSCACHE_ENABLED = True\nDNSCACHE_SIZE = 10000\nDNS_RESOLVER = \"scrapy.resolver.CachingThreadedResolver\"\nDNS_TIMEOUT = 60\n\nDOWNLOAD_DELAY = 0\n\nDOWNLOAD_HANDLERS = {}\nDOWNLOAD_HANDLERS_BASE = {\n \"data\": \"scrapy.core.downloader.handlers.datauri.DataURIDownloadHandler\",\n \"file\": \"scrapy.core.downloader.handlers.file.FileDownloadHandler\",\n \"http\": \"scrapy.core.downloader.handlers.http.HTTPDownloadHandler\",\n \"https\": \"scrapy.core.downloader.handlers.http.HTTPDownloadHandler\",\n \"s3\": \"scrapy.core.downloader.handlers.s3.S3DownloadHandler\",\n \"ftp\": \"scrapy.core.downloader.handlers.ftp.FTPDownloadHandler\",\n}\n\nDOWNLOAD_TIMEOUT = 180 # 3mins\n\nDOWNLOAD_MAXSIZE = 1024 * 1024 * 1024 # 1024m\nDOWNLOAD_WARNSIZE = 32 * 1024 * 1024 # 32m\n\nDOWNLOAD_FAIL_ON_DATALOSS = True\n\nDOWNLOADER = \"scrapy.core.downloader.Downloader\"\n\nDOWNLOADER_HTTPCLIENTFACTORY = (\n \"scrapy.core.downloader.webclient.ScrapyHTTPClientFactory\"\n)\nDOWNLOADER_CLIENTCONTEXTFACTORY = (\n \"scrapy.core.downloader.contextfactory.ScrapyClientContextFactory\"\n)\nDOWNLOADER_CLIENT_TLS_CIPHERS = \"DEFAULT\"\n# Use highest TLS/SSL protocol version supported by the platform, also allowing negotiation:\nDOWNLOADER_CLIENT_TLS_METHOD = \"TLS\"\nDOWNLOADER_CLIENT_TLS_VERBOSE_LOGGING = False\n\nDOWNLOADER_MIDDLEWARES = {}\n\nDOWNLOADER_MIDDLEWARES_BASE = {\n # Engine side\n \"scrapy.downloadermiddlewares.robotstxt.RobotsTxtMiddleware\": 100,\n \"scrapy.downloadermiddlewares.httpauth.HttpAuthMiddleware\": 300,\n \"scrapy.downloadermiddlewares.downloadtimeout.DownloadTimeoutMiddleware\": 350,\n \"scrapy.downloadermiddlewares.defaultheaders.DefaultHeadersMiddleware\": 400,\n \"scrapy.downloadermiddlewares.useragent.UserAgentMiddleware\": 500,\n \"scrapy.downloadermiddlewares.retry.RetryMiddleware\": 550,\n \"scrapy.downloadermiddlewares.ajaxcrawl.AjaxCrawlMiddleware\": 560,\n \"scrapy.downloadermiddlewares.redirect.MetaRefreshMiddleware\": 580,\n \"scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware\": 590,\n \"scrapy.downloadermiddlewares.redirect.RedirectMiddleware\": 600,\n \"scrapy.downloadermiddlewares.cookies.CookiesMiddleware\": 700,\n \"scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware\": 750,\n \"scrapy.downloadermiddlewares.stats.DownloaderStats\": 850,\n \"scrapy.downloadermiddlewares.httpcache.HttpCacheMiddleware\": 900,\n # Downloader side\n}\n\nDOWNLOADER_STATS = True\n\nDUPEFILTER_CLASS = \"scrapy.dupefilters.RFPDupeFilter\"\n\nEDITOR = \"vi\"\nif sys.platform == \"win32\":\n EDITOR = \"%s -m idlelib.idle\"\n\nEXTENSIONS = {}\n\nEXTENSIONS_BASE = {\n \"scrapy.extensions.corestats.CoreStats\": 0,\n \"scrapy.extensions.telnet.TelnetConsole\": 0,\n \"scrapy.extensions.memusage.MemoryUsage\": 0,\n \"scrapy.extensions.memdebug.MemoryDebugger\": 0,\n \"scrapy.extensions.closespider.CloseSpider\": 0,\n \"scrapy.extensions.feedexport.FeedExporter\": 0,\n \"scrapy.extensions.logstats.LogStats\": 0,\n \"scrapy.extensions.spiderstate.SpiderState\": 0,\n \"scrapy.extensions.throttle.AutoThrottle\": 0,\n}\n\nFEED_TEMPDIR = None\nFEEDS = {}\nFEED_URI_PARAMS = None # a function to extend uri arguments\nFEED_STORE_EMPTY = True\nFEED_EXPORT_ENCODING = None\nFEED_EXPORT_FIELDS = None\nFEED_STORAGES = {}\nFEED_STORAGES_BASE = {\n \"\": \"scrapy.extensions.feedexport.FileFeedStorage\",\n \"file\": \"scrapy.extensions.feedexport.FileFeedStorage\",\n \"ftp\": \"scrapy.extensions.feedexport.FTPFeedStorage\",\n \"gs\": \"scrapy.extensions.feedexport.GCSFeedStorage\",\n \"s3\": \"scrapy.extensions.feedexport.S3FeedStorage\",\n \"stdout\": \"scrapy.extensions.feedexport.StdoutFeedStorage\",\n}\nFEED_EXPORT_BATCH_ITEM_COUNT = 0\nFEED_EXPORTERS = {}\nFEED_EXPORTERS_BASE = {\n \"json\": \"scrapy.exporters.JsonItemExporter\",\n \"jsonlines\": \"scrapy.exporters.JsonLinesItemExporter\",\n \"jsonl\": \"scrapy.exporters.JsonLinesItemExporter\",\n \"jl\": \"scrapy.exporters.JsonLinesItemExporter\",\n \"csv\": \"scrapy.exporters.CsvItemExporter\",\n \"xml\": \"scrapy.exporters.XmlItemExporter\",\n \"marshal\": \"scrapy.exporters.MarshalItemExporter\",\n \"pickle\": \"scrapy.exporters.PickleItemExporter\",\n}\nFEED_EXPORT_INDENT = 0\n\nFEED_STORAGE_FTP_ACTIVE = False\nFEED_STORAGE_GCS_ACL = \"\"\nFEED_STORAGE_S3_ACL = \"\"\n\nFILES_STORE_S3_ACL = \"private\"\nFILES_STORE_GCS_ACL = \"\"\n\nFTP_USER = \"anonymous\"\nFTP_PASSWORD = \"guest\" # nosec\nFTP_PASSIVE_MODE = True\n\nGCS_PROJECT_ID = None\n\nHTTPCACHE_ENABLED = False\nHTTPCACHE_DIR = \"httpcache\"\nHTTPCACHE_IGNORE_MISSING = False\nHTTPCACHE_STORAGE = \"scrapy.extensions.httpcache.FilesystemCacheStorage\"\nHTTPCACHE_EXPIRATION_SECS = 0\nHTTPCACHE_ALWAYS_STORE = False\nHTTPCACHE_IGNORE_HTTP_CODES = []\nHTTPCACHE_IGNORE_SCHEMES = [\"file\"]\nHTTPCACHE_IGNORE_RESPONSE_CACHE_CONTROLS = []\nHTTPCACHE_DBM_MODULE = \"dbm\"\nHTTPCACHE_POLICY = \"scrapy.extensions.httpcache.DummyPolicy\"\nHTTPCACHE_GZIP = False\n\nHTTPPROXY_ENABLED = True\nHTTPPROXY_AUTH_ENCODING = \"latin-1\"\n\nIMAGES_STORE_S3_ACL = \"private\"\nIMAGES_STORE_GCS_ACL = \"\"\n\nITEM_PROCESSOR = \"scrapy.pipelines.ItemPipelineManager\"\n\nITEM_PIPELINES = {}\nITEM_PIPELINES_BASE = {}\n\nJOBDIR = None\n\nLOG_ENABLED = True\nLOG_ENCODING = \"utf-8\"\nLOG_FORMATTER = \"scrapy.logformatter.LogFormatter\"\nLOG_FORMAT = \"%(asctime)s [%(name)s] %(levelname)s: %(message)s\"\nLOG_DATEFORMAT = \"%Y-%m-%d %H:%M:%S\"\nLOG_STDOUT = False\nLOG_LEVEL = \"DEBUG\"\nLOG_FILE = None\nLOG_FILE_APPEND = True\nLOG_SHORT_NAMES = False\n\nSCHEDULER_DEBUG = False\n\nLOGSTATS_INTERVAL = 60.0\n\nMAIL_HOST = \"localhost\"\nMAIL_PORT = 25\nMAIL_FROM = \"scrapy@localhost\"\nMAIL_PASS = None\nMAIL_USER = None\n\nMEMDEBUG_ENABLED = False # enable memory debugging\nMEMDEBUG_NOTIFY = [] # send memory debugging report by mail at engine shutdown\n\nMEMUSAGE_CHECK_INTERVAL_SECONDS = 60.0\nMEMUSAGE_ENABLED = True\nMEMUSAGE_LIMIT_MB = 0\nMEMUSAGE_NOTIFY_MAIL = []\nMEMUSAGE_WARNING_MB = 0\n\nMETAREFRESH_ENABLED = True\nMETAREFRESH_IGNORE_TAGS = []\nMETAREFRESH_MAXDELAY = 100\n\nNEWSPIDER_MODULE = \"\"\n\nPERIODIC_LOG_DELTA = None\nPERIODIC_LOG_STATS = None\nPERIODIC_LOG_TIMING_ENABLED = False\n\nRANDOMIZE_DOWNLOAD_DELAY = True\n\nREACTOR_THREADPOOL_MAXSIZE = 10\n\nREDIRECT_ENABLED = True\nREDIRECT_MAX_TIMES = 20 # uses Firefox default setting\nREDIRECT_PRIORITY_ADJUST = +2\n\nREFERER_ENABLED = True\nREFERRER_POLICY = \"scrapy.spidermiddlewares.referer.DefaultReferrerPolicy\"\n\nREQUEST_FINGERPRINTER_CLASS = \"scrapy.utils.request.RequestFingerprinter\"\nREQUEST_FINGERPRINTER_IMPLEMENTATION = \"SENTINEL\"\n\nRETRY_ENABLED = True\nRETRY_TIMES = 2 # initial response + 2 retries = 3 requests\nRETRY_HTTP_CODES = [500, 502, 503, 504, 522, 524, 408, 429]\nRETRY_PRIORITY_ADJUST = -1\nRETRY_EXCEPTIONS = [\n \"twisted.internet.defer.TimeoutError\",\n \"twisted.internet.error.TimeoutError\",\n \"twisted.internet.error.DNSLookupError\",\n \"twisted.internet.error.ConnectionRefusedError\",\n \"twisted.internet.error.ConnectionDone\",\n \"twisted.internet.error.ConnectError\",\n \"twisted.internet.error.ConnectionLost\",\n \"twisted.internet.error.TCPTimedOutError\",\n \"twisted.web.client.ResponseFailed\",\n # OSError is raised by the HttpCompression middleware when trying to\n # decompress an empty response\n OSError,\n \"scrapy.core.downloader.handlers.http11.TunnelError\",\n]\n\nROBOTSTXT_OBEY = False\nROBOTSTXT_PARSER = \"scrapy.robotstxt.ProtegoRobotParser\"\nROBOTSTXT_USER_AGENT = None\n\nSCHEDULER = \"scrapy.core.scheduler.Scheduler\"\nSCHEDULER_DISK_QUEUE = \"scrapy.squeues.PickleLifoDiskQueue\"\nSCHEDULER_MEMORY_QUEUE = \"scrapy.squeues.LifoMemoryQueue\"\nSCHEDULER_PRIORITY_QUEUE = \"scrapy.pqueues.ScrapyPriorityQueue\"\n\nSCRAPER_SLOT_MAX_ACTIVE_SIZE = 5000000\n\nSPIDER_LOADER_CLASS = \"scrapy.spiderloader.SpiderLoader\"\nSPIDER_LOADER_WARN_ONLY = False\n\nSPIDER_MIDDLEWARES = {}\n\nSPIDER_MIDDLEWARES_BASE = {\n # Engine side\n \"scrapy.spidermiddlewares.httperror.HttpErrorMiddleware\": 50,\n \"scrapy.spidermiddlewares.offsite.OffsiteMiddleware\": 500,\n \"scrapy.spidermiddlewares.referer.RefererMiddleware\": 700,\n \"scrapy.spidermiddlewares.urllength.UrlLengthMiddleware\": 800,\n \"scrapy.spidermiddlewares.depth.DepthMiddleware\": 900,\n # Spider side\n}\n\nSPIDER_MODULES = []\n\nSTATS_CLASS = \"scrapy.statscollectors.MemoryStatsCollector\"\nSTATS_DUMP = True\n\nSTATSMAILER_RCPTS = []\n\nTEMPLATES_DIR = str((Path(__file__).parent / \"..\" / \"templates\").resolve())\n\nURLLENGTH_LIMIT = 2083\n\nUSER_AGENT = f'Scrapy/{import_module(\"scrapy\").__version__} (+https://scrapy.org)'\n\nTELNETCONSOLE_ENABLED = 1\nTELNETCONSOLE_PORT = [6023, 6073]\nTELNETCONSOLE_HOST = \"127.0.0.1\"\nTELNETCONSOLE_USERNAME = \"scrapy\"\nTELNETCONSOLE_PASSWORD = None\n\nTWISTED_REACTOR = None\n\nSPIDER_CONTRACTS = {}\nSPIDER_CONTRACTS_BASE = {\n \"scrapy.contracts.default.UrlContract\": 1,\n \"scrapy.contracts.default.CallbackKeywordArgumentsContract\": 1,\n \"scrapy.contracts.default.ReturnsContract\": 2,\n \"scrapy.contracts.default.ScrapesContract\": 3,\n}\n", "path": "scrapy/settings/default_settings.py" } ]
[ { "content": "\"\"\"\nThis module contains the default values for all settings used by Scrapy.\n\nFor more information about these settings you can read the settings\ndocumentation in docs/topics/settings.rst\n\nScrapy developers, if you add a setting here remember to:\n\n* add it in alphabetical order\n* group similar settings without leaving blank lines\n* add its documentation to the available settings documentation\n (docs/topics/settings.rst)\n\n\"\"\"\n\nimport sys\nfrom importlib import import_module\nfrom pathlib import Path\n\nADDONS = {}\n\nAJAXCRAWL_ENABLED = False\n\nASYNCIO_EVENT_LOOP = None\n\nAUTOTHROTTLE_ENABLED = False\nAUTOTHROTTLE_DEBUG = False\nAUTOTHROTTLE_MAX_DELAY = 60.0\nAUTOTHROTTLE_START_DELAY = 5.0\nAUTOTHROTTLE_TARGET_CONCURRENCY = 1.0\n\nBOT_NAME = \"scrapybot\"\n\nCLOSESPIDER_TIMEOUT = 0\nCLOSESPIDER_PAGECOUNT = 0\nCLOSESPIDER_ITEMCOUNT = 0\nCLOSESPIDER_ERRORCOUNT = 0\n\nCOMMANDS_MODULE = \"\"\n\nCOMPRESSION_ENABLED = True\n\nCONCURRENT_ITEMS = 100\n\nCONCURRENT_REQUESTS = 16\nCONCURRENT_REQUESTS_PER_DOMAIN = 8\nCONCURRENT_REQUESTS_PER_IP = 0\n\nCOOKIES_ENABLED = True\nCOOKIES_DEBUG = False\n\nDEFAULT_ITEM_CLASS = \"scrapy.item.Item\"\n\nDEFAULT_REQUEST_HEADERS = {\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n \"Accept-Language\": \"en\",\n}\n\nDEPTH_LIMIT = 0\nDEPTH_STATS_VERBOSE = False\nDEPTH_PRIORITY = 0\n\nDNSCACHE_ENABLED = True\nDNSCACHE_SIZE = 10000\nDNS_RESOLVER = \"scrapy.resolver.CachingThreadedResolver\"\nDNS_TIMEOUT = 60\n\nDOWNLOAD_DELAY = 0\n\nDOWNLOAD_HANDLERS = {}\nDOWNLOAD_HANDLERS_BASE = {\n \"data\": \"scrapy.core.downloader.handlers.datauri.DataURIDownloadHandler\",\n \"file\": \"scrapy.core.downloader.handlers.file.FileDownloadHandler\",\n \"http\": \"scrapy.core.downloader.handlers.http.HTTPDownloadHandler\",\n \"https\": \"scrapy.core.downloader.handlers.http.HTTPDownloadHandler\",\n \"s3\": \"scrapy.core.downloader.handlers.s3.S3DownloadHandler\",\n \"ftp\": \"scrapy.core.downloader.handlers.ftp.FTPDownloadHandler\",\n}\n\nDOWNLOAD_TIMEOUT = 180 # 3mins\n\nDOWNLOAD_MAXSIZE = 1024 * 1024 * 1024 # 1024m\nDOWNLOAD_WARNSIZE = 32 * 1024 * 1024 # 32m\n\nDOWNLOAD_FAIL_ON_DATALOSS = True\n\nDOWNLOADER = \"scrapy.core.downloader.Downloader\"\n\nDOWNLOADER_HTTPCLIENTFACTORY = (\n \"scrapy.core.downloader.webclient.ScrapyHTTPClientFactory\"\n)\nDOWNLOADER_CLIENTCONTEXTFACTORY = (\n \"scrapy.core.downloader.contextfactory.ScrapyClientContextFactory\"\n)\nDOWNLOADER_CLIENT_TLS_CIPHERS = \"DEFAULT\"\n# Use highest TLS/SSL protocol version supported by the platform, also allowing negotiation:\nDOWNLOADER_CLIENT_TLS_METHOD = \"TLS\"\nDOWNLOADER_CLIENT_TLS_VERBOSE_LOGGING = False\n\nDOWNLOADER_MIDDLEWARES = {}\n\nDOWNLOADER_MIDDLEWARES_BASE = {\n # Engine side\n \"scrapy.downloadermiddlewares.robotstxt.RobotsTxtMiddleware\": 100,\n \"scrapy.downloadermiddlewares.httpauth.HttpAuthMiddleware\": 300,\n \"scrapy.downloadermiddlewares.downloadtimeout.DownloadTimeoutMiddleware\": 350,\n \"scrapy.downloadermiddlewares.defaultheaders.DefaultHeadersMiddleware\": 400,\n \"scrapy.downloadermiddlewares.useragent.UserAgentMiddleware\": 500,\n \"scrapy.downloadermiddlewares.retry.RetryMiddleware\": 550,\n \"scrapy.downloadermiddlewares.ajaxcrawl.AjaxCrawlMiddleware\": 560,\n \"scrapy.downloadermiddlewares.redirect.MetaRefreshMiddleware\": 580,\n \"scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware\": 590,\n \"scrapy.downloadermiddlewares.redirect.RedirectMiddleware\": 600,\n \"scrapy.downloadermiddlewares.cookies.CookiesMiddleware\": 700,\n \"scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware\": 750,\n \"scrapy.downloadermiddlewares.stats.DownloaderStats\": 850,\n \"scrapy.downloadermiddlewares.httpcache.HttpCacheMiddleware\": 900,\n # Downloader side\n}\n\nDOWNLOADER_STATS = True\n\nDUPEFILTER_CLASS = \"scrapy.dupefilters.RFPDupeFilter\"\n\nEDITOR = \"vi\"\nif sys.platform == \"win32\":\n EDITOR = \"%s -m idlelib.idle\"\n\nEXTENSIONS = {}\n\nEXTENSIONS_BASE = {\n \"scrapy.extensions.corestats.CoreStats\": 0,\n \"scrapy.extensions.telnet.TelnetConsole\": 0,\n \"scrapy.extensions.memusage.MemoryUsage\": 0,\n \"scrapy.extensions.memdebug.MemoryDebugger\": 0,\n \"scrapy.extensions.closespider.CloseSpider\": 0,\n \"scrapy.extensions.feedexport.FeedExporter\": 0,\n \"scrapy.extensions.logstats.LogStats\": 0,\n \"scrapy.extensions.spiderstate.SpiderState\": 0,\n \"scrapy.extensions.throttle.AutoThrottle\": 0,\n}\n\nFEED_TEMPDIR = None\nFEEDS = {}\nFEED_URI_PARAMS = None # a function to extend uri arguments\nFEED_STORE_EMPTY = True\nFEED_EXPORT_ENCODING = None\nFEED_EXPORT_FIELDS = None\nFEED_STORAGES = {}\nFEED_STORAGES_BASE = {\n \"\": \"scrapy.extensions.feedexport.FileFeedStorage\",\n \"file\": \"scrapy.extensions.feedexport.FileFeedStorage\",\n \"ftp\": \"scrapy.extensions.feedexport.FTPFeedStorage\",\n \"gs\": \"scrapy.extensions.feedexport.GCSFeedStorage\",\n \"s3\": \"scrapy.extensions.feedexport.S3FeedStorage\",\n \"stdout\": \"scrapy.extensions.feedexport.StdoutFeedStorage\",\n}\nFEED_EXPORT_BATCH_ITEM_COUNT = 0\nFEED_EXPORTERS = {}\nFEED_EXPORTERS_BASE = {\n \"json\": \"scrapy.exporters.JsonItemExporter\",\n \"jsonlines\": \"scrapy.exporters.JsonLinesItemExporter\",\n \"jsonl\": \"scrapy.exporters.JsonLinesItemExporter\",\n \"jl\": \"scrapy.exporters.JsonLinesItemExporter\",\n \"csv\": \"scrapy.exporters.CsvItemExporter\",\n \"xml\": \"scrapy.exporters.XmlItemExporter\",\n \"marshal\": \"scrapy.exporters.MarshalItemExporter\",\n \"pickle\": \"scrapy.exporters.PickleItemExporter\",\n}\nFEED_EXPORT_INDENT = 0\n\nFEED_STORAGE_FTP_ACTIVE = False\nFEED_STORAGE_GCS_ACL = \"\"\nFEED_STORAGE_S3_ACL = \"\"\n\nFILES_STORE_S3_ACL = \"private\"\nFILES_STORE_GCS_ACL = \"\"\n\nFTP_USER = \"anonymous\"\nFTP_PASSWORD = \"guest\" # nosec\nFTP_PASSIVE_MODE = True\n\nGCS_PROJECT_ID = None\n\nHTTPCACHE_ENABLED = False\nHTTPCACHE_DIR = \"httpcache\"\nHTTPCACHE_IGNORE_MISSING = False\nHTTPCACHE_STORAGE = \"scrapy.extensions.httpcache.FilesystemCacheStorage\"\nHTTPCACHE_EXPIRATION_SECS = 0\nHTTPCACHE_ALWAYS_STORE = False\nHTTPCACHE_IGNORE_HTTP_CODES = []\nHTTPCACHE_IGNORE_SCHEMES = [\"file\"]\nHTTPCACHE_IGNORE_RESPONSE_CACHE_CONTROLS = []\nHTTPCACHE_DBM_MODULE = \"dbm\"\nHTTPCACHE_POLICY = \"scrapy.extensions.httpcache.DummyPolicy\"\nHTTPCACHE_GZIP = False\n\nHTTPPROXY_ENABLED = True\nHTTPPROXY_AUTH_ENCODING = \"latin-1\"\n\nIMAGES_STORE_S3_ACL = \"private\"\nIMAGES_STORE_GCS_ACL = \"\"\n\nITEM_PROCESSOR = \"scrapy.pipelines.ItemPipelineManager\"\n\nITEM_PIPELINES = {}\nITEM_PIPELINES_BASE = {}\n\nJOBDIR = None\n\nLOG_ENABLED = True\nLOG_ENCODING = \"utf-8\"\nLOG_FORMATTER = \"scrapy.logformatter.LogFormatter\"\nLOG_FORMAT = \"%(asctime)s [%(name)s] %(levelname)s: %(message)s\"\nLOG_DATEFORMAT = \"%Y-%m-%d %H:%M:%S\"\nLOG_STDOUT = False\nLOG_LEVEL = \"DEBUG\"\nLOG_FILE = None\nLOG_FILE_APPEND = True\nLOG_SHORT_NAMES = False\n\nSCHEDULER_DEBUG = False\n\nLOGSTATS_INTERVAL = 60.0\n\nMAIL_HOST = \"localhost\"\nMAIL_PORT = 25\nMAIL_FROM = \"scrapy@localhost\"\nMAIL_PASS = None\nMAIL_USER = None\n\nMEMDEBUG_ENABLED = False # enable memory debugging\nMEMDEBUG_NOTIFY = [] # send memory debugging report by mail at engine shutdown\n\nMEMUSAGE_CHECK_INTERVAL_SECONDS = 60.0\nMEMUSAGE_ENABLED = True\nMEMUSAGE_LIMIT_MB = 0\nMEMUSAGE_NOTIFY_MAIL = []\nMEMUSAGE_WARNING_MB = 0\n\nMETAREFRESH_ENABLED = True\nMETAREFRESH_IGNORE_TAGS = [\"noscript\"]\nMETAREFRESH_MAXDELAY = 100\n\nNEWSPIDER_MODULE = \"\"\n\nPERIODIC_LOG_DELTA = None\nPERIODIC_LOG_STATS = None\nPERIODIC_LOG_TIMING_ENABLED = False\n\nRANDOMIZE_DOWNLOAD_DELAY = True\n\nREACTOR_THREADPOOL_MAXSIZE = 10\n\nREDIRECT_ENABLED = True\nREDIRECT_MAX_TIMES = 20 # uses Firefox default setting\nREDIRECT_PRIORITY_ADJUST = +2\n\nREFERER_ENABLED = True\nREFERRER_POLICY = \"scrapy.spidermiddlewares.referer.DefaultReferrerPolicy\"\n\nREQUEST_FINGERPRINTER_CLASS = \"scrapy.utils.request.RequestFingerprinter\"\nREQUEST_FINGERPRINTER_IMPLEMENTATION = \"SENTINEL\"\n\nRETRY_ENABLED = True\nRETRY_TIMES = 2 # initial response + 2 retries = 3 requests\nRETRY_HTTP_CODES = [500, 502, 503, 504, 522, 524, 408, 429]\nRETRY_PRIORITY_ADJUST = -1\nRETRY_EXCEPTIONS = [\n \"twisted.internet.defer.TimeoutError\",\n \"twisted.internet.error.TimeoutError\",\n \"twisted.internet.error.DNSLookupError\",\n \"twisted.internet.error.ConnectionRefusedError\",\n \"twisted.internet.error.ConnectionDone\",\n \"twisted.internet.error.ConnectError\",\n \"twisted.internet.error.ConnectionLost\",\n \"twisted.internet.error.TCPTimedOutError\",\n \"twisted.web.client.ResponseFailed\",\n # OSError is raised by the HttpCompression middleware when trying to\n # decompress an empty response\n OSError,\n \"scrapy.core.downloader.handlers.http11.TunnelError\",\n]\n\nROBOTSTXT_OBEY = False\nROBOTSTXT_PARSER = \"scrapy.robotstxt.ProtegoRobotParser\"\nROBOTSTXT_USER_AGENT = None\n\nSCHEDULER = \"scrapy.core.scheduler.Scheduler\"\nSCHEDULER_DISK_QUEUE = \"scrapy.squeues.PickleLifoDiskQueue\"\nSCHEDULER_MEMORY_QUEUE = \"scrapy.squeues.LifoMemoryQueue\"\nSCHEDULER_PRIORITY_QUEUE = \"scrapy.pqueues.ScrapyPriorityQueue\"\n\nSCRAPER_SLOT_MAX_ACTIVE_SIZE = 5000000\n\nSPIDER_LOADER_CLASS = \"scrapy.spiderloader.SpiderLoader\"\nSPIDER_LOADER_WARN_ONLY = False\n\nSPIDER_MIDDLEWARES = {}\n\nSPIDER_MIDDLEWARES_BASE = {\n # Engine side\n \"scrapy.spidermiddlewares.httperror.HttpErrorMiddleware\": 50,\n \"scrapy.spidermiddlewares.offsite.OffsiteMiddleware\": 500,\n \"scrapy.spidermiddlewares.referer.RefererMiddleware\": 700,\n \"scrapy.spidermiddlewares.urllength.UrlLengthMiddleware\": 800,\n \"scrapy.spidermiddlewares.depth.DepthMiddleware\": 900,\n # Spider side\n}\n\nSPIDER_MODULES = []\n\nSTATS_CLASS = \"scrapy.statscollectors.MemoryStatsCollector\"\nSTATS_DUMP = True\n\nSTATSMAILER_RCPTS = []\n\nTEMPLATES_DIR = str((Path(__file__).parent / \"..\" / \"templates\").resolve())\n\nURLLENGTH_LIMIT = 2083\n\nUSER_AGENT = f'Scrapy/{import_module(\"scrapy\").__version__} (+https://scrapy.org)'\n\nTELNETCONSOLE_ENABLED = 1\nTELNETCONSOLE_PORT = [6023, 6073]\nTELNETCONSOLE_HOST = \"127.0.0.1\"\nTELNETCONSOLE_USERNAME = \"scrapy\"\nTELNETCONSOLE_PASSWORD = None\n\nTWISTED_REACTOR = None\n\nSPIDER_CONTRACTS = {}\nSPIDER_CONTRACTS_BASE = {\n \"scrapy.contracts.default.UrlContract\": 1,\n \"scrapy.contracts.default.CallbackKeywordArgumentsContract\": 1,\n \"scrapy.contracts.default.ReturnsContract\": 2,\n \"scrapy.contracts.default.ScrapesContract\": 3,\n}\n", "path": "scrapy/settings/default_settings.py" } ]
diff --git a/docs/topics/downloader-middleware.rst b/docs/topics/downloader-middleware.rst index 1abbc49684f..d4cd062fe38 100644 --- a/docs/topics/downloader-middleware.rst +++ b/docs/topics/downloader-middleware.rst @@ -884,6 +884,10 @@ Meta tags within these tags are ignored. The default value of :setting:`METAREFRESH_IGNORE_TAGS` changed from ``['script', 'noscript']`` to ``[]``. +.. versionchanged:: VERSION + The default value of :setting:`METAREFRESH_IGNORE_TAGS` changed from + ``[]`` to ``['noscript']``. + .. setting:: METAREFRESH_MAXDELAY METAREFRESH_MAXDELAY diff --git a/scrapy/settings/default_settings.py b/scrapy/settings/default_settings.py index 2b3d95a0e14..d7ac7ec350f 100644 --- a/scrapy/settings/default_settings.py +++ b/scrapy/settings/default_settings.py @@ -239,7 +239,7 @@ MEMUSAGE_WARNING_MB = 0 METAREFRESH_ENABLED = True -METAREFRESH_IGNORE_TAGS = [] +METAREFRESH_IGNORE_TAGS = ["noscript"] METAREFRESH_MAXDELAY = 100 NEWSPIDER_MODULE = "" diff --git a/tests/test_downloadermiddleware_redirect.py b/tests/test_downloadermiddleware_redirect.py index 10b8ca9afb9..83ff259823a 100644 --- a/tests/test_downloadermiddleware_redirect.py +++ b/tests/test_downloadermiddleware_redirect.py @@ -395,9 +395,8 @@ def test_ignore_tags_default(self): """content="0;URL='http://example.org/newpage'"></noscript>""" ) rsp = HtmlResponse(req.url, body=body.encode()) - req2 = self.mw.process_response(req, rsp, self.spider) - assert isinstance(req2, Request) - self.assertEqual(req2.url, "http://example.org/newpage") + response = self.mw.process_response(req, rsp, self.spider) + assert isinstance(response, Response) def test_ignore_tags_1_x_list(self): """Test that Scrapy 1.x behavior remains possible"""
dbt-labs__dbt-core-1826
Agate type inference is too clever ### Describe the bug We’re trying to set a value from a {% call statement %} and within the call, one line is SELECT 0 AS my_value...and it then treats it as a boolean (false) in the returned values. The same happens if we try SELECT 1 AS my_value, but as soon as we do SELECT 2 AS my_value it treats it like a number (as it should). ### Steps To Reproduce Create a call statement that selects 0, or 1. false, and true respectively will be returned. ### Expected behavior 0, or 1 to be returned, as integers. ### Screenshots and log output ### System information **Which database are you using dbt with?** - [ ] postgres - [ ] redshift - [x] bigquery - [ ] snowflake - [ ] other (specify: ____________) **The output of `dbt --version`:** ``` installed version: 0.15.0-a1 latest version: 0.14.2 Your version of dbt is ahead of the latest release! ``` FYI, we run a fork, but that shouldn't have affected anything here. **The operating system you're using:** Mojave **The output of `python --version`:** Python 3.7.1 ### Additional context We'd love a quick fix for this, even if it's ugly!
[ { "content": "from codecs import BOM_UTF8\n\nimport agate\nimport json\n\n\nBOM = BOM_UTF8.decode('utf-8') # '\\ufeff'\n\nDEFAULT_TYPE_TESTER = agate.TypeTester(types=[\n agate.data_types.Number(null_values=('null', '')),\n agate.data_types.TimeDelta(null_values=('null', '')),\n agate.data_types.Date(null_values=('null', '')),\n agate.data_types.DateTime(null_values=('null', '')),\n agate.data_types.Boolean(true_values=('true',),\n false_values=('false',),\n null_values=('null', '')),\n agate.data_types.Text(null_values=('null', ''))\n])\n\n\ndef table_from_data(data, column_names):\n \"Convert list of dictionaries into an Agate table\"\n\n # The agate table is generated from a list of dicts, so the column order\n # from `data` is not preserved. We can use `select` to reorder the columns\n #\n # If there is no data, create an empty table with the specified columns\n\n if len(data) == 0:\n return agate.Table([], column_names=column_names)\n else:\n table = agate.Table.from_object(data, column_types=DEFAULT_TYPE_TESTER)\n return table.select(column_names)\n\n\ndef table_from_data_flat(data, column_names):\n \"Convert list of dictionaries into an Agate table\"\n\n rows = []\n for _row in data:\n row = []\n for value in list(_row.values()):\n if isinstance(value, (dict, list, tuple)):\n row.append(json.dumps(value))\n else:\n row.append(value)\n rows.append(row)\n\n return agate.Table(rows, column_names)\n\n\ndef empty_table():\n \"Returns an empty Agate table. To be used in place of None\"\n\n return agate.Table(rows=[])\n\n\ndef as_matrix(table):\n \"Return an agate table as a matrix of data sans columns\"\n\n return [r.values() for r in table.rows.values()]\n\n\ndef from_csv(abspath):\n with open(abspath, encoding='utf-8') as fp:\n if fp.read(1) != BOM:\n fp.seek(0)\n return agate.Table.from_csv(fp, column_types=DEFAULT_TYPE_TESTER)\n", "path": "core/dbt/clients/agate_helper.py" } ]
[ { "content": "from codecs import BOM_UTF8\n\nimport agate\nimport json\n\n\nBOM = BOM_UTF8.decode('utf-8') # '\\ufeff'\n\nDEFAULT_TYPE_TESTER = agate.TypeTester(types=[\n agate.data_types.Number(null_values=('null', '')),\n agate.data_types.TimeDelta(null_values=('null', '')),\n agate.data_types.Date(null_values=('null', '')),\n agate.data_types.DateTime(null_values=('null', '')),\n agate.data_types.Boolean(true_values=('true',),\n false_values=('false',),\n null_values=('null', '')),\n agate.data_types.Text(null_values=('null', ''))\n])\n\n\ndef table_from_data(data, column_names):\n \"Convert list of dictionaries into an Agate table\"\n\n # The agate table is generated from a list of dicts, so the column order\n # from `data` is not preserved. We can use `select` to reorder the columns\n #\n # If there is no data, create an empty table with the specified columns\n\n if len(data) == 0:\n return agate.Table([], column_names=column_names)\n else:\n table = agate.Table.from_object(data, column_types=DEFAULT_TYPE_TESTER)\n return table.select(column_names)\n\n\ndef table_from_data_flat(data, column_names):\n \"Convert list of dictionaries into an Agate table\"\n\n rows = []\n for _row in data:\n row = []\n for value in list(_row.values()):\n if isinstance(value, (dict, list, tuple)):\n row.append(json.dumps(value))\n else:\n row.append(value)\n rows.append(row)\n\n return agate.Table(rows, column_names, column_types=DEFAULT_TYPE_TESTER)\n\n\ndef empty_table():\n \"Returns an empty Agate table. To be used in place of None\"\n\n return agate.Table(rows=[])\n\n\ndef as_matrix(table):\n \"Return an agate table as a matrix of data sans columns\"\n\n return [r.values() for r in table.rows.values()]\n\n\ndef from_csv(abspath):\n with open(abspath, encoding='utf-8') as fp:\n if fp.read(1) != BOM:\n fp.seek(0)\n return agate.Table.from_csv(fp, column_types=DEFAULT_TYPE_TESTER)\n", "path": "core/dbt/clients/agate_helper.py" } ]
diff --git a/core/dbt/clients/agate_helper.py b/core/dbt/clients/agate_helper.py index 8139a554853..215ceed7aaf 100644 --- a/core/dbt/clients/agate_helper.py +++ b/core/dbt/clients/agate_helper.py @@ -46,7 +46,7 @@ def table_from_data_flat(data, column_names): row.append(value) rows.append(row) - return agate.Table(rows, column_names) + return agate.Table(rows, column_names, column_types=DEFAULT_TYPE_TESTER) def empty_table(): diff --git a/test/integration/022_bigquery_test/macros/test_int_inference.sql b/test/integration/022_bigquery_test/macros/test_int_inference.sql new file mode 100644 index 00000000000..852accbb0e7 --- /dev/null +++ b/test/integration/022_bigquery_test/macros/test_int_inference.sql @@ -0,0 +1,13 @@ + +{% macro test_int_inference() %} + + {% set sql %} + select + 0 as int_0, + 1 as int_1, + 2 as int_2 + {% endset %} + + {% do return(run_query(sql)) %} + +{% endmacro %} diff --git a/test/integration/022_bigquery_test/test_bigquery_query_results.py b/test/integration/022_bigquery_test/test_bigquery_query_results.py new file mode 100644 index 00000000000..b7d6109f91e --- /dev/null +++ b/test/integration/022_bigquery_test/test_bigquery_query_results.py @@ -0,0 +1,39 @@ +from test.integration.base import DBTIntegrationTest, use_profile +import json + +class TestBaseBigQueryResults(DBTIntegrationTest): + + @property + def schema(self): + return "bigquery_test_022" + + @property + def models(self): + return "models" + + @property + def project_config(self): + return { + 'macro-paths': ['macros'], + } + + @use_profile('bigquery') + def test__bigquery_type_inference(self): + _, test_results = self.run_dbt(['run-operation', 'test_int_inference']) + self.assertEqual(len(test_results), 1) + + actual_0 = test_results.rows[0]['int_0'] + actual_1 = test_results.rows[0]['int_1'] + actual_2 = test_results.rows[0]['int_2'] + + self.assertEqual(actual_0, 0) + self.assertEqual(str(actual_0), '0') + self.assertEqual(actual_0 * 2, 0) # not 00 + + self.assertEqual(actual_1, 1) + self.assertEqual(str(actual_1), '1') + self.assertEqual(actual_1 * 2, 2) # not 11 + + self.assertEqual(actual_2, 2) + self.assertEqual(str(actual_2), '2') + self.assertEqual(actual_2 * 2, 4) # not 22
keras-team__keras-16145
EfficientNetV2 does not match google implementation Hi, I may be wrong, but checking the efficientnetv2 implementation I think there is a difference with the [google one](https://github.com/google/automl/blob/387d5ddb92bb8fbbec4b012e5636a81ea65fffda/efficientnetv2/effnetv2_model.py) The ["survival_probability"](https://github.com/keras-team/keras/blob/d8fcb9d4d4dad45080ecfdd575483653028f8eda/keras/applications/efficientnet_v2.py#L990) is defined as `survival_probability=drop_connect_rate * b / blocks` but b is set to zero, while according to the [google implementation](https://github.com/google/automl/blob/387d5ddb92bb8fbbec4b012e5636a81ea65fffda/efficientnetv2/effnetv2_model.py#L619) it should increase with the "number" of the block. I think that line should be replaced with: `survival_probability=drop_connect_rate * i / blocks` where i is the counter of the for loop
[ { "content": "# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n# pylint: disable=invalid-name\n# pylint: disable=missing-docstring\n\"\"\"EfficientNet V2 models for Keras.\n\nReference:\n- [EfficientNetV2: Smaller Models and Faster Training](\n https://arxiv.org/abs/2104.00298) (ICML 2021)\n\"\"\"\n\nimport copy\nimport math\n\nfrom keras import backend\nfrom keras import layers\nfrom keras.applications import imagenet_utils\nfrom keras.engine import training\nfrom keras.utils import data_utils\nfrom keras.utils import layer_utils\nimport tensorflow.compat.v2 as tf\n# pylint: disable=g-direct-tensorflow-import\nfrom tensorflow.python.util.tf_export import keras_export\n\nBASE_WEIGHTS_PATH = \"https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/\"\n\nWEIGHTS_HASHES = {\n \"b0\": (\"21ecbf6da12460d5c40bb2f29ceb2188\",\n \"893217f2bb855e2983157299931e43ff\"),\n \"b1\": (\"069f0534ff22adf035c89e2d9547a9dc\",\n \"0e80663031ca32d657f9caa404b6ec37\"),\n \"b2\": (\"424e49f28180edbde1e94797771950a7\",\n \"1dfe2e7a5d45b6632553a8961ea609eb\"),\n \"b3\": (\"1f1fc43bd98a6e4fd8fdfd551e02c7a0\",\n \"f6abf7b5849ac99a89b50dd3fd532856\"),\n \"-s\": (\"e1d88a8495beba45748fedd0cecbe016\",\n \"af0682fb74e8c54910f2d4393339c070\"),\n \"-m\": (\"a3bf6aa3276309f4fc6a34aa114c95cd\",\n \"1b8dc055df72dde80d614482840fe342\"),\n \"-l\": (\"27e6d408b53c7ebc868fefa357689935\",\n \"b0b66b5c863aef5b46e8608fe1711615\"),\n}\n\nDEFAULT_BLOCKS_ARGS = {\n \"efficientnetv2-s\": [{\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 24,\n \"output_filters\": 24,\n \"expand_ratio\": 1,\n \"se_ratio\": 0.0,\n \"strides\": 1,\n \"conv_type\": 1,\n }, {\n \"kernel_size\": 3,\n \"num_repeat\": 4,\n \"input_filters\": 24,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.0,\n \"strides\": 2,\n \"conv_type\": 1,\n }, {\n \"conv_type\": 1,\n \"expand_ratio\": 4,\n \"input_filters\": 48,\n \"kernel_size\": 3,\n \"num_repeat\": 4,\n \"output_filters\": 64,\n \"se_ratio\": 0,\n \"strides\": 2,\n }, {\n \"conv_type\": 0,\n \"expand_ratio\": 4,\n \"input_filters\": 64,\n \"kernel_size\": 3,\n \"num_repeat\": 6,\n \"output_filters\": 128,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n }, {\n \"conv_type\": 0,\n \"expand_ratio\": 6,\n \"input_filters\": 128,\n \"kernel_size\": 3,\n \"num_repeat\": 9,\n \"output_filters\": 160,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n }, {\n \"conv_type\": 0,\n \"expand_ratio\": 6,\n \"input_filters\": 160,\n \"kernel_size\": 3,\n \"num_repeat\": 15,\n \"output_filters\": 256,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n }],\n \"efficientnetv2-m\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 24,\n \"output_filters\": 24,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 24,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 48,\n \"output_filters\": 80,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 7,\n \"input_filters\": 80,\n \"output_filters\": 160,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 14,\n \"input_filters\": 160,\n \"output_filters\": 176,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 18,\n \"input_filters\": 176,\n \"output_filters\": 304,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 304,\n \"output_filters\": 512,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-l\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 4,\n \"input_filters\": 32,\n \"output_filters\": 32,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 7,\n \"input_filters\": 32,\n \"output_filters\": 64,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 7,\n \"input_filters\": 64,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 10,\n \"input_filters\": 96,\n \"output_filters\": 192,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 19,\n \"input_filters\": 192,\n \"output_filters\": 224,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 25,\n \"input_filters\": 224,\n \"output_filters\": 384,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 7,\n \"input_filters\": 384,\n \"output_filters\": 640,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-b0\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 1,\n \"input_filters\": 32,\n \"output_filters\": 16,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 16,\n \"output_filters\": 32,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 32,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 48,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 96,\n \"output_filters\": 112,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 8,\n \"input_filters\": 112,\n \"output_filters\": 192,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-b1\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 1,\n \"input_filters\": 32,\n \"output_filters\": 16,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 16,\n \"output_filters\": 32,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 32,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 48,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 96,\n \"output_filters\": 112,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 8,\n \"input_filters\": 112,\n \"output_filters\": 192,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-b2\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 1,\n \"input_filters\": 32,\n \"output_filters\": 16,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 16,\n \"output_filters\": 32,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 32,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 48,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 96,\n \"output_filters\": 112,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 8,\n \"input_filters\": 112,\n \"output_filters\": 192,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-b3\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 1,\n \"input_filters\": 32,\n \"output_filters\": 16,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 16,\n \"output_filters\": 32,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 32,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 48,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 96,\n \"output_filters\": 112,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 8,\n \"input_filters\": 112,\n \"output_filters\": 192,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n ],\n}\n\nCONV_KERNEL_INITIALIZER = {\n \"class_name\": \"VarianceScaling\",\n \"config\": {\n \"scale\": 2.0,\n \"mode\": \"fan_out\",\n \"distribution\": \"truncated_normal\"\n }\n}\n\nDENSE_KERNEL_INITIALIZER = {\n \"class_name\": \"VarianceScaling\",\n \"config\": {\n \"scale\": 1. / 3.,\n \"mode\": \"fan_out\",\n \"distribution\": \"uniform\"\n }\n}\n\nBASE_DOCSTRING = \"\"\"Instantiates the {name} architecture.\n\n Reference:\n - [EfficientNetV2: Smaller Models and Faster Training](\n https://arxiv.org/abs/2104.00298) (ICML 2021)\n\n This function returns a Keras image classification model,\n optionally loaded with weights pre-trained on ImageNet.\n\n For image classification use cases, see\n [this page for detailed examples](\n https://keras.io/api/applications/#usage-examples-for-image-classification-models).\n\n For transfer learning use cases, make sure to read the\n [guide to transfer learning & fine-tuning](\n https://keras.io/guides/transfer_learning/).\n\n Note: each Keras Application expects a specific kind of input preprocessing.\n For EfficientNetV2, by default input preprocessing is included as a part of the\n model (as a `Rescaling` layer), and thus\n `tf.keras.applications.efficientnet_v2.preprocess_input` is actually a\n pass-through function. In this use case, EfficientNetV2 models expect their inputs\n to be float tensors of pixels with values in the [0-255] range.\n At the same time, preprocessing as a part of the model (i.e. `Rescaling`\n layer) can be disabled by setting `include_preprocessing` argument to False.\n With preprocessing disabled EfficientNetV2 models expect their inputs to be float\n tensors of pixels with values in the [-1, 1] range.\n\n Args:\n include_top: Boolean, whether to include the fully-connected\n layer at the top of the network. Defaults to True.\n weights: One of `None` (random initialization),\n `\"imagenet\"` (pre-training on ImageNet),\n or the path to the weights file to be loaded. Defaults to `\"imagenet\"`.\n input_tensor: Optional Keras tensor\n (i.e. output of `layers.Input()`)\n to use as image input for the model.\n input_shape: Optional shape tuple, only to be specified\n if `include_top` is False.\n It should have exactly 3 inputs channels.\n pooling: Optional pooling mode for feature extraction\n when `include_top` is `False`. Defaults to None.\n - `None` means that the output of the model will be\n the 4D tensor output of the\n last convolutional layer.\n - `\"avg\"` means that global average pooling\n will be applied to the output of the\n last convolutional layer, and thus\n the output of the model will be a 2D tensor.\n - `\"max\"` means that global max pooling will\n be applied.\n classes: Optional number of classes to classify images\n into, only to be specified if `include_top` is True, and\n if no `weights` argument is specified. Defaults to 1000 (number of\n ImageNet classes).\n classifier_activation: A string or callable. The activation function to use\n on the `\"top\"` layer. Ignored unless `include_top=True`. Set\n `classifier_activation=None` to return the logits of the \"top\" layer.\n Defaults to `\"softmax\"`.\n When loading pretrained weights, `classifier_activation` can only\n be `None` or `\"softmax\"`.\n\n Returns:\n A `keras.Model` instance.\n\"\"\"\n\n\ndef round_filters(filters, width_coefficient, min_depth, depth_divisor):\n \"\"\"Round number of filters based on depth multiplier.\"\"\"\n filters *= width_coefficient\n minimum_depth = min_depth or depth_divisor\n new_filters = max(\n minimum_depth,\n int(filters + depth_divisor / 2) // depth_divisor * depth_divisor,\n )\n return int(new_filters)\n\n\ndef round_repeats(repeats, depth_coefficient):\n \"\"\"Round number of repeats based on depth multiplier.\"\"\"\n return int(math.ceil(depth_coefficient * repeats))\n\n\ndef MBConvBlock(\n input_filters: int,\n output_filters: int,\n expand_ratio=1,\n kernel_size=3,\n strides=1,\n se_ratio=0.0,\n bn_momentum=0.9,\n activation=\"swish\",\n survival_probability: float = 0.8,\n name=None,\n):\n \"\"\"MBConv block: Mobile Inverted Residual Bottleneck.\"\"\"\n bn_axis = 3 if backend.image_data_format() == \"channels_last\" else 1\n\n if name is None:\n name = backend.get_uid(\"block0\")\n\n def apply(inputs):\n # Expansion phase\n filters = input_filters * expand_ratio\n if expand_ratio != 1:\n x = layers.Conv2D(\n filters=filters,\n kernel_size=1,\n strides=1,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n data_format=\"channels_last\",\n use_bias=False,\n name=name + \"expand_conv\",\n )(inputs)\n x = layers.BatchNormalization(\n axis=bn_axis,\n momentum=bn_momentum,\n name=name + \"expand_bn\",\n )(x)\n x = layers.Activation(activation, name=name + \"expand_activation\")(x)\n else:\n x = inputs\n\n # Depthwise conv\n x = layers.DepthwiseConv2D(\n kernel_size=kernel_size,\n strides=strides,\n depthwise_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n data_format=\"channels_last\",\n use_bias=False,\n name=name + \"dwconv2\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis, momentum=bn_momentum, name=name + \"bn\")(x)\n x = layers.Activation(activation, name=name + \"activation\")(x)\n\n # Squeeze and excite\n if 0 < se_ratio <= 1:\n filters_se = max(1, int(input_filters * se_ratio))\n se = layers.GlobalAveragePooling2D(name=name + \"se_squeeze\")(x)\n if bn_axis == 1:\n se_shape = (filters, 1, 1)\n else:\n se_shape = (1, 1, filters)\n se = layers.Reshape(se_shape, name=name + \"se_reshape\")(se)\n\n se = layers.Conv2D(\n filters_se,\n 1,\n padding=\"same\",\n activation=activation,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n name=name + \"se_reduce\",\n )(se)\n se = layers.Conv2D(\n filters,\n 1,\n padding=\"same\",\n activation=\"sigmoid\",\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n name=name + \"se_expand\",\n )(se)\n\n x = layers.multiply([x, se], name=name + \"se_excite\")\n\n # Output phase\n x = layers.Conv2D(\n filters=output_filters,\n kernel_size=1,\n strides=1,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n data_format=\"channels_last\",\n use_bias=False,\n name=name + \"project_conv\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis, momentum=bn_momentum, name=name + \"project_bn\")(x)\n\n if strides == 1 and input_filters == output_filters:\n if survival_probability:\n x = layers.Dropout(\n survival_probability,\n noise_shape=(None, 1, 1, 1),\n name=name + \"drop\",\n )(x)\n x = layers.add([x, inputs], name=name + \"add\")\n return x\n\n return apply\n\n\ndef FusedMBConvBlock(\n input_filters: int,\n output_filters: int,\n expand_ratio=1,\n kernel_size=3,\n strides=1,\n se_ratio=0.0,\n bn_momentum=0.9,\n activation=\"swish\",\n survival_probability: float = 0.8,\n name=None,\n):\n \"\"\"Fused MBConv Block: Fusing the proj conv1x1 and depthwise_conv into a conv2d.\"\"\"\n bn_axis = 3 if backend.image_data_format() == \"channels_last\" else 1\n\n if name is None:\n name = backend.get_uid(\"block0\")\n\n def apply(inputs):\n filters = input_filters * expand_ratio\n if expand_ratio != 1:\n x = layers.Conv2D(\n filters,\n kernel_size=kernel_size,\n strides=strides,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n data_format=\"channels_last\",\n padding=\"same\",\n use_bias=False,\n name=name + \"expand_conv\",\n )(inputs)\n x = layers.BatchNormalization(\n axis=bn_axis, momentum=bn_momentum, name=name + \"expand_bn\")(x)\n x = layers.Activation(\n activation=activation, name=name + \"expand_activation\")(x)\n else:\n x = inputs\n\n # Squeeze and excite\n if 0 < se_ratio <= 1:\n filters_se = max(1, int(input_filters * se_ratio))\n se = layers.GlobalAveragePooling2D(name=name + \"se_squeeze\")(x)\n if bn_axis == 1:\n se_shape = (filters, 1, 1)\n else:\n se_shape = (1, 1, filters)\n\n se = layers.Reshape(se_shape, name=name + \"se_reshape\")(se)\n\n se = layers.Conv2D(\n filters_se,\n 1,\n padding=\"same\",\n activation=activation,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n name=name + \"se_reduce\",\n )(se)\n se = layers.Conv2D(\n filters,\n 1,\n padding=\"same\",\n activation=\"sigmoid\",\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n name=name + \"se_expand\",\n )(se)\n\n x = layers.multiply([x, se], name=name + \"se_excite\")\n\n # Output phase:\n x = layers.Conv2D(\n output_filters,\n kernel_size=1 if expand_ratio != 1 else kernel_size,\n strides=1 if expand_ratio != 1 else strides,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n use_bias=False,\n name=name + \"project_conv\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis, momentum=bn_momentum, name=name + \"project_bn\")(x)\n if expand_ratio == 1:\n x = layers.Activation(\n activation=activation, name=name + \"project_activation\")(x)\n\n # Residual:\n if strides == 1 and input_filters == output_filters:\n if survival_probability:\n x = layers.Dropout(\n survival_probability,\n noise_shape=(None, 1, 1, 1),\n name=name + \"drop\",\n )(x)\n x = layers.add([x, inputs], name=name + \"add\")\n return x\n\n return apply\n\n\ndef EfficientNetV2(\n width_coefficient,\n depth_coefficient,\n default_size,\n dropout_rate=0.2,\n drop_connect_rate=0.2,\n depth_divisor=8,\n min_depth=8,\n bn_momentum=0.9,\n activation=\"swish\",\n blocks_args=\"default\",\n model_name=\"efficientnetv2\",\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n \"\"\"Instantiates the EfficientNetV2 architecture using given scaling coefficients.\n\n Args:\n width_coefficient: float, scaling coefficient for network width.\n depth_coefficient: float, scaling coefficient for network depth.\n default_size: integer, default input image size.\n dropout_rate: float, dropout rate before final classifier layer.\n drop_connect_rate: float, dropout rate at skip connections.\n depth_divisor: integer, a unit of network width.\n min_depth: integer, minimum number of filters.\n bn_momentum: float. Momentum parameter for Batch Normalization layers.\n activation: activation function.\n blocks_args: list of dicts, parameters to construct block modules.\n model_name: string, model name.\n include_top: whether to include the fully-connected layer at the top of the\n network.\n weights: one of `None` (random initialization), `\"imagenet\"` (pre-training\n on ImageNet), or the path to the weights file to be loaded.\n input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) or\n numpy array to use as image input for the model.\n input_shape: optional shape tuple, only to be specified if `include_top` is\n False. It should have exactly 3 inputs channels.\n pooling: optional pooling mode for feature extraction when `include_top` is\n `False`. - `None` means that the output of the model will be the 4D tensor\n output of the last convolutional layer. - \"avg\" means that global average\n pooling will be applied to the output of the last convolutional layer, and\n thus the output of the model will be a 2D tensor. - `\"max\"` means that\n global max pooling will be applied.\n classes: optional number of classes to classify images into, only to be\n specified if `include_top` is True, and if no `weights` argument is\n specified.\n classifier_activation: A string or callable. The activation function to use\n on the `\"top\"` layer. Ignored unless `include_top=True`. Set\n `classifier_activation=None` to return the logits of the `\"top\"` layer.\n include_preprocessing: Boolean, whether to include the preprocessing layer\n (`Rescaling`) at the bottom of the network. Defaults to `True`.\n\n Returns:\n A `keras.Model` instance.\n\n Raises:\n ValueError: in case of invalid argument for `weights`,\n or invalid input shape.\n ValueError: if `classifier_activation` is not `\"softmax\"` or `None` when\n using a pretrained top layer.\n \"\"\"\n\n if blocks_args == \"default\":\n blocks_args = DEFAULT_BLOCKS_ARGS[model_name]\n\n if not (weights in {\"imagenet\", None} or tf.io.gfile.exists(weights)):\n raise ValueError(\"The `weights` argument should be either \"\n \"`None` (random initialization), `imagenet` \"\n \"(pre-training on ImageNet), \"\n \"or the path to the weights file to be loaded.\"\n f\"Received: weights={weights}\")\n\n if weights == \"imagenet\" and include_top and classes != 1000:\n raise ValueError(\"If using `weights` as `'imagenet'` with `include_top`\"\n \" as true, `classes` should be 1000\"\n f\"Received: classes={classes}\")\n\n # Determine proper input shape\n input_shape = imagenet_utils.obtain_input_shape(\n input_shape,\n default_size=default_size,\n min_size=32,\n data_format=backend.image_data_format(),\n require_flatten=include_top,\n weights=weights)\n\n if input_tensor is None:\n img_input = layers.Input(shape=input_shape)\n else:\n if not backend.is_keras_tensor(input_tensor):\n img_input = layers.Input(tensor=input_tensor, shape=input_shape)\n else:\n img_input = input_tensor\n\n bn_axis = 3 if backend.image_data_format() == \"channels_last\" else 1\n\n x = img_input\n\n if include_preprocessing:\n # Apply original V1 preprocessing for Bx variants\n # if number of channels allows it\n num_channels = input_shape[bn_axis - 1]\n if model_name.split(\"-\")[-1].startswith(\"b\") and num_channels == 3:\n x = layers.Rescaling(scale=1. / 255)(x)\n x = layers.Normalization(\n mean=[0.485, 0.456, 0.406],\n variance=[0.229**2, 0.224**2, 0.225**2],\n axis=bn_axis,\n )(x)\n else:\n x = layers.Rescaling(scale=1. / 128.0, offset=-1)(x)\n\n # Build stem\n stem_filters = round_filters(\n filters=blocks_args[0][\"input_filters\"],\n width_coefficient=width_coefficient,\n min_depth=min_depth,\n depth_divisor=depth_divisor,\n )\n x = layers.Conv2D(\n filters=stem_filters,\n kernel_size=3,\n strides=2,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n use_bias=False,\n name=\"stem_conv\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis,\n momentum=bn_momentum,\n name=\"stem_bn\",\n )(x)\n x = layers.Activation(activation, name=\"stem_activation\")(x)\n\n # Build blocks\n blocks_args = copy.deepcopy(blocks_args)\n b = 0\n blocks = float(sum(args[\"num_repeat\"] for args in blocks_args))\n\n for (i, args) in enumerate(blocks_args):\n assert args[\"num_repeat\"] > 0\n\n # Update block input and output filters based on depth multiplier.\n args[\"input_filters\"] = round_filters(\n filters=args[\"input_filters\"],\n width_coefficient=width_coefficient,\n min_depth=min_depth,\n depth_divisor=depth_divisor)\n args[\"output_filters\"] = round_filters(\n filters=args[\"output_filters\"],\n width_coefficient=width_coefficient,\n min_depth=min_depth,\n depth_divisor=depth_divisor)\n\n # Determine which conv type to use:\n block = {0: MBConvBlock, 1: FusedMBConvBlock}[args.pop(\"conv_type\")]\n repeats = round_repeats(\n repeats=args.pop(\"num_repeat\"), depth_coefficient=depth_coefficient)\n for j in range(repeats):\n # The first block needs to take care of stride and filter size increase.\n if j > 0:\n args[\"strides\"] = 1\n args[\"input_filters\"] = args[\"output_filters\"]\n\n x = block(\n activation=activation,\n bn_momentum=bn_momentum,\n survival_probability=drop_connect_rate * b / blocks,\n name=\"block{}{}_\".format(i + 1, chr(j + 97)),\n **args,\n )(x)\n\n # Build top\n top_filters = round_filters(\n filters=1280,\n width_coefficient=width_coefficient,\n min_depth=min_depth,\n depth_divisor=depth_divisor)\n x = layers.Conv2D(\n filters=top_filters,\n kernel_size=1,\n strides=1,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n data_format=\"channels_last\",\n use_bias=False,\n name=\"top_conv\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis,\n momentum=bn_momentum,\n name=\"top_bn\",\n )(x)\n x = layers.Activation(activation=activation, name=\"top_activation\")(x)\n\n if include_top:\n x = layers.GlobalAveragePooling2D(name=\"avg_pool\")(x)\n if dropout_rate > 0:\n x = layers.Dropout(dropout_rate, name=\"top_dropout\")(x)\n imagenet_utils.validate_activation(classifier_activation, weights)\n x = layers.Dense(\n classes,\n activation=classifier_activation,\n kernel_initializer=DENSE_KERNEL_INITIALIZER,\n bias_initializer=tf.constant_initializer(0),\n name=\"predictions\")(x)\n else:\n if pooling == \"avg\":\n x = layers.GlobalAveragePooling2D(name=\"avg_pool\")(x)\n elif pooling == \"max\":\n x = layers.GlobalMaxPooling2D(name=\"max_pool\")(x)\n\n # Ensure that the model takes into account\n # any potential predecessors of `input_tensor`.\n if input_tensor is not None:\n inputs = layer_utils.get_source_inputs(input_tensor)\n else:\n inputs = img_input\n\n # Create model.\n model = training.Model(inputs, x, name=model_name)\n\n # Load weights.\n if weights == \"imagenet\":\n if include_top:\n file_suffix = \".h5\"\n file_hash = WEIGHTS_HASHES[model_name[-2:]][0]\n else:\n file_suffix = \"_notop.h5\"\n file_hash = WEIGHTS_HASHES[model_name[-2:]][1]\n file_name = model_name + file_suffix\n weights_path = data_utils.get_file(\n file_name,\n BASE_WEIGHTS_PATH + file_name,\n cache_subdir=\"models\",\n file_hash=file_hash)\n model.load_weights(weights_path)\n elif weights is not None:\n model.load_weights(weights)\n\n return model\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2B0\",\n \"keras.applications.EfficientNetV2B0\")\ndef EfficientNetV2B0(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.0,\n default_size=224,\n model_name=\"efficientnetv2-b0\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing)\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2B1\",\n \"keras.applications.EfficientNetV2B1\")\ndef EfficientNetV2B1(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.1,\n default_size=240,\n model_name=\"efficientnetv2-b1\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2B2\",\n \"keras.applications.EfficientNetV2B2\")\ndef EfficientNetV2B2(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.1,\n depth_coefficient=1.2,\n default_size=260,\n model_name=\"efficientnetv2-b2\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2B3\",\n \"keras.applications.EfficientNetV2B3\")\ndef EfficientNetV2B3(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.2,\n depth_coefficient=1.4,\n default_size=300,\n model_name=\"efficientnetv2-b3\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2S\",\n \"keras.applications.EfficientNetV2S\")\ndef EfficientNetV2S(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.0,\n default_size=384,\n model_name=\"efficientnetv2-s\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2M\",\n \"keras.applications.EfficientNetV2M\")\ndef EfficientNetV2M(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.0,\n default_size=480,\n model_name=\"efficientnetv2-m\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2L\",\n \"keras.applications.EfficientNetV2L\")\ndef EfficientNetV2L(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.0,\n default_size=480,\n model_name=\"efficientnetv2-l\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\nEfficientNetV2B0.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2B0\")\nEfficientNetV2B1.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2B1\")\nEfficientNetV2B2.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2B2\")\nEfficientNetV2B3.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2B3\")\nEfficientNetV2S.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2S\")\nEfficientNetV2M.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2M\")\nEfficientNetV2L.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2L\")\n\n\n@keras_export(\"keras.applications.efficientnet_v2.preprocess_input\")\ndef preprocess_input(x, data_format=None): # pylint: disable=unused-argument\n \"\"\"A placeholder method for backward compatibility.\n\n The preprocessing logic has been included in the EfficientNetV2 model\n implementation. Users are no longer required to call this method to normalize\n the input data. This method does nothing and only kept as a placeholder to\n align the API surface between old and new version of model.\n\n Args:\n x: A floating point `numpy.array` or a `tf.Tensor`.\n data_format: Optional data format of the image tensor/array. Defaults to\n None, in which case the global setting\n `tf.keras.backend.image_data_format()` is used (unless you changed it, it\n defaults to \"channels_last\").{mode}\n\n Returns:\n Unchanged `numpy.array` or `tf.Tensor`.\n \"\"\"\n return x\n\n\n@keras_export(\"keras.applications.efficientnet_v2.decode_predictions\")\ndef decode_predictions(preds, top=5):\n return imagenet_utils.decode_predictions(preds, top=top)\n\n\ndecode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__\n", "path": "keras/applications/efficientnet_v2.py" } ]
[ { "content": "# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n# pylint: disable=invalid-name\n# pylint: disable=missing-docstring\n\"\"\"EfficientNet V2 models for Keras.\n\nReference:\n- [EfficientNetV2: Smaller Models and Faster Training](\n https://arxiv.org/abs/2104.00298) (ICML 2021)\n\"\"\"\n\nimport copy\nimport math\n\nfrom keras import backend\nfrom keras import layers\nfrom keras.applications import imagenet_utils\nfrom keras.engine import training\nfrom keras.utils import data_utils\nfrom keras.utils import layer_utils\nimport tensorflow.compat.v2 as tf\n# pylint: disable=g-direct-tensorflow-import\nfrom tensorflow.python.util.tf_export import keras_export\n\nBASE_WEIGHTS_PATH = \"https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/\"\n\nWEIGHTS_HASHES = {\n \"b0\": (\"21ecbf6da12460d5c40bb2f29ceb2188\",\n \"893217f2bb855e2983157299931e43ff\"),\n \"b1\": (\"069f0534ff22adf035c89e2d9547a9dc\",\n \"0e80663031ca32d657f9caa404b6ec37\"),\n \"b2\": (\"424e49f28180edbde1e94797771950a7\",\n \"1dfe2e7a5d45b6632553a8961ea609eb\"),\n \"b3\": (\"1f1fc43bd98a6e4fd8fdfd551e02c7a0\",\n \"f6abf7b5849ac99a89b50dd3fd532856\"),\n \"-s\": (\"e1d88a8495beba45748fedd0cecbe016\",\n \"af0682fb74e8c54910f2d4393339c070\"),\n \"-m\": (\"a3bf6aa3276309f4fc6a34aa114c95cd\",\n \"1b8dc055df72dde80d614482840fe342\"),\n \"-l\": (\"27e6d408b53c7ebc868fefa357689935\",\n \"b0b66b5c863aef5b46e8608fe1711615\"),\n}\n\nDEFAULT_BLOCKS_ARGS = {\n \"efficientnetv2-s\": [{\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 24,\n \"output_filters\": 24,\n \"expand_ratio\": 1,\n \"se_ratio\": 0.0,\n \"strides\": 1,\n \"conv_type\": 1,\n }, {\n \"kernel_size\": 3,\n \"num_repeat\": 4,\n \"input_filters\": 24,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.0,\n \"strides\": 2,\n \"conv_type\": 1,\n }, {\n \"conv_type\": 1,\n \"expand_ratio\": 4,\n \"input_filters\": 48,\n \"kernel_size\": 3,\n \"num_repeat\": 4,\n \"output_filters\": 64,\n \"se_ratio\": 0,\n \"strides\": 2,\n }, {\n \"conv_type\": 0,\n \"expand_ratio\": 4,\n \"input_filters\": 64,\n \"kernel_size\": 3,\n \"num_repeat\": 6,\n \"output_filters\": 128,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n }, {\n \"conv_type\": 0,\n \"expand_ratio\": 6,\n \"input_filters\": 128,\n \"kernel_size\": 3,\n \"num_repeat\": 9,\n \"output_filters\": 160,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n }, {\n \"conv_type\": 0,\n \"expand_ratio\": 6,\n \"input_filters\": 160,\n \"kernel_size\": 3,\n \"num_repeat\": 15,\n \"output_filters\": 256,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n }],\n \"efficientnetv2-m\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 24,\n \"output_filters\": 24,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 24,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 48,\n \"output_filters\": 80,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 7,\n \"input_filters\": 80,\n \"output_filters\": 160,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 14,\n \"input_filters\": 160,\n \"output_filters\": 176,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 18,\n \"input_filters\": 176,\n \"output_filters\": 304,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 304,\n \"output_filters\": 512,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-l\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 4,\n \"input_filters\": 32,\n \"output_filters\": 32,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 7,\n \"input_filters\": 32,\n \"output_filters\": 64,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 7,\n \"input_filters\": 64,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 10,\n \"input_filters\": 96,\n \"output_filters\": 192,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 19,\n \"input_filters\": 192,\n \"output_filters\": 224,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 25,\n \"input_filters\": 224,\n \"output_filters\": 384,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 7,\n \"input_filters\": 384,\n \"output_filters\": 640,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-b0\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 1,\n \"input_filters\": 32,\n \"output_filters\": 16,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 16,\n \"output_filters\": 32,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 32,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 48,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 96,\n \"output_filters\": 112,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 8,\n \"input_filters\": 112,\n \"output_filters\": 192,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-b1\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 1,\n \"input_filters\": 32,\n \"output_filters\": 16,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 16,\n \"output_filters\": 32,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 32,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 48,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 96,\n \"output_filters\": 112,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 8,\n \"input_filters\": 112,\n \"output_filters\": 192,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-b2\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 1,\n \"input_filters\": 32,\n \"output_filters\": 16,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 16,\n \"output_filters\": 32,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 32,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 48,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 96,\n \"output_filters\": 112,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 8,\n \"input_filters\": 112,\n \"output_filters\": 192,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n ],\n \"efficientnetv2-b3\": [\n {\n \"kernel_size\": 3,\n \"num_repeat\": 1,\n \"input_filters\": 32,\n \"output_filters\": 16,\n \"expand_ratio\": 1,\n \"se_ratio\": 0,\n \"strides\": 1,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 16,\n \"output_filters\": 32,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 2,\n \"input_filters\": 32,\n \"output_filters\": 48,\n \"expand_ratio\": 4,\n \"se_ratio\": 0,\n \"strides\": 2,\n \"conv_type\": 1,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 3,\n \"input_filters\": 48,\n \"output_filters\": 96,\n \"expand_ratio\": 4,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 5,\n \"input_filters\": 96,\n \"output_filters\": 112,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 1,\n \"conv_type\": 0,\n },\n {\n \"kernel_size\": 3,\n \"num_repeat\": 8,\n \"input_filters\": 112,\n \"output_filters\": 192,\n \"expand_ratio\": 6,\n \"se_ratio\": 0.25,\n \"strides\": 2,\n \"conv_type\": 0,\n },\n ],\n}\n\nCONV_KERNEL_INITIALIZER = {\n \"class_name\": \"VarianceScaling\",\n \"config\": {\n \"scale\": 2.0,\n \"mode\": \"fan_out\",\n \"distribution\": \"truncated_normal\"\n }\n}\n\nDENSE_KERNEL_INITIALIZER = {\n \"class_name\": \"VarianceScaling\",\n \"config\": {\n \"scale\": 1. / 3.,\n \"mode\": \"fan_out\",\n \"distribution\": \"uniform\"\n }\n}\n\nBASE_DOCSTRING = \"\"\"Instantiates the {name} architecture.\n\n Reference:\n - [EfficientNetV2: Smaller Models and Faster Training](\n https://arxiv.org/abs/2104.00298) (ICML 2021)\n\n This function returns a Keras image classification model,\n optionally loaded with weights pre-trained on ImageNet.\n\n For image classification use cases, see\n [this page for detailed examples](\n https://keras.io/api/applications/#usage-examples-for-image-classification-models).\n\n For transfer learning use cases, make sure to read the\n [guide to transfer learning & fine-tuning](\n https://keras.io/guides/transfer_learning/).\n\n Note: each Keras Application expects a specific kind of input preprocessing.\n For EfficientNetV2, by default input preprocessing is included as a part of the\n model (as a `Rescaling` layer), and thus\n `tf.keras.applications.efficientnet_v2.preprocess_input` is actually a\n pass-through function. In this use case, EfficientNetV2 models expect their inputs\n to be float tensors of pixels with values in the [0-255] range.\n At the same time, preprocessing as a part of the model (i.e. `Rescaling`\n layer) can be disabled by setting `include_preprocessing` argument to False.\n With preprocessing disabled EfficientNetV2 models expect their inputs to be float\n tensors of pixels with values in the [-1, 1] range.\n\n Args:\n include_top: Boolean, whether to include the fully-connected\n layer at the top of the network. Defaults to True.\n weights: One of `None` (random initialization),\n `\"imagenet\"` (pre-training on ImageNet),\n or the path to the weights file to be loaded. Defaults to `\"imagenet\"`.\n input_tensor: Optional Keras tensor\n (i.e. output of `layers.Input()`)\n to use as image input for the model.\n input_shape: Optional shape tuple, only to be specified\n if `include_top` is False.\n It should have exactly 3 inputs channels.\n pooling: Optional pooling mode for feature extraction\n when `include_top` is `False`. Defaults to None.\n - `None` means that the output of the model will be\n the 4D tensor output of the\n last convolutional layer.\n - `\"avg\"` means that global average pooling\n will be applied to the output of the\n last convolutional layer, and thus\n the output of the model will be a 2D tensor.\n - `\"max\"` means that global max pooling will\n be applied.\n classes: Optional number of classes to classify images\n into, only to be specified if `include_top` is True, and\n if no `weights` argument is specified. Defaults to 1000 (number of\n ImageNet classes).\n classifier_activation: A string or callable. The activation function to use\n on the `\"top\"` layer. Ignored unless `include_top=True`. Set\n `classifier_activation=None` to return the logits of the \"top\" layer.\n Defaults to `\"softmax\"`.\n When loading pretrained weights, `classifier_activation` can only\n be `None` or `\"softmax\"`.\n\n Returns:\n A `keras.Model` instance.\n\"\"\"\n\n\ndef round_filters(filters, width_coefficient, min_depth, depth_divisor):\n \"\"\"Round number of filters based on depth multiplier.\"\"\"\n filters *= width_coefficient\n minimum_depth = min_depth or depth_divisor\n new_filters = max(\n minimum_depth,\n int(filters + depth_divisor / 2) // depth_divisor * depth_divisor,\n )\n return int(new_filters)\n\n\ndef round_repeats(repeats, depth_coefficient):\n \"\"\"Round number of repeats based on depth multiplier.\"\"\"\n return int(math.ceil(depth_coefficient * repeats))\n\n\ndef MBConvBlock(\n input_filters: int,\n output_filters: int,\n expand_ratio=1,\n kernel_size=3,\n strides=1,\n se_ratio=0.0,\n bn_momentum=0.9,\n activation=\"swish\",\n survival_probability: float = 0.8,\n name=None,\n):\n \"\"\"MBConv block: Mobile Inverted Residual Bottleneck.\"\"\"\n bn_axis = 3 if backend.image_data_format() == \"channels_last\" else 1\n\n if name is None:\n name = backend.get_uid(\"block0\")\n\n def apply(inputs):\n # Expansion phase\n filters = input_filters * expand_ratio\n if expand_ratio != 1:\n x = layers.Conv2D(\n filters=filters,\n kernel_size=1,\n strides=1,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n data_format=\"channels_last\",\n use_bias=False,\n name=name + \"expand_conv\",\n )(inputs)\n x = layers.BatchNormalization(\n axis=bn_axis,\n momentum=bn_momentum,\n name=name + \"expand_bn\",\n )(x)\n x = layers.Activation(activation, name=name + \"expand_activation\")(x)\n else:\n x = inputs\n\n # Depthwise conv\n x = layers.DepthwiseConv2D(\n kernel_size=kernel_size,\n strides=strides,\n depthwise_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n data_format=\"channels_last\",\n use_bias=False,\n name=name + \"dwconv2\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis, momentum=bn_momentum, name=name + \"bn\")(x)\n x = layers.Activation(activation, name=name + \"activation\")(x)\n\n # Squeeze and excite\n if 0 < se_ratio <= 1:\n filters_se = max(1, int(input_filters * se_ratio))\n se = layers.GlobalAveragePooling2D(name=name + \"se_squeeze\")(x)\n if bn_axis == 1:\n se_shape = (filters, 1, 1)\n else:\n se_shape = (1, 1, filters)\n se = layers.Reshape(se_shape, name=name + \"se_reshape\")(se)\n\n se = layers.Conv2D(\n filters_se,\n 1,\n padding=\"same\",\n activation=activation,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n name=name + \"se_reduce\",\n )(se)\n se = layers.Conv2D(\n filters,\n 1,\n padding=\"same\",\n activation=\"sigmoid\",\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n name=name + \"se_expand\",\n )(se)\n\n x = layers.multiply([x, se], name=name + \"se_excite\")\n\n # Output phase\n x = layers.Conv2D(\n filters=output_filters,\n kernel_size=1,\n strides=1,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n data_format=\"channels_last\",\n use_bias=False,\n name=name + \"project_conv\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis, momentum=bn_momentum, name=name + \"project_bn\")(x)\n\n if strides == 1 and input_filters == output_filters:\n if survival_probability:\n x = layers.Dropout(\n survival_probability,\n noise_shape=(None, 1, 1, 1),\n name=name + \"drop\",\n )(x)\n x = layers.add([x, inputs], name=name + \"add\")\n return x\n\n return apply\n\n\ndef FusedMBConvBlock(\n input_filters: int,\n output_filters: int,\n expand_ratio=1,\n kernel_size=3,\n strides=1,\n se_ratio=0.0,\n bn_momentum=0.9,\n activation=\"swish\",\n survival_probability: float = 0.8,\n name=None,\n):\n \"\"\"Fused MBConv Block: Fusing the proj conv1x1 and depthwise_conv into a conv2d.\"\"\"\n bn_axis = 3 if backend.image_data_format() == \"channels_last\" else 1\n\n if name is None:\n name = backend.get_uid(\"block0\")\n\n def apply(inputs):\n filters = input_filters * expand_ratio\n if expand_ratio != 1:\n x = layers.Conv2D(\n filters,\n kernel_size=kernel_size,\n strides=strides,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n data_format=\"channels_last\",\n padding=\"same\",\n use_bias=False,\n name=name + \"expand_conv\",\n )(inputs)\n x = layers.BatchNormalization(\n axis=bn_axis, momentum=bn_momentum, name=name + \"expand_bn\")(x)\n x = layers.Activation(\n activation=activation, name=name + \"expand_activation\")(x)\n else:\n x = inputs\n\n # Squeeze and excite\n if 0 < se_ratio <= 1:\n filters_se = max(1, int(input_filters * se_ratio))\n se = layers.GlobalAveragePooling2D(name=name + \"se_squeeze\")(x)\n if bn_axis == 1:\n se_shape = (filters, 1, 1)\n else:\n se_shape = (1, 1, filters)\n\n se = layers.Reshape(se_shape, name=name + \"se_reshape\")(se)\n\n se = layers.Conv2D(\n filters_se,\n 1,\n padding=\"same\",\n activation=activation,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n name=name + \"se_reduce\",\n )(se)\n se = layers.Conv2D(\n filters,\n 1,\n padding=\"same\",\n activation=\"sigmoid\",\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n name=name + \"se_expand\",\n )(se)\n\n x = layers.multiply([x, se], name=name + \"se_excite\")\n\n # Output phase:\n x = layers.Conv2D(\n output_filters,\n kernel_size=1 if expand_ratio != 1 else kernel_size,\n strides=1 if expand_ratio != 1 else strides,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n use_bias=False,\n name=name + \"project_conv\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis, momentum=bn_momentum, name=name + \"project_bn\")(x)\n if expand_ratio == 1:\n x = layers.Activation(\n activation=activation, name=name + \"project_activation\")(x)\n\n # Residual:\n if strides == 1 and input_filters == output_filters:\n if survival_probability:\n x = layers.Dropout(\n survival_probability,\n noise_shape=(None, 1, 1, 1),\n name=name + \"drop\",\n )(x)\n x = layers.add([x, inputs], name=name + \"add\")\n return x\n\n return apply\n\n\ndef EfficientNetV2(\n width_coefficient,\n depth_coefficient,\n default_size,\n dropout_rate=0.2,\n drop_connect_rate=0.2,\n depth_divisor=8,\n min_depth=8,\n bn_momentum=0.9,\n activation=\"swish\",\n blocks_args=\"default\",\n model_name=\"efficientnetv2\",\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n \"\"\"Instantiates the EfficientNetV2 architecture using given scaling coefficients.\n\n Args:\n width_coefficient: float, scaling coefficient for network width.\n depth_coefficient: float, scaling coefficient for network depth.\n default_size: integer, default input image size.\n dropout_rate: float, dropout rate before final classifier layer.\n drop_connect_rate: float, dropout rate at skip connections.\n depth_divisor: integer, a unit of network width.\n min_depth: integer, minimum number of filters.\n bn_momentum: float. Momentum parameter for Batch Normalization layers.\n activation: activation function.\n blocks_args: list of dicts, parameters to construct block modules.\n model_name: string, model name.\n include_top: whether to include the fully-connected layer at the top of the\n network.\n weights: one of `None` (random initialization), `\"imagenet\"` (pre-training\n on ImageNet), or the path to the weights file to be loaded.\n input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) or\n numpy array to use as image input for the model.\n input_shape: optional shape tuple, only to be specified if `include_top` is\n False. It should have exactly 3 inputs channels.\n pooling: optional pooling mode for feature extraction when `include_top` is\n `False`. - `None` means that the output of the model will be the 4D tensor\n output of the last convolutional layer. - \"avg\" means that global average\n pooling will be applied to the output of the last convolutional layer, and\n thus the output of the model will be a 2D tensor. - `\"max\"` means that\n global max pooling will be applied.\n classes: optional number of classes to classify images into, only to be\n specified if `include_top` is True, and if no `weights` argument is\n specified.\n classifier_activation: A string or callable. The activation function to use\n on the `\"top\"` layer. Ignored unless `include_top=True`. Set\n `classifier_activation=None` to return the logits of the `\"top\"` layer.\n include_preprocessing: Boolean, whether to include the preprocessing layer\n (`Rescaling`) at the bottom of the network. Defaults to `True`.\n\n Returns:\n A `keras.Model` instance.\n\n Raises:\n ValueError: in case of invalid argument for `weights`,\n or invalid input shape.\n ValueError: if `classifier_activation` is not `\"softmax\"` or `None` when\n using a pretrained top layer.\n \"\"\"\n\n if blocks_args == \"default\":\n blocks_args = DEFAULT_BLOCKS_ARGS[model_name]\n\n if not (weights in {\"imagenet\", None} or tf.io.gfile.exists(weights)):\n raise ValueError(\"The `weights` argument should be either \"\n \"`None` (random initialization), `imagenet` \"\n \"(pre-training on ImageNet), \"\n \"or the path to the weights file to be loaded.\"\n f\"Received: weights={weights}\")\n\n if weights == \"imagenet\" and include_top and classes != 1000:\n raise ValueError(\"If using `weights` as `'imagenet'` with `include_top`\"\n \" as true, `classes` should be 1000\"\n f\"Received: classes={classes}\")\n\n # Determine proper input shape\n input_shape = imagenet_utils.obtain_input_shape(\n input_shape,\n default_size=default_size,\n min_size=32,\n data_format=backend.image_data_format(),\n require_flatten=include_top,\n weights=weights)\n\n if input_tensor is None:\n img_input = layers.Input(shape=input_shape)\n else:\n if not backend.is_keras_tensor(input_tensor):\n img_input = layers.Input(tensor=input_tensor, shape=input_shape)\n else:\n img_input = input_tensor\n\n bn_axis = 3 if backend.image_data_format() == \"channels_last\" else 1\n\n x = img_input\n\n if include_preprocessing:\n # Apply original V1 preprocessing for Bx variants\n # if number of channels allows it\n num_channels = input_shape[bn_axis - 1]\n if model_name.split(\"-\")[-1].startswith(\"b\") and num_channels == 3:\n x = layers.Rescaling(scale=1. / 255)(x)\n x = layers.Normalization(\n mean=[0.485, 0.456, 0.406],\n variance=[0.229**2, 0.224**2, 0.225**2],\n axis=bn_axis,\n )(x)\n else:\n x = layers.Rescaling(scale=1. / 128.0, offset=-1)(x)\n\n # Build stem\n stem_filters = round_filters(\n filters=blocks_args[0][\"input_filters\"],\n width_coefficient=width_coefficient,\n min_depth=min_depth,\n depth_divisor=depth_divisor,\n )\n x = layers.Conv2D(\n filters=stem_filters,\n kernel_size=3,\n strides=2,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n use_bias=False,\n name=\"stem_conv\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis,\n momentum=bn_momentum,\n name=\"stem_bn\",\n )(x)\n x = layers.Activation(activation, name=\"stem_activation\")(x)\n\n # Build blocks\n blocks_args = copy.deepcopy(blocks_args)\n b = 0\n blocks = float(sum(args[\"num_repeat\"] for args in blocks_args))\n\n for (i, args) in enumerate(blocks_args):\n assert args[\"num_repeat\"] > 0\n\n # Update block input and output filters based on depth multiplier.\n args[\"input_filters\"] = round_filters(\n filters=args[\"input_filters\"],\n width_coefficient=width_coefficient,\n min_depth=min_depth,\n depth_divisor=depth_divisor)\n args[\"output_filters\"] = round_filters(\n filters=args[\"output_filters\"],\n width_coefficient=width_coefficient,\n min_depth=min_depth,\n depth_divisor=depth_divisor)\n\n # Determine which conv type to use:\n block = {0: MBConvBlock, 1: FusedMBConvBlock}[args.pop(\"conv_type\")]\n repeats = round_repeats(\n repeats=args.pop(\"num_repeat\"), depth_coefficient=depth_coefficient)\n for j in range(repeats):\n # The first block needs to take care of stride and filter size increase.\n if j > 0:\n args[\"strides\"] = 1\n args[\"input_filters\"] = args[\"output_filters\"]\n\n x = block(\n activation=activation,\n bn_momentum=bn_momentum,\n survival_probability=drop_connect_rate * b / blocks,\n name=\"block{}{}_\".format(i + 1, chr(j + 97)),\n **args,\n )(x)\n b += 1\n\n # Build top\n top_filters = round_filters(\n filters=1280,\n width_coefficient=width_coefficient,\n min_depth=min_depth,\n depth_divisor=depth_divisor)\n x = layers.Conv2D(\n filters=top_filters,\n kernel_size=1,\n strides=1,\n kernel_initializer=CONV_KERNEL_INITIALIZER,\n padding=\"same\",\n data_format=\"channels_last\",\n use_bias=False,\n name=\"top_conv\",\n )(x)\n x = layers.BatchNormalization(\n axis=bn_axis,\n momentum=bn_momentum,\n name=\"top_bn\",\n )(x)\n x = layers.Activation(activation=activation, name=\"top_activation\")(x)\n\n if include_top:\n x = layers.GlobalAveragePooling2D(name=\"avg_pool\")(x)\n if dropout_rate > 0:\n x = layers.Dropout(dropout_rate, name=\"top_dropout\")(x)\n imagenet_utils.validate_activation(classifier_activation, weights)\n x = layers.Dense(\n classes,\n activation=classifier_activation,\n kernel_initializer=DENSE_KERNEL_INITIALIZER,\n bias_initializer=tf.constant_initializer(0),\n name=\"predictions\")(x)\n else:\n if pooling == \"avg\":\n x = layers.GlobalAveragePooling2D(name=\"avg_pool\")(x)\n elif pooling == \"max\":\n x = layers.GlobalMaxPooling2D(name=\"max_pool\")(x)\n\n # Ensure that the model takes into account\n # any potential predecessors of `input_tensor`.\n if input_tensor is not None:\n inputs = layer_utils.get_source_inputs(input_tensor)\n else:\n inputs = img_input\n\n # Create model.\n model = training.Model(inputs, x, name=model_name)\n\n # Load weights.\n if weights == \"imagenet\":\n if include_top:\n file_suffix = \".h5\"\n file_hash = WEIGHTS_HASHES[model_name[-2:]][0]\n else:\n file_suffix = \"_notop.h5\"\n file_hash = WEIGHTS_HASHES[model_name[-2:]][1]\n file_name = model_name + file_suffix\n weights_path = data_utils.get_file(\n file_name,\n BASE_WEIGHTS_PATH + file_name,\n cache_subdir=\"models\",\n file_hash=file_hash)\n model.load_weights(weights_path)\n elif weights is not None:\n model.load_weights(weights)\n\n return model\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2B0\",\n \"keras.applications.EfficientNetV2B0\")\ndef EfficientNetV2B0(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.0,\n default_size=224,\n model_name=\"efficientnetv2-b0\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing)\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2B1\",\n \"keras.applications.EfficientNetV2B1\")\ndef EfficientNetV2B1(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.1,\n default_size=240,\n model_name=\"efficientnetv2-b1\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2B2\",\n \"keras.applications.EfficientNetV2B2\")\ndef EfficientNetV2B2(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.1,\n depth_coefficient=1.2,\n default_size=260,\n model_name=\"efficientnetv2-b2\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2B3\",\n \"keras.applications.EfficientNetV2B3\")\ndef EfficientNetV2B3(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.2,\n depth_coefficient=1.4,\n default_size=300,\n model_name=\"efficientnetv2-b3\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2S\",\n \"keras.applications.EfficientNetV2S\")\ndef EfficientNetV2S(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.0,\n default_size=384,\n model_name=\"efficientnetv2-s\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2M\",\n \"keras.applications.EfficientNetV2M\")\ndef EfficientNetV2M(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.0,\n default_size=480,\n model_name=\"efficientnetv2-m\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\n@keras_export(\"keras.applications.efficientnet_v2.EfficientNetV2L\",\n \"keras.applications.EfficientNetV2L\")\ndef EfficientNetV2L(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n classifier_activation=\"softmax\",\n include_preprocessing=True,\n):\n return EfficientNetV2(\n width_coefficient=1.0,\n depth_coefficient=1.0,\n default_size=480,\n model_name=\"efficientnetv2-l\",\n include_top=include_top,\n weights=weights,\n input_tensor=input_tensor,\n input_shape=input_shape,\n pooling=pooling,\n classes=classes,\n classifier_activation=classifier_activation,\n include_preprocessing=include_preprocessing,\n )\n\n\nEfficientNetV2B0.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2B0\")\nEfficientNetV2B1.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2B1\")\nEfficientNetV2B2.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2B2\")\nEfficientNetV2B3.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2B3\")\nEfficientNetV2S.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2S\")\nEfficientNetV2M.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2M\")\nEfficientNetV2L.__doc__ = BASE_DOCSTRING.format(name=\"EfficientNetV2L\")\n\n\n@keras_export(\"keras.applications.efficientnet_v2.preprocess_input\")\ndef preprocess_input(x, data_format=None): # pylint: disable=unused-argument\n \"\"\"A placeholder method for backward compatibility.\n\n The preprocessing logic has been included in the EfficientNetV2 model\n implementation. Users are no longer required to call this method to normalize\n the input data. This method does nothing and only kept as a placeholder to\n align the API surface between old and new version of model.\n\n Args:\n x: A floating point `numpy.array` or a `tf.Tensor`.\n data_format: Optional data format of the image tensor/array. Defaults to\n None, in which case the global setting\n `tf.keras.backend.image_data_format()` is used (unless you changed it, it\n defaults to \"channels_last\").{mode}\n\n Returns:\n Unchanged `numpy.array` or `tf.Tensor`.\n \"\"\"\n return x\n\n\n@keras_export(\"keras.applications.efficientnet_v2.decode_predictions\")\ndef decode_predictions(preds, top=5):\n return imagenet_utils.decode_predictions(preds, top=top)\n\n\ndecode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__\n", "path": "keras/applications/efficientnet_v2.py" } ]
diff --git a/keras/applications/efficientnet_v2.py b/keras/applications/efficientnet_v2.py index a329ed23e101..6a1b5be8d0fb 100644 --- a/keras/applications/efficientnet_v2.py +++ b/keras/applications/efficientnet_v2.py @@ -991,6 +991,7 @@ def EfficientNetV2( name="block{}{}_".format(i + 1, chr(j + 97)), **args, )(x) + b += 1 # Build top top_filters = round_filters(
xorbitsai__inference-1092
DOC: Add doc about installing from source code Note that the issue tracker is NOT the place for general support.
[ { "content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\n# import sys\n# sys.path.insert(0, os.path.abspath('.'))\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'Xinference'\ncopyright = '2023, Xorbits Inc.'\nauthor = 'xorbitsai'\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"sphinx.ext.mathjax\",\n \"sphinx.ext.ifconfig\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.viewcode\",\n \"sphinx.ext.githubpages\",\n \"sphinx.ext.autosummary\",\n \"sphinx.ext.napoleon\",\n \"sphinx_tabs.tabs\",\n \"sphinx_design\",\n \"IPython.sphinxext.ipython_directive\",\n \"IPython.sphinxext.ipython_console_highlighting\",\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = []\n\n# i18n\nlocale_dirs = [\"locale/\"] # path is example but recommended.\ngettext_compact = False # optional\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'pydata_sphinx_theme'\nhtml_title = \"Xinference\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Define the json_url for our version switcher.\nversion_match = os.environ.get(\"READTHEDOCS_LANGUAGE\")\njson_url = \"https://inference.readthedocs.io/en/latest/_static/switcher.json\"\nif not version_match:\n version_match = 'en'\n\nhtml_theme_options = {\n \"show_toc_level\": 2,\n \"header_links_before_dropdown\": 6,\n \"icon_links\": [\n {\n \"name\": \"GitHub\",\n \"url\": \"https://github.com/xorbitsai/inference\",\n \"icon\": \"fa-brands fa-github\",\n \"type\": \"fontawesome\",\n },\n ],\n \"navbar_align\": \"content\", # [left, content, right] For testing that the navbar items align properly\n \"navbar_start\": [\"navbar-logo\", \"version-switcher\"],\n \"navbar_center\": [\"navbar-nav\"],\n \"switcher\": {\n \"json_url\": json_url,\n \"version_match\": version_match,\n },\n}\n\n\nif version_match != 'zh-cn':\n html_theme_options['icon_links'].extend([{\n \"name\": \"Slack\",\n \"url\": \"https://join.slack.com/t/xorbitsio/shared_invite/zt-1o3z9ucdh-RbfhbPVpx7prOVdM1CAuxg\",\n \"icon\": \"fa-brands fa-slack\",\n \"type\": \"fontawesome\",\n },\n {\n \"name\": \"Twitter\",\n \"url\": \"https://twitter.com/xorbitsio\",\n \"icon\": \"fa-brands fa-twitter\",\n \"type\": \"fontawesome\",\n }])\nelse:\n html_theme_options['icon_links'].extend([{\n \"name\": \"WeChat\",\n \"url\": \"https://xorbits.cn/assets/images/wechat_work_qr.png\",\n \"icon\": \"fa-brands fa-weixin\",\n \"type\": \"fontawesome\",\n },\n {\n \"name\": \"Zhihu\",\n \"url\": \"https://zhihu.com/org/xorbits\",\n \"icon\": \"fa-brands fa-zhihu\",\n \"type\": \"fontawesome\",\n }])\n html_theme_options[\"external_links\"] = [\n {\"name\": \"产品官网\", \"url\": \"https://xorbits.cn/inference\"},\n ]\n\nhtml_favicon = \"_static/favicon.svg\"\n", "path": "doc/source/conf.py" } ]
[ { "content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\n# import sys\n# sys.path.insert(0, os.path.abspath('.'))\n\n\n# -- Project information -----------------------------------------------------\n\nproject = 'Xinference'\ncopyright = '2023, Xorbits Inc.'\nauthor = 'xorbitsai'\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"sphinx.ext.mathjax\",\n \"sphinx.ext.ifconfig\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.viewcode\",\n \"sphinx.ext.githubpages\",\n \"sphinx.ext.autosummary\",\n \"sphinx.ext.napoleon\",\n \"sphinx_tabs.tabs\",\n \"sphinx_design\",\n \"IPython.sphinxext.ipython_directive\",\n \"IPython.sphinxext.ipython_console_highlighting\",\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = []\n\n# i18n\nlocale_dirs = [\"locale/\"] # path is example but recommended.\ngettext_compact = False # optional\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'pydata_sphinx_theme'\nhtml_title = \"Xinference\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Define the json_url for our version switcher.\nversion_match = os.environ.get(\"READTHEDOCS_LANGUAGE\")\njson_url = \"https://inference.readthedocs.io/en/latest/_static/switcher.json\"\nif not version_match:\n version_match = 'en'\n\nhtml_theme_options = {\n \"show_toc_level\": 2,\n \"header_links_before_dropdown\": 7,\n \"icon_links\": [\n {\n \"name\": \"GitHub\",\n \"url\": \"https://github.com/xorbitsai/inference\",\n \"icon\": \"fa-brands fa-github\",\n \"type\": \"fontawesome\",\n },\n ],\n \"navbar_align\": \"content\", # [left, content, right] For testing that the navbar items align properly\n \"navbar_start\": [\"navbar-logo\", \"version-switcher\"],\n \"navbar_center\": [\"navbar-nav\"],\n \"switcher\": {\n \"json_url\": json_url,\n \"version_match\": version_match,\n },\n}\n\n\nif version_match != 'zh-cn':\n html_theme_options['icon_links'].extend([{\n \"name\": \"Slack\",\n \"url\": \"https://join.slack.com/t/xorbitsio/shared_invite/zt-1o3z9ucdh-RbfhbPVpx7prOVdM1CAuxg\",\n \"icon\": \"fa-brands fa-slack\",\n \"type\": \"fontawesome\",\n },\n {\n \"name\": \"Twitter\",\n \"url\": \"https://twitter.com/xorbitsio\",\n \"icon\": \"fa-brands fa-twitter\",\n \"type\": \"fontawesome\",\n }])\nelse:\n html_theme_options['icon_links'].extend([{\n \"name\": \"WeChat\",\n \"url\": \"https://xorbits.cn/assets/images/wechat_work_qr.png\",\n \"icon\": \"fa-brands fa-weixin\",\n \"type\": \"fontawesome\",\n },\n {\n \"name\": \"Zhihu\",\n \"url\": \"https://zhihu.com/org/xorbits\",\n \"icon\": \"fa-brands fa-zhihu\",\n \"type\": \"fontawesome\",\n }])\n html_theme_options[\"external_links\"] = [\n {\"name\": \"产品官网\", \"url\": \"https://xorbits.cn/inference\"},\n ]\n\nhtml_favicon = \"_static/favicon.svg\"\n", "path": "doc/source/conf.py" } ]
diff --git a/doc/source/conf.py b/doc/source/conf.py index b22c286a07..0c5441f239 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -75,7 +75,7 @@ html_theme_options = { "show_toc_level": 2, - "header_links_before_dropdown": 6, + "header_links_before_dropdown": 7, "icon_links": [ { "name": "GitHub", diff --git a/doc/source/development/contributing_codebase.rst b/doc/source/development/contributing_codebase.rst new file mode 100644 index 0000000000..ace1729d21 --- /dev/null +++ b/doc/source/development/contributing_codebase.rst @@ -0,0 +1,100 @@ +============================= +Contributing to the code base +============================= + +.. contents:: Table of contents: + :local: + +Code standards +-------------- + +Writing good code is not just about what you write. It is also about *how* you write it. +During Continuous Integration testing, several tools will be run to check your code for stylistic errors. +Good style is a requirement for submitting code to Xinference. + +In addition, it is important that we do not make sudden changes to the code that +could have the potential to break a lot of user code as a result. Therefore +we need it to be as backwards compatible as possible to avoid mass breakages. + +Autofixing formatting errors +---------------------------- + +Moreover, Continuous Integration will run code formatting checks +like ``black``, ``flake8``, ``isort``, and others using `pre-commit hooks <https://pre-commit.com/>`_ +Any warnings generated by these checks will cause the Continuous Integration to fail. Therefore, +it is advisable to run the check yourself before submitting code. This +can be done by installing ``pre-commit``:: + + pip install pre-commit + +and then running:: + + pre-commit install + +from the root of the Xinference repository. This setup ensures that all styling checks are +automatically executed each time you commit changes without your needing to run each one manually. +In addition, using ``pre-commit`` will also allow you to more easily +remain up-to-date with our code checks as they change. + +Note that if needed, you can skip these checks with ``git commit --no-verify``. + +If you don't want to use ``pre-commit`` as part of your workflow, you can still use it +to run its checks with:: + + pre-commit run --files <files you have modified> + +without needing to have done ``pre-commit install`` beforehand. + +If you want to run checks on all recently committed files on upstream/main you can use:: + + pre-commit run --from-ref=upstream/main --to-ref=HEAD --all-files + +without needing to have done ``pre-commit install`` beforehand. + +.. note:: + + You may consider periodically running ``pre-commit gc`` to clean up repos + which are no longer used. + +.. note:: + + If you have conflicting installations of ``virtualenv``, if could lead to + errors - refer to `here <https://github.com/pypa/virtualenv/issues/1875>`_. + + Also, due to a `bug in virtualenv <https://github.com/pypa/virtualenv/issues/1986>`_, + you may run into issues if you're using conda. To solve this, you can downgrade + ``virtualenv`` to version ``20.0.33``. + +Backwards compatibility +----------------------- + +Please try to maintain backward compatibility. If you think breakage is necessary, +clearly state why as part of the pull request. Also, be careful when changing method +signatures and add deprecation warnings where needed. Also, add the deprecated sphinx +directive to the deprecated functions or methods. + +You'll also need to + +1. Write a new test that asserts a warning is issued when calling with the deprecated argument +2. Update all of Xinference existing tests and code to use the new argument + +Type hints +---------- + +Xinference strongly encourages the use of :pep:`484` style type hints. New development should +contain type hints and pull requests to annotate existing code are accepted as well! + +Test-driven development +----------------------- + +Xinference is serious about testing and strongly encourages contributors to embrace +`test-driven development (TDD) <https://en.wikipedia.org/wiki/Test-driven_development>`_. +This development process "relies on the repetition of a very short development cycle: +first the developer writes an (initially failing) automated test case that defines a desired +improvement or new function, then produces the minimum amount of code to pass that test." +So, before actually writing any code, you should write your tests. Often the test can be +taken from the original GitHub issue. However, it is always worth considering additional +use cases and writing corresponding tests. + +Adding tests is frequently requested after code is pushed to Xinference. Thus, +it is worth getting in the habit of writing tests ahead of time so this is never an issue. \ No newline at end of file diff --git a/doc/source/development/contributing_environment.rst b/doc/source/development/contributing_environment.rst new file mode 100644 index 0000000000..80aa266dfb --- /dev/null +++ b/doc/source/development/contributing_environment.rst @@ -0,0 +1,114 @@ +================================== +Creating a development environment +================================== + +.. contents:: Table of contents: + :local: + +Before proceeding with any code modifications, it's essential to set up the necessary environment for Xinference development, +which includes familiarizing yourself with Git usage, establishing an isolated environment, installing Xinference, and compiling the frontend. + +Getting startted with Git +------------------------- + +Now that you have identified an issue you wish to resolve, an enhancement to incorporate, or documentation to enhance, +it's crucial to acquaint yourself with GitHub and the Xinference codebase. + +To the new user, working with Git is one of the more intimidating aspects of contributing to Xinference. +It can very quickly become overwhelming, but sticking to the guidelines below will help simplify the process +and minimize potential issues. As always, if you are having difficulties please +feel free to ask for help. + +The code is hosted on `GitHub <https://github.com/xorbitsai/inference>`_. To +contribute you will need to sign up for a `free GitHub account +<https://github.com/signup/free>`_. We use `Git <https://git-scm.com/>`_ for +version control to allow many people to work together on the project. + +`GitHub has instructions <https://help.github.com/set-up-git-redirect>`__ for installing git, +setting up your SSH key, and configuring git. All these steps need to be completed before +you can work seamlessly between your local repository and GitHub. + +Some great resources for learning Git: + +* `Official Git Documentation <https://git-scm.com/doc>`_ +* `Pro Git Book <https://git-scm.com/book/en/v2>`_ +* `Git Tutorial by Atlassian <https://www.atlassian.com/git/tutorials>`_ +* `Git - Concise Guide <http://rogerdudler.github.io/git-guide/index.zh.html>`_ + +.. note:: + If the speed of ``git clone`` is slow, you can use the following command + to add a proxy: + + :: + + export https_proxy=YourProxyAddress + +Creating an isolated environment +-------------------------------- + +Before formally installing Xinference, it's recommended to create an isolated +environment, using Conda recommended, for ease of subsequent operations. + +:: + + conda create --name xinf + conda activate xinf + +``xinf`` can be replaced with a custom Conda environment name. + +Afterward, you'll need to install Python and Node.js (npm) in the newly created +Conda environment. Here are the commands: + +:: + + conda install python=3.10 + conda install nodejs + +Install from source code +------------------------ + +Before we begin, please make sure that you have cloned the repository. +Suppose you clone the repository as ``inference`` directory, ``cd`` to this directory +where the ``setup.cfg`` and ``setup.py`` files are located, and run the following command: + +:: + + pip install -e . + xinference-local + +If the commands run successfully, you can use Xinference normally. For +detailed usage instructions, refer to +`using_xinference <https://inference.readthedocs.io/en/latest/getting_started/using_xinference.html>`__. + +If errors occur or the process freezes during execution, the next step +is to compile the frontend. + +Frontend Compilation +-------------------- + +Navigate to the ``inference/xinference/web/ui`` directory. Then, execute the following command +to clear the cache: + +:: + + npm cache clean + +If the command fails to execute, you can try adding the ``--force`` option. + +.. note:: + If the ``node_modules`` folder already exists in this directory, + it's recommended to manually delete it before cleaning the cache. + +Next, execute the following command in this directory to compile the +frontend: + +:: + + npm install + npm run build + +Still, if the first command fails to execute, you can try adding the ``--force`` option. + +After compiling the frontend, you can ``cd`` back to the directory +where the ``setup.cfg`` and ``setup.py`` files are located, +and install Xinference via ``pip install -e .``. diff --git a/doc/source/development/index.rst b/doc/source/development/index.rst new file mode 100644 index 0000000000..9b423ae03a --- /dev/null +++ b/doc/source/development/index.rst @@ -0,0 +1,11 @@ +.. _development_index: + +=========== +Development +=========== + +.. toctree:: + :maxdepth: 2 + + contributing_environment + contributing_codebase \ No newline at end of file diff --git a/doc/source/index.rst b/doc/source/index.rst index e5ed21d85c..d5f3bce3e9 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -13,6 +13,7 @@ Welcome to Xinference! user_guide/index examples/index reference/index + development/index Xorbits Inference (Xinference) is an open-source platform to streamline the operation and integration diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_codebase.po b/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_codebase.po new file mode 100644 index 0000000000..e2138c7e45 --- /dev/null +++ b/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_codebase.po @@ -0,0 +1,202 @@ +# SOME DESCRIPTIVE TITLE. +# Copyright (C) 2023, Xorbits Inc. +# This file is distributed under the same license as the Xinference package. +# FIRST AUTHOR <EMAIL@ADDRESS>, 2024. +# +#, fuzzy +msgid "" +msgstr "" +"Project-Id-Version: Xinference \n" +"Report-Msgid-Bugs-To: \n" +"POT-Creation-Date: 2024-03-07 15:03+0800\n" +"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" +"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n" +"Language: zh_CN\n" +"Language-Team: zh_CN <[email protected]>\n" +"Plural-Forms: nplurals=1; plural=0;\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=utf-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Generated-By: Babel 2.14.0\n" + +#: ../../source/development/contributing_codebase.rst:3 +msgid "Contributing to the code base" +msgstr "代码库开发指南" + +#: ../../source/development/contributing_codebase.rst:6 +msgid "Table of contents:" +msgstr "目录" + +#: ../../source/development/contributing_codebase.rst:9 +msgid "Code standards" +msgstr "代码规范" + +#: ../../source/development/contributing_codebase.rst:11 +msgid "" +"Writing good code is not just about what you write. It is also about " +"*how* you write it. During Continuous Integration testing, several tools " +"will be run to check your code for stylistic errors. Good style is a " +"requirement for submitting code to Xinference." +msgstr "" +"写出好的代码不仅在于你写了什么，更在于你是如何写的。在持续集成测试期间，会有多个工具来检查您的代码是否存在风格错误。良好的编程风格是提交代码到 " +"Xinference 的要求之一。" + +#: ../../source/development/contributing_codebase.rst:15 +msgid "" +"In addition, it is important that we do not make sudden changes to the " +"code that could have the potential to break a lot of user code as a " +"result. Therefore we need it to be as backwards compatible as possible to" +" avoid mass breakages." +msgstr "此外，不要对代码进行突然的更改，这可能会导致大量用户代码出现问题。所以，我们需要尽可能地保持向后兼容，以避免大规模的故障。" + +#: ../../source/development/contributing_codebase.rst:20 +msgid "Autofixing formatting errors" +msgstr "自动修复格式错误" + +#: ../../source/development/contributing_codebase.rst:22 +msgid "" +"Moreover, Continuous Integration will run code formatting checks like " +"``black``, ``flake8``, ``isort``, and others using `pre-commit hooks " +"<https://pre-commit.com/>`_ Any warnings generated by these checks will " +"cause the Continuous Integration to fail. Therefore, it is advisable to " +"run the check yourself before submitting code. This can be done by " +"installing ``pre-commit``::" +msgstr "" +"此外，持续集成将使用 `pre-commit hooks <https://pre-commit.com/>`_ 运行诸如 ``black``、``flake8``、``isort`` " +"等代码格式检查工具。任何由这些检查生成的警告都将导致持续集成失败。因此，建议在提交代码之前自行运行这些检查。" +"可以通过在 Xinference 仓库的根目录下安装 ``pre-commit`` 来完成这一操作：" + +#: ../../source/development/contributing_codebase.rst:30 +msgid "and then running::" +msgstr "然后执行命令：" + +#: ../../source/development/contributing_codebase.rst:34 +msgid "" +"from the root of the Xinference repository. This setup ensures that all " +"styling checks are automatically executed each time you commit changes " +"without your needing to run each one manually. In addition, using ``pre-" +"commit`` will also allow you to more easily remain up-to-date with our " +"code checks as they change." +msgstr "" +"安装好了以后就能确保每次提交更改时都会自动执行所有样式检查，无需手动逐个运行。" +"此外，使用 ``pre-commit`` 也能让您更轻松地在我们的代码检查发生更改的时候保持同步。" + +#: ../../source/development/contributing_codebase.rst:39 +msgid "" +"Note that if needed, you can skip these checks with ``git commit --no-" +"verify``." +msgstr "请注意，如果需要，您可以通过使用 ``git commit --no-verify`` 命令来跳过这些检查。" + +#: ../../source/development/contributing_codebase.rst:41 +msgid "" +"If you don't want to use ``pre-commit`` as part of your workflow, you can" +" still use it to run its checks with::" +msgstr "如果您不想将 ``pre-commit`` 作为工作流程的一部分，仍然可以运行如下命令来使用它进行检查：" + +#: ../../source/development/contributing_codebase.rst:46 +#: ../../source/development/contributing_codebase.rst:52 +msgid "without needing to have done ``pre-commit install`` beforehand." +msgstr "而不需要事先执行 ``pre-commit install``。" + +#: ../../source/development/contributing_codebase.rst:48 +msgid "" +"If you want to run checks on all recently committed files on " +"upstream/main you can use::" +msgstr "如果您想在所有最近提交的文件上运行检查，您可以使用以下命令：" + +#: ../../source/development/contributing_codebase.rst:56 +msgid "" +"You may consider periodically running ``pre-commit gc`` to clean up repos" +" which are no longer used." +msgstr "您可以考虑定期运行 ``pre-commit gc`` 命令来清理不再使用的存储库。" + +#: ../../source/development/contributing_codebase.rst:61 +msgid "" +"If you have conflicting installations of ``virtualenv``, if could lead to" +" errors - refer to `here " +"<https://github.com/pypa/virtualenv/issues/1875>`_." +msgstr "如果您安装了冲突的 ``virtualenv`` 版本，可能会导致错误 - 可以参考" +" `这里 <https://github.com/pypa/virtualenv/issues/1875>`_ 。" + +#: ../../source/development/contributing_codebase.rst:64 +msgid "" +"Also, due to a `bug in virtualenv " +"<https://github.com/pypa/virtualenv/issues/1986>`_, you may run into " +"issues if you're using conda. To solve this, you can downgrade " +"``virtualenv`` to version ``20.0.33``." +msgstr "" +"此外，由于 ``virtualenv`` 中的一个 `错误 <https://github.com/pypa/virtualenv/issues/1986>`_ ，如果您使用 conda，可能会遇到问题。" +"要解决这个问题，您可以将 ``virtualenv`` 降级到版本 ``20.0.33``。" + +#: ../../source/development/contributing_codebase.rst:69 +msgid "Backwards compatibility" +msgstr "向后兼容" + +#: ../../source/development/contributing_codebase.rst:71 +msgid "" +"Please try to maintain backward compatibility. If you think breakage is " +"necessary, clearly state why as part of the pull request. Also, be " +"careful when changing method signatures and add deprecation warnings " +"where needed. Also, add the deprecated sphinx directive to the deprecated" +" functions or methods." +msgstr "" +"请尽量保持向后兼容性。如果您认为必须进行更改，请在拉取请求中说明清楚原因。同时，在更改方法签名时要小心，并在需要时添加弃用警告。此外，为弃用的函数或方法添加弃用的" +" sphinx 指令。" + +#: ../../source/development/contributing_codebase.rst:76 +msgid "You'll also need to" +msgstr "同时你还需要" + +#: ../../source/development/contributing_codebase.rst:78 +msgid "" +"Write a new test that asserts a warning is issued when calling with the " +"deprecated argument" +msgstr "编写一个新的测试样例，在调用带有弃用参数时会发出警告。" + +#: ../../source/development/contributing_codebase.rst:79 +msgid "Update all of Xinference existing tests and code to use the new argument" +msgstr "更新所有 Xinference 现有的测试样例和代码，以使用新的参数。" + +#: ../../source/development/contributing_codebase.rst:82 +msgid "Type hints" +msgstr "类型提示" + +#: ../../source/development/contributing_codebase.rst:84 +msgid "" +"Xinference strongly encourages the use of :pep:`484` style type hints. " +"New development should contain type hints and pull requests to annotate " +"existing code are accepted as well!" +msgstr "Xinference 强烈鼓励使用 :pep:`484` 风格的类型提示。新的开发应包含类型提示，并且对现有代码进行注释的拉取请求也是可以接受的！" + +#: ../../source/development/contributing_codebase.rst:88 +msgid "Test-driven development" +msgstr "测试驱动开发" + +#: ../../source/development/contributing_codebase.rst:90 +msgid "" +"Xinference is serious about testing and strongly encourages contributors " +"to embrace `test-driven development (TDD) <https://en.wikipedia.org/wiki" +"/Test-driven_development>`_. This development process \"relies on the " +"repetition of a very short development cycle: first the developer writes " +"an (initially failing) automated test case that defines a desired " +"improvement or new function, then produces the minimum amount of code to " +"pass that test.\" So, before actually writing any code, you should write " +"your tests. Often the test can be taken from the original GitHub issue. " +"However, it is always worth considering additional use cases and writing " +"corresponding tests." +msgstr "" +"Xinference 非常重视测试，并强烈鼓励贡献者采用 `测试驱动开发(TDD) <https://en.wikipedia.org/wiki" +"/Test-driven_development>`_ 。这种开发过程 " +"\"依赖于非常短的开发周期的重复：首先，开发者编写一个（初始为失败的）自动化测试样例来定义所需的改进或新功能，然后用最少的代码来通过该测试。\"因此，在实际编写任何代码之前，您应该编写您的测试样例。通常，测试样例可以从原始的" +" GitHub issue 中获取。然而，值得考虑额外的情况并编写相应的测试样例。" + +#: ../../source/development/contributing_codebase.rst:99 +msgid "" +"Adding tests is frequently requested after code is pushed to Xinference. " +"Thus, it is worth getting in the habit of writing tests ahead of time so " +"this is never an issue." +msgstr "在将代码推送到 Xinference 之后，经常会要求添加测试样例。因此，养成提前编写测试样例的习惯非常重要，这样就不会出现问题。" + +#~ msgid "Pre-commit" +#~ msgstr "Pre-commit" + diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_environment.po b/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_environment.po new file mode 100644 index 0000000000..8ced444523 --- /dev/null +++ b/doc/source/locale/zh_CN/LC_MESSAGES/development/contributing_environment.po @@ -0,0 +1,202 @@ +# SOME DESCRIPTIVE TITLE. +# Copyright (C) 2023, Xorbits Inc. +# This file is distributed under the same license as the Xinference package. +# FIRST AUTHOR <EMAIL@ADDRESS>, 2024. +# +#, fuzzy +msgid "" +msgstr "" +"Project-Id-Version: Xinference \n" +"Report-Msgid-Bugs-To: \n" +"POT-Creation-Date: 2024-03-06 16:29+0800\n" +"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" +"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n" +"Language: zh_CN\n" +"Language-Team: zh_CN <[email protected]>\n" +"Plural-Forms: nplurals=1; plural=0;\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=utf-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Generated-By: Babel 2.14.0\n" + +#: ../../source/development/contributing_environment.rst:3 +msgid "Creating a development environment" +msgstr "创建开发环境" + +#: ../../source/development/contributing_environment.rst:6 +msgid "Table of contents:" +msgstr "目录" + +#: ../../source/development/contributing_environment.rst:8 +msgid "" +"Before proceeding with any code modifications, it's essential to set up " +"the necessary environment for Xinference development, which includes " +"familiarizing yourself with Git usage, establishing an isolated " +"environment, installing Xinference, and compiling the frontend." +msgstr "" +"在进行任何代码修改之前，建立起适用于 Xinference 开发的必要环境至关重要。包括熟悉 Git 的使用、建立一个独立的环境、安装 " +"Xinference 以及前端部分的编译。" + +#: ../../source/development/contributing_environment.rst:12 +msgid "Getting startted with Git" +msgstr "Git 的使用" + +#: ../../source/development/contributing_environment.rst:14 +msgid "" +"Now that you have identified an issue you wish to resolve, an enhancement" +" to incorporate, or documentation to enhance, it's crucial to acquaint " +"yourself with GitHub and the Xinference codebase." +msgstr "当你有一个需要修复的问题、需要添加的增强功能或需要改进的文档时，熟悉 GitHub 和 Xinference 代码库很重要。" + +#: ../../source/development/contributing_environment.rst:17 +msgid "" +"To the new user, working with Git is one of the more intimidating aspects" +" of contributing to Xinference. It can very quickly become overwhelming, " +"but sticking to the guidelines below will help simplify the process and " +"minimize potential issues. As always, if you are having difficulties " +"please feel free to ask for help." +msgstr "" +"对新用户来说，使用 Git 是参与 Xinference " +"开发最令人畏惧的方面之一。很快就会感到压力山大，但以下指南将有助于简化流程并减少潜在问题。如果您遇到难以解决的问题，欢迎在社区寻求帮助。" + +#: ../../source/development/contributing_environment.rst:22 +msgid "" +"The code is hosted on `GitHub <https://github.com/xorbitsai/inference>`_." +" To contribute you will need to sign up for a `free GitHub account " +"<https://github.com/signup/free>`_. We use `Git <https://git-scm.com/>`_ " +"for version control to allow many people to work together on the project." +msgstr "" +"Xinference 的代码托管在 `GitHub <https://github.com/xorbitsai/inference>`_ 。要参与" +" Xinference 代码贡献，你需要注册一个 `免费的 GitHub 账户 " +"<https://github.com/signup/free>`_ 。我们使用 `Git <https://git-scm.com/>`_ " +"进行版本控制，以便大家共同参与项目的开发。" + +#: ../../source/development/contributing_environment.rst:27 +msgid "" +"`GitHub has instructions <https://help.github.com/set-up-git-redirect>`__" +" for installing git, setting up your SSH key, and configuring git. All " +"these steps need to be completed before you can work seamlessly between " +"your local repository and GitHub." +msgstr "" +"你可以参考 `GitHub 指南 <https://help.github.com/set-up-git-redirect>`_ 来安装 " +"git，设置 SSH 密钥以及配置 git。你需要完成这些步骤以确保你的本地仓库和 GitHub 可以正常工作，后续的工作才可以顺利进行。" + +#: ../../source/development/contributing_environment.rst:31 +msgid "Some great resources for learning Git:" +msgstr "以下是一些很好的学习 Git 的资源：" + +#: ../../source/development/contributing_environment.rst:33 +msgid "`Official Git Documentation <https://git-scm.com/doc>`_" +msgstr "`Git 官方文档 <https://git-scm.com/doc>`_" + +#: ../../source/development/contributing_environment.rst:34 +msgid "`Pro Git Book <https://git-scm.com/book/en/v2>`_" +msgstr "`Pro Git 书籍 <https://git-scm.com/book/en/v2>`_" + +#: ../../source/development/contributing_environment.rst:35 +msgid "`Git Tutorial by Atlassian <https://www.atlassian.com/git/tutorials>`_" +msgstr "`Atlassian 提供的 Git 教程 <https://www.atlassian.com/git/tutorials>`_" + +#: ../../source/development/contributing_environment.rst:36 +msgid "" +"`Git - Concise Guide <http://rogerdudler.github.io/git-" +"guide/index.zh.html>`_" +msgstr "`Git-简明指南 <http://rogerdudler.github.io/git-guide/index.zh.html>`_" + +#: ../../source/development/contributing_environment.rst:39 +msgid "" +"If the speed of ``git clone`` is slow, you can use the following command " +"to add a proxy:" +msgstr "如果在 ``git clone`` 代码的时候速度较慢，可以通过如下命令添加代理" + +#: ../../source/development/contributing_environment.rst:47 +msgid "Creating an isolated environment" +msgstr "创建一个隔离环境" + +#: ../../source/development/contributing_environment.rst:49 +msgid "" +"Before formally installing Xinference, it's recommended to create an " +"isolated environment, using Conda recommended, for ease of subsequent " +"operations." +msgstr "在正式安装Xinference之前，建议使用 Conda 创建一个隔离环境方便后续操作。" + +#: ../../source/development/contributing_environment.rst:57 +msgid "``xinf`` can be replaced with a custom Conda environment name." +msgstr "``xinf`` 可替换为自定义的 Conda 环境名。" + +#: ../../source/development/contributing_environment.rst:59 +msgid "" +"Afterward, you'll need to install Python and Node.js (npm) in the newly " +"created Conda environment. Here are the commands:" +msgstr "随后需要在新建的 Conda 环境中安装 Python 以及 Node.js (npm)。命令如下：" + +#: ../../source/development/contributing_environment.rst:68 +msgid "Install from source code" +msgstr "从源码安装" + +#: ../../source/development/contributing_environment.rst:70 +msgid "" +"Before we begin, please make sure that you have cloned the repository. " +"Suppose you clone the repository as ``inference`` directory, ``cd`` to " +"this directory where the ``setup.cfg`` and ``setup.py`` files are " +"located, and run the following command:" +msgstr "" +"在开始之前，请确保您已经克隆了存储库。假设您将存储库克隆到名为 ``inference`` 的目录中，请进入该目录，其中包含 " +"``setup.cfg`` 和 ``setup.py`` 文件，并执行以下命令：" + +#: ../../source/development/contributing_environment.rst:79 +msgid "" +"If the commands run successfully, you can use Xinference normally. For " +"detailed usage instructions, refer to `using_xinference " +"<https://inference.readthedocs.io/en/latest/getting_started/using_xinference.html>`__." +msgstr "" +"如果命令能够成功运行，接下来就能正常使用 Xinference 了，使用教程详情见 `使用 " +"<https://inference.readthedocs.io/zh-cn/latest/getting_started/using_xinference.html>`__。" + +#: ../../source/development/contributing_environment.rst:83 +msgid "" +"If errors occur or the process freezes during execution, the next step is" +" to compile the frontend." +msgstr "如果出现报错或者在运行过程中卡死，那就需要进行下一步前端编译。" + +#: ../../source/development/contributing_environment.rst:87 +msgid "Frontend Compilation" +msgstr "前端编译" + +#: ../../source/development/contributing_environment.rst:89 +msgid "" +"Navigate to the ``inference/xinference/web/ui`` directory. Then, execute " +"the following command to clear the cache:" +msgstr "首先需要进入 ``inference/xinference/web/ui`` 目录下，随后执行如下命令清除缓存：" + +#: ../../source/development/contributing_environment.rst:96 +msgid "" +"If the command fails to execute, you can try adding the ``--force`` " +"option." +msgstr "如果命令执行失败，您可以尝试添加 ``--force`` 选项" + +#: ../../source/development/contributing_environment.rst:99 +msgid "" +"If the ``node_modules`` folder already exists in this directory, it's " +"recommended to manually delete it before cleaning the cache." +msgstr "如果该目录下已经存在 ``node_modules`` 文件夹的话建议先手动删除该文件夹" + +#: ../../source/development/contributing_environment.rst:102 +msgid "" +"Next, execute the following command in this directory to compile the " +"frontend:" +msgstr "接着在该目录下执行以下命令进行前端编译：" + +#: ../../source/development/contributing_environment.rst:110 +msgid "" +"Still, if the first command fails to execute, you can try adding the " +"``--force`` option." +msgstr "如果第一个命令执行失败，您仍然可以尝试通过添加 ``--force`` 选项解决" + +#: ../../source/development/contributing_environment.rst:112 +msgid "" +"After compiling the frontend, you can ``cd`` back to the directory where " +"the ``setup.cfg`` and ``setup.py`` files are located, and install " +"Xinference via ``pip install -e .``." +msgstr "编译完前端后，您可以返回到包含 ``setup.cfg`` 和 ``setup.py`` 文件的目录，然后通过 ``pip install -e .`` 安装 Xinference。" + diff --git a/doc/source/locale/zh_CN/LC_MESSAGES/development/index.po b/doc/source/locale/zh_CN/LC_MESSAGES/development/index.po new file mode 100644 index 0000000000..54e58a5b8e --- /dev/null +++ b/doc/source/locale/zh_CN/LC_MESSAGES/development/index.po @@ -0,0 +1,25 @@ +# SOME DESCRIPTIVE TITLE. +# Copyright (C) 2023, Xorbits Inc. +# This file is distributed under the same license as the Xinference package. +# FIRST AUTHOR <EMAIL@ADDRESS>, 2024. +# +#, fuzzy +msgid "" +msgstr "" +"Project-Id-Version: Xinference \n" +"Report-Msgid-Bugs-To: \n" +"POT-Creation-Date: 2024-03-06 12:05+0800\n" +"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" +"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n" +"Language: zh_CN\n" +"Language-Team: zh_CN <[email protected]>\n" +"Plural-Forms: nplurals=1; plural=0;\n" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=utf-8\n" +"Content-Transfer-Encoding: 8bit\n" +"Generated-By: Babel 2.14.0\n" + +#: ../../source/development/index.rst:5 +msgid "Development" +msgstr "开发指南" +
open-telemetry__opentelemetry-python-contrib-1541
Add readthedocs documentation tortoiseorm instrumentation Part of [1491](https://github.com/open-telemetry/opentelemetry-python-contrib/issues/1491)
[ { "content": "# Copyright The OpenTelemetry Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nInstrument `tortoise-orm`_ to report SQL queries.\n\nUsage\n-----\n\n.. code:: python\n\n from fastapi import FastAPI\n from tortoise.contrib.fastapi import register_tortoise\n from opentelemetry.sdk.resources import SERVICE_NAME, Resource\n from opentelemetry.sdk.trace import TracerProvider\n from opentelemetry.instrumentation.tortoiseorm import TortoiseORMInstrumentor\n\n app = FastAPI()\n tracer = TracerProvider(resource=Resource({SERVICE_NAME: \"FastAPI\"}))\n TortoiseORMInstrumentor().instrument(tracer_provider=tracer)\n\n register_tortoise(\n app,\n db_url=\"sqlite://sample.db\",\n modules={\"models\": [\"example_app.db_models\"]}\n )\n\nAPI\n---\n\"\"\"\nfrom typing import Collection\n\nimport wrapt\n\nfrom opentelemetry import trace\nfrom opentelemetry.instrumentation.instrumentor import BaseInstrumentor\nfrom opentelemetry.instrumentation.tortoiseorm.package import _instruments\nfrom opentelemetry.instrumentation.tortoiseorm.version import __version__\nfrom opentelemetry.instrumentation.utils import unwrap\nfrom opentelemetry.semconv.trace import DbSystemValues, SpanAttributes\nfrom opentelemetry.trace import SpanKind\nfrom opentelemetry.trace.status import Status, StatusCode\n\ntry:\n import tortoise.backends.asyncpg.client\n\n TORTOISE_POSTGRES_SUPPORT = True\nexcept ModuleNotFoundError:\n TORTOISE_POSTGRES_SUPPORT = False\n\ntry:\n import tortoise.backends.mysql.client\n\n TORTOISE_MYSQL_SUPPORT = True\nexcept ModuleNotFoundError:\n TORTOISE_MYSQL_SUPPORT = False\n\ntry:\n import tortoise.backends.sqlite.client\n\n TORTOISE_SQLITE_SUPPORT = True\nexcept ModuleNotFoundError:\n TORTOISE_SQLITE_SUPPORT = False\n\nimport tortoise.contrib.pydantic.base\n\n\nclass TortoiseORMInstrumentor(BaseInstrumentor):\n \"\"\"An instrumentor for Tortoise-ORM\n See `BaseInstrumentor`\n \"\"\"\n\n def instrumentation_dependencies(self) -> Collection[str]:\n return _instruments\n\n def _instrument(self, **kwargs):\n \"\"\"Instruments Tortoise ORM backend methods.\n Args:\n **kwargs: Optional arguments\n ``tracer_provider``: a TracerProvider, defaults to global\n ``capture_parameters``: set to True to capture SQL query parameters\n Returns:\n None\n \"\"\"\n tracer_provider = kwargs.get(\"tracer_provider\")\n # pylint: disable=attribute-defined-outside-init\n self._tracer = trace.get_tracer(__name__, __version__, tracer_provider)\n self.capture_parameters = kwargs.get(\"capture_parameters\", False)\n if TORTOISE_SQLITE_SUPPORT:\n funcs = [\n \"SqliteClient.execute_many\",\n \"SqliteClient.execute_query\",\n \"SqliteClient.execute_insert\",\n \"SqliteClient.execute_query_dict\",\n \"SqliteClient.execute_script\",\n ]\n for func in funcs:\n wrapt.wrap_function_wrapper(\n \"tortoise.backends.sqlite.client\",\n func,\n self._do_execute,\n )\n\n if TORTOISE_POSTGRES_SUPPORT:\n funcs = [\n \"AsyncpgDBClient.execute_many\",\n \"AsyncpgDBClient.execute_query\",\n \"AsyncpgDBClient.execute_insert\",\n \"AsyncpgDBClient.execute_query_dict\",\n \"AsyncpgDBClient.execute_script\",\n ]\n for func in funcs:\n wrapt.wrap_function_wrapper(\n \"tortoise.backends.asyncpg.client\",\n func,\n self._do_execute,\n )\n\n if TORTOISE_MYSQL_SUPPORT:\n funcs = [\n \"MySQLClient.execute_many\",\n \"MySQLClient.execute_query\",\n \"MySQLClient.execute_insert\",\n \"MySQLClient.execute_query_dict\",\n \"MySQLClient.execute_script\",\n ]\n for func in funcs:\n wrapt.wrap_function_wrapper(\n \"tortoise.backends.mysql.client\",\n func,\n self._do_execute,\n )\n wrapt.wrap_function_wrapper(\n \"tortoise.contrib.pydantic.base\",\n \"PydanticModel.from_queryset\",\n self._from_queryset,\n )\n wrapt.wrap_function_wrapper(\n \"tortoise.contrib.pydantic.base\",\n \"PydanticModel.from_queryset_single\",\n self._from_queryset,\n )\n wrapt.wrap_function_wrapper(\n \"tortoise.contrib.pydantic.base\",\n \"PydanticListModel.from_queryset\",\n self._from_queryset,\n )\n\n def _uninstrument(self, **kwargs):\n if TORTOISE_SQLITE_SUPPORT:\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient, \"execute_query\"\n )\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient, \"execute_many\"\n )\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient, \"execute_insert\"\n )\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient,\n \"execute_query_dict\",\n )\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient, \"execute_script\"\n )\n if TORTOISE_MYSQL_SUPPORT:\n unwrap(tortoise.backends.mysql.client.MySQLClient, \"execute_query\")\n unwrap(tortoise.backends.mysql.client.MySQLClient, \"execute_many\")\n unwrap(\n tortoise.backends.mysql.client.MySQLClient, \"execute_insert\"\n )\n unwrap(\n tortoise.backends.mysql.client.MySQLClient,\n \"execute_query_dict\",\n )\n unwrap(\n tortoise.backends.mysql.client.MySQLClient, \"execute_script\"\n )\n if TORTOISE_POSTGRES_SUPPORT:\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_query\",\n )\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_many\",\n )\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_insert\",\n )\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_query_dict\",\n )\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_script\",\n )\n unwrap(tortoise.contrib.pydantic.base.PydanticModel, \"from_queryset\")\n unwrap(\n tortoise.contrib.pydantic.base.PydanticModel,\n \"from_queryset_single\",\n )\n unwrap(\n tortoise.contrib.pydantic.base.PydanticListModel, \"from_queryset\"\n )\n\n def _hydrate_span_from_args(self, connection, query, parameters) -> dict:\n \"\"\"Get network and database attributes from connection.\"\"\"\n span_attributes = {}\n capabilities = getattr(connection, \"capabilities\", None)\n if capabilities is not None:\n if capabilities.dialect == \"sqlite\":\n span_attributes[\n SpanAttributes.DB_SYSTEM\n ] = DbSystemValues.SQLITE.value\n elif capabilities.dialect == \"postgres\":\n span_attributes[\n SpanAttributes.DB_SYSTEM\n ] = DbSystemValues.POSTGRESQL.value\n elif capabilities.dialect == \"mysql\":\n span_attributes[\n SpanAttributes.DB_SYSTEM\n ] = DbSystemValues.MYSQL.value\n dbname = getattr(connection, \"filename\", None)\n if dbname:\n span_attributes[SpanAttributes.DB_NAME] = dbname\n dbname = getattr(connection, \"database\", None)\n if dbname:\n span_attributes[SpanAttributes.DB_NAME] = dbname\n if query is not None:\n span_attributes[SpanAttributes.DB_STATEMENT] = query\n user = getattr(connection, \"user\", None)\n if user:\n span_attributes[SpanAttributes.DB_USER] = user\n host = getattr(connection, \"host\", None)\n if host:\n span_attributes[SpanAttributes.NET_PEER_NAME] = host\n port = getattr(connection, \"port\", None)\n if port:\n span_attributes[SpanAttributes.NET_PEER_PORT] = port\n\n if self.capture_parameters:\n if parameters is not None and len(parameters) > 0:\n span_attributes[\"db.statement.parameters\"] = str(parameters)\n\n return span_attributes\n\n async def _do_execute(self, func, instance, args, kwargs):\n\n exception = None\n name = args[0].split()[0]\n\n with self._tracer.start_as_current_span(\n name, kind=SpanKind.CLIENT\n ) as span:\n if span.is_recording():\n span_attributes = self._hydrate_span_from_args(\n instance,\n args[0],\n args[1:],\n )\n for attribute, value in span_attributes.items():\n span.set_attribute(attribute, value)\n\n try:\n result = await func(*args, **kwargs)\n except Exception as exc: # pylint: disable=W0703\n exception = exc\n raise\n finally:\n if span.is_recording() and exception is not None:\n span.set_status(Status(StatusCode.ERROR))\n\n return result\n\n async def _from_queryset(self, func, modelcls, args, kwargs):\n\n exception = None\n name = f\"pydantic.{func.__name__}\"\n\n with self._tracer.start_as_current_span(\n name, kind=SpanKind.INTERNAL\n ) as span:\n if span.is_recording():\n span_attributes = {}\n\n model_config = getattr(modelcls, \"Config\", None)\n if model_config:\n model_title = getattr(modelcls.Config, \"title\")\n if model_title:\n span_attributes[\"pydantic.model\"] = model_title\n\n for attribute, value in span_attributes.items():\n span.set_attribute(attribute, value)\n\n try:\n result = await func(*args, **kwargs)\n except Exception as exc: # pylint: disable=W0703\n exception = exc\n raise\n finally:\n if span.is_recording() and exception is not None:\n span.set_status(Status(StatusCode.ERROR))\n\n return result\n", "path": "instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py" } ]
[ { "content": "# Copyright The OpenTelemetry Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nInstrument tortoise-orm to report SQL queries.\n\nUsage\n-----\n\n.. code:: python\n\n from fastapi import FastAPI\n from tortoise.contrib.fastapi import register_tortoise\n from opentelemetry.sdk.resources import SERVICE_NAME, Resource\n from opentelemetry.sdk.trace import TracerProvider\n from opentelemetry.instrumentation.tortoiseorm import TortoiseORMInstrumentor\n\n app = FastAPI()\n tracer = TracerProvider(resource=Resource({SERVICE_NAME: \"FastAPI\"}))\n TortoiseORMInstrumentor().instrument(tracer_provider=tracer)\n\n register_tortoise(\n app,\n db_url=\"sqlite://sample.db\",\n modules={\"models\": [\"example_app.db_models\"]}\n )\n\nAPI\n---\n\"\"\"\nfrom typing import Collection\n\nimport wrapt\n\nfrom opentelemetry import trace\nfrom opentelemetry.instrumentation.instrumentor import BaseInstrumentor\nfrom opentelemetry.instrumentation.tortoiseorm.package import _instruments\nfrom opentelemetry.instrumentation.tortoiseorm.version import __version__\nfrom opentelemetry.instrumentation.utils import unwrap\nfrom opentelemetry.semconv.trace import DbSystemValues, SpanAttributes\nfrom opentelemetry.trace import SpanKind\nfrom opentelemetry.trace.status import Status, StatusCode\n\ntry:\n import tortoise.backends.asyncpg.client\n\n TORTOISE_POSTGRES_SUPPORT = True\nexcept ModuleNotFoundError:\n TORTOISE_POSTGRES_SUPPORT = False\n\ntry:\n import tortoise.backends.mysql.client\n\n TORTOISE_MYSQL_SUPPORT = True\nexcept ModuleNotFoundError:\n TORTOISE_MYSQL_SUPPORT = False\n\ntry:\n import tortoise.backends.sqlite.client\n\n TORTOISE_SQLITE_SUPPORT = True\nexcept ModuleNotFoundError:\n TORTOISE_SQLITE_SUPPORT = False\n\nimport tortoise.contrib.pydantic.base\n\n\nclass TortoiseORMInstrumentor(BaseInstrumentor):\n \"\"\"An instrumentor for Tortoise-ORM\n See `BaseInstrumentor`\n \"\"\"\n\n def instrumentation_dependencies(self) -> Collection[str]:\n return _instruments\n\n def _instrument(self, **kwargs):\n \"\"\"Instruments Tortoise ORM backend methods.\n Args:\n **kwargs: Optional arguments\n ``tracer_provider``: a TracerProvider, defaults to global\n ``capture_parameters``: set to True to capture SQL query parameters\n Returns:\n None\n \"\"\"\n tracer_provider = kwargs.get(\"tracer_provider\")\n # pylint: disable=attribute-defined-outside-init\n self._tracer = trace.get_tracer(__name__, __version__, tracer_provider)\n self.capture_parameters = kwargs.get(\"capture_parameters\", False)\n if TORTOISE_SQLITE_SUPPORT:\n funcs = [\n \"SqliteClient.execute_many\",\n \"SqliteClient.execute_query\",\n \"SqliteClient.execute_insert\",\n \"SqliteClient.execute_query_dict\",\n \"SqliteClient.execute_script\",\n ]\n for func in funcs:\n wrapt.wrap_function_wrapper(\n \"tortoise.backends.sqlite.client\",\n func,\n self._do_execute,\n )\n\n if TORTOISE_POSTGRES_SUPPORT:\n funcs = [\n \"AsyncpgDBClient.execute_many\",\n \"AsyncpgDBClient.execute_query\",\n \"AsyncpgDBClient.execute_insert\",\n \"AsyncpgDBClient.execute_query_dict\",\n \"AsyncpgDBClient.execute_script\",\n ]\n for func in funcs:\n wrapt.wrap_function_wrapper(\n \"tortoise.backends.asyncpg.client\",\n func,\n self._do_execute,\n )\n\n if TORTOISE_MYSQL_SUPPORT:\n funcs = [\n \"MySQLClient.execute_many\",\n \"MySQLClient.execute_query\",\n \"MySQLClient.execute_insert\",\n \"MySQLClient.execute_query_dict\",\n \"MySQLClient.execute_script\",\n ]\n for func in funcs:\n wrapt.wrap_function_wrapper(\n \"tortoise.backends.mysql.client\",\n func,\n self._do_execute,\n )\n wrapt.wrap_function_wrapper(\n \"tortoise.contrib.pydantic.base\",\n \"PydanticModel.from_queryset\",\n self._from_queryset,\n )\n wrapt.wrap_function_wrapper(\n \"tortoise.contrib.pydantic.base\",\n \"PydanticModel.from_queryset_single\",\n self._from_queryset,\n )\n wrapt.wrap_function_wrapper(\n \"tortoise.contrib.pydantic.base\",\n \"PydanticListModel.from_queryset\",\n self._from_queryset,\n )\n\n def _uninstrument(self, **kwargs):\n if TORTOISE_SQLITE_SUPPORT:\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient, \"execute_query\"\n )\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient, \"execute_many\"\n )\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient, \"execute_insert\"\n )\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient,\n \"execute_query_dict\",\n )\n unwrap(\n tortoise.backends.sqlite.client.SqliteClient, \"execute_script\"\n )\n if TORTOISE_MYSQL_SUPPORT:\n unwrap(tortoise.backends.mysql.client.MySQLClient, \"execute_query\")\n unwrap(tortoise.backends.mysql.client.MySQLClient, \"execute_many\")\n unwrap(\n tortoise.backends.mysql.client.MySQLClient, \"execute_insert\"\n )\n unwrap(\n tortoise.backends.mysql.client.MySQLClient,\n \"execute_query_dict\",\n )\n unwrap(\n tortoise.backends.mysql.client.MySQLClient, \"execute_script\"\n )\n if TORTOISE_POSTGRES_SUPPORT:\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_query\",\n )\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_many\",\n )\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_insert\",\n )\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_query_dict\",\n )\n unwrap(\n tortoise.backends.asyncpg.client.AsyncpgDBClient,\n \"execute_script\",\n )\n unwrap(tortoise.contrib.pydantic.base.PydanticModel, \"from_queryset\")\n unwrap(\n tortoise.contrib.pydantic.base.PydanticModel,\n \"from_queryset_single\",\n )\n unwrap(\n tortoise.contrib.pydantic.base.PydanticListModel, \"from_queryset\"\n )\n\n def _hydrate_span_from_args(self, connection, query, parameters) -> dict:\n \"\"\"Get network and database attributes from connection.\"\"\"\n span_attributes = {}\n capabilities = getattr(connection, \"capabilities\", None)\n if capabilities is not None:\n if capabilities.dialect == \"sqlite\":\n span_attributes[\n SpanAttributes.DB_SYSTEM\n ] = DbSystemValues.SQLITE.value\n elif capabilities.dialect == \"postgres\":\n span_attributes[\n SpanAttributes.DB_SYSTEM\n ] = DbSystemValues.POSTGRESQL.value\n elif capabilities.dialect == \"mysql\":\n span_attributes[\n SpanAttributes.DB_SYSTEM\n ] = DbSystemValues.MYSQL.value\n dbname = getattr(connection, \"filename\", None)\n if dbname:\n span_attributes[SpanAttributes.DB_NAME] = dbname\n dbname = getattr(connection, \"database\", None)\n if dbname:\n span_attributes[SpanAttributes.DB_NAME] = dbname\n if query is not None:\n span_attributes[SpanAttributes.DB_STATEMENT] = query\n user = getattr(connection, \"user\", None)\n if user:\n span_attributes[SpanAttributes.DB_USER] = user\n host = getattr(connection, \"host\", None)\n if host:\n span_attributes[SpanAttributes.NET_PEER_NAME] = host\n port = getattr(connection, \"port\", None)\n if port:\n span_attributes[SpanAttributes.NET_PEER_PORT] = port\n\n if self.capture_parameters:\n if parameters is not None and len(parameters) > 0:\n span_attributes[\"db.statement.parameters\"] = str(parameters)\n\n return span_attributes\n\n async def _do_execute(self, func, instance, args, kwargs):\n\n exception = None\n name = args[0].split()[0]\n\n with self._tracer.start_as_current_span(\n name, kind=SpanKind.CLIENT\n ) as span:\n if span.is_recording():\n span_attributes = self._hydrate_span_from_args(\n instance,\n args[0],\n args[1:],\n )\n for attribute, value in span_attributes.items():\n span.set_attribute(attribute, value)\n\n try:\n result = await func(*args, **kwargs)\n except Exception as exc: # pylint: disable=W0703\n exception = exc\n raise\n finally:\n if span.is_recording() and exception is not None:\n span.set_status(Status(StatusCode.ERROR))\n\n return result\n\n async def _from_queryset(self, func, modelcls, args, kwargs):\n\n exception = None\n name = f\"pydantic.{func.__name__}\"\n\n with self._tracer.start_as_current_span(\n name, kind=SpanKind.INTERNAL\n ) as span:\n if span.is_recording():\n span_attributes = {}\n\n model_config = getattr(modelcls, \"Config\", None)\n if model_config:\n model_title = getattr(modelcls.Config, \"title\")\n if model_title:\n span_attributes[\"pydantic.model\"] = model_title\n\n for attribute, value in span_attributes.items():\n span.set_attribute(attribute, value)\n\n try:\n result = await func(*args, **kwargs)\n except Exception as exc: # pylint: disable=W0703\n exception = exc\n raise\n finally:\n if span.is_recording() and exception is not None:\n span.set_status(Status(StatusCode.ERROR))\n\n return result\n", "path": "instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py" } ]
diff --git a/docs-requirements.txt b/docs-requirements.txt index a1b55877a1..ed47dddac0 100644 --- a/docs-requirements.txt +++ b/docs-requirements.txt @@ -37,6 +37,7 @@ redis>=2.6 remoulade>=0.50 sqlalchemy>=1.0 tornado>=5.1.1 +tortoise-orm>=0.17.0 ddtrace>=0.34.0 httpx>=0.18.0 diff --git a/docs/instrumentation/tortoiseorm/tortoiseorm.rst b/docs/instrumentation/tortoiseorm/tortoiseorm.rst new file mode 100644 index 0000000000..af84ebf65a --- /dev/null +++ b/docs/instrumentation/tortoiseorm/tortoiseorm.rst @@ -0,0 +1,6 @@ +.. include:: ../../../instrumentation/opentelemetry-instrumentation-tortoiseorm/README.rst + +.. automodule:: opentelemetry.instrumentation.tortoiseorm + :members: + :undoc-members: + :show-inheritance: diff --git a/instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py b/instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py index a8061a99cc..3b0e58c928 100644 --- a/instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py +++ b/instrumentation/opentelemetry-instrumentation-tortoiseorm/src/opentelemetry/instrumentation/tortoiseorm/__init__.py @@ -13,7 +13,7 @@ # limitations under the License. """ -Instrument `tortoise-orm`_ to report SQL queries. +Instrument tortoise-orm to report SQL queries. Usage -----
pypa__setuptools-3715
[FR] The way to not overwrite but inherit DEFAULT_EXCLUDE when define find package exclude ### What's the problem this feature will solve? Setuptools users become able to omit redundant definition when they want to add exclude directory or file in `flat-layout`. And they also become able to manage many packages because they can reduce toils of maintain `pyproject.toml` in each project. <details> <summary>pyproject.toml when exclude as same as DEFAULT_EXCLUDE by tool.setuptools.packages.find.exclude</summary> ```toml [tool.setuptools.packages.find] where = ["."] exclude = [ # Additional ecludsion from Setuptools default. "htmlcov", # Setuptools default. # see: # - setuptools/discovery.py at 92ebeed732b08ac29576634ad4814b9efd07bb37 · pypa/setuptools # https://github.com/pypa/setuptools/blob/92ebeed732b08ac29576634ad4814b9efd07bb37/setuptools/discovery.py # FlatLayoutPackageFinder "ci", "ci.*", "bin", "bin.*", "doc", "doc.*", "docs", "docs.*", "documentation", "documentation.*", "manpages", "manpages.*", "news", "news.*", "changelog", "changelog.*", "test", "test.*", "tests", "tests.*", "unit_test", "unit_test.*", "unit_tests", "unit_tests.*", "example", "example.*", "examples", "examples.*", "scripts", "scripts.*", "tools", "tools.*", "util", "util.*", "utils", "utils.*", "python", "python.*", "build", "build.*", "dist", "dist.*", "venv", "venv.*", "env", "env.*", "requirements", "requirements.*", # ---- Task runners / Build tools ---- "tasks", # invoke "tasks.*", # invoke "fabfile", # fabric "fabfile.*", # fabric "site_scons", # SCons "site_scons.*", # SCons # ---- Other tools ---- "benchmark", "benchmark.*", "benchmarks", "benchmarks.*", "exercise", "exercise.*", "exercises", "exercises.*", # ---- Hidden directories/Private packages ---- "[._]*", # FlatLayoutModuleFinder "setup", "setup.*", "conftest", "conftest.*", "test", "test.*", "tests", "tests.*", "example", "example.*", "examples", "examples.*", "build", "build.*", # ---- Task runners ---- "toxfile", "toxfile.*", "noxfile", "noxfile.*", "pavement", "pavement.*", "dodo", "dodo.*", "tasks", "tasks.*", "fabfile", "fabfile.*", # ---- Other tools ---- "[Ss][Cc]onstruct", # SCons "[Ss][Cc]onstruct.*", # SCons "conanfile", # Connan: C/C++ build tool "conanfile.*", # Connan: C/C++ build tool "manage", # Django "manage.*", # Django "benchmark", "benchmark.*", "benchmarks", "benchmarks.*", "exercise", "exercise.*", "exercises", "exercises.*", # ---- Hidden files/Private modules ---- "[._]*", ] ``` </details> cf. [Package Discovery and Namespace Packages - setuptools 65.3.0.post20220826 documentation](https://setuptools.pypa.io/en/latest/userguide/package_discovery.html) ### Describe the solution you'd like For example, adds parameter `additional_exclude` which inherits `DEFAULT_EXCLUDE` . ### Alternative Solutions #### Alternative Solution A (If it can) User rename target directory with starting `.` . Because Setuptools ignore by `DEFAULT_EXCLUDE` . However, directory starting with `.` behaves as hidden by system. It's intension isn't always same as excluding when build. #### Alternative Solution B Setuptools adds directory to exclude when user report in GitHub Issues. However, Setuptools shouldn't cover every tools because there are infinite minor tools. ### Additional context In my case, I would to exclude `htmlcov` directory additionally which is dump of coverage. see: [Command line usage — Coverage.py 6.3.2 documentation](https://coverage.readthedocs.io/en/6.3.2/cmd.html#html-annotation-coverage-html) ### Code of Conduct - [X] I agree to follow the PSF Code of Conduct
[ { "content": "\"\"\"Automatic discovery of Python modules and packages (for inclusion in the\ndistribution) and other config values.\n\nFor the purposes of this module, the following nomenclature is used:\n\n- \"src-layout\": a directory representing a Python project that contains a \"src\"\n folder. Everything under the \"src\" folder is meant to be included in the\n distribution when packaging the project. Example::\n\n .\n ├── tox.ini\n ├── pyproject.toml\n └── src/\n └── mypkg/\n ├── __init__.py\n ├── mymodule.py\n └── my_data_file.txt\n\n- \"flat-layout\": a Python project that does not use \"src-layout\" but instead\n have a directory under the project root for each package::\n\n .\n ├── tox.ini\n ├── pyproject.toml\n └── mypkg/\n ├── __init__.py\n ├── mymodule.py\n └── my_data_file.txt\n\n- \"single-module\": a project that contains a single Python script direct under\n the project root (no directory used)::\n\n .\n ├── tox.ini\n ├── pyproject.toml\n └── mymodule.py\n\n\"\"\"\n\nimport itertools\nimport os\nfrom fnmatch import fnmatchcase\nfrom glob import glob\nfrom pathlib import Path\nfrom typing import (\n TYPE_CHECKING,\n Callable,\n Dict,\n Iterable,\n Iterator,\n List,\n Mapping,\n Optional,\n Tuple,\n Union\n)\n\nimport _distutils_hack.override # noqa: F401\n\nfrom distutils import log\nfrom distutils.util import convert_path\n\n_Path = Union[str, os.PathLike]\n_Filter = Callable[[str], bool]\nStrIter = Iterator[str]\n\nchain_iter = itertools.chain.from_iterable\n\nif TYPE_CHECKING:\n from setuptools import Distribution # noqa\n\n\ndef _valid_name(path: _Path) -> bool:\n # Ignore invalid names that cannot be imported directly\n return os.path.basename(path).isidentifier()\n\n\nclass _Finder:\n \"\"\"Base class that exposes functionality for module/package finders\"\"\"\n\n ALWAYS_EXCLUDE: Tuple[str, ...] = ()\n DEFAULT_EXCLUDE: Tuple[str, ...] = ()\n\n @classmethod\n def find(\n cls,\n where: _Path = '.',\n exclude: Iterable[str] = (),\n include: Iterable[str] = ('*',)\n ) -> List[str]:\n \"\"\"Return a list of all Python items (packages or modules, depending on\n the finder implementation) found within directory 'where'.\n\n 'where' is the root directory which will be searched.\n It should be supplied as a \"cross-platform\" (i.e. URL-style) path;\n it will be converted to the appropriate local path syntax.\n\n 'exclude' is a sequence of names to exclude; '*' can be used\n as a wildcard in the names.\n When finding packages, 'foo.*' will exclude all subpackages of 'foo'\n (but not 'foo' itself).\n\n 'include' is a sequence of names to include.\n If it's specified, only the named items will be included.\n If it's not specified, all found items will be included.\n 'include' can contain shell style wildcard patterns just like\n 'exclude'.\n \"\"\"\n\n exclude = exclude or cls.DEFAULT_EXCLUDE\n return list(\n cls._find_iter(\n convert_path(str(where)),\n cls._build_filter(*cls.ALWAYS_EXCLUDE, *exclude),\n cls._build_filter(*include),\n )\n )\n\n @classmethod\n def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:\n raise NotImplementedError\n\n @staticmethod\n def _build_filter(*patterns: str) -> _Filter:\n \"\"\"\n Given a list of patterns, return a callable that will be true only if\n the input matches at least one of the patterns.\n \"\"\"\n return lambda name: any(fnmatchcase(name, pat) for pat in patterns)\n\n\nclass PackageFinder(_Finder):\n \"\"\"\n Generate a list of all Python packages found within a directory\n \"\"\"\n\n ALWAYS_EXCLUDE = (\"ez_setup\", \"*__pycache__\")\n\n @classmethod\n def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:\n \"\"\"\n All the packages found in 'where' that pass the 'include' filter, but\n not the 'exclude' filter.\n \"\"\"\n for root, dirs, files in os.walk(str(where), followlinks=True):\n # Copy dirs to iterate over it, then empty dirs.\n all_dirs = dirs[:]\n dirs[:] = []\n\n for dir in all_dirs:\n full_path = os.path.join(root, dir)\n rel_path = os.path.relpath(full_path, where)\n package = rel_path.replace(os.path.sep, '.')\n\n # Skip directory trees that are not valid packages\n if '.' in dir or not cls._looks_like_package(full_path, package):\n continue\n\n # Should this package be included?\n if include(package) and not exclude(package):\n yield package\n\n # Keep searching subdirectories, as there may be more packages\n # down there, even if the parent was excluded.\n dirs.append(dir)\n\n @staticmethod\n def _looks_like_package(path: _Path, _package_name: str) -> bool:\n \"\"\"Does a directory look like a package?\"\"\"\n return os.path.isfile(os.path.join(path, '__init__.py'))\n\n\nclass PEP420PackageFinder(PackageFinder):\n @staticmethod\n def _looks_like_package(_path: _Path, _package_name: str) -> bool:\n return True\n\n\nclass ModuleFinder(_Finder):\n \"\"\"Find isolated Python modules.\n This function will **not** recurse subdirectories.\n \"\"\"\n\n @classmethod\n def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:\n for file in glob(os.path.join(where, \"*.py\")):\n module, _ext = os.path.splitext(os.path.basename(file))\n\n if not cls._looks_like_module(module):\n continue\n\n if include(module) and not exclude(module):\n yield module\n\n _looks_like_module = staticmethod(_valid_name)\n\n\n# We have to be extra careful in the case of flat layout to not include files\n# and directories not meant for distribution (e.g. tool-related)\n\n\nclass FlatLayoutPackageFinder(PEP420PackageFinder):\n _EXCLUDE = (\n \"ci\",\n \"bin\",\n \"doc\",\n \"docs\",\n \"documentation\",\n \"manpages\",\n \"news\",\n \"changelog\",\n \"test\",\n \"tests\",\n \"unit_test\",\n \"unit_tests\",\n \"example\",\n \"examples\",\n \"scripts\",\n \"tools\",\n \"util\",\n \"utils\",\n \"python\",\n \"build\",\n \"dist\",\n \"venv\",\n \"env\",\n \"requirements\",\n # ---- Task runners / Build tools ----\n \"tasks\", # invoke\n \"fabfile\", # fabric\n \"site_scons\", # SCons\n # ---- Other tools ----\n \"benchmark\",\n \"benchmarks\",\n \"exercise\",\n \"exercises\",\n # ---- Hidden directories/Private packages ----\n \"[._]*\",\n )\n\n DEFAULT_EXCLUDE = tuple(chain_iter((p, f\"{p}.*\") for p in _EXCLUDE))\n \"\"\"Reserved package names\"\"\"\n\n @staticmethod\n def _looks_like_package(_path: _Path, package_name: str) -> bool:\n names = package_name.split('.')\n # Consider PEP 561\n root_pkg_is_valid = names[0].isidentifier() or names[0].endswith(\"-stubs\")\n return root_pkg_is_valid and all(name.isidentifier() for name in names[1:])\n\n\nclass FlatLayoutModuleFinder(ModuleFinder):\n DEFAULT_EXCLUDE = (\n \"setup\",\n \"conftest\",\n \"test\",\n \"tests\",\n \"example\",\n \"examples\",\n \"build\",\n # ---- Task runners ----\n \"toxfile\",\n \"noxfile\",\n \"pavement\",\n \"dodo\",\n \"tasks\",\n \"fabfile\",\n # ---- Other tools ----\n \"[Ss][Cc]onstruct\", # SCons\n \"conanfile\", # Connan: C/C++ build tool\n \"manage\", # Django\n \"benchmark\",\n \"benchmarks\",\n \"exercise\",\n \"exercises\",\n # ---- Hidden files/Private modules ----\n \"[._]*\",\n )\n \"\"\"Reserved top-level module names\"\"\"\n\n\ndef _find_packages_within(root_pkg: str, pkg_dir: _Path) -> List[str]:\n nested = PEP420PackageFinder.find(pkg_dir)\n return [root_pkg] + [\".\".join((root_pkg, n)) for n in nested]\n\n\nclass ConfigDiscovery:\n \"\"\"Fill-in metadata and options that can be automatically derived\n (from other metadata/options, the file system or conventions)\n \"\"\"\n\n def __init__(self, distribution: \"Distribution\"):\n self.dist = distribution\n self._called = False\n self._disabled = False\n self._skip_ext_modules = False\n\n def _disable(self):\n \"\"\"Internal API to disable automatic discovery\"\"\"\n self._disabled = True\n\n def _ignore_ext_modules(self):\n \"\"\"Internal API to disregard ext_modules.\n\n Normally auto-discovery would not be triggered if ``ext_modules`` are set\n (this is done for backward compatibility with existing packages relying on\n ``setup.py`` or ``setup.cfg``). However, ``setuptools`` can call this function\n to ignore given ``ext_modules`` and proceed with the auto-discovery if\n ``packages`` and ``py_modules`` are not given (e.g. when using pyproject.toml\n metadata).\n \"\"\"\n self._skip_ext_modules = True\n\n @property\n def _root_dir(self) -> _Path:\n # The best is to wait until `src_root` is set in dist, before using _root_dir.\n return self.dist.src_root or os.curdir\n\n @property\n def _package_dir(self) -> Dict[str, str]:\n if self.dist.package_dir is None:\n return {}\n return self.dist.package_dir\n\n def __call__(self, force=False, name=True, ignore_ext_modules=False):\n \"\"\"Automatically discover missing configuration fields\n and modifies the given ``distribution`` object in-place.\n\n Note that by default this will only have an effect the first time the\n ``ConfigDiscovery`` object is called.\n\n To repeatedly invoke automatic discovery (e.g. when the project\n directory changes), please use ``force=True`` (or create a new\n ``ConfigDiscovery`` instance).\n \"\"\"\n if force is False and (self._called or self._disabled):\n # Avoid overhead of multiple calls\n return\n\n self._analyse_package_layout(ignore_ext_modules)\n if name:\n self.analyse_name() # depends on ``packages`` and ``py_modules``\n\n self._called = True\n\n def _explicitly_specified(self, ignore_ext_modules: bool) -> bool:\n \"\"\"``True`` if the user has specified some form of package/module listing\"\"\"\n ignore_ext_modules = ignore_ext_modules or self._skip_ext_modules\n ext_modules = not (self.dist.ext_modules is None or ignore_ext_modules)\n return (\n self.dist.packages is not None\n or self.dist.py_modules is not None\n or ext_modules\n or hasattr(self.dist, \"configuration\") and self.dist.configuration\n # ^ Some projects use numpy.distutils.misc_util.Configuration\n )\n\n def _analyse_package_layout(self, ignore_ext_modules: bool) -> bool:\n if self._explicitly_specified(ignore_ext_modules):\n # For backward compatibility, just try to find modules/packages\n # when nothing is given\n return True\n\n log.debug(\n \"No `packages` or `py_modules` configuration, performing \"\n \"automatic discovery.\"\n )\n\n return (\n self._analyse_explicit_layout()\n or self._analyse_src_layout()\n # flat-layout is the trickiest for discovery so it should be last\n or self._analyse_flat_layout()\n )\n\n def _analyse_explicit_layout(self) -> bool:\n \"\"\"The user can explicitly give a package layout via ``package_dir``\"\"\"\n package_dir = self._package_dir.copy() # don't modify directly\n package_dir.pop(\"\", None) # This falls under the \"src-layout\" umbrella\n root_dir = self._root_dir\n\n if not package_dir:\n return False\n\n log.debug(f\"`explicit-layout` detected -- analysing {package_dir}\")\n pkgs = chain_iter(\n _find_packages_within(pkg, os.path.join(root_dir, parent_dir))\n for pkg, parent_dir in package_dir.items()\n )\n self.dist.packages = list(pkgs)\n log.debug(f\"discovered packages -- {self.dist.packages}\")\n return True\n\n def _analyse_src_layout(self) -> bool:\n \"\"\"Try to find all packages or modules under the ``src`` directory\n (or anything pointed by ``package_dir[\"\"]``).\n\n The \"src-layout\" is relatively safe for automatic discovery.\n We assume that everything within is meant to be included in the\n distribution.\n\n If ``package_dir[\"\"]`` is not given, but the ``src`` directory exists,\n this function will set ``package_dir[\"\"] = \"src\"``.\n \"\"\"\n package_dir = self._package_dir\n src_dir = os.path.join(self._root_dir, package_dir.get(\"\", \"src\"))\n if not os.path.isdir(src_dir):\n return False\n\n log.debug(f\"`src-layout` detected -- analysing {src_dir}\")\n package_dir.setdefault(\"\", os.path.basename(src_dir))\n self.dist.package_dir = package_dir # persist eventual modifications\n self.dist.packages = PEP420PackageFinder.find(src_dir)\n self.dist.py_modules = ModuleFinder.find(src_dir)\n log.debug(f\"discovered packages -- {self.dist.packages}\")\n log.debug(f\"discovered py_modules -- {self.dist.py_modules}\")\n return True\n\n def _analyse_flat_layout(self) -> bool:\n \"\"\"Try to find all packages and modules under the project root.\n\n Since the ``flat-layout`` is more dangerous in terms of accidentally including\n extra files/directories, this function is more conservative and will raise an\n error if multiple packages or modules are found.\n\n This assumes that multi-package dists are uncommon and refuse to support that\n use case in order to be able to prevent unintended errors.\n \"\"\"\n log.debug(f\"`flat-layout` detected -- analysing {self._root_dir}\")\n return self._analyse_flat_packages() or self._analyse_flat_modules()\n\n def _analyse_flat_packages(self) -> bool:\n self.dist.packages = FlatLayoutPackageFinder.find(self._root_dir)\n top_level = remove_nested_packages(remove_stubs(self.dist.packages))\n log.debug(f\"discovered packages -- {self.dist.packages}\")\n self._ensure_no_accidental_inclusion(top_level, \"packages\")\n return bool(top_level)\n\n def _analyse_flat_modules(self) -> bool:\n self.dist.py_modules = FlatLayoutModuleFinder.find(self._root_dir)\n log.debug(f\"discovered py_modules -- {self.dist.py_modules}\")\n self._ensure_no_accidental_inclusion(self.dist.py_modules, \"modules\")\n return bool(self.dist.py_modules)\n\n def _ensure_no_accidental_inclusion(self, detected: List[str], kind: str):\n if len(detected) > 1:\n from inspect import cleandoc\n\n from setuptools.errors import PackageDiscoveryError\n\n msg = f\"\"\"Multiple top-level {kind} discovered in a flat-layout: {detected}.\n\n To avoid accidental inclusion of unwanted files or directories,\n setuptools will not proceed with this build.\n\n If you are trying to create a single distribution with multiple {kind}\n on purpose, you should not rely on automatic discovery.\n Instead, consider the following options:\n\n 1. set up custom discovery (`find` directive with `include` or `exclude`)\n 2. use a `src-layout`\n 3. explicitly set `py_modules` or `packages` with a list of names\n\n To find more information, look for \"package discovery\" on setuptools docs.\n \"\"\"\n raise PackageDiscoveryError(cleandoc(msg))\n\n def analyse_name(self):\n \"\"\"The packages/modules are the essential contribution of the author.\n Therefore the name of the distribution can be derived from them.\n \"\"\"\n if self.dist.metadata.name or self.dist.name:\n # get_name() is not reliable (can return \"UNKNOWN\")\n return None\n\n log.debug(\"No `name` configuration, performing automatic discovery\")\n\n name = (\n self._find_name_single_package_or_module()\n or self._find_name_from_packages()\n )\n if name:\n self.dist.metadata.name = name\n\n def _find_name_single_package_or_module(self) -> Optional[str]:\n \"\"\"Exactly one module or package\"\"\"\n for field in ('packages', 'py_modules'):\n items = getattr(self.dist, field, None) or []\n if items and len(items) == 1:\n log.debug(f\"Single module/package detected, name: {items[0]}\")\n return items[0]\n\n return None\n\n def _find_name_from_packages(self) -> Optional[str]:\n \"\"\"Try to find the root package that is not a PEP 420 namespace\"\"\"\n if not self.dist.packages:\n return None\n\n packages = remove_stubs(sorted(self.dist.packages, key=len))\n package_dir = self.dist.package_dir or {}\n\n parent_pkg = find_parent_package(packages, package_dir, self._root_dir)\n if parent_pkg:\n log.debug(f\"Common parent package detected, name: {parent_pkg}\")\n return parent_pkg\n\n log.warn(\"No parent package detected, impossible to derive `name`\")\n return None\n\n\ndef remove_nested_packages(packages: List[str]) -> List[str]:\n \"\"\"Remove nested packages from a list of packages.\n\n >>> remove_nested_packages([\"a\", \"a.b1\", \"a.b2\", \"a.b1.c1\"])\n ['a']\n >>> remove_nested_packages([\"a\", \"b\", \"c.d\", \"c.d.e.f\", \"g.h\", \"a.a1\"])\n ['a', 'b', 'c.d', 'g.h']\n \"\"\"\n pkgs = sorted(packages, key=len)\n top_level = pkgs[:]\n size = len(pkgs)\n for i, name in enumerate(reversed(pkgs)):\n if any(name.startswith(f\"{other}.\") for other in top_level):\n top_level.pop(size - i - 1)\n\n return top_level\n\n\ndef remove_stubs(packages: List[str]) -> List[str]:\n \"\"\"Remove type stubs (:pep:`561`) from a list of packages.\n\n >>> remove_stubs([\"a\", \"a.b\", \"a-stubs\", \"a-stubs.b.c\", \"b\", \"c-stubs\"])\n ['a', 'a.b', 'b']\n \"\"\"\n return [pkg for pkg in packages if not pkg.split(\".\")[0].endswith(\"-stubs\")]\n\n\ndef find_parent_package(\n packages: List[str], package_dir: Mapping[str, str], root_dir: _Path\n) -> Optional[str]:\n \"\"\"Find the parent package that is not a namespace.\"\"\"\n packages = sorted(packages, key=len)\n common_ancestors = []\n for i, name in enumerate(packages):\n if not all(n.startswith(f\"{name}.\") for n in packages[i+1:]):\n # Since packages are sorted by length, this condition is able\n # to find a list of all common ancestors.\n # When there is divergence (e.g. multiple root packages)\n # the list will be empty\n break\n common_ancestors.append(name)\n\n for name in common_ancestors:\n pkg_path = find_package_path(name, package_dir, root_dir)\n init = os.path.join(pkg_path, \"__init__.py\")\n if os.path.isfile(init):\n return name\n\n return None\n\n\ndef find_package_path(\n name: str, package_dir: Mapping[str, str], root_dir: _Path\n) -> str:\n \"\"\"Given a package name, return the path where it should be found on\n disk, considering the ``package_dir`` option.\n\n >>> path = find_package_path(\"my.pkg\", {\"\": \"root/is/nested\"}, \".\")\n >>> path.replace(os.sep, \"/\")\n './root/is/nested/my/pkg'\n\n >>> path = find_package_path(\"my.pkg\", {\"my\": \"root/is/nested\"}, \".\")\n >>> path.replace(os.sep, \"/\")\n './root/is/nested/pkg'\n\n >>> path = find_package_path(\"my.pkg\", {\"my.pkg\": \"root/is/nested\"}, \".\")\n >>> path.replace(os.sep, \"/\")\n './root/is/nested'\n\n >>> path = find_package_path(\"other.pkg\", {\"my.pkg\": \"root/is/nested\"}, \".\")\n >>> path.replace(os.sep, \"/\")\n './other/pkg'\n \"\"\"\n parts = name.split(\".\")\n for i in range(len(parts), 0, -1):\n # Look backwards, the most specific package_dir first\n partial_name = \".\".join(parts[:i])\n if partial_name in package_dir:\n parent = package_dir[partial_name]\n return os.path.join(root_dir, parent, *parts[i:])\n\n parent = package_dir.get(\"\") or \"\"\n return os.path.join(root_dir, *parent.split(\"/\"), *parts)\n\n\ndef construct_package_dir(packages: List[str], package_path: _Path) -> Dict[str, str]:\n parent_pkgs = remove_nested_packages(packages)\n prefix = Path(package_path).parts\n return {pkg: \"/\".join([*prefix, *pkg.split(\".\")]) for pkg in parent_pkgs}\n", "path": "setuptools/discovery.py" } ]
[ { "content": "\"\"\"Automatic discovery of Python modules and packages (for inclusion in the\ndistribution) and other config values.\n\nFor the purposes of this module, the following nomenclature is used:\n\n- \"src-layout\": a directory representing a Python project that contains a \"src\"\n folder. Everything under the \"src\" folder is meant to be included in the\n distribution when packaging the project. Example::\n\n .\n ├── tox.ini\n ├── pyproject.toml\n └── src/\n └── mypkg/\n ├── __init__.py\n ├── mymodule.py\n └── my_data_file.txt\n\n- \"flat-layout\": a Python project that does not use \"src-layout\" but instead\n have a directory under the project root for each package::\n\n .\n ├── tox.ini\n ├── pyproject.toml\n └── mypkg/\n ├── __init__.py\n ├── mymodule.py\n └── my_data_file.txt\n\n- \"single-module\": a project that contains a single Python script direct under\n the project root (no directory used)::\n\n .\n ├── tox.ini\n ├── pyproject.toml\n └── mymodule.py\n\n\"\"\"\n\nimport itertools\nimport os\nfrom fnmatch import fnmatchcase\nfrom glob import glob\nfrom pathlib import Path\nfrom typing import (\n TYPE_CHECKING,\n Callable,\n Dict,\n Iterable,\n Iterator,\n List,\n Mapping,\n Optional,\n Tuple,\n Union\n)\n\nimport _distutils_hack.override # noqa: F401\n\nfrom distutils import log\nfrom distutils.util import convert_path\n\n_Path = Union[str, os.PathLike]\n_Filter = Callable[[str], bool]\nStrIter = Iterator[str]\n\nchain_iter = itertools.chain.from_iterable\n\nif TYPE_CHECKING:\n from setuptools import Distribution # noqa\n\n\ndef _valid_name(path: _Path) -> bool:\n # Ignore invalid names that cannot be imported directly\n return os.path.basename(path).isidentifier()\n\n\nclass _Finder:\n \"\"\"Base class that exposes functionality for module/package finders\"\"\"\n\n ALWAYS_EXCLUDE: Tuple[str, ...] = ()\n DEFAULT_EXCLUDE: Tuple[str, ...] = ()\n\n @classmethod\n def find(\n cls,\n where: _Path = '.',\n exclude: Iterable[str] = (),\n include: Iterable[str] = ('*',)\n ) -> List[str]:\n \"\"\"Return a list of all Python items (packages or modules, depending on\n the finder implementation) found within directory 'where'.\n\n 'where' is the root directory which will be searched.\n It should be supplied as a \"cross-platform\" (i.e. URL-style) path;\n it will be converted to the appropriate local path syntax.\n\n 'exclude' is a sequence of names to exclude; '*' can be used\n as a wildcard in the names.\n When finding packages, 'foo.*' will exclude all subpackages of 'foo'\n (but not 'foo' itself).\n\n 'include' is a sequence of names to include.\n If it's specified, only the named items will be included.\n If it's not specified, all found items will be included.\n 'include' can contain shell style wildcard patterns just like\n 'exclude'.\n \"\"\"\n\n exclude = exclude or cls.DEFAULT_EXCLUDE\n return list(\n cls._find_iter(\n convert_path(str(where)),\n cls._build_filter(*cls.ALWAYS_EXCLUDE, *exclude),\n cls._build_filter(*include),\n )\n )\n\n @classmethod\n def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:\n raise NotImplementedError\n\n @staticmethod\n def _build_filter(*patterns: str) -> _Filter:\n \"\"\"\n Given a list of patterns, return a callable that will be true only if\n the input matches at least one of the patterns.\n \"\"\"\n return lambda name: any(fnmatchcase(name, pat) for pat in patterns)\n\n\nclass PackageFinder(_Finder):\n \"\"\"\n Generate a list of all Python packages found within a directory\n \"\"\"\n\n ALWAYS_EXCLUDE = (\"ez_setup\", \"*__pycache__\")\n\n @classmethod\n def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:\n \"\"\"\n All the packages found in 'where' that pass the 'include' filter, but\n not the 'exclude' filter.\n \"\"\"\n for root, dirs, files in os.walk(str(where), followlinks=True):\n # Copy dirs to iterate over it, then empty dirs.\n all_dirs = dirs[:]\n dirs[:] = []\n\n for dir in all_dirs:\n full_path = os.path.join(root, dir)\n rel_path = os.path.relpath(full_path, where)\n package = rel_path.replace(os.path.sep, '.')\n\n # Skip directory trees that are not valid packages\n if '.' in dir or not cls._looks_like_package(full_path, package):\n continue\n\n # Should this package be included?\n if include(package) and not exclude(package):\n yield package\n\n # Keep searching subdirectories, as there may be more packages\n # down there, even if the parent was excluded.\n dirs.append(dir)\n\n @staticmethod\n def _looks_like_package(path: _Path, _package_name: str) -> bool:\n \"\"\"Does a directory look like a package?\"\"\"\n return os.path.isfile(os.path.join(path, '__init__.py'))\n\n\nclass PEP420PackageFinder(PackageFinder):\n @staticmethod\n def _looks_like_package(_path: _Path, _package_name: str) -> bool:\n return True\n\n\nclass ModuleFinder(_Finder):\n \"\"\"Find isolated Python modules.\n This function will **not** recurse subdirectories.\n \"\"\"\n\n @classmethod\n def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:\n for file in glob(os.path.join(where, \"*.py\")):\n module, _ext = os.path.splitext(os.path.basename(file))\n\n if not cls._looks_like_module(module):\n continue\n\n if include(module) and not exclude(module):\n yield module\n\n _looks_like_module = staticmethod(_valid_name)\n\n\n# We have to be extra careful in the case of flat layout to not include files\n# and directories not meant for distribution (e.g. tool-related)\n\n\nclass FlatLayoutPackageFinder(PEP420PackageFinder):\n _EXCLUDE = (\n \"ci\",\n \"bin\",\n \"doc\",\n \"docs\",\n \"documentation\",\n \"manpages\",\n \"news\",\n \"changelog\",\n \"test\",\n \"tests\",\n \"unit_test\",\n \"unit_tests\",\n \"example\",\n \"examples\",\n \"scripts\",\n \"tools\",\n \"util\",\n \"utils\",\n \"python\",\n \"build\",\n \"dist\",\n \"venv\",\n \"env\",\n \"requirements\",\n # ---- Task runners / Build tools ----\n \"tasks\", # invoke\n \"fabfile\", # fabric\n \"site_scons\", # SCons\n # ---- Other tools ----\n \"benchmark\",\n \"benchmarks\",\n \"exercise\",\n \"exercises\",\n # ---- Hidden directories/Private packages ----\n \"[._]*\",\n )\n\n DEFAULT_EXCLUDE = tuple(chain_iter((p, f\"{p}.*\") for p in _EXCLUDE))\n \"\"\"Reserved package names\"\"\"\n\n @staticmethod\n def _looks_like_package(_path: _Path, package_name: str) -> bool:\n names = package_name.split('.')\n # Consider PEP 561\n root_pkg_is_valid = names[0].isidentifier() or names[0].endswith(\"-stubs\")\n return root_pkg_is_valid and all(name.isidentifier() for name in names[1:])\n\n\nclass FlatLayoutModuleFinder(ModuleFinder):\n DEFAULT_EXCLUDE = (\n \"setup\",\n \"conftest\",\n \"test\",\n \"tests\",\n \"example\",\n \"examples\",\n \"build\",\n # ---- Task runners ----\n \"toxfile\",\n \"noxfile\",\n \"pavement\",\n \"dodo\",\n \"tasks\",\n \"fabfile\",\n # ---- Other tools ----\n \"[Ss][Cc]onstruct\", # SCons\n \"conanfile\", # Connan: C/C++ build tool\n \"manage\", # Django\n \"benchmark\",\n \"benchmarks\",\n \"exercise\",\n \"exercises\",\n \"htmlcov\",\n # ---- Hidden files/Private modules ----\n \"[._]*\",\n )\n \"\"\"Reserved top-level module names\"\"\"\n\n\ndef _find_packages_within(root_pkg: str, pkg_dir: _Path) -> List[str]:\n nested = PEP420PackageFinder.find(pkg_dir)\n return [root_pkg] + [\".\".join((root_pkg, n)) for n in nested]\n\n\nclass ConfigDiscovery:\n \"\"\"Fill-in metadata and options that can be automatically derived\n (from other metadata/options, the file system or conventions)\n \"\"\"\n\n def __init__(self, distribution: \"Distribution\"):\n self.dist = distribution\n self._called = False\n self._disabled = False\n self._skip_ext_modules = False\n\n def _disable(self):\n \"\"\"Internal API to disable automatic discovery\"\"\"\n self._disabled = True\n\n def _ignore_ext_modules(self):\n \"\"\"Internal API to disregard ext_modules.\n\n Normally auto-discovery would not be triggered if ``ext_modules`` are set\n (this is done for backward compatibility with existing packages relying on\n ``setup.py`` or ``setup.cfg``). However, ``setuptools`` can call this function\n to ignore given ``ext_modules`` and proceed with the auto-discovery if\n ``packages`` and ``py_modules`` are not given (e.g. when using pyproject.toml\n metadata).\n \"\"\"\n self._skip_ext_modules = True\n\n @property\n def _root_dir(self) -> _Path:\n # The best is to wait until `src_root` is set in dist, before using _root_dir.\n return self.dist.src_root or os.curdir\n\n @property\n def _package_dir(self) -> Dict[str, str]:\n if self.dist.package_dir is None:\n return {}\n return self.dist.package_dir\n\n def __call__(self, force=False, name=True, ignore_ext_modules=False):\n \"\"\"Automatically discover missing configuration fields\n and modifies the given ``distribution`` object in-place.\n\n Note that by default this will only have an effect the first time the\n ``ConfigDiscovery`` object is called.\n\n To repeatedly invoke automatic discovery (e.g. when the project\n directory changes), please use ``force=True`` (or create a new\n ``ConfigDiscovery`` instance).\n \"\"\"\n if force is False and (self._called or self._disabled):\n # Avoid overhead of multiple calls\n return\n\n self._analyse_package_layout(ignore_ext_modules)\n if name:\n self.analyse_name() # depends on ``packages`` and ``py_modules``\n\n self._called = True\n\n def _explicitly_specified(self, ignore_ext_modules: bool) -> bool:\n \"\"\"``True`` if the user has specified some form of package/module listing\"\"\"\n ignore_ext_modules = ignore_ext_modules or self._skip_ext_modules\n ext_modules = not (self.dist.ext_modules is None or ignore_ext_modules)\n return (\n self.dist.packages is not None\n or self.dist.py_modules is not None\n or ext_modules\n or hasattr(self.dist, \"configuration\") and self.dist.configuration\n # ^ Some projects use numpy.distutils.misc_util.Configuration\n )\n\n def _analyse_package_layout(self, ignore_ext_modules: bool) -> bool:\n if self._explicitly_specified(ignore_ext_modules):\n # For backward compatibility, just try to find modules/packages\n # when nothing is given\n return True\n\n log.debug(\n \"No `packages` or `py_modules` configuration, performing \"\n \"automatic discovery.\"\n )\n\n return (\n self._analyse_explicit_layout()\n or self._analyse_src_layout()\n # flat-layout is the trickiest for discovery so it should be last\n or self._analyse_flat_layout()\n )\n\n def _analyse_explicit_layout(self) -> bool:\n \"\"\"The user can explicitly give a package layout via ``package_dir``\"\"\"\n package_dir = self._package_dir.copy() # don't modify directly\n package_dir.pop(\"\", None) # This falls under the \"src-layout\" umbrella\n root_dir = self._root_dir\n\n if not package_dir:\n return False\n\n log.debug(f\"`explicit-layout` detected -- analysing {package_dir}\")\n pkgs = chain_iter(\n _find_packages_within(pkg, os.path.join(root_dir, parent_dir))\n for pkg, parent_dir in package_dir.items()\n )\n self.dist.packages = list(pkgs)\n log.debug(f\"discovered packages -- {self.dist.packages}\")\n return True\n\n def _analyse_src_layout(self) -> bool:\n \"\"\"Try to find all packages or modules under the ``src`` directory\n (or anything pointed by ``package_dir[\"\"]``).\n\n The \"src-layout\" is relatively safe for automatic discovery.\n We assume that everything within is meant to be included in the\n distribution.\n\n If ``package_dir[\"\"]`` is not given, but the ``src`` directory exists,\n this function will set ``package_dir[\"\"] = \"src\"``.\n \"\"\"\n package_dir = self._package_dir\n src_dir = os.path.join(self._root_dir, package_dir.get(\"\", \"src\"))\n if not os.path.isdir(src_dir):\n return False\n\n log.debug(f\"`src-layout` detected -- analysing {src_dir}\")\n package_dir.setdefault(\"\", os.path.basename(src_dir))\n self.dist.package_dir = package_dir # persist eventual modifications\n self.dist.packages = PEP420PackageFinder.find(src_dir)\n self.dist.py_modules = ModuleFinder.find(src_dir)\n log.debug(f\"discovered packages -- {self.dist.packages}\")\n log.debug(f\"discovered py_modules -- {self.dist.py_modules}\")\n return True\n\n def _analyse_flat_layout(self) -> bool:\n \"\"\"Try to find all packages and modules under the project root.\n\n Since the ``flat-layout`` is more dangerous in terms of accidentally including\n extra files/directories, this function is more conservative and will raise an\n error if multiple packages or modules are found.\n\n This assumes that multi-package dists are uncommon and refuse to support that\n use case in order to be able to prevent unintended errors.\n \"\"\"\n log.debug(f\"`flat-layout` detected -- analysing {self._root_dir}\")\n return self._analyse_flat_packages() or self._analyse_flat_modules()\n\n def _analyse_flat_packages(self) -> bool:\n self.dist.packages = FlatLayoutPackageFinder.find(self._root_dir)\n top_level = remove_nested_packages(remove_stubs(self.dist.packages))\n log.debug(f\"discovered packages -- {self.dist.packages}\")\n self._ensure_no_accidental_inclusion(top_level, \"packages\")\n return bool(top_level)\n\n def _analyse_flat_modules(self) -> bool:\n self.dist.py_modules = FlatLayoutModuleFinder.find(self._root_dir)\n log.debug(f\"discovered py_modules -- {self.dist.py_modules}\")\n self._ensure_no_accidental_inclusion(self.dist.py_modules, \"modules\")\n return bool(self.dist.py_modules)\n\n def _ensure_no_accidental_inclusion(self, detected: List[str], kind: str):\n if len(detected) > 1:\n from inspect import cleandoc\n\n from setuptools.errors import PackageDiscoveryError\n\n msg = f\"\"\"Multiple top-level {kind} discovered in a flat-layout: {detected}.\n\n To avoid accidental inclusion of unwanted files or directories,\n setuptools will not proceed with this build.\n\n If you are trying to create a single distribution with multiple {kind}\n on purpose, you should not rely on automatic discovery.\n Instead, consider the following options:\n\n 1. set up custom discovery (`find` directive with `include` or `exclude`)\n 2. use a `src-layout`\n 3. explicitly set `py_modules` or `packages` with a list of names\n\n To find more information, look for \"package discovery\" on setuptools docs.\n \"\"\"\n raise PackageDiscoveryError(cleandoc(msg))\n\n def analyse_name(self):\n \"\"\"The packages/modules are the essential contribution of the author.\n Therefore the name of the distribution can be derived from them.\n \"\"\"\n if self.dist.metadata.name or self.dist.name:\n # get_name() is not reliable (can return \"UNKNOWN\")\n return None\n\n log.debug(\"No `name` configuration, performing automatic discovery\")\n\n name = (\n self._find_name_single_package_or_module()\n or self._find_name_from_packages()\n )\n if name:\n self.dist.metadata.name = name\n\n def _find_name_single_package_or_module(self) -> Optional[str]:\n \"\"\"Exactly one module or package\"\"\"\n for field in ('packages', 'py_modules'):\n items = getattr(self.dist, field, None) or []\n if items and len(items) == 1:\n log.debug(f\"Single module/package detected, name: {items[0]}\")\n return items[0]\n\n return None\n\n def _find_name_from_packages(self) -> Optional[str]:\n \"\"\"Try to find the root package that is not a PEP 420 namespace\"\"\"\n if not self.dist.packages:\n return None\n\n packages = remove_stubs(sorted(self.dist.packages, key=len))\n package_dir = self.dist.package_dir or {}\n\n parent_pkg = find_parent_package(packages, package_dir, self._root_dir)\n if parent_pkg:\n log.debug(f\"Common parent package detected, name: {parent_pkg}\")\n return parent_pkg\n\n log.warn(\"No parent package detected, impossible to derive `name`\")\n return None\n\n\ndef remove_nested_packages(packages: List[str]) -> List[str]:\n \"\"\"Remove nested packages from a list of packages.\n\n >>> remove_nested_packages([\"a\", \"a.b1\", \"a.b2\", \"a.b1.c1\"])\n ['a']\n >>> remove_nested_packages([\"a\", \"b\", \"c.d\", \"c.d.e.f\", \"g.h\", \"a.a1\"])\n ['a', 'b', 'c.d', 'g.h']\n \"\"\"\n pkgs = sorted(packages, key=len)\n top_level = pkgs[:]\n size = len(pkgs)\n for i, name in enumerate(reversed(pkgs)):\n if any(name.startswith(f\"{other}.\") for other in top_level):\n top_level.pop(size - i - 1)\n\n return top_level\n\n\ndef remove_stubs(packages: List[str]) -> List[str]:\n \"\"\"Remove type stubs (:pep:`561`) from a list of packages.\n\n >>> remove_stubs([\"a\", \"a.b\", \"a-stubs\", \"a-stubs.b.c\", \"b\", \"c-stubs\"])\n ['a', 'a.b', 'b']\n \"\"\"\n return [pkg for pkg in packages if not pkg.split(\".\")[0].endswith(\"-stubs\")]\n\n\ndef find_parent_package(\n packages: List[str], package_dir: Mapping[str, str], root_dir: _Path\n) -> Optional[str]:\n \"\"\"Find the parent package that is not a namespace.\"\"\"\n packages = sorted(packages, key=len)\n common_ancestors = []\n for i, name in enumerate(packages):\n if not all(n.startswith(f\"{name}.\") for n in packages[i+1:]):\n # Since packages are sorted by length, this condition is able\n # to find a list of all common ancestors.\n # When there is divergence (e.g. multiple root packages)\n # the list will be empty\n break\n common_ancestors.append(name)\n\n for name in common_ancestors:\n pkg_path = find_package_path(name, package_dir, root_dir)\n init = os.path.join(pkg_path, \"__init__.py\")\n if os.path.isfile(init):\n return name\n\n return None\n\n\ndef find_package_path(\n name: str, package_dir: Mapping[str, str], root_dir: _Path\n) -> str:\n \"\"\"Given a package name, return the path where it should be found on\n disk, considering the ``package_dir`` option.\n\n >>> path = find_package_path(\"my.pkg\", {\"\": \"root/is/nested\"}, \".\")\n >>> path.replace(os.sep, \"/\")\n './root/is/nested/my/pkg'\n\n >>> path = find_package_path(\"my.pkg\", {\"my\": \"root/is/nested\"}, \".\")\n >>> path.replace(os.sep, \"/\")\n './root/is/nested/pkg'\n\n >>> path = find_package_path(\"my.pkg\", {\"my.pkg\": \"root/is/nested\"}, \".\")\n >>> path.replace(os.sep, \"/\")\n './root/is/nested'\n\n >>> path = find_package_path(\"other.pkg\", {\"my.pkg\": \"root/is/nested\"}, \".\")\n >>> path.replace(os.sep, \"/\")\n './other/pkg'\n \"\"\"\n parts = name.split(\".\")\n for i in range(len(parts), 0, -1):\n # Look backwards, the most specific package_dir first\n partial_name = \".\".join(parts[:i])\n if partial_name in package_dir:\n parent = package_dir[partial_name]\n return os.path.join(root_dir, parent, *parts[i:])\n\n parent = package_dir.get(\"\") or \"\"\n return os.path.join(root_dir, *parent.split(\"/\"), *parts)\n\n\ndef construct_package_dir(packages: List[str], package_path: _Path) -> Dict[str, str]:\n parent_pkgs = remove_nested_packages(packages)\n prefix = Path(package_path).parts\n return {pkg: \"/\".join([*prefix, *pkg.split(\".\")]) for pkg in parent_pkgs}\n", "path": "setuptools/discovery.py" } ]
diff --git a/changelog.d/3594.change.rst b/changelog.d/3594.change.rst new file mode 100644 index 0000000000..c0642d783a --- /dev/null +++ b/changelog.d/3594.change.rst @@ -0,0 +1 @@ +Added ``htmlcov`` to FlatLayoutModuleFinder.DEFAULT_EXCLUDE -- by :user:`demianbrecht` diff --git a/setuptools/discovery.py b/setuptools/discovery.py index 98fc2a7f48..6244a18558 100644 --- a/setuptools/discovery.py +++ b/setuptools/discovery.py @@ -273,6 +273,7 @@ class FlatLayoutModuleFinder(ModuleFinder): "benchmarks", "exercise", "exercises", + "htmlcov", # ---- Hidden files/Private modules ---- "[._]*", )
certbot__certbot-3432
Nginx plugin selection We want to slowly roll out the Nginx plugin to make sure it's working for people. To do this we should: - Mark the nginx plugin as hidden (we did this for the manual plugin) - Disable automatic selection if it's the only available configurator (if not already the case, we should disable automatic selection of hidden plugins). - Make sure the description of the Nginx plugin is sane (it used to say it was broken).
[ { "content": "\"\"\"Nginx Configuration\"\"\"\nimport logging\nimport os\nimport re\nimport shutil\nimport socket\nimport subprocess\nimport time\n\nimport OpenSSL\nimport zope.interface\n\nfrom acme import challenges\nfrom acme import crypto_util as acme_crypto_util\n\nfrom certbot import constants as core_constants\nfrom certbot import crypto_util\nfrom certbot import errors\nfrom certbot import interfaces\nfrom certbot import util\nfrom certbot import reverter\n\nfrom certbot.plugins import common\n\nfrom certbot_nginx import constants\nfrom certbot_nginx import tls_sni_01\nfrom certbot_nginx import obj\nfrom certbot_nginx import parser\n\n\nlogger = logging.getLogger(__name__)\n\n\[email protected](interfaces.IAuthenticator, interfaces.IInstaller)\[email protected](interfaces.IPluginFactory)\nclass NginxConfigurator(common.Plugin):\n # pylint: disable=too-many-instance-attributes,too-many-public-methods\n \"\"\"Nginx configurator.\n\n .. todo:: Add proper support for comments in the config. Currently,\n config files modified by the configurator will lose all their comments.\n\n :ivar config: Configuration.\n :type config: :class:`~certbot.interfaces.IConfig`\n\n :ivar parser: Handles low level parsing\n :type parser: :class:`~certbot_nginx.parser`\n\n :ivar str save_notes: Human-readable config change notes\n\n :ivar reverter: saves and reverts checkpoints\n :type reverter: :class:`certbot.reverter.Reverter`\n\n :ivar tup version: version of Nginx\n\n \"\"\"\n\n description = \"Nginx Web Server - currently doesn't work\"\n\n @classmethod\n def add_parser_arguments(cls, add):\n add(\"server-root\", default=constants.CLI_DEFAULTS[\"server_root\"],\n help=\"Nginx server root directory.\")\n add(\"ctl\", default=constants.CLI_DEFAULTS[\"ctl\"], help=\"Path to the \"\n \"'nginx' binary, used for 'configtest' and retrieving nginx \"\n \"version number.\")\n\n @property\n def nginx_conf(self):\n \"\"\"Nginx config file path.\"\"\"\n return os.path.join(self.conf(\"server_root\"), \"nginx.conf\")\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initialize an Nginx Configurator.\n\n :param tup version: version of Nginx as a tuple (1, 4, 7)\n (used mostly for unittesting)\n\n \"\"\"\n version = kwargs.pop(\"version\", None)\n super(NginxConfigurator, self).__init__(*args, **kwargs)\n\n # Verify that all directories and files exist with proper permissions\n self._verify_setup()\n\n # Files to save\n self.save_notes = \"\"\n\n # Add number of outstanding challenges\n self._chall_out = 0\n\n # These will be set in the prepare function\n self.parser = None\n self.version = version\n self._enhance_func = {\"redirect\": self._enable_redirect}\n\n # Set up reverter\n self.reverter = reverter.Reverter(self.config)\n self.reverter.recovery_routine()\n\n @property\n def mod_ssl_conf(self):\n \"\"\"Full absolute path to SSL configuration file.\"\"\"\n return os.path.join(self.config.config_dir, constants.MOD_SSL_CONF_DEST)\n\n # This is called in determine_authenticator and determine_installer\n def prepare(self):\n \"\"\"Prepare the authenticator/installer.\n\n :raises .errors.NoInstallationError: If Nginx ctl cannot be found\n :raises .errors.MisconfigurationError: If Nginx is misconfigured\n \"\"\"\n # Verify Nginx is installed\n if not util.exe_exists(self.conf('ctl')):\n raise errors.NoInstallationError\n\n # Make sure configuration is valid\n self.config_test()\n\n self.parser = parser.NginxParser(\n self.conf('server-root'), self.mod_ssl_conf)\n\n # Set Version\n if self.version is None:\n self.version = self.get_version()\n\n temp_install(self.mod_ssl_conf)\n\n # Entry point in main.py for installing cert\n def deploy_cert(self, domain, cert_path, key_path,\n chain_path=None, fullchain_path=None):\n # pylint: disable=unused-argument\n \"\"\"Deploys certificate to specified virtual host.\n\n .. note:: Aborts if the vhost is missing ssl_certificate or\n ssl_certificate_key.\n\n .. note:: Nginx doesn't have a cert chain directive.\n It expects the cert file to have the concatenated chain.\n However, we use the chain file as input to the\n ssl_trusted_certificate directive, used for verify OCSP responses.\n\n .. note:: This doesn't save the config files!\n\n :raises errors.PluginError: When unable to deploy certificate due to\n a lack of directives or configuration\n\n \"\"\"\n if not fullchain_path:\n raise errors.PluginError(\n \"The nginx plugin currently requires --fullchain-path to \"\n \"install a cert.\")\n\n vhost = self.choose_vhost(domain)\n cert_directives = [['\\n', 'ssl_certificate', ' ', fullchain_path],\n ['\\n', 'ssl_certificate_key', ' ', key_path]]\n\n # OCSP stapling was introduced in Nginx 1.3.7. If we have that version\n # or greater, add config settings for it.\n stapling_directives = []\n if self.version >= (1, 3, 7):\n stapling_directives = [\n ['\\n ', 'ssl_trusted_certificate', ' ', chain_path],\n ['\\n ', 'ssl_stapling', ' ', 'on'],\n ['\\n ', 'ssl_stapling_verify', ' ', 'on'], ['\\n']]\n\n if len(stapling_directives) != 0 and not chain_path:\n raise errors.PluginError(\n \"--chain-path is required to enable \"\n \"Online Certificate Status Protocol (OCSP) stapling \"\n \"on nginx >= 1.3.7.\")\n\n try:\n self.parser.add_server_directives(vhost.filep, vhost.names,\n cert_directives, replace=True)\n self.parser.add_server_directives(vhost.filep, vhost.names,\n stapling_directives, replace=False)\n logger.info(\"Deployed Certificate to VirtualHost %s for %s\",\n vhost.filep, vhost.names)\n except errors.MisconfigurationError as error:\n logger.debug(error)\n logger.warning(\n \"Cannot find a cert or key directive in %s for %s. \"\n \"VirtualHost was not modified.\", vhost.filep, vhost.names)\n # Presumably break here so that the virtualhost is not modified\n return False\n\n self.save_notes += (\"Changed vhost at %s with addresses of %s\\n\" %\n (vhost.filep,\n \", \".join(str(addr) for addr in vhost.addrs)))\n self.save_notes += \"\\tssl_certificate %s\\n\" % fullchain_path\n self.save_notes += \"\\tssl_certificate_key %s\\n\" % key_path\n if len(stapling_directives) > 0:\n self.save_notes += \"\\tssl_trusted_certificate %s\\n\" % chain_path\n self.save_notes += \"\\tssl_stapling on\\n\"\n self.save_notes += \"\\tssl_stapling_verify on\\n\"\n\n\n\n #######################\n # Vhost parsing methods\n #######################\n def choose_vhost(self, target_name):\n \"\"\"Chooses a virtual host based on the given domain name.\n\n .. note:: This makes the vhost SSL-enabled if it isn't already. Follows\n Nginx's server block selection rules preferring blocks that are\n already SSL.\n\n .. todo:: This should maybe return list if no obvious answer\n is presented.\n\n .. todo:: The special name \"$hostname\" corresponds to the machine's\n hostname. Currently we just ignore this.\n\n :param str target_name: domain name\n\n :returns: ssl vhost associated with name\n :rtype: :class:`~certbot_nginx.obj.VirtualHost`\n\n \"\"\"\n vhost = None\n\n matches = self._get_ranked_matches(target_name)\n if not matches:\n # No matches. Create a new vhost with this name in nginx.conf.\n filep = self.parser.loc[\"root\"]\n new_block = [['server'], [['\\n', 'server_name', ' ', target_name]]]\n self.parser.add_http_directives(filep, new_block)\n vhost = obj.VirtualHost(filep, set([]), False, True,\n set([target_name]), list(new_block[1]))\n elif matches[0]['rank'] in xrange(2, 6):\n # Wildcard match - need to find the longest one\n rank = matches[0]['rank']\n wildcards = [x for x in matches if x['rank'] == rank]\n vhost = max(wildcards, key=lambda x: len(x['name']))['vhost']\n else:\n vhost = matches[0]['vhost']\n\n if vhost is not None:\n if not vhost.ssl:\n self._make_server_ssl(vhost)\n\n return vhost\n\n def _get_ranked_matches(self, target_name):\n \"\"\"Returns a ranked list of vhosts that match target_name.\n The ranking gives preference to SSL vhosts.\n\n :param str target_name: The name to match\n :returns: list of dicts containing the vhost, the matching name, and\n the numerical rank\n :rtype: list\n\n \"\"\"\n # Nginx chooses a matching server name for a request with precedence:\n # 1. exact name match\n # 2. longest wildcard name starting with *\n # 3. longest wildcard name ending with *\n # 4. first matching regex in order of appearance in the file\n matches = []\n for vhost in self.parser.get_vhosts():\n name_type, name = parser.get_best_match(target_name, vhost.names)\n if name_type == 'exact':\n matches.append({'vhost': vhost,\n 'name': name,\n 'rank': 0 if vhost.ssl else 1})\n elif name_type == 'wildcard_start':\n matches.append({'vhost': vhost,\n 'name': name,\n 'rank': 2 if vhost.ssl else 3})\n elif name_type == 'wildcard_end':\n matches.append({'vhost': vhost,\n 'name': name,\n 'rank': 4 if vhost.ssl else 5})\n elif name_type == 'regex':\n matches.append({'vhost': vhost,\n 'name': name,\n 'rank': 6 if vhost.ssl else 7})\n return sorted(matches, key=lambda x: x['rank'])\n\n def get_all_names(self):\n \"\"\"Returns all names found in the Nginx Configuration.\n\n :returns: All ServerNames, ServerAliases, and reverse DNS entries for\n virtual host addresses\n :rtype: set\n\n \"\"\"\n all_names = set()\n\n for vhost in self.parser.get_vhosts():\n all_names.update(vhost.names)\n\n for addr in vhost.addrs:\n host = addr.get_addr()\n if common.hostname_regex.match(host):\n # If it's a hostname, add it to the names.\n all_names.add(host)\n elif not common.private_ips_regex.match(host):\n # If it isn't a private IP, do a reverse DNS lookup\n # TODO: IPv6 support\n try:\n socket.inet_aton(host)\n all_names.add(socket.gethostbyaddr(host)[0])\n except (socket.error, socket.herror, socket.timeout):\n continue\n\n return all_names\n\n def _get_snakeoil_paths(self):\n # TODO: generate only once\n tmp_dir = os.path.join(self.config.work_dir, \"snakeoil\")\n le_key = crypto_util.init_save_key(\n key_size=1024, key_dir=tmp_dir, keyname=\"key.pem\")\n key = OpenSSL.crypto.load_privatekey(\n OpenSSL.crypto.FILETYPE_PEM, le_key.pem)\n cert = acme_crypto_util.gen_ss_cert(key, domains=[socket.gethostname()])\n cert_pem = OpenSSL.crypto.dump_certificate(\n OpenSSL.crypto.FILETYPE_PEM, cert)\n cert_file, cert_path = util.unique_file(os.path.join(tmp_dir, \"cert.pem\"))\n with cert_file:\n cert_file.write(cert_pem)\n return cert_path, le_key.file\n\n def _make_server_ssl(self, vhost):\n \"\"\"Make a server SSL.\n\n Make a server SSL based on server_name and filename by adding a\n ``listen IConfig.tls_sni_01_port ssl`` directive to the server block.\n\n .. todo:: Maybe this should create a new block instead of modifying\n the existing one?\n\n :param vhost: The vhost to add SSL to.\n :type vhost: :class:`~certbot_nginx.obj.VirtualHost`\n\n \"\"\"\n snakeoil_cert, snakeoil_key = self._get_snakeoil_paths()\n ssl_block = [['\\n ', 'listen', ' ', '{0} ssl'.format(self.config.tls_sni_01_port)],\n ['\\n ', 'ssl_certificate', ' ', snakeoil_cert],\n ['\\n ', 'ssl_certificate_key', ' ', snakeoil_key],\n ['\\n ', 'include', ' ', self.parser.loc[\"ssl_options\"]]]\n self.parser.add_server_directives(\n vhost.filep, vhost.names, ssl_block, replace=False)\n vhost.ssl = True\n vhost.raw.extend(ssl_block)\n vhost.addrs.add(obj.Addr(\n '', str(self.config.tls_sni_01_port), True, False))\n\n def get_all_certs_keys(self):\n \"\"\"Find all existing keys, certs from configuration.\n\n :returns: list of tuples with form [(cert, key, path)]\n cert - str path to certificate file\n key - str path to associated key file\n path - File path to configuration file.\n :rtype: set\n\n \"\"\"\n return self.parser.get_all_certs_keys()\n\n ##################################\n # enhancement methods (IInstaller)\n ##################################\n def supported_enhancements(self): # pylint: disable=no-self-use\n \"\"\"Returns currently supported enhancements.\"\"\"\n return ['redirect']\n\n def enhance(self, domain, enhancement, options=None):\n \"\"\"Enhance configuration.\n\n :param str domain: domain to enhance\n :param str enhancement: enhancement type defined in\n :const:`~certbot.constants.ENHANCEMENTS`\n :param options: options for the enhancement\n See :const:`~certbot.constants.ENHANCEMENTS`\n documentation for appropriate parameter.\n\n \"\"\"\n try:\n return self._enhance_func[enhancement](\n self.choose_vhost(domain), options)\n except (KeyError, ValueError):\n raise errors.PluginError(\n \"Unsupported enhancement: {0}\".format(enhancement))\n except errors.PluginError:\n logger.warning(\"Failed %s for %s\", enhancement, domain)\n\n def _enable_redirect(self, vhost, unused_options):\n \"\"\"Redirect all equivalent HTTP traffic to ssl_vhost.\n\n Add rewrite directive to non https traffic\n\n .. note:: This function saves the configuration\n\n :param vhost: Destination of traffic, an ssl enabled vhost\n :type vhost: :class:`~certbot_nginx.obj.VirtualHost`\n\n :param unused_options: Not currently used\n :type unused_options: Not Available\n \"\"\"\n redirect_block = [[\n ['\\n ', 'if', ' ', '($scheme != \"https\") '],\n [['\\n ', 'return', ' ', '301 https://$host$request_uri'],\n '\\n ']\n ], ['\\n']]\n self.parser.add_server_directives(\n vhost.filep, vhost.names, redirect_block, replace=False)\n logger.info(\"Redirecting all traffic to ssl in %s\", vhost.filep)\n\n ######################################\n # Nginx server management (IInstaller)\n ######################################\n def restart(self):\n \"\"\"Restarts nginx server.\n\n :raises .errors.MisconfigurationError: If either the reload fails.\n\n \"\"\"\n nginx_restart(self.conf('ctl'), self.nginx_conf)\n\n def config_test(self): # pylint: disable=no-self-use\n \"\"\"Check the configuration of Nginx for errors.\n\n :raises .errors.MisconfigurationError: If config_test fails\n\n \"\"\"\n try:\n util.run_script([self.conf('ctl'), \"-c\", self.nginx_conf, \"-t\"])\n except errors.SubprocessError as err:\n raise errors.MisconfigurationError(str(err))\n\n def _verify_setup(self):\n \"\"\"Verify the setup to ensure safe operating environment.\n\n Make sure that files/directories are setup with appropriate permissions\n Aim for defensive coding... make sure all input files\n have permissions of root.\n\n \"\"\"\n uid = os.geteuid()\n util.make_or_verify_dir(\n self.config.work_dir, core_constants.CONFIG_DIRS_MODE, uid)\n util.make_or_verify_dir(\n self.config.backup_dir, core_constants.CONFIG_DIRS_MODE, uid)\n util.make_or_verify_dir(\n self.config.config_dir, core_constants.CONFIG_DIRS_MODE, uid)\n\n def get_version(self):\n \"\"\"Return version of Nginx Server.\n\n Version is returned as tuple. (ie. 2.4.7 = (2, 4, 7))\n\n :returns: version\n :rtype: tuple\n\n :raises .PluginError:\n Unable to find Nginx version or version is unsupported\n\n \"\"\"\n try:\n proc = subprocess.Popen(\n [self.conf('ctl'), \"-c\", self.nginx_conf, \"-V\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n text = proc.communicate()[1] # nginx prints output to stderr\n except (OSError, ValueError) as error:\n logging.debug(error, exc_info=True)\n raise errors.PluginError(\n \"Unable to run %s -V\" % self.conf('ctl'))\n\n version_regex = re.compile(r\"nginx/([0-9\\.]*)\", re.IGNORECASE)\n version_matches = version_regex.findall(text)\n\n sni_regex = re.compile(r\"TLS SNI support enabled\", re.IGNORECASE)\n sni_matches = sni_regex.findall(text)\n\n ssl_regex = re.compile(r\" --with-http_ssl_module\")\n ssl_matches = ssl_regex.findall(text)\n\n if not version_matches:\n raise errors.PluginError(\"Unable to find Nginx version\")\n if not ssl_matches:\n raise errors.PluginError(\n \"Nginx build is missing SSL module (--with-http_ssl_module).\")\n if not sni_matches:\n raise errors.PluginError(\"Nginx build doesn't support SNI\")\n\n nginx_version = tuple([int(i) for i in version_matches[0].split(\".\")])\n\n # nginx < 0.8.48 uses machine hostname as default server_name instead of\n # the empty string\n if nginx_version < (0, 8, 48):\n raise errors.NotSupportedError(\"Nginx version must be 0.8.48+\")\n\n return nginx_version\n\n def more_info(self):\n \"\"\"Human-readable string to help understand the module\"\"\"\n return (\n \"Configures Nginx to authenticate and install HTTPS.{0}\"\n \"Server root: {root}{0}\"\n \"Version: {version}\".format(\n os.linesep, root=self.parser.loc[\"root\"],\n version=\".\".join(str(i) for i in self.version))\n )\n\n ###################################################\n # Wrapper functions for Reverter class (IInstaller)\n ###################################################\n def save(self, title=None, temporary=False):\n \"\"\"Saves all changes to the configuration files.\n\n :param str title: The title of the save. If a title is given, the\n configuration will be saved as a new checkpoint and put in a\n timestamped directory.\n\n :param bool temporary: Indicates whether the changes made will\n be quickly reversed in the future (ie. challenges)\n\n :raises .errors.PluginError: If there was an error in\n an attempt to save the configuration, or an error creating a\n checkpoint\n\n \"\"\"\n save_files = set(self.parser.parsed.keys())\n\n try:\n # Create Checkpoint\n if temporary:\n self.reverter.add_to_temp_checkpoint(\n save_files, self.save_notes)\n else:\n self.reverter.add_to_checkpoint(save_files,\n self.save_notes)\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n\n self.save_notes = \"\"\n\n # Change 'ext' to something else to not override existing conf files\n self.parser.filedump(ext='')\n if title and not temporary:\n try:\n self.reverter.finalize_checkpoint(title)\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n\n return True\n\n def recovery_routine(self):\n \"\"\"Revert all previously modified files.\n\n Reverts all modified files that have not been saved as a checkpoint\n\n :raises .errors.PluginError: If unable to recover the configuration\n\n \"\"\"\n try:\n self.reverter.recovery_routine()\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n self.parser.load()\n\n def revert_challenge_config(self):\n \"\"\"Used to cleanup challenge configurations.\n\n :raises .errors.PluginError: If unable to revert the challenge config.\n\n \"\"\"\n try:\n self.reverter.revert_temporary_config()\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n self.parser.load()\n\n def rollback_checkpoints(self, rollback=1):\n \"\"\"Rollback saved checkpoints.\n\n :param int rollback: Number of checkpoints to revert\n\n :raises .errors.PluginError: If there is a problem with the input or\n the function is unable to correctly revert the configuration\n\n \"\"\"\n try:\n self.reverter.rollback_checkpoints(rollback)\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n self.parser.load()\n\n def view_config_changes(self):\n \"\"\"Show all of the configuration changes that have taken place.\n\n :raises .errors.PluginError: If there is a problem while processing\n the checkpoints directories.\n\n \"\"\"\n try:\n self.reverter.view_config_changes()\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n\n ###########################################################################\n # Challenges Section for IAuthenticator\n ###########################################################################\n def get_chall_pref(self, unused_domain): # pylint: disable=no-self-use\n \"\"\"Return list of challenge preferences.\"\"\"\n return [challenges.TLSSNI01]\n\n # Entry point in main.py for performing challenges\n def perform(self, achalls):\n \"\"\"Perform the configuration related challenge.\n\n This function currently assumes all challenges will be fulfilled.\n If this turns out not to be the case in the future. Cleanup and\n outstanding challenges will have to be designed better.\n\n \"\"\"\n self._chall_out += len(achalls)\n responses = [None] * len(achalls)\n chall_doer = tls_sni_01.NginxTlsSni01(self)\n\n for i, achall in enumerate(achalls):\n # Currently also have chall_doer hold associated index of the\n # challenge. This helps to put all of the responses back together\n # when they are all complete.\n chall_doer.add_chall(achall, i)\n\n sni_response = chall_doer.perform()\n # Must restart in order to activate the challenges.\n # Handled here because we may be able to load up other challenge types\n self.restart()\n\n # Go through all of the challenges and assign them to the proper place\n # in the responses return value. All responses must be in the same order\n # as the original challenges.\n for i, resp in enumerate(sni_response):\n responses[chall_doer.indices[i]] = resp\n\n return responses\n\n # called after challenges are performed\n def cleanup(self, achalls):\n \"\"\"Revert all challenges.\"\"\"\n self._chall_out -= len(achalls)\n\n # If all of the challenges have been finished, clean up everything\n if self._chall_out <= 0:\n self.revert_challenge_config()\n self.restart()\n\n\ndef nginx_restart(nginx_ctl, nginx_conf=\"/etc/nginx.conf\"):\n \"\"\"Restarts the Nginx Server.\n\n .. todo:: Nginx restart is fatal if the configuration references\n non-existent SSL cert/key files. Remove references to /etc/letsencrypt\n before restart.\n\n :param str nginx_ctl: Path to the Nginx binary.\n\n \"\"\"\n try:\n proc = subprocess.Popen([nginx_ctl, \"-c\", nginx_conf, \"-s\", \"reload\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n stdout, stderr = proc.communicate()\n\n if proc.returncode != 0:\n # Maybe Nginx isn't running\n nginx_proc = subprocess.Popen([nginx_ctl, \"-c\", nginx_conf],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n stdout, stderr = nginx_proc.communicate()\n\n if nginx_proc.returncode != 0:\n # Enter recovery routine...\n raise errors.MisconfigurationError(\n \"nginx restart failed:\\n%s\\n%s\" % (stdout, stderr))\n\n except (OSError, ValueError):\n raise errors.MisconfigurationError(\"nginx restart failed\")\n # Nginx can take a moment to recognize a newly added TLS SNI servername, so sleep\n # for a second. TODO: Check for expected servername and loop until it\n # appears or return an error if looping too long.\n time.sleep(1)\n\n\ndef temp_install(options_ssl):\n \"\"\"Temporary install for convenience.\"\"\"\n # Check to make sure options-ssl.conf is installed\n if not os.path.isfile(options_ssl):\n shutil.copyfile(constants.MOD_SSL_CONF_SRC, options_ssl)\n", "path": "certbot-nginx/certbot_nginx/configurator.py" } ]
[ { "content": "\"\"\"Nginx Configuration\"\"\"\nimport logging\nimport os\nimport re\nimport shutil\nimport socket\nimport subprocess\nimport time\n\nimport OpenSSL\nimport zope.interface\n\nfrom acme import challenges\nfrom acme import crypto_util as acme_crypto_util\n\nfrom certbot import constants as core_constants\nfrom certbot import crypto_util\nfrom certbot import errors\nfrom certbot import interfaces\nfrom certbot import util\nfrom certbot import reverter\n\nfrom certbot.plugins import common\n\nfrom certbot_nginx import constants\nfrom certbot_nginx import tls_sni_01\nfrom certbot_nginx import obj\nfrom certbot_nginx import parser\n\n\nlogger = logging.getLogger(__name__)\n\n\[email protected](interfaces.IAuthenticator, interfaces.IInstaller)\[email protected](interfaces.IPluginFactory)\nclass NginxConfigurator(common.Plugin):\n # pylint: disable=too-many-instance-attributes,too-many-public-methods\n \"\"\"Nginx configurator.\n\n .. todo:: Add proper support for comments in the config. Currently,\n config files modified by the configurator will lose all their comments.\n\n :ivar config: Configuration.\n :type config: :class:`~certbot.interfaces.IConfig`\n\n :ivar parser: Handles low level parsing\n :type parser: :class:`~certbot_nginx.parser`\n\n :ivar str save_notes: Human-readable config change notes\n\n :ivar reverter: saves and reverts checkpoints\n :type reverter: :class:`certbot.reverter.Reverter`\n\n :ivar tup version: version of Nginx\n\n \"\"\"\n\n description = \"Nginx Web Server plugin - Alpha\"\n\n hidden = True\n\n @classmethod\n def add_parser_arguments(cls, add):\n add(\"server-root\", default=constants.CLI_DEFAULTS[\"server_root\"],\n help=\"Nginx server root directory.\")\n add(\"ctl\", default=constants.CLI_DEFAULTS[\"ctl\"], help=\"Path to the \"\n \"'nginx' binary, used for 'configtest' and retrieving nginx \"\n \"version number.\")\n\n @property\n def nginx_conf(self):\n \"\"\"Nginx config file path.\"\"\"\n return os.path.join(self.conf(\"server_root\"), \"nginx.conf\")\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initialize an Nginx Configurator.\n\n :param tup version: version of Nginx as a tuple (1, 4, 7)\n (used mostly for unittesting)\n\n \"\"\"\n version = kwargs.pop(\"version\", None)\n super(NginxConfigurator, self).__init__(*args, **kwargs)\n\n # Verify that all directories and files exist with proper permissions\n self._verify_setup()\n\n # Files to save\n self.save_notes = \"\"\n\n # Add number of outstanding challenges\n self._chall_out = 0\n\n # These will be set in the prepare function\n self.parser = None\n self.version = version\n self._enhance_func = {\"redirect\": self._enable_redirect}\n\n # Set up reverter\n self.reverter = reverter.Reverter(self.config)\n self.reverter.recovery_routine()\n\n @property\n def mod_ssl_conf(self):\n \"\"\"Full absolute path to SSL configuration file.\"\"\"\n return os.path.join(self.config.config_dir, constants.MOD_SSL_CONF_DEST)\n\n # This is called in determine_authenticator and determine_installer\n def prepare(self):\n \"\"\"Prepare the authenticator/installer.\n\n :raises .errors.NoInstallationError: If Nginx ctl cannot be found\n :raises .errors.MisconfigurationError: If Nginx is misconfigured\n \"\"\"\n # Verify Nginx is installed\n if not util.exe_exists(self.conf('ctl')):\n raise errors.NoInstallationError\n\n # Make sure configuration is valid\n self.config_test()\n\n self.parser = parser.NginxParser(\n self.conf('server-root'), self.mod_ssl_conf)\n\n # Set Version\n if self.version is None:\n self.version = self.get_version()\n\n temp_install(self.mod_ssl_conf)\n\n # Entry point in main.py for installing cert\n def deploy_cert(self, domain, cert_path, key_path,\n chain_path=None, fullchain_path=None):\n # pylint: disable=unused-argument\n \"\"\"Deploys certificate to specified virtual host.\n\n .. note:: Aborts if the vhost is missing ssl_certificate or\n ssl_certificate_key.\n\n .. note:: Nginx doesn't have a cert chain directive.\n It expects the cert file to have the concatenated chain.\n However, we use the chain file as input to the\n ssl_trusted_certificate directive, used for verify OCSP responses.\n\n .. note:: This doesn't save the config files!\n\n :raises errors.PluginError: When unable to deploy certificate due to\n a lack of directives or configuration\n\n \"\"\"\n if not fullchain_path:\n raise errors.PluginError(\n \"The nginx plugin currently requires --fullchain-path to \"\n \"install a cert.\")\n\n vhost = self.choose_vhost(domain)\n cert_directives = [['\\n', 'ssl_certificate', ' ', fullchain_path],\n ['\\n', 'ssl_certificate_key', ' ', key_path]]\n\n # OCSP stapling was introduced in Nginx 1.3.7. If we have that version\n # or greater, add config settings for it.\n stapling_directives = []\n if self.version >= (1, 3, 7):\n stapling_directives = [\n ['\\n ', 'ssl_trusted_certificate', ' ', chain_path],\n ['\\n ', 'ssl_stapling', ' ', 'on'],\n ['\\n ', 'ssl_stapling_verify', ' ', 'on'], ['\\n']]\n\n if len(stapling_directives) != 0 and not chain_path:\n raise errors.PluginError(\n \"--chain-path is required to enable \"\n \"Online Certificate Status Protocol (OCSP) stapling \"\n \"on nginx >= 1.3.7.\")\n\n try:\n self.parser.add_server_directives(vhost.filep, vhost.names,\n cert_directives, replace=True)\n self.parser.add_server_directives(vhost.filep, vhost.names,\n stapling_directives, replace=False)\n logger.info(\"Deployed Certificate to VirtualHost %s for %s\",\n vhost.filep, vhost.names)\n except errors.MisconfigurationError as error:\n logger.debug(error)\n logger.warning(\n \"Cannot find a cert or key directive in %s for %s. \"\n \"VirtualHost was not modified.\", vhost.filep, vhost.names)\n # Presumably break here so that the virtualhost is not modified\n return False\n\n self.save_notes += (\"Changed vhost at %s with addresses of %s\\n\" %\n (vhost.filep,\n \", \".join(str(addr) for addr in vhost.addrs)))\n self.save_notes += \"\\tssl_certificate %s\\n\" % fullchain_path\n self.save_notes += \"\\tssl_certificate_key %s\\n\" % key_path\n if len(stapling_directives) > 0:\n self.save_notes += \"\\tssl_trusted_certificate %s\\n\" % chain_path\n self.save_notes += \"\\tssl_stapling on\\n\"\n self.save_notes += \"\\tssl_stapling_verify on\\n\"\n\n\n\n #######################\n # Vhost parsing methods\n #######################\n def choose_vhost(self, target_name):\n \"\"\"Chooses a virtual host based on the given domain name.\n\n .. note:: This makes the vhost SSL-enabled if it isn't already. Follows\n Nginx's server block selection rules preferring blocks that are\n already SSL.\n\n .. todo:: This should maybe return list if no obvious answer\n is presented.\n\n .. todo:: The special name \"$hostname\" corresponds to the machine's\n hostname. Currently we just ignore this.\n\n :param str target_name: domain name\n\n :returns: ssl vhost associated with name\n :rtype: :class:`~certbot_nginx.obj.VirtualHost`\n\n \"\"\"\n vhost = None\n\n matches = self._get_ranked_matches(target_name)\n if not matches:\n # No matches. Create a new vhost with this name in nginx.conf.\n filep = self.parser.loc[\"root\"]\n new_block = [['server'], [['\\n', 'server_name', ' ', target_name]]]\n self.parser.add_http_directives(filep, new_block)\n vhost = obj.VirtualHost(filep, set([]), False, True,\n set([target_name]), list(new_block[1]))\n elif matches[0]['rank'] in xrange(2, 6):\n # Wildcard match - need to find the longest one\n rank = matches[0]['rank']\n wildcards = [x for x in matches if x['rank'] == rank]\n vhost = max(wildcards, key=lambda x: len(x['name']))['vhost']\n else:\n vhost = matches[0]['vhost']\n\n if vhost is not None:\n if not vhost.ssl:\n self._make_server_ssl(vhost)\n\n return vhost\n\n def _get_ranked_matches(self, target_name):\n \"\"\"Returns a ranked list of vhosts that match target_name.\n The ranking gives preference to SSL vhosts.\n\n :param str target_name: The name to match\n :returns: list of dicts containing the vhost, the matching name, and\n the numerical rank\n :rtype: list\n\n \"\"\"\n # Nginx chooses a matching server name for a request with precedence:\n # 1. exact name match\n # 2. longest wildcard name starting with *\n # 3. longest wildcard name ending with *\n # 4. first matching regex in order of appearance in the file\n matches = []\n for vhost in self.parser.get_vhosts():\n name_type, name = parser.get_best_match(target_name, vhost.names)\n if name_type == 'exact':\n matches.append({'vhost': vhost,\n 'name': name,\n 'rank': 0 if vhost.ssl else 1})\n elif name_type == 'wildcard_start':\n matches.append({'vhost': vhost,\n 'name': name,\n 'rank': 2 if vhost.ssl else 3})\n elif name_type == 'wildcard_end':\n matches.append({'vhost': vhost,\n 'name': name,\n 'rank': 4 if vhost.ssl else 5})\n elif name_type == 'regex':\n matches.append({'vhost': vhost,\n 'name': name,\n 'rank': 6 if vhost.ssl else 7})\n return sorted(matches, key=lambda x: x['rank'])\n\n def get_all_names(self):\n \"\"\"Returns all names found in the Nginx Configuration.\n\n :returns: All ServerNames, ServerAliases, and reverse DNS entries for\n virtual host addresses\n :rtype: set\n\n \"\"\"\n all_names = set()\n\n for vhost in self.parser.get_vhosts():\n all_names.update(vhost.names)\n\n for addr in vhost.addrs:\n host = addr.get_addr()\n if common.hostname_regex.match(host):\n # If it's a hostname, add it to the names.\n all_names.add(host)\n elif not common.private_ips_regex.match(host):\n # If it isn't a private IP, do a reverse DNS lookup\n # TODO: IPv6 support\n try:\n socket.inet_aton(host)\n all_names.add(socket.gethostbyaddr(host)[0])\n except (socket.error, socket.herror, socket.timeout):\n continue\n\n return all_names\n\n def _get_snakeoil_paths(self):\n # TODO: generate only once\n tmp_dir = os.path.join(self.config.work_dir, \"snakeoil\")\n le_key = crypto_util.init_save_key(\n key_size=1024, key_dir=tmp_dir, keyname=\"key.pem\")\n key = OpenSSL.crypto.load_privatekey(\n OpenSSL.crypto.FILETYPE_PEM, le_key.pem)\n cert = acme_crypto_util.gen_ss_cert(key, domains=[socket.gethostname()])\n cert_pem = OpenSSL.crypto.dump_certificate(\n OpenSSL.crypto.FILETYPE_PEM, cert)\n cert_file, cert_path = util.unique_file(os.path.join(tmp_dir, \"cert.pem\"))\n with cert_file:\n cert_file.write(cert_pem)\n return cert_path, le_key.file\n\n def _make_server_ssl(self, vhost):\n \"\"\"Make a server SSL.\n\n Make a server SSL based on server_name and filename by adding a\n ``listen IConfig.tls_sni_01_port ssl`` directive to the server block.\n\n .. todo:: Maybe this should create a new block instead of modifying\n the existing one?\n\n :param vhost: The vhost to add SSL to.\n :type vhost: :class:`~certbot_nginx.obj.VirtualHost`\n\n \"\"\"\n snakeoil_cert, snakeoil_key = self._get_snakeoil_paths()\n ssl_block = [['\\n ', 'listen', ' ', '{0} ssl'.format(self.config.tls_sni_01_port)],\n ['\\n ', 'ssl_certificate', ' ', snakeoil_cert],\n ['\\n ', 'ssl_certificate_key', ' ', snakeoil_key],\n ['\\n ', 'include', ' ', self.parser.loc[\"ssl_options\"]]]\n self.parser.add_server_directives(\n vhost.filep, vhost.names, ssl_block, replace=False)\n vhost.ssl = True\n vhost.raw.extend(ssl_block)\n vhost.addrs.add(obj.Addr(\n '', str(self.config.tls_sni_01_port), True, False))\n\n def get_all_certs_keys(self):\n \"\"\"Find all existing keys, certs from configuration.\n\n :returns: list of tuples with form [(cert, key, path)]\n cert - str path to certificate file\n key - str path to associated key file\n path - File path to configuration file.\n :rtype: set\n\n \"\"\"\n return self.parser.get_all_certs_keys()\n\n ##################################\n # enhancement methods (IInstaller)\n ##################################\n def supported_enhancements(self): # pylint: disable=no-self-use\n \"\"\"Returns currently supported enhancements.\"\"\"\n return ['redirect']\n\n def enhance(self, domain, enhancement, options=None):\n \"\"\"Enhance configuration.\n\n :param str domain: domain to enhance\n :param str enhancement: enhancement type defined in\n :const:`~certbot.constants.ENHANCEMENTS`\n :param options: options for the enhancement\n See :const:`~certbot.constants.ENHANCEMENTS`\n documentation for appropriate parameter.\n\n \"\"\"\n try:\n return self._enhance_func[enhancement](\n self.choose_vhost(domain), options)\n except (KeyError, ValueError):\n raise errors.PluginError(\n \"Unsupported enhancement: {0}\".format(enhancement))\n except errors.PluginError:\n logger.warning(\"Failed %s for %s\", enhancement, domain)\n\n def _enable_redirect(self, vhost, unused_options):\n \"\"\"Redirect all equivalent HTTP traffic to ssl_vhost.\n\n Add rewrite directive to non https traffic\n\n .. note:: This function saves the configuration\n\n :param vhost: Destination of traffic, an ssl enabled vhost\n :type vhost: :class:`~certbot_nginx.obj.VirtualHost`\n\n :param unused_options: Not currently used\n :type unused_options: Not Available\n \"\"\"\n redirect_block = [[\n ['\\n ', 'if', ' ', '($scheme != \"https\") '],\n [['\\n ', 'return', ' ', '301 https://$host$request_uri'],\n '\\n ']\n ], ['\\n']]\n self.parser.add_server_directives(\n vhost.filep, vhost.names, redirect_block, replace=False)\n logger.info(\"Redirecting all traffic to ssl in %s\", vhost.filep)\n\n ######################################\n # Nginx server management (IInstaller)\n ######################################\n def restart(self):\n \"\"\"Restarts nginx server.\n\n :raises .errors.MisconfigurationError: If either the reload fails.\n\n \"\"\"\n nginx_restart(self.conf('ctl'), self.nginx_conf)\n\n def config_test(self): # pylint: disable=no-self-use\n \"\"\"Check the configuration of Nginx for errors.\n\n :raises .errors.MisconfigurationError: If config_test fails\n\n \"\"\"\n try:\n util.run_script([self.conf('ctl'), \"-c\", self.nginx_conf, \"-t\"])\n except errors.SubprocessError as err:\n raise errors.MisconfigurationError(str(err))\n\n def _verify_setup(self):\n \"\"\"Verify the setup to ensure safe operating environment.\n\n Make sure that files/directories are setup with appropriate permissions\n Aim for defensive coding... make sure all input files\n have permissions of root.\n\n \"\"\"\n uid = os.geteuid()\n util.make_or_verify_dir(\n self.config.work_dir, core_constants.CONFIG_DIRS_MODE, uid)\n util.make_or_verify_dir(\n self.config.backup_dir, core_constants.CONFIG_DIRS_MODE, uid)\n util.make_or_verify_dir(\n self.config.config_dir, core_constants.CONFIG_DIRS_MODE, uid)\n\n def get_version(self):\n \"\"\"Return version of Nginx Server.\n\n Version is returned as tuple. (ie. 2.4.7 = (2, 4, 7))\n\n :returns: version\n :rtype: tuple\n\n :raises .PluginError:\n Unable to find Nginx version or version is unsupported\n\n \"\"\"\n try:\n proc = subprocess.Popen(\n [self.conf('ctl'), \"-c\", self.nginx_conf, \"-V\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n text = proc.communicate()[1] # nginx prints output to stderr\n except (OSError, ValueError) as error:\n logging.debug(error, exc_info=True)\n raise errors.PluginError(\n \"Unable to run %s -V\" % self.conf('ctl'))\n\n version_regex = re.compile(r\"nginx/([0-9\\.]*)\", re.IGNORECASE)\n version_matches = version_regex.findall(text)\n\n sni_regex = re.compile(r\"TLS SNI support enabled\", re.IGNORECASE)\n sni_matches = sni_regex.findall(text)\n\n ssl_regex = re.compile(r\" --with-http_ssl_module\")\n ssl_matches = ssl_regex.findall(text)\n\n if not version_matches:\n raise errors.PluginError(\"Unable to find Nginx version\")\n if not ssl_matches:\n raise errors.PluginError(\n \"Nginx build is missing SSL module (--with-http_ssl_module).\")\n if not sni_matches:\n raise errors.PluginError(\"Nginx build doesn't support SNI\")\n\n nginx_version = tuple([int(i) for i in version_matches[0].split(\".\")])\n\n # nginx < 0.8.48 uses machine hostname as default server_name instead of\n # the empty string\n if nginx_version < (0, 8, 48):\n raise errors.NotSupportedError(\"Nginx version must be 0.8.48+\")\n\n return nginx_version\n\n def more_info(self):\n \"\"\"Human-readable string to help understand the module\"\"\"\n return (\n \"Configures Nginx to authenticate and install HTTPS.{0}\"\n \"Server root: {root}{0}\"\n \"Version: {version}\".format(\n os.linesep, root=self.parser.loc[\"root\"],\n version=\".\".join(str(i) for i in self.version))\n )\n\n ###################################################\n # Wrapper functions for Reverter class (IInstaller)\n ###################################################\n def save(self, title=None, temporary=False):\n \"\"\"Saves all changes to the configuration files.\n\n :param str title: The title of the save. If a title is given, the\n configuration will be saved as a new checkpoint and put in a\n timestamped directory.\n\n :param bool temporary: Indicates whether the changes made will\n be quickly reversed in the future (ie. challenges)\n\n :raises .errors.PluginError: If there was an error in\n an attempt to save the configuration, or an error creating a\n checkpoint\n\n \"\"\"\n save_files = set(self.parser.parsed.keys())\n\n try:\n # Create Checkpoint\n if temporary:\n self.reverter.add_to_temp_checkpoint(\n save_files, self.save_notes)\n else:\n self.reverter.add_to_checkpoint(save_files,\n self.save_notes)\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n\n self.save_notes = \"\"\n\n # Change 'ext' to something else to not override existing conf files\n self.parser.filedump(ext='')\n if title and not temporary:\n try:\n self.reverter.finalize_checkpoint(title)\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n\n return True\n\n def recovery_routine(self):\n \"\"\"Revert all previously modified files.\n\n Reverts all modified files that have not been saved as a checkpoint\n\n :raises .errors.PluginError: If unable to recover the configuration\n\n \"\"\"\n try:\n self.reverter.recovery_routine()\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n self.parser.load()\n\n def revert_challenge_config(self):\n \"\"\"Used to cleanup challenge configurations.\n\n :raises .errors.PluginError: If unable to revert the challenge config.\n\n \"\"\"\n try:\n self.reverter.revert_temporary_config()\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n self.parser.load()\n\n def rollback_checkpoints(self, rollback=1):\n \"\"\"Rollback saved checkpoints.\n\n :param int rollback: Number of checkpoints to revert\n\n :raises .errors.PluginError: If there is a problem with the input or\n the function is unable to correctly revert the configuration\n\n \"\"\"\n try:\n self.reverter.rollback_checkpoints(rollback)\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n self.parser.load()\n\n def view_config_changes(self):\n \"\"\"Show all of the configuration changes that have taken place.\n\n :raises .errors.PluginError: If there is a problem while processing\n the checkpoints directories.\n\n \"\"\"\n try:\n self.reverter.view_config_changes()\n except errors.ReverterError as err:\n raise errors.PluginError(str(err))\n\n ###########################################################################\n # Challenges Section for IAuthenticator\n ###########################################################################\n def get_chall_pref(self, unused_domain): # pylint: disable=no-self-use\n \"\"\"Return list of challenge preferences.\"\"\"\n return [challenges.TLSSNI01]\n\n # Entry point in main.py for performing challenges\n def perform(self, achalls):\n \"\"\"Perform the configuration related challenge.\n\n This function currently assumes all challenges will be fulfilled.\n If this turns out not to be the case in the future. Cleanup and\n outstanding challenges will have to be designed better.\n\n \"\"\"\n self._chall_out += len(achalls)\n responses = [None] * len(achalls)\n chall_doer = tls_sni_01.NginxTlsSni01(self)\n\n for i, achall in enumerate(achalls):\n # Currently also have chall_doer hold associated index of the\n # challenge. This helps to put all of the responses back together\n # when they are all complete.\n chall_doer.add_chall(achall, i)\n\n sni_response = chall_doer.perform()\n # Must restart in order to activate the challenges.\n # Handled here because we may be able to load up other challenge types\n self.restart()\n\n # Go through all of the challenges and assign them to the proper place\n # in the responses return value. All responses must be in the same order\n # as the original challenges.\n for i, resp in enumerate(sni_response):\n responses[chall_doer.indices[i]] = resp\n\n return responses\n\n # called after challenges are performed\n def cleanup(self, achalls):\n \"\"\"Revert all challenges.\"\"\"\n self._chall_out -= len(achalls)\n\n # If all of the challenges have been finished, clean up everything\n if self._chall_out <= 0:\n self.revert_challenge_config()\n self.restart()\n\n\ndef nginx_restart(nginx_ctl, nginx_conf=\"/etc/nginx.conf\"):\n \"\"\"Restarts the Nginx Server.\n\n .. todo:: Nginx restart is fatal if the configuration references\n non-existent SSL cert/key files. Remove references to /etc/letsencrypt\n before restart.\n\n :param str nginx_ctl: Path to the Nginx binary.\n\n \"\"\"\n try:\n proc = subprocess.Popen([nginx_ctl, \"-c\", nginx_conf, \"-s\", \"reload\"],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n stdout, stderr = proc.communicate()\n\n if proc.returncode != 0:\n # Maybe Nginx isn't running\n nginx_proc = subprocess.Popen([nginx_ctl, \"-c\", nginx_conf],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n stdout, stderr = nginx_proc.communicate()\n\n if nginx_proc.returncode != 0:\n # Enter recovery routine...\n raise errors.MisconfigurationError(\n \"nginx restart failed:\\n%s\\n%s\" % (stdout, stderr))\n\n except (OSError, ValueError):\n raise errors.MisconfigurationError(\"nginx restart failed\")\n # Nginx can take a moment to recognize a newly added TLS SNI servername, so sleep\n # for a second. TODO: Check for expected servername and loop until it\n # appears or return an error if looping too long.\n time.sleep(1)\n\n\ndef temp_install(options_ssl):\n \"\"\"Temporary install for convenience.\"\"\"\n # Check to make sure options-ssl.conf is installed\n if not os.path.isfile(options_ssl):\n shutil.copyfile(constants.MOD_SSL_CONF_SRC, options_ssl)\n", "path": "certbot-nginx/certbot_nginx/configurator.py" } ]
diff --git a/certbot-nginx/certbot_nginx/configurator.py b/certbot-nginx/certbot_nginx/configurator.py index a1c24b5c8df..049ba9a2013 100644 --- a/certbot-nginx/certbot_nginx/configurator.py +++ b/certbot-nginx/certbot_nginx/configurator.py @@ -55,7 +55,9 @@ class NginxConfigurator(common.Plugin): """ - description = "Nginx Web Server - currently doesn't work" + description = "Nginx Web Server plugin - Alpha" + + hidden = True @classmethod def add_parser_arguments(cls, add):
mne-tools__mne-bids-pipeline-680
Doc deployment step failing The latest CI run failed to execute documentation deployment: https://app.circleci.com/pipelines/github/mne-tools/mne-bids-pipeline/3557/workflows/3458e5cc-c471-4664-8d0a-b0cc4961f9eb/jobs/41986/parallel-runs/0/steps/0-107 ```shell #!/bin/bash -eo pipefail ./.circleci/setup_bash.sh CIRCLE_JOB=deploy_docs COMMIT_MESSAGE=68c63d6878992fb7c298f24420f1d349c6811079 MAINT: Use mike for doc deployment (#676) COMMIT_MESSAGE_ESCAPED=68c63d6878992fb7c298f24420f1d349c6811079 MAINT: Use mike for doc deployment (#676) CIRCLE_REQUESTED_JOB= Running job deploy_docs for main branch ./.circleci/setup_bash.sh: line 35: sudo: command not found Exited with code exit status 127 CircleCI received exit code 127 ```
[ { "content": "#!/bin/env python\n\"\"\"Generate steps.md.\"\"\"\n\nimport importlib\nfrom pathlib import Path\nfrom mne_bids_pipeline._config_utils import _get_step_modules\n\npre = \"\"\"\\\n# Processing steps\n\nThe following table provides a concise summary of each step in the Study\nTemplate. All steps exist in the `steps`/ directory.\n\"\"\"\n\nstep_modules = _get_step_modules()\n\n# Construct the lines of steps.md\nlines = [pre]\nfor di, (dir_, modules) in enumerate(step_modules.items(), 1):\n if dir_ == 'all':\n continue # this is an alias\n dir_module = importlib.import_module(f'mne_bids_pipeline.steps.{dir_}')\n dir_header = dir_module.__doc__.split('\\n')[0].rstrip('.')\n dir_body = dir_module.__doc__.split('\\n', maxsplit=1)\n if len(dir_body) > 1:\n dir_body = dir_body[1].strip()\n else:\n dir_body = ''\n lines.append(f'## {di}. {dir_header}\\n')\n if dir_body:\n lines.append(f'{dir_body}\\n')\n lines.append('| Processing step | Description |')\n lines.append('|:----------------|:------------|')\n # the \"all\" option\n dir_name, step_title = dir_, f'Run all {dir_header.lower()} steps.'\n lines.append(f'`{dir_name}` | {step_title} |')\n for module in modules:\n step_name = f'{dir_name}/{Path(module.__file__).name}'[:-3]\n step_title = module.__doc__.split('\\n')[0]\n lines.append(f'`{step_name}` | {step_title} |')\n lines.append('')\nwith open(Path(__file__).parent / 'steps.md', 'w') as fid:\n fid.write('\\n'.join(lines))\n", "path": "docs/source/features/gen_steps.py" } ]
[ { "content": "#!/bin/env python\n\"\"\"Generate steps.md.\"\"\"\n\nimport importlib\nfrom pathlib import Path\nfrom mne_bids_pipeline._config_utils import _get_step_modules\n\npre = \"\"\"\\\n# Processing steps\n\nThe following table provides a concise summary of each step in the Study\nTemplate. All steps exist in the `steps`/ directory.\n\"\"\"\n\nprint('Generating steps …')\nstep_modules = _get_step_modules()\n\n# Construct the lines of steps.md\nlines = [pre]\nfor di, (dir_, modules) in enumerate(step_modules.items(), 1):\n if dir_ == 'all':\n continue # this is an alias\n dir_module = importlib.import_module(f'mne_bids_pipeline.steps.{dir_}')\n dir_header = dir_module.__doc__.split('\\n')[0].rstrip('.')\n dir_body = dir_module.__doc__.split('\\n', maxsplit=1)\n if len(dir_body) > 1:\n dir_body = dir_body[1].strip()\n else:\n dir_body = ''\n lines.append(f'## {di}. {dir_header}\\n')\n if dir_body:\n lines.append(f'{dir_body}\\n')\n lines.append('| Processing step | Description |')\n lines.append('|:----------------|:------------|')\n # the \"all\" option\n dir_name, step_title = dir_, f'Run all {dir_header.lower()} steps.'\n lines.append(f'`{dir_name}` | {step_title} |')\n for module in modules:\n step_name = f'{dir_name}/{Path(module.__file__).name}'[:-3]\n step_title = module.__doc__.split('\\n')[0]\n lines.append(f'`{step_name}` | {step_title} |')\n lines.append('')\nwith open(Path(__file__).parent / 'steps.md', 'w') as fid:\n fid.write('\\n'.join(lines))\n", "path": "docs/source/features/gen_steps.py" } ]
diff --git a/.circleci/config.yml b/.circleci/config.yml index 63076d580..bb419bcfe 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1132,13 +1132,9 @@ jobs: - store_artifacts: path: docs/site destination: site - - persist_to_workspace: # For documentation deployment to gh-pages - root: ~/ - paths: project/docs/site deploy_docs: - docker: - - image: node:10 + <<: *imageconfig steps: - add_ssh_keys: fingerprints: @@ -1157,15 +1153,23 @@ jobs: <<: *git - run: <<: *bashenv + - run: + # This is a bit computationally inefficient, but it should be much + # faster to "cp" directly on the machine rather than persist + # 1GB doc build to workspace then go retrieve it + name: Build documentation again + command: | + make doc - run: name: Deploy docs to gh-pages branch # https://github.com/jimporter/mike command: | - git config --global user.email "[email protected]" - git config --global user.name "Circle CI" # Arguments used in all mike commands ARGS="--config-file docs/mkdocs.yml" - echo "Deploying dev" + # First we need to actually check out our current version of + # gh-pages so we don't remove it! + git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" + git fetch origin # If it's tagged as v*, deploy as "v*" and "stable" as well if git describe --tags --exact-match $(git rev-parse HEAD); then VERSION="$(git describe --tags --exact-match $(git rev-parse HEAD))" @@ -1184,7 +1188,7 @@ jobs: fi git checkout gh-pages git reset $(git commit-tree HEAD^{tree} -m "Deploy and squash docs [ci skip]") - git log -n1 + git log -n3 # should just be one, but let's be sure git push origin --force gh-pages diff --git a/README.md b/README.md index 94ffe6ef0..662062cdd 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,27 @@ -[![CircleCI](https://circleci.com/gh/mne-tools/mne-bids-pipeline.svg?style=svg)](https://circleci.com/gh/mne-tools/mne-bids-pipeline) +# <img src="https://raw.github.com/mne-tools/mne-bids-pipeline/main/docs/source/assets/mne.svg" alt="MNE Logo" height="20"> The MNE-BIDS-Pipeline -# MNE-BIDS-Pipeline +**The MNE-BIDS-Pipeline is a full-flegded processing pipeline for your MEG and +EEG data.** It operates on data stored according to the [Brain Imaging Data +Structure (BIDS)](https://bids.neuroimaging.io/). Under the hood, it uses [MNE-Python](https://mne.tools). -The MNE-BIDS-Pipeline is a full-flegded processing pipeline for your MEG and -EEG data. It operates on data stored according to the Brain Imaging Data -Structure (BIDS). +## 💡 Basic concepts and features -# Documentation +* 🐾 Data processing as a sequence of processing steps. +* ⏏ Your data can be "ejected" from the pipeline at **any** stage. No lock-in! +* 🧾 Extensive processing and analysis summary reports. +* 🎬 Process just a single participant, or as many as several hundreds of participants – in parallel. +* 🛠 Configuration via a simple text file. +* 💻 Execution via an easy-to-use command-line utility. +* 🆘 Helpful error messages in case something goes wrong. -Please find the installation and usage instructions at -[mne.tools/mne-bids-pipeline](https://mne.tools/mne-bids-pipeline). +## 📘 Installation and usage instructions -# Acknowledgments +Please find the documentation at +[**mne.tools/mne-bids-pipeline**](https://mne.tools/mne-bids-pipeline). -The original pipeline for MEG/EEG data processing with MNE python was built +## ❤ Acknowledgments + +The original pipeline for MEG/EEG data processing with MNE-Python was built jointly by the [Cognition and Brain Dynamics Team](https://brainthemind.com/) and the [MNE Python Team](https://mne.tools), based on scripts originally developed for this publication: diff --git a/docs/source/features/gen_steps.py b/docs/source/features/gen_steps.py index 463d32bf4..e7c82ca7a 100755 --- a/docs/source/features/gen_steps.py +++ b/docs/source/features/gen_steps.py @@ -12,6 +12,7 @@ Template. All steps exist in the `steps`/ directory. """ +print('Generating steps …') step_modules = _get_step_modules() # Construct the lines of steps.md
liqd__a4-opin-766
cannot delete user in django admin if user has not uploaded avatar
[ { "content": "from django.db.models import signals\nfrom django.dispatch import receiver\n\nfrom adhocracy4.images import services\n\nfrom . import models\n\n\n@receiver(signals.post_init, sender=models.User)\ndef backup_image_path(sender, instance, **kwargs):\n instance._current_image_file = instance.avatar\n\n\n@receiver(signals.post_save, sender=models.User)\ndef delete_old_image(sender, instance, **kwargs):\n if hasattr(instance, '_current_image_file'):\n if instance._current_image_file != instance.avatar:\n services.delete_images([instance._current_image_file])\n\n\n@receiver(signals.post_delete, sender=models.User)\ndef delete_images_for_User(sender, instance, **kwargs):\n services.delete_images([instance.avatar])\n", "path": "euth/users/signals.py" } ]
[ { "content": "from django.db.models import signals\nfrom django.dispatch import receiver\n\nfrom adhocracy4.images import services\n\nfrom . import models\n\n\n@receiver(signals.post_init, sender=models.User)\ndef backup_image_path(sender, instance, **kwargs):\n instance._current_image_file = instance.avatar\n\n\n@receiver(signals.post_save, sender=models.User)\ndef delete_old_image(sender, instance, **kwargs):\n if hasattr(instance, '_current_image_file'):\n if instance._current_image_file != instance.avatar:\n services.delete_images([instance._current_image_file])\n\n\n@receiver(signals.post_delete, sender=models.User)\ndef delete_images_for_User(sender, instance, **kwargs):\n services.delete_images([instance._avatar])\n", "path": "euth/users/signals.py" } ]
diff --git a/euth/users/signals.py b/euth/users/signals.py index e29186dff..d74a64923 100644 --- a/euth/users/signals.py +++ b/euth/users/signals.py @@ -20,4 +20,4 @@ def delete_old_image(sender, instance, **kwargs): @receiver(signals.post_delete, sender=models.User) def delete_images_for_User(sender, instance, **kwargs): - services.delete_images([instance.avatar]) + services.delete_images([instance._avatar])
networkx__networkx-3958
Misleading description in the doc In this page https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.structuralholes.effective_size.html The description of *Return* is "Dictionary with nodes as keys and the constraint on the node as values." But this is effective size. I think it should be "Dictionary with nodes as keys and the **effective size of** the node as values."
[ { "content": "\"\"\"Functions for computing measures of structural holes.\"\"\"\n\nimport networkx as nx\n\n__all__ = ['constraint', 'local_constraint', 'effective_size']\n\n\ndef mutual_weight(G, u, v, weight=None):\n \"\"\"Returns the sum of the weights of the edge from `u` to `v` and\n the edge from `v` to `u` in `G`.\n\n `weight` is the edge data key that represents the edge weight. If\n the specified key is `None` or is not in the edge data for an edge,\n that edge is assumed to have weight 1.\n\n Pre-conditions: `u` and `v` must both be in `G`.\n\n \"\"\"\n try:\n a_uv = G[u][v].get(weight, 1)\n except KeyError:\n a_uv = 0\n try:\n a_vu = G[v][u].get(weight, 1)\n except KeyError:\n a_vu = 0\n return a_uv + a_vu\n\n\ndef normalized_mutual_weight(G, u, v, norm=sum, weight=None):\n \"\"\"Returns normalized mutual weight of the edges from `u` to `v`\n with respect to the mutual weights of the neighbors of `u` in `G`.\n\n `norm` specifies how the normalization factor is computed. It must\n be a function that takes a single argument and returns a number.\n The argument will be an iterable of mutual weights\n of pairs ``(u, w)``, where ``w`` ranges over each (in- and\n out-)neighbor of ``u``. Commons values for `normalization` are\n ``sum`` and ``max``.\n\n `weight` can be ``None`` or a string, if None, all edge weights\n are considered equal. Otherwise holds the name of the edge\n attribute used as weight.\n\n \"\"\"\n scale = norm(mutual_weight(G, u, w, weight)\n for w in set(nx.all_neighbors(G, u)))\n return 0 if scale == 0 else mutual_weight(G, u, v, weight) / scale\n\n\ndef effective_size(G, nodes=None, weight=None):\n r\"\"\"Returns the effective size of all nodes in the graph ``G``.\n\n The *effective size* of a node's ego network is based on the concept\n of redundancy. A person's ego network has redundancy to the extent\n that her contacts are connected to each other as well. The\n nonredundant part of a person's relationships it's the effective\n size of her ego network [1]_. Formally, the effective size of a\n node $u$, denoted $e(u)$, is defined by\n\n .. math::\n\n e(u) = \\sum_{v \\in N(u) \\setminus \\{u\\}}\n \\left(1 - \\sum_{w \\in N(v)} p_{uw} m_{vw}\\right)\n\n where $N(u)$ is the set of neighbors of $u$ and $p_{uw}$ is the\n normalized mutual weight of the (directed or undirected) edges\n joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. And $m_{vw}$\n is the mutual weight of $v$ and $w$ divided by $v$ highest mutual\n weight with any of its neighbors. The *mutual weight* of $u$ and $v$\n is the sum of the weights of edges joining them (edge weights are\n assumed to be one if the graph is unweighted).\n\n For the case of unweighted and undirected graphs, Borgatti proposed\n a simplified formula to compute effective size [2]_\n\n .. math::\n\n e(u) = n - \\frac{2t}{n}\n\n where `t` is the number of ties in the ego network (not including\n ties to ego) and `n` is the number of nodes (excluding ego).\n\n Parameters\n ----------\n G : NetworkX graph\n The graph containing ``v``. Directed graphs are treated like\n undirected graphs when computing neighbors of ``v``.\n\n nodes : container, optional\n Container of nodes in the graph ``G`` to compute the effective size.\n If None, the effective size of every node is computed.\n\n weight : None or string, optional\n If None, all edge weights are considered equal.\n Otherwise holds the name of the edge attribute used as weight.\n\n Returns\n -------\n dict\n Dictionary with nodes as keys and the constraint on the node as values.\n\n Notes\n -----\n Burt also defined the related concept of *efficiency* of a node's ego\n network, which is its effective size divided by the degree of that\n node [1]_. So you can easily compute efficiency:\n\n >>> G = nx.DiGraph()\n >>> G.add_edges_from([(0, 1), (0, 2), (1, 0), (2, 1)])\n >>> esize = nx.effective_size(G)\n >>> efficiency = {n: v / G.degree(n) for n, v in esize.items()}\n\n See also\n --------\n constraint\n\n References\n ----------\n .. [1] Burt, Ronald S.\n *Structural Holes: The Social Structure of Competition.*\n Cambridge: Harvard University Press, 1995.\n\n .. [2] Borgatti, S.\n \"Structural Holes: Unpacking Burt's Redundancy Measures\"\n CONNECTIONS 20(1):35-38.\n http://www.analytictech.com/connections/v20(1)/holes.htm\n\n \"\"\"\n def redundancy(G, u, v, weight=None):\n nmw = normalized_mutual_weight\n r = sum(nmw(G, u, w, weight=weight) * nmw(G, v, w, norm=max, weight=weight)\n for w in set(nx.all_neighbors(G, u)))\n return 1 - r\n effective_size = {}\n if nodes is None:\n nodes = G\n # Use Borgatti's simplified formula for unweighted and undirected graphs\n if not G.is_directed() and weight is None:\n for v in nodes:\n # Effective size is not defined for isolated nodes\n if len(G[v]) == 0:\n effective_size[v] = float('nan')\n continue\n E = nx.ego_graph(G, v, center=False, undirected=True)\n effective_size[v] = len(E) - (2 * E.size()) / len(E)\n else:\n for v in nodes:\n # Effective size is not defined for isolated nodes\n if len(G[v]) == 0:\n effective_size[v] = float('nan')\n continue\n effective_size[v] = sum(redundancy(G, v, u, weight)\n for u in set(nx.all_neighbors(G, v)))\n return effective_size\n\n\ndef constraint(G, nodes=None, weight=None):\n r\"\"\"Returns the constraint on all nodes in the graph ``G``.\n\n The *constraint* is a measure of the extent to which a node *v* is\n invested in those nodes that are themselves invested in the\n neighbors of *v*. Formally, the *constraint on v*, denoted `c(v)`,\n is defined by\n\n .. math::\n\n c(v) = \\sum_{w \\in N(v) \\setminus \\{v\\}} \\ell(v, w)\n\n where `N(v)` is the subset of the neighbors of `v` that are either\n predecessors or successors of `v` and `\\ell(v, w)` is the local\n constraint on `v` with respect to `w` [1]_. For the definition of local\n constraint, see :func:`local_constraint`.\n\n Parameters\n ----------\n G : NetworkX graph\n The graph containing ``v``. This can be either directed or undirected.\n\n nodes : container, optional\n Container of nodes in the graph ``G`` to compute the constraint. If\n None, the constraint of every node is computed.\n\n weight : None or string, optional\n If None, all edge weights are considered equal.\n Otherwise holds the name of the edge attribute used as weight.\n\n Returns\n -------\n dict\n Dictionary with nodes as keys and the constraint on the node as values.\n\n See also\n --------\n local_constraint\n\n References\n ----------\n .. [1] Burt, Ronald S.\n \"Structural holes and good ideas\".\n American Journal of Sociology (110): 349–399.\n\n \"\"\"\n if nodes is None:\n nodes = G\n constraint = {}\n for v in nodes:\n # Constraint is not defined for isolated nodes\n if len(G[v]) == 0:\n constraint[v] = float('nan')\n continue\n constraint[v] = sum(local_constraint(G, v, n, weight)\n for n in set(nx.all_neighbors(G, v)))\n return constraint\n\n\ndef local_constraint(G, u, v, weight=None):\n r\"\"\"Returns the local constraint on the node ``u`` with respect to\n the node ``v`` in the graph ``G``.\n\n Formally, the *local constraint on u with respect to v*, denoted\n $\\ell(v)$, is defined by\n\n .. math::\n\n \\ell(u, v) = \\left(p_{uv} + \\sum_{w \\in N(v)} p_{uw} p{wv}\\right)^2,\n\n where $N(v)$ is the set of neighbors of $v$ and $p_{uv}$ is the\n normalized mutual weight of the (directed or undirected) edges\n joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. The *mutual\n weight* of $u$ and $v$ is the sum of the weights of edges joining\n them (edge weights are assumed to be one if the graph is\n unweighted).\n\n Parameters\n ----------\n G : NetworkX graph\n The graph containing ``u`` and ``v``. This can be either\n directed or undirected.\n\n u : node\n A node in the graph ``G``.\n\n v : node\n A node in the graph ``G``.\n\n weight : None or string, optional\n If None, all edge weights are considered equal.\n Otherwise holds the name of the edge attribute used as weight.\n\n Returns\n -------\n float\n The constraint of the node ``v`` in the graph ``G``.\n\n See also\n --------\n constraint\n\n References\n ----------\n .. [1] Burt, Ronald S.\n \"Structural holes and good ideas\".\n American Journal of Sociology (110): 349–399.\n\n \"\"\"\n nmw = normalized_mutual_weight\n direct = nmw(G, u, v, weight=weight)\n indirect = sum(nmw(G, u, w, weight=weight) * nmw(G, w, v, weight=weight)\n for w in set(nx.all_neighbors(G, u)))\n return (direct + indirect) ** 2\n", "path": "networkx/algorithms/structuralholes.py" } ]
[ { "content": "\"\"\"Functions for computing measures of structural holes.\"\"\"\n\nimport networkx as nx\n\n__all__ = ['constraint', 'local_constraint', 'effective_size']\n\n\ndef mutual_weight(G, u, v, weight=None):\n \"\"\"Returns the sum of the weights of the edge from `u` to `v` and\n the edge from `v` to `u` in `G`.\n\n `weight` is the edge data key that represents the edge weight. If\n the specified key is `None` or is not in the edge data for an edge,\n that edge is assumed to have weight 1.\n\n Pre-conditions: `u` and `v` must both be in `G`.\n\n \"\"\"\n try:\n a_uv = G[u][v].get(weight, 1)\n except KeyError:\n a_uv = 0\n try:\n a_vu = G[v][u].get(weight, 1)\n except KeyError:\n a_vu = 0\n return a_uv + a_vu\n\n\ndef normalized_mutual_weight(G, u, v, norm=sum, weight=None):\n \"\"\"Returns normalized mutual weight of the edges from `u` to `v`\n with respect to the mutual weights of the neighbors of `u` in `G`.\n\n `norm` specifies how the normalization factor is computed. It must\n be a function that takes a single argument and returns a number.\n The argument will be an iterable of mutual weights\n of pairs ``(u, w)``, where ``w`` ranges over each (in- and\n out-)neighbor of ``u``. Commons values for `normalization` are\n ``sum`` and ``max``.\n\n `weight` can be ``None`` or a string, if None, all edge weights\n are considered equal. Otherwise holds the name of the edge\n attribute used as weight.\n\n \"\"\"\n scale = norm(mutual_weight(G, u, w, weight)\n for w in set(nx.all_neighbors(G, u)))\n return 0 if scale == 0 else mutual_weight(G, u, v, weight) / scale\n\n\ndef effective_size(G, nodes=None, weight=None):\n r\"\"\"Returns the effective size of all nodes in the graph ``G``.\n\n The *effective size* of a node's ego network is based on the concept\n of redundancy. A person's ego network has redundancy to the extent\n that her contacts are connected to each other as well. The\n nonredundant part of a person's relationships it's the effective\n size of her ego network [1]_. Formally, the effective size of a\n node $u$, denoted $e(u)$, is defined by\n\n .. math::\n\n e(u) = \\sum_{v \\in N(u) \\setminus \\{u\\}}\n \\left(1 - \\sum_{w \\in N(v)} p_{uw} m_{vw}\\right)\n\n where $N(u)$ is the set of neighbors of $u$ and $p_{uw}$ is the\n normalized mutual weight of the (directed or undirected) edges\n joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. And $m_{vw}$\n is the mutual weight of $v$ and $w$ divided by $v$ highest mutual\n weight with any of its neighbors. The *mutual weight* of $u$ and $v$\n is the sum of the weights of edges joining them (edge weights are\n assumed to be one if the graph is unweighted).\n\n For the case of unweighted and undirected graphs, Borgatti proposed\n a simplified formula to compute effective size [2]_\n\n .. math::\n\n e(u) = n - \\frac{2t}{n}\n\n where `t` is the number of ties in the ego network (not including\n ties to ego) and `n` is the number of nodes (excluding ego).\n\n Parameters\n ----------\n G : NetworkX graph\n The graph containing ``v``. Directed graphs are treated like\n undirected graphs when computing neighbors of ``v``.\n\n nodes : container, optional\n Container of nodes in the graph ``G`` to compute the effective size.\n If None, the effective size of every node is computed.\n\n weight : None or string, optional\n If None, all edge weights are considered equal.\n Otherwise holds the name of the edge attribute used as weight.\n\n Returns\n -------\n dict\n Dictionary with nodes as keys and the effective size of the node as values.\n\n Notes\n -----\n Burt also defined the related concept of *efficiency* of a node's ego\n network, which is its effective size divided by the degree of that\n node [1]_. So you can easily compute efficiency:\n\n >>> G = nx.DiGraph()\n >>> G.add_edges_from([(0, 1), (0, 2), (1, 0), (2, 1)])\n >>> esize = nx.effective_size(G)\n >>> efficiency = {n: v / G.degree(n) for n, v in esize.items()}\n\n See also\n --------\n constraint\n\n References\n ----------\n .. [1] Burt, Ronald S.\n *Structural Holes: The Social Structure of Competition.*\n Cambridge: Harvard University Press, 1995.\n\n .. [2] Borgatti, S.\n \"Structural Holes: Unpacking Burt's Redundancy Measures\"\n CONNECTIONS 20(1):35-38.\n http://www.analytictech.com/connections/v20(1)/holes.htm\n\n \"\"\"\n def redundancy(G, u, v, weight=None):\n nmw = normalized_mutual_weight\n r = sum(nmw(G, u, w, weight=weight) * nmw(G, v, w, norm=max, weight=weight)\n for w in set(nx.all_neighbors(G, u)))\n return 1 - r\n effective_size = {}\n if nodes is None:\n nodes = G\n # Use Borgatti's simplified formula for unweighted and undirected graphs\n if not G.is_directed() and weight is None:\n for v in nodes:\n # Effective size is not defined for isolated nodes\n if len(G[v]) == 0:\n effective_size[v] = float('nan')\n continue\n E = nx.ego_graph(G, v, center=False, undirected=True)\n effective_size[v] = len(E) - (2 * E.size()) / len(E)\n else:\n for v in nodes:\n # Effective size is not defined for isolated nodes\n if len(G[v]) == 0:\n effective_size[v] = float('nan')\n continue\n effective_size[v] = sum(redundancy(G, v, u, weight)\n for u in set(nx.all_neighbors(G, v)))\n return effective_size\n\n\ndef constraint(G, nodes=None, weight=None):\n r\"\"\"Returns the constraint on all nodes in the graph ``G``.\n\n The *constraint* is a measure of the extent to which a node *v* is\n invested in those nodes that are themselves invested in the\n neighbors of *v*. Formally, the *constraint on v*, denoted `c(v)`,\n is defined by\n\n .. math::\n\n c(v) = \\sum_{w \\in N(v) \\setminus \\{v\\}} \\ell(v, w)\n\n where `N(v)` is the subset of the neighbors of `v` that are either\n predecessors or successors of `v` and `\\ell(v, w)` is the local\n constraint on `v` with respect to `w` [1]_. For the definition of local\n constraint, see :func:`local_constraint`.\n\n Parameters\n ----------\n G : NetworkX graph\n The graph containing ``v``. This can be either directed or undirected.\n\n nodes : container, optional\n Container of nodes in the graph ``G`` to compute the constraint. If\n None, the constraint of every node is computed.\n\n weight : None or string, optional\n If None, all edge weights are considered equal.\n Otherwise holds the name of the edge attribute used as weight.\n\n Returns\n -------\n dict\n Dictionary with nodes as keys and the constraint on the node as values.\n\n See also\n --------\n local_constraint\n\n References\n ----------\n .. [1] Burt, Ronald S.\n \"Structural holes and good ideas\".\n American Journal of Sociology (110): 349–399.\n\n \"\"\"\n if nodes is None:\n nodes = G\n constraint = {}\n for v in nodes:\n # Constraint is not defined for isolated nodes\n if len(G[v]) == 0:\n constraint[v] = float('nan')\n continue\n constraint[v] = sum(local_constraint(G, v, n, weight)\n for n in set(nx.all_neighbors(G, v)))\n return constraint\n\n\ndef local_constraint(G, u, v, weight=None):\n r\"\"\"Returns the local constraint on the node ``u`` with respect to\n the node ``v`` in the graph ``G``.\n\n Formally, the *local constraint on u with respect to v*, denoted\n $\\ell(v)$, is defined by\n\n .. math::\n\n \\ell(u, v) = \\left(p_{uv} + \\sum_{w \\in N(v)} p_{uw} p{wv}\\right)^2,\n\n where $N(v)$ is the set of neighbors of $v$ and $p_{uv}$ is the\n normalized mutual weight of the (directed or undirected) edges\n joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. The *mutual\n weight* of $u$ and $v$ is the sum of the weights of edges joining\n them (edge weights are assumed to be one if the graph is\n unweighted).\n\n Parameters\n ----------\n G : NetworkX graph\n The graph containing ``u`` and ``v``. This can be either\n directed or undirected.\n\n u : node\n A node in the graph ``G``.\n\n v : node\n A node in the graph ``G``.\n\n weight : None or string, optional\n If None, all edge weights are considered equal.\n Otherwise holds the name of the edge attribute used as weight.\n\n Returns\n -------\n float\n The constraint of the node ``v`` in the graph ``G``.\n\n See also\n --------\n constraint\n\n References\n ----------\n .. [1] Burt, Ronald S.\n \"Structural holes and good ideas\".\n American Journal of Sociology (110): 349–399.\n\n \"\"\"\n nmw = normalized_mutual_weight\n direct = nmw(G, u, v, weight=weight)\n indirect = sum(nmw(G, u, w, weight=weight) * nmw(G, w, v, weight=weight)\n for w in set(nx.all_neighbors(G, u)))\n return (direct + indirect) ** 2\n", "path": "networkx/algorithms/structuralholes.py" } ]
diff --git a/networkx/algorithms/structuralholes.py b/networkx/algorithms/structuralholes.py index cdd799703fe..3938faa258e 100644 --- a/networkx/algorithms/structuralholes.py +++ b/networkx/algorithms/structuralholes.py @@ -98,7 +98,7 @@ def effective_size(G, nodes=None, weight=None): Returns ------- dict - Dictionary with nodes as keys and the constraint on the node as values. + Dictionary with nodes as keys and the effective size of the node as values. Notes -----
liberapay__liberapay.com-2234
Enabling or disabling a specific visibility level as a creator This issue is for the upcoming feature mentioned in <https://medium.com/liberapay-blog/lifting-the-veil-of-anonymity-479dadd369be>. Patrons page doesn't mention the lack of support for secret donations through PayPal I just clicked the option to explictly not show who my patrons are in the settings. On the settings page it shows "You've chosen not to see who your patrons are." However on the donation page it shows "This donation won't be secret, you will appear in bjorn3's private list of patrons." Which of those two statements is true?
[ { "content": "from base64 import b64decode, b64encode\nfrom binascii import hexlify, unhexlify\nfrom datetime import date, datetime, timedelta\nimport errno\nimport fnmatch\nfrom hashlib import sha256\nimport hmac\nfrom operator import getitem\nimport os\nimport re\nimport socket\n\nfrom pando import Response, json\nfrom pando.utils import to_rfc822, utcnow\nfrom markupsafe import Markup\n\nfrom liberapay.constants import CURRENCIES, CURRENCY_REPLACEMENTS, SAFE_METHODS\nfrom liberapay.elsewhere._paginators import _modify_query\nfrom liberapay.exceptions import (\n AuthRequired, ClosedAccount, LoginRequired, TooManyAdminActions,\n)\nfrom liberapay.models.community import Community\nfrom liberapay.i18n.base import LOCALE_EN, add_helpers_to_context\nfrom liberapay.website import website\nfrom liberapay.utils import cbor\n\n\nBEGINNING_OF_EPOCH = to_rfc822(datetime(1970, 1, 1)).encode('ascii')\n\n\ndef get_participant(\n state, restrict=True, allow_member=False, redirect_canon=True, redirect_stub=True,\n):\n \"\"\"Get a participant from the ID or username in the request path.\n\n Args:\n restrict (bool): the page is private, restrict access to it\n allow_member (bool): allow members of a team to access this page\n redirect_canon (bool): allow redirecting the request to the canonical URL\n redirect_stub (bool): allow redirecting the request to the pledge page\n\n Returns a `Participant` or raises a `Response`.\n\n \"\"\"\n request = state['request']\n response = state['response']\n user = state['user']\n slug = request.path['username']\n _ = state['_']\n\n if restrict and user.ANON:\n raise LoginRequired\n\n if slug.startswith('~'):\n try:\n value = int(slug[1:])\n except ValueError:\n raise response.error(404)\n participant = user if user and user.id == value else None\n elif slug:\n value = slug.lower()\n participant = user if user and user.username.lower() == value else None\n else:\n raise response.error(404)\n\n if participant is None:\n if type(value) is int:\n participant = website.db.Participant.from_id(value, _raise=False)\n else:\n participant = website.db.Participant.from_username(value)\n if participant is None:\n if type(value) is str:\n look_up_redirections(request, response)\n raise response.error(404)\n elif participant.kind == 'community':\n c_name = website.db.one(\"\"\"\n SELECT name\n FROM communities\n WHERE participant = %s\n \"\"\", (participant.id,))\n raise response.redirect('/for/%s' % c_name)\n\n if request.method in SAFE_METHODS:\n if redirect_canon and slug != participant.username:\n canon = '/' + participant.username + request.line.uri.decoded[len(slug)+1:]\n raise response.redirect(canon)\n else:\n if restrict:\n user.require_write_permission()\n\n is_blocked = participant.is_suspended\n if (restrict or is_blocked) and participant != user:\n if allow_member and participant.kind == 'group' and user.member_of(participant):\n pass\n elif user.is_acting_as('admin'):\n log_admin_request(user, participant, request)\n elif restrict:\n raise response.error(403, _(\"You are not authorized to access this page.\"))\n elif is_blocked:\n raise response.render('simplates/blocked-profile.spt', state)\n\n status = participant.status\n if status == 'closed':\n if not user.is_acting_as('admin'):\n raise ClosedAccount(participant)\n elif status == 'stub':\n if redirect_stub:\n to = participant.resolve_stub()\n if not to:\n # Account has been taken over\n raise response.error(404)\n raise response.redirect(to)\n\n if allow_member and (user == participant or participant.kind == 'group' and user.member_of(participant)):\n state['can_switch_account'] = True\n\n return participant\n\n\ndef get_community(state, restrict=False):\n request, response = state['request'], state['response']\n user = state['user']\n name = request.path['name']\n\n c = Community.from_name(name)\n if not c:\n raise response.error(404)\n if request.method in SAFE_METHODS:\n if c.name != name:\n response.redirect('/for/' + c.name + request.line.uri.decoded[5+len(name):])\n elif user.ANON:\n raise AuthRequired\n else:\n user.require_write_permission()\n\n is_blocked = c.participant.is_suspended\n if (restrict or is_blocked):\n if user.id == c.creator:\n pass\n elif user.is_acting_as('admin'):\n log_admin_request(user, c.participant, request)\n elif restrict:\n if user.ANON:\n raise LoginRequired\n else:\n _ = state['_']\n raise response.error(403, _(\"You are not authorized to access this page.\"))\n elif is_blocked:\n raise response.render('simplates/blocked-profile.spt', state)\n\n return c\n\n\ndef log_admin_request(admin, participant, request):\n if request.method not in SAFE_METHODS:\n website.db.hit_rate_limit('admin.http-unsafe', admin.id, TooManyAdminActions)\n action_data = {\n 'method': request.method,\n 'path': request.path.raw,\n 'qs': dict(request.qs),\n 'body': {\n k: (v[0] if len(v) == 1 else v)\n for k, v in request.body.items()\n if k != 'csrf_token'\n },\n }\n participant.add_event(website.db, 'admin_request', action_data, admin.id)\n\n\ndef look_up_redirections(request, response):\n path = request.path.raw\n if not path.endswith('/'):\n path += '/'\n r = website.db.one(\"\"\"\n SELECT *\n FROM redirections\n WHERE starts_with(%s, from_prefix)\n ORDER BY length(from_prefix) DESC\n LIMIT 1\n \"\"\", (path.lower(),))\n if r:\n location = r.to_prefix + path[len(r.from_prefix.rstrip('%')):]\n response.redirect(location.rstrip('/'))\n\n\ndef form_post_success(state, msg='', redirect_url=None):\n \"\"\"This function is meant to be called after a successful form POST.\n \"\"\"\n request, response = state['request'], state['response']\n if request.headers.get(b'X-Requested-With') == b'XMLHttpRequest':\n raise response.json({\"msg\": msg} if msg else {})\n else:\n if not redirect_url:\n redirect_url = request.body.get('back_to') or request.line.uri.decoded\n redirect_url = response.sanitize_untrusted_url(redirect_url)\n redirect_url = _modify_query(redirect_url, 'success', b64encode_s(msg))\n response.redirect(redirect_url)\n\n\ndef b64decode_s(s, **kw):\n def error():\n if 'default' in kw:\n return kw['default']\n raise Response(400, \"invalid base64 input\")\n\n try:\n s = s.encode('ascii') if hasattr(s, 'encode') else s\n except UnicodeError:\n return error()\n\n udecode = lambda a: a.decode('utf8')\n if s[:1] == b'.':\n udecode = lambda a: a\n s = s[1:]\n s = s.replace(b'~', b'=')\n try:\n return udecode(b64decode(s, '-_'))\n except Exception:\n try:\n # For retrocompatibility\n return udecode(b64decode(s))\n except Exception:\n pass\n return error()\n\n\ndef b64encode_s(s):\n prefix = b''\n if not isinstance(s, bytes):\n s = s.encode('utf8')\n else:\n # Check whether the string is binary or already utf8\n try:\n s.decode('utf8')\n except UnicodeError:\n prefix = b'.'\n r = prefix + b64encode(s, b'-_').replace(b'=', b'~')\n return r.decode('ascii')\n\n\ndef excerpt_intro(text, length=175):\n if not text:\n return ''\n if isinstance(text, Markup):\n i = text.find('</p>')\n if i != -1:\n text = text[:i]\n text = text.striptags().strip()\n else:\n text = text.lstrip().split('\\n', 1)[0].rstrip()\n if len(text) > length:\n text = text[:length]\n if text[-1] == '.':\n # don't add an ellipsis directly after a dot\n return text + ' […]'\n if text[-1] != ' ':\n # try to avoid cutting a word\n i = text.rfind(' ')\n if i > 0.9 * length:\n text = text[:i+1]\n return text + '…'\n return text\n\n\ndef is_card_expired(exp_year, exp_month):\n today = date.today()\n cur_year, cur_month = today.year, today.month\n return exp_year < cur_year or exp_year == cur_year and exp_month < cur_month\n\n\ndef get_owner_name(account):\n if not account:\n return ''\n if account.PersonType == 'NATURAL':\n return account.FirstName + ' ' + account.LastName\n else:\n return account.Name\n\n\ndef get_owner_address(bank_account, mp_account):\n if not mp_account:\n return ''\n if bank_account:\n addr = bank_account.OwnerAddress\n elif mp_account.PersonType == 'NATURAL':\n addr = mp_account.Address\n else:\n addr = mp_account.HeadquartersAddress\n if not addr.Country:\n return None\n return addr\n\n\ndef obfuscate(n, x, y):\n return n[:x] + 'x'*len(n[x:y]) + n[y:]\n\n\ndef ensure_str(s):\n if isinstance(s, str):\n return s\n return s.decode('ascii') if isinstance(s, bytes) else s.encode('ascii')\n\n\ndef set_cookie(cookies, key, value, expires=None, httponly=True, path='/', samesite='lax'):\n key = ensure_str(key)\n cookies[key] = ensure_str(value)\n cookie = cookies[key]\n if expires:\n if isinstance(expires, timedelta):\n expires += utcnow()\n if isinstance(expires, datetime):\n expires = to_rfc822(expires)\n cookie['expires'] = ensure_str(expires)\n if httponly:\n cookie['httponly'] = True\n if path:\n cookie['path'] = ensure_str(path)\n if samesite:\n cookie['samesite'] = ensure_str(samesite)\n if website.cookie_domain:\n cookie['domain'] = ensure_str(website.cookie_domain)\n if website.canonical_scheme == 'https':\n cookie['secure'] = True\n\n\ndef erase_cookie(cookies, key, **kw):\n set_cookie(cookies, key, '', BEGINNING_OF_EPOCH, **kw)\n\n\ndef to_javascript(obj):\n \"\"\"For when you want to inject an object into a <script> tag.\n \"\"\"\n return json.dumps(obj).replace('</', '<\\\\/')\n\n\nsvg_attrs_re = re.compile(r'\\s+(?:height|width|x|y|xmlns)=([\"\\']).*?\\1')\n\ndef include_svg(svg, height, width, x=None, y=None):\n \"\"\"For when you want to include an SVG in an HTML page or in another SVG.\n \"\"\"\n assert svg.startswith('<svg')\n i = svg.find('>')\n assert i != -1\n d = locals()\n attrs = svg_attrs_re.sub('', svg[4:i])\n for a in ('height', 'width', 'x', 'y'):\n v = d[a]\n if v is None:\n continue\n attrs += ' %s=\"%s\"' % (a, v)\n return Markup(svg[:4] + attrs + svg[i:])\n\n\ndef group_by(iterable, key, attr=False, ignored_exceptions=KeyError):\n r = {}\n if callable(key):\n for obj in iterable:\n k = key(obj)\n r.setdefault(k, []).append(obj)\n return r\n f = getattr if attr else getitem\n for obj in iterable:\n try:\n k = f(obj, key)\n except ignored_exceptions:\n continue\n r.setdefault(k, []).append(obj)\n return r\n\n\ndef find_files(directory, pattern):\n for root, dirs, files in os.walk(directory):\n for filename in fnmatch.filter(files, pattern):\n yield os.path.join(root, filename)\n\n\ndef serialize(context):\n for k, v in context.items():\n if callable(getattr(v, '_asdict', None)):\n context[k] = v._asdict()\n return b'\\\\x' + hexlify(cbor.dumps(context, canonical=True))\n\n\ndef deserialize(context):\n if isinstance(context, memoryview) and context[:2].tobytes() == b'\\\\x':\n context = unhexlify(context[2:])\n return cbor.loads(context)\n\n\ndef pid_exists(pid):\n \"\"\"Check whether pid exists in the current process table. UNIX only.\n\n Source: http://stackoverflow.com/a/6940314/2729778\n \"\"\"\n if not pid > 0:\n raise ValueError(\"bad PID %s\" % pid)\n try:\n os.kill(pid, 0)\n except OSError as err:\n if err.errno == errno.ESRCH:\n # ESRCH == No such process\n return False\n elif err.errno == errno.EPERM:\n # EPERM clearly means there's a process to deny access to\n return True\n else:\n # According to \"man 2 kill\" possible error values are\n # (EINVAL, EPERM, ESRCH)\n raise\n else:\n return True\n\n\ndef build_s3_object_url(key):\n now = utcnow()\n timestamp = now.strftime('%Y%m%dT%H%M%SZ')\n today = timestamp.split('T', 1)[0]\n region = website.app_conf.s3_region\n access_key = website.app_conf.s3_public_access_key\n endpoint = website.app_conf.s3_endpoint\n assert endpoint.startswith('https://')\n host = endpoint[8:]\n querystring = (\n f\"X-Amz-Algorithm=AWS4-HMAC-SHA256&\"\n f\"X-Amz-Credential={access_key}%2F{today}%2F{region}%2Fs3%2Faws4_request&\"\n f\"X-Amz-Date={timestamp}&\"\n f\"X-Amz-Expires=86400&\"\n f\"X-Amz-SignedHeaders=host\"\n )\n canonical_request = (\n f\"GET\\n\"\n f\"/{key}\\n\"\n f\"{querystring}\\n\"\n f\"host:{host}\\n\"\n f\"\\n\"\n f\"host\\n\"\n f\"UNSIGNED-PAYLOAD\"\n ).encode()\n canonical_request_hash = sha256(canonical_request).hexdigest()\n string_to_sign = (\n f\"AWS4-HMAC-SHA256\\n\"\n f\"{timestamp}\\n\"\n f\"{today}/{region}/s3/aws4_request\\n\"\n f\"{canonical_request_hash}\"\n ).encode()\n aws4_secret_key = b\"AWS4\" + website.app_conf.s3_secret_key.encode()\n sig_key = hmac.new(aws4_secret_key, today.encode(), sha256).digest()\n sig_key = hmac.new(sig_key, region.encode(), sha256).digest()\n sig_key = hmac.new(sig_key, b\"s3\", sha256).digest()\n sig_key = hmac.new(sig_key, b\"aws4_request\", sha256).digest()\n signature = hmac.new(sig_key, string_to_sign, sha256).hexdigest()\n return endpoint + \"/\" + key + \"?\" + querystring + \"&X-Amz-Signature=\" + signature\n\n\nNO_DEFAULT = object()\n\n\ndef get_int(d, k, default=NO_DEFAULT, minimum=0, maximum=2**64-1):\n try:\n r = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n try:\n r = int(r)\n except (ValueError, TypeError):\n raise Response().error(400, \"`%s` value %r is not a valid integer\" % (k, r))\n if minimum is not None and r < minimum:\n raise Response().error(400, \"`%s` value %r is less than %i\" % (k, r, minimum))\n if maximum is not None and r > maximum:\n raise Response().error(400, \"`%s` value %r is greater than %i\" % (k, r, maximum))\n return r\n\n\ndef get_currency(d, k, default=NO_DEFAULT, phased_out='allow'):\n try:\n currency = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if currency not in CURRENCIES:\n replacement = CURRENCY_REPLACEMENTS.get(currency)\n if replacement and phased_out in ('allow', 'replace'):\n if phased_out == 'replace':\n currency = replacement[1]\n else:\n raise Response().error(\n 400, \"`%s` value %r isn't a supported currency code\" % (k, currency)\n )\n return currency\n\n\ndef get_money_amount(d, k, currency, default=NO_DEFAULT):\n try:\n r = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n return LOCALE_EN.parse_money_amount(r, currency)\n\n\ndef get_choice(d, k, choices, default=NO_DEFAULT):\n try:\n r = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if r not in choices:\n raise Response().error(400, \"`%s` value %r is invalid. Choices: %r\" % (k, r, choices))\n return r\n\n\ncolor_re = re.compile(r\"^[0-9a-f]{6}$\")\n\n\ndef get_color(d, k, default=NO_DEFAULT):\n try:\n r = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if not color_re.match(r):\n raise Response().error(400, \"`%s` value %r is not a valid hexadecimal color\" % (k, r))\n return r\n\n\ndef word(mapping, k, pattern=r'^\\w+$', unicode=False):\n r = mapping[k]\n if not r:\n raise Response().error(400, \"`%s` value %r is empty\" % (k, r))\n if not re.match(pattern, r, re.UNICODE if unicode else re.ASCII):\n raise Response().error(400, \"`%s` value %r contains forbidden characters\" % (k, r))\n return r\n\n\nFALSEISH = {'0', 'f', 'false', 'n', 'no'}\nTRUEISH = {'1', 't', 'true', 'y', 'yes'}\nNULLISH = {'', 'null', 'none'}\n\n\ndef parse_boolean(mapping, k, default=NO_DEFAULT):\n try:\n r = mapping[k].lower()\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if r in TRUEISH:\n return True\n if r in FALSEISH:\n return False\n raise Response().error(400, \"`%s` value %r is invalid\" % (k, r))\n\n\ndef parse_ternary(mapping, k, default=NO_DEFAULT):\n try:\n r = mapping[k].lower()\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if r in TRUEISH:\n return True\n if r in FALSEISH:\n return False\n if r in NULLISH:\n return None\n raise Response().error(400, \"`%s` value %r is invalid\" % (k, r))\n\n\ndef parse_date(mapping, k, default=NO_DEFAULT, sep='-'):\n try:\n r = mapping[k]\n if r:\n r = r.split(sep)\n elif default is not NO_DEFAULT:\n return default\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n try:\n year, month, day = map(int, r)\n # the above raises ValueError if the number of parts isn't 3\n # or if any part isn't an integer\n r = date(year, month, day)\n except (ValueError, TypeError):\n raise Response().error(400, \"`%s` value %r is invalid\" % (k, mapping[k]))\n return r\n\n\ndef parse_list(mapping, k, cast, default=NO_DEFAULT, sep=','):\n try:\n r = mapping[k].split(sep)\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n try:\n r = [cast(v) for v in r]\n except (ValueError, TypeError):\n raise Response().error(400, \"`%s` value %r is invalid\" % (k, mapping[k]))\n return r\n\n\ndef parse_int(o, **kw):\n try:\n return int(o)\n except (ValueError, TypeError):\n if 'default' in kw:\n return kw['default']\n raise Response().error(400, \"%r is not a valid integer\" % o)\n\n\ndef check_address(addr):\n for k in ('AddressLine1', 'City', 'PostalCode', 'Country'):\n if not addr.get(k):\n return False\n if addr['Country'] == 'US' and not addr.get('Region'):\n return False\n return True\n\n\ndef check_address_v2(addr):\n if not addr:\n return False\n for k in ('country', 'city', 'postal_code', 'local_address'):\n if not addr.get(k):\n return False\n if addr['country'] == 'US' and not addr.get('region'):\n # FIXME This is simplistic, `region` can be required in other countries too.\n # Related: https://github.com/liberapay/liberapay.com/issues/1056\n return False\n return True\n\n\ndef render_postal_address(addr, single_line=False):\n if not check_address_v2(addr):\n return\n # FIXME The rendering below is simplistic, we should implement\n # https://github.com/liberapay/liberapay.com/issues/1056\n elements = [addr['local_address'], addr['city'], addr['postal_code']]\n if addr.get('region'):\n elements.append(addr['region'])\n elements.append(LOCALE_EN.countries[addr['country']])\n if single_line:\n return ', '.join(elements)\n else:\n return '\\n'.join(elements)\n\n\ndef mkdir_p(path):\n try:\n os.makedirs(path)\n except OSError as e:\n if e.errno == errno.EEXIST and os.path.isdir(path):\n return\n raise\n\n\ndef get_ip_net(addr):\n if addr.max_prefixlen == 32:\n return '.'.join(str(addr).split('.', 2)[:2])\n else:\n return ':'.join(str(addr).split(':', 2)[:2])\n\n\ndef render(context, allow_partial_i18n=True):\n \"\"\"Render the next page and return the output.\n\n This function is meant to be used in the second page of a simplate, e.g.:\n\n ```\n from liberapay.utils import render\n [---]\n output.body = render(globals(), allow_partial_i18n=False)\n [---] text/html\n ...\n ```\n\n If `allow_partial_i18n` is `False` and the output is a partially translated\n page then a second rendering is done so that the final output is entirely in\n English.\n \"\"\"\n output, resource = context['output'], context['resource']\n r = resource.renderers[output.media_type](context)\n if allow_partial_i18n or not context['state'].get('partial_translation'):\n return r\n else:\n # Fall back to English\n add_helpers_to_context(context, LOCALE_EN)\n return resource.renderers[output.media_type](context)\n\n\ndef resolve(domain, port):\n try:\n return socket.getaddrinfo(domain, port)\n except socket.gaierror:\n return\n\n\ndef partition(l, predicate):\n a, b = [], []\n for e in l:\n if predicate(e):\n a.append(e)\n else:\n b.append(e)\n return a, b\n\n\ndef get_recordable_headers(request):\n decode = lambda b: b.decode('ascii', 'backslashreplace')\n return {\n decode(k): decode(b', '.join(v))\n for k, v in request.headers.items()\n if k != b'Cookie'\n }\n", "path": "liberapay/utils/__init__.py" } ]
[ { "content": "from base64 import b64decode, b64encode\nfrom binascii import hexlify, unhexlify\nfrom datetime import date, datetime, timedelta\nimport errno\nimport fnmatch\nfrom hashlib import sha256\nimport hmac\nfrom operator import getitem\nimport os\nimport re\nimport socket\n\nfrom pando import Response, json\nfrom pando.utils import to_rfc822, utcnow\nfrom markupsafe import Markup\n\nfrom liberapay.constants import CURRENCIES, CURRENCY_REPLACEMENTS, SAFE_METHODS\nfrom liberapay.elsewhere._paginators import _modify_query\nfrom liberapay.exceptions import (\n AuthRequired, ClosedAccount, LoginRequired, TooManyAdminActions,\n)\nfrom liberapay.models.community import Community\nfrom liberapay.i18n.base import LOCALE_EN, add_helpers_to_context\nfrom liberapay.website import website\nfrom liberapay.utils import cbor\n\n\nBEGINNING_OF_EPOCH = to_rfc822(datetime(1970, 1, 1)).encode('ascii')\n\n\ndef get_participant(\n state, restrict=True, allow_member=False, redirect_canon=True, redirect_stub=True,\n):\n \"\"\"Get a participant from the ID or username in the request path.\n\n Args:\n restrict (bool): the page is private, restrict access to it\n allow_member (bool): allow members of a team to access this page\n redirect_canon (bool): allow redirecting the request to the canonical URL\n redirect_stub (bool): allow redirecting the request to the pledge page\n\n Returns a `Participant` or raises a `Response`.\n\n \"\"\"\n request = state['request']\n response = state['response']\n user = state['user']\n slug = request.path['username']\n _ = state['_']\n\n if restrict and user.ANON:\n raise LoginRequired\n\n if slug.startswith('~'):\n try:\n value = int(slug[1:])\n except ValueError:\n raise response.error(404)\n participant = user if user and user.id == value else None\n elif slug:\n value = slug.lower()\n participant = user if user and user.username.lower() == value else None\n else:\n raise response.error(404)\n\n if participant is None:\n if type(value) is int:\n participant = website.db.Participant.from_id(value, _raise=False)\n else:\n participant = website.db.Participant.from_username(value)\n if participant is None:\n if type(value) is str:\n look_up_redirections(request, response)\n raise response.error(404)\n elif participant.kind == 'community':\n c_name = website.db.one(\"\"\"\n SELECT name\n FROM communities\n WHERE participant = %s\n \"\"\", (participant.id,))\n raise response.redirect('/for/%s' % c_name)\n\n if request.method in SAFE_METHODS:\n if redirect_canon and slug != participant.username:\n canon = '/' + participant.username + request.line.uri.decoded[len(slug)+1:]\n raise response.redirect(canon)\n else:\n if restrict:\n user.require_write_permission()\n\n is_blocked = participant.is_suspended\n if (restrict or is_blocked) and participant != user:\n if allow_member and participant.kind == 'group' and user.member_of(participant):\n pass\n elif user.is_acting_as('admin'):\n log_admin_request(user, participant, request)\n elif restrict:\n raise response.error(403, _(\"You are not authorized to access this page.\"))\n elif is_blocked:\n raise response.render('simplates/blocked-profile.spt', state)\n\n status = participant.status\n if status == 'closed':\n if not user.is_acting_as('admin'):\n raise ClosedAccount(participant)\n elif status == 'stub':\n if redirect_stub:\n to = participant.resolve_stub()\n if not to:\n # Account has been taken over\n raise response.error(404)\n raise response.redirect(to)\n\n if allow_member and (user == participant or participant.kind == 'group' and user.member_of(participant)):\n state['can_switch_account'] = True\n\n return participant\n\n\ndef get_community(state, restrict=False):\n request, response = state['request'], state['response']\n user = state['user']\n name = request.path['name']\n\n c = Community.from_name(name)\n if not c:\n raise response.error(404)\n if request.method in SAFE_METHODS:\n if c.name != name:\n response.redirect('/for/' + c.name + request.line.uri.decoded[5+len(name):])\n elif user.ANON:\n raise AuthRequired\n else:\n user.require_write_permission()\n\n is_blocked = c.participant.is_suspended\n if (restrict or is_blocked):\n if user.id == c.creator:\n pass\n elif user.is_acting_as('admin'):\n log_admin_request(user, c.participant, request)\n elif restrict:\n if user.ANON:\n raise LoginRequired\n else:\n _ = state['_']\n raise response.error(403, _(\"You are not authorized to access this page.\"))\n elif is_blocked:\n raise response.render('simplates/blocked-profile.spt', state)\n\n return c\n\n\ndef log_admin_request(admin, participant, request):\n if request.method not in SAFE_METHODS:\n website.db.hit_rate_limit('admin.http-unsafe', admin.id, TooManyAdminActions)\n action_data = {\n 'method': request.method,\n 'path': request.path.raw,\n 'qs': dict(request.qs),\n 'body': {\n k: (v[0] if len(v) == 1 else v)\n for k, v in request.body.items()\n if k != 'csrf_token'\n },\n }\n participant.add_event(website.db, 'admin_request', action_data, admin.id)\n\n\ndef look_up_redirections(request, response):\n path = request.path.raw\n if not path.endswith('/'):\n path += '/'\n r = website.db.one(\"\"\"\n SELECT *\n FROM redirections\n WHERE starts_with(%s, from_prefix)\n ORDER BY length(from_prefix) DESC\n LIMIT 1\n \"\"\", (path.lower(),))\n if r:\n location = r.to_prefix + path[len(r.from_prefix.rstrip('%')):]\n response.redirect(location.rstrip('/'))\n\n\ndef form_post_success(state, msg='', redirect_url=None):\n \"\"\"This function is meant to be called after a successful form POST.\n \"\"\"\n request, response = state['request'], state['response']\n if request.headers.get(b'X-Requested-With') == b'XMLHttpRequest':\n raise response.json({\"msg\": msg} if msg else {})\n else:\n if not redirect_url:\n redirect_url = request.body.get('back_to') or request.line.uri.decoded\n redirect_url = response.sanitize_untrusted_url(redirect_url)\n redirect_url = _modify_query(redirect_url, 'success', b64encode_s(msg))\n response.redirect(redirect_url)\n\n\ndef b64decode_s(s, **kw):\n def error():\n if 'default' in kw:\n return kw['default']\n raise Response(400, \"invalid base64 input\")\n\n try:\n s = s.encode('ascii') if hasattr(s, 'encode') else s\n except UnicodeError:\n return error()\n\n udecode = lambda a: a.decode('utf8')\n if s[:1] == b'.':\n udecode = lambda a: a\n s = s[1:]\n s = s.replace(b'~', b'=')\n try:\n return udecode(b64decode(s, '-_'))\n except Exception:\n try:\n # For retrocompatibility\n return udecode(b64decode(s))\n except Exception:\n pass\n return error()\n\n\ndef b64encode_s(s):\n prefix = b''\n if not isinstance(s, bytes):\n s = s.encode('utf8')\n else:\n # Check whether the string is binary or already utf8\n try:\n s.decode('utf8')\n except UnicodeError:\n prefix = b'.'\n r = prefix + b64encode(s, b'-_').replace(b'=', b'~')\n return r.decode('ascii')\n\n\ndef excerpt_intro(text, length=175):\n if not text:\n return ''\n if isinstance(text, Markup):\n i = text.find('</p>')\n if i != -1:\n text = text[:i]\n text = text.striptags().strip()\n else:\n text = text.lstrip().split('\\n', 1)[0].rstrip()\n if len(text) > length:\n text = text[:length]\n if text[-1] == '.':\n # don't add an ellipsis directly after a dot\n return text + ' […]'\n if text[-1] != ' ':\n # try to avoid cutting a word\n i = text.rfind(' ')\n if i > 0.9 * length:\n text = text[:i+1]\n return text + '…'\n return text\n\n\ndef is_card_expired(exp_year, exp_month):\n today = date.today()\n cur_year, cur_month = today.year, today.month\n return exp_year < cur_year or exp_year == cur_year and exp_month < cur_month\n\n\ndef get_owner_name(account):\n if not account:\n return ''\n if account.PersonType == 'NATURAL':\n return account.FirstName + ' ' + account.LastName\n else:\n return account.Name\n\n\ndef get_owner_address(bank_account, mp_account):\n if not mp_account:\n return ''\n if bank_account:\n addr = bank_account.OwnerAddress\n elif mp_account.PersonType == 'NATURAL':\n addr = mp_account.Address\n else:\n addr = mp_account.HeadquartersAddress\n if not addr.Country:\n return None\n return addr\n\n\ndef obfuscate(n, x, y):\n return n[:x] + 'x'*len(n[x:y]) + n[y:]\n\n\ndef ensure_str(s):\n if isinstance(s, str):\n return s\n return s.decode('ascii') if isinstance(s, bytes) else s.encode('ascii')\n\n\ndef set_cookie(cookies, key, value, expires=None, httponly=True, path='/', samesite='lax'):\n key = ensure_str(key)\n cookies[key] = ensure_str(value)\n cookie = cookies[key]\n if expires:\n if isinstance(expires, timedelta):\n expires += utcnow()\n if isinstance(expires, datetime):\n expires = to_rfc822(expires)\n cookie['expires'] = ensure_str(expires)\n if httponly:\n cookie['httponly'] = True\n if path:\n cookie['path'] = ensure_str(path)\n if samesite:\n cookie['samesite'] = ensure_str(samesite)\n if website.cookie_domain:\n cookie['domain'] = ensure_str(website.cookie_domain)\n if website.canonical_scheme == 'https':\n cookie['secure'] = True\n\n\ndef erase_cookie(cookies, key, **kw):\n set_cookie(cookies, key, '', BEGINNING_OF_EPOCH, **kw)\n\n\ndef to_javascript(obj):\n \"\"\"For when you want to inject an object into a <script> tag.\n \"\"\"\n return json.dumps(obj).replace('</', '<\\\\/')\n\n\nsvg_attrs_re = re.compile(r'\\s+(?:height|width|x|y|xmlns)=([\"\\']).*?\\1')\n\ndef include_svg(svg, height, width, x=None, y=None):\n \"\"\"For when you want to include an SVG in an HTML page or in another SVG.\n \"\"\"\n assert svg.startswith('<svg')\n i = svg.find('>')\n assert i != -1\n d = locals()\n attrs = svg_attrs_re.sub('', svg[4:i])\n for a in ('height', 'width', 'x', 'y'):\n v = d[a]\n if v is None:\n continue\n attrs += ' %s=\"%s\"' % (a, v)\n return Markup(svg[:4] + attrs + svg[i:])\n\n\ndef group_by(iterable, key, attr=False, ignored_exceptions=KeyError):\n r = {}\n if callable(key):\n for obj in iterable:\n k = key(obj)\n r.setdefault(k, []).append(obj)\n return r\n f = getattr if attr else getitem\n for obj in iterable:\n try:\n k = f(obj, key)\n except ignored_exceptions:\n continue\n r.setdefault(k, []).append(obj)\n return r\n\n\ndef find_files(directory, pattern):\n for root, dirs, files in os.walk(directory):\n for filename in fnmatch.filter(files, pattern):\n yield os.path.join(root, filename)\n\n\ndef serialize(context):\n for k, v in context.items():\n if callable(getattr(v, '_asdict', None)):\n context[k] = v._asdict()\n return b'\\\\x' + hexlify(cbor.dumps(context, canonical=True))\n\n\ndef deserialize(context):\n if isinstance(context, memoryview) and context[:2].tobytes() == b'\\\\x':\n context = unhexlify(context[2:])\n return cbor.loads(context)\n\n\ndef pid_exists(pid):\n \"\"\"Check whether pid exists in the current process table. UNIX only.\n\n Source: http://stackoverflow.com/a/6940314/2729778\n \"\"\"\n if not pid > 0:\n raise ValueError(\"bad PID %s\" % pid)\n try:\n os.kill(pid, 0)\n except OSError as err:\n if err.errno == errno.ESRCH:\n # ESRCH == No such process\n return False\n elif err.errno == errno.EPERM:\n # EPERM clearly means there's a process to deny access to\n return True\n else:\n # According to \"man 2 kill\" possible error values are\n # (EINVAL, EPERM, ESRCH)\n raise\n else:\n return True\n\n\ndef build_s3_object_url(key):\n now = utcnow()\n timestamp = now.strftime('%Y%m%dT%H%M%SZ')\n today = timestamp.split('T', 1)[0]\n region = website.app_conf.s3_region\n access_key = website.app_conf.s3_public_access_key\n endpoint = website.app_conf.s3_endpoint\n assert endpoint.startswith('https://')\n host = endpoint[8:]\n querystring = (\n f\"X-Amz-Algorithm=AWS4-HMAC-SHA256&\"\n f\"X-Amz-Credential={access_key}%2F{today}%2F{region}%2Fs3%2Faws4_request&\"\n f\"X-Amz-Date={timestamp}&\"\n f\"X-Amz-Expires=86400&\"\n f\"X-Amz-SignedHeaders=host\"\n )\n canonical_request = (\n f\"GET\\n\"\n f\"/{key}\\n\"\n f\"{querystring}\\n\"\n f\"host:{host}\\n\"\n f\"\\n\"\n f\"host\\n\"\n f\"UNSIGNED-PAYLOAD\"\n ).encode()\n canonical_request_hash = sha256(canonical_request).hexdigest()\n string_to_sign = (\n f\"AWS4-HMAC-SHA256\\n\"\n f\"{timestamp}\\n\"\n f\"{today}/{region}/s3/aws4_request\\n\"\n f\"{canonical_request_hash}\"\n ).encode()\n aws4_secret_key = b\"AWS4\" + website.app_conf.s3_secret_key.encode()\n sig_key = hmac.new(aws4_secret_key, today.encode(), sha256).digest()\n sig_key = hmac.new(sig_key, region.encode(), sha256).digest()\n sig_key = hmac.new(sig_key, b\"s3\", sha256).digest()\n sig_key = hmac.new(sig_key, b\"aws4_request\", sha256).digest()\n signature = hmac.new(sig_key, string_to_sign, sha256).hexdigest()\n return endpoint + \"/\" + key + \"?\" + querystring + \"&X-Amz-Signature=\" + signature\n\n\nNO_DEFAULT = object()\n\n\ndef get_int(d, k, default=NO_DEFAULT, minimum=0, maximum=2**64-1):\n try:\n r = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n try:\n r = int(r)\n except (ValueError, TypeError):\n raise Response().error(400, \"`%s` value %r is not a valid integer\" % (k, r))\n if minimum is not None and r < minimum:\n raise Response().error(400, \"`%s` value %r is less than %i\" % (k, r, minimum))\n if maximum is not None and r > maximum:\n raise Response().error(400, \"`%s` value %r is greater than %i\" % (k, r, maximum))\n return r\n\n\ndef get_currency(d, k, default=NO_DEFAULT, phased_out='allow'):\n try:\n currency = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if currency not in CURRENCIES:\n replacement = CURRENCY_REPLACEMENTS.get(currency)\n if replacement and phased_out in ('allow', 'replace'):\n if phased_out == 'replace':\n currency = replacement[1]\n else:\n raise Response().error(\n 400, \"`%s` value %r isn't a supported currency code\" % (k, currency)\n )\n return currency\n\n\ndef get_money_amount(d, k, currency, default=NO_DEFAULT):\n try:\n r = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n return LOCALE_EN.parse_money_amount(r, currency)\n\n\ndef get_choice(d, k, choices, default=NO_DEFAULT):\n try:\n r = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if r not in choices:\n raise Response().error(400, \"`%s` value %r is invalid. Choices: %r\" % (k, r, choices))\n return r\n\n\ncolor_re = re.compile(r\"^[0-9a-f]{6}$\")\n\n\ndef get_color(d, k, default=NO_DEFAULT):\n try:\n r = d[k]\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if not color_re.match(r):\n raise Response().error(400, \"`%s` value %r is not a valid hexadecimal color\" % (k, r))\n return r\n\n\ndef word(mapping, k, pattern=r'^\\w+$', unicode=False):\n r = mapping[k]\n if not r:\n raise Response().error(400, \"`%s` value %r is empty\" % (k, r))\n if not re.match(pattern, r, re.UNICODE if unicode else re.ASCII):\n raise Response().error(400, \"`%s` value %r contains forbidden characters\" % (k, r))\n return r\n\n\nFALSEISH = {'0', 'f', 'false', 'n', 'no', 'off'}\nTRUEISH = {'1', 't', 'true', 'y', 'yes', 'on'}\nNULLISH = {'', 'null', 'none'}\n\n\ndef parse_boolean(mapping, k, default=NO_DEFAULT):\n try:\n r = mapping[k].lower()\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if r in TRUEISH:\n return True\n if r in FALSEISH:\n return False\n raise Response().error(400, \"`%s` value %r is invalid\" % (k, r))\n\n\ndef parse_ternary(mapping, k, default=NO_DEFAULT):\n try:\n r = mapping[k].lower()\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n if r in TRUEISH:\n return True\n if r in FALSEISH:\n return False\n if r in NULLISH:\n return None\n raise Response().error(400, \"`%s` value %r is invalid\" % (k, r))\n\n\ndef parse_date(mapping, k, default=NO_DEFAULT, sep='-'):\n try:\n r = mapping[k]\n if r:\n r = r.split(sep)\n elif default is not NO_DEFAULT:\n return default\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n try:\n year, month, day = map(int, r)\n # the above raises ValueError if the number of parts isn't 3\n # or if any part isn't an integer\n r = date(year, month, day)\n except (ValueError, TypeError):\n raise Response().error(400, \"`%s` value %r is invalid\" % (k, mapping[k]))\n return r\n\n\ndef parse_list(mapping, k, cast, default=NO_DEFAULT, sep=','):\n try:\n r = mapping[k].split(sep)\n except (KeyError, Response):\n if default is NO_DEFAULT:\n raise\n return default\n try:\n r = [cast(v) for v in r]\n except (ValueError, TypeError):\n raise Response().error(400, \"`%s` value %r is invalid\" % (k, mapping[k]))\n return r\n\n\ndef parse_int(o, **kw):\n try:\n return int(o)\n except (ValueError, TypeError):\n if 'default' in kw:\n return kw['default']\n raise Response().error(400, \"%r is not a valid integer\" % o)\n\n\ndef check_address(addr):\n for k in ('AddressLine1', 'City', 'PostalCode', 'Country'):\n if not addr.get(k):\n return False\n if addr['Country'] == 'US' and not addr.get('Region'):\n return False\n return True\n\n\ndef check_address_v2(addr):\n if not addr:\n return False\n for k in ('country', 'city', 'postal_code', 'local_address'):\n if not addr.get(k):\n return False\n if addr['country'] == 'US' and not addr.get('region'):\n # FIXME This is simplistic, `region` can be required in other countries too.\n # Related: https://github.com/liberapay/liberapay.com/issues/1056\n return False\n return True\n\n\ndef render_postal_address(addr, single_line=False):\n if not check_address_v2(addr):\n return\n # FIXME The rendering below is simplistic, we should implement\n # https://github.com/liberapay/liberapay.com/issues/1056\n elements = [addr['local_address'], addr['city'], addr['postal_code']]\n if addr.get('region'):\n elements.append(addr['region'])\n elements.append(LOCALE_EN.countries[addr['country']])\n if single_line:\n return ', '.join(elements)\n else:\n return '\\n'.join(elements)\n\n\ndef mkdir_p(path):\n try:\n os.makedirs(path)\n except OSError as e:\n if e.errno == errno.EEXIST and os.path.isdir(path):\n return\n raise\n\n\ndef get_ip_net(addr):\n if addr.max_prefixlen == 32:\n return '.'.join(str(addr).split('.', 2)[:2])\n else:\n return ':'.join(str(addr).split(':', 2)[:2])\n\n\ndef render(context, allow_partial_i18n=True):\n \"\"\"Render the next page and return the output.\n\n This function is meant to be used in the second page of a simplate, e.g.:\n\n ```\n from liberapay.utils import render\n [---]\n output.body = render(globals(), allow_partial_i18n=False)\n [---] text/html\n ...\n ```\n\n If `allow_partial_i18n` is `False` and the output is a partially translated\n page then a second rendering is done so that the final output is entirely in\n English.\n \"\"\"\n output, resource = context['output'], context['resource']\n r = resource.renderers[output.media_type](context)\n if allow_partial_i18n or not context['state'].get('partial_translation'):\n return r\n else:\n # Fall back to English\n add_helpers_to_context(context, LOCALE_EN)\n return resource.renderers[output.media_type](context)\n\n\ndef resolve(domain, port):\n try:\n return socket.getaddrinfo(domain, port)\n except socket.gaierror:\n return\n\n\ndef partition(l, predicate):\n a, b = [], []\n for e in l:\n if predicate(e):\n a.append(e)\n else:\n b.append(e)\n return a, b\n\n\ndef get_recordable_headers(request):\n decode = lambda b: b.decode('ascii', 'backslashreplace')\n return {\n decode(k): decode(b', '.join(v))\n for k, v in request.headers.items()\n if k != b'Cookie'\n }\n", "path": "liberapay/utils/__init__.py" } ]
diff --git a/liberapay/utils/__init__.py b/liberapay/utils/__init__.py index 9112e9e826..1a678ca68a 100644 --- a/liberapay/utils/__init__.py +++ b/liberapay/utils/__init__.py @@ -538,8 +538,8 @@ def word(mapping, k, pattern=r'^\w+$', unicode=False): return r -FALSEISH = {'0', 'f', 'false', 'n', 'no'} -TRUEISH = {'1', 't', 'true', 'y', 'yes'} +FALSEISH = {'0', 'f', 'false', 'n', 'no', 'off'} +TRUEISH = {'1', 't', 'true', 'y', 'yes', 'on'} NULLISH = {'', 'null', 'none'} diff --git a/style/base/base.scss b/style/base/base.scss index c5b7b43e8c..6326b2dcac 100644 --- a/style/base/base.scss +++ b/style/base/base.scss @@ -929,3 +929,8 @@ abbr[title] { /* Bootstrap 3.3.6 adds a bottom border but doesn't disable text-decoration */ text-decoration: none; } + +.preview { + border-left: 2px solid $gray-lighter; + padding: 11px 0 1px 2ex; +} diff --git a/style/base/utils.scss b/style/base/utils.scss index da69bf9c58..5ef82676ee 100644 --- a/style/base/utils.scss +++ b/style/base/utils.scss @@ -85,6 +85,10 @@ max-width: 500px; } +.max-width-750 { + max-width: 750px; +} + @for $i from 0 through 100 { .width-#{$i} { width: $i * 1%; diff --git a/templates/macros/your-tip.html b/templates/macros/your-tip.html index 7799db4355..97f123c52b 100644 --- a/templates/macros/your-tip.html +++ b/templates/macros/your-tip.html @@ -252,8 +252,37 @@ <h5 class="list-group-item-heading">{{ _("Manual renewal") }}</h5> </label> </li> </ul> - % set patron_visibilities = tippee_p.recipient_settings.patron_visibilities - % set paypal_only = tippee_p.payment_providers == 2 + {{ tip_visibility_choice( + tippee_name, + tippee_p.recipient_settings.patron_visibilities, + tippee_p.payment_providers, + tip + ) }} + <br> + <button class="btn btn-primary btn-lg btn-block" {{ disabled }}>{{ + (_("Modify your pledge") if pledging else _("Modify your donation")) + if tip.renewal_mode > 0 else + (_("Pledge") if pledging else _("Donate")) + }}</button> + % if tip.renewal_mode > 0 + <br> + <button class="btn btn-danger btn-lg btn-block" name="selected_amount" value="0">{{ + _("Cancel the pledge") if pledging else _("Discontinue the donation") + }}</button> + % elif tippee_p.payment_providers + <p class="text-center">{{ payment_methods_icons(tippee_p, new_currency) }}</p> + % elif not pledging + <p class="text-muted">{{ glyphicon('info-sign') }} {{ _( + "{username} hasn't configured any payment method yet, so your donation " + "cannot actually be processed right now. We will notify you when payment " + "becomes possible.", + username=tippee_name + ) }}</p> + % endif +% endmacro + +% macro tip_visibility_choice(tippee_name, patron_visibilities, payment_providers, tip) + % set paypal_only = payment_providers == 2 % if paypal_only % if patron_visibilities == 0 % set patron_visibilities = 2 @@ -332,25 +361,4 @@ <h5 class="list-group-item-heading">{{ _("Public donation") }}</h5> username=tippee_name ) }}</p> % endif - <br> - <button class="btn btn-primary btn-lg btn-block" {{ disabled }}>{{ - (_("Modify your pledge") if pledging else _("Modify your donation")) - if tip.renewal_mode > 0 else - (_("Pledge") if pledging else _("Donate")) - }}</button> - % if tip.renewal_mode > 0 - <br> - <button class="btn btn-danger btn-lg btn-block" name="selected_amount" value="0">{{ - _("Cancel the pledge") if pledging else _("Discontinue the donation") - }}</button> - % elif tippee_p.payment_providers - <p class="text-center">{{ payment_methods_icons(tippee_p, new_currency) }}</p> - % elif not pledging - <p class="text-muted">{{ glyphicon('info-sign') }} {{ _( - "{username} hasn't configured any payment method yet, so your donation " - "cannot actually be processed right now. We will notify you when payment " - "becomes possible.", - username=tippee_name - ) }}</p> - % endif % endmacro diff --git a/www/%username/patrons/index.spt b/www/%username/patrons/index.spt index b0963f04bd..dc997c5fbf 100644 --- a/www/%username/patrons/index.spt +++ b/www/%username/patrons/index.spt @@ -7,10 +7,27 @@ if user != participant and user.recipient_settings.patron_visibilities < 2: if not user.is_acting_as('admin'): response.redirect(user.path('patrons/')) +patron_visibilities = participant.recipient_settings.patron_visibilities if request.method == 'POST': - see_patrons = request.body.parse_boolean('see_patrons') - participant.update_recipient_settings(patron_visibilities=(7 if see_patrons else 1)) - form_post_success(state) + if 'see_patrons' in request.body: + # Temporary support for legacy form + see_patrons = request.body.parse_boolean('see_patrons') + participant.update_recipient_settings(patron_visibilities=(7 if see_patrons else 1)) + form_post_success(state) + new_patron_visibilities=( + (1 if request.body.get('allow_secret_donations') == 'on' else 0) | + (2 if request.body.get('allow_private_donations') == 'on' else 0) | + (4 if request.body.get('allow_public_donations') == 'on' else 0) + ) + if new_patron_visibilities == 0: + raise response.error(400, _("You have to check at least one box.")) + elif new_patron_visibilities == patron_visibilities: + raise response.redirect(participant.path('patrons/')) + if request.body.parse_boolean('confirmed', default=False): + participant.update_recipient_settings(patron_visibilities=new_patron_visibilities) + form_post_success(state) +else: + new_patron_visibilities = patron_visibilities or 1 title = participant.username subhead = _("Patrons") @@ -42,37 +59,57 @@ subhead = _("Patrons") ) }}</p> % endif -% set patron_visibilities = participant.recipient_settings.patron_visibilities -<form action="" method="POST"> +<h3>{{ _("Visibility levels") }}</h3> +<form action="" method="POST" id="patron_visibilities"> <input type="hidden" name="csrf_token" value="{{ csrf_token }}" /> - % if patron_visibilities == 0 - <p class="text-info">{{ glyphicon('info-sign') }} {{ _( - "Liberapay now supports non-anonymous donations, do you want to know " - "who your patrons are?" - ) }}</p> - <p class="buttons"> - <button class="btn btn-primary" name="see_patrons" value="yes">{{ _( - "Enable non-anonymous donations" - ) }}</button> - &nbsp;&nbsp; - <button class="btn btn-default" name="see_patrons" value="no">{{ _("No") }}</button> - </p> - % elif patron_visibilities > 1 - <p class="text-muted">{{ _( - "You have opted-in to see who your patrons are. If you change your mind, " - "then {link_start}click here to disable non-anonymous donations{link_end}.", - link_start='<button class="link" name="see_patrons" value="no">'|safe, - link_end='</button>'|safe - ) }}</p> + <p>{{ _( + "Liberapay supports three visibility levels for donations. Each level can " + "be turned on or off, but at least one of them must be enabled." + ) }}</p> + % if participant.payment_providers == 2 and new_patron_visibilities._and__(1) + <p class="text-danger">{{ glyphicon('exclamation-sign') }} {{ _( + "Secret donations aren't possible with PayPal. You should either disable " + "secret donations or {link_start}add a Stripe account{link_end}.", + link_start=('<a href="%s">'|safe) % participant.path('payment/'), + link_end='</a>'|safe, + ) }}</p> % else - <p class="text-muted">{{ _( - "You've chosen not to see who your patrons are. If you change your mind, " - "then {link_start}click here to enable non-anonymous donations{link_end}.", - link_start='<button class="link" name="see_patrons" value="yes">'|safe, - link_end='</button>'|safe - ) }}</p> + <p class="text-warning">{{ glyphicon('info-sign') }} {{ _( + "Secret donations aren't possible when the payer uses PayPal." + ) }}</p> % endif + <div class="checkbox"> + <label><input type="checkbox" name="allow_secret_donations"{% if new_patron_visibilities.__and__(1) %} checked{% endif %} /> {{ _("Allow secret donations") }}</label><br> + <label><input type="checkbox" name="allow_private_donations"{% if new_patron_visibilities.__and__(2) %} checked{% endif %} /> {{ _("Allow private donations") }}</label><br> + <label><input type="checkbox" name="allow_public_donations"{% if new_patron_visibilities.__and__(4) %} checked{% endif %} /> {{ _("Allow public donations") }}</label> + </div> + <button class="btn btn-primary">{{ _("Preview") }}</button> </form> +<br> +% from "templates/macros/your-tip.html" import tip_visibility_choice with context +<p>{{ _("This is what your prospective donors currently see:") }}</p> +<div class="preview max-width-750">{{ + tip_visibility_choice( + participant.username, + patron_visibilities, + participant.payment_providers, + participant.get_tip_to(participant) + ) +}}</div> +% if request.method == 'POST' + <br> + <p>{{ _("This is what your prospective donors will see with the new settings:") }}</p> + <div class="preview max-width-750">{{ + tip_visibility_choice( + participant.username, + new_patron_visibilities, + participant.payment_providers, + participant.get_tip_to(participant) + ) + }}</div> + <br> + <button class="btn btn-success btn-lg" form="patron_visibilities" name="confirmed" value="true">{{ _("Save") }}</button> +% endif % if patron_visibilities > 1 <h3>{{ _("Data export") }}</h3>
pypi__warehouse-1177
Permanent URL (Heroku "No such app" error) I noticed that https://warehouse.python.org/ produces a `Heroku | No such app` error at the moment. Is this intentional? Are we permanently at https://pypi.io/ now? If so, we should probably update the URL in a few places: https://github.com/pypa/warehouse/search?utf8=%E2%9C%93&q=%22warehouse.python.org%22
[ { "content": "# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os.path\n\n__all__ = [\n \"__title__\", \"__summary__\", \"__uri__\", \"__version__\", \"__commit__\",\n \"__author__\", \"__email__\", \"__license__\", \"__copyright__\",\n]\n\n\ntry:\n base_dir = os.path.dirname(os.path.abspath(__file__))\nexcept NameError:\n base_dir = None\n\n\n__title__ = \"warehouse\"\n__summary__ = \"Next Generation Python Package Repository\"\n__uri__ = \"https://warehouse.python.org/\"\n\n__version__ = \"15.0.dev0\"\n\nif base_dir is not None and os.path.exists(os.path.join(base_dir, \".commit\")):\n with open(os.path.join(base_dir, \".commit\")) as fp:\n __commit__ = fp.read().strip()\nelse:\n __commit__ = None\n\n__author__ = \"The Python Packaging Authority\"\n__email__ = \"[email protected]\"\n\n__license__ = \"Apache License, Version 2.0\"\n__copyright__ = \"2015 %s\" % __author__\n", "path": "warehouse/__about__.py" } ]
[ { "content": "# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os.path\n\n__all__ = [\n \"__title__\", \"__summary__\", \"__uri__\", \"__version__\", \"__commit__\",\n \"__author__\", \"__email__\", \"__license__\", \"__copyright__\",\n]\n\n\ntry:\n base_dir = os.path.dirname(os.path.abspath(__file__))\nexcept NameError:\n base_dir = None\n\n\n__title__ = \"warehouse\"\n__summary__ = \"Next Generation Python Package Repository\"\n__uri__ = \"https://pypi.io/\"\n\n__version__ = \"15.0.dev0\"\n\nif base_dir is not None and os.path.exists(os.path.join(base_dir, \".commit\")):\n with open(os.path.join(base_dir, \".commit\")) as fp:\n __commit__ = fp.read().strip()\nelse:\n __commit__ = None\n\n__author__ = \"The Python Packaging Authority\"\n__email__ = \"[email protected]\"\n\n__license__ = \"Apache License, Version 2.0\"\n__copyright__ = \"2015 %s\" % __author__\n", "path": "warehouse/__about__.py" } ]
diff --git a/docs/index.rst b/docs/index.rst index 369425f7bc30..2d108d74a33f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,7 +15,7 @@ Warehouse is a new code base that implements a Python package repository. It is being actively developed and the plan is that it will eventually power PyPI_ and replace an older code base that is currently powering PyPI. -You can see Warehouse in production at https://warehouse.python.org +You can see Warehouse in production at https://pypi.io The goal is to improve PyPI by making it: diff --git a/warehouse/__about__.py b/warehouse/__about__.py index 5bc3396e5461..1c67a3bb22c8 100644 --- a/warehouse/__about__.py +++ b/warehouse/__about__.py @@ -26,7 +26,7 @@ __title__ = "warehouse" __summary__ = "Next Generation Python Package Repository" -__uri__ = "https://warehouse.python.org/" +__uri__ = "https://pypi.io/" __version__ = "15.0.dev0"
dask__distributed-529
consider truncating huge strings in logging error when deserialization fails I was getting enormous errors due to this line, which made it difficult to debug. May want to truncate the string logged here if it is huge: https://github.com/dask/distributed/blob/master/distributed/core.py#L76
[ { "content": "from __future__ import print_function, division, absolute_import\n\nfrom collections import defaultdict\nfrom datetime import timedelta\nimport logging\nimport six\nimport socket\nimport struct\nfrom time import time\nimport traceback\nimport uuid\n\nfrom toolz import assoc, first\n\ntry:\n import cPickle as pickle\nexcept ImportError:\n import pickle\nimport cloudpickle\nfrom tornado import gen\nfrom tornado.locks import Event\nfrom tornado.tcpserver import TCPServer\nfrom tornado.tcpclient import TCPClient\nfrom tornado.ioloop import IOLoop\nfrom tornado.iostream import IOStream, StreamClosedError\n\nfrom .compatibility import PY3, unicode, WINDOWS\nfrom .utils import get_traceback, truncate_exception, ignoring\nfrom . import protocol\n\npickle_types = [str, bytes]\nwith ignoring(ImportError):\n import numpy as np\n pickle_types.append(np.ndarray)\nwith ignoring(ImportError):\n import pandas as pd\n pickle_types.append(pd.core.generic.NDFrame)\npickle_types = tuple(pickle_types)\n\n\nclass RPCClosed(IOError):\n pass\n\n\ndef dumps(x):\n \"\"\" Manage between cloudpickle and pickle\n\n 1. Try pickle\n 2. If it is short then check if it contains __main__\n 3. If it is long, then first check type, then check __main__\n \"\"\"\n try:\n result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n if len(result) < 1000:\n if b'__main__' in result:\n return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n else:\n return result\n else:\n if isinstance(x, pickle_types) or b'__main__' not in result:\n return result\n else:\n return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n except:\n try:\n return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n except Exception:\n logger.info(\"Failed to serialize %s\", x, exc_info=True)\n raise\n\n\ndef loads(x):\n try:\n return pickle.loads(x)\n except Exception:\n logger.info(\"Failed to deserialize %s\", x, exc_info=True)\n raise\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_total_physical_memory():\n try:\n import psutil\n return psutil.virtual_memory().total / 2\n except ImportError:\n return 2e9\n\n\nMAX_BUFFER_SIZE = get_total_physical_memory()\n\n\ndef handle_signal(sig, frame):\n IOLoop.instance().add_callback(IOLoop.instance().stop)\n\n\nclass Server(TCPServer):\n \"\"\" Distributed TCP Server\n\n Superclass for both Worker and Scheduler objects.\n Inherits from ``tornado.tcpserver.TCPServer``, adding a protocol for RPC.\n\n **Handlers**\n\n Servers define operations with a ``handlers`` dict mapping operation names\n to functions. The first argument of a handler function must be a stream for\n the connection to the client. Other arguments will receive inputs from the\n keys of the incoming message which will always be a dictionary.\n\n >>> def pingpong(stream):\n ... return b'pong'\n\n >>> def add(stream, x, y):\n ... return x + y\n\n >>> handlers = {'ping': pingpong, 'add': add}\n >>> server = Server(handlers) # doctest: +SKIP\n >>> server.listen(8000) # doctest: +SKIP\n\n **Message Format**\n\n The server expects messages to be dictionaries with a special key, `'op'`\n that corresponds to the name of the operation, and other key-value pairs as\n required by the function.\n\n So in the example above the following would be good messages.\n\n * ``{'op': 'ping'}``\n * ``{'op': 'add': 'x': 10, 'y': 20}``\n \"\"\"\n default_port = 0\n\n def __init__(self, handlers, max_buffer_size=MAX_BUFFER_SIZE, **kwargs):\n self.handlers = assoc(handlers, 'identity', self.identity)\n self.id = str(uuid.uuid1())\n self._port = None\n self.rpc = ConnectionPool()\n super(Server, self).__init__(max_buffer_size=max_buffer_size, **kwargs)\n\n @property\n def port(self):\n if not self._port:\n try:\n self._port = first(self._sockets.values()).getsockname()[1]\n except StopIteration:\n raise OSError(\"Server has no port. Please call .listen first\")\n return self._port\n\n def identity(self, stream):\n return {'type': type(self).__name__, 'id': self.id}\n\n def listen(self, port=None):\n if port is None:\n port = self.default_port\n while True:\n try:\n super(Server, self).listen(port)\n break\n except (socket.error, OSError):\n if port:\n raise\n else:\n logger.info('Randomly assigned port taken for %s. Retrying',\n type(self).__name__)\n\n @gen.coroutine\n def handle_stream(self, stream, address):\n \"\"\" Dispatch new connections to coroutine-handlers\n\n Handlers is a dictionary mapping operation names to functions or\n coroutines.\n\n {'get_data': get_data,\n 'ping': pingpong}\n\n Coroutines should expect a single IOStream object.\n \"\"\"\n stream.set_nodelay(True)\n ip, port = address\n logger.info(\"Connection from %s:%d to %s\", ip, port,\n type(self).__name__)\n try:\n while True:\n try:\n msg = yield read(stream)\n logger.debug(\"Message from %s:%d: %s\", ip, port, msg)\n except StreamClosedError:\n logger.info(\"Lost connection: %s\", str(address))\n break\n except Exception as e:\n yield write(stream, error_message(e, status='uncaught-error'))\n continue\n if not isinstance(msg, dict):\n raise TypeError(\"Bad message type. Expected dict, got\\n \"\n + str(msg))\n op = msg.pop('op')\n close = msg.pop('close', False)\n reply = msg.pop('reply', True)\n if op == 'close':\n if reply:\n yield write(stream, 'OK')\n break\n try:\n handler = self.handlers[op]\n except KeyError:\n result = \"No handler found: %s\" % op\n logger.warn(result, exc_info=True)\n else:\n logger.debug(\"Calling into handler %s\", handler.__name__)\n try:\n result = yield gen.maybe_future(handler(stream, **msg))\n except StreamClosedError as e:\n logger.info(\"%s\", e)\n result = error_message(e, status='uncaught-error')\n except Exception as e:\n logger.exception(e)\n result = error_message(e, status='uncaught-error')\n if reply:\n try:\n yield write(stream, result)\n except StreamClosedError:\n logger.info(\"Lost connection: %s\" % str(address))\n break\n if close:\n break\n finally:\n try:\n stream.close()\n except Exception as e:\n logger.warn(\"Failed while closing writer\", exc_info=True)\n logger.info(\"Close connection from %s:%d to %s\", address[0], address[1],\n type(self).__name__)\n\n\n\[email protected]\ndef read(stream):\n \"\"\" Read a message from a stream \"\"\"\n if isinstance(stream, BatchedStream):\n msg = yield stream.recv()\n raise gen.Return(msg)\n else:\n n_frames = yield stream.read_bytes(8)\n n_frames = struct.unpack('Q', n_frames)[0]\n\n lengths = yield stream.read_bytes(8 * n_frames)\n lengths = struct.unpack('Q' * n_frames, lengths)\n\n frames = []\n for length in lengths:\n if length:\n frame = yield stream.read_bytes(length)\n else:\n frame = b''\n frames.append(frame)\n\n msg = protocol.loads(frames)\n raise gen.Return(msg)\n\n\[email protected]\ndef write(stream, msg):\n \"\"\" Write a message to a stream \"\"\"\n if isinstance(stream, BatchedStream):\n stream.send(msg)\n else:\n try:\n frames = protocol.dumps(msg)\n except Exception as e:\n logger.info(\"Unserializable Message: %s\", msg)\n logger.exception(e)\n raise\n\n futures = []\n\n lengths = ([struct.pack('Q', len(frames))] +\n [struct.pack('Q', len(frame)) for frame in frames])\n futures.append(stream.write(b''.join(lengths)))\n\n for frame in frames[:-1]:\n futures.append(stream.write(frame))\n\n futures.append(stream.write(frames[-1]))\n\n if WINDOWS:\n yield futures[-1]\n else:\n yield futures\n\n\ndef pingpong(stream):\n return b'pong'\n\n\[email protected]\ndef connect(ip, port, timeout=3):\n client = TCPClient()\n start = time()\n while True:\n future = client.connect(ip, port, max_buffer_size=MAX_BUFFER_SIZE)\n try:\n stream = yield gen.with_timeout(timedelta(seconds=timeout), future)\n stream.set_nodelay(True)\n raise gen.Return(stream)\n except StreamClosedError:\n if time() - start < timeout:\n yield gen.sleep(0.01)\n logger.debug(\"sleeping on connect\")\n else:\n raise\n except gen.TimeoutError:\n raise IOError(\"Timed out while connecting to %s:%d\" % (ip, port))\n\n\[email protected]\ndef send_recv(stream=None, arg=None, ip=None, port=None, addr=None, reply=True, **kwargs):\n \"\"\" Send and recv with a stream\n\n Keyword arguments turn into the message\n\n response = yield send_recv(stream, op='ping', reply=True)\n \"\"\"\n if arg:\n if isinstance(arg, (unicode, bytes)):\n addr = arg\n if isinstance(arg, tuple):\n ip, port = arg\n if addr:\n assert not ip and not port\n if PY3 and isinstance(addr, bytes):\n addr = addr.decode()\n ip, port = addr.rsplit(':', 1)\n port = int(port)\n if PY3 and isinstance(ip, bytes):\n ip = ip.decode()\n if stream is None:\n stream = yield connect(ip, port)\n\n msg = kwargs\n msg['reply'] = reply\n\n yield write(stream, msg)\n\n if reply:\n response = yield read(stream)\n if isinstance(response, dict) and response.get('status') == 'uncaught-error':\n six.reraise(*clean_exception(**response))\n else:\n response = None\n if kwargs.get('close'):\n stream.close()\n raise gen.Return(response)\n\n\ndef ip_port_from_args(arg=None, addr=None, ip=None, port=None):\n if arg:\n if isinstance(arg, (unicode, bytes)):\n addr = arg\n if isinstance(arg, tuple):\n ip, port = arg\n if addr:\n if PY3 and isinstance(addr, bytes):\n addr = addr.decode()\n assert not ip and not port\n ip, port = addr.rsplit(':', 1)\n port = int(port)\n if PY3 and isinstance(ip, bytes):\n ip = ip.decode()\n\n return ip, port\n\n\nclass rpc(object):\n \"\"\" Conveniently interact with a remote server\n\n Normally we construct messages as dictionaries and send them with read/write\n\n >>> stream = yield connect(ip, port) # doctest: +SKIP\n >>> msg = {'op': 'add', 'x': 10, 'y': 20} # doctest: +SKIP\n >>> yield write(stream, msg) # doctest: +SKIP\n >>> response = yield read(stream) # doctest: +SKIP\n\n To reduce verbosity we use an ``rpc`` object.\n\n >>> remote = rpc(ip=ip, port=port) # doctest: +SKIP\n >>> response = yield remote.add(x=10, y=20) # doctest: +SKIP\n\n One rpc object can be reused for several interactions.\n Additionally, this object creates and destroys many streams as necessary\n and so is safe to use in multiple overlapping communications.\n\n When done, close streams explicitly.\n\n >>> remote.close_streams() # doctest: +SKIP\n \"\"\"\n def __init__(self, arg=None, stream=None, ip=None, port=None, addr=None,\n timeout=3):\n ip, port = ip_port_from_args(arg=arg, addr=addr, ip=ip, port=port)\n self.streams = dict()\n self.ip = ip\n self.port = port\n self.timeout = timeout\n self.status = 'running'\n assert self.ip\n assert self.port\n\n @property\n def address(self):\n return '%s:%d' % (self.ip, self.port)\n\n @gen.coroutine\n def live_stream(self):\n \"\"\" Get an open stream\n\n Some streams to the ip/port target may be in current use by other\n coroutines. We track this with the `streams` dict\n\n :: {stream: True/False if open and ready for use}\n\n This function produces an open stream, either by taking one that we've\n already made or making a new one if they are all taken. This also\n removes streams that have been closed.\n\n When the caller is done with the stream they should set\n\n self.streams[stream] = True\n\n As is done in __getattr__ below.\n \"\"\"\n if self.status == 'closed':\n raise RPCClosed(\"RPC Closed\")\n to_clear = set()\n open = False\n for stream, open in self.streams.items():\n if stream.closed():\n to_clear.add(stream)\n if open:\n break\n if not open or stream.closed():\n stream = yield connect(self.ip, self.port, timeout=self.timeout)\n for s in to_clear:\n del self.streams[s]\n self.streams[stream] = False # mark as taken\n raise gen.Return(stream)\n\n def close_streams(self):\n for stream in self.streams:\n if stream and not stream.closed():\n try:\n stream.close()\n except (OSError, IOError, StreamClosedError):\n pass\n\n def __getattr__(self, key):\n @gen.coroutine\n def send_recv_from_rpc(**kwargs):\n stream = yield self.live_stream()\n result = yield send_recv(stream=stream, op=key, **kwargs)\n self.streams[stream] = True # mark as open\n raise gen.Return(result)\n return send_recv_from_rpc\n\n def __del__(self):\n self.close_streams()\n\n def close_rpc(self):\n self.status = 'closed'\n self.close_streams()\n\n\nclass RPCCall(object):\n \"\"\" The result of ConnectionPool()('host:port')\n\n See Also:\n ConnectionPool\n \"\"\"\n def __init__(self, ip, port, pool):\n self.ip = ip\n self.port = port\n self.pool = pool\n\n def __getattr__(self, key):\n @gen.coroutine\n def send_recv_from_rpc(**kwargs):\n stream = yield self.pool.connect(self.ip, self.port)\n try:\n result = yield send_recv(stream=stream, op=key, **kwargs)\n finally:\n if not stream.closed():\n self.pool.available[self.ip, self.port].add(stream)\n self.pool.occupied[self.ip, self.port].remove(stream)\n self.pool.active -= 1\n\n raise gen.Return(result)\n return send_recv_from_rpc\n\n\nclass ConnectionPool(object):\n \"\"\" A maximum sized pool of Tornado IOStreams\n\n This provides a connect method that mirrors the normal distributed.connect\n method, but provides connection sharing and tracks connection limits.\n\n This object provides an ``rpc`` like interface::\n\n >>> rpc = ConnectionPool(limit=512)\n >>> scheduler = rpc('127.0.0.1:8786')\n >>> workers = [rpc(ip=ip, port=port) for ip, port in ...]\n\n >>> info = yield scheduler.identity()\n\n It creates enough streams to satisfy concurrent connections to any\n particular address::\n\n >>> a, b = yield [scheduler.who_has(), scheduler.has_what()]\n\n It reuses existing streams so that we don't have to continuously reconnect.\n\n It also maintains a stream limit to avoid \"too many open file handle\"\n issues. Whenever this maximum is reached we clear out all idling streams.\n If that doesn't do the trick then we wait until one of the occupied streams\n closes.\n \"\"\"\n def __init__(self, limit=512):\n self.open = 0\n self.active = 0\n self.limit = limit\n self.available = defaultdict(set)\n self.occupied = defaultdict(set)\n self.event = Event()\n\n def __str__(self):\n return \"<ConnectionPool: open=%d, active=%d>\" % (self.open,\n self.active)\n\n __repr__ = __str__\n\n def __call__(self, arg=None, ip=None, port=None, addr=None):\n \"\"\" Cached rpc objects \"\"\"\n ip, port = ip_port_from_args(arg=arg, addr=addr, ip=ip, port=port)\n return RPCCall(ip, port, self)\n\n @gen.coroutine\n def connect(self, ip, port, timeout=3):\n if self.available.get((ip, port)):\n stream = self.available[ip, port].pop()\n self.active += 1\n self.occupied[ip, port].add(stream)\n raise gen.Return(stream)\n\n while self.open >= self.limit:\n self.event.clear()\n self.collect()\n yield self.event.wait()\n\n self.open += 1\n stream = yield connect(ip=ip, port=port, timeout=timeout)\n stream.set_close_callback(lambda: self.on_close(ip, port, stream))\n self.active += 1\n self.occupied[ip, port].add(stream)\n\n if self.open >= self.limit:\n self.event.clear()\n\n raise gen.Return(stream)\n\n def on_close(self, ip, port, stream):\n self.open -= 1\n\n if stream in self.available[ip, port]:\n self.available[ip, port].remove(stream)\n if stream in self.occupied[ip, port]:\n self.occupied[ip, port].remove(stream)\n self.active -= 1\n\n if self.open <= self.limit:\n self.event.set()\n\n def collect(self):\n logger.info(\"Collecting unused streams. open: %d, active: %d\",\n self.open, self.active)\n for k, streams in list(self.available.items()):\n for stream in streams:\n stream.close()\n\n\ndef coerce_to_address(o, out=str):\n if PY3 and isinstance(o, bytes):\n o = o.decode()\n if isinstance(o, (unicode, str)):\n ip, port = o.rsplit(':', 1)\n port = int(port)\n o = (ip, port)\n if isinstance(o, list):\n o = tuple(o)\n if isinstance(o, tuple) and isinstance(o[0], bytes):\n o = (o[0].decode(), o[1])\n\n if out == str:\n o = '%s:%s' % o\n\n return o\n\n\ndef coerce_to_rpc(o, **kwargs):\n if isinstance(o, (bytes, str, tuple, list)):\n ip, port = coerce_to_address(o, out=tuple)\n return rpc(ip=ip, port=int(port), **kwargs)\n elif isinstance(o, IOStream):\n return rpc(stream=o, **kwargs)\n elif isinstance(o, rpc):\n return o\n else:\n raise TypeError()\n\n\ndef error_message(e, status='error'):\n \"\"\" Produce message to send back given an exception has occurred\n\n This does the following:\n\n 1. Gets the traceback\n 2. Truncates the exception and the traceback\n 3. Serializes the exception and traceback or\n 4. If they can't be serialized send string versions\n 5. Format a message and return\n\n See Also\n --------\n clean_exception: deserialize and unpack message into exception/traceback\n six.reraise: raise exception/traceback\n \"\"\"\n tb = get_traceback()\n e2 = truncate_exception(e, 1000)\n try:\n e3 = dumps(e2)\n loads(e3)\n except Exception:\n e3 = Exception(str(e2))\n e3 = dumps(e3)\n try:\n tb2 = dumps(tb)\n except Exception:\n tb2 = ''.join(traceback.format_tb(tb))\n tb2 = dumps(tb2)\n\n if len(tb2) > 10000:\n tb2 = None\n\n return {'status': status, 'exception': e3, 'traceback': tb2}\n\n\ndef clean_exception(exception, traceback, **kwargs):\n \"\"\" Reraise exception and traceback. Deserialize if necessary\n\n See Also\n --------\n error_message: create and serialize errors into message\n \"\"\"\n if isinstance(exception, bytes):\n exception = loads(exception)\n if isinstance(traceback, bytes):\n traceback = loads(traceback)\n if isinstance(traceback, str):\n traceback = None\n return type(exception), exception, traceback\n\n\nfrom .batched import BatchedStream\n", "path": "distributed/core.py" } ]
[ { "content": "from __future__ import print_function, division, absolute_import\n\nfrom collections import defaultdict\nfrom datetime import timedelta\nimport logging\nimport six\nimport socket\nimport struct\nfrom time import time\nimport traceback\nimport uuid\n\nfrom toolz import assoc, first\n\ntry:\n import cPickle as pickle\nexcept ImportError:\n import pickle\nimport cloudpickle\nfrom tornado import gen\nfrom tornado.locks import Event\nfrom tornado.tcpserver import TCPServer\nfrom tornado.tcpclient import TCPClient\nfrom tornado.ioloop import IOLoop\nfrom tornado.iostream import IOStream, StreamClosedError\n\nfrom .compatibility import PY3, unicode, WINDOWS\nfrom .utils import get_traceback, truncate_exception, ignoring\nfrom . import protocol\n\npickle_types = [str, bytes]\nwith ignoring(ImportError):\n import numpy as np\n pickle_types.append(np.ndarray)\nwith ignoring(ImportError):\n import pandas as pd\n pickle_types.append(pd.core.generic.NDFrame)\npickle_types = tuple(pickle_types)\n\n\nclass RPCClosed(IOError):\n pass\n\n\ndef dumps(x):\n \"\"\" Manage between cloudpickle and pickle\n\n 1. Try pickle\n 2. If it is short then check if it contains __main__\n 3. If it is long, then first check type, then check __main__\n \"\"\"\n try:\n result = pickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n if len(result) < 1000:\n if b'__main__' in result:\n return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n else:\n return result\n else:\n if isinstance(x, pickle_types) or b'__main__' not in result:\n return result\n else:\n return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n except:\n try:\n return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)\n except Exception:\n logger.info(\"Failed to serialize %s\", x, exc_info=True)\n raise\n\n\ndef loads(x):\n try:\n return pickle.loads(x)\n except Exception:\n logger.info(\"Failed to deserialize %s\", x[:10000], exc_info=True)\n raise\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_total_physical_memory():\n try:\n import psutil\n return psutil.virtual_memory().total / 2\n except ImportError:\n return 2e9\n\n\nMAX_BUFFER_SIZE = get_total_physical_memory()\n\n\ndef handle_signal(sig, frame):\n IOLoop.instance().add_callback(IOLoop.instance().stop)\n\n\nclass Server(TCPServer):\n \"\"\" Distributed TCP Server\n\n Superclass for both Worker and Scheduler objects.\n Inherits from ``tornado.tcpserver.TCPServer``, adding a protocol for RPC.\n\n **Handlers**\n\n Servers define operations with a ``handlers`` dict mapping operation names\n to functions. The first argument of a handler function must be a stream for\n the connection to the client. Other arguments will receive inputs from the\n keys of the incoming message which will always be a dictionary.\n\n >>> def pingpong(stream):\n ... return b'pong'\n\n >>> def add(stream, x, y):\n ... return x + y\n\n >>> handlers = {'ping': pingpong, 'add': add}\n >>> server = Server(handlers) # doctest: +SKIP\n >>> server.listen(8000) # doctest: +SKIP\n\n **Message Format**\n\n The server expects messages to be dictionaries with a special key, `'op'`\n that corresponds to the name of the operation, and other key-value pairs as\n required by the function.\n\n So in the example above the following would be good messages.\n\n * ``{'op': 'ping'}``\n * ``{'op': 'add': 'x': 10, 'y': 20}``\n \"\"\"\n default_port = 0\n\n def __init__(self, handlers, max_buffer_size=MAX_BUFFER_SIZE, **kwargs):\n self.handlers = assoc(handlers, 'identity', self.identity)\n self.id = str(uuid.uuid1())\n self._port = None\n self.rpc = ConnectionPool()\n super(Server, self).__init__(max_buffer_size=max_buffer_size, **kwargs)\n\n @property\n def port(self):\n if not self._port:\n try:\n self._port = first(self._sockets.values()).getsockname()[1]\n except StopIteration:\n raise OSError(\"Server has no port. Please call .listen first\")\n return self._port\n\n def identity(self, stream):\n return {'type': type(self).__name__, 'id': self.id}\n\n def listen(self, port=None):\n if port is None:\n port = self.default_port\n while True:\n try:\n super(Server, self).listen(port)\n break\n except (socket.error, OSError):\n if port:\n raise\n else:\n logger.info('Randomly assigned port taken for %s. Retrying',\n type(self).__name__)\n\n @gen.coroutine\n def handle_stream(self, stream, address):\n \"\"\" Dispatch new connections to coroutine-handlers\n\n Handlers is a dictionary mapping operation names to functions or\n coroutines.\n\n {'get_data': get_data,\n 'ping': pingpong}\n\n Coroutines should expect a single IOStream object.\n \"\"\"\n stream.set_nodelay(True)\n ip, port = address\n logger.info(\"Connection from %s:%d to %s\", ip, port,\n type(self).__name__)\n try:\n while True:\n try:\n msg = yield read(stream)\n logger.debug(\"Message from %s:%d: %s\", ip, port, msg)\n except StreamClosedError:\n logger.info(\"Lost connection: %s\", str(address))\n break\n except Exception as e:\n yield write(stream, error_message(e, status='uncaught-error'))\n continue\n if not isinstance(msg, dict):\n raise TypeError(\"Bad message type. Expected dict, got\\n \"\n + str(msg))\n op = msg.pop('op')\n close = msg.pop('close', False)\n reply = msg.pop('reply', True)\n if op == 'close':\n if reply:\n yield write(stream, 'OK')\n break\n try:\n handler = self.handlers[op]\n except KeyError:\n result = \"No handler found: %s\" % op\n logger.warn(result, exc_info=True)\n else:\n logger.debug(\"Calling into handler %s\", handler.__name__)\n try:\n result = yield gen.maybe_future(handler(stream, **msg))\n except StreamClosedError as e:\n logger.info(\"%s\", e)\n result = error_message(e, status='uncaught-error')\n except Exception as e:\n logger.exception(e)\n result = error_message(e, status='uncaught-error')\n if reply:\n try:\n yield write(stream, result)\n except StreamClosedError:\n logger.info(\"Lost connection: %s\" % str(address))\n break\n if close:\n break\n finally:\n try:\n stream.close()\n except Exception as e:\n logger.warn(\"Failed while closing writer\", exc_info=True)\n logger.info(\"Close connection from %s:%d to %s\", address[0], address[1],\n type(self).__name__)\n\n\n\[email protected]\ndef read(stream):\n \"\"\" Read a message from a stream \"\"\"\n if isinstance(stream, BatchedStream):\n msg = yield stream.recv()\n raise gen.Return(msg)\n else:\n n_frames = yield stream.read_bytes(8)\n n_frames = struct.unpack('Q', n_frames)[0]\n\n lengths = yield stream.read_bytes(8 * n_frames)\n lengths = struct.unpack('Q' * n_frames, lengths)\n\n frames = []\n for length in lengths:\n if length:\n frame = yield stream.read_bytes(length)\n else:\n frame = b''\n frames.append(frame)\n\n msg = protocol.loads(frames)\n raise gen.Return(msg)\n\n\[email protected]\ndef write(stream, msg):\n \"\"\" Write a message to a stream \"\"\"\n if isinstance(stream, BatchedStream):\n stream.send(msg)\n else:\n try:\n frames = protocol.dumps(msg)\n except Exception as e:\n logger.info(\"Unserializable Message: %s\", msg)\n logger.exception(e)\n raise\n\n futures = []\n\n lengths = ([struct.pack('Q', len(frames))] +\n [struct.pack('Q', len(frame)) for frame in frames])\n futures.append(stream.write(b''.join(lengths)))\n\n for frame in frames[:-1]:\n futures.append(stream.write(frame))\n\n futures.append(stream.write(frames[-1]))\n\n if WINDOWS:\n yield futures[-1]\n else:\n yield futures\n\n\ndef pingpong(stream):\n return b'pong'\n\n\[email protected]\ndef connect(ip, port, timeout=3):\n client = TCPClient()\n start = time()\n while True:\n future = client.connect(ip, port, max_buffer_size=MAX_BUFFER_SIZE)\n try:\n stream = yield gen.with_timeout(timedelta(seconds=timeout), future)\n stream.set_nodelay(True)\n raise gen.Return(stream)\n except StreamClosedError:\n if time() - start < timeout:\n yield gen.sleep(0.01)\n logger.debug(\"sleeping on connect\")\n else:\n raise\n except gen.TimeoutError:\n raise IOError(\"Timed out while connecting to %s:%d\" % (ip, port))\n\n\[email protected]\ndef send_recv(stream=None, arg=None, ip=None, port=None, addr=None, reply=True, **kwargs):\n \"\"\" Send and recv with a stream\n\n Keyword arguments turn into the message\n\n response = yield send_recv(stream, op='ping', reply=True)\n \"\"\"\n if arg:\n if isinstance(arg, (unicode, bytes)):\n addr = arg\n if isinstance(arg, tuple):\n ip, port = arg\n if addr:\n assert not ip and not port\n if PY3 and isinstance(addr, bytes):\n addr = addr.decode()\n ip, port = addr.rsplit(':', 1)\n port = int(port)\n if PY3 and isinstance(ip, bytes):\n ip = ip.decode()\n if stream is None:\n stream = yield connect(ip, port)\n\n msg = kwargs\n msg['reply'] = reply\n\n yield write(stream, msg)\n\n if reply:\n response = yield read(stream)\n if isinstance(response, dict) and response.get('status') == 'uncaught-error':\n six.reraise(*clean_exception(**response))\n else:\n response = None\n if kwargs.get('close'):\n stream.close()\n raise gen.Return(response)\n\n\ndef ip_port_from_args(arg=None, addr=None, ip=None, port=None):\n if arg:\n if isinstance(arg, (unicode, bytes)):\n addr = arg\n if isinstance(arg, tuple):\n ip, port = arg\n if addr:\n if PY3 and isinstance(addr, bytes):\n addr = addr.decode()\n assert not ip and not port\n ip, port = addr.rsplit(':', 1)\n port = int(port)\n if PY3 and isinstance(ip, bytes):\n ip = ip.decode()\n\n return ip, port\n\n\nclass rpc(object):\n \"\"\" Conveniently interact with a remote server\n\n Normally we construct messages as dictionaries and send them with read/write\n\n >>> stream = yield connect(ip, port) # doctest: +SKIP\n >>> msg = {'op': 'add', 'x': 10, 'y': 20} # doctest: +SKIP\n >>> yield write(stream, msg) # doctest: +SKIP\n >>> response = yield read(stream) # doctest: +SKIP\n\n To reduce verbosity we use an ``rpc`` object.\n\n >>> remote = rpc(ip=ip, port=port) # doctest: +SKIP\n >>> response = yield remote.add(x=10, y=20) # doctest: +SKIP\n\n One rpc object can be reused for several interactions.\n Additionally, this object creates and destroys many streams as necessary\n and so is safe to use in multiple overlapping communications.\n\n When done, close streams explicitly.\n\n >>> remote.close_streams() # doctest: +SKIP\n \"\"\"\n def __init__(self, arg=None, stream=None, ip=None, port=None, addr=None,\n timeout=3):\n ip, port = ip_port_from_args(arg=arg, addr=addr, ip=ip, port=port)\n self.streams = dict()\n self.ip = ip\n self.port = port\n self.timeout = timeout\n self.status = 'running'\n assert self.ip\n assert self.port\n\n @property\n def address(self):\n return '%s:%d' % (self.ip, self.port)\n\n @gen.coroutine\n def live_stream(self):\n \"\"\" Get an open stream\n\n Some streams to the ip/port target may be in current use by other\n coroutines. We track this with the `streams` dict\n\n :: {stream: True/False if open and ready for use}\n\n This function produces an open stream, either by taking one that we've\n already made or making a new one if they are all taken. This also\n removes streams that have been closed.\n\n When the caller is done with the stream they should set\n\n self.streams[stream] = True\n\n As is done in __getattr__ below.\n \"\"\"\n if self.status == 'closed':\n raise RPCClosed(\"RPC Closed\")\n to_clear = set()\n open = False\n for stream, open in self.streams.items():\n if stream.closed():\n to_clear.add(stream)\n if open:\n break\n if not open or stream.closed():\n stream = yield connect(self.ip, self.port, timeout=self.timeout)\n for s in to_clear:\n del self.streams[s]\n self.streams[stream] = False # mark as taken\n raise gen.Return(stream)\n\n def close_streams(self):\n for stream in self.streams:\n if stream and not stream.closed():\n try:\n stream.close()\n except (OSError, IOError, StreamClosedError):\n pass\n\n def __getattr__(self, key):\n @gen.coroutine\n def send_recv_from_rpc(**kwargs):\n stream = yield self.live_stream()\n result = yield send_recv(stream=stream, op=key, **kwargs)\n self.streams[stream] = True # mark as open\n raise gen.Return(result)\n return send_recv_from_rpc\n\n def __del__(self):\n self.close_streams()\n\n def close_rpc(self):\n self.status = 'closed'\n self.close_streams()\n\n\nclass RPCCall(object):\n \"\"\" The result of ConnectionPool()('host:port')\n\n See Also:\n ConnectionPool\n \"\"\"\n def __init__(self, ip, port, pool):\n self.ip = ip\n self.port = port\n self.pool = pool\n\n def __getattr__(self, key):\n @gen.coroutine\n def send_recv_from_rpc(**kwargs):\n stream = yield self.pool.connect(self.ip, self.port)\n try:\n result = yield send_recv(stream=stream, op=key, **kwargs)\n finally:\n if not stream.closed():\n self.pool.available[self.ip, self.port].add(stream)\n self.pool.occupied[self.ip, self.port].remove(stream)\n self.pool.active -= 1\n\n raise gen.Return(result)\n return send_recv_from_rpc\n\n\nclass ConnectionPool(object):\n \"\"\" A maximum sized pool of Tornado IOStreams\n\n This provides a connect method that mirrors the normal distributed.connect\n method, but provides connection sharing and tracks connection limits.\n\n This object provides an ``rpc`` like interface::\n\n >>> rpc = ConnectionPool(limit=512)\n >>> scheduler = rpc('127.0.0.1:8786')\n >>> workers = [rpc(ip=ip, port=port) for ip, port in ...]\n\n >>> info = yield scheduler.identity()\n\n It creates enough streams to satisfy concurrent connections to any\n particular address::\n\n >>> a, b = yield [scheduler.who_has(), scheduler.has_what()]\n\n It reuses existing streams so that we don't have to continuously reconnect.\n\n It also maintains a stream limit to avoid \"too many open file handle\"\n issues. Whenever this maximum is reached we clear out all idling streams.\n If that doesn't do the trick then we wait until one of the occupied streams\n closes.\n \"\"\"\n def __init__(self, limit=512):\n self.open = 0\n self.active = 0\n self.limit = limit\n self.available = defaultdict(set)\n self.occupied = defaultdict(set)\n self.event = Event()\n\n def __str__(self):\n return \"<ConnectionPool: open=%d, active=%d>\" % (self.open,\n self.active)\n\n __repr__ = __str__\n\n def __call__(self, arg=None, ip=None, port=None, addr=None):\n \"\"\" Cached rpc objects \"\"\"\n ip, port = ip_port_from_args(arg=arg, addr=addr, ip=ip, port=port)\n return RPCCall(ip, port, self)\n\n @gen.coroutine\n def connect(self, ip, port, timeout=3):\n if self.available.get((ip, port)):\n stream = self.available[ip, port].pop()\n self.active += 1\n self.occupied[ip, port].add(stream)\n raise gen.Return(stream)\n\n while self.open >= self.limit:\n self.event.clear()\n self.collect()\n yield self.event.wait()\n\n self.open += 1\n stream = yield connect(ip=ip, port=port, timeout=timeout)\n stream.set_close_callback(lambda: self.on_close(ip, port, stream))\n self.active += 1\n self.occupied[ip, port].add(stream)\n\n if self.open >= self.limit:\n self.event.clear()\n\n raise gen.Return(stream)\n\n def on_close(self, ip, port, stream):\n self.open -= 1\n\n if stream in self.available[ip, port]:\n self.available[ip, port].remove(stream)\n if stream in self.occupied[ip, port]:\n self.occupied[ip, port].remove(stream)\n self.active -= 1\n\n if self.open <= self.limit:\n self.event.set()\n\n def collect(self):\n logger.info(\"Collecting unused streams. open: %d, active: %d\",\n self.open, self.active)\n for k, streams in list(self.available.items()):\n for stream in streams:\n stream.close()\n\n\ndef coerce_to_address(o, out=str):\n if PY3 and isinstance(o, bytes):\n o = o.decode()\n if isinstance(o, (unicode, str)):\n ip, port = o.rsplit(':', 1)\n port = int(port)\n o = (ip, port)\n if isinstance(o, list):\n o = tuple(o)\n if isinstance(o, tuple) and isinstance(o[0], bytes):\n o = (o[0].decode(), o[1])\n\n if out == str:\n o = '%s:%s' % o\n\n return o\n\n\ndef coerce_to_rpc(o, **kwargs):\n if isinstance(o, (bytes, str, tuple, list)):\n ip, port = coerce_to_address(o, out=tuple)\n return rpc(ip=ip, port=int(port), **kwargs)\n elif isinstance(o, IOStream):\n return rpc(stream=o, **kwargs)\n elif isinstance(o, rpc):\n return o\n else:\n raise TypeError()\n\n\ndef error_message(e, status='error'):\n \"\"\" Produce message to send back given an exception has occurred\n\n This does the following:\n\n 1. Gets the traceback\n 2. Truncates the exception and the traceback\n 3. Serializes the exception and traceback or\n 4. If they can't be serialized send string versions\n 5. Format a message and return\n\n See Also\n --------\n clean_exception: deserialize and unpack message into exception/traceback\n six.reraise: raise exception/traceback\n \"\"\"\n tb = get_traceback()\n e2 = truncate_exception(e, 1000)\n try:\n e3 = dumps(e2)\n loads(e3)\n except Exception:\n e3 = Exception(str(e2))\n e3 = dumps(e3)\n try:\n tb2 = dumps(tb)\n except Exception:\n tb2 = ''.join(traceback.format_tb(tb))\n tb2 = dumps(tb2)\n\n if len(tb2) > 10000:\n tb2 = None\n\n return {'status': status, 'exception': e3, 'traceback': tb2}\n\n\ndef clean_exception(exception, traceback, **kwargs):\n \"\"\" Reraise exception and traceback. Deserialize if necessary\n\n See Also\n --------\n error_message: create and serialize errors into message\n \"\"\"\n if isinstance(exception, bytes):\n exception = loads(exception)\n if isinstance(traceback, bytes):\n traceback = loads(traceback)\n if isinstance(traceback, str):\n traceback = None\n return type(exception), exception, traceback\n\n\nfrom .batched import BatchedStream\n", "path": "distributed/core.py" } ]
diff --git a/distributed/core.py b/distributed/core.py index d1907462085..4a08ee63300 100644 --- a/distributed/core.py +++ b/distributed/core.py @@ -73,7 +73,7 @@ def loads(x): try: return pickle.loads(x) except Exception: - logger.info("Failed to deserialize %s", x, exc_info=True) + logger.info("Failed to deserialize %s", x[:10000], exc_info=True) raise
rasterio__rasterio-1477
Python crashes while building overviews After performing the below code Python crashes: ```python import rasterio from rasterio.enums import Resampling factors = [2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] dst = rasterio.open('rasterio/tests/data/RGB.byte.tif', 'r+') dst.build_overviews(factors, Resampling.average) ``` ``` *** Error in `python': malloc(): memory corruption: 0x0000000002e0f9c0 *** ======= Backtrace: ========= /lib/x86_64-linux-gnu/libc.so.6(+0x777e5)[0x7fbe1c3fd7e5] /lib/x86_64-linux-gnu/libc.so.6(+0x8213e)[0x7fbe1c40813e] /lib/x86_64-linux-gnu/libc.so.6(__libc_malloc+0x54)[0x7fbe1c40a184] /home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/.libs/libgdal-acedaae2.so.20.3.1(CPLMalloc+0x20)[0x7fbe19ab2700] /home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/.libs/libgdal-acedaae2.so.20.3.1(CPLCalloc+0x1c)[0x7fbe19ab27ac] /home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/.libs/libgdal-acedaae2.so.20.3.1(_ZN12GTiffDataset15IBuildOverviewsEPKciPiiS2_PFidS1_PvES3_+0x10f0)[0x7fbe19554bd0] /home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/.libs/libgdal-acedaae2.so.20.3.1(_ZN11GDALDataset14BuildOverviewsEPKciPiiS2_PFidS1_PvES3_+0x38)[0x7fbe198059f8] /home/rykov/sandbox/env/lib/python3.5/site-packages/rasterio/_io.cpython-35m-x86_64-linux-gnu.so(+0x3613a)[0x7fbe0595713a] python(PyCFunction_Call+0x77)[0x4e9ba7] python(PyEval_EvalFrameEx+0x614)[0x5372f4] python[0x540199] python(PyEval_EvalCode+0x1f)[0x540e4f] python[0x60c272] python(PyRun_InteractiveOneObject+0x2b1)[0x46b89f] python(PyRun_InteractiveLoopFlags+0xe8)[0x46ba48] python[0x46cfa0] python[0x4cf2bd] python(main+0xe1)[0x4cfeb1] /lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf0)[0x7fbe1c3a6830] python(_start+0x29)[0x5d6049] ```
[ { "content": "\"\"\"Errors and Warnings.\"\"\"\n\nfrom click import FileError\n\n\nclass RasterioError(Exception):\n \"\"\"Root exception class\"\"\"\n\n\nclass WindowError(RasterioError):\n \"\"\"Raised when errors occur during window operations\"\"\"\n\n\nclass CRSError(ValueError):\n \"\"\"Raised when a CRS string or mapping is invalid or cannot serve\n to define a coordinate transformation.\"\"\"\n\n\nclass EnvError(RasterioError):\n \"\"\"Raised when the state of GDAL/AWS environment cannot be created\n or modified.\"\"\"\n\n\nclass DriverRegistrationError(ValueError):\n \"\"\"Raised when a format driver is requested but is not registered.\"\"\"\n\n\nclass FileOverwriteError(FileError):\n \"\"\"Raised when Rasterio's CLI refuses to clobber output files.\"\"\"\n\n def __init__(self, message):\n \"\"\"Raise FileOverwriteError with message as hint.\"\"\"\n super(FileOverwriteError, self).__init__('', hint=message)\n\n\nclass RasterioIOError(IOError):\n \"\"\"Raised when a dataset cannot be opened using one of the\n registered format drivers.\"\"\"\n\n\nclass NodataShadowWarning(UserWarning):\n \"\"\"Warn that a dataset's nodata attribute is shadowing its alpha band.\"\"\"\n\n def __str__(self):\n return (\"The dataset's nodata attribute is shadowing \"\n \"the alpha band. All masks will be determined \"\n \"by the nodata attribute\")\n\n\nclass NotGeoreferencedWarning(UserWarning):\n \"\"\"Warn that a dataset isn't georeferenced.\"\"\"\n\n\nclass GDALBehaviorChangeException(RuntimeError):\n \"\"\"Raised when GDAL's behavior differs from the given arguments. For\n example, antimeridian cutting is always on as of GDAL 2.2.0. Users\n expecting it to be off will be presented with a MultiPolygon when the\n rest of their code expects a Polygon.\n\n # Raises an exception on GDAL >= 2.2.0\n rasterio.warp.transform_geometry(\n src_crs, dst_crs, antimeridian_cutting=False)\n \"\"\"\n\n\nclass GDALOptionNotImplementedError(RasterioError):\n \"\"\"A dataset opening or dataset creation option can't be supported\n\n This will be raised from Rasterio's shim modules. For example, when\n a user passes arguments to open_dataset() that can't be evaluated\n by GDAL 1.x.\n \"\"\"\n\nclass GDALVersionError(RasterioError):\n \"\"\"Raised if the runtime version of GDAL does not meet the required\n version of GDAL.\"\"\"\n\n\nclass WindowEvaluationError(ValueError):\n \"\"\"Raised when window evaluation fails\"\"\"\n\n\nclass RasterioDeprecationWarning(UserWarning):\n \"\"\"Rasterio module deprecations\"\"\"\n\n\nclass RasterBlockError(RasterioError):\n \"\"\"Raised when raster block access fails\"\"\"\n\n\nclass BandOverviewError(UserWarning):\n \"\"\"Raised when a band overview access fails.\"\"\"\n\n\nclass WarpOptionsError(RasterioError):\n \"\"\"Raised when options for a warp operation are invalid\"\"\"\n\n\nclass UnsupportedOperation(RasterioError):\n \"\"\"Raised when reading from a file opened in 'w' mode\"\"\"\n", "path": "rasterio/errors.py" } ]
[ { "content": "\"\"\"Errors and Warnings.\"\"\"\n\nfrom click import FileError\n\n\nclass RasterioError(Exception):\n \"\"\"Root exception class\"\"\"\n\n\nclass WindowError(RasterioError):\n \"\"\"Raised when errors occur during window operations\"\"\"\n\n\nclass CRSError(ValueError):\n \"\"\"Raised when a CRS string or mapping is invalid or cannot serve\n to define a coordinate transformation.\"\"\"\n\n\nclass EnvError(RasterioError):\n \"\"\"Raised when the state of GDAL/AWS environment cannot be created\n or modified.\"\"\"\n\n\nclass DriverRegistrationError(ValueError):\n \"\"\"Raised when a format driver is requested but is not registered.\"\"\"\n\n\nclass FileOverwriteError(FileError):\n \"\"\"Raised when Rasterio's CLI refuses to clobber output files.\"\"\"\n\n def __init__(self, message):\n \"\"\"Raise FileOverwriteError with message as hint.\"\"\"\n super(FileOverwriteError, self).__init__('', hint=message)\n\n\nclass RasterioIOError(IOError):\n \"\"\"Raised when a dataset cannot be opened using one of the\n registered format drivers.\"\"\"\n\n\nclass NodataShadowWarning(UserWarning):\n \"\"\"Warn that a dataset's nodata attribute is shadowing its alpha band.\"\"\"\n\n def __str__(self):\n return (\"The dataset's nodata attribute is shadowing \"\n \"the alpha band. All masks will be determined \"\n \"by the nodata attribute\")\n\n\nclass NotGeoreferencedWarning(UserWarning):\n \"\"\"Warn that a dataset isn't georeferenced.\"\"\"\n\n\nclass GDALBehaviorChangeException(RuntimeError):\n \"\"\"Raised when GDAL's behavior differs from the given arguments. For\n example, antimeridian cutting is always on as of GDAL 2.2.0. Users\n expecting it to be off will be presented with a MultiPolygon when the\n rest of their code expects a Polygon.\n\n # Raises an exception on GDAL >= 2.2.0\n rasterio.warp.transform_geometry(\n src_crs, dst_crs, antimeridian_cutting=False)\n \"\"\"\n\n\nclass GDALOptionNotImplementedError(RasterioError):\n \"\"\"A dataset opening or dataset creation option can't be supported\n\n This will be raised from Rasterio's shim modules. For example, when\n a user passes arguments to open_dataset() that can't be evaluated\n by GDAL 1.x.\n \"\"\"\n\nclass GDALVersionError(RasterioError):\n \"\"\"Raised if the runtime version of GDAL does not meet the required\n version of GDAL.\"\"\"\n\n\nclass WindowEvaluationError(ValueError):\n \"\"\"Raised when window evaluation fails\"\"\"\n\n\nclass RasterioDeprecationWarning(UserWarning):\n \"\"\"Rasterio module deprecations\"\"\"\n\n\nclass RasterBlockError(RasterioError):\n \"\"\"Raised when raster block access fails\"\"\"\n\n\nclass BandOverviewError(UserWarning):\n \"\"\"Raised when a band overview access fails.\"\"\"\n\n\nclass WarpOptionsError(RasterioError):\n \"\"\"Raised when options for a warp operation are invalid\"\"\"\n\n\nclass UnsupportedOperation(RasterioError):\n \"\"\"Raised when reading from a file opened in 'w' mode\"\"\"\n\n\nclass OverviewCreationError(RasterioError):\n \"\"\"Raised when creation of an overview fails\"\"\"\n", "path": "rasterio/errors.py" } ]
diff --git a/CHANGES.txt b/CHANGES.txt index 51549d733..61843b88d 100644 --- a/CHANGES.txt +++ b/CHANGES.txt @@ -6,6 +6,9 @@ Changes Bug fixes: +- If the build_overviews method of a dataset is passed a list of factors that + specify more than one 1x1 pixel overview (#1333), Rasterio raises an + exception. - Calling calculate_default_transform for large extents should no longer result in the out of memory error reported in #1131. The rio-warp command should also now run more quickly and with a smaller memory footprint. diff --git a/rasterio/_io.pyx b/rasterio/_io.pyx index 79f744b4c..062e1f4a2 100644 --- a/rasterio/_io.pyx +++ b/rasterio/_io.pyx @@ -6,6 +6,7 @@ from __future__ import absolute_import include "directives.pxi" include "gdal.pxi" +from collections import Counter import logging import sys import uuid @@ -24,7 +25,7 @@ from rasterio.enums import ColorInterp, MaskFlags, Resampling from rasterio.errors import ( CRSError, DriverRegistrationError, RasterioIOError, NotGeoreferencedWarning, NodataShadowWarning, WindowError, - UnsupportedOperation + UnsupportedOperation, OverviewCreationError ) from rasterio.sample import sample_gen from rasterio.transform import Affine @@ -1549,6 +1550,11 @@ cdef class DatasetWriterBase(DatasetReaderBase): ['Resampling.{0}'.format(Resampling(k).name) for k in resampling_map.keys()]))) + # Check factors + ovr_shapes = Counter([(int((self.height + f - 1) / f), int((self.width + f - 1) / f)) for f in factors]) + if ovr_shapes[(1, 1)] > 1: + raise OverviewCreationError("Too many overviews levels of 1x1 dimension were requested") + # Allocate arrays. if factors: factors_c = <int *>CPLMalloc(len(factors)*sizeof(int)) diff --git a/rasterio/errors.py b/rasterio/errors.py index 7e80f8817..97f63303f 100644 --- a/rasterio/errors.py +++ b/rasterio/errors.py @@ -98,3 +98,7 @@ class WarpOptionsError(RasterioError): class UnsupportedOperation(RasterioError): """Raised when reading from a file opened in 'w' mode""" + + +class OverviewCreationError(RasterioError): + """Raised when creation of an overview fails""" diff --git a/tests/test_overviews.py b/tests/test_overviews.py index d0098605d..4b26d9e33 100644 --- a/tests/test_overviews.py +++ b/tests/test_overviews.py @@ -9,6 +9,8 @@ import rasterio from rasterio.enums import Resampling from rasterio.env import GDALVersion +from rasterio.errors import OverviewCreationError + gdal_version = GDALVersion() @@ -79,3 +81,12 @@ def test_test_unsupported_algo(data): with rasterio.open(inputfile, 'r+') as src: overview_factors = [2, 4] src.build_overviews(overview_factors, resampling=Resampling.q1) + + +def test_issue1333(data): + """Fail if asked to create more than one 1x1 overview""" + inputfile = str(data.join('RGB.byte.tif')) + with pytest.raises(OverviewCreationError): + with rasterio.open(inputfile, 'r+') as src: + overview_factors = [1024, 2048] + src.build_overviews(overview_factors, resampling=Resampling.average)
dotkom__onlineweb4-425
"Startet studie" in Profile -> Medlemskap requires defined format without specifying it "Started studie" is a datefield. The problem is that most browsers (like FF, Chrome) don't render these fields with any additional tools which makes filling them out a pain in the ass (Safari@iOS has that fancy datepicker-shit). The field requires the format 'yyyy-mm-dd', but does not specify this anywhere. This should be fixed somehow.
[ { "content": "# -*- coding: utf-8 -*-\n\nfrom django import forms\nfrom django.utils.translation import ugettext as _\n\nfrom apps.profiles.models import Privacy\nfrom apps.authentication.models import OnlineUser, FIELD_OF_STUDY_CHOICES\n\nclass ProfileForm(forms.ModelForm):\n\n class Meta:\n model = OnlineUser\n\n fields = ['nickname', 'website', 'phone_number', 'address', 'zip_code', 'allergies', 'mark_rules', ]\n widgets = {\n 'allergies' : forms.Textarea(attrs={'id' : 'allergies'}),\n }\n\n def clean(self):\n super(ProfileForm, self).clean()\n\n cleaned_data = self.cleaned_data\n\n # ZIP code digits only\n zip_code = cleaned_data['zip_code']\n if len(zip_code) != 0 and (len(zip_code) != 4 or not zip_code.isdigit()):\n self._errors['zip_code'] = self.error_class([_(u\"Postnummer må bestå av fire siffer.\")])\n\n return cleaned_data\n\nclass ImageForm(forms.ModelForm):\n\n class Meta:\n model = OnlineUser\n\n fields = ['image']\n widgets = {\n 'image': forms.FileInput(attrs={'class' : 'hidden-input', 'id' : 'image'}),\n }\n\nclass PrivacyForm(forms.ModelForm):\n\n class Meta:\n model = Privacy\n exclude = ['user']\n\n\nclass MailSettingsForm(forms.ModelForm):\n\n class Meta:\n model = OnlineUser\n fields = ['infomail', ]\n\n\nclass MembershipSettingsForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(MembershipSettingsForm, self).__init__(*args, **kwargs)\n self.fields['started_date'].widget.attrs['class'] = 'hasDatePicker'\n\n class Meta:\n model = OnlineUser\n fields = ['field_of_study', 'started_date', ]\n", "path": "apps/profiles/forms.py" } ]
[ { "content": "# -*- coding: utf-8 -*-\n\nfrom django import forms\nfrom django.utils.translation import ugettext as _\n\nfrom apps.profiles.models import Privacy\nfrom apps.authentication.models import OnlineUser, FIELD_OF_STUDY_CHOICES\n\nclass ProfileForm(forms.ModelForm):\n\n class Meta:\n model = OnlineUser\n\n fields = ['nickname', 'website', 'phone_number', 'address', 'zip_code', 'allergies', 'mark_rules', ]\n widgets = {\n 'allergies' : forms.Textarea(attrs={'id' : 'allergies'}),\n }\n\n def clean(self):\n super(ProfileForm, self).clean()\n\n cleaned_data = self.cleaned_data\n\n # ZIP code digits only\n zip_code = cleaned_data['zip_code']\n if len(zip_code) != 0 and (len(zip_code) != 4 or not zip_code.isdigit()):\n self._errors['zip_code'] = self.error_class([_(u\"Postnummer må bestå av fire siffer.\")])\n\n return cleaned_data\n\nclass ImageForm(forms.ModelForm):\n\n class Meta:\n model = OnlineUser\n\n fields = ['image']\n widgets = {\n 'image': forms.FileInput(attrs={'class' : 'hidden-input', 'id' : 'image'}),\n }\n\nclass PrivacyForm(forms.ModelForm):\n\n class Meta:\n model = Privacy\n exclude = ['user']\n\n\nclass MailSettingsForm(forms.ModelForm):\n\n class Meta:\n model = OnlineUser\n fields = ['infomail', ]\n\n\nclass MembershipSettingsForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(MembershipSettingsForm, self).__init__(*args, **kwargs)\n self.fields['started_date'].widget.attrs['class'] = 'hasDatePicker'\n\n class Meta:\n model = OnlineUser\n fields = ['field_of_study', 'started_date', ]\n\n widgets = {\n 'started_date' : forms.TextInput(attrs={'placeholder' : 'YYYY-MM-DD'}),\n }\n", "path": "apps/profiles/forms.py" } ]
diff --git a/apps/profiles/forms.py b/apps/profiles/forms.py index 6e2b39a1f..ed1080b7d 100644 --- a/apps/profiles/forms.py +++ b/apps/profiles/forms.py @@ -61,3 +61,7 @@ def __init__(self, *args, **kwargs): class Meta: model = OnlineUser fields = ['field_of_study', 'started_date', ] + + widgets = { + 'started_date' : forms.TextInput(attrs={'placeholder' : 'YYYY-MM-DD'}), + } diff --git a/files/static/js/profiles.js b/files/static/js/profiles.js index b78c63825..12e372443 100755 --- a/files/static/js/profiles.js +++ b/files/static/js/profiles.js @@ -316,7 +316,11 @@ $(document).ready(function() { JS for membership */ - $(".hasDatePcker").datepicker({ dateFormat: "yy-mm-dd" }); + $(".hasDatePicker").datepicker({ + yearRange: "2004:" + new Date().getFullYear(), + changeYear: true, + dateFormat: "yy-mm-dd" + }); $("#membership-details").submit(function(event) { event.preventDefault(); diff --git a/templates/profiles/membership.html b/templates/profiles/membership.html index 497d0c476..9a40b4746 100644 --- a/templates/profiles/membership.html +++ b/templates/profiles/membership.html @@ -15,7 +15,7 @@ <h3>Medlemskap</h3> </form> {% else %} {% if user.ntnu_username %} - Du er ikke registrert som medlem av Linjeforeningen Online. + <p>Du er ikke registrert som medlem av Linjeforeningen Online.</p> {% else %} <p>For å aktivere ditt medlemskap kreves det at du har knyttet din studentkonto til din profil.</p> <p>Gå til Epost-innstillinger og registrer din @stud.ntnu.no epost.</p>
comic__grand-challenge.org-2027
Integrate Forums into challenges Navigating to the forum of a challenge currently takes the participant outside of the challenge environment. Navigating back to the challenge is not possible through the breadcrumbs on the forum page and instead requires going via the Challenge tab and searching for the respective Challenge again. It would be nicer if the forums were visually integrated into the challenge page layout and if the breadcrumbs reflected their nesting in the challenge rather than their nesting under all forum on GC. See here: https://github.com/DIAGNijmegen/rse-roadmap/issues/83#issuecomment-919250835
[ { "content": "from actstream.models import Follow\r\nfrom django import template\r\nfrom django.contrib.contenttypes.models import ContentType\r\n\r\nfrom grandchallenge.notifications.forms import FollowForm\r\n\r\nregister = template.Library()\r\n\r\n\r\[email protected]_tag\r\ndef get_follow_object_pk(user, follow_object):\r\n object_follows_for_user = Follow.objects.filter(\r\n user=user,\r\n content_type=ContentType.objects.get(\r\n app_label=follow_object._meta.app_label,\r\n model=follow_object._meta.model_name,\r\n ),\r\n ).all()\r\n\r\n if not object_follows_for_user:\r\n current_follow_object = []\r\n else:\r\n current_follow_object = []\r\n for obj in object_follows_for_user:\r\n if not obj.follow_object:\r\n continue\r\n elif obj.follow_object.id == follow_object.id:\r\n current_follow_object = obj.pk\r\n return current_follow_object\r\n\r\n\r\[email protected]_tag\r\ndef follow_form(*, user, object_id, content_type):\r\n return FollowForm(\r\n user=user,\r\n initial={\r\n \"object_id\": object_id,\r\n \"content_type\": content_type,\r\n \"actor_only\": False,\r\n },\r\n )\r\n\r\n\r\[email protected]_tag()\r\ndef get_content_type(follow_object):\r\n try:\r\n ct = ContentType.objects.get(\r\n app_label=follow_object._meta.app_label,\r\n model=follow_object._meta.model_name,\r\n )\r\n except AttributeError:\r\n ct = None\r\n return ct\r\n", "path": "app/grandchallenge/forum_conversation/templatetags/forum_extras.py" } ]
[ { "content": "from actstream.models import Follow\r\nfrom django import template\r\nfrom django.contrib.contenttypes.models import ContentType\r\n\r\nfrom grandchallenge.notifications.forms import FollowForm\r\n\r\nregister = template.Library()\r\n\r\n\r\[email protected]_tag\r\ndef get_follow_object_pk(user, follow_object):\r\n object_follows_for_user = Follow.objects.filter(\r\n user=user,\r\n content_type=ContentType.objects.get(\r\n app_label=follow_object._meta.app_label,\r\n model=follow_object._meta.model_name,\r\n ),\r\n ).all()\r\n\r\n if not object_follows_for_user:\r\n current_follow_object = []\r\n else:\r\n current_follow_object = []\r\n for obj in object_follows_for_user:\r\n if not obj.follow_object:\r\n continue\r\n elif obj.follow_object.id == follow_object.id:\r\n current_follow_object = obj.pk\r\n return current_follow_object\r\n\r\n\r\[email protected]_tag\r\ndef follow_form(*, user, object_id, content_type):\r\n return FollowForm(\r\n user=user,\r\n initial={\r\n \"object_id\": object_id,\r\n \"content_type\": content_type,\r\n \"actor_only\": False,\r\n },\r\n )\r\n\r\n\r\[email protected]_tag()\r\ndef get_content_type(follow_object):\r\n try:\r\n ct = ContentType.objects.get(\r\n app_label=follow_object._meta.app_label,\r\n model=follow_object._meta.model_name,\r\n )\r\n except AttributeError:\r\n ct = None\r\n return ct\r\n\r\n\r\[email protected]_tag()\r\ndef is_participant(user, challenge):\r\n if challenge.is_participant(user):\r\n return True\r\n", "path": "app/grandchallenge/forum_conversation/templatetags/forum_extras.py" } ]
diff --git a/app/grandchallenge/forum_conversation/templatetags/forum_extras.py b/app/grandchallenge/forum_conversation/templatetags/forum_extras.py index deb1d274f8..18b12b1d6b 100644 --- a/app/grandchallenge/forum_conversation/templatetags/forum_extras.py +++ b/app/grandchallenge/forum_conversation/templatetags/forum_extras.py @@ -51,3 +51,9 @@ def get_content_type(follow_object): except AttributeError: ct = None return ct + + [email protected]_tag() +def is_participant(user, challenge): + if challenge.is_participant(user): + return True diff --git a/app/grandchallenge/forums/templates/board_base.html b/app/grandchallenge/forums/templates/board_base.html index ecf95b9ac3..56268e2f07 100644 --- a/app/grandchallenge/forums/templates/board_base.html +++ b/app/grandchallenge/forums/templates/board_base.html @@ -2,6 +2,8 @@ {% load static %} {% load i18n %} {% load forum_permission_tags %} +{% load forum_extras %} +{% load guardian_tags %} {% block title %}{% block sub_title %}{% endblock sub_title %} - {{ request.site.name }} Forums{% endblock title %} @@ -12,31 +14,63 @@ {% block body %} {% include "grandchallenge/partials/navbar.html" with hide_userlinks=False %} - {% include "partials/breadcrumb.html" %} -<div class="my-5 container" id="main_container"> - <div class="row"> - <div class="col-12"> - <div class="float-right controls-link-wrapper"> - {% if not request.user.is_anonymous %} - <a href="{% url 'notifications:follow-list' %}" class="d-inline-block ml-3"><i class="fas fa-bookmark">&nbsp;</i>{% trans "Subscriptions" %}</a> - <a href="{% url 'forum_member:user_posts' request.user.id %}" class="d-inline-block ml-3"><i class="fas fa-comments">&nbsp;</i>{% trans "View my posts" %}</a> - {% endif %} - {% get_permission 'can_access_moderation_queue' request.user as can_access_moderation_queue %} - {% if can_access_moderation_queue %} - <a href="{% url 'forum_moderation:queue' %}" class="d-inline-block ml-3"><i class="fas fa-gavel">&nbsp;</i>{% trans "Moderation queue" %}</a> - {% endif %} + {% if forum.challenge %} + <div class="container-fluid bg-primary"> + <div class="container"> + <nav aria-label="breadcrumb"> + <ol class="breadcrumb"> + <li class="breadcrumb-item"><a href="{% url 'challenges:list' %}">Challenges</a></li> + <li class="breadcrumb-item"><a href="{{ forum.challenge.get_absolute_url }}"> + {% firstof forum.challenge.title forum.challenge.short_name %}</a></li> + <li class="breadcrumb-item"><a + href="{% url 'forum:forum' forum.slug forum.id %}">Forum</a></li> + {% if topic %} + <li class="breadcrumb-item"><a + href="{% url 'forum_conversation:topic' forum.slug forum.id topic.slug topic.id %}">{{ topic.subject }}</a> + </li> + {% endif %} + </ol> + </nav> </div> </div> - </div> - <div class="row"> - <div class="col-12"> - <br /> - {% block messages %}{% include "partials/messages.html" %}{% endblock messages %} + {% else %} + {% include "partials/breadcrumb.html" %} + {% endif %} + <div class="container pb-3 mt-3"> + {% block outer_content %} + {% block messages %} + {% if forum.challenge %} + {% include 'challenges/challenge_banner.html' with challenge=forum.challenge %} + {% endif %} + {% include "grandchallenge/partials/messages.html" %} + {% endblock %} + {% if forum.challenge %} + {% block topbar %} + {% get_obj_perms request.user for forum.challenge as "user_perms" %} + {% is_participant request.user forum.challenge as is_challenge_participant %} + {% include 'challenges/challenge_topbar.html' with challenge=forum.challenge challenge_perms=user_perms user_is_participant=is_challenge_participant %} + {% endblock %} + {% endif %} + {% endblock %} + + <div class="row"> + <div class="col-12"> + <div class="float-right controls-link-wrapper"> + {% if not request.user.is_anonymous %} + <a href="{% url 'notifications:follow-list' %}" class="d-inline-block ml-3"><i class="fas fa-bookmark">&nbsp;</i>{% trans "Subscriptions" %}</a> + <a href="{% url 'forum_member:user_posts' request.user.id %}" class="d-inline-block ml-3"><i class="fas fa-comments">&nbsp;</i>{% trans "View my posts" %}</a> + {% endif %} + {% get_permission 'can_access_moderation_queue' request.user as can_access_moderation_queue %} + {% if can_access_moderation_queue %} + <a href="{% url 'forum_moderation:queue' %}" class="d-inline-block ml-3"><i class="fas fa-gavel">&nbsp;</i>{% trans "Moderation queue" %}</a> + {% endif %} + </div> + </div> </div> + + {% block content %} + {% endblock content %} </div> - {% block content %} - {% endblock content %} -</div> {% endblock %} {% block js %}
speechbrain__speechbrain-1504
Torch 1.12 not compatible? working to install speechbrain 0.5.12, and getting the error that "speechbrain 0.5.12 requires torch<=1.11,>=1.7, but you have torch 1.12.0 which is incompatible." read elsewhere that it should work with >=1.7.
[ { "content": "#!/usr/bin/env python3\nimport os\nimport sys\nimport site\nimport setuptools\nfrom distutils.core import setup\n\n\n# Editable install in user site directory can be allowed with this hack:\n# https://github.com/pypa/pip/issues/7953.\nsite.ENABLE_USER_SITE = \"--user\" in sys.argv[1:]\n\nwith open(\"README.md\") as f:\n long_description = f.read()\n\nwith open(os.path.join(\"speechbrain\", \"version.txt\")) as f:\n version = f.read().strip()\n\nsetup(\n name=\"speechbrain\",\n version=version,\n description=\"All-in-one speech toolkit in pure Python and Pytorch\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n author=\"Mirco Ravanelli & Others\",\n author_email=\"[email protected]\",\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: Apache Software License\",\n ],\n packages=setuptools.find_packages(),\n package_data={\"speechbrain\": [\"version.txt\", \"log-config.yaml\"]},\n install_requires=[\n \"hyperpyyaml\",\n \"joblib\",\n \"numpy\",\n \"packaging\",\n \"scipy\",\n \"sentencepiece\",\n \"torch>=1.7,<=1.11\",\n \"torchaudio\",\n \"tqdm\",\n \"huggingface_hub\",\n ],\n python_requires=\">=3.7\",\n url=\"https://speechbrain.github.io/\",\n)\n", "path": "setup.py" } ]
[ { "content": "#!/usr/bin/env python3\nimport os\nimport sys\nimport site\nimport setuptools\nfrom distutils.core import setup\n\n\n# Editable install in user site directory can be allowed with this hack:\n# https://github.com/pypa/pip/issues/7953.\nsite.ENABLE_USER_SITE = \"--user\" in sys.argv[1:]\n\nwith open(\"README.md\") as f:\n long_description = f.read()\n\nwith open(os.path.join(\"speechbrain\", \"version.txt\")) as f:\n version = f.read().strip()\n\nsetup(\n name=\"speechbrain\",\n version=version,\n description=\"All-in-one speech toolkit in pure Python and Pytorch\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n author=\"Mirco Ravanelli & Others\",\n author_email=\"[email protected]\",\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: Apache Software License\",\n ],\n packages=setuptools.find_packages(),\n package_data={\"speechbrain\": [\"version.txt\", \"log-config.yaml\"]},\n install_requires=[\n \"hyperpyyaml\",\n \"joblib\",\n \"numpy\",\n \"packaging\",\n \"scipy\",\n \"sentencepiece\",\n \"torch>=1.9\",\n \"torchaudio\",\n \"tqdm\",\n \"huggingface_hub\",\n ],\n python_requires=\">=3.7\",\n url=\"https://speechbrain.github.io/\",\n)\n", "path": "setup.py" } ]
diff --git a/requirements.txt b/requirements.txt index 6f78e41680..7a9360a981 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,6 +8,6 @@ pre-commit>=2.3.0 scipy>=1.4.1 sentencepiece>=0.1.91 SoundFile; sys_platform == 'win32' -torch>=1.9.0,<=1.11.1 -torchaudio>=0.9.0,<=0.11.1 +torch>=1.9.0 +torchaudio>=0.9.0 tqdm>=4.42.0 diff --git a/setup.py b/setup.py index 49aab61b1b..92d2cd2b40 100644 --- a/setup.py +++ b/setup.py @@ -37,7 +37,7 @@ "packaging", "scipy", "sentencepiece", - "torch>=1.7,<=1.11", + "torch>=1.9", "torchaudio", "tqdm", "huggingface_hub",
mlcommons__GaNDLF-453
Pickle5 may cause setup errors on Python 3.8 (future-proofing) **Describe the bug** When installing GaNDLF on Python 3.8, an error occurs when installing the dependency "pickle5". Note that pickle5 is redundant in 3.8 -- the backported functionality is the default/standard [[ref](https://github.com/pitrou/pickle5-backport/issues/12)]. You can solve this by adding this annotation in setup.py so that pickle5 is only installed on Python versions 3.7 or lower (example of this syntax: https://stackoverflow.com/a/32643122). If pickle5 is imported directly in your code, you may also need to do a version check at import time, something like this: ``` python # Both these should come standard if you have setuptools anyway import platform from packaging import version if version.parse(platform.python_version()) < version.parse("3.8.0"): import pickle5 as pickle else: import pickle ``` **To Reproduce** Steps to reproduce the behavior: 1. Create a Python 3.8 environment using your mechanism of choice. 2. Install GaNDLF per instructions. 3. Receive error message while installing pickle5. **GaNDLF Version** Latest master (0.0.14.dev0 I think) **Desktop (please complete the following information):** Occurs in any system with Python 3.8 or greater. At least for me on Ubuntu-based machines. **Additional context** This issue is just a heads up for supporting 3.8 and greater. Hope this helps.
[ { "content": "#!/usr/bin/env python\n\n\"\"\"The setup script.\"\"\"\n\n\nimport os\nfrom setuptools import setup, find_packages\nfrom setuptools.command.install import install\nfrom setuptools.command.develop import develop\nfrom setuptools.command.egg_info import egg_info\n\nwith open(\"README.md\") as readme_file:\n readme = readme_file.read()\n\n\ndef git_submodule_update():\n ## submodule update\n os.system(\"git submodule update --init --recursive\")\n\n\nclass CustomInstallCommand(install):\n def run(self):\n install.run(self)\n git_submodule_update()\n\n\nclass CustomDevelopCommand(develop):\n def run(self):\n develop.run(self)\n git_submodule_update()\n\n\nclass CustomEggInfoCommand(egg_info):\n def run(self):\n egg_info.run(self)\n git_submodule_update()\n\n\n# read version.py\nimport sys, re\n\ntry:\n filepath = \"GANDLF/version.py\"\n version_file = open(filepath)\n (__version__,) = re.findall('__version__ = \"(.*)\"', version_file.read())\n\nexcept Exception as error:\n __version__ = \"0.0.1\"\n sys.stderr.write(\"Warning: Could not open '%s' due %s\\n\" % (filepath, error))\n\nrequirements = [\n \"black\",\n \"numpy==1.21.0\",\n \"scipy\",\n \"SimpleITK!=2.0.*\",\n \"torchvision\",\n \"tqdm\",\n \"torchio==0.18.57\",\n \"pandas\",\n \"pylint\",\n \"scikit-learn>=0.23.2\",\n \"scikit-image>=0.19.1\",\n \"pickle5>=0.0.11\",\n \"setuptools\",\n \"seaborn\",\n \"pyyaml\",\n \"tiffslide\",\n \"matplotlib\",\n \"requests>=2.25.0\",\n \"pyvips\",\n \"pytest\",\n \"coverage\",\n \"pytest-cov\",\n \"psutil\",\n \"medcam\",\n \"opencv-python\",\n \"torchmetrics==0.5.1\", # newer versions have changed api for f1 invocation\n \"OpenPatchMiner==0.1.8\",\n \"zarr==2.10.3\",\n \"pydicom\",\n \"onnx\",\n]\n\n# pytorch doesn't have LTS support on OSX - https://github.com/CBICA/GaNDLF/issues/389\nif sys.platform == \"darwin\":\n requirements.append(\"torch==1.9.0\")\nelse:\n requirements.append(\"torch==1.8.2\")\n\nsetup(\n name=\"GANDLF\",\n version=__version__,\n author=\"Jose Agraz, Vinayak Ahluwalia, Bhakti Baheti, Spyridon Bakas, Ujjwal Baid, Megh Bhalerao, Brandon Edwards, Karol Gotkowski, Caleb Grenko, Orhun Güley, Ibrahim Ethem Hamamci, Sarthak Pati, Micah Sheller, Juliia Skobleva, Siddhesh Thakur, Spiros Thermos\", # alphabetical order\n author_email=\"[email protected]\",\n python_requires=\">=3.7\",\n packages=find_packages(),\n cmdclass={ # this ensures git_submodule_update is called during install\n \"install\": CustomInstallCommand,\n \"develop\": CustomDevelopCommand,\n \"egg_info\": CustomEggInfoCommand,\n },\n scripts=[\n \"gandlf_run\",\n \"gandlf_constructCSV\",\n \"gandlf_collectStats\",\n \"gandlf_patchMiner\",\n \"gandlf_preprocess\",\n \"gandlf_anonymizer\",\n \"gandlf_verifyInstall\",\n ],\n classifiers=[\n \"Development Status :: 3 - Alpha\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: BSD License\",\n \"Natural Language :: English\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Topic :: Scientific/Engineering :: Medical Science Apps\",\n ],\n description=(\n \"PyTorch-based framework that handles segmentation/regression/classification using various DL architectures for medical imaging.\"\n ),\n install_requires=requirements,\n license=\"BSD-3-Clause License\",\n long_description=readme,\n long_description_content_type=\"text/markdown\",\n include_package_data=True,\n keywords=\"semantic, segmentation, regression, classification, data-augmentation, medical-imaging\",\n zip_safe=False,\n)\n\n## windows vips installation\nif os.name == \"nt\": # proceed for windows\n from pathlib import Path\n\n # download and extract if main dll is absent\n if not Path(\"./vips/vips-dev-8.10/bin/libvips-42.dll\").exists():\n print(\"Downloading and extracting VIPS for Windows\")\n url = \"https://github.com/libvips/libvips/releases/download/v8.10.2/vips-dev-w64-all-8.10.2.zip\"\n zip_to_extract = \"./vips.zip\"\n import urllib.request, zipfile\n\n urllib.request.urlretrieve(url, zip_to_extract)\n z = zipfile.ZipFile(zip_to_extract)\n z.extractall(\"./vips\")\n z.close()\n os.remove(zip_to_extract)\n", "path": "setup.py" } ]
[ { "content": "#!/usr/bin/env python\n\n\"\"\"The setup script.\"\"\"\n\n\nimport os\nfrom setuptools import setup, find_packages\nfrom setuptools.command.install import install\nfrom setuptools.command.develop import develop\nfrom setuptools.command.egg_info import egg_info\n\nwith open(\"README.md\") as readme_file:\n readme = readme_file.read()\n\n\ndef git_submodule_update():\n ## submodule update\n os.system(\"git submodule update --init --recursive\")\n\n\nclass CustomInstallCommand(install):\n def run(self):\n install.run(self)\n git_submodule_update()\n\n\nclass CustomDevelopCommand(develop):\n def run(self):\n develop.run(self)\n git_submodule_update()\n\n\nclass CustomEggInfoCommand(egg_info):\n def run(self):\n egg_info.run(self)\n git_submodule_update()\n\n\n# read version.py\nimport sys, re\n\ntry:\n filepath = \"GANDLF/version.py\"\n version_file = open(filepath)\n (__version__,) = re.findall('__version__ = \"(.*)\"', version_file.read())\n\nexcept Exception as error:\n __version__ = \"0.0.1\"\n sys.stderr.write(\"Warning: Could not open '%s' due %s\\n\" % (filepath, error))\n\nrequirements = [\n \"black\",\n \"numpy==1.21.0\",\n \"scipy\",\n \"SimpleITK!=2.0.*\",\n \"torchvision\",\n \"tqdm\",\n \"torchio==0.18.57\",\n \"pandas\",\n \"pylint\",\n \"scikit-learn>=0.23.2\",\n \"scikit-image>=0.19.1\",\n 'pickle5>=0.0.11; python_version < \"3.8.0\"',\n \"setuptools\",\n \"seaborn\",\n \"pyyaml\",\n \"tiffslide\",\n \"matplotlib\",\n \"requests>=2.25.0\",\n \"pyvips\",\n \"pytest\",\n \"coverage\",\n \"pytest-cov\",\n \"psutil\",\n \"medcam\",\n \"opencv-python\",\n \"torchmetrics==0.5.1\", # newer versions have changed api for f1 invocation\n \"OpenPatchMiner==0.1.8\",\n \"zarr==2.10.3\",\n \"pydicom\",\n \"onnx\",\n]\n\n# pytorch doesn't have LTS support on OSX - https://github.com/CBICA/GaNDLF/issues/389\nif sys.platform == \"darwin\":\n requirements.append(\"torch==1.9.0\")\nelse:\n requirements.append(\"torch==1.8.2\")\n\nsetup(\n name=\"GANDLF\",\n version=__version__,\n author=\"Jose Agraz, Vinayak Ahluwalia, Bhakti Baheti, Spyridon Bakas, Ujjwal Baid, Megh Bhalerao, Brandon Edwards, Karol Gotkowski, Caleb Grenko, Orhun Güley, Ibrahim Ethem Hamamci, Sarthak Pati, Micah Sheller, Juliia Skobleva, Siddhesh Thakur, Spiros Thermos\", # alphabetical order\n author_email=\"[email protected]\",\n python_requires=\">=3.7\",\n packages=find_packages(),\n cmdclass={ # this ensures git_submodule_update is called during install\n \"install\": CustomInstallCommand,\n \"develop\": CustomDevelopCommand,\n \"egg_info\": CustomEggInfoCommand,\n },\n scripts=[\n \"gandlf_run\",\n \"gandlf_constructCSV\",\n \"gandlf_collectStats\",\n \"gandlf_patchMiner\",\n \"gandlf_preprocess\",\n \"gandlf_anonymizer\",\n \"gandlf_verifyInstall\",\n ],\n classifiers=[\n \"Development Status :: 3 - Alpha\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: BSD License\",\n \"Natural Language :: English\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Topic :: Scientific/Engineering :: Medical Science Apps\",\n ],\n description=(\n \"PyTorch-based framework that handles segmentation/regression/classification using various DL architectures for medical imaging.\"\n ),\n install_requires=requirements,\n license=\"BSD-3-Clause License\",\n long_description=readme,\n long_description_content_type=\"text/markdown\",\n include_package_data=True,\n keywords=\"semantic, segmentation, regression, classification, data-augmentation, medical-imaging\",\n zip_safe=False,\n)\n\n## windows vips installation\nif os.name == \"nt\": # proceed for windows\n from pathlib import Path\n\n # download and extract if main dll is absent\n if not Path(\"./vips/vips-dev-8.10/bin/libvips-42.dll\").exists():\n print(\"Downloading and extracting VIPS for Windows\")\n url = \"https://github.com/libvips/libvips/releases/download/v8.10.2/vips-dev-w64-all-8.10.2.zip\"\n zip_to_extract = \"./vips.zip\"\n import urllib.request, zipfile\n\n urllib.request.urlretrieve(url, zip_to_extract)\n z = zipfile.ZipFile(zip_to_extract)\n z.extractall(\"./vips\")\n z.close()\n os.remove(zip_to_extract)\n", "path": "setup.py" } ]
diff --git a/Dockerfile-CPU b/Dockerfile-CPU index 5a5c6a80f..411dc67b5 100644 --- a/Dockerfile-CPU +++ b/Dockerfile-CPU @@ -2,19 +2,20 @@ FROM ubuntu:18.04 LABEL github="https://github.com/CBICA/GaNDLF" LABEL docs="https://cbica.github.io/GaNDLF/" LABEL version=1.0 + # Install fresh Python and dependencies for build-from-source -RUN apt-get update && apt-get install -y python3.7 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.7-dev libffi-dev -RUN python3.7 -m pip install --upgrade pip +RUN apt-get update && apt-get install -y python3.8 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.8-dev libffi-dev +RUN python3.8 -m pip install --upgrade pip # EXPLICITLY install cpu versions of torch/torchvision (not all versions have +cpu modes on PyPI...) -RUN python3.7 -m pip install torch==1.10.0+cpu torchvision==0.11.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html +RUN python3.8 -m pip install torch==1.8.2+cpu torchvision==0.9.2+cpu torchaudio===0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cpu COPY . /GaNDLF WORKDIR /GaNDLF -RUN python3.7 -m pip install --upgrade pip && python3.7 -m pip install openvino-dev==2022.1.0 && python3.7 -m pip install torch==1.8.2+cpu torchvision==0.9.2+cpu torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html -RUN python3.7 -m pip install -e . +RUN python3.8 -m pip install openvino-dev==2022.1.0 +RUN python3.8 -m pip install -e . # Entrypoint forces all commands given via "docker run" to go through python, CMD forces the default entrypoint script argument to be gandlf_run # If a user calls "docker run gandlf:[tag] gandlf_anonymize", it will resolve to running "python gandlf_anonymize" instead. # CMD is inherently overridden by args to "docker run", entrypoint is constant. -ENTRYPOINT python3.7 +ENTRYPOINT python3.8 CMD gandlf_run # The below force the container commands to run as a nonroot user with UID > 10000. diff --git a/Dockerfile-CUDA10.2 b/Dockerfile-CUDA10.2 index 011fcca7f..79649a93d 100644 --- a/Dockerfile-CUDA10.2 +++ b/Dockerfile-CUDA10.2 @@ -6,11 +6,15 @@ LABEL version=1.0 # Install instructions for NVIDIA Container Toolkit allowing you to use the host's GPU: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html # Note that to do this on a Windows host you need experimental feature "CUDA on WSL" -- not yet stable. -RUN python3 -m pip install --upgrade pip +# Explicitly install python3.8 +RUN apt-get update && apt-get install -y python3.8 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.8-dev libffi-dev +RUN python3.8 -m pip install --upgrade pip +RUN python3.8 -m pip install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html COPY . /GaNDLF WORKDIR /GaNDLF -RUN python3 -m pip install --upgrade pip && python3 -m pip install openvino-dev==2022.1.0 && python3 -m pip install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html -RUN python3 -m pip install -e . +RUN python3.8 -m pip install openvino-dev==2022.1.0 +RUN python3.8 -m pip install -e . + # Entrypoint forces all commands given via "docker run" to go through python, CMD forces the default entrypoint script argument to be gandlf_run # If a user calls "docker run gandlf:[tag] gandlf_anonymize", it will resolve to running "python gandlf_anonymize" instead. # CMD is inherently overridden by args to "docker run", entrypoint is constant. diff --git a/Dockerfile-CUDA11.3 b/Dockerfile-CUDA11.3 index f773f1999..66be6da58 100644 --- a/Dockerfile-CUDA11.3 +++ b/Dockerfile-CUDA11.3 @@ -6,18 +6,19 @@ LABEL version=1.0 # Install instructions for NVIDIA Container Toolkit allowing you to use the host's GPU: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html # Note that to do this on a Windows host you need experimental feature "CUDA on WSL" -- not yet stable. -# Install Python 3.7 -- base image has 3.8, but pickle5 is redundant in 3.8 (and will cause setup errors). This could be resolved at setup time but this simplifies the build for now. -RUN apt-get update && apt-get install -y python3.7 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.7-dev libffi-dev -RUN python3.7 -m pip install --upgrade pip -RUN python3.7 -m pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html +# Explicitly install python3.8 (this uses 11.1 for now, as PyTorch LTS 1.8.2 is built against it) +RUN apt-get update && apt-get install -y python3.8 python3-pip libvips libjpeg8-dev zlib1g-dev python3-dev libpython3.8-dev libffi-dev +RUN python3.8 -m pip install --upgrade pip +RUN python3.8 -m pip install torch==1.8.2 torchvision==0.9.2 torchaudio===0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cu111 COPY . /GaNDLF WORKDIR /GaNDLF -RUN python3.7 -m pip install --upgrade pip && python3.7 -m pip install openvino-dev==2022.1.0 && python3.7 -m pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html -RUN python3.7 -m pip install -e . +RUN python3.8 -m pip install openvino-dev==2022.1.0 +RUN python3.8 -m pip install -e . + # Entrypoint forces all commands given via "docker run" to go through python, CMD forces the default entrypoint script argument to be gandlf_run # If a user calls "docker run gandlf:[tag] gandlf_anonymize", it will resolve to running "python gandlf_anonymize" instead. # CMD is inherently overridden by args to "docker run", entrypoint is constant. -ENTRYPOINT python3.7 +ENTRYPOINT python3.8 CMD gandlf_run # The below force the container commands to run as a nonroot user with UID > 10000. diff --git a/Dockerfile-ROCm b/Dockerfile-ROCm index 51a406674..9cf61ee63 100644 --- a/Dockerfile-ROCm +++ b/Dockerfile-ROCm @@ -1,11 +1,12 @@ -FROM rocm/pytorch:rocm5.0.1_ubuntu18.04_py3.7_pytorch_1.9.0 +# NOTE: ROCm is NOT supported by PyTorch LTS (1.8.2) +FROM rocm/pytorch:rocm4.5.2_ubuntu18.04_py3.8_pytorch_1.10.0 LABEL github="https://github.com/CBICA/GaNDLF" LABEL docs="https://cbica.github.io/GaNDLF/" LABEL version=1.0 # Quick start instructions on using Docker with ROCm: https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/quick-start.md -# The base image contains ROCm, python 3.7 and pytorch already, no need to install those +# The base image contains ROCm, python 3.8 and pytorch already, no need to install those RUN python3 -m pip install --upgrade pip COPY . /GaNDLF WORKDIR /GaNDLF diff --git a/docs/setup.md b/docs/setup.md index 037d3f145..38c5171ff 100644 --- a/docs/setup.md +++ b/docs/setup.md @@ -18,12 +18,12 @@ ## Installation -The instructions assume a system using NVIDA GPUs with [CUDA 10.2](https://developer.nvidia.com/cuda-toolkit-archive) (for AMD, please make the appropriate change during PyTorch installation from [their installation page](https://pytorch.org/get-started/locally)). +The instructions assume a system using NVIDIA GPUs with [CUDA 10.2](https://developer.nvidia.com/cuda-toolkit-archive) (for AMD, please make the appropriate change during PyTorch installation from [their installation page](https://pytorch.org/get-started/locally)). ```bash git clone https://github.com/CBICA/GaNDLF.git cd GaNDLF -conda create -n venv_gandlf python=3.7 -y +conda create -n venv_gandlf python=3.8 -y conda activate venv_gandlf ### PyTorch LTS installation - https://pytorch.org/get-started/locally ## CUDA 10.2 diff --git a/setup.py b/setup.py index a85d0a251..0b153016c 100644 --- a/setup.py +++ b/setup.py @@ -60,7 +60,7 @@ def run(self): "pylint", "scikit-learn>=0.23.2", "scikit-image>=0.19.1", - "pickle5>=0.0.11", + 'pickle5>=0.0.11; python_version < "3.8.0"', "setuptools", "seaborn", "pyyaml",
pytorch__pytorch-116517
Missing packaging dependency in torch 2.1.x ### 🐛 Describe the bug Hi, [torch.utils.tensorboard requires "packaging"](https://github.com/pytorch/pytorch/blob/fa1ccc34c4f65756bc50c3e3ab135c88b175b18c/torch/utils/tensorboard/__init__.py#L2C1-L3C1) to be installed but that dependency is [missing on torch 2.1.x](https://github.com/pytorch/pytorch/blob/v2.1.2-rc1/requirements.txt). Here's some example code: ```python from torch.utils.tensorboard import SummaryWriter ``` The links above point to a RC version of 2.1.2 but this is also the case for 2.1.1. Would it be possible to make a patch release to add the dependency? ### Versions Python version: 3.9.16 (main, Dec 7 2022, 10:16:11) [Clang 14.0.0 (clang-140[0.0.29.202](http://0.0.29.202/) )] (64-bit runtime) Python platform: macOS-13.6.1-x86_64-i386-64bit Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Intel(R) Core(TM) i5-7287U CPU @ 3.30GHz Versions of relevant libraries: [pip3] numpy==1.23.5 [pip3] torch==2.1.1 [pip3] torchvision==0.16.1 [conda] Could not collect
[ { "content": "import tensorboard\nfrom packaging.version import Version\n\nif not hasattr(tensorboard, \"__version__\") or Version(\n tensorboard.__version__\n) < Version(\"1.15\"):\n raise ImportError(\"TensorBoard logging requires TensorBoard version 1.15 or above\")\n\ndel Version\ndel tensorboard\n\nfrom .writer import FileWriter, SummaryWriter # noqa: F401\nfrom tensorboard.summary.writer.record_writer import RecordWriter # noqa: F401\n", "path": "torch/utils/tensorboard/__init__.py" } ]
[ { "content": "import tensorboard\nfrom torch._vendor.packaging.version import Version\n\nif not hasattr(tensorboard, \"__version__\") or Version(\n tensorboard.__version__\n) < Version(\"1.15\"):\n raise ImportError(\"TensorBoard logging requires TensorBoard version 1.15 or above\")\n\ndel Version\ndel tensorboard\n\nfrom .writer import FileWriter, SummaryWriter # noqa: F401\nfrom tensorboard.summary.writer.record_writer import RecordWriter # noqa: F401\n", "path": "torch/utils/tensorboard/__init__.py" } ]
diff --git a/torch/utils/tensorboard/__init__.py b/torch/utils/tensorboard/__init__.py index 39ac891165696f..cca0fb95146039 100644 --- a/torch/utils/tensorboard/__init__.py +++ b/torch/utils/tensorboard/__init__.py @@ -1,5 +1,5 @@ import tensorboard -from packaging.version import Version +from torch._vendor.packaging.version import Version if not hasattr(tensorboard, "__version__") or Version( tensorboard.__version__
bookwyrm-social__bookwyrm-514
shelve actions are always posted and always public
[ { "content": "''' views for actions you can take in the application '''\nfrom io import BytesIO, TextIOWrapper\nfrom uuid import uuid4\nfrom PIL import Image\n\nimport dateutil.parser\nfrom dateutil.parser import ParserError\n\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.files.base import ContentFile\nfrom django.db import transaction\nfrom django.http import HttpResponseBadRequest, HttpResponseNotFound\nfrom django.shortcuts import get_object_or_404, redirect\nfrom django.template.response import TemplateResponse\nfrom django.utils import timezone\nfrom django.views.decorators.http import require_GET, require_POST\n\nfrom bookwyrm import forms, models, outgoing, goodreads_import\nfrom bookwyrm.connectors import connector_manager\nfrom bookwyrm.broadcast import broadcast\nfrom bookwyrm.emailing import password_reset_email\nfrom bookwyrm.settings import DOMAIN\nfrom bookwyrm.views import get_user_from_username, get_edition\n\n\n@require_POST\ndef user_login(request):\n ''' authenticate user login '''\n login_form = forms.LoginForm(request.POST)\n\n localname = login_form.data['localname']\n username = '%s@%s' % (localname, DOMAIN)\n password = login_form.data['password']\n user = authenticate(request, username=username, password=password)\n if user is not None:\n # successful login\n login(request, user)\n user.last_active_date = timezone.now()\n return redirect(request.GET.get('next', '/'))\n\n login_form.non_field_errors = 'Username or password are incorrect'\n register_form = forms.RegisterForm()\n data = {\n 'login_form': login_form,\n 'register_form': register_form\n }\n return TemplateResponse(request, 'login.html', data)\n\n\n@require_POST\ndef register(request):\n ''' join the server '''\n if not models.SiteSettings.get().allow_registration:\n invite_code = request.POST.get('invite_code')\n\n if not invite_code:\n raise PermissionDenied\n\n invite = get_object_or_404(models.SiteInvite, code=invite_code)\n if not invite.valid():\n raise PermissionDenied\n else:\n invite = None\n\n form = forms.RegisterForm(request.POST)\n errors = False\n if not form.is_valid():\n errors = True\n\n localname = form.data['localname'].strip()\n email = form.data['email']\n password = form.data['password']\n\n # check localname and email uniqueness\n if models.User.objects.filter(localname=localname).first():\n form.errors['localname'] = ['User with this username already exists']\n errors = True\n\n if errors:\n data = {\n 'login_form': forms.LoginForm(),\n 'register_form': form,\n 'invite': invite,\n 'valid': invite.valid() if invite else True,\n }\n if invite:\n return TemplateResponse(request, 'invite.html', data)\n return TemplateResponse(request, 'login.html', data)\n\n username = '%s@%s' % (localname, DOMAIN)\n user = models.User.objects.create_user(\n username, email, password, localname=localname, local=True)\n if invite:\n invite.times_used += 1\n invite.save()\n\n login(request, user)\n return redirect('/')\n\n\n@login_required\n@require_GET\ndef user_logout(request):\n ''' done with this place! outa here! '''\n logout(request)\n return redirect('/')\n\n\n@require_POST\ndef password_reset_request(request):\n ''' create a password reset token '''\n email = request.POST.get('email')\n try:\n user = models.User.objects.get(email=email)\n except models.User.DoesNotExist:\n return redirect('/password-reset')\n\n # remove any existing password reset cods for this user\n models.PasswordReset.objects.filter(user=user).all().delete()\n\n # create a new reset code\n code = models.PasswordReset.objects.create(user=user)\n password_reset_email(code)\n data = {'message': 'Password reset link sent to %s' % email}\n return TemplateResponse(request, 'password_reset_request.html', data)\n\n\n@require_POST\ndef password_reset(request):\n ''' allow a user to change their password through an emailed token '''\n try:\n reset_code = models.PasswordReset.objects.get(\n code=request.POST.get('reset-code')\n )\n except models.PasswordReset.DoesNotExist:\n data = {'errors': ['Invalid password reset link']}\n return TemplateResponse(request, 'password_reset.html', data)\n\n user = reset_code.user\n\n new_password = request.POST.get('password')\n confirm_password = request.POST.get('confirm-password')\n\n if new_password != confirm_password:\n data = {'errors': ['Passwords do not match']}\n return TemplateResponse(request, 'password_reset.html', data)\n\n user.set_password(new_password)\n user.save()\n login(request, user)\n reset_code.delete()\n return redirect('/')\n\n\n@login_required\n@require_POST\ndef password_change(request):\n ''' allow a user to change their password '''\n new_password = request.POST.get('password')\n confirm_password = request.POST.get('confirm-password')\n\n if new_password != confirm_password:\n return redirect('/user-edit')\n\n request.user.set_password(new_password)\n request.user.save()\n login(request, request.user)\n return redirect('/user/%s' % request.user.localname)\n\n\n@login_required\n@require_POST\ndef edit_profile(request):\n ''' les get fancy with images '''\n form = forms.EditUserForm(\n request.POST, request.FILES, instance=request.user)\n if not form.is_valid():\n data = {'form': form, 'user': request.user}\n return TemplateResponse(request, 'edit_user.html', data)\n\n user = form.save(commit=False)\n\n if 'avatar' in form.files:\n # crop and resize avatar upload\n image = Image.open(form.files['avatar'])\n target_size = 120\n width, height = image.size\n thumbnail_scale = height / (width / target_size) if height > width \\\n else width / (height / target_size)\n image.thumbnail([thumbnail_scale, thumbnail_scale])\n width, height = image.size\n\n width_diff = width - target_size\n height_diff = height - target_size\n cropped = image.crop((\n int(width_diff / 2),\n int(height_diff / 2),\n int(width - (width_diff / 2)),\n int(height - (height_diff / 2))\n ))\n output = BytesIO()\n cropped.save(output, format=image.format)\n ContentFile(output.getvalue())\n\n # set the name to a hash\n extension = form.files['avatar'].name.split('.')[-1]\n filename = '%s.%s' % (uuid4(), extension)\n user.avatar.save(filename, ContentFile(output.getvalue()))\n user.save()\n\n broadcast(user, user.to_update_activity(user))\n return redirect('/user/%s' % request.user.localname)\n\n\n@require_POST\ndef resolve_book(request):\n ''' figure out the local path to a book from a remote_id '''\n remote_id = request.POST.get('remote_id')\n connector = connector_manager.get_or_create_connector(remote_id)\n book = connector.get_or_create_book(remote_id)\n\n return redirect('/book/%d' % book.id)\n\n\n@login_required\n@permission_required('bookwyrm.edit_book', raise_exception=True)\n@require_POST\ndef edit_book(request, book_id):\n ''' edit a book cool '''\n book = get_object_or_404(models.Edition, id=book_id)\n\n form = forms.EditionForm(request.POST, request.FILES, instance=book)\n if not form.is_valid():\n data = {\n 'title': 'Edit Book',\n 'book': book,\n 'form': form\n }\n return TemplateResponse(request, 'edit_book.html', data)\n book = form.save()\n\n broadcast(request.user, book.to_update_activity(request.user))\n return redirect('/book/%s' % book.id)\n\n\n@login_required\n@require_POST\[email protected]\ndef switch_edition(request):\n ''' switch your copy of a book to a different edition '''\n edition_id = request.POST.get('edition')\n new_edition = get_object_or_404(models.Edition, id=edition_id)\n shelfbooks = models.ShelfBook.objects.filter(\n book__parent_work=new_edition.parent_work,\n shelf__user=request.user\n )\n for shelfbook in shelfbooks.all():\n broadcast(request.user, shelfbook.to_remove_activity(request.user))\n\n shelfbook.book = new_edition\n shelfbook.save()\n\n broadcast(request.user, shelfbook.to_add_activity(request.user))\n\n readthroughs = models.ReadThrough.objects.filter(\n book__parent_work=new_edition.parent_work,\n user=request.user\n )\n for readthrough in readthroughs.all():\n readthrough.book = new_edition\n readthrough.save()\n\n return redirect('/book/%d' % new_edition.id)\n\n\n@login_required\n@require_POST\ndef upload_cover(request, book_id):\n ''' upload a new cover '''\n book = get_object_or_404(models.Edition, id=book_id)\n\n form = forms.CoverForm(request.POST, request.FILES, instance=book)\n if not form.is_valid():\n return redirect('/book/%d' % book.id)\n\n book.cover = form.files['cover']\n book.save()\n\n broadcast(request.user, book.to_update_activity(request.user))\n return redirect('/book/%s' % book.id)\n\n\n@login_required\n@require_POST\n@permission_required('bookwyrm.edit_book', raise_exception=True)\ndef add_description(request, book_id):\n ''' upload a new cover '''\n if not request.method == 'POST':\n return redirect('/')\n\n book = get_object_or_404(models.Edition, id=book_id)\n\n description = request.POST.get('description')\n\n book.description = description\n book.save()\n\n broadcast(request.user, book.to_update_activity(request.user))\n return redirect('/book/%s' % book.id)\n\n\n@login_required\n@permission_required('bookwyrm.edit_book', raise_exception=True)\n@require_POST\ndef edit_author(request, author_id):\n ''' edit a author cool '''\n author = get_object_or_404(models.Author, id=author_id)\n\n form = forms.AuthorForm(request.POST, request.FILES, instance=author)\n if not form.is_valid():\n data = {\n 'title': 'Edit Author',\n 'author': author,\n 'form': form\n }\n return TemplateResponse(request, 'edit_author.html', data)\n author = form.save()\n\n broadcast(request.user, author.to_update_activity(request.user))\n return redirect('/author/%s' % author.id)\n\n\n@login_required\n@require_POST\ndef create_shelf(request):\n ''' user generated shelves '''\n form = forms.ShelfForm(request.POST)\n if not form.is_valid():\n return redirect(request.headers.get('Referer', '/'))\n\n shelf = form.save()\n return redirect('/user/%s/shelf/%s' % \\\n (request.user.localname, shelf.identifier))\n\n\n@login_required\n@require_POST\ndef edit_shelf(request, shelf_id):\n ''' user generated shelves '''\n shelf = get_object_or_404(models.Shelf, id=shelf_id)\n if request.user != shelf.user:\n return HttpResponseBadRequest()\n\n form = forms.ShelfForm(request.POST, instance=shelf)\n if not form.is_valid():\n return redirect(request.headers.get('Referer', '/'))\n shelf = form.save()\n return redirect('/user/%s/shelf/%s' % \\\n (request.user.localname, shelf.identifier))\n\n\n@login_required\n@require_POST\ndef delete_shelf(request, shelf_id):\n ''' user generated shelves '''\n shelf = get_object_or_404(models.Shelf, id=shelf_id)\n if request.user != shelf.user or not shelf.editable:\n return HttpResponseBadRequest()\n\n shelf.delete()\n return redirect('/user/%s/shelves' % request.user.localname)\n\n\n@login_required\n@require_POST\ndef shelve(request):\n ''' put a on a user's shelf '''\n book = get_edition(request.POST['book'])\n\n desired_shelf = models.Shelf.objects.filter(\n identifier=request.POST['shelf'],\n user=request.user\n ).first()\n\n if request.POST.get('reshelve', True):\n try:\n current_shelf = models.Shelf.objects.get(\n user=request.user,\n edition=book\n )\n outgoing.handle_unshelve(request.user, book, current_shelf)\n except models.Shelf.DoesNotExist:\n # this just means it isn't currently on the user's shelves\n pass\n outgoing.handle_shelve(request.user, book, desired_shelf)\n\n # post about \"want to read\" shelves\n if desired_shelf.identifier == 'to-read':\n outgoing.handle_reading_status(\n request.user,\n desired_shelf,\n book,\n privacy='public'\n )\n\n return redirect('/')\n\n\n@login_required\n@require_POST\ndef unshelve(request):\n ''' put a on a user's shelf '''\n book = models.Edition.objects.get(id=request.POST['book'])\n current_shelf = models.Shelf.objects.get(id=request.POST['shelf'])\n\n outgoing.handle_unshelve(request.user, book, current_shelf)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef start_reading(request, book_id):\n ''' begin reading a book '''\n book = get_edition(book_id)\n shelf = models.Shelf.objects.filter(\n identifier='reading',\n user=request.user\n ).first()\n\n # create a readthrough\n readthrough = update_readthrough(request, book=book)\n if readthrough.start_date:\n readthrough.save()\n\n # shelve the book\n if request.POST.get('reshelve', True):\n try:\n current_shelf = models.Shelf.objects.get(\n user=request.user,\n edition=book\n )\n outgoing.handle_unshelve(request.user, book, current_shelf)\n except models.Shelf.DoesNotExist:\n # this just means it isn't currently on the user's shelves\n pass\n outgoing.handle_shelve(request.user, book, shelf)\n\n # post about it (if you want)\n if request.POST.get('post-status'):\n privacy = request.POST.get('privacy')\n outgoing.handle_reading_status(request.user, shelf, book, privacy)\n\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef finish_reading(request, book_id):\n ''' a user completed a book, yay '''\n book = get_edition(book_id)\n shelf = models.Shelf.objects.filter(\n identifier='read',\n user=request.user\n ).first()\n\n # update or create a readthrough\n readthrough = update_readthrough(request, book=book)\n if readthrough.start_date or readthrough.finish_date:\n readthrough.save()\n\n # shelve the book\n if request.POST.get('reshelve', True):\n try:\n current_shelf = models.Shelf.objects.get(\n user=request.user,\n edition=book\n )\n outgoing.handle_unshelve(request.user, book, current_shelf)\n except models.Shelf.DoesNotExist:\n # this just means it isn't currently on the user's shelves\n pass\n outgoing.handle_shelve(request.user, book, shelf)\n\n # post about it (if you want)\n if request.POST.get('post-status'):\n privacy = request.POST.get('privacy')\n outgoing.handle_reading_status(request.user, shelf, book, privacy)\n\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef edit_readthrough(request):\n ''' can't use the form because the dates are too finnicky '''\n readthrough = update_readthrough(request, create=False)\n if not readthrough:\n return HttpResponseNotFound()\n\n # don't let people edit other people's data\n if request.user != readthrough.user:\n return HttpResponseBadRequest()\n readthrough.save()\n\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef delete_readthrough(request):\n ''' remove a readthrough '''\n readthrough = get_object_or_404(\n models.ReadThrough, id=request.POST.get('id'))\n\n # don't let people edit other people's data\n if request.user != readthrough.user:\n return HttpResponseBadRequest()\n\n readthrough.delete()\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef create_readthrough(request):\n ''' can't use the form because the dates are too finnicky '''\n book = get_object_or_404(models.Edition, id=request.POST.get('book'))\n readthrough = update_readthrough(request, create=True, book=book)\n if not readthrough:\n return redirect(book.local_path)\n readthrough.save()\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef rate(request):\n ''' just a star rating for a book '''\n form = forms.RatingForm(request.POST)\n return handle_status(request, form)\n\n\n@login_required\n@require_POST\ndef review(request):\n ''' create a book review '''\n form = forms.ReviewForm(request.POST)\n return handle_status(request, form)\n\n\n@login_required\n@require_POST\ndef quotate(request):\n ''' create a book quotation '''\n form = forms.QuotationForm(request.POST)\n return handle_status(request, form)\n\n\n@login_required\n@require_POST\ndef comment(request):\n ''' create a book comment '''\n form = forms.CommentForm(request.POST)\n return handle_status(request, form)\n\n\n@login_required\n@require_POST\ndef reply(request):\n ''' respond to a book review '''\n form = forms.ReplyForm(request.POST)\n return handle_status(request, form)\n\n\ndef handle_status(request, form):\n ''' all the \"create a status\" functions are the same '''\n if not form.is_valid():\n return redirect(request.headers.get('Referer', '/'))\n\n outgoing.handle_status(request.user, form)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef tag(request):\n ''' tag a book '''\n # I'm not using a form here because sometimes \"name\" is sent as a hidden\n # field which doesn't validate\n name = request.POST.get('name')\n book_id = request.POST.get('book')\n book = get_object_or_404(models.Edition, id=book_id)\n tag_obj, created = models.Tag.objects.get_or_create(\n name=name,\n )\n user_tag, _ = models.UserTag.objects.get_or_create(\n user=request.user,\n book=book,\n tag=tag_obj,\n )\n\n if created:\n broadcast(request.user, user_tag.to_add_activity(request.user))\n return redirect('/book/%s' % book_id)\n\n\n@login_required\n@require_POST\ndef untag(request):\n ''' untag a book '''\n name = request.POST.get('name')\n tag_obj = get_object_or_404(models.Tag, name=name)\n book_id = request.POST.get('book')\n book = get_object_or_404(models.Edition, id=book_id)\n\n user_tag = get_object_or_404(\n models.UserTag, tag=tag_obj, book=book, user=request.user)\n tag_activity = user_tag.to_remove_activity(request.user)\n user_tag.delete()\n\n broadcast(request.user, tag_activity)\n return redirect('/book/%s' % book_id)\n\n\n@login_required\n@require_POST\ndef favorite(request, status_id):\n ''' like a status '''\n status = models.Status.objects.get(id=status_id)\n outgoing.handle_favorite(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef unfavorite(request, status_id):\n ''' like a status '''\n status = models.Status.objects.get(id=status_id)\n outgoing.handle_unfavorite(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef boost(request, status_id):\n ''' boost a status '''\n status = models.Status.objects.get(id=status_id)\n outgoing.handle_boost(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef unboost(request, status_id):\n ''' boost a status '''\n status = models.Status.objects.get(id=status_id)\n outgoing.handle_unboost(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef delete_status(request, status_id):\n ''' delete and tombstone a status '''\n status = get_object_or_404(models.Status, id=status_id)\n\n # don't let people delete other people's statuses\n if status.user != request.user:\n return HttpResponseBadRequest()\n\n # perform deletion\n outgoing.handle_delete_status(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef follow(request):\n ''' follow another user, here or abroad '''\n username = request.POST['user']\n try:\n to_follow = get_user_from_username(username)\n except models.User.DoesNotExist:\n return HttpResponseBadRequest()\n\n outgoing.handle_follow(request.user, to_follow)\n user_slug = to_follow.localname if to_follow.localname \\\n else to_follow.username\n return redirect('/user/%s' % user_slug)\n\n\n@login_required\n@require_POST\ndef unfollow(request):\n ''' unfollow a user '''\n username = request.POST['user']\n try:\n to_unfollow = get_user_from_username(username)\n except models.User.DoesNotExist:\n return HttpResponseBadRequest()\n\n outgoing.handle_unfollow(request.user, to_unfollow)\n user_slug = to_unfollow.localname if to_unfollow.localname \\\n else to_unfollow.username\n return redirect('/user/%s' % user_slug)\n\n\n@login_required\ndef clear_notifications(request):\n ''' permanently delete notification for user '''\n request.user.notification_set.filter(read=True).delete()\n return redirect('/notifications')\n\n\n@login_required\n@require_POST\ndef accept_follow_request(request):\n ''' a user accepts a follow request '''\n username = request.POST['user']\n try:\n requester = get_user_from_username(username)\n except models.User.DoesNotExist:\n return HttpResponseBadRequest()\n\n try:\n follow_request = models.UserFollowRequest.objects.get(\n user_subject=requester,\n user_object=request.user\n )\n except models.UserFollowRequest.DoesNotExist:\n # Request already dealt with.\n pass\n else:\n outgoing.handle_accept(follow_request)\n\n return redirect('/user/%s' % request.user.localname)\n\n\n@login_required\n@require_POST\ndef delete_follow_request(request):\n ''' a user rejects a follow request '''\n username = request.POST['user']\n try:\n requester = get_user_from_username(username)\n except models.User.DoesNotExist:\n return HttpResponseBadRequest()\n\n try:\n follow_request = models.UserFollowRequest.objects.get(\n user_subject=requester,\n user_object=request.user\n )\n except models.UserFollowRequest.DoesNotExist:\n return HttpResponseBadRequest()\n\n outgoing.handle_reject(follow_request)\n return redirect('/user/%s' % request.user.localname)\n\n\n@login_required\n@require_POST\ndef import_data(request):\n ''' ingest a goodreads csv '''\n form = forms.ImportForm(request.POST, request.FILES)\n if form.is_valid():\n include_reviews = request.POST.get('include_reviews') == 'on'\n privacy = request.POST.get('privacy')\n try:\n job = goodreads_import.create_job(\n request.user,\n TextIOWrapper(\n request.FILES['csv_file'],\n encoding=request.encoding),\n include_reviews,\n privacy,\n )\n except (UnicodeDecodeError, ValueError):\n return HttpResponseBadRequest('Not a valid csv file')\n goodreads_import.start_import(job)\n return redirect('/import-status/%d' % job.id)\n return HttpResponseBadRequest()\n\n\n@login_required\n@require_POST\ndef retry_import(request):\n ''' ingest a goodreads csv '''\n job = get_object_or_404(models.ImportJob, id=request.POST.get('import_job'))\n items = []\n for item in request.POST.getlist('import_item'):\n items.append(get_object_or_404(models.ImportItem, id=item))\n\n job = goodreads_import.create_retry_job(\n request.user,\n job,\n items,\n )\n goodreads_import.start_import(job)\n return redirect('/import-status/%d' % job.id)\n\n\n@login_required\n@require_POST\n@permission_required('bookwyrm.create_invites', raise_exception=True)\ndef create_invite(request):\n ''' creates a user invite database entry '''\n form = forms.CreateInviteForm(request.POST)\n if not form.is_valid():\n return HttpResponseBadRequest(\"ERRORS : %s\" % (form.errors,))\n\n invite = form.save(commit=False)\n invite.user = request.user\n invite.save()\n\n return redirect('/invite')\n\n\ndef update_readthrough(request, book=None, create=True):\n ''' updates but does not save dates on a readthrough '''\n try:\n read_id = request.POST.get('id')\n if not read_id:\n raise models.ReadThrough.DoesNotExist\n readthrough = models.ReadThrough.objects.get(id=read_id)\n except models.ReadThrough.DoesNotExist:\n if not create or not book:\n return None\n readthrough = models.ReadThrough(\n user=request.user,\n book=book,\n )\n\n start_date = request.POST.get('start_date')\n if start_date:\n try:\n start_date = timezone.make_aware(dateutil.parser.parse(start_date))\n readthrough.start_date = start_date\n except ParserError:\n pass\n\n finish_date = request.POST.get('finish_date')\n if finish_date:\n try:\n finish_date = timezone.make_aware(\n dateutil.parser.parse(finish_date))\n readthrough.finish_date = finish_date\n except ParserError:\n pass\n\n if not readthrough.start_date and not readthrough.finish_date:\n return None\n\n return readthrough\n", "path": "bookwyrm/view_actions.py" } ]
[ { "content": "''' views for actions you can take in the application '''\nfrom io import BytesIO, TextIOWrapper\nfrom uuid import uuid4\nfrom PIL import Image\n\nimport dateutil.parser\nfrom dateutil.parser import ParserError\n\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.files.base import ContentFile\nfrom django.db import transaction\nfrom django.http import HttpResponseBadRequest, HttpResponseNotFound\nfrom django.shortcuts import get_object_or_404, redirect\nfrom django.template.response import TemplateResponse\nfrom django.utils import timezone\nfrom django.views.decorators.http import require_GET, require_POST\n\nfrom bookwyrm import forms, models, outgoing, goodreads_import\nfrom bookwyrm.connectors import connector_manager\nfrom bookwyrm.broadcast import broadcast\nfrom bookwyrm.emailing import password_reset_email\nfrom bookwyrm.settings import DOMAIN\nfrom bookwyrm.views import get_user_from_username, get_edition\n\n\n@require_POST\ndef user_login(request):\n ''' authenticate user login '''\n login_form = forms.LoginForm(request.POST)\n\n localname = login_form.data['localname']\n username = '%s@%s' % (localname, DOMAIN)\n password = login_form.data['password']\n user = authenticate(request, username=username, password=password)\n if user is not None:\n # successful login\n login(request, user)\n user.last_active_date = timezone.now()\n return redirect(request.GET.get('next', '/'))\n\n login_form.non_field_errors = 'Username or password are incorrect'\n register_form = forms.RegisterForm()\n data = {\n 'login_form': login_form,\n 'register_form': register_form\n }\n return TemplateResponse(request, 'login.html', data)\n\n\n@require_POST\ndef register(request):\n ''' join the server '''\n if not models.SiteSettings.get().allow_registration:\n invite_code = request.POST.get('invite_code')\n\n if not invite_code:\n raise PermissionDenied\n\n invite = get_object_or_404(models.SiteInvite, code=invite_code)\n if not invite.valid():\n raise PermissionDenied\n else:\n invite = None\n\n form = forms.RegisterForm(request.POST)\n errors = False\n if not form.is_valid():\n errors = True\n\n localname = form.data['localname'].strip()\n email = form.data['email']\n password = form.data['password']\n\n # check localname and email uniqueness\n if models.User.objects.filter(localname=localname).first():\n form.errors['localname'] = ['User with this username already exists']\n errors = True\n\n if errors:\n data = {\n 'login_form': forms.LoginForm(),\n 'register_form': form,\n 'invite': invite,\n 'valid': invite.valid() if invite else True,\n }\n if invite:\n return TemplateResponse(request, 'invite.html', data)\n return TemplateResponse(request, 'login.html', data)\n\n username = '%s@%s' % (localname, DOMAIN)\n user = models.User.objects.create_user(\n username, email, password, localname=localname, local=True)\n if invite:\n invite.times_used += 1\n invite.save()\n\n login(request, user)\n return redirect('/')\n\n\n@login_required\n@require_GET\ndef user_logout(request):\n ''' done with this place! outa here! '''\n logout(request)\n return redirect('/')\n\n\n@require_POST\ndef password_reset_request(request):\n ''' create a password reset token '''\n email = request.POST.get('email')\n try:\n user = models.User.objects.get(email=email)\n except models.User.DoesNotExist:\n return redirect('/password-reset')\n\n # remove any existing password reset cods for this user\n models.PasswordReset.objects.filter(user=user).all().delete()\n\n # create a new reset code\n code = models.PasswordReset.objects.create(user=user)\n password_reset_email(code)\n data = {'message': 'Password reset link sent to %s' % email}\n return TemplateResponse(request, 'password_reset_request.html', data)\n\n\n@require_POST\ndef password_reset(request):\n ''' allow a user to change their password through an emailed token '''\n try:\n reset_code = models.PasswordReset.objects.get(\n code=request.POST.get('reset-code')\n )\n except models.PasswordReset.DoesNotExist:\n data = {'errors': ['Invalid password reset link']}\n return TemplateResponse(request, 'password_reset.html', data)\n\n user = reset_code.user\n\n new_password = request.POST.get('password')\n confirm_password = request.POST.get('confirm-password')\n\n if new_password != confirm_password:\n data = {'errors': ['Passwords do not match']}\n return TemplateResponse(request, 'password_reset.html', data)\n\n user.set_password(new_password)\n user.save()\n login(request, user)\n reset_code.delete()\n return redirect('/')\n\n\n@login_required\n@require_POST\ndef password_change(request):\n ''' allow a user to change their password '''\n new_password = request.POST.get('password')\n confirm_password = request.POST.get('confirm-password')\n\n if new_password != confirm_password:\n return redirect('/user-edit')\n\n request.user.set_password(new_password)\n request.user.save()\n login(request, request.user)\n return redirect('/user/%s' % request.user.localname)\n\n\n@login_required\n@require_POST\ndef edit_profile(request):\n ''' les get fancy with images '''\n form = forms.EditUserForm(\n request.POST, request.FILES, instance=request.user)\n if not form.is_valid():\n data = {'form': form, 'user': request.user}\n return TemplateResponse(request, 'edit_user.html', data)\n\n user = form.save(commit=False)\n\n if 'avatar' in form.files:\n # crop and resize avatar upload\n image = Image.open(form.files['avatar'])\n target_size = 120\n width, height = image.size\n thumbnail_scale = height / (width / target_size) if height > width \\\n else width / (height / target_size)\n image.thumbnail([thumbnail_scale, thumbnail_scale])\n width, height = image.size\n\n width_diff = width - target_size\n height_diff = height - target_size\n cropped = image.crop((\n int(width_diff / 2),\n int(height_diff / 2),\n int(width - (width_diff / 2)),\n int(height - (height_diff / 2))\n ))\n output = BytesIO()\n cropped.save(output, format=image.format)\n ContentFile(output.getvalue())\n\n # set the name to a hash\n extension = form.files['avatar'].name.split('.')[-1]\n filename = '%s.%s' % (uuid4(), extension)\n user.avatar.save(filename, ContentFile(output.getvalue()))\n user.save()\n\n broadcast(user, user.to_update_activity(user))\n return redirect('/user/%s' % request.user.localname)\n\n\n@require_POST\ndef resolve_book(request):\n ''' figure out the local path to a book from a remote_id '''\n remote_id = request.POST.get('remote_id')\n connector = connector_manager.get_or_create_connector(remote_id)\n book = connector.get_or_create_book(remote_id)\n\n return redirect('/book/%d' % book.id)\n\n\n@login_required\n@permission_required('bookwyrm.edit_book', raise_exception=True)\n@require_POST\ndef edit_book(request, book_id):\n ''' edit a book cool '''\n book = get_object_or_404(models.Edition, id=book_id)\n\n form = forms.EditionForm(request.POST, request.FILES, instance=book)\n if not form.is_valid():\n data = {\n 'title': 'Edit Book',\n 'book': book,\n 'form': form\n }\n return TemplateResponse(request, 'edit_book.html', data)\n book = form.save()\n\n broadcast(request.user, book.to_update_activity(request.user))\n return redirect('/book/%s' % book.id)\n\n\n@login_required\n@require_POST\[email protected]\ndef switch_edition(request):\n ''' switch your copy of a book to a different edition '''\n edition_id = request.POST.get('edition')\n new_edition = get_object_or_404(models.Edition, id=edition_id)\n shelfbooks = models.ShelfBook.objects.filter(\n book__parent_work=new_edition.parent_work,\n shelf__user=request.user\n )\n for shelfbook in shelfbooks.all():\n broadcast(request.user, shelfbook.to_remove_activity(request.user))\n\n shelfbook.book = new_edition\n shelfbook.save()\n\n broadcast(request.user, shelfbook.to_add_activity(request.user))\n\n readthroughs = models.ReadThrough.objects.filter(\n book__parent_work=new_edition.parent_work,\n user=request.user\n )\n for readthrough in readthroughs.all():\n readthrough.book = new_edition\n readthrough.save()\n\n return redirect('/book/%d' % new_edition.id)\n\n\n@login_required\n@require_POST\ndef upload_cover(request, book_id):\n ''' upload a new cover '''\n book = get_object_or_404(models.Edition, id=book_id)\n\n form = forms.CoverForm(request.POST, request.FILES, instance=book)\n if not form.is_valid():\n return redirect('/book/%d' % book.id)\n\n book.cover = form.files['cover']\n book.save()\n\n broadcast(request.user, book.to_update_activity(request.user))\n return redirect('/book/%s' % book.id)\n\n\n@login_required\n@require_POST\n@permission_required('bookwyrm.edit_book', raise_exception=True)\ndef add_description(request, book_id):\n ''' upload a new cover '''\n if not request.method == 'POST':\n return redirect('/')\n\n book = get_object_or_404(models.Edition, id=book_id)\n\n description = request.POST.get('description')\n\n book.description = description\n book.save()\n\n broadcast(request.user, book.to_update_activity(request.user))\n return redirect('/book/%s' % book.id)\n\n\n@login_required\n@permission_required('bookwyrm.edit_book', raise_exception=True)\n@require_POST\ndef edit_author(request, author_id):\n ''' edit a author cool '''\n author = get_object_or_404(models.Author, id=author_id)\n\n form = forms.AuthorForm(request.POST, request.FILES, instance=author)\n if not form.is_valid():\n data = {\n 'title': 'Edit Author',\n 'author': author,\n 'form': form\n }\n return TemplateResponse(request, 'edit_author.html', data)\n author = form.save()\n\n broadcast(request.user, author.to_update_activity(request.user))\n return redirect('/author/%s' % author.id)\n\n\n@login_required\n@require_POST\ndef create_shelf(request):\n ''' user generated shelves '''\n form = forms.ShelfForm(request.POST)\n if not form.is_valid():\n return redirect(request.headers.get('Referer', '/'))\n\n shelf = form.save()\n return redirect('/user/%s/shelf/%s' % \\\n (request.user.localname, shelf.identifier))\n\n\n@login_required\n@require_POST\ndef edit_shelf(request, shelf_id):\n ''' user generated shelves '''\n shelf = get_object_or_404(models.Shelf, id=shelf_id)\n if request.user != shelf.user:\n return HttpResponseBadRequest()\n\n form = forms.ShelfForm(request.POST, instance=shelf)\n if not form.is_valid():\n return redirect(request.headers.get('Referer', '/'))\n shelf = form.save()\n return redirect('/user/%s/shelf/%s' % \\\n (request.user.localname, shelf.identifier))\n\n\n@login_required\n@require_POST\ndef delete_shelf(request, shelf_id):\n ''' user generated shelves '''\n shelf = get_object_or_404(models.Shelf, id=shelf_id)\n if request.user != shelf.user or not shelf.editable:\n return HttpResponseBadRequest()\n\n shelf.delete()\n return redirect('/user/%s/shelves' % request.user.localname)\n\n\n@login_required\n@require_POST\ndef shelve(request):\n ''' put a on a user's shelf '''\n book = get_edition(request.POST['book'])\n\n desired_shelf = models.Shelf.objects.filter(\n identifier=request.POST['shelf'],\n user=request.user\n ).first()\n\n if request.POST.get('reshelve', True):\n try:\n current_shelf = models.Shelf.objects.get(\n user=request.user,\n edition=book\n )\n outgoing.handle_unshelve(request.user, book, current_shelf)\n except models.Shelf.DoesNotExist:\n # this just means it isn't currently on the user's shelves\n pass\n outgoing.handle_shelve(request.user, book, desired_shelf)\n\n # post about \"want to read\" shelves\n if desired_shelf.identifier == 'to-read':\n outgoing.handle_reading_status(\n request.user,\n desired_shelf,\n book,\n privacy=desired_shelf.privacy\n )\n\n return redirect('/')\n\n\n@login_required\n@require_POST\ndef unshelve(request):\n ''' put a on a user's shelf '''\n book = models.Edition.objects.get(id=request.POST['book'])\n current_shelf = models.Shelf.objects.get(id=request.POST['shelf'])\n\n outgoing.handle_unshelve(request.user, book, current_shelf)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef start_reading(request, book_id):\n ''' begin reading a book '''\n book = get_edition(book_id)\n shelf = models.Shelf.objects.filter(\n identifier='reading',\n user=request.user\n ).first()\n\n # create a readthrough\n readthrough = update_readthrough(request, book=book)\n if readthrough.start_date:\n readthrough.save()\n\n # shelve the book\n if request.POST.get('reshelve', True):\n try:\n current_shelf = models.Shelf.objects.get(\n user=request.user,\n edition=book\n )\n outgoing.handle_unshelve(request.user, book, current_shelf)\n except models.Shelf.DoesNotExist:\n # this just means it isn't currently on the user's shelves\n pass\n outgoing.handle_shelve(request.user, book, shelf)\n\n # post about it (if you want)\n if request.POST.get('post-status'):\n privacy = request.POST.get('privacy')\n outgoing.handle_reading_status(request.user, shelf, book, privacy)\n\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef finish_reading(request, book_id):\n ''' a user completed a book, yay '''\n book = get_edition(book_id)\n shelf = models.Shelf.objects.filter(\n identifier='read',\n user=request.user\n ).first()\n\n # update or create a readthrough\n readthrough = update_readthrough(request, book=book)\n if readthrough.start_date or readthrough.finish_date:\n readthrough.save()\n\n # shelve the book\n if request.POST.get('reshelve', True):\n try:\n current_shelf = models.Shelf.objects.get(\n user=request.user,\n edition=book\n )\n outgoing.handle_unshelve(request.user, book, current_shelf)\n except models.Shelf.DoesNotExist:\n # this just means it isn't currently on the user's shelves\n pass\n outgoing.handle_shelve(request.user, book, shelf)\n\n # post about it (if you want)\n if request.POST.get('post-status'):\n privacy = request.POST.get('privacy')\n outgoing.handle_reading_status(request.user, shelf, book, privacy)\n\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef edit_readthrough(request):\n ''' can't use the form because the dates are too finnicky '''\n readthrough = update_readthrough(request, create=False)\n if not readthrough:\n return HttpResponseNotFound()\n\n # don't let people edit other people's data\n if request.user != readthrough.user:\n return HttpResponseBadRequest()\n readthrough.save()\n\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef delete_readthrough(request):\n ''' remove a readthrough '''\n readthrough = get_object_or_404(\n models.ReadThrough, id=request.POST.get('id'))\n\n # don't let people edit other people's data\n if request.user != readthrough.user:\n return HttpResponseBadRequest()\n\n readthrough.delete()\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef create_readthrough(request):\n ''' can't use the form because the dates are too finnicky '''\n book = get_object_or_404(models.Edition, id=request.POST.get('book'))\n readthrough = update_readthrough(request, create=True, book=book)\n if not readthrough:\n return redirect(book.local_path)\n readthrough.save()\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef rate(request):\n ''' just a star rating for a book '''\n form = forms.RatingForm(request.POST)\n return handle_status(request, form)\n\n\n@login_required\n@require_POST\ndef review(request):\n ''' create a book review '''\n form = forms.ReviewForm(request.POST)\n return handle_status(request, form)\n\n\n@login_required\n@require_POST\ndef quotate(request):\n ''' create a book quotation '''\n form = forms.QuotationForm(request.POST)\n return handle_status(request, form)\n\n\n@login_required\n@require_POST\ndef comment(request):\n ''' create a book comment '''\n form = forms.CommentForm(request.POST)\n return handle_status(request, form)\n\n\n@login_required\n@require_POST\ndef reply(request):\n ''' respond to a book review '''\n form = forms.ReplyForm(request.POST)\n return handle_status(request, form)\n\n\ndef handle_status(request, form):\n ''' all the \"create a status\" functions are the same '''\n if not form.is_valid():\n return redirect(request.headers.get('Referer', '/'))\n\n outgoing.handle_status(request.user, form)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef tag(request):\n ''' tag a book '''\n # I'm not using a form here because sometimes \"name\" is sent as a hidden\n # field which doesn't validate\n name = request.POST.get('name')\n book_id = request.POST.get('book')\n book = get_object_or_404(models.Edition, id=book_id)\n tag_obj, created = models.Tag.objects.get_or_create(\n name=name,\n )\n user_tag, _ = models.UserTag.objects.get_or_create(\n user=request.user,\n book=book,\n tag=tag_obj,\n )\n\n if created:\n broadcast(request.user, user_tag.to_add_activity(request.user))\n return redirect('/book/%s' % book_id)\n\n\n@login_required\n@require_POST\ndef untag(request):\n ''' untag a book '''\n name = request.POST.get('name')\n tag_obj = get_object_or_404(models.Tag, name=name)\n book_id = request.POST.get('book')\n book = get_object_or_404(models.Edition, id=book_id)\n\n user_tag = get_object_or_404(\n models.UserTag, tag=tag_obj, book=book, user=request.user)\n tag_activity = user_tag.to_remove_activity(request.user)\n user_tag.delete()\n\n broadcast(request.user, tag_activity)\n return redirect('/book/%s' % book_id)\n\n\n@login_required\n@require_POST\ndef favorite(request, status_id):\n ''' like a status '''\n status = models.Status.objects.get(id=status_id)\n outgoing.handle_favorite(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef unfavorite(request, status_id):\n ''' like a status '''\n status = models.Status.objects.get(id=status_id)\n outgoing.handle_unfavorite(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef boost(request, status_id):\n ''' boost a status '''\n status = models.Status.objects.get(id=status_id)\n outgoing.handle_boost(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef unboost(request, status_id):\n ''' boost a status '''\n status = models.Status.objects.get(id=status_id)\n outgoing.handle_unboost(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef delete_status(request, status_id):\n ''' delete and tombstone a status '''\n status = get_object_or_404(models.Status, id=status_id)\n\n # don't let people delete other people's statuses\n if status.user != request.user:\n return HttpResponseBadRequest()\n\n # perform deletion\n outgoing.handle_delete_status(request.user, status)\n return redirect(request.headers.get('Referer', '/'))\n\n\n@login_required\n@require_POST\ndef follow(request):\n ''' follow another user, here or abroad '''\n username = request.POST['user']\n try:\n to_follow = get_user_from_username(username)\n except models.User.DoesNotExist:\n return HttpResponseBadRequest()\n\n outgoing.handle_follow(request.user, to_follow)\n user_slug = to_follow.localname if to_follow.localname \\\n else to_follow.username\n return redirect('/user/%s' % user_slug)\n\n\n@login_required\n@require_POST\ndef unfollow(request):\n ''' unfollow a user '''\n username = request.POST['user']\n try:\n to_unfollow = get_user_from_username(username)\n except models.User.DoesNotExist:\n return HttpResponseBadRequest()\n\n outgoing.handle_unfollow(request.user, to_unfollow)\n user_slug = to_unfollow.localname if to_unfollow.localname \\\n else to_unfollow.username\n return redirect('/user/%s' % user_slug)\n\n\n@login_required\ndef clear_notifications(request):\n ''' permanently delete notification for user '''\n request.user.notification_set.filter(read=True).delete()\n return redirect('/notifications')\n\n\n@login_required\n@require_POST\ndef accept_follow_request(request):\n ''' a user accepts a follow request '''\n username = request.POST['user']\n try:\n requester = get_user_from_username(username)\n except models.User.DoesNotExist:\n return HttpResponseBadRequest()\n\n try:\n follow_request = models.UserFollowRequest.objects.get(\n user_subject=requester,\n user_object=request.user\n )\n except models.UserFollowRequest.DoesNotExist:\n # Request already dealt with.\n pass\n else:\n outgoing.handle_accept(follow_request)\n\n return redirect('/user/%s' % request.user.localname)\n\n\n@login_required\n@require_POST\ndef delete_follow_request(request):\n ''' a user rejects a follow request '''\n username = request.POST['user']\n try:\n requester = get_user_from_username(username)\n except models.User.DoesNotExist:\n return HttpResponseBadRequest()\n\n try:\n follow_request = models.UserFollowRequest.objects.get(\n user_subject=requester,\n user_object=request.user\n )\n except models.UserFollowRequest.DoesNotExist:\n return HttpResponseBadRequest()\n\n outgoing.handle_reject(follow_request)\n return redirect('/user/%s' % request.user.localname)\n\n\n@login_required\n@require_POST\ndef import_data(request):\n ''' ingest a goodreads csv '''\n form = forms.ImportForm(request.POST, request.FILES)\n if form.is_valid():\n include_reviews = request.POST.get('include_reviews') == 'on'\n privacy = request.POST.get('privacy')\n try:\n job = goodreads_import.create_job(\n request.user,\n TextIOWrapper(\n request.FILES['csv_file'],\n encoding=request.encoding),\n include_reviews,\n privacy,\n )\n except (UnicodeDecodeError, ValueError):\n return HttpResponseBadRequest('Not a valid csv file')\n goodreads_import.start_import(job)\n return redirect('/import-status/%d' % job.id)\n return HttpResponseBadRequest()\n\n\n@login_required\n@require_POST\ndef retry_import(request):\n ''' ingest a goodreads csv '''\n job = get_object_or_404(models.ImportJob, id=request.POST.get('import_job'))\n items = []\n for item in request.POST.getlist('import_item'):\n items.append(get_object_or_404(models.ImportItem, id=item))\n\n job = goodreads_import.create_retry_job(\n request.user,\n job,\n items,\n )\n goodreads_import.start_import(job)\n return redirect('/import-status/%d' % job.id)\n\n\n@login_required\n@require_POST\n@permission_required('bookwyrm.create_invites', raise_exception=True)\ndef create_invite(request):\n ''' creates a user invite database entry '''\n form = forms.CreateInviteForm(request.POST)\n if not form.is_valid():\n return HttpResponseBadRequest(\"ERRORS : %s\" % (form.errors,))\n\n invite = form.save(commit=False)\n invite.user = request.user\n invite.save()\n\n return redirect('/invite')\n\n\ndef update_readthrough(request, book=None, create=True):\n ''' updates but does not save dates on a readthrough '''\n try:\n read_id = request.POST.get('id')\n if not read_id:\n raise models.ReadThrough.DoesNotExist\n readthrough = models.ReadThrough.objects.get(id=read_id)\n except models.ReadThrough.DoesNotExist:\n if not create or not book:\n return None\n readthrough = models.ReadThrough(\n user=request.user,\n book=book,\n )\n\n start_date = request.POST.get('start_date')\n if start_date:\n try:\n start_date = timezone.make_aware(dateutil.parser.parse(start_date))\n readthrough.start_date = start_date\n except ParserError:\n pass\n\n finish_date = request.POST.get('finish_date')\n if finish_date:\n try:\n finish_date = timezone.make_aware(\n dateutil.parser.parse(finish_date))\n readthrough.finish_date = finish_date\n except ParserError:\n pass\n\n if not readthrough.start_date and not readthrough.finish_date:\n return None\n\n return readthrough\n", "path": "bookwyrm/view_actions.py" } ]
diff --git a/bookwyrm/view_actions.py b/bookwyrm/view_actions.py index 3de1dd9d74..b909a8f72c 100644 --- a/bookwyrm/view_actions.py +++ b/bookwyrm/view_actions.py @@ -402,7 +402,7 @@ def shelve(request): request.user, desired_shelf, book, - privacy='public' + privacy=desired_shelf.privacy ) return redirect('/')
ansible__molecule-3313
Question: accessing values of variables as they are being used for provisioning an instance inside Testinfra tests I want to use Testinfra tests to test my role. Inside an Testinfra test I would like to access the values of variables as they are being used for provisioning the machine when `playbook.yml` is converged for some instance. I need this since the instance's state, which I want to check, depends on the chosen values of the variables defined in role's `default/main.yml` or `vars/main.yml` file. I tried using the following ['trick' suggested by Testinfra maintainer](https://github.com/philpep/testinfra/issues/61#issuecomment-178503626), but it only works for Ansible facts and `group_vars`/`host_vars`, not for variables defined within a role, either in `default/main.yml` or `vars/main.yml`. Here is an example test for PostgreSQL service: ``` python def test_postgresql_running_and_enabled(Ansible, Service): postgresql_unit_name = Ansible("debug", "msg={{ postgresql_unit_name }}")["msg"] postgresql = Service(postgresql_unit_name) assert postgresql.is_running assert postgresql.is_enabled ``` Variable `postgresql_unit_name` is defined in role's `vars/main.yml`, but apparently the invocation of Ansible through Testinfra is unable to find it. Here is the error: ``` python def test_postgresql_running_and_enabled(Ansible, Service): postgresql_unit_name = Ansible("debug", "msg={{ postgresql_unit_name }}")["msg"] postgresql = Service(postgresql_unit_name) > assert postgresql.is_running E assert <service 'postgresql_unit_name' is undefined>.is_running ``` Any ideas how I could achieve this?
[ { "content": "# Copyright (c) 2015-2018 Cisco Systems, Inc.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to\n# deal in the Software without restriction, including without limitation the\n# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n# sell copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n\"\"\"Molecule Utils Module.\"\"\"\n\nfrom __future__ import print_function\n\nimport contextlib\nimport copy\nimport fnmatch\nimport logging\nimport os\nimport re\nimport sys\nfrom dataclasses import dataclass\nfrom subprocess import CalledProcessError, CompletedProcess\nfrom typing import Any, Dict, Iterable, List, MutableMapping, NoReturn, Optional, Union\nfrom warnings import WarningMessage\n\nimport jinja2\nimport yaml\nfrom ansible_compat.ports import cache\nfrom rich.syntax import Syntax\nfrom subprocess_tee import run\n\nfrom molecule.console import console\nfrom molecule.constants import MOLECULE_HEADER\n\nLOG = logging.getLogger(__name__)\n\n\nclass SafeDumper(yaml.SafeDumper):\n \"\"\"SafeDumper YAML Class.\"\"\"\n\n def increase_indent(self, flow=False, indentless=False):\n return super(SafeDumper, self).increase_indent(flow, False)\n\n\ndef print_debug(title: str, data: str) -> None:\n \"\"\"Print debug information.\"\"\"\n console.print(f\"DEBUG: {title}:\\n{data}\")\n\n\ndef print_environment_vars(env: Optional[Dict[str, str]]) -> None:\n \"\"\"\n Print ``Ansible`` and ``Molecule`` environment variables and returns None.\n\n :param env: A dict containing the shell's environment as collected by\n ``os.environ``.\n :return: None\n \"\"\"\n if env:\n ansible_env = {k: v for (k, v) in env.items() if \"ANSIBLE_\" in k}\n print_debug(\"ANSIBLE ENVIRONMENT\", safe_dump(ansible_env, explicit_start=False))\n\n molecule_env = {k: v for (k, v) in env.items() if \"MOLECULE_\" in k}\n print_debug(\n \"MOLECULE ENVIRONMENT\", safe_dump(molecule_env, explicit_start=False)\n )\n\n combined_env = ansible_env.copy()\n combined_env.update(molecule_env)\n print_debug(\n \"SHELL REPLAY\",\n \" \".join([f\"{k}={v}\" for (k, v) in sorted(combined_env.items())]),\n )\n print()\n\n\ndef do_report() -> None:\n \"\"\"Dump html report atexit.\"\"\"\n report_file = os.environ[\"MOLECULE_REPORT\"]\n LOG.info(\"Writing %s report.\", report_file)\n with open(report_file, \"w\") as f:\n f.write(console.export_html())\n f.close()\n\n\ndef sysexit(code: int = 1) -> NoReturn:\n \"\"\"Perform a system exit with given code, default 1.\"\"\"\n sys.exit(code)\n\n\ndef sysexit_with_message(\n msg: str,\n code: int = 1,\n detail: Optional[MutableMapping] = None,\n warns: Iterable[WarningMessage] = (),\n) -> None:\n \"\"\"Exit with an error message.\"\"\"\n # detail is usually a multi-line string which is not suitable for normal\n # logger.\n if detail:\n if isinstance(detail, dict):\n detail_str = safe_dump(detail)\n else:\n detail_str = str(detail)\n print(detail_str)\n LOG.critical(msg)\n\n for warn in warns:\n LOG.warning(warn.__dict__[\"message\"].args[0])\n sysexit(code)\n\n\ndef run_command(\n cmd, env=None, debug=False, echo=False, quiet=False, check=False, cwd=None\n) -> CompletedProcess:\n \"\"\"\n Execute the given command and returns None.\n\n :param cmd: :\n - a string or list of strings (similar to subprocess.run)\n - a BakedCommand object (\n :param debug: An optional bool to toggle debug output.\n \"\"\"\n args = []\n stdout = None\n stderr = None\n if cmd.__class__.__name__ == \"Command\":\n raise RuntimeError(\n \"Molecule 3.2.0 dropped use of sh library, update plugin code to use new API. \"\n \"See https://github.com/ansible-community/molecule/issues/2678\"\n )\n elif cmd.__class__.__name__ == \"BakedCommand\":\n if cmd.env and env:\n env = dict(cmd.env, **env)\n else:\n env = cmd.env or env\n args = cmd.cmd\n stdout = cmd.stdout\n stderr = cmd.stderr\n else:\n args = cmd\n\n if debug:\n print_environment_vars(env)\n\n result = run(\n args,\n env=env,\n stdout=stdout,\n stderr=stderr,\n echo=echo or debug,\n quiet=quiet,\n cwd=cwd,\n )\n if result.returncode != 0 and check:\n raise CalledProcessError(\n returncode=result.returncode,\n cmd=result.args,\n output=result.stdout,\n stderr=result.stderr,\n )\n return result\n\n\ndef os_walk(directory, pattern, excludes=[], followlinks=False):\n \"\"\"Navigate recursively and retried files based on pattern.\"\"\"\n for root, dirs, files in os.walk(directory, topdown=True, followlinks=followlinks):\n dirs[:] = [d for d in dirs if d not in excludes]\n for basename in files:\n if fnmatch.fnmatch(basename, pattern):\n filename = os.path.join(root, basename)\n\n yield filename\n\n\ndef render_template(template, **kwargs):\n \"\"\"Render a jinaj2 template.\"\"\"\n t = jinja2.Environment()\n t = t.from_string(template)\n\n return t.render(kwargs)\n\n\ndef write_file(filename: str, content: str):\n \"\"\"\n Write a file with the given filename and content and returns None.\n\n :param filename: A string containing the target filename.\n :param content: A string containing the data to be written.\n :return: None\n \"\"\"\n with open_file(filename, \"w\") as f:\n f.write(content)\n\n file_prepender(filename)\n\n\ndef molecule_prepender(content: str):\n \"\"\"Return molecule identification header.\"\"\"\n return MOLECULE_HEADER + \"\\n\\n\" + content\n\n\ndef file_prepender(filename: str):\n \"\"\"\n Prepend an informational header on files managed by Molecule and returns \\\n None.\n\n :param filename: A string containing the target filename.\n :return: None\n \"\"\"\n with open_file(filename, \"r+\") as f:\n content = f.read()\n f.seek(0, 0)\n f.write(molecule_prepender(content))\n\n\ndef safe_dump(data: Any, explicit_start=True) -> str:\n \"\"\"\n Dump the provided data to a YAML document and returns a string.\n\n :param data: A string containing an absolute path to the file to parse.\n :return: str\n \"\"\"\n return yaml.dump(\n data, Dumper=SafeDumper, default_flow_style=False, explicit_start=explicit_start\n )\n\n\ndef safe_load(string) -> Dict:\n \"\"\"\n Parse the provided string returns a dict.\n\n :param string: A string to be parsed.\n :return: dict\n \"\"\"\n try:\n return yaml.safe_load(string) or {}\n except yaml.scanner.ScannerError as e:\n sysexit_with_message(str(e))\n return {}\n\n\ndef safe_load_file(filename: str):\n \"\"\"\n Parse the provided YAML file and returns a dict.\n\n :param filename: A string containing an absolute path to the file to parse.\n :return: dict\n \"\"\"\n with open_file(filename) as stream:\n return safe_load(stream)\n\n\[email protected]\ndef open_file(filename, mode=\"r\"):\n \"\"\"\n Open the provide file safely and returns a file type.\n\n :param filename: A string containing an absolute path to the file to open.\n :param mode: A string describing the way in which the file will be used.\n :return: file type\n \"\"\"\n with open(filename, mode) as stream:\n yield stream\n\n\ndef instance_with_scenario_name(instance_name, scenario_name):\n \"\"\"Format instance name that includes scenario.\"\"\"\n return f\"{instance_name}-{scenario_name}\"\n\n\ndef verbose_flag(options):\n \"\"\"Return computed verbosity flag.\"\"\"\n verbose = \"v\"\n verbose_flag = []\n for i in range(0, 3):\n if options.get(verbose):\n verbose_flag = [f\"-{verbose}\"]\n del options[verbose]\n if options.get(\"verbose\"):\n del options[\"verbose\"]\n break\n verbose = verbose + \"v\"\n\n return verbose_flag\n\n\ndef filter_verbose_permutation(options):\n \"\"\"Clean verbose information.\"\"\"\n return {k: options[k] for k in options if not re.match(\"^[v]+$\", k)}\n\n\ndef abs_path(path: str) -> Optional[str]:\n \"\"\"Return absolute path.\"\"\"\n if path:\n return os.path.abspath(path)\n return None\n\n\ndef merge_dicts(a: MutableMapping, b: MutableMapping) -> MutableMapping:\n \"\"\"\n Merge the values of b into a and returns a new dict.\n\n This function uses the same algorithm as Ansible's `combine(recursive=True)` filter.\n\n :param a: the target dictionary\n :param b: the dictionary to import\n :return: dict\n \"\"\"\n result = copy.deepcopy(a)\n\n for k, v in b.items():\n if k in a and isinstance(a[k], dict) and isinstance(v, dict):\n result[k] = merge_dicts(a[k], v)\n else:\n result[k] = v\n\n return result\n\n\ndef validate_parallel_cmd_args(cmd_args):\n \"\"\"Prevents use of options incompatible with parallel mode.\"\"\"\n if cmd_args.get(\"parallel\") and cmd_args.get(\"destroy\") == \"never\":\n msg = 'Combining \"--parallel\" and \"--destroy=never\" is not supported'\n sysexit_with_message(msg)\n\n\ndef _parallelize_platforms(config, run_uuid):\n def parallelize(platform):\n platform[\"name\"] = f\"{platform['name']}-{run_uuid}\"\n return platform\n\n return [parallelize(platform) for platform in config[\"platforms\"]]\n\n\n@cache\ndef find_vcs_root(location=\"\", dirs=(\".git\", \".hg\", \".svn\"), default=None) -> str:\n \"\"\"Return current repository root directory.\"\"\"\n if not location:\n location = os.getcwd()\n prev, location = None, os.path.abspath(location)\n while prev != location:\n if any(os.path.isdir(os.path.join(location, d)) for d in dirs):\n return location\n prev, location = location, os.path.abspath(os.path.join(location, os.pardir))\n return default\n\n\ndef lookup_config_file(filename: str) -> Optional[str]:\n \"\"\"Return config file PATH.\"\"\"\n for path in [find_vcs_root(default=\"~\"), \"~\"]:\n f = os.path.expanduser(f\"{path}/{filename}\")\n if os.path.isfile(f):\n LOG.info(\"Found config file %s\", f)\n return f\n return None\n\n\ndef boolean(value: Any, strict=True) -> bool:\n \"\"\"Evaluate any object as boolean matching ansible behavior.\"\"\"\n # Based on https://github.com/ansible/ansible/blob/devel/lib/ansible/module_utils/parsing/convert_bool.py\n\n BOOLEANS_TRUE = frozenset((\"y\", \"yes\", \"on\", \"1\", \"true\", \"t\", 1, 1.0, True))\n BOOLEANS_FALSE = frozenset((\"n\", \"no\", \"off\", \"0\", \"false\", \"f\", 0, 0.0, False))\n BOOLEANS = BOOLEANS_TRUE.union(BOOLEANS_FALSE)\n\n if isinstance(value, bool):\n return value\n\n normalized_value = value\n if isinstance(value, (str, bytes)):\n normalized_value = str(value).lower().strip()\n\n if normalized_value in BOOLEANS_TRUE:\n return True\n elif normalized_value in BOOLEANS_FALSE or not strict:\n return False\n\n raise TypeError(\n f\"The value '{value!s}' is not a valid boolean. Valid booleans include: {', '.join(repr(i) for i in BOOLEANS)!s}\"\n )\n\n\n@dataclass\nclass BakedCommand:\n \"\"\"Define a subprocess command to be executed.\"\"\"\n\n cmd: Union[str, List[str]]\n env: Optional[Dict]\n cwd: Optional[str] = None\n stdout: Any = None\n stderr: Any = None\n\n\ndef dict2args(data: Dict) -> List[str]:\n \"\"\"Convert a dictionary of options to command like arguments.\"\"\"\n result = []\n # keep sorting in order to achieve a predictable behavior\n for k, v in sorted(data.items()):\n if v is not False:\n prefix = \"-\" if len(k) == 1 else \"--\"\n result.append(f\"{prefix}{k}\".replace(\"_\", \"-\"))\n if v is not True:\n # { foo: True } should produce --foo without any values\n result.append(v)\n return result\n\n\ndef bool2args(data: bool) -> List[str]:\n \"\"\"Convert a boolean value to command line argument (flag).\"\"\"\n return []\n\n\ndef print_as_yaml(data: Any) -> None:\n \"\"\"Render python object as yaml on console.\"\"\"\n result = Syntax(safe_dump(data), \"yaml\")\n console.print(result)\n", "path": "src/molecule/util.py" } ]
[ { "content": "# Copyright (c) 2015-2018 Cisco Systems, Inc.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to\n# deal in the Software without restriction, including without limitation the\n# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n# sell copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n\"\"\"Molecule Utils Module.\"\"\"\n\nfrom __future__ import print_function\n\nimport contextlib\nimport copy\nimport fnmatch\nimport logging\nimport os\nimport re\nimport sys\nfrom dataclasses import dataclass\nfrom subprocess import CalledProcessError, CompletedProcess\nfrom typing import Any, Dict, Iterable, List, MutableMapping, NoReturn, Optional, Union\nfrom warnings import WarningMessage\n\nimport jinja2\nimport yaml\nfrom ansible_compat.ports import cache\nfrom rich.syntax import Syntax\nfrom subprocess_tee import run\n\nfrom molecule.console import console\nfrom molecule.constants import MOLECULE_HEADER\n\nLOG = logging.getLogger(__name__)\n\n\nclass SafeDumper(yaml.SafeDumper):\n \"\"\"SafeDumper YAML Class.\"\"\"\n\n def increase_indent(self, flow=False, indentless=False):\n return super(SafeDumper, self).increase_indent(flow, False)\n\n\ndef print_debug(title: str, data: str) -> None:\n \"\"\"Print debug information.\"\"\"\n console.print(f\"DEBUG: {title}:\\n{data}\")\n\n\ndef print_environment_vars(env: Optional[Dict[str, str]]) -> None:\n \"\"\"\n Print ``Ansible`` and ``Molecule`` environment variables and returns None.\n\n :param env: A dict containing the shell's environment as collected by\n ``os.environ``.\n :return: None\n \"\"\"\n if env:\n ansible_env = {k: v for (k, v) in env.items() if \"ANSIBLE_\" in k}\n print_debug(\"ANSIBLE ENVIRONMENT\", safe_dump(ansible_env, explicit_start=False))\n\n molecule_env = {k: v for (k, v) in env.items() if \"MOLECULE_\" in k}\n print_debug(\n \"MOLECULE ENVIRONMENT\", safe_dump(molecule_env, explicit_start=False)\n )\n\n combined_env = ansible_env.copy()\n combined_env.update(molecule_env)\n print_debug(\n \"SHELL REPLAY\",\n \" \".join([f\"{k}={v}\" for (k, v) in sorted(combined_env.items())]),\n )\n print()\n\n\ndef do_report() -> None:\n \"\"\"Dump html report atexit.\"\"\"\n report_file = os.environ[\"MOLECULE_REPORT\"]\n LOG.info(\"Writing %s report.\", report_file)\n with open(report_file, \"w\") as f:\n f.write(console.export_html())\n f.close()\n\n\ndef sysexit(code: int = 1) -> NoReturn:\n \"\"\"Perform a system exit with given code, default 1.\"\"\"\n sys.exit(code)\n\n\ndef sysexit_with_message(\n msg: str,\n code: int = 1,\n detail: Optional[MutableMapping] = None,\n warns: Iterable[WarningMessage] = (),\n) -> None:\n \"\"\"Exit with an error message.\"\"\"\n # detail is usually a multi-line string which is not suitable for normal\n # logger.\n if detail:\n if isinstance(detail, dict):\n detail_str = safe_dump(detail)\n else:\n detail_str = str(detail)\n print(detail_str)\n LOG.critical(msg)\n\n for warn in warns:\n LOG.warning(warn.__dict__[\"message\"].args[0])\n sysexit(code)\n\n\ndef run_command(\n cmd, env=None, debug=False, echo=False, quiet=False, check=False, cwd=None\n) -> CompletedProcess:\n \"\"\"\n Execute the given command and returns None.\n\n :param cmd: :\n - a string or list of strings (similar to subprocess.run)\n - a BakedCommand object (\n :param debug: An optional bool to toggle debug output.\n \"\"\"\n args = []\n stdout = None\n stderr = None\n if cmd.__class__.__name__ == \"Command\":\n raise RuntimeError(\n \"Molecule 3.2.0 dropped use of sh library, update plugin code to use new API. \"\n \"See https://github.com/ansible-community/molecule/issues/2678\"\n )\n elif cmd.__class__.__name__ == \"BakedCommand\":\n if cmd.env and env:\n env = dict(cmd.env, **env)\n else:\n env = cmd.env or env\n args = cmd.cmd\n cwd = cmd.cwd\n stdout = cmd.stdout\n stderr = cmd.stderr\n else:\n args = cmd\n\n if debug:\n print_environment_vars(env)\n\n result = run(\n args,\n env=env,\n stdout=stdout,\n stderr=stderr,\n echo=echo or debug,\n quiet=quiet,\n cwd=cwd,\n )\n if result.returncode != 0 and check:\n raise CalledProcessError(\n returncode=result.returncode,\n cmd=result.args,\n output=result.stdout,\n stderr=result.stderr,\n )\n return result\n\n\ndef os_walk(directory, pattern, excludes=[], followlinks=False):\n \"\"\"Navigate recursively and retried files based on pattern.\"\"\"\n for root, dirs, files in os.walk(directory, topdown=True, followlinks=followlinks):\n dirs[:] = [d for d in dirs if d not in excludes]\n for basename in files:\n if fnmatch.fnmatch(basename, pattern):\n filename = os.path.join(root, basename)\n\n yield filename\n\n\ndef render_template(template, **kwargs):\n \"\"\"Render a jinaj2 template.\"\"\"\n t = jinja2.Environment()\n t = t.from_string(template)\n\n return t.render(kwargs)\n\n\ndef write_file(filename: str, content: str):\n \"\"\"\n Write a file with the given filename and content and returns None.\n\n :param filename: A string containing the target filename.\n :param content: A string containing the data to be written.\n :return: None\n \"\"\"\n with open_file(filename, \"w\") as f:\n f.write(content)\n\n file_prepender(filename)\n\n\ndef molecule_prepender(content: str):\n \"\"\"Return molecule identification header.\"\"\"\n return MOLECULE_HEADER + \"\\n\\n\" + content\n\n\ndef file_prepender(filename: str):\n \"\"\"\n Prepend an informational header on files managed by Molecule and returns \\\n None.\n\n :param filename: A string containing the target filename.\n :return: None\n \"\"\"\n with open_file(filename, \"r+\") as f:\n content = f.read()\n f.seek(0, 0)\n f.write(molecule_prepender(content))\n\n\ndef safe_dump(data: Any, explicit_start=True) -> str:\n \"\"\"\n Dump the provided data to a YAML document and returns a string.\n\n :param data: A string containing an absolute path to the file to parse.\n :return: str\n \"\"\"\n return yaml.dump(\n data, Dumper=SafeDumper, default_flow_style=False, explicit_start=explicit_start\n )\n\n\ndef safe_load(string) -> Dict:\n \"\"\"\n Parse the provided string returns a dict.\n\n :param string: A string to be parsed.\n :return: dict\n \"\"\"\n try:\n return yaml.safe_load(string) or {}\n except yaml.scanner.ScannerError as e:\n sysexit_with_message(str(e))\n return {}\n\n\ndef safe_load_file(filename: str):\n \"\"\"\n Parse the provided YAML file and returns a dict.\n\n :param filename: A string containing an absolute path to the file to parse.\n :return: dict\n \"\"\"\n with open_file(filename) as stream:\n return safe_load(stream)\n\n\[email protected]\ndef open_file(filename, mode=\"r\"):\n \"\"\"\n Open the provide file safely and returns a file type.\n\n :param filename: A string containing an absolute path to the file to open.\n :param mode: A string describing the way in which the file will be used.\n :return: file type\n \"\"\"\n with open(filename, mode) as stream:\n yield stream\n\n\ndef instance_with_scenario_name(instance_name, scenario_name):\n \"\"\"Format instance name that includes scenario.\"\"\"\n return f\"{instance_name}-{scenario_name}\"\n\n\ndef verbose_flag(options):\n \"\"\"Return computed verbosity flag.\"\"\"\n verbose = \"v\"\n verbose_flag = []\n for i in range(0, 3):\n if options.get(verbose):\n verbose_flag = [f\"-{verbose}\"]\n del options[verbose]\n if options.get(\"verbose\"):\n del options[\"verbose\"]\n break\n verbose = verbose + \"v\"\n\n return verbose_flag\n\n\ndef filter_verbose_permutation(options):\n \"\"\"Clean verbose information.\"\"\"\n return {k: options[k] for k in options if not re.match(\"^[v]+$\", k)}\n\n\ndef abs_path(path: str) -> Optional[str]:\n \"\"\"Return absolute path.\"\"\"\n if path:\n return os.path.abspath(path)\n return None\n\n\ndef merge_dicts(a: MutableMapping, b: MutableMapping) -> MutableMapping:\n \"\"\"\n Merge the values of b into a and returns a new dict.\n\n This function uses the same algorithm as Ansible's `combine(recursive=True)` filter.\n\n :param a: the target dictionary\n :param b: the dictionary to import\n :return: dict\n \"\"\"\n result = copy.deepcopy(a)\n\n for k, v in b.items():\n if k in a and isinstance(a[k], dict) and isinstance(v, dict):\n result[k] = merge_dicts(a[k], v)\n else:\n result[k] = v\n\n return result\n\n\ndef validate_parallel_cmd_args(cmd_args):\n \"\"\"Prevents use of options incompatible with parallel mode.\"\"\"\n if cmd_args.get(\"parallel\") and cmd_args.get(\"destroy\") == \"never\":\n msg = 'Combining \"--parallel\" and \"--destroy=never\" is not supported'\n sysexit_with_message(msg)\n\n\ndef _parallelize_platforms(config, run_uuid):\n def parallelize(platform):\n platform[\"name\"] = f\"{platform['name']}-{run_uuid}\"\n return platform\n\n return [parallelize(platform) for platform in config[\"platforms\"]]\n\n\n@cache\ndef find_vcs_root(location=\"\", dirs=(\".git\", \".hg\", \".svn\"), default=None) -> str:\n \"\"\"Return current repository root directory.\"\"\"\n if not location:\n location = os.getcwd()\n prev, location = None, os.path.abspath(location)\n while prev != location:\n if any(os.path.isdir(os.path.join(location, d)) for d in dirs):\n return location\n prev, location = location, os.path.abspath(os.path.join(location, os.pardir))\n return default\n\n\ndef lookup_config_file(filename: str) -> Optional[str]:\n \"\"\"Return config file PATH.\"\"\"\n for path in [find_vcs_root(default=\"~\"), \"~\"]:\n f = os.path.expanduser(f\"{path}/{filename}\")\n if os.path.isfile(f):\n LOG.info(\"Found config file %s\", f)\n return f\n return None\n\n\ndef boolean(value: Any, strict=True) -> bool:\n \"\"\"Evaluate any object as boolean matching ansible behavior.\"\"\"\n # Based on https://github.com/ansible/ansible/blob/devel/lib/ansible/module_utils/parsing/convert_bool.py\n\n BOOLEANS_TRUE = frozenset((\"y\", \"yes\", \"on\", \"1\", \"true\", \"t\", 1, 1.0, True))\n BOOLEANS_FALSE = frozenset((\"n\", \"no\", \"off\", \"0\", \"false\", \"f\", 0, 0.0, False))\n BOOLEANS = BOOLEANS_TRUE.union(BOOLEANS_FALSE)\n\n if isinstance(value, bool):\n return value\n\n normalized_value = value\n if isinstance(value, (str, bytes)):\n normalized_value = str(value).lower().strip()\n\n if normalized_value in BOOLEANS_TRUE:\n return True\n elif normalized_value in BOOLEANS_FALSE or not strict:\n return False\n\n raise TypeError(\n f\"The value '{value!s}' is not a valid boolean. Valid booleans include: {', '.join(repr(i) for i in BOOLEANS)!s}\"\n )\n\n\n@dataclass\nclass BakedCommand:\n \"\"\"Define a subprocess command to be executed.\"\"\"\n\n cmd: Union[str, List[str]]\n env: Optional[Dict]\n cwd: Optional[str] = None\n stdout: Any = None\n stderr: Any = None\n\n\ndef dict2args(data: Dict) -> List[str]:\n \"\"\"Convert a dictionary of options to command like arguments.\"\"\"\n result = []\n # keep sorting in order to achieve a predictable behavior\n for k, v in sorted(data.items()):\n if v is not False:\n prefix = \"-\" if len(k) == 1 else \"--\"\n result.append(f\"{prefix}{k}\".replace(\"_\", \"-\"))\n if v is not True:\n # { foo: True } should produce --foo without any values\n result.append(v)\n return result\n\n\ndef bool2args(data: bool) -> List[str]:\n \"\"\"Convert a boolean value to command line argument (flag).\"\"\"\n return []\n\n\ndef print_as_yaml(data: Any) -> None:\n \"\"\"Render python object as yaml on console.\"\"\"\n result = Syntax(safe_dump(data), \"yaml\")\n console.print(result)\n", "path": "src/molecule/util.py" } ]
diff --git a/src/molecule/util.py b/src/molecule/util.py index 3dc1c528f1..68df87e257 100644 --- a/src/molecule/util.py +++ b/src/molecule/util.py @@ -143,6 +143,7 @@ def run_command( else: env = cmd.env or env args = cmd.cmd + cwd = cmd.cwd stdout = cmd.stdout stderr = cmd.stderr else:
python-trio__trio-2334
Embedded use vs. signal handler If you install your own signal handler from C and then run an embedded Python interpreter, you can't use Trio: ``` Traceback (most recent call last): File "/etc/kamailio/main.py", line 65, in background trio.run(bg_main) File "/usr/lib/python3/dist-packages/trio/_core/_run.py", line 1929, in run runner = setup_runner( File "/usr/lib/python3/dist-packages/trio/_core/_run.py", line 1846, in setup_runner ki_manager.install(runner.deliver_ki, restrict_keyboard_interrupt_to_checkpoints) File "/usr/lib/python3/dist-packages/trio/_core/_ki.py", line 180, in install not is_main_thread() File "/usr/lib/python3/dist-packages/trio/_util.py", line 79, in is_main_thread signal.signal(signal.SIGINT, signal.getsignal(signal.SIGINT)) File "/usr/lib/python3.9/signal.py", line 56, in signal handler = _signal.signal(_enum_to_int(signalnum), _enum_to_int(handler)) TypeError: signal handler must be signal.SIG_IGN, signal.SIG_DFL, or a callable object ```
[ { "content": "# coding: utf-8\n\n# Little utilities we use internally\n\nfrom abc import ABCMeta\nimport os\nimport signal\nimport sys\nimport pathlib\nfrom functools import wraps, update_wrapper\nimport typing as t\nimport threading\nimport collections\n\nfrom async_generator import isasyncgen\n\nimport trio\n\n# Equivalent to the C function raise(), which Python doesn't wrap\nif os.name == \"nt\":\n # On windows, os.kill exists but is really weird.\n #\n # If you give it CTRL_C_EVENT or CTRL_BREAK_EVENT, it tries to deliver\n # those using GenerateConsoleCtrlEvent. But I found that when I tried\n # to run my test normally, it would freeze waiting... unless I added\n # print statements, in which case the test suddenly worked. So I guess\n # these signals are only delivered if/when you access the console? I\n # don't really know what was going on there. From reading the\n # GenerateConsoleCtrlEvent docs I don't know how it worked at all.\n #\n # I later spent a bunch of time trying to make GenerateConsoleCtrlEvent\n # work for creating synthetic control-C events, and... failed\n # utterly. There are lots of details in the code and comments\n # removed/added at this commit:\n # https://github.com/python-trio/trio/commit/95843654173e3e826c34d70a90b369ba6edf2c23\n #\n # OTOH, if you pass os.kill any *other* signal number... then CPython\n # just calls TerminateProcess (wtf).\n #\n # So, anyway, os.kill is not so useful for testing purposes. Instead\n # we use raise():\n #\n # https://msdn.microsoft.com/en-us/library/dwwzkt4c.aspx\n #\n # Have to import cffi inside the 'if os.name' block because we don't\n # depend on cffi on non-Windows platforms. (It would be easy to switch\n # this to ctypes though if we ever remove the cffi dependency.)\n #\n # Some more information:\n # https://bugs.python.org/issue26350\n #\n # Anyway, we use this for two things:\n # - redelivering unhandled signals\n # - generating synthetic signals for tests\n # and for both of those purposes, 'raise' works fine.\n import cffi\n\n _ffi = cffi.FFI()\n _ffi.cdef(\"int raise(int);\")\n _lib = _ffi.dlopen(\"api-ms-win-crt-runtime-l1-1-0.dll\")\n signal_raise = getattr(_lib, \"raise\")\nelse:\n\n def signal_raise(signum):\n signal.pthread_kill(threading.get_ident(), signum)\n\n\n# See: #461 as to why this is needed.\n# The gist is that threading.main_thread() has the capability to lie to us\n# if somebody else edits the threading ident cache to replace the main\n# thread; causing threading.current_thread() to return a _DummyThread,\n# causing the C-c check to fail, and so on.\n# Trying to use signal out of the main thread will fail, so we can then\n# reliably check if this is the main thread without relying on a\n# potentially modified threading.\ndef is_main_thread():\n \"\"\"Attempt to reliably check if we are in the main thread.\"\"\"\n try:\n signal.signal(signal.SIGINT, signal.getsignal(signal.SIGINT))\n return True\n except ValueError:\n return False\n\n\n######\n# Call the function and get the coroutine object, while giving helpful\n# errors for common mistakes. Returns coroutine object.\n######\ndef coroutine_or_error(async_fn, *args):\n def _return_value_looks_like_wrong_library(value):\n # Returned by legacy @asyncio.coroutine functions, which includes\n # a surprising proportion of asyncio builtins.\n if isinstance(value, collections.abc.Generator):\n return True\n # The protocol for detecting an asyncio Future-like object\n if getattr(value, \"_asyncio_future_blocking\", None) is not None:\n return True\n # This janky check catches tornado Futures and twisted Deferreds.\n # By the time we're calling this function, we already know\n # something has gone wrong, so a heuristic is pretty safe.\n if value.__class__.__name__ in (\"Future\", \"Deferred\"):\n return True\n return False\n\n try:\n coro = async_fn(*args)\n\n except TypeError:\n # Give good error for: nursery.start_soon(trio.sleep(1))\n if isinstance(async_fn, collections.abc.Coroutine):\n # explicitly close coroutine to avoid RuntimeWarning\n async_fn.close()\n\n raise TypeError(\n \"Trio was expecting an async function, but instead it got \"\n \"a coroutine object {async_fn!r}\\n\"\n \"\\n\"\n \"Probably you did something like:\\n\"\n \"\\n\"\n \" trio.run({async_fn.__name__}(...)) # incorrect!\\n\"\n \" nursery.start_soon({async_fn.__name__}(...)) # incorrect!\\n\"\n \"\\n\"\n \"Instead, you want (notice the parentheses!):\\n\"\n \"\\n\"\n \" trio.run({async_fn.__name__}, ...) # correct!\\n\"\n \" nursery.start_soon({async_fn.__name__}, ...) # correct!\".format(\n async_fn=async_fn\n )\n ) from None\n\n # Give good error for: nursery.start_soon(future)\n if _return_value_looks_like_wrong_library(async_fn):\n raise TypeError(\n \"Trio was expecting an async function, but instead it got \"\n \"{!r} – are you trying to use a library written for \"\n \"asyncio/twisted/tornado or similar? That won't work \"\n \"without some sort of compatibility shim.\".format(async_fn)\n ) from None\n\n raise\n\n # We can't check iscoroutinefunction(async_fn), because that will fail\n # for things like functools.partial objects wrapping an async\n # function. So we have to just call it and then check whether the\n # return value is a coroutine object.\n if not isinstance(coro, collections.abc.Coroutine):\n # Give good error for: nursery.start_soon(func_returning_future)\n if _return_value_looks_like_wrong_library(coro):\n raise TypeError(\n \"Trio got unexpected {!r} – are you trying to use a \"\n \"library written for asyncio/twisted/tornado or similar? \"\n \"That won't work without some sort of compatibility shim.\".format(coro)\n )\n\n if isasyncgen(coro):\n raise TypeError(\n \"start_soon expected an async function but got an async \"\n \"generator {!r}\".format(coro)\n )\n\n # Give good error for: nursery.start_soon(some_sync_fn)\n raise TypeError(\n \"Trio expected an async function, but {!r} appears to be \"\n \"synchronous\".format(getattr(async_fn, \"__qualname__\", async_fn))\n )\n\n return coro\n\n\nclass ConflictDetector:\n \"\"\"Detect when two tasks are about to perform operations that would\n conflict.\n\n Use as a synchronous context manager; if two tasks enter it at the same\n time then the second one raises an error. You can use it when there are\n two pieces of code that *would* collide and need a lock if they ever were\n called at the same time, but that should never happen.\n\n We use this in particular for things like, making sure that two different\n tasks don't call sendall simultaneously on the same stream.\n\n \"\"\"\n\n def __init__(self, msg):\n self._msg = msg\n self._held = False\n\n def __enter__(self):\n if self._held:\n raise trio.BusyResourceError(self._msg)\n else:\n self._held = True\n\n def __exit__(self, *args):\n self._held = False\n\n\ndef async_wraps(cls, wrapped_cls, attr_name):\n \"\"\"Similar to wraps, but for async wrappers of non-async functions.\"\"\"\n\n def decorator(func):\n func.__name__ = attr_name\n func.__qualname__ = \".\".join((cls.__qualname__, attr_name))\n\n func.__doc__ = \"\"\"Like :meth:`~{}.{}.{}`, but async.\n\n \"\"\".format(\n wrapped_cls.__module__, wrapped_cls.__qualname__, attr_name\n )\n\n return func\n\n return decorator\n\n\ndef fixup_module_metadata(module_name, namespace):\n seen_ids = set()\n\n def fix_one(qualname, name, obj):\n # avoid infinite recursion (relevant when using\n # typing.Generic, for example)\n if id(obj) in seen_ids:\n return\n seen_ids.add(id(obj))\n\n mod = getattr(obj, \"__module__\", None)\n if mod is not None and mod.startswith(\"trio.\"):\n obj.__module__ = module_name\n # Modules, unlike everything else in Python, put fully-qualitied\n # names into their __name__ attribute. We check for \".\" to avoid\n # rewriting these.\n if hasattr(obj, \"__name__\") and \".\" not in obj.__name__:\n obj.__name__ = name\n obj.__qualname__ = qualname\n if isinstance(obj, type):\n for attr_name, attr_value in obj.__dict__.items():\n fix_one(objname + \".\" + attr_name, attr_name, attr_value)\n\n for objname, obj in namespace.items():\n if not objname.startswith(\"_\"): # ignore private attributes\n fix_one(objname, objname, obj)\n\n\nclass generic_function:\n \"\"\"Decorator that makes a function indexable, to communicate\n non-inferrable generic type parameters to a static type checker.\n\n If you write::\n\n @generic_function\n def open_memory_channel(max_buffer_size: int) -> Tuple[\n SendChannel[T], ReceiveChannel[T]\n ]: ...\n\n it is valid at runtime to say ``open_memory_channel[bytes](5)``.\n This behaves identically to ``open_memory_channel(5)`` at runtime,\n and currently won't type-check without a mypy plugin or clever stubs,\n but at least it becomes possible to write those.\n \"\"\"\n\n def __init__(self, fn):\n update_wrapper(self, fn)\n self._fn = fn\n\n def __call__(self, *args, **kwargs):\n return self._fn(*args, **kwargs)\n\n def __getitem__(self, _):\n return self\n\n\nclass Final(ABCMeta):\n \"\"\"Metaclass that enforces a class to be final (i.e., subclass not allowed).\n\n If a class uses this metaclass like this::\n\n class SomeClass(metaclass=Final):\n pass\n\n The metaclass will ensure that no sub class can be created.\n\n Raises\n ------\n - TypeError if a sub class is created\n \"\"\"\n\n def __new__(cls, name, bases, cls_namespace):\n for base in bases:\n if isinstance(base, Final):\n raise TypeError(\n f\"{base.__module__}.{base.__qualname__} does not support subclassing\"\n )\n return super().__new__(cls, name, bases, cls_namespace)\n\n\nT = t.TypeVar(\"T\")\n\n\nclass NoPublicConstructor(Final):\n \"\"\"Metaclass that enforces a class to be final (i.e., subclass not allowed)\n and ensures a private constructor.\n\n If a class uses this metaclass like this::\n\n class SomeClass(metaclass=NoPublicConstructor):\n pass\n\n The metaclass will ensure that no sub class can be created, and that no instance\n can be initialized.\n\n If you try to instantiate your class (SomeClass()), a TypeError will be thrown.\n\n Raises\n ------\n - TypeError if a sub class or an instance is created.\n \"\"\"\n\n def __call__(cls, *args, **kwargs):\n raise TypeError(\n f\"{cls.__module__}.{cls.__qualname__} has no public constructor\"\n )\n\n def _create(cls: t.Type[T], *args: t.Any, **kwargs: t.Any) -> T:\n return super().__call__(*args, **kwargs) # type: ignore\n\n\ndef name_asyncgen(agen):\n \"\"\"Return the fully-qualified name of the async generator function\n that produced the async generator iterator *agen*.\n \"\"\"\n if not hasattr(agen, \"ag_code\"): # pragma: no cover\n return repr(agen)\n try:\n module = agen.ag_frame.f_globals[\"__name__\"]\n except (AttributeError, KeyError):\n module = \"<{}>\".format(agen.ag_code.co_filename)\n try:\n qualname = agen.__qualname__\n except AttributeError:\n qualname = agen.ag_code.co_name\n return f\"{module}.{qualname}\"\n", "path": "trio/_util.py" } ]
[ { "content": "# coding: utf-8\n\n# Little utilities we use internally\n\nfrom abc import ABCMeta\nimport os\nimport signal\nimport sys\nimport pathlib\nfrom functools import wraps, update_wrapper\nimport typing as t\nimport threading\nimport collections\n\nfrom async_generator import isasyncgen\n\nimport trio\n\n# Equivalent to the C function raise(), which Python doesn't wrap\nif os.name == \"nt\":\n # On windows, os.kill exists but is really weird.\n #\n # If you give it CTRL_C_EVENT or CTRL_BREAK_EVENT, it tries to deliver\n # those using GenerateConsoleCtrlEvent. But I found that when I tried\n # to run my test normally, it would freeze waiting... unless I added\n # print statements, in which case the test suddenly worked. So I guess\n # these signals are only delivered if/when you access the console? I\n # don't really know what was going on there. From reading the\n # GenerateConsoleCtrlEvent docs I don't know how it worked at all.\n #\n # I later spent a bunch of time trying to make GenerateConsoleCtrlEvent\n # work for creating synthetic control-C events, and... failed\n # utterly. There are lots of details in the code and comments\n # removed/added at this commit:\n # https://github.com/python-trio/trio/commit/95843654173e3e826c34d70a90b369ba6edf2c23\n #\n # OTOH, if you pass os.kill any *other* signal number... then CPython\n # just calls TerminateProcess (wtf).\n #\n # So, anyway, os.kill is not so useful for testing purposes. Instead\n # we use raise():\n #\n # https://msdn.microsoft.com/en-us/library/dwwzkt4c.aspx\n #\n # Have to import cffi inside the 'if os.name' block because we don't\n # depend on cffi on non-Windows platforms. (It would be easy to switch\n # this to ctypes though if we ever remove the cffi dependency.)\n #\n # Some more information:\n # https://bugs.python.org/issue26350\n #\n # Anyway, we use this for two things:\n # - redelivering unhandled signals\n # - generating synthetic signals for tests\n # and for both of those purposes, 'raise' works fine.\n import cffi\n\n _ffi = cffi.FFI()\n _ffi.cdef(\"int raise(int);\")\n _lib = _ffi.dlopen(\"api-ms-win-crt-runtime-l1-1-0.dll\")\n signal_raise = getattr(_lib, \"raise\")\nelse:\n\n def signal_raise(signum):\n signal.pthread_kill(threading.get_ident(), signum)\n\n\n# See: #461 as to why this is needed.\n# The gist is that threading.main_thread() has the capability to lie to us\n# if somebody else edits the threading ident cache to replace the main\n# thread; causing threading.current_thread() to return a _DummyThread,\n# causing the C-c check to fail, and so on.\n# Trying to use signal out of the main thread will fail, so we can then\n# reliably check if this is the main thread without relying on a\n# potentially modified threading.\ndef is_main_thread():\n \"\"\"Attempt to reliably check if we are in the main thread.\"\"\"\n try:\n signal.signal(signal.SIGINT, signal.getsignal(signal.SIGINT))\n return True\n except (TypeError, ValueError):\n return False\n\n\n######\n# Call the function and get the coroutine object, while giving helpful\n# errors for common mistakes. Returns coroutine object.\n######\ndef coroutine_or_error(async_fn, *args):\n def _return_value_looks_like_wrong_library(value):\n # Returned by legacy @asyncio.coroutine functions, which includes\n # a surprising proportion of asyncio builtins.\n if isinstance(value, collections.abc.Generator):\n return True\n # The protocol for detecting an asyncio Future-like object\n if getattr(value, \"_asyncio_future_blocking\", None) is not None:\n return True\n # This janky check catches tornado Futures and twisted Deferreds.\n # By the time we're calling this function, we already know\n # something has gone wrong, so a heuristic is pretty safe.\n if value.__class__.__name__ in (\"Future\", \"Deferred\"):\n return True\n return False\n\n try:\n coro = async_fn(*args)\n\n except TypeError:\n # Give good error for: nursery.start_soon(trio.sleep(1))\n if isinstance(async_fn, collections.abc.Coroutine):\n # explicitly close coroutine to avoid RuntimeWarning\n async_fn.close()\n\n raise TypeError(\n \"Trio was expecting an async function, but instead it got \"\n \"a coroutine object {async_fn!r}\\n\"\n \"\\n\"\n \"Probably you did something like:\\n\"\n \"\\n\"\n \" trio.run({async_fn.__name__}(...)) # incorrect!\\n\"\n \" nursery.start_soon({async_fn.__name__}(...)) # incorrect!\\n\"\n \"\\n\"\n \"Instead, you want (notice the parentheses!):\\n\"\n \"\\n\"\n \" trio.run({async_fn.__name__}, ...) # correct!\\n\"\n \" nursery.start_soon({async_fn.__name__}, ...) # correct!\".format(\n async_fn=async_fn\n )\n ) from None\n\n # Give good error for: nursery.start_soon(future)\n if _return_value_looks_like_wrong_library(async_fn):\n raise TypeError(\n \"Trio was expecting an async function, but instead it got \"\n \"{!r} – are you trying to use a library written for \"\n \"asyncio/twisted/tornado or similar? That won't work \"\n \"without some sort of compatibility shim.\".format(async_fn)\n ) from None\n\n raise\n\n # We can't check iscoroutinefunction(async_fn), because that will fail\n # for things like functools.partial objects wrapping an async\n # function. So we have to just call it and then check whether the\n # return value is a coroutine object.\n if not isinstance(coro, collections.abc.Coroutine):\n # Give good error for: nursery.start_soon(func_returning_future)\n if _return_value_looks_like_wrong_library(coro):\n raise TypeError(\n \"Trio got unexpected {!r} – are you trying to use a \"\n \"library written for asyncio/twisted/tornado or similar? \"\n \"That won't work without some sort of compatibility shim.\".format(coro)\n )\n\n if isasyncgen(coro):\n raise TypeError(\n \"start_soon expected an async function but got an async \"\n \"generator {!r}\".format(coro)\n )\n\n # Give good error for: nursery.start_soon(some_sync_fn)\n raise TypeError(\n \"Trio expected an async function, but {!r} appears to be \"\n \"synchronous\".format(getattr(async_fn, \"__qualname__\", async_fn))\n )\n\n return coro\n\n\nclass ConflictDetector:\n \"\"\"Detect when two tasks are about to perform operations that would\n conflict.\n\n Use as a synchronous context manager; if two tasks enter it at the same\n time then the second one raises an error. You can use it when there are\n two pieces of code that *would* collide and need a lock if they ever were\n called at the same time, but that should never happen.\n\n We use this in particular for things like, making sure that two different\n tasks don't call sendall simultaneously on the same stream.\n\n \"\"\"\n\n def __init__(self, msg):\n self._msg = msg\n self._held = False\n\n def __enter__(self):\n if self._held:\n raise trio.BusyResourceError(self._msg)\n else:\n self._held = True\n\n def __exit__(self, *args):\n self._held = False\n\n\ndef async_wraps(cls, wrapped_cls, attr_name):\n \"\"\"Similar to wraps, but for async wrappers of non-async functions.\"\"\"\n\n def decorator(func):\n func.__name__ = attr_name\n func.__qualname__ = \".\".join((cls.__qualname__, attr_name))\n\n func.__doc__ = \"\"\"Like :meth:`~{}.{}.{}`, but async.\n\n \"\"\".format(\n wrapped_cls.__module__, wrapped_cls.__qualname__, attr_name\n )\n\n return func\n\n return decorator\n\n\ndef fixup_module_metadata(module_name, namespace):\n seen_ids = set()\n\n def fix_one(qualname, name, obj):\n # avoid infinite recursion (relevant when using\n # typing.Generic, for example)\n if id(obj) in seen_ids:\n return\n seen_ids.add(id(obj))\n\n mod = getattr(obj, \"__module__\", None)\n if mod is not None and mod.startswith(\"trio.\"):\n obj.__module__ = module_name\n # Modules, unlike everything else in Python, put fully-qualitied\n # names into their __name__ attribute. We check for \".\" to avoid\n # rewriting these.\n if hasattr(obj, \"__name__\") and \".\" not in obj.__name__:\n obj.__name__ = name\n obj.__qualname__ = qualname\n if isinstance(obj, type):\n for attr_name, attr_value in obj.__dict__.items():\n fix_one(objname + \".\" + attr_name, attr_name, attr_value)\n\n for objname, obj in namespace.items():\n if not objname.startswith(\"_\"): # ignore private attributes\n fix_one(objname, objname, obj)\n\n\nclass generic_function:\n \"\"\"Decorator that makes a function indexable, to communicate\n non-inferrable generic type parameters to a static type checker.\n\n If you write::\n\n @generic_function\n def open_memory_channel(max_buffer_size: int) -> Tuple[\n SendChannel[T], ReceiveChannel[T]\n ]: ...\n\n it is valid at runtime to say ``open_memory_channel[bytes](5)``.\n This behaves identically to ``open_memory_channel(5)`` at runtime,\n and currently won't type-check without a mypy plugin or clever stubs,\n but at least it becomes possible to write those.\n \"\"\"\n\n def __init__(self, fn):\n update_wrapper(self, fn)\n self._fn = fn\n\n def __call__(self, *args, **kwargs):\n return self._fn(*args, **kwargs)\n\n def __getitem__(self, _):\n return self\n\n\nclass Final(ABCMeta):\n \"\"\"Metaclass that enforces a class to be final (i.e., subclass not allowed).\n\n If a class uses this metaclass like this::\n\n class SomeClass(metaclass=Final):\n pass\n\n The metaclass will ensure that no sub class can be created.\n\n Raises\n ------\n - TypeError if a sub class is created\n \"\"\"\n\n def __new__(cls, name, bases, cls_namespace):\n for base in bases:\n if isinstance(base, Final):\n raise TypeError(\n f\"{base.__module__}.{base.__qualname__} does not support subclassing\"\n )\n return super().__new__(cls, name, bases, cls_namespace)\n\n\nT = t.TypeVar(\"T\")\n\n\nclass NoPublicConstructor(Final):\n \"\"\"Metaclass that enforces a class to be final (i.e., subclass not allowed)\n and ensures a private constructor.\n\n If a class uses this metaclass like this::\n\n class SomeClass(metaclass=NoPublicConstructor):\n pass\n\n The metaclass will ensure that no sub class can be created, and that no instance\n can be initialized.\n\n If you try to instantiate your class (SomeClass()), a TypeError will be thrown.\n\n Raises\n ------\n - TypeError if a sub class or an instance is created.\n \"\"\"\n\n def __call__(cls, *args, **kwargs):\n raise TypeError(\n f\"{cls.__module__}.{cls.__qualname__} has no public constructor\"\n )\n\n def _create(cls: t.Type[T], *args: t.Any, **kwargs: t.Any) -> T:\n return super().__call__(*args, **kwargs) # type: ignore\n\n\ndef name_asyncgen(agen):\n \"\"\"Return the fully-qualified name of the async generator function\n that produced the async generator iterator *agen*.\n \"\"\"\n if not hasattr(agen, \"ag_code\"): # pragma: no cover\n return repr(agen)\n try:\n module = agen.ag_frame.f_globals[\"__name__\"]\n except (AttributeError, KeyError):\n module = \"<{}>\".format(agen.ag_code.co_filename)\n try:\n qualname = agen.__qualname__\n except AttributeError:\n qualname = agen.ag_code.co_name\n return f\"{module}.{qualname}\"\n", "path": "trio/_util.py" } ]
diff --git a/newsfragments/2333.bugfix.rst b/newsfragments/2333.bugfix.rst new file mode 100644 index 000000000..efab3a638 --- /dev/null +++ b/newsfragments/2333.bugfix.rst @@ -0,0 +1 @@ +Python raises a `TypeError` if you try to (re-)install a C signal handler. diff --git a/trio/_util.py b/trio/_util.py index b5a2ceabd..539fc540f 100644 --- a/trio/_util.py +++ b/trio/_util.py @@ -78,7 +78,7 @@ def is_main_thread(): try: signal.signal(signal.SIGINT, signal.getsignal(signal.SIGINT)) return True - except ValueError: + except (TypeError, ValueError): return False
docker__docker-py-806
Auth fails with long passwords See https://github.com/docker/docker/issues/16840 docker-py is encoding `X-Registry-Auth` with regular base64 and not the url safe version of base64 that jwt tokens use.
[ { "content": "# Copyright 2013 dotCloud inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport base64\nimport fileinput\nimport json\nimport logging\nimport os\nimport warnings\n\nimport six\n\nfrom .. import constants\nfrom .. import errors\n\nINDEX_NAME = 'index.docker.io'\nINDEX_URL = 'https://{0}/v1/'.format(INDEX_NAME)\nDOCKER_CONFIG_FILENAME = os.path.join('.docker', 'config.json')\nLEGACY_DOCKER_CONFIG_FILENAME = '.dockercfg'\n\nlog = logging.getLogger(__name__)\n\n\ndef resolve_repository_name(repo_name, insecure=False):\n if insecure:\n warnings.warn(\n constants.INSECURE_REGISTRY_DEPRECATION_WARNING.format(\n 'resolve_repository_name()'\n ), DeprecationWarning\n )\n\n if '://' in repo_name:\n raise errors.InvalidRepository(\n 'Repository name cannot contain a scheme ({0})'.format(repo_name))\n parts = repo_name.split('/', 1)\n if '.' not in parts[0] and ':' not in parts[0] and parts[0] != 'localhost':\n # This is a docker index repo (ex: foo/bar or ubuntu)\n return INDEX_NAME, repo_name\n if len(parts) < 2:\n raise errors.InvalidRepository(\n 'Invalid repository name ({0})'.format(repo_name))\n\n if 'index.docker.io' in parts[0]:\n raise errors.InvalidRepository(\n 'Invalid repository name, try \"{0}\" instead'.format(parts[1])\n )\n\n return parts[0], parts[1]\n\n\ndef resolve_authconfig(authconfig, registry=None):\n \"\"\"\n Returns the authentication data from the given auth configuration for a\n specific registry. As with the Docker client, legacy entries in the config\n with full URLs are stripped down to hostnames before checking for a match.\n Returns None if no match was found.\n \"\"\"\n # Default to the public index server\n registry = convert_to_hostname(registry) if registry else INDEX_NAME\n log.debug(\"Looking for auth entry for {0}\".format(repr(registry)))\n\n if registry in authconfig:\n log.debug(\"Found {0}\".format(repr(registry)))\n return authconfig[registry]\n\n for key, config in six.iteritems(authconfig):\n if convert_to_hostname(key) == registry:\n log.debug(\"Found {0}\".format(repr(key)))\n return config\n\n log.debug(\"No entry found\")\n return None\n\n\ndef convert_to_hostname(url):\n return url.replace('http://', '').replace('https://', '').split('/', 1)[0]\n\n\ndef encode_auth(auth_info):\n return base64.b64encode(auth_info.get('username', '') + b':' +\n auth_info.get('password', ''))\n\n\ndef decode_auth(auth):\n if isinstance(auth, six.string_types):\n auth = auth.encode('ascii')\n s = base64.b64decode(auth)\n login, pwd = s.split(b':', 1)\n return login.decode('ascii'), pwd.decode('ascii')\n\n\ndef encode_header(auth):\n auth_json = json.dumps(auth).encode('ascii')\n return base64.b64encode(auth_json)\n\n\ndef parse_auth(entries):\n \"\"\"\n Parses authentication entries\n\n Args:\n entries: Dict of authentication entries.\n\n Returns:\n Authentication registry.\n \"\"\"\n\n conf = {}\n for registry, entry in six.iteritems(entries):\n username, password = decode_auth(entry['auth'])\n log.debug(\n 'Found entry (registry={0}, username={1})'\n .format(repr(registry), repr(username))\n )\n conf[registry] = {\n 'username': username,\n 'password': password,\n 'email': entry['email'],\n 'serveraddress': registry,\n }\n return conf\n\n\ndef load_config(config_path=None):\n \"\"\"\n Loads authentication data from a Docker configuration file in the given\n root directory or if config_path is passed use given path.\n \"\"\"\n conf = {}\n data = None\n\n # Prefer ~/.docker/config.json.\n config_file = config_path or os.path.join(os.path.expanduser('~'),\n DOCKER_CONFIG_FILENAME)\n\n log.debug(\"Trying {0}\".format(config_file))\n\n if os.path.exists(config_file):\n try:\n with open(config_file) as f:\n for section, data in six.iteritems(json.load(f)):\n if section != 'auths':\n continue\n log.debug(\"Found 'auths' section\")\n return parse_auth(data)\n log.debug(\"Couldn't find 'auths' section\")\n except (IOError, KeyError, ValueError) as e:\n # Likely missing new Docker config file or it's in an\n # unknown format, continue to attempt to read old location\n # and format.\n log.debug(e)\n pass\n else:\n log.debug(\"File doesn't exist\")\n\n config_file = config_path or os.path.join(os.path.expanduser('~'),\n LEGACY_DOCKER_CONFIG_FILENAME)\n\n log.debug(\"Trying {0}\".format(config_file))\n\n if not os.path.exists(config_file):\n log.debug(\"File doesn't exist - returning empty config\")\n return {}\n\n log.debug(\"Attempting to parse as JSON\")\n try:\n with open(config_file) as f:\n return parse_auth(json.load(f))\n except Exception as e:\n log.debug(e)\n pass\n\n # If that fails, we assume the configuration file contains a single\n # authentication token for the public registry in the following format:\n #\n # auth = AUTH_TOKEN\n # email = [email protected]\n log.debug(\"Attempting to parse legacy auth file format\")\n try:\n data = []\n for line in fileinput.input(config_file):\n data.append(line.strip().split(' = ')[1])\n if len(data) < 2:\n # Not enough data\n raise errors.InvalidConfigFile(\n 'Invalid or empty configuration file!')\n\n username, password = decode_auth(data[0])\n conf[INDEX_NAME] = {\n 'username': username,\n 'password': password,\n 'email': data[1],\n 'serveraddress': INDEX_URL,\n }\n return conf\n except Exception as e:\n log.debug(e)\n pass\n\n log.debug(\"All parsing attempts failed - returning empty config\")\n return {}\n", "path": "docker/auth/auth.py" } ]
[ { "content": "# Copyright 2013 dotCloud inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport base64\nimport fileinput\nimport json\nimport logging\nimport os\nimport warnings\n\nimport six\n\nfrom .. import constants\nfrom .. import errors\n\nINDEX_NAME = 'index.docker.io'\nINDEX_URL = 'https://{0}/v1/'.format(INDEX_NAME)\nDOCKER_CONFIG_FILENAME = os.path.join('.docker', 'config.json')\nLEGACY_DOCKER_CONFIG_FILENAME = '.dockercfg'\n\nlog = logging.getLogger(__name__)\n\n\ndef resolve_repository_name(repo_name, insecure=False):\n if insecure:\n warnings.warn(\n constants.INSECURE_REGISTRY_DEPRECATION_WARNING.format(\n 'resolve_repository_name()'\n ), DeprecationWarning\n )\n\n if '://' in repo_name:\n raise errors.InvalidRepository(\n 'Repository name cannot contain a scheme ({0})'.format(repo_name))\n parts = repo_name.split('/', 1)\n if '.' not in parts[0] and ':' not in parts[0] and parts[0] != 'localhost':\n # This is a docker index repo (ex: foo/bar or ubuntu)\n return INDEX_NAME, repo_name\n if len(parts) < 2:\n raise errors.InvalidRepository(\n 'Invalid repository name ({0})'.format(repo_name))\n\n if 'index.docker.io' in parts[0]:\n raise errors.InvalidRepository(\n 'Invalid repository name, try \"{0}\" instead'.format(parts[1])\n )\n\n return parts[0], parts[1]\n\n\ndef resolve_authconfig(authconfig, registry=None):\n \"\"\"\n Returns the authentication data from the given auth configuration for a\n specific registry. As with the Docker client, legacy entries in the config\n with full URLs are stripped down to hostnames before checking for a match.\n Returns None if no match was found.\n \"\"\"\n # Default to the public index server\n registry = convert_to_hostname(registry) if registry else INDEX_NAME\n log.debug(\"Looking for auth entry for {0}\".format(repr(registry)))\n\n if registry in authconfig:\n log.debug(\"Found {0}\".format(repr(registry)))\n return authconfig[registry]\n\n for key, config in six.iteritems(authconfig):\n if convert_to_hostname(key) == registry:\n log.debug(\"Found {0}\".format(repr(key)))\n return config\n\n log.debug(\"No entry found\")\n return None\n\n\ndef convert_to_hostname(url):\n return url.replace('http://', '').replace('https://', '').split('/', 1)[0]\n\n\ndef encode_auth(auth_info):\n return base64.b64encode(auth_info.get('username', '') + b':' +\n auth_info.get('password', ''))\n\n\ndef decode_auth(auth):\n if isinstance(auth, six.string_types):\n auth = auth.encode('ascii')\n s = base64.b64decode(auth)\n login, pwd = s.split(b':', 1)\n return login.decode('ascii'), pwd.decode('ascii')\n\n\ndef encode_header(auth):\n auth_json = json.dumps(auth).encode('ascii')\n return base64.urlsafe_b64encode(auth_json)\n\n\ndef parse_auth(entries):\n \"\"\"\n Parses authentication entries\n\n Args:\n entries: Dict of authentication entries.\n\n Returns:\n Authentication registry.\n \"\"\"\n\n conf = {}\n for registry, entry in six.iteritems(entries):\n username, password = decode_auth(entry['auth'])\n log.debug(\n 'Found entry (registry={0}, username={1})'\n .format(repr(registry), repr(username))\n )\n conf[registry] = {\n 'username': username,\n 'password': password,\n 'email': entry['email'],\n 'serveraddress': registry,\n }\n return conf\n\n\ndef load_config(config_path=None):\n \"\"\"\n Loads authentication data from a Docker configuration file in the given\n root directory or if config_path is passed use given path.\n \"\"\"\n conf = {}\n data = None\n\n # Prefer ~/.docker/config.json.\n config_file = config_path or os.path.join(os.path.expanduser('~'),\n DOCKER_CONFIG_FILENAME)\n\n log.debug(\"Trying {0}\".format(config_file))\n\n if os.path.exists(config_file):\n try:\n with open(config_file) as f:\n for section, data in six.iteritems(json.load(f)):\n if section != 'auths':\n continue\n log.debug(\"Found 'auths' section\")\n return parse_auth(data)\n log.debug(\"Couldn't find 'auths' section\")\n except (IOError, KeyError, ValueError) as e:\n # Likely missing new Docker config file or it's in an\n # unknown format, continue to attempt to read old location\n # and format.\n log.debug(e)\n pass\n else:\n log.debug(\"File doesn't exist\")\n\n config_file = config_path or os.path.join(os.path.expanduser('~'),\n LEGACY_DOCKER_CONFIG_FILENAME)\n\n log.debug(\"Trying {0}\".format(config_file))\n\n if not os.path.exists(config_file):\n log.debug(\"File doesn't exist - returning empty config\")\n return {}\n\n log.debug(\"Attempting to parse as JSON\")\n try:\n with open(config_file) as f:\n return parse_auth(json.load(f))\n except Exception as e:\n log.debug(e)\n pass\n\n # If that fails, we assume the configuration file contains a single\n # authentication token for the public registry in the following format:\n #\n # auth = AUTH_TOKEN\n # email = [email protected]\n log.debug(\"Attempting to parse legacy auth file format\")\n try:\n data = []\n for line in fileinput.input(config_file):\n data.append(line.strip().split(' = ')[1])\n if len(data) < 2:\n # Not enough data\n raise errors.InvalidConfigFile(\n 'Invalid or empty configuration file!')\n\n username, password = decode_auth(data[0])\n conf[INDEX_NAME] = {\n 'username': username,\n 'password': password,\n 'email': data[1],\n 'serveraddress': INDEX_URL,\n }\n return conf\n except Exception as e:\n log.debug(e)\n pass\n\n log.debug(\"All parsing attempts failed - returning empty config\")\n return {}\n", "path": "docker/auth/auth.py" } ]
diff --git a/docker/auth/auth.py b/docker/auth/auth.py index 366bc67e9..1ee9f8125 100644 --- a/docker/auth/auth.py +++ b/docker/auth/auth.py @@ -102,7 +102,7 @@ def decode_auth(auth): def encode_header(auth): auth_json = json.dumps(auth).encode('ascii') - return base64.b64encode(auth_json) + return base64.urlsafe_b64encode(auth_json) def parse_auth(entries): diff --git a/tests/utils_test.py b/tests/utils_test.py index b1adde267..04183f9f8 100644 --- a/tests/utils_test.py +++ b/tests/utils_test.py @@ -19,7 +19,9 @@ exclude_paths, convert_volume_binds, decode_json_header ) from docker.utils.ports import build_port_bindings, split_port -from docker.auth import resolve_repository_name, resolve_authconfig +from docker.auth import ( + resolve_repository_name, resolve_authconfig, encode_header +) from . import base from .helpers import make_tree @@ -376,12 +378,21 @@ def test_decode_json_header(self): obj = {'a': 'b', 'c': 1} data = None if six.PY3: - data = base64.b64encode(bytes(json.dumps(obj), 'utf-8')) + data = base64.urlsafe_b64encode(bytes(json.dumps(obj), 'utf-8')) else: - data = base64.b64encode(json.dumps(obj)) + data = base64.urlsafe_b64encode(json.dumps(obj)) decoded_data = decode_json_header(data) self.assertEqual(obj, decoded_data) + def test_803_urlsafe_encode(self): + auth_data = { + 'username': 'root', + 'password': 'GR?XGR?XGR?XGR?X' + } + encoded = encode_header(auth_data) + assert b'/' not in encoded + assert b'_' in encoded + def test_resolve_repository_name(self): # docker hub library image self.assertEqual(
tornadoweb__tornado-2649
LogFormatter DEFAULT_COLORS needs a logging.CRITICAL entry https://github.com/tornadoweb/tornado/blob/c447875a1058f4768c2995ddd9ebb0eaddd3f32e/tornado/log.py#L108-L113 Currently `ERROR` will log red, but `CRITICAL` will log in white. Curses `COLOR_MAGENTA` (5) is probably the best option.
[ { "content": "#\n# Copyright 2012 Facebook\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\"\"\"Logging support for Tornado.\n\nTornado uses three logger streams:\n\n* ``tornado.access``: Per-request logging for Tornado's HTTP servers (and\n potentially other servers in the future)\n* ``tornado.application``: Logging of errors from application code (i.e.\n uncaught exceptions from callbacks)\n* ``tornado.general``: General-purpose logging, including any errors\n or warnings from Tornado itself.\n\nThese streams may be configured independently using the standard library's\n`logging` module. For example, you may wish to send ``tornado.access`` logs\nto a separate file for analysis.\n\"\"\"\nimport logging\nimport logging.handlers\nimport sys\n\nfrom tornado.escape import _unicode\nfrom tornado.util import unicode_type, basestring_type\n\ntry:\n import colorama # type: ignore\nexcept ImportError:\n colorama = None\n\ntry:\n import curses\nexcept ImportError:\n curses = None # type: ignore\n\nfrom typing import Dict, Any, cast\n\n# Logger objects for internal tornado use\naccess_log = logging.getLogger(\"tornado.access\")\napp_log = logging.getLogger(\"tornado.application\")\ngen_log = logging.getLogger(\"tornado.general\")\n\n\ndef _stderr_supports_color() -> bool:\n try:\n if hasattr(sys.stderr, \"isatty\") and sys.stderr.isatty():\n if curses:\n curses.setupterm()\n if curses.tigetnum(\"colors\") > 0:\n return True\n elif colorama:\n if sys.stderr is getattr(\n colorama.initialise, \"wrapped_stderr\", object()\n ):\n return True\n except Exception:\n # Very broad exception handling because it's always better to\n # fall back to non-colored logs than to break at startup.\n pass\n return False\n\n\ndef _safe_unicode(s: Any) -> str:\n try:\n return _unicode(s)\n except UnicodeDecodeError:\n return repr(s)\n\n\nclass LogFormatter(logging.Formatter):\n \"\"\"Log formatter used in Tornado.\n\n Key features of this formatter are:\n\n * Color support when logging to a terminal that supports it.\n * Timestamps on every log line.\n * Robust against str/bytes encoding problems.\n\n This formatter is enabled automatically by\n `tornado.options.parse_command_line` or `tornado.options.parse_config_file`\n (unless ``--logging=none`` is used).\n\n Color support on Windows versions that do not support ANSI color codes is\n enabled by use of the colorama__ library. Applications that wish to use\n this must first initialize colorama with a call to ``colorama.init``.\n See the colorama documentation for details.\n\n __ https://pypi.python.org/pypi/colorama\n\n .. versionchanged:: 4.5\n Added support for ``colorama``. Changed the constructor\n signature to be compatible with `logging.config.dictConfig`.\n \"\"\"\n\n DEFAULT_FORMAT = \"%(color)s[%(levelname)1.1s %(asctime)s %(module)s:%(lineno)d]%(end_color)s %(message)s\" # noqa: E501\n DEFAULT_DATE_FORMAT = \"%y%m%d %H:%M:%S\"\n DEFAULT_COLORS = {\n logging.DEBUG: 4, # Blue\n logging.INFO: 2, # Green\n logging.WARNING: 3, # Yellow\n logging.ERROR: 1, # Red\n }\n\n def __init__(\n self,\n fmt: str = DEFAULT_FORMAT,\n datefmt: str = DEFAULT_DATE_FORMAT,\n style: str = \"%\",\n color: bool = True,\n colors: Dict[int, int] = DEFAULT_COLORS,\n ) -> None:\n r\"\"\"\n :arg bool color: Enables color support.\n :arg str fmt: Log message format.\n It will be applied to the attributes dict of log records. The\n text between ``%(color)s`` and ``%(end_color)s`` will be colored\n depending on the level if color support is on.\n :arg dict colors: color mappings from logging level to terminal color\n code\n :arg str datefmt: Datetime format.\n Used for formatting ``(asctime)`` placeholder in ``prefix_fmt``.\n\n .. versionchanged:: 3.2\n\n Added ``fmt`` and ``datefmt`` arguments.\n \"\"\"\n logging.Formatter.__init__(self, datefmt=datefmt)\n self._fmt = fmt\n\n self._colors = {} # type: Dict[int, str]\n if color and _stderr_supports_color():\n if curses is not None:\n fg_color = curses.tigetstr(\"setaf\") or curses.tigetstr(\"setf\") or b\"\"\n\n for levelno, code in colors.items():\n # Convert the terminal control characters from\n # bytes to unicode strings for easier use with the\n # logging module.\n self._colors[levelno] = unicode_type(\n curses.tparm(fg_color, code), \"ascii\"\n )\n self._normal = unicode_type(curses.tigetstr(\"sgr0\"), \"ascii\")\n else:\n # If curses is not present (currently we'll only get here for\n # colorama on windows), assume hard-coded ANSI color codes.\n for levelno, code in colors.items():\n self._colors[levelno] = \"\\033[2;3%dm\" % code\n self._normal = \"\\033[0m\"\n else:\n self._normal = \"\"\n\n def format(self, record: Any) -> str:\n try:\n message = record.getMessage()\n assert isinstance(message, basestring_type) # guaranteed by logging\n # Encoding notes: The logging module prefers to work with character\n # strings, but only enforces that log messages are instances of\n # basestring. In python 2, non-ascii bytestrings will make\n # their way through the logging framework until they blow up with\n # an unhelpful decoding error (with this formatter it happens\n # when we attach the prefix, but there are other opportunities for\n # exceptions further along in the framework).\n #\n # If a byte string makes it this far, convert it to unicode to\n # ensure it will make it out to the logs. Use repr() as a fallback\n # to ensure that all byte strings can be converted successfully,\n # but don't do it by default so we don't add extra quotes to ascii\n # bytestrings. This is a bit of a hacky place to do this, but\n # it's worth it since the encoding errors that would otherwise\n # result are so useless (and tornado is fond of using utf8-encoded\n # byte strings wherever possible).\n record.message = _safe_unicode(message)\n except Exception as e:\n record.message = \"Bad message (%r): %r\" % (e, record.__dict__)\n\n record.asctime = self.formatTime(record, cast(str, self.datefmt))\n\n if record.levelno in self._colors:\n record.color = self._colors[record.levelno]\n record.end_color = self._normal\n else:\n record.color = record.end_color = \"\"\n\n formatted = self._fmt % record.__dict__\n\n if record.exc_info:\n if not record.exc_text:\n record.exc_text = self.formatException(record.exc_info)\n if record.exc_text:\n # exc_text contains multiple lines. We need to _safe_unicode\n # each line separately so that non-utf8 bytes don't cause\n # all the newlines to turn into '\\n'.\n lines = [formatted.rstrip()]\n lines.extend(_safe_unicode(ln) for ln in record.exc_text.split(\"\\n\"))\n formatted = \"\\n\".join(lines)\n return formatted.replace(\"\\n\", \"\\n \")\n\n\ndef enable_pretty_logging(options: Any = None, logger: logging.Logger = None) -> None:\n \"\"\"Turns on formatted logging output as configured.\n\n This is called automatically by `tornado.options.parse_command_line`\n and `tornado.options.parse_config_file`.\n \"\"\"\n if options is None:\n import tornado.options\n\n options = tornado.options.options\n if options.logging is None or options.logging.lower() == \"none\":\n return\n if logger is None:\n logger = logging.getLogger()\n logger.setLevel(getattr(logging, options.logging.upper()))\n if options.log_file_prefix:\n rotate_mode = options.log_rotate_mode\n if rotate_mode == \"size\":\n channel = logging.handlers.RotatingFileHandler(\n filename=options.log_file_prefix,\n maxBytes=options.log_file_max_size,\n backupCount=options.log_file_num_backups,\n encoding=\"utf-8\",\n ) # type: logging.Handler\n elif rotate_mode == \"time\":\n channel = logging.handlers.TimedRotatingFileHandler(\n filename=options.log_file_prefix,\n when=options.log_rotate_when,\n interval=options.log_rotate_interval,\n backupCount=options.log_file_num_backups,\n encoding=\"utf-8\",\n )\n else:\n error_message = (\n \"The value of log_rotate_mode option should be \"\n + '\"size\" or \"time\", not \"%s\".' % rotate_mode\n )\n raise ValueError(error_message)\n channel.setFormatter(LogFormatter(color=False))\n logger.addHandler(channel)\n\n if options.log_to_stderr or (options.log_to_stderr is None and not logger.handlers):\n # Set up color if we are in a tty and curses is installed\n channel = logging.StreamHandler()\n channel.setFormatter(LogFormatter())\n logger.addHandler(channel)\n\n\ndef define_logging_options(options: Any = None) -> None:\n \"\"\"Add logging-related flags to ``options``.\n\n These options are present automatically on the default options instance;\n this method is only necessary if you have created your own `.OptionParser`.\n\n .. versionadded:: 4.2\n This function existed in prior versions but was broken and undocumented until 4.2.\n \"\"\"\n if options is None:\n # late import to prevent cycle\n import tornado.options\n\n options = tornado.options.options\n options.define(\n \"logging\",\n default=\"info\",\n help=(\n \"Set the Python log level. If 'none', tornado won't touch the \"\n \"logging configuration.\"\n ),\n metavar=\"debug|info|warning|error|none\",\n )\n options.define(\n \"log_to_stderr\",\n type=bool,\n default=None,\n help=(\n \"Send log output to stderr (colorized if possible). \"\n \"By default use stderr if --log_file_prefix is not set and \"\n \"no other logging is configured.\"\n ),\n )\n options.define(\n \"log_file_prefix\",\n type=str,\n default=None,\n metavar=\"PATH\",\n help=(\n \"Path prefix for log files. \"\n \"Note that if you are running multiple tornado processes, \"\n \"log_file_prefix must be different for each of them (e.g. \"\n \"include the port number)\"\n ),\n )\n options.define(\n \"log_file_max_size\",\n type=int,\n default=100 * 1000 * 1000,\n help=\"max size of log files before rollover\",\n )\n options.define(\n \"log_file_num_backups\", type=int, default=10, help=\"number of log files to keep\"\n )\n\n options.define(\n \"log_rotate_when\",\n type=str,\n default=\"midnight\",\n help=(\n \"specify the type of TimedRotatingFileHandler interval \"\n \"other options:('S', 'M', 'H', 'D', 'W0'-'W6')\"\n ),\n )\n options.define(\n \"log_rotate_interval\",\n type=int,\n default=1,\n help=\"The interval value of timed rotating\",\n )\n\n options.define(\n \"log_rotate_mode\",\n type=str,\n default=\"size\",\n help=\"The mode of rotating files(time or size)\",\n )\n\n options.add_parse_callback(lambda: enable_pretty_logging(options))\n", "path": "tornado/log.py" } ]
[ { "content": "#\n# Copyright 2012 Facebook\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\"\"\"Logging support for Tornado.\n\nTornado uses three logger streams:\n\n* ``tornado.access``: Per-request logging for Tornado's HTTP servers (and\n potentially other servers in the future)\n* ``tornado.application``: Logging of errors from application code (i.e.\n uncaught exceptions from callbacks)\n* ``tornado.general``: General-purpose logging, including any errors\n or warnings from Tornado itself.\n\nThese streams may be configured independently using the standard library's\n`logging` module. For example, you may wish to send ``tornado.access`` logs\nto a separate file for analysis.\n\"\"\"\nimport logging\nimport logging.handlers\nimport sys\n\nfrom tornado.escape import _unicode\nfrom tornado.util import unicode_type, basestring_type\n\ntry:\n import colorama # type: ignore\nexcept ImportError:\n colorama = None\n\ntry:\n import curses\nexcept ImportError:\n curses = None # type: ignore\n\nfrom typing import Dict, Any, cast\n\n# Logger objects for internal tornado use\naccess_log = logging.getLogger(\"tornado.access\")\napp_log = logging.getLogger(\"tornado.application\")\ngen_log = logging.getLogger(\"tornado.general\")\n\n\ndef _stderr_supports_color() -> bool:\n try:\n if hasattr(sys.stderr, \"isatty\") and sys.stderr.isatty():\n if curses:\n curses.setupterm()\n if curses.tigetnum(\"colors\") > 0:\n return True\n elif colorama:\n if sys.stderr is getattr(\n colorama.initialise, \"wrapped_stderr\", object()\n ):\n return True\n except Exception:\n # Very broad exception handling because it's always better to\n # fall back to non-colored logs than to break at startup.\n pass\n return False\n\n\ndef _safe_unicode(s: Any) -> str:\n try:\n return _unicode(s)\n except UnicodeDecodeError:\n return repr(s)\n\n\nclass LogFormatter(logging.Formatter):\n \"\"\"Log formatter used in Tornado.\n\n Key features of this formatter are:\n\n * Color support when logging to a terminal that supports it.\n * Timestamps on every log line.\n * Robust against str/bytes encoding problems.\n\n This formatter is enabled automatically by\n `tornado.options.parse_command_line` or `tornado.options.parse_config_file`\n (unless ``--logging=none`` is used).\n\n Color support on Windows versions that do not support ANSI color codes is\n enabled by use of the colorama__ library. Applications that wish to use\n this must first initialize colorama with a call to ``colorama.init``.\n See the colorama documentation for details.\n\n __ https://pypi.python.org/pypi/colorama\n\n .. versionchanged:: 4.5\n Added support for ``colorama``. Changed the constructor\n signature to be compatible with `logging.config.dictConfig`.\n \"\"\"\n\n DEFAULT_FORMAT = \"%(color)s[%(levelname)1.1s %(asctime)s %(module)s:%(lineno)d]%(end_color)s %(message)s\" # noqa: E501\n DEFAULT_DATE_FORMAT = \"%y%m%d %H:%M:%S\"\n DEFAULT_COLORS = {\n logging.DEBUG: 4, # Blue\n logging.INFO: 2, # Green\n logging.WARNING: 3, # Yellow\n logging.ERROR: 1, # Red\n logging.CRITICAL: 5, # Magenta\n }\n\n def __init__(\n self,\n fmt: str = DEFAULT_FORMAT,\n datefmt: str = DEFAULT_DATE_FORMAT,\n style: str = \"%\",\n color: bool = True,\n colors: Dict[int, int] = DEFAULT_COLORS,\n ) -> None:\n r\"\"\"\n :arg bool color: Enables color support.\n :arg str fmt: Log message format.\n It will be applied to the attributes dict of log records. The\n text between ``%(color)s`` and ``%(end_color)s`` will be colored\n depending on the level if color support is on.\n :arg dict colors: color mappings from logging level to terminal color\n code\n :arg str datefmt: Datetime format.\n Used for formatting ``(asctime)`` placeholder in ``prefix_fmt``.\n\n .. versionchanged:: 3.2\n\n Added ``fmt`` and ``datefmt`` arguments.\n \"\"\"\n logging.Formatter.__init__(self, datefmt=datefmt)\n self._fmt = fmt\n\n self._colors = {} # type: Dict[int, str]\n if color and _stderr_supports_color():\n if curses is not None:\n fg_color = curses.tigetstr(\"setaf\") or curses.tigetstr(\"setf\") or b\"\"\n\n for levelno, code in colors.items():\n # Convert the terminal control characters from\n # bytes to unicode strings for easier use with the\n # logging module.\n self._colors[levelno] = unicode_type(\n curses.tparm(fg_color, code), \"ascii\"\n )\n self._normal = unicode_type(curses.tigetstr(\"sgr0\"), \"ascii\")\n else:\n # If curses is not present (currently we'll only get here for\n # colorama on windows), assume hard-coded ANSI color codes.\n for levelno, code in colors.items():\n self._colors[levelno] = \"\\033[2;3%dm\" % code\n self._normal = \"\\033[0m\"\n else:\n self._normal = \"\"\n\n def format(self, record: Any) -> str:\n try:\n message = record.getMessage()\n assert isinstance(message, basestring_type) # guaranteed by logging\n # Encoding notes: The logging module prefers to work with character\n # strings, but only enforces that log messages are instances of\n # basestring. In python 2, non-ascii bytestrings will make\n # their way through the logging framework until they blow up with\n # an unhelpful decoding error (with this formatter it happens\n # when we attach the prefix, but there are other opportunities for\n # exceptions further along in the framework).\n #\n # If a byte string makes it this far, convert it to unicode to\n # ensure it will make it out to the logs. Use repr() as a fallback\n # to ensure that all byte strings can be converted successfully,\n # but don't do it by default so we don't add extra quotes to ascii\n # bytestrings. This is a bit of a hacky place to do this, but\n # it's worth it since the encoding errors that would otherwise\n # result are so useless (and tornado is fond of using utf8-encoded\n # byte strings wherever possible).\n record.message = _safe_unicode(message)\n except Exception as e:\n record.message = \"Bad message (%r): %r\" % (e, record.__dict__)\n\n record.asctime = self.formatTime(record, cast(str, self.datefmt))\n\n if record.levelno in self._colors:\n record.color = self._colors[record.levelno]\n record.end_color = self._normal\n else:\n record.color = record.end_color = \"\"\n\n formatted = self._fmt % record.__dict__\n\n if record.exc_info:\n if not record.exc_text:\n record.exc_text = self.formatException(record.exc_info)\n if record.exc_text:\n # exc_text contains multiple lines. We need to _safe_unicode\n # each line separately so that non-utf8 bytes don't cause\n # all the newlines to turn into '\\n'.\n lines = [formatted.rstrip()]\n lines.extend(_safe_unicode(ln) for ln in record.exc_text.split(\"\\n\"))\n formatted = \"\\n\".join(lines)\n return formatted.replace(\"\\n\", \"\\n \")\n\n\ndef enable_pretty_logging(options: Any = None, logger: logging.Logger = None) -> None:\n \"\"\"Turns on formatted logging output as configured.\n\n This is called automatically by `tornado.options.parse_command_line`\n and `tornado.options.parse_config_file`.\n \"\"\"\n if options is None:\n import tornado.options\n\n options = tornado.options.options\n if options.logging is None or options.logging.lower() == \"none\":\n return\n if logger is None:\n logger = logging.getLogger()\n logger.setLevel(getattr(logging, options.logging.upper()))\n if options.log_file_prefix:\n rotate_mode = options.log_rotate_mode\n if rotate_mode == \"size\":\n channel = logging.handlers.RotatingFileHandler(\n filename=options.log_file_prefix,\n maxBytes=options.log_file_max_size,\n backupCount=options.log_file_num_backups,\n encoding=\"utf-8\",\n ) # type: logging.Handler\n elif rotate_mode == \"time\":\n channel = logging.handlers.TimedRotatingFileHandler(\n filename=options.log_file_prefix,\n when=options.log_rotate_when,\n interval=options.log_rotate_interval,\n backupCount=options.log_file_num_backups,\n encoding=\"utf-8\",\n )\n else:\n error_message = (\n \"The value of log_rotate_mode option should be \"\n + '\"size\" or \"time\", not \"%s\".' % rotate_mode\n )\n raise ValueError(error_message)\n channel.setFormatter(LogFormatter(color=False))\n logger.addHandler(channel)\n\n if options.log_to_stderr or (options.log_to_stderr is None and not logger.handlers):\n # Set up color if we are in a tty and curses is installed\n channel = logging.StreamHandler()\n channel.setFormatter(LogFormatter())\n logger.addHandler(channel)\n\n\ndef define_logging_options(options: Any = None) -> None:\n \"\"\"Add logging-related flags to ``options``.\n\n These options are present automatically on the default options instance;\n this method is only necessary if you have created your own `.OptionParser`.\n\n .. versionadded:: 4.2\n This function existed in prior versions but was broken and undocumented until 4.2.\n \"\"\"\n if options is None:\n # late import to prevent cycle\n import tornado.options\n\n options = tornado.options.options\n options.define(\n \"logging\",\n default=\"info\",\n help=(\n \"Set the Python log level. If 'none', tornado won't touch the \"\n \"logging configuration.\"\n ),\n metavar=\"debug|info|warning|error|none\",\n )\n options.define(\n \"log_to_stderr\",\n type=bool,\n default=None,\n help=(\n \"Send log output to stderr (colorized if possible). \"\n \"By default use stderr if --log_file_prefix is not set and \"\n \"no other logging is configured.\"\n ),\n )\n options.define(\n \"log_file_prefix\",\n type=str,\n default=None,\n metavar=\"PATH\",\n help=(\n \"Path prefix for log files. \"\n \"Note that if you are running multiple tornado processes, \"\n \"log_file_prefix must be different for each of them (e.g. \"\n \"include the port number)\"\n ),\n )\n options.define(\n \"log_file_max_size\",\n type=int,\n default=100 * 1000 * 1000,\n help=\"max size of log files before rollover\",\n )\n options.define(\n \"log_file_num_backups\", type=int, default=10, help=\"number of log files to keep\"\n )\n\n options.define(\n \"log_rotate_when\",\n type=str,\n default=\"midnight\",\n help=(\n \"specify the type of TimedRotatingFileHandler interval \"\n \"other options:('S', 'M', 'H', 'D', 'W0'-'W6')\"\n ),\n )\n options.define(\n \"log_rotate_interval\",\n type=int,\n default=1,\n help=\"The interval value of timed rotating\",\n )\n\n options.define(\n \"log_rotate_mode\",\n type=str,\n default=\"size\",\n help=\"The mode of rotating files(time or size)\",\n )\n\n options.add_parse_callback(lambda: enable_pretty_logging(options))\n", "path": "tornado/log.py" } ]
diff --git a/tornado/log.py b/tornado/log.py index 435cd71858..e1f7c6a677 100644 --- a/tornado/log.py +++ b/tornado/log.py @@ -110,6 +110,7 @@ class LogFormatter(logging.Formatter): logging.INFO: 2, # Green logging.WARNING: 3, # Yellow logging.ERROR: 1, # Red + logging.CRITICAL: 5, # Magenta } def __init__(
ansible-collections__community.aws-990
Invalid import path for BotoCoreError in redshift_info module ### Summary In case of any AWS related error (like missing permissions) the module will throw a gigantic python stack trace with error summary as: ``` line 304, in find_clusters NameError: name 'BotoCoreError' is not defined ``` This is due to an invalid import path that is present in the module https://github.com/ansible-collections/community.aws/blob/main/plugins/modules/redshift_info.py#L280 Instead of `from botocore.exception` it should be `from botocore.exceptions`. Once that is done, ansible no longer hides the real error with the stack trace. ### Issue Type Bug Report ### Component Name redshift_info ### Ansible Version ```console (paste below) $ ansible --version ansible 2.10.8 config file = None configured module search path = ['/home/wojtek/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules'] ansible python module location = /usr/local/lib/python3.6/dist-packages/ansible executable location = /usr/local/bin/ansible python version = 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0] ``` ### Collection Versions Non-relevant ### AWS SDK versions ```console (paste below) $ pip show boto boto3 botocore Name: boto Version: 2.49.0 Summary: Amazon Web Services Library Home-page: https://github.com/boto/boto/ Author: Mitch Garnaat Author-email: [email protected] License: MIT Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: --- Name: boto3 Version: 1.20.54 Summary: The AWS SDK for Python Home-page: https://github.com/boto/boto3 Author: Amazon Web Services Author-email: None License: Apache License 2.0 Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: jmespath, s3transfer, botocore --- Name: botocore Version: 1.23.54 Summary: Low-level, data-driven core of boto 3. Home-page: https://github.com/boto/botocore Author: Amazon Web Services Author-email: None License: Apache License 2.0 Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: jmespath, urllib3, python-dateutil ``` ### Configuration ```console (paste below) $ ansible-config dump --only-changed ``` ### OS / Environment Ubuntu 20.04 ### Steps to Reproduce Run the module without DescribeClusters permission. ### Expected Results AWS API error on missing permissions is shown. ### Actual Results Python stack trace ending with ``` line 304, in find_clusters NameError: name 'BotoCoreError' is not defined ``` ### Code of Conduct - [X] I agree to follow the Ansible Code of Conduct
[ { "content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: redshift_info\nversion_added: 1.0.0\nauthor: \"Jens Carl (@j-carl)\"\nshort_description: Gather information about Redshift cluster(s)\ndescription:\n - Gather information about Redshift cluster(s).\noptions:\n cluster_identifier:\n description:\n - The prefix of cluster identifier of the Redshift cluster you are searching for.\n - \"This is a regular expression match with implicit '^'. Append '$' for a complete match.\"\n required: false\n aliases: ['name', 'identifier']\n type: str\n tags:\n description:\n - \"A dictionary/hash of tags in the format { tag1_name: 'tag1_value', tag2_name: 'tag2_value' }\n to match against the security group(s) you are searching for.\"\n required: false\n type: dict\nextends_documentation_fragment:\n- amazon.aws.ec2\n- amazon.aws.aws\n\n'''\n\nEXAMPLES = '''\n# Note: These examples do net set authentication details, see the AWS guide for details.\n\n- name: Find all clusters\n community.aws.redshift_info:\n register: redshift\n\n- name: Find cluster(s) with matching tags\n community.aws.redshift_info:\n tags:\n env: prd\n stack: monitoring\n register: redshift_tags\n\n- name: Find cluster(s) with matching name/prefix and tags\n community.aws.redshift_info:\n tags:\n env: dev\n stack: web\n name: user-\n register: redshift_web\n\n- name: Fail if no cluster(s) is/are found\n community.aws.redshift_info:\n tags:\n env: stg\n stack: db\n register: redshift_user\n failed_when: \"{{ redshift_user.results | length == 0 }}\"\n'''\n\nRETURN = '''\n# For more information see U(http://boto3.readthedocs.io/en/latest/reference/services/redshift.html#Redshift.Client.describe_clusters)\n---\ncluster_identifier:\n description: Unique key to identify the cluster.\n returned: success\n type: str\n sample: \"redshift-identifier\"\nnode_type:\n description: The node type for nodes in the cluster.\n returned: success\n type: str\n sample: \"ds2.xlarge\"\ncluster_status:\n description: Current state of the cluster.\n returned: success\n type: str\n sample: \"available\"\nmodify_status:\n description: The status of a modify operation.\n returned: optional\n type: str\n sample: \"\"\nmaster_username:\n description: The master user name for the cluster.\n returned: success\n type: str\n sample: \"admin\"\ndb_name:\n description: The name of the initial database that was created when the cluster was created.\n returned: success\n type: str\n sample: \"dev\"\nendpoint:\n description: The connection endpoint.\n returned: success\n type: str\n sample: {\n \"address\": \"cluster-ds2.ocmugla0rf.us-east-1.redshift.amazonaws.com\",\n \"port\": 5439\n }\ncluster_create_time:\n description: The date and time that the cluster was created.\n returned: success\n type: str\n sample: \"2016-05-10T08:33:16.629000+00:00\"\nautomated_snapshot_retention_period:\n description: The number of days that automatic cluster snapshots are retained.\n returned: success\n type: int\n sample: 1\ncluster_security_groups:\n description: A list of cluster security groups that are associated with the cluster.\n returned: success\n type: list\n sample: []\nvpc_security_groups:\n description: A list of VPC security groups the are associated with the cluster.\n returned: success\n type: list\n sample: [\n {\n \"status\": \"active\",\n \"vpc_security_group_id\": \"sg-12cghhg\"\n }\n ]\ncluster_paramater_groups:\n description: The list of cluster parameters that are associated with this cluster.\n returned: success\n type: list\n sample: [\n {\n \"cluster_parameter_status_list\": [\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"statement_timeout\"\n },\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"require_ssl\"\n }\n ],\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_group_name\": \"tuba\"\n }\n ]\ncluster_subnet_group_name:\n description: The name of the subnet group that is associated with the cluster.\n returned: success\n type: str\n sample: \"redshift-subnet\"\nvpc_id:\n description: The identifier of the VPC the cluster is in, if the cluster is in a VPC.\n returned: success\n type: str\n sample: \"vpc-1234567\"\navailability_zone:\n description: The name of the Availability Zone in which the cluster is located.\n returned: success\n type: str\n sample: \"us-east-1b\"\npreferred_maintenance_window:\n description: The weekly time range, in Universal Coordinated Time (UTC), during which system maintenance can occur.\n returned: success\n type: str\n sample: \"tue:07:30-tue:08:00\"\npending_modified_values:\n description: A value that, if present, indicates that changes to the cluster are pending.\n returned: success\n type: dict\n sample: {}\ncluster_version:\n description: The version ID of the Amazon Redshift engine that is running on the cluster.\n returned: success\n type: str\n sample: \"1.0\"\nallow_version_upgrade:\n description: >\n A Boolean value that, if true, indicates that major version upgrades will be applied\n automatically to the cluster during the maintenance window.\n returned: success\n type: bool\n sample: true|false\nnumber_of_nodes:\n description: The number of compute nodes in the cluster.\n returned: success\n type: int\n sample: 12\npublicly_accessible:\n description: A Boolean value that, if true , indicates that the cluster can be accessed from a public network.\n returned: success\n type: bool\n sample: true|false\nencrypted:\n description: Boolean value that, if true , indicates that data in the cluster is encrypted at rest.\n returned: success\n type: bool\n sample: true|false\nrestore_status:\n description: A value that describes the status of a cluster restore action.\n returned: success\n type: dict\n sample: {}\nhsm_status:\n description: >\n A value that reports whether the Amazon Redshift cluster has finished applying any hardware\n security module (HSM) settings changes specified in a modify cluster command.\n returned: success\n type: dict\n sample: {}\ncluster_snapshot_copy_status:\n description: A value that returns the destination region and retention period that are configured for cross-region snapshot copy.\n returned: success\n type: dict\n sample: {}\ncluster_public_keys:\n description: The public key for the cluster.\n returned: success\n type: str\n sample: \"ssh-rsa anjigfam Amazon-Redshift\\n\"\ncluster_nodes:\n description: The nodes in the cluster.\n returned: success\n type: list\n sample: [\n {\n \"node_role\": \"LEADER\",\n \"private_ip_address\": \"10.0.0.1\",\n \"public_ip_address\": \"x.x.x.x\"\n },\n {\n \"node_role\": \"COMPUTE-1\",\n \"private_ip_address\": \"10.0.0.3\",\n \"public_ip_address\": \"x.x.x.x\"\n }\n ]\nelastic_ip_status:\n description: The status of the elastic IP (EIP) address.\n returned: success\n type: dict\n sample: {}\ncluster_revision_number:\n description: The specific revision number of the database in the cluster.\n returned: success\n type: str\n sample: \"1231\"\ntags:\n description: The list of tags for the cluster.\n returned: success\n type: list\n sample: []\nkms_key_id:\n description: The AWS Key Management Service (AWS KMS) key ID of the encryption key used to encrypt data in the cluster.\n returned: success\n type: str\n sample: \"\"\nenhanced_vpc_routing:\n description: An option that specifies whether to create the cluster with enhanced VPC routing enabled.\n returned: success\n type: bool\n sample: true|false\niam_roles:\n description: List of IAM roles attached to the cluster.\n returned: success\n type: list\n sample: []\n'''\n\nimport re\n\ntry:\n from botocore.exception import BotoCoreError, ClientError\nexcept ImportError:\n pass # caught by AnsibleAWSModule\n\nfrom ansible_collections.amazon.aws.plugins.module_utils.core import AnsibleAWSModule\nfrom ansible_collections.amazon.aws.plugins.module_utils.ec2 import camel_dict_to_snake_dict\n\n\ndef match_tags(tags_to_match, cluster):\n for key, value in tags_to_match.items():\n for tag in cluster['Tags']:\n if key == tag['Key'] and value == tag['Value']:\n return True\n\n return False\n\n\ndef find_clusters(conn, module, identifier=None, tags=None):\n\n try:\n cluster_paginator = conn.get_paginator('describe_clusters')\n clusters = cluster_paginator.paginate().build_full_result()\n except (BotoCoreError, ClientError) as e:\n module.fail_json_aws(e, msg='Failed to fetch clusters.')\n\n matched_clusters = []\n\n if identifier is not None:\n identifier_prog = re.compile('^' + identifier)\n\n for cluster in clusters['Clusters']:\n\n matched_identifier = True\n if identifier:\n matched_identifier = identifier_prog.search(cluster['ClusterIdentifier'])\n\n matched_tags = True\n if tags:\n matched_tags = match_tags(tags, cluster)\n\n if matched_identifier and matched_tags:\n matched_clusters.append(camel_dict_to_snake_dict(cluster))\n\n return matched_clusters\n\n\ndef main():\n\n argument_spec = dict(\n cluster_identifier=dict(type='str', aliases=['identifier', 'name']),\n tags=dict(type='dict')\n )\n module = AnsibleAWSModule(\n argument_spec=argument_spec,\n supports_check_mode=True\n )\n\n cluster_identifier = module.params.get('cluster_identifier')\n cluster_tags = module.params.get('tags')\n\n redshift = module.client('redshift')\n\n results = find_clusters(redshift, module, identifier=cluster_identifier, tags=cluster_tags)\n module.exit_json(results=results)\n\n\nif __name__ == '__main__':\n main()\n", "path": "plugins/modules/redshift_info.py" } ]
[ { "content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: redshift_info\nversion_added: 1.0.0\nauthor: \"Jens Carl (@j-carl)\"\nshort_description: Gather information about Redshift cluster(s)\ndescription:\n - Gather information about Redshift cluster(s).\noptions:\n cluster_identifier:\n description:\n - The prefix of cluster identifier of the Redshift cluster you are searching for.\n - \"This is a regular expression match with implicit '^'. Append '$' for a complete match.\"\n required: false\n aliases: ['name', 'identifier']\n type: str\n tags:\n description:\n - \"A dictionary/hash of tags in the format { tag1_name: 'tag1_value', tag2_name: 'tag2_value' }\n to match against the security group(s) you are searching for.\"\n required: false\n type: dict\nextends_documentation_fragment:\n- amazon.aws.ec2\n- amazon.aws.aws\n\n'''\n\nEXAMPLES = '''\n# Note: These examples do net set authentication details, see the AWS guide for details.\n\n- name: Find all clusters\n community.aws.redshift_info:\n register: redshift\n\n- name: Find cluster(s) with matching tags\n community.aws.redshift_info:\n tags:\n env: prd\n stack: monitoring\n register: redshift_tags\n\n- name: Find cluster(s) with matching name/prefix and tags\n community.aws.redshift_info:\n tags:\n env: dev\n stack: web\n name: user-\n register: redshift_web\n\n- name: Fail if no cluster(s) is/are found\n community.aws.redshift_info:\n tags:\n env: stg\n stack: db\n register: redshift_user\n failed_when: \"{{ redshift_user.results | length == 0 }}\"\n'''\n\nRETURN = '''\n# For more information see U(http://boto3.readthedocs.io/en/latest/reference/services/redshift.html#Redshift.Client.describe_clusters)\n---\ncluster_identifier:\n description: Unique key to identify the cluster.\n returned: success\n type: str\n sample: \"redshift-identifier\"\nnode_type:\n description: The node type for nodes in the cluster.\n returned: success\n type: str\n sample: \"ds2.xlarge\"\ncluster_status:\n description: Current state of the cluster.\n returned: success\n type: str\n sample: \"available\"\nmodify_status:\n description: The status of a modify operation.\n returned: optional\n type: str\n sample: \"\"\nmaster_username:\n description: The master user name for the cluster.\n returned: success\n type: str\n sample: \"admin\"\ndb_name:\n description: The name of the initial database that was created when the cluster was created.\n returned: success\n type: str\n sample: \"dev\"\nendpoint:\n description: The connection endpoint.\n returned: success\n type: str\n sample: {\n \"address\": \"cluster-ds2.ocmugla0rf.us-east-1.redshift.amazonaws.com\",\n \"port\": 5439\n }\ncluster_create_time:\n description: The date and time that the cluster was created.\n returned: success\n type: str\n sample: \"2016-05-10T08:33:16.629000+00:00\"\nautomated_snapshot_retention_period:\n description: The number of days that automatic cluster snapshots are retained.\n returned: success\n type: int\n sample: 1\ncluster_security_groups:\n description: A list of cluster security groups that are associated with the cluster.\n returned: success\n type: list\n sample: []\nvpc_security_groups:\n description: A list of VPC security groups the are associated with the cluster.\n returned: success\n type: list\n sample: [\n {\n \"status\": \"active\",\n \"vpc_security_group_id\": \"sg-12cghhg\"\n }\n ]\ncluster_paramater_groups:\n description: The list of cluster parameters that are associated with this cluster.\n returned: success\n type: list\n sample: [\n {\n \"cluster_parameter_status_list\": [\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"statement_timeout\"\n },\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"require_ssl\"\n }\n ],\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_group_name\": \"tuba\"\n }\n ]\ncluster_subnet_group_name:\n description: The name of the subnet group that is associated with the cluster.\n returned: success\n type: str\n sample: \"redshift-subnet\"\nvpc_id:\n description: The identifier of the VPC the cluster is in, if the cluster is in a VPC.\n returned: success\n type: str\n sample: \"vpc-1234567\"\navailability_zone:\n description: The name of the Availability Zone in which the cluster is located.\n returned: success\n type: str\n sample: \"us-east-1b\"\npreferred_maintenance_window:\n description: The weekly time range, in Universal Coordinated Time (UTC), during which system maintenance can occur.\n returned: success\n type: str\n sample: \"tue:07:30-tue:08:00\"\npending_modified_values:\n description: A value that, if present, indicates that changes to the cluster are pending.\n returned: success\n type: dict\n sample: {}\ncluster_version:\n description: The version ID of the Amazon Redshift engine that is running on the cluster.\n returned: success\n type: str\n sample: \"1.0\"\nallow_version_upgrade:\n description: >\n A Boolean value that, if true, indicates that major version upgrades will be applied\n automatically to the cluster during the maintenance window.\n returned: success\n type: bool\n sample: true|false\nnumber_of_nodes:\n description: The number of compute nodes in the cluster.\n returned: success\n type: int\n sample: 12\npublicly_accessible:\n description: A Boolean value that, if true , indicates that the cluster can be accessed from a public network.\n returned: success\n type: bool\n sample: true|false\nencrypted:\n description: Boolean value that, if true , indicates that data in the cluster is encrypted at rest.\n returned: success\n type: bool\n sample: true|false\nrestore_status:\n description: A value that describes the status of a cluster restore action.\n returned: success\n type: dict\n sample: {}\nhsm_status:\n description: >\n A value that reports whether the Amazon Redshift cluster has finished applying any hardware\n security module (HSM) settings changes specified in a modify cluster command.\n returned: success\n type: dict\n sample: {}\ncluster_snapshot_copy_status:\n description: A value that returns the destination region and retention period that are configured for cross-region snapshot copy.\n returned: success\n type: dict\n sample: {}\ncluster_public_keys:\n description: The public key for the cluster.\n returned: success\n type: str\n sample: \"ssh-rsa anjigfam Amazon-Redshift\\n\"\ncluster_nodes:\n description: The nodes in the cluster.\n returned: success\n type: list\n sample: [\n {\n \"node_role\": \"LEADER\",\n \"private_ip_address\": \"10.0.0.1\",\n \"public_ip_address\": \"x.x.x.x\"\n },\n {\n \"node_role\": \"COMPUTE-1\",\n \"private_ip_address\": \"10.0.0.3\",\n \"public_ip_address\": \"x.x.x.x\"\n }\n ]\nelastic_ip_status:\n description: The status of the elastic IP (EIP) address.\n returned: success\n type: dict\n sample: {}\ncluster_revision_number:\n description: The specific revision number of the database in the cluster.\n returned: success\n type: str\n sample: \"1231\"\ntags:\n description: The list of tags for the cluster.\n returned: success\n type: list\n sample: []\nkms_key_id:\n description: The AWS Key Management Service (AWS KMS) key ID of the encryption key used to encrypt data in the cluster.\n returned: success\n type: str\n sample: \"\"\nenhanced_vpc_routing:\n description: An option that specifies whether to create the cluster with enhanced VPC routing enabled.\n returned: success\n type: bool\n sample: true|false\niam_roles:\n description: List of IAM roles attached to the cluster.\n returned: success\n type: list\n sample: []\n'''\n\nimport re\n\ntry:\n from botocore.exceptions import BotoCoreError, ClientError\nexcept ImportError:\n pass # caught by AnsibleAWSModule\n\nfrom ansible_collections.amazon.aws.plugins.module_utils.core import AnsibleAWSModule\nfrom ansible_collections.amazon.aws.plugins.module_utils.ec2 import camel_dict_to_snake_dict\n\n\ndef match_tags(tags_to_match, cluster):\n for key, value in tags_to_match.items():\n for tag in cluster['Tags']:\n if key == tag['Key'] and value == tag['Value']:\n return True\n\n return False\n\n\ndef find_clusters(conn, module, identifier=None, tags=None):\n\n try:\n cluster_paginator = conn.get_paginator('describe_clusters')\n clusters = cluster_paginator.paginate().build_full_result()\n except (BotoCoreError, ClientError) as e:\n module.fail_json_aws(e, msg='Failed to fetch clusters.')\n\n matched_clusters = []\n\n if identifier is not None:\n identifier_prog = re.compile('^' + identifier)\n\n for cluster in clusters['Clusters']:\n\n matched_identifier = True\n if identifier:\n matched_identifier = identifier_prog.search(cluster['ClusterIdentifier'])\n\n matched_tags = True\n if tags:\n matched_tags = match_tags(tags, cluster)\n\n if matched_identifier and matched_tags:\n matched_clusters.append(camel_dict_to_snake_dict(cluster))\n\n return matched_clusters\n\n\ndef main():\n\n argument_spec = dict(\n cluster_identifier=dict(type='str', aliases=['identifier', 'name']),\n tags=dict(type='dict')\n )\n module = AnsibleAWSModule(\n argument_spec=argument_spec,\n supports_check_mode=True\n )\n\n cluster_identifier = module.params.get('cluster_identifier')\n cluster_tags = module.params.get('tags')\n\n redshift = module.client('redshift')\n\n results = find_clusters(redshift, module, identifier=cluster_identifier, tags=cluster_tags)\n module.exit_json(results=results)\n\n\nif __name__ == '__main__':\n main()\n", "path": "plugins/modules/redshift_info.py" } ]
diff --git a/changelogs/fragments/970-redshift_info-boto-import.yml b/changelogs/fragments/970-redshift_info-boto-import.yml new file mode 100644 index 00000000000..568c6cdf605 --- /dev/null +++ b/changelogs/fragments/970-redshift_info-boto-import.yml @@ -0,0 +1,2 @@ +bugfixes: + - redshift_info - fix invalid import path for botocore exceptions (https://github.com/ansible-collections/community.aws/issues/968). diff --git a/plugins/modules/redshift_info.py b/plugins/modules/redshift_info.py index b79b28b3074..a6a8a578a37 100644 --- a/plugins/modules/redshift_info.py +++ b/plugins/modules/redshift_info.py @@ -277,7 +277,7 @@ import re try: - from botocore.exception import BotoCoreError, ClientError + from botocore.exceptions import BotoCoreError, ClientError except ImportError: pass # caught by AnsibleAWSModule
django-json-api__django-rest-framework-json-api-817
Tag new version to release in pip Hi @sliverc, great work on DJA. May I know whether we can have a new release? I'm keen to use #781. Thanks 😄
[ { "content": "# -*- coding: utf-8 -*-\n\n__title__ = 'djangorestframework-jsonapi'\n__version__ = '3.1.0'\n__author__ = ''\n__license__ = 'BSD'\n__copyright__ = ''\n\n# Version synonym\nVERSION = __version__\n", "path": "rest_framework_json_api/__init__.py" } ]
[ { "content": "# -*- coding: utf-8 -*-\n\n__title__ = 'djangorestframework-jsonapi'\n__version__ = '3.2.0'\n__author__ = ''\n__license__ = 'BSD'\n__copyright__ = ''\n\n# Version synonym\nVERSION = __version__\n", "path": "rest_framework_json_api/__init__.py" } ]
diff --git a/CHANGELOG.md b/CHANGELOG.md index 97546f10..51c13dc1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,17 +8,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 Note that in line with [Django REST Framework policy](http://www.django-rest-framework.org/topics/release-notes/), any parts of the framework not mentioned in the documentation should generally be considered private API, and may be subject to change. -## [Unreleased] +## [3.2.0] - 2020-08-26 ### Added * Added support for serializing nested serializers as attribute json value introducing setting `JSON_API_SERIALIZE_NESTED_SERIALIZERS_AS_ATTRIBUTE` + * Note: As keys of nested serializers are not json api spec field names they are not inflected by format field names option. ### Fixed * Avoid `AttributeError` for PUT and PATCH methods when using `APIView` * Clear many-to-many relationships instead of deleting related objects during PATCH on `RelationshipView` -* Allow POST, PATCH, DELETE for actions in `ReadOnlyModelViewSet`. It was problematic since 2.8.0. +* Allow POST, PATCH, DELETE for actions in `ReadOnlyModelViewSet`. Regression since version `2.8.0`. * Properly format nested errors ### Changed diff --git a/rest_framework_json_api/__init__.py b/rest_framework_json_api/__init__.py index a15ece29..28a440ce 100644 --- a/rest_framework_json_api/__init__.py +++ b/rest_framework_json_api/__init__.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- __title__ = 'djangorestframework-jsonapi' -__version__ = '3.1.0' +__version__ = '3.2.0' __author__ = '' __license__ = 'BSD' __copyright__ = ''
graspologic-org__graspologic-176
change semipar and nonpar names? What do people think? @jovo brought up that the current names are uninformative. I agree, but don't really have a strong opinion on it
[ { "content": "from .semipar import SemiparametricTest\nfrom .nonpar import NonparametricTest\n\n__all__ = [\"SemiparametricTest\", \"NonparametricTest\"]\n", "path": "graspy/inference/__init__.py" } ]
[ { "content": "from .latent_position_test import LatentPositionTest\nfrom .latent_distribution_test import LatentDistributionTest\n\n__all__ = [\"LatentPositionTest\", \"LatentDistributionTest\"]\n", "path": "graspy/inference/__init__.py" } ]
diff --git a/docs/reference/inference.rst b/docs/reference/inference.rst index b388033c4..025b4e8fe 100644 --- a/docs/reference/inference.rst +++ b/docs/reference/inference.rst @@ -6,6 +6,6 @@ Inference Two-graph hypothesis testing ---------------------------- -.. autoclass:: SemiparametricTest +.. autoclass:: LatentPositionTest -.. autoclass:: NonparametricTest \ No newline at end of file +.. autoclass:: LatentDistributionTest \ No newline at end of file diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 8fd7311bc..774ac66d4 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -33,8 +33,8 @@ are tutorials for robust statistical hypothesis testing on multiple graphs. .. toctree:: :maxdepth: 1 - tutorials/inference/semipar - tutorials/inference/nonpar + tutorials/inference/latent_position_test + tutorials/inference/latent_distribution_test Plotting ======== diff --git a/docs/tutorials/inference/latent_distribution_test.ipynb b/docs/tutorials/inference/latent_distribution_test.ipynb new file mode 100644 index 000000000..1cf86e3a7 --- /dev/null +++ b/docs/tutorials/inference/latent_distribution_test.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Latent Distribution Two-Graph Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "np.random.seed(8888)\n", + "\n", + "from graspy.inference import LatentDistributionTest\n", + "from graspy.embed import AdjacencySpectralEmbed\n", + "from graspy.simulations import sbm, rdpg\n", + "from graspy.utils import symmetrize\n", + "from graspy.plot import heatmap, pairplot\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate a stochastic block model graph\n", + "\n", + "We generate a stochastic block model graph (SBM), which is shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x128644518>" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x12881a048>" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 20 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_components = 4 # the number of embedding dimensions for ASE\n", + "P = np.array([[0.9, 0.11, 0.13, 0.2],\n", + " [0, 0.7, 0.1, 0.1],\n", + " [0, 0, 0.8, 0.1],\n", + " [0, 0, 0, 0.85]])\n", + "\n", + "P = symmetrize(P)\n", + "csize = [50] * 4\n", + "A = sbm(csize, P)\n", + "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n", + "heatmap(A, title='4-block SBM adjacency matrix')\n", + "pairplot(X, title='4-block adjacency spectral embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Latent distribution test where null is true\n", + "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between SBM 1 and 2 (up to a rotation) are no greater than those differences observed by chance.\n", + "\n", + "In other words, we are testing\n", + "\n", + "\\begin{align*}\n", + "H_0:&X_1 = X_2\\\\\n", + "H_\\alpha:& X_1 \\neq X_2\n", + "\\end{align*}\n", + "\n", + "and want to see that the p-value for the unmatched test is high (fail to reject the null)\n", + "\n", + "We generate a second SBM in the same way, and run an unmatched test on it, generating a distance between the two graphs as well as a null distribution of distances between permutations of the graph. We can see this below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x12d1420f0>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x12d52da90>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 20 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "A1 = sbm(csize, P)\n", + "heatmap(A1, title='4-block SBM adjacency matrix A1')\n", + "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n", + "pairplot(X1, title='4-block adjacency spectral embedding A1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot of Null Distribution\n", + "\n", + "We plot the null distribution shown in blue and the test statistic shown red vertical line. We see that the test static is small, resulting in p-value of 0.94. Thus, we cannot reject the null hypothesis that the two graphs come from the same generating distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kmu5+0YpNlyU5Z358TpLXbX55AAA7264F9nlUkqcleU9VXTmve16SC5O8qqqekeSvknzP1pQIALBzrRumuvvtSWqNzY/b3HIAAJaLGdABAAYIUwAAA4QpAIABwhQAwIBFPs3HDrfn/MsX2u/AhWducSUAcOzRMwUAMECYAgAYIEwBAAwQpgAABghTAAADhCkAgAHCFADAAGEKAGCAMAUAMECYAgAYIEwBAAwQpgAABghTAAADhCkAgAHCFADAAGEKAGCAMAUAMECYAgAYIEwBAAwQpgAABghTAAADhCkAgAHCFADAAGEKAGCAMAUAMECYAgAYIEwBAAwQpgAABghTAAADhCkAgAHCFADAAGEKAGCAMAUAMECYAgAYIEwBAAxYN0xV1Uur6saqumrFuguq6iNVdeX8851bWyYAwM60SM/UviRnHGb9Rd192vzz+s0tCwBgOawbprr7j5PcvA21AAAsnZExU+dV1V/MtwFPXGunqjq3qvZX1f6DBw8OXA4AYOfZaJh6SZKvTXJakuuT/NJaO3b3xd29t7v37t69e4OXAwDYmTYUprr7hu6+vbvvSPKbSU7f3LIAAJbDhsJUVZ204umTkly11r4AAF/Kdq23Q1W9PMljktyvqq5L8jNJHlNVpyXpJAeSPGsLawQA2LHWDVPd/dTDrL5kC2oBAFg6ZkAHABggTAEADBCmAAAGCFMAAAOEKQCAAcIUAMAAYQoAYIAwBQAwQJgCABggTAEADBCmAAAGCFMAAAOEKQCAAcIUAMCAXUe7gGWw5/zLj3YJAMAOpWcKAGCAMAUAMECYAgAYIEwBAAwQpgAABghTAAADhCkAgAHCFADAAJN2HkMWnXz0wIVnHtVzAsAy0TMFADBAmAIAGCBMAQAMEKYAAAYIUwAAA4QpAIABwhQAwABhCgBggDAFADBAmAIAGCBMAQAMEKYAAAYIUwAAA4QpAIABwhQAwIB1w1RVvbSqbqyqq1asu09Vvamq3j8vT9zaMgEAdqZFeqb2JTlj1brzk7y5ux+S5M3zcwCAY866Yaq7/zjJzatWn5Xk0vnxpUnO3uS6AACWwkbHTN2/u6+fH/91kvtvUj0AAEtleAB6d3eSXmt7VZ1bVfurav/BgwdHLwcAsKNsNEzdUFUnJcm8vHGtHbv74u7e2917d+/evcHLAQDsTBsNU5clOWd+fE6S121OOQAAy2WRqRFenuRPkzy0qq6rqmckuTDJE6rq/UkePz8HADjm7Fpvh+5+6hqbHrfJtQAALB0zoAMADBCmAAAGCFMAAAOEKQCAAesOQOfYs+f8y492CQCwNPRMAQAMEKYAAAYIUwAAA4QpAIABwhQAwABhCgBggDAFADBAmAIAGCBMAQAMMAM622LRWdUPXHjmFlcCAJtLzxQAwABhCgBggDAFADBAmAIAGCBMAQAMEKYAAAYIUwAAA4QpAIABwhQAwABhCgBggDAFADBAmAIAGCBMAQAMEKYAAAYIUwAAA4QpAIABwhQAwABhCgBggDAFADBg19Eu4GjZc/7l6+7zig99bHrwTVtcDHdapF2OxIELz9zU8wHAanqmAAAGCFMAAAOEKQCAAcIUAMAAYQoAYMDQp/mq6kCSv0lye5LbunvvZhQFALAsNmNqhG/r7ps24TwAAEvHbT4AgAGjPVOd5H9VVSf5je6+ePUOVXVuknOT5OSTTx68HByZRScBNbknABs12jP16O5+ZJLvSPLsqvrW1Tt098Xdvbe79+7evXvwcgAAO8tQmOruj8zLG5O8Nsnpm1EUAMCy2HCYqqp7VtWXH3qc5IlJrtqswgAAlsHImKn7J3ltVR06z+919xs2pSoAgCWx4TDV3R9K8vWbWAsAwNIxNQIAwABhCgBggDAFADBAmAIAGLAZ3823oyw64zVsxJH892VWdYBjg54pAIABwhQAwABhCgBggDAFADBAmAIAGCBMAQAMEKYAAAYIUwAAA4QpAIABX3IzoMNOsehs6WZKB1hueqYAAAYIUwAAA4QpAIABwhQAwABhCgBggDAFADBAmAIAGCBMAQAMMGknHGUm9wRYbnqmAAAGCFMAAAOEKQCAAcIUAMAAYQoAYIAwBQAwQJgCABggTAEADBCmAAAGmAEdsvgs5EfTZs+UvhWv2SztwIgjeV/aSe83eqYAAAYIUwAAA4QpAIABwhQAwABhCgBgwFCYqqozquraqvpAVZ2/WUUBACyLDYepqjouyYuTfEeSU5M8tapO3azCAACWwUjP1OlJPtDdH+ruzyZ5RZKzNqcsAIDlMBKmHpDk/614ft28DgDgmFHdvbEDq56c5Izufub8/GlJvrG7z1u137lJzp2fPjTJtRsvlwH3S3LT0S6CIdpw+WnD5acNvzQs2o5f3d2719tp5OtkPpLkQSueP3Be93m6++IkFw9ch01QVfu7e+/RroON04bLTxsuP234pWGz23HkNt87kjykqk6pqrsmeUqSyzanLACA5bDhnqnuvq2qzkvyxiTHJXlpd1+9aZUBACyBkdt86e7XJ3n9JtXC1nKrdflpw+WnDZefNvzSsKntuOEB6AAA+DoZAIAhwtSSW+8rfarqblX1ynn7FVW1Z8W2n5zXX1tV376ddfP3NtqGVXXfqnprVd1aVb+23XXz+Qba8QlV9c6qes+8fOx2185koA1Pr6or558/r6onbXftTEb+Js7bT57fU3/8SK4rTC2xBb/S5xlJbunuBye5KMkvzMeemukTmA9PckaSX5/PxzYaacMkn07y00mO6JeezTfYjjcl+e7u/sdJzkny29tTNSsNtuFVSfZ292mZ3k9/o6qGxiRz5Abb8JAXJfnDI722MLXcFvlKn7OSXDo//v0kj6uqmte/ors/090fTvKB+Xxsrw23YXd/qrvfnilUcXSNtOO7u/uj8/qrk9yjqu62LVWz0kgb/m133zavv3sSg5GPjpG/iamqs5N8ONPv4RERppbbIl/pc+c+8y/7J5Lcd8Fj2XojbcjOsVnt+C+TvKu7P7NFdbK2oTasqm+sqquTvCfJD64IV2yfDbdhVd0ryU8kecFGLixMAewAVfXwTLccnnW0a+HIdfcV3f3wJN+Q5Cer6u5HuyaOyAVJLuruWzdysDC13Bb5Sp8795nv4d87yccWPJatN9KG7BxD7VhVD0zy2iRP7+4Pbnm1HM6m/C529zVJbk3ydVtWKWsZacNvTPKfq+pAkucmed48MflChKnltshX+lyWaVBrkjw5yVt6mlzssiRPmT/ZcEqShyT5s22qm7830obsHBtux6o6IcnlSc7v7v+9bRWz2kgbnnJowHlVfXWShyU5sD1ls8KG27C7v6W793T3niS/nOTnu3vhT0n7tMESW+srfarqhUn2d/dlSS5J8ttV9YEkN2f6jyvzfq9K8t4ktyV5dnffflReyDFspA2TZP6/qH+Q5K7z4Mkndvd7t/t1HOsG2/G8JA9O8vyqev687ondfeP2vopj22AbPjrJ+VX1uSR3JPmh7r5p+1/FsW30/XSEGdABAAa4zQcAMECYAgAYIEwBAAwQpgAABghTAAADhCkAgAHCFADAAGEKAGDA/wdYoZR4M1L20wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 720x432 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ldt = LatentDistributionTest()\n", + "p = ldt.fit(A, A1)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "ax.hist(ldt.null_distribution_, 50)\n", + "ax.axvline(ldt.sample_T_statistic_, color='r')\n", + "ax.set_title(\"P-value = {}\".format(p), fontsize=20)\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Latent distribution test where null is false\n", + "\n", + "We generate a seconds SBM with different block probabilities, and run a latent distribution test comaring the previous graph with the new one." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x1286af128>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x12e1eaf98>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 20 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "P2 = np.array([[0.8, 0.2, 0.2, 0.5],\n", + " [0, 0.9, 0.3, 0.2],\n", + " [0, 0, 0.5, 0.2],\n", + " [0, 0, 0, 0.5]])\n", + "\n", + "P2 = symmetrize(P2)\n", + "A2 = sbm(csize, P2)\n", + "heatmap(A2, title='4-block SBM adjacency matrix A2')\n", + "X2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n", + "pairplot(X2, title='4-block adjacency spectral embedding A2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot of Null Distribution\n", + "\n", + "We plot the null distribution shown in blue and the test statistic shown red vertical line. We see that the test static is small, resulting in p-value of 0. Thus, we reject the null hypothesis that the two graphs come from the same generating distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x432 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ldt = LatentDistributionTest()\n", + "p = ldt.fit(A, A2)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "ax.hist(ldt.null_distribution_, 50)\n", + "ax.axvline(ldt.sample_T_statistic_, color='r')\n", + "ax.set_title(\"P-value = {}\".format(p), fontsize=20)\n", + "plt.show();" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/inference/latent_position_test.ipynb b/docs/tutorials/inference/latent_position_test.ipynb new file mode 100644 index 000000000..4232d1391 --- /dev/null +++ b/docs/tutorials/inference/latent_position_test.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Latent Position Two-Graph Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.random.seed(88889999)\n", + "\n", + "from graspy.inference import LatentPositionTest\n", + "from graspy.embed import AdjacencySpectralEmbed\n", + "from graspy.simulations import sbm, rdpg\n", + "from graspy.utils import symmetrize\n", + "from graspy.plot import heatmap, pairplot\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate a stochastic block model graph to model as a random dot product graph\n", + "To start, we generate a binary stochastic block model graph (SBM). An SBM is composed of 'communities' or 'blocks,' where a node's block membership in a graph determines its probability of connection to the other nodes in the graph." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x127829400>" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x127a0c7b8>" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 20 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_components = 4 # the number of embedding dimensions for ASE\n", + "P = np.array([[0.9, 0.11, 0.13, 0.2],\n", + " [0, 0.7, 0.1, 0.1], \n", + " [0, 0, 0.8, 0.1],\n", + " [0, 0, 0, 0.85]])\n", + "\n", + "P = symmetrize(P)\n", + "csize = [50] * 4\n", + "A = sbm(csize, P)\n", + "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n", + "heatmap(A, title='4-block SBM adjacency matrix')\n", + "pairplot(X, title='4-block adjacency spectral embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. ASE(A) recovers an estimate of the **latent positions** of $A $. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n", + "\n", + "For an adjacency matrix $A \\in \\mathbb{R}^{n x n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n x n}$\n", + "\n", + "$P = XX^T$, where $X \\in \\mathbb{R}^{n x d} $ and is referred to as the latent positions of the graph. $X$ is referred to as the latent positions of the graph because each node $n_i$ is modeled as having a hidden, usually unobserved location in $\\mathbb{R}^d$ (we'll call it $x_i$). The probability of an edge existing between $n_i$ and $n_j$ is equal to the dot product $x_i \\cdot x_j$\n", + "\n", + "ASE is one way to obtain an estimate of the latent positions of a graph, $\\hat{X}$\n", + "\n", + "In the above embedding, we see 4 clusters of nodes corresponding to the 4 blocks that we prescribed. ASE recovers the fact that all of the nodes in a block have similar latent positions. So, RDPGs can also model an SBM graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sample new RDPGs from this latent position\n", + "Given the estimate of X, we now sample two new RDPGs from the same latent position above" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x12c535390>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x12c6287b8>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x12bf77a20>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x1279c46d8>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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aVLuUl6ERJlmXfzKqG/9NqB7k3wXwaHSHDC9DK8ywPn4QOkIXcXcY5UGNY/vUx56GSpX4Iypz0/kA9uxo+17jqI8/BFX00DJUDq7Xogrn/QCAB3ec489R3USvrOfn0e2x9czpZMwvmPL57vXnR7fmyjT+3YpqIXESqpDudaa0ZQB8x+H7XgXVA/qk+tzuqL/L8+rz3N6hLdPzz2aeVke1I/5DPde/Q+XLsytaIeV1+U1QLXD+Wv/7NqpF1r2usynHtka10Li+PuelqJzIj0G1RmmP7xGoHI7/Ul8zy1EtTB7TKvfoutzldbkr6+vn39EIKe65Npd0zRmq0Oh3oFqYrUT1e/sFgP/X0YbU82cAPMnhO+zse2C8k+t7l8ax9VE59C6vx3o+gH/tKluXPxCVae+OZv/oCWvu+l5ROTffhSqfSns+vlO3v6PtfPAf/2n+E2PUTZ6kUGoF4GgATzHGLM07GjIWRORPAH5rjOnymZhJRORCVGnpXcOECSERoA2SEDKV2sdofVSms7lARJ6Kygn8M7nHQgipoM8KIaST2i/nH1GZUH4wUHz01IuUzVD5XV0NLlYIKQYuVggh0zgU1T3ivajS9s86/wFgJ1QRey81CR1rCSH90GeFEEIIIUUTrKysXLHCAMDrV99yqKgTh99aZYSftDv5f9u+2vX7yvS1O20cNu1qjbOvrM05DLVHSC66ft8l0Pd71xrrtHMvdU5CiXVe2u3atrfa4sWpcynNNXSwJYQQQkjRBJuB9pcl1bvlPVSKPlzK9tXvG49LX6HjCSFW37l3brO6gwwhZE66dv997Q315aI0dpUP/V5t2hvLNWQzTpf5czlvG1Uo5TzGVlhc2/YdD5WVtFBZIYQQQkjRZFVWXP1QhrDZ5fWNq28sPuqGryKSqq/Sd6OhaO8+iR1j8U3Q6ivWdZbDT6ZJiLqnpQq163ThOzdDzwj6rJQFlRVCGnChQsjsk8OcT8LgYoUQQgghRaNmBmqjbeLxJZaJZxYoXUWIaZrJ4ZgZIn/HNGVNC9MNlddTmmsmlObUq+1YG9qXBrNmMqWD7TigskIIIYSQoommrDQpVbnwSahW6rn4oO08SMZFrKRkY75mYjnWpmhHo89QR1uXUPkc11lfcAeVlbKhskIIIYSQoik2KZwW2uHSsdvrK9NFSJ8uxAgdDCHFbjQkDNM1QVsOxpruPYXPhEu6/VkKqR7zdx56XbjOAZWVtFBZIYQQQkjRJPFZaTNLfh999NltXer3RQuEtFfqLiolpftnlPJdxUr2RcJwTb45rX7O79U1UWeqaCcmhSsLKiuEEEIIKRouVgghhBBSNOpmIBcTT8zEcT7jsHEinQXTVekyfUzZmYmz/Ejxplyf9mMl2nMJwdUiRQK5PsdhjfZ8y2jjm0xxWp2u+/4RZhnNQAmhskIIIYSQosniYNtFTuUipG9XdagkhWYWdvw5mDXVJAfau22XUONpdW3Hk9OReJYcwn3HEpJcTlMxo4NtWqisEEIIIaRoilFWJsRSHvradfks9vhitG3TZ+mUFBar9cLAWHW0oILkhvZ89SUWdOnHNTR4qO3QBHmhCfemzUFMNa0LKitpobJCCCGEkKIJVlZWrlhxjwa0VAFfJcSlvVT+IzlUExti+QukYIwRQ6G7zgla6d5DzjO2z8msEqrK2dR18cso4VUBoan0U75WoDkORgOlhcoKIQ1KWkwSQgipKM5npY2WKuGSu6AUYis/KXNIdBF7Z+9i7/bt36adnLvZaWNI2Y6vn1is8cRsZ6j9JrH8WmL3mZtSfL/os5KW0SgrpS4oyPzBazEeuR2JCSFlMprFCiGEEELmEzUH2xxOqtpOuF19tfu0aV871b8NIU7HzXqzIBPPOi5mJZvPJozF/KLlbJkCn2R1Wk7VsV4ZUIqzq9a4bH5PXcdoBkoLlRVCCCGEDCIiG4rIB0XkJyJyk4gYEdnFof6WIvJ9EblZRK4Tkc+LyPpWdUt1sPVRLkoLEdZ2kI3VHpkNfENAU6okQ2pOKQ63oZTiBOqKVgI5befelAkKbb+HeVRW6oXJTwBcAuAqAE8A8BRjzFKLug8CcDaAGwB8AsCaAN4EYBmAHY0xd/TVXyVg3IQQQgiZH34FYH1jzLUi8hwAJzjUfTuAxQC2NcZcDgAichaAkwHsBeBzfZWLVVbauPqskOmE2rBLSAEfK2Fbs+0QvwDXnWCqOdXaocbaOedWRHIQkkLfNxFgG5+U9a59+iR8c2k31NfHdU7nUVlp0lis2CorVwL4kTFmz9bx3wK4zBjzjL76VFYIaTCPD0tCyHwhIjcMlTHGrKPY38YAHgDglx0fnwVgt8E2xqKs9OGrsNjUK129maYCuEQijZkcO0GiR6lp+3Mm7usiZ3RNaNTZUJnQ33BKtbVJamVF81n7X/jDjUNlhhYrLsqKiGwP4BcA9jTGfLn12aEA3gxgFWPMndPaoLJCSINSF6WEEKKFpmpiyeL6720dn61slLl5WgMMXSaEEEJITFbUfxd1fLZaq0wn6magFGYTm9DlCVomnhzmoBzvBrIhtuzqm+gqVp8lkCN0s0np8xOCb8h3+7PcTt8+TvLT2rdtx6fvWNjMl6Zjcuq3LmuagTTG7mgG2hjAnwG80Rjz0dZnxwHYzRizQV8bNAMRQgghhbNwxLFHxpjLReRqANt3fLwDgHOG2lBLtz8hNN17SFmXVMq+pHDKHdq59e0gfNqPiXbK7zGrOj6p17XJ7cw45GQ5ZgUnx/fpSsowaQ1yqK22faR2sH3VAj1l5VN3xVVWRGQzADDGXNo49mlU+VQe0cizsiuAUwD8qzHms339UVkhhBBCiBUi8s76Pycr371EZCcANxhjPlkf+1H9d0mj6sEAXgjgJyIyyWD7ZgDnAjh2sF8tnxWXl0Dl9Ptw9WvJgYZaYqO+aKkdvqTa5cW0w4fMqauvRBvtENB2OymujxJCjX2/B5dkZC7j8FXeXO6zIcpW7oSHGmkZfJNANkmtrLx24UPVlJWP33mZ99hFZNo4/mCMWVKXWQYAk/9v1H0UgI8C2AnA7QC+A+ANxpirh/qlskIIIYQQK4wxgwud9iKlcfxCALv79BstGqiJlj+FTztaqk7fLjY1NnPsWqZEn4FSfQByRObk9jPy2XmX9J2F4hsN5KOWhM5/15hj9alxXYRGY9n0FeP6nVdlJRejUVZKM9UQQgghqRhzNJAGTApHCCGEkKJJ+m6gEMfRUPNNn8wZC61Q42ntarbjg4+D7Bj6ciG2yWMWTSpNUjrzalOqs3FKXNJI2LQR2/FX07SV2gx0wCp6ZqDD/kYzECGEEEKUWSijW1+oopYUTjtJmotDq28yOBfnK5dwOR+1I6Xi09dnCbu6UpxKp32WInQzdAcYUj9W4jdfSro2Q9EOk+67b6RQTm0cWX3VEds6Nu35jG/IQTm1svKm+2yqpqx8+I7fj27lQ58VQgghhBSNerr9PlxWzTkSx+Xou2sM0+ZJa1yzsEMtjVna/U9IEaJdqi9SG+2Ee77tzMJ1ppXYMfdcpFZW3rKqnrJyyO1UVgghhBBCVImWFG4MeVF8UpynJLbiU/ruTCuNuRY5fDm009E3Gdqh+vrmuNRJSe6d+BCxxqcVbdPXto3aYaMY+ySFi3WfGBoXlZW0MBqIEEIIKRxGAyXMsxJKLN8NFw97bVKck815ulD6DnWWKPWVA1popZYndqRSv0pTRW1wve5SKytvX7SZ2rP24NsuHd3KZzQ+K6Vf6ITEZF6u/1JMsISQsqAZiBBCCCmceX83UPBiJZVpJuUYXJ25SqJrfD5j7pOLtZ30YlNK2KNPn76J6DTGlcIEleP7SNGXZlr30D5T3rNCEsDZ1otFqfcvUjEaMxAhhBBC5hM1B1uf1by2I2uMxGo5dymxw8Bj7uhKV1smhKbSH2qv67Ou9kPmy2WHWprDbugrAzTH0EdKxaerT63rw+WeEitE2Cc5aIrQaldSO9getPhhag62B624ZHRGJSorhBBCCCmapC8ynGBTNlRNmLaS1m7Xt8y0Os16TLNfBmNRici4iPUiyb52fPxatBVYl3Zd7rMx1bmudqispIXRQIQQQkjhzLsZZK6TwoVQ0lh8SfEiMa2XloW0p1XfV03TePGdza5Ra3es9b36RoZo+wqFjMf3up2Q49UKJB7N7/gIsyypOvEeRWXlP0aorIxmscYf8PzB73z+4HdOCOmCZiBCCCGkcPhuoIRmIBupfGxmlTG9R2iWEnDlCHktLYlVHzmSksVu12cMKccxJjNQSEhvLFOna/82SQxjmvNSO9i+f/XN1cxA77j1d6Nb+YzGDEQIIYSQ+WRUDrbalPCqgHbd0Pq+4dLtvm125H3taBOyQ7IJC8/tsDtUP4YDqtaclkCq7yFFO6HkcA7OiY1SH+NcUisrH1xDT1l56y1UVgghhBBCVEmSFE4DV+Uhlg9MyqRwNu1O68ulvVBy7LZjhsXGStHtm4pfu6+hMinOVwsX/wXbuqFjaVKCwtJFSapJKYqUK1RW0sJoIEIajM3BmxAyHzAaSNlnJZbyoFV/TNE7sUkZSVMa83KePoxJWcnRVwilqwgpxhdL0Uqt4KVWVj605sPVlJU333zx6FY+VFYIIYSQwlk4uuWFLnSwJYQQQkjRZAldTmEqKglf01PsOfANYx2j5D4hJEQ4hwNlKXOt5aQ95EhcupkkBTkSHrbbD+3D5f1epXznrk7uqc1AH72vnhnoDTfRDEQIIYQQZehgW0hSuFRKSkoH277+S1CMxqiixNp1aodCa+9Qczi7pjxP1/FM67N0p16XlPApEwGGXv82qRNyJjN0qWvbfmpl5WNrPUJNWXndX387upUPfVYIIYQQUjTFKCvzRkjiuFBVJodKUppCUxqp5kcrWZrmDnWecPHlcG1vWrvziEtiR19V6QizLKk68cm19ZSVV99IZYUQQgghRJWk6fZzJoPz6adJ7Igcl/lrEjKuFOnjY/uP+NYrNSJBi5L8BErB5jvXfu1BTmxe8Dftc9t2Q/ze+q4zl/a1+7TtO7XPyrwrK6OJBhrzTYOMB15n8we/czIGGA1UmM9KymgZmzwa0+q4RCa4jidnlJILKfwWQuz42sqAq7+BxthdfRJKSmUeI5rFp0wIY1LcfFSJ0Ot2FhQ3X1IrK59eZwu1Z+0rb7hodCsf+qwUwjz+2IfQWrRpL/5cpfJUaPep1d4sXNuzcA42lJBSgZAuRmMGIoQQQuaVeX83UJa3Lqcg1CSTk5yOxGNmXiXpUs7bx1GxBIfW3KYemzmIPU8+CeSan9k420+r0/dZKSbPrrGmDl0+cl09M9ArrqcZiBBCCCFElWJfZBhStolW0rUQpcYmTM51PBp95t6JT9ByDHTpS9vhM5a6UWoYcWnjKmFOfIn1+/RRO0L7smkvNG1A7N9a11i6+kztYHvUenrKysuvo7JCCCGEEKKKmrJiowKU4s/SpnT/lljzNmvhiqnCbHP7OLRxsf1rjz1FgrDSfV76iO1XEXP+h9poYuPXYjuWXLiOh8pKWhgNREiDUm6chBDShEnhRpRu36WdHBE1Ngm9uuoNlW3X0Uo2V1oyN5t2UtirS1ywxBhfyG46pY9Du05MXw6Ndvva7oukGarrWtZlXD5l++rnjqpK4TuWWlk55n5bqikr+1z7m9GtfOizQoolhzmuxIUKIYTMOzQDEUIIIYXDpHAJQ5dzOrKGhjXHwse043ouPnJzTkp4J8wYGMv3qUXK79wlUVssE5avWSOkLy2zkkviuNJMsLZzkdoMdNz6emagva6hGYgQQgghRJWkDrax6Avjc3F6jTmeGO13oZWsLtYOZ8whwhNcHKeb5XM6LOZQJXKoEaWgoW70Xf85HeBjJnHzCVe3GYv27whIn27/S/d/pJqysufVv6ayQgghhBCiibqykjKBmUu9WVBU+vqcEOr7QuaPUn0JYvtetOu41otFigRtNikBbK6LaX36UpKaNnRdpPZZmXdlhdFAhBBCSOEwGijDiwy16VM0QqNtSogYcsHlfHMnfMtBThWhhHkrTUVwwWb37zvHPmnoY3+fpSQCdPmtpEi4N63v1KRWVr76AD1l5Z+uGp+yQp8VQhrkvgHkgAstAAAgAElEQVSS9IxtQ0LIPEIzECGEEFI4fDfQDJiBbBijo21IO1phydrJokLJkQgt5btkfBLiuThHuowr5txq9xHrukh5/ZdgJvRlLGPXNOulNgN9fYNHqT1rX3DlhaNb+dAMRAghhJBBRGSRiBwiIstFZIWInCEiu1rWfZqILBWRa0XkehE5XUReZN33rCsrPqG9pTrY+r4yIHa6fe30+NpKgTYp3u7q076Ng7lLaKqWk2pfmdBrp4TrovTXQ5TonNpkDE7fXd9famXlhAfqKSvP/YufsiIiXwbwfACHA7gEwD4AtgewszHm9J56zwLwLQA/B/CV+vCLATwRwH7GmKOG+qbPCiGEEEJ6EZEdUC0wDjDGHF4fOxbABQAOAfDknuqvAnAFgF2NMbfVdT8D4PcA9gYwuFjJoqzkSJ6mhc1OtVRi+Ti44PIStBg7e62+bNv1bTtnWvoS1ArbcZQyVsDt2u4r01fPJ0TbRXHzVWFsXhWg/fsM6VNDzZk3ZUVEDgXwegDrGWNubhx/G4D3A9jYGHPFlLqnAVjbGPPo1vELAPzZGPOMof6prBBCCCGFoxkNJCI3DJUxxqzTOrQdgIuaC5WaswAIgG1RqSdd/BTA20TkvQCOqY/tA+DhAA6wGnNKZUVbldBKBherbIp2htrv6qOEXWgXJe2SS0bb70NjDKUxq5E5oePKcR1oJe7z6Tuk3S5y+qx8a8NHqykr//iXC28cKtNerNQqyOXGmN1bxx8J4EL0+J6IyBoAPgfghagWNgBwM4AXGmO+bzNmKiuEEELIHNGhmtiwGMBtHcdXNj6fxm0ALgbwNQAnAFgI4BUAjheRXY0xvxjqnIsVQgghpHAW5E8KtwLAoo7jqzU+n8YnAOwA4HHGmLsAQESOR6XIHI4qKqgXtbcukzByh+8NSbU20qirOc7HFBYSiuvSvms9l3Z8Q9D7+gghpcmjBPNKqU7VLn2UZp6aBUpPCvedjbZSe9Y+a/n5Pg62JwPYwBizdev4rgBOAbCHMeakjnqrArgFwMHGmHe1PvsYgP8HYLEx5m99/TMpHCGEEEKGOAfAFiKyZuv4jvXfc6fUux8qK87Cjs/uU382uHgajbISGgpXeriuTf3QpFg+oX6+ztAaqolLna56sZyYQ/t0UVZ8v3ONa1sr/NSmD+3QWW365qKLWVY+XO9nJVyLMX4zqZWV7z54a7Vn7TP/dJ6PsrIjgDNwzzwri1DlWbnSGLNTfWwTAKsbYy6q/38hgGtQRQptY4y5oz6+JoDfALjBGLPVUP/0WSGEEEJIL8aYM0XkawAOFZENAVwK4KUAHoIqDHnCsQB2Rq2WGGPuFJEPA3gfgNNF5AuoVJaXA3gQgDfZ9F+8slJaqJ5LXyn6jhUG6OJnEZoIykad8ElipaUAhSo0NnOq4TPUZN58G+btfEtj3vyegPlTVgBARFYD8F4ALwGwLoDzALzdGHNKo8xSVOn3pVV3TwCvQ5VbZVFd9xBjzAk2fVNZIYQQQgpnwcLs0UAwxqwE8Ob637Qyu0w5/iUAX/Ltu3hlJTelrOKBeP4CzTZddvQ+aEXb9LXtovi4tKeNr7JS0rXYhbYvjZa6OqGE+XMlRyK1VN+VzbVdYqRVamXlpCXbqD1r/37ZuflXPo4wGogQQgghRZNEWSlJnXBlrGnKfT3229hErmhFwMTKP6L9aoQY0UDT2nMZ36ww1vtFzN1/CXPio8rYlo81Du0+mxxhliVVJ76/2XZqysozLj2bygohWsQyv5By4XdOCOmCixVCCCGEFE1SB9sSpEwtZuFc+qRan0R0NmHENk6lXWNK6fjbxqdvLZOWax8+lHotc1x5+yyhby20nKyb7aQ2A/3g4Y9RMwPtfvH/0gxECCGEEKLJqPOsxArvtGnHV3HQTrcfUtamnk368z7VJGRX1tW3jfrSN95pZXzTvNvMl8b3F9OXo9Qd87yPq5Rw9dD7WYr711A7WvNX6jU5D4x6sUIIIYTMAyUkhcvJqBcr7VWuVkid9g7ANVTV57x8ytqOJ6TdPp+VoTZtQoRd+u6q16dYuKgZPonofEOY521nP2/Y+HflxHUM2vevEhRskh76rBBCCCGkaIpNtx9rlRuaLC2l3VULm/EMpXvvKjtBK726S9I1G2yik7oISS9ucw59fdpEHpWeGMznHGIlX3RtM/b36TOGlPQpgqXcz3yIcQ6p0+2f8qjHqT1rn3bhL0ZnU6KyQoolR4KwMd+QCSFkVuFihRBCCCFFU6wZiITh+iZTF3zMZ111UyknoY6sLiYjF+dI7b6HxqFRNie+76aZEOv8SjPbxOojNJFa6Un0XFNhpDYD/XjrHdSetU897yyagQghhBBCNKGyYklsh98cO2DfkN42LuG6XfXafbrUDXWijZ3Gv6/tPvWldEfbMeIS8qqVnt2HUpSusSoiqaCykpZR51khhBBC5gGZ86RwM6usxF5ta+90cyfgGlIUbFSAvno+4Z42+PqjhIyjL5w7lkKTe5cdwhj8Kkpn1s8vFb5+TyX4rPxkux3VnrVPOfvM0a186LNCSAM+DAghpDxmVlkpnZQ7JZdoFBufCR9/D5vkctrE9EPR6Cs0Mko7yZ+LL8e8LepSzsUszHFupTgFqZWVpds/Xu1Zu8svT6eyQgghhBCiCRcrhBBCCCkamoFGTkjoc8xwWJvQTx8H1nYZV/NJrBDtdns2SeFcxhFTSi/J7KD1Th+tccQy8di0HToXPnW12plVp+rmeR1hliU1pZy64xPUnrVPPvPnNAMRQgghhGiipqyU5myWM6nTGPEJvfVJrtVVP1TdmPaZdtkJKc7ThhKu6TErISUpSkOEjLU0Z9eQ78q1nkt7ruNK7WA778oKk8IRQgghhSMLRre+UGXmfVbGtHtqkyOx3TR8kyiFKjQuhKTQD/EJ0OhLO3SZjIeS7lEx/NhcVO5Yrx+JUTa1svKzxz9R7Vn7pNNPG93Khz4rhBBCCCmamVJWSrPNhpIiImHC0AvcYqWub5bpG6dLsrqu+i7tlIRLEjdfSvCHKEldyIVL1JhvGc5zhc11W1q6/dN2epLas/aJ//MzKiuEEEIIIZpkVVZyKyFj3lH6vpCrWcdFeZhWflp7Q0rN0GdDffoSS33xademna42tW307Tqu4wpBS01IMQ7NfprYRJuVQM7vwXVOXF434QuVlbQwGiiQnIss9qlPSQ+HJqWaqWaBUue21GuR5EEWjm59oQrNQIQQQggpmmhmoNKkzFLk5qG++/r3TeVu0+cQfX2GtqNRtqteyh2zVuhyrHBrbbR+K2N0+BzjmCdo3C/GeN5a5Ey3//Odn6xmBnrCT08dnUxDMxAhhBBSOPNuBioudDllIrRpTlyhL/4qbedh47Q2IWcor4vjb/t432ez4JPgmqzLRUn0Gc+YKdVhd1bvPxpohBqHtNdVNrWD7elP3VntWfv4H/90dCsf+qwQQgghpGiKU1Zc8AktHVPIcUhd31C/acRQMEJ9Xobq+ig1Nn3FDL/WmBMtbJLyDdUbKusyDpsQ+Zw+NKX56eUk91xoKFulJYU78+lPUXvW7njyT6isEEIIIYRoklRZCUlkZlsvNTl2eVr221CfFZtxafuYuOzo+wjxj9FOpufblxal+zrEGl9M/7WuPkLa00pypnFeMe95Ln3alI+VQBGgspKa0UQDlXojJbMFr7P5g985GQPzHg1EMxAhhBBCimbUDrYTSpexx0RIMrKYodA+Dp4uphTf8U0bl42Joa+dvvGmktxj9klH1mHGdJ6uzqljYMisnTop3FnPeKras3aH7/94dDLNaMxAhBBCyLyyYMHo1heqzISyMiZsHOW0nOls++7Cx9HWVTFwUWiG+vZtx0X1sCHUwTbEwdlmPNo73piOjy51S1BXc6gJpSkYJSlwsUntYPvLPXZVe9Zu/70fjW7lQ58VQgghhBQNlZVAtJPMhbYT2r+ND0YbXz+NoXZs1Imu+u06vgpGLH8UF2Ughf+ICyHJtmzq5f4d2BCSUsCn3ZiUrFykxnUuUisrv/qHp6s9ax/77ZOprBBCCCGEaKKurMSyWWq2GYtYO66U2JzDhFC/Fhf1xUVxsBmfi9rRR0i0k4vi4xL9RNwJUYw472nJEQ3XBZWVtIwmGog3BEJIDELD6glJwQImhSOEEEIIKZeZd7ANlQw15GEbM1DusEcXbEwVOXerKU08IWVC52ve3hHUxbyYb8ZsYo4137kT0aU2A539nN3UnrXbnfjD0ck0ozEDEUIIIfPKvL8bKKuy4qI4lLZrKDUluc/bRrtwUQxc2rZxiLUZnws2yopP8rpp5Yfqhya9i+nEPtTu2MKmtfrKrXxqJ4gk4aRWVs55/u5qysq2//2D0a186LNCCCGEkKKJZgay2YmMYXcwbSfTNXab8Frb9ofqTSPWbtumbLNvH18Om/nymcvQsOvQVPgaClFfGykVPZ8dfoqkcD7qUF/ZkJD0rr58FLIUiQBjJ/mbNZrnfoRZlrRvWTjf2sJ8nz0hhBBCiieLz0rsXULMtku159tgoxBo7Sh98OnbN9Knr0+NOfBVVkJ9hUpn6Dcy5l17yrGXdq/RepVEKnwjNFvKSlK/j3Nf9PdqPivbHH/S6HxWGA1ESINSbv6EENJk3pPCFZ9uv48Qe7BtGW2mqRuuESca0VOuykFoev1pZXzHM9SnTSSH7/xrM+uKigZjUhM02m22HXLfSHF/LCF6KgZ9c5A6Guj8f95DTVnZ6svfG93Khz4rIyTHj3/MN5xZgPM/f/A7zwtfw1AWXKwQQgghhSMLRO2f9xhEFonIISKyXERWiMgZIrKrQ/09ReQsEblFRK4TkZ+KyA5WdUtNtx8iQZY2njFIokO7CJtkbr4OuzbmG412+8p04WPu0gpvdgm/HjOlmXZSkjOJZKlm8lhohWjndLC94F+eqfasffQXv+s1dhH5MoDnAzgcwCUA9gGwPYCdjTGnD9R9H4C3ADgOwM8BrAFgGwAnGmO+NdQ3HWwJIYSQwlmQOc9KrYC8GMABxpjD62PHArgAwCEAntxT9wkA3g7g+caYE7z6T6ms5NhJzNLuQBsftaOrfozw4ZCyfWOfVs/FGdd3XNPanTbWkPZyUtrvMqeC0Vcn1rgmlKJK244hdByh7djMX05l5dd7/4OasvLIY7/tPHYRORTA6wGsZ4y5uXH8bQDeD2BjY8wVU+p+FcASY8yOIrIAwOrNNmygzwohhBBChtgOwEUdi4yzAAiAbXvq7grgFyJyMIAbAdwkIstE5F9sOy/WZyUFWjskjXa1cU161C6r7T/SV689Hl8fGO2xusybCy5KlM35djGW67TNGPy7xkIpyTdLShExwTW1Qwmhy7/Z99l6ysox375xqIwxZp3m/4vIBQAuN8bs3jr+SAAXAtjPGHNUux0RWRfAdQCuBXAngHfX//8qADsBeJ6NaYg+K4QQQggZYjGA2zqOr2x83sWa9d/7Afg7Y8yZACAiJ6By0v0PAIOLlZlVVmKpJu2yTUJSiNskgtLGx68ktD2t5Gu+PiYaPiFafiSh4xuj8uCzw9Vqd9Z95kpUynLhkkTPd97GrKxsefS3fHxWfJWV9QFcDeAyY8ymrc8OA/A6AGsN+bBQWSGEEEIKp4C3Ll8BYMOO45Njy6fUuw6VInNlx2dXovJ3WRtA72Il+9kTQgghpHjOAbCFiKzZOr5j/ffcrkrGmLvquht3fPwgVH4s1w11XowZaCjRWKi0qR066GsGGirbV1/7HR9DSY/6jtuW8XW+jdWXxnhsriHf77z0UOXSTB8uaP3ehxzDc6ExDtfzzHk9hJjtNcxBqc1AF+33HDUz0BafPdHHDLQjgDNwzzwri1DlWbnSGLNTfWwTVKHJFzXqvhHAhwHsZow5uT62FiqflYuMMVNztEygGYgQQggpHFmQ1xBijDlTRL4G4FAR2RDApQBeCuAhqDLZTjgWwM6ozDsTPg1gPwD/XfupXA/g5QDWAfA2m/6zJIWbEBr25rOq105AFDPJk+84XOu64rPj0kogNzSWIYb6skkg19e/tqrjG7qcilJDjVOOq5R7wTRK/Y5s8FVPYgVWNEmtrPz2Fc9TU1YeceQ3fNPtrwbgvQBeAmBdAOcBeLsx5pRGmaWo0u9Lq+4DAXwIwDNRRQ79qq57qk3fVFYIIYQQMogxZiWAN9f/ppXZZcrxvwDYy7fvYnxWYjEWG7tv6HJOZcVFLbFRxnzacw1ddsEnoZSWb860sTT7iB3SHnM369NXX5/avm3t9mPgM8+lKyNjGacvzesjdbr9373yBWrP2s0//fWkY9eA0UCEEEIIKZqZV1a0cFEBxkJoNFCs6BuXPl19TEL7H+qzT/HRmoOucYydMf+exjx2F2IkVmvWbZJDbSo9GmjelRX6rBDSYNYfOISQcVJAUriszPfZE0IIIaR4soQua4UR55Rffft2SdCmTUj4sKv062NCscEnsZ1rO0N1XNu3mYt22VCn6nkxTYyZVM68Y77PtseQchxDfaY2A13y6hepmYEe9snjaQYihBBCiC7zbgZKulhpr05DnahSJJWbhu/qflo93/ZczsElJXyfetCnkA21E+p42rcbTZWIrg/fkGqbOfUhR3hsaDjyhJzp2V3aCR37tLJaaoLWfTZlSHssSlCJiB/zvVQjhBBCSPFE81kJTXLWrutKyO7OJjRVu28tfHc/LtgkSwvxWdH2DfFtu6+vNj4hxi6h2a471ZTJzYYo0d8gZf+uY/C5T3aVT3kfCvGjivFdDfXle39s1kvts/L7A/ZU81nZ9LAvjc5nhcoKIYQQQoqm+KRwpdgYSxlHbEKSnPkqKyHt9fUR6vehrU65RCBp+UNoE+t30KdqpvShiRVpNZb7R25Fygbt+XfxdWtCZSUtjAYihBBCCkcWLsw9hKwUr6z4ksNmX9ruKVU6ahffi772fO3w2n46LtEZLr4qPpFMNuMi4cTypyjNb4TcTeh8pVZWLnvTXmrP2od++LjRKSv0WZlRtENzY4X69hHTSTikT60yLvABFI+QBSwZJznuZyQMmoEIIYSQwmFSuBETmjBOq692nyWkQ49tEukq02yvLxmc7ThsEqv5OuG20U7G1sTl/Gz6H5vc72sm1O5/Wh8x+tYI5bUxnYaOQZvSxzfBd26b1/IRZpn6uMh05nupRgghhJDiUXOwTeXMOQZC041rnLOLs6prGRtiJV+b4OKAGhoibOP82oePk/Cs+1GM+fftgnbIbMp7S47Q8aE2tNpxbaOrXmoH2z++bV81B9tNPnA0HWwJIYQQQjSZ2dBlG+Ztdzch1D/DJSxZo/2+Pm0UEd/+J7ioHjZz4dJOzGRpY6WUxGU+SojL2Es5z1Bm9XqlspKWUTvYEqLNrN1QCSGzwbxHAyV9kWH7M5ddqC8hSbv6+g9NRGaTfCp2VJHNzs1XufBRN3wTx7kQktDOZS5C/VGG2m/30SbEt6GUBdtYIkt8KWHM2vexUokx16mVlT+98+VqysqD33fU6JSV+V6qkaIpNREdIYSQtNAMRAghhBQOzUCFOthqheiFyn+ppGgXk4x2eHizzVjp622SuIWajoaSbPX1EdPpeFqbvuGsJZgPCJkVfH9Pqc1Af37Xv6o9ax/07s/QDEQIIYQQokmxykqb3ImNchBLqXFJOKbloOyjOIQ68GqdQ1+dUAdi7XZKQCtdvDZav6cJKZOljfk+NlaGnjmplZXL3/1vas/ajd/1X1RWCCGEEEI0GY2yEgONnVbOHWKM/n1SgKck5XhKO/dpaIVzx/ZXSl1/qB3t83ZNjlj6deVCqI/hhDHdi6mspIXRQIQQQkjhMBqoUGWltCiInDt6n5TdLhEws0qpvhNaaCfMa+ObKMymTReVw+X8tF+1MCFG5Nws+96lVH9ztZdaWVn+vleqPWs3euenR6esjGapliNBGCGEEELyQzMQIYQQUjg0AymZgULeLupKjpC/2I6nvu9Scml7LBIyscPmfUTtz7TNGb5h3C7thYapD4Wna5/D0HimjatUSrt/pBpPaaHLV3zgVWpmoA3f9imagQghhBBCNCnWwTYFsZJElZDsyfctvTZlbdoIUYdmwblaa0dt4yTc10cKFaEEtJxwXZIExnLmDSXktxZTER8aQ8o+NUitrFx56GvUnrUbHPgJKiuEEEIIIZpE81nRprTVN5OTlUep8xQrZDxFKK9t+01iqTlaryvw6TNH6LJvigIXUiqesVXkUJ++CbbtUFlJC6OBCCGEkMKRBfNtCCneZyWlopIi6iZHQqMSUuiXqnqMGe1ooJTJ12K34xpVlCMyapZ9QlJEg8Zq35bUyspVH36d2rP2AW/62OiUldEs1fiQI4TEgPcWQsqneGWlCxcvftcb0bR6KfOqhJRtlp+3m/C8nXdo3pFZjRSK7afjgo3apY2vqmEzvlg+KqERdzl+86mVlasPO0DtWXv/Aw6jspKD3O+ICMGlLy5USCgu19AYKWmh0iTHeLRC7dtltK+hlO9YIuNlJhYrhBBCCJldkoQu+8ifKZIVaagQKdNpa5u2xsTYksKlQMvBdlqd3ISoJK5zkYMQ82+oyuHibO9zb7cJ9S497cPQ9XGEWZbUlHLNx9+oZgZa/7UfoRmIEEIIIUSTaA622kqIa/r4kD5KSfPuQmwHtdAEVaFO0Vrh16WEPWqile7dJcRXezwuDsEx/W5cru2QkG9XxpZCPxSt++5QwETfZ0N9p3awnXdlhUnhCCGEkMJhUrgRhi6T2SZmmHibsdjNbfwF+vDxN4gRyhyiSti011emjZYSot1OyqSSLoxFZQ6lVGXl2k++We1Ze79Xf2h0ysp8L9UIIYQQUjxJfFbapEyGZBMxEXvHqxUh5UJuNSGEmAnz2vVKiizz7TMUF0XFxn/EN1ldTnKMSyMKsfTf8hjwncvUysr1n36rmrKy7is/SGWFEC14I85LaQuKWPA6I6R8uFghhBBCSNEkSQrnglYSH613YqQyF4SGF4aOMyT0OeV7Qih/342Wk+qEEHNQKLNmHrJxJB4K74/hYO4Srl6CE69vuHos83GT1GagG458u5oZaJ1XHEwzECGEEEKIJlmUlRQJ3qb1mXO30NV/LHViXsi9E8yJtrLSh0/IsU+ytNDXAaSciwmxdv99fcS6F4zp9xT7fjg0F1RW0sKkcIQQQkjhyML5NoSoLVZcVrcpQkzH8tKsUnYvscK4Ndrva8fVr0VjHLn9i7SxUU1sxtyuF6q6TPusz5cmJNV/V32b78hXzXG5DlJdK76/mRzXdOy+Svl9kor5XqoRQgghpHiKiwaa0BeRENtua0OonTrnbqXUnVLKcZRyfiFo+Y/YJFAcGoNrOy7X4FD7XfSpJbb1+sbiO57SooFyksN3UZPUPit/Pfo/1HxW1tr3PaPzWRmNslJaSCOJD79zQgghwIgWK4QQQgjJh4gsEpFDRGS5iKwQkTNEZFePdr4nIkZEDreuM+a3Lo9Ryo+dfE2LFH1qm/VKeD9PKX2HmIhCzS2xw4d9TTxd9W3b6DNl+aLh5K3tsF4aJY8vtRnopmMPUnvW3nfvg7zGLiJfBvB8AIcDuATAPgC2B7CzMeZ0yzaeCeCrANYA8DFjzOtt6lFZIYQQQkgvIrIDgBcDONAYc6Ax5kgATwXwRwCHWLaxKoDDABzq2r9a6HLK5GZjdrr0SZo3q7un9nhCx5fj/Eqa0xQ+Pn2qhI2D7ZD64uJw21XPZg5s2umr0w6b1wrRHsv1a+Owq+XUa6NoxQzqsPlsjngBgDsAfHZywBizUkSOAvB+EdnQGHPFQBuvA7AYwIcBvNulcyaFI4QQQgqngKRw2wG4yBhzc+v4WQAEwLYApi5WROSBAP4dwKuMMbeKuFmi1EOX+8i5Ko0VCh0jLHBsKfhTjqGEvly/81hh/SUQ6rOi5d8Sy0/GJ+TbtW0ynRS+Ob6pHFL7rNz8xffo+ay85F03DpUxxqzT/H8RuQDA5caY3VvHHwngQgD7GWOOmtaeiBwN4NEAdjDGGBExcPBZobJCCCGEkCEWA7it4/jKxued1P4ue6NyxPVadI06GignNiv1CTar+bEnSEpFCUpSaaT0VdFIsNbVTqiCoVVmqK5vfdu2Q9BSAqe1a9OGloJh01dshuYttbJyy5ffp/asXeOf3+k8dl9lRSp7z88B/MkY86LGcSorhBBCCFHlCgAbdhyfHFs+pd5zAewA4O0isqT12Vr1sSuNMSv6Os/usUMIIYSQ4jkHwBYismbr+I7133On1NsE1VrjxwAua/wDgH3r/955qPPizUCU/cuntJC/Ut99pD1PKR1Ybdq1cVQPGYf2u4F850bbKViLkGvat26OPkPQvDekNgPd+tUPqD1rV/+nt/mYgXYEcAaAA4wxh9fHFgG4AJUyslN9bBMAqxtjLqr/fzMAW3U0eQKA7wA4CsDPjTFX9fVPMxAhhBBCejHGnCkiXwNwqIhsCOBSAC8F8BBUmWwnHItKKZG63qV12XtQhy5faow50ab/US5WSt05T0PLwU0z7G5oXFrzV9quaYJPuHronMQKXe7b6Yc4xLoqB9PKuyaOG2rXVbEJCaXuquvjHGwzzhzXV4o+p/WRI9XDWJ4ZBbM3gPfWf9cFcB6APYwxp8XueJSLFUIIIWSuWLAw9whgjFkJ4M31v2lldrFsy8kUVYzPytgSobmglTjOZ45C+4yJxvcY8zw1kgaG9mlTNiVaSdJslLwQnxztUGPf5Hc5UhOkSKRmW1djHNPazn3fSO6z8rVD9XxWXnhg0rFrwGggQgghhBRNsLKyvywxQLzVt4sd2DX5mo19OuWuesKQj0kMj/2S1KrcCfJCInN8xxkrGmiCdtI0rSRuE3yVFZ++fdprojXvqVRW4kdpSeFu/e8P6ykrz38TlRVCxkwO0wohhJB+svqs2ESjNAlRVGzG4dJOzJ1NDtmRRq8AAByjSURBVF+JEEqwkZdCn0Kg/X26+E70EbL7d1VqNHxMtP1RutrWmlubvkonljJbil+d7++RykpaRhMNNJYfNiGzQo7fHJWt+YP3djtkYf5ooJzQDEQIIYSQoikmdNmF2OYCbXmyFLmzj1TmrhRz4SJb24wlh/OsS7sTYppH2riYeLrqtdFysO3r08e04+Jg6xt2PY3c942cZtnYvxkNM21qM9CKEw9Te9Yufs4BNAMRQgghRJkCksLlRE1ZKSG016Wua/2cao6NA/C0uq7t2ISQl6oOjQXtBIgxHGt9ysYOj/ZVboZ2zq7pDKb15aqmlOhIn+P+mPveMhYH2xXf+riesvLs145OWaHPCiGEEEKKZpTKihZjHLMPOZKchaClJM06LvPURyzVRCvst095CFFoXNoIHVefihOCTXu+qq1PmS5S/UZjzm1Xu8mVle98Sk9ZedarqKwQQgghhGiiHg00L2qFL1q23Qkpk9UNMQbbc6odbwq0fE18iBVR09dHzARtQ2Noop2MryR8EgOG9uXiuxVzPK5QWUkLo4EIaVDKjZAQQprIgvk2hMz32RNCCCGkeJImhRtzeJsWNJOFYRN2moNYyfNCQ5ZThRPbjmvo3UB9ZggtB1ubsi7t+7Y3S9erFi7h3CGpGDRIbQZa+b1Pq5mBVtvjlTQDEUIIIUQZJoWLE7ocGlLXLlPaDqCPkFDhrnqhScTmPdy3lN2sNtrKiku7vsRSLvqucZv0+JplbQm5FlPcF30dYKfVyaE+hrQzdN9Irqx8/0g9ZeUZr6CyQgghhBBlqKzE8Vlx3c3G2imkbFc7fXaIIuI7/z592bTrkrbcpT3bz0LKavftQmiCPJf5j6UUpAg1jp3ozbU9m/HMkso3S9j+lpMrKz88Sk9Z2e3lo1NWGA1ECCGEkKJJGg0UylAEwRh2KrF34F2MYV5SM7ZXEHThs/u3UQL76qVMLpcjisenHS31hcTH9zfcVS+1snLbKUerPWsXPW1fKiuxSJm1khBCCCHlMJrFCiGEEELmk2LMQKVJ7LZ0ObKWfi6xQgiblHru2sR26rVpz4a+lAI+qmXou4EmhJqeXEKXtZ1nteafjJPkZqAfH6tnBnrq3jQDEUIIIYRoUoyyQspCO7HdGMkZ9q7Vngsp3mo81IdrKgANB1jXBHIhZaaVb9Yp5bejlc5AO6VDDopwsJ1zZYVJ4QghhJDSYVI4KisTcvgflLrL0EpoV9p5heDrm5PKV8XmFQspw5ND29PwqWnW105+p51ifhZ/MzHQfg2AL8mVlaVf1FNWdvmX0Skr9FkhhBBCSNFkVVbG4ONQ0m4n5g69hPPrQ8Ono1m/pO/VF+1ImpAoF9d6Lu3lUHxSKFElXnu+94R5VKVTKyu3/+wrasrKqk96MZUVQgghhBBN6LMSAdqp7dCOgElZvyRyZnd2jZIZKttXzyUlv2/0jos/ylD7Q5QeDTSN3IqszTxNK6OZE4rKSloYDUQImWv4Kg8yChbMtyFkvs+eEEIIIcVTjBloSLZzDVPTdqQMkR5j9DWtzoTcUnJOE492O7EIldNdTB8+6fFD34ScAg1H2BgJ37rqtevm/G2MmVRm9qHvKrkZ6LTj9cxAT3wRzUCEEEIIUYZJ4eIoK7P2cruUqdfHRspzKHW+Sgjd9H1JofaL+UJePGjTns1rCnzP2yacO8Txt6tMm1LunSUoxb6/p5AU/7Zlkysrp/+3nrLy+OePTlmhzwohhBBCiibYDGSTuMlntxIyhhTk3OGXZvd2STMeOvZQX6QSlJnSVKGUTFMauo67KD0u32vfvWpaO64+K1qJ9nKicU/2bcelTlfZvu9Ps++UyJybgaisEEIIIaRoiokGKp3YO3KXnYiv/TwWNjbj0ihhfKHflY+q2YVNBJ6PUuaisvahdZ42+PiRlKKalBARGBrJqK0U+8xJqT4rd5x5otqz9j47Pmd0PiuMBiKEEEJKh0nhCCGEEELKJdgMtL8sMUDakM2h9pt92MiAJZgEcpPqu8k9xxpSeSnvRtHGJ/TYNxGdi+mprz2NcOm+cdiMKxQt00eIqa4Us40Poe3Z1O8qk9wM9Itv6ZmBHvdsmoEIIYQQosu8RwMlSQqXM/lRKTv6saO9Owvt06ZejmRWNu1o7QQnxHKwtSnj6tQYS5VwSRwXmkwvVIGaNi4bSrifxVQWtX9zE3yVo776qZWVv/3qu2rKyiqPfebolBX6rBBCCCGkaJKELpewG5hQir9BrIRLOZKl+dp8Y+MTHttXPuU5uMxpFyFhtS6Kp2vossv4XBRZF2x8OlKES2vcA3LfU0sZRw6SKytnf19PWdnuGVRWCBkz83jTJYSQ0lFXVrRttC74RgPNOrGVjxyJ6HLimuwshU9PCC4KgZaaEKsvreRwLvj2meo3EppwMiSBX9exWM+I1OoolZW0MBqIEEIIKR0mhSOEEEIIKZfi3g1UQuiaT/td5AzrC3UadOmzFMfTmO8BCSGl+XMaKc03IdiYD0LaDGnHpR+f+0Mpv6NU7cQ0mcYOZAAymIHO/aGeGWib3WgGIoQQQoguspBJ4YIayPHW5RJ2IK5OlrGYl9DBUs8z1PnQpx2buhrqgY16VQqxkrlpl9FSKEv9PUwoLRVAjD5TKyt3XvAjtWftwkfvOjplhT4rhBBCCCmarOn2XQhtr/SdSB+lj32MO6Qcqc590rz7JoWbhq/6oq04tPEZS7NeiF9LDD+ZdtspwvuZpsEejedTcmXl10v1lJVH7kJlhRBCCCFEEzVlJXbiK+4S7sZ1VzCLczjmKJ5YEW+h+CTt8k2EFqLq2KhDLmqJbxRPqDIVEg2UQlm0mVvtPm3GkuM+1jV2KitpYTQQIYQQUjoLGA0U1MC0dPuAXox7m1gREyl20CF9zoLfjkuUQF+ZWSdk5+tKqjwkrmjndglRRJr1YkUDpSRWZNo8kVxZuehnesrKFk8anbISzWeFF3o85mVu5+U8SfmUsoCLzbycJ/FDRBaJyCEislxEVojIGSKyq0W954nIV0XkMhG5VUQuEpEPicjatn3TDEQIIYQUjpTxbqBjADwfwOEALgGwD4CTRGRnY8zpPfWOBLAcwHEA/ghgKwCvBfD3IrK9MWblUMfBZqD9ZYkBZncX7PLGUKJDTudZ3xBTn1cZ9PVr41RaiqOhRtkY9TWJGXocEm49JiVk2jVd+isgpnGEWZbUlHLXxaepmYEWPPyJzmMXkR0AnAngAGPM4fWx1QBcAGC5MebJPXV3McYsbR3bG8DnAexrjDlmcMyuAyaEEELI3PECAHcA+OzkQK2IHAVgJxHZcFrF9kKl5oT6r9VqM0lSuAmlhoCmxMdxctZVnVk8r3l0Ep7F7zEmIa/ysMEnSV/MJHhahCg0muNLrqxccoaeg+3mj79xqIwxZp3m/4vIyQA2MMZs3Tq+K4BTAOxhjDnJdgwisjmAiwEcaIz50FB5KiuEEEIIGWJDAFd0HJ8c28ixvbcAuBPAN2wKqznY9u2qtENU26ti3yRWLkmZQn0S2mW0E4P17ZBK3+n6JCPro4Qd/hj9NEJ/K9rfTazkebGUShtFJMarGlzG41LGJ4leitcdhPTpkk5iTP5ArrRVE0sWA7it4/jKxudWiMieAF4O4APGmEtt6jAaiBBCCCkdyW4IWQFgUcfx1RqfDyIiT0Ll5/JdAP9u27laNNCEnBEcNn4gvlEVOZK5la4m2Oy4fHaWOVShFH2OLRGXVmr4MZHyvIbS2YdGyfT1E5LYznUMIZFuLmVsxu6b5K+L5D4rl56lFw202Q4+0UDBPisisg2Apah8VZ5qjLnFesyuA87FLEtypJscD8JZe/gSQu4NnydenANgCxFZs3V8x/rvuX2VRWQzAN8HcBWAZ7osVIARLVYIIYSQuUUW6P3z4+sA7gNgv/8bksgiAPsCOM0Ys7w+tomIbHGPoYs8EMAPAdwFYHdjzDXOpx/r3UDaDoY2knQTbadNLTT6jOnI59K/Tx/a5hbX9sbyncd2wNbsQ5PcjuElmIEm9JlSXMq44NqeTfDEtDqlqBu+iSFTvxvort//Us8MtOn2XmMXkeMBPAfAYQAuBfBSAI8D8BRjzGl1maUAdjbGSKPeOQC2AXAogPNbzV46kP0WAB1sCSGEEGLH3gDeW/9dF8B5qHxVThuot03998COzz4PYHCxEi0pXCixwgrHEFIaq68Qh+ImLipOrJ19qMNuiANw6HhCyTH2ElWYsePi4BniNNv320uJRjK3vvK+zrO+40j+1uXL/lcvKdxDH8O3LhNCCCGEaJJUWQldUdu2b1svJBzZhVC/Cu1x+ia0i03p/jYxSXUt2oyhtLmJTS6/J5dkZDa+ISHKRR+l+piEhFRrnEvq0OV5V1bos0IIIYSUTv6kcFlRU1ZCIh6a9Vx8HMbkZ6CBVmK7sZxvH7F2txptDvVVyvy7JFnUUAFcxpCqvms/zb607lUuu3wb3xUfur7P0hSVCbHG5dpucmVl2Tl6ysqSbUenrIxmqVbaD4bMJqUsJAghhNwNzUCEEEJI6cjoxBBVsoQuz6vpoon2HJQwT6Um9Mo9rvY4Sk9IVxqzcA5dxEoKpx2uazNGm3fwuIzBZ3w25nEbE6ctyc1AfzhXzwz0kG1Gt/IZjRmIEEIIIfNJNGXFJj2+dsK3Jlrp9lMlaAvd/c/q7nNCDkfKCVrOpC59zQKzcH4xHYi1HWtD1AgtxcEnLcVY/RGTKyt/Ol9PWXnwVlRWCCGEEEI0mZt0+yn60Egfb1O/FB8MH1IqbqGUNh4XXPx36EsznZih1T6+HNPKdvWVUrEYu0rSxDaMm8pKWhgNRAghhBSOYVK49On2J/ikxLetF9KnTXu+fi4+yaJK3326UKoqlELxmbfvmvQzdC8I9UvxSfQWqtTY9Dlm5ac5P6lfZPi3P1+opqys8qBHjU5Zme+lGiEtuFgghJDyyJpuv4vQlPLT2mvi81Kwobpd7bgqRyG+Ki628VIeyLOgJmirJbGjz5pt21wXIblrtFS0Uq4T7Tw+IVE7rpTkU+I7Fu1zCG0vtc/K3y7/jZ6ysvGWVFa00b5R2bST46bo0mfumzYZpoSHQhfaD9NSf0/aaM1FDkodlwva5zALczJvFL9YIYQQQsh8o+5g62sCmUaoI6tNOzbjcgnzzGkacEnulCMZXh8uifJKMQ2MGc5hXmKbgVI4u9okq4vdnksgg007tueQ3Ay0/Ld6ZqCNHkEzECGEEEKIJklClzV2cL67Ay2n3GntxUyT76LihIyBzEZq+HlhzOHvNoQmhXPpo69uLIfWdp++55AbKitpYVI4QgghpHSYFC59un2bXayPb0hulcOHUP8Yn75K2o3GINSvyKVMbHzHEBKW3NWGVnhySeHcLsRQc7STpNm0Y/M9+rTnM/YU4dTaPjVNkisrV/xOT1nZcPPRKSvzvVQjhBBCSPGoJ4Wb4OqhHVtN6BtXSDuuvg5DKbZtyqSYN6JHSd9NDn+PUn1MchOirKRMkObyWagaox2tZINvn6mVlTv+cqmasnKfB25GZYUQQgghRBMuVgghhBBSNEmSwoUmNZtWZoKWg63NuPraDwmldnFmDDVlxZqb0sYVi5iO3FpovMcmdzi3jclUo91QtBNhTnAN6dUwobgmk/Qxj2ubdHxDvdu4PnuSm4GuvEzPDLTBQ2kGIoQQQgjRZDRJ4fra8w1DLTWMMmRcE0LDr2OpJaU6B+dWEdrjGIvK5IuP8uP7G0553bu0O/SbjRUybNtXTsff2A7EGonoqKykhUnhCCGEkNKR0a0vVMmaFC63OjEvu1gNbHa8TbSSiI2VMYQIx1YWtcqW7hMVquy64OOfEVo2hipREr4qTnJl5aplesrKA5aMbuVDnxVCCCGEFE0WZUWbUpSaWSTHeZfiPzKNGH4VQ31pqQquYw9hXn8zXcyCKtGneuUYR5/PT4pxJVdWrv6jnrJy/02orBBCCCGEaMLFCiGEEEKKJpoZyNfB0Cak0aXd2ImkQp0abcwGPkmeXM+3hPBOF1I6XeY83xLm2pYxjRUIT+amdU9pt5fTPOQbFu7Sdizzja+ZyicFBpDeDHT7NX9WMwOtuv6DaAYihBBCCNEkq4Ottvpi21ebkORkWiGqoY6PY1EKZoExhV/HDAl2HUOKvlLi4rzcV38aoanv+8q7KNgu7bn0GZrQLreDMpWVtDApHCGEEFI6C+bbEDJToctNSk1K5mMHDgl5bdafpUR5Wj4E2r4IKZWMHMnSSkpH7zueUq5xDWWlr55WCv3QPmOR+1ySKyvXLddTVtbbaHTKynwv1QhpkfsBRggh5N6oKyu+SbFi7aKG+mm2p53Qq69/rcihkPa1xlkKY47aGeN8D5HiWiw1lb5P26FRMrGiB7VUHZs+QyKkXOZLIwIpubJy/V/0lJV1H0hlJRa5nakIIYQQkofRLFYIIYQQMp+M0sE2lfSr1Udoe6EhktPaiVWnr50mOediqF1NSjJVdFGCiVOrrBYpzHpD4cjapo+UpBiXS+JQrbDpJsnNQDdcpWcGWucBNAMRQgghhGgy6qRwNnV9nPNihjfbOM3G3nXG6jOl4uOCdsK9FNdHrPZzU7pKklJ9jK2EpAgn9kkKNytQWUkLk8IRQgghpSPzbQgJVlb2lyUG0A/t094NpSwTylAfpSoYvpQ6Lm1KUxVK8KUZQx8a2HwPfWirEyHh0qHJ6nzqlkhyZeXGa/SUlbXXH52yMt9LNUIIIYQUj5rPipatPefuc4JLsiKXdm3rteunSG2eg1LGMa/My/z7JFec1kZXPW2fFV/1xaUdrSiZkCRwocqKzWsF2vgqQV31Uisrt/31OjVlZdFa61FZIYQQQgjRJEk00Jj8Rab1OSGFyqGxEyR2lDK3OXPg+ODjU9ZX3qVM7u9qGr5KaojPSqgKk8oXpq+sTfm+slp9us5FcmXlphv0lJX7rkNlJRZjd8YihAxT6kKExCPHvZ3Pk/ExmsUKIYQQQuaTLEnhXML4XNJVu6a2tnGU8wkjDk0iNnRevu2NxcRQCtrzpj2nORMezktYcswxuPzOtQkx/2iaUobacynTVy/UkbiL5Gagm2/UMwOtuTbNQIQQQgghmhSnrKSkhHGEjiFHenZtZ+hZcige89jbhKowsUPvY7Tt0r9P6GxXOykZctTN7cuR0+HXFSoraWG6fUIIIaR0mG4/34sMc1PCLtjHT4YQTUpKPliaapKirwk+Piuuvhw+Yb4lKUBa7Wkk3kuurNxyk56yssZ9R6eszPdSjRBCCCHFU5yyorWz0Y6GGPPrBEroOwU5kwbO4pz6JnOLPSe51RctcvuHNNFSMmIpIk1CIpjGnG5/5a23qD1rV1t9DSorhBBCCJk9RGSRiBwiIstFZIWInCEiu1rW3VhEjheRG0TkryJyoog81LZvLlYIIYQQYsMxAA4A8AUArwNwF4CTROTxfZVEZE0APwHwJADvB/AuAI8BsFRE1rXpWN0MpJ3wTSu5VopxlYDv+HKEDw+Z6sb8vqTQcNZSzysnY56bvmu6TV8ySe0kbjnxvRenOofizECKLherLV7sPHYR2QHAmQAOMMYcXh9bDcAFAJYbY57cU/dAAB8E8FhjzNn1sS3qugcbY/5jqH8qK4QQQggZ4gUA7gDw2ckBY8xKAEcB2ElENhyoe8ZkoVLXvQjAjwC8yKbzpMpKH0NJikpxaNVKpa9VVltxcGmv9J1uqeOLdU02sVGrtNG+trXRSuaWcjxtUqbkn9a3luKjOQ6Nsq6MWVlZvPrqNw6VMcas0/x/ETkZwAbGmK1bx3cFcAqAPYwxJ7XbEZEFAG4FcKQx5rWtz94L4B0A1jTG3No3HiaFI4QQQgrHx3TTww0edTYEcHnH8SvqvxtNqbcegEWNcu26Urd9aV/nSZSVWDsurbTvpe3AY8/XhByJ6GYh1LWvvdKupQmhKkJJv6MU55KSUlPfu9D3W451fiHhyBpzm1pZyY2IXArgQmPMs1vHN0W10HiNMeaTHfUeDOCPAN5ojPlo67OXoTIjbWWMuaCvf/qsEEIIIWSIFagUkjarNT6fVg+edf+PYGVlf1liAP0kbqHqgk8St9DdY0kRNbkpdVyzRElqhy2ljadNSr8WH5+VUIb60ooc8o16Cumr77uK4Q80h8pKVp8VKiuEEEIIGeIcAFvUOVOa7Fj/PberkjHmLgDnA9i+4+MdAfxuaKECcLFCCCGEkGG+DuA+APabHBCRRQD2BXCaMWZ5fWyTOodKu+7fich2jbqPAPBUAF+z6VzNwTa2s6uNY6ZvGZfxTRtvk9iOoqWGccckpC9fB9sxmlk0KeUdPKXPsVa6hj6mmTNczTZayeWm9ZnTOdjXhOU7t/NmBgIAETkewHMAHIbKqfalAB4H4CnGmNPqMksB7GyMkUa9+wI4G8AaAD4C4G8A3oAqEmhbY8y1Q30zdJkQQgghNuwN4L3133UBnIfKV+W0vkrGmJtEZBdUi5x/R2XV+QmA19ssVIDMb10O3bmVnoSqDx8H4FJwUb8022/3EdqP63hC+yztGhwrMa8zbfXXpv82pYcuhzrhpji/FH3Mo7KSE/qsEEIIIaRosior2qRIfd9uP3fKbhe4sydNxqzu+eCjiIQmubQZjw2xfFZiKRAuSeFsFFSb+jb3Yp8Q5mljVc4oSwagskIIIYSQoileWdFSLkpTFbR3Z7HbiZliPkcyPZ/xdDFvvlY2lDpmH3+UmOeioWaEprXXSqjmgs/vyncM2u0069NnJS1UVghpUKpTIyGEzDNJlJVYOxqXHCrtOk1S7nBT7dxi5MgIyUNT2i7bBZ/rrKu+TZ1ZmK82MX3JQuqWFlXU1057nKVH0rik22+i4cdjO66QvgEqK6mhskKsoOJAUpA7mVhJcC4IuRsuVgghhBBSNOpmoHlOCV86pc2Fi0kstrksx5yUns4+5vhKuBZTjMEnwaGvWcPG3DLUdg7TTMx2prXbxLcPmoHSQmWFEEIIIUWj/iLDPkJ2bqU4jGoxbwm5tMnx3WklCAup01VPW5HKoY76hoenug5i3n/aaDqB2mJz7dg4AGuPJ5bK1PV90sG2bKisEEIIIaRokiaF80mP7NKeb5lpdVzr+bTnslvRPgebRFDaoeN99W2I9Z27tBMjQd4s+OSk/G5mARf/Ea0Q5hShz7aU5t/i2g6VlbRQWSGEEEJI0QQrK/vLEgOE7chtCfHzCFVxbNrVVjBiRWmUtrstVVUodVzaxPafSuGbljJpo3ZfE3IqDS5RMn1zkTM3jM19Vqs9gC8yTM1olJUSZEtCCCGEpGc0ixVCCCGEzCfFv3XZBi2nxhKccX1xGXts80azba1x2UjmISaxLrSui2nt+qJlHox9zWiZhGMnC9SaN992SkI7yVxffZ90F67jCjFPDZ0nHWzTQmWFEEIIIUWTVVnRdrxzdaKNnTBL25E4h4JUijNoKeMIobTQXu2+Yjnqhl6vOdQXl3FNw1fBiKkmhIwrJ6EOwF31qaykhcoKIYQQQoqmWGWldP+RmGGxJSkf2iHfMdW0EJ8SmwRcNu2VFiZdWp/kblyUFds6XfXHon7EgKHLswOVFUIazPONnRBCSoWLFUIIIYQUTbAZiBBCCCEkJlRWCCGEEFI0XKwQQgghpGi4WCGEEEJI0XCxQgghhJCi4WKFEEIIIUXDxQohhBBCioaLFUIIIYQUDRcrhBBCCCkaLlYIIYQQUjRcrBBCCCGkaLhYIYQQQkjRcLFCCCGEkKLhYoUQQgghRcPFCiGEEEKKhosVQgghhBTN/wf1ww8vOKIUdgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 720x720 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 20 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 20 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "A1 = rdpg(X,\n", + " loops=False,\n", + " rescale=False,\n", + " directed=False)\n", + "A2 = rdpg(X,\n", + " loops=False,\n", + " rescale=False,\n", + " directed=False)\n", + "\n", + "Xhat1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n", + "Xhat2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n", + "\n", + "heatmap(A1, title='Sampled RDPG 1 adjacency matrix')\n", + "heatmap(A2, title='Sampled RDPG 2 adjacency matrix')\n", + "pairplot(Xhat1, title='Sampled RDPG 1 adjacency spectral embedding')\n", + "pairplot(Xhat2, title='Sampled RDPG 2 adjacency spectral embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qualitatively, both of the simulated RDPGs above match the behavior we would expect, with 4 clear blocks and the corresponding 4 clusters in the embedded space. But, can we say they were generated from the same latent positions?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Latent position test where null is true\n", + "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between Sampled RDPG 1 and 2 (up to a rotation, see below) are no greater than those differences observed by chance. In this case, we will use the `LatentPositionTest` in `GraSPy` because we know the true alignment between the vertices of the two graphs we are testing. In other words, node $i$ in graph 1 can be thought of as equivalent to node $i$ in graph 2 because of the way we generated these graphs. \n", + "\n", + "In other words, we are testing $$ H_0: X_1 = X_2 R$$$$ H_a: X_1 \\neq X_2 R$$\n", + "\n", + "and want to see that the p-value for the latent position test is high (fail to reject the null)\n", + "\n", + "Here, R is an orthogonal rotation matrix found from solving the [orthogonal procrustes problem](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.linalg.orthogonal_procrustes.html) (Note: this constraint can be relaxed for other versions of semipar)\n", + "\n", + "Note that LatentPositionTest.fit() may take several minutes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8325" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p = 0.8325\n" + ] + } + ], + "source": [ + "lpt = LatentPositionTest(n_bootstraps=200, n_components=n_components)\n", + "lpt.fit(A1, A2)\n", + "print('p = {}'.format(lpt.p_value_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the corresponding p-value is high, indicating that the observed differences between latent positions of Sampled RDPG 1 and 2 are likely due to chance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matched test where the null is false" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we distort the latent position of one of the sampled graphs by adding noise. The matched test should have a low p-value, indicating that we should reject the null hypothesis" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x12dc56438>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x12e084470>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x12bf6e860>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x12e0a4080>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 720x720 with 2 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 20 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x720 with 20 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "A3 = rdpg(X,\n", + " loops=False,\n", + " rescale=False,\n", + " directed=False)\n", + "A4 = rdpg(X + np.random.normal(0.05, 0.02, size=(X.shape)),\n", + " loops=False,\n", + " rescale=False,\n", + " directed=False)\n", + "\n", + "Xhat3 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A3)\n", + "Xhat4 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A4)\n", + "\n", + "heatmap(A3, title='Sampled RDPG 3 adjacency matrix')\n", + "heatmap(A4, title='Sampled RDPG 4 (distorted) adjacency matrix')\n", + "pairplot(Xhat3, title='Sampled RDPG 3 adjacency spectral embedding')\n", + "pairplot(Xhat4, title='Sampled RDPG 4 (distorted) adjacency spectral embedding')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0175" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p = 0.0175\n" + ] + } + ], + "source": [ + "lpt = LatentPositionTest(n_bootstraps=200, n_components=n_components)\n", + "lpt.fit(A3, A4)\n", + "print('p = {}'.format(lpt.p_value_))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/tutorials/inference/nonpar.ipynb b/docs/tutorials/inference/nonpar.ipynb deleted file mode 100644 index 59cb2dc6b..000000000 --- a/docs/tutorials/inference/nonpar.ipynb +++ /dev/null @@ -1,188 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nonparametric Two-Graph Testing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "np.random.seed(8888)\n", - "\n", - "from graspy.inference import NonparametricTest\n", - "from graspy.embed import AdjacencySpectralEmbed\n", - "from graspy.simulations import sbm, rdpg\n", - "from graspy.utils import symmetrize\n", - "from graspy.plot import heatmap, pairplot\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate a stochastic block model graph\n", - "\n", - "We generate a stochastic block model graph (SBM), which is shown below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n_components = 4 # the number of embedding dimensions for ASE\n", - "P = np.array([[0.9, 0.11, 0.13, 0.2],\n", - " [0, 0.7, 0.1, 0.1],\n", - " [0, 0, 0.8, 0.1],\n", - " [0, 0, 0, 0.85]])\n", - "\n", - "P = symmetrize(P)\n", - "csize = [50] * 4\n", - "A = sbm(csize, P)\n", - "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n", - "heatmap(A, title='4-block SBM adjacency matrix')\n", - "pairplot(X, title='4-block adjacency spectral embedding')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nonparametric test where null is true\n", - "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between SBM 1 and 2 (up to a rotation) are no greater than those differences observed by chance.\n", - "\n", - "In other words, we are testing\n", - "\n", - "\\begin{align*}\n", - "H_0:&X_1 = X_2\\\\\n", - "H_\\alpha:& X_1 \\neq X_2\n", - "\\end{align*}\n", - "\n", - "and want to see that the p-value for the nonparametric test is high (fail to reject the null)\n", - "\n", - "We generate a second SBM in the same way, and run a Nonparametric test on it, generating a distance between the two graphs as well as a null distribution of distances between permutations of the graph. We can see this below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A1 = sbm(csize, P)\n", - "heatmap(A1, title='4-block SBM adjacency matrix A1')\n", - "X1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n", - "pairplot(X1, title='4-block adjacency spectral embedding A1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot of Null Distribution\n", - "\n", - "We plot the null distribution shown in blue and the test statistic shown red vertical line. We see that the test static is small, resulting in p-value of 0.94. Thus, we cannot reject the null hypothesis that the two graphs come from the same generating distributions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nonpar = NonparametricTest()\n", - "p = nonpar.fit(A, A1)\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "ax.hist(nonpar.null_distribution_, 50)\n", - "ax.axvline(nonpar.sample_T_statistic_, color='r')\n", - "ax.set_title(\"P-value = {}\".format(p), fontsize=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nonparametric test where null is false\n", - "\n", - "We generate a seconds SBM with different block probabilities, and run a Nonparametric test comaring the previous graph with the new one." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "P2 = np.array([[0.8, 0.2, 0.2, 0.5],\n", - " [0, 0.9, 0.3, 0.2],\n", - " [0, 0, 0.5, 0.2],\n", - " [0, 0, 0, 0.5]])\n", - "\n", - "P2 = symmetrize(P2)\n", - "A2 = sbm(csize, P2)\n", - "heatmap(A2, title='4-block SBM adjacency matrix A2')\n", - "X2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n", - "pairplot(X2, title='4-block adjacency spectral embedding A2')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot of Null Distribution\n", - "\n", - "We plot the null distribution shown in blue and the test statistic shown red vertical line. We see that the test static is small, resulting in p-value of 0. Thus, we reject the null hypothesis that the two graphs come from the same generating distributions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nonpar = NonparametricTest()\n", - "p = nonpar.fit(A, A2)\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "ax.hist(nonpar.null_distribution_, 50)\n", - "ax.axvline(nonpar.sample_T_statistic_, color='r')\n", - "ax.set_title(\"P-value = {}\".format(p), fontsize=20)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/tutorials/inference/semipar.ipynb b/docs/tutorials/inference/semipar.ipynb deleted file mode 100644 index 2ca652061..000000000 --- a/docs/tutorials/inference/semipar.ipynb +++ /dev/null @@ -1,390 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Semiparametric Two-Graph Testing" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "np.random.seed(88889999)\n", - "\n", - "from graspy.inference import SemiparametricTest\n", - "from graspy.embed import AdjacencySpectralEmbed\n", - "from graspy.simulations import sbm, rdpg\n", - "from graspy.utils import symmetrize\n", - "from graspy.plot import heatmap, pairplot\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate a stochastic block model graph to model as a random dot product graph\n", - "To start, we generate a binary stochastic block model graph (SBM). An SBM is composed of 'communities' or 'blocks,' where a node's block membership in a graph determines its probability of connection to the other nodes in the graph." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<seaborn.axisgrid.PairGrid at 0x254c28370b8>" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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QucOYFUIIIYSYJllZuXrzZgcAz966qVH1mp811dwX/99l7LUpFn1D+6fYDBk/p41cdOe+oP8+pqI9ngW67/MqnNec36NScx/7PtO2Efu5XNbGyv2acg4W2XKrrUKqepNEGGBLCCGEENMku4EOlx0dkF/BCCVkPrnUICssO4cauxcru7wxNHZ3Q+eptWtcpmKG7lin+pVQE7SwsCMf+46IUUtyflYsXC8fUtWYkP4LfNse4zZRWSkIlRVCOsx5UUoIIatKsrISErOyYKiNRjzL2E4kZNezCj9YobE0q7xzm4Oa08fS9etidV65iFGkNG359hnrl1Mh84mTKW07x3hDrzFmpSxUVgghhBBiGi5WCCGEEGIatQDbPlopvasQ4GqVElK+9VToEm6N3G6DBTmviY+tlPNMvUZjbuga7hoL7rLUwNOpcWP75xovNqg9JmAdoBuoNFRWCCGEEGKabMpKFwvqyNjuQmvcuak/6xYkCdjaCdYgdu65Co5ZUCByoJ3+vkBbzalZziBkvtrqi8Z5UlkpC5UVQgghhJhGPXV5jJRCbbUVjBrpzVqKVExRuFXd8S6IiXHQilUoEcNS6n3TVmpKYMF2CfvaKcsh4/mUkSgRX7QMDZtUVspCZYWQDqu6OCOEkDmjHrNSoyicDxbiZmLJ9TgA6z/MqSXr51QufhmxKoAF9UA7C8UaubNtcpaYnxp3rH+NuKfaMStD3yUst18WKiuEEEIIMQ0XK4QQQggxTZHU5WXUCJqNtanlblnWP6SN1rWqHWBr6RkrMeN3sVr8a1nbsfbaBdpCxp5DsGWuwoQhc0i1NRX0GtJXkxBXUe0CkQywLQuVFUIIIYSYZm0CbMkNKVGefU5l8TWZc4pq6jzmksJcgzkoR3MhVwq07+eJykpZqKwQQgghxDRVY1aGyBWX4bNanlOhNw1yKiEaaD2YLNW+1jgLSioP1t7XmPiaXDEKIZR4rEDMfaL9GckZG2LtXlyGj1cAoLJSGiorhBBCCDFNUWUlpdx+DiypHDXwydaYy24oJ6kPiNNWEUq9J7WzZHywcH/GKhlTSkqOGIyp+0sr/ql2bE4frfPs9mNRuLJQWSGEEEKIabIpK1o1VGrURZmah++4IWXya9ecWWY7Vk1I6W9tF2812ylXPIvW+zjU10LcSe1aIpq2c6hf2veihc9KDhizUpbZKCvr6qoh9uC9SFYV3tvEKrNZrBBCCCFkPUl2A129ebMD8gfN+gSmxe4KrAba5p5XiGS7qoWltLDuYgihZKpqLltj92sJd0sMuYqcxdjW7Lfs+yv2vUp9/0L7LoNuoLJQWSGEEELIJCJyBxF5rYj8p4hcKSJORPYM6H9fEfmkiFwlIpeIyL+LyG969bVWFM4Hq0pIKcZ2GSEBu9Z3+KlY3UH72K+pbIWkgPqMUzP92ofYa6tVxM13/Kk+ua9prvFT04hr3UPrqKy0C5P3AvgKgJ8D2BfAXs65jR59dwbwRQBfAvAKADcD8DI0osluzrkrx/rfKGXihBBCCFkbPuOc2wEAROSxaBYrvrwYwBUA9nHOXdWOcRaAswH8GYB/HOs8S2VlQUhqcP/4sn6WqBGzUhOtVNwFFsrkd/vVTrfWGG+BtXtnwZzSZMdiVKZs5ooxyXHfphQ89FFdaimo66isdGkXKyfAQ1kRkRsDuAzAG51zz+q99jkAN3bO3W9sDCorhBBCyBohIpdOtXHObato8s4AtgJw1sBrZwI4ZGoAdWUlpxoQsgPxidPQmOOclJoFFkqTj82jdtzIMkIyE6yewxAa2RWh/dcN7aynBTUebeBjP+Rzrh3rE0rI70n3tdLKiqYX41/x3cum2kwtVgKVlQcC+ByAxzvnju+99jIALwSwtXNu87IxZqOs8Itw/eB7Tggh+iirJkGmI19j6jIhhBBCsnJR++/2A69tB2Czc+7qsQFmHWC7IMQNtKqkpi7HBN75YDXgMWWcGgXCtMfNMXYMJYKONWzUvm4xxde02o711bimoW6gZZR+NtKc3UAaT4xWDLD9TwA3mQqwpbJCCCGEGGcL0fsrjXPulwA+AuBAEdl6cVxEfgfAAwB8YGoMNWVlLMAwhZj05KHXxko7+6TLaaFtQ+sRBhaYSxGxWGJ2dVrlxvt9Y/tbpeb9EXvfWlAd55KSnpNYtaW0svJnG/SUlX++Nl5ZEZHHtf/5+wCeD+BINLVSrnLO/UfbZhMAOOd27PS7B5qicF8A8EpcVxTuxgB+1zl3xZjd2QTYEkIIIaQ67+v9/5Htv98FsOOyTs65c0RkLzTF394P4JcAPg7gL6cWKkCGBxmmKgcllQdLKkcNasZ9aBH6ELSx/hrjlLimJYqH5SalUFisrQWpxc5iYi1Czis1pTe16Fp/vBhiHytgqcz+1LxKKyvP2uJOasrK0ddcMLuCdoxZIaSDtR91QgghhbKBLMSGhO6up8bTapujv+94WuWzUwnZZeeal5VYjpS4AysLrZLzCSnKp6WIhLTRGi+kDH1MkbNc5+vbPsSG9nWf6rusP5WVsjBmhZAOVn7wCSGkS40sHkvQDUQIIYQQ01RxA/WPD73mQ0i6tPXCcanXItVmDBYKtGmPY8UdFEOJ56fUDmq0ZDP3NdGal/b51SgK5zPOECnun6nfDI3CaiE850Z6bqBX/4puIEIIIYQos4XMbn2hirly+1rF5ZatqH1SXbXIlTatpcKUVA5KBcWl4rNr1GqjRY3rlctmamCspn1fmz7p7z79U1OPY+aaGsiqrb7kLpyoqcKUDrB97o3vrPZb+8pffmd2Kx/GrBBCCCHENObK7eeKbxmzlWvc2J3JsnmEFHIa26GO9VtXYnfHWmnXq8QqXINc6fldtOMpUrCuZuawFWJz6PqUVlb+6iZ6yso//oLKCiGEEEKIKuoxKyUKvvkQM4/UwnFzI3ZHYWEXNsdde+6Cb9ayNPrjaY1pdTxr19/ns5s7cyi1rbZtzf5UVsrCbCBCCCHEOMwGMpYNVBOfWiwxcSMhO5upsacIyUDKuRPRKnPdZxUyXnIx5xox1qixW9e2WeL+rZGplTtex/dzVFpZeeFN76L2W3vUz789u5UPY1YIIYSYpoYbngt+W9ANRAghhBhn3Z8NpJ66POdA1Ni03xLz6B7vkuJWsiYPxxTkqo3VYndzc2GFEFo4rmZ6ec2S/LnuzZKF+2q6sqbSnEu7gf52Sz030N9fTTcQIYQQQogqRQNsU3b/qcpG6jgWlCPtOZRUA6zs9EulTcbuPmNUgJA2XVjIri65FZ/U9zxG2R0bRyvYuFQJgKmEi9IPMjxyq7uqKStHbv4WlRVCCCGEEE2qKiu5+qQypviE+DVDXysFd9Bl0UjjXtUy6FR1lpOzWJ12aYGQ2KF+n1Cby/ppxe/4UjpmZd2VFWYDEUIIIcZZdzcIi8IFkqqs1CBmN8Ud73VoFamzdv01bITueJe1zbn7z61oWf+s5FTIcik0VqkZs/ISRWXl72aorKz7Yo0QYgjrP1aEkDrQDUQIIYQYh88GSnQDXb15swPCnpWjTU23i89zf6ySMx3ZujTeZ27znSIlLTb3s598bOcY24JNq89v0vosh7h2xkpYxNhKTcMOpXSA7cu23knNDfQ3Pzt/disfuoEIIYQQYpqVD7AtqXKsgqJS42mxKTZWfSdeg9Qdb6qtUvgUpyxxT6fYKp2uW8umFqmPFehSWln5fzfTU1b++ioqK4QQQgghqqy8srIgpFBb7E4rpvR6TUrsnGMokdI4Fl9UqnhbSEqvhfelBLFpzlo2a8SslIwnyp2ubvV+XYWicOuurDAbiJAO1r5kCSEEYDaQeWVlTOXQGjvXuAu0xteer/Uf5tAdb8l4g6k5rGp5fGvMJZbGquJQgxpZhDkymkorK6/Y5nfUfmufd+V5s1v5zEZZseA2IYQQQmqwxeyWF7owwJYQQgghpqnqBsrp4omZh0+Abcr4Q+NZKWgXgo+7RSPglJL5OLmLt2k/r2fMRs2A0VAs2dQOdtV6z625JHPcQ6XdQP90cz030P+5gm4gQgghhCjDAFtjAbZaSkPIONptQ9Kka1K73H6NAnTWAx1jrptmoavQ8XyY0y67Jjmve5/Ue6c/Tr+Nz3dC7Pn6PNZEIzV7qrRDaWXltbe4m9pv7V9cfu7sVj6MWSGEEEKIacwpK31yKhE+u4JcCkjNxwBol/W2SskddEyROW2VYw6PHtAugJaLVbW97PvGSjzQMtsWv39KKyuvv6WesnLEZVRWCCGEEEJUMa+srAKh5fundj8+GQCh87KOVqaQdrnx1PmUxieOwad/jZ24byzBKhMTE9IlRMFbxawnzfuXykpZmA1ECCGEGIfZQJWVldTsGg2bY4pF7piSEGWl9DyWUbM2Rs4MggVWd+k16tDEZHDk2umWjFvIeY9Pja0V7+SDdWUqVVHJeX6llZU3bHt3NWXlmZd+c3Yrn6oxKz43UK6FSmobEkeNhdgquMasX7eSWH2PfNC+F+d8LUJYl/Mky6EbiBBCCDHOuj8bqLobqBSxCk2NIm4+bqplbWPsdKlRqC0FiymN64DWe16iiFgK1u7tkqXvU9wssUHouQsehtieorQb6Nhb6bmBnnEJ3UCEEEIIIaoUVVa0VICQYMk5E5OWHKLCpKYplthd57atPV7NQnSl+1tkFc/Jl9TS+f2+2p+JIRv917UDirVSoS2U23/TdnrKymEXU1khhBBCCFGlSszKqisiqUztHFKvm7XS2svQ2P2Q5VhVp3ztDNnSUpuWjZ9K6biKMfupcwjtbyH1PrVP9xyOcZuorBSE2UCEEEKIcVgUrmI20NCuuAYh5alj6iSkFrxLmcOYjRIR9jHMseBVzQJmPqQWarOQmROLtprQH4cqn/53S2rWU8w8QrOeSsesvHX7ndWUlUMv+q/ZrXwYs0K8qPGluy5f9ISsM/ycEx/oBiKEEEKMw6JwMywKpxWgq+WmKY2W+2xOaboxxMrCU+NZPV+S75k+Vp7Xk/setPb8pRr4zqu0G+gdt9ZzAz3lp3QDEUIIIYSosrKpyxYVkSHGAjJTi8D59g1Fq9BS6Pi5+oViLXh2FUhVwVIVlZA2uShh26cY3LI+U6m8KW1ibGsRq6aVVlbedZt7qCkrf/KTc6isEEIIIYRoslIxKzlSoXMrNCXjb5b1sYZVX/YYJR8wl5s5Xv8a+Dz2Iyb2JWYOoeOFqLYx89FWWHxtlITKSlmYDUQIIYQYh9lAM1RWtPEp7lSDFLUkdOeUsluxstNJQTtzKCcWHvzYH9d37Kn4gLnvthfkjtFKfWihhXnNndLKynt20FNWnvDj+SkrjFkhhBBCiGnoBiKEEEKMw2cDKbuBaqYlzzHANhc+16KG5J4zqNSqKyvFJRAi4ae6ZLRcF9rXspRLJbRfKquYJl3jeVwLSj+bqbQb6Pjb7qLmBnrc/5w9u5UP3UCEEEIImUREthGRo0XkQhHZLCJniMi+nn0PFJHPi8gl7d9pInKQt20tZcV66XqfnWquQFat/rE2fZSoKWo/fblPzUDM1GtRQ3koGcCqHZhZY7euVfAw13u9bC5jbX3mkeNzvqpBt6WVlRNup6es7P+jOGVFRE4BcB8AzwdwAYBDATwJwD7OuZNH+h0C4K0A3g/gTe3hwwAcCOAw59ybp2wzZoUQQggho4jI3gAeBuAA59wJ7bFTAdwZwKsALF2sAHgqgO8COMg5d23b92MAvgPgYACTixWmLg8w59TlkPFyFm7yIVca6xzRUC6sFJnLoYh1x60dD1GDFEWl9twXaN23JdLw++MP2Vw3ZUVEjgPweADbLRYc7fGnAzgWwC7OuXOW9D0FwG845+7ZO34WgP92zj1yyj6VFUIIIcQ4mtlAInLpVBvn3La9Q/cEcE53odJyZvf1JcO9HsAHRORvAPwrAAHwDAB3A/BcrzmvgrJSMhbGZ1cQUpBNu60P1rI9tMgdV0Hi8In3WPb6UNvSWR+5SM1cCu0bMm7s2DWzsUrHkpVWVj70G/dU+63d70dnXzbVpr9YEZHzAJznnHtM7/hOAM4D8L+dc29YNp6IPBrAOwEsxr0KwJOdcyf6zJnKCiGEELJGDKgm3l1jXhORhwN4F4B3owmy3QJNYO52IkQ2AAAgAElEQVS7ReRxzrmPTBnmYoUQQggxzob6ReEuArD9wPHt2n8vHuokIgLgbQA+5Zw7vPPSR0XkDgBeB2BysZLsBrp68+brDTBHWdbCnGsHR6Y8rTn0abPL2loovJdaTC/XeY7NK9VVZ+H+nxO8XjpYT6eforQb6MO3v5eaG+gxP/xGTIDtG9GkGm/fC7B9GoDjsCTAVkRuB+BCAC9yzr2s99or0MSsbOWcu3rMPovCEUIIIWSKE9DEm+zTO34wgHOXZQIBuATA1QDuN/DaHwC4aGqhAmRQVlKxWso9Zh6pc4gJpot9GutU+rBWILF2WnjseLnTwmPbhhRXXAW0Pytj92uM7Vzzq82qntcUmvMtrax85I67qv3WPvr7Z8YoKwLgkwB2xXVF4Q5Bs1jZzzl3UttuI4A9nHPS6ftqAM9GUxDueDQxKwcDOAgDissQjFkhhBBCyCjOOScijwVwVPu3LZpU5QMWC5URngvgm2jSlR8H4Fo0GURPAfBvPvbVy+1rMcedpfXdSsjuM2bX6aMUjKWdhsR71ExXH8NHQfIpxtdva/XRAyXHLckqnMOqUlIRG2PdlJXaUFkhhBBCjLNhi9mtL1QpGrOSy69cA6vzGiMl4yd2nBgbueJbQklRc0LUEx9bOQuhhShkKePOiRJz14jPq505OLfYwNjrZqHc/n/seG81ZeWPNn19disfZgMRQgghxDTmsoEW1Ng5WC8xr72jiX2A2LI+sdktVvFRVsYUkD7a8TZzVCxC0Hrw3dT4qePkGs8KGo8FCO2vzSrUWfnoXXZT+6191Le/SmWF5Ke29EvIKrFKCwtCVhUuVgghhBBiGrNuoD4hwbhjbaxQ0q3kI6cv8ElhDgkC9cHn/VzWJ7bUf4wrJtV9E5PibT2F39pnLtWVa8Hdax1r73kImu6p0m6gj/3OfdR+ax953lfoBiKEEEII0WQ2ykoqIbuqLqUC72J3KzFBiLHXImS8lLLzMantPuP5zmtqvJC+Y/OJVY6GbJDypAae8v1rKBGMq/VedY8d4zZRWSkIi8IRQgghxmFRuBVXVmIK0cWMPzZe7M4hd+rmsjG7pKYrjo0Xozho41O8LUa1GrOlFeMzl/gK7VRjq6UFfIhNe58zGueXeg+tQuryKTvfV+239uH/9eXZrXwYs0JIB6ZoE0KIPcwoK6uwu9A6h1JluFOzUmJtLOsfM59QpcYnrqU/nrbiExOPUmNnqR1HNfSaz/ugNS+f+aW0zYnW53HZuCljrBq+73lpZeUTu/y+mrLysLO/RGWFEC34xUkIIQTgYoUQQgghxjHjBlqGFRnWKhouIx9CgxpTUpd9bPq2X9ZHO304V6xL7H1v6XNT+xk86+Lq8HGVaowb22aqb2x/n/nEuCatuYE+tev91H5rH3LmF+kGIoQQQgjRRF1ZiV1hT6XpltwNhQSvaaUu1y4WlaIMhASyxozbHccnwDalrH2OYm4h93RIUO8qKQSpAaPa3xM1lanYIPlVuh8sMHVtqayUhUXhCCGEEOMIi8KlLdYOlx0dYEMp4C5DD620Xa1CaDG2fNqOzSVGSclVRK9ESfJU1iU2ZG6sY6p3CUorK6fudn81ZWWvr54+u5UPY1YIIYQQYhrz2UA5WYUdQ6lsoFRqltJPfQzAUJs+uTOGamR0lIi1GrO5LuXoSzK37zyf2LRalFZWNv7eA9R+a/c84zQqK4TMGZbbJ4QQe3CxQgghhBDTqLmB5pLqZ5UahbN80mvH0ApA1bKV0ifWxbOsf2jK8VTq/lT/GCx+bmILvlk6h1hKnkvMc7R80vKto3mNS7uBPnP/B6q5gR58+ufpBiKEEEII0aRqgO2cV+ixWFKgYoNCU9QSn7Yhtn1s5BwnhpjrNYfUZR80AsJDH/2gZUOzz6qh9V1gne65HOM2UVkpCIvCEUIIIcaRDbNbX6iyNqnLc9yZ1vBh+5SGjylDP4ZWWnOMohJTtC60sN2y8wsZx2LqJllOzceEhDySImb8LrmLzJUktKhh6ZiVzz7gQWq/tbuf9rnZrXwYs0IIIYQQ05hXVmL91DV92MvG0JiPJrFZPFql+JfZHJtfieJyPlkQ/bYLQuYcck2prAwTU+wudbyalHykQUyGmvXrp0lpZeVzf7i72m/tg/7zs1RWCJkz6/AlSwghc8N8gO3Yj8fYaj4k9kI76yPX7mKsBkL/eOq8+uM+e+udf91PO8ZkLDZEI5YjRO2IPSctpWfZOCUq66bcH779YmyN4dM/xIZWDEau7wCtz3euOcSq3NpzL/EdfIzbpDo2Gcf8YoXckBq7/5qp1oSQ1YWfcz9ki9l5blShG4gQQgghpqkSYFsiMDaF1IBY7SC41GsS4+4qWareZ1ztdOmYecSkQk/1D2mzrE8NLKTmxs7HQkmAdaRkcPDUHOZYbv/zezxYLcD2gZ/+zOxkGrqBCCGEEOOsuxvITOqypQCrWGXFaqlujcBTnzY5CqotsxlTLj/U5tQchmyU9L/nCh70OU8tG1pYV0vmrLBYv7YpdoZs+c6htLJy2kP2UFNWHvCpT89u5cOYFUIIIYSYJpuyErtyraGIaNiOtaFVwjrEZv94KLniNEoWhctV0C42xmdKidIudT7U32rch9Y8LKg6Id9RJYpJWlBNfM6zxLXwoZe6XFSdOP3he6kpK/c/5VQqK4QQQgghmpiJWVmGlRV1DXJHz8eWhh/rr83U7j9Wccg999BMppBdtk+flF2szzzmHHPiQ8355Mr+0xhbE0tzGWLqs1I6ZmXdlZXZZANZvaFJPvieE0JIw7pnA9ENRAghhBDTmHcDpWIhgKzWOL52urZi3COxAbYh40217eIjg/vYnAoA9hl3jJDxQu8FCymgFsYbGjtX8H5q8HJKSnSXdVMkc13bKUq7gb74qIeo/dbe76Ofmp1MMxs3ECGEELKubNgwu/WFKrNWVmJTBheU2nXmVFpiAilzpf0OBZXmKjU/ZHMZWinaQzZDgmdDlKJYVUmTVUud9aFkcUpLhTBj7JSwlUrO1PbSysoZez9U7bf2907+5OxWPoxZIYQQQohpqrqBYuMDYrC+AxhjbO7LXvPpE1Pu3rdfbDn80L6huzyfgnZTfcbaDt2/MeqLdtG6GLR3/ENjWivmVvJ7Yq7fSXOat4UyBlrIFuutLaz32RNCCCHEPFWUlbGdkvZOq6ZPfF0i/32yblIzaUKK1YXgo5Ism8tU/6H2Psd9x82NVmxCzcJqPvafvfXO6plMvrY1sfBdMEesl/gnDcwGIoSsNfwhInNgA4vCEUIIIYTYRT11eSzAMHcQ7Zyp8TRW7ZTenPjcOylum9Sg19Tn9MRgofhg6L24yp/9VXMbpLjba3yfpWI9dfmrj32EWurybid+fHYyDd1AhBBCiHHW/dlAsy4KV5JcBd5K7DRz7ewXlCyE5tMnV4G8VDXHZ84xKdVjaJciX2VlJBbr12ROj0SImUcthbG0svK1Ax+p9lv7u+//2OxWPoxZIYQQQohpiioruXcgc17xl5y7z85+2euhbULUBJ+5pMbLaCgroenKIcpWSpxMqAoTk14eowiuQtn+HNc/ZR5WVZwFse+51fMbmldpZeXrB/2RmrJy7/f+B5UVQgghhBBNsikrc1Y5alDjemllo2jtOnPG0KS01VJzxpSMqbF9lJo5kKI6Wjtf7QKFMbZLKFsWrr9P3Fns/EJUw+48jnGbqKwUhNlAhBBCiHHWvShctsXK2Kq0ZvntHPPKleEzl5L8Q8qBz65z0UZb4QlRMMbqAvnY7DPUNyTDx9fOlG1LKkTtejI+xHzWata50Z7n2PeGVRUstf7RMsb6WPpcrRuMWUmkZGE0ckO0vzxSFipknvAzTIh9uFghhBBCjCMbRO0veg4i24jI0SJyoYhsFpEzRGRfz74iIs8QkS+LyM9E5FIR+YKIPNCrv1aAbU35L6bvEKuwY469Nim7y5CicLEpvaWITdGOteE7zqoE2KZg5bxLzcOqW28Mq9+vOd6z0qnLZz3p0WoBtvf8t49EzV1ETgFwHwDPB3ABgEMBPAnAPs65kyf6vgnAgQBeDuDzAG4G4L4ATnPOnTJlmwG2hBBCiHE2bFHXESIiewN4GIADnHMntMdOBXBnAK8CsHSxIiIHolnY/KFz7rTOSx/xtm+t3L6F0t9z3NHEELILSlVNfNMBQwktI5+rCJzPvGJSl2ODBy2lCI/dOyH3xyp/FoeoESw/hrVCgClozK+0snLOwfuo/dbe4+0nBc9dRI4D8HgA2znnru0cfzqAYwHs4pw7Z0nfjQCcc26vuBkzZoUQQggh09wTwDndhUrLmZ3Xb4CI3BjAHwD4hogcJSL/IyK/EpGzReQQX+Pm3EA+u/YQcqUkrupuz0dNCIk1WZYqPNTfRynwOa5Vhj5FHdIqIJc6zhj988l1Lw+NO5fPWM45TI2dGhs11DflPKx81/l8/0zN1cq5hKD51GURuXSqjXNu296h7QGcN9D04s7rQ2wP4KYADgHwAwBHALgUwGEA3ioiN3HOHTc1H3OLFUJqwjRWQghZypgratlrCw/OlgD2ds59FwBE5BNo4l3+DsDkYmUlHmRYYwdmocBdqv1Yn3hIMaaUgmhj4w3N1yd7p492DEvINfHpHxKzUjJOwFo8RQjaRSBj21pQjqwxp9+I0jEr//XUfdViVnZ+y4diYlZOQxN38sDe8fsD+AKAJzjn3jvQbysAVwH4hnPu3r3XXgLgbwHc1jn34zH7VFYIIYQQ40jlbCAAZwM4UEQ29OJW7tX+e9ZQJ+fcZhH51pIxF4umfhzMDah+9oQQQggxzwkAtgWwT+/4wQDOXZYJ1PIBADuLyI6LAyIiAP4IwHeccz+dMp7sBjpcdnRAXBqlj2ydM11Oo7hcarGuEpJ0TBvt2I1U98jUuF3GziUkgNsnwLZGjIvW/TpXtD7nNYNnte0M2Ur5PtIaL5SUxIrSvyel3UDffNpj1dxAd3/jiTFuIAHwSQC74rqicIegWazs55w7qW23EcAezjnp9N0ewNfQuINejOsCbA8E8MfOufdM2acbiBBCCDGObKjrCHHOORF5LICj2r9tAZyDpkjcSRN9LxKR3QG8AsC/ANgKwDcA7O+cO9HHftWicNqBojnUCa0U6hjbJZlSEUKCaJcdWzZOfzyfeQ710SguF6r8aAUS+6RHL9BI3ZwDIanjMeOlkvu7oZatKWKVylWjtLJy7jMOUFNW7nbsB4rOXQPGrBBCCCHENObK7S8jdtdoNa1ZO74l965xiBgVYUwJiU3TTbEZ0sbHRkxaso/NOe5US372Qj5zC3KppL5jW1FVu1iJWbF0TZZRWlk5/5mPU/ut3ekNx1NZIWTOsCgcIYTYYyWKwqVitRBR7uuVSz3p9uvbilUTUuaaM66lVPG7sbbrjNXvlJpoFa3Lbds61rKB1l1ZYTYQIYQQYhwDReGqst5nTwghhBDzqLmBtF0eJQLm5iaXlgiinSqm1G2T042UMm5sIakQG1NoBewOjbEKErs2Vl25WrYsuFdqzi/0c17iepV2A33riIPU3EB3ff176QYihBBCiC7r7gbKFmA7p1TjEKzPb0FI6ekhUlKFQ23GFIMbsxkS7JrrMQBD4yzQUplq3IO5FYwuVtP6czO3+Wqide4pSr3vPVRaWfn2X/yxmrJyl9f+++yUlfVeqhFCCCHEPGZiVhaElBLX3k35pNdqlf62Rkwaca4iblrExiJNFY6LLck/Nf4YPo8yGCIkritX0bTahcbIDZnzowembPvY14gBK62sfOc5f6KmrNz51e+iskLInGFROEIIsQeLwq0BIWrVsj6xhdBCVDAffFSKEL90zliVZeSKhRkjt/I51GZBrCq0buSKu7GeSRYbX1czZgsAjnGbqKwUhNlAhHSw+GVOCCGyxRa1p1CV2TzIcE6kRp7nsrmsTxetGio+pe9j5uij9PjML2Y+2jVZtGuxxGLhXtTqvwrKjRUlZBWuZU5Kx6xc8NynqP3W3umV75idssKYFWIWPh9n/WDM0HXw3ibkOugGIoQQQoyz7kXhzC5WaqbClUD7fLQeGeBTTGlxLGYXnOom8TkHn9dSzmFsvBC0XFk+NmKvVwwp92LqXMbchCmElk6YC1ou0rFxcxcQ1E7hJzZZ76UaIYQQQswzywDbmPTJBTWC/nKM42tnzJbPzl6bkIJ7vuP0SQ0OnrKlHWirNa8xrKgAMQXtas95GbmKUg5RKtg49XujZmp16cDk0gG233vBU9V+a3/rH97CAFtCCCGEEE3UlZWaCkTJlbX1XZ8PIQqBz+4xJAZjyv4UqaXvtRSkXOepnXZtBY3PTern3Oe+XdY2dV5W0pJXkZDCcaHvw9B9QGWlLGYDbAkhhBDSsO7ZQFViVnKVlU4dJ6QkfGrBt9zKTGoJ65QS+N3+2ipHSPyIzzhj2U7L2uRQkLSK6M2VdVEcrJ3nKinE2r8ZU+9VaWXl+y86TE1ZueNL3zQ7ZWW9l2qE9GBRsrrM+UeTEJIPuoEIIYQQ49ANtKKpy3OjRBEr3/bdPlrPuvFxBy3rG2sztH8fC2nNOV2lucbzGXcu57DOrOI11XTRl3YD/eD/Pl3tt/YOLz6ObiBCCCGEEE3UlBVrOyWrgYrLzku7MJJWqvEYIaqET9ByDFqp0Kk2fQJ2U2yVTK9dBWqU24+1mfJdpZ3GrdW2NiXmWlpZ+e8X/y81ZeU3/++/UlkhhBBCCNEkW8xKCaWgRH8NUndcJdObV3WntcpoxwGVRLtIXyo+qonGeKHM+bPmoz5axFrq8rorK8wGIoQQQozDbKBMMSupRcm08Sn+Fdpfax65mctOhvihkblVEp8YH42xh8YfsxESRxUbgxSSBVeTkplbWmgV8Yy1VVpZ+eFLn6mmrNz+RW+YnbKy3ks1QgghhJiHbiBCCCHEOHQDFSwKZ0Fq9EkB7VIqjVib1ODZVScm6C/VlahNyecRablHpq53qFsoxJ1UMng25NlYyyiZZj7n74Zacy/tBrrwH/5MzQ30Gy/4Z7qBCCGEEEI0MffU5RrjpNiYc5Gt2iX+tWzF2NQsu12bsWuhFXRrYTwflSJXkHGO4oMpxf2GsP7YBKufudjU6tLKyv+8/M/VlJXbPv91VFYIIYQQQjQx9yBDa2X7NedQex6k7mMYcsUwje30U2I6fNqEpo1OXf/UR0D42NRCK+ZFi5h7yKraMUZuBWkIC0Xh1l1ZYTYQIYQQYhzZsN6OELMPMrSGT/aChYj9Ghkrc9ydpZDzgXUxMQA++NjSUmh85qFdfE3Dtlaf2LHn+NlIUXOGsHoNLBSF+/Er/0JNWdnhua+dnbKy3ks1QgghhJinSMxKyXojuWIScmYwzXlnVRrrZctLoBUjUaM8v7VHA4TE4OTKiBpDK9tGO2unJrHfAdrnVVpZ+cmrn6OmrNzmOa+mshKC9heWlS/AEKx/MViH180PXqcb4rP4sFT0r8Q4c/wOXYUFGJmGbiBCCCGEmMZ8gK3VFXGJwnYpLq3UINCSjwHITeqjB6zfgwu0yu2HjBdbYt6nEFeNINxl88xpo0+qO6dkam9IsUyt1Gorn8fSbqCfHv2Xam6gWz/rVXQDEUIIIYRokqysHC47OsBG0bWaZfetjRtie8i+ld2LNjWLwvUJUZlSi6X5qB1jNlNtaPTVKsIWowDlYOozV0KFtPD9E2tbq5BirH0qK2VhUThCCCHEOCwKZ6zcPtHBWon/1BiRGopIrrT6XGn6Q4QoFqnjhigXMfMcmm9IDIcPJdO7LXwutSlZWqC2+ltaWbno9c9T+63d/ohXzE5ZWe+lGiGEEELMk01Z8dnZh7bpYyF+JHQnoeFnLVlcbxV3f7UoWQAxJZ4lZJzYDCSNeVohRr0aQivTh5/d5aTGiXUpraxc8oa/VlNWbvXM/0dlhRCyGlhbFBCiBRdy84OLFUIIIYSYRi0bqC89+qxcQ4LiShaCGiPmPLVcYmO2LKTg5pSmY2xYk8OtzGPBMvdFSIBz6Gdx2efHJ304xk0VSkyQsM8cfNzFWvfHmKutRrDr1Ocw5/xyul6PcZtUxvRFtlhvbWG9z54QQggh5slWbj9nCluNMvQhY1tL1bOgMFiYQxdr84khpICaVkE1H9s+46QoNbFBvSkqTKyCWjJBoD+P1PHnXDAuFz1lpWiQ6qXHvlAtwHbbZxw1uwBbFoUjhBBCjLPubiD11OV1Kgrki09Z+y7Wz8cCc1KZcsXSlFIVuuP0baemS8f0HRsv1zUZK0QXey1yKbu5xifXp3Tq8mVvepGasnLLw146O2VlvZdqhBBCCDHPSpTbr7nLzhEDs2xeMSXrNedVCp/MkBr+eGtKXqlMmCFbY1kfy8aLnc+YndzK0VhbreuvVTCuBrk+RxbO11pRuMvf8ndqv7W3eOpLqKwQQsicsLYAIITcEC5WCCGEEDKJiGwjIkeLyIUisllEzhCRfQPHEBH5lIg4EXmNb78i2UBWA7803D85ipzFFIXTmtfUuLFjx8zL59rEFtyzcg9q4hMEOtR+WdG11ODv1GDXkGJrKa6Z0PTiXEUpY74TctjKTWrB0FJYmEMXI9lAJwC4D4DnA7gAwKEAThCRfZxzJ3uO8XQAdw81zNRlQgghhIwiInsDeBiAA5xzJ7THTgVwZwCvAjC5WBGR3wTwcgCHATg+yH7Npy4PtbdaKj0Xcwk2i2XOc7eOVppu7oJvOftqB7vGoKWuWvisxCqoFuY+RsrjAJZROsD2ircfqRZge/ODjwyeu4gcB+DxALZzzl3bOf50AMcC2MU5d87EGB8CAOfcviLiALzWOfdsH/tUVgghhBDjGHAD3RPAOd2FSsuZ3deXdRaRJwLYC8A9YoxnW6z4KAVj7Uumqlrw347taFJiRMZsjvVbpV2VNWoUThxCW5UIiR+ZipfxndeyNj7jaJ2/Vmp2ajxQSMyW75x852H1s299frUQkUun2jjntu0d2h7AeQNNL+68vszerQG8FsDfOOe+7zvPLtWXaoQQQgiZBWOuqLHXjkYTkPv6WMPmi8LVeBDYqhJTVI7X2I/c1zZHIbQ+2iXrS2bShNjWKlaXep0sZCOGzCVXsca5xjeWjlm56t0vVfutvdkTXxQTs3IaAOece2Dv+P0BfAHAE5xz7x3o93AAHwXwEABf77x0CYBjALwAwJXOuV+N2aeyQgghhJApzgaws4j01w33av89a0m/XdCsNTaiWaAs/gDg8Pa/HzZlnAG2hBBCCJniBDQpx/sA+GDn+MEAzh3JBDoewNcGjp8K4P1oXENnDrx+Pcy7gUpSwk1S072iXZxvTFa3kH4ZEkg8FuCZ+5lPWuP5yPQlyemKWWYr9Rosa5tacM9nnFUg1+dI+7slNUUbAI5xm4q6gX72nn9Q+63d+gkviHEDCYBPAtgV1xWFOwTNYmU/59xJbbuNAPZwzo3aYOoyIYQQQlRxzjkReSyAo9q/bdGkKh+wWKjkpIqyEhKoZXX3kbPcfgo1rlcJlSlXAF+NR0H4pNH7zMdn154yTo6g3qnPeUjfsTZdpsbWCgQOHUfjXvRRCFJVCavfwbUpHWBbW1mpDZUVQgghxDobtqg9g6oUVVZKFHjLZZOUw8p7VnIeU7bGVA5tJSQ11iQkDihEHYpFQxUKnVeMCpT6vWblc1Ma7evmO15xZeV9L9dTVh7//NkpK0xdJoQQQohpiigrMdkZy15PbZOKT7zBmC/aZ+5TbVPR3pVpvL8aWN9ZloxT0sgUCt19xigOIePFqDo50I7fWWD9/s2FlSzCUIorK+9/pZ6ycuBzqawQQgghhGiSTVmxslpepawbn3lo+mRj55DDho/NXDYW1Mj8is0ciokxWTZu6DipcTIh2Tu56rb42k+xWfu7Y4qUWkQ5v/+1FOzUeVFZKQuzgQghZqhRvI6sH9YXikPIFuudDUQ3ECGEEEJMU7Xcfg1XkXYgq09KaU6suKP6hAR4atjRGi8HuaTyEBUiZ6E3H1vL2iyIDdgNsZlCDtdTSLBxKXzuDyufs5jrpHltS7uBNp/4ajU30FaPfQ7dQIQQQghRhkXhbD3I0KpS0CcmhXmsTcnU5RDm8n7kJPW91rK1rO0YuYJea6QIh6hDqZ+9lHmG9tNG657MHcjqE8ht+XunuLLyoaP1lJV9nzU7ZYUxK4QQQggxjTllRZuSu+KpOZSwNWSztO0hasTxLIgtVmd9d1czc6ZGqnBMDEuXFEXFJ5bGR4nSIrd6UgsfNadPybl353GM21RWWfnwP+spK4/5MyorhBBCCCGamFNWrK30c1Nyx6C920t9r6yU6U+xaSVzo0+IeuATG5IyfqytlJgVbWKzlELHLkWpoo1jNqxnTU6dQ/GYlTVXVpgNRMxSw83BomSEEIvIhvV2hKz32RNCCCHEPObcQDXResZNzXRWbUJcNV1W2Y1XImA69rqHEFOULHfRtRC3ldZ8tIJnx+4La58RbRdsjfFqU9oNdPXJb1D7rd1y72fSDUQIIYQQZVgULm2xdrjs6AC7qXm5xgux2cWnzHhuLCg1Q/atBtyGYCVosFTAac5g11wBttpl8mP7lAo8zWlTQy1JVSq1VKvQa1BcWfnosXrKyqOeQWWFEEIIIcpQWdGJWfHZ/aQQOt6qFk3qExJvEDJOah8NlSSnnz+3cqc99zE1oaTiMHafxcR7DLWZauvTP6dqom0rNzWKUmpjLeYHqKCsfPxNesrKIw6bnbLCbCBCCCGEmGbW2UDWVY9UYrIOhvrWULbINDl89SmUKLAWU4AuZNwFoVlFIf2WjROqbFnMBuLn3D8rtLSy8vNPvEXtt/amD3sqlRVCCCGEEE24WCGEEEKIabK5gay5IVYhFTonq3Y+wLzOKSYFdEHI83XGKOGSiS3+Fjqv0CJuIWi5suZwX1rBonuquBvoU2/XcwM95GC6gQghhBBCNJl1gG0fn4Z+micAABkiSURBVPTO2DEtrOYtzWUIa+pXLmql0Y/NIzcpqdBdQtSIkunIMW1Tx7FeSFMLq/OaYupzTmWlLCwKRwghhFiHReFsKytaysiCua3uu/im1OW0PWarhF85d/HB2PnUmMOY7VxxLSXJVb5/QUjxO59xQq8/i8KVJ7VoZpdj3KayysrGf9NTVvZ80uyUFcasEEIIIcQ0RZWVFBWg1u7AYhaRtZ1SiZgM6+dZ4z6poYRYi/9Y9lqI+lhCbUpVVuYa9xFL7vPViDsrHbPyi8/+u5qycpPd/5jKCiGEEEKIJuZjVlIpuSOZ8+5nlcpvl4gjsfBe+yg9fcYUh5wqQqlxUrNwSsb6aKirq6Ayj41p+fubykpZmA1EvJjLQqUE1gJRCZkz/G7xZMN6O0LW++wJIYQQYp6qbqAawWZjNmPGzVGIToM5um9qYuE96xISYFsj9Ti1xHzKXLVK6cc+dTkkqDfUfrevRdfHOmD1qcu/+Nx79dxADzqIbiBCCCGEKMOicLYDbHOu/C2mJfuM61OoSnteOZSaZee1Co9GKEmuB/zVVjBC0AqEDQnQjVFWUlXbkoR8niyoQVrXy/f7tbiyctr79ZSVBxw4O2WFMSuEEEIIMY15ZUUL7ZLYPn7NkCJi2rE0vnNe1mbZXDRtpbCuKkqXGHUiZJe8auQqt+8zXsh8rCkZPpT8jrJCaWXll6efqPZbe+P7P5bKCiGEEEKIJurKigXf5dwpFfuiZXNV34cFWlliuWzmKn0fGoMxN0UmNsbER0ENydRapc+c9mfFNzMnB1OfYSorZWE2ECFkrZnbIousKSwKRwghhBBil2Q30OGyowPyp/+GBL1qzscSoSnL/X6rfk1KFRbMQYrbZyxIO7Vw2VC/ZfPVeB5RqNyfWxXJmXZd6t4r8RlZl+Dg7rU8xm0q6wb60of03EC/vy/dQIQQQgjRRVgUTldZiU31W+UUV+1iRdoqluaYuShVwK9ECrkWNcvrpxRPG2qzIEf6b4ia0ye2yJzPecXMR4uYgoyxCoSVzwugG+hcOsD2V1/+iJqycqP7Pnp2ygpjVgghhBBimipF4WqUVY+N9yhFyVTXVKwpDClYKfGfutvv9wlRQHxIUUBSU4RDbIQoPSFqcA6FxZLKGmszt+Jp+bumuLLy1Y/qKSu7PYrKCiGEEEKIJmbL7acWMNP2wWr2jSXWptXdyVTciHbxuqH+VpSVFNvahGQXTfXTnkf3+NRry9qk2E5tO9RPixTF2ec9t/b94UPO3wEqK2VhNhAhhBBiHRaFI4QQQgixi5obaI5S4ZyCubqUlGqtXxOfVOMuua5XyfFCAmy18QlK1XYVaQcJh7iOYtxBY+2tfo58yJnevEAj6Lg7Ts7vr+JuoK9/XM8NdO9H0A1ECCGEEF1kCxaFSxpgmbJSclerTY2CRj42Q3bbVq/tELkUrlyBujnvj9SdaS5SVJOYNGyfuYzZCg0OjrGvXRCvBrmUEKtF4TQpraxcc9Yn1ZSVLe750NkpK4xZIYQQQohpVjZmRUtpsHZeMcz1HEJ3ZzE7+pQ0T8uUVF0WhBRd07CTY+xltkJjo3zahqTsWyJEBR5rU4JVilm55pyNesrKPfakskIIIYQQoom5onAxikhqlPq6ZtJ0sT7XqQJyQ6+tOjUyfnL0Sxk3NlvHt0/svHxsWvi+KVFgM0Rpm9NnmMpKWZgNRAghhFhnA7OBkgYIUVa01A1rNTI0sllSfdkllZGamUfWFSAtYjMvNIiNCYkpj58ybg5SbIXWWbFwL+dWlcfGtpJ1OZdy+9d887N6ysrdd5+dssKYlURqBDKS9aPGDxrvbVKCGvf2qm92ciEi24jI0SJyoYhsFpEzRGRfj35PE5EPich3237nt+Pcxtc2FyuEEEKIcWTDBrW/BE4A8CQALwLwaADnADhBRPae6PdiAJcDeAGARwH4JwAHAfiSiGzrY9hs6rJWUNeCnAXGQtqUYk5uJS00Cr3F3ktzvF4p1AgqrXGNc32vdce0pGCFuuxiApxzp7aX4hi3qagr5drzPqfmBtrwOw8Knnu7IPkIgAOccye0xwTAZwFs75xb+kaKyA7OuR/3ju0BYCOAZznnXjc559AJE0IIIWTt2B/AZQA+uDjgGrXjbQDuLiL3WNaxv1Bp+VL77x18jFdNXa5dICnnrklz3Jg5lAy0XRc1wQqrXNBuDqnouR4H4ROYHKNG1A5wnjq/1HGHKKHWFFdWvvUFvQDbnR5w2VQb59z13DMiclpz2D2wd/z+AL4A4AnOuff6zkFEHgPgJAAHO+feMdWeygohhBBCptgewMUDxy/uvO6FiGwH4GgA5wPwWuAkKyuHy44OiEsD80kj8ykEpZ3urJneljKPGuPlRnvnbHUnHqsalijSNdU29rOyIGbHa+m9A8qUWdBWGizgc54h4+RSkDQe2TBnZWXDXf8gJmblPADnOuf26R3fCcB5AJ7pnDvGY5ytAXwUwK4AHuycO9PHPovCEUIIIdaR6o6QizCsnmzX/jukulwPEdkKwIcA7Abgkb4LFaBSzErJ4nAxZaBrxHuEKARW1YQuua/lnBQln0cF9FnVmKPcqlDOeWkzpTKlZg5ZU2q0s4FCss6G3t/Uwn+li8Jd++0v6ikrd7lfjLLyRgAHosn8ubZz/GkAjgOwi3PunJH+W6JZqDwIwN7OuU8HzTl0woQQkosaP6xWfsxJOfieR3ECgG0B7NM7fjAa99DYQuWmAE4EsDuA/UIXKgDdQIQQQoh96ruBTgZwKoA3icj2AC4AcAiAPwSw36KRiGwEsIdzrqveHA/gkQBeAuBKEfmDzms/cc59e8p4kaJwGqmWoYF8U+Om9p9TSvUqUcq91MWam2Sq71j/nMHjq4AVV3B/DlpKgIZrKMe8NAgJ7tUoflc8wPY7Z+i5ge78e1FzF5FbADgKwOPQqCznAHiJc+7ETpuN6C1WRGRs7m9zzh06ZZvKCiGEEEImcc5dDuCI9m9Zmz0HjiUv7NRTl4cola5bu8jcMqwExOa6Bqu6M08ps1/iXrRwT8/xPbcy95QA21iFJGYcn3tbW2EJKV1Ri9LKyjUXfEWvKNyd7sOnLhNCCCGEaFK13L4WsTulXOXxU1Mvp1KqQ5WaVSjEZdWWlV16DCEp1XM8P+vEqCRjcUYpac25Supr9xkbZ4icsT5UVsrCmBVCCCHEOvWzgapirihczDgLSsbNaKMV1xKj1PiOo9G2xDjWba46KbE+Y+PlirUaGlv7HKbsT6GthIQQo+aklr6vEQszNJ/+vLoUV1Y2fU1PWdnxd2enrKz3Uo0QsvZwoUqIfegGIoQQQqwjsxNDVFF3A2lJo1oFq3wKLeV2h5RIXbaWxlpqPjXSwmunolt3XWkXiFxVUp5NoxVgO9a3lKspZ5G5nIG/xd1A3/26nhvot+89u5UP3UCEEEIIMc3KFoVbUKMkf4ytVdiFxl63FDUtNFBuXVimKFp53MQc09RTxk9VQnzQVhF8vl81sFa+36yy8v1v6Ckrd7wXlRVCCCGEEE3MpS6n7IJCy0CvYvzIGFbnFYPVc7E6ryGmFMqxHe8qKCK1ya1U+Hz3+XwXl6RmirYP3eu05VZbUVkpCLOBCCGEEOM4FoXLo6z4PISrJCF+5TFiYnN8Iu1zXpNSu2KtmBXr5Ixpmtu1IPFMFVTTKkOfmunjk91oVQnpMxa7FTrn0jErv/rB2WrKyo3usMvslJX1XqoRQgghxDzq2UA+Jax9YlWs7V61Yl98zjNX3I5VRaskMfdZrmw2zTFj7OcqdV8ywy1ENdSuAZVKSTVijoqHBcbmVVxZ+e//0lNWfnNnKisLtD7QOZ8HooHV87SyOJgLNa5XzYWKdttVZ87Xwvp3gdX5WZ3XukI3ECGEEEJMU7XcfqoEn1ouX7u0f4jNBalSfK5z0EbbNWD1PAnxZUqtyZFOvMytUcMNE3J+qY8VCAkk9nX5l05d/tUPz9VzA93+bnQDEUIIIYRooqasaAXIpqTZhgb3ppR1L1Fu3MemVln1UkpF7eDSmqyiGhQbUJ9qq5TNsXlofQeEMKagppRO6B+PnafVQFktqKzUg0XhCCGEEOuwKFz+cvs1du19rKgLIUqIlvqSG+04JS1bNYidX0zpe617Otc1LREDpkGOe1I7LbmmEjJHtaREbE7x1OULz9dTVn5jp9kpK+u9VCOEEEKIeaoqKznjKnyiyn3GnbKZUynIVbwrd3GrOVI7lkarEOCCOb8XIdS49yzFrIS2sZQNFEvMXHNkVpVWVn75o2+rKSs3vt1dqKzkYg4fIjJ/1uVHnpB1hr8n82M2ixVCCCGErCdF3EB9QoqmLchRFC6lbQjaLobQYFxLheNqu1uWkRokrPXcmf642uS8/hr31RwDsLXf+1RSXEWxxddCiJlfiXktm8Mym8XdQP9zgZ4b6LZ3ohuIEEIIIUSTKsqKDyVUjgU1g1KH5qBRGM+SagHUTcO2ruYA0/OqfQ6l0odDi5yVUody2NRQCGIDRy2lLIeqJtrzWqbiTI1PZaUsLApHCCGEWEdmt75QxZyyYiHGJMSWz47LepyA1vg+sRyx18J64bKa+Pj1rZxf7lIANYiNnQvZyYd8nlLGjW1jlZxzL66s/HiTnrKyw46zW/kwZoUQQgghpimqrOTaZdcsKR6KlXlYwuo1CVHTtO7tlHnVGkdj/BqF7VK/f/p9Q88hpqhZ7j4+4w0RGu8R2rbEOKEUV1Z+8j09ZeU2v0VlhRBC5oS1RTIh5IZwsUIIIYQQ02TLBhqTWFPl55jgvFh5PsTllDv1tmahKy23Rs77QtsduKyNT3ptznNYRSUgtuijts2UfkP3Rco5+ARMj7k+rAbEpr6f/XOvUVSvBk7WW1tY77MnhBBCiHnUA2znUrK+O2ZIAGWfHLu+GunbFoJcLcxhVYgJQo8Zv0uKrdj51bxntD97Y1gPsF0QYyu0KJwVSgfY/uKnP1ALsL3Jre8wuwBbFoUjhBBCrLNhvR0h5orC9ald+M3Sbn8u8wNsFfWzUp59Tin2QN74rpLzih27O36qTZ/CfT5Yi6cYu+9925RUVjSvX3Fl5eIf6ikr291+dsrKei/VCCGEEGIe88qKNWJKWY/1T7E99JrPPGqrVRpttYktShaTLWZBNRlijkpIbmIL28XEd+SKMfGJLwoZr0tMgTzt0v7a5+lLcWXlkh/pKSu3uh2VFUIIIYQQTbhYIYQQQohpiqYu53IJxBZq05C9faRfq4GnPliaSywhwa6hbj0tLBX+s5IGbGl+XUICpRdou0CsBdr2sZKOrHWdhsYp7ga69Md6bqBtd6AbiBBCCCFEE/NF4bSLT6XOo6StBamprrmDerVLy1vY4deyH0Lu61TiEQslsKDIhChHcyQkyNXC+WooP1RWysKicIQQQoh11vzZQMnKyuGyowP0Cl+FFNmqQS41YaztgtCYnBTbJZiaT2xhNTK/AnQhxKaZWyV3CnMJNIrD5WClUpcv+6mesnLLW89OWVnvpRohhBBCzFO0KJxG7ES3f674GK3dqNb8VnWXPKUKWVHTrFKz4N4c77cUrJTbz43P/ELPYep6hcYlaihRoYU1hyitrPz88ovVlJWb3mI7KiuEEEIIIZoUUVZKZi0s0Kp5omFzqF+quhRi02e8ue2Q121nn8ocHwOQu86K9v0/Nl5IDEfO8vEp/TXUCM1xxsYtUaumuLJyxaV6ysrNt6WyQgghc8LaIm3dqOEO43s+P7hYIYQQQohpsrmBxgJi+8d9yOl2iZGXS0jnuQvs5SrmljL22PhTr02NlzovLWq4ZGpct1xlDELbpBBbasCn0GGKohBT6n+szZgNn0cEhIw7NkZMkGytQOXibqArL9NzA21zS7qBCCGEEEI0US8KF0Lszi1GIfApdx1D6G6q1LxyUmN+FlJvQ1MsU2ylYv0eCsHq9dMqEKlNTcUhVwCwz+eq9PlSWSkLy+0TQggh1mG5/XJF4eaMpZ39KuyW54y1GJgS5E4j9rHZPx5r38rnKKS42bLrrq0mlChaFxILozWHHNeruLJy1RV6ysrNbj47ZWW9l2qEEEIIMY+aslJjpzXHjJwpO11qlFHPxVzmuU6s+3vio9DkjEVaxZgVbULOpaQqBABbbrVVUXXi6p9dpaasbLn1zais5GIVPniEEHus62KNkFBEZBsROVpELhSRzSJyhojs69n3LiJyoohcJiJXiMjJInIPX9uzWawQQgghpConAHgSgBcBeDSAcwCcICJ7j3USkR0AfBbAjgAOAfBEANsB+LSI3MHHcJVnA2k94yakWFHsuDHpyKE2lo1nXaa3mlIaM96CEs+dsf6+alOjuNwcgqAtphbXDrD1SV1OKTwX8oymKUoH2Goms8S4sNoFyUcAHOCcO6E9JmgWIds755ZeOBF5OYA/B3AX59wP22PbA7gAwL855545ZZ/KCiGEEEKm2B/AZQA+uDjgGrXjbQDuPuHS2R/AKYuFStv3IgAnATjAx3iROiv9latWIS6fFbHPCt1nftqKis842mnN2oWuaqRda++SrQQxlyqzr6VqxtjWauuD1qMkrKhgWkGzU4rKkCKlrXKk9tEo8a/Zf46IyKVTbZxz2/YO3RPAOc65a3vHz+y+PmBrKwB3AfC+ATNnAvgTEdnBOffjsfmwKBwhhBBiHOXso8nFygDbAzhv4PjFndeHuBUA6bRb1nd0saIesxL74K8FKbuwGn5qLZvacw9JyxxqY2VHqY2le2fIfu4iZ6E759KMzW/V78Xa5eNrEarUhCgrOa9t6ZiV2ojIeQDOdc7t0zu+E5pFzDOdc8cM9Ls9gP8G8Fzn3Kt6rz0dwLEAdnbOfXPMPmNWCOmwaj+IhBCixEUYVk+2a/8dUk4A4BIALrLvr5lVUbg+MRkAsQ+jm5qDb79SPvHaSkEp5lR6PfcjFqy95zkLqmmPU4MYZaWm6qJVJl/7/FL6jPWbarOGysobARyIJvPn2s7xpwE4DsAuzrkbxKy0bb6FJt5l397xdwJ4hHNuhyn7VFYIIYQQMsUJALYFsE/v+MFo3EODC5VO34eLyO0WB0Rku3asD/gY52KFEEIIIVOcDOBUAG8SkT8Vkb1E5K0A/hDA8xaNRGSjiPRdNq9Ek/Z8sojsJyKPRlOz5VcAjvIxnq0o3Kq6R7SeBxKTPkzpPB8hLovYeyjkvY8Zf87E3pO5igSmFqnUSBQYK7OwCsG3Wm6lfttu+5zXa93cQAAgIrdAs7h4HBqV5RwAL3HOndhpsxHAHs456fXdCc2iZS80Qsln0QTdnu1jm6nLhBBCCJnEOXc5gCPav2Vt9lxy/HwA+8XaLlJuP4YSysqCuexeSwRbltxZ5piDFZup91mM0jOX+5g0+AR29ttqKwWrpNR0KZEOvo7KSk0Ys0IIIYQQ02RLXa4dG5J7PG2sF+uaA9bf41xYi+9at/chthxCSOn7ZcQWVMuVGlxTFRqLWekf9xlnap5UVspCZYUQQgghpjEbs9KndsGrZSqHjyIy1M8CIUX0xnZwWmpaDDlsamdjpWSAaZPzPcp9Dqum2FiKE7EWu5KiTC1rP9Vnme1lUFkpC5UVQjpY+bImhBByHUWUFa3shal+sXUOQvpoj2Nlt+jzHmnMVasWRU1KqjmhbTTn0iW3ClPDZo1xtOJQtEvfh84xxpY2ucvsU1mxxWyUFas7Xqvz0qbGea7LtfWB12L94GcuH+tynqvEbBYrhBBCCFlPigbYxhQ1yymLW08NtuQCsRLgvAqF4qy5v0oFxua0EYLW94aWO2mBjwt2jky5/1PPLdUdFDKPbr8tt9qKbqCCUFkhhBBCiGlWoihcbDBoSBGgkkGlU+NoB71qYWUHHVOqfoGF66hBTSVqgYVgYS1iC7/5jJOL1KDXXEGzuQJjQ2xpFK9jgG1ZqKwQQgghxDTqysqC0F2GtuIwF0qqJVZVhFIxABo2YtBIda0dX2GVmtc2lRpxKNaKv/lSIn4n9NpQWSkLlRVCCCGEmCZbNlCNAm0h5eND28TswlJVE22loJRiE2qjZkG02rtrX2oVPMyFtfezxvVKUQh8Piup+MR7WFdoYmIWGbNiEyorhJC1xvoPLtGH7/n84GKFEEIIIaYx89TlXDJs6rgpKdQlXAxWi6WVKjRW03U0NHbJQnK5roGPW8/qfZfar+RnWMMNFFrIbKr9nFw8C3KnWC8bm26gslBZIYQQQohp1JWV1N1ezUBWrfnE2ppqWzKNdS6Bp7FYL2ev/V6t+vu5wOezvKB2anuMyjGX1OM5KjRjDF13KitlobJCCCGEENNkK7c/htV4A59+JeNRltmujXYRt5B4iJpKlxbW3s9lpKakD41Ts3ibRgE5jXkMjdklZ5n8ZZ81q9SKR/HpxwcZloXKCiGEEEJMw8UKIYQQQkyT7AYihBBCCMkJlRVCCCGEmIaLFUIIIYSYhosVQgghhJiGixVCCCGEmIaLFUIIIYSYhosVQgghhJiGixVCCCGEmIaLFUIIIYSYhosVQgghhJiGixVCCCGEmIaLFUIIIYSYhosVQgghhJiGixVCCCGEmIaLFUIIIYSYhosVQgghhJjm/wMuFr/ikzJV1QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "<Figure size 720x720 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 20 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_components = 4 # the number of embedding dimensions for ASE\n", - "P = np.array([[0.9, 0.11, 0.13, 0.2],\n", - " [0, 0.7, 0.1, 0.1], \n", - " [0, 0, 0.8, 0.1],\n", - " [0, 0, 0, 0.85]])\n", - "\n", - "P = symmetrize(P)\n", - "csize = [50] * 4\n", - "A = sbm(csize, P)\n", - "X = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A)\n", - "heatmap(A, title='4-block SBM adjacency matrix')\n", - "pairplot(X, title='4-block adjacency spectral embedding')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the adjacency matrix above, there is a clearly defined block structrure corresponding to the 4 communities in the graph that we established. On the right, we see the **adjacency spectral embedding (ASE)** of this graph. ASE(A) recovers an estimate of the **latent positions** of $A $. Latent positions refer to the idea of a **random dot product graph (RDPG)** which can be modeled as follows:\n", - "\n", - "For an adjacency matrix $A \\in \\mathbb{R}^{n x n}$, the probability of an edge existing between node $i$ and node $j$ (aka whether or not $A_{ij}$ is a 1) is determined by the matrix $P \\in \\mathbb{R}^{n x n}$\n", - "\n", - "$P = XX^T$, where $X \\in \\mathbb{R}^{n x d} $ and is referred to as the latent positions of the graph. $X$ is referred to as the latent positions of the graph because each node $n_i$ is modeled as having a hidden, usually unobserved location in $\\mathbb{R}^d$ (we'll call it $x_i$). The probability of an edge existing between $n_i$ and $n_j$ is equal to the dot product $x_i \\cdot x_j$\n", - "\n", - "ASE is one way to obtain an estimate of the latent positions of a graph, $\\hat{X}$\n", - "\n", - "In the above embedding, we see 4 clusters of nodes corresponding to the 4 blocks that we prescribed. ASE recovers the fact that all of the nodes in a block have similar latent positions. So, RDPGs can also model an SBM graph." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sample new RDPGs from this latent position\n", - "Given the estimate of X, we now sample two new RDPGs from the same latent position above" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<seaborn.axisgrid.PairGrid at 0x254c828d860>" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 20 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 20 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "A1 = rdpg(X,\n", - " loops=False,\n", - " rescale=False,\n", - " directed=False)\n", - "A2 = rdpg(X,\n", - " loops=False,\n", - " rescale=False,\n", - " directed=False)\n", - "\n", - "Xhat1 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A1)\n", - "Xhat2 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A2)\n", - "\n", - "heatmap(A1, title='Sampled RDPG 1 adjacency matrix')\n", - "heatmap(A2, title='Sampled RDPG 2 adjacency matrix')\n", - "pairplot(Xhat1, title='Sampled RDPG 1 adjacency spectral embedding')\n", - "pairplot(Xhat2, title='Sampled RDPG 2 adjacency spectral embedding')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Qualitatively, both of the simulated RDPGs above match the behavior we would expect, with 4 clear blocks and the corresponding 4 clusters in the embedded space. But, can we say they were generated from the same latent positions?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Semiparametric test where null is true\n", - "Now, we want to know whether the above two graphs were generated from the same latent position. We know that they were, so the test should predict that the differences between Sampled RDPG 1 and 2 (up to a rotation, see below) are no greater than those differences observed by chance\n", - "\n", - "In other words, we are testing $$ H_0: X_1 = X_2 R$$$$ H_a: X_1 \\neq X_2 R$$\n", - "\n", - "and want to see that the p-value for the semiparametric test is high (fail to reject the null)\n", - "\n", - "Here, R is an orthogonal rotation matrix found from solving the [orthogonal procrustes problem](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.linalg.orthogonal_procrustes.html) (Note: this constraint can be relaxed for other versions of semipar)\n", - "\n", - "Note that the SemiparametricTest.fit() may take several minutes" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "p = 0.8325\n" - ] - } - ], - "source": [ - "spt = SemiparametricTest(n_bootstraps=200, n_components=n_components)\n", - "spt.fit(A1, A2)\n", - "print('p = {}'.format(spt.p_value_))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the corresponding p-value is high, indicating that the observed differences between latent positions of Sampled RDPG 1 and 2 are likely due to chance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Semiparametric test where the null is false" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we distort the latent position of one of the sampled graphs by adding noise. The semiparametric test should have a low p-value, indicating that we should reject the null hypothesis" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<seaborn.axisgrid.PairGrid at 0x254dba1e5f8>" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 20 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 20 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "A3 = rdpg(X,\n", - " loops=False,\n", - " rescale=False,\n", - " directed=False)\n", - "A4 = rdpg(X + np.random.normal(0.1, 0.1, size=(X.shape)),\n", - " loops=False,\n", - " rescale=False,\n", - " directed=False)\n", - "\n", - "Xhat3 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A3)\n", - "Xhat4 = AdjacencySpectralEmbed(n_components=n_components).fit_transform(A4)\n", - "\n", - "heatmap(A3, title='Sampled RDPG 3 adjacency matrix')\n", - "heatmap(A4, title='Sampled RDPG 4 (distorted) adjacency matrix')\n", - "pairplot(Xhat3, title='Sampled RDPG 3 adjacency spectral embedding')\n", - "pairplot(Xhat4, title='Sampled RDPG 4 (distorted) adjacency spectral embedding')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "p = 0.0025\n" - ] - } - ], - "source": [ - "spt = SemiparametricTest(n_bootstraps=200, n_components=n_components)\n", - "spt.fit(A3, A4)\n", - "print('p = {}'.format(spt.p_value_))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/graspy/inference/__init__.py b/graspy/inference/__init__.py index a0becca8e..05a8feb12 100644 --- a/graspy/inference/__init__.py +++ b/graspy/inference/__init__.py @@ -1,4 +1,4 @@ -from .semipar import SemiparametricTest -from .nonpar import NonparametricTest +from .latent_position_test import LatentPositionTest +from .latent_distribution_test import LatentDistributionTest -__all__ = ["SemiparametricTest", "NonparametricTest"] +__all__ = ["LatentPositionTest", "LatentDistributionTest"] diff --git a/graspy/inference/nonpar.py b/graspy/inference/latent_distribution_test.py similarity index 92% rename from graspy/inference/nonpar.py rename to graspy/inference/latent_distribution_test.py index 77612f58a..ea4ee3550 100644 --- a/graspy/inference/nonpar.py +++ b/graspy/inference/latent_distribution_test.py @@ -19,11 +19,13 @@ from .base import BaseInference -class NonparametricTest(BaseInference): +class LatentDistributionTest(BaseInference): """ - Two sample hypothesis test for the nonparamatric problem of determining - whether two random dot product graphs have the same latent positions [2]_. - + Two-sample hypothesis test for the problem of determining whether two random + dot product graphs have the same distributions of latent positions [2]_. + + This test can operate on two graphs where there is no known matching between + the vertices of the two graphs, or even when the number of vertices is different. Currently, testing is only supported for undirected graphs. Parameters @@ -46,7 +48,7 @@ class NonparametricTest(BaseInference): input graphs. p_ : float - The overall p value from the nonparametric test. + The overall p value from the test. null_distribution_ : ndarray, shape (n_bootstraps, ) The distribution of T statistics generated under the null. @@ -81,7 +83,7 @@ def __init__(self, n_components=None, n_bootstraps=200, bandwidth=None): def _gaussian_covariance(self, X, Y): diffs = np.expand_dims(X, 1) - np.expand_dims(Y, 0) - if self.bandwidth == None: + if self.bandwidth is None: self.bandwidth = 0.5 return np.exp(-0.5 * np.sum(diffs ** 2, axis=2) / self.bandwidth ** 2) diff --git a/graspy/inference/semipar.py b/graspy/inference/latent_position_test.py similarity index 93% rename from graspy/inference/semipar.py rename to graspy/inference/latent_position_test.py index aaa3705b3..9ca4c6d56 100644 --- a/graspy/inference/semipar.py +++ b/graspy/inference/latent_position_test.py @@ -14,7 +14,6 @@ import numpy as np from scipy.linalg import orthogonal_procrustes -from scipy.spatial import procrustes from ..embed import AdjacencySpectralEmbed, OmnibusEmbed, select_dimension from ..simulations import rdpg @@ -22,12 +21,15 @@ from .base import BaseInference -class SemiparametricTest(BaseInference): +class LatentPositionTest(BaseInference): r""" - Two sample hypothesis test for the semiparametric problem of determining - whether two random dot product graphs have the same latent positions [1]_. + Two-sample hypothesis test for the problem of determining whether two random + dot product graphs have the same latent positions [1]_. - Currently, the function only supports undirected graphs + This this test assumes that the two input graphs are vertex aligned, that is, + there is a known mapping between vertices in the two graphs and the input graphs + have their vertices sorted in the same order. Currently, the function only + supports undirected graphs. Parameters ---------- @@ -78,12 +80,11 @@ class SemiparametricTest(BaseInference): The p value estimated from the null distributions from sample 1 and sample 2. p_ : float - The overall p value from the semiparametric test; this is the max of p_value_1_ - and p_value_2_ + The overall p value from the test; this is the max of p_value_1_ and p_value_2_ Examples -------- - >>> spt = SemiparametricTest(n_components=2, test_case='rotation') + >>> spt = LatentPositionTest(n_components=2, test_case='rotation') >>> p = spt.fit(A1, A2) See also diff --git a/tests/test_nonpar.py b/tests/test_latentdistributiontest.py similarity index 70% rename from tests/test_nonpar.py rename to tests/test_latentdistributiontest.py index dc78b64af..4d7e8f143 100644 --- a/tests/test_nonpar.py +++ b/tests/test_latentdistributiontest.py @@ -6,12 +6,11 @@ import numpy as np -from graspy.embed import AdjacencySpectralEmbed, LaplacianSpectralEmbed -from graspy.inference import NonparametricTest +from graspy.inference import LatentDistributionTest from graspy.simulations import er_np, sbm -class TestNonparametricTest(unittest.TestCase): +class TestLatentDistributionTest(unittest.TestCase): @classmethod def setUpClass(cls): np.random.seed(123456) @@ -19,28 +18,28 @@ def setUpClass(cls): cls.A2 = er_np(20, 0.3) def test_fit_p_ase_works(self): - npt = NonparametricTest() + npt = LatentDistributionTest() p = npt.fit(self.A1, self.A2) def test_bad_kwargs(self): with self.assertRaises(ValueError): - NonparametricTest(n_components=-100) + LatentDistributionTest(n_components=-100) with self.assertRaises(ValueError): - NonparametricTest(n_bootstraps=-100) + LatentDistributionTest(n_bootstraps=-100) with self.assertRaises(TypeError): - NonparametricTest(n_bootstraps=0.5) + LatentDistributionTest(n_bootstraps=0.5) with self.assertRaises(TypeError): - NonparametricTest(n_components=0.5) + LatentDistributionTest(n_components=0.5) with self.assertRaises(TypeError): - NonparametricTest(bandwidth="oops") + LatentDistributionTest(bandwidth="oops") def test_n_bootstraps(self): - npt = NonparametricTest(n_bootstraps=234, n_components=None) + npt = LatentDistributionTest(n_bootstraps=234, n_components=None) npt.fit(self.A1, self.A2) self.assertEqual(npt.null_distribution_.shape[0], 234) def test_bad_matrix_inputs(self): - npt = NonparametricTest() + npt = LatentDistributionTest() bad_matrix = [[1, 2]] with self.assertRaises(TypeError): @@ -51,7 +50,7 @@ def test_directed_inputs(self): A = er_np(100, 0.3, directed=True) B = er_np(100, 0.3, directed=True) - npt = NonparametricTest() + npt = LatentDistributionTest() with self.assertRaises(NotImplementedError): npt.fit(A, B) @@ -60,7 +59,7 @@ def test_different_sizes(self): A = er_np(50, 0.3) B = er_np(100, 0.3) - npt = NonparametricTest() + npt = LatentDistributionTest() with self.assertRaises(ValueError): npt.fit(A, B) @@ -74,8 +73,8 @@ def test_SBM_epsilon(self): A2 = sbm(2 * [b_size], B1) A3 = sbm(2 * [b_size], B2) - npt_null = NonparametricTest(n_components=2, n_bootstraps=100) - npt_alt = NonparametricTest(n_components=2, n_bootstraps=100) + npt_null = LatentDistributionTest(n_components=2, n_bootstraps=100) + npt_alt = LatentDistributionTest(n_components=2, n_bootstraps=100) p_null = npt_null.fit(A1, A2) p_alt = npt_alt.fit(A1, A3) self.assertTrue(p_null > 0.05) diff --git a/tests/test_semipar.py b/tests/test_latentpositiontest.py similarity index 74% rename from tests/test_semipar.py rename to tests/test_latentpositiontest.py index 52b8144b1..60e7b0748 100644 --- a/tests/test_semipar.py +++ b/tests/test_latentpositiontest.py @@ -4,13 +4,12 @@ import unittest import numpy as np -from graspy.inference import SemiparametricTest -from graspy.embed import AdjacencySpectralEmbed, LaplacianSpectralEmbed +from graspy.inference import LatentPositionTest from graspy.simulations import er_np, sbm from graspy.utils import * -class TestSemiparametricTest(unittest.TestCase): +class TestLatentPositionTest(unittest.TestCase): @classmethod def setUpClass(cls): np.random.seed(1234556) @@ -18,42 +17,42 @@ def setUpClass(cls): cls.A2 = er_np(20, 0.3) def test_fit_p_ase_works(self): - spt = SemiparametricTest() + spt = LatentPositionTest() p = spt.fit(self.A1, self.A2) pass def test_fit_p_omni_works(self): - spt = SemiparametricTest(embedding="omnibus") + spt = LatentPositionTest(embedding="omnibus") p = spt.fit(self.A1, self.A2) pass def test_bad_kwargs(self): with self.assertRaises(ValueError): - SemiparametricTest(n_components=-100) + LatentPositionTest(n_components=-100) with self.assertRaises(ValueError): - SemiparametricTest(n_components=-100) + LatentPositionTest(n_components=-100) with self.assertRaises(ValueError): - SemiparametricTest(test_case="oops") + LatentPositionTest(test_case="oops") with self.assertRaises(ValueError): - SemiparametricTest(n_bootstraps=-100) + LatentPositionTest(n_bootstraps=-100) with self.assertRaises(ValueError): - SemiparametricTest(embedding="oops") + LatentPositionTest(embedding="oops") with self.assertRaises(TypeError): - SemiparametricTest(n_bootstraps=0.5) + LatentPositionTest(n_bootstraps=0.5) with self.assertRaises(TypeError): - SemiparametricTest(n_components=0.5) + LatentPositionTest(n_components=0.5) with self.assertRaises(TypeError): - SemiparametricTest(embedding=6) + LatentPositionTest(embedding=6) with self.assertRaises(TypeError): - SemiparametricTest(test_case=6) + LatentPositionTest(test_case=6) def test_n_bootstraps(self): - spt = SemiparametricTest(n_bootstraps=234, n_components=None) + spt = LatentPositionTest(n_bootstraps=234, n_components=None) spt.fit(self.A1, self.A2) self.assertEqual(spt.null_distribution_1_.shape[0], 234) def test_bad_matrix_inputs(self): - spt = SemiparametricTest() + spt = LatentPositionTest() A1 = self.A1.copy() A1[2, 0] = 1 # make asymmetric with self.assertRaises(NotImplementedError): # TODO : remove when we implement @@ -72,7 +71,7 @@ def test_rotation_norm(self): rotation = np.array([[0, 1], [-1, 0]]) points2 = np.dot(points1, rotation) - spt = SemiparametricTest(embedding="ase", test_case="rotation") + spt = LatentPositionTest(embedding="ase", test_case="rotation") n = spt._difference_norm(points1, points2) self.assertAlmostEqual(n, 0) @@ -86,7 +85,7 @@ def test_diagonal_rotation_norm(self): diagonal = np.array([[2, 0, 0], [0, 3, 0], [0, 0, 2]]) points2 = np.dot(diagonal, points2) - spt = SemiparametricTest(embedding="ase", test_case="diagonal-rotation") + spt = LatentPositionTest(embedding="ase", test_case="diagonal-rotation") n = spt._difference_norm(points1, points2) self.assertAlmostEqual(n, 0) @@ -99,7 +98,7 @@ def test_scalar_rotation_norm(self): # scaled points2 = 2 * points2 - spt = SemiparametricTest(embedding="ase", test_case="scalar-rotation") + spt = LatentPositionTest(embedding="ase", test_case="scalar-rotation") n = spt._difference_norm(points1, points2) self.assertAlmostEqual(n, 0) @@ -113,8 +112,8 @@ def test_SBM_epsilon(self): A2 = sbm(2 * [b_size], B1) A3 = sbm(2 * [b_size], B2) - spt_null = SemiparametricTest(n_components=2, n_bootstraps=10) - spt_alt = SemiparametricTest(n_components=2, n_bootstraps=10) + spt_null = LatentPositionTest(n_components=2, n_bootstraps=10) + spt_alt = LatentPositionTest(n_components=2, n_bootstraps=10) p_null = spt_null.fit(A1, A2) p_alt = spt_alt.fit(A1, A3) self.assertTrue(p_null > 0.05)
django-json-api__django-rest-framework-json-api-768
Tag new version for Django 3.0/DRF 3.11/Python 3.8 support Is there any chance we will see a new version in pip any time soon now that #752 is merged? Thanks!
[ { "content": "# -*- coding: utf-8 -*-\n\n__title__ = 'djangorestframework-jsonapi'\n__version__ = '3.0.0'\n__author__ = ''\n__license__ = 'BSD'\n__copyright__ = ''\n\n# Version synonym\nVERSION = __version__\n", "path": "rest_framework_json_api/__init__.py" } ]
[ { "content": "# -*- coding: utf-8 -*-\n\n__title__ = 'djangorestframework-jsonapi'\n__version__ = '3.1.0'\n__author__ = ''\n__license__ = 'BSD'\n__copyright__ = ''\n\n# Version synonym\nVERSION = __version__\n", "path": "rest_framework_json_api/__init__.py" } ]
diff --git a/CHANGELOG.md b/CHANGELOG.md index 7b2b34f9..f2dc90b7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 Note that in line with [Django REST Framework policy](http://www.django-rest-framework.org/topics/release-notes/), any parts of the framework not mentioned in the documentation should generally be considered private API, and may be subject to change. -## [Unreleased] +## [3.1.0] - 2020-02-08 ### Added @@ -18,10 +18,10 @@ any parts of the framework not mentioned in the documentation should generally b ### Fixed -* Ensure that `409 Conflict` is returned when processing a `PATCH` request in which the resource object’s type and id do not match the server’s endpoint properly as outlined in [JSON:API](https://jsonapi.org/format/#crud-updating-responses-409) spec. +* Ensured that `409 Conflict` is returned when processing a `PATCH` request in which the resource object’s type and id do not match the server’s endpoint as outlined in [JSON:API](https://jsonapi.org/format/#crud-updating-responses-409) spec. * Properly return parser error when primary data is of invalid type -* Pass instance to child serializer when `PolymorphicModelSerializer` inits it in `to_internal_value` -* Handle serialization of related resources on inherited polymorphic models that are absent on the base model +* Pass instance to child serializers when using `PolymorphicModelSerializer` +* Properly resolve related resource type when using `PolymorphicModelSerializer` ## [3.0.0] - 2019-10-14 diff --git a/rest_framework_json_api/__init__.py b/rest_framework_json_api/__init__.py index 619fd5ad..a15ece29 100644 --- a/rest_framework_json_api/__init__.py +++ b/rest_framework_json_api/__init__.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- __title__ = 'djangorestframework-jsonapi' -__version__ = '3.0.0' +__version__ = '3.1.0' __author__ = '' __license__ = 'BSD' __copyright__ = ''
certbot__certbot-7766
Required pyparsing version I've been experimenting with writing tests using the oldest allowed versions of our Python dependencies. `setup.py` for `letsencrypt-nginx` says it requires `pyparsing>=1.5.5` but when I pin version 1.5.5, I encounter problems. You can see Travis logs of the issue [here](https://travis-ci.org/letsencrypt/letsencrypt/jobs/100739657) and [here](https://travis-ci.org/letsencrypt/letsencrypt/jobs/100739658). We should determine what version we require and update `setup.py` accordingly.
[ { "content": "import sys\n\nfrom setuptools import find_packages\nfrom setuptools import setup\nfrom setuptools.command.test import test as TestCommand\n\nversion = '1.3.0.dev0'\n\n# Remember to update local-oldest-requirements.txt when changing the minimum\n# acme/certbot version.\ninstall_requires = [\n 'acme>=1.0.0',\n 'certbot>=1.1.0',\n 'mock',\n 'PyOpenSSL',\n 'pyparsing>=1.5.5', # Python3 support; perhaps unnecessary?\n 'setuptools',\n 'zope.interface',\n]\n\n\nclass PyTest(TestCommand):\n user_options = []\n\n def initialize_options(self):\n TestCommand.initialize_options(self)\n self.pytest_args = ''\n\n def run_tests(self):\n import shlex\n # import here, cause outside the eggs aren't loaded\n import pytest\n errno = pytest.main(shlex.split(self.pytest_args))\n sys.exit(errno)\n\n\nsetup(\n name='certbot-nginx',\n version=version,\n description=\"Nginx plugin for Certbot\",\n url='https://github.com/letsencrypt/letsencrypt',\n author=\"Certbot Project\",\n author_email='[email protected]',\n license='Apache License 2.0',\n python_requires='>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*',\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Environment :: Plugins',\n 'Intended Audience :: System Administrators',\n 'License :: OSI Approved :: Apache Software License',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Topic :: Internet :: WWW/HTTP',\n 'Topic :: Security',\n 'Topic :: System :: Installation/Setup',\n 'Topic :: System :: Networking',\n 'Topic :: System :: Systems Administration',\n 'Topic :: Utilities',\n ],\n\n packages=find_packages(),\n include_package_data=True,\n install_requires=install_requires,\n entry_points={\n 'certbot.plugins': [\n 'nginx = certbot_nginx._internal.configurator:NginxConfigurator',\n ],\n },\n test_suite='certbot_nginx',\n tests_require=[\"pytest\"],\n cmdclass={\"test\": PyTest},\n)\n", "path": "certbot-nginx/setup.py" } ]
[ { "content": "import sys\n\nfrom setuptools import find_packages\nfrom setuptools import setup\nfrom setuptools.command.test import test as TestCommand\n\nversion = '1.3.0.dev0'\n\n# Remember to update local-oldest-requirements.txt when changing the minimum\n# acme/certbot version.\ninstall_requires = [\n 'acme>=1.0.0',\n 'certbot>=1.1.0',\n 'mock',\n 'PyOpenSSL',\n 'pyparsing>=1.5.5', # Python3 support\n 'setuptools',\n 'zope.interface',\n]\n\n\nclass PyTest(TestCommand):\n user_options = []\n\n def initialize_options(self):\n TestCommand.initialize_options(self)\n self.pytest_args = ''\n\n def run_tests(self):\n import shlex\n # import here, cause outside the eggs aren't loaded\n import pytest\n errno = pytest.main(shlex.split(self.pytest_args))\n sys.exit(errno)\n\n\nsetup(\n name='certbot-nginx',\n version=version,\n description=\"Nginx plugin for Certbot\",\n url='https://github.com/letsencrypt/letsencrypt',\n author=\"Certbot Project\",\n author_email='[email protected]',\n license='Apache License 2.0',\n python_requires='>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*',\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Environment :: Plugins',\n 'Intended Audience :: System Administrators',\n 'License :: OSI Approved :: Apache Software License',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Topic :: Internet :: WWW/HTTP',\n 'Topic :: Security',\n 'Topic :: System :: Installation/Setup',\n 'Topic :: System :: Networking',\n 'Topic :: System :: Systems Administration',\n 'Topic :: Utilities',\n ],\n\n packages=find_packages(),\n include_package_data=True,\n install_requires=install_requires,\n entry_points={\n 'certbot.plugins': [\n 'nginx = certbot_nginx._internal.configurator:NginxConfigurator',\n ],\n },\n test_suite='certbot_nginx',\n tests_require=[\"pytest\"],\n cmdclass={\"test\": PyTest},\n)\n", "path": "certbot-nginx/setup.py" } ]
diff --git a/certbot-nginx/setup.py b/certbot-nginx/setup.py index 3b75a34247d..b180fe06a9e 100644 --- a/certbot-nginx/setup.py +++ b/certbot-nginx/setup.py @@ -13,7 +13,7 @@ 'certbot>=1.1.0', 'mock', 'PyOpenSSL', - 'pyparsing>=1.5.5', # Python3 support; perhaps unnecessary? + 'pyparsing>=1.5.5', # Python3 support 'setuptools', 'zope.interface', ] diff --git a/tools/oldest_constraints.txt b/tools/oldest_constraints.txt index 6154b497a4e..85d05879627 100644 --- a/tools/oldest_constraints.txt +++ b/tools/oldest_constraints.txt @@ -13,7 +13,6 @@ ply==3.4 pyasn1==0.1.9 pycparser==2.14 pyOpenSSL==0.13.1 -pyparsing==1.5.6 pyRFC3339==1.0 python-augeas==0.5.0 oauth2client==4.0.0
ansible-collections__community.aws-989
Invalid import path for BotoCoreError in redshift_info module ### Summary In case of any AWS related error (like missing permissions) the module will throw a gigantic python stack trace with error summary as: ``` line 304, in find_clusters NameError: name 'BotoCoreError' is not defined ``` This is due to an invalid import path that is present in the module https://github.com/ansible-collections/community.aws/blob/main/plugins/modules/redshift_info.py#L280 Instead of `from botocore.exception` it should be `from botocore.exceptions`. Once that is done, ansible no longer hides the real error with the stack trace. ### Issue Type Bug Report ### Component Name redshift_info ### Ansible Version ```console (paste below) $ ansible --version ansible 2.10.8 config file = None configured module search path = ['/home/wojtek/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules'] ansible python module location = /usr/local/lib/python3.6/dist-packages/ansible executable location = /usr/local/bin/ansible python version = 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0] ``` ### Collection Versions Non-relevant ### AWS SDK versions ```console (paste below) $ pip show boto boto3 botocore Name: boto Version: 2.49.0 Summary: Amazon Web Services Library Home-page: https://github.com/boto/boto/ Author: Mitch Garnaat Author-email: [email protected] License: MIT Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: --- Name: boto3 Version: 1.20.54 Summary: The AWS SDK for Python Home-page: https://github.com/boto/boto3 Author: Amazon Web Services Author-email: None License: Apache License 2.0 Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: jmespath, s3transfer, botocore --- Name: botocore Version: 1.23.54 Summary: Low-level, data-driven core of boto 3. Home-page: https://github.com/boto/botocore Author: Amazon Web Services Author-email: None License: Apache License 2.0 Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: jmespath, urllib3, python-dateutil ``` ### Configuration ```console (paste below) $ ansible-config dump --only-changed ``` ### OS / Environment Ubuntu 20.04 ### Steps to Reproduce Run the module without DescribeClusters permission. ### Expected Results AWS API error on missing permissions is shown. ### Actual Results Python stack trace ending with ``` line 304, in find_clusters NameError: name 'BotoCoreError' is not defined ``` ### Code of Conduct - [X] I agree to follow the Ansible Code of Conduct
[ { "content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: redshift_info\nversion_added: 1.0.0\nauthor: \"Jens Carl (@j-carl)\"\nshort_description: Gather information about Redshift cluster(s)\ndescription:\n - Gather information about Redshift cluster(s).\n - This module was called C(redshift_facts) before Ansible 2.9. The usage did not change.\noptions:\n cluster_identifier:\n description:\n - The prefix of cluster identifier of the Redshift cluster you are searching for.\n - \"This is a regular expression match with implicit '^'. Append '$' for a complete match.\"\n required: false\n aliases: ['name', 'identifier']\n type: str\n tags:\n description:\n - \"A dictionary/hash of tags in the format { tag1_name: 'tag1_value', tag2_name: 'tag2_value' }\n to match against the security group(s) you are searching for.\"\n required: false\n type: dict\nextends_documentation_fragment:\n- amazon.aws.ec2\n- amazon.aws.aws\n\n'''\n\nEXAMPLES = '''\n# Note: These examples do net set authentication details, see the AWS guide for details.\n\n- name: Find all clusters\n community.aws.redshift_info:\n register: redshift\n\n- name: Find cluster(s) with matching tags\n community.aws.redshift_info:\n tags:\n env: prd\n stack: monitoring\n register: redshift_tags\n\n- name: Find cluster(s) with matching name/prefix and tags\n community.aws.redshift_info:\n tags:\n env: dev\n stack: web\n name: user-\n register: redshift_web\n\n- name: Fail if no cluster(s) is/are found\n community.aws.redshift_info:\n tags:\n env: stg\n stack: db\n register: redshift_user\n failed_when: \"{{ redshift_user.results | length == 0 }}\"\n'''\n\nRETURN = '''\n# For more information see U(http://boto3.readthedocs.io/en/latest/reference/services/redshift.html#Redshift.Client.describe_clusters)\n---\ncluster_identifier:\n description: Unique key to identify the cluster.\n returned: success\n type: str\n sample: \"redshift-identifier\"\nnode_type:\n description: The node type for nodes in the cluster.\n returned: success\n type: str\n sample: \"ds2.xlarge\"\ncluster_status:\n description: Current state of the cluster.\n returned: success\n type: str\n sample: \"available\"\nmodify_status:\n description: The status of a modify operation.\n returned: optional\n type: str\n sample: \"\"\nmaster_username:\n description: The master user name for the cluster.\n returned: success\n type: str\n sample: \"admin\"\ndb_name:\n description: The name of the initial database that was created when the cluster was created.\n returned: success\n type: str\n sample: \"dev\"\nendpoint:\n description: The connection endpoint.\n returned: success\n type: str\n sample: {\n \"address\": \"cluster-ds2.ocmugla0rf.us-east-1.redshift.amazonaws.com\",\n \"port\": 5439\n }\ncluster_create_time:\n description: The date and time that the cluster was created.\n returned: success\n type: str\n sample: \"2016-05-10T08:33:16.629000+00:00\"\nautomated_snapshot_retention_period:\n description: The number of days that automatic cluster snapshots are retained.\n returned: success\n type: int\n sample: 1\ncluster_security_groups:\n description: A list of cluster security groups that are associated with the cluster.\n returned: success\n type: list\n sample: []\nvpc_security_groups:\n description: A list of VPC security groups the are associated with the cluster.\n returned: success\n type: list\n sample: [\n {\n \"status\": \"active\",\n \"vpc_security_group_id\": \"sg-12cghhg\"\n }\n ]\ncluster_paramater_groups:\n description: The list of cluster parameters that are associated with this cluster.\n returned: success\n type: list\n sample: [\n {\n \"cluster_parameter_status_list\": [\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"statement_timeout\"\n },\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"require_ssl\"\n }\n ],\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_group_name\": \"tuba\"\n }\n ]\ncluster_subnet_group_name:\n description: The name of the subnet group that is associated with the cluster.\n returned: success\n type: str\n sample: \"redshift-subnet\"\nvpc_id:\n description: The identifier of the VPC the cluster is in, if the cluster is in a VPC.\n returned: success\n type: str\n sample: \"vpc-1234567\"\navailability_zone:\n description: The name of the Availability Zone in which the cluster is located.\n returned: success\n type: str\n sample: \"us-east-1b\"\npreferred_maintenance_window:\n description: The weekly time range, in Universal Coordinated Time (UTC), during which system maintenance can occur.\n returned: success\n type: str\n sample: \"tue:07:30-tue:08:00\"\npending_modified_values:\n description: A value that, if present, indicates that changes to the cluster are pending.\n returned: success\n type: dict\n sample: {}\ncluster_version:\n description: The version ID of the Amazon Redshift engine that is running on the cluster.\n returned: success\n type: str\n sample: \"1.0\"\nallow_version_upgrade:\n description: >\n A Boolean value that, if true, indicates that major version upgrades will be applied\n automatically to the cluster during the maintenance window.\n returned: success\n type: bool\n sample: true|false\nnumber_of_nodes:\n description: The number of compute nodes in the cluster.\n returned: success\n type: int\n sample: 12\npublicly_accessible:\n description: A Boolean value that, if true , indicates that the cluster can be accessed from a public network.\n returned: success\n type: bool\n sample: true|false\nencrypted:\n description: Boolean value that, if true , indicates that data in the cluster is encrypted at rest.\n returned: success\n type: bool\n sample: true|false\nrestore_status:\n description: A value that describes the status of a cluster restore action.\n returned: success\n type: dict\n sample: {}\nhsm_status:\n description: >\n A value that reports whether the Amazon Redshift cluster has finished applying any hardware\n security module (HSM) settings changes specified in a modify cluster command.\n returned: success\n type: dict\n sample: {}\ncluster_snapshot_copy_status:\n description: A value that returns the destination region and retention period that are configured for cross-region snapshot copy.\n returned: success\n type: dict\n sample: {}\ncluster_public_keys:\n description: The public key for the cluster.\n returned: success\n type: str\n sample: \"ssh-rsa anjigfam Amazon-Redshift\\n\"\ncluster_nodes:\n description: The nodes in the cluster.\n returned: success\n type: list\n sample: [\n {\n \"node_role\": \"LEADER\",\n \"private_ip_address\": \"10.0.0.1\",\n \"public_ip_address\": \"x.x.x.x\"\n },\n {\n \"node_role\": \"COMPUTE-1\",\n \"private_ip_address\": \"10.0.0.3\",\n \"public_ip_address\": \"x.x.x.x\"\n }\n ]\nelastic_ip_status:\n description: The status of the elastic IP (EIP) address.\n returned: success\n type: dict\n sample: {}\ncluster_revision_number:\n description: The specific revision number of the database in the cluster.\n returned: success\n type: str\n sample: \"1231\"\ntags:\n description: The list of tags for the cluster.\n returned: success\n type: list\n sample: []\nkms_key_id:\n description: The AWS Key Management Service (AWS KMS) key ID of the encryption key used to encrypt data in the cluster.\n returned: success\n type: str\n sample: \"\"\nenhanced_vpc_routing:\n description: An option that specifies whether to create the cluster with enhanced VPC routing enabled.\n returned: success\n type: bool\n sample: true|false\niam_roles:\n description: List of IAM roles attached to the cluster.\n returned: success\n type: list\n sample: []\n'''\n\nimport re\n\ntry:\n from botocore.exception import BotoCoreError, ClientError\nexcept ImportError:\n pass # caught by AnsibleAWSModule\n\nfrom ansible_collections.amazon.aws.plugins.module_utils.core import AnsibleAWSModule\nfrom ansible_collections.amazon.aws.plugins.module_utils.ec2 import camel_dict_to_snake_dict\n\n\ndef match_tags(tags_to_match, cluster):\n for key, value in tags_to_match.items():\n for tag in cluster['Tags']:\n if key == tag['Key'] and value == tag['Value']:\n return True\n\n return False\n\n\ndef find_clusters(conn, module, identifier=None, tags=None):\n\n try:\n cluster_paginator = conn.get_paginator('describe_clusters')\n clusters = cluster_paginator.paginate().build_full_result()\n except (BotoCoreError, ClientError) as e:\n module.fail_json_aws(e, msg='Failed to fetch clusters.')\n\n matched_clusters = []\n\n if identifier is not None:\n identifier_prog = re.compile('^' + identifier)\n\n for cluster in clusters['Clusters']:\n\n matched_identifier = True\n if identifier:\n matched_identifier = identifier_prog.search(cluster['ClusterIdentifier'])\n\n matched_tags = True\n if tags:\n matched_tags = match_tags(tags, cluster)\n\n if matched_identifier and matched_tags:\n matched_clusters.append(camel_dict_to_snake_dict(cluster))\n\n return matched_clusters\n\n\ndef main():\n\n argument_spec = dict(\n cluster_identifier=dict(type='str', aliases=['identifier', 'name']),\n tags=dict(type='dict')\n )\n module = AnsibleAWSModule(\n argument_spec=argument_spec,\n supports_check_mode=True\n )\n if module._name == 'redshift_facts':\n module.deprecate(\"The 'redshift_facts' module has been renamed to 'redshift_info'\", date='2021-12-01', collection_name='community.aws')\n\n cluster_identifier = module.params.get('cluster_identifier')\n cluster_tags = module.params.get('tags')\n\n redshift = module.client('redshift')\n\n results = find_clusters(redshift, module, identifier=cluster_identifier, tags=cluster_tags)\n module.exit_json(results=results)\n\n\nif __name__ == '__main__':\n main()\n", "path": "plugins/modules/redshift_info.py" } ]
[ { "content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: redshift_info\nversion_added: 1.0.0\nauthor: \"Jens Carl (@j-carl)\"\nshort_description: Gather information about Redshift cluster(s)\ndescription:\n - Gather information about Redshift cluster(s).\n - This module was called C(redshift_facts) before Ansible 2.9. The usage did not change.\noptions:\n cluster_identifier:\n description:\n - The prefix of cluster identifier of the Redshift cluster you are searching for.\n - \"This is a regular expression match with implicit '^'. Append '$' for a complete match.\"\n required: false\n aliases: ['name', 'identifier']\n type: str\n tags:\n description:\n - \"A dictionary/hash of tags in the format { tag1_name: 'tag1_value', tag2_name: 'tag2_value' }\n to match against the security group(s) you are searching for.\"\n required: false\n type: dict\nextends_documentation_fragment:\n- amazon.aws.ec2\n- amazon.aws.aws\n\n'''\n\nEXAMPLES = '''\n# Note: These examples do net set authentication details, see the AWS guide for details.\n\n- name: Find all clusters\n community.aws.redshift_info:\n register: redshift\n\n- name: Find cluster(s) with matching tags\n community.aws.redshift_info:\n tags:\n env: prd\n stack: monitoring\n register: redshift_tags\n\n- name: Find cluster(s) with matching name/prefix and tags\n community.aws.redshift_info:\n tags:\n env: dev\n stack: web\n name: user-\n register: redshift_web\n\n- name: Fail if no cluster(s) is/are found\n community.aws.redshift_info:\n tags:\n env: stg\n stack: db\n register: redshift_user\n failed_when: \"{{ redshift_user.results | length == 0 }}\"\n'''\n\nRETURN = '''\n# For more information see U(http://boto3.readthedocs.io/en/latest/reference/services/redshift.html#Redshift.Client.describe_clusters)\n---\ncluster_identifier:\n description: Unique key to identify the cluster.\n returned: success\n type: str\n sample: \"redshift-identifier\"\nnode_type:\n description: The node type for nodes in the cluster.\n returned: success\n type: str\n sample: \"ds2.xlarge\"\ncluster_status:\n description: Current state of the cluster.\n returned: success\n type: str\n sample: \"available\"\nmodify_status:\n description: The status of a modify operation.\n returned: optional\n type: str\n sample: \"\"\nmaster_username:\n description: The master user name for the cluster.\n returned: success\n type: str\n sample: \"admin\"\ndb_name:\n description: The name of the initial database that was created when the cluster was created.\n returned: success\n type: str\n sample: \"dev\"\nendpoint:\n description: The connection endpoint.\n returned: success\n type: str\n sample: {\n \"address\": \"cluster-ds2.ocmugla0rf.us-east-1.redshift.amazonaws.com\",\n \"port\": 5439\n }\ncluster_create_time:\n description: The date and time that the cluster was created.\n returned: success\n type: str\n sample: \"2016-05-10T08:33:16.629000+00:00\"\nautomated_snapshot_retention_period:\n description: The number of days that automatic cluster snapshots are retained.\n returned: success\n type: int\n sample: 1\ncluster_security_groups:\n description: A list of cluster security groups that are associated with the cluster.\n returned: success\n type: list\n sample: []\nvpc_security_groups:\n description: A list of VPC security groups the are associated with the cluster.\n returned: success\n type: list\n sample: [\n {\n \"status\": \"active\",\n \"vpc_security_group_id\": \"sg-12cghhg\"\n }\n ]\ncluster_paramater_groups:\n description: The list of cluster parameters that are associated with this cluster.\n returned: success\n type: list\n sample: [\n {\n \"cluster_parameter_status_list\": [\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"statement_timeout\"\n },\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"require_ssl\"\n }\n ],\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_group_name\": \"tuba\"\n }\n ]\ncluster_subnet_group_name:\n description: The name of the subnet group that is associated with the cluster.\n returned: success\n type: str\n sample: \"redshift-subnet\"\nvpc_id:\n description: The identifier of the VPC the cluster is in, if the cluster is in a VPC.\n returned: success\n type: str\n sample: \"vpc-1234567\"\navailability_zone:\n description: The name of the Availability Zone in which the cluster is located.\n returned: success\n type: str\n sample: \"us-east-1b\"\npreferred_maintenance_window:\n description: The weekly time range, in Universal Coordinated Time (UTC), during which system maintenance can occur.\n returned: success\n type: str\n sample: \"tue:07:30-tue:08:00\"\npending_modified_values:\n description: A value that, if present, indicates that changes to the cluster are pending.\n returned: success\n type: dict\n sample: {}\ncluster_version:\n description: The version ID of the Amazon Redshift engine that is running on the cluster.\n returned: success\n type: str\n sample: \"1.0\"\nallow_version_upgrade:\n description: >\n A Boolean value that, if true, indicates that major version upgrades will be applied\n automatically to the cluster during the maintenance window.\n returned: success\n type: bool\n sample: true|false\nnumber_of_nodes:\n description: The number of compute nodes in the cluster.\n returned: success\n type: int\n sample: 12\npublicly_accessible:\n description: A Boolean value that, if true , indicates that the cluster can be accessed from a public network.\n returned: success\n type: bool\n sample: true|false\nencrypted:\n description: Boolean value that, if true , indicates that data in the cluster is encrypted at rest.\n returned: success\n type: bool\n sample: true|false\nrestore_status:\n description: A value that describes the status of a cluster restore action.\n returned: success\n type: dict\n sample: {}\nhsm_status:\n description: >\n A value that reports whether the Amazon Redshift cluster has finished applying any hardware\n security module (HSM) settings changes specified in a modify cluster command.\n returned: success\n type: dict\n sample: {}\ncluster_snapshot_copy_status:\n description: A value that returns the destination region and retention period that are configured for cross-region snapshot copy.\n returned: success\n type: dict\n sample: {}\ncluster_public_keys:\n description: The public key for the cluster.\n returned: success\n type: str\n sample: \"ssh-rsa anjigfam Amazon-Redshift\\n\"\ncluster_nodes:\n description: The nodes in the cluster.\n returned: success\n type: list\n sample: [\n {\n \"node_role\": \"LEADER\",\n \"private_ip_address\": \"10.0.0.1\",\n \"public_ip_address\": \"x.x.x.x\"\n },\n {\n \"node_role\": \"COMPUTE-1\",\n \"private_ip_address\": \"10.0.0.3\",\n \"public_ip_address\": \"x.x.x.x\"\n }\n ]\nelastic_ip_status:\n description: The status of the elastic IP (EIP) address.\n returned: success\n type: dict\n sample: {}\ncluster_revision_number:\n description: The specific revision number of the database in the cluster.\n returned: success\n type: str\n sample: \"1231\"\ntags:\n description: The list of tags for the cluster.\n returned: success\n type: list\n sample: []\nkms_key_id:\n description: The AWS Key Management Service (AWS KMS) key ID of the encryption key used to encrypt data in the cluster.\n returned: success\n type: str\n sample: \"\"\nenhanced_vpc_routing:\n description: An option that specifies whether to create the cluster with enhanced VPC routing enabled.\n returned: success\n type: bool\n sample: true|false\niam_roles:\n description: List of IAM roles attached to the cluster.\n returned: success\n type: list\n sample: []\n'''\n\nimport re\n\ntry:\n from botocore.exceptions import BotoCoreError, ClientError\nexcept ImportError:\n pass # caught by AnsibleAWSModule\n\nfrom ansible_collections.amazon.aws.plugins.module_utils.core import AnsibleAWSModule\nfrom ansible_collections.amazon.aws.plugins.module_utils.ec2 import camel_dict_to_snake_dict\n\n\ndef match_tags(tags_to_match, cluster):\n for key, value in tags_to_match.items():\n for tag in cluster['Tags']:\n if key == tag['Key'] and value == tag['Value']:\n return True\n\n return False\n\n\ndef find_clusters(conn, module, identifier=None, tags=None):\n\n try:\n cluster_paginator = conn.get_paginator('describe_clusters')\n clusters = cluster_paginator.paginate().build_full_result()\n except (BotoCoreError, ClientError) as e:\n module.fail_json_aws(e, msg='Failed to fetch clusters.')\n\n matched_clusters = []\n\n if identifier is not None:\n identifier_prog = re.compile('^' + identifier)\n\n for cluster in clusters['Clusters']:\n\n matched_identifier = True\n if identifier:\n matched_identifier = identifier_prog.search(cluster['ClusterIdentifier'])\n\n matched_tags = True\n if tags:\n matched_tags = match_tags(tags, cluster)\n\n if matched_identifier and matched_tags:\n matched_clusters.append(camel_dict_to_snake_dict(cluster))\n\n return matched_clusters\n\n\ndef main():\n\n argument_spec = dict(\n cluster_identifier=dict(type='str', aliases=['identifier', 'name']),\n tags=dict(type='dict')\n )\n module = AnsibleAWSModule(\n argument_spec=argument_spec,\n supports_check_mode=True\n )\n if module._name == 'redshift_facts':\n module.deprecate(\"The 'redshift_facts' module has been renamed to 'redshift_info'\", date='2021-12-01', collection_name='community.aws')\n\n cluster_identifier = module.params.get('cluster_identifier')\n cluster_tags = module.params.get('tags')\n\n redshift = module.client('redshift')\n\n results = find_clusters(redshift, module, identifier=cluster_identifier, tags=cluster_tags)\n module.exit_json(results=results)\n\n\nif __name__ == '__main__':\n main()\n", "path": "plugins/modules/redshift_info.py" } ]
diff --git a/changelogs/fragments/970-redshift_info-boto-import.yml b/changelogs/fragments/970-redshift_info-boto-import.yml new file mode 100644 index 00000000000..568c6cdf605 --- /dev/null +++ b/changelogs/fragments/970-redshift_info-boto-import.yml @@ -0,0 +1,2 @@ +bugfixes: + - redshift_info - fix invalid import path for botocore exceptions (https://github.com/ansible-collections/community.aws/issues/968). diff --git a/plugins/modules/redshift_info.py b/plugins/modules/redshift_info.py index bc4cb021840..9a6784ec506 100644 --- a/plugins/modules/redshift_info.py +++ b/plugins/modules/redshift_info.py @@ -278,7 +278,7 @@ import re try: - from botocore.exception import BotoCoreError, ClientError + from botocore.exceptions import BotoCoreError, ClientError except ImportError: pass # caught by AnsibleAWSModule
ansible-collections__community.aws-970
Invalid import path for BotoCoreError in redshift_info module ### Summary In case of any AWS related error (like missing permissions) the module will throw a gigantic python stack trace with error summary as: ``` line 304, in find_clusters NameError: name 'BotoCoreError' is not defined ``` This is due to an invalid import path that is present in the module https://github.com/ansible-collections/community.aws/blob/main/plugins/modules/redshift_info.py#L280 Instead of `from botocore.exception` it should be `from botocore.exceptions`. Once that is done, ansible no longer hides the real error with the stack trace. ### Issue Type Bug Report ### Component Name redshift_info ### Ansible Version ```console (paste below) $ ansible --version ansible 2.10.8 config file = None configured module search path = ['/home/wojtek/.ansible/plugins/modules', '/usr/share/ansible/plugins/modules'] ansible python module location = /usr/local/lib/python3.6/dist-packages/ansible executable location = /usr/local/bin/ansible python version = 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0] ``` ### Collection Versions Non-relevant ### AWS SDK versions ```console (paste below) $ pip show boto boto3 botocore Name: boto Version: 2.49.0 Summary: Amazon Web Services Library Home-page: https://github.com/boto/boto/ Author: Mitch Garnaat Author-email: [email protected] License: MIT Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: --- Name: boto3 Version: 1.20.54 Summary: The AWS SDK for Python Home-page: https://github.com/boto/boto3 Author: Amazon Web Services Author-email: None License: Apache License 2.0 Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: jmespath, s3transfer, botocore --- Name: botocore Version: 1.23.54 Summary: Low-level, data-driven core of boto 3. Home-page: https://github.com/boto/botocore Author: Amazon Web Services Author-email: None License: Apache License 2.0 Location: /home/wojtek/.local/lib/python3.6/site-packages Requires: jmespath, urllib3, python-dateutil ``` ### Configuration ```console (paste below) $ ansible-config dump --only-changed ``` ### OS / Environment Ubuntu 20.04 ### Steps to Reproduce Run the module without DescribeClusters permission. ### Expected Results AWS API error on missing permissions is shown. ### Actual Results Python stack trace ending with ``` line 304, in find_clusters NameError: name 'BotoCoreError' is not defined ``` ### Code of Conduct - [X] I agree to follow the Ansible Code of Conduct
[ { "content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: redshift_info\nversion_added: 1.0.0\nauthor: \"Jens Carl (@j-carl)\"\nshort_description: Gather information about Redshift cluster(s)\ndescription:\n - Gather information about Redshift cluster(s).\noptions:\n cluster_identifier:\n description:\n - The prefix of cluster identifier of the Redshift cluster you are searching for.\n - \"This is a regular expression match with implicit '^'. Append '$' for a complete match.\"\n required: false\n aliases: ['name', 'identifier']\n type: str\n tags:\n description:\n - \"A dictionary/hash of tags in the format { tag1_name: 'tag1_value', tag2_name: 'tag2_value' }\n to match against the security group(s) you are searching for.\"\n required: false\n type: dict\nextends_documentation_fragment:\n- amazon.aws.ec2\n- amazon.aws.aws\n\n'''\n\nEXAMPLES = '''\n# Note: These examples do net set authentication details, see the AWS guide for details.\n\n- name: Find all clusters\n community.aws.redshift_info:\n register: redshift\n\n- name: Find cluster(s) with matching tags\n community.aws.redshift_info:\n tags:\n env: prd\n stack: monitoring\n register: redshift_tags\n\n- name: Find cluster(s) with matching name/prefix and tags\n community.aws.redshift_info:\n tags:\n env: dev\n stack: web\n name: user-\n register: redshift_web\n\n- name: Fail if no cluster(s) is/are found\n community.aws.redshift_info:\n tags:\n env: stg\n stack: db\n register: redshift_user\n failed_when: \"{{ redshift_user.results | length == 0 }}\"\n'''\n\nRETURN = '''\n# For more information see U(http://boto3.readthedocs.io/en/latest/reference/services/redshift.html#Redshift.Client.describe_clusters)\n---\ncluster_identifier:\n description: Unique key to identify the cluster.\n returned: success\n type: str\n sample: \"redshift-identifier\"\nnode_type:\n description: The node type for nodes in the cluster.\n returned: success\n type: str\n sample: \"ds2.xlarge\"\ncluster_status:\n description: Current state of the cluster.\n returned: success\n type: str\n sample: \"available\"\nmodify_status:\n description: The status of a modify operation.\n returned: optional\n type: str\n sample: \"\"\nmaster_username:\n description: The master user name for the cluster.\n returned: success\n type: str\n sample: \"admin\"\ndb_name:\n description: The name of the initial database that was created when the cluster was created.\n returned: success\n type: str\n sample: \"dev\"\nendpoint:\n description: The connection endpoint.\n returned: success\n type: str\n sample: {\n \"address\": \"cluster-ds2.ocmugla0rf.us-east-1.redshift.amazonaws.com\",\n \"port\": 5439\n }\ncluster_create_time:\n description: The date and time that the cluster was created.\n returned: success\n type: str\n sample: \"2016-05-10T08:33:16.629000+00:00\"\nautomated_snapshot_retention_period:\n description: The number of days that automatic cluster snapshots are retained.\n returned: success\n type: int\n sample: 1\ncluster_security_groups:\n description: A list of cluster security groups that are associated with the cluster.\n returned: success\n type: list\n sample: []\nvpc_security_groups:\n description: A list of VPC security groups the are associated with the cluster.\n returned: success\n type: list\n sample: [\n {\n \"status\": \"active\",\n \"vpc_security_group_id\": \"sg-12cghhg\"\n }\n ]\ncluster_paramater_groups:\n description: The list of cluster parameters that are associated with this cluster.\n returned: success\n type: list\n sample: [\n {\n \"cluster_parameter_status_list\": [\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"statement_timeout\"\n },\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"require_ssl\"\n }\n ],\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_group_name\": \"tuba\"\n }\n ]\ncluster_subnet_group_name:\n description: The name of the subnet group that is associated with the cluster.\n returned: success\n type: str\n sample: \"redshift-subnet\"\nvpc_id:\n description: The identifier of the VPC the cluster is in, if the cluster is in a VPC.\n returned: success\n type: str\n sample: \"vpc-1234567\"\navailability_zone:\n description: The name of the Availability Zone in which the cluster is located.\n returned: success\n type: str\n sample: \"us-east-1b\"\npreferred_maintenance_window:\n description: The weekly time range, in Universal Coordinated Time (UTC), during which system maintenance can occur.\n returned: success\n type: str\n sample: \"tue:07:30-tue:08:00\"\npending_modified_values:\n description: A value that, if present, indicates that changes to the cluster are pending.\n returned: success\n type: dict\n sample: {}\ncluster_version:\n description: The version ID of the Amazon Redshift engine that is running on the cluster.\n returned: success\n type: str\n sample: \"1.0\"\nallow_version_upgrade:\n description: >\n A Boolean value that, if true, indicates that major version upgrades will be applied\n automatically to the cluster during the maintenance window.\n returned: success\n type: bool\n sample: true|false\nnumber_of_nodes:\n description: The number of compute nodes in the cluster.\n returned: success\n type: int\n sample: 12\npublicly_accessible:\n description: A Boolean value that, if true , indicates that the cluster can be accessed from a public network.\n returned: success\n type: bool\n sample: true|false\nencrypted:\n description: Boolean value that, if true , indicates that data in the cluster is encrypted at rest.\n returned: success\n type: bool\n sample: true|false\nrestore_status:\n description: A value that describes the status of a cluster restore action.\n returned: success\n type: dict\n sample: {}\nhsm_status:\n description: >\n A value that reports whether the Amazon Redshift cluster has finished applying any hardware\n security module (HSM) settings changes specified in a modify cluster command.\n returned: success\n type: dict\n sample: {}\ncluster_snapshot_copy_status:\n description: A value that returns the destination region and retention period that are configured for cross-region snapshot copy.\n returned: success\n type: dict\n sample: {}\ncluster_public_keys:\n description: The public key for the cluster.\n returned: success\n type: str\n sample: \"ssh-rsa anjigfam Amazon-Redshift\\n\"\ncluster_nodes:\n description: The nodes in the cluster.\n returned: success\n type: list\n sample: [\n {\n \"node_role\": \"LEADER\",\n \"private_ip_address\": \"10.0.0.1\",\n \"public_ip_address\": \"x.x.x.x\"\n },\n {\n \"node_role\": \"COMPUTE-1\",\n \"private_ip_address\": \"10.0.0.3\",\n \"public_ip_address\": \"x.x.x.x\"\n }\n ]\nelastic_ip_status:\n description: The status of the elastic IP (EIP) address.\n returned: success\n type: dict\n sample: {}\ncluster_revision_number:\n description: The specific revision number of the database in the cluster.\n returned: success\n type: str\n sample: \"1231\"\ntags:\n description: The list of tags for the cluster.\n returned: success\n type: list\n sample: []\nkms_key_id:\n description: The AWS Key Management Service (AWS KMS) key ID of the encryption key used to encrypt data in the cluster.\n returned: success\n type: str\n sample: \"\"\nenhanced_vpc_routing:\n description: An option that specifies whether to create the cluster with enhanced VPC routing enabled.\n returned: success\n type: bool\n sample: true|false\niam_roles:\n description: List of IAM roles attached to the cluster.\n returned: success\n type: list\n sample: []\n'''\n\nimport re\n\ntry:\n from botocore.exception import BotoCoreError, ClientError\nexcept ImportError:\n pass # caught by AnsibleAWSModule\n\nfrom ansible_collections.amazon.aws.plugins.module_utils.core import AnsibleAWSModule\nfrom ansible_collections.amazon.aws.plugins.module_utils.ec2 import camel_dict_to_snake_dict\n\n\ndef match_tags(tags_to_match, cluster):\n for key, value in tags_to_match.items():\n for tag in cluster['Tags']:\n if key == tag['Key'] and value == tag['Value']:\n return True\n\n return False\n\n\ndef find_clusters(conn, module, identifier=None, tags=None):\n\n try:\n cluster_paginator = conn.get_paginator('describe_clusters')\n clusters = cluster_paginator.paginate().build_full_result()\n except (BotoCoreError, ClientError) as e:\n module.fail_json_aws(e, msg='Failed to fetch clusters.')\n\n matched_clusters = []\n\n if identifier is not None:\n identifier_prog = re.compile('^' + identifier)\n\n for cluster in clusters['Clusters']:\n\n matched_identifier = True\n if identifier:\n matched_identifier = identifier_prog.search(cluster['ClusterIdentifier'])\n\n matched_tags = True\n if tags:\n matched_tags = match_tags(tags, cluster)\n\n if matched_identifier and matched_tags:\n matched_clusters.append(camel_dict_to_snake_dict(cluster))\n\n return matched_clusters\n\n\ndef main():\n\n argument_spec = dict(\n cluster_identifier=dict(type='str', aliases=['identifier', 'name']),\n tags=dict(type='dict')\n )\n module = AnsibleAWSModule(\n argument_spec=argument_spec,\n supports_check_mode=True\n )\n\n cluster_identifier = module.params.get('cluster_identifier')\n cluster_tags = module.params.get('tags')\n\n redshift = module.client('redshift')\n\n results = find_clusters(redshift, module, identifier=cluster_identifier, tags=cluster_tags)\n module.exit_json(results=results)\n\n\nif __name__ == '__main__':\n main()\n", "path": "plugins/modules/redshift_info.py" } ]
[ { "content": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Copyright: Ansible Project\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nDOCUMENTATION = '''\n---\nmodule: redshift_info\nversion_added: 1.0.0\nauthor: \"Jens Carl (@j-carl)\"\nshort_description: Gather information about Redshift cluster(s)\ndescription:\n - Gather information about Redshift cluster(s).\noptions:\n cluster_identifier:\n description:\n - The prefix of cluster identifier of the Redshift cluster you are searching for.\n - \"This is a regular expression match with implicit '^'. Append '$' for a complete match.\"\n required: false\n aliases: ['name', 'identifier']\n type: str\n tags:\n description:\n - \"A dictionary/hash of tags in the format { tag1_name: 'tag1_value', tag2_name: 'tag2_value' }\n to match against the security group(s) you are searching for.\"\n required: false\n type: dict\nextends_documentation_fragment:\n- amazon.aws.ec2\n- amazon.aws.aws\n\n'''\n\nEXAMPLES = '''\n# Note: These examples do net set authentication details, see the AWS guide for details.\n\n- name: Find all clusters\n community.aws.redshift_info:\n register: redshift\n\n- name: Find cluster(s) with matching tags\n community.aws.redshift_info:\n tags:\n env: prd\n stack: monitoring\n register: redshift_tags\n\n- name: Find cluster(s) with matching name/prefix and tags\n community.aws.redshift_info:\n tags:\n env: dev\n stack: web\n name: user-\n register: redshift_web\n\n- name: Fail if no cluster(s) is/are found\n community.aws.redshift_info:\n tags:\n env: stg\n stack: db\n register: redshift_user\n failed_when: \"{{ redshift_user.results | length == 0 }}\"\n'''\n\nRETURN = '''\n# For more information see U(http://boto3.readthedocs.io/en/latest/reference/services/redshift.html#Redshift.Client.describe_clusters)\n---\ncluster_identifier:\n description: Unique key to identify the cluster.\n returned: success\n type: str\n sample: \"redshift-identifier\"\nnode_type:\n description: The node type for nodes in the cluster.\n returned: success\n type: str\n sample: \"ds2.xlarge\"\ncluster_status:\n description: Current state of the cluster.\n returned: success\n type: str\n sample: \"available\"\nmodify_status:\n description: The status of a modify operation.\n returned: optional\n type: str\n sample: \"\"\nmaster_username:\n description: The master user name for the cluster.\n returned: success\n type: str\n sample: \"admin\"\ndb_name:\n description: The name of the initial database that was created when the cluster was created.\n returned: success\n type: str\n sample: \"dev\"\nendpoint:\n description: The connection endpoint.\n returned: success\n type: str\n sample: {\n \"address\": \"cluster-ds2.ocmugla0rf.us-east-1.redshift.amazonaws.com\",\n \"port\": 5439\n }\ncluster_create_time:\n description: The date and time that the cluster was created.\n returned: success\n type: str\n sample: \"2016-05-10T08:33:16.629000+00:00\"\nautomated_snapshot_retention_period:\n description: The number of days that automatic cluster snapshots are retained.\n returned: success\n type: int\n sample: 1\ncluster_security_groups:\n description: A list of cluster security groups that are associated with the cluster.\n returned: success\n type: list\n sample: []\nvpc_security_groups:\n description: A list of VPC security groups the are associated with the cluster.\n returned: success\n type: list\n sample: [\n {\n \"status\": \"active\",\n \"vpc_security_group_id\": \"sg-12cghhg\"\n }\n ]\ncluster_paramater_groups:\n description: The list of cluster parameters that are associated with this cluster.\n returned: success\n type: list\n sample: [\n {\n \"cluster_parameter_status_list\": [\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"statement_timeout\"\n },\n {\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_name\": \"require_ssl\"\n }\n ],\n \"parameter_apply_status\": \"in-sync\",\n \"parameter_group_name\": \"tuba\"\n }\n ]\ncluster_subnet_group_name:\n description: The name of the subnet group that is associated with the cluster.\n returned: success\n type: str\n sample: \"redshift-subnet\"\nvpc_id:\n description: The identifier of the VPC the cluster is in, if the cluster is in a VPC.\n returned: success\n type: str\n sample: \"vpc-1234567\"\navailability_zone:\n description: The name of the Availability Zone in which the cluster is located.\n returned: success\n type: str\n sample: \"us-east-1b\"\npreferred_maintenance_window:\n description: The weekly time range, in Universal Coordinated Time (UTC), during which system maintenance can occur.\n returned: success\n type: str\n sample: \"tue:07:30-tue:08:00\"\npending_modified_values:\n description: A value that, if present, indicates that changes to the cluster are pending.\n returned: success\n type: dict\n sample: {}\ncluster_version:\n description: The version ID of the Amazon Redshift engine that is running on the cluster.\n returned: success\n type: str\n sample: \"1.0\"\nallow_version_upgrade:\n description: >\n A Boolean value that, if true, indicates that major version upgrades will be applied\n automatically to the cluster during the maintenance window.\n returned: success\n type: bool\n sample: true|false\nnumber_of_nodes:\n description: The number of compute nodes in the cluster.\n returned: success\n type: int\n sample: 12\npublicly_accessible:\n description: A Boolean value that, if true , indicates that the cluster can be accessed from a public network.\n returned: success\n type: bool\n sample: true|false\nencrypted:\n description: Boolean value that, if true , indicates that data in the cluster is encrypted at rest.\n returned: success\n type: bool\n sample: true|false\nrestore_status:\n description: A value that describes the status of a cluster restore action.\n returned: success\n type: dict\n sample: {}\nhsm_status:\n description: >\n A value that reports whether the Amazon Redshift cluster has finished applying any hardware\n security module (HSM) settings changes specified in a modify cluster command.\n returned: success\n type: dict\n sample: {}\ncluster_snapshot_copy_status:\n description: A value that returns the destination region and retention period that are configured for cross-region snapshot copy.\n returned: success\n type: dict\n sample: {}\ncluster_public_keys:\n description: The public key for the cluster.\n returned: success\n type: str\n sample: \"ssh-rsa anjigfam Amazon-Redshift\\n\"\ncluster_nodes:\n description: The nodes in the cluster.\n returned: success\n type: list\n sample: [\n {\n \"node_role\": \"LEADER\",\n \"private_ip_address\": \"10.0.0.1\",\n \"public_ip_address\": \"x.x.x.x\"\n },\n {\n \"node_role\": \"COMPUTE-1\",\n \"private_ip_address\": \"10.0.0.3\",\n \"public_ip_address\": \"x.x.x.x\"\n }\n ]\nelastic_ip_status:\n description: The status of the elastic IP (EIP) address.\n returned: success\n type: dict\n sample: {}\ncluster_revision_number:\n description: The specific revision number of the database in the cluster.\n returned: success\n type: str\n sample: \"1231\"\ntags:\n description: The list of tags for the cluster.\n returned: success\n type: list\n sample: []\nkms_key_id:\n description: The AWS Key Management Service (AWS KMS) key ID of the encryption key used to encrypt data in the cluster.\n returned: success\n type: str\n sample: \"\"\nenhanced_vpc_routing:\n description: An option that specifies whether to create the cluster with enhanced VPC routing enabled.\n returned: success\n type: bool\n sample: true|false\niam_roles:\n description: List of IAM roles attached to the cluster.\n returned: success\n type: list\n sample: []\n'''\n\nimport re\n\ntry:\n from botocore.exceptions import BotoCoreError, ClientError\nexcept ImportError:\n pass # caught by AnsibleAWSModule\n\nfrom ansible_collections.amazon.aws.plugins.module_utils.core import AnsibleAWSModule\nfrom ansible_collections.amazon.aws.plugins.module_utils.ec2 import camel_dict_to_snake_dict\n\n\ndef match_tags(tags_to_match, cluster):\n for key, value in tags_to_match.items():\n for tag in cluster['Tags']:\n if key == tag['Key'] and value == tag['Value']:\n return True\n\n return False\n\n\ndef find_clusters(conn, module, identifier=None, tags=None):\n\n try:\n cluster_paginator = conn.get_paginator('describe_clusters')\n clusters = cluster_paginator.paginate().build_full_result()\n except (BotoCoreError, ClientError) as e:\n module.fail_json_aws(e, msg='Failed to fetch clusters.')\n\n matched_clusters = []\n\n if identifier is not None:\n identifier_prog = re.compile('^' + identifier)\n\n for cluster in clusters['Clusters']:\n\n matched_identifier = True\n if identifier:\n matched_identifier = identifier_prog.search(cluster['ClusterIdentifier'])\n\n matched_tags = True\n if tags:\n matched_tags = match_tags(tags, cluster)\n\n if matched_identifier and matched_tags:\n matched_clusters.append(camel_dict_to_snake_dict(cluster))\n\n return matched_clusters\n\n\ndef main():\n\n argument_spec = dict(\n cluster_identifier=dict(type='str', aliases=['identifier', 'name']),\n tags=dict(type='dict')\n )\n module = AnsibleAWSModule(\n argument_spec=argument_spec,\n supports_check_mode=True\n )\n\n cluster_identifier = module.params.get('cluster_identifier')\n cluster_tags = module.params.get('tags')\n\n redshift = module.client('redshift')\n\n results = find_clusters(redshift, module, identifier=cluster_identifier, tags=cluster_tags)\n module.exit_json(results=results)\n\n\nif __name__ == '__main__':\n main()\n", "path": "plugins/modules/redshift_info.py" } ]
diff --git a/changelogs/fragments/970-redshift_info-boto-import.yml b/changelogs/fragments/970-redshift_info-boto-import.yml new file mode 100644 index 00000000000..568c6cdf605 --- /dev/null +++ b/changelogs/fragments/970-redshift_info-boto-import.yml @@ -0,0 +1,2 @@ +bugfixes: + - redshift_info - fix invalid import path for botocore exceptions (https://github.com/ansible-collections/community.aws/issues/968). diff --git a/plugins/modules/redshift_info.py b/plugins/modules/redshift_info.py index b79b28b3074..a6a8a578a37 100644 --- a/plugins/modules/redshift_info.py +++ b/plugins/modules/redshift_info.py @@ -277,7 +277,7 @@ import re try: - from botocore.exception import BotoCoreError, ClientError + from botocore.exceptions import BotoCoreError, ClientError except ImportError: pass # caught by AnsibleAWSModule
sherlock-project__sherlock-77
Version Number System I do not think that the version number system should use the date. There are multiple systems out there, but they are all flavors of major.minor.maintenance. This allows the version number to have some meaning to other people. https://github.com/sdushantha/sherlock/blob/e2c4dbf1ef69db80a9c6ebf591be874686e04301/sherlock.py#L20
[ { "content": "#! /usr/bin/env python3\n\n\"\"\"\nSherlock: Find Usernames Across Social Networks Module\n\nThis module contains the main logic to search for usernames at social\nnetworks.\n\"\"\"\n\nimport csv\nimport json\nimport os\nimport platform\nimport re\nfrom argparse import ArgumentParser, RawDescriptionHelpFormatter\nfrom concurrent.futures import ThreadPoolExecutor\n\nimport requests\nfrom colorama import Back, Fore, Style, init\nfrom requests_futures.sessions import FuturesSession\nfrom torrequest import TorRequest\n\nmodule_name = \"Sherlock: Find Usernames Across Social Networks\"\n__version__ = \"2018.01.04\"\namount=0\n\n# TODO: fix tumblr\n\n\ndef write_to_file(url, fname):\n with open(fname, \"a\") as f:\n f.write(url + \"\\n\")\n\ndef final_score(amount, fname):\n with open(fname, \"a\") as f:\n f.write(\"Total: \"+str(amount) + \"\\n\")\n\ndef print_error(err, errstr, var, debug=False):\n print(f\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[91;1m {errstr}\\033[93;1m {err if debug else var}\")\n\n\ndef get_response(request_future, error_type, social_network, verbose=False):\n try:\n rsp = request_future.result()\n if rsp.status_code:\n return rsp, error_type\n except requests.exceptions.HTTPError as errh:\n print_error(errh, \"HTTP Error:\", social_network, verbose)\n except requests.exceptions.ConnectionError as errc:\n print_error(errc, \"Error Connecting:\", social_network, verbose)\n except requests.exceptions.Timeout as errt:\n print_error(errt, \"Timeout Error:\", social_network, verbose)\n except requests.exceptions.RequestException as err:\n print_error(err, \"Unknown error:\", social_network, verbose)\n return None, \"\"\n\n\ndef sherlock(username, verbose=False, tor=False, unique_tor=False):\n \"\"\"Run Sherlock Analysis.\n\n Checks for existence of username on various social media sites.\n\n Keyword Arguments:\n username -- String indicating username that report\n should be created against.\n verbose -- Boolean indicating whether to give verbose output.\n tor -- Boolean indicating whether to use a tor circuit for the requests.\n unique_tor -- Boolean indicating whether to use a new tor circuit for each request.\n\n Return Value:\n Dictionary containing results from report. Key of dictionary is the name\n of the social network site, and the value is another dictionary with\n the following keys:\n url_main: URL of main site.\n url_user: URL of user on site (if account exists).\n exists: String indicating results of test for account existence.\n http_status: HTTP status code of query which checked for existence on\n site.\n response_text: Text that came back from request. May be None if\n there was an HTTP error when checking for existence.\n \"\"\"\n global amount\n fname = username + \".txt\"\n\n if os.path.isfile(fname):\n os.remove(fname)\n print(\"\\033[1;92m[\\033[0m\\033[1;77m*\\033[0m\\033[1;92m] Removing previous file:\\033[1;37m {}\\033[0m\".format(fname))\n\n print(\"\\033[1;92m[\\033[0m\\033[1;77m*\\033[0m\\033[1;92m] Checking username\\033[0m\\033[1;37m {}\\033[0m\\033[1;92m on: \\033[0m\".format(username))\n\n # A user agent is needed because some sites don't\n # return the correct information since they think that\n # we are bots\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.12; rv:55.0) Gecko/20100101 Firefox/55.0'\n }\n\n # Load the data\n data_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"data.json\")\n with open(data_file_path, \"r\", encoding=\"utf-8\") as raw:\n data = json.load(raw)\n\n # Allow 1 thread for each external service, so `len(data)` threads total\n executor = ThreadPoolExecutor(max_workers=len(data))\n\n # Create session based on request methodology\n underlying_session = requests.session()\n underlying_request = requests.Request()\n if tor or unique_tor:\n underlying_request = TorRequest()\n underlying_session = underlying_request.session()\n\n # Create multi-threaded session for all requests\n session = FuturesSession(executor=executor, session=underlying_session)\n\n # Results from analysis of all sites\n results_total = {}\n\n # First create futures for all requests. This allows for the requests to run in parallel\n for social_network, net_info in data.items():\n\n # Results from analysis of this specific site\n results_site = {}\n\n # Record URL of main site\n results_site['url_main'] = net_info.get(\"urlMain\")\n\n # Don't make request if username is invalid for the site\n regex_check = net_info.get(\"regexCheck\")\n if regex_check and re.search(regex_check, username) is None:\n # No need to do the check at the site: this user name is not allowed.\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Illegal Username Format For This Site!\".format(social_network))\n results_site[\"exists\"] = \"illegal\"\n else:\n # URL of user on site (if it exists)\n url = net_info[\"url\"].format(username)\n results_site[\"url_user\"] = url\n\n # If only the status_code is needed don't download the body\n if net_info[\"errorType\"] == 'status_code':\n request_method = session.head\n else:\n request_method = session.get\n\n # This future starts running the request in a new thread, doesn't block the main thread\n future = request_method(url=url, headers=headers)\n\n # Store future in data for access later\n net_info[\"request_future\"] = future\n\n # Reset identify for tor (if needed)\n if unique_tor:\n underlying_request.reset_identity()\n\n # Add this site's results into final dictionary with all of the other results.\n results_total[social_network] = results_site\n\n # Core logic: If tor requests, make them here. If multi-threaded requests, wait for responses\n for social_network, net_info in data.items():\n\n # Retrieve results again\n results_site = results_total.get(social_network)\n\n # Retrieve other site information again\n url = results_site.get(\"url_user\")\n exists = results_site.get(\"exists\")\n if exists is not None:\n # We have already determined the user doesn't exist here\n continue\n\n # Get the expected error type\n error_type = net_info[\"errorType\"]\n\n # Default data in case there are any failures in doing a request.\n http_status = \"?\"\n response_text = \"\"\n\n # Retrieve future and ensure it has finished\n future = net_info[\"request_future\"]\n r, error_type = get_response(request_future=future,\n error_type=error_type,\n social_network=social_network,\n verbose=verbose)\n\n # Attempt to get request information\n try:\n http_status = r.status_code\n except:\n pass\n try:\n response_text = r.text.encode(r.encoding)\n except:\n pass\n\n if error_type == \"message\":\n error = net_info.get(\"errorMsg\")\n # Checks if the error message is in the HTML\n if not error in r.text:\n\n print(\"\\033[37;1m[\\033[92;1m+\\033[37;1m]\\033[92;1m {}:\\033[0m\".format(social_network), url)\n write_to_file(url, fname)\n exists = \"yes\"\n amount=amount+1\n else:\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Not Found!\".format(social_network))\n exists = \"no\"\n\n elif error_type == \"status_code\":\n # Checks if the status code of the response is 404\n if not r.status_code == 404:\n\n print(\"\\033[37;1m[\\033[92;1m+\\033[37;1m]\\033[92;1m {}:\\033[0m\".format(social_network), url)\n write_to_file(url, fname)\n exists = \"yes\"\n amount=amount+1\n else:\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Not Found!\".format(social_network))\n exists = \"no\"\n\n elif error_type == \"response_url\":\n error = net_info.get(\"errorUrl\")\n # Checks if the redirect url is the same as the one defined in data.json\n if not error in r.url:\n\n print(\"\\033[37;1m[\\033[92;1m+\\033[37;1m]\\033[92;1m {}:\\033[0m\".format(social_network), url)\n write_to_file(url, fname)\n exists = \"yes\"\n amount=amount+1\n else:\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Not Found!\".format(social_network))\n exists = \"no\"\n\n elif error_type == \"\":\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Error!\".format(social_network))\n exists = \"error\"\n\n # Save exists flag\n results_site['exists'] = exists\n\n # Save results from request\n results_site['http_status'] = http_status\n results_site['response_text'] = response_text\n\n # Add this site's results into final dictionary with all of the other results.\n results_total[social_network] = results_site\n\n print(\"\\033[1;92m[\\033[0m\\033[1;77m*\\033[0m\\033[1;92m] Saved: \\033[37;1m{}\\033[0m\".format(username+\".txt\"))\n\n final_score(amount, fname)\n return results_total\n\n\ndef main():\n # Colorama module's initialization.\n init()\n\n version_string = f\"%(prog)s {__version__}\\n\" + \\\n f\"{requests.__description__}: {requests.__version__}\\n\" + \\\n f\"Python: {platform.python_version()}\"\n\n parser = ArgumentParser(formatter_class=RawDescriptionHelpFormatter,\n description=f\"{module_name} (Version {__version__})\"\n )\n parser.add_argument(\"--version\",\n action=\"version\", version=version_string,\n help=\"Display version information and dependencies.\"\n )\n parser.add_argument(\"--verbose\", \"-v\", \"-d\", \"--debug\",\n action=\"store_true\", dest=\"verbose\", default=False,\n help=\"Display extra debugging information.\"\n )\n parser.add_argument(\"--quiet\", \"-q\",\n action=\"store_false\", dest=\"verbose\",\n help=\"Disable debugging information (Default Option).\"\n )\n parser.add_argument(\"--tor\", \"-t\",\n action=\"store_true\", dest=\"tor\", default=False,\n help=\"Make requests over TOR; increases runtime; requires TOR to be installed and in system path.\")\n parser.add_argument(\"--unique-tor\", \"-u\",\n action=\"store_true\", dest=\"unique_tor\", default=False,\n help=\"Make requests over TOR with new TOR circuit after each request; increases runtime; requires TOR to be installed and in system path.\")\n parser.add_argument(\"--csv\",\n action=\"store_true\", dest=\"csv\", default=False,\n help=\"Create Comma-Separated Values (CSV) File.\"\n )\n parser.add_argument(\"username\",\n nargs='+', metavar='USERNAMES',\n action=\"store\",\n help=\"One or more usernames to check with social networks.\"\n )\n\n args = parser.parse_args()\n\n # Banner\n print(\n\"\"\"\\033[37;1m .\\\"\\\"\\\"-.\n\\033[37;1m / \\\\\n\\033[37;1m ____ _ _ _ | _..--'-.\n\\033[37;1m/ ___|| |__ ___ _ __| | ___ ___| |__ >.`__.-\\\"\\\"\\;\\\"`\n\\033[37;1m\\___ \\| '_ \\ / _ \\ '__| |/ _ \\ / __| |/ / / /( ^\\\\\n\\033[37;1m ___) | | | | __/ | | | (_) | (__| < '-`) =|-.\n\\033[37;1m|____/|_| |_|\\___|_| |_|\\___/ \\___|_|\\_\\ /`--.'--' \\ .-.\n\\033[37;1m .'`-._ `.\\ | J /\n\\033[37;1m / `--.| \\__/\\033[0m\"\"\")\n\n if args.tor or args.unique_tor:\n print(\"Warning: some websites might refuse connecting over TOR, so note that using this option might increase connection errors.\")\n\n # Run report on all specified users.\n for username in args.username:\n print()\n results = sherlock(username, verbose=args.verbose, tor=args.tor, unique_tor=args.unique_tor)\n\n if args.csv == True:\n with open(username + \".csv\", \"w\", newline='') as csv_report:\n writer = csv.writer(csv_report)\n writer.writerow(['username',\n 'name',\n 'url_main',\n 'url_user',\n 'exists',\n 'http_status'\n ]\n )\n for site in results:\n writer.writerow([username,\n site,\n results[site]['url_main'],\n results[site]['url_user'],\n results[site]['exists'],\n results[site]['http_status']\n ]\n )\n\nif __name__ == \"__main__\":\n main()\n", "path": "sherlock.py" } ]
[ { "content": "#! /usr/bin/env python3\n\n\"\"\"\nSherlock: Find Usernames Across Social Networks Module\n\nThis module contains the main logic to search for usernames at social\nnetworks.\n\"\"\"\n\nimport csv\nimport json\nimport os\nimport platform\nimport re\nfrom argparse import ArgumentParser, RawDescriptionHelpFormatter\nfrom concurrent.futures import ThreadPoolExecutor\n\nimport requests\nfrom colorama import Back, Fore, Style, init\nfrom requests_futures.sessions import FuturesSession\nfrom torrequest import TorRequest\n\nmodule_name = \"Sherlock: Find Usernames Across Social Networks\"\n__version__ = \"0.1.0\"\namount=0\n\n# TODO: fix tumblr\n\n\ndef write_to_file(url, fname):\n with open(fname, \"a\") as f:\n f.write(url + \"\\n\")\n\ndef final_score(amount, fname):\n with open(fname, \"a\") as f:\n f.write(\"Total: \"+str(amount) + \"\\n\")\n\ndef print_error(err, errstr, var, debug=False):\n print(f\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[91;1m {errstr}\\033[93;1m {err if debug else var}\")\n\n\ndef get_response(request_future, error_type, social_network, verbose=False):\n try:\n rsp = request_future.result()\n if rsp.status_code:\n return rsp, error_type\n except requests.exceptions.HTTPError as errh:\n print_error(errh, \"HTTP Error:\", social_network, verbose)\n except requests.exceptions.ConnectionError as errc:\n print_error(errc, \"Error Connecting:\", social_network, verbose)\n except requests.exceptions.Timeout as errt:\n print_error(errt, \"Timeout Error:\", social_network, verbose)\n except requests.exceptions.RequestException as err:\n print_error(err, \"Unknown error:\", social_network, verbose)\n return None, \"\"\n\n\ndef sherlock(username, verbose=False, tor=False, unique_tor=False):\n \"\"\"Run Sherlock Analysis.\n\n Checks for existence of username on various social media sites.\n\n Keyword Arguments:\n username -- String indicating username that report\n should be created against.\n verbose -- Boolean indicating whether to give verbose output.\n tor -- Boolean indicating whether to use a tor circuit for the requests.\n unique_tor -- Boolean indicating whether to use a new tor circuit for each request.\n\n Return Value:\n Dictionary containing results from report. Key of dictionary is the name\n of the social network site, and the value is another dictionary with\n the following keys:\n url_main: URL of main site.\n url_user: URL of user on site (if account exists).\n exists: String indicating results of test for account existence.\n http_status: HTTP status code of query which checked for existence on\n site.\n response_text: Text that came back from request. May be None if\n there was an HTTP error when checking for existence.\n \"\"\"\n global amount\n fname = username + \".txt\"\n\n if os.path.isfile(fname):\n os.remove(fname)\n print(\"\\033[1;92m[\\033[0m\\033[1;77m*\\033[0m\\033[1;92m] Removing previous file:\\033[1;37m {}\\033[0m\".format(fname))\n\n print(\"\\033[1;92m[\\033[0m\\033[1;77m*\\033[0m\\033[1;92m] Checking username\\033[0m\\033[1;37m {}\\033[0m\\033[1;92m on: \\033[0m\".format(username))\n\n # A user agent is needed because some sites don't\n # return the correct information since they think that\n # we are bots\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.12; rv:55.0) Gecko/20100101 Firefox/55.0'\n }\n\n # Load the data\n data_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), \"data.json\")\n with open(data_file_path, \"r\", encoding=\"utf-8\") as raw:\n data = json.load(raw)\n\n # Allow 1 thread for each external service, so `len(data)` threads total\n executor = ThreadPoolExecutor(max_workers=len(data))\n\n # Create session based on request methodology\n underlying_session = requests.session()\n underlying_request = requests.Request()\n if tor or unique_tor:\n underlying_request = TorRequest()\n underlying_session = underlying_request.session()\n\n # Create multi-threaded session for all requests\n session = FuturesSession(executor=executor, session=underlying_session)\n\n # Results from analysis of all sites\n results_total = {}\n\n # First create futures for all requests. This allows for the requests to run in parallel\n for social_network, net_info in data.items():\n\n # Results from analysis of this specific site\n results_site = {}\n\n # Record URL of main site\n results_site['url_main'] = net_info.get(\"urlMain\")\n\n # Don't make request if username is invalid for the site\n regex_check = net_info.get(\"regexCheck\")\n if regex_check and re.search(regex_check, username) is None:\n # No need to do the check at the site: this user name is not allowed.\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Illegal Username Format For This Site!\".format(social_network))\n results_site[\"exists\"] = \"illegal\"\n else:\n # URL of user on site (if it exists)\n url = net_info[\"url\"].format(username)\n results_site[\"url_user\"] = url\n\n # If only the status_code is needed don't download the body\n if net_info[\"errorType\"] == 'status_code':\n request_method = session.head\n else:\n request_method = session.get\n\n # This future starts running the request in a new thread, doesn't block the main thread\n future = request_method(url=url, headers=headers)\n\n # Store future in data for access later\n net_info[\"request_future\"] = future\n\n # Reset identify for tor (if needed)\n if unique_tor:\n underlying_request.reset_identity()\n\n # Add this site's results into final dictionary with all of the other results.\n results_total[social_network] = results_site\n\n # Core logic: If tor requests, make them here. If multi-threaded requests, wait for responses\n for social_network, net_info in data.items():\n\n # Retrieve results again\n results_site = results_total.get(social_network)\n\n # Retrieve other site information again\n url = results_site.get(\"url_user\")\n exists = results_site.get(\"exists\")\n if exists is not None:\n # We have already determined the user doesn't exist here\n continue\n\n # Get the expected error type\n error_type = net_info[\"errorType\"]\n\n # Default data in case there are any failures in doing a request.\n http_status = \"?\"\n response_text = \"\"\n\n # Retrieve future and ensure it has finished\n future = net_info[\"request_future\"]\n r, error_type = get_response(request_future=future,\n error_type=error_type,\n social_network=social_network,\n verbose=verbose)\n\n # Attempt to get request information\n try:\n http_status = r.status_code\n except:\n pass\n try:\n response_text = r.text.encode(r.encoding)\n except:\n pass\n\n if error_type == \"message\":\n error = net_info.get(\"errorMsg\")\n # Checks if the error message is in the HTML\n if not error in r.text:\n\n print(\"\\033[37;1m[\\033[92;1m+\\033[37;1m]\\033[92;1m {}:\\033[0m\".format(social_network), url)\n write_to_file(url, fname)\n exists = \"yes\"\n amount=amount+1\n else:\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Not Found!\".format(social_network))\n exists = \"no\"\n\n elif error_type == \"status_code\":\n # Checks if the status code of the response is 404\n if not r.status_code == 404:\n\n print(\"\\033[37;1m[\\033[92;1m+\\033[37;1m]\\033[92;1m {}:\\033[0m\".format(social_network), url)\n write_to_file(url, fname)\n exists = \"yes\"\n amount=amount+1\n else:\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Not Found!\".format(social_network))\n exists = \"no\"\n\n elif error_type == \"response_url\":\n error = net_info.get(\"errorUrl\")\n # Checks if the redirect url is the same as the one defined in data.json\n if not error in r.url:\n\n print(\"\\033[37;1m[\\033[92;1m+\\033[37;1m]\\033[92;1m {}:\\033[0m\".format(social_network), url)\n write_to_file(url, fname)\n exists = \"yes\"\n amount=amount+1\n else:\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Not Found!\".format(social_network))\n exists = \"no\"\n\n elif error_type == \"\":\n print(\"\\033[37;1m[\\033[91;1m-\\033[37;1m]\\033[92;1m {}:\\033[93;1m Error!\".format(social_network))\n exists = \"error\"\n\n # Save exists flag\n results_site['exists'] = exists\n\n # Save results from request\n results_site['http_status'] = http_status\n results_site['response_text'] = response_text\n\n # Add this site's results into final dictionary with all of the other results.\n results_total[social_network] = results_site\n\n print(\"\\033[1;92m[\\033[0m\\033[1;77m*\\033[0m\\033[1;92m] Saved: \\033[37;1m{}\\033[0m\".format(username+\".txt\"))\n\n final_score(amount, fname)\n return results_total\n\n\ndef main():\n # Colorama module's initialization.\n init()\n\n version_string = f\"%(prog)s {__version__}\\n\" + \\\n f\"{requests.__description__}: {requests.__version__}\\n\" + \\\n f\"Python: {platform.python_version()}\"\n\n parser = ArgumentParser(formatter_class=RawDescriptionHelpFormatter,\n description=f\"{module_name} (Version {__version__})\"\n )\n parser.add_argument(\"--version\",\n action=\"version\", version=version_string,\n help=\"Display version information and dependencies.\"\n )\n parser.add_argument(\"--verbose\", \"-v\", \"-d\", \"--debug\",\n action=\"store_true\", dest=\"verbose\", default=False,\n help=\"Display extra debugging information.\"\n )\n parser.add_argument(\"--quiet\", \"-q\",\n action=\"store_false\", dest=\"verbose\",\n help=\"Disable debugging information (Default Option).\"\n )\n parser.add_argument(\"--tor\", \"-t\",\n action=\"store_true\", dest=\"tor\", default=False,\n help=\"Make requests over TOR; increases runtime; requires TOR to be installed and in system path.\")\n parser.add_argument(\"--unique-tor\", \"-u\",\n action=\"store_true\", dest=\"unique_tor\", default=False,\n help=\"Make requests over TOR with new TOR circuit after each request; increases runtime; requires TOR to be installed and in system path.\")\n parser.add_argument(\"--csv\",\n action=\"store_true\", dest=\"csv\", default=False,\n help=\"Create Comma-Separated Values (CSV) File.\"\n )\n parser.add_argument(\"username\",\n nargs='+', metavar='USERNAMES',\n action=\"store\",\n help=\"One or more usernames to check with social networks.\"\n )\n\n args = parser.parse_args()\n\n # Banner\n print(\n\"\"\"\\033[37;1m .\\\"\\\"\\\"-.\n\\033[37;1m / \\\\\n\\033[37;1m ____ _ _ _ | _..--'-.\n\\033[37;1m/ ___|| |__ ___ _ __| | ___ ___| |__ >.`__.-\\\"\\\"\\;\\\"`\n\\033[37;1m\\___ \\| '_ \\ / _ \\ '__| |/ _ \\ / __| |/ / / /( ^\\\\\n\\033[37;1m ___) | | | | __/ | | | (_) | (__| < '-`) =|-.\n\\033[37;1m|____/|_| |_|\\___|_| |_|\\___/ \\___|_|\\_\\ /`--.'--' \\ .-.\n\\033[37;1m .'`-._ `.\\ | J /\n\\033[37;1m / `--.| \\__/\\033[0m\"\"\")\n\n if args.tor or args.unique_tor:\n print(\"Warning: some websites might refuse connecting over TOR, so note that using this option might increase connection errors.\")\n\n # Run report on all specified users.\n for username in args.username:\n print()\n results = sherlock(username, verbose=args.verbose, tor=args.tor, unique_tor=args.unique_tor)\n\n if args.csv == True:\n with open(username + \".csv\", \"w\", newline='') as csv_report:\n writer = csv.writer(csv_report)\n writer.writerow(['username',\n 'name',\n 'url_main',\n 'url_user',\n 'exists',\n 'http_status'\n ]\n )\n for site in results:\n writer.writerow([username,\n site,\n results[site]['url_main'],\n results[site]['url_user'],\n results[site]['exists'],\n results[site]['http_status']\n ]\n )\n\nif __name__ == \"__main__\":\n main()\n", "path": "sherlock.py" } ]
diff --git a/sherlock.py b/sherlock.py index d9302cb20..93ea02967 100644 --- a/sherlock.py +++ b/sherlock.py @@ -21,7 +21,7 @@ from torrequest import TorRequest module_name = "Sherlock: Find Usernames Across Social Networks" -__version__ = "2018.01.04" +__version__ = "0.1.0" amount=0 # TODO: fix tumblr
streamlink__streamlink-3952
Add lxml dependency ### Checklist - [X] This is a feature request and not a different kind of issue - [X] [I have read the contribution guidelines](https://github.com/streamlink/streamlink/blob/master/CONTRIBUTING.md#contributing-to-streamlink) - [X] [I have checked the list of open and recently closed plugin requests](https://github.com/streamlink/streamlink/issues?q=is%3Aissue+label%3A%22feature+request%22) ### Description Streamlink should finally switch to a proper HTML/XML parser for extracting data instead of using cheap regex workarounds which don't work properly. I've already commented on this issue last year: https://github.com/streamlink/streamlink/issues/3241#issuecomment-706486239 The reason why I'm suggesting this again right now is that I was trying to fix the deutschewelle plugin (https://dw.com) yesterday and ran into issues with the `itertags` utility method, which is based on simple regexes for iterating HTML nodes and their attributes+body. `itertags` for example does not work with nested nodes, which makes adding ridiculous custom regexes necessary. Just take a look at this madness: https://github.com/streamlink/streamlink/blob/3668770d608f0fab54d40a46acd6720a97f63775/src/streamlink/plugins/deutschewelle.py#L18-L29 With `lxml` (https://lxml.de/), HTML page contents can be parsed and the data extracted via XPath queries and/or the respective API methods. The methods are similar to python's native `xml.etree.ElementTree`, which itself is considered too slow and unsafe in certain cases. I am by no means an expert regarding python's standard library though, so if someone has better insight here, please share. In regards to packaging, this lib is available on basically every packaging system and adding it as a dependency here only has benefits. I'd suggest that we add `lxml` as a dependency now and start using it for extracting data from HTML documents. The validation schema methods could be improved for this as well. There's also the `parse_xml` utility method, which is currently based on the native module. Comments?
[ { "content": "#!/usr/bin/env python\nimport codecs\nfrom os import environ, path\nfrom sys import argv, path as sys_path\n\nfrom setuptools import find_packages, setup\n\nimport versioneer\n\n\ndata_files = []\ndeps = [\n \"requests>=2.26.0,<3.0\",\n \"isodate\",\n \"websocket-client>=0.58.0\",\n # Support for SOCKS proxies\n \"PySocks!=1.5.7,>=1.5.6\",\n]\n\n# for encrypted streams\nif environ.get(\"STREAMLINK_USE_PYCRYPTO\"):\n deps.append(\"pycrypto\")\nelse:\n # this version of pycryptodome is known to work and has a Windows wheel for py2.7, py3.3-3.6\n deps.append(\"pycryptodome>=3.4.3,<4\")\n\n# for localization\nif environ.get(\"STREAMLINK_USE_PYCOUNTRY\"):\n deps.append(\"pycountry\")\nelse:\n deps.append(\"iso-639\")\n deps.append(\"iso3166\")\n\n# When we build an egg for the Win32 bootstrap we don\"t want dependency\n# information built into it.\nif environ.get(\"NO_DEPS\"):\n deps = []\n\nthis_directory = path.abspath(path.dirname(__file__))\nsrcdir = path.join(this_directory, \"src/\")\nsys_path.insert(0, srcdir)\n\nwith codecs.open(path.join(this_directory, \"README.md\"), 'r', \"utf8\") as f:\n long_description = f.read()\n\n\ndef is_wheel_for_windows():\n if \"bdist_wheel\" in argv:\n names = [\"win32\", \"win-amd64\", \"cygwin\"]\n length = len(argv)\n for pos in range(argv.index(\"bdist_wheel\") + 1, length):\n if argv[pos] == \"--plat-name\" and pos + 1 < length:\n return argv[pos + 1] in names\n elif argv[pos][:12] == \"--plat-name=\":\n return argv[pos][12:] in names\n return False\n\n\nentry_points = {\n \"console_scripts\": [\"streamlink=streamlink_cli.main:main\"]\n}\n\nif is_wheel_for_windows():\n entry_points[\"gui_scripts\"] = [\"streamlinkw=streamlink_cli.main:main\"]\n\n\nadditional_files = [\n (\"share/man/man1\", [\"docs/_build/man/streamlink.1\"])\n]\n\nfor destdir, srcfiles in additional_files:\n files = []\n for srcfile in srcfiles:\n if path.exists(srcfile):\n files.append(srcfile)\n if files:\n data_files.append((destdir, files))\n\n\nsetup(name=\"streamlink\",\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n description=\"Streamlink is a command-line utility that extracts streams \"\n \"from various services and pipes them into a video player of \"\n \"choice.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/streamlink/streamlink\",\n project_urls={\n \"Documentation\": \"https://streamlink.github.io/\",\n \"Tracker\": \"https://github.com/streamlink/streamlink/issues\",\n \"Source\": \"https://github.com/streamlink/streamlink\",\n \"Funding\": \"https://opencollective.com/streamlink\"\n },\n author=\"Streamlink\",\n # temp until we have a mailing list / global email\n author_email=\"[email protected]\",\n license=\"Simplified BSD\",\n packages=find_packages(\"src\"),\n package_dir={\"\": \"src\"},\n package_data={\"streamlink.plugins\": [\".removed\"]},\n entry_points=entry_points,\n data_files=data_files,\n install_requires=deps,\n test_suite=\"tests\",\n python_requires=\">=3.6, <4\",\n classifiers=[\"Development Status :: 5 - Production/Stable\",\n \"License :: OSI Approved :: BSD License\",\n \"Environment :: Console\",\n \"Intended Audience :: End Users/Desktop\",\n \"Operating System :: POSIX\",\n \"Operating System :: Microsoft :: Windows\",\n \"Operating System :: MacOS\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3 :: Only\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Topic :: Internet :: WWW/HTTP\",\n \"Topic :: Multimedia :: Sound/Audio\",\n \"Topic :: Multimedia :: Video\",\n \"Topic :: Utilities\"])\n", "path": "setup.py" } ]
[ { "content": "#!/usr/bin/env python\nimport codecs\nfrom os import environ, path\nfrom sys import argv, path as sys_path\n\nfrom setuptools import find_packages, setup\n\nimport versioneer\n\n\ndata_files = []\ndeps = [\n \"requests>=2.26.0,<3.0\",\n \"isodate\",\n \"lxml>=4.6.3\",\n \"websocket-client>=0.58.0\",\n # Support for SOCKS proxies\n \"PySocks!=1.5.7,>=1.5.6\",\n]\n\n# for encrypted streams\nif environ.get(\"STREAMLINK_USE_PYCRYPTO\"):\n deps.append(\"pycrypto\")\nelse:\n # this version of pycryptodome is known to work and has a Windows wheel for py2.7, py3.3-3.6\n deps.append(\"pycryptodome>=3.4.3,<4\")\n\n# for localization\nif environ.get(\"STREAMLINK_USE_PYCOUNTRY\"):\n deps.append(\"pycountry\")\nelse:\n deps.append(\"iso-639\")\n deps.append(\"iso3166\")\n\n# When we build an egg for the Win32 bootstrap we don\"t want dependency\n# information built into it.\nif environ.get(\"NO_DEPS\"):\n deps = []\n\nthis_directory = path.abspath(path.dirname(__file__))\nsrcdir = path.join(this_directory, \"src/\")\nsys_path.insert(0, srcdir)\n\nwith codecs.open(path.join(this_directory, \"README.md\"), 'r', \"utf8\") as f:\n long_description = f.read()\n\n\ndef is_wheel_for_windows():\n if \"bdist_wheel\" in argv:\n names = [\"win32\", \"win-amd64\", \"cygwin\"]\n length = len(argv)\n for pos in range(argv.index(\"bdist_wheel\") + 1, length):\n if argv[pos] == \"--plat-name\" and pos + 1 < length:\n return argv[pos + 1] in names\n elif argv[pos][:12] == \"--plat-name=\":\n return argv[pos][12:] in names\n return False\n\n\nentry_points = {\n \"console_scripts\": [\"streamlink=streamlink_cli.main:main\"]\n}\n\nif is_wheel_for_windows():\n entry_points[\"gui_scripts\"] = [\"streamlinkw=streamlink_cli.main:main\"]\n\n\nadditional_files = [\n (\"share/man/man1\", [\"docs/_build/man/streamlink.1\"])\n]\n\nfor destdir, srcfiles in additional_files:\n files = []\n for srcfile in srcfiles:\n if path.exists(srcfile):\n files.append(srcfile)\n if files:\n data_files.append((destdir, files))\n\n\nsetup(name=\"streamlink\",\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n description=\"Streamlink is a command-line utility that extracts streams \"\n \"from various services and pipes them into a video player of \"\n \"choice.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/streamlink/streamlink\",\n project_urls={\n \"Documentation\": \"https://streamlink.github.io/\",\n \"Tracker\": \"https://github.com/streamlink/streamlink/issues\",\n \"Source\": \"https://github.com/streamlink/streamlink\",\n \"Funding\": \"https://opencollective.com/streamlink\"\n },\n author=\"Streamlink\",\n # temp until we have a mailing list / global email\n author_email=\"[email protected]\",\n license=\"Simplified BSD\",\n packages=find_packages(\"src\"),\n package_dir={\"\": \"src\"},\n package_data={\"streamlink.plugins\": [\".removed\"]},\n entry_points=entry_points,\n data_files=data_files,\n install_requires=deps,\n test_suite=\"tests\",\n python_requires=\">=3.6, <4\",\n classifiers=[\"Development Status :: 5 - Production/Stable\",\n \"License :: OSI Approved :: BSD License\",\n \"Environment :: Console\",\n \"Intended Audience :: End Users/Desktop\",\n \"Operating System :: POSIX\",\n \"Operating System :: Microsoft :: Windows\",\n \"Operating System :: MacOS\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3 :: Only\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Topic :: Internet :: WWW/HTTP\",\n \"Topic :: Multimedia :: Sound/Audio\",\n \"Topic :: Multimedia :: Video\",\n \"Topic :: Utilities\"])\n", "path": "setup.py" } ]
diff --git a/docs/install.rst b/docs/install.rst index b6f528def71..08f56fb7722 100644 --- a/docs/install.rst +++ b/docs/install.rst @@ -289,6 +289,7 @@ Name Notes `iso-639`_ Used for localization settings, provides language information `iso3166`_ Used for localization settings, provides country information `isodate`_ Used for MPEG-DASH streams +`lxml`_ Used for processing HTML and XML data `PySocks`_ Used for SOCKS Proxies `websocket-client`_ At least version **0.58.0**. (used for some plugins) @@ -321,6 +322,7 @@ With these two environment variables it is possible to use `pycrypto`_ instead o .. _iso-639: https://pypi.org/project/iso-639/ .. _iso3166: https://pypi.org/project/iso3166/ .. _isodate: https://pypi.org/project/isodate/ +.. _lxml: https://lxml.de/ .. _PySocks: https://github.com/Anorov/PySocks .. _websocket-client: https://pypi.org/project/websocket-client/ diff --git a/script/makeinstaller.sh b/script/makeinstaller.sh index f9921218cb2..6c8a0d07cd5 100755 --- a/script/makeinstaller.sh +++ b/script/makeinstaller.sh @@ -96,6 +96,7 @@ pypi_wheels=certifi==2021.5.30 idna==3.2 iso3166==1.0.1 isodate==0.6.0 + lxml==4.6.3 pycryptodome==3.10.1 PySocks==1.7.1 requests==2.26.0 diff --git a/setup.py b/setup.py index 37e627c90a6..6f79f045db8 100644 --- a/setup.py +++ b/setup.py @@ -12,6 +12,7 @@ deps = [ "requests>=2.26.0,<3.0", "isodate", + "lxml>=4.6.3", "websocket-client>=0.58.0", # Support for SOCKS proxies "PySocks!=1.5.7,>=1.5.6",
HypothesisWorks__hypothesis-3148
clarification on `note` https://hypothesis.readthedocs.io/en/latest/details.html#hypothesis.note states `Report this value in the final execution.` From my test, `note` wasn't printed on successful run and was printed on falsified run. Please help me understand this functionality
[ { "content": "# This file is part of Hypothesis, which may be found at\n# https://github.com/HypothesisWorks/hypothesis/\n#\n# Most of this work is copyright (C) 2013-2021 David R. MacIver\n# ([email protected]), but it contains contributions by others. See\n# CONTRIBUTING.rst for a full list of people who may hold copyright, and\n# consult the git log if you need to determine who owns an individual\n# contribution.\n#\n# This Source Code Form is subject to the terms of the Mozilla Public License,\n# v. 2.0. If a copy of the MPL was not distributed with this file, You can\n# obtain one at https://mozilla.org/MPL/2.0/.\n#\n# END HEADER\n\nimport math\nimport traceback\nfrom typing import NoReturn, Union\n\nfrom hypothesis import Verbosity, settings\nfrom hypothesis.errors import CleanupFailed, InvalidArgument, UnsatisfiedAssumption\nfrom hypothesis.internal.conjecture.data import ConjectureData\nfrom hypothesis.internal.validation import check_type\nfrom hypothesis.reporting import report, verbose_report\nfrom hypothesis.utils.dynamicvariables import DynamicVariable\n\n\ndef reject() -> NoReturn:\n raise UnsatisfiedAssumption()\n\n\ndef assume(condition: object) -> bool:\n \"\"\"Calling ``assume`` is like an :ref:`assert <python:assert>` that marks\n the example as bad, rather than failing the test.\n\n This allows you to specify properties that you *assume* will be\n true, and let Hypothesis try to avoid similar examples in future.\n \"\"\"\n if not condition:\n raise UnsatisfiedAssumption()\n return True\n\n\n_current_build_context = DynamicVariable(None)\n\n\ndef currently_in_test_context() -> bool:\n \"\"\"Return ``True`` if the calling code is currently running inside an\n :func:`@given <hypothesis.given>` or :doc:`stateful <stateful>` test,\n ``False`` otherwise.\n\n This is useful for third-party integrations and assertion helpers which\n may be called from traditional or property-based tests, but can only use\n :func:`~hypothesis.assume` or :func:`~hypothesis.target` in the latter case.\n \"\"\"\n return _current_build_context.value is not None\n\n\ndef current_build_context():\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\"No build context registered\")\n return context\n\n\nclass BuildContext:\n def __init__(self, data, is_final=False, close_on_capture=True):\n assert isinstance(data, ConjectureData)\n self.data = data\n self.tasks = []\n self.is_final = is_final\n self.close_on_capture = close_on_capture\n self.close_on_del = False\n\n def __enter__(self):\n self.assign_variable = _current_build_context.with_value(self)\n self.assign_variable.__enter__()\n return self\n\n def __exit__(self, exc_type, exc_value, tb):\n self.assign_variable.__exit__(exc_type, exc_value, tb)\n if self.close() and exc_type is None:\n raise CleanupFailed()\n\n def close(self):\n any_failed = False\n for task in self.tasks:\n try:\n task()\n except BaseException:\n any_failed = True\n report(traceback.format_exc())\n return any_failed\n\n\ndef cleanup(teardown):\n \"\"\"Register a function to be called when the current test has finished\n executing. Any exceptions thrown in teardown will be printed but not\n rethrown.\n\n Inside a test this isn't very interesting, because you can just use\n a finally block, but note that you can use this inside map, flatmap,\n etc. in order to e.g. insist that a value is closed at the end.\n \"\"\"\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\"Cannot register cleanup outside of build context\")\n context.tasks.append(teardown)\n\n\ndef should_note():\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\"Cannot make notes outside of a test\")\n return context.is_final or settings.default.verbosity >= Verbosity.verbose\n\n\ndef note(value: str) -> None:\n \"\"\"Report this value in the final execution.\"\"\"\n if should_note():\n report(value)\n\n\ndef event(value: str) -> None:\n \"\"\"Record an event that occurred this test. Statistics on number of test\n runs with each event will be reported at the end if you run Hypothesis in\n statistics reporting mode.\n\n Events should be strings or convertible to them.\n \"\"\"\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\"Cannot make record events outside of a test\")\n\n context.data.note_event(value)\n\n\ndef target(observation: Union[int, float], *, label: str = \"\") -> Union[int, float]:\n \"\"\"Calling this function with an ``int`` or ``float`` observation gives it feedback\n with which to guide our search for inputs that will cause an error, in\n addition to all the usual heuristics. Observations must always be finite.\n\n Hypothesis will try to maximize the observed value over several examples;\n almost any metric will work so long as it makes sense to increase it.\n For example, ``-abs(error)`` is a metric that increases as ``error``\n approaches zero.\n\n Example metrics:\n\n - Number of elements in a collection, or tasks in a queue\n - Mean or maximum runtime of a task (or both, if you use ``label``)\n - Compression ratio for data (perhaps per-algorithm or per-level)\n - Number of steps taken by a state machine\n\n The optional ``label`` argument can be used to distinguish between\n and therefore separately optimise distinct observations, such as the\n mean and standard deviation of a dataset. It is an error to call\n ``target()`` with any label more than once per test case.\n\n .. note::\n **The more examples you run, the better this technique works.**\n\n As a rule of thumb, the targeting effect is noticeable above\n :obj:`max_examples=1000 <hypothesis.settings.max_examples>`,\n and immediately obvious by around ten thousand examples\n *per label* used by your test.\n\n :ref:`statistics` include the best score seen for each label,\n which can help avoid `the threshold problem\n <https://hypothesis.works/articles/threshold-problem/>`__ when the minimal\n example shrinks right down to the threshold of failure (:issue:`2180`).\n \"\"\"\n check_type((int, float), observation, \"observation\")\n if not math.isfinite(observation):\n raise InvalidArgument(f\"observation={observation!r} must be a finite float.\")\n check_type(str, label, \"label\")\n\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\n \"Calling target() outside of a test is invalid. \"\n \"Consider guarding this call with `if currently_in_test_context(): ...`\"\n )\n verbose_report(f\"Saw target(observation={observation!r}, label={label!r})\")\n\n if label in context.data.target_observations:\n raise InvalidArgument(\n f\"Calling target({observation!r}, label={label!r}) would overwrite \"\n f\"target({context.data.target_observations[label]!r}, label={label!r})\"\n )\n else:\n context.data.target_observations[label] = observation\n\n return observation\n", "path": "hypothesis-python/src/hypothesis/control.py" } ]
[ { "content": "# This file is part of Hypothesis, which may be found at\n# https://github.com/HypothesisWorks/hypothesis/\n#\n# Most of this work is copyright (C) 2013-2021 David R. MacIver\n# ([email protected]), but it contains contributions by others. See\n# CONTRIBUTING.rst for a full list of people who may hold copyright, and\n# consult the git log if you need to determine who owns an individual\n# contribution.\n#\n# This Source Code Form is subject to the terms of the Mozilla Public License,\n# v. 2.0. If a copy of the MPL was not distributed with this file, You can\n# obtain one at https://mozilla.org/MPL/2.0/.\n#\n# END HEADER\n\nimport math\nimport traceback\nfrom typing import NoReturn, Union\n\nfrom hypothesis import Verbosity, settings\nfrom hypothesis.errors import CleanupFailed, InvalidArgument, UnsatisfiedAssumption\nfrom hypothesis.internal.conjecture.data import ConjectureData\nfrom hypothesis.internal.validation import check_type\nfrom hypothesis.reporting import report, verbose_report\nfrom hypothesis.utils.dynamicvariables import DynamicVariable\n\n\ndef reject() -> NoReturn:\n raise UnsatisfiedAssumption()\n\n\ndef assume(condition: object) -> bool:\n \"\"\"Calling ``assume`` is like an :ref:`assert <python:assert>` that marks\n the example as bad, rather than failing the test.\n\n This allows you to specify properties that you *assume* will be\n true, and let Hypothesis try to avoid similar examples in future.\n \"\"\"\n if not condition:\n raise UnsatisfiedAssumption()\n return True\n\n\n_current_build_context = DynamicVariable(None)\n\n\ndef currently_in_test_context() -> bool:\n \"\"\"Return ``True`` if the calling code is currently running inside an\n :func:`@given <hypothesis.given>` or :doc:`stateful <stateful>` test,\n ``False`` otherwise.\n\n This is useful for third-party integrations and assertion helpers which\n may be called from traditional or property-based tests, but can only use\n :func:`~hypothesis.assume` or :func:`~hypothesis.target` in the latter case.\n \"\"\"\n return _current_build_context.value is not None\n\n\ndef current_build_context():\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\"No build context registered\")\n return context\n\n\nclass BuildContext:\n def __init__(self, data, is_final=False, close_on_capture=True):\n assert isinstance(data, ConjectureData)\n self.data = data\n self.tasks = []\n self.is_final = is_final\n self.close_on_capture = close_on_capture\n self.close_on_del = False\n\n def __enter__(self):\n self.assign_variable = _current_build_context.with_value(self)\n self.assign_variable.__enter__()\n return self\n\n def __exit__(self, exc_type, exc_value, tb):\n self.assign_variable.__exit__(exc_type, exc_value, tb)\n if self.close() and exc_type is None:\n raise CleanupFailed()\n\n def close(self):\n any_failed = False\n for task in self.tasks:\n try:\n task()\n except BaseException:\n any_failed = True\n report(traceback.format_exc())\n return any_failed\n\n\ndef cleanup(teardown):\n \"\"\"Register a function to be called when the current test has finished\n executing. Any exceptions thrown in teardown will be printed but not\n rethrown.\n\n Inside a test this isn't very interesting, because you can just use\n a finally block, but note that you can use this inside map, flatmap,\n etc. in order to e.g. insist that a value is closed at the end.\n \"\"\"\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\"Cannot register cleanup outside of build context\")\n context.tasks.append(teardown)\n\n\ndef should_note():\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\"Cannot make notes outside of a test\")\n return context.is_final or settings.default.verbosity >= Verbosity.verbose\n\n\ndef note(value: str) -> None:\n \"\"\"Report this value for the minimal failing example.\"\"\"\n if should_note():\n report(value)\n\n\ndef event(value: str) -> None:\n \"\"\"Record an event that occurred this test. Statistics on number of test\n runs with each event will be reported at the end if you run Hypothesis in\n statistics reporting mode.\n\n Events should be strings or convertible to them.\n \"\"\"\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\"Cannot make record events outside of a test\")\n\n context.data.note_event(value)\n\n\ndef target(observation: Union[int, float], *, label: str = \"\") -> Union[int, float]:\n \"\"\"Calling this function with an ``int`` or ``float`` observation gives it feedback\n with which to guide our search for inputs that will cause an error, in\n addition to all the usual heuristics. Observations must always be finite.\n\n Hypothesis will try to maximize the observed value over several examples;\n almost any metric will work so long as it makes sense to increase it.\n For example, ``-abs(error)`` is a metric that increases as ``error``\n approaches zero.\n\n Example metrics:\n\n - Number of elements in a collection, or tasks in a queue\n - Mean or maximum runtime of a task (or both, if you use ``label``)\n - Compression ratio for data (perhaps per-algorithm or per-level)\n - Number of steps taken by a state machine\n\n The optional ``label`` argument can be used to distinguish between\n and therefore separately optimise distinct observations, such as the\n mean and standard deviation of a dataset. It is an error to call\n ``target()`` with any label more than once per test case.\n\n .. note::\n **The more examples you run, the better this technique works.**\n\n As a rule of thumb, the targeting effect is noticeable above\n :obj:`max_examples=1000 <hypothesis.settings.max_examples>`,\n and immediately obvious by around ten thousand examples\n *per label* used by your test.\n\n :ref:`statistics` include the best score seen for each label,\n which can help avoid `the threshold problem\n <https://hypothesis.works/articles/threshold-problem/>`__ when the minimal\n example shrinks right down to the threshold of failure (:issue:`2180`).\n \"\"\"\n check_type((int, float), observation, \"observation\")\n if not math.isfinite(observation):\n raise InvalidArgument(f\"observation={observation!r} must be a finite float.\")\n check_type(str, label, \"label\")\n\n context = _current_build_context.value\n if context is None:\n raise InvalidArgument(\n \"Calling target() outside of a test is invalid. \"\n \"Consider guarding this call with `if currently_in_test_context(): ...`\"\n )\n verbose_report(f\"Saw target(observation={observation!r}, label={label!r})\")\n\n if label in context.data.target_observations:\n raise InvalidArgument(\n f\"Calling target({observation!r}, label={label!r}) would overwrite \"\n f\"target({context.data.target_observations[label]!r}, label={label!r})\"\n )\n else:\n context.data.target_observations[label] = observation\n\n return observation\n", "path": "hypothesis-python/src/hypothesis/control.py" } ]
diff --git a/hypothesis-python/RELEASE.rst b/hypothesis-python/RELEASE.rst new file mode 100644 index 0000000000..ff8b57ab04 --- /dev/null +++ b/hypothesis-python/RELEASE.rst @@ -0,0 +1,4 @@ +RELEASE_TYPE: patch + +This patch updates documentation of :func:`~hypothesis.note` +(:issue:`3147`). \ No newline at end of file diff --git a/hypothesis-python/docs/details.rst b/hypothesis-python/docs/details.rst index a3ba31def2..30bddc07d3 100644 --- a/hypothesis-python/docs/details.rst +++ b/hypothesis-python/docs/details.rst @@ -43,7 +43,7 @@ intermediate steps of your test. That's where the ``note`` function comes in: Shuffle: [1, 0] ls != ls2 -The note is printed in the final run of the test in order to include any +The note is printed for the minimal failing example of the test in order to include any additional information you might need in your test. diff --git a/hypothesis-python/src/hypothesis/control.py b/hypothesis-python/src/hypothesis/control.py index 3132e8c76b..e042136eda 100644 --- a/hypothesis-python/src/hypothesis/control.py +++ b/hypothesis-python/src/hypothesis/control.py @@ -116,7 +116,7 @@ def should_note(): def note(value: str) -> None: - """Report this value in the final execution.""" + """Report this value for the minimal failing example.""" if should_note(): report(value)
LMFDB__lmfdb-5795
Half integeral weight page visible on prod https://www.lmfdb.org/ModularForm/GL2/Q/holomorphic/half/ should redirect to beta, but it doesn't since the whitelist thinks it's inside CMFs.
[ { "content": "# -*- coding: utf-8 -*-\n\nfrom lmfdb.app import app\nfrom lmfdb.logger import make_logger\nfrom flask import Blueprint\n\nhiwf_page = Blueprint(\"hiwf\", __name__, template_folder='templates', static_folder=\"static\")\nhiwf_logger = make_logger(hiwf_page)\n\n\n@hiwf_page.context_processor\ndef body_class():\n return {'body_class': 'hiwf'}\n\nfrom . import half_integral_form\nassert half_integral_form\n\napp.register_blueprint(hiwf_page, url_prefix=\"/ModularForm/GL2/Q/holomorphic/half\")\n", "path": "lmfdb/half_integral_weight_forms/__init__.py" } ]
[ { "content": "# -*- coding: utf-8 -*-\n\nfrom lmfdb.app import app\nfrom lmfdb.logger import make_logger\nfrom flask import Blueprint\n\nhiwf_page = Blueprint(\"hiwf\", __name__, template_folder='templates', static_folder=\"static\")\nhiwf_logger = make_logger(hiwf_page)\n\n\n@hiwf_page.context_processor\ndef body_class():\n return {'body_class': 'hiwf'}\n\nfrom . import half_integral_form\nassert half_integral_form\n\napp.register_blueprint(hiwf_page, url_prefix=\"/ModularForm/GL2/Q/holomorphic_half\")\n", "path": "lmfdb/half_integral_weight_forms/__init__.py" } ]
diff --git a/lmfdb/half_integral_weight_forms/__init__.py b/lmfdb/half_integral_weight_forms/__init__.py index 048237c441..0ef40b41c7 100644 --- a/lmfdb/half_integral_weight_forms/__init__.py +++ b/lmfdb/half_integral_weight_forms/__init__.py @@ -15,4 +15,4 @@ def body_class(): from . import half_integral_form assert half_integral_form -app.register_blueprint(hiwf_page, url_prefix="/ModularForm/GL2/Q/holomorphic/half") +app.register_blueprint(hiwf_page, url_prefix="/ModularForm/GL2/Q/holomorphic_half")
nltk__nltk-3205
`corpus_bleu` function does not catch all the expections when calling `weights[0][0]` In your codes https://github.com/nltk/nltk/blob/e2d368e00ef806121aaa39f6e5f90d9f8243631b/nltk/translate/bleu_score.py#L201 I pass in `weights = array([0.25, 0.25, 0.25, 0.25])` and find this error: ``` File "/home/cyzhao/miniconda3/envs/prompt/lib/python3.11/site-packages/nltk/translate/bleu_score.py", line 200, in corpus_bleu weights[0][0] ~~~~~~~~~~^^^ IndexError: invalid index to scalar variable. """ ``` I then find out the reason why. Not all exceptions are completely caught. The `weights` passed in by the framework are `array([0.25, 0.25, 0.25, 0.25])`, and for `ndarray` the error is `IndexError: invalid index to scalar variable`. Hence, these codes haven't caught all the exceptions, leading to the situation where one must pass a tuple `(0.25, 0.25, 0.25, 0.25)` to be caught by this try-except block.
[ { "content": "# Natural Language Toolkit: BLEU Score\n#\n# Copyright (C) 2001-2023 NLTK Project\n# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim\n# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan\n# URL: <https://www.nltk.org/>\n# For license information, see LICENSE.TXT\n\n\"\"\"BLEU score implementation.\"\"\"\n\nimport math\nimport sys\nimport warnings\nfrom collections import Counter\nfrom fractions import Fraction\n\nfrom nltk.util import ngrams\n\n\ndef sentence_bleu(\n references,\n hypothesis,\n weights=(0.25, 0.25, 0.25, 0.25),\n smoothing_function=None,\n auto_reweigh=False,\n):\n \"\"\"\n Calculate BLEU score (Bilingual Evaluation Understudy) from\n Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002.\n \"BLEU: a method for automatic evaluation of machine translation.\"\n In Proceedings of ACL. https://www.aclweb.org/anthology/P02-1040.pdf\n\n >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'military', 'always',\n ... 'obeys', 'the', 'commands', 'of', 'the', 'party']\n\n >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',\n ... 'forever', 'hearing', 'the', 'activity', 'guidebook',\n ... 'that', 'party', 'direct']\n\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will', 'forever',\n ... 'heed', 'Party', 'commands']\n\n >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the',\n ... 'Party']\n\n >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n\n >>> sentence_bleu([reference1, reference2, reference3], hypothesis1) # doctest: +ELLIPSIS\n 0.5045...\n\n If there is no ngrams overlap for any order of n-grams, BLEU returns the\n value 0. This is because the precision for the order of n-grams without\n overlap is 0, and the geometric mean in the final BLEU score computation\n multiplies the 0 with the precision of other n-grams. This results in 0\n (independently of the precision of the other n-gram orders). The following\n example has zero 3-gram and 4-gram overlaps:\n\n >>> round(sentence_bleu([reference1, reference2, reference3], hypothesis2),4) # doctest: +ELLIPSIS\n 0.0\n\n To avoid this harsh behaviour when no ngram overlaps are found a smoothing\n function can be used.\n\n >>> chencherry = SmoothingFunction()\n >>> sentence_bleu([reference1, reference2, reference3], hypothesis2,\n ... smoothing_function=chencherry.method1) # doctest: +ELLIPSIS\n 0.0370...\n\n The default BLEU calculates a score for up to 4-grams using uniform\n weights (this is called BLEU-4). To evaluate your translations with\n higher/lower order ngrams, use customized weights. E.g. when accounting\n for up to 5-grams with uniform weights (this is called BLEU-5) use:\n\n >>> weights = (1./5., 1./5., 1./5., 1./5., 1./5.)\n >>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS\n 0.3920...\n\n Multiple BLEU scores can be computed at once, by supplying a list of weights.\n E.g. for computing BLEU-2, BLEU-3 *and* BLEU-4 in one computation, use:\n >>> weights = [\n ... (1./2., 1./2.),\n ... (1./3., 1./3., 1./3.),\n ... (1./4., 1./4., 1./4., 1./4.)\n ... ]\n >>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS\n [0.7453..., 0.6240..., 0.5045...]\n\n :param references: reference sentences\n :type references: list(list(str))\n :param hypothesis: a hypothesis sentence\n :type hypothesis: list(str)\n :param weights: weights for unigrams, bigrams, trigrams and so on (one or a list of weights)\n :type weights: tuple(float) / list(tuple(float))\n :param smoothing_function:\n :type smoothing_function: SmoothingFunction\n :param auto_reweigh: Option to re-normalize the weights uniformly.\n :type auto_reweigh: bool\n :return: The sentence-level BLEU score. Returns a list if multiple weights were supplied.\n :rtype: float / list(float)\n \"\"\"\n return corpus_bleu(\n [references], [hypothesis], weights, smoothing_function, auto_reweigh\n )\n\n\ndef corpus_bleu(\n list_of_references,\n hypotheses,\n weights=(0.25, 0.25, 0.25, 0.25),\n smoothing_function=None,\n auto_reweigh=False,\n):\n \"\"\"\n Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all\n the hypotheses and their respective references.\n\n Instead of averaging the sentence level BLEU scores (i.e. macro-average\n precision), the original BLEU metric (Papineni et al. 2002) accounts for\n the micro-average precision (i.e. summing the numerators and denominators\n for each hypothesis-reference(s) pairs before the division).\n\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'military', 'always',\n ... 'obeys', 'the', 'commands', 'of', 'the', 'party']\n >>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will', 'forever',\n ... 'heed', 'Party', 'commands']\n >>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'Party']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS\n 0.5920...\n\n The example below show that corpus_bleu() is different from averaging\n sentence_bleu() for hypotheses\n\n >>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1)\n >>> score2 = sentence_bleu([ref2a], hyp2)\n >>> (score1 + score2) / 2 # doctest: +ELLIPSIS\n 0.6223...\n\n Custom weights may be supplied to fine-tune the BLEU score further.\n A tuple of float weights for unigrams, bigrams, trigrams and so on can be given.\n >>> weights = (0.1, 0.3, 0.5, 0.1)\n >>> corpus_bleu(list_of_references, hypotheses, weights=weights) # doctest: +ELLIPSIS\n 0.5818...\n\n This particular weight gave extra value to trigrams.\n Furthermore, multiple weights can be given, resulting in multiple BLEU scores.\n >>> weights = [\n ... (0.5, 0.5),\n ... (0.333, 0.333, 0.334),\n ... (0.25, 0.25, 0.25, 0.25),\n ... (0.2, 0.2, 0.2, 0.2, 0.2)\n ... ]\n >>> corpus_bleu(list_of_references, hypotheses, weights=weights) # doctest: +ELLIPSIS\n [0.8242..., 0.7067..., 0.5920..., 0.4719...]\n\n :param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses\n :type list_of_references: list(list(list(str)))\n :param hypotheses: a list of hypothesis sentences\n :type hypotheses: list(list(str))\n :param weights: weights for unigrams, bigrams, trigrams and so on (one or a list of weights)\n :type weights: tuple(float) / list(tuple(float))\n :param smoothing_function:\n :type smoothing_function: SmoothingFunction\n :param auto_reweigh: Option to re-normalize the weights uniformly.\n :type auto_reweigh: bool\n :return: The corpus-level BLEU score.\n :rtype: float\n \"\"\"\n # Before proceeding to compute BLEU, perform sanity checks.\n\n p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches.\n p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref.\n hyp_lengths, ref_lengths = 0, 0\n\n assert len(list_of_references) == len(hypotheses), (\n \"The number of hypotheses and their reference(s) should be the \" \"same \"\n )\n\n try:\n weights[0][0]\n except TypeError:\n weights = [weights]\n max_weight_length = max(len(weight) for weight in weights)\n\n # Iterate through each hypothesis and their corresponding references.\n for references, hypothesis in zip(list_of_references, hypotheses):\n # For each order of ngram, calculate the numerator and\n # denominator for the corpus-level modified precision.\n for i in range(1, max_weight_length + 1):\n p_i = modified_precision(references, hypothesis, i)\n p_numerators[i] += p_i.numerator\n p_denominators[i] += p_i.denominator\n\n # Calculate the hypothesis length and the closest reference length.\n # Adds them to the corpus-level hypothesis and reference counts.\n hyp_len = len(hypothesis)\n hyp_lengths += hyp_len\n ref_lengths += closest_ref_length(references, hyp_len)\n\n # Calculate corpus-level brevity penalty.\n bp = brevity_penalty(ref_lengths, hyp_lengths)\n\n # Collects the various precision values for the different ngram orders.\n p_n = [\n Fraction(p_numerators[i], p_denominators[i], _normalize=False)\n for i in range(1, max_weight_length + 1)\n ]\n\n # Returns 0 if there's no matching n-grams\n # We only need to check for p_numerators[1] == 0, since if there's\n # no unigrams, there won't be any higher order ngrams.\n if p_numerators[1] == 0:\n return 0 if len(weights) == 1 else [0] * len(weights)\n\n # If there's no smoothing, set use method0 from SmoothinFunction class.\n if not smoothing_function:\n smoothing_function = SmoothingFunction().method0\n # Smoothen the modified precision.\n # Note: smoothing_function() may convert values into floats;\n # it tries to retain the Fraction object as much as the\n # smoothing method allows.\n p_n = smoothing_function(\n p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths\n )\n\n bleu_scores = []\n for weight in weights:\n # Uniformly re-weighting based on maximum hypothesis lengths if largest\n # order of n-grams < 4 and weights is set at default.\n if auto_reweigh:\n if hyp_lengths < 4 and weight == (0.25, 0.25, 0.25, 0.25):\n weight = (1 / hyp_lengths,) * hyp_lengths\n\n s = (w_i * math.log(p_i) for w_i, p_i in zip(weight, p_n) if p_i > 0)\n s = bp * math.exp(math.fsum(s))\n bleu_scores.append(s)\n return bleu_scores[0] if len(weights) == 1 else bleu_scores\n\n\ndef modified_precision(references, hypothesis, n):\n \"\"\"\n Calculate modified ngram precision.\n\n The normal precision method may lead to some wrong translations with\n high-precision, e.g., the translation, in which a word of reference\n repeats several times, has very high precision.\n\n This function only returns the Fraction object that contains the numerator\n and denominator necessary to calculate the corpus-level precision.\n To calculate the modified precision for a single pair of hypothesis and\n references, cast the Fraction object into a float.\n\n The famous \"the the the ... \" example shows that you can get BLEU precision\n by duplicating high frequency words.\n\n >>> reference1 = 'the cat is on the mat'.split()\n >>> reference2 = 'there is a cat on the mat'.split()\n >>> hypothesis1 = 'the the the the the the the'.split()\n >>> references = [reference1, reference2]\n >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS\n 0.2857...\n\n In the modified n-gram precision, a reference word will be considered\n exhausted after a matching hypothesis word is identified, e.g.\n\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will',\n ... 'forever', 'heed', 'Party', 'commands']\n >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the',\n ... 'Party']\n >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n >>> hypothesis = 'of the'.split()\n >>> references = [reference1, reference2, reference3]\n >>> float(modified_precision(references, hypothesis, n=1))\n 1.0\n >>> float(modified_precision(references, hypothesis, n=2))\n 1.0\n\n An example of a normal machine translation hypothesis:\n\n >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'military', 'always',\n ... 'obeys', 'the', 'commands', 'of', 'the', 'party']\n\n >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',\n ... 'forever', 'hearing', 'the', 'activity', 'guidebook',\n ... 'that', 'party', 'direct']\n\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will',\n ... 'forever', 'heed', 'Party', 'commands']\n\n >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the',\n ... 'Party']\n\n >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n >>> references = [reference1, reference2, reference3]\n >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS\n 0.9444...\n >>> float(modified_precision(references, hypothesis2, n=1)) # doctest: +ELLIPSIS\n 0.5714...\n >>> float(modified_precision(references, hypothesis1, n=2)) # doctest: +ELLIPSIS\n 0.5882352941176471\n >>> float(modified_precision(references, hypothesis2, n=2)) # doctest: +ELLIPSIS\n 0.07692...\n\n\n :param references: A list of reference translations.\n :type references: list(list(str))\n :param hypothesis: A hypothesis translation.\n :type hypothesis: list(str)\n :param n: The ngram order.\n :type n: int\n :return: BLEU's modified precision for the nth order ngram.\n :rtype: Fraction\n \"\"\"\n # Extracts all ngrams in hypothesis\n # Set an empty Counter if hypothesis is empty.\n counts = Counter(ngrams(hypothesis, n)) if len(hypothesis) >= n else Counter()\n # Extract a union of references' counts.\n # max_counts = reduce(or_, [Counter(ngrams(ref, n)) for ref in references])\n max_counts = {}\n for reference in references:\n reference_counts = (\n Counter(ngrams(reference, n)) if len(reference) >= n else Counter()\n )\n for ngram in counts:\n max_counts[ngram] = max(max_counts.get(ngram, 0), reference_counts[ngram])\n\n # Assigns the intersection between hypothesis and references' counts.\n clipped_counts = {\n ngram: min(count, max_counts[ngram]) for ngram, count in counts.items()\n }\n\n numerator = sum(clipped_counts.values())\n # Ensures that denominator is minimum 1 to avoid ZeroDivisionError.\n # Usually this happens when the ngram order is > len(reference).\n denominator = max(1, sum(counts.values()))\n\n return Fraction(numerator, denominator, _normalize=False)\n\n\ndef closest_ref_length(references, hyp_len):\n \"\"\"\n This function finds the reference that is the closest length to the\n hypothesis. The closest reference length is referred to as *r* variable\n from the brevity penalty formula in Papineni et. al. (2002)\n\n :param references: A list of reference translations.\n :type references: list(list(str))\n :param hyp_len: The length of the hypothesis.\n :type hyp_len: int\n :return: The length of the reference that's closest to the hypothesis.\n :rtype: int\n \"\"\"\n ref_lens = (len(reference) for reference in references)\n closest_ref_len = min(\n ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len)\n )\n return closest_ref_len\n\n\ndef brevity_penalty(closest_ref_len, hyp_len):\n \"\"\"\n Calculate brevity penalty.\n\n As the modified n-gram precision still has the problem from the short\n length sentence, brevity penalty is used to modify the overall BLEU\n score according to length.\n\n An example from the paper. There are three references with length 12, 15\n and 17. And a concise hypothesis of the length 12. The brevity penalty is 1.\n\n >>> reference1 = list('aaaaaaaaaaaa') # i.e. ['a'] * 12\n >>> reference2 = list('aaaaaaaaaaaaaaa') # i.e. ['a'] * 15\n >>> reference3 = list('aaaaaaaaaaaaaaaaa') # i.e. ['a'] * 17\n >>> hypothesis = list('aaaaaaaaaaaa') # i.e. ['a'] * 12\n >>> references = [reference1, reference2, reference3]\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len)\n 1.0\n\n In case a hypothesis translation is shorter than the references, penalty is\n applied.\n\n >>> references = [['a'] * 28, ['a'] * 28]\n >>> hypothesis = ['a'] * 12\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len)\n 0.2635971381157267\n\n The length of the closest reference is used to compute the penalty. If the\n length of a hypothesis is 12, and the reference lengths are 13 and 2, the\n penalty is applied because the hypothesis length (12) is less then the\n closest reference length (13).\n\n >>> references = [['a'] * 13, ['a'] * 2]\n >>> hypothesis = ['a'] * 12\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS\n 0.9200...\n\n The brevity penalty doesn't depend on reference order. More importantly,\n when two reference sentences are at the same distance, the shortest\n reference sentence length is used.\n\n >>> references = [['a'] * 13, ['a'] * 11]\n >>> hypothesis = ['a'] * 12\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> bp1 = brevity_penalty(closest_ref_len, hyp_len)\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(reversed(references), hyp_len)\n >>> bp2 = brevity_penalty(closest_ref_len, hyp_len)\n >>> bp1 == bp2 == 1\n True\n\n A test example from mteval-v13a.pl (starting from the line 705):\n\n >>> references = [['a'] * 11, ['a'] * 8]\n >>> hypothesis = ['a'] * 7\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS\n 0.8668...\n\n >>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7]\n >>> hypothesis = ['a'] * 7\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len)\n 1.0\n\n :param hyp_len: The length of the hypothesis for a single sentence OR the\n sum of all the hypotheses' lengths for a corpus\n :type hyp_len: int\n :param closest_ref_len: The length of the closest reference for a single\n hypothesis OR the sum of all the closest references for every hypotheses.\n :type closest_ref_len: int\n :return: BLEU's brevity penalty.\n :rtype: float\n \"\"\"\n if hyp_len > closest_ref_len:\n return 1\n # If hypothesis is empty, brevity penalty = 0 should result in BLEU = 0.0\n elif hyp_len == 0:\n return 0\n else:\n return math.exp(1 - closest_ref_len / hyp_len)\n\n\nclass SmoothingFunction:\n \"\"\"\n This is an implementation of the smoothing techniques\n for segment-level BLEU scores that was presented in\n Boxing Chen and Collin Cherry (2014) A Systematic Comparison of\n Smoothing Techniques for Sentence-Level BLEU. In WMT14.\n http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf\n \"\"\"\n\n def __init__(self, epsilon=0.1, alpha=5, k=5):\n \"\"\"\n This will initialize the parameters required for the various smoothing\n techniques, the default values are set to the numbers used in the\n experiments from Chen and Cherry (2014).\n\n >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures',\n ... 'that', 'the', 'military', 'always', 'obeys', 'the',\n ... 'commands', 'of', 'the', 'party']\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures',\n ... 'that', 'the', 'military', 'will', 'forever', 'heed',\n ... 'Party', 'commands']\n\n >>> chencherry = SmoothingFunction()\n >>> print(sentence_bleu([reference1], hypothesis1)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method0)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method1)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method2)) # doctest: +ELLIPSIS\n 0.4452...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method3)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method4)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method5)) # doctest: +ELLIPSIS\n 0.4905...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method6)) # doctest: +ELLIPSIS\n 0.4135...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method7)) # doctest: +ELLIPSIS\n 0.4905...\n\n :param epsilon: the epsilon value use in method 1\n :type epsilon: float\n :param alpha: the alpha value use in method 6\n :type alpha: int\n :param k: the k value use in method 4\n :type k: int\n \"\"\"\n self.epsilon = epsilon\n self.alpha = alpha\n self.k = k\n\n def method0(self, p_n, *args, **kwargs):\n \"\"\"\n No smoothing.\n \"\"\"\n p_n_new = []\n for i, p_i in enumerate(p_n):\n if p_i.numerator != 0:\n p_n_new.append(p_i)\n else:\n _msg = str(\n \"\\nThe hypothesis contains 0 counts of {}-gram overlaps.\\n\"\n \"Therefore the BLEU score evaluates to 0, independently of\\n\"\n \"how many N-gram overlaps of lower order it contains.\\n\"\n \"Consider using lower n-gram order or use \"\n \"SmoothingFunction()\"\n ).format(i + 1)\n warnings.warn(_msg)\n # When numerator==0 where denonminator==0 or !=0, the result\n # for the precision score should be equal to 0 or undefined.\n # Due to BLEU geometric mean computation in logarithm space,\n # we we need to take the return sys.float_info.min such that\n # math.log(sys.float_info.min) returns a 0 precision score.\n p_n_new.append(sys.float_info.min)\n return p_n_new\n\n def method1(self, p_n, *args, **kwargs):\n \"\"\"\n Smoothing method 1: Add *epsilon* counts to precision with 0 counts.\n \"\"\"\n return [\n (p_i.numerator + self.epsilon) / p_i.denominator\n if p_i.numerator == 0\n else p_i\n for p_i in p_n\n ]\n\n def method2(self, p_n, *args, **kwargs):\n \"\"\"\n Smoothing method 2: Add 1 to both numerator and denominator from\n Chin-Yew Lin and Franz Josef Och (2004) ORANGE: a Method for\n Evaluating Automatic Evaluation Metrics for Machine Translation.\n In COLING 2004.\n \"\"\"\n return [\n Fraction(p_n[i].numerator + 1, p_n[i].denominator + 1, _normalize=False)\n if i != 0\n else p_n[0]\n for i in range(len(p_n))\n ]\n\n def method3(self, p_n, *args, **kwargs):\n \"\"\"\n Smoothing method 3: NIST geometric sequence smoothing\n The smoothing is computed by taking 1 / ( 2^k ), instead of 0, for each\n precision score whose matching n-gram count is null.\n k is 1 for the first 'n' value for which the n-gram match count is null/\n\n For example, if the text contains:\n\n - one 2-gram match\n - and (consequently) two 1-gram matches\n\n the n-gram count for each individual precision score would be:\n\n - n=1 => prec_count = 2 (two unigrams)\n - n=2 => prec_count = 1 (one bigram)\n - n=3 => prec_count = 1/2 (no trigram, taking 'smoothed' value of 1 / ( 2^k ), with k=1)\n - n=4 => prec_count = 1/4 (no fourgram, taking 'smoothed' value of 1 / ( 2^k ), with k=2)\n \"\"\"\n incvnt = 1 # From the mteval-v13a.pl, it's referred to as k.\n for i, p_i in enumerate(p_n):\n if p_i.numerator == 0:\n p_n[i] = 1 / (2**incvnt * p_i.denominator)\n incvnt += 1\n return p_n\n\n def method4(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):\n \"\"\"\n Smoothing method 4:\n Shorter translations may have inflated precision values due to having\n smaller denominators; therefore, we give them proportionally\n smaller smoothed counts. Instead of scaling to 1/(2^k), Chen and Cherry\n suggests dividing by 1/ln(len(T)), where T is the length of the translation.\n \"\"\"\n incvnt = 1\n hyp_len = hyp_len if hyp_len else len(hypothesis)\n for i, p_i in enumerate(p_n):\n if p_i.numerator == 0 and hyp_len > 1:\n # incvnt = i + 1 * self.k / math.log(\n # hyp_len\n # ) # Note that this K is different from the K from NIST.\n # p_n[i] = incvnt / p_i.denominator\\\n numerator = 1 / (2**incvnt * self.k / math.log(hyp_len))\n p_n[i] = numerator / p_i.denominator\n incvnt += 1\n return p_n\n\n def method5(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):\n \"\"\"\n Smoothing method 5:\n The matched counts for similar values of n should be similar. To a\n calculate the n-gram matched count, it averages the n−1, n and n+1 gram\n matched counts.\n \"\"\"\n hyp_len = hyp_len if hyp_len else len(hypothesis)\n m = {}\n # Requires an precision value for an addition ngram order.\n p_n_plus1 = p_n + [modified_precision(references, hypothesis, 5)]\n m[-1] = p_n[0] + 1\n for i, p_i in enumerate(p_n):\n p_n[i] = (m[i - 1] + p_i + p_n_plus1[i + 1]) / 3\n m[i] = p_n[i]\n return p_n\n\n def method6(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):\n \"\"\"\n Smoothing method 6:\n Interpolates the maximum likelihood estimate of the precision *p_n* with\n a prior estimate *pi0*. The prior is estimated by assuming that the ratio\n between pn and pn−1 will be the same as that between pn−1 and pn−2; from\n Gao and He (2013) Training MRF-Based Phrase Translation Models using\n Gradient Ascent. In NAACL.\n \"\"\"\n hyp_len = hyp_len if hyp_len else len(hypothesis)\n # This smoothing only works when p_1 and p_2 is non-zero.\n # Raise an error with an appropriate message when the input is too short\n # to use this smoothing technique.\n assert p_n[2], \"This smoothing method requires non-zero precision for bigrams.\"\n for i, p_i in enumerate(p_n):\n if i in [0, 1]: # Skips the first 2 orders of ngrams.\n continue\n else:\n pi0 = 0 if p_n[i - 2] == 0 else p_n[i - 1] ** 2 / p_n[i - 2]\n # No. of ngrams in translation that matches the reference.\n m = p_i.numerator\n # No. of ngrams in translation.\n l = sum(1 for _ in ngrams(hypothesis, i + 1))\n # Calculates the interpolated precision.\n p_n[i] = (m + self.alpha * pi0) / (l + self.alpha)\n return p_n\n\n def method7(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):\n \"\"\"\n Smoothing method 7:\n Interpolates methods 4 and 5.\n \"\"\"\n hyp_len = hyp_len if hyp_len else len(hypothesis)\n p_n = self.method4(p_n, references, hypothesis, hyp_len)\n p_n = self.method5(p_n, references, hypothesis, hyp_len)\n return p_n\n", "path": "nltk/translate/bleu_score.py" } ]
[ { "content": "# Natural Language Toolkit: BLEU Score\n#\n# Copyright (C) 2001-2023 NLTK Project\n# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim\n# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan\n# URL: <https://www.nltk.org/>\n# For license information, see LICENSE.TXT\n\n\"\"\"BLEU score implementation.\"\"\"\n\nimport math\nimport sys\nimport warnings\nfrom collections import Counter\nfrom fractions import Fraction\n\nfrom nltk.util import ngrams\n\n\ndef sentence_bleu(\n references,\n hypothesis,\n weights=(0.25, 0.25, 0.25, 0.25),\n smoothing_function=None,\n auto_reweigh=False,\n):\n \"\"\"\n Calculate BLEU score (Bilingual Evaluation Understudy) from\n Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002.\n \"BLEU: a method for automatic evaluation of machine translation.\"\n In Proceedings of ACL. https://www.aclweb.org/anthology/P02-1040.pdf\n\n >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'military', 'always',\n ... 'obeys', 'the', 'commands', 'of', 'the', 'party']\n\n >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',\n ... 'forever', 'hearing', 'the', 'activity', 'guidebook',\n ... 'that', 'party', 'direct']\n\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will', 'forever',\n ... 'heed', 'Party', 'commands']\n\n >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the',\n ... 'Party']\n\n >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n\n >>> sentence_bleu([reference1, reference2, reference3], hypothesis1) # doctest: +ELLIPSIS\n 0.5045...\n\n If there is no ngrams overlap for any order of n-grams, BLEU returns the\n value 0. This is because the precision for the order of n-grams without\n overlap is 0, and the geometric mean in the final BLEU score computation\n multiplies the 0 with the precision of other n-grams. This results in 0\n (independently of the precision of the other n-gram orders). The following\n example has zero 3-gram and 4-gram overlaps:\n\n >>> round(sentence_bleu([reference1, reference2, reference3], hypothesis2),4) # doctest: +ELLIPSIS\n 0.0\n\n To avoid this harsh behaviour when no ngram overlaps are found a smoothing\n function can be used.\n\n >>> chencherry = SmoothingFunction()\n >>> sentence_bleu([reference1, reference2, reference3], hypothesis2,\n ... smoothing_function=chencherry.method1) # doctest: +ELLIPSIS\n 0.0370...\n\n The default BLEU calculates a score for up to 4-grams using uniform\n weights (this is called BLEU-4). To evaluate your translations with\n higher/lower order ngrams, use customized weights. E.g. when accounting\n for up to 5-grams with uniform weights (this is called BLEU-5) use:\n\n >>> weights = (1./5., 1./5., 1./5., 1./5., 1./5.)\n >>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS\n 0.3920...\n\n Multiple BLEU scores can be computed at once, by supplying a list of weights.\n E.g. for computing BLEU-2, BLEU-3 *and* BLEU-4 in one computation, use:\n >>> weights = [\n ... (1./2., 1./2.),\n ... (1./3., 1./3., 1./3.),\n ... (1./4., 1./4., 1./4., 1./4.)\n ... ]\n >>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS\n [0.7453..., 0.6240..., 0.5045...]\n\n :param references: reference sentences\n :type references: list(list(str))\n :param hypothesis: a hypothesis sentence\n :type hypothesis: list(str)\n :param weights: weights for unigrams, bigrams, trigrams and so on (one or a list of weights)\n :type weights: tuple(float) / list(tuple(float))\n :param smoothing_function:\n :type smoothing_function: SmoothingFunction\n :param auto_reweigh: Option to re-normalize the weights uniformly.\n :type auto_reweigh: bool\n :return: The sentence-level BLEU score. Returns a list if multiple weights were supplied.\n :rtype: float / list(float)\n \"\"\"\n return corpus_bleu(\n [references], [hypothesis], weights, smoothing_function, auto_reweigh\n )\n\n\ndef corpus_bleu(\n list_of_references,\n hypotheses,\n weights=(0.25, 0.25, 0.25, 0.25),\n smoothing_function=None,\n auto_reweigh=False,\n):\n \"\"\"\n Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all\n the hypotheses and their respective references.\n\n Instead of averaging the sentence level BLEU scores (i.e. macro-average\n precision), the original BLEU metric (Papineni et al. 2002) accounts for\n the micro-average precision (i.e. summing the numerators and denominators\n for each hypothesis-reference(s) pairs before the division).\n\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'military', 'always',\n ... 'obeys', 'the', 'commands', 'of', 'the', 'party']\n >>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will', 'forever',\n ... 'heed', 'Party', 'commands']\n >>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'Party']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS\n 0.5920...\n\n The example below show that corpus_bleu() is different from averaging\n sentence_bleu() for hypotheses\n\n >>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1)\n >>> score2 = sentence_bleu([ref2a], hyp2)\n >>> (score1 + score2) / 2 # doctest: +ELLIPSIS\n 0.6223...\n\n Custom weights may be supplied to fine-tune the BLEU score further.\n A tuple of float weights for unigrams, bigrams, trigrams and so on can be given.\n >>> weights = (0.1, 0.3, 0.5, 0.1)\n >>> corpus_bleu(list_of_references, hypotheses, weights=weights) # doctest: +ELLIPSIS\n 0.5818...\n\n This particular weight gave extra value to trigrams.\n Furthermore, multiple weights can be given, resulting in multiple BLEU scores.\n >>> weights = [\n ... (0.5, 0.5),\n ... (0.333, 0.333, 0.334),\n ... (0.25, 0.25, 0.25, 0.25),\n ... (0.2, 0.2, 0.2, 0.2, 0.2)\n ... ]\n >>> corpus_bleu(list_of_references, hypotheses, weights=weights) # doctest: +ELLIPSIS\n [0.8242..., 0.7067..., 0.5920..., 0.4719...]\n\n :param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses\n :type list_of_references: list(list(list(str)))\n :param hypotheses: a list of hypothesis sentences\n :type hypotheses: list(list(str))\n :param weights: weights for unigrams, bigrams, trigrams and so on (one or a list of weights)\n :type weights: tuple(float) / list(tuple(float))\n :param smoothing_function:\n :type smoothing_function: SmoothingFunction\n :param auto_reweigh: Option to re-normalize the weights uniformly.\n :type auto_reweigh: bool\n :return: The corpus-level BLEU score.\n :rtype: float\n \"\"\"\n # Before proceeding to compute BLEU, perform sanity checks.\n\n p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches.\n p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref.\n hyp_lengths, ref_lengths = 0, 0\n\n assert len(list_of_references) == len(hypotheses), (\n \"The number of hypotheses and their reference(s) should be the \" \"same \"\n )\n\n try:\n weights[0][0]\n except:\n weights = [weights]\n max_weight_length = max(len(weight) for weight in weights)\n\n # Iterate through each hypothesis and their corresponding references.\n for references, hypothesis in zip(list_of_references, hypotheses):\n # For each order of ngram, calculate the numerator and\n # denominator for the corpus-level modified precision.\n for i in range(1, max_weight_length + 1):\n p_i = modified_precision(references, hypothesis, i)\n p_numerators[i] += p_i.numerator\n p_denominators[i] += p_i.denominator\n\n # Calculate the hypothesis length and the closest reference length.\n # Adds them to the corpus-level hypothesis and reference counts.\n hyp_len = len(hypothesis)\n hyp_lengths += hyp_len\n ref_lengths += closest_ref_length(references, hyp_len)\n\n # Calculate corpus-level brevity penalty.\n bp = brevity_penalty(ref_lengths, hyp_lengths)\n\n # Collects the various precision values for the different ngram orders.\n p_n = [\n Fraction(p_numerators[i], p_denominators[i], _normalize=False)\n for i in range(1, max_weight_length + 1)\n ]\n\n # Returns 0 if there's no matching n-grams\n # We only need to check for p_numerators[1] == 0, since if there's\n # no unigrams, there won't be any higher order ngrams.\n if p_numerators[1] == 0:\n return 0 if len(weights) == 1 else [0] * len(weights)\n\n # If there's no smoothing, set use method0 from SmoothinFunction class.\n if not smoothing_function:\n smoothing_function = SmoothingFunction().method0\n # Smoothen the modified precision.\n # Note: smoothing_function() may convert values into floats;\n # it tries to retain the Fraction object as much as the\n # smoothing method allows.\n p_n = smoothing_function(\n p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths\n )\n\n bleu_scores = []\n for weight in weights:\n # Uniformly re-weighting based on maximum hypothesis lengths if largest\n # order of n-grams < 4 and weights is set at default.\n if auto_reweigh:\n if hyp_lengths < 4 and weight == (0.25, 0.25, 0.25, 0.25):\n weight = (1 / hyp_lengths,) * hyp_lengths\n\n s = (w_i * math.log(p_i) for w_i, p_i in zip(weight, p_n) if p_i > 0)\n s = bp * math.exp(math.fsum(s))\n bleu_scores.append(s)\n return bleu_scores[0] if len(weights) == 1 else bleu_scores\n\n\ndef modified_precision(references, hypothesis, n):\n \"\"\"\n Calculate modified ngram precision.\n\n The normal precision method may lead to some wrong translations with\n high-precision, e.g., the translation, in which a word of reference\n repeats several times, has very high precision.\n\n This function only returns the Fraction object that contains the numerator\n and denominator necessary to calculate the corpus-level precision.\n To calculate the modified precision for a single pair of hypothesis and\n references, cast the Fraction object into a float.\n\n The famous \"the the the ... \" example shows that you can get BLEU precision\n by duplicating high frequency words.\n\n >>> reference1 = 'the cat is on the mat'.split()\n >>> reference2 = 'there is a cat on the mat'.split()\n >>> hypothesis1 = 'the the the the the the the'.split()\n >>> references = [reference1, reference2]\n >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS\n 0.2857...\n\n In the modified n-gram precision, a reference word will be considered\n exhausted after a matching hypothesis word is identified, e.g.\n\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will',\n ... 'forever', 'heed', 'Party', 'commands']\n >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the',\n ... 'Party']\n >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n >>> hypothesis = 'of the'.split()\n >>> references = [reference1, reference2, reference3]\n >>> float(modified_precision(references, hypothesis, n=1))\n 1.0\n >>> float(modified_precision(references, hypothesis, n=2))\n 1.0\n\n An example of a normal machine translation hypothesis:\n\n >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'military', 'always',\n ... 'obeys', 'the', 'commands', 'of', 'the', 'party']\n\n >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',\n ... 'forever', 'hearing', 'the', 'activity', 'guidebook',\n ... 'that', 'party', 'direct']\n\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'military', 'will',\n ... 'forever', 'heed', 'Party', 'commands']\n\n >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'military', 'forces', 'always',\n ... 'being', 'under', 'the', 'command', 'of', 'the',\n ... 'Party']\n\n >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'army', 'always', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'party']\n >>> references = [reference1, reference2, reference3]\n >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS\n 0.9444...\n >>> float(modified_precision(references, hypothesis2, n=1)) # doctest: +ELLIPSIS\n 0.5714...\n >>> float(modified_precision(references, hypothesis1, n=2)) # doctest: +ELLIPSIS\n 0.5882352941176471\n >>> float(modified_precision(references, hypothesis2, n=2)) # doctest: +ELLIPSIS\n 0.07692...\n\n\n :param references: A list of reference translations.\n :type references: list(list(str))\n :param hypothesis: A hypothesis translation.\n :type hypothesis: list(str)\n :param n: The ngram order.\n :type n: int\n :return: BLEU's modified precision for the nth order ngram.\n :rtype: Fraction\n \"\"\"\n # Extracts all ngrams in hypothesis\n # Set an empty Counter if hypothesis is empty.\n counts = Counter(ngrams(hypothesis, n)) if len(hypothesis) >= n else Counter()\n # Extract a union of references' counts.\n # max_counts = reduce(or_, [Counter(ngrams(ref, n)) for ref in references])\n max_counts = {}\n for reference in references:\n reference_counts = (\n Counter(ngrams(reference, n)) if len(reference) >= n else Counter()\n )\n for ngram in counts:\n max_counts[ngram] = max(max_counts.get(ngram, 0), reference_counts[ngram])\n\n # Assigns the intersection between hypothesis and references' counts.\n clipped_counts = {\n ngram: min(count, max_counts[ngram]) for ngram, count in counts.items()\n }\n\n numerator = sum(clipped_counts.values())\n # Ensures that denominator is minimum 1 to avoid ZeroDivisionError.\n # Usually this happens when the ngram order is > len(reference).\n denominator = max(1, sum(counts.values()))\n\n return Fraction(numerator, denominator, _normalize=False)\n\n\ndef closest_ref_length(references, hyp_len):\n \"\"\"\n This function finds the reference that is the closest length to the\n hypothesis. The closest reference length is referred to as *r* variable\n from the brevity penalty formula in Papineni et. al. (2002)\n\n :param references: A list of reference translations.\n :type references: list(list(str))\n :param hyp_len: The length of the hypothesis.\n :type hyp_len: int\n :return: The length of the reference that's closest to the hypothesis.\n :rtype: int\n \"\"\"\n ref_lens = (len(reference) for reference in references)\n closest_ref_len = min(\n ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len)\n )\n return closest_ref_len\n\n\ndef brevity_penalty(closest_ref_len, hyp_len):\n \"\"\"\n Calculate brevity penalty.\n\n As the modified n-gram precision still has the problem from the short\n length sentence, brevity penalty is used to modify the overall BLEU\n score according to length.\n\n An example from the paper. There are three references with length 12, 15\n and 17. And a concise hypothesis of the length 12. The brevity penalty is 1.\n\n >>> reference1 = list('aaaaaaaaaaaa') # i.e. ['a'] * 12\n >>> reference2 = list('aaaaaaaaaaaaaaa') # i.e. ['a'] * 15\n >>> reference3 = list('aaaaaaaaaaaaaaaaa') # i.e. ['a'] * 17\n >>> hypothesis = list('aaaaaaaaaaaa') # i.e. ['a'] * 12\n >>> references = [reference1, reference2, reference3]\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len)\n 1.0\n\n In case a hypothesis translation is shorter than the references, penalty is\n applied.\n\n >>> references = [['a'] * 28, ['a'] * 28]\n >>> hypothesis = ['a'] * 12\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len)\n 0.2635971381157267\n\n The length of the closest reference is used to compute the penalty. If the\n length of a hypothesis is 12, and the reference lengths are 13 and 2, the\n penalty is applied because the hypothesis length (12) is less then the\n closest reference length (13).\n\n >>> references = [['a'] * 13, ['a'] * 2]\n >>> hypothesis = ['a'] * 12\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS\n 0.9200...\n\n The brevity penalty doesn't depend on reference order. More importantly,\n when two reference sentences are at the same distance, the shortest\n reference sentence length is used.\n\n >>> references = [['a'] * 13, ['a'] * 11]\n >>> hypothesis = ['a'] * 12\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> bp1 = brevity_penalty(closest_ref_len, hyp_len)\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(reversed(references), hyp_len)\n >>> bp2 = brevity_penalty(closest_ref_len, hyp_len)\n >>> bp1 == bp2 == 1\n True\n\n A test example from mteval-v13a.pl (starting from the line 705):\n\n >>> references = [['a'] * 11, ['a'] * 8]\n >>> hypothesis = ['a'] * 7\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS\n 0.8668...\n\n >>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7]\n >>> hypothesis = ['a'] * 7\n >>> hyp_len = len(hypothesis)\n >>> closest_ref_len = closest_ref_length(references, hyp_len)\n >>> brevity_penalty(closest_ref_len, hyp_len)\n 1.0\n\n :param hyp_len: The length of the hypothesis for a single sentence OR the\n sum of all the hypotheses' lengths for a corpus\n :type hyp_len: int\n :param closest_ref_len: The length of the closest reference for a single\n hypothesis OR the sum of all the closest references for every hypotheses.\n :type closest_ref_len: int\n :return: BLEU's brevity penalty.\n :rtype: float\n \"\"\"\n if hyp_len > closest_ref_len:\n return 1\n # If hypothesis is empty, brevity penalty = 0 should result in BLEU = 0.0\n elif hyp_len == 0:\n return 0\n else:\n return math.exp(1 - closest_ref_len / hyp_len)\n\n\nclass SmoothingFunction:\n \"\"\"\n This is an implementation of the smoothing techniques\n for segment-level BLEU scores that was presented in\n Boxing Chen and Collin Cherry (2014) A Systematic Comparison of\n Smoothing Techniques for Sentence-Level BLEU. In WMT14.\n http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf\n \"\"\"\n\n def __init__(self, epsilon=0.1, alpha=5, k=5):\n \"\"\"\n This will initialize the parameters required for the various smoothing\n techniques, the default values are set to the numbers used in the\n experiments from Chen and Cherry (2014).\n\n >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures',\n ... 'that', 'the', 'military', 'always', 'obeys', 'the',\n ... 'commands', 'of', 'the', 'party']\n >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures',\n ... 'that', 'the', 'military', 'will', 'forever', 'heed',\n ... 'Party', 'commands']\n\n >>> chencherry = SmoothingFunction()\n >>> print(sentence_bleu([reference1], hypothesis1)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method0)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method1)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method2)) # doctest: +ELLIPSIS\n 0.4452...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method3)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method4)) # doctest: +ELLIPSIS\n 0.4118...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method5)) # doctest: +ELLIPSIS\n 0.4905...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method6)) # doctest: +ELLIPSIS\n 0.4135...\n >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method7)) # doctest: +ELLIPSIS\n 0.4905...\n\n :param epsilon: the epsilon value use in method 1\n :type epsilon: float\n :param alpha: the alpha value use in method 6\n :type alpha: int\n :param k: the k value use in method 4\n :type k: int\n \"\"\"\n self.epsilon = epsilon\n self.alpha = alpha\n self.k = k\n\n def method0(self, p_n, *args, **kwargs):\n \"\"\"\n No smoothing.\n \"\"\"\n p_n_new = []\n for i, p_i in enumerate(p_n):\n if p_i.numerator != 0:\n p_n_new.append(p_i)\n else:\n _msg = str(\n \"\\nThe hypothesis contains 0 counts of {}-gram overlaps.\\n\"\n \"Therefore the BLEU score evaluates to 0, independently of\\n\"\n \"how many N-gram overlaps of lower order it contains.\\n\"\n \"Consider using lower n-gram order or use \"\n \"SmoothingFunction()\"\n ).format(i + 1)\n warnings.warn(_msg)\n # When numerator==0 where denonminator==0 or !=0, the result\n # for the precision score should be equal to 0 or undefined.\n # Due to BLEU geometric mean computation in logarithm space,\n # we we need to take the return sys.float_info.min such that\n # math.log(sys.float_info.min) returns a 0 precision score.\n p_n_new.append(sys.float_info.min)\n return p_n_new\n\n def method1(self, p_n, *args, **kwargs):\n \"\"\"\n Smoothing method 1: Add *epsilon* counts to precision with 0 counts.\n \"\"\"\n return [\n (p_i.numerator + self.epsilon) / p_i.denominator\n if p_i.numerator == 0\n else p_i\n for p_i in p_n\n ]\n\n def method2(self, p_n, *args, **kwargs):\n \"\"\"\n Smoothing method 2: Add 1 to both numerator and denominator from\n Chin-Yew Lin and Franz Josef Och (2004) ORANGE: a Method for\n Evaluating Automatic Evaluation Metrics for Machine Translation.\n In COLING 2004.\n \"\"\"\n return [\n Fraction(p_n[i].numerator + 1, p_n[i].denominator + 1, _normalize=False)\n if i != 0\n else p_n[0]\n for i in range(len(p_n))\n ]\n\n def method3(self, p_n, *args, **kwargs):\n \"\"\"\n Smoothing method 3: NIST geometric sequence smoothing\n The smoothing is computed by taking 1 / ( 2^k ), instead of 0, for each\n precision score whose matching n-gram count is null.\n k is 1 for the first 'n' value for which the n-gram match count is null/\n\n For example, if the text contains:\n\n - one 2-gram match\n - and (consequently) two 1-gram matches\n\n the n-gram count for each individual precision score would be:\n\n - n=1 => prec_count = 2 (two unigrams)\n - n=2 => prec_count = 1 (one bigram)\n - n=3 => prec_count = 1/2 (no trigram, taking 'smoothed' value of 1 / ( 2^k ), with k=1)\n - n=4 => prec_count = 1/4 (no fourgram, taking 'smoothed' value of 1 / ( 2^k ), with k=2)\n \"\"\"\n incvnt = 1 # From the mteval-v13a.pl, it's referred to as k.\n for i, p_i in enumerate(p_n):\n if p_i.numerator == 0:\n p_n[i] = 1 / (2**incvnt * p_i.denominator)\n incvnt += 1\n return p_n\n\n def method4(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):\n \"\"\"\n Smoothing method 4:\n Shorter translations may have inflated precision values due to having\n smaller denominators; therefore, we give them proportionally\n smaller smoothed counts. Instead of scaling to 1/(2^k), Chen and Cherry\n suggests dividing by 1/ln(len(T)), where T is the length of the translation.\n \"\"\"\n incvnt = 1\n hyp_len = hyp_len if hyp_len else len(hypothesis)\n for i, p_i in enumerate(p_n):\n if p_i.numerator == 0 and hyp_len > 1:\n # incvnt = i + 1 * self.k / math.log(\n # hyp_len\n # ) # Note that this K is different from the K from NIST.\n # p_n[i] = incvnt / p_i.denominator\\\n numerator = 1 / (2**incvnt * self.k / math.log(hyp_len))\n p_n[i] = numerator / p_i.denominator\n incvnt += 1\n return p_n\n\n def method5(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):\n \"\"\"\n Smoothing method 5:\n The matched counts for similar values of n should be similar. To a\n calculate the n-gram matched count, it averages the n−1, n and n+1 gram\n matched counts.\n \"\"\"\n hyp_len = hyp_len if hyp_len else len(hypothesis)\n m = {}\n # Requires an precision value for an addition ngram order.\n p_n_plus1 = p_n + [modified_precision(references, hypothesis, 5)]\n m[-1] = p_n[0] + 1\n for i, p_i in enumerate(p_n):\n p_n[i] = (m[i - 1] + p_i + p_n_plus1[i + 1]) / 3\n m[i] = p_n[i]\n return p_n\n\n def method6(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):\n \"\"\"\n Smoothing method 6:\n Interpolates the maximum likelihood estimate of the precision *p_n* with\n a prior estimate *pi0*. The prior is estimated by assuming that the ratio\n between pn and pn−1 will be the same as that between pn−1 and pn−2; from\n Gao and He (2013) Training MRF-Based Phrase Translation Models using\n Gradient Ascent. In NAACL.\n \"\"\"\n hyp_len = hyp_len if hyp_len else len(hypothesis)\n # This smoothing only works when p_1 and p_2 is non-zero.\n # Raise an error with an appropriate message when the input is too short\n # to use this smoothing technique.\n assert p_n[2], \"This smoothing method requires non-zero precision for bigrams.\"\n for i, p_i in enumerate(p_n):\n if i in [0, 1]: # Skips the first 2 orders of ngrams.\n continue\n else:\n pi0 = 0 if p_n[i - 2] == 0 else p_n[i - 1] ** 2 / p_n[i - 2]\n # No. of ngrams in translation that matches the reference.\n m = p_i.numerator\n # No. of ngrams in translation.\n l = sum(1 for _ in ngrams(hypothesis, i + 1))\n # Calculates the interpolated precision.\n p_n[i] = (m + self.alpha * pi0) / (l + self.alpha)\n return p_n\n\n def method7(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):\n \"\"\"\n Smoothing method 7:\n Interpolates methods 4 and 5.\n \"\"\"\n hyp_len = hyp_len if hyp_len else len(hypothesis)\n p_n = self.method4(p_n, references, hypothesis, hyp_len)\n p_n = self.method5(p_n, references, hypothesis, hyp_len)\n return p_n\n", "path": "nltk/translate/bleu_score.py" } ]
diff --git a/nltk/test/unit/translate/test_bleu.py b/nltk/test/unit/translate/test_bleu.py index ac3565e74b..990b76406a 100644 --- a/nltk/test/unit/translate/test_bleu.py +++ b/nltk/test/unit/translate/test_bleu.py @@ -5,6 +5,8 @@ import io import unittest +import numpy as np + from nltk.data import find from nltk.translate.bleu_score import ( SmoothingFunction, @@ -217,6 +219,14 @@ def test_reference_or_hypothesis_shorter_than_fourgrams(self): except AttributeError: pass # unittest.TestCase.assertWarns is only supported in Python >= 3.2. + def test_numpy_weights(self): + # Test case where there's 0 matches + references = ["The candidate has no alignment to any of the references".split()] + hypothesis = "John loves Mary".split() + + weights = np.array([0.25] * 4) + assert sentence_bleu(references, hypothesis, weights) == 0 + class TestBLEUvsMteval13a(unittest.TestCase): def test_corpus_bleu(self): diff --git a/nltk/translate/bleu_score.py b/nltk/translate/bleu_score.py index f6c232e1cb..da445bc3ec 100644 --- a/nltk/translate/bleu_score.py +++ b/nltk/translate/bleu_score.py @@ -198,7 +198,7 @@ def corpus_bleu( try: weights[0][0] - except TypeError: + except: weights = [weights] max_weight_length = max(len(weight) for weight in weights)
microsoft__botbuilder-python-1507
Python 81.skills-skilldialog throwing error: [on_turn_error] unhandled error: Cannot deserialize content-type: text/plain ## Sample information 1. Sample type: \samples\ 2. Sample language: python 3. Sample name: 81.skills-skilldialog ## Describe the bug When you run the sample as per the instructions, the skill bot is throwing the following error: ======== Running on http://localhost:39783 ======== (Press CTRL+C to quit) [on_turn_error] unhandled error: Cannot deserialize content-type: text/plain Traceback (most recent call last): File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/bot_adapter.py", line 128, in run_pipeline context, callback File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/middleware_set.py", line 69, in receive_activity_with_status return await self.receive_activity_internal(context, callback) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/middleware_set.py", line 79, in receive_activity_internal return await callback(context) File "/Users/tim/Documents/Sourcetree/BotBuilderSamples/samples/python/81.skills-skilldialog/dialog-skill-bot/bots/skill_bot.py", line 21, in on_turn self._conversation_state.create_property("DialogState"), File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/dialog_extensions.py", line 68, in run_dialog result = await dialog_context.begin_dialog(dialog.id) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/dialog_context.py", line 91, in begin_dialog return await dialog.begin_dialog(self, options) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/component_dialog.py", line 67, in begin_dialog turn_result = await self.on_begin_dialog(inner_dc, options) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/component_dialog.py", line 221, in on_begin_dialog return await inner_dc.begin_dialog(self.initial_dialog_id, options) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/dialog_context.py", line 91, in begin_dialog return await dialog.begin_dialog(self, options) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/waterfall_dialog.py", line 65, in begin_dialog return await self.run_step(dialog_context, 0, DialogReason.BeginCalled, None) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/waterfall_dialog.py", line 156, in run_step return await self.on_step(step_context) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/dialogs/waterfall_dialog.py", line 132, in on_step return await self._steps[step_context.index](step_context) File "/Users/tim/Documents/Sourcetree/BotBuilderSamples/samples/python/81.skills-skilldialog/dialog-skill-bot/dialogs/activity_router_dialog.py", line 50, in process_activity return await self._on_event_activity(step_context) File "/Users/tim/Documents/Sourcetree/BotBuilderSamples/samples/python/81.skills-skilldialog/dialog-skill-bot/dialogs/activity_router_dialog.py", line 77, in _on_event_activity return await self._begin_get_weather(step_context) File "/Users/tim/Documents/Sourcetree/BotBuilderSamples/samples/python/81.skills-skilldialog/dialog-skill-bot/dialogs/activity_router_dialog.py", line 156, in _begin_get_weather get_weather_message, get_weather_message, InputHints.ignoring_input, File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/turn_context.py", line 174, in send_activity result = await self.send_activities([activity_or_text]) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/turn_context.py", line 226, in send_activities return await self._emit(self._on_send_activities, output, logic()) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/turn_context.py", line 304, in _emit return await logic File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/turn_context.py", line 221, in logic responses = await self.adapter.send_activities(self, output) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/bot_framework_adapter.py", line 729, in send_activities raise error File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botbuilder/core/bot_framework_adapter.py", line 715, in send_activities activity.conversation.id, activity.reply_to_id, activity File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/botframework/connector/aio/operations_async/_conversations_operations_async.py", line 529, in reply_to_activity request, stream=False, **operation_config File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/async_client.py", line 115, in async_send pipeline_response = await self.config.pipeline.run(request, **kwargs) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/async_abc.py", line 159, in run return await first_node.send(pipeline_request, **kwargs) # type: ignore File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/async_abc.py", line 79, in send response = await self.next.send(request, **kwargs) # type: ignore File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/async_requests.py", line 106, in send return await self.next.send(request, **kwargs) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/async_abc.py", line 84, in send self._policy.on_response(request, response, **kwargs) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/universal.py", line 252, in on_response http_response.headers File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/universal.py", line 226, in deserialize_from_http_generics return cls.deserialize_from_text(body_bytes, content_type) File "/Users/tim/.pyenv/versions/bot379/lib/python3.7/site-packages/msrest/pipeline/universal.py", line 203, in deserialize_from_text raise DeserializationError("Cannot deserialize content-type: {}".format(content_type)) msrest.exceptions.DeserializationError: Cannot deserialize content-type: text/plain ## To Reproduce Steps to reproduce the behavior: 1. Run the root & skill bots as per the instructions from the sample readme 2. Start the bot framework emulator & connect 3. Choose the DialogSkillBot 4. Enter activity 3 ## Expected behavior Error not returned
[ { "content": "# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nimport os\nfrom setuptools import setup\n\nVERSION = os.environ[\"packageVersion\"] if \"packageVersion\" in os.environ else \"4.12.0\"\nREQUIRES = [\n \"botbuilder-schema==4.12.0\",\n \"botframework-connector==4.12.0\",\n \"botbuilder-core==4.12.0\",\n \"aiohttp==3.6.2\",\n]\n\nroot = os.path.abspath(os.path.dirname(__file__))\n\nwith open(os.path.join(root, \"botbuilder\", \"integration\", \"aiohttp\", \"about.py\")) as f:\n package_info = {}\n info = f.read()\n exec(info, package_info)\n\nwith open(os.path.join(root, \"README.rst\"), encoding=\"utf-8\") as f:\n long_description = f.read()\n\nsetup(\n name=package_info[\"__title__\"],\n version=package_info[\"__version__\"],\n url=package_info[\"__uri__\"],\n author=package_info[\"__author__\"],\n description=package_info[\"__description__\"],\n keywords=[\n \"BotBuilderIntegrationAiohttp\",\n \"bots\",\n \"ai\",\n \"botframework\",\n \"botbuilder\",\n ],\n long_description=long_description,\n long_description_content_type=\"text/x-rst\",\n license=package_info[\"__license__\"],\n packages=[\n \"botbuilder.integration.aiohttp\",\n \"botbuilder.integration.aiohttp.skills\",\n ],\n install_requires=REQUIRES,\n classifiers=[\n \"Programming Language :: Python :: 3.7\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Development Status :: 5 - Production/Stable\",\n \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n ],\n)\n", "path": "libraries/botbuilder-integration-aiohttp/setup.py" } ]
[ { "content": "# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\nimport os\nfrom setuptools import setup\n\nVERSION = os.environ[\"packageVersion\"] if \"packageVersion\" in os.environ else \"4.12.0\"\nREQUIRES = [\n \"botbuilder-schema==4.12.0\",\n \"botframework-connector==4.12.0\",\n \"botbuilder-core==4.12.0\",\n \"yarl<=1.4.2\",\n \"aiohttp==3.6.2\",\n]\n\nroot = os.path.abspath(os.path.dirname(__file__))\n\nwith open(os.path.join(root, \"botbuilder\", \"integration\", \"aiohttp\", \"about.py\")) as f:\n package_info = {}\n info = f.read()\n exec(info, package_info)\n\nwith open(os.path.join(root, \"README.rst\"), encoding=\"utf-8\") as f:\n long_description = f.read()\n\nsetup(\n name=package_info[\"__title__\"],\n version=package_info[\"__version__\"],\n url=package_info[\"__uri__\"],\n author=package_info[\"__author__\"],\n description=package_info[\"__description__\"],\n keywords=[\n \"BotBuilderIntegrationAiohttp\",\n \"bots\",\n \"ai\",\n \"botframework\",\n \"botbuilder\",\n ],\n long_description=long_description,\n long_description_content_type=\"text/x-rst\",\n license=package_info[\"__license__\"],\n packages=[\n \"botbuilder.integration.aiohttp\",\n \"botbuilder.integration.aiohttp.skills\",\n ],\n install_requires=REQUIRES,\n classifiers=[\n \"Programming Language :: Python :: 3.7\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Development Status :: 5 - Production/Stable\",\n \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n ],\n)\n", "path": "libraries/botbuilder-integration-aiohttp/setup.py" } ]
diff --git a/libraries/botbuilder-integration-aiohttp/setup.py b/libraries/botbuilder-integration-aiohttp/setup.py index f45e67ec8..e1e06e54b 100644 --- a/libraries/botbuilder-integration-aiohttp/setup.py +++ b/libraries/botbuilder-integration-aiohttp/setup.py @@ -9,6 +9,7 @@ "botbuilder-schema==4.12.0", "botframework-connector==4.12.0", "botbuilder-core==4.12.0", + "yarl<=1.4.2", "aiohttp==3.6.2", ]
Gallopsled__pwntools-200
pwnlib.util.fiddling.hexdump() no longer has colors The colors used to be so pretty but now they are gone because of this piece of code: https://github.com/Gallopsled/pwntools/blob/master/pwnlib/util/fiddling.py#L460 It looks like it was added as a quick-fix to overcome some issue, but I can't figure out which. Thoughts, @zachriggle?
[ { "content": "# -*- coding: utf-8 -*-\nimport re, base64, random, string, sys\nfrom . import packing, lists\nfrom .cyclic import cyclic_find\nfrom ..context import context\nfrom ..term import text\n\ndef unhex(s):\n \"\"\"unhex(s) -> str\n\n Hex-decodes a string.\n\n Example:\n\n >>> unhex(\"74657374\")\n 'test'\n\"\"\"\n return s.decode('hex')\n\ndef enhex(x):\n \"\"\"enhex(x) -> str\n\n Hex-encodes a string.\n\n Example:\n\n >>> enhex(\"test\")\n '74657374'\n\"\"\"\n return x.encode('hex')\n\ndef urlencode(s):\n \"\"\"urlencode(s) -> str\n\n URL-encodes a string.\n\n Example:\n\n >>> urlencode(\"test\")\n '%74%65%73%74'\n\"\"\"\n return ''.join(['%%%02x' % ord(c) for c in s])\n\ndef urldecode(s, ignore_invalid = False):\n \"\"\"urldecode(s, ignore_invalid = False) -> str\n\n URL-decodes a string.\n\n Example:\n\n >>> urldecode(\"test%20%41\")\n 'test A'\n >>> urldecode(\"%qq\")\n Traceback (most recent call last):\n ...\n ValueError: Invalid input to urldecode\n >>> urldecode(\"%qq\", ignore_invalid = True)\n '%qq'\n\"\"\"\n res = ''\n n = 0\n while n < len(s):\n if s[n] != '%':\n res += s[n]\n n += 1\n else:\n cur = s[n+1:n+3]\n if re.match('[0-9a-fA-F]{2}', cur):\n res += chr(int(cur, 16))\n n += 3\n elif ignore_invalid:\n res += '%'\n n += 1\n else:\n raise ValueError(\"Invalid input to urldecode\")\n return res\n\ndef bits(s, endian = 'big', zero = 0, one = 1):\n \"\"\"bits(s, endian = 'big', zero = 0, one = 1) -> list\n\n Converts the argument a list of bits.\n\n Args:\n s: A string or number to be converted into bits.\n endian (str): The binary endian, default 'big'.\n zero: The representing a 0-bit.\n one: The representing a 1-bit.\n\n Returns:\n A list consisting of the values specified in `zero` and `one`.\n\n Examples:\n\n >>> bits(511, zero = \"+\", one = \"-\")\n ['+', '+', '+', '+', '+', '+', '+', '-', '-', '-', '-', '-', '-', '-', '-', '-']\n >>> sum(bits(\"test\"))\n 17\n\"\"\"\n\n\n if endian not in ['little', 'big']:\n raise ValueError(\"bits(): 'endian' must be either 'little' or 'big'\")\n else:\n little = endian == 'little'\n\n out = []\n if isinstance(s, str):\n for c in s:\n b = ord(c)\n byte = []\n for _ in range(8):\n byte.append(one if b & 1 else zero)\n b >>= 1\n if little:\n out += byte\n else:\n out += byte[::-1]\n elif isinstance(s, (int, long)):\n while s:\n bit, s = one if s & 1 else zero, s >> 1\n out.append(bit)\n while len(out) % 8:\n out.append(zero)\n if not little:\n out = out[::-1]\n else:\n raise ValueError(\"bits(): 's' must be either a string or a number\")\n\n return out\n\ndef bits_str(s, endian = 'big', zero = '0', one = '1'):\n \"\"\"bits_str(s, endian = 'big', zero = '0', one = '1') -> str\n\n A wrapper around :func:`bits`, which converts the output into a string.\n\n Examples:\n\n >>> bits_str(511)\n '0000000111111111'\n >>> bits_str(\"bits_str\", endian = \"little\")\n '0100011010010110001011101100111011111010110011100010111001001110'\n\"\"\"\n return ''.join(bits(s, endian, zero, one))\n\ndef unbits(s, endian = 'big'):\n \"\"\"unbits(s, endian = 'big') -> str\n\n Converts an iterable of bits into a string.\n\n Args:\n s: Iterable of bits\n endian (str): The string \"little\" or \"big\", which specifies the bits endianness.\n\n Returns:\n A string of the decoded bits.\n\n Example:\n >>> unbits([1])\n '\\\\x80'\n >>> unbits([1], endian = 'little')\n '\\\\x01'\n >>> unbits(bits('hello'), endian = 'little')\n '\\\\x16\\\\xa666\\\\xf6'\n \"\"\"\n if endian == 'little':\n u = lambda s: chr(int(s[::-1], 2))\n elif endian == 'big':\n u = lambda s: chr(int(s, 2))\n else:\n raise ValueError(\"unbits(): 'endian' must be either 'little' or 'big'\")\n\n out = ''\n cur = ''\n\n for c in s:\n if c in ['1', 1, True]:\n cur += '1'\n elif c in ['0', 0, False]:\n cur += '0'\n else:\n raise ValueError(\"unbits(): cannot decode the value %r into a bit\" % c)\n\n if len(cur) == 8:\n out += u(cur)\n cur = ''\n if cur:\n out += u(cur.ljust(8, '0'))\n\n return ''.join(out)\n\n\ndef bitswap(s):\n \"\"\"bitswap(s) -> str\n\n Reverses the bits in every byte of a given string.\n\n Example:\n >>> bitswap(\"1234\")\n '\\\\x8cL\\\\xcc,'\n\"\"\"\n\n out = []\n\n for c in s:\n out.append(unbits(bits_str(c)[::-1]))\n\n return ''.join(out)\n\ndef bitswap_int(n, width):\n \"\"\"bitswap_int(n) -> int\n\n Reverses the bits of a numbers and returns the result as a new number.\n\n Args:\n n (int): The number to swap.\n width (int): The width of the integer\n\n Examples:\n >>> hex(bitswap_int(0x1234, 8))\n '0x2c'\n >>> hex(bitswap_int(0x1234, 16))\n '0x2c48'\n >>> hex(bitswap_int(0x1234, 24))\n '0x2c4800'\n >>> hex(bitswap_int(0x1234, 25))\n '0x589000'\n\"\"\"\n # Make n fit inside the width\n n &= (1 << width) - 1\n\n # Convert into bits\n s = bits_str(n, endian = 'little').ljust(width, '0')[:width]\n\n # Convert back\n return int(s, 2)\n\n\ndef b64e(s):\n \"\"\"b64e(s) -> str\n\n Base64 encodes a string\n\n Example:\n\n >>> b64e(\"test\")\n 'dGVzdA=='\n \"\"\"\n return base64.b64encode(s)\n\ndef b64d(s):\n \"\"\"b64d(s) -> str\n\n Base64 decodes a string\n\n Example:\n\n >>> b64d('dGVzdA==')\n 'test'\n \"\"\"\n return base64.b64decode(s)\n\n# misc binary functions\ndef xor(*args, **kwargs):\n \"\"\"xor(*args, cut = 'max') -> str\n\n Flattens its arguments using :func:`pwnlib.util.packing.flat` and\n then xors them together. If the end of a string is reached, it wraps\n around in the string.\n\n Args:\n args: The arguments to be xor'ed together.\n cut: How long a string should be returned.\n Can be either 'min'/'max'/'left'/'right' or a number.\n\n Returns:\n The string of the arguments xor'ed together.\n\n Example:\n >>> xor('lol', 'hello', 42)\n '. ***'\n\"\"\"\n\n cut = kwargs.pop('cut', 'max')\n\n if kwargs != {}:\n raise TypeError(\"xor() got an unexpected keyword argument '%s'\" % kwargs.pop()[0])\n\n if len(args) == 0:\n raise ValueError(\"Must have something to xor\")\n\n strs = [packing.flat(s, word_size = 8, sign = False, endianness = 'little') for s in args]\n strs = [[ord(c) for c in s] for s in strs if s != '']\n\n if strs == []:\n return ''\n\n if isinstance(cut, (int, long)):\n cut = cut\n elif cut == 'left':\n cut = len(strs[0])\n elif cut == 'right':\n cut = len(strs[-1])\n elif cut == 'min':\n cut = min(len(s) for s in strs)\n elif cut == 'max':\n cut = max(len(s) for s in strs)\n else:\n raise ValueError(\"Not a valid argument for 'cut'\")\n\n def get(n):\n return chr(reduce(lambda x, y: x ^ y, [s[n % len(s)] for s in strs]))\n\n return ''.join(get(n) for n in range(cut))\n\n_default_alphabet = ''.join(chr(n) for n in range(256) if n not in [0, 0xa])\n_default_avoid = '\\x00\\n'\n\ndef xor_pair(data, avoid = None):\n \"\"\"xor_pair(data, avoid = None) -> None or (str, str)\n\n Finds two strings that will xor into a given string, while only\n using a given alphabet.\n\n Args:\n data (str): The desired string.\n avoid: The list of disallowed characters. Defaults to nulls and newlines.\n\n Returns:\n Two strings which will xor to the given string. If no such two strings exist, then None is returned.\n\n Example:\n\n >>> xor_pair(\"test\")\n ('\\\\x01\\\\x01\\\\x01\\\\x01', 'udru')\n\"\"\"\n\n avoid = avoid or _default_avoid\n alphabet = ''.join(chr(n) for n in range(256) if chr(n) not in avoid)\n\n res1 = ''\n res2 = ''\n\n for c1 in data:\n for c2 in alphabet:\n c3 = chr(ord(c1) ^ ord(c2))\n if c3 in alphabet:\n res1 += c2\n res2 += c3\n break\n else:\n return None\n\n return res1, res2\n\n\ndef randoms(count, alphabet = None):\n \"\"\"randoms(count, alphabet = None) -> str\n\n Returns a random string of a given length using only the specified alphabet.\n\n Args:\n count (int): The length of the desired string.\n alphabet: The alphabet of allowed characters. Defaults to all characters except nulls and newlines.\n\n Returns:\n A random string.\"\"\"\n\n return ''.join(random.sample(alphabet or _default_alphabet, count))\n\n\ndef rol(n, k, word_size = None):\n \"\"\"Returns a rotation by `k` of `n`.\n\n When `n` is a number, then means ``((n << k) | (n >> (word_size - k)))`` truncated to `word_size` bits.\n\n When `n` is a list, tuple or string, this is ``n[k % len(n):] + n[:k % len(n)]``.\n\n Args:\n n: The value to rotate.\n k(int): The rotation amount. Can be a positive or negative number.\n word_size(int): If `n` is a number, then this is the assumed bitsize of `n`. Defaults to :data:`pwnlib.context.word_size` if `None` .\n\n Example:\n\n >>> rol('abcdefg', 2)\n 'cdefgab'\n >>> rol('abcdefg', -2)\n 'fgabcde'\n >>> hex(rol(0x86, 3, 8))\n '0x34'\n >>> hex(rol(0x86, -3, 8))\n '0xd0'\n\"\"\"\n\n word_size = word_size or context.word_size\n\n if not isinstance(word_size, (int, long)) or word_size <= 0:\n raise ValueError(\"rol(): 'word_size' must be a strictly positive integer\")\n\n if not isinstance(k, (int, long)):\n raise ValueError(\"rol(): 'k' must be an integer\")\n\n if isinstance(n, (str, unicode, list, tuple)):\n return n[k % len(n):] + n[:k % len(n)]\n elif isinstance(n, (int, long)):\n k = k % word_size\n n = (n << k) | (n >> (word_size - k))\n n &= (1 << word_size) - 1\n\n return n\n else:\n raise ValueError(\"rol(): 'n' must be an integer, string, list or tuple\")\n\ndef ror(n, k, word_size = None):\n \"\"\"A simple wrapper around :func:`rol`, which negates the values of `k`.\"\"\"\n\n return ror(n, -k, word_size)\n\ndef isprint(c):\n \"\"\"isprint(c) -> bool\n\n Return True if a character is printable\"\"\"\n return c in string.ascii_letters + string.digits + string.punctuation\n\n\ndef hexii(s, width = 16, skip = True):\n \"\"\"hexii(s, width = 16, skip = True) -> str\n\n Return a HEXII-dump of a string.\n\n Args:\n s(str): The string to dump\n width(int): The number of characters per line\n skip(bool): Should repeated lines be replaced by a \"*\"\n\n Returns:\n A HEXII-dump in the form of a string.\n\"\"\"\n\n return hexdump(s, width, skip, True)\n\ndef _hexiichar(c):\n HEXII = string.punctuation + string.digits + string.letters\n if c in HEXII:\n return \".%c \" % c\n elif c == '\\0':\n return \" \"\n elif c == '\\xff':\n return \"## \"\n else:\n return \"%02x \" % ord(c)\n\ndefault_style = {\n 'marker': text.gray if text.has_gray else text.blue,\n 'nonprintable': text.gray if text.has_gray else text.blue,\n '00': text.red,\n 'ff': text.green,\n}\n\nif 1 or not sys.stdout.isatty():\n default_style = {\n 'marker': lambda x:x,\n 'nonprintable': lambda x:x,\n }\n\n\ndef sequential_lines(a,b):\n if len(a) != len(b) or len(a) < 4:\n return False\n\n all_chars = sorted(set(a+b))\n\n alphabet = ''\n if all(a in string.lowercase for a in all_chars):\n alphabet = string.lowercase\n if all(a in string.uppercase for a in all_chars):\n alphabet = string.uppercase\n\n # Check each set of four\n for i in range(0, len(a)-3):\n A = cyclic_find(a[i:i+4], alphabet)\n B = cyclic_find(b[i:i+4], alphabet)\n if A+len(a) != B:\n return False\n return True\n\ndef hexdump_iter(s, width = 16, skip = True, hexii = False, begin = 0,\n style = None, highlight = None):\n \"\"\"hexdump_iter(s, width = 16, skip = True, hexii = False, begin = 0,\n style = {}, highlight = []) -> str generator\n\n Return a hexdump-dump of a string as a generator of lines.\n\n Args:\n s(str): The string to dump\n width(int): The number of characters per line\n skip(bool): Set to True, if repeated lines should be replaced by a \"*\"\n hexii(bool): Set to True, if a hexii-dump should be returned instead of a hexdump.\n begin(int): Offset of the first byte to print in the left column\n style(dict): Color scheme to use.\n highlight(iterable): Byte values to highlight.\n\n Returns:\n A hexdump-dump in the form of a string.\n\"\"\"\n style = style or {}\n highlight = highlight or []\n\n for b in highlight:\n if isinstance(b, str):\n b = ord(b)\n style['%02x' % b] = text.white_on_red\n _style = style\n style = default_style.copy()\n style.update(_style)\n\n skipping = False\n lines = []\n last_unique = ''\n byte_width = len('00 ')\n column_sep = ' '\n line_fmt = '%%(offset)08x %%(hexbytes)-%is │%%(printable)s│' % (len(column_sep)+(width*byte_width))\n spacer = ' '\n marker = (style.get('marker') or (lambda s:s))('│')\n\n if hexii:\n column_sep = ''\n line_fmt = '%%(offset)08x %%(hexbytes)-%is│' % (len(column_sep)+(width*byte_width))\n else:\n def style_byte(b):\n hbyte = '%02x' % ord(b)\n abyte = b if isprint(b) else ' '\n if hbyte in style:\n st = style[hbyte]\n elif isprint(b):\n st = style.get('printable')\n else:\n st = style.get('nonprintable')\n if st:\n hbyte = st(hbyte)\n abyte = st(abyte)\n return hbyte, abyte\n cache = [style_byte(chr(b)) for b in range(256)]\n\n for line, chunk in enumerate(lists.group(width, s)):\n # If this chunk is the same as the last unique chunk,\n # use a '*' instead.\n if skip and (last_unique == chunk or sequential_lines(last_unique, chunk)):\n last_unique = chunk\n if not skipping:\n yield '*'\n skipping = True\n continue\n\n # Chunk is unique, save for next iteration\n last_unique = chunk\n skipping = False\n\n # Cenerate contents for line\n offset = begin+line*width\n hexbytes = ''\n printable = ''\n for i, b in enumerate(chunk):\n if not hexii:\n hbyte, abyte = cache[ord(b)]\n else:\n hbyte, abyte = _hexiichar(b), ''\n\n if i % 4 == 3 and i < width - 1:\n hbyte += spacer\n abyte += marker\n\n hexbytes += hbyte + ' '\n printable += abyte\n\n if i + 1 < width:\n delta = width - i - 1\n hexbytes += ' ' * (byte_width * delta + (delta - 1) // 4)\n\n line = line_fmt % {'offset': offset, 'hexbytes': hexbytes, 'printable': printable}\n yield line\n\n line = \"%08x\" % (len(s) + begin)\n yield line\n\ndef hexdump(s, width = 16, skip = True, hexii = False, begin = 0,\n style = None, highlight = None):\n return '\\n'.join(hexdump_iter(s, width, skip, hexii, begin, style, highlight))\n", "path": "pwnlib/util/fiddling.py" } ]
[ { "content": "# -*- coding: utf-8 -*-\nimport re, base64, random, string, sys\nfrom . import packing, lists\nfrom .cyclic import cyclic_find\nfrom ..context import context\nfrom ..term import text\n\ndef unhex(s):\n \"\"\"unhex(s) -> str\n\n Hex-decodes a string.\n\n Example:\n\n >>> unhex(\"74657374\")\n 'test'\n\"\"\"\n return s.decode('hex')\n\ndef enhex(x):\n \"\"\"enhex(x) -> str\n\n Hex-encodes a string.\n\n Example:\n\n >>> enhex(\"test\")\n '74657374'\n\"\"\"\n return x.encode('hex')\n\ndef urlencode(s):\n \"\"\"urlencode(s) -> str\n\n URL-encodes a string.\n\n Example:\n\n >>> urlencode(\"test\")\n '%74%65%73%74'\n\"\"\"\n return ''.join(['%%%02x' % ord(c) for c in s])\n\ndef urldecode(s, ignore_invalid = False):\n \"\"\"urldecode(s, ignore_invalid = False) -> str\n\n URL-decodes a string.\n\n Example:\n\n >>> urldecode(\"test%20%41\")\n 'test A'\n >>> urldecode(\"%qq\")\n Traceback (most recent call last):\n ...\n ValueError: Invalid input to urldecode\n >>> urldecode(\"%qq\", ignore_invalid = True)\n '%qq'\n\"\"\"\n res = ''\n n = 0\n while n < len(s):\n if s[n] != '%':\n res += s[n]\n n += 1\n else:\n cur = s[n+1:n+3]\n if re.match('[0-9a-fA-F]{2}', cur):\n res += chr(int(cur, 16))\n n += 3\n elif ignore_invalid:\n res += '%'\n n += 1\n else:\n raise ValueError(\"Invalid input to urldecode\")\n return res\n\ndef bits(s, endian = 'big', zero = 0, one = 1):\n \"\"\"bits(s, endian = 'big', zero = 0, one = 1) -> list\n\n Converts the argument a list of bits.\n\n Args:\n s: A string or number to be converted into bits.\n endian (str): The binary endian, default 'big'.\n zero: The representing a 0-bit.\n one: The representing a 1-bit.\n\n Returns:\n A list consisting of the values specified in `zero` and `one`.\n\n Examples:\n\n >>> bits(511, zero = \"+\", one = \"-\")\n ['+', '+', '+', '+', '+', '+', '+', '-', '-', '-', '-', '-', '-', '-', '-', '-']\n >>> sum(bits(\"test\"))\n 17\n\"\"\"\n\n\n if endian not in ['little', 'big']:\n raise ValueError(\"bits(): 'endian' must be either 'little' or 'big'\")\n else:\n little = endian == 'little'\n\n out = []\n if isinstance(s, str):\n for c in s:\n b = ord(c)\n byte = []\n for _ in range(8):\n byte.append(one if b & 1 else zero)\n b >>= 1\n if little:\n out += byte\n else:\n out += byte[::-1]\n elif isinstance(s, (int, long)):\n while s:\n bit, s = one if s & 1 else zero, s >> 1\n out.append(bit)\n while len(out) % 8:\n out.append(zero)\n if not little:\n out = out[::-1]\n else:\n raise ValueError(\"bits(): 's' must be either a string or a number\")\n\n return out\n\ndef bits_str(s, endian = 'big', zero = '0', one = '1'):\n \"\"\"bits_str(s, endian = 'big', zero = '0', one = '1') -> str\n\n A wrapper around :func:`bits`, which converts the output into a string.\n\n Examples:\n\n >>> bits_str(511)\n '0000000111111111'\n >>> bits_str(\"bits_str\", endian = \"little\")\n '0100011010010110001011101100111011111010110011100010111001001110'\n\"\"\"\n return ''.join(bits(s, endian, zero, one))\n\ndef unbits(s, endian = 'big'):\n \"\"\"unbits(s, endian = 'big') -> str\n\n Converts an iterable of bits into a string.\n\n Args:\n s: Iterable of bits\n endian (str): The string \"little\" or \"big\", which specifies the bits endianness.\n\n Returns:\n A string of the decoded bits.\n\n Example:\n >>> unbits([1])\n '\\\\x80'\n >>> unbits([1], endian = 'little')\n '\\\\x01'\n >>> unbits(bits('hello'), endian = 'little')\n '\\\\x16\\\\xa666\\\\xf6'\n \"\"\"\n if endian == 'little':\n u = lambda s: chr(int(s[::-1], 2))\n elif endian == 'big':\n u = lambda s: chr(int(s, 2))\n else:\n raise ValueError(\"unbits(): 'endian' must be either 'little' or 'big'\")\n\n out = ''\n cur = ''\n\n for c in s:\n if c in ['1', 1, True]:\n cur += '1'\n elif c in ['0', 0, False]:\n cur += '0'\n else:\n raise ValueError(\"unbits(): cannot decode the value %r into a bit\" % c)\n\n if len(cur) == 8:\n out += u(cur)\n cur = ''\n if cur:\n out += u(cur.ljust(8, '0'))\n\n return ''.join(out)\n\n\ndef bitswap(s):\n \"\"\"bitswap(s) -> str\n\n Reverses the bits in every byte of a given string.\n\n Example:\n >>> bitswap(\"1234\")\n '\\\\x8cL\\\\xcc,'\n\"\"\"\n\n out = []\n\n for c in s:\n out.append(unbits(bits_str(c)[::-1]))\n\n return ''.join(out)\n\ndef bitswap_int(n, width):\n \"\"\"bitswap_int(n) -> int\n\n Reverses the bits of a numbers and returns the result as a new number.\n\n Args:\n n (int): The number to swap.\n width (int): The width of the integer\n\n Examples:\n >>> hex(bitswap_int(0x1234, 8))\n '0x2c'\n >>> hex(bitswap_int(0x1234, 16))\n '0x2c48'\n >>> hex(bitswap_int(0x1234, 24))\n '0x2c4800'\n >>> hex(bitswap_int(0x1234, 25))\n '0x589000'\n\"\"\"\n # Make n fit inside the width\n n &= (1 << width) - 1\n\n # Convert into bits\n s = bits_str(n, endian = 'little').ljust(width, '0')[:width]\n\n # Convert back\n return int(s, 2)\n\n\ndef b64e(s):\n \"\"\"b64e(s) -> str\n\n Base64 encodes a string\n\n Example:\n\n >>> b64e(\"test\")\n 'dGVzdA=='\n \"\"\"\n return base64.b64encode(s)\n\ndef b64d(s):\n \"\"\"b64d(s) -> str\n\n Base64 decodes a string\n\n Example:\n\n >>> b64d('dGVzdA==')\n 'test'\n \"\"\"\n return base64.b64decode(s)\n\n# misc binary functions\ndef xor(*args, **kwargs):\n \"\"\"xor(*args, cut = 'max') -> str\n\n Flattens its arguments using :func:`pwnlib.util.packing.flat` and\n then xors them together. If the end of a string is reached, it wraps\n around in the string.\n\n Args:\n args: The arguments to be xor'ed together.\n cut: How long a string should be returned.\n Can be either 'min'/'max'/'left'/'right' or a number.\n\n Returns:\n The string of the arguments xor'ed together.\n\n Example:\n >>> xor('lol', 'hello', 42)\n '. ***'\n\"\"\"\n\n cut = kwargs.pop('cut', 'max')\n\n if kwargs != {}:\n raise TypeError(\"xor() got an unexpected keyword argument '%s'\" % kwargs.pop()[0])\n\n if len(args) == 0:\n raise ValueError(\"Must have something to xor\")\n\n strs = [packing.flat(s, word_size = 8, sign = False, endianness = 'little') for s in args]\n strs = [[ord(c) for c in s] for s in strs if s != '']\n\n if strs == []:\n return ''\n\n if isinstance(cut, (int, long)):\n cut = cut\n elif cut == 'left':\n cut = len(strs[0])\n elif cut == 'right':\n cut = len(strs[-1])\n elif cut == 'min':\n cut = min(len(s) for s in strs)\n elif cut == 'max':\n cut = max(len(s) for s in strs)\n else:\n raise ValueError(\"Not a valid argument for 'cut'\")\n\n def get(n):\n return chr(reduce(lambda x, y: x ^ y, [s[n % len(s)] for s in strs]))\n\n return ''.join(get(n) for n in range(cut))\n\n_default_alphabet = ''.join(chr(n) for n in range(256) if n not in [0, 0xa])\n_default_avoid = '\\x00\\n'\n\ndef xor_pair(data, avoid = None):\n \"\"\"xor_pair(data, avoid = None) -> None or (str, str)\n\n Finds two strings that will xor into a given string, while only\n using a given alphabet.\n\n Args:\n data (str): The desired string.\n avoid: The list of disallowed characters. Defaults to nulls and newlines.\n\n Returns:\n Two strings which will xor to the given string. If no such two strings exist, then None is returned.\n\n Example:\n\n >>> xor_pair(\"test\")\n ('\\\\x01\\\\x01\\\\x01\\\\x01', 'udru')\n\"\"\"\n\n avoid = avoid or _default_avoid\n alphabet = ''.join(chr(n) for n in range(256) if chr(n) not in avoid)\n\n res1 = ''\n res2 = ''\n\n for c1 in data:\n for c2 in alphabet:\n c3 = chr(ord(c1) ^ ord(c2))\n if c3 in alphabet:\n res1 += c2\n res2 += c3\n break\n else:\n return None\n\n return res1, res2\n\n\ndef randoms(count, alphabet = None):\n \"\"\"randoms(count, alphabet = None) -> str\n\n Returns a random string of a given length using only the specified alphabet.\n\n Args:\n count (int): The length of the desired string.\n alphabet: The alphabet of allowed characters. Defaults to all characters except nulls and newlines.\n\n Returns:\n A random string.\"\"\"\n\n return ''.join(random.sample(alphabet or _default_alphabet, count))\n\n\ndef rol(n, k, word_size = None):\n \"\"\"Returns a rotation by `k` of `n`.\n\n When `n` is a number, then means ``((n << k) | (n >> (word_size - k)))`` truncated to `word_size` bits.\n\n When `n` is a list, tuple or string, this is ``n[k % len(n):] + n[:k % len(n)]``.\n\n Args:\n n: The value to rotate.\n k(int): The rotation amount. Can be a positive or negative number.\n word_size(int): If `n` is a number, then this is the assumed bitsize of `n`. Defaults to :data:`pwnlib.context.word_size` if `None` .\n\n Example:\n\n >>> rol('abcdefg', 2)\n 'cdefgab'\n >>> rol('abcdefg', -2)\n 'fgabcde'\n >>> hex(rol(0x86, 3, 8))\n '0x34'\n >>> hex(rol(0x86, -3, 8))\n '0xd0'\n\"\"\"\n\n word_size = word_size or context.word_size\n\n if not isinstance(word_size, (int, long)) or word_size <= 0:\n raise ValueError(\"rol(): 'word_size' must be a strictly positive integer\")\n\n if not isinstance(k, (int, long)):\n raise ValueError(\"rol(): 'k' must be an integer\")\n\n if isinstance(n, (str, unicode, list, tuple)):\n return n[k % len(n):] + n[:k % len(n)]\n elif isinstance(n, (int, long)):\n k = k % word_size\n n = (n << k) | (n >> (word_size - k))\n n &= (1 << word_size) - 1\n\n return n\n else:\n raise ValueError(\"rol(): 'n' must be an integer, string, list or tuple\")\n\ndef ror(n, k, word_size = None):\n \"\"\"A simple wrapper around :func:`rol`, which negates the values of `k`.\"\"\"\n\n return ror(n, -k, word_size)\n\ndef isprint(c):\n \"\"\"isprint(c) -> bool\n\n Return True if a character is printable\"\"\"\n return c in string.ascii_letters + string.digits + string.punctuation\n\n\ndef hexii(s, width = 16, skip = True):\n \"\"\"hexii(s, width = 16, skip = True) -> str\n\n Return a HEXII-dump of a string.\n\n Args:\n s(str): The string to dump\n width(int): The number of characters per line\n skip(bool): Should repeated lines be replaced by a \"*\"\n\n Returns:\n A HEXII-dump in the form of a string.\n\"\"\"\n\n return hexdump(s, width, skip, True)\n\ndef _hexiichar(c):\n HEXII = string.punctuation + string.digits + string.letters\n if c in HEXII:\n return \".%c \" % c\n elif c == '\\0':\n return \" \"\n elif c == '\\xff':\n return \"## \"\n else:\n return \"%02x \" % ord(c)\n\ndefault_style = {\n 'marker': text.gray if text.has_gray else text.blue,\n 'nonprintable': text.gray if text.has_gray else text.blue,\n '00': text.red,\n 'ff': text.green,\n}\n\nif not sys.stdout.isatty():\n default_style = {\n 'marker': lambda x:x,\n 'nonprintable': lambda x:x,\n }\n\n\ndef sequential_lines(a,b):\n if len(a) != len(b) or len(a) < 4:\n return False\n\n all_chars = sorted(set(a+b))\n\n alphabet = ''\n if all(a in string.lowercase for a in all_chars):\n alphabet = string.lowercase\n if all(a in string.uppercase for a in all_chars):\n alphabet = string.uppercase\n\n # Check each set of four\n for i in range(0, len(a)-3):\n A = cyclic_find(a[i:i+4], alphabet)\n B = cyclic_find(b[i:i+4], alphabet)\n if A+len(a) != B:\n return False\n return True\n\ndef hexdump_iter(s, width = 16, skip = True, hexii = False, begin = 0,\n style = None, highlight = None):\n \"\"\"hexdump_iter(s, width = 16, skip = True, hexii = False, begin = 0,\n style = {}, highlight = []) -> str generator\n\n Return a hexdump-dump of a string as a generator of lines.\n\n Args:\n s(str): The string to dump\n width(int): The number of characters per line\n skip(bool): Set to True, if repeated lines should be replaced by a \"*\"\n hexii(bool): Set to True, if a hexii-dump should be returned instead of a hexdump.\n begin(int): Offset of the first byte to print in the left column\n style(dict): Color scheme to use.\n highlight(iterable): Byte values to highlight.\n\n Returns:\n A hexdump-dump in the form of a string.\n\"\"\"\n style = style or {}\n highlight = highlight or []\n\n for b in highlight:\n if isinstance(b, str):\n b = ord(b)\n style['%02x' % b] = text.white_on_red\n _style = style\n style = default_style.copy()\n style.update(_style)\n\n skipping = False\n lines = []\n last_unique = ''\n byte_width = len('00 ')\n column_sep = ' '\n line_fmt = '%%(offset)08x %%(hexbytes)-%is │%%(printable)s│' % (len(column_sep)+(width*byte_width))\n spacer = ' '\n marker = (style.get('marker') or (lambda s:s))('│')\n\n if hexii:\n column_sep = ''\n line_fmt = '%%(offset)08x %%(hexbytes)-%is│' % (len(column_sep)+(width*byte_width))\n else:\n def style_byte(b):\n hbyte = '%02x' % ord(b)\n abyte = b if isprint(b) else ' '\n if hbyte in style:\n st = style[hbyte]\n elif isprint(b):\n st = style.get('printable')\n else:\n st = style.get('nonprintable')\n if st:\n hbyte = st(hbyte)\n abyte = st(abyte)\n return hbyte, abyte\n cache = [style_byte(chr(b)) for b in range(256)]\n\n for line, chunk in enumerate(lists.group(width, s)):\n # If this chunk is the same as the last unique chunk,\n # use a '*' instead.\n if skip and (last_unique == chunk or sequential_lines(last_unique, chunk)):\n last_unique = chunk\n if not skipping:\n yield '*'\n skipping = True\n continue\n\n # Chunk is unique, save for next iteration\n last_unique = chunk\n skipping = False\n\n # Cenerate contents for line\n offset = begin+line*width\n hexbytes = ''\n printable = ''\n for i, b in enumerate(chunk):\n if not hexii:\n hbyte, abyte = cache[ord(b)]\n else:\n hbyte, abyte = _hexiichar(b), ''\n\n if i % 4 == 3 and i < width - 1:\n hbyte += spacer\n abyte += marker\n\n hexbytes += hbyte + ' '\n printable += abyte\n\n if i + 1 < width:\n delta = width - i - 1\n hexbytes += ' ' * (byte_width * delta + (delta - 1) // 4)\n\n line = line_fmt % {'offset': offset, 'hexbytes': hexbytes, 'printable': printable}\n yield line\n\n line = \"%08x\" % (len(s) + begin)\n yield line\n\ndef hexdump(s, width = 16, skip = True, hexii = False, begin = 0,\n style = None, highlight = None):\n return '\\n'.join(hexdump_iter(s, width, skip, hexii, begin, style, highlight))\n", "path": "pwnlib/util/fiddling.py" } ]
diff --git a/pwnlib/util/fiddling.py b/pwnlib/util/fiddling.py index 2a6fc8925..320d42d72 100644 --- a/pwnlib/util/fiddling.py +++ b/pwnlib/util/fiddling.py @@ -457,7 +457,7 @@ def _hexiichar(c): 'ff': text.green, } -if 1 or not sys.stdout.isatty(): +if not sys.stdout.isatty(): default_style = { 'marker': lambda x:x, 'nonprintable': lambda x:x,
pytorch__vision-3472
Investigate inconsistent casting inside functional_tensor.py The operators in [functional_tensor.py](https://github.com/pytorch/vision/blob/9e71fdafd871e3de9e72a6022291b49100945e29/torchvision/transforms/functional_tensor.py) perform casting in two ways: - Using the `tensor.to(dtype=dtype)` PyTorch method - Using the `convert_image_dtype()` Transformation method The first method does direct casting from one type to the other. The latter method has more complex logic that handles corner-cases and performs rescaling. Sometimes both are used on the same operator, for example: https://github.com/pytorch/vision/blob/9e71fdafd871e3de9e72a6022291b49100945e29/torchvision/transforms/functional_tensor.py#L397-L406 We should investigate if the use of the two different approaches across operators is justified and fix any potential inconsistencies. cc @vfdev-5
[ { "content": "import warnings\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad\nfrom torch.jit.annotations import BroadcastingList2\nfrom typing import Optional, Tuple, List\n\n\ndef _is_tensor_a_torch_image(x: Tensor) -> bool:\n return x.ndim >= 2\n\n\ndef _assert_image_tensor(img):\n if not _is_tensor_a_torch_image(img):\n raise TypeError(\"Tensor is not a torch image.\")\n\n\ndef _get_image_size(img: Tensor) -> List[int]:\n # Returns (w, h) of tensor image\n _assert_image_tensor(img)\n return [img.shape[-1], img.shape[-2]]\n\n\ndef _get_image_num_channels(img: Tensor) -> int:\n if img.ndim == 2:\n return 1\n elif img.ndim > 2:\n return img.shape[-3]\n\n raise TypeError(\"Input ndim should be 2 or more. Got {}\".format(img.ndim))\n\n\ndef _max_value(dtype: torch.dtype) -> float:\n # TODO: replace this method with torch.iinfo when it gets torchscript support.\n # https://github.com/pytorch/pytorch/issues/41492\n\n a = torch.tensor(2, dtype=dtype)\n signed = 1 if torch.tensor(0, dtype=dtype).is_signed() else 0\n bits = 1\n max_value = torch.tensor(-signed, dtype=torch.long)\n while True:\n next_value = a.pow(bits - signed).sub(1)\n if next_value > max_value:\n max_value = next_value\n bits *= 2\n else:\n break\n return max_value.item()\n\n\ndef _assert_channels(img: Tensor, permitted: List[int]) -> None:\n c = _get_image_num_channels(img)\n if c not in permitted:\n raise TypeError(\"Input image tensor permitted channel values are {}, but found {}\".format(permitted, c))\n\n\ndef convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:\n if image.dtype == dtype:\n return image\n\n if image.is_floating_point():\n\n # TODO: replace with dtype.is_floating_point when torchscript supports it\n if torch.tensor(0, dtype=dtype).is_floating_point():\n return image.to(dtype)\n\n # float to int\n if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or (\n image.dtype == torch.float64 and dtype == torch.int64\n ):\n msg = f\"The cast from {image.dtype} to {dtype} cannot be performed safely.\"\n raise RuntimeError(msg)\n\n # https://github.com/pytorch/vision/pull/2078#issuecomment-612045321\n # For data in the range 0-1, (float * 255).to(uint) is only 255\n # when float is exactly 1.0.\n # `max + 1 - epsilon` provides more evenly distributed mapping of\n # ranges of floats to ints.\n eps = 1e-3\n max_val = _max_value(dtype)\n result = image.mul(max_val + 1.0 - eps)\n return result.to(dtype)\n else:\n input_max = _max_value(image.dtype)\n\n # int to float\n # TODO: replace with dtype.is_floating_point when torchscript supports it\n if torch.tensor(0, dtype=dtype).is_floating_point():\n image = image.to(dtype)\n return image / input_max\n\n output_max = _max_value(dtype)\n\n # int to int\n if input_max > output_max:\n # factor should be forced to int for torch jit script\n # otherwise factor is a float and image // factor can produce different results\n factor = int((input_max + 1) // (output_max + 1))\n image = image // factor\n return image.to(dtype)\n else:\n # factor should be forced to int for torch jit script\n # otherwise factor is a float and image * factor can produce different results\n factor = int((output_max + 1) // (input_max + 1))\n image = image.to(dtype)\n return image * factor\n\n\ndef vflip(img: Tensor) -> Tensor:\n _assert_image_tensor(img)\n\n return img.flip(-2)\n\n\ndef hflip(img: Tensor) -> Tensor:\n _assert_image_tensor(img)\n\n return img.flip(-1)\n\n\ndef crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:\n _assert_image_tensor(img)\n\n return img[..., top:top + height, left:left + width]\n\n\ndef rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor:\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n _assert_channels(img, [3])\n\n if num_output_channels not in (1, 3):\n raise ValueError('num_output_channels should be either 1 or 3')\n\n r, g, b = img.unbind(dim=-3)\n # This implementation closely follows the TF one:\n # https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/ops/image_ops_impl.py#L2105-L2138\n l_img = (0.2989 * r + 0.587 * g + 0.114 * b).to(img.dtype)\n l_img = l_img.unsqueeze(dim=-3)\n\n if num_output_channels == 3:\n return l_img.expand(img.shape)\n\n return l_img\n\n\ndef adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:\n if brightness_factor < 0:\n raise ValueError('brightness_factor ({}) is not non-negative.'.format(brightness_factor))\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [1, 3])\n\n return _blend(img, torch.zeros_like(img), brightness_factor)\n\n\ndef adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:\n if contrast_factor < 0:\n raise ValueError('contrast_factor ({}) is not non-negative.'.format(contrast_factor))\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [3])\n\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n mean = torch.mean(rgb_to_grayscale(img).to(dtype), dim=(-3, -2, -1), keepdim=True)\n\n return _blend(img, mean, contrast_factor)\n\n\ndef adjust_hue(img: Tensor, hue_factor: float) -> Tensor:\n if not (-0.5 <= hue_factor <= 0.5):\n raise ValueError('hue_factor ({}) is not in [-0.5, 0.5].'.format(hue_factor))\n\n if not (isinstance(img, torch.Tensor)):\n raise TypeError('Input img should be Tensor image')\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [1, 3])\n if _get_image_num_channels(img) == 1: # Match PIL behaviour\n return img\n\n orig_dtype = img.dtype\n if img.dtype == torch.uint8:\n img = img.to(dtype=torch.float32) / 255.0\n\n img = _rgb2hsv(img)\n h, s, v = img.unbind(dim=-3)\n h = (h + hue_factor) % 1.0\n img = torch.stack((h, s, v), dim=-3)\n img_hue_adj = _hsv2rgb(img)\n\n if orig_dtype == torch.uint8:\n img_hue_adj = (img_hue_adj * 255.0).to(dtype=orig_dtype)\n\n return img_hue_adj\n\n\ndef adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor:\n if saturation_factor < 0:\n raise ValueError('saturation_factor ({}) is not non-negative.'.format(saturation_factor))\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [3])\n\n return _blend(img, rgb_to_grayscale(img), saturation_factor)\n\n\ndef adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:\n if not isinstance(img, torch.Tensor):\n raise TypeError('Input img should be a Tensor.')\n\n _assert_channels(img, [1, 3])\n\n if gamma < 0:\n raise ValueError('Gamma should be a non-negative real number')\n\n result = img\n dtype = img.dtype\n if not torch.is_floating_point(img):\n result = convert_image_dtype(result, torch.float32)\n\n result = (gain * result ** gamma).clamp(0, 1)\n\n result = convert_image_dtype(result, dtype)\n result = result.to(dtype)\n return result\n\n\ndef center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor:\n \"\"\"DEPRECATED\n \"\"\"\n warnings.warn(\n \"This method is deprecated and will be removed in future releases. \"\n \"Please, use ``F.center_crop`` instead.\"\n )\n\n _assert_image_tensor(img)\n\n _, image_width, image_height = img.size()\n crop_height, crop_width = output_size\n # crop_top = int(round((image_height - crop_height) / 2.))\n # Result can be different between python func and scripted func\n # Temporary workaround:\n crop_top = int((image_height - crop_height + 1) * 0.5)\n # crop_left = int(round((image_width - crop_width) / 2.))\n # Result can be different between python func and scripted func\n # Temporary workaround:\n crop_left = int((image_width - crop_width + 1) * 0.5)\n\n return crop(img, crop_top, crop_left, crop_height, crop_width)\n\n\ndef five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]:\n \"\"\"DEPRECATED\n \"\"\"\n warnings.warn(\n \"This method is deprecated and will be removed in future releases. \"\n \"Please, use ``F.five_crop`` instead.\"\n )\n\n _assert_image_tensor(img)\n\n assert len(size) == 2, \"Please provide only two dimensions (h, w) for size.\"\n\n _, image_width, image_height = img.size()\n crop_height, crop_width = size\n if crop_width > image_width or crop_height > image_height:\n msg = \"Requested crop size {} is bigger than input size {}\"\n raise ValueError(msg.format(size, (image_height, image_width)))\n\n tl = crop(img, 0, 0, crop_width, crop_height)\n tr = crop(img, image_width - crop_width, 0, image_width, crop_height)\n bl = crop(img, 0, image_height - crop_height, crop_width, image_height)\n br = crop(img, image_width - crop_width, image_height - crop_height, image_width, image_height)\n center = center_crop(img, (crop_height, crop_width))\n\n return [tl, tr, bl, br, center]\n\n\ndef ten_crop(img: Tensor, size: BroadcastingList2[int], vertical_flip: bool = False) -> List[Tensor]:\n \"\"\"DEPRECATED\n \"\"\"\n warnings.warn(\n \"This method is deprecated and will be removed in future releases. \"\n \"Please, use ``F.ten_crop`` instead.\"\n )\n\n _assert_image_tensor(img)\n\n assert len(size) == 2, \"Please provide only two dimensions (h, w) for size.\"\n first_five = five_crop(img, size)\n\n if vertical_flip:\n img = vflip(img)\n else:\n img = hflip(img)\n\n second_five = five_crop(img, size)\n\n return first_five + second_five\n\n\ndef _blend(img1: Tensor, img2: Tensor, ratio: float) -> Tensor:\n ratio = float(ratio)\n bound = 1.0 if img1.is_floating_point() else 255.0\n return (ratio * img1 + (1.0 - ratio) * img2).clamp(0, bound).to(img1.dtype)\n\n\ndef _rgb2hsv(img):\n r, g, b = img.unbind(dim=-3)\n\n # Implementation is based on https://github.com/python-pillow/Pillow/blob/4174d4267616897df3746d315d5a2d0f82c656ee/\n # src/libImaging/Convert.c#L330\n maxc = torch.max(img, dim=-3).values\n minc = torch.min(img, dim=-3).values\n\n # The algorithm erases S and H channel where `maxc = minc`. This avoids NaN\n # from happening in the results, because\n # + S channel has division by `maxc`, which is zero only if `maxc = minc`\n # + H channel has division by `(maxc - minc)`.\n #\n # Instead of overwriting NaN afterwards, we just prevent it from occuring so\n # we don't need to deal with it in case we save the NaN in a buffer in\n # backprop, if it is ever supported, but it doesn't hurt to do so.\n eqc = maxc == minc\n\n cr = maxc - minc\n # Since `eqc => cr = 0`, replacing denominator with 1 when `eqc` is fine.\n ones = torch.ones_like(maxc)\n s = cr / torch.where(eqc, ones, maxc)\n # Note that `eqc => maxc = minc = r = g = b`. So the following calculation\n # of `h` would reduce to `bc - gc + 2 + rc - bc + 4 + rc - bc = 6` so it\n # would not matter what values `rc`, `gc`, and `bc` have here, and thus\n # replacing denominator with 1 when `eqc` is fine.\n cr_divisor = torch.where(eqc, ones, cr)\n rc = (maxc - r) / cr_divisor\n gc = (maxc - g) / cr_divisor\n bc = (maxc - b) / cr_divisor\n\n hr = (maxc == r) * (bc - gc)\n hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc)\n hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc)\n h = (hr + hg + hb)\n h = torch.fmod((h / 6.0 + 1.0), 1.0)\n return torch.stack((h, s, maxc), dim=-3)\n\n\ndef _hsv2rgb(img):\n h, s, v = img.unbind(dim=-3)\n i = torch.floor(h * 6.0)\n f = (h * 6.0) - i\n i = i.to(dtype=torch.int32)\n\n p = torch.clamp((v * (1.0 - s)), 0.0, 1.0)\n q = torch.clamp((v * (1.0 - s * f)), 0.0, 1.0)\n t = torch.clamp((v * (1.0 - s * (1.0 - f))), 0.0, 1.0)\n i = i % 6\n\n mask = i.unsqueeze(dim=-3) == torch.arange(6, device=i.device).view(-1, 1, 1)\n\n a1 = torch.stack((v, q, p, p, t, v), dim=-3)\n a2 = torch.stack((t, v, v, q, p, p), dim=-3)\n a3 = torch.stack((p, p, t, v, v, q), dim=-3)\n a4 = torch.stack((a1, a2, a3), dim=-4)\n\n return torch.einsum(\"...ijk, ...xijk -> ...xjk\", mask.to(dtype=img.dtype), a4)\n\n\ndef _pad_symmetric(img: Tensor, padding: List[int]) -> Tensor:\n # padding is left, right, top, bottom\n\n # crop if needed\n if padding[0] < 0 or padding[1] < 0 or padding[2] < 0 or padding[3] < 0:\n crop_left, crop_right, crop_top, crop_bottom = [-min(x, 0) for x in padding]\n img = img[..., crop_top:img.shape[-2] - crop_bottom, crop_left:img.shape[-1] - crop_right]\n padding = [max(x, 0) for x in padding]\n\n in_sizes = img.size()\n\n x_indices = [i for i in range(in_sizes[-1])] # [0, 1, 2, 3, ...]\n left_indices = [i for i in range(padding[0] - 1, -1, -1)] # e.g. [3, 2, 1, 0]\n right_indices = [-(i + 1) for i in range(padding[1])] # e.g. [-1, -2, -3]\n x_indices = torch.tensor(left_indices + x_indices + right_indices)\n\n y_indices = [i for i in range(in_sizes[-2])]\n top_indices = [i for i in range(padding[2] - 1, -1, -1)]\n bottom_indices = [-(i + 1) for i in range(padding[3])]\n y_indices = torch.tensor(top_indices + y_indices + bottom_indices)\n\n ndim = img.ndim\n if ndim == 3:\n return img[:, y_indices[:, None], x_indices[None, :]]\n elif ndim == 4:\n return img[:, :, y_indices[:, None], x_indices[None, :]]\n else:\n raise RuntimeError(\"Symmetric padding of N-D tensors are not supported yet\")\n\n\ndef pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = \"constant\") -> Tensor:\n _assert_image_tensor(img)\n\n if not isinstance(padding, (int, tuple, list)):\n raise TypeError(\"Got inappropriate padding arg\")\n if not isinstance(fill, (int, float)):\n raise TypeError(\"Got inappropriate fill arg\")\n if not isinstance(padding_mode, str):\n raise TypeError(\"Got inappropriate padding_mode arg\")\n\n if isinstance(padding, tuple):\n padding = list(padding)\n\n if isinstance(padding, list) and len(padding) not in [1, 2, 4]:\n raise ValueError(\"Padding must be an int or a 1, 2, or 4 element tuple, not a \" +\n \"{} element tuple\".format(len(padding)))\n\n if padding_mode not in [\"constant\", \"edge\", \"reflect\", \"symmetric\"]:\n raise ValueError(\"Padding mode should be either constant, edge, reflect or symmetric\")\n\n if isinstance(padding, int):\n if torch.jit.is_scripting():\n # This maybe unreachable\n raise ValueError(\"padding can't be an int while torchscripting, set it as a list [value, ]\")\n pad_left = pad_right = pad_top = pad_bottom = padding\n elif len(padding) == 1:\n pad_left = pad_right = pad_top = pad_bottom = padding[0]\n elif len(padding) == 2:\n pad_left = pad_right = padding[0]\n pad_top = pad_bottom = padding[1]\n else:\n pad_left = padding[0]\n pad_top = padding[1]\n pad_right = padding[2]\n pad_bottom = padding[3]\n\n p = [pad_left, pad_right, pad_top, pad_bottom]\n\n if padding_mode == \"edge\":\n # remap padding_mode str\n padding_mode = \"replicate\"\n elif padding_mode == \"symmetric\":\n # route to another implementation\n return _pad_symmetric(img, p)\n\n need_squeeze = False\n if img.ndim < 4:\n img = img.unsqueeze(dim=0)\n need_squeeze = True\n\n out_dtype = img.dtype\n need_cast = False\n if (padding_mode != \"constant\") and img.dtype not in (torch.float32, torch.float64):\n # Here we temporary cast input tensor to float\n # until pytorch issue is resolved :\n # https://github.com/pytorch/pytorch/issues/40763\n need_cast = True\n img = img.to(torch.float32)\n\n img = torch_pad(img, p, mode=padding_mode, value=float(fill))\n\n if need_squeeze:\n img = img.squeeze(dim=0)\n\n if need_cast:\n img = img.to(out_dtype)\n\n return img\n\n\ndef resize(img: Tensor, size: List[int], interpolation: str = \"bilinear\") -> Tensor:\n _assert_image_tensor(img)\n\n if not isinstance(size, (int, tuple, list)):\n raise TypeError(\"Got inappropriate size arg\")\n if not isinstance(interpolation, str):\n raise TypeError(\"Got inappropriate interpolation arg\")\n\n if interpolation not in [\"nearest\", \"bilinear\", \"bicubic\"]:\n raise ValueError(\"This interpolation mode is unsupported with Tensor input\")\n\n if isinstance(size, tuple):\n size = list(size)\n\n if isinstance(size, list) and len(size) not in [1, 2]:\n raise ValueError(\"Size must be an int or a 1 or 2 element tuple/list, not a \"\n \"{} element tuple/list\".format(len(size)))\n\n w, h = _get_image_size(img)\n\n if isinstance(size, int):\n size_w, size_h = size, size\n elif len(size) < 2:\n size_w, size_h = size[0], size[0]\n else:\n size_w, size_h = size[1], size[0] # Convention (h, w)\n\n if isinstance(size, int) or len(size) < 2:\n if w < h:\n size_h = int(size_w * h / w)\n else:\n size_w = int(size_h * w / h)\n\n if (w <= h and w == size_w) or (h <= w and h == size_h):\n return img\n\n img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [torch.float32, torch.float64])\n\n # Define align_corners to avoid warnings\n align_corners = False if interpolation in [\"bilinear\", \"bicubic\"] else None\n\n img = interpolate(img, size=[size_h, size_w], mode=interpolation, align_corners=align_corners)\n\n if interpolation == \"bicubic\" and out_dtype == torch.uint8:\n img = img.clamp(min=0, max=255)\n\n img = _cast_squeeze_out(img, need_cast=need_cast, need_squeeze=need_squeeze, out_dtype=out_dtype)\n\n return img\n\n\ndef _assert_grid_transform_inputs(\n img: Tensor,\n matrix: Optional[List[float]],\n interpolation: str,\n fill: Optional[List[float]],\n supported_interpolation_modes: List[str],\n coeffs: Optional[List[float]] = None,\n):\n\n if not (isinstance(img, torch.Tensor)):\n raise TypeError(\"Input img should be Tensor\")\n\n _assert_image_tensor(img)\n\n if matrix is not None and not isinstance(matrix, list):\n raise TypeError(\"Argument matrix should be a list\")\n\n if matrix is not None and len(matrix) != 6:\n raise ValueError(\"Argument matrix should have 6 float values\")\n\n if coeffs is not None and len(coeffs) != 8:\n raise ValueError(\"Argument coeffs should have 8 float values\")\n\n if fill is not None and not isinstance(fill, (int, float, tuple, list)):\n warnings.warn(\"Argument fill should be either int, float, tuple or list\")\n\n # Check fill\n num_channels = _get_image_num_channels(img)\n if isinstance(fill, (tuple, list)) and (len(fill) > 1 and len(fill) != num_channels):\n msg = (\"The number of elements in 'fill' cannot broadcast to match the number of \"\n \"channels of the image ({} != {})\")\n raise ValueError(msg.format(len(fill), num_channels))\n\n if interpolation not in supported_interpolation_modes:\n raise ValueError(\"Interpolation mode '{}' is unsupported with Tensor input\".format(interpolation))\n\n\ndef _cast_squeeze_in(img: Tensor, req_dtypes: List[torch.dtype]) -> Tuple[Tensor, bool, bool, torch.dtype]:\n need_squeeze = False\n # make image NCHW\n if img.ndim < 4:\n img = img.unsqueeze(dim=0)\n need_squeeze = True\n\n out_dtype = img.dtype\n need_cast = False\n if out_dtype not in req_dtypes:\n need_cast = True\n req_dtype = req_dtypes[0]\n img = img.to(req_dtype)\n return img, need_cast, need_squeeze, out_dtype\n\n\ndef _cast_squeeze_out(img: Tensor, need_cast: bool, need_squeeze: bool, out_dtype: torch.dtype):\n if need_squeeze:\n img = img.squeeze(dim=0)\n\n if need_cast:\n if out_dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):\n # it is better to round before cast\n img = torch.round(img)\n img = img.to(out_dtype)\n\n return img\n\n\ndef _apply_grid_transform(img: Tensor, grid: Tensor, mode: str, fill: Optional[List[float]]) -> Tensor:\n\n img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [grid.dtype, ])\n\n if img.shape[0] > 1:\n # Apply same grid to a batch of images\n grid = grid.expand(img.shape[0], grid.shape[1], grid.shape[2], grid.shape[3])\n\n # Append a dummy mask for customized fill colors, should be faster than grid_sample() twice\n if fill is not None:\n dummy = torch.ones((img.shape[0], 1, img.shape[2], img.shape[3]), dtype=img.dtype, device=img.device)\n img = torch.cat((img, dummy), dim=1)\n\n img = grid_sample(img, grid, mode=mode, padding_mode=\"zeros\", align_corners=False)\n\n # Fill with required color\n if fill is not None:\n mask = img[:, -1:, :, :] # N * 1 * H * W\n img = img[:, :-1, :, :] # N * C * H * W\n mask = mask.expand_as(img)\n len_fill = len(fill) if isinstance(fill, (tuple, list)) else 1\n fill_img = torch.tensor(fill, dtype=img.dtype, device=img.device).view(1, len_fill, 1, 1).expand_as(img)\n if mode == 'nearest':\n mask = mask < 0.5\n img[mask] = fill_img[mask]\n else: # 'bilinear'\n img = img * mask + (1.0 - mask) * fill_img\n\n img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype)\n return img\n\n\ndef _gen_affine_grid(\n theta: Tensor, w: int, h: int, ow: int, oh: int,\n) -> Tensor:\n # https://github.com/pytorch/pytorch/blob/74b65c32be68b15dc7c9e8bb62459efbfbde33d8/aten/src/ATen/native/\n # AffineGridGenerator.cpp#L18\n # Difference with AffineGridGenerator is that:\n # 1) we normalize grid values after applying theta\n # 2) we can normalize by other image size, such that it covers \"extend\" option like in PIL.Image.rotate\n\n d = 0.5\n base_grid = torch.empty(1, oh, ow, 3, dtype=theta.dtype, device=theta.device)\n x_grid = torch.linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, steps=ow, device=theta.device)\n base_grid[..., 0].copy_(x_grid)\n y_grid = torch.linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, steps=oh, device=theta.device).unsqueeze_(-1)\n base_grid[..., 1].copy_(y_grid)\n base_grid[..., 2].fill_(1)\n\n rescaled_theta = theta.transpose(1, 2) / torch.tensor([0.5 * w, 0.5 * h], dtype=theta.dtype, device=theta.device)\n output_grid = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta)\n return output_grid.view(1, oh, ow, 2)\n\n\ndef affine(\n img: Tensor, matrix: List[float], interpolation: str = \"nearest\", fill: Optional[List[float]] = None\n) -> Tensor:\n _assert_grid_transform_inputs(img, matrix, interpolation, fill, [\"nearest\", \"bilinear\"])\n\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3)\n shape = img.shape\n # grid will be generated on the same device as theta and img\n grid = _gen_affine_grid(theta, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2])\n return _apply_grid_transform(img, grid, interpolation, fill=fill)\n\n\ndef _compute_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]:\n\n # Inspired of PIL implementation:\n # https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054\n\n # pts are Top-Left, Top-Right, Bottom-Left, Bottom-Right points.\n pts = torch.tensor([\n [-0.5 * w, -0.5 * h, 1.0],\n [-0.5 * w, 0.5 * h, 1.0],\n [0.5 * w, 0.5 * h, 1.0],\n [0.5 * w, -0.5 * h, 1.0],\n ])\n theta = torch.tensor(matrix, dtype=torch.float).reshape(1, 2, 3)\n new_pts = pts.view(1, 4, 3).bmm(theta.transpose(1, 2)).view(4, 2)\n min_vals, _ = new_pts.min(dim=0)\n max_vals, _ = new_pts.max(dim=0)\n\n # Truncate precision to 1e-4 to avoid ceil of Xe-15 to 1.0\n tol = 1e-4\n cmax = torch.ceil((max_vals / tol).trunc_() * tol)\n cmin = torch.floor((min_vals / tol).trunc_() * tol)\n size = cmax - cmin\n return int(size[0]), int(size[1])\n\n\ndef rotate(\n img: Tensor, matrix: List[float], interpolation: str = \"nearest\",\n expand: bool = False, fill: Optional[List[float]] = None\n) -> Tensor:\n _assert_grid_transform_inputs(img, matrix, interpolation, fill, [\"nearest\", \"bilinear\"])\n w, h = img.shape[-1], img.shape[-2]\n ow, oh = _compute_output_size(matrix, w, h) if expand else (w, h)\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3)\n # grid will be generated on the same device as theta and img\n grid = _gen_affine_grid(theta, w=w, h=h, ow=ow, oh=oh)\n\n return _apply_grid_transform(img, grid, interpolation, fill=fill)\n\n\ndef _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype, device: torch.device):\n # https://github.com/python-pillow/Pillow/blob/4634eafe3c695a014267eefdce830b4a825beed7/\n # src/libImaging/Geometry.c#L394\n\n #\n # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1)\n # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1)\n #\n theta1 = torch.tensor([[\n [coeffs[0], coeffs[1], coeffs[2]],\n [coeffs[3], coeffs[4], coeffs[5]]\n ]], dtype=dtype, device=device)\n theta2 = torch.tensor([[\n [coeffs[6], coeffs[7], 1.0],\n [coeffs[6], coeffs[7], 1.0]\n ]], dtype=dtype, device=device)\n\n d = 0.5\n base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device)\n x_grid = torch.linspace(d, ow * 1.0 + d - 1.0, steps=ow, device=device)\n base_grid[..., 0].copy_(x_grid)\n y_grid = torch.linspace(d, oh * 1.0 + d - 1.0, steps=oh, device=device).unsqueeze_(-1)\n base_grid[..., 1].copy_(y_grid)\n base_grid[..., 2].fill_(1)\n\n rescaled_theta1 = theta1.transpose(1, 2) / torch.tensor([0.5 * ow, 0.5 * oh], dtype=dtype, device=device)\n output_grid1 = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta1)\n output_grid2 = base_grid.view(1, oh * ow, 3).bmm(theta2.transpose(1, 2))\n\n output_grid = output_grid1 / output_grid2 - 1.0\n return output_grid.view(1, oh, ow, 2)\n\n\ndef perspective(\n img: Tensor, perspective_coeffs: List[float], interpolation: str = \"bilinear\", fill: Optional[List[float]] = None\n) -> Tensor:\n if not (isinstance(img, torch.Tensor)):\n raise TypeError('Input img should be Tensor.')\n\n _assert_image_tensor(img)\n\n _assert_grid_transform_inputs(\n img,\n matrix=None,\n interpolation=interpolation,\n fill=fill,\n supported_interpolation_modes=[\"nearest\", \"bilinear\"],\n coeffs=perspective_coeffs\n )\n\n ow, oh = img.shape[-1], img.shape[-2]\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n grid = _perspective_grid(perspective_coeffs, ow=ow, oh=oh, dtype=dtype, device=img.device)\n return _apply_grid_transform(img, grid, interpolation, fill=fill)\n\n\ndef _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Tensor:\n ksize_half = (kernel_size - 1) * 0.5\n\n x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)\n pdf = torch.exp(-0.5 * (x / sigma).pow(2))\n kernel1d = pdf / pdf.sum()\n\n return kernel1d\n\n\ndef _get_gaussian_kernel2d(\n kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device\n) -> Tensor:\n kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0]).to(device, dtype=dtype)\n kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1]).to(device, dtype=dtype)\n kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :])\n return kernel2d\n\n\ndef gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Tensor:\n if not (isinstance(img, torch.Tensor)):\n raise TypeError('img should be Tensor. Got {}'.format(type(img)))\n\n _assert_image_tensor(img)\n\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype, device=img.device)\n kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1])\n\n img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype, ])\n\n # padding = (left, right, top, bottom)\n padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2]\n img = torch_pad(img, padding, mode=\"reflect\")\n img = conv2d(img, kernel, groups=img.shape[-3])\n\n img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype)\n return img\n\n\ndef invert(img: Tensor) -> Tensor:\n\n _assert_image_tensor(img)\n\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n\n _assert_channels(img, [1, 3])\n\n bound = torch.tensor(1 if img.is_floating_point() else 255, dtype=img.dtype, device=img.device)\n return bound - img\n\n\ndef posterize(img: Tensor, bits: int) -> Tensor:\n\n _assert_image_tensor(img)\n\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n if img.dtype != torch.uint8:\n raise TypeError(\"Only torch.uint8 image tensors are supported, but found {}\".format(img.dtype))\n\n _assert_channels(img, [1, 3])\n mask = -int(2**(8 - bits)) # JIT-friendly for: ~(2 ** (8 - bits) - 1)\n return img & mask\n\n\ndef solarize(img: Tensor, threshold: float) -> Tensor:\n\n _assert_image_tensor(img)\n\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n\n _assert_channels(img, [1, 3])\n\n inverted_img = invert(img)\n return torch.where(img >= threshold, inverted_img, img)\n\n\ndef _blurred_degenerate_image(img: Tensor) -> Tensor:\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n\n kernel = torch.ones((3, 3), dtype=dtype, device=img.device)\n kernel[1, 1] = 5.0\n kernel /= kernel.sum()\n kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1])\n\n result_tmp, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype, ])\n result_tmp = conv2d(result_tmp, kernel, groups=result_tmp.shape[-3])\n result_tmp = _cast_squeeze_out(result_tmp, need_cast, need_squeeze, out_dtype)\n\n result = img.clone()\n result[..., 1:-1, 1:-1] = result_tmp\n\n return result\n\n\ndef adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:\n if sharpness_factor < 0:\n raise ValueError('sharpness_factor ({}) is not non-negative.'.format(sharpness_factor))\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [1, 3])\n\n if img.size(-1) <= 2 or img.size(-2) <= 2:\n return img\n\n return _blend(img, _blurred_degenerate_image(img), sharpness_factor)\n\n\ndef autocontrast(img: Tensor) -> Tensor:\n\n _assert_image_tensor(img)\n\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n\n _assert_channels(img, [1, 3])\n\n bound = 1.0 if img.is_floating_point() else 255.0\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n\n minimum = img.amin(dim=(-2, -1), keepdim=True).to(dtype)\n maximum = img.amax(dim=(-2, -1), keepdim=True).to(dtype)\n eq_idxs = torch.where(minimum == maximum)[0]\n minimum[eq_idxs] = 0\n maximum[eq_idxs] = bound\n scale = bound / (maximum - minimum)\n\n return ((img - minimum) * scale).clamp(0, bound).to(img.dtype)\n\n\ndef _scale_channel(img_chan):\n hist = torch.histc(img_chan.to(torch.float32), bins=256, min=0, max=255)\n\n nonzero_hist = hist[hist != 0]\n step = nonzero_hist[:-1].sum() // 255\n if step == 0:\n return img_chan\n\n lut = (torch.cumsum(hist, 0) + (step // 2)) // step\n lut = torch.nn.functional.pad(lut, [1, 0])[:-1].clamp(0, 255)\n\n return lut[img_chan.to(torch.int64)].to(torch.uint8)\n\n\ndef _equalize_single_image(img: Tensor) -> Tensor:\n return torch.stack([_scale_channel(img[c]) for c in range(img.size(0))])\n\n\ndef equalize(img: Tensor) -> Tensor:\n\n _assert_image_tensor(img)\n\n if not (3 <= img.ndim <= 4):\n raise TypeError(\"Input image tensor should have 3 or 4 dimensions, but found {}\".format(img.ndim))\n if img.dtype != torch.uint8:\n raise TypeError(\"Only torch.uint8 image tensors are supported, but found {}\".format(img.dtype))\n\n _assert_channels(img, [1, 3])\n\n if img.ndim == 3:\n return _equalize_single_image(img)\n\n return torch.stack([_equalize_single_image(x) for x in img])\n", "path": "torchvision/transforms/functional_tensor.py" } ]
[ { "content": "import warnings\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn.functional import grid_sample, conv2d, interpolate, pad as torch_pad\nfrom torch.jit.annotations import BroadcastingList2\nfrom typing import Optional, Tuple, List\n\n\ndef _is_tensor_a_torch_image(x: Tensor) -> bool:\n return x.ndim >= 2\n\n\ndef _assert_image_tensor(img):\n if not _is_tensor_a_torch_image(img):\n raise TypeError(\"Tensor is not a torch image.\")\n\n\ndef _get_image_size(img: Tensor) -> List[int]:\n # Returns (w, h) of tensor image\n _assert_image_tensor(img)\n return [img.shape[-1], img.shape[-2]]\n\n\ndef _get_image_num_channels(img: Tensor) -> int:\n if img.ndim == 2:\n return 1\n elif img.ndim > 2:\n return img.shape[-3]\n\n raise TypeError(\"Input ndim should be 2 or more. Got {}\".format(img.ndim))\n\n\ndef _max_value(dtype: torch.dtype) -> float:\n # TODO: replace this method with torch.iinfo when it gets torchscript support.\n # https://github.com/pytorch/pytorch/issues/41492\n\n a = torch.tensor(2, dtype=dtype)\n signed = 1 if torch.tensor(0, dtype=dtype).is_signed() else 0\n bits = 1\n max_value = torch.tensor(-signed, dtype=torch.long)\n while True:\n next_value = a.pow(bits - signed).sub(1)\n if next_value > max_value:\n max_value = next_value\n bits *= 2\n else:\n break\n return max_value.item()\n\n\ndef _assert_channels(img: Tensor, permitted: List[int]) -> None:\n c = _get_image_num_channels(img)\n if c not in permitted:\n raise TypeError(\"Input image tensor permitted channel values are {}, but found {}\".format(permitted, c))\n\n\ndef convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:\n if image.dtype == dtype:\n return image\n\n if image.is_floating_point():\n\n # TODO: replace with dtype.is_floating_point when torchscript supports it\n if torch.tensor(0, dtype=dtype).is_floating_point():\n return image.to(dtype)\n\n # float to int\n if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or (\n image.dtype == torch.float64 and dtype == torch.int64\n ):\n msg = f\"The cast from {image.dtype} to {dtype} cannot be performed safely.\"\n raise RuntimeError(msg)\n\n # https://github.com/pytorch/vision/pull/2078#issuecomment-612045321\n # For data in the range 0-1, (float * 255).to(uint) is only 255\n # when float is exactly 1.0.\n # `max + 1 - epsilon` provides more evenly distributed mapping of\n # ranges of floats to ints.\n eps = 1e-3\n max_val = _max_value(dtype)\n result = image.mul(max_val + 1.0 - eps)\n return result.to(dtype)\n else:\n input_max = _max_value(image.dtype)\n\n # int to float\n # TODO: replace with dtype.is_floating_point when torchscript supports it\n if torch.tensor(0, dtype=dtype).is_floating_point():\n image = image.to(dtype)\n return image / input_max\n\n output_max = _max_value(dtype)\n\n # int to int\n if input_max > output_max:\n # factor should be forced to int for torch jit script\n # otherwise factor is a float and image // factor can produce different results\n factor = int((input_max + 1) // (output_max + 1))\n image = image // factor\n return image.to(dtype)\n else:\n # factor should be forced to int for torch jit script\n # otherwise factor is a float and image * factor can produce different results\n factor = int((output_max + 1) // (input_max + 1))\n image = image.to(dtype)\n return image * factor\n\n\ndef vflip(img: Tensor) -> Tensor:\n _assert_image_tensor(img)\n\n return img.flip(-2)\n\n\ndef hflip(img: Tensor) -> Tensor:\n _assert_image_tensor(img)\n\n return img.flip(-1)\n\n\ndef crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:\n _assert_image_tensor(img)\n\n return img[..., top:top + height, left:left + width]\n\n\ndef rgb_to_grayscale(img: Tensor, num_output_channels: int = 1) -> Tensor:\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n _assert_channels(img, [3])\n\n if num_output_channels not in (1, 3):\n raise ValueError('num_output_channels should be either 1 or 3')\n\n r, g, b = img.unbind(dim=-3)\n # This implementation closely follows the TF one:\n # https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/ops/image_ops_impl.py#L2105-L2138\n l_img = (0.2989 * r + 0.587 * g + 0.114 * b).to(img.dtype)\n l_img = l_img.unsqueeze(dim=-3)\n\n if num_output_channels == 3:\n return l_img.expand(img.shape)\n\n return l_img\n\n\ndef adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:\n if brightness_factor < 0:\n raise ValueError('brightness_factor ({}) is not non-negative.'.format(brightness_factor))\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [1, 3])\n\n return _blend(img, torch.zeros_like(img), brightness_factor)\n\n\ndef adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:\n if contrast_factor < 0:\n raise ValueError('contrast_factor ({}) is not non-negative.'.format(contrast_factor))\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [3])\n\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n mean = torch.mean(rgb_to_grayscale(img).to(dtype), dim=(-3, -2, -1), keepdim=True)\n\n return _blend(img, mean, contrast_factor)\n\n\ndef adjust_hue(img: Tensor, hue_factor: float) -> Tensor:\n if not (-0.5 <= hue_factor <= 0.5):\n raise ValueError('hue_factor ({}) is not in [-0.5, 0.5].'.format(hue_factor))\n\n if not (isinstance(img, torch.Tensor)):\n raise TypeError('Input img should be Tensor image')\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [1, 3])\n if _get_image_num_channels(img) == 1: # Match PIL behaviour\n return img\n\n orig_dtype = img.dtype\n if img.dtype == torch.uint8:\n img = img.to(dtype=torch.float32) / 255.0\n\n img = _rgb2hsv(img)\n h, s, v = img.unbind(dim=-3)\n h = (h + hue_factor) % 1.0\n img = torch.stack((h, s, v), dim=-3)\n img_hue_adj = _hsv2rgb(img)\n\n if orig_dtype == torch.uint8:\n img_hue_adj = (img_hue_adj * 255.0).to(dtype=orig_dtype)\n\n return img_hue_adj\n\n\ndef adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor:\n if saturation_factor < 0:\n raise ValueError('saturation_factor ({}) is not non-negative.'.format(saturation_factor))\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [3])\n\n return _blend(img, rgb_to_grayscale(img), saturation_factor)\n\n\ndef adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:\n if not isinstance(img, torch.Tensor):\n raise TypeError('Input img should be a Tensor.')\n\n _assert_channels(img, [1, 3])\n\n if gamma < 0:\n raise ValueError('Gamma should be a non-negative real number')\n\n result = img\n dtype = img.dtype\n if not torch.is_floating_point(img):\n result = convert_image_dtype(result, torch.float32)\n\n result = (gain * result ** gamma).clamp(0, 1)\n\n result = convert_image_dtype(result, dtype)\n return result\n\n\ndef center_crop(img: Tensor, output_size: BroadcastingList2[int]) -> Tensor:\n \"\"\"DEPRECATED\n \"\"\"\n warnings.warn(\n \"This method is deprecated and will be removed in future releases. \"\n \"Please, use ``F.center_crop`` instead.\"\n )\n\n _assert_image_tensor(img)\n\n _, image_width, image_height = img.size()\n crop_height, crop_width = output_size\n # crop_top = int(round((image_height - crop_height) / 2.))\n # Result can be different between python func and scripted func\n # Temporary workaround:\n crop_top = int((image_height - crop_height + 1) * 0.5)\n # crop_left = int(round((image_width - crop_width) / 2.))\n # Result can be different between python func and scripted func\n # Temporary workaround:\n crop_left = int((image_width - crop_width + 1) * 0.5)\n\n return crop(img, crop_top, crop_left, crop_height, crop_width)\n\n\ndef five_crop(img: Tensor, size: BroadcastingList2[int]) -> List[Tensor]:\n \"\"\"DEPRECATED\n \"\"\"\n warnings.warn(\n \"This method is deprecated and will be removed in future releases. \"\n \"Please, use ``F.five_crop`` instead.\"\n )\n\n _assert_image_tensor(img)\n\n assert len(size) == 2, \"Please provide only two dimensions (h, w) for size.\"\n\n _, image_width, image_height = img.size()\n crop_height, crop_width = size\n if crop_width > image_width or crop_height > image_height:\n msg = \"Requested crop size {} is bigger than input size {}\"\n raise ValueError(msg.format(size, (image_height, image_width)))\n\n tl = crop(img, 0, 0, crop_width, crop_height)\n tr = crop(img, image_width - crop_width, 0, image_width, crop_height)\n bl = crop(img, 0, image_height - crop_height, crop_width, image_height)\n br = crop(img, image_width - crop_width, image_height - crop_height, image_width, image_height)\n center = center_crop(img, (crop_height, crop_width))\n\n return [tl, tr, bl, br, center]\n\n\ndef ten_crop(img: Tensor, size: BroadcastingList2[int], vertical_flip: bool = False) -> List[Tensor]:\n \"\"\"DEPRECATED\n \"\"\"\n warnings.warn(\n \"This method is deprecated and will be removed in future releases. \"\n \"Please, use ``F.ten_crop`` instead.\"\n )\n\n _assert_image_tensor(img)\n\n assert len(size) == 2, \"Please provide only two dimensions (h, w) for size.\"\n first_five = five_crop(img, size)\n\n if vertical_flip:\n img = vflip(img)\n else:\n img = hflip(img)\n\n second_five = five_crop(img, size)\n\n return first_five + second_five\n\n\ndef _blend(img1: Tensor, img2: Tensor, ratio: float) -> Tensor:\n ratio = float(ratio)\n bound = 1.0 if img1.is_floating_point() else 255.0\n return (ratio * img1 + (1.0 - ratio) * img2).clamp(0, bound).to(img1.dtype)\n\n\ndef _rgb2hsv(img):\n r, g, b = img.unbind(dim=-3)\n\n # Implementation is based on https://github.com/python-pillow/Pillow/blob/4174d4267616897df3746d315d5a2d0f82c656ee/\n # src/libImaging/Convert.c#L330\n maxc = torch.max(img, dim=-3).values\n minc = torch.min(img, dim=-3).values\n\n # The algorithm erases S and H channel where `maxc = minc`. This avoids NaN\n # from happening in the results, because\n # + S channel has division by `maxc`, which is zero only if `maxc = minc`\n # + H channel has division by `(maxc - minc)`.\n #\n # Instead of overwriting NaN afterwards, we just prevent it from occuring so\n # we don't need to deal with it in case we save the NaN in a buffer in\n # backprop, if it is ever supported, but it doesn't hurt to do so.\n eqc = maxc == minc\n\n cr = maxc - minc\n # Since `eqc => cr = 0`, replacing denominator with 1 when `eqc` is fine.\n ones = torch.ones_like(maxc)\n s = cr / torch.where(eqc, ones, maxc)\n # Note that `eqc => maxc = minc = r = g = b`. So the following calculation\n # of `h` would reduce to `bc - gc + 2 + rc - bc + 4 + rc - bc = 6` so it\n # would not matter what values `rc`, `gc`, and `bc` have here, and thus\n # replacing denominator with 1 when `eqc` is fine.\n cr_divisor = torch.where(eqc, ones, cr)\n rc = (maxc - r) / cr_divisor\n gc = (maxc - g) / cr_divisor\n bc = (maxc - b) / cr_divisor\n\n hr = (maxc == r) * (bc - gc)\n hg = ((maxc == g) & (maxc != r)) * (2.0 + rc - bc)\n hb = ((maxc != g) & (maxc != r)) * (4.0 + gc - rc)\n h = (hr + hg + hb)\n h = torch.fmod((h / 6.0 + 1.0), 1.0)\n return torch.stack((h, s, maxc), dim=-3)\n\n\ndef _hsv2rgb(img):\n h, s, v = img.unbind(dim=-3)\n i = torch.floor(h * 6.0)\n f = (h * 6.0) - i\n i = i.to(dtype=torch.int32)\n\n p = torch.clamp((v * (1.0 - s)), 0.0, 1.0)\n q = torch.clamp((v * (1.0 - s * f)), 0.0, 1.0)\n t = torch.clamp((v * (1.0 - s * (1.0 - f))), 0.0, 1.0)\n i = i % 6\n\n mask = i.unsqueeze(dim=-3) == torch.arange(6, device=i.device).view(-1, 1, 1)\n\n a1 = torch.stack((v, q, p, p, t, v), dim=-3)\n a2 = torch.stack((t, v, v, q, p, p), dim=-3)\n a3 = torch.stack((p, p, t, v, v, q), dim=-3)\n a4 = torch.stack((a1, a2, a3), dim=-4)\n\n return torch.einsum(\"...ijk, ...xijk -> ...xjk\", mask.to(dtype=img.dtype), a4)\n\n\ndef _pad_symmetric(img: Tensor, padding: List[int]) -> Tensor:\n # padding is left, right, top, bottom\n\n # crop if needed\n if padding[0] < 0 or padding[1] < 0 or padding[2] < 0 or padding[3] < 0:\n crop_left, crop_right, crop_top, crop_bottom = [-min(x, 0) for x in padding]\n img = img[..., crop_top:img.shape[-2] - crop_bottom, crop_left:img.shape[-1] - crop_right]\n padding = [max(x, 0) for x in padding]\n\n in_sizes = img.size()\n\n x_indices = [i for i in range(in_sizes[-1])] # [0, 1, 2, 3, ...]\n left_indices = [i for i in range(padding[0] - 1, -1, -1)] # e.g. [3, 2, 1, 0]\n right_indices = [-(i + 1) for i in range(padding[1])] # e.g. [-1, -2, -3]\n x_indices = torch.tensor(left_indices + x_indices + right_indices)\n\n y_indices = [i for i in range(in_sizes[-2])]\n top_indices = [i for i in range(padding[2] - 1, -1, -1)]\n bottom_indices = [-(i + 1) for i in range(padding[3])]\n y_indices = torch.tensor(top_indices + y_indices + bottom_indices)\n\n ndim = img.ndim\n if ndim == 3:\n return img[:, y_indices[:, None], x_indices[None, :]]\n elif ndim == 4:\n return img[:, :, y_indices[:, None], x_indices[None, :]]\n else:\n raise RuntimeError(\"Symmetric padding of N-D tensors are not supported yet\")\n\n\ndef pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = \"constant\") -> Tensor:\n _assert_image_tensor(img)\n\n if not isinstance(padding, (int, tuple, list)):\n raise TypeError(\"Got inappropriate padding arg\")\n if not isinstance(fill, (int, float)):\n raise TypeError(\"Got inappropriate fill arg\")\n if not isinstance(padding_mode, str):\n raise TypeError(\"Got inappropriate padding_mode arg\")\n\n if isinstance(padding, tuple):\n padding = list(padding)\n\n if isinstance(padding, list) and len(padding) not in [1, 2, 4]:\n raise ValueError(\"Padding must be an int or a 1, 2, or 4 element tuple, not a \" +\n \"{} element tuple\".format(len(padding)))\n\n if padding_mode not in [\"constant\", \"edge\", \"reflect\", \"symmetric\"]:\n raise ValueError(\"Padding mode should be either constant, edge, reflect or symmetric\")\n\n if isinstance(padding, int):\n if torch.jit.is_scripting():\n # This maybe unreachable\n raise ValueError(\"padding can't be an int while torchscripting, set it as a list [value, ]\")\n pad_left = pad_right = pad_top = pad_bottom = padding\n elif len(padding) == 1:\n pad_left = pad_right = pad_top = pad_bottom = padding[0]\n elif len(padding) == 2:\n pad_left = pad_right = padding[0]\n pad_top = pad_bottom = padding[1]\n else:\n pad_left = padding[0]\n pad_top = padding[1]\n pad_right = padding[2]\n pad_bottom = padding[3]\n\n p = [pad_left, pad_right, pad_top, pad_bottom]\n\n if padding_mode == \"edge\":\n # remap padding_mode str\n padding_mode = \"replicate\"\n elif padding_mode == \"symmetric\":\n # route to another implementation\n return _pad_symmetric(img, p)\n\n need_squeeze = False\n if img.ndim < 4:\n img = img.unsqueeze(dim=0)\n need_squeeze = True\n\n out_dtype = img.dtype\n need_cast = False\n if (padding_mode != \"constant\") and img.dtype not in (torch.float32, torch.float64):\n # Here we temporary cast input tensor to float\n # until pytorch issue is resolved :\n # https://github.com/pytorch/pytorch/issues/40763\n need_cast = True\n img = img.to(torch.float32)\n\n img = torch_pad(img, p, mode=padding_mode, value=float(fill))\n\n if need_squeeze:\n img = img.squeeze(dim=0)\n\n if need_cast:\n img = img.to(out_dtype)\n\n return img\n\n\ndef resize(img: Tensor, size: List[int], interpolation: str = \"bilinear\") -> Tensor:\n _assert_image_tensor(img)\n\n if not isinstance(size, (int, tuple, list)):\n raise TypeError(\"Got inappropriate size arg\")\n if not isinstance(interpolation, str):\n raise TypeError(\"Got inappropriate interpolation arg\")\n\n if interpolation not in [\"nearest\", \"bilinear\", \"bicubic\"]:\n raise ValueError(\"This interpolation mode is unsupported with Tensor input\")\n\n if isinstance(size, tuple):\n size = list(size)\n\n if isinstance(size, list) and len(size) not in [1, 2]:\n raise ValueError(\"Size must be an int or a 1 or 2 element tuple/list, not a \"\n \"{} element tuple/list\".format(len(size)))\n\n w, h = _get_image_size(img)\n\n if isinstance(size, int):\n size_w, size_h = size, size\n elif len(size) < 2:\n size_w, size_h = size[0], size[0]\n else:\n size_w, size_h = size[1], size[0] # Convention (h, w)\n\n if isinstance(size, int) or len(size) < 2:\n if w < h:\n size_h = int(size_w * h / w)\n else:\n size_w = int(size_h * w / h)\n\n if (w <= h and w == size_w) or (h <= w and h == size_h):\n return img\n\n img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [torch.float32, torch.float64])\n\n # Define align_corners to avoid warnings\n align_corners = False if interpolation in [\"bilinear\", \"bicubic\"] else None\n\n img = interpolate(img, size=[size_h, size_w], mode=interpolation, align_corners=align_corners)\n\n if interpolation == \"bicubic\" and out_dtype == torch.uint8:\n img = img.clamp(min=0, max=255)\n\n img = _cast_squeeze_out(img, need_cast=need_cast, need_squeeze=need_squeeze, out_dtype=out_dtype)\n\n return img\n\n\ndef _assert_grid_transform_inputs(\n img: Tensor,\n matrix: Optional[List[float]],\n interpolation: str,\n fill: Optional[List[float]],\n supported_interpolation_modes: List[str],\n coeffs: Optional[List[float]] = None,\n):\n\n if not (isinstance(img, torch.Tensor)):\n raise TypeError(\"Input img should be Tensor\")\n\n _assert_image_tensor(img)\n\n if matrix is not None and not isinstance(matrix, list):\n raise TypeError(\"Argument matrix should be a list\")\n\n if matrix is not None and len(matrix) != 6:\n raise ValueError(\"Argument matrix should have 6 float values\")\n\n if coeffs is not None and len(coeffs) != 8:\n raise ValueError(\"Argument coeffs should have 8 float values\")\n\n if fill is not None and not isinstance(fill, (int, float, tuple, list)):\n warnings.warn(\"Argument fill should be either int, float, tuple or list\")\n\n # Check fill\n num_channels = _get_image_num_channels(img)\n if isinstance(fill, (tuple, list)) and (len(fill) > 1 and len(fill) != num_channels):\n msg = (\"The number of elements in 'fill' cannot broadcast to match the number of \"\n \"channels of the image ({} != {})\")\n raise ValueError(msg.format(len(fill), num_channels))\n\n if interpolation not in supported_interpolation_modes:\n raise ValueError(\"Interpolation mode '{}' is unsupported with Tensor input\".format(interpolation))\n\n\ndef _cast_squeeze_in(img: Tensor, req_dtypes: List[torch.dtype]) -> Tuple[Tensor, bool, bool, torch.dtype]:\n need_squeeze = False\n # make image NCHW\n if img.ndim < 4:\n img = img.unsqueeze(dim=0)\n need_squeeze = True\n\n out_dtype = img.dtype\n need_cast = False\n if out_dtype not in req_dtypes:\n need_cast = True\n req_dtype = req_dtypes[0]\n img = img.to(req_dtype)\n return img, need_cast, need_squeeze, out_dtype\n\n\ndef _cast_squeeze_out(img: Tensor, need_cast: bool, need_squeeze: bool, out_dtype: torch.dtype):\n if need_squeeze:\n img = img.squeeze(dim=0)\n\n if need_cast:\n if out_dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64):\n # it is better to round before cast\n img = torch.round(img)\n img = img.to(out_dtype)\n\n return img\n\n\ndef _apply_grid_transform(img: Tensor, grid: Tensor, mode: str, fill: Optional[List[float]]) -> Tensor:\n\n img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [grid.dtype, ])\n\n if img.shape[0] > 1:\n # Apply same grid to a batch of images\n grid = grid.expand(img.shape[0], grid.shape[1], grid.shape[2], grid.shape[3])\n\n # Append a dummy mask for customized fill colors, should be faster than grid_sample() twice\n if fill is not None:\n dummy = torch.ones((img.shape[0], 1, img.shape[2], img.shape[3]), dtype=img.dtype, device=img.device)\n img = torch.cat((img, dummy), dim=1)\n\n img = grid_sample(img, grid, mode=mode, padding_mode=\"zeros\", align_corners=False)\n\n # Fill with required color\n if fill is not None:\n mask = img[:, -1:, :, :] # N * 1 * H * W\n img = img[:, :-1, :, :] # N * C * H * W\n mask = mask.expand_as(img)\n len_fill = len(fill) if isinstance(fill, (tuple, list)) else 1\n fill_img = torch.tensor(fill, dtype=img.dtype, device=img.device).view(1, len_fill, 1, 1).expand_as(img)\n if mode == 'nearest':\n mask = mask < 0.5\n img[mask] = fill_img[mask]\n else: # 'bilinear'\n img = img * mask + (1.0 - mask) * fill_img\n\n img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype)\n return img\n\n\ndef _gen_affine_grid(\n theta: Tensor, w: int, h: int, ow: int, oh: int,\n) -> Tensor:\n # https://github.com/pytorch/pytorch/blob/74b65c32be68b15dc7c9e8bb62459efbfbde33d8/aten/src/ATen/native/\n # AffineGridGenerator.cpp#L18\n # Difference with AffineGridGenerator is that:\n # 1) we normalize grid values after applying theta\n # 2) we can normalize by other image size, such that it covers \"extend\" option like in PIL.Image.rotate\n\n d = 0.5\n base_grid = torch.empty(1, oh, ow, 3, dtype=theta.dtype, device=theta.device)\n x_grid = torch.linspace(-ow * 0.5 + d, ow * 0.5 + d - 1, steps=ow, device=theta.device)\n base_grid[..., 0].copy_(x_grid)\n y_grid = torch.linspace(-oh * 0.5 + d, oh * 0.5 + d - 1, steps=oh, device=theta.device).unsqueeze_(-1)\n base_grid[..., 1].copy_(y_grid)\n base_grid[..., 2].fill_(1)\n\n rescaled_theta = theta.transpose(1, 2) / torch.tensor([0.5 * w, 0.5 * h], dtype=theta.dtype, device=theta.device)\n output_grid = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta)\n return output_grid.view(1, oh, ow, 2)\n\n\ndef affine(\n img: Tensor, matrix: List[float], interpolation: str = \"nearest\", fill: Optional[List[float]] = None\n) -> Tensor:\n _assert_grid_transform_inputs(img, matrix, interpolation, fill, [\"nearest\", \"bilinear\"])\n\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3)\n shape = img.shape\n # grid will be generated on the same device as theta and img\n grid = _gen_affine_grid(theta, w=shape[-1], h=shape[-2], ow=shape[-1], oh=shape[-2])\n return _apply_grid_transform(img, grid, interpolation, fill=fill)\n\n\ndef _compute_output_size(matrix: List[float], w: int, h: int) -> Tuple[int, int]:\n\n # Inspired of PIL implementation:\n # https://github.com/python-pillow/Pillow/blob/11de3318867e4398057373ee9f12dcb33db7335c/src/PIL/Image.py#L2054\n\n # pts are Top-Left, Top-Right, Bottom-Left, Bottom-Right points.\n pts = torch.tensor([\n [-0.5 * w, -0.5 * h, 1.0],\n [-0.5 * w, 0.5 * h, 1.0],\n [0.5 * w, 0.5 * h, 1.0],\n [0.5 * w, -0.5 * h, 1.0],\n ])\n theta = torch.tensor(matrix, dtype=torch.float).reshape(1, 2, 3)\n new_pts = pts.view(1, 4, 3).bmm(theta.transpose(1, 2)).view(4, 2)\n min_vals, _ = new_pts.min(dim=0)\n max_vals, _ = new_pts.max(dim=0)\n\n # Truncate precision to 1e-4 to avoid ceil of Xe-15 to 1.0\n tol = 1e-4\n cmax = torch.ceil((max_vals / tol).trunc_() * tol)\n cmin = torch.floor((min_vals / tol).trunc_() * tol)\n size = cmax - cmin\n return int(size[0]), int(size[1])\n\n\ndef rotate(\n img: Tensor, matrix: List[float], interpolation: str = \"nearest\",\n expand: bool = False, fill: Optional[List[float]] = None\n) -> Tensor:\n _assert_grid_transform_inputs(img, matrix, interpolation, fill, [\"nearest\", \"bilinear\"])\n w, h = img.shape[-1], img.shape[-2]\n ow, oh = _compute_output_size(matrix, w, h) if expand else (w, h)\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n theta = torch.tensor(matrix, dtype=dtype, device=img.device).reshape(1, 2, 3)\n # grid will be generated on the same device as theta and img\n grid = _gen_affine_grid(theta, w=w, h=h, ow=ow, oh=oh)\n\n return _apply_grid_transform(img, grid, interpolation, fill=fill)\n\n\ndef _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype, device: torch.device):\n # https://github.com/python-pillow/Pillow/blob/4634eafe3c695a014267eefdce830b4a825beed7/\n # src/libImaging/Geometry.c#L394\n\n #\n # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1)\n # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1)\n #\n theta1 = torch.tensor([[\n [coeffs[0], coeffs[1], coeffs[2]],\n [coeffs[3], coeffs[4], coeffs[5]]\n ]], dtype=dtype, device=device)\n theta2 = torch.tensor([[\n [coeffs[6], coeffs[7], 1.0],\n [coeffs[6], coeffs[7], 1.0]\n ]], dtype=dtype, device=device)\n\n d = 0.5\n base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device)\n x_grid = torch.linspace(d, ow * 1.0 + d - 1.0, steps=ow, device=device)\n base_grid[..., 0].copy_(x_grid)\n y_grid = torch.linspace(d, oh * 1.0 + d - 1.0, steps=oh, device=device).unsqueeze_(-1)\n base_grid[..., 1].copy_(y_grid)\n base_grid[..., 2].fill_(1)\n\n rescaled_theta1 = theta1.transpose(1, 2) / torch.tensor([0.5 * ow, 0.5 * oh], dtype=dtype, device=device)\n output_grid1 = base_grid.view(1, oh * ow, 3).bmm(rescaled_theta1)\n output_grid2 = base_grid.view(1, oh * ow, 3).bmm(theta2.transpose(1, 2))\n\n output_grid = output_grid1 / output_grid2 - 1.0\n return output_grid.view(1, oh, ow, 2)\n\n\ndef perspective(\n img: Tensor, perspective_coeffs: List[float], interpolation: str = \"bilinear\", fill: Optional[List[float]] = None\n) -> Tensor:\n if not (isinstance(img, torch.Tensor)):\n raise TypeError('Input img should be Tensor.')\n\n _assert_image_tensor(img)\n\n _assert_grid_transform_inputs(\n img,\n matrix=None,\n interpolation=interpolation,\n fill=fill,\n supported_interpolation_modes=[\"nearest\", \"bilinear\"],\n coeffs=perspective_coeffs\n )\n\n ow, oh = img.shape[-1], img.shape[-2]\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n grid = _perspective_grid(perspective_coeffs, ow=ow, oh=oh, dtype=dtype, device=img.device)\n return _apply_grid_transform(img, grid, interpolation, fill=fill)\n\n\ndef _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Tensor:\n ksize_half = (kernel_size - 1) * 0.5\n\n x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)\n pdf = torch.exp(-0.5 * (x / sigma).pow(2))\n kernel1d = pdf / pdf.sum()\n\n return kernel1d\n\n\ndef _get_gaussian_kernel2d(\n kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device\n) -> Tensor:\n kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0]).to(device, dtype=dtype)\n kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1]).to(device, dtype=dtype)\n kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :])\n return kernel2d\n\n\ndef gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Tensor:\n if not (isinstance(img, torch.Tensor)):\n raise TypeError('img should be Tensor. Got {}'.format(type(img)))\n\n _assert_image_tensor(img)\n\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype, device=img.device)\n kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1])\n\n img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype, ])\n\n # padding = (left, right, top, bottom)\n padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2]\n img = torch_pad(img, padding, mode=\"reflect\")\n img = conv2d(img, kernel, groups=img.shape[-3])\n\n img = _cast_squeeze_out(img, need_cast, need_squeeze, out_dtype)\n return img\n\n\ndef invert(img: Tensor) -> Tensor:\n\n _assert_image_tensor(img)\n\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n\n _assert_channels(img, [1, 3])\n\n bound = torch.tensor(1 if img.is_floating_point() else 255, dtype=img.dtype, device=img.device)\n return bound - img\n\n\ndef posterize(img: Tensor, bits: int) -> Tensor:\n\n _assert_image_tensor(img)\n\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n if img.dtype != torch.uint8:\n raise TypeError(\"Only torch.uint8 image tensors are supported, but found {}\".format(img.dtype))\n\n _assert_channels(img, [1, 3])\n mask = -int(2**(8 - bits)) # JIT-friendly for: ~(2 ** (8 - bits) - 1)\n return img & mask\n\n\ndef solarize(img: Tensor, threshold: float) -> Tensor:\n\n _assert_image_tensor(img)\n\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n\n _assert_channels(img, [1, 3])\n\n inverted_img = invert(img)\n return torch.where(img >= threshold, inverted_img, img)\n\n\ndef _blurred_degenerate_image(img: Tensor) -> Tensor:\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n\n kernel = torch.ones((3, 3), dtype=dtype, device=img.device)\n kernel[1, 1] = 5.0\n kernel /= kernel.sum()\n kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1])\n\n result_tmp, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype, ])\n result_tmp = conv2d(result_tmp, kernel, groups=result_tmp.shape[-3])\n result_tmp = _cast_squeeze_out(result_tmp, need_cast, need_squeeze, out_dtype)\n\n result = img.clone()\n result[..., 1:-1, 1:-1] = result_tmp\n\n return result\n\n\ndef adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:\n if sharpness_factor < 0:\n raise ValueError('sharpness_factor ({}) is not non-negative.'.format(sharpness_factor))\n\n _assert_image_tensor(img)\n\n _assert_channels(img, [1, 3])\n\n if img.size(-1) <= 2 or img.size(-2) <= 2:\n return img\n\n return _blend(img, _blurred_degenerate_image(img), sharpness_factor)\n\n\ndef autocontrast(img: Tensor) -> Tensor:\n\n _assert_image_tensor(img)\n\n if img.ndim < 3:\n raise TypeError(\"Input image tensor should have at least 3 dimensions, but found {}\".format(img.ndim))\n\n _assert_channels(img, [1, 3])\n\n bound = 1.0 if img.is_floating_point() else 255.0\n dtype = img.dtype if torch.is_floating_point(img) else torch.float32\n\n minimum = img.amin(dim=(-2, -1), keepdim=True).to(dtype)\n maximum = img.amax(dim=(-2, -1), keepdim=True).to(dtype)\n eq_idxs = torch.where(minimum == maximum)[0]\n minimum[eq_idxs] = 0\n maximum[eq_idxs] = bound\n scale = bound / (maximum - minimum)\n\n return ((img - minimum) * scale).clamp(0, bound).to(img.dtype)\n\n\ndef _scale_channel(img_chan):\n hist = torch.histc(img_chan.to(torch.float32), bins=256, min=0, max=255)\n\n nonzero_hist = hist[hist != 0]\n step = nonzero_hist[:-1].sum() // 255\n if step == 0:\n return img_chan\n\n lut = (torch.cumsum(hist, 0) + (step // 2)) // step\n lut = torch.nn.functional.pad(lut, [1, 0])[:-1].clamp(0, 255)\n\n return lut[img_chan.to(torch.int64)].to(torch.uint8)\n\n\ndef _equalize_single_image(img: Tensor) -> Tensor:\n return torch.stack([_scale_channel(img[c]) for c in range(img.size(0))])\n\n\ndef equalize(img: Tensor) -> Tensor:\n\n _assert_image_tensor(img)\n\n if not (3 <= img.ndim <= 4):\n raise TypeError(\"Input image tensor should have 3 or 4 dimensions, but found {}\".format(img.ndim))\n if img.dtype != torch.uint8:\n raise TypeError(\"Only torch.uint8 image tensors are supported, but found {}\".format(img.dtype))\n\n _assert_channels(img, [1, 3])\n\n if img.ndim == 3:\n return _equalize_single_image(img)\n\n return torch.stack([_equalize_single_image(x) for x in img])\n", "path": "torchvision/transforms/functional_tensor.py" } ]
diff --git a/torchvision/transforms/functional_tensor.py b/torchvision/transforms/functional_tensor.py index 69445e6a231..d20d24a8413 100644 --- a/torchvision/transforms/functional_tensor.py +++ b/torchvision/transforms/functional_tensor.py @@ -227,7 +227,6 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor: result = (gain * result ** gamma).clamp(0, 1) result = convert_image_dtype(result, dtype) - result = result.to(dtype) return result