Datasets:

semeru

/

Galeras-3k_deduped

like 0

keras/datasets/imdb.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

imdb.py

def get_word_index(path="imdb_word_index.json"): origin_folder = ( "https://storage.googleapis.com/tensorflow/tf-keras-datasets/" ) path = get_file( path, origin=origin_folder + "imdb_word_index.json", file_hash="bfafd718b763782e994055a2d397834f", ) with open(path) as f: return json.load(f)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def get_word_index(path="imdb_word_index.json"): origin_folder = ( "https://storage.googleapis.com/tensorflow/tf-keras-datasets/" ) path = get_file( path, origin=origin_folder + "imdb_word_index.json", file_hash="bfafd718b763782e994055a2d3978

get_word_index

mindsdb/api/mysql/mysql_proxy/mysql_proxy.py

DESCRIBE to accept [predictor_name].[features, model, etc] syntax (#1938) * DESCRIBE to accept [predictor_name].[features, model, etc] syntax

mindsdb

Python

mysql_proxy.py

def answer_describe_predictor(self, predictor_value): predictor_attr = None if isinstance(predictor_value, (list, tuple)): predictor_name = predictor_value[0] predictor_attr = predictor_value[1] else: predictor_name = predictor_value model_interface = self.session.model_interface models = model_interface.get_models() if predictor_name not in [x['name'] for x in models]: raise ErBadTableError(f"Can't describe predictor. There is no predictor with name '{predictor_name}'") description = model_interface.get_model_description(predictor_name) if predictor_attr is None: description = [ description['accuracies'], description['column_importances'], description['outputs'], description['inputs'], description['datasource'], description['model'] ] packages = self.get_tabel_packets( columns=[{ 'table_name': '', 'name': 'accuracies', 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': 'column_importances', 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': "outputs", 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': 'inputs', 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': 'datasource', 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': 'model', 'type': TYPES.MYSQL_TYPE_VAR_STRING }], data=[description] ) else: data = model_interface.get_model_data(predictor_name) if predictor_attr == "features": data = self._get_features_info(data) packages = self.get_tabel_packets( columns=[{ 'table_name': '', 'name': 'column', 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': 'type', 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': "encoder", 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': 'role', 'type': TYPES.MYSQL_TYPE_VAR_STRING }], data=data ) elif predictor_attr == "model": data = self._get_model_info(data) packages = self.get_tabel_packets( columns=[{ 'table_name': '', 'name': 'name', 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': 'performance', 'type': TYPES.MYSQL_TYPE_VAR_STRING }, { 'table_name': '', 'name': "selected", 'type': TYPES.MYSQL_TYPE_VAR_STRING }], data=data ) else: raise ErNotSupportedYet("DESCRIBE '%s' predictor attribute is not supported yet" % predictor_attr) packages.append(self.last_packet()) self.send_package_group(packages)

1552c3b72ed13e12e86be90506fa34504298695c

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

def answer_describe_predictor(self, predictor_value): predictor_attr

answer_describe_predictor

src/accelerate/test_utils/testing.py

Create Cross-Validation example (#317)

accelerate

Python

testing.py

def slow(test_case): if not _run_slow_tests: return unittest.skip("test is slow")(test_case) else: return test_case

2d7fbbdc73670b96dbc8b3f875cfe147db4d9241

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

def slow(test_case): if not _run_slow_tests: return unittest.skip("test is slow")(test_case) else: return test_case

slow

python/ray/util/sgd/tf/examples/tensorflow_train_example.py

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

ray

Python

tensorflow_train_example.py

def train_example(num_replicas=1, batch_size=128, use_gpu=False): trainer = TFTrainer( model_creator=simple_model, data_creator=simple_dataset, num_replicas=num_replicas, use_gpu=use_gpu, verbose=True, config=create_config(batch_size), ) # model baseline performance start_stats = trainer.validate() print(start_stats) # train for 2 epochs trainer.train() trainer.train() # model performance after training (should improve) end_stats = trainer.validate() print(end_stats) # sanity check that training worked dloss = end_stats["validation_loss"] - start_stats["validation_loss"] dmse = ( end_stats["validation_mean_squared_error"] - start_stats["validation_mean_squared_error"] ) print(f"dLoss: {dloss}, dMSE: {dmse}") if dloss > 0 or dmse > 0: print("training sanity check failed. loss increased!") else: print("success!")

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def train_example(num_replicas=1, batch_size=128, use_gpu=False): trainer = TFTrainer( model_creator=simple_model, data_creator=simple_dataset, num_replicas=num_replicas, use_gpu=use_gpu, verbose=True, config=create_config(batch_size), ) # model baseline performance start_stats = trainer.validate() print(start_stats) # train for 2 epochs trainer.train() trainer.train() # model performance after training (should improve) end_stats = trainer.validate() print(end_stats) # sanity check that training worked dloss = end_stats["validation_loss"] - start_stats["validation_loss"] dmse = ( end_stats["validation_mean_squared_error"] - start_stats["validation_

train_example

pandas/core/indexes/base.py

CLN: assorted (#49590)

pandas

Python

base.py

def _convert_can_do_setop(self, other) -> tuple[Index, Hashable]: if not isinstance(other, Index): other = Index(other, name=self.name) result_name = self.name else: result_name = get_op_result_name(self, other) return other, result_name # -------------------------------------------------------------------- # Indexing Methods

ceebce6f4f074887ce2c27f2342d8d618b4037e0

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

def _convert_can_do_setop(self, other) -> tuple[Index, Hashable]: if not isinstance(other, Index): other = Index(other, name=self.name) result_name = self.name else: result_name = get_op_result_name(self, other) return other, result_name # -------------------------------------------------------------------- # Indexing Methods

_convert_can_do_setop

modules/image/text_to_image/disco_diffusion_ernievil_base/vit_b_16x/ernievil2/transformers/beam.py

add disco_diffusion_ernievil_base

PaddleHub

Python

beam.py

def _expand_to_beam_size(self, x): r check_type(x, 'x', (Variable), 'BeamSearchDecoder._expand_to_beam_size') x = nn.unsqueeze(x, [1]) expand_times = [1] * len(x.shape) expand_times[1] = self.beam_size x = paddle.tile(x, expand_times) return x

ffcde21305c61d950a9f93e57e6180c9a9665b87

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

def _expand_to_beam_size(self, x): r check_type(x, 'x', (Variable), 'BeamSearchDecoder._expand_to_be

_expand_to_beam_size

tests/nightly/gpu/test_style_gen.py

[Style-Controlled Generation] Open-source a second style classifier (#4380) * Add model to model list * Curr only classifier download page * Add test case * Update version * Update with some results * Wording

ParlAI

Python

test_style_gen.py

def test_curr_only_accuracy(self): _, test = testing_utils.eval_model( opt={ 'batchsize': 4, 'fp16': True, 'num_examples': 16, 'model_file': 'zoo:style_gen/curr_only_classifier/model', 'model': 'projects.style_gen.classifier:ClassifierAgent', 'classes_from_file': 'image_chat_personalities_file', 'task': 'style_gen:CurrUttOnlyStyle', 'wrapper_task': 'style_gen:LabeledBlendedSkillTalk', }, skip_valid=True, ) self.assertAlmostEqual(test['accuracy'], 0.4375, delta=0.0)

82df52b4431f3573ca2c93dd4bb3098992968acc

https://github.com/facebookresearch/ParlAI.git

def test_curr_only_accuracy(self): _, test = testing_utils.eval_model( opt={ 'batchsize': 4, 'fp16': True, 'num_examples': 16, 'model_file': 'zoo:style_gen/cu

test_curr_only_accuracy

homeassistant/components/matter/config_flow.py

Add matter integration BETA (#83064) * Add matter base (#79372) Co-authored-by: Marcel van der Veldt <m.vanderveldt@outlook.com> * Add matter server add-on flow (#82698) * Add matter server add-on flow * Fix stale error argument * Clean docstrings * Use localhost as default address * Add matter websocket api foundation (#82848) * Add matter config entry add-on management (#82865) * Use matter refactored server/client library (#83003) Co-authored-by: Martin Hjelmare <marhje52@gmail.com> * Bump python-matter-server to 1.0.6 (#83059) * Extend matter websocket api (#82948) * Extend matter websocket api * Finish docstring * Fix pin type * Adjust api after new client * Adjust api to frontend for now Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

core

Python

config_flow.py

async def _async_start_addon(self) -> None: addon_manager: AddonManager = get_addon_manager(self.hass) try: await addon_manager.async_schedule_start_addon() # Sleep some seconds to let the add-on start properly before connecting. for _ in range(ADDON_SETUP_TIMEOUT_ROUNDS): await asyncio.sleep(ADDON_SETUP_TIMEOUT) try: if not (ws_address := self.ws_address): discovery_info = await self._async_get_addon_discovery_info() ws_address = self.ws_address = build_ws_address( discovery_info["host"], discovery_info["port"] ) await validate_input(self.hass, {CONF_URL: ws_address}) except (AbortFlow, CannotConnect) as err: LOGGER.debug( "Add-on not ready yet, waiting %s seconds: %s", ADDON_SETUP_TIMEOUT, err, ) else: break else: raise CannotConnect("Failed to start Matter Server add-on: timeout") finally: # Continue the flow after show progress when the task is done. self.hass.async_create_task( self.hass.config_entries.flow.async_configure(flow_id=self.flow_id) )

e2308fd15cec4dfdd25d843b72cd3071657fd5b8

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

async def _async_start_addon(self) -> None: addon_manager: AddonManager = get_addon_manager(self.hass) try: await addon_manager.async_schedule_start_addon() # Sleep some seconds to let the add-on start properly before connecting. for _ in range(ADDON_SETUP_TIMEOUT_ROUN

_async_start_addon

python3.10.4/Lib/hashlib.py

add python 3.10.4 for windows

XX-Net

Python

hashlib.py

def __py_new(name, data=b'', **kwargs): return __get_builtin_constructor(name)(data, **kwargs)

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def __py_new(name, data=b'', **kwargs): return __get_builtin_constructor(name)(data, **kwa

__py_new

homeassistant/components/jellyfin/media_player.py

Add media_player platform to Jellyfin (#76801)

core

Python

media_player.py

def _handle_coordinator_update(self) -> None: self.session_data = ( self.coordinator.data.get(self.session_id) if self.coordinator.data is not None else None ) if self.session_data is not None: self.now_playing = self.session_data.get("NowPlayingItem") self.play_state = self.session_data.get("PlayState") else: self.now_playing = None self.play_state = None self._update_from_session_data() super()._handle_coordinator_update()

3759be09df09be61a4b880eaa58c7d9d8a099080

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

def _handle_coordinator_update(self) -> None: self.session_data = ( self.coordinator.data.get(se

_handle_coordinator_update

tests/gamestonk_terminal/stocks/options/test_yfinance_model.py

Tests : Stocks > Options (#1125) * Update tests : conftest * Updating tests : stocks/options * Updating tests : fix typing * Updating tests : black * Updating tests : pyupgrade * Updating tests : black * Updating tests : mock dates in cassettes * Updating tests : conftest * Updating tests : black * Updating tests : force single threading * Updating tests : skip * Updating tests : black * Updating tests : conftest * Update tests : skip stocks/options/controller * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : fixing issue * Updating tests : add init * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : conftest * Updating tests : skip * Updating tests : skip * Updating tests : skip * Updating tests : skip

OpenBBTerminal

Python

test_yfinance_model.py

def test_get_option_chain(recorder): result_tuple = yfinance_model.get_option_chain( ticker="PM", expiration="2022-01-07", ) result_tuple = (result_tuple.calls, result_tuple.puts) recorder.capture_list(result_tuple) @pytest.mark.vcr @pytest.mark.parametrize( "func", [ "option_expirations", "get_dividend", "get_price", "get_info", "get_closing", ], )

000d1e93d7187299dce5653f781345031a9ad96f

@pytest.mark.vcr @pytest.mark.parametrize( "func", [ "option_expirations", "get_dividend", "get_price", "get_info", "get_closing", ], )

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

def test_get_option_chain(recorder): result_tuple = yfinance_model.get_option_chain( ticker="PM", expiration="2022-01-07", ) result_tuple = (result_tuple.calls, result_tuple.puts) recorder.capture_list(result_tuple) @pytest.mark.vcr @pytest.mark.parametri

test_get_option_chain

wagtail/admin/views/pages/create.py

Reformat with black

wagtail

Python

create.py

def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context.update( { "content_type": self.page_content_type, "page_class": self.page_class, "parent_page": self.parent_page, "edit_handler": self.edit_handler, "action_menu": PageActionMenu( self.request, view="create", parent_page=self.parent_page ), "preview_modes": self.page.preview_modes, "form": self.form, "next": self.next_url, "has_unsaved_changes": self.has_unsaved_changes, "locale": None, "translations": [], } ) if getattr(settings, "WAGTAIL_I18N_ENABLED", False): # Pages can be created in any language at the root level if self.parent_page.is_root(): translations = [ { "locale": locale, "url": reverse( "wagtailadmin_pages:add", args=[ self.page_content_type.app_label, self.page_content_type.model, self.parent_page.id, ], ) + "?" + urlencode({"locale": locale.language_code}), } for locale in Locale.objects.all() ] else: user_perms = UserPagePermissionsProxy(self.request.user) translations = [ { "locale": translation.locale, "url": reverse( "wagtailadmin_pages:add", args=[ self.page_content_type.app_label, self.page_content_type.model, translation.id, ], ), } for translation in self.parent_page.get_translations() .only("id", "locale") .select_related("locale") if user_perms.for_page(translation).can_add_subpage() and self.page_class in translation.specific_class.creatable_subpage_models() and self.page_class.can_create_at(translation) ] context.update( { "locale": self.locale, "translations": translations, } ) return context

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context.update( { "content_type": self.page_content_type, "page_class": self.page_class, "parent_page": self.parent_page, "edit_handler": self.edit_handler, "action_menu": PageActionMenu( self.request, view="create", parent_page=self.parent_page ), "preview_modes": self.page.preview_modes, "form": self.form, "next": self.next_url, "has_unsaved_changes": self.has_unsaved_changes, "locale": None, "translations": [], } ) if getattr(settings, "WAGTAIL_I18N_ENABLED", False): # Pages can be created in any language at the root level if self.parent_page.is_root(): translations = [ { "locale": locale, "url": reverse( "wagtailadmin_pages:add", args=[ self.page_content_type.app_label, self.page_content_type.model, self.parent_page.id, ],

get_context_data

tests/checkpointing/test_model_checkpoint.py

Update `tests/checkpointing/*.py` to use `devices` instead of `gpus` or `ipus` (#11408) Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

lightning

Python

test_model_checkpoint.py

def test_model_checkpoint_no_extraneous_invocations(tmpdir): model = LogInTwoMethods() num_epochs = 4 model_checkpoint = ModelCheckpointTestInvocations(monitor="early_stop_on", expected_count=num_epochs, save_top_k=-1) trainer = Trainer( strategy="ddp_spawn", accelerator="cpu", devices=2, default_root_dir=tmpdir, callbacks=[model_checkpoint], max_epochs=num_epochs, ) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}"

d2d284fd6e3e8f53e9a44ab233771850af1e4dab

https://github.com/Lightning-AI/lightning.git

def test_model_checkpoint_no_extraneous_invocations(tmpdir): model = LogInTwoMethods() num_epochs = 4 model_checkpoint = ModelCheckpointTestInvocations(monitor="early_stop_on", expected_count=num_epochs, sa

test_model_checkpoint_no_extraneous_invocations

homeassistant/components/humidifier/__init__.py

Adjust HumidifierEntity type hints (#82248)

core

Python

__init__.py

def supported_features(self) -> HumidifierEntityFeature | int: return self._attr_supported_features

2453f95b2442036200a07d862d98bfd3a401e726

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

def supported_features(self) -> HumidifierEntityFeature | int: return self._attr_supported_features

supported_features

saleor/graphql/product/tests/queries/test_product_type_query.py

Split test_product.py and test_variant.py into multiple files (#11173) * Split test_product.py into multiple files * Split test_variant.py into multiple files

saleor

Python

test_product_type_query.py

def test_query_product_type_for_federation(api_client, product, channel_USD): product_type = product.product_type product_type_id = graphene.Node.to_global_id("ProductType", product_type.pk) variables = { "representations": [ { "__typename": "ProductType", "id": product_type_id, }, ], } query = response = api_client.post_graphql(query, variables) content = get_graphql_content(response) assert content["data"]["_entities"] == [ { "__typename": "ProductType", "id": product_type_id, "name": product_type.name, } ]

d90be220d6b687d08153934a51354011a3cb5ca1

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

def test_query_product_type_for_federation(api_client, product, channel_USD): product_type = product.product_type product_type_id = graphene.Node.to_global_id("ProductType", product_type.pk) variables = { "representations": [ { "__typename": "ProductType", "id": product_type_id, }, ], } query = response = api_client.post_graphql(query, variables) content = get_graphql_content(response) assert content["data"]["_entities"] == [ { "__typename":

test_query_product_type_for_federation

src/transformers/utils/fx.py

Fx with meta (#16836) * Add meta proxy * Uses meta data to trace data dependent control-flow * Remove commented class * Handles torch creating functions * Added type annotation to fix tracing * Tracing works for everything but T5 and GPT-J * Almost all previously supported models pass * All architectures can be traced except T5 * Intermediate commit to have a trace of the comparison operators for HFProxy * Everything works, except loss computation * Everything works * Removed unused import * Overriden methods do not use underlying ops (linear and torch.matmul), and model attributes are copied to the traced version * Fix torch_matmul_override * Change attributes reference to deepcopy * Remove breakpoint and add torch_index_override * Small fix * Fix typo * Replace asserts by explicit exceptions

transformers

Python

fx.py

def __contains__(self, key): # To handle cases such as : # `"some_key" in kwargs` if self.node.op == "placeholder": return False return super().__contains__(key)

2c2a2169b6524f18b37d7b4b64c64fb6a29a35a2

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

def __contains__(self, key): # To handle cases such as : # `"some_key" in kwargs` if self.node.op == "placeholder":

__contains__

airflow/models/taskinstance.py

Make `airflow dags test` be able to execute Mapped Tasks (#21210) * Make `airflow dags test` be able to execute Mapped Tasks In order to do this there were two steps required: - The BackfillJob needs to know about mapped tasks, both to expand them, and in order to update it's TI tracking - The DebugExecutor needed to "unmap" the mapped task to get the real operator back I was testing this with the following dag: ``` from airflow import DAG from airflow.decorators import task from airflow.operators.python import PythonOperator import pendulum @task def make_list(): return list(map(lambda a: f'echo "{a!r}"', [1, 2, {'a': 'b'}])) def consumer(*args): print(repr(args)) with DAG(dag_id='maptest', start_date=pendulum.DateTime(2022, 1, 18)) as dag: PythonOperator(task_id='consumer', python_callable=consumer).map(op_args=make_list()) ``` It can't "unmap" decorated operators successfully yet, so we're using old-school PythonOperator We also just pass the whole value to the operator, not just the current mapping value(s) * Always have a `task_group` property on DAGNodes And since TaskGroup is a DAGNode, we don't need to store parent group directly anymore -- it'll already be stored * Add "integation" tests for running mapped tasks via BackfillJob * Only show "Map Index" in Backfill report when relevant Co-authored-by: Tzu-ping Chung <uranusjr@gmail.com>

airflow

Python

taskinstance.py

def key(self) -> TaskInstanceKey: return TaskInstanceKey(self.dag_id, self.task_id, self.run_id, self.try_number, self.map_index)

6fc6edf6af7f676bfa54ff3a2e6e6d2edb938f2e

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

def key(self) -> TaskInstanceKey: return TaskInstanceKey(self.dag_id, self.task_id, self.run_id, self.try_number, self.m

key

airbyte-integrations/connectors/source-zendesk-support/unit_tests/unit_test.py

🐛 Source Zendesk-Support: fixed bug when `Tickets` stream didn't return removed records (#11349)

airbyte

Python

unit_test.py

def test_check_start_time_param(): expected = 1626936955 start_time = calendar.timegm(pendulum.parse(DATETIME_STR).utctimetuple()) output = SourceZendeskTicketExportStream.check_start_time_param(start_time) assert output == expected

a305e4913060b919f02f3db57b9e17f82f48c425

https://github.com/airbytehq/airbyte.git

def test_check_start_time_param():

test_check_start_time_param

sklearn/cluster/tests/test_k_means.py

MNT Update black to stable version (#22474)

scikit-learn

Python

test_k_means.py

def test_euclidean_distance(dtype, squared): # Check that the _euclidean_(dense/sparse)_dense helpers produce correct # results rng = np.random.RandomState(0) a_sparse = sp.random( 1, 100, density=0.5, format="csr", random_state=rng, dtype=dtype ) a_dense = a_sparse.toarray().reshape(-1) b = rng.randn(100).astype(dtype, copy=False) b_squared_norm = (b**2).sum() expected = ((a_dense - b) ** 2).sum() expected = expected if squared else np.sqrt(expected) distance_dense_dense = _euclidean_dense_dense_wrapper(a_dense, b, squared) distance_sparse_dense = _euclidean_sparse_dense_wrapper( a_sparse.data, a_sparse.indices, b, b_squared_norm, squared ) assert_allclose(distance_dense_dense, distance_sparse_dense, rtol=1e-6) assert_allclose(distance_dense_dense, expected, rtol=1e-6) assert_allclose(distance_sparse_dense, expected, rtol=1e-6) @pytest.mark.parametrize("dtype", [np.float32, np.float64])

1fc86b6aacd89da44a3b4e8abf7c3e2ba4336ffe

@pytest.mark.parametrize("dtype", [np.float32, np.float64])

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

def test_euclidean_distance(dtype, squared): # Check that the _euclidean_(dense/sparse)_dense helpers produce correct # results rng = np.random.RandomState(0) a_sparse = sp.random( 1, 100, density=0.5, format="csr", random_state=rng, dtype=dtype ) a_dense = a_sparse.toarray().reshape(-1) b = rng.randn(100).astype(dtype, copy=False) b_squared_norm = (b**2).sum() expected = ((a_dense - b) ** 2).sum() expected = expected if squared else np.sqrt(expected) distance_dense_dense = _euclidean_dense_dense_wrapper(a_dense, b, squared) distance_sparse_dense = _euclidean_sparse_dense_wrapper( a_sparse.data, a_sparse.indices, b, b_squared_norm, squared ) assert_allclose(distance_dense_dense, distance_sparse_dense, rtol

test_euclidean_distance

netbox/extras/scripts.py

Iterate base classes when searching for ScriptVariables

netbox

Python

scripts.py

def _get_vars(cls): vars = {} # Iterate all base classes looking for ScriptVariables for base_class in inspect.getmro(cls): # When object is reached there's no reason to continue if base_class is object: break for name, attr in base_class.__dict__.items(): if name not in vars and issubclass(attr.__class__, ScriptVariable): vars[name] = attr # Order variables according to field_order field_order = getattr(cls.Meta, 'field_order', None) if not field_order: return vars ordered_vars = { field: vars.pop(field) for field in field_order if field in vars } ordered_vars.update(vars) return ordered_vars

fe899d9d7cdb458298b92c2f46792adaf211851d

https://github.com/netbox-community/netbox.git

def _get_vars(cls): vars = {} # Iterate all base classes looking for ScriptVariables for base_class in inspect.getmro(cls): # When object is reached there's no reason to continue if base_class is object: break for name, attr in base_class.__dict__.items(): if name not in vars and issubclass(attr.__class__, ScriptVariable): vars[name] = attr # Order variables according to field_order field_order = getattr(cls.Meta, 'field_order', None) if not field_order: return vars ordered_vars = { field: vars.pop(field) for field in field_order if field in vars } ordered_vars.update(vars) r

_get_vars

tests/test_unpaper.py

unpaper: issue warning if image too large to clean

OCRmyPDF

Python

test_unpaper.py

def test_unpaper_image_too_big(resources, outdir, caplog): with patch('ocrmypdf._exec.unpaper.UNPAPER_IMAGE_PIXEL_LIMIT', 42): infile = resources / 'crom.png' unpaper.clean(infile, outdir / 'out.png', dpi=300) == infile assert any( 'too large for cleaning' in rec.message for rec in caplog.get_records('call') if rec.levelno == logging.WARNING )

ea69e868ed95a335b362a3708628c0372cb7abb8

https://github.com/ocrmypdf/OCRmyPDF.git

def test_unpaper_image_too_big(resources, outdir, caplog): with patch('ocrmypdf._exec.unpaper.UNPAPER_IMAGE_PIXEL_LIMIT', 42): infile = resources / 'crom.png' u

test_unpaper_image_too_big

tests/maskformer/test_feature_extraction_maskformer.py

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

transformers

Python

test_feature_extraction_maskformer.py

def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "max_size"))

d83d22f578276e9f201b0b3b0f8f9bd68e86c133

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

def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTru

test_feat_extract_properties

openbb_terminal/settings_controller.py

Default env for packaged apps (#1693) * Remove defaults json in favor of a .env in a cross platform specfile * Use ENV_FILE from obff across the app * Add venv packaging support to the specfile * Make silencing explicit * Fix bug in integration tests report printout Co-authored-by: piiq <piiq@tinag.ru>

OpenBBTerminal

Python

settings_controller.py

def call_cls(self, _): obbff.USE_CLEAR_AFTER_CMD = not obbff.USE_CLEAR_AFTER_CMD set_key( obbff.ENV_FILE, "OPENBB_USE_CLEAR_AFTER_CMD", str(obbff.USE_CLEAR_AFTER_CMD), ) console.print("")

a5b414bf1a91f05f370886748845077d4cec03e7

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

def call_cls(self, _): obbff.USE_CLEAR_AFTER_CMD = not obbff.USE_CLEAR_AFTER_CMD set_key(

call_cls

plugins/extract/detect/s3fd.py

Update all Keras Imports to be conditional (#1214) * Remove custom keras importer * first round keras imports fix * launcher.py: Remove KerasFinder references * 2nd round keras imports update (lib and extract) * 3rd round keras imports update (train) * remove KerasFinder from tests * 4th round keras imports update (tests)

faceswap

Python

s3fd.py

def _post_process(self, bboxlist): retval = [] for i in range(len(bboxlist) // 2): bboxlist[i * 2] = self.softmax(bboxlist[i * 2], axis=3) for i in range(len(bboxlist) // 2): ocls, oreg = bboxlist[i * 2], bboxlist[i * 2 + 1] stride = 2 ** (i + 2) # 4,8,16,32,64,128 poss = zip(*np.where(ocls[:, :, :, 1] > 0.05)) for _, hindex, windex in poss: axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride score = ocls[0, hindex, windex, 1] if score >= self.confidence: loc = np.ascontiguousarray(oreg[0, hindex, windex, :]).reshape((1, 4)) priors = np.array([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) box = self.decode(loc, priors) x_1, y_1, x_2, y_2 = box[0] * 1.0 retval.append([x_1, y_1, x_2, y_2, score]) return_numpy = np.array(retval) if len(retval) != 0 else np.zeros((1, 5)) return return_numpy

aa39234538a8f83e6aa2b60b8275a570e8876ac2

https://github.com/deepfakes/faceswap.git

def _post_process(self, bboxlist):

_post_process

lib/streamlit/app_session.py

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

streamlit

Python

app_session.py

def handle_stop_script_request(self): self._enqueue_script_request(ScriptRequest.STOP)

704eab3478cf69847825b23dabf15813a8ac9fa2

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

def handle_stop_script_request(self): self._enqueue_script_request(ScriptRequest.STOP)

handle_stop_script_request

dask/array/tests/test_overlap.py

Finish making ``map_overlap`` default boundary ``kwarg`` ``'none'`` (#8743) Followup to PR https://github.com/dask/dask/pull/8397 We've had a FutureWarning up for a few months about an upcoming change to the default 'boundary' kwarg value in `map_overlap`, so now is the time to change it. Previous default was `"reflect"`, new default will be "None". The reason for this change is that it makes the code run a lot faster, and for most people the overlap depth is sufficient and they should not require additional boundary handling. See https://github.com/dask/dask/issues/8391 for a full discussion.

dask

Python

test_overlap.py

def test_map_overlap_no_depth(boundary): x = da.arange(10, chunks=5) y = x.map_overlap(lambda i: i, depth=0, boundary=boundary, dtype=x.dtype) assert_eq(y, x)

c7e069947b9b720df03aca2e4f7682faa2d9876f

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

def test_map_overlap_no_depth(boundary): x = da.arange(10, chunks=5) y = x.map_overlap(lambda i: i, depth=0, boundary=bound

test_map_overlap_no_depth

homeassistant/components/media_player/browse_media.py

Restore children media class (#67409)

core

Python

browse_media.py

def calculate_children_class(self) -> None: self.children_media_class = MEDIA_CLASS_DIRECTORY assert self.children is not None proposed_class = self.children[0].media_class if all(child.media_class == proposed_class for child in self.children): self.children_media_class = proposed_class

d68ada74ccebaa0c1b6986b3be9cf4d73eca7cae

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

def calculate_children_class(self) -> None: self.children_media_class = MEDIA_CLASS

calculate_children_class

tests/gamestonk_terminal/cryptocurrency/defi/test_coindix_model.py

Tests + Fix : Cryptocurrency > Defi (#1284) * Updating tests : crypto/defi * Updating tests : stocks/prediction_techniques * Updating tests : economy * Updating tests : conftest * Updating tests : economy/wsj * Updating crypto : fixing defi/coindix_view * Updating crypto : fix crypto/defi/defipulse_model * Updating tests : crypto/defi * Updating tests : crypto/defi * Updating crypto : crypto/defi/graph_model * Updating tests : crypto/defi * Updating tests : crypto/defi * Updating tests : crypto/defi * Updating tests : black * Updating tests : economy/fred/prediction * Updating tests : crypto/defi/graph * Updating tests : linting

OpenBBTerminal

Python

test_coindix_model.py

def test_get_defi_vaults_value_error(mocker): # MOCK GET attrs = { "status_code": 200, "json.side_effect": UnicodeDecodeError, } mock_response = mocker.Mock(**attrs) mocker.patch(target="requests.get", new=mocker.Mock(return_value=mock_response)) with pytest.raises(ValueError) as _: coindix_model.get_defi_vaults( chain=None, protocol=None, kind=None, ) @pytest.mark.vcr(record_mode="none")

ccfe98e19dd36702047fd8130e9b299e8f7cadcc

@pytest.mark.vcr(record_mode="none")

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

def test_get_defi_vaults_value_error(mocker): # MOCK GET attrs = { "status_code": 200, "json.side_effect": UnicodeDecodeError, } mock_response = mocker.Mock(**attrs) mocker.patch(target="requests.get", new=mocker.

test_get_defi_vaults_value_error

homeassistant/components/powerwall/__init__.py

Enable strict typing for powerwall (#65577)

core

Python

__init__.py

async def async_update_data(self) -> PowerwallData: # Check if we had an error before _LOGGER.debug("Checking if update failed") if self.api_changed: raise UpdateFailed("The powerwall api has changed") return await self.hass.async_add_executor_job(self._update_data)

e1989e285896e07fb6f4a5f09dcf5039c722a16e

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

async def async_update_data(self) -> PowerwallData: # Check if we had an error before _LO

async_update_data

homeassistant/components/itunes/media_player.py

Use new media player enums [i-l] (#78054)

core

Python

media_player.py

def state(self): if self.player_state == "offline" or self.player_state is None: return "offline" if self.player_state == "error": return "error" if self.player_state == "stopped": return MediaPlayerState.IDLE if self.player_state == "paused": return MediaPlayerState.PAUSED return MediaPlayerState.PLAYING

823e7e8830118a8c500a0492c9cc8905bf5effb4

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

def state(self): if self.player_state == "offline" or self.player_state is None: return "offline" if self.player_state == "error": return "error" if self.player_state == "stopped": return MediaPlayerState.IDLE if self.player_s

state

pandas/tests/plotting/frame/test_frame.py

TST: Clean tests/plotting (#45992)

pandas

Python

test_frame.py

def test_boxplot_vertical(self, hist_df): df = hist_df numeric_cols = df._get_numeric_data().columns labels = [pprint_thing(c) for c in numeric_cols] # if horizontal, yticklabels are rotated ax = df.plot.box(rot=50, fontsize=8, vert=False) self._check_ticks_props(ax, xrot=0, yrot=50, ylabelsize=8) self._check_text_labels(ax.get_yticklabels(), labels) assert len(ax.lines) == 7 * len(numeric_cols) axes = _check_plot_works( df.plot.box, default_axes=True, subplots=True, vert=False, logx=True, ) self._check_axes_shape(axes, axes_num=3, layout=(1, 3)) self._check_ax_scales(axes, xaxis="log") for ax, label in zip(axes, labels): self._check_text_labels(ax.get_yticklabels(), [label]) assert len(ax.lines) == 7 positions = np.array([3, 2, 8]) ax = df.plot.box(positions=positions, vert=False) self._check_text_labels(ax.get_yticklabels(), labels) tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), positions) assert len(ax.lines) == 7 * len(numeric_cols)

03fef5f0e35200aa5828975b62782bcf11faa0d2

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

def test_boxplot_vertical(self, hist_df): df = hist_df numeric_cols = df._get_numeric_data().columns labels = [pprint_thing(c) for c in numeric_cols] # if horizontal, yticklabels are rotated ax = df.plot.box(rot=50, fontsize=8, vert=False) self._check_ticks_props(ax, xrot=0, yrot=50, ylabelsize=8) self._check_text_labels(ax.get_yticklabels(), labels) assert len(ax.lines) == 7 * len(numeric_cols) axes = _check_p

test_boxplot_vertical

homeassistant/components/esphome/media_player.py

Initial implementation of ESPHome media players (#72047) Co-authored-by: Paulus Schoutsen <balloob@gmail.com> Co-authored-by: Franck Nijhof <git@frenck.dev>

core

Python

media_player.py

def supported_features(self) -> int: flags = ( MediaPlayerEntityFeature.PLAY_MEDIA | MediaPlayerEntityFeature.BROWSE_MEDIA | MediaPlayerEntityFeature.STOP | MediaPlayerEntityFeature.VOLUME_SET | MediaPlayerEntityFeature.VOLUME_MUTE ) if self._static_info.supports_pause: flags |= MediaPlayerEntityFeature.PAUSE | MediaPlayerEntityFeature.PLAY return flags

8ff0ced846e505a0c33a848e21b19820861e6884

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

def supported_features(self) -> int: flags = ( MediaPlayerEntityFeature.PLAY_MEDIA | MediaPlayerEn

supported_features

awx/main/scheduler/task_manager.py

Consume control capacity (#11665) * Select control node before start task Consume capacity on control nodes for controlling tasks and consider remainging capacity on control nodes before selecting them. This depends on the requirement that control and hybrid nodes should all be in the instance group named 'controlplane'. Many tests do not satisfy that requirement. I'll update the tests in another commit. * update tests to use controlplane We don't start any tasks if we don't have a controlplane instance group Due to updates to fixtures, update tests to set node type and capacity explicitly so they get expected result. * Fixes for accounting of control capacity consumed Update method is used to account for currently consumed capacity for instance groups in the in-memory capacity tracking data structure we initialize in after_lock_init and then update via calculate_capacity_consumed (both in task_manager.py) Also update fit_task_to_instance to consider control impact on instances Trust that these functions do the right thing looking for a node with capacity, and cut out redundant check for the whole group's capacity per Alan's reccomendation. * Refactor now redundant code Deal with control type tasks before we loop over the preferred instance groups, which cuts out the need for some redundant logic. Also, fix a bug where I was missing assigning the execution node in one case! * set job explanation on tasks that need capacity move the job explanation for jobs that need capacity to a function so we can re-use it in the three places we need it. * project updates always run on the controlplane Instance group ordering makes no sense on project updates because they always need to run on the control plane. Also, since hybrid nodes should always run the control processes for the jobs running on them as execution nodes, account for this when looking for a execution node. * fix misleading message the variables and wording were both misleading, fix to be more accurate description in the two different cases where this log may be emitted. * use settings correctly use settings.DEFAULT_CONTROL_PLANE_QUEUE_NAME instead of a hardcoded name cache the controlplane_ig object during the after lock init to avoid an uneccesary query eliminate mistakenly duplicated AWX_CONTROL_PLANE_TASK_IMPACT and use only AWX_CONTROL_NODE_TASK_IMPACT * add test for control capacity consumption add test to verify that when there are 2 jobs and only capacity for one that one will move into waiting and the other stays in pending * add test for hybrid node capacity consumption assert that the hybrid node is used for both control and execution and capacity is deducted correctly * add test for task.capacity_type = control Test that control type tasks have the right capacity consumed and get assigned to the right instance group Also fix lint in the tests * jobs_running not accurate for control nodes We can either NOT use "idle instances" for control nodes, or we need to update the jobs_running property on the Instance model to count jobs where the node is the controller_node. I didn't do that because it may be an expensive query, and it would be hard to make it match with jobs_running on the InstanceGroup which filters on tasks assigned to the instance group. This change chooses to stop considering "idle" control nodes an option, since we can't acurrately identify them. The way things are without any change, is we are continuing to over consume capacity on control nodes because this method sees all control nodes as "idle" at the beginning of the task manager run, and then only counts jobs started in that run in the in-memory tracking. So jobs which last over a number of task manager runs build up consuming capacity, which is accurately reported via Instance.consumed_capacity * Reduce default task impact for control nodes This is something we can experiment with as far as what users want at install time, but start with just 1 for now. * update capacity docs Describe usage of the new setting and the concept of control impact. Co-authored-by: Alan Rominger <arominge@redhat.com> Co-authored-by: Rebeccah <rhunter@redhat.com>

awx

Python

task_manager.py

def task_needs_capacity(self, task, tasks_to_update_job_explanation): task.log_lifecycle("needs_capacity") job_explanation = gettext_noop("This job is not ready to start because there is not enough available capacity.") if task.job_explanation != job_explanation: if task.created < (tz_now() - self.time_delta_job_explanation): # Many launched jobs are immediately blocked, but most blocks will resolve in a few seconds. # Therefore we should only update the job_explanation after some time has elapsed to # prevent excessive task saves. task.job_explanation = job_explanation tasks_to_update_job_explanation.append(task) logger.debug("{} couldn't be scheduled on graph, waiting for next cycle".format(task.log_format))

604cbc17376620dc67df35386421835d43732a4e

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

def task_needs_capacity(self, task, tasks_to_update_job_explanation): task.log_lifecycle("needs_capacity") job_explanation = gettext_noop("This job is not ready to start because there is not enough available capacity.") if task.job_explanation != job_explanation: if task.created < (tz_now() - self.time_delta_job_explanation): # Many launched jobs are immediately blocked, but most blocks will resolve in a few seconds. # Therefore we should only update the job_explanation after some time has elapsed to # prev

task_needs_capacity

tests/sentry/notifications/utils/test_participants.py

ref(hybrid-cloud): Add user services. Start tagging some model tests as stable (#40614) Notifications uses new hybrid cloud APIUser Co-authored-by: Mike Ihbe <mike.ihbe@sentry.io> Co-authored-by: Zachary Collins <zachary.collins@sentry.io> Co-authored-by: Zach Collins <recursive.cookie.jar@gmail.com>

sentry

Python

test_participants.py

def test_other_org_user(self): org_2 = self.create_organization() user_2 = self.create_user() team_2 = self.create_team(org_2, members=[user_2]) team_3 = self.create_team(org_2, members=[user_2]) project_2 = self.create_project(organization=org_2, teams=[team_2, team_3]) assert self.get_send_to_member(project_2, user_2.id) == { ExternalProviders.EMAIL: {user_service.serialize_user(user_2)}, ExternalProviders.SLACK: {user_service.serialize_user(user_2)}, } assert self.get_send_to_member(self.project, user_2.id) == {}

b38f59d9f6d9eedd7ce0606805df7c072addb000

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

def test_other_org_user(self): org_2 = self.create_organizatio

test_other_org_user

tests/rest/admin/test_device.py

Use literals in place of `HTTPStatus` constants in tests (#13479) Replace - `HTTPStatus.NOT_FOUND` - `HTTPStatus.FORBIDDEN` - `HTTPStatus.UNAUTHORIZED` - `HTTPStatus.CONFLICT` - `HTTPStatus.CREATED` Signed-off-by: Dirk Klimpel <dirk@klimpel.org>

synapse

Python

test_device.py

def test_no_auth(self) -> None: channel = self.make_request("GET", self.url, b"{}") self.assertEqual( 401, channel.code, msg=channel.json_body, ) self.assertEqual(Codes.MISSING_TOKEN, channel.json_body["errcode"])

1595052b2681fb86c1c1b9a6028c1bc0d38a2e4b

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

def test_no_auth(self) -> None: channel = self.make_request("GET", self.url, b"{}") self.assertEqual( 401, channel.code, msg=channel.json_body, ) s

test_no_auth

tests/unit/serve/gateway/test_gateway.py

feat: allow passing custom gateway in Flow (#5189)

jina

Python

test_gateway.py

def _start_gateway_runtime(uses, uses_with, worker_port): port = random_port() p = multiprocessing.Process( target=_create_gateway_runtime, args=(port, uses, uses_with, worker_port), daemon=True, ) p.start() time.sleep(1) return port, p

cdaf7f87ececf9e13b517379ca183b17f0d7b007

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

def _start_gateway_runtime(uses, uses_with, worker_port): port = random_port() p = multiprocessing.Process(

_start_gateway_runtime

pandas/tests/plotting/frame/test_frame_subplots.py

TST: Don't mark all plotting tests as slow (#46003)

pandas

Python

test_frame_subplots.py

def test_bar_barwidth_position_int(self, w): # GH 12979 df = DataFrame(np.random.randn(5, 5)) ax = df.plot.bar(stacked=True, width=w) ticks = ax.xaxis.get_ticklocs() tm.assert_numpy_array_equal(ticks, np.array([0, 1, 2, 3, 4])) assert ax.get_xlim() == (-0.75, 4.75) # check left-edge of bars assert ax.patches[0].get_x() == -0.5 assert ax.patches[-1].get_x() == 3.5

63616a622186068e487b3fd5304022c27f6aa6db

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

def test_bar_barwidth_position_int(self, w): # GH 12979 df = DataFrame(np.random.randn(5, 5)) ax = df.plot.bar(stacked=True, width=w) ticks = ax.xaxis.get_ticklocs() tm.assert_numpy_array_equal(ticks, np.array([0, 1, 2, 3, 4])) assert ax.get_xlim() == (-0.75, 4.75) # check left-edge of bars assert ax.patches[0].get_x() == -0.5 assert ax.patches[-1].get_x() == 3.5

test_bar_barwidth_position_int

pandas/tests/indexes/test_base.py

BUG: do not replace all nulls with "NaN"-string in Series index (#45283)

pandas

Python

test_base.py

def test_format_missing(self, vals, nulls_fixture): # 2845 vals = list(vals) # Copy for each iteration vals.append(nulls_fixture) index = Index(vals) formatted = index.format() null_repr = "NaN" if isinstance(nulls_fixture, float) else str(nulls_fixture) expected = [str(index[0]), str(index[1]), str(index[2]), null_repr] assert formatted == expected assert index[3] is nulls_fixture

b8cce91ee7bcc86877d4679cd8a9454b5995c2c6

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

def test_format_missing(self, vals, nulls_fixture): # 2845 vals = list(vals) # Copy for each iteration vals.append(nulls_fixture)

test_format_missing

src/PIL/Jpeg2KImagePlugin.py

Improve exception traceback readability

Pillow

Python

Jpeg2KImagePlugin.py

def _open(self): sig = self.fp.read(4) if sig == b"\xff\x4f\xff\x51": self.codec = "j2k" self._size, self.mode = _parse_codestream(self.fp) else: sig = sig + self.fp.read(8) if sig == b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a": self.codec = "jp2" header = _parse_jp2_header(self.fp) self._size, self.mode, self.custom_mimetype, dpi = header if dpi is not None: self.info["dpi"] = dpi else: msg = "not a JPEG 2000 file" raise SyntaxError(msg) if self.size is None or self.mode is None: msg = "unable to determine size/mode" raise SyntaxError(msg) self._reduce = 0 self.layers = 0 fd = -1 length = -1 try: fd = self.fp.fileno() length = os.fstat(fd).st_size except Exception: fd = -1 try: pos = self.fp.tell() self.fp.seek(0, io.SEEK_END) length = self.fp.tell() self.fp.seek(pos) except Exception: length = -1 self.tile = [ ( "jpeg2k", (0, 0) + self.size, 0, (self.codec, self._reduce, self.layers, fd, length), ) ]

2ae55ccbdad9c842929fb238ea1eb81d1f999024

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

def _open(self): sig = self.fp.read(4) if sig == b"\xff\x4f\xff\x51": self.codec = "j2k" self._size, self.mode = _parse_codestream(self.fp) else: sig = sig + self.fp.read(8) if sig == b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a": self.codec = "jp2" header = _parse_jp2_header(self.fp) self._size, self.mode, self.custom_mimetype, dpi = header if dpi is not None: self.info["dpi"] = dpi else: msg = "not a JPEG 2000 file" raise SyntaxError(msg) if self.size is None or self.mode is None: msg = "unable to determine size/mode" raise S

_open

zerver/tests/test_message_topics.py

tests: Refactor away result.json() calls with helpers. Signed-off-by: Zixuan James Li <p359101898@gmail.com>

zulip

Python

test_message_topics.py

def test_get_topics_web_public_stream_web_public_request(self) -> None: iago = self.example_user("iago") stream = self.make_stream("web-public-stream", is_web_public=True) self.subscribe(iago, stream.name) for i in range(3): self.send_stream_message(iago, stream.name, topic_name="topic" + str(i)) endpoint = f"/json/users/me/{stream.id}/topics" result = self.client_get(endpoint) history = self.assert_json_success(result)["topics"] self.assertEqual( [topic["name"] for topic in history], [ "topic2", "topic1", "topic0", ], )

a142fbff85302c5e3acb2e204eca2e9c75dbc74b

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

def test_get_topics_web_public_stream_web_public_request(self) -> None: iago = self.example_user("iago") stream = self.make_stream("web-public-stream", is_web_public=True) self.subscribe(iago, stream.name) for i in range(3): self.send_stream_message(iago,

test_get_topics_web_public_stream_web_public_request

ludwig/datasets/kaggle.py

Config-first Datasets API (ludwig.datasets refactor) (#2479) * Adds README and stub for reading dataset configs. * Adds __init__.py for configs, moves circular import into function scope in ludwig/datasets/__init__.py * Print config files in datasets folder. * First pass at automatic archive extraction. * Implemented downloading and extract. * Refactor DatasetConfig into its own file. * Fixed bugs downloading kaggle dataset. * Makes registry store dataset instances, not classes. Also comments out import_submodules for testing. * Typo fix. * Only pass data files on to load_unprocessed_dataframe, symlink directories. * Downloading dataset files into existing directory if exists. * Refactor: make datasets fully config-first, lazy load dataset loaders. * Implemented agnews custom loader. * Implements train/validation/test split by files, and globbing support * Adds _glob_multiple * Adds adult_census_income, agnews, allstate_claims_severity. * Implements sha256 verification, adds more datasets up to creditcard_fraud. * Adds checksums, dbpedia, electricity * Fixes gzip file name returned as string not list, adds up to forest_cover dataset. * Adds datasets up to reuters_r8 * Adds all datasets which don't require a custom class. * Restore dataset import behavior by implementing module __getattr__ * Adds KDD datasets. * Adds ieee_fraud. * Adds imbalanced_insurance, insurance_lite. * Adds mnist. * Completes implementation of all of the built-in datasets. * Made cache_dir optional, read from environment variable if set. * Upgrades datasets tests. * Adds test for new dataset config API. Also adds scripts for dataset link checking. * Fixes loading allstate claims severity dataset. * Use @lru_cache(1), @cache not supported in python < 3.9 * Deletes dataset registry, updates automl test utils * Fix imports of datasets API. * Adds more detail to sha256: docstring and basic README * Copy-paste link oops. * Fixes handling of nested archive types like .tar.bz Also adds a LUDWIG_CACHE and export to the README * Adds link for twitter bots. * Fix order of splits in README.md * typo * Adds verify as a phase in doc string. * Support .pqt, .pq extensions for parquet. * Handle nested archives with longer file extensions like .csv.zip * Handle nested .gz types properly too. Check all extensions with .endswith * Handle all archive types with .endswith * Update ludwig/datasets/loaders/split_loaders.py Co-authored-by: Joppe Geluykens <joppe@rvrie.com> * Adds explanation for export, fixes preserve_paths (should be relative to processed_dataset_dir) * Resolve preserved paths relative to raw dataset dir before move. * Catch runtime exception from extracting sub-archives. Co-authored-by: Daniel Treiman <daniel@predibase.com> Co-authored-by: Joppe Geluykens <joppe@rvrie.com>

ludwig

Python

kaggle.py

def create_kaggle_client(): # Need to import here to prevent Kaggle from authenticating on import from kaggle import api return api @contextmanager

e4fc06f986e03919d9aef3ab55c05fee5a6b9d3a

@contextmanager

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

def create_kaggle_client(): # Need

create_kaggle_client

python/ccxt/async_support/huobi.py

1.66.21 [ci skip]

ccxt

Python

huobi.py

def parse_trade(self, trade, market=None): # # spot fetchTrades(public) # # { # "amount": 0.010411000000000000, # "trade-id": 102090736910, # "ts": 1583497692182, # "id": 10500517034273194594947, # "price": 9096.050000000000000000, # "direction": "sell" # } # # spot fetchMyTrades(private) # # { # 'symbol': 'swftcbtc', # 'fee-currency': 'swftc', # 'filled-fees': '0', # 'source': 'spot-api', # 'id': 83789509854000, # 'type': 'buy-limit', # 'order-id': 83711103204909, # 'filled-points': '0.005826843283532154', # 'fee-deduct-currency': 'ht', # 'filled-amount': '45941.53', # 'price': '0.0000001401', # 'created-at': 1597933260729, # 'match-id': 100087455560, # 'role': 'maker', # 'trade-id': 100050305348 # } # # linear swap isolated margin fetchOrder details # # { # "trade_id": 131560927, # "trade_price": 13059.800000000000000000, # "trade_volume": 1.000000000000000000, # "trade_turnover": 13.059800000000000000, # "trade_fee": -0.005223920000000000, # "created_at": 1603703614715, # "role": "taker", # "fee_asset": "USDT", # "profit": 0, # "real_profit": 0, # "id": "131560927-770334322963152896-1" # } # marketId = self.safe_string(trade, 'symbol') market = self.safe_market(marketId, market) symbol = market['symbol'] timestamp = self.safe_integer_2(trade, 'ts', 'created-at') timestamp = self.safe_integer(trade, 'created_at', timestamp) order = self.safe_string(trade, 'order-id') side = self.safe_string(trade, 'direction') type = self.safe_string(trade, 'type') if type is not None: typeParts = type.split('-') side = typeParts[0] type = typeParts[1] takerOrMaker = self.safe_string(trade, 'role') priceString = self.safe_string_2(trade, 'price', 'trade_price') amountString = self.safe_string_2(trade, 'filled-amount', 'amount') amountString = self.safe_string(trade, 'trade_volume', amountString) costString = self.safe_string(trade, 'trade_turnover') fee = None feeCost = self.safe_string_2(trade, 'filled-fees', 'trade_fee') feeCurrencyId = self.safe_string_2(trade, 'fee-currency', 'fee_asset') feeCurrency = self.safe_currency_code(feeCurrencyId) filledPoints = self.safe_string(trade, 'filled-points') if filledPoints is not None: if (feeCost is None) or Precise.string_equals(feeCost, '0'): feeCost = filledPoints feeCurrency = self.safe_currency_code(self.safe_string(trade, 'fee-deduct-currency')) if feeCost is not None: fee = { 'cost': feeCost, 'currency': feeCurrency, } tradeId = self.safe_string_2(trade, 'trade-id', 'tradeId') id = self.safe_string_2(trade, 'trade_id', 'id', tradeId) return self.safe_trade({ 'id': id, 'info': trade, 'order': order, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'type': type, 'side': side, 'takerOrMaker': takerOrMaker, 'price': priceString, 'amount': amountString, 'cost': costString, 'fee': fee, }, market)

dbd6d1c306421a24581647dd50f82f3e11dadf4e

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

def parse_trade(self, trade, market=None): # # spot fetchTrades(public) # # { # "amount": 0.010411000000000000, # "trade-id": 102090736910, # "ts": 1583497692182, # "id": 10500517034273194594947, # "price": 9096.050000000000000000, # "direction": "sell" # } # # spot fetchMyTrades(private) # # { # 'symbol': 'swftcbtc', # 'fee-currency': 'swftc', # 'filled-fees': '0', # 'source': 'spot-api', # 'id': 83789509854000, # 'type': 'buy-limit', # 'order-id': 83711103204909, # 'filled-points': '0.005826843283532154', # 'fee-deduct-currency': 'ht', # 'filled-amount': '45941.53',

parse_trade

pandas/tests/base/test_value_counts.py

TST: Filter/test pyarrow PerformanceWarnings (#48093)

pandas

Python

test_value_counts.py

def test_value_counts_null(null_obj, index_or_series_obj): orig = index_or_series_obj obj = orig.copy() if not allow_na_ops(obj): pytest.skip("type doesn't allow for NA operations") elif len(obj) < 1: pytest.skip("Test doesn't make sense on empty data") elif isinstance(orig, pd.MultiIndex): pytest.skip(f"MultiIndex can't hold '{null_obj}'") values = obj._values values[0:2] = null_obj klass = type(obj) repeated_values = np.repeat(values, range(1, len(values) + 1)) obj = klass(repeated_values, dtype=obj.dtype) # because np.nan == np.nan is False, but None == None is True # np.nan would be duplicated, whereas None wouldn't counter = collections.Counter(obj.dropna()) expected = Series(dict(counter.most_common()), dtype=np.int64) expected.index = expected.index.astype(obj.dtype) result = obj.value_counts() if obj.duplicated().any(): # TODO(GH#32514): # Order of entries with the same count is inconsistent on CI (gh-32449) with tm.maybe_produces_warning( PerformanceWarning, pa_version_under7p0 and getattr(obj.dtype, "storage", "") == "pyarrow", ): expected = expected.sort_index() with tm.maybe_produces_warning( PerformanceWarning, pa_version_under7p0 and getattr(obj.dtype, "storage", "") == "pyarrow", ): result = result.sort_index() if not isinstance(result.dtype, np.dtype): # i.e IntegerDtype expected = expected.astype("Int64") tm.assert_series_equal(result, expected) expected[null_obj] = 3 result = obj.value_counts(dropna=False) if obj.duplicated().any(): # TODO(GH#32514): # Order of entries with the same count is inconsistent on CI (gh-32449) with tm.maybe_produces_warning( PerformanceWarning, pa_version_under7p0 and getattr(obj.dtype, "storage", "") == "pyarrow", ): expected = expected.sort_index() with tm.maybe_produces_warning( PerformanceWarning, pa_version_under7p0 and getattr(obj.dtype, "storage", "") == "pyarrow", ): result = result.sort_index() tm.assert_series_equal(result, expected)

786c28fe929ed65298bfc723aa1cdbe49a68ae0c

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

def test_value_counts_null(null_obj, index_or_series_obj): orig = index_or_series_obj obj = orig.copy() if not allow_na_ops(obj): pytest.skip("type doesn't allow for NA operations") elif len(obj) < 1: pytest.skip("Test doesn't make sense on empty data") elif isinstance(orig, pd.MultiIndex): pytest.skip(f"MultiIndex can't hold '{null_obj}'") values = obj._values values[0:2] = null_obj klass = type(obj) repeated_values = np.repeat(values, range(1, len(values) + 1)) obj = klass(repeated_values, dtype=obj.dtype) # because np.nan == np.nan is False, but None == None is True # np.nan would be duplicated, whereas None wouldn't counter = collections.Counter(obj.dropna()) expected = Series(dict(counter.most_common()), dtype=np.int64) expected.index = expected.index.astype(obj.dtype) result = obj.value_counts() if obj.duplicated().any(): # TODO(GH#32514): # Order of entries with the same count is inconsistent on CI (gh-32449) with tm.maybe_produces_warning( PerformanceWarning, pa_version_under7p0 and getattr(obj.dtype, "storage", "") == "pyarrow", ): expected = expected.sort_index() with tm.maybe_produces_warning( PerformanceWarning, pa_version_under7p0 and getattr(obj.dtype, "storage", "") == "pyarrow",

test_value_counts_null

wagtail/admin/tests/test_page_chooser.py

Reformat with black

wagtail

Python

test_page_chooser.py

def test_type_missing(self): self.assertEqual( self.get_best_root({"page_type": "tests.BusinessIndex"}), self.tree_root )

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def test_type_missing(self): self.assertEqual( self.get_best_root({"page_type": "tests.B

test_type_missing

erpnext/manufacturing/report/work_order_consumed_materials/work_order_consumed_materials.py

feat: provision to return non consumed components against the work order

erpnext

Python

work_order_consumed_materials.py

def get_returned_materials(work_orders): raw_materials_qty = defaultdict(float) raw_materials = frappe.get_all( "Stock Entry", fields=["`tabStock Entry Detail`.`item_code`", "`tabStock Entry Detail`.`qty`"], filters=[ ["Stock Entry", "is_return", "=", 1], ["Stock Entry Detail", "docstatus", "=", 1], ["Stock Entry", "work_order", "in", [d.name for d in work_orders]], ], ) for d in raw_materials: raw_materials_qty[d.item_code] += d.qty for row in work_orders: row.returned_qty = 0.0 if raw_materials_qty.get(row.raw_material_item_code): row.returned_qty = raw_materials_qty.get(row.raw_material_item_code)

d59ed24e6ca2a1ff62963c282882a2d52691b7c6

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

def get_returned_materials(work_orders): raw_materials_qty = defaultdict(float) raw_materials = frappe.get_all( "Stock Entry", fields=["`tabStock Entry Detail`.`item_code`", "`tabStock Entry Detail`.`qty`"], filters=[ ["Stock Entry", "is_return", "=", 1], ["Stock Entry Detail", "docstatus", "=", 1], ["Stock Entry", "work_order", "in", [d.name for d in work_orders]], ], ) for d in raw_materials: raw_materials_qty[d.item_code] += d.qty for row in work_orders: row.returned_qty = 0.0 if raw_materials_qty.get(row.raw_material_item_code): row.returned_qty = raw_materials_qty.get(row.raw_material_item_code)

get_returned_materials

tests/integration_tests/utils.py

Removes/renames some references to legacy config keys (#1775) * regularizer settings no longer supported for modules. * s/fc_size/output_size * Removes regularize parameter. Co-authored-by: Daniel Treiman <daniel@predibase.com>

ludwig

Python

utils.py

def audio_feature(folder, **kwargs): feature = { "name": "audio_" + random_string(), "type": "audio", "preprocessing": { "audio_feature": { "type": "fbank", "window_length_in_s": 0.04, "window_shift_in_s": 0.02, "num_filter_bands": 80, }, "audio_file_length_limit_in_s": 3.0, }, "encoder": "stacked_cnn", "should_embed": False, "conv_layers": [ {"filter_size": 400, "pool_size": 16, "num_filters": 32}, {"filter_size": 40, "pool_size": 10, "num_filters": 64}, ], "output_size": 16, "destination_folder": folder, } feature.update(kwargs) feature[COLUMN] = feature[NAME] feature[PROC_COLUMN] = compute_feature_hash(feature) return feature

b77b6ca0afa3439103ab164d80be61652bee21dc

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

def audio_feature(folder, **kwargs): feature = { "name": "audio_" + random_string(), "type": "audio", "preprocessing": { "audio_feature": { "type": "fbank", "window_length_in_s": 0.04, "window_shift_in_s": 0.02, "num_filter_bands": 80, }, "audio_file_length_limit_in_s": 3.0, }, "encoder": "s

audio_feature

reconstruction/ostec/external/stylegan2/training/misc.py

initialize ostec

insightface

Python

misc.py

def parse_config_for_previous_run(run_dir): with open(os.path.join(run_dir, 'submit_config.pkl'), 'rb') as f: data = pickle.load(f) data = data.get('run_func_kwargs', {}) return dict(train=data, dataset=data.get('dataset_args', {})) #---------------------------------------------------------------------------- # Size and contents of the image snapshot grids that are exported # periodically during training.

7375ee364e0df2a417f92593e09557f1b2a3575a

https://github.com/deepinsight/insightface.git

def parse_config_for_previous_run(run_dir): with open(os.path.join(run_dir, 'submit_config.pkl'),

parse_config_for_previous_run

tests/core/training/test_story_conflict.py

Update dependencies in 3.0 to align with rasa-sdk (#10667) * align dependencies * use black 21.7b0 * apply black and docstring reformatting * add changelog

rasa

Python

test_story_conflict.py

async def test_get_previous_event(): assert _get_previous_event( {PREVIOUS_ACTION: {"action_name": "utter_greet"}, USER: {"intent": "greet"}} ) == ("action", "utter_greet") assert _get_previous_event( {PREVIOUS_ACTION: {"action_text": "this is a test"}, USER: {"intent": "greet"}} ) == ("bot utterance", "this is a test") assert ( _get_previous_event( { PREVIOUS_ACTION: {"action_name": ACTION_LISTEN_NAME}, USER: {"intent": "greet"}, } ) == ("intent", "greet") )

36eb9c9a5fcca2160e54a6cde5076c93db5bd70b

https://github.com/RasaHQ/rasa.git

async def test_get_previous_event(): assert _get_previous_event( {PREVIOUS_ACTION: {"action_name": "utter_greet"}, USER: {"intent": "greet"}} ) == ("action", "utter_greet") assert _get_previous_event( {PREVIOUS_ACTION: {"action_text": "this is a test"}, USER: {"intent": "greet"}} ) == ("bot utterance", "this is a test"

test_get_previous_event

tests/ludwig/backend/test_ray.py

Allow explicitly plumbing through nics (#2605)

ludwig

Python

test_ray.py

def test_get_trainer_kwargs(trainer_config, cluster_resources, num_nodes, expected_kwargs): with patch("ludwig.backend.ray.ray.cluster_resources", return_value=cluster_resources): with patch("ludwig.backend.ray._num_nodes", return_value=num_nodes): trainer_config_copy = copy.deepcopy(trainer_config) actual_kwargs = get_trainer_kwargs(**trainer_config_copy) # Function should not modify the original input assert trainer_config_copy == trainer_config actual_backend = actual_kwargs.pop("backend") expected_backend = expected_kwargs.pop("backend") assert type(actual_backend) == type(expected_backend) assert actual_backend.nics == expected_backend.nics assert actual_kwargs == expected_kwargs @pytest.mark.parametrize( "trainer_kwargs,current_env_value,expected_env_value", [ ({"use_gpu": False, "num_workers": 2}, None, "1"), ({"use_gpu": False, "num_workers": 1}, None, None), ({"use_gpu": True, "num_workers": 2}, None, None), ({"use_gpu": True, "num_workers": 2}, "1", "1"), ({"use_gpu": True, "num_workers": 2}, "", ""), ], )

c99cab3a674e31885e5608a4aed73a64b1901c55

@pytest.mark.parametrize( "trainer_kwargs,current_env_value,expected_env_value", [ ({"use_gpu": False, "num_workers": 2}, None, "1"), ({"use_gpu": False, "num_workers": 1}, None, None), ({"use_gpu": True, "num_workers": 2}, None, None), ({"use_gpu": True, "num_workers": 2}, "1", "1"), ({"use_gpu": True, "num_workers": 2}, "", ""), ], )

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

def test_get_trainer_kwargs(trainer_config, cluster_resources, num_nodes, expected_kwargs): with patch("ludwig.backend.ray.ray.cluster_resources", return_value=cluster_resources): with patch("ludwig.backend.ray._num_nodes", return_value=num_nodes): trainer_config_copy = copy.deepcopy(trainer_config) actual_kwargs = get_trainer_kwargs(**trainer_config_copy) # Function should not modify the original input assert trainer_config_copy == trainer_config actual_backend = actual_kwargs.pop("backend") expected_backend = expected_kwargs.pop("backend") assert type(actual_backend) == type(expected_backend) assert actual_backend.nics == expected_backend.nics assert

test_get_trainer_kwargs

test/pipelines/test_pipeline.py

Fix YAML validation for `ElasticsearchDocumentStore.custom_query` (#2789) * Add exception for in the validation code * Update Documentation & Code Style * Add tests * Update Documentation & Code Style Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

haystack

Python

test_pipeline.py

def test_validate_pipeline_config_component_with_json_input_invalid_value(): with pytest.raises(PipelineConfigError, match="does not contain valid JSON"): validate_config_strings( { "components": [ {"name": "test", "type": "test", "params": {"custom_query": "this is surely not JSON! :)"}} ] } )

4d2a06989db0b8bff5570624b13c734dfc1e3d68

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

def test_validate_pipeline_config_component_with_json_input_invalid_value(): with pytest.raises(PipelineConfigError, match="does not contain valid JSON"): validate_config_strings( { "components": [ {"name": "test", "type": "test", "params": {"custom_query": "this is surely not JSON! :)"}} ] }

test_validate_pipeline_config_component_with_json_input_invalid_value

lib/gui/display_command.py

Typing - lib.gui.display_command

faceswap

Python

display_command.py

def display_item_set(self) -> None: logger.trace("Loading latest preview") # type:ignore size = 256 if self.command == "convert" else 128 get_images().load_latest_preview(thumbnail_size=int(size * get_config().scaling_factor), frame_dims=(self.winfo_width(), self.winfo_height())) self.display_item = get_images().previewoutput

dab823a3eb7a5257cb1e0818ee10ed234d3de97f

https://github.com/deepfakes/faceswap.git

def display_item_set(self) -> None: logger.trace("Loadin

display_item_set

python/ray/data/tests/conftest.py

[AIR] Add `batch_size` arg for `BatchMapper`. (#29193) The default batch_size of 4096 at the Datasets level doesn't suffice for all use cases: it can be too large for wide tables and large images, leading to DRAM/GRAM OOms; it can be too small for narrow tables, leading to unnecessary batch slicing overhead and suboptimal vectorized operations in their UDFs. We should allow users to configure the batch_size at the AIR level. Closes #29168 Signed-off-by: Amog Kamsetty <amogkamsetty@yahoo.com> Signed-off-by: Amog Kamsetty <amogkam@users.noreply.github.com> Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com> Co-authored-by: Amog Kamsetty <amogkam@users.noreply.github.com>

ray

Python

conftest.py

def ds_pandas_list_multi_column_format(): in_df = pd.DataFrame({"column_1": [1], "column_2": [1]}) yield ray.data.from_pandas([in_df] * 4) # ===== Arrow dataset formats ===== @pytest.fixture(scope="function")

28a295968b445635efd1105b900cc624312fc49e

@pytest.fixture(scope="function")

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

def ds_pandas_list_multi_column_format(): in_df = pd.DataFrame

ds_pandas_list_multi_column_format

mmdet/models/detectors/base.py

Modify RetinaNet model interface

mmdetection

Python

base.py

def preprocss_aug_testing_data(self, data): num_augs = len(data[0]['img']) batch_size = len(data) aug_batch_imgs = [] aug_batch_data_samples = [] # adjust `images` and `data_samples` to a list of list # outer list is test-time augmentation and inter list # is batch dimension for aug_index in range(num_augs): batch_imgs = [] batch_data_samples = [] for batch_index in range(batch_size): single_img = data[batch_index]['img'][aug_index] # to gpu and normalize single_img = single_img.to(self.device) if self.preprocess_cfg is None: # YOLOX does not need preprocess_cfg single_img = single_img.float() else: if self.to_rgb and single_img[0].size(0) == 3: single_img = single_img[[2, 1, 0], ...] single_img = (single_img - self.pixel_mean) / self.pixel_std batch_imgs.append(single_img) batch_data_samples.append( data[batch_index]['data_sample'][aug_index]) aug_batch_imgs.append( stack_batch(batch_imgs, self.pad_size_divisor, self.pad_value)) aug_batch_data_samples.append(batch_data_samples) return aug_batch_imgs, aug_batch_data_samples

924c381a78eb70cede198e042ef34e038e05c15a

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

def preprocss_aug_testing_data(self, data): num_augs = len(data[0]['img']) batch_size = len(data) aug_batch_imgs = [] aug_batch_data_samples = [] # adjust `images` and `data_samples` to a list of list # outer list is test-time augmentation and inter list # is batch dimension for aug_index in range(num_augs): batch_imgs = [] batch_data_samples = [] for batch_index in range(batch_size): single_img = data[batch_index]['img'][aug_index] # to gpu and normalize single_img = single_img.to(self.device) if self.preprocess_cfg is None: # YOLOX does not need preprocess_cfg single_img = single_img.float() else: if self.to_rgb and single_img[0].size(0) == 3: single_img = single_img[[2, 1, 0], ..

preprocss_aug_testing_data

sympy/polys/numberfields/minpoly.py

Moved definition of illegal

sympy

Python

minpoly.py

def _choose_factor(factors, x, v, dom=QQ, prec=200, bound=5): if isinstance(factors[0], tuple): factors = [f[0] for f in factors] if len(factors) == 1: return factors[0] prec1 = 10 points = {} symbols = dom.symbols if hasattr(dom, 'symbols') else [] while prec1 <= prec: # when dealing with non-Rational numbers we usually evaluate # with `subs` argument but we only need a ballpark evaluation xv = {x:v if not v.is_number else v.n(prec1)} fe = [f.as_expr().xreplace(xv) for f in factors] # assign integers [0, n) to symbols (if any) for n in subsets(range(bound), k=len(symbols), repetition=True): for s, i in zip(symbols, n): points[s] = i # evaluate the expression at these points candidates = [(abs(f.subs(points).n(prec1)), i) for i,f in enumerate(fe)] # if we get invalid numbers (e.g. from division by zero) # we try again if any(i in _illegal for i, _ in candidates): continue # find the smallest two -- if they differ significantly # then we assume we have found the factor that becomes # 0 when v is substituted into it can = sorted(candidates) (a, ix), (b, _) = can[:2] if b > a * 10**6: # XXX what to use? return factors[ix] prec1 *= 2 raise NotImplementedError("multiple candidates for the minimal polynomial of %s" % v)

117f9554466e08aa4178137ad65fae1f2d49b340

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

def _choose_factor(factors, x, v, dom=QQ, prec=200, bound=5): if isinstance(factors[0], tuple): factors = [f[0] for f in factors] if len(factors) == 1: return factors[0] prec1 = 10 points = {} symbols = dom.symbols if hasattr(dom, 'symbols') else [] while prec1 <= prec: # when dealing with non-Rational numbers we usually evaluate # with `subs` argument but we only need a ballpark evaluation xv = {x:v if not v.is_number else v.n(prec1)} fe = [f.as_expr().xreplace(xv) for f in factors] # assign integers [0, n) to symbols (if any) for n in subsets(range(bound), k=len(symbols), repetition=True): for s, i in zip(symbols, n): points[s] = i # evaluate the expression at these points candidates = [

_choose_factor

python/ray/serve/controller.py

[Serve] add alpha gRPC support (#28175)

ray

Python

controller.py

def get_root_url(self): if self.http_state is None: return None http_config = self.get_http_config() if http_config.root_url == "": if SERVE_ROOT_URL_ENV_KEY in os.environ: return os.environ[SERVE_ROOT_URL_ENV_KEY] else: return ( f"http://{http_config.host}:{http_config.port}" f"{http_config.root_path}" ) return http_config.root_url

65d0c0aa48be8f9f7faae857d3ab71444997755a

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

def get_root_url(self): if self.http_state is None: return None http_config

get_root_url

timm/models/convnext.py

Significant model refactor and additions: * All models updated with revised foward_features / forward_head interface * Vision transformer and MLP based models consistently output sequence from forward_features (pooling or token selection considered part of 'head') * WIP param grouping interface to allow consistent grouping of parameters for layer-wise decay across all model types * Add gradient checkpointing support to a significant % of models, especially popular architectures * Formatting and interface consistency improvements across models * layer-wise LR decay impl part of optimizer factory w/ scale support in scheduler * Poolformer and Volo architectures added

pytorch-image-models

Python

convnext.py

def set_grad_checkpointing(self, enable=True): for s in self.stages: s.grad_checkpointing = enable

372ad5fa0dbeb74dcec81db06e9ff69b3d5a2eb6

https://github.com/huggingface/pytorch-image-models.git

def set_grad_checkpointing(self, enable=True): for s in s

set_grad_checkpointing

pytorch_lightning/loops/fit_loop.py

Add DETAIL logs for batch use cases (#11008)

lightning

Python

fit_loop.py

def advance(self) -> None: # type: ignore[override] log.detail(f"{self.__class__.__name__}: advancing loop") assert self.trainer.train_dataloader is not None dataloader = self.trainer.strategy.process_dataloader(self.trainer.train_dataloader) data_fetcher = self.trainer._data_connector.get_profiled_dataloader(dataloader) with self.trainer.profiler.profile("run_training_epoch"): self._outputs = self.epoch_loop.run(data_fetcher)

6107ce8e0d2feaed0263c0a60fc6c031603fd9ea

https://github.com/Lightning-AI/lightning.git

def advance(self) -> None: # type: ignore[override] log.detail(f"{self.__class__.__name__}: advancing loop") assert self.trainer.train_dataloader is not None dataloader = self.trainer.strategy.process_dataloader(self.trainer.train_dataloader) data_fetcher = self.trainer._data_connector.get_profiled_dataloader(dataloader) with self.train

advance

python3.10.4/Lib/inspect.py

add python 3.10.4 for windows

XX-Net

Python

inspect.py

def cleandoc(doc): try: lines = doc.expandtabs().split('\n') except UnicodeError: return None else: # Find minimum indentation of any non-blank lines after first line. margin = sys.maxsize for line in lines[1:]: content = len(line.lstrip()) if content: indent = len(line) - content margin = min(margin, indent) # Remove indentation. if lines: lines[0] = lines[0].lstrip() if margin < sys.maxsize: for i in range(1, len(lines)): lines[i] = lines[i][margin:] # Remove any trailing or leading blank lines. while lines and not lines[-1]: lines.pop() while lines and not lines[0]: lines.pop(0) return '\n'.join(lines)

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def cleandoc(doc): try: lines = doc.expandtabs().split('\n') except UnicodeError: return None else: # Find minimum indentation of any non-blank lines after first line. margin = sys.maxsize for line in lines[1:]: content = len(line.lstrip()) if content: indent = len(line) - content margin = min(margin, indent) # Remove indentation. if lines: lines[0] = lines[0].lstrip() if margin < sys.maxsize: for i in range(1, len(lines)): lines[i] = lines[i][margin:] # Remove any trailing or leading blank lines. while lines and not lines[-1]: lines.pop() while lines and not lines[0]: lines.pop(0)

cleandoc

test/test_utils.py

Format The Codebase - black formatting - isort formatting

gradio

Python

test_utils.py

def test_should_fail_with_distribution_not_found(self, mock_require): mock_require.side_effect = pkg_resources.DistributionNotFound() with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") version_check() self.assertEqual( str(w[-1].message), "gradio is not setup or installed properly. Unable to get version info.", )

cc0cff893f9d7d472788adc2510c123967b384fe

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

def test_should_fail_with_distribution_not_found(self, mock_require): mock_require.side_effect = pkg_resources.DistributionNotFound() with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") version_check() self.assertEqual( str(w[-1].message), "gradio is not setup or installed properly. Unable to get

test_should_fail_with_distribution_not_found

test/test_components.py

Support for iterative outputs (#2189) * Support for iterative outputs (#2162) (#2188) * added generator demo * fixed demo structure * fixes * fix failing tests due to refactor * test components * adding generators * fixes * iterative * formatting * add all * added demo * demo * formatting * fixed frontend * 3.2.1b release * removed test queue * iterative * formatting * formatting * Support for iterative outputs (#2149) * added generator demo * fixed demo structure * fixes * fix failing tests due to refactor * test components * adding generators * fixes * iterative * formatting * add all * added demo * demo * formatting * fixed frontend * 3.2.1b release * iterative * formatting * formatting * reverted queue everywhere * added queue to demos * added fake diffusion with gif * add to demos * more complex counter * fixes * image gif * fixes * version * merged * added support for state * formatting * generating animation * fix * tests, iterator * tests * formatting * tests for queuing * version * generating orange border animation * testings * added to documentation Co-authored-by: Ali Abid <aabid94@gmail.com>

gradio

Python

test_components.py

async def test_in_blocks(self): with gr.Blocks() as demo: score = gr.State() btn = gr.Button() btn.click(lambda x: x + 1, score, score) result = await demo.call_function(0, [0]) assert result["prediction"] == 1 result = await demo.call_function(0, [result["prediction"]]) assert result["prediction"] == 2

bf1510165ddd8c0d5b29adf67dfed967995e8a5b

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

async def test_in_blocks(self): with gr.Blocks() as demo: score = gr.State()

test_in_blocks

python/ccxt/stex.py

1.70.39 [ci skip]

ccxt

Python

stex.py

def fetch_markets(self, params={}): request = { 'code': 'ALL', } response = self.publicGetCurrencyPairsListCode(self.extend(request, params)) # # { # "success":true, # "data":[ # { # "id":935, # "currency_id":662, # "currency_code":"ABET", # "currency_name":"Altbet", # "market_currency_id":1, # "market_code":"BTC", # "market_name":"Bitcoin", # "min_order_amount":"0.00000010", # "min_buy_price":"0.00000001", # "min_sell_price":"0.00000001", # "buy_fee_percent":"0.20000000", # "sell_fee_percent":"0.20000000", # "active":true, # "delisted":false, # "pair_message":"", # "currency_precision":8, # "market_precision":8, # "symbol":"ABET_BTC", # "group_name":"BTC", # "group_id":1 # } # ] # } # result = [] markets = self.safe_value(response, 'data', []) for i in range(0, len(markets)): market = markets[i] id = self.safe_string(market, 'id') numericId = self.safe_integer(market, 'id') baseId = self.safe_string(market, 'currency_id') quoteId = self.safe_string(market, 'market_currency_id') baseNumericId = self.safe_integer(market, 'currency_id') quoteNumericId = self.safe_integer(market, 'market_currency_id') base = self.safe_currency_code(self.safe_string(market, 'currency_code')) quote = self.safe_currency_code(self.safe_string(market, 'market_code')) minBuyPrice = self.safe_string(market, 'min_buy_price') minSellPrice = self.safe_string(market, 'min_sell_price') minPrice = Precise.string_max(minBuyPrice, minSellPrice) buyFee = Precise.string_div(self.safe_string(market, 'buy_fee_percent'), '100') sellFee = Precise.string_div(self.safe_string(market, 'sell_fee_percent'), '100') fee = Precise.string_max(buyFee, sellFee) result.append({ 'id': id, 'numericId': numericId, 'symbol': base + '/' + quote, 'base': base, 'quote': quote, 'settle': None, 'baseId': baseId, 'quoteId': quoteId, 'settleId': None, 'baseNumericId': baseNumericId, 'quoteNumericId': quoteNumericId, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': self.safe_value(market, 'active'), 'contract': False, 'linear': None, 'inverse': None, 'taker': fee, 'maker': fee, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': { 'price': self.safe_integer(market, 'market_precision'), 'amount': self.safe_integer(market, 'currency_precision'), }, 'limits': { 'leverage': { 'min': None, 'max': None, }, 'amount': { 'min': self.safe_number(market, 'min_order_amount'), 'max': None, }, 'price': { 'min': minPrice, 'max': None, }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, }) return result

599367bddf0348d9491990623efcf32c1158d48f

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

def fetch_markets(self, params={}): request = { 'code': 'ALL', } response = self.publicGetCurrencyPairsListCode(self.extend(request, params)) # # { # "success":true, # "data":[ # { # "id":935, # "currency_id":662, # "currency_code":"ABET", # "currency_name":"Altbet", # "market_currency_id":1, # "market_code":"BTC", # "market_name":"Bitcoin", # "min_order_amount":"0.00000010", # "min_buy_price":"0.00000001", # "min_sell_price":"0.00000001", # "buy_fee_percent":"0.20000000", # "sell_fee_percent":"0.20000000", # "active":true, # "delisted":false, # "pair_message":"", # "currency_precision":8, # "market_precision":8, # "symbol":"ABET_BTC", # "group_name":"BTC", # "group_id":1 # } # ] # } # result = [] markets = self.safe_value(response, 'data', []) for i in range(0, len(markets)): market = markets[i] id = self.safe_string(market, 'id') numericId = self.safe_integer(market, 'id') baseId = self.safe_string(market, 'currency_id') quoteId = self.safe_string(market, 'market_currency_id') baseNumericId = self.safe_integer(market, 'currency_id') quoteNumericId = self.safe_integer(market, 'market_currency_id') base = self.safe_currency_code(self.safe_string(market, 'currency_code')) quote = self.safe_currency_code(self.safe_string(market, 'market_code')) minBuyPrice = self.safe_string(market, 'min_buy_price') minSellPrice = self.safe_string(market, 'min_sell_price') minPrice = Precise.string_max(minBuyPrice, minSellPrice) buyFee = Precise.string_div(self.safe_string(market, 'buy_fee_percent'), '100') sellFee = Precise.string_div(self.safe_string(market, 'sell_fee_percent'), '100') fee = Precise.string_max(buyFee, sellFee) result.append({ 'id': id, 'numericId': numericId, 'symbol': base + '/' + quote, 'base': base, 'quote': quote, 'settle': None, 'baseId': baseId, 'quoteId': quoteId, 'settleId': None, 'baseNumericId': baseNumericId, 'quoteNumericId': quoteNumericId, 'type': 'spot', 'spot': True, 'margin': False, 'swap': False, 'future': False, 'option': False, 'active': self.safe_value(market, 'active'), 'contract': False, 'linear': None, 'inverse': None, 'taker': fee, 'maker': fee, 'contractSize': None, 'expiry': None, 'expiryDatetime': None, 'strike': None, 'optionType': None, 'precision': { 'price': self.safe_integer(market, 'market_precision'), 'amount': self.safe_integer(market, 'currency_precision'), }, 'limits': { 'leverage': {

fetch_markets

pandas/core/indexes/interval.py

Revert Interval/IntervalIndex/interval_range.inclusive deprecation (#48116) * Revert "Cln tests interval wrt inclusive (#47775)" This reverts commit 2d6e0b251955d3a2c0c88f7e6ddb57b335ed09b7. * Revert "CLN: Rename private variables to inclusive (#47655)" This reverts commit 102b3ca2119df822e2b0f346fa936d0fe9f17501. * Revert "TYP: Improve typing interval inclusive (#47646)" This reverts commit 55064763e8ba55f6ff5370a8dd083767a189d7a4. * Revert "DEPR: Deprecate set_closed and add set_incluive (#47636)" This reverts commit bd4ff395cbbf4cbde1fc8f1f746cae064a401638. * Revert "DEPR: Remove deprecation from private class IntervalTree (#47637)" This reverts commit f6658ef9fdef5972214fdc338e2c6b5ee308dbf4. * Revert "Revert inclusive default change of IntervalDtype (#47367)" This reverts commit d9dd1289e07d86928d144e53beb3d5b8ab3c2215. * Revert "ENH: consistency of input args for boundaries - Interval (#46522)" This reverts commit 7e23a37e1c5bda81234801a6584563e2880769eb. * Revert "ENH: consistency of input args for boundaries - pd.interval_range (#46355)" This reverts commit 073b3535d7a5171102e5915c38b57c21d13795ae. * Fix ArrowIntervalType manually * Remove unused import * Fix doctest and leftover usage * Fix remaining tests * Fix wording in doctoring Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com>

pandas

Python

interval.py

def __reduce__(self): d = { "left": self.left, "right": self.right, "closed": self.closed, "name": self.name, } return _new_IntervalIndex, (type(self), d), None

252ae0555abf488522f947107dcdee684be6ac8a

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

def __reduce__(self): d = { "left": self.left, "right": self

__reduce__

ludwig/serve.py

Serve json numpy encoding (#2316)

ludwig

Python

serve.py

def server(model, allowed_origins=None): middleware = [Middleware(CORSMiddleware, allow_origins=allowed_origins)] if allowed_origins else None app = FastAPI(middleware=middleware) input_features = {f[COLUMN] for f in model.config["input_features"]}

5069f19bc289592c3d57969531e56271cb0bc538

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

def server(model, allowed_origins=None): middleware = [Middleware(CORSMiddleware, allow_o

server

wagtail/search/tests/test_backends.py

Fix warnings from flake8-comprehensions.

wagtail

Python

test_backends.py

def assertUnsortedListEqual(self, a, b): self.assertListEqual(sorted(a), sorted(b)) # SEARCH TESTS

de3fcba9e95818e9634ab7de6bfcb1f4221f2775

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

def assertUnsortedListEqual(self, a, b):

assertUnsortedListEqual

wagtail/admin/tests/api/test_images.py

Reformat with black

wagtail

Python

test_images.py

def test_thumbnail(self): # Add a new image with source file image = get_image_model().objects.create( title="Test image", file=get_test_image_file(), ) response = self.get_response(image.id) content = json.loads(response.content.decode("UTF-8")) self.assertIn("thumbnail", content) self.assertEqual(content["thumbnail"]["width"], 165) self.assertEqual(content["thumbnail"]["height"], 123) self.assertTrue(content["thumbnail"]["url"].startswith("/media/images/test")) # Check that source_image_error didn't appear self.assertNotIn("source_image_error", content["meta"]) # Overwrite imported test cases do Django doesn't run them TestImageDetail = None TestImageListing = None

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def test_thumbnail(self): # Add a new image with source file image = get_image_model().objects.create(

test_thumbnail

python3.10.4/Lib/distutils/_msvccompiler.py

add python 3.10.4 for windows

XX-Net

Python

_msvccompiler.py

def _find_vc2017(): root = os.environ.get("ProgramFiles(x86)") or os.environ.get("ProgramFiles") if not root: return None, None try: path = subprocess.check_output([ os.path.join(root, "Microsoft Visual Studio", "Installer", "vswhere.exe"), "-latest", "-prerelease", "-requires", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", "-property", "installationPath", "-products", "*", ], encoding="mbcs", errors="strict").strip() except (subprocess.CalledProcessError, OSError, UnicodeDecodeError): return None, None path = os.path.join(path, "VC", "Auxiliary", "Build") if os.path.isdir(path): return 15, path return None, None PLAT_SPEC_TO_RUNTIME = { 'x86' : 'x86', 'x86_amd64' : 'x64', 'x86_arm' : 'arm', 'x86_arm64' : 'arm64' }

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def _find_vc2017(): root = os.environ.get("ProgramFiles(x86)") or os.environ.get("ProgramFiles") if not root: return None, None try: path = subprocess.check_output([ os.path.join(root, "Microsoft Visual Studio", "Installer", "vswhere.exe"), "-latest", "-prerelease", "-requires", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", "-property", "installationPath", "-products", "*", ], encoding="m

_find_vc2017

seaborn/tests/_core/test_mappings.py

Thoroughly update scaling logic and internal API

seaborn

Python

test_mappings.py

def test_categorical_multi_lookup_categorical(self): x = pd.Series(["a", "b", "c"]).astype("category") colors = color_palette(n_colors=len(x)) scale = get_default_scale(x) m = ColorSemantic().setup(x, scale) assert_series_equal(m(x), pd.Series(colors))

6f3077f12b7837106ba0a79740fbfd547628291b

https://github.com/mwaskom/seaborn.git

def test_categorical_multi_lookup_categorical(self): x = pd.Series(["a", "b", "c"]).astype("category") colors = color_palette(n_colors=len(x)) scale = get_default_scale(x) m = ColorSemantic().setup(x, scale) assert_series_equal(m

test_categorical_multi_lookup_categorical

mkdocs/tests/config/config_options_tests.py

Refactor tests for ConfigOption errors

mkdocs

Python

config_options_tests.py

def test_deprecated_option_move(self): option = config_options.Deprecated(moved_to='new') config = {'old': 'value'} option.pre_validation(config, 'old') self.assertEqual( option.warnings, [ "The configuration option 'old' has been deprecated and will be removed in a " "future release of MkDocs. Use 'new' instead." ], ) self.assertEqual(config, {'new': 'value'})

13b9c0dbd18d5a1b9705ca171a9e3b383a8e7d97

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

def test_deprecated_option_move(self): option = config_options.Deprecated(moved_to='new') config = {'old': 'value'} option.pre_validation(config, 'old') self.assertEqual( option.warnings, [ "The configuration option 'old' has been deprecated and will be removed in a " "future release of MkD

test_deprecated_option_move

homeassistant/components/forked_daapd/media_player.py

Add browse media to forked-daapd (#79009) * Add browse media to forked-daapd * Use elif in async_browse_image * Add tests * Add tests * Add test * Fix test

core

Python

media_player.py

async def async_turn_on(self) -> None: # restore state await self.api.set_volume(volume=self._last_volume * 100) if self._last_outputs: futures: list[asyncio.Task[int]] = [] for output in self._last_outputs: futures.append( asyncio.create_task( self.api.change_output( output["id"], selected=output["selected"], volume=output["volume"], ) ) ) await asyncio.wait(futures) else: # enable all outputs await self.api.set_enabled_outputs( [output["id"] for output in self._outputs] )

499c3410d1177eeec478af366e275a41b3e6ea60

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

async def async_turn_on(self) -> None: # restore state await self.api.set_volume(volume=self._last_volume * 100) if self._last_outputs: futures: list[asyncio.Task[int]] = [] for output in self._last_outputs: futures.append( asyncio.create_task( self.api.change_output( output["id"], selected=output["selected"], volume=output["volume"], ) ) ) await asyncio.w

async_turn_on

nuitka/tools/quality/Git.py

Quality: Fix formatting when adding files on Windows * These have the wrong newlines potentially, so try again in case of failure after cleaning the newlines in checkout.

Nuitka

Python

Git.py

def updateWorkingFile(path, orig_object_hash, new_object_hash): patch = check_output( ["git", "diff", "--no-color", orig_object_hash, new_object_hash] ) git_path = path.replace(os.path.sep, "/").encode("utf8")

033d29fee17fbf13a53bf89f89ca5c444ff3dd0b

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

def updateWorkingFile(path, orig_object_hash, new_object_hash): patch = check_output( ["git", "diff", "--no-color", orig_object_hash, new_object_hash] ) git_path = path.replace(os.path.sep, "/").encode("utf8")

updateWorkingFile

pyxel/editor/music_editor.py

Renamed the sounds property of Music

pyxel

Python

music_editor.py

def get_field(self, index): if index >= pyxel.NUM_CHANNELS: return music = pyxel.music(self.music_no_var) return music.snds_list[index]

4f4459f6f8d37d3b687f7844e63abb0f672d8a98

https://github.com/kitao/pyxel.git

def get_field(self, index): if index >= pyxel.NUM_CHANNELS: return music = pyxel.music(self.music_no_var) return music.snds_list[index]

get_field

gradio/documentation.py

Fix default value in docs for objects (#1900)

gradio

Python

documentation.py

def document_fn(fn): doc_str = inspect.getdoc(fn) doc_lines = doc_str.split("\n") signature = inspect.signature(fn) description, parameters, returns, examples = [], {}, [], [] mode = "description" for line in doc_lines: line = line.rstrip() if line == "Parameters:": mode = "parameter" elif line == "Example:": mode = "example" elif line == "Returns:": mode = "return" else: if mode == "description": description.append(line if line.strip() else "<br>") continue assert line.startswith( " " ), f"Documentation format for {fn.__name__} has format error in line: {line}" line = line[4:] if mode == "parameter": colon_index = line.index(": ") assert ( colon_index > -1 ), f"Documentation format for {fn.__name__} has format error in line: {line}" parameter = line[:colon_index] parameter_doc = line[colon_index + 2 :] parameters[parameter] = parameter_doc elif mode == "return": returns.append(line) elif mode == "example": examples.append(line) description_doc = " ".join(description) parameter_docs = [] for param_name, param in signature.parameters.items(): if param_name.startswith("_"): continue if param_name == "kwargs" and param_name not in parameters: continue parameter_doc = { "name": param_name, "annotation": param.annotation, "kind": param.kind.description, "doc": parameters.get(param_name), } if param_name in parameters: del parameters[param_name] if param.default != inspect.Parameter.empty: default = param.default if type(default) == str: default = '"' + default + '"' if default.__class__.__module__ != "builtins": default = f"{default.__class__.__name__}()" parameter_doc["default"] = default elif parameter_doc["doc"] is not None and "kwargs" in parameter_doc["doc"]: parameter_doc["kwargs"] = True parameter_docs.append(parameter_doc) assert ( len(parameters) == 0 ), f"Documentation format for {fn.__name__} documents nonexistent parameters: {''.join(parameters.keys())}" if len(returns) == 0: return_docs = {} elif len(returns) == 1: return_docs = {"annotation": signature.return_annotation, "doc": returns[0]} else: return_docs = {} # raise ValueError("Does not support multiple returns yet.") examples_doc = "\n".join(examples) if len(examples) > 0 else None return description_doc, parameter_docs, return_docs, examples_doc

3ef4d4da4c1d39818d8bde82701f5e75b4b2cbe8

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

def document_fn(fn): doc_str = inspect.getdoc(fn) doc_lines = doc_str.split("\n") signature = inspect.signature(fn) description, parameters, returns, examples = [], {}, [], [] mode = "description" for line in doc_lines: line = line.rstrip() if line == "Parameters:": mode = "parameter" elif line == "Example:": mode = "example" elif line == "Returns:": mode = "return" else: if mode == "description": description.append(line if line.strip() else "<br>") continue assert line.startswith( " " ), f"Documentation format for {fn.__name__} has format error in line: {line}" line = line[4:] if mode == "parameter": colon_index = line.index(": ") assert ( colon_index > -1 ), f"Documentation format for {fn.__name__} has format error in line: {line}" parameter = line[:colon_index] parameter_doc = line[colon_index + 2 :] parameters[parameter] = parameter_doc elif mode == "return": returns.append(line) elif mode == "example": examples.append(line) description_doc = " ".join(description) parameter_docs = [] for param_name, param in signature.parameters.items(): if param_name.startswith("_"): continue if param_name == "kwargs" and param_name not in parameters: continue parameter_doc = { "name": param_name, "annotation": param.annotation, "kind": param.kind.description, "doc": parameters.get(param_name), } if param_name in parameters:

document_fn

pandas/tests/indexing/test_indexing.py

DEPR: df.iloc[:, foo] = bar attempt to set inplace (#45333)

pandas

Python

test_indexing.py

def test_astype_assignment(self): # GH4312 (iloc) df_orig = DataFrame( [["1", "2", "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") ) df = df_orig.copy() msg = "will attempt to set the values inplace instead" with tm.assert_produces_warning(FutureWarning, match=msg): df.iloc[:, 0:2] = df.iloc[:, 0:2].astype(np.int64) expected = DataFrame( [[1, 2, "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") ) tm.assert_frame_equal(df, expected) df = df_orig.copy() with tm.assert_produces_warning(FutureWarning, match=msg): df.iloc[:, 0:2] = df.iloc[:, 0:2]._convert(datetime=True, numeric=True) expected = DataFrame( [[1, 2, "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") ) tm.assert_frame_equal(df, expected) # GH5702 (loc) df = df_orig.copy() with tm.assert_produces_warning(FutureWarning, match=msg): df.loc[:, "A"] = df.loc[:, "A"].astype(np.int64) expected = DataFrame( [[1, "2", "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") ) tm.assert_frame_equal(df, expected) df = df_orig.copy() with tm.assert_produces_warning(FutureWarning, match=msg): df.loc[:, ["B", "C"]] = df.loc[:, ["B", "C"]].astype(np.int64) expected = DataFrame( [["1", 2, 3, ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") ) tm.assert_frame_equal(df, expected)

46bcf3740b38339f62b94e66ec29537a28a17140

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

def test_astype_assignment(self): # GH4312 (iloc) df_orig = DataFrame( [["1", "2", "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") ) df = df_orig.copy() msg = "will attempt to set the values inplace instead" with tm.assert_produces_warning(Futu

test_astype_assignment

wagtail/images/tests/test_jinja2.py

Reformat with black

wagtail

Python

test_jinja2.py

def render(self, string, context=None, request_context=True): if context is None: context = {} # Add a request to the template, to simulate a RequestContext if request_context: site = Site.objects.get(is_default_site=True) request = self.client.get("/test/", HTTP_HOST=site.hostname) context["request"] = request template = self.engine.from_string(string) return template.render(context)

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def render(self, string, context=None, request_context=True): if context is None: context = {} # Add a request to the template, to simulate a RequestContext if request_context:

render

tests/snuba/api/endpoints/test_organization_events.py

test(perf-issues): Improve how performance issues are created in tests (#39293) This PR updates `Factories.store_event` and `load_data` to support creation of performance groups for transactions. Once this PR is merged, I will update all instances of `hack_pull_out_data`. Resolved ISP-16

sentry

Python

test_organization_events.py

def test_has_performance_issue_ids(self): data = load_data( platform="transaction", fingerprint=[f"{GroupType.PERFORMANCE_N_PLUS_ONE_DB_QUERIES.value}-group1"], ) self.store_event(data=data, project_id=self.project.id) query = { "field": ["count()"], "statsPeriod": "1h", "query": "has:performance.issue_ids", } response = self.do_request(query) assert response.status_code == 200, response.content assert response.data["data"][0]["count()"] == 1 query = { "field": ["count()"], "statsPeriod": "1h", "query": "!has:performance.issue_ids", } response = self.do_request(query) assert response.status_code == 200, response.content assert response.data["data"][0]["count()"] == 0

5d8a666bebd4d4b0b0200af5ed37ba504e0895be

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

def test_has_performance_issue_ids(self): data = load_data( platform="transaction", fingerprint=[f"{GroupType.PERFORMANCE_N_PLUS_ONE_DB_QUERIES.value}-group1"], ) self.store_event(data=data, project_id=self.project.id) query = { "field": ["c

test_has_performance_issue_ids

recommenders/models/ncf/dataset.py

fix docstrings

recommenders

Python

dataset.py

def __next__(self): if self.next_row: self.row = self.next_row elif self.line_num == 0: self.row = self._extract_row_data(next(self.reader, None)) if self.row is None: raise EmptyFileException("{} is empty.".format(self.filename)) else: raise StopIteration # end of file self.next_row = self._extract_row_data(next(self.reader, None)) self.line_num += 1 return self.row

87970de68431d511a1ea28f838be1f9eba9b4c02

https://github.com/microsoft/recommenders.git

def __next__(self): if self.next_row: self.row = self.next_row elif self.line_num == 0: self.row = self._extract_row_data(next(self.reader, None)) if self.row is None: raise EmptyFileException("{} is empty.".format(self.filename)) else: raise StopIteration # end of file self.next_row = self._extract_

__next__

lib/matplotlib/backends/_backend_tk.py

Make it easier to improve UI event metadata. Currently, UI events (MouseEvent, KeyEvent, etc.) are generated by letting the GUI-specific backends massage the native event objects into a list of args/kwargs and then call `FigureCanvasBase.motion_notify_event`/`.key_press_event`/etc. This makes it a bit tricky to improve the metadata on the events, because one needs to change the signature on both the `FigureCanvasBase` method and the event class. Moreover, the `motion_notify_event`/etc. methods are directly bound as event handlers in the gtk3 and tk backends, and thus have incompatible signatures there. Instead, the native GUI handlers can directly construct the relevant event objects and trigger the events themselves; a new `Event._process` helper method makes this even shorter (and allows to keep factoring some common functionality e.g. for tracking the last pressed button or key). As an example, this PR also updates figure_leave_event to always correctly set the event location based on the *current* cursor position, instead of the last triggered location event (which may be outdated); this can now easily be done on a backend-by-backend basis, instead of coordinating the change with FigureCanvasBase.figure_leave_event. This also exposed another (minor) issue, in that resize events often trigger *two* calls to draw_idle -- one in the GUI-specific handler, and one in FigureCanvasBase.draw_idle (now moved to ResizeEvent._process, but should perhaps instead be a callback autoconnected to "resize_event") -- could probably be fixed later.

matplotlib

Python

_backend_tk.py

def scroll_event_windows(self, event): # need to find the window that contains the mouse w = event.widget.winfo_containing(event.x_root, event.y_root) if w != self._tkcanvas: return x = self._tkcanvas.canvasx(event.x_root - w.winfo_rootx()) y = (self.figure.bbox.height - self._tkcanvas.canvasy(event.y_root - w.winfo_rooty())) step = event.delta / 120 MouseEvent("scroll_event", self, x, y, step=step, guiEvent=event)._process()

4e21912d2938b0e8812c4d1f7cd902c080062ff2

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

def scroll_event_windows(self, event): # need to find the window that co

scroll_event_windows

dashboard/datacenter.py

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

ray

Python

datacenter.py

async def _get_actor(actor): actor = dict(actor) worker_id = actor["address"]["workerId"] core_worker_stats = DataSource.core_worker_stats.get(worker_id, {}) actor_constructor = core_worker_stats.get( "actorTitle", "Unknown actor constructor" ) actor["actorConstructor"] = actor_constructor actor.update(core_worker_stats) # TODO(fyrestone): remove this, give a link from actor # info to worker info in front-end. node_id = actor["address"]["rayletId"] pid = core_worker_stats.get("pid") node_physical_stats = DataSource.node_physical_stats.get(node_id, {}) actor_process_stats = None actor_process_gpu_stats = [] if pid: for process_stats in node_physical_stats.get("workers", []): if process_stats["pid"] == pid: actor_process_stats = process_stats break for gpu_stats in node_physical_stats.get("gpus", []): for process in gpu_stats.get("processes", []): if process["pid"] == pid: actor_process_gpu_stats.append(gpu_stats) break actor["gpus"] = actor_process_gpu_stats actor["processStats"] = actor_process_stats return actor

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

async def _get_actor(actor): actor = dict(actor) worker_id = actor["address"]["workerId"] core_w

_get_actor

jina/orchestrate/deployments/config/docker_compose.py

feat: add default volume to dockererized executors (#4554)

jina

Python

docker_compose.py

def get_runtime_config(self) -> List[Dict]: # One Dict for replica replica_configs = [] for i_rep in range(self.service_args.replicas): cargs = copy.copy(self.service_args) cargs.name = ( f'{cargs.name}/rep-{i_rep}' if self.service_args.replicas > 1 else cargs.name ) env = cargs.env image_name = self._get_image_name(cargs.uses) container_args = self._get_container_args(cargs) config = { 'image': image_name, 'entrypoint': ['jina'], 'command': container_args, 'healthcheck': { 'test': f'python -m jina.resources.health_check.pod localhost:{cargs.port}', 'interval': '2s', }, 'environment': [ f'JINA_LOG_LEVEL={os.getenv("JINA_LOG_LEVEL", "INFO")}' ], } if env is not None: config['environment'] = [f'{k}={v}' for k, v in env.items()] if self.service_args.pod_role == PodRoleType.WORKER: config = self._update_config_with_volumes( config, auto_volume=not self.common_args.disable_auto_volume ) replica_configs.append(config) return replica_configs

984e743734b18c1117bbbc2eda49d7eceaa9343f

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

def get_runtime_config(self) -> List[Dict]: # One Dict for replica replica_configs = [] for i_rep in range(self.service_args.replicas): cargs = copy.copy(self.service_args) cargs.name = ( f'{cargs.name}/rep-{i_rep

get_runtime_config

keras/legacy_tf_layers/core_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

core_test.py

def testDropoutProperties(self): dp = core_layers.Dropout(0.5, name="dropout") self.assertEqual(dp.rate, 0.5) self.assertEqual(dp.noise_shape, None) dp(tf.ones(())) self.assertEqual(dp.name, "dropout")

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def testDropoutProperties(self): dp = core_layers

testDropoutProperties

dashboard/modules/healthz/tests/test_healthz.py

[dashboard][2/2] Add endpoints to dashboard and dashboard_agent for liveness check of raylet and gcs (#26408) ## Why are these changes needed? As in this https://github.com/ray-project/ray/pull/26405 we added the health check for gcs and raylets. This PR expose them in the endpoint in dashboard and dashboard agent. For dashboard, we added `http://host:port/api/gcs_healthz` and it'll send RPC to GCS directly to see whether the GCS is alive or not. For agent, we added `http://host:port/api/local_raylet_healthz` and it'll send RPC to GCS to check whether raylet is alive or not. We think raylet is live if - GCS is dead - GCS is alive but GCS think the raylet is dead If GCS is dead for more than X seconds (60 by default), raylet will just crash itself, so KubeRay can still catch it.

ray

Python

test_healthz.py

def test_healthz_head(ray_start_cluster): dashboard_port = find_free_port() h = ray_start_cluster.add_node(dashboard_port=dashboard_port) uri = f"http://localhost:{dashboard_port}/api/gcs_healthz" wait_for_condition(lambda: requests.get(uri).status_code == 200) h.all_processes[ray_constants.PROCESS_TYPE_GCS_SERVER][0].process.kill() # It'll either timeout or just return an error try: wait_for_condition(lambda: requests.get(uri, timeout=1) != 200, timeout=4) except RuntimeError as e: assert "Read timed out" in str(e)

a68c02a15d041f987359c73781fb38202041a16f

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

def test_healthz_head(ray_start_cluster): dashboard_port = find_free_port() h = ray_start_cluster.add_node(dashboard_port=dashboard_port) uri = f"http://localhost:{dashboard_port}/api/gcs_healthz" wait_for_condition(lambda: requests.get(uri).status_code == 200) h.all_processes[ray_constants.PROCESS_TYPE_GCS_SERVER][0].process.kill() # It'll either timeout or just return an error try: wait_for_condition(lambda: requests.get(uri, timeout=1) != 200, timeout=4) except RuntimeError as e: assert "Read timed out" in str(e)

test_healthz_head

tests/sentry/api_gateway/test_proxy.py

feat(api-gateway): Proxy GET requests (#39595) This change introduces a proxy manager that proxies requests to a region silo given an org slug Currently, this only handles GETs and JSON response bodies. Later PRs will handle other methods and body types.

sentry

Python

test_proxy.py

def test_query_params(self, region_fnc_patch): query_param_dict = dict(foo="bar", numlist=["1", "2", "3"]) query_param_str = urlencode(query_param_dict, doseq=True) request = RequestFactory().get(f"http://sentry.io/echo?{query_param_str}") region_fnc_patch.return_value = SENTRY_REGION_CONFIG[0] resp = proxy_request(request, self.organization.slug) resp_json = json.loads(b"".join(resp.streaming_content)) assert resp.status_code == 200 # parse_qs returns everything in a list, including single arguments assert query_param_dict["foo"] == resp_json["foo"][0] assert query_param_dict["numlist"] == resp_json["numlist"]

ff8ef470d2fdb80df0a57890ead1e4a792ac99a2

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

def test_query_params(self, region_fnc_patch): query_param_dict = dict(foo="bar", numlist=["1", "2", "3"]) query_param_str = url

test_query_params

modules/safe.py

added guard for torch.load to prevent loading pickles with unknown content

stable-diffusion-webui

Python

safe.py

def find_class(self, module, name): if module == 'collections' and name == 'OrderedDict': return getattr(collections, name) if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']: return getattr(torch._utils, name) if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage']: return getattr(torch, name) if module == 'torch.nn.modules.container' and name in ['ParameterDict']: return getattr(torch.nn.modules.container, name) if module == 'numpy.core.multiarray' and name == 'scalar': return numpy.core.multiarray.scalar if module == 'numpy' and name == 'dtype': return numpy.dtype if module == '_codecs' and name == 'encode': return encode if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint': import pytorch_lightning.callbacks return pytorch_lightning.callbacks.model_checkpoint if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint': import pytorch_lightning.callbacks.model_checkpoint return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint if module == "__builtin__" and name == 'set': return set # Forbid everything else. raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")

875ddfeecfaffad9eee24813301637cba310337d

https://github.com/AUTOMATIC1111/stable-diffusion-webui.git

def find_class(self, module, name): if module == 'collections' and name == 'OrderedDict': return getattr(collections, name) if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']: return getattr(torch._utils, name) if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage']: return getattr(torch, name) if module == 'torch.nn.modules.container' and name in ['ParameterDict']: return getattr(torch.nn.modules.container, name) if module == 'numpy.core.multiarray' and name == 'scalar': return numpy.core.multiarray.scalar if module == 'numpy' and name == 'dtype': return numpy.dtype if module == '_codecs' and name == 'encode': return encode if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint': import pytorch_lightning.callbacks return pytorch_lightning.callbacks.model_checkpoint if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint': import pytorch_lightning.callbacks.model_checkpoint return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint

find_class

tests/utils/test_metadata.py

Create test_metadata.py

spotify-downloader

Python

test_metadata.py

def test_embed_metadata(tmpdir, monkeypatch, output_format): monkeypatch.chdir(tmpdir) monkeypatch.setattr(spotdl.utils.ffmpeg, "get_spotdl_path", lambda *_: tmpdir) yt = YoutubeDL( { "format": "bestaudio", "encoding": "UTF-8", } ) download_info = yt.extract_info( "https://www.youtube.com/watch?v=h-nHdqC3pPs", download=False ) song = Song.from_data_dump( ) output_file = Path(tmpdir / f"test.{output_format}") assert convert( input_file=(download_info["url"], download_info["ext"]), output_file=output_file, output_format=output_format, ) == (True, None) embed_metadata(output_file, song, output_format)

a96db2512e1533287684d5563d0a6b7dd065a8b7

https://github.com/spotDL/spotify-downloader.git

def test_embed_metadata(tmpdir, monkeypatch, output_format): monkeypatch.chdir(tmpdir) monkeypatch.setattr(spotdl.utils.ffmpeg, "get_spotdl_path", lambda *_: tmpdir) yt = YoutubeDL( { "format": "bestaudio", "encoding": "UTF-8", } ) download_info = yt.extract_info( "https://www.youtube.com/watch?v=h-nHdqC3pPs", download=False ) song = Song.from_data_dump( ) output_file = Path(tmpdir / f"test.{output_format}") assert convert( input_file=(download_info["url"], download_info["ext"]), output_file=output_file, output_format=output_format, ) == (True, None)

test_embed_metadata

sympy/assumptions/assume.py

Fix various typos Found via `codespell -q 3 -L aboves,aline,ans,aother,arithmetics,assum,atleast,braket,clen,declar,declars,dorder,dum,enew,fo,fro,inout,iself,ist,ket,lamda,lightyear,lightyears,nd,numer,numers,orderd,ot,pring,rcall,rever,ro,ser,siz,splitted,sring,supercedes,te,tht,unequality,upto,vas,versin,whet`

sympy

Python

assume.py

def function(self): # Will be changed to self.args[0] after args overriding is removed return self._args[0]

24f1e7730119fe958cc8e28411f790c9a5ec04eb

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

def function(self): # Will be changed to self.args[0

function

src/prefect/utilities/importtools.py

Fix attribute getter support

prefect

Python

importtools.py

def __getattr__(self, attr): if attr in ("__class__", "__file__", "__frame_data", "__help_message"): super().__getattr__(attr) else: fd = self.__frame_data raise ModuleNotFoundError( f"No module named '{fd['spec']}'\n\n" "This module was originally imported at:\n" f' File "{fd["filename"]}", line {fd["lineno"]}, in {fd["function"]}\n\n' f' {"".join(fd["code_context"]).strip()}\n' + self.__help_message )

d238c7b16097895006eff9e3f081958af15cd3e5

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

def __getattr__(self, attr): if attr in ("__class__", "__file_

__getattr__

src/diffusers/models/resnet.py

Simplify FirUp/down, unet sde (#71) * refactor fir up/down sample * remove variance scaling * remove variance scaling from unet sde * refactor Linear * style * actually remove variance scaling * add back upsample_2d, downsample_2d * style * fix FirUpsample2D

diffusers

Python

resnet.py

def forward(self, x): if self.use_conv: h = self._upsample_2d(x, self.Conv2d_0.weight, k=self.fir_kernel) h = h + self.Conv2d_0.bias.reshape(1, -1, 1, 1) else: h = self._upsample_2d(x, k=self.fir_kernel, factor=2) return h

53a42d0a0cab99e9a905b117b9893052c6849e10

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

def forward(self, x): if self.use_conv: h = self._upsample_2d(x, self.Conv2d_0.weight, k=self.fir_kernel) h = h + self.Conv2d_0.bias.reshape(1, -1, 1, 1) else: h = self._upsample_2d(x, k=self.fir_kernel, factor=2)

forward

python/ray/ml/preprocessors/scaler.py

[ml] add more preprocessors (#23904) Adding some more common preprocessors: * MaxAbsScaler * RobustScaler * PowerTransformer * Normalizer * FeatureHasher * Tokenizer * HashingVectorizer * CountVectorizer API docs: https://ray--23904.org.readthedocs.build/en/23904/ray-air/getting-started.html Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com>

ray

Python

scaler.py

def __repr__(self): stats = getattr(self, "stats_", None) return ( f"StandardScaler(columns={self.columns}, ddof={self.ddof}, stats={stats})" )

cc08c01adedad6cd89f3ab310ed58100ed6dbc26

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

def __repr__(self):

__repr__

.venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py

upd; format

transferlearning

Python

pyparsing.py

def addCondition(self, *fns, **kwargs): for fn in fns: self.parseAction.append(conditionAsParseAction(fn, message=kwargs.get('message'), fatal=kwargs.get('fatal', False))) self.callDuringTry = self.callDuringTry or kwargs.get("callDuringTry", False) return self

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def addCondition(self, *fns, **kwargs): for fn in fns: self.parseAction.append(conditionAsParseAction(fn, message=kwargs.get('message'), fa

addCondition

tests/test_categorical.py

Revert unnecessary (and broken) backwards compat in catplot (#2839)

seaborn

Python

test_categorical.py

def test_plot_elements(self): g = cat.catplot(x="g", y="y", data=self.df, kind="point") assert len(g.ax.collections) == 1 want_lines = self.g.unique().size + 1 assert len(g.ax.lines) == want_lines g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="point") want_collections = self.h.unique().size assert len(g.ax.collections) == want_collections want_lines = (self.g.unique().size + 1) * self.h.unique().size assert len(g.ax.lines) == want_lines g = cat.catplot(x="g", y="y", data=self.df, kind="bar") want_elements = self.g.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == want_elements g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="bar") want_elements = self.g.unique().size * self.h.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == want_elements g = cat.catplot(x="g", data=self.df, kind="count") want_elements = self.g.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == 0 g = cat.catplot(x="g", hue="h", data=self.df, kind="count") want_elements = self.g.unique().size * self.h.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == 0 g = cat.catplot(y="y", data=self.df, kind="box") want_artists = 1 assert len(self.get_box_artists(g.ax)) == want_artists g = cat.catplot(x="g", y="y", data=self.df, kind="box") want_artists = self.g.unique().size assert len(self.get_box_artists(g.ax)) == want_artists g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="box") want_artists = self.g.unique().size * self.h.unique().size assert len(self.get_box_artists(g.ax)) == want_artists g = cat.catplot(x="g", y="y", data=self.df, kind="violin", inner=None) want_elements = self.g.unique().size assert len(g.ax.collections) == want_elements g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="violin", inner=None) want_elements = self.g.unique().size * self.h.unique().size assert len(g.ax.collections) == want_elements g = cat.catplot(x="g", y="y", data=self.df, kind="strip") want_elements = self.g.unique().size assert len(g.ax.collections) == want_elements g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="strip") want_elements = self.g.unique().size + self.h.unique().size assert len(g.ax.collections) == want_elements

de1ecf0e0d0064982ebf4f13e1b1afddd27c80ff

https://github.com/mwaskom/seaborn.git

def test_plot_elements(self): g = cat.catplot(x="g", y="y", data=self.df, kind="point") assert len(g.ax.collections) == 1 want_lines = self.g.unique().size + 1 assert len(g.ax.lines) == want_lines g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="point") want_collections = self.h.unique().size assert len(g.ax.collections) == want_collections want_lines = (self.g.unique().size + 1) * self.h.unique().size assert len(g.ax.lines) == want_lines g = cat.catplot(x="g", y="y", data=self.df, kind="bar") want_elements = self.g.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == want_elements g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="bar") want_elements = self.g.unique().size * self.h.unique().size assert

test_plot_elements

dashboard/modules/job/common.py

[jobs] Monitor jobs in the background to avoid requiring clients to poll (#22180)

ray

Python

common.py

def get_all_jobs(self) -> Dict[str, JobStatusInfo]: raw_job_ids = _internal_kv_list(self.JOB_STATUS_KEY_PREFIX) job_ids = [job_id.decode() for job_id in raw_job_ids] return {job_id: self.get_status(job_id) for job_id in job_ids}

8806b2d5c43f256188632c245dd741774776dad0

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

def get_all_jobs(self) -> Dict[str, JobStatusInfo]: raw_job_ids = _internal_kv_list(self.JOB_STATUS_KEY_PREFIX) job_ids = [job_id.decode() for job_id in raw_job_ids] return {job_id: self.get_status(job_id) for job_id in job_ids}

get_all_jobs

tests/integration_tests/flows/test_mysql_api_pytest_based.py

It mysql api test pytest (#3694) * migration to pytest * Tests start passing * Fully working tests * Increase timeout for mindsdb start * reduce amount of logs * show logs only for failed tests

mindsdb

Python

test_mysql_api_pytest_based.py

def get_record(self, key, value): if key in self: for x in self: if x[key] == value: return x return None

ae4fa77a2c0a9fa57cc9c8bc7e8961dd01e4067e

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

def get_record(self, key, value): if key in self: for x in self: if x[key] == value: return x r

get_record

tests/sentry/api/endpoints/test_organization_sdk_updates.py

fix(sdk): Do not error if a project is using an unknown version (#32206)

sentry

Python

test_organization_sdk_updates.py

def test_unknown_version(self, mock_index_state): min_ago = iso_format(before_now(minutes=1)) self.store_event( data={ "event_id": "a" * 32, "message": "oh no", "timestamp": min_ago, "fingerprint": ["group-1"], "sdk": {"name": "example.sdk", "version": "dev-master@32e5415"}, }, project_id=self.project.id, assert_no_errors=False, ) self.store_event( data={ "event_id": "b" * 32, "message": "b", "timestamp": min_ago, "fingerprint": ["group-2"], "sdk": {"name": "example.sdk", "version": "2.0.0"}, }, project_id=self.project.id, assert_no_errors=False, ) with self.feature(self.features): response = self.client.get(self.url) update_suggestions = response.data assert len(update_suggestions) == 0

3ba27f5b5845de0a5a89d5cbf2e5df752915d9d7

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

def test_unknown_version(self, mock_index_state): min_ago = iso_format(before_now(minutes=1)) self.store_event( data={

test_unknown_version

numpy/lib/shape_base.py

ENH: Maintain subclass info for `np.kron` * Replace `*` call with `multiply` * Handle `mat` cases to perform reshape * Remove use result wrapping to maintain consistency with ufuncs

numpy

Python

shape_base.py

def kron(a, b): b = asanyarray(b) a = array(a, copy=False, subok=True, ndmin=b.ndim) ndb, nda = b.ndim, a.ndim nd = max(ndb, nda) if (nda == 0 or ndb == 0): return _nx.multiply(a, b) as_ = a.shape bs = b.shape if not a.flags.contiguous: a = reshape(a, as_) if not b.flags.contiguous: b = reshape(b, bs) # Equalise the shapes by prepending smaller one with 1s as_ = (1,)*max(0, ndb-nda) + as_ bs = (1,)*max(0, nda-ndb) + bs # Compute the product a_arr = a.reshape(a.size, 1) b_arr = b.reshape(1, b.size) is_any_mat = isinstance(a_arr, matrix) or isinstance(b_arr, matrix) # In case of `mat`, convert result to `array` result = _nx.multiply(a_arr, b_arr, subok=(not is_any_mat)) # Reshape back result = result.reshape(as_+bs) transposer = _nx.arange(nd*2).reshape([2, nd]).ravel(order='f') result = result.transpose(transposer) result = result.reshape(_nx.multiply(as_, bs)) return result if not is_any_mat else matrix(result, copy=False)

730f3154f48e33f22b2ea8814eb10a45aa273e17

https://github.com/numpy/numpy.git

def kron(a, b): b = asanyarray(b) a = array(a, copy=False, subok=True, ndmin=b.ndim) ndb, nda = b.ndim, a.ndim nd = max(ndb, nda) if (nda == 0 or ndb == 0): return _nx.multiply(a, b) as_ = a.shape bs = b.shape if not a.flags.contiguous: a = reshape(a, as_) if not b.flags.contiguous: b = reshape(b, bs) # Equalise the shapes by prepending smaller one with 1s as_ = (1,)*max(0, ndb-nda) + as_ bs = (1,)*max(0, nda-ndb) + bs # Compute the product a_arr = a.reshape(a.size, 1) b_arr = b.reshape(1, b.s

kron

.venv/lib/python3.8/site-packages/pip/_internal/resolution/resolvelib/base.py

upd; format

transferlearning

Python

base.py

def get_candidate_lookup(self): # type: () -> CandidateLookup raise NotImplementedError("Subclass should override")

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def get_candidate_lookup(self): # type: () -> CandidateLookup raise NotImplementedError("Subclass should override")

get_candidate_lookup

tests/components/utility_meter/test_sensor.py

Remove EVENT_TIME_CHANGED and EVENT_TIMER_OUT_OF_SYNC (#69643) Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

core

Python

test_sensor.py

async def test_self_reset_hourly(hass): await _test_self_reset( hass, gen_config("hourly"), "2017-12-31T23:59:00.000000+00:00" )

fe6a4bfb1dbb37dd16a0d73d776ad5f604154670

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

async def test_self_reset_hourly(hass): await _test_self_reset( hass, gen_config("hourly"), "2017-12-31T23

test_self_reset_hourly

rllib/agents/a3c/tests/test_a2c.py

[RLlib] A2/3C Config objects (A2CConfig and A3CConfig). (#24332)

ray

Python

test_a2c.py

def test_a2c_compilation(self): config = a3c.A2CConfig().rollouts(num_rollout_workers=2, num_envs_per_worker=2) num_iterations = 1 # Test against all frameworks. for _ in framework_iterator(config, with_eager_tracing=True): for env in ["CartPole-v0", "Pendulum-v1", "PongDeterministic-v0"]: trainer = config.build(env=env) for i in range(num_iterations): results = trainer.train() check_train_results(results) print(results) check_compute_single_action(trainer) trainer.stop()

b2b1c95aa5f94c74d192caca0d86945f2b4ce986

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

def test_a2c_compilation(self): config = a3c.A2CConfig().rollouts(num_rollout_workers=2, num_envs_per_worker=2) num_iterations = 1 # Test against all frameworks. for _ in framework_iterator(config, with_eager_tracing=True): for env in ["CartPole-v0", "Pendulum-v1", "PongDeterministic-v0"]: trainer = config.build(env=env) for i in range(num_iterations): resu

test_a2c_compilation

django/db/models/sql/where.py

Refs #33476 -- Reformatted code with Black.

django

Python

where.py

def _resolve_leaf(expr, query, *args, **kwargs): if hasattr(expr, "resolve_expression"): expr = expr.resolve_expression(query, *args, **kwargs) return expr

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def _resolve_leaf(expr, query, *args, **kwargs): if hasattr(expr,

_resolve_leaf

tools/preview/preview.py

Bugfix: convert - Gif Writer - Fix non-launch error on Gif Writer - convert plugins - linting - convert/fs_media/preview/queue_manager - typing - Change convert items from dict to Dataclass

faceswap

Python

preview.py

def update_tk_image(self) -> None: logger.trace("Updating tk image") # type: ignore self._build_faces_image() img = np.vstack((self._faces_source, self._faces_dest)) size = self._get_scale_size(img) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) pilimg = Image.fromarray(img) pilimg = pilimg.resize(size, Image.ANTIALIAS) self._tk_image = ImageTk.PhotoImage(pilimg) self._tk_vars["refresh"].set(False) logger.trace("Updated tk image") # type: ignore

1022651eb8a7741014f5d2ec7cbfe882120dfa5f

https://github.com/deepfakes/faceswap.git

def update_tk_image(self) -> None: logger.trace("Updating tk image") # type: ignore self._build_faces_image() img = np.vstack((self._

update_tk_image

tests/sentry/middleware/test_ratelimit_middleware.py

feat(ratelimits): Add headers with rate limit details (#30951) The headers allow API users to know where they are in terms of their rate limits. It'll be returned for every API that can be rate limited except when there is an internal exception. At the time of this commit, rate limits are not enforced except for some specific endpoints. * Several improvements to rate limit headers Headers now track how many requests are left in the current window, and when the next window starts Also, rate limit metadata is in a dataclass

sentry

Python

test_ratelimit_middleware.py

def get(self, request): return Response({"ok": True}) urlpatterns = [ url(r"^/ratelimit$", RateLimitHeaderTestEndpoint.as_view(), name="ratelimit-header-endpoint") ] @override_settings(ROOT_URLCONF="tests.sentry.middleware.test_ratelimit_middleware")

68b1cdf3b1bcb7990834a890b8a32a021bc75666

@override_settings(ROOT_URLCONF="tests.sentry.middleware.test_ratelimit_middleware")

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

def get(self, request): return Response({"ok": True}) urlpatt

get