Datasets:

semeru

/

Galeras-3k_deduped

like 0

python/ray/autoscaler/_private/gcp/config.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

config.py

def _configure_project(config, crm): config = copy.deepcopy(config) project_id = config["provider"].get("project_id") assert config["provider"]["project_id"] is not None, ( "'project_id' must be set in the 'provider' section of the autoscaler" " config. Notice that the project id must be globally unique." ) project = _get_project(project_id, crm) if project is None: # Project not found, try creating it _create_project(project_id, crm) project = _get_project(project_id, crm) assert project is not None, "Failed to create project" assert ( project["lifecycleState"] == "ACTIVE" ), "Project status needs to be ACTIVE, got {}".format(project["lifecycleState"]) config["provider"]["project_id"] = project["projectId"] return config

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def _configure_project(config, crm): config = copy.deepcopy(config) project_id = config["provider"].get("project_id") assert config["provider"]["project_id"] is not None, ( "'project_id' must be set in the 'provider' section of the autoscaler" " config. Notice that the project id must be globally unique." ) project = _get_project(project_id, crm) if project is None: # Project not found, try creating it _create_project(project_id, crm) project = _get_project(project_id, crm) assert proje

_configure_project

test/mitmproxy/addons/test_intercept.py

Add support for raw UDP. (#5414)

mitmproxy

Python

test_intercept.py

async def test_udp(): r = intercept.Intercept() with taddons.context(r) as tctx: tctx.configure(r, intercept="~udp") f = tflow.tudpflow() await tctx.cycle(r, f) assert f.intercepted tctx.configure(r, intercept_active=False) f = tflow.tudpflow() await tctx.cycle(r, f) assert not f.intercepted

cd4a74fae7cbd8119afc3900597f798ec1604db7

https://github.com/mitmproxy/mitmproxy.git

async def test_udp(): r = intercept.Intercept() with taddons.context(r) as tctx: tctx.configure(r, intercept="~udp") f = tflow.tudpflow() await tctx.cycle(r, f) assert f.intercepted tctx.configure(r, intercept_active=False) f = tflow

test_udp

Tests/test_imagemath.py

some pylint warnings Fixed some pylint issues

Pillow

Python

test_imagemath.py

def pixel(im): if hasattr(im, "im"): return f"{im.mode} {repr(im.getpixel((0, 0)))}" if isinstance(im, int): return int(im) # hack to deal with booleans print(im) A = Image.new("L", (1, 1), 1) B = Image.new("L", (1, 1), 2) Z = Image.new("L", (1, 1), 0) # Z for zero F = Image.new("F", (1, 1), 3) I = Image.new("I", (1, 1), 4) # noqa: E741 A2 = A.resize((2, 2)) B2 = B.resize((2, 2)) images = {"A": A, "B": B, "F": F, "I": I}

69baeccf2ee7850ccfb9b2b05ab584b87ad50fe1

https://github.com/python-pillow/Pillow.git

def pixel(im): if hasattr(im, "im"): return f"{im.mode} {repr(im.getpixel((0, 0)))}" if isinstance(im, int): return int(im) # hack to deal with booleans print(im) A = Image.new("L", (1, 1), 1) B = Image.new("L", (1, 1), 2) Z = Image.new("L", (1, 1), 0) # Z for zero F = Image.new("F", (1, 1), 3) I = Image.new("I", (1, 1), 4) # noqa: E741 A2 = A.resize((2, 2)) B2 = B.resize((2, 2)) images = {"A": A, "B": B, "F": F, "I": I

pixel

test/mitmproxy/test_http.py

make it black!

mitmproxy

Python

test_http.py

def test_get_multipart_form(self): request = treq(content=b"foobar") assert not request.multipart_form request.headers["Content-Type"] = "multipart/form-data" assert list(request.multipart_form.items()) == [] with mock.patch("mitmproxy.net.http.multipart.decode") as m: m.side_effect = ValueError assert list(request.multipart_form.items()) == []

b3587b52b25077f68116b9852b041d33e7fc6601

https://github.com/mitmproxy/mitmproxy.git

def test_get_multipart_form(self):

test_get_multipart_form

tests/sentry/helpers/test_deprecation.py

feat(api): Add initial skeleton for deprecation decorator (#34980) * feat(api): Add initial skeleton for deprecation decorator The decorator will eventually handle all the overhead for a deprecated endpoint including * Metric tracking * Brownout * Deprecation Headers Later PRs will introduce crontab logic for brownout periods and unit tests * Added a header for suggested API and an exit for self hosted * Added the base for the deprecation test * Added unit tests, but am running into issues with 403s * tell pytest to ignore the endpoint * Remove test prefix * Got tests passing and handled time boundary corner case * typo

sentry

Python

test_deprecation.py

def head(self, request): return Response({"ok": True}) dummy_endpoint = DummyEndpoint.as_view()

a7f6111fcf15ef09696991027e473dc42520dadc

https://github.com/getsentry/sentry.git

def head(self, request): return Re

head

python/ray/workflow/workflow_storage.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

workflow_storage.py

def get_entrypoint_step_id(self) -> StepID: # empty StepID represents the workflow driver try: return asyncio_run(self._locate_output_step_id("")) except Exception as e: raise ValueError( "Fail to get entrypoint step ID from workflow" f"[id={self._workflow_id}]" ) from e

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def get_entrypoint_step_id(self) -> StepID: # empty StepID represents the workflow driver try:

get_entrypoint_step_id

lib/matplotlib/figure.py

Emit "axes not compatible with tight_layout" in a single place. ... instead of triplicating it on the caller side.

matplotlib

Python

figure.py

def tight_layout(self, *, pad=1.08, h_pad=None, w_pad=None, rect=None): # note that here we do not permanently set the figures engine to # tight_layout but rather just perform the layout in place and remove # any previous engines. engine = TightLayoutEngine(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect) try: self.set_layout_engine(engine) engine.execute(self) finally: self.set_layout_engine(None)

7d2503b422f98686bef42e7caebe025540ca6aaa

https://github.com/matplotlib/matplotlib.git

def tight_layout(self, *, pad=1.08, h_pad=None, w_pad=None, rect=None): # note that here we do not permanently set the figures engine to # tight_layout but rather just perform the layout in place and remove # any previous engines. engine = TightLayoutEngine(pad=pad, h_pad=h_pad, w_pad=w_pad

tight_layout

tests/maskformer/test_modeling_maskformer.py

Maskformer (#15682) * maskformer * conflicts * conflicts * minor fixes * feature extractor test fix refactor MaskFormerLoss following conversation MaskFormer related types should not trigger a module time import error missed one removed all the types that are not used update config mapping minor updates in the doc resolved conversation that doesn't need a discussion minor changes resolved conversations fixed DetrDecoder * minor changes minor changes fixed mdx file test feature_extractor return types functional losses -> classes removed the return type test for the feature extractor minor changes + style + quality * conflicts? * rebase master * readme * added missing files * deleded poolformers test that where in the wrong palce * CI * minor changes * Apply suggestions from code review Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * resolved conversations * minor changes * conversations [Unispeech] Fix slow tests (#15818) * remove soundfile old way of loading audio * Adapt slow test [Barthez Tokenizer] Fix saving (#15815) [TFXLNet] Correct tf xlnet generate (#15822) * [TFXLNet] Correct tf xlnet * adapt test comment Fix the push run (#15807) Fix semantic segmentation pipeline test (#15826) Fix dummy_inputs() to dummy_inputs in symbolic_trace doc (#15776) Add model specific output classes to PoolFormer model docs (#15746) * Added model specific output classes to poolformer docs * Fixed Segformer typo in Poolformer docs Adding the option to return_timestamps on pure CTC ASR models. (#15792) * Adding the option to return_timestamps on pure CTC ASR models. * Remove `math.prod` which was introduced in Python 3.8 * int are not floats. * Reworking the PR to support "char" vs "word" output. * Fixup! * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/pipelines/automatic_speech_recognition.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Quality. Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> HFTracer.trace should use/return self.graph to be compatible with torch.fx.Tracer (#15824) Fix tf.concatenate + test past_key_values for TF models (#15774) * fix wrong method name tf.concatenate * add tests related to causal LM / decoder * make style and quality * clean-up * Fix TFBertModel's extended_attention_mask when past_key_values is provided * Fix tests * fix copies * More tf.int8 -> tf.int32 in TF test template * clean-up * Update TF test template * revert the previous commit + update the TF test template * Fix TF template extended_attention_mask when past_key_values is provided * Fix some styles manually * clean-up * Fix ValueError: too many values to unpack in the test * Fix more: too many values to unpack in the test * Add a comment for extended_attention_mask when there is past_key_values * Fix TFElectra extended_attention_mask when past_key_values is provided * Add tests to other TF models * Fix for TF Electra test: add prepare_config_and_inputs_for_decoder * Fix not passing training arg to lm_head in TFRobertaForCausalLM * Fix tests (with past) for TF Roberta * add testing for pask_key_values for TFElectra model Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> [examples/summarization and translation] fix readme (#15833) Add ONNX Runtime quantization for text classification notebook (#15817) Re-enable doctests for the quicktour (#15828) * Re-enable doctests for the quicktour * Re-enable doctests for task_summary (#15830) * Remove & Framework split model report (#15825) Add TFConvNextModel (#15750) * feat: initial implementation of convnext in tensorflow. * fix: sample code for the classification model. * chore: added checked for from the classification model. * chore: set bias initializer in the classification head. * chore: updated license terms. * chore: removed ununsed imports * feat: enabled argument during using drop_path. * chore: replaced tf.identity with layers.Activation(linear). * chore: edited default checkpoint. * fix: minor bugs in the initializations. * partial-fix: tf model errors for loading pretrained pt weights. * partial-fix: call method updated * partial-fix: cross loading of weights (4x3 variables to be matched) * chore: removed unneeded comment. * removed playground.py * rebasing * rebasing and removing playground.py. * fix: renaming TFConvNextStage conv and layer norm layers * chore: added initializers and other minor additions. * chore: added initializers and other minor additions. * add: tests for convnext. * fix: integration tester class. * fix: issues mentioned in pr feedback (round 1). * fix: how output_hidden_states arg is propoagated inside the network. * feat: handling of arg for pure cnn models. * chore: added a note on equal contribution in model docs. * rebasing * rebasing and removing playground.py. * feat: encapsulation for the convnext trunk. * Fix variable naming; Test-related corrections; Run make fixup * chore: added Joao as a contributor to convnext. * rebasing * rebasing and removing playground.py. * rebasing * rebasing and removing playground.py. * chore: corrected copyright year and added comment on NHWC. * chore: fixed the black version and ran formatting. * chore: ran make style. * chore: removed from_pt argument from test, ran make style. * rebasing * rebasing and removing playground.py. * rebasing * rebasing and removing playground.py. * fix: tests in the convnext subclass, ran make style. * rebasing * rebasing and removing playground.py. * rebasing * rebasing and removing playground.py. * chore: moved convnext test to the correct location * fix: locations for the test file of convnext. * fix: convnext tests. * chore: applied sgugger's suggestion for dealing w/ output_attentions. * chore: added comments. * chore: applied updated quality enviornment style. * chore: applied formatting with quality enviornment. * chore: revert to the previous tests/test_modeling_common.py. * chore: revert to the original test_modeling_common.py * chore: revert to previous states for test_modeling_tf_common.py and modeling_tf_utils.py * fix: tests for convnext. * chore: removed output_attentions argument from convnext config. * chore: revert to the earlier tf utils. * fix: output shapes of the hidden states * chore: removed unnecessary comment * chore: reverting to the right test_modeling_tf_common.py. * Styling nits Co-authored-by: ariG23498 <aritra.born2fly@gmail.com> Co-authored-by: Joao Gante <joao@huggingface.co> Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com> * minor changes * doc fix in feature extractor * doc * typose * removed detr logic from config * removed detr logic from config * removed num_labels * small fix in the config * auxilary -> auxiliary * make style * some test is failing * fix a weird char in config prevending doc-builder * retry to fix the doc-builder issue * make style * new try to fix the doc builder * CI * change weights to facebook Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: ariG23498 <aritra.born2fly@gmail.com> Co-authored-by: Joao Gante <joao@huggingface.co> Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>

transformers

Python

test_modeling_maskformer.py

def test_inference_instance_segmentation_head(self): model = MaskFormerForInstanceSegmentation.from_pretrained(self.model_checkpoints).to(torch_device).eval() feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(image, return_tensors="pt").to(torch_device) inputs_shape = inputs["pixel_values"].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0) # check size self.assertEqual(inputs_shape, (1, 3, 800, 1088)) with torch.no_grad(): outputs = model(**inputs) # masks_queries_logits masks_queries_logits = outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape, (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ) expected_slice = torch.tensor( [[-1.3738, -1.7725, -1.9365], [-1.5978, -1.9869, -2.1524], [-1.5796, -1.9271, -2.0940]] ) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], expected_slice, atol=TOLERANCE)) # class_queries_logits class_queries_logits = outputs.class_queries_logits self.assertEqual(class_queries_logits.shape, (1, model.config.num_queries, model.config.num_labels + 1)) expected_slice = torch.tensor( [ [1.6512e00, -5.2572e00, -3.3519e00], [3.6169e-02, -5.9025e00, -2.9313e00], [1.0766e-04, -7.7630e00, -5.1263e00], ] ) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_slice, atol=TOLERANCE))

d83d22f578276e9f201b0b3b0f8f9bd68e86c133

https://github.com/huggingface/transformers.git

def test_inference_instance_segmentation_head(self): mo

test_inference_instance_segmentation_head

chart/tests/test_migrate_database_job.py

Add support for custom command and args in jobs (#20864)

airflow

Python

test_migrate_database_job.py

def test_default_command_and_args_airflow_version(self, airflow_version, expected_arg): docs = render_chart( values={ "airflowVersion": airflow_version, }, show_only=["templates/jobs/migrate-database-job.yaml"], ) assert jmespath.search("spec.template.spec.containers[0].command", docs[0]) is None assert [ "bash", "-c", f"exec \\\n{expected_arg}", ] == jmespath.search("spec.template.spec.containers[0].args", docs[0])

4e17528fd2ed69020c3cdc6672e3093254f1477f

https://github.com/apache/airflow.git

def test_default_command_and_args_airflow_version(self, airflow_version, expected_arg): docs = render_chart( values={ "airflowVersion": airflow_version, }, show_only=["templates/jobs/

test_default_command_and_args_airflow_version

.venv/lib/python3.8/site-packages/pip/_internal/cli/parser.py

upd; format

transferlearning

Python

parser.py

def error(self, msg): # type: (str) -> None self.print_usage(sys.stderr) self.exit(UNKNOWN_ERROR, f"{msg}\n")

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

https://github.com/jindongwang/transferlearning.git

def error(self, msg): # type: (str) -> None

error

wagtail/contrib/redirects/tests/test_redirects.py

Reformat with black

wagtail

Python

test_redirects.py

def test_edit_duplicate(self): models.Redirect.objects.create( old_path="/othertest", site=None, redirect_link="http://elsewhere.com/" ) response = self.post( { "old_path": "/othertest", "is_permanent": "on", "site": "", "redirect_link": "http://www.test.com/ive-been-edited", } ) # Should not redirect to index self.assertEqual(response.status_code, 200)

d10f15e55806c6944827d801cd9c2d53f5da4186

https://github.com/wagtail/wagtail.git

def test_edit_duplicate(self): models.Redirect.objects.create( old_path="/othertest", site=None, redirect_link="h

test_edit_duplicate

yt_dlp/extractor/instagram.py

[instagram] Fix bug in 013322a95e396ab21c8febc3e560d8a177c87f4a Closes #2552

yt-dlp

Python

instagram.py

def _extract_product_media(self, product_media): media_id = product_media.get('code') or product_media.get('id') vcodec = product_media.get('video_codec') dash_manifest_raw = product_media.get('video_dash_manifest') videos_list = product_media.get('video_versions') if not (dash_manifest_raw or videos_list): return {} formats = [{ 'format_id': format.get('id'), 'url': format.get('url'), 'width': format.get('width'), 'height': format.get('height'), 'vcodec': vcodec, } for format in videos_list or []] if dash_manifest_raw: formats.extend(self._parse_mpd_formats(self._parse_xml(dash_manifest_raw, media_id), mpd_id='dash')) self._sort_formats(formats) thumbnails = [{ 'url': thumbnail.get('url'), 'width': thumbnail.get('width'), 'height': thumbnail.get('height') } for thumbnail in traverse_obj(product_media, ('image_versions2', 'candidates')) or []] return { 'id': media_id, 'duration': float_or_none(product_media.get('video_duration')), 'formats': formats, 'thumbnails': thumbnails }

c5332d7fbb654a7127aeb080b91f8e85b48796b4

https://github.com/yt-dlp/yt-dlp.git

def _extract_product_media(self, product_media): media_id = product_media.get('code') or product_media.get('id') vcodec = product_media.get('video_codec') dash_manifest_raw = product_media.get('video_dash_manifest') videos_list = product_media.get('video_versions')

_extract_product_media

wagtail/contrib/forms/views.py

Initialize locale attribute in SafePaginateListView.__init__

wagtail

Python

views.py

def get(self, request, *args, **kwargs): if getattr(settings, "WAGTAIL_I18N_ENABLED", False): if request.GET.get("locale"): self.locale = get_object_or_404( Locale, language_code=request.GET["locale"] ) else: self.locale = Locale.get_default() return super().get(request, *args, **kwargs)

e777c22d70191382668efeb04981b4b4acb29905

https://github.com/wagtail/wagtail.git

def get(self, request, *args, **kwargs): if getattr(settings, "WAGTAIL_I18N_ENABLED", False): if request.GET.get("locale"): self.locale = get_object_or_404( Locale, language_code=request.GET["locale"] ) else: self.locale = Locale.get_default() r

get

src/prefect/orion/schemas/schedules.py

Ensure that UTC offsets dont get parsed as timezones (PrefectHQ/orion#2551)

prefect

Python

schedules.py

def default_timezone(cls, v, *, values, **kwargs): # if was provided, make sure its a valid IANA string if v and v not in pendulum.tz.timezones: raise ValueError(f'Invalid timezone: "{v}"') # otherwise infer the timezone from the anchor date elif v is None and values.get("anchor_date"): tz = values["anchor_date"].tz.name if tz in pendulum.tz.timezones: return tz # sometimes anchor dates have "timezones" that are UTC offsets # like "-04:00". This happens when parsing ISO8601 strings. # In this case we, the correct inferred localization is "UTC". else: return "UTC" return v

076fb7d6472874eeec670239590c78a763e0f72d

https://github.com/PrefectHQ/prefect.git

def default_timezone(cls, v, *, values, **kwargs): # if was provided, make sure its a valid IANA string if v and v not in pendulum.tz.timezones: raise ValueError(f'Invalid timezone: "{v}"') # otherwise infer the timezone from the anchor date elif v is None and values.get("anchor_date"): tz = values["anchor_date"].tz.name if tz in pendulum.tz.timezones: return tz # sometimes anchor dates have "timezones" that

default_timezone

modules/image/classification/mobilenet_v2_dishes/test.py

update mobilenet_v2_dishes (#2018)

PaddleHub

Python

test.py

def test_classification2(self): results = self.module.classification( images=[cv2.imread('tests/test.jpg')] ) data = results[0] self.assertTrue('海鲜面' in data) self.assertTrue(data['海鲜面'] > 0.01)

94949b0e9120b4bca5888a4d19ff9759a05dd54f

https://github.com/PaddlePaddle/PaddleHub.git

def test_classification2(self):

test_classification2

homeassistant/components/google/calendar.py

Move google calendar integration to aiohttp (#70173) * Use new aiohttp based google client library in gcal_sync. * Use base url in tests for shorter string * Remove unnecessary line of code * Jump to gcal-sync-0.4.1 * Update tests/components/google/conftest.py Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Update to gcal_sync 0.5.0 incorporating PR feedback Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

core

Python

calendar.py

async def async_update(self) -> None: request = ListEventsRequest(calendar_id=self._calendar_id, search=self._search) try: result = await self._calendar_service.async_list_events(request) except ApiException as err: _LOGGER.error("Unable to connect to Google: %s", err) return # Pick the first visible event and apply offset calculations. valid_items = filter(self._event_filter, result.items) event = copy.deepcopy(next(valid_items, None)) if event: (event.summary, offset) = extract_offset(event.summary, self._offset) self._event = _get_calendar_event(event) self._offset_value = offset else: self._event = None

0e0c0ce22b0a87e0c709abab2091dc5bfddb42bb

https://github.com/home-assistant/core.git

async def async_update(self) -> None: request = ListEventsRequest(calendar_id=self._calendar_id, search=self._search) try: result = await self._calendar_service.async_list_events(request) except ApiException as err: _LOGGER.error("Unable to connect to Google: %s", err) return # Pick the first visible event and apply offset calculations. valid_items = filter(self._event_filter, result.items) event

async_update

src/prefect/flow_runners.py

Kubernetes flow runner (PrefectHQ/orion#780) Add a Kubernetes flow runner

prefect

Python

flow_runners.py

def _get_environment_variables(self): env = self.env.copy() env.setdefault("PREFECT_ORION_HOST", "http://orion:4200/api") return env

be671cbecee46c621dc08ed47bb520f795b34a42

https://github.com/PrefectHQ/prefect.git

def _get_environment_variables(self): env = self.env.copy() env.setdefault("PREFECT_ORION_HOST", "http://orion:4

_get_environment_variables

test/prototype_transforms_kernel_infos.py

fix prototype transforms tests with set agg_method (#6934) * fix prototype transforms tests with set agg_method * use individual tolerances * refactor PIL reference test * increase tolerance for elastic_mask * fix autocontrast tolerances * increase tolerance for RandomAutocontrast

vision

Python

prototype_transforms_kernel_infos.py

def reference_inputs_convert_color_space_image_tensor(): for args_kwargs in sample_inputs_convert_color_space_image_tensor(): (image_loader, *other_args), kwargs = args_kwargs if len(image_loader.shape) == 3 and image_loader.dtype == torch.uint8: yield args_kwargs

65769ab7662263a032a14c77e7b0890abb7c3001

https://github.com/pytorch/vision.git

def reference_inputs_convert_color_space_image_tensor(): for args_kwargs in sample_inputs_convert_color_space_image_tensor(): (image_loader, *other_args), kwargs = args_kwargs if le

reference_inputs_convert_color_space_image_tensor

spacy/tests/doc/test_json_doc_conversion.py

Support custom attributes for tokens and spans in json conversion (#11125) * Add token and span custom attributes to to_json() * Change logic for to_json * Add functionality to from_json * Small adjustments * Move token/span attributes to new dict key * Fix test * Fix the same test but much better * Add backwards compatibility tests and adjust logic * Add test to check if attributes not set in underscore are not saved in the json * Add tests for json compatibility * Adjust test names * Fix tests and clean up code * Fix assert json tests * small adjustment * adjust naming and code readability * Adjust naming, added more tests and changed logic * Fix typo * Adjust errors, naming, and small test optimization * Fix byte tests * Fix bytes tests * Change naming and json structure * update schema * Update spacy/schemas.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/tokens/doc.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/tokens/doc.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/schemas.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update schema for underscore attributes * Adjust underscore schema * adjust schema tests Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

spaCy

Python

test_json_doc_conversion.py

def test_doc_to_json_with_token_attributes_missing(doc): Token.set_extension("token_test", default=False) Span.set_extension("span_test", default=False) doc[0:1]._.span_test = "span_attribute" doc[0]._.token_test = 117 json_doc = doc.to_json(underscore=["span_test"]) assert "underscore_token" in json_doc assert "underscore_span" in json_doc assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute" assert "token_test" not in json_doc["underscore_token"] assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0

5afa98aabfc18a23f19b07b13e2cd12ddb6ee009

https://github.com/explosion/spaCy.git

def test_doc_to_json_with_token_attributes_missing(doc): Token.set_extension("token_test", default=False) Span.set_extension("span_test", default=False) doc[0:1]._.span_test = "span_attribute" doc[0]._.token_test = 117 json_doc = doc.to_json(underscore=["span_test"]) assert "underscore_token" in json_doc assert "underscore_span" in json_doc assert json_doc["underscore_span"]["span_test"]["value

test_doc_to_json_with_token_attributes_missing

test/test_pipeline.py

Generate code from pipeline (pipeline.to_code()) (#2214) * pipeline.to_code() with jupyter support * Update Documentation & Code Style * add imports * refactoring * Update Documentation & Code Style * docstrings added and refactoring * Update Documentation & Code Style * improve imports code generation * add comment param * Update Documentation & Code Style * add simple test * add to_notebook_cell() * Update Documentation & Code Style * introduce helper classes for code gen and eval report gen * add more tests * Update Documentation & Code Style * fix Dict typings * Update Documentation & Code Style * validate user input before code gen * enable urls for to_code() * Update Documentation & Code Style * remove all chars except colon from validation regex Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

haystack

Python

test_pipeline.py

def test_PipelineCodeGen_dual_retriever_pipeline(): es_doc_store = ElasticsearchDocumentStore(index="my-index") es_retriever = ElasticsearchRetriever(document_store=es_doc_store, top_k=20) dense_doc_store = InMemoryDocumentStore(index="my-index") emb_retriever = EmbeddingRetriever( document_store=dense_doc_store, embedding_model="sentence-transformers/all-MiniLM-L6-v2" ) p_ensemble = Pipeline() p_ensemble.add_node(component=es_retriever, name="EsRetriever", inputs=["Query"]) p_ensemble.add_node(component=emb_retriever, name="EmbeddingRetriever", inputs=["Query"]) p_ensemble.add_node( component=JoinDocuments(join_mode="merge"), name="JoinResults", inputs=["EsRetriever", "EmbeddingRetriever"] ) code = _PipelineCodeGen.generate_code(pipeline=p_ensemble, pipeline_variable_name="p", generate_imports=False) assert code == ( 'elasticsearch_document_store = ElasticsearchDocumentStore(index="my-index")\n' "es_retriever = ElasticsearchRetriever(document_store=elasticsearch_document_store, top_k=20)\n" 'in_memory_document_store = InMemoryDocumentStore(index="my-index")\n' 'embedding_retriever = EmbeddingRetriever(document_store=in_memory_document_store, embedding_model="sentence-transformers/all-MiniLM-L6-v2")\n' 'join_results = JoinDocuments(join_mode="merge")\n' "\n" "p = Pipeline()\n" 'p.add_node(component=es_retriever, name="EsRetriever", inputs=["Query"])\n' 'p.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["Query"])\n' 'p.add_node(component=join_results, name="JoinResults", inputs=["EsRetriever", "EmbeddingRetriever"])' ) @pytest.mark.elasticsearch

e20f2e0d541805c3afb1f0948fa85f88b2a4f434

@pytest.mark.elasticsearch

https://github.com/deepset-ai/haystack.git

def test_PipelineCodeGen_dual_retriever_pipeline(): es_doc_store = ElasticsearchDocumentStore(index="my-index") es_retriever = ElasticsearchRetriever(document_store=es_doc_store, top_k=20) dense_doc_store = InMemoryDocumentStore(index="my-index") emb_retriever = EmbeddingRetriever( document_store=dense_doc_store, embedding_model="sentence-transformers/all-MiniLM-L6-v2" ) p_ensemble = Pipeline() p_ensemble.add_node(component=es_retriever, name="EsRetriever", inputs=["Query"]) p_ensemble.add_node(component=emb_retriever, name="EmbeddingRetriever", inputs=["Query"]) p_ensemble.add_node( component=JoinDocuments(join_mode="merge"), name="JoinResults", inputs=["EsRetriever", "EmbeddingRetriever"] ) code = _PipelineCodeGen.generate_code(pipeline=p_ensemble, pipeline_variable_name="p", generate_imports=False) assert code == ( 'elasticsearch_document_store = ElasticsearchDocumentStore(index="my-index")\n' "es_retriever = ElasticsearchRetriever(document_store=elasticsearch_document_store, top_k=20)\n" 'in_memory_document_store = InMemoryDocumentStore(index="my-index")\n' 'embedding_retriever = EmbeddingRetriever(document_store=in_memory_document_store, embedding_model="sentence-transformers/all-MiniLM-L6-v2")\n' 'join_results = JoinDocuments(join_mode="merge")\n' "\n" "p = Pipeline()\n" 'p.add_node(component=es_retriever, name="EsRetriever", inputs=["Query"])\n' 'p.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["Query"])\n' 'p.add_node(component=join_results, name="JoinResults", inputs=["EsRetriever

test_PipelineCodeGen_dual_retriever_pipeline

mindsdb/interfaces/stream/stream.py

fix

mindsdb

Python

stream.py

def setup(self, db_alias): try: integration = self.integration_controller.get(db_alias) if integration is None: raise Exception(f'Unkonw database integration: {db_alias}') if integration.get('type') not in self.known_dbs: raise Exception(f'Unkonw database integration type for: {db_alias}') self.known_dbs[integration['type']](self.config, db_alias, integration).setup() except Exception as e: logger.warning('Failed to setup stream for ' + db_alias + f', error: {e}')

2fa2805fd6bbcf3819e8da4f3aba3d4618db082f

https://github.com/mindsdb/mindsdb.git

def setup(self, db_alias): try: integration = self.integration_co

setup

jina/serve/instrumentation/__init__.py

fix: health check exception with opentelemetry tracing interceptors (#5392)

jina

Python

__init__.py

def _new_timer(self): return self.__class__( self._summary_metric, self._histogram, self._histogram_metric_labels )

f738d34bfc75437c7203f5746cc602145088d220

https://github.com/jina-ai/jina.git

def _new_timer(self): return self.__class__( self._summary_metric, self._histogram, self._histo

_new_timer

python/ray/autoscaler/_private/aliyun/utils.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

utils.py

def describe_vpcs(self): request = DescribeVpcsRequest() response = self._send_request(request) if response is not None: return response.get("Vpcs").get("Vpc") return None

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def describe_vpcs(self): request

describe_vpcs

tests/test_signal_handler.py

Reformat to 80 chars and mypy.ini

thumbor

Python

test_signal_handler.py

def test_signal_handler_calls_add_callback_from_signal(self, ioloop_mock): ioloop_instance_mock = mock.Mock() ioloop_mock.return_value = ioloop_instance_mock signal_handler(mock.Mock(), mock.Mock(), signal.SIGTERM, mock.Mock()) ioloop_instance_mock.add_callback_from_signal.assert_called_with( mock.ANY )

301124c5b377fa56b940d298900dbc5816dbc24e

https://github.com/thumbor/thumbor.git

def test_signal_handler_calls_add_callback_from_signal(self, ioloop_mock): ioloop_instance_mo

test_signal_handler_calls_add_callback_from_signal

freqtrade/exchange/exchange.py

exchange.get_leverage_tiers and exchange.get_market_leverage_tiers

freqtrade

Python

exchange.py

def get_market_leverage_tiers(self, symbol) -> List[Dict]: try: return self._api.fetch_market_leverage_tiers(symbol) except ccxt.DDoSProtection as e: raise DDosProtection(e) from e except (ccxt.NetworkError, ccxt.ExchangeError) as e: raise TemporaryError( f'Could not load leverage tiers for {symbol}' f' due to {e.__class__.__name__}. Message: {e}' ) from e except ccxt.BaseError as e: raise OperationalException(e) from e

6cd01c45d5e57d357a6b1c3495ec035e0610fd78

https://github.com/freqtrade/freqtrade.git

def get_market_leverage_tiers(self, symbol) -> List[Dict]: try: return self._api.fetch_market_leverage_tiers(symbol) except ccxt.DDoSProtection as e: raise DDosProtection(e) from e except (ccxt.NetworkError, ccxt.ExchangeError) as e: raise TemporaryError( f'Could not load leverage tiers for {symbol}' f' due to {e.__class__.__name_

get_market_leverage_tiers

tests/packaged_modules/test_audiofolder.py

Add support for CSV metadata files to ImageFolder (#4837) * Add support for CSV metadata files to ImageFolder * Add tests * Update doc * Add one more test * Add identical tests for audiofolder * Docs for audiofolder * Address review comments * Minor adjustments

datasets

Python

test_audiofolder.py

def test_data_files_with_metadata_and_multiple_splits(streaming, cache_dir, data_files_with_two_splits_and_metadata): data_files = data_files_with_two_splits_and_metadata audiofolder = AudioFolder(data_files=data_files, cache_dir=cache_dir) audiofolder.download_and_prepare() datasets = audiofolder.as_streaming_dataset() if streaming else audiofolder.as_dataset() for split, data_files in data_files.items(): expected_num_of_audios = len(data_files) - 1 # don't count the metadata file assert split in datasets dataset = list(datasets[split]) assert len(dataset) == expected_num_of_audios # make sure each sample has its own audio and metadata assert len(set(example["audio"]["path"] for example in dataset)) == expected_num_of_audios assert len(set(example["text"] for example in dataset)) == expected_num_of_audios assert all(example["text"] is not None for example in dataset) @require_sndfile @pytest.mark.parametrize("streaming", [False, True])

7380140accf522a4363bb56c0b77a4190f49bed6

@require_sndfile @pytest.mark.parametrize("streaming", [False, True])

https://github.com/huggingface/datasets.git

def test_data_files_with_metadata_and_multiple_splits(streaming, cache_dir, data_files_with_two_splits_and_metadata): data_files = data_files_with_two_splits_and_metadata audiofolder = AudioFolder(data_files=data_files, cache_dir=cache_dir) audiofolder.download_and_prepare() datasets = audiofolder.as_streaming_dataset() if streaming else audiofolder.as_dataset() for split, data_files in data_files.items(): expected_num_of_audio

test_data_files_with_metadata_and_multiple_splits

pandas/tests/scalar/interval/test_interval.py

ENH: consistency of input args for boundaries - Interval (#46522)

pandas

Python

test_interval.py

def test_is_empty(self, left, right, closed): # GH27219 # non-empty always return False iv = Interval(left, right, closed) assert iv.is_empty is False # same endpoint is empty except when inclusive='both' (contains one point) iv = Interval(left, left, closed) result = iv.is_empty expected = closed != "both" assert result is expected

7e23a37e1c5bda81234801a6584563e2880769eb

https://github.com/pandas-dev/pandas.git

def test_is_empty(self, left, right, closed): # GH27219 # non-empty always return False iv = Interval(left, right, closed) assert iv.is_empty is False # same endpoint is empty except when inclusive='both' (contains one point) iv = Interval(left, left, closed) result = iv.is_empty expected = closed != "both" assert

test_is_empty

tests/components/websocket_api/test_commands.py

Websocket api to subscribe to entities (payloads reduced by ~80%+ vs state_changed events) (#67891)

core

Python

test_commands.py

async def test_subscribe_unsubscribe_entities(hass, websocket_client, hass_admin_user): hass.states.async_set("light.permitted", "off", {"color": "red"}) original_state = hass.states.get("light.permitted") assert isinstance(original_state, State) state_dict = { "attributes": dict(original_state.attributes), "context": dict(original_state.context.as_dict()), "entity_id": original_state.entity_id, "last_changed": original_state.last_changed.isoformat(), "last_updated": original_state.last_updated.isoformat(), "state": original_state.state, } hass_admin_user.groups = [] hass_admin_user.mock_policy({"entities": {"entity_ids": {"light.permitted": True}}}) await websocket_client.send_json({"id": 7, "type": "subscribe_entities"}) msg = await websocket_client.receive_json() assert msg["id"] == 7 assert msg["type"] == const.TYPE_RESULT assert msg["success"] msg = await websocket_client.receive_json() assert msg["id"] == 7 assert msg["type"] == "event" assert isinstance(msg["event"]["a"]["light.permitted"]["c"], str) assert msg["event"] == { "a": { "light.permitted": { "a": {"color": "red"}, "c": ANY, "lc": ANY, "s": "off", } } } hass.states.async_set("light.not_permitted", "on") hass.states.async_set("light.permitted", "on", {"color": "blue"}) hass.states.async_set("light.permitted", "on", {"effect": "help"}) hass.states.async_set( "light.permitted", "on", {"effect": "help", "color": ["blue", "green"]} ) hass.states.async_remove("light.permitted") hass.states.async_set("light.permitted", "on", {"effect": "help", "color": "blue"}) msg = await websocket_client.receive_json() assert msg["id"] == 7 assert msg["type"] == "event" assert msg["event"] == { "c": { "light.permitted": { "+": { "a": {"color": "blue"}, "c": ANY, "lc": ANY, "s": "on", } } } } change_set = msg["event"]["c"]["light.permitted"] additions = deepcopy(change_set["+"]) _apply_entities_changes(state_dict, change_set) assert state_dict == { "attributes": {"color": "blue"}, "context": { "id": additions["c"], "parent_id": None, "user_id": None, }, "entity_id": "light.permitted", "last_changed": additions["lc"], "last_updated": additions["lc"], "state": "on", } msg = await websocket_client.receive_json() assert msg["id"] == 7 assert msg["type"] == "event" assert msg["event"] == { "c": { "light.permitted": { "+": { "a": {"effect": "help"}, "c": ANY, "lu": ANY, }, "-": {"a": ["color"]}, } } } change_set = msg["event"]["c"]["light.permitted"] additions = deepcopy(change_set["+"]) _apply_entities_changes(state_dict, change_set) assert state_dict == { "attributes": {"effect": "help"}, "context": { "id": additions["c"], "parent_id": None, "user_id": None, }, "entity_id": "light.permitted", "last_changed": ANY, "last_updated": additions["lu"], "state": "on", } msg = await websocket_client.receive_json() assert msg["id"] == 7 assert msg["type"] == "event" assert msg["event"] == { "c": { "light.permitted": { "+": { "a": {"color": ["blue", "green"]}, "c": ANY, "lu": ANY, } } } } change_set = msg["event"]["c"]["light.permitted"] additions = deepcopy(change_set["+"]) _apply_entities_changes(state_dict, change_set) assert state_dict == { "attributes": {"effect": "help", "color": ["blue", "green"]}, "context": { "id": additions["c"], "parent_id": None, "user_id": None, }, "entity_id": "light.permitted", "last_changed": ANY, "last_updated": additions["lu"], "state": "on", } msg = await websocket_client.receive_json() assert msg["id"] == 7 assert msg["type"] == "event" assert msg["event"] == {"r": ["light.permitted"]} msg = await websocket_client.receive_json() assert msg["id"] == 7 assert msg["type"] == "event" assert msg["event"] == { "a": { "light.permitted": { "a": {"color": "blue", "effect": "help"}, "c": ANY, "lc": ANY, "s": "on", } } }

0d8f649bd65c8c54cd3503dd75485d3ec35d6076

https://github.com/home-assistant/core.git

async def test_subscribe_unsubscribe_entities(hass, websocket_client, hass_admin_user): hass.states.async_set("light.permitted", "off", {"color": "red"}) original_state = hass.states.get("light.permitted") assert isinstance(original_state, State) state_dict = { "attributes": dict(original_state.attributes), "context": dict(original_state.context.as_dict()), "entity_id": original_state.entity_id, "la

test_subscribe_unsubscribe_entities

tests/sentry/relay/test_config.py

feat(dyn-sampling): Switch to new feature flag multiplexer in projectconfig (#40498) This PR switch to new feature flag multiplexer in projectconfig.

sentry

Python

test_config.py

def test_get_experimental_config(mock_sentry_sdk, _, default_project): keys = ProjectKey.objects.filter(project=default_project) with Feature( {"organizations:dynamic-sampling": True, "organizations:server-side-sampling": True} ): # Does not raise: cfg = get_project_config(default_project, full_config=True, project_keys=keys) # Key is missing from config: assert "dynamicSampling" not in cfg.to_dict()["config"] assert mock_sentry_sdk.capture_exception.call_args == mock.call(SOME_EXCEPTION) @pytest.mark.django_db @pytest.mark.parametrize("has_custom_filters", [False, True])

c8bfd65f261769da2565ca4240f11da6e820a7e4

@pytest.mark.django_db @pytest.mark.parametrize("has_custom_filters", [False, True])

https://github.com/getsentry/sentry.git

def test_get_experimental_config(mock_sentry_sdk, _, default_project): keys = ProjectKey.objects.filter(project=default_project) with Feature( {"organizations:dynamic-sampling": True, "organizations:server-side-sampling": True} ): # Does not raise:

test_get_experimental_config

examples/addons/options-configure.py

use Python 3.9+ typing

mitmproxy

Python

options-configure.py

def load(self, loader): loader.add_option( name = "addheader", typespec = Optional[int], default = None, help = "Add a header to responses", )

fdde9ba3b3caaa2654048cec0af07bfcc3a6a3f8

https://github.com/mitmproxy/mitmproxy.git

def load(self, loader): loader.add_option( name = "addhe

load

modin/core/execution/unidist/implementations/pandas_on_unidist/partitioning/partition.py

FEAT-#5053: Add pandas on unidist execution with MPI backend (#5059) Signed-off-by: Igoshev, Iaroslav <iaroslav.igoshev@intel.com>

modin

Python

partition.py

def _get_index_and_columns_size(df): return len(df.index), len(df.columns) @unidist.remote(num_returns=4)

193505fdf0c984743397ba3df56262f30aee13a8

@unidist.remote(num_returns=4)

https://github.com/modin-project/modin.git

def _get_index_and_columns_size(df): return len(df.index), len(df.columns) @unidist.remote(num_returns=4)

_get_index_and_columns_size

sandbox/will/basic.py

more docs

textual

Python

basic.py

def compose(self) -> ComposeResult: table = DataTable() self.scroll_to_target = Tweet(TweetBody()) yield Static( Text.from_markup( "[b]This is a [u]Textual[/u] app, running in the terminal" ), id="header", ) yield from ( Tweet(TweetBody()), Widget( Static( Syntax(CODE, "python", line_numbers=True, indent_guides=True), classes="code", ), classes="scrollable", ), table, Error(), Tweet(TweetBody(), classes="scrollbar-size-custom"), Warning(), Tweet(TweetBody(), classes="scroll-horizontal"), Success(), Tweet(TweetBody(), classes="scroll-horizontal"), Tweet(TweetBody(), classes="scroll-horizontal"), Tweet(TweetBody(), classes="scroll-horizontal"), Tweet(TweetBody(), classes="scroll-horizontal"), Tweet(TweetBody(), classes="scroll-horizontal"), ) yield Widget(id="footer") yield Widget( Widget(classes="title"), Widget(classes="user"), OptionItem(), OptionItem(), OptionItem(), Widget(classes="content"), id="sidebar", ) table.add_column("Foo", width=20) table.add_column("Bar", width=20) table.add_column("Baz", width=20) table.add_column("Foo", width=20) table.add_column("Bar", width=20) table.add_column("Baz", width=20) table.zebra_stripes = True for n in range(100): table.add_row(*[f"Cell ([b]{n}[/b], {col})" for col in range(6)])

fa4b971bffb9f488d155981794b7f0bf3b657c72

https://github.com/Textualize/textual.git

def compose(self) -> ComposeResult: table = DataTable() self.scroll_to_target = Tweet(TweetBody()) yield Static( Text.from_markup( "[b]This is a [u]Textual[/u] app, running in the terminal" ), id="header", ) yield from ( Tweet(TweetBody()), Widget( Static( Syntax(CODE, "python", line_numbers=True, indent_guides=True), classes="code", ), classes="scrollable", ), table, Error(), Tweet(TweetBody(), classes="scrollbar-size-custom"), Warning(), Tweet(TweetBody(), classes="scroll-horizontal"), Success(), Tweet(TweetBody(), classes="scroll-horizontal"), Tweet(TweetBody(), classes="scroll-horizontal"), Tweet(TweetBody(), classes="scroll-horizontal"), Tweet(TweetBody(), classes="scroll-horizontal"), Tweet(TweetBody(), classes="scroll-horizontal"), ) yield Widget(id="footer") yield Widget( Widget(classes="title"), Widget(classes="user"), OptionItem(), OptionItem(), OptionItem(), Widget(classes="content"), id="sidebar", ) table.add_column("Foo", width=20) table.add_column("Bar", width=20) table.add

compose

homeassistant/components/sia/sia_entity_base.py

Add Connectivity sensor to SIA (#64305) * implemented connectivity sensor * further cleanup off update code * cleanup and tighter behaviour for attributes * added seperate connectivity class to binary sensor * callbacks and keys * redid name and unique_id logic, non-breaking result * using entry more in inits * Fix import * fix ping_interval in sia_entity_base * added ping_interval default to next * fixed next Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

core

Python

sia_entity_base.py

async def async_added_to_hass(self) -> None: self.async_on_remove( async_dispatcher_connect( self.hass, SIA_EVENT.format(self.port, self.account), self.async_handle_event, ) ) self.handle_last_state(await self.async_get_last_state()) if self._attr_available: self.async_create_post_interval_update_cb()

af4e37339a39badd5596e8bc9ba86d6c1994aa1b

https://github.com/home-assistant/core.git

async def async_added_to_hass(self) -> None: self.async_on_remove( async_dispatcher_connect( self.hass,

async_added_to_hass

keras/saving/saved_model/saved_model_test.py

resolve line-too-long in saving

keras

Python

saved_model_test.py

def test_custom_metric_model(self): # TODO(b/134519980): Issue with `model.fit` if the model call function # uses a `tf.function` in graph mode. if not tf.executing_eagerly(): return x = np.random.random((1, 3)) y = np.random.random((1, 4))

b0ffc0031e9c1964e7398ca47c6666bbfc0d5086

https://github.com/keras-team/keras.git

def test_custom_metric_model(self): # TODO(b/134519980): Issue with `model.fit` if the model call function # uses a `tf.function` in graph mode.

test_custom_metric_model

keras/saving/save_weights_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

save_weights_test.py

def test_sequential_weight_loading(self): if h5py is None: return h5_path = self._save_model_dir("test.h5") num_hidden = 5 input_dim = 3 batch_size = 5 num_classes = 2 with self.cached_session(): model = keras.models.Sequential() model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) model.add(keras.layers.Dense(num_classes)) x = np.random.random((batch_size, input_dim)) ref_y = model.predict(x) model.save_weights(h5_path) model = keras.models.Sequential() model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) model.add(keras.layers.Dense(num_classes)) model.load_weights(h5_path) y = model.predict(x) self.assertAllClose(y, ref_y)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

https://github.com/keras-team/keras.git

def test_sequential_weight_loading(self): if h5py is None:

test_sequential_weight_loading

sympy/series/gruntz.py

Reordered imports 2

sympy

Python

gruntz.py

def sign(e, x): if not isinstance(e, Basic): raise TypeError("e should be an instance of Basic") if e.is_positive: return 1 elif e.is_negative: return -1 elif e.is_zero: return 0 elif not e.has(x): from sympy.simplify import logcombine e = logcombine(e) return _sign(e) elif e == x: return 1 elif e.is_Mul: a, b = e.as_two_terms() sa = sign(a, x) if not sa: return 0 return sa * sign(b, x) elif isinstance(e, exp): return 1 elif e.is_Pow: if e.base == S.Exp1: return 1 s = sign(e.base, x) if s == 1: return 1 if e.exp.is_Integer: return s**e.exp elif isinstance(e, log): return sign(e.args[0] - 1, x) # if all else fails, do it the hard way c0, e0 = mrv_leadterm(e, x) return sign(c0, x) @debug @timeit @cacheit

f757f3daae6e11ea0cfb7dadc133274d8d74315f

@debug @timeit @cacheit

https://github.com/sympy/sympy.git

def sign(e, x): if not isinstance(e, Basic): raise TypeError("e should be an instance of Basic")

sign

tests/sentry/tasks/test_relay.py

feat(proj-config): Add v3 of proj config endpoint (#34746) This new version of the endpoint doesn't perform any computation on the project configs. Instead, it performs computation async, as follows: - If a requested project config exists in the cache, the endpoint returns it. - If a requested project config doesn't exist in the cache, the endpoint schedules a new task to compute that config, and returns a pending response. - If a requested project config doesn't exist in the cache, but a task is already scheduled, the endpoint returns a pending request. Tasks are debounced based on the public key of the project. Pending projects are returned as a list part of the config in the response, for example: ``` { proj1_key: { proj1_config }, proj2_key: { proj2_config }, pending: [proj3_key, proj4_key] } ``` The `pending` entry only exists if there is at least one pending project. **Redis cache changes** Redis is now a requirement for the project configs cache, since the endpoint can't operate without redis anymore. On the other hand, the debouncing cache hasn't been updated because it's not needed for local development (requests will never be debounced and always processed).

sentry

Python

test_relay.py

def redis_cache(monkeypatch): monkeypatch.setattr( "django.conf.settings.SENTRY_RELAY_PROJECTCONFIG_CACHE", "sentry.relay.projectconfig_cache.redis.RedisProjectConfigCache", ) cache = RedisProjectConfigCache() monkeypatch.setattr("sentry.relay.projectconfig_cache.set_many", cache.set_many) monkeypatch.setattr("sentry.relay.projectconfig_cache.delete_many", cache.delete_many) monkeypatch.setattr("sentry.relay.projectconfig_cache.get", cache.get) monkeypatch.setattr( "django.conf.settings.SENTRY_RELAY_PROJECTCONFIG_DEBOUNCE_CACHE", "sentry.relay.projectconfig_debounce_cache.redis.RedisProjectConfigDebounceCache", ) return cache @pytest.fixture

d9e850f2723a9d4919b0038d2f6cb59321eef295

@pytest.fixture

https://github.com/getsentry/sentry.git

def redis_cache(monkeypatch): monkeypatch.set

redis_cache

fastai/metrics.py

Upgrading to support latest Pytorch version

DeOldify

Python

metrics.py

def _precision(self): prec = torch.diag(self.cm) / self.cm.sum(dim=0) if self.average is None: return prec else: weights = self._weights(avg=self.average) return (prec * weights).sum()

4fc3616712edb19179b17dd270ad6cf63abf99c2

https://github.com/jantic/DeOldify.git

def _precision(self): prec = torch.diag(self.cm) / self.cm.sum(dim=0) if self.average is None: return prec else: weights = self._w

_precision

python/ray/experimental/state/state_manager.py

[State Observability] Ray log alpha API (#24964) This is the PR to implement ray log to the server side. The PR is continued from #24068. The PR supports two endpoints; /api/v0/logs # list logs of the node id filtered by the given glob. /api/v0/logs/{[file | stream]}?filename&pid&actor_id&task_id&interval&lines # Stream the requested file log. The filename can be inferred by pid/actor_id/task_id Some tests need to be re-written, I will do it soon. As a follow-up after this PR, there will be 2 PRs. PR to add actual CLI PR to remove in-memory cached logs and do on-demand query for actor/worker logs

ray

Python

state_manager.py

def get_all_registered_agent_ids(self) -> List[str]: assert len(self._log_agent_stub) == len(self._runtime_env_agent_stub) return self._runtime_env_agent_stub.keys()

00e3fd75f33d762387ab6fa379743cd21c409ea6

https://github.com/ray-project/ray.git

def get_all_registered_agent_ids(self) -> List[str]: assert len(self._log_agent_stub) == len(self._runtime_env_agent_stub) return self._runtim

get_all_registered_agent_ids

networkx/linalg/laplacianmatrix.py

Use scipy.sparse array datastructure (#5139) * Step 1: use sparse arrays in nx.to_scipy_sparse_matrix. Seems like a reasonable place to start. nx.to_scipy_sparse_matrix is one of the primary interfaces to scipy.sparse from within NetworkX. * 1: Use np.outer instead of mult col/row vectors Fix two instances in modularitymatrix where a new 2D array was being created via an outer product of two \"vectors\". In the matrix case, this was a row vector \* a column vector. In the array case this can be disambiguated by being explicit with np.outer. * Update _transition_matrix in laplacianmatrix module - A few instances of matrix multiplication operator - Add np.newaxis + transpose to get shape right for broadcasting - Explicitly convert e.g. sp.sparse.spdiags to a csr_array. * Update directed_combinitorial_laplacian w/ sparse array. - Wrap spdiags in csr_array and update matmul operators. * Rm matrix-specific code from lgc and hmn modules - Replace .A call with appropriate array semantics - wrap sparse.diags in csr_array. * Change hits to use sparse array semantics. - Replace * with @ - Remove superfluous calls to flatten. * Update sparse matrix usage in layout module. - Simplify lil.getrowview call - Wrap spdiags in csr_array. * lil_matrix -> lil_array in graphmatrix.py. * WIP: Start working on algebraic connectivity module. * Incorporate auth mat varname feedback. * Revert 1D slice and comment for 1D sparse future. * Add TODOs: rm csr_array wrapper around spdiags etc. * WIP: cleanup algebraicconn: tracemin_fiedler. * Typo. * Finish reviewing algebraicconnectivity. * Convert bethe_hessian matrix to use sparse arrays. * WIP: update laplacian. Update undirected laplacian functions. * WIP: laplacian - add comment about _transition_matrix return types. * Finish laplacianmatrix review. * Update attrmatrix. * Switch to official laplacian function. * Update pagerank to use sparse array. * Switch bipartite matrix to sparse arrays. * Check from_scipy_sparse_matrix works with arrays. Modifies test suite. * Apply changes from review. * Fix failing docstring tests. * Fix missing axis for in-place multiplication. * Use scipy==1.8rc2 * Use matrix multiplication * Fix PyPy CI * [MRG] Create plot_subgraphs.py example (#5165) * Create plot_subgraphs.py https://github.com/networkx/networkx/issues/4220 * Update plot_subgraphs.py black * Update plot_subgraphs.py lint plus font_size * Update plot_subgraphs.py added more plots * Update plot_subgraphs.py removed plots from the unit test and added comments * Update plot_subgraphs.py lint * Update plot_subgraphs.py typos fixed * Update plot_subgraphs.py added nodes to the plot of the edges removed that was commented out for whatever reason * Update plot_subgraphs.py revert the latest commit - the line was commented out for a reason - it's broken * Update plot_subgraphs.py fixed node color issue * Update plot_subgraphs.py format fix * Update plot_subgraphs.py forgot to draw the nodes... now fixed * Fix sphinx warnings about heading length. * Update examples/algorithms/plot_subgraphs.py * Update examples/algorithms/plot_subgraphs.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> Co-authored-by: Dan Schult <dschult@colgate.edu> * Add traveling salesman problem to example gallery (#4874) Adds an example of the using Christofides to solve the TSP problem to the example galery. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() (#5037) * Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() * Resolved Requested Changes * Revert changes to degree docstrings. * Update comments in example. * Apply wording to edges method in all graph classes. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Compatibility updates from testing with numpy/scipy/pytest rc's (#5226) * Rm deprecated scipy subpkg access. * Use recwarn fixture in place of deprecated pytest pattern. * Rm unnecessary try/except from tests. * Replace internal `close` fn with `math.isclose`. (#5224) * Replace internal close fn with math.isclose. * Fix lines in docstring examples. * Fix Python 3.10 deprecation warning w/ int div. (#5231) * Touchups and suggestions for subgraph gallery example (#5225) * Simplify construction of G with edges rm'd * Rm unused graph attribute. * Shorten categorization by node type. * Simplify node coloring. * Simplify isomorphism check. * Rm unit test. * Rm redundant plotting of each subgraph. * Use new package name (#5234) * Allowing None edges in weight function of bidirectional Dijkstra (#5232) * added following feature also to bidirectional dijkstra: The weight function can be used to hide edges by returning None. * changed syntax for better readability and code duplicate avoidance Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de> * Add an FAQ about assigning issues. (#5182) * Add FAQ about assigning issues. * Add note about linking issues from new PRs. * Update dev deps (#5243) * Update minor doc issues with tex notation (#5244) * Add FutureWarnings to fns that return sparse matrices - biadjacency_matrix. - bethe_hessian_matrix. - incidence_matrix. - laplacian functions. - modularity_matrix functions. - adjacency_matrix. * Add to_scipy_sparse_array and use it everywhere. Add a new conversion function to preserve array semantics internally while not altering behavior for users. Also adds FutureWarning to to_scipy_sparse_matrix. * Add from_scipy_sparse_array. Supercedes from_scipy_sparse_matrix. * Handle deprecations in separate PR. * Fix docstring examples. Co-authored-by: Mridul Seth <mail@mriduls.com> Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com> Co-authored-by: Andrew Knyazev <andrew.knyazev@ucdenver.edu> Co-authored-by: Dan Schult <dschult@colgate.edu> Co-authored-by: eskountis <56514439+eskountis@users.noreply.github.com> Co-authored-by: Anutosh Bhat <87052487+anutosh491@users.noreply.github.com> Co-authored-by: NikHoh <nikhoh@web.de> Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de> Co-authored-by: Sultan Orazbayev <contact@econpoint.com> Co-authored-by: Mridul Seth <mail@mriduls.com>

networkx

Python

laplacianmatrix.py

def normalized_laplacian_matrix(G, nodelist=None, weight="weight"): r import numpy as np import scipy as sp import scipy.sparse # call as sp.sparse if nodelist is None: nodelist = list(G) A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr") n, m = A.shape diags = A.sum(axis=1) # TODO: rm csr_array wrapper when spdiags can produce arrays D = sp.sparse.csr_array(sp.sparse.spdiags(diags, 0, m, n, format="csr")) L = D - A with sp.errstate(divide="ignore"): diags_sqrt = 1.0 / np.sqrt(diags) diags_sqrt[np.isinf(diags_sqrt)] = 0 # TODO: rm csr_array wrapper when spdiags can produce arrays DH = sp.sparse.csr_array(sp.sparse.spdiags(diags_sqrt, 0, m, n, format="csr")) import warnings warnings.warn( "normalized_laplacian_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.", FutureWarning, stacklevel=2, ) # TODO: rm csr_matrix wrapper for NX 3.0 return sp.sparse.csr_matrix(DH @ (L @ DH)) ############################################################################### # Code based on # https://bitbucket.org/bedwards/networkx-community/src/370bd69fc02f/networkx/algorithms/community/ @not_implemented_for("undirected") @not_implemented_for("multigraph")

5dfd57af2a141a013ae3753e160180b82bec9469

@not_implemented_for("undirected") @not_implemented_for("multigraph")

https://github.com/networkx/networkx.git

def normalized_laplacian_matrix(G, nodelist=None, weight="weight"): r import numpy as np import scipy as sp import scipy.sparse # call as sp.sparse if nodelist is None: nodelist = list(G) A =

normalized_laplacian_matrix

keras/preprocessing/image.py

Copy image utils from keras_preprocessing directly into core keras This is not new code, we are just moving these utilities directly into keras from keras-preprocessing. For the library code, just fixed linting errors. For the test code, had to do more major changes to port from pytest, but hopefully any errors have been caught by the tests themselves. PiperOrigin-RevId: 427274651

keras

Python

image.py

def random_channel_shift(x, intensity_range, channel_axis=0): intensity = np.random.uniform(-intensity_range, intensity_range) return apply_channel_shift(x, intensity, channel_axis=channel_axis) @keras_export('keras.preprocessing.image.apply_brightness_shift')

373ad97c72ed1ac4b6898e85b2cfd7b016e4b469

@keras_export('keras.preprocessing.image.apply_brightness_shift')

https://github.com/keras-team/keras.git

def random_channel_shift(x, intensity_range, channel_axis=0): intensi

random_channel_shift

erpnext/patches/v14_0/migrate_remarks_from_gl_to_payment_ledger.py

refactor: remove duplicate entries on remarks migration patch

erpnext

Python

migrate_remarks_from_gl_to_payment_ledger.py

def execute(): if frappe.reload_doc("accounts", "doctype", "payment_ledger_entry"): gle = qb.DocType("GL Entry") ple = qb.DocType("Payment Ledger Entry") # get ple and their remarks from GL Entry pl_entries = ( qb.from_(ple) .left_join(gle) .on( (ple.account == gle.account) & (ple.party_type == gle.party_type) & (ple.party == gle.party) & (ple.voucher_type == gle.voucher_type) & (ple.voucher_no == gle.voucher_no) & (ple.company == gle.company) ) .select( ple.company, ple.account, ple.party_type, ple.party, ple.voucher_type, ple.voucher_no, gle.remarks.as_("gle_remarks"), ) .where((ple.delinked == 0) & (gle.is_cancelled == 0)) .run(as_dict=True) ) pl_entries = remove_duplicate_entries(pl_entries) if pl_entries: # split into multiple batches, update and commit for each batch batch_size = 1000 for batch in create_batch(pl_entries, batch_size): for entry in batch: query = ( qb.update(ple) .set(ple.remarks, entry.gle_remarks) .where( (ple.company == entry.company) & (ple.account == entry.account) & (ple.party_type == entry.party_type) & (ple.party == entry.party) & (ple.voucher_type == entry.voucher_type) & (ple.voucher_no == entry.voucher_no) ) ) query.run() frappe.db.commit()

518ab93e039d68827506c3ac92db3c09aea644e3

https://github.com/frappe/erpnext.git

def execute(): if frappe.reload_doc("accounts", "doctype", "payment_ledger_entry"): gle = qb.DocType("GL Entry") ple = qb.DocType("Payment Ledger Entry") # get ple and their remarks from GL Entry pl_entries = ( qb.from_(ple) .left_join(gle) .on( (ple.account == gle.account) & (ple.party_type == gle.party_type) & (ple.party == gle.party) & (ple.voucher_type == gle.voucher_type) & (ple.voucher_no == gle.voucher_no) & (ple.company == gle.company) ) .select( ple.company, ple.account, ple.party_type, ple.party, ple.voucher_type, ple.voucher_no, gle.remarks.as_("gle_remarks"), ) .where((ple.delinked == 0) & (gle.is_cancelled == 0)) .run(as_dict=True) ) pl_entries = remove_duplicate_entries(pl_entries) if pl_entries: # split into multiple batches, update and commit for each batch batch_size = 1000 for batch in create_batch(pl_entries, batch_size): for entry in batch: query = ( qb.update(ple) .set(ple.remarks, entry.gle_remarks) .where( (ple.company == entry.company) & (ple.account == entry.account) & (ple.party_type == entry.party_type) & (ple.party == entry.party) & (ple.voucher_type == entry.vouche

execute

terminal.py

Remember Contexts (#1187) * Refacotred classes * Handling for new instance desired * Added feature flag * Converted all menu calls

OpenBBTerminal

Python

terminal.py

def call_funds(self, _): from gamestonk_terminal.mutual_funds.mutual_fund_controller import ( FundController, ) self.queue = self.load_class(FundController, self.queue)

9e671aeba98dacc69ecbbfec1f087aca3b139ee7

https://github.com/OpenBB-finance/OpenBBTerminal.git

def call_funds(self, _): from ga

call_funds

homeassistant/components/elgato/light.py

Improve Elgato error handling (#73444)

core

Python

light.py

async def async_identify(self) -> None: try: await self.client.identify() except ElgatoError as error: raise HomeAssistantError( "An error occurred while identifying the Elgato Light" ) from error

05d7d31dfd4d4eb4a8b10a84319d7f2778e65298

https://github.com/home-assistant/core.git

async def async_identify(self) -> None: try: await self.client.ident

async_identify

mypy/test/teststubtest.py

stubtest: fix literal type construction (#11931) Co-authored-by: hauntsaninja <>

mypy

Python

teststubtest.py

def test_bad_literal(self) -> Iterator[Case]: yield Case("from typing_extensions import Literal", "", None) # dummy case yield Case( stub="INT_FLOAT_MISMATCH: Literal[1]", runtime="INT_FLOAT_MISMATCH = 1.0", error="INT_FLOAT_MISMATCH", ) yield Case( stub="WRONG_INT: Literal[1]", runtime="WRONG_INT = 2", error="WRONG_INT", ) yield Case( stub="WRONG_STR: Literal['a']", runtime="WRONG_STR = 'b'", error="WRONG_STR", ) yield Case( stub="BYTES_STR_MISMATCH: Literal[b'value']", runtime="BYTES_STR_MISMATCH = 'value'", error="BYTES_STR_MISMATCH", ) yield Case( stub="STR_BYTES_MISMATCH: Literal['value']", runtime="STR_BYTES_MISMATCH = b'value'", error="STR_BYTES_MISMATCH", ) yield Case( stub="WRONG_BYTES: Literal[b'abc']", runtime="WRONG_BYTES = b'xyz'", error="WRONG_BYTES", ) yield Case( stub="WRONG_BOOL_1: Literal[True]", runtime="WRONG_BOOL_1 = False", error='WRONG_BOOL_1', ) yield Case( stub="WRONG_BOOL_2: Literal[False]", runtime="WRONG_BOOL_2 = True", error='WRONG_BOOL_2', )

e40877d4306787acb15985888e1f33ad4bdd9912

https://github.com/python/mypy.git

def test_bad_literal(self) -> Iterator[Case]: yield Case("from typing_extensions import Literal", "", None) # dummy case yield Case( stub="INT_FLOAT_MISMATCH: Literal[1]", runtime="INT_FLOAT_MISMATCH = 1.0", error="INT_FLOAT_MISMATCH", ) yield Case( stub="WRONG_INT: Literal[1]", runtime="WRONG_INT = 2", error="WRONG_INT", ) yield Case( stub="WRONG_STR: Literal['a']", runtime="WRONG_STR = 'b'", error="WRONG_STR", ) yield Case( stub="BYTES_STR_MISMATCH: Literal[b'value']", runtime="BYTES_STR_MISMATCH = 'value'", error="BYTES_STR_MISMA

test_bad_literal

erpnext/accounts/doctype/pricing_rule/utils.py

fix: Reapply pricing rule on qty change

erpnext

Python

utils.py

def apply_pricing_rule_for_free_items(doc, pricing_rule_args): if pricing_rule_args: items = tuple((d.item_code, d.pricing_rules) for d in doc.items if d.is_free_item) for args in pricing_rule_args: if not items or (args.get("item_code"), args.get("pricing_rules")) not in items: doc.append("items", args)

b741ae143c514cf832a435db1902986a915efde4

https://github.com/frappe/erpnext.git

def apply_pricing_rule_for_free_items(doc, pricing_rule_args): if pricing_rule_args: items = tuple((d.item_code, d.pricing_rules) for d in doc.items if d.is_free_item) for args in pricing_rule_args: if not items or (args.get("item_code"), args.get("pricing_rules")) not in items: do

apply_pricing_rule_for_free_items

test_tipc/supplementary/config.py

add supplementary

PaddleOCR

Python

config.py

def parse_args(self, argv=None): args = super(ArgsParser, self).parse_args(argv) assert args.config is not None, \ "Please specify --config=configure_file_path." args.opt = self._parse_opt(args.opt) return args

11f6ff38dcc61348aa4aae8ad2fbbe42b0eab34d

https://github.com/PaddlePaddle/PaddleOCR.git

def parse_args(self, argv=None): args = super(ArgsParser, self).parse_args(argv) assert args.config is not None, \ "Please specify --config=configure_file_path." args.opt = self._parse_opt(args.opt) return args

parse_args

test/lib/ansible_test/_internal/commands/sanity/import.py

ansible-test - Code cleanup and refactoring. (#77169) * Remove unnecessary PyCharm ignores. * Ignore intentional undefined attribute usage. * Add missing type hints. Fix existing type hints. * Fix docstrings and comments. * Use function to register completion handler. * Pass strings to display functions. * Fix CompositeAction handling of dest argument. * Use consistent types in expressions/assignments. * Use custom function to keep linters happy. * Add missing raise for custom exception. * Clean up key/value type handling in cloud plugins. * Use dataclass instead of dict for results. * Add custom type_guard function to check lists. * Ignore return type that can't be checked (yet). * Avoid changing types on local variables.

ansible

Python

import.py

def test(self, args, targets, python): # type: (SanityConfig, SanityTargets, PythonConfig) -> TestResult settings = self.load_processor(args, python.version) paths = [target.path for target in targets.include] if python.version.startswith('2.') and (get_virtualenv_version(args, python.path) or (0,)) < (13,): # hack to make sure that virtualenv is available under Python 2.x # on Python 3.x we can use the built-in venv # version 13+ is required to use the `--no-wheel` option try: install_requirements(args, python, virtualenv=True, controller=False) # sanity (import) except PipUnavailableError as ex: display.warning(str(ex)) temp_root = os.path.join(ResultType.TMP.path, 'sanity', 'import') messages = [] for import_type, test in ( ('module', _get_module_test(True)), ('plugin', _get_module_test(False)), ): if import_type == 'plugin' and python.version in REMOTE_ONLY_PYTHON_VERSIONS: continue data = '\n'.join([path for path in paths if test(path)]) if not data and not args.prime_venvs: continue virtualenv_python = create_sanity_virtualenv(args, python, f'{self.name}.{import_type}', coverage=args.coverage, minimize=True) if not virtualenv_python: display.warning(f'Skipping sanity test "{self.name}" on Python {python.version} due to missing virtual environment support.') return SanitySkipped(self.name, python.version) virtualenv_yaml = check_sanity_virtualenv_yaml(virtualenv_python) if virtualenv_yaml is False: display.warning(f'Sanity test "{self.name}" ({import_type}) on Python {python.version} may be slow due to missing libyaml support in PyYAML.') env = ansible_environment(args, color=False) env.update( SANITY_TEMP_PATH=ResultType.TMP.path, SANITY_IMPORTER_TYPE=import_type, ) if data_context().content.collection: external_python = create_sanity_virtualenv(args, args.controller_python, self.name) env.update( SANITY_COLLECTION_FULL_NAME=data_context().content.collection.full_name, SANITY_EXTERNAL_PYTHON=external_python.path, SANITY_YAML_TO_JSON=os.path.join(ANSIBLE_TEST_TOOLS_ROOT, 'yaml_to_json.py'), ANSIBLE_CONTROLLER_MIN_PYTHON_VERSION=CONTROLLER_MIN_PYTHON_VERSION, PYTHONPATH=':'.join((get_ansible_test_python_path(), env["PYTHONPATH"])), ) if args.prime_venvs: continue display.info(import_type + ': ' + data, verbosity=4) cmd = ['importer.py'] # add the importer to the path so it can be accessed through the coverage injector env.update( PATH=os.pathsep.join([os.path.join(TARGET_SANITY_ROOT, 'import'), env['PATH']]), ) try: stdout, stderr = cover_python(args, virtualenv_python, cmd, self.name, env, capture=True, data=data) if stdout or stderr: raise SubprocessError(cmd, stdout=stdout, stderr=stderr) except SubprocessError as ex: if ex.status != 10 or ex.stderr or not ex.stdout: raise pattern = r'^(?P<path>[^:]*):(?P<line>[0-9]+):(?P<column>[0-9]+): (?P<message>.*)$' parsed = parse_to_list_of_dict(pattern, ex.stdout) relative_temp_root = os.path.relpath(temp_root, data_context().content.root) + os.path.sep messages += [SanityMessage( message=r['message'], path=os.path.relpath(r['path'], relative_temp_root) if r['path'].startswith(relative_temp_root) else r['path'], line=int(r['line']), column=int(r['column']), ) for r in parsed] if args.prime_venvs: return SanitySkipped(self.name, python_version=python.version) results = settings.process_errors(messages, paths) if results: return SanityFailure(self.name, messages=results, python_version=python.version) return SanitySuccess(self.name, python_version=python.version) @cache

a06fa496d3f837cca3c437ab6e9858525633d147

@cache

https://github.com/ansible/ansible.git

def test(self, args, targets, python): # type: (SanityConfig, SanityTargets, PythonConfig) -> TestResult settings = self.load_processor(args, python.version) paths = [target.path for target in targets.include] if python.version.startswith('2.') and (get_virtualenv_version(args, python.path) or (0,)) < (13,): # hack to make sure that virtualenv is available under Python 2.x # on Python 3.x we can use the built-in venv # version 13+ is required to use the `--no-wheel` option try: install_requirements(args, python, virtualenv=True, controller=False) # sanity (import) except PipUnavailableError as ex: display.warning(str(ex)) temp_root = os.path.join(ResultType.TMP.path, 'sanity', 'import') messages = [] for import_type, test in ( ('module', _get_module_test(True)), ('plugin', _get_module_test(False)), ): if import_type == 'plugin' and python.version in REMOTE_ONLY_PYTHON_VERSIONS: continue data = '\n'.join([path for path in paths if test(path)]) if not data and not args.prime_venvs: continue virtualenv_python = create_sanity_virtualenv(args, python, f'{self.name}.{import_type}', coverage=args.coverage, minimize=True) if not virtualenv_python: display.warning(f'Skipping sanity test "{self.name}" on Python {python.version} due to missing virtual environment support.') return SanitySkipped(self.name, python.version) virtualenv_yaml = check_sanity_virtualenv_yaml(virtualenv_python) if virtualenv_yaml is False: display.warning(f'Sanity test "{self.name}" ({import_type}) on Python {python.version} may be slow due to missing libyaml support in PyYAML.') env = ansible_environment(args, color=False) env.update( SANITY_TEMP_PATH=ResultType.TMP.path, SANITY_IMPORTER_TYPE=import_type, ) if data_context().content.collection: external_python = create_sanity_virtualenv(args, args.controller_python, self.name) env.update( SANITY_COLLECTION_FULL_NAME=data_context().content.collection.full_name, SANITY_EXTERNAL_PYTHON=external_python.path, SANITY_YAML_TO_JSON=os.path.join(ANSIBLE_TEST_TOOLS_ROOT, 'yaml_to_json.py'), ANSIBL

test

website/homepage/src/guides/__init__.py

Use underscore instead of parentheses (#2625)

gradio

Python

__init__.py

def format_name(guide_name): index = None if re.match("^[0-9]+_", guide_name): index = int(guide_name[: guide_name.index("_")]) guide_name = guide_name[guide_name.index("_") + 1 :] if guide_name.lower().endswith(".md"): guide_name = guide_name[:-3] pretty_guide_name = " ".join([word[0].upper() + word[1:] for word in guide_name.split("_")]) return index, guide_name, pretty_guide_name guide_folders = sorted(os.listdir(GUIDES_DIR)) guide_folders.remove("CONTRIBUTING.md") guide_folders.remove("assets") guides = [] guides_by_category = [] absolute_index = 0 for guide_folder in guide_folders: guide_list = sorted(os.listdir(os.path.join(GUIDES_DIR, guide_folder))) _, guide_category, pretty_guide_category = format_name(guide_folder) guides_by_category.append({"category": pretty_guide_category, "guides": []}) for guide_file in guide_list: guide_index, guide_name, pretty_guide_name = format_name(guide_file) with open(os.path.join(GUIDES_DIR, guide_folder, guide_file), "r") as f: guide_content = f.read() title = guide_content.split("\n")[0] metadata_labels = []

27d60792ead5152672d613282c8012cebc39af19

https://github.com/gradio-app/gradio.git

def format_name(guide_name): index = None if re.match("^[0-9]+_", guide_name):

format_name

python3.10.4/Lib/lib2to3/btm_utils.py

add python 3.10.4 for windows

XX-Net

Python

btm_utils.py

def get_linear_subpattern(self): for l in self.leaves(): subp = l.leaf_to_root() if subp: return subp

8198943edd73a363c266633e1aa5b2a9e9c9f526

https://github.com/XX-net/XX-Net.git

def get_linear_subpattern(self): for l in self.leaves(): subp = l.leaf_to_root(

get_linear_subpattern

tests/exchange/test_exchange.py

add "date_minus_candles" method

freqtrade

Python

test_exchange.py

def test_date_minus_candles(): date = datetime(2019, 8, 12, 13, 25, 0, tzinfo=timezone.utc) assert date_minus_candles("5m", 3, date) == date - timedelta(minutes=15) assert date_minus_candles("5m", 5, date) == date - timedelta(minutes=25) assert date_minus_candles("1m", 6, date) == date - timedelta(minutes=6) assert date_minus_candles("1h", 3, date) == date - timedelta(hours=3, minutes=25) assert date_minus_candles("1h", 3) == timeframe_to_prev_date('1h') - timedelta(hours=3) @pytest.mark.parametrize( "market_symbol,base,quote,exchange,spot,margin,futures,trademode,add_dict,expected_result", [ ("BTC/USDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True), ("USDT/BTC", 'USDT', 'BTC', "binance", True, False, False, 'spot', {}, True), # No seperating / ("BTCUSDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True), ("BTCUSDT", None, "USDT", "binance", True, False, False, 'spot', {}, False), ("USDT/BTC", "BTC", None, "binance", True, False, False, 'spot', {}, False), ("BTCUSDT", "BTC", None, "binance", True, False, False, 'spot', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'spot', {}, True), # Futures mode, spot pair ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'futures', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'margin', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, True, True, 'margin', {}, True), ("BTC/USDT", "BTC", "USDT", "binance", False, True, False, 'margin', {}, True), # Futures mode, futures pair ("BTC/USDT", "BTC", "USDT", "binance", False, False, True, 'futures', {}, True), # Futures market ("BTC/UNK", "BTC", 'UNK', "binance", False, False, True, 'spot', {}, False), ("BTC/EUR", 'BTC', 'EUR', "kraken", True, False, False, 'spot', {"darkpool": False}, True), ("EUR/BTC", 'EUR', 'BTC', "kraken", True, False, False, 'spot', {"darkpool": False}, True), # no darkpools ("BTC/EUR", 'BTC', 'EUR', "kraken", True, False, False, 'spot', {"darkpool": True}, False), # no darkpools ("BTC/EUR.d", 'BTC', 'EUR', "kraken", True, False, False, 'spot', {"darkpool": True}, False), ("BTC/USD", 'BTC', 'USD', "ftx", True, False, False, 'spot', {}, True), ("USD/BTC", 'USD', 'BTC', "ftx", True, False, False, 'spot', {}, True), # Can only trade spot markets ("BTC/USD", 'BTC', 'USD', "ftx", False, False, True, 'spot', {}, False), ("BTC/USD", 'BTC', 'USD', "ftx", False, False, True, 'futures', {}, True), # Can only trade spot markets ("BTC-PERP", 'BTC', 'USD', "ftx", False, False, True, 'spot', {}, False), ("BTC-PERP", 'BTC', 'USD', "ftx", False, False, True, 'margin', {}, False), ("BTC-PERP", 'BTC', 'USD', "ftx", False, False, True, 'futures', {}, True), ("BTC/USDT:USDT", 'BTC', 'USD', "okx", False, False, True, 'spot', {}, False), ("BTC/USDT:USDT", 'BTC', 'USD', "okx", False, False, True, 'margin', {}, False), ("BTC/USDT:USDT", 'BTC', 'USD', "okx", False, False, True, 'futures', {}, True), ])

116b58e97cad2b86aff5e20d97f494b5ef9abd41

@pytest.mark.parametrize( "market_symbol,base,quote,exchange,spot,margin,futures,trademode,add_dict,expected_result", [ ("BTC/USDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True), ("USDT/BTC", 'USDT', 'BTC', "binance", True, False, False, 'spot', {}, True), # No seperating / ("BTCUSDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True), ("BTCUSDT", None, "USDT", "binance", True, False, False, 'spot', {}, False), ("USDT/BTC", "BTC", None, "binance", True, False, False, 'spot', {}, False), ("BTCUSDT", "BTC", None, "binance", True, False, False, 'spot', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'spot', {}, True), # Futures mode, spot pair ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'futures', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'margin', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, True, True, 'margin', {}, True), ("BTC/USDT", "BTC", "USDT", "binance", False, True, False, 'margin', {}, True), # Futures mode, futures pair ("BTC/USDT", "BTC", "USDT", "binance", False, False, True, 'futures', {}, True), # Futures market ("BTC/UNK", "BTC", 'UNK', "binance", False, False, True, 'spot', {}, False), ("BTC/EUR", 'BTC', 'EUR', "kraken", True, False, False, 'spot', {"darkpool": False}, True), ("EUR/BTC", 'EUR', 'BTC', "kraken", True, False, False, 'spot', {"darkpool": False}, True), # no darkpools ("BTC/EUR", 'BTC', 'EUR', "kraken", True, False, False, 'spot', {"darkpool": True}, False), # no darkpools ("BTC/EUR.d", 'BTC', 'EUR', "kraken", True, False, False, 'spot', {"darkpool": True}, False), ("BTC/USD", 'BTC', 'USD', "ftx", True, False, False, 'spot', {}, True), ("USD/BTC", 'USD', 'BTC', "ftx", True, False, False, 'spot', {}, True), # Can only trade spot markets ("BTC/USD", 'BTC', 'USD', "ftx", False, False, True, 'spot', {}, False), ("BTC/USD", 'BTC', 'USD', "ftx", False, False, True, 'futures', {}, True), # Can only trade spot markets ("BTC-PERP", 'BTC', 'USD', "ftx", False, False, True, 'spot', {}, False), ("BTC-PERP", 'BTC', 'USD', "ftx", False, False, True, 'margin', {}, False), ("BTC-PERP", 'BTC', 'USD', "ftx", False, False, True, 'futures', {}, True), ("BTC/USDT:USDT", 'BTC', 'USD', "okx", False, False, True, 'spot', {}, False), ("BTC/USDT:USDT", 'BTC', 'USD', "okx", False, False, True, 'margin', {}, False), ("BTC/USDT:USDT", 'BTC', 'USD', "okx", False, False, True, 'futures', {}, True), ])

https://github.com/freqtrade/freqtrade.git

def test_date_minus_candles(): date = datetime(2019, 8, 12, 13, 25, 0, tzinfo=timezone.utc) assert date_minus_candles("5m", 3, date) == date - timedelta(minutes=15) assert date_minus_candles("5m", 5, date) == date - timedelta(minutes=25) assert date_minus_candles("1m", 6, date) == date - timedelta(minutes=6) assert date_minus_candles("1h", 3, date) == date - timedelta(hours=3, minutes=25) assert date_minus_candles("1h", 3) == timeframe_to_prev_date('1h') - timedelta(hours=3) @pytest.mark.parametrize( "market_symbol,base,quote,exchange,spot,margin,futures,trademode,add_dict,expected_result", [ ("BTC/USDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True), ("USDT/BTC", 'USDT', 'BTC', "binance", True, False, False, 'spot', {}, True), # No seperating / ("BTCUSDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True), ("BTCUSDT", None, "USDT", "binance", True, False, False, 'spot', {}, False), ("USDT/BTC", "BTC", None, "binance", True, False, False, 'spot', {}, False), ("BTCUSDT", "BTC", None, "binance", True, False, False, 'spot', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'spot', {}, True), # Futures mode, spot pair ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'futures', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, False, False, 'margin', {}, False), ("BTC/USDT", "BTC", "USDT", "binance", True, True, True, 'margin', {}, True), ("BTC/USDT", "BTC", "USDT", "binance", False, True, False, 'margin', {}, True), # Futures mode, futures pair ("BTC/USDT", "BTC", "USDT", "binance", False, False, True, 'futures', {}, True), # Futures market ("BTC/UNK", "BTC", 'UNK', "binance", False, False, True, 'spot', {}, False), ("BTC/EUR", 'BTC', 'EUR', "kraken", True, False, False, 'spot', {"darkpool": False}, True), ("EUR/BTC", 'EUR', 'BTC', "kraken", True

test_date_minus_candles

saleor/checkout/utils.py

Allow to pass metadata directly to line data in checkoutCreate and checkoutLinesAdd mutations (#10592)

saleor

Python

utils.py

def _append_line_to_create(to_create, checkout, variant, line_data, line): if line is None: if line_data.quantity > 0: checkout_line = CheckoutLine( checkout=checkout, variant=variant, quantity=line_data.quantity, currency=checkout.currency, price_override=line_data.custom_price, ) if line_data.metadata_list: checkout_line.store_value_in_metadata( {data.key: data.value for data in line_data.metadata_list} ) to_create.append(checkout_line)

577d43f21b7d6774dd272fedbb5719e101b92308

https://github.com/saleor/saleor.git

def _append_line_to_create(to_create, checkout, variant, line_data, line): if line is None: if line_data.quantity > 0: checkout_line = CheckoutLine( checkout=checkout, variant=variant, quantity=line_data.quantity, currency=checkout.currency, price_override=line_data.custom_price, ) if line_data.metadata_list:

_append_line_to_create

tests/integration/reduce/test_reduce.py

refactor: unify port args (#4382)

jina

Python

test_reduce.py

def test_uses_before_no_reduce_real_executor_uses(): flow = ( Flow(port=exposed_port) .add(uses=Executor1, name='pod0') .add(uses=Executor2, needs='gateway', name='pod1') .add(uses=Executor3, needs='gateway', name='pod2') .add(needs=['pod0', 'pod1', 'pod2'], name='pod3', uses=DummyExecutor) ) with flow as f: da = DocumentArray([Document() for _ in range(5)]) resp = Client(port=exposed_port, return_responses=True).post('/', inputs=da) # assert no reduce happened assert len(resp[0].docs) == 1 assert resp[0].docs[0].id == 'fake_document'

51403a57d03f0b1ddfd7fc533ccee78e23f5faa1

https://github.com/jina-ai/jina.git

def test_uses_before_no_reduce_real_executor_uses(): flow = ( Flow(port=exposed_port) .add(uses=Executor1, name='pod0') .add(uses=Executor2, needs='gateway', name='pod1') .add(uses=Executor3, needs='gateway', name='pod2') .add(needs=['pod0', 'pod1', 'pod2'], name='pod3', uses=DummyExecutor) ) with flow as f: da = DocumentArray([Document() for _ in range(5)]) resp = Client(port=exposed_port, return_responses=True).post('/', inputs=da) # assert no reduce happened assert len(resp[0].docs) == 1 assert resp[0].docs[0].id == 'fake_document'

test_uses_before_no_reduce_real_executor_uses

saleor/graphql/checkout/mutations/order_create_from_checkout.py

Use dataloader for discounts instead of lazy object (#10512) * Use dataloader for discounts instead of lazy object * Rename load function to be in par with other loaders

saleor

Python

order_create_from_checkout.py

def perform_mutation(cls, _root, info, **data): checkout_id = data.get("id") checkout = cls.get_node_or_error( info, checkout_id, field="id", only_type=Checkout, code=OrderCreateFromCheckoutErrorCode.CHECKOUT_NOT_FOUND.value, ) tracking_code = analytics.get_client_id(info.context) discounts = load_discounts(info.context) manager = info.context.plugins checkout_lines, unavailable_variant_pks = fetch_checkout_lines(checkout) checkout_info = fetch_checkout_info( checkout, checkout_lines, discounts, manager ) validate_checkout( checkout_info=checkout_info, lines=checkout_lines, unavailable_variant_pks=unavailable_variant_pks, discounts=discounts, manager=manager, ) app = load_app(info.context) try: order = create_order_from_checkout( checkout_info=checkout_info, checkout_lines=checkout_lines, discounts=discounts, manager=info.context.plugins, user=info.context.user, app=app, tracking_code=tracking_code, delete_checkout=data["remove_checkout"], ) except NotApplicable: code = OrderCreateFromCheckoutErrorCode.VOUCHER_NOT_APPLICABLE.value raise ValidationError( { "voucher_code": ValidationError( "Voucher not applicable", code=code, ) } ) except InsufficientStock as e: error = prepare_insufficient_stock_checkout_validation_error(e) raise error except GiftCardNotApplicable as e: raise ValidationError({"gift_cards": e}) return OrderCreateFromCheckout(order=order)

a579eb534e06a854805f037a587f13258f22fdf5

https://github.com/saleor/saleor.git

def perform_mutation(cls, _root, info, **data): checkout_id = data.get("id") checkout = cls.get_node_or_error( info, checkout_id, field="id", only_type=Checkout, code=OrderCreateFromCheckoutErrorCode.CHECKOUT_NOT_FOUND.value, ) tracking_code = analytics.get_client_id(info.context) discounts = load_discounts(info.context) man

perform_mutation

homeassistant/components/deconz/gateway.py

Make type checking pass for deCONZ init, gateway and services (#66054) * Type and enable type checking for init, config_flow, diagnostics, gateway and services * Fix import * Fix review comment

core

Python

gateway.py

def master(self) -> bool: return cast(bool, self.config_entry.options[CONF_MASTER_GATEWAY]) # Options

dd88a05cb400d416a68a1be16fee8ee2ab48a70f

https://github.com/home-assistant/core.git

def master(self) -> bool: retur

master

tests/components/homewizard/test_coordinator.py

Add HomeWizard Energy integration (#55812) Co-authored-by: Matthias Alphart <farmio@alphart.net> Co-authored-by: Paulus Schoutsen <paulus@home-assistant.io>

core

Python

test_coordinator.py

async def test_coordinator_failed_to_update(aioclient_mock, hass): # Update failed by internal error meter = get_mock_device(product_type="p1_meter")

8f6e24aa1ea7061e9b52085deb57c49e9ccf4a86

https://github.com/home-assistant/core.git

async def test_coordinator_failed_to_update(aioclient_mock, hass): # Update failed by internal error meter = get_mock_device(product_type="p

test_coordinator_failed_to_update

gradio/blocks.py

blocks-with-fix - add support for "with gr.Blocks() as demo:" usage

gradio

Python

blocks.py

def __enter__(self): self.parent = Context.block Context.block = self return self

da4a59459f7eaa7312b50687a4a61cef1fb411d6

https://github.com/gradio-app/gradio.git

def __enter__(self):

__enter__

wagtail/admin/views/generic/models.py

Add breadcrumbs and new Page Editor side panels to Snippets views (#8623)

wagtail

Python

models.py

def save_instance(self): instance = self.form.save() revision = None self.has_content_changes = self.form.has_changed() # Save revision if the model inherits from RevisionMixin if self.revision_enabled: revision = instance.save_revision( user=self.request.user, changed=self.has_content_changes, ) log( instance=instance, action="wagtail.edit", revision=revision, content_changed=self.has_content_changes, ) return instance

952edd84c7a0fd9249257591bc92cd55cf59c0f8

https://github.com/wagtail/wagtail.git

def save_instance(self): instance = self.form.save() revision = None self.has_content_changes = self.form.has_changed() # Save revision if the model inherits from RevisionMixin if self.revision_enabled: revision = instance.sa

save_instance

tests/rest/admin/test_room.py

Use literals in place of `HTTPStatus` constants in tests (#13469)

synapse

Python

test_room.py

def test_context_as_admin(self) -> None: # Create a room. We're not part of it. user_id = self.register_user("test", "test") user_tok = self.login("test", "test") room_id = self.helper.create_room_as(user_id, tok=user_tok) # Populate the room with events. events = [] for i in range(30): events.append( self.helper.send_event( room_id, "com.example.test", content={"index": i}, tok=user_tok ) ) # Now let's fetch the context for this room. midway = (len(events) - 1) // 2 channel = self.make_request( "GET", "/_synapse/admin/v1/rooms/%s/context/%s" % (room_id, events[midway]["event_id"]), access_token=self.admin_user_tok, ) self.assertEqual(200, channel.code, msg=channel.json_body) self.assertEqual( channel.json_body["event"]["event_id"], events[midway]["event_id"] ) for found_event in channel.json_body["events_before"]: for j, posted_event in enumerate(events): if found_event["event_id"] == posted_event["event_id"]: self.assertTrue(j < midway) break else: self.fail("Event %s from events_before not found" % j) for found_event in channel.json_body["events_after"]: for j, posted_event in enumerate(events): if found_event["event_id"] == posted_event["event_id"]: self.assertTrue(j > midway) break else: self.fail("Event %s from events_after not found" % j)

c97042f7eef3748e17c90e48a4122389a89c4735

https://github.com/matrix-org/synapse.git

def test_context_as_admin(self) -> None: # Create a room. We're not part of it. user_id = self.register_user("test", "test") user_tok = self.login("test", "test") room_id = self.helper.create_room_as(user_id, tok=user_tok) # Populate the room with events. events = [] for i in range(30): events.append( self.helper.send_event( room_id, "com.example.test", content={"index": i}, tok=user_tok ) ) # Now let's fetch the context for this room. midway = (len(events) - 1) // 2 channel = self.make_request( "GET", "/_synapse/admin/v1/rooms/%s/context/%s" % (room_id, events[midw

test_context_as_admin

airflow/migrations/versions/3c20cacc0044_add_dagrun_run_type.py

Autogenerate migration reference doc (#21601) * document airflow version in each alembic migration module and use this to autogen the doc * update each migration module to have the same description used in migration ref (so it can be used in autogen)

airflow

Python

3c20cacc0044_add_dagrun_run_type.py

def upgrade(): run_type_col_type = sa.String(length=50) conn = op.get_bind() inspector = Inspector.from_engine(conn) dag_run_columns = [col.get('name') for col in inspector.get_columns("dag_run")] if "run_type" not in dag_run_columns: # Add nullable column with op.batch_alter_table("dag_run") as batch_op: batch_op.add_column(sa.Column("run_type", run_type_col_type, nullable=True)) # Generate run type for existing records sessionmaker = sa.orm.sessionmaker() session = sessionmaker(bind=conn) for run_type in DagRunType: session.query(DagRun).filter(DagRun.run_id.like(f"{run_type.value}__%")).update( {DagRun.run_type: run_type.value}, synchronize_session=False ) session.query(DagRun).filter(DagRun.run_type.is_(None)).update( {DagRun.run_type: DagRunType.MANUAL.value}, synchronize_session=False ) session.commit() # Make run_type not nullable with op.batch_alter_table("dag_run") as batch_op: batch_op.alter_column( "run_type", existing_type=run_type_col_type, type_=run_type_col_type, nullable=False )

69f6f9e01b6df76c3c8fa266d460324163957887

https://github.com/apache/airflow.git

def upgrade(): run_type_col_type = sa.String(length=50) conn = op.get_bind() inspector = Inspector.from_engine(conn) dag_run_columns = [col.get('name') for col in inspector.get_columns("dag_run")] if "run_type" not in dag_run_columns: # Add nullable column with op.batch_alter_table("dag_run") as batch_op: batch_op.add_column(sa.Column("run_type", run_type_col_type, nullable=True)) # Generate run type for existing records sessionmaker = sa.orm.sessionmaker() session = sessionmaker(bind=conn) for run_type in DagRunType: session.query(DagRun).filter(DagRun.run_id.like(f"{run_type.value}__%")).update( {DagRun.run_type: run_type.value}, synchronize_session=False ) session.query(DagRun).filter(DagRun.run_type.is_(None)).update( {DagRun.run_type: DagRunType.MANUAL.value}, synchron

upgrade

dask/tests/test_distributed.py

Move creation of sqlalchemy connection for picklability (#8745)

dask

Python

test_distributed.py

async def test_to_sql_engine_kwargs(c, s, a, b): # https://github.com/dask/dask/issues/8738 pd = pytest.importorskip("pandas") dd = pytest.importorskip("dask.dataframe") pytest.importorskip("sqlalchemy") df = pd.DataFrame({"a": range(10), "b": range(10)}) df.index.name = "index" ddf = dd.from_pandas(df, npartitions=1) with tmpfile() as f: uri = f"sqlite:///{f}" result = ddf.to_sql( "test", uri, index=True, engine_kwargs={"echo": False}, compute=False ) await c.compute(result) dd.utils.assert_eq( ddf, dd.read_sql_table("test", uri, "index"), check_divisions=False, )

48820dfc4565d0dcd299409553e6721c246a9f88

https://github.com/dask/dask.git

async def test_to_sql_engine_kwargs(c, s, a, b): # https://github.com/dask/dask/issues/8738 pd = pytest.importorskip("pandas") dd = pytest.importorskip("dask.dataframe") pytest.importorskip("sqlalchemy") df = pd.DataFrame({"a": range(10), "b": range(10)}) df.index.name = "index" ddf = dd.from_pandas(df, npartitions=1) with tmpfile() as f: uri = f"sqlite:///{f}" result = ddf.to_sql( "test"

test_to_sql_engine_kwargs

apps/orgs/models.py

fix: fix rbac to dev (#7636) * feat: 添加 RBAC 应用模块 * feat: 添加 RBAC Model、API * feat: 添加 RBAC Model、API 2 * feat: 添加 RBAC Model、API 3 * feat: 添加 RBAC Model、API 4 * feat: RBAC * feat: RBAC * feat: RBAC * feat: RBAC * feat: RBAC * feat: RBAC 整理权限位 * feat: RBAC 整理权限位2 * feat: RBAC 整理权限位2 * feat: RBAC 整理权限位 * feat: RBAC 添加默认角色 * feat: RBAC 添加迁移文件；迁移用户角色->用户角色绑定 * feat: RBAC 添加迁移文件；迁移用户角色->用户角色绑定 * feat: RBAC 修改用户模块API * feat: RBAC 添加组织模块迁移文件 & 修改组织模块API * feat: RBAC 添加组织模块迁移文件 & 修改组织模块API * feat: RBAC 修改用户角色属性的使用 * feat: RBAC No.1 * xxx * perf: 暂存 * perf: ... * perf(rbac): 添加 perms 到 profile serializer 中 * stash * perf: 使用init * perf: 修改migrations * perf: rbac * stash * stash * pref: 修改rbac * stash it * stash: 先去修复其他bug * perf: 修改 role 添加 users * pref: 修改 RBAC Model * feat: 添加权限的 tree api * stash: 暂存一下 * stash: 暂存一下 * perf: 修改 model verbose name * feat: 添加model各种 verbose name * perf: 生成 migrations * perf: 优化权限位 * perf: 添加迁移脚本 * feat: 添加组织角色迁移 * perf: 添加迁移脚本 * stash * perf: 添加migrateion * perf: 暂存一下 * perf: 修改rbac * perf: stash it * fix: 迁移冲突 * fix: 迁移冲突 * perf: 暂存一下 * perf: 修改 rbac 逻辑 * stash: 暂存一下 * perf: 修改内置角色 * perf: 解决 root 组织的问题 * perf: stash it * perf: 优化 rbac * perf: 优化 rolebinding 处理 * perf: 完成用户离开组织的问题 * perf: 暂存一下 * perf: 修改翻译 * perf: 去掉了 IsSuperUser * perf: IsAppUser 去掉完成 * perf: 修改 connection token 的权限 * perf: 去掉导入的问题 * perf: perms define 格式，修改 app 用户 的全新啊 * perf: 修改 permission * perf: 去掉一些 org admin * perf: 去掉部分 org admin * perf: 再去掉点 org admin role * perf: 再去掉部分 org admin * perf: user 角色搜索 * perf: 去掉很多 js * perf: 添加权限位 * perf: 修改权限 * perf: 去掉一个 todo * merge: with dev * fix: 修复冲突 Co-authored-by: Bai <bugatti_it@163.com> Co-authored-by: Michael Bai <baijiangjie@gmail.com> Co-authored-by: ibuler <ibuler@qq.com>

jumpserver

Python

models.py

def get_total_resources_amount(self): from django.apps import apps from orgs.mixins.models import OrgModelMixin summary = {'users.Members': self.get_members().count()} for app_name, app_config in apps.app_configs.items(): models_cls = app_config.get_models() for model in models_cls: if not issubclass(model, OrgModelMixin): continue key = '{}.{}'.format(app_name, model.__name__) summary[key] = self.get_resource_amount(model) return summary

e259d2a9e9167c58fa75a78d1050dd5dcfde96f4

https://github.com/jumpserver/jumpserver.git

def get_total_resources_amount(self): from django.apps import apps from orgs.mixins.models import OrgModelMixin summary = {'users.Members': self.get_members().count()} for app_name, app_config in apps.app_configs.items(): models_cls = app_config.get_models() for model in models_cls: if not issubclass(model, OrgModelMixin

get_total_resources_amount

pandas/tests/io/excel/test_odf.py

BUG: error in read_excel with some ods files #45598 (#46050) * BUG: error in read_excel with some ods files #45598 * BUG: use hasattr instead of dir * DOC: add issue number in new test case * DOC: remove comment Co-authored-by: Dimitra Karadima <dkaradima@convertgroup.com>

pandas

Python

test_odf.py

def test_read_newlines_between_xml_elements_table(): # GH#45598 expected = pd.DataFrame( [[1.0, 4.0, 7], [np.nan, np.nan, 8], [3.0, 6.0, 9]], columns=["Column 1", "Column 2", "Column 3"], ) result = pd.read_excel("test_newlines.ods") tm.assert_frame_equal(result, expected)

004b4c58779612a91972e3d9b1ce3c8e045d8e14

https://github.com/pandas-dev/pandas.git

def test_read_newlines_between_xml_elements_table(): # GH#45598 expected = pd.DataFrame( [[1.0, 4.0, 7], [np.nan, np.nan, 8], [3.0, 6.0, 9]], columns=["Column 1", "Column 2", "Column 3"], ) result = pd.read_excel("test_newlines.ods") tm.assert_frame_equal(result, expected)

test_read_newlines_between_xml_elements_table

tests/test_iterable_dataset.py

Add `concatenate_datasets` for iterable datasets (#4500) * add concatenate_datasets for iterable datasets * fix * infer features * fill missing rowzs and columns * comments * only check for duplicate keys once * comments * keep concatenate_datasets in arrow_dataset (to be deprecated) * style * comments, typing, fix missing token_per_repo_id * style

datasets

Python

test_iterable_dataset.py

def test_concatenate_datasets_with_different_columns(): ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label": 10}) dataset1 = IterableDataset(ex_iterable1) ex_iterable2 = ExamplesIterable(generate_examples_fn, {}) dataset2 = IterableDataset(ex_iterable2) # missing column "label" -> it should be replaced with nulls extended_dataset2_list = [{"label": None, **x} for x in dataset2] concatenated_dataset = concatenate_datasets([dataset1, dataset2]) assert list(concatenated_dataset) == list(dataset1) + extended_dataset2_list # change order concatenated_dataset = concatenate_datasets([dataset2, dataset1]) assert list(concatenated_dataset) == extended_dataset2_list + list(dataset1)

f5826eff9b06ab10dba1adfa52543341ef1e6009

https://github.com/huggingface/datasets.git

def test_concatenate_datasets_with_different_columns(): ex_iterable1 = ExamplesIterable(generate_examples_fn, {"label": 10}) dataset1 = It

test_concatenate_datasets_with_different_columns

sympy/solvers/tests/test_decompogen.py

made changes

sympy

Python

test_decompogen.py

def test_decompogen(): assert decompogen(sin(cos(x)), x) == [sin(x), cos(x)] assert decompogen(sin(x)**2 + sin(x) + 1, x) == [x**2 + x + 1, sin(x)] assert decompogen(sqrt(6*x**2 - 5), x) == [sqrt(x), 6*x**2 - 5] assert decompogen(sin(sqrt(cos(x**2 + 1))), x) == [sin(x), sqrt(x), cos(x), x**2 + 1] assert decompogen(Abs(cos(x)**2 + 3*cos(x) - 4), x) == [Abs(x), x**2 + 3*x - 4, cos(x)] assert decompogen(sin(x)**2 + sin(x) - sqrt(3)/2, x) == [x**2 + x - sqrt(3)/2, sin(x)] assert decompogen(Abs(cos(y)**2 + 3*cos(x) - 4), x) == [Abs(x), 3*x + cos(y)**2 - 4, cos(x)] assert decompogen(x, y) == [x] assert decompogen(1, x) == [1] assert decompogen(Max(3, x), x) == [Max(3, x)] raises(TypeError, lambda: decompogen(x < 5, x)) u = 2*x + 3 assert decompogen(Max(sqrt(u),(u)**2), x) == [Max(sqrt(x), x**2), u] assert decompogen(Max(u, u**2, y), x) == [Max(x, x**2, y), u]

bbca83fd553f5f14251ab08ae06cbd7524d2bbc1

https://github.com/sympy/sympy.git

def test_decompogen(): assert decompogen(sin(cos(x)), x) == [sin(x), cos(x)] assert decompogen(sin(x)**2 + sin(x) + 1, x) == [x**2 + x + 1, sin(x)] assert decompogen(sqrt(6*x**2 - 5), x) == [sqrt(x), 6*x**2 - 5] assert decompogen(sin(sqrt(cos(x**2 + 1))), x) == [sin(x), sqrt(x), cos(x), x**2 + 1] assert decompogen(Abs(cos(x)**2 + 3*cos(x) - 4), x) == [Abs(x), x**2 + 3*x - 4, cos(x)] assert decompogen(sin(x)**2 + sin(x) - sqrt(3)/2, x) == [x**2 + x - sqrt(3)/2, sin(x)] assert decompogen(Abs(cos(y)**2 + 3*cos(x) - 4), x) == [Abs(x), 3*x + cos(y)**2 - 4, cos(x)] assert decompogen(x, y) == [x] assert decompogen(1, x) == [1] assert decompogen

test_decompogen

pandas/tests/groupby/test_groupby.py

DEPR: Enforce numeric_only=False in groupby sum/mean (#49829) * DEPR: Enforce numeric_only=False in groupby sum/mean * cleanup * Refinements * whatsnew fixup

pandas

Python

test_groupby.py

def test_frame_set_name_single(df): grouped = df.groupby("A") msg = "The default value of numeric_only" with pytest.raises(TypeError, match="Could not convert"): grouped.mean() result = grouped.mean(numeric_only=True) assert result.index.name == "A" with pytest.raises(TypeError, match="Could not convert"): df.groupby("A", as_index=False).mean() result = df.groupby("A", as_index=False).mean(numeric_only=True) assert result.index.name != "A" with pytest.raises(TypeError, match="Could not convert"): grouped.agg(np.mean) result = grouped[["C", "D"]].agg(np.mean) assert result.index.name == "A" result = grouped.agg({"C": np.mean, "D": np.std}) assert result.index.name == "A" result = grouped["C"].mean() assert result.index.name == "A" result = grouped["C"].agg(np.mean) assert result.index.name == "A" result = grouped["C"].agg([np.mean, np.std]) assert result.index.name == "A" msg = r"nested renamer is not supported" with pytest.raises(SpecificationError, match=msg): grouped["C"].agg({"foo": np.mean, "bar": np.std})

3fffb6d49abe20ebc4a3380181f90103fb9ce22e

https://github.com/pandas-dev/pandas.git

def test_frame_set_name_single(df): grouped = df.groupby("A") msg = "The default value of numeric_only" with pytest.raises(TypeError, match="Could not convert"): grouped.mean() result = grouped.mean(numeric_only=True) assert result.index.name == "A" with pytest.raises(TypeError, match="Could not convert"): df.groupby("A", as_index=False).mean() result = df.groupby("A", as_index=False).mean(numeric_only=True) assert result.index.name != "A" with pytest.raises(TypeError, match="Could not convert"): grouped.agg(np.mean) result = grouped[["C", "D"]].agg(np.mean) assert result.index.name == "A" result = grouped.agg({"C": np.mean, "D": np.std}) assert result.index.name == "A" result = grouped["C"].mean() assert result.index.name == "A" result = grouped["C"].agg(np.mean) assert result.index.name == "A" result = grouped["C"].agg([np.mean, np.std]) assert result.index.name == "A" msg = r"nested renamer is not supported" with pytest.raises(Specificatio

test_frame_set_name_single

dask/dataframe/io/tests/test_sql.py

Add engine_kwargs support to dask.dataframe.to_sql (#8609) ### Use case: SSL This was the original [use case] that motivated #8596: to force SSL on the db connection. Whether the new `engine_kwargs` argument is helpful/necessary for this use case depends on the db driver used by SQLAlchemy: * [MySQL] (helpful): either use `connect_args={'ssl': ...}` as engine kwarg or add `?ssl_cert=...&ssl_key=...` to URI. * [psycopg2] (not helpful): must use `?sslmode=require` in connection URI, not supported as engine argument. * [pg8000] (necessary): must use `connect_args={'ssl_context': ...}` as engine kwarg. [use case]: https://github.com/coiled/dask-community/issues/186 [MySQL]: https://docs.sqlalchemy.org/en/14/dialects/mysql.html#ssl-connections [psycopg2]: https://docs.sqlalchemy.org/en/14/dialects/postgresql.html#ssl-connections [pg8000]: https://docs.sqlalchemy.org/en/14/dialects/postgresql.html#pg8000-ssl

dask

Python

test_sql.py

def test_to_sql_engine_kwargs(caplog): ddf = dd.from_pandas(df, 2) with tmp_db_uri() as uri: ddf.to_sql("test", uri, engine_kwargs={"echo": False}) logs = "\n".join(r.message for r in caplog.records) assert logs == "" assert_eq(df, read_sql_table("test", uri, "number")) with tmp_db_uri() as uri: ddf.to_sql("test", uri, engine_kwargs={"echo": True}) logs = "\n".join(r.message for r in caplog.records) assert "CREATE" in logs assert "INSERT" in logs assert_eq(df, read_sql_table("test", uri, "number"))

80f9821f4d5a4badc2179dbd17f1fc7730cc9f50

https://github.com/dask/dask.git

def test_to_sql_engine_kwargs(caplog): ddf = dd.from_pandas(df, 2) with tmp_db_uri() as uri: ddf.to_sql("test", uri, engine_kwargs={"echo": False}) logs = "\n".join(r.message for r in caplog.records) assert logs == "" assert_eq(df, read_sql_table("test", uri, "number")) with tmp_db_uri() as uri: ddf.to_sql("test", uri, engine_kwargs={"echo": True}) logs = "\n".join(r.message for r in caplog.records) assert "CREATE" in logs

test_to_sql_engine_kwargs

tests/db_functions/comparison/test_greatest.py

Refs #33476 -- Reformatted code with Black.

django

Python

test_greatest.py

def test_decimal_filter(self): obj = DecimalModel.objects.create(n1=Decimal("1.1"), n2=Decimal("1.2")) self.assertCountEqual( DecimalModel.objects.annotate( greatest=Greatest("n1", "n2"), ).filter(greatest=Decimal("1.2")), [obj], )

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def test_decimal_filter(self): obj = DecimalModel.objects.create(n1=Decimal("1.1"), n2=Decimal("1.2")) self.assertCountEqual( DecimalModel.objects.annotate( greatest=Greatest("n1", "n2"), ).

test_decimal_filter

django/db/models/lookups.py

Refs #33476 -- Reformatted code with Black.

django

Python

lookups.py

def get_bilateral_transforms(self): if hasattr(self.lhs, "get_bilateral_transforms"): bilateral_transforms = self.lhs.get_bilateral_transforms() else: bilateral_transforms = [] if self.bilateral: bilateral_transforms.append(self.__class__) return bilateral_transforms

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def get_bilateral_transforms(self): if hasattr(self.lhs, "get_bilateral_transforms"): bilateral_transforms = self.lhs.get_bilateral_transforms() else: bilateral_transforms = [] if self.bilateral: bilateral_transforms.append(self.__class__) return bilateral_transforms

get_bilateral_transforms

saleor/graphql/order/schema.py

Stricter signatures for resolvers and mutations (#9649)

saleor

Python

schema.py

def resolve_homepage_events(_root, info, **kwargs): qs = resolve_homepage_events() return create_connection_slice(qs, info, kwargs, OrderEventCountableConnection)

513fc80bc698c177b87774b3aff3da7b9aedbe06

https://github.com/saleor/saleor.git

def resolve_homepage_events(_root, info, **kwargs): qs = resolve_homepage_events() return create_connection_slice(qs, info, kwargs, OrderEventCountableConnection)

resolve_homepage_events

zerver/tests/test_digest.py

testing: 100% coverage for zerver/tests/test_digest.py.

zulip

Python

test_digest.py

def test_no_logging(self) -> None: hamlet = self.example_user("hamlet") startlen = len(RealmAuditLog.objects.all()) bulk_write_realm_audit_logs([]) self.assert_length(RealmAuditLog.objects.all(), startlen) bulk_write_realm_audit_logs([hamlet]) self.assert_length(RealmAuditLog.objects.all(), startlen + 1)

ba5cf331a2c65bc5d09be28892327e59698eda0e

https://github.com/zulip/zulip.git

def test_no_logging(self) -> None: hamlet = self.example_user("hamlet") startlen = len(RealmAuditLog.objects.all()) bulk_write_realm_audit_logs([]) self.a

test_no_logging

langchain/llms/cohere.py

model laboratory (#95)

langchain

Python

cohere.py

def _default_params(self) -> Mapping[str, Any]: return { "max_tokens": self.max_tokens, "temperature": self.temperature, "k": self.k, "p": self.p, "frequency_penalty": self.frequency_penalty, "presence_penalty": self.presence_penalty, }

db37bd089fc18c8215da42202dfadc397b20d26c

https://github.com/hwchase17/langchain.git

def _default_params(self) -> Mapping[str, Any]: return { "max_tokens": self.max_tokens, "temperature": self.temperature, "k": self.k, "p": self.p, "frequency_penalty": self.frequency_penalty, "presence_penalty": self.presence_penalty, }

_default_params

openbb_terminal/portfolio/portfolio_controller.py

Feature/attribution toolkit (#3156) * add attribution toolkit * add attrib to test script for portfolio * removes yahooquery dependency and early rounding * Update _index.md * update feature to include raw and type flags, graph always shows, table output optional, one type of output at a time * Linting * Update index * Update index 2 * Update tests * changes argument descriptions * Small fix * Formatting Black Co-authored-by: S3908818 <S3908818@student.rmit.edu.au> Co-authored-by: Louise Platts (S3908818) <88080425+s3908818@users.noreply.github.com> Co-authored-by: Jeroen Bouma <jer.bouma@gmail.com> Co-authored-by: James Maslek <jmaslek11@gmail.com> Co-authored-by: Louise Amy <74476622+louiseamy4@users.noreply.github.com> Co-authored-by: Jeroen Bouma <jeroenbouma@Jeroens-MacBook-Pro.local>

OpenBBTerminal

Python

portfolio_controller.py

def print_help(self): mt = MenuText("portfolio/") mt.add_menu("bro") mt.add_menu("po") mt.add_raw("\n") mt.add_cmd("load") mt.add_cmd("show") mt.add_cmd("bench") mt.add_raw("\n") mt.add_param("_loaded", self.portfolio_name) mt.add_param("_riskfreerate", self.portfolio_name) mt.add_param("_benchmark", self.benchmark_name) mt.add_raw("\n") mt.add_info("_graphs_") mt.add_cmd("holdv", self.portfolio_name and self.benchmark_name) mt.add_cmd("holdp", self.portfolio_name and self.benchmark_name) mt.add_cmd("yret", self.portfolio_name and self.benchmark_name) mt.add_cmd("mret", self.portfolio_name and self.benchmark_name) mt.add_cmd("dret", self.portfolio_name and self.benchmark_name) mt.add_cmd("distr", self.portfolio_name and self.benchmark_name) mt.add_cmd("maxdd", self.portfolio_name and self.benchmark_name) mt.add_cmd("rvol", self.portfolio_name and self.benchmark_name) mt.add_cmd("rsharpe", self.portfolio_name and self.benchmark_name) mt.add_cmd("rsort", self.portfolio_name and self.benchmark_name) mt.add_cmd("rbeta", self.portfolio_name and self.benchmark_name) mt.add_info("_metrics_") mt.add_cmd("alloc", self.portfolio_name and self.benchmark_name) mt.add_cmd("attrib", self.portfolio_name and self.benchmark_name) mt.add_cmd("summary", self.portfolio_name and self.benchmark_name) mt.add_cmd("alloc", self.portfolio_name and self.benchmark_name) mt.add_cmd("attrib", self.portfolio_name and self.benchmark_name) mt.add_cmd("metric", self.portfolio_name and self.benchmark_name) mt.add_cmd("perf", self.portfolio_name and self.benchmark_name) mt.add_info("_risk_") mt.add_cmd("var", self.portfolio_name and self.benchmark_name) mt.add_cmd("es", self.portfolio_name and self.benchmark_name) mt.add_cmd("os", self.portfolio_name and self.benchmark_name) port = bool(self.portfolio_name) port_bench = bool(self.portfolio_name) and bool(self.benchmark_name) help_text = f # TODO: Clean up the reports inputs # TODO: Edit the allocation to allow the different asset classes # [info]Reports:[/info] # ar annual report for performance of a given portfolio console.print(text=help_text, menu="Portfolio") self.update_choices()

aed683f44015cb5aa6cae9c2ce719c956cda7b46

https://github.com/OpenBB-finance/OpenBBTerminal.git

def print_help(self): mt = MenuText("portfolio/") mt.add_menu("bro") mt.add_menu("po") mt.add_raw("\n") mt.add_cmd("load") mt.add_cmd("show") mt.add_cmd("bench") mt.add_raw("\n") mt.add_param("_loaded", self.portfolio_name) mt.add_param("_riskfreerate", self.portfolio_name) mt.add_param("_benchmark", self.benchmark_name) mt.add_raw("\n") mt.add_info("_graphs_") mt.add_cmd("holdv", self.portfolio_name and self.benchmark_name) mt.add_cmd("holdp", self.portfolio_name and self.benchmark_name) mt.add_cmd("yret", self.portfolio_name and self.benchmark_name) mt.add_cmd("mret", self.portfolio_name and self.benchmark_name) mt.add_cmd("dret", self.portfolio_name and self.benchmark_name) mt.add_cmd("distr", self.portfolio_name and self.benchmark_name) mt.add_cmd("maxdd", self.portfolio_name and self.benchmark_name) mt.add_cmd("rvol", self.portfolio_name and self.benchmark_name) mt.add_cmd("rsharpe", self.portfolio_name and self.benchmark_name) mt.add_cmd("rsort", self.portfolio_name and self.benchmark_name) mt.add_cmd("rbeta", self.portfolio_name and self.benchmark_name) mt.add_info("_metrics_") mt.add_cmd("alloc", self.portfolio_name and self.benchmark_name) mt.add_cmd("attrib", self.portfolio_name and self.benchmark_name) mt.add_cmd("summary", self.portfolio_name and self.benchmark_name) mt.add_cmd("alloc", self.portfolio_name and self.benchmark_name) mt.add_cmd("attrib", self.portfolio_name and self.benchmark_name) mt.add_cmd("metric", self.portfolio_name and self.benchmark_name) mt.add_cmd("perf", self.portfolio_name and self.benchmark_name) mt.add_info("_risk_") mt.add_cmd("var", self.portfolio_name and self.benchmark_name) mt.add_cmd("es", self.portfolio_name and self.benchmark_name) mt.add_cmd("os", self.portfolio_name and self.benchmark_name) port = bool(self.portfolio_name) port_bench = bool(self.portfolio_name) and bool(self.benchmark_name) help_text = f # TODO: Clean up the reports inputs # TODO: Edit the allocation to allow the different asset classes # [info]Reports:[/info] # ar annual report for performance of a given portfolio console.print(text=help_text, menu

print_help

test/test_prototype_transforms_functional.py

Cleanup prototype transforms tests (#6984) * minor cleanup of the prototype transforms tests * refactor ImagePair * pretty format enum

vision

Python

test_prototype_transforms_functional.py

def test_float32_vs_uint8(self, test_id, info, args_kwargs): (input, *other_args), kwargs = args_kwargs.load("cpu") if input.dtype != torch.uint8: pytest.skip(f"Input dtype is {input.dtype}.") adapted_other_args, adapted_kwargs = info.float32_vs_uint8(other_args, kwargs) actual = info.kernel( F.convert_dtype_image_tensor(input, dtype=torch.float32), *adapted_other_args, **adapted_kwargs, ) expected = F.convert_dtype_image_tensor(info.kernel(input, *other_args, **kwargs), dtype=torch.float32) assert_close( actual, expected, **info.get_closeness_kwargs(test_id, dtype=torch.float32, device=input.device), msg=parametrized_error_message(*other_args, **kwargs), ) @pytest.fixture

74ea933c29898f838991acdf49a70cb20b4ec3ad

@pytest.fixture

https://github.com/pytorch/vision.git

def test_float32_vs_uint8(self, test_id, info, args_kwargs):

test_float32_vs_uint8

tests/api/common/test_mark_tasks.py

Use `DagRun.run_id` instead of `execution_date` when updating state of TIs(UI & REST API) (#18724) We can now use run_id as well as execution_date to update states of task instances Co-authored-by: Tzu-ping Chung <uranusjr@gmail.com> Co-authored-by: Ash Berlin-Taylor <ash_github@firemirror.com>

airflow

Python

test_mark_tasks.py

def _create_test_dag_run(self, state, date): return self.dag1.create_dagrun( run_type=DagRunType.MANUAL, state=state, start_date=date, execution_date=date )

2b4bf7fe67fc656ceb7bdaad36453b7a5b83ef04

https://github.com/apache/airflow.git

def _create_test_dag_run(self, state, date): return self.dag1.create_dagrun( run_type=DagRunType.MANUAL,

_create_test_dag_run

python3.10.4/Lib/asyncio/queues.py

add python 3.10.4 for windows

XX-Net

Python

queues.py

def put_nowait(self, item): if self.full(): raise QueueFull self._put(item) self._unfinished_tasks += 1 self._finished.clear() self._wakeup_next(self._getters)

8198943edd73a363c266633e1aa5b2a9e9c9f526

https://github.com/XX-net/XX-Net.git

def put_nowait(self, item):

put_nowait

src/datasets/arrow_writer.py

Multiprocessed dataset builder [WIP] (#5107) * multiprocessing-compatible naming scheme and refactor * multiprocessed shard writing for GeneratorBasedBuilder * multiprocessed shard writing for ArrowBasedBuilder * style * multiprocessed dataset loading * compatibility with non-sharded datasets * bugfix * bugfix * removed unused import * fixed bad ordering * less misleading tqdm * fix gen_kwargs distribution + read shards * minor * minor2 * support beam datasets * docstrings + minor * add iflatmap_unordered for parallel write & progress updates * use 1 tqdm bar receiving updates from subprocesses * docs * add test_iflatmap_unordered * style * test arrow_reader.py * fix test_iflatmap_unordered * add Beam test_download_and_prepare_sharded * test gen_kwargs distribution * test download_and_prepare with num_proc * style * improve test * don't close the pool * fix multiprocessing on windows * keep multiprocessing disabled by default * again + docs * more docs * more docs * some var renaming * style * Apply suggestions from code review Co-authored-by: Mario Šaško <mariosasko777@gmail.com> * Apply suggestions from code review Co-authored-by: Mario Šaško <mariosasko777@gmail.com> * added utils/sharding.py * style * style Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com> Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> Co-authored-by: Mario Šaško <mariosasko777@gmail.com>

datasets

Python

arrow_writer.py

def get_parquet_lengths(sources) -> List[int]: shard_lengths = [] disable = not logging.is_progress_bar_enabled() for source in logging.tqdm(sources, unit="parquet files", disable=disable): parquet_file = pa.parquet.ParquetFile(source) shard_lengths.append(parquet_file.metadata.num_rows) return shard_lengths

2945690ea731f85a356220a71cdc630281c676f4

https://github.com/huggingface/datasets.git

def get_parquet_lengths(sources) -> List[int]: shard_lengths = [] disable = not logging.is_progress_bar_enabled() for source in logging.tqdm(sources, unit="

get_parquet_lengths

keras/datasets/mnist.py

Add f-string format and lint with flynt on the whole codebase

keras

Python

mnist.py

def load_data(path="mnist.npz"): origin_folder = ( "https://storage.googleapis.com/tensorflow/tf-keras-datasets/" ) path = get_file( path, origin=origin_folder + "mnist.npz", file_hash=( # noqa: E501 "731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1" ), ) with np.load(path, allow_pickle=True) as f: x_train, y_train = f["x_train"], f["y_train"] x_test, y_test = f["x_test"], f["y_test"] return (x_train, y_train), (x_test, y_test)

be73ac1a1e25d9abd4d793cba9707098d7adf231

https://github.com/keras-team/keras.git

def load_data(path="mnist.npz"): origin_folder = ( "https://storage.googleapis.com/tensorflow/tf-keras-datasets/" ) path = get_file( path, origin=origin_folder + "mnist.npz", file_hash=( # noqa: E501 "731c5ac602752760c8e48fbffcf8c3b850d9dc2a2aedcf2cc48468fc17b673d1" ), ) with np.load(path, allow_pickle=True) as f: x_train, y_train = f["x_train"]

load_data

python/ray/tune/tests/test_checkpoint_manager.py

[tune] Treat checkpoints with nan value as worst (#23862) Changes the logic in CheckpointManager to consider checkpoints with nan value of the metric as worst values, meaning they will be deleted first if keep_checkpoints_num is set.

ray

Python

test_checkpoint_manager.py

def testBestCheckpointsWithNan(self): keep_checkpoints_num = 2 checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num) checkpoints = [ _TuneCheckpoint( _TuneCheckpoint.PERSISTENT, None, self.mock_result(float("nan"), i) ) for i in range(2) ] checkpoints += [ _TuneCheckpoint(_TuneCheckpoint.PERSISTENT, 3, self.mock_result(0, 3)) ] random.shuffle(checkpoints) for checkpoint in checkpoints: checkpoint_manager.on_checkpoint(checkpoint) best_checkpoints = checkpoint_manager.best_checkpoints() # best_checkpoints is sorted from worst to best self.assertEqual(len(best_checkpoints), keep_checkpoints_num) self.assertEqual(best_checkpoints[0].value, None) self.assertEqual(best_checkpoints[1].value, 3)

52eaf020bcd4e8ebeb94af11a8039313a37488d1

https://github.com/ray-project/ray.git

def testBestCheckpointsWithNan(self): keep_checkpoints_num = 2 checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num) checkpoints = [ _TuneCheckpoint( _TuneCheckpoint.PERSISTENT, None, self.mock_result(float("nan"), i) ) for i in range(2) ] checkpoints += [ _TuneCheckpoint(_TuneCheckpoint.PERSISTENT, 3, self.mock_result(0, 3)) ] random.shuffle(checkpoints) for checkpoint in checkpoints: checkpoint_manager.on_checkpoint(checkpoint) best_checkpoints = checkpoint_manager.best_checkpoints

testBestCheckpointsWithNan

nuitka/freezer/Standalone.py

macOS: Massive improvements for dependency scans * Was not recursively scanning dependencies and therefore could miss some of them. * Made internal functions private. * Make sure to pass proper "package" value to DLL scans, so it can include the needed directories. * Do not mutate information of DLL map, it is used later for other things and we now detect errors in that.

Nuitka

Python

Standalone.py

def copyDllsUsed(source_dir, dist_dir, standalone_entry_points): # This is complex, because we also need to handle OS specifics. used_dlls = detectUsedDLLs( source_dir=source_dir, standalone_entry_points=standalone_entry_points, use_cache=not Options.shallNotUseDependsExeCachedResults() and not Options.getWindowsDependencyTool() == "depends.exe", update_cache=not Options.shallNotStoreDependsExeCachedResults() and not Options.getWindowsDependencyTool() == "depends.exe", ) _removeDuplicateDlls(used_dlls=used_dlls) dll_map = _copyDllsUsed(dist_dir=dist_dir, used_dlls=used_dlls) # TODO: This belongs inside _copyDllsUsed if Utils.isMacOS(): # For macOS, the binary and the DLLs needs to be changed to reflect # the relative DLL location in the ".dist" folder. for standalone_entry_point in standalone_entry_points: _fixupBinaryDLLPathsMacOS( binary_filename=standalone_entry_point.dest_path, package_name=standalone_entry_point.package_name, dll_map=dll_map, original_location=standalone_entry_point.source_path, ) for original_path, package_name, dll_filename in dll_map: _fixupBinaryDLLPathsMacOS( binary_filename=os.path.join(dist_dir, dll_filename), package_name=package_name, dll_map=dll_map, original_location=original_path, ) # Remove or update rpath settings. if Utils.getOS() in ("Linux", "Darwin"): # For Linux, the "rpath" of libraries may be an issue and must be # removed. if Utils.isMacOS(): start = 0 else: start = 1 for standalone_entry_point in standalone_entry_points[start:]: count = relpath( path=standalone_entry_point.dest_path, start=dist_dir ).count(os.path.sep) rpath = os.path.join("$ORIGIN", *([".."] * count)) setSharedLibraryRPATH(standalone_entry_point.dest_path, rpath) for _original_path, _package_name, dll_filename in dll_map: setSharedLibraryRPATH(os.path.join(dist_dir, dll_filename), "$ORIGIN") if Utils.isMacOS(): addMacOSCodeSignature( filenames=[ standalone_entry_point.dest_path for standalone_entry_point in standalone_entry_points ] + [ os.path.join(dist_dir, dll_filename) for _original_path, _package_name, dll_filename in dll_map ] ) Plugins.onCopiedDLLs(dist_dir=dist_dir, used_dlls=used_dlls)

a470b75c8e045312ea22dbfb6c5fc6702835b31c

https://github.com/Nuitka/Nuitka.git

def copyDllsUsed(source_dir, dist_dir, standalone_entry_points): # This is complex, because we also need to handle OS specifics. used_dlls = detectUsedDLLs( source_dir=source_dir, standalone_entry_points=standalone_entry_points, use_cache=not Options.shallNotUseDependsExeCachedResults()

copyDllsUsed

homeassistant/components/samsungtv/media_player.py

Add Upnp volume control/status to SamsungTV (#68663) Co-authored-by: epenet <epenet@users.noreply.github.com> Co-authored-by: J. Nick Koston <nick@koston.org>

core

Python

media_player.py

async def async_update(self) -> None: if self._auth_failed or self.hass.is_stopping: return if self._power_off_in_progress(): self._attr_state = STATE_OFF else: self._attr_state = ( STATE_ON if await self._bridge.async_is_on() else STATE_OFF ) if self._attr_state != STATE_ON: return startup_tasks: list[Coroutine[Any, Any, None]] = [] if not self._app_list_event.is_set(): startup_tasks.append(self._async_startup_app_list()) if not self._upnp_device and self._ssdp_rendering_control_location: startup_tasks.append(self._async_startup_upnp()) if startup_tasks: await asyncio.gather(*startup_tasks) if not (service := self._get_upnp_service()): return get_volume, get_mute = await asyncio.gather( service.action("GetVolume").async_call(InstanceID=0, Channel="Master"), service.action("GetMute").async_call(InstanceID=0, Channel="Master"), ) LOGGER.debug("Upnp GetVolume on %s: %s", self._host, get_volume) if (volume_level := get_volume.get("CurrentVolume")) is not None: self._attr_volume_level = volume_level / 100 LOGGER.debug("Upnp GetMute on %s: %s", self._host, get_mute) if (is_muted := get_mute.get("CurrentMute")) is not None: self._attr_is_volume_muted = is_muted

c024033dae98f01380842dac35be743fbefa0a36

https://github.com/home-assistant/core.git

async def async_update(self) -> None: if self._auth_failed or self.hass.is_stopping: return if self._power_off_in_progress(): self._attr_state = STATE_OFF else: self._attr_state = ( STATE_ON if await self._bridge.async_is_on() else STATE_OFF ) if self._attr_state != STATE_ON: return startup_tasks: list[Coroutine[Any, Any, None]] = [] if not self._app_list_event.is_set(): startup_tasks.append(self._async_startup_app_list()) if not self._upnp_device and self._ssdp_rendering_control_location: startup_tasks.append(self._async_startup_upnp()) if startup_tasks: await asyncio.gather(*startup_tasks) if not (service := self._get_upnp_service()): return get_volume, get_mute = await asyncio.gather( service.action("GetVolume").async_call(InstanceID=0, Channel="Master"), service.action("GetMute").async_call(InstanceID=0, Channel="Master"), ) LOGGER.debug(

async_update

pandas/io/excel/_xlwt.py

DEP: Protect some ExcelWriter attributes (#45795) * DEP: Deprecate ExcelWriter attributes * DEP: Deprecate ExcelWriter attributes * Fixup for test * Move tests and restore check_extension y * Deprecate xlwt fm_date and fm_datetime; doc improvements

pandas

Python

_xlwt.py

def fm_datetime(self): self._deprecate("fm_datetime") return self._fm_datetime

047137ce2619cfe2027e3999dfb92eb614d9a485

https://github.com/pandas-dev/pandas.git

def fm_datetime(self): self._deprecate("fm_datetime") r

fm_datetime

keras/layers/preprocessing/index_lookup.py

Add Preprocessing Layer support in Keras v3 saving (IntegerLookup, StringLoop, TextVectorization). PiperOrigin-RevId: 491682409

keras

Python

index_lookup.py

def _load_assets(self, dir_path): if self.input_vocabulary: # Vocab saved in config. # TODO: consider unifying both paths. return vocabulary_filepath = tf.io.gfile.join(dir_path, "vocabulary.txt") # TODO: fix bug with include_special_tokens and set reload from file. with open(vocabulary_filepath, "r") as f: lines = f.read().split("\n") if tf.as_dtype(self.vocabulary_dtype) == tf.string: values = [str(line) for line in lines] else: values = [int(line) for line in lines] if self.output_mode == TF_IDF: self.set_vocabulary(values, idf_weights=False) else: self.set_vocabulary(values)

dc1fe7f95b389e1bda9056ba53e739821fbe8e6e

https://github.com/keras-team/keras.git

def _load_assets(self, dir_path): if self.input_vocabulary: # Vocab saved in config. # TODO: consider unifying both paths. return

_load_assets

tests/test_schema.py

Fix regression in handling of nested dataclasses in `get_flat_models_from_field` (#3819) * add test for nested python dataclass schema generation * fix handling of dataclasses in `get_flat_models_from_field` * add change note

pydantic

Python

test_schema.py

def test_nested_python_dataclasses(): from dataclasses import dataclass as python_dataclass

faee3301eb2c0d4157150a2f4cde2b4edb32ac8e

https://github.com/pydantic/pydantic.git

def test_nested_python_dataclasses(): from dataclasses import dataclass as python_dataclass

test_nested_python_dataclasses

IPython/conftest.py

make sure to run async tests there are some `async def` tests, but they are skipped without `mark("asyncio")`

ipython

Python

conftest.py

def pytest_collection_modifyitems(items): for item in items: if inspect.iscoroutinefunction(item.obj): item.add_marker("asyncio") assert not inspect.isasyncgenfunction(item.obj)

82d1a374575d9785708f144976cf139c76c7acb7

https://github.com/ipython/ipython.git

def pytest_collection_modifyitems(items): for item in items: if inspect.iscoroutinefunction(item.obj): item.add_marker("asyncio") assert not inspect.isasync

pytest_collection_modifyitems

ivy/backends/numpy/core/image.py

renamed dev_str arg to dev for all methods.

ivy

Python

image.py

def gradient_image(x): x_shape = _ivy.shape(x) batch_shape = x_shape[:-3] image_dims = x_shape[-3:-1] dev = _ivy.dev(x) # to list batch_shape = list(batch_shape) image_dims = list(image_dims) num_dims = x_shape[-1] # BS x H-1 x W x D dy = x[..., 1:, :, :] - x[..., :-1, :, :] # BS x H x W-1 x D dx = x[..., :, 1:, :] - x[..., :, :-1, :] # BS x H x W x D dy = _ivy.concatenate((dy, _ivy.zeros(batch_shape + [1, image_dims[1], num_dims], dev=dev)), -3) dx = _ivy.concatenate((dx, _ivy.zeros(batch_shape + [image_dims[0], 1, num_dims], dev=dev)), -2) # BS x H x W x D, BS x H x W x D return dy, dx

d743336b1f3654cd0315f380f43eed4116997c1d

https://github.com/unifyai/ivy.git

def gradient_image(x): x_shape = _ivy.shape(x) batch_shape = x_shape[:-3] image_dims = x_shape[-3:-1] dev = _ivy.dev(x) # to list batch_sh

gradient_image

homeassistant/components/http/__init__.py

String formatting and max line length - Part 2 (#84393)

core

Python

__init__.py

def _create_ssl_context(self) -> ssl.SSLContext | None: context: ssl.SSLContext | None = None assert self.ssl_certificate is not None try: if self.ssl_profile == SSL_INTERMEDIATE: context = ssl_util.server_context_intermediate() else: context = ssl_util.server_context_modern() context.load_cert_chain(self.ssl_certificate, self.ssl_key) except OSError as error: if not self.hass.config.safe_mode: raise HomeAssistantError( f"Could not use SSL certificate from {self.ssl_certificate}:" f" {error}" ) from error _LOGGER.error( "Could not read SSL certificate from %s: %s", self.ssl_certificate, error, ) try: context = self._create_emergency_ssl_context() except OSError as error2: _LOGGER.error( "Could not create an emergency self signed ssl certificate: %s", error2, ) context = None else: _LOGGER.critical( "Home Assistant is running in safe mode with an emergency self" " signed ssl certificate because the configured SSL certificate was" " not usable" ) return context if self.ssl_peer_certificate: if context is None: raise HomeAssistantError( "Failed to create ssl context, no fallback available because a peer" " certificate is required." ) context.verify_mode = ssl.CERT_REQUIRED context.load_verify_locations(self.ssl_peer_certificate) return context

cb13418babd21a1e9584978b0c523f1b1e4e1cb0

https://github.com/home-assistant/core.git

def _create_ssl_context(self) -> ssl.SSLContext | None: context: ssl.SSLContext | None = None assert self.ssl_certificate is not None try: if self.ssl_profile == SSL_INTERMEDIATE: context = ssl_util.server_context_intermediate() else: context = ssl_util.server_context_modern() contex

_create_ssl_context

certbot-nginx/certbot_nginx/_internal/configurator.py

Fully type certbot-nginx module (#9124) * Work in progress * Fix type * Work in progress * Work in progress * Work in progress * Work in progress * Work in progress * Oups. * Fix typing in UnspacedList * Fix logic * Finish typing * List certbot-nginx as fully typed in tox * Fix lint * Fix checks * Organize imports * Fix typing for Python 3.6 * Fix checks * Fix lint * Update certbot-nginx/certbot_nginx/_internal/configurator.py Co-authored-by: alexzorin <alex@zor.io> * Update certbot-nginx/certbot_nginx/_internal/configurator.py Co-authored-by: alexzorin <alex@zor.io> * Fix signature of deploy_cert regarding the installer interface * Update certbot-nginx/certbot_nginx/_internal/obj.py Co-authored-by: alexzorin <alex@zor.io> * Fix types * Update certbot-nginx/certbot_nginx/_internal/parser.py Co-authored-by: alexzorin <alex@zor.io> * Precise type * Precise _coerce possible inputs/outputs * Fix type * Update certbot-nginx/certbot_nginx/_internal/http_01.py Co-authored-by: ohemorange <ebportnoy@gmail.com> * Fix type * Remove an undesirable implementation. * Fix type Co-authored-by: alexzorin <alex@zor.io> Co-authored-by: ohemorange <ebportnoy@gmail.com>

certbot

Python

configurator.py

def recovery_routine(self) -> None: super().recovery_routine() self.new_vhost = None self.parser.load()

16aad35d31a887dab157f9d4f5e0fe9218d06064

https://github.com/certbot/certbot.git

def recovery_routine(self) -> None: super().recovery_routine() self.new_vho

recovery_routine

tests/indices/embedding/test_base.py

Add general embedding abstraction (#79) Co-authored-by: Jerry Liu <jerry@robustintelligence.com>

llama_index

Python

test_base.py

def test_embedding_similarity() -> None: embed_model = OpenAIEmbedding() text_embedding = [3.0, 4.0, 0.0] query_embedding = [0.0, 1.0, 0.0] cosine = embed_model.similarity(query_embedding, text_embedding) assert cosine == 0.8 @pytest.fixture

a48611ee12b6758d752b4ca2f3f640f94234522d

@pytest.fixture

https://github.com/jerryjliu/llama_index.git

def test_embedding_similarity() -> None: embed_model = OpenAIEmbedding() text_embedding = [3.0, 4.0, 0.0] query_embedding = [0.0, 1.0, 0.0] cosine = embed_model.similarity(query_embeddin

test_embedding_similarity

sympy/integrals/rubi/tests/test_utility_function.py

Fixed stuff in rubi

sympy

Python

test_utility_function.py

def test_TrigReduce(): assert TrigReduce(cos(x)**2) == cos(2*x)/2 + S.Half assert TrigReduce(cos(x)**2*sin(x)) == sin(x)/4 + sin(3*x)/4 assert TrigReduce(cos(x)**2+sin(x)) == sin(x) + cos(2*x)/2 + S.Half assert TrigReduce(cos(x)**2*sin(x)**5) == 5*sin(x)/64 + sin(3*x)/64 - 3*sin(5*x)/64 + sin(7*x)/64 assert TrigReduce(2*sin(x)*cos(x) + 2*cos(x)**2) == sin(2*x) + cos(2*x) + 1 assert TrigReduce(sinh(a + b*x)**2) == cosh(2*a + 2*b*x)/2 - S.Half assert TrigReduce(sinh(a + b*x)*cosh(a + b*x)) == sinh(2*a + 2*b*x)/2

ed4b2f2458b02f18df0e4449be38269ef88b90f6

https://github.com/sympy/sympy.git

def test_TrigReduce(): assert TrigReduce(cos(x)**2) == cos(2*x)/2 + S.Half assert TrigReduce(cos(x)**2*sin(x

test_TrigReduce

lib/streamlit/in_memory_file_manager.py

Rename and refactor `Report` machinery (#4141) This refactor renames (almost) everything related to the outdated "report" concept with more precise concepts that we use throughout our code, primarily "script run", "session", and "app".

streamlit

Python

in_memory_file_manager.py

def _get_session_id() -> str: ctx = get_script_run_ctx() if ctx is None: # This is only None when running "python myscript.py" rather than # "streamlit run myscript.py". In which case the session ID doesn't # matter and can just be a constant, as there's only ever "session". return "dontcare" else: return ctx.session_id

704eab3478cf69847825b23dabf15813a8ac9fa2

https://github.com/streamlit/streamlit.git

def _get_session_id() -> str: ctx

_get_session_id

homeassistant/components/mqtt/climate.py

Use climate enums in mqtt (#70696)

core

Python

climate.py

async def async_set_swing_mode(self, swing_mode): # CONF_SEND_IF_OFF is deprecated, support will be removed with release 2022.9 if self._send_if_off or self._current_operation != HVACMode.OFF: payload = self._command_templates[CONF_SWING_MODE_COMMAND_TEMPLATE]( swing_mode ) await self._publish(CONF_SWING_MODE_COMMAND_TOPIC, payload) if self._topic[CONF_SWING_MODE_STATE_TOPIC] is None: self._current_swing_mode = swing_mode self.async_write_ha_state()

1be2438ef67c7f523654bdb849cbed5f4c865365

https://github.com/home-assistant/core.git

async def async_set_swing_mode(self, swing_mode): # CONF_SEND_IF_OFF is deprecated, support will be removed with release 2022.9 if self._send_if_off or self._current_operation != HVACMode.OFF: payload = self._command_templates[CONF_SWING_MODE_COMMAND_TEMPLATE]( swing_mode ) await self._publish(CONF_SWING_MODE_COMMAND_TOPIC, payload) if self._topic[CONF_SWING_MODE_STATE_

async_set_swing_mode

keras/benchmarks/model_components_benchmarks_test.py

Code reformated

keras

Python

model_components_benchmarks_test.py

def _run(self, func, num_iters, execution_mode=None): total_time = run_benchmark(func, num_iters, execution_mode) mean_us = total_time * 1e6 / num_iters self.report_benchmark( iters=num_iters, wall_time=mean_us, metrics=[ { "name": "exp_per_sec", "value": float(f"{num_iters / total_time:.3f}"), }, { "name": "us_per_exp", "value": float(f"{total_time * 1000000.0 / num_iters:.3f}"), }, ], )

afd86e95fc91b98dfb30eac27933b1e10b201b97

https://github.com/keras-team/keras.git

def _run(self, func, num_iters, execution_mode=None): total_time = run_benchmark(func, num_iters, execution_mode) mean_us = total_time * 1e6 / num_iters self.report_benchmark( iters=num_iters, wall_time=mean_us, metrics=[ { "name": "exp_per_sec", "value": float(f"{num_iters / total_time:.3f}"), }, { "name": "us_per_exp", "value": float(f"{total_time * 1000000.0 / num_iters:.3f}"), }, ], )

_run

plugins/dbms/maxdb/enumeration.py

Fixing DeprecationWarning (logger.warn)

sqlmap

Python

enumeration.py

def getHostname(self): warnMsg = "on SAP MaxDB it is not possible to enumerate the hostname" logger.warning(warnMsg)

df4293473d2fb6e887e31522cab5aff95e201581

https://github.com/sqlmapproject/sqlmap.git

def getHostname(self): warnMsg = "on SAP MaxDB it is not possible to enumerate the hostname" logger.

getHostname

sklearn/impute/tests/test_common.py

ENH keep features with all missing values during imputation (#24770) Co-authored-by: Chiara Marmo <cmarmo@users.noreply.github.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Vitor SRG <vitorssrg@gmail.com> Fixes https://github.com/scikit-learn/scikit-learn/pull/16695 Fixes https://github.com/scikit-learn/scikit-learn/issues/16426 Fixes https://github.com/scikit-learn/scikit-learn/issues/16977

scikit-learn

Python

test_common.py

def test_keep_empty_features(imputer, keep_empty_features): X = np.array([[np.nan, 1], [np.nan, 2], [np.nan, 3]]) imputer = imputer.set_params( add_indicator=False, keep_empty_features=keep_empty_features ) for method in ["fit_transform", "transform"]: X_imputed = getattr(imputer, method)(X) if keep_empty_features: assert X_imputed.shape == X.shape else: assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)

d8fa96c29828e3ca79ddd5d7466521ac4d95213c

https://github.com/scikit-learn/scikit-learn.git

def test_keep_empty_features(imputer, keep_empty_features): X = np.array([[np.nan, 1], [np.nan, 2], [np.nan, 3]]) imputer = imputer.set_params( add_indicator=False, keep_empty_features=keep_empty_features ) for method in ["fit_transform", "transform"]: X_imputed = getattr(imputer, method)(X) if keep_empty_features: assert X_imputed.shape == X.shape else: assert X_imputed.shap

test_keep_empty_features

tests/test_plotting.py

Fix some tests after drawdown calculation change

freqtrade

Python

test_plotting.py

def test_generate_profit_graph(testdatadir): filename = testdatadir / "backtest-result_test.json" trades = load_backtest_data(filename) timerange = TimeRange.parse_timerange("20180110-20180112") pairs = ["TRX/BTC", "XLM/BTC"] trades = trades[trades['close_date'] < pd.Timestamp('2018-01-12', tz='UTC')] data = history.load_data(datadir=testdatadir, pairs=pairs, timeframe='5m', timerange=timerange) trades = trades[trades['pair'].isin(pairs)] fig = generate_profit_graph(pairs, data, trades, timeframe="5m", stake_currency='BTC') assert isinstance(fig, go.Figure) assert fig.layout.title.text == "Freqtrade Profit plot" assert fig.layout.yaxis.title.text == "Price" assert fig.layout.yaxis2.title.text == "Profit BTC" assert fig.layout.yaxis3.title.text == "Profit BTC" figure = fig.layout.figure assert len(figure.data) == 7 avgclose = find_trace_in_fig_data(figure.data, "Avg close price") assert isinstance(avgclose, go.Scatter) profit = find_trace_in_fig_data(figure.data, "Profit") assert isinstance(profit, go.Scatter) drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 35.69%") assert isinstance(drawdown, go.Scatter) parallel = find_trace_in_fig_data(figure.data, "Parallel trades") assert isinstance(parallel, go.Scatter) underwater = find_trace_in_fig_data(figure.data, "Underwater Plot") assert isinstance(underwater, go.Scatter) for pair in pairs: profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}") assert isinstance(profit_pair, go.Scatter) with pytest.raises(OperationalException, match=r"No trades found.*"): # Pair cannot be empty - so it's an empty dataframe. generate_profit_graph(pairs, data, trades.loc[trades['pair'].isnull()], timeframe="5m", stake_currency='BTC')

09fae25c9426cd03df2f2d031d16a6e3b1533a55

https://github.com/freqtrade/freqtrade.git

def test_generate_profit_graph(testdatadir): filename = testdatadir / "backtest-result_test.json" trades = load_backtest_data(filename) timerange = TimeRange.parse_timerange("20180110-20180112") pairs = ["TRX/BTC", "XLM/BTC"] trades = trades[trades['close_date'] < pd.Timestamp('2018-01-12', tz='UTC')] data = history.load_data(datadir=testdatadir, pairs=pairs, timeframe='5m', timerange=timerange) trades = trades[trades['pair'].isin(pairs)] fig = generate_profit_graph(pairs, data, trades, timef

test_generate_profit_graph

nltk/tokenize/util.py

Docstring tests (#3050) * fixed pytests * fixed more pytests * fixed more pytest and changed multiline pytest issues fixes for snowball.py and causal.py * fixed pytests (mainly multiline or rounding issues) * fixed treebank pytests, removed test for return_string=True (deprecated) * fixed destructive.py pytests, removed test for return_string=True (deprecated) * fixed pytest (rounding issues) * fixed pytest (initialised missing object) * fixed pytest (formatting issues) * fixed pytest (formatting issues) * fixed pytest (formatting issues) * added pytest +SKIP for deprecated module stanford * updated AUTHORS.md * changed docstring corrections by usage of ELLIPSIS and different roundings * fixed AUTHORS.md to be consistent * Fix framenet doctest formatting with pprint * Change docstring on MultiListBox.__init__ I believe the original typo was misinterpreted and changed to something that was not originally intended. Co-authored-by: Jan Lennartz <jan.lennartz@ing.com> Co-authored-by: Tom Aarsen <37621491+tomaarsen@users.noreply.github.com> Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>

nltk

Python

util.py

def regexp_span_tokenize(s, regexp): rGood muffins cost $3.88\nin New York. Please buy me ... two of them.\n\nThanks. left = 0 for m in finditer(regexp, s): right, next = m.span() if right != left: yield left, right left = next yield left, len(s)

8a4cf5d94eb94b6427c5d1d7907ba07b119932c5

https://github.com/nltk/nltk.git

def regexp_span_tokenize(s, regexp): rGood muffins cost $3.88\nin New York. Please buy me ... two of them.\n\nThanks.

regexp_span_tokenize

wagtail/images/tests/test_admin_views.py

Allow images to be sorted by date, title or file size

wagtail

Python

test_admin_views.py

def test_default_ordering_used_if_invalid_ordering_provided(self): response = self.get({"ordering": "bogus"}) self.assertEqual(response.status_code, 200) context = response.context default_ordering = "-created_at" self.assertEqual(context["current_ordering"], default_ordering) self.assertEqual( context["images"].object_list.query.order_by, (default_ordering,) )

b1614930eb74e3bdab28c5f04949347f43ae6fa7

https://github.com/wagtail/wagtail.git

def test_default_ordering_used_if_invalid_ordering_provided(self): response = self.get({"ordering": "bogus"}) self.assertEqual(response.status_code, 200) context =

test_default_ordering_used_if_invalid_ordering_provided

airflow/providers/elasticsearch/log/es_task_handler.py

Update black precommit (#22521) Use latest version of black, drop py36, and add py310.

airflow

Python

es_task_handler.py

def emit(self, record): if self.handler: record.offset = int(time() * (10**9)) self.handler.emit(record)

c063fc688cf20c37ed830de5e3dac4a664fd8241

https://github.com/apache/airflow.git

def emit(self, record): if self.handler: record.of

emit

mmdet/models/dense_heads/maskformer_head.py

[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

mmdetection

Python

maskformer_head.py

def preprocess_gt(self, gt_labels_list, gt_masks_list, gt_semantic_segs): num_things_list = [self.num_things_classes] * len(gt_labels_list) num_stuff_list = [self.num_stuff_classes] * len(gt_labels_list) targets = multi_apply(preprocess_panoptic_gt, gt_labels_list, gt_masks_list, gt_semantic_segs, num_things_list, num_stuff_list) labels, masks = targets return labels, masks

cac356380d505bf15587f07c0529218cc36b9652

https://github.com/open-mmlab/mmdetection.git

def preprocess_gt(self, gt_labels_list, gt_masks_list, gt_semantic_segs): num_things_list = [self.num_things_classes] * len(gt_labels_list) num_stuff_list = [self.num_stuff_classes] * len(gt_labels_list) targets = multi_apply(preprocess_panoptic_gt, gt_labels_list, gt_masks_list, gt_semantic_segs, num_things_list, num_stuff_list) labels, masks = targets return labels, mas

preprocess_gt