id
int64 20
338k
| vocab_size
int64 2
671
| ast_levels
int64 4
32
| nloc
int64 1
451
| n_ast_nodes
int64 12
5.6k
| n_identifiers
int64 1
186
| n_ast_errors
int64 0
10
| n_words
int64 2
2.17k
| n_whitespaces
int64 2
13.8k
| fun_name
stringlengths 2
73
| commit_message
stringlengths 51
15.3k
| url
stringlengths 31
59
| code
stringlengths 51
31k
| ast_errors
stringlengths 0
1.46k
| token_counts
int64 6
3.32k
| file_name
stringlengths 5
56
| language
stringclasses 1
value | path
stringlengths 7
134
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stringlengths 40
40
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stringlengths 3
28
| complexity
int64 1
153
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
310,816
| 33
| 16
| 7
| 76
| 8
| 0
| 36
| 93
|
set_myzone
|
Start depreciation of custom services in Advantage Air (#58777)
Co-authored-by: J. Nick Koston <nick@koston.org>
|
https://github.com/home-assistant/core.git
|
async def set_myzone(self, **kwargs):
_LOGGER.warning(
"The advantage_air.set_myzone service has been deprecated and will be removed in a future version, please use the select.select_option service on the MyZone entity"
)
await self.async_change(
{self.ac_key: {"info": {"myZone": self._zone["number"]}}}
)
| 41
|
climate.py
|
Python
|
homeassistant/components/advantage_air/climate.py
|
0cd3302ebc5951c9ecd00ab1e6cd9ae28173fab5
|
core
| 1
|
|
314,495
| 19
| 8
| 15
| 148
| 17
| 0
| 36
| 158
|
device_class
|
Adjust CoverEntity property type hints in components (#73943)
* Adjust CoverEntity property type hints in components
* Revert changes to rflink
* Revert changes to wilight
|
https://github.com/home-assistant/core.git
|
def device_class(self) -> CoverDeviceClass:
if isinstance(self.node, Awning):
return CoverDeviceClass.AWNING
if isinstance(self.node, Blind):
return CoverDeviceClass.BLIND
if isinstance(self.node, GarageDoor):
return CoverDeviceClass.GARAGE
if isinstance(self.node, Gate):
return CoverDeviceClass.GATE
if isinstance(self.node, RollerShutter):
return CoverDeviceClass.SHUTTER
if isinstance(self.node, Window):
return CoverDeviceClass.WINDOW
return CoverDeviceClass.WINDOW
| 96
|
cover.py
|
Python
|
homeassistant/components/velux/cover.py
|
10dc38e0ec27f7bef990ee431459342f9c3c52b4
|
core
| 7
|
|
127,340
| 21
| 12
| 13
| 92
| 13
| 0
| 27
| 107
|
get_object_refs_from_last_execute
|
[serve] Visualize Deployment Graph with Gradio (#27897)
|
https://github.com/ray-project/ray.git
|
async def get_object_refs_from_last_execute(self) -> Dict[str, Any]:
cache = {}
for node_uuid, value in self.cache_from_last_execute.items():
if isinstance(value, asyncio.Task):
cache[node_uuid] = await value
else:
cache[node_uuid] = value
return cache
| 57
|
dag_node.py
|
Python
|
python/ray/dag/dag_node.py
|
4c970cc88247f7cfa7351297b8b5050f2372742e
|
ray
| 3
|
|
101,280
| 55
| 15
| 20
| 269
| 28
| 0
| 81
| 258
|
read_image_batch
|
Data Augmentation update (#1263)
- lib.detected_face
- Subclass Masks for Landmark based masks
- Add training mask propery + methods to DetectedFace
- lib.training_training
- subclass TrainingDataGenerator for training and preview data
- Split cache into own module
- Reduce thread count to 1 to prevent image corruption + data re-use
- Process on largest model input/output size rather than stored image size
- Size and crop masks during caching stage
- Implement ring buffer for data flow
- Fix preview reload bug
- augmentation
- typing
- switch color aug order
- better initialization
- Fix warp + landmark warp to correctly apply at different image scales
- Slightly improved warp caching
- Don't store whether image is_preview. Handle all data as training images implicitly
- plugins.trainer: Typing and fixes to work with trainingdata refactor
|
https://github.com/deepfakes/faceswap.git
|
def read_image_batch(filenames, with_metadata=False):
logger.trace("Requested batch: '%s'", filenames)
batch = [None for _ in range(len(filenames))]
if with_metadata:
meta = [None for _ in range(len(filenames))]
with futures.ThreadPoolExecutor() as executor:
images = {executor.submit(read_image, filename,
raise_error=True, with_metadata=with_metadata): idx
for idx, filename in enumerate(filenames)}
for future in futures.as_completed(images):
ret_idx = images[future]
if with_metadata:
batch[ret_idx], meta[ret_idx] = future.result()
else:
batch[ret_idx] = future.result()
batch = np.array(batch)
retval = (batch, meta) if with_metadata else batch
logger.trace("Returning images: (filenames: %s, batch shape: %s, with_metadata: %s)",
filenames, batch.shape, with_metadata)
return retval
| 172
|
image.py
|
Python
|
lib/image.py
|
2beceffad9b15c1fd78f06b9b272563321c5a41e
|
faceswap
| 8
|
|
153,457
| 23
| 16
| 9
| 96
| 10
| 0
| 28
| 135
|
_dialect_is_microsoft_sql
|
FEAT-#979: Enable reading from SQL server. (#4279)
Co-authored-by: eavidan <eran.avidan@intel.com>
Co-authored-by: Devin Petersohn <devin-petersohn@users.noreply.github.com>
Signed-off-by: mvashishtha <mahesh@ponder.io>
|
https://github.com/modin-project/modin.git
|
def _dialect_is_microsoft_sql(self):
if self._dialect_is_microsoft_sql_cache is None:
self._dialect_is_microsoft_sql_cache = False
if self.lib == _SQLALCHEMY_LIB_NAME:
from sqlalchemy import create_engine
self._dialect_is_microsoft_sql_cache = create_engine(
*self.args, **self.kwargs
).driver in ("pymssql", "pyodbc")
return self._dialect_is_microsoft_sql_cache
| 57
|
db_conn.py
|
Python
|
modin/db_conn.py
|
2d40797b2b700d81d4db4a4cd023d563edf6431f
|
modin
| 3
|
|
47,565
| 32
| 11
| 17
| 235
| 15
| 0
| 66
| 209
|
test_next_dagrun_after_auto_align
|
Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator`
|
https://github.com/apache/airflow.git
|
def test_next_dagrun_after_auto_align(self):
dag = DAG(
dag_id='test_scheduler_auto_align_1',
start_date=timezone.datetime(2016, 1, 1, 10, 10, 0),
schedule_interval="4 5 * * *",
)
EmptyOperator(task_id='dummy', dag=dag, owner='airflow')
next_info = dag.next_dagrun_info(None)
assert next_info and next_info.logical_date == timezone.datetime(2016, 1, 2, 5, 4)
dag = DAG(
dag_id='test_scheduler_auto_align_2',
start_date=timezone.datetime(2016, 1, 1, 10, 10, 0),
schedule_interval="10 10 * * *",
)
EmptyOperator(task_id='dummy', dag=dag, owner='airflow')
next_info = dag.next_dagrun_info(None)
assert next_info and next_info.logical_date == timezone.datetime(2016, 1, 1, 10, 10)
| 156
|
test_dag.py
|
Python
|
tests/models/test_dag.py
|
49e336ae0302b386a2f47269a6d13988382d975f
|
airflow
| 3
|
|
3,401
| 15
| 8
| 4
| 50
| 9
| 0
| 16
| 28
|
test_stream_unsupported_by_bulk
|
Source Salesforce: Deprecate API Type parameter (#9302)
* use BULK for the first sync, REST for incremental sync
* if stream contains compound data or/and base64 use always REST
* fix get stream state from connector state
* fix integration test
* refactor catalog name
* format code
* refactor unit tests
* refactor unit tests 2
* format code 2
* Set additionalProperties to true not to break test temporarily
* fix unit test and remove unnecessary filtering fields
* bump version
* updated spec and def yaml
Co-authored-by: auganbay <auganenu@gmail.com>
|
https://github.com/airbytehq/airbyte.git
|
def test_stream_unsupported_by_bulk(stream_config, stream_api, caplog):
stream_name = "AcceptedEventRelation"
stream = _generate_stream(stream_name, stream_config, stream_api)
assert not isinstance(stream, BulkSalesforceStream)
| 31
|
unit_test.py
|
Python
|
airbyte-integrations/connectors/source-salesforce/unit_tests/unit_test.py
|
0a3713a5a52995dc0dc205d8edfd097bf625899f
|
airbyte
| 1
|
|
40,284
| 105
| 21
| 30
| 400
| 39
| 0
| 152
| 739
|
comp_data
|
Avoid stall/crash on duplicate index in comp_data (#2776)
* Avoid stall/crash on duplicate index in comp_data
* Don't fail on empty data; update release notes
|
https://github.com/mwaskom/seaborn.git
|
def comp_data(self):
if not hasattr(self, "ax"):
# Probably a good idea, but will need a bunch of tests updated
# Most of these tests should just use the external interface
# Then this can be re-enabled.
# raise AttributeError("No Axes attached to plotter")
return self.plot_data
if not hasattr(self, "_comp_data"):
comp_data = (
self.plot_data
.copy(deep=False)
.drop(["x", "y"], axis=1, errors="ignore")
)
for var in "yx":
if var not in self.variables:
continue
parts = []
grouped = self.plot_data[var].groupby(self.converters[var], sort=False)
for converter, orig in grouped:
with pd.option_context('mode.use_inf_as_null', True):
orig = orig.dropna()
if var in self.var_levels:
# TODO this should happen in some centralized location
# it is similar to GH2419, but more complicated because
# supporting `order` in categorical plots is tricky
orig = orig[orig.isin(self.var_levels[var])]
comp = pd.to_numeric(converter.convert_units(orig))
if converter.get_scale() == "log":
comp = np.log10(comp)
parts.append(pd.Series(comp, orig.index, name=orig.name))
if parts:
comp_col = pd.concat(parts)
else:
comp_col = pd.Series(dtype=float, name=var)
comp_data.insert(0, var, comp_col)
self._comp_data = comp_data
return self._comp_data
| 243
|
_core.py
|
Python
|
seaborn/_core.py
|
8605afd2ab1c6474c50ba90e478f4dc2decc8c7a
|
seaborn
| 9
|
|
272,692
| 5
| 9
| 2
| 32
| 4
| 0
| 5
| 11
|
minimum
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def minimum(inputs, **kwargs):
return Minimum(**kwargs)(inputs)
| 18
|
minimum.py
|
Python
|
keras/layers/merging/minimum.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 1
|
|
160,868
| 32
| 14
| 9
| 189
| 21
| 0
| 39
| 172
|
trace
|
ENH: Adding __array_ufunc__ capability to MaskedArrays.
This enables any ufunc numpy operations that are called on a
MaskedArray to use the masked version of that function automatically
without needing to resort to np.ma.func() calls.
|
https://github.com/numpy/numpy.git
|
def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
#!!!: implement out + test!
m = self._mask
if m is nomask:
result = self.view(np.ndarray).trace(offset=offset, axis1=axis1,
axis2=axis2, out=out)
return result.astype(dtype)
else:
D = self.diagonal(offset=offset, axis1=axis1, axis2=axis2)
return D.astype(dtype).filled(0).sum(axis=-1, out=out)
trace.__doc__ = ndarray.trace.__doc__
| 116
|
core.py
|
Python
|
numpy/ma/core.py
|
6d77c591c59b5678f14ae5af2127eebb7d2415bc
|
numpy
| 2
|
|
25,708
| 12
| 10
| 4
| 44
| 4
| 0
| 13
| 29
|
get_duplicates_items
|
Feature/gift cards post mvp (#7977)
* Add giftCardBulkCreate mutation
* Extend OrderFilter with giftCardUsed and giftCardBought fields
* Allow exporting gift cards
* Update the name of the email template for export
* Add exportGiftCards muttaion
* Add used gift card filter
* Export only unused gift cards
* Block mutations for expired gift cards (#8115)
* Block mutations for expired gift cards
* Block only resending and activating expired gift cards
* Add celery schedule task for deactivate expired cards (#8100)
* Add gift card section to invoice (#8148)
* Add filtering on gift card events (#8090)
* Add filtering on gift card events
* Filter gift card events by orders instead of order_id
* Update populatedb with gift card data (#8016)
* Generate gift cards with events in populate db
* Set product types kinds and add placeholder for gift card product
* Add dedicated gift card product images
* Change order of order emails (#8168)
* Drop duplicated kind field from producttype in populatedb (#8224)
* Change gift card display_code field to last_4 (#8445)
* Change gift card display_code field to last_4
* Change last4 to last4CodeChars
* Fix github test env action configuration
* Drop filtering gift cards by tag
* Fix export gift card tags test
* Re-add gift card tags query (#8412)
* Update populatedb with gift card data (#8016)
* Generate gift cards with events in populate db
* Set product types kinds and add placeholder for gift card product
* Add dedicated gift card product images
* Add giftCardTags model
* Add giftCardTags query
Co-authored-by: Iga Karbowiak <40886528+IKarbowiak@users.noreply.github.com>
Co-authored-by: IKarbowiak <iga.karbowiak@mirumee.com>
* Do not create EXPIRY_DATE_UPDATED gift card event when expiry date is not changed (#8882)
Co-authored-by: Marcin Gębala <5421321+maarcingebala@users.noreply.github.com>
|
https://github.com/saleor/saleor.git
|
def get_duplicates_items(first_list, second_list):
if first_list and second_list:
return set(first_list) & set(second_list)
return []
| 26
|
__init__.py
|
Python
|
saleor/graphql/core/utils/__init__.py
|
f5a45de4a22fecacfcd5b2cd18c07e5cf95ce27c
|
saleor
| 3
|
|
130,222
| 43
| 12
| 13
| 128
| 11
| 0
| 49
| 120
|
recover_args
|
[CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes.
|
https://github.com/ray-project/ray.git
|
def recover_args(flattened_args):
assert (
len(flattened_args) % 2 == 0
), "Flattened arguments need to be even-numbered. See `flatten_args`."
args = []
kwargs = {}
for name_index in range(0, len(flattened_args), 2):
name, arg = flattened_args[name_index], flattened_args[name_index + 1]
if name == DUMMY_TYPE:
args.append(arg)
else:
kwargs[name] = arg
return args, kwargs
| 80
|
signature.py
|
Python
|
python/ray/_private/signature.py
|
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
|
ray
| 3
|
|
241,561
| 20
| 10
| 7
| 157
| 23
| 1
| 21
| 41
|
test_ddp_spawn_configure_ddp
|
Rename training plugin test files & names to strategy (#11303)
|
https://github.com/Lightning-AI/lightning.git
|
def test_ddp_spawn_configure_ddp(tmpdir):
trainer = Trainer(default_root_dir=tmpdir, num_processes=2, strategy="ddp_spawn", fast_dev_run=True)
model = BoringModelDDP()
trainer.fit(model)
trainer.validate(model, dataloaders=model.val_dataloader())
trainer.test(model, dataloaders=model.test_dataloader())
trainer.predict(model, dataloaders=model.predict_dataloader())
@pytest.mark.parametrize("trainer_fn", [TrainerFn.FITTING, "other"])
|
@pytest.mark.parametrize("trainer_fn", [TrainerFn.FITTING, "other"])
| 79
|
test_ddp_spawn_strategy.py
|
Python
|
tests/strategies/test_ddp_spawn_strategy.py
|
650c710efacd633fa283955145342bb64063c883
|
lightning
| 1
|
59,215
| 16
| 9
| 7
| 77
| 7
| 0
| 29
| 50
|
test_result_literal_null_is_distinguishable_from_none
|
Implement `prefect.result` types and utilities (#6973)
|
https://github.com/PrefectHQ/prefect.git
|
async def test_result_literal_null_is_distinguishable_from_none():
result = await ResultLiteral.create(None)
assert result is not None
serialized = result.json()
assert serialized is not None
assert serialized != "null"
assert json.loads(serialized) is not None
| 45
|
test_result_literal.py
|
Python
|
tests/results/test_result_literal.py
|
a0c1df21a43c5045c8306d6f8ef912155d9a86f4
|
prefect
| 1
|
|
144,152
| 24
| 11
| 17
| 113
| 18
| 0
| 27
| 166
|
_replace_alarm_config_variables
|
[autoscaler] Add AWS Autoscaler CloudWatch Alarm support (#21523)
These changes add a set of improvements to enable automatic creation and update of CloudWatch alarms when provisioning AWS Autoscaling clusters. Successful implementation of these improvements will allow AWS Autoscaler users to:
Setup alarms against Ray CloudWatch metrics to get notified about increased load, service outage.
Update their CloudWatch alarm JSON configuration files during Ray up execution time.
Notes:
This PR is a follow-up PR for #20266, which adds CloudWatch alarm support.
|
https://github.com/ray-project/ray.git
|
def _replace_alarm_config_variables(self) -> List[Dict[str, Any]]:
data = self._load_config_file("alarm")
param_data = []
for item in data:
item_out = copy.deepcopy(item)
self._replace_all_config_variables(
item_out,
self.node_id,
self.cluster_name,
self.provider_config["region"],
)
param_data.append(item_out)
return param_data
| 71
|
cloudwatch_helper.py
|
Python
|
python/ray/autoscaler/_private/aws/cloudwatch/cloudwatch_helper.py
|
8237c6228fe884f463ae68fbb744f98decad6a85
|
ray
| 2
|
|
120,519
| 204
| 18
| 5
| 748
| 71
| 0
| 309
| 610
|
convert_scatter_jax_to_tf
|
feat: support subset of scatters with no XLA
Supports:
- Scatters with unique indices e.g..at[:, ((3,4),(5,5)), 3].add
- Contiguous scatters with non-unique indices of single depth e.g..at[:, ((2,2,2),)].add
|
https://github.com/google/jax.git
|
def convert_scatter_jax_to_tf(update_op, unsorted_segment_op=None):
def error(msg):
suffix = ("See source code for the precise conditions under which "
"scatter_(update/add/multiply/min/max) ops can be converted without XLA.")
return _xla_disabled_error("scatter_(update/add/multiply/min/max)", f"{msg} - {suffix}")
def _sparse_scatter(
operand,
scatter_indices,
updates,
update_jaxpr,
update_consts,
dimension_numbers,
indices_are_sorted: bool,
unique_indices: bool,
mode,
_in_avals: Sequence[core.ShapedArray],
_out_aval: core.ShapedArray):
# Infer unique indices from lack of batch dimension
unique_indices = unique_indices or (len(scatter_indices.shape) == 1)
if unique_indices:
suboperand = tf.gather_nd(operand, scatter_indices)
updated_suboperand = update_op(suboperand, updates)
# add a batch dim if none exist
if len(scatter_indices.shape) == 1:
scatter_indices = scatter_indices[None]
updated_suboperand = updated_suboperand[None]
y = tf.tensor_scatter_nd_update(operand, scatter_indices, updated_suboperand)
else:
if (scatter_indices.shape[-1] == 1) and (unsorted_segment_op != None):
# If only indexing into the first dimension, it's a segment op
operand_update = unsorted_segment_op(updates, tf.squeeze(scatter_indices, -1), operand.shape[0])
y = update_op(operand, operand_update)
else:
raise error("Scatter supports unique indices. Scatter also supports non-unique indices with indexing into only one dimension for (add, mul, min, max)")
return y
def sparse_scatter(
operand,
scatter_indices,
updates,
update_jaxpr,
update_consts,
dimension_numbers,
indices_are_sorted: bool,
unique_indices: bool,
mode,
_in_avals: Sequence[core.ShapedArray],
_out_aval: core.ShapedArray):
ud = dimension_numbers.update_window_dims
wd = dimension_numbers.inserted_window_dims
sd = dimension_numbers.scatter_dims_to_operand_dims
dtype = operand.dtype # assume updates has same dtype as operand
if dtype in [tf.bool, tf.complex64]:
raise error(f"Scatter does not support operands of type {dtype}")
if not (wd == sd):
raise error("Complex scatters are not supported")
if not (mode == lax.GatherScatterMode.PROMISE_IN_BOUNDS):
raise error("Only scatter mode `PROMISE_IN_BOUNDS` is supported")
# Shift axes to the front to match tf syntax, inverse afterwards
fwd = partial(shift_axes_forward, axes=sd)
inv = partial(fwd, inverse=True)
# shift update value axes to the back, so batch are at the front
updates_shifted = shift_axes_forward(updates, axes=ud, forward=False)
return inv(_sparse_scatter(
fwd(operand),
scatter_indices,
updates_shifted,
update_jaxpr,
update_consts,
dimension_numbers,
indices_are_sorted,
unique_indices,
mode,
_in_avals,
_out_aval,
))
return sparse_scatter
tf_impl_no_xla[lax.scatter_p] = convert_scatter_jax_to_tf(lambda x,y: y) # just replace with the update
tf_impl_no_xla[lax.scatter_add_p] = convert_scatter_jax_to_tf(tf.add, tf.math.unsorted_segment_sum)
tf_impl_no_xla[lax.scatter_mul_p] = convert_scatter_jax_to_tf(tf.multiply, tf.math.unsorted_segment_prod)
tf_impl_no_xla[lax.scatter_min_p] = convert_scatter_jax_to_tf(tf.minimum, tf.math.unsorted_segment_min)
tf_impl_no_xla[lax.scatter_max_p] = convert_scatter_jax_to_tf(tf.maximum, tf.math.unsorted_segment_max)
tf_impl_no_xla[lax.sort_p] = _unimplemented("sort")
| 17
|
impl_no_xla.py
|
Python
|
jax/experimental/jax2tf/impl_no_xla.py
|
17f8638fd451e46fc387ef6f32a8db13b2a92e5a
|
jax
| 1
|
|
43,685
| 11
| 7
| 2
| 61
| 10
| 0
| 12
| 31
|
node_id
|
Map and Partial DAG authoring interface for Dynamic Task Mapping (#19965)
* Make DAGNode a proper Abstract Base Class
* Prevent mapping an already mapped Task/TaskGroup
Also prevent calls like .partial(...).partial(...). It is uncertain
whether these kinds of repeated partial/map calls have utility, so let's
disable them entirely for now to simplify implementation. We can always
add them if they are proven useful.
Co-authored-by: Tzu-ping Chung <tp@astronomer.io>
|
https://github.com/apache/airflow.git
|
def node_id(self) -> str:
raise NotImplementedError()
task_group: Optional["TaskGroup"]
start_date: Optional[pendulum.DateTime]
end_date: Optional[pendulum.DateTime]
| 11
|
taskmixin.py
|
Python
|
airflow/models/taskmixin.py
|
e9226139c2727a4754d734f19ec625c4d23028b3
|
airflow
| 1
|
|
111,419
| 9
| 12
| 5
| 80
| 11
| 0
| 9
| 28
|
test_json_to_doc_attribute_consistency
|
Add Doc.from_json() (#10688)
* Implement Doc.from_json: rough draft.
* Implement Doc.from_json: first draft with tests.
* Implement Doc.from_json: added documentation on website for Doc.to_json(), Doc.from_json().
* Implement Doc.from_json: formatting changes.
* Implement Doc.to_json(): reverting unrelated formatting changes.
* Implement Doc.to_json(): fixing entity and span conversion. Moving fixture and doc <-> json conversion tests into single file.
* Implement Doc.from_json(): replaced entity/span converters with doc.char_span() calls.
* Implement Doc.from_json(): handling sentence boundaries in spans.
* Implementing Doc.from_json(): added parser-free sentence boundaries transfer.
* Implementing Doc.from_json(): added parser-free sentence boundaries transfer.
* Implementing Doc.from_json(): incorporated various PR feedback.
* Renaming fixture for document without dependencies.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implementing Doc.from_json(): using two sent_starts instead of one.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implementing Doc.from_json(): doc_without_dependency_parser() -> doc_without_deps.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implementing Doc.from_json(): incorporating various PR feedback. Rebased on latest master.
* Implementing Doc.from_json(): refactored Doc.from_json() to work with annotation IDs instead of their string representations.
* Implement Doc.from_json(): reverting unwanted formatting/rebasing changes.
* Implement Doc.from_json(): added check for char_span() calculation for entities.
* Update spacy/tokens/doc.pyx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): minor refactoring, additional check for token attribute consistency with corresponding test.
* Implement Doc.from_json(): removed redundancy in annotation type key naming.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): Simplifying setting annotation values.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement doc.from_json(): renaming annot_types to token_attrs.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): adjustments for renaming of annot_types to token_attrs.
* Implement Doc.from_json(): removing default categories.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): simplifying lexeme initialization.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): simplifying lexeme initialization.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): refactoring to only have keys for present annotations.
* Implement Doc.from_json(): fix check for tokens' HEAD attributes.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): refactoring Doc.from_json().
* Implement Doc.from_json(): fixing span_group retrieval.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): fixing span retrieval.
* Implement Doc.from_json(): added schema for Doc JSON format. Minor refactoring in Doc.from_json().
* Implement Doc.from_json(): added comment regarding Token and Span extension support.
* Implement Doc.from_json(): renaming inconsistent_props to partial_attrs..
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): adjusting error message.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): extending E1038 message.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): added params to E1038 raises.
* Implement Doc.from_json(): combined attribute collection with partial attributes check.
* Implement Doc.from_json(): added optional schema validation.
* Implement Doc.from_json(): fixed optional fields in schema, tests.
* Implement Doc.from_json(): removed redundant None check for DEP.
* Implement Doc.from_json(): added passing of schema validatoin message to E1037..
* Implement Doc.from_json(): removing redundant error E1040.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): changing message for E1037.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): adjusted website docs and docstring of Doc.from_json().
* Update spacy/tests/doc/test_json_doc_conversion.py
* Implement Doc.from_json(): docstring update.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): docstring update.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): website docs update.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): docstring formatting.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): docstring formatting.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): fixing Doc reference in website docs.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): reformatted website/docs/api/doc.md.
* Implement Doc.from_json(): bumped IDs of new errors to avoid merge conflicts.
* Implement Doc.from_json(): fixing bug in tests.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Implement Doc.from_json(): fix setting of sentence starts for docs without DEP.
* Implement Doc.from_json(): add check for valid char spans when manually setting sentence boundaries. Refactor sentence boundary setting slightly. Move error message for lack of support for partial token annotations to errors.py.
* Implement Doc.from_json(): simplify token sentence start manipulation.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Combine related error messages
* Update spacy/tests/doc/test_json_doc_conversion.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
|
https://github.com/explosion/spaCy.git
|
def test_json_to_doc_attribute_consistency(doc):
doc_json = doc.to_json()
doc_json["tokens"][1].pop("morph")
with pytest.raises(ValueError):
Doc(doc.vocab).from_json(doc_json)
| 44
|
test_json_doc_conversion.py
|
Python
|
spacy/tests/doc/test_json_doc_conversion.py
|
8387ce4c01db48d92ac5638e18316c0f1fc8861e
|
spaCy
| 1
|
|
108,031
| 43
| 13
| 12
| 159
| 13
| 0
| 59
| 187
|
_get_font_family_and_reduced
|
Move towards making texmanager stateless.
Previously, TexManager needed to call get_font_config at a specific
place in the middle of processing to update some internal attributes
before proceeding with TeX source generation. Instead, move towards
making TexManager stateless (except for caching), i.e. the user facing
API should be thought of as a bunch of independently callable functions
`make_tex()`, `make_dvi()`, etc. (they will probably stay as methods on
a "empty" TexManager object for a long time for backcompat, in fact).
|
https://github.com/matplotlib/matplotlib.git
|
def _get_font_family_and_reduced(cls):
ff = rcParams['font.family']
ff_val = ff[0].lower() if len(ff) == 1 else None
if len(ff) == 1 and ff_val in cls._font_families:
return ff_val, False
elif len(ff) == 1 and ff_val in cls._font_preambles:
return cls._font_types[ff_val], True
else:
_log.info('font.family must be one of (%s) when text.usetex is '
'True. serif will be used by default.',
', '.join(cls._font_families))
return 'serif', False
| 94
|
texmanager.py
|
Python
|
lib/matplotlib/texmanager.py
|
13147992b317c29c6e832ca7f6d05bf48aeb0718
|
matplotlib
| 6
|
|
293,342
| 9
| 9
| 3
| 45
| 8
| 0
| 9
| 23
|
toggle
|
Add switch platform to bosch_shc integration (#62315)
* Add support for switch platform.
* Add untested files to .coveragerc.
* Differ between Light Switch and Smart Plug.
Bumped to boschshcpy==0.2.27
* Removed duplicated code.
Fixed suggestions from code review.
* Fixed pylint errors
* Fix pylint issue.
* Add property statement
* Fixed wrong attribute access
* Apply suggestions from code review
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* Move switch function to base class.
Changes from code review.
* Apply suggestions from code review
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* Merged camera switch into SHCSwitch class
* Type hint
* Removed deprecated sensor entities in switch device.
Added routing switch entity.
* Apply suggestions from code review
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
* Icon and EntityCategory as class attributes
Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>
|
https://github.com/home-assistant/core.git
|
def toggle(self, **kwargs) -> None:
setattr(self._device, self.entity_description.on_key, not self.is_on)
| 28
|
switch.py
|
Python
|
homeassistant/components/bosch_shc/switch.py
|
0b7b1baf30182fd12e55ee9fe66fa3788fee71ba
|
core
| 1
|
|
338,161
| 86
| 14
| 31
| 550
| 43
| 0
| 146
| 349
|
load_accelerator_state
|
Megatron-LM integration (#667)
* Megatron-LM integration
* add code and resolve comment
Co-Authored-By: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add code
* add code
* fix many :bug:
* add code
* add code and reverting tracker processes
* updating logging utilities, fixing Pipeline Parallelism and dataset/dataloader :bug: s
1. Fixing bugs related to Pipeline Parallelism
2. Fixing bugs related to dataloaders/datasets.
3. Fixing logging utilities so that all logging and tracking happens on last process when using Megatron.
* addressing comments
* resolving comments
* update code
* refactoring and adding code to support custom implementation of`AbstractTrainStep` class
* minor change
* Many fixes for supporting custom TrainStep and Megatron Indexed Datasets
* Add code, :bug: fixes and a initial doc file with headings
* fixing a big :bug: related to loading checkpoints
* adding doc and an example
* example test CI
* docs
* more docs
* more doc changes
* more doc changes
* docs
* more docs
* doc fixing
* trying if we can directly import megatronlm utils
* doc fixing and throwing error if megatron isn't available.
* resolving comments
* fixes to bert and t5 and more docs
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
|
https://github.com/huggingface/accelerate.git
|
def load_accelerator_state(input_dir, models, optimizers, schedulers, process_index, scaler=None):
# Model states
for i, model in enumerate(models):
weights_name = f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
input_model_file = os.path.join(input_dir, weights_name)
models[i].load_state_dict(torch.load(input_model_file, map_location="cpu"))
logger.info("All model weights loaded successfully")
# Optimizer states
for i, opt in enumerate(optimizers):
optimizer_name = f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
input_optimizer_file = os.path.join(input_dir, optimizer_name)
optimizers[i].load_state_dict(torch.load(input_optimizer_file, map_location="cpu"))
logger.info("All optimizer states loaded successfully")
# Scheduler states
for i, scheduler in enumerate(schedulers):
scheduler_name = f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
input_scheduler_file = os.path.join(input_dir, scheduler_name)
scheduler.load_state_dict(torch.load(input_scheduler_file))
logger.info("All scheduler states loaded successfully")
# GradScaler state
if scaler is not None:
input_scaler_file = os.path.join(input_dir, SCALER_NAME)
scaler.load_state_dict(torch.load(input_scaler_file))
logger.info("GradScaler state loaded successfully")
# Random states
try:
states = torch.load(os.path.join(input_dir, f"{RNG_STATE_NAME}_{process_index}.pkl"))
random.setstate(states["random_state"])
np.random.set_state(states["numpy_random_seed"])
torch.set_rng_state(states["torch_manual_seed"])
torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"])
# ^^ safe to call this function even if cuda is not available
if is_tpu_available():
xm.set_rng_state(states["xm_seed"])
logger.info("All random states loaded successfully")
except:
logger.info("Could not load random states")
| 305
|
checkpointing.py
|
Python
|
src/accelerate/checkpointing.py
|
e3ebf176b85a57f1d9f082b2aeda22df21e2d395
|
accelerate
| 10
|
|
56,454
| 36
| 13
| 30
| 232
| 17
| 0
| 82
| 433
|
test_to_block_document_anonymous_errors
|
Improve anonymous name generation and check client anonymous blocks
|
https://github.com/PrefectHQ/prefect.git
|
def test_to_block_document_anonymous_errors(self, block_type_x):
# explicit false
with pytest.raises(
ValueError,
match="(No name provided, either as an argument or on the block)",
):
self.MyRegisteredBlock(x="x")._to_block_document(
block_schema_id=uuid4(),
block_type_id=block_type_x.id,
is_anonymous=False,
)
# none with no fallback
with pytest.raises(
ValueError,
match="(No name provided, either as an argument or on the block)",
):
self.MyRegisteredBlock(x="x")._to_block_document(
block_schema_id=uuid4(),
block_type_id=block_type_x.id,
is_anonymous=None,
)
# none with False fallback
anon_block_4 = self.MyRegisteredBlock(x="x")
anon_block_4._is_anonymous = False
with pytest.raises(
ValueError,
match="(No name provided, either as an argument or on the block)",
):
anon_block_4._to_block_document(
block_schema_id=uuid4(),
block_type_id=block_type_x.id,
is_anonymous=None,
)
| 139
|
test_core.py
|
Python
|
tests/blocks/test_core.py
|
539d90ed73c1332590d0345daf5e2b2ae4b49b92
|
prefect
| 1
|
|
21,650
| 64
| 18
| 36
| 327
| 36
| 0
| 86
| 514
|
from_requirement
|
Vendor in latest requirements lib and pip-shims in order to drop packaging and resolve differences in sourcing it.
|
https://github.com/pypa/pipenv.git
|
def from_requirement(cls, requirement, parent=None):
name = requirement.normalized_name
specifiers = requirement.ireq.specifier if not requirement.editable else ""
markers = requirement.ireq.markers
extras = requirement.ireq.extras
is_pinned = is_pinned_requirement(requirement.ireq)
is_constraint = bool(parent)
_, finder = get_finder(sources=None)
candidates = []
if not is_pinned and not requirement.editable:
for r in requirement.find_all_matches(finder=finder):
req = make_install_requirement(
name,
r.version,
extras=extras,
markers=markers,
constraint=is_constraint,
)
req.req.link = getattr(r, "location", getattr(r, "link", None))
req.parent = parent
candidates.append(req)
candidates = sorted(
set(candidates),
key=lambda k: parse(version_from_ireq(k)),
)
else:
candidates = [requirement.ireq]
return cls(
name=name,
specifiers=specifiers,
markers=markers,
candidates=candidates,
requirement=requirement,
parent=parent,
finder=finder,
)
| 213
|
dependencies.py
|
Python
|
pipenv/vendor/requirementslib/models/dependencies.py
|
8a4d2eb130fd173466310f59df607ea59bfc44a5
|
pipenv
| 5
|
|
101,186
| 50
| 14
| 21
| 316
| 37
| 0
| 62
| 397
|
estimate_blur
|
lib.detected_face.Mask
- Add source + target offset and coverage to set_sub_crop method
|
https://github.com/deepfakes/faceswap.git
|
def estimate_blur(cls, image, metadata=None):
if metadata is not None:
alignments = metadata["alignments"]
det_face = DetectedFace()
det_face.from_png_meta(alignments)
aln_face = AlignedFace(np.array(alignments["landmarks_xy"], dtype="float32"),
image=image,
centering="legacy",
size=256,
is_aligned=True)
mask = det_face.mask["components"]
mask.set_sub_crop(aln_face.pose.offset[mask.stored_centering],
aln_face.pose.offset["legacy"],
centering="legacy")
mask = cv2.resize(mask.mask, (256, 256), interpolation=cv2.INTER_CUBIC)[..., None]
image = np.minimum(aln_face.face, mask)
if image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur_map = cv2.Laplacian(image, cv2.CV_32F)
score = np.var(blur_map) / np.sqrt(image.shape[0] * image.shape[1])
return score
| 204
|
sort.py
|
Python
|
tools/sort/sort.py
|
32950897376b48e0f08b46385602e4df902cf49e
|
faceswap
| 3
|
|
336,897
| 13
| 13
| 4
| 66
| 10
| 0
| 14
| 46
|
enable_gradient_checkpointing
|
[UNet2DConditionModel] add gradient checkpointing (#461)
* add grad ckpt to downsample blocks
* make it work
* don't pass gradient_checkpointing to upsample block
* add tests for UNet2DConditionModel
* add test_gradient_checkpointing
* add gradient_checkpointing for up and down blocks
* add functions to enable and disable grad ckpt
* remove the forward argument
* better naming
* make supports_gradient_checkpointing private
|
https://github.com/huggingface/diffusers.git
|
def enable_gradient_checkpointing(self):
if not self._supports_gradient_checkpointing:
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
self.apply(partial(self._set_gradient_checkpointing, value=True))
| 33
|
modeling_utils.py
|
Python
|
src/diffusers/modeling_utils.py
|
e7120bae955949355e50a4c2adef8353790fda0d
|
diffusers
| 2
|
|
260,258
| 24
| 10
| 6
| 56
| 8
| 0
| 24
| 50
|
sqeuclidean_row_norms
|
MAINT Create private `_pairwise_distances_reductions` submodule (#23724)
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
|
https://github.com/scikit-learn/scikit-learn.git
|
def sqeuclidean_row_norms(X, num_threads):
if X.dtype == np.float64:
return _sqeuclidean_row_norms64(X, num_threads)
raise ValueError(
f"Only 64bit float datasets are supported at this time, got: X.dtype={X.dtype}."
)
| 30
|
_dispatcher.py
|
Python
|
sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py
|
325130f205f446fcdea333d40bd9197605e3bccc
|
scikit-learn
| 2
|
|
45,563
| 59
| 14
| 25
| 132
| 13
| 0
| 82
| 357
|
_validate_extra
|
Deprecate non-JSON conn.extra (#21816)
Connection extra field is generally assumed to be JSON but we don't actually require it. Here we deprecate non-JSON extra so that in 3.0 we can require it. Further, we require that it not just be any json but must also parse as dict, because a string value such as '"hi"' or '[1,2,3]' is json, but a very bad practice.
|
https://github.com/apache/airflow.git
|
def _validate_extra(extra, conn_id) -> None:
if extra is None:
return None
try:
extra_parsed = json.loads(extra)
if not isinstance(extra_parsed, dict):
warnings.warn(
"Encountered JSON value in `extra` which does not parse as a dictionary in "
f"connection {conn_id!r}. From Airflow 3.0, the `extra` field must contain a JSON "
"representation of a Python dict.",
DeprecationWarning,
stacklevel=3,
)
except json.JSONDecodeError:
warnings.warn(
f"Encountered non-JSON in `extra` field for connection {conn_id!r}. Support for "
"non-JSON `extra` will be removed in Airflow 3.0",
DeprecationWarning,
stacklevel=2,
)
return None
| 74
|
connection.py
|
Python
|
airflow/models/connection.py
|
3aebb21c523c0eea0d4a1518d502ff95fd98011b
|
airflow
| 4
|
|
249,314
| 23
| 10
| 14
| 115
| 13
| 0
| 24
| 117
|
test_missing_parameter
|
Use literals in place of `HTTPStatus` constants in tests (#13488)
* Use literals in place of `HTTPStatus` constants in tests
* newsfile
* code style
* code style
|
https://github.com/matrix-org/synapse.git
|
def test_missing_parameter(self) -> None:
channel = self.make_request(
"POST",
self.url,
access_token=self.admin_user_tok,
)
self.assertEqual(400, channel.code, msg=channel.json_body)
self.assertEqual(Codes.MISSING_PARAM, channel.json_body["errcode"])
self.assertEqual(
"Missing integer query parameter 'before_ts'", channel.json_body["error"]
)
| 71
|
test_media.py
|
Python
|
tests/rest/admin/test_media.py
|
2281427175e4c93a30c39607fb4ac23c2a1f399f
|
synapse
| 1
|
|
288,079
| 37
| 9
| 14
| 135
| 15
| 1
| 41
| 98
|
test_discovery_incl_nodeid
|
Move MQTT discovery hass.data globals to dataclass (#78706)
* Add MQTT discovery hass.data globals to dataclass
* isort
* Additional rework
* Add hass.data["mqtt_tags"] to dataclass
* Follow-up comment
* Corrections
|
https://github.com/home-assistant/core.git
|
async def test_discovery_incl_nodeid(hass, mqtt_mock_entry_no_yaml_config, caplog):
await mqtt_mock_entry_no_yaml_config()
async_fire_mqtt_message(
hass,
"homeassistant/binary_sensor/my_node_id/bla/config",
'{ "name": "Beer", "state_topic": "test-topic" }',
)
await hass.async_block_till_done()
state = hass.states.get("binary_sensor.beer")
assert state is not None
assert state.name == "Beer"
assert ("binary_sensor", "my_node_id bla") in hass.data[
"mqtt"
].discovery_already_discovered
@patch("homeassistant.components.mqtt.PLATFORMS", [Platform.BINARY_SENSOR])
|
@patch("homeassistant.components.mqtt.PLATFORMS", [Platform.BINARY_SENSOR])
| 65
|
test_discovery.py
|
Python
|
tests/components/mqtt/test_discovery.py
|
84b2c74746b694d217fe6d448a8dfff4bc2d7a9e
|
core
| 1
|
60,425
| 93
| 22
| 25
| 295
| 13
| 0
| 179
| 381
|
CleanseRawStrings
|
Balanced joint maximum mean discrepancy for deep transfer learning
|
https://github.com/jindongwang/transferlearning.git
|
def CleanseRawStrings(raw_lines):
delimiter = None
lines_without_raw_strings = []
for line in raw_lines:
if delimiter:
# Inside a raw string, look for the end
end = line.find(delimiter)
if end >= 0:
# Found the end of the string, match leading space for this
# line and resume copying the original lines, and also insert
# a "" on the last line.
leading_space = Match(r'^(\s*)\S', line)
line = leading_space.group(1) + '""' + line[end + len(delimiter):]
delimiter = None
else:
# Haven't found the end yet, append a blank line.
line = ''
else:
# Look for beginning of a raw string.
# See 2.14.15 [lex.string] for syntax.
matched = Match(r'^(.*)\b(?:R|u8R|uR|UR|LR)"([^\s\\()]*)\((.*)$', line)
if matched:
delimiter = ')' + matched.group(2) + '"'
end = matched.group(3).find(delimiter)
if end >= 0:
# Raw string ended on same line
line = (matched.group(1) + '""' +
matched.group(3)[end + len(delimiter):])
delimiter = None
else:
# Start of a multi-line raw string
line = matched.group(1) + '""'
lines_without_raw_strings.append(line)
# TODO(unknown): if delimiter is not None here, we might want to
# emit a warning for unterminated string.
return lines_without_raw_strings
| 167
|
cpp_lint.py
|
Python
|
code/deep/BJMMD/caffe/scripts/cpp_lint.py
|
cc4d0564756ca067516f71718a3d135996525909
|
transferlearning
| 6
|
|
135,997
| 30
| 12
| 17
| 105
| 18
| 0
| 31
| 128
|
_worker_health_check
|
[RLlib] Refactor `WorkerSet` on top of `FaultTolerantActorManager`. (#29938)
Signed-off-by: Jun Gong <jungong@anyscale.com>
|
https://github.com/ray-project/ray.git
|
def _worker_health_check(self) -> List[int]:
logger.info("Health checking all workers ...")
remote_results = self.__worker_manager.foreach_actor(
lambda w: w.sample_with_count(),
healthy_only=False,
)
return [
r.actor_id
for r in remote_results
if not r.ok and isinstance(r.get(), RayError)
]
| 65
|
worker_set.py
|
Python
|
rllib/evaluation/worker_set.py
|
e707ce4fb3717e3c05118c57f503dfbd03552ca9
|
ray
| 4
|
|
8,637
| 6
| 9
| 2
| 27
| 4
| 0
| 6
| 20
|
auto_window_size
|
Enable dataset window autosizing (#2721)
* set windowed shuffle for large datasets
* documentation
* update to automatic windowing flag
* address reviews
* address reviews
* update logging info and add auto_window flag passthrough
* update tests to use flag passthrough
* more descriptive test class name
* todo to add link to windowing docs
* local test handling for dask import
* handle RayDataset import in local tests
* bad type annotation
* bad type annotation
|
https://github.com/ludwig-ai/ludwig.git
|
def auto_window_size(self):
return int(self.object_store_size // 5)
| 13
|
test_ray.py
|
Python
|
tests/integration_tests/test_ray.py
|
0d19a48cff0958ed77926a0712cbdb6485d4034a
|
ludwig
| 1
|
|
38,306
| 85
| 13
| 28
| 176
| 23
| 0
| 130
| 382
|
test_diverse_beam_search
|
Black preview (#17217)
* Black preview
* Fixup too!
* Fix check copies
* Use the same version as the CI
* Bump black
|
https://github.com/huggingface/transformers.git
|
def test_diverse_beam_search(self):
article =
bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn").to(torch_device)
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
outputs = bart_model.generate(
input_ids,
num_beams=4,
num_return_sequences=2,
num_beam_groups=4,
diversity_penalty=2.0,
remove_invalid_values=True,
)
generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"The couple announced the birth of their son, Silas Randall Timberlake, in a statement. Silas was the"
" middle name of Timberlake's maternal grandfather Bill Bomar. Randall is the musician's own middle"
" name, as well as his father's first. It is the first baby for both of them.",
"Justin Timberlake and Jessica Biel have a son. The baby is named Silas Randall Timberlake. It is the"
" first child for both. The couple announced the pregnancy in January. The name Silas is the middle"
" name of Timberlake's maternal grandfather. It's also his own middle name.",
],
)
| 107
|
test_generation_utils.py
|
Python
|
tests/generation/test_generation_utils.py
|
afe5d42d8d1d80af911ed980c2936bfe887078f6
|
transformers
| 1
|
|
96,684
| 45
| 14
| 22
| 195
| 20
| 0
| 58
| 325
|
test_sessions_metrics_equal_num_keys
|
fix(metrics): Zero-fill response when there's no data [INGEST-941] (#32157)
When there isn't any metrics data, the `groups` of the response is empty.
However, the absence of data must be represented with an appropriate value.
For example, `count_unique(user)` must return `0` when there aren't any
users, instead of returning no data. The value representing the absence of
data is `0` for sums and counts, and `None` for everything else
(such as `p50`).
|
https://github.com/getsentry/sentry.git
|
def test_sessions_metrics_equal_num_keys(self):
empty_groupbyes = ["project", "release", "environment", "session.status"]
interval_days = "1d"
for groupby in empty_groupbyes:
with patch(
"sentry.api.endpoints.organization_sessions.release_health",
SessionsReleaseHealthBackend(),
):
sessions_data = result_sorted(self.get_sessions_data(groupby, interval_days))
with patch(
"sentry.release_health.metrics_sessions_v2.indexer.resolve", MockIndexer().resolve
), patch(
"sentry.api.endpoints.organization_sessions.release_health",
MetricsReleaseHealthBackend(),
):
metrics_data = result_sorted(self.get_sessions_data(groupby, interval_days))
errors = compare_results(
sessions=sessions_data,
metrics=metrics_data,
rollup=interval_days * 24 * 60 * 60, # days to seconds
)
assert len(errors) == 0
| 114
|
test_metrics_sessions_v2.py
|
Python
|
tests/sentry/release_health/test_metrics_sessions_v2.py
|
cfdb7fdc1fef7f8a364bbfef050cdcfc66c82371
|
sentry
| 2
|
|
128,542
| 44
| 16
| 20
| 374
| 21
| 0
| 75
| 271
|
test_recurrent_unroll_and_filter
|
[RLlib] Add torch models (#29043)
1. converted class attributes to setters
2. use override decorator
3. SimpleModel should not have any T dimension, it can confuse folks. So I removed it.
4. I merged all the unittests under one class and separated them by methods names. It will be easier to use -k filter to run pytests later if we don't allow repetative method names.
Signed-off-by: Kourosh Hakhamaneshi <kourosh@anyscale.com>
Signed-off-by: Steven Morad <smorad@anyscale.com>
|
https://github.com/ray-project/ray.git
|
def test_recurrent_unroll_and_filter(self):
inputs = TensorDict(
{
"in": torch.arange(B * T * 2).reshape(B, T, 2),
"bork": torch.arange(5 * 4).reshape(5, 4),
}
)
states = TensorDict(
{
"in": torch.arange(B * 4).reshape(B, 4),
"bork": torch.arange(5 * 4).reshape(5, 4),
}
)
outputs, out_states = SimpleRecurrentModel(ModelConfig()).unroll(inputs, states)
desired = TensorDict({"out": torch.arange(B * T * 3).reshape(B, T, 3)})
desired_states = TensorDict({"out": torch.arange(B * 5).reshape(B, 5)})
for k in outputs.flatten().keys() | desired.flatten().keys():
check(outputs[k], desired[k])
for k in out_states.flatten().keys() | desired_states.flatten().keys():
check(out_states[k], desired_states[k])
| 235
|
test_torch_model.py
|
Python
|
rllib/models/tests/test_torch_model.py
|
b0945548e874642287b81514b71432a2330de1d3
|
ray
| 3
|
|
299,250
| 17
| 10
| 6
| 63
| 7
| 0
| 20
| 66
|
_async_validate_input
|
Sabnzbd config flow (#68138)
Co-authored-by: Paulus Schoutsen <paulus@home-assistant.io>
|
https://github.com/home-assistant/core.git
|
async def _async_validate_input(self, user_input):
errors = {}
sab_api = await get_client(self.hass, user_input)
if not sab_api:
errors["base"] = "cannot_connect"
return errors
| 35
|
config_flow.py
|
Python
|
homeassistant/components/sabnzbd/config_flow.py
|
3f5027834b2334eaf5eaab7884376b3cfb089a63
|
core
| 2
|
|
100,516
| 33
| 14
| 17
| 247
| 24
| 0
| 39
| 396
|
_crop_source_faces
|
bugfix: Preview Tool, ensure all config items are written
|
https://github.com/deepfakes/faceswap.git
|
def _crop_source_faces(self):
logger.debug("Updating source faces")
self._faces = {}
for image in self.source:
detected_face = image["detected_faces"][0]
src_img = image["image"]
detected_face.load_aligned(src_img, size=self._size, centering=self._centering)
matrix = detected_face.aligned.matrix
self._faces.setdefault("filenames",
[]).append(os.path.splitext(image["filename"])[0])
self._faces.setdefault("matrix", []).append(matrix)
self._faces.setdefault("src", []).append(transform_image(src_img,
matrix,
self._size,
self._padding))
self.update_source = False
logger.debug("Updated source faces")
| 150
|
preview.py
|
Python
|
tools/preview/preview.py
|
71c20252c2e747f692289cdefe80ad0d5a456ea6
|
faceswap
| 2
|
|
258,754
| 65
| 12
| 65
| 357
| 47
| 0
| 92
| 380
|
fit
|
DOC Clarify splitting behavior of `fit_params` (#22379)
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Tom Augspurger <tom.w.augspurger@gmail.com>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
|
https://github.com/scikit-learn/scikit-learn.git
|
def fit(self, X, y=None, *, groups=None, **fit_params):
estimator = self.estimator
refit_metric = "score"
if callable(self.scoring):
scorers = self.scoring
elif self.scoring is None or isinstance(self.scoring, str):
scorers = check_scoring(self.estimator, self.scoring)
else:
scorers = _check_multimetric_scoring(self.estimator, self.scoring)
self._check_refit_for_multimetric(scorers)
refit_metric = self.refit
X, y, groups = indexable(X, y, groups)
fit_params = _check_fit_params(X, fit_params)
cv_orig = check_cv(self.cv, y, classifier=is_classifier(estimator))
n_splits = cv_orig.get_n_splits(X, y, groups)
base_estimator = clone(self.estimator)
parallel = Parallel(n_jobs=self.n_jobs, pre_dispatch=self.pre_dispatch)
fit_and_score_kwargs = dict(
scorer=scorers,
fit_params=fit_params,
return_train_score=self.return_train_score,
return_n_test_samples=True,
return_times=True,
return_parameters=False,
error_score=self.error_score,
verbose=self.verbose,
)
results = {}
with parallel:
all_candidate_params = []
all_out = []
all_more_results = defaultdict(list)
| 459
|
_search.py
|
Python
|
sklearn/model_selection/_search.py
|
bdb7db524a5cb584d975b3bb1823494bcd5eb92f
|
scikit-learn
| 12
|
|
37,626
| 33
| 10
| 5
| 119
| 12
| 0
| 39
| 53
|
corners_to_center_format
|
Add YOLOS (#16848)
* First draft
* Add YolosForObjectDetection
* Make forward pass work
* Add mid position embeddings
* Add interpolation of position encodings
* Add expected values
* Add YOLOS to tests
* Add integration test
* Support tiny model as well
* Support all models in conversion script
* Remove mid_pe_size attribute
* Make more tests pass
* Add model to README and fix config
* Add copied from statements
* Rename base_model_prefix to vit
* Add missing YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP
* Apply suggestions from code review
* Apply more suggestions from code review
* Convert remaining checkpoints
* Improve docstrings
* Add YolosFeatureExtractor
* Add feature extractor to docs
* Add corresponding tests
* Fix style
* Fix docs
* Apply suggestion from code review
* Fix bad rebase
* Fix some more bad rebase
* Fix missing character
* Improve docs and variable names
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
|
https://github.com/huggingface/transformers.git
|
def corners_to_center_format(x):
x_transposed = x.T
x0, y0, x1, y1 = x_transposed[0], x_transposed[1], x_transposed[2], x_transposed[3]
b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
return np.stack(b, axis=-1)
# Copied from transformers.models.detr.feature_extraction_detr.masks_to_boxes
| 81
|
feature_extraction_yolos.py
|
Python
|
src/transformers/models/yolos/feature_extraction_yolos.py
|
1ac698744c4dbdf1495d303246d08ffacdf4f5b8
|
transformers
| 1
|
|
145,261
| 25
| 8
| 5
| 83
| 12
| 1
| 32
| 51
|
test_usage_lib_cluster_metadata_generation_usage_disabled
|
[Usage Stats] Implement usage stats report "Turned off by default". (#22249)
This is the second PR to implement usage stats on Ray. Please refer to the file usage_lib.py for more details.
The full specification is here https://docs.google.com/document/d/1ZT-l9YbGHh-iWRUC91jS-ssQ5Qe2UQ43Lsoc1edCalc/edit#heading=h.17dss3b9evbj.
This adds a dashboard module to enable usage stats. **Usage stats report is turned off by default** after this PR. We can control the report (enablement, report period, and URL. Note that URL is strictly for testing) using the env variable.
## NOTE
This requires us to add `requests` to the default library. `requests` must be okay to be included because
1. it is extremely lightweight. It is implemented only with built-in libs.
2. It is really stable. The project basically claims they are "deprecated", meaning no new features will be added there.
cc @edoakes @richardliaw for the approval
For the HTTP request, I was alternatively considered httpx, but it was not as lightweight as `requests`. So I decided to implement async requests using the thread pool.
|
https://github.com/ray-project/ray.git
|
def test_usage_lib_cluster_metadata_generation_usage_disabled(shutdown_only):
meta = ray_usage_lib._generate_cluster_metadata()
assert "ray_version" in meta
assert "python_version" in meta
assert len(meta) == 2
@pytest.mark.skipif(
sys.platform == "win32",
reason="Test depends on runtime env feature not supported on Windows.",
)
|
@pytest.mark.skipif(
sys.platform == "win32",
reason="Test depends on runtime env feature not supported on Windows.",
)
| 28
|
test_usage_stats.py
|
Python
|
python/ray/tests/test_usage_stats.py
|
36a31cb6fde95d490c81c6de5d9f911b4cac8af2
|
ray
| 1
|
194,923
| 9
| 8
| 7
| 41
| 7
| 0
| 10
| 38
|
get_additional_agent_args
|
SeeKeR (#4447)
* seeker
* todo
* readme updates; add test
* small config changes
* various updates
* readme fix
* model card
* add arxiv link
* surround spacy with try catch
* more protected
* more protection of imports
* lint
|
https://github.com/facebookresearch/ParlAI.git
|
def get_additional_agent_args(cls) -> ParlaiParser:
additional_agent_parser = SeekerAgent.get_additional_agent_args()
GPT2ComboAgent.add_cmdline_args(additional_agent_parser)
return additional_agent_parser
| 23
|
gpt2_seeker.py
|
Python
|
projects/seeker/agents/gpt2_seeker.py
|
7e453008fde751aff0cfd752662e19fe2adc7410
|
ParlAI
| 1
|
|
268,886
| 32
| 11
| 11
| 123
| 19
| 1
| 36
| 68
|
deserialize
|
Reorganize `advanced_activations.py` into smaller logically organized files hosted under an `activation` directory.
PiperOrigin-RevId: 425465077
|
https://github.com/keras-team/keras.git
|
def deserialize(name, custom_objects=None):
globs = globals()
# only replace missing activations
activation_globs = activation_layers.get_globals()
for key, val in activation_globs.items():
if key not in globs:
globs[key] = val
return deserialize_keras_object(
name,
module_objects=globs,
custom_objects=custom_objects,
printable_module_name='activation function')
@keras_export('keras.activations.get')
@tf.__internal__.dispatch.add_dispatch_support
|
@keras_export('keras.activations.get')
@tf.__internal__.dispatch.add_dispatch_support
| 62
|
activations.py
|
Python
|
keras/activations.py
|
c06d3d133469eb8adab5ee89a30dd8659865d303
|
keras
| 3
|
283,073
| 8
| 9
| 4
| 30
| 5
| 0
| 9
| 18
|
get_app_id
|
Logging : refactor log generation/collection (#1538)
* Update only_send_file_size
* Updating only_send_file_size feature
* Updating only_send_file_size feature
* Updating only_send_file_size feature
* Updating settings_controller
* Update settings_controller : help message
* Data Collection : move boto3 as discord dependencies
* Update dependencies
* Refactoring log collection
* Refactoring logging
* Logging : add rolling clock
* Logging : enable daemon for logging clock
* Logging : fix linting and type
* Logging : update linting
* Logging : add comment
* Logging : remove empty log files
* Logging : adding PII filter
* Logging removing : unlink
* Logging : terminal.py
* Logging : linting
* Logging : linting
* minor changes
* Logging : update
* Logging : linting
* added userId
* userId NA
Co-authored-by: LBolte29 <lbolte@gmx.net>
|
https://github.com/OpenBB-finance/OpenBBTerminal.git
|
def get_app_id() -> str:
app_id = get_log_dir().stem
return app_id
| 16
|
loggers.py
|
Python
|
gamestonk_terminal/loggers.py
|
56a9d76b805157e80aff7e8d8b0b5ca27875b107
|
OpenBBTerminal
| 1
|
|
33,479
| 19
| 13
| 5
| 72
| 11
| 1
| 23
| 53
|
_reorder_cache
|
add task_type_id to BERT to support ERNIE-2.0 and ERNIE-3.0 models (#18686)
* add_ernie
* remove Tokenizer in ernie
* polish code
* format code style
* polish code
* fix style
* update doc
* make fix-copies
* change model name
* change model name
* fix dependency
* add more copied from
* rename ErnieLMHeadModel to ErnieForCausalLM
do not expose ErnieLayer
update doc
* fix
* make style
* polish code
* polish code
* fix
* fix
* fix
* fix
* fix
* final fix
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
|
https://github.com/huggingface/transformers.git
|
def _reorder_cache(self, past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
@add_start_docstrings(, ERNIE_START_DOCSTRING)
|
@add_start_docstrings("""Ernie Model with a `language modeling` head on top.""", ERNIE_START_DOCSTRING)
| 40
|
modeling_ernie.py
|
Python
|
src/transformers/models/ernie/modeling_ernie.py
|
22f72185601d5167a747104b4aca102d0e92524c
|
transformers
| 3
|
213,070
| 13
| 9
| 4
| 42
| 4
| 0
| 14
| 46
|
setdefault
|
fix: Py27hash fix (#2182)
* Add third party py27hash code
* Add Py27UniStr and unit tests
* Add py27hash_fix utils and tests
* Add to_py27_compatible_template and tests
* Apply py27hash fix to wherever it is needed
* Apply py27hash fix, all tests pass except api_with_any_method_in_swagger
* apply py27hash fix in openapi + run black
* remove py27 testing
* remove other py27 references
* black fixes
* fixes/typos
* remove py27 from tox.ini
* refactoring
* third party notice
* black
* Fix py27hash fix to deal with null events
* Fix Py27UniStr repr for unicode literals
* black reformat
* Update _template_has_api_resource to check data type more defensively
* Apply py27Dict in _get_authorizers
* Apply Py27Dict to authorizers and gateway responses which will go into swagger
* Update to_py27_compatible_template to handle parameter_values; Add Py27LongInt class
* Rename _convert_to_py27_dict to _convert_to_py27_type
* Apply Py27UniStr to path param name
* Handle HttpApi resource under to_py27_compatible_template
* Fix InvalidDocumentException to not sort different exceptions
* black reformat
* Remove unnecessary test files
Co-authored-by: Wing Fung Lau <4760060+hawflau@users.noreply.github.com>
|
https://github.com/aws/serverless-application-model.git
|
def setdefault(self, key, default):
if key not in self:
self[key] = default
return self[key]
| 27
|
py27hash_fix.py
|
Python
|
samtranslator/utils/py27hash_fix.py
|
a5db070f446b7cfebdaa6ad2e3dcf78f6105a272
|
serverless-application-model
| 2
|
|
271,198
| 11
| 10
| 4
| 50
| 6
| 0
| 12
| 44
|
on_epoch_end
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def on_epoch_end(self):
self._current_epoch += 1
if self._current_epoch < self._epochs:
self._current_batch_size = self._linearly_increasing_batch_size()
| 29
|
data_adapter_test.py
|
Python
|
keras/engine/data_adapter_test.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 2
|
|
42,731
| 36
| 13
| 21
| 135
| 16
| 0
| 48
| 167
|
print_warning_if_setup_changed
|
Replace generation of docker volumes to be done from python (#23985)
The pre-commit to generate docker volumes in docker compose
file is now written in Python and it also uses the newer "volume:"
syntax to define the volumes mounted in the docker-compose.
|
https://github.com/apache/airflow.git
|
def print_warning_if_setup_changed() -> bool:
try:
package_hash = get_package_setup_metadata_hash()
except ModuleNotFoundError as e:
if "importlib_metadata" in e.msg:
return False
sources_hash = get_installation_sources_config_metadata_hash()
if sources_hash != package_hash:
installation_sources = get_installation_airflow_sources()
if installation_sources is not None:
breeze_sources = installation_sources / "dev" / "breeze"
warn_dependencies_changed()
set_forced_answer_for_upgrade_check()
ask_to_reinstall_breeze(breeze_sources)
set_forced_answer(None)
return True
return False
| 74
|
path_utils.py
|
Python
|
dev/breeze/src/airflow_breeze/utils/path_utils.py
|
882535a8a2699af7d1d079ecebd8c31aa7fbaba9
|
airflow
| 5
|
|
247,541
| 36
| 11
| 17
| 139
| 13
| 0
| 38
| 175
|
test_blacklisted_ip_specific
|
Add type hints to `tests/rest`. (#12208)
Co-authored-by: Patrick Cloke <clokep@users.noreply.github.com>
|
https://github.com/matrix-org/synapse.git
|
def test_blacklisted_ip_specific(self) -> None:
self.lookups["example.com"] = [(IPv4Address, "192.168.1.1")]
channel = self.make_request(
"GET", "preview_url?url=http://example.com", shorthand=False
)
# No requests made.
self.assertEqual(len(self.reactor.tcpClients), 0)
self.assertEqual(channel.code, 502)
self.assertEqual(
channel.json_body,
{
"errcode": "M_UNKNOWN",
"error": "DNS resolution failure during URL preview generation",
},
)
| 81
|
test_url_preview.py
|
Python
|
tests/rest/media/v1/test_url_preview.py
|
32c828d0f760492711a98b11376e229d795fd1b3
|
synapse
| 1
|
|
260,403
| 172
| 15
| 77
| 766
| 65
| 1
| 284
| 716
|
glm_dataset
|
TST tight tests for GLMs (#23619)
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
|
https://github.com/scikit-learn/scikit-learn.git
|
def glm_dataset(global_random_seed, request):
data_type, model = request.param
# Make larger dim more than double as big as the smaller one.
# This helps when constructing singular matrices like (X, X).
if data_type == "long":
n_samples, n_features = 12, 4
else:
n_samples, n_features = 4, 12
k = min(n_samples, n_features)
rng = np.random.RandomState(global_random_seed)
X = make_low_rank_matrix(
n_samples=n_samples,
n_features=n_features,
effective_rank=k,
tail_strength=0.1,
random_state=rng,
)
X[:, -1] = 1 # last columns acts as intercept
U, s, Vt = linalg.svd(X, full_matrices=False)
assert np.all(s > 1e-3) # to be sure
assert np.max(s) / np.min(s) < 100 # condition number of X
if data_type == "long":
coef_unpenalized = rng.uniform(low=1, high=3, size=n_features)
coef_unpenalized *= rng.choice([-1, 1], size=n_features)
raw_prediction = X @ coef_unpenalized
else:
raw_prediction = rng.uniform(low=-3, high=3, size=n_samples)
# minimum norm solution min ||w||_2 such that raw_prediction = X w:
# w = X'(XX')^-1 raw_prediction = V s^-1 U' raw_prediction
coef_unpenalized = Vt.T @ np.diag(1 / s) @ U.T @ raw_prediction
linear_loss = LinearModelLoss(base_loss=model._get_loss(), fit_intercept=True)
sw = np.full(shape=n_samples, fill_value=1 / n_samples)
y = linear_loss.base_loss.link.inverse(raw_prediction)
# Add penalty l2_reg_strength * ||coef||_2^2 for l2_reg_strength=1 and solve with
# optimizer. Note that the problem is well conditioned such that we get accurate
# results.
l2_reg_strength = 1
fun = partial(
linear_loss.loss,
X=X[:, :-1],
y=y,
sample_weight=sw,
l2_reg_strength=l2_reg_strength,
)
grad = partial(
linear_loss.gradient,
X=X[:, :-1],
y=y,
sample_weight=sw,
l2_reg_strength=l2_reg_strength,
)
coef_penalized_with_intercept = _special_minimize(
fun, grad, coef_unpenalized, tol_NM=1e-6, tol=1e-14
)
linear_loss = LinearModelLoss(base_loss=model._get_loss(), fit_intercept=False)
fun = partial(
linear_loss.loss,
X=X[:, :-1],
y=y,
sample_weight=sw,
l2_reg_strength=l2_reg_strength,
)
grad = partial(
linear_loss.gradient,
X=X[:, :-1],
y=y,
sample_weight=sw,
l2_reg_strength=l2_reg_strength,
)
coef_penalized_without_intercept = _special_minimize(
fun, grad, coef_unpenalized[:-1], tol_NM=1e-6, tol=1e-14
)
# To be sure
assert np.linalg.norm(coef_penalized_with_intercept) < np.linalg.norm(
coef_unpenalized
)
return (
model,
X,
y,
coef_unpenalized,
coef_penalized_with_intercept,
coef_penalized_without_intercept,
l2_reg_strength,
)
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("fit_intercept", [False, True])
|
@pytest.mark.parametrize("solver", SOLVERS)
@pytest.mark.parametrize("fit_intercept", [False, True])
| 495
|
test_glm.py
|
Python
|
sklearn/linear_model/_glm/tests/test_glm.py
|
9d863aba2b6dab9c9cbbcf2f7c3b7a99b6ad168f
|
scikit-learn
| 3
|
180,769
| 58
| 20
| 25
| 308
| 17
| 0
| 85
| 378
|
_get_dataset_features_info
|
Add a flagging callback to save json files to a hugging face dataset (#1821)
* work on saving flags in JSON format
* explained what I did more clearly
* final updates + added test case
* reviews to flagging.py for HuggingFaceDatasetJSONSaver
* formatted imports
* used uuid for random ids
* used uuid for random + function to get dataset infos
* reformmated flagging.py
* fix examples test
* formatting
* async examples
* working on mix
* comment out failing test
* fixed interface problem
* final updates to HuggingFaceDatasetJSONSaver flagging.py
* final updates to HuggingFaceDatasetJSONSaver flagging.py
* formatting
* some tweaks
* tweaks
* tweaks
* omar's fixes
* added back test.init
* restored test init
Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
|
https://github.com/gradio-app/gradio.git
|
def _get_dataset_features_info(is_new, components):
infos = {"flagged": {"features": {}}}
# File previews for certain input and output types
file_preview_types = {gr.Audio: "Audio", gr.Image: "Image"}
headers = []
# Generate the headers and dataset_infos
if is_new:
for component in components:
headers.append(component.label)
infos["flagged"]["features"][component.label] = {
"dtype": "string",
"_type": "Value",
}
if isinstance(component, tuple(file_preview_types)):
headers.append(component.label + " file")
for _component, _type in file_preview_types.items():
if isinstance(component, _component):
infos["flagged"]["features"][component.label + " file"] = {
"_type": _type
}
break
headers.append("flag")
infos["flagged"]["features"]["flag"] = {
"dtype": "string",
"_type": "Value",
}
return infos, file_preview_types, headers
| 172
|
flagging.py
|
Python
|
gradio/flagging.py
|
9c4dc6c183b418eb82d6b062eeb0feef9cd52740
|
gradio
| 6
|
|
292,710
| 4
| 6
| 2
| 19
| 3
| 0
| 4
| 18
|
media_image_hash
|
Correctly handle missing mpd albumart (#66771)
Co-authored-by: Martin Hjelmare <marhje52@gmail.com>
|
https://github.com/home-assistant/core.git
|
def media_image_hash(self):
return self._media_image_hash
| 10
|
media_player.py
|
Python
|
homeassistant/components/mpd/media_player.py
|
facf22c2ddccbf9b205a2d8b26da457330b53ba6
|
core
| 1
|
|
82,298
| 13
| 13
| 27
| 69
| 14
| 0
| 16
| 51
|
test_processors
|
Enabled isort workflow (#7200)
* Ran isort
* Enabled isort workflow
Co-authored-by: Vinit Kumar <mail@vinitkumar.me>
|
https://github.com/django-cms/django-cms.git
|
def test_processors(self):
from djangocms_text_ckeditor.cms_plugins import TextPlugin
from cms.plugin_pool import plugin_pool
instance = CMSPlugin.objects.all()[0].get_plugin_instance()[0]
load_from_string = self.load_template_from_string
| 169
|
test_rendering.py
|
Python
|
cms/tests/test_rendering.py
|
a3110e1ff24085373898c7d2a85f628abeb8518d
|
django-cms
| 1
|
|
118,873
| 32
| 12
| 9
| 122
| 17
| 0
| 39
| 83
|
get_current_version
|
[STLT-24] 🔄 (CircleCI RC Pipeline) Add release candidate pipeline (#4425)
|
https://github.com/streamlit/streamlit.git
|
def get_current_version():
filename = os.path.join(BASE_DIR, "lib/setup.py")
regex = r"(?P<pre>.*VERSION = \")(.*)(?P<post>\" # PEP-440$)"
pattern = re.compile(regex)
for line in fileinput.input(filename):
match = pattern.match(line.rstrip())
if match:
return match.groups()[1]
raise Exception('Did not find regex "%s" for version in setup.py' % (regex))
| 72
|
get_prerelease_version.py
|
Python
|
scripts/get_prerelease_version.py
|
967fb9b856b94ee22f7d125139f8b8536cc75e99
|
streamlit
| 3
|
|
297,732
| 28
| 12
| 13
| 122
| 15
| 1
| 30
| 157
|
_data_to_save
|
Add aliases to area registry items (#84294)
* Add aliases to area registry items
* Update test
* Fix WS API
|
https://github.com/home-assistant/core.git
|
def _data_to_save(self) -> dict[str, list[dict[str, Any]]]:
data = {}
data["areas"] = [
{
"aliases": list(entry.aliases),
"name": entry.name,
"id": entry.id,
"picture": entry.picture,
}
for entry in self.areas.values()
]
return data
@callback
|
@callback
| 73
|
area_registry.py
|
Python
|
homeassistant/helpers/area_registry.py
|
1a42bd5c4cb51ffbfcaf8d5389b80a228712ac81
|
core
| 2
|
266,515
| 37
| 11
| 18
| 212
| 20
| 0
| 63
| 221
|
_create_role_list
|
updated metadata dump to do full docs dump (#76170)
* minor refactor in other options by pushing common code into functions
* consolidate coll_filter
* more normalizing loader
* dont pass plugin_loader, its global import
* Also dump roles and collections
* adjusted tests to new err msg
* disable namespace filter (unused)
|
https://github.com/ansible/ansible.git
|
def _create_role_list(self):
roles_path = self._get_roles_path()
collection_filter = self._get_collection_filter()
if not collection_filter:
roles = self._find_all_normal_roles(roles_path)
else:
roles = []
collroles = self._find_all_collection_roles(collection_filter=collection_filter)
result = {}
for role, role_path in roles:
argspec = self._load_argspec(role, role_path=role_path)
fqcn, summary = self._build_summary(role, '', argspec)
result[fqcn] = summary
for role, collection, collection_path in collroles:
argspec = self._load_argspec(role, collection_path=collection_path)
fqcn, summary = self._build_summary(role, collection, argspec)
result[fqcn] = summary
return result
| 134
|
doc.py
|
Python
|
lib/ansible/cli/doc.py
|
29b5eb6ba9fb652ddd8dd06cdd8f2e80e2098063
|
ansible
| 4
|
|
100,464
| 29
| 14
| 16
| 283
| 16
| 0
| 58
| 194
|
decoder
|
Update all Keras Imports to be conditional (#1214)
* Remove custom keras importer
* first round keras imports fix
* launcher.py: Remove KerasFinder references
* 2nd round keras imports update (lib and extract)
* 3rd round keras imports update (train)
* remove KerasFinder from tests
* 4th round keras imports update (tests)
|
https://github.com/deepfakes/faceswap.git
|
def decoder(self, side):
input_ = Input(shape=(8, 8, 512))
var_x = input_
var_x = UpscaleBlock(256, activation="leakyrelu")(var_x)
var_x = UpscaleBlock(128, activation="leakyrelu")(var_x)
var_x = UpscaleBlock(64, activation="leakyrelu")(var_x)
var_x = Conv2DOutput(3, 5, name=f"face_out_{side}")(var_x)
outputs = [var_x]
if self.learn_mask:
var_y = input_
var_y = UpscaleBlock(256, activation="leakyrelu")(var_y)
var_y = UpscaleBlock(128, activation="leakyrelu")(var_y)
var_y = UpscaleBlock(64, activation="leakyrelu")(var_y)
var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y)
outputs.append(var_y)
return KerasModel(input_, outputs=outputs, name=f"decoder_{side}")
| 168
|
original.py
|
Python
|
plugins/train/model/original.py
|
aa39234538a8f83e6aa2b60b8275a570e8876ac2
|
faceswap
| 2
|
|
37,492
| 7
| 10
| 2
| 37
| 5
| 0
| 7
| 13
|
require_tokenizers
|
Update all require decorators to use skipUnless when possible (#16999)
|
https://github.com/huggingface/transformers.git
|
def require_tokenizers(test_case):
return unittest.skipUnless(is_tokenizers_available(), "test requires tokenizers")(test_case)
| 20
|
testing_utils.py
|
Python
|
src/transformers/testing_utils.py
|
57e6464ac9a31156f1c93e59107323e6ec01309e
|
transformers
| 1
|
|
47,528
| 31
| 11
| 19
| 222
| 33
| 0
| 43
| 180
|
test_execute_task_instances_backfill_tasks_wont_execute
|
Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator`
|
https://github.com/apache/airflow.git
|
def test_execute_task_instances_backfill_tasks_wont_execute(self, dag_maker):
dag_id = 'SchedulerJobTest.test_execute_task_instances_backfill_tasks_wont_execute'
task_id_1 = 'dummy_task'
with dag_maker(dag_id=dag_id):
task1 = EmptyOperator(task_id=task_id_1)
self.scheduler_job = SchedulerJob(subdir=os.devnull)
session = settings.Session()
dr1 = dag_maker.create_dagrun(run_type=DagRunType.BACKFILL_JOB)
ti1 = TaskInstance(task1, run_id=dr1.run_id)
ti1.refresh_from_db()
ti1.state = State.SCHEDULED
session.merge(ti1)
session.flush()
assert dr1.is_backfill
self.scheduler_job._critical_section_execute_task_instances(session)
session.flush()
ti1.refresh_from_db()
assert State.SCHEDULED == ti1.state
session.rollback()
| 131
|
test_scheduler_job.py
|
Python
|
tests/jobs/test_scheduler_job.py
|
49e336ae0302b386a2f47269a6d13988382d975f
|
airflow
| 1
|
|
275,776
| 17
| 10
| 7
| 82
| 11
| 1
| 18
| 74
|
to_json
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def to_json(self, **kwargs):
config = self.get_config()
timeseries_generator_config = {
"class_name": self.__class__.__name__,
"config": config,
}
return json.dumps(timeseries_generator_config, **kwargs)
@keras_export("keras.preprocessing.sequence.make_sampling_table")
|
@keras_export("keras.preprocessing.sequence.make_sampling_table")
| 42
|
sequence.py
|
Python
|
keras/preprocessing/sequence.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 1
|
176,193
| 63
| 13
| 15
| 203
| 26
| 1
| 79
| 146
|
laplacian_matrix
|
Use scipy.sparse array datastructure (#5139)
* Step 1: use sparse arrays in nx.to_scipy_sparse_matrix.
Seems like a reasonable place to start.
nx.to_scipy_sparse_matrix is one of the primary interfaces to
scipy.sparse from within NetworkX.
* 1: Use np.outer instead of mult col/row vectors
Fix two instances in modularitymatrix where a new 2D array was being
created via an outer product of two \"vectors\".
In the matrix case, this was a row vector \* a column vector. In the
array case this can be disambiguated by being explicit with np.outer.
* Update _transition_matrix in laplacianmatrix module
- A few instances of matrix multiplication operator
- Add np.newaxis + transpose to get shape right for broadcasting
- Explicitly convert e.g. sp.sparse.spdiags to a csr_array.
* Update directed_combinitorial_laplacian w/ sparse array.
- Wrap spdiags in csr_array and update matmul operators.
* Rm matrix-specific code from lgc and hmn modules
- Replace .A call with appropriate array semantics
- wrap sparse.diags in csr_array.
* Change hits to use sparse array semantics.
- Replace * with @
- Remove superfluous calls to flatten.
* Update sparse matrix usage in layout module.
- Simplify lil.getrowview call
- Wrap spdiags in csr_array.
* lil_matrix -> lil_array in graphmatrix.py.
* WIP: Start working on algebraic connectivity module.
* Incorporate auth mat varname feedback.
* Revert 1D slice and comment for 1D sparse future.
* Add TODOs: rm csr_array wrapper around spdiags etc.
* WIP: cleanup algebraicconn: tracemin_fiedler.
* Typo.
* Finish reviewing algebraicconnectivity.
* Convert bethe_hessian matrix to use sparse arrays.
* WIP: update laplacian.
Update undirected laplacian functions.
* WIP: laplacian - add comment about _transition_matrix return types.
* Finish laplacianmatrix review.
* Update attrmatrix.
* Switch to official laplacian function.
* Update pagerank to use sparse array.
* Switch bipartite matrix to sparse arrays.
* Check from_scipy_sparse_matrix works with arrays.
Modifies test suite.
* Apply changes from review.
* Fix failing docstring tests.
* Fix missing axis for in-place multiplication.
* Use scipy==1.8rc2
* Use matrix multiplication
* Fix PyPy CI
* [MRG] Create plot_subgraphs.py example (#5165)
* Create plot_subgraphs.py
https://github.com/networkx/networkx/issues/4220
* Update plot_subgraphs.py
black
* Update plot_subgraphs.py
lint plus font_size
* Update plot_subgraphs.py
added more plots
* Update plot_subgraphs.py
removed plots from the unit test and added comments
* Update plot_subgraphs.py
lint
* Update plot_subgraphs.py
typos fixed
* Update plot_subgraphs.py
added nodes to the plot of the edges removed that was commented out for whatever reason
* Update plot_subgraphs.py
revert the latest commit - the line was commented out for a reason - it's broken
* Update plot_subgraphs.py
fixed node color issue
* Update plot_subgraphs.py
format fix
* Update plot_subgraphs.py
forgot to draw the nodes... now fixed
* Fix sphinx warnings about heading length.
* Update examples/algorithms/plot_subgraphs.py
* Update examples/algorithms/plot_subgraphs.py
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
Co-authored-by: Dan Schult <dschult@colgate.edu>
* Add traveling salesman problem to example gallery (#4874)
Adds an example of the using Christofides to solve the TSP problem to the example galery.
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
* Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() (#5037)
* Fixed inconsistent documentation for nbunch parameter in DiGraph.edges()
* Resolved Requested Changes
* Revert changes to degree docstrings.
* Update comments in example.
* Apply wording to edges method in all graph classes.
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
* Compatibility updates from testing with numpy/scipy/pytest rc's (#5226)
* Rm deprecated scipy subpkg access.
* Use recwarn fixture in place of deprecated pytest pattern.
* Rm unnecessary try/except from tests.
* Replace internal `close` fn with `math.isclose`. (#5224)
* Replace internal close fn with math.isclose.
* Fix lines in docstring examples.
* Fix Python 3.10 deprecation warning w/ int div. (#5231)
* Touchups and suggestions for subgraph gallery example (#5225)
* Simplify construction of G with edges rm'd
* Rm unused graph attribute.
* Shorten categorization by node type.
* Simplify node coloring.
* Simplify isomorphism check.
* Rm unit test.
* Rm redundant plotting of each subgraph.
* Use new package name (#5234)
* Allowing None edges in weight function of bidirectional Dijkstra (#5232)
* added following feature also to bidirectional dijkstra: The weight function can be used to hide edges by returning None.
* changed syntax for better readability and code duplicate avoidance
Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de>
* Add an FAQ about assigning issues. (#5182)
* Add FAQ about assigning issues.
* Add note about linking issues from new PRs.
* Update dev deps (#5243)
* Update minor doc issues with tex notation (#5244)
* Add FutureWarnings to fns that return sparse matrices
- biadjacency_matrix.
- bethe_hessian_matrix.
- incidence_matrix.
- laplacian functions.
- modularity_matrix functions.
- adjacency_matrix.
* Add to_scipy_sparse_array and use it everywhere.
Add a new conversion function to preserve array semantics internally
while not altering behavior for users.
Also adds FutureWarning to to_scipy_sparse_matrix.
* Add from_scipy_sparse_array. Supercedes from_scipy_sparse_matrix.
* Handle deprecations in separate PR.
* Fix docstring examples.
Co-authored-by: Mridul Seth <mail@mriduls.com>
Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com>
Co-authored-by: Andrew Knyazev <andrew.knyazev@ucdenver.edu>
Co-authored-by: Dan Schult <dschult@colgate.edu>
Co-authored-by: eskountis <56514439+eskountis@users.noreply.github.com>
Co-authored-by: Anutosh Bhat <87052487+anutosh491@users.noreply.github.com>
Co-authored-by: NikHoh <nikhoh@web.de>
Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de>
Co-authored-by: Sultan Orazbayev <contact@econpoint.com>
Co-authored-by: Mridul Seth <mail@mriduls.com>
|
https://github.com/networkx/networkx.git
|
def laplacian_matrix(G, nodelist=None, weight="weight"):
import scipy as sp
import scipy.sparse # call as sp.sparse
if nodelist is None:
nodelist = list(G)
A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
n, m = A.shape
# TODO: rm csr_array wrapper when spdiags can produce arrays
D = sp.sparse.csr_array(sp.sparse.spdiags(A.sum(axis=1), 0, m, n, format="csr"))
import warnings
warnings.warn(
"laplacian_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.",
FutureWarning,
stacklevel=2,
)
# TODO: rm sp.sparse.csr_matrix in version 3.0
return sp.sparse.csr_matrix(D - A)
@not_implemented_for("directed")
|
@not_implemented_for("directed")
| 120
|
laplacianmatrix.py
|
Python
|
networkx/linalg/laplacianmatrix.py
|
5dfd57af2a141a013ae3753e160180b82bec9469
|
networkx
| 2
|
38,418
| 40
| 17
| 11
| 205
| 17
| 0
| 55
| 98
|
get_all_tests
|
Update self-push workflow (#17177)
* update push ci
* install git-python
* update comment
* update deepspeed jobs
* fix report
* skip 2 more tests that require fairscale
* Fix changes in test_fetcher.py (to deal with `setup.py` is changed)
* set RUN_PT_TF_CROSS_TESTS=1 and final clean-up
* remove SIGOPT_API_TOKEN
* remove echo "$matrix_folders"
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
|
https://github.com/huggingface/transformers.git
|
def get_all_tests():
test_root_dir = os.path.join(PATH_TO_TRANFORMERS, "tests")
# test folders/files directly under `tests` folder
tests = os.listdir(test_root_dir)
tests = sorted(
list(filter(lambda x: os.path.isdir(x) or x.startswith("tests/test_"), [f"tests/{x}" for x in tests]))
)
# model specific test folders
model_tests_folders = os.listdir(os.path.join(test_root_dir, "models"))
model_test_folders = sorted(list(filter(os.path.isdir, [f"tests/models/{x}" for x in model_tests_folders])))
tests.remove("tests/models")
tests = model_test_folders + tests
return tests
| 118
|
tests_fetcher.py
|
Python
|
utils/tests_fetcher.py
|
38043d8453b82a9c712f8d5c98323150fbee7503
|
transformers
| 4
|
|
10,922
| 75
| 10
| 29
| 171
| 18
| 0
| 110
| 276
|
mixin_base_deployment_parser
|
refactor: rename pod to deployment (#4230)
* refactor: rename pod to deployment
* style: fix overload and cli autocomplete
* fix: undo daemon mistake
* refactor: leftover cleanup
* fix: more test fixes
* fix: more fixes
* fix: more fixes
* fix: more fixes
* fix: more tests
* fix: fix more tests
* refactor: fix more tests
* refactor: more tests fixes
* refactor: rename pea to pod
* refactor: adjust docs
* refactor: complete pea renaming
* refactor: more fixes
* fix: pea_type in k8s yamls
* fix: adjust pod args name
* refactor: rename peapods parser folder
* fix: da init
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
|
https://github.com/jina-ai/jina.git
|
def mixin_base_deployment_parser(parser):
gp = add_arg_group(parser, title='Deployment')
gp.add_argument(
'--uses-before',
type=str,
help='The executor attached after the Pods described by --uses, typically before sending to all '
'shards, accepted type follows `--uses`',
)
gp.add_argument(
'--uses-after',
type=str,
help='The executor attached after the Pods described by --uses, typically used for receiving from '
'all shards, accepted type follows `--uses`',
)
gp.add_argument(
'--external',
action='store_true',
default=False,
help='The Deployment will be considered an external Deployment that has been started independently from the Flow.'
'This Deployment will not be context managed by the Flow.',
)
# hidden CLI used for internal only
gp.add_argument(
'--deployment-role',
type=DeploymentRoleType.from_string,
choices=list(DeploymentRoleType),
help='The role of this deployment in the flow'
if _SHOW_ALL_ARGS
else argparse.SUPPRESS,
)
| 98
|
deployment.py
|
Python
|
jina/parsers/orchestrate/deployment.py
|
13edc16d806fb5d77a6849551178ccc75937f25f
|
jina
| 2
|
|
300,754
| 14
| 10
| 5
| 54
| 8
| 0
| 14
| 49
|
load_ignored_devices
|
Streamline setup of deCONZ binary sensor platform (#71820)
|
https://github.com/home-assistant/core.git
|
def load_ignored_devices(self) -> None:
for add_entities, device_id in self.ignored_devices:
add_entities(EventType.ADDED, device_id)
self.ignored_devices.clear()
# Callbacks
| 32
|
gateway.py
|
Python
|
homeassistant/components/deconz/gateway.py
|
007c6d22366b39c166bc4c511a145382d8fd7551
|
core
| 2
|
|
203,224
| 16
| 9
| 18
| 63
| 8
| 0
| 17
| 66
|
get_primary_key_column
|
Refs #33476 -- Refactored problematic code before reformatting by Black.
In these cases Black produces unexpected results, e.g.
def make_random_password(
self,
length=10,
allowed_chars='abcdefghjkmnpqrstuvwxyz' 'ABCDEFGHJKLMNPQRSTUVWXYZ' '23456789',
):
or
cursor.execute("""
SELECT ...
""",
[table name],
)
|
https://github.com/django/django.git
|
def get_primary_key_column(self, cursor, table_name):
cursor.execute(
,
[table_name],
)
row = cursor.fetchone()
return self.identifier_converter(row[0]) if row else None
| 41
|
introspection.py
|
Python
|
django/db/backends/oracle/introspection.py
|
c5cd8783825b5f6384417dac5f3889b4210b7d08
|
django
| 2
|
|
294,912
| 68
| 13
| 28
| 213
| 20
| 0
| 100
| 361
|
fill_out_missing_chromecast_info
|
Get cast type and manufacturer via http protocol (#68863)
|
https://github.com/home-assistant/core.git
|
def fill_out_missing_chromecast_info(self) -> ChromecastInfo:
cast_info = self.cast_info
if self.cast_info.cast_type is None or self.cast_info.manufacturer is None:
# Manufacturer and cast type is not available in mDNS data, get it over http
cast_info = dial.get_cast_type(
cast_info,
zconf=ChromeCastZeroconf.get_zeroconf(),
)
if not self.is_audio_group or self.is_dynamic_group is not None:
# We have all information, no need to check HTTP API.
return ChromecastInfo(cast_info=cast_info)
# Fill out missing group information via HTTP API.
is_dynamic_group = False
http_group_status = None
http_group_status = dial.get_multizone_status(
None,
services=self.cast_info.services,
zconf=ChromeCastZeroconf.get_zeroconf(),
)
if http_group_status is not None:
is_dynamic_group = any(
g.uuid == self.cast_info.uuid for g in http_group_status.dynamic_groups
)
return ChromecastInfo(
cast_info=cast_info,
is_dynamic_group=is_dynamic_group,
)
| 136
|
helpers.py
|
Python
|
homeassistant/components/cast/helpers.py
|
cf39a61aa125c77a5a05ac48292467fcdccb2f12
|
core
| 7
|
|
60,252
| 27
| 11
| 6
| 81
| 9
| 0
| 29
| 95
|
set_transpose
|
Balanced joint maximum mean discrepancy for deep transfer learning
|
https://github.com/jindongwang/transferlearning.git
|
def set_transpose(self, in_, order):
self.__check_input(in_)
if len(order) != len(self.inputs[in_]) - 1:
raise Exception('Transpose order needs to have the same number of '
'dimensions as the input.')
self.transpose[in_] = order
| 48
|
io.py
|
Python
|
code/deep/BJMMD/caffe/python/caffe/io.py
|
cc4d0564756ca067516f71718a3d135996525909
|
transferlearning
| 2
|
|
186,687
| 37
| 13
| 8
| 93
| 13
| 0
| 46
| 140
|
update_includes
|
Add typing to certbot.apache (#9071)
* Add typing to certbot.apache
Co-authored-by: Adrien Ferrand <ferrand.ad@gmail.com>
|
https://github.com/certbot/certbot.git
|
def update_includes(self) -> None:
# Find_dir iterates over configuration for Include and IncludeOptional
# directives to make sure we see the full include tree present in the
# configuration files
_ = self.find_dir("Include")
matches = apache_util.parse_includes(self.configurator.options.ctl)
if matches:
for i in matches:
if not self.parsed_in_current(i):
self.parse_file(i)
| 53
|
parser.py
|
Python
|
certbot-apache/certbot_apache/_internal/parser.py
|
7d9e9a49005de7961e84d2a7c608db57dbab3046
|
certbot
| 4
|
|
106,057
| 4
| 6
| 2
| 19
| 3
| 0
| 4
| 18
|
info
|
Clean up Dataset and DatasetDict (#5344)
* clean up docstrings
* make style
* apply review
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
|
https://github.com/huggingface/datasets.git
|
def info(self):
return self._info
| 10
|
arrow_dataset.py
|
Python
|
src/datasets/arrow_dataset.py
|
cd3169f3f35afcf73a36a8276113e1881d92e5e0
|
datasets
| 1
|
|
264,370
| 75
| 15
| 23
| 282
| 24
| 0
| 132
| 312
|
get_selected_values
|
Fixes #8498: Fix display of selected content type filters in object list views
|
https://github.com/netbox-community/netbox.git
|
def get_selected_values(form, field_name):
if not hasattr(form, 'cleaned_data'):
form.is_valid()
filter_data = form.cleaned_data.get(field_name)
field = form.fields[field_name]
# Non-selection field
if not hasattr(field, 'choices'):
return [str(filter_data)]
# Model choice field
if type(field.choices) is forms.models.ModelChoiceIterator:
# If this is a single-choice field, wrap its value in a list
if not hasattr(filter_data, '__iter__'):
values = [filter_data]
else:
values = filter_data
else:
# Static selection field
choices = unpack_grouped_choices(field.choices)
if type(filter_data) not in (list, tuple):
filter_data = [filter_data] # Ensure filter data is iterable
values = [
label for value, label in choices if str(value) in filter_data or None in filter_data
]
if hasattr(field, 'null_option'):
# If the field has a `null_option` attribute set and it is selected,
# add it to the field's grouped choices.
if field.null_option is not None and None in filter_data:
values.append(field.null_option)
return values
| 169
|
utils.py
|
Python
|
netbox/utilities/forms/utils.py
|
e20ac803f306871d1397a40ed8d54b03b6748045
|
netbox
| 12
|
|
102,162
| 10
| 8
| 7
| 47
| 6
| 0
| 11
| 32
|
test_missing_cpp_namespace
|
Revert "Revert D32498569: allow external backend codegen to toggle whether to generate out= and inplace kernels" (#69950)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69950
This reverts commit f6cad53443704dfe5a20cc62bee14d91e3bffcaa.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33113545
Pulled By: bdhirsh
fbshipit-source-id: d6590294662588d36c09662dea65919ad4e1e288
|
https://github.com/pytorch/pytorch.git
|
def test_missing_cpp_namespace(self) -> None:
yaml_str =
output_error = self.get_errors_from_gen_backend_stubs(yaml_str)
self.assertExpectedInline(output_error, )
| 26
|
test_gen_backend_stubs.py
|
Python
|
tools/test/test_gen_backend_stubs.py
|
bb5b4cceb6f737448eaaa6817cd773b6f4b0e77d
|
pytorch
| 1
|
|
78,145
| 17
| 13
| 6
| 77
| 10
| 0
| 17
| 67
|
get_indented_choices
|
Use a CollectionQuerySet method to simplify displaying indented collection lists
|
https://github.com/wagtail/wagtail.git
|
def get_indented_choices(self):
min_depth = self.aggregate(models.Min("depth"))["depth__min"] or 2
return [
(collection.pk, collection.get_indented_name(min_depth, html=True))
for collection in self
]
| 47
|
collections.py
|
Python
|
wagtail/models/collections.py
|
7fd45a669317df14b40b9d661834d482caf911c1
|
wagtail
| 3
|
|
212,859
| 32
| 10
| 11
| 121
| 12
| 0
| 43
| 142
|
update
|
Fixed bug in Tree and Table when visible changed in the layout. Added Canvas.update method
|
https://github.com/PySimpleGUI/PySimpleGUI.git
|
def update(self, background_color=None, visible=None):
if not self._widget_was_created(): # if widget hasn't been created yet, then don't allow
return
if background_color not in (None, COLOR_SYSTEM_DEFAULT):
self._TKCanvas.configure(background=background_color)
if visible is False:
self._pack_forget_save_settings()
elif visible is True:
self._pack_restore_settings()
if visible is not None:
self._visible = visible
| 74
|
PySimpleGUI.py
|
Python
|
PySimpleGUI.py
|
6c4dc01f4acc7396387008510f7574db9f1b6201
|
PySimpleGUI
| 6
|
|
320,781
| 22
| 10
| 6
| 84
| 12
| 0
| 24
| 82
|
set_pattern
|
mypy: Upgrade to PyQt5-stubs 5.15.6.0
For some unknown reason, those new stubs cause a *lot* of things now to be
checked by mypy which formerly probably got skipped due to Any being implied
somewhere.
The stubs themselves mainly improved, with a couple of regressions too.
In total, there were some 337 (!) new mypy errors. This commit fixes almost all
of them, and the next commit improves a fix to get things down to 0 errors
again.
Overview of the changes:
==== qutebrowser/app.py
- Drop type ignore due to improved stubs.
==== qutebrowser/browser/browsertab.py
- Specify the type of _widget members more closely than just QWidget.
This is debatable: I suppose the abstract stuff shouldn't need to know
anything about the concrete backends at all. But it seems like we cut some
corners when initially implementing things, and put some code in browsertab.py
just because the APIs of both backends happened to be compatible. Perhaps
something to reconsider once we drop QtWebKit and hopefully implement a dummy
backend.
- Add an additional assertion in AbstractAction.run_string. This is already
covered by the isinstance(member, self.action_base) above it, but that's too
dynamic for mypy to understand.
- Fix the return type of AbstractScroller.pos_px, which is a QPoint (with x
and y components), not a single int.
- Fix the return type of AbstractScroller.pos_perc, which is a Tuple (with x
and y components), not a single int.
- Fix the argument types of AbstractScroller.to_perc, as it's possible to pass
fractional percentages too.
- Specify the type for AbstractHistoryPrivate._history. See above (_widget) re
this being debatable.
- Fix the return type of AbstractTabPrivate.event_target(), which can be None
(see #3888).
- Fix the return type of AbstractTabPrivate.run_js_sync, which is Any (the JS
return value), not None.
- Fix the argument type for AbstractTabPrivate.toggle_inspector: position can
be None to use the last used position.
- Declare the type of sub-objects of AbstractTab.
- Fix the return value of AbstractTab.icon(), which is the QIcon, not None.
==== qutebrowser/browser/commands.py
- Make sure the active window is a MainWindow (with a .win_id attribute).
==== qutebrowser/browser/downloadview.py
- Add _model() which makes sure that self.model() is a DownloadModel, not None
or any other model. This is needed because other methods access a variety of
custom attributes on it, e.g. last_index().
==== qutebrowser/browser/greasemonkey.py
- Add an ignore for AbstractDownload.requested_url which we patch onto the
downloads. Probably would be nicer to add it as a proper attribute which always
gets set by the DownloadManager.
==== qutebrowser/browser/hints.py
- Remove type ignores for QUrl.toString().
- Add a new type ignore for combining different URL flags (which works, but is
not exactly type safe... still probably a regression in the stubs).
- Make sure the things we get back from self._get_keyparser are what we actually
expect. Probably should introduce a TypedDict (and/or overloads for
_get_keyparser with typing.Literal) to teach mypy about the exact return value.
See #7098.
This is needed because we access Hint/NormalKeyParser-specific attributes such
as .set_inhibited_timout() or .update_bindings().
==== qutebrowser/browser/inspector.py
- Similar changes than in browsertab.py to make some types where we share API
(e.g. .setPage()) more concrete. Didn't work out unfortunately, see next
commit.
==== qutebrowser/browser/network/pac.py
- Remove now unneeded type ignore for signal.
==== qutebrowser/browser/qtnetworkdownloads.py
- Make sure that downloads is a qtnetworkdownloads.DownloadItem (rather than an
AbstractDownload), so that we can call ._uses_nam() on it.
==== qutebrowser/browser/qutescheme.py
- Remove now unneeded type ignore for QUrl flags.
==== qutebrowser/browser/urlmarks.py
- Specify the type of UrlMarkManager._lineparser, as those only get initialized
in _init_lineparser of subclasses, so mypy doesn't know it's supposed to exist.
==== qutebrowser/browser/webelem.py
- New casts to turn single KeyboardModifier (enum) entries into
KeyboardModifiers (flags). Might not be needed anymore with Qt 6.
- With that, casting the final value is now unneeded.
==== qutebrowser/browser/webengine/notification.py
- Remove now unneeded type ignore for signal.
- Make sure the self.sender() we get in HerbeNotificationAdapter._on_finished()
is a QProcess, not just any QObject.
==== qutebrowser/browser/webengine/webenginedownloads.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/browser/webengine/webengineelem.py
- Specify the type of WebEngineElement._tab.
- Remove now unneeded type ignore for mixed flags.
==== qutebrowser/browser/webengine/webengineinspector.py
- See changes to inspector.py and next commit.
- Remove now unneeded type ignore for signal.
==== qutebrowser/browser/webengine/webenginequtescheme.py
- Remove now unneeded type ignore for mixed flags.
==== qutebrowser/browser/webengine/webenginesettings.py
- Ignore access of .setter attribute which we patch onto QWebEngineProfile.
Would be nice to have a subclass or wrapper-class instead.
==== qutebrowser/browser/webengine/webenginetab.py
- Specified the type of _widget members more closely than just QWidget.
See browsertab.py changes for details.
- Remove some now-unneeded type ignores for creating FindFlags.
- Specify more concrete types for WebEngineTab members where we actually need to
access WebEngine-specific attributes.
- Make sure the page we get is our custom WebEnginePage subclass, not just any
QWebEnginePage. This is needed because we access custom attributes on it.
==== qutebrowser/browser/webengine/webview.py
- Make sure the page we get is our custom WebEnginePage subclass, not just any
QWebEnginePage. This is needed because we access custom attributes on it.
==== qutebrowser/browser/webkit/network/networkreply.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/browser/webkit/webkitinspector.py
- See changes to inspector.py and next commit.
==== qutebrowser/browser/webkit/webkittab.py
- Specify the type of _widget members more closely than just QWidget.
See browsertab.py changes for details.
- Add a type ignore for WebKitAction because our workaround needs to
treat them as ints (which is allowed by PyQt, even if not type-safe).
- Add new ignores for findText calls: The text is a QString and can be None; the
flags are valid despite mypy thinking they aren't (stubs regression?).
- Specify the type for WebKitHistoryPrivate._history, because we access
WebKit-specific attributes. See above (_widget) re this being debatable.
- Make mypy aware that .currentFrame() and .frameAt() can return None (stubs
regression?).
- Make sure the .page() and .page().networkAccessManager() are our subclasses
rather than the more generic QtWebKit objects, as we use custom attributes.
- Add new type ignores for signals (stubs regression!)
==== qutebrowser/browser/webkit/webpage.py
- Make sure the .networkAccessManager() is our subclass rather than the more
generic QtWebKit object, as we use custom attributes.
- Replace a cast by a type ignore. The cast didn't work anymore.
==== qutebrowser/browser/webkit/webview.py
- Make sure the .page() is our subclass rather than the more generic QtWebKit
object, as we use custom attributes.
==== qutebrowser/commands/userscripts.py
- Remove now unneeded type ignore for signal.
==== qutebrowser/completion/completer.py
- Add a new _completion() getter (which ensures it actually gets the completion
view) rather than accessing the .parent() directly (which could be any QObject).
==== qutebrowser/completion/completiondelegate.py
- Make sure self.parent() is a CompletionView (no helper method as there is only
one instance).
- Remove a now-unneeded type ignore for adding QSizes.
==== qutebrowser/completion/completionwidget.py
- Add a ._model() getter which ensures that we get a CompletionModel (with
custom attributes) rather than Qt's .model() which can be any QAbstractItemModel
(or None).
- Removed a now-unneeded type ignore for OR-ing flags.
==== qutebrowser/completion/models/completionmodel.py
- Remove now unneeded type ignores for signals.
- Ignore a complaint about .set_pattern() not being defined. Completion
categories don't share any common parent class, so it would be good to introduce
a typing.Protocol for this. See #7098.
==== qutebrowser/components/misccommands.py
- Removed a now-unneeded type ignore for OR-ing flags.
==== qutebrowser/components/readlinecommands.py
- Make sure QApplication.instance() is a QApplication (and not just a
QCoreApplication). This includes the former "not None" check.
==== qutebrowser/components/scrollcommands.py
- Add basic annotation for "funcs" dict. Could have a callable protocol to
specify it needs a count kwarg, see #7098.
==== qutebrowser/config/stylesheet.py
- Correctly specify that stylesheet apply to QWidgets, not any QObject.
- Ignore an attr-defined for obj.STYLESHEET. Perhaps could somehow teach mypy
about this with overloads and protocols (stylesheet for set_register being None
=> STYLESHEET needs to be defined, otherwise anything goes), but perhaps not
worth the troble. See #7098.
==== qutebrowser/keyinput/keyutils.py
- Remove some now-unneeded type ignores and add a cast for using a single enum
value as flags. Might need to look at this again with Qt 6 support.
==== qutebrowser/keyinput/modeman.py
- Add a FIXME for using a TypedDict, see comments for hints.py above.
==== qutebrowser/mainwindow/mainwindow.py
- Remove now-unneeded type ignores for calling with OR-ed flags.
- Improve where we cast from WindowType to WindowFlags, no int needed
- Use new .tab_bar() getter, see below.
==== qutebrowser/mainwindow/prompt.py
- Remove now-unneeded type ignores for calling with OR-ed flags.
==== qutebrowser/mainwindow/statusbar/bar.py
- Adjust type ignores around @pyqtProperty. The fact one is still needed seems
like a stub regression.
==== qutebrowser/mainwindow/statusbar/command.py
- Fix type for setText() override (from QLineEdit): text can be None
(QString in C++).
==== qutebrowser/mainwindow/statusbar/url.py
- Adjust type ignores around @pyqtProperty. The fact one is still needed seems
like a stub regression.
==== qutebrowser/mainwindow/tabbedbrowser.py
- Specify that TabDeque manages browser tabs, not any QWidgets. It accesses
AbstractTab-specific attributes.
- Make sure that the .tabBar() we get is a tabwidget.TabBar, as we access
.maybe_hide.
- Fix the annotations for stored marks: Scroll positions are a QPoint, not int.
- Add _current_tab() and _tab_by_idx() wrappers for .currentWidget() and
.widget(), which ensures that the return values are valid AbstractTabs (or None
for _tab_by_idx). This is needed because we access AbstractTab-specific
attributes.
- For some places, where the tab can be None, continue using .currentTab() but
add asserts.
- Remove some now-unneeded [unreachable] ignores, as mypy knows about the None
possibility now.
==== qutebrowser/mainwindow/tabwidget.py
- Add new tab_bar() and _tab_by_idx() helpers which check that the .tabBar() and
.widget() are of type TabBar and AbstractTab, respectively.
- Add additional assertions where we expect ._tab_by_idx() to never be None.
- Remove dead code in get_tab_fields for handling a None y scroll position. I
was unable to find any place in the code where this could be set to None.
- Remove some now-unneeded type ignores and casts, as mypy now knows that
_type_by_idx() could be None.
- Work around a strange instance where mypy complains about not being able to
find the type of TabBar.drag_in_progress from TabWidget._toggle_visibility,
despite it clearly being shown as a bool *inside* that class without any
annotation.
- Add a ._tab_widget() getter in TabBar which ensures that the .parent() is in
fact a TabWidget.
==== qutebrowser/misc/crashsignal.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/misc/editor.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/misc/ipc.py
- Remove now unneeded type ignores for signals.
- Add new type ignores for .error() which is both a signal and a getter
(stub regression?). Won't be relevant for Qt 6 anymore, as the signal was
renamed to errorOccurred in 5.15.
==== qutebrowser/misc/objects.py
- Make sure mypy knows that objects.app is our custom Application (with custom
attributes) rather than any QApplication.
==== qutebrowser/utils/objreg.py
- Ignore attr-defined for .win_id attributes. Maybe could add a typing.Protocol,
but ideally, the whole objreg stuff should die one day anyways.
==== tests/unit/completion/test_completer.py
- Make CompletionWidgetStub inherit from CompletionView so that it passes the
new isinstance() asserts in completer.py (see above).
|
https://github.com/qutebrowser/qutebrowser.git
|
def set_pattern(self, pattern):
log.completion.debug("Setting completion pattern '{}'".format(pattern))
self.layoutAboutToBeChanged.emit()
for cat in self._categories:
# FIXME:mypy define a Protocol for set_pattern?
cat.set_pattern(pattern) # type: ignore[attr-defined]
self.layoutChanged.emit()
| 48
|
completionmodel.py
|
Python
|
qutebrowser/completion/models/completionmodel.py
|
a20bb67a878b2e68abf8268c1b0a27f018d01352
|
qutebrowser
| 2
|
|
41,392
| 5
| 7
| 2
| 26
| 5
| 0
| 5
| 19
|
_forward
|
Transition mappings->properties, leaving a few loose ends
|
https://github.com/mwaskom/seaborn.git
|
def _forward(self, values):
return np.square(values)
| 15
|
properties.py
|
Python
|
seaborn/_core/properties.py
|
a07ef69882ed76e09a0ed43d6f3ea33780c1b2be
|
seaborn
| 1
|
|
337,586
| 16
| 14
| 9
| 108
| 18
| 0
| 17
| 56
|
test_tracking
|
Refactor tests to use accelerate launch (#373)
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
|
https://github.com/huggingface/accelerate.git
|
def test_tracking(self):
with tempfile.TemporaryDirectory() as tmpdir:
testargs = f.split()
_ = subprocess.run(self._launch_args + testargs, stdout=subprocess.PIPE)
self.assertTrue(os.path.exists(os.path.join(tmpdir, "tracking")))
| 62
|
test_examples.py
|
Python
|
tests/test_examples.py
|
23c0341262bd396a3ba9265614b3818d6e08a6c1
|
accelerate
| 1
|
|
281,498
| 9
| 9
| 18
| 40
| 7
| 0
| 10
| 32
|
print_help
|
Terminal Wide Rich (#1161)
* My idea for how we handle Rich moving forward
* remove independent consoles
* FIxed pylint issues
* add a few vars
* Switched print to console
* More transitions
* Changed more prints
* Replaced all prints
* Fixing tabulate
* Finished replace tabulate
* Finished removing rich from Tabulate
* add Panel around menu
* add GST watermark under feature flag
* Fixed 46 tests
* Delete test_screener[False].yaml
* Delete test_screener[True].yaml
* Fixed the rest of the tests
* add help and source color vars and use rgb
* rich on stocks/options
* update rich on disc, dps, sia
* rich in gov, ins and scr menus
* ba and ca menus with rich
* Fixed import issue
* Fixed some tests
* removed termcolor
* Removed prettytable
* add rich to remaining stocks menus
* FIxed linting issue
* Added James' changes
* Updated dependencies
* Add rich to cryptocurrency menu
* refactor economy and forex
* refactor etf with rich
* refactor mfunds
* refactor rich rest
* not specify style so default color works well on any background
* Fixing mypy issues
* Updated tests
* More test fixes
* James' test fixes
* Updating tests : stocks/screener - fix cassettes using BR
* Updating tests : crypto
* Updating tests : disable DEBUG_MODE
* Updating tests : stocks/fa/yfinance
* minor fixes that escape
* Improve the rich table function (that replaces tabulate :D )
* Fixed bad code
* delete rogue file + dcf fix + NoConsole
* sia mypy
* fuck you linter
* fuck you linter pt 2
* skip hehe
* i hate the black linter
* ubuntu mypy attempt
* Update : rich_config + gtff
* Updating tests : conftest
* Updating tests : stocks
* Update : rich_config
* Updating : rich_config
* make panel configurable for Theodore :b
* colors update
* Merged
* Updating : rich_config + feature_flags
* Updating : rich_config
* Updating tests : stocks
* Updating : feature_flags
Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt>
Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
Co-authored-by: james <jmaslek11@gmail.com>
Co-authored-by: jose-donato <zmcdonato@gmail.com>
|
https://github.com/OpenBB-finance/OpenBBTerminal.git
|
def print_help(self):
help_text =
console.print(text=help_text, menu="Portfolio - Brokers - Degiro")
| 21
|
degiro_controller.py
|
Python
|
gamestonk_terminal/portfolio/brokers/degiro/degiro_controller.py
|
82747072c511beb1b2672846ae2ee4aec53eb562
|
OpenBBTerminal
| 1
|
|
212,818
| 19
| 10
| 7
| 80
| 9
| 0
| 26
| 91
|
widget_to_element
|
Added key and widget Element properties, new focus methods Element.get_next_focus, Element.get_previous_focus. New Window method Window.widget_to_element
|
https://github.com/PySimpleGUI/PySimpleGUI.git
|
def widget_to_element(self, widget):
if self.AllKeysDict is None or len(self.AllKeysDict) == 0:
return None
for key, element in self.AllKeysDict.items():
if element.Widget == widget:
return element
return None
| 50
|
PySimpleGUI.py
|
Python
|
PySimpleGUI.py
|
9b814f003b0685757d76ce56ee9c98eae114d346
|
PySimpleGUI
| 5
|
|
19,953
| 16
| 12
| 7
| 63
| 9
| 0
| 16
| 37
|
is_pinned
|
check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4
|
https://github.com/pypa/pipenv.git
|
def is_pinned(self) -> bool:
specifiers = self.specifier
return len(specifiers) == 1 and next(iter(specifiers)).operator in {"==", "==="}
| 36
|
req_install.py
|
Python
|
pipenv/patched/notpip/_internal/req/req_install.py
|
f3166e673fe8d40277b804d35d77dcdb760fc3b3
|
pipenv
| 2
|
|
337,071
| 48
| 15
| 33
| 221
| 22
| 0
| 70
| 144
|
upsample_2d
|
Clean up resnet.py file (#780)
* clean up resnet.py
* make style and quality
* minor formatting
|
https://github.com/huggingface/diffusers.git
|
def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
r
assert isinstance(factor, int) and factor >= 1
if kernel is None:
kernel = [1] * factor
kernel = torch.tensor(kernel, dtype=torch.float32)
if kernel.ndim == 1:
kernel = torch.outer(kernel, kernel)
kernel /= torch.sum(kernel)
kernel = kernel * (gain * (factor**2))
pad_value = kernel.shape[0] - factor
output = upfirdn2d_native(
hidden_states,
kernel.to(device=hidden_states.device),
up=factor,
pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
)
return output
| 138
|
resnet.py
|
Python
|
src/diffusers/models/resnet.py
|
a73f8b725105b12a60a9b22918bda68f8b6d26c3
|
diffusers
| 4
|
|
181,839
| 43
| 13
| 19
| 139
| 16
| 0
| 46
| 239
|
export
|
Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068.
|
https://github.com/EpistasisLab/tpot.git
|
def export(self, output_file_name="", data_file_path=""):
if self._optimized_pipeline is None:
raise RuntimeError(
"A pipeline has not yet been optimized. Please call fit() first."
)
to_write = export_pipeline(
self._optimized_pipeline,
self.operators,
self._pset,
self._imputed,
self._optimized_pipeline_score,
self.random_state,
data_file_path=data_file_path,
)
if output_file_name != "":
with open(output_file_name, "w") as output_file:
output_file.write(to_write)
else:
return to_write
| 84
|
base.py
|
Python
|
tpot/base.py
|
388616b6247ca4ea8de4e2f340d6206aee523541
|
tpot
| 3
|
|
313,273
| 16
| 11
| 6
| 67
| 9
| 1
| 16
| 38
|
should_retry
|
Improve stream robustness by always retrying worker (#66417)
Improve stream robustness by always retrying in the worker on failure, rather than only when
keepalive is enabled.
This will make cloud cameras like nest more robust, since they have a tendency to be flaky. This
is also needed to improve client side retry behavior because when the client attempts to retry,
the stream token is already revoked because the worker stopped.
The worker will still idle timeout if no streams are present, so it won't go on forever if no
frontend is viewing the stream.
|
https://github.com/home-assistant/core.git
|
def should_retry() -> Generator[Mock, None, None]:
with patch(
"homeassistant.components.stream._should_retry", return_value=False
) as mock_should_retry:
yield mock_should_retry
@pytest.fixture(scope="package")
|
@pytest.fixture(scope="package")
| 28
|
conftest.py
|
Python
|
tests/components/stream/conftest.py
|
0a128d006f0c0de4bdfe2acda66beb8fa5731463
|
core
| 1
|
259,147
| 8
| 10
| 2
| 49
| 7
| 0
| 8
| 22
|
fit_transform
|
MNT Refactor KMeans and MiniBatchKMeans to inherit from a common base class (#22723)
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
|
https://github.com/scikit-learn/scikit-learn.git
|
def fit_transform(self, X, y=None, sample_weight=None):
return self.fit(X, sample_weight=sample_weight)._transform(X)
| 32
|
_kmeans.py
|
Python
|
sklearn/cluster/_kmeans.py
|
6ab950ec081044a1f32c2d082772635bb56144d8
|
scikit-learn
| 1
|
|
20,836
| 7
| 8
| 8
| 40
| 4
| 0
| 7
| 28
|
pop
|
check point progress on only bringing in pip==22.0.4 (#4966)
* vendor in pip==22.0.4
* updating vendor packaging version
* update pipdeptree to fix pipenv graph with new version of pip.
* Vendoring of pip-shims 0.7.0
* Vendoring of requirementslib 1.6.3
* Update pip index safety restrictions patch for pip==22.0.4
* Update patches
* exclude pyptoject.toml from black to see if that helps.
* Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4
|
https://github.com/pypa/pipenv.git
|
def pop(self) -> Style:
self._stack.pop()
return self._stack[-1]
| 23
|
style.py
|
Python
|
pipenv/patched/notpip/_vendor/rich/style.py
|
f3166e673fe8d40277b804d35d77dcdb760fc3b3
|
pipenv
| 1
|
|
246,790
| 89
| 15
| 39
| 438
| 34
| 0
| 132
| 557
|
test_context_as_admin
|
Replace assertEquals and friends with non-deprecated versions. (#12092)
|
https://github.com/matrix-org/synapse.git
|
def test_context_as_admin(self) -> None:
# Create a room. We're not part of it.
user_id = self.register_user("test", "test")
user_tok = self.login("test", "test")
room_id = self.helper.create_room_as(user_id, tok=user_tok)
# Populate the room with events.
events = []
for i in range(30):
events.append(
self.helper.send_event(
room_id, "com.example.test", content={"index": i}, tok=user_tok
)
)
# Now let's fetch the context for this room.
midway = (len(events) - 1) // 2
channel = self.make_request(
"GET",
"/_synapse/admin/v1/rooms/%s/context/%s"
% (room_id, events[midway]["event_id"]),
access_token=self.admin_user_tok,
)
self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body)
self.assertEqual(
channel.json_body["event"]["event_id"], events[midway]["event_id"]
)
for found_event in channel.json_body["events_before"]:
for j, posted_event in enumerate(events):
if found_event["event_id"] == posted_event["event_id"]:
self.assertTrue(j < midway)
break
else:
self.fail("Event %s from events_before not found" % j)
for found_event in channel.json_body["events_after"]:
for j, posted_event in enumerate(events):
if found_event["event_id"] == posted_event["event_id"]:
self.assertTrue(j > midway)
break
else:
self.fail("Event %s from events_after not found" % j)
| 261
|
test_room.py
|
Python
|
tests/rest/admin/test_room.py
|
02d708568b476f2f7716000b35c0adfa4cbd31b3
|
synapse
| 8
|
|
212,752
| 16
| 15
| 5
| 76
| 8
| 0
| 17
| 60
|
mapping
|
More demo programs updates 🤦♂️ wow.....I thought for sure these were checked in....
|
https://github.com/PySimpleGUI/PySimpleGUI.git
|
def mapping(func, sequence, *argc):
if isinstance(sequence, list):
return list(map(lambda i: func(i, *argc), sequence))
else:
return func(sequence, *argc)
| 48
|
Demo_Desktop_Widget_Drive_Usage_Gauges.py
|
Python
|
DemoPrograms/Demo_Desktop_Widget_Drive_Usage_Gauges.py
|
430d1bc77fcdc0969a66ff370ec5e7e590423c83
|
PySimpleGUI
| 2
|
|
156,670
| 33
| 15
| 13
| 142
| 17
| 0
| 36
| 130
|
_register_entry_point_plugins
|
Feat: read entrypoints in ``dask.sizeof`` (#7688)
Entry-points are only used when there is no dispatch for a given type which eliminates the cost for applications which do not need entry-point based implementations.
|
https://github.com/dask/dask.git
|
def _register_entry_point_plugins():
if sys.version_info >= (3, 10):
sizeof_entry_points = importlib.metadata.entry_points(group="dask.sizeof")
else:
sizeof_entry_points = importlib.metadata.entry_points().get("dask.sizeof", [])
for entry_point in sizeof_entry_points:
registrar = entry_point.load()
try:
registrar(sizeof)
except Exception:
logger.exception(
f"Failed to register sizeof entry point {entry_point.name}"
)
_register_entry_point_plugins()
| 75
|
sizeof.py
|
Python
|
dask/sizeof.py
|
0a7c41dd1ffd84ea56e387eea51b7df1dbb81140
|
dask
| 4
|
|
269,475
| 9
| 7
| 2
| 57
| 10
| 1
| 9
| 12
|
minimum
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def minimum(x, y):
return tf.minimum(x, y)
@keras_export("keras.backend.sin")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs
|
@keras_export("keras.backend.sin")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs
| 17
|
backend.py
|
Python
|
keras/backend.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 1
|
153,818
| 6
| 11
| 2
| 42
| 9
| 0
| 6
| 20
|
dt_strftime
|
REFACTOR-#4513: Fix spelling mistakes in docs and docstrings (#4514)
Co-authored-by: Rehan Sohail Durrani <rdurrani@berkeley.edu>
Signed-off-by: jeffreykennethli <jkli@ponder.io>
|
https://github.com/modin-project/modin.git
|
def dt_strftime(self, date_format):
return DateTimeDefault.register(pandas.Series.dt.strftime)(self, date_format)
| 26
|
query_compiler.py
|
Python
|
modin/core/storage_formats/base/query_compiler.py
|
57e29bc5d82348006c5170ef9ac0a9eedcd9acf9
|
modin
| 1
|
|
148,053
| 5
| 9
| 3
| 31
| 4
| 0
| 5
| 30
|
compute_first_block
|
[Datasets] [Out-of-Band Serialization: 1/3] Refactor `LazyBlockList`. (#23821)
This PR refactors `LazyBlockList` in service of out-of-band serialization (see [mono-PR](https://github.com/ray-project/ray/pull/22616)) and is a precursor to an execution plan refactor (PR #2) and adding the actual out-of-band serialization APIs (PR #3). The following is included in this refactor:
1. `ReadTask`s are now a first-class concept, replacing calls;
2. read stage progress tracking is consolidated into `LazyBlockList._get_blocks_with_metadta()` and more of the read task complexity, e.g. the read remote function, was pushed into `LazyBlockList` to make `ray.data.read_datasource()` simpler;
3. we are a bit smarter with how we progressively launch tasks and fetch and cache metadata, including fetching the metadata for read tasks in `.iter_blocks_with_metadata()` instead of relying on the pre-read task metadata (which will be less accurate), and we also fix some small bugs in the lazy ramp-up around progressive metadata fetching.
(1) is the most important item for supporting out-of-band serialization and fundamentally changes the `LazyBlockList` data model. This is required since we need to be able to reference the underlying read tasks when rewriting read stages during optimization and when serializing the lineage of the Dataset. See the [mono-PR](https://github.com/ray-project/ray/pull/22616) for more context.
Other changes:
1. Changed stats actor to a global named actor singleton in order to obviate the need for serializing the actor handle with the Dataset stats; without this, we were encountering serialization failures.
|
https://github.com/ray-project/ray.git
|
def compute_first_block(self):
if self._tasks:
self._get_or_compute(0)
| 17
|
lazy_block_list.py
|
Python
|
python/ray/data/impl/lazy_block_list.py
|
efc5ac5ddfb1a83db931e015ce4697280e73a16d
|
ray
| 2
|
|
260,894
| 14
| 13
| 5
| 82
| 16
| 0
| 16
| 31
|
test_gpr_predict_input_not_modified
|
FIX Ensure that GaussianProcessRegressor predict method does not modify input (#24405)
|
https://github.com/scikit-learn/scikit-learn.git
|
def test_gpr_predict_input_not_modified():
gpr = GaussianProcessRegressor(kernel=CustomKernel()).fit(X, y)
X2_copy = np.copy(X2)
_, _ = gpr.predict(X2, return_std=True)
assert_allclose(X2, X2_copy)
| 50
|
test_gpr.py
|
Python
|
sklearn/gaussian_process/tests/test_gpr.py
|
e04fb58e5d65cbb9a44840bf7c20f850d4081cb2
|
scikit-learn
| 1
|
|
76,543
| 7
| 9
| 4
| 35
| 3
| 0
| 8
| 40
|
classes
|
Move wagtail.admin.edit_handlers to wagtail.admin.panels
|
https://github.com/wagtail/wagtail.git
|
def classes(self):
if self.classname:
return [self.classname]
return []
| 20
|
panels.py
|
Python
|
wagtail/admin/panels.py
|
b189ab8382cc2247129b221e80599df86acd5bb3
|
wagtail
| 2
|
|
68,939
| 45
| 14
| 22
| 183
| 14
| 0
| 53
| 37
|
validate_house_rent_dates
|
chore: keep back code to be a part of other apps / to be ported later
|
https://github.com/frappe/erpnext.git
|
def validate_house_rent_dates(doc):
if not doc.rented_to_date or not doc.rented_from_date:
frappe.throw(_("House rented dates required for exemption calculation"))
if date_diff(doc.rented_to_date, doc.rented_from_date) < 14:
frappe.throw(_("House rented dates should be atleast 15 days apart"))
proofs = frappe.db.sql(
,
{
"employee": doc.employee,
"payroll_period": doc.payroll_period,
"from_date": doc.rented_from_date,
"to_date": doc.rented_to_date,
},
)
if proofs:
frappe.throw(_("House rent paid days overlapping with {0}").format(proofs[0][0]))
| 109
|
utils.py
|
Python
|
erpnext/regional/india/utils.py
|
12b7e14fded587abc0f7821e3c3dfbea64498a7d
|
erpnext
| 5
|
|
150,737
| 54
| 14
| 14
| 312
| 22
| 0
| 203
| 601
|
calculate_reward
|
make base 3ac and base 5ac environments. TDQN defaults to 3AC.
|
https://github.com/freqtrade/freqtrade.git
|
def calculate_reward(self, action):
if self._last_trade_tick is None:
return 0.
# close long
if (action == Actions.Short.value or
action == Actions.Neutral.value) and self._position == Positions.Long:
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(current_price) - np.log(last_trade_price))
# close short
if (action == Actions.Long.value or
action == Actions.Neutral.value) and self._position == Positions.Short:
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
return float(np.log(last_trade_price) - np.log(current_price))
return 0.
# User can inherit and customize 5 action environment
# class MyRLEnv(Base5ActionRLEnv):
#
# def calculate_reward(self, action):
# if self._last_trade_tick is None:
# return 0.
# # close long
# if action == Actions.Long_sell.value and self._position == Positions.Long:
# last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
# current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
# return float(np.log(current_price) - np.log(last_trade_price))
# if action == Actions.Long_sell.value and self._position == Positions.Long:
# if self.close_trade_profit[-1] > self.profit_aim * self.rr:
# last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
# current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
# return float((np.log(current_price) - np.log(last_trade_price)) * 2)
# # close short
# if action == Actions.Short_buy.value and self._position == Positions.Short:
# last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
# current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
# return float(np.log(last_trade_price) - np.log(current_price))
# if action == Actions.Short_buy.value and self._position == Positions.Short:
# if self.close_trade_profit[-1] > self.profit_aim * self.rr:
# last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
# current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
# return float((np.log(last_trade_price) - np.log(current_price)) * 2)
# return 0.
| 184
|
ReinforcementLearningTDQN.py
|
Python
|
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
|
926023935f52a69d7e8830843729978121b68bc3
|
freqtrade
| 8
|
|
244,263
| 115
| 17
| 58
| 715
| 45
| 0
| 184
| 984
|
add_params
|
[Feature] Support ConvNeXt (#7281)
* update
* update
* fix init_cfg
* update
* update
* update
* update
* update1
* final
* update
* update
* fix lint
* fix backbone config
* update cascade_mask_rcnn
* update and fix lint
* update
* fix DefaultOptimizerConstructor error
* update
* update
* update
* fix year
* update
* fix lint
|
https://github.com/open-mmlab/mmdetection.git
|
def add_params(self, params, module, **kwargs):
logger = get_root_logger()
parameter_groups = {}
logger.info(f'self.paramwise_cfg is {self.paramwise_cfg}')
num_layers = self.paramwise_cfg.get('num_layers') + 2
decay_rate = self.paramwise_cfg.get('decay_rate')
decay_type = self.paramwise_cfg.get('decay_type', 'layer_wise')
logger.info('Build LearningRateDecayOptimizerConstructor '
f'{decay_type} {decay_rate} - {num_layers}')
weight_decay = self.base_wd
for name, param in module.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith('.bias') or name in (
'pos_embed', 'cls_token'):
group_name = 'no_decay'
this_weight_decay = 0.
else:
group_name = 'decay'
this_weight_decay = weight_decay
if 'layer_wise' in decay_type:
if 'ConvNeXt' in module.backbone.__class__.__name__:
layer_id = get_layer_id_for_convnext(
name, self.paramwise_cfg.get('num_layers'))
logger.info(f'set param {name} as id {layer_id}')
else:
raise NotImplementedError()
elif decay_type == 'stage_wise':
if 'ConvNeXt' in module.backbone.__class__.__name__:
layer_id = get_stage_id_for_convnext(name, num_layers)
logger.info(f'set param {name} as id {layer_id}')
else:
raise NotImplementedError()
group_name = f'layer_{layer_id}_{group_name}'
if group_name not in parameter_groups:
scale = decay_rate**(num_layers - layer_id - 1)
parameter_groups[group_name] = {
'weight_decay': this_weight_decay,
'params': [],
'param_names': [],
'lr_scale': scale,
'group_name': group_name,
'lr': scale * self.base_lr,
}
parameter_groups[group_name]['params'].append(param)
parameter_groups[group_name]['param_names'].append(name)
rank, _ = get_dist_info()
if rank == 0:
to_display = {}
for key in parameter_groups:
to_display[key] = {
'param_names': parameter_groups[key]['param_names'],
'lr_scale': parameter_groups[key]['lr_scale'],
'lr': parameter_groups[key]['lr'],
'weight_decay': parameter_groups[key]['weight_decay'],
}
logger.info(f'Param groups = {json.dumps(to_display, indent=2)}')
params.extend(parameter_groups.values())
| 382
|
layer_decay_optimizer_constructor.py
|
Python
|
mmdet/core/optimizers/layer_decay_optimizer_constructor.py
|
1fd48f7318ac70bab6de371025c74a76c5219e1c
|
mmdetection
| 13
|
|
260,178
| 32
| 10
| 9
| 153
| 24
| 1
| 35
| 69
|
test_fastica_eigh_low_rank_warning
|
ENH Add `eigh` solver to `FastICA` (#22527)
Co-authored-by: Pierre Ablin <pierreablin@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
|
https://github.com/scikit-learn/scikit-learn.git
|
def test_fastica_eigh_low_rank_warning(global_random_seed):
rng = np.random.RandomState(global_random_seed)
X = make_low_rank_matrix(
n_samples=10, n_features=10, random_state=rng, effective_rank=2
)
ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver="eigh")
msg = "There are some small singular values"
with pytest.warns(UserWarning, match=msg):
ica.fit(X)
@pytest.mark.parametrize("whiten_solver", ["this_should_fail", "test", 1, None])
|
@pytest.mark.parametrize("whiten_solver", ["this_should_fail", "test", 1, None])
| 73
|
test_fastica.py
|
Python
|
sklearn/decomposition/tests/test_fastica.py
|
54c150392fd3ccf28b8d843bbc41311f1da711df
|
scikit-learn
| 1
|
291,225
| 10
| 7
| 4
| 35
| 5
| 0
| 10
| 32
|
last_changed
|
Add type hints to template states (#82582)
* Add type hints to template states
* Undo rename
* Remove invalid mypy issue link
|
https://github.com/home-assistant/core.git
|
def last_changed(self) -> datetime: # type: ignore[override]
self._collect_state()
return self._state.last_changed
| 19
|
template.py
|
Python
|
homeassistant/helpers/template.py
|
aa02a53ac667d08c66a536baf139993bcfe4d7d6
|
core
| 1
|
|
197,105
| 17
| 9
| 13
| 79
| 11
| 0
| 17
| 92
|
_collect_factor_and_dimension
|
Update the deprecation warnings in sympy.physics.units
|
https://github.com/sympy/sympy.git
|
def _collect_factor_and_dimension(expr, unit_system="SI"):
sympy_deprecation_warning(
,
deprecated_since_version="1.5",
active_deprecations_target="deprecated-quantity-methods",
)
from sympy.physics.units import UnitSystem
unit_system = UnitSystem.get_unit_system(unit_system)
return unit_system._collect_factor_and_dimension(expr)
| 46
|
quantities.py
|
Python
|
sympy/physics/units/quantities.py
|
905eb426131ca9542a6b258462d9ae984e5b2563
|
sympy
| 1
|
|
267,051
| 53
| 12
| 6
| 126
| 10
| 0
| 69
| 140
|
set_attributes_from_cli
|
Deprecate PlayContext.verbosity (#77507)
display.verbosity should be used instead
|
https://github.com/ansible/ansible.git
|
def set_attributes_from_cli(self):
if context.CLIARGS.get('timeout', False):
self.timeout = int(context.CLIARGS['timeout'])
# From the command line. These should probably be used directly by plugins instead
# For now, they are likely to be moved to FieldAttribute defaults
self.private_key_file = context.CLIARGS.get('private_key_file') # Else default
self._internal_verbosity = context.CLIARGS.get('verbosity') # Else default
# Not every cli that uses PlayContext has these command line args so have a default
self.start_at_task = context.CLIARGS.get('start_at_task', None) # Else default
| 69
|
play_context.py
|
Python
|
lib/ansible/playbook/play_context.py
|
494f9327734b3ef9d2b4e4f2a32d1d4c363cd400
|
ansible
| 2
|
|
46,261
| 8
| 11
| 4
| 52
| 7
| 0
| 9
| 41
|
full_path
|
Add cgroupspy to _vendor folder (#22206)
This is just importing existing cgroupspy library without
code modifications. In the next step we will modify the cgroupspy
code to work from the new location, then we will fix it to
implement Python 3.10 compatibility and finally we will change
airflow to use the vendored package instead of the original
package.
This is part of the effort needed to implement Python 3.10
compatibility: #22050
|
https://github.com/apache/airflow.git
|
def full_path(self):
if self.parent:
return os.path.join(self.parent.full_path, self.name)
return self.name
| 32
|
nodes.py
|
Python
|
airflow/_vendor/cgroupspy/nodes.py
|
68aa01936c37f9b03468cc83e3f32766ae0ba5cb
|
airflow
| 2
|
|
294,411
| 78
| 16
| 50
| 433
| 53
| 0
| 111
| 820
|
async_step_connect
|
Motion allow changing ip (#68589)
Co-authored-by: J. Nick Koston <nick@koston.org>
|
https://github.com/home-assistant/core.git
|
async def async_step_connect(self, user_input=None):
errors = {}
if user_input is not None:
key = user_input[CONF_API_KEY]
multicast_interface = user_input[CONF_INTERFACE]
# check socket interface
if multicast_interface != DEFAULT_INTERFACE:
motion_multicast = AsyncMotionMulticast(interface=multicast_interface)
try:
await motion_multicast.Start_listen()
motion_multicast.Stop_listen()
except gaierror:
errors[CONF_INTERFACE] = "invalid_interface"
return self.async_show_form(
step_id="connect",
data_schema=self._config_settings,
errors=errors,
)
connect_gateway_class = ConnectMotionGateway(self.hass, multicast=None)
if not await connect_gateway_class.async_connect_gateway(self._host, key):
return self.async_abort(reason="connection_error")
motion_gateway = connect_gateway_class.gateway_device
mac_address = motion_gateway.mac
await self.async_set_unique_id(mac_address)
self._abort_if_unique_id_configured(
updates={
CONF_HOST: self._host,
CONF_API_KEY: key,
CONF_INTERFACE: multicast_interface,
}
)
return self.async_create_entry(
title=DEFAULT_GATEWAY_NAME,
data={
CONF_HOST: self._host,
CONF_API_KEY: key,
CONF_INTERFACE: multicast_interface,
},
)
(interfaces, default_interface) = await self.async_get_interfaces()
self._config_settings = vol.Schema(
{
vol.Required(CONF_API_KEY): vol.All(str, vol.Length(min=16, max=16)),
vol.Optional(CONF_INTERFACE, default=default_interface): vol.In(
interfaces
),
}
)
return self.async_show_form(
step_id="connect", data_schema=self._config_settings, errors=errors
)
| 278
|
config_flow.py
|
Python
|
homeassistant/components/motion_blinds/config_flow.py
|
faf1f229e1117090df69c0af526c1320f2e495c3
|
core
| 5
|
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