Datasets:

semeru

/

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

like 0

wagtail/admin/tests/test_page_chooser.py

def test_type_eventpage_two_indexes(self): self.make_event_section("Other events") self.assertEqual( self.get_best_root({"page_type": "tests.EventPage"}), se

Reformat with black

test_type_eventpage_two_indexes

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_page_chooser.py

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

Python

def test_type_eventpage_two_indexes(self): self.make_event_section("Other events") self.assertEqual( self.get_best_root({"page_type": "tests.EventPage"}), self.home_page )

.venv/lib/python3.8/site-packages/pip/_internal/req/req_file.py

def parse(self, filename, constraint): # type: (

upd; format

parse

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

req_file.py

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

Python

def parse(self, filename, constraint): # type: (str, bool) -> Iterator[ParsedLine] yield from self._parse_and_recurse(filename, constraint)

keras/layers/preprocessing/preprocessing_utils.py

def sparse_bincount(inputs, depth, binary_output, dtype, count_weights=None): result = tf.sparse.bincount( inputs, weights=count_weights, minlength=depth, maxlength=depth, axis=-1, binary_output=binary_output, ) result = tf.cast(result, dtype) if inputs.shape.rank == 1: output_shape = (depth,) else: batch_size = tf.shape(result)[0] output_shape = (batch_size, depth) result = tf

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

sparse_bincount

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

preprocessing_utils.py

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

Python

def sparse_bincount(inputs, depth, binary_output, dtype, count_weights=None): result = tf.sparse.bincount( inputs, weights=count_weights, minlength=depth, maxlength=depth, axis=-1, binary_output=binary_output, ) result = tf.cast(result, dtype) if inputs.shape.rank == 1: output_shape = (depth,) else: batch_size = tf.shape(result)[0] output_shape = (batch_size, depth) result = tf.SparseTensor( indices=result.indices, values=result.values, dense_shape=output_shape ) return result

lib/serializer.py

def unmarshal(self, serialized_data): logger.debug("data type: %s", type(serialized_data)) try: retval = self._unmarshal(serialized_data) except Exception as err: msg

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

unmarshal

bad5025aea1adb9126580e14e064e6c99089243d

faceswap

serializer.py

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

Python

def unmarshal(self, serialized_data): logger.debug("data type: %s", type(serialized_data)) try: retval = self._unmarshal(serialized_data) except Exception as err: msg = f"Error unserializing data for type {type(serialized_data)}: {str(err)}" raise FaceswapError(msg) from err logger.debug("returned data type: %s", type(retval)) return retval

nuitka/freezer/IncludedDataFiles.py

def copyDataFiles(): for included_datafile in getIncludedDataFiles(): # TODO: directories should be resolved

Plugins: Massive cleanup of data file handling * Move data file handling out of standalone only, allowing support for other modes as well. * Attach logger and tags to data file objects.

copyDataFiles

abfb99b0a05dd76d2ecc6ebc20732a271857c6c8

Nuitka

IncludedDataFiles.py

https://github.com/Nuitka/Nuitka.git

Python

def copyDataFiles(): for included_datafile in getIncludedDataFiles(): # TODO: directories should be resolved to files. if ( not isinstance(included_datafile, (IncludedDataFile)) or included_datafile.needsCopy() ): _handleDataFile( included_datafile, )

mitmproxy/optmanager.py

def toggler(self, attr): if attr not in self._options: raise KeyErr

make it black!

toggler

b3587b52b25077f68116b9852b041d33e7fc6601

mitmproxy

optmanager.py

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

Python

def toggler(self, attr): if attr not in self._options: raise KeyError("No such option: %s" % attr) o = self._options[attr] if o.typespec != bool: raise ValueError("Toggler can only be used with boolean options")

src/prefect/testing/cli.py

def disable_terminal_wrapping(monkeypatch): monkeypatch.setattr( "prefect.cli.profile.console", rich.console.Console(soft_wrap=True) )

Continue moving objects to sensible locations

disable_terminal_wrapping

05b2cf58e0610cedcea27e4d8cb96ad95307a068

prefect

cli.py

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

Python

def disable_terminal_wrapping(monkeypatch): monkeypatch.setattr( "prefect.cli.profile.console", rich.console.Console(soft_wrap=True) )

sympy/combinatorics/permutations.py

def __add__(self, other): rank = (self.rank() + other) % self.cardinality rv = self.unrank_lex(self.size, rank) rv._rank = rank

Updated import locations

__add__

498015021131af4dbb07eb110e5badaba8250c7b

sympy

permutations.py

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

Python

def __add__(self, other): rank = (self.rank() + other) % self.cardinality rv = self.unrank_lex(self.size, rank) rv._rank = rank return rv

tests/handlers/test_presence.py

def test_set_presence_from_syncing_not_set(self): user_id = "@test:server" status_msg = "I'm here!" self._set_presencestate_with

Prevent a sync request from removing a user's busy presence status (#12213) In trying to use the MSC3026 busy presence status, the user's status would be set back to 'online' next time they synced. This change makes it so that syncing does not affect a user's presence status if it is currently set to 'busy': it must be removed through the presence API. The MSC defers to implementations on the behaviour of busy presence, so this ought to remain compatible with the MSC.

test_set_presence_from_syncing_not_set

73d8ded0b030a81e828c07bb134c08db67569e5d

synapse

test_presence.py

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

Python

def test_set_presence_from_syncing_not_set(self): user_id = "@test:server" status_msg = "I'm here!" self._set_presencestate_with_status_msg( user_id, PresenceState.UNAVAILABLE, status_msg ) self.get_success( self.presence_handler.user_syncing(user_id, False, PresenceState.ONLINE) ) state = self.get_success( self.presence_handler.get_state(UserID.from_string(user_id)) ) # we should still be unavailable self.assertEqual(state.state, PresenceState.UNAVAILABLE) # and status message should still be the same self.assertEqual(state.status_msg, status_msg)

tests/events/test_utils.py

def test_stringy_integers(self): input = { "a": "100", "b": { "foo": 99, "

Convert stringy power levels to integers on room upgrade (#12657)

test_stringy_integers

051a1c3f220938a0ea1a5b328c268bdb3d1ad592

synapse

test_utils.py

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

Python

def test_stringy_integers(self): input = { "a": "100", "b": { "foo": 99, "bar": "-98", }, "d": "0999", } output = copy_and_fixup_power_levels_contents(input) expected_output = { "a": 100, "b": { "foo": 99, "bar": -98, }, "d": 999, } self.assertEqual(output, expected_output)

erpnext/accounts/doctype/bank_reconciliation_tool/bank_reconciliation_tool.py

def get_ec_matching_query(bank_account, company, amount_condition): # get matching Expense Claim query mod

style: format code with black

get_ec_matching_query

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

bank_reconciliation_tool.py

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

Python

def get_ec_matching_query(bank_account, company, amount_condition): # get matching Expense Claim query mode_of_payments = [ x["parent"] for x in frappe.db.get_all( "Mode of Payment Account", filters={"default_account": bank_account}, fields=["parent"] ) ] mode_of_payments = "('" + "', '".join(mode_of_payments) + "' )" company_currency = get_company_currency(company) return f

modin/experimental/core/execution/native/implementations/hdk_on_native/partitioning/partition_manager.py

def run_exec_plan(cls, plan, index_cols, dtypes, columns): omniSession = DbWorker() # First step is to make sure all partitions are in

FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <Poolliver868@mail.ru> Signed-off-by: Andrey Pavlenko <andrey.a.pavlenko@gmail.com>

run_exec_plan

e5b1888cd932909e49194d58035da34b210b91c4

modin

partition_manager.py

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

Python

def run_exec_plan(cls, plan, index_cols, dtypes, columns): omniSession = DbWorker() # First step is to make sure all partitions are in HDK. frames = plan.collect_frames() for frame in frames: if frame._partitions.size != 1: raise NotImplementedError( "HdkOnNative engine doesn't suport partitioned frames" ) for p in frame._partitions.flatten(): if p.frame_id is None: obj = p.get() if isinstance(obj, (pandas.DataFrame, pandas.Series)): p.frame_id = omniSession.import_pandas_dataframe(obj) else: assert isinstance(obj, pyarrow.Table) p.frame_id = omniSession.import_arrow_table(obj) calcite_plan = CalciteBuilder().build(plan) calcite_json = CalciteSerializer().serialize(calcite_plan) cmd_prefix = "execute relalg " if DoUseCalcite.get(): cmd_prefix = "execute calcite " at = omniSession.executeRA(cmd_prefix + calcite_json) res = np.empty((1, 1), dtype=np.dtype(object)) # workaround for https://github.com/modin-project/modin/issues/1851 if DoUseCalcite.get(): at = at.rename_columns(["F_" + str(c) for c in columns]) res[0][0] = cls._partition_class.put_arrow(at) return res

jax/_src/dtypes.py

def to_numeric_dtype(dtype): dtype = np.dt

jax.numpy: improve support for boolean inputs

to_numeric_dtype

3f0619599499fc0751cd6181c04d50245ef5dcce

jax

dtypes.py

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

Python

def to_numeric_dtype(dtype): dtype = np.dtype(dtype) return np.dtype('int32') if dtype == np.dtype('bool') else dtype

tests/jobs/test_scheduler_job.py

def test_do_schedule_max_active_runs_task_removed(self, session, dag_maker): with dag_maker( dag_id='test_do_schedule_max_active_runs_task_removed', start_date=DEFAULT_DATE, schedule_interval='@once', max_active_runs=1, session=session, ): # Can't use EmptyOperator as that goes straight to success BashOperator(task_id='dummy1', bash_command='true') run1 = dag_maker.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=DEFAULT_DATE + timedelta(hours=1), state=State.RUNNING, ) self.scheduler_job = Schedule

Replace usage of `DummyOperator` with `EmptyOperator` (#22974) * Replace usage of `DummyOperator` with `EmptyOperator`

test_do_schedule_max_active_runs_task_removed

49e336ae0302b386a2f47269a6d13988382d975f

airflow

test_scheduler_job.py

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

Python

def test_do_schedule_max_active_runs_task_removed(self, session, dag_maker): with dag_maker( dag_id='test_do_schedule_max_active_runs_task_removed', start_date=DEFAULT_DATE, schedule_interval='@once', max_active_runs=1, session=session, ): # Can't use EmptyOperator as that goes straight to success BashOperator(task_id='dummy1', bash_command='true') run1 = dag_maker.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=DEFAULT_DATE + timedelta(hours=1), state=State.RUNNING, ) self.scheduler_job = SchedulerJob(subdir=os.devnull) self.scheduler_job.executor = MockExecutor(do_update=False) self.scheduler_job.processor_agent = mock.MagicMock(spec=DagFileProcessorAgent) num_queued = self.scheduler_job._do_scheduling(session) assert num_queued == 1 session.flush() ti = run1.task_instances[0] ti.refresh_from_db(session=session) assert ti.state == State.QUEUED

ppg_extractor/encoder/encoder.py

def forward(self, xs, masks): if isinstance(self.embed, (Conv2dSubsampling, VGG2L)): xs, masks = self.embed(xs, masks) else: xs = self.embed(xs) xs, masks = self.encoders(xs, masks) if isinstance(xs, tuple): xs = xs[0] if self.normalize_before:

Init ppg extractor and ppg2mel (#375) * Init ppg extractor and ppg2mel * add preprocess and training * FIx known issues * Update __init__.py Allow to gen audio * Fix length issue * Fix bug of preparing fid * Fix sample issues * Add UI usage of PPG-vc

forward

b617a87ee40ab384767a27335313c2c65ee094ec

MockingBird

encoder.py

https://github.com/babysor/MockingBird.git

Python

def forward(self, xs, masks): if isinstance(self.embed, (Conv2dSubsampling, VGG2L)): xs, masks = self.embed(xs, masks) else: xs = self.embed(xs) xs, masks = self.encoders(xs, masks) if isinstance(xs, tuple): xs = xs[0] if self.normalize_before: xs = self.after_norm(xs) return xs, masks

gamestonk_terminal/parent_classes.py

def save_class(self): if gtff.REMEMBER_CONTEXTS:

Remember Contexts (#1187) * Refacotred classes * Handling for new instance desired * Added feature flag * Converted all menu calls

save_class

9e671aeba98dacc69ecbbfec1f087aca3b139ee7

OpenBBTerminal

parent_classes.py

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

Python

def save_class(self): if gtff.REMEMBER_CONTEXTS: controllers[self.PATH] = self

salt/transport/zeromq.py

def _decode_messages(self, messages): messages_len = len(messages) # if it was one message, then its old style if messages_len == 1: payload = salt.payload.loads(messages[0]) # 2 includes a header which says who should do it elif messages_len == 2: message_target = salt.utils.stringutils.to_str(messages[0]) if ( self.opts.get("__role") != "syndic" and message_target not in ("broadcast", self.hexid) ) or ( self.opts.get("__role") == "syndic" and message_target not in ("broadcast", "syndic") ): log.debug("Publish received for not this minion: %s", message_target) raise salt.ext.tornado.gen.Return(None) payload = salt.payload.loads(messages[1]) else: raise Exception(

Refactor into transports and channels

_decode_messages

d4e6111086ff713eb6609dc6c98cec98aded2564

salt

zeromq.py

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

Python

def _decode_messages(self, messages): messages_len = len(messages) # if it was one message, then its old style if messages_len == 1: payload = salt.payload.loads(messages[0]) # 2 includes a header which says who should do it elif messages_len == 2: message_target = salt.utils.stringutils.to_str(messages[0]) if ( self.opts.get("__role") != "syndic" and message_target not in ("broadcast", self.hexid) ) or ( self.opts.get("__role") == "syndic" and message_target not in ("broadcast", "syndic") ): log.debug("Publish received for not this minion: %s", message_target) raise salt.ext.tornado.gen.Return(None) payload = salt.payload.loads(messages[1]) else: raise Exception( "Invalid number of messages ({}) in zeromq pubmessage from master".format( len(messages_len) ) ) # Yield control back to the caller. When the payload has been decoded, assign # the decoded payload to 'ret' and resume operation raise salt.ext.tornado.gen.Return(payload)

python/ray/tests/test_runtime_env.py

def test_get_release_wheel_url(): # This should be a commit for which wheels have al

[runtime env] Support python 3.10 for runtime_env conda (#30970) Signed-off-by: Archit Kulkarni <architkulkarni@users.noreply.github.com> conda environments are isolated, so when runtime_env sets up a conda environment it must download the Ray wheel into the conda environment. It must download the wheel that matches the current Python and Ray version running, otherwise there will be incompatibility issues between the workers that use this runtime_env and the other workers and Ray processes. This PR updates the wheel name format logic to support Python 3.10.

test_get_release_wheel_url

98fef7732852cdb3e9377cd87c1ee1085b894928

ray

test_runtime_env.py

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

Python

def test_get_release_wheel_url(): # This should be a commit for which wheels have already been built for # all platforms and python versions at # `s3://ray-wheels/releases/2.2.0/<commit>/`. test_commits = {"2.2.0": "b6af0887ee5f2e460202133791ad941a41f15beb"} for sys_platform in ["darwin", "linux", "win32"]: for py_version in ray_constants.RUNTIME_ENV_CONDA_PY_VERSIONS: for version, commit in test_commits.items(): if sys_platform == "win32" and py_version == (3, 6): # Windows wheels are not built for py3.6 anymore continue url = get_release_wheel_url(commit, sys_platform, version, py_version) assert requests.head(url).status_code == 200, url

tests/rest/admin/test_media.py

def test_keep_media_by_date(self) -> None: # timestamp before upload now_ms = self.clock.time_msec() server_and_media_id = self._create_media() self._access_media(server_and_media_id) channel = self.make_request( "POST", self.url + "?before_ts=" + str(now_ms), access_token=self.admin_user_tok, ) self.assertEqual(200, channel.code, msg=channel.json_body) self.assertEqual(0, channel.json_body["total"]) self._access_media(server_and_media_id) # timestamp after upload now_ms = sel

Use literals in place of `HTTPStatus` constants in tests (#13469)

test_keep_media_by_date

c97042f7eef3748e17c90e48a4122389a89c4735

synapse

test_media.py

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

Python

def test_keep_media_by_date(self) -> None: # timestamp before upload now_ms = self.clock.time_msec() server_and_media_id = self._create_media() self._access_media(server_and_media_id) channel = self.make_request( "POST", self.url + "?before_ts=" + str(now_ms), access_token=self.admin_user_tok, ) self.assertEqual(200, channel.code, msg=channel.json_body) self.assertEqual(0, channel.json_body["total"]) self._access_media(server_and_media_id) # timestamp after upload now_ms = self.clock.time_msec() channel = self.make_request( "POST", self.url + "?before_ts=" + str(now_ms), access_token=self.admin_user_tok, ) self.assertEqual(200, channel.code, msg=channel.json_body) self.assertEqual(1, channel.json_body["total"]) self.assertEqual( server_and_media_id.split("/")[1], channel.json_body["deleted_media"][0], ) self._access_media(server_and_media_id, False)

@pytest.mark.filterwarnings("ignore:Version 1 of dataset Australian is inactive") # TODO(1.4): remove this filterwarning decorator for `parser` @pytest.mark.filterwarnings("ignore:The default value of `parser` will change") @pytest.mark.parametrize( "params, err_msg", [ ( {"parser": "pandas"}, "Sparse ARFF datasets cannot be loaded with parser='pandas'", ), ( {"as_frame": True}, "Sparse ARFF datasets cannot be loaded with as_frame=True.", ), ( {"parser": "pandas", "as_frame": True}, "Sparse ARFF datasets cannot be loaded with as_frame=True.", ), ], )

sklearn/datasets/tests/test_openml.py

def test_fetch_openml_requires_pandas_in_future(monkeypatch): params = {"as_frame": False, "parser": "auto"} data_id = 1119 try: check_pandas_support("test_fetch_openml_requires_pandas") except ImportError: _monkey_patch_webbased_functions(monk

ENH improve ARFF parser using pandas (#21938) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@gmail.com> Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>

test_fetch_openml_requires_pandas_in_future

a47d569e670fd4102af37c3165c9b1ddf6fd3005

scikit-learn

test_openml.py

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

Python

def test_fetch_openml_requires_pandas_in_future(monkeypatch): params = {"as_frame": False, "parser": "auto"} data_id = 1119 try: check_pandas_support("test_fetch_openml_requires_pandas") except ImportError: _monkey_patch_webbased_functions(monkeypatch, data_id, True) warn_msg = ( "From version 1.4, `parser='auto'` with `as_frame=False` will use pandas" ) with pytest.warns(FutureWarning, match=warn_msg): fetch_openml(data_id=data_id, **params) else: raise SkipTest("This test requires pandas to not be installed.") @pytest.mark.filterwarnings("ignore:Version 1 of dataset Australian is inactive") # TODO(1.4): remove this filterwarning decorator for `parser` @pytest.mark.filterwarnings("ignore:The default value of `parser` will change") @pytest.mark.parametrize( "params, err_msg", [ ( {"parser": "pandas"}, "Sparse ARFF datasets cannot be loaded with parser='pandas'", ), ( {"as_frame": True}, "Sparse ARFF datasets cannot be loaded with as_frame=True.", ), ( {"parser": "pandas", "as_frame": True}, "Sparse ARFF datasets cannot be loaded with as_frame=True.", ), ], )

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

def get_connection(self, url, proxies=None): proxy = select_proxy(url, proxies) if proxy: proxy = prepend_scheme_if_needed(proxy, "http") proxy_url = parse_url(proxy) if not proxy_url.host: raise InvalidProxyURL( "Please check proxy URL. It is malformed " "and could be missing the host."

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

get_connection

cd5a9683be69c86c8f3adcd13385a9bc5db198ec

pipenv

adapters.py

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

Python

def get_connection(self, url, proxies=None): proxy = select_proxy(url, proxies) if proxy: proxy = prepend_scheme_if_needed(proxy, "http") proxy_url = parse_url(proxy) if not proxy_url.host: raise InvalidProxyURL( "Please check proxy URL. It is malformed " "and could be missing the host." ) proxy_manager = self.proxy_manager_for(proxy) conn = proxy_manager.connection_from_url(url) else: # Only scheme should be lower case parsed = urlparse(url) url = parsed.geturl() conn = self.poolmanager.connection_from_url(url) return conn

freqtrade/templates/FreqaiExampleStrategy.py

def feature_engineering_expand_all(self, dataframe, period, **kwargs): dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period) dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] dataframe["%-close-bb_lower-period"] = ( dataframe["close"] / dataframe["bb_lowerband-period"] ) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-relative_volume-period"] = ( dataframe["volume"] / dataframe["volume"].rolling(period).mean() ) return dataframe

improve doc, update test strats, change function names

feature_engineering_expand_all

c2936d551b8ad6ccf7b57e2ac6cb55d8550622cf

freqtrade

FreqaiExampleStrategy.py

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

Python

def feature_engineering_expand_all(self, dataframe, period, **kwargs): dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period) dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] dataframe["%-close-bb_lower-period"] = ( dataframe["close"] / dataframe["bb_lowerband-period"] ) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-relative_volume-period"] = ( dataframe["volume"] / dataframe["volume"].rolling(period).mean() ) return dataframe

django/core/handlers/asgi.py

def get_script_prefix(self, scope): if settings

Refs #33476 -- Reformatted code with Black.

get_script_prefix

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

asgi.py

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

Python

def get_script_prefix(self, scope): if settings.FORCE_SCRIPT_NAME: return settings.FORCE_SCRIPT_NAME return scope.get("root_path", "") or ""

tests/cli/test_storage_cli.py

def test_get_first_menu_and_fail(): part_one = f part_two = f command = ["storage", "create"] invoke_and_assert_in( command=command, desired_contents=(part_one, part_two), e

Update tests

test_get_first_menu_and_fail

11638691240b7595c0d02542af506a96d344ae8b

prefect

test_storage_cli.py

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

Python

def test_get_first_menu_and_fail(): part_one = f part_two = f command = ["storage", "create"] invoke_and_assert_in( command=command, desired_contents=(part_one, part_two), expected_code=1, user_input=f"{INVALID_OPTION}\n", )

networkx/algorithms/planarity.py

def check_planarity(G, counterexample=False): planarity_state = LRPlanarity(G) embedding = planarity_state.lr_planarity() if embedding is None: # graph is not planar if counterexample: return False, get_counterexample(G) else: return False, None else:

Improve documentation of PlanarEmbedding class (#5523) * Improve documentation of PlanarEmbedding * Fix type * Make suggested changes * rst formatting nits. * Update networkx/algorithms/planarity.py Co-authored-by: Dan Schult <dschult@colgate.edu> * Run black for formatting Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> Co-authored-by: Dan Schult <dschult@colgate.edu>

check_planarity

1af7d49d70869081e5cb64d17165652f1b26c57b

networkx

planarity.py

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

Python

def check_planarity(G, counterexample=False): planarity_state = LRPlanarity(G) embedding = planarity_state.lr_planarity() if embedding is None: # graph is not planar if counterexample: return False, get_counterexample(G) else: return False, None else: # graph is planar return True, embedding

airflow/providers/cncf/kubernetes/hooks/kubernetes.py

def _get_bool(val) -> Optional[bool]: if isinstance(val, bool): return val elif isinstance(val, str): if val.strip().lower() == 'true': return True

Use KubernetesHook to create api client in KubernetesPodOperator (#20578) Add support for k8s hook in KPO; use it always (even when no conn id); continue to consider the core k8s settings that KPO already takes into account but emit deprecation warning about them. KPO historically takes into account a few settings from core airflow cfg (e.g. verify ssl, tcp keepalive, context, config file, and in_cluster). So to use the hook to generate the client, somehow the hook has to take these settings into account. But we don't want the hook to consider these settings in general. So we read them in KPO and if necessary patch the hook and warn.

_get_bool

60eb9e106f5915398eafd6aa339ec710c102dc09

airflow

kubernetes.py

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

Python

def _get_bool(val) -> Optional[bool]: if isinstance(val, bool): return val elif isinstance(val, str): if val.strip().lower() == 'true': return True elif val.strip().lower() == 'false': return False return None

d2l/mxnet.py

def load_data_ptb(batch_size, max_window_size, num_noise_words): sentences = read_ptb() vocab = d2l.Vocab(sentences, min_freq=10) subsampled, counter = subsample(sentences, vocab) corpus = [vocab[line] for line in subsampled] all_centers, all_contexts = get_centers_and_contexts( corpus, max_window_size) all_negatives = get_negatives( all_contexts, vocab, counter, num_noise_words) dataset = gluon.data.ArrayDataset( all_centers, all_contexts, all_negatives) data_iter = gluon.data.DataLoader( dataset, batch_size, shuffle=True,batchify_fn=batc

[PaddlePaddle] Merge master into Paddle branch (#1186) * change 15.2 title in chinese version (#1109) change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘ * 修改部分语义表述 (#1105) * Update r0.17.5 (#1120) * Bump versions in installation * 94行typo: （“bert.mall”）->（“bert.small”） (#1129) * line 313: "bert.mall" -> "bert.small" (#1130) * fix: update language as native reader (#1114) * Fix the translation of "stride" (#1115) * Update index.md (#1118) 修改部分语义表述 * Update self-attention-and-positional-encoding.md (#1133) 依照本书的翻译习惯，将pooling翻译成汇聚 * maybe a comment false (#1149) * maybe a little false * maybe a little false * A minor bug in the rcnn section (Chinese edition) (#1148) * Update bert.md (#1137) 一个笔误 # 假设batch_size=2，num_pred_positions=3 # 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1] * Update calculus.md (#1135) * fix typo in git documentation (#1106) * fix: Update the Chinese translation in lr-scheduler.md (#1136) * Update lr-scheduler.md * Update chapter_optimization/lr-scheduler.md Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * fix translation for kaggle-house-price.md (#1107) * fix translation for kaggle-house-price.md * fix translation for kaggle-house-price.md Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com> * Update weight-decay.md (#1150) * Update weight-decay.md 关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解 关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。 并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释 解释为何会增加复杂性以及为何需要细粒度工具。 * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Fix a spelling error (#1161) * Update gru.md (#1152) The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state. 翻译错误 * Unify the function naming (#1113) Unify naming of the function 'init_xavier()'. * Update mlp-concise.md (#1166) * Update mlp-concise.md 语句不通顺 * Update environment.md 语序异常 * Update config.ini * fix the imprecise description (#1168) Co-authored-by: yuande <yuande> * fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175) * Fix some typos. (#1163) * Update batch-norm.md (#1170) fixing typos u->x in article * Update linear-regression.md (#1090) We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that 原译文把who也直接翻译出来了。 * Update mlp.md (#1117) * Update mlp.md 修改部分语义表述 * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Correct a translation error. (#1091) * Correct a translation error. * Update chapter_computer-vision/image-augmentation.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update aws.md (#1121) * Update aws.md * Update chapter_appendix-tools-for-deep-learning/aws.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update image-augmentation.md (#1093) * Update anchor.md (#1088) fix a minor issue in code * Update anchor.md * Update image-augmentation.md * fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087) * Avoid `torch.meshgrid` user warning (#1174) Avoids the following user warning: ```python ~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] ``` * bump to 2.0.0-beta1 * Update sequence.md * bump beta1 on readme * Add latex code block background to config * BLD: Bump python support version 3.9 (#1183) * BLD: Bump python support version 3.9 * Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4 * BLD: Bump torch and tensorflow * Update Jenkinsfile * Update chapter_installation/index.md * Update chapter_installation/index.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update config.ini * Update INFO.md * Update INFO.md * Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187) * resolve the conflicts * revise from publisher (#1089) * revise from publisher * d2l api * post_latex * revise from publisher * revise ch11 * Delete d2l-Copy1.bib * clear cache * rm d2lbook clear * debug anchor * keep original d2l doc Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> * 重复语句 (#1188) Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Improve expression for chapter_preliminaries/pandas.md (#1184) * Update pandas.md * Improve expression * Improve expression * Update chapter_preliminaries/pandas.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Improce expression for chapter_preliminaries/linear-algebra.md (#1185) * Improce expression * Improve code comments * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Fix multibox_detection bugs * Update d2l to 0.17.5 version * restore older version * Upgrade pandas * change to python3.8 * Test warning log * relocate warning log * test logs filtering * Update gru.md * Add DeprecationWarning filter * Test warning log * Update attention mechanisms & computational performance * Update multilayer perceptron& linear & convolution networks & computer vision * Update recurrent&optimition&nlp pretraining & nlp applications * ignore warnings * Update index.md * Update linear networks * Update multilayer perceptrons&deep learning computation * Update preliminaries * Check and Add warning filter * Update kaggle-cifar10.md * Update object-detection-dataset.md * Update ssd.md fcn.md * Update hybridize.md * Update hybridize.md Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com> Co-authored-by: zhou201505013 <39976863+zhou201505013@users.noreply.github.com> Co-authored-by: Xinwei Liu <xinzone@outlook.com> Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: hugo_han <57249629+HugoHann@users.noreply.github.com> Co-authored-by: gyro永不抽风 <1247006353@qq.com> Co-authored-by: CanChengZheng <zcc550169544@163.com> Co-authored-by: linlin <jajupmochi@gmail.com> Co-authored-by: iuk <liukun0104@gmail.com> Co-authored-by: yoos <49556860+liyunlongaaa@users.noreply.github.com> Co-authored-by: Mr. Justice Lawrence John Wargrave <65226618+RUCWargrave@users.noreply.github.com> Co-authored-by: Chiyuan Fu <fuchiyuan2019@outlook.com> Co-authored-by: Sunhuashan <48636870+Sunhuashan@users.noreply.github.com> Co-authored-by: Haiker Sun <haizhou.uestc2011@gmail.com> Co-authored-by: Ming Liu <akira.liu@njnu.edu.cn> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: silenceZheng66 <13754430639@163.com> Co-authored-by: Wenchao Yan <56541797+YWonchall@users.noreply.github.com> Co-authored-by: Kiki2049 <55939997+Kiki2049@users.noreply.github.com> Co-authored-by: Krahets <krahets@163.com> Co-authored-by: friedmainfunction <73703265+friedmainfunction@users.noreply.github.com> Co-authored-by: Jameson <miraclecome@gmail.com> Co-authored-by: P. Yao <12227516+YaoPengCN@users.noreply.github.com> Co-authored-by: Yulv-git <34329208+Yulv-git@users.noreply.github.com> Co-authored-by: Liu,Xiao <45966993+liuxiao916@users.noreply.github.com> Co-authored-by: YIN, Gang <1246410+yingang@users.noreply.github.com> Co-authored-by: Joe-HZ <58297431+Joe-HZ@users.noreply.github.com> Co-authored-by: lybloveyou <102609904+lybloveyou@users.noreply.github.com> Co-authored-by: VigourJiang <jiangfuqiang154@163.com> Co-authored-by: zxhd863943427 <74853597+zxhd863943427@users.noreply.github.com> Co-authored-by: LYF <27893441+liyufan@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> Co-authored-by: xiaotinghe <xiaotih@amazon.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Holly-Max <60691735+Holly-Max@users.noreply.github.com> Co-authored-by: HinGwenWoong <peterhuang0323@qq.com> Co-authored-by: Shuai Zhang <cheungdaven@gmail.com>

load_data_ptb

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

d2l-zh

mxnet.py

https://github.com/d2l-ai/d2l-zh.git

Python

def load_data_ptb(batch_size, max_window_size, num_noise_words): sentences = read_ptb() vocab = d2l.Vocab(sentences, min_freq=10) subsampled, counter = subsample(sentences, vocab) corpus = [vocab[line] for line in subsampled] all_centers, all_contexts = get_centers_and_contexts( corpus, max_window_size) all_negatives = get_negatives( all_contexts, vocab, counter, num_noise_words) dataset = gluon.data.ArrayDataset( all_centers, all_contexts, all_negatives) data_iter = gluon.data.DataLoader( dataset, batch_size, shuffle=True,batchify_fn=batchify, num_workers=d2l.get_dataloader_workers()) return data_iter, vocab d2l.DATA_HUB['glove.6b.50d'] = (d2l.DATA_URL + 'glove.6B.50d.zip', '0b8703943ccdb6eb788e6f091b8946e82231bc4d') d2l.DATA_HUB['glove.6b.100d'] = (d2l.DATA_URL + 'glove.6B.100d.zip', 'cd43bfb07e44e6f27cbcc7bc9ae3d80284fdaf5a') d2l.DATA_HUB['glove.42b.300d'] = (d2l.DATA_URL + 'glove.42B.300d.zip', 'b5116e234e9eb9076672cfeabf5469f3eec904fa') d2l.DATA_HUB['wiki.en'] = (d2l.DATA_URL + 'wiki.en.zip', 'c1816da3821ae9f43899be655002f6c723e91b88')

homeassistant/components/homekit/__init__.py

async def async_config_changed(self) -> None: assert self.driver is not None await self.hass.async_add_executor_job(self.driver.config_changed)

Add support for restoring HomeKit IIDs (#79913)

async_config_changed

3b33e0d832b238b40360383099391e2093ea05cb

core

__init__.py

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

Python

async def async_config_changed(self) -> None: assert self.driver is not None await self.hass.async_add_executor_job(self.driver.config_changed)

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

def setDebugActions(self, startAction, successAction, exceptionAction): self.debugActions = (startAction or _defaultStartDebug

upd; format

setDebugActions

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

pyparsing.py

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

Python

def setDebugActions(self, startAction, successAction, exceptionAction): self.debugActions = (startAction or _defaultStartDebugAction, successAction or _defaultSuccessDebugAction, exceptionAction or _defaultExceptionDebugAction) self.debug = True return self

erpnext/accounts/doctype/pricing_rule/utils.py

def get_qty_amount_data_for_cumulative(pr_doc, doc, items=None): if items is None: items = [] sum_qty, sum_amt = [0, 0] doctype = doc.get("parenttype") or doc.doctype date_field = ( "transaction_date" if frappe.get_meta(doctype).has_field("transaction_date") else "posting_date" ) child_doctype = "{0} Item".format(doct

style: format code with black

get_qty_amount_data_for_cumulative

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

utils.py

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

Python

def get_qty_amount_data_for_cumulative(pr_doc, doc, items=None): if items is None: items = [] sum_qty, sum_amt = [0, 0] doctype = doc.get("parenttype") or doc.doctype date_field = ( "transaction_date" if frappe.get_meta(doctype).has_field("transaction_date") else "posting_date" ) child_doctype = "{0} Item".format(doctype) apply_on = frappe.scrub(pr_doc.get("apply_on")) values = [pr_doc.valid_from, pr_doc.valid_upto] condition = "" if pr_doc.warehouse: warehouses = get_child_warehouses(pr_doc.warehouse) condition += .format( child_doc=child_doctype, warehouses=",".join(["%s"] * len(warehouses)) ) values.extend(warehouses) if items: condition = " and `tab{child_doc}`.{apply_on} in ({items})".format( child_doc=child_doctype, apply_on=apply_on, items=",".join(["%s"] * len(items)) ) values.extend(items) data_set = frappe.db.sql( .format( parent_doc=doctype, child_doc=child_doctype, condition=condition, date_field=date_field ), tuple(values), as_dict=1, ) for data in data_set: sum_qty += data.get("stock_qty") sum_amt += data.get("amount") return [sum_qty, sum_amt]

scipy/sparse/linalg/_isolve/utils.py

def make_system(A, M, x0, b): A_ = A A = aslinearoperator(A) if A.shape[0] != A.shape[1]: raise ValueError(f'expected square matrix, but got shape={(A.shape,)}') N = A.shape[0] b = asanyarray(b) if not (b.shape == (N,1) or b.shape == (N,)): raise ValueError(f'shapes of A {A.shape} and b {b.shape} are ' 'incompatib

MAINT: sparse.linalg: Remove unnecessary operations

make_system

5628849933f1ba002f34b88b4d3af24f68008b39

scipy

utils.py

https://github.com/scipy/scipy.git

Python

def make_system(A, M, x0, b): A_ = A A = aslinearoperator(A) if A.shape[0] != A.shape[1]: raise ValueError(f'expected square matrix, but got shape={(A.shape,)}') N = A.shape[0] b = asanyarray(b) if not (b.shape == (N,1) or b.shape == (N,)): raise ValueError(f'shapes of A {A.shape} and b {b.shape} are ' 'incompatible') if b.dtype.char not in 'fdFD': b = b.astype('d') # upcast non-FP types to double

networkx/generators/small.py

def dodecahedral_graph(create_using=None): G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using) G.name = "Dodecahedral Graph" re

Docstrings for the small.py module (#5240) * added description for the first 5 small graphs * modified descriptions based on comment and added description for two more functions * added doctrings to all the functions * Minor touchups. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

dodecahedral_graph

dec723f072eb997a497a159dbe8674cd39999ee9

networkx

small.py

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

Python

def dodecahedral_graph(create_using=None): G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using) G.name = "Dodecahedral Graph" return G

homeassistant/helpers/template.py

def arc_tangent(value, default=_SENTINEL): try: return math.atan(float(value)) except (ValueError, TypeError): if default is _SENTINEL: ra

Fail template functions when no default specified (#71687)

arc_tangent

4885331509eeffe50f42d76b234996467b06170f

core

template.py

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

Python

def arc_tangent(value, default=_SENTINEL): try: return math.atan(float(value)) except (ValueError, TypeError): if default is _SENTINEL: raise_no_default("atan", value) return default

keras/utils/metrics_utils.py

def sparse_top_k_categorical_matches(y_true, y_pred, k=5): reshape_matches = False y_true = tf.convert_to_tensor(y_true) y_pred = tf.convert_to_tensor(y_pred) y_true_rank = y_true.shape.ndims y_pred_rank = y_pred.shape.ndims y_true_org_shape = tf.shape(y_true) # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,) if (y_true_rank is not None) and (y_pred_rank is not None): if y_pred_rank > 2: y_pred = tf.reshape(y_pred, [-1, y_pred.shape[-1]]) if y_true_rank > 1: reshape_matches = True y_true = tf.reshape(y_true, [-1]) matches = tf.cast( tf.math.in_top_k( predictions=y_pred, ta

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

sparse_top_k_categorical_matches

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

metrics_utils.py

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

Python

def sparse_top_k_categorical_matches(y_true, y_pred, k=5): reshape_matches = False y_true = tf.convert_to_tensor(y_true) y_pred = tf.convert_to_tensor(y_pred) y_true_rank = y_true.shape.ndims y_pred_rank = y_pred.shape.ndims y_true_org_shape = tf.shape(y_true) # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,) if (y_true_rank is not None) and (y_pred_rank is not None): if y_pred_rank > 2: y_pred = tf.reshape(y_pred, [-1, y_pred.shape[-1]]) if y_true_rank > 1: reshape_matches = True y_true = tf.reshape(y_true, [-1]) matches = tf.cast( tf.math.in_top_k( predictions=y_pred, targets=tf.cast(y_true, "int32"), k=k ), dtype=backend.floatx(), ) # returned matches is expected to have same shape as y_true input if reshape_matches: return tf.reshape(matches, shape=y_true_org_shape) return matches

@frappe.whitelist() @frappe.validate_and_sanitize_search_inputs

erpnext/controllers/queries.py

def tax_account_query(doctype, txt, searchfield, start, page_len, filters): company_currency = erpnext.get_company_currency(filters.get("company")) def get_accounts(with_account_type_filter): account_type_condition = "" if with_account_type_filter: account_type_condition = "AND account_type in %(account_types)s" accounts = frappe.db.sql( .format( account_type_condition=account_type_condition, searchfield=searchfield, mcond=get_match_cond(doctype), ), dict( account_types=filters.get("account_type"), company=filters.get("company"), disabled=filters.get("disabled", 0), currency=company_currency, txt="%{}%".format(txt), offset=start, limit=page_len, ), ) return accounts tax_accounts = get_accounts(True) if not tax_accounts: tax_accounts = get_accounts(False) return tax_accounts

fix: user can select disabled accounts in taxes table

tax_account_query

a1e3ae8869194a487acccc706a381db74c4aa1ff

erpnext

queries.py

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

Python

def tax_account_query(doctype, txt, searchfield, start, page_len, filters): company_currency = erpnext.get_company_currency(filters.get("company")) def get_accounts(with_account_type_filter): account_type_condition = "" if with_account_type_filter: account_type_condition = "AND account_type in %(account_types)s" accounts = frappe.db.sql( .format( account_type_condition=account_type_condition, searchfield=searchfield, mcond=get_match_cond(doctype), ), dict( account_types=filters.get("account_type"), company=filters.get("company"), disabled=filters.get("disabled", 0), currency=company_currency, txt="%{}%".format(txt), offset=start, limit=page_len, ), ) return accounts tax_accounts = get_accounts(True) if not tax_accounts: tax_accounts = get_accounts(False) return tax_accounts @frappe.whitelist() @frappe.validate_and_sanitize_search_inputs

wagtail/images/tests/test_admin_views.py

def test_add_post_duplicate_choose_permission(self): # Create group with access to admin and add permission. bakers_group = Group.objects.create(name="Bakers") access_admin_perm = Permission.objects.get( content_type__app_label="wagtailadmin", codename="access_admin" ) bakers_group.permissions.add(access_admin_perm) # Create the "Bakery" Collection and grant "add" permission to the Bakers group. root = Collection.objects.get(id=get_root_collection_id()) bak

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

test_add_post_duplicate_choose_permission

c136f461bc052cef362991458e1bd1fca37a3da9

wagtail

test_admin_views.py

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

Python

def test_add_post_duplicate_choose_permission(self): # Create group with access to admin and add permission. bakers_group = Group.objects.create(name="Bakers") access_admin_perm = Permission.objects.get( content_type__app_label="wagtailadmin", codename="access_admin" ) bakers_group.permissions.add(access_admin_perm) # Create the "Bakery" Collection and grant "add" permission to the Bakers group. root = Collection.objects.get(id=get_root_collection_id()) bakery_collection = root.add_child(instance=Collection(name="Bakery")) GroupCollectionPermission.objects.create( group=bakers_group, collection=bakery_collection, permission=Permission.objects.get( content_type__app_label="wagtailimages", codename="add_image" ), )

@proxy_napalm_wrap

salt/modules/napalm_mod.py

def netmiko_commands(*commands, **kwargs): conn = _netmiko_conn(**kwargs) ret = [] for cmd in commands: ret.append(conn.send_command(cmd))

Deprecated netmiko_conn and pyeapi_conn in napalm_mod.py as these function should not be called from the CLI

netmiko_commands

d8305bfaa7b98d898f5963b01ca75f277c266322

salt

napalm_mod.py

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

Python

def netmiko_commands(*commands, **kwargs): conn = _netmiko_conn(**kwargs) ret = [] for cmd in commands: ret.append(conn.send_command(cmd)) return ret @proxy_napalm_wrap

plugins/train/model/original.py

def decoder(self, side): input_ = Input(shape=(8, 8, 512)) var_x = input_ var_x = UpscaleBlock(256, activation="leakyrelu")(var_x) var_x = UpscaleBlock(128, activation="leakyrelu")(var_x) var_x = UpscaleBlock(64, activation="leakyrelu")(var_x) var_x = Conv2DOutput(3, 5, name=f"face_out_{side}")(var_x) outputs = [var_x] if self.learn_mask: var_y = input_ var_y = UpscaleBlock(256, activation="leakyrelu")(var_y) var_y = UpscaleBlock(128, activation="leakyrelu")(var_y) var_y = UpscaleBlock(64, activation="leakyrelu")(var_y) var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y) outputs.append(var_y) return KerasModel(input_, outputs=outputs, name=f"dec

Update all Keras Imports to be conditional (#1214) * Remove custom keras importer * first round keras imports fix * launcher.py: Remove KerasFinder references * 2nd round keras imports update (lib and extract) * 3rd round keras imports update (train) * remove KerasFinder from tests * 4th round keras imports update (tests)

decoder

aa39234538a8f83e6aa2b60b8275a570e8876ac2

faceswap

original.py

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

Python

def decoder(self, side): input_ = Input(shape=(8, 8, 512)) var_x = input_ var_x = UpscaleBlock(256, activation="leakyrelu")(var_x) var_x = UpscaleBlock(128, activation="leakyrelu")(var_x) var_x = UpscaleBlock(64, activation="leakyrelu")(var_x) var_x = Conv2DOutput(3, 5, name=f"face_out_{side}")(var_x) outputs = [var_x] if self.learn_mask: var_y = input_ var_y = UpscaleBlock(256, activation="leakyrelu")(var_y) var_y = UpscaleBlock(128, activation="leakyrelu")(var_y) var_y = UpscaleBlock(64, activation="leakyrelu")(var_y) var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y) outputs.append(var_y) return KerasModel(input_, outputs=outputs, name=f"decoder_{side}")

zerver/tests/test_subs.py

def test_users_getting_add_peer_event(self) -> None: streams_to_sub = ["multi_user_stream"] othello = self.example_user("othello") cordelia = self.example_user("cordelia") iago = self.example_user("iago") orig_user_ids_to_subscribe = [self.test_user.id, othello.id] self.common_subscribe_to_streams( self.test_user, streams_to_sub, dict(principals=orjson.dumps(orig_user_ids_to_subscribe).decode()), ) new_user_ids_to_subscribe = [iago.id, cordelia.id] events: List[Mapping[str, Any]] = [] with self.tornado_redirected_to_list(events, expected_num_events=5): self.common_subscribe_to_streams( self.test_user, streams_to_sub, dict(principals=orjson.dumps(new_user_ids_to_subscribe).decode()), ) add_peer_events = [event for event in events if event["event"].get("op") == "peer_add"] (add_peer_event,) = add_peer_events self.assertEqual(add_peer_event["event"]["type"], "subscription") self.a

Correctly hyphenate “non-”. Signed-off-by: Anders Kaseorg <anders@zulip.com>

test_users_getting_add_peer_event

6331a314d464f9c49a612023a5969e5d7b8e00a0

zulip

test_subs.py

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

Python

def test_users_getting_add_peer_event(self) -> None: streams_to_sub = ["multi_user_stream"] othello = self.example_user("othello") cordelia = self.example_user("cordelia") iago = self.example_user("iago") orig_user_ids_to_subscribe = [self.test_user.id, othello.id] self.common_subscribe_to_streams( self.test_user, streams_to_sub, dict(principals=orjson.dumps(orig_user_ids_to_subscribe).decode()), ) new_user_ids_to_subscribe = [iago.id, cordelia.id] events: List[Mapping[str, Any]] = [] with self.tornado_redirected_to_list(events, expected_num_events=5): self.common_subscribe_to_streams( self.test_user, streams_to_sub, dict(principals=orjson.dumps(new_user_ids_to_subscribe).decode()), ) add_peer_events = [event for event in events if event["event"].get("op") == "peer_add"] (add_peer_event,) = add_peer_events self.assertEqual(add_peer_event["event"]["type"], "subscription") self.assertEqual(add_peer_event["event"]["op"], "peer_add") event_sent_to_ids = add_peer_event["users"] for user_id in new_user_ids_to_subscribe: # Make sure new users subscribed to stream is not in # peer_add event recipient list self.assertNotIn(user_id, event_sent_to_ids) for old_user in orig_user_ids_to_subscribe: # Check non-new users are in peer_add event recipient list. self.assertIn(old_user, event_sent_to_ids)

d2l/mxnet.py

def show_trace_2d(f, results): d2l.set_figsize() d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e') x1, x2 = d2

[PaddlePaddle] Merge master into Paddle branch (#1186) * change 15.2 title in chinese version (#1109) change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘ * 修改部分语义表述 (#1105) * Update r0.17.5 (#1120) * Bump versions in installation * 94行typo: （“bert.mall”）->（“bert.small”） (#1129) * line 313: "bert.mall" -> "bert.small" (#1130) * fix: update language as native reader (#1114) * Fix the translation of "stride" (#1115) * Update index.md (#1118) 修改部分语义表述 * Update self-attention-and-positional-encoding.md (#1133) 依照本书的翻译习惯，将pooling翻译成汇聚 * maybe a comment false (#1149) * maybe a little false * maybe a little false * A minor bug in the rcnn section (Chinese edition) (#1148) * Update bert.md (#1137) 一个笔误 # 假设batch_size=2，num_pred_positions=3 # 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1] * Update calculus.md (#1135) * fix typo in git documentation (#1106) * fix: Update the Chinese translation in lr-scheduler.md (#1136) * Update lr-scheduler.md * Update chapter_optimization/lr-scheduler.md Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * fix translation for kaggle-house-price.md (#1107) * fix translation for kaggle-house-price.md * fix translation for kaggle-house-price.md Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com> * Update weight-decay.md (#1150) * Update weight-decay.md 关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解 关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。 并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释 解释为何会增加复杂性以及为何需要细粒度工具。 * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Fix a spelling error (#1161) * Update gru.md (#1152) The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state. 翻译错误 * Unify the function naming (#1113) Unify naming of the function 'init_xavier()'. * Update mlp-concise.md (#1166) * Update mlp-concise.md 语句不通顺 * Update environment.md 语序异常 * Update config.ini * fix the imprecise description (#1168) Co-authored-by: yuande <yuande> * fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175) * Fix some typos. (#1163) * Update batch-norm.md (#1170) fixing typos u->x in article * Update linear-regression.md (#1090) We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that 原译文把who也直接翻译出来了。 * Update mlp.md (#1117) * Update mlp.md 修改部分语义表述 * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Correct a translation error. (#1091) * Correct a translation error. * Update chapter_computer-vision/image-augmentation.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update aws.md (#1121) * Update aws.md * Update chapter_appendix-tools-for-deep-learning/aws.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update image-augmentation.md (#1093) * Update anchor.md (#1088) fix a minor issue in code * Update anchor.md * Update image-augmentation.md * fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087) * Avoid `torch.meshgrid` user warning (#1174) Avoids the following user warning: ```python ~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] ``` * bump to 2.0.0-beta1 * Update sequence.md * bump beta1 on readme * Add latex code block background to config * BLD: Bump python support version 3.9 (#1183) * BLD: Bump python support version 3.9 * Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4 * BLD: Bump torch and tensorflow * Update Jenkinsfile * Update chapter_installation/index.md * Update chapter_installation/index.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update config.ini * Update INFO.md * Update INFO.md * Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187) * resolve the conflicts * revise from publisher (#1089) * revise from publisher * d2l api * post_latex * revise from publisher * revise ch11 * Delete d2l-Copy1.bib * clear cache * rm d2lbook clear * debug anchor * keep original d2l doc Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> * 重复语句 (#1188) Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Improve expression for chapter_preliminaries/pandas.md (#1184) * Update pandas.md * Improve expression * Improve expression * Update chapter_preliminaries/pandas.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Improce expression for chapter_preliminaries/linear-algebra.md (#1185) * Improce expression * Improve code comments * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Fix multibox_detection bugs * Update d2l to 0.17.5 version * restore older version * Upgrade pandas * change to python3.8 * Test warning log * relocate warning log * test logs filtering * Update gru.md * Add DeprecationWarning filter * Test warning log * Update attention mechanisms & computational performance * Update multilayer perceptron& linear & convolution networks & computer vision * Update recurrent&optimition&nlp pretraining & nlp applications * ignore warnings * Update index.md * Update linear networks * Update multilayer perceptrons&deep learning computation * Update preliminaries * Check and Add warning filter * Update kaggle-cifar10.md * Update object-detection-dataset.md * Update ssd.md fcn.md * Update hybridize.md * Update hybridize.md Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com> Co-authored-by: zhou201505013 <39976863+zhou201505013@users.noreply.github.com> Co-authored-by: Xinwei Liu <xinzone@outlook.com> Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: hugo_han <57249629+HugoHann@users.noreply.github.com> Co-authored-by: gyro永不抽风 <1247006353@qq.com> Co-authored-by: CanChengZheng <zcc550169544@163.com> Co-authored-by: linlin <jajupmochi@gmail.com> Co-authored-by: iuk <liukun0104@gmail.com> Co-authored-by: yoos <49556860+liyunlongaaa@users.noreply.github.com> Co-authored-by: Mr. Justice Lawrence John Wargrave <65226618+RUCWargrave@users.noreply.github.com> Co-authored-by: Chiyuan Fu <fuchiyuan2019@outlook.com> Co-authored-by: Sunhuashan <48636870+Sunhuashan@users.noreply.github.com> Co-authored-by: Haiker Sun <haizhou.uestc2011@gmail.com> Co-authored-by: Ming Liu <akira.liu@njnu.edu.cn> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: silenceZheng66 <13754430639@163.com> Co-authored-by: Wenchao Yan <56541797+YWonchall@users.noreply.github.com> Co-authored-by: Kiki2049 <55939997+Kiki2049@users.noreply.github.com> Co-authored-by: Krahets <krahets@163.com> Co-authored-by: friedmainfunction <73703265+friedmainfunction@users.noreply.github.com> Co-authored-by: Jameson <miraclecome@gmail.com> Co-authored-by: P. Yao <12227516+YaoPengCN@users.noreply.github.com> Co-authored-by: Yulv-git <34329208+Yulv-git@users.noreply.github.com> Co-authored-by: Liu,Xiao <45966993+liuxiao916@users.noreply.github.com> Co-authored-by: YIN, Gang <1246410+yingang@users.noreply.github.com> Co-authored-by: Joe-HZ <58297431+Joe-HZ@users.noreply.github.com> Co-authored-by: lybloveyou <102609904+lybloveyou@users.noreply.github.com> Co-authored-by: VigourJiang <jiangfuqiang154@163.com> Co-authored-by: zxhd863943427 <74853597+zxhd863943427@users.noreply.github.com> Co-authored-by: LYF <27893441+liyufan@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> Co-authored-by: xiaotinghe <xiaotih@amazon.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Holly-Max <60691735+Holly-Max@users.noreply.github.com> Co-authored-by: HinGwenWoong <peterhuang0323@qq.com> Co-authored-by: Shuai Zhang <cheungdaven@gmail.com>

show_trace_2d

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

d2l-zh

mxnet.py

https://github.com/d2l-ai/d2l-zh.git

Python

def show_trace_2d(f, results): d2l.set_figsize() d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e') x1, x2 = d2l.meshgrid(d2l.arange(-5.5, 1.0, 0.1), d2l.arange(-3.0, 1.0, 0.1)) d2l.plt.contour(x1, x2, f(x1, x2), colors='#1f77b4') d2l.plt.xlabel('x1') d2l.plt.ylabel('x2') d2l.DATA_HUB['airfoil'] = (d2l.DATA_URL + 'airfoil_self_noise.dat', '76e5be1548fd8222e5074cf0faae75edff8cf93f')

@pytest.mark.parametrize("use_local", [True, False])

python/ray/util/sgd/tests/test_torch_2.py

def test_dataset(ray_start_4_cpus, use_local): model_creator = mlp_identity.model_creator optimizer_creator = mlp_identity.optimizer_creator dataset_creator = mlp_identity.dataset_creator DatasetOperator = TrainingOperator.from_creators( model_creator=model_creator, optimizer_creator=optimizer_creator, loss_creator=nn.MSELoss, ) trainer = TorchTrainer( training_operator_cls=DatasetOperator, use_local=use_local, num_workers=2, ) dataset = dataset_creator() for i in range(5): trainer.train(dataset=dataset, num_steps=100) x = mlp_identity.to_mat(0.5) prediction = float(trainer.get_model()(x)[0][0]) assert 0.4 <= prediction <= 0.6 trainer.shutdown(

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

test_dataset

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

test_torch_2.py

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

Python

def test_dataset(ray_start_4_cpus, use_local): model_creator = mlp_identity.model_creator optimizer_creator = mlp_identity.optimizer_creator dataset_creator = mlp_identity.dataset_creator DatasetOperator = TrainingOperator.from_creators( model_creator=model_creator, optimizer_creator=optimizer_creator, loss_creator=nn.MSELoss, ) trainer = TorchTrainer( training_operator_cls=DatasetOperator, use_local=use_local, num_workers=2, ) dataset = dataset_creator() for i in range(5): trainer.train(dataset=dataset, num_steps=100) x = mlp_identity.to_mat(0.5) prediction = float(trainer.get_model()(x)[0][0]) assert 0.4 <= prediction <= 0.6 trainer.shutdown() @pytest.mark.parametrize("use_local", [True, False])

parsing/dml_csr/loss/lovasz_softmax.py

def lovasz_softmax_flat(probas, labels, classes='present', weighted=None): if probas.numel() == 0: # only void pixels, the gradients should be 0 return probas * 0. C = probas.size(1) losses = [] class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes for c in class_to_sum: fg = (labels == c).float() # foreground for class c if (classes is 'present' and fg.sum() == 0): continue if C == 1: if len(classes) > 1: raise ValueError('Sigmoid output possible only with 1 class') class_pred = probas[:, 0] else: class_pred = probas[:, c] errors = (Variable(fg) - class_pred).abs() errors_sorted, perm = torch.sort(errors, 0, descending=True) perm = perm.data fg_sorted = fg[perm] if weighted is not None: losses.append(wei

Create lovasz_softmax.py

lovasz_softmax_flat

db307ffb12d6ba1f8eaeeafd29ee6d4a3fd6fa97

insightface

lovasz_softmax.py

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

Python

def lovasz_softmax_flat(probas, labels, classes='present', weighted=None): if probas.numel() == 0: # only void pixels, the gradients should be 0 return probas * 0. C = probas.size(1) losses = [] class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes for c in class_to_sum: fg = (labels == c).float() # foreground for class c if (classes is 'present' and fg.sum() == 0): continue if C == 1: if len(classes) > 1: raise ValueError('Sigmoid output possible only with 1 class') class_pred = probas[:, 0] else: class_pred = probas[:, c] errors = (Variable(fg) - class_pred).abs() errors_sorted, perm = torch.sort(errors, 0, descending=True) perm = perm.data fg_sorted = fg[perm] if weighted is not None: losses.append(weighted[c]*torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted)))) else: losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted)))) return mean(losses)

jina/parsers/orchestrate/runtimes/distributed.py

def mixin_distributed_feature_parser(parser): gp = add_arg_group(parser, title='Distributed') gp.add_argument( '--quiet-remote-logs', action='store_true', default=False, help='Do not display the streaming of remote logs on local console', ) gp.add_argument( '--upload-files', type=str, nargs='*', metavar='FILE', help=, ) gp.add_argument(

refactor: rename pod to deployment (#4230) * refactor: rename pod to deployment * style: fix overload and cli autocomplete * fix: undo daemon mistake * refactor: leftover cleanup * fix: more test fixes * fix: more fixes * fix: more fixes * fix: more fixes * fix: more tests * fix: fix more tests * refactor: fix more tests * refactor: more tests fixes * refactor: rename pea to pod * refactor: adjust docs * refactor: complete pea renaming * refactor: more fixes * fix: pea_type in k8s yamls * fix: adjust pod args name * refactor: rename peapods parser folder * fix: da init Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>

mixin_distributed_feature_parser

13edc16d806fb5d77a6849551178ccc75937f25f

jina

distributed.py

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

Python

def mixin_distributed_feature_parser(parser): gp = add_arg_group(parser, title='Distributed') gp.add_argument( '--quiet-remote-logs', action='store_true', default=False, help='Do not display the streaming of remote logs on local console', ) gp.add_argument( '--upload-files', type=str, nargs='*', metavar='FILE', help=, ) gp.add_argument( '--disable-remote', action='store_true', default=False, help='If set, remote pod invocation is avoided. This is used by pods created by JinaD' if _SHOW_ALL_ARGS else argparse.SUPPRESS, )

PyInstaller/depend/analysis.py

def get_bootstrap_modules(): # Import 'struct' modules to get real paths to module file names. mod_struct = __import__('struct') # Basic modules necessary for the bootstrap process. loader_mods = TOC() loaderpath = os.path.join(HOMEPATH, 'PyInstaller', 'loader') # On some platforms (Windows, Debian/Ubuntu) '_struct' and zlib modules are built-in modules (linked statically) # and thus does not have attribute __file__. 'struct' module is required for reading Python bytecode from # executable. 'zlib' is required to decompress this bytecode. for mod_name in ['_struct', 'zlib']: mod = __import__(mod_name) # C extension. if hasattr(mod, '__file__'): mod_file = os.path.abspath(mod.__file__) if os.path.basename(os.path.dirname(mod_file)) == 'lib-dynload': # Divert extensions originating from python's lib-dynload directory, to match behavior of #5604. mod_name = os.path.join('lib-dynload', mod_name) loader_mods.append((mod_name, mod_file, 'EXTEN

utils: remove compile_py_files helper The only remaining use is in `PYZ.__init__`, and that can be replaced with a loop that uses the new `compile_pymodule` helper. This change, however, requires `get_boostrap_modules()` helper from `PyInstaller.depend˙ to return paths to source `.py` files instead of non-existing `.pyc` files (the old `compile_py_files` helper went to great lengths to convert these back to source file names...).

get_bootstrap_modules

83193a1897232e133966d15e30758a149de50407

pyinstaller

analysis.py

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

Python

def get_bootstrap_modules(): # Import 'struct' modules to get real paths to module file names. mod_struct = __import__('struct') # Basic modules necessary for the bootstrap process. loader_mods = TOC() loaderpath = os.path.join(HOMEPATH, 'PyInstaller', 'loader') # On some platforms (Windows, Debian/Ubuntu) '_struct' and zlib modules are built-in modules (linked statically) # and thus does not have attribute __file__. 'struct' module is required for reading Python bytecode from # executable. 'zlib' is required to decompress this bytecode. for mod_name in ['_struct', 'zlib']: mod = __import__(mod_name) # C extension. if hasattr(mod, '__file__'): mod_file = os.path.abspath(mod.__file__) if os.path.basename(os.path.dirname(mod_file)) == 'lib-dynload': # Divert extensions originating from python's lib-dynload directory, to match behavior of #5604. mod_name = os.path.join('lib-dynload', mod_name) loader_mods.append((mod_name, mod_file, 'EXTENSION')) # NOTE:These modules should be kept simple without any complicated dependencies. loader_mods += [ ('struct', os.path.abspath(mod_struct.__file__), 'PYMODULE'), ('pyimod01_os_path', os.path.join(loaderpath, 'pyimod01_os_path.py'), 'PYMODULE'), ('pyimod02_archive', os.path.join(loaderpath, 'pyimod02_archive.py'), 'PYMODULE'), ('pyimod03_importers', os.path.join(loaderpath, 'pyimod03_importers.py'), 'PYMODULE'), ('pyimod04_ctypes', os.path.join(loaderpath, 'pyimod04_ctypes.py'), 'PYMODULE'), ('pyiboot01_bootstrap', os.path.join(loaderpath, 'pyiboot01_bootstrap.py'), 'PYSOURCE'), ] return loader_mods

django/contrib/gis/db/backends/postgis/adapter.py

def getquoted(self):

Refs #33476 -- Reformatted code with Black.

getquoted

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

adapter.py

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

Python

def getquoted(self): if self.is_geometry: # Psycopg will figure out whether to use E'\\000' or '\000'. return b"%s(%s)" % ( b"ST_GeogFromWKB" if self.geography else b"ST_GeomFromEWKB", self._adapter.getquoted(), ) else: # For rasters, add explicit type cast to WKB string. return b"'%s'::raster" % self.ewkb.encode()

scripts/extract.py

def _set_skip_list(self) -> None: if self._skip_num == 1 and not self._alignments.data: logger.debug("No frames to be skipped") return skip_list = [] for idx, filename in enumerate(self._images.file_list): if idx % self._skip_num != 0: logger.trace("Adding image '%s' to skip list due to extract_every_n = %s", filename, self._skip_num) skip_list.append

bugfix: extract - stop progress bar from going over max value

_set_skip_list

0d23714875f81ddabdbe8f4e40bef6e5f29eeb19

faceswap

extract.py

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

Python

def _set_skip_list(self) -> None: if self._skip_num == 1 and not self._alignments.data: logger.debug("No frames to be skipped") return skip_list = [] for idx, filename in enumerate(self._images.file_list): if idx % self._skip_num != 0: logger.trace("Adding image '%s' to skip list due to extract_every_n = %s", filename, self._skip_num) skip_list.append(idx) # Items may be in the alignments file if skip-existing[-faces] is selected elif os.path.basename(filename) in self._alignments.data: self._existing_count += 1 logger.trace("Removing image: '%s' due to previously existing", filename) skip_list.append(idx) if self._existing_count != 0: logger.info("Skipping %s frames due to skip_existing/skip_existing_faces.", self._existing_count) logger.debug("Adding skip list: %s", skip_list) self._images.add_skip_list(skip_list)

tests/conftest.py

def caplog(caplog): config = setup_logging() for name, logg

Update logging setup to support incremental configuration (#7569)

caplog

8ac2498a0203d3ccb9070d30d7b3a0c475afab92

prefect

conftest.py

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

Python

def caplog(caplog): config = setup_logging() for name, logger_config in config["loggers"].items(): if not logger_config.get("propagate", True): logger = get_logger(name) logger.handlers.append(caplog.handler) yield caplog

tools/manual/faceviewer/viewport.py

def _show_mesh(self, mesh_ids, face_index, detected_face, top_left): state = "normal" if (self._tk_vars["selected_editor"].get() != "Mask" or self._optional_annotations["mesh"]) else "hidden" kwargs = dict(polygon=dict(fill="", width=2, outline=self._canvas.control_colors["Mesh"]), line=dict(fill=self._canvas.control_colors["Mesh"], width=2)) edi

lib.align updates: - alignments.py - Add typed dicts for imported alignments - Explicitly check for presence of thumb value in alignments dict - linting - detected_face.py - Typing - Linting - Legacy support for pre-aligned face - Update dependencies to new property names

_show_mesh

5e73437be47f2410439a3c6716de96354e6a0c94

faceswap

viewport.py

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

Python

def _show_mesh(self, mesh_ids, face_index, detected_face, top_left): state = "normal" if (self._tk_vars["selected_editor"].get() != "Mask" or self._optional_annotations["mesh"]) else "hidden" kwargs = dict(polygon=dict(fill="", width=2, outline=self._canvas.control_colors["Mesh"]), line=dict(fill=self._canvas.control_colors["Mesh"], width=2)) edited = (self._tk_vars["edited"].get() and self._tk_vars["selected_editor"].get() not in ("Mask", "View")) landmarks = self._viewport.get_landmarks(self.frame_index, face_index, detected_face, top_left, edited) for key, kwarg in kwargs.items(): for idx, mesh_id in enumerate(mesh_ids[key]): self._canvas.coords(mesh_id, *landmarks[key][idx].flatten()) self._canvas.itemconfig(mesh_id, state=state, **kwarg) self._canvas.addtag_withtag(f"active_mesh_{key}", mesh_id)

python/ray/serve/deployment_state.py

def _get_curr_status(self) -> Tuple[DeploymentStatusInfo, bool]: # TODO(edoakes): we could make this more efficient in steady-state by # having a "healthy" flag that gets flipped if an update or replica # failure happens. target_version = self._target_version target_replica_count = self._target_replicas all_running_replica_cnt = self._replicas.count(states=[ReplicaState.RUNNING]) running_at_target_version_replica_cnt = self._replicas.count( states=[ReplicaState.RUNNING], version=target_version ) failed_to_start_count = self._replica_constructor_retry_counter failed_to_start_threshold = min( MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_CO

[serve] Introduce DeploymentStatus, poll for statuses instead of using async goals (#22121)

_get_curr_status

48adb6f7bb335b28fb0fb0d1190bd6c5dfc8ddfa

ray

deployment_state.py

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

Python

def _get_curr_status(self) -> Tuple[DeploymentStatusInfo, bool]: # TODO(edoakes): we could make this more efficient in steady-state by # having a "healthy" flag that gets flipped if an update or replica # failure happens. target_version = self._target_version target_replica_count = self._target_replicas all_running_replica_cnt = self._replicas.count(states=[ReplicaState.RUNNING]) running_at_target_version_replica_cnt = self._replicas.count( states=[ReplicaState.RUNNING], version=target_version ) failed_to_start_count = self._replica_constructor_retry_counter failed_to_start_threshold = min( MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_COUNT, target_replica_count * 3 ) # Got to make a call to complete current deploy() goal after # start failure threshold reached, while we might still have # pending replicas in current goal. if ( failed_to_start_count >= failed_to_start_threshold and failed_to_start_threshold != 0 ): if running_at_target_version_replica_cnt > 0: # At least one RUNNING replica at target state, partial # success; We can stop tracking constructor failures and # leave it to the controller to fully scale to target # number of replicas and only return as completed once # reached target replica count self._replica_constructor_retry_counter = -1 else: return ( DeploymentStatusInfo( status=DeploymentStatus.FAILED, message=( "The Deployment constructor failed " f"{failed_to_start_count} times in a row. See " "logs for details." ), ), False, ) # If we have pending ops, the current goal is *not* ready. if ( self._replicas.count( states=[ ReplicaState.STARTING, ReplicaState.UPDATING, ReplicaState.RECOVERING, ReplicaState.STOPPING, ] ) == 0 ): # Check for deleting. if target_replica_count == 0 and all_running_replica_cnt == 0: return DeploymentStatusInfo(status=DeploymentStatus.UPDATING), True # Check for a non-zero number of deployments. elif target_replica_count == running_at_target_version_replica_cnt: return DeploymentStatusInfo(status=DeploymentStatus.RUNNING), False return ( DeploymentStatusInfo( status=DeploymentStatus.UPDATING, message=( f"Running replicas of target version: " f"{running_at_target_version_replica_cnt}, target " "replicas: {target_replica_count}" ), ), False, )

examples/model_interpretation/task/transformer.py

def generate_square_subsequent_mask(self, length): return paddle.tensor.triu( (paddle.ones( (lengt

Add NLP model interpretation (#1752) * upload NLP interpretation * fix problems and relocate project * remove abandoned picture * remove abandoned picture * fix dead link in README * fix dead link in README * fix code style problems * fix CR round 1 * remove .gitkeep files * fix code style * fix file encoding problem * fix code style * delete duplicated files due to directory rebuild * fix CR round 2 * fix code style * fix ernie tokenizer * fix code style * fix problem from CR round 1 * fix bugs * fix README * remove duplicated files * deal with diff of old and new tokenizer results * fix CR round 4 * fix code style * add missing dependence * fix broken import path * move some data file to cloud * MRC upper case to lower case Co-authored-by: Zeyu Chen <chenzeyu01@baidu.com> Co-authored-by: binlinquge <xxx> Co-authored-by: Guo Sheng <guosheng@baidu.com>

generate_square_subsequent_mask

93cae49c0c572b5c1ac972759140fbe924b0374d

PaddleNLP

transformer.py

https://github.com/PaddlePaddle/PaddleNLP.git

Python

def generate_square_subsequent_mask(self, length): return paddle.tensor.triu( (paddle.ones( (length, length), dtype=paddle.get_default_dtype()) * -np.inf), 1)

.venv/lib/python3.8/site-packages/pip/_internal/index/package_finder.py

def find_requirement(self, req, upgrade): # type: (InstallRequirement, bool) -> Optional[InstallationCandidate] hashes = req.hashes(trust_internet=False) best_candidate_result = self.find_best_candidate( req.name, specifier=req.specifier, hashes=hashes, ) best_candidate = best_candidate_result.best_candidate

upd; format

find_requirement

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

package_finder.py

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

Python

def find_requirement(self, req, upgrade): # type: (InstallRequirement, bool) -> Optional[InstallationCandidate] hashes = req.hashes(trust_internet=False) best_candidate_result = self.find_best_candidate( req.name, specifier=req.specifier, hashes=hashes, ) best_candidate = best_candidate_result.best_candidate installed_version = None # type: Optional[_BaseVersion] if req.satisfied_by is not None: installed_version = parse_version(req.satisfied_by.version)

pipenv/patched/notpip/_vendor/pygments/formatters/html.py

def wrap(self, source, outfile): if s

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

wrap

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

html.py

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

Python

def wrap(self, source, outfile): if self.wrapcode: return self._wrap_div(self._wrap_pre(self._wrap_code(source))) else: return self._wrap_div(self._wrap_pre(source))

@frappe.whitelist()

erpnext/education/api.py

def get_assessment_criteria(course): return frappe.get_all( "Course Assessment Criteria", fields=["assessment_criteria", "weightage"], filt

style: format code with black

get_assessment_criteria

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

api.py

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

Python

def get_assessment_criteria(course): return frappe.get_all( "Course Assessment Criteria", fields=["assessment_criteria", "weightage"], filters={"parent": course}, order_by="idx", ) @frappe.whitelist()

salt/modules/mount.py

def rm_filesystems(name, device, config="/etc/filesystems"): modified = False view_lines = [] if "AIX" not in __grains__["kernel"]: return modified criteria = _FileSystemsEntry(name=name, dev=device) try: fsys_filedict = _filesystems(config, False) for fsys_view in fsys_filedict.items(): try: if criteria.match(fsys_view): modified = True else: view_lines.append(fsys_view) except _FileSystemsEntry.ParseError: view_lines.append(fsys_view) except OSError as exc: raise CommandExecutionError("Couldn't read from {}: {}".format(config, exc)) if modified: try: with salt.utils.files.fopen(config, "wb") as ofile: for fsys_view in view_lines: entry = fsys_view[1] list_strgs = _FileSystemsEntry.dict_to_list_lines(entry) ofile.writelines(salt.utils.data.encode(list_strgs)) except OSError as exc: raise CommandExecutionError("Couldn't write to {}: {}".format(config, exc)) except Exception as exc: raise CommandExecutionError("rm_filesystems error exception {exc}") return modified

Convert Py 2'isms to Python 3, and add tests for set_filesystems on AIX

rm_filesystems

9354c15e0818715d055242d14b1308643a6918d7

salt

mount.py

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

Python

def rm_filesystems(name, device, config="/etc/filesystems"): modified = False view_lines = [] if "AIX" not in __grains__["kernel"]: return modified criteria = _FileSystemsEntry(name=name, dev=device) try: fsys_filedict = _filesystems(config, False) for fsys_view in fsys_filedict.items(): try: if criteria.match(fsys_view): modified = True else: view_lines.append(fsys_view) except _FileSystemsEntry.ParseError: view_lines.append(fsys_view) except OSError as exc: raise CommandExecutionError("Couldn't read from {}: {}".format(config, exc)) if modified: try: with salt.utils.files.fopen(config, "wb") as ofile: for fsys_view in view_lines: entry = fsys_view[1] list_strgs = _FileSystemsEntry.dict_to_list_lines(entry) ofile.writelines(salt.utils.data.encode(list_strgs)) except OSError as exc: raise CommandExecutionError("Couldn't write to {}: {}".format(config, exc)) except Exception as exc: raise CommandExecutionError("rm_filesystems error exception {exc}") return modified

networkx/generators/classic.py

def complete_graph(n, create_using=None): _, nodes = n G = empty_graph(nodes, create_using) if len(nodes) > 1: if G.is_directed(): edges = itertools.permutations(nodes, 2) else: edges = itertools.combinations(nodes, 2) G.add_edges_from(edges) return G

Adjust the usage of nodes_or_number decorator (#5599) * recorrect typo in decorators.py * Update tests to show troubles in current code * fix troubles with usage of nodes_or_number * fix typo * remove nodes_or_number where that makes sense * Reinclude nodes_or_numbers and add some tests for nonstandard usage * fix typowq * hopefully final tweaks (no behavior changes * Update test_classic.py Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com>

complete_graph

de1d00f20e0bc14f1cc911b3486e50225a8fa168

networkx

classic.py

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

Python

def complete_graph(n, create_using=None): _, nodes = n G = empty_graph(nodes, create_using) if len(nodes) > 1: if G.is_directed(): edges = itertools.permutations(nodes, 2) else: edges = itertools.combinations(nodes, 2) G.add_edges_from(edges) return G

sklearn/pipeline.py

def fit_predict(self, X, y=None, **fit_params): self._validate_params() fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) fit_params_last_step = fit_params_steps[self.steps[-1][0]] with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): y_pred = self.steps[-1][1].fit_predict(Xt, y, **fit_params_last_step) r

MAINT validate parameters of Pipeline (#25133)

fit_predict

754bd5245aa46b89a1d686a3326c2b853012ff3e

scikit-learn

pipeline.py

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

Python

def fit_predict(self, X, y=None, **fit_params): self._validate_params() fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) fit_params_last_step = fit_params_steps[self.steps[-1][0]] with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): y_pred = self.steps[-1][1].fit_predict(Xt, y, **fit_params_last_step) return y_pred

@pytest.mark.issue(5918) @pytest.mark.parametrize("entity_ruler_factory", ENTITY_RULERS)

spacy/tests/pipeline/test_entity_ruler.py

def test_issue4849(entity_ruler_factory): nlp = English() patterns = [ {"label": "PERSON", "pattern": "joe biden", "id": "joe-biden"}, {"label": "PERSON", "pattern": "bernie sanders", "id": "bernie-sanders"}, ] ruler = nlp.add_pipe( entity_ruler_factory, name="entity_ruler", config={"phrase_matcher_attr": "LOWER"}, ) ruler.add_patterns(patterns) text = # USING 1 PROCESS count_ents = 0 for doc in nlp.pipe([text], n_process=1): count_ents += len([ent for ent in doc.ents if ent.ent_id > 0]) asser

Add SpanRuler component (#9880) * Add SpanRuler component Add a `SpanRuler` component similar to `EntityRuler` that saves a list of matched spans to `Doc.spans[spans_key]`. The matches from the token and phrase matchers are deduplicated and sorted before assignment but are not otherwise filtered. * Update spacy/pipeline/span_ruler.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fix cast * Add self.key property * Use number of patterns as length * Remove patterns kwarg from init * Update spacy/tests/pipeline/test_span_ruler.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Add options for spans filter and setting to ents * Add `spans_filter` option as a registered function' * Make `spans_key` optional and if `None`, set to `doc.ents` instead of `doc.spans[spans_key]`. * Update and generalize tests * Add test for setting doc.ents, fix key property type * Fix typing * Allow independent doc.spans and doc.ents * If `spans_key` is set, set `doc.spans` with `spans_filter`. * If `annotate_ents` is set, set `doc.ents` with `ents_fitler`. * Use `util.filter_spans` by default as `ents_filter`. * Use a custom warning if the filter does not work for `doc.ents`. * Enable use of SpanC.id in Span * Support id in SpanRuler as Span.id * Update types * `id` can only be provided as string (already by `PatternType` definition) * Update all uses of Span.id/ent_id in Doc * Rename Span id kwarg to span_id * Update types and docs * Add ents filter to mimic EntityRuler overwrite_ents * Refactor `ents_filter` to take `entities, spans` args for more filtering options * Give registered filters more descriptive names * Allow registered `filter_spans` filter (`spacy.first_longest_spans_filter.v1`) to take any number of `Iterable[Span]` objects as args so it can be used for spans filter or ents filter * Implement future entity ruler as span ruler Implement a compatible `entity_ruler` as `future_entity_ruler` using `SpanRuler` as the underlying component: * Add `sort_key` and `sort_reverse` to allow the sorting behavior to be customized. (Necessary for the same sorting/filtering as in `EntityRuler`.) * Implement `overwrite_overlapping_ents_filter` and `preserve_existing_ents_filter` to support `EntityRuler.overwrite_ents` settings. * Add `remove_by_id` to support `EntityRuler.remove` functionality. * Refactor `entity_ruler` tests to parametrize all tests to test both `entity_ruler` and `future_entity_ruler` * Implement `SpanRuler.token_patterns` and `SpanRuler.phrase_patterns` properties. Additional changes: * Move all config settings to top-level attributes to avoid duplicating settings in the config vs. `span_ruler/cfg`. (Also avoids a lot of casting.) * Format * Fix filter make method name * Refactor to use same error for removing by label or ID * Also provide existing spans to spans filter * Support ids property * Remove token_patterns and phrase_patterns * Update docstrings * Add span ruler docs * Fix types * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Move sorting into filters * Check for all tokens in seen tokens in entity ruler filters * Remove registered sort key * Set Token.ent_id in a backwards-compatible way in Doc.set_ents * Remove sort options from API docs * Update docstrings * Rename entity ruler filters * Fix and parameterize scoring * Add id to Span API docs * Fix typo in API docs * Include explicit labeled=True for scorer Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

test_issue4849

a322d6d5f2f85c2da6cded4fcd6143d41b5a9e96

spaCy

test_entity_ruler.py

https://github.com/explosion/spaCy.git

Python

def test_issue4849(entity_ruler_factory): nlp = English() patterns = [ {"label": "PERSON", "pattern": "joe biden", "id": "joe-biden"}, {"label": "PERSON", "pattern": "bernie sanders", "id": "bernie-sanders"}, ] ruler = nlp.add_pipe( entity_ruler_factory, name="entity_ruler", config={"phrase_matcher_attr": "LOWER"}, ) ruler.add_patterns(patterns) text = # USING 1 PROCESS count_ents = 0 for doc in nlp.pipe([text], n_process=1): count_ents += len([ent for ent in doc.ents if ent.ent_id > 0]) assert count_ents == 2 # USING 2 PROCESSES if isinstance(get_current_ops, NumpyOps): count_ents = 0 for doc in nlp.pipe([text], n_process=2): count_ents += len([ent for ent in doc.ents if ent.ent_id > 0]) assert count_ents == 2 @pytest.mark.issue(5918) @pytest.mark.parametrize("entity_ruler_factory", ENTITY_RULERS)

netbox/netbox/api/viewsets/__init__.py

def get_serializer_context(self): context = super().get_serializer_context() if hasattr(self.queryset.model, 'custom_fields'): content_type = ContentType.objects.get_for_model(self.queryset.model) context.update({ 'custom_fields': content_type.custom_fields.all(),

Move CustomFieldModelViewSet functionality into NetBoxModelViewSet

get_serializer_context

bbdeae0ed9bcc06fb96ffa2970272e1a3447448c

netbox

__init__.py

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

Python

def get_serializer_context(self): context = super().get_serializer_context() if hasattr(self.queryset.model, 'custom_fields'): content_type = ContentType.objects.get_for_model(self.queryset.model) context.update({ 'custom_fields': content_type.custom_fields.all(), }) return context

python3.10.4/Lib/distutils/util.py

def execute (func, args, msg=None, verbose=0, dry_run=0): if msg is None: msg = "%s%r" % (func.__name__, args) if msg[-2:] == ',)': # correct for singleton tuple msg = msg[0:-2] + ')' log.info(msg) if not dry_run:

add python 3.10.4 for windows

execute

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

util.py

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

Python

def execute (func, args, msg=None, verbose=0, dry_run=0): if msg is None: msg = "%s%r" % (func.__name__, args) if msg[-2:] == ',)': # correct for singleton tuple msg = msg[0:-2] + ')' log.info(msg) if not dry_run: func(*args)

keras/feature_column/dense_features.py

def call(self, features, cols_to_output_tensors=None, training=None): if training is None: training = backend.learning_phase() if not isinstance(features, dict): raise ValueError( "We expected a dictionary here. Instead we got: ", features ) transformation_cache = ( tf.__internal__.feature_column.FeatureTransformationCache(features) ) output_tens

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

call

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

dense_features.py

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

Python

def call(self, features, cols_to_output_tensors=None, training=None): if training is None: training = backend.learning_phase() if not isinstance(features, dict): raise ValueError( "We expected a dictionary here. Instead we got: ", features ) transformation_cache = ( tf.__internal__.feature_column.FeatureTransformationCache(features) ) output_tensors = [] for column in self._feature_columns: with backend.name_scope(column.name): try: tensor = column.get_dense_tensor( transformation_cache, self._state_manager, training=training, ) except TypeError: tensor = column.get_dense_tensor( transformation_cache, self._state_manager ) processed_tensors = self._process_dense_tensor(column, tensor) if cols_to_output_tensors is not None: cols_to_output_tensors[column] = processed_tensors output_tensors.append(processed_tensors) return self._verify_and_concat_tensors(output_tensors)

pandas/compat/pickle_compat.py

def patch_pickle() -> Iterator[None]: orig_loads = pkl.loads try: setattr(pkl, "loads", loads) yield finally: setattr(pkl, "loads", orig_loads)

TYP: misc return type annotations (#47558)

patch_pickle

f538568afc2c76c2d738d32e3544cf9fe6742960

pandas

pickle_compat.py

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

Python

def patch_pickle() -> Iterator[None]: orig_loads = pkl.loads try: setattr(pkl, "loads", loads) yield finally: setattr(pkl, "loads", orig_loads)

dev/breeze/src/airflow_breeze/utils/run_utils.py

def get_filesystem_type(filepath): # We import it locally so that click autocomplete wor

Prepare Breeze2 for prime time :) (#22713) This is a review and clean-up for all the parameters and commands for Breeze2 in order to prepare it for being used by the contribugors. There are various small fixes here and there, removal of duplicated code, refactoring and moving code around as well as cleanup and review all the parameters used for all implemented commands. The parameters, default values and their behaviours were updated to match "new" life of Breeze rather than old one. Some improvements are made to the autocomplete and click help messages printed. Full list of choices is always displayed, parameters are groups according to their target audience, and they were sorted according to importance and frequency of use. Various messages have been colourised according to their meaning - warnings as yellow, errors as red and informational messages as bright_blue. The `dry-run` option has been added to just show what would have been run without actually running some potentially "write" commands (read commands are still executed) so that you can easily verify and manually copy and execute the commands with option to modify them before. The `dry_run` and `verbose` options are now used for all commands. The "main" command now runs "shell" by default similarly as the original Breeze. All "shortcut" parameters have been standardized - i.e common options (verbose/dry run/help) have one and all common flags that are likely to be used often have an assigned shortcute. The "stop" and "cleanup" command have been added as they are necessary for average user to complete the regular usage cycle. Documentation for all the important methods have been updated.

get_filesystem_type

4ffd4f09532fceb67675fce4c1f5cd383eff992e

airflow

run_utils.py

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

Python

def get_filesystem_type(filepath): # We import it locally so that click autocomplete works import psutil root_type = "unknown" for part in psutil.disk_partitions(): if part.mountpoint == '/': root_type = part.fstype continue if filepath.startswith(part.mountpoint): return part.fstype return root_type

networkx/algorithms/similarity.py

def panther_similarity(G, source, k=5, path_length=5, c=0.5, delta=0.1, eps=None): r import numpy as np num_nodes = G.number_of_nodes() if num_nodes < k: warnings.warn( f"Number of nodes is {num_nodes}, but requested k is {k}. " "Setting k to number of nodes." ) k = num_nodes # According to [1], they empirically determined # a good value for ``eps`` to be sqrt( 1 / |E| ) if eps is None: eps = np.sqrt(1.0 / G.number_of_edges()) inv_node_map = {name: index for index, name in enumerate(G.nodes)} node_map = np.array(G) # Calculate the sample size ``R`` for how many paths # to randomly generate t_choose_2 = math.comb(path_length, 2) sample_size = int((c / eps**2) * (np.log2(t_choose_2) + 1 + np.log(1 / delta))) index_map = {} _ = list( generate_random_paths( G, sample_size, path_length=path_length, index_map=index_map ) ) S = np.zeros(num_nodes) inv_sample_size = 1 / sample_size source_paths = set(index_map[source]) # Calculate the path similarities # between ``source`` (v) and ``node`` (v_j) # using our inverted index mapping of # vertices to paths for node, paths in index_map.items(): # Only consider paths where both # ``node`` and ``source`` are present common_paths = source_paths.intersection(paths) S[inv_node_map[node]] = len(common_paths) * inv_sample_size # Retrieve top ``k`` similar # Note: the below performed anywhere from 4-10x faster # (depending on input sizes) vs the equivalent ``np.argsort(S)[::-1]`` top_k_unsorted = np.argpartition(S, -k)[-k:] top_k_sorte

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

panther_similarity

f6755ffa00211b523c6c0bec5398bc6c3c43c8b1

networkx

similarity.py

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

Python

def panther_similarity(G, source, k=5, path_length=5, c=0.5, delta=0.1, eps=None): r import numpy as np num_nodes = G.number_of_nodes() if num_nodes < k: warnings.warn( f"Number of nodes is {num_nodes}, but requested k is {k}. " "Setting k to number of nodes." ) k = num_nodes # According to [1], they empirically determined # a good value for ``eps`` to be sqrt( 1 / |E| ) if eps is None: eps = np.sqrt(1.0 / G.number_of_edges()) inv_node_map = {name: index for index, name in enumerate(G.nodes)} node_map = np.array(G) # Calculate the sample size ``R`` for how many paths # to randomly generate t_choose_2 = math.comb(path_length, 2) sample_size = int((c / eps**2) * (np.log2(t_choose_2) + 1 + np.log(1 / delta))) index_map = {} _ = list( generate_random_paths( G, sample_size, path_length=path_length, index_map=index_map ) ) S = np.zeros(num_nodes) inv_sample_size = 1 / sample_size source_paths = set(index_map[source]) # Calculate the path similarities # between ``source`` (v) and ``node`` (v_j) # using our inverted index mapping of # vertices to paths for node, paths in index_map.items(): # Only consider paths where both # ``node`` and ``source`` are present common_paths = source_paths.intersection(paths) S[inv_node_map[node]] = len(common_paths) * inv_sample_size # Retrieve top ``k`` similar # Note: the below performed anywhere from 4-10x faster # (depending on input sizes) vs the equivalent ``np.argsort(S)[::-1]`` top_k_unsorted = np.argpartition(S, -k)[-k:] top_k_sorted = top_k_unsorted[np.argsort(S[top_k_unsorted])][::-1] # Add back the similarity scores top_k_sorted_names = map(lambda n: node_map[n], top_k_sorted) top_k_with_val = dict(zip(top_k_sorted_names, S[top_k_sorted])) # Remove the self-similarity top_k_with_val.pop(source, None) return top_k_with_val

TTS/tts/utils/text/phonemizers/base.py

def _phonemize_preprocess(self, text) -> Tuple[List[str], List]: text = text.strip() if self._keep_puncs: # a tuple (text, punctuation marks) return self._punctuator.strip_to_restore(text) return [self._punctuator.strip(text)], []

Fix BasePhonemizer

_phonemize_preprocess

ff7c3858389ba250f761d76592cb060ac6be05c0

TTS

base.py

https://github.com/coqui-ai/TTS.git

Python

def _phonemize_preprocess(self, text) -> Tuple[List[str], List]: text = text.strip() if self._keep_puncs: # a tuple (text, punctuation marks) return self._punctuator.strip_to_restore(text) return [self._punctuator.strip(text)], []

spacy/cli/_util.py

def ensure_pathy(path): from pathy import Pathy # noqa: F811 return Pathy.fluid(path)

Support local filesystem remotes for projects (#11762) * Support local filesystem remotes for projects * Fix support for local filesystem remotes for projects * Use `FluidPath` instead of `Pathy` to support both filesystem and remote paths * Create missing parent directories if required for local filesystem * Add a more general `_file_exists` method to support both `Pathy`, `Path`, and `smart_open`-compatible URLs * Add explicit `smart_open` dependency starting with support for `compression` flag * Update `pathy` dependency to exclude older versions that aren't compatible with required `smart_open` version * Update docs to refer to `Pathy` instead of `smart_open` for project remotes (technically you can still push to any `smart_open`-compatible path but you can't pull from them) * Add tests for local filesystem remotes * Update pathy for general BlobStat sorting * Add import * Remove _file_exists since only Pathy remotes are supported * Format CLI docs * Clean up merge

ensure_pathy

1ebe7db07c8dbb1a55dafb09131b1d08242b79c5

spaCy

_util.py

https://github.com/explosion/spaCy.git

Python

def ensure_pathy(path): from pathy import Pathy # noqa: F811 return Pathy.fluid(path)

django/contrib/admin/sites.py

def index(self, request, extra_context=None): app_list = self.get_app_list(request) context = { **self.each_context(request), "title": self.index_title, "subtitle": None, "app_list": app_list, **(extra_context or {}), }

Refs #33476 -- Reformatted code with Black.

index

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

sites.py

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

Python

def index(self, request, extra_context=None): app_list = self.get_app_list(request) context = { **self.each_context(request), "title": self.index_title, "subtitle": None, "app_list": app_list, **(extra_context or {}), } request.current_app = self.name return TemplateResponse( request, self.index_template or "admin/index.html", context )

docker/start.py

def generate_config_from_template(config_dir, config_path, environ, ownership): for v in ("SYNAPSE_SERVER_NAME", "SYNAPSE_REPORT_STATS"): if v not in environ: error( "Environment variable '%s' is mandatory when generating a config file." % (v,) ) # populate some params from data files (if they exist, else create new ones) environ = environ.copy() secrets = { "registration": "SYNAPSE_REGISTRATION_SHARED_SECRET", "macaroon": "SYNAPSE_MACAROON_SECRET_KEY", } for name, secret in secrets.items(): if secret not in environ: filename = "/data/%s.%s.key" % (environ["SYNAPSE_SERVER_NAME"], name) # if the file already exists, load in the existing value; otherwise, # generate a new secret and write

Poetry: use locked environment in Docker images (#12385)

generate_config_from_template

3a7e97c7ade17a47517aadc0e9e305a1894119ac

synapse

start.py

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

Python

def generate_config_from_template(config_dir, config_path, environ, ownership): for v in ("SYNAPSE_SERVER_NAME", "SYNAPSE_REPORT_STATS"): if v not in environ: error( "Environment variable '%s' is mandatory when generating a config file." % (v,) ) # populate some params from data files (if they exist, else create new ones) environ = environ.copy() secrets = { "registration": "SYNAPSE_REGISTRATION_SHARED_SECRET", "macaroon": "SYNAPSE_MACAROON_SECRET_KEY", } for name, secret in secrets.items(): if secret not in environ: filename = "/data/%s.%s.key" % (environ["SYNAPSE_SERVER_NAME"], name) # if the file already exists, load in the existing value; otherwise, # generate a new secret and write it to a file if os.path.exists(filename): log("Reading %s from %s" % (secret, filename)) with open(filename) as handle: value = handle.read() else: log("Generating a random secret for {}".format(secret)) value = codecs.encode(os.urandom(32), "hex").decode() with open(filename, "w") as handle: handle.write(value) environ[secret] = value environ["SYNAPSE_APPSERVICES"] = glob.glob("/data/appservices/*.yaml") if not os.path.exists(config_dir): os.mkdir(config_dir) # Convert SYNAPSE_NO_TLS to boolean if exists if "SYNAPSE_NO_TLS" in environ: tlsanswerstring = str.lower(environ["SYNAPSE_NO_TLS"]) if tlsanswerstring in ("true", "on", "1", "yes"): environ["SYNAPSE_NO_TLS"] = True else: if tlsanswerstring in ("false", "off", "0", "no"): environ["SYNAPSE_NO_TLS"] = False else: error( 'Environment variable "SYNAPSE_NO_TLS" found but value "' + tlsanswerstring + '" unrecognized; exiting.' ) if "SYNAPSE_LOG_CONFIG" not in environ: environ["SYNAPSE_LOG_CONFIG"] = config_dir + "/log.config" log("Generating synapse config file " + config_path) convert("/conf/homeserver.yaml", config_path, environ) log_config_file = environ["SYNAPSE_LOG_CONFIG"] log("Generating log config file " + log_config_file) convert("/conf/log.config", log_config_file, environ) # Hopefully we already have a signing key, but generate one if not. args = [ sys.executable, "-m", "synapse.app.homeserver", "--config-path", config_path, # tell synapse to put generated keys in /data rather than /compiled "--keys-directory", config_dir, "--generate-keys", ] if ownership is not None: log(f"Setting ownership on /data to {ownership}") subprocess.check_output(["chown", "-R", ownership, "/data"]) args = ["gosu", ownership] + args subprocess.check_output(args)

jax/_src/api_util.py

def donation_vector(donate_argnums, args, kwargs) -> Tuple[bool, ...]: res: List[bool] = [] for i, arg in enumerate(args): donate = bool(i in donate_argnums) res.extend((donate,) * tree_structur

Annotate tree_util

donation_vector

b742b04380ebe2e824403e603924ca505173bf7a

jax

api_util.py

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

Python

def donation_vector(donate_argnums, args, kwargs) -> Tuple[bool, ...]: res: List[bool] = [] for i, arg in enumerate(args): donate = bool(i in donate_argnums) res.extend((donate,) * tree_structure(arg).num_leaves) res.extend((False,) * tree_structure(kwargs).num_leaves) return tuple(res)

python3.10.4/Lib/asyncio/futures.py

def set_exception(self, exception): if self._state != _PENDING: raise exceptions.InvalidStateError(f'{self._state}: {self!r}') if isinstance(exception, type): exception = exception() if type(exception) is StopIteration: raise TypeError("StopIteration interacts badly with generators " "and cannot be raised into a Future") self._exception = exception

add python 3.10.4 for windows

set_exception

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

futures.py

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

Python

def set_exception(self, exception): if self._state != _PENDING: raise exceptions.InvalidStateError(f'{self._state}: {self!r}') if isinstance(exception, type): exception = exception() if type(exception) is StopIteration: raise TypeError("StopIteration interacts badly with generators " "and cannot be raised into a Future") self._exception = exception self._state = _FINISHED self.__schedule_callbacks() self.__log_traceback = True

django/contrib/gis/gdal/raster/band.py

def statistics(self, refresh=False, approximate=False): # Prepare array with arguments for capi function smin, smax

Refs #33476 -- Reformatted code with Black.

statistics

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

band.py

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

Python

def statistics(self, refresh=False, approximate=False): # Prepare array with arguments for capi function smin, smax, smean, sstd = c_double(), c_double(), c_double(), c_double() stats_args = [ self._ptr, c_int(approximate), byref(smin), byref(smax), byref(smean), byref(sstd), c_void_p(), c_void_p(), ] if refresh or self._stats_refresh: func = capi.compute_band_statistics else: # Add additional argument to force computation if there is no # existing PAM file to take the values from. force = True stats_args.insert(2, c_int(force)) func = capi.get_band_statistics # Computation of statistics fails for empty bands. try: func(*stats_args) result = smin.value, smax.value, smean.value, sstd.value except GDALException: result = (None, None, None, None) self._stats_refresh = False return result

tests/storage/databases/test_state_store.py

def test_smaller_request_deduplicated(self) -> None: req1 = ensureDeferred( self.state_datastore._get_state_for_group_using_inflight_cache( 42, StateFilter.from_types((("test.type", None),)) ) ) self.pump(by=0.1) # This should have gone to the database self.assertEqual(len(self.get_state_group_calls), 1) self.assertFalse(req1.called) req2 = ensureDeferred( self.state_datastore._get_state_for_group_using_inflight_cache( 42, StateFilter.from_types((("test.type", "b"),)) ) ) self.pump(by=0.1) # No more calls should have gone to the database, because the second # request was already in the i

Add more tests for in-flight state query duplication. (#12033)

test_smaller_request_deduplicated

546b9c9e648f5e2b25bb7c8350570787ff9befae

synapse

test_state_store.py

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

Python

def test_smaller_request_deduplicated(self) -> None: req1 = ensureDeferred( self.state_datastore._get_state_for_group_using_inflight_cache( 42, StateFilter.from_types((("test.type", None),)) ) ) self.pump(by=0.1) # This should have gone to the database self.assertEqual(len(self.get_state_group_calls), 1) self.assertFalse(req1.called) req2 = ensureDeferred( self.state_datastore._get_state_for_group_using_inflight_cache( 42, StateFilter.from_types((("test.type", "b"),)) ) ) self.pump(by=0.1) # No more calls should have gone to the database, because the second # request was already in the in-flight cache! self.assertEqual(len(self.get_state_group_calls), 1) self.assertFalse(req1.called) self.assertFalse(req2.called) groups, sf, d = self.get_state_group_calls[0] self.assertEqual(groups, (42,)) # The state filter is expanded internally for increased cache hit rate, # so we the database sees a wider state filter than requested. self.assertEqual(sf, ALL_NON_MEMBERS_STATE_FILTER) # Now we can complete the request self._complete_request_fake(groups, sf, d) self.assertEqual( self.get_success(req1), {("test.type", "a"): "AAA", ("test.type", "b"): "BBB"}, ) self.assertEqual(self.get_success(req2), {("test.type", "b"): "BBB"})

rllib/examples/simulators/sumo/marlenvironment.py

def get_observation(self, agent): speed = 0 distance = self._config["scenari

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

get_observation

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

marlenvironment.py

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

Python

def get_observation(self, agent): speed = 0 distance = self._config["scenario_config"]["misc"]["max_distance"] if agent in self.simulation.veh_subscriptions: speed = round( self.simulation.veh_subscriptions[agent][tc.VAR_SPEED] * MS_TO_KMH ) leader = self.simulation.veh_subscriptions[agent][tc.VAR_LEADER] if leader: # compatible with traci veh, dist = leader if veh: # compatible with libsumo distance = round(dist) ret = [speed, distance] logger.debug("Agent %s --> Obs: %s", agent, pformat(ret)) return ret

lib/matplotlib/colors.py

def register(self, name, color_list): if name in self._BUILTIN_COLOR_SEQUENCES: raise ValueError(f"{name!r} is a reserved name for a builtin " "color sequence") color_list = list(color_list) # force copy and coerce type to list for color in color_list: try: to_rgba(color) except ValueError: raise ValueError( f"{color!r} is not a valid color specification") self._color_sequences[name] = color_list

Add a registry for color sequences Color sequences are simply lists of colors, that we store by name in a registry. The registry is modelled similar to the ColormapRegistry to 1) support immutable builtin color sequences and 2) to return copies so that one cannot mess with the global definition of the color sequence through an obtained instance. For now, I've made the sequences used for `ListedColormap`s available as builtin sequences, but that's open for discussion. More usage documentation should be added in the color examples and/or tutorials, but I'll wait with that till after the general approval of the structure and API. One common use case will be ``` plt.rc_params['axes.prop_cycle'] = plt.cycler(color=plt.color_sequences['Pastel1') ``` Co-authored-by: Elliott Sales de Andrade <quantum.analyst@gmail.com>

register

0abe0ce2f2748d1d0383154d045da3609a4b871b

matplotlib

colors.py

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

Python

def register(self, name, color_list): if name in self._BUILTIN_COLOR_SEQUENCES: raise ValueError(f"{name!r} is a reserved name for a builtin " "color sequence") color_list = list(color_list) # force copy and coerce type to list for color in color_list: try: to_rgba(color) except ValueError: raise ValueError( f"{color!r} is not a valid color specification") self._color_sequences[name] = color_list

@pytest.mark.parametrize( "attributes, no_attributes, limit", [ ({"attr": True}, False, 5000), ({}, True, 5000), ({"attr": True}, False, 3), ({}, True, 3), ], )

tests/components/recorder/test_history.py

def test_get_states_no_attributes(hass_recorder): hass = hass_recorder() now, future, states = _setup_get_states(hass) for state in states: state.attributes = {} # Get states returns everything before POINT for all entities for state1, state2 in zip( states, sorted( history.get_states(hass, future, no_attributes=True), key=lambda state: state.entity_id, ), ): assert state1 == state2 # Get states returns everything before POINT for tested entities entities = [f"test.point_in_time_{i % 5}

Avoid selecting attributes in the history api when `no_attributes` is passed (#68352)

test_get_states_no_attributes

816695cc96c19110ccda10431d92160ea6064d32

core

test_history.py

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

Python

def test_get_states_no_attributes(hass_recorder): hass = hass_recorder() now, future, states = _setup_get_states(hass) for state in states: state.attributes = {} # Get states returns everything before POINT for all entities for state1, state2 in zip( states, sorted( history.get_states(hass, future, no_attributes=True), key=lambda state: state.entity_id, ), ): assert state1 == state2 # Get states returns everything before POINT for tested entities entities = [f"test.point_in_time_{i % 5}" for i in range(5)] for state1, state2 in zip( states, sorted( history.get_states(hass, future, entities, no_attributes=True), key=lambda state: state.entity_id, ), ): assert state1 == state2 # Test get_state here because we have a DB setup assert states[0] == history.get_state( hass, future, states[0].entity_id, no_attributes=True ) time_before_recorder_ran = now - timedelta(days=1000) assert history.get_states(hass, time_before_recorder_ran, no_attributes=True) == [] assert ( history.get_state(hass, time_before_recorder_ran, "demo.id", no_attributes=True) is None ) @pytest.mark.parametrize( "attributes, no_attributes, limit", [ ({"attr": True}, False, 5000), ({}, True, 5000), ({"attr": True}, False, 3), ({}, True, 3), ], )

python3.10.4/Lib/bdb.py

def set_until(self, frame, lineno=None): # the name "until" is borrowed from gdb if lineno is None: lineno = frame.f_lineno + 1 self._set_stopinfo(frame, frame, lineno)

add python 3.10.4 for windows

set_until

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

bdb.py

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

Python

def set_until(self, frame, lineno=None): # the name "until" is borrowed from gdb if lineno is None: lineno = frame.f_lineno + 1 self._set_stopinfo(frame, frame, lineno)

homeassistant/components/rest/switch.py

async def get_device_state(self, hass): websession = async_get_clientsession(hass, self._verify_ssl) rendered_headers = template.render_complex(self._headers, parse_res

Use _attr_is_on in rest (#81305)

get_device_state

fee3898f648d4fffdf9dbec748aab2410a0bd227

core

switch.py

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

Python

async def get_device_state(self, hass): websession = async_get_clientsession(hass, self._verify_ssl) rendered_headers = template.render_complex(self._headers, parse_result=False) rendered_params = template.render_complex(self._params)

python/ray/tests/test_client_reconnect.py

def reset_channel(self) -> None: if self.channel: self.

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

reset_channel

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

test_client_reconnect.py

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

Python

def reset_channel(self) -> None: if self.channel: self.channel.close() self.channel = grpc.insecure_channel(self.real_addr, options=GRPC_OPTIONS) grpc.channel_ready_future(self.channel) self.task_servicer.set_channel(self.channel) self.data_servicer.set_channel(self.channel) self.logs_servicer.set_channel(self.channel)

tests/end2end/fixtures/quteprocess.py

def wait_scroll_pos_changed(self, x=None, y=None): __tracebackhide__ = (lambda e: e.errisinstance(testprocess.WaitForTimeout)) if (x is None and y is not None) or (y is None and x is not None): raise ValueError("Either both x/y or neither must be given!") if x is None and y is None: point = 'Py*.QtCore.QPoint(*, *)' # not counting 0/0 here elif x == '0' and y == '0':

qt6 tests: Fix remaining PyQt5 references

wait_scroll_pos_changed

deb21acdebd77c6dc6d5fe4d8cad75e4ca074138

qutebrowser

quteprocess.py

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

Python

def wait_scroll_pos_changed(self, x=None, y=None): __tracebackhide__ = (lambda e: e.errisinstance(testprocess.WaitForTimeout)) if (x is None and y is not None) or (y is None and x is not None): raise ValueError("Either both x/y or neither must be given!") if x is None and y is None: point = 'Py*.QtCore.QPoint(*, *)' # not counting 0/0 here elif x == '0' and y == '0': point = 'Py*.QtCore.QPoint()' else: point = 'Py*.QtCore.QPoint({}, {})'.format(x, y) self.wait_for(category='webview', message='Scroll position changed to ' + point)

python/ray/data/tests/test_dataset_tfrecords.py

def test_write_tfrecords(ray_start_regular_shared, tmp_path): import tensorflow as tf # The dataset we will write to a .tfrecords file. ds = ray.data.from_items( [ # Row one. { "int_item": 1, "int_list": [2, 2, 3], "float_item": 1.0, "float_list": [2.0, 3.0, 4.0], "bytes_item": b"abc", "bytes_list": [b"abc", b"1234"], }, # Row two. { "int_item": 2, "int_list": [3, 3, 4], "float_item": 2.0, "float_list": [2.0, 2.0, 3.0], "bytes_item": b"def", "bytes_list": [b"def", b"1234"], }, ] ) # The corresponding tf.train.Example that we would expect to read # from this dataset. expected_records = [ # Record one (corresponding to row one). tf.train.Example( features=tf.train.Features( feature={ "int_item": tf.train.Feature( int64_list=tf.train.Int64List(value=[1]) ), "int_list": tf.train.Feature( int64_list=tf.train.Int64List(value=[2, 2, 3]) ), "float_item": tf.train.Feature( float_list=tf.train.FloatList(value=[1.0]) ), "float_l

[Datasets] Add writer for TFRecords. (#29448) This PR enables users to write TFRecords from datasets. In particular, the master branch already includes an API for reading TFRecords from datasets. Users have requested the ability to write these datasets back to TFRecords.

test_write_tfrecords

9fab504fe776f96fecf85e12ea006264cbe92f4a

ray

test_dataset_tfrecords.py

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

Python

def test_write_tfrecords(ray_start_regular_shared, tmp_path): import tensorflow as tf # The dataset we will write to a .tfrecords file. ds = ray.data.from_items( [ # Row one. { "int_item": 1, "int_list": [2, 2, 3], "float_item": 1.0, "float_list": [2.0, 3.0, 4.0], "bytes_item": b"abc", "bytes_list": [b"abc", b"1234"], }, # Row two. { "int_item": 2, "int_list": [3, 3, 4], "float_item": 2.0, "float_list": [2.0, 2.0, 3.0], "bytes_item": b"def", "bytes_list": [b"def", b"1234"], }, ] ) # The corresponding tf.train.Example that we would expect to read # from this dataset. expected_records = [ # Record one (corresponding to row one). tf.train.Example( features=tf.train.Features( feature={ "int_item": tf.train.Feature( int64_list=tf.train.Int64List(value=[1]) ), "int_list": tf.train.Feature( int64_list=tf.train.Int64List(value=[2, 2, 3]) ), "float_item": tf.train.Feature( float_list=tf.train.FloatList(value=[1.0]) ), "float_list": tf.train.Feature( float_list=tf.train.FloatList(value=[2.0, 3.0, 4.0]) ), "bytes_item": tf.train.Feature( bytes_list=tf.train.BytesList(value=[b"abc"]) ), "bytes_list": tf.train.Feature( bytes_list=tf.train.BytesList(value=[b"abc", b"1234"]) ), } ) ), # Record two (corresponding to row two). tf.train.Example( features=tf.train.Features( feature={ "int_item": tf.train.Feature( int64_list=tf.train.Int64List(value=[2]) ), "int_list": tf.train.Feature( int64_list=tf.train.Int64List(value=[3, 3, 4]) ), "float_item": tf.train.Feature( float_list=tf.train.FloatList(value=[2.0]) ), "float_list": tf.train.Feature( float_list=tf.train.FloatList(value=[2.0, 2.0, 3.0]) ), "bytes_item": tf.train.Feature( bytes_list=tf.train.BytesList(value=[b"def"]) ), "bytes_list": tf.train.Feature( bytes_list=tf.train.BytesList(value=[b"def", b"1234"]) ), } ) ), ] # Perform the test. # Write the dataset to a .tfrecords file. ds.write_tfrecords(tmp_path) # Read the Examples back out from the .tfrecords file. # This follows the offical TFRecords tutorial: # https://www.tensorflow.org/tutorials/load_data/tfrecord#reading_a_tfrecord_file_2 filenames = sorted(os.listdir(tmp_path)) filepaths = [os.path.join(tmp_path, filename) for filename in filenames] raw_dataset = tf.data.TFRecordDataset(filepaths) tfrecords = [] for raw_record in raw_dataset: example = tf.train.Example() example.ParseFromString(raw_record.numpy()) tfrecords.append(example) assert tfrecords == expected_records

test/test_processing_utils.py

def test_float_conversion_dtype(self): x = np.a

Format The Codebase - black formatting - isort formatting

test_float_conversion_dtype

cc0cff893f9d7d472788adc2510c123967b384fe

gradio

test_processing_utils.py

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

Python

def test_float_conversion_dtype(self): x = np.array([-1, 1]) # Test all combinations of dtypes conversions dtype_combin = np.array( np.meshgrid( OutputPreprocessing.float_dtype_list, OutputPreprocessing.float_dtype_list, ) ).T.reshape(-1, 2) for dtype_in, dtype_out in dtype_combin: x = x.astype(dtype_in) y = gr.processing_utils._convert(x, dtype_out) assert y.dtype == np.dtype(dtype_out)

@app.server.websocket("/api/ws")

spotdl/console/web.py

def fix_mime_types(): # Known to be problematic when Visual Studio is installed: # <https://github.com/tensorflow/tensorboard/issues/3120> # https://github.com/spotDL/spotify-downloader/issues/1540 mimetypes.add_type("application/javascript", ".js") # Not known to be problematic, but used by spotDL: mimetypes.add_type("text/css", ".css") mimetypes.add_type("image/

fix: broken mimetypes #1540

fix_mime_types

de31601550e5b6b243f7a00b2bc82300f43f2d9d

spotify-downloader

web.py

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

Python

def fix_mime_types(): # Known to be problematic when Visual Studio is installed: # <https://github.com/tensorflow/tensorboard/issues/3120> # https://github.com/spotDL/spotify-downloader/issues/1540 mimetypes.add_type("application/javascript", ".js") # Not known to be problematic, but used by spotDL: mimetypes.add_type("text/css", ".css") mimetypes.add_type("image/svg+xml", ".svg") mimetypes.add_type("text/html", ".html") @app.server.websocket("/api/ws")

scripts/convert.py

def _get_threads(self) -> MultiThread: # TODO Check if multiple threads actually speeds anything up save_queue = queue_manager.get_queue("convert_out") patch_queue = queue_manager.get_queue("patch") return MultiThread(self._converter.process, patch_queue, save_queue, thread_count=self._pool_p

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

_get_threads

1022651eb8a7741014f5d2ec7cbfe882120dfa5f

faceswap

convert.py

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

Python

def _get_threads(self) -> MultiThread: # TODO Check if multiple threads actually speeds anything up save_queue = queue_manager.get_queue("convert_out") patch_queue = queue_manager.get_queue("patch") return MultiThread(self._converter.process, patch_queue, save_queue, thread_count=self._pool_processes, name="patch")

python3.10.4/Lib/distutils/command/register.py

def verify_metadata(self): # send the info to the server and report the result (code, result) = self.post_to_server(self.build_post_da

add python 3.10.4 for windows

verify_metadata

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

register.py

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

Python

def verify_metadata(self): # send the info to the server and report the result (code, result) = self.post_to_server(self.build_post_data('verify')) log.info('Server response (%s): %s', code, result)

zerver/tests/test_subs.py

def test_subscriptions_add_for_principal_invite_only(self) -> None: invitee = self.example_user("iago") current_streams = self.get_streams(invitee) invite_streams = self.make_random_stream_names(current_streams) self.assert_adding_subscriptions_for_principal( invitee.id, invitee.realm, invite_streams, invite_only=True,

stream_settings: Show stream privacy & description in stream events. Provide stream privacy and description in stream notification events when stream is created. In function "send_messages_for_new_subscribers" for when stream is created, put policy name and description of the stream. Fixes #21004

test_subscriptions_add_for_principal_invite_only

4b9770e270823b7ed2bbbeda0e4450f0ba6a288b

zulip

test_subs.py

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

Python

def test_subscriptions_add_for_principal_invite_only(self) -> None: invitee = self.example_user("iago") current_streams = self.get_streams(invitee) invite_streams = self.make_random_stream_names(current_streams) self.assert_adding_subscriptions_for_principal( invitee.id, invitee.realm, invite_streams, invite_only=True, policy_name="Private, protected history", )

ludwig/features/feature_utils.py

def get_module_dict_key_from_name(name): key = name.replace(".", "__ludwig_punct_peri

Enable feature names with periods in them. (#1787) * Enable feature names with periods in them. * Simplify new unit test.

get_module_dict_key_from_name

c9b6f4dfa32631c320d122ad07f09013769d9d5d

ludwig

feature_utils.py

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

Python

def get_module_dict_key_from_name(name): key = name.replace(".", "__ludwig_punct_period__") return key + FEATURE_NAME_SUFFIX

@pytest.mark.parametrize( "import_strategy, expected_to_fail", [ pytest.param( """ from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker import pyarrow.gandiva """, True, id="import_pydbe_first-pyarrow_gandiva_second", ), pytest.param( """ import pyarrow.gandiva from modin.experimental.core.execution.native.implementations.hdk_on_native.db_worker import DbWorker """, False, id="import_pyarrow_gandiva_first-pydbe_second", ), ], )

modin/test/storage_formats/hdk/test_internals.py

def test_hdk_import(import_strategy, has_other_engines): remove_other_engines = if not has_other_engines: import_strategy = f"{remove_oth

FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <Poolliver868@mail.ru> Signed-off-by: Andrey Pavlenko <andrey.a.pavlenko@gmail.com>

test_hdk_import

e5b1888cd932909e49194d58035da34b210b91c4

modin

test_internals.py

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

Python

def test_hdk_import(import_strategy, has_other_engines): remove_other_engines = if not has_other_engines: import_strategy = f"{remove_other_engines}\n{import_strategy}" res = subprocess.run( [sys.executable, "-c", import_strategy], stderr=subprocess.PIPE, stdout=subprocess.PIPE, ) if res.returncode != 0: pytest.fail(str(res.stderr)) @pytest.mark.parametrize( "import_strategy, expected_to_fail", [ pytest.param( , True, id="import_pydbe_first-pyarrow_gandiva_second", ), pytest.param( , False, id="import_pyarrow_gandiva_first-pydbe_second", ), ], )

python/ray/_private/thirdparty/pathspec/util.py

def _iter_tree_entries_next(root_full, dir_rel, memo, on_error, follow_links): dir_full = os.path.join(root_full, dir_rel) dir_real = os.path.realpath(dir_full) # Remember each encountered ancestor directory and its canonical # (real) path. If a canonical path is encountered more than once, # recursion has occurred. if dir_real not in memo:

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

_iter_tree_entries_next

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

util.py

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

Python

def _iter_tree_entries_next(root_full, dir_rel, memo, on_error, follow_links): dir_full = os.path.join(root_full, dir_rel) dir_real = os.path.realpath(dir_full) # Remember each encountered ancestor directory and its canonical # (real) path. If a canonical path is encountered more than once, # recursion has occurred. if dir_real not in memo: memo[dir_real] = dir_rel else: raise RecursionError( real_path=dir_real, first_path=memo[dir_real], second_path=dir_rel ) for node_name in os.listdir(dir_full): node_rel = os.path.join(dir_rel, node_name) node_full = os.path.join(root_full, node_rel) # Inspect child node. try: node_lstat = os.lstat(node_full) except OSError as e: if on_error is not None: on_error(e) continue if stat.S_ISLNK(node_lstat.st_mode): # Child node is a link, inspect the target node. is_link = True try: node_stat = os.stat(node_full) except OSError as e: if on_error is not None: on_error(e) continue else: is_link = False node_stat = node_lstat if stat.S_ISDIR(node_stat.st_mode) and (follow_links or not is_link): # Child node is a directory, recurse into it and yield its # descendant files. yield TreeEntry(node_name, node_rel, node_lstat, node_stat) for entry in _iter_tree_entries_next( root_full, node_rel, memo, on_error, follow_links ): yield entry elif stat.S_ISREG(node_stat.st_mode) or is_link: # Child node is either a file or an unfollowed link, yield it. yield TreeEntry(node_name, node_rel, node_lstat, node_stat) # NOTE: Make sure to remove the canonical (real) path of the directory # from the ancestors memo once we are done with it. This allows the # same directory to appear multiple times. If this is not done, the # second occurrence of the directory will be incorrectly interpreted # as a recursion. See <https://github.com/cpburnz/python-path-specification/pull/7>. del memo[dir_real]

sympy/combinatorics/util.py

def _remove_gens(base, strong_gens, basic_orbits=None, strong_gens_distr=None): from sympy.combinatorics.perm_groups import _orbit base_len = len(base) degree = strong_gens[0].size if strong_gens_distr is None: strong_gens_distr = _distribute_gens_by_base(base, strong_gens) if basic_orbits is None: basic_orbits = [] for i in range(base_len): basic_orbit = _orbit(degree, strong_gens_distr[i], base[i]) basic_orbits.append(basic_orbit) strong_gens_distr.append([]) res = strong_gens[:] for i in range(base_len - 1, -1, -1): gens_copy = strong_gens_distr[i][:] for gen in strong_gens_distr[i]: if gen not in strong_gens_distr[i + 1]: temp_gens = gens_copy[:] temp_gens.remove(gen) if temp_gens == []: continue temp_orbit = _orbit(degree, temp_gens,

Updated import locations

_remove_gens

498015021131af4dbb07eb110e5badaba8250c7b

sympy

util.py

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

Python

def _remove_gens(base, strong_gens, basic_orbits=None, strong_gens_distr=None): from sympy.combinatorics.perm_groups import _orbit base_len = len(base) degree = strong_gens[0].size if strong_gens_distr is None: strong_gens_distr = _distribute_gens_by_base(base, strong_gens) if basic_orbits is None: basic_orbits = [] for i in range(base_len): basic_orbit = _orbit(degree, strong_gens_distr[i], base[i]) basic_orbits.append(basic_orbit) strong_gens_distr.append([]) res = strong_gens[:] for i in range(base_len - 1, -1, -1): gens_copy = strong_gens_distr[i][:] for gen in strong_gens_distr[i]: if gen not in strong_gens_distr[i + 1]: temp_gens = gens_copy[:] temp_gens.remove(gen) if temp_gens == []: continue temp_orbit = _orbit(degree, temp_gens, base[i]) if temp_orbit == basic_orbits[i]: gens_copy.remove(gen) res.remove(gen) return res

django/core/files/uploadedfile.py

def from_dict(cls, file_dict): return cls( file_dict["filename"], file_dict["content"], file_dict.get("con

Refs #33476 -- Reformatted code with Black.

from_dict

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

uploadedfile.py

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

Python

def from_dict(cls, file_dict): return cls( file_dict["filename"], file_dict["content"], file_dict.get("content-type", "text/plain"), )

tests/admin_views/tests.py

def test_change_list_sorting_callable(self): response = self.client.get( reverse("admin:admin_views_article_changelist"), {"o": 2} ) self.assertContentBefore

Refs #33476 -- Reformatted code with Black.

test_change_list_sorting_callable

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_change_list_sorting_callable(self): response = self.client.get( reverse("admin:admin_views_article_changelist"), {"o": 2} ) self.assertContentBefore( response, "Oldest content", "Middle content", "Results of sorting on callable are out of order.", ) self.assertContentBefore( response, "Middle content", "Newest content", "Results of sorting on callable are out of order.", )

scripts/convert.py

def pre_encode(self) -> Optional[Callable[[np.ndarray], List[bytes]]]: dummy = np.zeros((20, 20, 3), dtype="uint8") test = self._writer.pre_encode(dummy) retval: O

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

pre_encode

1022651eb8a7741014f5d2ec7cbfe882120dfa5f

faceswap

convert.py

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

Python

def pre_encode(self) -> Optional[Callable[[np.ndarray], List[bytes]]]: dummy = np.zeros((20, 20, 3), dtype="uint8") test = self._writer.pre_encode(dummy) retval: Optional[Callable[[np.ndarray], List[bytes]]] = None if test is None else self._writer.pre_encode logger.debug("Writer pre_encode function: %s", retval) return retval

mindsdb/integrations/handlers/sqlite_handler/sqlite_handler.py

def disconnect(self):

added connection_args and connection_args_example dicts

disconnect

fc9776d9b342f873cbb3f36fd39955b9e1ea6f76

mindsdb

sqlite_handler.py

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

Python

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

python/ray/workflow/step_executor.py

def resolve(self) -> Tuple[List, Dict]: objects_mapping = [] for obj_ref in self.workflow_outputs: obj, ref = _resolve_object_ref(obj_ref.ref) objects_mapping.append(o

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

resolve

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

step_executor.py

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

Python

def resolve(self) -> Tuple[List, Dict]: objects_mapping = [] for obj_ref in self.workflow_outputs: obj, ref = _resolve_object_ref(obj_ref.ref) objects_mapping.append(obj) workflow_ref_mapping = _resolve_dynamic_workflow_refs(self.workflow_refs) with serialization_context.workflow_args_resolving_context( objects_mapping, workflow_ref_mapping ): # reconstruct input arguments under correct serialization context flattened_args: List[Any] = ray.get(self.args) # dereference arguments like Ray remote functions flattened_args = [ ray.get(a) if isinstance(a, ObjectRef) else a for a in flattened_args ] return signature.recover_args(flattened_args)

keras/engine/base_layer.py

def _maybe_create_attribute(self, name, default_value):

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

_maybe_create_attribute

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

base_layer.py

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

Python

def _maybe_create_attribute(self, name, default_value): if not hasattr(self, name): self.__setattr__(name, default_value)

networkx/classes/digraph.py

def add_nodes_from(self, nodes_for_adding, **attr): for n in nodes_for_adding: try: newnode = n not in self._node newdict = attr except Type

doc: update documentation when providing an iterator over current graph to add/remove_edges_from. (#6268) * doc for add_edges_from * doc for digraph * doc for multigraph * multigraph.add_nodes_from returns keylist * update docs for graph - edges * doc update: graph.add_nodes_from * doc update: graph.remove_nodes_from * doc update: graph.add_edges_from * doc update: rewording for graph.add_edges_from * doc update: graph.add_weighted_edges_from rewording * doc update: digraph updated as graph * doc update: digraph minor sync * doc update: multigraph same as graph * Update graph.py * Update digraph.py * Update multigraph.py

add_nodes_from

979d54acba7c3d372c93d44c6c149700608ce8b0

networkx

digraph.py

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