id
int64 20
338k
| vocab_size
int64 2
671
| ast_levels
int64 4
32
| nloc
int64 1
451
| n_ast_nodes
int64 12
5.6k
| n_identifiers
int64 1
186
| n_ast_errors
int64 0
10
| n_words
int64 2
2.17k
| n_whitespaces
int64 2
13.8k
| fun_name
stringlengths 2
73
| commit_message
stringlengths 51
15.3k
| url
stringlengths 31
59
| code
stringlengths 51
31k
| ast_errors
stringlengths 0
1.46k
| token_counts
int64 6
3.32k
| file_name
stringlengths 5
56
| language
stringclasses 1
value | path
stringlengths 7
134
| commit_id
stringlengths 40
40
| repo
stringlengths 3
28
| complexity
int64 1
153
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
289,232 | 35 | 11 | 17 | 161 | 24 | 0 | 40 | 123 | test_setup_devices_exception | Update to iaqualink 0.5.0 (#80304)
* Update to iaqualink 0.5.0.
* Boolean conditional style fix
Co-authored-by: Martin Hjelmare <[email protected]>
* Fix black formatting
* Update iaqualink tests after update to 0.5.x
* Remove debug print statements
Co-authored-by: Martin Hjelmare <[email protected]> | https://github.com/home-assistant/core.git | async def test_setup_devices_exception(hass, config_entry, client):
config_entry.add_to_hass(hass)
system = get_aqualink_system(client, cls=IaquaSystem)
systems = {system.serial: system}
with patch(
"homeassistant.components.iaqualink.AqualinkClient.login",
return_value=None,
), patch(
"homeassistant.components.iaqualink.AqualinkClient.get_systems",
return_value=systems,
), patch.object(
system, "get_devices"
) as mock_get_devices:
mock_get_devices.side_effect = AqualinkServiceException
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
assert config_entry.state is ConfigEntryState.SETUP_RETRY
| 97 | test_init.py | Python | tests/components/iaqualink/test_init.py | abec592a248607869dc1d495f956ca397dc189f4 | core | 1 |
|
107,739 | 35 | 13 | 11 | 132 | 12 | 0 | 43 | 157 | set_rotation | Deprecate toplevel mpl.text.get_rotation; normalize rotations early.
get_rotation had been made a toplevel function a long time ago to be
used in TextWithDash, which has now been removed, so there isn't much
justification to have it separate. Also, note that while the toplevel
get_rotation's implementation also supported string-form of floats (see
test_text.test_get_rotation_string), this was not consistent with the
docstring, and, more importantly, it was not possible to set a Text's
rotation to e.g. "15." because Text.set_rotation() would first reject
that anyways.
Also, we can directly normalize angles in Text.set_rotation, rather than
doing it again and again in Text.get_rotation. Again, this made the old
inconsistency (on supporting string-form floats) clearer. | https://github.com/matplotlib/matplotlib.git | def set_rotation(self, s):
if isinstance(s, numbers.Real):
self._rotation = float(s) % 360
elif cbook._str_equal(s, 'horizontal') or s is None:
self._rotation = 0.
elif cbook._str_equal(s, 'vertical'):
self._rotation = 90.
else:
raise ValueError("rotation must be 'vertical', 'horizontal' or "
f"a number, not {s}")
self.stale = True
| 78 | text.py | Python | lib/matplotlib/text.py | 1f8f50e522f7e623279626381d650f8ff63627e3 | matplotlib | 5 |
|
141,966 | 51 | 15 | 23 | 348 | 29 | 0 | 83 | 261 | test_syncer_callback_sync_period | [tune] Refactor Syncer / deprecate Sync client (#25655)
This PR includes / depends on #25709
The two concepts of Syncer and SyncClient are confusing, as is the current API for passing custom sync functions.
This PR refactors Tune's syncing behavior. The Sync client concept is hard deprecated. Instead, we offer a well defined Syncer API that can be extended to provide own syncing functionality. However, the default will be to use Ray AIRs file transfer utilities.
New API:
- Users can pass `syncer=CustomSyncer` which implements the `Syncer` API
- Otherwise our off-the-shelf syncing is used
- As before, syncing to cloud disables syncing to driver
Changes:
- Sync client is removed
- Syncer interface introduced
- _DefaultSyncer is a wrapper around the URI upload/download API from Ray AIR
- SyncerCallback only uses remote tasks to synchronize data
- Rsync syncing is fully depracated and removed
- Docker and kubernetes-specific syncing is fully deprecated and removed
- Testing is improved to use `file://` URIs instead of mock sync clients | https://github.com/ray-project/ray.git | def test_syncer_callback_sync_period(ray_start_2_cpus, temp_data_dirs):
tmp_source, tmp_target = temp_data_dirs
with freeze_time() as frozen:
syncer_callback = TestSyncerCallback(
sync_period=60, local_logdir_override=tmp_target
)
trial1 = MockTrial(trial_id="a", logdir=tmp_source)
syncer_callback.on_trial_result(iteration=1, trials=[], trial=trial1, result={})
syncer_callback.wait_for_all()
assert_file(True, tmp_target, "level0.txt")
assert_file(False, tmp_target, "level0_new.txt")
# Add new file to source directory
with open(os.path.join(tmp_source, "level0_new.txt"), "w") as f:
f.write("Data\n")
frozen.tick(30)
# Should not sync after 30 seconds
syncer_callback.on_trial_result(iteration=2, trials=[], trial=trial1, result={})
syncer_callback.wait_for_all()
assert_file(True, tmp_target, "level0.txt")
assert_file(False, tmp_target, "level0_new.txt")
frozen.tick(30)
# Should sync after 60 seconds
syncer_callback.on_trial_result(iteration=3, trials=[], trial=trial1, result={})
syncer_callback.wait_for_all()
assert_file(True, tmp_target, "level0.txt")
assert_file(True, tmp_target, "level0_new.txt")
| 210 | test_syncer_callback.py | Python | python/ray/tune/tests/test_syncer_callback.py | 6313ddc47cf9df4df8c8907997df559850a1b874 | ray | 1 |
|
43,203 | 25 | 11 | 19 | 116 | 19 | 0 | 26 | 231 | test_confirm | Don't rely on current ORM structure for db clean command (#23574)
For command DB clean, by not relying on the ORM models, we will be able to use the command even when the metadatabase is not yet upgraded to the version of Airflow you have installed.
Additionally we archive all rows before deletion. | https://github.com/apache/airflow.git | def test_confirm(self, run_cleanup_mock, confirm_arg, expected):
args = self.parser.parse_args(
[
'db',
'clean',
'--clean-before-timestamp',
'2021-01-01',
*confirm_arg,
]
)
db_command.cleanup_tables(args)
run_cleanup_mock.assert_called_once_with(
table_names=None,
dry_run=False,
clean_before_timestamp=pendulum.parse('2021-01-01 00:00:00Z'),
verbose=False,
confirm=expected,
skip_archive=False,
)
| 74 | test_db_command.py | Python | tests/cli/commands/test_db_command.py | 95bd6b71cc9f5da377e272707f7b68000d980939 | airflow | 1 |
|
60,240 | 61 | 18 | 24 | 362 | 31 | 0 | 105 | 454 | configure_crop | Balanced joint maximum mean discrepancy for deep transfer learning | https://github.com/jindongwang/transferlearning.git | def configure_crop(self, context_pad):
# crop dimensions
in_ = self.inputs[0]
tpose = self.transformer.transpose[in_]
inv_tpose = [tpose[t] for t in tpose]
self.crop_dims = np.array(self.blobs[in_].data.shape[1:])[inv_tpose]
#.transpose(inv_tpose)
# context padding
self.context_pad = context_pad
if self.context_pad:
in_ = self.inputs[0]
transpose = self.transformer.transpose.get(in_)
channel_order = self.transformer.channel_swap.get(in_)
raw_scale = self.transformer.raw_scale.get(in_)
# Padding context crops needs the mean in unprocessed input space.
mean = self.transformer.mean.get(in_)
if mean is not None:
inv_transpose = [transpose[t] for t in transpose]
crop_mean = mean.copy().transpose(inv_transpose)
if channel_order is not None:
channel_order_inverse = [channel_order.index(i)
for i in range(crop_mean.shape[2])]
crop_mean = crop_mean[:, :, channel_order_inverse]
if raw_scale is not None:
crop_mean /= raw_scale
self.crop_mean = crop_mean
else:
self.crop_mean = np.zeros(self.crop_dims, dtype=np.float32)
| 233 | detector.py | Python | code/deep/BJMMD/caffe/python/caffe/detector.py | cc4d0564756ca067516f71718a3d135996525909 | transferlearning | 8 |
|
258,625 | 98 | 14 | 35 | 495 | 40 | 0 | 137 | 456 | fit | ENH Replaced RandomState with Generator compatible calls (#22271) | https://github.com/scikit-learn/scikit-learn.git | def fit(self, X, y):
y = self._validate_data(X="no_validation", y=y)
if self.code_size <= 0:
raise ValueError(
"code_size should be greater than 0, got {0}".format(self.code_size)
)
_check_estimator(self.estimator)
random_state = check_random_state(self.random_state)
check_classification_targets(y)
self.classes_ = np.unique(y)
n_classes = self.classes_.shape[0]
if n_classes == 0:
raise ValueError(
"OutputCodeClassifier can not be fit when no class is present."
)
code_size_ = int(n_classes * self.code_size)
# FIXME: there are more elaborate methods than generating the codebook
# randomly.
self.code_book_ = random_state.uniform(size=(n_classes, code_size_))
self.code_book_[self.code_book_ > 0.5] = 1
if hasattr(self.estimator, "decision_function"):
self.code_book_[self.code_book_ != 1] = -1
else:
self.code_book_[self.code_book_ != 1] = 0
classes_index = {c: i for i, c in enumerate(self.classes_)}
Y = np.array(
[self.code_book_[classes_index[y[i]]] for i in range(_num_samples(y))],
dtype=int,
)
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_binary)(self.estimator, X, Y[:, i]) for i in range(Y.shape[1])
)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return self
| 318 | multiclass.py | Python | sklearn/multiclass.py | 254ea8c453cd2100ade07644648f1f00392611a6 | scikit-learn | 9 |
|
269,246 | 70 | 22 | 43 | 498 | 25 | 0 | 184 | 640 | _get_data_iterator_from_dataset | fixed import random, removed keras import ,fixed grammer issues | https://github.com/keras-team/keras.git | def _get_data_iterator_from_dataset(dataset,dataset_type_spec) :
if dataset_type_spec == list:
if len(dataset) == 0:
raise ValueError('Received an empty list dataset. '
'Please provide a non-empty list of arrays.')
if _get_type_spec(dataset[0]) is np.ndarray:
expected_shape = dataset[0].shape
for i,element in enumerate(dataset):
if not np.array(element).shape[0] == expected_shape[0]:
raise ValueError('Received a list of numpy arrays with different '
f'lengths. Mismatch found at index {i}, '
f'Expected shape={expected_shape} '
f'Received shape={np.array(element).shape}.'
f'Please provide a list of numpy arrays with '
f'same length.')
else:
raise ValueError('Expected a list of numpy.ndarrays,'
'Received: {}'.format(type(dataset[0])))
return iter(zip(*dataset))
elif dataset_type_spec == tuple:
if len(dataset) == 0:
raise ValueError('Received an empty list dataset.'
'Please provide a non-empty tuple of arrays.')
if _get_type_spec(dataset[0]) is np.ndarray:
expected_shape = dataset[0].shape
for i,element in enumerate(dataset):
if not np.array(element).shape[0] == expected_shape[0]:
raise ValueError('Received a tuple of numpy arrays with different '
f'lengths. Mismatch found at index {i}, '
f'Expected shape={expected_shape} '
f'Received shape={np.array(element).shape}.'
f'Please provide a tuple of numpy arrays with '
'same length.')
else:
raise ValueError('Expected a tuple of numpy.ndarrays, '
'Received: {}'.format(type(dataset[0])))
return iter(zip(*dataset))
elif dataset_type_spec == tf.data.Dataset:
if is_batched(dataset):
dataset = dataset.unbatch()
return iter(dataset)
elif dataset_type_spec == np.ndarray:
return iter(dataset)
| 270 | dataset_utils.py | Python | keras/utils/dataset_utils.py | c3a27a6642c03c6380aca22c6e3d73d0b29bb271 | keras | 14 |
|
176,593 | 12 | 13 | 6 | 80 | 8 | 1 | 14 | 48 | is_strongly_connected | Added examples in connected and strongly connected functions (#5559)
* added examples
* Update networkx/algorithms/components/connected.py
Co-authored-by: Ross Barnowski <[email protected]>
Co-authored-by: Ross Barnowski <[email protected]> | https://github.com/networkx/networkx.git | def is_strongly_connected(G):
if len(G) == 0:
raise nx.NetworkXPointlessConcept(
)
return len(list(strongly_connected_components(G))[0]) == len(G)
@not_implemented_for("undirected") | @not_implemented_for("undirected") | 40 | strongly_connected.py | Python | networkx/algorithms/components/strongly_connected.py | 7cad29b3542ad867f1eb5b7b6a9087495f252749 | networkx | 2 |
27,800 | 78 | 15 | 93 | 595 | 45 | 0 | 129 | 330 | test_orderline_query | Metadata added to checkout and order lines (#10040)
* Metadata added to checkout and order lines
* CHANGELOG.md update
* Missing tests added | https://github.com/saleor/saleor.git | def test_orderline_query(staff_api_client, permission_manage_orders, fulfilled_order):
order = fulfilled_order
query =
line = order.lines.first()
metadata_key = "md key"
metadata_value = "md value"
line.store_value_in_private_metadata({metadata_key: metadata_value})
line.store_value_in_metadata({metadata_key: metadata_value})
line.save()
staff_api_client.user.user_permissions.add(permission_manage_orders)
response = staff_api_client.post_graphql(query)
content = get_graphql_content(response)
order_data = content["data"]["orders"]["edges"][0]["node"]
first_order_data_line = order_data["lines"][0]
variant_id = graphene.Node.to_global_id("ProductVariant", line.variant.pk)
assert first_order_data_line["thumbnail"] is None
assert first_order_data_line["variant"]["id"] == variant_id
assert first_order_data_line["quantity"] == line.quantity
assert first_order_data_line["unitPrice"]["currency"] == line.unit_price.currency
assert first_order_data_line["metadata"] == [
{"key": metadata_key, "value": metadata_value}
]
assert first_order_data_line["privateMetadata"] == [
{"key": metadata_key, "value": metadata_value}
]
expected_unit_price = Money(
amount=str(first_order_data_line["unitPrice"]["gross"]["amount"]),
currency="USD",
)
assert first_order_data_line["totalPrice"]["currency"] == line.unit_price.currency
assert expected_unit_price == line.unit_price.gross
expected_total_price = Money(
amount=str(first_order_data_line["totalPrice"]["gross"]["amount"]),
currency="USD",
)
assert expected_total_price == line.unit_price.gross * line.quantity
allocation = line.allocations.first()
allocation_id = graphene.Node.to_global_id("Allocation", allocation.pk)
warehouse_id = graphene.Node.to_global_id(
"Warehouse", allocation.stock.warehouse.pk
)
assert first_order_data_line["allocations"] == [
{
"id": allocation_id,
"quantity": allocation.quantity_allocated,
"warehouse": {"id": warehouse_id},
}
]
| 349 | test_order.py | Python | saleor/graphql/order/tests/test_order.py | a68553e1a55e3a1bd32826cdce294d27f74175e9 | saleor | 1 |
|
209,520 | 34 | 9 | 6 | 58 | 6 | 0 | 40 | 115 | recap | E275 - Missing whitespace after keyword (#3711)
Co-authored-by: Alexander Aring <[email protected]>
Co-authored-by: Anmol Sarma <[email protected]>
Co-authored-by: antoine.torre <[email protected]>
Co-authored-by: Antoine Vacher <[email protected]>
Co-authored-by: Arnaud Ebalard <[email protected]>
Co-authored-by: atlowl <[email protected]>
Co-authored-by: Brian Bienvenu <[email protected]>
Co-authored-by: Chris Packham <[email protected]>
Co-authored-by: CQ <[email protected]>
Co-authored-by: Daniel Collins <[email protected]>
Co-authored-by: Federico Maggi <[email protected]>
Co-authored-by: Florian Maury <[email protected]>
Co-authored-by: _Frky <[email protected]>
Co-authored-by: g-mahieux <[email protected]>
Co-authored-by: gpotter2 <[email protected]>
Co-authored-by: Guillaume Valadon <[email protected]>
Co-authored-by: Hao Zheng <[email protected]>
Co-authored-by: Haresh Khandelwal <[email protected]>
Co-authored-by: Harri Hämäläinen <[email protected]>
Co-authored-by: hecke <[email protected]>
Co-authored-by: Jan Romann <[email protected]>
Co-authored-by: Jan Sebechlebsky <[email protected]>
Co-authored-by: jdiog0 <[email protected]>
Co-authored-by: jockque <[email protected]>
Co-authored-by: Julien Bedel <[email protected]>
Co-authored-by: Keith Scott <[email protected]>
Co-authored-by: Kfir Gollan <[email protected]>
Co-authored-by: Lars Munch <[email protected]>
Co-authored-by: ldp77 <[email protected]>
Co-authored-by: Leonard Crestez <[email protected]>
Co-authored-by: Marcel Patzlaff <[email protected]>
Co-authored-by: Martijn Thé <[email protected]>
Co-authored-by: Martine Lenders <[email protected]>
Co-authored-by: Michael Farrell <[email protected]>
Co-authored-by: Michał Mirosław <[email protected]>
Co-authored-by: mkaliszan <[email protected]>
Co-authored-by: mtury <[email protected]>
Co-authored-by: Neale Ranns <[email protected]>
Co-authored-by: Octavian Toader <[email protected]>
Co-authored-by: Peter Eisenlohr <[email protected]>
Co-authored-by: Phil <[email protected]>
Co-authored-by: Pierre Lalet <[email protected]>
Co-authored-by: Pierre Lorinquer <[email protected]>
Co-authored-by: piersoh <[email protected]>
Co-authored-by: plorinquer <[email protected]>
Co-authored-by: pvinci <[email protected]>
Co-authored-by: Rahul Jadhav <[email protected]>
Co-authored-by: Robin Jarry <[email protected]>
Co-authored-by: romain-perez <[email protected]>
Co-authored-by: rperez <rperez@debian>
Co-authored-by: Sabrina Dubroca <[email protected]>
Co-authored-by: Sebastian Baar <[email protected]>
Co-authored-by: sebastien mainand <[email protected]>
Co-authored-by: smehner1 <[email protected]>
Co-authored-by: speakinghedge <[email protected]>
Co-authored-by: Steven Van Acker <[email protected]>
Co-authored-by: Thomas Faivre <[email protected]>
Co-authored-by: Tran Tien Dat <[email protected]>
Co-authored-by: Wael Mahlous <[email protected]>
Co-authored-by: waeva <[email protected]>
Co-authored-by: Alexander Aring <[email protected]>
Co-authored-by: Anmol Sarma <[email protected]>
Co-authored-by: antoine.torre <[email protected]>
Co-authored-by: Antoine Vacher <[email protected]>
Co-authored-by: Arnaud Ebalard <[email protected]>
Co-authored-by: atlowl <[email protected]>
Co-authored-by: Brian Bienvenu <[email protected]>
Co-authored-by: Chris Packham <[email protected]>
Co-authored-by: CQ <[email protected]>
Co-authored-by: Daniel Collins <[email protected]>
Co-authored-by: Federico Maggi <[email protected]>
Co-authored-by: Florian Maury <[email protected]>
Co-authored-by: _Frky <[email protected]>
Co-authored-by: g-mahieux <[email protected]>
Co-authored-by: gpotter2 <[email protected]>
Co-authored-by: Guillaume Valadon <[email protected]>
Co-authored-by: Hao Zheng <[email protected]>
Co-authored-by: Haresh Khandelwal <[email protected]>
Co-authored-by: Harri Hämäläinen <[email protected]>
Co-authored-by: hecke <[email protected]>
Co-authored-by: Jan Romann <[email protected]>
Co-authored-by: Jan Sebechlebsky <[email protected]>
Co-authored-by: jdiog0 <[email protected]>
Co-authored-by: jockque <[email protected]>
Co-authored-by: Julien Bedel <[email protected]>
Co-authored-by: Keith Scott <[email protected]>
Co-authored-by: Kfir Gollan <[email protected]>
Co-authored-by: Lars Munch <[email protected]>
Co-authored-by: ldp77 <[email protected]>
Co-authored-by: Leonard Crestez <[email protected]>
Co-authored-by: Marcel Patzlaff <[email protected]>
Co-authored-by: Martijn Thé <[email protected]>
Co-authored-by: Martine Lenders <[email protected]>
Co-authored-by: Michael Farrell <[email protected]>
Co-authored-by: Michał Mirosław <[email protected]>
Co-authored-by: mkaliszan <[email protected]>
Co-authored-by: mtury <[email protected]>
Co-authored-by: Neale Ranns <[email protected]>
Co-authored-by: Octavian Toader <[email protected]>
Co-authored-by: Peter Eisenlohr <[email protected]>
Co-authored-by: Phil <[email protected]>
Co-authored-by: Pierre Lalet <[email protected]>
Co-authored-by: Pierre Lorinquer <[email protected]>
Co-authored-by: piersoh <[email protected]>
Co-authored-by: pvinci <[email protected]>
Co-authored-by: Rahul Jadhav <[email protected]>
Co-authored-by: Robin Jarry <[email protected]>
Co-authored-by: romain-perez <[email protected]>
Co-authored-by: rperez <rperez@debian>
Co-authored-by: Sabrina Dubroca <[email protected]>
Co-authored-by: Sebastian Baar <[email protected]>
Co-authored-by: sebastien mainand <[email protected]>
Co-authored-by: smehner1 <[email protected]>
Co-authored-by: Steven Van Acker <[email protected]>
Co-authored-by: Thomas Faivre <[email protected]>
Co-authored-by: Tran Tien Dat <[email protected]>
Co-authored-by: Wael Mahlous <[email protected]>
Co-authored-by: waeva <[email protected]> | https://github.com/secdev/scapy.git | def recap(self, nc):
# type: (int) -> None
assert nc >= 0
t = self._dynamic_table_cap_size > nc
self._dynamic_table_cap_size = nc
if t:
# The RFC is not clear about whether this resize should happen;
# we do it anyway
self.resize(nc)
| 33 | http2.py | Python | scapy/contrib/http2.py | 08b1f9d67c8e716fd44036a027bdc90dcb9fcfdf | scapy | 2 |
|
215,674 | 202 | 20 | 160 | 1,811 | 55 | 0 | 479 | 2,383 | test_modify | Adding the ability to add, delete, purge, and modify Salt scheduler jobs when the Salt minion is not running. | https://github.com/saltstack/salt.git | def test_modify(sock_dir, job1, schedule_config_file):
current_job1 = {
"function": "salt",
"seconds": "3600",
"maxrunning": 1,
"name": "job1",
"enabled": True,
"jid_include": True,
}
new_job1 = {
"function": "salt",
"seconds": "60",
"maxrunning": 1,
"name": "job1",
"enabled": True,
"jid_include": True,
}
comm1 = "Modified job: job1 in schedule."
changes1 = {
"job1": {
"new": salt.utils.odict.OrderedDict(new_job1),
"old": salt.utils.odict.OrderedDict(current_job1),
}
}
new_job4 = {
"function": "test.version",
"seconds": "3600",
"maxrunning": 1,
"name": "job1",
"enabled": True,
"jid_include": True,
}
changes4 = {
"job1": {
"new": salt.utils.odict.OrderedDict(new_job4),
"old": salt.utils.odict.OrderedDict(current_job1),
}
}
expected1 = {"comment": comm1, "changes": changes1, "result": True}
comm2 = (
'Error: Unable to use "seconds", "minutes", "hours", '
'or "days" with "when" option.'
)
expected2 = {"comment": comm2, "changes": {}, "result": False}
comm3 = 'Unable to use "when" and "cron" options together. Ignoring.'
expected3 = {"comment": comm3, "changes": {}, "result": False}
comm4 = "Job: job1 would be modified in schedule."
expected4 = {"comment": comm4, "changes": changes4, "result": True}
comm5 = "Job job2 does not exist in schedule."
expected5 = {"comment": comm5, "changes": {}, "result": False}
with patch.dict(
schedule.__opts__, {"schedule": {"job1": current_job1}, "sock_dir": sock_dir}
):
mock = MagicMock(return_value=True)
with patch.dict(schedule.__salt__, {"event.fire": mock}):
_ret_value = {"complete": True, "schedule": {"job1": current_job1}}
with patch.object(SaltEvent, "get_event", return_value=_ret_value):
ret = schedule.modify("job1", seconds="60")
assert "job1" in ret["changes"]
assert "new" in ret["changes"]["job1"]
assert "old" in ret["changes"]["job1"]
for key in [
"maxrunning",
"function",
"seconds",
"jid_include",
"name",
"enabled",
]:
assert (
ret["changes"]["job1"]["new"][key]
== expected1["changes"]["job1"]["new"][key]
)
assert (
ret["changes"]["job1"]["old"][key]
== expected1["changes"]["job1"]["old"][key]
)
assert ret["comment"] == expected1["comment"]
assert ret["result"] == expected1["result"]
_ret_value = {"complete": True, "schedule": {"job1": current_job1}}
with patch.object(SaltEvent, "get_event", return_value=_ret_value):
ret = schedule.modify(
"job1", function="test.ping", seconds=3600, when="2400"
)
assert ret == expected2
_ret_value = {"complete": True, "schedule": {"job1": current_job1}}
with patch.object(SaltEvent, "get_event", return_value=_ret_value):
ret = schedule.modify(
"job1", function="test.ping", when="2400", cron="2"
)
assert ret == expected3
_ret_value = {"complete": True, "schedule": {"job1": current_job1}}
with patch.object(SaltEvent, "get_event", return_value=_ret_value):
ret = schedule.modify("job1", function="test.version", test=True)
assert "job1" in ret["changes"]
assert "new" in ret["changes"]["job1"]
assert "old" in ret["changes"]["job1"]
for key in [
"maxrunning",
"function",
"jid_include",
"name",
"enabled",
]:
assert (
ret["changes"]["job1"]["new"][key]
== expected4["changes"]["job1"]["new"][key]
)
assert (
ret["changes"]["job1"]["old"][key]
== expected4["changes"]["job1"]["old"][key]
)
assert ret["comment"] == expected4["comment"]
assert ret["result"] == expected4["result"]
_ret_value = {"complete": True, "schedule": {}}
with patch.object(SaltEvent, "get_event", return_value=_ret_value):
ret = schedule.modify("job2", function="test.version", test=True)
assert ret == expected5
_schedule_data = {"job1": job1}
comm = "Modified job: job1 in schedule."
changes = {"job1": "removed"}
changes = {
"job1": {
"new": OrderedDict(
[
("function", "test.version"),
("maxrunning", 1),
("name", "job1"),
("enabled", True),
("jid_include", True),
]
),
"old": OrderedDict(
[
("function", "test.ping"),
("maxrunning", 1),
("name", "job1"),
("jid_include", True),
("enabled", True),
]
),
}
}
with patch.dict(
schedule.__opts__, {"schedule": {"job1": "salt"}, "sock_dir": sock_dir}
):
with patch("salt.utils.files.fopen", mock_open(read_data="")) as fopen_mock:
with patch.object(
schedule, "list_", MagicMock(return_value=_schedule_data)
):
assert schedule.modify(
"job1", function="test.version", offline="True"
) == {"comment": comm, "changes": changes, "result": True}
_call = call(
b"schedule:\n job1: {enabled: true, function: test.version, jid_include: true, maxrunning: 1,\n name: job1}\n"
)
write_calls = fopen_mock.filehandles[schedule_config_file][
0
].write._mock_mock_calls
assert _call in write_calls
# 'is_enabled' function tests: 1
| 1,004 | test_schedule.py | Python | tests/pytests/unit/modules/test_schedule.py | 62908a04f5166e0a26f69ff1a5296a19bad351ad | salt | 3 |
|
125,720 | 79 | 15 | 42 | 543 | 29 | 0 | 172 | 425 | test_component_activities_hook | [dashboard] Update cluster_activities endpoint to use pydantic. (#26609)
Update cluster_activities endpoint to use pydantic so we have better data validation.
Make timestamp a required field.
Add pydantic to ray[default] requirements | https://github.com/ray-project/ray.git | def test_component_activities_hook(set_ray_cluster_activity_hook, call_ray_start):
external_hook = set_ray_cluster_activity_hook
response = requests.get("http://127.0.0.1:8265/api/component_activities")
response.raise_for_status()
# Validate schema of response
data = response.json()
schema_path = os.path.join(
os.path.dirname(dashboard.__file__),
"modules/snapshot/component_activities_schema.json",
)
pprint.pprint(data)
jsonschema.validate(instance=data, schema=json.load(open(schema_path)))
# Validate driver response can be cast to RayActivityResponse object
# and that there are no active drivers.
driver_ray_activity_response = RayActivityResponse(**data["driver"])
assert driver_ray_activity_response.is_active == "INACTIVE"
assert driver_ray_activity_response.reason is None
# Validate external component response can be cast to RayActivityResponse object
if external_hook[-1] == "5":
external_activity_response = RayActivityResponse(**data["test_component5"])
assert external_activity_response.is_active == "ACTIVE"
assert external_activity_response.reason == "Counter: 1"
elif external_hook[-1] == "4":
external_activity_response = RayActivityResponse(**data["external_component"])
assert external_activity_response.is_active == "ERROR"
assert (
"'Error in external cluster activity hook'"
in external_activity_response.reason
)
elif external_hook[-1] == "3":
external_activity_response = RayActivityResponse(**data["external_component"])
assert external_activity_response.is_active == "ERROR"
elif external_hook[-1] == "2":
external_activity_response = RayActivityResponse(**data["test_component2"])
assert external_activity_response.is_active == "ERROR"
elif external_hook[-1] == "1":
external_activity_response = RayActivityResponse(**data["test_component1"])
assert external_activity_response.is_active == "ACTIVE"
assert external_activity_response.reason == "Counter: 1"
# Call endpoint again to validate different response
response = requests.get("http://127.0.0.1:8265/api/component_activities")
response.raise_for_status()
data = response.json()
jsonschema.validate(instance=data, schema=json.load(open(schema_path)))
external_activity_response = RayActivityResponse(**data["test_component1"])
assert external_activity_response.is_active == "ACTIVE"
assert external_activity_response.reason == "Counter: 2"
| 308 | test_snapshot.py | Python | dashboard/modules/snapshot/tests/test_snapshot.py | e8222ff600f79cc7c5cc28f43a951215c4b5460c | ray | 6 |
|
86,843 | 11 | 9 | 4 | 48 | 8 | 0 | 11 | 43 | drop | feat(replays): Remove usage of the attachment cache (#39987)
closes https://github.com/getsentry/replay-backend/issues/154 | https://github.com/getsentry/sentry.git | def drop(self):
part = RecordingSegmentPart(self.prefix)
for i in range(self.num_parts):
del part[i]
| 29 | cache.py | Python | src/sentry/replays/cache.py | 5113b00cb01ae85a9b0578bed8f9eb7a1a66967a | sentry | 2 |
|
2,529 | 20 | 11 | 23 | 115 | 14 | 0 | 22 | 142 | _object2proto | Changes for publishing dataset and passing actions to enclave | https://github.com/OpenMined/PySyft.git | def _object2proto(self) -> PublishDatasetMessage_PB:
return PublishDatasetMessage_PB(
msg_id=serialize(self.id),
address=serialize(self.address),
reply_to=serialize(self.reply_to),
dataset_id=self.dataset_id,
deployment_id=self.deployment_id,
host_or_ip = self.host_or_ip,
protocol = self.protocol,
port = self.port,
client=serialize(self.client),
)
| 78 | oblv_messages.py | Python | packages/syft/src/syft/core/node/common/node_service/oblv/oblv_messages.py | 54c0a2f6738090252dc2b2863eb3c13b1bcb9e6a | PySyft | 1 |
|
126,133 | 2 | 6 | 16 | 13 | 2 | 0 | 2 | 5 | test_receive_event_by_http | [workflow] http_event_provider and accompanied listener (#26010)
### Why are these changes needed?
This PR enhances workflow functionality to receive external events from a Serve based HTTP endpoint. A workflow can then consume events asynchronously as they arrive.
### Design Logic
A `workflow.wait_for_event` node subscribes to the endpoint instantiated by a Ray Serve deployment of class `http_event_provider.HTTPEventProvider`. The subscription is made through a helper class `http_event_provider.HTTPListener`. `HTTPListener` implements the methods of `EventListener` to poll from and confirm event checkpointing to `HTTPEventProvider`, before `HTTPEventProvider`acknowledges success or error to the event submitter.
### Architecture Improvement
The logic of this enhancement conforms with existing workflow runtime design. | https://github.com/ray-project/ray.git | def test_receive_event_by_http(workflow_start_regular_shared_serve):
| 92 | test_http_events.py | Python | python/ray/workflow/tests/test_http_events.py | 659d25a3a9c4794db9dbe8f428ec587470b261b0 | ray | 4 |
|
288,138 | 19 | 8 | 7 | 72 | 10 | 0 | 20 | 63 | _async_ble_device_disconnected | Add ESPHome BleakClient (#78911)
Co-authored-by: Paulus Schoutsen <[email protected]> | https://github.com/home-assistant/core.git | def _async_ble_device_disconnected(self) -> None:
_LOGGER.debug("%s: BLE device disconnected", self._source)
self._is_connected = False
self.services = BleakGATTServiceCollection() # type: ignore[no-untyped-call]
self._async_call_bleak_disconnected_callback()
self._unsubscribe_connection_state()
| 40 | client.py | Python | homeassistant/components/esphome/bluetooth/client.py | 7042d6d35be54865b1252c0b28a50cce1a92eabc | core | 1 |
|
337,299 | 58 | 15 | 28 | 236 | 20 | 0 | 82 | 354 | recursively_apply | Convert documentation to the new front (#271)
* Main conversion
* Doc styling
* Style
* New front deploy
* Fixes
* Fixes
* Fix new docstrings
* Style | https://github.com/huggingface/accelerate.git | def recursively_apply(func, data, *args, test_type=is_torch_tensor, error_on_other_type=False, **kwargs):
if isinstance(data, (tuple, list)):
return honor_type(
data,
(
recursively_apply(
func, o, *args, test_type=test_type, error_on_other_type=error_on_other_type, **kwargs
)
for o in data
),
)
elif isinstance(data, Mapping):
return type(data)(
{
k: recursively_apply(
func, v, *args, test_type=test_type, error_on_other_type=error_on_other_type, **kwargs
)
for k, v in data.items()
}
)
elif test_type(data):
return func(data, *args, **kwargs)
elif error_on_other_type:
raise TypeError(
f"Can't apply {func.__name__} on object of type {type(data)}, only of nested list/tuple/dicts of objects "
f"that satisfy {test_type.__name__}."
)
return data
| 146 | utils.py | Python | src/accelerate/utils.py | fb5ed62c102c0323486b89805e1888495de3db15 | accelerate | 7 |
|
90,708 | 7 | 9 | 24 | 26 | 3 | 0 | 7 | 26 | mixed_payload | feat(metrics): functionality for the indexer-last-seen-updater (#34865)
* update meta structure to support last_seen updater better
* use metrics to record last_seen_updater info
* actually produce headers in indexer output message | https://github.com/getsentry/sentry.git | def mixed_payload():
return bytes(
,
encoding="utf-8",
)
| 14 | test_last_seen_updater.py | Python | tests/sentry/sentry_metrics/test_last_seen_updater.py | 261437e3bbb102732344817f67762142c0d6977e | sentry | 1 |
|
322,882 | 25 | 13 | 9 | 81 | 10 | 0 | 26 | 113 | available_labels | Add NLP model interpretation (#1752)
* upload NLP interpretation
* fix problems and relocate project
* remove abandoned picture
* remove abandoned picture
* fix dead link in README
* fix dead link in README
* fix code style problems
* fix CR round 1
* remove .gitkeep files
* fix code style
* fix file encoding problem
* fix code style
* delete duplicated files due to directory rebuild
* fix CR round 2
* fix code style
* fix ernie tokenizer
* fix code style
* fix problem from CR round 1
* fix bugs
* fix README
* remove duplicated files
* deal with diff of old and new tokenizer results
* fix CR round 4
* fix code style
* add missing dependence
* fix broken import path
* move some data file to cloud
* MRC upper case to lower case
Co-authored-by: Zeyu Chen <[email protected]>
Co-authored-by: binlinquge <xxx>
Co-authored-by: Guo Sheng <[email protected]> | https://github.com/PaddlePaddle/PaddleNLP.git | def available_labels(self):
try:
assert self.mode == "classification"
except AssertionError:
raise NotImplementedError(
'Not supported for regression explanations.')
else:
ans = self.top_labels if self.top_labels else self.local_exp.keys()
return list(ans)
| 46 | explanation.py | Python | examples/model_interpretation/task/senti/LIME/explanation.py | 93cae49c0c572b5c1ac972759140fbe924b0374d | PaddleNLP | 3 |
|
181,596 | 62 | 11 | 23 | 232 | 20 | 0 | 67 | 287 | test_driver_5 | Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | https://github.com/EpistasisLab/tpot.git | def test_driver_5():
# Catch FutureWarning https://github.com/scikit-learn/scikit-learn/issues/11785
if (np.__version__ >= LooseVersion("1.15.0") and
sklearn.__version__ <= LooseVersion("0.20.0")):
raise nose.SkipTest("Warning raised by scikit-learn")
args_list = [
'tests/tests.csv',
'-is', ',',
'-target', 'class',
'-o', 'test_export.py',
'-g', '1',
'-p', '2',
'-cv', '3',
'-s', '42',
'-config', 'TPOT light',
'-v', '0'
]
args = _get_arg_parser().parse_args(args_list)
with captured_output() as (out, err):
tpot_driver(args)
ret_stdout = out.getvalue()
assert ret_stdout == ""
assert path.isfile("test_export.py")
remove("test_export.py") # clean up exported file
| 121 | driver_tests.py | Python | tests/driver_tests.py | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | 3 |
|
245,559 | 42 | 13 | 20 | 278 | 26 | 0 | 57 | 245 | get_whs_and_shapes | [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 get_whs_and_shapes(self):
self.logger.info('Collecting bboxes from annotation...')
bbox_whs = []
img_shapes = []
prog_bar = ProgressBar(len(self.dataset))
for idx in range(len(self.dataset)):
ann = self.dataset.get_ann_info(idx)
data_info = self.dataset.data_infos[idx]
img_shape = np.array([data_info['width'], data_info['height']])
gt_bboxes = ann['bboxes']
for bbox in gt_bboxes:
wh = bbox[2:4] - bbox[0:2]
img_shapes.append(img_shape)
bbox_whs.append(wh)
prog_bar.update()
print('\n')
bbox_whs = np.array(bbox_whs)
img_shapes = np.array(img_shapes)
self.logger.info(f'Collected {bbox_whs.shape[0]} bboxes.')
return bbox_whs, img_shapes
| 160 | optimize_anchors.py | Python | tools/analysis_tools/optimize_anchors.py | d0695e68654ca242be54e655491aef8c959ac345 | mmdetection | 3 |
|
77,101 | 40 | 13 | 31 | 176 | 25 | 0 | 49 | 196 | test_add_post_duplicate_choose_permission | Add duplicate detection to multiple image upload view
Add utility function to find an image's potential duplicates
Add logic to detect duplicates on multiple images upload view
Add template shown when a user is prompted to confirm a duplicate upload
Add client-side logic to confirm a duplicate upload
Add/update styles
Add tests for duplicate image uploads
Index Image file_hash field
Ensure that a user can choose an image from duplicates returned by find_image_duplicates
Use CSS classes instead of HTML elements to hide edit form on duplicate upload
Add ImagesPermissionPolicy helper to retrieve the permission policy dynamically
This allows test cases that override the base image model to pick up the corresponding permission policy, should they need it.
Remove usage of sibling selector
Use wagtail image templatetag to generate image
Renamed ImagesPermissionPolicy to ImagesPermissionPolicyGetter
Fail loudly when setting permission policy and a wromg image model is provided
Add decorator to disconnect a signal's receiver during a test execution and use it in get_image_model tests
Improve warning message on duplicate upload in multiple upload view
Show matching form when confirming a duplicate upload | https://github.com/wagtail/wagtail.git | def test_add_post_duplicate_choose_permission(self):
# Create group with access to admin and add permission.
bakers_group = Group.objects.create(name="Bakers")
access_admin_perm = Permission.objects.get(
content_type__app_label="wagtailadmin", codename="access_admin"
)
bakers_group.permissions.add(access_admin_perm)
# Create the "Bakery" Collection and grant "add" permission to the Bakers group.
root = Collection.objects.get(id=get_root_collection_id())
bakery_collection = root.add_child(instance=Collection(name="Bakery"))
GroupCollectionPermission.objects.create(
group=bakers_group,
collection=bakery_collection,
permission=Permission.objects.get(
content_type__app_label="wagtailimages", codename="add_image"
),
)
| 221 | test_admin_views.py | Python | wagtail/images/tests/test_admin_views.py | c136f461bc052cef362991458e1bd1fca37a3da9 | wagtail | 1 |
|
305,067 | 24 | 14 | 30 | 138 | 13 | 0 | 36 | 118 | extra_state_attributes | Awair local use config entry name + add measurement state class (#77383) | https://github.com/home-assistant/core.git | def extra_state_attributes(self) -> dict:
sensor_type = self.entity_description.key
attrs: dict = {}
if not self._air_data:
return attrs
if sensor_type in self._air_data.indices:
attrs["awair_index"] = abs(self._air_data.indices[sensor_type])
elif sensor_type in DUST_ALIASES and API_DUST in self._air_data.indices:
attrs["awair_index"] = abs(self._air_data.indices.dust)
return attrs
| 84 | sensor.py | Python | homeassistant/components/awair/sensor.py | 79b5147b46a16b65404c74df5dd9a10ce16ea216 | core | 5 |
|
186,353 | 12 | 8 | 3 | 45 | 4 | 0 | 12 | 21 | parse_includes | 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 parse_includes(apachectl):
inc_cmd = [apachectl, "-t", "-D", "DUMP_INCLUDES"]
return parse_from_subprocess(inc_cmd, r"\(.*\) (.*)")
| 25 | apache_util.py | Python | certbot-apache/certbot_apache/_internal/apache_util.py | eeca208c8f57304590ac1af80b496e61021aaa45 | certbot | 1 |
|
195,097 | 20 | 10 | 5 | 50 | 7 | 0 | 23 | 66 | _get_batch_context | Added director agent and safety experiment commands. (#4602)
* Added director agent and safety.
* ran autoformat.sh | https://github.com/facebookresearch/ParlAI.git | def _get_batch_context(self, batch):
if 'full_text_vec' not in batch:
logging.warn('Batch does not have full text vec, resorting to text vec')
return batch.text_vec
return batch.full_text_vec
| 28 | director_agent.py | Python | projects/director/director_agent.py | 2ef5586ed0d644abe18cd3ff45ef9fa01981e87c | ParlAI | 2 |
|
186,742 | 6 | 7 | 7 | 25 | 4 | 0 | 6 | 20 | must_staple | Must staple: check for OCSP support (#9226)
* Must staple: check for OCSP support
* Expand error message
* s/Must Staple/Must-Staple
* Broaden the term webserver
* Improve error message | https://github.com/certbot/certbot.git | def must_staple(self) -> bool:
return self.namespace.must_staple
| 14 | configuration.py | Python | certbot/certbot/configuration.py | a513b57e5e3667365f77b040e070cbec05212174 | certbot | 1 |
|
139,475 | 7 | 6 | 7 | 27 | 5 | 0 | 7 | 21 | extra_learn_fetches_fn | [RLlib] Introduce new policy base classes. (#24742) | https://github.com/ray-project/ray.git | def extra_learn_fetches_fn(self) -> Dict[str, TensorType]:
return {}
| 16 | dynamic_tf_policy_v2.py | Python | rllib/policy/dynamic_tf_policy_v2.py | bc3a1d35cf6e9a5fd7eef908a8e76aefb80ce6a9 | ray | 1 |
|
259,850 | 30 | 11 | 7 | 121 | 19 | 0 | 32 | 68 | test_dataframe_support | FIX DecisionBoundaryPlot should not raise spurious warning (#23318) | https://github.com/scikit-learn/scikit-learn.git | def test_dataframe_support():
pd = pytest.importorskip("pandas")
df = pd.DataFrame(X, columns=["col_x", "col_y"])
estimator = LogisticRegression().fit(df, y)
with warnings.catch_warnings():
# no warnings linked to feature names validation should be raised
warnings.simplefilter("error", UserWarning)
DecisionBoundaryDisplay.from_estimator(estimator, df, response_method="predict")
| 68 | test_boundary_decision_display.py | Python | sklearn/inspection/_plot/tests/test_boundary_decision_display.py | b0b8a39d8bb80611398e4c57895420d5cb1dfe09 | scikit-learn | 1 |
|
60,423 | 41 | 13 | 6 | 90 | 12 | 0 | 45 | 80 | CheckForCopyright | Balanced joint maximum mean discrepancy for deep transfer learning | https://github.com/jindongwang/transferlearning.git | def CheckForCopyright(filename, lines, error):
# We'll check up to line 10. Don't forget there's a
# dummy line at the front.
for line in xrange(1, min(len(lines), 11)):
if _RE_COPYRIGHT.search(lines[line], re.I):
error(filename, 0, 'legal/copyright', 5,
'Copyright message found. '
'You should not include a copyright line.')
| 56 | cpp_lint.py | Python | code/deep/BJMMD/caffe/scripts/cpp_lint.py | cc4d0564756ca067516f71718a3d135996525909 | transferlearning | 3 |
|
19,899 | 13 | 8 | 10 | 47 | 5 | 0 | 14 | 53 | installed_as_egg | 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 installed_as_egg(self) -> bool:
location = self.location
if not location:
return False
return location.endswith(".egg")
| 26 | base.py | Python | pipenv/patched/notpip/_internal/metadata/base.py | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | 2 |
|
5,874 | 70 | 12 | 32 | 326 | 37 | 0 | 90 | 257 | test_visualization_compare_performance_output_saved | Use tempfile to automatically garbage collect data and modeling artifacts in ludwig integration tests. (#1642)
* Use tmpdir to automatically garbage collect data and modeling artifacts in ludwig integration tests. | https://github.com/ludwig-ai/ludwig.git | def test_visualization_compare_performance_output_saved(csv_filename):
input_features = [text_feature(encoder="parallel_cnn")]
output_features = [category_feature()]
# Generate test data
rel_path = generate_data(input_features, output_features, csv_filename)
input_features[0]["encoder"] = "parallel_cnn"
exp_dir_name = run_experiment_with_visualization(input_features, output_features, dataset=rel_path)
vis_output_pattern_pdf = os.path.join(exp_dir_name, "*.pdf")
vis_output_pattern_png = os.path.join(exp_dir_name, "*.png")
test_stats = os.path.join(exp_dir_name, TEST_STATISTICS_FILE_NAME)
test_cmd_pdf = [
"python",
"-m",
"ludwig.visualize",
"--visualization",
"compare_performance",
"--test_statistics",
test_stats,
test_stats,
"-m",
"Model1",
"Model2",
"-od",
exp_dir_name,
]
test_cmd_png = test_cmd_pdf.copy() + ["-ff", "png"]
commands = [test_cmd_pdf, test_cmd_png]
vis_patterns = [vis_output_pattern_pdf, vis_output_pattern_png]
for command, viz_pattern in zip(commands, vis_patterns):
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
figure_cnt = glob.glob(viz_pattern)
assert 0 == result.returncode
assert 1 == len(figure_cnt)
| 200 | test_visualization.py | Python | tests/integration_tests/test_visualization.py | 4fb8f63181f5153b4f6778c6ef8dad61022c4f3f | ludwig | 2 |
|
269,142 | 166 | 15 | 44 | 462 | 47 | 0 | 285 | 553 | wrap_layer_functions | Support Keras saving/loading for ShardedVariables with arbitrary partitions.
PiperOrigin-RevId: 439837516 | https://github.com/keras-team/keras.git | def wrap_layer_functions(layer, serialization_cache):
# Since Sequential models may be modified in place using model.add() or
# model.pop(), don't use saved functions.
if (isinstance(layer, keras_load.RevivedLayer) and
not isinstance(layer, sequential_lib.Sequential)):
return {
fn_name: getattr(layer.keras_api, fn_name, None)
for fn_name in serialized_attributes.LayerAttributes.all_functions
}
# Reset the losses of the layer and its children. The call function in each
# child layer is replaced with tf.functions.
original_fns = _replace_child_layer_functions(layer, serialization_cache)
original_losses = _reset_layer_losses(layer)
# Wrap all the layer call and activity regularizer functions.
# Use LayerCallCollection to ensure that all layer call functions (__call__,
# call with losses) are traced with the same inputs.
call_collection = LayerCallCollection(layer)
call_fn_with_losses = call_collection.add_function(
_wrap_call_and_conditional_losses(layer),
'{}_layer_call_and_return_conditional_losses'.format(layer.name),
# If any of this layer's child layers use the training arg, the traced
# call functions of this layer will have a training keyword argument. If
# the original layer does not expect the training arg, then it will have
# to be removed (by setting `match_layer_training_arg`).
match_layer_training_arg=True)
call_fn = call_collection.add_function(
_extract_outputs_from_fn(layer, call_fn_with_losses),
'{}_layer_call_fn'.format(layer.name),
# Since `call_fn` wraps call_fn_with_losses and not the original call
# function, `match_layer_training_arg` should be set to False.
match_layer_training_arg=False)
fns = {
'call_and_return_conditional_losses': call_fn_with_losses,
'__call__': call_fn
}
if layer._activity_regularizer is not None: # pylint: disable=protected-access
fns['activity_regularizer_fn'] = _wrap_activity_regularizer(layer)
fns['call_and_return_all_conditional_losses'] = (
call_collection.add_function(
_append_activity_regularizer_loss(layer, call_fn_with_losses,
fns['activity_regularizer_fn']),
'{}_layer_call_and_return_all_conditional_losses'.format(
layer.name),
match_layer_training_arg=False))
else:
fns['activity_regularizer_fn'] = None
fns['call_and_return_all_conditional_losses'] = call_fn_with_losses
# Manually trigger traces before restoring the overwritten functions. The
# functions are traced within the layer call context to ensure that layer
# functions (e.g. add_loss) behave as though running in graph mode.
with tracing_scope():
call_collection.trace_with_input_signature()
with base_layer_utils.call_context().enter(
layer, inputs=None, build_graph=True, training=None, saving=True):
for fn in fns.values():
if fn is not None and not isinstance(fn, LayerCall):
fn.get_concrete_function()
# Restore overwritten functions and losses
_restore_child_layer_functions(original_fns)
_restore_layer_losses(original_losses)
return fns
| 277 | save_impl.py | Python | keras/saving/saved_model/save_impl.py | e61cbc52fd3b0170769c120e9b8dabc8c4205322 | keras | 8 |
|
178,270 | 41 | 16 | 29 | 266 | 28 | 0 | 63 | 382 | url | fix: DEV-3911: Move persistent storages to OS (#3377)
* fix: DEV-3911: Move persistent storages to OS
* Fix
* Add deps
* Back header
* Move DownloadStorageData handler
* Update all urls json
* Fix import
* add nginx config
* Fix GSC storage
Co-authored-by: Sergei Ivashchenko <[email protected]>
Co-authored-by: Sergey Zhuk <[email protected]> | https://github.com/heartexlabs/label-studio.git | def url(self, name):
name = self._normalize_name(clean_name(name))
blob = self.bucket.blob(name)
blob_params = self.get_object_parameters(name)
no_signed_url = (
blob_params.get('acl', self.default_acl) == 'publicRead' or not self.querystring_auth)
if not self.custom_endpoint and no_signed_url:
return blob.public_url
elif no_signed_url:
out = '{storage_base_url}/{quoted_name}'.format(
storage_base_url=self.custom_endpoint,
quoted_name=_quote(name, safe=b"/~"),
)
return out
elif not self.custom_endpoint:
out2 = blob.generate_signed_url(
expiration=self.expiration,
version="v4",
**self._get_signing_kwargs()
)
return out2
else:
out3 = blob.generate_signed_url(
bucket_bound_hostname=self.custom_endpoint,
expiration=self.expiration,
version="v4",
**self._get_signing_kwargs()
)
return out3
| 164 | storage.py | Python | label_studio/core/storage.py | 92314e4a9c431c407533e4a064481acf3c5983ab | label-studio | 6 |
|
250,580 | 13 | 10 | 6 | 55 | 5 | 0 | 14 | 64 | intercept | Flow.intercept: use an Event instead of the reply system
This is patch 3/4 of the reply-ectomy. | https://github.com/mitmproxy/mitmproxy.git | def intercept(self):
if self.intercepted:
return
self.intercepted = True
if self._resume_event is not None:
self._resume_event.clear()
| 32 | flow.py | Python | mitmproxy/flow.py | ede269fce40ec4000a4717d5f5aec7835d9931c2 | mitmproxy | 3 |
|
258,441 | 17 | 12 | 17 | 89 | 11 | 0 | 17 | 71 | get_openapi_specs | bug: fix the docs rest api reference url (#3775)
* bug: fix the docs rest api reference url
* revert openapi json changes
* remove last line on json files
* Add explanation about `servers` and remove `servers` parameter from FastAPI
* generate openapi schema without empty end line | https://github.com/deepset-ai/haystack.git | def get_openapi_specs() -> dict:
app = get_app()
return get_openapi(
title=app.title,
version=app.version,
openapi_version=app.openapi_version,
description=app.description,
routes=app.routes,
servers=[{"url": "http://localhost:8000"}],
)
| 56 | utils.py | Python | rest_api/rest_api/utils.py | 86ade4817eda3142d2ddef65a0b1e29ffee770e3 | haystack | 1 |
|
212,208 | 23 | 12 | 8 | 84 | 14 | 0 | 26 | 46 | _clone | Discover unstable defaults in `HasProps.__init__()` (#11959)
* Discover unstable defaults in HasProps.__init__()
* Make HasProps.__getattr__() fail properly
* More sensible implementation of HasProps._clone()
* Make InstanceDefault a generic class
* Fix recursive model definition in tests
* Fix default override in test_document
* Add unit tests | https://github.com/bokeh/bokeh.git | def _clone(self) -> HasProps:
attrs = self.properties_with_values(include_defaults=False, include_undefined=True)
return self.__class__(**{key: val for key, val in attrs.items() if val is not Undefined})
KindRef = Any # TODO
| 49 | has_props.py | Python | bokeh/core/has_props.py | b23a3b77447ede916b31756fca997cbb1b806de7 | bokeh | 3 |
|
95,638 | 26 | 10 | 10 | 117 | 16 | 0 | 29 | 111 | test_unsupported_null_response | ref(webhooks): Handle unexpected webhook responses (#31143) | https://github.com/getsentry/sentry.git | def test_unsupported_null_response(self):
responses.add(
responses.POST, "http://example.com", body="null", content_type="application/json"
)
try:
self.plugin.notify(self.notification)
except Exception as exc:
assert False, f"'self.plugin.notify' raised an exception {exc}"
assert len(responses.calls) == 1
assert responses.calls[0].response.status_code == 200
| 68 | test_plugin.py | Python | tests/sentry/plugins/sentry_webhooks/test_plugin.py | 03c688897205e936302f528dc72fc391b3ef5904 | sentry | 2 |
|
208,774 | 22 | 13 | 7 | 128 | 9 | 0 | 40 | 73 | find_entry_points | Added additional entrypoint script.
Added a third entrypoint to use python's minor version as well.
This can help when testing out differences of python versions. One could easily open "ipython3.10" and test it's differences with "ipython3.8". | https://github.com/ipython/ipython.git | def find_entry_points():
ep = [
'ipython%s = IPython:start_ipython',
]
major_suffix = str(sys.version_info[0])
minor_suffix = ".".join([str(sys.version_info[0]), str(sys.version_info[1])])
return [e % '' for e in ep] + [e % major_suffix for e in ep] + [e % minor_suffix for e in ep]
| 80 | setupbase.py | Python | setupbase.py | 1db65d02e89f31c28c221197a2ed04f3ade3b195 | ipython | 4 |
|
176,920 | 53 | 12 | 20 | 276 | 27 | 0 | 91 | 179 | _hits_numpy | Make HITS numpy and scipy private functions (#5771)
* Make HITS numpy and scipy private functions
* fix examples with correct imports
* remove functions from TOC | https://github.com/networkx/networkx.git | def _hits_numpy(G, normalized=True):
import numpy as np
if len(G) == 0:
return {}, {}
adj_ary = nx.to_numpy_array(G)
# Hub matrix
H = adj_ary @ adj_ary.T
e, ev = np.linalg.eig(H)
h = ev[:, np.argmax(e)] # eigenvector corresponding to the maximum eigenvalue
# Authority matrix
A = adj_ary.T @ adj_ary
e, ev = np.linalg.eig(A)
a = ev[:, np.argmax(e)] # eigenvector corresponding to the maximum eigenvalue
if normalized:
h /= h.sum()
a /= a.sum()
else:
h /= h.max()
a /= a.max()
hubs = dict(zip(G, map(float, h)))
authorities = dict(zip(G, map(float, a)))
return hubs, authorities
| 173 | hits_alg.py | Python | networkx/algorithms/link_analysis/hits_alg.py | e5f1edb82a379ceb6afcf421fa5f6b4cb43cfbaf | networkx | 3 |
|
297,831 | 24 | 8 | 34 | 101 | 16 | 1 | 24 | 95 | async_start | String formatting and max line length - Part 1 (#84390)
Co-authored-by: Erik Montnemery <[email protected]> | https://github.com/home-assistant/core.git | async def async_start(self) -> None:
_LOGGER.info("Starting Home Assistant")
setattr(self.loop, "_thread_ident", threading.get_ident())
self.state = CoreState.starting
self.bus.async_fire(EVENT_CORE_CONFIG_UPDATE)
self.bus.async_fire(EVENT_HOMEASSISTANT_START)
try:
# Only block for EVENT_HOMEASSISTANT_START listener
self.async_stop_track_tasks() | async def async_start(self) -> None:
"""Finalize startup from inside the event loop.
This method is a coroutine.
"""
_LOGGER.info("Starting Home Assistant")
setattr(self.loop, "_thread_ident", threading.get_ident())
self.state = CoreState.starting
self.bus.async_fire(EVENT_CORE_CONFIG_UPDATE)
self.bus.async_fire(EVENT_HOMEASSISTANT_START)
try:
# Only block for EVENT_HOMEASSISTANT_START listener
self.async_stop_track_tasks() | 150 | core.py | Python | homeassistant/core.py | b0cee0bc46cbd7efe0e6421da18d91595c7a25ad | core | 3 |
196,005 | 211 | 17 | 60 | 658 | 37 | 0 | 472 | 1,095 | multiset_derangements | make remap canonical; more inline comments
In addition, clean-up of multiset_derangement's rv is
done to encourage proper use of the function: if you want
to see the output you have to copy it since the derangements
are generated in place.
If you don't want this or find it a hassle, then use
generate_derangements. | https://github.com/sympy/sympy.git | def multiset_derangements(s):
from sympy.core.sorting import ordered
# create multiset dictionary of hashable elements or else
# remap elements to integers
try:
ms = multiset(s)
except TypeError:
# give each element a canonical integer value
key = dict(enumerate(ordered(uniq(s))))
h = []
for si in s:
for k in key:
if key[k] == si:
h.append(k)
break
for i in multiset_derangements(h):
yield [key[j] for j in i]
return
mx = max(ms.values()) # max repetition of any element
n = len(s) # the number of elements
## special cases
# 1) one element has more than half the total cardinality of s: no
# derangements are possible.
if mx*2 > n:
return
# 2) all elements appear once: singletons
if len(ms) == n:
yield from _set_derangements(s)
return
# find the first element that is repeated the most to place
# in the following two special cases where the selection
# is unambiguous: either there are two elements with multiplicity
# of mx or else there is only one with multiplicity mx
for M in ms:
if ms[M] == mx:
break
inonM = [i for i in range(n) if s[i] != M] # location of non-M
iM = [i for i in range(n) if s[i] == M] # locations of M
rv = [None]*n
# 3) half are the same
if 2*mx == n:
# M goes into non-M locations
for i in inonM:
rv[i] = M
# permutations of non-M go to M locations
for p in multiset_permutations([s[i] for i in inonM]):
for i, pi in zip(iM, p):
rv[i] = pi
yield rv
# clean-up (and encourages proper use of routine)
rv[:] = [None]*n
return
# 4) single repeat covers all but 1 of the non-repeats:
# if there is one repeat then the multiset of the values
# of ms would be {mx: 1, 1: n - mx}, i.e. there would
# be n - mx + 1 values with the condition that n - 2*mx = 1
if n - 2*mx == 1 and len(ms.values()) == n - mx + 1:
for i in range(len(inonM)):
i1 = inonM[i]
ifill = inonM[:i] + inonM[i+1:]
for j in ifill:
rv[j] = M
for p in permutations([s[j] for j in ifill]):
rv[i1] = s[i1]
for j, pi in zip(iM, p):
rv[j] = pi
k = i1
for j in iM:
rv[j], rv[k] = rv[k], rv[j]
yield rv
k = j
# clean-up (and encourages proper use of routine)
rv[:] = [None]*n
return
## general case is handled with 3 helpers:
# 1) `finish_derangements` will place the last two elements
# which have arbitrary multiplicities, e.g. for multiset
# {c: 3, a: 2, b: 2}, the last two elements are a and b
# 2) `iopen` will tell where a given element can be placed
# 3) `do` will recursively place elements into subsets of
# valid locations
| 461 | iterables.py | Python | sympy/utilities/iterables.py | 94d6e4fc1ac2c516d328fdad20933d3d92bf52bb | sympy | 28 |
|
155,525 | 221 | 16 | 68 | 962 | 68 | 0 | 366 | 834 | merge_percentiles | Replace `interpolation` with `method` and `method` with `internal_method` (#8525)
Following the change in numpy 1.22.0
Co-authored-by: James Bourbeau <[email protected]> | https://github.com/dask/dask.git | def merge_percentiles(finalq, qs, vals, method="lower", Ns=None, raise_on_nan=True):
from .utils import array_safe
if isinstance(finalq, Iterator):
finalq = list(finalq)
finalq = array_safe(finalq, like=finalq)
qs = list(map(list, qs))
vals = list(vals)
if Ns is None:
vals, Ns = zip(*vals)
Ns = list(Ns)
L = list(zip(*[(q, val, N) for q, val, N in zip(qs, vals, Ns) if N]))
if not L:
if raise_on_nan:
raise ValueError("No non-trivial arrays found")
return np.full(len(qs[0]) - 2, np.nan)
qs, vals, Ns = L
# TODO: Perform this check above in percentile once dtype checking is easy
# Here we silently change meaning
if vals[0].dtype.name == "category":
result = merge_percentiles(
finalq, qs, [v.codes for v in vals], method, Ns, raise_on_nan
)
import pandas as pd
return pd.Categorical.from_codes(result, vals[0].categories, vals[0].ordered)
if not np.issubdtype(vals[0].dtype, np.number):
method = "nearest"
if len(vals) != len(qs) or len(Ns) != len(qs):
raise ValueError("qs, vals, and Ns parameters must be the same length")
# transform qs and Ns into number of observations between percentiles
counts = []
for q, N in zip(qs, Ns):
count = np.empty_like(finalq, shape=len(q))
count[1:] = np.diff(array_safe(q, like=q[0]))
count[0] = q[0]
count *= N
counts.append(count)
# Sort by calculated percentile values, then number of observations.
combined_vals = np.concatenate(vals)
combined_counts = array_safe(np.concatenate(counts), like=combined_vals)
sort_order = np.argsort(combined_vals)
combined_vals = np.take(combined_vals, sort_order)
combined_counts = np.take(combined_counts, sort_order)
# percentile-like, but scaled by total number of observations
combined_q = np.cumsum(combined_counts)
# rescale finalq percentiles to match combined_q
finalq = array_safe(finalq, like=combined_vals)
desired_q = finalq * sum(Ns)
# the behavior of different interpolation methods should be
# investigated further.
if method == "linear":
rv = np.interp(desired_q, combined_q, combined_vals)
else:
left = np.searchsorted(combined_q, desired_q, side="left")
right = np.searchsorted(combined_q, desired_q, side="right") - 1
np.minimum(left, len(combined_vals) - 1, left) # don't exceed max index
lower = np.minimum(left, right)
upper = np.maximum(left, right)
if method == "lower":
rv = combined_vals[lower]
elif method == "higher":
rv = combined_vals[upper]
elif method == "midpoint":
rv = 0.5 * (combined_vals[lower] + combined_vals[upper])
elif method == "nearest":
lower_residual = np.abs(combined_q[lower] - desired_q)
upper_residual = np.abs(combined_q[upper] - desired_q)
mask = lower_residual > upper_residual
index = lower # alias; we no longer need lower
index[mask] = upper[mask]
rv = combined_vals[index]
else:
raise ValueError(
"interpolation method can only be 'linear', 'lower', "
"'higher', 'midpoint', or 'nearest'"
)
return rv
| 612 | percentile.py | Python | dask/array/percentile.py | 3c46e89aea2af010e69049cd638094fea2ddd576 | dask | 18 |
|
136,218 | 13 | 9 | 8 | 71 | 11 | 0 | 13 | 34 | poll | [Dashboard] Optimize and backpressure actor_head.py (#29580)
Signed-off-by: SangBin Cho <[email protected]>
This optimizes the actor head CPU usage and guarantees a stable API response from the dashboard under lots of actor events published to drivers. The below script is used for testing, and I could reproduce the same level of delay as many_nodes_actor_test (250 nodes + 10k actors) | https://github.com/ray-project/ray.git | async def poll(self, timeout=None, batch_size=500) -> List[Tuple[bytes, str]]:
await self._poll(timeout=timeout)
return self._pop_actors(self._queue, batch_size=batch_size)
| 46 | gcs_pubsub.py | Python | python/ray/_private/gcs_pubsub.py | 9da53e3e5a6ec7b9bea32cd68f00f3e9468056ef | ray | 1 |
|
296,364 | 5 | 6 | 2 | 17 | 2 | 0 | 5 | 12 | async_tear_down | Refactor MQTT discovery (#67966)
* Proof of concept
* remove notify platform
* remove loose test
* Add rework from #67912 (#1)
* Move notify serviceupdater to Mixins
* Move tag discovery handler to Mixins
* fix tests
* Add typing for async_load_platform_helper
* Add add entry unload support for notify platform
* Simplify discovery updates
* Remove not needed extra logic
* Cleanup inrelevant or duplicate code
* reuse update_device and move to mixins
* Remove notify platform
* revert changes to notify platform
* Rename update class
* unify tag entry setup
* Use shared code for device_trigger `update_device`
* PoC shared dispatcher for device_trigger
* Fix bugs
* Improve typing - remove async_update
* Unload config_entry and tests
* Release dispatcher after setup and deduplicate
* closures to methods, revert `in` to `=`, updates
* Re-add update support for tag platform
* Re-add update support for device-trigger platform
* Cleanup rediscovery code revert related changes
* Undo discovery code shift
* Update homeassistant/components/mqtt/mixins.py
Co-authored-by: Erik Montnemery <[email protected]>
* Update homeassistant/components/mqtt/device_trigger.py
Co-authored-by: Erik Montnemery <[email protected]>
* Update homeassistant/components/mqtt/mixins.py
Co-authored-by: Erik Montnemery <[email protected]>
* revert doc string changes
* move conditions
* typing and check config_entry_id
* Update homeassistant/components/mqtt/mixins.py
Co-authored-by: Erik Montnemery <[email protected]>
* cleanup not used attribute
* Remove entry_unload code and tests
* update comment
* add second comment
Co-authored-by: Erik Montnemery <[email protected]> | https://github.com/home-assistant/core.git | async def async_tear_down(self) -> None:
| 8 | mixins.py | Python | homeassistant/components/mqtt/mixins.py | 3b2aae5045f9f08dc8f174c5d975852588e1a132 | core | 1 |
|
141,872 | 10 | 12 | 8 | 50 | 7 | 0 | 11 | 32 | remote_execution_api | [tune/ci] Multinode support killing nodes in Ray client mode (#25709)
The multi node testing utility currently does not support controlling cluster state from within Ray tasks or actors., but it currently requires Ray client. This makes it impossible to properly test e.g. fault tolerance, as the driver has to be executed on the client machine in order to control cluster state. However, this client machine is not part of the Ray cluster and can't schedule tasks on the local node - which is required by some utilities, e.g. checkpoint to driver syncing.
This PR introduces a remote control API for the multi node cluster utility that utilizes a Ray queue to communicate with an execution thread. That way we can instruct cluster commands from within the Ray cluster. | https://github.com/ray-project/ray.git | def remote_execution_api(self) -> "RemoteAPI":
self._execution_queue = Queue(actor_options={"num_cpus": 0})
stop_event = self._execution_event
| 55 | test_utils.py | Python | python/ray/autoscaler/_private/fake_multi_node/test_utils.py | b574f75a8f5a28d2f9e55696ca5c40b2e095d9f9 | ray | 1 |
|
213,073 | 10 | 9 | 2 | 31 | 4 | 0 | 10 | 31 | __reduce__ | fix: Py27hash fix (#2182)
* Add third party py27hash code
* Add Py27UniStr and unit tests
* Add py27hash_fix utils and tests
* Add to_py27_compatible_template and tests
* Apply py27hash fix to wherever it is needed
* Apply py27hash fix, all tests pass except api_with_any_method_in_swagger
* apply py27hash fix in openapi + run black
* remove py27 testing
* remove other py27 references
* black fixes
* fixes/typos
* remove py27 from tox.ini
* refactoring
* third party notice
* black
* Fix py27hash fix to deal with null events
* Fix Py27UniStr repr for unicode literals
* black reformat
* Update _template_has_api_resource to check data type more defensively
* Apply py27Dict in _get_authorizers
* Apply Py27Dict to authorizers and gateway responses which will go into swagger
* Update to_py27_compatible_template to handle parameter_values; Add Py27LongInt class
* Rename _convert_to_py27_dict to _convert_to_py27_type
* Apply Py27UniStr to path param name
* Handle HttpApi resource under to_py27_compatible_template
* Fix InvalidDocumentException to not sort different exceptions
* black reformat
* Remove unnecessary test files
Co-authored-by: Wing Fung Lau <[email protected]> | https://github.com/aws/serverless-application-model.git | def __reduce__(self):
# pylint: disable = W0235
return super(Py27Dict, self).__reduce__()
| 17 | py27hash_fix.py | Python | samtranslator/utils/py27hash_fix.py | a5db070f446b7cfebdaa6ad2e3dcf78f6105a272 | serverless-application-model | 1 |
|
177,008 | 66 | 16 | 30 | 340 | 22 | 1 | 112 | 408 | naive_all_pairs_lowest_common_ancestor | Naive lowest common ancestor implementation (#5736)
* Add naive lca methods
* Naive algorithm implementation for LCA
* Modify naive lca functions
* Correct parameters of nx.ancestors
* Update lowest_common_ancestors.py
* Parametrize tests
* Apply suggestions from code review
Co-authored-by: Dan Schult <[email protected]>
* Yield instead of append
* Tests for naive lca
* Correct test cases for naive lca algorithms
* Apply suggestions from code review
Co-authored-by: Mridul Seth <[email protected]>
* Fix function name -when calling
* Make requested changes
* Inlining _get_a_lowest_common_ancestor
Co-authored-by: dtuncturk <[email protected]>
Co-authored-by: Dan Schult <[email protected]>
Co-authored-by: Mridul Seth <[email protected]> | https://github.com/networkx/networkx.git | def naive_all_pairs_lowest_common_ancestor(G, pairs=None):
if not nx.is_directed_acyclic_graph(G):
raise nx.NetworkXError("LCA only defined on directed acyclic graphs.")
elif len(G) == 0:
raise nx.NetworkXPointlessConcept("LCA meaningless on null graphs.")
elif None in G:
raise nx.NetworkXError("None is not a valid node.")
ancestor_cache = {}
if pairs is None:
pairs = combinations_with_replacement(G, 2)
for v, w in pairs:
if v not in ancestor_cache:
ancestor_cache[v] = nx.ancestors(G, v)
ancestor_cache[v].add(v)
if w not in ancestor_cache:
ancestor_cache[w] = nx.ancestors(G, w)
ancestor_cache[w].add(w)
common_ancestors = ancestor_cache[v] & ancestor_cache[w]
if common_ancestors:
common_ancestor = next(iter(common_ancestors))
while True:
successor = None
for lower_ancestor in G.successors(common_ancestor):
if lower_ancestor in common_ancestors:
successor = lower_ancestor
break
if successor is None:
break
common_ancestor = successor
yield ((v, w), common_ancestor)
@not_implemented_for("undirected")
@not_implemented_for("multigraph") | @not_implemented_for("undirected")
@not_implemented_for("multigraph") | 200 | lowest_common_ancestors.py | Python | networkx/algorithms/lowest_common_ancestors.py | b2f91c34a23058dd70b41784af0d87890216026a | networkx | 13 |
54,484 | 6 | 10 | 4 | 41 | 4 | 0 | 6 | 26 | assert_does_not_warn | Fix deprecated use of pytest.warn to assert warnings are not raised | https://github.com/PrefectHQ/prefect.git | def assert_does_not_warn():
with warnings.catch_warnings():
warnings.simplefilter("error")
yield
| 19 | testing.py | Python | src/prefect/utilities/testing.py | 2b2f421054df5bb301a13322f420b5bb44fcd2aa | prefect | 1 |
|
183,774 | 8 | 8 | 3 | 31 | 6 | 0 | 8 | 22 | _cursor_at_right_edge | Support for bracketed paste mode (#567)
* Detecting bracketed paste, sending paste events
* Bracketed pasting support in TextInput
* Restore debugging conditional
* Handle pasting of text in text-input, improve scrolling
* Fix ordering of handling in parser for bracketed pastes
* Docstrings
* Add docstrings | https://github.com/Textualize/textual.git | def _cursor_at_right_edge(self) -> bool:
return self._visible_content_to_cursor_cell_len == self.content_region.width
| 18 | text_input.py | Python | src/textual/widgets/text_input.py | fe151a7f25cfd7f1134ebafbddc7eeade1c18ccb | textual | 1 |
|
69,293 | 73 | 20 | 37 | 455 | 37 | 0 | 91 | 54 | get_price_list | refactor: rewrite `Item Prices Report` queries in `QB` | https://github.com/frappe/erpnext.git | def get_price_list():
rate = {}
ip = frappe.qb.DocType("Item Price")
pl = frappe.qb.DocType("Price List")
cu = frappe.qb.DocType("Currency")
price_list = (
frappe.qb.from_(ip)
.from_(pl)
.from_(cu)
.select(
ip.item_code,
ip.buying,
ip.selling,
(IfNull(cu.symbol, ip.currency)).as_("currency"),
ip.price_list_rate,
ip.price_list,
)
.where((ip.price_list == pl.name) & (pl.currency == cu.name) & (pl.enabled == 1))
).run(as_dict=True)
for d in price_list:
d.update(
{"price": "{0} {1} - {2}".format(d.currency, round(d.price_list_rate, 2), d.price_list)}
)
d.pop("currency")
d.pop("price_list_rate")
d.pop("price_list")
if d.price:
rate.setdefault(d.item_code, {}).setdefault("Buying" if d.buying else "Selling", []).append(
d.price
)
item_rate_map = {}
for item in rate:
for buying_or_selling in rate[item]:
item_rate_map.setdefault(item, {}).setdefault(
buying_or_selling, ", ".join(rate[item].get(buying_or_selling, []))
)
return item_rate_map
| 282 | item_prices.py | Python | erpnext/stock/report/item_prices/item_prices.py | e312d17eae2a957b71c3a11480cad1d5dd848077 | erpnext | 6 |
|
132,856 | 53 | 13 | 6 | 98 | 9 | 0 | 72 | 150 | is_finished | [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 is_finished(self):
# The checks here are partly redundant but optimized for quick
# evaluation. Specifically, if there are live trials, we check
# these live trials first. Only if none of the live trials is
# live anymore do we loop over all trials for a final check.
trials_done = (
len(self._live_trials) == 0
or all(trial.is_finished() for trial in self._live_trials)
) and all(trial.is_finished() for trial in self._trials)
return trials_done and self._search_alg.is_finished()
| 58 | trial_runner.py | Python | python/ray/tune/trial_runner.py | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | 6 |
|
260,358 | 9 | 8 | 4 | 47 | 7 | 0 | 9 | 37 | fit | MAINT Use _validate_params in FastICA (#23711)
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: jeremiedbb <[email protected]> | https://github.com/scikit-learn/scikit-learn.git | def fit(self, X, y=None):
self._validate_params()
self._fit_transform(X, compute_sources=False)
return self
| 29 | _fastica.py | Python | sklearn/decomposition/_fastica.py | 4cc347d4d0cbbfdcbd353f08842e0668fed78c9f | scikit-learn | 1 |
|
269,454 | 65 | 14 | 30 | 478 | 37 | 1 | 103 | 346 | dot | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def dot(x, y):
if ndim(x) is not None and (ndim(x) > 2 or ndim(y) > 2):
x_shape = []
for i, s in zip(int_shape(x), tf.unstack(tf.shape(x))):
if i is not None:
x_shape.append(i)
else:
x_shape.append(s)
x_shape = tuple(x_shape)
y_shape = []
for i, s in zip(int_shape(y), tf.unstack(tf.shape(y))):
if i is not None:
y_shape.append(i)
else:
y_shape.append(s)
y_shape = tuple(y_shape)
y_permute_dim = list(range(ndim(y)))
y_permute_dim = [y_permute_dim.pop(-2)] + y_permute_dim
xt = tf.reshape(x, [-1, x_shape[-1]])
yt = tf.reshape(
tf.compat.v1.transpose(y, perm=y_permute_dim), [y_shape[-2], -1]
)
return tf.reshape(
tf.matmul(xt, yt), x_shape[:-1] + y_shape[:-2] + y_shape[-1:]
)
if is_sparse(x):
out = tf.sparse.sparse_dense_matmul(x, y)
else:
out = tf.matmul(x, y)
return out
@keras_export("keras.backend.batch_dot")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | @keras_export("keras.backend.batch_dot")
@tf.__internal__.dispatch.add_dispatch_support
@doc_controls.do_not_generate_docs | 286 | backend.py | Python | keras/backend.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 9 |
125,559 | 4 | 8 | 2 | 22 | 2 | 0 | 4 | 10 | find_gcs_addresses | [core] ray.init defaults to an existing Ray instance if there is one (#26678)
ray.init() will currently start a new Ray instance even if one is already existing, which is very confusing if you are a new user trying to go from local development to a cluster. This PR changes it so that, when no address is specified, we first try to find an existing Ray cluster that was created through `ray start`. If none is found, we will start a new one.
This makes two changes to the ray.init() resolution order:
1. When `ray start` is called, the started cluster address was already written to a file called `/tmp/ray/ray_current_cluster`. For ray.init() and ray.init(address="auto"), we will first check this local file for an existing cluster address. The file is deleted on `ray stop`. If the file is empty, autodetect any running cluster (legacy behavior) if address="auto", or we will start a new local Ray instance if address=None.
2. When ray.init(address="local") is called, we will create a new local Ray instance, even if one is already existing. This behavior seems to be necessary mainly for `ray.client` use cases.
This also surfaces the logs about which Ray instance we are connecting to. Previously these were hidden because we didn't set up the log until after connecting to Ray. So now Ray will log one of the following messages during ray.init:
```
(Connecting to existing Ray cluster at address: <IP>...)
...connection...
(Started a local Ray cluster.| Connected to Ray Cluster.)( View the dashboard at <URL>)
```
Note that this changes the dashboard URL to be printed with `ray.init()` instead of when the dashboard is first started.
Co-authored-by: Eric Liang <[email protected]> | https://github.com/ray-project/ray.git | def find_gcs_addresses():
return _find_address_from_flag("--gcs-address")
| 10 | services.py | Python | python/ray/_private/services.py | 55a0f7bb2db941d8c6ff93f55e4b3193f404ddf0 | ray | 1 |
|
3,682 | 39 | 13 | 17 | 250 | 30 | 0 | 48 | 119 | test_page_token_expired_retry_succeeds | Source Google Ads: handle page token expired exception (#9812)
* dynamic date range
* raise exception if exites the cycle without error
* if range days is 1 already do not retry
* added unit tests
* added comments
* added comments
* common mock classes are moved to common module
* change read_records
* refactored get_date_params
* handle corner case
* added parse_dates function
* added test_streams
* check mock calls
* fix unit tests for chunk date range refactoring
* removed commented codes
* remove commented line
* refactor test_streams
* refactor CustomQuery.get_query
* remove TODO
* deleted unused json
* format
* fix chunk_date_range
* added docstring
* set range_days to 15 for ShoppingPerformanceReport
* refactor chunk_date_range
* format code 2
* call parent read_records method
* add return type in get_date_params
* change e to exception
* set start_date as end_date
* log page token has expired
* bump version
* updated spec and def yaml
Co-authored-by: auganbay <[email protected]> | https://github.com/airbytehq/airbyte.git | def test_page_token_expired_retry_succeeds(mock_ads_client, test_config):
stream_slice = {"start_date": "2021-01-01", "end_date": "2021-01-15"}
google_api = MockGoogleAds(credentials=test_config["credentials"], customer_id=test_config["customer_id"])
incremental_stream_config = dict(
api=google_api,
conversion_window_days=test_config["conversion_window_days"],
start_date=test_config["start_date"],
time_zone="local",
end_date="2021-04-04",
)
stream = ClickView(**incremental_stream_config)
stream.get_query = Mock()
stream.get_query.return_value = "query"
result = list(stream.read_records(sync_mode=SyncMode.incremental, cursor_field=["segments.date"], stream_slice=stream_slice))
assert len(result) == 9
assert stream.get_query.call_count == 2
stream.get_query.assert_called_with({"start_date": "2021-01-03", "end_date": "2021-01-15"})
| 145 | test_streams.py | Python | airbyte-integrations/connectors/source-google-ads/unit_tests/test_streams.py | 359fcd801128239b39297828d39821f631ce00c0 | airbyte | 1 |
|
310,597 | 13 | 10 | 5 | 42 | 5 | 0 | 13 | 45 | _async_change_light | Migrate amcrest integration to new async API (#56294) | https://github.com/home-assistant/core.git | async def _async_change_light(self) -> None:
await self._async_change_setting(
self._audio_enabled or self.is_streaming, "indicator light"
)
| 23 | camera.py | Python | homeassistant/components/amcrest/camera.py | 7781e308cd7b28c67b6cf339f9b115c7190456fe | core | 2 |
|
261,218 | 14 | 11 | 4 | 68 | 8 | 0 | 15 | 31 | axis0_safe_slice | DOC Ensure that sklearn.utils.axis0_safe_slice passes numpydoc (#24561) | https://github.com/scikit-learn/scikit-learn.git | def axis0_safe_slice(X, mask, len_mask):
if len_mask != 0:
return X[safe_mask(X, mask), :]
return np.zeros(shape=(0, X.shape[1]))
| 45 | __init__.py | Python | sklearn/utils/__init__.py | 537c325f2927895449ce418b3a77750135c0ba7b | scikit-learn | 2 |
|
170,649 | 7 | 6 | 3 | 25 | 6 | 0 | 7 | 14 | obj_to_write | issue 48855 enable pylint unnecessary-pass (#49418)
issue 48855 enable unnecessary-pass | https://github.com/pandas-dev/pandas.git | def obj_to_write(self) -> NDFrame | Mapping[IndexLabel, Any]:
| 16 | _json.py | Python | pandas/io/json/_json.py | 76923d7b58d8f25329e779a40b87e2b6959f9cea | pandas | 1 |
|
213,001 | 131 | 21 | 57 | 973 | 33 | 0 | 258 | 945 | cut_ansi_string_into_parts | Removed old code that used Popen and instead uses the PySimpleGUI Exec API calls for an all-in-one demo. Added expansion of the Multilline and a SizeGrip so that it's obvious to user the window is resizable. | https://github.com/PySimpleGUI/PySimpleGUI.git | def cut_ansi_string_into_parts(string_with_ansi_codes):
color_codes_english = ['Black', 'Red', 'Green', 'Yellow', 'Blue', 'Magenta', 'Cyan', 'White', 'Reset']
color_codes = ["30m", "31m", "32m", "33m", "34m", "35m", "36m", "37m", "0m"]
effect_codes_english = ['Italic', 'Underline', 'Slow Blink', 'Rapid Blink', 'Crossed Out']
effect_codes = ["3m", "4m", "5m", "6m", "9m"]
background_codes = ["40m", "41m", "42m", "43m", "44m", "45m", "46m", "47m"]
background_codes_english = ["Black", "Red", "Green", "Yellow", "Blue", "Magenta", "Cyan", "White"]
ansi_codes = color_codes + effect_codes
tuple_list = []
string_list = string_with_ansi_codes.split("\u001b[")
if (len(string_list)) == 1:
string_list = string_with_ansi_codes.split("\033[")
for teststring in string_list:
if teststring == string_with_ansi_codes:
tuple_list += [(teststring, None, None, None)]
break
if any(code in teststring for code in ansi_codes):
static_string = None
color_used = None
effect_used = None
background_used = None
for color in range(0, len(color_codes)):
if teststring.startswith(color_codes[color]):
working_thread = teststring.split(color_codes[color])
ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
static_string = ansi_strip.sub('', working_thread[1])
color_used = color_codes_english[color]
for effect in range(0, len(effect_codes)):
if teststring.startswith(effect_codes[effect]):
working_thread = teststring.split(effect_codes[effect])
ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
static_string = ansi_strip.sub('', working_thread[1])
effect_used = effect_codes_english[effect]
for background in range(0, len(background_codes)):
if teststring.startswith(background_codes[background]):
working_thread = teststring.split(background_codes[background])
ansi_strip = re.compile(r'\x1B[@-_][0-?]*[ -/]*[@-~]')
static_string = ansi_strip.sub('', working_thread[1])
background_used = background_codes_english[background]
try:
if not tuple_list[len(tuple_list) - 1][0]:
if not tuple_list[len(tuple_list) - 1][1] == None:
color_used = tuple_list[len(tuple_list) - 1][1]
if not tuple_list[len(tuple_list) - 1][2] == None:
background_used = tuple_list[len(tuple_list) - 1][2]
if not tuple_list[len(tuple_list) - 1][3] == None:
effect_used = tuple_list[len(tuple_list) - 1][3]
tuple_list += [(static_string, color_used, background_used, effect_used)]
else:
tuple_list += [(static_string, color_used, background_used, effect_used)]
except Exception:
tuple_list += [(static_string, color_used, background_used, effect_used)]
new_tuple_list = []
for x in range(0, len(tuple_list)):
if tuple_list[x][0]:
new_tuple_list += [[tuple_list[x][0], tuple_list[x][1], tuple_list[x][2], tuple_list[x][3]]]
return new_tuple_list
| 603 | Demo_Script_Launcher_ANSI_Color_Output.py | Python | DemoPrograms/Demo_Script_Launcher_ANSI_Color_Output.py | a35687ac51dac5a2a0664ca20e7dd7cba6836c7b | PySimpleGUI | 19 |
|
153,298 | 62 | 12 | 19 | 244 | 16 | 0 | 90 | 298 | _read | REFACTOR-#3900: add flake8-no-implicit-concat plugin and refactor flake8 error codes (#3901)
Signed-off-by: jeffreykennethli <[email protected]> | https://github.com/modin-project/modin.git | def _read(cls, path_or_buf, **kwargs):
if cls._validate_hdf_format(path_or_buf=path_or_buf) is None:
ErrorMessage.default_to_pandas(
"File format seems to be `fixed`. For better distribution consider "
+ "saving the file in `table` format. df.to_hdf(format=`table`)."
)
return cls.single_worker_read(path_or_buf, **kwargs)
columns = kwargs.pop("columns", None)
# Have to do this because of Dask's keyword arguments
kwargs["_key"] = kwargs.pop("key", None)
if not columns:
start = kwargs.pop("start", None)
stop = kwargs.pop("stop", None)
empty_pd_df = pandas.read_hdf(path_or_buf, start=0, stop=0, **kwargs)
if start is not None:
kwargs["start"] = start
if stop is not None:
kwargs["stop"] = stop
columns = empty_pd_df.columns
return cls.build_query_compiler(path_or_buf, columns, **kwargs)
| 148 | hdf_dispatcher.py | Python | modin/core/io/column_stores/hdf_dispatcher.py | e5e9634357e60925a5a70e56a1d4882d269f533a | modin | 5 |
|
43,494 | 74 | 12 | 97 | 1,393 | 23 | 0 | 246 | 983 | upgrade | Have consistent types between the ORM and the migration files (#24044)
We currently don't compare column types between ORM and the migration files. Some columns in the migration files have different types from the same columns in the ORM.
Here, I made effort to match the types in migration files with the
types in ORM, using the migration files as the source of truth in most cases.
I couldn't convert the MySQL VARCHAR collation in db(utf8_bin) to use the one in ORM(utf8mb3_bin). It seems it's not possible to convert a collation of an already existing column in MySQL. | https://github.com/apache/airflow.git | def upgrade():
conn = op.get_bind()
with op.batch_alter_table('connection', schema=None) as batch_op:
batch_op.alter_column(
'extra',
existing_type=sa.TEXT(),
type_=sa.Text(),
existing_nullable=True,
)
with op.batch_alter_table('log_template', schema=None) as batch_op:
batch_op.alter_column(
'created_at', existing_type=sa.DateTime(), type_=TIMESTAMP(), existing_nullable=False
)
with op.batch_alter_table('serialized_dag', schema=None) as batch_op:
# drop server_default
batch_op.alter_column(
'dag_hash',
existing_type=sa.String(32),
server_default=None,
type_=sa.String(32),
existing_nullable=False,
)
with op.batch_alter_table('trigger', schema=None) as batch_op:
batch_op.alter_column(
'created_date', existing_type=sa.DateTime(), type_=TIMESTAMP(), existing_nullable=False
)
if conn.dialect.name != 'sqlite':
return
with op.batch_alter_table('serialized_dag', schema=None) as batch_op:
batch_op.alter_column('fileloc_hash', existing_type=sa.Integer, type_=sa.BigInteger())
# Some sqlite date are not in db_types.TIMESTAMP. Convert these to TIMESTAMP.
with op.batch_alter_table('dag', schema=None) as batch_op:
batch_op.alter_column(
'last_pickled', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
batch_op.alter_column(
'last_expired', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
with op.batch_alter_table('dag_pickle', schema=None) as batch_op:
batch_op.alter_column(
'created_dttm', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
with op.batch_alter_table('dag_run', schema=None) as batch_op:
batch_op.alter_column(
'execution_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=False
)
batch_op.alter_column(
'start_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
batch_op.alter_column(
'end_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
with op.batch_alter_table('import_error', schema=None) as batch_op:
batch_op.alter_column(
'timestamp', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
with op.batch_alter_table('job', schema=None) as batch_op:
batch_op.alter_column(
'start_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
batch_op.alter_column(
'end_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
batch_op.alter_column(
'latest_heartbeat', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
with op.batch_alter_table('log', schema=None) as batch_op:
batch_op.alter_column('dttm', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True)
batch_op.alter_column(
'execution_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
with op.batch_alter_table('serialized_dag', schema=None) as batch_op:
batch_op.alter_column(
'last_updated', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=False
)
with op.batch_alter_table('sla_miss', schema=None) as batch_op:
batch_op.alter_column(
'execution_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=False
)
batch_op.alter_column(
'timestamp', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
with op.batch_alter_table('task_fail', schema=None) as batch_op:
batch_op.alter_column(
'start_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
batch_op.alter_column(
'end_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
with op.batch_alter_table('task_instance', schema=None) as batch_op:
batch_op.alter_column(
'start_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
batch_op.alter_column(
'end_date', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
batch_op.alter_column(
'queued_dttm', existing_type=sa.DATETIME(), type_=TIMESTAMP(), existing_nullable=True
)
| 840 | 0113_2_4_0_compare_types_between_orm_and_db.py | Python | airflow/migrations/versions/0113_2_4_0_compare_types_between_orm_and_db.py | 25537acfa28eebc82a90274840e0e6fb5c91e271 | airflow | 2 |
|
279,720 | 33 | 12 | 9 | 148 | 25 | 1 | 37 | 122 | _load_state | Move optimizer methods not related to distributed training to the base class.
PiperOrigin-RevId: 471880396 | https://github.com/keras-team/keras.git | def _load_state(self, dir_path):
# To avoid circular import
from keras.saving.experimental import saving_lib
file_path = tf.io.gfile.join(dir_path, saving_lib.STATE_FILENAME)
if tf.io.gfile.exists(file_path):
loaded_npz = np.load(file_path)
logging.debug(f"Loaded state from {file_path}")
self._set_state(
{file: loaded_npz[file] for file in loaded_npz.files}
)
base_optimizer_keyword_args =
@keras_export("keras.optimizers.experimental.Optimizer", v1=[]) | @keras_export("keras.optimizers.experimental.Optimizer", v1=[]) | 77 | optimizer.py | Python | keras/optimizers/optimizer_experimental/optimizer.py | 3ba4d8dadb4db52cf066662f5068e4f99ebd87ee | keras | 3 |
34,475 | 64 | 13 | 16 | 168 | 14 | 0 | 92 | 236 | simplify_replacements | Add model like (#14992)
* Add new model like command
* Bad doc-styler
* black and doc-styler, stop fighting!
* black and doc-styler, stop fighting!
* At last
* Clean up
* Typo
* Bad doc-styler
* Bad doc-styler
* All good maybe?
* Use constants
* Add doc and type hints
* More cleaning
* Add doc
* Fix Copied from
* Doc template
* Use typing.Pattern instead
* Framework-specific files
* Fixes
* Select frameworks clean model init
* Deal with frameworks in main init
* fixes
* Last fix
* Prompt user for info
* Delete exemple config
* Last fixes
* Add test config
* Fix bug with model_type included in each other
* Fixes
* More fixes
* More fixes
* Adapt config
* Remove print statements
* Will fix tokenization later, leave it broken for now
* Add test
* Quality
* Try this way
* Debug
* Maybe by setting the path?
* Let's try another way
* It should go better when actually passing the arg...
* Remove debug statements and style
* Fix config
* Add tests
* Test require the three backends
* intermediate commit
* Revamp pattern replacements and start work on feature extractors
* Adapt model info
* Finalize code for processors
* Fix in main init additions
* Finish questionnaire for processing classes
* Fix file name
* Fix for real
* Fix patterns
* Style
* Remove needless warnings
* Copied from should work now.
* Include Copied form in blocks
* Add test
* More fixes and tests
* Apply suggestions from code review
Co-authored-by: Lysandre Debut <[email protected]>
* Address review comment
Co-authored-by: Lysandre Debut <[email protected]> | https://github.com/huggingface/transformers.git | def simplify_replacements(replacements):
if len(replacements) <= 1:
# Nothing to simplify
return replacements
# Next let's sort replacements by length as a replacement can only "imply" another replacement if it's shorter.
replacements.sort(key=lambda x: len(x[0]))
idx = 0
while idx < len(replacements):
old, new = replacements[idx]
# Loop through all replacements after
j = idx + 1
while j < len(replacements):
old_2, new_2 = replacements[j]
# If the replacement is implied by the current one, we can drop it.
if old_2.replace(old, new) == new_2:
replacements.pop(j)
else:
j += 1
idx += 1
return replacements
| 101 | add_new_model_like.py | Python | src/transformers/commands/add_new_model_like.py | 81156d20cd76c1a43ed44fdbc785e237d60b6896 | transformers | 5 |
|
269,031 | 50 | 16 | 12 | 128 | 12 | 0 | 67 | 138 | _set_object_by_path | Expose keras/dtensor package to public
PiperOrigin-RevId: 430366845 | https://github.com/keras-team/keras.git | def _set_object_by_path(object_to_set, path, value):
for i, attr_name in enumerate(path):
if i == len(path) - 1:
# We found the actual attribute to set
if isinstance(attr_name, int):
# This means we are trying to set an element in the array, make sure the
# instance is array like object.
object_to_set[attr_name] = value
else:
setattr(object_to_set, attr_name, value)
else:
if isinstance(attr_name, int):
object_to_set = object_to_set[attr_name]
else:
object_to_set = getattr(object_to_set, attr_name)
| 80 | layout_map.py | Python | keras/dtensor/layout_map.py | a179ed22f002e2f4a43ae4770348a9b8e1d5a051 | keras | 5 |
|
241,679 | 15 | 15 | 10 | 97 | 13 | 0 | 18 | 68 | val_batch_idx | Integrate progress tracking into the progress bar (#11213) | https://github.com/Lightning-AI/lightning.git | def val_batch_idx(self) -> int:
if self.trainer is None:
return 0
if self.trainer.state.fn == "fit":
return self.trainer.fit_loop.epoch_loop.val_loop.epoch_loop.batch_progress.current.processed
return self.trainer.validate_loop.epoch_loop.batch_progress.current.processed
| 60 | base.py | Python | pytorch_lightning/callbacks/progress/base.py | 8a549a550cb10189ff1db382f546a40cd1c6c5b3 | lightning | 3 |
|
100,776 | 18 | 14 | 7 | 65 | 11 | 0 | 22 | 73 | is_admin | Add Apple M1 to setup.py
add libblas to requirements | https://github.com/deepfakes/faceswap.git | def is_admin(self) -> bool:
try:
retval = os.getuid() == 0
except AttributeError:
retval = ctypes.windll.shell32.IsUserAnAdmin() != 0 # type: ignore
return retval
| 37 | setup.py | Python | setup.py | a586ef6bf3db26752fc1164835e46b6e375576ca | faceswap | 2 |
|
165,785 | 8 | 7 | 6 | 28 | 5 | 0 | 8 | 22 | length | TYP: fix mid and length for Interval and Intervalarray (#46472) | https://github.com/pandas-dev/pandas.git | def length(self) -> Index:
return self.right - self.left
| 16 | interval.py | Python | pandas/core/arrays/interval.py | 6d7e004b1fc69942390d953bf21098a786c12c92 | pandas | 1 |
|
292,161 | 73 | 15 | 30 | 318 | 27 | 0 | 101 | 456 | async_step_link | Ensure lutron caseta imports set the unique id (#66754) | https://github.com/home-assistant/core.git | async def async_step_link(self, user_input=None):
errors = {}
# Abort if existing entry with matching host exists.
self._async_abort_entries_match({CONF_HOST: self.data[CONF_HOST]})
self._configure_tls_assets()
if (
not self.attempted_tls_validation
and await self.hass.async_add_executor_job(self._tls_assets_exist)
and await self.async_get_lutron_id()
):
self.tls_assets_validated = True
self.attempted_tls_validation = True
if user_input is not None:
if self.tls_assets_validated:
# If we previous paired and the tls assets already exist,
# we do not need to go though pairing again.
return self.async_create_entry(title=self.bridge_id, data=self.data)
assets = None
try:
assets = await async_pair(self.data[CONF_HOST])
except (asyncio.TimeoutError, OSError):
errors["base"] = "cannot_connect"
if not errors:
await self.hass.async_add_executor_job(self._write_tls_assets, assets)
return self.async_create_entry(title=self.bridge_id, data=self.data)
return self.async_show_form(
step_id="link",
errors=errors,
description_placeholders={
CONF_NAME: self.bridge_id,
CONF_HOST: self.data[CONF_HOST],
},
)
| 199 | config_flow.py | Python | homeassistant/components/lutron_caseta/config_flow.py | 64277058b5ba6fb10029553422695964204f0ebb | core | 8 |
|
225,776 | 29 | 8 | 14 | 72 | 4 | 0 | 33 | 104 | test_expand_tokens_with_subtokens | add more unit tests for keyword table (#45)
Co-authored-by: Jerry Liu <[email protected]> | https://github.com/jerryjliu/llama_index.git | def test_expand_tokens_with_subtokens() -> None:
response = "foo bar, baz, Hello hello wOrld bye"
keywords = extract_keywords_given_response(response)
assert keywords == {
"foo bar",
"foo",
"bar",
"baz",
"hello hello world bye",
"hello",
"world",
"bye",
}
| 37 | test_utils.py | Python | tests/indices/keyword_table/test_utils.py | 3c7e1ad1ea0d6feace926d9749c73c7870397714 | llama_index | 1 |
|
138,395 | 80 | 16 | 32 | 234 | 31 | 0 | 107 | 518 | get_objects | [State Observability] Tasks and Objects API (#23912)
This PR implements ray list tasks and ray list objects APIs.
NOTE: You can ignore the merge conflict for now. It is because the first PR was reverted. There's a fix PR open now. | https://github.com/ray-project/ray.git | async def get_objects(self) -> dict:
replies = await asyncio.gather(
*[
self._client.get_object_info(node_id, timeout=DEFAULT_RPC_TIMEOUT)
for node_id in self._client.get_all_registered_raylet_ids()
]
)
worker_stats = []
for reply in replies:
for core_worker_stat in reply.core_workers_stats:
# NOTE: Set preserving_proto_field_name=False here because
# `construct_memory_table` requires a dictionary that has
# modified protobuf name
# (e.g., workerId instead of worker_id) as a key.
worker_stats.append(
self._message_to_dict(
message=core_worker_stat,
fields_to_decode=["object_id"],
preserving_proto_field_name=False,
)
)
result = {}
memory_table = memory_utils.construct_memory_table(worker_stats)
for entry in memory_table.table:
data = entry.as_dict()
# `construct_memory_table` returns object_ref field which is indeed
# object_id. We do transformation here.
# TODO(sang): Refactor `construct_memory_table`.
data["object_id"] = data["object_ref"]
del data["object_ref"]
data = filter_fields(data, ObjectState)
result[data["object_id"]] = data
return result
| 140 | state_aggregator.py | Python | dashboard/state_aggregator.py | 30ab5458a7e4ba2351d5e1beef8c8797b5946493 | ray | 5 |
|
246,122 | 4 | 6 | 9 | 16 | 3 | 0 | 4 | 11 | _setup_get_username_for_registration | Add a module callback to set username at registration (#11790)
This is in the context of mainlining the Tchap fork of Synapse. Currently in Tchap usernames are derived from the user's email address (extracted from the UIA results, more specifically the m.login.email.identity step).
This change also exports the check_username method from the registration handler as part of the module API, so that a module can check if the username it's trying to generate is correct and doesn't conflict with an existing one, and fallback gracefully if not.
Co-authored-by: David Robertson <[email protected]> | https://github.com/matrix-org/synapse.git | def _setup_get_username_for_registration(self) -> Mock:
| 38 | test_password_providers.py | Python | tests/handlers/test_password_providers.py | 2d3bd9aa670eedd299cc03093459929adec41918 | synapse | 1 |
|
288,152 | 4 | 6 | 1 | 17 | 4 | 0 | 4 | 11 | attribute_updated | Add configuration entities and device actions for Inovelli Blue Series switch to ZHA (#79106)
* Add Inovelli configutation entities to ZHA
* add device actions
* fix attribute name collision
* add device action tests
* disable remote protection per Inovelli request
* expect_reply to false
* update test for expect_reply change
* inovelli feedback
* translation keys and strings
* clean up numbers
* prevent double events
* remove individual LED defaults per inovelli
* redundant check
* update test | https://github.com/home-assistant/core.git | def attribute_updated(self, attrid, value):
| 10 | manufacturerspecific.py | Python | homeassistant/components/zha/core/channels/manufacturerspecific.py | 2ed48a9b28f10784a5b8fc27ddae5ae299b43deb | core | 1 |
|
154,845 | 51 | 9 | 7 | 76 | 7 | 0 | 61 | 91 | get_keywords | REFACTOR-#5012: Add mypy checks for singleton files in base modin directory (#5013)
Signed-off-by: Jonathan Shi <[email protected]> | https://github.com/modin-project/modin.git | def get_keywords() -> Dict[str, str]:
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
git_refnames = "$Format:%d$"
git_full = "$Format:%H$"
git_date = "$Format:%ci$"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
| 38 | _version.py | Python | modin/_version.py | 446148dbf9b66debd0a0dbf9ce778253380d5921 | modin | 1 |
|
34,282 | 42 | 15 | 19 | 186 | 15 | 0 | 57 | 262 | _run_split_on_punc | Add FastTokenizer to REALM (#15211)
* Remove BertTokenizer abstraction
* Add FastTokenizer to REALM
* Fix config archive map
* Fix copies
* Update realm.mdx
* Apply suggestions from code review | https://github.com/huggingface/transformers.git | def _run_split_on_punc(self, text, never_split=None):
if never_split is not None and text in never_split:
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
| 114 | tokenization_realm.py | Python | src/transformers/models/realm/tokenization_realm.py | 841d979190319098adc8101f9820a02ee3be4c8b | transformers | 7 |
|
285,675 | 18 | 10 | 21 | 91 | 17 | 0 | 22 | 75 | copy_func | Next release : reports on steroids (#2349)
* fix gov tests
* refactor insider
* new virtual path extraction
* removed some symbol default params as they're considered critical
* little adjustments
* portfolio refactor
* merge API factory
* add helpers, stocks, crypto, forex
* minor forex changes
* include forex api paths
* add 2 missing forex funcs
* portfolio brokers refactor
* display help on api func call
* add econometrics virtual paths to api
* add api unit test
* fixed report for the new api
* minor portfolio refactorings
* added gdapps
* anchor_yield path
* some more crypto path fixes
* small change
* fixed wrong param
* minor fixes
* wip - inital commit for forex report
* add bw as a model, we'll get better solution afterwards
* added ema with dummy model as it adds great functionality to the report
* minor fixes
* wip - added functions to forex report
* add feedparser news path
* add new virtual paths to api
* adding commands to equity report
* revert to old paths, new ones were breaking
* Add in very basic ETF report
* Add candle chart to ETF report
* add etf load
* allow use of candle without data
* add raw to candle
* added forex report
* ongoing equity report
* equity report change
* fix some portfolio bugs and add docstrings
* include portfolio paths and coin class
* add crypto paths
* change event dates to str
* starting economy report
* window for limit
* equity report and refactor newsapi
* add helper to api
* update on economy report
* equity report
* update economy report
* refactor some docstrings
* change maturities helper
* refactor newsapi
* refactor futures command
* add some sauce to ycrv plot
* black
* update report
* refactor alphavantage
* refactor wsj
* update economy report
* ycrv tenor
* map avaiable_indices
* map economy helpers
* fix econdb docstring
* add plots on economy report
* minor fixes
* wip - crypto report
* update economy report
* added same default args as view
* added view to explicity use chart=True when suing the api
* adjustments - removed rich tables to use only df
* final version economy report
* change report name
* equity report for review
* linting
* add etf symbols endpoint
* incorporate feedback economy report
* fix reports launch by adding tag to economy report
* fix equity bug
* remove analyst name
* fix
* fix news
* make links hyperlinks for equity
* click links
* fixed arg name
* improved news
* small improves
* Fix light terminal stylesheet that would prevent using it in notebooks (#2473)
* improved report
* run reports in installer
* fix #2209
* minor ycrv refactoring
* refactor portfolio/holdv virtual path
* refactor benchmark trades
* fix events args
* adapt economy report to changes
* fix portfolio controller bug
* holdv refactor
* refactor perf command
* start portfolio report
* remove perf view
* refactor holp
* add textwrap3 to poetry (doesn't solve the error)
* fix equity after merge
* add some rolling commands
* fix equity after save button
* improved crypto report, plus minor fixes
* minor fixes on the reports
* add maxdd and distr
* refactor qa
* var command
* refactor qa expected shortfall
* add es command
* add es command
* fix qa percentile bug
* fix economy rendering
* refactor qa omega
* add om command
* add summary command
* add dret command
* add mret command
* add yret command
* add metrics
* add allocs to report
* remove bro and po commands, add later
* fixed some tests
* adjustments to crypto report
* Fix docstring for VSCode
Added a note about installing Jupyter PowerToys extension for optimal API usage in Jupyter VSCode, in the API_README.md.
* minor adjustment
* remove nft calendar model virtual paths
* Add in Portfolio report
* fix external axes portfolio view
* Update portfolio report with rolling plots
* Details for ETF and Portfolio
* fix economy report
* change analyst to openbb
* floppy
* fixed unmatched axis in reports
* Speed up tests
* fix file and load on po
* get_news output
* add some po paths
* Add integration tests for Reports menu
* refactor maxsharpe
* open maxsharpe
* open minrisk
* open maxutil
* open maxret
* Added fixes
* black
* remove useless views
* Fixed small issue
* refactor ef
* open ef api
* portfolio optimization report
* Added fixes
* unblock api loading
* add more endpoints
* update po report
* unblock api loading
* update po report
* expose herc
* expose property endpoint
* Added fixes
* More api fixes
* flake8
* Fixed some mypy
* news api model
* flake8
* mypy fix
* mypy
* black
* pylint
* fix tests
* markdown
* markdown
* Added fixes
* fix economy report
* merge
* fix economy report
* remove empty notebook
* expose nco
* remove jupyter notebook
* expose plot endpoint
* remove po report, just used for tests
* api v paths plot
* remove api_old
* change loading msg
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: hjoaquim <[email protected]>
Co-authored-by: montezdesousa <[email protected]>
Co-authored-by: Om Gupta <[email protected]>
Co-authored-by: minhhoang1023 <[email protected]>
Co-authored-by: JerBouma <[email protected]>
Co-authored-by: Theodore Aptekarev <[email protected]>
Co-authored-by: Om Gupta <[email protected]>
Co-authored-by: Diogo Sousa <[email protected]>
Co-authored-by: Colin Delahunty <[email protected]>
Co-authored-by: northern-64bit <[email protected]>
Co-authored-by: colin99d <[email protected]>
Co-authored-by: Minh Hoang <[email protected]> | https://github.com/OpenBB-finance/OpenBBTerminal.git | def copy_func(f) -> Callable:
g = types.FunctionType(
f.__code__,
f.__globals__,
name=f.__name__,
argdefs=f.__defaults__,
closure=f.__closure__,
)
g = functools.update_wrapper(g, f)
g.__kwdefaults__ = f.__kwdefaults__
return g
| 60 | api.py | Python | openbb_terminal/api.py | 72b0a9f1ee8b91ad9fd9e76d80d2ccab51ee6d21 | OpenBBTerminal | 1 |
|
20,389 | 95 | 17 | 47 | 575 | 36 | 0 | 169 | 795 | format_unencoded | 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 format_unencoded(self, tokensource, outfile):
x = self.xoffset
y = self.yoffset
if not self.nowrap:
if self.encoding:
outfile.write('<?xml version="1.0" encoding="%s"?>\n' %
self.encoding)
else:
outfile.write('<?xml version="1.0"?>\n')
outfile.write('<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.0//EN" '
'"http://www.w3.org/TR/2001/REC-SVG-20010904/DTD/'
'svg10.dtd">\n')
outfile.write('<svg xmlns="http://www.w3.org/2000/svg">\n')
outfile.write('<g font-family="%s" font-size="%s">\n' %
(self.fontfamily, self.fontsize))
counter = self.linenostart
counter_step = self.linenostep
counter_style = self._get_style(Comment)
line_x = x
if self.linenos:
if counter % counter_step == 0:
outfile.write('<text x="%s" y="%s" %s text-anchor="end">%s</text>' %
(x+self.linenowidth,y,counter_style,counter))
line_x += self.linenowidth + self.ystep
counter += 1
outfile.write('<text x="%s" y="%s" xml:space="preserve">' % (line_x, y))
for ttype, value in tokensource:
style = self._get_style(ttype)
tspan = style and '<tspan' + style + '>' or ''
tspanend = tspan and '</tspan>' or ''
value = escape_html(value)
if self.spacehack:
value = value.expandtabs().replace(' ', ' ')
parts = value.split('\n')
for part in parts[:-1]:
outfile.write(tspan + part + tspanend)
y += self.ystep
outfile.write('</text>\n')
if self.linenos and counter % counter_step == 0:
outfile.write('<text x="%s" y="%s" text-anchor="end" %s>%s</text>' %
(x+self.linenowidth,y,counter_style,counter))
counter += 1
outfile.write('<text x="%s" y="%s" ' 'xml:space="preserve">' % (line_x,y))
outfile.write(tspan + parts[-1] + tspanend)
outfile.write('</text>')
if not self.nowrap:
outfile.write('</g></svg>\n')
| 332 | svg.py | Python | pipenv/patched/notpip/_vendor/pygments/formatters/svg.py | f3166e673fe8d40277b804d35d77dcdb760fc3b3 | pipenv | 15 |
|
279,959 | 18 | 14 | 7 | 88 | 8 | 0 | 19 | 100 | from_config | Some changes on the new optimizer:
1. Include `custom_objects` in `from_config` for deserializing custom learning rate.
2. Handle the error of seeing unrecognized variable with a better error message.
PiperOrigin-RevId: 476505974 | https://github.com/keras-team/keras.git | def from_config(cls, config, custom_objects=None):
if "learning_rate" in config:
if isinstance(config["learning_rate"], dict):
config["learning_rate"] = learning_rate_schedule.deserialize(
config["learning_rate"], custom_objects=custom_objects
)
return cls(**config)
| 52 | optimizer.py | Python | keras/optimizers/optimizer_experimental/optimizer.py | 51a6050b936ec87cd684fc1a052f79785ec9aaec | keras | 3 |
|
33,933 | 40 | 10 | 40 | 196 | 17 | 1 | 67 | 246 | forward | [Fix doc examples] Add missing from_pretrained (#15044)
* fix doc example - ValueError: Parameter config should be an instance of class `PretrainedConfig`
* Update src/transformers/models/segformer/modeling_segformer.py
Co-authored-by: NielsRogge <[email protected]>
* update
Co-authored-by: ydshieh <[email protected]>
Co-authored-by: NielsRogge <[email protected]> | https://github.com/huggingface/transformers.git | def forward(self, pixel_values, output_attentions=None, output_hidden_states=None, return_dict=None):
r
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_outputs = self.encoder(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
,
SEGFORMER_START_DOCSTRING,
) | @add_start_docstrings(
"""
SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden
states) e.g. for ImageNet.
""",
SEGFORMER_START_DOCSTRING,
) | 127 | modeling_segformer.py | Python | src/transformers/models/segformer/modeling_segformer.py | ac224bb0797c1ee6522d814139f3eb0a8947267b | transformers | 5 |
297,740 | 42 | 11 | 25 | 319 | 18 | 0 | 88 | 197 | test_create_area | Add aliases to area registry items (#84294)
* Add aliases to area registry items
* Update test
* Fix WS API | https://github.com/home-assistant/core.git | async def test_create_area(hass, registry, update_events):
# Create area with only mandatory parameters
area = registry.async_create("mock")
assert area == area_registry.AreaEntry(
name="mock", normalized_name=ANY, aliases=set(), id=ANY, picture=None
)
assert len(registry.areas) == 1
await hass.async_block_till_done()
assert len(update_events) == 1
assert update_events[-1]["action"] == "create"
assert update_events[-1]["area_id"] == area.id
# Create area with all parameters
area = registry.async_create(
"mock 2", aliases={"alias_1", "alias_2"}, picture="/image/example.png"
)
assert area == area_registry.AreaEntry(
name="mock 2",
normalized_name=ANY,
aliases={"alias_1", "alias_2"},
id=ANY,
picture="/image/example.png",
)
assert len(registry.areas) == 2
await hass.async_block_till_done()
assert len(update_events) == 2
assert update_events[-1]["action"] == "create"
assert update_events[-1]["area_id"] == area.id
| 191 | test_area_registry.py | Python | tests/helpers/test_area_registry.py | 1a42bd5c4cb51ffbfcaf8d5389b80a228712ac81 | core | 1 |
|
273,987 | 4 | 6 | 3 | 17 | 4 | 0 | 4 | 7 | _zero_state_tensors | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def _zero_state_tensors(state_size, batch_size, dtype):
| 23 | legacy_cells.py | Python | keras/layers/rnn/legacy_cells.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 1 |
|
30,229 | 71 | 19 | 23 | 198 | 19 | 0 | 112 | 277 | create_github_url | update web code
Co-Authored-By: Peyton Creery <[email protected]> | https://github.com/spotDL/spotify-downloader.git | def create_github_url(url):
repo_only_url = re.compile(
r"https:\/\/github\.com\/[a-z\d](?:[a-z\d]|-(?=[a-z\d])){0,38}\/[a-zA-Z0-9]+$"
)
re_branch = re.compile("/(tree|blob)/(.+?)/")
# Check if the given url is a url to a GitHub repo. If it is, tell the
# user to use 'git clone' to download it
if re.match(repo_only_url, url):
print(
"✘ The given url is a complete repository. Use 'git clone' to download the repository",
"red",
)
sys.exit()
# extract the branch name from the given url (e.g master)
branch = re_branch.search(url)
if branch:
download_dirs = url[branch.end() :]
api_url = (
url[: branch.start()].replace("github.com", "api.github.com/repos", 1)
+ "/contents/"
+ download_dirs
+ "?ref="
+ branch.group(2)
)
return api_url, download_dirs
raise ValueError("The given url is not a valid GitHub url")
# Modification of https://github.com/sdushantha/gitdir/blob/master/gitdir/gitdir.py | 111 | web.py | Python | spotdl/console/web.py | bbb7a02ef889134af71593102bc6f65035ab14cb | spotify-downloader | 3 |
|
7,623 | 37 | 15 | 14 | 139 | 20 | 0 | 40 | 114 | load_data_for_viz | Encoder refactor V2 (#2370)
* Added base files and some initial code
* More files created, fleshing out binary feature and corresponding encoders
* Added more schema infra
* Registered all feature encoders
* Separated feature utils infra
* Added all preprocessing classes
* Filled out rest of schema configs
* Fixed preproc dataclass
* Fixed small errors blocking import
* Tests should be passing
* Deleted unnecesssary files and removed commented out code
* fixed flake8
* Fixed most tests
* fixed pattern validation
* Fixed missing val strategies and solved custom encoder update issue
* Removed preprocessing from features due to schema SSOT
* fix flake 8
* Started encoder schema work
* Parallel CNN Encoder
* StackedCNN Encoder
* Added image encoders
* Finished sequence encoders
* Partway through text encoders
* Added text encoders
* Bag Encoders
* Binary and Date Encoders
* category, date, h3, and set encoders
* Wired up encoder schemas
* Switched input feature encoder schema definitions
* Fixed handful of issues
* Fix schema issues
* Refactored a bunch of test configs
* Small changes
* Removed default param from register_encoder
* Schema working now, working on refactoring
* Finished decoder schemas
* Removed default param from register_decoder
* Added some default params to output features and more decoder work
* Refactored all input feature encoder/decoder referencing
* Refactored pretty much all the tests
* Added back constants
* Solved gbm issue
* Fixed save_load test
* various fixes
* Fixed import issue
* Flake 8 and various fixes
* Solved more failed tests
* Refactored missed tests
* Removed commented lines
* Added init file for decoders schema
* Fixed failing tests
* Fixed hyperopt shared params test
* Added backwards compatability logic and test
* Flake 8
* removed comment
* Added base files and some initial code
* More files created, fleshing out binary feature and corresponding encoders
* Added more schema infra
* Registered all feature encoders
* Separated feature utils infra
* Added all preprocessing classes
* Filled out rest of schema configs
* Fixed preproc dataclass
* Fixed small errors blocking import
* Tests should be passing
* Deleted unnecesssary files and removed commented out code
* fixed flake8
* Fixed most tests
* fixed pattern validation
* Fixed missing val strategies and solved custom encoder update issue
* Removed preprocessing from features due to schema SSOT
* fix flake 8
* Started encoder schema work
* Parallel CNN Encoder
* StackedCNN Encoder
* Added image encoders
* Finished sequence encoders
* Partway through text encoders
* Added text encoders
* Bag Encoders
* Binary and Date Encoders
* category, date, h3, and set encoders
* Wired up encoder schemas
* Switched input feature encoder schema definitions
* Fixed handful of issues
* Fix schema issues
* Refactored a bunch of test configs
* Small changes
* Removed default param from register_encoder
* Schema working now, working on refactoring
* Finished decoder schemas
* Removed default param from register_decoder
* Added some default params to output features and more decoder work
* Refactored all input feature encoder/decoder referencing
* Refactored pretty much all the tests
* Added back constants
* Solved gbm issue
* Fixed save_load test
* various fixes
* Fixed import issue
* Flake 8 and various fixes
* Solved more failed tests
* Refactored missed tests
* Removed commented lines
* Added init file for decoders schema
* Fixed failing tests
* Fixed hyperopt shared params test
* Added backwards compatability logic and test
* Flake 8
* removed comment
* Skipping CTRL Encoder test since it's blasting memory
* Fixed audio_feature test
* Addressed failing tests
* Fixed backwards compatability
* Fixed more failing tests
* Flake 8
* Fixed more tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Refactored default logic for all features
* Fixed H3 weighted_sum encoder wrong type
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix import issue
* Mark slow HF tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fixed defaults tests
* Pin Ray nightly version
* fix link
* pin torch to 07/26
* cleanup
* upgrade ray pinned version to enable parquet partition filtering
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* downgrade Ray to ensure TensorDtypes are not inferred during Ray Dataset <=> Dask conversions
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Removed custom encoder decoder helper method
* unpin torch
* Flake 8
* Daniel feedback
* Small fixes
* Fixed default weights init
* Added test with encoder dependencies for global defaults
* Fixed Arnav's test
* Addressed Arnav's feedback
* Address nit
* Addressed feedback
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address nit
* Fix test
* Initial feedback refactor
* More refactoring
* Added vocab field to all text_encoder configs
* More refactoring
* Fixed more tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix audio feature test, also s/logging/logger.
* param names should start with lowercase s/N/n
* Re-added schema utils used in encoder refactor.
* Removes unused overwrite_defaults()
* Oops, name is passed to feature as a kwarg not a member of the feature config. Why? Probably should change that.
* Change lowercase default back to True. Fixes test_strings_utils
* Set feature validation error with output size 1.
* MLP mixer encoder needs num_channels.
* Use schema.dump instead of .__dict__ to convert marshmallow dataclass to dict
* (x,) in python is a tuple with a single element x. Watch out for this when defining schemas.
* Construct features by using build_single_input/output to share code for deserializing feature configs. Also changes ECD to BaseModel, IMO its confusing to import ECD to use a class method from BaseModel.
* Fix test_trainer_utils, adds convenience method BaseFeature.load_from_dictionary
* Use feature load_from_dictionary instead of BaseModel in feature tests.
* Populate encoder and decoder types in shared test fixtures, fixes error expectations in test_validate_config_combiner.py
* Fixes test_validate_config_misc.py by ensuring only one option of OneOf allows None, because OneOf fails validation if more than one condition match.
* Updates test_defaults.py
* Adds type, column, proc_column to feature schemas. Revert feature tests by passing in config dict again.
* decorate feature base classes with @dataclass, fixes failure building input features in trainer.
* Implement _serialize for PreprocessingDataclassField.
* use type(feature) to get schema class.
* Fix test_trainer_utils.py
* audio_feature requires embedding_size, but passthrough encoder does not have this property. Technically, passthrough encoder is not supported for audio features.
* Wow, apparently the order of elements in the oneOf affects which error message we get from jsonschema.
* Get default encoders from feature schema.
* Get encoder defaults from schema in config_utils.py
* Make number feature allow decoders without clip property
* s/list/List
* Adds reduce_output to h3 encoder.
* Moves decoder params into nested decoder.
* Update processing parameters with computed_fill_value.
* Removes test code.
* Adds input_size to decoder base because some features assume decoders have an input_size
* dense encoder not supported for bag features, changed to embed.
* Adds input_size param to dense encoder schema, since its a required parameter of dense encoder.
* Fixes vector feature input_size in encoder metadata.
* Fixes test reducers, set sequence reduce mode in output feature base.
* Don't nest encoder parameters in decoder
* Fixes test_torchscript, get num_classes from encoder config.
* Audio feature padding is float, not int.
* Adds temp check for threshold to fix GBM tests.
* Adds missing value strategy drop_row for vector feature in test.
* Drop row should work even if computed_fill_value is an empty string
* Removes duplicated TOP_K constant.
* Consolidated set_default_values
* Removes commented-out defaults.
* Remove load_config from OutputFeature, it isn't doing anything here.
* Removes comment.
* Fix type annotations for input/output feature constructors.
* Fixes output feature dependencies being ignored.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Adds test for construction of output features with dependencies.
* Encoder/Decoder config now lives on encoder/decoder object
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fixes decoder params to match their respective classes. Moves fc_stack params and threshold back to output feature.
* Make clip property of number output feature again.
* Adds threshold property to set feature schema, use this property instead of storing it in the decoder.
* input_size in output_feature instead of decoder.
* Made vector_size property of vector_feature.
* Fixed gbm tests
* Fixed flake 8
* Re-adds num_classes as member of category output feature.
* Makes vocab_size match vocab used in preprocessing.
* num_classes in CategoryOutputFeature.
* Moves num_classes from decoder to category output feature.
* Fixes test_model_training_options. Copies fc_layer keys into decoder if they are present on output features.
* Adds field descriptors for fc_layers params in BaseOutputFeatureConfig.
Co-authored-by: connor-mccorm <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: connor-mccorm <[email protected]>
Co-authored-by: Geoffrey Angus <[email protected]>
Co-authored-by: Arnav Garg <[email protected]>
Co-authored-by: Daniel Treiman <[email protected]> | https://github.com/ludwig-ai/ludwig.git | def load_data_for_viz(load_type, model_file_statistics, **kwargs):
supported_load_types = dict(
load_json=load_json,
load_from_file=partial(
load_from_file, dtype=kwargs.get("dtype", int), ground_truth_split=kwargs.get("ground_truth_split", 2)
),
)
loader = supported_load_types[load_type]
try:
stats_per_model = [loader(stats_f) for stats_f in model_file_statistics]
except (TypeError, AttributeError):
logger.exception(f"Unable to open model statistics file {model_file_statistics}!")
raise
return stats_per_model
| 86 | visualize.py | Python | ludwig/visualize.py | 03b4ab273abd7e22a56bb550b56f3d667200abf9 | ludwig | 3 |
|
78,237 | 12 | 10 | 2 | 61 | 8 | 1 | 12 | 17 | classnames | add classnames template tag for generating classnames
- use classnames template tag in shared header template
- add classname as documented variable for the shared header template | https://github.com/wagtail/wagtail.git | def classnames(*classes):
return " ".join([classname.strip() for classname in classes if classname])
@register.simple_tag(takes_context=True) | @register.simple_tag(takes_context=True) | 26 | wagtailadmin_tags.py | Python | wagtail/admin/templatetags/wagtailadmin_tags.py | e2d4cb77458878d7d7076a7aa8b6d590deb99463 | wagtail | 3 |
156,111 | 61 | 14 | 17 | 249 | 30 | 0 | 80 | 183 | repartition | absolufy-imports - No relative - PEP8 (#8796)
Conversation in https://github.com/dask/distributed/issues/5889 | https://github.com/dask/dask.git | def repartition(df, divisions=None, force=False):
token = tokenize(df, divisions)
if isinstance(df, _Frame):
tmp = "repartition-split-" + token
out = "repartition-merge-" + token
dsk = repartition_divisions(
df.divisions, divisions, df._name, tmp, out, force=force
)
graph = HighLevelGraph.from_collections(out, dsk, dependencies=[df])
return new_dd_object(graph, out, df._meta, divisions)
elif is_dataframe_like(df) or is_series_like(df):
name = "repartition-dataframe-" + token
from dask.dataframe.utils import shard_df_on_index
dfs = shard_df_on_index(df, divisions[1:-1])
dsk = {(name, i): df for i, df in enumerate(dfs)}
return new_dd_object(dsk, name, df, divisions)
raise ValueError("Data must be DataFrame or Series")
| 165 | core.py | Python | dask/dataframe/core.py | cccb9d8d8e33a891396b1275c2448c352ef40c27 | dask | 5 |
|
293,984 | 8 | 6 | 7 | 25 | 4 | 0 | 8 | 22 | title | Add update entity platform (#68248)
Co-authored-by: Glenn Waters <[email protected]> | https://github.com/home-assistant/core.git | def title(self) -> str | None:
return self._attr_title
| 14 | __init__.py | Python | homeassistant/components/update/__init__.py | 073fb40b79cf8aa06790fdceb23b6857db888c99 | core | 1 |
|
299,378 | 19 | 8 | 2 | 28 | 3 | 0 | 19 | 40 | shuffle | Improve repeat and shuffle support for Squeezebox (#70941) | https://github.com/home-assistant/core.git | def shuffle(self):
# Squeezebox has a third shuffle mode (album) not recognized by Home Assistant
return self._player.shuffle == "song"
| 14 | media_player.py | Python | homeassistant/components/squeezebox/media_player.py | 0264f060e4fc988f3a0442ba8f951677816c11ea | core | 1 |
|
255,418 | 4 | 6 | 9 | 16 | 2 | 0 | 4 | 11 | test_case_connect_partially_no_name_collision | Use Python type annotations rather than comments (#3962)
* These have been supported since Python 3.5.
ONNX doesn't support Python < 3.6, so we can use the annotations.
Diffs generated by https://pypi.org/project/com2ann/.
Signed-off-by: Gary Miguel <[email protected]>
* Remove MYPY conditional logic in gen_proto.py
It breaks the type annotations and shouldn't be needed.
Signed-off-by: Gary Miguel <[email protected]>
* Get rid of MYPY bool from more scripts
Signed-off-by: Gary Miguel <[email protected]>
* move Descriptors class above where its referenced in type annotation
Signed-off-by: Gary Miguel <[email protected]>
* fixes
Signed-off-by: Gary Miguel <[email protected]>
* remove extra blank line
Signed-off-by: Gary Miguel <[email protected]>
* fix type annotations
Signed-off-by: Gary Miguel <[email protected]>
* fix type annotation in gen_docs
Signed-off-by: Gary Miguel <[email protected]>
* fix Operators.md
Signed-off-by: Gary Miguel <[email protected]>
* fix TestCoverage.md
Signed-off-by: Gary Miguel <[email protected]>
* fix protoc-gen-mypy.py
Signed-off-by: Gary Miguel <[email protected]> | https://github.com/onnx/onnx.git | def test_case_connect_partially_no_name_collision(self) -> None:
| 37 | compose_test.py | Python | onnx/test/compose_test.py | 83fa57c74edfd13ddac9548b8a12f9e3e2ed05bd | onnx | 1 |
|
35,663 | 9 | 9 | 3 | 38 | 6 | 0 | 9 | 34 | freeze_base_model | Add Data2Vec (#15507)
* Add data2vec model cloned from roberta
* Add checkpoint conversion script
* Fix copies
* Update docs
* Add checkpoint conversion script
* Remove fairseq data2vec_text script and fix format
* Add comment on where to get data2vec_text.py
* Remove mock implementation cheat.py and fix style
* Fix copies
* Remove TF and Flax classes from init
* Add back copy from fairseq data2vec_text.py and fix style
* Update model name in docs/source/index.mdx to be CamelCase
* Revert model name in table to lower-case to get check_table test to pass
* Update src/transformers/models/data2vec/__init__.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/convert_data2vec_original_pytorch_checkpoint_to_pytorch.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update docs/source/model_doc/data2vec.mdx
Co-authored-by: Sylvain Gugger <[email protected]>
* Update docs/source/model_doc/data2vec.mdx
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/auto/configuration_auto.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/configuration_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update tests/test_modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/configuration_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update documentation
* Copy-paste Data2VecConfig from BertConfig
* Update config checkpoint to point to edugp/data2vec-nlp-base. Fix style and repo-consistency
* Update config special tokens to match RoBERTa
* Split multiple assertions and add individual error messages
* Rename Data2VecModel to Data2VecForTextModel
* Add Data2Vec to _toctree.yml
* Rename Data2VecEmbeddings to Data2VecForTextEmbeddings
* Add initial Data2VecForAudio model (unfinished). Only matching fairseq's implementation up to the feature encoder (before positional encoding).
* finish audio model
* finish audio file
* Update names and fix style, quality and repo consistency
* Remove Data2VecAudioForPretraining. Add tests for Data2VecAudio, mimicking the Wav2Vec2 test suite. Fix bias initilization in positional conv layers. Move back configurations for audio and text to separate files.
* add inputs to logits to data2vec'
* correct autio models
* correct config auto
* correct tok auto
* Update utils/tests_fetcher.py
* delete unnecessary files
* delete unnecessary files
* further renaming
* make all tests pass
* finish
* remove useless test file
* Update tests/test_modeling_common.py
* Update utils/check_repo.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec_text.py
Co-authored-by: Patrick von Platen <[email protected]>
* Fix copies
* Update docs
* Remove fairseq data2vec_text script and fix format
* Add comment on where to get data2vec_text.py
* Remove mock implementation cheat.py and fix style
* Fix copies
* Remove TF and Flax classes from init
* Add back copy from fairseq data2vec_text.py and fix style
* Update model name in docs/source/index.mdx to be CamelCase
* Revert model name in table to lower-case to get check_table test to pass
* Update documentation
* Update src/transformers/models/data2vec/__init__.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/convert_data2vec_original_pytorch_checkpoint_to_pytorch.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/auto/configuration_auto.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/configuration_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update tests/test_modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/configuration_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Copy-paste Data2VecConfig from BertConfig
* Update config checkpoint to point to edugp/data2vec-nlp-base. Fix style and repo-consistency
* Update config special tokens to match RoBERTa
* Split multiple assertions and add individual error messages
* Rename Data2VecModel to Data2VecForTextModel
* Add Data2Vec to _toctree.yml
* Rename Data2VecEmbeddings to Data2VecForTextEmbeddings
* Add initial Data2VecForAudio model (unfinished). Only matching fairseq's implementation up to the feature encoder (before positional encoding).
* finish audio model
* finish audio file
* add inputs to logits to data2vec'
* Update names and fix style, quality and repo consistency
* Remove Data2VecAudioForPretraining. Add tests for Data2VecAudio, mimicking the Wav2Vec2 test suite. Fix bias initilization in positional conv layers. Move back configurations for audio and text to separate files.
* correct autio models
* correct config auto
* correct tok auto
* delete unnecessary files
* delete unnecessary files
* Update utils/tests_fetcher.py
* further renaming
* make all tests pass
* finish
* remove useless test file
* Update tests/test_modeling_common.py
* Update utils/check_repo.py
Co-authored-by: Patrick von Platen <[email protected]>
* Update src/transformers/models/data2vec/modeling_data2vec_text.py
Co-authored-by: Patrick von Platen <[email protected]>
* Move data2vec tests to new structure
* Fix test imports for text tests
* Remove fairseq files
* Change paper link to arxiv
* Modify Data2Vec documentation to reflect that the encoder is not shared across the audio and text models in the current implementation.
* Update text model checkpoint to be facebook/data2vec-text-base
* Add 'Copy from' statements and update paper links and docs
* fix copy from statements
* improve copied from
* correct more copied from statements
* finish copied from stuff
* make style
* add model to README
* add to master
Co-authored-by: Eduardo Gonzalez Ponferrada <[email protected]>
Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: Sylvain Gugger <[email protected]> | https://github.com/huggingface/transformers.git | def freeze_base_model(self):
for param in self.data2vec_audio.parameters():
param.requires_grad = False
| 22 | modeling_data2vec_audio.py | Python | src/transformers/models/data2vec/modeling_data2vec_audio.py | df5a4094a6e3f98f2cb2058cdb688fcc3f453220 | transformers | 2 |
|
244,211 | 11 | 6 | 10 | 18 | 4 | 0 | 11 | 17 | split_batch | [Tools] Support respliting data_batch with tag (#7641)
* support respliting data_batch with tag
* add citations
* add a unit test
* fix lint | https://github.com/open-mmlab/mmdetection.git | def split_batch(img, img_metas, kwargs):
# only stack img in the batch | 94 | split_batch.py | Python | mmdet/utils/split_batch.py | c6f467fe9baccc281b0695368c1eae14d5d21fd5 | mmdetection | 4 |
|
181,691 | 16 | 10 | 11 | 69 | 14 | 0 | 16 | 77 | test_memory_6 | Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | https://github.com/EpistasisLab/tpot.git | def test_memory_6():
tpot_obj = TPOTClassifier(
random_state=42,
population_size=1,
offspring_size=2,
generations=1,
config_dict='TPOT light',
memory=str,
verbosity=0
)
assert_raises(ValueError, tpot_obj._setup_memory)
| 45 | tpot_tests.py | Python | tests/tpot_tests.py | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | 1 |
|
181,726 | 9 | 9 | 3 | 34 | 5 | 0 | 9 | 18 | test_conf_dict_2 | Revert "Deployed 7ccda9a with MkDocs version: 1.3.0"
This reverts commit bd9629c40e01241766197119b581a99409b07068. | https://github.com/EpistasisLab/tpot.git | def test_conf_dict_2():
tpot_obj = TPOTClassifier(config_dict=tpot_mdr_classifier_config_dict)
assert tpot_obj.config_dict == tpot_mdr_classifier_config_dict
| 19 | tpot_tests.py | Python | tests/tpot_tests.py | 388616b6247ca4ea8de4e2f340d6206aee523541 | tpot | 1 |
|
281,429 | 65 | 18 | 37 | 334 | 28 | 0 | 75 | 258 | get_defi_protocols | Features(crypto): added new commands to defi menu (#1169)
* adding new defi features and refactoring existing ones
* added tests and docs
* added zlot to ignored words
* markdown lint
* fixed pr issues
* fix hugo main.yml
* fix hugo main.yml
* added tests
* fixed prt issue
* added iv_surface failing tests
* added mocking to llama tests
* new tests
* skipped rich test for now | https://github.com/OpenBB-finance/OpenBBTerminal.git | def get_defi_protocols() -> pd.DataFrame:
response = requests.get(API_URL + "/protocols")
columns = [
"name",
"symbol",
"category",
"chains",
"change_1h",
"change_1d",
"change_7d",
"tvl",
"url",
"description",
"chain",
]
if response.status_code != 200:
raise Exception(f"Status code: {response.status_code}. Reason: {response.text}")
try:
df = pd.DataFrame(response.json())
df.replace({float(np.nan): None}, inplace=True)
df["chains"] = df["chains"].apply(
lambda x: "\n".join(textwrap.wrap(", ".join(x), width=50))
)
df["description"] = df["description"].apply(
lambda x: "\n".join(textwrap.wrap(x, width=70)) if isinstance(x, str) else x
)
return df[columns]
except Exception as e:
raise ValueError("Wrong response type\n") from e
| 184 | llama_model.py | Python | gamestonk_terminal/cryptocurrency/defi/llama_model.py | d334d5e0878961d2b6cfda82271693d457047bee | OpenBBTerminal | 4 |
|
126,674 | 27 | 13 | 10 | 112 | 14 | 0 | 32 | 110 | test_failed_runtime_env_setup | Convert job_manager to be async (#27123)
Updates jobs api
Updates snapshot api
Updates state api
Increases jobs api version to 2
Signed-off-by: Alan Guo [email protected]
Why are these changes needed?
follow-up for #25902 (comment) | https://github.com/ray-project/ray.git | async def test_failed_runtime_env_setup(self, job_manager):
run_cmd = f"python {_driver_script_path('override_env_var.py')}"
job_id = await job_manager.submit_job(
entrypoint=run_cmd, runtime_env={"working_dir": "s3://does_not_exist.zip"}
)
await async_wait_for_condition_async_predicate(
check_job_failed, job_manager=job_manager, job_id=job_id
)
data = await job_manager.get_job_info(job_id)
assert "runtime_env setup failed" in data.message
| 59 | test_job_manager.py | Python | dashboard/modules/job/tests/test_job_manager.py | 326b5bd1acc6d3d00ab0546e4ae45da6bed501f7 | ray | 1 |
|
48,723 | 2 | 6 | 6 | 13 | 2 | 0 | 2 | 9 | test_empty_html_checkbox_not_required | Fix BooleanField's allow_null behavior (#8614)
* Fix BooleanField's allow_null behavior
* Update rest_framework.fields
- Use .get with default value for 'allow_null' kwarg in BooleanField's
init | https://github.com/encode/django-rest-framework.git | def test_empty_html_checkbox_not_required(self):
| 51 | test_fields.py | Python | tests/test_fields.py | 1fbe16a8d26ff5be64797cafb7004898f72ca52b | django-rest-framework | 1 |
|
130,091 | 89 | 12 | 6 | 125 | 14 | 0 | 118 | 151 | get_incremental_data | [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 get_incremental_data(self, day=0):
start = self._get_day_slice(day - 1)
end = self._get_day_slice(day)
available_data = Subset(self.dataset, list(range(start, end)))
train_n = int(0.8 * (end - start)) # 80% train data, 20% validation data
return random_split(available_data, [train_n, end - start - train_n])
#######################################################################
# PyTorch neural network classifier
# ---------------------------------
# Next, we will introduce our PyTorch neural network model and the
# train and test function. These are adapted directly from
# our :doc:`PyTorch MNIST example </tune/examples/mnist_pytorch>`.
# We only introduced an additional neural network layer with a configurable
# layer size. This is not strictly needed for learning good performance on
# MNIST, but it is useful to demonstrate scenarios where your hyperparameter
# search space affects the model complexity. | 75 | tune-serve-integration-mnist.py | Python | doc/source/tune/_tutorials/tune-serve-integration-mnist.py | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | 1 |
|
45,040 | 7 | 8 | 16 | 26 | 6 | 0 | 7 | 21 | test_set_serialize_call_old_signature | Add params dag_id, task_id etc to XCom.serialize_value (#19505)
When implementing a custom XCom backend, in order to store XCom objects organized by dag_id, run_id etc, we need to pass those params to `serialize_value`. | https://github.com/apache/airflow.git | def test_set_serialize_call_old_signature(self, get_import, session):
serialize_watcher = MagicMock()
| 82 | test_xcom.py | Python | tests/models/test_xcom.py | 56285eee04285d8b6fac90911248d7e9dd5504d8 | airflow | 1 |
|
120,210 | 9 | 8 | 2 | 31 | 3 | 0 | 9 | 11 | mock_4x8x16_devices | [mesh_utils] Support creating device meshes for hybrid networks
Also makes some NFCs to other mesh_utils code.
PiperOrigin-RevId: 442581767 | https://github.com/google/jax.git | def mock_4x8x16_devices(one_device_per_chip):
return mock_devices(4, 8, 16, 'TPU v4', one_device_per_chip)
| 19 | mesh_utils_test.py | Python | tests/mesh_utils_test.py | 3f9e45e0c5b035de27b14588cd3b4cfd5f3c1f04 | jax | 1 |
|
248,552 | 30 | 10 | 20 | 135 | 14 | 0 | 49 | 247 | test_random_users_cannot_send_state_before_first_pl | EventAuthTestCase: build events for the right room version
In practice, when we run the auth rules, all of the events have the right room
version. Let's stop building Room V1 events for these tests and use the right
version. | https://github.com/matrix-org/synapse.git | def test_random_users_cannot_send_state_before_first_pl(self):
creator = "@creator:example.com"
joiner = "@joiner:example.com"
auth_events = [
_create_event(RoomVersions.V1, creator),
_join_event(RoomVersions.V1, creator),
_join_event(RoomVersions.V1, joiner),
]
# creator should be able to send state
event_auth.check_auth_rules_for_event(
RoomVersions.V1,
_random_state_event(RoomVersions.V1, creator),
auth_events,
)
# joiner should not be able to send state
self.assertRaises(
AuthError,
event_auth.check_auth_rules_for_event,
RoomVersions.V1,
_random_state_event(RoomVersions.V1, joiner),
auth_events,
)
| 89 | test_event_auth.py | Python | tests/test_event_auth.py | 2959184a42398277ff916206235b844a8f7be5d7 | synapse | 1 |
|
276,845 | 23 | 8 | 4 | 41 | 4 | 0 | 25 | 71 | get | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def get(self, object_id):
# Explicitly check for `None` internally to make external calling code a
# bit cleaner.
if object_id is None:
return
return self._obj_ids_to_obj.get(object_id)
| 23 | generic_utils.py | Python | keras/utils/generic_utils.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 2 |
|
274,615 | 20 | 12 | 9 | 89 | 8 | 0 | 24 | 67 | get | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | https://github.com/keras-team/keras.git | def get(identifier):
if isinstance(identifier, dict):
return deserialize(identifier)
elif isinstance(identifier, str):
return deserialize(str(identifier))
elif callable(identifier):
return identifier
else:
raise ValueError(f"Could not interpret metric identifier: {identifier}")
| 51 | __init__.py | Python | keras/metrics/__init__.py | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | 4 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.