Datasets:

semeru

/

Galeras-3k_deduped

like 0

scripts-dev/check_pydantic_models.py

Reject non-strict types in Pydantic models (#13502)

synapse

Python

check_pydantic_models.py

def test_annotation_without_strict_raises(self) -> None: with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException): run_test_snippet( )

ba8938b090c7e1908cfa4feac75f08f3bc1183e8

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

def test_annotation_without_strict_raises(self) -> None: with monkeypatch_pydantic(), self.assertRaises(ModelCheckerException): run_test_snippet(

test_annotation_without_strict_raises

pandas/tests/series/indexing/test_setitem.py

TST: tests for setitem-like casting issues (#45154)

pandas

Python

test_setitem.py

def test_37477(): # fixed by GH#45121 orig = DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]}) expected = DataFrame({"A": [1, 2, 3], "B": [3, 1.2, 5]}) df = orig.copy() df.at[1, "B"] = 1.2 tm.assert_frame_equal(df, expected) df = orig.copy() df.loc[1, "B"] = 1.2 tm.assert_frame_equal(df, expected) df = orig.copy() df.iat[1, 1] = 1.2 tm.assert_frame_equal(df, expected) df = orig.copy() df.iloc[1, 1] = 1.2 tm.assert_frame_equal(df, expected)

d70b95bc0e17d18bbefee8ac8a07e4fa5f33513c

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

def test_37477(): # fixed by GH#45121 orig = DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]}) expe

test_37477

tests/util/test_treecache.py

Add missing types to tests.util. (#14597) Removes files under tests.util from the ignored by list, then fully types all tests/util/*.py files.

synapse

Python

test_treecache.py

def test_pop_twolevel(self) -> None: cache = TreeCache() cache[("a", "a")] = "AA" cache[("a", "b")] = "AB" cache[("b", "a")] = "BA" self.assertEqual(cache.pop(("a", "a")), "AA") self.assertEqual(cache.get(("a", "a")), None) self.assertEqual(cache.get(("a", "b")), "AB") self.assertEqual(cache.pop(("b", "a")), "BA") self.assertEqual(cache.pop(("b", "a")), None) self.assertEqual(len(cache), 1)

acea4d7a2ff61b5beda420b54a8451088060a8cd

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

def test_pop_twolevel(self) -> None: cache = TreeCache() cache[("a", "a")] = "AA" cache[("a", "b")] = "AB" cache[("b", "a")] = "BA" self.assertEqual(cache.pop(("a", "a")), "AA") self.assertEqual(cache.get(("a", "a")), None)

test_pop_twolevel

tests/test_event_auth.py

EventAuthTestCase: build events for the right room version In practice, when we run the auth rules, all of the events have the right room version. Let's stop building Room V1 events for these tests and use the right version.

synapse

Python

test_event_auth.py

def test_random_users_cannot_send_state_before_first_pl(self): creator = "@creator:example.com" joiner = "@joiner:example.com" auth_events = [ _create_event(RoomVersions.V1, creator), _join_event(RoomVersions.V1, creator), _join_event(RoomVersions.V1, joiner), ] # creator should be able to send state event_auth.check_auth_rules_for_event( RoomVersions.V1, _random_state_event(RoomVersions.V1, creator), auth_events, ) # joiner should not be able to send state self.assertRaises( AuthError, event_auth.check_auth_rules_for_event, RoomVersions.V1, _random_state_event(RoomVersions.V1, joiner), auth_events, )

2959184a42398277ff916206235b844a8f7be5d7

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

def test_random_users_cannot_send_state_before_first_pl(self): creator = "@creator:example.com" joiner = "@joiner:example.com" auth_events = [ _create_event(RoomVersions.V1, creator), _join_event(RoomVersions.V1, creator),

test_random_users_cannot_send_state_before_first_pl

scapy/contrib/automotive/scanner/enumerator.py

Improve reduce function for Automotive Scanner Enumerators (#3740)

scapy

Python

enumerator.py

def _get_retry_iterator(self, state): # type: (EcuState) -> Iterable[Packet] retry_entry = self._retry_pkt[state] if isinstance(retry_entry, Packet): log_automotive.debug("Provide retry packet") return [retry_entry] else: log_automotive.debug("Provide retry iterator") # assume self.retry_pkt is a generator or list return retry_entry

799f272bc04c361841d01e9c0087950e0eb86610

https://github.com/secdev/scapy.git

def _get_retry_iterator(self, state): # type: (EcuState) -> Iterable[Packet] retry_entry = self._retry_pkt[state] if isinstance(retry_entry, Packet): log_automotive.debug("Provide retry packet") return [retry_entry] else: log_automotive.debug("Provide retry iterator") # assume self.retry_pkt is a gene

_get_retry_iterator

src/transformers/trainer.py

fix bug for the log of RNG states are not properly loaded exception. (#15638) Co-authored-by: muz <muzhi1991@limuzhideMBP-2.lan>

transformers

Python

trainer.py

def _load_rng_state(self, checkpoint): # Load RNG states from `checkpoint` if checkpoint is None: return local_rank = xm.get_local_ordinal() if is_torch_tpu_available() else self.args.local_rank if local_rank != -1: rng_file = os.path.join(checkpoint, f"rng_state_{local_rank}.pth") if not os.path.isfile(os.path.join(checkpoint, rng_file)): logger.info( f"Didn't find an RNG file for process {local_rank}, if you are resuming a training that " "wasn't launched in a distributed fashion, reproducibility is not guaranteed." ) return else: rng_file = os.path.join(checkpoint, "rng_state.pth") if not os.path.isfile(rng_file): logger.info( "Didn't find an RNG file, if you are resuming a training that was launched in a distributed " "fashion, reproducibility is not guaranteed." ) return checkpoint_rng_state = torch.load(rng_file) random.setstate(checkpoint_rng_state["python"]) np.random.set_state(checkpoint_rng_state["numpy"]) torch.random.set_rng_state(checkpoint_rng_state["cpu"]) if torch.cuda.is_available(): if self.args.local_rank != -1: torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"]) else: try: torch.cuda.random.set_rng_state_all(checkpoint_rng_state["cuda"]) except Exception as e: logger.info( f"Didn't manage to set back the RNG states of the GPU because of the following error:\n {e}" "\nThis won't yield the same results as if the training had not been interrupted." ) if is_torch_tpu_available(): xm.set_rng_state(checkpoint_rng_state["xla"])

e314c19a3ff52b39f33453ab6c7f7b3c6c12413e

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

def _load_rng_state(self, checkpoint): # Load RNG states from `checkpoint` if checkpoint is None: return local_rank = xm.get_local_ordinal() if is_torch_tpu_available() else self.args.local_rank if local_rank != -1: rng_file = os.path.join(checkpoint, f"rng_state_{local_rank}.pth") if not os.path.isfile(os.path.join(checkpoint, rng_file)): logger.info( f"Didn't find an RNG file for process {local_rank}, if you are resuming a training that " "wasn't launched in a distributed fashion, reproducibility is not guaranteed." ) return else: rng_file = os.path.join(checkpoint, "rng_state.pth") if not os.path.isfile(rng_file): logger.info( "Didn't find an RNG file, if you are resuming a training that was launched in a distributed " "fashion, reproducibility is not guaranteed." ) return checkpoint_rng_state = torch.load(rng_file) random.setstate(checkpoint_rng_state["python"]) np.random.set_state(checkpoint_rng_state["numpy"]) torch.random.set_rng_state(checkpoint_rng_state["cpu"]) if torch.cuda.is_available(): if self.args.local_rank != -1: torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"]) else:

_load_rng_state

wagtail/images/__init__.py

Reformat with black

wagtail

Python

__init__.py

def get_image_model(): from django.apps import apps model_string = get_image_model_string() try: return apps.get_model(model_string, require_ready=False) except ValueError: raise ImproperlyConfigured( "WAGTAILIMAGES_IMAGE_MODEL must be of the form 'app_label.model_name'" ) except LookupError: raise ImproperlyConfigured( "WAGTAILIMAGES_IMAGE_MODEL refers to model '%s' that has not been installed" % model_string )

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def get_image_model(): from django.apps import apps model_string = get_image_model_string() try: return apps.get_model(model_string, require_ready=False) except ValueError: raise ImproperlyConfigured( "WAGTAILIMAGES_IMAGE_MODEL must be of the form 'app_label.model_name'" ) except LookupError: raise I

get_image_model

test/lib/ansible_test/_internal/config.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

config.py

def only_targets(self, target_type): # type: (t.Type[THostConfig]) -> t.List[THostConfig] if not self.targets: raise Exception('There must be one or more targets.') assert type_guard(self.targets, target_type) return t.cast(t.List[THostConfig], self.targets)

a06fa496d3f837cca3c437ab6e9858525633d147

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

def only_targets(self, target_type): # type: (t.Type[THostConfig]) -> t.List[THostConfig] if not self.targets: raise Exception('There must be

only_targets

test/prototype_transforms_kernel_infos.py

[prototype] Switch to `spatial_size` (#6736) * Change `image_size` to `spatial_size` * Fix linter * Fixing more tests. * Adding get_num_channels_video and get_spatial_size_* kernels for video, masks and bboxes. * Refactor get_spatial_size * Reduce the usage of `query_chw` where possible * Rename `query_chw` to `query_spatial_size` * Adding `get_num_frames` dispatcher and kernel. * Adding jit-scriptability tests

vision

Python

prototype_transforms_kernel_infos.py

def sample_inputs_rotate_bounding_box(): for bounding_box_loader in make_bounding_box_loaders(): yield ArgsKwargs( bounding_box_loader, format=bounding_box_loader.format, spatial_size=bounding_box_loader.spatial_size, angle=_ROTATE_ANGLES[0], )

4d4711d970f5cbd0a9e1adb465dca2703c8efbfd

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

def sample_inputs_rotate_bounding_box(): for bounding_box_loader in make_bounding_box_loaders(): yield ArgsKwargs( bounding_box_loader,

sample_inputs_rotate_bounding_box

test/test_outputs.py

Format The Codebase - black formatting - isort formatting

gradio

Python

test_outputs.py

def test_as_component(self): y = "happy" label_output = gr.outputs.Label() label = label_output.postprocess(y) self.assertDictEqual(label, {"label": "happy"}) self.assertEqual(label_output.deserialize(y), y) self.assertEqual(label_output.deserialize(label), y) with tempfile.TemporaryDirectory() as tmpdir: to_save = label_output.save_flagged(tmpdir, "label_output", label, None) self.assertEqual(to_save, y) y = {3: 0.7, 1: 0.2, 0: 0.1} label_output = gr.outputs.Label() label = label_output.postprocess(y) self.assertDictEqual( label, { "label": 3, "confidences": [ {"label": 3, "confidence": 0.7}, {"label": 1, "confidence": 0.2}, {"label": 0, "confidence": 0.1}, ], }, ) label_output = gr.outputs.Label(num_top_classes=2) label = label_output.postprocess(y) self.assertDictEqual( label, { "label": 3, "confidences": [ {"label": 3, "confidence": 0.7}, {"label": 1, "confidence": 0.2}, ], }, ) with self.assertRaises(ValueError): label_output.postprocess([1, 2, 3]) with tempfile.TemporaryDirectory() as tmpdir: to_save = label_output.save_flagged(tmpdir, "label_output", label, None) self.assertEqual(to_save, '{"3": 0.7, "1": 0.2}') self.assertEqual( label_output.restore_flagged(tmpdir, to_save, None), { "label": "3", "confidences": [ {"label": "3", "confidence": 0.7}, {"label": "1", "confidence": 0.2}, ], }, ) with self.assertRaises(ValueError): label_output = gr.outputs.Label(type="unknown") label_output.deserialize([1, 2, 3])

cc0cff893f9d7d472788adc2510c123967b384fe

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

def test_as_component(self): y = "happy" label_output = gr.outputs.Label() label = label_output.postprocess(y) self.assertDictEqual(label, {"label": "happy"}) self.assertEqual(label_output.deserialize(y), y)

test_as_component

tests/orion/api/test_work_queues.py

Add work queue backend

prefect

Python

test_work_queues.py

async def scheduled_flow_runs(self, session, deployment, work_queue, work_queue_2): for i in range(3): for wq in [work_queue, work_queue_2]: await models.flow_runs.create_flow_run( session=session, flow_run=schemas.core.FlowRun( flow_id=deployment.flow_id, deployment_id=deployment.id, work_queue_name=wq.name, state=schemas.states.State( type="SCHEDULED", timestamp=pendulum.now("UTC").add(minutes=i), state_details=dict( scheduled_time=pendulum.now("UTC").add(minutes=i) ), ), ), ) await session.commit()

2649fa325433aa219d6569ed77ef018f79480479

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

async def scheduled_flow_runs(self, session, deployment, work_queue, work_queue_2): for i in range(3): for wq in [work_queue, work_queue_2]: await models.flow_runs.create_flow_run( session=session, flow_run=schemas.core.FlowRun( flow_id=deployment.flow_id, deployment_id=deployment.id, work_queue_name=wq.name, state=schemas.states.State( type="SCHEDULED", timestamp=pendulum.now("UTC").add(minutes=i), state_details=dict( scheduled_time=pendulum.now("UTC").add(minutes=i) ), ), ),

scheduled_flow_runs

pandas/tests/io/xml/test_to_xml.py

TST: Remove unused fixtures (#45692) * TST: Remove unused fixtures * Undo a removed fixture * Add back other fixtures * Undo a file * Try undoing this? * Revert "Try undoing this?" This reverts commit 0e56cb04f5e8cb1f7b2ac4c5e6191485bb2fe1ab.

pandas

Python

test_to_xml.py

def test_attrs_cols_prefix(parser): expected = output = geom_df.to_xml( attr_cols=["index", "shape", "degrees", "sides"], namespaces={"doc": "http://example.xom"}, prefix="doc", parser=parser, ) output = equalize_decl(output) assert output == expected

f46df091df3afea25a273f491d1f6b2c7d20b32c

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

def test_attrs_cols_prefix(parser): expected = output = geom_df.to_xml( attr_cols=["index", "shape", "degrees", "sides"], namespaces={"doc": "http://example.xom"}, prefix="doc", parser=p

test_attrs_cols_prefix

bootloader/waflib/Tools/c_preproc.py

Bootloader: Building: Unpack waf's lib archive. Doing so makes it easier to modify. This is a temporary measure until the next waf version is released (although I'm tempted to keep it since it's much more IDE completion friendly).

pyinstaller

Python

c_preproc.py

def filter_comments(self, node): code = node.read() if use_trigraphs: for (a, b) in trig_def: code = code.split(a).join(b) code = re_nl.sub('', code) code = re_cpp.sub(repl, code) return re_lines.findall(code)

64ccb7aea824fbec57f7ed1bbe483ec486183c13

https://github.com/pyinstaller/pyinstaller.git

def filter_comments(self, node): c

filter_comments

tests/integration_tests/test_mlflow.py

Config Object (#2426) * Fixed loss instances across features * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed binary OneOfImplementation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Flake 8 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix custom loss components * Fix gbm category * Remove config object code, out of scope * Fixed more tests * Fixed incorrect text preproc default, added clip to category feature level * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixes additional tests * Cache jsonschema validator to reduce memory pressure * Fix imports * Skip neuropod test * Added upgrade audio to default preproc back compat and cleaned up * Small nits * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Change backfill constant for audio * Add docstring to compute feature hash * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Unused import * Another backfill constant change * Unused import * remove default population functions * Added config object test * rewired build_inputs * rewired combiner in ecd, added logic to config object * Refactored ecd.py * Fixing up merge_with_defaults, need metadata changes in master * Refactored defaults section and mega upgraded config obj * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed some formatting * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed feature col, proc col, and render config from defaults.py * Fix duplicate import * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added config initializer to merge defaults flow * Refactored update_config_with_metadata * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added dict conversion method to config object and refactored merge config function in config_utils * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Refactored until preproc entrypoint * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed update_config_with_metadata * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Removed load config base feature method - no longer necessary * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Formatting * Fixed input size assignment * Temp fix * Fixed pretrained encoder path referencing temp until preproc refactor * Solved the WORST BUG EVER * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Switch reduce_input to None for sequence tagger * Fixed another one * Fixed typo * Various test fixes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Flake 8 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed excess defaults params issue * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Minor fixes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed some defaults tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed more tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed more tests * Formatting * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * More test fixes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed defaults tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix more tests * Flake 8 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix more tests * Fixed more tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed more tests * Fixed more tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fixing ghost tests attempt * Deep copy to smash the ghost failures * Copied top level modules now too * Started fixing hyperopt * Fixed Hyperopt Issues * Flake 8 * Remove commented out code * Address Piero feedback * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Flake 8 * Removed merge with defaults * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed various issues with preprocessing and splitting positioning * Fixed hyperopt issues * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Refactored api pipeline to use all config obj references * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed more tests * Flake 8 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix more tests * Fixed auto tune learning rate and batch size * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed sequence feature tests * Fixed image feature test * Fixed last test * flake 8 * Marshmallowify Config object, remove manual to dict method, add Factory method constructors * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Validate config within config object * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * All Travis feedback addressed * Using all new constructors now * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * removed from class attributes * Added deep copies back and piped repr inheritance * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Format * Small error fix, moved back compat into Config Object * Flake8 * Docstring for hyperopt defaults method * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Address Joppe feedback * Revert "Address Joppe feedback" This reverts commit 42f1665ef917d062a010550bb960594c355285ff. * Fix tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Flake8 * fix test * Small improvement * Changed repr for input features, added feature enabling/disabling * Added feature enabling/disabling, and better reprs for SDK dev * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Flake 8 * Added rich to requirements.txt * Add some more CO tests and comment more on CO code * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix explain issue * Julian feedback * Added TODOs for future refactor PRs * Fix explain test failure, test shared state improvement and bug fix, remove unncessary code from convert_submodules * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * implement Daniel's feedback * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix residual errors * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Error fix * Using mixins now so no loose attributes on defaults, fixed height width schema restrictions * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Removed unnecessary filtering from defaults schema logic * Piero's simplification and cleanup * Flake 8 * Fix test and update docstrings from Pieros change * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Address most of Justin's feedback * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix tests and more feedback implementation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Address feedback * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Renamed files to correspond to ModelConfig class name * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Missing constant import * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed incorrect merge conflict resolution * Flake8 * Fix remaining tests (except old models training from trainer type removal) * Fixed old models not validating trainer type * Add output_feature=False to test_hyperopt_ray.py * Implement Kabir's feedback * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Travis Addair <tgaddair@gmail.com> Co-authored-by: w4nderlust <w4nderlust@gmail.com>

ludwig

Python

test_mlflow.py

def test_export_mlflow_local(tmpdir): epochs = 2 batch_size = 8 num_examples = 32 input_features = [sequence_feature(reduce_output="sum")] output_features = [category_feature(vocab_size=2, reduce_input="sum", output_feature=True)] config = { "input_features": input_features, "output_features": output_features, "combiner": {"type": "concat", "output_size": 14}, TRAINER: {"epochs": epochs, "batch_size": batch_size}, } data_csv = generate_data( input_features, output_features, os.path.join(tmpdir, "train.csv"), num_examples=num_examples ) exp_name = "mlflow_test" output_dir = os.path.join(tmpdir, "output") model = LudwigModel(config, backend=FakeRemoteBackend()) _, _, output_directory = model.train(training_set=data_csv, experiment_name=exp_name, output_directory=output_dir) model_path = os.path.join(output_directory, "model") output_path = os.path.join(tmpdir, "data/results/mlflow") export_mlflow(model_path, output_path) assert set(os.listdir(output_path)) == {"MLmodel", "model", "conda.yaml"}

4d2d81f9fdefc52eea6a9bf0826a6f2ffc8d681b

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

def test_export_mlflow_local(tmpdir): epochs = 2 batch_size = 8 num_examples = 32 input_features = [sequence_feature(reduce_output="sum")] output_features = [category_feature(vocab_size=2, reduce_input="sum", output_feature=True)] config = { "input_features": input_features, "output_features": output_features, "combiner": {"type": "concat", "output_size": 14}, TRAINER: {"epochs": epochs, "batch_size": batch_size}, } data_csv = generate_data(

test_export_mlflow_local

rest_framework/utils/encoders.py

Refactor: Replace try/except with contextlib.suppress() (#8676)

django-rest-framework

Python

encoders.py

def default(self, obj): # For Date Time string spec, see ECMA 262 # https://ecma-international.org/ecma-262/5.1/#sec-15.9.1.15 if isinstance(obj, Promise): return force_str(obj) elif isinstance(obj, datetime.datetime): representation = obj.isoformat() if representation.endswith('+00:00'): representation = representation[:-6] + 'Z' return representation elif isinstance(obj, datetime.date): return obj.isoformat() elif isinstance(obj, datetime.time): if timezone and timezone.is_aware(obj): raise ValueError("JSON can't represent timezone-aware times.") representation = obj.isoformat() return representation elif isinstance(obj, datetime.timedelta): return str(obj.total_seconds()) elif isinstance(obj, decimal.Decimal): # Serializers will coerce decimals to strings by default. return float(obj) elif isinstance(obj, uuid.UUID): return str(obj) elif isinstance(obj, QuerySet): return tuple(obj) elif isinstance(obj, bytes): # Best-effort for binary blobs. See #4187. return obj.decode() elif hasattr(obj, 'tolist'): # Numpy arrays and array scalars. return obj.tolist() elif (coreapi is not None) and isinstance(obj, (coreapi.Document, coreapi.Error)): raise RuntimeError( 'Cannot return a coreapi object from a JSON view. ' 'You should be using a schema renderer instead for this view.' ) elif hasattr(obj, '__getitem__'): cls = (list if isinstance(obj, (list, tuple)) else dict) with contextlib.suppress(Exception): return cls(obj) elif hasattr(obj, '__iter__'): return tuple(item for item in obj) return super().default(obj)

c10f2266222c434485889b08cc1463acdb8fa169

https://github.com/encode/django-rest-framework.git

def default(self, obj): # For Date Time string spec, see ECMA 262 # https://ecma-international.org/ecma-262/5.1/#sec-15.9.1.15 if isinstance(obj, Promise): return force_str(obj) elif isinstance(obj, datetime.datetime): representation = obj.isoformat() if representation.endswith('+00:00'): representation = representation[:-6] + 'Z' return representation elif isinstance(obj, datetime.date): return obj.isoformat() elif isinstance(obj, datetime.time): if timezone and timezone.is_aware(obj): raise ValueError("JSON can't represent timezone-aware times.") representation = obj.isoformat() return represen

default

python/ray/serve/tests/test_healthcheck.py

[serve] Improve health check failure semantics (#22297)

ray

Python

test_healthcheck.py

def test_user_defined_method_fails(serve_instance): Patient.deploy() h = Patient.get_handle() actor = ray.get(h.remote()) ray.get(h.set_should_fail.remote()) wait_for_condition(check_new_actor_started, handle=h, original_actors=actor) ray.get([h.remote() for _ in range(100)])

610930ae6aeafb37be75851a8c1b9ff39d5f7d22

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

def test_user_defined_method_fails(serve_instance): Patient.deploy() h = Patient.get_handle() actor = ray.get(h.remote()) ray.get(h.set_should_fail.remote()) wait_fo

test_user_defined_method_fails

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

upd; format

transferlearning

Python

wheel_builder.py

def _clean_one_legacy(req, global_options): # type: (InstallRequirement, List[str]) -> bool clean_args = make_setuptools_clean_args( req.setup_py_path, global_options=global_options, ) logger.info('Running setup.py clean for %s', req.name) try: call_subprocess(clean_args, cwd=req.source_dir) return True except Exception: logger.error('Failed cleaning build dir for %s', req.name) return False

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def _clean_one_legacy(req, global_options): # type: (InstallRequirement, List[str]) -> bool clean_args = make_setuptools_clean_args( req.setup_py_path, global_options=global_options, ) lo

_clean_one_legacy

python/ray/train/tests/test_huggingface_gpu.py

[Train] `HuggingFacePredictor` & docs improvements (#30945) This PR introduces 2 changes: Removes a confusing suggestion in the docstring of HuggingFaceCheckpoint - checkpoints created using from_checkpoint will not work for prediction as intended. Adds use_gpu argument and logic to automatically use GPU if one is available to HuggingFacePredictor. Signed-off-by: Antoni Baum <antoni.baum@protonmail.com>

ray

Python

test_huggingface_gpu.py

def create_checkpoint(): with tempfile.TemporaryDirectory() as tmpdir: model_config = AutoConfig.from_pretrained(model_checkpoint) model = AutoModelForCausalLM.from_config(model_config) tokenizer = AutoTokenizer.from_pretrained(tokenizer_checkpoint) checkpoint = HuggingFaceCheckpoint.from_model(model, tokenizer, path=tmpdir) # Serialize to dict so we can remove the temporary directory return HuggingFaceCheckpoint.from_dict(checkpoint.to_dict()) # TODO(ml-team): Add np.ndarray to batch_type @pytest.mark.parametrize("batch_type", [pd.DataFrame]) @pytest.mark.parametrize("device", [None, 0])

81237e05838757dde196688a20631daad48010dd

@pytest.mark.parametrize("batch_type", [pd.DataFrame]) @pytest.mark.parametrize("device", [None, 0])

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

def create_checkpoint(): with tempfile.TemporaryDirectory() as tmpdir: model_config = AutoConfig.from_pretrained(model_checkpoint) model = AutoModelForCausalLM.from_c

create_checkpoint

tests/sentry/search/events/test_builder.py

ref: replace self.assertRaises with pytest.raises (#35685) * add flake8 plugin to detect assertRaises * ref: replace self.assertRaises with pytest.raises * non-sed fixes

sentry

Python

test_builder.py

def test_limit_validation(self): # 51 is ok MetricsQueryBuilder(self.params, limit=51) # None is ok, defaults to 50 query = MetricsQueryBuilder(self.params) assert query.limit.limit == 50 # anything higher should throw an error with pytest.raises(IncompatibleMetricsQuery): MetricsQueryBuilder(self.params, limit=10_000)

284e980df0018f8baee659999268bdd4c7d08255

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

def test_limit_validation(self): # 51 is ok MetricsQueryBuilder(self.params, limit=51) # None is ok, defaults to 50 query = MetricsQueryBuilder(self.params) assert query.limit.limit == 50 # anything higher should throw an error with pytest.raises(IncompatibleMe

test_limit_validation

tests/infrastructure/test_docker_container.py

Update default infrastructure command to be set at runtime Add commands to Docker container tests with no command

prefect

Python

test_docker_container.py

def test_adds_docker_host_gateway_on_linux(mock_docker_client, monkeypatch): monkeypatch.setattr("sys.platform", "linux") DockerContainer( command=["echo", "hello"], ).run() mock_docker_client.containers.create.assert_called_once() call_extra_hosts = mock_docker_client.containers.create.call_args[1].get( "extra_hosts" ) assert call_extra_hosts == {"host.docker.internal": "host-gateway"}

c02383e4a879c95586cfbc19787904da2d4be22b

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

def test_adds_docker_host_gateway_on_linux(mock_docker_client, monkeypatch): monkeypatch.setattr("sys.platform", "linux") DockerContainer( command=["echo", "hello"], ).run() mock_docker_client.containers.create.assert_called_once() call_extra_hosts = mock_docker_client.containers.create.call_args[1].get( "extra_host

test_adds_docker_host_gateway_on_linux

modin/pandas/indexing.py

FIX-#3764: Ensure df.loc with a scalar out of bounds appends to df (#3765) Co-authored-by: Devin Petersohn <devin-petersohn@users.noreply.github.com> Co-authored-by: Bill Wang <billiam@ponder.io> Co-authored-by: Vasily Litvinov <fam1ly.n4me@yandex.ru>

modin

Python

indexing.py

def _set_item_existing_loc(self, row_loc, col_loc, item): row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc) self._setitem_positional( row_lookup, col_lookup, item, axis=self._determine_setitem_axis( row_lookup, col_lookup, is_scalar(row_loc), is_scalar(col_loc) ), )

11ba4811e6db11740e11fd33d3cdfba8ce5bec54

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

def _set_item_existing_loc(self, row_loc, col_loc, item): row_lookup, col_lookup = self._compute_lookup(row_loc, col_loc) self._setitem_positional( row_lookup,

_set_item_existing_loc

mindsdb/integrations/mysql_handler/mysql_handler/mysql_handler.py

test: move testing logic into unittest modules; CI still pending

mindsdb

Python

mysql_handler.py

def check_status(self): try: return self.connection.is_connected() except Exception: return False

76a30708e24bca37169df44d8b31573c7b5beb43

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

def check_status(self): try: return self.connection.is_connected() except Exception: return Fals

check_status

wagtail/admin/views/pages/moderation.py

Reformat with black

wagtail

Python

moderation.py

def reject_moderation(request, revision_id): revision = get_object_or_404(PageRevision, id=revision_id) if not revision.page.permissions_for_user(request.user).can_publish(): raise PermissionDenied if not revision.submitted_for_moderation: messages.error( request, _("The page '{0}' is not currently awaiting moderation.").format( revision.page.specific_deferred.get_admin_display_title() ), ) return redirect("wagtailadmin_home") if request.method == "POST": revision.reject_moderation(user=request.user) messages.success( request, _("Page '{0}' rejected for publication.").format( revision.page.specific_deferred.get_admin_display_title() ), buttons=[ messages.button( reverse("wagtailadmin_pages:edit", args=(revision.page.id,)), _("Edit"), ) ], ) if not send_moderation_notification(revision, "rejected", request.user): messages.error(request, _("Failed to send rejection notifications")) return redirect("wagtailadmin_home") @require_GET

d10f15e55806c6944827d801cd9c2d53f5da4186

@require_GET

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

def reject_moderation(request, revision_id): revision = get_object_or_404(PageRevision, id=revision_id) if not revision.page.permissions_for_user(request.user).can_publish(): raise PermissionDenied if not revision.submitted_for_moderation: messages.error( request, _("The page '{0}' is not currently awaiting moderation.").format( revision.page.specific_deferred.get_admin_display_title() ), ) return redirect("wagtailadmin_home") if request.method == "POST": revision.reject_moderation(user=request.user) messages.success( request, _("Page '{0}' rejected for publication.").format( revision.page.specific_deferred.get_admin_display_title() ), buttons=[ messages.button( reverse("wagtailadmin_pages:edit", args=(revision.page.id,)), _("Edit"), ) ], ) if not send_moderation_notification(revision, "rejected", request.user): messages.error(request, _("Failed to send rejection notifications")) return redirect("wagtailadmin_home")

reject_moderation

tests/components/calendar/test_trigger.py

Add initial implementation of a calendar trigger (#68674) * Add initial implementation of calendar trigger This is an initial implementation of a calendar trigger, that supports triggering on calendar start time. See architecture proposal in: https://github.com/home-assistant/architecture/discussions/700 * Address reviewer feedback * Use f-strings for all tests * Apply suggestions from code review Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Remove logging f-strings, and move to main code * Remove mypy ignore * Apply suggestions from code review Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Update calendar triggers to use new calendar data model * Update tests/components/calendar/test_trigger.py Co-authored-by: Franck Nijhof <frenck@frenck.nl> * Rewrite tests using freezegun Rewrite tests using freezegun and improve edge case handling, and use utc consistently for all alarms. * Update homeassistant/components/calendar/trigger.py Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Update homeassistant/components/calendar/trigger.py Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Increase test coverage based on pr feedback Co-authored-by: Martin Hjelmare <marhje52@gmail.com> Co-authored-by: Franck Nijhof <frenck@frenck.nl>

core

Python

test_trigger.py

async def test_event_payload(hass, calls, fake_schedule): event_data = fake_schedule.create_event( start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"), end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"), description="Description", location="Location", ) await create_automation(hass, EVENT_START) assert len(calls()) == 0 await fake_schedule.fire_until( datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00") ) assert calls() == [ { "platform": "calendar", "event": EVENT_START, "calendar_event": event_data, } ]

a2c74b978664b627bafc4a43b26aa2be7b15b229

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

async def test_event_payload(hass, calls, fake_schedule): event_data = fake_schedule.create_event( start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"), end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"), description="Description", location="Location", ) await create_automation(hass, EVENT_START) assert len(calls()) == 0 await fake_schedule.fire_until( datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00") ) assert calls() == [ { "platform":

test_event_payload

packages/syft/src/syft/core/tensor/nn/model.py

update domain update script to add branch name during hagrid launch add loss to parameter list in model publish print loss during model training

PySyft

Python

model.py

def publish(self, deduct_epsilon_for_user, get_budget_for_user, ledger, sigma): print("Publish Model Weights") # relative from ..autodp.gamma_tensor import GammaTensor parameters = {} for i, layer in enumerate(self.layers): print("Layer", str(layer)) print("Before Publish") for param in layer.params: print(param.shape, end=" ") print() if hasattr(layer, "params"): parameters[str(layer) + str(i)] = [ param.publish( deduct_epsilon_for_user=deduct_epsilon_for_user, get_budget_for_user=get_budget_for_user, ledger=ledger, sigma=sigma, ) if isinstance(param, (GammaTensor)) else param for param in layer.params ] print("After Publish") for param in parameters[str(layer) + str(i)]: print(param.shape, end=" ") print() parameters["loss"] = self.aggregated_loss return parameters

b480217f5bc07d97a691bfed74eb7489667788dd

https://github.com/OpenMined/PySyft.git

def publish(self, deduct_epsilon_for_user, get_budget_for_user, ledger, sigma): print("Publish Model Weights") # relative from ..autodp.gamma_tensor import GammaTensor parameters = {} for i, layer in enumerate(self.layers): print("Layer", str(layer)) print("Before Publish") for param in layer.params: print(param.shape, end=" ") print() if hasattr(layer, "params"): parameters[str(layer

publish

kubernetes_tests/test_kubernetes_pod_operator_backcompat.py

KubernetesPodOperator should patch "already checked" always (#22734) When not configured to delete pods, at end of task execution the current behavior is to patch the pod as "already checked", but only if pod not successful. We should also patch when successful so it isn't "reattached" to after a task clear.

airflow

Python

test_kubernetes_pod_operator_backcompat.py

def test_volume_mount(self): with patch.object(PodManager, 'log') as mock_logger: volume_mount = VolumeMount( 'test-volume', mount_path='/tmp/test_volume', sub_path=None, read_only=False ) volume_config = {'persistentVolumeClaim': {'claimName': 'test-volume'}} volume = Volume(name='test-volume', configs=volume_config) args = [ "echo \"retrieved from mount\" > /tmp/test_volume/test.txt " "&& cat /tmp/test_volume/test.txt" ] k = KubernetesPodOperator( namespace='default', image="ubuntu:16.04", cmds=["bash", "-cx"], arguments=args, labels={"foo": "bar"}, volume_mounts=[volume_mount], volumes=[volume], is_delete_operator_pod=False, name="test", task_id="task", in_cluster=False, do_xcom_push=False, ) context = create_context(k) k.execute(context=context) mock_logger.info.assert_any_call('retrieved from mount') actual_pod = self.api_client.sanitize_for_serialization(k.pod) expected_pod = copy(self.expected_pod) expected_pod['spec']['containers'][0]['args'] = args expected_pod['spec']['containers'][0]['volumeMounts'] = [ {'name': 'test-volume', 'mountPath': '/tmp/test_volume', 'readOnly': False} ] expected_pod['spec']['volumes'] = [ {'name': 'test-volume', 'persistentVolumeClaim': {'claimName': 'test-volume'}} ] expected_pod['metadata']['labels']['already_checked'] = 'True' assert expected_pod == actual_pod

c3d883a971a8e4e65ccc774891928daaaa0f4442

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

def test_volume_mount(self): with patch.object(PodManager, 'log') as mock_logger: volume_mount = VolumeMount( 'test-volume', mount_path='/tmp/test_volume', sub_path=None, read_only=False ) volume_config = {'persistentVolumeClaim': {'claimName': 'test-volume'}} volume = Volume(name='test-volume', configs=volume_config) args = [ "echo \"retrieved from mount\" > /tmp/test_volume/test.txt " "&& cat /tmp/test_volume/test.txt" ] k = KubernetesPodOperator( namespace='default', image="ubuntu:16.04", cmds=["bash", "-cx"], arguments=args, labels={"foo": "bar"}, volume_mounts=[volume_mount], volumes=[volume], is_delete_operator_pod=False, name="test", task_id="task", in_cluster=False, do_xcom_push=False, ) context = create_context(k) k.execute(context=context) mock_logger.info.assert_any_call('retrieved from mount') actual_pod = self.api_client.sanitize_for_serialization(k.pod) expected_pod = copy(self.expected_pod) expected_pod['spec']['containers'][0]['args'] = args

test_volume_mount

d2l/mxnet.py

Refactor Multihead Attn, Self Attn, and Transformer (#2096) * multihead attn * self attn and pos encoding * simplify * before EncoderBlock * before tmencoder * before decoder block * before training * transformer code * rm seq2seq encoder old * fix bahdanau attn map * transformer done, perf tuned * clean super

d2l-en

Python

mxnet.py

def forward(self, X, valid_lens): # Since positional encoding values are between -1 and 1, the embedding # values are multiplied by the square root of the embedding dimension # to rescale before they are summed up X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens)) self.attention_weights = [None] * len(self.blks) for i, blk in enumerate(self.blks): X = blk(X, valid_lens) self.attention_weights[ i] = blk.attention.attention.attention_weights return X

f0be7e672bc0a7c77005d5c79452d796cfe1a06b

https://github.com/d2l-ai/d2l-en.git

def forward(self, X, valid_lens): # Since positional encoding values are between -1 and 1, the embedding # values are multiplied by the square root of the embedding dimension # to rescale before they are summed up X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens)) self.attention_weights = [None] * len(self.blks) for i, blk in enumerate(self.blks): X = blk(X, valid_lens) self.attention_weights[ i] = blk.attention.attention.attention_wei

forward

tests/unit/executor_test_base.py

executor base test

mindsdb

Python

executor_test_base.py

def clear_db(db): # drop db.Base.metadata.drop_all(db.engine) # create db.Base.metadata.create_all(db.engine) # fill with data r = db.Integration(name='files', data={}, engine='files') db.session.add(r) r = db.Integration(name='views', data={}, engine='views') db.session.add(r) db.session.commit() return db

d304fa61c43e5248c0cb111d5553db653be92cff

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

def clear_db(db): # drop db.Base.metadata.drop_all(db.engine) # create db.Base.metadata.create_all(db.engine) # fill with data

clear_db

mitmproxy/net/udp.py

[dns] rewrite of udp, merge dnsserver>proxyserver

mitmproxy

Python

udp.py

def resume_writing(self) -> None: assert self._paused > 0 self._paused = self._paused - 1 if self._paused == 0: self._can_write.set()

ef3f9e492e8f1d197ddab24bf5f80a76d2fe566d

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

def resume_writing(self) -> None: assert self._paused > 0 self._paused = self._paused - 1 if self._pa

resume_writing

configs/rotate/tools/slicebase.py

Refactor rbox (#6704) * refactor rbox * modify the code of save results * fix some problem * add .gitignore in dataset/dota * fix test anno path

PaddleDetection

Python

slicebase.py

def get_poly4_from_poly5(self, poly): distances = [ cal_line_length((poly[i * 2], poly[i * 2 + 1]), (poly[(i + 1) * 2], poly[(i + 1) * 2 + 1])) for i in range(int(len(poly) / 2 - 1)) ] distances.append( cal_line_length((poly[0], poly[1]), (poly[8], poly[9]))) pos = np.array(distances).argsort()[0] count = 0 out_poly = [] while count < 5: if (count == pos): out_poly.append( (poly[count * 2] + poly[(count * 2 + 2) % 10]) / 2) out_poly.append( (poly[(count * 2 + 1) % 10] + poly[(count * 2 + 3) % 10]) / 2) count = count + 1 elif (count == (pos + 1) % 5): count = count + 1 continue else: out_poly.append(poly[count * 2]) out_poly.append(poly[count * 2 + 1]) count = count + 1 return out_poly

e55e41945d42db787a0f7c557d53d06a6b24536b

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

def get_poly4_from_poly5(self, poly): distances = [ cal_line_length((poly[i * 2], poly[i * 2 + 1]), (poly[(i + 1) * 2], poly[(i + 1) * 2 + 1])) for i in range(int(len(poly) / 2 - 1)) ] distances.append( cal_line_length((poly[0], poly[1]), (poly[8], poly[9]))) pos = np.array(distances).argsort()[0] count = 0 out_poly = [] while count < 5: if (count == pos): out_poly.append( (poly[count * 2] + poly[(count * 2 + 2) % 10]) / 2) out_poly.append( (poly[(count * 2 + 1) % 10] + poly[(count * 2 + 3) % 10]) / 2) count = count + 1 elif (count == (pos + 1) % 5): count = count + 1 continue else

get_poly4_from_poly5

sklearn/utils/tests/test_estimator_html_repr.py

FIX Show a HTML repr for meta-estimatosr with invalid parameters (#24015) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

scikit-learn

Python

test_estimator_html_repr.py

def test_invalid_parameters_in_stacking(): stacker = StackingClassifier(estimators=[]) html_output = estimator_html_repr(stacker) assert html.escape(str(stacker)) in html_output

84c6421a9067de7d1b54b7a6d8e21ce38e1f0eca

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

def test_invalid_parameters_in_stacking(): stacker = StackingClassifier(estimators=[]) html_output = estimator_html_rep

test_invalid_parameters_in_stacking

python/ccxt/coinex.py

1.71.68 [ci skip]

ccxt

Python

coinex.py

def fetch_markets(self, params={}): response = self.publicGetMarketInfo(params) # # { # "code": 0, # "data": { # "WAVESBTC": { # "name": "WAVESBTC", # "min_amount": "1", # "maker_fee_rate": "0.001", # "taker_fee_rate": "0.001", # "pricing_name": "BTC", # "pricing_decimal": 8, # "trading_name": "WAVES", # "trading_decimal": 8 # } # } # } # markets = self.safe_value(response, 'data', {}) result = [] keys = list(markets.keys()) for i in range(0, len(keys)): key = keys[i] market = markets[key] id = self.safe_string(market, 'name') tradingName = self.safe_string(market, 'trading_name') baseId = tradingName quoteId = self.safe_string(market, 'pricing_name') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote if tradingName == id: symbol = id result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'settle': None, 'baseId': baseId, 'quoteId': quoteId, 'settleId': None, 'type': 'spot', 'spot': True, 'margin': None, 'swap': False, 'future': False, 'option': False, 'active': None, 'contract': False, 'linear': None, 'inverse': None, 'taker': self.safe_number(market, 'taker_fee_rate'), 'maker': self.safe_number(market, 'maker_fee_rate'), 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': { 'price': self.safe_integer(market, 'pricing_decimal'), 'amount': self.safe_integer(market, 'trading_decimal'), }, 'limits': { 'leverage': { 'min': None, 'max': None, }, 'amount': { 'min': self.safe_number(market, 'min_amount'), 'max': None, }, 'price': { 'min': None, 'max': None, }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, }) return result

c9b141d8b46d6bc771d9305e403440654bbe03b2

https://github.com/ccxt/ccxt.git

def fetch_markets(self, params={}): response = self.publicGetMarketInfo(params) # # { # "code": 0, # "data": { # "WAVESBTC": { # "name": "WAVESBTC", # "min_amount": "1", # "maker_fee_rate": "0.001", # "taker_fee_rate": "0.001", # "pricing_name": "BTC", # "pricing_decimal": 8, # "trading_name": "WAVES", # "trading_decimal": 8 # } # } # } # markets = self.safe_value(response, 'data', {}) result = [] keys = list(markets.keys()) for i in range(0, len(keys)): key = keys[i] market = markets[key] id = self.safe_string(market, 'name') tradingName = self.safe_string(market, 'trading_name') baseId = tradingName quoteId = self.safe_string(market, 'pricing_name') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) symbol = base + '/' + quote if tradingName == id: symbol = id result.append({ 'id': id, 'symbol': symbol,

fetch_markets

tests/models/layoutlmv3/test_modeling_tf_layoutlmv3.py

[LayoutLMv3] Add TensorFlow implementation (#18678) Co-authored-by: Esben Toke Christensen <esben.christensen@visma.com> Co-authored-by: Lasse Reedtz <lasse.reedtz@visma.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

transformers

Python

test_modeling_tf_layoutlmv3.py

def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict: inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)) if isinstance(v, tf.Tensor) and v.ndim > 0 else v for k, v in inputs_dict.items() } if return_labels: if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING): inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING): inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING): inputs_dict["labels"] = tf.zeros( (self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=tf.int32 ) return inputs_dict

de8548ebf3242305d0f9792dacb6f86b196a3a33

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

def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict: inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)) if isinstance(v, tf.Tensor) and v.ndim > 0 else v for k, v in inputs_dict.items() } if return_labels: if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING): inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING): inputs_dict["labels"] = tf.zeros(self

_prepare_for_class

tests/test_modeling_utils.py

[SDE] Merge to unconditional model (#89) * up * more * uP * make dummy test pass * save intermediate * p * p * finish * finish * finish

diffusers

Python

test_modeling_utils.py

def test_score_sde_ve_pipeline(self): model = UNetUnconditionalModel.from_pretrained("fusing/ffhq_ncsnpp", sde=True) torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) scheduler = ScoreSdeVeScheduler.from_config("fusing/ffhq_ncsnpp") sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler) torch.manual_seed(0) image = sde_ve(num_inference_steps=2) if model.device.type == "cpu": expected_image_sum = 3384805632.0 expected_image_mean = 1076.000732421875 else: expected_image_sum = 3382849024.0 expected_image_mean = 1075.3787841796875 assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2 assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4

ba3c9a9a3a9cf76e4ff8292b66d7cc3206732627

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

def test_score_sde_ve_pipeline(self): model = UNetUncondi

test_score_sde_ve_pipeline

test/lib/ansible_test/_internal/host_profiles.py

Add `use_rsa_sha2_algorithms` option for paramiko (#78789) Fixes #76737 Fixes #77673 Co-authored-by: Matt Clay <matt@mystile.com>

ansible

Python

host_profiles.py

def get_inventory_variables(self) -> dict[str, t.Optional[t.Union[str, int]]]: core_ci = self.wait_for_instance() connection = core_ci.connection variables: dict[str, t.Optional[t.Union[str, int]]] = dict( ansible_connection=self.config.connection, ansible_pipelining='yes', ansible_host=connection.hostname, ansible_port=connection.port, ansible_user=connection.username, ansible_ssh_private_key_file=core_ci.ssh_key.key, ansible_paramiko_use_rsa_sha2_algorithms='no', ansible_network_os=f'{self.config.collection}.{self.config.platform}' if self.config.collection else self.config.platform, ) return variables

76b746655a36807fa9198064ca9fe7c6cc00083a

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

def get_inventory_variables(self) -> dict[str, t.Optional[t.Union[str, int]]]: core_ci = self.wait_for_instance() connection = core_ci.connection variables: dict[str, t.Optional[t.Union[str, int]]] = dict( ansible_connection=self.config.connection, ansible_pipelining='yes', ansible_host=connection.hostname, ansible_port=connection.port, ansible_user=connection.username, ansible_ssh_private_key_file=core_ci.ssh_key.key, ansible_paramiko_use_rsa_sha2_algorithms='no', ansible_network_os

get_inventory_variables

rest_api/test/test_rest_api.py

API tests (#2738) * clean up tests and run earlier * use change detection * better naming, skip ES * more cleanup * fix job name * dummy commit to trigger the CI * mock away the PDF converter * make the test compatible with 3.7 * removed leftover * always run the api tests, use a matrix for the OS * refactor all the tests * remove outdated dependency * pylint * new abstract method * adjust for older python versions * rename pipeline file * address PR comments

haystack

Python

test_rest_api.py

def test_file_upload_with_wrong_meta(client): file_to_upload = {"files": (Path(__file__).parent / "samples" / "pdf" / "sample_pdf_1.pdf").open("rb")} response = client.post(url="/file-upload", files=file_to_upload, data={"meta": "1"}) assert 500 == response.status_code # Ensure the `convert` method was never called MockPDFToTextConverter.mocker.convert.assert_not_called()

82df677ebf853340d331ff0868304cc958307ee0

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

def test_file_upload_with_wrong_meta(client): file_to_upload = {"files": (Path(__file__).parent / "samples" / "pdf" / "sample_pdf_1.pdf").open("rb")} response = client.post(url="/file-upload", files=file_to_upload, data={"meta": "1"}) assert 500 == response.status_code # E

test_file_upload_with_wrong_meta

homeassistant/components/skybell/sensor.py

Add strict typing to Skybell (#79800)

core

Python

sensor.py

def native_value(self) -> StateType | datetime: return self.entity_description.value_fn(self._device)

9850709b37fdfa704ac3db4c45a2660880a7ca65

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

def native_value(self) -> StateType | datetime:

native_value

label_studio/projects/models.py

fix: DEV-3164: Remove potential data exposure from logs (#2828) * Remove potential data exposure from logs * Bump converter & tools pip versions Co-authored-by: nik <nik@heartex.net>

label-studio

Python

models.py

def _get_annotation_key(self, result): result_type = result.get('type', None) if result_type in ('relation', 'pairwise', None): return None if 'from_name' not in result or 'to_name' not in result: logger.error( 'Unexpected annotation.result format: "from_name" or "to_name" not found', extra={'sentry_skip': True}, ) return None result_from_name = result['from_name'] key = get_annotation_tuple(result_from_name, result['to_name'], result_type or '') return key

5a0415ea99e3ef95bdbb2d6b62577c0c868b9540

https://github.com/heartexlabs/label-studio.git

def _get_annotation_key(self, result): result_type = result.get('type', None) if result_type in ('relation', 'pairwise', None): return None if 'from_name' not in result or 'to_name' not in result: logger.error( 'Unexpected annotation.result format: "from_name" or "to_name" not found', extra={'sentry_skip': True}, ) return None result_from_name = result['from_name'] key = get_annotation_

_get_annotation_key

tests/snuba/api/endpoints/test_organization_events.py

fix(tests): Fix dnd backend test flakes (#37916) This PR fixes 3 major flakes: Fixes SENTRY-TESTS-3J5: Just sort the project id order Fixes SENTRY-TESTS-3HQ: Flakes because we calculate the retention in the test once and the value returned in the response is calculated a little while after. We don't need to test for seconds granularity so replacing seconds to 0. Fixes SENTRY-TESTS-3J0: Successively calling before_now results in some flakes particularly in tests that are calculating aggregates on transaction.duration. Introduced a load_data method that takes a datetime object timestamp and a timedelta duration calculates the offset based on timestamp to get start_timestamp.

sentry

Python

test_organization_events.py

def test_stack_wildcard_condition(self): data = self.load_data(platform="javascript") data["timestamp"] = self.ten_mins_ago self.store_event(data=data, project_id=self.project.id) query = {"field": ["stack.filename", "message"], "query": "stack.filename:*.js"} response = self.do_request(query) assert response.status_code == 200, response.content assert len(response.data["data"]) == 1 assert response.data["meta"]["fields"]["message"] == "string"

ab993b32614bb83d17d10e1041817e43dd6f5980

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

def test_stack_wildcard_condition(self): data = self.load_data(platform="javascript") data["timestamp"] = self.ten_mins_ago self.store_event(data=data, project_id=self.project.id) query = {"field": ["stack.filename", "message"], "query": "stack.filename:*.js"} response = self.do_request(query) assert response.status_code == 200, response.content assert len(response.data["data"]) == 1 assert respons

test_stack_wildcard_condition

tests/integration_tests/test_hyperopt_ray_horovod.py

Fix ray hyperopt (#1999) * WIP fix ray hyperopt * Fixed kwargs * Updated the nones * Placement groups * Updated test cpus * Test with dynamic resource allocation * Using 0 CPUs for evaluation and using dask annotate * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Updates to ray backend and hyperopt execution * Added dask global config * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Uncommented tests * Disabled async hyperband tests * Responded to comments * Fixed all hyperopt horovod tests to use 10 CPUs * Moved dask config setting to ray backend * Calculate stats for distributed datasets (#2016) * Fixed tests, responded to comments * Responded to comments * Updated horovod hyperopt tests to be consistent with the hyperopt refactor, added a df_engine attribute to RayPredictor * Added parentheses on pandas * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: Travis Addair <tgaddair@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ludwig

Python

test_hyperopt_ray_horovod.py

def test_hyperopt_run_hyperopt(csv_filename, ray_mock_dir): input_features = [number_feature(), number_feature()] output_features = [binary_feature()] csv_filename = os.path.join(ray_mock_dir, "dataset.csv") dataset_csv = generate_data(input_features, output_features, csv_filename, num_examples=100) dataset_parquet = create_data_set_to_use("parquet", dataset_csv) config = { "input_features": input_features, "output_features": output_features, "combiner": {"type": "concat", "num_fc_layers": 2}, TRAINER: {"epochs": 4, "learning_rate": 0.001}, "backend": {"type": "ray", **RAY_BACKEND_KWARGS}, } output_feature_name = output_features[0]["name"] hyperopt_configs = { "parameters": { "trainer.learning_rate": { "space": "loguniform", "lower": 0.001, "upper": 0.1, }, output_feature_name + ".output_size": {"space": "randint", "lower": 2, "upper": 32}, output_feature_name + ".num_fc_layers": {"space": "randint", "lower": 2, "upper": 6}, }, "goal": "minimize", "output_feature": output_feature_name, "validation_metrics": "loss", "executor": {"type": "ray", "num_samples": 2}, "search_alg": {"type": "variant_generator"}, } # add hyperopt parameter space to the config config["hyperopt"] = hyperopt_configs run_hyperopt(config, dataset_parquet, ray_mock_dir) @spawn

b59ce782e675d1c4511fad9f13b12fc3f2f02e90

@spawn

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

def test_hyperopt_run_hyperopt(csv_filename, ray_mock_dir): input_features = [number_feature(), number_feature()] output_features = [binary_feature()] csv_filename = os.path.join(ray_mock_dir, "dataset.csv") dataset_csv = generate_data(input_features, output_features, csv_filename, num_examples=100) dataset_parquet = create_data_set_to_use("parquet", dataset_csv) config = { "input_features": input_features, "output_features": output_features, "combiner": {"type": "concat", "num_fc_layers": 2}, TRAINER: {"epochs": 4, "learning_rate": 0.001}, "backend": {"type": "ray", **RAY_BACKEND_KWARGS}, } output_feature_name = output_features[0]["name"] hyperopt_config

test_hyperopt_run_hyperopt

code/default/gae_proxy/local/web_control.py

v4.6.0 compactiable with python 2.7.

XX-Net

Python

web_control.py

def req_importip_handler(self): req = urlparse(self.path).query reqs = parse_qs(req, keep_blank_values=True) data = '' if reqs['cmd'] == ['importip']: count = 0 ip_list = self.postvars['ipList'][0] lines = ip_list.split("\n") for line in lines: addresses = line.split('|') for ip in addresses: ip = ip.strip() if not utils.check_ip_valid(ip): continue if front.ip_manager.add_ip(ip, 100, "google.com", "gws"): count += 1 data = '{"res":"%s"}' % count front.ip_manager.save(force=True) elif reqs['cmd'] == ['exportip']: data = '{"res":"' for ip in front.ip_manager.ip_list: if front.ip_manager.ip_dict[ip]['fail_times'] > 0: continue data += "%s|" % ip data = data[0: len(data) - 1] data += '"}' self.send_response_nc('text/html', data)

0820c040ec2815f40bd0e469e27c2bf4d2cc33bc

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

def req_importip_handler(self): req = urlparse(self.path).query reqs = parse_qs(req, keep_blank_values=True) data = '' if reqs['cmd'] == ['importip']: count = 0 ip_list = self.postvars['ipList'][0] lines = ip_list.split("\n") for line in lines: addresses = line.split('|') for ip in addresses: ip = ip.strip() if not utils.check_ip_valid(ip): continue if front.ip_manager.add_ip(ip, 100, "google.com", "gws"): count += 1 data = '{"res":"%s"}' % c

req_importip_handler

tests/blocks/test_core.py

Block capabilities (PrefectHQ/orion#1898) * Add capabilities to BlockSchemas * Remove type field from BlockSchemas * Create postgres migration, bump API version

prefect

Python

test_core.py

async def test_block_load(self, test_block, block_document): my_block = await test_block.load(block_document.name) assert my_block._block_document_name == block_document.name assert my_block._block_document_id == block_document.id assert my_block._block_type_id == block_document.block_type_id assert my_block._block_schema_id == block_document.block_schema_id assert my_block.foo == "bar"

168483e9cf038a3629f880f838b5aa9291a48411

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

async def test_block_load(self, test_block, block_document): my_block = await test_block.load(block_document.name) assert my_block._block_document_name == block_document.name assert my_block._block_document_id == block_document.id a

test_block_load

tests/components/blebox/test_climate.py

Blebox add thermoBox to climate (#81090) Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

core

Python

test_climate.py

async def test_reding_hvac_actions(saunabox, hass, caplog): caplog.set_level(logging.ERROR) feature_mock, entity_id = saunabox await async_setup_entity(hass, entity_id)

923fa473e171fcdf396556ea200612e378f9b0a5

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

async def test_reding_hvac_actions(saunabox, hass, caplog): caplog.set_level(logging.ERROR) feature_mock, entity_id = saunabox await async_setup_entity(hass, entity_id)

test_reding_hvac_actions

saleor/plugins/tests/test_manager.py

Revert "Add fix for multiplied prices on Avatax side (#9699)" (#9750) This reverts commit 5dc3a30ef3bb8dfce67ede276fa465e2c420d003.

saleor

Python

test_manager.py

def test_manager_calculates_order_line_total(order_line, plugins): currency = order_line.order.currency expected_total = ( TaxedMoney(Money("1.0", currency), Money("1.0", currency)) if plugins else quantize_price(order_line.unit_price * order_line.quantity, currency) ) taxed_total = ( PluginsManager(plugins=plugins) .calculate_order_line_total( order_line.order, order_line, order_line.variant, order_line.variant.product ) .price_with_discounts ) assert expected_total == taxed_total

ab7e4e203fd23a5fec1d27d0774905c52c509dc3

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

def test_manager_calculates_order_line_total(order_line, plugins): currency = order_line.order.currency expected_total = ( TaxedMoney(Money("1.0", currency), Money("1.0", currency)) if plugins else quantize_price(order_line.unit_price * order_line.quantity, currency) ) taxed_total = ( PluginsManager(plugins=plugins) .cal

test_manager_calculates_order_line_total

thumbor/filters/blur.py

Reformat to 80 chars and mypy.ini

thumbor

Python

blur.py

def apply_blur(mode, data, size, radius, sigma=0): if sigma == 0: sigma = radius radius = min(radius, MAX_RADIUS) matrix, matrix_size = generate_1d_matrix(sigma, radius) data = _convolution.apply( mode, data, size[0], size[1], matrix, matrix_size, True ) return _convolution.apply(mode, data, size[0], size[1], matrix, 1, True)

301124c5b377fa56b940d298900dbc5816dbc24e

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

def apply_blur(mode, data, size, radius, sigma=0): if sigma

apply_blur

wagtail/documents/tests/test_views.py

Reformat with black

wagtail

Python

test_views.py

def test_content(self): self.assertEqual( b"".join(self.get().streaming_content), b"A boring example document" )

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def test_content(self): self.assertEqual(

test_content

test/test_components.py

blocks-components-tests - move gradio/test_data to test/test_data/media_data

gradio

Python

test_components.py

def test_tokenize(self): x_wav = media_data.BASE64_AUDIO audio_input = gr.Audio() tokens, _, _ = audio_input.tokenize(x_wav) self.assertEquals(len(tokens), audio_input.interpretation_segments) x_new = audio_input.get_masked_inputs(tokens, [[1] * len(tokens)])[0] similarity = SequenceMatcher(a=x_wav["data"], b=x_new).ratio() self.assertGreater(similarity, 0.9)

070b8a96b5b8448e306bd40f2b12d44b759afd48

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

def test_tokenize(self): x_wav = media_data.BASE64_AUDIO audio_input = gr.Audio() tokens, _, _ = audio_input.tokenize(x_wav) self.assertEquals(len(tokens), audio_input.interpretation_segments) x_new = audio_input.get_masked_inputs(tokens, [[1

test_tokenize

django/core/signing.py

Refs #33476 -- Reformatted code with Black.

django

Python

signing.py

def signature(self, value, key=None): key = key or self.key return base64_hmac(self.salt + "signer", value, key, algorithm=self.algorithm)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def signature(self, value, key=None): key = key or self.key return b

signature

sympy/tensor/tests/test_tensor.py

Tests for subs and xreplace with dummy index conflicts see https://github.com/sympy/sympy/issues/24337

sympy

Python

test_tensor.py

def test_TensMul_subs(): R3 = TensorIndexType('R3', dim=3) p, q, r = tensor_indices("p q r", R3) K = TensorHead("K", [R3]) V = TensorHead("V", [R3]) C0 = TensorIndex(R3.dummy_name + "_0", R3, True) assert ( K(p)*V(r)*K(-p) ).subs({V(r): K(q)*K(-q)}) == K(p)*K(q)*K(-q)*K(-p) assert ( K(p)*V(r)*K(-p) ).xreplace({V(r): K(q)*K(-q)}) == K(p)*K(q)*K(-q)*K(-p) assert ( K(p)*V(r) ).xreplace({p: C0, V(r): K(q)*K(-q)}) == K(C0)*K(q)*K(-q)

e31e048fe4834f7259193c5e13e7e7b0d5fcd230

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

def test_TensMul_subs(): R3 = TensorIndexType('R3', dim=3) p, q, r = tensor_indices("p q r", R3) K = TensorHead("K", [R3]) V = TensorHead("V", [R3])

test_TensMul_subs

python/ray/tests/kuberay/test_autoscaling_config.py

[autoscaler][kuberay] Never request more than maxReplicas worker pods (#29770) Partially addresses ray-project/kuberay#560, in which it was observed that "replicas" was being set higher than "maxReplicas" in the KubeRay CR. Applies a surface-level fix by making sure that the autoscaler does not set replicas higher the maxReplicas when creating nodes. Signed-off-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>

ray

Python

test_autoscaling_config.py

def test_cr_image_consistency(): cr = get_basic_ray_cr() group_specs = [cr["spec"]["headGroupSpec"]] + cr["spec"]["workerGroupSpecs"] # Head, CPU group, GPU group. assert len(group_specs) == 3 ray_containers = [ group_spec["template"]["spec"]["containers"][0] for group_spec in group_specs ] # All Ray containers in the example config have "ray-" in their name. assert all("ray-" in ray_container["name"] for ray_container in ray_containers) # All Ray images are from the Ray repo. assert all( "rayproject/ray" in ray_container["image"] for ray_container in ray_containers ) # All Ray images are the same. assert len({ray_container["image"] for ray_container in ray_containers}) == 1 @pytest.mark.parametrize("exception", [Exception, requests.HTTPError]) @pytest.mark.parametrize("num_exceptions", range(6))

9c9977f814facdebc1828fa576531fc95f553172

@pytest.mark.parametrize("exception", [Exception, requests.HTTPError]) @pytest.mark.parametrize("num_exceptions", range(6))

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

def test_cr_image_consistency(): cr = get_basic_ray_cr() group_specs = [cr["spec"]["headGroupSpec"]] + cr["spec"]["workerGroupSpecs"] # Head, CPU group, GPU group. assert len(group_specs) == 3 ray_containers = [ group_spec["template"]["spec"]["containers"][0] for group_spec in group_specs ] # All Ray containers in the example config have "ray-" in their name. assert all("ray-" in ray_container["name"] for ray_container in ray_containers) # All Ray images are from the Ray repo. assert all( "rayproject/

test_cr_image_consistency

tests/freqai/test_freqai_interface.py

skip darwin in RL tests, remove example scripts, improve doc

freqtrade

Python

test_freqai_interface.py

def is_mac() -> bool: machine = platform.system() return "Darwin" in machine @pytest.mark.parametrize('model', [ 'LightGBMRegressor', 'XGBoostRegressor', 'CatboostRegressor', 'ReinforcementLearner', 'ReinforcementLearner_multiproc' ])

eeebb78a5c772b0c3e569fd476587facb1f8a9dc

@pytest.mark.parametrize('model', [ 'LightGBMRegressor', 'XGBoostRegressor', 'CatboostRegressor', 'ReinforcementLearner', 'ReinforcementLearner_multiproc' ])

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

def is_mac() -> bool: machine = platform.system() return "Darwin" in machine @pytest.mark.parametrize('model', [ 'LightGBMReg

is_mac

src/sentry/eventstore/models.py

feat(perf_issues): Add `GroupEvent` and split some functionality in `Event` into a base class. (#38143) Since we can now have events with multiple groups, we can no longer rely on the `Event.group` property. This pr adds in a `GroupEvent` subclass that should be passed around wherever we expect an event to have a single `Group` associated with it. `Event` has been split up into `BaseEvent` and `Event`. We will deprecate and remove uses of `group_id` and `group` in the `Event` class going forward. If we need an event with a `Group`, we can use `build_group_events` to fetch all `GroupEvents` associated with the `Event`, or `for_group` if we just need a specific `Event`/`Group` pairing. Going forward, the plan is to store all groups in the `groups` property. This means that error events being sent via eventstream will have their group included in `groups` as well. We'll need to update the errors processor in snuba to look there instead of `group_id`. This seems cleaner long term, instead of having both `group_id` and `group_ids` passed through. To figure out where we need to use `build_group_events` and `for_group` we can do a mix of searching the codebase and commenting out the `group_id` and `group` properties and see how CI goes.

sentry

Python

models.py

def groups(self) -> Sequence[Group]: from sentry.models import Group if getattr(self, "_groups_cache"): return self._groups_cache if self._group_ids is not None: group_ids = self._group_ids else: snuba_group_id = self.group_id # TODO: Replace `snuba_group_id` with this once we deprecate `group_id`. # snuba_group_id = self._snuba_data.get(self._get_column_name(Columns.GROUP_ID)) snuba_group_ids = self._snuba_data.get(self._get_column_name(Columns.GROUP_IDS)) group_ids = [] if snuba_group_id: group_ids.append(snuba_group_id) if snuba_group_ids: group_ids.extend(snuba_group_ids) if group_ids: groups = list(Group.objects.filter(id__in=group_ids)) else: groups = [] self._groups_cache = groups return groups

6aaaf5089b2c39757883179df5a8512db3b0c716

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

def groups(self) -> Sequence[Group]: from sentry.models import Group if getattr(self, "_groups_cache"): return self._groups_cache if self._group_ids is not None: group_ids = self._group_ids else: snuba_group_id = self.group_id # TODO: Replace `snuba_group_id` with this once we deprecate `group_id`. # snuba_group_id = self._snuba_data.get(self._get_column_name(C

groups

src/datasets/iterable_dataset.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

iterable_dataset.py

def __iter__(self): rng = deepcopy(self.generator) kwargs_with_shuffled_shards = _shuffle_gen_kwargs(rng, self.kwargs) yield from self.generate_examples_fn(**kwargs_with_shuffled_shards)

2945690ea731f85a356220a71cdc630281c676f4

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

def __iter__(self): rng = deepcopy(self.generator) kwargs_with_shuff

__iter__

tests/utils/test_cache.py

chore: remove unnecessary collection.OrderedDict - Replace collection.OrderedDict with builtins.dict where possible: Python 3.7+ ensures the correct order in builtins.dict objects and is no longer an implementation detail of cpython. - Fix OrderedDict type annotation in streamlink.utils.cache.LRUCache - Add unit test for streamlink.utils.cache.LRUCache

streamlink

Python

test_cache.py

def test_lru_cache(): cache = LRUCache(num=3) assert cache.get("foo") is None, "Getter returns None for unknown items" cache.set("foo", "FOO") assert list(cache.cache.items()) == [("foo", "FOO")], "Setter adds new items" assert cache.get("foo") == "FOO", "Getter returns correct value of known items" cache.set("bar", "BAR") cache.set("baz", "BAZ") cache.set("qux", "QUX") assert list(cache.cache.items()) == [("bar", "BAR"), ("baz", "BAZ"), ("qux", "QUX")], "Setter respects max queue size" cache.get("bar") assert list(cache.cache.items()) == [("baz", "BAZ"), ("qux", "QUX"), ("bar", "BAR")], "Getter moves known items to the end" cache.get("unknown") assert list(cache.cache.items()) == [("baz", "BAZ"), ("qux", "QUX"), ("bar", "BAR")], "Getter keeps order on unknown items" cache.set("foo", "FOO") assert list(cache.cache.items()) == [("qux", "QUX"), ("bar", "BAR"), ("foo", "FOO")], "Setter moves new items to the end" cache.set("qux", "QUUX") assert list(cache.cache.items()) == [("bar", "BAR"), ("foo", "FOO"), ("qux", "QUUX")], "Setter moves known items to the end"

6325c74e6869b45051ec111e4243d77cc536ba66

https://github.com/streamlink/streamlink.git

def test_lru_cache(): cache = LRUCache(num=3) assert cache.get("foo") is None, "Getter returns None for unknown items" cache.set("foo", "FOO") assert list(cache.cache.items()) == [("foo", "FOO")

test_lru_cache

fastai/gen_doc/nbtest.py

Upgrading to support latest Pytorch version

DeOldify

Python

nbtest.py

def get_qualname(elt): return elt.__qualname__ if hasattr(elt, '__qualname__') else fn_name(elt)

4fc3616712edb19179b17dd270ad6cf63abf99c2

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

def get_qualname(elt): return elt.__qualname__

get_qualname

networkx/generators/small.py

Use from_dict_of_lists instead of make_small_graph in generators.small (#5267) * Add test for digraph creation behavior. * Use from_dict_of_lists instead of make_small_graph * Make sure generators don't support digraph. * Rm redundant create_using check.

networkx

Python

small.py

def truncated_cube_graph(create_using=None): G = nx.from_dict_of_lists( { 0: [1, 2, 4], 1: [11, 14], 2: [3, 4], 3: [6, 8], 4: [5], 5: [16, 18], 6: [7, 8], 7: [10, 12], 8: [9], 9: [17, 20], 10: [11, 12], 11: [14], 12: [13], 13: [21, 22], 14: [15], 15: [19, 23], 16: [17, 18], 17: [20], 18: [19], 19: [23], 20: [21], 21: [22], 22: [23], }, create_using=create_using, ) G.name = "Truncated Cube Graph" return G

7669e7f2f31485015f3ea7cdd535e086467fa433

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

def truncated_cube_graph(create_using=None): G = nx.from_dict_of_lists( { 0: [1, 2, 4], 1: [11, 14], 2: [3, 4], 3: [6, 8], 4: [5],

truncated_cube_graph

src/prefect/cli/profile.py

Remove extra "f" (#6384)

prefect

Python

profile.py

async def check_orion_connection(profile_name): with use_profile(profile_name, include_current_context=False): httpx_settings = dict(timeout=3) try: # attempt to infer Cloud 2.0 API from the connection URL cloud_client = get_cloud_client( httpx_settings=httpx_settings, infer_cloud_url=True ) res = await cloud_client.api_healthcheck() exit_method, msg = ( exit_with_success, f"Connected to Prefect Cloud using profile {profile_name!r}", ) except CloudUnauthorizedError: # if the Cloud 2.0 API exists and fails to authenticate, notify the user exit_method, msg = ( exit_with_error, f"Error authenticating with Prefect Cloud using profile {profile_name!r}", ) except httpx.HTTPStatusError as exc: if exc.response.status_code == status.HTTP_404_NOT_FOUND: # if the route does not exist, attmpt to connect as a hosted Orion instance try: # inform the user if Prefect Orion endpoints exist, but there are # connection issues client = get_client(httpx_settings=httpx_settings) connect_error = await client.api_healthcheck() if connect_error is not None: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Orion using profile {profile_name!r}", ) elif await client.using_ephemeral_app(): # if the client is using an ephemeral Orion app, inform the user exit_method, msg = ( exit_with_success, f"No Prefect Orion instance specified using profile {profile_name!r}. " f"Flow run metadata will be stored at the locally configured database: {prefect.settings.PREFECT_ORION_DATABASE_CONNECTION_URL.value()}", ) else: exit_method, msg = ( exit_with_success, f"Connected to Prefect Orion using profile {profile_name!r}", ) except Exception as exc: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Orion using profile {profile_name!r}", ) else: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Cloud: {exc!r}", ) except TypeError: # if no Prefect Orion API URL has been set, httpx will throw a TypeError try: # try to connect with the client anyway, it will likely use an # ephemeral Orion instance client = get_client(httpx_settings=httpx_settings) connect_error = await client.api_healthcheck() if connect_error is not None: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Orion using profile {profile_name!r}", ) elif await client.using_ephemeral_app(): exit_method, msg = ( exit_with_success, f"No Prefect Orion instance specified using profile {profile_name!r}. " f"Flow run metadata will be stored at the locally configured database: {prefect.settings.PREFECT_ORION_DATABASE_CONNECTION_URL.value()}", ) else: exit_method, msg = ( exit_with_success, f"Connected to Prefect Orion using profile {profile_name!r}", ) except Exception as exc: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Orion using profile {profile_name!r}", ) except (httpx.ConnectError, httpx.UnsupportedProtocol) as exc: exit_method, msg = exit_with_error, "Invalid Prefect API URL" return exit_method, msg @profile_app.command()

2f22824cd7af9bb89c103698c05036f2542caff1

@profile_app.command()

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

async def check_orion_connection(profile_name): with use_profile(profile_name, include_current_context=False): httpx_settings = dict(timeout=3) try: # attempt to infer Cloud 2.0 API from the connection URL cloud_client = get_cloud_client( httpx_settings=httpx_settings, infer_cloud_url=True ) res = await cloud_client.api_healthcheck() exit_method, msg = ( exit_with_success, f"Connected to Prefect Cloud using profile {profile_name!r}", ) except CloudUnauthorizedError: # if the Cloud 2.0 API exists and fails to authenticate, notify the user exit_method, msg = ( exit_with_error, f"Error authenticating with Prefect Cloud using profile {profile_name!r}", ) except httpx.HTTPStatusError as exc: if exc.response.status_code == status.HTTP_404_NOT_FOUND: # if the route does not exist, attmpt to connect as a hosted Orion instance try: # inform the user if Prefect Orion endpoints exist, but there are # connection issues client = get_client(httpx_settings=httpx_settings) connect_error = await client.api_healthcheck() if connect_error is not None: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Orion using profile {profile_name!r}", ) elif await client.using_ephemeral_app(): # if the client is using an ephemeral Orion app, inform the user exit_method, msg = ( exit_with_success, f"No Prefect Orion instance specified using profile {profile_name!r}. " f"Flow run metadata will be stored at the locally configured database: {prefect.settings.PREFECT_ORION_DATABASE_CONNECTION_URL.value()}", ) else: exit_method, msg = ( exit_with_success, f"Connected to Prefect Orion using profile {profile_name!r}", ) except Exception as exc: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Orion using profile {profile_name!r}", ) else: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Cloud: {exc!r}", ) except TypeError: # if no Prefect Orion API URL has been set, httpx will throw a TypeError try: # try to connect with the client anyway, it will likely use an # ephemeral Orion instance client = get_client(httpx_settings=httpx_settings) connect_error = await client.api_healthcheck() if connect_error is not None: exit_method, msg = ( exit_with_error, f"Error connecting to Prefect Orion using profile {profile_name!r}", ) elif await client.using_ephemeral_app(): exit_method, msg = ( exit_with_success, f"No Prefect Orion instance specified using profile {profile_name!r}. " f"Flow run metadata will be stored at the locally configured database: {prefect.settings.PREFECT_ORION_DATABASE_CONNECTION_URL.value()}", ) else: exit_method, msg = ( exit_with_success, f"Connected to Prefect Orion using profile {profile_name!r}", ) except Exception as exc: exit_method, msg = ( exit_with_er

check_orion_connection

src/prefect/utilities/logging.py

Update src/prefect/utilities/logging.py Co-authored-by: Michael Adkins <madkinszane@gmail.com>

prefect

Python

logging.py

def process(self, msg, kwargs): kwargs["extra"] = {**self.extra, **(kwargs.get("extra") or {})} return (msg, kwargs)

3a2d581ec0540dab8efc5e30c1bc10dfa321f2b5

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

def process(self, msg, kwargs): kwargs["extra"] = {**self.extra, **(kwarg

process

keras/legacy_tf_layers/variable_scope_shim_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

variable_scope_shim_test.py

def call(self, inputs): with tf.compat.v1.variable_scope("foo"): return self.scale_by_y(inputs)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def call(self, inputs): with tf.compat.v1.variable_scope(

call

test/test_prototype_transforms.py

rename features._Feature to datapoints._Datapoint (#7002) * rename features._Feature to datapoints.Datapoint * _Datapoint to Datapoint * move is_simple_tensor to transforms.utils * fix CI * move Datapoint out of public namespace

vision

Python

test_prototype_transforms.py

def test__get_params(self, padding, pad_if_needed, size, mocker): image = mocker.MagicMock(spec=datapoints.Image) image.num_channels = 3 image.spatial_size = (24, 32) h, w = image.spatial_size transform = transforms.RandomCrop(size, padding=padding, pad_if_needed=pad_if_needed) params = transform._get_params([image]) if padding is not None: if isinstance(padding, int): pad_top = pad_bottom = pad_left = pad_right = padding elif isinstance(padding, list) and len(padding) == 2: pad_left = pad_right = padding[0] pad_top = pad_bottom = padding[1] elif isinstance(padding, list) and len(padding) == 4: pad_left, pad_top, pad_right, pad_bottom = padding h += pad_top + pad_bottom w += pad_left + pad_right else: pad_left = pad_right = pad_top = pad_bottom = 0 if pad_if_needed: if w < size[1]: diff = size[1] - w pad_left += diff pad_right += diff w += 2 * diff if h < size[0]: diff = size[0] - h pad_top += diff pad_bottom += diff h += 2 * diff padding = [pad_left, pad_top, pad_right, pad_bottom] assert 0 <= params["top"] <= h - size[0] + 1 assert 0 <= params["left"] <= w - size[1] + 1 assert params["height"] == size[0] assert params["width"] == size[1] assert params["needs_pad"] is any(padding) assert params["padding"] == padding

a8007dcdfb5159a711fa343d2ac4bb7df826975f

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

def test__get_params(self, padding, pad_if_needed, size, mocker): image = mocker.MagicMock(spec=datapoints.Image) image.num_channels = 3 image.spatial_size = (24, 32) h, w = image.spatial_size transform = transforms.RandomCrop(size, padding=padding, pad_if_needed=pad_if_needed) params = transform._get_params([image]) if padding is not None: if isinstance(padding, int): pad_top = pad_bottom = pad_left = pad_right = padding elif isinstance(padding, list) and len(padding) == 2:

test__get_params

tests/components/mqtt/test_discovery.py

Move MQTT discovery hass.data globals to dataclass (#78706) * Add MQTT discovery hass.data globals to dataclass * isort * Additional rework * Add hass.data["mqtt_tags"] to dataclass * Follow-up comment * Corrections

core

Python

test_discovery.py

async def test_discovery_expansion(hass, mqtt_mock_entry_no_yaml_config, caplog): await mqtt_mock_entry_no_yaml_config() data = ( '{ "~": "some/base/topic",' ' "name": "DiscoveryExpansionTest1",' ' "stat_t": "test_topic/~",' ' "cmd_t": "~/test_topic",' ' "availability": [' " {" ' "topic":"~/avail_item1",' ' "payload_available": "available",' ' "payload_not_available": "not_available"' " }," " {" ' "topic":"avail_item2/~",' ' "payload_available": "available",' ' "payload_not_available": "not_available"' " }" " ]," ' "dev":{' ' "ids":["5706DF"],' ' "name":"DiscoveryExpansionTest1 Device",' ' "mdl":"Generic",' ' "hw":"rev1",' ' "sw":"1.2.3.4",' ' "mf":"None",' ' "sa":"default_area"' " }" "}" ) async_fire_mqtt_message(hass, "homeassistant/switch/bla/config", data) await hass.async_block_till_done() state = hass.states.get("switch.DiscoveryExpansionTest1") assert state.state == STATE_UNAVAILABLE async_fire_mqtt_message(hass, "avail_item2/some/base/topic", "available") await hass.async_block_till_done() state = hass.states.get("switch.DiscoveryExpansionTest1") assert state is not None assert state.name == "DiscoveryExpansionTest1" assert ("switch", "bla") in hass.data["mqtt"].discovery_already_discovered assert state.state == STATE_UNKNOWN async_fire_mqtt_message(hass, "test_topic/some/base/topic", "ON") state = hass.states.get("switch.DiscoveryExpansionTest1") assert state.state == STATE_ON async_fire_mqtt_message(hass, "some/base/topic/avail_item1", "not_available") await hass.async_block_till_done() state = hass.states.get("switch.DiscoveryExpansionTest1") assert state.state == STATE_UNAVAILABLE @patch("homeassistant.components.mqtt.PLATFORMS", [Platform.SWITCH])

84b2c74746b694d217fe6d448a8dfff4bc2d7a9e

@patch("homeassistant.components.mqtt.PLATFORMS", [Platform.SWITCH])

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

async def test_discovery_expansion(hass, mqtt_mock_entry_no_yaml_config, caplog): await mqtt_mock_entry_no_yaml_config() data = ( '{ "~": "some/base/topic",' ' "name": "DiscoveryExpansionTest1",' ' "stat_t": "test_topic/~",' ' "cmd_t": "~/test_topic",' ' "availability": [' " {" ' "topic":"~/avail_item1",' ' "payload_available": "available",' ' "payload_not_available": "not_available"' " }," " {" ' "topic":"avail_item2/~",' ' "payload_available": "available",' ' "payload_not_ava

test_discovery_expansion

pipenv/patched/notpip/_vendor/distlib/_backport/tarfile.py

Vendor in pip 22.1.2

pipenv

Python

tarfile.py

def __iter__(self): while True: line = self.readline() if not line: break yield line #class ExFileObject #------------------ # Exported Classes #------------------

c69d55f7c82d5ae2cce542bcfb98d043ca4836a0

https://github.com/pypa/pipenv.git

def __iter__(self): while True: line = self.readline() if not line: break

__iter__

pipenv/patched/notpip/_vendor/platformdirs/android.py

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

pipenv

Python

android.py

def _android_folder() -> str: try: # First try to get path to android app via pyjnius from jnius import autoclass Context = autoclass("android.content.Context") # noqa: N806 result: str = Context.getFilesDir().getParentFile().getAbsolutePath() except Exception: # if fails find an android folder looking path on the sys.path pattern = re.compile(r"/data/(data|user/\d+)/(.+)/files") for path in sys.path: if pattern.match(path): result = path.split("/files")[0] break else: raise OSError("Cannot find path to android app folder") return result @lru_cache(maxsize=1)

f3166e673fe8d40277b804d35d77dcdb760fc3b3

@lru_cache(maxsize=1)

https://github.com/pypa/pipenv.git

def _android_folder() -> str: try: # First try to get path to android app via pyjnius from jnius import autoclass Context = autoclass("android.content.Context") # noqa: N806 result: str = Context.getFilesDir().getParentFile().getAbsolutePath() except Exception: # if fails find an android folder looking path on the sys.path pattern = re.compile(r"/data/(data|user/\d+)/(.+)/files") for path in sys.path: if pattern.match(path): re

_android_folder

python/ray/data/tests/test_dataset.py

[Dataset] [DataFrame 2/n] Add pandas block format implementation (partial) (#20988) This PR adds pandas block format support by implementing `PandasRow`, `PandasBlockBuilder`, `PandasBlockAccessor`. Note that `sort_and_partition`, `combine`, `merge_sorted_blocks`, `aggregate_combined_blocks` in `PandasBlockAccessor` redirects to arrow block format implementation for now. They'll be implemented in a later PR. Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com> Co-authored-by: Eric Liang <ekhliang@gmail.com>

ray

Python

test_dataset.py

def test_from_pandas_refs(ray_start_regular_shared, enable_pandas_block): ctx = ray.data.context.DatasetContext.get_current() old_enable_pandas_block = ctx.enable_pandas_block ctx.enable_pandas_block = enable_pandas_block try: df1 = pd.DataFrame({"one": [1, 2, 3], "two": ["a", "b", "c"]}) df2 = pd.DataFrame({"one": [4, 5, 6], "two": ["e", "f", "g"]}) ds = ray.data.from_pandas_refs([ray.put(df1), ray.put(df2)]) assert ds._dataset_format( ) == "pandas" if enable_pandas_block else "arrow" values = [(r["one"], r["two"]) for r in ds.take(6)] rows = [(r.one, r.two) for _, r in pd.concat([df1, df2]).iterrows()] assert values == rows # test from single pandas dataframe ref ds = ray.data.from_pandas_refs(ray.put(df1)) assert ds._dataset_format( ) == "pandas" if enable_pandas_block else "arrow" values = [(r["one"], r["two"]) for r in ds.take(3)] rows = [(r.one, r.two) for _, r in df1.iterrows()] assert values == rows finally: ctx.enable_pandas_block = old_enable_pandas_block

4a55d10bb1b70971f50a3872421f2c1eebd84e64

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

def test_from_pandas_refs(ray_start_regular_shared, enable_pandas_block): ctx = ray.data.context.DatasetContext.get_current() old_enable_pandas_block = ctx.enable_pandas_block ctx.enable_pandas_block = enable_pandas_block try: df1 = pd.DataFrame({"one": [1, 2, 3], "two": ["a", "b", "c"]}) df2 = pd.DataFrame({"one": [4, 5, 6], "two": ["e", "f", "g"]}) ds = ray.data.from_pandas_refs([ray.put(df1), ray.put(df2)]) assert ds._dataset_format( ) == "pandas" if enable_pandas_block else "arrow" values = [(r["one"], r["two"]) for r in ds.take(6)] rows = [(r.one, r.two) for _, r in pd.concat([df1, df

test_from_pandas_refs

homeassistant/components/zha/switch.py

Add configurable zha switch entity (#71784) * add configurable zha switch entity * final zha configurable switch * fix codecov * replaced errorneous cluster with local quirk * test fix * minor changes

core

Python

switch.py

async def async_turn_on(self, **kwargs) -> None: await self.async_turn_on_off(True)

0c2f22d4780612545c483627da729e44d46ee9fd

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

async def async_turn_on(self, **kwargs) -> None: await self.async_turn_on_off(True)

async_turn_on

homeassistant/components/fibaro/climate.py

Support hvacsystem in fibaro integration (#78234) fixes undefined

core

Python

climate.py

def hvac_action(self) -> HVACAction | None: if not self._op_mode_device: return None prop = self._op_mode_device.fibaro_device.properties if "thermostatOperatingState" in prop: with suppress(ValueError): return HVACAction(prop.thermostatOperatingState.lower()) return None

cd2377bc054ebe4c5c0432aac525d768dcfbe57a

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

def hvac_action(self) -> HVACAction | None: if not self._op_mode_device: return None prop = self._op_mode_device.fibaro_device.properties if "thermostatOperatingState" in prop: with suppress(ValueError):

hvac_action

tests/cli/test_cloud.py

Add login with a browser to `prefect cloud login` (#7334)

prefect

Python

test_cloud.py

def test_login_with_invalid_key(key, expected_output, respx_mock): respx_mock.get(PREFECT_CLOUD_API_URL.value() + "/me/workspaces").mock( return_value=httpx.Response(status.HTTP_403_FORBIDDEN) ) invoke_and_assert( ["cloud", "login", "--key", key, "--workspace", "foo"], expected_code=1, expected_output=expected_output, )

1a6dee5e9eb71e6e6d1d3492002e9cd674ab9f9b

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

def test_login_with_invalid_key(key, expected_output, respx_mock): respx_mock.get(PREFECT_CLOUD_API_URL.value() + "/me/workspaces").mock( return_value=httpx.Response(status.HTTP_403_FORBIDDEN) ) invoke_and_assert( ["cloud", "login", "--key", key, "--workspace", "foo"], expected_code=1, expe

test_login_with_invalid_key

fastai/data_block.py

Upgrading to support latest Pytorch version

DeOldify

Python

data_block.py

def _decode(df): return np.array([[df.columns[i] for i,t in enumerate(x) if t==1] for x in df.values], dtype=np.object)

4fc3616712edb19179b17dd270ad6cf63abf99c2

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

def _decode(df): return np.array([[df.columns[i] for i,t in enumerate(x) if t==1] for x i

_decode

keras/backend.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

backend.py

def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): if ndim(x) == 4 and list(reduction_axes) in [[0, 1, 2], [0, 2, 3]]: if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]: return _broadcast_normalize_batch_in_training( x, gamma, beta, reduction_axes, epsilon=epsilon ) return _fused_normalize_batch_in_training( x, gamma, beta, reduction_axes, epsilon=epsilon ) else: if sorted(reduction_axes) == list(range(ndim(x)))[:-1]: return _regular_normalize_batch_in_training( x, gamma, beta, reduction_axes, epsilon=epsilon ) else: return _broadcast_normalize_batch_in_training( x, gamma, beta, reduction_axes, epsilon=epsilon ) @keras_export("keras.backend.batch_normalization") @tf.__internal__.dispatch.add_dispatch_support @doc_controls.do_not_generate_docs

84afc5193d38057e2e2badf9c889ea87d80d8fbf

@keras_export("keras.backend.batch_normalization") @tf.__internal__.dispatch.add_dispatch_support @doc_controls.do_not_generate_docs

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

def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3): if ndim(x) == 4 and list(reduction_axes) in [[0, 1, 2], [0, 2, 3]]: if not _has_nchw_support() and list(reduction_axes) == [0, 2, 3]: return _broadcast_normalize_batch_in_training( x, gamma, beta, reduction_axes, epsilon=epsilon ) return _fused_

normalize_batch_in_training

mitmproxy/tools/console/grideditor/base.py

Replace blinker with custom implementation (#5528) * replace blinker with custom implementation The major benefit here is type checking, followed by proper support for async receivers. * fix compatibility with Python 3.9 * fix nits * try harder to force gc * try harderer * coverage++ * coverage++ * nits

mitmproxy

Python

base.py

def set_current_value(self, val) -> None: errors = self.lst[self.focus][1] emsg = self.editor.is_error(self.focus_col, val) if emsg: signals.status_message.send(message=emsg, expire=5) errors.add(self.focus_col) else: errors.discard(self.focus_col) self.set_value(val, self.focus, self.focus_col, errors)

f4dc2f2cfdb40e04022e4deb4aa67578deff5d23

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

def set_current_value(self, val) -> None: errors = self.lst[self.focus][1] emsg = self.editor.is_error(self.focus_col, val) if emsg: signals.status_message.send(message=emsg, expire=5)

set_current_value

python3.10.4/Lib/calendar.py

add python 3.10.4 for windows

XX-Net

Python

calendar.py

def formatmonthname(self, theyear, themonth, withyear=True): if withyear: s = '%s %s' % (month_name[themonth], theyear) else: s = '%s' % month_name[themonth] return '<tr><th colspan="7" class="%s">%s</th></tr>' % ( self.cssclass_month_head, s)

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def formatmonthname(self, theyear, themonth, withyear=True): if withyear: s = '%s %s' % (month_name[themonth], theyear) else: s = '%s' % month_name[themonth] return '<tr><th colspan="7" class="%s">%s</th></tr>' % ( self.cssclass_month_head, s)

formatmonthname

modules/image/Image_editing/colorization/deoldify/test.py

update deoldify (#1992) * update deoldify * add clean func * update README * update format

PaddleHub

Python

test.py

def test_predict1(self): pred_img, out_path = self.module.predict(input='tests/test.jpg') self.assertIsInstance(pred_img, np.ndarray) self.assertIsInstance(out_path, str)

ca09b195daa8033a6f85bccf27362d0b114f9706

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

def test_predict1(self): pr

test_predict1

homeassistant/components/axis/device.py

Improve type hints in axis (#75910)

core

Python

device.py

async def async_reset(self) -> bool: self.disconnect_from_stream() return await self.hass.config_entries.async_unload_platforms( self.config_entry, PLATFORMS )

8181da70901c6b848ebc2efb2d39a7a3536599f3

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

async def async_reset(self) -> bool: self.disconnect_from_stream() return await self.hass.config_entries.async_unload_platforms( self.config_entry, PLATFORMS

async_reset

tests/test_builder.py

Set builder name from module instead of class (#4388) * Set builder name from module instead of class * Fix tests * Rename dummy_builder to builder in tests

datasets

Python

test_builder.py

def test_cache_dir_for_features(self): with tempfile.TemporaryDirectory() as tmp_dir: f1 = Features({"id": Value("int8")}) f2 = Features({"id": Value("int32")}) builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f1) other_builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f1) self.assertEqual(builder.cache_dir, other_builder.cache_dir) other_builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f2) self.assertNotEqual(builder.cache_dir, other_builder.cache_dir)

d6ae1ea3f93a48d03eab78eecf7b6599144143e1

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

def test_cache_dir_for_features(self): with tempfile.TemporaryDirectory() as tmp_dir: f1 = Features({"id": Value("int8")}) f2 = Features({"id": Value("int32")}) builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f1) other_builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f1) self.assertEqual(builder.cache_dir, other_builder.cache_dir) other_builder = DummyGeneratorBasedBuilderWithIntegers(cache_dir=tmp_dir, name="dummy", features=f2) self.assertNotEqual(builder.cache_dir, other_builder.cache_dir)

test_cache_dir_for_features

src/sentry/snuba/metrics/query_builder.py

ref(metrics): Honor snuba group limits without orderBy [TET-5] (#34287) * ref(metrics): Honor snuba group limits without orderBy This PR fixes the else branch to apply similar session V2 limits without explicit orderBy. Essentially how we achieve this now is through the following logic: Let's say fields across the three different entities are requested with a limit of 3, groupBy project and no orderBy clause - If the results of query to entity 1, hits the limit then we use the project groups as filters for subsequent queries - If the results of query to entity 1 do not hit the limit, but results of query 2 does, then we nuke the groups from query 1 that do not exist in query 2 results and apply those as a filter to query 3 - If the results of all three queries to all three entities don't hit the limit, then at the very end, we might end up with an extra number of groups greater than the limit, which is why we nuke the excess groups

sentry

Python

query_builder.py

def _parse_limit(self, paginator_kwargs) -> Optional[Limit]: if "limit" not in paginator_kwargs: return return Limit(paginator_kwargs["limit"])

1b1e1ed83fa3ee7da1009b927efbd7af94609301

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

def _parse_limit(self, paginator_kwargs) -> Optional[Limit]: if "limit" not in paginator_kwargs: return return Limit(paginator_kwargs["limit"])

_parse_limit

test/test_examples.py

Fix bug with gr.update and interactive=True (#2639) * Fix update interactivity * Lint * CHANGELOG * Fix * Undo interactive=True * Do not call update twice * Add unit test * Revert change * Lint

gradio

Python

test_examples.py

async def test_caching_with_dict(self): text = gr.Textbox() out = gr.Label() io = gr.Interface( lambda _: {text: gr.update(lines=4, interactive=False), out: "lion"}, "textbox", [text, out], examples=["abc"], cache_examples=True, ) prediction = await io.examples_handler.load_from_cache(0) assert not any(d["trigger"] == "fake_event" for d in io.config["dependencies"]) assert prediction == [ {"lines": 4, "__type__": "update", "mode": "static"}, {"label": "lion"}, ]

e6336d688259494205ff4616ff2c03d5460b36bc

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

async def test_caching_with_dict(self): text = gr.Textbox() out = gr.Label() io = gr.Interface( lambda _: {text: gr.update(lines=4, interactive=False), out: "lion"}, "textbox", [text, out], examples=["abc"], cache_examples=True, ) prediction = await io.examples_handler.load

test_caching_with_dict

homeassistant/components/sonarr/sensor.py

Use aiopyarr for sonarr (#65349)

core

Python

sensor.py

def native_value(self) -> StateType: key = self.entity_description.key if key == "diskspace" and self.data.get(key) is not None: total_free = sum(disk.freeSpace for disk in self.data[key]) free = total_free / 1024**3 return f"{free:.2f}" if key == "commands" and self.data.get(key) is not None: return len(self.data[key]) if key == "queue" and self.data.get(key) is not None: return self.data[key].totalRecords if key == "series" and self.data.get(key) is not None: return len(self.data[key]) if key == "upcoming" and self.data.get(key) is not None: return len(self.data[key]) if key == "wanted" and self.data.get(key) is not None: return self.data[key].totalRecords return None

f30681dae7efffd8980b3ee3ae7f355c603b842c

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

def native_value(self) -> StateType: key = self.entity_description.key if key == "diskspace" and self.data.get(key) is not None: total_free = sum(disk.freeSpace for disk in self.data[key]) free = total_free / 1024**3 return f"{free:.2f}" if key == "commands" and self.data.get(key) is not None: return len(self.data[key]) if key == "queue" and self.data.get(key) is not None: return self.data[key].totalRecords if key == "series" and self.data.get(key) is not No

native_value

tests/providers/google/cloud/operators/test_dataplex.py

Add Dataplex operators (#20377)

airflow

Python

test_dataplex.py

def test_execute(self, hook_mock): op = DataplexDeleteTaskOperator( project_id=PROJECT_ID, region=REGION, lake_id=LAKE_ID, dataplex_task_id=DATAPLEX_TASK_ID, task_id="delete_dataplex_task", api_version=API_VERSION, gcp_conn_id=GCP_CONN_ID, delegate_to=DELEGATE_TO, impersonation_chain=IMPERSONATION_CHAIN, ) op.execute(context=None) hook_mock.assert_called_once_with( gcp_conn_id=GCP_CONN_ID, delegate_to=DELEGATE_TO, api_version=API_VERSION, impersonation_chain=IMPERSONATION_CHAIN, ) hook_mock.return_value.delete_task.assert_called_once_with( project_id=PROJECT_ID, region=REGION, lake_id=LAKE_ID, dataplex_task_id=DATAPLEX_TASK_ID, retry=None, timeout=None, metadata=(), )

87c1246b79769f20214a339aadc6a8270d453953

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

def test_execute(self, hook_mock): op = DataplexDeleteTaskOperator( project_id=PROJECT_ID, region=REGION, lake_id=LAKE_ID, dataplex_task_id=DATAPLEX_TASK_ID, task_id="delete_dataplex_task", api_version=API_VERSION, gcp_conn_id=GCP_CONN_ID, delegate_to=DELEGATE_TO, impersonation_chain=IMPERSONATION_CHAIN, ) op.execute(context=None) hook_mock.assert_called_once_with( gcp_conn_id=GCP_CONN_ID, delegate_to=DELEGATE_TO, ap

test_execute

tests/gamestonk_terminal/stocks/insider/test_openinsider_view.py

Tests : Stocks (#1240) * Updating tests : stocks/sector_industry_analysis * Updating tests : stocks/prediction_techniques * Updating tests : doc * Updating tests : black * Updating tests : stocks/sector_industry_analysis * Updating tests : stocks/technical_analysis * Updating tests : etf/technical_analysis * Updating tests : black * Updating tests : stocks/quantitative_analysis * Updating tests : stocks/quantitative_analysis * Updating tests : stocks/options * Updating tests : stocks/options * Updating tests : stocks * Updating tests : black * Updating tests : stocks/prediction_techniques * Updating tests : black * Updating tests : stocks * Updating tests : etf * Updating tests : stocks * Updating tests : black * Updating tests : fundamental_analysis * Updating tests : dark_pool_shorts/finra_model * Updating tests : black * Updating tests : stocks/dark_pook_shorts * Updating tests : stocks/discovery * Updating tests : stocks/insider * Updating tests : stocks * Updating tests : black * Updating tests : stocks/options/yfinance_model * Updating tests : stocks * Updating tests : stocks/insider

OpenBBTerminal

Python

test_openinsider_view.py

def test_print_insider_filter_no_table(mocker): # MOCK SOUP mocker.patch( target="gamestonk_terminal.stocks.insider.openinsider_view.get_open_insider_link", return_value=None, ) openinsider_view.print_insider_filter( preset_loaded="whales", ticker="", limit=10, links=False, export="", ) @pytest.mark.default_cassette("test_print_insider_data") @pytest.mark.vcr @pytest.mark.parametrize( "color", [True, False], )

379cf31cfe7473c6b5747861bb2ec2dbb9974b5d

@pytest.mark.default_cassette("test_print_insider_data") @pytest.mark.vcr @pytest.mark.parametrize( "color", [True, False], )

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

def test_print_insider_filter_no_table(mocker): # MOCK SOUP mocker.patch( target="gamestonk_terminal.stocks.insider.openinsider_view.get_open_insider_link", return_value=None, ) openinsider_vi

test_print_insider_filter_no_table

pandas/tests/scalar/timedelta/test_constructors.py

REF: _reso->_creso (#49107)

pandas

Python

test_constructors.py

def test_overflow_on_construction(): # GH#3374 value = Timedelta("1day").value * 20169940 msg = "Cannot cast 1742682816000000000000 from ns to 'ns' without overflow" with pytest.raises(OutOfBoundsTimedelta, match=msg): Timedelta(value) # xref GH#17637 msg = "Cannot cast 139993 from D to 'ns' without overflow" with pytest.raises(OutOfBoundsTimedelta, match=msg): Timedelta(7 * 19999, unit="D") # used to overflow before non-ns support td = Timedelta(timedelta(days=13 * 19999)) assert td._creso == NpyDatetimeUnit.NPY_FR_us.value assert td.days == 13 * 19999 @pytest.mark.parametrize( "val, unit", [ (3508, "M"), (15251, "W"), # 1 (106752, "D"), # change from previous: (2562048, "h"), # 0 hours (153722868, "m"), # 13 minutes (9223372037, "s"), # 44 seconds ], )

90b4add77859d1349530fff3c8cadeef95f36f39

@pytest.mark.parametrize( "val, unit", [ (3508, "M"), (15251, "W"), # 1 (106752, "D"), # change from previous: (2562048, "h"), # 0 hours (153722868, "m"), # 13 minutes (9223372037, "s"), # 44 seconds ], )

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

def test_overflow_on_construction(): # GH#3374 value = Timedelta("1day").value * 20169940 msg = "Cannot cast 1742682816000000000000 from ns to 'ns' without overflow" with pytest.raises(OutOfBoundsTimedelta, match=msg): Timedelta(value) # xref GH#17637 msg = "Cannot cast 139993 from D to 'ns' without overflow" with pytest.raises(OutOfBoundsTimedelta, match=msg): Timedelta(7 * 19999, unit="D") # used to overflow before non-ns support td = Timedelta(timedelta(days=13 * 19999)) assert td._creso == NpyDatetimeUnit.NPY_FR_us.value assert td.days == 13 * 19999 @pytest.mark.parametrize( "val, unit", [ (3508, "M"), (15251, "W"), # 1 (106752, "D"), # change from previous: (2562048, "h"), # 0 hours (153722868, "m"), # 13 minutes (9223372037, "s"), # 44

test_overflow_on_construction

homeassistant/components/motion_blinds/cover.py

Motion Blinds API lock (#68587)

core

Python

cover.py

async def async_set_cover_position(self, **kwargs): position = kwargs[ATTR_POSITION]

425b825ae990b054838fea09b86202407d14dae1

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

async def async_set_cover_position(self, **kwargs): position = kwargs[ATTR_POSITION]

async_set_cover_position

django/db/backends/base/base.py

Refs #33476 -- Reformatted code with Black.

django

Python

base.py

def dec_thread_sharing(self): with self._thread_sharing_lock: if self._thread_sharing_count <= 0: raise RuntimeError( "Cannot decrement the thread sharing count below zero." ) self._thread_sharing_count -= 1

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def dec_thread_sharing(self): with self._thread_

dec_thread_sharing

nuitka/OutputDirectories.py

macOS: Added support for mixing --onefile and --macos-create-app-bundle * For some software, e.g. PySide2 it will actually be the only way to get it working.

Nuitka

Python

OutputDirectories.py

def getResultFullpath(onefile): result = getResultBasepath(onefile=onefile) if Options.shallMakeModule(): result += getSharedLibrarySuffix(preferred=True) else: output_filename = Options.getOutputFilename() if Options.isOnefileMode() and output_filename is not None: if onefile: result = output_filename else: result = os.path.join( getStandaloneDirectoryPath(), os.path.basename(output_filename), ) elif output_filename is not None: result = output_filename elif getOS() == "Windows": result += ".exe" elif ( not Options.isStandaloneMode() or onefile and not Options.shallCreateAppBundle() ): result += ".bin" return result

053c207229292b7f011937964a69cdf271d47532

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

def getResultFullpath(onefile): result = getResultBasepath(onefile=onefile) if Options.shallMakeModule(): result += getSharedLibrarySuffix(preferred=True) else: output_filename = Options.getOutputFilename() if Options.isOnefileMode() and output_filename is not None: if onefile: result = output_filename else: result = os.path.join( getStandaloneDirectoryPath(), os.path.basename(output_filename), ) elif output_filename is not None: result = output_filename elif getOS() == "Windows": result += ".exe" elif ( n

getResultFullpath

tests/exchange/test_exchange.py

Change to precise casing instead of .lower()

freqtrade

Python

test_exchange.py

async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog): caplog.set_level(logging.INFO) api_mock = MagicMock() api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection( "kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?" "symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735" "429 Too Many Requests" '{"code":"429000","msg":"Too Many Requests"}')) exchange = get_patched_exchange(mocker, default_conf, api_mock, id="KuCoin") mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='KuCoin')) msg = "Kucoin 429 error, avoid triggering DDosProtection backoff delay" assert not num_log_has_re(msg, caplog) for _ in range(3): with pytest.raises(DDosProtection, match=r'429 Too Many Requests'): await exchange._async_get_candle_history( "ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3) assert num_log_has_re(msg, caplog) == 3 caplog.clear() # Test regular non-kucoin message api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection( "kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?" "symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735" "429 Too Many Requests" '{"code":"2222222","msg":"Too Many Requests"}')) msg = r'_async_get_candle_history\(\) returned exception: .*' msg2 = r'Applying DDosProtection backoff delay: .*' with patch('freqtrade.exchange.common.asyncio.sleep', get_mock_coro(None)): for _ in range(3): with pytest.raises(DDosProtection, match=r'429 Too Many Requests'): await exchange._async_get_candle_history( "ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3) # Expect the "returned exception" message 12 times (4 retries * 3 (loop)) assert num_log_has_re(msg, caplog) == 12 assert num_log_has_re(msg2, caplog) == 9 @pytest.mark.asyncio

39d925c2950aa3c734c454535fef70d89353211e

@pytest.mark.asyncio

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

async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog): caplog.set_level(logging.INFO) api_mock = MagicMock() api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection( "kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?" "symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735" "429 Too Many Requests" '{"code":"429000","msg":"Too Many Requests"}')) exchange = get_patched_exchange(mocker, default_conf, api_mock, id="KuCoin") mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='KuCoin')) msg = "Kucoin 429 error, avoid triggering DDosProtection backoff delay" assert not num_log_has_re(msg, caplog) for _ in range(3): with pytest.raises(DDosProtection, match=r'429 Too Many Requests'): await exchange._async_get_candle_history( "ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3) assert num_log_has_re(msg, caplog) == 3 caplog.clear() # Test regular non-kucoin message api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection( "kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?" "symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735" "429 Too Many Requests" '{"code":"2222222","msg":"Too Many Requests"}')) msg = r'_async_get_candle_history\(\) returned exception: .*' msg2 = r'Applying DDosProtection backoff delay: .*' with patch('freqtrade.exchange.common.asyncio.sleep', get_mock_c

test__async_kucoin_get_candle_history

homeassistant/components/risco/sensor.py

Clean up accessing entity_registry.async_get_registry helper via hass (#72005)

core

Python

sensor.py

async def async_added_to_hass(self): self._entity_registry = er.async_get(self.hass) self.async_on_remove( self.coordinator.async_add_listener(self._refresh_from_coordinator) )

69cc6ab5f1d58adc586c3b300a4f7f0cde2cd0c2

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

async def async_added_to_hass(self): self._entity_registry = er.async_get(self.hass) self.async_on_remo

async_added_to_hass

homeassistant/components/london_air/sensor.py

Improve entity type hints [l] (#77655)

core

Python

sensor.py

def update(self) -> None: sites_status = [] self._api_data.update() if self._api_data.data: self._site_data = self._api_data.data[self._name] self._updated = self._site_data[0]["updated"] for site in self._site_data: if site["pollutants_status"] != "no_species_data": sites_status.append(site["pollutants_status"]) if sites_status: self._state = max(set(sites_status), key=sites_status.count) else: self._state = None

d1ecd74a1a153b85b829acf45b5c6a5ea79df5c1

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

def update(self) -> None: sites_status = [] self._api_data.update() if self._api_data.data: self._site_data = self._api_data.d

update

python3.10.4/Lib/ast.py

add python 3.10.4 for windows

XX-Net

Python

ast.py

def set_precedence(self, precedence, *nodes): for node in nodes: self._precedences[node] = precedence

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def set_precedence(self, precedence, *nodes): for node in nodes: self._precedences[nod

set_precedence

rllib/algorithms/apex_dqn/apex_dqn.py

[RLlib] Move all config validation logic into AlgorithmConfig classes. (#29854)

ray

Python

apex_dqn.py

def update_target_networks(self, num_new_trained_samples) -> None: self._num_ts_trained_since_last_target_update += num_new_trained_samples if ( self._num_ts_trained_since_last_target_update >= self.config["target_network_update_freq"] ): self._num_ts_trained_since_last_target_update = 0 with self._timers[TARGET_NET_UPDATE_TIMER]: to_update = self.workers.local_worker().get_policies_to_train() self.workers.local_worker().foreach_policy_to_train( lambda p, pid: pid in to_update and p.update_target() ) self._counters[NUM_TARGET_UPDATES] += 1 self._counters[LAST_TARGET_UPDATE_TS] = self._counters[ NUM_AGENT_STEPS_TRAINED if self.config.count_steps_by == "agent_steps" else NUM_ENV_STEPS_TRAINED ]

2ed09c54459cc3f74e2dab13406018698559856c

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

def update_target_networks(self, num_new_trained_samples) -> None: self._num_ts_trained_since_last_target_update += num_new_trained_samples if ( self._num_ts_trained_since_last_target_update >= self.config["target_network_update_freq"] ): self._num_ts_trained_since_last_target_update = 0 with self._timers[TARGET_NET_UPDATE_TIMER]: to_update = self.workers.local_worker().get_policies_to_train() self.workers.local_worker().foreach_policy_to_train( lambda p, pid: pid in to_update and p.update_target() ) self._counters[NUM_TARGET_UPDATES] += 1 self._counters[LAST_TARGET_UPDATE_TS] = self._counters[

update_target_networks

saleor/payment/gateways/np_atobarai/tests/test_utils.py

Port NP Atobarai gateway to 3.1 (#8684) * Port net protections (#8640) to 3.1 * Add NP final code review feedback onto 3.1 * Fix optional sku in NP payload & add docstrings * Refactor tracking_number_updated * Change NetProtections timeout value to 20 * Do not use f-strings in logger warnings * Trace only http requests * Simplify code * Add comment about longer than usual timeout period * Remove order from process payment * Add comment for 400 status code * Reduce scope of Posuto context manager * Refactor voucher and shipping amount for payment lines data * Update PaymentResult.psp_reference type to Optional[str] * Add handler for report error in transaction reregistration * Add docstrings to goods functions * Add FOR_REREGISTRATION payment status * Refactor create_refund_data * Fix refund data * Add docstrings to goods functions * Add prefetch to _create_refund_manual_amount * Move refund logic to NP * Fix billing amount for partial refunds * Fix multiple shipping refunds * Set currency to JPY * WIP fix refunds * Clean up code * Refactor * Fix get_goods_with_refunds for all returned products Co-authored-by: Mateusz Grzyb <grzybmateusz@protonmail.com>

saleor

Python

test_utils.py

def test_get_fulfillment_for_order_no_refundable_fulfillment(order): # given order.fulfillments.create(tracking_number="123", status=FulfillmentStatus.REFUNDED) # then with pytest.raises(PaymentError, match=r".* not exist .*"): # when get_fulfillment_for_order(order)

bf654a5f958fcf0611b61cf43ac13c886761b80a

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

def test_get_fulfillment_for_order_no_refundable_fulfillment(order): # given order.fulfillments.create(tracking_number="123", status=Fulfillmen

test_get_fulfillment_for_order_no_refundable_fulfillment

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

upd; format

transferlearning

Python

parser.py

def format_heading(self, heading): # type: (str) -> str if heading == "Options": return "" return heading + ":\n"

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def format_heading(self, heading): # type: (str) -> str if heading == "Options":

format_heading

tests/admin_widgets/tests.py

Refs #33476 -- Reformatted code with Black.

django

Python

tests.py

def test_m2m_related_model_not_in_admin(self): # M2M relationship with model not registered with admin site. Raw ID # widget should have no magnifying glass link. See #16542 consultor1 = Advisor.objects.create(name="Rockstar Techie") c1 = Company.objects.create(name="Doodle") c2 = Company.objects.create(name="Pear") consultor1.companies.add(c1, c2) rel = Advisor._meta.get_field("companies").remote_field w = widgets.ManyToManyRawIdWidget(rel, widget_admin_site) self.assertHTMLEqual( w.render("company_widget1", [c1.pk, c2.pk], attrs={}), '<input type="text" name="company_widget1" value="%(c1pk)s,%(c2pk)s">' % {"c1pk": c1.pk, "c2pk": c2.pk}, ) self.assertHTMLEqual( w.render("company_widget2", [c1.pk]), '<input type="text" name="company_widget2" value="%(c1pk)s">' % {"c1pk": c1.pk}, ) @override_settings(ROOT_URLCONF="admin_widgets.urls")

9c19aff7c7561e3a82978a272ecdaad40dda5c00

@override_settings(ROOT_URLCONF="admin_widgets.urls")

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

def test_m2m_related_model_not_in_admin(self): # M2M relationship with model not registered with admin site. Raw ID # widget should have no magnifying glass link. See #16542 consultor1 = Advisor.objects.create(name="Rockstar Techie") c1 = Company.objects.create(name="Doodle") c2 = Company.objects.create(name="Pear") consultor1.companies.add(c1, c2) rel = Advisor._meta.get_field("companies").remote_field w = widgets.ManyToManyRawIdWidget(rel, widget_admin_site) self.assertHTMLEqual( w.render("co

test_m2m_related_model_not_in_admin

tests/lobpcg_test.py

Add initial LOBPCG top-k eigenvalue solver (#3112) This initial version is f32-only for accelerators, since it relies on an eigh call (which itself is f32 at most) in its inner loop. For details, see jax.experimental.linalg.standard_lobpcg documentation. This is a partial implementation of the similar [scipy lobpcg function](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.lobpcg.html).

jax

Python

lobpcg_test.py

def _make_concrete_cases(f64): dtype = np.float64 if f64 else np.float32 example_names = list(_concrete_generators(dtype)) cases = [] for name in example_names: nkm = [(100, 10, 20)] if not flags.FLAGS.jax_skip_slow_tests: nkm.append((1000, 100, 200)) for n, k, m in nkm: if name == 'ring laplacian': m *= 3 if name.startswith('linear'): m *= 2 if f64: m *= 2 case = [('matrix_name', name), ('n', n), ('k', k), ('m', m)] clean_matrix_name = _clean_matrix_name(name) case.append(('testcase_name', f'{clean_matrix_name}_n{n}')) cases.append(dict(case)) assert len({c['testcase_name'] for c in cases}) == len(cases) return cases

76fcf63fb4e53fd82faece677ed46db8b0c71707

https://github.com/google/jax.git

def _make_concrete_cases(f64): dtype = np.float64 if f64 else np.float32 example_names = list(_concrete_generators(dtype)) cases = [] for name in example_names: nkm = [(100, 10, 20)] if not flags.FLAGS.jax_skip_slow_tests: nkm.append((1000, 100, 200)) for n, k, m in nkm: if name == 'ring laplacian': m *= 3 if name.startswith('linear'): m *= 2 if f64:

_make_concrete_cases

src/streamlink/plugins/funimationnow.py

plugins.funimationnow: replace itertags

streamlink

Python

funimationnow.py

def login_csrf(self): return self.session.http.get(self.login_url, schema=validate.Schema( validate.parse_html(), validate.xml_xpath_string(f".//input[@name='{self.CSRF_NAME}'][1]/@value") ))

b2557361f734304fbd80b4985c753668fed00db5

https://github.com/streamlink/streamlink.git

def login_csrf(self): return self.session.http.get(self.login_url, schema=validate.Schema( validate.p

login_csrf

tests/test_edgeql_scope.py

Always include the definition context namespace in computable contexts (#3331) We need to include the *original* source namespace in our ctx namespace when compiling computables. The current mechanism of trying to look up in view_sets or failing that using the source namespace from the computable use, but this was failing to find it in some cases with FOR. Fix this by instead directly pulling in the namespace from qlctx. The inclusion of qlctx's namespace nicely allows us to ditch so later logic as well. Additionally we need to merge the namespace into *both* sides in get_view_map_remapping, to handle cases like referencing a `FOR` variable where the current ns doesn't get merged in. Fixes #3323.

edgedb

Python

test_edgeql_scope.py

async def test_edgeql_scope_ref_outer_02a(self): await self.assert_query_result( , [{ "cards": [ {"tag": ["Alice"]}, {"tag": ["Alice"]}, {"tag": ["Alice"]}, {"tag": ["Alice"]} ] }], )

0dada08f4eedb104bfa40932b576e44d82218547

https://github.com/edgedb/edgedb.git

async def test_edgeql_scope_ref_outer_02a(self): await self.assert_query_result( , [{ "cards": [ {"tag": ["Alice"]}, {"tag": ["Alice"]}, {"tag": ["Alice"]}, {"tag": ["Alice"]} ] }], )

test_edgeql_scope_ref_outer_02a

python/ray/ml/preprocessor.py

[air - preprocessor] Add BatchMapper. (#23700) Add BatchMapper preprocessor. Update the semantics of preprocessor.fit() to allow for multiple fit. This is to follow scikitlearn example. Introduce FitStatus to explicitly incorporate Chain case.

ray

Python

preprocessor.py

def _check_is_fitted(self) -> bool: fitted_vars = [v for v in vars(self) if v.endswith("_")] return bool(fitted_vars)

06a57b20de12c840406a3bac69751c83a44f008c

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

def _check_is_fitted(self) -> bool: fitted_vars = [v for v in vars(self)

_check_is_fitted

mkdocs/tests/config/config_options_tests.py

Add tests for new class-based configs The old-style tests are intentionally kept at config_options_legacy_tests.py

mkdocs

Python

config_options_tests.py

def test_valid_dir(self) -> None: for cls in c.Dir, c.FilesystemObject: with self.subTest(cls): d = os.path.dirname(__file__)

ff8552a57abf2c32f2d0344ef12707b88e008493

https://github.com/mkdocs/mkdocs.git

def test_valid_dir(self) -> None: for cls in c.Dir, c.FilesystemObject: with self.subTest(cls): d = os.path.dirname(__file__)

test_valid_dir

tests/gamestonk_terminal/etf/test_yfinance_model.py

ETF tests (#1208) * etf tests for stockanalysis * add financedatabase etf tests * fix financedatabase etf documentation * yfinance etf tests * add etf/discovery tests * add tests to etf/screener * add etf controller tests * add etf/ta tests * remove tabulate and use rich table * etf/pred * add more etf tests, thanks Chavi * update about us website * Updating tests : etf Co-authored-by: Chavithra PARANA <chavithra@gmail.com>

OpenBBTerminal

Python

test_yfinance_model.py

def test_get_etf_summary_description(recorder, name): result = yfinance_model.get_etf_summary_description(name) recorder.capture(result)

d8ca7556edde9a700706c7802a229cb4439304c5

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

def test_get_etf_summary_description(recorder, name): result = yfinance_model.get

test_get_etf_summary_description

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

Add Wav2Vec2Conformer (#16812) * save intermediate * add wav2vec2 conformer * add more code * more * first test passes * make all checkpoints work * update * up * more clean ups * save clean-up * save clean-up * save more * remove bogus * finalize design conformer * remove vision * finish all tests * more changes * finish code * add doc tests * add slow tests * fix autoconfig test * up * correct docstring * up * update * fix * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * Update docs/source/en/model_doc/wav2vec2-conformer.mdx * upload * save copied from * correct configs * fix model outputs * add to docs * fix imports * finish * finish code * correct copied from * correct again * correct make fix * improve make fix copies * save * correct fix copy from * correct init structure * correct * fix import * apply suggestions Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

transformers

Python

modeling_wav2vec2_conformer.py

def _compute_perplexity(probs, mask=None): if mask is not None: mask_extended = mask.flatten()[:, None, None].expand(probs.shape) probs = torch.where(mask_extended, probs, torch.zeros_like(probs)) marginal_probs = probs.sum(dim=0) / mask.sum() else: marginal_probs = probs.mean(dim=0) perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum() return perplexity

5a9957358cebd616e58b2d1ab3b887c2f2793b45

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

def _compute_perplexity(probs, mask=None): if mask is not None: mask_extended = mask.flatten()[:, None,

_compute_perplexity

jax/experimental/maps.py

Add experimental support for SPMD lowering of xmap via MANUAL sharding annotations Note that it's still limited and turns out to be a bit hard (partly due to unclear XLA semantics at this point). Using constants that are not xmap inputs is likely to cause SPMD partitioner errors and cross-replica collectives don't seem to work either. In any case, the next step will be to allow nesting those xmaps inside pjits. PiperOrigin-RevId: 426447989

jax

Python

maps.py

def _ensure_supports_manual_and(f): def update(v): if v and not hasattr(xc.OpSharding.Type, "MANUAL"): raise RuntimeError("This flag requires a version of jaxlib that supports MANUAL sharding type") return f(v) return update try: config.define_bool_state( name="experimental_xmap_spmd_lowering", default=False, help=("When set, multi-device xmap computations will be compiled through " "the XLA SPMD partitioner instead of explicit cross-replica collectives. " "Not supported on CPU!"), update_global_hook=_clear_compilation_cache, update_thread_local_hook=_thread_local_flag_unsupported) config.define_bool_state( name="experimental_xmap_spmd_lowering_manual", default=False, help=("When set, multi-device xmap computations will be compiled using " "the MANUAL partitioning feature of the XLA SPMD partitioner instead of " "sharding constraints on vectorized code. " "Requires experimental_xmap_spmd_lowering!"), update_global_hook=_ensure_supports_manual_and(_ensure_spmd_and(_clear_compilation_cache)), update_thread_local_hook=_thread_local_flag_unsupported) config.define_bool_state( name="experimental_xmap_ensure_fixed_sharding", default=False, help=("When set and `experimental_xmap_spmd_lowering` is enabled, the lowering will " "try to limit the flexibility of the automated SPMD partitioner heuristics " "by emitting additional sharding annotations for program intermediates."), update_global_hook=_ensure_spmd_and(_clear_compilation_cache), update_thread_local_hook=_thread_local_flag_unsupported) except Exception: raise ImportError("jax.experimental.maps has to be imported before JAX flags " "are parsed")

086a607d8c8ea8487a59d6ced8aaf59834b8846c

https://github.com/google/jax.git

def _ensure_supports_manual_and(f): def update(v): if v and not hasattr(xc.OpSharding.Type, "MANUAL"): raise RuntimeError("This flag requires a version of jaxlib that supports MANUAL sharding type") return f(v) return update try: config.define_bool_state( name="experimental_xmap_spmd_lowering", default=False, help=("When set, multi-device xmap computations will be compiled through " "the XLA SPMD partitioner instead of explicit cross-replica collectives. " "Not supported on CPU!"), update_global_hook=_clear_compilation_cache, update_thread_local_hook=_thread_local_flag_unsupported)

_ensure_supports_manual_and

test/test_youtube_lists.py

Fix test_youtube_mix

youtube-dl

Python

test_youtube_lists.py

def test_youtube_mix(self): dl = FakeYDL() dl.params['format'] = 'best' ie = YoutubeTabIE(dl) result = dl.extract_info('https://www.youtube.com/watch?v=uVJ0Il5WvbE&list=PLhQjrBD2T381k8ul4WQ8SQ165XqY149WW', download=False, ie_key=ie.ie_key(), process=True) entries = (result or {}).get('entries', [{'id': 'not_found', }]) self.assertTrue(len(entries) >= 50) original_video = entries[0] self.assertEqual(original_video['id'], 'uVJ0Il5WvbE')

2c2c2bd348b7dce0aad55a6fc37a18c6f9a000e3

https://github.com/ytdl-org/youtube-dl.git

def test_youtube_mix(self): dl = FakeYDL()

test_youtube_mix