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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
299,377 | 18 | 10 | 8 | 72 | 7 | 0 | 24 | 92 | async_set_repeat | Improve repeat and shuffle support for Squeezebox (#70941) | https://github.com/home-assistant/core.git | async def async_set_repeat(self, repeat):
if repeat == REPEAT_MODE_ALL:
repeat_mode = "playlist"
elif repeat == REPEAT_MODE_ONE:
repeat_mode = "song"
else:
repeat_mode = "none"
await self._player.async_set_repeat(repeat_mode)
| 38 | media_player.py | Python | homeassistant/components/squeezebox/media_player.py | 0264f060e4fc988f3a0442ba8f951677816c11ea | core | 3 |
|
213,043 | 106 | 13 | 56 | 542 | 30 | 0 | 195 | 789 | add_resource_policy | fix: Py27hash fix (#2182)
* Add third party py27hash code
* Add Py27UniStr and unit tests
* Add py27hash_fix utils and tests
* Add to_py27_compatible_template and tests
* Apply py27hash fix to wherever it is needed
* Apply py27hash fix, all tests pass except api_with_any_method_in_swagger
* apply py27hash fix in openapi + run black
* remove py27 testing
* remove other py27 references
* black fixes
* fixes/typos
* remove py27 from tox.ini
* refactoring
* third party notice
* black
* Fix py27hash fix to deal with null events
* Fix Py27UniStr repr for unicode literals
* black reformat
* Update _template_has_api_resource to check data type more defensively
* Apply py27Dict in _get_authorizers
* Apply Py27Dict to authorizers and gateway responses which will go into swagger
* Update to_py27_compatible_template to handle parameter_values; Add Py27LongInt class
* Rename _convert_to_py27_dict to _convert_to_py27_type
* Apply Py27UniStr to path param name
* Handle HttpApi resource under to_py27_compatible_template
* Fix InvalidDocumentException to not sort different exceptions
* black reformat
* Remove unnecessary test files
Co-authored-by: Wing Fung Lau <[email protected]> | https://github.com/aws/serverless-application-model.git | def add_resource_policy(self, resource_policy, path, stage):
if resource_policy is None:
return
SwaggerEditor.validate_is_dict(resource_policy, "Resource Policy is not a valid dictionary.")
aws_account_whitelist = resource_policy.get("AwsAccountWhitelist")
aws_account_blacklist = resource_policy.get("AwsAccountBlacklist")
ip_range_whitelist = resource_policy.get("IpRangeWhitelist")
ip_range_blacklist = resource_policy.get("IpRangeBlacklist")
source_vpc_whitelist = resource_policy.get("SourceVpcWhitelist")
source_vpc_blacklist = resource_policy.get("SourceVpcBlacklist")
# Intrinsic's supported in these properties
source_vpc_intrinsic_whitelist = resource_policy.get("IntrinsicVpcWhitelist")
source_vpce_intrinsic_whitelist = resource_policy.get("IntrinsicVpceWhitelist")
source_vpc_intrinsic_blacklist = resource_policy.get("IntrinsicVpcBlacklist")
source_vpce_intrinsic_blacklist = resource_policy.get("IntrinsicVpceBlacklist")
if aws_account_whitelist is not None:
resource_list = self._get_method_path_uri_list(path, stage)
self._add_iam_resource_policy_for_method(aws_account_whitelist, "Allow", resource_list)
if aws_account_blacklist is not None:
resource_list = self._get_method_path_uri_list(path, stage)
self._add_iam_resource_policy_for_method(aws_account_blacklist, "Deny", resource_list)
if ip_range_whitelist is not None:
resource_list = self._get_method_path_uri_list(path, stage)
self._add_ip_resource_policy_for_method(ip_range_whitelist, "NotIpAddress", resource_list)
if ip_range_blacklist is not None:
resource_list = self._get_method_path_uri_list(path, stage)
self._add_ip_resource_policy_for_method(ip_range_blacklist, "IpAddress", resource_list)
if not SwaggerEditor._validate_list_property_is_resolved(source_vpc_blacklist):
raise InvalidDocumentException(
[
InvalidTemplateException(
"SourceVpcBlacklist must be a list of strings. Use IntrinsicVpcBlacklist instead for values that use Intrinsic Functions"
)
]
)
# FIXME: check if this requires py27 dict?
blacklist_dict = {
"StringEndpointList": source_vpc_blacklist,
"IntrinsicVpcList": source_vpc_intrinsic_blacklist,
"IntrinsicVpceList": source_vpce_intrinsic_blacklist,
}
resource_list = self._get_method_path_uri_list(path, stage)
self._add_vpc_resource_policy_for_method(blacklist_dict, "StringEquals", resource_list)
if not SwaggerEditor._validate_list_property_is_resolved(source_vpc_whitelist):
raise InvalidDocumentException(
[
InvalidTemplateException(
"SourceVpcWhitelist must be a list of strings. Use IntrinsicVpcWhitelist instead for values that use Intrinsic Functions"
)
]
)
whitelist_dict = {
"StringEndpointList": source_vpc_whitelist,
"IntrinsicVpcList": source_vpc_intrinsic_whitelist,
"IntrinsicVpceList": source_vpce_intrinsic_whitelist,
}
self._add_vpc_resource_policy_for_method(whitelist_dict, "StringNotEquals", resource_list)
self._doc[self._X_APIGW_POLICY] = self.resource_policy
| 322 | swagger.py | Python | samtranslator/swagger/swagger.py | a5db070f446b7cfebdaa6ad2e3dcf78f6105a272 | serverless-application-model | 8 |
|
101,298 | 27 | 11 | 8 | 106 | 9 | 0 | 34 | 88 | toggle_mask_display | Data Augmentation update (#1263)
- lib.detected_face
- Subclass Masks for Landmark based masks
- Add training mask propery + methods to DetectedFace
- lib.training_training
- subclass TrainingDataGenerator for training and preview data
- Split cache into own module
- Reduce thread count to 1 to prevent image corruption + data re-use
- Process on largest model input/output size rather than stored image size
- Size and crop masks during caching stage
- Implement ring buffer for data flow
- Fix preview reload bug
- augmentation
- typing
- switch color aug order
- better initialization
- Fix warp + landmark warp to correctly apply at different image scales
- Slightly improved warp caching
- Don't store whether image is_preview. Handle all data as training images implicitly
- plugins.trainer: Typing and fixes to work with trainingdata refactor | https://github.com/deepfakes/faceswap.git | def toggle_mask_display(self) -> None:
if not (self._model.config["learn_mask"] or self._model.config["penalized_mask_loss"]):
return
display_mask = not self._display_mask
print("") # Break to not garble loss output
logger.info("Toggling mask display %s...", "on" if display_mask else "off")
self._display_mask = display_mask
| 58 | _base.py | Python | plugins/train/trainer/_base.py | 2beceffad9b15c1fd78f06b9b272563321c5a41e | faceswap | 4 |
|
168,323 | 7 | 9 | 15 | 46 | 8 | 0 | 8 | 22 | isin | TYP: pandas.core.series annotations from pandas-stubs (#47926)
* TYP: pandas.core.series annotations from pandas-stubs
* and DataFrame
* more compatibility with pandas-stub tests
* mypy address line-off-by-one (merge?) issue | https://github.com/pandas-dev/pandas.git | def isin(self, values) -> npt.NDArray[np.bool_]:
return isin(np.asarray(self), values)
| 29 | base.py | Python | pandas/core/arrays/base.py | 50c2af1a6322375359a8bacfd79056ca4ab02df2 | pandas | 1 |
|
158,111 | 47 | 15 | 10 | 183 | 27 | 0 | 60 | 157 | evaluate_accuracy_gpus | [PaddlePaddle] Merge master into Paddle branch (#1186)
* change 15.2 title in chinese version (#1109)
change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘
* 修改部分语义表述 (#1105)
* Update r0.17.5 (#1120)
* Bump versions in installation
* 94行typo: （“bert.mall”）->（“bert.small”） (#1129)
* line 313: "bert.mall" -> "bert.small" (#1130)
* fix: update language as native reader (#1114)
* Fix the translation of "stride" (#1115)
* Update index.md (#1118)
修改部分语义表述
* Update self-attention-and-positional-encoding.md (#1133)
依照本书的翻译习惯，将pooling翻译成汇聚
* maybe a comment false (#1149)
* maybe a little false
* maybe a little false
* A minor bug in the rcnn section (Chinese edition) (#1148)
* Update bert.md (#1137)
一个笔误
# 假设batch_size=2，num_pred_positions=3
# 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1]
* Update calculus.md (#1135)
* fix typo in git documentation (#1106)
* fix: Update the Chinese translation in lr-scheduler.md (#1136)
* Update lr-scheduler.md
* Update chapter_optimization/lr-scheduler.md
Co-authored-by: goldmermaid <[email protected]>
Co-authored-by: goldmermaid <[email protected]>
* fix translation for kaggle-house-price.md (#1107)
* fix translation for kaggle-house-price.md
* fix translation for kaggle-house-price.md
Signed-off-by: sunhaizhou <[email protected]>
* Update weight-decay.md (#1150)
* Update weight-decay.md
关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解
关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。
并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释
解释为何会增加复杂性以及为何需要细粒度工具。
* Update chapter_multilayer-perceptrons/weight-decay.md
yep
Co-authored-by: goldmermaid <[email protected]>
* Update chapter_multilayer-perceptrons/weight-decay.md
yep
Co-authored-by: goldmermaid <[email protected]>
Co-authored-by: goldmermaid <[email protected]>
* Fix a spelling error (#1161)
* Update gru.md (#1152)
The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state.
翻译错误
* Unify the function naming (#1113)
Unify naming of the function 'init_xavier()'.
* Update mlp-concise.md (#1166)
* Update mlp-concise.md
语句不通顺
* Update environment.md
语序异常
* Update config.ini
* fix the imprecise description (#1168)
Co-authored-by: yuande <yuande>
* fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175)
* Fix some typos. (#1163)
* Update batch-norm.md (#1170)
fixing typos u->x in article
* Update linear-regression.md (#1090)
We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that
原译文把who也直接翻译出来了。
* Update mlp.md (#1117)
* Update mlp.md
修改部分语义表述
* Update chapter_multilayer-perceptrons/mlp.md
Co-authored-by: goldmermaid <[email protected]>
* Update chapter_multilayer-perceptrons/mlp.md
Co-authored-by: Aston Zhang <[email protected]>
Co-authored-by: goldmermaid <[email protected]>
* Correct a translation error. (#1091)
* Correct a translation error.
* Update chapter_computer-vision/image-augmentation.md
Co-authored-by: Aston Zhang <[email protected]>
* Update aws.md (#1121)
* Update aws.md
* Update chapter_appendix-tools-for-deep-learning/aws.md
Co-authored-by: Aston Zhang <[email protected]>
* Update image-augmentation.md (#1093)
* Update anchor.md (#1088)
fix a minor issue in code
* Update anchor.md
* Update image-augmentation.md
* fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087)
* Avoid `torch.meshgrid` user warning (#1174)
Avoids the following user warning:
```python
~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
```
* bump to 2.0.0-beta1
* Update sequence.md
* bump beta1 on readme
* Add latex code block background to config
* BLD: Bump python support version 3.9 (#1183)
* BLD: Bump python support version 3.9
* Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4
* BLD: Bump torch and tensorflow
* Update Jenkinsfile
* Update chapter_installation/index.md
* Update chapter_installation/index.md
Co-authored-by: Aston Zhang <[email protected]>
* Update config.ini
* Update INFO.md
* Update INFO.md
* Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187)
* resolve the conflicts
* revise from publisher (#1089)
* revise from publisher
* d2l api
* post_latex
* revise from publisher
* revise ch11
* Delete d2l-Copy1.bib
* clear cache
* rm d2lbook clear
* debug anchor
* keep original d2l doc
Co-authored-by: Ubuntu <[email protected]>
Co-authored-by: Aston Zhang <[email protected]>
Co-authored-by: Aston Zhang <[email protected]>
* 重复语句 (#1188)
Co-authored-by: Aston Zhang <[email protected]>
* Improve expression for chapter_preliminaries/pandas.md (#1184)
* Update pandas.md
* Improve expression
* Improve expression
* Update chapter_preliminaries/pandas.md
Co-authored-by: Aston Zhang <[email protected]>
* Improce expression for chapter_preliminaries/linear-algebra.md (#1185)
* Improce expression
* Improve code comments
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
Co-authored-by: Aston Zhang <[email protected]>
* Fix multibox_detection bugs
* Update d2l to 0.17.5 version
* restore older version
* Upgrade pandas
* change to python3.8
* Test warning log
* relocate warning log
* test logs filtering
* Update gru.md
* Add DeprecationWarning filter
* Test warning log
* Update attention mechanisms & computational performance
* Update multilayer perceptron& linear & convolution networks & computer vision
* Update recurrent&optimition&nlp pretraining & nlp applications
* ignore warnings
* Update index.md
* Update linear networks
* Update multilayer perceptrons&deep learning computation
* Update preliminaries
* Check and Add warning filter
* Update kaggle-cifar10.md
* Update object-detection-dataset.md
* Update ssd.md fcn.md
* Update hybridize.md
* Update hybridize.md
Signed-off-by: sunhaizhou <[email protected]>
Co-authored-by: zhou201505013 <[email protected]>
Co-authored-by: Xinwei Liu <[email protected]>
Co-authored-by: Anirudh Dagar <[email protected]>
Co-authored-by: Aston Zhang <[email protected]>
Co-authored-by: hugo_han <[email protected]>
Co-authored-by: gyro永不抽风 <[email protected]>
Co-authored-by: CanChengZheng <[email protected]>
Co-authored-by: linlin <[email protected]>
Co-authored-by: iuk <[email protected]>
Co-authored-by: yoos <[email protected]>
Co-authored-by: Mr. Justice Lawrence John Wargrave <[email protected]>
Co-authored-by: Chiyuan Fu <[email protected]>
Co-authored-by: Sunhuashan <[email protected]>
Co-authored-by: Haiker Sun <[email protected]>
Co-authored-by: Ming Liu <[email protected]>
Co-authored-by: goldmermaid <[email protected]>
Co-authored-by: silenceZheng66 <[email protected]>
Co-authored-by: Wenchao Yan <[email protected]>
Co-authored-by: Kiki2049 <[email protected]>
Co-authored-by: Krahets <[email protected]>
Co-authored-by: friedmainfunction <[email protected]>
Co-authored-by: Jameson <[email protected]>
Co-authored-by: P. Yao <[email protected]>
Co-authored-by: Yulv-git <[email protected]>
Co-authored-by: Liu,Xiao <[email protected]>
Co-authored-by: YIN, Gang <[email protected]>
Co-authored-by: Joe-HZ <[email protected]>
Co-authored-by: lybloveyou <[email protected]>
Co-authored-by: VigourJiang <[email protected]>
Co-authored-by: zxhd863943427 <[email protected]>
Co-authored-by: LYF <[email protected]>
Co-authored-by: Aston Zhang <[email protected]>
Co-authored-by: xiaotinghe <[email protected]>
Co-authored-by: Ubuntu <[email protected]>
Co-authored-by: Holly-Max <[email protected]>
Co-authored-by: HinGwenWoong <[email protected]>
Co-authored-by: Shuai Zhang <[email protected]> | https://github.com/d2l-ai/d2l-zh.git | def evaluate_accuracy_gpus(net, data_iter, split_f=d2l.split_batch):
# Query the list of devices
devices = list(net.collect_params().values())[0].list_ctx()
# No. of correct predictions, no. of predictions
metric = d2l.Accumulator(2)
for features, labels in data_iter:
X_shards, y_shards = split_f(features, labels, devices)
# Run in parallel
pred_shards = [net(X_shard) for X_shard in X_shards]
metric.add(sum(float(d2l.accuracy(pred_shard, y_shard)) for
pred_shard, y_shard in zip(
pred_shards, y_shards)), labels.size)
return metric[0] / metric[1]
| 118 | mxnet.py | Python | d2l/mxnet.py | b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2 | d2l-zh | 4 |
|
107,880 | 55 | 12 | 17 | 352 | 33 | 1 | 74 | 294 | test_colorbar_extension_inverted_axis | FIX: Handle inverted colorbar axes with extensions
This fixes the colorbar extensions to use the proper color when the
long axis is inverted. | https://github.com/matplotlib/matplotlib.git | def test_colorbar_extension_inverted_axis(orientation, extend, expected):
data = np.arange(12).reshape(3, 4)
fig, ax = plt.subplots()
cmap = plt.get_cmap("viridis").with_extremes(under=(0, 0, 0, 1),
over=(1, 1, 1, 1))
im = ax.imshow(data, cmap=cmap)
cbar = fig.colorbar(im, orientation=orientation, extend=extend)
if orientation == "horizontal":
cbar.ax.invert_xaxis()
else:
cbar.ax.invert_yaxis()
assert cbar._extend_patches[0].get_facecolor() == expected
if extend == "both":
assert len(cbar._extend_patches) == 2
assert cbar._extend_patches[1].get_facecolor() == (0, 0, 0, 1)
else:
assert len(cbar._extend_patches) == 1
@pytest.mark.parametrize('use_gridspec', [True, False])
@image_comparison(['cbar_with_orientation',
'cbar_locationing',
'double_cbar',
'cbar_sharing',
],
extensions=['png'], remove_text=True,
savefig_kwarg={'dpi': 40}) | @pytest.mark.parametrize('use_gridspec', [True, False])
@image_comparison(['cbar_with_orientation',
'cbar_locationing',
'double_cbar',
'cbar_sharing',
],
extensions=['png'], remove_text=True,
savefig_kwarg={'dpi': 40}) | 177 | test_colorbar.py | Python | lib/matplotlib/tests/test_colorbar.py | ec374f5148631e4d392ed7e6d4c454d163a62f21 | matplotlib | 3 |
191,688 | 12 | 11 | 5 | 71 | 9 | 0 | 12 | 28 | test_run_args_and_kwargs_error | change run to use args and kwargs (#367)
Before, `run` was not able to be called with multiple arguments. This
expands the functionality. | https://github.com/hwchase17/langchain.git | def test_run_args_and_kwargs_error() -> None:
chain = FakeChain(the_input_keys=["foo", "bar"])
with pytest.raises(ValueError):
chain.run("bar", foo="bar")
| 37 | test_base.py | Python | tests/unit_tests/chains/test_base.py | 8d0869c6d3ed63b2b15d4f75ea664e089dcc569d | langchain | 1 |
|
31,016 | 13 | 9 | 3 | 53 | 9 | 0 | 16 | 31 | _set_gradient_checkpointing | M-CTC-T Model (#16402)
* added cbs to notebooks, made copy-paste error fix in generation_utils
* initial push for mctc model
* mctc feature extractor done
* added processor, tokenizer and their tests for MCTC. Have added an MCTC modeling test, adjusting model code accordingly.
* added processor, tokenizer and their tests for MCTC. Have added an MCTC modeling test, adjusting model code accordingly.
* passing attention, now struggling to figure out how attention masks make sense here
* works when excluding attention masks. ask later how one would integrate attention maskshere
* bizarre configuration error (model prefix comes first in config dict json and messes up the order)
* all passing but bizzarre config dict ordering issue when to_dict
* passing all major tests
* feature extraction, processor, tokenizer added & tests passing
* style & consistency & other logistical fixes
* copy paste fix
* model after feature extraction working
* commiting final feature extraction results; need to fix normalization
* feature extraction passing tests; probably should add tests on the specific flashlight-copied functions?
* delete print ; format code a bit
* fixing tests
* passing major tests
* fixing styles
* completed tokenization test with real example; not sure if these values are entirely correct.
* last test fixes from local
* reverting accidentally included custom setup configs
* remove load tf weights; fix config error
* testing couldnt import featureextractor
* fix docs
* fix docs
* resolving comments
* style fixes
* style fixes
* Update to MCTCConv1dSubSampler
Co-authored-by: Patrick von Platen <[email protected]>
* relposemb fixes
* conv1d name issue; expecting config fail with paraentheses
* fix config issue
* fix config issue
* fix config issue
* change everything to MCTCT
* fixing naming change errors
* archive list
* copyrights and docs
* copyrights and docs
* copyrights and docs
* merge resolution
* move tests, fix to changed optionaldependency structure
* test directories changed
* fixing tests
* how to avoid tf tests?
* how to avoid tf tests?
* tests passing locally
* allow mctctprocessor imported any env
* allow mctctprocessor imported any env
* fixed second round of feedback, need to fix docs
* doc changes not being applied
* all fixed
* style fix
* feedback fixes
* fix copies and feature extraction style fix
* Update tests/models/visual_bert/test_modeling_visual_bert.py
Co-authored-by: Sylvain Gugger <[email protected]>
* copy paste huggingface:main visual bert
* added eof newline to visual bert; all tests are passing otherwise
* fix slow tests by adding attention mask
* change model id to speechbrain
* make fix-copies
* fix readme unwanted deletes
* fixing readmes, make fix-copies
* consistent M-CTC-T naming
* Update src/transformers/models/mctct/__init__.py
Co-authored-by: Patrick von Platen <[email protected]>
* all fixed but variable naming
* adjust double quotes
* fixed variable names
* copyright and mr quilter
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <[email protected]>
* correct slow tests
* make fix-copies
* Update src/transformers/models/mctct/configuration_mctct.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/mctct/configuration_mctct.py
Co-authored-by: Sylvain Gugger <[email protected]>
* m-ctc-t not mctct
Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: Sylvain Gugger <[email protected]> | https://github.com/huggingface/transformers.git | def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (MCTCTEncoder)):
module.gradient_checkpointing = value
MCTCT_START_DOCSTRING = r
MCTCT_INPUTS_DOCSTRING = r
| 26 | modeling_mctct.py | Python | src/transformers/models/mctct/modeling_mctct.py | 119e3c0fc83db5803d20d0749eef1220f27cfdc8 | transformers | 2 |
|
264,638 | 23 | 11 | 10 | 120 | 19 | 0 | 25 | 119 | enqueue_job | Allow setting individual timeouts on scripts and reports | https://github.com/netbox-community/netbox.git | def enqueue_job(cls, func, name, obj_type, user, *args, **kwargs):
job_result = cls.objects.create(
name=name,
obj_type=obj_type,
user=user,
job_id=uuid.uuid4()
)
queue = django_rq.get_queue("default")
queue.enqueue(func, job_id=str(job_result.job_id), job_result=job_result, **kwargs)
return job_result
| 80 | models.py | Python | netbox/extras/models/models.py | 36d6ae33d15e93cc552827cdea363a9c00c7f823 | netbox | 1 |
|
273,842 | 9 | 9 | 7 | 51 | 8 | 0 | 12 | 69 | _create_non_trackable_mask_cache | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def _create_non_trackable_mask_cache(self):
self._dropout_mask_cache = backend.ContextValueCache(
self._create_dropout_mask
)
self._recurrent_dropout_mask_cache = backend.ContextValueCache(
self._create_recurrent_dropout_mask
)
| 30 | dropout_rnn_cell_mixin.py | Python | keras/layers/rnn/dropout_rnn_cell_mixin.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
|
296,571 | 23 | 12 | 7 | 78 | 14 | 0 | 23 | 73 | _get_bytes | Add missing typing in AsusWRT router class (#70189)
* Add missing typing in AsusWRT router class
* Fix typing in device tracker
* Fix mypy incompatible type | https://github.com/home-assistant/core.git | async def _get_bytes(self) -> dict[str, Any]:
try:
datas = await self._api.async_get_bytes_total()
except (OSError, ValueError) as exc:
raise UpdateFailed(exc) from exc
return _get_dict(SENSORS_BYTES, datas)
| 48 | router.py | Python | homeassistant/components/asuswrt/router.py | b7c1fbc842ef362672a32c762c5bfb5cb84326ab | core | 2 |
|
259,605 | 85 | 17 | 33 | 328 | 24 | 0 | 125 | 603 | predict_proba | DEP loss "log" in favor of "log loss" in SGDClassifier (#23046)
Co-authored-by: Julien Jerphanion <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def predict_proba(self, X):
check_is_fitted(self)
# TODO(1.3): Remove "log"
if self.loss in ("log_loss", "log"):
return self._predict_proba_lr(X)
elif self.loss == "modified_huber":
binary = len(self.classes_) == 2
scores = self.decision_function(X)
if binary:
prob2 = np.ones((scores.shape[0], 2))
prob = prob2[:, 1]
else:
prob = scores
np.clip(scores, -1, 1, prob)
prob += 1.0
prob /= 2.0
if binary:
prob2[:, 0] -= prob
prob = prob2
else:
# the above might assign zero to all classes, which doesn't
# normalize neatly; work around this to produce uniform
# probabilities
prob_sum = prob.sum(axis=1)
all_zero = prob_sum == 0
if np.any(all_zero):
prob[all_zero, :] = 1
prob_sum[all_zero] = len(self.classes_)
# normalize
prob /= prob_sum.reshape((prob.shape[0], -1))
return prob
else:
raise NotImplementedError(
"predict_(log_)proba only supported when"
" loss='log_loss' or loss='modified_huber' "
"(%r given)"
% self.loss
)
| 204 | _stochastic_gradient.py | Python | sklearn/linear_model/_stochastic_gradient.py | 0c20ba744966d23ede67cffd7c5d2e0d01cd0658 | scikit-learn | 6 |
|
299,281 | 6 | 8 | 3 | 33 | 6 | 0 | 6 | 20 | is_online | Use shorthand attributes in sharkiq vacuum (#70844) | https://github.com/home-assistant/core.git | def is_online(self) -> bool:
return self.coordinator.device_is_online(self._serial_number)
| 19 | vacuum.py | Python | homeassistant/components/sharkiq/vacuum.py | 0425f8bc4e03beb848bac99ff3ee8f60ad45c74c | core | 1 |
|
43,586 | 93 | 12 | 3 | 333 | 49 | 0 | 137 | 344 | upload_file | Migrate Google example DAG s3_to_gcs to new design AIP-47 (#24641)
related: #22447, #22430 | https://github.com/apache/airflow.git | def upload_file():
s3_hook = S3Hook()
s3_hook.load_file(filename=UPLOAD_FILE, key=PREFIX, bucket_name=BUCKET_NAME)
with models.DAG(
DAG_ID,
schedule_interval='@once',
start_date=datetime(2021, 1, 1),
catchup=False,
tags=['example', 's3'],
) as dag:
create_s3_bucket = S3CreateBucketOperator(
task_id="create_s3_bucket", bucket_name=BUCKET_NAME, region_name='us-east-1'
)
create_gcs_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=BUCKET_NAME,
project_id=GCP_PROJECT_ID,
)
# [START howto_transfer_s3togcs_operator]
transfer_to_gcs = S3ToGCSOperator(
task_id='s3_to_gcs_task', bucket=BUCKET_NAME, prefix=PREFIX, dest_gcs=GCS_BUCKET_URL
)
# [END howto_transfer_s3togcs_operator]
delete_s3_bucket = S3DeleteBucketOperator(
task_id='delete_s3_bucket',
bucket_name=BUCKET_NAME,
force_delete=True,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_gcs_bucket = GCSDeleteBucketOperator(
task_id='delete_gcs_bucket', bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE
)
(
# TEST SETUP
create_gcs_bucket
>> create_s3_bucket
>> upload_file()
# TEST BODY
>> transfer_to_gcs
# TEST TEARDOWN
>> delete_s3_bucket
>> delete_gcs_bucket
)
from tests.system.utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "tearDown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests.system.utils import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
test_run = get_test_run(dag)
| 26 | example_s3_to_gcs.py | Python | tests/system/providers/google/cloud/gcs/example_s3_to_gcs.py | 7a7ca5016019f93ebee052a2bf99772145b7fc03 | airflow | 1 |
|
153,578 | 33 | 12 | 7 | 114 | 13 | 0 | 35 | 93 | idxmax | DOCS-#3099: Fix `BasePandasDataSet` docstrings warnings (#4333)
Co-authored-by: Yaroslav Igoshev <[email protected]>
Signed-off-by: Alexander Myskov <[email protected]> | https://github.com/modin-project/modin.git | def idxmax(self, axis=0, skipna=True): # noqa: PR01, RT01, D200
if not all(d != np.dtype("O") for d in self._get_dtypes()):
raise TypeError("reduce operation 'argmax' not allowed for this dtype")
axis = self._get_axis_number(axis)
return self._reduce_dimension(
self._query_compiler.idxmax(axis=axis, skipna=skipna)
)
| 69 | base.py | Python | modin/pandas/base.py | 605efa618e7994681f57b11d04d417f353ef8d50 | modin | 3 |
|
9,813 | 36 | 15 | 17 | 121 | 11 | 0 | 40 | 171 | ports | feat: star routing (#3900)
* feat(proto): adjust proto for star routing (#3844)
* feat(proto): adjust proto for star routing
* feat(proto): generate proto files
* feat(grpc): refactor grpclet interface (#3846)
* feat: refactor connection pool for star routing (#3872)
* feat(k8s): add more labels to k8s deployments
* feat(network): refactor connection pool
* feat(network): refactor k8s pool
* feat: star routing graph gateway (#3877)
* feat: star routing - refactor grpc data runtime (#3887)
* feat(runtimes): refactor grpc dataruntime
* fix(tests): adapt worker runtime tests
* fix(import): fix import
* feat(proto): enable sending multiple lists (#3891)
* feat: star routing gateway (#3893)
* feat: star routing gateway all protocols (#3897)
* test: add streaming and prefetch tests (#3901)
* feat(head): new head runtime for star routing (#3899)
* feat(head): new head runtime
* feat(head): new head runtime
* style: fix overload and cli autocomplete
* feat(network): improve proto comments
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(worker): merge docs in worker runtime (#3905)
* feat(worker): merge docs in worker runtime
* feat(tests): assert after clean up
* feat(tests): star routing runtime integration tests (#3908)
* fix(tests): fix integration tests
* test: test runtimes fast slow request (#3910)
* feat(zmq): purge zmq, zed, routing_table (#3915)
* feat(zmq): purge zmq, zed, routing_table
* style: fix overload and cli autocomplete
* feat(zmq): adapt comment in dependency list
* style: fix overload and cli autocomplete
* fix(tests): fix type tests
Co-authored-by: Jina Dev Bot <[email protected]>
* test: add test gateway to worker connection (#3921)
* feat(pea): adapt peas for star routing (#3918)
* feat(pea): adapt peas for star routing
* style: fix overload and cli autocomplete
* feat(pea): add tests
* feat(tests): add failing head pea test
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(tests): integration tests for peas (#3923)
* feat(tests): integration tests for peas
* feat(pea): remove _inner_pea function
* feat: star routing container pea (#3922)
* test: rescue tests (#3942)
* fix: fix streaming tests (#3945)
* refactor: move docker run to run (#3948)
* feat: star routing pods (#3940)
* feat(pod): adapt pods for star routing
* feat(pods): adapt basepod to star routing
* feat(pod): merge pod and compound pod
* feat(tests): fix tests
* style: fix overload and cli autocomplete
* feat(test): add container pea int test
* feat(ci): remove more unnecessary tests
* fix(tests): remove jinad runtime
* feat(ci): remove latency tracking
* fix(ci): fix ci def
* fix(runtime): enable runtime to be exited
* fix(tests): wrap runtime test in process
* fix(runtimes): remove unused runtimes
* feat(runtimes): improve cancel wait
* fix(ci): build test pip again in ci
* fix(tests): fix a test
* fix(test): run async in its own process
* feat(pod): include shard in activate msg
* fix(pea): dont join
* feat(pod): more debug out
* feat(grpc): manage channels properly
* feat(pods): remove exitfifo
* feat(network): add simple send retry mechanism
* fix(network): await pool close
* fix(test): always close grpc server in worker
* fix(tests): remove container pea from tests
* fix(tests): reorder tests
* fix(ci): split tests
* fix(ci): allow alias setting
* fix(test): skip a test
* feat(pods): address comments
Co-authored-by: Jina Dev Bot <[email protected]>
* test: unblock skipped test (#3957)
* feat: jinad pea (#3949)
* feat: jinad pea
* feat: jinad pea
* test: remote peas
* test: toplogy tests with jinad
* ci: parallel jobs
* feat(tests): add pod integration tests (#3958)
* feat(tests): add pod integration tests
* fix(tests): make tests less flaky
* fix(test): fix test
* test(pea): remote pea topologies (#3961)
* test(pea): remote pea simple topology
* test: remote pea topologies
* refactor: refactor streamer result handling (#3960)
* feat(k8s): adapt K8s Pod for StarRouting (#3964)
* test: optimize k8s test
* test: increase timeout and use different namespace
* test: optimize k8s test
* test: build and load image when needed
* test: refactor k8s test
* test: fix image name error
* test: fix k8s image load
* test: fix typoe port expose
* test: update tests in connection pool and handling
* test: remove unused fixture
* test: parameterize docker images
* test: parameterize docker images
* test: parameterize docker images
* feat(k8s): adapt k8s pod for star routing
* fix(k8s): dont overwrite add/remove function in pool
* fix(k8s): some fixes
* fix(k8s): some more fixes
* fix(k8s): linting
* fix(tests): fix tests
* fix(tests): fix k8s unit tests
* feat(k8s): complete k8s integration test
* feat(k8s): finish k8s tests
* feat(k8s): fix test
* fix(tests): fix test with no name
* feat(k8s): unify create/replace interface
* feat(k8s): extract k8s port constants
* fix(tests): fix tests
* fix(tests): wait for runtime being ready in tests
* feat(k8s): address comments
Co-authored-by: bwanglzu <[email protected]>
* feat(flow): adapt Flow for StarRouting (#3986)
* feat(flow): add routes
* feat(flow): adapt flow to star routing
* style: fix overload and cli autocomplete
* feat(flow): handle empty topologies
* feat(k8s): allow k8s pool disabling
* style: fix overload and cli autocomplete
* fix(test): fix test with mock
* fix(tests): fix more tests
* feat(flow): clean up tests
* style: fix overload and cli autocomplete
* fix(tests): fix more tests
* feat: add plot function (#3994)
* fix(tests): avoid hanging tests
* feat(flow): add type hinting
* fix(test): fix duplicate exec name in test
* fix(tests): fix more tests
* fix(tests): enable jinad test again
* fix(tests): random port fixture
* fix(style): replace quotes
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* feat(ci): bring back ci (#3997)
* feat(ci): enable ci again
* style: fix overload and cli autocomplete
* feat(ci): add latency tracking
* feat(ci): bring back some tests
* fix(tests): remove invalid port test
* feat(ci): disable daemon and distributed tests
* fix(tests): fix entrypoint in hub test
* fix(tests): wait for gateway to be ready
* fix(test): fix more tests
* feat(flow): do rolling update and scale sequentially
* fix(tests): fix more tests
* style: fix overload and cli autocomplete
* feat: star routing hanging pods (#4011)
* fix: try to handle hanging pods better
* test: hanging pods test work
* fix: fix topology graph problem
* test: add unit test to graph
* fix(tests): fix k8s tests
* fix(test): fix k8s test
* fix(test): fix k8s pool test
* fix(test): fix k8s test
* fix(test): fix k8s connection pool setting
* fix(tests): make runtime test more reliable
* fix(test): fix routes test
* fix(tests): make rolling update test less flaky
* feat(network): gurantee unique ports
* feat(network): do round robin for shards
* fix(ci): increase pytest timeout to 10 min
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* fix(ci): fix ci file
* feat(daemon): jinad pod for star routing
* Revert "feat(daemon): jinad pod for star routing"
This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92.
* feat(daemon): remote jinad pod support (#4042)
* feat(daemon): add pod tests for star routing
* feat(daemon): add remote pod test
* test(daemon): add remote pod arguments test
* test(daemon): add async scale test
* test(daemon): add rolling update test
* test(daemon): fix host
* feat(proto): remove message proto (#4051)
* feat(proto): remove message proto
* fix(tests): fix tests
* fix(tests): fix some more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* feat(proto): put docs back in data
* fix(proto): clean up
* feat(proto): clean up
* fix(tests): skip latency tracking
* fix(test): fix hub test
* fix(tests): fix k8s test
* fix(test): some test clean up
* fix(style): clean up style issues
* feat(proto): adjust for rebase
* fix(tests): bring back latency tracking
* fix(tests): fix merge accident
* feat(proto): skip request serialization (#4074)
* feat: add reduce to star routing (#4070)
* feat: add reduce on shards to head runtime
* test: add reduce integration tests with fixed order
* feat: add reduce on needs
* chore: get_docs_matrix_from_request becomes public
* style: fix overload and cli autocomplete
* docs: remove undeterministic results warning
* fix: fix uses_after
* test: assert correct num docs after reducing in test_external_pod
* test: correct asserts after reduce in test_rolling_update
* fix: no reduce if uses_after_address is set
* fix: get_docs_from_request only if needed
* fix: fix tests after merge
* refactor: move reduce from data_request_handler to head
* style: fix overload and cli autocomplete
* chore: apply suggestions
* fix: fix asserts
* chore: minor test fix
* chore: apply suggestions
* test: remove flow tests with external executor (pea)
* fix: fix test_expected_messages_routing
* fix: fix test_func_joiner
* test: adapt k8s test
Co-authored-by: Jina Dev Bot <[email protected]>
* fix(k8s): fix static pool config
* fix: use custom protoc doc generator image (#4088)
* fix: use custom protoc doc generator image
* fix(docs): minor doc improvement
* fix(docs): use custom image
* fix(docs): copy docarray
* fix: doc building local only
* fix: timeout doc building
* fix: use updated args when building ContainerPea
* test: add container PeaFactory test
* fix: force pea close on windows (#4098)
* fix: dont reduce if uses exist (#4099)
* fix: dont use reduce if uses exist
* fix: adjust reduce tests
* fix: adjust more reduce tests
* fix: fix more tests
* fix: adjust more tests
* fix: ignore non jina resources (#4101)
* feat(executor): enable async executors (#4102)
* feat(daemon): daemon flow on star routing (#4096)
* test(daemon): add remote flow test
* feat(daemon): call scale in daemon
* feat(daemon): remove tail args and identity
* test(daemon): rename scalable executor
* test(daemon): add a small delay in async test
* feat(daemon): scale partial flow only
* feat(daemon): call scale directly in partial flow store
* test(daemon): use asyncio sleep
* feat(daemon): enable flow level distributed tests
* test(daemon): fix jinad env workspace config
* test(daemon): fix pod test use new port rolling update
* feat(daemon): enable distribuetd tests
* test(daemon): remove duplicate tests and zed runtime test
* test(daemon): fix stores unit test
* feat(daemon): enable part of distributed tests
* feat(daemon): enable part of distributed tests
* test: correct test paths
* test(daemon): add client test for remote flows
* test(daemon): send a request with jina client
* test(daemon): assert async generator
* test(daemon): small interval between tests
* test(daemon): add flow test for container runtime
* test(daemon): add flow test for container runtime
* test(daemon): fix executor name
* test(daemon): fix executor name
* test(daemon): use async client fetch result
* test(daemon): finish container flow test
* test(daemon): enable distributed in ci
* test(daemon): enable distributed in ci
* test(daemon): decare flows and pods
* test(daemon): debug ci if else
* test(daemon): debug ci if else
* test(daemon): decare flows and pods
* test(daemon): correct test paths
* test(daemon): add small delay for async tests
* fix: star routing fixes (#4100)
* docs: update docs
* fix: fix Request.__repr__
* docs: update flow remarks
* docs: fix typo
* test: add non_empty_fields test
* chore: remove non_empty_fields test
* feat: polling per endpoint (#4111)
* feat(polling): polling per endpoint configurable
* fix: adjust tests
* feat(polling): extend documentation
* style: fix overload and cli autocomplete
* fix: clean up
* fix: adjust more tests
* fix: remove repeat from flaky test
* fix: k8s test
* feat(polling): address pr feedback
* feat: improve docs
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(grpc): support connect grpc server via ssl tunnel (#4092)
* feat(grpc): support ssl grpc connect if port is 443
* fix(grpc): use https option instead of detect port automatically
* chore: fix typo
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* test(networking): add test for peapods networking
* fix: address comments
Co-authored-by: Joan Fontanals <[email protected]>
* feat(polling): unify polling args (#4113)
* fix: several issues for jinad pods (#4119)
* fix: activate for jinad pods
* fix: dont expose worker pod in partial daemon
* fix: workspace setting
* fix: containerized flows
* fix: hub test
* feat(daemon): remote peas on star routing (#4112)
* test(daemon): fix request in peas
* test(daemon): fix request in peas
* test(daemon): fix sync async client test
* test(daemon): enable remote peas test
* test(daemon): replace send message to send request
* test(daemon): declare pea tests in ci
* test(daemon): use pea args fixture
* test(daemon): head pea use default host
* test(daemon): fix peas topologies
* test(daemon): fix pseudo naming
* test(daemon): use default host as host
* test(daemon): fix executor path
* test(daemon): add remote worker back
* test(daemon): skip local remote remote topology
* fix: jinad pea test setup
* fix: jinad pea tests
* fix: remove invalid assertion
Co-authored-by: jacobowitz <[email protected]>
* feat: enable daemon tests again (#4132)
* feat: enable daemon tests again
* fix: remove bogy empty script file
* fix: more jinad test fixes
* style: fix overload and cli autocomplete
* fix: scale and ru in jinad
* fix: fix more jinad tests
Co-authored-by: Jina Dev Bot <[email protected]>
* fix: fix flow test
* fix: improve pea tests reliability (#4136)
Co-authored-by: Joan Fontanals <[email protected]>
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Deepankar Mahapatro <[email protected]>
Co-authored-by: bwanglzu <[email protected]>
Co-authored-by: AlaeddineAbdessalem <[email protected]>
Co-authored-by: Zhaofeng Miao <[email protected]> | https://github.com/jina-ai/jina.git | def ports(self) -> Dict:
# Container peas are started in separate docker containers, so we should not expose port_in here
if (
(
self.params.pea_role
and PeaRoleType.from_string(self.params.pea_role) != PeaRoleType.HEAD
)
and self.params.uses
and self.params.uses.startswith('docker://')
):
return {}
else:
return {f'{self.params.port_in}/tcp': self.params.port_in}
| 67 | dependencies.py | Python | daemon/api/dependencies.py | 933415bfa1f9eb89f935037014dfed816eb9815d | jina | 5 |
|
261,869 | 47 | 15 | 16 | 233 | 23 | 0 | 60 | 219 | common_voice | Update TTS.tts formatters (#1228)
* Return Dict from tts formatters
* Make style | https://github.com/coqui-ai/TTS.git | def common_voice(root_path, meta_file, ignored_speakers=None):
txt_file = os.path.join(root_path, meta_file)
items = []
with open(txt_file, "r", encoding="utf-8") as ttf:
for line in ttf:
if line.startswith("client_id"):
continue
cols = line.split("\t")
text = cols[2]
speaker_name = cols[0]
# ignore speakers
if isinstance(ignored_speakers, list):
if speaker_name in ignored_speakers:
continue
wav_file = os.path.join(root_path, "clips", cols[1].replace(".mp3", ".wav"))
items.append({"text": text, "audio_file": wav_file, "speaker_name": "MCV_" + speaker_name})
return items
| 136 | formatters.py | Python | TTS/tts/datasets/formatters.py | 127118c6378168e3d36a1e5d19ede777fd20684f | TTS | 5 |
|
89,804 | 7 | 8 | 6 | 39 | 6 | 0 | 7 | 28 | get_response_from_control_silo | feat(hybrid-cloud): Create a base parser and middleware for webhooks (#42267)
See [HC-468](https://getsentry.atlassian.net/browse/HC-468)
Requires https://github.com/getsentry/sentry/pull/42260
This PR establishes the base parser that will be inherited from to
forward webhooks to the appropriate integration. It is a slightly
modified, portion of this [much larger
PR](https://github.com/getsentry/sentry/pull/39169). It was split off in
order to update that PR and make it more reviewable.
Some background:
The IntegrationControlMiddleware catches any incoming requests to the
control silo with the `/extensions/` path prefix. It parses the provider
out of the URL (e.g. `sentry.io/extensions/slack/something`), and passes
the request along to that parser to determine how we handle the request
(e.g. do we forward it to a region, multiple regions, handle it async,
respond immediately from control, etc.)
The BaseRequestParser provides a bunch of helpful methods to these
parsers to make the actual integration-specific parsers as minimal as
possible. They only need to implement a method for identifying the
integration (e.g. from headers, from a signature, from a payload, etc),
and how we respond to the webhook (allowing for different behaviour from
different webhooks). | https://github.com/getsentry/sentry.git | def get_response_from_control_silo(self) -> HttpResponse:
self._ensure_control_silo()
return self.response_handler(self.request)
| 22 | base.py | Python | src/sentry/middleware/integrations/parsers/base.py | d8609112d6e2f373692b414acff6d4a2f7466750 | sentry | 1 |
|
286,575 | 9 | 8 | 9 | 52 | 8 | 0 | 9 | 23 | get_categories | [IMPROVE] Fix Docstring formatting/Fix missing, incomplete type hints (#3412)
* Fixes
* Update stocks_helper.py
* update git-actions set-output to new format
* Update stocks_helper.py
* Update terminal_helper.py
* removed LineAnnotateDrawer from qa_view
* lint
* few changes
* updates
* sdk auto gen modules done
* Update stocks_helper.py
* updates to changed imports, and remove first sdk_modules
* Update generate_sdk.py
* Update generate_sdk.py
* pylint
* revert stocks_helper
* Update generate_sdk.py
* Update sdk.py
* Update generate_sdk.py
* full auto generation, added sdk.py/controllers creation
* missed enable forecasting
* added running black in subprocess after sdk files generation completes
* removed deleted sdk_arg_logger
* comment out tests
* property doc fix
* clean up
* Update generate_sdk.py
* make trailmap classes useable for doc generation
* Update generate_sdk.py
* added lineon to trailmap class for linking to func in markdown
* changed lineon to dict
* added full_path to trailmap for linking in docs
* updated portfolio
* feat: initial files
* feat: added meta head
* feat: added funcdef
* added func_def to trailmap attributes for markdown in docs, added missing type hints to covid functions
* feat: added view and merged with jaun
* Update generate_sdk.py
* Update generate_sdk.py
* Update generate_sdk.py
* Update generate_sdk.py
* init
* fix returns
* fix: random stuff
* fix: random
* fixed encoding issue on windows
* fix: generate tabs
* update
* Update generate_sdk_markdown.py
* Create .pydocstyle.ini
* added type hint classes for views
* fixes
* alt, ba
* alt-economy
* Update finviz_compare_model.py
* fixs
* Update substack_model.py
* Update generate_sdk.py
* last of my section
* porfolio
* po
* Update optimizer_model.py
* fixing more things
* few more
* keys done
* update
* fixes
* Update generate_sdk_markdown.py
* Update generate_sdk_markdown.py
* mypy forecast fix
* Update generate_sdk_markdown.py
* Update generate_sdk_markdown.py
* Update generate_sdk_markdown.py
* fixes
* forecast fixes
* one more fix
* Update coinbase_model.py
* Update generate_sdk_markdown.py
Co-authored-by: Colin Delahunty <[email protected]>
Co-authored-by: James Maslek <[email protected]>
Co-authored-by: jose-donato <[email protected]>
Co-authored-by: andrewkenreich <[email protected]> | https://github.com/OpenBB-finance/OpenBBTerminal.git | def get_categories(self) -> Union[Dict[Any, Any], List[Any]]:
return self.coin.get("categories", {})
| 33 | pycoingecko_model.py | Python | openbb_terminal/cryptocurrency/due_diligence/pycoingecko_model.py | 59d8b36bb0467a1a99513b10e8b8471afaa56fd6 | OpenBBTerminal | 1 |
|
156,683 | 30 | 11 | 9 | 61 | 6 | 0 | 32 | 132 | codes | Add ``AttributeNotImplementedError`` for properties so IPython glob search works (#9231) | https://github.com/dask/dask.git | def codes(self):
if not self.known:
msg = (
"`df.column.cat.codes` with unknown categories is not "
"supported. Please use `column.cat.as_known()` or "
"`df.categorize()` beforehand to ensure known categories"
)
raise AttributeNotImplementedError(msg)
return self._property_map("codes")
| 31 | categorical.py | Python | dask/dataframe/categorical.py | ecbab9d7cb52a2d96cb1b8dc397a87811e6f8059 | dask | 2 |
|
276,382 | 50 | 19 | 30 | 313 | 26 | 0 | 75 | 333 | generate_combinations_with_testcase_name | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def generate_combinations_with_testcase_name(**kwargs):
sort_by_key = lambda k: k[0]
combinations = []
for key, values in sorted(kwargs.items(), key=sort_by_key):
if not isinstance(values, list):
values = [values]
combinations.append([(key, value) for value in values])
combinations = [
collections.OrderedDict(result)
for result in itertools.product(*combinations)
]
named_combinations = []
for combination in combinations:
assert isinstance(combination, collections.OrderedDict)
name = "".join(
[
"_{}_{}".format(
"".join(filter(str.isalnum, key)),
"".join(filter(str.isalnum, str(value))),
)
for key, value in combination.items()
]
)
named_combinations.append(
collections.OrderedDict(
list(combination.items())
+ [("testcase_name", "_test{}".format(name))]
)
)
return named_combinations
| 194 | test_utils.py | Python | keras/testing_infra/test_utils.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 7 |
|
290,665 | 77 | 14 | 31 | 470 | 34 | 0 | 116 | 437 | _parse_routing_response | Use HERE API v8 (#80892)
* Use HERE API v8
Signed-off-by: Kevin Stillhammer <[email protected]>
* Add migration
Signed-off-by: Kevin Stillhammer <[email protected]>
* Catch correct voluptuous error
Signed-off-by: Kevin Stillhammer <[email protected]>
* Use list comprehension for transit values
* Add migration alternative
Signed-off-by: Kevin Stillhammer <[email protected]> | https://github.com/home-assistant/core.git | def _parse_routing_response(self, response) -> HERETravelTimeData:
section: dict = response["routes"][0]["sections"][0]
summary: dict = section["summary"]
mapped_origin_lat: float = section["departure"]["place"]["location"]["lat"]
mapped_origin_lon: float = section["departure"]["place"]["location"]["lng"]
mapped_destination_lat: float = section["arrival"]["place"]["location"]["lat"]
mapped_destination_lon: float = section["arrival"]["place"]["location"]["lng"]
distance: float = summary["length"]
if self.config.units == IMPERIAL_UNITS:
# Convert to miles.
distance = DistanceConverter.convert(distance, LENGTH_METERS, LENGTH_MILES)
else:
# Convert to kilometers
distance = distance / 1000
origin_name: str | None = None
if (names := section["spans"][0].get("names")) is not None:
origin_name = names[0]["value"]
destination_name: str | None = None
if (names := section["spans"][-1].get("names")) is not None:
destination_name = names[0]["value"]
return HERETravelTimeData(
{
ATTR_ATTRIBUTION: None,
ATTR_DURATION: round(summary["baseDuration"] / 60), # type: ignore[misc]
ATTR_DURATION_IN_TRAFFIC: round(summary["duration"] / 60),
ATTR_DISTANCE: distance,
ATTR_ORIGIN: f"{mapped_origin_lat},{mapped_origin_lon}",
ATTR_DESTINATION: f"{mapped_destination_lat},{mapped_destination_lon}",
ATTR_ORIGIN_NAME: origin_name,
ATTR_DESTINATION_NAME: destination_name,
}
)
| 270 | coordinator.py | Python | homeassistant/components/here_travel_time/coordinator.py | aedbfdabee67e3e7b915b842d28f151a08cb3d7f | core | 4 |
|
20,353 | 83 | 14 | 24 | 366 | 44 | 0 | 107 | 371 | format | 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 format(self, tokensource, outfile):
self._create_drawables(tokensource)
self._draw_line_numbers()
im = Image.new(
'RGB',
self._get_image_size(self.maxlinelength, self.maxlineno),
self.background_color
)
self._paint_line_number_bg(im)
draw = ImageDraw.Draw(im)
# Highlight
if self.hl_lines:
x = self.image_pad + self.line_number_width - self.line_number_pad + 1
recth = self._get_line_height()
rectw = im.size[0] - x
for linenumber in self.hl_lines:
y = self._get_line_y(linenumber - 1)
draw.rectangle([(x, y), (x + rectw, y + recth)],
fill=self.hl_color)
for pos, value, font, text_fg, text_bg in self.drawables:
if text_bg:
text_size = draw.textsize(text=value, font=font)
draw.rectangle([pos[0], pos[1], pos[0] + text_size[0], pos[1] + text_size[1]], fill=text_bg)
draw.text(pos, value, font=font, fill=text_fg)
im.save(outfile, self.image_format.upper())
# Add one formatter per format, so that the "-f gif" option gives the correct result
# when used in pygmentize.
| 244 | img.py | Python | pipenv/patched/notpip/_vendor/pygments/formatters/img.py | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | 5 |
|
180,363 | 22 | 13 | 9 | 119 | 14 | 0 | 31 | 102 | call_function | Callable blocks (#1437)
* format backend
* blocks callable
* call blocks
* format
* fixed upload
* fix mix
* formatting
* formatting
* formatting
* added serialization/deserialization for video
* formatting
* blocks
* formatting
* fix tests
* formatting | https://github.com/gradio-app/gradio.git | async def call_function(self, fn_index, processed_input):
block_fn = self.fns[fn_index]
start = time.time()
if inspect.iscoroutinefunction(block_fn.fn):
prediction = await block_fn.fn(*processed_input)
else:
prediction = await run_in_threadpool(block_fn.fn, *processed_input)
duration = time.time() - start
return prediction, duration
| 72 | blocks.py | Python | gradio/blocks.py | 71bcfdbe929c83c10d761018042078041e956b81 | gradio | 2 |
|
113,058 | 47 | 12 | 22 | 236 | 27 | 1 | 62 | 194 | test_multi_trial | [retiarii] fix experiment does not exit after done (#4916) | https://github.com/microsoft/nni.git | def test_multi_trial():
evaluator_kwargs = {
'max_epochs': 1
}
to_test = [
# (model, evaluator)
_mnist_net('simple', evaluator_kwargs),
_mnist_net('simple_value_choice', evaluator_kwargs),
_mnist_net('value_choice', evaluator_kwargs),
_mnist_net('repeat', evaluator_kwargs),
_mnist_net('custom_op', evaluator_kwargs),
]
for base_model, evaluator in to_test:
search_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnist_unittest'
exp_config.trial_concurrency = 1
exp_config.max_trial_number = 1
exp_config.training_service.use_active_gpu = False
exp.run(exp_config, 8080)
assert isinstance(exp.export_top_models()[0], dict)
exp.stop()
python_script =
@pytest.mark.timeout(600) | @pytest.mark.timeout(600) | 131 | test_multitrial.py | Python | test/ut/retiarii/test_multitrial.py | 2bc984412c0fec3dae84e6f6a8253a615d2b6ebd | nni | 2 |
260,489 | 90 | 15 | 31 | 331 | 41 | 0 | 123 | 439 | fit | MAINT validate parameter in `EmpiricalCovariance`, `MinCovDet`, and `EllipticEnvelope` (#23842)
Co-authored-by: Jérémie du Boisberranger <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def fit(self, X, y=None):
self._validate_params()
X = self._validate_data(X, ensure_min_samples=2, estimator="MinCovDet")
random_state = check_random_state(self.random_state)
n_samples, n_features = X.shape
# check that the empirical covariance is full rank
if (linalg.svdvals(np.dot(X.T, X)) > 1e-8).sum() != n_features:
warnings.warn(
"The covariance matrix associated to your dataset is not full rank"
)
# compute and store raw estimates
raw_location, raw_covariance, raw_support, raw_dist = fast_mcd(
X,
support_fraction=self.support_fraction,
cov_computation_method=self._nonrobust_covariance,
random_state=random_state,
)
if self.assume_centered:
raw_location = np.zeros(n_features)
raw_covariance = self._nonrobust_covariance(
X[raw_support], assume_centered=True
)
# get precision matrix in an optimized way
precision = linalg.pinvh(raw_covariance)
raw_dist = np.sum(np.dot(X, precision) * X, 1)
self.raw_location_ = raw_location
self.raw_covariance_ = raw_covariance
self.raw_support_ = raw_support
self.location_ = raw_location
self.support_ = raw_support
self.dist_ = raw_dist
# obtain consistency at normal models
self.correct_covariance(X)
# re-weight estimator
self.reweight_covariance(X)
return self
| 211 | _robust_covariance.py | Python | sklearn/covariance/_robust_covariance.py | 47dd54f727f09a47e84fb4cb53d33f9b0f239edb | scikit-learn | 3 |
|
101,691 | 6 | 6 | 4 | 24 | 3 | 0 | 6 | 20 | thumbnails | Alignments update:
- Store face embeddings in PNG header when sorting
- typing + refactor
- Update alignments keys for 'identity' and 'video_meta' + bump to v2.3
- General typing fixes | https://github.com/deepfakes/faceswap.git | def thumbnails(self) -> "Thumbnails":
return self._thumbnails
| 12 | alignments.py | Python | lib/align/alignments.py | e5356a417e7c2124e75c4a2994ed604fc0a3cc74 | faceswap | 1 |
|
129,565 | 62 | 13 | 25 | 350 | 28 | 0 | 100 | 388 | test_apex_dqn_compilation_and_per_worker_epsilon_values | [RLlib] Preparatory PR for multi-agent multi-GPU learner (alpha-star style) #03 (#21652) | https://github.com/ray-project/ray.git | def test_apex_dqn_compilation_and_per_worker_epsilon_values(self):
config = apex.APEX_DEFAULT_CONFIG.copy()
config["num_workers"] = 3
config["num_gpus"] = 0
config["learning_starts"] = 1000
config["prioritized_replay"] = True
config["timesteps_per_iteration"] = 100
config["min_time_s_per_reporting"] = 1
config["optimizer"]["num_replay_buffer_shards"] = 1
for _ in framework_iterator(config, with_eager_tracing=True):
plain_config = config.copy()
trainer = apex.ApexTrainer(config=plain_config, env="CartPole-v0")
# Test per-worker epsilon distribution.
infos = trainer.workers.foreach_policy(
lambda p, _: p.get_exploration_state())
expected = [0.4, 0.016190862, 0.00065536]
check([i["cur_epsilon"] for i in infos], [0.0] + expected)
check_compute_single_action(trainer)
for i in range(2):
results = trainer.train()
check_train_results(results)
print(results)
# Test again per-worker epsilon distribution
# (should not have changed).
infos = trainer.workers.foreach_policy(
lambda p, _: p.get_exploration_state())
check([i["cur_epsilon"] for i in infos], [0.0] + expected)
trainer.stop()
| 220 | test_apex_dqn.py | Python | rllib/agents/dqn/tests/test_apex_dqn.py | d5bfb7b7da6f8ec505dd8ed69f0be419decfdcc0 | ray | 5 |
|
303,357 | 51 | 13 | 24 | 246 | 21 | 0 | 63 | 307 | async_update | Remove attribution from extra state attributes (#76172) | https://github.com/home-assistant/core.git | async def async_update(self):
try:
await self.device.update()
if self._removed:
_LOGGER.debug("%s reacquired", self.name)
self._removed = False
except AgentError:
# server still available - camera error
if self.device.client.is_available and not self._removed:
_LOGGER.error("%s lost", self.name)
self._removed = True
self._attr_icon = "mdi:camcorder-off"
if self.is_on:
self._attr_icon = "mdi:camcorder"
self._attr_available = self.device.client.is_available
self._attr_extra_state_attributes = {
"editable": False,
"enabled": self.is_on,
"connected": self.connected,
"detected": self.is_detected,
"alerted": self.is_alerted,
"has_ptz": self.device.has_ptz,
"alerts_enabled": self.device.alerts_active,
}
| 148 | camera.py | Python | homeassistant/components/agent_dvr/camera.py | 22eba6ce1ba3611421c526b61432ac90592eff08 | core | 6 |
|
298,007 | 38 | 12 | 19 | 126 | 10 | 0 | 51 | 272 | _update_effect_list | String formatting and max line length - Part 6 (#84525) | https://github.com/home-assistant/core.git | def _update_effect_list(self, effect_list):
if effect_list in (None, "None", ""):
self._effect_list = None
return
if not isinstance(effect_list, list):
_LOGGER.error(
(
"Received invalid effect list: %s for entity %s. Expected list of"
" strings"
),
effect_list,
self.entity_id,
)
self._effect_list = None
return
if len(effect_list) == 0:
self._effect_list = None
return
self._effect_list = effect_list
| 75 | light.py | Python | homeassistant/components/template/light.py | 8819634b613f6bfd55885283bab86c3852ae40c4 | core | 4 |
|
26,067 | 14 | 8 | 4 | 50 | 7 | 0 | 17 | 38 | test_is_user_address_limit_reached_false | Limit number of user addresses (#9205)
* Add migration for clearing user addresses
* Adjust checkout flow - do not save address for client with more than max addresses value
* Remove the oldest user address when new address above limit is added | https://github.com/saleor/saleor.git | def test_is_user_address_limit_reached_false(customer_user, address):
# given
customer_user.addresses.set([address])
# when
limit_reached = is_user_address_limit_reached(customer_user)
# then
assert limit_reached is False
| 28 | test_utils.py | Python | saleor/account/tests/test_utils.py | 9ebd28ab8a3b7f6c341ef8474820ab225548fe0e | saleor | 1 |
|
212,377 | 19 | 13 | 8 | 81 | 13 | 0 | 22 | 72 | port | 12089 support unix socket (#12091)
* Support unix socket with bokeh server
This commit adds a command line argument to bokeh serve to allow bokeh server to listen to a unix socket.
Usage: bokeh serve --unix-socket /path/to/socket
Reference: https://github.com/bokeh/bokeh/issues/12089
* Fix failing tests
* Fix test
* Address review comments
* Add end-to-end testing
* Update env
* Remove temp files
* Fix failing testS
* Fix test
* Fix mypy fail
* Address review comments
* Fix tests
* Fix tests
* Address review comments
* Fix tests
* Fix tests
* Reduce sleep for unit tests
* Fix test for windows
* Use absolute path
* Disable test in windows
* Fix code quality tests
* Raise error on win32
* Fix tests
* Fix tests
* Address review comments
* Fix tests
* Fix mypy fails
* Fix tests | https://github.com/bokeh/bokeh.git | def port(self) -> int | None:
sock = next(
sock for sock in self._http._sockets.values()
if sock.family in (socket.AF_INET, socket.AF_INET6)
)
return sock.getsockname()[1]
| 51 | server.py | Python | bokeh/server/server.py | dd9668e0b61d961c9e4bb76d9d0951d4f82e1dad | bokeh | 3 |
|
129,371 | 30 | 16 | 30 | 162 | 8 | 0 | 47 | 393 | testAutoscalingNewNode | Tune test autoscaler / fix stale node detection bug (#21516)
See #21458. Currently, Tune keeps its own list of alive node IPs, but this information is only updated every 10 seconds and is usually stale when a new node is added. Because of this, the first trial scheduled on this node is usually marked as failed. This PR adds a test confirming this behavior and gets rid of the unneeded code path.
Co-authored-by: Xiaowei Jiang <[email protected]>
Co-authored-by: xwjiang2010 <[email protected]> | https://github.com/ray-project/ray.git | def testAutoscalingNewNode(self):
self.cluster.update_config({
"provider": {
"head_resources": {
"CPU": 4,
"GPU": 0
}
},
"available_node_types": {
"ray.worker.cpu": {
"resources": {
"CPU": 4
},
"min_workers": 0, # No minimum nodes
"max_workers": 2,
},
"ray.worker.gpu": {
"min_workers": 0,
"max_workers": 0, # No GPU nodes
}
},
})
self.cluster.start()
self.cluster.connect(client=True, timeout=120)
| 114 | test_multinode_sync.py | Python | python/ray/tune/tests/test_multinode_sync.py | 8fd5b7a5a80e88f7f70c4ebb179129c8eed65b72 | ray | 1 |
|
93,204 | 110 | 11 | 60 | 694 | 41 | 0 | 176 | 581 | test_issue_message_builder | test(msteams): Add tests for building group card (#36834)
Add tests for build_group_card which builds issues cards. Does NOT test all visual aspects of the card. Only ensures that certain important elements are present and the basic structure of the card is correct. | https://github.com/getsentry/sentry.git | def test_issue_message_builder(self):
self.event1.data["metadata"].update({"value": "some error"})
self.group1.data["metadata"].update({"value": "some error"})
self.event1.data["type"] = self.group1.data["type"] = "error"
issue_card = build_group_card(
group=self.group1, event=self.event1, rules=self.rules, integration=self.integration
)
body = issue_card["body"]
assert 4 == len(body)
title = body[0]
assert "oh no" in title["text"]
assert TextSize.LARGE == title["size"]
assert TextWeight.BOLDER == title["weight"]
description = body[1]
assert "some error" == description["text"]
assert TextWeight.BOLDER == description["weight"]
footer = body[2]
assert "ColumnSet" == footer["type"]
assert 3 == len(footer["columns"])
logo = footer["columns"][0]["items"][0]
assert "20px" == logo["height"]
issue_id_and_rule = footer["columns"][1]["items"][0]
assert self.group1.qualified_short_id in issue_id_and_rule["text"]
assert "rule1" in issue_id_and_rule["text"]
assert "+1 other" in issue_id_and_rule["text"]
date = footer["columns"][2]["items"][0]
assert (
re.match(
r,
date["text"],
re.VERBOSE,
)
is not None
)
actions_container = body[3]
assert "Container" == actions_container["type"]
action_set = actions_container["items"][0]
assert "ActionSet" == action_set["type"]
actions = action_set["actions"]
for action in actions:
assert ActionType.SHOW_CARD == action["type"]
card_body = action["card"]["body"]
assert 1 <= len(card_body)
assert "Input.ChoiceSet" == card_body[-1]["type"]
resolve_action, ignore_action, assign_action = actions
assert "Resolve" == resolve_action["title"]
assert "Ignore" == ignore_action["title"]
assert "Assign" == assign_action["title"]
# Check if card is serializable to json
card_json = json.dumps(issue_card)
assert card_json[0] == "{" and card_json[-1] == "}"
| 402 | test_message_builder.py | Python | tests/sentry/integrations/msteams/test_message_builder.py | db35e231ceababe8c9f5ca7b5d2ca685f07c7d5b | sentry | 3 |
|
267,974 | 6 | 7 | 3 | 28 | 4 | 0 | 6 | 20 | id | ansible-test - Use more native type hints. (#78435)
* ansible-test - Use more native type hints.
Simple search and replace to switch from comments to native type hints for return types of functions with no arguments.
* ansible-test - Use more native type hints.
Conversion of simple single-line function annotation type comments to native type hints.
* ansible-test - Use more native type hints.
Conversion of single-line function annotation type comments with default values to native type hints.
* ansible-test - Use more native type hints.
Manual conversion of type annotation comments for functions which have pylint directives. | https://github.com/ansible/ansible.git | def id(self) -> str:
return self.inspection['Id']
| 15 | docker_util.py | Python | test/lib/ansible_test/_internal/docker_util.py | 3eb0485dd92c88cc92152d3656d94492db44b183 | ansible | 1 |
|
19,966 | 39 | 14 | 16 | 194 | 20 | 0 | 49 | 238 | rollback | 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 rollback(self) -> None:
for p in self._moves:
logger.info("Moving to %s\n from %s", *p)
for new_path, path in self._moves:
try:
logger.debug("Replacing %s from %s", new_path, path)
if os.path.isfile(new_path) or os.path.islink(new_path):
os.unlink(new_path)
elif os.path.isdir(new_path):
rmtree(new_path)
renames(path, new_path)
except OSError as ex:
logger.error("Failed to restore %s", new_path)
logger.debug("Exception: %s", ex)
self.commit()
| 116 | req_uninstall.py | Python | pipenv/patched/notpip/_internal/req/req_uninstall.py | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | 7 |
|
100,929 | 43 | 13 | 13 | 157 | 12 | 0 | 52 | 189 | _get_tooltip | Core updates
- Change loss loading mechanism
- Autosize tooltips based on content size
- Random linting + code modernisation | https://github.com/deepfakes/faceswap.git | def _get_tooltip(widget, text=None, text_variable=None):
_RECREATE_OBJECTS["tooltips"][str(widget)] = {"text": text,
"text_variable": text_variable}
logger.debug("Adding to tooltips dict: (widget: %s. text: '%s')", widget, text)
wrap_length = 400
if text is not None:
while True:
if len(text) < wrap_length * 5:
break
if wrap_length > 720:
break
wrap_length = int(wrap_length * 1.10)
return Tooltip(widget, text=text, text_variable=text_variable, wrap_length=wrap_length)
| 100 | control_helper.py | Python | lib/gui/control_helper.py | bad5025aea1adb9126580e14e064e6c99089243d | faceswap | 5 |
|
271,893 | 6 | 8 | 2 | 27 | 3 | 0 | 6 | 12 | is_feature_layer | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def is_feature_layer(layer):
return getattr(layer, "_is_feature_layer", False)
| 15 | training_utils_v1.py | Python | keras/engine/training_utils_v1.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
|
152,834 | 23 | 9 | 9 | 100 | 13 | 0 | 24 | 56 | get_deepbooru_tags | refactored the deepbooru module to improve speed on running multiple interogations in a row. Added the option to generate deepbooru tags for textual inversion preproccessing. | https://github.com/AUTOMATIC1111/stable-diffusion-webui.git | def get_deepbooru_tags(pil_image, threshold=0.5):
from modules import shared # prevents circular reference
create_deepbooru_process(threshold)
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_process_queue.put(pil_image)
while shared.deepbooru_process_return["value"] == -1:
time.sleep(0.2)
release_process()
return ret
| 61 | deepbooru.py | Python | modules/deepbooru.py | 1f92336be768d235c18a82acb2195b7135101ae7 | stable-diffusion-webui | 2 |
|
274,533 | 50 | 17 | 15 | 160 | 24 | 0 | 59 | 246 | __call__ | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def __call__(self, y_true, y_pred, sample_weight=None):
# If we are wrapping a lambda function strip '<>' from the name as it is not
# accepted in scope name.
graph_ctx = tf_utils.graph_context_for_symbolic_tensors(
y_true, y_pred, sample_weight
)
with backend.name_scope(self._name_scope), graph_ctx:
if tf.executing_eagerly():
call_fn = self.call
else:
call_fn = tf.__internal__.autograph.tf_convert(
self.call, tf.__internal__.autograph.control_status_ctx()
)
losses = call_fn(y_true, y_pred)
return losses_utils.compute_weighted_loss(
losses, sample_weight, reduction=self._get_reduction()
)
| 101 | losses.py | Python | keras/losses.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 2 |
|
107,434 | 32 | 9 | 24 | 84 | 8 | 0 | 38 | 71 | use | Rename outdated seaborn styles.
They are kept available under a versioned name for backcompat. | https://github.com/matplotlib/matplotlib.git | def use(style):
if isinstance(style, (str, Path)) or hasattr(style, 'keys'):
# If name is a single str, Path or dict, make it a single element list.
styles = [style]
else:
styles = style
style_alias = {'mpl20': 'default', 'mpl15': 'classic'}
| 144 | core.py | Python | lib/matplotlib/style/core.py | 06c92b8a8b81bbdacc3baf7ff0f1566c53fbe1b0 | matplotlib | 8 |
|
256,254 | 126 | 16 | 35 | 328 | 27 | 0 | 226 | 629 | _words_to_tokens | Apply black formatting (#2115)
* Testing black on ui/
* Applying black on docstores
* Add latest docstring and tutorial changes
* Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too
* Remove comments
* Relax constraints on pydoc-markdown
* Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade
* Fix a couple of bugs
* Add a type: ignore that was missing somehow
* Give path to black
* Apply Black
* Apply Black
* Relocate a couple of type: ignore
* Update documentation
* Make Linux CI run after applying Black
* Triggering Black
* Apply Black
* Remove dependency, does not work well
* Remove manually double trailing commas
* Update documentation
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | https://github.com/deepset-ai/haystack.git | def _words_to_tokens(words, word_offsets, tokenizer):
tokens = []
token_offsets = []
start_of_word = []
idx = 0
for w, w_off in zip(words, word_offsets):
idx += 1
if idx % 500000 == 0:
logger.info(idx)
# Get (subword) tokens of single word.
# empty / pure whitespace
if len(w) == 0:
continue
# For the first word of a text: we just call the regular tokenize function.
# For later words: we need to call it with add_prefix_space=True to get the same results with roberta / gpt2 tokenizer
# see discussion here. https://github.com/huggingface/transformers/issues/1196
elif len(tokens) == 0:
tokens_word = tokenizer.tokenize(w)
else:
if type(tokenizer) == RobertaTokenizer:
tokens_word = tokenizer.tokenize(w, add_prefix_space=True)
else:
tokens_word = tokenizer.tokenize(w)
# Sometimes the tokenizer returns no tokens
if len(tokens_word) == 0:
continue
tokens += tokens_word
# get global offset for each token in word + save marker for first tokens of a word
first_tok = True
for tok in tokens_word:
token_offsets.append(w_off)
# Depending on the tokenizer type special chars are added to distinguish tokens with preceeding
# whitespace (=> "start of a word"). We need to get rid of these to calculate the original length of the token
orig_tok = re.sub(SPECIAL_TOKENIZER_CHARS, "", tok)
# Don't use length of unk token for offset calculation
if orig_tok == tokenizer.special_tokens_map["unk_token"]:
w_off += 1
else:
w_off += len(orig_tok)
if first_tok:
start_of_word.append(True)
first_tok = False
else:
start_of_word.append(False)
return tokens, token_offsets, start_of_word
| 195 | tokenization.py | Python | haystack/modeling/model/tokenization.py | a59bca366174d9c692fa19750c24d65f47660ef7 | haystack | 10 |
|
291,784 | 72 | 13 | 45 | 567 | 22 | 0 | 162 | 705 | handle_event | Create a UUID from given LG soundbar device name (#81918)
Co-authored-by: Martin Hjelmare <[email protected]>
Fixes https://github.com/home-assistant/core/issues/77524
fixes undefined | https://github.com/home-assistant/core.git | def handle_event(self, response):
data = response["data"] if "data" in response else {}
if response["msg"] == "EQ_VIEW_INFO":
if "i_bass" in data:
self._bass = data["i_bass"]
if "i_treble" in data:
self._treble = data["i_treble"]
if "ai_eq_list" in data:
self._equalisers = data["ai_eq_list"]
if "i_curr_eq" in data:
self._equaliser = data["i_curr_eq"]
elif response["msg"] == "SPK_LIST_VIEW_INFO":
if "i_vol" in data:
self._volume = data["i_vol"]
if "i_vol_min" in data:
self._volume_min = data["i_vol_min"]
if "i_vol_max" in data:
self._volume_max = data["i_vol_max"]
if "b_mute" in data:
self._mute = data["b_mute"]
if "i_curr_func" in data:
self._function = data["i_curr_func"]
elif response["msg"] == "FUNC_VIEW_INFO":
if "i_curr_func" in data:
self._function = data["i_curr_func"]
if "ai_func_list" in data:
self._functions = data["ai_func_list"]
elif response["msg"] == "SETTING_VIEW_INFO":
if "i_rear_min" in data:
self._rear_volume_min = data["i_rear_min"]
if "i_rear_max" in data:
self._rear_volume_max = data["i_rear_max"]
if "i_rear_level" in data:
self._rear_volume = data["i_rear_level"]
if "i_woofer_min" in data:
self._woofer_volume_min = data["i_woofer_min"]
if "i_woofer_max" in data:
self._woofer_volume_max = data["i_woofer_max"]
if "i_woofer_level" in data:
self._woofer_volume = data["i_woofer_level"]
if "i_curr_eq" in data:
self._equaliser = data["i_curr_eq"]
if "s_user_name" in data:
self._attr_name = data["s_user_name"]
self.schedule_update_ha_state()
| 305 | media_player.py | Python | homeassistant/components/lg_soundbar/media_player.py | 4167edc52d92577d9bceb800d6699167a1a8b4c8 | core | 25 |
|
266,053 | 6 | 6 | 2 | 20 | 4 | 0 | 6 | 20 | prep_related_object_data | 4347 Add JSON/YAML import support for all objects (#10367)
* 4347 initial code for json import
* 4347 initial code for json import
* Clean up form processing logic
* Consolidate import forms
* Consolidate object import/update logic
* Clean up bulk import view
Co-authored-by: jeremystretch <[email protected]> | https://github.com/netbox-community/netbox.git | def prep_related_object_data(self, parent, data):
return data
| 12 | bulk_views.py | Python | netbox/netbox/views/generic/bulk_views.py | 93e7457e0d84ad24cba22cc5c0811777ddebf94e | netbox | 1 |
|
102,252 | 40 | 11 | 120 | 198 | 25 | 0 | 54 | 108 | infer_concrete_type_builder | Per-overload torch.ops API (#67254)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
TODO: disallow `default` as an overload name for aten operators.
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33262228
Pulled By: anjali411
fbshipit-source-id: 600dbf511514ea9b41aea3e6b1bc1102dab08909 | https://github.com/pytorch/pytorch.git | def infer_concrete_type_builder(nn_module, share_types=True):
concrete_type_builder = torch._C.ConcreteModuleTypeBuilder(type(nn_module))
if isinstance(nn_module, (torch.nn.ModuleDict)):
concrete_type_builder.set_module_dict()
if isinstance(nn_module, (torch.nn.ModuleList, torch.nn.Sequential)):
concrete_type_builder.set_module_list()
class_annotations = getattr(nn_module, '__annotations__', {})
if isinstance(nn_module, (torch.ao.quantization.QuantWrapper)):
class_annotations = {}
# Get user-annotated ignored attributes.
user_annotated_ignored_attributes = getattr(nn_module, "__jit_ignored_attributes__", list())
concrete_type_builder.add_ignored_attributes(user_annotated_ignored_attributes)
ignored_properties = jit_ignored_properties(nn_module)
# try to infer the type from type annotation or from the object itself | 846 | _recursive.py | Python | torch/jit/_recursive.py | 8e6d1738a41cb28c36db3dc5e10fd812417673de | pytorch | 37 |
|
293,923 | 27 | 13 | 15 | 126 | 14 | 0 | 35 | 113 | test_from_event_to_delete_state | Convert unindexed domain queries to entity_id queries (#68404) | https://github.com/home-assistant/core.git | def test_from_event_to_delete_state():
event = ha.Event(
EVENT_STATE_CHANGED,
{
"entity_id": "sensor.temperature",
"old_state": ha.State("sensor.temperature", "18"),
"new_state": None,
},
)
db_state = States.from_event(event)
assert db_state.entity_id == "sensor.temperature"
assert db_state.state == ""
assert db_state.last_changed == event.time_fired
assert db_state.last_updated == event.time_fired
| 78 | test_models.py | Python | tests/components/recorder/test_models.py | 3150915cb72df2ca94a67fa29ebc9164d8deabf1 | core | 1 |
|
120,239 | 145 | 14 | 34 | 580 | 55 | 1 | 218 | 280 | _lstsq | remove numpy.linalg._promote_arg_dtypes
in favor of numpy.util._promote_dtypes_inexact | https://github.com/google/jax.git | def _lstsq(a, b, rcond, *, numpy_resid=False):
# TODO: add lstsq to lax_linalg and implement this function via those wrappers.
# TODO: add custom jvp rule for more robust lstsq differentiation
a, b = _promote_dtypes_inexact(a, b)
if a.shape[0] != b.shape[0]:
raise ValueError("Leading dimensions of input arrays must match")
b_orig_ndim = b.ndim
if b_orig_ndim == 1:
b = b[:, None]
if a.ndim != 2:
raise TypeError(
f"{a.ndim}-dimensional array given. Array must be two-dimensional")
if b.ndim != 2:
raise TypeError(
f"{b.ndim}-dimensional array given. Array must be one or two-dimensional")
m, n = a.shape
dtype = a.dtype
if rcond is None:
rcond = jnp.finfo(dtype).eps * max(n, m)
else:
rcond = jnp.where(rcond < 0, jnp.finfo(dtype).eps, rcond)
u, s, vt = svd(a, full_matrices=False)
mask = s >= rcond * s[0]
rank = mask.sum()
safe_s = jnp.where(mask, s, 1)
s_inv = jnp.where(mask, 1 / safe_s, 0)[:, jnp.newaxis]
uTb = jnp.matmul(u.conj().T, b, precision=lax.Precision.HIGHEST)
x = jnp.matmul(vt.conj().T, s_inv * uTb, precision=lax.Precision.HIGHEST)
# Numpy returns empty residuals in some cases. To allow compilation, we
# default to returning full residuals in all cases.
if numpy_resid and (rank < n or m <= n):
resid = jnp.asarray([])
else:
b_estimate = jnp.matmul(a, x, precision=lax.Precision.HIGHEST)
resid = norm(b - b_estimate, axis=0) ** 2
if b_orig_ndim == 1:
x = x.ravel()
return x, resid, rank, s
_jit_lstsq = jit(partial(_lstsq, numpy_resid=False))
@_wraps(np.linalg.lstsq, lax_description=textwrap.dedent()) | @_wraps(np.linalg.lstsq, lax_description=textwrap.dedent("""\
It has two important differences:
1. In `numpy.linalg.lstsq`, the default `rcond` is `-1`, and warns that in the future
the default will be `None`. Here, the default rcond is `None`.
2. In `np.linalg.lstsq` the returned residuals are empty for low-rank or over-determined
solutions. Here, the residuals are returned in all cases, to make the function
compatible with jit. The non-jit compatible numpy behavior can be recovered by
passing numpy_resid=True.
The lstsq function does not currently have a custom JVP rule, so the gradient is
poorly behaved for some inputs, particularly for low-rank `a`.
""")) | 336 | linalg.py | Python | jax/_src/numpy/linalg.py | bb2682db6df5b9388ce0b161e3f449624238718b | jax | 10 |
19,913 | 17 | 10 | 18 | 74 | 9 | 0 | 22 | 97 | installed_location | 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 installed_location(self) -> Optional[str]:
egg_link = egg_link_path_from_location(self.raw_name)
if egg_link:
location = egg_link
elif self.location:
location = self.location
else:
return None
return normalize_path(location)
| 44 | base.py | Python | pipenv/patched/notpip/_internal/metadata/base.py | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | 3 |
|
188,416 | 61 | 16 | 15 | 214 | 22 | 0 | 74 | 154 | validate_and_return_id_token | Fix rbac (#7699)
* perf: 优化 suggesstion
* perf: 修改 migrations
* feat: 添加OIDC认证逻辑
* perf: 修改 backend
* perf: 优化认证backends
* perf: 优化认证backends
* perf: 优化CAS认证, 用户多域名进行访问时回调到各自域名
Co-authored-by: ibuler <[email protected]> | https://github.com/jumpserver/jumpserver.git | def validate_and_return_id_token(jws, nonce=None, validate_nonce=True):
log_prompt = "Validate ID Token: {}"
logger.debug(log_prompt.format('Get shared key'))
shared_key = settings.AUTH_OPENID_CLIENT_ID \
if settings.AUTH_OPENID_PROVIDER_SIGNATURE_ALG == 'HS256' \
else settings.AUTH_OPENID_PROVIDER_SIGNATURE_KEY # RS256
try:
# Decodes the JSON Web Token and raise an error if the signature is invalid.
logger.debug(log_prompt.format('Verify compact jwk'))
id_token = JWS().verify_compact(force_bytes(jws), _get_jwks_keys(shared_key))
except JWKESTException as e:
logger.debug(log_prompt.format('Verify compact jwkest exception: {}'.format(str(e))))
return
# Validates the claims embedded in the id_token.
logger.debug(log_prompt.format('Validate claims'))
_validate_claims(id_token, nonce=nonce, validate_nonce=validate_nonce)
return id_token
| 126 | utils.py | Python | apps/authentication/backends/oidc/utils.py | edfca5eb2486c2f006257723ffeda6f56b170170 | jumpserver | 3 |
|
153,580 | 37 | 11 | 9 | 140 | 13 | 0 | 51 | 137 | aggregate | DOCS-#3099: Fix `BasePandasDataSet` docstrings warnings (#4333)
Co-authored-by: Yaroslav Igoshev <[email protected]>
Signed-off-by: Alexander Myskov <[email protected]> | https://github.com/modin-project/modin.git | def aggregate(self, func=None, axis=0, *args, **kwargs): # noqa: PR01, RT01, D200
axis = self._get_axis_number(axis)
result = None
if axis == 0:
result = self._aggregate(func, _axis=axis, *args, **kwargs)
# TODO: handle case when axis == 1
if result is None:
kwargs.pop("is_transform", None)
return self.apply(func, axis=axis, args=args, **kwargs)
return result
agg = aggregate
| 87 | base.py | Python | modin/pandas/base.py | 605efa618e7994681f57b11d04d417f353ef8d50 | modin | 3 |
|
288,763 | 10 | 9 | 5 | 38 | 6 | 0 | 10 | 42 | open_position | Powerview refactor prep for all shade types (#79862) | https://github.com/home-assistant/core.git | def open_position(self) -> PowerviewShadeMove:
return PowerviewShadeMove(
self._shade.open_position, {POS_KIND_VANE: MIN_POSITION}
)
| 23 | cover.py | Python | homeassistant/components/hunterdouglas_powerview/cover.py | 3ab294e8efc00c9f3cda2993318bb582ba675f8c | core | 1 |
|
282,602 | 19 | 12 | 5 | 104 | 8 | 0 | 27 | 43 | lambda_int_or_round_float | adjusted format of logs (#1292)
adjusted format of logs | https://github.com/OpenBB-finance/OpenBBTerminal.git | def lambda_int_or_round_float(x) -> str:
if (x - int(x) < -sys.float_info.epsilon) or (x - int(x) > sys.float_info.epsilon):
return " " + str(round(x, 2))
return " " + str(int(x))
| 62 | helper_funcs.py | Python | gamestonk_terminal/helper_funcs.py | fd5821928265429d1ffb6e6d53f019915b3afbbc | OpenBBTerminal | 3 |
|
293,605 | 6 | 8 | 3 | 27 | 4 | 0 | 6 | 20 | extra_restore_state_data | Restore state of trigger based template binary sensor (#67538) | https://github.com/home-assistant/core.git | def extra_restore_state_data(self) -> AutoOffExtraStoredData:
return AutoOffExtraStoredData(self._auto_off_time)
| 15 | binary_sensor.py | Python | homeassistant/components/template/binary_sensor.py | 7fc0ffd5c591429cef805aca707acdda0ca304e6 | core | 1 |
|
19,288 | 55 | 17 | 23 | 332 | 30 | 0 | 76 | 418 | planning | Add optional robot radius to RRT/RRTStar path planners (#655)
* Add optional robot radius to RRT/RRTStar path planners.
* update __init__ and check_collision to include radius
* during animation, if a robot radius is given then it is drawn
* Add test for robot radius
* Correct import error
* Correct missing robot_radius errors
* Address "expected 2 blank lines, found 1" error
* Address "line too long" errors
* Add missing argument description.
* Remove collision_check_with_xy and replace with check_collision
* Fix "missing whitespace after ','" error
* Update PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
Co-authored-by: Atsushi Sakai <[email protected]>
Co-authored-by: Atsushi Sakai <[email protected]> | https://github.com/AtsushiSakai/PythonRobotics.git | def planning(self, animation=True):
self.node_list = [self.start]
for i in range(self.max_iter):
rnd_node = self.get_random_node()
nearest_ind = self.get_nearest_node_index(self.node_list, rnd_node)
nearest_node = self.node_list[nearest_ind]
new_node = self.steer(nearest_node, rnd_node, self.expand_dis)
if self.check_if_outside_play_area(new_node, self.play_area) and \
self.check_collision(
new_node, self.obstacle_list, self.robot_radius):
self.node_list.append(new_node)
if animation and i % 5 == 0:
self.draw_graph(rnd_node)
if self.calc_dist_to_goal(self.node_list[-1].x,
self.node_list[-1].y) <= self.expand_dis:
final_node = self.steer(self.node_list[-1], self.end,
self.expand_dis)
if self.check_collision(
final_node, self.obstacle_list, self.robot_radius):
return self.generate_final_course(len(self.node_list) - 1)
if animation and i % 5:
self.draw_graph(rnd_node)
return None # cannot find path
| 218 | rrt.py | Python | PathPlanning/RRT/rrt.py | b53fdf75f66ccb63b5cfaadaa81253d43f01805a | PythonRobotics | 10 |
|
125,798 | 3 | 8 | 2 | 26 | 4 | 0 | 3 | 17 | testTerminateUnhealthyWorkers | [autoscaler][kuberay] Disable autoscaler health check and drain functionality (#26764)
Signed-off-by: Dmitri Gekhtman <[email protected]>
For KubeRay,
Disables autoscaler's RPC drain of worker nodes prior to termination.
Disables autoscaler's termination of nodes disconnected from the GCS. | https://github.com/ray-project/ray.git | def testTerminateUnhealthyWorkers(self):
self.unhealthyWorkerHelper(disable_liveness_check=False)
| 14 | test_autoscaler.py | Python | python/ray/tests/test_autoscaler.py | b2b11316cd0be45857b157d39ab4a89ffaf059b3 | ray | 1 |
|
152,690 | 3 | 6 | 6 | 15 | 3 | 0 | 3 | 6 | get_learned_conditioning_prompt_schedules | prompt_parser: allow spaces in schedules, add test, log/ignore errors
Only build the parser once (at import time) instead of for each step.
doctest is run by simply executing modules/prompt_parser.py | https://github.com/AUTOMATIC1111/stable-diffusion-webui.git | def get_learned_conditioning_prompt_schedules(prompts, steps):
| 42 | prompt_parser.py | Python | modules/prompt_parser.py | 90e911fd546e76f879b38a764473569911a0f845 | stable-diffusion-webui | 3 |
|
256,283 | 17 | 15 | 17 | 94 | 11 | 0 | 19 | 80 | get_nodes_by_class | Apply black formatting (#2115)
* Testing black on ui/
* Applying black on docstores
* Add latest docstring and tutorial changes
* Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too
* Remove comments
* Relax constraints on pydoc-markdown
* Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade
* Fix a couple of bugs
* Add a type: ignore that was missing somehow
* Give path to black
* Apply Black
* Apply Black
* Relocate a couple of type: ignore
* Update documentation
* Make Linux CI run after applying Black
* Triggering Black
* Apply Black
* Remove dependency, does not work well
* Remove manually double trailing commas
* Update documentation
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | https://github.com/deepset-ai/haystack.git | def get_nodes_by_class(self, class_type) -> List[Any]:
matches = [
self.graph.nodes.get(node)["component"]
for node in self.graph.nodes
if isinstance(self.graph.nodes.get(node)["component"], class_type)
]
return matches
| 59 | base.py | Python | haystack/pipelines/base.py | a59bca366174d9c692fa19750c24d65f47660ef7 | haystack | 3 |
|
244,036 | 19 | 8 | 10 | 88 | 9 | 0 | 19 | 117 | info | [Feature] Add Maskformer to mmdet (#7212)
* first commit
* add README
* move model description from config to readme
add description for binary_input
add description for dice loss
add a independent panoptic gt processing function
add a independent panoptic gt processing function
remove compatibility of pretrain in maskformer
* update comments in maskformer_head
* update docs format | https://github.com/open-mmlab/mmdetection.git | def info(self):
return {
'pos_inds': self.pos_inds,
'neg_inds': self.neg_inds,
'pos_masks': self.pos_masks,
'neg_masks': self.neg_masks,
'pos_is_gt': self.pos_is_gt,
'num_gts': self.num_gts,
'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
}
| 51 | mask_sampling_result.py | Python | mmdet/core/bbox/samplers/mask_sampling_result.py | cac356380d505bf15587f07c0529218cc36b9652 | mmdetection | 1 |
|
260,080 | 8 | 8 | 9 | 34 | 7 | 0 | 9 | 18 | test_calibration_without_sample_weight_base_estimator | API Rename base_estimator in CalibratedClassifierCV (#22054)
Co-authored-by: Kevin Roice <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def test_calibration_without_sample_weight_base_estimator(data):
X, y = data
sample_weight = np.ones_like(y)
| 58 | test_calibration.py | Python | sklearn/tests/test_calibration.py | effdd6e215c67f2ae8ed1e378ea1661e936059a4 | scikit-learn | 1 |
|
4,306 | 107 | 19 | 42 | 514 | 24 | 0 | 178 | 524 | test_search_based_stream_should_not_attempt_to_get_more_than_10k_records | 🐛 Source Hubspot: Handled 10K+ search-endpoint queries (#10700)
* Handled search queries that would output more than 10K records
* Getting CRM search objects in ascending chronological ortder
* Fixed stream
* Fixed rebase
* Fixed condition
* Added unit test
* Removed unused import
* Started a new query when reached 10K records
* Moved comment | https://github.com/airbytehq/airbyte.git | def test_search_based_stream_should_not_attempt_to_get_more_than_10k_records(requests_mock, common_params, fake_properties_list):
responses = [
{
"json": {
"results": [{"id": f"{y}", "updatedAt": "2022-02-25T16:43:11Z"} for y in range(100)],
"paging": {"next": {"after": f"{x*100}",}}
},
"status_code": 200,
}
for x in range(1, 101)
]
# After reaching 10K records, it performs a new search query.
responses.extend([
{
"json": {
"results": [{"id": f"{y}", "updatedAt": "2022-03-01T00:00:00Z"} for y in range(100)],
"paging": {"next": {"after": f"{x*100}",}}
},
"status_code": 200,
}
for x in range(1, 10)
])
# Last page... it does not have paging->next->after
responses.append({
"json": {
"results": [{"id": f"{y}", "updatedAt": "2022-03-01T00:00:00Z"} for y in range(100)],
"paging": {}
},
"status_code": 200,
})
properties_response = [{
"json": [
{"name": property_name, "type": "string", "updatedAt": 1571085954360, "createdAt": 1565059306048}
for property_name in fake_properties_list
],
"status_code": 200
}]
# Create test_stream instance with some state
test_stream = Companies(**common_params)
test_stream.state = {"updatedAt": "2022-02-24T16:43:11Z"}
# Mocking Request
requests_mock.register_uri("POST", test_stream.url, responses)
requests_mock.register_uri("GET", "/properties/v2/company/properties", properties_response)
records = list(test_stream.read_records(sync_mode=SyncMode.incremental))
# The stream should not attempt to get more than 10K records.
# Instead, it should use the new state to start a new search query.
assert len(records) == 11000
assert test_stream.state['updatedAt'] == '2022-03-01T00:00:00+00:00'
| 277 | test_source.py | Python | airbyte-integrations/connectors/source-hubspot/unit_tests/test_source.py | 710543a9abacc7578238cb5edaa47f43ed7c0431 | airbyte | 7 |
|
265,988 | 69 | 15 | 29 | 370 | 36 | 0 | 91 | 283 | applied_filters | Closes #9623: Implement saved filters (#10801)
* Initial work on saved filters
* Return only enabled/shared filters
* Add tests
* Clean up filtering of usable SavedFilters | https://github.com/netbox-community/netbox.git | def applied_filters(context, model, form, query_params):
user = context['request'].user
form.is_valid() # Ensure cleaned_data has been set
applied_filters = []
for filter_name in form.changed_data:
if filter_name not in form.cleaned_data:
continue
querydict = query_params.copy()
if filter_name not in querydict:
continue
bound_field = form.fields[filter_name].get_bound_field(form, filter_name)
querydict.pop(filter_name)
display_value = ', '.join([str(v) for v in get_selected_values(form, filter_name)])
applied_filters.append({
'name': filter_name,
'value': form.cleaned_data[filter_name],
'link_url': f'?{querydict.urlencode()}',
'link_text': f'{bound_field.label}: {display_value}',
})
save_link = None
if user.has_perm('extras.add_savedfilter') and 'filter' not in context['request'].GET:
content_type = ContentType.objects.get_for_model(model).pk
parameters = context['request'].GET.urlencode()
url = reverse('extras:savedfilter_add')
save_link = f"{url}?content_types={content_type}¶meters={quote(parameters)}"
return {
'applied_filters': applied_filters,
'save_link': save_link,
}
| 198 | helpers.py | Python | netbox/utilities/templatetags/helpers.py | 484efdaf75f267a43f9321b938fda1bc967b9e53 | netbox | 7 |
|
39,434 | 134 | 16 | 118 | 712 | 27 | 0 | 264 | 887 | fit | Reformat SAR+ SQL queries (#1772)
1. Quote SQL names to allow spaces in names
2. Uppercase SQL keywords and functions
3. Lowercase SQL alias and table names
4. Wrap long lines | https://github.com/microsoft/recommenders.git | def fit(self, df):
df.createOrReplaceTempView(self._format("{prefix}df_train_input"))
if self.timedecay_formula:
# With time decay, we compute a sum over ratings given by
# a user in the case when T=np.inf, so user gets a
# cumulative sum of ratings for a particular item and not
# the last rating. Time Decay does a group by on user
# item pairs and apply the formula for time decay there
# Time T parameter is in days and input time is in
# seconds, so we do dt/60/(T*24*60)=dt/(T*24*3600) the
# following is the query which we want to run
if self.header["time_now"] is None:
query = self._format()
self.header["time_now"] = self.spark.sql(query).first()[0]
query = self._format()
# replace with time-decayed version
df = self.spark.sql(query)
else:
if self.header["col_timestamp"] in df.columns:
# we need to de-duplicate items by using the latest item
query = self._format()
df = self.spark.sql(query)
df.createOrReplaceTempView(self._format("{prefix}df_train"))
log.info("sarplus.fit 1/2: compute item cooccurrences...")
# compute cooccurrence above minimum threshold
query = self._format()
item_cooccurrence = self.spark.sql(query)
item_cooccurrence.write.mode("overwrite").saveAsTable(
self._format("{prefix}item_cooccurrence")
)
# compute the diagonal used later for Jaccard and Lift
if self.similarity_type == SIM_LIFT or self.similarity_type == SIM_JACCARD:
query = self._format()
item_marginal = self.spark.sql(query)
item_marginal.createOrReplaceTempView(self._format("{prefix}item_marginal"))
if self.similarity_type == SIM_COOCCUR:
self.item_similarity = item_cooccurrence
elif self.similarity_type == SIM_JACCARD:
query = self._format()
self.item_similarity = self.spark.sql(query)
elif self.similarity_type == SIM_LIFT:
query = self._format()
self.item_similarity = self.spark.sql(query)
else:
raise ValueError(
"Unknown similarity type: {0}".format(self.similarity_type)
)
# store upper triangular
log.info(
"sarplus.fit 2/2: compute similarity metric %s..." % self.similarity_type
)
self.item_similarity.write.mode("overwrite").saveAsTable(
self._format("{prefix}item_similarity_upper")
)
# expand upper triangular to full matrix
query = self._format()
self.item_similarity = self.spark.sql(query)
self.item_similarity.write.mode("overwrite").saveAsTable(
self._format("{prefix}item_similarity")
)
# free space
self.spark.sql(self._format("DROP TABLE `{prefix}item_cooccurrence`"))
self.spark.sql(self._format("DROP TABLE `{prefix}item_similarity_upper`"))
self.item_similarity = self.spark.table(
self._format("{prefix}item_similarity")
)
| 402 | SARPlus.py | Python | contrib/sarplus/python/pysarplus/SARPlus.py | c8522b45ca90d521ab9054563cbaccd92668eb52 | recommenders | 9 |
|
19,577 | 32 | 13 | 9 | 113 | 15 | 1 | 38 | 98 | find_requirements | Code reorg utils into utils module reduces complexity (#4990)
* Split apart the massive utils.py into a utils module | https://github.com/pypa/pipenv.git | def find_requirements(max_depth=3):
i = 0
for c, d, f in walk_up(os.getcwd()):
i += 1
if i < max_depth:
r = os.path.join(c, "requirements.txt")
if os.path.isfile(r):
return r
raise RuntimeError("No requirements.txt found!")
# Borrowed from Pew.
# See https://github.com/berdario/pew/blob/master/pew/_utils.py#L82
@contextmanager | @contextmanager | 64 | shell.py | Python | pipenv/utils/shell.py | 3387881a6d4fc2d8bdc0f05c484cb2f7222acfb8 | pipenv | 4 |
176,538 | 27 | 13 | 8 | 147 | 14 | 0 | 38 | 74 | is_forest | Added examples in is_forest() and is_tree() (#5524)
* examples
* examples
* examples
* Example changed
* improved styling
* revised
* edge labels
* improved styling
* spacing
* error testing
* examples
* styling
* add_nodes removed
* spaceing
* spacing
* spacing | https://github.com/networkx/networkx.git | def is_forest(G):
if len(G) == 0:
raise nx.exception.NetworkXPointlessConcept("G has no nodes.")
if G.is_directed():
components = (G.subgraph(c) for c in nx.weakly_connected_components(G))
else:
components = (G.subgraph(c) for c in nx.connected_components(G))
return all(len(c) - 1 == c.number_of_edges() for c in components)
| 90 | recognition.py | Python | networkx/algorithms/tree/recognition.py | 1e0d829afa67885e3eeaeb191c7dd824e6ae7e21 | networkx | 6 |
|
168,264 | 46 | 14 | 133 | 138 | 24 | 0 | 47 | 156 | contains | PERF cache find_stack_level (#48023)
cache stacklevel | https://github.com/pandas-dev/pandas.git | def contains(self, pat, case=True, flags=0, na=None, regex=True):
r
if regex and re.compile(pat).groups:
warnings.warn(
"This pattern is interpreted as a regular expression, and has "
"match groups. To actually get the groups, use str.extract.",
UserWarning,
stacklevel=find_stack_level(inspect.currentframe()),
)
result = self._data.array._str_contains(pat, case, flags, na, regex)
return self._wrap_result(result, fill_value=na, returns_string=False)
| 93 | accessor.py | Python | pandas/core/strings/accessor.py | 2f8d0a36703e81e4dca52ca9fe4f58c910c1b304 | pandas | 3 |
|
261,716 | 41 | 10 | 17 | 164 | 22 | 0 | 57 | 124 | test_class_wrapper_param_validation | MAINT Allow partial param validation for functions (#25087) | https://github.com/scikit-learn/scikit-learn.git | def test_class_wrapper_param_validation(func_module, class_module):
func, func_name, func_params, required_params = _get_func_info(func_module)
module_name, class_name = class_module.rsplit(".", 1)
module = import_module(module_name)
klass = getattr(module, class_name)
parameter_constraints_func = getattr(func, "_skl_parameter_constraints")
parameter_constraints_class = getattr(klass, "_parameter_constraints")
parameter_constraints = {
**parameter_constraints_class,
**parameter_constraints_func,
}
parameter_constraints = {
k: v for k, v in parameter_constraints.items() if k in func_params
}
_check_function_param_validation(
func, func_name, func_params, required_params, parameter_constraints
)
| 105 | test_public_functions.py | Python | sklearn/tests/test_public_functions.py | 14130f44eb6cba8a2fb2eff8383be8909783cad0 | scikit-learn | 3 |
|
3,984 | 47 | 18 | 17 | 224 | 23 | 0 | 56 | 259 | test_execute_in_batch_with_fails | 🐛 Source FB Marketing: fix `execute_in_batch` when batch is bigger than 50 (#10588)
* fix execute_in_batch
* add tests
* fix pre-commit config
Co-authored-by: Sherif A. Nada <[email protected]>
Co-authored-by: Eugene Kulak <[email protected]>
Co-authored-by: Sherif A. Nada <[email protected]> | https://github.com/airbytehq/airbyte.git | def test_execute_in_batch_with_fails(self, api, batch, mock_batch_responses):
mock_batch_responses(
[
{
"json": [
{"body": "{}", "code": 500, "headers": {}},
{"body": json.dumps({"name": "creative 1"}), "code": 200, "headers": {}},
],
}
]
)
stream = SomeTestStream(api=api)
requests = [FacebookRequest("node", "GET", "endpoint") for _ in range(5)]
with pytest.raises(RuntimeError, match="Batch request failed with response:"):
list(stream.execute_in_batch(requests))
assert batch.add_request.call_count == len(requests)
assert batch.execute.call_count == 1
| 130 | test_base_streams.py | Python | airbyte-integrations/connectors/source-facebook-marketing/unit_tests/test_base_streams.py | 6cd20e6879a42053692c18a16906eb410aad85db | airbyte | 2 |
|
164,647 | 19 | 12 | 16 | 73 | 10 | 0 | 19 | 92 | test_repeated_column_labels | TST: Don't use autouse fixture in test_stata (#45831) | https://github.com/pandas-dev/pandas.git | def test_repeated_column_labels(self, datapath):
# GH 13923, 25772
msg =
with pytest.raises(ValueError, match=msg):
read_stata(
datapath("io", "data", "stata", "stata15.dta"),
convert_categoricals=True,
)
| 40 | test_stata.py | Python | pandas/tests/io/test_stata.py | c055dc4e6be9fc1b68d873a1ace286322dadd5e1 | pandas | 1 |
|
267,461 | 115 | 21 | 51 | 587 | 41 | 0 | 194 | 1,014 | _run | Forked display via queue (#77056)
* Forked Display via queue
* Docs and simple code cleanup
* Only proxy Display.display
* Remove unused import
* comment
* Update deadlock comment, remove py3 check
* Don't flush display, and don't lock from forks
* clog frag
* ci_complete ci_coverage
* Add units for queue proxying
* Cleanup flush
* ci_complete
* Only lock the write, switch to RLock
* Remove unused import | https://github.com/ansible/ansible.git | def _run(self):
# import cProfile, pstats, StringIO
# pr = cProfile.Profile()
# pr.enable()
# Set the queue on Display so calls to Display.display are proxied over the queue
display.set_queue(self._final_q)
try:
# execute the task and build a TaskResult from the result
display.debug("running TaskExecutor() for %s/%s" % (self._host, self._task))
executor_result = TaskExecutor(
self._host,
self._task,
self._task_vars,
self._play_context,
self._new_stdin,
self._loader,
self._shared_loader_obj,
self._final_q
).run()
display.debug("done running TaskExecutor() for %s/%s [%s]" % (self._host, self._task, self._task._uuid))
self._host.vars = dict()
self._host.groups = []
# put the result on the result queue
display.debug("sending task result for task %s" % self._task._uuid)
self._final_q.send_task_result(
self._host.name,
self._task._uuid,
executor_result,
task_fields=self._task.dump_attrs(),
)
display.debug("done sending task result for task %s" % self._task._uuid)
except AnsibleConnectionFailure:
self._host.vars = dict()
self._host.groups = []
self._final_q.send_task_result(
self._host.name,
self._task._uuid,
dict(unreachable=True),
task_fields=self._task.dump_attrs(),
)
except Exception as e:
if not isinstance(e, (IOError, EOFError, KeyboardInterrupt, SystemExit)) or isinstance(e, TemplateNotFound):
try:
self._host.vars = dict()
self._host.groups = []
self._final_q.send_task_result(
self._host.name,
self._task._uuid,
dict(failed=True, exception=to_text(traceback.format_exc()), stdout=''),
task_fields=self._task.dump_attrs(),
)
except Exception:
display.debug(u"WORKER EXCEPTION: %s" % to_text(e))
display.debug(u"WORKER TRACEBACK: %s" % to_text(traceback.format_exc()))
finally:
self._clean_up()
display.debug("WORKER PROCESS EXITING")
# pr.disable()
# s = StringIO.StringIO()
# sortby = 'time'
# ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
# ps.print_stats()
# with open('worker_%06d.stats' % os.getpid(), 'w') as f:
# f.write(s.getvalue())
| 365 | worker.py | Python | lib/ansible/executor/process/worker.py | 5e369604e1930b1a2e071fecd7ec5276ebd12cb1 | ansible | 7 |
|
261,253 | 86 | 13 | 28 | 252 | 20 | 0 | 124 | 415 | _check_interaction_cst | ENH FEA add interaction constraints to HGBT (#21020)
Co-authored-by: Loïc Estève <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def _check_interaction_cst(self, n_features):
if self.interaction_cst is None:
return None
if not (
isinstance(self.interaction_cst, Iterable)
and all(isinstance(x, Iterable) for x in self.interaction_cst)
):
raise ValueError(
"Interaction constraints must be None or an iterable of iterables, "
f"got: {self.interaction_cst!r}."
)
invalid_indices = [
x
for cst_set in self.interaction_cst
for x in cst_set
if not (isinstance(x, Integral) and 0 <= x < n_features)
]
if invalid_indices:
raise ValueError(
"Interaction constraints must consist of integer indices in [0,"
f" n_features - 1] = [0, {n_features - 1}], specifying the position of"
f" features, got invalid indices: {invalid_indices!r}"
)
constraints = [set(group) for group in self.interaction_cst]
# Add all not listed features as own group by default.
rest = set(range(n_features)) - set().union(*constraints)
if len(rest) > 0:
constraints.append(rest)
return constraints
| 145 | gradient_boosting.py | Python | sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py | 5ceb8a6a031ddff26a7ede413db1b53edb64166a | scikit-learn | 12 |
|
101,331 | 11 | 9 | 4 | 45 | 7 | 0 | 11 | 43 | _flush_queues | Bugfix: convert - Gif Writer
- Fix non-launch error on Gif Writer
- convert plugins - linting
- convert/fs_media/preview/queue_manager - typing
- Change convert items from dict to Dataclass | https://github.com/deepfakes/faceswap.git | def _flush_queues(self):
for q_name in self.queues:
self.flush_queue(q_name)
logger.debug("QueueManager flushed all queues")
| 25 | queue_manager.py | Python | lib/queue_manager.py | 1022651eb8a7741014f5d2ec7cbfe882120dfa5f | faceswap | 2 |
|
133,519 | 36 | 12 | 25 | 174 | 20 | 0 | 53 | 159 | fetch_latest_alerts | [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 fetch_latest_alerts(rds_data_client):
schema = GLOBAL_CONFIG["RELEASE_AWS_DB_STATE_TABLE"]
sql = f
result = rds_data_client.execute_statement(
database=GLOBAL_CONFIG["RELEASE_AWS_DB_NAME"],
secretArn=GLOBAL_CONFIG["RELEASE_AWS_DB_SECRET_ARN"],
resourceArn=GLOBAL_CONFIG["RELEASE_AWS_DB_RESOURCE_ARN"],
schema=schema,
sql=sql,
)
for row in result["records"]:
category, test_suite, test_name, last_result_hash, last_notification_dt = (
r["stringValue"] if "stringValue" in r else None for r in row
)
last_notification_dt = datetime.datetime.strptime(
last_notification_dt, "%Y-%m-%d %H:%M:%S"
)
yield category, test_suite, test_name, last_result_hash, last_notification_dt
| 107 | alert.py | Python | release/alert.py | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | 4 |
|
296,838 | 23 | 11 | 8 | 82 | 12 | 1 | 24 | 62 | fake_schedule | Add initial implementation of a calendar trigger (#68674)
* Add initial implementation of calendar trigger
This is an initial implementation of a calendar trigger, that supports
triggering on calendar start time.
See architecture proposal in:
https://github.com/home-assistant/architecture/discussions/700
* Address reviewer feedback
* Use f-strings for all tests
* Apply suggestions from code review
Co-authored-by: Martin Hjelmare <[email protected]>
* Remove logging f-strings, and move to main code
* Remove mypy ignore
* Apply suggestions from code review
Co-authored-by: Martin Hjelmare <[email protected]>
* Update calendar triggers to use new calendar data model
* Update tests/components/calendar/test_trigger.py
Co-authored-by: Franck Nijhof <[email protected]>
* Rewrite tests using freezegun
Rewrite tests using freezegun and improve edge case handling, and use utc consistently for all alarms.
* Update homeassistant/components/calendar/trigger.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Update homeassistant/components/calendar/trigger.py
Co-authored-by: Martin Hjelmare <[email protected]>
* Increase test coverage based on pr feedback
Co-authored-by: Martin Hjelmare <[email protected]>
Co-authored-by: Franck Nijhof <[email protected]> | https://github.com/home-assistant/core.git | def fake_schedule(hass, freezer):
# Setup start time for all tests
freezer.move_to("2022-04-19 10:31:02+00:00")
schedule = FakeSchedule(hass, freezer)
with patch(
"homeassistant.components.demo.calendar.DemoCalendar.async_get_events",
new=schedule.async_get_events,
):
yield schedule
@pytest.fixture(autouse=True) | @pytest.fixture(autouse=True) | 37 | test_trigger.py | Python | tests/components/calendar/test_trigger.py | a2c74b978664b627bafc4a43b26aa2be7b15b229 | core | 1 |
262,772 | 28 | 12 | 5 | 110 | 14 | 0 | 29 | 48 | convert_binary_to_thin_arch | macOS: Remove the timeouts for codesigning/signature stripping/lipo. (#6644) | https://github.com/pyinstaller/pyinstaller.git | def convert_binary_to_thin_arch(filename, thin_arch):
cmd_args = ['lipo', '-thin', thin_arch, filename, '-output', filename]
p = subprocess.run(cmd_args, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
if p.returncode:
raise SystemError(f"lipo command ({cmd_args}) failed with error code {p.returncode}!\noutput: {p.stdout}")
| 58 | osx.py | Python | PyInstaller/utils/osx.py | 1cd3b73e2939052271a0bc26cf204eebee4dcd15 | pyinstaller | 2 |
|
195,558 | 7 | 7 | 5 | 28 | 5 | 0 | 7 | 21 | join_hook | [FSDP] Zero 3 Optimization Support (#4903)
* zero3 init commit
* minor cleanup:
* handle mpeval
* remove fairscale dependence
* fsdp avail
* update reqs
* better reqs
* autoformat
* autofromat | https://github.com/facebookresearch/ParlAI.git | def join_hook(self, **kwargs) -> JoinHook:
return TrainLoopJoinHook(self)
| 16 | fsdp.py | Python | parlai/utils/fsdp.py | 96aa1bb7cc28bae0a7367344f91d2c517485f686 | ParlAI | 1 |
|
133,959 | 2 | 6 | 4 | 13 | 2 | 0 | 2 | 5 | ray_start_client_server_for_address | [Test][Client] Only start ray once in client tests (#28835)
It looks like we're frequently starting and shutting down Ray in this test because `ray_start_client_server` isn't connecting to the Ray created by `ray_start_regular_shared`, and is instead starting a new Ray head process every time it launches.
Ray client tests are failing frequently with:
```
[2022-10-06 07:31:46,253 E 13235 13751] core_worker_process.cc:277: The core worker has already been shutdown. This happens when the language frontend accesses the Ray's worker after it is shutdown. The process will exit
```
Which is probably caused by having multiple ray clusters running simultaneous, with some shutting down asynchronously. This refactor forces all of the tests in the module to use the same Ray cluster.
Also fixes two other sources of potential flakiness:
* Joins the thread in test_client_thread_safe (seems like this has a bad interaction when the client server is cleaned up)
* Calls ray.get in `test_stdout_log_stream`, to make sure that the remote function is done running before we try searching for its output
Should also have the happy side effect of speeding up test_client.
Ran the `Small & Client` tests (regular and external redis) twice each, no flakes, and windows version of test_client. | https://github.com/ray-project/ray.git | def ray_start_client_server_for_address(address):
| 20 | ray_client_helpers.py | Python | python/ray/util/client/ray_client_helpers.py | 297341e107daee1ea3aff991ae8ea8c90993c683 | ray | 1 |
|
284,770 | 83 | 13 | 70 | 418 | 39 | 0 | 104 | 768 | call_cpsearch | Preferred data source for a given command and help docs for any command (#1928)
* Add a feature flag loaded from . env for preferred data source
* Update the load command to use the preferred source
* Add a test
* Black syntax checker updates
* Update test syntax
* Update load command documentation
* Add a variable to read a sources file from
* Add a default sources.json file
* Add a helper function to read the source file and get the most relevant value
* Add some comments
* Remove older preferred data source file setting
* Add a default source file for data and use it if no user preference specifies a specific file
* Remove duplicate source file and old settings values
* Update the test file
* Update documentation string
* Update tests/openbb_terminal/test_settings_controller.py
* Black syntax formatting issues
* Fix a test case
* Fix a typo
* Fix Flake8 errors
* Fix Flake8 errors
* Pyupgrade syntax fixes
* Additional linter corrections
* More pylint issues
* Improve predefined default sources
* refactor parse_known_args_and_warn for documentation
* Update openbb_terminal/helper_funcs.py
* Add a feature flag loaded from . env for preferred data source
* Update the load command to use the preferred source
* Add a test
* Black syntax checker updates
* Update test syntax
* Update load command documentation
* Add a variable to read a sources file from
* Add a default sources.json file
* Add a helper function to read the source file and get the most relevant value
* Add some comments
* Remove older preferred data source file setting
* Add a default source file for data and use it if no user preference specifies a specific file
* Remove duplicate source file and old settings values
* Update the test file
* Update documentation string
* Update tests/openbb_terminal/test_settings_controller.py
* Black syntax formatting issues
* Fix a test case
* Fix a typo
* Fix Flake8 errors
* Fix Flake8 errors
* Pyupgrade syntax fixes
* Additional linter corrections
* More pylint issues
* Improve predefined default sources
* refactor parse_known_args_and_warn for documentation
* Update openbb_terminal/helper_funcs.py
* Forex controller
* Tests : Fixing `parse_known_args_and_warn` issue
* Comparison Analysis controller
* Tests : Comparison Analysis
* Tests : Stocks/DarkPoolShorts + Stocks/Options
* Tests : fix call_load tests
* Linting : fixing line size
Co-authored-by: DidierRLopes <[email protected]>
Co-authored-by: Chavithra PARANA <[email protected]> | https://github.com/OpenBB-finance/OpenBBTerminal.git | def call_cpsearch(self, other_args):
parser = argparse.ArgumentParser(
add_help=False,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
prog="cpsearch",
description=,
)
parser.add_argument(
"-q",
"--query",
help="phrase for search",
dest="query",
nargs="+",
type=str,
required="-h" not in other_args,
)
parser.add_argument(
"-c",
"--cat",
help="Categories to search: currencies|exchanges|icos|people|tags|all. Default: all",
dest="category",
default="all",
type=str,
choices=coinpaprika_model.CATEGORIES,
)
parser.add_argument(
"-l",
"--limit",
default=10,
dest="limit",
help="Limit of records",
type=check_positive,
)
parser.add_argument(
"-s",
"--sort",
dest="sortby",
type=str,
help="Sort by given column. Default: id",
default="id",
choices=coinpaprika_model.FILTERS,
)
parser.add_argument(
"--descend",
action="store_false",
help="Flag to sort in descending order (lowest first)",
dest="descend",
default=True,
)
if other_args:
if not other_args[0][0] == "-":
other_args.insert(0, "-q")
ns_parser = self.parse_known_args_and_warn(
parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED
)
if ns_parser:
coinpaprika_view.display_search_results(
top=ns_parser.limit,
sortby=ns_parser.sortby,
descend=ns_parser.descend,
export=ns_parser.export,
query=" ".join(ns_parser.query),
category=ns_parser.category,
)
| 259 | discovery_controller.py | Python | openbb_terminal/cryptocurrency/discovery/discovery_controller.py | 497324d75f523f104bc98bd1e3865bf9d19eef3f | OpenBBTerminal | 4 |
|
290,683 | 7 | 8 | 2 | 24 | 3 | 0 | 7 | 13 | test_normalize_url_does_not_touch_valid_url | Normalize url entered in fibaro integration setup dialog (#81996)
* Normalize url entered in fibaro integration setup dialog
* Improvements as suggested in code review
* Fix spelling in comments | https://github.com/home-assistant/core.git | async def test_normalize_url_does_not_touch_valid_url():
assert _normalize_url(TEST_URL) == TEST_URL
| 12 | test_config_flow.py | Python | tests/components/fibaro/test_config_flow.py | ff1ec7a028f747de1f96521eb3df6f98d7426434 | core | 1 |
|
127,153 | 16 | 12 | 9 | 72 | 12 | 0 | 17 | 72 | get_dashboard_url | [core] Don't override external dashboard URL in internal KV store (#27901)
Fix 2.0.0 release blocker bug where Ray State API and Jobs not accessible if the override URL doesn't support adding additional subpaths. This PR keeps the localhost dashboard URL in the internal KV store and only overrides in values printed or returned to the user.
images.githubusercontent.com/6900234/184809934-8d150874-90fe-4b45-a13d-bce1807047de.png"> | https://github.com/ray-project/ray.git | def get_dashboard_url():
if ray_constants.RAY_OVERRIDE_DASHBOARD_URL in os.environ:
return _remove_protocol_from_url(
os.environ.get(ray_constants.RAY_OVERRIDE_DASHBOARD_URL)
)
else:
worker = global_worker
worker.check_connected()
return _global_node.webui_url
| 42 | worker.py | Python | python/ray/_private/worker.py | 4692e8d8023e789120d3f22b41ffb136b50f70ea | ray | 2 |
|
127,274 | 137 | 19 | 74 | 650 | 48 | 0 | 189 | 749 | start_rayservice | [nightly] Add serve ha chaos test into nightly test. (#27413)
This PR adds a serve ha test. The flow of the tests is:
1. check the kube ray build
2. start ray service
3. warm up the cluster
4. start killing nodes
5. get the stats and make sure it's good | https://github.com/ray-project/ray.git | def start_rayservice():
# step-1: generate the yaml file
print(f"Using ray image: {ray_image}")
solution = "\n".join(
[
f" {line}"
for line in pathlib.Path("./solution.py").read_text().splitlines()
]
)
locustfile = "\n".join(
[
f" {line}"
for line in pathlib.Path("./locustfile.py").read_text().splitlines()
]
)
template = (
pathlib.Path("ray_v1alpha1_rayservice_template.yaml")
.read_text()
.format(
cluster_id=cluster_id,
ray_image=ray_image,
solution=solution,
locustfile=locustfile,
)
)
print("=== YamlFile ===")
print(template)
tmp_yaml = pathlib.Path("/tmp/ray_v1alpha1_rayservice.yaml")
tmp_yaml.write_text(template)
print("=== Get Pods from ray-system ===")
print(
subprocess.check_output(
["kubectl", "get", "pods", "--namespace", "ray-system", "--no-headers"]
).decode()
)
# step-2: create the cluter
print(f"Creating cluster with id: {cluster_id}")
print(subprocess.check_output(["kubectl", "create", "-f", str(tmp_yaml)]).decode())
# step-3: make sure the ray cluster is up
w = watch.Watch()
start_time = time.time()
head_pod_name = None
for event in w.stream(
func=cli.list_namespaced_pod,
namespace="default",
label_selector=f"rayCluster={ray_cluster_name},ray.io/node-type=head",
timeout_seconds=60,
):
if event["object"].status.phase == "Running":
assert event["object"].kind == "Pod"
head_pod_name = event["object"].metadata.name
end_time = time.time()
print(f"{cluster_id} started in {end_time-start_time} sec")
print(f"head pod {head_pod_name}")
break
assert head_pod_name is not None
# step-4: e2e check it's alive
cmd =
while True:
try:
resp = (
subprocess.check_output(
f'kubectl exec {head_pod_name} -- python -c "{cmd}"', shell=True
)
.decode()
.strip()
)
if resp == "375":
print("Service is up now!")
break
else:
print(f"Failed with msg {resp}")
except Exception as e:
print("Error", e)
time.sleep(2)
| 343 | run_gcs_ft_on_k8s.py | Python | release/k8s_tests/run_gcs_ft_on_k8s.py | 4d91f516ca59abd2f4880f36e17630ad3effee11 | ray | 8 |
|
278,792 | 3 | 6 | 3 | 15 | 3 | 0 | 3 | 6 | _delegate_method | Remove pylint comments.
PiperOrigin-RevId: 452353044 | https://github.com/keras-team/keras.git | def _delegate_method(keras_tensor_cls, method_name):
| 18 | tf_op_layer.py | Python | keras/layers/core/tf_op_layer.py | 3613c3defc39c236fb1592c4f7ba1a9cc887343a | keras | 1 |
|
224,484 | 4 | 8 | 2 | 29 | 4 | 0 | 4 | 18 | is_css | Refactor URI handling to not have to deal with backslashes | https://github.com/mkdocs/mkdocs.git | def is_css(self):
return self.src_uri.endswith('.css')
| 15 | files.py | Python | mkdocs/structure/files.py | 1c50987f9c17b228fdf22456aa369b83bd6b11b9 | mkdocs | 1 |
|
289,759 | 8 | 9 | 3 | 34 | 4 | 0 | 8 | 22 | async_uninstall_addon | Refactor zwave_js add-on manager (#80883)
* Make addon slug an instance attribute
* Extract addon name and addon config
* Update docstrings | https://github.com/home-assistant/core.git | async def async_uninstall_addon(self) -> None:
await async_uninstall_addon(self._hass, self.addon_slug)
| 19 | addon.py | Python | homeassistant/components/zwave_js/addon.py | 838691f22f27852a05313809cdf9c51094ad3798 | core | 1 |
|
22,866 | 25 | 14 | 9 | 157 | 9 | 0 | 30 | 70 | print_n_speak_index | VoiceAssistant
This is Voice Assistant coded using Python which can do the following: -
1. Speak Text entered by User.
2. Search anything on Google.
3. Search anything on Wikipedia.
4. Read an MS Word(docx) document.
5. Read a book(PDF).
6. Can be used as a Dictator. | https://github.com/geekcomputers/Python.git | def print_n_speak_index(toc):
dash = "-"*(100 - 7)
space = " "*47
print(f"{space}INDEX")
print(f"\n\nName : {dash} PageNo.\n\n\n\n")
for topic in toc:
eq_dash = "-"*(100 - len(topic[1]))
print(f"{topic[1]} {eq_dash} {topic[2]}")
speak(f"{topic[1]} {topic[2]}")
| 60 | textRead.py | Python | VoiceAssistant/Project_Basic_struct/textRead.py | 39c49e07066b2a53e176d555af6a7bf8aabb8a9c | Python | 2 |
|
260,324 | 74 | 13 | 24 | 263 | 30 | 0 | 98 | 345 | fit | MAINT Use _validate_params in Power and Quantile Transformer (#23672)
Co-authored-by: Guillaume Lemaitre <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def fit(self, X, y=None):
self._validate_params()
if self.n_quantiles > self.subsample:
raise ValueError(
"The number of quantiles cannot be greater than"
" the number of samples used. Got {} quantiles"
" and {} samples.".format(self.n_quantiles, self.subsample)
)
X = self._check_inputs(X, in_fit=True, copy=False)
n_samples = X.shape[0]
if self.n_quantiles > n_samples:
warnings.warn(
"n_quantiles (%s) is greater than the total number "
"of samples (%s). n_quantiles is set to "
"n_samples." % (self.n_quantiles, n_samples)
)
self.n_quantiles_ = max(1, min(self.n_quantiles, n_samples))
rng = check_random_state(self.random_state)
# Create the quantiles of reference
self.references_ = np.linspace(0, 1, self.n_quantiles_, endpoint=True)
if sparse.issparse(X):
self._sparse_fit(X, rng)
else:
self._dense_fit(X, rng)
return self
| 164 | _data.py | Python | sklearn/preprocessing/_data.py | a63a827e6db3fcb65ea05172ee18f67e6a6a27d7 | scikit-learn | 4 |
|
35,044 | 164 | 18 | 46 | 797 | 48 | 0 | 338 | 1,099 | get_position_embeds | Upgrade black to version ~=22.0 (#15565)
* Upgrade black to version ~=22.0
* Check copies
* Fix code | https://github.com/huggingface/transformers.git | def get_position_embeds(self, seq_len, dtype, device):
d_model = self.config.d_model
if self.config.attention_type == "factorized":
# Notations from the paper, appending A.2.2, final formula.
# We need to create and return the matrices phi, psi, pi and omega.
pos_seq = torch.arange(0, seq_len, 1.0, dtype=dtype, device=device)
freq_seq = torch.arange(0, d_model // 2, 1.0, dtype=dtype, device=device)
inv_freq = 1 / (10000 ** (freq_seq / (d_model // 2)))
sinusoid = pos_seq[:, None] * inv_freq[None]
sin_embed = torch.sin(sinusoid)
sin_embed_d = self.sin_dropout(sin_embed)
cos_embed = torch.cos(sinusoid)
cos_embed_d = self.cos_dropout(cos_embed)
# This is different from the formula on the paper...
phi = torch.cat([sin_embed_d, sin_embed_d], dim=-1)
psi = torch.cat([cos_embed, sin_embed], dim=-1)
pi = torch.cat([cos_embed_d, cos_embed_d], dim=-1)
omega = torch.cat([-sin_embed, cos_embed], dim=-1)
return (phi, pi, psi, omega)
else:
# Notations from the paper, appending A.2.1, final formula.
# We need to create and return all the possible vectors R for all blocks and shifts.
freq_seq = torch.arange(0, d_model // 2, 1.0, dtype=dtype, device=device)
inv_freq = 1 / (10000 ** (freq_seq / (d_model // 2)))
# Maximum relative positions for the first input
rel_pos_id = torch.arange(-seq_len * 2, seq_len * 2, 1.0, dtype=dtype, device=device)
zero_offset = seq_len * 2
sinusoid = rel_pos_id[:, None] * inv_freq[None]
sin_embed = self.sin_dropout(torch.sin(sinusoid))
cos_embed = self.cos_dropout(torch.cos(sinusoid))
pos_embed = torch.cat([sin_embed, cos_embed], dim=-1)
pos = torch.arange(0, seq_len, dtype=dtype, device=device)
pooled_pos = pos
position_embeds_list = []
for block_index in range(0, self.config.num_blocks):
# For each block with block_index > 0, we need two types position embeddings:
# - Attention(pooled-q, unpooled-kv)
# - Attention(pooled-q, pooled-kv)
# For block_index = 0 we only need the second one and leave the first one as None.
# First type
if block_index == 0:
position_embeds_pooling = None
else:
pooled_pos = self.stride_pool_pos(pos, block_index)
# construct rel_pos_id
stride = 2 ** (block_index - 1)
rel_pos = self.relative_pos(pos, stride, pooled_pos, shift=2)
rel_pos = rel_pos[:, None] + zero_offset
rel_pos = rel_pos.expand(rel_pos.size(0), d_model)
position_embeds_pooling = torch.gather(pos_embed, 0, rel_pos)
# Second type
pos = pooled_pos
stride = 2**block_index
rel_pos = self.relative_pos(pos, stride)
rel_pos = rel_pos[:, None] + zero_offset
rel_pos = rel_pos.expand(rel_pos.size(0), d_model)
position_embeds_no_pooling = torch.gather(pos_embed, 0, rel_pos)
position_embeds_list.append([position_embeds_no_pooling, position_embeds_pooling])
return position_embeds_list
| 498 | modeling_funnel.py | Python | src/transformers/models/funnel/modeling_funnel.py | 7732d0fe7a759c9844215920e9f1c5540eafb1a6 | transformers | 4 |
|
266,444 | 8 | 12 | 2 | 44 | 6 | 0 | 8 | 14 | ansible_concat | Attach concat func to an environment class (#76282)
* Attach concat func to an environment class
ci_complete
* clog and docstrings | https://github.com/ansible/ansible.git | def ansible_concat(nodes):
return ''.join([to_text(_fail_on_undefined(v)) for v in nodes])
| 25 | native_helpers.py | Python | lib/ansible/template/native_helpers.py | 8febd37f325b049afe448af689064ee019d1099c | ansible | 2 |
|
178,662 | 110 | 12 | 45 | 428 | 37 | 0 | 159 | 485 | queryRuntimeInformationMultiple | Plugins: Add DLL specific plugin with configuration
* As a first one, this solves the shapely DLL issue with using the new
configuration.
* Existing codes should be migrated to this with time. | https://github.com/Nuitka/Nuitka.git | def queryRuntimeInformationMultiple(self, info_name, setup_codes, values):
info_name = self.plugin_name.replace("-", "_") + "_" + info_name
if info_name in self._runtime_information_cache:
return self._runtime_information_cache[info_name]
keys = []
query_codes = []
for key, value_expression in values:
keys.append(key)
query_codes.append("print(repr(%s))" % value_expression)
query_codes.append('print("-" * 27)')
if type(setup_codes) is str:
setup_codes = setup_codes.split("\n")
cmd = r % {
"setup_codes": "\n ".join(setup_codes),
"query_codes": "\n".join(query_codes),
}
try:
feedback = check_output([sys.executable, "-c", cmd])
except NuitkaCalledProcessError as e:
if e.returncode == 38:
return None
raise
if str is not bytes: # We want to work with strings, that's hopefully OK.
feedback = feedback.decode("utf8")
# Ignore Windows newlines difference.
feedback = [line.strip() for line in feedback.splitlines()]
if feedback.count("-" * 27) != len(keys):
self.sysexit(
"Error, mismatch in output retrieving %r information." % info_name
)
feedback = [line for line in feedback if line != "-" * 27]
NamedTupleResult = namedtuple(info_name, keys)
# We are being lazy here, the code is trusted, pylint: disable=eval-used
self._runtime_information_cache[info_name] = NamedTupleResult(
*(eval(value) for value in feedback)
)
return self._runtime_information_cache[info_name]
| 253 | PluginBase.py | Python | nuitka/plugins/PluginBase.py | e7e31cbdb430a1b5f9ec4e186f10a9e60908a4e9 | Nuitka | 12 |
|
271,565 | 22 | 12 | 11 | 116 | 10 | 0 | 33 | 94 | flatten_metrics_in_order | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def flatten_metrics_in_order(logs, metrics_names):
results = []
for name in metrics_names:
if name in logs:
results.append(logs[name])
for key in sorted(logs.keys()):
if key not in metrics_names:
results.append(logs[key])
if len(results) == 1:
return results[0]
return results
| 73 | training.py | Python | keras/engine/training.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 6 |
|
177,502 | 29 | 13 | 8 | 106 | 11 | 0 | 39 | 79 | multi_source_dijkstra_path_length | Hide edges with a weight of None in A*. (#5945)
* Hide edges with a weight of None in A*.
This matches the Dijkstra's weight interface.
* Update Dijkstra's and A* docs for weights of None.
* Add tests for A* with weight of None.
* Add another test for A* with a weight function.
* Document that None indicates a hidden edge. | https://github.com/networkx/networkx.git | def multi_source_dijkstra_path_length(G, sources, cutoff=None, weight="weight"):
if not sources:
raise ValueError("sources must not be empty")
for s in sources:
if s not in G:
raise nx.NodeNotFound(f"Node {s} not found in graph")
weight = _weight_function(G, weight)
return _dijkstra_multisource(G, sources, weight, cutoff=cutoff)
| 65 | weighted.py | Python | networkx/algorithms/shortest_paths/weighted.py | d82815dba6c8ddce19cd49f700298dc82a58f066 | networkx | 4 |
|
100,313 | 79 | 18 | 26 | 425 | 23 | 0 | 107 | 447 | _get_per_session_stats | Update code to support Tensorflow versions up to 2.8 (#1213)
* Update maximum tf version in setup + requirements
* - bump max version of tf version in launcher
- standardise tf version check
* update keras get_custom_objects for tf>2.6
* bugfix: force black text in GUI file dialogs (linux)
* dssim loss - Move to stock tf.ssim function
* Update optimizer imports for compatibility
* fix logging for tf2.8
* Fix GUI graphing for TF2.8
* update tests
* bump requirements.txt versions
* Remove limit on nvidia-ml-py
* Graphing bugfixes
- Prevent live graph from displaying if data not yet available
* bugfix: Live graph. Collect loss labels correctly
* fix: live graph - swallow inconsistent loss errors
* Bugfix: Prevent live graph from clearing during training
* Fix graphing for AMD | https://github.com/deepfakes/faceswap.git | def _get_per_session_stats(self):
if self._per_session_stats is None:
logger.debug("Collating per session stats")
compiled = []
for session_id, ts_data in self._time_stats.items():
logger.debug("Compiling session ID: %s", session_id)
if self._state is None:
logger.debug("Session state dict doesn't exist. Most likely task has been "
"terminated during compilation")
return
compiled.append(self._collate_stats(session_id, ts_data))
self._per_session_stats = list(sorted(compiled, key=lambda k: k["session"]))
elif self._session.is_training:
logger.debug("Collating per session stats for latest training data")
session_id = self._session.session_ids[-1]
ts_data = self._time_stats[session_id]
if session_id > len(self._per_session_stats):
self._per_session_stats.append(self._collate_stats(session_id, ts_data))
stats = self._per_session_stats[-1]
stats["start"] = ts_data["start_time"]
stats["end"] = ts_data["end_time"]
stats["elapsed"] = int(stats["end"] - stats["start"])
stats["iterations"] = ts_data["iterations"]
stats["rate"] = (((stats["batch"] * 2) * stats["iterations"])
/ stats["elapsed"] if stats["elapsed"] != 0 else 0)
logger.debug("per_session_stats: %s", self._per_session_stats)
| 249 | stats.py | Python | lib/gui/analysis/stats.py | c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf | faceswap | 7 |
|
113,522 | 6 | 10 | 3 | 32 | 5 | 0 | 6 | 20 | next_label | Mutable V3 (Stage 1) - Label namespace and utilities (#5194) | https://github.com/microsoft/nni.git | def next_label(self) -> str:
return str(uid(self.absolute_scope))
| 18 | utils.py | Python | nni/mutable/utils.py | 6641d309ac6e98c69295ac3d59bf7fa23bdb6588 | nni | 1 |
|
126,266 | 17 | 12 | 9 | 87 | 13 | 1 | 21 | 59 | _detect_config_single | [air] Add annotation for Tune module. (#27060)
Co-authored-by: Kai Fricke <[email protected]> | https://github.com/ray-project/ray.git | def _detect_config_single(func):
func_sig = inspect.signature(func)
use_config_single = True
try:
func_sig.bind({})
except Exception as e:
logger.debug(str(e))
use_config_single = False
return use_config_single
@PublicAPI() | @PublicAPI() | 45 | util.py | Python | python/ray/tune/utils/util.py | eb69c1ca286a2eec594f02ddaf546657a8127afd | ray | 2 |
245,439 | 44 | 14 | 22 | 323 | 30 | 0 | 62 | 272 | run | [Feature] Support TeacherStudentValLoop (#8503)
* Support MultiValLoop
* Rename as TeacherStudentVaLloop | https://github.com/open-mmlab/mmdetection.git | def run(self):
self.runner.call_hook('before_val')
self.runner.call_hook('before_val_epoch')
self.runner.model.eval()
model = self.runner.model
if is_model_wrapper(model):
model = model.module
assert hasattr(model, 'teacher')
assert hasattr(model, 'student')
predict_on = model.semi_test_cfg.get('predict_on', None)
multi_metrics = dict()
for _predict_on in ['teacher', 'student']:
model.semi_test_cfg['predict_on'] = _predict_on
for idx, data_batch in enumerate(self.dataloader):
self.run_iter(idx, data_batch)
# compute metrics
metrics = self.evaluator.evaluate(len(self.dataloader.dataset))
multi_metrics.update(
{'/'.join((_predict_on, k)): v
for k, v in metrics.items()})
model.semi_test_cfg['predict_on'] = predict_on
self.runner.call_hook('after_val_epoch', metrics=multi_metrics)
self.runner.call_hook('after_val')
| 191 | loops.py | Python | mmdet/engine/runner/loops.py | 4886ac46ee1e18fe5b0fc3fd5fe3a24dd5a5743f | mmdetection | 5 |
|
86,309 | 21 | 12 | 10 | 121 | 22 | 0 | 21 | 131 | test_user_rate_reached_perf_issues | ref(perf issues): Enable ignore in a time period (#39120)
Enable ignoring a performance issue in a time period e.g. ignore this
until it happens 10x / hr or ignore until 10 users experience it in an
hour. | https://github.com/getsentry/sentry.git | def test_user_rate_reached_perf_issues(self):
snooze = GroupSnooze.objects.create(group=self.perf_group, user_count=10, user_window=60)
for i in range(0, 10):
self.store_transaction(
environment=None,
project_id=self.project.id,
user_id=str(i),
groups=[self.perf_group],
)
assert not snooze.is_valid(test_rates=True)
| 80 | test_groupsnooze.py | Python | tests/sentry/models/test_groupsnooze.py | d745edbd591063f2c3241cd1960c361834058823 | sentry | 2 |
|
187,373 | 56 | 13 | 24 | 316 | 28 | 0 | 73 | 225 | log_current_versions | cli: list all dependencies in debug output
- Require importlib-metadata as fallback on Python < 3.8
- Add importlib_metadata to streamlink_cli.compat
- List all dependencies in `log_current_versions`
- Update tests | https://github.com/streamlink/streamlink.git | def log_current_versions():
if not logger.root.isEnabledFor(logging.DEBUG):
return
# macOS
if sys.platform == "darwin":
os_version = f"macOS {platform.mac_ver()[0]}"
# Windows
elif sys.platform == "win32":
os_version = f"{platform.system()} {platform.release()}"
# Linux / other
else:
os_version = platform.platform()
log.debug(f"OS: {os_version}")
log.debug(f"Python: {platform.python_version()}")
log.debug(f"Streamlink: {streamlink_version}")
# https://peps.python.org/pep-0508/#names
re_name = re.compile(r"[A-Z\d](?:[A-Z\d._-]*[A-Z\d])?", re.IGNORECASE)
log.debug("Dependencies:")
for name in [
match.group(0)
for match in map(re_name.match, importlib_metadata.requires("streamlink"))
if match is not None
]:
try:
version = importlib_metadata.version(name)
except importlib_metadata.PackageNotFoundError:
continue
log.debug(f" {name}: {version}")
| 146 | main.py | Python | src/streamlink_cli/main.py | c319aa445e7577134d61da587c6338730b82a4c8 | streamlink | 8 |
|
82,383 | 12 | 10 | 3 | 51 | 7 | 0 | 12 | 33 | get_instance_icon_alt | ci: Added codespell (#7355)
Co-authored-by: Christian Clauss <[email protected]>
* ci: codespell config taken from #7292 | https://github.com/django-cms/django-cms.git | def get_instance_icon_alt(self):
instance, plugin = self.get_plugin_instance()
return force_str(plugin.icon_alt(instance)) if instance else ''
| 29 | pluginmodel.py | Python | cms/models/pluginmodel.py | c1290c9ff89cb00caa5469129fd527e9d82cd820 | django-cms | 2 |
|
153,982 | 22 | 10 | 5 | 95 | 14 | 0 | 25 | 64 | _multiindex_possibly_contains_key | FIX-#4358: MultiIndex `loc` shouldn't drop levels for full-key lookups (#4608)
Signed-off-by: Karthik Velayutham <[email protected]> | https://github.com/modin-project/modin.git | def _multiindex_possibly_contains_key(self, axis, key):
if not self.qc.has_multiindex(axis=axis):
return False
multiindex = self.df.index if axis == 0 else self.df.columns
return isinstance(key, tuple) and len(key) == len(multiindex.levels)
| 61 | indexing.py | Python | modin/pandas/indexing.py | da7bd1a14752895a38342b2faa3220a8db4cb7ff | modin | 4 |
|
277,193 | 19 | 13 | 7 | 109 | 12 | 0 | 23 | 80 | predict | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def predict(self, x, **kwargs):
proba = self.model.predict(x, **kwargs)
if proba.shape[-1] > 1:
classes = proba.argmax(axis=-1)
else:
classes = (proba > 0.5).astype("int32")
return self.classes_[classes]
| 69 | scikit_learn.py | Python | keras/wrappers/scikit_learn.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 2 |
|
166,206 | 6 | 7 | 5 | 25 | 4 | 0 | 6 | 20 | size | ENH: Implement DataFrame interchange protocol (#46141) | https://github.com/pandas-dev/pandas.git | def size(self) -> int:
return self._col.size
| 14 | column.py | Python | pandas/core/exchange/column.py | 90140f055892a46f473bd26affab88a7f171e394 | pandas | 1 |
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