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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
177,558 | 34 | 10 | 5 | 61 | 11 | 0 | 37 | 106 | _get_storage_by_url | fix: DEV-1476: Resolving performance for project storages (#1910)
* Fix: DEV-1476: Resolving performance for project storages
* Rewrite cache
* Remove cache completely | https://github.com/heartexlabs/label-studio.git | def _get_storage_by_url(self, url, storage_objects):
from io_storages.models import get_storage_classes
for storage_object in storage_objects:
# check url is string because task can have int, float, dict, list
# and 'can_resolve_url' will fail
if isinstance(url, str) and storage_object.can_resolve_url(url):
return storage_object
| 38 | models.py | Python | label_studio/tasks/models.py | 6293c3226e3713bdae678603d6c1300e09c41448 | label-studio | 4 |
|
108,942 | 8 | 12 | 5 | 59 | 8 | 0 | 8 | 71 | _on_leave | Make it easier to improve UI event metadata.
Currently, UI events (MouseEvent, KeyEvent, etc.) are generated by
letting the GUI-specific backends massage the native event objects into
a list of args/kwargs and then call
`FigureCanvasBase.motion_notify_event`/`.key_press_event`/etc. This
makes it a bit tricky to improve the metadata on the events, because one
needs to change the signature on both the `FigureCanvasBase` method and
the event class. Moreover, the `motion_notify_event`/etc. methods are
directly bound as event handlers in the gtk3 and tk backends, and thus
have incompatible signatures there.
Instead, the native GUI handlers can directly construct the relevant
event objects and trigger the events themselves; a new `Event._process`
helper method makes this even shorter (and allows to keep factoring some
common functionality e.g. for tracking the last pressed button or key).
As an example, this PR also updates figure_leave_event to always
correctly set the event location based on the *current* cursor position,
instead of the last triggered location event (which may be outdated);
this can now easily be done on a backend-by-backend basis, instead of
coordinating the change with FigureCanvasBase.figure_leave_event.
This also exposed another (minor) issue, in that resize events
often trigger *two* calls to draw_idle -- one in the GUI-specific
handler, and one in FigureCanvasBase.draw_idle (now moved to
ResizeEvent._process, but should perhaps instead be a callback
autoconnected to "resize_event") -- could probably be fixed later. | https://github.com/matplotlib/matplotlib.git | def _on_leave(self, event):
event.Skip()
LocationEvent("figure_leave_event", self,
*self._mpl_coords(event),
guiEvent=event)._process()
| 35 | backend_wx.py | Python | lib/matplotlib/backends/backend_wx.py | 4e21912d2938b0e8812c4d1f7cd902c080062ff2 | matplotlib | 1 |
|
337,616 | 10 | 12 | 2 | 45 | 7 | 0 | 10 | 16 | require_cpu | Fix debug_launcher issues (#413)
* change to require_cpu only | https://github.com/huggingface/accelerate.git | def require_cpu(test_case):
return unittest.skipUnless(not torch.cuda.is_available(), "test requires only a CPU")(test_case)
| 25 | testing.py | Python | src/accelerate/test_utils/testing.py | 3b51d6e9ad8a3916e519eb27ed85ec70f6f862fc | accelerate | 1 |
|
189,393 | 18 | 13 | 5 | 98 | 13 | 0 | 22 | 69 | set_color_by_t2g | Update `Text` to use new ManimPango color setting (#2341)
* Find indexes in stripped text, not original text
* Add regression test
* Only run the test in linux environement
* Rewrite text2settings in Text to set text color via pango
* Make gradient in Text use pango coloring
* Bump manimpango to newest version
* Update test to use new frames_comparison
* Don't remove svg file on exception
* Bump manimpango
* Fix pre-commit errors
* Fix index bug
* Deprecate no longer used functions set_color_by_t2x
* Remove old commented out code
* Update poetry.lock | https://github.com/ManimCommunity/manim.git | def set_color_by_t2g(self, t2g=None):
t2g = t2g if t2g else self.t2g
for word, gradient in list(t2g.items()):
for start, end in self.find_indexes(word, self.text):
self.chars[start:end].set_color_by_gradient(*gradient)
| 63 | text_mobject.py | Python | manim/mobject/svg/text_mobject.py | 540dc70d2fd7a2f759a6da158303ef81a1ae53f8 | manim | 4 |
|
130,250 | 25 | 12 | 7 | 109 | 14 | 0 | 27 | 84 | match_files | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | https://github.com/ray-project/ray.git | def match_files(self, files, separators=None):
if not util._is_iterable(files):
raise TypeError("files:{!r} is not an iterable.".format(files))
file_map = util.normalize_files(files, separators=separators)
matched_files = util.match_files(self.patterns, iterkeys(file_map))
for path in matched_files:
yield file_map[path]
| 68 | pathspec.py | Python | python/ray/_private/thirdparty/pathspec/pathspec.py | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | 3 |
|
81,824 | 64 | 12 | 22 | 197 | 15 | 0 | 102 | 278 | check_related | DRY edits to access classes for new prompts
Remove if-not-data conditional from WFJTnode.can_change
these are cannonical for can_add, but this looks like a bug
Change JTaccess.can_unattach to call same method in super()
previously called can_attach, which is problematic
Better consolidate launch config m2m related checks
Test and fix pre-existing WFJT node RBAC bug
recognize not-provided instance group list on launch, avoiding bug where it fell back to default
fix bug where timeout field was saved on WFJT nodes after creating approval node
remove labels from schedule serializer summary_fields
remove unnecessary prefetch of credentials from WFJT node queryset | https://github.com/ansible/awx.git | def check_related(self, field, Model, data, role_field='admin_role', obj=None, mandatory=False):
new = None
changed = True
if data and 'reference_obj' in data:
# Use reference object's related fields, if given
new = getattr(data['reference_obj'], field)
elif data and field in data:
new = get_object_from_data(field, Model, data, obj=obj)
else:
changed = False
# Obtain existing related resource
current = None
if obj and (changed or mandatory):
current = getattr(obj, field)
if obj and new == current:
# Resource not changed, like a PUT request
changed = False
if (not new) and (not obj) and mandatory:
# Restrict ability to create resource without required field
return self.user.is_superuser
| 161 | access.py | Python | awx/main/access.py | 34e8087aeef0de19642e7dd9cd076adcdf5fbe9c | awx | 20 |
|
247,387 | 14 | 9 | 12 | 59 | 6 | 0 | 14 | 64 | test_unknown_invalid | Add type hints to `tests/rest` (#12146)
* Add type hints to `tests/rest`
* newsfile
* change import from `SigningKey` | https://github.com/matrix-org/synapse.git | def test_unknown_invalid(self) -> None:
encodings = _get_html_media_encodings(
b,
'text/html; charset="invalid"',
)
self.assertEqual(list(encodings), ["utf-8", "cp1252"])
| 33 | test_html_preview.py | Python | tests/rest/media/v1/test_html_preview.py | 7e91107be1a4287873266e588a3c5b415279f4c8 | synapse | 1 |
|
241,621 | 6 | 6 | 3 | 19 | 3 | 0 | 6 | 20 | creates_processes_externally | Modify LSFEnvironment to use more reliable environment variable (#10825)
Co-authored-by: thomas chaton <[email protected]>
Co-authored-by: Carlos Mocholí <[email protected]>
Co-authored-by: Adrian Wälchli <[email protected]>
Co-authored-by: Jirka Borovec <[email protected]> | https://github.com/Lightning-AI/lightning.git | def creates_processes_externally(self) -> bool:
return True
| 10 | lsf_environment.py | Python | pytorch_lightning/plugins/environments/lsf_environment.py | dbf1acd5a553ffc1546734be164cc89cef2b741d | lightning | 1 |
|
145,828 | 2 | 6 | 60 | 13 | 2 | 0 | 2 | 9 | test_timeslices_partially_overlapping_experiences | [RLlib] Issue 22625: `MultiAgentBatch.timeslices()` does not behave as expected. (#22657) | https://github.com/ray-project/ray.git | def test_timeslices_partially_overlapping_experiences(self):
| 254 | test_multi_agent_batch.py | Python | rllib/policy/tests/test_multi_agent_batch.py | c0ade5f0b7cfc9aeba46cde7af3b36068a6420df | ray | 3 |
|
250,614 | 12 | 11 | 43 | 63 | 15 | 0 | 14 | 26 | test_http_client_aborts | reintroduce `Flow.live`
We previously relied on the state of `Flow.reply` to check if a flow can be killed,
but this doesn't work anymore with `Flow.reply` being removed. Instead, we now
reintroduce the `Flow.live` attribute, which signals if we are on a live connection.
Killing still is not ideal (see comment in `Flow.killable`), but this paves the way. | https://github.com/mitmproxy/mitmproxy.git | def test_http_client_aborts(tctx, stream):
server = Placeholder(Server)
flow = Placeholder(HTTPFlow)
playbook = Playbook(http.HttpLayer(tctx, HTTPMode.regular), hooks=True)
| 191 | test_http.py | Python | test/mitmproxy/proxy/layers/http/test_http.py | 372a632161dee642d81542069507826e34466ba1 | mitmproxy | 3 |
|
109,257 | 18 | 7 | 2 | 84 | 8 | 0 | 25 | 54 | get_yaxis | Add discouraged admonitions
The [*Discouraged*] prefix in the summary line is added in analogy to
the [*Deprecated*] prefix we add automatically. We do this so that
these "labels" are prominently visible also in summary overviews of
the functions in the docs.
Since we rarely discourage whole functions, for now I just do this
manually. | https://github.com/matplotlib/matplotlib.git | def get_yaxis(self):
return self.yaxis
get_xgridlines = _axis_method_wrapper("xaxis", "get_gridlines")
get_xticklines = _axis_method_wrapper("xaxis", "get_ticklines")
get_ygridlines = _axis_method_wrapper("yaxis", "get_gridlines")
get_yticklines = _axis_method_wrapper("yaxis", "get_ticklines")
# Adding and tracking artists
| 10 | _base.py | Python | lib/matplotlib/axes/_base.py | 5af97515b3823b2efa1961253a11e2d77df88637 | matplotlib | 1 |
|
13,348 | 52 | 10 | 37 | 162 | 15 | 0 | 68 | 217 | mixin_scalable_deployment_parser | refactor: remove unnecessary parser args (#5328)
* refactor: refactor deployment mixin and remove polling and shards for gateway
* chore: rename executor to pod and move native and array type to worker args
* refactor: make exit-on-exceptions just a worker arg
* style: fix overload and cli autocomplete
* chore: apply suggestion
* chore: move native parameter to deployment group
* fix: fix pod init
* style: fix overload and cli autocomplete
* fix: fix shards and replicas in deployment
* chore: disable gpu and volumes for gateway
* style: fix overload and cli autocomplete
* fix: volume and gpus are optional for container pods
Co-authored-by: Jina Dev Bot <[email protected]> | https://github.com/jina-ai/jina.git | def mixin_scalable_deployment_parser(parser):
gp = mixin_base_deployment_parser(parser, title='Scalable Deployment')
gp.add_argument(
'--polling',
type=str,
default=PollingType.ANY.name,
help=,
)
gp.add_argument(
'--shards',
type=int,
default=1,
help='The number of shards in the deployment running at the same time. For more details check '
'https://docs.jina.ai/fundamentals/flow/create-flow/#complex-flow-topologies',
)
gp.add_argument(
'--replicas',
type=int,
default=1,
help='The number of replicas in the deployment',
)
gp.add_argument(
'--native',
action='store_true',
default=False,
help='If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.',
)
| 97 | base.py | Python | jina/parsers/orchestrate/base.py | bd8003508da0b35713361484f5801ebc818bd0c3 | jina | 1 |
|
271,620 | 11 | 8 | 39 | 41 | 4 | 0 | 13 | 38 | make_train_function | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def make_train_function(self, force=False):
if self.train_function is not None and not force:
return self.train_function
| 204 | training.py | Python | keras/engine/training.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 10 |
|
274,931 | 4 | 7 | 2 | 22 | 3 | 0 | 4 | 18 | dtype | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def dtype(self):
return self._variable.dtype
| 12 | autocast_variable.py | Python | keras/mixed_precision/autocast_variable.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
|
171,304 | 37 | 12 | 17 | 169 | 15 | 0 | 59 | 244 | __new__ | STYLE enable pylint's redefined-outer-name (#49671)
* fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes.py, pandas/core/indexes/base.py
* fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes.py, pandas/core/indexes/base.py
* fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes.py, pandas/core/indexes/base.py
* fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes.py, pandas/core/indexes/base.py
Co-authored-by: bishwas jha <[email protected]> | https://github.com/pandas-dev/pandas.git | def __new__(cls, freq=None):
if isinstance(freq, PeriodDtype):
return freq
elif freq is None:
# empty constructor for pickle compat
# -10_000 corresponds to PeriodDtypeCode.UNDEFINED
u = PeriodDtypeBase.__new__(cls, -10_000)
u._freq = None
return u
if not isinstance(freq, BaseOffset):
freq = cls._parse_dtype_strict(freq)
try:
return cls._cache_dtypes[freq.freqstr]
except KeyError:
dtype_code = freq._period_dtype_code
u = PeriodDtypeBase.__new__(cls, dtype_code)
u._freq = freq
cls._cache_dtypes[freq.freqstr] = u
return u
| 106 | dtypes.py | Python | pandas/core/dtypes/dtypes.py | c7010a7adec1c47a4642fa068544699fc8e1ea6a | pandas | 5 |
|
100,733 | 55 | 11 | 9 | 134 | 7 | 0 | 79 | 231 | _rewrite_warnings | Bugfixes:
- Stats graph - Handle NaNs in data
- logger - de-elevate matplotlib font messages | https://github.com/deepfakes/faceswap.git | def _rewrite_warnings(cls, record):
if record.levelno == 30 and record.funcName == "warn" and record.module == "ag_logging":
# TF 2.3 in Conda is imported with the wrong gast(0.4 when 0.3.3 should be used). This
# causes warnings in autograph. They don't appear to impact performance so de-elevate
# warning to debug
record.levelno = 10
record.levelname = "DEBUG"
if record.levelno == 30 and (record.funcName == "_tfmw_add_deprecation_warning" or
record.module in ("deprecation", "deprecation_wrapper")):
# Keras Deprecations.
record.levelno = 10
record.levelname = "DEBUG"
return record
| 74 | logger.py | Python | lib/logger.py | afec52309326304f4323029039e49bfcf928ef43 | faceswap | 7 |
|
163,113 | 22 | 11 | 51 | 53 | 6 | 0 | 24 | 97 | get_loc | BUG: Index.get_loc always raise InvalidIndexError on listlike (#45181) | https://github.com/pandas-dev/pandas.git | def get_loc(self, key, method=None):
if method is not None:
raise NotImplementedError(
"only the default get_loc method is "
"currently supported for MultiIndex"
)
self._check_indexing_error(key)
| 324 | multi.py | Python | pandas/core/indexes/multi.py | 46ddb8ef882940fa3da58813e0b7a2df1061031e | pandas | 15 |
|
225,597 | 77 | 15 | 22 | 422 | 32 | 1 | 145 | 242 | bbox_rotate | Implement Ellipse Method For Bounding Box Rotation (#1203)
* implement ellipse method
* black formatting
* fix serialization and update docs
* apply reviews | https://github.com/albumentations-team/albumentations.git | def bbox_rotate(bbox, angle, method, rows, cols):
x_min, y_min, x_max, y_max = bbox[:4]
scale = cols / float(rows)
if method == "largest_box":
x = np.array([x_min, x_max, x_max, x_min]) - 0.5
y = np.array([y_min, y_min, y_max, y_max]) - 0.5
elif method == "ellipse":
w = (x_max - x_min) / 2
h = (y_max - y_min) / 2
data = np.arange(0, 360, dtype=np.float32)
x = w * np.sin(np.radians(data)) + (w + x_min - 0.5)
y = h * np.cos(np.radians(data)) + (h + y_min - 0.5)
else:
raise ValueError(f"Method {method} is not a valid rotation method.")
angle = np.deg2rad(angle)
x_t = (np.cos(angle) * x * scale + np.sin(angle) * y) / scale
y_t = -np.sin(angle) * x * scale + np.cos(angle) * y
x_t = x_t + 0.5
y_t = y_t + 0.5
x_min, x_max = min(x_t), max(x_t)
y_min, y_max = min(y_t), max(y_t)
return x_min, y_min, x_max, y_max
@angle_2pi_range | @angle_2pi_range | 280 | functional.py | Python | albumentations/augmentations/geometric/functional.py | a3a8fd99b564663e26a741c6d59013f2f213c799 | albumentations | 3 |
68,215 | 26 | 11 | 12 | 105 | 9 | 0 | 32 | 24 | get_message | feat: add colors for attendance status to lessen the cognitive load
- legend with colors and full form for status abbreviations | https://github.com/frappe/erpnext.git | def get_message() -> str:
message = ''
colors = ['green', 'red', 'orange', 'green', '#318AD8', '', '']
count = 0
for status, abbr in status_map.items():
message += f
count += 1
return message
| 49 | monthly_attendance_sheet.py | Python | erpnext/hr/report/monthly_attendance_sheet/monthly_attendance_sheet.py | 865204a541651c284979a824576cdfcc4d789056 | erpnext | 2 |
|
270,970 | 27 | 10 | 7 | 117 | 12 | 0 | 50 | 121 | get_losses_for | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def get_losses_for(self, inputs):
if inputs is None:
# Requesting unconditional losses.
return [l for l in self.losses if l._unconditional_loss]
# Requesting input-conditional losses.
losses = [l for l in self.losses if not l._unconditional_loss]
inputs = tf.nest.flatten(inputs)
reachable = tf_utils.get_reachable_from_inputs(inputs, losses)
return [l for l in losses if l in reachable]
| 75 | base_layer_v1.py | Python | keras/engine/base_layer_v1.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 8 |
|
209,501 | 70 | 9 | 6 | 132 | 26 | 0 | 103 | 101 | GetIcmpStatistics | E275 - Missing whitespace after keyword (#3711)
Co-authored-by: Alexander Aring <[email protected]>
Co-authored-by: Anmol Sarma <[email protected]>
Co-authored-by: antoine.torre <[email protected]>
Co-authored-by: Antoine Vacher <[email protected]>
Co-authored-by: Arnaud Ebalard <[email protected]>
Co-authored-by: atlowl <[email protected]>
Co-authored-by: Brian Bienvenu <[email protected]>
Co-authored-by: Chris Packham <[email protected]>
Co-authored-by: CQ <[email protected]>
Co-authored-by: Daniel Collins <[email protected]>
Co-authored-by: Federico Maggi <[email protected]>
Co-authored-by: Florian Maury <[email protected]>
Co-authored-by: _Frky <[email protected]>
Co-authored-by: g-mahieux <[email protected]>
Co-authored-by: gpotter2 <[email protected]>
Co-authored-by: Guillaume Valadon <[email protected]>
Co-authored-by: Hao Zheng <[email protected]>
Co-authored-by: Haresh Khandelwal <[email protected]>
Co-authored-by: Harri Hämäläinen <[email protected]>
Co-authored-by: hecke <[email protected]>
Co-authored-by: Jan Romann <[email protected]>
Co-authored-by: Jan Sebechlebsky <[email protected]>
Co-authored-by: jdiog0 <[email protected]>
Co-authored-by: jockque <[email protected]>
Co-authored-by: Julien Bedel <[email protected]>
Co-authored-by: Keith Scott <[email protected]>
Co-authored-by: Kfir Gollan <[email protected]>
Co-authored-by: Lars Munch <[email protected]>
Co-authored-by: ldp77 <[email protected]>
Co-authored-by: Leonard Crestez <[email protected]>
Co-authored-by: Marcel Patzlaff <[email protected]>
Co-authored-by: Martijn Thé <[email protected]>
Co-authored-by: Martine Lenders <[email protected]>
Co-authored-by: Michael Farrell <[email protected]>
Co-authored-by: Michał Mirosław <[email protected]>
Co-authored-by: mkaliszan <[email protected]>
Co-authored-by: mtury <[email protected]>
Co-authored-by: Neale Ranns <[email protected]>
Co-authored-by: Octavian Toader <[email protected]>
Co-authored-by: Peter Eisenlohr <[email protected]>
Co-authored-by: Phil <[email protected]>
Co-authored-by: Pierre Lalet <[email protected]>
Co-authored-by: Pierre Lorinquer <[email protected]>
Co-authored-by: piersoh <[email protected]>
Co-authored-by: plorinquer <[email protected]>
Co-authored-by: pvinci <[email protected]>
Co-authored-by: Rahul Jadhav <[email protected]>
Co-authored-by: Robin Jarry <[email protected]>
Co-authored-by: romain-perez <[email protected]>
Co-authored-by: rperez <rperez@debian>
Co-authored-by: Sabrina Dubroca <[email protected]>
Co-authored-by: Sebastian Baar <[email protected]>
Co-authored-by: sebastien mainand <[email protected]>
Co-authored-by: smehner1 <[email protected]>
Co-authored-by: speakinghedge <[email protected]>
Co-authored-by: Steven Van Acker <[email protected]>
Co-authored-by: Thomas Faivre <[email protected]>
Co-authored-by: Tran Tien Dat <[email protected]>
Co-authored-by: Wael Mahlous <[email protected]>
Co-authored-by: waeva <[email protected]>
Co-authored-by: Alexander Aring <[email protected]>
Co-authored-by: Anmol Sarma <[email protected]>
Co-authored-by: antoine.torre <[email protected]>
Co-authored-by: Antoine Vacher <[email protected]>
Co-authored-by: Arnaud Ebalard <[email protected]>
Co-authored-by: atlowl <[email protected]>
Co-authored-by: Brian Bienvenu <[email protected]>
Co-authored-by: Chris Packham <[email protected]>
Co-authored-by: CQ <[email protected]>
Co-authored-by: Daniel Collins <[email protected]>
Co-authored-by: Federico Maggi <[email protected]>
Co-authored-by: Florian Maury <[email protected]>
Co-authored-by: _Frky <[email protected]>
Co-authored-by: g-mahieux <[email protected]>
Co-authored-by: gpotter2 <[email protected]>
Co-authored-by: Guillaume Valadon <[email protected]>
Co-authored-by: Hao Zheng <[email protected]>
Co-authored-by: Haresh Khandelwal <[email protected]>
Co-authored-by: Harri Hämäläinen <[email protected]>
Co-authored-by: hecke <[email protected]>
Co-authored-by: Jan Romann <[email protected]>
Co-authored-by: Jan Sebechlebsky <[email protected]>
Co-authored-by: jdiog0 <[email protected]>
Co-authored-by: jockque <[email protected]>
Co-authored-by: Julien Bedel <[email protected]>
Co-authored-by: Keith Scott <[email protected]>
Co-authored-by: Kfir Gollan <[email protected]>
Co-authored-by: Lars Munch <[email protected]>
Co-authored-by: ldp77 <[email protected]>
Co-authored-by: Leonard Crestez <[email protected]>
Co-authored-by: Marcel Patzlaff <[email protected]>
Co-authored-by: Martijn Thé <[email protected]>
Co-authored-by: Martine Lenders <[email protected]>
Co-authored-by: Michael Farrell <[email protected]>
Co-authored-by: Michał Mirosław <[email protected]>
Co-authored-by: mkaliszan <[email protected]>
Co-authored-by: mtury <[email protected]>
Co-authored-by: Neale Ranns <[email protected]>
Co-authored-by: Octavian Toader <[email protected]>
Co-authored-by: Peter Eisenlohr <[email protected]>
Co-authored-by: Phil <[email protected]>
Co-authored-by: Pierre Lalet <[email protected]>
Co-authored-by: Pierre Lorinquer <[email protected]>
Co-authored-by: piersoh <[email protected]>
Co-authored-by: pvinci <[email protected]>
Co-authored-by: Rahul Jadhav <[email protected]>
Co-authored-by: Robin Jarry <[email protected]>
Co-authored-by: romain-perez <[email protected]>
Co-authored-by: rperez <rperez@debian>
Co-authored-by: Sabrina Dubroca <[email protected]>
Co-authored-by: Sebastian Baar <[email protected]>
Co-authored-by: sebastien mainand <[email protected]>
Co-authored-by: smehner1 <[email protected]>
Co-authored-by: Steven Van Acker <[email protected]>
Co-authored-by: Thomas Faivre <[email protected]>
Co-authored-by: Tran Tien Dat <[email protected]>
Co-authored-by: Wael Mahlous <[email protected]>
Co-authored-by: waeva <[email protected]> | https://github.com/secdev/scapy.git | def GetIcmpStatistics():
statistics = MIB_ICMP()
_GetIcmpStatistics(byref(statistics))
results = _struct_to_dict(statistics)
del statistics
return results
##############################
##### Adapters Addresses #####
##############################
# Our GetAdaptersAddresses implementation is inspired by
# @sphaero 's gist: https://gist.github.com/sphaero/f9da6ebb9a7a6f679157
# published under a MPL 2.0 License (GPLv2 compatible)
# from iptypes.h
MAX_ADAPTER_ADDRESS_LENGTH = 8
MAX_DHCPV6_DUID_LENGTH = 130
GAA_FLAG_INCLUDE_PREFIX = 0x0010
GAA_FLAG_INCLUDE_ALL_INTERFACES = 0x0100
# for now, just use void * for pointers to unused structures
PIP_ADAPTER_WINS_SERVER_ADDRESS_LH = VOID
PIP_ADAPTER_GATEWAY_ADDRESS_LH = VOID
PIP_ADAPTER_DNS_SUFFIX = VOID
IF_OPER_STATUS = UINT
IF_LUID = UINT64
NET_IF_COMPARTMENT_ID = UINT32
GUID = BYTE * 16
NET_IF_NETWORK_GUID = GUID
NET_IF_CONNECTION_TYPE = UINT # enum
TUNNEL_TYPE = UINT # enum
| 27 | structures.py | Python | scapy/arch/windows/structures.py | 08b1f9d67c8e716fd44036a027bdc90dcb9fcfdf | scapy | 1 |
|
292,459 | 50 | 14 | 34 | 241 | 33 | 1 | 68 | 366 | upnp_factory_mock | Add dlna_dms integration to support DLNA Digital Media Servers (#66437) | https://github.com/home-assistant/core.git | def upnp_factory_mock() -> Iterable[Mock]:
with patch(
"homeassistant.components.dlna_dms.dms.UpnpFactory",
autospec=True,
spec_set=True,
) as upnp_factory:
upnp_device = create_autospec(UpnpDevice, instance=True)
upnp_device.name = MOCK_DEVICE_NAME
upnp_device.udn = MOCK_DEVICE_UDN
upnp_device.device_url = MOCK_DEVICE_LOCATION
upnp_device.device_type = MOCK_DEVICE_TYPE
upnp_device.available = True
upnp_device.parent_device = None
upnp_device.root_device = upnp_device
upnp_device.all_devices = [upnp_device]
upnp_device.services = {
"urn:schemas-upnp-org:service:ContentDirectory:1": create_autospec(
UpnpService,
instance=True,
service_type="urn:schemas-upnp-org:service:ContentDirectory:1",
service_id="urn:upnp-org:serviceId:ContentDirectory",
),
"urn:schemas-upnp-org:service:ConnectionManager:1": create_autospec(
UpnpService,
instance=True,
service_type="urn:schemas-upnp-org:service:ConnectionManager:1",
service_id="urn:upnp-org:serviceId:ConnectionManager",
),
}
seal(upnp_device)
upnp_factory_instance = upnp_factory.return_value
upnp_factory_instance.async_create_device.return_value = upnp_device
yield upnp_factory_instance
@pytest.fixture | @pytest.fixture | 143 | conftest.py | Python | tests/components/dlna_dms/conftest.py | b19bf9b147f4321e89d1f7f01e68337f2102f460 | core | 1 |
211,430 | 27 | 9 | 9 | 114 | 16 | 0 | 36 | 126 | forward | pose3d metro modeling (#6612)
* pose3d metro modeling
* delete extra comments | https://github.com/PaddlePaddle/PaddleDetection.git | def forward(self, pred3d, pred2d, inputs):
gt_3d_joints = inputs['joints_3d']
gt_2d_joints = inputs['joints_2d']
has_3d_joints = inputs['has_3d_joints']
has_2d_joints = inputs['has_2d_joints']
loss_3d = mpjpe(pred3d, gt_3d_joints, has_3d_joints)
loss_2d = keypoint_2d_loss(self.criterion_2dpose, pred2d, gt_2d_joints,
has_2d_joints)
return self.weight_3d * loss_3d + self.weight_2d * loss_2d
| 72 | pose3d_loss.py | Python | ppdet/modeling/losses/pose3d_loss.py | d4e34fe165c09db65fd00113708be1b711ac957c | PaddleDetection | 1 |
|
337,064 | 61 | 11 | 24 | 261 | 37 | 0 | 81 | 291 | test_stable_diffusion_fp16 | [img2img, inpainting] fix fp16 inference (#769)
* handle dtype in vae and image2image pipeline
* fix inpaint in fp16
* dtype should be handled in add_noise
* style
* address review comments
* add simple fast tests to check fp16
* fix test name
* put mask in fp16 | https://github.com/huggingface/diffusers.git | def test_stable_diffusion_fp16(self):
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
# put models in fp16
unet = unet.half()
vae = vae.half()
bert = bert.half()
# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images
assert image.shape == (1, 128, 128, 3)
| 165 | test_pipelines.py | Python | tests/test_pipelines.py | 92d70863663662669ee3c376909be1f876e00965 | diffusers | 1 |
|
209,387 | 17 | 9 | 7 | 56 | 3 | 0 | 23 | 58 | dce_rpc_endianess | Add SPDX License identifiers (#3655)
* Add SPDX License identifiers
* Relicense `ldp.py` with author consent
See https://github.com/secdev/scapy/issues/3478
* Apply guedou suggestions
* Relicense someim under GPL2
* DCE/RPC licensing | https://github.com/secdev/scapy.git | def dce_rpc_endianess(pkt):
if pkt.endianness == 0: # big endian
return ">"
elif pkt.endianness == 1: # little endian
return "<"
else:
return "!"
| 28 | dce_rpc.py | Python | scapy/contrib/dce_rpc.py | 9420c2229bf5330c2cc580f114f63f920a68db10 | scapy | 3 |
|
128,581 | 11 | 14 | 24 | 67 | 10 | 0 | 12 | 55 | get_results | [tune] Add Tuner.get_results() to retrieve results after restore (#29083)
At the moment, we need to call `tuner.fit()` to retrieve results. This PR adds a method `Tuner.get_results()` that will return the results again after fitting. It can also be used after restoring a run to get results without calling `fit()` (and potentially resuming failed trials).
Signed-off-by: Kai Fricke <[email protected]> | https://github.com/ray-project/ray.git | def get_results(self) -> ResultGrid:
if not self._is_ray_client:
return self._local_tuner.get_results()
else:
return ray.get(self._remote_tuner.fit.remote())
| 39 | tuner.py | Python | python/ray/tune/tuner.py | b510640f15a0fa4782b83ec2ea0749386b615b15 | ray | 2 |
|
276,451 | 12 | 9 | 4 | 42 | 8 | 0 | 12 | 44 | _get_tensors | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def _get_tensors(self, sess, tensor_list):
return [
sess.graph.get_tensor_by_name(tensor.name) for tensor in tensor_list
]
| 27 | graph_util_test.py | Python | keras/tests/graph_util_test.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 2 |
|
46,559 | 44 | 16 | 16 | 207 | 27 | 0 | 51 | 135 | pool_import_helper | More explicit messages for pools and exceptions (#22569) | https://github.com/apache/airflow.git | def pool_import_helper(filepath):
api_client = get_current_api_client()
with open(filepath) as poolfile:
data = poolfile.read()
try:
pools_json = json.loads(data)
except JSONDecodeError as e:
raise SystemExit(f"Invalid json file: {e}")
pools = []
failed = []
for k, v in pools_json.items():
if isinstance(v, dict) and len(v) == 2:
pools.append(api_client.create_pool(name=k, slots=v["slots"], description=v["description"]))
else:
failed.append(k)
return pools, failed
| 120 | pool_command.py | Python | airflow/cli/commands/pool_command.py | 7418720ce173ca5d0c5f5197c168e43258af8cc3 | airflow | 5 |
|
314,848 | 26 | 10 | 10 | 98 | 16 | 0 | 28 | 71 | test_dont_fire_on_unknown_module | Add tests for LCN sensor and binary_sensor platforms (#67263) | https://github.com/home-assistant/core.git | async def test_dont_fire_on_unknown_module(hass, lcn_connection):
inp = ModStatusAccessControl(
LcnAddr(0, 10, False), # unknown module
periphery=AccessControlPeriphery.FINGERPRINT,
code="aabbcc",
)
events = async_capture_events(hass, LCN_FINGERPRINT)
await lcn_connection.async_process_input(inp)
await hass.async_block_till_done()
assert len(events) == 0
| 60 | test_events.py | Python | tests/components/lcn/test_events.py | b7b8feda0ffb7487954545c96c50e7f64e2195bc | core | 1 |
|
310,210 | 22 | 10 | 13 | 101 | 13 | 0 | 28 | 81 | extra_state_attributes | Remove vera from mypy ignore list (#64474)
* Remove vera from mypy ignore list
* Fix pylint | https://github.com/home-assistant/core.git | def extra_state_attributes(self) -> dict[str, Any] | None:
data = super().extra_state_attributes or {}
last_user = self.vera_device.get_last_user_alert()
if last_user is not None:
data[ATTR_LAST_USER_NAME] = last_user[1]
data[ATTR_LOW_BATTERY] = self.vera_device.get_low_battery_alert()
return data
| 63 | lock.py | Python | homeassistant/components/vera/lock.py | 03bf2cdd56eb9a0a9ed56d7afb700d5f7d9cf75e | core | 3 |
|
33,645 | 68 | 12 | 24 | 337 | 36 | 0 | 84 | 284 | loss_labels | Add Deformable DETR (#17281)
* First draft
* More improvements
* Improve model, add custom CUDA code
* Import torch before
* Add script that imports custom layer
* Add everything in new ops directory
* Import custom layer in modeling file
* Fix ARCHIVE_MAP typo
* Creating the custom kernel on the fly.
* Import custom layer in modeling file
* More improvements
* Fix CUDA loading
* More improvements
* Improve conversion script
* Improve conversion script
* Make it work until encoder_outputs
* Make forward pass work
* More improvements
* Make logits match original implementation
* Make implementation also support single_scale model
* Add support for single_scale and dilation checkpoint
* Add support for with_box_refine model
* Support also two stage model
* Improve tests
* Fix more tests
* Make more tests pass
* Upload all models to the hub
* Clean up some code
* Improve decoder outputs
* Rename intermediate hidden states and reference points
* Improve model outputs
* Move tests to dedicated folder
* Improve model outputs
* Fix retain_grad test
* Improve docs
* Clean up and make test_initialization pass
* Improve variable names
* Add copied from statements
* Improve docs
* Fix style
* Improve docs
* Improve docs, move tests to model folder
* Fix rebase
* Remove DetrForSegmentation from auto mapping
* Apply suggestions from code review
* Improve variable names and docstrings
* Apply some more suggestions from code review
* Apply suggestion from code review
* better docs and variables names
* hint to num_queries and two_stage confusion
* remove asserts and code refactor
* add exception if two_stage is True and with_box_refine is False
* use f-strings
* Improve docs and variable names
* Fix code quality
* Fix rebase
* Add require_torch_gpu decorator
* Add pip install ninja to CI jobs
* Apply suggestion of @sgugger
* Remove DeformableDetrForObjectDetection from auto mapping
* Remove DeformableDetrModel from auto mapping
* Add model to toctree
* Add model back to mappings, skip model in pipeline tests
* Apply @sgugger's suggestion
* Fix imports in the init
* Fix copies
* Add CPU implementation
* Comment out GPU function
* Undo previous change
* Apply more suggestions
* Remove require_torch_gpu annotator
* Fix quality
* Add logger.info
* Fix logger
* Fix variable names
* Fix initializaztion
* Add missing initialization
* Update checkpoint name
* Add model to doc tests
* Add CPU/GPU equivalence test
* Add Deformable DETR to pipeline tests
* Skip model for object detection pipeline
Co-authored-by: Nicolas Patry <[email protected]>
Co-authored-by: Nouamane Tazi <[email protected]>
Co-authored-by: Sylvain Gugger <[email protected]> | https://github.com/huggingface/transformers.git | def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
if "logits" not in outputs:
raise ValueError("No logits were found in the outputs")
source_logits = outputs["logits"]
idx = self._get_source_permutation_idx(indices)
target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)])
target_classes = torch.full(
source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
)
target_classes[idx] = target_classes_o
target_classes_onehot = torch.zeros(
[source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1],
dtype=source_logits.dtype,
layout=source_logits.layout,
device=source_logits.device,
)
target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)
target_classes_onehot = target_classes_onehot[:, :, :-1]
loss_ce = (
sigmoid_focal_loss(source_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2)
* source_logits.shape[1]
)
losses = {"loss_ce": loss_ce}
return losses
| 226 | modeling_deformable_detr.py | Python | src/transformers/models/deformable_detr/modeling_deformable_detr.py | 59407bbeb31fff8340938768051c9daabd38d7a7 | transformers | 3 |
|
22,665 | 18 | 11 | 5 | 61 | 6 | 0 | 19 | 62 | component | refactor: clean code
Signed-off-by: slowy07 <[email protected]> | https://github.com/geekcomputers/Python.git | def component(self, i):
if i < len(self.__components) and i >= 0:
return self.__components[i]
else:
raise Exception("index out of range")
| 36 | lib.py | Python | linear-algebra-python/src/lib.py | f0af0c43340763724f139fa68aa1e5a9ffe458b4 | Python | 3 |
|
260,557 | 82 | 13 | 28 | 334 | 28 | 0 | 103 | 367 | fit | MAINT add parameter_constraints for MultiOutputClassifier and MultiOutputRegressor (#23902)
Co-authored-by: jeremiedbb <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def fit(self, X, y, sample_weight=None, **fit_params):
self._validate_params()
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = self._validate_data(X="no_validation", y=y, multi_output=True)
if is_classifier(self):
check_classification_targets(y)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
fit_params_validated = _check_fit_params(X, fit_params)
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params_validated
)
for i in range(y.shape[1])
)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self
| 209 | multioutput.py | Python | sklearn/multioutput.py | d942600e1f1979c431c24f59933a95155789f324 | scikit-learn | 9 |
|
181,911 | 13 | 10 | 6 | 74 | 12 | 0 | 16 | 34 | mutate_random_individual | Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | https://github.com/EpistasisLab/tpot.git | def mutate_random_individual(population, toolbox):
idx = np.random.randint(0,len(population))
ind = population[idx]
ind, = toolbox.mutate(ind)
del ind.fitness.values
return ind
| 46 | gp_deap.py | Python | tpot/gp_deap.py | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | 1 |
|
224,172 | 32 | 11 | 21 | 232 | 21 | 0 | 42 | 181 | test_nav_no_title | Some manual changes ahead of formatting code with Black | https://github.com/mkdocs/mkdocs.git | def test_nav_no_title(self):
nav_cfg = [
'index.md',
{'About': 'about.md'},
]
expected = dedent(
)
cfg = load_config(nav=nav_cfg, site_url='http://example.com/')
fs = [
File(nav_cfg[0], cfg['docs_dir'], cfg['site_dir'], cfg['use_directory_urls']),
File(nav_cfg[1]['About'], cfg['docs_dir'], cfg['site_dir'], cfg['use_directory_urls'])
]
files = Files(fs)
site_navigation = get_navigation(files, cfg)
self.assertEqual(str(site_navigation).strip(), expected)
self.assertEqual(len(site_navigation.items), 2)
self.assertEqual(len(site_navigation.pages), 2)
| 142 | nav_tests.py | Python | mkdocs/tests/structure/nav_tests.py | 372384d8102ddb4be6360f44d1bfddb8b45435a4 | mkdocs | 1 |
|
60,276 | 4 | 7 | 2 | 21 | 2 | 0 | 4 | 18 | to_proto | Balanced joint maximum mean discrepancy for deep transfer learning | https://github.com/jindongwang/transferlearning.git | def to_proto(self):
return to_proto(self)
| 11 | net_spec.py | Python | code/deep/BJMMD/caffe/python/caffe/net_spec.py | cc4d0564756ca067516f71718a3d135996525909 | transferlearning | 1 |
|
168,314 | 8 | 10 | 3 | 45 | 8 | 0 | 8 | 29 | get | DOC/TST: Clarify Series.str.get supports passing hashable label (#47918)
* gh 47911
* pre-commit issue
* add test and fix doc
* modified comment
* pep 8
* add more elements | https://github.com/pandas-dev/pandas.git | def get(self, i):
result = self._data.array._str_get(i)
return self._wrap_result(result)
| 27 | accessor.py | Python | pandas/core/strings/accessor.py | d5b4b33f1034b0fb0aa8a76cefe620794e28e851 | pandas | 1 |
|
296,122 | 6 | 7 | 3 | 25 | 4 | 0 | 6 | 20 | is_connected | Add missing type declaration to AsusWrt Scanner Entity (#69773) | https://github.com/home-assistant/core.git | def is_connected(self) -> bool:
return self._device.is_connected
| 14 | device_tracker.py | Python | homeassistant/components/asuswrt/device_tracker.py | bc2ba8e1c8c988ae24f6961ce64187782f5ba32d | core | 1 |
|
261,022 | 84 | 12 | 16 | 185 | 17 | 0 | 113 | 219 | get_namespace | ENH Adds Array API support to LinearDiscriminantAnalysis (#22554)
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Julien Jerphanion <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def get_namespace(*arrays):
# `arrays` contains one or more arrays, or possibly Python scalars (accepting
# those is a matter of taste, but doesn't seem unreasonable).
# Returns a tuple: (array_namespace, is_array_api)
if not get_config()["array_api_dispatch"]:
return _NumPyApiWrapper(), False
namespaces = {
x.__array_namespace__() if hasattr(x, "__array_namespace__") else None
for x in arrays
if not isinstance(x, (bool, int, float, complex))
}
if not namespaces:
# one could special-case np.ndarray above or use np.asarray here if
# older numpy versions need to be supported.
raise ValueError("Unrecognized array input")
if len(namespaces) != 1:
raise ValueError(f"Multiple namespaces for array inputs: {namespaces}")
(xp,) = namespaces
if xp is None:
# Use numpy as default
return _NumPyApiWrapper(), False
return _ArrayAPIWrapper(xp), True
| 107 | _array_api.py | Python | sklearn/utils/_array_api.py | 2710a9e7eefd2088ce35fd2fb6651d5f97e5ef8b | scikit-learn | 8 |
|
268,938 | 113 | 16 | 32 | 479 | 22 | 1 | 178 | 316 | array_to_img | Copy image utils from keras_preprocessing directly into core keras
This is not new code, we are just moving these utilities directly
into keras from keras-preprocessing.
For the library code, just fixed linting errors.
For the test code, had to do more major changes to port from pytest, but
hopefully any errors have been caught by the tests themselves.
PiperOrigin-RevId: 427274651 | https://github.com/keras-team/keras.git | def array_to_img(x, data_format=None, scale=True, dtype=None):
if data_format is None:
data_format = backend.image_data_format()
if dtype is None:
dtype = backend.floatx()
if pil_image is None:
raise ImportError('Could not import PIL.Image. '
'The use of `array_to_img` requires PIL.')
x = np.asarray(x, dtype=dtype)
if x.ndim != 3:
raise ValueError('Expected image array to have rank 3 (single image). '
f'Got array with shape: {x.shape}')
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError(f'Invalid data_format: {data_format}')
# Original Numpy array x has format (height, width, channel)
# or (channel, height, width)
# but target PIL image has format (width, height, channel)
if data_format == 'channels_first':
x = x.transpose(1, 2, 0)
if scale:
x = x - np.min(x)
x_max = np.max(x)
if x_max != 0:
x /= x_max
x *= 255
if x.shape[2] == 4:
# RGBA
return pil_image.fromarray(x.astype('uint8'), 'RGBA')
elif x.shape[2] == 3:
# RGB
return pil_image.fromarray(x.astype('uint8'), 'RGB')
elif x.shape[2] == 1:
# grayscale
if np.max(x) > 255:
# 32-bit signed integer grayscale image. PIL mode "I"
return pil_image.fromarray(x[:, :, 0].astype('int32'), 'I')
return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L')
else:
raise ValueError(f'Unsupported channel number: {x.shape[2]}')
@keras_export('keras.utils.img_to_array',
'keras.preprocessing.image.img_to_array') | @keras_export('keras.utils.img_to_array',
'keras.preprocessing.image.img_to_array') | 264 | image.py | Python | keras/preprocessing/image.py | 373ad97c72ed1ac4b6898e85b2cfd7b016e4b469 | keras | 13 |
276,040 | 34 | 11 | 21 | 177 | 23 | 0 | 50 | 225 | _infer_inputs | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def _infer_inputs(self, layer_node_id, convert_to_shapes=False):
call_fn_id = self._search_for_child_node(
layer_node_id, ["call_and_return_all_conditional_losses"]
)
if call_fn_id is None:
return None
concrete_functions = self._proto.nodes[
call_fn_id
].function.concrete_functions
if not concrete_functions:
return None
call_fn_name = concrete_functions[0]
call_fn_proto = self._proto.concrete_functions[call_fn_name]
structured_input_signature = tf.__internal__.saved_model.decode_proto(
call_fn_proto.canonicalized_input_signature
)
inputs = structured_input_signature[0][0]
if convert_to_shapes:
return tf.nest.map_structure(lambda spec: spec.shape, inputs)
else:
return inputs
| 113 | load.py | Python | keras/saving/saved_model/load.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 4 |
|
289,389 | 20 | 10 | 73 | 90 | 11 | 0 | 34 | 83 | test_measure_from_end_going_backwards | Ensure recorder test fixture is setup before hass fixture (#80528)
* Ensure recorder test fixture is setup before hass fixture
* Adjust more tests | https://github.com/home-assistant/core.git | async def test_measure_from_end_going_backwards(recorder_mock, hass):
start_time = dt_util.utcnow() - timedelta(minutes=60)
t0 = start_time + timedelta(minutes=20)
t1 = t0 + timedelta(minutes=10)
t2 = t1 + timedelta(minutes=10)
# Start t0 t1 t2 End
# |--20min--|--20min--|--10min--|--10min--|
# |---off---|---on----|---off---|---on----|
| 393 | test_sensor.py | Python | tests/components/history_stats/test_sensor.py | 31a787558fd312331b55e5c2c4b33341fc3601fc | core | 2 |
|
268,596 | 32 | 15 | 6 | 111 | 12 | 0 | 36 | 72 | _gen_candidate_chars | fix password lookup's use of f=v settings (#76551)
update tests | https://github.com/ansible/ansible.git | def _gen_candidate_chars(characters):
chars = []
for chars_spec in characters:
# getattr from string expands things like "ascii_letters" and "digits"
# into a set of characters.
chars.append(to_text(getattr(string, to_native(chars_spec), chars_spec), errors='strict'))
chars = u''.join(chars).replace(u'"', u'').replace(u"'", u'')
return chars
| 67 | password.py | Python | lib/ansible/plugins/lookup/password.py | 5d253a13807e884b7ce0b6b57a963a45e2f0322c | ansible | 2 |
|
245,934 | 54 | 11 | 20 | 256 | 26 | 1 | 76 | 166 | quality_focal_loss_tensor_target | [Feat]: adjust FocalLoss and QualityFocalLoss to allow different kinds of targets (#9481)
* Adjust FocalLoss and QualityFocalLoss for MMYOLO
* Adjust FocalLoss and QualityFocalLoss to fit MMYOLO
* Adjust FocalLoss and QualityFocalLoss to fit MMYOLO
* add comment
* Add docstring
* refine docstring
* add a new quality_focal_loss_tensor_target function to support any dim tensor target
* add activated condition
* Add a test unit to determine whether two losses are equal | https://github.com/open-mmlab/mmdetection.git | def quality_focal_loss_tensor_target(pred, target, beta=2.0, activated=False):
# pred and target should be of the same size
assert pred.size() == target.size()
if activated:
pred_sigmoid = pred
loss_function = F.binary_cross_entropy
else:
pred_sigmoid = pred.sigmoid()
loss_function = F.binary_cross_entropy_with_logits
scale_factor = pred_sigmoid
target = target.type_as(pred)
zerolabel = scale_factor.new_zeros(pred.shape)
loss = loss_function(
pred, zerolabel, reduction='none') * scale_factor.pow(beta)
pos = (target != 0)
scale_factor = target[pos] - pred_sigmoid[pos]
loss[pos] = loss_function(
pred[pos], target[pos],
reduction='none') * scale_factor.abs().pow(beta)
loss = loss.sum(dim=1, keepdim=False)
return loss
@weighted_loss | @weighted_loss | 161 | gfocal_loss.py | Python | mmdet/models/losses/gfocal_loss.py | 380d936098c051a639ed8403667d95a595145c2a | mmdetection | 2 |
291,850 | 4 | 6 | 2 | 16 | 3 | 0 | 4 | 18 | supported_options | Add dialect support to google_translate (#81768)
* Add TLD option support to google_translate
* Fix tests for added TLD option in google_translate
* Add Language to TLD mapping, Make tld configurable in google_translate
* Move const to dedicated file in google_translate | https://github.com/home-assistant/core.git | def supported_options(self):
return SUPPORT_OPTIONS
| 8 | tts.py | Python | homeassistant/components/google_translate/tts.py | 5533368171525f00beb7b2355f49c5b774408996 | core | 1 |
|
34,566 | 26 | 11 | 7 | 101 | 8 | 0 | 28 | 68 | maybe_append_new_line | [DocTests Speech] Add doc tests for all speech models (#15031)
* fix_torch_device_generate_test
* remove @
* doc tests
* up
* up
* fix doctests
* adapt files
* finish refactor
* up
* save intermediate
* add more logic
* new change
* improve
* next try
* next try
* next try
* next try
* fix final spaces
* fix final spaces
* improve
* renaming
* correct more bugs
* finish wavlm
* add comment
* run on test runner
* finish all speech models
* adapt
* finish | https://github.com/huggingface/transformers.git | def maybe_append_new_line(code):
lines = code.split("\n")
if lines[0] in ["py", "python"]:
# add new line before last line being ```
last_line = lines[-1]
lines.pop()
lines.append("\n" + last_line)
return "\n".join(lines)
| 53 | prepare_for_doc_test.py | Python | utils/prepare_for_doc_test.py | 9f831bdeaf965acca6c6097dfffb1364f4416c17 | transformers | 2 |
|
153,887 | 14 | 11 | 4 | 55 | 8 | 0 | 14 | 46 | add_to_apply_calls | REFACTOR-#4530: Standardize access to physical data in partitions (#4563)
Signed-off-by: Alexey Prutskov <[email protected]> | https://github.com/modin-project/modin.git | def add_to_apply_calls(self, func, *args, **kwargs):
return PandasOnRayDataframePartition(
self._data, call_queue=self.call_queue + [(func, args, kwargs)]
)
| 37 | partition.py | Python | modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py | 4ec7f6347903f9133c65ebc5b6e0e15553b98577 | modin | 1 |
|
290,592 | 29 | 12 | 62 | 215 | 15 | 0 | 46 | 202 | test_remote_scanner | Move bluetooth remote scanner implementation into a base class (#82012) | https://github.com/home-assistant/core.git | async def test_remote_scanner(hass):
manager = _get_manager()
switchbot_device = BLEDevice(
"44:44:33:11:23:45",
"wohand",
{},
rssi=-100,
)
switchbot_device_adv = generate_advertisement_data(
local_name="wohand",
service_uuids=["050a021a-0000-1000-8000-00805f9b34fb"],
service_data={"050a021a-0000-1000-8000-00805f9b34fb": b"\n\xff"},
manufacturer_data={1: b"\x01"},
rssi=-100,
)
switchbot_device_2 = BLEDevice(
"44:44:33:11:23:45",
"w",
{},
rssi=-100,
)
switchbot_device_adv_2 = generate_advertisement_data(
local_name="wohand",
service_uuids=["00000001-0000-1000-8000-00805f9b34fb"],
service_data={"00000001-0000-1000-8000-00805f9b34fb": b"\n\xff"},
manufacturer_data={1: b"\x01", 2: b"\x02"},
rssi=-100,
)
| 337 | test_models.py | Python | tests/components/bluetooth/test_models.py | f584efa0c24df19ef1f805ecf95a95cecec5ff99 | core | 1 |
|
20,526 | 45 | 10 | 3 | 84 | 14 | 0 | 51 | 128 | with_class | 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 | https://github.com/pypa/pipenv.git | def with_class(classname, namespace=""):
<div>
Some text
<div class="grid">1 4 0 1 0</div>
<div class="graph">1,3 2,3 1,1</div>
<div>this <div> has no class</div>
</div>
classattr = "{}:class".format(namespace) if namespace else "class"
return with_attribute(**{classattr: classname})
# pre-PEP8 compatibility symbols
replaceWith = replace_with
removeQuotes = remove_quotes
withAttribute = with_attribute
withClass = with_class
matchOnlyAtCol = match_only_at_col
| 32 | actions.py | Python | pipenv/patched/notpip/_vendor/pyparsing/actions.py | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | 2 |
|
1,721 | 13 | 10 | 6 | 56 | 8 | 0 | 18 | 67 | predict | fix pooling layers
add notebooks for model training | https://github.com/OpenMined/PySyft.git | def predict(self, X):
x_next = X
for layer in self.layers[:]:
x_next = layer.forward(x_next)
y_pred = x_next
return y_pred
| 34 | model.py | Python | packages/syft/src/syft/core/tensor/nn/model.py | f9c115a133e58935de7905dd8a19b6b0d6490500 | PySyft | 2 |
|
190,823 | 113 | 14 | 25 | 348 | 25 | 0 | 164 | 532 | getSubRectangles | Reformat of files using black
These files were not properly formatted. | https://github.com/thumbor/thumbor.git | def getSubRectangles(self, ims):
# Check image count
if len(ims) < 2:
return ims, [(0, 0) for i in ims]
# We need numpy
if np is None:
raise RuntimeError("Need Numpy to calculate sub-rectangles. ")
# Prepare
ims2 = [ims[0]]
xy = [(0, 0)]
# t0 = time.time()
# Iterate over images
prev = ims[0]
for im in ims[1:]:
# Get difference, sum over colors
diff = np.abs(im - prev)
if diff.ndim == 3:
diff = diff.sum(2)
# Get begin and end for both dimensions
X = np.argwhere(diff.sum(0))
Y = np.argwhere(diff.sum(1))
# Get rect coordinates
if X.size and Y.size:
x0, x1 = X[0], X[-1] + 1
y0, y1 = Y[0], Y[-1] + 1
else: # No change ... make it minimal
x0, x1 = 0, 2
y0, y1 = 0, 2
# Cut out and store
im2 = im[y0:y1, x0:x1]
prev = im
ims2.append(im2)
xy.append((x0, y0))
# Done
# print('%1.2f seconds to determine subrectangles of %i images' %
# (time.time()-t0, len(ims2)) )
return ims2, xy
| 215 | pil.py | Python | thumbor/engines/extensions/pil.py | 3c745ef193e9af9244cc406734e67815377472ed | thumbor | 8 |
|
244,171 | 5 | 6 | 27 | 19 | 5 | 0 | 5 | 8 | split_coco | [Feature] Support splitting COCO data for Semi-supervised object detection. (#7431)
* Split COCO data for Semi-supervised object detection.
* import mmcv and use f-string
* add a parser out_dir to set the path of semi-annos
* Support multiprocessing
* use mmcv.track_parallel_progress
* fix
* rename some variables | https://github.com/open-mmlab/mmdetection.git | def split_coco(data_root, out_dir, percent, fold):
| 214 | split_coco.py | Python | tools/misc/split_coco.py | 04db930cec2bb1bf628456ac57ec1aa396204b1b | mmdetection | 5 |
|
22,785 | 27 | 16 | 5 | 122 | 10 | 0 | 36 | 75 | expand_block | refactor: clean code
Signed-off-by: slowy07 <[email protected]> | https://github.com/geekcomputers/Python.git | def expand_block(self, block):
w = list(struct.unpack(">16L", block)) + [0] * 64
for i in range(16, 80):
w[i] = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]), 1)
return w
| 80 | sha1.py | Python | sha1.py | f0af0c43340763724f139fa68aa1e5a9ffe458b4 | Python | 2 |
|
309,481 | 6 | 6 | 12 | 19 | 4 | 0 | 6 | 20 | speed_count | Bump pytradfri to 8.0.1 and fix fan preset mode "Auto" bug (#63920)
* Move util functions
* Fix errors
* Revert changes
* Fix tests
* Use self.async_set_percentage()
* Fix calculation functions and associated tests
* Handle case of 0
* Update tests/components/tradfri/test_util.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Update tests/components/tradfri/test_util.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Update tests/components/tradfri/test_util.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Handle case of 0
* Update homeassistant/components/tradfri/fan.py
Co-authored-by: Martin Hjelmare <[email protected]>
Co-authored-by: Martin Hjelmare <[email protected]> | https://github.com/home-assistant/core.git | def speed_count(self) -> int:
return ATTR_MAX_FAN_STEPS
| 10 | fan.py | Python | homeassistant/components/tradfri/fan.py | b52a8ba37a5e5e05b80beddff06b116371941d86 | core | 1 |
|
181,816 | 26 | 8 | 7 | 55 | 7 | 0 | 29 | 83 | _combine_individual_stats | Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | https://github.com/EpistasisLab/tpot.git | def _combine_individual_stats(self, operator_count, cv_score, individual_stats):
stats = deepcopy(
individual_stats
) # Deepcopy, since the string reference to predecessor should be cloned
stats["operator_count"] = operator_count
stats["internal_cv_score"] = cv_score
return stats
| 32 | base.py | Python | tpot/base.py | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | 1 |
|
200,290 | 9 | 11 | 4 | 54 | 6 | 0 | 9 | 29 | raise_on_deprecated | runtests.py: Undo auto-formatting, re-add changes to blacklist for scipy, numpy | https://github.com/sympy/sympy.git | def raise_on_deprecated():
with warnings.catch_warnings():
warnings.filterwarnings('error', '.*', DeprecationWarning, module='sympy.*')
yield
| 27 | runtests.py | Python | sympy/testing/runtests.py | 6d2bbf80752549276a968fd4af78231c569d55c5 | sympy | 1 |
|
142,835 | 4 | 6 | 2 | 19 | 3 | 0 | 4 | 18 | get_live_trials | [tune/structure] Introduce execution package (#26015)
Execution-specific packages are moved to tune.execution.
Co-authored-by: Xiaowei Jiang <[email protected]> | https://github.com/ray-project/ray.git | def get_live_trials(self):
return self._live_trials
| 10 | trial_runner.py | Python | python/ray/tune/execution/trial_runner.py | 0959f44b6fc217a4f2766ed46a721eb79b067b2c | ray | 1 |
|
96,980 | 26 | 11 | 13 | 75 | 12 | 0 | 27 | 62 | is_guessed_to_be_created_on_project_creation | ref(types): Add types to conditions and filters (#32393) | https://github.com/getsentry/sentry.git | def is_guessed_to_be_created_on_project_creation(self) -> bool:
# TODO(mgaeta): Bug: Rule is optional.
delta = abs(self.rule.date_added - self.project.date_added)
guess: bool = delta.total_seconds() < 30 and self.rule.label == DEFAULT_RULE_LABEL
return guess
| 45 | event_frequency.py | Python | src/sentry/rules/conditions/event_frequency.py | 654c6627307359956c6d44f83791d6b177841363 | sentry | 2 |
|
248,089 | 16 | 12 | 12 | 140 | 6 | 0 | 18 | 93 | test_check_push_rules_actions | Add a module API to allow modules to edit push rule actions (#12406)
Co-authored-by: Richard van der Hoff <[email protected]> | https://github.com/matrix-org/synapse.git | def test_check_push_rules_actions(self) -> None:
with self.assertRaises(InvalidRuleException):
self.module_api.check_push_rule_actions(["foo"])
with self.assertRaises(InvalidRuleException):
self.module_api.check_push_rule_actions({"foo": "bar"})
self.module_api.check_push_rule_actions(["notify"])
self.module_api.check_push_rule_actions(
[{"set_tweak": "sound", "value": "default"}]
)
| 74 | test_api.py | Python | tests/module_api/test_api.py | 5ef673de4f0bf991402ee29235741a91a7cc9b02 | synapse | 1 |
|
43,887 | 53 | 17 | 31 | 292 | 32 | 0 | 83 | 452 | on_task_instance_state_session_flush | Add Listener Plugin API that tracks TaskInstance state changes (#20443)
This adds new Plugin API - "listeners". It enables plugin authors to write
[pluggy hook implementation][1] that will be called on certain formalized extension
points. To differentiate between current Airflow extension points, like
plugins, and current Airflow hooks, implementations of those hooks are called
listeners.
The API is ment to be called across all dags, and all operators - in contrast
to current on_success_callback, pre_execute and related family which are meant
to provide callbacks for particular dag authors, or operator creators.
pluggy mechanism enables us to execute multiple, or none, listeners that
implement particular extension point, so that users can use multiple listeners
seamlessly.
In this PR, three such extension points are added. When TaskInstance's state is
changed to RUNNING, on_task_instance_running hook is called. On change
toSUCCESS on_task_instance_success is called, similarly on FAILED
on_task_instance_failed is called.
Actual notification mechanism is be implemented using [SQLAlchemy’s events
mechanism][2]. This ensures that plugins will get every change of state,
regardless of where in the codebase it happened, and not require manual
annotation of TI state changes across the codebase.
To make sure that this change is not affecting performance, running this
mechanism on scheduler is disabled by default. The SQLAlchemy event mechanism
is also not affected by default - the event listener is only added if we have
any plugin which actually provides any listener.
[1]: https://pluggy.readthedocs.io/en/stable/
[2]: https://docs.sqlalchemy.org/en/13/orm/session_events.html#after-flush
Signed-off-by: Maciej Obuchowski <[email protected]> | https://github.com/apache/airflow.git | def on_task_instance_state_session_flush(session, flush_context):
logger = logging.getLogger(__name__)
if not get_listener_manager().has_listeners:
return
for state in flush_context.states:
if isinstance(state.object, TaskInstance) and session.is_modified(
state.object, include_collections=False
):
added, unchanged, deleted = flush_context.get_attribute_history(state, 'state')
logger.debug(
"session flush listener: added %s unchanged %s deleted %s - %s",
added,
unchanged,
deleted,
state.object,
)
if not added:
continue
previous_state = deleted[0] if deleted else State.NONE
if State.RUNNING in added:
get_listener_manager().hook.on_task_instance_running(
previous_state=previous_state, task_instance=state.object, session=session
)
elif State.FAILED in added:
get_listener_manager().hook.on_task_instance_failed(
previous_state=previous_state, task_instance=state.object, session=session
)
elif State.SUCCESS in added:
get_listener_manager().hook.on_task_instance_success(
previous_state=previous_state, task_instance=state.object, session=session
)
| 190 | events.py | Python | airflow/listeners/events.py | dba00ce6a32b7f50153887c6974f62985ca8023f | airflow | 10 |
|
277,101 | 8 | 11 | 5 | 49 | 9 | 0 | 8 | 21 | sync_to_numpy_or_python_type | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def sync_to_numpy_or_python_type(tensors):
if isinstance(tensors, tf.distribute.experimental.coordinator.RemoteValue):
tensors = tensors.fetch()
| 42 | tf_utils.py | Python | keras/utils/tf_utils.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 2 |
|
43,650 | 23 | 14 | 6 | 83 | 9 | 0 | 25 | 102 | cancel_triggers | Rename `to_delete` to `to_cancel` in TriggerRunner (#20658)
The queue's purpose is to track triggers that need to be canceled. The language `to_delete` was a bit confusing because for one it does not actually delete them but cancel them. The deletion work is actually in `cleanup_finished_triggers`. It seems that this method will usually not do anything and it's only for cancelling triggers that are currently running but for whatever reason no longer should be. E.g. when a task is killed and therefore the trigger is no longer needed, or some multi-triggerer scenarios. So putting cancel in the name also highlights that this is about stopping running triggers, not e.g. purging completed ones. | https://github.com/apache/airflow.git | async def cancel_triggers(self):
while self.to_cancel:
trigger_id = self.to_cancel.popleft()
if trigger_id in self.triggers:
# We only delete if it did not exit already
self.triggers[trigger_id]["task"].cancel()
await asyncio.sleep(0)
| 47 | triggerer_job.py | Python | airflow/jobs/triggerer_job.py | c20ad79b40ea2b213f6dca221221c6dbd55bd08f | airflow | 3 |
|
166,980 | 5 | 6 | 21 | 27 | 4 | 0 | 5 | 8 | parametrize_fixture_doc | TYP: pandas/_testing (#47037)
* TYP: bunch of type annotations
* change not needed | https://github.com/pandas-dev/pandas.git | def parametrize_fixture_doc(*args) -> Callable[[F], F]:
| 20 | _test_decorators.py | Python | pandas/util/_test_decorators.py | c9c6685c51ead26bbbb9a0dd565e82967cd839e8 | pandas | 1 |
|
102,175 | 10 | 8 | 8 | 47 | 6 | 0 | 11 | 32 | test_empty_backend | Revert "Revert D32498569: allow external backend codegen to toggle whether to generate out= and inplace kernels" (#69950)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69950
This reverts commit f6cad53443704dfe5a20cc62bee14d91e3bffcaa.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33113545
Pulled By: bdhirsh
fbshipit-source-id: d6590294662588d36c09662dea65919ad4e1e288 | https://github.com/pytorch/pytorch.git | def test_empty_backend(self) -> None:
yaml_str =
output_error = self.get_errors_from_gen_backend_stubs(yaml_str)
self.assertExpectedInline(output_error, )
| 26 | test_gen_backend_stubs.py | Python | tools/test/test_gen_backend_stubs.py | bb5b4cceb6f737448eaaa6817cd773b6f4b0e77d | pytorch | 1 |
|
270,204 | 25 | 12 | 9 | 84 | 10 | 0 | 29 | 84 | normalize_cluster_spec | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def normalize_cluster_spec(cluster_spec):
if isinstance(cluster_spec, (dict, cluster_pb2.ClusterDef)):
return tf.train.ClusterSpec(cluster_spec)
elif not isinstance(cluster_spec, tf.train.ClusterSpec):
raise ValueError(
"`cluster_spec' should be dict or a `tf.train.ClusterSpec` or a "
"`tf.train.ClusterDef` object"
)
return cluster_spec
| 50 | distribute_coordinator_utils.py | Python | keras/distribute/distribute_coordinator_utils.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 3 |
|
295,908 | 10 | 8 | 7 | 41 | 7 | 0 | 13 | 27 | available_tones | Add EntityFeature enum to Siren (#69585)
Co-authored-by: Franck Nijhof <[email protected]> | https://github.com/home-assistant/core.git | def available_tones(self) -> list[int | str] | dict[int, str] | None:
return self._attr_available_tones
| 26 | __init__.py | Python | homeassistant/components/siren/__init__.py | a61ac3ddc6d65522dfa1eb599adf73420a9267dc | core | 1 |
|
126,954 | 56 | 14 | 23 | 257 | 37 | 0 | 67 | 330 | test_ddpg_compilation | [RLlib] Move learning_starts logic from buffers into `training_step()`. (#26032) | https://github.com/ray-project/ray.git | def test_ddpg_compilation(self):
config = (
ddpg.DDPGConfig()
.training(num_steps_sampled_before_learning_starts=0)
.rollouts(num_rollout_workers=0, num_envs_per_worker=2)
)
explore = config.exploration_config.update({"random_timesteps": 100})
config.exploration(exploration_config=explore)
num_iterations = 1
# Test against all frameworks.
for _ in framework_iterator(config, with_eager_tracing=True):
algo = config.build(env="Pendulum-v1")
for i in range(num_iterations):
results = algo.train()
check_train_results(results)
print(results)
check_compute_single_action(algo)
# Ensure apply_gradient_fn is being called and updating global_step
pol = algo.get_policy()
if config.framework_str == "tf":
a = pol.get_session().run(pol.global_step)
else:
a = pol.global_step
check(a, 500)
algo.stop()
| 153 | test_ddpg.py | Python | rllib/algorithms/ddpg/tests/test_ddpg.py | 0dceddb912ed92286032b5563dd2e541a8a7031f | ray | 4 |
|
297,996 | 75 | 16 | 37 | 382 | 49 | 0 | 111 | 521 | async_step_user | Add blebox discovery/zeroconf (#83837)
Co-authored-by: J. Nick Koston <[email protected]> | https://github.com/home-assistant/core.git | async def async_step_user(self, user_input=None):
hass = self.hass
schema = create_schema(user_input)
if user_input is None:
return self.async_show_form(
step_id="user",
data_schema=schema,
errors={},
description_placeholders={},
)
addr = host_port(user_input)
for entry in self._async_current_entries():
if addr == host_port(entry.data):
host, port = addr
return self.async_abort(
reason=ADDRESS_ALREADY_CONFIGURED,
description_placeholders={"address": f"{host}:{port}"},
)
websession = async_get_clientsession(hass)
api_host = ApiHost(*addr, DEFAULT_SETUP_TIMEOUT, websession, hass.loop, _LOGGER)
try:
product = await Box.async_from_host(api_host)
except UnsupportedBoxVersion as ex:
return self.handle_step_exception(
"user", ex, schema, *addr, UNSUPPORTED_VERSION, _LOGGER.debug
)
except Error as ex:
return self.handle_step_exception(
"user", ex, schema, *addr, CANNOT_CONNECT, _LOGGER.warning
)
except RuntimeError as ex:
return self.handle_step_exception(
"user", ex, schema, *addr, UNKNOWN, _LOGGER.error
)
# Check if configured but IP changed since
await self.async_set_unique_id(product.unique_id, raise_on_progress=False)
self._abort_if_unique_id_configured()
return self.async_create_entry(title=product.name, data=user_input)
| 241 | config_flow.py | Python | homeassistant/components/blebox/config_flow.py | c737378ee14c12f988118dc9d23f1fc0b1da8ea1 | core | 7 |
|
167,373 | 61 | 18 | 26 | 240 | 21 | 0 | 96 | 488 | update_info | TYP: some return annotations in pytables.py (#47512) | https://github.com/pandas-dev/pandas.git | def update_info(self, info) -> None:
for key in self._info_fields:
value = getattr(self, key, None)
idx = info.setdefault(self.name, {})
existing_value = idx.get(key)
if key in idx and value is not None and existing_value != value:
# frequency/name just warn
if key in ["freq", "index_name"]:
ws = attribute_conflict_doc % (key, existing_value, value)
warnings.warn(
ws, AttributeConflictWarning, stacklevel=find_stack_level()
)
# reset
idx[key] = None
setattr(self, key, None)
else:
raise ValueError(
f"invalid info for [{self.name}] for [{key}], "
f"existing_value [{existing_value}] conflicts with "
f"new value [{value}]"
)
else:
if value is not None or existing_value is not None:
idx[key] = value
| 141 | pytables.py | Python | pandas/io/pytables.py | 7d2f9b8d59908fbf57c6453bc41891efbfe981a6 | pandas | 8 |
|
176,408 | 4 | 7 | 2 | 21 | 3 | 0 | 4 | 18 | out_degree | Updated MultiDiGraph documentation to include more examples of actually (#5387)
using parallel edges, and fixed references to things like G[u, v] where
G[u, v, k] is required for a MultiDigraph. Have not made parallel
changes in MultiGraph which should maybe also be made?
Docs tests pass on my end; no code outside of comments was changed.
-Peter Mawhorter | https://github.com/networkx/networkx.git | def out_degree(self):
return OutMultiDegreeView(self)
| 11 | multidigraph.py | Python | networkx/classes/multidigraph.py | 4d4cf1efd44326a858af33711cb0c631abc5105a | networkx | 1 |
|
264,675 | 6 | 8 | 2 | 31 | 4 | 0 | 6 | 12 | get_auth_backend_display | Closes #9123: Improve appearance of SSO login providers | https://github.com/netbox-community/netbox.git | def get_auth_backend_display(name):
return AUTH_BACKEND_ATTRS.get(name, (name, None))
| 19 | authentication.py | Python | netbox/netbox/authentication.py | d6df6b444f1bcc1b77b1b6ae6e726f3024e0abd4 | netbox | 1 |
|
45,743 | 24 | 10 | 9 | 101 | 10 | 0 | 30 | 94 | unmap | More explicit mapped argument validation (#21933)
* More explicit mapped argument validation
Instead of always using MagicMock to validate mapped arguments, this
implements a more sophisticated protocol that allows an operator to
implement a 'validate_mapped_arguments' to provide custom validation
logic. If an operator just wants to use __init__ for validation,
however, they can set a flag 'mapped_arguments_validated_by_init' to get
the behavior easily. (This does *not* use MagicMock, however, since any
custom validation logic should be able to handle those on its own).
The 'validate_mapped_arguments' flag is currently only set on
PythonOperator. It can likely be used on a lot more operators down the
road.
* Add flag to distinguish a validation-only init
There's just too much magic during a task's initialization that tries to
add it into the dependency graph. This flag is needed to work around all
that, I think. | https://github.com/apache/airflow.git | def unmap(self) -> "BaseOperator":
dag = self.dag
if not dag:
raise RuntimeError("Cannot unmap a task without a DAG")
dag._remove_task(self.task_id)
if isinstance(self.operator_class, str):
raise RuntimeError("Cannot unmap a deserialized operator")
return self.operator_class(**self._get_unmap_kwargs())
| 57 | mappedoperator.py | Python | airflow/models/mappedoperator.py | b65e52205a7045eb08d471289b85abda587442b7 | airflow | 3 |
|
154,520 | 26 | 11 | 12 | 150 | 17 | 0 | 39 | 143 | apply | REFACTOR-#5009: use RayWrapper.materialize instead of ray.get (#5010)
Signed-off-by: Myachev <[email protected]> | https://github.com/modin-project/modin.git | def apply(self, first, other, func, **kwargs):
df1 = self.cudf_dataframe_dict[first]
if not other:
result = func(df1, **kwargs)
return self.store_new_df(result)
if not isinstance(other, int):
assert isinstance(other, ray.ObjectRef)
df2 = RayWrapper.materialize(other)
else:
df2 = self.cudf_dataframe_dict[other]
result = func(df1, df2, **kwargs)
return self.store_new_df(result)
| 97 | gpu_manager.py | Python | modin/core/execution/ray/implementations/cudf_on_ray/partitioning/gpu_manager.py | 1dc16415333bf2428ee2b1f4d31ff94e66b9a0a6 | modin | 3 |
|
270,593 | 10 | 8 | 2 | 60 | 11 | 1 | 10 | 21 | get_default_mesh | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def get_default_mesh(self):
return self._default_mesh
LayoutMap.get.__doc__ = LayoutMap.__getitem__.__doc__
@keras_export("keras.dtensor.experimental.layout_map_scope", v1=[])
@contextlib.contextmanager | @keras_export("keras.dtensor.experimental.layout_map_scope", v1=[])
@contextlib.contextmanager | 10 | layout_map.py | Python | keras/dtensor/layout_map.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
308,560 | 51 | 13 | 27 | 261 | 18 | 0 | 77 | 246 | test_local_push_only | Allow mobile app registrations only supporting websocket push (#63208) | https://github.com/home-assistant/core.git | async def test_local_push_only(hass, hass_ws_client, setup_websocket_channel_only_push):
with pytest.raises(HomeAssistantError) as e_info:
assert await hass.services.async_call(
"notify",
"mobile_app_websocket_push_name",
{"message": "Not connected"},
blocking=True,
)
assert str(e_info.value) == "Device not connected to local push notifications"
client = await hass_ws_client(hass)
await client.send_json(
{
"id": 5,
"type": "mobile_app/push_notification_channel",
"webhook_id": "websocket-push-webhook-id",
}
)
sub_result = await client.receive_json()
assert sub_result["success"]
assert await hass.services.async_call(
"notify",
"mobile_app_websocket_push_name",
{"message": "Hello world 1"},
blocking=True,
)
msg = await client.receive_json()
assert msg == {"id": 5, "type": "event", "event": {"message": "Hello world 1"}}
| 143 | test_notify.py | Python | tests/components/mobile_app/test_notify.py | ad8af5fc7a52a66a584bc31c535f100fb7c71919 | core | 1 |
|
241,648 | 50 | 16 | 11 | 303 | 25 | 1 | 63 | 220 | test_error_raised_with_float_limited_eval_batches | Deprecate `TrainerDataLoadingMixin` and move logic to `DataConnector` (#11282)
Co-authored-by: Rohit Gupta <[email protected]>
Co-authored-by: Aki Nitta <[email protected]>
Co-authored-by: Carlos Mocholí <[email protected]> | https://github.com/Lightning-AI/lightning.git | def test_error_raised_with_float_limited_eval_batches():
model = BoringModel()
dl_size = len(model.val_dataloader())
limit_val_batches = 1 / (dl_size + 2)
trainer = Trainer(limit_val_batches=limit_val_batches)
trainer._data_connector.attach_data(model)
with pytest.raises(
MisconfigurationException,
match=fr"{limit_val_batches} \* {dl_size} < 1. Please increase the `limit_val_batches`",
):
trainer._data_connector._reset_eval_dataloader(RunningStage.VALIDATING, model)
@pytest.mark.parametrize(
"val_dl",
[
DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
CombinedLoader(DataLoader(dataset=RandomDataset(32, 64), shuffle=True)),
CombinedLoader(
[DataLoader(dataset=RandomDataset(32, 64)), DataLoader(dataset=RandomDataset(32, 64), shuffle=True)]
),
CombinedLoader(
{
"dl1": DataLoader(dataset=RandomDataset(32, 64)),
"dl2": DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
}
),
],
) | @pytest.mark.parametrize(
"val_dl",
[
DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
CombinedLoader(DataLoader(dataset=RandomDataset(32, 64), shuffle=True)),
CombinedLoader(
[DataLoader(dataset=RandomDataset(32, 64)), DataLoader(dataset=RandomDataset(32, 64), shuffle=True)]
),
CombinedLoader(
{
"dl1": DataLoader(dataset=RandomDataset(32, 64)),
"dl2": DataLoader(dataset=RandomDataset(32, 64), shuffle=True),
}
),
],
) | 71 | test_data_loading.py | Python | tests/trainer/test_data_loading.py | 5b59c951e28ddc8bb884f044b1f46fb54c23a8b8 | lightning | 1 |
8,060 | 23 | 11 | 6 | 99 | 15 | 0 | 30 | 45 | _get_dataset_configs | Config-first Datasets API (ludwig.datasets refactor) (#2479)
* Adds README and stub for reading dataset configs.
* Adds __init__.py for configs, moves circular import into function scope in ludwig/datasets/__init__.py
* Print config files in datasets folder.
* First pass at automatic archive extraction.
* Implemented downloading and extract.
* Refactor DatasetConfig into its own file.
* Fixed bugs downloading kaggle dataset.
* Makes registry store dataset instances, not classes. Also comments out import_submodules for testing.
* Typo fix.
* Only pass data files on to load_unprocessed_dataframe, symlink directories.
* Downloading dataset files into existing directory if exists.
* Refactor: make datasets fully config-first, lazy load dataset loaders.
* Implemented agnews custom loader.
* Implements train/validation/test split by files, and globbing support
* Adds _glob_multiple
* Adds adult_census_income, agnews, allstate_claims_severity.
* Implements sha256 verification, adds more datasets up to creditcard_fraud.
* Adds checksums, dbpedia, electricity
* Fixes gzip file name returned as string not list, adds up to forest_cover dataset.
* Adds datasets up to reuters_r8
* Adds all datasets which don't require a custom class.
* Restore dataset import behavior by implementing module __getattr__
* Adds KDD datasets.
* Adds ieee_fraud.
* Adds imbalanced_insurance, insurance_lite.
* Adds mnist.
* Completes implementation of all of the built-in datasets.
* Made cache_dir optional, read from environment variable if set.
* Upgrades datasets tests.
* Adds test for new dataset config API. Also adds scripts for dataset link checking.
* Fixes loading allstate claims severity dataset.
* Use @lru_cache(1), @cache not supported in python < 3.9
* Deletes dataset registry, updates automl test utils
* Fix imports of datasets API.
* Adds more detail to sha256: docstring and basic README
* Copy-paste link oops.
* Fixes handling of nested archive types like .tar.bz Also adds a LUDWIG_CACHE and export to the README
* Adds link for twitter bots.
* Fix order of splits in README.md
* typo
* Adds verify as a phase in doc string.
* Support .pqt, .pq extensions for parquet.
* Handle nested archives with longer file extensions like .csv.zip
* Handle nested .gz types properly too. Check all extensions with .endswith
* Handle all archive types with .endswith
* Update ludwig/datasets/loaders/split_loaders.py
Co-authored-by: Joppe Geluykens <[email protected]>
* Adds explanation for export, fixes preserve_paths (should be relative to processed_dataset_dir)
* Resolve preserved paths relative to raw dataset dir before move.
* Catch runtime exception from extracting sub-archives.
Co-authored-by: Daniel Treiman <[email protected]>
Co-authored-by: Joppe Geluykens <[email protected]> | https://github.com/ludwig-ai/ludwig.git | def _get_dataset_configs() -> Dict[str, DatasetConfig]:
import importlib.resources
config_files = [f for f in importlib.resources.contents(configs) if f.endswith(".yaml")]
config_objects = [load_dataset_config(f) for f in config_files]
return {c.name: c for c in config_objects}
| 63 | __init__.py | Python | ludwig/datasets/__init__.py | e4fc06f986e03919d9aef3ab55c05fee5a6b9d3a | ludwig | 5 |
|
291,214 | 18 | 11 | 5 | 64 | 9 | 0 | 20 | 52 | sound_mode_list | Bump to Arcam 1.0.1 and make strictly typed (#82487)
* Make arcam_fmj strictly typed
* Add test for invalid UDN | https://github.com/home-assistant/core.git | def sound_mode_list(self) -> list[str] | None:
if (values := self._state.get_decode_modes()) is None:
return None
return [x.name for x in values]
| 40 | media_player.py | Python | homeassistant/components/arcam_fmj/media_player.py | a55fb445b0ed4efd625227b4f13a01a0f469c358 | core | 3 |
|
288,186 | 17 | 9 | 7 | 74 | 9 | 0 | 19 | 65 | wait_for_ble_connections_free | Wait for disconnect when we are out of connection ble slots in esphome (#79246) | https://github.com/home-assistant/core.git | async def wait_for_ble_connections_free(self) -> int:
if self.ble_connections_free > 0:
return self.ble_connections_free
fut: asyncio.Future[int] = asyncio.Future()
self._ble_connection_free_futures.append(fut)
return await fut
| 44 | entry_data.py | Python | homeassistant/components/esphome/entry_data.py | 0b5289f7483dde5911f4a268233fea2ce3b417ff | core | 2 |
|
110,032 | 11 | 13 | 4 | 77 | 11 | 0 | 11 | 64 | update_from_data_y | Remove unnecessary np.{,as}array / astype calls.
Quite often numpy will call asarray for us, saving us the need to call
asarray explicitly.
When we do call asarray (or array) ourselves, a dtype can directly be
passed in, rather than immediately calling astype immediately after.
Passing the dtype makes it unnecessary for asarray to infer the dtype
of the passed-in container, and can also save an extra array allocation
if asarray first has to allocate an array of a type and astype
immediately has to allocate an array of another type. | https://github.com/matplotlib/matplotlib.git | def update_from_data_y(self, y, ignore=None):
y = np.ravel(y)
self.update_from_data_xy(np.column_stack([np.ones(y.size), y]),
ignore=ignore, updatex=False)
| 50 | transforms.py | Python | lib/matplotlib/transforms.py | 1068a6faa19767724437461bcfb88c6852ec435c | matplotlib | 1 |
|
87,234 | 5 | 7 | 2 | 28 | 3 | 0 | 5 | 11 | generate_cache_key_for_observed_release | feat(ds): Implements release boosting functionality for ds [TET-496] (#40403)
Sets releases that should be boosted with ds into the cache when a
transaction is observed in the event manager. The logic is as follows
once a transaction from a release that wasn't observed in the previous
24 hours is received, a cache key for that release is set with an
expiration of one day and then that release is set into a list of
boosted releases into the cache with an expiration of 1h, then the
project config is invalidated so we recompute the project config with
new dynamic sampling rule to boost that release with a hardcoded
interval for one hour. If that release doesn't send any transactions in
the next 24 hours i.e. after the 24 hour cache key expires and then
starts sending transaction again, we want to start boosting the release
again for an hour. This PR is one part of two parts, and only handles
the setting of the cache and the invalidation of the project config, but
does not include the dynamic sampling rules to be sent to relay. This is
by design so we can merge this into production and monitor the
performance impact of this logic before committing to adding the dynamic
sampling rules
As a follow up, add a PR that only runs this logic if the feature flags
for dynamic sampling are enabled, however we want to merge this without
that check to monitor production load | https://github.com/getsentry/sentry.git | def generate_cache_key_for_observed_release(project_id, release_id):
return f"ds::p:{project_id}:r:{release_id}"
| 11 | latest_release_booster.py | Python | src/sentry/dynamic_sampling/latest_release_booster.py | 0fc7bab05d499d4df4faea2d11f49d2be8214776 | sentry | 1 |
|
101,208 | 79 | 13 | 15 | 213 | 22 | 0 | 98 | 301 | _update_file_format | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | https://github.com/deepfakes/faceswap.git | def _update_file_format(self, folder, filename):
logger.info("Reformatting legacy alignments file...")
old_location = os.path.join(str(folder), filename)
new_location = f"{os.path.splitext(old_location)[0]}.{self._serializer.file_extension}"
if os.path.exists(old_location):
if os.path.exists(new_location):
logger.info("Using existing updated alignments file found at '%s'. If you do not "
"wish to use this existing file then you should delete or rename it.",
new_location)
else:
logger.info("Old location: '%s', New location: '%s'", old_location, new_location)
load_serializer = get_serializer_from_filename(old_location)
data = load_serializer.load(old_location)
self._serializer.save(new_location, data)
return os.path.basename(new_location)
# <Structure> #
# Alignments were structured: {frame_name: <list of faces>}. We need to be able to store
# information at the frame level, so new structure is: {frame_name: {faces: <list of faces>}} | 109 | alignments.py | Python | lib/align/alignments.py | 5e73437be47f2410439a3c6716de96354e6a0c94 | faceswap | 3 |
|
297,977 | 16 | 9 | 6 | 47 | 6 | 0 | 16 | 66 | async_sync | String formatting and max line length - Part 5 (#84501)
Co-authored-by: jjlawren <[email protected]> | https://github.com/home-assistant/core.git | async def async_sync(self, other_player):
_LOGGER.warning(
"Service squeezebox.sync is deprecated; use media_player.join_players"
" instead"
)
await self.async_join_players([other_player])
| 24 | media_player.py | Python | homeassistant/components/squeezebox/media_player.py | f39f3b612a8c1a12504f2f1d54fb1c9872216d12 | core | 1 |
|
10,842 | 38 | 12 | 8 | 59 | 10 | 0 | 48 | 134 | get_worker_host | refactor: rename pod to deployment (#4230)
* refactor: rename pod to deployment
* style: fix overload and cli autocomplete
* fix: undo daemon mistake
* refactor: leftover cleanup
* fix: more test fixes
* fix: more fixes
* fix: more fixes
* fix: more fixes
* fix: more tests
* fix: fix more tests
* refactor: fix more tests
* refactor: more tests fixes
* refactor: rename pea to pod
* refactor: adjust docs
* refactor: complete pea renaming
* refactor: more fixes
* fix: pea_type in k8s yamls
* fix: adjust pod args name
* refactor: rename peapods parser folder
* fix: da init
Co-authored-by: Jina Dev Bot <[email protected]> | https://github.com/jina-ai/jina.git | def get_worker_host(pod_args, pod, head_pod):
# Check if the current pod and head are both containerized on the same host
# If so __docker_host__ needs to be advertised as the worker's address to the head
worker_host = (
__docker_host__
if Deployment._is_container_to_container(pod, head_pod)
and host_is_local(pod_args.host)
else pod_args.host
)
return worker_host
| 37 | __init__.py | Python | jina/orchestrate/deployments/__init__.py | 13edc16d806fb5d77a6849551178ccc75937f25f | jina | 3 |
|
175,178 | 105 | 12 | 37 | 278 | 28 | 0 | 142 | 549 | test_co_positions_artificial_instructions | bpo-46202: Remove opcode POP_EXCEPT_AND_RERAISE (GH-30302)
* bpo-46202: remove opcode POP_EXCEPT_AND_RERAISE
* do not assume that an exception group is truthy | https://github.com/python/cpython.git | def test_co_positions_artificial_instructions(self):
import dis
namespace = {}
exec(textwrap.dedent(), namespace)
exc = namespace['exc']
traceback = exc.__traceback__
code = traceback.tb_frame.f_code
artificial_instructions = []
for instr, positions in zip(
dis.get_instructions(code),
code.co_positions(),
strict=True
):
# If any of the positions is None, then all have to
# be None as well for the case above. There are still
# some places in the compiler, where the artificial instructions
# get assigned the first_lineno but they don't have other positions.
# There is no easy way of inferring them at that stage, so for now
# we don't support it.
self.assertTrue(positions.count(None) in [0, 4])
if not any(positions):
artificial_instructions.append(instr)
self.assertEqual(
[
(instruction.opname, instruction.argval)
for instruction in artificial_instructions
],
[
("PUSH_EXC_INFO", None),
("LOAD_CONST", None), # artificial 'None'
("STORE_NAME", "e"), # XX: we know the location for this
("DELETE_NAME", "e"),
("RERAISE", 1),
("COPY", 3),
("POP_EXCEPT", None),
("RERAISE", 1)
]
)
| 169 | test_code.py | Python | Lib/test/test_code.py | a94461d7189d7f1147ab304a332c8684263dc17e | cpython | 4 |
|
281,540 | 8 | 9 | 31 | 40 | 7 | 0 | 8 | 30 | print_help | Terminal Wide Rich (#1161)
* My idea for how we handle Rich moving forward
* remove independent consoles
* FIxed pylint issues
* add a few vars
* Switched print to console
* More transitions
* Changed more prints
* Replaced all prints
* Fixing tabulate
* Finished replace tabulate
* Finished removing rich from Tabulate
* add Panel around menu
* add GST watermark under feature flag
* Fixed 46 tests
* Delete test_screener[False].yaml
* Delete test_screener[True].yaml
* Fixed the rest of the tests
* add help and source color vars and use rgb
* rich on stocks/options
* update rich on disc, dps, sia
* rich in gov, ins and scr menus
* ba and ca menus with rich
* Fixed import issue
* Fixed some tests
* removed termcolor
* Removed prettytable
* add rich to remaining stocks menus
* FIxed linting issue
* Added James' changes
* Updated dependencies
* Add rich to cryptocurrency menu
* refactor economy and forex
* refactor etf with rich
* refactor mfunds
* refactor rich rest
* not specify style so default color works well on any background
* Fixing mypy issues
* Updated tests
* More test fixes
* James' test fixes
* Updating tests : stocks/screener - fix cassettes using BR
* Updating tests : crypto
* Updating tests : disable DEBUG_MODE
* Updating tests : stocks/fa/yfinance
* minor fixes that escape
* Improve the rich table function (that replaces tabulate :D )
* Fixed bad code
* delete rogue file + dcf fix + NoConsole
* sia mypy
* fuck you linter
* fuck you linter pt 2
* skip hehe
* i hate the black linter
* ubuntu mypy attempt
* Update : rich_config + gtff
* Updating tests : conftest
* Updating tests : stocks
* Update : rich_config
* Updating : rich_config
* make panel configurable for Theodore :b
* colors update
* Merged
* Updating : rich_config + feature_flags
* Updating : rich_config
* Updating tests : stocks
* Updating : feature_flags
Co-authored-by: DidierRLopes <[email protected]>
Co-authored-by: Chavithra PARANA <[email protected]>
Co-authored-by: james <[email protected]>
Co-authored-by: jose-donato <[email protected]> | https://github.com/OpenBB-finance/OpenBBTerminal.git | def print_help(self):
help_text =
console.print(text=help_text, menu="Stocks - Discovery")
| 21 | disc_controller.py | Python | gamestonk_terminal/stocks/discovery/disc_controller.py | 82747072c511beb1b2672846ae2ee4aec53eb562 | OpenBBTerminal | 1 |
|
142,459 | 35 | 10 | 7 | 81 | 9 | 0 | 38 | 107 | task_id | [api] Annotate as public / move ray-core APIs to _private and add enforcement rule (#25695)
Enable checking of the ray core module, excluding serve, workflows, and tune, in ./ci/lint/check_api_annotations.py. This required moving many files to ray._private and associated fixes. | https://github.com/ray-project/ray.git | def task_id(self):
# only worker mode has actor_id
assert (
self.worker.mode == ray._private.worker.WORKER_MODE
), f"This method is only available when the process is a\
worker. Current mode: {self.worker.mode}"
task_id = self.worker.current_task_id
return task_id if not task_id.is_nil() else None
| 43 | runtime_context.py | Python | python/ray/runtime_context.py | 43aa2299e6623c8f8c7c4a1b80133459d0aa68b0 | ray | 2 |
|
203,277 | 61 | 12 | 17 | 156 | 13 | 0 | 70 | 250 | test_body_after_POST_multipart_form_data | Refs #33476 -- Refactored problematic code before reformatting by Black.
In these cases Black produces unexpected results, e.g.
def make_random_password(
self,
length=10,
allowed_chars='abcdefghjkmnpqrstuvwxyz' 'ABCDEFGHJKLMNPQRSTUVWXYZ' '23456789',
):
or
cursor.execute("""
SELECT ...
""",
[table name],
) | https://github.com/django/django.git | def test_body_after_POST_multipart_form_data(self):
# Because multipart is used for large amounts of data i.e. file uploads,
# we don't want the data held in memory twice, and we don't want to
# silence the error by setting body = '' either.
payload = FakePayload("\r\n".join([
'--boundary',
'Content-Disposition: form-data; name="name"',
'',
'value',
'--boundary--'
]))
request = WSGIRequest({
'REQUEST_METHOD': 'POST',
'CONTENT_TYPE': 'multipart/form-data; boundary=boundary',
'CONTENT_LENGTH': len(payload),
'wsgi.input': payload,
})
self.assertEqual(request.POST, {'name': ['value']})
with self.assertRaises(RawPostDataException):
request.body
| 80 | tests.py | Python | tests/requests/tests.py | c5cd8783825b5f6384417dac5f3889b4210b7d08 | django | 1 |
|
297,694 | 26 | 13 | 12 | 165 | 12 | 0 | 37 | 150 | update | Use UnitOfTemperature in integrations (e-h) (#84305) | https://github.com/home-assistant/core.git | def update(self) -> None:
self.hddtemp.update()
if self.hddtemp.data and self.disk in self.hddtemp.data:
self._details = self.hddtemp.data[self.disk].split("|")
self._attr_native_value = self._details[2]
if self._details is not None and self._details[3] == "F":
self._attr_native_unit_of_measurement = UnitOfTemperature.FAHRENHEIT
else:
self._attr_native_unit_of_measurement = UnitOfTemperature.CELSIUS
else:
self._attr_native_value = None
| 101 | sensor.py | Python | homeassistant/components/hddtemp/sensor.py | 9580c4f1ec5e45e5090d927792feea4ecf7c96e7 | core | 5 |
|
269,419 | 32 | 14 | 11 | 145 | 14 | 1 | 43 | 131 | stack3 | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def stack3(x, filters, blocks, stride1=2, groups=32, name=None):
x = block3(x, filters, stride=stride1, groups=groups, name=name + "_block1")
for i in range(2, blocks + 1):
x = block3(
x,
filters,
groups=groups,
conv_shortcut=False,
name=name + "_block" + str(i),
)
return x
@keras_export(
"keras.applications.resnet50.ResNet50",
"keras.applications.resnet.ResNet50",
"keras.applications.ResNet50",
) | @keras_export(
"keras.applications.resnet50.ResNet50",
"keras.applications.resnet.ResNet50",
"keras.applications.ResNet50",
) | 86 | resnet.py | Python | keras/applications/resnet.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 2 |
308,886 | 4 | 6 | 2 | 16 | 2 | 0 | 4 | 11 | async_update_group_state | Simplify groups (#63477)
* Simplify group
* Rename async_update to async_update_group_state and mark it as callback
* Simplify _async_start | https://github.com/home-assistant/core.git | def async_update_group_state(self) -> None:
| 8 | __init__.py | Python | homeassistant/components/group/__init__.py | 8bf8709d9928b714e70d32a383ba4e1a2849d353 | core | 1 |
|
268,806 | 43 | 8 | 7 | 78 | 9 | 0 | 56 | 123 | add_locals | Simplify AnsibleJ2Vars by using ChainMap for vars (#78713)
Co-authored-by: Matt Martz <[email protected]> | https://github.com/ansible/ansible.git | def add_locals(self, locals):
if locals is None:
return self
current_locals = self.maps[0]
current_globals = self.maps[2]
# prior to version 2.9, locals contained all of the vars and not just the current
# local vars so this was not necessary for locals to propagate down to nested includes
new_locals = current_locals | locals
return AnsibleJ2Vars(self._templar, current_globals, locals=new_locals)
| 49 | vars.py | Python | lib/ansible/template/vars.py | 60f76436c144a08aa6b74bfefd559ac0188202f6 | ansible | 2 |
|
104,587 | 36 | 12 | 24 | 131 | 17 | 0 | 42 | 63 | _parse_and_clean_wikicode | Improve Wikipedia Loading Script (#3435)
* Improve Wikipedia Loading Script (#3400)
* More structured approach to detecting redirects
* Remove redundant template filter code (covered by strip_code)
* Add language-specific lists of additional media namespace aliases for filtering
* Add language-specific lists of category namespace aliases for new link text cleaning step
* Remove magic words (parser directions like __TOC__ that occasionally occur in text)
With support from @albertvillanova
* Update wikipedia.py
Co-authored-by: Albert Villanova del Moral <[email protected]> | https://github.com/huggingface/datasets.git | def _parse_and_clean_wikicode(raw_content, parser, language):
wikicode = parser.parse(raw_content)
# Filters for magic words that are parser instructions -- e.g., __NOTOC__
re_rm_magic = re.compile("__[A-Z]*__", flags=re.UNICODE)
# Filters for file/image links.
media_prefixes = "|".join(["File", "Image", "Media"] + MEDIA_ALIASES.get(language, []))
re_rm_wikilink = re.compile(f"^(?:{media_prefixes}):", flags=re.IGNORECASE | re.UNICODE)
| 236 | wikipedia.py | Python | datasets/wikipedia/wikipedia.py | 7e30308f49f8c85dc7a2ab5aafbff04b5d2f38e2 | datasets | 6 |
|
275,541 | 18 | 15 | 12 | 110 | 15 | 0 | 20 | 188 | iterations | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def iterations(self):
if self._iterations is None:
with self._distribution_strategy_scope():
self._iterations = self.add_weight(
"iter",
shape=[],
dtype=tf.int64,
trainable=False,
aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
)
self._weights.append(self._iterations)
return self._iterations
| 68 | optimizer_v2.py | Python | keras/optimizers/optimizer_v2/optimizer_v2.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 2 |
|
319,942 | 108 | 20 | 43 | 463 | 56 | 0 | 141 | 662 | update_document_archive_file | Implements a better re-do of OCR by making the document archiver function common. Actually creates updated file now | https://github.com/paperless-ngx/paperless-ngx.git | def update_document_archive_file(document_id):
document = Document.objects.get(id=document_id)
mime_type = document.mime_type
parser_class: Type[DocumentParser] = get_parser_class_for_mime_type(mime_type)
if not parser_class:
logger.error(
f"No parser found for mime type {mime_type}, cannot "
f"archive document {document} (ID: {document_id})",
)
return
parser: DocumentParser = parser_class(logging_group=uuid.uuid4())
try:
parser.parse(document.source_path, mime_type, document.get_public_filename())
thumbnail = parser.get_thumbnail(
document.source_path,
mime_type,
document.get_public_filename(),
)
if parser.get_archive_path():
with transaction.atomic():
with open(parser.get_archive_path(), "rb") as f:
checksum = hashlib.md5(f.read()).hexdigest()
# I'm going to save first so that in case the file move
# fails, the database is rolled back.
# We also don't use save() since that triggers the filehandling
# logic, and we don't want that yet (file not yet in place)
document.archive_filename = generate_unique_filename(
document,
archive_filename=True,
)
Document.objects.filter(pk=document.pk).update(
archive_checksum=checksum,
content=parser.get_text(),
archive_filename=document.archive_filename,
)
with FileLock(settings.MEDIA_LOCK):
create_source_path_directory(document.archive_path)
shutil.move(parser.get_archive_path(), document.archive_path)
shutil.move(thumbnail, document.thumbnail_path)
with index.open_index_writer() as writer:
index.update_document(writer, document)
except Exception:
logger.exception(
f"Error while parsing document {document} " f"(ID: {document_id})",
)
finally:
parser.cleanup()
| 266 | tasks.py | Python | src/documents/tasks.py | ab761e837c4be4974f699c8c97560a4291a8d298 | paperless-ngx | 5 |
|
181,659 | 8 | 9 | 6 | 38 | 6 | 0 | 8 | 38 | test_sparse1_with_non_sparse_components | Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | https://github.com/EpistasisLab/tpot.git | def test_sparse1_with_non_sparse_components():
fit_then_transform(
sparse1_paratial_1h.todense(),
sparse1,
categorical_features=[True, False]
)
| 23 | one_hot_encoder_tests.py | Python | tests/one_hot_encoder_tests.py | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | 1 |
|
267,386 | 20 | 11 | 3 | 56 | 8 | 0 | 22 | 35 | get_generic_type | ansible-test - Code cleanup.
This helps prepare for a future pylint upgrade. | https://github.com/ansible/ansible.git | def get_generic_type(base_type, generic_base_type): # type: (t.Type, t.Type[TValue]) -> t.Optional[t.Type[TValue]]
# noinspection PyUnresolvedReferences
type_arg = t.get_args(base_type.__orig_bases__[0])[0]
return None if isinstance(type_arg, generic_base_type) else type_arg
| 35 | util.py | Python | test/lib/ansible_test/_internal/util.py | 86779cc90376ea70bafa7044b12ce5132409fd63 | ansible | 2 |
|
166,227 | 4 | 8 | 2 | 23 | 3 | 0 | 4 | 18 | __dlpack__ | ENH: Implement DataFrame interchange protocol (#46141) | https://github.com/pandas-dev/pandas.git | def __dlpack__(self):
raise NotImplementedError("__dlpack__")
| 11 | dataframe_protocol.py | Python | pandas/core/exchange/dataframe_protocol.py | 90140f055892a46f473bd26affab88a7f171e394 | pandas | 1 |
|
82,287 | 46 | 15 | 22 | 232 | 21 | 0 | 71 | 314 | for_page | Enabled isort workflow (#7200)
* Ran isort
* Enabled isort workflow
Co-authored-by: Vinit Kumar <[email protected]> | https://github.com/django-cms/django-cms.git | def for_page(self, page):
# permissions should be managed on the draft page only
from cms.models import (
ACCESS_CHILDREN, ACCESS_DESCENDANTS, ACCESS_PAGE,
ACCESS_PAGE_AND_CHILDREN, ACCESS_PAGE_AND_DESCENDANTS,
)
page = page.get_draft_object()
paths = page.node.get_ancestor_paths()
# Ancestors
query = (
Q(page__node__path__in=paths) & (
Q(grant_on=ACCESS_DESCENDANTS) | Q(grant_on=ACCESS_PAGE_AND_DESCENDANTS)
)
)
if page.parent_page:
# Direct parent
query |= (
Q(page=page.parent_page) & (
Q(grant_on=ACCESS_CHILDREN) | Q(grant_on=ACCESS_PAGE_AND_CHILDREN)
)
)
query |= Q(page=page) & (
Q(grant_on=ACCESS_PAGE_AND_DESCENDANTS) | Q(grant_on=ACCESS_PAGE_AND_CHILDREN) | Q(grant_on=ACCESS_PAGE)
)
return self.filter(query).order_by('page__node__depth')
| 143 | managers.py | Python | cms/models/managers.py | a3110e1ff24085373898c7d2a85f628abeb8518d | django-cms | 2 |
|
20,773 | 27 | 15 | 20 | 105 | 10 | 0 | 33 | 250 | position_cursor | 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 | https://github.com/pypa/pipenv.git | def position_cursor(self) -> Control:
if self._shape is not None:
_, height = self._shape
return Control(
ControlType.CARRIAGE_RETURN,
(ControlType.ERASE_IN_LINE, 2),
*(
(
(ControlType.CURSOR_UP, 1),
(ControlType.ERASE_IN_LINE, 2),
)
* (height - 1)
)
)
return Control()
| 70 | live_render.py | Python | pipenv/patched/notpip/_vendor/rich/live_render.py | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | 2 |
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