Datasets:

semeru

/

Galeras-3k_deduped

like 0

tests/sentry/rules/history/test_preview.py

feat(alert-preview): last triggered (#42098) Attaches `last_triggered` to group info. `preview` now returns a mapping of group_ids to triggers, updated tests to reflect that.

sentry

Python

test_preview.py

def test_frequency_condition_alone(self): prev_hour = timezone.now() - timedelta(hours=1) group = None for i in range(5): group = self.store_event( project_id=self.project.id, data={"timestamp": iso_format(prev_hour)} ).group conditions = [ { "id": "sentry.rules.conditions.event_frequency.EventFrequencyCondition", "value": 4, "interval": "5m", } ] result = preview(self.project, conditions, [], *MATCH_ARGS) assert group.id in result conditions[0]["value"] = 5 result = preview(self.project, conditions, [], *MATCH_ARGS) assert group.id not in result

583a7ec15744b2ca8a9c56df484516111dbf783d

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

def test_frequency_condition_alone(self): prev_hour = timezone.now() - timedelta(hours=1) group = None for i in range(5): group = self.store_event( project_id=self.project.id, data={"timestamp": iso_format(prev_hour)} ).group conditions = [ {

test_frequency_condition_alone

modin/pandas/test/test_series.py

TEST-#3655: Check that Modin is defaulting to Pandas. (#3656) Co-authored-by: Dmitry Chigarev <62142979+dchigarev@users.noreply.github.com> Co-authored-by: Devin Petersohn <devin-petersohn@users.noreply.github.com> Signed-off-by: mvashishtha <mahesh@ponder.io>

modin

Python

test_series.py

def test_expanding(data): modin_series, _ = create_test_series(data) # noqa: F841 with warns_that_defaulting_to_pandas(): modin_series.expanding() @pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)

be2716f393fddd2f669f26616f80e051fc7ceee6

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)

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

def test_expanding(data): modin_series, _ = create_

test_expanding

pandas/tests/frame/test_query_eval.py

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

pandas

Python

test_query_eval.py

def setup_method(self): self.df = DataFrame({"A": [1, 2, 3]}) self.expected1 = self.df[self.df.A > 0] self.expected2 = self.df.A + 1

f46df091df3afea25a273f491d1f6b2c7d20b32c

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

def setup_method(self): self.df = DataFrame({"A": [1, 2, 3]}) self.expected1 = self.df[self.df.A > 0] self.expected2 = self.df.A + 1

setup_method

t/integration/test_canvas.py

Fixed bug when chaining a chord with a group (#7919) * Reproduced Bug from Issue #5958 * Fixed Issue #5958 * Added unit test: test_chord__or__group_of_single_task() * Added unit test: test_chord_upgrade_on_chaining() * Added unit test: test_chain_of_chord__or__group_of_single_task() * Added unit test: test_chain_of_chord_upgrade_on_chaining()

celery

Python

test_canvas.py

def test_chaining_upgraded_chords_mixed_canvas(self, manager, subtests): try: manager.app.backend.ensure_chords_allowed() except NotImplementedError as e: raise pytest.skip(e.args[0]) if not manager.app.conf.result_backend.startswith('redis'): raise pytest.skip('Requires redis result backend.') redis_connection = get_redis_connection() redis_key = 'echo_chamber' c = chain( chord(group([redis_echo.si('1', redis_key=redis_key), redis_echo.si('2', redis_key=redis_key), redis_echo.si('3', redis_key=redis_key)]), group([redis_echo.si('4', redis_key=redis_key), redis_echo.si('5', redis_key=redis_key), redis_echo.si('6', redis_key=redis_key)])), redis_echo.si('7', redis_key=redis_key), group( redis_echo.si('8', redis_key=redis_key), ), redis_echo.si('9', redis_key=redis_key), redis_echo.si('Done', redis_key='Done'), ) with subtests.test(msg='Run the chain and wait for completion'): redis_connection.delete(redis_key, 'Done') c.delay().get(timeout=TIMEOUT) await_redis_list_message_length(1, redis_key='Done', timeout=10) with subtests.test(msg='All tasks are executed once'): actual = [sig.decode('utf-8') for sig in redis_connection.lrange(redis_key, 0, -1)] expected = [str(i) for i in range(1, 10)] with subtests.test(msg='All tasks are executed once'): assert sorted(actual) == sorted(expected) # Cleanup redis_connection.delete(redis_key, 'Done')

87613c780ccd92c8b2694becfb50511a6052e8f1

https://github.com/celery/celery.git

def test_chaining_upgraded_chords_mixed_canvas(self, manager, subtests): try: manager.app.backend.ensure_chords_allowed() except NotImplementedError as e: raise pytest.skip(e.args[0]) if not manager.app.conf.result_backend.startswith('redis'): raise pytest.skip('Requires redis result backend.') redis_connection = get_redis_connection() redis_key = 'echo_chamber' c = chain( chord(group([redis_echo.si('1', redis_key=redis_key), redis_echo.si('2', redis_key=redis_key), redis_echo.si('3', redis_key=redis_key)]), group([redis_echo.si('4', redis_key=redis_key), redis_echo.si('5', redis_key=redis_key), redis_echo.si('6', redis_key=redis_key)])), redis_echo.si('7', redis_key=redis_key), group( redis_echo.si('8', redis_key=redis_key), ), redis_echo.si('9', redis_key=redis_key), redis_echo.si('Done', redis_key='Done'), ) with subtests.test(msg='Run the chain and wait for completion'): redis_connection.delete(redis_key, 'Done') c.delay().get(timeout=TIMEOUT) await_redis_list_message_length(1, redis_key='Done', timeout=10) with subtests.test(msg='All tasks are executed once'): actual = [sig.decode('utf-8') for sig

test_chaining_upgraded_chords_mixed_canvas

jax/experimental/jax2tf/impl_no_xla.py

[jax2tf] Fix conv1d padding; it's already normalized before the _pad_spatial_dims call. Enable non-XLA tests of conv1d. PiperOrigin-RevId: 461556553

jax

Python

impl_no_xla.py

def _pad_spatial_dims(x, x_shape, padding): # Add empty padding for batch and feature dimensions. no_pad = ((0, 0),) padding = tuple(padding) padding = no_pad + padding + no_pad x = tf.pad(x, padding) assert len(x.shape) == len(padding) x_shape = tuple(p0 + xs + p1 for xs, (p0, p1) in zip(x_shape, padding)) jax2tf._assert_matching_abstract_shape(x, x_shape) return x, x_shape

ae4aee762a6ab18b17d61b68d8ee32d2c4e3b957

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

def _pad_spatial_dims(x, x_shape, padding): # Add empty padding for batch and feature dimensions. no_pad = ((0, 0),) padding = tuple(padding) padding = no_pad + padding + no_pad x = tf.pad(x, padding) assert len(x.shape) == len(p

_pad_spatial_dims

.venv/lib/python3.8/site-packages/pip/_vendor/urllib3/_collections.py

upd; format

transferlearning

Python

_collections.py

def iteritems(self): for key in self: vals = self._container[key.lower()] for val in vals[1:]: yield vals[0], val

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def iteritems(self): fo

iteritems

tests/components/sleepiq/conftest.py

Refactor sleepiq as async with config flow (#64850) Co-authored-by: J. Nick Koston <nick@koston.org>

core

Python

conftest.py

def mock_sleepers(): return [ Sleeper(sleeper) for sleeper in json.loads(load_fixture("sleeper.json", "sleepiq"))["sleepers"] ]

0bd0b4766e8221584a74bffc7c2f0430c23169df

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

def mock_sleepers(): return [ Sleeper(sleeper) for sleeper in json.loads(load_fixture("sleeper.json", "sleepiq"))["sleepers

mock_sleepers

homeassistant/components/plugwise/coordinator.py

String formatting and max line length - Part 4 (#84445) Co-authored-by: jjlawren <jjlawren@users.noreply.github.com>

core

Python

coordinator.py

async def _async_update_data(self) -> PlugwiseData: try: if not self._connected: await self._connect() data = await self.api.async_update() except InvalidAuthentication as err: raise ConfigEntryError("Invalid username or Smile ID") from err except (InvalidXMLError, ResponseError) as err: raise UpdateFailed( "Invalid XML data, or error indication received for the Plugwise" " Adam/Smile/Stretch" ) from err except UnsupportedDeviceError as err: raise ConfigEntryError("Device with unsupported firmware") from err except ConnectionFailedError as err: raise UpdateFailed("Failed to connect to the Plugwise Smile") from err return PlugwiseData( gateway=cast(GatewayData, data[0]), devices=cast(dict[str, DeviceData], data[1]), )

94755a5773f8197153ab9bffe83b9711f3a76d9d

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

async def _async_update_data(self) -> PlugwiseData: try: if not self._connected: await self._connect() data = await self.api.async_update() except InvalidAuthentication as err: raise ConfigEntryError("Invalid username or Smile ID") from err except (InvalidXMLError, ResponseError) as err: raise UpdateFailed( "Invalid XML data, or error indication received for the Plugwise" " Adam/Smile/Stretch" ) from err

_async_update_data

jax/_src/lax/lax.py

[dynamic-shapes] revive basic bounded int machinery, add tests

jax

Python

lax.py

def _iota_abstract_eval(*, dtype, shape, dimension): _check_shapelike("iota", "shape", shape) if not any(dtypes.issubdtype(dtype, t) for t in _num): msg = 'iota does not accept dtype {}. Accepted dtypes are subtypes of {}.' typename = str(np.dtype(dtype).name) accepted_typenames = (t.__name__ for t in _num) raise TypeError(msg.format(typename, ', '.join(accepted_typenames))) if not 0 <= dimension < len(shape): raise ValueError("iota dimension must be between 0 and len(shape), got " f"dimension={dimension} for shape {shape}") if not any(isinstance(d, core.BInt) for d in shape): return ShapedArray(shape, dtype) # TODO(mattjj): unify DShapedArray with ShapedArray, and remove this code return core.DShapedArray(shape, dtype, False) iota_p = Primitive('iota') iota_p.def_impl(partial(xla.apply_primitive, iota_p)) iota_p.def_abstract_eval(_iota_abstract_eval)

98e71fe31de8f6ea26be76488d41fb471fef56eb

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

def _iota_abstract_eval(*, dtype, shape, dimension): _check_shapelike("iota", "shape", shape) if not any(dtypes.issubdtype(dtype, t) for t in _num): msg = 'iota does not accept dtyp

_iota_abstract_eval

thumbor/filters/redeye.py

Feature/optional opencv (#1400) * Removed opencv dependency Now OpenCV is optional and detectors are smart to skip if cv2 could not be imported. Also refactored face detector a bit to make it more maintainable. Now thumbor can be installed with pip install thumbor pip install thumbor[all] pip install thumbor[opencv] pip install thumbor[tests]

thumbor

Python

redeye.py

def cascade(self) -> None: if not hasattr(self, "_cascade"): setattr(self, "_cascade", cv2.CascadeClassifier(CASCADE_FILE_PATH)) return getattr(self, "_cascade")

d34fd16034e307b545c3e3adfa4d9d472a582cc6

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

def cascade(self) -> None: if not hasattr(self, "_cascade"): setattr(self, "_cascade", cv2.CascadeClassifier(CASCADE_FILE_PATH)) return getattr(self, "_cas

cascade

django/db/models/query.py

Refs #33476 -- Reformatted code with Black.

django

Python

query.py

def __deepcopy__(self, memo): obj = self.__class__() for k, v in self.__dict__.items(): if k == "_result_cache": obj.__dict__[k] = None else: obj.__dict__[k] = copy.deepcopy(v, memo) return obj

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def __deepcopy__(self, memo): obj = self.__class__() for k, v in self.

__deepcopy__

python/ray/data/_internal/lazy_block_list.py

Make execution plan/blocklist aware of the memory ownership and who runs the plan (#26650) Having the indicator about who's running the stage and who created a blocklist will enable the eager memory releasing. This is an alternative with better abstraction to https://github.com/ray-project/ray/pull/26196. Note: this doesn't work for Dataset.split() yet, will do in a followup PR.

ray

Python

lazy_block_list.py

def copy(self) -> "LazyBlockList": return LazyBlockList( self._tasks.copy(), block_partition_refs=self._block_partition_refs.copy(), block_partition_meta_refs=self._block_partition_meta_refs.copy(), cached_metadata=self._cached_metadata, ray_remote_args=self._remote_args.copy(), owned_by_consumer=self._owned_by_consumer, stats_uuid=self._stats_uuid, )

8553df49bba654a9edd6befce198be90d6524fca

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

def copy(self) -> "LazyBlockList": return LazyBlockList( self._tasks.copy(), block_partition_refs=self._block_partition_refs.copy(), block_partition_meta_refs=self._block_partition_meta_refs.copy(),

copy

mindsdb/integrations/handlers/couchbase_handler/couchbase_handler.py

Initial commit for the couchbase handler

mindsdb

Python

couchbase_handler.py

def get_tables(self) -> Response: cluster = self.connect() bucket = cluster.bucket(self.bucket_name) collections = [] for _scope in bucket.collections().get_all_scopes(): for __collections in _scope.collections: collections.append(__collections.name) collections_ar = [ [i] for i in collections ] df = pd.DataFrame(collections_ar, columns=['TABLE_NAME']) response = Response( RESPONSE_TYPE.TABLE, df ) return response

41f58415fbd45c9ce0fb47962949e40e488424c6

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

def get_tables(self) -> Response: cluster = self.connect() bucket = cluster.bucket(self.bucket_name) collections = [] for _scope in bucket.collections().get_all_scopes(): for __collections in _scope.collections: collections.append(__collections.name) collections_ar = [ [i] for i in collections ] df = pd.DataFrame(collections_ar, col

get_tables

datasets/quickdraw/quickdraw.py

Add QuickDraw dataset (#3592) * Add QuickDraw dataset * Style * Add infos file, dummy data, improve script * Add info and dummy data * Test readme * Finish readme * Delete generate_dummy.py * Remove whitespace

datasets

Python

quickdraw.py

def process_struct(fileobj): (key_id,) = struct.unpack("Q", fileobj.read(8)) (country_code,) = struct.unpack("2s", fileobj.read(2)) (recognized,) = struct.unpack("b", fileobj.read(1)) (timestamp,) = struct.unpack("I", fileobj.read(4)) (n_strokes,) = struct.unpack("H", fileobj.read(2)) drawing = [] for _ in range(n_strokes): (n_points,) = struct.unpack("H", fileobj.read(2)) fmt = str(n_points) + "B" x = struct.unpack(fmt, fileobj.read(n_points)) y = struct.unpack(fmt, fileobj.read(n_points)) drawing.append({"x": list(x), "y": list(y)}) return { "key_id": str(key_id), "recognized": recognized, "timestamp": datetime.fromtimestamp(timestamp), "countrycode": country_code.decode("utf-8"), "drawing": drawing, }

1c1eaf96d5ef4623e36c9124d49e88ab476dd655

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

def process_struct(fileobj): (key_id,) = struct.unpack("Q", fileobj.read(8)) (country_code,) = struct.unpack("2s", fileobj.read(2)) (recognized,) = struct.unpack("b", fileobj.read(1)) (timestamp,) = struct.unpack("I", fileobj.read(4)) (n_strokes,) = struct.unpack("H", fileobj.read(2)) drawing = [] for _ in range(n_str

process_struct

py/visdom/__init__.py

apply black py to all python files

visdom

Python

__init__.py

def check_connection(self, timeout_seconds=0): while not self._has_connection() and timeout_seconds > 0: time.sleep(0.1) timeout_seconds -= 0.1 print("waiting") return self._has_connection()

5b8b7f267cfaf76a2a39a727ef31a62b3909a093

https://github.com/fossasia/visdom.git

def check_connection(self, timeout_seconds=0):

check_connection

python/ray/tests/test_namespace.py

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

ray

Python

test_namespace.py

def test_namespace_client(): cluster = Cluster() cluster.add_node(num_cpus=4, ray_client_server_port=8080) cluster.wait_for_nodes(1) template =

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

template = """ import ray ray.util.connect("{address}", namespace="{namespace}")@ray.remote

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

def test_namespace_client(): cluster = Cluster() cluster.add_node(num_cpus=4, ray_client_server_port=8080) cluster.wait_for_nodes(1) template =

test_namespace_client

tests/backends/base/test_operations.py

Refs #33476 -- Reformatted code with Black.

django

Python

test_operations.py

def test_adapt_unknown_value_decimal(self): value = decimal.Decimal("3.14") self.assertEqual( self.ops.adapt_unknown_value(value), self.ops.adapt_decimalfield_value(value), )

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def test_adapt_unknown_value_decimal(self): value = decimal.Decimal("3.14") self.assertEqual(

test_adapt_unknown_value_decimal

tests/pylint/test_enforce_type_hints.py

Adjust pylint plugin to enforce device_tracker type hints (#64903) * Adjust pylint plugin to enforce device_tracker type hints * Use a constant for the type hint matchers * Add tests * Add x_of_y match * Adjust bluetooth_tracker * Adjust mysensors * Adjust tile Co-authored-by: epenet <epenet@users.noreply.github.com>

core

Python

test_enforce_type_hints.py

def test_regex_x_of_y_comma_z(string, expected_x, expected_y, expected_z): assert (match := _TYPE_HINT_MATCHERS["x_of_y_comma_z"].match(string)) assert match.group(0) == string assert match.group(1) == expected_x assert match.group(2) == expected_y assert match.group(3) == expected_z @pytest.mark.parametrize( ("string", "expected_a", "expected_b"), [("DiscoveryInfoType | None", "DiscoveryInfoType", "None")], )

367521e369839e6504989603b1282c2ba31dad49

@pytest.mark.parametrize( ("string", "expected_a", "expected_b"), [("DiscoveryInfoType | None", "DiscoveryInfoType", "None")], )

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

def test_regex_x_of_y_comma_z(string, expected_x, expected_y, expected_z):

test_regex_x_of_y_comma_z

keras/engine/base_layer_v1.py

fix the rest

keras

Python

base_layer_v1.py

def add_update(self, updates): call_context = base_layer_utils.call_context() if ( tf.distribute.has_strategy() and tf.distribute.in_cross_replica_context() # When saving the model, the distribution strategy context should be # ignored, following the default path for adding updates. and not call_context.saving ): # Updates don't need to be run in a cross-replica context. return updates = generic_utils.to_list(updates) if call_context.in_call: relevant_inputs = call_context.inputs else: inbound_nodes = getattr(self, "_inbound_nodes", []) relevant_inputs = [node.input_tensors for node in inbound_nodes]

5cf72f4934f3104ac2378c8b9b3638afea38ba1e

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

def add_update(self, updates): call_context = base_layer_utils.call_context() if ( tf.distribute.has_strategy() and tf.distribute.in_cross_replica_context()

add_update

ppdet/modeling/transformers/detr_transformer.py

[dev] fix export model bug in DETR (#7120)

PaddleDetection

Python

detr_transformer.py

def forward(self, src, src_mask=None, pos_embed=None): residual = src if self.normalize_before: src = self.norm1(src) q = k = self.with_pos_embed(src, pos_embed) src = self.self_attn(q, k, value=src, attn_mask=src_mask) src = residual + self.dropout1(src) if not self.normalize_before: src = self.norm1(src) residual = src if self.normalize_before: src = self.norm2(src) src = self.linear2(self.dropout(self.activation(self.linear1(src)))) src = residual + self.dropout2(src) if not self.normalize_before: src = self.norm2(src) return src

fa67fb9f88ff7b03ca24a4f80e0fde2ef6d80384

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

def forward(self, src, src_mask=None, pos_embed=None): residual = src if self.normalize_before: src = self.norm1(src) q = k = self.with_pos_embed(src, pos_embed) src = self.self_attn(q, k, value=src, attn_mask=src_mask) src = residual + self.dropout1(src) if not self.normalize_before: src = self.norm1(src) residual = src if self.normalize_before: src = self.norm2(src) src = self.linear2(self.dropout(self.activation(self.linear1(src)))) src = residual + self.dropout2(src)

forward

django/db/models/base.py

Refs #33476 -- Reformatted code with Black.

django

Python

base.py

def _check_m2m_through_same_relationship(cls): errors = [] seen_intermediary_signatures = [] fields = cls._meta.local_many_to_many # Skip when the target model wasn't found. fields = (f for f in fields if isinstance(f.remote_field.model, ModelBase)) # Skip when the relationship model wasn't found. fields = (f for f in fields if isinstance(f.remote_field.through, ModelBase)) for f in fields: signature = ( f.remote_field.model, cls, f.remote_field.through, f.remote_field.through_fields, ) if signature in seen_intermediary_signatures: errors.append( checks.Error( "The model has two identical many-to-many relations " "through the intermediate model '%s'." % f.remote_field.through._meta.label, obj=cls, id="models.E003", ) ) else: seen_intermediary_signatures.append(signature) return errors

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def _check_m2m_through_same_relationship(cls): errors = [] seen_intermediary_signatures = [] fields = cls._meta.local_many_to_many # Skip when the target model wasn't found. fields = (f for f in fields if isinstance(f.remote_field.model, ModelBase)) # Skip when the relationship model wasn't found. fields = (f for f in fields if isinstance(f.remote_field.through, ModelBase)) for f in fields: signature = ( f.remote_field.model, cls, f.remote_field.through, f.remote_field.through_fields, ) if signature in seen_intermediary_signatures:

_check_m2m_through_same_relationship

sympy/physics/mechanics/tests/test_rigidbody.py

Add optional frame argument to parallel axis method

sympy

Python

test_rigidbody.py

def test_parallel_axis(): N = ReferenceFrame('N') m, Ix, Iy, Iz, a, b = symbols('m, I_x, I_y, I_z, a, b') Io = inertia(N, Ix, Iy, Iz) o = Point('o') p = o.locatenew('p', a * N.x + b * N.y) R = RigidBody('R', o, N, m, (Io, o)) Ip = R.parallel_axis(p) Ip_expected = inertia(N, Ix + m * b**2, Iy + m * a**2, Iz + m * (a**2 + b**2), ixy=-m * a * b) assert Ip == Ip_expected A = ReferenceFrame('A') A.orient_axis(N, N.z, 1) assert (R.parallel_axis(p, A).to_matrix(A) - Ip_expected.to_matrix(A)).simplify() == zeros(3, 3)

801e149d69d5f88919a735f8b55b6024f97c6950

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

def test_parallel_axis(): N = Refe

test_parallel_axis

ivy_tests/test_core/test_general.py

renamed dev_str arg to dev for all methods.

ivy

Python

test_general.py

def test_set_framework(fw_str, dev, call): ivy.set_framework(fw_str) ivy.unset_framework() # use_framework

d743336b1f3654cd0315f380f43eed4116997c1d

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

def test_set_framework(fw_str, dev, call): ivy.set_framework(fw_str) ivy.unset_framework() # use_framework

test_set_framework

keras/layers/rnn/gru_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

gru_test.py

def test_recurrent_dropout_with_implementation_restriction(self): layer = keras.layers.GRU(2, recurrent_dropout=0.1, implementation=2) # The implementation is force to 1 due to the limit of recurrent_dropout. self.assertEqual(layer.implementation, 1)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def test_recurrent_dropout_with_implementation_restriction(self): laye

test_recurrent_dropout_with_implementation_restriction

ppocr/losses/rec_vl_loss.py

add vl

PaddleOCR

Python

rec_vl_loss.py

def forward(self, predicts, batch): text_pre = predicts[0] target = batch[1].astype('int64') label_flatten, length = self.flatten_label(target) text_pre = self._flatten(text_pre, length) if self.mode == 'LF_1': loss = self.loss_func(text_pre, label_flatten) else: text_rem = predicts[1] text_mas = predicts[2] target_res = batch[2].astype('int64') target_sub = batch[3].astype('int64') label_flatten_res, length_res = self.flatten_label(target_res) label_flatten_sub, length_sub = self.flatten_label(target_sub) text_rem = self._flatten(text_rem, length_res) text_mas = self._flatten(text_mas, length_sub) loss_ori = self.loss_func(text_pre, label_flatten) loss_res = self.loss_func(text_rem, label_flatten_res) loss_mas = self.loss_func(text_mas, label_flatten_sub) loss = loss_ori + loss_res * self.weight_res + loss_mas * self.weight_mas return {'loss': loss}

a3a095150e8e1f56dd03d88ac71db6ad6262611a

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

def forward(self, predicts, batch): text_pre = predicts[0] target = batch[1].astype('int64') label_flatten, length = self.flatten_label(target) text_pre = self._flatten(text_pre, length) if self.mode == 'LF_1': loss = self.loss_func(text_pre, label_flatten) else: text_rem = predicts[1] text_mas = predicts[2]

forward

seaborn/tests/_core/test_moves.py

Add move concept, with Dodge and Jitter, and ordered GroupBy

seaborn

Python

test_moves.py

def test_height(self, df, groupby): df["height"] = df["width"] height = .4 res = Jitter(height=height)(df, groupby, "y") self.check_same(res, df, "y", "grp2", "width") self.check_pos(res, df, "x", height * df["height"])

430cb8fe332a752b79fb74bd618038ac51e82df8

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

def test_height(self, df, groupby): df["height"] = df["width"] height = .4 res = Jitter(height=height)(df, groupby, "y") self.check_same(res, df, "y", "grp2", "width") self.check_pos(res, df, "x", height *

test_height

jax/interpreters/batching.py

add a basic prototype of piles, behind jax_dynamic_shapes Co-authored-by: Adam Paszke <apaszke@google.com> Co-authored-by: Dougal Maclaurin <dougalm@google.com>

jax

Python

batching.py

def _pile_flatten(pile): lengths = [] new_shape = [lengths.append(d.lengths) or d.replace(lengths=len(lengths)) if type(d) is IndexedAxisSize else d for d in pile.aval.elt_ty.shape] elt_ty = pile.aval.elt_ty.update(shape=tuple(new_shape)) aval = pile.aval.replace(elt_ty=elt_ty) return (lengths, pile.data), aval

f2f2faa4fa166f40a4a93bc966379cf1ebb720d1

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

def _pile_flatten(pile): lengths = [] new_shape = [lengths.append(d.lengths) or d.replace(lengths=len(lengths)) if type(d)

_pile_flatten

awx/main/tasks/receptor.py

Fix fallout from turning off work signing in docker-compose

awx

Python

receptor.py

def get_receptor_ctl(config_data=None): if config_data is None: config_data = read_receptor_config() receptor_sockfile = get_receptor_sockfile(config_data) try: return ReceptorControl(receptor_sockfile, config=__RECEPTOR_CONF, tlsclient=get_tls_client(config_data, True)) except RuntimeError: return ReceptorControl(receptor_sockfile)

84f2b91105c959c4d89a63063cca441f3d67fc0f

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

def get_receptor_ctl(config_data=None): if config_data is None: config_data = read_receptor_config() receptor_sockfile = get_receptor_sockfile(config_data) try: return ReceptorControl(receptor_sockfile, config=__RECEPTOR_CONF, tlsclient=get_tls_client(config_data, True)) except RuntimeError: r

get_receptor_ctl

tests/snuba/api/endpoints/test_organization_events_mep.py

chore(discover): Cleanup events tests (#36797) - Delete the deprecated eventsv2 tests - Move MEP tests to its own file

sentry

Python

test_organization_events_mep.py

def test_failed_dry_run_does_not_error(self, mock_builder): with self.feature("organizations:performance-dry-run-mep"): mock_builder.side_effect = InvalidSearchQuery("Something bad") query = { "field": ["count()"], "project": [self.project.id], } response = self.do_request(query) assert response.status_code == 200, response.content assert len(mock_builder.mock_calls) == 1 assert mock_builder.call_args.kwargs["dry_run"] mock_builder.side_effect = IncompatibleMetricsQuery("Something bad") query = { "field": ["count()"], "project": [self.project.id], } response = self.do_request(query) assert response.status_code == 200, response.content assert len(mock_builder.mock_calls) == 2 assert mock_builder.call_args.kwargs["dry_run"] mock_builder.side_effect = InvalidConditionError("Something bad") query = { "field": ["count()"], "project": [self.project.id], } response = self.do_request(query) assert response.status_code == 200, response.content assert len(mock_builder.mock_calls) == 3 assert mock_builder.call_args.kwargs["dry_run"]

d3b8c9dd7bef6bccb5e70d2ccf3cda8463444a34

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

def test_failed_dry_run_does_not_error(self, mock_builder): with self.feature("organizations:performance-dry-run-mep"): mock_builder.side_effect = InvalidSearchQuery("Something bad") query = { "field": ["count()"], "project": [self.project.id], } response = self.do_request(query) assert response.status_code == 200, response.content assert len(mock_builder.mock_calls) == 1 assert mock_builder.call_args.kwargs["dry_run"] mock_builder.side_effect = IncompatibleMetricsQuery("Something bad") query = { "field": ["count()"], "project": [self.project.id], } response = self.do_request(query) assert response.status_code == 200, response.content assert len(mock_builder.mock_calls) == 2 assert mock_builder.call_args.kwargs["dry_run"] mock_builder.side_effect = InvalidConditionError("Something bad") query = { "field": ["count()"], "project": [self.project.id]

test_failed_dry_run_does_not_error

pipenv/utils/resolver.py

Improve documentation around index restrictions (#5029) * Improve documentation around index restrictions * Update docs/advanced.rst * Refine index documentation updates. Factor out and re-use method before closing down other PR. * Fully remove the --extra-index-url argument Co-authored-by: Yusuke Nishioka <yusuke.nishioka.0713@gmail.com>

pipenv

Python

resolver.py

def collect_hashes(self, ireq): link = ireq.link # Handle VCS and file links first if link and (link.is_vcs or (link.is_file and link.is_existing_dir())): return set() if not is_pinned_requirement(ireq): return set() sources = self.sources # Enforce index restrictions if ireq.name in self.index_lookup: sources = list( filter(lambda s: s.get("name") == self.index_lookup[ireq.name], sources) ) if any(is_pypi_url(source["url"]) for source in sources): hashes = self._get_hashes_from_pypi(ireq) if hashes: return hashes applicable_candidates = self.ignore_compatibility_finder.find_best_candidate( ireq.name, ireq.specifier ).iter_applicable() applicable_candidates = list(applicable_candidates) if applicable_candidates: return { self._get_hash_from_link(candidate.link) for candidate in applicable_candidates } if link: return {self._get_hash_from_link(link)} if ireq.original_link: return {self._get_hash_from_link(ireq.original_link)} return set()

99cf729dd52100efba406b9c6af585425de0788c

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

def collect_hashes(self, ireq): link = ireq.link # Handle VCS and file links first if link and (link.is_vcs or (link.is_file and link.is_existing_dir())): return set() if not is_pinned_requirement(ireq): return set() sources = self.sources # Enforc

collect_hashes

tests/integration_tests/test_api.py

Rename fc_size to output_size (#1641) * Rename fc_size to output_size * Responding to comments

ludwig

Python

test_api.py

def test_api_callbacks(csv_filename): mock_callback = mock.Mock() epochs = 2 batch_size = 8 num_examples = 32 with tempfile.TemporaryDirectory() as output_dir: input_features = [sequence_feature(reduce_output="sum")] output_features = [category_feature(vocab_size=5, reduce_input="sum")] config = { "input_features": input_features, "output_features": output_features, "combiner": {"type": "concat", "output_size": 14}, "training": {"epochs": epochs, "batch_size": batch_size}, } model = LudwigModel(config, callbacks=[mock_callback]) data_csv = generate_data( input_features, output_features, os.path.join(output_dir, csv_filename), num_examples=num_examples ) val_csv = shutil.copyfile(data_csv, os.path.join(output_dir, "validation.csv")) test_csv = shutil.copyfile(data_csv, os.path.join(output_dir, "test.csv")) model.train(training_set=data_csv, validation_set=val_csv, test_set=test_csv) assert mock_callback.on_epoch_start.call_count == epochs assert mock_callback.on_epoch_end.call_count == epochs assert mock_callback.on_validation_start.call_count == epochs assert mock_callback.on_validation_end.call_count == epochs assert mock_callback.on_test_start.call_count == epochs assert mock_callback.on_test_end.call_count == epochs assert mock_callback.on_batch_start.call_count == epochs * (num_examples / batch_size) assert mock_callback.on_batch_end.call_count == epochs * (num_examples / batch_size)

69604268c2ddc06a4ee0b3dce0e05a8fb73b5d16

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

def test_api_callbacks(csv_filename): mock_callback = mock.Mock() epochs = 2 batch_size = 8 num_examples = 32 with tempfile.TemporaryDirectory() as output_dir: input_features = [sequence_feature(reduce_output="sum")] output_features = [category_feature(vocab_size=5, reduce_input="sum")] config = { "input_features": input_features, "output_features": output_features, "combiner": {"type": "concat", "output_size": 14}, "training": {"epochs": epochs, "batch_size": batch_size}, } model = LudwigModel(config, callbacks=[mock_callback]) data_csv = generate_data( input_features, output_features, os.path.join(output_dir, csv_filename), num_examples=num_examples ) val_csv = shutil.copyfile(data_csv, os.path.join(output_dir, "validation.csv")) test_csv = shutil.copyfile(data_csv, os.path.join(output_dir, "test.csv")) model.

test_api_callbacks

TTS/tts/datasets/formatters.py

Implement bucketed weighted sampling for VITS (#1871)

TTS

Python

formatters.py

def mls(root_path, meta_files=None, ignored_speakers=None): items = [] with open(os.path.join(root_path, meta_files), "r", encoding="utf-8") as meta: for line in meta: file, text = line.split("\t") text = text[:-1] speaker, book, *_ = file.split("_") wav_file = os.path.join(root_path, os.path.dirname(meta_files), "audio", speaker, book, file + ".wav") # ignore speakers if isinstance(ignored_speakers, list): if speaker in ignored_speakers: continue items.append( {"text": text, "audio_file": wav_file, "speaker_name": "MLS_" + speaker, "root_path": root_path} ) return items # ======================================== VOX CELEB ===========================================

bfc63829ac869f479bf9e8bf0fb75a2fb6d04959

https://github.com/coqui-ai/TTS.git

def mls(root_path, meta_files=None, ignored_speakers=None): items = [] with open(os.path.join(root_path, meta_files), "r", encoding="utf-8") as meta: for line in meta: file, text = line.split("\t") text = text[:-1] speaker, book, *_ = file.split("_") wav_file = os.path.join(root_path, os.path.dirname(meta_files), "audio", speaker, book, file + ".wav") # ignore speakers if isinstance(ignored_speakers, list): if speaker in ignored_speakers: continue items.append( {"text": text, "audio_

mls

pandas/tests/io/xml/test_xml_dtypes.py

ENH: Add dtypes/converters arguments for pandas.read_xml (#45411)

pandas

Python

test_xml_dtypes.py

def test_dtype_float(parser): df_result = read_xml(xml_types, dtype={"degrees": "float"}, parser=parser) df_expected = DataFrame( { "shape": ["square", "circle", "triangle"], "degrees": Series([360, 360, 180]).astype("float"), "sides": [4.0, float("nan"), 3.0], } ) tm.assert_frame_equal(df_result, df_expected)

d2d7ffb56f0f12c412c36c0c867ab3bb240d04ca

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

def test_dtype_float(parser): df_resul

test_dtype_float

python/ray/tests/test_object_manager.py

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

ray

Python

test_object_manager.py

def test_actor_broadcast(ray_start_cluster_with_resource): cluster, num_nodes = ray_start_cluster_with_resource

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def test_actor_broadcast(ray_start_cluster_with_resource): cluster, num_nodes = ray_start_cluster_with_resource

test_actor_broadcast

keras/utils/dataset_utils.py

adds mnist dataset test case

keras

Python

dataset_utils.py

def _rescale_dataset_split_sizes(left_size,right_size,total_length): left_size_type = type(left_size) right_size_type = type(right_size) # check both left_size and right_size are integers or floats if ((left_size is not None and left_size_type not in [int,float]) and (right_size is not None and right_size_type not in [int,float])): raise TypeError('Invalid `left_size` and `right_size` Types. Expected: ' 'integer or float or None, Received: type(left_size)=' f'{left_size_type} and type(right_size)={right_size_type}') # check left_size is a integer or float if left_size is not None and left_size_type not in [int,float]: raise TypeError('Invalid `left_size` Type.Expected: int or float or None, ' f'Received: type(left_size)={left_size_type}. ') # check right_size is a integer or float if right_size is not None and right_size_type not in [int,float]: raise TypeError(f'Invalid `right_size` Type.Expected: int or float or None,' f'Received: type(right_size)={right_size_type}. ') # check left_size and right_size are non-zero if left_size == 0 and right_size == 0: raise ValueError('Both `left_size` and `right_size` are zero. ' 'Atleast one of the split sizes must be non-zero.') # check left_size is non-negative and less than 1 and less than total_length if (left_size_type == int and (left_size <= 0 or left_size>= total_length) or left_size_type == float and (left_size <= 0 or left_size>= 1) ): raise ValueError('`left_size` should be either a positive integer ' f'and smaller than {total_length} or a float ' 'within the range `[0, 1]`. Received: left_size=' f'{left_size}') # check right_size is non-negative and less than 1 and less than total_length if (right_size_type == int and (right_size <= 0 or right_size>= total_length) or right_size_type == float and (right_size <= 0 or right_size>= 1)): raise ValueError('`right_size` should be either a positive integer ' f'and smaller than {total_length} or a float ' 'within the range `[0, 1]`. Received: right_size=' f'{right_size}') # check sum of left_size and right_size is less than or equal to total_length if right_size_type == left_size_type == float and right_size + left_size > 1: raise ValueError('The sum of `left_size` and `right_size` is greater ' 'than 1. It must be less than or equal to 1.') if left_size_type == float: left_size = round(left_size*total_length) elif left_size_type == int: left_size = float(left_size) if right_size_type == float: right_size = round(right_size*total_length) elif right_size_type == int: right_size = float(right_size) if left_size is None: left_size = total_length - right_size elif right_size is None: right_size = total_length - left_size if left_size + right_size > total_length: raise ValueError('The sum of `left_size` and `right_size` should ' 'be smaller than the {total_length}. ' f'Received: left_size + right_size = {left_size+right_size}' f'and total_length = {total_length}') for split,side in [(left_size,'left'),(right_size,'right')]: if split == 0: raise ValueError(f'With `dataset` of length={total_length}, `left_size`=' '{left_size} and `right_size`={right_size}.' f'Resulting {side} side dataset split will be empty. ' 'Adjust any of the aforementioned parameters') left_size,right_size = int(left_size) ,int(right_size) return left_size,right_size

3337f8716967b9b5c9c575e73c66cef0a17e891f

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

def _rescale_dataset_split_sizes(left_size,right_size,total_length): left_size_type = type(left_size) right_size_type = type(right_size) # check both left_size and right_size are integers or floats if ((left_size is not None and left_size_type not in [int,float]) and (right_size is not None and right_size_type not in [int,float])): raise TypeError('Invalid `left_size` and `right_size` Types. Expected: ' 'integer or float or None, Received: type(left_size)=' f'{left_size_type} and type(right_size)={right_size_type}') # check left_size is a integer or float if left_size is not None and left_size_type not in [int,float]: raise TypeError('Invalid `left_size` Type.Expected: int or float or None,

_rescale_dataset_split_sizes

keras/backend.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

backend.py

def flatten(x): return tf.reshape(x, [-1]) @keras_export("keras.backend.batch_flatten") @tf.__internal__.dispatch.add_dispatch_support @doc_controls.do_not_generate_docs

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

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

def flatten(x): return tf.reshape(x, [-1]) @keras_export("keras.backend.batch_flatten") @tf.__internal__.dispatch.add_

flatten

saleor/graphql/discount/schema.py

Stricter signatures for resolvers and mutations (#9649)

saleor

Python

schema.py

def resolve_voucher(_root, _info, *, id, channel=None): _, id = from_global_id_or_error(id, Voucher) return resolve_voucher(id, channel)

513fc80bc698c177b87774b3aff3da7b9aedbe06

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

def resolve_voucher(_root, _info, *, id, channel=None): _, id = from_global_id_or_error(id, Voucher)

resolve_voucher

wagtail/api/v2/tests/test_pages.py

Reformat with black

wagtail

Python

test_pages.py

def test_descendant_of_filter(self): response = self.get_response(descendant_of=6) content = json.loads(response.content.decode("UTF-8")) page_id_list = self.get_page_id_list(content) self.assertEqual(page_id_list, [10, 15, 17, 21, 22, 23])

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def test_descendant_of_filter(self): response = self.get_response(descendant_of=6) content = json.loads(response.content.decode("UTF-8")) page_id_list = self.get_page_id_list(content) self.assertEqual(page_id_list, [10, 15, 17, 21, 22,

test_descendant_of_filter

jax/_src/third_party/numpy/linalg.py

Call _check_arraylike for jnp.linalg & jnp.fft functions

jax

Python

linalg.py

def _multi_dot_matrix_chain_order(arrays, return_costs=False): n = len(arrays) # p stores the dimensions of the matrices # Example for p: A_{10x100}, B_{100x5}, C_{5x50} --> p = [10, 100, 5, 50] p = [a.shape[0] for a in arrays] + [arrays[-1].shape[1]] # m is a matrix of costs of the subproblems # m[i,j]: min number of scalar multiplications needed to compute A_{i..j} m = np.zeros((n, n), dtype=np.double) # s is the actual ordering # s[i, j] is the value of k at which we split the product A_i..A_j s = np.empty((n, n), dtype=np.intp) for l in range(1, n): for i in range(n - l): j = i + l m[i, j] = jnp.inf for k in range(i, j): q = m[i, k] + m[k+1, j] + p[i]*p[k+1]*p[j+1] if q < m[i, j]: m[i, j] = q s[i, j] = k # Note that Cormen uses 1-based index return (s, m) if return_costs else s

2416d154355f19e77b5c1ddf1de1f8552e4a98ad

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

def _multi_dot_matrix_chain_order(arrays, return_costs=False): n = len(arrays) # p stores the dimensions of the matrices

_multi_dot_matrix_chain_order

Lib/test/test_posix.py

bpo-46426: Improve tests for the dir_fd argument (GH-30668) Ensure that directory file descriptors refer to directories different from the current directory, and that src_dir_fd and dst_dir_fd refer to different directories. Add context manager open_dir_fd() in test.support.os_helper.

cpython

Python

test_posix.py

def test_chmod_dir_fd(self): with self.prepare_file() as (dir_fd, name, fullname): posix.chmod(fullname, stat.S_IRUSR) posix.chmod(name, stat.S_IRUSR | stat.S_IWUSR, dir_fd=dir_fd) s = posix.stat(fullname) self.assertEqual(s.st_mode & stat.S_IRWXU, stat.S_IRUSR | stat.S_IWUSR)

54610bb448a9cf5be77d53b66169fca4c11be6cb

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

def test_chmod_dir_fd(self): with self.prepare_file() as (dir_fd, name, fullname): posix.chmod(fullname, stat.S_I

test_chmod_dir_fd

wagtail/admin/views/pages/revisions.py

Extract generic RevisionsCompareView from page revisions_compare view

wagtail

Python

revisions.py

def dispatch(self, request, *args, **kwargs): return super().dispatch(request, *args, **kwargs)

2664a4c1fc7df471225d3e71355802401217889a

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

def dispatch(self, request, *args, **kwargs): return super().dispatch(request, *args, **kwargs)

dispatch

tests/components/homekit/test_util.py

Add hardware revision support to homekit (#63336)

core

Python

test_util.py

async def test_format_version(): assert format_version("soho+3.6.8+soho-release-rt120+10") == "3.6.8" assert format_version("undefined-undefined-1.6.8") == "1.6.8" assert format_version("56.0-76060") == "56.0.76060" assert format_version(3.6) == "3.6" assert format_version("AK001-ZJ100") == "001.100" assert format_version("HF-LPB100-") == "100" assert format_version("AK001-ZJ2149") == "001.2149" assert format_version("0.1") == "0.1" assert format_version("unknown") is None

5c8271552a3023808e272125f71ba79f3a1e97d8

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

async def test_format_version(): assert format_version("soho+3.6.8+soho-release-rt120+10") == "3.6.8" assert format_version("undefined-undefined-1.6.8") == "1.6.8" assert format_version("56.0-76060") == "56.0.76060" assert format_version(3.6) == "3.6" assert format_version("AK001-ZJ100") == "001.100" assert format_version("HF-LPB100-") == "100" assert

test_format_version

tools/alignments/jobs.py

Bugfix: Alignments tool - don't error on from-faces job

faceswap

Python

jobs.py

def _set_skip_list(self) -> Optional[List[int]]: skip_num = self._arguments.extract_every_n if skip_num == 1: logger.debug("Not skipping any frames") return None skip_list = [] for idx, item in enumerate(self._frames.file_list_sorted): if idx % skip_num != 0: logger.trace("Adding image '%s' to skip list due to " # type:ignore "extract_every_n = %s", item["frame_fullname"], skip_num) skip_list.append(idx) logger.debug("Adding skip list: %s", skip_list) return skip_list

892d8626ed4e7f834ac5607af59f14f5476d5997

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

def _set_skip_list(self) -> Optional[List[int]]: skip_num = self._arguments.extract_every_n if skip_num == 1: logger.debug("Not skipping any frames") return None skip_list = [] for idx, item in enumerate(s

_set_skip_list

saleor/graphql/order/mutations/draft_order_create.py

Simple (flat rate) taxes API (#9784) * Add empty tax module * Add tax models (#9839) * Add tax API queries (#9856) * Add MANAGE_TAXES permission * Add tax configuration queries * Create tax configuration when channel is created * Drop sorters for now * Add TaxConfigurationPerCountry type * Update migration * Add metadata to TaxConfiguration type * Add tests for tax configuration queries * Add TaxClass types * Improve tests * Add queries for tax configuration per country * Fix query in tests * Update query cost map * Add tax API mutations (#9934) * Add taxConfigurationUpdate mutation * Update schema * Add tax class CRUD mutations * Add mutations to update/delete tax class rates per country * Review fixes * Add taxClass field to ProductType type (#9999) * Add taxClass field to ProductType type * Add taxClass field to Product type * Add taxClass field to shipping method type * Add displayGrossPrices to ProductPricingInfo (#10008) * Add displayGrossPrices to ProductPricingInfo * Add displayGrossPrices to Checkout * Add displayGrossPrices to Order * Add tests * Add ADDED_IN_35 label to new fields' descriptions * Use new display_gross_prices flag (#10121) * Use new display_gross_prices flag * Update tests * Add tests * Review fixes * Drop Vatlayer (#10335) * Add migration from Vatlayer to simple taxes * Review fixes * Review fixes * Drop usages of global include_taxes_in_prices flag (#10406) * Drop `include_taxes_in_prices` function from site settings * Adjust tests * Review fixes * Drop the `charge_taxes_on_shipping` flag from site settings. (#10466) * Include migrating Avatax tax codes in tax class migration * Drop `charge_taxes_on_shipping` function * Add tax_class to ShippingMethodData * Review fixes * Always calculate shipping tax with Avalara * Add default country rate (#10497) * Allow setting default tax rate for a country (without providing a tax class) * Add validation to allow settings only one default rate at once * Code review fixes * Add taxCalculationStrategy field * Add tests * CR fixes * Adjust resolver to use new tax configuration (#10533) * CR fixes * Add database router to fix false positives on relation mismatch. (#10524) * Add database router to fix false positives on relation mismatch. * The db router should have only 'allow_relation' implemented. * The 'db_for_write' part should stay. * Subscription for sync tax webooks (#10433) * Add proposed changes to schema * Add base implementation for sync tax subscription * Add test for empty order * Add clean up and missing part for tests * Use subscription for tax webhooks. Add more tests * Improve descriptions for tax objects * Adjust resolver to use new tax configuration (#10533) * Add taxCalculationStrategy field (#10532) * Add taxCalculationStrategy field * Add tests * CR fixes * CR fixes * Add datamigration to populate taxCalculationStrategy * Migrate Product.charge_taxes to new tax configuration (#10585) * Migrate Product.charge_taxes field to new tax configuration * Rename function * Fix tests * Change assign_tax_code_to_object_meta function to support tax classes * Update tax class fixtures * Improve dataloader * CR fixes * CR fixes * Add deprecation notice to dataloader * Allow removing country rates in the `taxCountryConfigurationUpdate` mutation. (#10647) * Allow deleting rates in taxCountryConfigurationUpdate mutation * Change tax rates ordering to keep default rates first (with null tax classes) * Update existing migration * Remove TaxClass.is_default field (#10660) * Change tax rates ordering to keep default rates first (with null tax classes) * Update existing migration * Drop is_default field from TaxClass model * Drop extra Avalara config (#10673) * Drop extra Avatax config options * Adjust tests * Use flat rates in tax calculations (#10747) * WIP Use new tax configuration in tax calculations * Use new tax calculations for checkout * Adjust tests * Add flat rates calculations for checkout and order * Calculate flat rates in product pricing objects * Adjust tests * Add tests for order calculations * Add tests for product queries tax calculations * Add tests for order calculations * Use base calculations to get default checkout shipping price * Add tests for using tax_class from product_type * Add tests for get_order_country * Adjust tests * Code review fixes * Drop update_taxes_for_order_lines (#11000) * Fix calls to Avalara not validating order (#11012) * Add validation to disallow creating negative rates (#11010) * Add missing recalculation of order.undiscounted_total (#11039) * Optimize getting tax class country rates (#11040) * Tax API adjustments for dashboard (#11042) * Ignore null rates in taxCountryConfigurationUpdate mutation * Allow to pass null rates in taxClassUpdate mutation * Improve tests * Update saleor/graphql/tax/mutations/tax_class_update.py Co-authored-by: Krzysztof Waliczek <krzysztof.waliczek@saleor.io> * Update schema Co-authored-by: Krzysztof Waliczek <krzysztof.waliczek@saleor.io> * Cleanup before release (#11049) * Update ADDED_IN labels * Fix skippeded test * Regenerate migrations * Deprecate CountryDisplay.vat field * Add changelog * Update order.undiscounted_total calculation to not include taxes (#11068) * Fix assigning rates to tax classes (#11105) * Allow all staff users and apps to query tax-related data (#11113) * Bump dependencies for origin/SALEOR-6391-simple-taxes (#11127) Bumps: - cryptography to 38.0.3 - pillow to 9.3.0 * Fix using tax code from product and product type's tax class (#11111) * Fix using tax code from product and product type's tax class * Extract function * Replace synchronous load_site with promise (#11165) * Denormalize tax class for order lines and orders (#11172) * WIP Denormalize tax class for order lines and orders * Add denormalized fields in GraphQL types * Add tests for denormalized API fields * Return 0 rate in API when rate is null * Add preview/version notes in new field descriptions * Update changelog Co-authored-by: Dominik Kozaczko <dominik@kozaczko.info> Co-authored-by: Maciej Korycinski <maciej@mirumee.com> Co-authored-by: Krzysztof Waliczek <krzysztof.waliczek@saleor.io> Co-authored-by: Mika <6186720+NyanKiyoshi@users.noreply.github.com> Co-authored-by: Krzysztof Kwaśniak <mr.brzys@gmail.com>

saleor

Python

draft_order_create.py

def _save_lines(info, instance, lines_data, app, manager): if lines_data: lines = [] for line_data in lines_data: new_line = create_order_line( instance, line_data, manager, ) lines.append(new_line) # New event events.order_added_products_event( order=instance, user=info.context.user, app=app, order_lines=lines, )

67df28935c555fdd673f17e8c9183e24dde7c51f

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

def _save_lines(info, instance, lines_data, app, manager): if lines_data: lines = [] for line_data in lines_data: new_line = create_order_line( instance, line_data, manager, ) lines.append(new_line) # New event events.order_added_products_event( order=instance, user=info.context.user, app=app, order_lines=lines,

_save_lines

wagtail/documents/tests/test_admin_views.py

Reformat with black

wagtail

Python

test_admin_views.py

def test_reupload_different_file_size_and_file_hash(self): # Build a fake file, and create it through the admin view # since self.document doesn't have a file_size set. fake_file = SimpleUploadedFile("some_file.txt", b"this is the content") post_data = { "title": "My doc", "file": fake_file, } self.client.post(reverse("wagtaildocs:add"), post_data) document = models.Document.objects.get(title="My doc") old_file_size, old_file_hash = document.file_size, document.file_hash new_file = SimpleUploadedFile(document.filename, b"less content") self.client.post( reverse("wagtaildocs:edit", args=(document.pk,)), { "title": document.title, "file": new_file, }, ) document.refresh_from_db() self.assertNotEqual(document.file_size, old_file_size) self.assertNotEqual(document.file_hash, old_file_hash)

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def test_reupload_different_file_size_and_file_hash(self): # Build a fake file, and create it through the admin view # since self.document doesn't have a file_size set. fake_file = SimpleUploaded

test_reupload_different_file_size_and_file_hash

python/ray/util/rpdb.py

[api] Annotate as public / move ray-core APIs to _private and add enforcement rule (#25695) Enable checking of the ray core module, excluding serve, workflows, and tune, in ./ci/lint/check_api_annotations.py. This required moving many files to ray._private and associated fixes.

ray

Python

rpdb.py

def do_remote(self, arg): # Tell the next task to drop into the debugger. ray._private.worker.global_worker.debugger_breakpoint = self._breakpoint_uuid # Tell the debug loop to connect to the next task. data = json.dumps( { "job_id": ray.get_runtime_context().job_id.hex(), } ) _internal_kv_put( "RAY_PDB_CONTINUE_{}".format(self._breakpoint_uuid), data, namespace=ray_constants.KV_NAMESPACE_PDB, ) self.__restore() self.handle.connection.close() return Pdb.do_continue(self, arg)

43aa2299e6623c8f8c7c4a1b80133459d0aa68b0

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

def do_remote(self, arg): # Tell the next task to drop into the debugger. ray._private.worker.global_worker.debugger_breakpoint = self._breakpoint_uuid # Tell the debug loop to connect to the next task. data = json.dumps( { "job_id": ray.get_runtime_context().job_id.hex(), } ) _internal_kv_put( "RAY_PDB_CONTINUE_{}".format(self._breakpoint_uuid), data, namespace=ray_constants.KV_NAMESPACE_PDB, ) self.__restore() self.handle.connection.close() return Pdb.do_continue(self, arg)

do_remote

mkdocs/tests/config/base_tests.py

Rework ConfigOption schemas as class-based This is NOT a breaking change, the old style keeps working. Now developers can make a subclass of Config, declare the schema of the config as fields of the class, and instances of this class will hold the processed config. This better represents the relationship between what a config definition and a config instance is, now you think of configs definitions as classes and parsed configs as instances. We also can write these fields as descriptors and enable safe attribute-based access. Static analysis will be able to see when a missing fields is accessed. And in followup changes I plan to add type annotations which will make even type checking fully sound.

mkdocs

Python

base_tests.py

def test_missing_required(self): conf = defaults.MkDocsConfig() errors, warnings = conf.validate() self.assertEqual( errors, [('site_name', ValidationError('Required configuration not provided.'))] ) self.assertEqual(warnings, [])

73e8fef5068d47ab7bdc4c49bc4abcc74434b57e

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

def test_missing_required(self): conf = defa

test_missing_required

tests/openbb_terminal/cryptocurrency/test_cryptocurrency_helpers.py

Refactored Crypto Tests (#1743) * Refactored tests * Removed unused command * Added tests * Tests : remove cassettes files + add fixture * Black * Tests : skip tests Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt> Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>

OpenBBTerminal

Python

test_cryptocurrency_helpers.py

def test_read_data_file(recorder): file = read_data_file("coinbase_gecko_map.json") recorder.capture(file)

9068ad01249c1e1adaca3ef9a704d70da7e3a17b

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

def test_read_data_file(recorder): file = read_data_file("coinbase_gecko_map.json") recorder.captur

test_read_data_file

tests/components/anthemav/conftest.py

Refactor and improve anthemav (#75852)

core

Python

conftest.py

def mock_anthemav() -> AsyncMock: avr = AsyncMock() avr.protocol.macaddress = "000000000001" avr.protocol.model = "MRX 520" avr.reconnect = AsyncMock() avr.close = MagicMock() avr.protocol.input_list = [] avr.protocol.audio_listening_mode_list = [] avr.protocol.power = False return avr @pytest.fixture

bbd7041a73572547be49ead53b183aa1e55a6d75

@pytest.fixture

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

def mock_anthemav() -> AsyncMock: avr = AsyncMock() avr.protocol.macaddress = "000000000001" avr.protocol.model = "MRX 520" avr.reconnect = AsyncMock() avr.close = MagicMock() avr.protocol.input_list = [] avr.protocol.audio_listening_mode_list = [] avr.protocol.power = False return avr @pytest.fixture

mock_anthemav

dask/bag/tests/test_random.py

Bag: add implementation for reservoir sampling (#7068) (#7636) - Implement the [L algorithm](https://en.wikipedia.org/wiki/Reservoir_sampling#An_optimal_algorithm) for reservoir sampling without replacement. - Use the **k** reservoir of size 1 strategy for sampling with replacement (see [reference](http://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf)) of **k** items

dask

Python

test_random.py

def test_reservoir_sample_with_replacement_map_partitions_correctness(): N, k = 20, 10 seq = list(range(N)) distribution = [0 for _ in range(N)] expected_distribution = [0 for _ in range(N)] reps = 2000 for _ in range(reps): picks, _ = random._sample_with_replacement_map_partitions(seq, k) for pick in picks: distribution[pick] += 1 for pick in rnd.choices(seq, k=k): expected_distribution[pick] += 1 # convert to probabilities distribution = [c / (reps * k) for c in distribution] expected_distribution = [c / (reps * k) for c in expected_distribution] # use bhattacharyya distance to asses the similarity of distributions assert math.isclose( 0.0, bhattacharyya(distribution, expected_distribution), abs_tol=1e-2 )

4e5dfe7463028a39a90e026c7fb9220969093ab3

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

def test_reservoir_sample_with_replacement_map_partitions_correctness(): N, k = 20, 10 seq = list(range(N)) distribution = [0 for _ in range(N)] expected_distribution = [0 for _ in range(N)] reps = 2000 for _ in range(reps): picks, _ = random._sample_with_replacement_map_partitions(seq, k) for pick in picks: distribution[pick] += 1 for pick in rnd.choices(seq, k=k): expected_distribution[

test_reservoir_sample_with_replacement_map_partitions_correctness

pandas/io/formats/latex.py

TYP: Autotyping (#48191) * annotate-magics * annotate-imprecise-magics * none-return * scalar-return * pyi files * ignore vendored file * manual changes * ignore pyright in pickle_compat (these errors would be legit if the current __new__ methods were called but I think these pickle tests call older __new__ methods which allowed providing multiple positional arguments) * run autotyping in pre-commit * remove final and expand safe (and add annotate-imprecise-magics)

pandas

Python

latex.py

def _empty_info_line(self) -> str: return ( f"Empty {type(self.frame).__name__}\n" f"Columns: {self.frame.columns}\n" f"Index: {self.frame.index}" )

54347fe684e0f7844bf407b1fb958a5269646825

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

def _empty_info_line(self) -> str: return ( f"Empty {type(self.frame).__name__}\n" f"Columns: {self.frame.columns}\n" f"Index: {self.frame.index

_empty_info_line

sympy/physics/units/tests/test_quantities.py

feat(physics.units): add `is_prefixed` property to `Quantity`

sympy

Python

test_quantities.py

def test_prefixed_property(): assert not meter.is_prefixed assert not joule.is_prefixed assert not day.is_prefixed assert not second.is_prefixed assert centimeter.is_prefixed assert kilometer.is_prefixed assert kilogram.is_prefixed assert pebibyte.is_prefixed

40a89803dbe877edc8ab6672819715f959273e60

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

def test_prefixed_property(): assert not meter.is_prefixed assert not joule.is_prefixed assert not day.is_prefixed assert not second.is_prefixed assert centimeter.is_prefixed assert kilometer.is_prefixed

test_prefixed_property

python3.10.4/Lib/ctypes/test/test_python_api.py

add python 3.10.4 for windows

XX-Net

Python

test_python_api.py

def test_PyObj_FromPtr(self): s = "abc def ghi jkl" ref = grc(s) # id(python-object) is the address pyobj = PyObj_FromPtr(id(s)) self.assertIs(s, pyobj) self.assertEqual(grc(s), ref + 1) del pyobj self.assertEqual(grc(s), ref)

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def test_PyObj_FromPtr(self): s = "abc def ghi jkl" ref = grc(s) # id(python-object) is the address pyobj = PyObj_FromPtr(id(s)) self.assertIs(s, pyobj) self.assertEqual(grc(s), ref + 1) del pyobj self.assertEqual(grc(s), ref)

test_PyObj_FromPtr

keras/integration_test/preprocessing_applied_in_model_test.py

resolve line-too-long in integration_test

keras

Python

preprocessing_applied_in_model_test.py

def testDistributedModelFit(self, strategy): if not tf.__internal__.tf2.enabled() and isinstance( strategy, tf.distribute.experimental.ParameterServerStrategy ): self.skipTest( "Parameter Server strategy with dataset creator need to be run " "when eager execution is enabled." ) with strategy.scope(): preprocessing_model = utils.make_preprocessing_model( self.get_temp_dir() ) training_model = utils.make_training_model() # Merge the two separate models into a single model for training. inputs = preprocessing_model.inputs outputs = training_model(preprocessing_model(inputs)) merged_model = tf.keras.Model(inputs, outputs) merged_model.compile(optimizer="sgd", loss="binary_crossentropy")

4f1d333ded256b0315cf02eee067d6fa902b748d

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

def testDistributedModelFit(self, strategy): if not tf.__internal__.tf2.enabled() and isinstance( strategy, tf.distribute.experimental.ParameterServerStrategy ): self.skipTest( "Parameter Server strategy with dataset creator need to be run " "when eager execution is enabled." )

testDistributedModelFit

saleor/order/tests/test_order_utils.py

Simple (flat rate) taxes API (#9784) * Add empty tax module * Add tax models (#9839) * Add tax API queries (#9856) * Add MANAGE_TAXES permission * Add tax configuration queries * Create tax configuration when channel is created * Drop sorters for now * Add TaxConfigurationPerCountry type * Update migration * Add metadata to TaxConfiguration type * Add tests for tax configuration queries * Add TaxClass types * Improve tests * Add queries for tax configuration per country * Fix query in tests * Update query cost map * Add tax API mutations (#9934) * Add taxConfigurationUpdate mutation * Update schema * Add tax class CRUD mutations * Add mutations to update/delete tax class rates per country * Review fixes * Add taxClass field to ProductType type (#9999) * Add taxClass field to ProductType type * Add taxClass field to Product type * Add taxClass field to shipping method type * Add displayGrossPrices to ProductPricingInfo (#10008) * Add displayGrossPrices to ProductPricingInfo * Add displayGrossPrices to Checkout * Add displayGrossPrices to Order * Add tests * Add ADDED_IN_35 label to new fields' descriptions * Use new display_gross_prices flag (#10121) * Use new display_gross_prices flag * Update tests * Add tests * Review fixes * Drop Vatlayer (#10335) * Add migration from Vatlayer to simple taxes * Review fixes * Review fixes * Drop usages of global include_taxes_in_prices flag (#10406) * Drop `include_taxes_in_prices` function from site settings * Adjust tests * Review fixes * Drop the `charge_taxes_on_shipping` flag from site settings. (#10466) * Include migrating Avatax tax codes in tax class migration * Drop `charge_taxes_on_shipping` function * Add tax_class to ShippingMethodData * Review fixes * Always calculate shipping tax with Avalara * Add default country rate (#10497) * Allow setting default tax rate for a country (without providing a tax class) * Add validation to allow settings only one default rate at once * Code review fixes * Add taxCalculationStrategy field * Add tests * CR fixes * Adjust resolver to use new tax configuration (#10533) * CR fixes * Add database router to fix false positives on relation mismatch. (#10524) * Add database router to fix false positives on relation mismatch. * The db router should have only 'allow_relation' implemented. * The 'db_for_write' part should stay. * Subscription for sync tax webooks (#10433) * Add proposed changes to schema * Add base implementation for sync tax subscription * Add test for empty order * Add clean up and missing part for tests * Use subscription for tax webhooks. Add more tests * Improve descriptions for tax objects * Adjust resolver to use new tax configuration (#10533) * Add taxCalculationStrategy field (#10532) * Add taxCalculationStrategy field * Add tests * CR fixes * CR fixes * Add datamigration to populate taxCalculationStrategy * Migrate Product.charge_taxes to new tax configuration (#10585) * Migrate Product.charge_taxes field to new tax configuration * Rename function * Fix tests * Change assign_tax_code_to_object_meta function to support tax classes * Update tax class fixtures * Improve dataloader * CR fixes * CR fixes * Add deprecation notice to dataloader * Allow removing country rates in the `taxCountryConfigurationUpdate` mutation. (#10647) * Allow deleting rates in taxCountryConfigurationUpdate mutation * Change tax rates ordering to keep default rates first (with null tax classes) * Update existing migration * Remove TaxClass.is_default field (#10660) * Change tax rates ordering to keep default rates first (with null tax classes) * Update existing migration * Drop is_default field from TaxClass model * Drop extra Avalara config (#10673) * Drop extra Avatax config options * Adjust tests * Use flat rates in tax calculations (#10747) * WIP Use new tax configuration in tax calculations * Use new tax calculations for checkout * Adjust tests * Add flat rates calculations for checkout and order * Calculate flat rates in product pricing objects * Adjust tests * Add tests for order calculations * Add tests for product queries tax calculations * Add tests for order calculations * Use base calculations to get default checkout shipping price * Add tests for using tax_class from product_type * Add tests for get_order_country * Adjust tests * Code review fixes * Drop update_taxes_for_order_lines (#11000) * Fix calls to Avalara not validating order (#11012) * Add validation to disallow creating negative rates (#11010) * Add missing recalculation of order.undiscounted_total (#11039) * Optimize getting tax class country rates (#11040) * Tax API adjustments for dashboard (#11042) * Ignore null rates in taxCountryConfigurationUpdate mutation * Allow to pass null rates in taxClassUpdate mutation * Improve tests * Update saleor/graphql/tax/mutations/tax_class_update.py Co-authored-by: Krzysztof Waliczek <krzysztof.waliczek@saleor.io> * Update schema Co-authored-by: Krzysztof Waliczek <krzysztof.waliczek@saleor.io> * Cleanup before release (#11049) * Update ADDED_IN labels * Fix skippeded test * Regenerate migrations * Deprecate CountryDisplay.vat field * Add changelog * Update order.undiscounted_total calculation to not include taxes (#11068) * Fix assigning rates to tax classes (#11105) * Allow all staff users and apps to query tax-related data (#11113) * Bump dependencies for origin/SALEOR-6391-simple-taxes (#11127) Bumps: - cryptography to 38.0.3 - pillow to 9.3.0 * Fix using tax code from product and product type's tax class (#11111) * Fix using tax code from product and product type's tax class * Extract function * Replace synchronous load_site with promise (#11165) * Denormalize tax class for order lines and orders (#11172) * WIP Denormalize tax class for order lines and orders * Add denormalized fields in GraphQL types * Add tests for denormalized API fields * Return 0 rate in API when rate is null * Add preview/version notes in new field descriptions * Update changelog Co-authored-by: Dominik Kozaczko <dominik@kozaczko.info> Co-authored-by: Maciej Korycinski <maciej@mirumee.com> Co-authored-by: Krzysztof Waliczek <krzysztof.waliczek@saleor.io> Co-authored-by: Mika <6186720+NyanKiyoshi@users.noreply.github.com> Co-authored-by: Krzysztof Kwaśniak <mr.brzys@gmail.com>

saleor

Python

test_order_utils.py

def test_update_order_display_gross_prices_use_country_specific_tax_settings(order): # given country_code = "PT" tax_config = order.channel.tax_configuration tax_config.display_gross_prices = False tax_config.save() tax_config.country_exceptions.create( country=country_code, display_gross_prices=True ) order.display_gross_prices = False order.save(update_fields=["display_gross_prices"]) order.shipping_address.country = country_code order.shipping_address.save() # when update_order_display_gross_prices(order) # then assert order.display_gross_prices

67df28935c555fdd673f17e8c9183e24dde7c51f

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

def test_update_order_display_gross_prices_use_country_specific_tax_settings(order): # given country_code = "PT" tax_config = order.channel.tax_configuration tax_config.display_gross_prices = False tax_config.save() tax_config.country_exceptions.create( country=country_code, display_gross_prices=True ) order.display_gross_prices = False order.save(update_fields=["display_gross_prices"]) order.shipping_address.country = c

test_update_order_display_gross_prices_use_country_specific_tax_settings

modin/pandas/test/test_io.py

FEAT-#4766: Support fsspec URLs in `read_csv` and `read_csv_glob` (#4898) Signed-off-by: Karthik Velayutham <vkarthik@ponder.io>

modin

Python

test_io.py

def test_read_csv_google_cloud_storage(self): eval_io( fn_name="read_csv", # read_csv kwargs filepath_or_buffer="gs://modin-testing/testing/multiple_csv/test_data0.csv", )

c5107e5be29089720528c6c0ec4f96bc2a6a1eb3

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

def test_read_csv_google_cloud_storage(self): eval_io( fn_name="read_csv", # read_csv kwargs filepath_or_buffer="gs://modin-testing/testing/multiple_csv/tes

test_read_csv_google_cloud_storage

dask/array/tests/test_creation.py

increased performance of k-diagonal extraction in da.diag() and da.diagonal() (#8689) * added support for extracting k-diagonals from a 2d-array * included heterogeneous chunks in test_diag() * fixed linting errors in test_diag() * improved efficiency of diagonal extractor a bit * stole @TAdeJong's simple padding solution for diag(v, k) when v is 1d * reduced complexity of `diagonal()` from O(N**2) to O(N) diag() now calls diagonal() * fixed linting errors in diagonal() * reorganized tests and ensured coverage of diag() & diagonal() as per @jcrist's advice * catered for cupy type input arrays to diagonal()

dask

Python

test_creation.py

def test_diag_2d_array_creation(k): # when input 1d-array is a numpy array: v = np.arange(11) assert_eq(da.diag(v, k), np.diag(v, k)) # when input 1d-array is a dask array: v = da.arange(11, chunks=3) darr = da.diag(v, k) nparr = np.diag(v, k) assert_eq(darr, nparr) assert sorted(da.diag(v, k).dask) == sorted(da.diag(v, k).dask) v = v + v + 3 darr = da.diag(v, k) nparr = np.diag(v, k) assert_eq(darr, nparr) v = da.arange(11, chunks=11) darr = da.diag(v, k) nparr = np.diag(v, k) assert_eq(darr, nparr) assert sorted(da.diag(v, k).dask) == sorted(da.diag(v, k).dask) @pytest.mark.parametrize("k", [0, 3, -3, 8])

e3b3259419c21d0d412b9d5f12531ebe5ad6967a

@pytest.mark.parametrize("k", [0, 3, -3, 8])

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

def test_diag_2d_array_creation(k): # when input 1d-array is a numpy array: v = np.arange(11) assert_eq(da.diag(v, k), np.diag(v, k)) # when input 1d-array is a dask array: v = da.arange(11, chunks=3) darr = da.diag(v, k) nparr = np.diag(v, k) assert_eq(darr, nparr) assert sorted(da.diag(v, k).dask) == sorted(da.diag(v, k).dask) v = v + v + 3 darr = da.diag(v, k) nparr = np.diag(v, k) assert_eq(darr, nparr) v = da.arange(11, chunks=11) darr = da.diag(v, k) nparr

test_diag_2d_array_creation

datasets/imagenet_sketch/imagenet_sketch.py

Add ImageNet-Sketch dataset (#4301) * :sparkles: Add ImageNet-Sketch dataset * :memo: add data splits to dataset card * Update datasets/imagenet_sketch/README.md * :sparkles: labels->label and use HF hosted download link * Apply suggestions from code review Co-authored-by: Mario Šaško <mariosasko777@gmail.com> * :memo: update imagenet_sketch README.md * Use dataset repo data url Co-authored-by: Mario Šaško <mariosasko777@gmail.com>

datasets

Python

imagenet_sketch.py

def _split_generators(self, dl_manager): data_files = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": dl_manager.iter_files([data_files]), }, ), ]

78941675d6f39c269f9d445121718c6c27c511dc

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

def _split_generators(self, dl_manager): data_files = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": dl_manager.iter_files([data_files]), }, ), ]

_split_generators

torchvision/datasets/utils.py

support confirming no virus scan on GDrive download (#5645) * support confirming no virus scan on GDrive download * put gen_bar_updater back * Update torchvision/datasets/utils.py Co-authored-by: Nicolas Hug <contact@nicolas-hug.com> Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

vision

Python

utils.py

def gen_bar_updater() -> Callable[[int, int, int], None]: warnings.warn("The function `gen_bar_update` is deprecated since 0.13 and will be removed in 0.15.") pbar = tqdm(total=None)

96f2c0d47f00371dd066c84f69c34fde07e876c3

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

def gen_bar_updater() -> Callable[[int, int, int], None]: warnings.warn("The function `gen_bar_update`

gen_bar_updater

wagtail/admin/tests/api/test_pages.py

Reformat with black

wagtail

Python

test_pages.py

def test_revert_to_page_revision(self): self.assertEqual(self.events_page.title, "Evenements") response = self.get_response( self.events_page.id, {"revision_id": self.first_revision.id} ) self.assertEqual(response.status_code, 200) self.events_page.get_latest_revision().publish() self.events_page.refresh_from_db() self.assertEqual(self.events_page.title, "Events")

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def test_revert_to_page_revision(self): self.assertEqual(self.events_page.title, "Evenements") response = self.get_response( self.events_page.id, {"revision_id": self.first_revision.id} ) self.assertEqual(response.status_code, 200) self.events_page.get_latest_

test_revert_to_page_revision

freqtrade/optimize/backtesting.py

Merge index and mark rates as part of dataload

freqtrade

Python

backtesting.py

def load_bt_data_detail(self) -> None: if self.timeframe_detail: self.detail_data = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.timeframe_detail, timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=self.config.get('candle_type_def', CandleType.SPOT) ) else: self.detail_data = {} if self.trading_mode == TradingMode.FUTURES: # Load additional futures data. funding_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.FUNDING_RATE ) # For simplicity, assign to CandleType.Mark (might contian index candles!) mark_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.from_string(self.exchange._ft_has["mark_ohlcv_price"]) ) # Combine data to avoid combining the data per trade. for pair in self.pairlists.whitelist: self.futures_data[pair] = funding_rates_dict[pair].merge( mark_rates_dict[pair], on='date', how="inner", suffixes=["_fund", "_mark"]) else: self.futures_data = {}

f26cd191466b792123f3d0b1a18b3b117a23a638

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

def load_bt_data_detail(self) -> None: if self.timeframe_detail: self.detail_data = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.timeframe_detail, timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=self.config.get('candle_type_def', CandleType.SPOT) ) else: self.detail_data = {} if self.trading_mode == TradingMode.FUTURES: # Load additional futures data. funding_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.FUNDING_RATE ) # For simplicity, assign to CandleType.Mark (might contian index candles!) mark_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.from_string(se

load_bt_data_detail

homeassistant/components/bond/light.py

String formatting and max line length - Part 1 (#84390) Co-authored-by: Erik Montnemery <erik@montnemery.com>

core

Python

light.py

async def async_stop(self) -> None: _LOGGER.warning( "The bond.stop service is deprecated and has been replaced with a button;" " Call the button.press service instead" ) self._async_has_action_or_raise(Action.STOP) await self._hub.bond.action(self._device.device_id, Action(Action.STOP))

b0cee0bc46cbd7efe0e6421da18d91595c7a25ad

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

async def async_stop(self) -> None: _LOGGER.warning( "The bond.stop service is deprecated and has been replaced with a button;" " Call the button.press service instead" ) self._async_has_a

async_stop

homeassistant/components/zwave_js/sensor.py

Clean zwave_js platform typing (#72439) * Fix binary sensor * Fix climate * Fix cover * Fix fan * Fix light * Fix lock * Fix number * Fix select * Fix sensor * Add back type ignore until library bump

core

Python

sensor.py

def extra_state_attributes(self) -> dict[str, str] | None: if (value := self.info.primary_value.value) is None: return None # add the value's int value as property for multi-value (list) items return {ATTR_VALUE: value}

6cac1dadeba6cb81285960db1ab6ec6239547cd9

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

def extra_state_attributes(self) -> dict[str, str] | None: if (value := se

extra_state_attributes

homeassistant/components/smartthings/cover.py

Adjust smartthings cover type hints (#73948)

core

Python

cover.py

def current_cover_position(self) -> int | None: if not self._attr_supported_features & CoverEntityFeature.SET_POSITION: return None return self._device.status.level

3743d42ade80528325d36357ca6f9629d4970eaa

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

def current_cover_position(self) -> int | None:

current_cover_position

ivy_tests/test_nn/test_functional/test_activations.py

created backends sub-folder for all backend implementations.

ivy

Python

test_activations.py

def test_softmax(x, dtype_str, tensor_fn, dev_str, call): # smoke test x = tensor_fn(x, dtype_str, dev_str) ret = ivy.softmax(x) # type test assert ivy.is_array(ret) # cardinality test assert ret.shape == x.shape # value test assert np.allclose(call(ivy.softmax, x), ivy.backends.numpy.softmax(ivy.to_numpy(x))) # compilation test if not ivy.wrapped_mode(): helpers.assert_compilable(ivy.softmax) # softplus @pytest.mark.parametrize( "x", [[[-1., 1., 2.]]]) @pytest.mark.parametrize( "dtype_str", ['float32']) @pytest.mark.parametrize( "tensor_fn", [ivy.array, helpers.var_fn])

b50046a631badcf15ee25b6355a2d2052f6f5bf9

@pytest.mark.parametrize( "x", [[[-1., 1., 2.]]]) @pytest.mark.parametrize( "dtype_str", ['float32']) @pytest.mark.parametrize( "tensor_fn", [ivy.array, helpers.var_fn])

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

def test_softmax(x, dtype_str, tensor_fn, dev_str, call): # smoke test x = tensor_fn(x, dtype_str, dev_str) ret = ivy.softmax(x) # type test assert ivy.is_array(ret) # cardinality test assert ret.shape == x.shape # value test assert np.allclose(call(ivy.softmax, x), ivy.backends.numpy.softmax(ivy.to_numpy(x))) # compilation test if not ivy.wrapped_mode(): helpers.assert_compilable(ivy.soft

test_softmax

jax/_src/lax/convolution.py

Fix batching rule for convolution for batch dimensions of size 0.

jax

Python

convolution.py

def _reshape_axis_into(src, dst, x): # NB: `dst` is the number of the dimension that we should reshape into # *after* `src` is removed from `x`'s list of dimensions. For example, if # `src` is an added batch dimension, `dst` might name a target dimension in # the unbatched list of dimensions. perm = [i for i in range(x.ndim) if i != src] perm.insert(dst, src) new_shape = list(np.delete(x.shape, src)) new_shape[dst] *= x.shape[src] return lax.reshape(x, new_shape, perm)

ece9b999fb5f85eee6570e5f987ad6704c130503

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

def _reshape_axis_into(src, dst, x): # NB: `dst` is the number of the dimension that we should reshape into # *after* `src` is removed from `x`'s list of dimensions. For example, if # `src` is an added batch dimension, `dst` might name a target dimension in # the

_reshape_axis_into

bootloader/waflib/TaskGen.py

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

pyinstaller

Python

TaskGen.py

def force_permissions(self): if getattr(self.generator, 'chmod', None): for x in self.outputs: os.chmod(x.abspath(), self.generator.chmod)

64ccb7aea824fbec57f7ed1bbe483ec486183c13

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

def force_permissions(self): if getattr(self.generator, 'chm

force_permissions

tests/components/generic/test_diagnostics.py

Fix diagnostics export for generic camera (#75665) Fix url redaction and add tests Co-authored-by: Dave T <davet2001@users.noreply.github.com>

core

Python

test_diagnostics.py

def test_redact_url(url_in, url_out_expected): url_out = redact_url(url_in) assert url_out == url_out_expected

7075032bf743f8702d942410c0c41214c90c212b

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

def test_redact_url(url_in, url_out_expected): url_out = redact_url(

test_redact_url

numpy/lib/tests/test_io.py

Port over tests from npreadtext test suite - Add test for parsing scientific notation. - Add multiple-char comment test. - Port over tests for structured dtypes. - Add tests for exceptions on skiprows/max_rows. - port over ndmin tests. - Make structured data reusable, add unpack tests. - Port over delimiter tests. - Port over maxrows test w/ various dtypes. - Port over test of exception msg on parse failure. - Port over test for converters w/neg indices. - Port over usecols tests - Port over unicode tests. - Port over more converter tests. - Port over test for large rows. - Port over test for string-len discovery. - Port over float conversion accuracy test. - Port over bool test. - Add test for implicit float->int conversion. - Port over complex parsing tests. - Port over tests for reading from generator. - Port over object cleanup test. - Port over bytes incompat test. - Port over converters tests. Co-authored-by: Warren Weckesser <warren.weckesser@gmail.com> Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>

numpy

Python

test_io.py

def test_loadtxt_converters_negative_indices(): txt = TextIO('1.5,2.5\n3.0,XXX\n5.5,6.0') conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)} expected = np.array([[1.5, 2.5], [3.0, np.nan], [5.5, 6.0]]) res = np.loadtxt( txt, dtype=np.float64, delimiter=",", converters=conv, encoding=None ) assert_equal(res, expected)

66a61b03658f3c9f312505dcf7eab07e4cf91ac6

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

def test_loadtxt_converters_negative_indices(): txt = TextIO('1.5,2.5\n3.0,XXX\n5.5,6.0') conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)} expected = np.array([[1.5, 2

test_loadtxt_converters_negative_indices

python/ray/tests/test_autoscaler_yaml.py

[Autoscaler][Local Node Provider] Log a warning if max_workers < len(worker_ips) (#24635) Logs a warning when a user sets max_workers for local node provider less than the number of available ips. Also removes defaults of 0 for min_workers and max_workers from example configs to help prevent users inadvertantly setting max_workers=0 again.

ray

Python

test_autoscaler_yaml.py

def testValidateLocal(self): local_config_path = os.path.join( RAY_PATH, "autoscaler/local/example-minimal-manual.yaml" ) base_config = yaml.safe_load(open(local_config_path).read()) base_config["provider"]["head_ip"] = "xxx.yyy" base_config["provider"]["worker_ips"] = ["aaa.bbb", "ccc.ddd", "eee.fff"] base_config["auth"]["ssh_user"] = "user" base_config["auth"]["ssh_private_key"] = "~/.ssh/id_rsa" test_prepare_config = copy.deepcopy(base_config) prepared_config = prepare_config(test_prepare_config) try: validate_config(prepared_config) except Exception: self.fail("Failed to validate local/example-minimal-manual.yaml") expected_prepared = yaml.safe_load(EXPECTED_LOCAL_CONFIG_STR) assert prepared_config == expected_prepared no_worker_config = copy.deepcopy(base_config) del no_worker_config["provider"]["worker_ips"] with pytest.raises(ClickException): prepare_config(no_worker_config) no_head_config = copy.deepcopy(base_config) del no_head_config["provider"]["head_ip"] with pytest.raises(ClickException): prepare_config(no_head_config) for field in "head_node", "worker_nodes", "available_node_types": faulty_config = copy.deepcopy(base_config) faulty_config[field] = "This field shouldn't be in here." with pytest.raises(ClickException): prepare_config(faulty_config) too_many_workers_config = copy.deepcopy(base_config) # More workers requested than the three available ips. too_many_workers_config["max_workers"] = 10 too_many_workers_config["min_workers"] = 10 prepared_config = prepare_config(too_many_workers_config) # Check that worker config numbers were clipped to 3. assert prepared_config == expected_prepared not_enough_workers_config = copy.deepcopy(base_config) # Max workers is less than than the three available ips. # The user is probably has probably made an error. Make sure we log a warning. not_enough_workers_config["max_workers"] = 0 not_enough_workers_config["min_workers"] = 0 with mock.patch( "ray.autoscaler._private.local.config.cli_logger.warning" ) as warning: prepared_config = prepare_config(not_enough_workers_config) warning.assert_called_with( "The value of `max_workers` supplied (0) is less" " than the number of available worker ips (3)." " At most 0 Ray worker nodes will connect to the cluster." ) expected_prepared = yaml.safe_load(EXPECTED_LOCAL_CONFIG_STR) # We logged a warning. # However, prepare_config does not repair the strange config setting: expected_prepared["max_workers"] = 0 expected_prepared["available_node_types"]["local.cluster.node"][ "max_workers" ] = 0 expected_prepared["available_node_types"]["local.cluster.node"][ "min_workers" ] = 0 assert prepared_config == expected_prepared

29eebdfef2acb7d278042f38247a7d82473c3fd6

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

def testValidateLocal(self): local_config_path = os.path.join( RAY_PATH, "autoscaler/local/example-minimal-manual.yaml" ) base_config = yaml.safe_load(open(local_config_path).read()) base_config["provider"]["head_ip"] = "xxx.yyy" base_config["provider"]["worker_ips"] = ["aaa.bbb", "ccc.ddd", "eee.fff"] base_config["

testValidateLocal

wagtail/contrib/sitemaps/tests.py

Reformat with black

wagtail

Python

tests.py

def get_request_and_django_site(self, url): request = RequestFactory().get(url) request.META["HTTP_HOST"] = self.site.hostname request.META["SERVER_PORT"] = self.site.port return request, get_current_site(request)

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def get_request_and_django_site(self, url): request = RequestFactory().get(url) request.META["HTTP_HOST"] = self.site.hostname request.META["SERVER_PORT"] = self.site.port return request, get_current_site(request)

get_request_and_django_site

jax/_src/ad_checkpoint.py

Fix lax imports

jax

Python

ad_checkpoint.py

def dot_with_no_batch_dims(prim, *_, **params) -> bool: # This is a useful heuristic for transformers. if prim is lax_internal.dot_general_p: (_, _), (lhs_b, rhs_b) = params['dimension_numbers'] if not lhs_b and not rhs_b: return True return False name_p = core.Primitive('name')

1d895b2c85e17b9f563cd41d9a340528179d29aa

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

def dot_with_no_batch_dims(prim, *_, **params) -> bool: # This is a useful heuristic for transformers. if prim is lax_inter

dot_with_no_batch_dims

python/ray/data/impl/block_list.py

[Datasets] Provide more efficient + intuitive block clearing semantics for different execution modes (#24127) **TL;DR:** Don't clear for eager, clear all but non-lazy input blocks if lazy, clear everything if pipelining. This PR provides more efficient and intuitive block clearing semantics for eager mode, lazy mode, and pipelining, while still supporting multiple operations applied to the same base dataset, i.e. fan-out. For example, two different map operations are applied to the same base `ds` in this example: ```python ds = ray.data.range(10).map(lambda x: x+1) ds1 = ds.map(lambda x: 2*x) ds2 = ds.map(lambda x: 3*x) ``` If naively clear the blocks when executing the map to produce `ds1`, the map producing `ds2` will fail. ### Desired Semantics - **Eager mode** - don’t clear input blocks, thereby supporting fan-out from cached data at any point in the stage chain without triggering unexpected recomputation. - **Lazy mode** - if lazy datasource, clear the input blocks for every stage, relying on recomputing via stage lineage if fan-out occurs; if non-lazy datasource, do not clear source blocks for execution plan when executing first stage, but do clear input blocks for every subsequent stage. - **Pipelines** - Same as lazy mode, although the only fan-out that can occur is from the pipeline source blocks when repeating a dataset/pipeline, so unintended intermediate recomputation will never happen.

ray

Python

block_list.py

def _check_if_cleared(self) -> None: if self.is_cleared(): raise ValueError( "This Dataset's blocks have been moved, which means that you " "can no longer use this Dataset." )

f72555262afbbfc1aabb87c9e40839aaaee3ba0b

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

def _check_if_cleared(self) -> None: if self.is_cleared(): raise ValueError(

_check_if_cleared

dask/dataframe/tests/test_groupby.py

Implement {Series,DataFrame}GroupBy `fillna` methods (#8869) Co-authored-by: Ian Rose <ian.r.rose@gmail.com>

dask

Python

test_groupby.py

def test_bfill(): df = pd.DataFrame( { "A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": [np.nan, 3, np.nan, np.nan], "D": [np.nan, 4, np.nan, 5], "E": [np.nan, 6, np.nan, 7], } ) ddf = dd.from_pandas(df, npartitions=2) assert_eq( df.groupby("A").bfill(), ddf.groupby("A").bfill(), ) assert_eq( df.groupby("A").B.bfill(), ddf.groupby("A").B.bfill(), ) assert_eq( df.groupby(["A", "B"]).bfill(), ddf.groupby(["A", "B"]).bfill(), ) @pytest.mark.parametrize( "grouper", [ lambda df: ["a"], lambda df: ["a", "b"], lambda df: df["a"], lambda df: [df["a"], df["b"]], lambda df: [df["a"] > 2, df["b"] > 1], ], )

5fbda77cfc5bc1b8f1453a2dbb034b048fc10726

@pytest.mark.parametrize( "grouper", [ lambda df: ["a"], lambda df: ["a", "b"], lambda df: df["a"], lambda df: [df["a"], df["b"]], lambda df: [df["a"] > 2, df["b"] > 1], ], )

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

def test_bfill(): df = pd.DataFrame( { "A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": [np.nan, 3, np.nan, np.nan], "D": [np.nan, 4, np.nan, 5], "E": [np.nan, 6, np.nan, 7], } ) ddf = dd.from_pandas(df, npartit

test_bfill

example/components/plot_line.py

test: split demo.py into seperate files and functions

visdom

Python

plot_line.py

def plot_line_stackedarea(viz, env): Y = np.linspace(0, 4, 200) return viz.line( Y=np.column_stack((np.sqrt(Y), np.sqrt(Y) + 2)), X=np.column_stack((Y, Y)), opts=dict( fillarea=True, showlegend=False, width=800, height=800, xlabel='Time', ylabel='Volume', ytype='log', title='Stacked area plot', marginleft=30, marginright=30, marginbottom=80, margintop=30, ), ) # Assure that the stacked area plot isn't giant

b4115c0337b1bacc876bef1ece97e8fa8b3e2834

https://github.com/fossasia/visdom.git

def plot_line_stackedarea(viz, env): Y = np.linspace(0, 4, 200)

plot_line_stackedarea

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

upd; format

transferlearning

Python

provider.py

def identify(self, requirement_or_candidate): # type: (Union[Requirement, Candidate]) -> str return requirement_or_candidate.name

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def identify(self, requirement_or_candidate): # type: (Union[Requirement, Candidate]) -> str return requirement_or_candidate.na

identify

code/deep/BJMMD/caffe/scripts/cpp_lint.py

Balanced joint maximum mean discrepancy for deep transfer learning

transferlearning

Python

cpp_lint.py

def CleanseComments(line): commentpos = line.find('//') if commentpos != -1 and not IsCppString(line[:commentpos]): line = line[:commentpos].rstrip() # get rid of /* ... */ return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line)

cc4d0564756ca067516f71718a3d135996525909

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

def CleanseComments(line): comment

CleanseComments

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

upd; format

transferlearning

Python

pkg_resources.py

def from_wheel(cls, path, name): # type: (str, str) -> Distribution with zipfile.ZipFile(path, allowZip64=True) as zf: dist = pkg_resources_distribution_for_wheel(zf, name, path) return cls(dist)

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def from_wheel(cls, path, name): # type: (str, str) -> Distribution with zipfile.ZipFile(path, allowZip64=True) as zf: d

from_wheel

airflow/utils/sqlalchemy.py

Have consistent types between the ORM and the migration files (#24044) We currently don't compare column types between ORM and the migration files. Some columns in the migration files have different types from the same columns in the ORM. Here, I made effort to match the types in migration files with the types in ORM, using the migration files as the source of truth in most cases. I couldn't convert the MySQL VARCHAR collation in db(utf8_bin) to use the one in ORM(utf8mb3_bin). It seems it's not possible to convert a collation of an already existing column in MySQL.

airflow

Python

sqlalchemy.py

def load_dialect_impl(self, dialect): if dialect.name == 'mssql': return mssql.DATETIME2(precision=6) elif dialect.name == 'mysql': return mysql.TIMESTAMP(fsp=6) return super().load_dialect_impl(dialect)

25537acfa28eebc82a90274840e0e6fb5c91e271

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

def load_dialect_impl(self, dialect): if dialect.name == 'mssq

load_dialect_impl

tests/sentry/api/endpoints/test_organization_release_details.py

ref(models): `ActivityType` (#34978) ## Objective: We want to separate enum logic from Model logic. This breaks a lot of circular dependencies.

sentry

Python

test_organization_release_details.py

def test_activity_generation_long_release(self): user = self.create_user(is_staff=False, is_superuser=False) org = self.organization org.flags.allow_joinleave = False org.save() team = self.create_team(organization=org) project = self.create_project(teams=[team], organization=org) release = Release.objects.create(organization_id=org.id, version="x" * 65) release.add_project(project) self.create_member(teams=[team], user=user, organization=org) self.login_as(user=user) url = reverse( "sentry-api-0-organization-release-details", kwargs={"organization_slug": org.slug, "version": release.version}, ) response = self.client.put(url, data={"dateReleased": datetime.utcnow().isoformat() + "Z"}) assert response.status_code == 200, (response.status_code, response.content) release = Release.objects.get(id=release.id) assert release.date_released activity = Activity.objects.filter( type=ActivityType.RELEASE.value, project=project, ident=release.version[:64] ) assert activity.exists()

b9f5a910dc841b85f58d46266ec049ae5a7fd305

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

def test_activity_generation_long_release(self): user = self.create_user(is_staff=False, is_superuser=False) org = self.organization org.flags.allow_joinleave = False org.save() team = self.create_team(organization=org) project = self.create_project(teams=[team], organization=org) release = Release.objects.create(organization_id=org.id, version="x" * 65) release.add_project(project) self.create_member(teams=[team], user=user, organization=org) self.login_as(user=user) url = reverse( "sentry-api-0-organization-release-details", kwargs={"organization_slug": org.slug, "version": release.version}, ) response = self.client.put(url, data={"dateRel

test_activity_generation_long_release

fastai/data_block.py

Upgrading to support latest Pytorch version

DeOldify

Python

data_block.py

def split_by_list(self, train, valid): "Split the data between `train` and `valid`." return self._split(self.path, train, valid)

4fc3616712edb19179b17dd270ad6cf63abf99c2

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

def split_by_list(self, train, valid): "Split the data between `train` and `val

split_by_list

tests/integration/gateway_clients/test_clients_gateways.py

fix: fix endpoint discovery tries (#5014)

jina

Python

test_clients_gateways.py

def test_grpc_gateway_runtime_lazy_request_access(linear_graph_dict, monkeypatch): call_counts = multiprocessing.Queue() monkeypatch.setattr( networking.GrpcConnectionPool, 'send_requests_once', DummyNoDocAccessMockConnectionPool.send_requests_once, ) monkeypatch.setattr( networking.GrpcConnectionPool, 'send_discover_endpoint', DummyMockConnectionPool.send_discover_endpoint, ) port = random_port()

6f5b3f2a9b13c2eae78b746531132cbfcdc8c2da

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

def test_grpc_gateway_runtime_lazy_request_access(linear_graph_dict, monkeypatch): call_counts = multiprocessing.Queue() monkeypatch.setattr( networking.GrpcConnectionPool, 'send_requests_once', DummyNoDocAccessMockConnectionPool.send_requests_once, ) monkeypatch.setattr( networking.GrpcConnectionPool, 'send

test_grpc_gateway_runtime_lazy_request_access

ludwig/utils/defaults.py

Comprehensive configs for trainer and combiner. (#2118)

ludwig

Python

defaults.py

def _perform_sanity_checks(config): assert "input_features" in config, "config does not define any input features" assert "output_features" in config, "config does not define any output features" assert isinstance(config["input_features"], list), ( "Ludwig expects input features in a list. Check your model " "config format" ) assert isinstance(config["output_features"], list), ( "Ludwig expects output features in a list. Check your model " "config format" ) assert len(config["input_features"]) > 0, "config needs to have at least one input feature" assert len(config["output_features"]) > 0, "config needs to have at least one output feature" if TRAINER in config: assert isinstance(config[TRAINER], dict), ( "There is an issue while reading the training section of the " "config. The parameters are expected to be" "read as a dictionary. Please check your config format." ) if "preprocessing" in config: assert isinstance(config["preprocessing"], dict), ( "There is an issue while reading the preprocessing section of the " "config. The parameters are expected to be read" "as a dictionary. Please check your config format." ) if COMBINER in config: assert isinstance(config[COMBINER], dict), ( "There is an issue while reading the combiner section of the " "config. The parameters are expected to be read" "as a dictionary. Please check your config format." )

ae25cc4c5a229bbc44339249e1f94bf256f18317

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

def _perform_sanity_checks(config): assert "input_features" in config, "config does not define any input features" assert "output_features" in config, "config does not define any output features" assert isinstance(config["input_features"], list), ( "Ludwig expects input features in a list. Check your model " "config format" ) assert isinstance(config["output_features"], list), ( "Ludwig expects output features in a list. Check your model " "config format" ) assert len(config["input_fe

_perform_sanity_checks

test/units/plugins/callback/test_callback.py

Avoid deprecated TestCase functions in unit tests. (#76678) * Avoid deprecated TestCase functions in unit tests. * Add assertRaisesRegex for Python 2.7. * Fix indentation.

ansible

Python

test_callback.py

def test_host_label(self): result = TaskResult(host=Host('host1'), task=mock_task, return_data={}) self.assertEqual(CallbackBase.host_label(result), 'host1')

97104f1221b64ef36cf42cb90c5a0eff263a2adb

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

def test_host_label(self):

test_host_label

dask/dataframe/io/tests/test_parquet.py

Remove pyarrow-legacy engine from parquet API (#8835) * remove pyarrow-legacy * Small fixup * Small fixup for pyarrow < 5 Co-authored-by: Jim Crist-Harif <jcristharif@gmail.com>

dask

Python

test_parquet.py

def test_writing_parquet_with_kwargs(tmpdir, engine): fn = str(tmpdir) path1 = os.path.join(fn, "normal") path2 = os.path.join(fn, "partitioned") df = pd.DataFrame( { "a": np.random.choice(["A", "B", "C"], size=100), "b": np.random.random(size=100), "c": np.random.randint(1, 5, size=100), } ) df.index.name = "index" ddf = dd.from_pandas(df, npartitions=3) engine_kwargs = { "pyarrow": { "compression": "snappy", "coerce_timestamps": None, "use_dictionary": True, }, "fastparquet": {"compression": "snappy", "times": "int64", "fixed_text": None}, } ddf.to_parquet(path1, engine=engine, **engine_kwargs[engine]) out = dd.read_parquet(path1, engine=engine) assert_eq(out, ddf, check_index=(engine != "fastparquet")) # Avoid race condition in pyarrow 0.8.0 on writing partitioned datasets with dask.config.set(scheduler="sync"): ddf.to_parquet( path2, engine=engine, partition_on=["a"], **engine_kwargs[engine] ) out = dd.read_parquet(path2, engine=engine).compute() for val in df.a.unique(): assert set(df.b[df.a == val]) == set(out.b[out.a == val])

0b36d7fcaf54ee9a78fff4b07f124cb0c8741cdf

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

def test_writing_parquet_with_kwargs(tmpdir, engine): fn = str(tmpdir) path1 = os.path.join(fn, "normal") path2 = os.path.join(fn, "partitioned") df = pd.DataFrame( { "a": np.random.choice(["A", "B", "C"], size=100), "b": np.random.random(size=100), "c": np.random.randint(1, 5, size=100), } ) df.index.name = "index" ddf = dd.from_pandas(df, npartitions=3) engine_kwargs = { "pyarrow": { "compression": "snappy", "coerce_timestamps": None, "use_dictionary": True, }, "fastparquet": {"compression": "snappy", "times": "int64", "fixed_text": None}, } ddf.to_parquet(path1, engine=engine, **engine_kwargs[engine

test_writing_parquet_with_kwargs

saleor/plugins/webhook/tests/subscription_webhooks/fixtures.py

GraphQL subscription support for synchronous webhook events (#9763) * WIP add sync webhooks subscription payload handling * add tests, fix minor things * update schema * remove unneeded code * add fix for circular field resolve * fix-filter-shipping-methods-payload * added_in added to desription * add missing types * revert refactor, precommit issues * fixes after review * cosmetix fixes post-review * subscription types description fixes * remove unneeded description from PaymentBase * add validation for creating webhook with two top level fields, add tests for shippingListMethodsForCheckout * add docstring, refactor prevent_sync_event_circular_wuery wrapper * fix docstring of revent_sync_event_circular_query * fix linters

saleor

Python

fixtures.py

def subscription_order_fulfilled_webhook(subscription_webhook): return subscription_webhook( queries.ORDER_FULFILLED, WebhookEventAsyncType.ORDER_FULFILLED ) @pytest.fixture

8201efcde2d7aacccf3512c544cceea6780a0598

@pytest.fixture

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

def subscription_order_fulfilled_webhook(subscription_webhook): return subscription_webhook( queries.ORDER_FULFILLED, WebhookEventAsyncType.ORDER_FULFILLED ) @pytest.fixture

subscription_order_fulfilled_webhook

packages/syft/src/syft/core/tensor/autodp/phi_tensor.py

add data subject and data subject shape serialization to GammaTensor

PySyft

Python

phi_tensor.py

def _object2bytes(self) -> bytes: schema = get_capnp_schema(schema_file="phi_tensor.capnp") pt_struct: CapnpModule = schema.PT # type: ignore pt_msg = pt_struct.new_message() # this is how we dispatch correct deserialization of bytes pt_msg.magicHeader = serde_magic_header(type(self)) # We always have FPT as the child of an PT in the tensor chain. chunk_bytes(serialize(self.child, to_bytes=True), "child", pt_msg) # type: ignore pt_msg.minVals = serialize(self.min_vals, to_bytes=True) pt_msg.maxVals = serialize(self.max_vals, to_bytes=True) pt_msg.dataSubjects = serialize( dslarraytonumpyutf8(self.data_subjects), to_bytes=True ) pt_msg.dataSubjectsShape = serialize(self.data_subjects.shape, to_bytes=True) # to pack or not to pack? # to_bytes = pt_msg.to_bytes() return pt_msg.to_bytes_packed()

a81b66ea18721dc36c77aefac733dd224f48cc87

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

def _object2bytes(self) -> bytes: schema = get_capnp_schema(schema_file="phi_tensor.capnp") pt_struct: CapnpModule = schema.PT # type: ignore pt_msg = pt_struct.new_message() # this is how we dispatch correct deserialization of bytes pt_msg.magicHeader = serde_magic_header(type(self)) # We always have FPT as the child of an PT in the tensor chain. chunk_bytes(serialize(self.child, to_bytes=True), "child", pt_msg) # type: ignore pt_msg.minVals = serialize(self.min_vals, to_bytes=True) pt_msg.maxVals = serialize(self.max_vals, to_bytes=True) pt_msg.dataSubjects = serialize( dslarraytonumpyutf8(self.da

_object2bytes

homeassistant/components/tplink/light.py

Use ColorMode enum in tplink (#70542)

core

Python

light.py

def supported_color_modes(self) -> set[ColorMode | str] | None: modes: set[ColorMode | str] = set() if self.device.is_variable_color_temp: modes.add(ColorMode.COLOR_TEMP) if self.device.is_color: modes.add(ColorMode.HS) if self.device.is_dimmable: modes.add(ColorMode.BRIGHTNESS) if not modes: modes.add(ColorMode.ONOFF) return modes

121d2008c2e98c94775f0379ccd4eedc15476d7d

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

def supported_color_modes(self) -> set[ColorMode | str] | None: modes: set[ColorMode | str] = set() if self.device.is_variable_color_temp: modes.add(ColorMode.COLOR_TEMP) if self.device.is_color: m

supported_color_modes

tests/test_patching.py

Support DataLoader with num_workers > 0 in streaming mode (#4375) * make TorchIterableDataset work in parallel - make it picklable - paralellize over the shards when num_workers is passed * start writing some tests * fix streaming extension and fsspec issues in subprocesses * fix some tests * fix more tests * fix import * fix and add tests * fix patch (handle successive patches and builtins) * revert unnecessary change to enriched_web_blg * style * use open locally to fix win permission errors * keep file opened in read_csv * fix compression for read_csv * consistency of read_csv: don't infer compression for file-like objects * stringify Path objects * comments + raise error if sharding is ambiguous * minor * Update src/datasets/iterable_dataset.py Co-authored-by: Mario Šaško <mariosasko777@gmail.com> Co-authored-by: Mario Šaško <mariosasko777@gmail.com>

datasets

Python

test_patching.py

def test_patch_submodule_missing_builtin(): # builtin should always be mocked even if they're not in the globals # in case they're loaded at one point mock = "__test_patch_submodule_missing_builtin_mock__" # _test_patching doesn't have "len" in its globals assert getattr(_test_patching, "len", None) is None with patch_submodule(_test_patching, "len", mock): assert _test_patching.len is mock assert _test_patching.len is len

ab7d3045ac9154e9c1c2602d0869130defdc6dc7

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

def test_patch_submodule_missing_builtin(): # builtin should always be mocked even if they're not in the globals # in case they're loaded at one point mock = "__test

test_patch_submodule_missing_builtin

tests/openbb_terminal/stocks/fundamental_analysis/test_market_watch_view.py

Updating some names (#1575) * quick econ fix * black * keys and feature flags * terminal name :eyes: * some more replacements * some more replacements * edit pyproject * gst -> openbb * add example portfolios back to git * Update api from gst * sorry. skipping some tests * another round of names * another round of test edits * Missed some .gst refs and update timezone * water mark stuff * Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS * fix more GST to OpenBB Terminal * Logging : merge conflicts with main * Revert wrong files Co-authored-by: Andrew <andrew.kenreich@gmail.com> Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>

OpenBBTerminal

Python

test_market_watch_view.py

def test_call_func_no_parser(func, mocker): mocker.patch( "openbb_terminal.stocks.fundamental_analysis.market_watch_view.parse_known_args_and_warn", return_value=None, ) func_result = getattr(market_watch_view, func)(other_args=list(), ticker="TSLA") assert func_result is None getattr(market_watch_view, "parse_known_args_and_warn").assert_called_once() @pytest.mark.vcr @pytest.mark.record_stdout @pytest.mark.parametrize( "func", [ "income", "balance", "cash", ], ) @pytest.mark.parametrize( "use_color", [True, False], )

b71abcfbf4d7e8ac1855522aff0378e13c8b5362

@pytest.mark.vcr @pytest.mark.record_stdout @pytest.mark.parametrize( "func", [ "income", "balance", "cash", ], ) @pytest.mark.parametrize( "use_color", [True, False], )

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

def test_call_func_no_parser(func, mocker): mocker.patch( "openbb_terminal.stocks.fundamental_analysis.market_watch_view.parse_known_args_and_warn", return_value=None, ) func_result = getattr(market_watch_view, func)(other_args=list(), ticker="TSLA") assert func_result is None getattr(market_watch_view, "parse_known_args_and_warn").assert_called_once() @pytest.mark.vcr @pytest.mark.record_stdout @pytest.mark.parametrize(

test_call_func_no_parser

wagtail/admin/tests/api/test_pages.py

Reformat with black

wagtail

Python

test_pages.py

def test_all_nested_fields(self): response = self.get_response( type="demosite.BlogEntryPage", fields="feed_image(*)" ) content = json.loads(response.content.decode("UTF-8")) for page in content["items"]: self.assertEqual( set(page["feed_image"].keys()), {"id", "meta", "title", "width", "height", "thumbnail"}, )

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def test_all_nested_fields(self): response = self.get_response( type="demosite.BlogEntryPage", fields="feed_image(*)" ) content = json.loads(response.content.decode("UTF-8")) for page in content["items"]: self.assertEqual( set(page["feed_image"].keys()),

test_all_nested_fields

tests/snuba/sessions/test_sessions_v2.py

fix(sessions): Order results by timestamp and log error if snuba limit exceeded (#31214) As described in https://getsentry.atlassian.net/browse/ISSUE-1372, gaps occur in sessions_v2 time series when the number of releases is large. This seems to be caused by the fact that snuba applies a default limit of 1000. The sessions API queries these series without an orderBy constraint, so a random subset of entries default to zero. This PR logs an error if this limit is actually reached. Furthermore, we add an order by clause to the snuba query, such that at least the most recent part of the time series is complete.

sentry

Python

test_sessions_v2.py

def test_massage_simple_timeseries(): query = _make_query("statsPeriod=1d&interval=6h&field=sum(session)") result_totals = [{"sessions": 4}] # snuba returns the datetimes as strings for now result_timeseries = [ {"sessions": 2, "bucketed_started": "2020-12-18T06:00:00+00:00"}, {"sessions": 2, "bucketed_started": "2020-12-17T12:00:00+00:00"}, ] expected_result = { "start": "2020-12-17T12:00:00Z", "end": "2020-12-18T11:15:00Z", "query": "", "intervals": [ "2020-12-17T12:00:00Z", "2020-12-17T18:00:00Z", "2020-12-18T00:00:00Z", "2020-12-18T06:00:00Z", ], "groups": [ {"by": {}, "series": {"sum(session)": [2, 0, 0, 2]}, "totals": {"sum(session)": 4}} ], } actual_result = result_sorted(massage_sessions_result(query, result_totals, result_timeseries)) assert actual_result == expected_result @freeze_time("2020-12-18T11:14:17.105Z")

7fbf708470ba13992a5d53b088be2388a8ed93df

@freeze_time("2020-12-18T11:14:17.105Z")

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

def test_massage_simple_timeseries(): query = _make_query("statsPeriod=1d&interval=6h&field=sum(session)") result_totals = [{"sessions": 4}] # snuba returns the datetimes as strings for now result_timeseries = [ {"sessions": 2, "bucketed_started": "2020-12-18T06:00:00+00:00"}, {"sessions": 2, "bucketed_started": "2020-12-17T12:00:00+00:00"}, ] expected_result = { "start": "2020-12-17T12:00:00Z", "end": "2020-12-18T11:15:00Z", "query": "", "intervals": [ "2020-12-17T12:00:00Z", "2020-12-1

test_massage_simple_timeseries

sklearn/metrics/tests/test_pairwise_distances_reduction.py

FEA Fused sparse-dense support for `PairwiseDistancesReduction` (#23585) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Meekail Zain <Micky774@users.noreply.github.com>

scikit-learn

Python

test_pairwise_distances_reduction.py

def test_pairwise_distances_reduction_is_usable_for(): rng = np.random.RandomState(0) X = rng.rand(100, 10) Y = rng.rand(100, 10) X_csr = csr_matrix(X) Y_csr = csr_matrix(Y) metric = "manhattan" # Must be usable for all possible pair of {dense, sparse} datasets assert BaseDistanceReductionDispatcher.is_usable_for(X, Y, metric) assert BaseDistanceReductionDispatcher.is_usable_for(X_csr, Y_csr, metric) assert BaseDistanceReductionDispatcher.is_usable_for(X_csr, Y, metric) assert BaseDistanceReductionDispatcher.is_usable_for(X, Y_csr, metric) assert BaseDistanceReductionDispatcher.is_usable_for( X.astype(np.float64), Y.astype(np.float64), metric ) assert BaseDistanceReductionDispatcher.is_usable_for( X.astype(np.float32), Y.astype(np.float32), metric ) assert not BaseDistanceReductionDispatcher.is_usable_for( X.astype(np.int64), Y.astype(np.int64), metric ) assert not BaseDistanceReductionDispatcher.is_usable_for(X, Y, metric="pyfunc") assert not BaseDistanceReductionDispatcher.is_usable_for( X.astype(np.float32), Y, metric ) assert not BaseDistanceReductionDispatcher.is_usable_for( X, Y.astype(np.int32), metric ) # F-ordered arrays are not supported assert not BaseDistanceReductionDispatcher.is_usable_for( np.asfortranarray(X), Y, metric ) # We prefer not to use those implementations for fused sparse-dense when # metric="(sq)euclidean" because it's not yet the most efficient one on # all configurations of datasets. # See: https://github.com/scikit-learn/scikit-learn/pull/23585#issuecomment-1247996669 # noqa # TODO: implement specialisation for (sq)euclidean on fused sparse-dense # using sparse-dense routines for matrix-vector multiplications. assert not BaseDistanceReductionDispatcher.is_usable_for( X_csr, Y, metric="euclidean" ) assert not BaseDistanceReductionDispatcher.is_usable_for( X_csr, Y_csr, metric="sqeuclidean" ) # CSR matrices without non-zeros elements aren't currently supported # TODO: support CSR matrices without non-zeros elements X_csr_0_nnz = csr_matrix(X * 0) assert not BaseDistanceReductionDispatcher.is_usable_for(X_csr_0_nnz, Y, metric) # CSR matrices with int64 indices and indptr (e.g. large nnz, or large n_features) # aren't supported as of now. # See: https://github.com/scikit-learn/scikit-learn/issues/23653 # TODO: support CSR matrices with int64 indices and indptr X_csr_int64 = csr_matrix(X) X_csr_int64.indices = X_csr_int64.indices.astype(np.int64) assert not BaseDistanceReductionDispatcher.is_usable_for(X_csr_int64, Y, metric)

60cc5b596f38d0d236dab34e02c05d98b5a72bad

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

def test_pairwise_distances_reduction_is_usable_for(): rng = np.random.RandomState(0) X = rng.rand(100, 10) Y = rng.rand(100, 10) X_csr = csr_matrix(X) Y_csr = csr_matrix(Y) metric = "manhattan" # Must be usable for all possible pair of {dense, sparse} datasets assert BaseDistanceReductionDispatcher.is_usable_for(X, Y, metric) assert BaseDistanceReductionDispatcher.is_usable_for(X_csr, Y_csr, metric) assert BaseDistanceReductionDispatcher.is_usable_for(X_csr, Y, metric) assert BaseDistanceReductionDispatcher.is_usable_for(X, Y_csr, metric) assert BaseDistanceReductionDispatcher.is_usable_for( X.astype(np.float64), Y.astype(np.float64), metric ) assert BaseDistanceReductionDispatcher.is_usable_for( X.astype(np.float32), Y.astype(np.float32), metric ) assert not BaseDistanceReductionDispatcher.is_usable_for( X.astype(np.int64), Y.astype(np.int64), metric ) assert not BaseDistanceReductionDispatcher.is_usable_for(X, Y, metric="pyfunc") assert not BaseDistanceReductionDispatcher.is_usable_for( X.astype(np.float32), Y, metric ) assert not BaseDistanceReductionDispatcher.is_usable_for( X, Y.astype(np.int32), metric ) # F-ordered arrays are not supported assert not BaseDistanceReductionDispatcher.is_usable_for( np.asfortranarray(X), Y, metric ) # We prefer not to use those implementations for fused sparse-dense when # metric="(sq)euclidean" because it's not yet the most efficient o

test_pairwise_distances_reduction_is_usable_for

airflow/providers/arangodb/hooks/arangodb.py

Adding ArangoDB Provider (#22548) * Adding ArangoDB Provider

airflow

Python

arangodb.py

def create_database(self, name): if not self.db_conn.has_database(name): self.db_conn.create_database(name) return True else: self.log.info('Database already exists: %s', name) return False

c758c76ac336c054fd17d4b878378aa893b7a979

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

def create_database(self, name): if not self.db_conn.has_database(name): self.db_conn.create_database(name)

create_database

erpnext/manufacturing/doctype/bom_update_tool/bom_update_tool.py

refactor: Add exception handling in background job within BOM Update Tool

erpnext

Python

bom_update_tool.py

def replace_bom(args): try: frappe.db.auto_commit_on_many_writes = 1 args = frappe._dict(args) doc = frappe.get_doc("BOM Update Tool") doc.current_bom = args.current_bom doc.new_bom = args.new_bom doc.replace_bom() except Exception: frappe.log_error( msg=frappe.get_traceback(), title=_("BOM Update Tool Error") ) finally: frappe.db.auto_commit_on_many_writes = 0

f57725f8fa016b9826e8fdf2f14dbf1a3d9991f7

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

def replace_bom(args): try: frappe.db.auto_commit_on_many_writes = 1 args = frappe._dict(args) doc = frappe.get_doc("BOM Update Tool") doc.current_bom

replace_bom

mmdet/models/dense_heads/ddod_head.py

[Feature] Support DDOD: Disentangle Your Dense Object Detector(ACM MM2021 oral) (#7279) * add ddod feature * add ddod feature * modify new * [Feature] modify ddod code0225 * [Feature] modify ddod code0226 * [Feature] modify ddod code0228 * [Feature] modify ddod code0228#7279 * [Feature] modify ddod code0301 * [Feature] modify ddod code0301 test draft * [Feature] modify ddod code0301 test * [Feature] modify ddod code0301 extra * [Feature] modify ddod code0301 delete src/mmtrack * [Feature] modify ddod code0302 * [Feature] modify ddod code0302(2) * [Feature] modify ddod code0303 * [Feature] modify ddod code0303(2) * [Feature] modify ddod code0303(3) * [Feature] modify ddod code0305 * [Feature] modify ddod code0305(2) delete diou * [Feature] modify ddod code0305(3) * modify ddod code0306 * [Feature] modify ddod code0307 * [Feature] modify ddod code0311 * [Feature] modify ddod code0311(2) * [Feature] modify ddod code0313 * update * [Feature] modify ddod code0319 * fix * fix lint * [Feature] modify ddod code0321 * update readme * [0502] compute common vars at once for get_target * [0504] update ddod conflicts * [0518] seperate reg and cls loss and get_target compute * [0518] merge ATSSCostAssigner to ATSSAssigner * [0518] refine ATSSAssigner * [0518] refine ATSSAssigner 2 * [0518] refine ATSSAssigner 2 * [0518] refine ATSSAssigner 3 * [0519] fix bugs * update * fix lr * update weight Co-authored-by: hha <1286304229@qq.com>

mmdetection

Python

ddod_head.py

def forward_single(self, x, scale): cls_feat = x reg_feat = x for cls_conv in self.cls_convs: cls_feat = cls_conv(cls_feat) for reg_conv in self.reg_convs: reg_feat = reg_conv(reg_feat) cls_score = self.atss_cls(cls_feat) # we just follow atss, not apply exp in bbox_pred bbox_pred = scale(self.atss_reg(reg_feat)).float() iou_pred = self.atss_iou(reg_feat) return cls_score, bbox_pred, iou_pred

151a803ed0119560f59dbe7b73824dbdcae08fc6

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

def forward_single(self, x, scale): cls_feat = x reg_feat = x for cls_conv in self.cls_convs: cls_feat = cls_conv(cls_feat) for reg_conv in self.reg_convs: reg_feat = reg_conv(reg_feat) cls_score = self.atss_cls(cls_feat) # we just follow atss, not apply exp in bbox_pred bbox_pred = scale(self.atss_reg(reg_feat)).float() iou_pred = self.atss_iou(reg_f

forward_single

erpnext/patches/v14_0/update_batch_valuation_flag.py

refactor: use qb for patching flag

erpnext

Python

update_batch_valuation_flag.py

def execute(): batch = frappe.qb.DocType("Batch") frappe.qb.update(batch).set(batch.use_batchwise_valuation, 0).run()

312db429e4605d6d0ce47d1034662fdf0ec053b7

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

def execute(): batch = frappe.qb.D

execute

PyInstaller/utils/hooks/tcl_tk.py

hookutils: tcl/tk: port to PyInstaller.isolated framework

pyinstaller

Python

tcl_tk.py

def _get_tcl_tk_info(): try: import tkinter from _tkinter import TCL_VERSION, TK_VERSION except ImportError: # tkinter unavailable return None, None, None, False tcl = tkinter.Tcl() # Query the location of Tcl library/data directory. tcl_dir = tcl.eval("info library") # Check if Tcl/Tk is built with multi-threaded support (built with --enable-threads), as indicated by the presence # of optional `threaded` member in `tcl_platform` array. try: tcl.getvar("tcl_platform(threaded)") # Ignore the actual value. tcl_threaded = True except tkinter.TclError: tcl_threaded = False return tcl_dir, TCL_VERSION, TK_VERSION, tcl_threaded # Populate the variables. If `tkinter` is unavailable, the values are set to `None` or `False`. ( tcl_dir, tcl_version, tk_version, tcl_threaded, ) = _get_tcl_tk_info()

2b2559af1c7790596e7b2040f48e56baef608f9d

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

def _get_tcl_tk_info(): try: import tkinter from _tkinter import TCL_VERSION, TK_VERSION except ImportError: # tkinter unavailable return None, None, None, False tcl = tkinter.Tcl() # Query the location of Tcl library/data directory. t

_get_tcl_tk_info

tests/sentry/utils/locking/backends/test_redis.py

ref(locks): Make the post_process locks backend configurable (#36328)

sentry

Python

test_redis.py

def test_cluster_as_str(self): assert RedisLockBackend(cluster="default").cluster == self.cluster

5cf12753665512f60b32a99dd8fd9aa27d0a4a3a

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

def test_cluster_as_str(self): assert RedisLockBackend(cluster="defau

test_cluster_as_str

datasets/crd3/crd3.py

Fix yield for crd3 (#4240) * yielding example per chunk id * changing data type for turns * removing unused variable * Update crd3.py Co-authored-by: Shanya Sharma - s0s0cr3 <Shanya.Sharma@walmartlabs.com>

datasets

Python

crd3.py

def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "chunk": datasets.Value("string"), "chunk_id": datasets.Value("int32"), "turn_start": datasets.Value("int32"), "turn_end": datasets.Value("int32"), "alignment_score": datasets.Value("float32"), "turns": [ { "names": datasets.features.Sequence(datasets.Value("string")), "utterances": datasets.features.Sequence(datasets.Value("string")), "number": datasets.Value("int32"), } ], } ), homepage="https://github.com/RevanthRameshkumar/CRD3", citation=_CITATION, )

23efe55f5547c640f9efdcb2bc678fb7b76e663e

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

def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "chunk": datasets.Value("string"), "chunk_id": datasets.Value("int32"), "turn_start": datasets.Value("int32"), "turn_end": datasets.Value("int32"), "alignment_score": datasets.Value("float32"), "turns": [ { "names": datasets.features.Sequence(datas

_info