Datasets:

semeru

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code-text-galeras-code-summarization-3k-deduped

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Semeru Lab 7

filter_queryset

def filter_queryset(self, request, queryset, view): fields = set(view.get_available_fields(queryset.model, db_fields_only=True)) # Locale is a database field, but we provide a separate filter for it if "locale" in fields: fields.remove("locale") for field_name, value in request.GET.items(): if field_name in fields: try: field = queryset.model._meta.get_field(field_name) except LookupError: field = None # Convert value into python try: if isinstance( field, (models.BooleanField, models.NullBooleanField) ): value = parse_boolean(value) elif isinstance(field, (models.IntegerField, models.AutoField)): value = int(value) elif isinstance(field, models.ForeignKey): value = field.target_field.get_prep_value(value) except ValueError as e: raise BadRequestError( "field filter error. '%s' is not a valid value for %s (%s)" % (value, field_name, str(e)) ) if isinstance(field, TaggableManager): for tag in value.split(","): queryset = queryset.filter(**{field_name + "__name": tag}) # Stick a message on the queryset to indicate that tag filtering has been performed # This will let the do_search method know that it must raise an error as searching # and tag filtering at the same time is not supported queryset._filtered_by_tag = True else: queryset = queryset.filter(**{field_name: value}) return queryset

d10f15e55806c6944827d801cd9c2d53f5da4186

filters.py

Reformat with black

wagtail

wagtail/api/v2/filters.py

Python

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

generate_operation_regex

def generate_operation_regex(): operations = [] for item in OP_TO_SNUBA_FUNCTION.values(): operations += list(item.keys()) return rf"({'|'.join(map(str, operations))})" OP_REGEX = generate_operation_regex() AVAILABLE_OPERATIONS = { type_: sorted(mapping.keys()) for type_, mapping in OP_TO_SNUBA_FUNCTION.items() } OPERATIONS_TO_ENTITY = { op: entity for entity, operations in AVAILABLE_OPERATIONS.items() for op in operations } # ToDo add guages/summaries METRIC_TYPE_TO_ENTITY: Mapping[MetricType, EntityKey] = { "counter": EntityKey.MetricsCounters, "set": EntityKey.MetricsSets, "distribution": EntityKey.MetricsDistributions, } FIELD_ALIAS_MAPPINGS = {"project": "project_id"}

3535fa83a743f6967a92e0afdbba2b633236bf67

utils.py

fix(metrics): Restrict public name regex [TET-111] (#35305) * fix(metrics): Restrict public name regex Restricts the public name regex for metrics by separating out the public name regex and the mri name regex from the shared field regex and removing characters that are not expected to be in public facing names * Update mri regex and public regex + add tests * Add better tests * PR comments

sentry

src/sentry/snuba/metrics/utils.py

Python

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

getbbox

def getbbox(self, text, *args, **kwargs): width, height = self.font.getsize(text) return 0, 0, width, height

c854bf8d1c05022bec4309fbf6b547e494db9373

ImageFont.py

add getbbox and getlength to basic ImageFont and update related tests

Pillow

src/PIL/ImageFont.py

Python

https://github.com/python-pillow/Pillow.git

_validate

def _validate(self) -> None: if self._job == "missing-frames" and self._output == "move": logger.warning("Missing_frames was selected with move output, but there will " "be nothing to move. Defaulting to output: console") self._output = "console" if self._type == "faces" and self._job != "multi-faces": logger.error("The selected folder is not valid. Faces folder (-fc) is only " "supported for 'multi-faces'") sys.exit(1)

e2a77e7c6e84e81f642cb22f528e25e3f2d2dbc1

jobs.py

Alignments Tool - Typing, Documentation + Re-org

faceswap

tools/alignments/jobs.py

Python

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

pagerank_numpy

def pagerank_numpy(G, alpha=0.85, personalization=None, weight="weight", dangling=None): msg = "networkx.pagerank_numpy is deprecated and will be removed in NetworkX 3.0, use networkx.pagerank instead." warn(msg, DeprecationWarning, stacklevel=2) import numpy as np if len(G) == 0: return {} M = google_matrix( G, alpha, personalization=personalization, weight=weight, dangling=dangling ) # use numpy LAPACK solver eigenvalues, eigenvectors = np.linalg.eig(M.T) ind = np.argmax(eigenvalues) # eigenvector of largest eigenvalue is at ind, normalized largest = np.array(eigenvectors[:, ind]).flatten().real norm = largest.sum() return dict(zip(G, map(float, largest / norm)))

2a05ccdb07cff88e56661dee8a9271859354027f

pagerank_alg.py

Remove redundant py2 numeric conversions (#5661) * Remove redundant float conversion * Remove redundant int conversion * Use integer division Co-authored-by: Miroslav Šedivý <[email protected]>

networkx

networkx/algorithms/link_analysis/pagerank_alg.py

Python

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

decode_predictions

def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top) preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( mode="", ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TORCH, error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC, ) decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__ DOC = setattr(DenseNet121, "__doc__", DenseNet121.__doc__ + DOC) setattr(DenseNet169, "__doc__", DenseNet169.__doc__ + DOC) setattr(DenseNet201, "__doc__", DenseNet201.__doc__ + DOC)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

densenet.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/applications/densenet.py

Python

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

test_tika_parse_unreachable

def test_tika_parse_unreachable(self): html = '<html><head><meta http-equiv="content-type" content="text/html; charset=UTF-8"></head><body><p>Some Text</p></body></html>' # Check if exception is raised when Tika cannot be reached. self.parser.tika_server = "" self.assertRaises(ParseError, self.parser.tika_parse, html)

4aa318598fd0dc6c5d4e08dd2a13e7bf614511ec

test_parsers.py

add test comments

paperless-ngx

src/paperless_mail/tests/test_parsers.py

Python

https://github.com/paperless-ngx/paperless-ngx.git

test_train_model_from_files

def test_train_model_from_files(self): df = pd.DataFrame({ 'x1': [x for x in range(100, 210)] + [x for x in range(100, 210)], 'x2': [x * 2 for x in range(100, 210)] + [x * 3 for x in range(100, 210)], 'y': [x * 3 for x in range(100, 210)] + [x * 2 for x in range(100, 210)] }) file_predictor_name = "predictor_from_file" self.upload_ds(df, self.file_datasource_name) self.verify_file_ds(self.file_datasource_name) _query = f self.query(_query) self.check_predictor_readiness(file_predictor_name)

7c02e15aa403a4ca1fa34489dd2df9136d6c961c

test_mysql_api.py

Projects structure (#3532) Projects structure

mindsdb

tests/integration_tests/flows/test_mysql_api.py

Python

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

_search_cb

def _search_cb(self, found, *, tab, old_match, options, text, prev): # :search/:search-next without reverse -> down # :search/:search-next with reverse -> up # :search-prev without reverse -> up # :search-prev with reverse -> down going_up = options['reverse'] ^ prev if found: if not config.val.search.wrap_messages: return # Check if the match count change is opposite to the search direction if old_match.current > 0: if not going_up: if old_match.current > tab.search.match.current: message.info("Search hit BOTTOM, continuing at TOP", replace="search-hit-msg") elif old_match.current == tab.search.match.current: message.info("Search hit BOTTOM", replace="search-hit-msg") elif going_up: if old_match.current < tab.search.match.current: message.info("Search hit TOP, continuing at BOTTOM", replace="search-hit-msg") elif old_match.current == tab.search.match.current: message.info("Search hit TOP", replace="search-hit-msg") else: message.warning(f"Text '{text}' not found on page!", replace='find-in-page')

265b018c172f8c1f6d9e7f8850256363f0629f82

commands.py

Add a SearchMatch helper class

qutebrowser

qutebrowser/browser/commands.py

Python

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

from_decimal

def from_decimal(cls, dec): from decimal import Decimal if isinstance(dec, numbers.Integral): dec = Decimal(int(dec)) elif not isinstance(dec, Decimal): raise TypeError( "%s.from_decimal() only takes Decimals, not %r (%s)" % (cls.__name__, dec, type(dec).__name__)) return cls(*dec.as_integer_ratio())

8198943edd73a363c266633e1aa5b2a9e9c9f526

fractions.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/fractions.py

Python

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

forward

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

0932ab787d58eead15b5f823fbcca5351ceb90f7

@PLUGIN_LAYERS.register_module()

pixel_decoder.py

add msdeformattn pixel decoder (#7466) fix typo rm img_metas rename in pixel_decoder update comments rename fix typo generae points with MlvlPointGenerator

mmdetection

mmdet/models/plugins/pixel_decoder.py

Python

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

primary_calendar_error

async def primary_calendar_error() -> ClientError | None: return None @pytest.fixture(autouse=True)

3f8c896cb20526ef59e285b1f0eeb9b4e734efee

@pytest.fixture(autouse=True)

test_config_flow.py

Set user friendly name for Google Calendar config entry (#72243) * Set user friendly name for Google Calendar config entry * Add a new auth implementation for use during the config flow

core

tests/components/google/test_config_flow.py

Python

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

test_get_release_wheel_url

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

98fef7732852cdb3e9377cd87c1ee1085b894928

test_runtime_env.py

[runtime env] Support python 3.10 for runtime_env conda (#30970) Signed-off-by: Archit Kulkarni <[email protected]> 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.

ray

python/ray/tests/test_runtime_env.py

Python

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

_validate_extra

def _validate_extra(extra, conn_id) -> None: if extra is None: return None try: extra_parsed = json.loads(extra) if not isinstance(extra_parsed, dict): warnings.warn( "Encountered JSON value in `extra` which does not parse as a dictionary in " f"connection {conn_id!r}. From Airflow 3.0, the `extra` field must contain a JSON " "representation of a Python dict.", DeprecationWarning, stacklevel=3, ) except json.JSONDecodeError: warnings.warn( f"Encountered non-JSON in `extra` field for connection {conn_id!r}. Support for " "non-JSON `extra` will be removed in Airflow 3.0", DeprecationWarning, stacklevel=2, ) return None

3aebb21c523c0eea0d4a1518d502ff95fd98011b

connection.py

Deprecate non-JSON conn.extra (#21816) Connection extra field is generally assumed to be JSON but we don't actually require it. Here we deprecate non-JSON extra so that in 3.0 we can require it. Further, we require that it not just be any json but must also parse as dict, because a string value such as '"hi"' or '[1,2,3]' is json, but a very bad practice.

airflow

airflow/models/connection.py

Python

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

algdiv

def algdiv(a, b): c0 = 0.833333333333333e-01 c1 = -0.277777777760991e-02 c2 = 0.793650666825390e-03 c3 = -0.595202931351870e-03 c4 = 0.837308034031215e-03 c5 = -0.165322962780713e-02 h = a / b c = h / (1 + h) x = h / (1 + h) d = b + (a - 0.5) # Set sN = (1 - x**n)/(1 - x) x2 = x * x s3 = 1.0 + (x + x2) s5 = 1.0 + (x + x2 * s3) s7 = 1.0 + (x + x2 * s5) s9 = 1.0 + (x + x2 * s7) s11 = 1.0 + (x + x2 * s9) # Set w = del(b) - del(a + b) # where del(x) is defined by ln(gamma(x)) = (x - 0.5)*ln(x) - x + 0.5*ln(2*pi) + del(x) t = (1.0 / b) ** 2 w = ((((c5 * s11 * t + c4 * s9) * t + c3 * s7) * t + c2 * s5) * t + c1 * s3) * t + c0 w = w * (c / b) # Combine the results u = d * lax.log1p(a / b) v = a * (lax.log(b) - 1.0) return jnp.where(u <= v, (w - v) - u, (w - u) - v)

9f811ba54d16d1acc4c0950dd3608d0b59d1af82

betaln.py

Address drastic slowdown in mypy runtime

jax

jax/_src/third_party/scipy/betaln.py

Python

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

_disabled

def _disabled(self, *args, **kwargs) -> NoReturn: raise TypeError(f"'{type(self).__name__}' does not support mutable operations.")

6ba2a67556526db2e5b0b60a566b5f2039cf4a46

frozen.py

TYP: annotate functions that always error with NoReturn (#48002)

pandas

pandas/core/indexes/frozen.py

Python

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

_set_preview_feed

def _set_preview_feed(self): retval = {} for idx, side in enumerate(("a", "b")): logger.debug("Setting preview feed: (side: '%s')", side) preview_images = self._config.get("preview_images", 14) preview_images = min(max(preview_images, 2), 16) batchsize = min(len(self._images[side]), preview_images) retval[side] = self._load_generator(idx).minibatch_ab(self._images[side], batchsize, side, do_shuffle=True, is_preview=True) logger.debug("Set preview feed. Batchsize: %s", batchsize) return retval

c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf

_base.py

Update code to support Tensorflow versions up to 2.8 (#1213) * Update maximum tf version in setup + requirements * - bump max version of tf version in launcher - standardise tf version check * update keras get_custom_objects for tf>2.6 * bugfix: force black text in GUI file dialogs (linux) * dssim loss - Move to stock tf.ssim function * Update optimizer imports for compatibility * fix logging for tf2.8 * Fix GUI graphing for TF2.8 * update tests * bump requirements.txt versions * Remove limit on nvidia-ml-py * Graphing bugfixes - Prevent live graph from displaying if data not yet available * bugfix: Live graph. Collect loss labels correctly * fix: live graph - swallow inconsistent loss errors * Bugfix: Prevent live graph from clearing during training * Fix graphing for AMD

faceswap

plugins/train/trainer/_base.py

Python

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

test_multiple_containers

def test_multiple_containers(self): rq = ForwardMsgQueue() self.assertTrue(rq.is_empty()) rq.enqueue(NEW_SESSION_MSG)

704eab3478cf69847825b23dabf15813a8ac9fa2

forward_msg_queue_test.py

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

streamlit

lib/tests/streamlit/forward_msg_queue_test.py

Python

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

test_simple_class_node_json_serde

def test_simple_class_node_json_serde(serve_instance): hello_actor = ClassHello.bind() original_dag_node = hello_actor.hello.bind() _test_deployment_json_serde_helper( original_dag_node, expected_num_deployments=1, ) model_actor = Model.bind(1) original_dag_node = model_actor.forward.bind(1) _test_deployment_json_serde_helper( original_dag_node, expected_num_deployments=1, ) model_actor = Model.bind(1, ratio=0.5) original_dag_node = model_actor.forward.bind(1) _test_deployment_json_serde_helper( original_dag_node, expected_num_deployments=1, )

89b0b82c13568254d179e63cb1a43b95fe632bd9

test_json_serde.py

[Deployment Graph] Move `Deployment` creation outside to build function (#26129)

ray

python/ray/serve/tests/test_json_serde.py

Python

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

evaluate

def evaluate(self, system_id=1, rouge_args=None): self.write_config(system_id=system_id) options = self.__get_options(rouge_args) command = [self._bin_path] + options self.log.info("Running ROUGE with command {}".format(" ".join(command))) rouge_output = check_output(command).decode("UTF-8") return rouge_output

487162262196bead8d9b4c2306f313b8f64edf9b

bs_pyrouge.py

Add model Prohetnet (#1698) * add Prohetnet model * update prohetnet * update format * pre commit * add prophetnet example * update tokenizer.py,run_train.sh,train_prophetnet.py * remove evaluate/gigaword/__init__.py Co-authored-by: smallv0221 <[email protected]>

PaddleNLP

examples/text_summarization/prophetnet/evaluate/cnndm/bs_pyrouge.py

Python

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

execute_test_case

def execute_test_case(self, test_case, kill_time=None): # type: (AutomotiveTestCaseABC, Optional[float]) -> None test_case.pre_execute( self.socket, self.target_state, self.configuration) try: test_case_kwargs = self.configuration[test_case.__class__.__name__] except KeyError: test_case_kwargs = dict() if kill_time: max_execution_time = max(int(kill_time - time.time()), 5) cur_execution_time = test_case_kwargs.get("execution_time", 1200) test_case_kwargs["execution_time"] = min(max_execution_time, cur_execution_time) log_interactive.debug("[i] Execute test_case %s with args %s", test_case.__class__.__name__, test_case_kwargs) test_case.execute(self.socket, self.target_state, **test_case_kwargs) test_case.post_execute( self.socket, self.target_state, self.configuration) self.check_new_states(test_case) self.check_new_testcases(test_case)

e6eaa484b8fa3d10051e82f5a784fe8dedbd5592

executor.py

Add assert to GMLAN Scanner to enforce fast fail on to many open TestSockets Fix bugs in TestSocket Fix bugs in the AutomotiveScanner execution_time handling Simplify test code for UDS_Scanner and reuse ObjectPipes to avoid mass creation

scapy

scapy/contrib/automotive/scanner/executor.py

Python

https://github.com/secdev/scapy.git

update

def update(self): if not self.ax.get_visible() or self.ax.figure._cachedRenderer is None: return False if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) else: self.update_background(None) for artist in self.artists + self._get_animated_artists(): if artist.stale: self.ax.draw_artist(artist) self.canvas.blit(self.ax.bbox) else: self.canvas.draw_idle() return False

da31ed386482845629a8505f81810ddb341514fb

widgets.py

Fix drawing animated artists changed in selector callback

matplotlib

lib/matplotlib/widgets.py

Python

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

synchronize_labels

def synchronize_labels(self, axis=None): if axis is None: self._deferred_index = True self._deferred_column = True elif axis == 0: self._deferred_index = True else: self._deferred_column = True

3c740dbfcdd69ddc3ab45a42be996e5c61104342

dataframe.py

FEAT-#3111: Ensure relabeling Modin Frame does not lose partition shape (#3662) Co-authored-by: Devin Petersohn <[email protected]> Signed-off-by: Naren Krishna <[email protected]>

modin

modin/core/dataframe/pandas/dataframe/dataframe.py

Python

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

_impute_values

def _impute_values(self, features): if self.verbosity > 1: print("Imputing missing values in feature set") if self._fitted_imputer is None: self._fitted_imputer = SimpleImputer(strategy="median") self._fitted_imputer.fit(features) return self._fitted_imputer.transform(features)

388616b6247ca4ea8de4e2f340d6206aee523541

base.py

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

tpot

tpot/base.py

Python

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

_multi_dot_three

def _multi_dot_three(A, B, C, precision): a0, a1b0 = A.shape b1c0, c1 = C.shape # cost1 = cost((AB)C) = a0*a1b0*b1c0 + a0*b1c0*c1 cost1 = a0 * b1c0 * (a1b0 + c1) # cost2 = cost(A(BC)) = a1b0*b1c0*c1 + a0*a1b0*c1 cost2 = a1b0 * c1 * (a0 + b1c0) if cost1 < cost2: return jnp.dot(jnp.dot(A, B, precision=precision), C, precision=precision) else: return jnp.dot(A, jnp.dot(B, C, precision=precision), precision=precision)

2416d154355f19e77b5c1ddf1de1f8552e4a98ad

linalg.py

Call _check_arraylike for jnp.linalg & jnp.fft functions

jax

jax/_src/third_party/numpy/linalg.py

Python

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

test_dqn

def test_dqn(self): config = dqn.DQNConfig() # Soft-Q for DQN. config.exploration(exploration_config={"type": "SoftQ", "temperature": 0.5}) config.debugging(seed=42) do_test_log_likelihood(dqn.DQN, config)

2ed09c54459cc3f74e2dab13406018698559856c

test_compute_log_likelihoods.py

[RLlib] Move all config validation logic into AlgorithmConfig classes. (#29854)

ray

rllib/policy/tests/test_compute_log_likelihoods.py

Python

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

_extract_future_flags

def _extract_future_flags(globs): flags = 0 for fname in __future__.all_feature_names: feature = globs.get(fname, None) if feature is getattr(__future__, fname): flags |= feature.compiler_flag return flags

8198943edd73a363c266633e1aa5b2a9e9c9f526

doctest.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/doctest.py

Python

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

test_send_html_email

def test_send_html_email(self): send_mail( "Test HTML subject", "TEXT content", ["[email protected]"], html_message="<h2>Test HTML content</h2>", ) send_mail("Test TEXT subject", "TEXT content", ["[email protected]"]) # Check that the emails were sent self.assertEqual(len(mail.outbox), 2) # check that the first email is the HTML email email_message = mail.outbox[0] self.assertEqual(email_message.subject, "Test HTML subject") self.assertEqual( email_message.alternatives, [("<h2>Test HTML content</h2>", "text/html")] ) self.assertEqual( email_message.body, "TEXT content" ) # note: plain text will always be added to body, even with alternatives self.assertEqual(email_message.to, ["[email protected]"]) # confirm that without html_message kwarg we do not get 'alternatives' email_message = mail.outbox[1] self.assertEqual(email_message.subject, "Test TEXT subject") self.assertEqual(email_message.alternatives, []) self.assertEqual(email_message.body, "TEXT content") self.assertEqual(email_message.to, ["[email protected]"])

d10f15e55806c6944827d801cd9c2d53f5da4186

tests.py

Reformat with black

wagtail

wagtail/admin/tests/tests.py

Python

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

set_expired_status

def set_expired_status(): # filter out submitted non expired quotations whose validity has been ended cond = "`tabQuotation`.docstatus = 1 and `tabQuotation`.status != 'Expired' and `tabQuotation`.valid_till < %s" # check if those QUO have SO against it so_against_quo = # if not exists any SO, set status as Expired frappe.db.multisql( { "mariadb": .format( cond=cond, so_against_quo=so_against_quo ), "postgres": .format( cond=cond, so_against_quo=so_against_quo ), }, (nowdate()), ) @frappe.whitelist()

74a782d81d8f8c4a4d9214a9c06377e5e6e464dd

@frappe.whitelist()

quotation.py

refactor: DB independent quoting and truthy/falsy values (#31358) * refactor: DB independent quoting and truthy/falsy values * style: reformat to black spec * fix: ifnull -> coalesce * fix: coalesce -> Coalesce * fix: revert pypika comparison * refactor: convert queries to QB * fix: incorrect value types for query `=` query makes no sense with list of values * fix: remove warehouse docstatus condition * fix: keep using base rate as rate Co-authored-by: Ankush Menat <[email protected]>

erpnext

erpnext/selling/doctype/quotation/quotation.py

Python

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

check_freezing_date

def check_freezing_date(posting_date, adv_adj=False): if not adv_adj: acc_frozen_upto = frappe.db.get_value("Accounts Settings", None, "acc_frozen_upto") if acc_frozen_upto: frozen_accounts_modifier = frappe.db.get_value( "Accounts Settings", None, "frozen_accounts_modifier" ) if getdate(posting_date) <= getdate(acc_frozen_upto) and ( frozen_accounts_modifier not in frappe.get_roles() or frappe.session.user == "Administrator" ): frappe.throw( _("You are not authorized to add or update entries before {0}").format( formatdate(acc_frozen_upto) ) )

494bd9ef78313436f0424b918f200dab8fc7c20b

general_ledger.py

style: format code with black

erpnext

erpnext/accounts/general_ledger.py

Python

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

test_run_binary

def test_run_binary(self, df, flex, comparison_op): arith = comparison_op.__name__ with option_context("compute.use_numexpr", False): other = df.copy() + 1 expr._MIN_ELEMENTS = 0 expr.set_test_mode(True) result, expected = self.call_op(df, other, flex, arith) used_numexpr = expr.get_test_result() assert used_numexpr, "Did not use numexpr as expected." tm.assert_equal(expected, result) # FIXME: dont leave commented-out # series doesn't uses vec_compare instead of numexpr... # for i in range(len(df.columns)): # binary_comp = other.iloc[:, i] + 1 # self.run_binary(df.iloc[:, i], binary_comp, flex)

2e5b05e5773f02b653d66373787b493bc3cf3abc

test_expressions.py

STYLE: use option_context almost always (#45407)

pandas

pandas/tests/test_expressions.py

Python

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

wagtail_site

def wagtail_site(context): try: request = context["request"] except KeyError: return None return Site.find_for_request(request=request)

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtailcore_tags.py

Reformat with black

wagtail

wagtail/core/templatetags/wagtailcore_tags.py

Python

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

get_user_configured_option_names

def get_user_configured_option_names(self) -> Set[str]: return { field for field, value in self.dict().items() if value is not DEFAULT.VALUE } @PublicAPI(stability="beta")

18b38c5e23fc54bf0baf7e7dbfcb07640e81f5ef

@PublicAPI(stability="beta")

schema.py

[Serve] Track user-configured options in Serve deployments (#28313)

ray

python/ray/serve/schema.py

Python

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

setmodulation

def setmodulation(self, modu): # type: (int) -> bool # According to https://nmap.org/npcap/guide/npcap-devguide.html#npcap-feature-dot11 # noqa: E501 self._check_npcap_requirement() _modus = { 0: "dsss", 1: "fhss", 2: "irbaseband", 3: "ofdm", 4: "hrdss", 5: "erp", 6: "ht", 7: "vht", 8: "ihv", 9: "mimo-ofdm", 10: "mimo-ofdm", } m = _modus.get(modu, "unknown") if isinstance(modu, int) else modu return self._npcap_set("modu", str(m))

a2b7a28faff1db058dd22ce097a268e0ad5d1d33

__init__.py

[Hinty] Core typing: windows (#3684) * Core typing: windows Co-authored-by: Pierre <[email protected]>

scapy

scapy/arch/windows/__init__.py

Python

https://github.com/secdev/scapy.git

make_distribution_for_install_requirement

def make_distribution_for_install_requirement(install_req): # type: (InstallRequirement) -> AbstractDistribution # Editable requirements will always be source distributions. They use the # legacy logic until we create a modern standard for them. if install_req.editable: return SourceDistribution(install_req) # If it's a wheel, it's a WheelDistribution if install_req.is_wheel: return WheelDistribution(install_req) # Otherwise, a SourceDistribution return SourceDistribution(install_req)

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

__init__.py

upd; format

transferlearning

.venv/lib/python3.8/site-packages/pip/_internal/distributions/__init__.py

Python

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

_discover_all_providers_from_packages

def _discover_all_providers_from_packages(self) -> None: for entry_point, dist in entry_points_with_dist('apache_airflow_provider'): package_name = dist.metadata['name'] if self._provider_dict.get(package_name) is not None: continue log.debug("Loading %s from package %s", entry_point, package_name) version = dist.version provider_info = entry_point.load()() self._provider_schema_validator.validate(provider_info) provider_info_package_name = provider_info['package-name'] if package_name != provider_info_package_name: raise Exception( f"The package '{package_name}' from setuptools and " f"{provider_info_package_name} do not match. Please make sure they are aligned" ) if package_name not in self._provider_dict: self._provider_dict[package_name] = ProviderInfo(version, provider_info, 'package') else: log.warning( "The provider for package '%s' could not be registered from because providers for that " "package name have already been registered", package_name, )

b5a786b38148295c492da8ab731d5e2f6f86ccf7

providers_manager.py

Suppress import errors for providers from sources (#22579) When we are running airflow locally with providers installed from sources, often many providers will be discovered which we haven't installed the deps for. This generally results in a very large amount of traceback logging, which has a very negative effect on usefulness of terminal output. Here we suppress this error logging for providers that are installed from sources.

airflow

airflow/providers_manager.py

Python

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

load

def load(self, loader): loader.add_option( "modify_body", Sequence[str], [], )

fdde9ba3b3caaa2654048cec0af07bfcc3a6a3f8

modifybody.py

use Python 3.9+ typing

mitmproxy

mitmproxy/addons/modifybody.py

Python

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

get_ready

def get_ready(self) -> Dict[ActorHandle, List[Any]]: ready_requests_dict = defaultdict(list) ready_requests, self._pending_remotes = ray.wait( self._pending_remotes, timeout=self._ray_wait_timeout_s, num_returns=len(self._pending_remotes), ) if not self._return_object_refs: objs = ray.get(ready_requests) else: objs = ready_requests for req, obj in zip(ready_requests, objs): actor = self._pending_to_actor[req] self._remote_requests_in_flight[actor].remove(req) ready_requests_dict[actor].append(obj) del self._pending_to_actor[req] del ready_requests return dict(ready_requests_dict)

eaed256d6863c529b8ada42514f7fba12d146f22

parallel_requests.py

[RLlib] Async parallel execution manager. (#24423)

ray

rllib/execution/parallel_requests.py

Python

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

set_link_objects

def set_link_objects(self, objects): self.objects = objects[:] # -- Private utility methods -------------------------------------- # (here for the convenience of subclasses) # Helper method to prep compiler in subclass compile() methods

8198943edd73a363c266633e1aa5b2a9e9c9f526

ccompiler.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/distutils/ccompiler.py

Python

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

get_serve_client

def get_serve_client(self): from ray.serve.context import get_global_client from ray.serve.exceptions import RayServeException try: return get_global_client(_health_check_controller=True) except RayServeException: logger.debug("There's no Serve app running on this Ray cluster.") return None

4ab97399cda80be1e146946c43d6fb2926248b28

serve_agent.py

[Serve] Only start Serve in the CLI through the `serve deploy` command (#27063) These Serve CLI commands start Serve if it's not already running: * `serve deploy` * `serve config` * `serve status` * `serve shutdown` #27026 introduces the ability to specify a `host` and `port` in the Serve config file. However, once Serve starts running, changing these options requires tearing down the entire Serve application and relaunching it. This limitation is an issue because users can inadvertently start Serve by running one of the `GET`-based CLI commands (i.e. `serve config` or `serve status`) before running `serve deploy`. This change makes `serve deploy` the only CLI command that can start a Serve application on a Ray cluster. The other commands have updated behavior when Serve is not yet running on the cluster. * `serve config`: prints an empty config body. ```yaml import_path: '' runtime_env: {} deployments: [] ``` * `serve status`: prints an empty status body, with a new `app_status` `status` value: `NOT_STARTED`. ```yaml app_status: status: NOT_STARTED message: '' deployment_timestamp: 0 deployment_statuses: [] ``` * `serve shutdown`: performs a no-op.

ray

dashboard/modules/serve/serve_agent.py

Python

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

resize_image_type0

def resize_image_type0(self, img): limit_side_len = self.max_side_len h, w, _ = img.shape # limit the max side if max(h, w) > limit_side_len: if h > w: ratio = float(limit_side_len) / h else: ratio = float(limit_side_len) / w else: ratio = 1. resize_h = int(h * ratio) resize_w = int(w * ratio) resize_h = int(round(resize_h / 32) * 32) resize_w = int(round(resize_w / 32) * 32) try: if int(resize_w) <= 0 or int(resize_h) <= 0: return None, (None, None) img = cv2.resize(img, (int(resize_w), int(resize_h))) except: print(img.shape, resize_w, resize_h) sys.exit(0) ratio_h = resize_h / float(h) ratio_w = resize_w / float(w) # return img, np.array([h, w]) return img, [ratio_h, ratio_w]

9b3119dfb63c4cbb7acfb9f1f1c09ac24e6d68d2

processor.py

add module

PaddleHub

modules/image/text_recognition/ppocrv3_det_ch/processor.py

Python

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

test_finds_message

def test_finds_message(self) -> None: # The other user sends some messages self.helper.send(self.room, body="Hi!", tok=self.other_access_token) self.helper.send(self.room, body="There!", tok=self.other_access_token) channel = self.make_request( "POST", "/search?access_token=%s" % (self.access_token,), { "search_categories": { "room_events": {"keys": ["content.body"], "search_term": "Hi"} } }, ) # Check we get the results we expect -- one search result, of the sent # messages self.assertEqual(channel.code, 200) results = channel.json_body["search_categories"]["room_events"] self.assertEqual(results["count"], 1) self.assertEqual(results["results"][0]["result"]["content"]["body"], "Hi!") # No context was requested, so we should get none. self.assertEqual(results["results"][0]["context"], {})

2ffaf30803f93273a4d8a65c9e6c3110c8433488

test_rooms.py

Add type hints to `tests/rest/client` (#12108) * Add type hints to `tests/rest/client` * newsfile * fix imports * add `test_account.py` * Remove one type hint in `test_report_event.py` * change `on_create_room` to `async` * update new functions in `test_third_party_rules.py` * Add `test_filter.py` * add `test_rooms.py` * change to `assertEquals` to `assertEqual` * lint

synapse

tests/rest/client/test_rooms.py

Python

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

conv_block

def conv_block(cls, inputs, filters, idx, recursions): name = f"conv{idx}" var_x = inputs for i in range(1, recursions + 1): rec_name = f"{name}_{i}" var_x = ZeroPadding2D(1, name=f"{rec_name}.zeropad")(var_x) var_x = Conv2D(filters, kernel_size=3, strides=1, activation="relu", name=rec_name)(var_x) return var_x

aa39234538a8f83e6aa2b60b8275a570e8876ac2

s3fd.py

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)

faceswap

plugins/extract/detect/s3fd.py

Python

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

get_bazel_path

def get_bazel_path(bazel_path_flag): for path in filter(None, get_bazel_paths(bazel_path_flag)): version = get_bazel_version(path) if version is not None and version >= (5, 0, 0): return path, ".".join(map(str, version)) print("Cannot find or download a suitable version of bazel." "Please install bazel >= 5.0.0.") sys.exit(-1)

2388e353da9768a5e714a83b360bac1e920ff7ae

build.py

Increase bazel version to 5.0.0 to match TensorFlow (https://github.com/tensorflow/tensorflow/commit/8871926b0aa9d5b0e819d12f6945bce752fde610).

jax

build/build.py

Python

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

build_ddp

def build_ddp(model, device='cuda', *args, **kwargs): assert device in ['cuda', 'mlu'], 'Only available for cuda or mlu devices.' if device == 'cuda': model = model.cuda() elif device == 'mlu': from mmcv.device.mlu import MLUDistributedDataParallel ddp_factory['mlu'] = MLUDistributedDataParallel model = model.mlu() return ddp_factory[device](model, *args, **kwargs)

24f2fdb38481e6c013a588660c044e410148ce1e

util_distribution.py

fix lint (#7793)

mmdetection

mmdet/utils/util_distribution.py

Python

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

detect_current_mode

def detect_current_mode(cls) -> _FaultTolerantMode: env_value = os.getenv("PL_FAULT_TOLERANT_TRAINING", "0").lower() # the int values are kept for backwards compatibility, but long-term we want to keep only the strings if env_value in ("0", "disabled"): return _FaultTolerantMode.DISABLED elif env_value in ("1", "automatic"): return _FaultTolerantMode.AUTOMATIC elif env_value in ("2", "manual"): return _FaultTolerantMode.MANUAL raise MisconfigurationException( "The environment flag `PL_FAULT_TOLERANT_TRAINING` should be either 'disabled', 'automatic', or 'manual'." )

a610e043d797ca0bae1ce186829fece79077407a

enums.py

Add typing for utilities/enums.py (#11298)

lightning

pytorch_lightning/utilities/enums.py

Python

https://github.com/Lightning-AI/lightning.git

_path_from_module

def _path_from_module(self, module): # See #21874 for extended discussion of the behavior of this method in # various cases. # Convert to list because __path__ may not support indexing. paths = list(getattr(module, "__path__", [])) if len(paths) != 1: filename = getattr(module, "__file__", None) if filename is not None: paths = [os.path.dirname(filename)] else: # For unknown reasons, sometimes the list returned by __path__ # contains duplicates that must be removed (#25246). paths = list(set(paths)) if len(paths) > 1: raise ImproperlyConfigured( "The app module %r has multiple filesystem locations (%r); " "you must configure this app with an AppConfig subclass " "with a 'path' class attribute." % (module, paths) ) elif not paths: raise ImproperlyConfigured( "The app module %r has no filesystem location, " "you must configure this app with an AppConfig subclass " "with a 'path' class attribute." % module ) return paths[0]

9c19aff7c7561e3a82978a272ecdaad40dda5c00

config.py

Refs #33476 -- Reformatted code with Black.

django

django/apps/config.py

Python

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

value_as_datetime

def value_as_datetime(self) -> tp.Tuple[datetime, datetime] | None: if self.value is None: return None v1, v2 = self.value if isinstance(v1, numbers.Number): d1 = datetime.utcfromtimestamp(v1 / 1000) else: d1 = v1 if isinstance(v2, numbers.Number): d2 = datetime.utcfromtimestamp(v2 / 1000) else: d2 = v2 return d1, d2 value = NonNullable(Tuple(Datetime, Datetime), help=) value_throttled = Readonly(NonNullable(Tuple(Datetime, Datetime)), help=) start = NonNullable(Datetime, help=) end = NonNullable(Datetime, help=) step = Int(default=3_600_000, help=) format = Override(default="%d %b %Y %H:%M:%S") #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------

c9751009161f092b2e403d8cccccf5252c0dce1a

sliders.py

Add DatetimeRangeSlider (#12034) * Add DatetimeRangeSlider * Add tests * Add docs

bokeh

bokeh/models/widgets/sliders.py

Python

https://github.com/bokeh/bokeh.git

spectral_graph_forge

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

8bea55e3071ed71eab4fb6650a45f0cdf5c911d4

spectral_graph_forge.py

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

networkx

networkx/generators/spectral_graph_forge.py

Python

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

find_loader

def find_loader(self, fullname): _warnings.warn("FileFinder.find_loader() is deprecated and " "slated for removal in Python 3.12; use find_spec() instead", DeprecationWarning) spec = self.find_spec(fullname) if spec is None: return None, [] return spec.loader, spec.submodule_search_locations or []

8198943edd73a363c266633e1aa5b2a9e9c9f526

_bootstrap_external.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/importlib/_bootstrap_external.py

Python

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

sys_path

def sys_path(self): # type: () -> List[str] from .vendor.vistir.compat import JSONDecodeError current_executable = Path(sys.executable).as_posix() if not self.python or self.python == current_executable: return sys.path elif any([sys.prefix == self.prefix, not self.is_venv]): return sys.path cmd_args = [self.python, "-c", "import json, sys; print(json.dumps(sys.path))"] path, _ = vistir.misc.run( cmd_args, return_object=False, nospin=True, block=True, combine_stderr=False, write_to_stdout=False, ) try: path = json.loads(path.strip()) except JSONDecodeError: path = sys.path return path

9a3b3ce70621af6f9adaa9eeac9cf83fa149319c

environment.py

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

pipenv

pipenv/environment.py

Python

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

parquet_to_arrow

def parquet_to_arrow(sources, destination): stream = None if isinstance(destination, str) else destination disable = not utils.is_progress_bar_enabled() with ArrowWriter(path=destination, stream=stream) as writer: for source in utils.tqdm(sources, unit="sources", disable=disable): pf = pa.parquet.ParquetFile(source) for i in utils.tqdm(range(pf.num_row_groups), unit="row_groups", leave=False, disable=disable): df = pf.read_row_group(i).to_pandas() for col in df.columns: df[col] = df[col].apply(json.loads) reconstructed_table = pa.Table.from_pandas(df) writer.write_table(reconstructed_table) return destination

6ed6ac9448311930557810383d2cfd4fe6aae269

arrow_writer.py

Better TQDM output (#3654) * Show progress bar when generating examples * Consistent utils.is_progress_bar_enabled calls * Fix tqdm in notebook * Add missing params to DatasetDict.map * Specify total in tqdm progress bar in map * Fix total computation * Small fix * Add desc to map_nested * Add more precise descriptions to download * Address comments * Fix docstring * Final changes * Minor change

datasets

src/datasets/arrow_writer.py

Python

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

cursor_width

def cursor_width(self) -> int: if self.placeholder and not self.value: return cell_len(self.placeholder) return self._position_to_cell(len(self.value)) + 1

e61eaf7597a1050e80e7ce029737b5544743a2f5

_input.py

replace TextInput with Input

textual

src/textual/widgets/_input.py

Python

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

test_bundled_aggregations_with_filter

def test_bundled_aggregations_with_filter(self) -> None: self._send_relation(RelationTypes.ANNOTATION, "m.reaction", "a") # Note that the sync filter does not include "unsigned" as a field. filter = urllib.parse.quote_plus( b'{"event_fields": ["content", "event_id"], "room": {"timeline": {"limit": 3}}}' ) channel = self.make_request( "GET", f"/sync?filter={filter}", access_token=self.user_token ) self.assertEqual(200, channel.code, channel.json_body) # Ensure the timeline is limited, find the parent event. room_timeline = channel.json_body["rooms"]["join"][self.room]["timeline"] self.assertTrue(room_timeline["limited"]) parent_event = self._find_event_in_chunk(room_timeline["events"]) # Ensure there's bundled aggregations on it. self.assertIn("unsigned", parent_event) self.assertIn("m.relations", parent_event["unsigned"])

96274565ff0dbb7d21b02b04fcef115330426707

test_relations.py

Fix bundling aggregations if unsigned is not a returned event field. (#12234) An error occured if a filter was supplied with `event_fields` which did not include `unsigned`. In that case, bundled aggregations are still added as the spec states it is allowed for servers to add additional fields.

synapse

tests/rest/client/test_relations.py

Python

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

sample_crop_box

def sample_crop_box(self, img_size, results): assert isinstance(img_size, tuple) h, w = img_size[:2] key_masks = results['polys'] x_valid_array = np.ones(w, dtype=np.int32) y_valid_array = np.ones(h, dtype=np.int32) selected_mask = key_masks[np.random.randint(0, len(key_masks))] selected_mask = selected_mask.reshape((-1, 2)).astype(np.int32) max_x_start = max(np.min(selected_mask[:, 0]) - 2, 0) min_x_end = min(np.max(selected_mask[:, 0]) + 3, w - 1) max_y_start = max(np.min(selected_mask[:, 1]) - 2, 0) min_y_end = min(np.max(selected_mask[:, 1]) + 3, h - 1) # for key in results.get('mask_fields', []): # if len(results[key].masks) == 0: # continue # masks = results[key].masks for mask in key_masks: # assert len(mask) == 1 mask = mask.reshape((-1, 2)).astype(np.int32) clip_x = np.clip(mask[:, 0], 0, w - 1) clip_y = np.clip(mask[:, 1], 0, h - 1) min_x, max_x = np.min(clip_x), np.max(clip_x) min_y, max_y = np.min(clip_y), np.max(clip_y) x_valid_array[min_x - 2:max_x + 3] = 0 y_valid_array[min_y - 2:max_y + 3] = 0 min_w = int(w * self.min_side_ratio) min_h = int(h * self.min_side_ratio) x1, x2 = self.sample_valid_start_end(x_valid_array, min_w, max_x_start, min_x_end) y1, y2 = self.sample_valid_start_end(y_valid_array, min_h, max_y_start, min_y_end) return np.array([x1, y1, x2, y2])

9f62b610dea6161627200ed85d92e19b1923279a

fce_aug.py

add fcenet

PaddleOCR

ppocr/data/imaug/fce_aug.py

Python

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

dt64arr_to_periodarr

def dt64arr_to_periodarr(data, freq, tz=None): if not isinstance(data.dtype, np.dtype) or data.dtype.kind != "M": raise ValueError(f"Wrong dtype: {data.dtype}") if freq is None: if isinstance(data, ABCIndex): data, freq = data._values, data.freq elif isinstance(data, ABCSeries): data, freq = data._values, data.dt.freq elif isinstance(data, (ABCIndex, ABCSeries)): data = data._values reso = get_unit_from_dtype(data.dtype) freq = Period._maybe_convert_freq(freq) base = freq._period_dtype_code return c_dt64arr_to_periodarr(data.view("i8"), base, tz, reso=reso), freq

25749d29dbbb8c6ae7a05f4661948d03c17b20ae

period.py

ENH: DTA.to_period support non-nano (#47324) * ENH: DTA.to_period support non-nano * update test

pandas

pandas/core/arrays/period.py

Python

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

get_2d_sincos_pos_embed

def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False): grid_h = tf.range(grid_size, dtype=tf.float32) grid_w = tf.range(grid_size, dtype=tf.float32) grid = tf.meshgrid(grid_w, grid_h) # here w goes first grid = tf.stack(grid, axis=0) grid = tf.reshape(grid, [2, 1, grid_size, grid_size]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if add_cls_token: pos_embed = tf.concat([tf.zeros((1, embed_dim)), pos_embed], axis=0) return pos_embed

5b40a37bc4da9dc6cd33876ce9bb3f7f48450a03

modeling_tf_vit_mae.py

Add TF ViT MAE (#16255) * ported TFViTMAEIntermediate and TFViTMAEOutput. * added TFViTMAEModel and TFViTMAEDecoder. * feat: added a noise argument in the implementation for reproducibility. * feat: vit mae models with an additional noise argument for reproducibility. Co-authored-by: ariG23498 <[email protected]> Co-authored-by: ydshieh <[email protected]>

transformers

src/transformers/models/vit_mae/modeling_tf_vit_mae.py

Python

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

df_update

def df_update(self, other, **kwargs): # noqa: PR02 return BinaryDefault.register(pandas.DataFrame.update, inplace=True)( self, other=other, **kwargs )

57e29bc5d82348006c5170ef9ac0a9eedcd9acf9

query_compiler.py

REFACTOR-#4513: Fix spelling mistakes in docs and docstrings (#4514) Co-authored-by: Rehan Sohail Durrani <[email protected]> Signed-off-by: jeffreykennethli <[email protected]>

modin

modin/core/storage_formats/base/query_compiler.py

Python

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

total_seconds

def total_seconds(self) -> npt.NDArray[np.float64]: return self._maybe_mask_results(1e-9 * self.asi8, fill_value=None)

3350f95c017a68644e8577651af743413532356f

timedeltas.py

REF: share DTA/TDA/PA arithmetic methods (#47205) * REF: share DTA/TDA/PA arithmetic methods * troubleshoot npdev build

pandas

pandas/core/arrays/timedeltas.py

Python

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

batch_shuffle

def batch_shuffle(index_array, batch_size): batch_count = int(len(index_array) / batch_size) # to reshape we need to be cleanly divisible by batch size # we stash extra items and reappend them after shuffling last_batch = index_array[batch_count * batch_size :] index_array = index_array[: batch_count * batch_size] index_array = index_array.reshape((batch_count, batch_size)) np.random.shuffle(index_array) index_array = index_array.flatten() return np.append(index_array, last_batch)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

training_utils_v1.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/engine/training_utils_v1.py

Python

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

_intervals

def _intervals(self, sym): from sympy.solvers.inequalities import _solve_inequality assert isinstance(self, Piecewise)

498015021131af4dbb07eb110e5badaba8250c7b

piecewise.py

Updated import locations

sympy

sympy/functions/elementary/piecewise.py

Python

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

get

def get(identifier): if isinstance(identifier, dict): return deserialize(identifier) elif isinstance(identifier, str): return deserialize(str(identifier)) elif callable(identifier): return identifier else: raise ValueError( f'Could not interpret metric identifier: {identifier}')

b4dca51d0558e788f62a96d1009a07f773a202f4

__init__.py

Refactor disparate metrics-related files into a single metrics folder. Further work may be needed to split up the long file with individual metric definitions. However having a single file per metric may be too granular. TBD. PiperOrigin-RevId: 425248502

keras

keras/metrics/__init__.py

Python

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

get_attrtext

def get_attrtext(value): m = _non_attribute_end_matcher(value) if not m: raise errors.HeaderParseError( "expected attrtext but found {!r}".format(value)) attrtext = m.group() value = value[len(attrtext):] attrtext = ValueTerminal(attrtext, 'attrtext') _validate_xtext(attrtext) return attrtext, value

8198943edd73a363c266633e1aa5b2a9e9c9f526

_header_value_parser.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/email/_header_value_parser.py

Python

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

from_dict

def from_dict(data, npartitions, orient="columns", dtype=None, columns=None): collection_types = {type(v) for v in data.values() if is_dask_collection(v)} if collection_types: raise NotImplementedError( "from_dict doesn't currently support Dask collections as inputs. " f"Objects of type {collection_types} were given in the input dict." ) return from_pandas( pd.DataFrame.from_dict(data, orient, dtype, columns), npartitions, )

c4d35f5515191409913827fd4faa3b69a3d7399a

io.py

Backend library dispatching for IO in Dask-Array and Dask-DataFrame (#9475)

dask

dask/dataframe/io/io.py

Python

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

_is_unpacked_egg

def _is_unpacked_egg(path): return ( _is_egg_path(path) and os.path.isfile(os.path.join(path, 'EGG-INFO', 'PKG-INFO')) )

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

__init__.py

upd; format

transferlearning

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

Python

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

shuffle

def shuffle(self, items, key): hashes = {} for item in items: hashed = self._hash_item(item, key) if hashed in hashes: msg = "item {!r} has same hash {!r} as item {!r}".format( item, hashed, hashes[hashed], ) raise RuntimeError(msg) hashes[hashed] = item return [hashes[hashed] for hashed in sorted(hashes)]

9c19aff7c7561e3a82978a272ecdaad40dda5c00

runner.py

Refs #33476 -- Reformatted code with Black.

django

django/test/runner.py

Python

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

test_transform_inverse_transform_round_trip

def test_transform_inverse_transform_round_trip(SPCA): rng = np.random.RandomState(0) n_samples, n_features = 10, 5 X = rng.randn(n_samples, n_features) n_components = n_features spca = SPCA( n_components=n_components, alpha=1e-12, ridge_alpha=1e-12, random_state=0 ) X_trans_spca = spca.fit_transform(X) assert_allclose(spca.inverse_transform(X_trans_spca), X)

01fcf8a0acc7e6517faa4fc6887eb45f5d2ea77b

test_sparse_pca.py

ENH add inverse_transform in *SparsePCA (#23905)

scikit-learn

sklearn/decomposition/tests/test_sparse_pca.py

Python

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

extract_operations

def extract_operations(self) -> List[str]: if not self.step: return [] try: operations = re.findall(r'[-+*^/]', self.step) except TypeError as e: print(f"TYPE: {type(self.step)}") print(f"STEP: {self.step}") raise e return operations

0f129e9c38b6b10d80982ecc412785db62842938

step_by_step.py

ROSCOE suite of metrics (#4839) * ROSCOE suite of metrics * updating tests * lint * fixing protobuf version to stop cleaninstall failures * updating requirements * convert to absolute path * moving tests because of the dependency issues * adding new dependencies in tests * add test dependencies * fixing deps * updating task list * checklist deps can't be installed on circleci * actually fix protobuf version * protobuf range * protobuf conflict with google-api-core * return tests * convert imports to absolute path * trying checklist again * trying to avoid checklist failures * checklist to teacher tests * add user option to avoid installation failure * jupiter as well * typo * moving into virtual env setup * user param not allowed in virtual env * move spacy to circleCI because it's big * replace local model with HF * fixes based on comments * remove unused nli scores, fix tests * Added path to BART model Co-authored-by: Spencer Poff <[email protected]>

ParlAI

parlai/tasks/reasoning/reason_types/step_by_step.py

Python

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

less_equal

def less_equal(x, y): return tf.less_equal(x, y) @keras_export("keras.backend.maximum") @tf.__internal__.dispatch.add_dispatch_support @doc_controls.do_not_generate_docs

84afc5193d38057e2e2badf9c889ea87d80d8fbf

@keras_export("keras.backend.maximum") @tf.__internal__.dispatch.add_dispatch_support @doc_controls.do_not_generate_docs

backend.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/backend.py

Python

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

update_reference_in_journal_entry

def update_reference_in_journal_entry(d, journal_entry, do_not_save=False): jv_detail = journal_entry.get("accounts", {"name": d["voucher_detail_no"]})[0] if flt(d["unadjusted_amount"]) - flt(d["allocated_amount"]) != 0: # adjust the unreconciled balance amount_in_account_currency = flt(d["unadjusted_amount"]) - flt(d["allocated_amount"]) amount_in_company_currency = amount_in_account_currency * flt(jv_detail.exchange_rate) jv_detail.set(d["dr_or_cr"], amount_in_account_currency) jv_detail.set( "debit" if d["dr_or_cr"] == "debit_in_account_currency" else "credit", amount_in_company_currency, ) else: journal_entry.remove(jv_detail) # new row with references new_row = journal_entry.append("accounts") new_row.update((frappe.copy_doc(jv_detail)).as_dict()) new_row.set(d["dr_or_cr"], d["allocated_amount"]) new_row.set( "debit" if d["dr_or_cr"] == "debit_in_account_currency" else "credit", d["allocated_amount"] * flt(jv_detail.exchange_rate), ) new_row.set( "credit_in_account_currency" if d["dr_or_cr"] == "debit_in_account_currency" else "debit_in_account_currency", 0, ) new_row.set("credit" if d["dr_or_cr"] == "debit_in_account_currency" else "debit", 0) new_row.set("reference_type", d["against_voucher_type"]) new_row.set("reference_name", d["against_voucher"]) new_row.against_account = cstr(jv_detail.against_account) new_row.is_advance = cstr(jv_detail.is_advance) new_row.docstatus = 1 # will work as update after submit journal_entry.flags.ignore_validate_update_after_submit = True if not do_not_save: journal_entry.save(ignore_permissions=True)

494bd9ef78313436f0424b918f200dab8fc7c20b

utils.py

style: format code with black

erpnext

erpnext/accounts/utils.py

Python

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

test_sync_call_healthy_only

def test_sync_call_healthy_only(self): actors = [Actor.remote(i) for i in range(4)] manager = FaultTolerantActorManager(actors=actors) results = [] for _ in range(10): results.extend( manager.foreach_actor( lambda w: w.call(), healthy_only=True ).ignore_errors() ) # Wait for actors to recover. wait_for_restore() # Notice that since we only fire calls against healthy actors, # we wouldn't be aware that the actors have been recovered. # So once an actor is taken out of the lineup (10% chance), # it will not go back in, and we should have few results here. # Basically takes us 10 calls to kill all the actors. # Note that we can hardcode 10 here because we are using deterministic # sequences of random numbers. self.assertEqual(len(results), 10)

d329147ae28c57b290f6b932f9f3044523f67c4e

test_actor_manager.py

[RLlib] Introduce FaultTolerantActorManager (#29703) Signed-off-by: Jun Gong <[email protected]>

ray

rllib/utils/tests/test_actor_manager.py

Python

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

test_base_form_class_used

def test_base_form_class_used(self): edit_url = reverse( "wagtailadmin_pages:add", args=("tests", "formclassadditionalfieldpage", self.test_page.id), ) response = self.client.get(edit_url) self.assertContains( response, '<input type="text" name="code" required id="id_code" maxlength="5" />', html=True, )

d10f15e55806c6944827d801cd9c2d53f5da4186

test_revisions.py

Reformat with black

wagtail

wagtail/admin/tests/pages/test_revisions.py

Python

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

components

def components(self) -> Dict[str, BaseComponent]: all_components = self._find_all_components() return {component.name: component for component in all_components if component.name is not None}

f6e3a639063887f9f5b27f574a04c7fe602b3185

base.py

Prevent losing names of utilized components when loaded from config (#2525) * Prevent losing names of utilized components when loaded from config * Update Documentation & Code Style * update test * fix failing tests * Update Documentation & Code Style * fix even more tests * Update Documentation & Code Style * incorporate review feedback Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

haystack

haystack/pipelines/base.py

Python

https://github.com/deepset-ai/haystack.git

_ensure_iterable_column_indexer

def _ensure_iterable_column_indexer(self, column_indexer): ilocs: Sequence[int] if is_integer(column_indexer): ilocs = [column_indexer] elif isinstance(column_indexer, slice): ilocs = np.arange(len(self.obj.columns))[column_indexer] elif isinstance(column_indexer, np.ndarray) and is_bool_dtype( column_indexer.dtype ): ilocs = np.arange(len(column_indexer))[column_indexer] else: ilocs = column_indexer return ilocs

b5c6e4713ae4397cd047cb41f11aca4d27fb6096

indexing.py

CLN: suppress warnings (#45212)

pandas

pandas/core/indexing.py

Python

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

_start

def _start(self) -> int: warnings.warn( self._deprecation_message.format("_start", "start"), FutureWarning, stacklevel=find_stack_level(inspect.currentframe()), ) return self.start

2f8d0a36703e81e4dca52ca9fe4f58c910c1b304

range.py

PERF cache find_stack_level (#48023) cache stacklevel

pandas

pandas/core/indexes/range.py

Python

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

test_update_notice_user_name_when_changed

def test_update_notice_user_name_when_changed(self) -> None: server_notice_request_content = { "user_id": self.other_user, "content": {"msgtype": "m.text", "body": "test msg one"}, } self.make_request( "POST", self.url, access_token=self.admin_user_tok, content=server_notice_request_content, ) # simulate a change in server config after a server restart. new_display_name = "new display name" self.server_notices_manager._config.servernotices.server_notices_mxid_display_name = ( new_display_name ) self.server_notices_manager.get_or_create_notice_room_for_user.cache.invalidate_all() self.make_request( "POST", self.url, access_token=self.admin_user_tok, content=server_notice_request_content, ) invited_rooms = self._check_invite_and_join_status(self.other_user, 1, 0) notice_room_id = invited_rooms[0].room_id self.helper.join( room=notice_room_id, user=self.other_user, tok=self.other_user_token ) notice_user_state_in_room = self.helper.get_state( notice_room_id, "m.room.member", self.other_user_token, state_key="@notices:test", ) self.assertEqual(notice_user_state_in_room["displayname"], new_display_name)

2e2d8cc2f9b9af5f8b48d75e22c474e08feca236

test_server_notice.py

Update the server notices user profile in room if changed. (#12115)

synapse

tests/rest/admin/test_server_notice.py

Python

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

_create_basic_room

def _create_basic_room(self) -> Tuple[int, object]: channel = self.make_request( "POST", "/createRoom", {}, ) return channel.code, channel.json_body

6a6e1e8c0711939338f25d8d41d1e4d33d984949

test_rooms.py

Fix room creation being rate limited too aggressively since Synapse v1.69.0. (#14314) * Introduce a test for the old behaviour which we want to restore * Reintroduce the old behaviour in a simpler way * Newsfile Signed-off-by: Olivier Wilkinson (reivilibre) <[email protected]> * Use 1 credit instead of 2 for creating a room: be more lenient than before Notably, the UI in Element Web was still broken after restoring to prior behaviour. After discussion, we agreed that it would be sensible to increase the limit. Signed-off-by: Olivier Wilkinson (reivilibre) <[email protected]>

synapse

tests/rest/client/test_rooms.py

Python

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

boundary_slice

def boundary_slice(df, start, stop, right_boundary=True, left_boundary=True, kind=None): if len(df.index) == 0: return df if PANDAS_GT_131: if kind is not None: warnings.warn( "The `kind` argument is no longer used/supported. " "It will be dropped in a future release.", category=FutureWarning, ) kind_opts = {} kind = "loc" else: kind = kind or "loc" kind_opts = {"kind": kind} if kind == "loc" and not df.index.is_monotonic_increasing: # Pandas treats missing keys differently for label-slicing # on monotonic vs. non-monotonic indexes # If the index is monotonic, `df.loc[start:stop]` is fine. # If it's not, `df.loc[start:stop]` raises when `start` is missing if start is not None: if left_boundary: df = df[df.index >= start] else: df = df[df.index > start] if stop is not None: if right_boundary: df = df[df.index <= stop] else: df = df[df.index < stop] return df result = getattr(df, kind)[start:stop] if not right_boundary and stop is not None: right_index = result.index.get_slice_bound(stop, "left", **kind_opts) result = result.iloc[:right_index] if not left_boundary and start is not None: left_index = result.index.get_slice_bound(start, "right", **kind_opts) result = result.iloc[left_index:] return result

5e8a4813cf948250608b16747773a7dc52088eb6

methods.py

Deprecate `is_monotonic` (#8653) This PR deprecates `is_monotonic` to follow what `pandas` is doing upstream. This also resolves some test failures in our `upstream` build

dask

dask/dataframe/methods.py

Python

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

_ray

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

f97603bba836c215e153d7d3d5b3b9de4d0ae822

task_runners.py

First draft `RayTaskRunner` implementation

prefect

src/prefect/task_runners.py

Python

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

preprocess_WEBP

def preprocess_WEBP(self, image): save_kwargs = { "quality": self.WEBP_QUAL, "lossless": self.LOSSLESS_WEBP, "icc_profile": image.info.get("icc_profile", ""), } return (image, save_kwargs)

5d1a36b9aaf408016957db04f86397b2e53c2500

utils.py

Better media thumbnails including WebP support (#9988) * Add thumbnail app * Update get_thumbnail_size method and add tests * Add logic for creating thumbnails * Update logic for getting thumbnail * Allow defining format for tumbnail generation * Clear handle_thumbnail views * Add prepare_image_proxy_url method * Use ImageField for user avatar * Allow defining thumbnail format when querying user avatar * Use ImageField for category backgound_image * Use ImageField for Collection backgound_image * Use ImageField for ProductMedia image * Ensure that thumbnails are deleted when category background_image is changed or deleted * Ensure that thumbnails are deleted when collection background_image is changed or deleted * Update product media deleteion task and failing tests * Delete thumbnail from storage when thumbnail objects is deleted * Fix import in product test_bulk_delete * Drop create_thumbnails command * Update Product.thumbnail resolver * Update OrderLine thumbnail resolver * Add missing ADDED_IN_35 and PREVIEW_FEATURE labels * Update account and product signals - ensure the image is deleted from storage * Refactor product_images methods * Add signal for product media image delete * Drop create_thumbnails method and not longer valid settings fields * Clean the ProcessedImage class * Drop versatileimagefield from INSTALLED_APPS * Update changelog * Drop comments from ThumbnailFormat * Add get_image_or_proxy_url method * Apply reiew suggestions - add ThumbnailField and use get_image_or_proxy_ur when it's possible * Update changelog * Replace ADDED_IN_35 with ADDED_IN_36 label * Update changelog Co-authored-by: Marcin Gębala <[email protected]>

saleor

saleor/thumbnail/utils.py

Python

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

spherical_bessel_fn

def spherical_bessel_fn(n, x=None, polys=False): if x is None: x = Dummy("x") f = dup_spherical_bessel_fn_minus if n < 0 else dup_spherical_bessel_fn return named_poly(abs(n), f, ZZ, "", (QQ(1)/x,), polys)

d1d46df73ebaad94089847558d00a8b7269f554d

orthopolys.py

Run orthopolys and appellseqs through a common interface Including unifying the two Chebyshev generators into one function. There are also two kinds of Hermite polynomials, and they too share the same recurrence, but the second type He_n(x) (aka the probabilist, reduced or small polynomials) will not be added here.

sympy

sympy/polys/orthopolys.py

Python

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

_try_import_backends

def _try_import_backends(self) -> _BackendImports: # pylint: disable=unused-import results = _BackendImports() try: from qutebrowser.qt import webkit as QtWebKit from qutebrowser.qt.webkit import qWebKitVersion from qutebrowser.qt import webkit as QtWebKitWidgets except (ImportError, ValueError) as e: results.webkit_error = str(e) else: if not qtutils.is_new_qtwebkit(): results.webkit_error = "Unsupported legacy QtWebKit found" try: from qutebrowser.qt import webengine as QtWebEngineWidgets except (ImportError, ValueError) as e: results.webengine_error = str(e) return results

d47cfd99d7374a41b4c228c21221c7850e65d0b1

backendproblem.py

Run scripts/dev/rewrite_qt_imports.sh

qutebrowser

qutebrowser/misc/backendproblem.py

Python

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

test_sync_in_area

async def test_sync_in_area(area_on_device, hass, registries): area = registries.area.async_create("Living Room") device = registries.device.async_get_or_create( config_entry_id="1234", manufacturer="Someone", model="Some model", sw_version="Some Version", connections={(device_registry.CONNECTION_NETWORK_MAC, "12:34:56:AB:CD:EF")}, ) registries.device.async_update_device( device.id, area_id=area.id if area_on_device else None ) entity = registries.entity.async_get_or_create( "light", "test", "1235", suggested_object_id="demo_light", device_id=device.id, ) entity = registries.entity.async_update_entity( entity.entity_id, area_id=area.id if not area_on_device else None ) light = DemoLight( None, "Demo Light", state=False, hs_color=(180, 75), effect_list=LIGHT_EFFECT_LIST, effect=LIGHT_EFFECT_LIST[0], ) light.hass = hass light.entity_id = entity.entity_id await light.async_update_ha_state() config = MockConfig(should_expose=lambda _: True, entity_config={}) events = async_capture_events(hass, EVENT_SYNC_RECEIVED) result = await sh.async_handle_message( hass, config, "test-agent", {"requestId": REQ_ID, "inputs": [{"intent": "action.devices.SYNC"}]}, const.SOURCE_CLOUD, ) assert result == { "requestId": REQ_ID, "payload": { "agentUserId": "test-agent", "devices": [ { "id": "light.demo_light", "name": {"name": "Demo Light"}, "traits": [ trait.TRAIT_BRIGHTNESS, trait.TRAIT_ONOFF, trait.TRAIT_COLOR_SETTING, trait.TRAIT_MODES, ], "type": const.TYPE_LIGHT, "willReportState": False, "attributes": { "availableModes": [ { "name": "effect", "name_values": [ {"lang": "en", "name_synonym": ["effect"]} ], "ordered": False, "settings": [ { "setting_name": "rainbow", "setting_values": [ { "lang": "en", "setting_synonym": ["rainbow"], } ], }, { "setting_name": "none", "setting_values": [ {"lang": "en", "setting_synonym": ["none"]} ], }, ], } ], "colorModel": "hsv", "colorTemperatureRange": { "temperatureMinK": 2000, "temperatureMaxK": 6535, }, }, "deviceInfo": { "manufacturer": "Someone", "model": "Some model", "swVersion": "Some Version", }, "roomHint": "Living Room", } ], }, } await hass.async_block_till_done() assert len(events) == 1 assert events[0].event_type == EVENT_SYNC_RECEIVED assert events[0].data == {"request_id": REQ_ID, "source": "cloud"}

1bc8770b51658f0dc1bd076b392d70be5a7433bc

test_smart_home.py

Remove area_id from entity_registry.async_get_or_create (#77700) * Remove area_id from entity_registry.async_get_or_create * Adjust tests * Fix lying comment in test

core

tests/components/google_assistant/test_smart_home.py

Python

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

get_document_chosen_response

def get_document_chosen_response(request, document): return render_modal_workflow( request, None, None, None, json_data={ "step": "document_chosen", "result": { "id": document.id, "title": document.title, "url": document.url, "filename": document.filename, "edit_link": reverse("wagtaildocs:edit", args=(document.id,)), }, }, )

d10f15e55806c6944827d801cd9c2d53f5da4186

chooser.py

Reformat with black

wagtail

wagtail/documents/views/chooser.py

Python

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

call

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

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

__init__.py

upd; format

transferlearning

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

Python

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

execute

def execute(filters=None): filters = filters if isinstance(filters, frappe._dict) else frappe._dict(filters) if not filters: filters.setdefault('fiscal_year', get_fiscal_year(nowdate())[0]) filters.setdefault('company', frappe.db.get_default("company")) region = frappe.db.get_value("Company", filters={"name": filters.company}, fieldname=["country"]) if region != 'United States': return [], [] columns = get_columns() conditions = "" if filters.supplier_group: conditions += "AND s.supplier_group = %s" %frappe.db.escape(filters.get("supplier_group")) data = frappe.db.sql(.format(conditions=conditions), { "fiscal_year": filters.fiscal_year, "company": filters.company }, as_dict=True) return columns, data

fe4b6771b5fd935ed278cf553c864a18e3356a33

irs_1099.py

refactor: Remove dead code (#30140)

erpnext

erpnext/regional/report/irs_1099/irs_1099.py

Python

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

get_student_group_strength

def get_student_group_strength(student_group): student_group_strength = frappe.db.sql( , student_group, )[0][0] return student_group_strength

494bd9ef78313436f0424b918f200dab8fc7c20b

student_batch_wise_attendance.py

style: format code with black

erpnext

erpnext/education/report/student_batch_wise_attendance/student_batch_wise_attendance.py

Python

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

source

def source(self) -> "np.ndarray": assert self._source is not None return self._source

7da2cc3dd266aabebf41a31384cc2e0e7e5af6e5

preview_tk.py

Training - Use custom preview pop-out

faceswap

lib/training/preview_tk.py

Python

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

summer

def summer(): set_cmap('summer') # Autogenerated by boilerplate.py. Do not edit as changes will be lost.

032316bc6c7798fca6c82de24167c975f237687f

pyplot.py

Cleanup documentation generation for pyplot - remove the awkward `pyplot.plotting()` function, which only served as a namespace to take up the docs for pyplot and output them via `.. autofunction` - Instead generate the same information using `.. autosummary::`. We have to list the desired methods here explicitly. I've added a test that these are the same as previously auto-generated in the `plotting()` docstring. If we change anything in pyplot, we'll be notified through the test failure that we have to adapt the autosummary list. - Removed the docstring generation logic `_setup_pyplot_info_docstrings()`. Apart from generating the `plotting()` docstring, this added docstrings to the pyplot colormap setters. Instead, we now add these docstrings directly via boilerplate.py Co-authored-by: Elliott Sales de Andrade <[email protected]>

matplotlib

lib/matplotlib/pyplot.py

Python

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

test_all_users

def test_all_users(self) -> None: self._create_users(2) channel = self.make_request( "GET", self.url + "?deactivated=true", {}, access_token=self.admin_user_tok, ) self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body) self.assertEqual(3, len(channel.json_body["users"])) self.assertEqual(3, channel.json_body["total"]) # Check that all fields are available self._check_fields(channel.json_body["users"])

901b264c0c88f39cbfb8b2229e0dc57968882658

test_user.py

Add type hints to `tests/rest/admin` (#11851)

synapse

tests/rest/admin/test_user.py

Python

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

test_ssh_sync

def test_ssh_sync(): experiment_name = "cloud_ssh_sync" indicator_file = f"/tmp/{experiment_name}_indicator"

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

run_cloud_test.py

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

ray

release/tune_tests/cloud_tests/workloads/run_cloud_test.py

Python

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

parse_time_mapped_ti_count

def parse_time_mapped_ti_count(self) -> Optional[int]: total = 0 for value in self._get_expansion_kwargs().values(): if not isinstance(value, MAPPABLE_LITERAL_TYPES): # None literal type encountered, so give up return None total += len(value) return total

91832a42d8124b040073481fd93c54e9e64c2609

mappedoperator.py

Expand mapped tasks at DagRun.Veriy_integrity (#22679) Create the necessary task instances for a mapped task at dagrun.verify_integrity Co-authored-by: Ash Berlin-Taylor <[email protected]>

airflow

airflow/models/mappedoperator.py

Python

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

convert_path

def convert_path(pathname): if os.sep == '/': return pathname if not pathname: return pathname if pathname[0] == '/': raise ValueError("path '%s' cannot be absolute" % pathname) if pathname[-1] == '/': raise ValueError("path '%s' cannot end with '/'" % pathname) paths = pathname.split('/') while os.curdir in paths: paths.remove(os.curdir) if not paths: return os.curdir return os.path.join(*paths)

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

util.py

upd; format

transferlearning

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

Python

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

_async_update_data

async def _async_update_data(self) -> _DataT: interval = self.update_interval if ( interval is not None and self.last_update_success and self.data and self.push_coordinator.active(interval) ): data = self.data else: data = await super()._async_update_data() return data

653805584bac4907ba6da527071bceb9d7313019

coordinator.py

Improve `lookin` generic typing (#84636)

core

homeassistant/components/lookin/coordinator.py

Python

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

gs_distill

def gs_distill(tmpfile, eps=False, ptype='letter', bbox=None, rotated=False): if eps: paper_option = "-dEPSCrop" else: paper_option = "-sPAPERSIZE=%s" % ptype psfile = tmpfile + '.ps' dpi = mpl.rcParams['ps.distiller.res'] cbook._check_and_log_subprocess( [mpl._get_executable_info("gs").executable, "-dBATCH", "-dNOPAUSE", "-r%d" % dpi, "-sDEVICE=ps2write", paper_option, "-sOutputFile=%s" % psfile, tmpfile], _log) os.remove(tmpfile) shutil.move(psfile, tmpfile) # While it is best if above steps preserve the original bounding # box, there seem to be cases when it is not. For those cases, # the original bbox can be restored during the pstoeps step. if eps: # For some versions of gs, above steps result in a ps file where the # original bbox is no more correct. Do not adjust bbox for now. pstoeps(tmpfile, bbox, rotated=rotated)

383de519505964ed879c40b23ef36e90c17ebe0d

backend_ps.py

[Doc] fix more spelling and grammar

matplotlib

lib/matplotlib/backends/backend_ps.py

Python

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

_get_const_info

def _get_const_info(const_index, const_list): argval = const_index if const_list is not None: argval = const_list[const_index] return argval, repr(argval)

8198943edd73a363c266633e1aa5b2a9e9c9f526

dis.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/dis.py

Python

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

draw

def draw(self, subs_dict=None): if not numpy: raise ImportError("To use this function numpy module is required") x = Symbol('x') markers = [] annotations = [] rectangles = [] node_markers = self._draw_nodes(subs_dict) markers += node_markers member_rectangles = self._draw_members() rectangles += member_rectangles support_markers = self._draw_supports() markers += support_markers load_annotations = self._draw_loads() annotations += load_annotations xmax = -INF xmin = INF ymax = -INF ymin = INF for node in list(self._node_coordinates): xmax = max(xmax, self._node_coordinates[node][0]) xmin = min(xmin, self._node_coordinates[node][0]) ymax = max(ymax, self._node_coordinates[node][1]) ymin = min(ymin, self._node_coordinates[node][1]) lim = max(xmax*1.1-xmin*0.8+1, ymax*1.1-ymin*0.8+1) if lim==xmax*1.1-xmin*0.8+1: sing_plot = plot(1, (x, 1, 1), markers=markers, show=False, annotations=annotations, xlim=(xmin-0.05*lim, xmax*1.1), ylim=(xmin-0.05*lim, xmax*1.1), axis=False, rectangles=rectangles) else: sing_plot = plot(1, (x, 1, 1), markers=markers, show=False, annotations=annotations, xlim=(ymin-0.05*lim, ymax*1.1), ylim=(ymin-0.05*lim, ymax*1.1), axis=False, rectangles=rectangles) return sing_plot

2ddc46704dffa81a5a2a8df4348bf98dff07ebd5

truss.py

subs_dict added to the draw method

sympy

sympy/physics/continuum_mechanics/truss.py

Python

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

center_crop

def center_crop(self, image, size): self._ensure_format_supported(image) if not isinstance(size, tuple): size = (size, size) # PIL Image.size is (width, height) but NumPy array and torch Tensors have (height, width) if is_torch_tensor(image) or isinstance(image, np.ndarray): if image.ndim == 2: image = self.expand_dims(image) image_shape = image.shape[1:] if image.shape[0] in [1, 3] else image.shape[:2] else: image_shape = (image.size[1], image.size[0]) top = (image_shape[0] - size[0]) // 2 bottom = top + size[0] # In case size is odd, (image_shape[0] + size[0]) // 2 won't give the proper result. left = (image_shape[1] - size[1]) // 2 right = left + size[1] # In case size is odd, (image_shape[1] + size[1]) // 2 won't give the proper result. # For PIL Images we have a method to crop directly. if isinstance(image, PIL.Image.Image): return image.crop((left, top, right, bottom)) # Check if image is in (n_channels, height, width) or (height, width, n_channels) format channel_first = True if image.shape[0] in [1, 3] else False # Transpose (height, width, n_channels) format images if not channel_first: if isinstance(image, np.ndarray): image = image.transpose(2, 0, 1) if is_torch_tensor(image): image = image.permute(2, 0, 1) # Check if cropped area is within image boundaries if top >= 0 and bottom <= image_shape[0] and left >= 0 and right <= image_shape[1]: return image[..., top:bottom, left:right] # Otherwise, we may need to pad if the image is too small. Oh joy... new_shape = image.shape[:-2] + (max(size[0], image_shape[0]), max(size[1], image_shape[1])) if isinstance(image, np.ndarray): new_image = np.zeros_like(image, shape=new_shape) elif is_torch_tensor(image): new_image = image.new_zeros(new_shape) top_pad = (new_shape[-2] - image_shape[0]) // 2 bottom_pad = top_pad + image_shape[0] left_pad = (new_shape[-1] - image_shape[1]) // 2 right_pad = left_pad + image_shape[1] new_image[..., top_pad:bottom_pad, left_pad:right_pad] = image top += top_pad bottom += top_pad left += left_pad right += left_pad new_image = new_image[ ..., max(0, top) : min(new_image.shape[-2], bottom), max(0, left) : min(new_image.shape[-1], right) ] return new_image

49becbaa5549b477b0d96c55f207614773c0ab42

image_utils.py

Enable crop_center method to handle (W, H, C) images (#17626) * enable crop_center method to handle (W, H, C) images * minor style and comment edits

transformers

src/transformers/image_utils.py

Python

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

input

def input(self): if not self._inbound_nodes: raise AttributeError( "Layer " + self.name + " is not connected, no input to return." ) return self._get_node_attribute_at_index(0, "input_tensors", "input")

84afc5193d38057e2e2badf9c889ea87d80d8fbf

base_layer.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/engine/base_layer.py

Python

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