Datasets:

semeru

/

text-code-galeras-code-generation-from-docstring-3k-deduped

like 0

def get_patches(self): r return silent_list('Patch', [h for h in self.legend_handles if isinstance(h, Patch)])

lib/matplotlib/legend.py

matplotlib

Python

2a1a1a6e47e41b8992d462c48491d2ce347694cd

legend.py

get_patches

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

API/DOC: Document legend_handles and legend_handlers - deprecate legendHandles

def pop(): conn = sqlite3.connect(DB_FILE) c = conn.cursor() c.execute("BEGIN EXCLUSIVE") c.execute( ) result = c.fetchone() if result is None: conn.commit() return None queue_index = result[0] c.execute( , (queue_index,), ) conn.commit() return result[0], result[1], json.loads(result[2]), result[3]

gradio/queueing.py

gradio

Python

cc0cff893f9d7d472788adc2510c123967b384fe

queueing.py

pop

https://github.com/gradio-app/gradio.git

Format The Codebase - black formatting - isort formatting

def __call__(self, name, value): return self[name](name, value)

python3.10.4/Lib/email/headerregistry.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

headerregistry.py

__call__

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

add python 3.10.4 for windows

def disconnect(self): if self.is_connected is False: return self.connection.close() self.is_connected = False return self.is_connected

mindsdb/integrations/handlers/sqlite_handler/sqlite_handler.py

mindsdb

Python

fc9776d9b342f873cbb3f36fd39955b9e1ea6f76

sqlite_handler.py

disconnect

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

added connection_args and connection_args_example dicts

def call_mkt(self, other_args): parser = argparse.ArgumentParser( prog="mkt", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=, ) parser.add_argument( "--vs", help="Quoted currency. Default USD", dest="vs", default="USD", type=str, choices=coinpaprika_view.CURRENCIES, ) parser.add_argument( "-l", "--limit", default=20, dest="limit", help="Limit of records", type=check_positive, ) parser.add_argument( "-s", "--sort", dest="sortby", type=str, help="Sort by given column. Default: pct_volume_share", default="pct_volume_share", choices=coinpaprika_view.MARKET_FILTERS, ) parser.add_argument( "-r", "--reverse", action="store_true", dest="reverse", default=False, help=( "Data is sorted in descending order by default. " "Reverse flag will sort it in an ascending way. " "Only works when raw data is displayed." ), ) parser.add_argument( "-u", "--urls", dest="urls", action="store_true", help=, default=False, ) ns_parser = self.parse_known_args_and_warn( parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED ) if ns_parser: if self.symbol: coinpaprika_view.display_markets( from_symbol=self.symbol, to_symbol=ns_parser.vs, limit=ns_parser.limit, sortby=ns_parser.sortby, ascend=ns_parser.reverse, links=ns_parser.urls, export=ns_parser.export, )

openbb_terminal/cryptocurrency/due_diligence/dd_controller.py

OpenBBTerminal

Python

0ae89d6cc20be84bf49c31e437fda38a845ebc68

dd_controller.py

call_mkt

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

Style fixing: removing --ascend/--descend (#3395) * stocks candle to use reverse * qa raw to use reverse * etf candle to use reverse * oss rossix to use reverse * crypto/defi to use reverse * crypto/disc to use reverse * added test * crypto/dd to use reverse * crypto/onchain to use reverse * crypto/ov to use revert * forex candle to use revert * conibase controller to use revert * tests to use reverse * covid to use reverse * removing ascend * removing ascend from econ * more removing ascend * more removing ascend * more removing ascend * fixing stuff on .md files * fixed economy controller tests * fixed screener tests * fa controller to use comma separated when multiple inputs

def nseries(self, x=None, x0=0, n=6, dir='+', logx=None, cdir=0): if x and x not in self.free_symbols: return self if x is None or x0 or dir != '+': # {see XPOS above} or (x.is_positive == x.is_negative == None): return self.series(x, x0, n, dir, cdir=cdir) else: return self._eval_nseries(x, n=n, logx=logx, cdir=cdir)

sympy/core/expr.py

sympy

Python

46ba104ee0f9cb35b54c2f5f5591cfabb26d0301

expr.py

nseries

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

Fixed failing doctest

def _process_triggers(self) -> None: if self._triggers is None: # Don't need triggers for GUI return logger.debug("Processing triggers") root = self._canvas.winfo_toplevel() for key in self._keymaps: bindkey = "Return" if key == "enter" else key logger.debug("Adding trigger for key: '%s'", bindkey) root.bind(f"<{bindkey}>", self._on_keypress) logger.debug("Processed triggers")

lib/training/preview_tk.py

faceswap

Python

7da2cc3dd266aabebf41a31384cc2e0e7e5af6e5

preview_tk.py

_process_triggers

https://github.com/deepfakes/faceswap.git

Training - Use custom preview pop-out

def to_arrow_refs(self) -> List[ObjectRef["pyarrow.Table"]]: blocks: List[ObjectRef[Block]] = self.get_internal_block_refs() if self.dataset_format() == BlockFormat.ARROW: # Zero-copy path. return blocks block_to_arrow = cached_remote_fn(_block_to_arrow) return [block_to_arrow.remote(block) for block in blocks]

python/ray/data/dataset.py

ray

Python

326d84f1149319809191e7887155df7f04f6f46a

dataset.py

to_arrow_refs

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

[AIR][Predictor] Enable numpy based predictor (#28917) Co-authored-by: Clark Zinzow <clarkzinzow@gmail.com> Co-authored-by: Amog Kamsetty <amogkam@users.noreply.github.com>

def _get_kernel(self) -> tf.Tensor: coords = np.arange(self._filter_size, dtype="float32") coords -= (self._filter_size - 1) / 2. kernel = np.square(coords) kernel *= -0.5 / np.square(self._filter_sigma) kernel = np.reshape(kernel, (1, -1)) + np.reshape(kernel, (-1, 1)) kernel = K.constant(np.reshape(kernel, (1, -1))) kernel = K.softmax(kernel) kernel = K.reshape(kernel, (self._filter_size, self._filter_size, 1, 1)) return kernel

lib/model/losses_tf.py

faceswap

Python

04337e0c5efd442c1ce3e2da193dd8749f1e30d8

losses_tf.py

_get_kernel

https://github.com/deepfakes/faceswap.git

SSIM Updates - Standardize DSSIM Function - Implement MSSIM function for AMD

def test_validate_subscription_query_invalid(): result = validate_subscription_query("invalid_query") assert result is False TEST_VALID_SUBSCRIPTION_QUERY_WITH_FRAGMENT =

saleor/plugins/webhook/tests/subscription_webhooks/test_create_deliveries_for_subscription.py

saleor

Python

aca6418d6c36956bc1ab530e6ef7e146ec9df90c

test_create_deliveries_for_subscription.py

test_validate_subscription_query_invalid

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

Add Webhook payload via graphql subscriptions (#9394) * Add PoC of webhook subscriptions * add async webhooks subscription payloads feature * remove unneeded file * add translations subscription handling, fixes after review * remove todo * add descriptions * add descriptions, move subsrciption_payloads.py * refactor * fix imports, add changelog * check_document_is_single_subscription refactor Co-authored-by: Maciej Korycinski <maciej@mirumee.com> Co-authored-by: Marcin Gębala <5421321+maarcingebala@users.noreply.github.com>

def find_module(module, path=None, imp=None): if imp is None: imp = import_module with cwd_in_path(): try: return imp(module) except ImportError: # Raise a more specific error if the problem is that one of the # dot-separated segments of the module name is not a package. if '.' in module: parts = module.split('.') for i, part in enumerate(parts[:-1]): package = '.'.join(parts[:i + 1]) try: mpart = imp(package) except ImportError: # Break out and re-raise the original ImportError # instead. break try: mpart.__path__ except AttributeError: raise NotAPackage(package) raise

celery/utils/imports.py

celery

Python

59263b0409e3f02dc16ca8a3bd1e42b5a3eba36d

imports.py

find_module

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

Minor refactors, found by static analysis (#7587) * Remove deprecated methods in `celery.local.Proxy` * Collapse conditionals for readability * Remove unused parameter `uuid` * Remove unused import `ClusterOptions` * Remove dangerous mutable default argument Continues work from #5478 * Remove always `None` and unused global variable * Remove unreachable `elif` block * Consolidate import statements * Add missing parameter to `os._exit()` * Add missing assert statement * Remove unused global `WindowsError` * Use `mkstemp` instead of deprecated `mktemp` * No need for `for..else` constructs in loops that don't break In these cases where the loop returns or raises instead of breaking, it is simpler to just put the code that runs after the loop completes right after the loop instead. * Use the previously unused parameter `compat_modules` Previously this parameter was always overwritten by the value of `COMPAT_MODULES.get(name, ())`, which was very likely unintentional. * Remove unused local variable `tz` * Make `assert_received` actually check for `is_received` Previously, it called `is_accepted`, which was likely a copy-paste mistake from the `assert_accepted` method. * Use previously unused `args` and `kwargs` params Unlike other backends' `__reduce__` methods, the one from `RedisBackend` simply overwrites `args` and `kwargs` instead of adding to them. This change makes it more in line with other backends. * Update celery/backends/filesystem.py Co-authored-by: Gabriel Soldani <1268700+gabrielsoldani@users.noreply.github.com> Co-authored-by: Asif Saif Uddin <auvipy@gmail.com>

def test_from_is_negative(self) -> None: channel = self.make_request( "GET", self.url + "?from=-5", access_token=self.admin_user_tok, ) self.assertEqual(400, channel.code, msg=channel.json_body) self.assertEqual(Codes.INVALID_PARAM, channel.json_body["errcode"])

tests/rest/admin/test_event_reports.py

synapse

Python

2281427175e4c93a30c39607fb4ac23c2a1f399f

test_event_reports.py

test_from_is_negative

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

Use literals in place of `HTTPStatus` constants in tests (#13488) * Use literals in place of `HTTPStatus` constants in tests * newsfile * code style * code style

def test__compress_ids_not_dict(): data = ["malformed"] actual_output = highstate._compress_ids(data) assert actual_output == data

tests/pytests/unit/output/test_highstate.py

salt

Python

7e1c2baa659ee2a975cbe4ed0f6d85e34ec91e50

test_highstate.py

test__compress_ids_not_dict

https://github.com/saltstack/salt.git

fixes saltstack/salt#61549 allow roll-up of duplicate IDs with different names

def _populate_directed_relation_graph(self): related_objects_graph = defaultdict(list) all_models = self.apps.get_models(include_auto_created=True) for model in all_models: opts = model._meta # Abstract model's fields are copied to child models, hence we will # see the fields from the child models. if opts.abstract: continue fields_with_relations = ( f for f in opts._get_fields(reverse=False, include_parents=False) if f.is_relation and f.related_model is not None ) for f in fields_with_relations: if not isinstance(f.remote_field.model, str): remote_label = f.remote_field.model._meta.concrete_model._meta.label related_objects_graph[remote_label].append(f) for model in all_models: # Set the relation_tree using the internal __dict__. In this way # we avoid calling the cached property. In attribute lookup, # __dict__ takes precedence over a data descriptor (such as # @cached_property). This means that the _meta._relation_tree is # only called if related_objects is not in __dict__. related_objects = related_objects_graph[ model._meta.concrete_model._meta.label ] model._meta.__dict__["_relation_tree"] = related_objects # It seems it is possible that self is not in all_models, so guard # against that with default for get(). return self.__dict__.get("_relation_tree", EMPTY_RELATION_TREE)

django/db/models/options.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

options.py

_populate_directed_relation_graph

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

Refs #33476 -- Reformatted code with Black.

def downgrade(): conn = op.get_bind() if conn.dialect.name == "mysql": op.alter_column( table_name=TABLE_NAME, column_name=COLUMN_NAME, type_=mysql.TIMESTAMP(), nullable=False )

airflow/migrations/versions/a66efa278eea_add_precision_to_execution_date_in_mysql.py

airflow

Python

69f6f9e01b6df76c3c8fa266d460324163957887

a66efa278eea_add_precision_to_execution_date_in_mysql.py

downgrade

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

Autogenerate migration reference doc (#21601) * document airflow version in each alembic migration module and use this to autogen the doc * update each migration module to have the same description used in migration ref (so it can be used in autogen)

def colors(self): return self["colors"]

packages/python/plotly/plotly/graph_objs/funnelarea/_marker.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_marker.py

colors

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

switch to black .22

def post_fork(self, payload_handler, io_loop): if not io_loop: raise ValueError("io_loop must be set") self.payload_handler = payload_handler self.io_loop = io_loop self._rmq_nonblocking_connection_wrapper = RMQNonBlockingConnectionWrapper( self.opts, io_loop=io_loop ) self._rmq_nonblocking_connection_wrapper.register_message_callback( self.handle_message ) self._rmq_nonblocking_connection_wrapper.connect()

salt/transport/rabbitmq.py

salt

Python

ab4803984bce4a4de7cc10910e7310c4babf557e

rabbitmq.py

post_fork

https://github.com/saltstack/salt.git

Start to add base class defs

def to(self, device=None, dtype=None) -> None: r # .to() on the tensors handles None correctly self.shadow_params = [ p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device) for p in self.shadow_params ]

examples/text_to_image/train_text_to_image.py

diffusers

Python

008b608f1551dbcf521284ed0e7a6722cd02ef07

train_text_to_image.py

to

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

[train_text2image] Fix EMA and make it compatible with deepspeed. (#813) * fix ema * style * add comment about copy * style * quality

def _try_state_query_expect_rate_limit(api_func, res_q, start_q=None): try: # Indicate start of the process if start_q is not None: start_q.put(1) api_func() except RayStateApiException as e: # Other exceptions will be thrown if "Max number of in-progress requests" in str(e): res_q.put(1) else: res_q.put(e) except Exception as e: res_q.put(e) else: res_q.put(0) @pytest.mark.skipif( sys.platform == "win32", reason="Lambda test functions could not be pickled on Windows", )

python/ray/tests/test_state_api.py

@pytest.mark.skipif( sys.platform == "win32", reason="Lambda test functions could not be pickled on Windows", )

ray

Python

365ffe21e592589880e3116302705b5e08a5b81f

test_state_api.py

_try_state_query_expect_rate_limit

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

[Core | State Observability] Implement API Server (Dashboard) HTTP Requests Throttling (#26257) This is to limit the max number of HTTP requests the dashboard (API server) will accept before rejecting more requests. This will make sure the observability requests do not overload the downstream systems (raylet/gcs) when delegating too many concurrent state observability requests to the cluster.

async def handle_webhook(hass, webhook_id, request): data = dict(await request.post()) data["webhook_id"] = webhook_id hass.bus.async_fire(RECEIVED_DATA, dict(data)) return web.Response(text="")

homeassistant/components/twilio/__init__.py

core

Python

44befe5f11390365e2ff0a7ce03133c1edd838a9

__init__.py

handle_webhook

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

Fix Twilio webhook content type (#66561)

def manhattan_distances(X, Y=None, *, sum_over_features="deprecated"): # TODO(1.4): remove sum_over_features if sum_over_features != "deprecated": warnings.warn( "`sum_over_features` is deprecated in version 1.2 and will be" " removed in version 1.4.", FutureWarning, ) else: sum_over_features = True X, Y = check_pairwise_arrays(X, Y) if issparse(X) or issparse(Y): if not sum_over_features: raise TypeError( "sum_over_features=%r not supported for sparse matrices" % sum_over_features ) X = csr_matrix(X, copy=False) Y = csr_matrix(Y, copy=False) X.sum_duplicates() # this also sorts indices in-place Y.sum_duplicates() D = np.zeros((X.shape[0], Y.shape[0])) _sparse_manhattan(X.data, X.indices, X.indptr, Y.data, Y.indices, Y.indptr, D) return D if sum_over_features: return distance.cdist(X, Y, "cityblock") D = X[:, np.newaxis, :] - Y[np.newaxis, :, :] D = np.abs(D, D) return D.reshape((-1, X.shape[1]))

sklearn/metrics/pairwise.py

scikit-learn

Python

7cf938c78ff0e38a231a7cb3a2a7fa412bb47966

pairwise.py

manhattan_distances

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

API Remove `sklearn.metrics.manhattan_distances` option `sum_over_features` (#24630)

def set_axis_direction(self, label_direction): self.set_default_alignment(label_direction) self.set_default_angle(label_direction) self._axis_direction = label_direction

lib/mpl_toolkits/axisartist/axis_artist.py

matplotlib

Python

df6f95703b60348e01603f98a439b133da2938a0

axis_artist.py

set_axis_direction

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

Improve mpl_toolkit documentation

def prep_related_object_data(self, parent, data): return data

netbox/netbox/views/generic/bulk_views.py

netbox

Python

93e7457e0d84ad24cba22cc5c0811777ddebf94e

bulk_views.py

prep_related_object_data

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

4347 Add JSON/YAML import support for all objects (#10367) * 4347 initial code for json import * 4347 initial code for json import * Clean up form processing logic * Consolidate import forms * Consolidate object import/update logic * Clean up bulk import view Co-authored-by: jeremystretch <jstretch@ns1.com>

def euler_poly(n, x=None, polys=False): if n < 0: raise ValueError("Cannot generate Euler polynomial of degree %s" % n) poly = DMP(dup_euler(int(n), QQ), QQ) if x is not None: poly = Poly.new(poly, x) else: poly = PurePoly.new(poly, Dummy('x')) return poly if polys else poly.as_expr()

sympy/polys/appellseqs.py

sympy

Python

93e4d381d35cd4c21a3a8d713c157f8fb21f725b

appellseqs.py

euler_poly

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

Custom Appell sequence functions and a doctest

def iterencode(self, o, _one_shot=False): if self.check_circular: markers = {} else: markers = None if self.ensure_ascii: _encoder = encode_basestring_ascii else: _encoder = encode_basestring

python3.10.4/Lib/json/encoder.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

encoder.py

iterencode

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

add python 3.10.4 for windows

def clear(self) -> None: self.row_count = 0 self._clear_caches() self._y_offsets.clear() self.data.clear() self.rows.clear() self._line_no = 0 self._require_update_dimensions = True self.refresh()

src/textual/widgets/_data_table.py

textual

Python

b524fa08eecadc83b0b694278db1c79d90feb9d8

_data_table.py

clear

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

ffixed table refresh on add row

def test_string_target(pyplot): iris = load_iris() X = iris.data[:, [0, 1]] # Use strings as target y = iris.target_names[iris.target] log_reg = LogisticRegression().fit(X, y) # Does not raise DecisionBoundaryDisplay.from_estimator( log_reg, X, grid_resolution=5, response_method="predict", )

sklearn/inspection/_plot/tests/test_boundary_decision_display.py

scikit-learn

Python

d400723a2112f15c5d5b4d40dfac2ed8a19cca5c

test_boundary_decision_display.py

test_string_target

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

FEA Add DecisionBoundaryDisplay (#16061) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Loïc Estève <loic.esteve@ymail.com>

def setdefault(cls, key, default, description=None, deserialize_json=False): obj = Variable.get(key, default_var=None, deserialize_json=deserialize_json) if obj is None: if default is not None: Variable.set(key, default, description=description, serialize_json=deserialize_json) return default else: raise ValueError('Default Value must be set') else: return obj

airflow/models/variable.py

airflow

Python

602abe8394fafe7de54df7e73af56de848cdf617

variable.py

setdefault

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

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.

def _update_trackables(self): for trackable_obj in self._self_tracked_trackables: if isinstance( trackable_obj, tf.__internal__.tracking.TrackableDataStructure ): self._track_variables(trackable_obj)

keras/engine/base_layer.py

keras

Python

00524152437b957ca4e850a5db014e223d3c6826

base_layer.py

_update_trackables

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

isort, black and flake8 checked

def assertCountSeleniumElements(self, selector, count, root_element=None): from selenium.webdriver.common.by import By root_element = root_element or self.selenium self.assertEqual( len(root_element.find_elements(By.CSS_SELECTOR, selector)), count )

django/contrib/admin/tests.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

tests.py

assertCountSeleniumElements

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

Refs #33476 -- Reformatted code with Black.

async def action_pop_screen(self) -> None: self.pop_screen()

src/textual/app.py

textual

Python

cf14b812ed47982463062e5b51bce506ad6ede1f

app.py

action_pop_screen

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

words

def test_empty_string_topic(self) -> None: self.login("hamlet") result = self.client_post( "/json/messages", { "type": "stream", "to": "Verona", "client": "test suite", "content": "Test message", "topic": "", }, ) self.assert_json_error(result, "Topic can't be empty!")

zerver/tests/test_message_send.py

zulip

Python

4f482c234c3ab72d264e7bff7835dad5207b9d07

test_message_send.py

test_empty_string_topic

https://github.com/zulip/zulip.git

string_validation: Standardize missing topic with missing stream name. Co-authored-by: Shlok Patel <shlokcpatel2001@gmail.com>

def generate_metric_ids(self) -> Set[Any]: raise NotImplementedError

src/sentry/snuba/metrics/fields/base.py

sentry

Python

3e8115c4a681e9c4adeafb1f15eb669a9342b93c

base.py

generate_metric_ids

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

feat(metrics): Add initial framework for derived metrics [INGEST-924] (#32451) * feat(metrics): Add initial framework for derived metrics Adds support for derived metrics composed of constituent metrics that span one entity * Adds logic/test for when metric does not exist * Fix failing test + incorporate PR feedback * Rename snql functions to their snuba name

def angleref(self): return self["angleref"]

packages/python/plotly/plotly/graph_objs/scatter/_marker.py

plotly.py

Python

d5a345d01507f8b6792c51507d1d8f35d7386d29

_marker.py

angleref

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

update to plotly.js 2.16.1

def ask_question(self, question, blocking): log.prompt.debug("Asking question {}, blocking {}, loops {}, queue " "{}".format(question, blocking, self._loops, self._queue)) if self._shutting_down: # If we're currently shutting down we have to ignore this question # to avoid segfaults - see # https://github.com/qutebrowser/qutebrowser/issues/95 log.prompt.debug("Ignoring question because we're shutting down.") question.abort() return None if self._question is not None and not blocking: # We got an async question, but we're already busy with one, so we # just queue it up for later. log.prompt.debug("Adding {} to queue.".format(question)) self._queue.append(question) return None if blocking: # If we're blocking we save the old question on the stack, so we # can restore it after exec, if exec gets called multiple times. log.prompt.debug("New question is blocking, saving {}".format( self._question)) old_question = self._question if old_question is not None: old_question.interrupted = True self._question = question self.show_prompts.emit(question) if blocking: loop = qtutils.EventLoop() self._loops.append(loop) loop.destroyed.connect(lambda: self._loops.remove(loop)) question.completed.connect(loop.quit) question.completed.connect(loop.deleteLater) log.prompt.debug("Starting loop.exec() for {}".format(question)) flags = cast(QEventLoop.ProcessEventsFlags, QEventLoop.ProcessEventsFlag.ExcludeSocketNotifiers) loop.exec(flags) log.prompt.debug("Ending loop.exec() for {}".format(question)) log.prompt.debug("Restoring old question {}".format(old_question)) self._question = old_question self.show_prompts.emit(old_question) if old_question is None: # Nothing left to restore, so we can go back to popping async # questions. if self._queue: self._pop_later() return question.answer else: question.completed.connect(self._pop_later) return None

qutebrowser/mainwindow/prompt.py

qutebrowser

Python

0877fb0d78635692e481c8bde224fac5ad0dd430

prompt.py

ask_question

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

Run scripts/dev/rewrite_enums.py

def prettify_exc(error): errors = [] for exc in KNOWN_EXCEPTIONS: search_string = exc.match_string if exc.match_string else exc.exception_name split_string = ( exc.show_from_string if exc.show_from_string else exc.exception_name ) if search_string in error: # for known exceptions with no display rules and no prefix # we should simply show nothing if not exc.show_from_string and not exc.prefix: errors.append("") continue elif exc.prefix and exc.prefix in error: _, error, info = error.rpartition(exc.prefix) else: _, error, info = error.rpartition(split_string) errors.append(f"{error} {info}") if not errors: return f"{vistir.misc.decode_for_output(error)}" return "\n".join(errors)

pipenv/exceptions.py

pipenv

Python

9a3b3ce70621af6f9adaa9eeac9cf83fa149319c

exceptions.py

prettify_exc

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

Issue 4993 Add standard pre commit hooks and apply linting. (#4994) * Add .pre-commit-config.yaml to the project and exclude tests (for now). This does not include the MyPy linting that pip does but does include everything else.

def get_filename(self): return getattr(self.model_admin, "export_filename", super().get_filename())

wagtail/contrib/modeladmin/views.py

wagtail

Python

d10f15e55806c6944827d801cd9c2d53f5da4186

views.py

get_filename

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

Reformat with black

def ensure_future(coro_or_future, *, loop=None): return _ensure_future(coro_or_future, loop=loop)

python3.10.4/Lib/asyncio/tasks.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

tasks.py

ensure_future

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

add python 3.10.4 for windows

def _get_permissions(self, user_obj, obj, from_name): if not user_obj.is_active or user_obj.is_anonymous or obj is not None: return set() perm_cache_name = "_%s_perm_cache" % from_name if not hasattr(user_obj, perm_cache_name): if user_obj.is_superuser: perms = Permission.objects.all() else: perms = getattr(self, "_get_%s_permissions" % from_name)(user_obj) perms = perms.values_list("content_type__app_label", "codename").order_by() setattr( user_obj, perm_cache_name, {"%s.%s" % (ct, name) for ct, name in perms} ) return getattr(user_obj, perm_cache_name)

django/contrib/auth/backends.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

backends.py

_get_permissions

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

Refs #33476 -- Reformatted code with Black.

def __hash__(self): # type: () -> int

.venv/lib/python3.8/site-packages/pip/_vendor/packaging/specifiers.py

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

specifiers.py

__hash__

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

upd; format

def get_cost_of_delayed_shipments(scorecard): return get_total_cost_of_shipments(scorecard) - get_cost_of_on_time_shipments(scorecard)

erpnext/buying/doctype/supplier_scorecard_variable/supplier_scorecard_variable.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

supplier_scorecard_variable.py

get_cost_of_delayed_shipments

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

style: format code with black

def check(self, pattern): if self.eos: raise EndOfText() if pattern not in self._re_cache: self._re_cache[pattern] = re.compile(pattern, self.flags) return self._re_cache[pattern].match(self.data, self.pos)

pipenv/patched/notpip/_vendor/pygments/scanner.py

pipenv

Python

f3166e673fe8d40277b804d35d77dcdb760fc3b3

scanner.py

check

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

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

def test_clear_not_launched_queued_tasks_mapped_task(self, dag_maker, session):

tests/executors/test_kubernetes_executor.py

airflow

Python

98d52af7074e9a82457515588bdf9cdd6de70f35

test_kubernetes_executor.py

test_clear_not_launched_queued_tasks_mapped_task

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

Use map_index when clearing not launched tasks in k8s (#23224)

def vertexcolorsrc(self): return self["vertexcolorsrc"]

packages/python/plotly/plotly/graph_objs/_mesh3d.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_mesh3d.py

vertexcolorsrc

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

switch to black .22

def widthsrc(self): return self["widthsrc"]

packages/python/plotly/plotly/graph_objs/_bar.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_bar.py

widthsrc

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

switch to black .22

def _dotprodsimp(expr, withsimp=False): from sympy.simplify.simplify import dotprodsimp as dps return dps(expr, withsimp=withsimp)

sympy/matrices/utilities.py

sympy

Python

f757f3daae6e11ea0cfb7dadc133274d8d74315f

utilities.py

_dotprodsimp

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

Reordered imports 2

def libc_ver() -> Tuple[str, str]: glibc_version = glibc_version_string() if glibc_version is None: return ("", "") else: return ("glibc", glibc_version)

pipenv/patched/notpip/_internal/utils/glibc.py

pipenv

Python

f3166e673fe8d40277b804d35d77dcdb760fc3b3

glibc.py

libc_ver

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

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

def select_query(self, targets, from_stmt, where_stmt) -> pd.DataFrame: # noqa raise NotImplementedError()

mindsdb/integrations/libs/base_handler.py

mindsdb

Python

0fd3b436c38f38bcae6fed9e14dc4d2a12e90793

base_handler.py

select_query

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

fix tests and reformat

def test_bad_return_in_train_loop(ray_start_4_cpus): # Simulates what happens with eg. torch models

python/ray/train/tests/test_data_parallel_trainer.py

ray

Python

8bb67427c18887f43721cf9726d6836c3b40cafb

test_data_parallel_trainer.py

test_bad_return_in_train_loop

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

[AIR] Discard returns of train loops in Trainers (#26448) Discards returns of user defined train loop functions to prevent deser issues with eg. torch models. Those returns are not used anywhere in AIR, so there is no loss of functionality.

def cone(self): return self["cone"]

packages/python/plotly/plotly/graph_objs/layout/template/_data.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_data.py

cone

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

switch to black .22

def get_purchased_items_cost(): pr_items = frappe.db.sql( , as_dict=1, ) pr_item_map = {} for item in pr_items: pr_item_map.setdefault(item.project, item.amount) return pr_item_map

erpnext/projects/report/project_wise_stock_tracking/project_wise_stock_tracking.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

project_wise_stock_tracking.py

get_purchased_items_cost

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

style: format code with black

def test_identity_weighted_graph_matrix(self): A = nx.to_scipy_sparse_array(self.G3) self.identity_conversion(self.G3, A, nx.Graph())

networkx/tests/test_convert_scipy.py

networkx

Python

5dfd57af2a141a013ae3753e160180b82bec9469

test_convert_scipy.py

test_identity_weighted_graph_matrix

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

Use scipy.sparse array datastructure (#5139) * Step 1: use sparse arrays in nx.to_scipy_sparse_matrix. Seems like a reasonable place to start. nx.to_scipy_sparse_matrix is one of the primary interfaces to scipy.sparse from within NetworkX. * 1: Use np.outer instead of mult col/row vectors Fix two instances in modularitymatrix where a new 2D array was being created via an outer product of two \"vectors\". In the matrix case, this was a row vector \* a column vector. In the array case this can be disambiguated by being explicit with np.outer. * Update _transition_matrix in laplacianmatrix module - A few instances of matrix multiplication operator - Add np.newaxis + transpose to get shape right for broadcasting - Explicitly convert e.g. sp.sparse.spdiags to a csr_array. * Update directed_combinitorial_laplacian w/ sparse array. - Wrap spdiags in csr_array and update matmul operators. * Rm matrix-specific code from lgc and hmn modules - Replace .A call with appropriate array semantics - wrap sparse.diags in csr_array. * Change hits to use sparse array semantics. - Replace * with @ - Remove superfluous calls to flatten. * Update sparse matrix usage in layout module. - Simplify lil.getrowview call - Wrap spdiags in csr_array. * lil_matrix -> lil_array in graphmatrix.py. * WIP: Start working on algebraic connectivity module. * Incorporate auth mat varname feedback. * Revert 1D slice and comment for 1D sparse future. * Add TODOs: rm csr_array wrapper around spdiags etc. * WIP: cleanup algebraicconn: tracemin_fiedler. * Typo. * Finish reviewing algebraicconnectivity. * Convert bethe_hessian matrix to use sparse arrays. * WIP: update laplacian. Update undirected laplacian functions. * WIP: laplacian - add comment about _transition_matrix return types. * Finish laplacianmatrix review. * Update attrmatrix. * Switch to official laplacian function. * Update pagerank to use sparse array. * Switch bipartite matrix to sparse arrays. * Check from_scipy_sparse_matrix works with arrays. Modifies test suite. * Apply changes from review. * Fix failing docstring tests. * Fix missing axis for in-place multiplication. * Use scipy==1.8rc2 * Use matrix multiplication * Fix PyPy CI * [MRG] Create plot_subgraphs.py example (#5165) * Create plot_subgraphs.py https://github.com/networkx/networkx/issues/4220 * Update plot_subgraphs.py black * Update plot_subgraphs.py lint plus font_size * Update plot_subgraphs.py added more plots * Update plot_subgraphs.py removed plots from the unit test and added comments * Update plot_subgraphs.py lint * Update plot_subgraphs.py typos fixed * Update plot_subgraphs.py added nodes to the plot of the edges removed that was commented out for whatever reason * Update plot_subgraphs.py revert the latest commit - the line was commented out for a reason - it's broken * Update plot_subgraphs.py fixed node color issue * Update plot_subgraphs.py format fix * Update plot_subgraphs.py forgot to draw the nodes... now fixed * Fix sphinx warnings about heading length. * Update examples/algorithms/plot_subgraphs.py * Update examples/algorithms/plot_subgraphs.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> Co-authored-by: Dan Schult <dschult@colgate.edu> * Add traveling salesman problem to example gallery (#4874) Adds an example of the using Christofides to solve the TSP problem to the example galery. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() (#5037) * Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() * Resolved Requested Changes * Revert changes to degree docstrings. * Update comments in example. * Apply wording to edges method in all graph classes. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Compatibility updates from testing with numpy/scipy/pytest rc's (#5226) * Rm deprecated scipy subpkg access. * Use recwarn fixture in place of deprecated pytest pattern. * Rm unnecessary try/except from tests. * Replace internal `close` fn with `math.isclose`. (#5224) * Replace internal close fn with math.isclose. * Fix lines in docstring examples. * Fix Python 3.10 deprecation warning w/ int div. (#5231) * Touchups and suggestions for subgraph gallery example (#5225) * Simplify construction of G with edges rm'd * Rm unused graph attribute. * Shorten categorization by node type. * Simplify node coloring. * Simplify isomorphism check. * Rm unit test. * Rm redundant plotting of each subgraph. * Use new package name (#5234) * Allowing None edges in weight function of bidirectional Dijkstra (#5232) * added following feature also to bidirectional dijkstra: The weight function can be used to hide edges by returning None. * changed syntax for better readability and code duplicate avoidance Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de> * Add an FAQ about assigning issues. (#5182) * Add FAQ about assigning issues. * Add note about linking issues from new PRs. * Update dev deps (#5243) * Update minor doc issues with tex notation (#5244) * Add FutureWarnings to fns that return sparse matrices - biadjacency_matrix. - bethe_hessian_matrix. - incidence_matrix. - laplacian functions. - modularity_matrix functions. - adjacency_matrix. * Add to_scipy_sparse_array and use it everywhere. Add a new conversion function to preserve array semantics internally while not altering behavior for users. Also adds FutureWarning to to_scipy_sparse_matrix. * Add from_scipy_sparse_array. Supercedes from_scipy_sparse_matrix. * Handle deprecations in separate PR. * Fix docstring examples. Co-authored-by: Mridul Seth <mail@mriduls.com> Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com> Co-authored-by: Andrew Knyazev <andrew.knyazev@ucdenver.edu> Co-authored-by: Dan Schult <dschult@colgate.edu> Co-authored-by: eskountis <56514439+eskountis@users.noreply.github.com> Co-authored-by: Anutosh Bhat <87052487+anutosh491@users.noreply.github.com> Co-authored-by: NikHoh <nikhoh@web.de> Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de> Co-authored-by: Sultan Orazbayev <contact@econpoint.com> Co-authored-by: Mridul Seth <mail@mriduls.com>

def _merge_dataframes(transactions_df, articles_df, customers_df): # Merge the transactions and articles dataframes transactions_df = pd.merge( transactions_df, articles_df, how="left", left_on="article_id", right_on="article_id", ) # Merge the transactions and customers dataframes transactions_df = pd.merge( transactions_df, customers_df, how="left", left_on="customer_id", right_on="customer_id", ) return transactions_df

ludwig/datasets/loaders/hm_fashion_recommendations.py

ludwig

Python

abfdc05018cc4dec5a2fed20ad09e94f1749fca9

hm_fashion_recommendations.py

_merge_dataframes

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

Add H&M fashion recommendation dataset (#2708) * allow individual file downloads from kaggle * pipe download_filenames to kaggle download fn * add dataset config for H&M Fashion Recommendations * add custom loader * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * use local backend instead of mock * add docstring for sample * fix titanic test * move negative_sample to ludwig.data * do not negative sample in loader Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

def _get_trainable_state(self): trainable_state = weakref.WeakKeyDictionary() for layer in self._flatten_layers(): trainable_state[layer] = layer.trainable return trainable_state

keras/engine/base_layer.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

base_layer.py

_get_trainable_state

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def get_field_type(self, connection, table_name, row): field_params = {} field_notes = [] try: field_type = connection.introspection.get_field_type(row.type_code, row) except KeyError: field_type = "TextField" field_notes.append("This field type is a guess.") # Add max_length for all CharFields. if field_type == "CharField" and row.internal_size: field_params["max_length"] = int(row.internal_size) if field_type in {"CharField", "TextField"} and row.collation: field_params["db_collation"] = row.collation if field_type == "DecimalField": if row.precision is None or row.scale is None: field_notes.append( "max_digits and decimal_places have been guessed, as this " "database handles decimal fields as float" ) field_params["max_digits"] = ( row.precision if row.precision is not None else 10 ) field_params["decimal_places"] = ( row.scale if row.scale is not None else 5 ) else: field_params["max_digits"] = row.precision field_params["decimal_places"] = row.scale return field_type, field_params, field_notes

django/core/management/commands/inspectdb.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

inspectdb.py

get_field_type

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

Refs #33476 -- Reformatted code with Black.

def solve_undetermined_coeffs(equ, coeffs, *syms, **flags): r if not (coeffs and all(i.is_Symbol for i in coeffs)): raise ValueError('must provide symbols for coeffs') if isinstance(equ, Eq): eq = equ.lhs - equ.rhs else: eq = equ ceq = cancel(eq) xeq = _mexpand(ceq.as_numer_denom()[0], recursive=True) free = xeq.free_symbols coeffs = free & set(coeffs) if not coeffs: return ([], {}) if flags.get('set', None) else [] # solve(0, x) -> [] if not syms: # e.g. A*exp(x) + B - (exp(x) + y) separated into parts that # don't/do depend on coeffs gives # -(exp(x) + y), A*exp(x) + B # then see what symbols are common to both # {x} = {x, A, B} - {x, y} ind, dep = xeq.as_independent(*coeffs, as_Add=True) dfree = dep.free_symbols syms = dfree & ind.free_symbols if not syms: # but if the system looks like (a + b)*x + b - c # then {} = {a, b, x} - c # so calculate {x} = {a, b, x} - {a, b} syms = dfree - set(coeffs) if not syms: syms = [Dummy()] else: if len(syms) == 1 and iterable(syms[0]): syms = syms[0] e, s, _ = recast_to_symbols([xeq], syms) xeq = e[0] syms = s # find the functional forms in which symbols appear gens = set(xeq.as_coefficients_dict(*syms).keys()) - {1} cset = set(coeffs) if any(g.has_xfree(cset) for g in gens): return # a generator contained a coefficient symbol # make sure we are working with symbols for generators e, gens, _ = recast_to_symbols([xeq], list(gens)) xeq = e[0] # collect coefficients in front of generators system = list(collect(xeq, gens, evaluate=False).values()) # get a solution soln = solve(system, coeffs, **flags) # unpack unless told otherwise if length is 1 settings = flags.get('dict', None) or flags.get('set', None) if type(soln) is dict or settings or len(soln) != 1: return soln return soln[0]

sympy/solvers/solvers.py

sympy

Python

2163f938f26e75e10f2d25b92321511988eff502

solvers.py

solve_undetermined_coeffs

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

mv solve_undetermined_coeffs and legacy behavior

def populate_any_indicators(self, pair, df, tf, informative=None,coin=''): if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) informative[coin+'rsi'] = ta.RSI(informative, timeperiod=14) informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25) informative[coin+'adx'] = ta.ADX(informative, window=20) informative[coin+'20sma'] = ta.SMA(informative,timeperiod=20) informative[coin+'21ema'] = ta.EMA(informative,timeperiod=21) informative[coin+'bmsb'] = np.where(informative[coin+'20sma'].lt(informative[coin+'21ema']),1,0) informative[coin+'close_over_20sma'] = informative['close']/informative[coin+'20sma'] informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25) informative[coin+'ema21'] = ta.EMA(informative, timeperiod=21) informative[coin+'sma20'] = ta.SMA(informative, timeperiod=20) stoch = ta.STOCHRSI(informative, 15, 20, 2, 2) informative[coin+'srsi-fk'] = stoch['fastk'] informative[coin+'srsi-fd'] = stoch['fastd'] bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2) informative[coin+'bb_lowerband'] = bollinger['lower'] informative[coin+'bb_middleband'] = bollinger['mid'] informative[coin+'bb_upperband'] = bollinger['upper'] informative[coin+'bb_width'] = ((informative[coin+"bb_upperband"] - informative[coin+"bb_lowerband"]) / informative[coin+"bb_middleband"]) informative[coin+'close-bb_lower'] = informative['close'] / informative[coin+'bb_lowerband'] informative[coin+'roc'] = ta.ROC(informative, timeperiod=3) informative[coin+'adx'] = ta.ADX(informative, window=14) macd = ta.MACD(informative) informative[coin+'macd'] = macd['macd'] informative[coin+'pct-change'] = informative['close'].pct_change() informative[coin+'relative_volume'] = informative['volume'] / informative['volume'].rolling(10).mean() informative[coin+'pct-change'] = informative['close'].pct_change() indicators = [col for col in informative if col.startswith(coin)] for n in range(self.freqai_info['feature_parameters']['shift']+1): if n==0: continue informative_shift = informative[indicators].shift(n) informative_shift = informative_shift.add_suffix('_shift-'+str(n)) informative = pd.concat((informative,informative_shift),axis=1) df = merge_informative_pair(df, informative, self.config['timeframe'], tf, ffill=True) skip_columns = [(s + '_'+tf) for s in ['date', 'open', 'high', 'low', 'close', 'volume']] df = df.drop(columns=skip_columns) return df

freqtrade/templates/FreqaiExampleStrategy.py

freqtrade

Python

fc837c4daa27a18ff0e86128f4d52089b88fa5fb

FreqaiExampleStrategy.py

populate_any_indicators

https://github.com/freqtrade/freqtrade.git

add freqao backend machinery, user interface, documentation

def shuffle(*arrays, random_state=None, n_samples=None): return resample( *arrays, replace=False, n_samples=n_samples, random_state=random_state )

sklearn/utils/__init__.py

scikit-learn

Python

49279c3267c0c54cdba80a571820c46f25fbe883

__init__.py

shuffle

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

DOC ensures sklearn.utils.shuffle passes numpydoc validation (#24367) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

def _eigh(*args, **kwargs): eigvals = kwargs.pop("subset_by_index", None) return scipy.linalg.eigh(*args, eigvals=eigvals, **kwargs) # remove when https://github.com/joblib/joblib/issues/1071 is fixed

sklearn/utils/fixes.py

scikit-learn

Python

b0bf2315a771ed10b10d1f6a24a48ebdba34cf16

fixes.py

_eigh

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

MAINT fix deprecation raised in scipy-dev build (#25175) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Loïc Estève <loic.esteve@ymail.com>

def compute_dict_delta(old_dict, new_dict) -> Tuple[dict, dict, dict]: added_keys, removed_keys, updated_keys = compute_iterable_delta( old_dict.keys(), new_dict.keys() ) return ( {k: new_dict[k] for k in added_keys}, {k: old_dict[k] for k in removed_keys}, {k: new_dict[k] for k in updated_keys}, )

python/ray/serve/utils.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

utils.py

compute_dict_delta

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

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

def convert_shapes(input_shape, to_tuples=True):

keras/utils/tf_utils.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

tf_utils.py

convert_shapes

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def get_performance_issue_description_data(self, event): spans, matched_problem = get_span_and_problem(event) if not matched_problem: return "" parent_span, repeating_spans = get_parent_and_repeating_spans(spans, matched_problem) transaction_name = get_span_evidence_value_problem(matched_problem) parent_span = get_span_evidence_value(parent_span) repeating_spans = get_span_evidence_value(repeating_spans) num_repeating_spans = ( str(len(matched_problem.offender_span_ids)) if matched_problem.offender_span_ids else "" ) return (transaction_name, parent_span, num_repeating_spans, repeating_spans)

src/sentry/integrations/mixins/issues.py

sentry

Python

0711b240a4efe79f06629914d5836cd6acbfcf78

issues.py

get_performance_issue_description_data

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

feat(github): Add span evidence to performance issues (#41041) Add span evidence to the description of a GitHub issue created from a performance issue. Currently the GitHub issue is fairly empty as for an error issue it shows the stacktrace, but for a performance issue it's just a link back to the Sentry issue. <img width="1019" alt="Screen Shot 2022-11-04 at 2 03 29 PM" src="https://user-images.githubusercontent.com/29959063/200081055-f5119f8a-3467-490f-b5a4-f30f179a9620.png">

def install_src(collection, b_collection_path, b_collection_output_path, artifacts_manager): r collection_meta = artifacts_manager.get_direct_collection_meta(collection) if 'build_ignore' not in collection_meta: # installed collection, not src # FIXME: optimize this? use a different process? copy instead of build? collection_meta['build_ignore'] = [] collection_manifest = _build_manifest(**collection_meta) file_manifest = _build_files_manifest( b_collection_path, collection_meta['namespace'], collection_meta['name'], collection_meta['build_ignore'], ) collection_output_path = _build_collection_dir( b_collection_path, b_collection_output_path, collection_manifest, file_manifest, ) display.display( 'Created collection for {coll!s} at {path!s}'. format(coll=collection, path=collection_output_path) )

lib/ansible/galaxy/collection/__init__.py

ansible

Python

b439e41a915ccec0ccbabecc966919ea406db74e

__init__.py

install_src

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

expand ansible-doc coverage (#74963) * Expand ansible-doc to tests/filters and fix existing issues enable filter/test docs if in single file or companion yaml add docs for several filters/tests plugins allow .yml companion for docs for other plugins, must be colocated verify plugins are valid (not modules, cannot) fix 'per collection' filtering limit old style deprecation (_ prefix) to builtin/legacy start move to pathlib for saner path handling moved some funcitons, kept backwards compat shims with deprecation notice Co-authored-by: Abhijeet Kasurde <akasurde@redhat.com> Co-authored-by: Felix Fontein <felix@fontein.de> Co-authored-by: Sandra McCann <samccann@redhat.com>

def in1d(ar1, ar2, assume_unique=False, invert=False, method='auto'): # Ravel both arrays, behavior for the first array could be different ar1 = np.asarray(ar1).ravel() ar2 = np.asarray(ar2).ravel() # Ensure that iteration through object arrays yields size-1 arrays if ar2.dtype == object: ar2 = ar2.reshape(-1, 1) # Convert booleans to uint8 so we can use the fast integer algorithm if ar1.dtype == bool: ar1 = ar1.view(np.uint8) if ar2.dtype == bool: ar2 = ar2.view(np.uint8) # Check if we can use a fast integer algorithm: integer_arrays = (np.issubdtype(ar1.dtype, np.integer) and np.issubdtype(ar2.dtype, np.integer)) if method not in {'auto', 'sort', 'dictionary'}: raise ValueError( "Invalid method: {0}. ".format(method) + "Please use 'auto', 'sort' or 'dictionary'.") if integer_arrays and method in {'auto', 'dictionary'}: ar2_min = np.min(ar2) ar2_max = np.max(ar2) ar2_size = ar2.size # Check for integer overflow with np.errstate(over='raise'): try: ar2_range = ar2_max - ar2_min # Optimal performance is for approximately # log10(size) > (log10(range) - 2.27) / 0.927. # See discussion on # https://github.com/numpy/numpy/pull/12065 optimal_parameters = ( np.log10(ar2_size) > ((np.log10(ar2_range + 1.0) - 2.27) / 0.927) ) except FloatingPointError: optimal_parameters = False # Use the fast integer algorithm if optimal_parameters or method == 'dictionary': if invert: outgoing_array = np.ones_like(ar1, dtype=bool) else: outgoing_array = np.zeros_like(ar1, dtype=bool) # Make elements 1 where the integer exists in ar2 if invert: isin_helper_ar = np.ones(ar2_range + 1, dtype=bool) isin_helper_ar[ar2 - ar2_min] = 0 else: isin_helper_ar = np.zeros(ar2_range + 1, dtype=bool) isin_helper_ar[ar2 - ar2_min] = 1 # Mask out elements we know won't work basic_mask = (ar1 <= ar2_max) & (ar1 >= ar2_min) outgoing_array[basic_mask] = isin_helper_ar[ar1[basic_mask] - ar2_min] return outgoing_array elif method == 'dictionary': raise ValueError( "'dictionary' method is only " "supported for boolean or integer arrays. " "Please select 'sort' or 'auto' for the method." ) # Check if one of the arrays may contain arbitrary objects contains_object = ar1.dtype.hasobject or ar2.dtype.hasobject # This code is run when # a) the first condition is true, making the code significantly faster # b) the second condition is true (i.e. `ar1` or `ar2` may contain # arbitrary objects), since then sorting is not guaranteed to work if len(ar2) < 10 * len(ar1) ** 0.145 or contains_object: if invert: mask = np.ones(len(ar1), dtype=bool) for a in ar2: mask &= (ar1 != a) else: mask = np.zeros(len(ar1), dtype=bool) for a in ar2: mask |= (ar1 == a) return mask # Otherwise use sorting if not assume_unique: ar1, rev_idx = np.unique(ar1, return_inverse=True) ar2 = np.unique(ar2) ar = np.concatenate((ar1, ar2)) # We need this to be a stable sort, so always use 'mergesort' # here. The values from the first array should always come before # the values from the second array. order = ar.argsort(kind='mergesort') sar = ar[order] if invert: bool_ar = (sar[1:] != sar[:-1]) else: bool_ar = (sar[1:] == sar[:-1]) flag = np.concatenate((bool_ar, [invert])) ret = np.empty(ar.shape, dtype=bool) ret[order] = flag if assume_unique: return ret[:len(ar1)] else: return ret[rev_idx]

numpy/lib/arraysetops.py

numpy

Python

d7e2582cd33b22a767286e8a3d95b336dfe51a34

arraysetops.py

in1d

https://github.com/numpy/numpy.git

MAINT: bool instead of np.bool_ dtype

def world_size(self): if is_torch_tpu_available(): return xm.xrt_world_size() elif is_sagemaker_mp_enabled(): return smp.dp_size() if not smp.state.cfg.prescaled_batch else smp.rdp_size() elif is_sagemaker_dp_enabled(): return sm_dist.get_world_size() elif self.local_rank != -1: return torch.distributed.get_world_size() return 1

src/transformers/training_args.py

transformers

Python

2eb7bb15e771f13192968cd4657c78f76b0799fe

training_args.py

world_size

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

Updates in Trainer to support new features in SM Model Parallel library (#15877) * Create optimizer after model creation for SMP * update dp_rank to rdp_rank for opt_state_dict * update world_size and process_index for smp * Address comments * Lint fix Co-authored-by: Cavdar <dcavdar@a07817b12d7e.ant.amazon.com>

def _is_packed(dtype): align = dtype.isalignedstruct max_alignment = 1 total_offset = 0 for name in dtype.names: fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name]) if align: total_offset = _aligned_offset(total_offset, fld_dtype.alignment) max_alignment = max(max_alignment, fld_dtype.alignment) if fld_offset != total_offset: return False total_offset += fld_dtype.itemsize if align: total_offset = _aligned_offset(total_offset, max_alignment) if total_offset != dtype.itemsize: return False return True

numpy/core/_dtype.py

numpy

Python

a0c2e826738daa0cbd83aba85852405b73878f5b

_dtype.py

_is_packed

https://github.com/numpy/numpy.git

API: Fix structured dtype cast-safety, promotion, and comparison This PR replaces the old gh-15509 implementing proper type promotion for structured voids. It further fixes the casting safety to consider casts with equivalent field number and matching order as "safe" and if the names, titles, and offsets match as "equiv". The change perculates into the void comparison, and since it fixes the order, it removes the current FutureWarning there as well. This addresses https://github.com/liberfa/pyerfa/issues/77 and replaces gh-15509 (the implementation has changed too much). Fixes gh-15494 (and probably a few more) Co-authored-by: Allan Haldane <allan.haldane@gmail.com>

async def _async_stop(self) -> None: if self._cancel_watchdog: self._cancel_watchdog() self._cancel_watchdog = None await self._async_stop_scanner()

homeassistant/components/bluetooth/scanner.py

core

Python

ced8278e3222501dde7d769ea4b57aae75f62438

scanner.py

_async_stop

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

Auto recover when the Bluetooth adapter stops responding (#77043)

def netdev():

salt/modules/status.py

salt

Python

fe48a85e8204f3840264f16235ea3bde3e664c65

status.py

netdev

https://github.com/saltstack/salt.git

Allow for Python 3 using view objects for a dictionary keys() function

def remove_module_load(state_dict): new_state_dict = OrderedDict() for k, v in state_dict.items(): new_state_dict[k[7:]] = v return new_state_dict

fastai/torch_core.py

DeOldify

Python

4fc3616712edb19179b17dd270ad6cf63abf99c2

torch_core.py

remove_module_load

https://github.com/jantic/DeOldify.git

Upgrading to support latest Pytorch version

def in_place_subclassed_model_state_restoration(model): assert not model._is_graph_network # Restore layers and build attributes if ( hasattr(model, "_original_attributes_cache") and model._original_attributes_cache is not None ): # Models have sticky attribute assignment, so we want to be careful to # add back the previous attributes and track Layers by their original # names without adding dependencies on "utility" attributes which Models # exempt when they're constructed. setattr_tracking = model._setattr_tracking model._setattr_tracking = False model._self_tracked_trackables = [] for name, value in model._original_attributes_cache.items(): setattr(model, name, value) if isinstance(value, Layer): model._self_tracked_trackables.append(value) model._original_attributes_cache = None model._setattr_tracking = setattr_tracking else: # Restore to the state of a never-called model. _reset_build_compile_trackers(model) @keras_export("keras.__internal__.models.clone_and_build_model", v1=[])

keras/models/cloning.py

@keras_export("keras.__internal__.models.clone_and_build_model", v1=[])

keras

Python

f0fc6f798937a7a5fdab469c0f16bdde7cfc4ccd

cloning.py

in_place_subclassed_model_state_restoration

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

resolve line-too-long in models

def _make_inc_temp(self, suffix="", prefix="", directory_name=None): if directory_name is None: directory_name = ray._private.utils.get_ray_temp_dir() directory_name = os.path.expanduser(directory_name) index = self._incremental_dict[suffix, prefix, directory_name] # `tempfile.TMP_MAX` could be extremely large, # so using `range` in Python2.x should be avoided. while index < tempfile.TMP_MAX: if index == 0: filename = os.path.join(directory_name, prefix + suffix) else: filename = os.path.join( directory_name, prefix + "." + str(index) + suffix ) index += 1 if not os.path.exists(filename): # Save the index. self._incremental_dict[suffix, prefix, directory_name] = index return filename raise FileExistsError(errno.EEXIST, "No usable temporary filename found")

python/ray/node.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

node.py

_make_inc_temp

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

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

def captured_stdout() -> ContextManager[StreamWrapper]: return captured_output("stdout")

pipenv/patched/notpip/_internal/utils/misc.py

pipenv

Python

f3166e673fe8d40277b804d35d77dcdb760fc3b3

misc.py

captured_stdout

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

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

def test_a3c_compilation(self): config = a3c.DEFAULT_CONFIG.copy() config["num_workers"] = 2 config["num_envs_per_worker"] = 2 num_iterations = 1 # Test against all frameworks. for _ in framework_iterator(config, with_eager_tracing=True): for env in ["CartPole-v1", "Pendulum-v1", "PongDeterministic-v0"]: print("env={}".format(env)) config["model"]["use_lstm"] = env == "CartPole-v1" trainer = a3c.A3CTrainer(config=config, env=env) for i in range(num_iterations): results = trainer.train() check_train_results(results) print(results) check_compute_single_action( trainer, include_state=config["model"]["use_lstm"] ) trainer.stop()

rllib/agents/a3c/tests/test_a3c.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

test_a3c.py

test_a3c_compilation

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

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

def test_author_name_present(self): response = self.get_for_author(1) self.assertContains(response, "J. R. R. Tolkien", 2)

wagtail/contrib/modeladmin/tests/test_simple_modeladmin.py

wagtail

Python

d10f15e55806c6944827d801cd9c2d53f5da4186

test_simple_modeladmin.py

test_author_name_present

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

Reformat with black

def _update_title_position(self, renderer): if self._autotitlepos is not None and not self._autotitlepos: _log.debug('title position was updated manually, not adjusting') return titles = (self.title, self._left_title, self._right_title) for title in titles: x, _ = title.get_position() # need to start again in case of window resizing title.set_position((x, 1.0)) # need to check all our twins too... axs = self._twinned_axes.get_siblings(self) # and all the children for ax in self.child_axes: if ax is not None: locator = ax.get_axes_locator() if locator: pos = locator(self, renderer) ax.apply_aspect(pos) else: ax.apply_aspect() axs = axs + [ax] top = -np.Inf for ax in axs: bb = None if (ax.xaxis.get_ticks_position() in ['top', 'unknown'] or ax.xaxis.get_label_position() == 'top'): bb = ax.xaxis.get_tightbbox(renderer) if bb is None: bb = ax.get_window_extent(renderer) top = max(top, bb.ymax) if title.get_text(): ax.yaxis.get_tightbbox(renderer) # update offsetText if ax.yaxis.offsetText.get_text(): bb = ax.yaxis.offsetText.get_tightbbox(renderer) if bb.intersection(title.get_tightbbox(renderer), bb): top = bb.ymax if top < 0: # the top of Axes is not even on the figure, so don't try and # automatically place it. _log.debug('top of Axes not in the figure, so title not moved') return if title.get_window_extent(renderer).ymin < top: _, y = self.transAxes.inverted().transform((0, top)) title.set_position((x, y)) # empirically, this doesn't always get the min to top, # so we need to adjust again. if title.get_window_extent(renderer).ymin < top: _, y = self.transAxes.inverted().transform( (0., 2 * top - title.get_window_extent(renderer).ymin)) title.set_position((x, y)) ymax = max(title.get_position()[1] for title in titles) for title in titles: # now line up all the titles at the highest baseline. x, _ = title.get_position() title.set_position((x, ymax)) # Drawing

lib/matplotlib/axes/_base.py

matplotlib

Python

cfabe79945743dd375db4fe8bcdbaab00330dfe8

_base.py

_update_title_position

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

FIX: Autoposition title when yaxis has offset Move any title above the y axis offset text it would overlap with the offset. If multiple titles are present, they are vertically aligned to the highest one.

def geturl(self): return self.url

python3.10.4/Lib/http/client.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

client.py

geturl

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

add python 3.10.4 for windows

def get_updated_history(self, current_stream_state, latest_record_datetime, latest_record, current_parsed_datetime, state_date): history = current_stream_state.get("history", {}) file_modification_date = latest_record_datetime.strftime("%Y-%m-%d") # add record to history if record modified date in range delta start from state if latest_record_datetime.date() + timedelta(days=self.buffer_days) >= state_date: history_item = set(history.setdefault(file_modification_date, set())) history_item.add(latest_record[self.ab_file_name_col]) history[file_modification_date] = history_item # reset history to new date state if current_parsed_datetime.date() != state_date: history = { date: history[date] for date in history if datetime.strptime(date, "%Y-%m-%d").date() + timedelta(days=self.buffer_days) >= state_date } return history

airbyte-integrations/connectors/source-s3/source_s3/source_files_abstract/stream.py

airbyte

Python

f9348b22517556e1af5d1831db7187b912ee0126

stream.py

get_updated_history

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

🐛 Source Amazon S3: solve possible case of files being missed during incremental syncs (#12568) * Added history to state * Deleted unused import * Rollback abnormal state file * Rollback abnormal state file * Fixed type error issue * Fix state issue * Updated after review * Bumped version

def test_inheritance(self): should_contain = [ '<li>Villain: <a href="%s">Bob</a>' % reverse("admin:admin_views_villain_change", args=(self.sv1.pk,)), '<li>Super villain: <a href="%s">Bob</a>' % reverse("admin:admin_views_supervillain_change", args=(self.sv1.pk,)), "<li>Secret hideout: floating castle", "<li>Super secret hideout: super floating castle!", ] response = self.client.get( reverse("admin:admin_views_villain_delete", args=(self.sv1.pk,)) ) for should in should_contain: self.assertContains(response, should, 1) response = self.client.get( reverse("admin:admin_views_supervillain_delete", args=(self.sv1.pk,)) ) for should in should_contain: self.assertContains(response, should, 1)

tests/admin_views/tests.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

tests.py

test_inheritance

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

Refs #33476 -- Reformatted code with Black.

def store_or_execute(self, block, name): if name: # If storing it for further editing self.shell.user_ns[name] = SList(block.splitlines()) print("Block assigned to '%s'" % name) else: b = self.preclean_input(block) self.shell.user_ns['pasted_block'] = b self.shell.using_paste_magics = True try: self.shell.run_cell(b, store_history=True) finally: self.shell.using_paste_magics = False

IPython/terminal/magics.py

ipython

Python

75b3d1cc6d5e1e629705d8a7233a374f1e4235e7

magics.py

store_or_execute

https://github.com/ipython/ipython.git

Get history from sql. Fixes #13585 By getting history from sql we can get the transformed history. This also skip storing history if `%paste` is used and `%paste` itself will insert the pasted value in history which is more conveninent.

def _count_righthand_zero_bits(number, bits): if number == 0: return bits return min(bits, (~number & (number-1)).bit_length())

python3.10.4/Lib/ipaddress.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

ipaddress.py

_count_righthand_zero_bits

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

add python 3.10.4 for windows

def _deserialize_metric(metric_config): from keras import ( metrics as metrics_module, ) # pylint:disable=g-import-not-at-top if metric_config in ["accuracy", "acc", "crossentropy", "ce"]: # Do not deserialize accuracy and cross-entropy strings as we have special # case handling for these in compile, based on model output shape. return metric_config return metrics_module.deserialize(metric_config)

keras/saving/saving_utils.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

saving_utils.py

_deserialize_metric

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def __call__(self, w): return w

keras/constraints.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

constraints.py

__call__

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def on_click(self) -> None: self.cycle_variant()

src/textual/widgets/_placeholder.py

textual

Python

4b5fd43423a327e4cd6d477a66bebc9588fd1488

_placeholder.py

on_click

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

Add scaffolding for the Placeholder widget.

def set_tunnel(self, host, port=None, headers=None): if self.sock: raise RuntimeError("Can't set up tunnel for established connection") self._tunnel_host, self._tunnel_port = self._get_hostport(host, port) if headers: self._tunnel_headers = headers else: self._tunnel_headers.clear()

python3.10.4/Lib/http/client.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

client.py

set_tunnel

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

add python 3.10.4 for windows

def get_ann_info(self, idx): img_id = self.data_infos[idx]['img_id'] bboxes = [] labels = [] bboxes_ignore = [] labels_ignore = [] is_occludeds = [] is_truncateds = [] is_group_ofs = [] is_depictions = [] is_insides = [] for obj in self.ann_infos[img_id]: label = int(obj['label']) bbox = [ float(obj['bbox'][0]), float(obj['bbox'][1]), float(obj['bbox'][2]), float(obj['bbox'][3]) ] bboxes.append(bbox) labels.append(label) # Other parameters is_occludeds.append(obj['is_occluded']) is_truncateds.append(obj['is_truncated']) is_group_ofs.append(obj['is_group_of']) is_depictions.append(obj['is_depiction']) is_insides.append(obj['is_inside']) if not bboxes: bboxes = np.zeros((0, 4)) labels = np.zeros((0, )) else: bboxes = np.array(bboxes) labels = np.array(labels) if not bboxes_ignore: bboxes_ignore = np.zeros((0, 4)) labels_ignore = np.zeros((0, )) else: bboxes_ignore = np.array(bboxes_ignore) labels_ignore = np.array(labels_ignore) assert len(is_group_ofs) == len(labels) == len(bboxes) gt_is_group_ofs = np.array(is_group_ofs, dtype=np.bool) # These parameters is not used yet. is_occludeds = np.array(is_occludeds, dtype=np.bool) is_truncateds = np.array(is_truncateds, dtype=np.bool) is_depictions = np.array(is_depictions, dtype=np.bool) is_insides = np.array(is_insides, dtype=np.bool) ann = dict( bboxes=bboxes.astype(np.float32), labels=labels.astype(np.int64), bboxes_ignore=bboxes_ignore.astype(np.float32), labels_ignore=labels_ignore.astype(np.int64), gt_is_group_ofs=gt_is_group_ofs, is_occludeds=is_occludeds, is_truncateds=is_truncateds, is_depictions=is_depictions, is_insides=is_insides) return ann

mmdet/datasets/openimages.py

mmdetection

Python

1516986a616fee8bb741d0ab2be40683045efccd

openimages.py

get_ann_info

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

[Feature] Support OpenImages Dataset (#6331) * [Feature] support openimage group of eval * [Feature] support openimage group of eval * support openimage dataset * support openimage challenge dataset * fully support OpenImages-V6 and OpenImages Challenge 2019 * Fix some logic error * update config file * fix get data_infos error * fully support OpenImages evaluation * update OpenImages config files * [Feature] support OpenImages datasets * fix bug * support load image metas from pipeline * fix bug * fix get classes logic error * update code * support get image metas * support openimags * support collect image metas * support Open Images * fix openimages logic * minor fix * add a new function to compute openimages tpfp * minor fix * fix ci error * minor fix * fix indication * minor fix * fix returns * fix returns * fix returns * fix returns * fix returns * minor fix * update readme * support loading image level labels and fix some logic * minor fix * minor fix * add class names * minor fix * minor fix * minor fix * add openimages test unit * minor fix * minor fix * fix test unit * minor fix * fix logic error * minor fix * fully support openimages * minor fix * fix docstring * fix docstrings in readthedocs * update get image metas script * label_description_file -> label_file * update openimages readme * fix test unit * fix test unit * minor fix * update readme file * Update get_image_metas.py

def test_chord_clone_kwargs(self, subtests): with subtests.test(msg='Verify chord cloning clones kwargs correctly'): c = chord([signature('g'), signature('h')], signature('i'), kwargs={'U': 6}) c2 = c.clone() assert c2.kwargs == c.kwargs with subtests.test(msg='Cloning the chord with overridden kwargs'): override_kw = {'X': 2} c3 = c.clone(args=(1,), kwargs=override_kw) with subtests.test(msg='Verify the overridden kwargs were cloned correctly'): new_kw = c.kwargs.copy() new_kw.update(override_kw) assert c3.kwargs == new_kw

t/unit/tasks/test_canvas.py

celery

Python

c3c6594b4cdea898abba218f576a669700dba98d

test_canvas.py

test_chord_clone_kwargs

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

BLM-2: Adding unit tests to chord clone (#7668) * Added .python-version and .vscode to .gitignore * Added test_chord_clone_kwargs() to verify chord cloning treats kwargs correctly * Happify linter

def save(self, filename, data): logger.debug("filename: %s, data type: %s", filename, type(data)) filename = self._check_extension(filename) try: with open(filename, self._write_option) as s_file: s_file.write(self.marshal(data)) except IOError as err: msg = f"Error writing to '{filename}': {err.strerror}" raise FaceswapError(msg) from err

lib/serializer.py

faceswap

Python

bad5025aea1adb9126580e14e064e6c99089243d

serializer.py

save

https://github.com/deepfakes/faceswap.git

Core updates - Change loss loading mechanism - Autosize tooltips based on content size - Random linting + code modernisation

def _media_status(self): media_status = self.media_status media_status_received = self.media_status_received if ( media_status is None or media_status.player_state == MEDIA_PLAYER_STATE_UNKNOWN ): groups = self.mz_media_status for k, val in groups.items(): if val and val.player_state != MEDIA_PLAYER_STATE_UNKNOWN: media_status = val media_status_received = self.mz_media_status_received[k] break return (media_status, media_status_received)

homeassistant/components/cast/media_player.py

core

Python

66551e6fcbd063e53c13adc8a6462b8e00ce1450

media_player.py

_media_status

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

Add state buffering to media_player and use it in cast (#70802)

def no_manual_dependency_tracking_scope(obj):

keras/feature_column/dense_features_v2.py

"""A context that disables manual dependency tracking for the given `obj`. Sometimes library methods might track objects on their own and we might want to disable that and do the tracking on our own. One can then use this context manager to disable the tracking the library method does and do your own tracking. For example:the given `obj`. Sometimes library methods might track objects on their own and we might want to disable that and do the tracking on our own. One can then use this context manager to disable the tracking the library method does and do your own

keras

Python

0c959a0670a2bcb12dc7a1717ce7416ff1f7cc27

dense_features_v2.py

no_manual_dependency_tracking_scope

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

Remove deprecated TF1 Layer APIs `apply()`, `get_updates_for()`, `get_losses_for()`, and remove the `inputs` argument in the `add_loss()` method. PiperOrigin-RevId: 428134172

def load(self) -> Generator[Tuple[str, np.ndarray], None, None]: iterator = self._load_video_frames if self._is_video else self._load_disk_frames for filename, image in iterator(): yield filename, image

scripts/fsmedia.py

faceswap

Python

1022651eb8a7741014f5d2ec7cbfe882120dfa5f

fsmedia.py

load

https://github.com/deepfakes/faceswap.git

Bugfix: convert - Gif Writer - Fix non-launch error on Gif Writer - convert plugins - linting - convert/fs_media/preview/queue_manager - typing - Change convert items from dict to Dataclass

def _execute_task(self, context, task_copy): # If the task has been deferred and is being executed due to a trigger, # then we need to pick the right method to come back to, otherwise # we go for the default execute execute_callable = task_copy.execute if self.next_method: # __fail__ is a special signal value for next_method that indicates # this task was scheduled specifically to fail. if self.next_method == "__fail__": next_kwargs = self.next_kwargs or {} raise TaskDeferralError(next_kwargs.get("error", "Unknown")) # Grab the callable off the Operator/Task and add in any kwargs execute_callable = getattr(task_copy, self.next_method) if self.next_kwargs: execute_callable = partial(execute_callable, **self.next_kwargs) # If a timeout is specified for the task, make it fail # if it goes beyond if task_copy.execution_timeout: # If we are coming in with a next_method (i.e. from a deferral), # calculate the timeout from our start_date. if self.next_method: timeout_seconds = ( task_copy.execution_timeout - (timezone.utcnow() - self.start_date) ).total_seconds() else: timeout_seconds = task_copy.execution_timeout.total_seconds() try: # It's possible we're already timed out, so fast-fail if true if timeout_seconds <= 0: raise AirflowTaskTimeout() # Run task in timeout wrapper with timeout(timeout_seconds): result = execute_callable(context=context) except AirflowTaskTimeout: task_copy.on_kill() raise else: result = execute_callable(context=context) # If the task returns a result, push an XCom containing it if task_copy.do_xcom_push and result is not None: with create_session() as session: self.xcom_push(key=XCOM_RETURN_KEY, value=result, session=session) self._record_task_map_for_downstreams(result, session=session) return result

airflow/models/taskinstance.py

airflow

Python

d48a3a357fd89ec805d086d5b6c1f1d4daf77b9a

taskinstance.py

_execute_task

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

Add TaskMap and TaskInstance.map_id (#20286) Co-authored-by: Ash Berlin-Taylor <ash_github@firemirror.com>

def update_pdfjs(target_version=None, legacy=False, gh_token=None): if target_version is None: version, url = get_latest_pdfjs_url(gh_token, legacy=legacy) else: # We need target_version as x.y.z, without the 'v' prefix, though the # user might give it on the command line if target_version.startswith('v'): target_version = target_version[1:] # version should have the prefix to be consistent with the return value # of get_latest_pdfjs_url() version = 'v' + target_version suffix = "-legacy" if legacy else "" url = ('https://github.com/mozilla/pdf.js/releases/download/' f'{version}/pdfjs-{target_version}{suffix}-dist.zip') os.chdir(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..')) target_path = os.path.join('qutebrowser', '3rdparty', 'pdfjs') print(f"=> Downloading pdf.js {version}{' (legacy)' if legacy else ''}") try: (archive_path, _headers) = urllib.request.urlretrieve(url) except urllib.error.HTTPError as error: print("Could not retrieve pdfjs {}: {}".format(version, error)) return if os.path.isdir(target_path): print("Removing old version in {}".format(target_path)) shutil.rmtree(target_path) os.makedirs(target_path) print("Extracting new version") shutil.unpack_archive(archive_path, target_path, 'zip') urllib.request.urlcleanup()

scripts/dev/update_3rdparty.py

qutebrowser

Python

f6a365172afe127a4ba770e14569f2d3cd7569b4

update_3rdparty.py

update_pdfjs

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

Use legacy PDF.js build for macOS/Windows releases Fixes #7108

def test_callback_error(self) -> None: request = Mock(args={}) request.args[b"error"] = [b"invalid_client"] self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("invalid_client", "") request.args[b"error_description"] = [b"some description"] self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("invalid_client", "some description")

tests/handlers/test_oidc.py

synapse

Python

5dd949bee6158a8b651db9f2ae417a62c8184bfd

test_oidc.py

test_callback_error

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

Add type hints to some tests/handlers files. (#12224)

def _Filters(): return _cpplint_state.filters

code/deep/BJMMD/caffe/scripts/cpp_lint.py

transferlearning

Python

cc4d0564756ca067516f71718a3d135996525909

cpp_lint.py

_Filters

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

Balanced joint maximum mean discrepancy for deep transfer learning

def notchspansrc(self): return self["notchspansrc"]

packages/python/plotly/plotly/graph_objs/_box.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_box.py

notchspansrc

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

switch to black .22

def get_random_transform(self, img_shape, seed=None): img_row_axis = self.row_axis - 1 img_col_axis = self.col_axis - 1 if seed is not None: np.random.seed(seed) if self.rotation_range: theta = np.random.uniform(-self.rotation_range, self.rotation_range) else: theta = 0 if self.height_shift_range: try: # 1-D array-like or int tx = np.random.choice(self.height_shift_range) tx *= np.random.choice([-1, 1]) except ValueError: # floating point tx = np.random.uniform( -self.height_shift_range, self.height_shift_range ) if np.max(self.height_shift_range) < 1: tx *= img_shape[img_row_axis] else: tx = 0 if self.width_shift_range: try: # 1-D array-like or int ty = np.random.choice(self.width_shift_range) ty *= np.random.choice([-1, 1]) except ValueError: # floating point ty = np.random.uniform( -self.width_shift_range, self.width_shift_range ) if np.max(self.width_shift_range) < 1: ty *= img_shape[img_col_axis] else: ty = 0 if self.shear_range: shear = np.random.uniform(-self.shear_range, self.shear_range) else: shear = 0 if self.zoom_range[0] == 1 and self.zoom_range[1] == 1: zx, zy = 1, 1 else: zx, zy = np.random.uniform( self.zoom_range[0], self.zoom_range[1], 2 ) flip_horizontal = (np.random.random() < 0.5) * self.horizontal_flip flip_vertical = (np.random.random() < 0.5) * self.vertical_flip channel_shift_intensity = None if self.channel_shift_range != 0: channel_shift_intensity = np.random.uniform( -self.channel_shift_range, self.channel_shift_range ) brightness = None if self.brightness_range is not None: brightness = np.random.uniform( self.brightness_range[0], self.brightness_range[1] ) transform_parameters = { "theta": theta, "tx": tx, "ty": ty, "shear": shear, "zx": zx, "zy": zy, "flip_horizontal": flip_horizontal, "flip_vertical": flip_vertical, "channel_shift_intensity": channel_shift_intensity, "brightness": brightness, } return transform_parameters

keras/preprocessing/image.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

image.py

get_random_transform

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def test_dynamic_event_by_http(workflow_start_regular_shared_serve):

python/ray/workflow/tests/test_http_events.py

ray

Python

659d25a3a9c4794db9dbe8f428ec587470b261b0

test_http_events.py

test_dynamic_event_by_http

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

[workflow] http_event_provider and accompanied listener (#26010) ### Why are these changes needed? This PR enhances workflow functionality to receive external events from a Serve based HTTP endpoint. A workflow can then consume events asynchronously as they arrive. ### Design Logic A `workflow.wait_for_event` node subscribes to the endpoint instantiated by a Ray Serve deployment of class `http_event_provider.HTTPEventProvider`. The subscription is made through a helper class `http_event_provider.HTTPListener`. `HTTPListener` implements the methods of `EventListener` to poll from and confirm event checkpointing to `HTTPEventProvider`, before `HTTPEventProvider`acknowledges success or error to the event submitter. ### Architecture Improvement The logic of this enhancement conforms with existing workflow runtime design.

def select_query(self, targets, from_stmt, where_stmt): query = f"SELECT {','.join([t.__str__() for t in targets])} FROM {from_stmt.parts[-1]}" if where_stmt: query += f" WHERE {str(where_stmt)}" result = self.run_native_query(query) return result #TODO: JOIN, SELECT INTO

mindsdb/integrations/postgres_handler/postgres_handler.py

mindsdb

Python

32edb0b1468a705d89af89ed2b3dca2a459dc23f

postgres_handler.py

select_query

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

Select query

def patch(url, data=None, **kwargs): r return request("patch", url, data=data, **kwargs)

pipenv/patched/pip/_vendor/requests/api.py

pipenv

Python

cd5a9683be69c86c8f3adcd13385a9bc5db198ec

api.py

patch

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

Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir.

def test_install_venv_project_directory(PipenvInstance): with PipenvInstance(chdir=True) as p: with temp_environ(), TemporaryDirectory( prefix="pipenv-", suffix="temp_workon_home" ) as workon_home: os.environ["WORKON_HOME"] = workon_home c = p.pipenv("install six") assert c.returncode == 0 venv_loc = None for line in c.stderr.splitlines(): if line.startswith("Virtualenv location:"): venv_loc = Path(line.split(":", 1)[-1].strip()) assert venv_loc is not None assert venv_loc.joinpath(".project").exists() @pytest.mark.cli @pytest.mark.deploy @pytest.mark.system

tests/integration/test_install_basic.py

@pytest.mark.cli @pytest.mark.deploy @pytest.mark.system

pipenv

Python

949ee95d6748e8777bed589f0d990aa4792b28f8

test_install_basic.py