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55,049 | 217,975 | 109 | python3.10.4/Lib/imaplib.py | 39 | 8 | def xatom(self, name, *args):
name = name.upper()
#if not name in self.capabilities: # Let the server decide!
# raise self.error('unknown extension command: %s' % name)
if not name in Commands:
Commands[name] = (self.state,)
return self._simple_command(name, *args)
# Private | add python 3.10.4 for windows | xatom | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | imaplib.py | 10 | 5 | https://github.com/XX-net/XX-Net.git | 2 | 45 | 0 | 32 | 75 | Python | {
"docstring": "Allow simple extension commands\n notified by server in CAPABILITY response.\n\n Assumes command is legal in current state.\n\n (typ, [data]) = <instance>.xatom(name, arg, ...)\n\n Returns response appropriate to extension command `name'.\n ",
"language": "en",
"n_whitespaces": 73,
"n_words": 30,
"vocab_size": 27
} | def xatom(self, name, *args):
name = name.upper()
#if not name in self.capabilities: # Let the server decide!
# raise self.error('unknown extension command: %s' % name)
if not name in Commands:
Commands[name] = (self.state,)
return self._simple_command(name, *args)
# Private methods
|
|
199 | 1,493 | 69 | packages/syft/src/syft/core/tensor/nn/loss.py | 34 | 11 | def forward(self, outputs, targets):
outputs = outputs.clip(self.epsilon, 1 - self.epsilon)
log_loss = targets * dp_log(outputs) + ((targets * -1) + 1) * dp_log((outputs * -1) + 1)
log_loss = log_loss.sum(axi | Moved all code from notebook to codebase
Took 19 minutes | forward | f3b8f6f1196e6f8a92620b4efc190715273fecab | PySyft | loss.py | 14 | 5 | https://github.com/OpenMined/PySyft.git | 1 | 76 | 0 | 23 | 123 | Python | {
"docstring": "Forward pass.\n\n .. math:: L = -t \\\\log(p) - (1 - t) \\\\log(1 - p)\n\n Parameters\n ----------\n outputs : numpy.array\n Predictions in (0, 1), such as sigmoidal output of a neural network.\n targets : numpy.array\n Targets in [0, 1], such as ground truth labels.\n ",
"language": "en",
"n_whitespaces": 108,
"n_words": 44,
"vocab_size": 37
} | def forward(self, outputs, targets):
outputs = outputs.clip(self.epsilon, 1 - self.epsilon)
log_loss = targets * dp_log(outputs) + ((targets * -1) + 1) * dp_log((outputs * -1) + 1)
log_loss = log_loss.sum(axis=1) * -1
return log_loss.mean()
|
|
41,834 | 176,320 | 61 | networkx/algorithms/assortativity/correlation.py | 36 | 13 | def numeric_assortativity_coefficient(G, attribute, nodes=None):
if nodes is None:
nodes | MAINT: Cleanup assortativity module, remove unused variables (#5301)
Remove unused variables, sort imports,
raise errors instead of accepting invalid arguments silently
Co-authored-by: Dan Schult <[email protected]> | numeric_assortativity_coefficient | 34d9d630bb02426d297d3e20fedb7da8c3ced03a | networkx | correlation.py | 10 | 7 | https://github.com/networkx/networkx.git | 4 | 75 | 0 | 28 | 111 | Python | {
"docstring": "Compute assortativity for numerical node attributes.\n\n Assortativity measures the similarity of connections\n in the graph with respect to the given numeric attribute.\n\n Parameters\n ----------\n G : NetworkX graph\n\n attribute : string\n Node attribute key.\n\n nodes: list or iterable (optional)\n Compute numeric assortativity only for attributes of nodes in\n container. The default is all nodes.\n\n Returns\n -------\n r: float\n Assortativity of graph for given attribute\n\n Examples\n --------\n >>> G = nx.Graph()\n >>> G.add_nodes_from([0, 1], size=2)\n >>> G.add_nodes_from([2, 3], size=3)\n >>> G.add_edges_from([(0, 1), (2, 3)])\n >>> print(nx.numeric_assortativity_coefficient(G, \"size\"))\n 1.0\n\n Notes\n -----\n This computes Eq. (21) in Ref. [1]_ , which is the Pearson correlation\n coefficient of the specified (scalar valued) attribute across edges.\n\n References\n ----------\n .. [1] M. E. J. Newman, Mixing patterns in networks\n Physical Review E, 67 026126, 2003\n ",
"language": "en",
"n_whitespaces": 244,
"n_words": 129,
"vocab_size": 99
} | def numeric_assortativity_coefficient(G, attribute, nodes=None):
if nodes is None:
nodes = G.nodes
vals = {G.nodes[n][attribute] for n in nodes}
mapping = {d: i for i, d, in enumerate(vals)}
M = attribute_mixing_matrix(G, attribute, nodes, mapping)
return _numeric_ac(M, mapping)
|
|
80,425 | 270,311 | 25 | keras/distribute/distributed_file_utils.py | 13 | 11 | def write_filepath(filepath, strategy):
dir | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | write_filepath | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | distributed_file_utils.py | 9 | 4 | https://github.com/keras-team/keras.git | 1 | 44 | 0 | 12 | 70 | Python | {
"docstring": "Returns the writing file path to be used to save file distributedly.\n\n Directory to contain `filepath` would be created if it doesn't exist.\n\n Args:\n filepath: Original filepath that would be used without distribution.\n strategy: The tf.distribute strategy object currently used.\n\n Returns:\n The writing filepath that should be used to save file with distribution.\n ",
"language": "en",
"n_whitespaces": 80,
"n_words": 53,
"vocab_size": 36
} | def write_filepath(filepath, strategy):
dirpath = os.path.dirname(filepath)
base = os.path.basename(filepath)
return os.path.join(write_dirpath(dirpath, strategy), base)
|
|
@add_start_docstrings(
"The bare ConvNext model outputting raw features without any specific head on top.",
CONVNEXT_START_DOCSTRING,
) | 5,922 | 32,423 | 53 | src/transformers/models/convnext/modeling_tf_convnext.py | 30 | 9 | def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
CONVNEXT_START_DOCSTRING = r
CONVNEXT_INPUTS_DOCSTRING = r
@add_start_docstrings(
"The bare | Update serving code to enable `saved_model=True` (#18153)
* Add serving_output and serving methods to some vision models
* Add serving outputs for DeiT
* Don't convert hidden states - differing shapes
* Make saveable
* Fix up
* Make swin saveable
* Add in tests
* Fix funnel tests (can't convert to tensor)
* Fix numpy call
* Tidy up a bit
* Add in hidden states - resnet
* Remove numpy
* Fix failing tests - tensor shape and skipping tests
* Remove duplicated function
* PR comments - formatting and var names
* PR comments
Add suggestions made by Joao Gante:
* Use tf.shape instead of shape_list
* Use @tooslow decorator on tests
* Simplify some of the logic
* PR comments
Address Yih-Dar Sheih comments - making tensor names consistent and make types float
* Types consistent with docs; disable test on swin (slow)
* CI trigger
* Change input_features to float32
* Add serving_output for segformer
* Fixup
Co-authored-by: Amy Roberts <[email protected]> | serving | 8e8384663d716d4b5a4f510070ff954fc0ba4a52 | transformers | modeling_tf_convnext.py | 8 | 3 | https://github.com/huggingface/transformers.git | 1 | 23 | 1 | 27 | 67 | Python | {
"docstring": "\n Method used for serving the model.\n\n Args:\n inputs (`Dict[str, tf.Tensor]`):\n The input of the saved model as a dictionary of tensors.\n \n This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the\n library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n etc.)\n\n This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it\n as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and\n behavior.\n\n <Tip>\n\n TF 2.0 models accepts two formats as inputs:\n\n - having all inputs as keyword arguments (like PyTorch models), or\n - having all inputs as a list, tuple or dict in the first positional arguments.\n\n This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the\n tensors in the first argument of the model call function: `model(inputs)`.\n\n </Tip>\n\n Parameters:\n config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n\n Args:\n pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`ConvNextFeatureExtractor`]. See\n [`ConvNextFeatureExtractor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail. This argument can be used only in eager mode, in graph mode the value in the config will be\n used instead.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in\n eager mode, in graph mode the value will always be set to True.\n",
"language": "en",
"n_whitespaces": 518,
"n_words": 298,
"vocab_size": 171
} | def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
CONVNEXT_START_DOCSTRING = r
CONVNEXT_INPUTS_DOCSTRING = r
@add_start_docstrings(
"The bare ConvNext model outputting raw features without any specific head on top.",
CONVNEXT_START_DOCSTRING,
) |
55,333 | 218,477 | 224 | python3.10.4/Lib/inspect.py | 71 | 12 | def getclasstree(classes, unique=False):
children = {}
roots = []
for c in classes:
if c.__bases__:
for parent in c.__bases__:
if parent not in children:
children[parent] = []
if c not in children[parent]:
children[parent].append(c)
if unique and parent in classes: break
elif c not in roots:
roots.append(c)
for parent in | add python 3.10.4 for windows | getclasstree | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | XX-Net | inspect.py | 16 | 17 | https://github.com/XX-net/XX-Net.git | 11 | 112 | 0 | 41 | 191 | Python | {
"docstring": "Arrange the given list of classes into a hierarchy of nested lists.\n\n Where a nested list appears, it contains classes derived from the class\n whose entry immediately precedes the list. Each entry is a 2-tuple\n containing a class and a tuple of its base classes. If the 'unique'\n argument is true, exactly one entry appears in the returned structure\n for each class in the given list. Otherwise, classes using multiple\n inheritance and their descendants will appear multiple times.",
"language": "en",
"n_whitespaces": 98,
"n_words": 78,
"vocab_size": 53
} | def getclasstree(classes, unique=False):
children = {}
roots = []
for c in classes:
if c.__bases__:
for parent in c.__bases__:
if parent not in children:
children[parent] = []
if c not in children[parent]:
children[parent].append(c)
if unique and parent in classes: break
elif c not in roots:
roots.append(c)
for parent in children:
if parent not in classes:
roots.append(parent)
return walktree(roots, children, None)
# ------------------------------------------------ argument list extraction
Arguments = namedtuple('Arguments', 'args, varargs, varkw')
|
|
12,574 | 61,435 | 31 | .venv/lib/python3.8/site-packages/pip/_internal/vcs/versioncontrol.py | 10 | 4 | def get_revision(cls, location):
# type: (s | upd; format | get_revision | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | versioncontrol.py | 6 | 2 | https://github.com/jindongwang/transferlearning.git | 1 | 10 | 0 | 10 | 19 | Python | {
"docstring": "\n Return the current commit id of the files at the given location.\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 12,
"vocab_size": 10
} | def get_revision(cls, location):
# type: (str) -> str
raise NotImplementedError
|
|
11,185 | 55,038 | 42 | src/prefect/settings.py | 20 | 9 | def get_current_settings() -> Settings:
from prefect.context import ProfileContext
profile = ProfileCo | Rewrite temporary settings to use copy_with_update | get_current_settings | 95b47e807fa5ccc626a06efc2cced0d8ff8eadfa | prefect | settings.py | 8 | 10 | https://github.com/PrefectHQ/prefect.git | 2 | 34 | 0 | 18 | 58 | Python | {
"docstring": "\n Returns a settings object populated with values from the current profile or, if no\n profile is active, the environment.\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 19,
"vocab_size": 17
} | def get_current_settings() -> Settings:
from prefect.context import ProfileContext
profile = ProfileContext.get()
if profile is not None:
return profile.settings
return get_settings_from_env()
|
|
46,020 | 189,215 | 456 | tests/unit/customizations/s3/test_comparator.py | 101 | 31 | def test_compare_key_greater(self):
self.not_at_dest_sync_strategy.determine_should_sync.return_value = False
# Try when the sync strategy says to sync the file.
self.not_at_src_syn | Delete extra whitespace
A correction that does not affect the operation. | test_compare_key_greater | 8a16d7d8ce5e3f97fb100af7a960224f7f80137d | aws-cli | test_comparator.py | 10 | 30 | https://github.com/aws/aws-cli.git | 3 | 230 | 0 | 53 | 378 | Python | {
"docstring": "\n Confirm the appropriate action is taken when the soruce compare key\n is greater than the destination compare key.\n ",
"language": "en",
"n_whitespaces": 40,
"n_words": 18,
"vocab_size": 14
} | def test_compare_key_greater(self):
self.not_at_dest_sync_strategy.determine_should_sync.return_value = False
# Try when the sync strategy says to sync the file.
self.not_at_src_sync_strategy.determine_should_sync.return_value = True
src_files = []
dest_files = []
ref_list = []
result_list = []
time = datetime.datetime.now()
src_file = FileStat(src='', dest='',
compare_key='domparator_test.py', size=10,
last_update=time, src_type='local',
dest_type='s3', operation_name='upload')
dest_file = FileStat(src='', dest='',
compare_key='comparator_test.py', size=10,
last_update=time, src_type='s3',
dest_type='local', operation_name='')
src_files.append(src_file)
dest_files.append(dest_file)
ref_list.append(dest_file)
files = self.comparator.call(iter(src_files), iter(dest_files))
for filename in files:
result_list.append(filename)
self.assertEqual(result_list, ref_list)
# Now try when the sync strategy says not to sync the file.
self.not_at_src_sync_strategy.determine_should_sync.return_value = False
result_list = []
ref_list = []
files = self.comparator.call(iter(src_files), iter(dest_files))
for filename in files:
result_list.append(filename)
self.assertEqual(result_list, ref_list)
|
|
50,133 | 202,469 | 95 | tests/custom_lookups/tests.py | 20 | 18 | def test_custom_exact_lookup_none_rhs(self):
field = Author._meta.get_field("birthdate")
OldExactLookup = field.get_lookup("exact")
author = Author.objects.create(name="author", birthdate=None)
try:
field.register_lookup(Exactly, "exact" | Refs #33476 -- Reformatted code with Black. | test_custom_exact_lookup_none_rhs | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | tests.py | 12 | 9 | https://github.com/django/django.git | 2 | 77 | 0 | 17 | 134 | Python | {
"docstring": "\n __exact=None is transformed to __isnull=True if a custom lookup class\n with lookup_name != 'exact' is registered as the `exact` lookup.\n ",
"language": "en",
"n_whitespaces": 42,
"n_words": 20,
"vocab_size": 19
} | def test_custom_exact_lookup_none_rhs(self):
field = Author._meta.get_field("birthdate")
OldExactLookup = field.get_lookup("exact")
author = Author.objects.create(name="author", birthdate=None)
try:
field.register_lookup(Exactly, "exact")
self.assertEqual(Author.objects.get(birthdate__exact=None), author)
finally:
field.register_lookup(OldExactLookup, "exact")
|
|
1,621 | 9,461 | 127 | reconstruction/ostec/external/stylegan2/metrics/precision_recall.py | 63 | 13 | def batch_pairwise_distances(U, V):
with tf.variable_scope('pairwise_dist_block'):
# Squared norms of each row in U and V.
norm_u = tf.reduce_sum(tf.square(U), 1)
norm_v = tf.reduce_sum(tf.square(V), 1)
# norm_u as a row and norm_v as a column vectors.
norm_u = tf.reshape(norm_u, [-1, 1])
norm_v = tf.reshape(norm_v, [1, -1])
# Pairwise squared Euclidean distances.
D = tf.maximum(norm_u - 2*tf.matmul(U, V, False, True) + norm_v, 0.0)
return D
#----------------------------- | initialize ostec | batch_pairwise_distances | 7375ee364e0df2a417f92593e09557f1b2a3575a | insightface | precision_recall.py | 15 | 8 | https://github.com/deepinsight/insightface.git | 1 | 107 | 0 | 47 | 167 | Python | {
"docstring": " Compute pairwise distances between two batches of feature vectors.",
"language": "en",
"n_whitespaces": 9,
"n_words": 9,
"vocab_size": 9
} | def batch_pairwise_distances(U, V):
with tf.variable_scope('pairwise_dist_block'):
# Squared norms of each row in U and V.
norm_u = tf.reduce_sum(tf.square(U), 1)
norm_v = tf.reduce_sum(tf.square(V), 1)
# norm_u as a row and norm_v as a column vectors.
norm_u = tf.reshape(norm_u, [-1, 1])
norm_v = tf.reshape(norm_v, [1, -1])
# Pairwise squared Euclidean distances.
D = tf.maximum(norm_u - 2*tf.matmul(U, V, False, True) + norm_v, 0.0)
return D
#----------------------------------------------------------------------------
|
|
5,702 | 31,219 | 109 | src/transformers/image_utils.py | 27 | 11 | def expand_dims(self, image):
self._ensure_format | Enable crop_center method to handle (W, H, C) images (#17626)
* enable crop_center method to handle (W, H, C) images
* minor style and comment edits | expand_dims | 49becbaa5549b477b0d96c55f207614773c0ab42 | transformers | image_utils.py | 12 | 9 | https://github.com/huggingface/transformers.git | 3 | 58 | 0 | 19 | 95 | Python | {
"docstring": "\n Expands 2-dimensional `image` to 3 dimensions.\n\n Args:\n image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):\n The image to expand.\n ",
"language": "en",
"n_whitespaces": 65,
"n_words": 17,
"vocab_size": 14
} | def expand_dims(self, image):
self._ensure_format_supported(image)
# Do nothing if PIL image
if isinstance(image, PIL.Image.Image):
return image
if is_torch_tensor(image):
image = image.unsqueeze(0)
else:
image = np.expand_dims(image, axis=0)
return image
|
|
9,128 | 47,487 | 238 | tests/jobs/test_scheduler_job.py | 68 | 38 | def test_find_executable_task_instances_order_execution_date(self, dag_maker):
dag_id_1 = 'SchedulerJobTest.test_find_executable_task_instances_or | Replace usage of `DummyOperator` with `EmptyOperator` (#22974)
* Replace usage of `DummyOperator` with `EmptyOperator` | test_find_executable_task_instances_order_execution_date | 49e336ae0302b386a2f47269a6d13988382d975f | airflow | test_scheduler_job.py | 13 | 22 | https://github.com/apache/airflow.git | 3 | 193 | 0 | 48 | 314 | Python | {
"docstring": "\n Test that task instances follow execution_date order priority. If two dagruns with\n different execution dates are scheduled, tasks with earliest dagrun execution date will first\n be executed\n ",
"language": "en",
"n_whitespaces": 56,
"n_words": 27,
"vocab_size": 25
} | def test_find_executable_task_instances_order_execution_date(self, dag_maker):
dag_id_1 = 'SchedulerJobTest.test_find_executable_task_instances_order_execution_date-a'
dag_id_2 = 'SchedulerJobTest.test_find_executable_task_instances_order_execution_date-b'
task_id = 'task-a'
session = settings.Session()
with dag_maker(dag_id=dag_id_1, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id)
dr1 = dag_maker.create_dagrun(execution_date=DEFAULT_DATE + timedelta(hours=1))
with dag_maker(dag_id=dag_id_2, max_active_tasks=16, session=session):
EmptyOperator(task_id=task_id)
dr2 = dag_maker.create_dagrun()
dr1 = session.merge(dr1, load=False)
self.scheduler_job = SchedulerJob(subdir=os.devnull)
tis = dr1.task_instances + dr2.task_instances
for ti in tis:
ti.state = State.SCHEDULED
session.merge(ti)
session.flush()
res = self.scheduler_job._executable_task_instances_to_queued(max_tis=1, session=session)
session.flush()
assert [ti.key for ti in res] == [tis[1].key]
session.rollback()
|
|
18,290 | 87,377 | 332 | src/sentry/web/frontend/base.py | 89 | 30 | def dispatch(self, request, *args, **kwargs):
self.determine_active_organization(request, kwargs.get("organization_slug", None))
if self.csrf_protect:
if hasattr(self.dispatch.__func__, "csrf_exempt"):
delattr(self.dispatch.__func__, "csrf_exempt")
response = self.test_csrf(request)
if response:
return response
if self.is_auth_required(request, *args, **kwargs):
return self.handle_auth_required(request, *args, **kwargs)
if self.is_sudo_required(request, *args, **kwargs):
return self.handle_sudo_required(request, *a | chore(hybrid-cloud): Refactor Organization ORM out of views and auth (#40362)
For hybrid cloud, the organization and related models will not exist in the control silo, but will be necessary for certain auth related flows. This change is the first of many to make the core auth flows compatible with a split silo world by introducing a service object that captures existing needs for an organization arond the `get_active_organization` method. Behavior should remain identical, except that the pure ORM object is not available in many places. Those places have been updated to use a new thinner model object that corresponds with future control silo's data availability.
Co-authored-by: getsantry[bot] <66042841+getsantry[bot]@users.noreply.github.com> | dispatch | a882713d1b8fc6f30ba7e8717252334d6720caa9 | sentry | base.py | 13 | 25 | https://github.com/getsentry/sentry.git | 10 | 268 | 0 | 47 | 415 | Python | {
"docstring": "\n A note on the CSRF protection process.\n\n Because the CSRF decorators don't work well with view subclasses, we\n allow them to control whether a CSRF check is done by setting\n self.csrf_protect. This has a couple of implications:\n\n 1. We need to mark this method as @csrf_exempt so that when the CSRF\n middleware checks it as part of the regular middleware sequence, it\n always passes.\n 2. If self.csrf_protect is set, we will re-run the CSRF check ourselves\n using CsrfViewMiddleware().process_view()\n 3. But first we must remove the csrf_exempt attribute that was set by\n the decorator so that the middleware doesn't shortcut and pass the\n check unconditionally again.\n\n ",
"language": "en",
"n_whitespaces": 212,
"n_words": 105,
"vocab_size": 77
} | def dispatch(self, request, *args, **kwargs):
self.determine_active_organization(request, kwargs.get("organization_slug", None))
if self.csrf_protect:
if hasattr(self.dispatch.__func__, "csrf_exempt"):
delattr(self.dispatch.__func__, "csrf_exempt")
response = self.test_csrf(request)
if response:
return response
if self.is_auth_required(request, *args, **kwargs):
return self.handle_auth_required(request, *args, **kwargs)
if self.is_sudo_required(request, *args, **kwargs):
return self.handle_sudo_required(request, *args, **kwargs)
args, kwargs = self.convert_args(request, *args, **kwargs)
request.access = self.get_access(request, *args, **kwargs)
if not self.has_permission(request, *args, **kwargs):
return self.handle_permission_required(request, *args, **kwargs)
if "organization" in kwargs:
org = kwargs["organization"]
if self.is_member_disabled_from_limit(request, org):
return self.handle_disabled_member(org)
if self.is_not_2fa_compliant(request, org):
return self.handle_not_2fa_compliant(request, *args, **kwargs)
self.request = request
self.default_context = self.get_context_data(request, *args, **kwargs)
return self.handle(request, *args, **kwargs)
|
|
20,640 | 101,220 | 32 | lib/align/detected_face.py | 11 | 4 | def interpolator(self) -> int:
assert self._interpolator is | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | interpolator | 5e73437be47f2410439a3c6716de96354e6a0c94 | faceswap | detected_face.py | 7 | 4 | https://github.com/deepfakes/faceswap.git | 1 | 19 | 0 | 10 | 32 | Python | {
"docstring": " int: The cv2 interpolator required to transpose the mask to a full frame. ",
"language": "en",
"n_whitespaces": 14,
"n_words": 13,
"vocab_size": 12
} | def interpolator(self) -> int:
assert self._interpolator is not None
return self._interpolator
|
|
28,754 | 128,590 | 29 | python/ray/tune/tests/test_cluster.py | 17 | 6 | def test_cluster_interrupt(start_connected_cluster, tmpdir):
cluster = start_connected_cluster
dirpath = str(tmpdir)
# Needs to be in scope for pytest | [tune] Store sync config/checkpoint config in experiment, trial (#29019)
This is some clean-up required for future changes to the syncing/checkpointing behavior. At the moment we pass single attributes of these configs to the Experiment class, and then subsequently to the Trial class, from which it is passed on to the trainable. If we extend the configurability in the future (e.g. provide fallback mechanisms in the checkpoint config, or make retry wait times configurable in the sync config), we would have to add more and more attributes to these intermediate classes. Instead, we should just pass and store the full config.
As a next follow-up, we can pass these configs to the Trainable.
Signed-off-by: Kai Fricke <[email protected]> | test_cluster_interrupt | e142be077f0c727ab11ba51ecaba9a98b7bfe474 | ray | test_cluster.py | 8 | 75 | https://github.com/ray-project/ray.git | 11 | 335 | 0 | 16 | 31 | Python | {
"docstring": "Tests run_experiment on cluster shutdown with actual interrupt.\n\n This is an end-to-end test.\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 13
} | def test_cluster_interrupt(start_connected_cluster, tmpdir):
cluster = start_connected_cluster
dirpath = str(tmpdir)
# Needs to be in scope for pytest |
|
71,656 | 247,400 | 200 | tests/rest/media/v1/test_oembed.py | 85 | 8 | def test_version(self) -> None:
for version in ("1.0", 1.0, 1):
result = self.parse_response({"version": version, "type": "link"})
# An empty Open Graph response is an error, ensure the URL is included.
self.assertIn("og:url", result.open_graph_result)
# A missing version should be treated as 1.0.
result = self.parse_response({"type": "link"})
| Add type hints to `tests/rest` (#12146)
* Add type hints to `tests/rest`
* newsfile
* change import from `SigningKey` | test_version | 7e91107be1a4287873266e588a3c5b415279f4c8 | synapse | test_oembed.py | 13 | 10 | https://github.com/matrix-org/synapse.git | 3 | 119 | 0 | 50 | 200 | Python | {
"docstring": "Accept versions that are similar to 1.0 as a string or int (or missing).",
"language": "en",
"n_whitespaces": 13,
"n_words": 14,
"vocab_size": 14
} | def test_version(self) -> None:
for version in ("1.0", 1.0, 1):
result = self.parse_response({"version": version, "type": "link"})
# An empty Open Graph response is an error, ensure the URL is included.
self.assertIn("og:url", result.open_graph_result)
# A missing version should be treated as 1.0.
result = self.parse_response({"type": "link"})
self.assertIn("og:url", result.open_graph_result)
# Invalid versions should be rejected.
for version in ("2.0", "1", 1.1, 0, None, {}, []):
result = self.parse_response({"version": version, "type": "link"})
# An empty Open Graph response is an error, ensure the URL is included.
self.assertEqual({}, result.open_graph_result)
|
|
15,845 | 72,190 | 100 | wagtail/admin/tests/test_userbar.py | 30 | 14 | def test_page_allowing_subpages(self):
response = self.client.get(
reverse("wagtailadmin_userbar_frontend", args=(self.event_index.id,))
)
# page allows subpages, so the 'add page' button should show
expected_url = reverse(
| Reformat with black | test_page_allowing_subpages | d10f15e55806c6944827d801cd9c2d53f5da4186 | wagtail | test_userbar.py | 14 | 16 | https://github.com/wagtail/wagtail.git | 1 | 63 | 0 | 27 | 106 | Python | {
"docstring": "\n <a href=\"{expected_url}\" target=\"_parent\" role=\"menuitem\">\n <svg class=\"icon icon-plus wagtail-action-icon\" aria-hidden=\"true\" focusable=\"false\">\n <use href=\"#icon-plus\"></use>\n </svg>\n Add a child page\n </a>\n ",
"language": "en",
"n_whitespaces": 116,
"n_words": 18,
"vocab_size": 18
} | def test_page_allowing_subpages(self):
response = self.client.get(
reverse("wagtailadmin_userbar_frontend", args=(self.event_index.id,))
)
# page allows subpages, so the 'add page' button should show
expected_url = reverse(
"wagtailadmin_pages:add_subpage", args=(self.event_index.id,)
)
needle = f
self.assertTagInHTML(needle, str(response.content))
|
|
38,944 | 161,199 | 132 | mkgui/app.py | 39 | 21 | def render_output_ui(self, streamlit_app, input) -> None: # type: ignore
src, result = self.__root__
streamlit_app.subheader("Synthesized Audio")
streamlit_app.audio(result.content, format="audio/wav")
fig, ax = plt.subplots()
ax.imshow(src.mel, aspect="equal", interpolation="none")
ax.set_title("mel spectrogram(Source Audio)")
streamlit_app.pyplot(fig)
fig, ax = plt.subplots()
ax.imshow(result.mel, aspect="equal", interpolation="none")
ax.set_title("mel spectrogram(Result | Upgrade to new web service (#529)
* Init new GUI
* Remove unused codes
* Reset layout
* Add samples
* Make framework to support multiple pages
* Add vc mode
* Add preprocessing mode
* Add training mode
* Remove text input in vc mode
* Add entry for GUI and revise readme
* Move requirement together
* Add error raise when no model folder found
* Add readme | render_output_ui | c5d03fb3cbf5105aa45dc131474260cf140b748b | MockingBird | app.py | 9 | 15 | https://github.com/babysor/MockingBird.git | 1 | 111 | 0 | 29 | 192 | Python | {
"docstring": "Custom output UI.\n If this method is implmeneted, it will be used instead of the default Output UI renderer.\n ",
"language": "en",
"n_whitespaces": 33,
"n_words": 19,
"vocab_size": 19
} | def render_output_ui(self, streamlit_app, input) -> None: # type: ignore
src, result = self.__root__
streamlit_app.subheader("Synthesized Audio")
streamlit_app.audio(result.content, format="audio/wav")
fig, ax = plt.subplots()
ax.imshow(src.mel, aspect="equal", interpolation="none")
ax.set_title("mel spectrogram(Source Audio)")
streamlit_app.pyplot(fig)
fig, ax = plt.subplots()
ax.imshow(result.mel, aspect="equal", interpolation="none")
ax.set_title("mel spectrogram(Result Audio)")
streamlit_app.pyplot(fig)
|
|
@keras_export("keras.dtensor.experimental.layout_map_scope", v1=[])
@contextlib.contextmanager | 80,492 | 270,593 | 21 | keras/dtensor/layout_map.py | 10 | 11 | def get_default_mesh(self):
return self._default_mesh
LayoutMap.get.__doc__ = LayoutMap | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | get_default_mesh | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | keras | layout_map.py | 8 | 2 | https://github.com/keras-team/keras.git | 1 | 10 | 1 | 10 | 60 | Python | {
"docstring": "Return the default `Mesh` set at instance creation.\n\n The `Mesh` can be used to create default replicated `Layout` when there\n isn't a match of the input string query.\n ",
"language": "en",
"n_whitespaces": 49,
"n_words": 28,
"vocab_size": 25
} | def get_default_mesh(self):
return self._default_mesh
LayoutMap.get.__doc__ = LayoutMap.__getitem__.__doc__
@keras_export("keras.dtensor.experimental.layout_map_scope", v1=[])
@contextlib.contextmanager |
35,845 | 154,186 | 27 | modin/pandas/indexing.py | 12 | 5 | def __setitem__(self, key, item): # pragma: no cover
raise NotImplementedError("Implemented by subclasses")
| REFACTOR-#4730: make Indexers immutable (#4731)
Signed-off-by: Brock Mendel <[email protected]> | __setitem__ | 8e1190c9979a1df26ea570f3ad2ccd822ad54c8e | modin | indexing.py | 8 | 2 | https://github.com/modin-project/modin.git | 1 | 15 | 0 | 12 | 28 | Python | {
"docstring": "\n Assign `item` value to dataset located by `key`.\n\n Parameters\n ----------\n key : callable or tuple\n The global row numbers to assign data to.\n item : modin.pandas.DataFrame, modin.pandas.Series or scalar\n Value that should be assigned to located dataset.\n\n See Also\n --------\n pandas.DataFrame.iloc\n ",
"language": "en",
"n_whitespaces": 127,
"n_words": 41,
"vocab_size": 36
} | def __setitem__(self, key, item): # pragma: no cover
raise NotImplementedError("Implemented by subclasses")
|
|
50,113 | 202,407 | 130 | tests/csrf_tests/tests.py | 42 | 21 | def test_bad_origin_cannot_be_parsed(self):
req = self._get_POST_request_with_token()
req.META["HTTP_HOST"] = "www.example.com"
req.META["HTTP_ORIGIN"] = "https://["
mw = CsrfViewMiddleware(post_form_view)
self._check_referer_rejects(mw, req)
self.assertIs(mw._origin_verified(req), False)
with self.assertLogs("django.security.csrf", "WARNING") as cm:
response = mw.process_view(req, post_form_view, (), {})
self.assertEqual(response.status_code, 403)
msg = REASON_BAD_ORIGIN % req.META[ | Refs #33476 -- Reformatted code with Black. | test_bad_origin_cannot_be_parsed | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | tests.py | 11 | 12 | https://github.com/django/django.git | 1 | 123 | 0 | 35 | 210 | Python | {
"docstring": "\n A POST request with an origin that can't be parsed by urlparse() is\n rejected.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 14
} | def test_bad_origin_cannot_be_parsed(self):
req = self._get_POST_request_with_token()
req.META["HTTP_HOST"] = "www.example.com"
req.META["HTTP_ORIGIN"] = "https://["
mw = CsrfViewMiddleware(post_form_view)
self._check_referer_rejects(mw, req)
self.assertIs(mw._origin_verified(req), False)
with self.assertLogs("django.security.csrf", "WARNING") as cm:
response = mw.process_view(req, post_form_view, (), {})
self.assertEqual(response.status_code, 403)
msg = REASON_BAD_ORIGIN % req.META["HTTP_ORIGIN"]
self.assertEqual(cm.records[0].getMessage(), "Forbidden (%s): " % msg)
|
|
23,516 | 109,299 | 18 | lib/mpl_toolkits/mplot3d/axis3d.py | 12 | 6 | def move_from_center(coord, centers, deltas, axmask=(True, True, True)):
return _move_from_center | Deprecate helper functions in axis3d | move_from_center | b89ed5752c2a3b4eb9c9a3bf57848f543765fd6d | matplotlib | axis3d.py | 8 | 6 | https://github.com/matplotlib/matplotlib.git | 1 | 33 | 0 | 10 | 46 | Python | {
"docstring": "\n For each coordinate where *axmask* is True, move *coord* away from\n *centers* by *deltas*.\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 14,
"vocab_size": 14
} | def move_from_center(coord, centers, deltas, axmask=(True, True, True)):
return _move_from_center(coord, centers, deltas, axmask=axmask)
|
|
29,113 | 130,122 | 497 | python/ray/_private/function_manager.py | 162 | 17 | def get_execution_info(self, job_id, function_descriptor):
function_id = function_descriptor.function_id
# If the function has already been loaded,
# There's no need to load again
if function_id in self._function_execution_info:
return self._function_execution_info[function_id]
if self._worker.load_code_from_local:
# Load function from local code.
if not function_descriptor.is_actor_method():
# If the function is not able to be loaded,
# try to load it from GCS,
# even if load_code_from_local is set True
if self._load_function_from_local(function_descriptor) is True:
return self._function_execution_info[function_id]
# Load function from GCS.
# Wait until the function to be executed has actually been
# registered on this worker. We will push w | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | get_execution_info | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | function_manager.py | 13 | 21 | https://github.com/ray-project/ray.git | 6 | 118 | 0 | 99 | 206 | Python | {
"docstring": "Get the FunctionExecutionInfo of a remote function.\n Args:\n job_id: ID of the job that the function belongs to.\n function_descriptor: The FunctionDescriptor of the function to get.\n Returns:\n A FunctionExecutionInfo object.\n ",
"language": "en",
"n_whitespaces": 84,
"n_words": 30,
"vocab_size": 23
} | def get_execution_info(self, job_id, function_descriptor):
function_id = function_descriptor.function_id
# If the function has already been loaded,
# There's no need to load again
if function_id in self._function_execution_info:
return self._function_execution_info[function_id]
if self._worker.load_code_from_local:
# Load function from local code.
if not function_descriptor.is_actor_method():
# If the function is not able to be loaded,
# try to load it from GCS,
# even if load_code_from_local is set True
if self._load_function_from_local(function_descriptor) is True:
return self._function_execution_info[function_id]
# Load function from GCS.
# Wait until the function to be executed has actually been
# registered on this worker. We will push warnings to the user if
# we spend too long in this loop.
# The driver function may not be found in sys.path. Try to load
# the function from GCS.
with profiling.profile("wait_for_function"):
self._wait_for_function(function_descriptor, job_id)
try:
function_id = function_descriptor.function_id
info = self._function_execution_info[function_id]
except KeyError as e:
message = (
"Error occurs in get_execution_info: "
"job_id: %s, function_descriptor: %s. Message: %s"
% (job_id, function_descriptor, e)
)
raise KeyError(message)
return info
|
|
19,997 | 100,533 | 26 | lib/gpu_stats/_base.py | 12 | 8 | def exclude_all_devices(self) -> bool:
return all(idx in _EXCLUDE_DEVICES for idx in range(self._device_count))
| Refactor lib.gpu_stats (#1218)
* inital gpu_stats refactor
* Add dummy CPU Backend
* Update Sphinx documentation | exclude_all_devices | bdbbad4d310fb606b6f412aa81e9f57ccd994e97 | faceswap | _base.py | 11 | 3 | https://github.com/deepfakes/faceswap.git | 2 | 24 | 0 | 11 | 40 | Python | {
"docstring": " bool: ``True`` if all GPU devices have been explicitly disabled otherwise ``False`` ",
"language": "en",
"n_whitespaces": 13,
"n_words": 12,
"vocab_size": 12
} | def exclude_all_devices(self) -> bool:
return all(idx in _EXCLUDE_DEVICES for idx in range(self._device_count))
|
|
93,467 | 294,430 | 126 | homeassistant/components/alexa/resources.py | 35 | 9 | def serialize_labels(self, resources):
labels = []
for label in resources:
if label in AlexaGlobalCatalog.__dict__.values():
label = {"@type": "asset", "value": {"assetId": label}}
else:
label = {"@type": "text", "va | Update pylint to 2.13.0 (#68656) | serialize_labels | 53245c65238e3009dd1f3412f7f9bef10385f64e | core | resources.py | 16 | 9 | https://github.com/home-assistant/core.git | 3 | 76 | 0 | 27 | 141 | Python | {
"docstring": "Return resource label objects for friendlyNames serialized for an API response.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | def serialize_labels(self, resources):
labels = []
for label in resources:
if label in AlexaGlobalCatalog.__dict__.values():
label = {"@type": "asset", "value": {"assetId": label}}
else:
label = {"@type": "text", "value": {"text": label, "locale": "en-US"}}
labels.append(label)
return {"friendlyNames": labels}
|
|
69,953 | 243,007 | 400 | src/PIL/PpmImagePlugin.py | 104 | 24 | def _decode_bitonal(self):
data = bytearray()
total_bytes = self.state.xsize * self.state.ysize
comment_spans = False
while len(data) != total_bytes:
block = self._read_block() # read next block
if not block:
# eof
break
while block and comment_spans:
comment_end = self._find_comment_end(block)
if comment_end != -1: # comment ends in this block
block = block[comment_end + 1 :] # delete tail of previous comment
break
| Added support for PPM arbitrary maxval in plain formats | _decode_bitonal | c4d51fb2681c2434fd324098d116a66013549de7 | Pillow | PpmImagePlugin.py | 18 | 23 | https://github.com/python-pillow/Pillow.git | 8 | 159 | 0 | 65 | 279 | Python | {
"docstring": "\n This is a separate method because in the plain PBM format, all data tokens are\n exactly one byte, so the inter-token whitespace is optional.\n ",
"language": "en",
"n_whitespaces": 46,
"n_words": 24,
"vocab_size": 22
} | def _decode_bitonal(self):
data = bytearray()
total_bytes = self.state.xsize * self.state.ysize
comment_spans = False
while len(data) != total_bytes:
block = self._read_block() # read next block
if not block:
# eof
break
while block and comment_spans:
comment_end = self._find_comment_end(block)
if comment_end != -1: # comment ends in this block
block = block[comment_end + 1 :] # delete tail of previous comment
break
else: # comment spans whole block
block = self._read_block()
block, comment_spans = self._ignore_comments(block)
tokens = b"".join(block.split())
for token in tokens:
if token not in (48, 49):
raise ValueError(f"Invalid token for this mode: {bytes([token])}")
data = (data + tokens)[:total_bytes]
invert = bytes.maketrans(b"01", b"\xFF\x00")
return data.translate(invert)
|
|
48,995 | 198,543 | 200 | sympy/solvers/solvers.py | 88 | 25 | def recast_to_symbols(eqs, symbols):
if not iterable(eqs) and iterable(symbols):
raise ValueError('Both eqs and symbols must be iterable')
orig = list(symbols)
symbols = list(ordered(symbols))
swap_sym = {}
i = 0
for j, s in e | ordered swaps and dict | recast_to_symbols | 883f3c95de8eaa79e04a6b78199e07f0d9fbba6c | sympy | solvers.py | 14 | 20 | https://github.com/sympy/sympy.git | 10 | 163 | 0 | 59 | 262 | Python | {
"docstring": "\n Return (e, s, d) where e and s are versions of *eqs* and\n *symbols* in which any non-Symbol objects in *symbols* have\n been replaced with generic Dummy symbols and d is a dictionary\n that can be used to restore the original expressions.\n\n Examples\n ========\n\n >>> from sympy.solvers.solvers import recast_to_symbols\n >>> from sympy import symbols, Function\n >>> x, y = symbols('x y')\n >>> fx = Function('f')(x)\n >>> eqs, syms = [fx + 1, x, y], [fx, y]\n >>> e, s, d = recast_to_symbols(eqs, syms); (e, s, d)\n ([_X0 + 1, x, y], [_X0, y], {_X0: f(x)})\n\n The original equations and symbols can be restored using d:\n\n >>> assert [i.xreplace(d) for i in eqs] == eqs\n >>> assert [d.get(i, i) for i in s] == syms\n\n ",
"language": "en",
"n_whitespaces": 176,
"n_words": 124,
"vocab_size": 85
} | def recast_to_symbols(eqs, symbols):
if not iterable(eqs) and iterable(symbols):
raise ValueError('Both eqs and symbols must be iterable')
orig = list(symbols)
symbols = list(ordered(symbols))
swap_sym = {}
i = 0
for j, s in enumerate(symbols):
if not isinstance(s, Symbol) and s not in swap_sym:
swap_sym[s] = Dummy('X%d' % i)
i += 1
new_f = []
for i in eqs:
isubs = getattr(i, 'subs', None)
if isubs is not None:
new_f.append(isubs(swap_sym))
else:
new_f.append(i)
restore = {v: k for k, v in swap_sym.items()}
return new_f, [swap_sym.get(i, i) for i in orig], restore
|
|
16,955 | 79,676 | 466 | wagtail/models/reference_index.py | 91 | 20 | def model_is_indexable(cls, model, allow_child_models=False):
if getattr(model, "wagtail_reference_index_ignore", False):
return False
# Don't check any models that have a parental key, references from these will be collected from the parent
if not allow_child_models and any(
[isinstance(field, ParentalKey) for field in model._meta.get_fields()]
):
return False
for field in model._meta.get_fields():
if field.is_relation and field.many_to_one:
if getattr(field, "wagtail_reference_index_ignore", False):
continue
if getattr(
field.related_model, "wagtail_reference_index_ignore", False
):
continue
if isinstance(field, (ParentalKey, GenericRel)):
continue
return True
if hasattr(field, "extract_references"):
return True
if issubclass(model, ClusterableModel):
for child_relation in get_all_child_relations(model):
if cls.model_is_indexable(
child_relation.related_model,
allow_child_models=True,
):
return True
return False
| Check field for .extract_references method instead of field type
Co-authored-by: Matt Westcott <[email protected]> | model_is_indexable | c8689acb3724dc12fb09a0bfc14d7e4755a1ea0f | wagtail | reference_index.py | 13 | 28 | https://github.com/wagtail/wagtail.git | 15 | 156 | 0 | 59 | 244 | Python | {
"docstring": "\n Returns True if the given model may have outbound references that we would be interested in recording in the index.\n\n\n Args:\n model (type): a Django model class\n allow_child_models (boolean): Child models are not indexable on their own. If you are looking at\n a child model from the perspective of indexing it through its parent,\n set this to True to disable checking for this. Default False.\n ",
"language": "en",
"n_whitespaces": 191,
"n_words": 65,
"vocab_size": 55
} | def model_is_indexable(cls, model, allow_child_models=False):
if getattr(model, "wagtail_reference_index_ignore", False):
return False
# Don't check any models that have a parental key, references from these will be collected from the parent
if not allow_child_models and any(
[isinstance(field, ParentalKey) for field in model._meta.get_fields()]
):
return False
for field in model._meta.get_fields():
if field.is_relation and field.many_to_one:
if getattr(field, "wagtail_reference_index_ignore", False):
continue
if getattr(
field.related_model, "wagtail_reference_index_ignore", False
):
continue
if isinstance(field, (ParentalKey, GenericRel)):
continue
return True
if hasattr(field, "extract_references"):
return True
if issubclass(model, ClusterableModel):
for child_relation in get_all_child_relations(model):
if cls.model_is_indexable(
child_relation.related_model,
allow_child_models=True,
):
return True
return False
|
|
4,204 | 22,132 | 150 | pipenv/patched/pip/_vendor/requests/utils.py | 51 | 13 | def get_encodings_from_content(content):
warnings.warn(
(
"In requests 3.0, get_encodings_from_content will be removed. For "
"more information, please see the discussion on issue #2266. (This"
" warning should only appear once.)"
),
DeprecationWarning,
)
charset_re = re.compile(r'<meta.*?charset=["\']*(.+?)["\'>]', flags=re.I | Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir. | get_encodings_from_content | cd5a9683be69c86c8f3adcd13385a9bc5db198ec | pipenv | utils.py | 10 | 17 | https://github.com/pypa/pipenv.git | 1 | 81 | 0 | 44 | 135 | Python | {
"docstring": "Returns encodings from given content string.\n\n :param content: bytestring to extract encodings from.\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 12
} | def get_encodings_from_content(content):
warnings.warn(
(
"In requests 3.0, get_encodings_from_content will be removed. For "
"more information, please see the discussion on issue #2266. (This"
" warning should only appear once.)"
),
DeprecationWarning,
)
charset_re = re.compile(r'<meta.*?charset=["\']*(.+?)["\'>]', flags=re.I)
pragma_re = re.compile(r'<meta.*?content=["\']*;?charset=(.+?)["\'>]', flags=re.I)
xml_re = re.compile(r'^<\?xml.*?encoding=["\']*(.+?)["\'>]')
return (
charset_re.findall(content)
+ pragma_re.findall(content)
+ xml_re.findall(content)
)
|
|
23,734 | 109,752 | 177 | lib/mpl_toolkits/mplot3d/axes3d.py | 79 | 21 | def _scale_axis_limits(self, scale_x, scale_y, scale_z):
# Get the axis limits and centers
minx, maxx, miny, maxy, minz, maxz = self.get_w_lims()
cx = (maxx + minx)/2
cy = (maxy + miny)/2
cz = (maxz + minz)/2
# Scale the data range
dx = (maxx - minx)*scale_x
dy = (maxy - miny)*scale_y
dz = (maxz - minz)*scale_z
# Set the scaled axis limits
self.set_xlim3d(cx - dx/2, cx + dx/2)
self.set_ylim3d(cy - dy/2, cy + dy/2)
| Add pan and zoom toolbar handling to 3D Axes (Replaces PR#22614) (#23449)
* ENH: Add pan and zoom toolbar handling to 3D Axes
1) This moves the pan logic that was already in the mouse move handler
into the "drag_pan" method to make it available from the toolbar.
2) This expands upon the panning logic to enable a zoom-to-box feature.
The zoom-to-box is done relative to the Axes, so it shrinks/expands
the box as a fraction of each delta, from lower-left Axes to lower-left
zoom-box. Thus, it tries to handle non-centered zooms, which adds more
cases to handle versus the current right-click zoom only scaling from
the center of the projection.
* Rewrite zooming with bounding box
* Rewrite 3d panning to work with a roll angle
* Whats new for zoom and pan buttons
* Make pan button configurable
* Do not jump when zooming and mouse goes over other subplot
* Rework zooming for 3d plots
* Handle x/y lock when zooming and panning
* Update tests
* Docstrings
* Dont assume a scale_z
* Limit zoom box
* Test zoom pan key modifiers
* Save some calculation by saving view axes
* Deprecation warnings for Axes3D.eye, .vvec
* Remove Axes3D._prepare_view_from_bbox for now
* Comments and docstrings
* Switch from uvn to uvw
* Save aspect to axes
* Constrain zooming with mouse when one of the equal aspect ratios is set
* Cleanup
* Cleanup
* Consolidate finding equal aspect axis indices
* linting
* More intuitive scaling
* Box zoom keeps existing aspect ratios
* Linting
* Code review comments
* Revert parameters for view_transformation
* Fix new 3d pan/zoom view going on view stack twice
* Better clipping
* Test 3d toolbar navigation
* Privatize helper functions
* Deprecations
* Code review changes
* Deprecation note
* Undeprecate proj3d.view_transformation
* Undeprecate proj3d.view_transformation
* Update doc/api/next_api_changes/deprecations/23449-SS.rst
Co-authored-by: Greg Lucas <[email protected]>
Co-authored-by: Scott Shambaugh <[email protected]>
Co-authored-by: Oscar Gustafsson <[email protected]> | _scale_axis_limits | 4896ec1a2cfb8c454e385632d8df213c915ced52 | matplotlib | axes3d.py | 9 | 11 | https://github.com/matplotlib/matplotlib.git | 1 | 131 | 0 | 51 | 201 | Python | {
"docstring": "\n Keeping the center of the x, y, and z data axes fixed, scale their\n limits by scale factors. A scale factor > 1 zooms out and a scale\n factor < 1 zooms in.\n\n Parameters\n ----------\n scale_x : float\n Scale factor for the x data axis.\n scale_y : float\n Scale factor for the y data axis.\n scale_z : float\n Scale factor for the z data axis.\n ",
"language": "en",
"n_whitespaces": 162,
"n_words": 65,
"vocab_size": 37
} | def _scale_axis_limits(self, scale_x, scale_y, scale_z):
# Get the axis limits and centers
minx, maxx, miny, maxy, minz, maxz = self.get_w_lims()
cx = (maxx + minx)/2
cy = (maxy + miny)/2
cz = (maxz + minz)/2
# Scale the data range
dx = (maxx - minx)*scale_x
dy = (maxy - miny)*scale_y
dz = (maxz - minz)*scale_z
# Set the scaled axis limits
self.set_xlim3d(cx - dx/2, cx + dx/2)
self.set_ylim3d(cy - dy/2, cy + dy/2)
self.set_zlim3d(cz - dz/2, cz + dz/2)
|
|
44,489 | 184,113 | 93 | src/textual/widget.py | 20 | 10 | def layers(self) -> tuple[str, ...]:
for node in se | layers and docks | layers | c98e1b96049369f6af013a133f204ae0a286f2c7 | textual | widget.py | 11 | 12 | https://github.com/Textualize/textual.git | 4 | 51 | 0 | 18 | 84 | Python | {
"docstring": "Layers of from parent.\n\n Returns:\n tuple[str, ...]: Tuple of layer names.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 11,
"vocab_size": 10
} | def layers(self) -> tuple[str, ...]:
for node in self.ancestors:
if not isinstance(node, Widget):
break
if node.styles.has_rule("layers"):
return node.styles.layers
return ("default",)
|
|
70,663 | 245,114 | 57 | mmdet/testing/_utils.py | 33 | 13 | def get_roi_head_cfg(fname):
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
roi_head = model.roi_head
train_cfg = None if model.train_cfg is None else model.train_cfg.rcnn
test_cfg = None if model.test_cfg is None else model.test_cfg.rcnn
roi_head.update(dict(train_cfg=train_cfg, test_cfg=test_cfg))
return roi_head
| Refactor Double Head, MS, Dynamic, Trident. | get_roi_head_cfg | cd4e9ed8269b0c767e129169b7268b0ced7e60c9 | mmdetection | _utils.py | 10 | 8 | https://github.com/open-mmlab/mmdetection.git | 3 | 74 | 0 | 22 | 117 | Python | {
"docstring": "Grab configs necessary to create a roi_head.\n\n These are deep copied to allow for safe modification of parameters without\n influencing other tests.\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 22,
"vocab_size": 21
} | def get_roi_head_cfg(fname):
config = _get_config_module(fname)
model = copy.deepcopy(config.model)
roi_head = model.roi_head
train_cfg = None if model.train_cfg is None else model.train_cfg.rcnn
test_cfg = None if model.test_cfg is None else model.test_cfg.rcnn
roi_head.update(dict(train_cfg=train_cfg, test_cfg=test_cfg))
return roi_head
|
|
@contextmanager | 11,963 | 59,935 | 95 | src/prefect/logging/loggers.py | 56 | 19 | def print_as_log(*args, **kwargs):
from prefect.context import FlowRunContext, T | Add `log_prints` option to redirect print to logs (#7580)
Co-authored-by: Will Raphaelson <[email protected]>
Co-authored-by: Will Raphaelson <[email protected]>
Co-authored-by: Nathan Nowack <[email protected]>
Co-authored-by: Terrence Dorsey <[email protected]> | print_as_log | 298554b26fa5d866d34fed5f6e8646edb56984a4 | prefect | loggers.py | 11 | 10 | https://github.com/PrefectHQ/prefect.git | 4 | 89 | 1 | 43 | 157 | Python | {
"docstring": "\n A patch for `print` to send printed messages to the Prefect run logger.\n\n If no run is active, `print` will behave as if it were not patched.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 27,
"vocab_size": 24
} | def print_as_log(*args, **kwargs):
from prefect.context import FlowRunContext, TaskRunContext
context = TaskRunContext.get() or FlowRunContext.get()
if not context or not context.log_prints:
return print(*args, **kwargs)
logger = get_run_logger()
# Print to an in-memory buffer; so we do not need to implement `print`
buffer = io.StringIO()
kwargs["file"] = buffer
print(*args, **kwargs)
# Remove trailing whitespace to prevent duplicates
logger.info(buffer.getvalue().rstrip())
@contextmanager |
62,617 | 230,975 | 110 | packages/python/plotly/plotly/matplotlylib/mplexporter/tests/test_basic.py | 55 | 23 | def test_image():
# Test fails for mat | Updated distutils.Version to packaging.Version | test_image | 1d82b8822120db088bfeb6c8eae7ec8df9703783 | plotly.py | test_basic.py | 11 | 18 | https://github.com/plotly/plotly.py.git | 2 | 94 | 0 | 45 | 159 | Python | {
"docstring": "\n opening figure\n opening axes\n draw image of size {image_size} \n closing axes\n closing figure\n ",
"language": "en",
"n_whitespaces": 159,
"n_words": 13,
"vocab_size": 9
} | def test_image():
# Test fails for matplotlib 1.5+ because the size of the image
# generated by matplotlib has changed.
if Version(matplotlib.__version__) == Version("3.4.1"):
image_size = 432
else:
pytest.skip("Test fails for older matplotlib")
np.random.seed(0) # image size depends on the seed
fig, ax = plt.subplots(figsize=(2, 2))
ax.imshow(np.random.random((10, 10)), cmap=plt.cm.jet, interpolation="nearest")
_assert_output_equal(
fake_renderer_output(fig, FakeRenderer),
f,
)
|
|
54,361 | 216,055 | 112 | salt/cloud/clouds/proxmox.py | 48 | 17 | def ignore_cidr(vm_, ip):
from ipaddress import ip_address, ip_network
cidrs = config.get_cloud_config_value(
"ignore_cidr", vm_, __opts__, default=[], search_global=False
)
if cidrs and isinstance(cidrs, str):
cidrs = [cidrs]
for cidr in cidrs or []:
if ip_address(ip) in ip_network(cidr):
log.warning("IP %r found within %r; ignoring it.", ip, cidr)
return True
retur | Add support for get IP-address from agent | ignore_cidr | a5679caf65c7c79cd72841b6e5793b9b693744c9 | salt | proxmox.py | 12 | 12 | https://github.com/saltstack/salt.git | 6 | 83 | 0 | 41 | 131 | Python | {
"docstring": "\n Return True if we are to ignore the specified IP.\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 10,
"vocab_size": 10
} | def ignore_cidr(vm_, ip):
from ipaddress import ip_address, ip_network
cidrs = config.get_cloud_config_value(
"ignore_cidr", vm_, __opts__, default=[], search_global=False
)
if cidrs and isinstance(cidrs, str):
cidrs = [cidrs]
for cidr in cidrs or []:
if ip_address(ip) in ip_network(cidr):
log.warning("IP %r found within %r; ignoring it.", ip, cidr)
return True
return False
|
|
21,883 | 104,508 | 824 | src/datasets/features/features.py | 270 | 29 | def encode_nested_example(schema, obj):
# Nested structures: we allow dict, list/tuples, sequences
if isinstance(schema, dict):
return {k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in zip_dict(schema, obj)}
elif isinstance(schema, (list, tuple)):
sub_schema = schema[0]
if obj is None:
return None
else:
if len(obj) > 0:
for first_elmt in obj:
if _check_non_null_non_empty_recursive(first_elmt, sub_schema):
break
if encode_nested_example(sub_schema, first_elmt) != first_elmt:
return [encode_nested_example(sub_schema, o) for o in obj]
return list(obj)
elif isinstance(schema, Sequence):
# We allow to reverse list of dict => dict of list for compatiblity with tfds
if isinstance(schema.feature, dict):
# dict of list to fill
list_dict = {}
if isinstance(obj, (list, tuple)):
# obj is a list of dict
for k, dict_tuples in zip_dict(schema.feature, *obj):
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
return list_dict
else:
# obj is | Module namespace cleanup for v2.0 (#3875)
* Imports cleaning
* Small change
* Remove unused methods
* Small fix
* Additional fix
* Final fix
* Fix benchmark test
* Fix benchmark test #2 | encode_nested_example | ba4d30c42e0702bd894c36777d7d2c0adf74516c | datasets | features.py | 19 | 41 | https://github.com/huggingface/datasets.git | 27 | 356 | 0 | 134 | 541 | Python | {
"docstring": "Encode a nested example.\n This is used since some features (in particular ClassLabel) have some logic during encoding.\n\n To avoid iterating over possibly long lists, it first checks (recursively) if the first element that is not None or empty (if it is a sequence) has to be encoded.\n If the first element needs to be encoded, then all the elements of the list will be encoded, otherwise they'll stay the same.\n ",
"language": "en",
"n_whitespaces": 83,
"n_words": 71,
"vocab_size": 55
} | def encode_nested_example(schema, obj):
# Nested structures: we allow dict, list/tuples, sequences
if isinstance(schema, dict):
return {k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in zip_dict(schema, obj)}
elif isinstance(schema, (list, tuple)):
sub_schema = schema[0]
if obj is None:
return None
else:
if len(obj) > 0:
for first_elmt in obj:
if _check_non_null_non_empty_recursive(first_elmt, sub_schema):
break
if encode_nested_example(sub_schema, first_elmt) != first_elmt:
return [encode_nested_example(sub_schema, o) for o in obj]
return list(obj)
elif isinstance(schema, Sequence):
# We allow to reverse list of dict => dict of list for compatiblity with tfds
if isinstance(schema.feature, dict):
# dict of list to fill
list_dict = {}
if isinstance(obj, (list, tuple)):
# obj is a list of dict
for k, dict_tuples in zip_dict(schema.feature, *obj):
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
return list_dict
else:
# obj is a single dict
for k, (sub_schema, sub_objs) in zip_dict(schema.feature, obj):
list_dict[k] = [encode_nested_example(sub_schema, o) for o in sub_objs]
return list_dict
# schema.feature is not a dict
if isinstance(obj, str): # don't interpret a string as a list
raise ValueError(f"Got a string but expected a list instead: '{obj}'")
if obj is None:
return None
else:
if len(obj) > 0:
for first_elmt in obj:
if _check_non_null_non_empty_recursive(first_elmt, schema.feature):
break
# be careful when comparing tensors here
if not isinstance(first_elmt, list) or encode_nested_example(schema.feature, first_elmt) != first_elmt:
return [encode_nested_example(schema.feature, o) for o in obj]
return list(obj)
# Object with special encoding:
# ClassLabel will convert from string to int, TranslationVariableLanguages does some checks
elif isinstance(schema, (Audio, Image, ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):
return schema.encode_example(obj) if obj is not None else None
# Other object should be directly convertible to a native Arrow type (like Translation and Translation)
return obj
|
|
20,490 | 101,053 | 73 | scripts/train.py | 38 | 4 | def _configure_matplotlib(cls):
rcParams["keymap.fullscreen"] = [k for k in rcParams["keymap.fullscreen"] if k != "f" | bugfix: Stop preview window from stealing focus | _configure_matplotlib | c8122bc499afba4fcb99030e42e08bfb8d3a75e1 | faceswap | train.py | 10 | 5 | https://github.com/deepfakes/faceswap.git | 7 | 69 | 0 | 17 | 123 | Python | {
"docstring": " Remove `F`, 'S' and 'R' from their default bindings and stop Matplotlib from stealing\n focus ",
"language": "en",
"n_whitespaces": 23,
"n_words": 15,
"vocab_size": 13
} | def _configure_matplotlib(cls):
rcParams["keymap.fullscreen"] = [k for k in rcParams["keymap.fullscreen"] if k != "f"]
rcParams["keymap.save"] = [k for k in rcParams["keymap.save"] if k != "s"]
rcParams["keymap.home"] = [k for k in rcParams["keymap.home"] if k != "r"]
rcParams["figure.raise_window"] = False
|
|
17,337 | 82,284 | 68 | cms/cache/permissions.py | 27 | 13 | def set_permission_cache(user, key, value):
from django.core.cache import cache
# store this key, so we can clean it when required
cache_ke | Enabled isort workflow (#7200)
* Ran isort
* Enabled isort workflow
Co-authored-by: Vinit Kumar <[email protected]> | set_permission_cache | a3110e1ff24085373898c7d2a85f628abeb8518d | django-cms | permissions.py | 11 | 6 | https://github.com/django-cms/django-cms.git | 1 | 48 | 0 | 26 | 77 | Python | {
"docstring": "\n Helper method for storing values in cache. Stores used keys so\n all of them can be cleaned when clean_permission_cache gets called.\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 21,
"vocab_size": 21
} | def set_permission_cache(user, key, value):
from django.core.cache import cache
# store this key, so we can clean it when required
cache_key = get_cache_key(user, key)
cache.set(cache_key, value,
get_cms_setting('CACHE_DURATIONS')['permissions'],
version=get_cache_permission_version())
|
|
73,611 | 251,155 | 55 | mitmproxy/http.py | 12 | 10 | def cookies(self) -> multidict.MultiDictView[str, tuple[str, multidict.MultiDict[str, Optional[str]]]]:
return multidict.MultiDict | `pyupgrade --py39-plus **/*.py` | cookies | e83ec8390ad6be6a86cfcfc57bce14cb8861bf32 | mitmproxy | http.py | 8 | 14 | https://github.com/mitmproxy/mitmproxy.git | 1 | 43 | 0 | 12 | 63 | Python | {
"docstring": "\n The response cookies. A possibly empty `MultiDictView`, where the keys are cookie\n name strings, and values are `(cookie value, attributes)` tuples. Within\n attributes, unary attributes (e.g. `HTTPOnly`) are indicated by a `None` value.\n Modifications to the MultiDictView update `Response.headers`, and vice versa.\n\n *Warning:* Changes to `attributes` will not be picked up unless you also reassign\n the `(cookie value, attributes)` tuple directly in the `MultiDictView`.\n ",
"language": "en",
"n_whitespaces": 114,
"n_words": 64,
"vocab_size": 54
} | def cookies(self) -> multidict.MultiDictView[str, tuple[str, multidict.MultiDict[str, Optional[str]]]]:
return multidict.MultiDictView(
self._get_cookies,
self._set_cookies
)
|
|
34,221 | 148,285 | 11 | python/ray/_private/thirdparty/pathspec/util.py | 16 | 7 | def _normalize_entries(entries, separators=None):
norm_files = {}
for entry in entries:
norm_files[normalize_file(entry.path, separators=separators)] = entry
return norm_files
| [Bugfix] fix invalid excluding of Black (#24042)
- We should use `--force-exclude` when we pass code path explicitly https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html?highlight=--force-exclude#command-line-options
- Recover the files in `python/ray/_private/thirdparty` which has been formatted in the PR https://github.com/ray-project/ray/pull/21975 by mistake. | _normalize_entries | 0e6c042e29cbbe429d81c9c1af3c75c261f00980 | ray | util.py | 12 | 5 | https://github.com/ray-project/ray.git | 2 | 36 | 0 | 13 | 57 | Python | {
"docstring": "\n\tNormalizes the entry paths to use the POSIX path separator.\n\n\t*entries* (:class:`~collections.abc.Iterable` of :class:`.TreeEntry`)\n\tcontains the entries to be normalized.\n\n\t*separators* (:class:`~collections.abc.Collection` of :class:`str`; or\n\t:data:`None`) optionally contains the path separators to normalize.\n\tSee :func:`normalize_file` for more information.\n\n\tReturns a :class:`dict` mapping the each normalized file path (:class:`str`)\n\tto the entry (:class:`.TreeEntry`)\n\t",
"language": "en",
"n_whitespaces": 44,
"n_words": 52,
"vocab_size": 39
} | def _normalize_entries(entries, separators=None):
norm_files = {}
for entry in entries:
norm_files[normalize_file(entry.path, separators=separators)] = entry
return norm_files
|
|
51,528 | 206,460 | 200 | django/test/testcases.py | 45 | 13 | def assertXMLNotEqual(self, xml1, xml2, msg=None):
try:
result = compare_xml(xml1, xml2)
except Exception as e:
standardMsg = "First or second argument is not valid XML\n%s" % | Refs #33476 -- Reformatted code with Black. | assertXMLNotEqual | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | testcases.py | 15 | 13 | https://github.com/django/django.git | 3 | 85 | 0 | 38 | 137 | Python | {
"docstring": "\n Assert that two XML snippets are not semantically equivalent.\n Whitespace in most cases is ignored and attribute ordering is not\n significant. The arguments must be valid XML.\n ",
"language": "en",
"n_whitespaces": 56,
"n_words": 27,
"vocab_size": 25
} | def assertXMLNotEqual(self, xml1, xml2, msg=None):
try:
result = compare_xml(xml1, xml2)
except Exception as e:
standardMsg = "First or second argument is not valid XML\n%s" % e
self.fail(self._formatMessage(msg, standardMsg))
else:
if result:
standardMsg = "%s == %s" % (
safe_repr(xml1, True),
safe_repr(xml2, True),
)
self.fail(self._formatMessage(msg, standardMsg))
|
|
51,399 | 206,187 | 334 | django/template/base.py | 95 | 13 | def token_kwargs(bits, parser, support_legacy=False):
if not bits:
return {}
match = kwarg_re.match(bits[0])
kwarg_format = match and match[1]
if not kwarg_format:
if not support_legacy:
return {}
if len(bits) < 3 or bits[1] != "as":
return {}
kwargs = {}
while bits:
if kwarg_format:
match = kwarg_re.match(bits[0])
if not match or not match[1]:
return kwargs
key, value = match.groups()
del bits[:1]
else:
if len(bits) < 3 or bits[1] != "as":
return kwargs
key, value = bits[2], bits[0]
del bits[:3]
kwargs[key] = parser.compile_filter(value)
if bits and not kwarg_format:
if bits[0] != "and":
return kwargs
del bits[:1]
return kwargs
| Refs #33476 -- Reformatted code with Black. | token_kwargs | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | base.py | 14 | 29 | https://github.com/django/django.git | 16 | 188 | 0 | 40 | 303 | Python | {
"docstring": "\n Parse token keyword arguments and return a dictionary of the arguments\n retrieved from the ``bits`` token list.\n\n `bits` is a list containing the remainder of the token (split by spaces)\n that is to be checked for arguments. Valid arguments are removed from this\n list.\n\n `support_legacy` - if True, the legacy format ``1 as foo`` is accepted.\n Otherwise, only the standard ``foo=1`` format is allowed.\n\n There is no requirement for all remaining token ``bits`` to be keyword\n arguments, so return the dictionary as soon as an invalid argument format\n is reached.\n ",
"language": "en",
"n_whitespaces": 124,
"n_words": 90,
"vocab_size": 59
} | def token_kwargs(bits, parser, support_legacy=False):
if not bits:
return {}
match = kwarg_re.match(bits[0])
kwarg_format = match and match[1]
if not kwarg_format:
if not support_legacy:
return {}
if len(bits) < 3 or bits[1] != "as":
return {}
kwargs = {}
while bits:
if kwarg_format:
match = kwarg_re.match(bits[0])
if not match or not match[1]:
return kwargs
key, value = match.groups()
del bits[:1]
else:
if len(bits) < 3 or bits[1] != "as":
return kwargs
key, value = bits[2], bits[0]
del bits[:3]
kwargs[key] = parser.compile_filter(value)
if bits and not kwarg_format:
if bits[0] != "and":
return kwargs
del bits[:1]
return kwargs
|
|
13,924 | 65,547 | 4 | erpnext/buying/doctype/supplier_scorecard_variable/supplier_scorecard_variable.py | 6 | 4 | def get_cost_of_delayed_shipments(scorecard):
return get_total_cost_of_shipments(scorecard) - get_cost_of_on_time_shipments(scorec | style: format code with black | get_cost_of_delayed_shipments | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | supplier_scorecard_variable.py | 8 | 2 | https://github.com/frappe/erpnext.git | 1 | 16 | 0 | 6 | 29 | Python | {
"docstring": "Gets the total cost of all delayed shipments in the period (based on Purchase Receipts - POs)",
"language": "en",
"n_whitespaces": 16,
"n_words": 17,
"vocab_size": 16
} | def get_cost_of_delayed_shipments(scorecard):
return get_total_cost_of_shipments(scorecard) - get_cost_of_on_time_shipments(scorecard)
|
|
70,253 | 244,126 | 230 | mmdet/models/losses/utils.py | 112 | 12 | def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
# if weight is specified, apply element-wise weight
if weight is not None:
loss = loss * weight
# if avg_factor is not specified, just reduce the loss
if avg_factor is None:
loss = reduce_loss(loss, reduction)
els | [Fix] Fix reduction=mean in CELoss. (#7449)
* [Fix] Fix ignore in CELoss.
* add ut
* fix and add comments
* add avg_non_ignore option
* bce avg
* fix lint | weight_reduce_loss | 3b2e9655631a2edd28bb94c640bd6a74c0bfad55 | mmdetection | utils.py | 15 | 12 | https://github.com/open-mmlab/mmdetection.git | 5 | 86 | 0 | 69 | 150 | Python | {
"docstring": "Apply element-wise weight and reduce loss.\n\n Args:\n loss (Tensor): Element-wise loss.\n weight (Tensor): Element-wise weights.\n reduction (str): Same as built-in losses of PyTorch.\n avg_factor (float): Average factor when computing the mean of losses.\n\n Returns:\n Tensor: Processed loss values.\n ",
"language": "en",
"n_whitespaces": 82,
"n_words": 38,
"vocab_size": 32
} | def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
# if weight is specified, apply element-wise weight
if weight is not None:
loss = loss * weight
# if avg_factor is not specified, just reduce the loss
if avg_factor is None:
loss = reduce_loss(loss, reduction)
else:
# if reduction is mean, then average the loss by avg_factor
if reduction == 'mean':
# Avoid causing ZeroDivisionError when avg_factor is 0.0,
# i.e., all labels of an image belong to ignore index.
eps = torch.finfo(torch.float32).eps
loss = loss.sum() / (avg_factor + eps)
# if reduction is 'none', then do nothing, otherwise raise an error
elif reduction != 'none':
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
|
|
@pytest.mark.filterwarnings("ignore:The problem size") | 69,727 | 241,889 | 62 | scipy/sparse/linalg/_eigen/lobpcg/tests/test_lobpcg.py | 35 | 22 | def test_failure_to_run_iterations():
rnd = np.random.RandomState(0)
X = rnd.standard_normal((100, 10))
A = X @ X.T
Q = rnd.standard_normal((X.shape[0], 4))
with pytest.warns(UserWarning, | Update test_lobpcg.py
copy/paste from #15280 | test_failure_to_run_iterations | e477bab940324648c6f6e2fb53f0932dff19b11b | scipy | test_lobpcg.py | 11 | 8 | https://github.com/scipy/scipy.git | 1 | 88 | 1 | 30 | 158 | Python | {
"docstring": "Check that the code exists gracefully without breaking. Issue #10974.\n ",
"language": "en",
"n_whitespaces": 13,
"n_words": 10,
"vocab_size": 10
} | def test_failure_to_run_iterations():
rnd = np.random.RandomState(0)
X = rnd.standard_normal((100, 10))
A = X @ X.T
Q = rnd.standard_normal((X.shape[0], 4))
with pytest.warns(UserWarning, match="Exited at iteration"):
eigenvalues, _ = lobpcg(A, Q, maxiter=20)
assert(np.max(eigenvalues) > 0)
@pytest.mark.filterwarnings("ignore:The problem size") |
25,675 | 116,154 | 298 | tests/unit/test_executor.py | 82 | 22 | def test_predictor_tableau_header(self, mock_handler):
df = pd.DataFrame([
{'a': 1, 'b': 'one'},
{'a': 2, 'b': 'two'},
{'a': 1, 'b': 'three'},
])
self.set_handler(mock_handler, name='pg', tables={'tasks': df})
# --- use predictor ---
predicted_value = 5
predictor = {
'name': 'task_model',
'predict': 'p',
'dtypes': {
'p': dtype.float,
'a': dtype.integer,
'b': dtype.categorical
},
'predicted_value': predicted_value
}
self.set_predictor(predictor)
ret = self.command_executor.execute_command(parse_sql(f, dialect='mindsdb'))
# second column is having last value of 'b'
# 3: count rows, 4: sum of 'a', 5 max of pre | executor tests | test_predictor_tableau_header | 02a831997cdffafca7cb160eb1938e72020ee049 | mindsdb | test_executor.py | 12 | 32 | https://github.com/mindsdb/mindsdb.git | 1 | 143 | 0 | 64 | 250 | Python | {
"docstring": "\n SELECT \n SUM(1) AS `cnt__0B4A4E8BD11C48FFB4730D4D2C32191A_ok`,\n sum(`Custom SQL Query`.`a`) AS `sum_height_ok`,\n max(`Custom SQL Query`.`p`) AS `sum_length1_ok`\n FROM (\n SELECT res.a, res.p \n FROM pg.tasks as source\n JOIN mindsdb.task_model as res\n ) `Custom SQL Query`\n HAVING (COUNT(1) > 0)\n ",
"language": "en",
"n_whitespaces": 176,
"n_words": 35,
"vocab_size": 28
} | def test_predictor_tableau_header(self, mock_handler):
df = pd.DataFrame([
{'a': 1, 'b': 'one'},
{'a': 2, 'b': 'two'},
{'a': 1, 'b': 'three'},
])
self.set_handler(mock_handler, name='pg', tables={'tasks': df})
# --- use predictor ---
predicted_value = 5
predictor = {
'name': 'task_model',
'predict': 'p',
'dtypes': {
'p': dtype.float,
'a': dtype.integer,
'b': dtype.categorical
},
'predicted_value': predicted_value
}
self.set_predictor(predictor)
ret = self.command_executor.execute_command(parse_sql(f, dialect='mindsdb'))
# second column is having last value of 'b'
# 3: count rows, 4: sum of 'a', 5 max of prediction
assert ret.data[0] == [3, 4, 5]
|
|
9,584 | 48,733 | 41 | tests/test_routers.py | 9 | 10 | def test_conflicting_specified_basename_different_models(self):
self.router.register(r'notes', NoteViewSet)
with pytest.raises(ImproperlyConfigured):
self.router.register(r'notes_basename', BasenameViewSet, basename='routertestmodel')
| raise ImproperlyConfigured exception if `basename` is not unique (#8438)
* raise ImproperlyConfigured if basename already exists
* rename already_registered function; return True/False
* additional basename tests
* additional basename tests
* Update rest_framework/routers.py
Co-authored-by: David Graves <[email protected]>
Co-authored-by: Asif Saif Uddin <[email protected]> | test_conflicting_specified_basename_different_models | 48a21aa0eb3a95d32456c2a927eff9552a04231e | django-rest-framework | test_routers.py | 11 | 4 | https://github.com/encode/django-rest-framework.git | 1 | 40 | 0 | 9 | 69 | Python | {
"docstring": "\n Ensure 2 routers with different models, and a conflicting basename specified\n throws an exception\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 14
} | def test_conflicting_specified_basename_different_models(self):
self.router.register(r'notes', NoteViewSet)
with pytest.raises(ImproperlyConfigured):
self.router.register(r'notes_basename', BasenameViewSet, basename='routertestmodel')
|
|
18,525 | 89,279 | 220 | src/sentry/dynamic_sampling/latest_release_booster.py | 31 | 18 | def _get_boosted_releases(self) -> BoostedReleases:
boosted_releases = BoostedReleases()
for boosted_release_cache_key, timestamp in self.redis_client.hgetall(
self._generate_cache_key_for_boosted_releases_hash()
).items():
extracted_data = self._extr | ref(metrics): Change implementation of latest release [TET-555] (#41757) | _get_boosted_releases | 16b946cef6e851e40d552e1f9a9d44d0f7d31509 | sentry | latest_release_booster.py | 14 | 21 | https://github.com/getsentry/sentry.git | 3 | 77 | 0 | 27 | 123 | Python | {
"docstring": "\n Returns all the boosted releases and parses them based on key and value data.\n\n This method should not be called directly as the boosted releases are not extended, thus they contain only a\n subset of information.\n ",
"language": "en",
"n_whitespaces": 65,
"n_words": 36,
"vocab_size": 31
} | def _get_boosted_releases(self) -> BoostedReleases:
boosted_releases = BoostedReleases()
for boosted_release_cache_key, timestamp in self.redis_client.hgetall(
self._generate_cache_key_for_boosted_releases_hash()
).items():
extracted_data = self._extract_data_from_cache_key(boosted_release_cache_key)
if extracted_data:
release_id, environment = extracted_data
boosted_releases.add_release(
cache_key=boosted_release_cache_key,
id=release_id,
timestamp=float(timestamp),
environment=environment,
)
return boosted_releases
|
|
29,350 | 130,772 | 800 | python/ray/internal/internal_api.py | 194 | 20 | def store_stats_summary(reply):
store_summary = "--- Aggregate object store stats across all nodes ---\n"
# TODO(ekl) it would be nice if we could provide a full memory usage
# breakdown by type (e.g., pinned by worker, primar | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | store_stats_summary | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | internal_api.py | 17 | 56 | https://github.com/ray-project/ray.git | 6 | 272 | 0 | 101 | 438 | Python | {
"docstring": "Returns formatted string describing object store stats in all nodes.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def store_stats_summary(reply):
store_summary = "--- Aggregate object store stats across all nodes ---\n"
# TODO(ekl) it would be nice if we could provide a full memory usage
# breakdown by type (e.g., pinned by worker, primary, etc.)
store_summary += (
"Plasma memory usage {} MiB, {} objects, {}% full, {}% "
"needed\n".format(
int(reply.store_stats.object_store_bytes_used / (1024 * 1024)),
reply.store_stats.num_local_objects,
round(
100
* reply.store_stats.object_store_bytes_used
/ reply.store_stats.object_store_bytes_avail,
2,
),
round(
100
* reply.store_stats.object_store_bytes_primary_copy
/ reply.store_stats.object_store_bytes_avail,
2,
),
)
)
if reply.store_stats.object_store_bytes_fallback > 0:
store_summary += "Plasma filesystem mmap usage: {} MiB\n".format(
int(reply.store_stats.object_store_bytes_fallback / (1024 * 1024))
)
if reply.store_stats.spill_time_total_s > 0:
store_summary += (
"Spilled {} MiB, {} objects, avg write throughput {} MiB/s\n".format(
int(reply.store_stats.spilled_bytes_total / (1024 * 1024)),
reply.store_stats.spilled_objects_total,
int(
reply.store_stats.spilled_bytes_total
/ (1024 * 1024)
/ reply.store_stats.spill_time_total_s
),
)
)
if reply.store_stats.restore_time_total_s > 0:
store_summary += (
"Restored {} MiB, {} objects, avg read throughput {} MiB/s\n".format(
int(reply.store_stats.restored_bytes_total / (1024 * 1024)),
reply.store_stats.restored_objects_total,
int(
reply.store_stats.restored_bytes_total
/ (1024 * 1024)
/ reply.store_stats.restore_time_total_s
),
)
)
if reply.store_stats.consumed_bytes > 0:
store_summary += "Objects consumed by Ray tasks: {} MiB.\n".format(
int(reply.store_stats.consumed_bytes / (1024 * 1024))
)
if reply.store_stats.object_pulls_queued:
store_summary += "Object fetches queued, waiting for available memory."
return store_summary
|
|
73,416 | 250,395 | 20 | tests/handlers/test_register.py | 6 | 7 | def test_spam_checker_deny(self) -> None:
self.get_failure(self.handler.register_user(localpart="user"), SynapseError)
| Add missing type hints to tests.handlers. (#14680)
And do not allow untyped defs in tests.handlers. | test_spam_checker_deny | 652d1669c5a103b1c20478770c4aaf18849c09a3 | synapse | test_register.py | 11 | 3 | https://github.com/matrix-org/synapse.git | 1 | 25 | 0 | 6 | 44 | Python | {
"docstring": "A spam checker can deny registration, which results in an error.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def test_spam_checker_deny(self) -> None:
self.get_failure(self.handler.register_user(localpart="user"), SynapseError)
|
|
21,073 | 101,668 | 71 | lib/align/aligned_face.py | 28 | 19 | def _get_pitch_yaw_roll(self) -> None:
proj_matrix = np.zeros((3, 4), dtype="float32")
proj_matrix[:3, | sort tool: Add sort by roll | _get_pitch_yaw_roll | a7d0898f64adc9816427c4923074c7955ce95ac8 | faceswap | aligned_face.py | 12 | 7 | https://github.com/deepfakes/faceswap.git | 1 | 90 | 0 | 25 | 143 | Python | {
"docstring": " Obtain the yaw, roll and pitch from the :attr:`_rotation` in eular angles. ",
"language": "en",
"n_whitespaces": 13,
"n_words": 12,
"vocab_size": 11
} | def _get_pitch_yaw_roll(self) -> None:
proj_matrix = np.zeros((3, 4), dtype="float32")
proj_matrix[:3, :3] = cv2.Rodrigues(self._rotation)[0]
euler = cv2.decomposeProjectionMatrix(proj_matrix)[-1]
self._pitch_yaw_roll = cast(Tuple[float, float, float], tuple(euler.squeeze()))
logger.trace("yaw_pitch: %s", self._pitch_yaw_roll) # type: ignore
|
|
91,989 | 292,922 | 36 | tests/components/dlna_dmr/test_data.py | 13 | 6 | def aiohttp_notify_servers_mock() -> Iterable[Mock]:
with patch(
"homeassistant. | Bump async-upnp-client to 0.25.0 (#66414)
Co-authored-by: J. Nick Koston <[email protected]> | aiohttp_notify_servers_mock | dbbb5655e5df0d72ca6b5534af624b54027cbb6d | core | test_data.py | 11 | 17 | https://github.com/home-assistant/core.git | 2 | 50 | 0 | 13 | 44 | Python | {
"docstring": "Construct mock AiohttpNotifyServer on demand, eliminating network use.\n\n This fixture provides a list of the constructed servers.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 17,
"vocab_size": 17
} | def aiohttp_notify_servers_mock() -> Iterable[Mock]:
with patch(
"homeassistant.components.dlna_dmr.data.AiohttpNotifyServer"
) as mock_constructor:
servers = []
|
|
23,823 | 109,916 | 25 | lib/mpl_toolkits/mplot3d/art3d.py | 13 | 10 | def line_collection_2d_to_3d(col, zs=0, zdir='z'):
segments3d = _paths_to_3d_segments(col.get_p | Improve mpl_toolkit documentation | line_collection_2d_to_3d | df6f95703b60348e01603f98a439b133da2938a0 | matplotlib | art3d.py | 10 | 4 | https://github.com/matplotlib/matplotlib.git | 1 | 39 | 0 | 12 | 64 | Python | {
"docstring": "Convert a `.LineCollection` to a `.Line3DCollection` object.",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 6
} | def line_collection_2d_to_3d(col, zs=0, zdir='z'):
segments3d = _paths_to_3d_segments(col.get_paths(), zs, zdir)
col.__class__ = Line3DCollection
col.set_segments(segments3d)
|
|
13,581 | 64,235 | 259 | erpnext/patches/v13_0/convert_to_website_item_in_item_card_group_template.py | 69 | 23 | def execute():
frappe.reload_doc("e_commerce", "web_template", "item_card_group")
blocks = frappe.db.get_all(
"Web Page Block",
filters={"web_template": "Item Card Group"},
fields=["parent", "web_template_values", "name"]
)
fields = generate_fields_to_edit()
for block in blocks:
web_template_value = json.loads(block.get('web_template_values'))
for field in fields:
item = web_template_value.get(field)
if not item:
continue
if frappe.db.exists("Website Item", {"item_code": item}):
website_item = frappe.db.get_value("Website Item", {"item_c | fix: Convert Item links to Website Item links in Item Card Group template data
- Changed link option to Website Item in Item card group template
- patch to convert pre-existing data | execute | 456f27724c975685c2d5a92c20296737b11c084d | erpnext | convert_to_website_item_in_item_card_group_template.py | 17 | 22 | https://github.com/frappe/erpnext.git | 6 | 159 | 0 | 51 | 275 | Python | {
"docstring": "\n Convert all Item links to Website Item link values in\n exisitng 'Item Card Group' Web Page Block data.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 18,
"vocab_size": 17
} | def execute():
frappe.reload_doc("e_commerce", "web_template", "item_card_group")
blocks = frappe.db.get_all(
"Web Page Block",
filters={"web_template": "Item Card Group"},
fields=["parent", "web_template_values", "name"]
)
fields = generate_fields_to_edit()
for block in blocks:
web_template_value = json.loads(block.get('web_template_values'))
for field in fields:
item = web_template_value.get(field)
if not item:
continue
if frappe.db.exists("Website Item", {"item_code": item}):
website_item = frappe.db.get_value("Website Item", {"item_code": item})
else:
website_item = make_new_website_item(item, web_template_value, field)
continue
if website_item:
web_template_value[field] = website_item
frappe.db.set_value("Web Page Block", block.name, "web_template_values", json.dumps(web_template_value))
|
|
23,452 | 109,136 | 60 | lib/matplotlib/lines.py | 25 | 9 | def set_pickradius(self, pickradius):
if not isinstance(pickradius, Number) or pickradius < 0:
raise ValueError("pick radius should be a distance")
self._pickradius = pickradius
pickradius = property(ge | Unify set_pickradius argument | set_pickradius | 91f47d6eff63187f582c395c007d0152980be6b3 | matplotlib | lines.py | 10 | 4 | https://github.com/matplotlib/matplotlib.git | 3 | 31 | 0 | 22 | 65 | Python | {
"docstring": "\n Set the pick radius used for containment tests.\n\n See `.contains` for more details.\n\n Parameters\n ----------\n pickradius : float\n Pick radius, in points.\n ",
"language": "en",
"n_whitespaces": 76,
"n_words": 22,
"vocab_size": 21
} | def set_pickradius(self, pickradius):
if not isinstance(pickradius, Number) or pickradius < 0:
raise ValueError("pick radius should be a distance")
self._pickradius = pickradius
pickradius = property(get_pickradius, set_pickradius)
|
|
19,977 | 100,509 | 292 | tools/preview/preview.py | 57 | 18 | def _predict(self):
with self._lock:
self._predicted_images = []
for frame in self._input_images:
sel | bugfix: Preview Tool, ensure all config items are written | _predict | 71c20252c2e747f692289cdefe80ad0d5a456ea6 | faceswap | preview.py | 14 | 17 | https://github.com/deepfakes/faceswap.git | 5 | 117 | 0 | 40 | 198 | Python | {
"docstring": " Predict from the loaded frames.\n\n With a threading lock (to prevent stacking), run the selected faces through the Faceswap\n model predict function and add the output to :attr:`predicted`\n ",
"language": "en",
"n_whitespaces": 50,
"n_words": 28,
"vocab_size": 25
} | def _predict(self):
with self._lock:
self._predicted_images = []
for frame in self._input_images:
self._predictor.in_queue.put(frame)
idx = 0
while idx < self._sample_size:
logger.debug("Predicting face %s of %s", idx + 1, self._sample_size)
items = self._predictor.out_queue.get()
if items == "EOF":
logger.debug("Received EOF")
break
for item in items:
self._predicted_images.append(item)
logger.debug("Predicted face %s of %s", idx + 1, self._sample_size)
idx += 1
logger.debug("Predicted faces")
|
|
48,907 | 198,396 | 50 | sympy/polys/polytools.py | 22 | 10 | def exclude(f):
J, new = f.rep.exclude()
gens = [gen for j, gen in enumer | Cleanup loops and ranges | exclude | 7d773eb18daaef3c54f34d1ac6cbc5b83a5bb16c | sympy | polytools.py | 11 | 4 | https://github.com/sympy/sympy.git | 3 | 49 | 0 | 20 | 78 | Python | {
"docstring": "\n Remove unnecessary generators from ``f``.\n\n Examples\n ========\n\n >>> from sympy import Poly\n >>> from sympy.abc import a, b, c, d, x\n\n >>> Poly(a + x, a, b, c, d, x).exclude()\n Poly(a + x, a, x, domain='ZZ')\n\n ",
"language": "en",
"n_whitespaces": 93,
"n_words": 36,
"vocab_size": 22
} | def exclude(f):
J, new = f.rep.exclude()
gens = [gen for j, gen in enumerate(f.gens) if j not in J]
return f.per(new, gens=gens)
|
|
22,741 | 107,424 | 60 | lib/matplotlib/axis.py | 21 | 9 | def set_ticks(self, ticks, labels=None, *, minor=False, **kwargs):
result = self._set_tick_locations(ticks, minor=minor)
if labels is not None:
self.set_ticklabels(label | Expanded documentation of Axis.set_ticks as per discussion in issue #22262 (#22270)
* Expanded documentation of Axis.set_ticks()
* Fix flake8 W293 (blank line contains whitespace) warnings
* Expanded the documentation even more based on discussion in issue #22262
* Update lib/matplotlib/axis.py - @jklymak rewording
Co-authored-by: Jody Klymak <[email protected]>
* Reduced verbosity of doc by @jklymak 's suggestion.
* On second thought, the previous wording could be seen as very ambiguous.
* Update set_ticks docstring by @timhoffm compromise suggestion
Co-authored-by: Tim Hoffmann <[email protected]>
* Removed extra sentence as per @timhoffm review
* Blank line whitespace issue crept up again
* Update lib/matplotlib/axis.py as per correction by @timhoffm
Co-authored-by: Tim Hoffmann <[email protected]>
Co-authored-by: unknown <>
Co-authored-by: Jody Klymak <[email protected]>
Co-authored-by: Tim Hoffmann <[email protected]> | set_ticks | 695bc25c7a9b198e00c54496a8eed56a8a908cbf | matplotlib | axis.py | 10 | 5 | https://github.com/matplotlib/matplotlib.git | 2 | 54 | 0 | 20 | 81 | Python | {
"docstring": "\n Set this Axis' tick locations and optionally labels.\n\n If necessary, the view limits of the Axis are expanded so that all\n given ticks are visible.\n\n Parameters\n ----------\n ticks : list of floats\n List of tick locations. The axis `.Locator` is replaced by a\n `~.ticker.FixedLocator`.\n\n Some tick formatters will not label arbitrary tick positions;\n e.g. log formatters only label decade ticks by default. In\n such a case you can set a formatter explicitly on the axis\n using `.Axis.set_major_formatter` or provide formatted\n *labels* yourself.\n labels : list of str, optional\n List of tick labels. If not set, the labels are generated with\n the axis tick `.Formatter`.\n minor : bool, default: False\n If ``False``, set the major ticks; if ``True``, the minor ticks.\n **kwargs\n `.Text` properties for the labels. These take effect only if you\n pass *labels*. In other cases, please use `~.Axes.tick_params`.\n\n Notes\n -----\n The mandatory expansion of the view limits is an intentional design\n choice to prevent the surprise of a non-visible tick. If you need\n other limits, you should set the limits explicitly after setting the\n ticks.\n ",
"language": "en",
"n_whitespaces": 423,
"n_words": 177,
"vocab_size": 115
} | def set_ticks(self, ticks, labels=None, *, minor=False, **kwargs):
result = self._set_tick_locations(ticks, minor=minor)
if labels is not None:
self.set_ticklabels(labels, minor=minor, **kwargs)
return result
|
|
@frappe.whitelist() | 14,558 | 67,567 | 45 | erpnext/startup/leaderboard.py | 73 | 22 | def get_all_customers(date_range, company, field, limit=None):
if field == "outstanding_amount":
filters = [["docstatus", "=", "1"], ["company", "=", company]]
if date_range:
date_range = frappe.parse_json(date_range)
filters.append(["posting_date", ">=", "between", [date_range[0], date_range[1]]])
return frappe.db.get_all(
"Sales Invoice",
fields=["customer as name", "sum(outstanding_amount) as value"],
filters=filters,
group_by="customer",
order_by="value desc",
limit=limit,
)
else:
if field == "total_sales_amount":
select_field = "sum(so_item.base_net_amount)"
elif field == "total_qty_sold":
select_field = "sum(so_item.stock_qty)"
date_condition = get_date_condition(date_range, "so.transaction_date")
return frappe.db.sql(
.format(
select_field, dat | style: format code with black | get_all_customers | 494bd9ef78313436f0424b918f200dab8fc7c20b | erpnext | leaderboard.py | 14 | 35 | https://github.com/frappe/erpnext.git | 5 | 162 | 1 | 57 | 280 | Python | {
"docstring": "\n\t\t\tselect so.customer as name, {0} as value\n\t\t\tFROM `tabSales Order` as so JOIN `tabSales Order Item` as so_item\n\t\t\t\tON so.name = so_item.parent\n\t\t\twhere so.docstatus = 1 {1} and so.company = %s\n\t\t\tgroup by so.customer\n\t\t\torder by value DESC\n\t\t\tlimit %s\n\t\t",
"language": "en",
"n_whitespaces": 33,
"n_words": 40,
"vocab_size": 30
} | def get_all_customers(date_range, company, field, limit=None):
if field == "outstanding_amount":
filters = [["docstatus", "=", "1"], ["company", "=", company]]
if date_range:
date_range = frappe.parse_json(date_range)
filters.append(["posting_date", ">=", "between", [date_range[0], date_range[1]]])
return frappe.db.get_all(
"Sales Invoice",
fields=["customer as name", "sum(outstanding_amount) as value"],
filters=filters,
group_by="customer",
order_by="value desc",
limit=limit,
)
else:
if field == "total_sales_amount":
select_field = "sum(so_item.base_net_amount)"
elif field == "total_qty_sold":
select_field = "sum(so_item.stock_qty)"
date_condition = get_date_condition(date_range, "so.transaction_date")
return frappe.db.sql(
.format(
select_field, date_condition
),
(company, cint(limit)),
as_dict=1,
)
@frappe.whitelist() |
103,871 | 305,079 | 189 | homeassistant/components/zha/config_flow.py | 45 | 20 | async def _async_create_radio_entity(self) -> FlowResult:
assert self._title is not None
assert self._radio_type is not None
assert self._device_path is not None
assert self._device_settings is not None
device_settings = self._device_settings.copy()
device_settings[CONF_DEVICE_PATH] = await self.hass.async_add_executor_job(
usb.get_serial_by_id, self._device_path
)
return self.async_create_entry(
title=self._title,
data={
CONF_DEVICE: device_settings,
CONF_RADIO_TYPE: self._radio_type.name,
},
)
| ZHA backup/restore config flow (#77044) | _async_create_radio_entity | f78b39bdbfbe151e8bab72610b6fe03afc8c0747 | core | config_flow.py | 12 | 17 | https://github.com/home-assistant/core.git | 1 | 94 | 0 | 30 | 143 | Python | {
"docstring": "Create a config entity with the current flow state.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | async def _async_create_radio_entity(self) -> FlowResult:
assert self._title is not None
assert self._radio_type is not None
assert self._device_path is not None
assert self._device_settings is not None
device_settings = self._device_settings.copy()
device_settings[CONF_DEVICE_PATH] = await self.hass.async_add_executor_job(
usb.get_serial_by_id, self._device_path
)
return self.async_create_entry(
title=self._title,
data={
CONF_DEVICE: device_settings,
CONF_RADIO_TYPE: self._radio_type.name,
},
)
|
|
53,905 | 215,279 | 52 | salt/transport/zeromq.py | 17 | 14 | def publish_daemon(self, publish_payload, *args, **kwargs):
context = zmq.Context(1)
ioloop = salt.ext.tornado.ioloop.IOLoo | Refactor into transports and channels | publish_daemon | d4e6111086ff713eb6609dc6c98cec98aded2564 | salt | zeromq.py | 11 | 9 | https://github.com/saltstack/salt.git | 1 | 68 | 0 | 15 | 67 | Python | {
"docstring": "\n Bind to the interface specified in the configuration file\n ",
"language": "en",
"n_whitespaces": 24,
"n_words": 9,
"vocab_size": 8
} | def publish_daemon(self, publish_payload, *args, **kwargs):
context = zmq.Context(1)
ioloop = salt.ext.tornado.ioloop.IOLoop()
ioloop.make_current()
# Set up the context |
|
47,917 | 196,417 | 362 | sympy/printing/str.py | 124 | 23 | def _print_Pow(self, expr, rational=False):
PREC = precedence(expr)
if expr.exp is S.Half and not rational:
return "sqrt(%s)" % self._print(expr.base)
if expr.is_commutative:
if -expr.exp is S.Half and | Moved imports to higher level | _print_Pow | 59d22b6bb7287613d598611027f640d068ca5748 | sympy | str.py | 17 | 15 | https://github.com/sympy/sympy.git | 11 | 218 | 0 | 84 | 347 | Python | {
"docstring": "Printing helper function for ``Pow``\n\n Parameters\n ==========\n\n rational : bool, optional\n If ``True``, it will not attempt printing ``sqrt(x)`` or\n ``x**S.Half`` as ``sqrt``, and will use ``x**(1/2)``\n instead.\n\n See examples for additional details\n\n Examples\n ========\n\n >>> from sympy import sqrt, StrPrinter\n >>> from sympy.abc import x\n\n How ``rational`` keyword works with ``sqrt``:\n\n >>> printer = StrPrinter()\n >>> printer._print_Pow(sqrt(x), rational=True)\n 'x**(1/2)'\n >>> printer._print_Pow(sqrt(x), rational=False)\n 'sqrt(x)'\n >>> printer._print_Pow(1/sqrt(x), rational=True)\n 'x**(-1/2)'\n >>> printer._print_Pow(1/sqrt(x), rational=False)\n '1/sqrt(x)'\n\n Notes\n =====\n\n ``sqrt(x)`` is canonicalized as ``Pow(x, S.Half)`` in SymPy,\n so there is no need of defining a separate printer for ``sqrt``.\n Instead, it should be handled here as well.\n ",
"language": "en",
"n_whitespaces": 307,
"n_words": 102,
"vocab_size": 81
} | def _print_Pow(self, expr, rational=False):
PREC = precedence(expr)
if expr.exp is S.Half and not rational:
return "sqrt(%s)" % self._print(expr.base)
if expr.is_commutative:
if -expr.exp is S.Half and not rational:
# Note: Don't test "expr.exp == -S.Half" here, because that will
# match -0.5, which we don't want.
return "%s/sqrt(%s)" % tuple(map(lambda arg: self._print(arg), (S.One, expr.base)))
if expr.exp is -S.One:
# Similarly to the S.Half case, don't test with "==" here.
return '%s/%s' % (self._print(S.One),
self.parenthesize(expr.base, PREC, strict=False))
e = self.parenthesize(expr.exp, PREC, strict=False)
if self.printmethod == '_sympyrepr' and expr.exp.is_Rational and expr.exp.q != 1:
# the parenthesized exp should be '(Rational(a, b))' so strip parens,
# but just check to be sure.
if e.startswith('(Rational'):
return '%s**%s' % (self.parenthesize(expr.base, PREC, strict=False), e[1:-1])
return '%s**%s' % (self.parenthesize(expr.base, PREC, strict=False), e)
|
|
117,386 | 320,838 | 279 | qutebrowser/misc/sessions.py | 70 | 21 | def _save_tab(self, tab, active, minimal=False):
data: _JsonType = {'history': []}
if active:
data['active'] = True
if minimal:
history = [tab.history.current_item()]
else:
history = tab.history
for idx, item in enumerate(history):
qtutils.ensure_valid(item)
item_data = self._save_tab_item(tab, idx, item)
if item.url().scheme() == 'qute' and item.url().host() == 'back':
# don't add qute://back to the session file
if item_data.get('active', False) and data['history']:
# mark entry before qute://back as active
data['history'][-1] | Add --minimal option to session-save command
Currently the session-save commande make a dump of all tabs history and stores
them in the session file. --minimal flag adds the option to store only the last
item of the history.
Signed-off-by: shirenn <[email protected]> | _save_tab | 4026854f45b63ec71bdbef42d71831beb5f10714 | qutebrowser | sessions.py | 16 | 17 | https://github.com/qutebrowser/qutebrowser.git | 8 | 148 | 0 | 54 | 253 | Python | {
"docstring": "Get a dict with data for a single tab.\n\n Args:\n tab: The WebView to save.\n active: Whether the tab is currently active.\n ",
"language": "en",
"n_whitespaces": 58,
"n_words": 22,
"vocab_size": 21
} | def _save_tab(self, tab, active, minimal=False):
data: _JsonType = {'history': []}
if active:
data['active'] = True
if minimal:
history = [tab.history.current_item()]
else:
history = tab.history
for idx, item in enumerate(history):
qtutils.ensure_valid(item)
item_data = self._save_tab_item(tab, idx, item)
if item.url().scheme() == 'qute' and item.url().host() == 'back':
# don't add qute://back to the session file
if item_data.get('active', False) and data['history']:
# mark entry before qute://back as active
data['history'][-1]['active'] = True
else:
data['history'].append(item_data)
return data
|
|
29,751 | 132,415 | 141 | python/ray/tune/tests/test_checkpoint_manager.py | 38 | 18 | def testBestCheckpoints(self):
keep_checkpoints_num = 4
checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num)
checkpoints = [
Checkpoint(Checkpoint.PERSISTENT, i, self.mock_result(i)) for i in range(16)
]
random.shuffle(checkpoints)
for checkpoint in checkpoints:
checkpoint_manager.on_checkpoint(checkpoint)
best_checkpoints = checkpoint_manager.best_checkpoints()
self.assertEqual(len(best_checkpoints), keep_checkpoints_num)
for i in range(len(best_checkpoints)):
self.assertEqual(best_checkpoints[i].val | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | testBestCheckpoints | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | test_checkpoint_manager.py | 11 | 13 | https://github.com/ray-project/ray.git | 4 | 104 | 0 | 29 | 164 | Python | {
"docstring": "\n Tests that the best checkpoints are tracked and ordered correctly.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 10,
"vocab_size": 10
} | def testBestCheckpoints(self):
keep_checkpoints_num = 4
checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num)
checkpoints = [
Checkpoint(Checkpoint.PERSISTENT, i, self.mock_result(i)) for i in range(16)
]
random.shuffle(checkpoints)
for checkpoint in checkpoints:
checkpoint_manager.on_checkpoint(checkpoint)
best_checkpoints = checkpoint_manager.best_checkpoints()
self.assertEqual(len(best_checkpoints), keep_checkpoints_num)
for i in range(len(best_checkpoints)):
self.assertEqual(best_checkpoints[i].value, i + 12)
|
|
40,839 | 173,342 | 182 | cps/config_sql.py | 40 | 20 | def save(self):
s = self._read_from_storage() # type: _Settings
for k, v in self.__dict__.items():
if k[0] == '_':
continue
if hasattr(s, k):
setattr(s, k, v)
log.debug("_ConfigSQL updating storage")
self._session.merge(s)
try:
self._sessi | Code cosmetics | save | 4ea80e9810a14ca3617f08a4ae5cfa6b50482e9a | calibre-web | config_sql.py | 11 | 15 | https://github.com/janeczku/calibre-web.git | 5 | 99 | 0 | 38 | 170 | Python | {
"docstring": "Apply all configuration values to the underlying storage.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | def save(self):
s = self._read_from_storage() # type: _Settings
for k, v in self.__dict__.items():
if k[0] == '_':
continue
if hasattr(s, k):
setattr(s, k, v)
log.debug("_ConfigSQL updating storage")
self._session.merge(s)
try:
self._session.commit()
except OperationalError as e:
log.error('Database error: %s', e)
self._session.rollback()
self.load()
|
|
18,538 | 89,408 | 636 | tests/sentry/rules/history/test_preview.py | 153 | 36 | def test_transactions(self):
prev_hour = timezone.now() - timedelta(hours=1)
event = self.transaction_data.copy()
event.update(
{
"start_timestamp": iso_format(prev_hour - timedelta(minutes=1)),
"timestamp": iso_format(prev_hour),
"tags": {"foo": "bar"},
"transaction": "this is where a transaction's 'message' is stored",
}
)
transaction = self.store_event(project_id=self.project.id, data=event)
perf_issue = transaction.groups[0]
perf_issue.update(first_seen=prev_hour)
Activity.objects.create(
project=self.project,
group=perf_issue,
type=ActivityType.SET_REGRESSION.value,
datetime=prev_hour,
data={"event_id": transaction.event_id},
)
conditions = [{"id": "sentry.rules.conditions.regression_event.RegressionEventCondition"}]
filters = [
{
"id": "sentry.rules.filters.tagged_event.TaggedEventFilter",
"key": "foo",
"match": "eq",
"value": "bar",
}
]
result = preview(self.project, conditions, filters, "all", "all", 0)
assert perf_issue.id in result
filters[0]["value"] = "baz"
result = preview(self.project, conditions, filters, "all", "all", 0)
assert perf_issue.id not in result
filters = [
{
"id": "sentry.rules.filters.event_attribute.EventAttributeFilter",
"attribute": "message",
"match": "eq",
"value": "this is where a transaction's 'message' is stored",
}
]
result = preview(self.project, conditions, filters, "all", "all", 0)
assert perf_issue.id in result
filters[0]["value"] = "wrong message"
result = preview(self.project, conditions, filters, "all", "all", 0)
| feat(alert-preview): last triggered (#42098)
Attaches `last_triggered` to group info. `preview` now returns a mapping
of group_ids to triggers, updated tests to reflect that. | test_transactions | 583a7ec15744b2ca8a9c56df484516111dbf783d | sentry | test_preview.py | 15 | 59 | https://github.com/getsentry/sentry.git | 1 | 311 | 0 | 81 | 524 | Python | {
"docstring": "\n conditions = [{\"id\": \"sentry.rules.conditions.first_seen_event.FirstSeenEventCondition\"}]\n filters = [{\n \"id\": \"sentry.rules.filters.tagged_event.TaggedEventFilter\",\n \"key\": \"foo\",\n \"match\": \"eq\",\n \"value\": \"bar\",\n }]\n result = preview(self.project, conditions, filters, \"all\", \"all\", 0)\n assert perf_issue.id in result\n ",
"language": "en",
"n_whitespaces": 115,
"n_words": 28,
"vocab_size": 24
} | def test_transactions(self):
prev_hour = timezone.now() - timedelta(hours=1)
event = self.transaction_data.copy()
event.update(
{
"start_timestamp": iso_format(prev_hour - timedelta(minutes=1)),
"timestamp": iso_format(prev_hour),
"tags": {"foo": "bar"},
"transaction": "this is where a transaction's 'message' is stored",
}
)
transaction = self.store_event(project_id=self.project.id, data=event)
perf_issue = transaction.groups[0]
perf_issue.update(first_seen=prev_hour)
Activity.objects.create(
project=self.project,
group=perf_issue,
type=ActivityType.SET_REGRESSION.value,
datetime=prev_hour,
data={"event_id": transaction.event_id},
)
conditions = [{"id": "sentry.rules.conditions.regression_event.RegressionEventCondition"}]
filters = [
{
"id": "sentry.rules.filters.tagged_event.TaggedEventFilter",
"key": "foo",
"match": "eq",
"value": "bar",
}
]
result = preview(self.project, conditions, filters, "all", "all", 0)
assert perf_issue.id in result
filters[0]["value"] = "baz"
result = preview(self.project, conditions, filters, "all", "all", 0)
assert perf_issue.id not in result
filters = [
{
"id": "sentry.rules.filters.event_attribute.EventAttributeFilter",
"attribute": "message",
"match": "eq",
"value": "this is where a transaction's 'message' is stored",
}
]
result = preview(self.project, conditions, filters, "all", "all", 0)
assert perf_issue.id in result
filters[0]["value"] = "wrong message"
result = preview(self.project, conditions, filters, "all", "all", 0)
assert perf_issue.id not in result
# this can be tested when SNS-1891 is fixed
|
|
19,271 | 96,067 | 81 | tests/sentry/models/test_release.py | 12 | 9 | def test_follows_semver_all_releases_semver_and_missing_package_semver_release_version(self):
assert (
follows_semver_versioning_scheme(
org_id=self.org.id, project_id=self.proj_1.id, release_version="2.0.0"
)
is False
)
| fix(semver): Fixes semver check bug (#31567)
Fixes bug that considers a release
to be following semver even if the release
does not have a package | test_follows_semver_all_releases_semver_and_missing_package_semver_release_version | 8e70206e59a81fba2f9a833aed8aa762848c335c | sentry | test_release.py | 12 | 7 | https://github.com/getsentry/sentry.git | 1 | 33 | 0 | 11 | 54 | Python | {
"docstring": "\n Test that ensures that even if a project is following semver, then if the release_version\n supplied lacks a package, then for that specific release we opt the project out of being\n considered a semver project\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 35,
"vocab_size": 26
} | def test_follows_semver_all_releases_semver_and_missing_package_semver_release_version(self):
assert (
follows_semver_versioning_scheme(
org_id=self.org.id, project_id=self.proj_1.id, release_version="2.0.0"
)
is False
)
|
|
48,612 | 197,534 | 22 | sympy/stats/joint_rv_types.py | 18 | 7 | def MultivariateT(syms, mu, sigma, v):
return multivariate_rv(Mu | Improved some documentation in the stats module | MultivariateT | 7fe8e027ae1d7f683243c0229b961671a6cbb4c5 | sympy | joint_rv_types.py | 7 | 2 | https://github.com/sympy/sympy.git | 1 | 25 | 0 | 16 | 37 | Python | {
"docstring": "\n Creates a joint random variable with multivariate T-distribution.\n\n Parameters\n ==========\n\n syms : A symbol/str\n For identifying the random variable.\n mu : A list/matrix\n Representing the location vector\n sigma : The shape matrix for the distribution\n\n Examples\n ========\n\n >>> from sympy.stats import density, MultivariateT\n >>> from sympy import Symbol\n\n >>> x = Symbol(\"x\")\n >>> X = MultivariateT(\"x\", [1, 1], [[1, 0], [0, 1]], 2)\n\n >>> density(X)(1, 2)\n 2/(9*pi)\n\n Returns\n =======\n\n RandomSymbol\n\n ",
"language": "en",
"n_whitespaces": 139,
"n_words": 70,
"vocab_size": 56
} | def MultivariateT(syms, mu, sigma, v):
return multivariate_rv(MultivariateTDistribution, syms, mu, sigma, v)
#-------------------------------------------------------------------------------
# Multivariate Normal Gamma distribution ---------------------------------------
|
|
39,678 | 165,559 | 49 | pandas/core/indexes/base.py | 17 | 8 | def _can_hold_identifiers_and_holds_name(self, name) -> bool:
if self.is_object() or is_string_dtype(self.dtype) or self.is_categorical():
return name in self
return False
| BUG: DataFrame.getattribute raising if columns have dtype string (#46301) | _can_hold_identifiers_and_holds_name | 3aec1d5756f363e25062914dbb82bd8b25b399ce | pandas | base.py | 10 | 12 | https://github.com/pandas-dev/pandas.git | 4 | 36 | 0 | 15 | 60 | Python | {
"docstring": "\n Faster check for ``name in self`` when we know `name` is a Python\n identifier (e.g. in NDFrame.__getattr__, which hits this to support\n . key lookup). For indexes that can't hold identifiers (everything\n but object & categorical) we just return False.\n\n https://github.com/pandas-dev/pandas/issues/19764\n ",
"language": "en",
"n_whitespaces": 84,
"n_words": 41,
"vocab_size": 39
} | def _can_hold_identifiers_and_holds_name(self, name) -> bool:
if self.is_object() or is_string_dtype(self.dtype) or self.is_categorical():
return name in self
return False
|
|
71,773 | 247,605 | 282 | tests/handlers/test_directory.py | 63 | 15 | def test_remove_other_alias(self) -> None:
# Create a second alias.
other_test_alias = "#test2:test"
other_room_alias = self._add_alias(other_test_alias)
# Set the alias as the canonical alias for this room.
self._set_canonical_alias(
{
"alias": self.test_alias,
"alt_aliases": [self.test_alias, other_test_alias],
}
)
data = self._get_canonical_alias()
self.assertEqual(data["content"]["alias"], self.test_alias)
self.assertEqual(
data["content"]["alt_aliases"], [self.test_alias, other_test_alias]
)
# Delete the second alia | Add type hints to some tests/handlers files. (#12224) | test_remove_other_alias | 5dd949bee6158a8b651db9f2ae417a62c8184bfd | synapse | test_directory.py | 12 | 23 | https://github.com/matrix-org/synapse.git | 1 | 146 | 0 | 44 | 247 | Python | {
"docstring": "Removing an alias listed as in alt_aliases should remove it there too.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | def test_remove_other_alias(self) -> None:
# Create a second alias.
other_test_alias = "#test2:test"
other_room_alias = self._add_alias(other_test_alias)
# Set the alias as the canonical alias for this room.
self._set_canonical_alias(
{
"alias": self.test_alias,
"alt_aliases": [self.test_alias, other_test_alias],
}
)
data = self._get_canonical_alias()
self.assertEqual(data["content"]["alias"], self.test_alias)
self.assertEqual(
data["content"]["alt_aliases"], [self.test_alias, other_test_alias]
)
# Delete the second alias.
self.get_success(
self.handler.delete_association(
create_requester(self.admin_user), other_room_alias
)
)
data = self._get_canonical_alias()
self.assertEqual(data["content"]["alias"], self.test_alias)
self.assertEqual(data["content"]["alt_aliases"], [self.test_alias])
|
|
117,026 | 319,960 | 60 | src/documents/tests/test_api.py | 10 | 8 | def test_get_comments_no_doc(self):
response = self.client.get(
"/api/documents/500/comments/",
format="js | Starts on implementing tests for the new API | test_get_comments_no_doc | 6d5d308d6c7b7e359ba72964a300634e1065ace9 | paperless-ngx | test_api.py | 10 | 6 | https://github.com/paperless-ngx/paperless-ngx.git | 1 | 31 | 0 | 10 | 54 | Python | {
"docstring": "\n GIVEN:\n - A request to get comments from a non-existent document\n WHEN:\n - API request for document comments is made\n THEN:\n - HTTP 404 is returned\n ",
"language": "en",
"n_whitespaces": 88,
"n_words": 26,
"vocab_size": 20
} | def test_get_comments_no_doc(self):
response = self.client.get(
"/api/documents/500/comments/",
format="json",
)
self.assertEqual(response.status_code, 404)
|
|
117,678 | 321,351 | 62 | tests/unit/keyinput/test_basekeyparser.py | 20 | 20 | def test_mapping_keypad(self, config_stub, keyparser):
config_stub.val.bindings.commands = {'normal': {'a': 'nop'}}
config_stub.val.bindings.key_ | Run scripts/dev/rewrite_enums.py | test_mapping_keypad | 0877fb0d78635692e481c8bde224fac5ad0dd430 | qutebrowser | test_basekeyparser.py | 11 | 6 | https://github.com/qutebrowser/qutebrowser.git | 1 | 78 | 0 | 18 | 134 | Python | {
"docstring": "Make sure falling back to non-numpad keys works with mappings.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | def test_mapping_keypad(self, config_stub, keyparser):
config_stub.val.bindings.commands = {'normal': {'a': 'nop'}}
config_stub.val.bindings.key_mappings = {'1': 'a'}
info = keyutils.KeyInfo(Qt.Key.Key_1, Qt.KeyboardModifier.KeypadModifier)
keyparser.handle(info.to_event())
keyparser.execute.assert_called_once_with('nop', None)
|
|
826 | 5,799 | 71 | instapy/like_util.py | 32 | 14 | def verify_liked_image(browser, logger):
browser.refresh()
unlike_xpath = read_xpath(like_image.__name__, "un | PR - Fix `extract_text_from_element()`and `find_element*()` to `find_element()` (#6438)
* Updated getUserData() and find_element*
Signed-off-by: elulcao <[email protected]>
Thanks @breuerfelix for reviewing, 🚀
People in this thread please let me know if something is not OK, IG changed a lot these days. 🤗 @her | verify_liked_image | 2a157d452611d37cf50ccb7d56ff1a06e9790ecb | InstaPy | like_util.py | 11 | 9 | https://github.com/InstaPy/InstaPy.git | 2 | 55 | 0 | 30 | 94 | Python | {
"docstring": "Check for a ban on likes using the last liked image",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def verify_liked_image(browser, logger):
browser.refresh()
unlike_xpath = read_xpath(like_image.__name__, "unlike")
like_elem = browser.find_elements(By.XPATH, unlike_xpath)
if len(like_elem) == 1:
return True
else:
logger.warning("--> Image was NOT liked! You have a BLOCK on likes!")
return False
|
|
70,811 | 245,505 | 41 | mmdet/structures/mask/structures.py | 13 | 8 | def get_bboxes(self, dst_type='hbb'):
from ..bbox import get_box_type
| [Refactor] Refactor pipelines with boxlist. (#8562)
* Refactor pipelines and data_preprocesser by boxlist
* Refactor browse_dataset.py
* Update
* Update
* Update
* Update
* update
* Update
* Change with_box_wrapped to with_boxlist
* Fix comments
* Fix commits
* Update UT | get_bboxes | af063a6f25ddae4de90646f86b2db824f3d00138 | mmdetection | structures.py | 8 | 4 | https://github.com/open-mmlab/mmdetection.git | 1 | 31 | 0 | 13 | 55 | Python | {
"docstring": "Get the certain type boxes from masks.\n\n Please refer to ``mmdet.structures.bbox.box_type`` for more details of\n the box type.\n\n Args:\n dst_type: Destination box type.\n\n Returns:\n :obj:`BaseBoxes`: Certain type boxes.\n ",
"language": "en",
"n_whitespaces": 85,
"n_words": 28,
"vocab_size": 24
} | def get_bboxes(self, dst_type='hbb'):
from ..bbox import get_box_type
_, box_type_cls = get_box_type(dst_type)
return box_type_cls.from_instance_masks(self)
|
|
48,349 | 197,116 | 41 | sympy/tensor/tensor.py | 8 | 5 | def deprecate_call():
sympy_deprecation_warning(
,
deprecated_since_version="1.5",
active_deprecations_target="deprecated-tensor- | Update the various tensor deprecations | deprecate_call | cba899d4137b0b65f6850120ee42cd4fcd4f9dbf | sympy | tensor.py | 9 | 10 | https://github.com/sympy/sympy.git | 1 | 21 | 0 | 8 | 37 | Python | {
"docstring": "\n Calling a tensor like Tensor(*indices) is deprecated. Use\n Tensor.substitute_indices() instead.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 10,
"vocab_size": 10
} | def deprecate_call():
sympy_deprecation_warning(
,
deprecated_since_version="1.5",
active_deprecations_target="deprecated-tensor-fun-eval",
stacklevel=4,
)
|
|
12,059 | 60,271 | 194 | code/deep/BJMMD/caffe/python/caffe/net_spec.py | 59 | 19 | def assign_proto(proto, name, val):
is_repeated_field = hasattr(getattr(proto, name), 'extend')
if is_repeated_field and not isinstance(val, list):
val = [val]
if isinstance(val, list):
if isinstance(val[0], dict):
for item in val:
proto_item = getattr(proto, name).add()
for k, v in six.iteritems(item):
| Balanced joint maximum mean discrepancy for deep transfer learning | assign_proto | cc4d0564756ca067516f71718a3d135996525909 | transferlearning | net_spec.py | 16 | 17 | https://github.com/jindongwang/transferlearning.git | 9 | 151 | 0 | 37 | 230 | Python | {
"docstring": "Assign a Python object to a protobuf message, based on the Python\n type (in recursive fashion). Lists become repeated fields/messages, dicts\n become messages, and other types are assigned directly. For convenience,\n repeated fields whose values are not lists are converted to single-element\n lists; e.g., `my_repeated_int_field=3` is converted to\n `my_repeated_int_field=[3]`.",
"language": "en",
"n_whitespaces": 63,
"n_words": 49,
"vocab_size": 40
} | def assign_proto(proto, name, val):
is_repeated_field = hasattr(getattr(proto, name), 'extend')
if is_repeated_field and not isinstance(val, list):
val = [val]
if isinstance(val, list):
if isinstance(val[0], dict):
for item in val:
proto_item = getattr(proto, name).add()
for k, v in six.iteritems(item):
assign_proto(proto_item, k, v)
else:
getattr(proto, name).extend(val)
elif isinstance(val, dict):
for k, v in six.iteritems(val):
assign_proto(getattr(proto, name), k, v)
else:
setattr(proto, name, val)
|
|
48,155 | 196,759 | 66 | sympy/assumptions/handlers/common.py | 13 | 8 | def __new__(cls, *args, **kwargs):
sympy_deprecation_warning(
,
depr | Update the AskHandler deprecation warnings
n.b., the issue number in the original warning message was wrong. It should
have been #20837. | __new__ | ad766d1c02943e86f50559abfd0c72e582c9ca6a | sympy | common.py | 9 | 10 | https://github.com/sympy/sympy.git | 1 | 39 | 0 | 12 | 64 | Python | {
"docstring": "\n The AskHandler system is deprecated. The AskHandler class should\n be replaced with the multipledispatch handler of Predicate\n ",
"language": "en",
"n_whitespaces": 51,
"n_words": 17,
"vocab_size": 15
} | def __new__(cls, *args, **kwargs):
sympy_deprecation_warning(
,
deprecated_since_version="1.8",
active_deprecations_target='deprecated-askhandler',
)
return super().__new__(cls, *args, **kwargs)
|
|
76,613 | 260,996 | 308 | sklearn/utils/sparsefuncs.py | 121 | 22 | def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None):
_raise_error_wrong_axis(axis)
if not isinstance(X, (sp.csr_matrix, sp.csc_matrix)):
_raise_typeerror(X)
if np.size(last_n) == 1:
last_n = np.full(last_mean.shape, last_n, dtype=last_mean.dtype)
if not (np.size(last_mean) == np.size(last_var) == np.size(last_n)):
raise ValueError("last_mean, last_var, last_n do not have the same shapes.")
| DOC Ensures that incr_mean_variance_axis passes numpydoc validation (#24477) | incr_mean_variance_axis | 02e36b4d866d7c7b14397ab291cb3e97d1105d5c | scikit-learn | sparsefuncs.py | 17 | 26 | https://github.com/scikit-learn/scikit-learn.git | 9 | 206 | 0 | 74 | 366 | Python | {
"docstring": "Compute incremental mean and variance along an axis on a CSR or CSC matrix.\n\n last_mean, last_var are the statistics computed at the last step by this\n function. Both must be initialized to 0-arrays of the proper size, i.e.\n the number of features in X. last_n is the number of samples encountered\n until now.\n\n Parameters\n ----------\n X : CSR or CSC sparse matrix of shape (n_samples, n_features)\n Input data.\n\n axis : {0, 1}\n Axis along which the axis should be computed.\n\n last_mean : ndarray of shape (n_features,) or (n_samples,), dtype=floating\n Array of means to update with the new data X.\n Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1.\n\n last_var : ndarray of shape (n_features,) or (n_samples,), dtype=floating\n Array of variances to update with the new data X.\n Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1.\n\n last_n : float or ndarray of shape (n_features,) or (n_samples,), \\\n dtype=floating\n Sum of the weights seen so far, excluding the current weights\n If not float, it should be of shape (n_features,) if\n axis=0 or (n_samples,) if axis=1. If float it corresponds to\n having same weights for all samples (or features).\n\n weights : ndarray of shape (n_samples,) or (n_features,), default=None\n If axis is set to 0 shape is (n_samples,) or\n if axis is set to 1 shape is (n_features,).\n If it is set to None, then samples are equally weighted.\n\n .. versionadded:: 0.24\n\n Returns\n -------\n means : ndarray of shape (n_features,) or (n_samples,), dtype=floating\n Updated feature-wise means if axis = 0 or\n sample-wise means if axis = 1.\n\n variances : ndarray of shape (n_features,) or (n_samples,), dtype=floating\n Updated feature-wise variances if axis = 0 or\n sample-wise variances if axis = 1.\n\n n : ndarray of shape (n_features,) or (n_samples,), dtype=integral\n Updated number of seen samples per feature if axis=0\n or number of seen features per sample if axis=1.\n\n If weights is not None, n is a sum of the weights of the seen\n samples or features instead of the actual number of seen\n samples or features.\n\n Notes\n -----\n NaNs are ignored in the algorithm.\n ",
"language": "en",
"n_whitespaces": 579,
"n_words": 344,
"vocab_size": 134
} | def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None):
_raise_error_wrong_axis(axis)
if not isinstance(X, (sp.csr_matrix, sp.csc_matrix)):
_raise_typeerror(X)
if np.size(last_n) == 1:
last_n = np.full(last_mean.shape, last_n, dtype=last_mean.dtype)
if not (np.size(last_mean) == np.size(last_var) == np.size(last_n)):
raise ValueError("last_mean, last_var, last_n do not have the same shapes.")
if axis == 1:
if np.size(last_mean) != X.shape[0]:
raise ValueError(
"If axis=1, then last_mean, last_n, last_var should be of "
f"size n_samples {X.shape[0]} (Got {np.size(last_mean)})."
)
else: # axis == 0
if np.size(last_mean) != X.shape[1]:
raise ValueError(
"If axis=0, then last_mean, last_n, last_var should be of "
f"size n_features {X.shape[1]} (Got {np.size(last_mean)})."
)
X = X.T if axis == 1 else X
if weights is not None:
weights = _check_sample_weight(weights, X, dtype=X.dtype)
return _incr_mean_var_axis0(
X, last_mean=last_mean, last_var=last_var, last_n=last_n, weights=weights
)
|
|
106,866 | 308,105 | 346 | tests/components/homekit/test_type_thermostats.py | 118 | 38 | async def test_thermostat_with_no_off_after_recheck(hass, hk_driver, events):
entity_id = "climate.test"
# support_auto = True
hass.states.async_set(
entity_id,
HVACMode.COOL,
{
ATTR_SUPPORTED_FEATURES: SUPPORT_TARGET_TEMPERATURE
| SUPPORT_TARGET_TEMPERATURE_RANGE,
ATTR_HVAC_MODES: [],
},
)
await hass.async_block_till_done()
acc = Thermostat(hass, hk_driver, "Climate", entity_id, 1, None)
hk_driver.add_accessory(acc)
await acc.run()
await hass.async_block_till_done()
assert acc.char_cooling_thresh_temp.value == 23.0
assert acc.char_heating_thresh_temp.value == 19.0
assert acc.char_cooling_thresh_temp.properties[PROP_MAX_VALUE] == DEFAULT_MAX_TEMP
assert acc.char_cooling_thresh_temp.properties[PROP_MIN_VALUE] == 7.0
assert acc.char_cooling_thresh_temp.properties[PROP_MIN_STEP] == 0.1
assert acc.char_heating_thresh_temp.properties[PROP_MAX_VALUE] == DEFAULT_MAX_TEMP
assert acc.char_heating_thresh_temp.properties[PROP_MIN_VALUE] == 7.0
assert acc.char_heating_thresh_temp.properties[PROP_MIN_STEP] == 0.1
assert acc.char_target_heat_cool.value == 2
| Cleanup HVACAction and HVACMode in tests (#78813) | test_thermostat_with_no_off_after_recheck | f453726b1862d1d247f6aefdd5f23455b87c11cf | core | test_type_thermostats.py | 11 | 43 | https://github.com/home-assistant/core.git | 1 | 294 | 0 | 69 | 406 | Python | {
"docstring": "Test if a thermostat that is not ready when we first see it that actually does not have off.",
"language": "en",
"n_whitespaces": 18,
"n_words": 19,
"vocab_size": 17
} | async def test_thermostat_with_no_off_after_recheck(hass, hk_driver, events):
entity_id = "climate.test"
# support_auto = True
hass.states.async_set(
entity_id,
HVACMode.COOL,
{
ATTR_SUPPORTED_FEATURES: SUPPORT_TARGET_TEMPERATURE
| SUPPORT_TARGET_TEMPERATURE_RANGE,
ATTR_HVAC_MODES: [],
},
)
await hass.async_block_till_done()
acc = Thermostat(hass, hk_driver, "Climate", entity_id, 1, None)
hk_driver.add_accessory(acc)
await acc.run()
await hass.async_block_till_done()
assert acc.char_cooling_thresh_temp.value == 23.0
assert acc.char_heating_thresh_temp.value == 19.0
assert acc.char_cooling_thresh_temp.properties[PROP_MAX_VALUE] == DEFAULT_MAX_TEMP
assert acc.char_cooling_thresh_temp.properties[PROP_MIN_VALUE] == 7.0
assert acc.char_cooling_thresh_temp.properties[PROP_MIN_STEP] == 0.1
assert acc.char_heating_thresh_temp.properties[PROP_MAX_VALUE] == DEFAULT_MAX_TEMP
assert acc.char_heating_thresh_temp.properties[PROP_MIN_VALUE] == 7.0
assert acc.char_heating_thresh_temp.properties[PROP_MIN_STEP] == 0.1
assert acc.char_target_heat_cool.value == 2
hass.states.async_set(
entity_id,
HVACMode.HEAT_COOL,
{
ATTR_TARGET_TEMP_HIGH: 22.0,
ATTR_TARGET_TEMP_LOW: 20.0,
ATTR_CURRENT_TEMPERATURE: 18.0,
ATTR_HVAC_ACTION: HVACAction.HEATING,
ATTR_HVAC_MODES: [HVACMode.HEAT_COOL, HVACMode.AUTO],
},
)
await hass.async_block_till_done()
assert acc.char_heating_thresh_temp.value == 20.0
assert acc.char_cooling_thresh_temp.value == 22.0
assert acc.char_current_heat_cool.value == 1
assert acc.char_target_heat_cool.value == 3
assert acc.char_current_temp.value == 18.0
assert acc.char_display_units.value == 0
|
|
16,548 | 76,592 | 69 | wagtail/contrib/forms/models.py | 19 | 10 | def save(self, *args, **kwargs):
is_new = self.pk is None
| AbstractFormField.save - add to the docstring about clean name | save | 10f8e8d21640f019eeb22e91ba3ee1c5284c4574 | wagtail | models.py | 11 | 6 | https://github.com/wagtail/wagtail.git | 2 | 47 | 0 | 16 | 78 | Python | {
"docstring": "\n When new fields are created, generate a template safe ascii name to use as the\n JSON storage reference for this field. Previously created fields will be updated\n to use the legacy unidecode method via checks & _migrate_legacy_clean_name.\n We do not want to update the clean name on any subsequent changes to the label\n as this would invalidate any previously submitted data.\n ",
"language": "en",
"n_whitespaces": 104,
"n_words": 61,
"vocab_size": 49
} | def save(self, *args, **kwargs):
is_new = self.pk is None
if is_new:
clean_name = get_field_clean_name(self.label)
self.clean_name = clean_name
super().save(*args, **kwargs)
|
|
29,934 | 133,135 | 162 | python/ray/util/dask/scheduler.py | 77 | 17 | def dask_task_wrapper(func, repack, key, ray_pretask_cbs, ray_posttask_cbs, *args):
if ray_pretask_cbs is not None:
pre_states = [
cb(key, args) if cb is not None else None for cb in ray_pretask_cbs
]
repacked_args, repacked_deps = repack(args)
# Recursively execute Dask-inlined tasks.
actual_args = [_execute_task(a, repacked_deps) for a in repacked_args]
# Execute the actual underlying Dask task.
result = func(*actual_args)
if ray_posttask_cbs is not None:
for cb, pre_state in zip(ray_posttask_cbs, pre_states):
if cb is not None:
cb(key, result, pre_state)
return result
| [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | dask_task_wrapper | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | scheduler.py | 13 | 13 | https://github.com/ray-project/ray.git | 8 | 107 | 0 | 52 | 159 | Python | {
"docstring": "\n A Ray remote function acting as a Dask task wrapper. This function will\n repackage the given flat `args` into its original data structures using\n `repack`, execute any Dask subtasks within the repackaged arguments\n (inlined by Dask's optimization pass), and then pass the concrete task\n arguments to the provide Dask task function, `func`.\n\n Args:\n func (callable): The Dask task function to execute.\n repack (callable): A function that repackages the provided args into\n the original (possibly nested) Python objects.\n key (str): The Dask key for this task.\n ray_pretask_cbs (callable): Pre-task execution callbacks.\n ray_posttask_cbs (callable): Post-task execution callback.\n *args (ObjectRef): Ray object references representing the Dask task's\n arguments.\n\n Returns:\n The output of the Dask task. In the context of Ray, a\n dask_task_wrapper.remote() invocation will return a Ray object\n reference representing the Ray task's result.\n ",
"language": "en",
"n_whitespaces": 241,
"n_words": 131,
"vocab_size": 87
} | def dask_task_wrapper(func, repack, key, ray_pretask_cbs, ray_posttask_cbs, *args):
if ray_pretask_cbs is not None:
pre_states = [
cb(key, args) if cb is not None else None for cb in ray_pretask_cbs
]
repacked_args, repacked_deps = repack(args)
# Recursively execute Dask-inlined tasks.
actual_args = [_execute_task(a, repacked_deps) for a in repacked_args]
# Execute the actual underlying Dask task.
result = func(*actual_args)
if ray_posttask_cbs is not None:
for cb, pre_state in zip(ray_posttask_cbs, pre_states):
if cb is not None:
cb(key, result, pre_state)
return result
|
|
51,651 | 206,716 | 114 | django/utils/lorem_ipsum.py | 42 | 13 | def words(count, common=True):
word_list = list(COMMON_WORDS) if common else []
c = len(word_list)
if count > c:
count -= c
while count > 0:
c = min(count, len(WORDS))
| Refs #33476 -- Reformatted code with Black. | words | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | django | lorem_ipsum.py | 14 | 12 | https://github.com/django/django.git | 4 | 80 | 0 | 28 | 131 | Python | {
"docstring": "\n Return a string of `count` lorem ipsum words separated by a single space.\n\n If `common` is True, then the first 19 words will be the standard\n 'lorem ipsum' words. Otherwise, all words will be selected randomly.\n ",
"language": "en",
"n_whitespaces": 49,
"n_words": 36,
"vocab_size": 30
} | def words(count, common=True):
word_list = list(COMMON_WORDS) if common else []
c = len(word_list)
if count > c:
count -= c
while count > 0:
c = min(count, len(WORDS))
count -= c
word_list += random.sample(WORDS, c)
else:
word_list = word_list[:count]
return " ".join(word_list)
|
|
12,969 | 62,402 | 68 | .venv/lib/python3.8/site-packages/pip/_vendor/html5lib/_inputstream.py | 18 | 9 | def jumpTo(self, bytes):
| upd; format | jumpTo | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | transferlearning | _inputstream.py | 13 | 6 | https://github.com/jindongwang/transferlearning.git | 2 | 38 | 0 | 18 | 62 | Python | {
"docstring": "Look for the next sequence of bytes matching a given sequence. If\n a match is found advance the position to the last byte of the match",
"language": "en",
"n_whitespaces": 32,
"n_words": 26,
"vocab_size": 20
} | def jumpTo(self, bytes):
try:
self._position = self.index(bytes, self.position) + len(bytes) - 1
except ValueError:
raise StopIteration
return True
|
|
89,301 | 290,182 | 78 | homeassistant/components/mqtt/binary_sensor.py | 31 | 11 | def available(self) -> bool:
expire_after: int | None = self._config.get(CONF_EXPIRE_AFTER)
| Improve MQTT type hints part 1 (#80523)
* Improve typing alarm_control_panel
* Improve typing binary_sensor
* Improve typing button
* Add misssed annotation
* Move CONF_EXPIRE_AFTER to _setup_from_config
* Use CALL_BACK type
* Remove assert, improve code style | available | b4ad03784f1d02995da39f3094c80adb4a60492b | core | binary_sensor.py | 10 | 6 | https://github.com/home-assistant/core.git | 3 | 42 | 0 | 29 | 71 | Python | {
"docstring": "Return true if the device is available and value has not expired.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | def available(self) -> bool:
expire_after: int | None = self._config.get(CONF_EXPIRE_AFTER)
# mypy doesn't know about fget: https://github.com/python/mypy/issues/6185
return MqttAvailability.available.fget(self) and ( # type: ignore[attr-defined]
expire_after is None or not self._expired
)
|
|
48,109 | 196,691 | 18 | sympy/stats/crv_types.py | 15 | 6 | def Logistic(name, mu, s):
r
return rv(name, LogisticDistribution, (mu, s))
#----------- | Documentation cleanup 5 | Logistic | 9ad8ab9fe58051cf11626ba6654852fcfec60147 | sympy | crv_types.py | 8 | 49 | https://github.com/sympy/sympy.git | 1 | 24 | 0 | 15 | 36 | Python | {
"docstring": "\n Create a continuous random variable with a logistic distribution.\n\n Explanation\n ===========\n\n The density of the logistic distribution is given by\n\n .. math::\n f(x) := \\frac{e^{-(x-\\mu)/s}} {s\\left(1+e^{-(x-\\mu)/s}\\right)^2}\n\n Parameters\n ==========\n\n mu : Real number, the location (mean)\n s : Real number, `s > 0`, a scale\n\n Returns\n =======\n\n RandomSymbol\n\n Examples\n ========\n\n >>> from sympy.stats import Logistic, density, cdf\n >>> from sympy import Symbol\n\n >>> mu = Symbol(\"mu\", real=True)\n >>> s = Symbol(\"s\", positive=True)\n >>> z = Symbol(\"z\")\n\n >>> X = Logistic(\"x\", mu, s)\n\n >>> density(X)(z)\n exp((mu - z)/s)/(s*(exp((mu - z)/s) + 1)**2)\n\n >>> cdf(X)(z)\n 1/(exp((mu - z)/s) + 1)\n\n References\n ==========\n\n .. [1] https://en.wikipedia.org/wiki/Logistic_distribution\n .. [2] http://mathworld.wolfram.com/LogisticDistribution.html\n\n ",
"language": "en",
"n_whitespaces": 200,
"n_words": 105,
"vocab_size": 77
} | def Logistic(name, mu, s):
r
return rv(name, LogisticDistribution, (mu, s))
#-------------------------------------------------------------------------------
# Log-logistic distribution --------------------------------------------------------
|
|
4,867 | 25,205 | 845 | ppocr/modeling/heads/local_graph.py | 146 | 58 | def __call__(self, feat_maps, comp_attribs):
assert isinstance(feat_maps, paddle.Tensor)
assert comp_attribs.ndim == 3
assert comp_attribs.shape[2] == 8
sorted_dist_inds_batch = []
local_graph_batch = []
knn_batch = []
node_feat_batch = []
node_label_batch = []
for batch_ind in range(comp_attribs.shape[0]):
num_comps = int(comp_attribs[batch_ind, 0, 0])
comp_geo_attribs = comp_attribs[batch_ind, :num_comps, 1:7]
node_labels = comp_attribs[batch_ind, :num_comps, 7].astype(
np.int32)
comp_centers = comp_geo_attribs[:, 0:2]
distance_matrix = euclidean_distance_matrix(comp_centers,
comp_centers)
| add drrg | __call__ | 1f9400dd7374ce9cc47981372e324ff412e53ba3 | PaddleOCR | local_graph.py | 14 | 48 | https://github.com/PaddlePaddle/PaddleOCR.git | 2 | 406 | 0 | 103 | 607 | Python | {
"docstring": "Generate local graphs as GCN input.\n\n Args:\n feat_maps (Tensor): The feature maps to extract the content\n features of text components.\n comp_attribs (ndarray): The text component attributes.\n\n Returns:\n local_graphs_node_feat (Tensor): The node features of graph.\n adjacent_matrices (Tensor): The adjacent matrices of local graphs.\n pivots_knn_inds (Tensor): The k-nearest neighbor indices in local\n graph.\n gt_linkage (Tensor): The surpervision signal of GCN for linkage\n prediction.\n ",
"language": "en",
"n_whitespaces": 193,
"n_words": 61,
"vocab_size": 43
} | def __call__(self, feat_maps, comp_attribs):
assert isinstance(feat_maps, paddle.Tensor)
assert comp_attribs.ndim == 3
assert comp_attribs.shape[2] == 8
sorted_dist_inds_batch = []
local_graph_batch = []
knn_batch = []
node_feat_batch = []
node_label_batch = []
for batch_ind in range(comp_attribs.shape[0]):
num_comps = int(comp_attribs[batch_ind, 0, 0])
comp_geo_attribs = comp_attribs[batch_ind, :num_comps, 1:7]
node_labels = comp_attribs[batch_ind, :num_comps, 7].astype(
np.int32)
comp_centers = comp_geo_attribs[:, 0:2]
distance_matrix = euclidean_distance_matrix(comp_centers,
comp_centers)
batch_id = np.zeros(
(comp_geo_attribs.shape[0], 1), dtype=np.float32) * batch_ind
comp_geo_attribs[:, -2] = np.clip(comp_geo_attribs[:, -2], -1, 1)
angle = np.arccos(comp_geo_attribs[:, -2]) * np.sign(
comp_geo_attribs[:, -1])
angle = angle.reshape((-1, 1))
rotated_rois = np.hstack(
[batch_id, comp_geo_attribs[:, :-2], angle])
rois = paddle.to_tensor(rotated_rois)
content_feats = self.pooling(feat_maps[batch_ind].unsqueeze(0),
rois)
content_feats = content_feats.reshape([content_feats.shape[0], -1])
geo_feats = feature_embedding(comp_geo_attribs,
self.node_geo_feat_dim)
geo_feats = paddle.to_tensor(geo_feats)
node_feats = paddle.concat([content_feats, geo_feats], axis=-1)
sorted_dist_inds = np.argsort(distance_matrix, axis=1)
pivot_local_graphs, pivot_knns = self.generate_local_graphs(
sorted_dist_inds, node_labels)
node_feat_batch.append(node_feats)
node_label_batch.append(node_labels)
local_graph_batch.append(pivot_local_graphs)
knn_batch.append(pivot_knns)
sorted_dist_inds_batch.append(sorted_dist_inds)
(node_feats, adjacent_matrices, knn_inds, gt_linkage) = \
self.generate_gcn_input(node_feat_batch,
node_label_batch,
local_graph_batch,
knn_batch,
sorted_dist_inds_batch)
return node_feats, adjacent_matrices, knn_inds, gt_linkage
|
|
35,525 | 153,659 | 244 | modin/experimental/core/execution/native/implementations/omnisci_on_native/exchange/dataframe_protocol/dataframe.py | 85 | 16 | def _is_zero_copy_arrow_op(cls, op) -> bool:
is_zero_copy_op = False
if isinstance(op, (FrameNode, TransformNode, UnionNode)):
# - FrameNode: already materialized PyArrow table
# - TransformNode: select certain columns of the table, implemented zero-copy (``df._arrow_select``)
# - UnionNode: concatenate PyArrow tables, implemented zero-copy (``df._arrow_concat``)
is_zero_copy_op = True
elif isinstance(op, Mas | FEAT-#4244: Implement dataframe exchange protocol for OmniSci (#4269)
Co-authored-by: Yaroslav Igoshev <[email protected]>
Co-authored-by: Vasily Litvinov <[email protected]>
Signed-off-by: Dmitry Chigarev <[email protected]> | _is_zero_copy_arrow_op | 0c1a2129df64cf45bf1ff49c8ed92c510fdb1c82 | modin | dataframe.py | 12 | 23 | https://github.com/modin-project/modin.git | 7 | 83 | 0 | 64 | 133 | Python | {
"docstring": "\n Check whether the passed node of the delayed computation tree could be executed zero-copy via PyArrow execution.\n\n Parameters\n ----------\n op : DFAlgNode\n\n Returns\n -------\n bool\n ",
"language": "en",
"n_whitespaces": 82,
"n_words": 25,
"vocab_size": 24
} | def _is_zero_copy_arrow_op(cls, op) -> bool:
is_zero_copy_op = False
if isinstance(op, (FrameNode, TransformNode, UnionNode)):
# - FrameNode: already materialized PyArrow table
# - TransformNode: select certain columns of the table, implemented zero-copy (``df._arrow_select``)
# - UnionNode: concatenate PyArrow tables, implemented zero-copy (``df._arrow_concat``)
is_zero_copy_op = True
elif isinstance(op, MaskNode) and (
isinstance(op.row_positions, slice) or is_range_like(op.row_positions)
):
# Can select rows zero-copy if indexer is a slice-like (``df._arrow_row_slice``)
is_zero_copy_op = True
return is_zero_copy_op and all(
# Walk the computation tree
cls._is_zero_copy_arrow_op(_op)
for _op in getattr(op, "inputs", [])
)
|
|
@register | 53,129 | 211,688 | 29 | ppdet/modeling/assigners/uniform_assigner.py | 18 | 13 | def batch_p_dist(x, y, p=2):
x = x.unsqueeze(1)
diff = x - y
return paddle | support YOLOF (#7336) | batch_p_dist | 41d8be66e84d066d98cfabbe13d4c7a5877cb009 | PaddleDetection | uniform_assigner.py | 14 | 4 | https://github.com/PaddlePaddle/PaddleDetection.git | 1 | 52 | 1 | 16 | 85 | Python | {
"docstring": "\n calculate pairwise p_dist, the first index of x and y are batch\n return [x.shape[0], y.shape[0]]\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 15,
"vocab_size": 15
} | def batch_p_dist(x, y, p=2):
x = x.unsqueeze(1)
diff = x - y
return paddle.norm(diff, p=p, axis=list(range(2, diff.dim())))
@register |
14,737 | 68,200 | 67 | erpnext/hr/doctype/shift_assignment/shift_assignment.py | 82 | 16 | def get_employee_shift(employee, for_timestamp=None, consider_default_shift=False, next_shift_direction=None):
if for_timestamp is None:
for_timestamp = now_datetime()
shift_details = get_shift_for_timestamp(employee, for_timestamp)
# if shift assignment is not found, consider default shift
default_shift = frappe.db.get_value('Employee', employee, 'default_shift')
if not shift_details and consider_default_shift:
shift_details = get_shift_details(default_shift, for_timestamp.date())
# if its a holiday, reset
if | refactor: consider timeslots in `get_employee_shift` | get_employee_shift | 625a9f69f592be8c50c9b1bd1a16e0b7b9157988 | erpnext | shift_assignment.py | 12 | 12 | https://github.com/frappe/erpnext.git | 8 | 103 | 0 | 51 | 164 | Python | {
"docstring": "Returns a Shift Type for the given employee on the given date. (excluding the holidays)\n\n\t:param employee: Employee for which shift is required.\n\t:param for_timestamp: DateTime on which shift is required\n\t:param consider_default_shift: If set to true, default shift is taken when no shift assignment is found.\n\t:param next_shift_direction: One of: None, 'forward', 'reverse'. Direction to look for next shift if shift not found on given date.\n\t",
"language": "en",
"n_whitespaces": 62,
"n_words": 67,
"vocab_size": 45
} | def get_employee_shift(employee, for_timestamp=None, consider_default_shift=False, next_shift_direction=None):
if for_timestamp is None:
for_timestamp = now_datetime()
shift_details = get_shift_for_timestamp(employee, for_timestamp)
# if shift assignment is not found, consider default shift
default_shift = frappe.db.get_value('Employee', employee, 'default_shift')
if not shift_details and consider_default_shift:
shift_details = get_shift_details(default_shift, for_timestamp.date())
# if its a holiday, reset
if shift_details and is_holiday_date(employee, shift_details):
shift_details = None
# if no shift is found, find next or prev shift based on direction
if not shift_details and next_shift_direction:
shift_details = get_prev_or_next_shift(employee, for_timestamp, consider_default_shift, default_shift, next_shift_direction)
return shift_details
|
|
15,530 | 70,602 | 62 | wagtail/admin/views/workflows.py | 12 | 5 | def get_create_form_class(self):
self.create_model = self.get_create_model()
if self.create_model:
return ge | Split out data retrieval methods from BaseTaskChooserView.dispatch
This ensures that we don't do redundant setup for sub-views that don't need it, e.g. setting up creation forms for the results-only view. | get_create_form_class | fb48f9863d8ba1856e0697552fb454de162281b8 | wagtail | workflows.py | 10 | 6 | https://github.com/wagtail/wagtail.git | 2 | 31 | 0 | 11 | 54 | Python | {
"docstring": "\n To be called after dispatch(); returns the form class for creating a new task\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 14,
"vocab_size": 14
} | def get_create_form_class(self):
self.create_model = self.get_create_model()
if self.create_model:
return get_task_form_class(self.create_model)
else:
return None
|
|
5,151 | 27,995 | 40 | saleor/thumbnail/utils.py | 12 | 9 | def retrieve_image(self):
image = self.s | Better media thumbnails including WebP support (#9988)
* Add thumbnail app
* Update get_thumbnail_size method and add tests
* Add logic for creating thumbnails
* Update logic for getting thumbnail
* Allow defining format for tumbnail generation
* Clear handle_thumbnail views
* Add prepare_image_proxy_url method
* Use ImageField for user avatar
* Allow defining thumbnail format when querying user avatar
* Use ImageField for category backgound_image
* Use ImageField for Collection backgound_image
* Use ImageField for ProductMedia image
* Ensure that thumbnails are deleted when category background_image is changed or deleted
* Ensure that thumbnails are deleted when collection background_image is changed or deleted
* Update product media deleteion task and failing tests
* Delete thumbnail from storage when thumbnail objects is deleted
* Fix import in product test_bulk_delete
* Drop create_thumbnails command
* Update Product.thumbnail resolver
* Update OrderLine thumbnail resolver
* Add missing ADDED_IN_35 and PREVIEW_FEATURE labels
* Update account and product signals - ensure the image is deleted from storage
* Refactor product_images methods
* Add signal for product media image delete
* Drop create_thumbnails method and not longer valid settings fields
* Clean the ProcessedImage class
* Drop versatileimagefield from INSTALLED_APPS
* Update changelog
* Drop comments from ThumbnailFormat
* Add get_image_or_proxy_url method
* Apply reiew suggestions - add ThumbnailField and use get_image_or_proxy_ur when it's possible
* Update changelog
* Replace ADDED_IN_35 with ADDED_IN_36 label
* Update changelog
Co-authored-by: Marcin Gębala <[email protected]> | retrieve_image | 5d1a36b9aaf408016957db04f86397b2e53c2500 | saleor | utils.py | 9 | 4 | https://github.com/saleor/saleor.git | 1 | 39 | 0 | 11 | 65 | Python | {
"docstring": "Return a PIL Image instance stored at `image_path`.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | def retrieve_image(self):
image = self.storage.open(self.image_path, "rb")
image_format = self.get_image_metadata_from_file(image)
return (Image.open(image), image_format)
|
|
38,408 | 159,724 | 224 | numpy/polynomial/chebyshev.py | 87 | 27 | def chebval(x, c, tensor=True):
c = np.array(c, ndmin=1, copy=True)
if c.dtype.char in '?bBhHiIlLqQpP':
c = c.astype(np.double)
if isinstance(x, (tuple, list)):
x = np.asarray(x)
if isinstance(x, np.ndarray) and tensor:
c = c.reshape(c.shape + (1,)*x.ndim)
if len(c) == 1:
c0 = c[0]
c1 = 0
elif len(c) == 2:
c0 = c[0]
c1 = c[1]
else:
| MAINT, DOC: discard repeated words | chebval | 58dbe260a2e41c31f1ab03e1abdb1f01da4c1edc | numpy | chebyshev.py | 14 | 23 | https://github.com/numpy/numpy.git | 8 | 196 | 0 | 50 | 305 | Python | {
"docstring": "\n Evaluate a Chebyshev series at points x.\n\n If `c` is of length `n + 1`, this function returns the value:\n\n .. math:: p(x) = c_0 * T_0(x) + c_1 * T_1(x) + ... + c_n * T_n(x)\n\n The parameter `x` is converted to an array only if it is a tuple or a\n list, otherwise it is treated as a scalar. In either case, either `x`\n or its elements must support multiplication and addition both with\n themselves and with the elements of `c`.\n\n If `c` is a 1-D array, then `p(x)` will have the same shape as `x`. If\n `c` is multidimensional, then the shape of the result depends on the\n value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +\n x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that\n scalars have shape (,).\n\n Trailing zeros in the coefficients will be used in the evaluation, so\n they should be avoided if efficiency is a concern.\n\n Parameters\n ----------\n x : array_like, compatible object\n If `x` is a list or tuple, it is converted to an ndarray, otherwise\n it is left unchanged and treated as a scalar. In either case, `x`\n or its elements must support addition and multiplication with\n themselves and with the elements of `c`.\n c : array_like\n Array of coefficients ordered so that the coefficients for terms of\n degree n are contained in c[n]. If `c` is multidimensional the\n remaining indices enumerate multiple polynomials. In the two\n dimensional case the coefficients may be thought of as stored in\n the columns of `c`.\n tensor : boolean, optional\n If True, the shape of the coefficient array is extended with ones\n on the right, one for each dimension of `x`. Scalars have dimension 0\n for this action. The result is that every column of coefficients in\n `c` is evaluated for every element of `x`. If False, `x` is broadcast\n over the columns of `c` for the evaluation. This keyword is useful\n when `c` is multidimensional. The default value is True.\n\n .. versionadded:: 1.7.0\n\n Returns\n -------\n values : ndarray, algebra_like\n The shape of the return value is described above.\n\n See Also\n --------\n chebval2d, chebgrid2d, chebval3d, chebgrid3d\n\n Notes\n -----\n The evaluation uses Clenshaw recursion, aka synthetic division.\n\n ",
"language": "en",
"n_whitespaces": 578,
"n_words": 369,
"vocab_size": 191
} | def chebval(x, c, tensor=True):
c = np.array(c, ndmin=1, copy=True)
if c.dtype.char in '?bBhHiIlLqQpP':
c = c.astype(np.double)
if isinstance(x, (tuple, list)):
x = np.asarray(x)
if isinstance(x, np.ndarray) and tensor:
c = c.reshape(c.shape + (1,)*x.ndim)
if len(c) == 1:
c0 = c[0]
c1 = 0
elif len(c) == 2:
c0 = c[0]
c1 = c[1]
else:
x2 = 2*x
c0 = c[-2]
c1 = c[-1]
for i in range(3, len(c) + 1):
tmp = c0
c0 = c[-i] - c1
c1 = tmp + c1*x2
return c0 + c1*x
|
|
8,767 | 46,033 | 63 | airflow/www/views.py | 21 | 11 | def dagrun_queued(self):
dag_i | Add queue button to click-on-DagRun interface. (#21555)
* Initial implementation of adding Queue button to DagRun interface
* Implement the test cases
* FIX Add all required MyPy ignores
* FIX import
* Update airflow/www/views.py
FIX Documentation
Co-authored-by: Brent Bovenzi <[email protected]>
* update modal UI
Co-authored-by: Brent Bovenzi <[email protected]> | dagrun_queued | afd3c135c7d1815c56578d020625a33dc27fe640 | airflow | views.py | 11 | 6 | https://github.com/apache/airflow.git | 1 | 64 | 0 | 18 | 112 | Python | {
"docstring": "Queue DagRun so tasks that haven't run yet can be started.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | def dagrun_queued(self):
dag_id = request.form.get('dag_id')
dag_run_id = request.form.get('dag_run_id')
confirmed = request.form.get('confirmed') == 'true'
origin = get_safe_url(request.form.get('origin'))
return self._mark_dagrun_state_as_queued(dag_id, dag_run_id, confirmed, origin)
|
|
@pytest.mark.django_db
@pytest.mark.job_runtime_vars | 17,255 | 81,752 | 136 | awx/main/tests/functional/api/test_job_runtime_params.py | 70 | 20 | def data_to_internal(data):
internal = data.copy()
if 'extra_vars' in data:
internal['extra_vars'] = json.loads(data['extra_vars'])
if 'credentials' in data:
internal['credentials'] = set(Cr | JT param everything (#12646)
* Making almost all fields promptable on job templates and config models
* Adding EE, IG and label access checks
* Changing jobs preferred instance group function to handle the new IG cache field
* Adding new ask fields to job template modules
* Address unit/functional tests
* Adding migration file | data_to_internal | 33c0fb79d66f56374d7c042ba79887faa85e2885 | awx | test_job_runtime_params.py | 13 | 15 | https://github.com/ansible/awx.git | 10 | 168 | 1 | 41 | 314 | Python | {
"docstring": "\n returns internal representation, model objects, dictionaries, etc\n as opposed to integer primary keys and JSON strings\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 16,
"vocab_size": 16
} | def data_to_internal(data):
internal = data.copy()
if 'extra_vars' in data:
internal['extra_vars'] = json.loads(data['extra_vars'])
if 'credentials' in data:
internal['credentials'] = set(Credential.objects.get(pk=_id) for _id in data['credentials'])
if 'inventory' in data:
internal['inventory'] = Inventory.objects.get(pk=data['inventory'])
if 'execution_environment' in data:
internal['execution_environment'] = ExecutionEnvironment.objects.get(pk=data['execution_environment'])
if 'labels' in data:
internal['labels'] = [Label.objects.get(pk=_id) for _id in data['labels']]
if 'instance_groups' in data:
internal['instance_groups'] = [InstanceGroup.objects.get(pk=_id) for _id in data['instance_groups']]
return internal
# End of setup, tests start here
@pytest.mark.django_db
@pytest.mark.job_runtime_vars |
28,411 | 127,299 | 27 | python/ray/tune/progress_reporter.py | 11 | 5 | def _generate_sys_info_str(*sys_info) -> str:
if sys_info:
return "<br>".join(sys_info).replace("\n", "<br>")
return ""
| [Tune] Add rich output for ray tune progress updates in notebooks (#26263)
These changes are part of a series intended to improve integration with notebooks. This PR modifies the tune progress status shown to the user if tuning is run from a notebook.
Previously, part of the trial progress was reported in an HTML table before; now, all progress is displayed in an organized HTML template.
Signed-off-by: pdmurray <[email protected]> | _generate_sys_info_str | ffe12a5f103b9f06d728429fc0d930b76523726f | ray | progress_reporter.py | 12 | 10 | https://github.com/ray-project/ray.git | 2 | 28 | 0 | 10 | 56 | Python | {
"docstring": "Format system info into a string.\n *sys_info: System info strings to be included.\n\n Returns:\n Formatted string containing system information.\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 19,
"vocab_size": 17
} | def _generate_sys_info_str(*sys_info) -> str:
if sys_info:
return "<br>".join(sys_info).replace("\n", "<br>")
return ""
|
|
82,758 | 278,926 | 66 | keras/saving/saved_model/json_utils.py | 23 | 10 | def default(self, obj):
if isinstance(obj, tf.TensorShape):
items = obj.as_list() i | Remove pylint comments.
PiperOrigin-RevId: 452353044 | default | 3613c3defc39c236fb1592c4f7ba1a9cc887343a | keras | json_utils.py | 11 | 5 | https://github.com/keras-team/keras.git | 3 | 49 | 0 | 20 | 82 | Python | {
"docstring": "Encodes objects for types that aren't handled by the default\n encoder.",
"language": "en",
"n_whitespaces": 17,
"n_words": 11,
"vocab_size": 11
} | def default(self, obj):
if isinstance(obj, tf.TensorShape):
items = obj.as_list() if obj.rank is not None else None
return {"class_name": "TensorShape", "items": items}
return get_json_type(obj)
|
|
async def _pause_and_wait_for_callback(self):
"""Send pause and wait for the pause callback to be received."""
self._pause_requested = True
await self.async_media_pause()
try: | 106,437 | 307,669 | 37 | homeassistant/components/forked_daapd/media_player.py | 9 | 4 | async def _pause_and_wait_for_callback(self):
self._pause_requested = True
await self.async_media_pause()
try: | Use async_timeout in forked_daapd (#78451) | _pause_and_wait_for_callback | 26251895295d74fcd2c73e37804c23675c433247 | core | media_player.py | 7 | 9 | https://github.com/home-assistant/core.git | 2 | 53 | 1 | 9 | 34 | Python | {
"docstring": "Send pause and wait for the pause callback to be received.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 10
} | async def _pause_and_wait_for_callback(self):
self._pause_requested = True
await self.async_media_pause()
try: |
29,817 | 132,825 | 249 | python/ray/tune/trainable.py | 56 | 22 | def delete_checkpoint(self, checkpoint_path):
# Ensure TrialCheckpoints are converted
if isinstance(checkpoint_path, TrialCheckpoint):
checkpoint_path = checkpoint_path.local_path
try:
checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
except FileNotFoundError:
# The checkpoint won't exist locally if the
# trial was rescheduled to another worker.
logger.debug(
| [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | delete_checkpoint | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | trainable.py | 14 | 16 | https://github.com/ray-project/ray.git | 5 | 80 | 0 | 49 | 148 | Python | {
"docstring": "Deletes local copy of checkpoint.\n\n Args:\n checkpoint_path (str): Path to checkpoint.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 11,
"vocab_size": 10
} | def delete_checkpoint(self, checkpoint_path):
# Ensure TrialCheckpoints are converted
if isinstance(checkpoint_path, TrialCheckpoint):
checkpoint_path = checkpoint_path.local_path
try:
checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
except FileNotFoundError:
# The checkpoint won't exist locally if the
# trial was rescheduled to another worker.
logger.debug(
f"Local checkpoint not found during garbage collection: "
f"{self.trial_id} - {checkpoint_path}"
)
return
else:
if self.uses_cloud_checkpointing:
self.storage_client.delete(self._storage_path(checkpoint_dir))
if os.path.exists(checkpoint_dir):
shutil.rmtree(checkpoint_dir)
|
|
29,987 | 133,356 | 585 | python/ray/util/sgd/torch/torch_trainer.py | 119 | 33 | def _resize_worker_group(self, state_dict, max_retries=10):
old_workers = self.worker_group.num_workers
self.worker_group.reset()
time.sleep(1)
for i in range(max_retries):
new_workers = self.worker_group.new_workers_size()
if new_workers:
self._last_resize = time.time()
startup_success = self._start_workers(int(new_workers))
if not startup_success:
logger.info(
f"Worker startup failed. Retrying "
f"{max_retries-i-1} more times."
)
self.worker_group.reset()
continue
self.load_state_dict(state_dict, blocking=True)
if self.use_local and new_workers == 1 and old_workers > 1:
# Major hack. If we go from LocalDistributedRunner to a
# standard TorchRunner we have to manually reset the
# dummy actor handle global vars.
# TODO(amog): Refactor LocalDistributedTorchRunner to
# not use global variables for resource reservation.
ray.util.sgd.torch.distributed_torch | [CI] Format Python code with Black (#21975)
See #21316 and #21311 for the motivation behind these changes. | _resize_worker_group | 7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065 | ray | torch_trainer.py | 18 | 26 | https://github.com/ray-project/ray.git | 7 | 169 | 0 | 92 | 294 | Python | {
"docstring": "Resizes the number of remote workers based on available resources.\n Total number of workers will never exceed `num_workers` amount.\n\n Args:\n state_dict (dict): The state dict to load to all workers.\n max_retries (int): How many times to attempt to resize workers\n before failing.\n ",
"language": "en",
"n_whitespaces": 100,
"n_words": 42,
"vocab_size": 35
} | def _resize_worker_group(self, state_dict, max_retries=10):
old_workers = self.worker_group.num_workers
self.worker_group.reset()
time.sleep(1)
for i in range(max_retries):
new_workers = self.worker_group.new_workers_size()
if new_workers:
self._last_resize = time.time()
startup_success = self._start_workers(int(new_workers))
if not startup_success:
logger.info(
f"Worker startup failed. Retrying "
f"{max_retries-i-1} more times."
)
self.worker_group.reset()
continue
self.load_state_dict(state_dict, blocking=True)
if self.use_local and new_workers == 1 and old_workers > 1:
# Major hack. If we go from LocalDistributedRunner to a
# standard TorchRunner we have to manually reset the
# dummy actor handle global vars.
# TODO(amog): Refactor LocalDistributedTorchRunner to
# not use global variables for resource reservation.
ray.util.sgd.torch.distributed_torch_runner._dummy_cuda_actor = None
ray.util.sgd.torch.distributed_torch_runner._dummy_cpu_actor = None
return
else:
delay = 2 ** i
logger.warning("No new workers found. Retrying in %d sec." % delay)
time.sleep(delay)
raise RuntimeError("Exceeded max_retries for relaunching workers.")
|
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