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semeru

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code-code-galeras-code-completion-from-docstring-3k-deduped

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Semeru Lab 7

pandas/core/indexers/utils.py

def is_empty_indexer(indexer) -> bool: if is_list_like(indexer) and not len(indexer): return True if not isinstance(indexer, tuple): indexer = (indexer,)

REF: simplify Block.setitem (#45403)

is_empty_indexer

6b43a78f2f1036ebae205d2d35ab96f07549fe96

pandas

utils.py

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

Python

def is_empty_indexer(indexer) -> bool: if is_list_like(indexer) and not len(indexer): return True if not isinstance(indexer, tuple): indexer = (indexer,) return any(isinstance(idx, np.ndarray) and len(idx) == 0 for idx in indexer) # ----------------------------------------------------------- # Indexer Validation

lib/matplotlib/_constrained_layout.py

def make_layoutgrids_gs(layoutgrids, gs): if gs in layoutgrids or gs.figure is None: return layoutgrids # in order to do constrained_layout there has to be at least *one* # gridspec in the tree: layoutgrids['hasgrids'] = True if not hasattr(gs, '_subplot_spec'): # normal gridspec parent = layoutgrids[gs.figure] layoutgrids[gs] = mlayoutgrid.LayoutGrid( parent=parent, parent_inner=True, name='gridspec', ncols=gs._ncols, nrows=gs._nrows, width_ratios=gs.get_width_ratios(), height_ratios=gs.get_height_ratios()) else: # this is a gridspecfromsubplotspec: subplot_spec = g

FIX: better repr for subgridspecs

make_layoutgrids_gs

c682ca40c647770a967b6b8a7615eb91c7cb3fc9

matplotlib

_constrained_layout.py

https://github.com/matplotlib/matplotlib.git

Python

def make_layoutgrids_gs(layoutgrids, gs): if gs in layoutgrids or gs.figure is None: return layoutgrids # in order to do constrained_layout there has to be at least *one* # gridspec in the tree: layoutgrids['hasgrids'] = True if not hasattr(gs, '_subplot_spec'): # normal gridspec parent = layoutgrids[gs.figure] layoutgrids[gs] = mlayoutgrid.LayoutGrid( parent=parent, parent_inner=True, name='gridspec', ncols=gs._ncols, nrows=gs._nrows, width_ratios=gs.get_width_ratios(), height_ratios=gs.get_height_ratios()) else: # this is a gridspecfromsubplotspec: subplot_spec = gs._subplot_spec parentgs = subplot_spec.get_gridspec() # if a nested gridspec it is possible the parent is not in there yet: if parentgs not in layoutgrids: layoutgrids = make_layoutgrids_gs(layoutgrids, parentgs) subspeclb = layoutgrids[parentgs] # get a unique representation: rep = object.__repr__(gs) + 'top' # gridspecfromsubplotspec need an outer container: if rep not in layoutgrids: layoutgrids[rep] = mlayoutgrid.LayoutGrid( parent=subspeclb, name='top', nrows=1, ncols=1, parent_pos=(subplot_spec.rowspan, subplot_spec.colspan)) layoutgrids[gs] = mlayoutgrid.LayoutGrid( parent=layoutgrids[rep], name='gridspec', nrows=gs._nrows, ncols=gs._ncols, width_ratios=gs.get_width_ratios(), height_ratios=gs.get_height_ratios()) return layoutgrids

cms/tests/test_sitemap.py

def test_sitemap_published_titles(self): sitemap = CMSSitemap() locations = [] urlset = sitemap.get_urls() for item in urlset: locations.append(item['location']) for title in Title.objects.public(): page = title.page.get_public_object()

ci: Added codespell (#7355) Co-authored-by: Christian Clauss <cclauss@me.com> * ci: codespell config taken from #7292

test_sitemap_published_titles

c1290c9ff89cb00caa5469129fd527e9d82cd820

django-cms

test_sitemap.py

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

Python

def test_sitemap_published_titles(self): sitemap = CMSSitemap() locations = [] urlset = sitemap.get_urls() for item in urlset: locations.append(item['location']) for title in Title.objects.public(): page = title.page.get_public_object() if title.path: url = f'http://example.com/{title.language}/{title.path}/' else: url = f'http://example.com/{title.language}/{title.path}' if page.is_published('en') and not page.publisher_is_draft: self.assertTrue(url in locations) else: self.assertFalse(url in locations)

@derived_from(np)

dask/array/creation.py

def eye(N, chunks="auto", M=None, k=0, dtype=float): eye = {} if M is None: M = N if dtype is None: dtype = float if not isinstance(chunks, (int, str)): raise ValueError("chunks must be an int or string") vchunks, hchunks = normalize_chunks(chunks, shape=(N, M), dtype=dtype) chunks = vchunks[0] token = tokenize(N, chunks, M, k, dtype) name_eye = "eye-" + token for i, vchunk in enumerate(vchunks): for j, hchunk in enumerate(hchunks): if (j - i - 1) * chunks <= k <= (j - i + 1) * chunks: eye[name_eye, i, j] = ( np.eye, vchunk, hchunk, k - (j - i) * chunks, dtype, ) else: eye[name_eye, i, j] = (np.zeros, (vchunk, hchunk), dtype) return Array(eye, name_eye, shape=(N, M), chunks=(chunks, chunks), dtype=dtype)

Fix eye inconsistency with NumPy for dtype=None (#8669) (#8685)

eye

e25284dced9749f02bd5d8c80b6225153aa282d8

dask

creation.py

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

Python

def eye(N, chunks="auto", M=None, k=0, dtype=float): eye = {} if M is None: M = N if dtype is None: dtype = float if not isinstance(chunks, (int, str)): raise ValueError("chunks must be an int or string") vchunks, hchunks = normalize_chunks(chunks, shape=(N, M), dtype=dtype) chunks = vchunks[0] token = tokenize(N, chunks, M, k, dtype) name_eye = "eye-" + token for i, vchunk in enumerate(vchunks): for j, hchunk in enumerate(hchunks): if (j - i - 1) * chunks <= k <= (j - i + 1) * chunks: eye[name_eye, i, j] = ( np.eye, vchunk, hchunk, k - (j - i) * chunks, dtype, ) else: eye[name_eye, i, j] = (np.zeros, (vchunk, hchunk), dtype) return Array(eye, name_eye, shape=(N, M), chunks=(chunks, chunks), dtype=dtype) @derived_from(np)

keras/engine/training_utils_v1.py

def is_composite_or_composite_value(tensor): # TODO(b/125094323): This sho

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

is_composite_or_composite_value

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training_utils_v1.py

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

Python

def is_composite_or_composite_value(tensor): # TODO(b/125094323): This should be isinstance(CompositeTensor) or # isinstance(CompositeTensorValue) once we support that. return isinstance( tensor, ( tf.__internal__.CompositeTensor, tf.compat.v1.SparseTensorValue, tf.compat.v1.ragged.RaggedTensorValue, ), )

awx/main/utils/common.py

def create_partition(tblname, start=None, end=None, partition_label=None, minutely=False): current_time = now() if not start: if minutely: start = current_time.replace(microsecond=0, second=0) else: start = current_time.replace(microsecond=0, second=0, minute=0) if not end: if minutely: end = start.replace(microsecond=0, second=0) + timedelta(minutes=1) else: end = start.replace(microsecond=0, second=0, minute=0) + timedelta(hours=1) start_timestamp = str(start) end_timestamp = str(end) if not partition_label: if minutely: partition_label = start.strftime('%Y%m%d_%H%M') else: partition_label = start.strftime('%Y%m%d_%H') try: with transaction.atomic(): with connection.cursor() as cursor: cursor.execute( f'CREATE TABLE IF NOT EXISTS {tblname}_{partition_label} ' f'PARTITION OF {tblname} ' f'FOR VALUES FROM (\'{start_timestamp}\') to (\'{end_timestamp}\');' ) exce

Handle error for create_partition Occasionally the create_partition will error with, relation "main_projectupdateevent_20220323_19" already exists This change wraps the db command into a try except block with its own transaction

create_partition

24152555c5d1b52d5024197bcaf80fdb87b8b14e

awx

common.py

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

Python

def create_partition(tblname, start=None, end=None, partition_label=None, minutely=False): current_time = now() if not start: if minutely: start = current_time.replace(microsecond=0, second=0) else: start = current_time.replace(microsecond=0, second=0, minute=0) if not end: if minutely: end = start.replace(microsecond=0, second=0) + timedelta(minutes=1) else: end = start.replace(microsecond=0, second=0, minute=0) + timedelta(hours=1) start_timestamp = str(start) end_timestamp = str(end) if not partition_label: if minutely: partition_label = start.strftime('%Y%m%d_%H%M') else: partition_label = start.strftime('%Y%m%d_%H') try: with transaction.atomic(): with connection.cursor() as cursor: cursor.execute( f'CREATE TABLE IF NOT EXISTS {tblname}_{partition_label} ' f'PARTITION OF {tblname} ' f'FOR VALUES FROM (\'{start_timestamp}\') to (\'{end_timestamp}\');' ) except ProgrammingError as e: logger.debug(f'Caught known error due to existing partition: {e}')

sklearn/metrics/pairwise.py

def rbf_kernel(X, Y=None, gamma=None): X, Y = check_pairwise_arrays(X, Y)

DOC Ensure `rbf_kernel` passes numpydoc validation (#23954) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

rbf_kernel

095e46670a1e21e8c49972b23e75f2d2a48c6c93

scikit-learn

pairwise.py

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

Python

def rbf_kernel(X, Y=None, gamma=None): X, Y = check_pairwise_arrays(X, Y) if gamma is None: gamma = 1.0 / X.shape[1] K = euclidean_distances(X, Y, squared=True) K *= -gamma np.exp(K, K) # exponentiate K in-place return K

lib/ansible/module_utils/common/parameters.py

def _validate_argument_values(argument_spec, parameters, options_context=None, errors=None): if errors is None: errors = AnsibleValidationErrorMultiple() for param, spec in argument_spec.items(): choices = spec.get('choices') if choices is None: continue if isinstance(choices, (frozenset, KeysView, Sequence)) and not isinstance(choices, (binary_type, text_type)): if param in parameters: # Allow one or more when type='list' param with choices if isinstance(parameters[param], list):

parameters: handle blank values when argument is a list (#77119) Fixes: #77108 Signed-off-by: Abhijeet Kasurde <akasurde@redhat.com>

_validate_argument_values

4f48f375a0203b0d09c55522a86300a52da5b24a

ansible

parameters.py

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

Python

def _validate_argument_values(argument_spec, parameters, options_context=None, errors=None): if errors is None: errors = AnsibleValidationErrorMultiple() for param, spec in argument_spec.items(): choices = spec.get('choices') if choices is None: continue if isinstance(choices, (frozenset, KeysView, Sequence)) and not isinstance(choices, (binary_type, text_type)): if param in parameters: # Allow one or more when type='list' param with choices if isinstance(parameters[param], list): diff_list = [item for item in parameters[param] if item not in choices] if diff_list: choices_str = ", ".join([to_native(c) for c in choices]) diff_str = ", ".join(diff_list) msg = "value of %s must be one or more of: %s. Got no match for: %s" % (param, choices_str, diff_str) if options_context: msg = "{0} found in {1}".format(msg, " -> ".join(options_context)) errors.append(ArgumentValueError(msg)) elif parameters[param] not in choices: # PyYaml converts certain strings to bools. If we can unambiguously convert back, do so before checking # the value. If we can't figure this out, module author is responsible. if parameters[param] == 'False': overlap = BOOLEANS_FALSE.intersection(choices) if len(overlap) == 1: # Extract from a set (parameters[param],) = overlap if parameters[param] == 'True': overlap = BOOLEANS_TRUE.intersection(choices) if len(overlap) == 1: (parameters[param],) = overlap if parameters[param] not in choices: choices_str = ", ".join([to_native(c) for c in choices]) msg = "value of %s must be one of: %s, got: %s" % (param, choices_str, parameters[param]) if options_context: msg = "{0} found in {1}".format(msg, " -> ".join(options_context)) errors.append(ArgumentValueError(msg)) else: msg = "internal error: choices for argument %s are not iterable: %s" % (param, choices) if options_context: msg = "{0} found in {1}".format(msg, " -> ".join(options_context)) errors.append(ArgumentTypeError(msg))

mlflow/models/evaluation/base.py

def content(self): if self._content is None: self._load()

Improve evaluation api (#5256) * init Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * address comments Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update doc Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * add shap limitation on value type Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * fix format Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com> * update Signed-off-by: Weichen Xu <weichen.xu@databricks.com>

content

4c58179509e6f6047789efb0a95c2b0e20cb6c8f

mlflow

base.py

https://github.com/mlflow/mlflow.git

Python

def content(self): if self._content is None: self._load() return self._content

@frappe.whitelist(allow_guest=True)

erpnext/e_commerce/api.py

def get_product_filter_data(query_args=None): if isinstance(query_args, str): query_args = json.loads(query_args) query_args = frappe._dict(query_args) if query_args: search = query_args.get("search") field_filters = query_args.get("field_filters", {}) attribute_filters = query_args.get("attribute_filters", {}) start = cint(query_args.start) if query_args.get("start") else 0 item_group = query_args.get("item_group") from_filters = query_args.get("from_filters") else: search, attri

feat: Include child item group products in Item Group Page & cleanup - Added 'Include descendants' checkbox, which will pull child item group products too - Build item group filters in query engine file - Include logic in filter engine - Clean up Website section of Item Group page (UX) - Add util to fetch child item groups including self

get_product_filter_data

b2755f6fdddd3e1b0a305b57c18651c98fee8f7e

erpnext

api.py

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

Python

def get_product_filter_data(query_args=None): if isinstance(query_args, str): query_args = json.loads(query_args) query_args = frappe._dict(query_args) if query_args: search = query_args.get("search") field_filters = query_args.get("field_filters", {}) attribute_filters = query_args.get("attribute_filters", {}) start = cint(query_args.start) if query_args.get("start") else 0 item_group = query_args.get("item_group") from_filters = query_args.get("from_filters") else: search, attribute_filters, item_group, from_filters = None, None, None, None field_filters = {} start = 0 # if new filter is checked, reset start to show filtered items from page 1 if from_filters: start = 0 sub_categories = [] if item_group: sub_categories = get_child_groups_for_website(item_group, immediate=True) engine = ProductQuery() try: result = engine.query( attribute_filters, field_filters, search_term=search, start=start, item_group=item_group ) except Exception: traceback = frappe.get_traceback() frappe.log_error(traceback, frappe._("Product Engine Error")) return {"exc": "Something went wrong!"} # discount filter data filters = {} discounts = result["discounts"] if discounts: filter_engine = ProductFiltersBuilder() filters["discount_filters"] = filter_engine.get_discount_filters(discounts) return { "items": result["items"] or [], "filters": filters, "settings": engine.settings, "sub_categories": sub_categories, "items_count": result["items_count"] } @frappe.whitelist(allow_guest=True)

homeassistant/components/mqtt/light/schema_basic.py

async def async_turn_on(self, **kwargs): # noqa: C901 should

Use `_attr_` for MQTT light (#81465) * Schema basic * Schema json * Schema template * add color_mode - follow up comments * Fix regression * Follow up comments 2 * Fix mypy errors * Update homeassistant/components/mqtt/light/schema_template.py Co-authored-by: epenet <6771947+epenet@users.noreply.github.com> Co-authored-by: epenet <6771947+epenet@users.noreply.github.com>

async_turn_on

d66d079330b92c02c38fb1c9dca539617161fdbc

core

schema_basic.py

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

Python

async def async_turn_on(self, **kwargs): # noqa: C901 should_update = False on_command_type = self._config[CONF_ON_COMMAND_TYPE]

tests/unit/keyinput/test_keyutils.py

def test_fake_mac(self, modifiers, expected): seq = keyutils.KeySequence() info = keyutils.KeyInfo(key=Qt.K

Run scripts/dev/rewrite_enums.py

test_fake_mac

0877fb0d78635692e481c8bde224fac5ad0dd430

qutebrowser

test_keyutils.py

https://github.com/qutebrowser/qutebrowser.git

Python

def test_fake_mac(self, modifiers, expected): seq = keyutils.KeySequence() info = keyutils.KeyInfo(key=Qt.Key.Key_A, modifiers=modifiers) new = seq.append_event(info.to_event()) assert new[0] == keyutils.KeyInfo(Qt.Key.Key_A, expected)

airbyte-integrations/connectors/source-recurly/source_recurly/streams.py

def default_params(self) -> dict: return {"order": "asc", "sort": self.sort_key, "limit": self.limit}

🎉 Recurly Schema Revamp (#9866) * Cleanup Recurly connector schemas * Add more Recurly schemas to the connector - `billing_infos` - `shipping_addresses` - `shipping_methods` - `subscription_changes` * Add Recurly `add-on` resouce * Add Recurly's account notes resource schema * Add unique coupons to Recurly source * Add credit payments to Recurly connector * Add Recurly resources to integration tests configurations * Bump Recurly source version to `0.4.0` * Add `line_items` Recurly resource * Add `line_items` to Recurly documentation * Add missing `line_items` JSON schema * Replace Subscription Change Recurly API call with Subscription `pending_changes` field * Replace Recurly unique coupon codes API call with coupons `unique_coupon` field To avoid the extra API call to import unique coupon calls * Revert "Replace Recurly unique coupon codes API call with coupons `unique_coupon` field" This reverts commit 1c4592d82da3c5e5e0026dda8eb2ed7a896ac5b8. * Add `end_time` parameter to Recurly connector * Order Recurly specs * Set the Recurly `begin_time` and `end_time` to be optional * Add `order` to Recurly `source_spec.yaml` * Add `maxLength` to Recurly source schemas * Set `maxLength` for Recurly Subscription and Transaction `uuid` * Fix Recurly `export_dates` acceptance tests

default_params

63af98e3b999d4b223237b51472a819915c5a558

airbyte

streams.py

https://github.com/airbytehq/airbyte.git

Python

def default_params(self) -> dict: return {"order": "asc", "sort": self.sort_key, "limit": self.limit}

python/ray/serve/deployment_state.py

def _should_start_new_health_check(self) -> bool: if self._health_check_ref is not None: # There's already an active health check. return False # If there's no active health check, kick off another and reset # the timer if it's been long enough since the last health # check. Add some randomness to avo

[serve] Improve health check failure semantics (#22297)

_should_start_new_health_check

610930ae6aeafb37be75851a8c1b9ff39d5f7d22

ray

deployment_state.py

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

Python

def _should_start_new_health_check(self) -> bool: if self._health_check_ref is not None: # There's already an active health check. return False # If there's no active health check, kick off another and reset # the timer if it's been long enough since the last health # check. Add some randomness to avoid synchronizing across all # replicas. time_since_last = time.time() - self._last_health_check_time randomized_period = self._health_check_period_s * random.uniform(0.9, 1.1) return time_since_last > randomized_period

src/transformers/trainer_pt_utils.py

def numpy_pad_and_concatenate(array1, array2, padding_index=-100): array1 = atleas

Ensure tensors are at least 1d for pad and concat (#17179) * Ensure tensors are at least 1d for pad and concat * Compatibility * Fix * Fix * Add test * Retrigger CI * Consistency with master * Retrigger CI

numpy_pad_and_concatenate

47412c7d434f6ddfc02a9b7ecd6182b86ae0a164

transformers

trainer_pt_utils.py

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

Python

def numpy_pad_and_concatenate(array1, array2, padding_index=-100): array1 = atleast_1d(array1) array2 = atleast_1d(array2) if len(array1.shape) == 1 or array1.shape[1] == array2.shape[1]: return np.concatenate((array1, array2), axis=0) # Let's figure out the new shape new_shape = (array1.shape[0] + array2.shape[0], max(array1.shape[1], array2.shape[1])) + array1.shape[2:] # Now let's fill the result tensor result = np.full_like(array1, padding_index, shape=new_shape) result[: array1.shape[0], : array1.shape[1]] = array1 result[array1.shape[0] :, : array2.shape[1]] = array2 return result

src/prefect/task_runners.py

def _ray(self) -> "ray": global ray if r

First draft `RayTaskRunner` implementation

_ray

f97603bba836c215e153d7d3d5b3b9de4d0ae822

prefect

task_runners.py

https://github.com/PrefectHQ/prefect.git

Python

def _ray(self) -> "ray": global ray if ray is None: try: import ray except ImportError as exc: raise RuntimeError( "Using the `RayTaskRunner` requires `ray` to be installed." ) from exc return ray

homeassistant/components/zwave_js/climate.py

def temperature_unit(self) -> str: if ( self._unit_value and self._unit_v

Use enums instead of deprecated constants (#81591)

temperature_unit

9a747bafa398185eb3d4fe041c52acfbb8264372

core

climate.py

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

Python

def temperature_unit(self) -> str: if ( self._unit_value and self._unit_value.metadata.unit and "f" in self._unit_value.metadata.unit.lower() ): return UnitOfTemperature.FAHRENHEIT return UnitOfTemperature.CELSIUS

examples/text_labels_and_annotations/angles_on_bracket_arrows.py

def get_point_of_rotated_vertical(origin, line_length, degrees): rad = np.deg2rad(-degrees) return [origin[0] + line_length * np.sin(rad), origin[1] + line_length * np.cos(rad)] fig, ax = plt.subplots(figsize=(8, 7)) ax.set(xlim=(0, 6), ylim=(-1, 4)) ax.set_title("Orientation of the bracket arrows relative to angleA and angleB") for i, style in enumerate(["]-[", "|-|"]): for j, angle in enumerate([-40, 60]): y = 2*i + j arrow_centers = ((1, y), (5, y)) vlines = ((1, y + 0.5), (5, y + 0.5)) anglesAB = (angle, -angle) bracketstyle = f"{style}, angleA={anglesAB[0]}, angleB={anglesAB[1]}" bracket = FancyArrowPatch(*arrow_centers, arrowstyle=bracketstyle, mutation_scale=42) ax.add_patch(bracket) ax.text(3, y + 0.05, bracketstyle, ha="center", va="bottom") ax.vlines([i[0] for i in vlines], [y, y], [i[1] for i in vlines], linestyles="--", color="C0") # Get the top coordinates for the drawn patches at A and B patch_tops = [get_point_of_rotated_vertical(center, 0.5, angle) for center, angle in zip(arrow_centers, anglesAB)] # Define the connection directions for the annotation arrows connection_dirs = (1, -1) if angle > 0 else (-1, 1) # Add arrows and annotation text arrowstyle = "Simple, tail_width=0.5, head_width=4, head_length=8" for vline, dir, patch_top, angle in zip(vlines, connection_dirs, patch_tops, anglesAB): kw = dict(connectionstyle=f"arc3,rad={dir * 0.5}", arrowst

Updated Angles on Bracket arrow styles example to make angles clear #23176 (#24145) * removed AngleAnnotation from angle_on_bracket_arrow example * Fixes indentation mistake. * rebase to main, remove conflicting commit

get_point_of_rotated_vertical

f15aeee5e8d380c2ea04bcbed202a8940a7db1d0

matplotlib

angles_on_bracket_arrows.py

https://github.com/matplotlib/matplotlib.git

Python

def get_point_of_rotated_vertical(origin, line_length, degrees): rad = np.deg2rad(-degrees) return [origin[0] + line_length * np.sin(rad), origin[1] + line_length * np.cos(rad)] fig, ax = plt.subplots(figsize=(8, 7)) ax.set(xlim=(0, 6), ylim=(-1, 4)) ax.set_title("Orientation of the bracket arrows relative to angleA and angleB") for i, style in enumerate(["]-[", "|-|"]): for j, angle in enumerate([-40, 60]): y = 2*i + j arrow_centers = ((1, y), (5, y)) vlines = ((1, y + 0.5), (5, y + 0.5)) anglesAB = (angle, -angle) bracketstyle = f"{style}, angleA={anglesAB[0]}, angleB={anglesAB[1]}" bracket = FancyArrowPatch(*arrow_centers, arrowstyle=bracketstyle, mutation_scale=42) ax.add_patch(bracket) ax.text(3, y + 0.05, bracketstyle, ha="center", va="bottom") ax.vlines([i[0] for i in vlines], [y, y], [i[1] for i in vlines], linestyles="--", color="C0") # Get the top coordinates for the drawn patches at A and B patch_tops = [get_point_of_rotated_vertical(center, 0.5, angle) for center, angle in zip(arrow_centers, anglesAB)] # Define the connection directions for the annotation arrows connection_dirs = (1, -1) if angle > 0 else (-1, 1) # Add arrows and annotation text arrowstyle = "Simple, tail_width=0.5, head_width=4, head_length=8" for vline, dir, patch_top, angle in zip(vlines, connection_dirs, patch_tops, anglesAB): kw = dict(connectionstyle=f"arc3,rad={dir * 0.5}", arrowstyle=arrowstyle, color="C0") ax.add_patch(FancyArrowPatch(vline, patch_top, **kw)) ax.text(vline[0] - dir * 0.15, y + 0.3, f'{angle}°', ha="center", va="center") ############################################################################# # # .. admonition:: References # # The use of the following functions, methods, classes and modules is shown # in this example: # # - `matplotlib.patches.ArrowStyle`

.venv/lib/python3.8/site-packages/pip/_vendor/html5lib/_tokenizer.py

def processEntityInAttribute(self, allowedChar): self.consumeEntity(allowedChar=allowedChar, fromAttribute=True)

upd; format

processEntityInAttribute

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

_tokenizer.py

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

Python

def processEntityInAttribute(self, allowedChar): self.consumeEntity(allowedChar=allowedChar, fromAttribute=True)

sympy/physics/secondquant.py

def _get_ordered_dummies(mul, verbose=False): # setup dicts to avoid repeated calculations in key() args = Mul.make_args(mul) fac_dum = { fac: fac.atoms(Dummy) for fac in args } fac_repr = { fac: __kprint(fac) for fac in args } all_dums = set().union(*fac_dum.values()) mask = {} for d in all_dums: if d.assumptions0.get('below_fermi'): mask[d] = '0' elif d.assumptions0.get('above_fermi'): mask[d] = '1' else: mask[d] = '2' d

Fix various typos Found via `codespell -q 3 -L aboves,aline,ans,aother,arithmetics,assum,atleast,braket,clen,declar,declars,dorder,dum,enew,fo,fro,inout,iself,ist,ket,lamda,lightyear,lightyears,nd,numer,numers,orderd,ot,pring,rcall,rever,ro,ser,siz,splitted,sring,supercedes,te,tht,unequality,upto,vas,versin,whet`

_get_ordered_dummies

24f1e7730119fe958cc8e28411f790c9a5ec04eb

sympy

secondquant.py

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

Python

def _get_ordered_dummies(mul, verbose=False): # setup dicts to avoid repeated calculations in key() args = Mul.make_args(mul) fac_dum = { fac: fac.atoms(Dummy) for fac in args } fac_repr = { fac: __kprint(fac) for fac in args } all_dums = set().union(*fac_dum.values()) mask = {} for d in all_dums: if d.assumptions0.get('below_fermi'): mask[d] = '0' elif d.assumptions0.get('above_fermi'): mask[d] = '1' else: mask[d] = '2' dum_repr = {d: __kprint(d) for d in all_dums}

airflow/providers/amazon/aws/example_dags/example_athena.py

def read_results_from_s3(query_execution_id): s3_hook = S3Hook() file_obj = s3_hook.get_conn().get_object(Bucket=S3_BUCKET, Key=f'{S3_KEY}/{query_execution_id}.csv') file_content = file_obj['Body'].read().decode('utf-8') print(file_content) QUERY_CREATE_TABLE = f QUERY_READ_TABLE = f QUERY_DROP_TABLE = f with DAG( dag_id='example_athena', schedule_interval=None, start_date=datetime(2021, 1, 1), tags=['example'], catchup=False, ) as dag: upload_sample_data = S3CreateObjectOperator( task_id='upload_sample_data', s3_bucket=S3_BUCKET, s3_key=f'{S3_KEY}/{ATHENA_TABLE}/{SAMPLE_FILENAME}', data=SAMPLE_DATA, replace=True, ) create_table = AthenaOperator( task_id='create_table', query=QUERY_CREATE_TABLE, database=ATHENA_DATABASE, output_location=f's3://{S3_BUCKET}/{S3_KEY}', ) # [START howto_athena_operator] read_table = AthenaOperator( task_id='read_table', query=QUERY_READ_TABLE, database=ATHENA_DATABASE, output_location=f's3://{S3_BUCKET}/{

Update the Athena Sample DAG and Docs (#23428) * Update the Athena Sample DAG and Docs

read_results_from_s3

46af5baba810a07eec395e89db08fc5dab175e23

airflow

example_athena.py

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

Python

def read_results_from_s3(query_execution_id): s3_hook = S3Hook() file_obj = s3_hook.get_conn().get_object(Bucket=S3_BUCKET, Key=f'{S3_KEY}/{query_execution_id}.csv') file_content = file_obj['Body'].read().decode('utf-8') print(file_content) QUERY_CREATE_TABLE = f QUERY_READ_TABLE = f QUERY_DROP_TABLE = f with DAG( dag_id='example_athena', schedule_interval=None, start_date=datetime(2021, 1, 1), tags=['example'], catchup=False, ) as dag: upload_sample_data = S3CreateObjectOperator( task_id='upload_sample_data', s3_bucket=S3_BUCKET, s3_key=f'{S3_KEY}/{ATHENA_TABLE}/{SAMPLE_FILENAME}', data=SAMPLE_DATA, replace=True, ) create_table = AthenaOperator( task_id='create_table', query=QUERY_CREATE_TABLE, database=ATHENA_DATABASE, output_location=f's3://{S3_BUCKET}/{S3_KEY}', ) # [START howto_athena_operator] read_table = AthenaOperator( task_id='read_table', query=QUERY_READ_TABLE, database=ATHENA_DATABASE, output_location=f's3://{S3_BUCKET}/{S3_KEY}', ) # [END howto_athena_operator] # [START howto_athena_sensor] await_query = AthenaSensor( task_id='await_query', query_execution_id=read_table.output, ) # [END howto_athena_sensor] drop_table = AthenaOperator( task_id='drop_table', query=QUERY_DROP_TABLE, database=ATHENA_DATABASE, output_location=f's3://{S3_BUCKET}/{S3_KEY}', ) remove_s3_files = S3DeleteObjectsOperator( task_id='remove_s3_files', bucket=S3_BUCKET, prefix=S3_KEY, ) ( upload_sample_data >> create_table >> read_table >> await_query >> read_results_from_s3(read_table.output) >> drop_table >> remove_s3_files )

pandas/core/dtypes/common.py

def is_datetime64_ns_dtype(arr_or_dtype) -> bool: if arr_or_dtype is None: return False try: tipo = get_dtype(arr_or_dtype) except TypeError:

ENH: DTI/DTA.astype support non-nano (#47579) * ENH: DTI/DTA.astype support non-nano * whatsnew * GH ref * pyright fixup

is_datetime64_ns_dtype

67e8c4c3761ab1da4b0a341a472c0fe2ea393e8b

pandas

common.py

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

Python

def is_datetime64_ns_dtype(arr_or_dtype) -> bool: if arr_or_dtype is None: return False try: tipo = get_dtype(arr_or_dtype) except TypeError: if is_datetime64tz_dtype(arr_or_dtype): tipo = get_dtype(arr_or_dtype.dtype) else: return False return tipo == DT64NS_DTYPE or ( isinstance(tipo, DatetimeTZDtype) and tipo._unit == "ns" )

@register.tag

django/template/defaulttags.py

def regroup(parser, token): bits = token.split_contents() if len(bits) != 6: raise TemplateSyntaxError("'regroup' tag takes five arguments") target = parser.compile_filter(bits[1]) if bits[2] != 'by': raise TemplateSyntaxError("second argument to 'regroup' tag must be 'by'") if bits[4] != 'as': raise Template

Refs #33476 -- Refactored problematic code before reformatting by Black. In these cases Black produces unexpected results, e.g. def make_random_password( self, length=10, allowed_chars='abcdefghjkmnpqrstuvwxyz' 'ABCDEFGHJKLMNPQRSTUVWXYZ' '23456789', ): or cursor.execute(""" SELECT ... """, [table name], )

regroup

c5cd8783825b5f6384417dac5f3889b4210b7d08

django

defaulttags.py

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

Python

def regroup(parser, token): bits = token.split_contents() if len(bits) != 6: raise TemplateSyntaxError("'regroup' tag takes five arguments") target = parser.compile_filter(bits[1]) if bits[2] != 'by': raise TemplateSyntaxError("second argument to 'regroup' tag must be 'by'") if bits[4] != 'as': raise TemplateSyntaxError( "next-to-last argument to 'regroup' tag must be 'as'" ) var_name = bits[5] # RegroupNode will take each item in 'target', put it in the context under # 'var_name', evaluate 'var_name'.'expression' in the current context, and # group by the resulting value. After all items are processed, it will # save the final result in the context under 'var_name', thus clearing the # temporary values. This hack is necessary because the template engine # doesn't provide a context-aware equivalent of Python's getattr. expression = parser.compile_filter(var_name + VARIABLE_ATTRIBUTE_SEPARATOR + bits[3]) return RegroupNode(target, expression, var_name) @register.tag

spotdl/types/saved.py

def create_basic_list(cls) -> "Saved": metadata

fixed arguments for frozen env fixed pylint errors fixed arguments black fixed argument parser for all scenarios black docs black

create_basic_list

773398048b7990ab58e2998fe4d15355f7998774

spotify-downloader

saved.py

https://github.com/spotDL/spotify-downloader.git

Python

def create_basic_list(cls) -> "Saved": metadata = cls.get_metadata("saved") urls = cls.get_urls("saved") return cls(**metadata, urls=urls, songs=[])

tests/common.py

def assert_lists_same(a, b): assert len(a) == len(b) for i in a: assert i in b for i in b: assert i in a

Mark device actions from hidden or auxiliary entities as secondary (#70278)

assert_lists_same

64381acbaf2930cda5dfa538d00bfa9f5172e690

core

common.py

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

Python

def assert_lists_same(a, b): assert len(a) == len(b) for i in a: assert i in b for i in b: assert i in a

dask/dataframe/tests/test_format.py

def test_dataframe_format_with_index(): pytest.importorskip("jinja2") df = pd.DataFrame( { "A": [1, 2, 3, 4, 5, 6, 7, 8], "B": list("ABCDEFGH"), "C": pd.Categorical(list("AAABBBCC")), }, index=list("ABCDEFGH"), ) ddf = dd.from_pandas(df, 3) exp = ( "Dask DataFrame Structure:\n" " A B C\n" "npartitions=3 \n" "A int64 object category[known]\n" "D ... ... ...\n" "G ... ... ...\n" "H ... ... ...\n" "Dask Name: from_pandas, 1 graph layer" ) assert repr(ddf) == exp assert str(ddf) == exp exp_table = exp = .format( exp_table=exp_table ) assert ddf.to_html() == exp # table is boxed with div and has style exp = .format( style=style, exp_table=exp_table ) assert ddf._repr_html_() == exp

Change repr methods to avoid Layer materialization (#9289) * change task count to layer count in DataFrame and Array reprs * add test * address doctest failure * simplify test * support pluralization * use 'graph layers' instead of 'layers' to be more explicit

test_dataframe_format_with_index

ddcb841903f8f180aa359bd8db0054aa3b5964e3

dask

test_format.py

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

Python

def test_dataframe_format_with_index(): pytest.importorskip("jinja2") df = pd.DataFrame( { "A": [1, 2, 3, 4, 5, 6, 7, 8], "B": list("ABCDEFGH"), "C": pd.Categorical(list("AAABBBCC")), }, index=list("ABCDEFGH"), ) ddf = dd.from_pandas(df, 3) exp = ( "Dask DataFrame Structure:\n" " A B C\n" "npartitions=3 \n" "A int64 object category[known]\n" "D ... ... ...\n" "G ... ... ...\n" "H ... ... ...\n" "Dask Name: from_pandas, 1 graph layer" ) assert repr(ddf) == exp assert str(ddf) == exp exp_table = exp = .format( exp_table=exp_table ) assert ddf.to_html() == exp # table is boxed with div and has style exp = .format( style=style, exp_table=exp_table ) assert ddf._repr_html_() == exp

tests/admin_scripts/tests.py

def test_run_from_argv_closes_connections(self):

Refs #33476 -- Reformatted code with Black.

test_run_from_argv_closes_connections

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_run_from_argv_closes_connections(self): command = BaseCommand() command.check = lambda: [] command.handle = lambda *args, **kwargs: args with mock.patch("django.core.management.base.connections") as mock_connections: command.run_from_argv(["", ""]) # Test connections have been closed self.assertTrue(mock_connections.close_all.called)

tests/unit/test_project_structure.py

def test_version_managing(self, data_handler): # set up df = pd.DataFrame([ {'a': 1, 'b': dt.datetime(2020, 1, 1)}, {'a': 2, 'b': dt.datetime(2020, 1, 2)}, {'a': 1, 'b': dt.datetime(2020, 1, 3)}, ]) self.set_handler(data_handler, name='pg', tables={'tasks': df}) # ================= retrain cycles ===================== # create folder self.run_sql('create database proj') # -- create model -- self.run_sql( ) self.wait_predictor('proj', 'task_model') assert data_handler().native_query.call_args[0][0] == 'select * from tasks' # tag works in create model ret = self.run_sql('select * from proj.models') assert ret['TAG'][0] == 'first' # use model ret = self.run_sql() assert len(ret) == 3 assert ret.predicted[0] == 42 # -- retrain predictor with tag -- data_handler.reset_mock() self.run_sql( ) self.wait_predictor('proj', 'task_model', {'tag': 'second'}) # get current model ret = self.run_sql('select * from proj.models') # check target assert ret['PREDICT'][0] == 'b' # check label a

update and delete model version renaming (predictor->model)

test_version_managing

3f1a5c30c2ccbd78b21f1f41b7dfdfca87bb7135

mindsdb

test_project_structure.py

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

Python

def test_version_managing(self, data_handler): # set up df = pd.DataFrame([ {'a': 1, 'b': dt.datetime(2020, 1, 1)}, {'a': 2, 'b': dt.datetime(2020, 1, 2)}, {'a': 1, 'b': dt.datetime(2020, 1, 3)}, ]) self.set_handler(data_handler, name='pg', tables={'tasks': df}) # ================= retrain cycles ===================== # create folder self.run_sql('create database proj') # -- create model -- self.run_sql( ) self.wait_predictor('proj', 'task_model') assert data_handler().native_query.call_args[0][0] == 'select * from tasks' # tag works in create model ret = self.run_sql('select * from proj.models') assert ret['TAG'][0] == 'first' # use model ret = self.run_sql() assert len(ret) == 3 assert ret.predicted[0] == 42 # -- retrain predictor with tag -- data_handler.reset_mock() self.run_sql( ) self.wait_predictor('proj', 'task_model', {'tag': 'second'}) # get current model ret = self.run_sql('select * from proj.models') # check target assert ret['PREDICT'][0] == 'b' # check label assert ret['TAG'][0] == 'second' # check integration sql assert data_handler().native_query.call_args[0][0] == 'select * from tasks where a=2' # use model ret = self.run_sql() assert ret.predicted[0] == 42 # used model has tag 'second' models = self.get_models() model_id = ret.predictor_id[0] assert models[model_id].label == 'second' # -- retrain again with active=0 -- data_handler.reset_mock() self.run_sql( ) self.wait_predictor('proj', 'task_model', {'tag': 'third'}) ret = self.run_sql('select * from proj.models') # check target is from previous retrain assert ret['PREDICT'][0] == 'b' # use model ret = self.run_sql() # used model has tag 'second' (previous) models = self.get_models() model_id = ret.predictor_id[0] assert models[model_id].label == 'second' # ================ working with inactive versions ================= # run 3st version model and check used model version ret = self.run_sql() models = self.get_models() model_id = ret.predictor_id[0] assert models[model_id].label == 'third' # one-line query model by version ret = self.run_sql('SELECT * from proj.task_model.3 where a=1 and b=2') model_id = ret.predictor_id[0] assert models[model_id].label == 'third' # not existing version with pytest.raises(Exception) as exc_info: self.run_sql( 'SELECT * from proj.task_model.4 where a=1 and b=2', ) assert 'does not exists' in str(exc_info.value) # ================== managing versions ========================= # show models command # Show models <from | in> <project> where <expr> ret = self.run_sql('Show models') assert len(ret) == 1 and ret['NAME'][0] == 'task_model' ret = self.run_sql('Show models from proj') assert len(ret) == 1 and ret['NAME'][0] == 'task_model' ret = self.run_sql('Show models in proj') assert len(ret) == 1 and ret['NAME'][0] == 'task_model' ret = self.run_sql("Show models where name='task_model'") assert len(ret) == 1 and ret['NAME'][0] == 'task_model' ret = self.run_sql("Show models from proj where name='xxx'") assert len(ret) == 0 # ---------------- # See all versions ret = self.run_sql('select * from proj.models_versions') # we have all tags in versions assert set(ret['TAG']) == {'first', 'second', 'third'} # Set active selected version self.run_sql() # get active version ret = self.run_sql('select * from proj.models_versions where active = 1') assert ret['TAG'][0] == 'first' # use active version ? # Delete specific version self.run_sql() # deleted version not in list ret = self.run_sql('select * from proj.models_versions') assert len(ret) == 2 assert 'second' not in ret['TAG'] # try to use deleted version with pytest.raises(Exception) as exc_info: self.run_sql( 'SELECT * from proj.task_model.2 where a=1', ) assert 'does not exists' in str(exc_info.value) # exception with deleting active version with pytest.raises(Exception) as exc_info: self.run_sql() assert 'is not found' in str(exc_info.value) # drop predictor and check model is deleted and no versions self.run_sql('drop predictor proj.task_model') ret = self.run_sql('select * from proj.models') assert len(ret) == 0 ret = self.run_sql('select * from proj.models_versions') assert len(ret) == 0

@functools.lru_cache(maxsize=None)

pipenv/patched/notpip/_internal/locations/__init__.py

def _looks_like_red_hat_lib() -> bool: from distutils.command.install import INSTALL_SCHEMES # type: ignore return all( k in INSTALL_SCHEMES and _looks_like_red_hat_patched_platlib

Vendor in pip 21.2.4 release (from pip 21.2.2 prior). (#5009) * Vendor in pip 21.2.4 release (from pip 21.2.2 prior). * Add news fragment for pip 21.2.4 vendor update. * Add potentially missing LICENSE files

_looks_like_red_hat_lib

7e33fcae4384563b4c927fd44318c29dd524a097

pipenv

__init__.py

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

Python

def _looks_like_red_hat_lib() -> bool: from distutils.command.install import INSTALL_SCHEMES # type: ignore return all( k in INSTALL_SCHEMES and _looks_like_red_hat_patched_platlib_purelib(INSTALL_SCHEMES[k]) for k in ("unix_prefix", "unix_home") ) @functools.lru_cache(maxsize=None)

wagtail/contrib/modeladmin/helpers/permission.py

def get_valid_parent_pages(self, user): # Get queryset of pages where this page type can be added allowed_parent_page_content_types = list( ContentType.objects.get_for_models( *self.model.allowed_parent_page_models() ).values() ) allowed_parent_pages = Page.objects.filter( content_type__in=allowed_parent_page_content_types ) # Get queryset of pages where the user has permission to add subpages if user.is_superuser: pages_where_user_can_add = Page.objects.all() else: pages_where_user_can_add = Page.objects.none() user_perms = UserPagePermissionsProxy(user)

Reformat with black

get_valid_parent_pages

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

permission.py

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

Python

def get_valid_parent_pages(self, user): # Get queryset of pages where this page type can be added allowed_parent_page_content_types = list( ContentType.objects.get_for_models( *self.model.allowed_parent_page_models() ).values() ) allowed_parent_pages = Page.objects.filter( content_type__in=allowed_parent_page_content_types ) # Get queryset of pages where the user has permission to add subpages if user.is_superuser: pages_where_user_can_add = Page.objects.all() else: pages_where_user_can_add = Page.objects.none() user_perms = UserPagePermissionsProxy(user) for perm in user_perms.permissions.filter(permission_type="add"): # user has add permission on any subpage of perm.page # (including perm.page itself) pages_where_user_can_add |= Page.objects.descendant_of( perm.page, inclusive=True ) # Combine them return allowed_parent_pages & pages_where_user_can_add

erpnext/hr/report/monthly_attendance_sheet/monthly_attendance_sheet.py

def get_attendance_list(conditions, filters): attendance_list = frapp

style: format code with black

get_attendance_list

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

monthly_attendance_sheet.py

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

Python

def get_attendance_list(conditions, filters): attendance_list = frappe.db.sql( % conditions, filters, as_dict=1, ) if not attendance_list: msgprint(_("No attendance record found"), alert=True, indicator="orange") att_map = {} for d in attendance_list: att_map.setdefault(d.employee, frappe._dict()).setdefault(d.day_of_month, "") att_map[d.employee][d.day_of_month] = d.status return att_map

@add_start_docstrings( """ RemBERT Model transformer with a sequence classification/regression head on top e.g., for GLUE tasks. """, REMBERT_START_DOCSTRING, )

src/transformers/models/rembert/modeling_tf_rembert.py

def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(tf.gather(past_state, bea

TF generate refactor - past without encoder outputs (#15944) * Remove packed past from generation_tf_utils * update models with the new past format * update template accordingly

_reorder_cache

70203b59379b1841013980b6941bddfd34bfe816

transformers

modeling_tf_rembert.py

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

Python

def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(tf.gather(past_state, beam_idx, axis=0) for past_state in layer_past),) return reordered_past @add_start_docstrings( , REMBERT_START_DOCSTRING, )

tests/sentry/api/endpoints/test_team_details.py

def test_remove_as_admin_not_in_team(self): # an org with closed membership (byproduct of flags=0) org = self.create_organization(owner=self.user, flags=0) team = self.create_team(organization=org) admin_user = self.create_user(email="foo@example.com", is_superuser=False) self.create_member( organization=org, user=admin_user, role="admin", teams=[], # note that admin_user isn't a member of `team` ) self.login_as(admin_user) # first, try deleting the team with open membership off self.get_error_response(team.organization.slug, team.slug, status_code=403) self.assert_team_not_deleted(

ref(tests): Remove `get_valid_response()` (#34822)

test_remove_as_admin_not_in_team

096b5511e244eecd8799b2a0324655207ce8985e

sentry

test_team_details.py

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

Python

def test_remove_as_admin_not_in_team(self): # an org with closed membership (byproduct of flags=0) org = self.create_organization(owner=self.user, flags=0) team = self.create_team(organization=org) admin_user = self.create_user(email="foo@example.com", is_superuser=False) self.create_member( organization=org, user=admin_user, role="admin", teams=[], # note that admin_user isn't a member of `team` ) self.login_as(admin_user) # first, try deleting the team with open membership off self.get_error_response(team.organization.slug, team.slug, status_code=403) self.assert_team_not_deleted(team.id) # now, with open membership on org.flags.allow_joinleave = True org.save() self.get_success_response(team.organization.slug, team.slug, status_code=204) self.assert_team_deleted(team.id)

python/ray/tests/aws/test_aws_batch_tag_update.py

def batch_test(num_threads, delay): with mock.patch( "ray.autoscaler._private.aws.node_provider.make_ec2_client" ), mock.patch.object(AWSNodeProvider, "_create_tags", mock_create_tags): provider = AWSNodeProvider( provider_config={"region": "nowhere"}, cluster_name="default" ) provider.batch_counter = 0 provider.tag_update_count

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

batch_test

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

test_aws_batch_tag_update.py

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

Python

def batch_test(num_threads, delay): with mock.patch( "ray.autoscaler._private.aws.node_provider.make_ec2_client" ), mock.patch.object(AWSNodeProvider, "_create_tags", mock_create_tags): provider = AWSNodeProvider( provider_config={"region": "nowhere"}, cluster_name="default" ) provider.batch_counter = 0 provider.tag_update_counter = 0 provider.tag_cache = {str(x): {} for x in range(num_threads)} threads = [] for x in range(num_threads): thread = threading.Thread( target=provider.set_node_tags, args=(str(x), {"foo": "bar"}) ) threads.append(thread) for thread in threads: thread.start() time.sleep(delay) for thread in threads: thread.join() return provider.batch_counter, provider.tag_update_counter

@PLUGIN_LAYERS.register_module()

mmdet/models/plugins/pixel_decoder.py

def forward(self, feats, img_metas): y = self.last_feat_conv(feats[-1]) for i in range(self.num_inputs - 2, -1, -1): x = feats[i] cur_fpn = self.lateral_convs[i](x) y = cur_fpn + \ F.interpolate(y, size=cur_fpn.shape[-2:], mode='nearest') y = self.output_convs[i](y) mask_feature = self.mask_feature(y) memory = feats[-1] return mask_feature, memory @PLUGIN_LAYERS.register_module()

[Feature] Add Maskformer to mmdet (#7212) * first commit * add README * move model description from config to readme add description for binary_input add description for dice loss add a independent panoptic gt processing function add a independent panoptic gt processing function remove compatibility of pretrain in maskformer * update comments in maskformer_head * update docs format

forward

cac356380d505bf15587f07c0529218cc36b9652

mmdetection

pixel_decoder.py

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

Python

def forward(self, feats, img_metas): y = self.last_feat_conv(feats[-1]) for i in range(self.num_inputs - 2, -1, -1): x = feats[i] cur_fpn = self.lateral_convs[i](x) y = cur_fpn + \ F.interpolate(y, size=cur_fpn.shape[-2:], mode='nearest') y = self.output_convs[i](y) mask_feature = self.mask_feature(y) memory = feats[-1] return mask_feature, memory @PLUGIN_LAYERS.register_module()

tests/components/bluetooth/test_models.py

async def test_remote_scanner_expires_non_connectable(hass): manager = _get_manager() switchbot_device = BLEDevice(

Move bluetooth remote scanner implementation into a base class (#82012)

test_remote_scanner_expires_non_connectable

f584efa0c24df19ef1f805ecf95a95cecec5ff99

core

test_models.py

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

Python

async def test_remote_scanner_expires_non_connectable(hass): manager = _get_manager() switchbot_device = BLEDevice( "44:44:33:11:23:45", "wohand", {}, rssi=-100, ) switchbot_device_adv = generate_advertisement_data( local_name="wohand", service_uuids=[], manufacturer_data={1: b"\x01"}, rssi=-100, )

lib/ansible/plugins/inventory/toml.py

def convert_yaml_objects_to_native(obj): if isinstance(obj, dict):

Support for Python 3.11+ tomllib for inventory (#77435)

convert_yaml_objects_to_native

bcdc2e167af61cf978e589c753764f76e301a6fa

ansible

toml.py

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

Python

def convert_yaml_objects_to_native(obj): if isinstance(obj, dict): return dict((k, convert_yaml_objects_to_native(v)) for k, v in obj.items()) elif isinstance(obj, list): return [convert_yaml_objects_to_native(v) for v in obj] elif isinstance(obj, text_type): return text_type(obj) else: return obj

python/prophet/forecaster.py

def make_future_dataframe(self, periods, freq='D', include_history=True): if self.history_dates is None: raise Exception('Model has not been fit.') if freq is None: # taking the tail makes freq inference more reliable freq = pd.infer_freq(self.history_dates.tail(5)) # returns None if inference failed if freq is None: raise Exception('Unable to infer `freq`') last_date = self.history_dates.max() dates = pd.date_range( start=last_date, periods=periods + 1, # An extra in case we include start freq=freq) dates = dates[dates > last_date] # Drop start if equals last_date dates = dates[:per

Speed Up Uncertainty Predictions (#2186)

make_future_dataframe

8fbf8ba2a5bfcdb892e8ca596e338894614000b5

prophet

forecaster.py

https://github.com/facebook/prophet.git

Python

def make_future_dataframe(self, periods, freq='D', include_history=True): if self.history_dates is None: raise Exception('Model has not been fit.') if freq is None: # taking the tail makes freq inference more reliable freq = pd.infer_freq(self.history_dates.tail(5)) # returns None if inference failed if freq is None: raise Exception('Unable to infer `freq`') last_date = self.history_dates.max() dates = pd.date_range( start=last_date, periods=periods + 1, # An extra in case we include start freq=freq) dates = dates[dates > last_date] # Drop start if equals last_date dates = dates[:periods] # Return correct number of periods if include_history: dates = np.concatenate((np.array(self.history_dates), dates)) return pd.DataFrame({'ds': dates})

code/deep/BJMMD/caffe/python/caffe/io.py

def array_to_blobproto(arr, diff=None): blob = caffe_pb2.BlobProt

Balanced joint maximum mean discrepancy for deep transfer learning

array_to_blobproto

cc4d0564756ca067516f71718a3d135996525909

transferlearning

io.py

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

Python

def array_to_blobproto(arr, diff=None): blob = caffe_pb2.BlobProto() blob.shape.dim.extend(arr.shape) blob.data.extend(arr.astype(float).flat) if diff is not None: blob.diff.extend(diff.astype(float).flat) return blob

tests/test_federation.py

def test_cross_signing_keys_retry(self): remote_user_id = "@john:test_remote" remote_master_key = "85T7JXPFBAySB/jwby4S3lBPTqY3+Zg53nYuGmu1ggY" remote_self_signing_key = "QeIiFEjluPBtI7WQdG365QKZcFs9kqmHir6RBD0//nQ" # Register mock device list retrieval on the federation client. federation_client = self.homeserver.get_federation_client() federation_client.query_user_devices = Mock( return_value=make_awaitable( { "user_id": remote_user_id, "stream_id": 1, "devices": [], "master_key": { "user_id": remote_user_id, "usage": ["master"], "keys": {"ed25519:" + remote_master_key: remote_master_key}, }, "self_signing_key": { "user_id": remote_user_id, "usage": ["self_signing"], "keys": { "ed25

Prefer `make_awaitable` over `defer.succeed` in tests (#12505) When configuring the return values of mocks, prefer awaitables from `make_awaitable` over `defer.succeed`. `Deferred`s are only awaitable once, so it is inappropriate for a mock to return the same `Deferred` multiple times. Also update `run_in_background` to support functions that return arbitrary awaitables. Signed-off-by: Sean Quah <seanq@element.io>

test_cross_signing_keys_retry

78b99de7c206b106340e12cdee0af9aa246bd5ad

synapse

test_federation.py

https://github.com/matrix-org/synapse.git

Python

def test_cross_signing_keys_retry(self): remote_user_id = "@john:test_remote" remote_master_key = "85T7JXPFBAySB/jwby4S3lBPTqY3+Zg53nYuGmu1ggY" remote_self_signing_key = "QeIiFEjluPBtI7WQdG365QKZcFs9kqmHir6RBD0//nQ" # Register mock device list retrieval on the federation client. federation_client = self.homeserver.get_federation_client() federation_client.query_user_devices = Mock( return_value=make_awaitable( { "user_id": remote_user_id, "stream_id": 1, "devices": [], "master_key": { "user_id": remote_user_id, "usage": ["master"], "keys": {"ed25519:" + remote_master_key: remote_master_key}, }, "self_signing_key": { "user_id": remote_user_id, "usage": ["self_signing"], "keys": { "ed25519:" + remote_self_signing_key: remote_self_signing_key }, }, } ) ) # Resync the device list. device_handler = self.homeserver.get_device_handler() self.get_success( device_handler.device_list_updater.user_device_resync(remote_user_id), ) # Retrieve the cross-signing keys for this user. keys = self.get_success( self.store.get_e2e_cross_signing_keys_bulk(user_ids=[remote_user_id]), ) self.assertTrue(remote_user_id in keys) # Check that the master key is the one returned by the mock. master_key = keys[remote_user_id]["master"] self.assertEqual(len(master_key["keys"]), 1) self.assertTrue("ed25519:" + remote_master_key in master_key["keys"].keys()) self.assertTrue(remote_master_key in master_key["keys"].values()) # Check that the self-signing key is the one returned by the mock. self_signing_key = keys[remote_user_id]["self_signing"] self.assertEqual(len(self_signing_key["keys"]), 1) self.assertTrue( "ed25519:" + remote_self_signing_key in self_signing_key["keys"].keys(), ) self.assertTrue(remote_self_signing_key in self_signing_key["keys"].values())

tests/providers/databricks/operators/test_databricks.py

def test_exec_success(self, db_mock_class): run = { 'new_cluster': NEW_CLUSTER, 'notebook_task': NOTEBOOK_TASK, } op = DatabricksSubmitRunOperator(task_id=TASK_ID, json=run) db_mock = db_mock_class.return_value db_mock.submit_run.return_value = 1 db_mock.get_run_state.return_value = RunState('TERMINATED', 'SUCCESS', '') op.execute(None) expected = databricks_operator._deep_string_coerce( {'new_cluster': NEW_CLUSTER, 'notebook_task': NOTEBOOK_TASK, 'run_name': TASK_ID} ) db_mock_class.assert_called_once_with( DEFAULT_CONN_ID, retry_limit=op.databricks_retry_limit, retry_delay=op.databricks_retry_delay, retry_args=None, ) db_mock.submit_run.assert_called_once_with(expected) db_mock.get_run_page_url.assert_called_once_with(RUN_ID) db_mock.get_run_state.assert_called_once_with(RUN_ID) assert RUN_ID == op.run_

Databricks hook - retry on HTTP Status 429 as well (#21852) * Databricks hook - retry on HTTP Status 429 as well this fixes #21559 * Reimplement retries using tenacity it's now uses exponential backoff by default

test_exec_success

12e9e2c695f9ebb9d3dde9c0f7dfaa112654f0d6

airflow

test_databricks.py

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

Python

def test_exec_success(self, db_mock_class): run = { 'new_cluster': NEW_CLUSTER, 'notebook_task': NOTEBOOK_TASK, } op = DatabricksSubmitRunOperator(task_id=TASK_ID, json=run) db_mock = db_mock_class.return_value db_mock.submit_run.return_value = 1 db_mock.get_run_state.return_value = RunState('TERMINATED', 'SUCCESS', '') op.execute(None) expected = databricks_operator._deep_string_coerce( {'new_cluster': NEW_CLUSTER, 'notebook_task': NOTEBOOK_TASK, 'run_name': TASK_ID} ) db_mock_class.assert_called_once_with( DEFAULT_CONN_ID, retry_limit=op.databricks_retry_limit, retry_delay=op.databricks_retry_delay, retry_args=None, ) db_mock.submit_run.assert_called_once_with(expected) db_mock.get_run_page_url.assert_called_once_with(RUN_ID) db_mock.get_run_state.assert_called_once_with(RUN_ID) assert RUN_ID == op.run_id

seaborn/palettes.py

def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"): rgb = _color_to_rgb(color, input) h, s, l = husl.rgb_to_husl(*rgb) gray_s, gray_l = .15 * s, 15 gray = _color_to_rgb

Convert color palette docstrings to notebooks (#3034) * Convert color palette docstrings to notebooks and rerun all with py310 kernel * Add v0.12.1 release notes to index * Improve failure mode when ipywidgets is not involved * Update palettes docstrings * Remove all other doctest-style examples * Remove doctest-oriented testing infrastructure * Mention in release notes * Skip colormap patch test on matplotlib's where it's not relevant * Use more robust approach to mpl backcompat

dark_palette

e644793f0ac2b1be178425f20f529121f37f29de

seaborn

palettes.py

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

Python

def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"): rgb = _color_to_rgb(color, input) h, s, l = husl.rgb_to_husl(*rgb) gray_s, gray_l = .15 * s, 15 gray = _color_to_rgb((h, gray_s, gray_l), input="husl") colors = [rgb, gray] if reverse else [gray, rgb] return blend_palette(colors, n_colors, as_cmap)

py/visdom/__init__.py

def contour(self, X, win=None, env=None, opts=None): return self._surface(X=X, stype="contour", opts=opts, win=win, env=env

apply black py to all python files

contour

5b8b7f267cfaf76a2a39a727ef31a62b3909a093

visdom

__init__.py

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

Python

def contour(self, X, win=None, env=None, opts=None): return self._surface(X=X, stype="contour", opts=opts, win=win, env=env)

mkdocs/utils/__init__.py

def get_themes(): themes = {} eps = set(importlib_metadata.entry_points(group='mkdocs.themes')) builtins = {ep.name for ep in eps if ep.dist.name == 'mkdocs'} for theme in eps: if theme.name in builtins and

Remove spaces at the ends of docstrings, normalize quotes

get_themes

e7f07cc82ab2be920ab426ba07456d8b2592714d

mkdocs

__init__.py

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

Python

def get_themes(): themes = {} eps = set(importlib_metadata.entry_points(group='mkdocs.themes')) builtins = {ep.name for ep in eps if ep.dist.name == 'mkdocs'} for theme in eps: if theme.name in builtins and theme.dist.name != 'mkdocs': raise exceptions.ConfigurationError( f"The theme '{theme.name}' is a builtin theme but the package '{theme.dist.name}' " "attempts to provide a theme with the same name." ) elif theme.name in themes: log.warning( f"A theme named '{theme.name}' is provided by the Python packages '{theme.dist.name}' " f"and '{themes[theme.name].dist.name}'. The one in '{theme.dist.name}' will be used." ) themes[theme.name] = theme return themes

tests/sentry/api/endpoints/test_user_notification_details.py

def test_subscribe_by_default(self): NotificationSetting

ref(tests): Remove `get_valid_response()` (#34822)

test_subscribe_by_default

096b5511e244eecd8799b2a0324655207ce8985e

sentry

test_user_notification_details.py

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

Python

def test_subscribe_by_default(self): NotificationSetting.objects.update_settings( ExternalProviders.EMAIL, NotificationSettingTypes.ISSUE_ALERTS, NotificationSettingOptionValues.NEVER, user=self.user, ) response = self.get_success_response("me") assert response.data.get("subscribeByDefault") is False

wagtail/core/permission_policies/base.py

def _get_users_with_any_permission_codenames_filter(self, permission_codenames): permissions = Permission.objects.filter( content_type=self._content_type, codename__in=permission_codenames ) return ( Q(is_superuser=True)

Reformat with black

_get_users_with_any_permission_codenames_filter

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

base.py

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

Python

def _get_users_with_any_permission_codenames_filter(self, permission_codenames): permissions = Permission.objects.filter( content_type=self._content_type, codename__in=permission_codenames ) return ( Q(is_superuser=True) | Q(user_permissions__in=permissions) | Q(groups__permissions__in=permissions) ) & Q(is_active=True)

homeassistant/components/ibeacon/coordinator.py

def _async_update_rssi_and_transients(self) -> None: for ( unique_id, ibeacon_advert

Update ibeacon-ble to 1.0.1 (#80785)

_async_update_rssi_and_transients

e15f2e050e7afadbb19d32973104e4e2f5a172ae

core

coordinator.py

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

Python

def _async_update_rssi_and_transients(self) -> None: for ( unique_id, ibeacon_advertisement, ) in self._last_ibeacon_advertisement_by_unique_id.items(): address = unique_id.split("_")[-1] service_info = bluetooth.async_last_service_info( self.hass, address, connectable=False ) if not service_info: continue if address in self._transient_seen_count: self._transient_seen_count[address] += 1 if self._transient_seen_count[address] == MIN_SEEN_TRANSIENT_NEW: self._transient_seen_count.pop(address) _async_dispatch_update( self.hass, unique_id, service_info, ibeacon_advertisement, True, True, ) continue if service_info.rssi != ibeacon_advertisement.rssi: ibeacon_advertisement.update_rssi(service_info.rssi) async_dispatcher_send( self.hass, signal_seen(unique_id), ibeacon_advertisement, )

celery/contrib/testing/worker.py

def setup_app_for_worker(app, loglevel, logfile) -> None: # type: (Celery, Union[str, int], str) -> None app.finalize() app.set_current() app.set_default() type(app.log)._setup = False app.log.setup(loglevel=loglevel, logfile=logfile)

Add `mypy` to the pipeline (#7383) * Add typing to Celery This is a simple bootstrap of the process, adding some types to a few selected functions, based on comment annotations. MyPy is chosen as the default static analyzer for the types. * Add mypy to the pipeline * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove unused command from tox * Install mypy only on CPython * Remove wrong annotations * Update celery/utils/saferepr.py Co-authored-by: Mads Jensen <mje@inducks.org> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

setup_app_for_worker

fbda0089f08d7f2a8f00925dbc0b6e10bd779251

celery

worker.py

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

Python

def setup_app_for_worker(app, loglevel, logfile) -> None: # type: (Celery, Union[str, int], str) -> None app.finalize() app.set_current() app.set_default() type(app.log)._setup = False app.log.setup(loglevel=loglevel, logfile=logfile)

sympy/combinatorics/graycode.py

def rank(self): if self._rank is None: self._rank

Updated import locations

rank

498015021131af4dbb07eb110e5badaba8250c7b

sympy

graycode.py

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

Python

def rank(self): if self._rank is None: self._rank = int(gray_to_bin(self.current), 2) return self._rank

homeassistant/components/rest/data.py

async def async_update(self, log_errors=True): if not self._async_client: self._async_client = get_async_client( self._hass, verify_ssl=self._verify_ssl ) rendered_headers = template.render_complex(self._headers, parse_result=False) rendered_params = template.render_complex(self._params) _LOGGER.debug("Updating from %s", self._resource) try: response = await self._async_client.request( self._method, self._resource, headers=rendered_headers, params=rendered_params, auth=self._auth, content=self._request_data, timeout=self._timeout, follow_redirects=True, ) self.data = response.text self.headers = response.headers except httpx.TimeoutException as ex: if log_errors: _LOGGER.error("Timeout while fetching data: %s", self._resource) self.last_exception = ex self.data = None self.headers = None except httpx.RequestError as ex:

Fix payload in rest (#80544)

async_update

599d61a4da096227ce4d5ba1dc0eaabceea56f49

core

data.py

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

Python

async def async_update(self, log_errors=True): if not self._async_client: self._async_client = get_async_client( self._hass, verify_ssl=self._verify_ssl ) rendered_headers = template.render_complex(self._headers, parse_result=False) rendered_params = template.render_complex(self._params) _LOGGER.debug("Updating from %s", self._resource) try: response = await self._async_client.request( self._method, self._resource, headers=rendered_headers, params=rendered_params, auth=self._auth, content=self._request_data, timeout=self._timeout, follow_redirects=True, ) self.data = response.text self.headers = response.headers except httpx.TimeoutException as ex: if log_errors: _LOGGER.error("Timeout while fetching data: %s", self._resource) self.last_exception = ex self.data = None self.headers = None except httpx.RequestError as ex: if log_errors: _LOGGER.error( "Error fetching data: %s failed with %s", self._resource, ex ) self.last_exception = ex self.data = None self.headers = None

openbb_terminal/helper_funcs.py

def set_default_timezone() -> None: dotenv.load_dotenv(USER_ENV_FILE) user_tz = os.ge

[SDK] Allow silencing verbose output in commands that use stocks/load (#3180) * remove verbose on load * Revert implementation of the verbosity setting in stocks controller * Edit docstrings to comply with pydocstyle linting rules * Fix typos in variable names and help text * Add verbosity setting to forex load helper as it uses the stocks helper * Update docstrings to comply with pydocstyle linting rules * Update tests * Fix test relying on local sources settings * Remove old test cassettes * Add new test data * WIP: Fix futures tests * Clean up test file * Fix futures tests having a time component * Fix futures model tests Co-authored-by: James Maslek <jmaslek11@gmail.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com>

set_default_timezone

47549cbd9f52a436c06b040fda5b88a7d2bf700a

OpenBBTerminal

helper_funcs.py

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

Python

def set_default_timezone() -> None: dotenv.load_dotenv(USER_ENV_FILE) user_tz = os.getenv("OPENBB_TIMEZONE") if not user_tz: dotenv.set_key(USER_ENV_FILE, "OPENBB_TIMEZONE", "America/New_York")

langchain/agents/agent.py

def return_stopped_response(self) -> dict: return {k: "Agent stopped due to max iterations." for k in self.return_values}

add logic for agent stopping (#420)

return_stopped_response

d0f194de73c942cb89d731dbfa5ae809111fb07a

langchain

agent.py

https://github.com/hwchase17/langchain.git

Python

def return_stopped_response(self) -> dict: return {k: "Agent stopped due to max iterations." for k in self.return_values}

netbox/utilities/forms/fields/dynamic.py

def clean(self, value): if self.null_option is not None and value == settings.FILTERS_NULL_CHOICE_VALUE: return Non

Refactor & document supported form fields

clean

cf3ca5a661cc015baf4ef462be07e91c09db0ede

netbox

dynamic.py

https://github.com/netbox-community/netbox.git

Python

def clean(self, value): if self.null_option is not None and value == settings.FILTERS_NULL_CHOICE_VALUE: return None return super().clean(value)

tests/test_event_auth.py

def test_unexpected_auth_events(self): creator = "@creator:example.com" create_event = _create_event(RoomVersions.V9, creator) join_event = _join_event(RoomVersions.V9, creator) pl_event = _power_levels_event( RoomVersions.V9, creator, {"state_default": 30, "users": {"creator": 100}}, ) join_rules_event = _join_rules_event(RoomVersions.V9, creator, "public") event_store = _StubEventSourceStore() event_store.add_events([create_event, join_event, pl_event, join_rules_event]) good_event = _random_state_event( RoomVersions.V9, creator, [create_event, join_event, pl_event] ) # join rules

Fix inconsistencies in event validation (#13088)

test_unexpected_auth_events

d4b1c0d800eaa83c4d56a9cf17881ad362b9194b

synapse

test_event_auth.py

https://github.com/matrix-org/synapse.git

Python

def test_unexpected_auth_events(self): creator = "@creator:example.com" create_event = _create_event(RoomVersions.V9, creator) join_event = _join_event(RoomVersions.V9, creator) pl_event = _power_levels_event( RoomVersions.V9, creator, {"state_default": 30, "users": {"creator": 100}}, ) join_rules_event = _join_rules_event(RoomVersions.V9, creator, "public") event_store = _StubEventSourceStore() event_store.add_events([create_event, join_event, pl_event, join_rules_event]) good_event = _random_state_event( RoomVersions.V9, creator, [create_event, join_event, pl_event] ) # join rules should *not* be included in the auth events. bad_event = _random_state_event( RoomVersions.V9, creator, [create_event, join_event, pl_event, join_rules_event], ) get_awaitable_result( event_auth.check_state_independent_auth_rules(event_store, good_event) ) with self.assertRaises(AuthError): get_awaitable_result( event_auth.check_state_independent_auth_rules(event_store, bad_event) )

src/textual/devtools/service.py

async def _consume_incoming(self) -> None: while True: message_json = await self.incoming_queue.get() if message_json is None: self.incoming_queue.task_done() break type = message_json["type"] if type == "client_log": path = message_json["payload"]["path"] line_number = message_json["payload"]["line_number"] timestamp = message_json["payload"]["timestamp"] encoded_segments = message_json["payload"]["encoded_segments"] decoded_segments = base64.b64decode(encoded_segments) segments = pickle.loads(decoded_segments) self.service.console.print( DevtoolsLogMessage( segments=segments, path=path, line_number=line_number, unix_timestamp=timestamp, ) ) elif type == "client_spillover":

Seperate server and client handling logic into classes for devtools

_consume_incoming

a72e347ed99333a090377ee438eaf63477cbf98b

textual

service.py

https://github.com/Textualize/textual.git

Python

async def _consume_incoming(self) -> None: while True: message_json = await self.incoming_queue.get() if message_json is None: self.incoming_queue.task_done() break type = message_json["type"] if type == "client_log": path = message_json["payload"]["path"] line_number = message_json["payload"]["line_number"] timestamp = message_json["payload"]["timestamp"] encoded_segments = message_json["payload"]["encoded_segments"] decoded_segments = base64.b64decode(encoded_segments) segments = pickle.loads(decoded_segments) self.service.console.print( DevtoolsLogMessage( segments=segments, path=path, line_number=line_number, unix_timestamp=timestamp, ) ) elif type == "client_spillover": spillover = int(message_json["payload"]["spillover"]) info_renderable = DevtoolsInternalMessage( f"Discarded {spillover} messages", level="warning" ) self.service.console.print(info_renderable) self.incoming_queue.task_done()

docker/kuberay-autoscaler/test_autoscaling_config.py

def _get_basic_ray_cr() -> dict: cr_path = str( Path(__file__).resolve().paren

[KubeRay] Format autoscaling config based on RayCluster CR (#22348) Closes #21655. At the start of each autoscaler iteration, we read the Ray Cluster CR from K8s and use it to extract the autoscaling config.

_get_basic_ray_cr

a402e956a4e1ebe9bc4e2b404536466967c497af

ray

test_autoscaling_config.py

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

Python

def _get_basic_ray_cr() -> dict: cr_path = str( Path(__file__).resolve().parents[2] / "python" / "ray" / "autoscaler" / "kuberay" / "ray-cluster.complete.yaml" ) return yaml.safe_load(open(cr_path).read())

networkx/generators/spectral_graph_forge.py

def spectral_graph_forge(G, alpha, transformation="identity", seed=None): import numpy as np import scipy as sp import scipy.stats # call as sp.stats available_transformations = ["identity", "modularity"] alpha = np.clip(alpha, 0, 1) A = nx.to_numpy_array(G) n = A.shape[1] level = int(round(n * alpha)) if transformation not in available_transformations: msg = f"{transformation!r} is not a valid transformation. " msg += f"Transformations: {available_transformations}" raise nx.NetworkXError(msg) K = np.ones((1, n)) @ A B = A if transformation == "modularity": B -= K.T @ K / K.sum() # Compute low-rank approximation of B evals, evecs = np.

Remove `_mat_spect_approx` in favor of simpler procedure (#5624) * Replace _mat_spect_approx func internal usage. * Rm _mat_spect_approx helper function.

spectral_graph_forge

8bea55e3071ed71eab4fb6650a45f0cdf5c911d4

networkx

spectral_graph_forge.py

https://github.com/networkx/networkx.git

Python

def spectral_graph_forge(G, alpha, transformation="identity", seed=None): import numpy as np import scipy as sp import scipy.stats # call as sp.stats available_transformations = ["identity", "modularity"] alpha = np.clip(alpha, 0, 1) A = nx.to_numpy_array(G) n = A.shape[1] level = int(round(n * alpha)) if transformation not in available_transformations: msg = f"{transformation!r} is not a valid transformation. " msg += f"Transformations: {available_transformations}" raise nx.NetworkXError(msg) K = np.ones((1, n)) @ A B = A if transformation == "modularity": B -= K.T @ K / K.sum() # Compute low-rank approximation of B evals, evecs = np.linalg.eigh(B) k = np.argsort(np.abs(evals))[::-1] # indices of evals in descending order evecs[:, k[np.arange(level, n)]] = 0 # set smallest eigenvectors to 0 B = evecs @ np.diag(evals) @ evecs.T if transformation == "modularity": B += K.T @ K / K.sum() B = np.clip(B, 0, 1) np.fill_diagonal(B, 0) for i in range(n - 1): B[i, i + 1 :] = sp.stats.bernoulli.rvs(B[i, i + 1 :], random_state=seed) B[i + 1 :, i] = np.transpose(B[i, i + 1 :]) H = nx.from_numpy_array(B) return H

src/datasets/features/features.py

def encode_example(self, example): example = cast_to_python_objects(example) return encode_nested_exa

Clean up remaining Main Classes docstrings (#5349) clean up docstrings

encode_example

c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead

datasets

features.py

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

Python

def encode_example(self, example): example = cast_to_python_objects(example) return encode_nested_example(self, example)

keras/engine/training_v1.py

def _add_unique_metric_name(self, metric_name, metric_fn, output_index): # For multi-output models, prepend the output names to the metric name. if len(self.output_names) > 1: # If we're loading from an already-serialized model, we've already # prepended the output name, and we don't want to do it again. # # Alternatively, we may be receiving a stateless metric (e.g. the string # "accuracy") rather than a `Metric` object, in which case we want to # prepend the output name even if we are loading a serialized model. if not getattr(metric_fn

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

_add_unique_metric_name

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training_v1.py

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

Python

def _add_unique_metric_name(self, metric_name, metric_fn, output_index): # For multi-output models, prepend the output names to the metric name. if len(self.output_names) > 1: # If we're loading from an already-serialized model, we've already # prepended the output name, and we don't want to do it again. # # Alternatively, we may be receiving a stateless metric (e.g. the string # "accuracy") rather than a `Metric` object, in which case we want to # prepend the output name even if we are loading a serialized model. if not getattr(metric_fn, "_from_serialized", False): metric_name = "%s_%s" % ( self.output_names[output_index], metric_name, ) j = 1 base_metric_name = metric_name while metric_name in self.metrics_names: metric_name = "%s_%d" % (base_metric_name, j) j += 1 return metric_name

@not_implemented_for("undirected")

networkx/algorithms/components/strongly_connected.py

def strongly_connected_components(G): preorder = {} lowlink = {} scc_found = set() scc_queue = [] i = 0 # Preorder counter neighbors = {v: iter(G[v]) for v in G} for source in G: if source not in scc_found: queue = [source] while queue: v = queue[-1] if v not in preorder: i = i + 1 preorder[v] = i done = True for w in neighbors[v]: if w not in preorder: queue.append(w) done = False break if done: lowlink[v] = preorder[v] for w in G[v]: if w not in scc_found: if preorder[w] > preorder[v]: lowlink[v] = min([lowlink[v], lowlink[w]]) else:

Fixing Tarjan's strongly connected components algorithm implementation to have O(|E|+|V|) time complexity instead of O(|V|^3). (#5288) Prevent unnecessary traversal of edges multiple times

strongly_connected_components

77c49c16e10693dbe566d20601b28dd2b1e8df03

networkx

strongly_connected.py

https://github.com/networkx/networkx.git

Python

def strongly_connected_components(G): preorder = {} lowlink = {} scc_found = set() scc_queue = [] i = 0 # Preorder counter neighbors = {v: iter(G[v]) for v in G} for source in G: if source not in scc_found: queue = [source] while queue: v = queue[-1] if v not in preorder: i = i + 1 preorder[v] = i done = True for w in neighbors[v]: if w not in preorder: queue.append(w) done = False break if done: lowlink[v] = preorder[v] for w in G[v]: if w not in scc_found: if preorder[w] > preorder[v]: lowlink[v] = min([lowlink[v], lowlink[w]]) else: lowlink[v] = min([lowlink[v], preorder[w]]) queue.pop() if lowlink[v] == preorder[v]: scc = {v} while scc_queue and preorder[scc_queue[-1]] > preorder[v]: k = scc_queue.pop() scc.add(k) scc_found.update(scc) yield scc else: scc_queue.append(v) @not_implemented_for("undirected")

sklearn/linear_model/_glm/glm.py

def score(self, X, y, sample_weight=None): # TODO: Adapt link to User Guide in the docstring, once # https://github.com/scikit-learn/scikit-learn/pull/22118 is merged. # # Note, default score defined in RegressorMixin is R^2 score. # TODO: make D^2 a score function in module metrics (and thereby get # input validation and so on) raw_prediction = self._linear_predictor(X) # validates X # required by losses y = check_array(y, dtype=raw_prediction.dtype, order="C", ensure_2d=False) if sample_weight is not None: # Note that _check_sample_weight calls check_array(order="C") required by # losses. sample_weight = _check_sample_weight(sample_weight, X, dtype=y.dtype) base_loss = self._linear_loss.base_loss if not base_loss.in_y_true_range(y): raise ValueError( "Some value(s) of y are out of the valid range of the loss" f" {self._base_loss.__name__}." ) # Note that constant_to_optimal_zero is already multiplied by sample_weight. constant = np.mean(base_loss.constant_to_optimal_zero(y_true=y)) if sample_weight is not None: constant *= sample_weight.shape[0] / np.sum(sample_weight) # Missing factor of 2 in deviance cancels out. deviance = base_loss( y_true=y, raw_prediction=raw_prediction, sample_weight=sample_weight, n_threads=1, ) y_mean = base_loss.link.link(np.average(y, weights=sample_weight)) deviance_null = base_loss(

ENH migrate GLMs / TweedieRegressor to linear loss (#22548) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

score

75a94f518f7bd7d0bf581ffb67d9f961e3c4efbc

scikit-learn

glm.py

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

Python

def score(self, X, y, sample_weight=None): # TODO: Adapt link to User Guide in the docstring, once # https://github.com/scikit-learn/scikit-learn/pull/22118 is merged. # # Note, default score defined in RegressorMixin is R^2 score. # TODO: make D^2 a score function in module metrics (and thereby get # input validation and so on) raw_prediction = self._linear_predictor(X) # validates X # required by losses y = check_array(y, dtype=raw_prediction.dtype, order="C", ensure_2d=False) if sample_weight is not None: # Note that _check_sample_weight calls check_array(order="C") required by # losses. sample_weight = _check_sample_weight(sample_weight, X, dtype=y.dtype) base_loss = self._linear_loss.base_loss if not base_loss.in_y_true_range(y): raise ValueError( "Some value(s) of y are out of the valid range of the loss" f" {self._base_loss.__name__}." ) # Note that constant_to_optimal_zero is already multiplied by sample_weight. constant = np.mean(base_loss.constant_to_optimal_zero(y_true=y)) if sample_weight is not None: constant *= sample_weight.shape[0] / np.sum(sample_weight) # Missing factor of 2 in deviance cancels out. deviance = base_loss( y_true=y, raw_prediction=raw_prediction, sample_weight=sample_weight, n_threads=1, ) y_mean = base_loss.link.link(np.average(y, weights=sample_weight)) deviance_null = base_loss( y_true=y, raw_prediction=np.tile(y_mean, y.shape[0]), sample_weight=sample_weight, n_threads=1, ) return 1 - (deviance + constant) / (deviance_null + constant)

reconstruction/ostec/external/stylegan2/dnnlib/tflib/ops/upfirdn_2d.py

def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'): r assert isinstance(factor, int) and factor >= 1 # Check weight shape.

initialize ostec

upsample_conv_2d

7375ee364e0df2a417f92593e09557f1b2a3575a

insightface

upfirdn_2d.py

https://github.com/deepinsight/insightface.git

Python

def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'): r assert isinstance(factor, int) and factor >= 1 # Check weight shape. w = tf.convert_to_tensor(w) assert w.shape.rank == 4 convH = w.shape[0].value convW = w.shape[1].value inC = _shape(w, 2) outC = _shape(w, 3) assert convW == convH # Setup filter kernel. if k is None: k = [1] * factor k = _setup_kernel(k) * (gain * (factor ** 2)) p = (k.shape[0] - factor) - (convW - 1) # Determine data dimensions. if data_format == 'NCHW': stride = [1, 1, factor, factor] output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW] num_groups = _shape(x, 1) // inC else: stride = [1, factor, factor, 1] output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + convH, (_shape(x, 2) - 1) * factor + convW, outC] num_groups = _shape(x, 3) // inC # Transpose weights. w = tf.reshape(w, [convH, convW, inC, num_groups, -1]) w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2]) w = tf.reshape(w, [convH, convW, -1, num_groups * inC]) # Execute. x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format) return _simple_upfirdn_2d(x, k, pad0=(p+1)//2+factor-1, pad1=p//2+1, data_format=data_format, impl=impl) #----------------------------------------------------------------------------

ludwig/contribs/aim.py

def normalize_config(config): return json.loads(json.dumps(config, cls=NumpyEn

Fixes to serialization, and update to allow set repo location. (#2367) * Updating AimCallback to add init for optional repo. * Fixed numpy serialization for config objects. * Removed print statements, added logging for progress tracker.

normalize_config

7ec0cd13cf5e77d6fe68acbbeef9a7c694fc83c2

ludwig

aim.py

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

Python

def normalize_config(config): return json.loads(json.dumps(config, cls=NumpyEncoder))

thumbor/utils.py

def ensure_srgb(img, srgb_profile=None): img_info = dict(img.info) icc = img_info.pop("icc_profile", None) if not icc: return img if ImageCms is None: raise RuntimeError("ImageCms is required for color profile utilities") if srgb_profile is not None: srgb_profile = ImageCms.ImageCmsProfile(srgb_profile) else: srgb_profile = DEFAULT_SRGB_PROFILE buf = BytesIO(icc) try: orig_profile = ImageCms.ImageCmsProfile(buf) color_space = orig_profile.profile.xcolor_space except (AttributeError, OSError, TypeError, ValueError): return None finally: buf.close() if color_space == "RGB ": logger.debug("Returning img (RGB)") return img if color_space not in ("GRAY", "CMYK

feat: Support AVIF format encoding (#1476) * feat: Support AVIF format encoding * Increase test coverage * test coverage for remaining uncovered lines * Add simple integration test * Add "filters:format(avif)" integration test * Gracefully handle AVIF encoding when codec unavailable * Don't pass quality="keep" to AVIF save * Fix no-member pylint error

ensure_srgb

1d9deef4e99f52a08eed9aa973572c4567754f5a

thumbor

utils.py

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

Python

def ensure_srgb(img, srgb_profile=None): img_info = dict(img.info) icc = img_info.pop("icc_profile", None) if not icc: return img if ImageCms is None: raise RuntimeError("ImageCms is required for color profile utilities") if srgb_profile is not None: srgb_profile = ImageCms.ImageCmsProfile(srgb_profile) else: srgb_profile = DEFAULT_SRGB_PROFILE buf = BytesIO(icc) try: orig_profile = ImageCms.ImageCmsProfile(buf) color_space = orig_profile.profile.xcolor_space except (AttributeError, OSError, TypeError, ValueError): return None finally: buf.close() if color_space == "RGB ": logger.debug("Returning img (RGB)") return img if color_space not in ("GRAY", "CMYK"): # Other color spaces are rare, but best not to try to convert them. # Upstream understands a None return as meaning it should not # use it for the target encoder. logger.debug( "Cannot convert to sRGB; color space = %s", (color_space.strip()), ) return None # Probably not possible to have an animated image with CMYK or GRAY icc # profile, but best leave it alone if we have one if getattr(img, "is_animated", False): return None if color_space == "GRAY": pil_mode = "L" else: pil_mode = "CMYK" logger.debug("Converting from %s to sRGB", color_space) transform = ImageCms.ImageCmsTransform( orig_profile, srgb_profile, pil_mode, "RGBA", intent=ImageCms.INTENT_RELATIVE_COLORIMETRIC, flags=TRANSFORM_FLAGS, ) src_im = Image.new(pil_mode, img.size, "white") src_im.paste(img) dst_im = Image.new("RGBA", img.size, "white") dst_im.info = img_info dst_im = transform.apply(src_im, dst_im) dst_im = dst_im.convert("RGB") dst_im.info = img_info return dst_im

keras/engine/base_layer.py

def _cast_single_input(self, x): if self._should_cast_single_input(x): return tf.cast(x, self._compute_dtype_obje

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

_cast_single_input

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

base_layer.py

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

Python

def _cast_single_input(self, x): if self._should_cast_single_input(x): return tf.cast(x, self._compute_dtype_object) else: return x # _dtype used to be an attribute set in the constructor. We still expose it # because some clients still use it. # TODO(reedwm): Deprecate, then remove the _dtype property.

saleor/csv/utils/export.py

def queryset_in_batches(queryset): start_pk = 0 while True: qs = queryset.filter(pk__gt=start_pk)[:BATCH_SIZE] pks = list(qs.values_list("pk", flat=True)) if not pks: break yield pks

Feature/gift cards post mvp (#7977) * Add giftCardBulkCreate mutation * Extend OrderFilter with giftCardUsed and giftCardBought fields * Allow exporting gift cards * Update the name of the email template for export * Add exportGiftCards muttaion * Add used gift card filter * Export only unused gift cards * Block mutations for expired gift cards (#8115) * Block mutations for expired gift cards * Block only resending and activating expired gift cards * Add celery schedule task for deactivate expired cards (#8100) * Add gift card section to invoice (#8148) * Add filtering on gift card events (#8090) * Add filtering on gift card events * Filter gift card events by orders instead of order_id * Update populatedb with gift card data (#8016) * Generate gift cards with events in populate db * Set product types kinds and add placeholder for gift card product * Add dedicated gift card product images * Change order of order emails (#8168) * Drop duplicated kind field from producttype in populatedb (#8224) * Change gift card display_code field to last_4 (#8445) * Change gift card display_code field to last_4 * Change last4 to last4CodeChars * Fix github test env action configuration * Drop filtering gift cards by tag * Fix export gift card tags test * Re-add gift card tags query (#8412) * Update populatedb with gift card data (#8016) * Generate gift cards with events in populate db * Set product types kinds and add placeholder for gift card product * Add dedicated gift card product images * Add giftCardTags model * Add giftCardTags query Co-authored-by: Iga Karbowiak <40886528+IKarbowiak@users.noreply.github.com> Co-authored-by: IKarbowiak <iga.karbowiak@mirumee.com> * Do not create EXPIRY_DATE_UPDATED gift card event when expiry date is not changed (#8882) Co-authored-by: Marcin Gębala <5421321+maarcingebala@users.noreply.github.com>

queryset_in_batches

f5a45de4a22fecacfcd5b2cd18c07e5cf95ce27c

saleor

export.py

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

Python

def queryset_in_batches(queryset): start_pk = 0 while True: qs = queryset.filter(pk__gt=start_pk)[:BATCH_SIZE] pks = list(qs.values_list("pk", flat=True)) if not pks: break yield pks start_pk = pks[-1]

erpnext/patches/v13_0/update_returned_qty_in_pr_dn.py

def execute(): frappe.reload_doc("stock", "doctype", "purchase_receipt") frappe.reload_doc("stock", "doctype", "purchase_receipt_item") frappe.reload_doc("stock", "doctype", "delivery_note") frappe.reload_doc("stock", "doctype", "delivery_note_item") frappe.reload_doc("stock", "doctype", "stock_settings") def update_from_return_docs(doctype): for return_doc in frappe.get_all( doctype, filters={"is_return": 1, "docstatus":

style: format code with black

execute

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

update_returned_qty_in_pr_dn.py

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

Python

def execute(): frappe.reload_doc("stock", "doctype", "purchase_receipt") frappe.reload_doc("stock", "doctype", "purchase_receipt_item") frappe.reload_doc("stock", "doctype", "delivery_note") frappe.reload_doc("stock", "doctype", "delivery_note_item") frappe.reload_doc("stock", "doctype", "stock_settings") def update_from_return_docs(doctype): for return_doc in frappe.get_all( doctype, filters={"is_return": 1, "docstatus": 1, "return_against": ("!=", "")} ): # Update original receipt/delivery document from return return_doc = frappe.get_cached_doc(doctype, return_doc.name) try: return_doc.update_prevdoc_status() except OverAllowanceError: frappe.db.rollback() continue return_against = frappe.get_doc(doctype, return_doc.return_against) return_against.update_billing_status() frappe.db.commit() # Set received qty in stock uom in PR, as returned qty is checked against it frappe.db.sql( ) for doctype in ("Purchase Receipt", "Delivery Note"): update_from_return_docs(doctype)

openbb_terminal/cryptocurrency/discovery/discovery_controller.py

def call_dex(self, other_args): parser = argparse.ArgumentParser( prog="dex", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=, ) parser.add_argument( "-l", "--limit", dest="limit", type=check_positive, help="Number of records to display", default=15, ) parser.add_argument( "-s", "--sort", dest="sortby", nargs="+", help="Sort by given column. Default: Daily Volume [$]", default="Daily Volume [

Combining commands and renaming others (#3000) * Combining commands and renaming others Combining commands with different sources but the same functionality. I also removed indications of source from command names * Fix tests and hugo * Test fixes Co-authored-by: james <jmaslek11@gmail.com>

call_dex

38a53e5f43bccb716e6a6494605f97293077a679

OpenBBTerminal

discovery_controller.py

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

Python

def call_dex(self, other_args): parser = argparse.ArgumentParser( prog="dex", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=, ) parser.add_argument( "-l", "--limit", dest="limit", type=check_positive, help="Number of records to display", default=15, ) parser.add_argument( "-s", "--sort", dest="sortby", nargs="+", help="Sort by given column. Default: Daily Volume [$]", default="Daily Volume [$]", ) ns_parser = self.parse_known_args_and_warn( parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED ) if ns_parser: dappradar_view.display_top_dexes( sortby=" ".join(ns_parser.sortby), limit=ns_parser.limit, export=ns_parser.export, )

.venv/lib/python3.8/site-packages/pip/_internal/vcs/versioncontrol.py

def get_backend_for_dir(self, location): # type: (str) -> Optional[VersionControl] vcs_backends = {} for vcs_backend in self._registry.values(): repo_path = vcs_backend.get_repository_root(location) if not repo_path: continue logger.debug('Determine that %s uses VCS: %s', location, vcs_backend.name) vcs_backends[repo_path] = vcs_backend if not vcs_backends: return None # Choose the VCS in the inner-most directory. Since all repository # roots found here would be eith

upd; format

get_backend_for_dir

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

versioncontrol.py

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

Python

def get_backend_for_dir(self, location): # type: (str) -> Optional[VersionControl] vcs_backends = {} for vcs_backend in self._registry.values(): repo_path = vcs_backend.get_repository_root(location) if not repo_path: continue logger.debug('Determine that %s uses VCS: %s', location, vcs_backend.name) vcs_backends[repo_path] = vcs_backend if not vcs_backends: return None # Choose the VCS in the inner-most directory. Since all repository # roots found here would be either `location` or one of its # parents, the longest path should have the most path components, # i.e. the backend representing the inner-most repository. inner_most_repo_path = max(vcs_backends, key=len) return vcs_backends[inner_most_repo_path]

airflow/www/security.py

def has_access(self, action_name, resource_name, user=None) -> bool: if not user: user = g.user if

Remove `:type` lines now sphinx-autoapi supports typehints (#20951) * Remove `:type` lines now sphinx-autoapi supports typehints Since we have no updated sphinx-autoapi to a more recent version it supports showing type hints in the documentation, so we don't need to have the type hints _and_ the `:type` lines -- which is good, as the ones in the doc strings are easy to get out of date! The following settings have been set: `autodoc_typehints = 'description'` -- show types in description (where previous `:type` used to show up) `autodoc_typehints_description_target = 'documented'` -- only link to types that are documented. (Without this we have some missing return types that aren't documented, and aren't linked to in our current python API docs, so this caused a build failure) `autodoc_typehints_format = 'short'` -- Shorten type hints where possible, i.e. `StringIO` instead of `io.StringIO` * Add argument type names to local spelling dictionary Now that we are using the type hints in the docs, sphinxcontrib-spelling picks them up as words to be checked, so we have to ignore them. I've chosen to add the provider specific ones to local dictionary files rather than the global, as for example, `mgmt` is an error in most places, but not in some of the Azure provider.

has_access

602abe8394fafe7de54df7e73af56de848cdf617

airflow

security.py

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

Python

def has_access(self, action_name, resource_name, user=None) -> bool: if not user: user = g.user if user.is_anonymous: user.roles = self.get_user_roles(user) has_access = self._has_access(user, action_name, resource_name) # FAB built-in view access method. Won't work for AllDag access. if self.is_dag_resource(resource_name): if action_name == permissions.ACTION_CAN_READ: has_access |= self.can_read_dag(resource_name, user) elif action_name == permissions.ACTION_CAN_EDIT: has_access |= self.can_edit_dag(resource_name, user) return has_access

lib/streamlit/forward_msg_cache.py

def has_refs(self) -> bool: return len(self._session_report_run_counts) > 0

Rename and refactor `Report` machinery (#4141) This refactor renames (almost) everything related to the outdated "report" concept with more precise concepts that we use throughout our code, primarily "script run", "session", and "app".

has_refs

704eab3478cf69847825b23dabf15813a8ac9fa2

streamlit

forward_msg_cache.py

https://github.com/streamlit/streamlit.git

Python

def has_refs(self) -> bool: return len(self._session_report_run_counts) > 0

projects/director/director_agent.py

def _reshape_to_record_metrics(self, batch, losses, num_target_tokens, indices): val_id_shape = batch.

Added director agent and safety experiment commands. (#4602) * Added director agent and safety. * ran autoformat.sh

_reshape_to_record_metrics

2ef5586ed0d644abe18cd3ff45ef9fa01981e87c

ParlAI

director_agent.py

https://github.com/facebookresearch/ParlAI.git

Python

def _reshape_to_record_metrics(self, batch, losses, num_target_tokens, indices): val_id_shape = batch.valid_indices.shape reshaped_losses = torch.zeros( val_id_shape, device=losses.device, dtype=losses.dtype ) reshaped_num_target_tokens = torch.zeros( val_id_shape, device=num_target_tokens.device, dtype=num_target_tokens.dtype ) reshaped_losses[indices] = losses reshaped_num_target_tokens[indices] = num_target_tokens return (reshaped_losses, reshaped_num_target_tokens)

sympy/physics/continuum_mechanics/truss.py

def add_support(self, location, type): if location not in s

Truss class initialized and documentation added

add_support

158f441d4fae4bd406597a41ba8af142e5eeb593

sympy

truss.py

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

Python

def add_support(self, location, type): if location not in self._node_labels: raise ValueError("Support must be added on a known node") else: self._supports[location] = type

python3.10.4/Lib/ftplib.py

def makeport(self): sock = socket.create_server(("", 0), family=self.af, backlog=1) port = sock.getsockname()[1] # Get proper port host =

add python 3.10.4 for windows

makeport

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

ftplib.py

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

Python

def makeport(self): sock = socket.create_server(("", 0), family=self.af, backlog=1) port = sock.getsockname()[1] # Get proper port host = self.sock.getsockname()[0] # Get proper host if self.af == socket.AF_INET: resp = self.sendport(host, port) else: resp = self.sendeprt(host, port) if self.timeout is not _GLOBAL_DEFAULT_TIMEOUT: sock.settimeout(self.timeout) return sock

@pytest.mark.parametrize("url, expected_matches", [ # included ('http://trolol.com/', 1), # neither included nor excluded ('http://aaaaaaaaaa.com/', 0), # excluded ('https://badhost.xxx/', 0), ])

tests/unit/javascript/test_greasemonkey.py

def test_all(gm_manager): _save_script(test_gm_script, 'test.user.js') gm_manager.load_scripts() assert (gm_manager.all_scripts()[0].name == "qutebrowser test userscript") @pytest.mark.parametrize("url, expected_matches", [ # included ('http://trolol.com/', 1), # neither included nor excluded ('http://aaaaaaaaaa.com/', 0)

greasemonkey: Don't implicitly load scripts Needed for #7245 and also seems like cleaner code.

test_all

21419c9ef5a90ea36a27afaf2503a57f8f9f8536

qutebrowser

test_greasemonkey.py

https://github.com/qutebrowser/qutebrowser.git

Python

def test_all(gm_manager): _save_script(test_gm_script, 'test.user.js') gm_manager.load_scripts() assert (gm_manager.all_scripts()[0].name == "qutebrowser test userscript") @pytest.mark.parametrize("url, expected_matches", [ # included ('http://trolol.com/', 1), # neither included nor excluded ('http://aaaaaaaaaa.com/', 0), # excluded ('https://badhost.xxx/', 0), ])

bootloader/waflib/Utils.py

def split_path_msys(path): if path.startswith(('/', '\\')) and not path.startswith(('//', '\\\\')): global msysroot if not msysroot: msysroot = subprocess.check_output(['cygpath', '-w', '/']).decode(sys.stdout.encoding or 'latin-1') msysroot = msysroot.strip() path = os.path.normpath(msysroot + os.sep + path) return split_path_win32(path) if sys.platform == 'cygwin': split_path = split_path

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

split_path_msys

64ccb7aea824fbec57f7ed1bbe483ec486183c13

pyinstaller

Utils.py

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

Python

def split_path_msys(path): if path.startswith(('/', '\\')) and not path.startswith(('//', '\\\\')): global msysroot if not msysroot: msysroot = subprocess.check_output(['cygpath', '-w', '/']).decode(sys.stdout.encoding or 'latin-1') msysroot = msysroot.strip() path = os.path.normpath(msysroot + os.sep + path) return split_path_win32(path) if sys.platform == 'cygwin': split_path = split_path_cygwin elif is_win32: if os.environ.get('MSYSTEM') and sys.executable.startswith('/'): split_path = split_path_msys else: split_path = split_path_win32 else: split_path = split_path_unix split_path.__doc__ =

wagtail/images/utils.py

def find_image_duplicates(image, user, permission_policy): instances = permi

Add duplicate detection to multiple image upload view Add utility function to find an image's potential duplicates Add logic to detect duplicates on multiple images upload view Add template shown when a user is prompted to confirm a duplicate upload Add client-side logic to confirm a duplicate upload Add/update styles Add tests for duplicate image uploads Index Image file_hash field Ensure that a user can choose an image from duplicates returned by find_image_duplicates Use CSS classes instead of HTML elements to hide edit form on duplicate upload Add ImagesPermissionPolicy helper to retrieve the permission policy dynamically This allows test cases that override the base image model to pick up the corresponding permission policy, should they need it. Remove usage of sibling selector Use wagtail image templatetag to generate image Renamed ImagesPermissionPolicy to ImagesPermissionPolicyGetter Fail loudly when setting permission policy and a wromg image model is provided Add decorator to disconnect a signal's receiver during a test execution and use it in get_image_model tests Improve warning message on duplicate upload in multiple upload view Show matching form when confirming a duplicate upload

find_image_duplicates

c136f461bc052cef362991458e1bd1fca37a3da9

wagtail

utils.py

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

Python

def find_image_duplicates(image, user, permission_policy): instances = permission_policy.instances_user_has_permission_for(user, "choose") return instances.exclude(pk=image.pk).filter(file_hash=image.file_hash)

pipenv/patched/notpip/_vendor/pyparsing/core.py

def _trim_arity(func, maxargs=2): global _trim_arity_call_line if f

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

_trim_arity

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

core.py

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

Python

def _trim_arity(func, maxargs=2): global _trim_arity_call_line if func in _single_arg_builtins: return lambda s, l, t: func(t) limit = 0 found_arity = False

openbb_terminal/cryptocurrency/overview/overview_controller.py

def call_exmarkets(self, other_args): parser = argparse.ArgumentParser( prog="exmarkets", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=, ) parser.add_argument( "-e", "--exchange", help="Identifier of exchange e.g for Binance Exchange -> binance", dest="exchange", default="binance", type=str, ) parser.add_argument( "-l", "--limit", dest="limit", type=check_positive, help="display N number records", default=10, ) parser.add_argument( "-s", "--sortby", dest="sortby", type=str, help="Sort by given column. Default: reported_volume_24h_share", default="reported_volume_24h_share", choices=coinpaprika_model.EXMARKETS_FILTERS, ) parser.add_argument( "--descend", action="store_false", help="Flag to sort in descending order (lowest first)", dest="descend", default=False, ) parser.add_argument( "-u", "--urls", dest="urls", action="store_true", help=, default=False, ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-e") ns_pa

More Fixes to Crypto + key sort (#3244) * fix #3095 - autocomplete and command working + key sort * fix #3056 * fix [Bug] bugs #3048 * fix [Bug] bug #3017 * sort -> sortby, not ascend, tests * fix my goof ups Co-authored-by: james <jmaslek11@gmail.com>

call_exmarkets

09f753da1c2a2f03c41fe6a3ca2eb79f6ea58995

OpenBBTerminal

overview_controller.py

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

Python

def call_exmarkets(self, other_args): parser = argparse.ArgumentParser( prog="exmarkets", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=, ) parser.add_argument( "-e", "--exchange", help="Identifier of exchange e.g for Binance Exchange -> binance", dest="exchange", default="binance", type=str, ) parser.add_argument( "-l", "--limit", dest="limit", type=check_positive, help="display N number records", default=10, ) parser.add_argument( "-s", "--sortby", dest="sortby", type=str, help="Sort by given column. Default: reported_volume_24h_share", default="reported_volume_24h_share", choices=coinpaprika_model.EXMARKETS_FILTERS, ) parser.add_argument( "--descend", action="store_false", help="Flag to sort in descending order (lowest first)", dest="descend", default=False, ) parser.add_argument( "-u", "--urls", dest="urls", action="store_true", help=, default=False, ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-e") ns_parser = self.parse_known_args_and_warn( parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED ) if ns_parser: coinpaprika_view.display_exchange_markets( exchange=ns_parser.exchange, limit=ns_parser.limit, export=ns_parser.export, sortby=ns_parser.sortby, ascend=not ns_parser.descend, links=ns_parser.urls, )

tests/components/lcn/test_cover.py

async def test_unload_config_entry(hass, entry, lcn_connection): await hass.config_entries.async_unload(entry.entry_id) assert hass.states.get("cover.cover_outputs").state == STATE_UNAVAILABLE assert hass.s

Add tests for LCN cover platform (#64832)

test_unload_config_entry

684f01f4664ad490a314ae983194c0f439445a16

core

test_cover.py

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

Python

async def test_unload_config_entry(hass, entry, lcn_connection): await hass.config_entries.async_unload(entry.entry_id) assert hass.states.get("cover.cover_outputs").state == STATE_UNAVAILABLE assert hass.states.get("cover.cover_relays").state == STATE_UNAVAILABLE

dask/array/core.py

def topk(self, k, axis=-1, split_every=None): from dask.array.reductions import topk return topk(self, k, axis=axis, split_every

absolufy-imports - No relative - PEP8 (#8796) Conversation in https://github.com/dask/distributed/issues/5889

topk

cccb9d8d8e33a891396b1275c2448c352ef40c27

dask

core.py

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

Python

def topk(self, k, axis=-1, split_every=None): from dask.array.reductions import topk return topk(self, k, axis=axis, split_every=split_every)

packages/python/plotly/plotly/graph_objs/_figure.py

def for_each_ternary(self, fn, selector=None, row=None, col=None) -> "Figure": for obj in self.select_ternaries(selector=selector, row=row, col=col): fn(obj) r

type annotations for chainable Figure methods

for_each_ternary

c95b4fa4388f29e50b6966e45c94c5980013a01d

plotly.py

_figure.py

https://github.com/plotly/plotly.py.git

Python

def for_each_ternary(self, fn, selector=None, row=None, col=None) -> "Figure": for obj in self.select_ternaries(selector=selector, row=row, col=col): fn(obj) return self

.venv/lib/python3.8/site-packages/pip/_vendor/tenacity/__init__.py

def call(self, *args, **kwargs): warnings.warn(

upd; format

call

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

__init__.py

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

Python

def call(self, *args, **kwargs): warnings.warn( "'call()' method is deprecated. " + "Use '__call__()' instead", DeprecationWarning, ) return self.__call__(*args, **kwargs)

keras/distribute/distributed_training_utils_v1.py

def validate_per_replica_inputs(distribution_strategy, x): # Convert the inputs and targets into a list of PerReplica objects. per_replica_list = tf.nest.flatten(x) x_values_list = [] for x in per_replica_list: # At this point x should contain only tensors. x_values = distribution_strategy.unwrap(x) for value in x_values: if not tf.is_tensor(value): raise ValueError('Dataset input to the model should be tensors instead ' 'they are of type {}'.format(type(value))) if not tf.executing_eagerly(): # Validate that the shape and dtype of all the elements in x are

Rework a test to avoid instantiating DistributedValues directly. PiperOrigin-RevId: 438824819

validate_per_replica_inputs

2d7dc6080f0824200e317f255e3290da60e0f98a

keras

distributed_training_utils_v1.py

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

Python

def validate_per_replica_inputs(distribution_strategy, x): # Convert the inputs and targets into a list of PerReplica objects. per_replica_list = tf.nest.flatten(x) x_values_list = [] for x in per_replica_list: # At this point x should contain only tensors. x_values = distribution_strategy.unwrap(x) for value in x_values: if not tf.is_tensor(value): raise ValueError('Dataset input to the model should be tensors instead ' 'they are of type {}'.format(type(value))) if not tf.executing_eagerly(): # Validate that the shape and dtype of all the elements in x are the same. validate_all_tensor_shapes(x, x_values) validate_all_tensor_types(x, x_values) x_values_list.append(x_values[0]) return x_values_list

homeassistant/components/lifx/light.py

def get_mac_addr(self): if ( self.bulb.host_firmware_version and AwesomeVersion(self.bulb.host_firmware_version) >= FIX_MAC_FW ): octets = [int(octet, 16) for octet in self.mac_addr.split(":")] octets[5] = (octets[5] + 1) % 256 return ":".join(f"{octet:02x}" for octet in octets) return self.ma

Refactor LIFX discovery to prevent duplicate discovery response handling (#72213) * Partially revert #70458 and allow duplicate LIFX discoveries Signed-off-by: Avi Miller <me@dje.li> * Only process one discovery at a time * Revert all LIFX duplicate/inflight discovery checks Also remember LIFX Switches and do as little processing for them as possible. Signed-off-by: Avi Miller <me@dje.li> * Bump aiolifx version to support the latest LIFX devices LIFX added 22 new product definitions to their public product list at the end of January and those new products are defined in aiolifx v0.8.1, so bump the dependency version. Also switched to testing for relays instead of maintaining a seperate list of switch product IDs. Fixes #72894. Signed-off-by: Avi Miller <me@dje.li> * Refactor LIFX discovery to better handle duplicate responses Signed-off-by: Avi Miller <me@dje.li> * Update clear_inflight_discovery with review suggestion Signed-off-by: Avi Miller <me@dje.li> * Move the existing entity check to before the asyncio lock Signed-off-by: Avi Miller <me@dje.li> * Bail out of discovery early and if an entity was created Also ensure that the entity always has a unique ID even if the bulb was not successfully discovered. Signed-off-by: Avi Miller <me@dje.li> Co-authored-by: J. Nick Koston <nick@koston.org>

get_mac_addr

a0974e0c7297537149985f93544dd6f8ed8cfded

core

light.py

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

Python

def get_mac_addr(self): if ( self.bulb.host_firmware_version and AwesomeVersion(self.bulb.host_firmware_version) >= FIX_MAC_FW ): octets = [int(octet, 16) for octet in self.mac_addr.split(":")] octets[5] = (octets[5] + 1) % 256 return ":".join(f"{octet:02x}" for octet in octets) return self.mac_addr

tests/driver_tests.py

def test_positive_integer_or_none_4(): assert positive_integer_or_none('none') is None

Revert "Deployed 7ccda9a with MkDocs version: 1.3.0" This reverts commit bd9629c40e01241766197119b581a99409b07068.

test_positive_integer_or_none_4

388616b6247ca4ea8de4e2f340d6206aee523541

tpot

driver_tests.py

https://github.com/EpistasisLab/tpot.git

Python

def test_positive_integer_or_none_4(): assert positive_integer_or_none('none') is None assert positive_integer_or_none('None') is None

python3.10.4/Lib/email/_header_value_parser.py

def get_local_part(value): local_part = LocalPart() leader = None if value[0] in CFWS_LEADER: leader, value = get_cfws(value) if not value: raise errors.HeaderParseError( "expected local-part but found '{}'".format(value)) try: token, value = get_dot_atom(value) except errors.HeaderParseError: try: token, value = get_word(value) except errors.HeaderParseError: if value[0] != '\\' and value[0] in PHRASE_ENDS: raise token = TokenList() if leader is not None: token[:0] = [leader] local_part.append(token) if value and (value[0]=='\\' or value[0] not in PHRASE_ENDS): obs_local_part, value = get

add python 3.10.4 for windows

get_local_part

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

_header_value_parser.py

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

Python

def get_local_part(value): local_part = LocalPart() leader = None if value[0] in CFWS_LEADER: leader, value = get_cfws(value) if not value: raise errors.HeaderParseError( "expected local-part but found '{}'".format(value)) try: token, value = get_dot_atom(value) except errors.HeaderParseError: try: token, value = get_word(value) except errors.HeaderParseError: if value[0] != '\\' and value[0] in PHRASE_ENDS: raise token = TokenList() if leader is not None: token[:0] = [leader] local_part.append(token) if value and (value[0]=='\\' or value[0] not in PHRASE_ENDS): obs_local_part, value = get_obs_local_part(str(local_part) + value) if obs_local_part.token_type == 'invalid-obs-local-part': local_part.defects.append(errors.InvalidHeaderDefect( "local-part is not dot-atom, quoted-string, or obs-local-part")) else: local_part.defects.append(errors.ObsoleteHeaderDefect( "local-part is not a dot-atom (contains CFWS)")) local_part[0] = obs_local_part try: local_part.value.encode('ascii') except UnicodeEncodeError: local_part.defects.append(errors.NonASCIILocalPartDefect( "local-part contains non-ASCII characters)")) return local_part, value

tests/test_feature_extraction_common.py

def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False): assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" video_inputs = [] for i in range(feature_extract_te

Add VideoMAE (#17821) * First draft * Add VideoMAEForVideoClassification * Improve conversion script * Add VideoMAEForPreTraining * Add VideoMAEFeatureExtractor * Improve VideoMAEFeatureExtractor * Improve docs * Add first draft of model tests * Improve VideoMAEForPreTraining * Fix base_model_prefix * Make model take pixel_values of shape (B, T, C, H, W) * Add loss computation of VideoMAEForPreTraining * Improve tests * Improve model testsé * Make all tests pass * Add VideoMAE to main README * Add tests for VideoMAEFeatureExtractor * Add integration test * Improve conversion script * Rename patch embedding class * Remove VideoMAELayer from init * Update design of patch embeddings * Improve comments * Improve conversion script * Improve conversion script * Add conversion of pretrained model * Add loss verification of pretrained model * Add loss verification of unnormalized targets * Add integration test for pretraining model * Apply suggestions from code review * Fix bug to make feature extractor resize only shorter edge * Address more comments * Improve normalization of videos * Add doc examples * Move constants to dedicated script * Remove scripts * Transfer checkpoints, fix docs * Update script * Update image mean and std * Fix doc tests * Set return_tensors to NumPy by default * Revert the previous change Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

prepare_video_inputs

f9a0008d2d3082a665f711b24f5314e4a8205fab

transformers

test_feature_extraction_common.py

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

Python

def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False): assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" video_inputs = [] for i in range(feature_extract_tester.batch_size): if equal_resolution: width = height = feature_extract_tester.max_resolution else: width, height = np.random.choice( np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2 ) video = prepare_video( feature_extract_tester=feature_extract_tester, width=width, height=height, numpify=numpify, torchify=torchify, ) video_inputs.append(video) return video_inputs

certbot-apache/certbot_apache/_internal/configurator.py

def _verify_no_matching_http_header(self, ssl_vhost, header_substring): header_path = self.parser.find_dir("Header", None, start=ssl_vhost.path) if header_path: # "Existing Header directive for virtualhost" pat = '(?:[ "]|^)(%s)(?:[ "]|$)' % (header_substring.lower()) for match in header_path: if re.search(pat, self.parser.aug.get(matc

Various clean-ups in certbot-apache. Use f-strings. (#9132) * Various clean-ups in certbot-apache. Use f-strings. * Smaller tweaks

_verify_no_matching_http_header

eeca208c8f57304590ac1af80b496e61021aaa45

certbot

configurator.py

https://github.com/certbot/certbot.git

Python

def _verify_no_matching_http_header(self, ssl_vhost, header_substring): header_path = self.parser.find_dir("Header", None, start=ssl_vhost.path) if header_path: # "Existing Header directive for virtualhost" pat = '(?:[ "]|^)(%s)(?:[ "]|$)' % (header_substring.lower()) for match in header_path: if re.search(pat, self.parser.aug.get(match).lower()): raise errors.PluginEnhancementAlreadyPresent( "Existing %s header" % header_substring)

@_wraps(np.repeat, lax_description=_TOTAL_REPEAT_LENGTH_DOC)

jax/_src/numpy/lax_numpy.py

def indices(dimensions, dtype=int32, sparse=False): dimensions = tuple( core.concrete_or_error(operator.index, d, "dimensions argument of jnp.indices") for d in dimensions) N = len(dimensions) output = [] s = dimensio

replace int with operator.index part2 This change align the behavior of `ravel_multi_index`, `split` and `indices` to their `numpy` counterparts. Also ensure size argument of `nonzero` should be integer. The changes with `*space` are only simplification

indices

667d63aa2d4fbf7c9da73aab0e24c5c4c33cb5ba

jax

lax_numpy.py

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

Python

def indices(dimensions, dtype=int32, sparse=False): dimensions = tuple( core.concrete_or_error(operator.index, d, "dimensions argument of jnp.indices") for d in dimensions) N = len(dimensions) output = [] s = dimensions for i, dim in enumerate(dimensions): idx = lax.iota(dtype, dim) if sparse: s = (1,)*i + (dim,) + (1,)*(N - i - 1) output.append(lax.broadcast_in_dim(idx, s, (i,))) if sparse: return tuple(output) return stack(output, 0) if output else array([], dtype=dtype) _TOTAL_REPEAT_LENGTH_DOC = @_wraps(np.repeat, lax_description=_TOTAL_REPEAT_LENGTH_DOC)

test/mitmproxy/proxy/layers/test_tcp.py

def test_open_connection(tctx): assert Playbook(tcp.TCPLayer(tctx,

make it black!

test_open_connection

b3587b52b25077f68116b9852b041d33e7fc6601

mitmproxy

test_tcp.py

https://github.com/mitmproxy/mitmproxy.git

Python

def test_open_connection(tctx): assert Playbook(tcp.TCPLayer(tctx, True)) << OpenConnection(tctx.server) tctx.server.timestamp_start = 1624544785 assert Playbook(tcp.TCPLayer(tctx, True)) << None

netbox/dcim/api/serializers.py

def get_connected_endpoints(self, obj): endpoints = obj.connected_endpoints if endpoints: serializer = get_serializer_for_model(endpoints[0], prefix='Nested') context = {'request': self.context['request']}

Update connected_endpoint serializer field to support multiple objects

get_connected_endpoints

4c51dbba809b6b199a96da30f32f4dd3cd6ea6ed

netbox

serializers.py

https://github.com/netbox-community/netbox.git

Python

def get_connected_endpoints(self, obj): endpoints = obj.connected_endpoints if endpoints: serializer = get_serializer_for_model(endpoints[0], prefix='Nested') context = {'request': self.context['request']} return serializer(endpoints, many=True, context=context).data

networkx/algorithms/tree/tests/test_operations.py

def test_basic(self): trees = [(nx.full_rary_tree(2, 2**2 - 1), 0) for i in range(2)] actual = nx.join(trees) expected = nx.full_rary_tree(2, 2**3 - 1)

Update black (#5438) * CI: sync up black dev requirements version with precommit * Run black Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com>

test_basic

f6755ffa00211b523c6c0bec5398bc6c3c43c8b1

networkx

test_operations.py

https://github.com/networkx/networkx.git

Python

def test_basic(self): trees = [(nx.full_rary_tree(2, 2**2 - 1), 0) for i in range(2)] actual = nx.join(trees) expected = nx.full_rary_tree(2, 2**3 - 1) assert nx.is_isomorphic(actual, expected)

tests/admin_changelist/tests.py

def test_result_list_html(self): new_parent = Parent.objects.create(name="parent") new_child = Child.objects.create(name="name", parent=new_parent) request = self.factory.get("/child/") request.user = self.superuser m = ChildAdmin(Child, custom_site) cl = m.get_changelist_instance(request) cl.formset = None template = Template( "{% load admin_list %}{% spaceless %}{% result_list cl %}{% endspaceless %}

Refs #33476 -- Reformatted code with Black.

test_result_list_html

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_result_list_html(self): new_parent = Parent.objects.create(name="parent") new_child = Child.objects.create(name="name", parent=new_parent) request = self.factory.get("/child/") request.user = self.superuser m = ChildAdmin(Child, custom_site) cl = m.get_changelist_instance(request) cl.formset = None template = Template( "{% load admin_list %}{% spaceless %}{% result_list cl %}{% endspaceless %}" ) context = Context({"cl": cl, "opts": Child._meta}) table_output = template.render(context) link = reverse("admin:admin_changelist_child_change", args=(new_child.id,)) row_html = build_tbody_html( new_child.id, link, '<td class="field-parent nowrap">%s</td>' % new_parent ) self.assertNotEqual( table_output.find(row_html), -1, "Failed to find expected row element: %s" % table_output, )

wagtail/images/tests/test_admin_views.py

def test_delete_uploaded_image(self): # Send request response = self.client.post( reverse( "wagtailimages:delete_upload_multiple", args=(self.uploaded_image.id,) ) )

Reformat with black

test_delete_uploaded_image

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_admin_views.py

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