id
int64 20
338k
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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
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int64 1
153
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
160,530
| 42
| 14
| 16
| 215
| 22
| 0
| 58
| 157
|
openhook
|
ENH: Support character string arrays
TST: added test for issue #18684
ENH: f2py opens files with correct encoding, fixes #635
TST: added test for issue #6308
TST: added test for issue #4519
TST: added test for issue #3425
ENH: Implement user-defined hooks support for post-processing f2py data structure. Implement character BC hook.
ENH: Add support for detecting utf-16 and utf-32 encodings.
|
https://github.com/numpy/numpy.git
|
def openhook(filename, mode):
bytes = min(32, os.path.getsize(filename))
with open(filename, 'rb') as f:
raw = f.read(bytes)
if raw.startswith(codecs.BOM_UTF8):
encoding = 'UTF-8-SIG'
elif raw.startswith((codecs.BOM_UTF32_LE, codecs.BOM_UTF32_BE)):
encoding = 'UTF-32'
elif raw.startswith((codecs.BOM_LE, codecs.BOM_BE)):
encoding = 'UTF-16'
else:
if chardet is not None:
encoding = chardet.detect(raw)['encoding']
else:
# hint: install chardet to ensure correct encoding handling
encoding = 'ascii'
return open(filename, mode, encoding=encoding)
| 127
|
crackfortran.py
|
Python
|
numpy/f2py/crackfortran.py
|
d4e11c7a2eb64861275facb076d47ccd135fa28c
|
numpy
| 5
|
|
303,750
| 9
| 9
| 5
| 47
| 6
| 0
| 9
| 41
|
async_state_changed
|
Improve type hints in yeelight lights (#76018)
Co-authored-by: Franck Nijhof <frenck@frenck.nl>
|
https://github.com/home-assistant/core.git
|
def async_state_changed(self) -> None:
if not self._device.available:
self._async_cancel_pending_state_check()
self.async_write_ha_state()
| 26
|
light.py
|
Python
|
homeassistant/components/yeelight/light.py
|
66b742f110025013e60ca8cac7aeb3247bac8f47
|
core
| 2
|
|
243,137
| 39
| 12
| 7
| 158
| 18
| 1
| 48
| 138
|
test_sanity_ati2
|
Add support for ATI1/2(BC4/BC5) DDS files
This commit adds support for loading DDS with ATI1 and ATI2 fourcc pixel format
|
https://github.com/python-pillow/Pillow.git
|
def test_sanity_ati2():
with Image.open(TEST_FILE_ATI2) as im:
im.load()
assert im.format == "DDS"
assert im.mode == "RGB"
assert im.size == (128, 128)
assert_image_equal_tofile(im, TEST_FILE_ATI2.replace(".dds", ".png"))
@pytest.mark.parametrize(
("image_path", "expected_path"),
(
# hexeditted to be typeless
(TEST_FILE_DX10_BC5_TYPELESS, TEST_FILE_DX10_BC5_UNORM),
(TEST_FILE_DX10_BC5_UNORM, TEST_FILE_DX10_BC5_UNORM),
# hexeditted to use DX10 FourCC
(TEST_FILE_DX10_BC5_SNORM, TEST_FILE_BC5S),
(TEST_FILE_BC5S, TEST_FILE_BC5S),
),
)
|
@pytest.mark.parametrize(
("image_path", "expected_path"),
(
# hexeditted to be typeless
(TEST_FILE_DX10_BC5_TYPELESS, TEST_FILE_DX10_BC5_UNORM),
(TEST_FILE_DX10_BC5_UNORM, TEST_FILE_DX10_BC5_UNORM),
# hexeditted to use DX10 FourCC
(TEST_FILE_DX10_BC5_SNORM, TEST_FILE_BC5S),
(TEST_FILE_BC5S, TEST_FILE_BC5S),
),
)
| 55
|
test_file_dds.py
|
Python
|
Tests/test_file_dds.py
|
ad2c6a20fe874958d8d9adecbbfeb81856155f05
|
Pillow
| 1
|
60,238
| 26
| 11
| 5
| 92
| 9
| 0
| 28
| 44
|
crop_params
|
Balanced joint maximum mean discrepancy for deep transfer learning
|
https://github.com/jindongwang/transferlearning.git
|
def crop_params(fn):
params = fn.params.get('crop_param', fn.params)
axis = params.get('axis', 2) # default to spatial crop for N, C, H, W
offset = np.array(params.get('offset', 0), ndmin=1)
return (axis, offset)
| 55
|
coord_map.py
|
Python
|
code/deep/BJMMD/caffe/python/caffe/coord_map.py
|
cc4d0564756ca067516f71718a3d135996525909
|
transferlearning
| 1
|
|
290,734
| 30
| 13
| 17
| 161
| 20
| 0
| 45
| 189
|
_async_create_radio_entry
|
Minor refactor of zha config flow (#82200)
* Minor refactor of zha config flow
* Move ZhaRadioManager to a separate module
|
https://github.com/home-assistant/core.git
|
async def _async_create_radio_entry(self) -> FlowResult:
assert self._title is not None
assert self._radio_mgr.radio_type is not None
assert self._radio_mgr.device_path is not None
assert self._radio_mgr.device_settings is not None
device_settings = self._radio_mgr.device_settings.copy()
device_settings[CONF_DEVICE_PATH] = await self.hass.async_add_executor_job(
usb.get_serial_by_id, self._radio_mgr.device_path
)
return self.async_create_entry(
title=self._title,
data={
CONF_DEVICE: device_settings,
CONF_RADIO_TYPE: self._radio_mgr.radio_type.name,
},
)
| 106
|
config_flow.py
|
Python
|
homeassistant/components/zha/config_flow.py
|
bb64b39d0e6d41f531af9c63b69d1ce243a2751b
|
core
| 1
|
|
135,991
| 41
| 13
| 20
| 200
| 26
| 0
| 57
| 247
|
test_foreach_worker
|
[RLlib] Refactor `WorkerSet` on top of `FaultTolerantActorManager`. (#29938)
Signed-off-by: Jun Gong <jungong@anyscale.com>
|
https://github.com/ray-project/ray.git
|
def test_foreach_worker(self):
ws = WorkerSet(
env_creator=lambda _: gym.make("CartPole-v1"),
default_policy_class=RandomPolicy,
config=AlgorithmConfig().rollouts(num_rollout_workers=2),
num_workers=2,
)
policies = ws.foreach_worker(
lambda w: w.get_policy(DEFAULT_POLICY_ID),
local_worker=True,
)
# 3 policies including the one from the local worker.
self.assertEqual(len(policies), 3)
for p in policies:
self.assertIsInstance(p, RandomPolicy)
policies = ws.foreach_worker(
lambda w: w.get_policy(DEFAULT_POLICY_ID),
local_worker=False,
)
# 2 policies from only the remote workers.
self.assertEqual(len(policies), 2)
ws.stop()
| 126
|
test_worker_set.py
|
Python
|
rllib/evaluation/tests/test_worker_set.py
|
e707ce4fb3717e3c05118c57f503dfbd03552ca9
|
ray
| 2
|
|
280,144
| 46
| 11
| 39
| 219
| 40
| 4
| 61
| 253
|
get_config
|
Make default `Layer.get_config()` automatically work for a wide range of layers that do not override it.
PiperOrigin-RevId: 480781082
|
https://github.com/keras-team/keras.git
|
def get_config(self):
config = {
"name": self.name,
"trainable": self.trainable,
}
config["dtype"] = policy.serialize(self._dtype_policy)
if hasattr(self, "_batch_input_shape"):
config["batch_input_shape"] = self._batch_input_shape
if not generic_utils.is_default(self.get_config):
# In this case the subclass implements get_config()
return config
# In this case the subclass doesn't implement get_config():
# Let's see if we can autogenerate it.
if getattr(self, "_auto_get_config", False):
config.update(self._auto_config.config)
return config
else:
raise NotImplementedError(
textwrap.dedent(
f
|
raise NotImplementedError(
textwrap.dedent(
f"""
Layer {self.__class__.__name__} was created by passingargument valuesand therefore the layer must override `get_config()` in
order to be serializable. Please implement `get_config()`.
Example:order to be serializable. Please implement
| 99
|
base_layer.py
|
Python
|
keras/engine/base_layer.py
|
af1408d3255e3db9067522762e22a6c454c56654
|
keras
| 4
|
127,668
| 37
| 12
| 16
| 77
| 10
| 0
| 42
| 143
|
to_object_ref
|
[AIR] Deprecate `Checkpoint.to_object_ref` and `Checkpoint.from_object_ref` (#28318)
Before
object_ref = checkpoint.to_object_ref()
checkpoint.from_object_ref(object_ref)
After (this is already possible)
object_ref = ray.put(checkpoint)
ray.get(checkpoint)
Why are these changes needed?
We need to efficiently recover checkpoint type. ray.get already does this; from_object_ref can't. See [AIR] Maintain checkpoint subclass information during serialization #28134.
There are two ways to put checkpoints in the object store. You can either call to_object_ref or ray.put. We should standardize on the conventional way.
There should be one-- and preferably only one --obvious way to do it.
|
https://github.com/ray-project/ray.git
|
def to_object_ref(self) -> ray.ObjectRef:
warnings.warn(
"`to_object_ref` is deprecated and will be removed in a future Ray "
"version. To store the checkpoint in the Ray object store, call "
"`ray.put(ckpt)` instead of `ckpt.to_object_ref()`.",
DeprecationWarning,
)
if self._obj_ref:
return self._obj_ref
else:
return ray.put(self.to_dict())
| 43
|
checkpoint.py
|
Python
|
python/ray/air/checkpoint.py
|
c2bdee9fea6f354330545009d5e6caec3dd7eb26
|
ray
| 2
|
|
260,762
| 18
| 13
| 15
| 99
| 14
| 0
| 22
| 40
|
test_toy_example_collapse_points
|
MAINT Parameters validation for NeighborhoodComponentsAnalysis (#24195)
Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>
|
https://github.com/scikit-learn/scikit-learn.git
|
def test_toy_example_collapse_points():
rng = np.random.RandomState(42)
input_dim = 5
two_points = rng.randn(2, input_dim)
X = np.vstack([two_points, two_points.mean(axis=0)[np.newaxis, :]])
y = [0, 0, 1]
| 132
|
test_nca.py
|
Python
|
sklearn/neighbors/tests/test_nca.py
|
d7c978b764c6aafb65cc28757baf3f64da2cae34
|
scikit-learn
| 1
|
|
154,497
| 22
| 10
| 6
| 90
| 11
| 0
| 30
| 76
|
deploy
|
FIX-#4597: Refactor Partition handling of func, args, kwargs (#4715)
Co-authored-by: Iaroslav Igoshev <Poolliver868@mail.ru>
Signed-off-by: Jonathan Shi <jhshi@ponder.io>
|
https://github.com/modin-project/modin.git
|
def deploy(cls, func, f_args=None, f_kwargs=None, num_returns=1):
args = [] if f_args is None else f_args
kwargs = {} if f_kwargs is None else f_kwargs
return _deploy_ray_func.options(num_returns=num_returns).remote(
func, *args, **kwargs
)
| 60
|
engine_wrapper.py
|
Python
|
modin/core/execution/ray/common/engine_wrapper.py
|
d6d503ac7c3028d871c34d9e99e925ddb0746df6
|
modin
| 3
|
|
320,112
| 2
| 6
| 10
| 13
| 2
| 0
| 2
| 9
|
test_scan_file_for_separating_barcodes_pillow_transcode_error
|
In case pikepdf fails to convert an image to a PIL image, fall back to converting pages to PIL images
|
https://github.com/paperless-ngx/paperless-ngx.git
|
def test_scan_file_for_separating_barcodes_pillow_transcode_error(self):
| 69
|
test_barcodes.py
|
Python
|
src/documents/tests/test_barcodes.py
|
caf4b54bc7bf828ba170fcc329aa82a0c45da382
|
paperless-ngx
| 1
|
|
40,304
| 120
| 17
| 44
| 472
| 29
| 0
| 195
| 586
|
plotting_context
|
Use f-strings for string formatting (#2800)
Reformats all the text from the old "%-formatted" and .format(...) format to the newer f-string format, as defined in PEP 498. This requires Python 3.6+.
Flynt 0.76 was used to reformat the strings. 45 f-strings were created in 13 files.
F-strings are in general more readable, concise and performant. See also: https://www.python.org/dev/peps/pep-0498/#rationale
|
https://github.com/mwaskom/seaborn.git
|
def plotting_context(context=None, font_scale=1, rc=None):
if context is None:
context_dict = {k: mpl.rcParams[k] for k in _context_keys}
elif isinstance(context, dict):
context_dict = context
else:
contexts = ["paper", "notebook", "talk", "poster"]
if context not in contexts:
raise ValueError(f"context must be in {', '.join(contexts)}")
# Set up dictionary of default parameters
texts_base_context = {
"font.size": 12,
"axes.labelsize": 12,
"axes.titlesize": 12,
"xtick.labelsize": 11,
"ytick.labelsize": 11,
"legend.fontsize": 11,
"legend.title_fontsize": 12,
}
base_context = {
"axes.linewidth": 1.25,
"grid.linewidth": 1,
"lines.linewidth": 1.5,
"lines.markersize": 6,
"patch.linewidth": 1,
"xtick.major.width": 1.25,
"ytick.major.width": 1.25,
"xtick.minor.width": 1,
"ytick.minor.width": 1,
"xtick.major.size": 6,
"ytick.major.size": 6,
"xtick.minor.size": 4,
"ytick.minor.size": 4,
}
base_context.update(texts_base_context)
# Scale all the parameters by the same factor depending on the context
scaling = dict(paper=.8, notebook=1, talk=1.5, poster=2)[context]
context_dict = {k: v * scaling for k, v in base_context.items()}
# Now independently scale the fonts
font_keys = texts_base_context.keys()
font_dict = {k: context_dict[k] * font_scale for k in font_keys}
context_dict.update(font_dict)
# Override these settings with the provided rc dictionary
if rc is not None:
rc = {k: v for k, v in rc.items() if k in _context_keys}
context_dict.update(rc)
# Wrap in a _PlottingContext object so this can be used in a with statement
context_object = _PlottingContext(context_dict)
return context_object
| 290
|
rcmod.py
|
Python
|
seaborn/rcmod.py
|
f7e25e18983f2f36a1529cd9e4bda6fa008cbd6d
|
seaborn
| 10
|
|
47,420
| 29
| 13
| 8
| 194
| 22
| 0
| 41
| 81
|
create_test_pipeline
|
Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator`
|
https://github.com/apache/airflow.git
|
def create_test_pipeline(suffix, trigger_rule):
skip_operator = EmptySkipOperator(task_id=f'skip_operator_{suffix}')
always_true = EmptyOperator(task_id=f'always_true_{suffix}')
join = EmptyOperator(task_id=trigger_rule, trigger_rule=trigger_rule)
final = EmptyOperator(task_id=f'final_{suffix}')
skip_operator >> join
always_true >> join
join >> final
with DAG(
dag_id='example_skip_dag',
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
tags=['example'],
) as dag:
create_test_pipeline('1', TriggerRule.ALL_SUCCESS)
create_test_pipeline('2', TriggerRule.ONE_SUCCESS)
| 59
|
example_skip_dag.py
|
Python
|
airflow/example_dags/example_skip_dag.py
|
49e336ae0302b386a2f47269a6d13988382d975f
|
airflow
| 1
|
|
287,839
| 15
| 11
| 7
| 66
| 9
| 0
| 16
| 74
|
async_will_remove_from_hass
|
Netatmo refactor to use pyatmo 7.0.1 (#73482) (#78523)
Co-authored-by: Robert Svensson <Kane610@users.noreply.github.com>
|
https://github.com/home-assistant/core.git
|
async def async_will_remove_from_hass(self) -> None:
await super().async_will_remove_from_hass()
for publisher in self._publishers:
await self.data_handler.unsubscribe(
publisher[SIGNAL_NAME], self.async_update_callback
)
| 39
|
netatmo_entity_base.py
|
Python
|
homeassistant/components/netatmo/netatmo_entity_base.py
|
81abeac83ed85c5753cb8f2ac317caf079cf1868
|
core
| 2
|
|
53,074
| 27
| 11
| 14
| 49
| 5
| 0
| 29
| 105
|
import_distributed
|
Delay import of `distributed`
This improves `prefect` module import times which can be really slow with all of the distributed extras and allows configuration of a `DaskTaskRunner` on a machine without `distributed` installed
|
https://github.com/PrefectHQ/prefect.git
|
def import_distributed() -> "distributed":
try:
import distributed
except ImportError as exc:
raise RuntimeError(
"Using the Dask task runner requires Dask `distributed` to be installed."
) from exc
return distributed
| 25
|
task_runners.py
|
Python
|
src/prefect/task_runners.py
|
9de7f04816f1ef884d98ed817e869e73a9523ca1
|
prefect
| 2
|
|
115,994
| 72
| 14
| 20
| 295
| 26
| 0
| 126
| 403
|
get_columns
|
implemented the connection_args and connection_args_example dicts
|
https://github.com/mindsdb/mindsdb.git
|
def get_columns(self) -> StatusResponse:
query = "SELECT * FROM S3Object LIMIT 5"
df = self.native_query(query)
response = Response(
RESPONSE_TYPE.TABLE,
data_frame=pd.DataFrame(
{
'column_name': df.columns,
'data_type': df.dtypes
}
)
)
return response
connection_args = OrderedDict(
aws_access_key_id={
'type': ARG_TYPE.STR,
'description': 'The access key for the AWS account.'
},
aws_secret_access_key={
'type': ARG_TYPE.STR,
'description': 'The secret key for the AWS account.'
},
region_name={
'type': ARG_TYPE.STR,
'description': 'The AWS region where the S3 bucket is located.'
},
bucket={
'type': ARG_TYPE.STR,
'description': 'The name of the S3 bucket.'
},
key={
'type': ARG_TYPE.STR,
'description': 'The key of the object to be queried.'
},
input_serialization={
'type': ARG_TYPE.STR,
'description': 'The format of the data in the object that is to be queried.'
}
)
connection_args_example = OrderedDict(
aws_access_key_id='PCAQ2LJDOSWLNSQKOCPW',
aws_secret_access_key='U/VjewPlNopsDmmwItl34r2neyC6WhZpUiip57i',
region_name='us-east-1',
bucket='mindsdb-bucket',
key='iris.csv',
input_serialization="{'CSV': {'FileHeaderInfo': 'NONE'}}",
)
| 50
|
s3_handler.py
|
Python
|
mindsdb/integrations/handlers/s3_handler/s3_handler.py
|
4c20820d35782ed27f41e964ad2a429420b0eb67
|
mindsdb
| 1
|
|
216,271
| 30
| 15
| 11
| 124
| 10
| 0
| 36
| 164
|
_get_job_completion_ipc_path
|
Enable minion's IPC channel to aggregate results from spawned jobber processes. Use a long-running request channel in the minion parent process to communicate job results back to the master via broker-based or broker-less transport.
This is a necessary optimization for transports that prefer a sustained long-running connection because connection create/dispose operations are expensive. The working assumption is that this change benefits all supported transports.
Testing Done:
* this tests provide coverage for this use case:
.../salt/tests/pytests/integration/minion.*
|
https://github.com/saltstack/salt.git
|
def _get_job_completion_ipc_path(self):
if self.opts["ipc_mode"] == "tcp":
# try to find the port and fallback to something if not configured
uxd_path_or_tcp_port = int(
self.opts.get("tcp_job_completion_port", self.opts["tcp_pub_port"] + 1)
)
else:
uxd_path_or_tcp_port = os.path.join(
self.opts["sock_dir"],
"job_completion_minion-{}.ipc".format(self.opts["id"]),
)
return uxd_path_or_tcp_port
| 70
|
minion.py
|
Python
|
salt/minion.py
|
171926cc57618b51bf3fdc042b62212e681180fc
|
salt
| 2
|
|
22,117
| 10
| 8
| 10
| 48
| 6
| 0
| 11
| 24
|
patch
|
Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir.
|
https://github.com/pypa/pipenv.git
|
def patch(self, url, data=None, **kwargs):
r
return self.request("PATCH", url, data=data, **kwargs)
| 32
|
sessions.py
|
Python
|
pipenv/patched/pip/_vendor/requests/sessions.py
|
cd5a9683be69c86c8f3adcd13385a9bc5db198ec
|
pipenv
| 1
|
|
248,583
| 36
| 10
| 12
| 177
| 15
| 0
| 48
| 154
|
test_guest_access_token
|
Move the "email unsubscribe" resource, refactor the macaroon generator & simplify the access token verification logic. (#12986)
This simplifies the access token verification logic by removing the `rights`
parameter which was only ever used for the unsubscribe link in email
notifications. The latter has been moved under the `/_synapse` namespace,
since it is not a standard API.
This also makes the email verification link more secure, by embedding the
app_id and pushkey in the macaroon and verifying it. This prevents the user
from tampering the query parameters of that unsubscribe link.
Macaroon generation is refactored:
- Centralised all macaroon generation and verification logic to the
`MacaroonGenerator`
- Moved to `synapse.utils`
- Changed the constructor to require only a `Clock`, hostname, and a secret key
(instead of a full `Homeserver`).
- Added tests for all methods.
|
https://github.com/matrix-org/synapse.git
|
def test_guest_access_token(self):
token = self.macaroon_generator.generate_guest_access_token("@user:tesths")
user_id = self.macaroon_generator.verify_guest_token(token)
self.assertEqual(user_id, "@user:tesths")
# Raises with another secret key
with self.assertRaises(MacaroonVerificationFailedException):
self.other_macaroon_generator.verify_guest_token(token)
# Check that an old access token without the guest caveat does not work
macaroon = self.macaroon_generator._generate_base_macaroon("access")
macaroon.add_first_party_caveat(f"user_id = {user_id}")
macaroon.add_first_party_caveat("nonce = 0123456789abcdef")
token = macaroon.serialize()
with self.assertRaises(MacaroonVerificationFailedException):
self.macaroon_generator.verify_guest_token(token)
| 96
|
test_macaroons.py
|
Python
|
tests/util/test_macaroons.py
|
fe1daad67237c2154a3d8d8cdf6c603f0d33682e
|
synapse
| 1
|
|
160,733
| 31
| 10
| 6
| 48
| 5
| 0
| 34
| 73
|
no_nep50_warning
|
WIP: Add warning context manager and fix min_scalar for new promotion
Even the new promotion has to use the min-scalar logic to avoid
picking up a float16 loop for `np.int8(3) * 3.`.
|
https://github.com/numpy/numpy.git
|
def no_nep50_warning():
# TODO: We could skip the manager entirely if NumPy as a whole is not
# in the warning mode. (Which is NOT thread/context safe.)
token = NO_NEP50_WARNING.set(True)
try:
yield
finally:
NO_NEP50_WARNING.reset(token)
| 24
|
_ufunc_config.py
|
Python
|
numpy/core/_ufunc_config.py
|
baaeb9a16c9c28683db97c4fc3d047e86d32a0c5
|
numpy
| 2
|
|
224,136
| 19
| 15
| 17
| 130
| 19
| 0
| 20
| 136
|
test_invalid_config
|
Some manual changes ahead of formatting code with Black
|
https://github.com/mkdocs/mkdocs.git
|
def test_invalid_config(self):
file_contents = dedent(
)
config_file = tempfile.NamedTemporaryFile('w', delete=False)
try:
config_file.write(file_contents)
config_file.flush()
config_file.close()
with self.assertRaises(ConfigurationError):
config.load_config(config_file=open(config_file.name, 'rb'))
finally:
os.remove(config_file.name)
| 74
|
config_tests.py
|
Python
|
mkdocs/tests/config/config_tests.py
|
372384d8102ddb4be6360f44d1bfddb8b45435a4
|
mkdocs
| 2
|
|
203,227
| 13
| 12
| 9
| 72
| 8
| 0
| 13
| 82
|
_get_default_collation
|
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_default_collation(self, table_name):
with self.connection.cursor() as cursor:
cursor.execute(
,
[self.normalize_name(table_name)],
)
return cursor.fetchone()[0]
| 43
|
schema.py
|
Python
|
django/db/backends/oracle/schema.py
|
c5cd8783825b5f6384417dac5f3889b4210b7d08
|
django
| 1
|
|
272,375
| 44
| 16
| 15
| 153
| 17
| 0
| 60
| 184
|
_apply_scores
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def _apply_scores(self, scores, value, scores_mask=None, training=None):
if scores_mask is not None:
padding_mask = tf.logical_not(scores_mask)
# Bias so padding positions do not contribute to attention distribution.
# Note 65504. is the max float16 value.
if scores.dtype is tf.float16:
scores -= 65504.0 * tf.cast(padding_mask, dtype=scores.dtype)
else:
scores -= 1.0e9 * tf.cast(padding_mask, dtype=scores.dtype)
if training is None:
training = backend.learning_phase()
weights = tf.nn.softmax(scores)
| 133
|
base_dense_attention.py
|
Python
|
keras/layers/attention/base_dense_attention.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 4
|
|
133,351
| 12
| 11
| 4
| 52
| 9
| 0
| 12
| 44
|
update_scheduler
|
[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 update_scheduler(self, metric):
self.worker_group.apply_all_operators(
lambda op: [sched.step(metric) for sched in op._schedulers]
)
| 32
|
torch_trainer.py
|
Python
|
python/ray/util/sgd/torch/torch_trainer.py
|
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
|
ray
| 2
|
|
288,502
| 6
| 9
| 3
| 33
| 7
| 0
| 6
| 20
|
auto_update
|
Supervisor update entity auto update from api (#79611)
* Supervisor update entity auto update from api
* Update api mocks in tests
|
https://github.com/home-assistant/core.git
|
def auto_update(self) -> bool:
return self.coordinator.data[DATA_KEY_SUPERVISOR][ATTR_AUTO_UPDATE]
| 20
|
update.py
|
Python
|
homeassistant/components/hassio/update.py
|
416c10a793a982fb8c17259d36b99be458131cd0
|
core
| 1
|
|
272,119
| 64
| 15
| 38
| 329
| 29
| 0
| 114
| 680
|
test_shared_embedding_column_with_non_sequence_categorical
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def test_shared_embedding_column_with_non_sequence_categorical(self):
with tf.Graph().as_default():
vocabulary_size = 3
sparse_input_a = tf.compat.v1.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2),
)
sparse_input_b = tf.compat.v1.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2),
)
categorical_column_a = (
tf.feature_column.categorical_column_with_identity(
key="aaa", num_buckets=vocabulary_size
)
)
categorical_column_b = (
tf.feature_column.categorical_column_with_identity(
key="bbb", num_buckets=vocabulary_size
)
)
shared_embedding_columns = tf.feature_column.shared_embeddings(
[categorical_column_a, categorical_column_b], dimension=2
)
sequence_input_layer = ksfc.SequenceFeatures(
shared_embedding_columns
)
with self.assertRaisesRegex(
ValueError,
r"In embedding_column: aaa_shared_embedding\. "
r"categorical_column must "
r"be of type SequenceCategoricalColumn to use SequenceFeatures\.",
):
_, _ = sequence_input_layer(
{"aaa": sparse_input_a, "bbb": sparse_input_b}
)
| 216
|
sequence_feature_column_test.py
|
Python
|
keras/feature_column/sequence_feature_column_test.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 1
|
|
268,177
| 164
| 21
| 57
| 556
| 39
| 0
| 348
| 1,100
|
package_status
|
apt: include apt preferences (e.g. pinning) when selecting packages (#78327)
Fixes #77969
|
https://github.com/ansible/ansible.git
|
def package_status(m, pkgname, version_cmp, version, default_release, cache, state):
try:
# get the package from the cache, as well as the
# low-level apt_pkg.Package object which contains
# state fields not directly accessible from the
# higher-level apt.package.Package object.
pkg = cache[pkgname]
ll_pkg = cache._cache[pkgname] # the low-level package object
except KeyError:
if state == 'install':
try:
provided_packages = cache.get_providing_packages(pkgname)
if provided_packages:
# When this is a virtual package satisfied by only
# one installed package, return the status of the target
# package to avoid requesting re-install
if cache.is_virtual_package(pkgname) and len(provided_packages) == 1:
package = provided_packages[0]
installed, installed_version, version_installable, has_files = \
package_status(m, package.name, version_cmp, version, default_release, cache, state='install')
if installed:
return installed, installed_version, version_installable, has_files
# Otherwise return nothing so apt will sort out
# what package to satisfy this with
return False, False, True, False
m.fail_json(msg="No package matching '%s' is available" % pkgname)
except AttributeError:
# python-apt version too old to detect virtual packages
# mark as not installed and let apt-get install deal with it
return False, False, True, False
else:
return False, False, None, False
try:
has_files = len(pkg.installed_files) > 0
except UnicodeDecodeError:
has_files = True
except AttributeError:
has_files = False # older python-apt cannot be used to determine non-purged
try:
package_is_installed = ll_pkg.current_state == apt_pkg.CURSTATE_INSTALLED
except AttributeError: # python-apt 0.7.X has very weak low-level object
try:
# might not be necessary as python-apt post-0.7.X should have current_state property
package_is_installed = pkg.is_installed
except AttributeError:
# assume older version of python-apt is installed
package_is_installed = pkg.isInstalled
version_best = package_best_match(pkgname, version_cmp, version, default_release, cache._cache)
version_is_installed = False
version_installable = None
if package_is_installed:
try:
installed_version = pkg.installed.version
except AttributeError:
installed_version = pkg.installedVersion
if version_cmp == "=":
# check if the version is matched as well
version_is_installed = fnmatch.fnmatch(installed_version, version)
if version_best and installed_version != version_best and fnmatch.fnmatch(version_best, version):
version_installable = version_best
elif version_cmp == ">=":
version_is_installed = apt_pkg.version_compare(installed_version, version) >= 0
if version_best and installed_version != version_best and apt_pkg.version_compare(version_best, version) >= 0:
version_installable = version_best
else:
version_is_installed = True
if version_best and installed_version != version_best:
version_installable = version_best
else:
version_installable = version_best
return package_is_installed, version_is_installed, version_installable, has_files
| 350
|
apt.py
|
Python
|
lib/ansible/modules/apt.py
|
04e892757941bf77198692bbe37041d7a8cbf999
|
ansible
| 24
|
|
265,648
| 17
| 10
| 9
| 88
| 11
| 0
| 18
| 97
|
test_interface_label_count_valid
|
Fixes #10247: Allow changing selected device/VM when creating a new component (#10312)
* Initial work on #10247
* Continued work on #10247
* Clean up component creation tests
* Move valdiation of replicated field to form
* Clean up ordering of fields in component creation forms
* Omit fieldset header if none
* Clean up ordering of fields in component template creation forms
* View tests should not move component templates to new device type
* Define replication_fields on VMInterfaceCreateForm
* Clean up expandable field help texts
* Update comments
* Update component bulk update forms & views to support new replication fields
* Fix ModularDeviceComponentForm parent class
* Fix bulk creation of VM interfaces (thanks @kkthxbye-code!)
|
https://github.com/netbox-community/netbox.git
|
def test_interface_label_count_valid(self):
interface_data = {
'device': self.device.pk,
'name': 'eth[0-9]',
'label': 'Interface[0-9]',
'type': InterfaceTypeChoices.TYPE_1GE_GBIC,
}
form = InterfaceCreateForm(interface_data)
self.assertTrue(form.is_valid())
| 48
|
test_forms.py
|
Python
|
netbox/dcim/tests/test_forms.py
|
c4b7ab067a914349abd88398dd9bfef9f6c2f806
|
netbox
| 1
|
|
154,556
| 113
| 16
| 57
| 498
| 44
| 0
| 171
| 912
|
_join_by_index
|
FEAT-#4946: Replace OmniSci with HDK (#4947)
Co-authored-by: Iaroslav Igoshev <Poolliver868@mail.ru>
Signed-off-by: Andrey Pavlenko <andrey.a.pavlenko@gmail.com>
|
https://github.com/modin-project/modin.git
|
def _join_by_index(self, other_modin_frames, how, sort, ignore_index):
if how == "outer":
raise NotImplementedError("outer join is not supported in HDK engine")
lhs = self._maybe_materialize_rowid()
reset_index_names = False
for rhs in other_modin_frames:
rhs = rhs._maybe_materialize_rowid()
if len(lhs._index_cols) != len(rhs._index_cols):
raise NotImplementedError(
"join by indexes with different sizes is not supported"
)
reset_index_names = reset_index_names or lhs._index_cols != rhs._index_cols
condition = lhs._build_equi_join_condition(
rhs, lhs._index_cols, rhs._index_cols
)
exprs = lhs._index_exprs()
new_columns = lhs.columns.to_list()
for col in lhs.columns:
exprs[col] = lhs.ref(col)
for col in rhs.columns:
# Handle duplicating column names here. When user specifies
# suffixes to make a join, actual renaming is done in front-end.
new_col_name = col
rename_idx = 0
while new_col_name in exprs:
new_col_name = f"{col}{rename_idx}"
rename_idx += 1
exprs[new_col_name] = rhs.ref(col)
new_columns.append(new_col_name)
op = JoinNode(
lhs,
rhs,
how=how,
exprs=exprs,
condition=condition,
)
new_columns = Index.__new__(
Index, data=new_columns, dtype=self.columns.dtype
)
lhs = lhs.__constructor__(
dtypes=lhs._dtypes_for_exprs(exprs),
columns=new_columns,
index_cols=lhs._index_cols,
op=op,
force_execution_mode=self._force_execution_mode,
)
if sort:
lhs = lhs.sort_rows(
lhs._index_cols,
ascending=True,
ignore_index=False,
na_position="last",
)
if reset_index_names:
lhs = lhs._reset_index_names()
if ignore_index:
new_columns = Index.__new__(RangeIndex, data=range(len(lhs.columns)))
lhs = lhs._set_columns(new_columns)
return lhs
| 315
|
dataframe.py
|
Python
|
modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe/dataframe.py
|
e5b1888cd932909e49194d58035da34b210b91c4
|
modin
| 11
|
|
282,373
| 35
| 15
| 17
| 232
| 18
| 0
| 48
| 243
|
get_crypto_yfinance
|
Portfolio class (#1280)
* remerge into main ?
* tests again
* change the example csv
* squash all the bugs
* Improve `add` interface
* Add warning on loading portfolio with no cash
* left a rogue print
* oopsie. hugo
* oopsie. hugo
* Add back a new `al` function + port name
* test
Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com>
|
https://github.com/OpenBB-finance/OpenBBTerminal.git
|
def get_crypto_yfinance(self):
if self._crypto_tickers:
list_of_coins = [f"{coin}-USD" for coin in self._crypto_tickers]
self._historical_crypto = yf.download(
list_of_coins, start=self._start_date, progress=False
)["Close"]
if len(list_of_coins) == 1:
self._historical_crypto = pd.DataFrame(self._historical_crypto)
self._historical_crypto.columns = list_of_coins
self._historical_crypto.columns = pd.MultiIndex.from_product(
[["Close"], [col[:-4] for col in self._historical_crypto.columns]]
)
else:
self._historical_crypto = pd.DataFrame()
self._historical_crypto[
pd.MultiIndex.from_product([["Close"], ["crypto"]])
] = 0
| 142
|
portfolio_model.py
|
Python
|
gamestonk_terminal/portfolio/portfolio_model.py
|
2a998a5a417ba81b6ee3c4de90d2ffaca52b46fa
|
OpenBBTerminal
| 5
|
|
102,005
| 6
| 9
| 2
| 35
| 5
| 0
| 6
| 20
|
active
|
Update Face Filter
- Remove old face filter
- plugins.extract.pipeline: Expose plugins directly
- Change `is_aligned` from plugin level to ExtractMedia level
- Allow extract pipeline to take faceswap aligned images
- Add ability for recognition plugins to accept aligned faces as input
- Add face filter to recognition plugin
- Move extractor pipeline IO ops to own class
|
https://github.com/deepfakes/faceswap.git
|
def active(self):
return bool(self._filter_files) or bool(self._nfilter_files)
| 20
|
extract.py
|
Python
|
scripts/extract.py
|
1d1face00d9476896e7857d3976afce383585d1b
|
faceswap
| 2
|
|
276,023
| 18
| 13
| 6
| 93
| 9
| 0
| 20
| 72
|
_set_network_attributes_from_metadata
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def _set_network_attributes_from_metadata(revived_obj):
with utils.no_automatic_dependency_tracking_scope(revived_obj):
# pylint:disable=protected-access
metadata = revived_obj._serialized_attributes["metadata"]
if metadata.get("dtype") is not None:
revived_obj._set_dtype_policy(metadata["dtype"])
revived_obj._trainable = metadata["trainable"]
# pylint:enable=protected-access
| 50
|
load.py
|
Python
|
keras/saving/saved_model/load.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 2
|
|
283,220
| 9
| 11
| 4
| 51
| 9
| 0
| 11
| 39
|
_words_and_emoticons
|
Create a packaged app bundle with Pyinstaller (#1525)
* Add dashboard widget assets
* Add ipywidgets and ipyflex to project
* Add currencies dashboard notebook
* Update docs and docstrings
* Add pyinstaller to project deps
* Add pyinstaller artifacts to gitignore
* Fix linter errors in terminal.py
* Update cspell hook and action with a pyinstaller specific word
* Add pyinstaller specfile and artifacts
* Add splashscreen image
* Add app icon
* adding splash screen support to terminal.spec and terminal.py
* Restore the conda env build files
* Sync deps
* Add border to the splashscreen image
* Clean up terminal launcher
* Add support for default feature flags in packages apps
* Fix types and linting
* Add splashscreen management to app bootup
* Check prediction feature flag when entering crypto/pred
* Update pyinstaller spec file
* fix .spec file to work for splash and icon - removed the ".."
* Allows to export when using installer (#1568)
* fix export for packaged apps
* fix filename
* Git : replace commit_hash when it is set in config_terminal
* Add update of the git commit hash in gtff default during build
* Add packaged app name and feature flag to logs
* Add platform specific icon assignment
* Add macOS build assets
* Add tensorflow to hidden imports
* Move LOGGING_COMMIT_HASH to gtff
* Adding files/folders needed to .spec and pyinstaller folder. This will make certain commands work again.
* Linting
* Workflow : ignore ./build/pyinstaller from codespell
* Workflow : exclude ./build/pyinstaller from flake8
* Poetry + Workflow : add types-six
* Pyinstaller : remove property_cached, user_agent and vaderSentiment
* Revert "Pyinstaller : remove property_cached, user_agent and vaderSentiment"
This reverts commit dbb3e2b81086f97819ebd21457148c7160a4d703.
* Clean up local paths in specfile
* Validate deps have correct Jinja version (they do)
* Fix logging commit hash to be set correctly for the logger to see it
Co-authored-by: Andrew <andrew.kenreich@gmail.com>
Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt>
Co-authored-by: Chavithra PARANA <chavithra@gmail.com>
|
https://github.com/OpenBB-finance/OpenBBTerminal.git
|
def _words_and_emoticons(self):
wes = self.text.split()
stripped = list(map(self._strip_punc_if_word, wes))
return stripped
| 30
|
vaderSentiment.py
|
Python
|
build/pyinstaller/vaderSentiment/vaderSentiment.py
|
ab4de1dd70fba866930150e440a03e461a6ca6a8
|
OpenBBTerminal
| 1
|
|
126,370
| 8
| 11
| 4
| 59
| 11
| 0
| 9
| 37
|
_reset_replica_iterator
|
[Serve] ServeHandle detects ActorError and drop replicas from target group (#26685)
|
https://github.com/ray-project/ray.git
|
def _reset_replica_iterator(self):
replicas = list(self.in_flight_queries.keys())
random.shuffle(replicas)
self.replica_iterator = itertools.cycle(replicas)
| 34
|
router.py
|
Python
|
python/ray/serve/_private/router.py
|
545c51609f0f55b41cf99cec95a9c21bee6846de
|
ray
| 1
|
|
176,368
| 47
| 13
| 18
| 219
| 17
| 1
| 79
| 203
|
is_perfect_matching
|
Update matching functions for error validation and speed (#4897)
* First steps to update matching functions for #4644
Expand tests
Change API to raise NetworkXError when matching involves nodes not in G
Update is_*_matching to 100+ times faster.
* improve matching_dict_to_set and docs for min_weight_matching
* fix sphinx error
|
https://github.com/networkx/networkx.git
|
def is_perfect_matching(G, matching):
if isinstance(matching, dict):
matching = matching_dict_to_set(matching)
nodes = set()
for edge in matching:
if len(edge) != 2:
raise nx.NetworkXError(f"matching has non-2-tuple edge {edge}")
u, v = edge
if u not in G or v not in G:
raise nx.NetworkXError(f"matching contains edge {edge} with node not in G")
if u == v:
return False
if not G.has_edge(u, v):
return False
if u in nodes or v in nodes:
return False
nodes.update(edge)
return len(nodes) == len(G)
@not_implemented_for("multigraph")
@not_implemented_for("directed")
|
@not_implemented_for("multigraph")
@not_implemented_for("directed")
| 119
|
matching.py
|
Python
|
networkx/algorithms/matching.py
|
28b3014d68d2b4e40d3e02219770296a827bd55c
|
networkx
| 10
|
314,030
| 7
| 7
| 8
| 24
| 4
| 0
| 7
| 21
|
assumed_state
|
Enable polling for hardwired powerview devices (#73659)
* Enable polling for hardwired powerview devices
* Update homeassistant/components/hunterdouglas_powerview/cover.py
* Update homeassistant/components/hunterdouglas_powerview/cover.py
* docs were wrong
* Update homeassistant/components/hunterdouglas_powerview/cover.py
* Update homeassistant/components/hunterdouglas_powerview/sensor.py
|
https://github.com/home-assistant/core.git
|
def assumed_state(self) -> bool:
return not self._is_hard_wired
| 13
|
cover.py
|
Python
|
homeassistant/components/hunterdouglas_powerview/cover.py
|
120479acef9a8e9e52fa356f036e55465e441d31
|
core
| 1
|
|
293,838
| 60
| 11
| 20
| 281
| 23
| 1
| 76
| 170
|
test_matching_filter
|
Simplify time zone setting in tests (#68330)
* Simplify timezone setting in tests
* Fix typo
* Adjust caldav tests
* Adjust input_datetime tests
* Adjust time_date tests
* Adjust tod tests
* Adjust helper tests
* Adjust recorder tests
* Adjust risco tests
* Adjust aemet tests
* Adjust flux tests
* Adjust forecast_solar tests
* Revert unnecessary change in forecast_solar test
* Adjust climacell tests
* Adjust google tests
* Adjust sensor tests
* Adjust sonarr tests
* Adjust template tests
* Adjust zodiac tests
Co-authored-by: Martin Hjelmare <marhje52@gmail.com>
|
https://github.com/home-assistant/core.git
|
async def test_matching_filter(mock_now, hass, calendar, set_tz):
config = dict(CALDAV_CONFIG)
config["custom_calendars"] = [
{"name": "Private", "calendar": "Private", "search": "This is a normal event"}
]
assert await async_setup_component(hass, "calendar", {"calendar": config})
await hass.async_block_till_done()
state = hass.states.get("calendar.private_private")
assert state.name == calendar.name
assert state.state == STATE_OFF
assert dict(state.attributes) == {
"friendly_name": "Private",
"message": "This is a normal event",
"all_day": False,
"offset_reached": False,
"start_time": "2017-11-27 17:00:00",
"end_time": "2017-11-27 18:00:00",
"location": "Hamburg",
"description": "Surprisingly rainy",
}
@pytest.mark.parametrize("set_tz", ["utc"], indirect=True)
@patch("homeassistant.util.dt.now", return_value=_local_datetime(12, 00))
|
@pytest.mark.parametrize("set_tz", ["utc"], indirect=True)
@patch("homeassistant.util.dt.now", return_value=_local_datetime(12, 00))
| 124
|
test_calendar.py
|
Python
|
tests/components/caldav/test_calendar.py
|
cf4033b1bc853fc70828c6128ac91cdfb1d5bdaf
|
core
| 1
|
43,466
| 11
| 11
| 4
| 71
| 14
| 0
| 11
| 43
|
test_send_message_exception
|
Implement Azure Service Bus Queue Operators (#24038)
Implemented Azure Service Bus Queue based Operator's to create queue, send message to the queue and receive message(list of message or batch message) and delete queue in azure service
- Added `AzureServiceBusCreateQueueOperator`
- Added `AzureServiceBusSendMessageOperator`
- Added `AzureServiceBusReceiveMessageOperator`
- Added `AzureServiceBusDeleteQueueOperator`
- Added Example DAG
- Added Documentation
- Added hooks and connection type in - provider yaml file
- Added unit Test case, doc strings
|
https://github.com/apache/airflow.git
|
def test_send_message_exception(self, mock_sb_client):
hook = MessageHook(azure_service_bus_conn_id=self.conn_id)
with pytest.raises(TypeError):
hook.send_message(queue_name=None, messages="", batch_message_flag=False)
| 42
|
test_asb.py
|
Python
|
tests/providers/microsoft/azure/hooks/test_asb.py
|
09f38ad3f6872bae5059a1de226362eb358c4a7a
|
airflow
| 1
|
|
175,300
| 22
| 11
| 5
| 80
| 9
| 0
| 22
| 61
|
__setattr__
|
bpo-40066: [Enum] update str() and format() output (GH-30582)
Undo rejected PEP-663 changes:
- restore `repr()` to its 3.10 status
- restore `str()` to its 3.10 status
New changes:
- `IntEnum` and `IntFlag` now leave `__str__` as the original `int.__str__` so that str() and format() return the same result
- zero-valued flags without a name have a slightly changed repr(), e.g. `repr(Color(0)) == '<Color: 0>'`
- update `dir()` for mixed-in types to return all the methods and attributes of the mixed-in type
- added `_numeric_repr_` to `Flag` to control display of unnamed values
- enums without doc strings have a more comprehensive doc string added
- `ReprEnum` added -- inheriting from this makes it so only `__repr__` is replaced, not `__str__` nor `__format__`; `IntEnum`, `IntFlag`, and `StrEnum` all inherit from `ReprEnum`
|
https://github.com/python/cpython.git
|
def __setattr__(cls, name, value):
member_map = cls.__dict__.get('_member_map_', {})
if name in member_map:
raise AttributeError('cannot reassign member %r' % (name, ))
super().__setattr__(name, value)
| 48
|
enum.py
|
Python
|
Lib/enum.py
|
acf7403f9baea3ae1119fc6b4a3298522188bf96
|
cpython
| 2
|
|
22,039
| 12
| 10
| 7
| 50
| 6
| 0
| 13
| 46
|
unicode_is_ascii
|
Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir.
|
https://github.com/pypa/pipenv.git
|
def unicode_is_ascii(u_string):
assert isinstance(u_string, str)
try:
u_string.encode("ascii")
return True
except UnicodeEncodeError:
return False
| 28
|
_internal_utils.py
|
Python
|
pipenv/patched/pip/_vendor/requests/_internal_utils.py
|
cd5a9683be69c86c8f3adcd13385a9bc5db198ec
|
pipenv
| 2
|
|
303,773
| 11
| 9
| 5
| 45
| 3
| 0
| 13
| 49
|
_clean_up_listener
|
Add schedule helper (#76566)
Co-authored-by: Paulus Schoutsen <balloob@gmail.com>
|
https://github.com/home-assistant/core.git
|
def _clean_up_listener(self) -> None:
if self._unsub_update is not None:
self._unsub_update()
self._unsub_update = None
| 26
|
__init__.py
|
Python
|
homeassistant/components/schedule/__init__.py
|
f0827a20c3c0014de7e28dbeba76fc3f2e74fc70
|
core
| 2
|
|
245,544
| 35
| 12
| 14
| 118
| 10
| 0
| 46
| 113
|
update_data_root
|
[Fix] replace mmcv's function and modules imported with mmengine's (#8594)
* use mmengine's load_state_dict and load_checkpoint
* from mmengine import dump
* from mmengine import FileClient dump list_from_file
* remove redundant registry
* update
* update
* update
* replace _load_checkpoint with CheckpointLoad.load_checkpoint
* changes according to mmcv #2216
* changes due to mmengine #447
* changes due mmengine #447 and mmcv #2217
* changes due mmengine #447 and mmcv #2217
* update
* update
* update
|
https://github.com/open-mmlab/mmdetection.git
|
def update_data_root(cfg, logger=None):
assert isinstance(cfg, Config), \
f'cfg got wrong type: {type(cfg)}, expected mmengine.Config'
if 'MMDET_DATASETS' in os.environ:
dst_root = os.environ['MMDET_DATASETS']
print_log(f'MMDET_DATASETS has been set to be {dst_root}.'
f'Using {dst_root} as data root.')
else:
return
assert isinstance(cfg, Config), \
f'cfg got wrong type: {type(cfg)}, expected mmengine.Config'
| 76
|
misc.py
|
Python
|
mmdet/utils/misc.py
|
d0695e68654ca242be54e655491aef8c959ac345
|
mmdetection
| 2
|
|
287,795
| 76
| 22
| 131
| 795
| 31
| 0
| 184
| 2,873
|
test_ryse_smart_bridge_four_shades_setup
|
Handle battery services that only report low battery in HomeKit Controller (#79072)
|
https://github.com/home-assistant/core.git
|
async def test_ryse_smart_bridge_four_shades_setup(hass):
accessories = await setup_accessories_from_file(
hass, "ryse_smart_bridge_four_shades.json"
)
await setup_test_accessories(hass, accessories)
await assert_devices_and_entities_created(
hass,
DeviceTestInfo(
unique_id=HUB_TEST_ACCESSORY_ID,
name="RYSE SmartBridge",
model="RYSE SmartBridge",
manufacturer="RYSE Inc.",
sw_version="1.3.0",
hw_version="0401.3521.0679",
devices=[
DeviceTestInfo(
unique_id="00:00:00:00:00:00:aid:2",
name="LR Left",
model="RYSE Shade",
manufacturer="RYSE Inc.",
sw_version="3.0.8",
hw_version="1.0.0",
serial_number="",
devices=[],
entities=[
EntityTestInfo(
entity_id="cover.lr_left_ryse_shade",
friendly_name="LR Left RYSE Shade",
unique_id="homekit-00:00:00:00:00:00-2-48",
supported_features=RYSE_SUPPORTED_FEATURES,
state="closed",
),
EntityTestInfo(
entity_id="sensor.lr_left_ryse_shade_battery",
friendly_name="LR Left RYSE Shade Battery",
entity_category=EntityCategory.DIAGNOSTIC,
capabilities={"state_class": SensorStateClass.MEASUREMENT},
unique_id="homekit-00:00:00:00:00:00-2-64",
unit_of_measurement=PERCENTAGE,
state="89",
),
],
),
DeviceTestInfo(
unique_id="00:00:00:00:00:00:aid:3",
name="LR Right",
model="RYSE Shade",
manufacturer="RYSE Inc.",
sw_version="3.0.8",
hw_version="1.0.0",
serial_number="",
devices=[],
entities=[
EntityTestInfo(
entity_id="cover.lr_right_ryse_shade",
friendly_name="LR Right RYSE Shade",
unique_id="homekit-00:00:00:00:00:00-3-48",
supported_features=RYSE_SUPPORTED_FEATURES,
state="closed",
),
EntityTestInfo(
entity_id="sensor.lr_right_ryse_shade_battery",
friendly_name="LR Right RYSE Shade Battery",
entity_category=EntityCategory.DIAGNOSTIC,
capabilities={"state_class": SensorStateClass.MEASUREMENT},
unique_id="homekit-00:00:00:00:00:00-3-64",
unit_of_measurement=PERCENTAGE,
state="100",
),
],
),
DeviceTestInfo(
unique_id="00:00:00:00:00:00:aid:4",
name="BR Left",
model="RYSE Shade",
manufacturer="RYSE Inc.",
sw_version="3.0.8",
hw_version="1.0.0",
serial_number="",
devices=[],
entities=[
EntityTestInfo(
entity_id="cover.br_left_ryse_shade",
friendly_name="BR Left RYSE Shade",
unique_id="homekit-00:00:00:00:00:00-4-48",
supported_features=RYSE_SUPPORTED_FEATURES,
state="open",
),
EntityTestInfo(
entity_id="sensor.br_left_ryse_shade_battery",
friendly_name="BR Left RYSE Shade Battery",
entity_category=EntityCategory.DIAGNOSTIC,
capabilities={"state_class": SensorStateClass.MEASUREMENT},
unique_id="homekit-00:00:00:00:00:00-4-64",
unit_of_measurement=PERCENTAGE,
state="100",
),
],
),
DeviceTestInfo(
unique_id="00:00:00:00:00:00:aid:5",
name="RZSS",
model="RYSE Shade",
manufacturer="RYSE Inc.",
sw_version="3.0.8",
hw_version="1.0.0",
serial_number="",
devices=[],
entities=[
EntityTestInfo(
entity_id="cover.rzss_ryse_shade",
friendly_name="RZSS RYSE Shade",
unique_id="homekit-00:00:00:00:00:00-5-48",
supported_features=RYSE_SUPPORTED_FEATURES,
state="open",
),
EntityTestInfo(
entity_id="sensor.rzss_ryse_shade_battery",
entity_category=EntityCategory.DIAGNOSTIC,
capabilities={"state_class": SensorStateClass.MEASUREMENT},
friendly_name="RZSS RYSE Shade Battery",
unique_id="homekit-00:00:00:00:00:00-5-64",
unit_of_measurement=PERCENTAGE,
state="0",
),
],
),
],
entities=[],
),
)
| 490
|
test_ryse_smart_bridge.py
|
Python
|
tests/components/homekit_controller/specific_devices/test_ryse_smart_bridge.py
|
917cf674de2db2216681dfec3ef9d63df573ace8
|
core
| 1
|
|
293,754
| 54
| 13
| 14
| 127
| 18
| 0
| 70
| 311
|
to_native
|
Separate attrs into another table (reduces database size) (#68224)
|
https://github.com/home-assistant/core.git
|
def to_native(self, validate_entity_id=True):
try:
return State(
self.entity_id,
self.state,
# Join the state_attributes table on attributes_id to get the attributes
# for newer states
json.loads(self.attributes) if self.attributes else {},
process_timestamp(self.last_changed),
process_timestamp(self.last_updated),
# Join the events table on event_id to get the context instead
# as it will always be there for state_changed events
context=Context(id=None),
validate_entity_id=validate_entity_id,
)
except ValueError:
# When json.loads fails
_LOGGER.exception("Error converting row to state: %s", self)
return None
| 80
|
models.py
|
Python
|
homeassistant/components/recorder/models.py
|
9215702388eef03c7c3ed9f756ea0db533d5beec
|
core
| 3
|
|
224,316
| 17
| 13
| 10
| 120
| 17
| 0
| 17
| 115
|
test_load_missing_required
|
Format code with `black -l100 --skip-string-normalization`
|
https://github.com/mkdocs/mkdocs.git
|
def test_load_missing_required(self):
config_file = tempfile.NamedTemporaryFile('w', delete=False)
try:
config_file.write("site_dir: output\nsite_uri: https://www.mkdocs.org\n")
config_file.flush()
config_file.close()
with self.assertRaises(exceptions.Abort):
base.load_config(config_file=config_file.name)
finally:
os.remove(config_file.name)
| 66
|
base_tests.py
|
Python
|
mkdocs/tests/config/base_tests.py
|
dca7cbb43fcd6ea7c677c98ba585395b070d387b
|
mkdocs
| 2
|
|
127,791
| 21
| 10
| 8
| 105
| 15
| 0
| 21
| 92
|
_create_default_prometheus_configs
|
Export default configurations for grafana and prometheus (#28286)
|
https://github.com/ray-project/ray.git
|
def _create_default_prometheus_configs(self):
prometheus_config_output_path = os.path.join(
self.metrics_root, "prometheus", "prometheus.yml"
)
# Copy default prometheus configurations
if os.path.exists(prometheus_config_output_path):
os.remove(prometheus_config_output_path)
os.makedirs(os.path.dirname(prometheus_config_output_path), exist_ok=True)
shutil.copy(PROMETHEUS_CONFIG_INPUT_PATH, prometheus_config_output_path)
| 63
|
metrics_head.py
|
Python
|
dashboard/modules/metrics/metrics_head.py
|
42da4445e7a3cb358a1a02ae433a004e9fa836b5
|
ray
| 2
|
|
259,014
| 58
| 14
| 19
| 246
| 24
| 0
| 81
| 166
|
calinski_harabasz_score
|
FIX Calinski and Harabasz score description (#22605)
|
https://github.com/scikit-learn/scikit-learn.git
|
def calinski_harabasz_score(X, labels):
X, labels = check_X_y(X, labels)
le = LabelEncoder()
labels = le.fit_transform(labels)
n_samples, _ = X.shape
n_labels = len(le.classes_)
check_number_of_labels(n_labels, n_samples)
extra_disp, intra_disp = 0.0, 0.0
mean = np.mean(X, axis=0)
for k in range(n_labels):
cluster_k = X[labels == k]
mean_k = np.mean(cluster_k, axis=0)
extra_disp += len(cluster_k) * np.sum((mean_k - mean) ** 2)
intra_disp += np.sum((cluster_k - mean_k) ** 2)
return (
1.0
if intra_disp == 0.0
else extra_disp * (n_samples - n_labels) / (intra_disp * (n_labels - 1.0))
)
| 168
|
_unsupervised.py
|
Python
|
sklearn/metrics/cluster/_unsupervised.py
|
d548c77980c4a633780cee3671e54ecd2f8cecb4
|
scikit-learn
| 3
|
|
139,696
| 36
| 12
| 30
| 154
| 3
| 0
| 56
| 408
|
get_valid_runtime_envs
|
[Serve] Add deployment graph `import_path` and `runtime_env` to `ServeApplicationSchema` (#24814)
A newly planned version of the Serve schema (used in the REST API and CLI) requires the user to pass in their deployment graph's`import_path` and optionally a runtime_env containing that graph. This new schema can then pick up any `init_args` and `init_kwargs` values directly from the graph, instead of requiring them to be serialized and passed explicitly into the REST request.
This change:
* Adds the `import_path` and `runtime_env` fields to the `ServeApplicationSchema`.
* Updates or disables outdated unit tests.
Follow-up changes should:
* Update the status schemas (i.e. `DeploymentStatusSchema` and `ServeApplicationStatusSchema`).
* Remove deployment-level `import_path`s.
* Process the new `import_path` and `runtime_env` fields instead of silently ignoring them.
* Remove `init_args` and `init_kwargs` from `DeploymentSchema` afterwards.
Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
|
https://github.com/ray-project/ray.git
|
def get_valid_runtime_envs() -> List[Dict]:
return [
# Empty runtime_env
{},
# Runtime_env with remote_URIs
{
"working_dir": (
"https://github.com/shrekris-anyscale/test_module/archive/HEAD.zip"
),
"py_modules": [
(
"https://github.com/shrekris-anyscale/"
"test_deploy_group/archive/HEAD.zip"
),
],
},
# Runtime_env with extra options
{
"working_dir": (
"https://github.com/shrekris-anyscale/test_module/archive/HEAD.zip"
),
"py_modules": [
(
"https://github.com/shrekris-anyscale/"
"test_deploy_group/archive/HEAD.zip"
),
],
"pip": ["pandas", "numpy"],
"env_vars": {"OMP_NUM_THREADS": "32", "EXAMPLE_VAR": "hello"},
"excludes": "imaginary_file.txt",
},
]
| 78
|
test_schema.py
|
Python
|
python/ray/serve/tests/test_schema.py
|
3a2bd16ecae15d6e26585c32c113dcfe7469ccd7
|
ray
| 1
|
|
42,732
| 17
| 10
| 15
| 192
| 13
| 0
| 27
| 60
|
create_directories_and_files
|
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 create_directories_and_files() -> None:
BUILD_CACHE_DIR.mkdir(parents=True, exist_ok=True)
FILES_DIR.mkdir(parents=True, exist_ok=True)
MSSQL_DATA_VOLUME.mkdir(parents=True, exist_ok=True)
KUBE_DIR.mkdir(parents=True, exist_ok=True)
LOGS_DIR.mkdir(parents=True, exist_ok=True)
DIST_DIR.mkdir(parents=True, exist_ok=True)
OUTPUT_LOG.mkdir(parents=True, exist_ok=True)
(AIRFLOW_SOURCES_ROOT / ".bash_aliases").touch()
(AIRFLOW_SOURCES_ROOT / ".bash_history").touch()
(AIRFLOW_SOURCES_ROOT / ".inputrc").touch()
| 118
|
path_utils.py
|
Python
|
dev/breeze/src/airflow_breeze/utils/path_utils.py
|
882535a8a2699af7d1d079ecebd8c31aa7fbaba9
|
airflow
| 1
|
|
109,545
| 22
| 10
| 11
| 108
| 4
| 0
| 45
| 139
|
_equal_aspect_axis_indices
|
Provide `adjustable='box'` to 3D axes aspect ratio setting (#23552)
* Provided `adjustable='box'` option to set 3D aspect ratio.
* "What's New": `adjustable` argument of 3D plots aspect ratio.
|
https://github.com/matplotlib/matplotlib.git
|
def _equal_aspect_axis_indices(self, aspect):
ax_indices = [] # aspect == 'auto'
if aspect == 'equal':
ax_indices = [0, 1, 2]
elif aspect == 'equalxy':
ax_indices = [0, 1]
elif aspect == 'equalxz':
ax_indices = [0, 2]
elif aspect == 'equalyz':
ax_indices = [1, 2]
return ax_indices
| 64
|
axes3d.py
|
Python
|
lib/mpl_toolkits/mplot3d/axes3d.py
|
7c6a74c47accdfb8d66e526cbd0b63c29ffede12
|
matplotlib
| 5
|
|
45,599
| 133
| 17
| 54
| 595
| 49
| 0
| 185
| 744
|
clear_not_launched_queued_tasks
|
Add map_index to pods launched by KubernetesExecutor (#21871)
I also did a slight drive-by-refactor (sorry!) to rename `queued_tasks
and `task` inside `clear_not_launched_queued_tasks` to `queued_tis` and
`ti` to reflect what they are.
|
https://github.com/apache/airflow.git
|
def clear_not_launched_queued_tasks(self, session=None) -> None:
self.log.debug("Clearing tasks that have not been launched")
if not self.kube_client:
raise AirflowException(NOT_STARTED_MESSAGE)
queued_tis: List[TaskInstance] = (
session.query(TaskInstance).filter(TaskInstance.state == State.QUEUED).all()
)
self.log.info('Found %s queued task instances', len(queued_tis))
# Go through the "last seen" dictionary and clean out old entries
allowed_age = self.kube_config.worker_pods_queued_check_interval * 3
for key, timestamp in list(self.last_handled.items()):
if time.time() - timestamp > allowed_age:
del self.last_handled[key]
for ti in queued_tis:
self.log.debug("Checking task instance %s", ti)
# Check to see if we've handled it ourselves recently
if ti.key in self.last_handled:
continue
# Build the pod selector
base_label_selector = (
f"dag_id={pod_generator.make_safe_label_value(ti.dag_id)},"
f"task_id={pod_generator.make_safe_label_value(ti.task_id)},"
f"airflow-worker={pod_generator.make_safe_label_value(str(ti.queued_by_job_id))}"
)
if ti.map_index >= 0:
# Old tasks _couldn't_ be mapped, so we don't have to worry about compat
base_label_selector += f',map_index={ti.map_index}'
kwargs = dict(label_selector=base_label_selector)
if self.kube_config.kube_client_request_args:
kwargs.update(**self.kube_config.kube_client_request_args)
# Try run_id first
kwargs['label_selector'] += ',run_id=' + pod_generator.make_safe_label_value(ti.run_id)
pod_list = self.kube_client.list_namespaced_pod(self.kube_config.kube_namespace, **kwargs)
if pod_list.items:
continue
# Fallback to old style of using execution_date
kwargs['label_selector'] = (
f'{base_label_selector},'
f'execution_date={pod_generator.datetime_to_label_safe_datestring(ti.execution_date)}'
)
pod_list = self.kube_client.list_namespaced_pod(self.kube_config.kube_namespace, **kwargs)
if pod_list.items:
continue
self.log.info('TaskInstance: %s found in queued state but was not launched, rescheduling', ti)
session.query(TaskInstance).filter(
TaskInstance.dag_id == ti.dag_id,
TaskInstance.task_id == ti.task_id,
TaskInstance.run_id == ti.run_id,
).update({TaskInstance.state: State.SCHEDULED})
| 318
|
kubernetes_executor.py
|
Python
|
airflow/executors/kubernetes_executor.py
|
ac77c89018604a96ea4f5fba938f2fbd7c582793
|
airflow
| 10
|
|
133,790
| 168
| 12
| 34
| 306
| 7
| 0
| 264
| 743
|
validate_config
|
[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 validate_config(self, config):
# Call (base) PPO's config validation function first.
# Note that this will not touch or check on the train_batch_size=-1
# setting.
super().validate_config(config)
# Error if run on Win.
if sys.platform in ["win32", "cygwin"]:
raise ValueError(
"DD-PPO not supported on Win yet! " "Due to usage of torch.distributed."
)
# Auto-train_batch_size: Calculate from rollout len and
# envs-per-worker.
if config["train_batch_size"] == -1:
config["train_batch_size"] = (
config["rollout_fragment_length"] * config["num_envs_per_worker"]
)
# Users should not define `train_batch_size` directly (always -1).
else:
raise ValueError(
"Set rollout_fragment_length instead of train_batch_size " "for DDPPO."
)
# Only supported for PyTorch so far.
if config["framework"] != "torch":
raise ValueError("Distributed data parallel is only supported for PyTorch")
if config["torch_distributed_backend"] not in ("gloo", "mpi", "nccl"):
raise ValueError(
"Only gloo, mpi, or nccl is supported for "
"the backend of PyTorch distributed."
)
# `num_gpus` must be 0/None, since all optimization happens on Workers.
if config["num_gpus"]:
raise ValueError(
"When using distributed data parallel, you should set "
"num_gpus=0 since all optimization "
"is happening on workers. Enable GPUs for workers by setting "
"num_gpus_per_worker=1."
)
# `batch_mode` must be "truncate_episodes".
if config["batch_mode"] != "truncate_episodes":
raise ValueError(
"Distributed data parallel requires truncate_episodes " "batch mode."
)
# DDPPO doesn't support KL penalties like PPO-1.
# In order to support KL penalties, DDPPO would need to become
# undecentralized, which defeats the purpose of the algorithm.
# Users can still tune the entropy coefficient to control the
# policy entropy (similar to controlling the KL penalty).
if config["kl_coeff"] != 0.0 or config["kl_target"] != 0.0:
raise ValueError("DDPPO doesn't support KL penalties like PPO-1")
| 152
|
ddppo.py
|
Python
|
rllib/agents/ppo/ddppo.py
|
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
|
ray
| 9
|
|
331,644
| 11
| 10
| 4
| 43
| 5
| 0
| 12
| 28
|
get_pretrained_cfg_value
|
Transitioning default_cfg -> pretrained_cfg. Improving handling of pretrained_cfg source (HF-Hub, files, timm config, etc). Checkpoint handling tweaks.
|
https://github.com/huggingface/pytorch-image-models.git
|
def get_pretrained_cfg_value(model_name, cfg_key):
if model_name in _model_pretrained_cfgs:
return _model_pretrained_cfgs[model_name].get(cfg_key, None)
return None
| 27
|
registry.py
|
Python
|
timm/models/registry.py
|
abc9ba254430ef971ea3dbd12f2b4f1969da55be
|
pytorch-image-models
| 2
|
|
158,113
| 18
| 15
| 4
| 86
| 11
| 0
| 19
| 35
|
split_batch_multi_inputs
|
[PaddlePaddle] Merge master into Paddle branch (#1186)
* change 15.2 title in chinese version (#1109)
change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘
* 修改部分语义表述 (#1105)
* Update r0.17.5 (#1120)
* Bump versions in installation
* 94行typo: （“bert.mall”）->（“bert.small”） (#1129)
* line 313: "bert.mall" -> "bert.small" (#1130)
* fix: update language as native reader (#1114)
* Fix the translation of "stride" (#1115)
* Update index.md (#1118)
修改部分语义表述
* Update self-attention-and-positional-encoding.md (#1133)
依照本书的翻译习惯，将pooling翻译成汇聚
* maybe a comment false (#1149)
* maybe a little false
* maybe a little false
* A minor bug in the rcnn section (Chinese edition) (#1148)
* Update bert.md (#1137)
一个笔误
# 假设batch_size=2，num_pred_positions=3
# 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1]
* Update calculus.md (#1135)
* fix typo in git documentation (#1106)
* fix: Update the Chinese translation in lr-scheduler.md (#1136)
* Update lr-scheduler.md
* Update chapter_optimization/lr-scheduler.md
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* fix translation for kaggle-house-price.md (#1107)
* fix translation for kaggle-house-price.md
* fix translation for kaggle-house-price.md
Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com>
* Update weight-decay.md (#1150)
* Update weight-decay.md
关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解
关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。
并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释
解释为何会增加复杂性以及为何需要细粒度工具。
* Update chapter_multilayer-perceptrons/weight-decay.md
yep
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* Update chapter_multilayer-perceptrons/weight-decay.md
yep
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* Fix a spelling error (#1161)
* Update gru.md (#1152)
The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state.
翻译错误
* Unify the function naming (#1113)
Unify naming of the function 'init_xavier()'.
* Update mlp-concise.md (#1166)
* Update mlp-concise.md
语句不通顺
* Update environment.md
语序异常
* Update config.ini
* fix the imprecise description (#1168)
Co-authored-by: yuande <yuande>
* fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175)
* Fix some typos. (#1163)
* Update batch-norm.md (#1170)
fixing typos u->x in article
* Update linear-regression.md (#1090)
We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that
原译文把who也直接翻译出来了。
* Update mlp.md (#1117)
* Update mlp.md
修改部分语义表述
* Update chapter_multilayer-perceptrons/mlp.md
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* Update chapter_multilayer-perceptrons/mlp.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
* Correct a translation error. (#1091)
* Correct a translation error.
* Update chapter_computer-vision/image-augmentation.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Update aws.md (#1121)
* Update aws.md
* Update chapter_appendix-tools-for-deep-learning/aws.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Update image-augmentation.md (#1093)
* Update anchor.md (#1088)
fix a minor issue in code
* Update anchor.md
* Update image-augmentation.md
* fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087)
* Avoid `torch.meshgrid` user warning (#1174)
Avoids the following user warning:
```python
~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
```
* bump to 2.0.0-beta1
* Update sequence.md
* bump beta1 on readme
* Add latex code block background to config
* BLD: Bump python support version 3.9 (#1183)
* BLD: Bump python support version 3.9
* Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4
* BLD: Bump torch and tensorflow
* Update Jenkinsfile
* Update chapter_installation/index.md
* Update chapter_installation/index.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Update config.ini
* Update INFO.md
* Update INFO.md
* Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187)
* resolve the conflicts
* revise from publisher (#1089)
* revise from publisher
* d2l api
* post_latex
* revise from publisher
* revise ch11
* Delete d2l-Copy1.bib
* clear cache
* rm d2lbook clear
* debug anchor
* keep original d2l doc
Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal>
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Co-authored-by: Aston Zhang <asv325@gmail.com>
* 重复语句 (#1188)
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Improve expression for chapter_preliminaries/pandas.md (#1184)
* Update pandas.md
* Improve expression
* Improve expression
* Update chapter_preliminaries/pandas.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Improce expression for chapter_preliminaries/linear-algebra.md (#1185)
* Improce expression
* Improve code comments
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
* Update chapter_preliminaries/linear-algebra.md
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
* Fix multibox_detection bugs
* Update d2l to 0.17.5 version
* restore older version
* Upgrade pandas
* change to python3.8
* Test warning log
* relocate warning log
* test logs filtering
* Update gru.md
* Add DeprecationWarning filter
* Test warning log
* Update attention mechanisms & computational performance
* Update multilayer perceptron& linear & convolution networks & computer vision
* Update recurrent&optimition&nlp pretraining & nlp applications
* ignore warnings
* Update index.md
* Update linear networks
* Update multilayer perceptrons&deep learning computation
* Update preliminaries
* Check and Add warning filter
* Update kaggle-cifar10.md
* Update object-detection-dataset.md
* Update ssd.md fcn.md
* Update hybridize.md
* Update hybridize.md
Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com>
Co-authored-by: zhou201505013 <39976863+zhou201505013@users.noreply.github.com>
Co-authored-by: Xinwei Liu <xinzone@outlook.com>
Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com>
Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com>
Co-authored-by: hugo_han <57249629+HugoHann@users.noreply.github.com>
Co-authored-by: gyro永不抽风 <1247006353@qq.com>
Co-authored-by: CanChengZheng <zcc550169544@163.com>
Co-authored-by: linlin <jajupmochi@gmail.com>
Co-authored-by: iuk <liukun0104@gmail.com>
Co-authored-by: yoos <49556860+liyunlongaaa@users.noreply.github.com>
Co-authored-by: Mr. Justice Lawrence John Wargrave <65226618+RUCWargrave@users.noreply.github.com>
Co-authored-by: Chiyuan Fu <fuchiyuan2019@outlook.com>
Co-authored-by: Sunhuashan <48636870+Sunhuashan@users.noreply.github.com>
Co-authored-by: Haiker Sun <haizhou.uestc2011@gmail.com>
Co-authored-by: Ming Liu <akira.liu@njnu.edu.cn>
Co-authored-by: goldmermaid <goldpiggy@berkeley.edu>
Co-authored-by: silenceZheng66 <13754430639@163.com>
Co-authored-by: Wenchao Yan <56541797+YWonchall@users.noreply.github.com>
Co-authored-by: Kiki2049 <55939997+Kiki2049@users.noreply.github.com>
Co-authored-by: Krahets <krahets@163.com>
Co-authored-by: friedmainfunction <73703265+friedmainfunction@users.noreply.github.com>
Co-authored-by: Jameson <miraclecome@gmail.com>
Co-authored-by: P. Yao <12227516+YaoPengCN@users.noreply.github.com>
Co-authored-by: Yulv-git <34329208+Yulv-git@users.noreply.github.com>
Co-authored-by: Liu,Xiao <45966993+liuxiao916@users.noreply.github.com>
Co-authored-by: YIN, Gang <1246410+yingang@users.noreply.github.com>
Co-authored-by: Joe-HZ <58297431+Joe-HZ@users.noreply.github.com>
Co-authored-by: lybloveyou <102609904+lybloveyou@users.noreply.github.com>
Co-authored-by: VigourJiang <jiangfuqiang154@163.com>
Co-authored-by: zxhd863943427 <74853597+zxhd863943427@users.noreply.github.com>
Co-authored-by: LYF <27893441+liyufan@users.noreply.github.com>
Co-authored-by: Aston Zhang <asv325@gmail.com>
Co-authored-by: xiaotinghe <xiaotih@amazon.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal>
Co-authored-by: Holly-Max <60691735+Holly-Max@users.noreply.github.com>
Co-authored-by: HinGwenWoong <peterhuang0323@qq.com>
Co-authored-by: Shuai Zhang <cheungdaven@gmail.com>
|
https://github.com/d2l-ai/d2l-zh.git
|
def split_batch_multi_inputs(X, y, devices):
X = list(zip(*[gluon.utils.split_and_load(
feature, devices, even_split=False) for feature in X]))
return (X, gluon.utils.split_and_load(y, devices, even_split=False))
| 58
|
mxnet.py
|
Python
|
d2l/mxnet.py
|
b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2
|
d2l-zh
| 2
|
|
84,208
| 45
| 13
| 30
| 178
| 23
| 0
| 52
| 114
|
clean_archived_data
|
retention: Add docstring info on how archive cleaning works.
In particular, it's important to record the special treatment around
ArchivedAttachment rows not being deleted in this step.
|
https://github.com/zulip/zulip.git
|
def clean_archived_data() -> None:
logger.info("Cleaning old archive data.")
check_date = timezone_now() - timedelta(days=settings.ARCHIVED_DATA_VACUUMING_DELAY_DAYS)
# Associated archived objects will get deleted through the on_delete=CASCADE property:
count = 0
transaction_ids = list(
ArchiveTransaction.objects.filter(timestamp__lt=check_date).values_list("id", flat=True)
)
while len(transaction_ids) > 0:
transaction_block = transaction_ids[0:TRANSACTION_DELETION_BATCH_SIZE]
transaction_ids = transaction_ids[TRANSACTION_DELETION_BATCH_SIZE:]
ArchiveTransaction.objects.filter(id__in=transaction_block).delete()
count += len(transaction_block)
logger.info("Deleted %s old ArchiveTransactions.", count)
| 105
|
retention.py
|
Python
|
zerver/lib/retention.py
|
acfa55138ee2e5f43a0a96614aa0581b115fc714
|
zulip
| 2
|
|
250,533
| 33
| 9
| 44
| 162
| 20
| 0
| 47
| 211
|
test_get_multiple_keys_from_perspectives
|
Add missing type hints to tests. (#14687)
Adds type hints to tests.metrics and tests.crypto.
|
https://github.com/matrix-org/synapse.git
|
def test_get_multiple_keys_from_perspectives(self) -> None:
fetcher = PerspectivesKeyFetcher(self.hs)
SERVER_NAME = "server2"
testkey1 = signedjson.key.generate_signing_key("ver1")
testverifykey1 = signedjson.key.get_verify_key(testkey1)
testverifykey1_id = "ed25519:ver1"
testkey2 = signedjson.key.generate_signing_key("ver2")
testverifykey2 = signedjson.key.get_verify_key(testkey2)
testverifykey2_id = "ed25519:ver2"
VALID_UNTIL_TS = 200 * 1000
response1 = self.build_perspectives_response(
SERVER_NAME,
testkey1,
VALID_UNTIL_TS,
)
response2 = self.build_perspectives_response(
SERVER_NAME,
testkey2,
VALID_UNTIL_TS,
)
| 292
|
test_keyring.py
|
Python
|
tests/crypto/test_keyring.py
|
a4ca770655a6b067468de3d507292ec133fdc5ca
|
synapse
| 1
|
|
156,160
| 6
| 8
| 2
| 31
| 4
| 0
| 6
| 12
|
sample
|
Bag: add implementation for reservoir sampling (#7068) (#7636)
- Implement the [L algorithm](https://en.wikipedia.org/wiki/Reservoir_sampling#An_optimal_algorithm) for reservoir sampling without replacement.
- Use the **k** reservoir of size 1 strategy for sampling with replacement (see [reference](http://utopia.duth.gr/~pefraimi/research/data/2007EncOfAlg.pdf)) of **k** items
|
https://github.com/dask/dask.git
|
def sample(population, k):
return _sample(population=population, k=k)
| 19
|
random.py
|
Python
|
dask/bag/random.py
|
4e5dfe7463028a39a90e026c7fb9220969093ab3
|
dask
| 1
|
|
292,851
| 10
| 9
| 8
| 35
| 5
| 0
| 10
| 24
|
available
|
Fix powerwall data incompatibility with energy integration (#67245)
|
https://github.com/home-assistant/core.git
|
def available(self) -> bool:
return super().available and self.native_value != 0
| 20
|
sensor.py
|
Python
|
homeassistant/components/powerwall/sensor.py
|
3f16c6d6efad20b60a4a8d2114a0905ecd252820
|
core
| 2
|
|
298,591
| 24
| 9
| 14
| 170
| 18
| 0
| 43
| 85
|
test_restore_state_uncoherence_case
|
Use climate enums in generic_thermostat (#70656)
* Use climate enums in generic_thermostat
* Adjust tests
|
https://github.com/home-assistant/core.git
|
async def test_restore_state_uncoherence_case(hass):
_mock_restore_cache(hass, temperature=20)
calls = _setup_switch(hass, False)
_setup_sensor(hass, 15)
await _setup_climate(hass)
await hass.async_block_till_done()
state = hass.states.get(ENTITY)
assert state.attributes[ATTR_TEMPERATURE] == 20
assert state.state == HVACMode.OFF
assert len(calls) == 0
calls = _setup_switch(hass, False)
await hass.async_block_till_done()
state = hass.states.get(ENTITY)
assert state.state == HVACMode.OFF
| 105
|
test_climate.py
|
Python
|
tests/components/generic_thermostat/test_climate.py
|
b81f8e75eea3d1aaa8111f542519de1d58093200
|
core
| 1
|
|
269,571
| 17
| 14
| 8
| 84
| 10
| 1
| 21
| 67
|
int_shape
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def int_shape(x):
try:
shape = x.shape
if not isinstance(shape, tuple):
shape = tuple(shape.as_list())
return shape
except ValueError:
return None
@keras_export("keras.backend.ndim")
@doc_controls.do_not_generate_docs
|
@keras_export("keras.backend.ndim")
@doc_controls.do_not_generate_docs
| 39
|
backend.py
|
Python
|
keras/backend.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 3
|
247,935
| 31
| 8
| 11
| 126
| 15
| 0
| 39
| 116
|
test_linearizer
|
Convert `Linearizer` tests from `inlineCallbacks` to async (#12353)
Signed-off-by: Sean Quah <seanq@element.io>
|
https://github.com/matrix-org/synapse.git
|
def test_linearizer(self) -> None:
linearizer = Linearizer()
key = object()
_, acquired_d1, unblock1 = self._start_task(linearizer, key)
self.assertTrue(acquired_d1.called)
_, acquired_d2, unblock2 = self._start_task(linearizer, key)
self.assertFalse(acquired_d2.called)
# Once the first task is done, the second task can continue.
unblock1()
self.assertTrue(acquired_d2.called)
unblock2()
| 76
|
test_linearizer.py
|
Python
|
tests/util/test_linearizer.py
|
41b5f72677ea9763f3cf920d4f6df507653222f2
|
synapse
| 1
|
|
8,639
| 17
| 12
| 5
| 89
| 15
| 0
| 19
| 54
|
test_window_autosizing_disabled
|
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 test_window_autosizing_disabled(self, ray_cluster_small_object_store):
ds = self.create_dataset(self.object_store_size * 8, auto_window=False)
pipe = ds.pipeline()
rep = next(iter(pipe._base_iterable))()
assert rep.num_blocks() == self.num_partitions
| 54
|
test_ray.py
|
Python
|
tests/integration_tests/test_ray.py
|
0d19a48cff0958ed77926a0712cbdb6485d4034a
|
ludwig
| 1
|
|
186,362
| 47
| 10
| 7
| 111
| 10
| 0
| 54
| 125
|
pick_apache_config
|
Various clean-ups in certbot-apache. Use f-strings. (#9132)
* Various clean-ups in certbot-apache. Use f-strings.
* Smaller tweaks
|
https://github.com/certbot/certbot.git
|
def pick_apache_config(self, warn_on_no_mod_ssl=True):
# Disabling TLS session tickets is supported by Apache 2.4.11+ and OpenSSL 1.0.2l+.
# So for old versions of Apache we pick a configuration without this option.
min_openssl_version = util.parse_loose_version('1.0.2l')
openssl_version = self.openssl_version(warn_on_no_mod_ssl)
if self.version < (2, 4, 11) or not openssl_version or \
util.parse_loose_version(openssl_version) < min_openssl_version:
return apache_util.find_ssl_apache_conf("old")
return apache_util.find_ssl_apache_conf("current")
| 66
|
configurator.py
|
Python
|
certbot-apache/certbot_apache/_internal/configurator.py
|
eeca208c8f57304590ac1af80b496e61021aaa45
|
certbot
| 4
|
|
9,880
| 5
| 6
| 8
| 20
| 2
| 0
| 5
| 19
|
start
|
feat: star routing (#3900)
* feat(proto): adjust proto for star routing (#3844)
* feat(proto): adjust proto for star routing
* feat(proto): generate proto files
* feat(grpc): refactor grpclet interface (#3846)
* feat: refactor connection pool for star routing (#3872)
* feat(k8s): add more labels to k8s deployments
* feat(network): refactor connection pool
* feat(network): refactor k8s pool
* feat: star routing graph gateway (#3877)
* feat: star routing - refactor grpc data runtime (#3887)
* feat(runtimes): refactor grpc dataruntime
* fix(tests): adapt worker runtime tests
* fix(import): fix import
* feat(proto): enable sending multiple lists (#3891)
* feat: star routing gateway (#3893)
* feat: star routing gateway all protocols (#3897)
* test: add streaming and prefetch tests (#3901)
* feat(head): new head runtime for star routing (#3899)
* feat(head): new head runtime
* feat(head): new head runtime
* style: fix overload and cli autocomplete
* feat(network): improve proto comments
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* feat(worker): merge docs in worker runtime (#3905)
* feat(worker): merge docs in worker runtime
* feat(tests): assert after clean up
* feat(tests): star routing runtime integration tests (#3908)
* fix(tests): fix integration tests
* test: test runtimes fast slow request (#3910)
* feat(zmq): purge zmq, zed, routing_table (#3915)
* feat(zmq): purge zmq, zed, routing_table
* style: fix overload and cli autocomplete
* feat(zmq): adapt comment in dependency list
* style: fix overload and cli autocomplete
* fix(tests): fix type tests
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* test: add test gateway to worker connection (#3921)
* feat(pea): adapt peas for star routing (#3918)
* feat(pea): adapt peas for star routing
* style: fix overload and cli autocomplete
* feat(pea): add tests
* feat(tests): add failing head pea test
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* feat(tests): integration tests for peas (#3923)
* feat(tests): integration tests for peas
* feat(pea): remove _inner_pea function
* feat: star routing container pea (#3922)
* test: rescue tests (#3942)
* fix: fix streaming tests (#3945)
* refactor: move docker run to run (#3948)
* feat: star routing pods (#3940)
* feat(pod): adapt pods for star routing
* feat(pods): adapt basepod to star routing
* feat(pod): merge pod and compound pod
* feat(tests): fix tests
* style: fix overload and cli autocomplete
* feat(test): add container pea int test
* feat(ci): remove more unnecessary tests
* fix(tests): remove jinad runtime
* feat(ci): remove latency tracking
* fix(ci): fix ci def
* fix(runtime): enable runtime to be exited
* fix(tests): wrap runtime test in process
* fix(runtimes): remove unused runtimes
* feat(runtimes): improve cancel wait
* fix(ci): build test pip again in ci
* fix(tests): fix a test
* fix(test): run async in its own process
* feat(pod): include shard in activate msg
* fix(pea): dont join
* feat(pod): more debug out
* feat(grpc): manage channels properly
* feat(pods): remove exitfifo
* feat(network): add simple send retry mechanism
* fix(network): await pool close
* fix(test): always close grpc server in worker
* fix(tests): remove container pea from tests
* fix(tests): reorder tests
* fix(ci): split tests
* fix(ci): allow alias setting
* fix(test): skip a test
* feat(pods): address comments
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* test: unblock skipped test (#3957)
* feat: jinad pea (#3949)
* feat: jinad pea
* feat: jinad pea
* test: remote peas
* test: toplogy tests with jinad
* ci: parallel jobs
* feat(tests): add pod integration tests (#3958)
* feat(tests): add pod integration tests
* fix(tests): make tests less flaky
* fix(test): fix test
* test(pea): remote pea topologies (#3961)
* test(pea): remote pea simple topology
* test: remote pea topologies
* refactor: refactor streamer result handling (#3960)
* feat(k8s): adapt K8s Pod for StarRouting (#3964)
* test: optimize k8s test
* test: increase timeout and use different namespace
* test: optimize k8s test
* test: build and load image when needed
* test: refactor k8s test
* test: fix image name error
* test: fix k8s image load
* test: fix typoe port expose
* test: update tests in connection pool and handling
* test: remove unused fixture
* test: parameterize docker images
* test: parameterize docker images
* test: parameterize docker images
* feat(k8s): adapt k8s pod for star routing
* fix(k8s): dont overwrite add/remove function in pool
* fix(k8s): some fixes
* fix(k8s): some more fixes
* fix(k8s): linting
* fix(tests): fix tests
* fix(tests): fix k8s unit tests
* feat(k8s): complete k8s integration test
* feat(k8s): finish k8s tests
* feat(k8s): fix test
* fix(tests): fix test with no name
* feat(k8s): unify create/replace interface
* feat(k8s): extract k8s port constants
* fix(tests): fix tests
* fix(tests): wait for runtime being ready in tests
* feat(k8s): address comments
Co-authored-by: bwanglzu <bo.wang@jina.ai>
* feat(flow): adapt Flow for StarRouting (#3986)
* feat(flow): add routes
* feat(flow): adapt flow to star routing
* style: fix overload and cli autocomplete
* feat(flow): handle empty topologies
* feat(k8s): allow k8s pool disabling
* style: fix overload and cli autocomplete
* fix(test): fix test with mock
* fix(tests): fix more tests
* feat(flow): clean up tests
* style: fix overload and cli autocomplete
* fix(tests): fix more tests
* feat: add plot function (#3994)
* fix(tests): avoid hanging tests
* feat(flow): add type hinting
* fix(test): fix duplicate exec name in test
* fix(tests): fix more tests
* fix(tests): enable jinad test again
* fix(tests): random port fixture
* fix(style): replace quotes
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* feat(ci): bring back ci (#3997)
* feat(ci): enable ci again
* style: fix overload and cli autocomplete
* feat(ci): add latency tracking
* feat(ci): bring back some tests
* fix(tests): remove invalid port test
* feat(ci): disable daemon and distributed tests
* fix(tests): fix entrypoint in hub test
* fix(tests): wait for gateway to be ready
* fix(test): fix more tests
* feat(flow): do rolling update and scale sequentially
* fix(tests): fix more tests
* style: fix overload and cli autocomplete
* feat: star routing hanging pods (#4011)
* fix: try to handle hanging pods better
* test: hanging pods test work
* fix: fix topology graph problem
* test: add unit test to graph
* fix(tests): fix k8s tests
* fix(test): fix k8s test
* fix(test): fix k8s pool test
* fix(test): fix k8s test
* fix(test): fix k8s connection pool setting
* fix(tests): make runtime test more reliable
* fix(test): fix routes test
* fix(tests): make rolling update test less flaky
* feat(network): gurantee unique ports
* feat(network): do round robin for shards
* fix(ci): increase pytest timeout to 10 min
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* fix(ci): fix ci file
* feat(daemon): jinad pod for star routing
* Revert "feat(daemon): jinad pod for star routing"
This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92.
* feat(daemon): remote jinad pod support (#4042)
* feat(daemon): add pod tests for star routing
* feat(daemon): add remote pod test
* test(daemon): add remote pod arguments test
* test(daemon): add async scale test
* test(daemon): add rolling update test
* test(daemon): fix host
* feat(proto): remove message proto (#4051)
* feat(proto): remove message proto
* fix(tests): fix tests
* fix(tests): fix some more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* feat(proto): put docs back in data
* fix(proto): clean up
* feat(proto): clean up
* fix(tests): skip latency tracking
* fix(test): fix hub test
* fix(tests): fix k8s test
* fix(test): some test clean up
* fix(style): clean up style issues
* feat(proto): adjust for rebase
* fix(tests): bring back latency tracking
* fix(tests): fix merge accident
* feat(proto): skip request serialization (#4074)
* feat: add reduce to star routing (#4070)
* feat: add reduce on shards to head runtime
* test: add reduce integration tests with fixed order
* feat: add reduce on needs
* chore: get_docs_matrix_from_request becomes public
* style: fix overload and cli autocomplete
* docs: remove undeterministic results warning
* fix: fix uses_after
* test: assert correct num docs after reducing in test_external_pod
* test: correct asserts after reduce in test_rolling_update
* fix: no reduce if uses_after_address is set
* fix: get_docs_from_request only if needed
* fix: fix tests after merge
* refactor: move reduce from data_request_handler to head
* style: fix overload and cli autocomplete
* chore: apply suggestions
* fix: fix asserts
* chore: minor test fix
* chore: apply suggestions
* test: remove flow tests with external executor (pea)
* fix: fix test_expected_messages_routing
* fix: fix test_func_joiner
* test: adapt k8s test
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* fix(k8s): fix static pool config
* fix: use custom protoc doc generator image (#4088)
* fix: use custom protoc doc generator image
* fix(docs): minor doc improvement
* fix(docs): use custom image
* fix(docs): copy docarray
* fix: doc building local only
* fix: timeout doc building
* fix: use updated args when building ContainerPea
* test: add container PeaFactory test
* fix: force pea close on windows (#4098)
* fix: dont reduce if uses exist (#4099)
* fix: dont use reduce if uses exist
* fix: adjust reduce tests
* fix: adjust more reduce tests
* fix: fix more tests
* fix: adjust more tests
* fix: ignore non jina resources (#4101)
* feat(executor): enable async executors (#4102)
* feat(daemon): daemon flow on star routing (#4096)
* test(daemon): add remote flow test
* feat(daemon): call scale in daemon
* feat(daemon): remove tail args and identity
* test(daemon): rename scalable executor
* test(daemon): add a small delay in async test
* feat(daemon): scale partial flow only
* feat(daemon): call scale directly in partial flow store
* test(daemon): use asyncio sleep
* feat(daemon): enable flow level distributed tests
* test(daemon): fix jinad env workspace config
* test(daemon): fix pod test use new port rolling update
* feat(daemon): enable distribuetd tests
* test(daemon): remove duplicate tests and zed runtime test
* test(daemon): fix stores unit test
* feat(daemon): enable part of distributed tests
* feat(daemon): enable part of distributed tests
* test: correct test paths
* test(daemon): add client test for remote flows
* test(daemon): send a request with jina client
* test(daemon): assert async generator
* test(daemon): small interval between tests
* test(daemon): add flow test for container runtime
* test(daemon): add flow test for container runtime
* test(daemon): fix executor name
* test(daemon): fix executor name
* test(daemon): use async client fetch result
* test(daemon): finish container flow test
* test(daemon): enable distributed in ci
* test(daemon): enable distributed in ci
* test(daemon): decare flows and pods
* test(daemon): debug ci if else
* test(daemon): debug ci if else
* test(daemon): decare flows and pods
* test(daemon): correct test paths
* test(daemon): add small delay for async tests
* fix: star routing fixes (#4100)
* docs: update docs
* fix: fix Request.__repr__
* docs: update flow remarks
* docs: fix typo
* test: add non_empty_fields test
* chore: remove non_empty_fields test
* feat: polling per endpoint (#4111)
* feat(polling): polling per endpoint configurable
* fix: adjust tests
* feat(polling): extend documentation
* style: fix overload and cli autocomplete
* fix: clean up
* fix: adjust more tests
* fix: remove repeat from flaky test
* fix: k8s test
* feat(polling): address pr feedback
* feat: improve docs
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* feat(grpc): support connect grpc server via ssl tunnel (#4092)
* feat(grpc): support ssl grpc connect if port is 443
* fix(grpc): use https option instead of detect port automatically
* chore: fix typo
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* test(networking): add test for peapods networking
* fix: address comments
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
* feat(polling): unify polling args (#4113)
* fix: several issues for jinad pods (#4119)
* fix: activate for jinad pods
* fix: dont expose worker pod in partial daemon
* fix: workspace setting
* fix: containerized flows
* fix: hub test
* feat(daemon): remote peas on star routing (#4112)
* test(daemon): fix request in peas
* test(daemon): fix request in peas
* test(daemon): fix sync async client test
* test(daemon): enable remote peas test
* test(daemon): replace send message to send request
* test(daemon): declare pea tests in ci
* test(daemon): use pea args fixture
* test(daemon): head pea use default host
* test(daemon): fix peas topologies
* test(daemon): fix pseudo naming
* test(daemon): use default host as host
* test(daemon): fix executor path
* test(daemon): add remote worker back
* test(daemon): skip local remote remote topology
* fix: jinad pea test setup
* fix: jinad pea tests
* fix: remove invalid assertion
Co-authored-by: jacobowitz <tobias.jacobowitz@posteo.de>
* feat: enable daemon tests again (#4132)
* feat: enable daemon tests again
* fix: remove bogy empty script file
* fix: more jinad test fixes
* style: fix overload and cli autocomplete
* fix: scale and ru in jinad
* fix: fix more jinad tests
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
* fix: fix flow test
* fix: improve pea tests reliability (#4136)
Co-authored-by: Joan Fontanals <joan.martinez@jina.ai>
Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>
Co-authored-by: Deepankar Mahapatro <deepankar.mahapatro@jina.ai>
Co-authored-by: bwanglzu <bo.wang@jina.ai>
Co-authored-by: AlaeddineAbdessalem <alaeddine-13@live.fr>
Co-authored-by: Zhaofeng Miao <522856232@qq.com>
|
https://github.com/jina-ai/jina.git
|
def start(self) -> 'BasePod':
...
| 9
|
__init__.py
|
Python
|
jina/peapods/pods/__init__.py
|
933415bfa1f9eb89f935037014dfed816eb9815d
|
jina
| 1
|
|
104,813
| 12
| 13
| 6
| 89
| 12
| 0
| 13
| 39
|
xbasename
|
Add support for metadata files to `imagefolder` (#4069)
* Add support for metadata files to `imagefolder`
* Fix imagefolder drop_labels test
* Replace csv with jsonl
* Add test
* Correct resolution for nested metadata files
* Allow None as JSON Lines value
* Add comments
* Count path segments
* Address comments
* Improve test
* Update src/datasets/packaged_modules/imagefolder/imagefolder.py
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
* test e2e imagefolder with metadata
* add test for zip archives
* fix test
* add some debug logging to know which files are ignored
* add test for bad/malformed metadata file
* revert use of posix path to fix windows tests
* style
* Refactor tests for packaged modules Text and Csv
Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>
|
https://github.com/huggingface/datasets.git
|
def xbasename(a):
a, *b = str(a).split("::")
if is_local_path(a):
return os.path.basename(Path(a).as_posix())
else:
return posixpath.basename(a)
| 51
|
streaming_download_manager.py
|
Python
|
src/datasets/utils/streaming_download_manager.py
|
7017b0965f0a0cae603e7143de242c3425ecef91
|
datasets
| 2
|
|
142,253
| 86
| 10
| 16
| 182
| 20
| 0
| 120
| 305
|
__call__
|
[air] Consolidate Tune and Train report (#25558)
Consolidate tune/train report/checkpoint functionality by working with a unified Session interface.
The goal of this PR is to establish a solid Session and Session.report path.
In favor of having less merging conflict (as other folks are doing the whole package renaming) and control the scope of this PR, I have intentionally left out some migration. More PRs to follow. Feel free to comment on the ideal final state.
To give an idea of the final directory structure. This is a for 2-worker DP training.
```
├── TensorflowTrainer_ce44d_00000_0_2022-06-15_14-40-42
│ ├── checkpoint_000000
│ │ ├── _current_checkpoint_id.meta.pkl
│ │ ├── _preprocessor.meta.pkl
│ │ ├── _timestamp.meta.pkl
│ │ ├── assets
│ │ ├── keras_metadata.pb
│ │ ├── saved_model.pb
│ │ └── variables
│ │ ├── variables.data-00000-of-00001
│ │ └── variables.index
│ ├── events.out.tfevents.1655329242.xw
│ ├── params.json
│ ├── params.pkl
│ ├── progress.csv
│ ├── rank_0
│ │ └── my_model
│ │ ├── assets
│ │ ├── keras_metadata.pb
│ │ ├── saved_model.pb
│ │ └── variables
│ │ ├── variables.data-00000-of-00001
│ │ └── variables.index
│ ├── rank_1
│ │ └── my_model
│ │ ├── assets
│ │ ├── keras_metadata.pb
│ │ ├── saved_model.pb
│ │ └── variables
│ │ ├── variables.data-00000-of-00001
│ │ └── variables.index
│ └── result.json
├── basic-variant-state-2022-06-15_14-40-42.json
├── experiment_state-2022-06-15_14-40-42.json
├── trainable.pkl
└── tuner.pkl
```
Update:
1. Updated a few classes to be backward compatible - while legacy ray train deprecation is ongoing.
2. Marked all places in 1 using "# TODO(xwjiang): Legacy Ray Train trainer clean up!". So we can easily clean those up once Antoni's work is landed.
3. All CI and release tests are passing.
Co-authored-by: Eric Liang <ekhliang@gmail.com>
|
https://github.com/ray-project/ray.git
|
def __call__(self, _metric=None, **kwargs):
assert self._last_report_time is not None, (
"_StatusReporter._start() must be called before the first "
"report __call__ is made to ensure correct runtime metrics."
)
if _metric:
kwargs[DEFAULT_METRIC] = _metric
# time per iteration is recorded directly in the reporter to ensure
# any delays in logging results aren't counted
report_time = time.time()
if TIME_THIS_ITER_S not in kwargs:
kwargs[TIME_THIS_ITER_S] = report_time - self._last_report_time
self._last_report_time = report_time
# add results to a thread-safe queue
self._queue.put(kwargs.copy(), block=True)
# This blocks until notification from the FunctionRunner that the last
# result has been returned to Tune and that the function is safe to
# resume training.
self._continue_semaphore.acquire()
# If the trial should be terminated, exit gracefully.
if self._end_event.is_set():
self._end_event.clear()
sys.exit(0)
| 107
|
function_runner.py
|
Python
|
python/ray/tune/function_runner.py
|
97f42425dacc914fc90059a010f5a02a5ab3b8c7
|
ray
| 4
|
|
100,318
| 52
| 16
| 15
| 217
| 23
| 0
| 76
| 302
|
get_loss_keys
|
Update code to support Tensorflow versions up to 2.8 (#1213)
* Update maximum tf version in setup + requirements
* - bump max version of tf version in launcher
- standardise tf version check
* update keras get_custom_objects for tf>2.6
* bugfix: force black text in GUI file dialogs (linux)
* dssim loss - Move to stock tf.ssim function
* Update optimizer imports for compatibility
* fix logging for tf2.8
* Fix GUI graphing for TF2.8
* update tests
* bump requirements.txt versions
* Remove limit on nvidia-ml-py
* Graphing bugfixes
- Prevent live graph from displaying if data not yet available
* bugfix: Live graph. Collect loss labels correctly
* fix: live graph - swallow inconsistent loss errors
* Bugfix: Prevent live graph from clearing during training
* Fix graphing for AMD
|
https://github.com/deepfakes/faceswap.git
|
def get_loss_keys(self, session_id):
if get_backend() == "amd":
# We can't log the graph in Tensorboard logs for AMD so need to obtain from state file
loss_keys = {int(sess_id): [name for name in session["loss_names"] if name != "total"]
for sess_id, session in self._state["sessions"].items()}
else:
loss_keys = {sess_id: list(logs.keys())
for sess_id, logs
in self._tb_logs.get_loss(session_id=session_id).items()}
if session_id is None:
retval = list(set(loss_key
for session in loss_keys.values()
for loss_key in session))
else:
retval = loss_keys.get(session_id)
return retval
_SESSION = GlobalSession()
| 126
|
stats.py
|
Python
|
lib/gui/analysis/stats.py
|
c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf
|
faceswap
| 9
|
|
249,541
| 21
| 11
| 10
| 95
| 12
| 0
| 25
| 103
|
test_get_insertion_event_backward_extremities_in_room
|
Only try to backfill event if we haven't tried before recently (#13635)
Only try to backfill event if we haven't tried before recently (exponential backoff). No need to keep trying the same backfill point that fails over and over.
Fix https://github.com/matrix-org/synapse/issues/13622
Fix https://github.com/matrix-org/synapse/issues/8451
Follow-up to https://github.com/matrix-org/synapse/pull/13589
Part of https://github.com/matrix-org/synapse/issues/13356
|
https://github.com/matrix-org/synapse.git
|
def test_get_insertion_event_backward_extremities_in_room(self):
setup_info = self._setup_room_for_insertion_backfill_tests()
room_id = setup_info.room_id
backfill_points = self.get_success(
self.store.get_insertion_event_backward_extremities_in_room(room_id)
)
backfill_event_ids = [backfill_point[0] for backfill_point in backfill_points]
self.assertListEqual(
backfill_event_ids, ["insertion_eventB", "insertion_eventA"]
)
| 57
|
test_event_federation.py
|
Python
|
tests/storage/test_event_federation.py
|
ac1a31740b6d0dfda4d57a25762aaddfde981caf
|
synapse
| 2
|
|
274,643
| 30
| 13
| 10
| 115
| 13
| 0
| 33
| 147
|
merge_state
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def merge_state(self, metrics):
assign_add_ops = []
for metric in metrics:
if len(self.weights) != len(metric.weights):
raise ValueError(
f"Metric {metric} is not compatible with {self}"
)
for weight, weight_to_add in zip(self.weights, metric.weights):
assign_add_ops.append(weight.assign_add(weight_to_add))
return assign_add_ops
| 67
|
base_metric.py
|
Python
|
keras/metrics/base_metric.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 4
|
|
258,495
| 61
| 13
| 20
| 298
| 23
| 0
| 79
| 216
|
manhattan_distances
|
DOC Ensures that manhattan_distances passes numpydoc validation (#22139)
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
|
https://github.com/scikit-learn/scikit-learn.git
|
def manhattan_distances(X, Y=None, *, sum_over_features=True):
X, Y = check_pairwise_arrays(X, Y)
if issparse(X) or issparse(Y):
if not sum_over_features:
raise TypeError(
"sum_over_features=%r not supported for sparse matrices"
% sum_over_features
)
X = csr_matrix(X, copy=False)
Y = csr_matrix(Y, copy=False)
X.sum_duplicates() # this also sorts indices in-place
Y.sum_duplicates()
D = np.zeros((X.shape[0], Y.shape[0]))
_sparse_manhattan(X.data, X.indices, X.indptr, Y.data, Y.indices, Y.indptr, D)
return D
if sum_over_features:
return distance.cdist(X, Y, "cityblock")
D = X[:, np.newaxis, :] - Y[np.newaxis, :, :]
D = np.abs(D, D)
return D.reshape((-1, X.shape[1]))
| 194
|
pairwise.py
|
Python
|
sklearn/metrics/pairwise.py
|
ff09c8a579b116500deade618f93c4dc0d5750bd
|
scikit-learn
| 5
|
|
155,898
| 142
| 20
| 55
| 576
| 40
| 0
| 240
| 846
|
get_scheduler
|
Raise warning when using multiple types of schedulers where one is `distributed` (#8700)
Raise a warning when `compute` or `persist` are called with a scheduler different from "dask.distributed" or "distributed" in Dask.distributed mode
|
https://github.com/dask/dask.git
|
def get_scheduler(get=None, scheduler=None, collections=None, cls=None):
if get:
raise TypeError(get_err_msg)
if scheduler is not None:
if callable(scheduler):
return scheduler
elif "Client" in type(scheduler).__name__ and hasattr(scheduler, "get"):
return scheduler.get
elif isinstance(scheduler, str):
scheduler = scheduler.lower()
if scheduler in named_schedulers:
if config.get("scheduler", None) in ("dask.distributed", "distributed"):
warnings.warn(
"Running on a single-machine scheduler when a distributed client "
"is active might lead to unexpected results."
)
return named_schedulers[scheduler]
elif scheduler in ("dask.distributed", "distributed"):
from distributed.worker import get_client
return get_client().get
else:
raise ValueError(
"Expected one of [distributed, %s]"
% ", ".join(sorted(named_schedulers))
)
elif isinstance(scheduler, Executor):
# Get `num_workers` from `Executor`'s `_max_workers` attribute.
# If undefined, fallback to `config` or worst case CPU_COUNT.
num_workers = getattr(scheduler, "_max_workers", None)
if num_workers is None:
num_workers = config.get("num_workers", CPU_COUNT)
assert isinstance(num_workers, Integral) and num_workers > 0
return partial(local.get_async, scheduler.submit, num_workers)
else:
raise ValueError("Unexpected scheduler: %s" % repr(scheduler))
# else: # try to connect to remote scheduler with this name
# return get_client(scheduler).get
if config.get("scheduler", None):
return get_scheduler(scheduler=config.get("scheduler", None))
if config.get("get", None):
raise ValueError(get_err_msg)
if getattr(thread_state, "key", False):
from distributed.worker import get_worker
return get_worker().client.get
if cls is not None:
return cls.__dask_scheduler__
if collections:
collections = [c for c in collections if c is not None]
if collections:
get = collections[0].__dask_scheduler__
if not all(c.__dask_scheduler__ == get for c in collections):
raise ValueError(
"Compute called on multiple collections with "
"differing default schedulers. Please specify a "
"scheduler=` parameter explicitly in compute or "
"globally with `dask.config.set`."
)
return get
return None
| 346
|
base.py
|
Python
|
dask/base.py
|
277859ddfcc30a9070ca560c9e3e2720e5eed616
|
dask
| 23
|
|
101,370
| 79
| 14
| 37
| 262
| 25
| 0
| 112
| 531
|
_load_extractor
|
Bugfix: convert - Gif Writer
- Fix non-launch error on Gif Writer
- convert plugins - linting
- convert/fs_media/preview/queue_manager - typing
- Change convert items from dict to Dataclass
|
https://github.com/deepfakes/faceswap.git
|
def _load_extractor(self) -> Optional[Extractor]:
if not self._alignments.have_alignments_file and not self._args.on_the_fly:
logger.error("No alignments file found. Please provide an alignments file for your "
"destination video (recommended) or enable on-the-fly conversion (not "
"recommended).")
sys.exit(1)
if self._alignments.have_alignments_file:
if self._args.on_the_fly:
logger.info("On-The-Fly conversion selected, but an alignments file was found. "
"Using pre-existing alignments file: '%s'", self._alignments.file)
else:
logger.debug("Alignments file found: '%s'", self._alignments.file)
return None
logger.debug("Loading extractor")
logger.warning("On-The-Fly conversion selected. This will use the inferior cv2-dnn for "
"extraction and will produce poor results.")
logger.warning("It is recommended to generate an alignments file for your destination "
"video with Extract first for superior results.")
extractor = Extractor(detector="cv2-dnn",
aligner="cv2-dnn",
masker=self._args.mask_type,
multiprocess=True,
rotate_images=None,
min_size=20)
extractor.launch()
logger.debug("Loaded extractor")
return extractor
| 148
|
convert.py
|
Python
|
scripts/convert.py
|
1022651eb8a7741014f5d2ec7cbfe882120dfa5f
|
faceswap
| 5
|
|
107,814
| 7
| 8
| 2
| 29
| 5
| 0
| 7
| 13
|
test_strip_comment
|
Support quoted strings in matplotlibrc
This enables using the comment character # within strings.
Closes #19288.
Superseeds #22565.
|
https://github.com/matplotlib/matplotlib.git
|
def test_strip_comment(line, result):
assert cbook._strip_comment(line) == result
| 17
|
test_cbook.py
|
Python
|
lib/matplotlib/tests/test_cbook.py
|
7c378a8f3f30ce57c874a851f3af8af58f1ffdf6
|
matplotlib
| 1
|
|
153,666
| 50
| 15
| 19
| 112
| 9
| 0
| 71
| 245
|
_is_zero_copy_possible
|
FEAT-#4244: Implement dataframe exchange protocol for OmniSci (#4269)
Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru>
Co-authored-by: Vasily Litvinov <vasilij.n.litvinov@intel.com>
Signed-off-by: Dmitry Chigarev <dmitry.chigarev@intel.com>
|
https://github.com/modin-project/modin.git
|
def _is_zero_copy_possible(self) -> bool:
if self.__is_zero_copy_possible is None:
if self._df._has_arrow_table():
# If PyArrow table is already materialized then we can
# retrieve data zero-copy
self.__is_zero_copy_possible = True
elif not self._df._can_execute_arrow():
# When not able to execute the plan via PyArrow means
# that we have to involve OmniSci, so no zero-copy.
self.__is_zero_copy_possible = False
else:
# Check whether the plan for PyArrow can be executed zero-copy
self.__is_zero_copy_possible = self._is_zero_copy_arrow_op(self._df._op)
return self.__is_zero_copy_possible
| 64
|
dataframe.py
|
Python
|
modin/experimental/core/execution/native/implementations/omnisci_on_native/exchange/dataframe_protocol/dataframe.py
|
0c1a2129df64cf45bf1ff49c8ed92c510fdb1c82
|
modin
| 4
|
|
126,252
| 46
| 12
| 19
| 166
| 26
| 0
| 59
| 162
|
test_syncer_callback_noop_on_trial_cloud_checkpointing
|
[air] Add annotation for Tune module. (#27060)
Co-authored-by: Kai Fricke <kai@anyscale.com>
|
https://github.com/ray-project/ray.git
|
def test_syncer_callback_noop_on_trial_cloud_checkpointing():
callbacks = _create_default_callbacks(callbacks=[], sync_config=SyncConfig())
syncer_callback = None
for cb in callbacks:
if isinstance(cb, SyncerCallback):
syncer_callback = cb
trial1 = MockTrial(trial_id="a", logdir=None)
trial1.uses_cloud_checkpointing = True
assert syncer_callback
assert syncer_callback._enabled
# Cloud checkpointing set, so no-op
assert not syncer_callback._sync_trial_dir(trial1)
# This should not raise any error for not existing directory
syncer_callback.on_checkpoint(
iteration=1,
trials=[],
trial=trial1,
checkpoint=_TrackedCheckpoint(
dir_or_data="/does/not/exist", storage_mode=CheckpointStorage.PERSISTENT
),
)
| 103
|
test_syncer_callback.py
|
Python
|
python/ray/tune/tests/test_syncer_callback.py
|
eb69c1ca286a2eec594f02ddaf546657a8127afd
|
ray
| 3
|
|
241,536
| 23
| 9
| 6
| 96
| 7
| 0
| 30
| 48
|
test_prefix_metric_keys
|
Group metrics generated by `DeviceStatsMonitor` for better visualization (#11254)
Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
|
https://github.com/Lightning-AI/lightning.git
|
def test_prefix_metric_keys(tmpdir):
metrics = {"1": 1.0, "2": 2.0, "3": 3.0}
prefix = "foo"
separator = "."
converted_metrics = _prefix_metric_keys(metrics, prefix, separator)
assert converted_metrics == {"foo.1": 1.0, "foo.2": 2.0, "foo.3": 3.0}
| 65
|
test_device_stats_monitor.py
|
Python
|
tests/callbacks/test_device_stats_monitor.py
|
05ed9a201c24e08c2b4d3df4735296758ddcd6a5
|
lightning
| 1
|
|
273,619
| 4
| 6
| 2
| 16
| 3
| 0
| 4
| 18
|
output_size
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def output_size(self):
raise NotImplementedError
| 8
|
abstract_rnn_cell.py
|
Python
|
keras/layers/rnn/abstract_rnn_cell.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 1
|
|
269,586
| 136
| 14
| 31
| 405
| 40
| 1
| 189
| 444
|
categorical_crossentropy
|
Reformatting the codebase with black.
PiperOrigin-RevId: 450093126
|
https://github.com/keras-team/keras.git
|
def categorical_crossentropy(target, output, from_logits=False, axis=-1):
target = tf.convert_to_tensor(target)
output = tf.convert_to_tensor(output)
target.shape.assert_is_compatible_with(output.shape)
# Use logits whenever they are available. `softmax` and `sigmoid`
# activations cache logits on the `output` Tensor.
if hasattr(output, "_keras_logits"):
output = output._keras_logits # pylint: disable=protected-access
if from_logits:
warnings.warn(
'"`categorical_crossentropy` received `from_logits=True`, but '
"the `output` argument was produced by a sigmoid or softmax "
'activation and thus does not represent logits. Was this intended?"',
stacklevel=2,
)
from_logits = True
if from_logits:
return tf.nn.softmax_cross_entropy_with_logits(
labels=target, logits=output, axis=axis
)
if (
not isinstance(output, (tf.__internal__.EagerTensor, tf.Variable))
and output.op.type == "Softmax"
) and not hasattr(output, "_keras_history"):
# When softmax activation function is used for output operation, we
# use logits from the softmax function directly to compute loss in order
# to prevent collapsing zero when training.
# See b/117284466
assert len(output.op.inputs) == 1
output = output.op.inputs[0]
return tf.nn.softmax_cross_entropy_with_logits(
labels=target, logits=output, axis=axis
)
# scale preds so that the class probas of each sample sum to 1
output = output / tf.reduce_sum(output, axis, True)
# Compute cross entropy from probabilities.
epsilon_ = _constant_to_tensor(epsilon(), output.dtype.base_dtype)
output = tf.clip_by_value(output, epsilon_, 1.0 - epsilon_)
return -tf.reduce_sum(target * tf.math.log(output), axis)
@keras_export("keras.backend.sparse_categorical_crossentropy")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs
|
@keras_export("keras.backend.sparse_categorical_crossentropy")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs
| 237
|
backend.py
|
Python
|
keras/backend.py
|
84afc5193d38057e2e2badf9c889ea87d80d8fbf
|
keras
| 7
|
156,322
| 6
| 6
| 12
| 19
| 3
| 0
| 6
| 20
|
produces_keys
|
Use DataFrameIOLayer in DataFrame.from_delayed (#8852)
|
https://github.com/dask/dask.git
|
def produces_keys(self) -> bool:
return False
| 10
|
blockwise.py
|
Python
|
dask/blockwise.py
|
1ccd1a4f96afa1fe89ea93dcfe66517319b0664d
|
dask
| 1
|
|
22,678
| 16
| 11
| 5
| 50
| 6
| 0
| 16
| 59
|
set
|
refactor: clean code
Signed-off-by: slowy07 <slowy.arfy@gmail.com>
|
https://github.com/geekcomputers/Python.git
|
def set(self, components):
if len(components) > 0:
self.__components = components
else:
raise Exception("please give any vector")
| 28
|
lib.py
|
Python
|
linear-algebra-python/src/lib.py
|
f0af0c43340763724f139fa68aa1e5a9ffe458b4
|
Python
| 2
|
|
176,703
| 23
| 9
| 74
| 83
| 9
| 0
| 25
| 43
|
average_clustering
|
Remove redundant py2 numeric conversions (#5661)
* Remove redundant float conversion
* Remove redundant int conversion
* Use integer division
Co-authored-by: Miroslav Šedivý <6774676+eumiro@users.noreply.github.com>
|
https://github.com/networkx/networkx.git
|
def average_clustering(G, nodes=None, mode="dot"):
r
if nodes is None:
nodes = G
ccs = latapy_clustering(G, nodes=nodes, mode=mode)
return sum(ccs[v] for v in nodes) / len(nodes)
| 54
|
cluster.py
|
Python
|
networkx/algorithms/bipartite/cluster.py
|
2a05ccdb07cff88e56661dee8a9271859354027f
|
networkx
| 3
|
|
89,862
| 70
| 18
| 45
| 339
| 23
| 0
| 88
| 887
|
test_first_event_with_minified_stack_trace_received
|
ref(onboarding): Add function to record first event per project with min stack trace -(#42208)
|
https://github.com/getsentry/sentry.git
|
def test_first_event_with_minified_stack_trace_received(self, record_analytics):
now = timezone.now()
project = self.create_project(first_event=now)
project_created.send(project=project, user=self.user, sender=type(project))
url = "http://localhost:3000"
data = load_data("javascript")
data["tags"] = [("url", url)]
data["exception"] = {
"values": [
{
**data["exception"]["values"][0],
"raw_stacktrace": {
"frames": [
{
"function": "o",
"filename": "/_static/dist/sentry/chunks/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.255071ceadabfb67483c.js",
"abs_path": "https://s1.sentry-cdn.com/_static/dist/sentry/chunks/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.255071ceadabfb67483c.js",
"lineno": 2,
"colno": 37098,
"pre_context": [
"/*! For license information please see vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd. {snip}"
],
"context_line": "{snip} .apply(this,arguments);const i=o.map((e=>c(e,t)));return e.apply(this,i)}catch(e){throw l(),(0,i.$e)((n=>{n.addEventProcessor((e=>(t.mechani {snip}",
"post_context": [
"//# sourceMappingURL=../sourcemaps/vendors-node_modules_emotion_is-prop-valid_node_modules_emotion_memoize_dist_memoize_browser_-4fe4bd.fe32 {snip}"
],
"in_app": False,
},
],
},
}
]
}
self.store_event(
project_id=project.id,
data=data,
)
record_analytics.assert_called_with(
"first_event_with_minified_stack_trace_for_project.sent",
user_id=self.user.id,
organization_id=project.organization_id,
project_id=project.id,
platform=data["platform"],
url=url,
)
| 198
|
test_onboarding.py
|
Python
|
tests/sentry/receivers/test_onboarding.py
|
ce841204ef3b20d0f6ac812ebb06aebbc63547ac
|
sentry
| 1
|
|
260,279
| 9
| 9
| 4
| 49
| 7
| 0
| 9
| 37
|
fit
|
MAINT parameter validation for Normalizer (#23543)
Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>
|
https://github.com/scikit-learn/scikit-learn.git
|
def fit(self, X, y=None):
self._validate_params()
self._validate_data(X, accept_sparse="csr")
return self
| 29
|
_data.py
|
Python
|
sklearn/preprocessing/_data.py
|
40e055b362a337cef15645d4b1be046aa782c415
|
scikit-learn
| 1
|
|
86,878
| 15
| 14
| 14
| 69
| 10
| 0
| 18
| 59
|
get_region_to_control_producer
|
chore(hybrid-cloud): AuditLogEntry is a control silo model now (#39890)
In the control silo, creating an audit log entry writes to the db
directly, whilst in region silo mode creating an audit log entry will
instead push to a new kafka producer that consumes into the control silo
asynchronously.
|
https://github.com/getsentry/sentry.git
|
def get_region_to_control_producer() -> KafkaProducer:
global _publisher
if _publisher is None:
config = settings.KAFKA_TOPICS.get(settings.KAFKA_REGION_TO_CONTROL)
_publisher = KafkaProducer(
kafka_config.get_kafka_producer_cluster_options(config["cluster"])
)
| 48
|
producer.py
|
Python
|
src/sentry/region_to_control/producer.py
|
941184cd24186324fd9f7f304b7f713041834726
|
sentry
| 2
|
|
181,702
| 19
| 9
| 6
| 60
| 8
| 0
| 23
| 41
|
test_source_decode_2
|
Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068.
|
https://github.com/EpistasisLab/tpot.git
|
def test_source_decode_2():
import_str, op_str, op_obj = source_decode("sklearn.linear_model.LogisticReg")
from sklearn.linear_model import LogisticRegression
assert import_str == "sklearn.linear_model"
assert op_str == "LogisticReg"
assert op_obj is None
| 33
|
tpot_tests.py
|
Python
|
tests/tpot_tests.py
|
388616b6247ca4ea8de4e2f340d6206aee523541
|
tpot
| 1
|
|
20,964
| 47
| 14
| 16
| 171
| 18
| 0
| 58
| 157
|
get_allowed_args
|
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 get_allowed_args(fn_or_class):
# type: (Union[Callable, Type]) -> Tuple[List[str], Dict[str, Any]]
try:
signature = inspect.signature(fn_or_class)
except AttributeError:
import funcsigs
signature = funcsigs.signature(fn_or_class)
args = []
kwargs = {}
for arg, param in signature.parameters.items():
if (
param.kind in (param.POSITIONAL_OR_KEYWORD, param.POSITIONAL_ONLY)
) and param.default is param.empty:
args.append(arg)
else:
kwargs[arg] = param.default if param.default is not param.empty else None
return args, kwargs
| 106
|
utils.py
|
Python
|
pipenv/vendor/pip_shims/utils.py
|
f3166e673fe8d40277b804d35d77dcdb760fc3b3
|
pipenv
| 6
|
|
211,029
| 31
| 15
| 12
| 233
| 9
| 0
| 69
| 146
|
convert_x_to_bbox
|
[MOT] Add OC_SORT tracker (#6272)
* add ocsort tracker
* add ocsort deploy
* merge develop
* fix ocsort tracker codes
* fix doc, test=document_fix
* fix doc, test=document_fix
|
https://github.com/PaddlePaddle/PaddleDetection.git
|
def convert_x_to_bbox(x, score=None):
w = np.sqrt(x[2] * x[3])
h = x[2] / w
if (score == None):
return np.array(
[x[0] - w / 2., x[1] - h / 2., x[0] + w / 2.,
x[1] + h / 2.]).reshape((1, 4))
else:
score = np.array([score])
return np.array([
x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score
]).reshape((1, 5))
| 167
|
ocsort_tracker.py
|
Python
|
deploy/pptracking/python/mot/tracker/ocsort_tracker.py
|
c84153a355d9855fe55cf51d203b8b24e7d884e5
|
PaddleDetection
| 2
|
|
107,206
| 7
| 8
| 3
| 40
| 7
| 0
| 7
| 44
|
get_current_underline_thickness
|
Factor out underline-thickness lookups in mathtext.
Just adding a small helper function.
|
https://github.com/matplotlib/matplotlib.git
|
def get_current_underline_thickness(self):
return self.font_output.get_underline_thickness(
self.font, self.fontsize, self.dpi)
| 25
|
_mathtext.py
|
Python
|
lib/matplotlib/_mathtext.py
|
b71421e685733b1cade94113b588ca1a773ae558
|
matplotlib
| 1
|
|
86,136
| 23
| 14
| 8
| 112
| 13
| 0
| 23
| 95
|
get_form
|
fix(admin): Fix typo in admin user creation form (#38418)
This fixes the error reported in https://github.com/getsentry/sentry/issues/38303, which appears to be due to a typo in the django module name.
|
https://github.com/getsentry/sentry.git
|
def get_form(self, request, obj=None, **kwargs):
defaults = {}
if obj is None:
defaults.update(
{"form": self.add_form, "fields": admin.utils.flatten_fieldsets(self.add_fieldsets)}
)
defaults.update(kwargs)
return super().get_form(request, obj, **defaults)
| 69
|
admin.py
|
Python
|
src/sentry/admin.py
|
d522d620e5e6799000b918278c86cbaa0b1592a1
|
sentry
| 2
|
|
9,760
| 104
| 15
| 6
| 374
| 37
| 0
| 161
| 496
|
different
|
Check gallery up to date as part of CI (#3329)
* Check gallery up to date as part of CI
Fix #2916
* tweak check_gallery.py
* update CI workflow
* update stale doc cache
* update stale docs
|
https://github.com/RaRe-Technologies/gensim.git
|
def different(path1, path2):
with open(path1) as fin:
f1 = fin.read()
with open(path2) as fin:
f2 = fin.read()
return f1 != f2
curr_dir = os.path.dirname(__file__)
stale = []
for root, dirs, files in os.walk(os.path.join(curr_dir, 'gallery')):
for f in files:
if f.endswith('.py'):
source_path = os.path.join(root, f)
cache_path = source_path.replace('docs/src/gallery/', 'docs/src/auto_examples/')
#
# We check two things:
#
# 1) Actual file content
# 2) MD5 checksums
#
# We check 1) because that's the part that matters to the user -
# it's what will appear in the documentation. We check 2) because
# that's what Sphinx Gallery relies on to decide what it needs to
# rebuild. In practice, only one of these checks is necessary,
# but we run them both because it's trivial.
#
if different(source_path, cache_path):
stale.append(cache_path)
continue
actual_md5 = hashlib.md5()
with open(source_path, 'rb') as fin:
actual_md5.update(fin.read())
md5_path = cache_path + '.md5'
with open(md5_path) as fin:
expected_md5 = fin.read()
if actual_md5.hexdigest() != expected_md5:
stale.append(cache_path)
if stale:
print(f, file=sys.stderr)
sys.exit(1)
| 41
|
check_gallery.py
|
Python
|
docs/src/check_gallery.py
|
9bbf12c330275351e777b553c145066b7c397f95
|
gensim
| 1
|
|
106,118
| 52
| 14
| 22
| 326
| 17
| 0
| 85
| 222
|
fast_slice
|
Clean up Table class docstrings (#5355)
* clean table docstrings
* 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 fast_slice(self, offset=0, length=None) -> pa.Table:
if offset < 0:
raise IndexError("Offset must be non-negative")
elif offset >= self._offsets[-1] or (length is not None and length <= 0):
return pa.Table.from_batches([], schema=self._schema)
i = _interpolation_search(self._offsets, offset)
if length is None or length + offset >= self._offsets[-1]:
batches = self._batches[i:]
batches[0] = batches[0].slice(offset - self._offsets[i])
else:
j = _interpolation_search(self._offsets, offset + length - 1)
batches = self._batches[i : j + 1]
batches[-1] = batches[-1].slice(0, offset + length - self._offsets[j])
batches[0] = batches[0].slice(offset - self._offsets[i])
return pa.Table.from_batches(batches, schema=self._schema)
| 214
|
table.py
|
Python
|
src/datasets/table.py
|
c902456677116a081f762fa2b4aad13a0aa04d6e
|
datasets
| 7
|
|
133,814
| 41
| 11
| 10
| 198
| 18
| 0
| 55
| 93
|
_mac
|
[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 _mac(model, obs, h):
B, n_agents = obs.size(0), obs.size(1)
if not isinstance(obs, dict):
obs = {"obs": obs}
obs_agents_as_batches = {k: _drop_agent_dim(v) for k, v in obs.items()}
h_flat = [s.reshape([B * n_agents, -1]) for s in h]
q_flat, h_flat = model(obs_agents_as_batches, h_flat, None)
return q_flat.reshape([B, n_agents, -1]), [
s.reshape([B, n_agents, -1]) for s in h_flat
]
| 130
|
qmix_policy.py
|
Python
|
rllib/agents/qmix/qmix_policy.py
|
7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065
|
ray
| 5
|
|
136,639
| 10
| 10
| 5
| 45
| 5
| 0
| 10
| 42
|
post_process
|
KubeRay node provider refactor (#30281)
Implements KubeRay node provider as a "BatchingNodeProvider".
Builds on #29933.
Summary of design
An autoscaler update now works like this:
list pod data from k8s
check if it's safe to proceed with update. Abort the update if not.
do some internal calculation to determine desired scale
submit a single patch to the RayCluster CR if a scale change is required
Everything is single-threaded and there are O(1) K8s API calls per autoscaler update.
Signed-off-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>
|
https://github.com/ray-project/ray.git
|
def post_process(self) -> None:
if self.scale_change_needed:
self.submit_scale_request(self.scale_request)
self.scale_change_needed = False
| 26
|
batching_node_provider.py
|
Python
|
python/ray/autoscaler/batching_node_provider.py
|
c976799dfd96806ec9972a287835f7a034ec3d2c
|
ray
| 2
|
|
309,333
| 6
| 7
| 22
| 27
| 4
| 0
| 8
| 29
|
test_circular_import
|
Use Platform enum in load_platform [tests] (#63904)
* Use Platform enum in numato tests
* Use Platform enum in discovery tests
* Adjust load_platform argument
Co-authored-by: epenet <epenet@users.noreply.github.com>
|
https://github.com/home-assistant/core.git
|
def test_circular_import(self):
component_calls = []
platform_calls = []
| 131
|
test_discovery.py
|
Python
|
tests/helpers/test_discovery.py
|
b71a22557dc6ca87b6c1871a0e4d3c3a949759fc
|
core
| 1
|
|
126,889
| 24
| 8
| 3
| 46
| 7
| 0
| 27
| 62
|
__reduce__
|
Fix out-of-band deserialization of actor handle (#27700)
When we deserialize actor handle via pickle, we will register it with an outer object ref equaling to itself which is wrong. For out-of-band deserialization, there should be no outer object ref.
Signed-off-by: Jiajun Yao <jeromeyjj@gmail.com>
|
https://github.com/ray-project/ray.git
|
def __reduce__(self):
(serialized, _) = self._serialization_helper()
# There is no outer object ref when the actor handle is
# deserialized out-of-band using pickle.
return ActorHandle._deserialization_helper, (serialized, None)
| 27
|
actor.py
|
Python
|
python/ray/actor.py
|
f084546d41f0533c1e9e96a7249532d0eb4ff47d
|
ray
| 1
|
|
3,763
| 29
| 14
| 12
| 176
| 19
| 0
| 39
| 114
|
get_json_schema
|
🎉 🎉 Source FB Marketing: performance and reliability fixes (#9805)
* Facebook Marketing performance improvement
* add comments and little refactoring
* fix integration tests with the new config
* improve job status handling, limit concurrency to 10
* fix campaign jobs, refactor manager
* big refactoring of async jobs, support random order of slices
* update source _read_incremental to hook new state logic
* fix issues with timeout
* remove debugging and clean up, improve retry logic
* merge changes from #8234
* fix call super _read_increment
* generalize batch execution, add use_batch flag
* improve coverage, do some refactoring of spec
* update test, remove overrides of source
* add split by AdSet
* add smaller insights
* fix end_date < start_date case
* add account_id to PK
* add notes
* fix new streams
* fix reversed incremental stream
* update spec.json for SAT
* upgrade CDK and bump version
Co-authored-by: Dmytro Rezchykov <dmitry.rezchykov@zazmic.com>
Co-authored-by: Eugene Kulak <kulak.eugene@gmail.com>
|
https://github.com/airbytehq/airbyte.git
|
def get_json_schema(self) -> Mapping[str, Any]:
loader = ResourceSchemaLoader(package_name_from_class(self.__class__))
schema = loader.get_schema("ads_insights")
if self._fields:
schema["properties"] = {k: v for k, v in schema["properties"].items() if k in self._fields}
if self.breakdowns:
breakdowns_properties = loader.get_schema("ads_insights_breakdowns")["properties"]
schema["properties"].update({prop: breakdowns_properties[prop] for prop in self.breakdowns})
return schema
| 106
|
base_insight_streams.py
|
Python
|
airbyte-integrations/connectors/source-facebook-marketing/source_facebook_marketing/streams/base_insight_streams.py
|
a3aae8017a0a40ff2006e2567f71dccb04c997a5
|
airbyte
| 6
|
|
247,670
| 14
| 9
| 6
| 71
| 12
| 0
| 15
| 50
|
test_exception_callback
|
Add tests for database transaction callbacks (#12198)
Signed-off-by: Sean Quah <seanq@element.io>
|
https://github.com/matrix-org/synapse.git
|
def test_exception_callback(self) -> None:
_test_txn = Mock(side_effect=ZeroDivisionError)
after_callback, exception_callback = self._run_interaction(_test_txn)
after_callback.assert_not_called()
exception_callback.assert_called_once_with(987, 654, extra=321)
| 43
|
test_database.py
|
Python
|
tests/storage/test_database.py
|
dea577998f221297d3ff30bdf904f7147f3c3d8a
|
synapse
| 1
|
|
268,826
| 25
| 13
| 9
| 95
| 11
| 0
| 28
| 55
|
print_msg
|
Put absl logging control flag in a separate file.
Open the APIs for control the logging in Keras.
PiperOrigin-RevId: 419972643
|
https://github.com/keras-team/keras.git
|
def print_msg(message, line_break=True):
# Use `getattr` in case `INTERACTIVE_LOGGING`
# does not have the `enable` attribute.
if INTERACTIVE_LOGGING.enable:
if line_break:
sys.stdout.write(message + '\n')
else:
sys.stdout.write(message)
sys.stdout.flush()
else:
logging.info(message)
| 53
|
io_utils.py
|
Python
|
keras/utils/io_utils.py
|
f427e16d9e4a440b5e7e839001255f7cd87127f5
|
keras
| 3
|
|
288,323
| 63
| 12
| 29
| 253
| 17
| 0
| 74
| 289
|
test_webhook_person_event
|
Fix Netatmo scope issue with HA cloud (#79437)
Co-authored-by: Paulus Schoutsen <balloob@gmail.com>
|
https://github.com/home-assistant/core.git
|
async def test_webhook_person_event(hass, config_entry, netatmo_auth):
with selected_platforms(["camera"]):
assert await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
test_netatmo_event = async_capture_events(hass, NETATMO_EVENT)
assert not test_netatmo_event
fake_webhook_event = {
"persons": [
{
"id": "91827374-7e04-5298-83ad-a0cb8372dff1",
"face_id": "a1b2c3d4e5",
"face_key": "9876543",
"is_known": True,
"face_url": "https://netatmocameraimage.blob.core.windows.net/production/12345",
}
],
"snapshot_id": "123456789abc",
"snapshot_key": "foobar123",
"snapshot_url": "https://netatmocameraimage.blob.core.windows.net/production/12346",
"event_type": "person",
"camera_id": "12:34:56:00:f1:62",
"device_id": "12:34:56:00:f1:62",
"event_id": "1234567890",
"message": "MYHOME: John Doe has been seen by Indoor Camera ",
"push_type": "NACamera-person",
}
webhook_id = config_entry.data[CONF_WEBHOOK_ID]
await simulate_webhook(hass, webhook_id, fake_webhook_event)
assert test_netatmo_event
| 133
|
test_camera.py
|
Python
|
tests/components/netatmo/test_camera.py
|
3e411935bbe07ebe0e7a9f5323734448486d75d7
|
core
| 1
|
|
291,525
| 26
| 13
| 10
| 137
| 13
| 0
| 40
| 119
|
_is_today
|
Fix Sonos alarm 'scheduled_today' attribute logic (#82816)
fixes undefined
|
https://github.com/home-assistant/core.git
|
def _is_today(self) -> bool:
recurrence = self.alarm.recurrence
daynum = int(datetime.datetime.today().strftime("%w"))
return (
recurrence in ("DAILY", "ONCE")
or (recurrence == "WEEKENDS" and daynum in WEEKEND_DAYS)
or (recurrence == "WEEKDAYS" and daynum not in WEEKEND_DAYS)
or (recurrence.startswith("ON_") and str(daynum) in recurrence)
)
| 79
|
switch.py
|
Python
|
homeassistant/components/sonos/switch.py
|
f887aeedfe057682f8d5a3abd44082d02fe42758
|
core
| 7
|
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