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9 | 0 | 1 | 8 | scripts-dev/check_pydantic_models.py | 249,383 | Reject non-strict types in Pydantic models (#13502) | synapse | 10 | Python | 9 | check_pydantic_models.py | def test_annotation_without_strict_raises(self) -> None:
with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
run_test_snippet(
)
| ba8938b090c7e1908cfa4feac75f08f3bc1183e8 | 23 | https://github.com/matrix-org/synapse.git | 53 | def test_annotation_without_strict_raises(self) -> None:
with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException):
run_test_snippet(
| 6 | 43 | test_annotation_without_strict_raises |
|
62 | 0 | 1 | 15 | pandas/tests/series/indexing/test_setitem.py | 163,050 | TST: tests for setitem-like casting issues (#45154) | pandas | 11 | Python | 29 | test_setitem.py | def test_37477():
# fixed by GH#45121
orig = DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]})
expected = DataFrame({"A": [1, 2, 3], "B": [3, 1.2, 5]})
df = orig.copy()
df.at[1, "B"] = 1.2
tm.assert_frame_equal(df, expected)
df = orig.copy()
df.loc[1, "B"] = 1.2
tm.assert_frame_equal(df, expected)
df = orig.copy()
df.iat[1, 1] = 1.2
tm.assert_frame_equal(df, expected)
df = orig.copy()
df.iloc[1, 1] = 1.2
tm.assert_frame_equal(df, expected)
| d70b95bc0e17d18bbefee8ac8a07e4fa5f33513c | 166 | https://github.com/pandas-dev/pandas.git | 106 | def test_37477():
# fixed by GH#45121
orig = DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]})
expe | 12 | 243 | test_37477 |
|
36 | 0 | 1 | 11 | tests/util/test_treecache.py | 250,034 | Add missing types to tests.util. (#14597)
Removes files under tests.util from the ignored by list, then
fully types all tests/util/*.py files. | synapse | 11 | Python | 25 | test_treecache.py | def test_pop_twolevel(self) -> None:
cache = TreeCache()
cache[("a", "a")] = "AA"
cache[("a", "b")] = "AB"
cache[("b", "a")] = "BA"
self.assertEqual(cache.pop(("a", "a")), "AA")
self.assertEqual(cache.get(("a", "a")), None)
self.assertEqual(cache.get(("a", "b")), "AB")
self.assertEqual(cache.pop(("b", "a")), "BA")
self.assertEqual(cache.pop(("b", "a")), None)
self.assertEqual(len(cache), 1)
| acea4d7a2ff61b5beda420b54a8451088060a8cd | 138 | https://github.com/matrix-org/synapse.git | 105 | def test_pop_twolevel(self) -> None:
cache = TreeCache()
cache[("a", "a")] = "AA"
cache[("a", "b")] = "AB"
cache[("b", "a")] = "BA"
self.assertEqual(cache.pop(("a", "a")), "AA")
self.assertEqual(cache.get(("a", "a")), None)
| 8 | 249 | test_pop_twolevel |
|
49 | 0 | 1 | 20 | tests/test_event_auth.py | 248,552 | EventAuthTestCase: build events for the right room version
In practice, when we run the auth rules, all of the events have the right room
version. Let's stop building Room V1 events for these tests and use the right
version. | synapse | 10 | Python | 30 | test_event_auth.py | def test_random_users_cannot_send_state_before_first_pl(self):
creator = "@creator:example.com"
joiner = "@joiner:example.com"
auth_events = [
_create_event(RoomVersions.V1, creator),
_join_event(RoomVersions.V1, creator),
_join_event(RoomVersions.V1, joiner),
]
# creator should be able to send state
event_auth.check_auth_rules_for_event(
RoomVersions.V1,
_random_state_event(RoomVersions.V1, creator),
auth_events,
)
# joiner should not be able to send state
self.assertRaises(
AuthError,
event_auth.check_auth_rules_for_event,
RoomVersions.V1,
_random_state_event(RoomVersions.V1, joiner),
auth_events,
)
| 2959184a42398277ff916206235b844a8f7be5d7 | 89 | https://github.com/matrix-org/synapse.git | 247 | def test_random_users_cannot_send_state_before_first_pl(self):
creator = "@creator:example.com"
joiner = "@joiner:example.com"
auth_events = [
_create_event(RoomVersions.V1, creator),
_join_event(RoomVersions.V1, creator),
| 14 | 135 | test_random_users_cannot_send_state_before_first_pl |
|
33 | 0 | 2 | 8 | scapy/contrib/automotive/scanner/enumerator.py | 209,880 | Improve reduce function for Automotive Scanner Enumerators (#3740) | scapy | 11 | Python | 28 | enumerator.py | def _get_retry_iterator(self, state):
# type: (EcuState) -> Iterable[Packet]
retry_entry = self._retry_pkt[state]
if isinstance(retry_entry, Packet):
log_automotive.debug("Provide retry packet")
return [retry_entry]
else:
log_automotive.debug("Provide retry iterator")
# assume self.retry_pkt is a generator or list
return retry_entry
| 799f272bc04c361841d01e9c0087950e0eb86610 | 43 | https://github.com/secdev/scapy.git | 115 | def _get_retry_iterator(self, state):
# type: (EcuState) -> Iterable[Packet]
retry_entry = self._retry_pkt[state]
if isinstance(retry_entry, Packet):
log_automotive.debug("Provide retry packet")
return [retry_entry]
else:
log_automotive.debug("Provide retry iterator")
# assume self.retry_pkt is a gene | 9 | 74 | _get_retry_iterator |
|
152 | 0 | 10 | 37 | src/transformers/trainer.py | 35,175 | fix bug for the log of RNG states are not properly loaded exception. (#15638)
Co-authored-by: muz <[email protected]> | transformers | 18 | Python | 92 | trainer.py | def _load_rng_state(self, checkpoint):
# Load RNG states from `checkpoint`
if checkpoint is None:
return
local_rank = xm.get_local_ordinal() if is_torch_tpu_available() else self.args.local_rank
if local_rank != -1:
rng_file = os.path.join(checkpoint, f"rng_state_{local_rank}.pth")
if not os.path.isfile(os.path.join(checkpoint, rng_file)):
logger.info(
f"Didn't find an RNG file for process {local_rank}, if you are resuming a training that "
"wasn't launched in a distributed fashion, reproducibility is not guaranteed."
)
return
else:
rng_file = os.path.join(checkpoint, "rng_state.pth")
if not os.path.isfile(rng_file):
logger.info(
"Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
"fashion, reproducibility is not guaranteed."
)
return
checkpoint_rng_state = torch.load(rng_file)
random.setstate(checkpoint_rng_state["python"])
np.random.set_state(checkpoint_rng_state["numpy"])
torch.random.set_rng_state(checkpoint_rng_state["cpu"])
if torch.cuda.is_available():
if self.args.local_rank != -1:
torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"])
else:
try:
torch.cuda.random.set_rng_state_all(checkpoint_rng_state["cuda"])
except Exception as e:
logger.info(
f"Didn't manage to set back the RNG states of the GPU because of the following error:\n {e}"
"\nThis won't yield the same results as if the training had not been interrupted."
)
if is_torch_tpu_available():
xm.set_rng_state(checkpoint_rng_state["xla"])
| e314c19a3ff52b39f33453ab6c7f7b3c6c12413e | 226 | https://github.com/huggingface/transformers.git | 630 | def _load_rng_state(self, checkpoint):
# Load RNG states from `checkpoint`
if checkpoint is None:
return
local_rank = xm.get_local_ordinal() if is_torch_tpu_available() else self.args.local_rank
if local_rank != -1:
rng_file = os.path.join(checkpoint, f"rng_state_{local_rank}.pth")
if not os.path.isfile(os.path.join(checkpoint, rng_file)):
logger.info(
f"Didn't find an RNG file for process {local_rank}, if you are resuming a training that "
"wasn't launched in a distributed fashion, reproducibility is not guaranteed."
)
return
else:
rng_file = os.path.join(checkpoint, "rng_state.pth")
if not os.path.isfile(rng_file):
logger.info(
"Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
"fashion, reproducibility is not guaranteed."
)
return
checkpoint_rng_state = torch.load(rng_file)
random.setstate(checkpoint_rng_state["python"])
np.random.set_state(checkpoint_rng_state["numpy"])
torch.random.set_rng_state(checkpoint_rng_state["cpu"])
if torch.cuda.is_available():
if self.args.local_rank != -1:
torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"])
else:
| 28 | 402 | _load_rng_state |
|
42 | 0 | 3 | 14 | wagtail/images/__init__.py | 75,022 | Reformat with black | wagtail | 12 | Python | 36 | __init__.py | def get_image_model():
from django.apps import apps
model_string = get_image_model_string()
try:
return apps.get_model(model_string, require_ready=False)
except ValueError:
raise ImproperlyConfigured(
"WAGTAILIMAGES_IMAGE_MODEL must be of the form 'app_label.model_name'"
)
except LookupError:
raise ImproperlyConfigured(
"WAGTAILIMAGES_IMAGE_MODEL refers to model '%s' that has not been installed"
% model_string
)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 47 | https://github.com/wagtail/wagtail.git | 128 | def get_image_model():
from django.apps import apps
model_string = get_image_model_string()
try:
return apps.get_model(model_string, require_ready=False)
except ValueError:
raise ImproperlyConfigured(
"WAGTAILIMAGES_IMAGE_MODEL must be of the form 'app_label.model_name'"
)
except LookupError:
raise I | 10 | 83 | get_image_model |
|
25 | 0 | 2 | 5 | test/lib/ansible_test/_internal/config.py | 266,768 | ansible-test - Code cleanup and refactoring. (#77169)
* Remove unnecessary PyCharm ignores.
* Ignore intentional undefined attribute usage.
* Add missing type hints. Fix existing type hints.
* Fix docstrings and comments.
* Use function to register completion handler.
* Pass strings to display functions.
* Fix CompositeAction handling of dest argument.
* Use consistent types in expressions/assignments.
* Use custom function to keep linters happy.
* Add missing raise for custom exception.
* Clean up key/value type handling in cloud plugins.
* Use dataclass instead of dict for results.
* Add custom type_guard function to check lists.
* Ignore return type that can't be checked (yet).
* Avoid changing types on local variables. | ansible | 10 | Python | 25 | config.py | def only_targets(self, target_type): # type: (t.Type[THostConfig]) -> t.List[THostConfig]
if not self.targets:
raise Exception('There must be one or more targets.')
assert type_guard(self.targets, target_type)
return t.cast(t.List[THostConfig], self.targets)
| a06fa496d3f837cca3c437ab6e9858525633d147 | 44 | https://github.com/ansible/ansible.git | 65 | def only_targets(self, target_type): # type: (t.Type[THostConfig]) -> t.List[THostConfig]
if not self.targets:
raise Exception('There must be | 10 | 72 | only_targets |
|
13 | 0 | 2 | 8 | test/prototype_transforms_kernel_infos.py | 193,916 | [prototype] Switch to `spatial_size` (#6736)
* Change `image_size` to `spatial_size`
* Fix linter
* Fixing more tests.
* Adding get_num_channels_video and get_spatial_size_* kernels for video, masks and bboxes.
* Refactor get_spatial_size
* Reduce the usage of `query_chw` where possible
* Rename `query_chw` to `query_spatial_size`
* Adding `get_num_frames` dispatcher and kernel.
* Adding jit-scriptability tests | vision | 12 | Python | 13 | prototype_transforms_kernel_infos.py | def sample_inputs_rotate_bounding_box():
for bounding_box_loader in make_bounding_box_loaders():
yield ArgsKwargs(
bounding_box_loader,
format=bounding_box_loader.format,
spatial_size=bounding_box_loader.spatial_size,
angle=_ROTATE_ANGLES[0],
)
| 4d4711d970f5cbd0a9e1adb465dca2703c8efbfd | 36 | https://github.com/pytorch/vision.git | 73 | def sample_inputs_rotate_bounding_box():
for bounding_box_loader in make_bounding_box_loaders():
yield ArgsKwargs(
bounding_box_loader,
| 8 | 54 | sample_inputs_rotate_bounding_box |
|
138 | 0 | 1 | 54 | test/test_outputs.py | 179,364 | Format The Codebase
- black formatting
- isort formatting | gradio | 15 | Python | 62 | test_outputs.py | def test_as_component(self):
y = "happy"
label_output = gr.outputs.Label()
label = label_output.postprocess(y)
self.assertDictEqual(label, {"label": "happy"})
self.assertEqual(label_output.deserialize(y), y)
self.assertEqual(label_output.deserialize(label), y)
with tempfile.TemporaryDirectory() as tmpdir:
to_save = label_output.save_flagged(tmpdir, "label_output", label, None)
self.assertEqual(to_save, y)
y = {3: 0.7, 1: 0.2, 0: 0.1}
label_output = gr.outputs.Label()
label = label_output.postprocess(y)
self.assertDictEqual(
label,
{
"label": 3,
"confidences": [
{"label": 3, "confidence": 0.7},
{"label": 1, "confidence": 0.2},
{"label": 0, "confidence": 0.1},
],
},
)
label_output = gr.outputs.Label(num_top_classes=2)
label = label_output.postprocess(y)
self.assertDictEqual(
label,
{
"label": 3,
"confidences": [
{"label": 3, "confidence": 0.7},
{"label": 1, "confidence": 0.2},
],
},
)
with self.assertRaises(ValueError):
label_output.postprocess([1, 2, 3])
with tempfile.TemporaryDirectory() as tmpdir:
to_save = label_output.save_flagged(tmpdir, "label_output", label, None)
self.assertEqual(to_save, '{"3": 0.7, "1": 0.2}')
self.assertEqual(
label_output.restore_flagged(tmpdir, to_save, None),
{
"label": "3",
"confidences": [
{"label": "3", "confidence": 0.7},
{"label": "1", "confidence": 0.2},
],
},
)
with self.assertRaises(ValueError):
label_output = gr.outputs.Label(type="unknown")
label_output.deserialize([1, 2, 3])
| cc0cff893f9d7d472788adc2510c123967b384fe | 385 | https://github.com/gradio-app/gradio.git | 772 | def test_as_component(self):
y = "happy"
label_output = gr.outputs.Label()
label = label_output.postprocess(y)
self.assertDictEqual(label, {"label": "happy"})
self.assertEqual(label_output.deserialize(y), y)
| 22 | 606 | test_as_component |
|
34 | 0 | 3 | 19 | tests/orion/api/test_work_queues.py | 58,319 | Add work queue backend | prefect | 27 | Python | 29 | test_work_queues.py | async def scheduled_flow_runs(self, session, deployment, work_queue, work_queue_2):
for i in range(3):
for wq in [work_queue, work_queue_2]:
await models.flow_runs.create_flow_run(
session=session,
flow_run=schemas.core.FlowRun(
flow_id=deployment.flow_id,
deployment_id=deployment.id,
work_queue_name=wq.name,
state=schemas.states.State(
type="SCHEDULED",
timestamp=pendulum.now("UTC").add(minutes=i),
state_details=dict(
scheduled_time=pendulum.now("UTC").add(minutes=i)
),
),
),
)
await session.commit()
| 2649fa325433aa219d6569ed77ef018f79480479 | 127 | https://github.com/PrefectHQ/prefect.git | 399 | async def scheduled_flow_runs(self, session, deployment, work_queue, work_queue_2):
for i in range(3):
for wq in [work_queue, work_queue_2]:
await models.flow_runs.create_flow_run(
session=session,
flow_run=schemas.core.FlowRun(
flow_id=deployment.flow_id,
deployment_id=deployment.id,
work_queue_name=wq.name,
state=schemas.states.State(
type="SCHEDULED",
timestamp=pendulum.now("UTC").add(minutes=i),
state_details=dict(
scheduled_time=pendulum.now("UTC").add(minutes=i)
),
),
),
| 34 | 193 | scheduled_flow_runs |
|
23 | 0 | 1 | 19 | pandas/tests/io/xml/test_to_xml.py | 164,112 | TST: Remove unused fixtures (#45692)
* TST: Remove unused fixtures
* Undo a removed fixture
* Add back other fixtures
* Undo a file
* Try undoing this?
* Revert "Try undoing this?"
This reverts commit 0e56cb04f5e8cb1f7b2ac4c5e6191485bb2fe1ab. | pandas | 12 | Python | 18 | test_to_xml.py | def test_attrs_cols_prefix(parser):
expected =
output = geom_df.to_xml(
attr_cols=["index", "shape", "degrees", "sides"],
namespaces={"doc": "http://example.xom"},
prefix="doc",
parser=parser,
)
output = equalize_decl(output)
assert output == expected
| f46df091df3afea25a273f491d1f6b2c7d20b32c | 53 | https://github.com/pandas-dev/pandas.git | 66 | def test_attrs_cols_prefix(parser):
expected =
output = geom_df.to_xml(
attr_cols=["index", "shape", "degrees", "sides"],
namespaces={"doc": "http://example.xom"},
prefix="doc",
parser=p | 10 | 97 | test_attrs_cols_prefix |
|
26 | 0 | 3 | 8 | bootloader/waflib/Tools/c_preproc.py | 263,295 | Bootloader: Building: Unpack waf's lib archive.
Doing so makes it easier to modify. This is a temporary measure until the next
waf version is released (although I'm tempted to keep it since it's much more
IDE completion friendly). | pyinstaller | 14 | Python | 19 | c_preproc.py | def filter_comments(self, node):
code = node.read()
if use_trigraphs:
for (a, b) in trig_def:
code = code.split(a).join(b)
code = re_nl.sub('', code)
code = re_cpp.sub(repl, code)
return re_lines.findall(code)
| 64ccb7aea824fbec57f7ed1bbe483ec486183c13 | 66 | https://github.com/pyinstaller/pyinstaller.git | 86 | def filter_comments(self, node):
c | 17 | 104 | filter_comments |
|
80 | 0 | 1 | 23 | tests/integration_tests/test_mlflow.py | 8,454 | Config Object (#2426)
* Fixed loss instances across features
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix gbm category
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* Fixed more tests
* Fixed incorrect text preproc default, added clip to category feature level
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fixes additional tests
* Cache jsonschema validator to reduce memory pressure
* Fix imports
* Skip neuropod test
* Added upgrade audio to default preproc back compat and cleaned up
* Small nits
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Change backfill constant for audio
* Add docstring to compute feature hash
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Unused import
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* Unused import
* remove default population functions
* Added config object test
* rewired build_inputs
* rewired combiner in ecd, added logic to config object
* Refactored ecd.py
* Fixing up merge_with_defaults, need metadata changes in master
* Refactored defaults section and mega upgraded config obj
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Travis Addair <[email protected]>
Co-authored-by: w4nderlust <[email protected]> | ludwig | 11 | Python | 63 | test_mlflow.py | def test_export_mlflow_local(tmpdir):
epochs = 2
batch_size = 8
num_examples = 32
input_features = [sequence_feature(reduce_output="sum")]
output_features = [category_feature(vocab_size=2, reduce_input="sum", output_feature=True)]
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "output_size": 14},
TRAINER: {"epochs": epochs, "batch_size": batch_size},
}
data_csv = generate_data(
input_features, output_features, os.path.join(tmpdir, "train.csv"), num_examples=num_examples
)
exp_name = "mlflow_test"
output_dir = os.path.join(tmpdir, "output")
model = LudwigModel(config, backend=FakeRemoteBackend())
_, _, output_directory = model.train(training_set=data_csv, experiment_name=exp_name, output_directory=output_dir)
model_path = os.path.join(output_directory, "model")
output_path = os.path.join(tmpdir, "data/results/mlflow")
export_mlflow(model_path, output_path)
assert set(os.listdir(output_path)) == {"MLmodel", "model", "conda.yaml"}
| 4d2d81f9fdefc52eea6a9bf0826a6f2ffc8d681b | 198 | https://github.com/ludwig-ai/ludwig.git | 165 | def test_export_mlflow_local(tmpdir):
epochs = 2
batch_size = 8
num_examples = 32
input_features = [sequence_feature(reduce_output="sum")]
output_features = [category_feature(vocab_size=2, reduce_input="sum", output_feature=True)]
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "output_size": 14},
TRAINER: {"epochs": epochs, "batch_size": batch_size},
}
data_csv = generate_data(
| 36 | 327 | test_export_mlflow_local |
|
169 | 0 | 20 | 39 | rest_framework/utils/encoders.py | 48,674 | Refactor: Replace try/except with contextlib.suppress() (#8676) | django-rest-framework | 15 | Python | 110 | encoders.py | def default(self, obj):
# For Date Time string spec, see ECMA 262
# https://ecma-international.org/ecma-262/5.1/#sec-15.9.1.15
if isinstance(obj, Promise):
return force_str(obj)
elif isinstance(obj, datetime.datetime):
representation = obj.isoformat()
if representation.endswith('+00:00'):
representation = representation[:-6] + 'Z'
return representation
elif isinstance(obj, datetime.date):
return obj.isoformat()
elif isinstance(obj, datetime.time):
if timezone and timezone.is_aware(obj):
raise ValueError("JSON can't represent timezone-aware times.")
representation = obj.isoformat()
return representation
elif isinstance(obj, datetime.timedelta):
return str(obj.total_seconds())
elif isinstance(obj, decimal.Decimal):
# Serializers will coerce decimals to strings by default.
return float(obj)
elif isinstance(obj, uuid.UUID):
return str(obj)
elif isinstance(obj, QuerySet):
return tuple(obj)
elif isinstance(obj, bytes):
# Best-effort for binary blobs. See #4187.
return obj.decode()
elif hasattr(obj, 'tolist'):
# Numpy arrays and array scalars.
return obj.tolist()
elif (coreapi is not None) and isinstance(obj, (coreapi.Document, coreapi.Error)):
raise RuntimeError(
'Cannot return a coreapi object from a JSON view. '
'You should be using a schema renderer instead for this view.'
)
elif hasattr(obj, '__getitem__'):
cls = (list if isinstance(obj, (list, tuple)) else dict)
with contextlib.suppress(Exception):
return cls(obj)
elif hasattr(obj, '__iter__'):
return tuple(item for item in obj)
return super().default(obj)
| c10f2266222c434485889b08cc1463acdb8fa169 | 291 | https://github.com/encode/django-rest-framework.git | 597 | def default(self, obj):
# For Date Time string spec, see ECMA 262
# https://ecma-international.org/ecma-262/5.1/#sec-15.9.1.15
if isinstance(obj, Promise):
return force_str(obj)
elif isinstance(obj, datetime.datetime):
representation = obj.isoformat()
if representation.endswith('+00:00'):
representation = representation[:-6] + 'Z'
return representation
elif isinstance(obj, datetime.date):
return obj.isoformat()
elif isinstance(obj, datetime.time):
if timezone and timezone.is_aware(obj):
raise ValueError("JSON can't represent timezone-aware times.")
representation = obj.isoformat()
return represen | 41 | 473 | default |
|
18 | 0 | 2 | 7 | python/ray/serve/tests/test_healthcheck.py | 144,837 | [serve] Improve health check failure semantics (#22297) | ray | 11 | Python | 17 | test_healthcheck.py | def test_user_defined_method_fails(serve_instance):
Patient.deploy()
h = Patient.get_handle()
actor = ray.get(h.remote())
ray.get(h.set_should_fail.remote())
wait_for_condition(check_new_actor_started, handle=h, original_actors=actor)
ray.get([h.remote() for _ in range(100)])
| 610930ae6aeafb37be75851a8c1b9ff39d5f7d22 | 72 | https://github.com/ray-project/ray.git | 35 | def test_user_defined_method_fails(serve_instance):
Patient.deploy()
h = Patient.get_handle()
actor = ray.get(h.remote())
ray.get(h.set_should_fail.remote())
wait_fo | 17 | 117 | test_user_defined_method_fails |
|
37 | 0 | 2 | 12 | .venv/lib/python3.8/site-packages/pip/_internal/wheel_builder.py | 61,436 | upd; format | transferlearning | 11 | Python | 33 | wheel_builder.py | def _clean_one_legacy(req, global_options):
# type: (InstallRequirement, List[str]) -> bool
clean_args = make_setuptools_clean_args(
req.setup_py_path,
global_options=global_options,
)
logger.info('Running setup.py clean for %s', req.name)
try:
call_subprocess(clean_args, cwd=req.source_dir)
return True
except Exception:
logger.error('Failed cleaning build dir for %s', req.name)
return False
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 59 | https://github.com/jindongwang/transferlearning.git | 96 | def _clean_one_legacy(req, global_options):
# type: (InstallRequirement, List[str]) -> bool
clean_args = make_setuptools_clean_args(
req.setup_py_path,
global_options=global_options,
)
lo | 14 | 95 | _clean_one_legacy |
|
44 | 1 | 1 | 7 | python/ray/train/tests/test_huggingface_gpu.py | 137,364 | [Train] `HuggingFacePredictor` & docs improvements (#30945)
This PR introduces 2 changes:
Removes a confusing suggestion in the docstring of HuggingFaceCheckpoint - checkpoints created using from_checkpoint will not work for prediction as intended.
Adds use_gpu argument and logic to automatically use GPU if one is available to HuggingFacePredictor.
Signed-off-by: Antoni Baum <[email protected]> | ray | 11 | Python | 39 | test_huggingface_gpu.py | def create_checkpoint():
with tempfile.TemporaryDirectory() as tmpdir:
model_config = AutoConfig.from_pretrained(model_checkpoint)
model = AutoModelForCausalLM.from_config(model_config)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint)
checkpoint = HuggingFaceCheckpoint.from_model(model, tokenizer, path=tmpdir)
# Serialize to dict so we can remove the temporary directory
return HuggingFaceCheckpoint.from_dict(checkpoint.to_dict())
# TODO(ml-team): Add np.ndarray to batch_type
@pytest.mark.parametrize("batch_type", [pd.DataFrame])
@pytest.mark.parametrize("device", [None, 0]) | 81237e05838757dde196688a20631daad48010dd | @pytest.mark.parametrize("batch_type", [pd.DataFrame])
@pytest.mark.parametrize("device", [None, 0]) | 62 | https://github.com/ray-project/ray.git | 85 | def create_checkpoint():
with tempfile.TemporaryDirectory() as tmpdir:
model_config = AutoConfig.from_pretrained(model_checkpoint)
model = AutoModelForCausalLM.from_c | 25 | 153 | create_checkpoint |
33 | 0 | 1 | 6 | tests/sentry/search/events/test_builder.py | 91,455 | ref: replace self.assertRaises with pytest.raises (#35685)
* add flake8 plugin to detect assertRaises
* ref: replace self.assertRaises with pytest.raises
* non-sed fixes | sentry | 10 | Python | 28 | test_builder.py | def test_limit_validation(self):
# 51 is ok
MetricsQueryBuilder(self.params, limit=51)
# None is ok, defaults to 50
query = MetricsQueryBuilder(self.params)
assert query.limit.limit == 50
# anything higher should throw an error
with pytest.raises(IncompatibleMetricsQuery):
MetricsQueryBuilder(self.params, limit=10_000)
| 284e980df0018f8baee659999268bdd4c7d08255 | 49 | https://github.com/getsentry/sentry.git | 92 | def test_limit_validation(self):
# 51 is ok
MetricsQueryBuilder(self.params, limit=51)
# None is ok, defaults to 50
query = MetricsQueryBuilder(self.params)
assert query.limit.limit == 50
# anything higher should throw an error
with pytest.raises(IncompatibleMe | 9 | 82 | test_limit_validation |
|
20 | 0 | 1 | 10 | tests/infrastructure/test_docker_container.py | 58,882 | Update default infrastructure command to be set at runtime
Add commands to Docker container tests with no command | prefect | 12 | Python | 19 | test_docker_container.py | def test_adds_docker_host_gateway_on_linux(mock_docker_client, monkeypatch):
monkeypatch.setattr("sys.platform", "linux")
DockerContainer(
command=["echo", "hello"],
).run()
mock_docker_client.containers.create.assert_called_once()
call_extra_hosts = mock_docker_client.containers.create.call_args[1].get(
"extra_hosts"
)
assert call_extra_hosts == {"host.docker.internal": "host-gateway"}
| c02383e4a879c95586cfbc19787904da2d4be22b | 64 | https://github.com/PrefectHQ/prefect.git | 54 | def test_adds_docker_host_gateway_on_linux(mock_docker_client, monkeypatch):
monkeypatch.setattr("sys.platform", "linux")
DockerContainer(
command=["echo", "hello"],
).run()
mock_docker_client.containers.create.assert_called_once()
call_extra_hosts = mock_docker_client.containers.create.call_args[1].get(
"extra_host | 13 | 113 | test_adds_docker_host_gateway_on_linux |
|
21 | 0 | 1 | 10 | modin/pandas/indexing.py | 155,050 | FIX-#3764: Ensure df.loc with a scalar out of bounds appends to df (#3765)
Co-authored-by: Devin Petersohn <[email protected]>
Co-authored-by: Bill Wang <[email protected]>
Co-authored-by: Vasily Litvinov <[email protected]> | modin | 12 | Python | 18 | indexing.py | def _set_item_existing_loc(self, row_loc, col_loc, item):
row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc)
self._setitem_positional(
row_lookup,
col_lookup,
item,
axis=self._determine_setitem_axis(
row_lookup, col_lookup, is_scalar(row_loc), is_scalar(col_loc)
),
)
| 11ba4811e6db11740e11fd33d3cdfba8ce5bec54 | 56 | https://github.com/modin-project/modin.git | 119 | def _set_item_existing_loc(self, row_loc, col_loc, item):
row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc)
self._setitem_positional(
row_lookup,
| 12 | 81 | _set_item_existing_loc |
|
9 | 0 | 2 | 5 | mindsdb/integrations/mysql_handler/mysql_handler/mysql_handler.py | 114,412 | test: move testing logic into unittest modules; CI still pending | mindsdb | 10 | Python | 8 | mysql_handler.py | def check_status(self):
try:
return self.connection.is_connected()
except Exception:
return False
| 76a30708e24bca37169df44d8b31573c7b5beb43 | 20 | https://github.com/mindsdb/mindsdb.git | 44 | def check_status(self):
try:
return self.connection.is_connected()
except Exception:
return Fals | 5 | 34 | check_status |
|
66 | 1 | 5 | 29 | wagtail/admin/views/pages/moderation.py | 72,515 | Reformat with black | wagtail | 19 | Python | 52 | moderation.py | def reject_moderation(request, revision_id):
revision = get_object_or_404(PageRevision, id=revision_id)
if not revision.page.permissions_for_user(request.user).can_publish():
raise PermissionDenied
if not revision.submitted_for_moderation:
messages.error(
request,
_("The page '{0}' is not currently awaiting moderation.").format(
revision.page.specific_deferred.get_admin_display_title()
),
)
return redirect("wagtailadmin_home")
if request.method == "POST":
revision.reject_moderation(user=request.user)
messages.success(
request,
_("Page '{0}' rejected for publication.").format(
revision.page.specific_deferred.get_admin_display_title()
),
buttons=[
messages.button(
reverse("wagtailadmin_pages:edit", args=(revision.page.id,)),
_("Edit"),
)
],
)
if not send_moderation_notification(revision, "rejected", request.user):
messages.error(request, _("Failed to send rejection notifications"))
return redirect("wagtailadmin_home")
@require_GET | d10f15e55806c6944827d801cd9c2d53f5da4186 | @require_GET | 174 | https://github.com/wagtail/wagtail.git | 332 | def reject_moderation(request, revision_id):
revision = get_object_or_404(PageRevision, id=revision_id)
if not revision.page.permissions_for_user(request.user).can_publish():
raise PermissionDenied
if not revision.submitted_for_moderation:
messages.error(
request,
_("The page '{0}' is not currently awaiting moderation.").format(
revision.page.specific_deferred.get_admin_display_title()
),
)
return redirect("wagtailadmin_home")
if request.method == "POST":
revision.reject_moderation(user=request.user)
messages.success(
request,
_("Page '{0}' rejected for publication.").format(
revision.page.specific_deferred.get_admin_display_title()
),
buttons=[
messages.button(
reverse("wagtailadmin_pages:edit", args=(revision.page.id,)),
_("Edit"),
)
],
)
if not send_moderation_notification(revision, "rejected", request.user):
messages.error(request, _("Failed to send rejection notifications"))
return redirect("wagtailadmin_home") | 28 | 290 | reject_moderation |
40 | 0 | 1 | 19 | tests/components/calendar/test_trigger.py | 296,840 | Add initial implementation of a calendar trigger (#68674)
* Add initial implementation of calendar trigger
This is an initial implementation of a calendar trigger, that supports
triggering on calendar start time.
See architecture proposal in:
https://github.com/home-assistant/architecture/discussions/700
* Address reviewer feedback
* Use f-strings for all tests
* Apply suggestions from code review
Co-authored-by: Martin Hjelmare <[email protected]>
* Remove logging f-strings, and move to main code
* Remove mypy ignore
* Apply suggestions from code review
Co-authored-by: Martin Hjelmare <[email protected]>
* Update calendar triggers to use new calendar data model
* Update tests/components/calendar/test_trigger.py
Co-authored-by: Franck Nijhof <[email protected]>
* Rewrite tests using freezegun
Rewrite tests using freezegun and improve edge case handling, and use utc consistently for all alarms.
* Update homeassistant/components/calendar/trigger.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Update homeassistant/components/calendar/trigger.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Increase test coverage based on pr feedback
Co-authored-by: Martin Hjelmare <[email protected]>
Co-authored-by: Franck Nijhof <[email protected]> | core | 12 | Python | 36 | test_trigger.py | async def test_event_payload(hass, calls, fake_schedule):
event_data = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
description="Description",
location="Location",
)
await create_automation(hass, EVENT_START)
assert len(calls()) == 0
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data,
}
]
| a2c74b978664b627bafc4a43b26aa2be7b15b229 | 98 | https://github.com/home-assistant/core.git | 149 | async def test_event_payload(hass, calls, fake_schedule):
event_data = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
description="Description",
location="Location",
)
await create_automation(hass, EVENT_START)
assert len(calls()) == 0
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00")
)
assert calls() == [
{
"platform": | 16 | 169 | test_event_payload |
|
76 | 0 | 7 | 28 | packages/syft/src/syft/core/tensor/nn/model.py | 1,861 | update domain update script to add branch name during hagrid launch
add loss to parameter list in model publish
print loss during model training | PySyft | 15 | Python | 56 | model.py | def publish(self, deduct_epsilon_for_user, get_budget_for_user, ledger, sigma):
print("Publish Model Weights")
# relative
from ..autodp.gamma_tensor import GammaTensor
parameters = {}
for i, layer in enumerate(self.layers):
print("Layer", str(layer))
print("Before Publish")
for param in layer.params:
print(param.shape, end=" ")
print()
if hasattr(layer, "params"):
parameters[str(layer) + str(i)] = [
param.publish(
deduct_epsilon_for_user=deduct_epsilon_for_user,
get_budget_for_user=get_budget_for_user,
ledger=ledger,
sigma=sigma,
)
if isinstance(param, (GammaTensor))
else param
for param in layer.params
]
print("After Publish")
for param in parameters[str(layer) + str(i)]:
print(param.shape, end=" ")
print()
parameters["loss"] = self.aggregated_loss
return parameters
| b480217f5bc07d97a691bfed74eb7489667788dd | 178 | https://github.com/OpenMined/PySyft.git | 477 | def publish(self, deduct_epsilon_for_user, get_budget_for_user, ledger, sigma):
print("Publish Model Weights")
# relative
from ..autodp.gamma_tensor import GammaTensor
parameters = {}
for i, layer in enumerate(self.layers):
print("Layer", str(layer))
print("Before Publish")
for param in layer.params:
print(param.shape, end=" ")
print()
if hasattr(layer, "params"):
parameters[str(layer | 23 | 285 | publish |
|
98 | 0 | 1 | 39 | kubernetes_tests/test_kubernetes_pod_operator_backcompat.py | 47,749 | KubernetesPodOperator should patch "already checked" always (#22734)
When not configured to delete pods, at end of task execution the current behavior is to patch the pod as "already checked", but only if pod not successful. We should also patch when successful so it isn't "reattached" to after a task clear. | airflow | 14 | Python | 73 | test_kubernetes_pod_operator_backcompat.py | def test_volume_mount(self):
with patch.object(PodManager, 'log') as mock_logger:
volume_mount = VolumeMount(
'test-volume', mount_path='/tmp/test_volume', sub_path=None, read_only=False
)
volume_config = {'persistentVolumeClaim': {'claimName': 'test-volume'}}
volume = Volume(name='test-volume', configs=volume_config)
args = [
"echo \"retrieved from mount\" > /tmp/test_volume/test.txt "
"&& cat /tmp/test_volume/test.txt"
]
k = KubernetesPodOperator(
namespace='default',
image="ubuntu:16.04",
cmds=["bash", "-cx"],
arguments=args,
labels={"foo": "bar"},
volume_mounts=[volume_mount],
volumes=[volume],
is_delete_operator_pod=False,
name="test",
task_id="task",
in_cluster=False,
do_xcom_push=False,
)
context = create_context(k)
k.execute(context=context)
mock_logger.info.assert_any_call('retrieved from mount')
actual_pod = self.api_client.sanitize_for_serialization(k.pod)
expected_pod = copy(self.expected_pod)
expected_pod['spec']['containers'][0]['args'] = args
expected_pod['spec']['containers'][0]['volumeMounts'] = [
{'name': 'test-volume', 'mountPath': '/tmp/test_volume', 'readOnly': False}
]
expected_pod['spec']['volumes'] = [
{'name': 'test-volume', 'persistentVolumeClaim': {'claimName': 'test-volume'}}
]
expected_pod['metadata']['labels']['already_checked'] = 'True'
assert expected_pod == actual_pod
| c3d883a971a8e4e65ccc774891928daaaa0f4442 | 254 | https://github.com/apache/airflow.git | 579 | def test_volume_mount(self):
with patch.object(PodManager, 'log') as mock_logger:
volume_mount = VolumeMount(
'test-volume', mount_path='/tmp/test_volume', sub_path=None, read_only=False
)
volume_config = {'persistentVolumeClaim': {'claimName': 'test-volume'}}
volume = Volume(name='test-volume', configs=volume_config)
args = [
"echo \"retrieved from mount\" > /tmp/test_volume/test.txt "
"&& cat /tmp/test_volume/test.txt"
]
k = KubernetesPodOperator(
namespace='default',
image="ubuntu:16.04",
cmds=["bash", "-cx"],
arguments=args,
labels={"foo": "bar"},
volume_mounts=[volume_mount],
volumes=[volume],
is_delete_operator_pod=False,
name="test",
task_id="task",
in_cluster=False,
do_xcom_push=False,
)
context = create_context(k)
k.execute(context=context)
mock_logger.info.assert_any_call('retrieved from mount')
actual_pod = self.api_client.sanitize_for_serialization(k.pod)
expected_pod = copy(self.expected_pod)
expected_pod['spec']['containers'][0]['args'] = args
| 41 | 453 | test_volume_mount |
|
61 | 0 | 2 | 8 | d2l/mxnet.py | 253,762 | Refactor Multihead Attn, Self Attn, and Transformer (#2096)
* multihead attn
* self attn and pos encoding
* simplify
* before EncoderBlock
* before tmencoder
* before decoder block
* before training
* transformer code
* rm seq2seq encoder old
* fix bahdanau attn map
* transformer done, perf tuned
* clean super | d2l-en | 12 | Python | 47 | mxnet.py | def forward(self, X, valid_lens):
# Since positional encoding values are between -1 and 1, the embedding
# values are multiplied by the square root of the embedding dimension
# to rescale before they are summed up
X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))
self.attention_weights = [None] * len(self.blks)
for i, blk in enumerate(self.blks):
X = blk(X, valid_lens)
self.attention_weights[
i] = blk.attention.attention.attention_weights
return X
| f0be7e672bc0a7c77005d5c79452d796cfe1a06b | 81 | https://github.com/d2l-ai/d2l-en.git | 146 | def forward(self, X, valid_lens):
# Since positional encoding values are between -1 and 1, the embedding
# values are multiplied by the square root of the embedding dimension
# to rescale before they are summed up
X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))
self.attention_weights = [None] * len(self.blks)
for i, blk in enumerate(self.blks):
X = blk(X, valid_lens)
self.attention_weights[
i] = blk.attention.attention.attention_wei | 16 | 127 | forward |
|
27 | 0 | 1 | 9 | tests/unit/executor_test_base.py | 116,152 | executor base test | mindsdb | 10 | Python | 21 | executor_test_base.py | def clear_db(db):
# drop
db.Base.metadata.drop_all(db.engine)
# create
db.Base.metadata.create_all(db.engine)
# fill with data
r = db.Integration(name='files', data={}, engine='files')
db.session.add(r)
r = db.Integration(name='views', data={}, engine='views')
db.session.add(r)
db.session.commit()
return db
| d304fa61c43e5248c0cb111d5553db653be92cff | 92 | https://github.com/mindsdb/mindsdb.git | 103 | def clear_db(db):
# drop
db.Base.metadata.drop_all(db.engine)
# create
db.Base.metadata.create_all(db.engine)
# fill with data
| 14 | 155 | clear_db |
|
18 | 0 | 2 | 5 | mitmproxy/net/udp.py | 250,972 | [dns] rewrite of udp, merge dnsserver>proxyserver | mitmproxy | 10 | Python | 15 | udp.py | def resume_writing(self) -> None:
assert self._paused > 0
self._paused = self._paused - 1
if self._paused == 0:
self._can_write.set()
| ef3f9e492e8f1d197ddab24bf5f80a76d2fe566d | 36 | https://github.com/mitmproxy/mitmproxy.git | 49 | def resume_writing(self) -> None:
assert self._paused > 0
self._paused = self._paused - 1
if self._pa | 5 | 58 | resume_writing |
|
124 | 0 | 5 | 27 | configs/rotate/tools/slicebase.py | 211,333 | Refactor rbox (#6704)
* refactor rbox
* modify the code of save results
* fix some problem
* add .gitignore in dataset/dota
* fix test anno path | PaddleDetection | 19 | Python | 63 | slicebase.py | def get_poly4_from_poly5(self, poly):
distances = [
cal_line_length((poly[i * 2], poly[i * 2 + 1]),
(poly[(i + 1) * 2], poly[(i + 1) * 2 + 1]))
for i in range(int(len(poly) / 2 - 1))
]
distances.append(
cal_line_length((poly[0], poly[1]), (poly[8], poly[9])))
pos = np.array(distances).argsort()[0]
count = 0
out_poly = []
while count < 5:
if (count == pos):
out_poly.append(
(poly[count * 2] + poly[(count * 2 + 2) % 10]) / 2)
out_poly.append(
(poly[(count * 2 + 1) % 10] + poly[(count * 2 + 3) % 10]) /
2)
count = count + 1
elif (count == (pos + 1) % 5):
count = count + 1
continue
else:
out_poly.append(poly[count * 2])
out_poly.append(poly[count * 2 + 1])
count = count + 1
return out_poly
| e55e41945d42db787a0f7c557d53d06a6b24536b | 258 | https://github.com/PaddlePaddle/PaddleDetection.git | 449 | def get_poly4_from_poly5(self, poly):
distances = [
cal_line_length((poly[i * 2], poly[i * 2 + 1]),
(poly[(i + 1) * 2], poly[(i + 1) * 2 + 1]))
for i in range(int(len(poly) / 2 - 1))
]
distances.append(
cal_line_length((poly[0], poly[1]), (poly[8], poly[9])))
pos = np.array(distances).argsort()[0]
count = 0
out_poly = []
while count < 5:
if (count == pos):
out_poly.append(
(poly[count * 2] + poly[(count * 2 + 2) % 10]) / 2)
out_poly.append(
(poly[(count * 2 + 1) % 10] + poly[(count * 2 + 3) % 10]) /
2)
count = count + 1
elif (count == (pos + 1) % 5):
count = count + 1
continue
else | 16 | 389 | get_poly4_from_poly5 |
|
12 | 0 | 1 | 4 | sklearn/utils/tests/test_estimator_html_repr.py | 260,606 | FIX Show a HTML repr for meta-estimatosr with invalid parameters (#24015)
Co-authored-by: Jérémie du Boisberranger <[email protected]> | scikit-learn | 10 | Python | 10 | test_estimator_html_repr.py | def test_invalid_parameters_in_stacking():
stacker = StackingClassifier(estimators=[])
html_output = estimator_html_repr(stacker)
assert html.escape(str(stacker)) in html_output
| 84c6421a9067de7d1b54b7a6d8e21ce38e1f0eca | 32 | https://github.com/scikit-learn/scikit-learn.git | 24 | def test_invalid_parameters_in_stacking():
stacker = StackingClassifier(estimators=[])
html_output = estimator_html_rep | 9 | 56 | test_invalid_parameters_in_stacking |
|
205 | 0 | 3 | 68 | python/ccxt/coinex.py | 17,144 | 1.71.68
[ci skip] | ccxt | 18 | Python | 119 | coinex.py | def fetch_markets(self, params={}):
response = self.publicGetMarketInfo(params)
#
# {
# "code": 0,
# "data": {
# "WAVESBTC": {
# "name": "WAVESBTC",
# "min_amount": "1",
# "maker_fee_rate": "0.001",
# "taker_fee_rate": "0.001",
# "pricing_name": "BTC",
# "pricing_decimal": 8,
# "trading_name": "WAVES",
# "trading_decimal": 8
# }
# }
# }
#
markets = self.safe_value(response, 'data', {})
result = []
keys = list(markets.keys())
for i in range(0, len(keys)):
key = keys[i]
market = markets[key]
id = self.safe_string(market, 'name')
tradingName = self.safe_string(market, 'trading_name')
baseId = tradingName
quoteId = self.safe_string(market, 'pricing_name')
base = self.safe_currency_code(baseId)
quote = self.safe_currency_code(quoteId)
symbol = base + '/' + quote
if tradingName == id:
symbol = id
result.append({
'id': id,
'symbol': symbol,
'base': base,
'quote': quote,
'settle': None,
'baseId': baseId,
'quoteId': quoteId,
'settleId': None,
'type': 'spot',
'spot': True,
'margin': None,
'swap': False,
'future': False,
'option': False,
'active': None,
'contract': False,
'linear': None,
'inverse': None,
'taker': self.safe_number(market, 'taker_fee_rate'),
'maker': self.safe_number(market, 'maker_fee_rate'),
'contractSize': None,
'expiry': None,
'expiryDatetime': None,
'strike': None,
'optionType': None,
'precision': {
'price': self.safe_integer(market, 'pricing_decimal'),
'amount': self.safe_integer(market, 'trading_decimal'),
},
'limits': {
'leverage': {
'min': None,
'max': None,
},
'amount': {
'min': self.safe_number(market, 'min_amount'),
'max': None,
},
'price': {
'min': None,
'max': None,
},
'cost': {
'min': None,
'max': None,
},
},
'info': market,
})
return result
| c9b141d8b46d6bc771d9305e403440654bbe03b2 | 352 | https://github.com/ccxt/ccxt.git | 1,520 | def fetch_markets(self, params={}):
response = self.publicGetMarketInfo(params)
#
# {
# "code": 0,
# "data": {
# "WAVESBTC": {
# "name": "WAVESBTC",
# "min_amount": "1",
# "maker_fee_rate": "0.001",
# "taker_fee_rate": "0.001",
# "pricing_name": "BTC",
# "pricing_decimal": 8,
# "trading_name": "WAVES",
# "trading_decimal": 8
# }
# }
# }
#
markets = self.safe_value(response, 'data', {})
result = []
keys = list(markets.keys())
for i in range(0, len(keys)):
key = keys[i]
market = markets[key]
id = self.safe_string(market, 'name')
tradingName = self.safe_string(market, 'trading_name')
baseId = tradingName
quoteId = self.safe_string(market, 'pricing_name')
base = self.safe_currency_code(baseId)
quote = self.safe_currency_code(quoteId)
symbol = base + '/' + quote
if tradingName == id:
symbol = id
result.append({
'id': id,
'symbol': symbol,
| 27 | 619 | fetch_markets |
|
86 | 0 | 10 | 22 | tests/models/layoutlmv3/test_modeling_tf_layoutlmv3.py | 33,184 | [LayoutLMv3] Add TensorFlow implementation (#18678)
Co-authored-by: Esben Toke Christensen <[email protected]>
Co-authored-by: Lasse Reedtz <[email protected]>
Co-authored-by: amyeroberts <[email protected]>
Co-authored-by: Joao Gante <[email protected]> | transformers | 18 | Python | 55 | test_modeling_tf_layoutlmv3.py | def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
inputs_dict = copy.deepcopy(inputs_dict)
if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict = {
k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
if isinstance(v, tf.Tensor) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING):
inputs_dict["labels"] = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=tf.int32
)
return inputs_dict
| de8548ebf3242305d0f9792dacb6f86b196a3a33 | 250 | https://github.com/huggingface/transformers.git | 348 | def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
inputs_dict = copy.deepcopy(inputs_dict)
if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict = {
k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
if isinstance(v, tf.Tensor) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
inputs_dict["labels"] = tf.zeros(self | 30 | 383 | _prepare_for_class |
|
50 | 0 | 3 | 17 | tests/test_modeling_utils.py | 336,014 | [SDE] Merge to unconditional model (#89)
* up
* more
* uP
* make dummy test pass
* save intermediate
* p
* p
* finish
* finish
* finish | diffusers | 18 | Python | 36 | test_modeling_utils.py | def test_score_sde_ve_pipeline(self):
model = UNetUnconditionalModel.from_pretrained("fusing/ffhq_ncsnpp", sde=True)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
scheduler = ScoreSdeVeScheduler.from_config("fusing/ffhq_ncsnpp")
sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler)
torch.manual_seed(0)
image = sde_ve(num_inference_steps=2)
if model.device.type == "cpu":
expected_image_sum = 3384805632.0
expected_image_mean = 1076.000732421875
else:
expected_image_sum = 3382849024.0
expected_image_mean = 1075.3787841796875
assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2
assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4
| ba3c9a9a3a9cf76e4ff8292b66d7cc3206732627 | 165 | https://github.com/huggingface/diffusers.git | 181 | def test_score_sde_ve_pipeline(self):
model = UNetUncondi | 27 | 257 | test_score_sde_ve_pipeline |
|
33 | 0 | 2 | 15 | test/lib/ansible_test/_internal/host_profiles.py | 268,481 | Add `use_rsa_sha2_algorithms` option for paramiko (#78789)
Fixes #76737
Fixes #77673
Co-authored-by: Matt Clay <[email protected]> | ansible | 14 | Python | 29 | host_profiles.py | def get_inventory_variables(self) -> dict[str, t.Optional[t.Union[str, int]]]:
core_ci = self.wait_for_instance()
connection = core_ci.connection
variables: dict[str, t.Optional[t.Union[str, int]]] = dict(
ansible_connection=self.config.connection,
ansible_pipelining='yes',
ansible_host=connection.hostname,
ansible_port=connection.port,
ansible_user=connection.username,
ansible_ssh_private_key_file=core_ci.ssh_key.key,
ansible_paramiko_use_rsa_sha2_algorithms='no',
ansible_network_os=f'{self.config.collection}.{self.config.platform}' if self.config.collection else self.config.platform,
)
return variables
| 76b746655a36807fa9198064ca9fe7c6cc00083a | 122 | https://github.com/ansible/ansible.git | 163 | def get_inventory_variables(self) -> dict[str, t.Optional[t.Union[str, int]]]:
core_ci = self.wait_for_instance()
connection = core_ci.connection
variables: dict[str, t.Optional[t.Union[str, int]]] = dict(
ansible_connection=self.config.connection,
ansible_pipelining='yes',
ansible_host=connection.hostname,
ansible_port=connection.port,
ansible_user=connection.username,
ansible_ssh_private_key_file=core_ci.ssh_key.key,
ansible_paramiko_use_rsa_sha2_algorithms='no',
ansible_network_os | 28 | 199 | get_inventory_variables |
|
31 | 0 | 1 | 5 | rest_api/test/test_rest_api.py | 257,559 | API tests (#2738)
* clean up tests and run earlier
* use change detection
* better naming, skip ES
* more cleanup
* fix job name
* dummy commit to trigger the CI
* mock away the PDF converter
* make the test compatible with 3.7
* removed leftover
* always run the api tests, use a matrix for the OS
* refactor all the tests
* remove outdated dependency
* pylint
* new abstract method
* adjust for older python versions
* rename pipeline file
* address PR comments | haystack | 17 | Python | 28 | test_rest_api.py | def test_file_upload_with_wrong_meta(client):
file_to_upload = {"files": (Path(__file__).parent / "samples" / "pdf" / "sample_pdf_1.pdf").open("rb")}
response = client.post(url="/file-upload", files=file_to_upload, data={"meta": "1"})
assert 500 == response.status_code
# Ensure the `convert` method was never called
MockPDFToTextConverter.mocker.convert.assert_not_called()
| 82df677ebf853340d331ff0868304cc958307ee0 | 67 | https://github.com/deepset-ai/haystack.git | 45 | def test_file_upload_with_wrong_meta(client):
file_to_upload = {"files": (Path(__file__).parent / "samples" / "pdf" / "sample_pdf_1.pdf").open("rb")}
response = client.post(url="/file-upload", files=file_to_upload, data={"meta": "1"})
assert 500 == response.status_code
# E | 17 | 121 | test_file_upload_with_wrong_meta |
|
8 | 0 | 1 | 3 | homeassistant/components/skybell/sensor.py | 288,657 | Add strict typing to Skybell (#79800) | core | 8 | Python | 8 | sensor.py | def native_value(self) -> StateType | datetime:
return self.entity_description.value_fn(self._device)
| 9850709b37fdfa704ac3db4c45a2660880a7ca65 | 21 | https://github.com/home-assistant/core.git | 22 | def native_value(self) -> StateType | datetime:
| 7 | 36 | native_value |
|
51 | 0 | 5 | 13 | label_studio/projects/models.py | 178,057 | fix: DEV-3164: Remove potential data exposure from logs (#2828)
* Remove potential data exposure from logs
* Bump converter & tools pip versions
Co-authored-by: nik <[email protected]> | label-studio | 13 | Python | 36 | models.py | def _get_annotation_key(self, result):
result_type = result.get('type', None)
if result_type in ('relation', 'pairwise', None):
return None
if 'from_name' not in result or 'to_name' not in result:
logger.error(
'Unexpected annotation.result format: "from_name" or "to_name" not found',
extra={'sentry_skip': True},
)
return None
result_from_name = result['from_name']
key = get_annotation_tuple(result_from_name, result['to_name'], result_type or '')
return key
| 5a0415ea99e3ef95bdbb2d6b62577c0c868b9540 | 81 | https://github.com/heartexlabs/label-studio.git | 166 | def _get_annotation_key(self, result):
result_type = result.get('type', None)
if result_type in ('relation', 'pairwise', None):
return None
if 'from_name' not in result or 'to_name' not in result:
logger.error(
'Unexpected annotation.result format: "from_name" or "to_name" not found',
extra={'sentry_skip': True},
)
return None
result_from_name = result['from_name']
key = get_annotation_ | 11 | 138 | _get_annotation_key |
|
33 | 0 | 1 | 9 | tests/snuba/api/endpoints/test_organization_events.py | 94,829 | fix(tests): Fix dnd backend test flakes (#37916)
This PR fixes 3 major flakes:
Fixes SENTRY-TESTS-3J5: Just sort the project id order
Fixes SENTRY-TESTS-3HQ: Flakes because we calculate the retention
in the test once and the value returned in the response is calculated
a little while after. We don't need to test for seconds granularity
so replacing seconds to 0.
Fixes SENTRY-TESTS-3J0: Successively calling before_now results in some flakes
particularly in tests that are calculating aggregates
on transaction.duration. Introduced a load_data method
that takes a datetime object timestamp and a timedelta duration
calculates the offset based on timestamp to get start_timestamp. | sentry | 10 | Python | 26 | test_organization_events.py | def test_stack_wildcard_condition(self):
data = self.load_data(platform="javascript")
data["timestamp"] = self.ten_mins_ago
self.store_event(data=data, project_id=self.project.id)
query = {"field": ["stack.filename", "message"], "query": "stack.filename:*.js"}
response = self.do_request(query)
assert response.status_code == 200, response.content
assert len(response.data["data"]) == 1
assert response.data["meta"]["fields"]["message"] == "string"
| ab993b32614bb83d17d10e1041817e43dd6f5980 | 99 | https://github.com/getsentry/sentry.git | 88 | def test_stack_wildcard_condition(self):
data = self.load_data(platform="javascript")
data["timestamp"] = self.ten_mins_ago
self.store_event(data=data, project_id=self.project.id)
query = {"field": ["stack.filename", "message"], "query": "stack.filename:*.js"}
response = self.do_request(query)
assert response.status_code == 200, response.content
assert len(response.data["data"]) == 1
assert respons | 16 | 172 | test_stack_wildcard_condition |
|
112 | 1 | 1 | 32 | tests/integration_tests/test_hyperopt_ray_horovod.py | 6,877 | Fix ray hyperopt (#1999)
* WIP fix ray hyperopt
* Fixed kwargs
* Updated the nones
* Placement groups
* Updated test cpus
* Test with dynamic resource allocation
* Using 0 CPUs for evaluation and using dask annotate
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Updates to ray backend and hyperopt execution
* Added dask global config
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Uncommented tests
* Disabled async hyperband tests
* Responded to comments
* Fixed all hyperopt horovod tests to use 10 CPUs
* Moved dask config setting to ray backend
* Calculate stats for distributed datasets (#2016)
* Fixed tests, responded to comments
* Responded to comments
* Updated horovod hyperopt tests to be consistent with the hyperopt refactor, added a df_engine attribute to RayPredictor
* Added parentheses on pandas
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
Co-authored-by: Travis Addair <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> | ludwig | 13 | Python | 81 | test_hyperopt_ray_horovod.py | def test_hyperopt_run_hyperopt(csv_filename, ray_mock_dir):
input_features = [number_feature(), number_feature()]
output_features = [binary_feature()]
csv_filename = os.path.join(ray_mock_dir, "dataset.csv")
dataset_csv = generate_data(input_features, output_features, csv_filename, num_examples=100)
dataset_parquet = create_data_set_to_use("parquet", dataset_csv)
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "num_fc_layers": 2},
TRAINER: {"epochs": 4, "learning_rate": 0.001},
"backend": {"type": "ray", **RAY_BACKEND_KWARGS},
}
output_feature_name = output_features[0]["name"]
hyperopt_configs = {
"parameters": {
"trainer.learning_rate": {
"space": "loguniform",
"lower": 0.001,
"upper": 0.1,
},
output_feature_name + ".output_size": {"space": "randint", "lower": 2, "upper": 32},
output_feature_name + ".num_fc_layers": {"space": "randint", "lower": 2, "upper": 6},
},
"goal": "minimize",
"output_feature": output_feature_name,
"validation_metrics": "loss",
"executor": {"type": "ray", "num_samples": 2},
"search_alg": {"type": "variant_generator"},
}
# add hyperopt parameter space to the config
config["hyperopt"] = hyperopt_configs
run_hyperopt(config, dataset_parquet, ray_mock_dir)
@spawn | b59ce782e675d1c4511fad9f13b12fc3f2f02e90 | @spawn | 229 | https://github.com/ludwig-ai/ludwig.git | 322 | def test_hyperopt_run_hyperopt(csv_filename, ray_mock_dir):
input_features = [number_feature(), number_feature()]
output_features = [binary_feature()]
csv_filename = os.path.join(ray_mock_dir, "dataset.csv")
dataset_csv = generate_data(input_features, output_features, csv_filename, num_examples=100)
dataset_parquet = create_data_set_to_use("parquet", dataset_csv)
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "num_fc_layers": 2},
TRAINER: {"epochs": 4, "learning_rate": 0.001},
"backend": {"type": "ray", **RAY_BACKEND_KWARGS},
}
output_feature_name = output_features[0]["name"]
hyperopt_config | 22 | 404 | test_hyperopt_run_hyperopt |
89 | 0 | 9 | 27 | code/default/gae_proxy/local/web_control.py | 218,969 | v4.6.0 compactiable with python 2.7. | XX-Net | 15 | Python | 56 | web_control.py | def req_importip_handler(self):
req = urlparse(self.path).query
reqs = parse_qs(req, keep_blank_values=True)
data = ''
if reqs['cmd'] == ['importip']:
count = 0
ip_list = self.postvars['ipList'][0]
lines = ip_list.split("\n")
for line in lines:
addresses = line.split('|')
for ip in addresses:
ip = ip.strip()
if not utils.check_ip_valid(ip):
continue
if front.ip_manager.add_ip(ip, 100, "google.com", "gws"):
count += 1
data = '{"res":"%s"}' % count
front.ip_manager.save(force=True)
elif reqs['cmd'] == ['exportip']:
data = '{"res":"'
for ip in front.ip_manager.ip_list:
if front.ip_manager.ip_dict[ip]['fail_times'] > 0:
continue
data += "%s|" % ip
data = data[0: len(data) - 1]
data += '"}'
self.send_response_nc('text/html', data)
| 0820c040ec2815f40bd0e469e27c2bf4d2cc33bc | 196 | https://github.com/XX-net/XX-Net.git | 422 | def req_importip_handler(self):
req = urlparse(self.path).query
reqs = parse_qs(req, keep_blank_values=True)
data = ''
if reqs['cmd'] == ['importip']:
count = 0
ip_list = self.postvars['ipList'][0]
lines = ip_list.split("\n")
for line in lines:
addresses = line.split('|')
for ip in addresses:
ip = ip.strip()
if not utils.check_ip_valid(ip):
continue
if front.ip_manager.add_ip(ip, 100, "google.com", "gws"):
count += 1
data = '{"res":"%s"}' % c | 29 | 336 | req_importip_handler |
|
29 | 0 | 1 | 7 | tests/blocks/test_core.py | 55,912 | Block capabilities (PrefectHQ/orion#1898)
* Add capabilities to BlockSchemas
* Remove type field from BlockSchemas
* Create postgres migration, bump API version | prefect | 10 | Python | 21 | test_core.py | async def test_block_load(self, test_block, block_document):
my_block = await test_block.load(block_document.name)
assert my_block._block_document_name == block_document.name
assert my_block._block_document_id == block_document.id
assert my_block._block_type_id == block_document.block_type_id
assert my_block._block_schema_id == block_document.block_schema_id
assert my_block.foo == "bar"
| 168483e9cf038a3629f880f838b5aa9291a48411 | 58 | https://github.com/PrefectHQ/prefect.git | 70 | async def test_block_load(self, test_block, block_document):
my_block = await test_block.load(block_document.name)
assert my_block._block_document_name == block_document.name
assert my_block._block_document_id == block_document.id
a | 15 | 91 | test_block_load |
|
13 | 0 | 1 | 15 | tests/components/blebox/test_climate.py | 297,156 | Blebox add thermoBox to climate (#81090)
Co-authored-by: Martin Hjelmare <[email protected]> | core | 8 | Python | 13 | test_climate.py | async def test_reding_hvac_actions(saunabox, hass, caplog):
caplog.set_level(logging.ERROR)
feature_mock, entity_id = saunabox
await async_setup_entity(hass, entity_id)
| 923fa473e171fcdf396556ea200612e378f9b0a5 | 108 | https://github.com/home-assistant/core.git | 25 | async def test_reding_hvac_actions(saunabox, hass, caplog):
caplog.set_level(logging.ERROR)
feature_mock, entity_id = saunabox
await async_setup_entity(hass, entity_id)
| 10 | 50 | test_reding_hvac_actions |
|
37 | 0 | 2 | 15 | saleor/plugins/tests/test_manager.py | 27,323 | Revert "Add fix for multiplied prices on Avatax side (#9699)" (#9750)
This reverts commit 5dc3a30ef3bb8dfce67ede276fa465e2c420d003. | saleor | 13 | Python | 30 | test_manager.py | def test_manager_calculates_order_line_total(order_line, plugins):
currency = order_line.order.currency
expected_total = (
TaxedMoney(Money("1.0", currency), Money("1.0", currency))
if plugins
else quantize_price(order_line.unit_price * order_line.quantity, currency)
)
taxed_total = (
PluginsManager(plugins=plugins)
.calculate_order_line_total(
order_line.order, order_line, order_line.variant, order_line.variant.product
)
.price_with_discounts
)
assert expected_total == taxed_total
| ab7e4e203fd23a5fec1d27d0774905c52c509dc3 | 84 | https://github.com/saleor/saleor.git | 114 | def test_manager_calculates_order_line_total(order_line, plugins):
currency = order_line.order.currency
expected_total = (
TaxedMoney(Money("1.0", currency), Money("1.0", currency))
if plugins
else quantize_price(order_line.unit_price * order_line.quantity, currency)
)
taxed_total = (
PluginsManager(plugins=plugins)
.cal | 17 | 127 | test_manager_calculates_order_line_total |
|
41 | 0 | 2 | 9 | thumbor/filters/blur.py | 191,063 | Reformat to 80 chars and mypy.ini | thumbor | 9 | Python | 30 | blur.py | def apply_blur(mode, data, size, radius, sigma=0):
if sigma == 0:
sigma = radius
radius = min(radius, MAX_RADIUS)
matrix, matrix_size = generate_1d_matrix(sigma, radius)
data = _convolution.apply(
mode, data, size[0], size[1], matrix, matrix_size, True
)
return _convolution.apply(mode, data, size[0], size[1], matrix, 1, True)
| 301124c5b377fa56b940d298900dbc5816dbc24e | 92 | https://github.com/thumbor/thumbor.git | 72 | def apply_blur(mode, data, size, radius, sigma=0):
if sigma | 13 | 124 | apply_blur |
|
9 | 0 | 1 | 4 | wagtail/documents/tests/test_views.py | 74,876 | Reformat with black | wagtail | 12 | Python | 9 | test_views.py | def test_content(self):
self.assertEqual(
b"".join(self.get().streaming_content), b"A boring example document"
)
| d10f15e55806c6944827d801cd9c2d53f5da4186 | 26 | https://github.com/wagtail/wagtail.git | 33 | def test_content(self):
self.assertEqual(
| 6 | 43 | test_content |
|
27 | 0 | 1 | 8 | test/test_components.py | 179,939 | blocks-components-tests
- move gradio/test_data to test/test_data/media_data | gradio | 13 | Python | 23 | test_components.py | def test_tokenize(self):
x_wav = media_data.BASE64_AUDIO
audio_input = gr.Audio()
tokens, _, _ = audio_input.tokenize(x_wav)
self.assertEquals(len(tokens), audio_input.interpretation_segments)
x_new = audio_input.get_masked_inputs(tokens, [[1] * len(tokens)])[0]
similarity = SequenceMatcher(a=x_wav["data"], b=x_new).ratio()
self.assertGreater(similarity, 0.9)
| 070b8a96b5b8448e306bd40f2b12d44b759afd48 | 93 | https://github.com/gradio-app/gradio.git | 75 | def test_tokenize(self):
x_wav = media_data.BASE64_AUDIO
audio_input = gr.Audio()
tokens, _, _ = audio_input.tokenize(x_wav)
self.assertEquals(len(tokens), audio_input.interpretation_segments)
x_new = audio_input.get_masked_inputs(tokens, [[1 | 22 | 143 | test_tokenize |
|
16 | 0 | 2 | 3 | django/core/signing.py | 204,789 | Refs #33476 -- Reformatted code with Black. | django | 9 | Python | 14 | signing.py | def signature(self, value, key=None):
key = key or self.key
return base64_hmac(self.salt + "signer", value, key, algorithm=self.algorithm)
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 37 | https://github.com/django/django.git | 29 | def signature(self, value, key=None):
key = key or self.key
return b | 7 | 55 | signature |
|
52 | 0 | 1 | 9 | sympy/tensor/tests/test_tensor.py | 200,564 | Tests for subs and xreplace with dummy index conflicts
see https://github.com/sympy/sympy/issues/24337 | sympy | 14 | Python | 37 | test_tensor.py | def test_TensMul_subs():
R3 = TensorIndexType('R3', dim=3)
p, q, r = tensor_indices("p q r", R3)
K = TensorHead("K", [R3])
V = TensorHead("V", [R3])
C0 = TensorIndex(R3.dummy_name + "_0", R3, True)
assert ( K(p)*V(r)*K(-p) ).subs({V(r): K(q)*K(-q)}) == K(p)*K(q)*K(-q)*K(-p)
assert ( K(p)*V(r)*K(-p) ).xreplace({V(r): K(q)*K(-q)}) == K(p)*K(q)*K(-q)*K(-p)
assert ( K(p)*V(r) ).xreplace({p: C0, V(r): K(q)*K(-q)}) == K(C0)*K(q)*K(-q)
| e31e048fe4834f7259193c5e13e7e7b0d5fcd230 | 236 | https://github.com/sympy/sympy.git | 79 | def test_TensMul_subs():
R3 = TensorIndexType('R3', dim=3)
p, q, r = tensor_indices("p q r", R3)
K = TensorHead("K", [R3])
V = TensorHead("V", [R3])
| 16 | 386 | test_TensMul_subs |
|
89 | 1 | 5 | 12 | python/ray/tests/kuberay/test_autoscaling_config.py | 135,330 | [autoscaler][kuberay] Never request more than maxReplicas worker pods (#29770)
Partially addresses ray-project/kuberay#560, in which it was observed that "replicas" was being set higher than "maxReplicas" in the KubeRay CR.
Applies a surface-level fix by making sure that the autoscaler does not set replicas higher the maxReplicas when creating nodes.
Signed-off-by: Dmitri Gekhtman <[email protected]> | ray | 12 | Python | 57 | test_autoscaling_config.py | def test_cr_image_consistency():
cr = get_basic_ray_cr()
group_specs = [cr["spec"]["headGroupSpec"]] + cr["spec"]["workerGroupSpecs"]
# Head, CPU group, GPU group.
assert len(group_specs) == 3
ray_containers = [
group_spec["template"]["spec"]["containers"][0] for group_spec in group_specs
]
# All Ray containers in the example config have "ray-" in their name.
assert all("ray-" in ray_container["name"] for ray_container in ray_containers)
# All Ray images are from the Ray repo.
assert all(
"rayproject/ray" in ray_container["image"] for ray_container in ray_containers
)
# All Ray images are the same.
assert len({ray_container["image"] for ray_container in ray_containers}) == 1
@pytest.mark.parametrize("exception", [Exception, requests.HTTPError])
@pytest.mark.parametrize("num_exceptions", range(6)) | 9c9977f814facdebc1828fa576531fc95f553172 | @pytest.mark.parametrize("exception", [Exception, requests.HTTPError])
@pytest.mark.parametrize("num_exceptions", range(6)) | 101 | https://github.com/ray-project/ray.git | 143 | def test_cr_image_consistency():
cr = get_basic_ray_cr()
group_specs = [cr["spec"]["headGroupSpec"]] + cr["spec"]["workerGroupSpecs"]
# Head, CPU group, GPU group.
assert len(group_specs) == 3
ray_containers = [
group_spec["template"]["spec"]["containers"][0] for group_spec in group_specs
]
# All Ray containers in the example config have "ray-" in their name.
assert all("ray-" in ray_container["name"] for ray_container in ray_containers)
# All Ray images are from the Ray repo.
assert all(
"rayproject/ | 16 | 229 | test_cr_image_consistency |
19 | 1 | 1 | 3 | tests/freqai/test_freqai_interface.py | 151,238 | skip darwin in RL tests, remove example scripts, improve doc | freqtrade | 8 | Python | 18 | test_freqai_interface.py | def is_mac() -> bool:
machine = platform.system()
return "Darwin" in machine
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
'ReinforcementLearner',
'ReinforcementLearner_multiproc'
]) | eeebb78a5c772b0c3e569fd476587facb1f8a9dc | @pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
'ReinforcementLearner',
'ReinforcementLearner_multiproc'
]) | 17 | https://github.com/freqtrade/freqtrade.git | 41 | def is_mac() -> bool:
machine = platform.system()
return "Darwin" in machine
@pytest.mark.parametrize('model', [
'LightGBMReg | 8 | 71 | is_mac |
65 | 0 | 6 | 20 | src/sentry/eventstore/models.py | 85,387 | feat(perf_issues): Add `GroupEvent` and split some functionality in `Event` into a base class. (#38143)
Since we can now have events with multiple groups, we can no longer rely on the `Event.group`
property. This pr adds in a `GroupEvent` subclass that should be passed around wherever we expect an
event to have a single `Group` associated with it.
`Event` has been split up into `BaseEvent` and `Event`. We will deprecate and remove uses of
`group_id` and `group` in the `Event` class going forward. If we need an event with a `Group`, we
can use `build_group_events` to fetch all `GroupEvents` associated with the `Event`, or `for_group`
if we just need a specific `Event`/`Group` pairing.
Going forward, the plan is to store all groups in the `groups` property. This means that error
events being sent via eventstream will have their group included in `groups` as well. We'll
need to update the errors processor in snuba to look there instead of `group_id`. This seems cleaner
long term, instead of having both `group_id` and `group_ids` passed through.
To figure out where we need to use `build_group_events` and `for_group` we can do a mix of searching
the codebase and commenting out the `group_id` and `group` properties and see how CI goes. | sentry | 14 | Python | 43 | models.py | def groups(self) -> Sequence[Group]:
from sentry.models import Group
if getattr(self, "_groups_cache"):
return self._groups_cache
if self._group_ids is not None:
group_ids = self._group_ids
else:
snuba_group_id = self.group_id
# TODO: Replace `snuba_group_id` with this once we deprecate `group_id`.
# snuba_group_id = self._snuba_data.get(self._get_column_name(Columns.GROUP_ID))
snuba_group_ids = self._snuba_data.get(self._get_column_name(Columns.GROUP_IDS))
group_ids = []
if snuba_group_id:
group_ids.append(snuba_group_id)
if snuba_group_ids:
group_ids.extend(snuba_group_ids)
if group_ids:
groups = list(Group.objects.filter(id__in=group_ids))
else:
groups = []
self._groups_cache = groups
return groups
| 6aaaf5089b2c39757883179df5a8512db3b0c716 | 118 | https://github.com/getsentry/sentry.git | 271 | def groups(self) -> Sequence[Group]:
from sentry.models import Group
if getattr(self, "_groups_cache"):
return self._groups_cache
if self._group_ids is not None:
group_ids = self._group_ids
else:
snuba_group_id = self.group_id
# TODO: Replace `snuba_group_id` with this once we deprecate `group_id`.
# snuba_group_id = self._snuba_data.get(self._get_column_name(C | 24 | 194 | groups |
|
12 | 0 | 1 | 4 | src/datasets/iterable_dataset.py | 105,911 | Multiprocessed dataset builder [WIP] (#5107)
* multiprocessing-compatible naming scheme and refactor
* multiprocessed shard writing for GeneratorBasedBuilder
* multiprocessed shard writing for ArrowBasedBuilder
* style
* multiprocessed dataset loading
* compatibility with non-sharded datasets
* bugfix
* bugfix
* removed unused import
* fixed bad ordering
* less misleading tqdm
* fix gen_kwargs distribution + read shards
* minor
* minor2
* support beam datasets
* docstrings + minor
* add iflatmap_unordered for parallel write & progress updates
* use 1 tqdm bar receiving updates from subprocesses
* docs
* add test_iflatmap_unordered
* style
* test arrow_reader.py
* fix test_iflatmap_unordered
* add Beam test_download_and_prepare_sharded
* test gen_kwargs distribution
* test download_and_prepare with num_proc
* style
* improve test
* don't close the pool
* fix multiprocessing on windows
* keep multiprocessing disabled by default
* again + docs
* more docs
* more docs
* some var renaming
* style
* Apply suggestions from code review
Co-authored-by: Mario Šaško <[email protected]>
* Apply suggestions from code review
Co-authored-by: Mario Šaško <[email protected]>
* added utils/sharding.py
* style
* style
Co-authored-by: Quentin Lhoest <[email protected]>
Co-authored-by: Quentin Lhoest <[email protected]>
Co-authored-by: Mario Šaško <[email protected]> | datasets | 9 | Python | 11 | iterable_dataset.py | def __iter__(self):
rng = deepcopy(self.generator)
kwargs_with_shuffled_shards = _shuffle_gen_kwargs(rng, self.kwargs)
yield from self.generate_examples_fn(**kwargs_with_shuffled_shards)
| 2945690ea731f85a356220a71cdc630281c676f4 | 33 | https://github.com/huggingface/datasets.git | 40 | def __iter__(self):
rng = deepcopy(self.generator)
kwargs_with_shuff | 9 | 56 | __iter__ |
|
126 | 0 | 1 | 18 | tests/utils/test_cache.py | 187,056 | chore: remove unnecessary collection.OrderedDict
- Replace collection.OrderedDict with builtins.dict where possible:
Python 3.7+ ensures the correct order in builtins.dict objects and is
no longer an implementation detail of cpython.
- Fix OrderedDict type annotation in streamlink.utils.cache.LRUCache
- Add unit test for streamlink.utils.cache.LRUCache | streamlink | 11 | Python | 64 | test_cache.py | def test_lru_cache():
cache = LRUCache(num=3)
assert cache.get("foo") is None, "Getter returns None for unknown items"
cache.set("foo", "FOO")
assert list(cache.cache.items()) == [("foo", "FOO")], "Setter adds new items"
assert cache.get("foo") == "FOO", "Getter returns correct value of known items"
cache.set("bar", "BAR")
cache.set("baz", "BAZ")
cache.set("qux", "QUX")
assert list(cache.cache.items()) == [("bar", "BAR"), ("baz", "BAZ"), ("qux", "QUX")], "Setter respects max queue size"
cache.get("bar")
assert list(cache.cache.items()) == [("baz", "BAZ"), ("qux", "QUX"), ("bar", "BAR")], "Getter moves known items to the end"
cache.get("unknown")
assert list(cache.cache.items()) == [("baz", "BAZ"), ("qux", "QUX"), ("bar", "BAR")], "Getter keeps order on unknown items"
cache.set("foo", "FOO")
assert list(cache.cache.items()) == [("qux", "QUX"), ("bar", "BAR"), ("foo", "FOO")], "Setter moves new items to the end"
cache.set("qux", "QUUX")
assert list(cache.cache.items()) == [("bar", "BAR"), ("foo", "FOO"), ("qux", "QUUX")], "Setter moves known items to the end"
| 6325c74e6869b45051ec111e4243d77cc536ba66 | 280 | https://github.com/streamlink/streamlink.git | 176 | def test_lru_cache():
cache = LRUCache(num=3)
assert cache.get("foo") is None, "Getter returns None for unknown items"
cache.set("foo", "FOO")
assert list(cache.cache.items()) == [("foo", "FOO") | 8 | 515 | test_lru_cache |
|
9 | 0 | 2 | 2 | fastai/gen_doc/nbtest.py | 190,355 | Upgrading to support latest Pytorch version | DeOldify | 9 | Python | 9 | nbtest.py | def get_qualname(elt):
return elt.__qualname__ if hasattr(elt, '__qualname__') else fn_name(elt)
| 4fc3616712edb19179b17dd270ad6cf63abf99c2 | 21 | https://github.com/jantic/DeOldify.git | 11 | def get_qualname(elt):
return elt.__qualname__ | 5 | 34 | get_qualname |
|
75 | 0 | 1 | 31 | networkx/generators/small.py | 176,242 | Use from_dict_of_lists instead of make_small_graph in generators.small (#5267)
* Add test for digraph creation behavior.
* Use from_dict_of_lists instead of make_small_graph
* Make sure generators don't support digraph.
* Rm redundant create_using check. | networkx | 11 | Python | 66 | small.py | def truncated_cube_graph(create_using=None):
G = nx.from_dict_of_lists(
{
0: [1, 2, 4],
1: [11, 14],
2: [3, 4],
3: [6, 8],
4: [5],
5: [16, 18],
6: [7, 8],
7: [10, 12],
8: [9],
9: [17, 20],
10: [11, 12],
11: [14],
12: [13],
13: [21, 22],
14: [15],
15: [19, 23],
16: [17, 18],
17: [20],
18: [19],
19: [23],
20: [21],
21: [22],
22: [23],
},
create_using=create_using,
)
G.name = "Truncated Cube Graph"
return G
| 7669e7f2f31485015f3ea7cdd535e086467fa433 | 193 | https://github.com/networkx/networkx.git | 364 | def truncated_cube_graph(create_using=None):
G = nx.from_dict_of_lists(
{
0: [1, 2, 4],
1: [11, 14],
2: [3, 4],
3: [6, 8],
4: [5],
| 6 | 260 | truncated_cube_graph |
|
367 | 1 | 12 | 76 | src/prefect/cli/profile.py | 58,520 | Remove extra "f" (#6384) | prefect | 26 | Python | 122 | profile.py | async def check_orion_connection(profile_name):
with use_profile(profile_name, include_current_context=False):
httpx_settings = dict(timeout=3)
try:
# attempt to infer Cloud 2.0 API from the connection URL
cloud_client = get_cloud_client(
httpx_settings=httpx_settings, infer_cloud_url=True
)
res = await cloud_client.api_healthcheck()
exit_method, msg = (
exit_with_success,
f"Connected to Prefect Cloud using profile {profile_name!r}",
)
except CloudUnauthorizedError:
# if the Cloud 2.0 API exists and fails to authenticate, notify the user
exit_method, msg = (
exit_with_error,
f"Error authenticating with Prefect Cloud using profile {profile_name!r}",
)
except httpx.HTTPStatusError as exc:
if exc.response.status_code == status.HTTP_404_NOT_FOUND:
# if the route does not exist, attmpt to connect as a hosted Orion instance
try:
# inform the user if Prefect Orion endpoints exist, but there are
# connection issues
client = get_client(httpx_settings=httpx_settings)
connect_error = await client.api_healthcheck()
if connect_error is not None:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Orion using profile {profile_name!r}",
)
elif await client.using_ephemeral_app():
# if the client is using an ephemeral Orion app, inform the user
exit_method, msg = (
exit_with_success,
f"No Prefect Orion instance specified using profile {profile_name!r}. "
f"Flow run metadata will be stored at the locally configured database: {prefect.settings.PREFECT_ORION_DATABASE_CONNECTION_URL.value()}",
)
else:
exit_method, msg = (
exit_with_success,
f"Connected to Prefect Orion using profile {profile_name!r}",
)
except Exception as exc:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Orion using profile {profile_name!r}",
)
else:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Cloud: {exc!r}",
)
except TypeError:
# if no Prefect Orion API URL has been set, httpx will throw a TypeError
try:
# try to connect with the client anyway, it will likely use an
# ephemeral Orion instance
client = get_client(httpx_settings=httpx_settings)
connect_error = await client.api_healthcheck()
if connect_error is not None:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Orion using profile {profile_name!r}",
)
elif await client.using_ephemeral_app():
exit_method, msg = (
exit_with_success,
f"No Prefect Orion instance specified using profile {profile_name!r}. "
f"Flow run metadata will be stored at the locally configured database: {prefect.settings.PREFECT_ORION_DATABASE_CONNECTION_URL.value()}",
)
else:
exit_method, msg = (
exit_with_success,
f"Connected to Prefect Orion using profile {profile_name!r}",
)
except Exception as exc:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Orion using profile {profile_name!r}",
)
except (httpx.ConnectError, httpx.UnsupportedProtocol) as exc:
exit_method, msg = exit_with_error, "Invalid Prefect API URL"
return exit_method, msg
@profile_app.command() | 2f22824cd7af9bb89c103698c05036f2542caff1 | @profile_app.command() | 298 | https://github.com/PrefectHQ/prefect.git | 1,781 | async def check_orion_connection(profile_name):
with use_profile(profile_name, include_current_context=False):
httpx_settings = dict(timeout=3)
try:
# attempt to infer Cloud 2.0 API from the connection URL
cloud_client = get_cloud_client(
httpx_settings=httpx_settings, infer_cloud_url=True
)
res = await cloud_client.api_healthcheck()
exit_method, msg = (
exit_with_success,
f"Connected to Prefect Cloud using profile {profile_name!r}",
)
except CloudUnauthorizedError:
# if the Cloud 2.0 API exists and fails to authenticate, notify the user
exit_method, msg = (
exit_with_error,
f"Error authenticating with Prefect Cloud using profile {profile_name!r}",
)
except httpx.HTTPStatusError as exc:
if exc.response.status_code == status.HTTP_404_NOT_FOUND:
# if the route does not exist, attmpt to connect as a hosted Orion instance
try:
# inform the user if Prefect Orion endpoints exist, but there are
# connection issues
client = get_client(httpx_settings=httpx_settings)
connect_error = await client.api_healthcheck()
if connect_error is not None:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Orion using profile {profile_name!r}",
)
elif await client.using_ephemeral_app():
# if the client is using an ephemeral Orion app, inform the user
exit_method, msg = (
exit_with_success,
f"No Prefect Orion instance specified using profile {profile_name!r}. "
f"Flow run metadata will be stored at the locally configured database: {prefect.settings.PREFECT_ORION_DATABASE_CONNECTION_URL.value()}",
)
else:
exit_method, msg = (
exit_with_success,
f"Connected to Prefect Orion using profile {profile_name!r}",
)
except Exception as exc:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Orion using profile {profile_name!r}",
)
else:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Cloud: {exc!r}",
)
except TypeError:
# if no Prefect Orion API URL has been set, httpx will throw a TypeError
try:
# try to connect with the client anyway, it will likely use an
# ephemeral Orion instance
client = get_client(httpx_settings=httpx_settings)
connect_error = await client.api_healthcheck()
if connect_error is not None:
exit_method, msg = (
exit_with_error,
f"Error connecting to Prefect Orion using profile {profile_name!r}",
)
elif await client.using_ephemeral_app():
exit_method, msg = (
exit_with_success,
f"No Prefect Orion instance specified using profile {profile_name!r}. "
f"Flow run metadata will be stored at the locally configured database: {prefect.settings.PREFECT_ORION_DATABASE_CONNECTION_URL.value()}",
)
else:
exit_method, msg = (
exit_with_success,
f"Connected to Prefect Orion using profile {profile_name!r}",
)
except Exception as exc:
exit_method, msg = (
exit_with_er | 38 | 585 | check_orion_connection |
13 | 0 | 2 | 3 | src/prefect/utilities/logging.py | 53,044 | Update src/prefect/utilities/logging.py
Co-authored-by: Michael Adkins <[email protected]> | prefect | 13 | Python | 13 | logging.py | def process(self, msg, kwargs):
kwargs["extra"] = {**self.extra, **(kwargs.get("extra") or {})}
return (msg, kwargs)
| 3a2d581ec0540dab8efc5e30c1bc10dfa321f2b5 | 39 | https://github.com/PrefectHQ/prefect.git | 26 | def process(self, msg, kwargs):
kwargs["extra"] = {**self.extra, **(kwarg | 6 | 62 | process |
|
7 | 0 | 1 | 3 | keras/legacy_tf_layers/variable_scope_shim_test.py | 274,469 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 11 | Python | 7 | variable_scope_shim_test.py | def call(self, inputs):
with tf.compat.v1.variable_scope("foo"):
return self.scale_by_y(inputs)
| 84afc5193d38057e2e2badf9c889ea87d80d8fbf | 26 | https://github.com/keras-team/keras.git | 24 | def call(self, inputs):
with tf.compat.v1.variable_scope( | 8 | 45 | call |
|
179 | 0 | 10 | 37 | test/test_prototype_transforms.py | 194,353 | rename features._Feature to datapoints._Datapoint (#7002)
* rename features._Feature to datapoints.Datapoint
* _Datapoint to Datapoint
* move is_simple_tensor to transforms.utils
* fix CI
* move Datapoint out of public namespace | vision | 13 | Python | 72 | test_prototype_transforms.py | def test__get_params(self, padding, pad_if_needed, size, mocker):
image = mocker.MagicMock(spec=datapoints.Image)
image.num_channels = 3
image.spatial_size = (24, 32)
h, w = image.spatial_size
transform = transforms.RandomCrop(size, padding=padding, pad_if_needed=pad_if_needed)
params = transform._get_params([image])
if padding is not None:
if isinstance(padding, int):
pad_top = pad_bottom = pad_left = pad_right = padding
elif isinstance(padding, list) and len(padding) == 2:
pad_left = pad_right = padding[0]
pad_top = pad_bottom = padding[1]
elif isinstance(padding, list) and len(padding) == 4:
pad_left, pad_top, pad_right, pad_bottom = padding
h += pad_top + pad_bottom
w += pad_left + pad_right
else:
pad_left = pad_right = pad_top = pad_bottom = 0
if pad_if_needed:
if w < size[1]:
diff = size[1] - w
pad_left += diff
pad_right += diff
w += 2 * diff
if h < size[0]:
diff = size[0] - h
pad_top += diff
pad_bottom += diff
h += 2 * diff
padding = [pad_left, pad_top, pad_right, pad_bottom]
assert 0 <= params["top"] <= h - size[0] + 1
assert 0 <= params["left"] <= w - size[1] + 1
assert params["height"] == size[0]
assert params["width"] == size[1]
assert params["needs_pad"] is any(padding)
assert params["padding"] == padding
| a8007dcdfb5159a711fa343d2ac4bb7df826975f | 308 | https://github.com/pytorch/vision.git | 558 | def test__get_params(self, padding, pad_if_needed, size, mocker):
image = mocker.MagicMock(spec=datapoints.Image)
image.num_channels = 3
image.spatial_size = (24, 32)
h, w = image.spatial_size
transform = transforms.RandomCrop(size, padding=padding, pad_if_needed=pad_if_needed)
params = transform._get_params([image])
if padding is not None:
if isinstance(padding, int):
pad_top = pad_bottom = pad_left = pad_right = padding
elif isinstance(padding, list) and len(padding) == 2:
| 30 | 470 | test__get_params |
|
134 | 1 | 1 | 48 | tests/components/mqtt/test_discovery.py | 288,080 | Move MQTT discovery hass.data globals to dataclass (#78706)
* Add MQTT discovery hass.data globals to dataclass
* isort
* Additional rework
* Add hass.data["mqtt_tags"] to dataclass
* Follow-up comment
* Corrections | core | 9 | Python | 73 | test_discovery.py | async def test_discovery_expansion(hass, mqtt_mock_entry_no_yaml_config, caplog):
await mqtt_mock_entry_no_yaml_config()
data = (
'{ "~": "some/base/topic",'
' "name": "DiscoveryExpansionTest1",'
' "stat_t": "test_topic/~",'
' "cmd_t": "~/test_topic",'
' "availability": ['
" {"
' "topic":"~/avail_item1",'
' "payload_available": "available",'
' "payload_not_available": "not_available"'
" },"
" {"
' "topic":"avail_item2/~",'
' "payload_available": "available",'
' "payload_not_available": "not_available"'
" }"
" ],"
' "dev":{'
' "ids":["5706DF"],'
' "name":"DiscoveryExpansionTest1 Device",'
' "mdl":"Generic",'
' "hw":"rev1",'
' "sw":"1.2.3.4",'
' "mf":"None",'
' "sa":"default_area"'
" }"
"}"
)
async_fire_mqtt_message(hass, "homeassistant/switch/bla/config", data)
await hass.async_block_till_done()
state = hass.states.get("switch.DiscoveryExpansionTest1")
assert state.state == STATE_UNAVAILABLE
async_fire_mqtt_message(hass, "avail_item2/some/base/topic", "available")
await hass.async_block_till_done()
state = hass.states.get("switch.DiscoveryExpansionTest1")
assert state is not None
assert state.name == "DiscoveryExpansionTest1"
assert ("switch", "bla") in hass.data["mqtt"].discovery_already_discovered
assert state.state == STATE_UNKNOWN
async_fire_mqtt_message(hass, "test_topic/some/base/topic", "ON")
state = hass.states.get("switch.DiscoveryExpansionTest1")
assert state.state == STATE_ON
async_fire_mqtt_message(hass, "some/base/topic/avail_item1", "not_available")
await hass.async_block_till_done()
state = hass.states.get("switch.DiscoveryExpansionTest1")
assert state.state == STATE_UNAVAILABLE
@patch("homeassistant.components.mqtt.PLATFORMS", [Platform.SWITCH]) | 84b2c74746b694d217fe6d448a8dfff4bc2d7a9e | @patch("homeassistant.components.mqtt.PLATFORMS", [Platform.SWITCH]) | 184 | https://github.com/home-assistant/core.git | 451 | async def test_discovery_expansion(hass, mqtt_mock_entry_no_yaml_config, caplog):
await mqtt_mock_entry_no_yaml_config()
data = (
'{ "~": "some/base/topic",'
' "name": "DiscoveryExpansionTest1",'
' "stat_t": "test_topic/~",'
' "cmd_t": "~/test_topic",'
' "availability": ['
" {"
' "topic":"~/avail_item1",'
' "payload_available": "available",'
' "payload_not_available": "not_available"'
" },"
" {"
' "topic":"avail_item2/~",'
' "payload_available": "available",'
' "payload_not_ava | 18 | 375 | test_discovery_expansion |
20 | 0 | 3 | 6 | pipenv/patched/notpip/_vendor/distlib/_backport/tarfile.py | 21,493 | Vendor in pip 22.1.2 | pipenv | 10 | Python | 18 | tarfile.py | def __iter__(self):
while True:
line = self.readline()
if not line:
break
yield line
#class ExFileObject
#------------------
# Exported Classes
#------------------ | c69d55f7c82d5ae2cce542bcfb98d043ca4836a0 | 23 | https://github.com/pypa/pipenv.git | 78 | def __iter__(self):
while True:
line = self.readline()
if not line:
break
| 4 | 47 | __iter__ |
|
69 | 1 | 4 | 15 | pipenv/patched/notpip/_vendor/platformdirs/android.py | 20,198 | check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4 | pipenv | 17 | Python | 53 | android.py | def _android_folder() -> str:
try:
# First try to get path to android app via pyjnius
from jnius import autoclass
Context = autoclass("android.content.Context") # noqa: N806
result: str = Context.getFilesDir().getParentFile().getAbsolutePath()
except Exception:
# if fails find an android folder looking path on the sys.path
pattern = re.compile(r"/data/(data|user/\d+)/(.+)/files")
for path in sys.path:
if pattern.match(path):
result = path.split("/files")[0]
break
else:
raise OSError("Cannot find path to android app folder")
return result
@lru_cache(maxsize=1) | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | @lru_cache(maxsize=1) | 84 | https://github.com/pypa/pipenv.git | 189 | def _android_folder() -> str:
try:
# First try to get path to android app via pyjnius
from jnius import autoclass
Context = autoclass("android.content.Context") # noqa: N806
result: str = Context.getFilesDir().getParentFile().getAbsolutePath()
except Exception:
# if fails find an android folder looking path on the sys.path
pattern = re.compile(r"/data/(data|user/\d+)/(.+)/files")
for path in sys.path:
if pattern.match(path):
re | 20 | 163 | _android_folder |
112 | 0 | 8 | 21 | python/ray/data/tests/test_dataset.py | 129,300 | [Dataset] [DataFrame 2/n] Add pandas block format implementation (partial) (#20988)
This PR adds pandas block format support by implementing `PandasRow`, `PandasBlockBuilder`, `PandasBlockAccessor`.
Note that `sort_and_partition`, `combine`, `merge_sorted_blocks`, `aggregate_combined_blocks` in `PandasBlockAccessor` redirects to arrow block format implementation for now. They'll be implemented in a later PR.
Co-authored-by: Clark Zinzow <[email protected]>
Co-authored-by: Eric Liang <[email protected]> | ray | 15 | Python | 61 | test_dataset.py | def test_from_pandas_refs(ray_start_regular_shared, enable_pandas_block):
ctx = ray.data.context.DatasetContext.get_current()
old_enable_pandas_block = ctx.enable_pandas_block
ctx.enable_pandas_block = enable_pandas_block
try:
df1 = pd.DataFrame({"one": [1, 2, 3], "two": ["a", "b", "c"]})
df2 = pd.DataFrame({"one": [4, 5, 6], "two": ["e", "f", "g"]})
ds = ray.data.from_pandas_refs([ray.put(df1), ray.put(df2)])
assert ds._dataset_format(
) == "pandas" if enable_pandas_block else "arrow"
values = [(r["one"], r["two"]) for r in ds.take(6)]
rows = [(r.one, r.two) for _, r in pd.concat([df1, df2]).iterrows()]
assert values == rows
# test from single pandas dataframe ref
ds = ray.data.from_pandas_refs(ray.put(df1))
assert ds._dataset_format(
) == "pandas" if enable_pandas_block else "arrow"
values = [(r["one"], r["two"]) for r in ds.take(3)]
rows = [(r.one, r.two) for _, r in df1.iterrows()]
assert values == rows
finally:
ctx.enable_pandas_block = old_enable_pandas_block
| 4a55d10bb1b70971f50a3872421f2c1eebd84e64 | 269 | https://github.com/ray-project/ray.git | 238 | def test_from_pandas_refs(ray_start_regular_shared, enable_pandas_block):
ctx = ray.data.context.DatasetContext.get_current()
old_enable_pandas_block = ctx.enable_pandas_block
ctx.enable_pandas_block = enable_pandas_block
try:
df1 = pd.DataFrame({"one": [1, 2, 3], "two": ["a", "b", "c"]})
df2 = pd.DataFrame({"one": [4, 5, 6], "two": ["e", "f", "g"]})
ds = ray.data.from_pandas_refs([ray.put(df1), ray.put(df2)])
assert ds._dataset_format(
) == "pandas" if enable_pandas_block else "arrow"
values = [(r["one"], r["two"]) for r in ds.take(6)]
rows = [(r.one, r.two) for _, r in pd.concat([df1, df | 27 | 435 | test_from_pandas_refs |
|
8 | 0 | 1 | 3 | homeassistant/components/zha/switch.py | 301,326 | Add configurable zha switch entity (#71784)
* add configurable zha switch entity
* final zha configurable switch
* fix codecov
* replaced errorneous cluster with local quirk
* test fix
* minor changes | core | 8 | Python | 8 | switch.py | async def async_turn_on(self, **kwargs) -> None:
await self.async_turn_on_off(True)
| 0c2f22d4780612545c483627da729e44d46ee9fd | 18 | https://github.com/home-assistant/core.git | 22 | async def async_turn_on(self, **kwargs) -> None:
await self.async_turn_on_off(True)
| 4 | 33 | async_turn_on |
|
24 | 0 | 3 | 9 | homeassistant/components/fibaro/climate.py | 291,300 | Support hvacsystem in fibaro integration (#78234)
fixes undefined | core | 14 | Python | 20 | climate.py | def hvac_action(self) -> HVACAction | None:
if not self._op_mode_device:
return None
prop = self._op_mode_device.fibaro_device.properties
if "thermostatOperatingState" in prop:
with suppress(ValueError):
return HVACAction(prop.thermostatOperatingState.lower())
return None
| cd2377bc054ebe4c5c0432aac525d768dcfbe57a | 51 | https://github.com/home-assistant/core.git | 96 | def hvac_action(self) -> HVACAction | None:
if not self._op_mode_device:
return None
prop = self._op_mode_device.fibaro_device.properties
if "thermostatOperatingState" in prop:
with suppress(ValueError):
| 11 | 89 | hvac_action |
|
19 | 0 | 1 | 9 | tests/cli/test_cloud.py | 59,860 | Add login with a browser to `prefect cloud login` (#7334) | prefect | 12 | Python | 18 | test_cloud.py | def test_login_with_invalid_key(key, expected_output, respx_mock):
respx_mock.get(PREFECT_CLOUD_API_URL.value() + "/me/workspaces").mock(
return_value=httpx.Response(status.HTTP_403_FORBIDDEN)
)
invoke_and_assert(
["cloud", "login", "--key", key, "--workspace", "foo"],
expected_code=1,
expected_output=expected_output,
)
| 1a6dee5e9eb71e6e6d1d3492002e9cd674ab9f9b | 60 | https://github.com/PrefectHQ/prefect.git | 58 | def test_login_with_invalid_key(key, expected_output, respx_mock):
respx_mock.get(PREFECT_CLOUD_API_URL.value() + "/me/workspaces").mock(
return_value=httpx.Response(status.HTTP_403_FORBIDDEN)
)
invoke_and_assert(
["cloud", "login", "--key", key, "--workspace", "foo"],
expected_code=1,
expe | 15 | 98 | test_login_with_invalid_key |
|
15 | 0 | 4 | 2 | fastai/data_block.py | 190,254 | Upgrading to support latest Pytorch version | DeOldify | 12 | Python | 13 | data_block.py | def _decode(df):
return np.array([[df.columns[i] for i,t in enumerate(x) if t==1] for x in df.values], dtype=np.object)
| 4fc3616712edb19179b17dd270ad6cf63abf99c2 | 46 | https://github.com/jantic/DeOldify.git | 17 | def _decode(df):
return np.array([[df.columns[i] for i,t in enumerate(x) if t==1] for x i | 12 | 68 | _decode |
|
69 | 1 | 6 | 18 | keras/backend.py | 269,587 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | keras | 16 | Python | 35 | backend.py | def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3):
if ndim(x) == 4 and list(reduction_axes) in [[0, 1, 2], [0, 2, 3]]:
if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]:
return _broadcast_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon
)
return _fused_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon
)
else:
if sorted(reduction_axes) == list(range(ndim(x)))[:-1]:
return _regular_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon
)
else:
return _broadcast_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon
)
@keras_export("keras.backend.batch_normalization")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | @keras_export("keras.backend.batch_normalization")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | 154 | https://github.com/keras-team/keras.git | 232 | def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3):
if ndim(x) == 4 and list(reduction_axes) in [[0, 1, 2], [0, 2, 3]]:
if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]:
return _broadcast_normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=epsilon
)
return _fused_ | 21 | 245 | normalize_batch_in_training |
23 | 0 | 2 | 9 | mitmproxy/tools/console/grideditor/base.py | 252,592 | Replace blinker with custom implementation (#5528)
* replace blinker with custom implementation
The major benefit here is type checking, followed by proper support for async receivers.
* fix compatibility with Python 3.9
* fix nits
* try harder to force gc
* try harderer
* coverage++
* coverage++
* nits | mitmproxy | 11 | Python | 21 | base.py | def set_current_value(self, val) -> None:
errors = self.lst[self.focus][1]
emsg = self.editor.is_error(self.focus_col, val)
if emsg:
signals.status_message.send(message=emsg, expire=5)
errors.add(self.focus_col)
else:
errors.discard(self.focus_col)
self.set_value(val, self.focus, self.focus_col, errors)
| f4dc2f2cfdb40e04022e4deb4aa67578deff5d23 | 87 | https://github.com/mitmproxy/mitmproxy.git | 90 | def set_current_value(self, val) -> None:
errors = self.lst[self.focus][1]
emsg = self.editor.is_error(self.focus_col, val)
if emsg:
signals.status_message.send(message=emsg, expire=5)
| 18 | 132 | set_current_value |
|
28 | 0 | 2 | 7 | python3.10.4/Lib/calendar.py | 221,244 | add python 3.10.4 for windows | XX-Net | 11 | Python | 24 | calendar.py | def formatmonthname(self, theyear, themonth, withyear=True):
if withyear:
s = '%s %s' % (month_name[themonth], theyear)
else:
s = '%s' % month_name[themonth]
return '<tr><th colspan="7" class="%s">%s</th></tr>' % (
self.cssclass_month_head, s)
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 49 | https://github.com/XX-net/XX-Net.git | 89 | def formatmonthname(self, theyear, themonth, withyear=True):
if withyear:
s = '%s %s' % (month_name[themonth], theyear)
else:
s = '%s' % month_name[themonth]
return '<tr><th colspan="7" class="%s">%s</th></tr>' % (
self.cssclass_month_head, s)
| 8 | 80 | formatmonthname |
|
10 | 0 | 1 | 4 | modules/image/Image_editing/colorization/deoldify/test.py | 52,122 | update deoldify (#1992)
* update deoldify
* add clean func
* update README
* update format | PaddleHub | 10 | Python | 10 | test.py | def test_predict1(self):
pred_img, out_path = self.module.predict(input='tests/test.jpg')
self.assertIsInstance(pred_img, np.ndarray)
self.assertIsInstance(out_path, str)
| ca09b195daa8033a6f85bccf27362d0b114f9706 | 37 | https://github.com/PaddlePaddle/PaddleHub.git | 30 | def test_predict1(self):
pr | 11 | 60 | test_predict1 |
|
12 | 0 | 1 | 6 | homeassistant/components/axis/device.py | 318,150 | Improve type hints in axis (#75910) | core | 10 | Python | 12 | device.py | async def async_reset(self) -> bool:
self.disconnect_from_stream()
return await self.hass.config_entries.async_unload_platforms(
self.config_entry, PLATFORMS
)
| 8181da70901c6b848ebc2efb2d39a7a3536599f3 | 29 | https://github.com/home-assistant/core.git | 51 | async def async_reset(self) -> bool:
self.disconnect_from_stream()
return await self.hass.config_entries.async_unload_platforms(
self.config_entry, PLATFORMS
| 9 | 50 | async_reset |
|
33 | 0 | 1 | 9 | tests/test_builder.py | 104,939 | Set builder name from module instead of class (#4388)
* Set builder name from module instead of class
* Fix tests
* Rename dummy_builder to builder in tests | datasets | 15 | Python | 21 | test_builder.py | def test_cache_dir_for_features(self):
with tempfile.TemporaryDirectory() as tmp_dir:
f1 = Features({"id": Value("int8")})
f2 = Features({"id": Value("int32")})
builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f1)
other_builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f1)
self.assertEqual(builder.cache_dir, other_builder.cache_dir)
other_builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f2)
self.assertNotEqual(builder.cache_dir, other_builder.cache_dir)
| d6ae1ea3f93a48d03eab78eecf7b6599144143e1 | 112 | https://github.com/huggingface/datasets.git | 116 | def test_cache_dir_for_features(self):
with tempfile.TemporaryDirectory() as tmp_dir:
f1 = Features({"id": Value("int8")})
f2 = Features({"id": Value("int32")})
builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f1)
other_builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f1)
self.assertEqual(builder.cache_dir, other_builder.cache_dir)
other_builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f2)
self.assertNotEqual(builder.cache_dir, other_builder.cache_dir)
| 17 | 188 | test_cache_dir_for_features |
|
13 | 0 | 2 | 4 | src/sentry/snuba/metrics/query_builder.py | 99,664 | ref(metrics): Honor snuba group limits without orderBy [TET-5] (#34287)
* ref(metrics): Honor snuba group limits without orderBy
This PR fixes the else branch to apply similar session V2 limits without explicit orderBy. Essentially how we achieve this now is through the following logic:
Let's say fields across the three different entities are requested with a limit of 3, groupBy project and no orderBy clause
- If the results of query to entity 1, hits the limit then we use the project groups as filters for subsequent queries
- If the results of query to entity 1 do not hit the limit, but results of query 2 does, then we nuke the groups from query 1 that do not exist in query 2 results and apply those as a filter to query 3
- If the results of all three queries to all three entities don't hit the limit, then at the very end, we might end up with an extra number of groups greater than the limit, which is why we nuke the excess groups | sentry | 9 | Python | 12 | query_builder.py | def _parse_limit(self, paginator_kwargs) -> Optional[Limit]:
if "limit" not in paginator_kwargs:
return
return Limit(paginator_kwargs["limit"])
| 1b1e1ed83fa3ee7da1009b927efbd7af94609301 | 27 | https://github.com/getsentry/sentry.git | 37 | def _parse_limit(self, paginator_kwargs) -> Optional[Limit]:
if "limit" not in paginator_kwargs:
return
return Limit(paginator_kwargs["limit"])
| 5 | 45 | _parse_limit |
|
51 | 0 | 2 | 16 | test/test_examples.py | 181,237 | Fix bug with gr.update and interactive=True (#2639)
* Fix update interactivity
* Lint
* CHANGELOG
* Fix
* Undo interactive=True
* Do not call update twice
* Add unit test
* Revert change
* Lint | gradio | 14 | Python | 44 | test_examples.py | async def test_caching_with_dict(self):
text = gr.Textbox()
out = gr.Label()
io = gr.Interface(
lambda _: {text: gr.update(lines=4, interactive=False), out: "lion"},
"textbox",
[text, out],
examples=["abc"],
cache_examples=True,
)
prediction = await io.examples_handler.load_from_cache(0)
assert not any(d["trigger"] == "fake_event" for d in io.config["dependencies"])
assert prediction == [
{"lines": 4, "__type__": "update", "mode": "static"},
{"label": "lion"},
]
| e6336d688259494205ff4616ff2c03d5460b36bc | 124 | https://github.com/gradio-app/gradio.git | 183 | async def test_caching_with_dict(self):
text = gr.Textbox()
out = gr.Label()
io = gr.Interface(
lambda _: {text: gr.update(lines=4, interactive=False), out: "lion"},
"textbox",
[text, out],
examples=["abc"],
cache_examples=True,
)
prediction = await io.examples_handler.load | 21 | 209 | test_caching_with_dict |
|
87 | 0 | 14 | 18 | homeassistant/components/sonarr/sensor.py | 292,531 | Use aiopyarr for sonarr (#65349) | core | 13 | Python | 34 | sensor.py | def native_value(self) -> StateType:
key = self.entity_description.key
if key == "diskspace" and self.data.get(key) is not None:
total_free = sum(disk.freeSpace for disk in self.data[key])
free = total_free / 1024**3
return f"{free:.2f}"
if key == "commands" and self.data.get(key) is not None:
return len(self.data[key])
if key == "queue" and self.data.get(key) is not None:
return self.data[key].totalRecords
if key == "series" and self.data.get(key) is not None:
return len(self.data[key])
if key == "upcoming" and self.data.get(key) is not None:
return len(self.data[key])
if key == "wanted" and self.data.get(key) is not None:
return self.data[key].totalRecords
return None
| f30681dae7efffd8980b3ee3ae7f355c603b842c | 194 | https://github.com/home-assistant/core.git | 238 | def native_value(self) -> StateType:
key = self.entity_description.key
if key == "diskspace" and self.data.get(key) is not None:
total_free = sum(disk.freeSpace for disk in self.data[key])
free = total_free / 1024**3
return f"{free:.2f}"
if key == "commands" and self.data.get(key) is not None:
return len(self.data[key])
if key == "queue" and self.data.get(key) is not None:
return self.data[key].totalRecords
if key == "series" and self.data.get(key) is not No | 14 | 316 | native_value |
|
32 | 0 | 1 | 28 | tests/providers/google/cloud/operators/test_dataplex.py | 46,141 | Add Dataplex operators (#20377) | airflow | 10 | Python | 22 | test_dataplex.py | def test_execute(self, hook_mock):
op = DataplexDeleteTaskOperator(
project_id=PROJECT_ID,
region=REGION,
lake_id=LAKE_ID,
dataplex_task_id=DATAPLEX_TASK_ID,
task_id="delete_dataplex_task",
api_version=API_VERSION,
gcp_conn_id=GCP_CONN_ID,
delegate_to=DELEGATE_TO,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context=None)
hook_mock.assert_called_once_with(
gcp_conn_id=GCP_CONN_ID,
delegate_to=DELEGATE_TO,
api_version=API_VERSION,
impersonation_chain=IMPERSONATION_CHAIN,
)
hook_mock.return_value.delete_task.assert_called_once_with(
project_id=PROJECT_ID,
region=REGION,
lake_id=LAKE_ID,
dataplex_task_id=DATAPLEX_TASK_ID,
retry=None,
timeout=None,
metadata=(),
)
| 87c1246b79769f20214a339aadc6a8270d453953 | 115 | https://github.com/apache/airflow.git | 300 | def test_execute(self, hook_mock):
op = DataplexDeleteTaskOperator(
project_id=PROJECT_ID,
region=REGION,
lake_id=LAKE_ID,
dataplex_task_id=DATAPLEX_TASK_ID,
task_id="delete_dataplex_task",
api_version=API_VERSION,
gcp_conn_id=GCP_CONN_ID,
delegate_to=DELEGATE_TO,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context=None)
hook_mock.assert_called_once_with(
gcp_conn_id=GCP_CONN_ID,
delegate_to=DELEGATE_TO,
ap | 30 | 161 | test_execute |
|
23 | 1 | 1 | 12 | tests/gamestonk_terminal/stocks/insider/test_openinsider_view.py | 281,863 | Tests : Stocks (#1240)
* Updating tests : stocks/sector_industry_analysis
* Updating tests : stocks/prediction_techniques
* Updating tests : doc
* Updating tests : black
* Updating tests : stocks/sector_industry_analysis
* Updating tests : stocks/technical_analysis
* Updating tests : etf/technical_analysis
* Updating tests : black
* Updating tests : stocks/quantitative_analysis
* Updating tests : stocks/quantitative_analysis
* Updating tests : stocks/options
* Updating tests : stocks/options
* Updating tests : stocks
* Updating tests : black
* Updating tests : stocks/prediction_techniques
* Updating tests : black
* Updating tests : stocks
* Updating tests : etf
* Updating tests : stocks
* Updating tests : black
* Updating tests : fundamental_analysis
* Updating tests : dark_pool_shorts/finra_model
* Updating tests : black
* Updating tests : stocks/dark_pook_shorts
* Updating tests : stocks/discovery
* Updating tests : stocks/insider
* Updating tests : stocks
* Updating tests : black
* Updating tests : stocks/options/yfinance_model
* Updating tests : stocks
* Updating tests : stocks/insider | OpenBBTerminal | 9 | Python | 21 | test_openinsider_view.py | def test_print_insider_filter_no_table(mocker):
# MOCK SOUP
mocker.patch(
target="gamestonk_terminal.stocks.insider.openinsider_view.get_open_insider_link",
return_value=None,
)
openinsider_view.print_insider_filter(
preset_loaded="whales",
ticker="",
limit=10,
links=False,
export="",
)
@pytest.mark.default_cassette("test_print_insider_data")
@pytest.mark.vcr
@pytest.mark.parametrize(
"color",
[True, False],
) | 379cf31cfe7473c6b5747861bb2ec2dbb9974b5d | @pytest.mark.default_cassette("test_print_insider_data")
@pytest.mark.vcr
@pytest.mark.parametrize(
"color",
[True, False],
) | 43 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 88 | def test_print_insider_filter_no_table(mocker):
# MOCK SOUP
mocker.patch(
target="gamestonk_terminal.stocks.insider.openinsider_view.get_open_insider_link",
return_value=None,
)
openinsider_vi | 17 | 121 | test_print_insider_filter_no_table |
100 | 1 | 1 | 11 | pandas/tests/scalar/timedelta/test_constructors.py | 169,912 | REF: _reso->_creso (#49107) | pandas | 12 | Python | 71 | test_constructors.py | def test_overflow_on_construction():
# GH#3374
value = Timedelta("1day").value * 20169940
msg = "Cannot cast 1742682816000000000000 from ns to 'ns' without overflow"
with pytest.raises(OutOfBoundsTimedelta, match=msg):
Timedelta(value)
# xref GH#17637
msg = "Cannot cast 139993 from D to 'ns' without overflow"
with pytest.raises(OutOfBoundsTimedelta, match=msg):
Timedelta(7 * 19999, unit="D")
# used to overflow before non-ns support
td = Timedelta(timedelta(days=13 * 19999))
assert td._creso == NpyDatetimeUnit.NPY_FR_us.value
assert td.days == 13 * 19999
@pytest.mark.parametrize(
"val, unit",
[
(3508, "M"),
(15251, "W"), # 1
(106752, "D"), # change from previous:
(2562048, "h"), # 0 hours
(153722868, "m"), # 13 minutes
(9223372037, "s"), # 44 seconds
],
) | 90b4add77859d1349530fff3c8cadeef95f36f39 | @pytest.mark.parametrize(
"val, unit",
[
(3508, "M"),
(15251, "W"), # 1
(106752, "D"), # change from previous:
(2562048, "h"), # 0 hours
(153722868, "m"), # 13 minutes
(9223372037, "s"), # 44 seconds
],
) | 89 | https://github.com/pandas-dev/pandas.git | 200 | def test_overflow_on_construction():
# GH#3374
value = Timedelta("1day").value * 20169940
msg = "Cannot cast 1742682816000000000000 from ns to 'ns' without overflow"
with pytest.raises(OutOfBoundsTimedelta, match=msg):
Timedelta(value)
# xref GH#17637
msg = "Cannot cast 139993 from D to 'ns' without overflow"
with pytest.raises(OutOfBoundsTimedelta, match=msg):
Timedelta(7 * 19999, unit="D")
# used to overflow before non-ns support
td = Timedelta(timedelta(days=13 * 19999))
assert td._creso == NpyDatetimeUnit.NPY_FR_us.value
assert td.days == 13 * 19999
@pytest.mark.parametrize(
"val, unit",
[
(3508, "M"),
(15251, "W"), # 1
(106752, "D"), # change from previous:
(2562048, "h"), # 0 hours
(153722868, "m"), # 13 minutes
(9223372037, "s"), # 44 | 17 | 236 | test_overflow_on_construction |
7 | 0 | 1 | 7 | homeassistant/components/motion_blinds/cover.py | 294,802 | Motion Blinds API lock (#68587) | core | 7 | Python | 7 | cover.py | async def async_set_cover_position(self, **kwargs):
position = kwargs[ATTR_POSITION] | 425b825ae990b054838fea09b86202407d14dae1 | 44 | https://github.com/home-assistant/core.git | 21 | async def async_set_cover_position(self, **kwargs):
position = kwargs[ATTR_POSITION] | 5 | 27 | async_set_cover_position |
|
22 | 0 | 2 | 7 | django/db/backends/base/base.py | 204,804 | Refs #33476 -- Reformatted code with Black. | django | 12 | Python | 21 | base.py | def dec_thread_sharing(self):
with self._thread_sharing_lock:
if self._thread_sharing_count <= 0:
raise RuntimeError(
"Cannot decrement the thread sharing count below zero."
)
self._thread_sharing_count -= 1
| 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | 27 | https://github.com/django/django.git | 99 | def dec_thread_sharing(self):
with self._thread_ | 5 | 48 | dec_thread_sharing |
|
63 | 0 | 10 | 25 | nuitka/OutputDirectories.py | 178,706 | macOS: Added support for mixing --onefile and --macos-create-app-bundle
* For some software, e.g. PySide2 it will actually be the only way
to get it working. | Nuitka | 19 | Python | 35 | OutputDirectories.py | def getResultFullpath(onefile):
result = getResultBasepath(onefile=onefile)
if Options.shallMakeModule():
result += getSharedLibrarySuffix(preferred=True)
else:
output_filename = Options.getOutputFilename()
if Options.isOnefileMode() and output_filename is not None:
if onefile:
result = output_filename
else:
result = os.path.join(
getStandaloneDirectoryPath(),
os.path.basename(output_filename),
)
elif output_filename is not None:
result = output_filename
elif getOS() == "Windows":
result += ".exe"
elif (
not Options.isStandaloneMode()
or onefile
and not Options.shallCreateAppBundle()
):
result += ".bin"
return result
| 053c207229292b7f011937964a69cdf271d47532 | 123 | https://github.com/Nuitka/Nuitka.git | 298 | def getResultFullpath(onefile):
result = getResultBasepath(onefile=onefile)
if Options.shallMakeModule():
result += getSharedLibrarySuffix(preferred=True)
else:
output_filename = Options.getOutputFilename()
if Options.isOnefileMode() and output_filename is not None:
if onefile:
result = output_filename
else:
result = os.path.join(
getStandaloneDirectoryPath(),
os.path.basename(output_filename),
)
elif output_filename is not None:
result = output_filename
elif getOS() == "Windows":
result += ".exe"
elif (
n | 19 | 211 | getResultFullpath |
|
150 | 1 | 3 | 30 | tests/exchange/test_exchange.py | 149,366 | Change to precise casing instead of .lower() | freqtrade | 20 | Python | 89 | test_exchange.py | async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
api_mock = MagicMock()
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection(
"kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
"symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
"429 Too Many Requests" '{"code":"429000","msg":"Too Many Requests"}'))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id="KuCoin")
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='KuCoin'))
msg = "Kucoin 429 error, avoid triggering DDosProtection backoff delay"
assert not num_log_has_re(msg, caplog)
for _ in range(3):
with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
await exchange._async_get_candle_history(
"ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
assert num_log_has_re(msg, caplog) == 3
caplog.clear()
# Test regular non-kucoin message
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection(
"kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
"symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
"429 Too Many Requests" '{"code":"2222222","msg":"Too Many Requests"}'))
msg = r'_async_get_candle_history\(\) returned exception: .*'
msg2 = r'Applying DDosProtection backoff delay: .*'
with patch('freqtrade.exchange.common.asyncio.sleep', get_mock_coro(None)):
for _ in range(3):
with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
await exchange._async_get_candle_history(
"ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
# Expect the "returned exception" message 12 times (4 retries * 3 (loop))
assert num_log_has_re(msg, caplog) == 12
assert num_log_has_re(msg2, caplog) == 9
@pytest.mark.asyncio | 39d925c2950aa3c734c454535fef70d89353211e | @pytest.mark.asyncio | 243 | https://github.com/freqtrade/freqtrade.git | 341 | async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
api_mock = MagicMock()
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection(
"kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
"symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
"429 Too Many Requests" '{"code":"429000","msg":"Too Many Requests"}'))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id="KuCoin")
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='KuCoin'))
msg = "Kucoin 429 error, avoid triggering DDosProtection backoff delay"
assert not num_log_has_re(msg, caplog)
for _ in range(3):
with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
await exchange._async_get_candle_history(
"ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
assert num_log_has_re(msg, caplog) == 3
caplog.clear()
# Test regular non-kucoin message
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection(
"kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
"symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
"429 Too Many Requests" '{"code":"2222222","msg":"Too Many Requests"}'))
msg = r'_async_get_candle_history\(\) returned exception: .*'
msg2 = r'Applying DDosProtection backoff delay: .*'
with patch('freqtrade.exchange.common.asyncio.sleep', get_mock_c | 37 | 423 | test__async_kucoin_get_candle_history |
9 | 0 | 1 | 5 | homeassistant/components/risco/sensor.py | 300,807 | Clean up accessing entity_registry.async_get_registry helper via hass (#72005) | core | 10 | Python | 9 | sensor.py | async def async_added_to_hass(self):
self._entity_registry = er.async_get(self.hass)
self.async_on_remove(
self.coordinator.async_add_listener(self._refresh_from_coordinator)
)
| 69cc6ab5f1d58adc586c3b300a4f7f0cde2cd0c2 | 33 | https://github.com/home-assistant/core.git | 48 | async def async_added_to_hass(self):
self._entity_registry = er.async_get(self.hass)
self.async_on_remo | 10 | 57 | async_added_to_hass |
|
35 | 0 | 5 | 14 | homeassistant/components/london_air/sensor.py | 305,438 | Improve entity type hints [l] (#77655) | core | 15 | Python | 28 | sensor.py | def update(self) -> None:
sites_status = []
self._api_data.update()
if self._api_data.data:
self._site_data = self._api_data.data[self._name]
self._updated = self._site_data[0]["updated"]
for site in self._site_data:
if site["pollutants_status"] != "no_species_data":
sites_status.append(site["pollutants_status"])
if sites_status:
self._state = max(set(sites_status), key=sites_status.count)
else:
self._state = None
| d1ecd74a1a153b85b829acf45b5c6a5ea79df5c1 | 104 | https://github.com/home-assistant/core.git | 166 | def update(self) -> None:
sites_status = []
self._api_data.update()
if self._api_data.data:
self._site_data = self._api_data.d | 15 | 173 | update |
|
11 | 0 | 2 | 3 | python3.10.4/Lib/ast.py | 220,187 | add python 3.10.4 for windows | XX-Net | 10 | Python | 11 | ast.py | def set_precedence(self, precedence, *nodes):
for node in nodes:
self._precedences[node] = precedence
| 8198943edd73a363c266633e1aa5b2a9e9c9f526 | 23 | https://github.com/XX-net/XX-Net.git | 28 | def set_precedence(self, precedence, *nodes):
for node in nodes:
self._precedences[nod | 6 | 34 | set_precedence |
|
46 | 0 | 4 | 19 | rllib/algorithms/apex_dqn/apex_dqn.py | 135,780 | [RLlib] Move all config validation logic into AlgorithmConfig classes. (#29854) | ray | 15 | Python | 39 | apex_dqn.py | def update_target_networks(self, num_new_trained_samples) -> None:
self._num_ts_trained_since_last_target_update += num_new_trained_samples
if (
self._num_ts_trained_since_last_target_update
>= self.config["target_network_update_freq"]
):
self._num_ts_trained_since_last_target_update = 0
with self._timers[TARGET_NET_UPDATE_TIMER]:
to_update = self.workers.local_worker().get_policies_to_train()
self.workers.local_worker().foreach_policy_to_train(
lambda p, pid: pid in to_update and p.update_target()
)
self._counters[NUM_TARGET_UPDATES] += 1
self._counters[LAST_TARGET_UPDATE_TS] = self._counters[
NUM_AGENT_STEPS_TRAINED
if self.config.count_steps_by == "agent_steps"
else NUM_ENV_STEPS_TRAINED
]
| 2ed09c54459cc3f74e2dab13406018698559856c | 111 | https://github.com/ray-project/ray.git | 260 | def update_target_networks(self, num_new_trained_samples) -> None:
self._num_ts_trained_since_last_target_update += num_new_trained_samples
if (
self._num_ts_trained_since_last_target_update
>= self.config["target_network_update_freq"]
):
self._num_ts_trained_since_last_target_update = 0
with self._timers[TARGET_NET_UPDATE_TIMER]:
to_update = self.workers.local_worker().get_policies_to_train()
self.workers.local_worker().foreach_policy_to_train(
lambda p, pid: pid in to_update and p.update_target()
)
self._counters[NUM_TARGET_UPDATES] += 1
self._counters[LAST_TARGET_UPDATE_TS] = self._counters[
| 21 | 180 | update_target_networks |
|
17 | 0 | 1 | 4 | saleor/payment/gateways/np_atobarai/tests/test_utils.py | 26,106 | Port NP Atobarai gateway to 3.1 (#8684)
* Port net protections (#8640) to 3.1
* Add NP final code review feedback onto 3.1
* Fix optional sku in NP payload & add docstrings
* Refactor tracking_number_updated
* Change NetProtections timeout value to 20
* Do not use f-strings in logger warnings
* Trace only http requests
* Simplify code
* Add comment about longer than usual timeout period
* Remove order from process payment
* Add comment for 400 status code
* Reduce scope of Posuto context manager
* Refactor voucher and shipping amount for payment lines data
* Update PaymentResult.psp_reference type to Optional[str]
* Add handler for report error in transaction reregistration
* Add docstrings to goods functions
* Add FOR_REREGISTRATION payment status
* Refactor create_refund_data
* Fix refund data
* Add docstrings to goods functions
* Add prefetch to _create_refund_manual_amount
* Move refund logic to NP
* Fix billing amount for partial refunds
* Fix multiple shipping refunds
* Set currency to JPY
* WIP fix refunds
* Clean up code
* Refactor
* Fix get_goods_with_refunds for all returned products
Co-authored-by: Mateusz Grzyb <[email protected]> | saleor | 11 | Python | 15 | test_utils.py | def test_get_fulfillment_for_order_no_refundable_fulfillment(order):
# given
order.fulfillments.create(tracking_number="123", status=FulfillmentStatus.REFUNDED)
# then
with pytest.raises(PaymentError, match=r".* not exist .*"):
# when
get_fulfillment_for_order(order)
| bf654a5f958fcf0611b61cf43ac13c886761b80a | 38 | https://github.com/saleor/saleor.git | 42 | def test_get_fulfillment_for_order_no_refundable_fulfillment(order):
# given
order.fulfillments.create(tracking_number="123", status=Fulfillmen | 13 | 67 | test_get_fulfillment_for_order_no_refundable_fulfillment |
|
18 | 0 | 2 | 4 | .venv/lib/python3.8/site-packages/pip/_internal/cli/parser.py | 60,526 | upd; format | transferlearning | 8 | Python | 16 | parser.py | def format_heading(self, heading):
# type: (str) -> str
if heading == "Options":
return ""
return heading + ":\n"
| f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | 18 | https://github.com/jindongwang/transferlearning.git | 49 | def format_heading(self, heading):
# type: (str) -> str
if heading == "Options":
| 3 | 36 | format_heading |
|
70 | 1 | 1 | 17 | tests/admin_widgets/tests.py | 200,799 | Refs #33476 -- Reformatted code with Black. | django | 11 | Python | 58 | tests.py | def test_m2m_related_model_not_in_admin(self):
# M2M relationship with model not registered with admin site. Raw ID
# widget should have no magnifying glass link. See #16542
consultor1 = Advisor.objects.create(name="Rockstar Techie")
c1 = Company.objects.create(name="Doodle")
c2 = Company.objects.create(name="Pear")
consultor1.companies.add(c1, c2)
rel = Advisor._meta.get_field("companies").remote_field
w = widgets.ManyToManyRawIdWidget(rel, widget_admin_site)
self.assertHTMLEqual(
w.render("company_widget1", [c1.pk, c2.pk], attrs={}),
'<input type="text" name="company_widget1" value="%(c1pk)s,%(c2pk)s">'
% {"c1pk": c1.pk, "c2pk": c2.pk},
)
self.assertHTMLEqual(
w.render("company_widget2", [c1.pk]),
'<input type="text" name="company_widget2" value="%(c1pk)s">'
% {"c1pk": c1.pk},
)
@override_settings(ROOT_URLCONF="admin_widgets.urls") | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | @override_settings(ROOT_URLCONF="admin_widgets.urls") | 144 | https://github.com/django/django.git | 218 | def test_m2m_related_model_not_in_admin(self):
# M2M relationship with model not registered with admin site. Raw ID
# widget should have no magnifying glass link. See #16542
consultor1 = Advisor.objects.create(name="Rockstar Techie")
c1 = Company.objects.create(name="Doodle")
c2 = Company.objects.create(name="Pear")
consultor1.companies.add(c1, c2)
rel = Advisor._meta.get_field("companies").remote_field
w = widgets.ManyToManyRawIdWidget(rel, widget_admin_site)
self.assertHTMLEqual(
w.render("co | 26 | 255 | test_m2m_related_model_not_in_admin |
80 | 0 | 9 | 21 | tests/lobpcg_test.py | 121,070 | Add initial LOBPCG top-k eigenvalue solver (#3112)
This initial version is f32-only for accelerators, since it relies on an eigh call (which itself is f32 at most) in its inner loop.
For details, see jax.experimental.linalg.standard_lobpcg documentation.
This is a partial implementation of the similar [scipy lobpcg
function](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.lobpcg.html). | jax | 14 | Python | 58 | lobpcg_test.py | def _make_concrete_cases(f64):
dtype = np.float64 if f64 else np.float32
example_names = list(_concrete_generators(dtype))
cases = []
for name in example_names:
nkm = [(100, 10, 20)]
if not flags.FLAGS.jax_skip_slow_tests:
nkm.append((1000, 100, 200))
for n, k, m in nkm:
if name == 'ring laplacian':
m *= 3
if name.startswith('linear'):
m *= 2
if f64:
m *= 2
case = [('matrix_name', name), ('n', n), ('k', k), ('m', m)]
clean_matrix_name = _clean_matrix_name(name)
case.append(('testcase_name', f'{clean_matrix_name}_n{n}'))
cases.append(dict(case))
assert len({c['testcase_name'] for c in cases}) == len(cases)
return cases
| 76fcf63fb4e53fd82faece677ed46db8b0c71707 | 176 | https://github.com/google/jax.git | 155 | def _make_concrete_cases(f64):
dtype = np.float64 if f64 else np.float32
example_names = list(_concrete_generators(dtype))
cases = []
for name in example_names:
nkm = [(100, 10, 20)]
if not flags.FLAGS.jax_skip_slow_tests:
nkm.append((1000, 100, 200))
for n, k, m in nkm:
if name == 'ring laplacian':
m *= 3
if name.startswith('linear'):
m *= 2
if f64:
| 26 | 283 | _make_concrete_cases |
|
8 | 0 | 1 | 5 | src/streamlink/plugins/funimationnow.py | 187,049 | plugins.funimationnow: replace itertags | streamlink | 15 | Python | 8 | funimationnow.py | def login_csrf(self):
return self.session.http.get(self.login_url, schema=validate.Schema(
validate.parse_html(),
validate.xml_xpath_string(f".//input[@name='{self.CSRF_NAME}'][1]/@value")
))
| b2557361f734304fbd80b4985c753668fed00db5 | 39 | https://github.com/streamlink/streamlink.git | 43 | def login_csrf(self):
return self.session.http.get(self.login_url, schema=validate.Schema(
validate.p | 12 | 68 | login_csrf |
|
20 | 0 | 1 | 18 | tests/test_edgeql_scope.py | 176,144 | Always include the definition context namespace in computable contexts (#3331)
We need to include the *original* source namespace in our ctx
namespace when compiling computables. The current mechanism of trying
to look up in view_sets or failing that using the source namespace
from the computable use, but this was failing to find it in some cases
with FOR.
Fix this by instead directly pulling in the namespace from qlctx. The
inclusion of qlctx's namespace nicely allows us to ditch so later
logic as well.
Additionally we need to merge the namespace into *both* sides in
get_view_map_remapping, to handle cases like referencing a `FOR`
variable where the current ns doesn't get merged in.
Fixes #3323. | edgedb | 16 | Python | 15 | test_edgeql_scope.py | async def test_edgeql_scope_ref_outer_02a(self):
await self.assert_query_result(
,
[{
"cards": [
{"tag": ["Alice"]},
{"tag": ["Alice"]},
{"tag": ["Alice"]},
{"tag": ["Alice"]}
]
}],
)
| 0dada08f4eedb104bfa40932b576e44d82218547 | 53 | https://github.com/edgedb/edgedb.git | 172 | async def test_edgeql_scope_ref_outer_02a(self):
await self.assert_query_result(
,
[{
"cards": [
{"tag": ["Alice"]},
{"tag": ["Alice"]},
{"tag": ["Alice"]},
{"tag": ["Alice"]}
]
}],
)
| 3 | 99 | test_edgeql_scope_ref_outer_02a |
|
15 | 0 | 3 | 8 | python/ray/ml/preprocessor.py | 148,035 | [air - preprocessor] Add BatchMapper. (#23700)
Add BatchMapper preprocessor.
Update the semantics of preprocessor.fit() to allow for multiple fit. This is to follow scikitlearn example.
Introduce FitStatus to explicitly incorporate Chain case. | ray | 11 | Python | 15 | preprocessor.py | def _check_is_fitted(self) -> bool:
fitted_vars = [v for v in vars(self) if v.endswith("_")]
return bool(fitted_vars)
| 06a57b20de12c840406a3bac69751c83a44f008c | 32 | https://github.com/ray-project/ray.git | 36 | def _check_is_fitted(self) -> bool:
fitted_vars = [v for v in vars(self) | 7 | 55 | _check_is_fitted |
|
14 | 0 | 2 | 9 | mkdocs/tests/config/config_options_tests.py | 225,428 | Add tests for new class-based configs
The old-style tests are intentionally kept at config_options_legacy_tests.py | mkdocs | 13 | Python | 14 | config_options_tests.py | def test_valid_dir(self) -> None:
for cls in c.Dir, c.FilesystemObject:
with self.subTest(cls):
d = os.path.dirname(__file__)
| ff8552a57abf2c32f2d0344ef12707b88e008493 | 82 | https://github.com/mkdocs/mkdocs.git | 46 | def test_valid_dir(self) -> None:
for cls in c.Dir, c.FilesystemObject:
with self.subTest(cls):
d = os.path.dirname(__file__)
| 12 | 59 | test_valid_dir |
|
7 | 0 | 1 | 3 | tests/gamestonk_terminal/etf/test_yfinance_model.py | 281,802 | ETF tests (#1208)
* etf tests for stockanalysis
* add financedatabase etf tests
* fix financedatabase etf documentation
* yfinance etf tests
* add etf/discovery tests
* add tests to etf/screener
* add etf controller tests
* add etf/ta tests
* remove tabulate and use rich table
* etf/pred
* add more etf tests, thanks Chavi
* update about us website
* Updating tests : etf
Co-authored-by: Chavithra PARANA <[email protected]> | OpenBBTerminal | 8 | Python | 7 | test_yfinance_model.py | def test_get_etf_summary_description(recorder, name):
result = yfinance_model.get_etf_summary_description(name)
recorder.capture(result)
| d8ca7556edde9a700706c7802a229cb4439304c5 | 21 | https://github.com/OpenBB-finance/OpenBBTerminal.git | 12 | def test_get_etf_summary_description(recorder, name):
result = yfinance_model.get | 7 | 34 | test_get_etf_summary_description |
|
37 | 0 | 2 | 9 | src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | 38,499 | Add Wav2Vec2Conformer (#16812)
* save intermediate
* add wav2vec2 conformer
* add more code
* more
* first test passes
* make all checkpoints work
* update
* up
* more clean ups
* save clean-up
* save clean-up
* save more
* remove bogus
* finalize design conformer
* remove vision
* finish all tests
* more changes
* finish code
* add doc tests
* add slow tests
* fix autoconfig test
* up
* correct docstring
* up
* update
* fix
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
Co-authored-by: Anton Lozhkov <[email protected]>
* Update docs/source/en/model_doc/wav2vec2-conformer.mdx
* upload
* save copied from
* correct configs
* fix model outputs
* add to docs
* fix imports
* finish
* finish code
* correct copied from
* correct again
* correct make fix
* improve make fix copies
* save
* correct fix copy from
* correct init structure
* correct
* fix import
* apply suggestions
Co-authored-by: Sylvain Gugger <[email protected]>
Co-authored-by: Anton Lozhkov <[email protected]> | transformers | 17 | Python | 31 | modeling_wav2vec2_conformer.py | def _compute_perplexity(probs, mask=None):
if mask is not None:
mask_extended = mask.flatten()[:, None, None].expand(probs.shape)
probs = torch.where(mask_extended, probs, torch.zeros_like(probs))
marginal_probs = probs.sum(dim=0) / mask.sum()
else:
marginal_probs = probs.mean(dim=0)
perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum()
return perplexity
| 5a9957358cebd616e58b2d1ab3b887c2f2793b45 | 117 | https://github.com/huggingface/transformers.git | 108 | def _compute_perplexity(probs, mask=None):
if mask is not None:
mask_extended = mask.flatten()[:, None, | 17 | 180 | _compute_perplexity |
|
141 | 0 | 1 | 3 | jax/experimental/maps.py | 119,207 | Add experimental support for SPMD lowering of xmap via MANUAL sharding annotations
Note that it's still limited and turns out to be a bit hard (partly due to
unclear XLA semantics at this point). Using constants that are not xmap inputs
is likely to cause SPMD partitioner errors and cross-replica collectives don't seem
to work either.
In any case, the next step will be to allow nesting those xmaps inside pjits.
PiperOrigin-RevId: 426447989 | jax | 13 | Python | 95 | maps.py | def _ensure_supports_manual_and(f):
def update(v):
if v and not hasattr(xc.OpSharding.Type, "MANUAL"):
raise RuntimeError("This flag requires a version of jaxlib that supports MANUAL sharding type")
return f(v)
return update
try:
config.define_bool_state(
name="experimental_xmap_spmd_lowering",
default=False,
help=("When set, multi-device xmap computations will be compiled through "
"the XLA SPMD partitioner instead of explicit cross-replica collectives. "
"Not supported on CPU!"),
update_global_hook=_clear_compilation_cache,
update_thread_local_hook=_thread_local_flag_unsupported)
config.define_bool_state(
name="experimental_xmap_spmd_lowering_manual",
default=False,
help=("When set, multi-device xmap computations will be compiled using "
"the MANUAL partitioning feature of the XLA SPMD partitioner instead of "
"sharding constraints on vectorized code. "
"Requires experimental_xmap_spmd_lowering!"),
update_global_hook=_ensure_supports_manual_and(_ensure_spmd_and(_clear_compilation_cache)),
update_thread_local_hook=_thread_local_flag_unsupported)
config.define_bool_state(
name="experimental_xmap_ensure_fixed_sharding",
default=False,
help=("When set and `experimental_xmap_spmd_lowering` is enabled, the lowering will "
"try to limit the flexibility of the automated SPMD partitioner heuristics "
"by emitting additional sharding annotations for program intermediates."),
update_global_hook=_ensure_spmd_and(_clear_compilation_cache),
update_thread_local_hook=_thread_local_flag_unsupported)
except Exception:
raise ImportError("jax.experimental.maps has to be imported before JAX flags "
"are parsed")
| 086a607d8c8ea8487a59d6ced8aaf59834b8846c | 9 | https://github.com/google/jax.git | 326 | def _ensure_supports_manual_and(f):
def update(v):
if v and not hasattr(xc.OpSharding.Type, "MANUAL"):
raise RuntimeError("This flag requires a version of jaxlib that supports MANUAL sharding type")
return f(v)
return update
try:
config.define_bool_state(
name="experimental_xmap_spmd_lowering",
default=False,
help=("When set, multi-device xmap computations will be compiled through "
"the XLA SPMD partitioner instead of explicit cross-replica collectives. "
"Not supported on CPU!"),
update_global_hook=_clear_compilation_cache,
update_thread_local_hook=_thread_local_flag_unsupported) | 21 | 246 | _ensure_supports_manual_and |
|
33 | 0 | 2 | 10 | test/test_youtube_lists.py | 106,199 | Fix test_youtube_mix | youtube-dl | 12 | Python | 28 | test_youtube_lists.py | def test_youtube_mix(self):
dl = FakeYDL()
dl.params['format'] = 'best'
ie = YoutubeTabIE(dl)
result = dl.extract_info('https://www.youtube.com/watch?v=uVJ0Il5WvbE&list=PLhQjrBD2T381k8ul4WQ8SQ165XqY149WW',
download=False, ie_key=ie.ie_key(), process=True)
entries = (result or {}).get('entries', [{'id': 'not_found', }])
self.assertTrue(len(entries) >= 50)
original_video = entries[0]
self.assertEqual(original_video['id'], 'uVJ0Il5WvbE')
| 2c2c2bd348b7dce0aad55a6fc37a18c6f9a000e3 | 98 | https://github.com/ytdl-org/youtube-dl.git | 120 | def test_youtube_mix(self):
dl = FakeYDL()
| 18 | 167 | test_youtube_mix |
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