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semeru

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

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Semeru Lab 7

ppdet/modeling/rbox_utils.py

def box2corners(box): B = box.shape[0] x, y, w, h, alpha = paddle.split(box, 5, axis=-1) x4 = paddle.to_tensor( [0.5, 0.5, -0.5, -0.5], dtype=paddle.float32).reshape( (1, 1, 4)) # (1,1,4) x4 = x4 * w # (B, N, 4) y4 = paddle.to_tensor( [-0.5, 0.5, 0.5, -0.5], dtype=paddle.float32).reshape((1, 1, 4)) y4 = y4 * h # (B, N, 4) corners = paddle.stack([x4, y4], axis=-1) # (B, N, 4, 2) sin = paddle.sin(alpha) cos = paddle.cos(alpha) row1 = paddle.concat([cos, sin], axis=-1) row2 = paddle.concat([-sin, cos], axis=-1) # (B, N, 2) rot_T = paddle.stack([row1, row2], axis=-2) # (B, N, 2, 2) rotated = paddle.bmm(corners.reshape([-1, 4, 2]), rot_T.reshape([-1, 2, 2])) rotated = rotated.reshape([B, -1, 4, 2]) # (B*N, 4, 2) -> (B, N, 4, 2) rotated[..., 0] += x rotated[..., 1] += y return rotated

add fcosr model (#6765) * add fcosr * fix some problem * add docs for fcosr * modify code * modify focsr reader * finish tensorrt deployment with dynamic shape * modify according to review comment Co-authored-by: wangxinxin08 <>

box2corners

92078713cced4f0d9450a6fc80a449fa75fd8c10

PaddleDetection

rbox_utils.py

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

Python

def box2corners(box): B = box.shape[0] x, y, w, h, alpha = paddle.split(box, 5, axis=-1) x4 = paddle.to_tensor( [0.5, 0.5, -0.5, -0.5], dtype=paddle.float32).reshape( (1, 1, 4)) # (1,1,4) x4 = x4 * w # (B, N, 4) y4 = paddle.to_tensor( [-0.5, 0.5, 0.5, -0.5], dtype=paddle.float32).reshape((1, 1, 4)) y4 = y4 * h # (B, N, 4) corners = paddle.stack([x4, y4], axis=-1) # (B, N, 4, 2) sin = paddle.sin(alpha) cos = paddle.cos(alpha) row1 = paddle.concat([cos, sin], axis=-1) row2 = paddle.concat([-sin, cos], axis=-1) # (B, N, 2) rot_T = paddle.stack([row1, row2], axis=-2) # (B, N, 2, 2) rotated = paddle.bmm(corners.reshape([-1, 4, 2]), rot_T.reshape([-1, 2, 2])) rotated = rotated.reshape([B, -1, 4, 2]) # (B*N, 4, 2) -> (B, N, 4, 2) rotated[..., 0] += x rotated[..., 1] += y return rotated

src/sentry/incidents/subscription_processor.py

def get_crash_rate_alert_metrics_aggregation_value(self, subscription_update): rows = subscription_update["values"]["data"] if BaseMetricsEntitySubscription.is_crash_rate_format_v2(rows): version = "v2" result = self._get_crash_rate_alert_metrics_aggregation_value_v2(subscription_update) else: version = "v1" result = self._get_crash_rate_alert_metrics_aggregation_value_v1(subscription_update) metrics.incr( "incidents.alert_rules.get_crash_rate_alert_metrics_aggregation_value", tags={"format": version}, sample_rate=1.0, ) ret

fix(cra-metrics): Count all users in metrics alerts (#34957) Use conditional aggregates in order to get both the total user count and the number of crashed users in the same snuba query. To maintain compatibility until existing subscriptions have been migrated, make the subscription processor able to handle both the old and the new format. The actual migration of existing subscriptions will be in a separate PR.

get_crash_rate_alert_metrics_aggregation_value

65f43fd4e0f1821b468547fc08136bbad9cd8446

sentry

subscription_processor.py

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

Python

def get_crash_rate_alert_metrics_aggregation_value(self, subscription_update): rows = subscription_update["values"]["data"] if BaseMetricsEntitySubscription.is_crash_rate_format_v2(rows): version = "v2" result = self._get_crash_rate_alert_metrics_aggregation_value_v2(subscription_update) else: version = "v1" result = self._get_crash_rate_alert_metrics_aggregation_value_v1(subscription_update) metrics.incr( "incidents.alert_rules.get_crash_rate_alert_metrics_aggregation_value", tags={"format": version}, sample_rate=1.0, ) return result

jax/_src/test_util.py

def strict_promotion_if_dtypes_match(dtypes): if all(dtype == dtypes[0] for dtype in dtypes): return jax.

Add jtu.strict_promotion_if_dtypes_match utility

strict_promotion_if_dtypes_match

4c0d61a1435b70760814f1f678cb041d36b8408d

jax

test_util.py

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

Python

def strict_promotion_if_dtypes_match(dtypes): if all(dtype == dtypes[0] for dtype in dtypes): return jax.numpy_dtype_promotion('strict') return jax.numpy_dtype_promotion('standard')

sklearn/utils/tests/test_param_validation.py

def test_stroptions_deprecated_subset(): with pytest.raises(ValueError, match="deprecated options must be a subset"): StrOptions({"a", "b", "c"}, deprecated={"a", "d"})

MNT Param validation: Make it possible to mark a constraint as hidden (#23558)

test_stroptions_deprecated_subset

de659b9dee2054efb4830eff8e98ece60f4a1758

scikit-learn

test_param_validation.py

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

Python

def test_stroptions_deprecated_subset(): with pytest.raises(ValueError, match="deprecated options must be a subset"): StrOptions({"a", "b", "c"}, deprecated={"a", "d"})

keras/engine/training.py

def _validate_target_and_loss(self, y, loss): # `self.loss` references the loss added via `compile` call. If users have # provided such, the target must be provided; otherwise it's a user error. # Note that `self.loss` does not include losses added via `add_loss`, and it # is a valid use when such loss from `add_loss` exists and target does not. if self.loss and y is None: raise ValueError( "Target data is missing. Your model was compiled with " f"loss={self.loss}, " "and therefore expects target data to be provided in `fit()`." ) # For training, there must be compiled loss or regularization loss to exist # in ord

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

_validate_target_and_loss

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training.py

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

Python

def _validate_target_and_loss(self, y, loss): # `self.loss` references the loss added via `compile` call. If users have # provided such, the target must be provided; otherwise it's a user error. # Note that `self.loss` does not include losses added via `add_loss`, and it # is a valid use when such loss from `add_loss` exists and target does not. if self.loss and y is None: raise ValueError( "Target data is missing. Your model was compiled with " f"loss={self.loss}, " "and therefore expects target data to be provided in `fit()`." ) # For training, there must be compiled loss or regularization loss to exist # in order to apply the gradients. If one is not found, it means no loss # was supplied via `compile` or `add_loss`. elif loss is None: raise ValueError( "No loss found. You may have forgotten to provide a `loss` argument " "in the `compile()` method." )

pandas/core/window/rolling.py

def _validate_datetimelike_monotonic(self): # GH 46061 if self._on.hasnans: self._raise_monoton

BUG: groupby().rolling(freq) with monotonic dates within groups #46065 (#46567)

_validate_datetimelike_monotonic

d2aa44f50f6ac4789d4e351e4e52a53a358da42e

pandas

rolling.py

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

Python

def _validate_datetimelike_monotonic(self): # GH 46061 if self._on.hasnans: self._raise_monotonic_error("values must not have NaT") for group_indices in self._grouper.indices.values(): group_on = self._on.take(group_indices) if not ( group_on.is_monotonic_increasing or group_on.is_monotonic_decreasing ): on = "index" if self.on is None else self.on raise ValueError( f"Each group within {on} must be monotonic. " f"Sort the values in {on} first." )

python3.10.4/Lib/ctypes/macholib/framework.py

def framework_info(filename): is_framework = STRICT_FRAMEWORK_RE.match(filename) if not is_framework: return None return is_framewo

add python 3.10.4 for windows

framework_info

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

framework.py

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

Python

def framework_info(filename): is_framework = STRICT_FRAMEWORK_RE.match(filename) if not is_framework: return None return is_framework.groupdict()

.venv/lib/python3.8/site-packages/pip/_vendor/pep517/in_process/_in_process.py

def contained_in(filename, directory): filename = os.path.normcase(os.path.abspath(filename)) directory = os.path.normcase(os.path.abspath(directory)) return os.path.commonprefix([f

upd; format

contained_in

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

_in_process.py

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

Python

def contained_in(filename, directory): filename = os.path.normcase(os.path.abspath(filename)) directory = os.path.normcase(os.path.abspath(directory)) return os.path.commonprefix([filename, directory]) == directory

src/transformers/activations_tf.py

def glu(x, axis=-1): a, b = tf.split(x, 2, axis=axis) return a *

add TF glu activation function (#15146)

glu

c4f7eb124b218741d66dd1d86b5d744024a78f6f

transformers

activations_tf.py

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

Python

def glu(x, axis=-1): a, b = tf.split(x, 2, axis=axis) return a * tf.math.sigmoid(b) if version.parse(tf.version.VERSION) >= version.parse("2.4"):

py/visdom/__init__.py

def matplot(self, plot, opts=None, env=None, win=None): opts = {} if opts is None else opts _title2str(opts) _assert_opts(opts) # write plot to SVG buffer: buffer = StringIO() plot.savefig(buffer, format="svg") buffer.seek(0) svg = buffer.read() buffer.close() if opts.get("resizable", False): if not BS4_AVAILABLE: raise ImportError("No module named 'bs4'") else: try: soup = bs4.BeautifulSoup(svg, "x

apply black py to all python files

matplot

5b8b7f267cfaf76a2a39a727ef31a62b3909a093

visdom

__init__.py

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

Python

def matplot(self, plot, opts=None, env=None, win=None): opts = {} if opts is None else opts _title2str(opts) _assert_opts(opts) # write plot to SVG buffer: buffer = StringIO() plot.savefig(buffer, format="svg") buffer.seek(0) svg = buffer.read() buffer.close() if opts.get("resizable", False): if not BS4_AVAILABLE: raise ImportError("No module named 'bs4'") else: try: soup = bs4.BeautifulSoup(svg, "xml") except bs4.FeatureNotFound as e: import six six.raise_from(ImportError("No module named 'lxml'"), e) height = soup.svg.attrs.pop("height", None) width = soup.svg.attrs.pop("width", None) svg = str(soup) else: height = None width = None # show SVG: if "height" not in opts: height = height or re.search(r'height\="([0-9\.]*)pt"', svg) if height is not None: if not isstr(height): height = height.group(1) height = height.replace("pt", "00") opts["height"] = 1.4 * int(math.ceil(float(height))) if "width" not in opts: width = width or re.search(r'width\="([0-9\.]*)pt"', svg) if width is not None: if not isstr(width): width = width.group(1) width = width.replace("pt", "00") opts["width"] = 1.35 * int(math.ceil(float(width))) return self.svg(svgstr=svg, opts=opts, env=env, win=win)

salt/modules/consul.py

def acl_clone(consul_url=None, token=None, **kwargs): ret = {} data = {} if not consul_url: consul_url = _get_config() if not consul_url: log.error("No Consul URL found.") ret["message"] = "No Consul URL found." ret["res"] = False return ret if "id" not in kwargs: ret["message"] = 'Required parameter "id" is missing.' ret["res"] = False return ret function = "acl/clone/{}".format(kwargs["id"]) res = _query( consul_url=consul_url, token=token, data=data, method="PUT", function=function ) if res["res"]: ret["res"] = True ret["message"] = "ACL {} cloned.".format(kwargs["name"]) ret["ID"] = res["data"] else: ret["res"] = False ret["message"] = "Cloning ACL item {} failed.".format(kwargs["name"]) return ret

fix(consul): serialize to JSON only non string objects. Fixes 35215

acl_clone

50a17432015fb712ec4dc7d3ead79e8939e2bf96

salt

consul.py

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

Python

def acl_clone(consul_url=None, token=None, **kwargs): ret = {} data = {} if not consul_url: consul_url = _get_config() if not consul_url: log.error("No Consul URL found.") ret["message"] = "No Consul URL found." ret["res"] = False return ret if "id" not in kwargs: ret["message"] = 'Required parameter "id" is missing.' ret["res"] = False return ret function = "acl/clone/{}".format(kwargs["id"]) res = _query( consul_url=consul_url, token=token, data=data, method="PUT", function=function ) if res["res"]: ret["res"] = True ret["message"] = "ACL {} cloned.".format(kwargs["name"]) ret["ID"] = res["data"] else: ret["res"] = False ret["message"] = "Cloning ACL item {} failed.".format(kwargs["name"]) return ret

pandas/core/indexes/datetimes.py

def slice_indexer(self, start=None, end=None, step=None, kind=lib.no_default): self._deprecated_arg(kind, "kind", "slice_indexer") # For historical reasons DatetimeIndex supports slices between two # instances of datetime.time as if it were applying a slice mask to # an

PERF cache find_stack_level (#48023) cache stacklevel

slice_indexer

2f8d0a36703e81e4dca52ca9fe4f58c910c1b304

pandas

datetimes.py

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

Python

def slice_indexer(self, start=None, end=None, step=None, kind=lib.no_default): self._deprecated_arg(kind, "kind", "slice_indexer") # For historical reasons DatetimeIndex supports slices between two # instances of datetime.time as if it were applying a slice mask to # an array of (self.hour, self.minute, self.seconds, self.microsecond). if isinstance(start, time) and isinstance(end, time): if step is not None and step != 1: raise ValueError("Must have step size of 1 with time slices") return self.indexer_between_time(start, end) if isinstance(start, time) or isinstance(end, time): raise KeyError("Cannot mix time and non-time slice keys")

freqtrade/persistence/migrations.py

def fix_old_dry_orders(engine):

Cleanup old, left open dry-run orders

fix_old_dry_orders

c0ff554d5be871098cd10424fdd579322b5370df

freqtrade

migrations.py

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

Python

def fix_old_dry_orders(engine): with engine.begin() as connection: connection.execute( text( ) ) connection.execute( text( ) )

test/mitmproxy/proxy/layers/http/hyper_h2_test_helpers.py

def build_data_frame(self, data, flags=None, stream_id=1, padding_len=0): flags = set(flags) if flags is not None else set() f = DataFrame(stream_id) f.data = data f.flags = flags if padding_len: flags.add("PADDED") f.pad_length = padding_len return f

make it black!

build_data_frame

b3587b52b25077f68116b9852b041d33e7fc6601

mitmproxy

hyper_h2_test_helpers.py

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

Python

def build_data_frame(self, data, flags=None, stream_id=1, padding_len=0): flags = set(flags) if flags is not None else set() f = DataFrame(stream_id) f.data = data f.flags = flags if padding_len: flags.add("PADDED") f.pad_length = padding_len return f

jina/parsers/dryrun.py

def set_dryrun_parser(parser=None): if not parser: parser = set_base_parser() parser.add_argument( 'host', type=str, help='The full host address of the Gateway, e.g. grpc://localhost:12345', ) parser.add_argument( '--timeout', type=int, default=3000, help=, ) return parser

feat: add dryrun to cli (#5050) * feat: add dryrun to cli * style: fix overload and cli autocomplete * feat: add parsing for dryrun * feat: update checker dryrun * docs: add dryrun cli to healt check page * style: fix overload and cli autocomplete * feat: add exit Co-authored-by: Jina Dev Bot <[email protected]>

set_dryrun_parser

124045351137d80d118f9692de4295d50561f1e1

jina

dryrun.py

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

Python

def set_dryrun_parser(parser=None): if not parser: parser = set_base_parser() parser.add_argument( 'host', type=str, help='The full host address of the Gateway, e.g. grpc://localhost:12345', ) parser.add_argument( '--timeout', type=int, default=3000, help=, ) return parser

.venv/lib/python3.8/site-packages/pip/_internal/utils/wheel.py

def wheel_dist_info_dir(source, name): # type: (ZipFile, str) -> str # Zip file path separators must be / subdirs = {p.split("/", 1)[0] for p in source.namelist()} info_dirs = [s for s in subdirs if s.endswith(".dist-info")] if not info_dirs: raise UnsupportedWheel(".dist-info directory not found") if len(info_dirs) > 1: raise UnsupportedWheel( "multiple .dist-info directories found: {}".format(", ".join(info_dirs)) ) info_dir = info_dirs[0] info_dir_name = canonicalize_name(info_dir) canonical_name = canonicalize_name(name) if not info_dir_name.startswith(canonical_name): raise Unsupported

upd; format

wheel_dist_info_dir

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

wheel.py

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

Python

def wheel_dist_info_dir(source, name): # type: (ZipFile, str) -> str # Zip file path separators must be / subdirs = {p.split("/", 1)[0] for p in source.namelist()} info_dirs = [s for s in subdirs if s.endswith(".dist-info")] if not info_dirs: raise UnsupportedWheel(".dist-info directory not found") if len(info_dirs) > 1: raise UnsupportedWheel( "multiple .dist-info directories found: {}".format(", ".join(info_dirs)) ) info_dir = info_dirs[0] info_dir_name = canonicalize_name(info_dir) canonical_name = canonicalize_name(name) if not info_dir_name.startswith(canonical_name): raise UnsupportedWheel( ".dist-info directory {!r} does not start with {!r}".format( info_dir, canonical_name ) ) return info_dir

numpy/lib/arraysetops.py

def in1d(ar1, ar2, assume_unique=False, invert=False): # Ravel both arrays, behavior for the first array could be different ar1 = np.asarray(ar1).ravel() ar2 = np.asarray(ar2).ravel() # Ensure that iteration through object arrays yields size

MAINT: Optimize np.isin for integer arrays - This optimization indexes with an intermediary boolean array to speed up numpy.isin and numpy.in1d for integer arrays over a range of optimal parameters which are calculated.

in1d

cedba623b110caf83f46edfa38cb4fbc0191e285

numpy

arraysetops.py

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

Python

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

networkx/algorithms/centrality/degree_alg.py

def out_degree_centrality(G): if len(G) <= 1: return {n: 1 for n in G} s = 1.0 / (len(G) - 1.0) centrality = {n: d * s for n, d in G.out_degree()} return centralit

added examples to degree_alg.py (#5644) * added example on degree centrality * added example on in degree centrality * added example on out degree centrality * added opening braces

out_degree_centrality

b8d1438e4ea3d8190c650110b3b7d7c141224842

networkx

degree_alg.py

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

Python

def out_degree_centrality(G): if len(G) <= 1: return {n: 1 for n in G} s = 1.0 / (len(G) - 1.0) centrality = {n: d * s for n, d in G.out_degree()} return centrality

packages/python/plotly/plotly/tests/test_optional/test_offline/test_offline.py

def _read_html(self, file_url): with open(file_url.replace("file://", "").replace(" ", "")) as f: return f.read() if matplotlylib:

switch to black .22

_read_html

43e3a4011080911901176aab919c0ecf5046ddd3

plotly.py

test_offline.py

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

Python

def _read_html(self, file_url): with open(file_url.replace("file://", "").replace(" ", "")) as f: return f.read() if matplotlylib:

python/ray/_private/utils.py

def set_omp_num_threads_if_unset() -> bool: num_threads_from_env = os.environ.get("OMP_NUM_THREADS") if num_threads_from_env is not None: # No ops if it's set return False # If unset, try setting the correct CPU count assigned. runtime_ctx = ray.get_runtime_context() if runtime_ctx.worker.mode != ray._private.worker.WORKER_MODE: # Non worker mode, no ops. return False num_assigned_cpus = runtime_ctx.get_assigned_resources().get("CPU") if num_assigned_cpus is None: # This is an acto

[core] Set OMP_NUM_THREADS to `num_cpus` required by task/actors by default (#30496) Ray currently sets OMP_NUM_THREADS=1 when the environ variable is not set. This PR: Sets OMP_NUM_THREADS to the number of cpus assigned to the worker that runs a task before running, and reset it after running. If num_cpus is a fractional smaller than 1, it will set OMP_NUM_THREADS to 1. Doesn't override OMP_NUM_THREADS if it's already being specified in runtime env or through os.environ. Signed-off-by: Ricky Xu <[email protected]> Co-authored-by: Eric Liang <[email protected]> Co-authored-by: Simon Mo <[email protected]>

set_omp_num_threads_if_unset

7c8859f1428224710e4c2db2abf0d9ec28536301

ray

utils.py

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

Python

def set_omp_num_threads_if_unset() -> bool: num_threads_from_env = os.environ.get("OMP_NUM_THREADS") if num_threads_from_env is not None: # No ops if it's set return False # If unset, try setting the correct CPU count assigned. runtime_ctx = ray.get_runtime_context() if runtime_ctx.worker.mode != ray._private.worker.WORKER_MODE: # Non worker mode, no ops. return False num_assigned_cpus = runtime_ctx.get_assigned_resources().get("CPU") if num_assigned_cpus is None: # This is an actor task w/o any num_cpus specified, set it to 1 logger.debug( "[ray] Forcing OMP_NUM_THREADS=1 to avoid performance " "degradation with many workers (issue #6998). You can override this " "by explicitly setting OMP_NUM_THREADS, or changing num_cpus." ) num_assigned_cpus = 1 import math # For num_cpu < 1: Set to 1. # For num_cpus >= 1: Set to the floor of the actual assigned cpus. omp_num_threads = max(math.floor(num_assigned_cpus), 1) os.environ["OMP_NUM_THREADS"] = str(omp_num_threads) return True

tests/components/homekit/test_type_sensors.py

async def test_binary_device_classes(hass, hk_driver): entity_id = "binary_sensor.demo" aid = 1 for device_class, (service, char, _) in BINARY_SENSOR_SERVICE_MAP.items(): hass.states.async_set(entity_id, STATE_OFF, {ATTR_DEVICE_CLASS: device_class}) await hass.async_block_till_done() aid += 1 acc = BinarySensor(hass, hk_driver, "Binary Sensor", entity_id, aid, None) assert acc.get_service(

Add support for restoring HomeKit IIDs (#79913)

test_binary_device_classes

3b33e0d832b238b40360383099391e2093ea05cb

core

test_type_sensors.py

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

Python

async def test_binary_device_classes(hass, hk_driver): entity_id = "binary_sensor.demo" aid = 1 for device_class, (service, char, _) in BINARY_SENSOR_SERVICE_MAP.items(): hass.states.async_set(entity_id, STATE_OFF, {ATTR_DEVICE_CLASS: device_class}) await hass.async_block_till_done() aid += 1 acc = BinarySensor(hass, hk_driver, "Binary Sensor", entity_id, aid, None) assert acc.get_service(service).display_name == service assert acc.char_detected.display_name == char

python3.10.4/Lib/imaplib.py

def Internaldate2tuple(resp): mo = InternalDate.match(resp) if not mo: return

add python 3.10.4 for windows

Internaldate2tuple

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

imaplib.py

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

Python

def Internaldate2tuple(resp): mo = InternalDate.match(resp) if not mo: return None mon = Mon2num[mo.group('mon')] zonen = mo.group('zonen') day = int(mo.group('day')) year = int(mo.group('year')) hour = int(mo.group('hour')) min = int(mo.group('min')) sec = int(mo.group('sec')) zoneh = int(mo.group('zoneh')) zonem = int(mo.group('zonem')) # INTERNALDATE timezone must be subtracted to get UT zone = (zoneh*60 + zonem)*60 if zonen == b'-': zone = -zone tt = (year, mon, day, hour, min, sec, -1, -1, -1) utc = calendar.timegm(tt) - zone return time.localtime(utc)

wagtail/documents/tests/test_admin_views.py

def test_delete_get(self): # Send request response = self.client.get( reverse("wagtaildocs:delete_multiple", args=(self.doc.id,)) ) # Check response self.assertEqual(response.status_code, 405)

Reformat with black

test_delete_get

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_admin_views.py

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

Python

def test_delete_get(self): # Send request response = self.client.get( reverse("wagtaildocs:delete_multiple", args=(self.doc.id,)) ) # Check response self.assertEqual(response.status_code, 405)

zerver/tests/test_subs.py

def test_stream_admin_remove_others_from_public_stream(self) -> None: result = self.attempt_unsubscribe_of_principal( query_count=15, target_users=[self.example_user("cordelia")], is_realm_admin=False, is_stream_admin=True, is_subbed=True, invite_only=False, target_users_subbed=True, ) json = self.assert_json_success(result) self.assert_length(json["re

message_flags: Short-circuit if no messages changed. Omit sending an event, and updating the database, if there are no matching messages.

test_stream_admin_remove_others_from_public_stream

803982e87254e3b1ebcb16ed795e224afceea3a3

zulip

test_subs.py

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

Python

def test_stream_admin_remove_others_from_public_stream(self) -> None: result = self.attempt_unsubscribe_of_principal( query_count=15, target_users=[self.example_user("cordelia")], is_realm_admin=False, is_stream_admin=True, is_subbed=True, invite_only=False, target_users_subbed=True, ) json = self.assert_json_success(result) self.assert_length(json["removed"], 1) self.assert_length(json["not_removed"], 0)

mmdet/datasets/pipelines/transforms.py

def _mosaic_combine(self, loc, center_position_xy, img_shape_wh): assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right') if loc == 'top_left': # index0 to top left part of image x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \ max(center_position_xy[1] - img_shape_wh[1], 0), \ center_position_xy[0], \ center_position_xy[1] crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - ( y2 - y1), img_shape_wh[0], img_shape_wh[1] elif loc == 'top_right':

Refactor RandomCrop and SegRescale

_mosaic_combine

c407e970a8ee2544f27d1c233855a31129dca158

mmdetection

transforms.py

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

Python

def _mosaic_combine(self, loc, center_position_xy, img_shape_wh): assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right') if loc == 'top_left': # index0 to top left part of image x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \ max(center_position_xy[1] - img_shape_wh[1], 0), \ center_position_xy[0], \ center_position_xy[1] crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - ( y2 - y1), img_shape_wh[0], img_shape_wh[1] elif loc == 'top_right': # index1 to top right part of image x1, y1, x2, y2 = center_position_xy[0], \ max(center_position_xy[1] - img_shape_wh[1], 0), \ min(center_position_xy[0] + img_shape_wh[0], self.img_scale[1] * 2), \ center_position_xy[1] crop_coord = 0, img_shape_wh[1] - (y2 - y1), min( img_shape_wh[0], x2 - x1), img_shape_wh[1] elif loc == 'bottom_left': # index2 to bottom left part of image x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \ center_position_xy[1], \ center_position_xy[0], \ min(self.img_scale[0] * 2, center_position_xy[1] + img_shape_wh[1]) crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min( y2 - y1, img_shape_wh[1]) else: # index3 to bottom right part of image x1, y1, x2, y2 = center_position_xy[0], \ center_position_xy[1], \ min(center_position_xy[0] + img_shape_wh[0], self.img_scale[1] * 2), \ min(self.img_scale[0] * 2, center_position_xy[1] + img_shape_wh[1]) crop_coord = 0, 0, min(img_shape_wh[0], x2 - x1), min(y2 - y1, img_shape_wh[1]) paste_coord = x1, y1, x2, y2 return paste_coord, crop_coord

sympy/matrices/common.py

def permute(self, perm, orientation='rows', direction='forward'): r from sympy.combinatorics import Permutation # allow british variants and `columns` if direction == 'forwards': direction = 'forward' if direction == 'backwards': direction = 'backward' if orientation == 'columns': orientation = 'cols' if direction not in ('forward', 'backward'): raise TypeError("direction='{}' is an invalid kwarg. " "Try 'forward' or 'backward'".format(direction)) if orientation not in ('rows', 'cols'): raise TypeError("orientation='{}' is an invalid kwarg. " "Try 'rows' or 'cols'".format(orientation)) if not isinstance(perm, (Permutation, Iterable)): raise ValueError( "{} must be a list, a list of lists, " "or a SymPy permutation object.".format(perm)) # ensure all swaps are in range max_index = self.rows if orientation == 'rows' else self.cols if not all(0 <= t <= max_index for t in flatten(list(perm))): raise IndexError("`swap` indices out of range.") if perm and not isinstance(perm, Permutation) and \ isinstance(perm[0], Iterable): if direction == 'forward': perm = list(reversed(perm)) perm = Permutation(perm, size=max_index+1) else: perm = Permutation(perm, size=max_index+1) if orientation == 'rows': return self._eval_permute_rows(perm) if orientation == 'cols': return self._eval_permute_cols(perm)

Moved imports to higher level

permute

59d22b6bb7287613d598611027f640d068ca5748

sympy

common.py

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

Python

def permute(self, perm, orientation='rows', direction='forward'): r from sympy.combinatorics import Permutation # allow british variants and `columns` if direction == 'forwards': direction = 'forward' if direction == 'backwards': direction = 'backward' if orientation == 'columns': orientation = 'cols' if direction not in ('forward', 'backward'): raise TypeError("direction='{}' is an invalid kwarg. " "Try 'forward' or 'backward'".format(direction)) if orientation not in ('rows', 'cols'): raise TypeError("orientation='{}' is an invalid kwarg. " "Try 'rows' or 'cols'".format(orientation)) if not isinstance(perm, (Permutation, Iterable)): raise ValueError( "{} must be a list, a list of lists, " "or a SymPy permutation object.".format(perm)) # ensure all swaps are in range max_index = self.rows if orientation == 'rows' else self.cols if not all(0 <= t <= max_index for t in flatten(list(perm))): raise IndexError("`swap` indices out of range.") if perm and not isinstance(perm, Permutation) and \ isinstance(perm[0], Iterable): if direction == 'forward': perm = list(reversed(perm)) perm = Permutation(perm, size=max_index+1) else: perm = Permutation(perm, size=max_index+1) if orientation == 'rows': return self._eval_permute_rows(perm) if orientation == 'cols': return self._eval_permute_cols(perm)

seaborn/axisgrid.py

def apply(self, func, *args, **kwargs):

Add apply and pipe methods to Grid objects for fluent customization (#2928) * Return self from tight_layout and refline * Add apply and pipe methods to FacetGrid for fluent customization * Move apply/pipe down to base class so JointGrid/PaiGrid get them too * Tweak docstrings

apply

949dec3666ab12a366d2fc05ef18d6e90625b5fa

seaborn

axisgrid.py

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

Python

def apply(self, func, *args, **kwargs): func(self, *args, **kwargs) return self

awx/main/utils/common.py

def copy_m2m_relationships(obj1, obj2, fields, kwargs=None): for field_name in fields: if hasattr(obj1, field_name): try: field_obj = obj1._meta.get_field(field_name) except FieldDoesNotExist: continue if isinstance(field_obj, ManyToManyField): # Many to Many can be specified as field_name src_field_value = getattr(obj1, field_name) if kwargs and field_name in kwargs: override_field_val = kwargs[field_name] # TODO: Should we spike this our or just put the for loop inside the next if and make everything respect order? if field_name == 'instance_groups': # instance_groups are a list but we need to preserve the order for ig_id in override_field_val: getattr(obj2, field_name).add(ig_id) continue

JT param everything (#12646) * Making almost all fields promptable on job templates and config models * Adding EE, IG and label access checks * Changing jobs preferred instance group function to handle the new IG cache field * Adding new ask fields to job template modules * Address unit/functional tests * Adding migration file

copy_m2m_relationships

33c0fb79d66f56374d7c042ba79887faa85e2885

awx

common.py

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

Python

def copy_m2m_relationships(obj1, obj2, fields, kwargs=None): for field_name in fields: if hasattr(obj1, field_name): try: field_obj = obj1._meta.get_field(field_name) except FieldDoesNotExist: continue if isinstance(field_obj, ManyToManyField): # Many to Many can be specified as field_name src_field_value = getattr(obj1, field_name) if kwargs and field_name in kwargs: override_field_val = kwargs[field_name] # TODO: Should we spike this our or just put the for loop inside the next if and make everything respect order? if field_name == 'instance_groups': # instance_groups are a list but we need to preserve the order for ig_id in override_field_val: getattr(obj2, field_name).add(ig_id) continue if isinstance(override_field_val, (set, list, QuerySet)): getattr(obj2, field_name).add(*override_field_val) continue if override_field_val.__class__.__name__ == 'ManyRelatedManager': src_field_value = override_field_val dest_field = getattr(obj2, field_name) dest_field.add(*list(src_field_value.all().values_list('id', flat=True)))

gradio/external.py

def load_from_pipeline(pipeline): try: import transformers except ImportError: raise ImportError( "transformers not installed. Please try `pip install transformers`" ) if not isinstance(pipeline, transformers.Pipeline): raise ValueError("pipeline must be a transformers.Pipeline") # Handle the different pipelines. The has_attr() checks to make sure the pipeline exists in the # version of the transformers library that the user has installed. if hasattr(transformers, "AudioClassificationPipeline") and isinstance( pipeline, transformers.AudioClassificationPipeline ): pipeline_info = { "inputs": inputs.Audio(label="Input", source="microphone", type="filepath"), "outputs": outputs.Label(label="Class", type="confidences"), "preprocess": lambda i: {"inputs": i}, "postprocess": lambda r: {i["label"].split(", ")[0]: i["score"] for i in r}, } elif hasattr(transformers, "AutomaticSpeechRecognitionPipeline") and isinstance( pipeline, transformers.AutomaticSpeechRecognitionPipeline ): pipeline_info = { "inputs": inputs.Audio(label="Input", source="microphone", type="filepath"), "outputs": outputs.Textbox(label="Output"), "preprocess": lambda i: {"inputs": i}, "postprocess": lambda r: r["text"], } elif hasattr(transformers, "FeatureExtractionPipeline") and isinstance( pipeline, transformers.FeatureExtractionPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input"), "outputs": outputs.Dataframe(label="Output"), "preprocess": lambda x: {"inputs": x}, "postprocess": lambda r: r[0], } elif hasattr(transformers, "FillMaskPipeline") and isinstance( pipeline, transformers.FillMaskPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input"), "outputs": outputs.Label(label="Classification", type="confidences"), "preprocess": lambda x: {"inputs": x}, "postprocess": lambda r: {i["token_str"]: i["score"] for i in r}, } elif hasattr(transformers, "ImageClassificationPipeline") and isinstance( pipeline, transformers.ImageClassificationPipeline ): pipeline_info = { "inputs": inputs.Image(label="Input Image", type="filepath"), "outputs": outputs.Label(label="Classification", type="confidences"), "preprocess": lambda i: {"images": i}, "postprocess": lambda r: {i["label"].split(", ")[0]: i["score"] for i in r}, } elif hasattr(transformers, "QuestionAnsweringPipeline") and isinstance( pipeline, transformers.QuestionAnsweringPipeline ): pipeline_info = { "inputs": [ inputs.Textbox(label="Context", lines=7), inputs.Textbox(label="Question"), ], "outputs": [outputs.Textbox(label="Answer"), outputs.Label(label="Score")], "preprocess": lambda c, q: {"context": c, "question": q}, "postprocess": lambda r: (r["answer"], r["score"]), } elif hasattr(transformers, "SummarizationPipeline") and isinstance( pipeline, transformers.SummarizationPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input", lines=7), "outputs": outputs.Textbox(label="Summary"), "preprocess": lambda x: {"inputs": x}, "postprocess": lambda r: r[0]["summary_text"], } elif hasattr(transformers, "TextClassificationPipeline") and isinstance( pipeline, transformers.TextClassificationPipeline ):

Format The Codebase - black formatting - isort formatting

load_from_pipeline

cc0cff893f9d7d472788adc2510c123967b384fe

gradio

external.py

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

Python

def load_from_pipeline(pipeline): try: import transformers except ImportError: raise ImportError( "transformers not installed. Please try `pip install transformers`" ) if not isinstance(pipeline, transformers.Pipeline): raise ValueError("pipeline must be a transformers.Pipeline") # Handle the different pipelines. The has_attr() checks to make sure the pipeline exists in the # version of the transformers library that the user has installed. if hasattr(transformers, "AudioClassificationPipeline") and isinstance( pipeline, transformers.AudioClassificationPipeline ): pipeline_info = { "inputs": inputs.Audio(label="Input", source="microphone", type="filepath"), "outputs": outputs.Label(label="Class", type="confidences"), "preprocess": lambda i: {"inputs": i}, "postprocess": lambda r: {i["label"].split(", ")[0]: i["score"] for i in r}, } elif hasattr(transformers, "AutomaticSpeechRecognitionPipeline") and isinstance( pipeline, transformers.AutomaticSpeechRecognitionPipeline ): pipeline_info = { "inputs": inputs.Audio(label="Input", source="microphone", type="filepath"), "outputs": outputs.Textbox(label="Output"), "preprocess": lambda i: {"inputs": i}, "postprocess": lambda r: r["text"], } elif hasattr(transformers, "FeatureExtractionPipeline") and isinstance( pipeline, transformers.FeatureExtractionPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input"), "outputs": outputs.Dataframe(label="Output"), "preprocess": lambda x: {"inputs": x}, "postprocess": lambda r: r[0], } elif hasattr(transformers, "FillMaskPipeline") and isinstance( pipeline, transformers.FillMaskPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input"), "outputs": outputs.Label(label="Classification", type="confidences"), "preprocess": lambda x: {"inputs": x}, "postprocess": lambda r: {i["token_str"]: i["score"] for i in r}, } elif hasattr(transformers, "ImageClassificationPipeline") and isinstance( pipeline, transformers.ImageClassificationPipeline ): pipeline_info = { "inputs": inputs.Image(label="Input Image", type="filepath"), "outputs": outputs.Label(label="Classification", type="confidences"), "preprocess": lambda i: {"images": i}, "postprocess": lambda r: {i["label"].split(", ")[0]: i["score"] for i in r}, } elif hasattr(transformers, "QuestionAnsweringPipeline") and isinstance( pipeline, transformers.QuestionAnsweringPipeline ): pipeline_info = { "inputs": [ inputs.Textbox(label="Context", lines=7), inputs.Textbox(label="Question"), ], "outputs": [outputs.Textbox(label="Answer"), outputs.Label(label="Score")], "preprocess": lambda c, q: {"context": c, "question": q}, "postprocess": lambda r: (r["answer"], r["score"]), } elif hasattr(transformers, "SummarizationPipeline") and isinstance( pipeline, transformers.SummarizationPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input", lines=7), "outputs": outputs.Textbox(label="Summary"), "preprocess": lambda x: {"inputs": x}, "postprocess": lambda r: r[0]["summary_text"], } elif hasattr(transformers, "TextClassificationPipeline") and isinstance( pipeline, transformers.TextClassificationPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input"), "outputs": outputs.Label(label="Classification", type="confidences"), "preprocess": lambda x: [x], "postprocess": lambda r: {i["label"].split(", ")[0]: i["score"] for i in r}, } elif hasattr(transformers, "TextGenerationPipeline") and isinstance( pipeline, transformers.TextGenerationPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input"), "outputs": outputs.Textbox(label="Output"), "preprocess": lambda x: {"text_inputs": x}, "postprocess": lambda r: r[0]["generated_text"], } elif hasattr(transformers, "TranslationPipeline") and isinstance( pipeline, transformers.TranslationPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input"), "outputs": outputs.Textbox(label="Translation"), "preprocess": lambda x: [x], "postprocess": lambda r: r[0]["translation_text"], } elif hasattr(transformers, "Text2TextGenerationPipeline") and isinstance( pipeline, transformers.Text2TextGenerationPipeline ): pipeline_info = { "inputs": inputs.Textbox(label="Input"), "outputs": outputs.Textbox(label="Generated Text"), "preprocess": lambda x: [x], "postprocess": lambda r: r[0]["generated_text"], } elif hasattr(transformers, "ZeroShotClassificationPipeline") and isinstance( pipeline, transformers.ZeroShotClassificationPipeline ): pipeline_info = { "inputs": [ inputs.Textbox(label="Input"), inputs.Textbox(label="Possible class names (" "comma-separated)"), inputs.Checkbox(label="Allow multiple true classes"), ], "outputs": outputs.Label(label="Classification", type="confidences"), "preprocess": lambda i, c, m: { "sequences": i, "candidate_labels": c, "multi_label": m, }, "postprocess": lambda r: { r["labels"][i]: r["scores"][i] for i in range(len(r["labels"])) }, } else: raise ValueError("Unsupported pipeline type: {}".format(type(pipeline))) # define the function that will be called by the Interface

packages/syft/src/syft/core/adp/vectorized_publish.py

def calculate_bounds_for_mechanism(value_array, min_val_array, max_val_array): # TODO: Double check whether the iDPGaussianMechanism class squares its squared_l2_no

Implemented working vectorized_publish method into codebase Took 26 minutes

calculate_bounds_for_mechanism

56137bacda6fea5a0053c65eb6fd88688f5298cc

PySyft

vectorized_publish.py

https://github.com/OpenMined/PySyft.git

Python

def calculate_bounds_for_mechanism(value_array, min_val_array, max_val_array): # TODO: Double check whether the iDPGaussianMechanism class squares its squared_l2_norm values!! worst_case_l2_norm = np.sqrt(np.sum(np.square(max_val_array - min_val_array))) * np.ones_like(value_array) l2_norm = np.sqrt(np.sum(np.square(value_array))) * np.ones_like(value_array) # print(l2_norm.shape, worst_case_l2_norm.shape) # print(l2_norm.shape) return l2_norm, worst_case_l2_norm

erpnext/buying/report/procurement_tracker/procurement_tracker.py

def get_po_entries(conditions): ret

style: format code with black

get_po_entries

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

procurement_tracker.py

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

Python

def get_po_entries(conditions): return frappe.db.sql( .format( conditions=conditions ), as_dict=1, ) # nosec

django/views/generic/dates.py

def get_year(self): year = self.year if year is None: try: year = self.kwargs["year"]

Refs #33476 -- Reformatted code with Black.

get_year

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

dates.py

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

Python

def get_year(self): year = self.year if year is None: try: year = self.kwargs["year"] except KeyError: try: year = self.request.GET["year"] except KeyError: raise Http404(_("No year specified")) return year

src/documents/tests/test_management_retagger.py

def test_overwrite_storage_path(self): call_command("document_retagger", "--storage_path", "--overwrite") d_first, d_second, d_unrelated, d_auto = self.get_updated_docs() self.assertEqual(d_first.storage_path, self.sp2) self.assertEqual(d_auto.storage_path, self.sp1) self.assertIsNone(d_second.storage_path) self.assertEqual(d_unrelated.storage_path, self.sp2)

Adds the storage paths to the re-tagger command

test_overwrite_storage_path

c8e838e3a0828e82efac1fd93ebb9aba6a000ff8

paperless-ngx

test_management_retagger.py

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

Python

def test_overwrite_storage_path(self): call_command("document_retagger", "--storage_path", "--overwrite") d_first, d_second, d_unrelated, d_auto = self.get_updated_docs() self.assertEqual(d_first.storage_path, self.sp2) self.assertEqual(d_auto.storage_path, self.sp1) self.assertIsNone(d_second.storage_path) self.assertEqual(d_unrelated.storage_path, self.sp2)

modin/core/storage_formats/base/query_compiler.py

def invert(self): return DataFrameDefault.regis

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

invert

57e29bc5d82348006c5170ef9ac0a9eedcd9acf9

modin

query_compiler.py

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

Python

def invert(self): return DataFrameDefault.register(pandas.DataFrame.__invert__)(self)

tests/test_models/test_dense_heads/test_centernet_head.py

def test_center_head_loss(self): s = 256 img_metas = [{'batch_input_shape': (s, s, 3)}] test_cfg = dict(topK=100, max_per_img=100) centernet_head = CenterNetHead( num_classes=4, in_channels=1, feat_channels=4, test_cfg=test_cfg) feat = [torch.rand(1, 1, s, s)] center_out, wh_out, offset_out = centernet_head.forward(feat) # Test that empty ground truth encourages the network to # predict background gt_instances = InstanceData() gt_instances.bboxes = torch.empty((0, 4)) gt_instances.labels = torch.LongTensor([]) empty_gt_losses = centernet_head.loss(center_out, wh_out, offset_out, [gt_instances], img_metas) loss_center = empty_gt_losses['loss_center_heatmap'] loss_wh = empty_gt_losses['loss_wh'] loss_offset = empty_gt_losses['loss_offset'] assert loss_center.item() > 0, 'loss_center should be non-zero' assert loss_wh.item() == 0, ( 'there should be no loss_wh when there are no true

[Refactor] CenterNet

test_center_head_loss

96aa909c19dbe753852ac6dba13bbbc35329b99f

mmdetection

test_centernet_head.py

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

Python

def test_center_head_loss(self): s = 256 img_metas = [{'batch_input_shape': (s, s, 3)}] test_cfg = dict(topK=100, max_per_img=100) centernet_head = CenterNetHead( num_classes=4, in_channels=1, feat_channels=4, test_cfg=test_cfg) feat = [torch.rand(1, 1, s, s)] center_out, wh_out, offset_out = centernet_head.forward(feat) # Test that empty ground truth encourages the network to # predict background gt_instances = InstanceData() gt_instances.bboxes = torch.empty((0, 4)) gt_instances.labels = torch.LongTensor([]) empty_gt_losses = centernet_head.loss(center_out, wh_out, offset_out, [gt_instances], img_metas) loss_center = empty_gt_losses['loss_center_heatmap'] loss_wh = empty_gt_losses['loss_wh'] loss_offset = empty_gt_losses['loss_offset'] assert loss_center.item() > 0, 'loss_center should be non-zero' assert loss_wh.item() == 0, ( 'there should be no loss_wh when there are no true boxes') assert loss_offset.item() == 0, ( 'there should be no loss_offset when there are no true boxes') # When truth is non-empty then both cls and box loss # should be nonzero for random inputs gt_instances = InstanceData() gt_instances.bboxes = torch.Tensor( [[23.6667, 23.8757, 238.6326, 151.8874]]) gt_instances.labels = torch.LongTensor([2]) one_gt_losses = centernet_head.loss(center_out, wh_out, offset_out, [gt_instances], img_metas) loss_center = one_gt_losses['loss_center_heatmap'] loss_wh = one_gt_losses['loss_wh'] loss_offset = one_gt_losses['loss_offset'] assert loss_center.item() > 0, 'loss_center should be non-zero' assert loss_wh.item() > 0, 'loss_wh should be non-zero' assert loss_offset.item() > 0, 'loss_offset should be non-zero'

@contextlib.contextmanager

python3.10.4/Lib/importlib/_common.py

def from_package(package): spec = wrap_spec(package) reader = spec.loader.get_resource_reader(spec.name) return reader.files() @contextlib.contex

add python 3.10.4 for windows

from_package

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

_common.py

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

Python

def from_package(package): spec = wrap_spec(package) reader = spec.loader.get_resource_reader(spec.name) return reader.files() @contextlib.contextmanager

saleor/checkout/complete_checkout.py

def _is_refund_ongoing(payment): return ( payment.transactions.filter( kind=TransactionKind.REFUND_ONGOING, is_s

Fix payment flow (#9504) * Do not capture payment again when it should be refunded or voided * Do not create order when then is ongoing refund

_is_refund_ongoing

0881beec1ac02dfa97525c5173687defb356d85c

saleor

complete_checkout.py

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

Python

def _is_refund_ongoing(payment): return ( payment.transactions.filter( kind=TransactionKind.REFUND_ONGOING, is_success=True ).exists() if payment else False )

salt/states/win_wua.py

def installed(name, updates=None): if isinstance(updates, str): updates = [updates] if not updates: updates = name ret = {"name": name, "changes": {}, "result": True, "comment": ""} wua = salt.utils.win_update.WindowsUpdateAgent() # Search for updates install_list = wua.search(updates) # No updates found if install_list.count() == 0: ret["comment"] = "No updates found" return ret # List of updates to download download = salt.utils.win_update.Updates() for item in install_list.updates: if not salt.utils.data.is_true(item.IsDownloaded): download.updates.Add(item) # List of updates to install

Remove 40 character limit to update Title

installed

52c922760e8447f0c9efd23b12481ba1a7509dcd

salt

win_wua.py

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

Python

def installed(name, updates=None): if isinstance(updates, str): updates = [updates] if not updates: updates = name ret = {"name": name, "changes": {}, "result": True, "comment": ""} wua = salt.utils.win_update.WindowsUpdateAgent() # Search for updates install_list = wua.search(updates) # No updates found if install_list.count() == 0: ret["comment"] = "No updates found" return ret # List of updates to download download = salt.utils.win_update.Updates() for item in install_list.updates: if not salt.utils.data.is_true(item.IsDownloaded): download.updates.Add(item) # List of updates to install install = salt.utils.win_update.Updates() installed_updates = [] for item in install_list.updates: if not salt.utils.data.is_true(item.IsInstalled): install.updates.Add(item) else: installed_updates.extend("KB" + kb for kb in item.KBArticleIDs) if install.count() == 0: ret["comment"] = "Updates already installed: " ret["comment"] += "\n - ".join(installed_updates) return ret # Return comment of changes if test. if __opts__["test"]: ret["result"] = None ret["comment"] = "Updates will be installed:" for update in install.updates: ret["comment"] += "\n" ret["comment"] += ": ".join([update.Identity.UpdateID, update.Title]) return ret # Download updates wua.download(download) # Install updates wua.install(install) # Refresh windows update info wua.refresh() post_info = wua.updates().list() # Verify the installation for item in install.list(): if not salt.utils.data.is_true(post_info[item]["Installed"]): ret["changes"]["failed"] = { item: { "Title": post_info[item]["Title"], "KBs": post_info[item]["KBs"], } } ret["result"] = False else: ret["changes"]["installed"] = { item: { "Title": post_info[item]["Title"], "NeedsReboot": post_info[item]["NeedsReboot"], "KBs": post_info[item]["KBs"], } } if ret["changes"].get("failed", False): ret["comment"] = "Updates failed" else: ret["comment"] = "Updates installed successfully" return ret

nuitka/plugins/standard/KivyPlugin.py

def _getKivyInformation(self): setup_codes = r info = self.queryRuntimeInformationMultiple( info_name="kivy_info", setup_codes=setup_codes, values=( ("libs_loaded", "kivy.core.image.libs_loaded"), ("window_impl", "kivy.core.window.window_impl"), ("label_libs", "kivy.core.text.label_libs"), (

Plugins: Add DLL folders needed on Windows for Kivy plugin * Make DLL reporting code part of plugin base class. * Added new method to scan for DLLs in folders.

_getKivyInformation

6ed4d787519d7075d7ff492bc40a291bc12f088c

Nuitka

KivyPlugin.py

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

Python

def _getKivyInformation(self): setup_codes = r info = self.queryRuntimeInformationMultiple( info_name="kivy_info", setup_codes=setup_codes, values=( ("libs_loaded", "kivy.core.image.libs_loaded"), ("window_impl", "kivy.core.window.window_impl"), ("label_libs", "kivy.core.text.label_libs"), ("sdl2_dep_bins", "sdl2_dep_bins"), ("glew_dep_bins", "glew_dep_bins"), ), ) if info is None: self.sysexit("Error, it seems Kivy is not installed.") return info

pandas/core/generic.py

def __iter__(self) -> Iterator: return

TYP: Autotyping (#48191) * annotate-magics * annotate-imprecise-magics * none-return * scalar-return * pyi files * ignore vendored file * manual changes * ignore pyright in pickle_compat (these errors would be legit if the current __new__ methods were called but I think these pickle tests call older __new__ methods which allowed providing multiple positional arguments) * run autotyping in pre-commit * remove final and expand safe (and add annotate-imprecise-magics)

__iter__

54347fe684e0f7844bf407b1fb958a5269646825

pandas

generic.py

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

Python

def __iter__(self) -> Iterator: return iter(self._info_axis) # can we get a better explanation of this?

src/datasets/table.py

def cast(self, target_schema, *args, **kwargs): table = table_cast(self.table, target_schema, *args, **kwargs) blocks = [] for subtables in self.blocks: new_tables = [] fields = list(target_schema) for subtable in subtables: subfields = [] for name in subtable.column_names: subfields.append(fields.pop(next(i for i, field in enumerate(fields) if field.name == name)))

Update docs to new frontend/UI (#3690) * WIP: update docs to new UI * make style * Rm unused * inject_arrow_table_documentation __annotations__ * hasattr(arrow_table_method, "__annotations__") * Update task_template.rst * Codeblock PT-TF-SPLIT * Convert loading scripts * Convert docs to mdx * Fix mdx * Add <Tip> * Convert mdx tables * Fix codeblock * Rm unneded hashlinks * Update index.mdx * Redo dev change * Rm circle ci `build_doc` & `deploy_doc` * Rm unneeded files * Update docs reamde * Standardize to `Example::` * mdx logging levels doc * Table properties inject_arrow_table_documentation * ``` to ```py mdx * Add Tips mdx * important,None -> <Tip warning={true}> * More misc * Center imgs * Update instllation page * `setup.py` docs section * Rm imgs since they are in hf.co * Update docs/source/access.mdx Co-authored-by: Steven Liu <[email protected]> * Update index mdx * Update docs/source/access.mdx Co-authored-by: Steven Liu <[email protected]> * just `Dataset` obj * Addedversion just italics * Update ReadInstruction doc example syntax * Change docstring for `prepare_for_task` * Chore * Remove `code` syntax from headings * Rm `code` syntax from headings * Hashlink backward compatability * S3FileSystem doc * S3FileSystem doc updates * index.mdx updates * Add darkmode gifs * Index logo img css classes * Index mdx dataset logo img size * Docs for DownloadMode class * Doc DownloadMode table * format docstrings * style * Add doc builder scripts (#3790) * add doc builder scripts * fix docker image * Docs new UI actions no self hosted (#3793) * No self hosted * replace doc injection by actual docstrings * Docstring formatted Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: Mishig Davaadorj <[email protected]> Co-authored-by: Lysandre Debut <[email protected]> Co-authored-by: Mishig Davaadorj <[email protected]> * Rm notebooks from docs actions since they dont exi * Update tsting branch * More docstring * Chore * bump up node version * bump up node * ``` -> ```py for audio_process.mdx * Update .github/workflows/build_documentation.yml Co-authored-by: Quentin Lhoest <[email protected]> * Uodate dev doc build * remove run on PR * fix action * Fix gh doc workflow * forgot this change when merging master * Update build doc Co-authored-by: Steven Liu <[email protected]> Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: Lysandre Debut <[email protected]>

cast

e35be138148333078284b942ccc9ed7b1d826f97

datasets

table.py

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

Python

def cast(self, target_schema, *args, **kwargs): table = table_cast(self.table, target_schema, *args, **kwargs) blocks = [] for subtables in self.blocks: new_tables = [] fields = list(target_schema) for subtable in subtables: subfields = [] for name in subtable.column_names: subfields.append(fields.pop(next(i for i, field in enumerate(fields) if field.name == name))) subschema = pa.schema(subfields) new_tables.append(subtable.cast(subschema, *args, **kwargs)) blocks.append(new_tables) return ConcatenationTable(table, blocks)

magenta/models/coconet/lib_util.py

def softmax(p, axis=None, temperature=1): if axis is None: axis = p.ndim - 1 if temperature == 0.: # NOTE: in case o

Work around tensor2tensor/gym issues, fix pylint errors. PiperOrigin-RevId: 433019701

softmax

e833848c6dda95dbcf17e84d935dcdb8cff6f47d

magenta

lib_util.py

https://github.com/magenta/magenta.git

Python

def softmax(p, axis=None, temperature=1): if axis is None: axis = p.ndim - 1 if temperature == 0.: # NOTE: in case of multiple equal maxima, returns uniform distribution. p = p == np.max(p, axis=axis, keepdims=True) else: # oldp = p logp = np.log(p) logp /= temperature logp -= logp.max(axis=axis, keepdims=True) p = np.exp(logp) p /= p.sum(axis=axis, keepdims=True) if np.isnan(p).any(): pdb.set_trace() # pylint: disable=forgotten-debug-statement return p

ludwig/benchmarking/profiler.py

def _populate_static_information(self) -> None: self.info["ludwig_version"] = LUDWIG_VERSION self.info["start_disk_usage"] = shutil.disk_usage(os.path.expanduser("~")).used # CPU information cpu_info = get_my_cpu_info() self.info["cpu_architecture"] = cpu_info["arch"] self.info["num_cpu"] = psutil.cpu_count() self.info["cpu_name"] = cpu_info["brand_raw"] self.info["total_cpu_memory_size"] = psutil.virtual_memory().total # GPU information if self.cuda_is_ava

More precise resource usage tracking (#2363) * added `torch.profiler.record_function` decorator * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * export torch profiler metric draft/pseudocode * exporting cpu and cuda memory usage * exporting CPU and CUDA execution time * formatting * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * adding basic comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * removed `_str` entries in the exported JSONs * attempting to speed up result collection from kineto and function event lists * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * style improvements * speed improvements while calculating averages/summaries * style improvements * using variable defined in torch instead of hard coding it * specifying torch in the tracked metrics * added torch.profiler to ResrouceUsageTracker * combining torch.profiler metrics with ResourceUsageMetrics * handling multiple context mangers with exitstack * making it a decorator * flattening output dict * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * using logging instead of print * flake8 formatting * removed intermediary write/read to/from disk * cleaning after last change * adjusted number of args * replaced tag with code_block_tag * changed total_duration to total_execution_time * support nested use of the context manager and the decorator * remove torch.record_function decorator * adding LUDWIG_TAG to label torch profiler main events * using logging instead of print * style changes * preventing cases of empty list when code block execution is too quick * removing experimental code * fixed gpu tracking * style improvements * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove experimental code from trainer.py * style improvements * more accurate torch op cpu and cuda execution time. * flake8 fixes * rename to LudwigProfiler * updated docstrings * dont collect torch metrics when use_torch_profiler=False * test for LudwigProfiler * formatting improvements * update test to remove repetitive asserts * make the tag->os directory relationship more obvious * explaining what LUDWIG_TAG is used for * removing unncessary asserts * added explanation for `LUDWIG_TAG` * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * formatting fixes * removing `get_python_packages_and_versions` Co-authored-by: Joppe Geluykens <[email protected]> * dataclasses for base profiler * dataclasses for torch profiler * adding OOM event tracking * formatting * normalizing cpu_utilization * pull out flattening dataclass function * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added total CPU memory size and CPU memory available * adding system-wide CPU utilization Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Joppe Geluykens <[email protected]>

_populate_static_information

c50997c2b27e7f7f59a96c0158f3737e22419ed8

ludwig

profiler.py

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

Python

def _populate_static_information(self) -> None: self.info["ludwig_version"] = LUDWIG_VERSION self.info["start_disk_usage"] = shutil.disk_usage(os.path.expanduser("~")).used # CPU information cpu_info = get_my_cpu_info() self.info["cpu_architecture"] = cpu_info["arch"] self.info["num_cpu"] = psutil.cpu_count() self.info["cpu_name"] = cpu_info["brand_raw"] self.info["total_cpu_memory_size"] = psutil.virtual_memory().total # GPU information if self.cuda_is_available: gpu_infos = get_gpu_info() for i, gpu_info in enumerate(gpu_infos): gpu_key = f"cuda_{i}" self.info[f"{gpu_key}_memory_used"] = [] self.info[f"{gpu_key}_name"] = gpu_info["name"] self.info[f"{gpu_key}_total_memory"] = gpu_info["total_memory"] self.info[f"{gpu_key}_driver_version"] = gpu_info["driver_version"] self.info[f"{gpu_key}_cuda_version"] = gpu_info["cuda_version"] # recording in microseconds to be in line with torch profiler time recording. self.info["start_time"] = time.perf_counter_ns() / 1000

wagtail/tests/utils/page_tests.py

def assertCanNotCreateAt(self, parent_model, child_model, msg=None): if self._testCanCreateAt(parent_model, child_model): msg = self._formatMessage( msg, "Can create a %s.%s unde

Reformat with black

assertCanNotCreateAt

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

page_tests.py

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

Python

def assertCanNotCreateAt(self, parent_model, child_model, msg=None): if self._testCanCreateAt(parent_model, child_model): msg = self._formatMessage( msg, "Can create a %s.%s under a %s.%s" % ( child_model._meta.app_label, child_model._meta.model_name, parent_model._meta.app_label, parent_model._meta.model_name, ), ) raise self.failureException(msg)

src/sentry/features/manager.py

def get_feature_objects(self) -> Mapping[Project, Feature]: cls = self._manager._get_feature_class(self.feature_name) return {obj: cls(self.feature_name, obj) for obj in self.objects}

feat(notifications): add a feature flag to make slack the default for new users (#35652) We want to have some users automatically get notifications on Slack and email instead of just Slack. But we don't want to impact existing users so instead I introduce the concept of a UserFeature which isn't dependent on the user. I'm adding a flag called users:notification-slack-automatic which is controlled by the age of the user (see getsentry/getsentry#7644). If there is no value for a particular notification setting and provider, it will fall back to being enabled. This is different than the current behavior which defaults to being off for Slack for issue and workflow notifications. This PR is based off a previous PR which had a similar feature flag: #28190

get_feature_objects

f64e58203b6c6d5121a4f6685dace0a4627d49b0

sentry

manager.py

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

Python

def get_feature_objects(self) -> Mapping[Project, Feature]: cls = self._manager._get_feature_class(self.feature_name) return {obj: cls(self.feature_name, obj) for obj in self.objects}

tests/test_pipelines_automatic_speech_recognition.py

def require_ffmpeg(test_case): import subprocess try: s

Adding support for `microphone` streaming within pipeline. (#15046) * Adding support for `microphone` streaming within pipeline. - Uses `ffmpeg` to get microphone data. - Makes sure alignment is made to `size_of_sample`. - Works by sending `{"raw": ..data.., "stride": (n, left, right), "partial": bool}` directly to the pipeline enabling to stream partial results and still get inference. - Let's `partial` information flow through the pipeline to enable caller to get it back and choose to display text or not. - The striding reconstitution is bound to have errors since CTC does not keep previous state. Currently most of the errors are we don't know if there's a space or not between two chunks. Since we have some left striding info, we could use that during decoding to choose what to do with those spaces and even extra letters maybe (if the stride is long enough, it's bound to cover at least a few symbols) Fixing tests. Protecting with `require_torch`. `raw_ctc` support for nicer demo. Post rebase fixes. Revamp to split raw_mic_data from it's live chunking. - Requires a refactor to make everything a bit cleaner. Automatic resampling. Small fix. Small fix. * Post rebase fix (need to let super handle more logic, reorder args.) * Update docstrings * Docstring format. * Remove print. * Prevent flow of `input_values`. * Fixing `stride` too. * Fixing the PR by removing `raw_ctc`. * Better docstrings. * Fixing init. * Update src/transformers/pipelines/audio_utils.py Co-authored-by: Anton Lozhkov <[email protected]> * Update tests/test_pipelines_automatic_speech_recognition.py Co-authored-by: Anton Lozhkov <[email protected]> * Quality. Co-authored-by: Anton Lozhkov <[email protected]>

require_ffmpeg

623d8cb475804f2b0f85a47b04b8b2e522db06ef

transformers

test_pipelines_automatic_speech_recognition.py

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

Python

def require_ffmpeg(test_case): import subprocess try: subprocess.check_output(["ffmpeg", "-h"], stderr=subprocess.DEVNULL) return test_case except Exception: return unittest.skip("test requires ffmpeg")(test_case)

tests/system/providers/google/tasks/example_queue.py

def generate_random_string(): import random import string return "".join(random.choices(string.ascii_uppercase + string.digits, k=8)) random_string = generate_random_string() # [START create_queue] create_queue = CloudTasksQueueCreateOperator( location=LOCATION, task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=0.5)), queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", retry=Retry(maximum=10.0), timeout=5, task_id="create_queue", ) # [END create_queue] # [START delete_queue] delete_queue = CloudTasksQueueDeleteOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="delete_queue", ) # [END delete_queue] delete_queue.trigger_rule = TriggerRule.ALL_DONE # [START resume_queue] resume_queue = CloudTasksQueueResumeOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="resume_queue", ) # [END resume_queue] # [START pause_queue] pause_queue = CloudTasksQueuePauseOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}",

CloudTasks assets & system tests migration (AIP-47) (#23282)

generate_random_string

3977e1798d8294ba628b5f330f43702c1a5c79fc

airflow

example_queue.py

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

Python

def generate_random_string(): import random import string return "".join(random.choices(string.ascii_uppercase + string.digits, k=8)) random_string = generate_random_string() # [START create_queue] create_queue = CloudTasksQueueCreateOperator( location=LOCATION, task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=0.5)), queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", retry=Retry(maximum=10.0), timeout=5, task_id="create_queue", ) # [END create_queue] # [START delete_queue] delete_queue = CloudTasksQueueDeleteOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="delete_queue", ) # [END delete_queue] delete_queue.trigger_rule = TriggerRule.ALL_DONE # [START resume_queue] resume_queue = CloudTasksQueueResumeOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="resume_queue", ) # [END resume_queue] # [START pause_queue] pause_queue = CloudTasksQueuePauseOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="pause_queue", ) # [END pause_queue] # [START purge_queue] purge_queue = CloudTasksQueuePurgeOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="purge_queue", ) # [END purge_queue] # [START get_queue] get_queue = CloudTasksQueueGetOperator( location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", task_id="get_queue", ) get_queue_result = BashOperator( task_id="get_queue_result", bash_command=f"echo {get_queue.output}", ) # [END get_queue] # [START update_queue] update_queue = CloudTasksQueueUpdateOperator( task_queue=Queue(stackdriver_logging_config=dict(sampling_ratio=1)), location=LOCATION, queue_name=QUEUE_ID + "{{ task_instance.xcom_pull(task_ids='random_string') }}", update_mask=FieldMask(paths=["stackdriver_logging_config.sampling_ratio"]), task_id="update_queue", ) # [END update_queue] # [START list_queue] list_queue = CloudTasksQueuesListOperator(location=LOCATION, task_id="list_queue") # [END list_queue] chain( random_string, create_queue, update_queue, pause_queue, resume_queue, purge_queue, get_queue, get_queue_result, list_queue, delete_queue, ) from tests.system.utils.watcher import watcher # This test needs watcher in order to properly mark success/failure # when "tearDown" task with trigger rule is part of the DAG list(dag.tasks) >> watcher() from tests.system.utils import get_test_run # noqa: E402 # Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest) test_run = get_test_run(dag)

keras/optimizers/optimizer_v2/utils.py

def make_gradient_clipvalue_fn(clipvalue): if clipvalue is None:

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

make_gradient_clipvalue_fn

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

utils.py

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

Python

def make_gradient_clipvalue_fn(clipvalue): if clipvalue is None: return lambda grads_and_vars: grads_and_vars

jax/_src/scipy/signal.py

def odd_ext(x, n, axis=-1): if n < 1: return x if n > x.shape[axis] - 1: raise ValueError( f"The extension length n ({n}) is too big. " f"It must not exceed x.shape[axis]-1, which is {x.shape[axis] - 1}.") left_end = lax.slice_in_di

Add some functions for spectral analysis. This commit adds "stft", "csd", and "welch" functions in scipy.signal.

odd_ext

e085370ec4137cf0f73c5163cb664bc4e1c46082

jax

signal.py

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

Python

def odd_ext(x, n, axis=-1): if n < 1: return x if n > x.shape[axis] - 1: raise ValueError( f"The extension length n ({n}) is too big. " f"It must not exceed x.shape[axis]-1, which is {x.shape[axis] - 1}.") left_end = lax.slice_in_dim(x, 0, 1, axis=axis) left_ext = jnp.flip(lax.slice_in_dim(x, 1, n + 1, axis=axis), axis=axis) right_end = lax.slice_in_dim(x, -1, None, axis=axis) right_ext = jnp.flip(lax.slice_in_dim(x, -(n + 1), -1, axis=axis), axis=axis) ext = jnp.concatenate((2 * left_end - left_ext, x, 2 * right_end - right_ext), axis=axis) return ext

keras/tests/keras_doctest.py

def filter_on_submodules(all_modules, submodule): filtered_modules = [ mod for mod in all_modules if PACKAGE + submodule in mod.__name__ ] return filtered_modules

Add a keras doctest modeled on tensorflow doctest PiperOrigin-RevId: 424672415

filter_on_submodules

a449efe29b092e658a29cd847e0494979a47d252

keras

keras_doctest.py

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

Python

def filter_on_submodules(all_modules, submodule): filtered_modules = [ mod for mod in all_modules if PACKAGE + submodule in mod.__name__ ] return filtered_modules

erpnext/accounts/doctype/bank_reconciliation_tool/bank_reconciliation_tool.py

def get_pe_matching_query(amount_condition, account_from_to, transaction): # get matching payment entries query from_date = frappe.db.get_single_value("Bank Reconciliation Tool", "bank_statement_from_date") to_date = frappe.db.get_single_value("Bank Reconciliation Tool", "bank_statement_to_date") from_reference_date = frappe.db.get_single_value( "Bank Reconciliation Tool", "from_reference_date" ) to_reference_date = frappe.db.get_single_value("Bank Reconciliation Tool", "to_reference_date") filtered_by_reference_date = frappe.db.get_single_value( "Bank Reconcil

Feat:Filter on Payment Entries and Journal Entries Applying filters on Payement entries and Journal Entries as per reference date and posting date

get_pe_matching_query

408c89df030998fe36df135570c9edd90a522996

erpnext

bank_reconciliation_tool.py

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

Python

def get_pe_matching_query(amount_condition, account_from_to, transaction): # get matching payment entries query from_date = frappe.db.get_single_value("Bank Reconciliation Tool", "bank_statement_from_date") to_date = frappe.db.get_single_value("Bank Reconciliation Tool", "bank_statement_to_date") from_reference_date = frappe.db.get_single_value( "Bank Reconciliation Tool", "from_reference_date" ) to_reference_date = frappe.db.get_single_value("Bank Reconciliation Tool", "to_reference_date") filtered_by_reference_date = frappe.db.get_single_value( "Bank Reconciliation Tool", "filtered_by_reference_date" ) if transaction.deposit > 0: currency_field = "paid_to_account_currency as currency" else: currency_field = "paid_from_account_currency as currency" cond_filtered_from_ref_date = "" cond_filtered_to_ref_date = "" cond_filtered_from_posting_date = "" cond_filtered_to_posting_date = "" from_ref_date ="" to_ref_date ="" from_post_date = "" to_post_date = "" if(filtered_by_reference_date): cond_filtered_from_ref_date = " AND reference_date >=" cond_filtered_to_ref_date = " AND reference_date <=" from_ref_date = from_reference_date to_ref_date = to_reference_date elif(not filtered_by_reference_date): cond_filtered_from_posting_date = " AND posting_date >=" cond_filtered_to_posting_date = " AND posting_date <=" from_post_date = from_date to_post_date = to_date pe_data= f return pe_data

pandas/core/arrays/interval.py

def closed(self) -> IntervalInclusiveType: warnings.warn( "Attribute `closed` is deprecated in favor of `inclusive`.", FutureWarning, stacklevel=find_stack_level(inspect.currentframe()), ) return self.dtype.inclusive _interval_shared_docs["set_closed"] = textwrap.dedent( )

PERF cache find_stack_level (#48023) cache stacklevel

closed

2f8d0a36703e81e4dca52ca9fe4f58c910c1b304

pandas

interval.py

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

Python

def closed(self) -> IntervalInclusiveType: warnings.warn( "Attribute `closed` is deprecated in favor of `inclusive`.", FutureWarning, stacklevel=find_stack_level(inspect.currentframe()), ) return self.dtype.inclusive _interval_shared_docs["set_closed"] = textwrap.dedent( )

sympy/functions/elementary/exponential.py

def as_real_imag(self, deep=True, **hints): from sympy.functions.elementary.trigonometric import cos, sin re, im = self.a

Updated import locations

as_real_imag

498015021131af4dbb07eb110e5badaba8250c7b

sympy

exponential.py

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

Python

def as_real_imag(self, deep=True, **hints): from sympy.functions.elementary.trigonometric import cos, sin re, im = self.args[0].as_real_imag() if deep: re = re.expand(deep, **hints) im = im.expand(deep, **hints) cos, sin = cos(im), sin(im) return (exp(re)*cos, exp(re)*sin)

rasa/core/exporter.py

async def _get_conversation_ids_to_process(self) -> Set[Text]: conversation_ids_in_tracker_store = ( await self._get_conversation_ids_in_tracker() ) if not self.requested_conversation_ids: return conversation_ids_in_tracker_store self._validate_all_requested_ids_exist(conversation_ids_in_tracker_store) conversation_ids_to_process = conversation_ids_in_tracker_store & set( self.request

Async Tracker Store Support (#10696) Make `TrackerStore` interface methods asynchronous and supply an `AwaitableTrackerstore` wrapper for custom tracker stores which do not implement the methods as asynchronous. Squashed commits: * refactor tracker store and tests to be async * update core modules with async tracker store calls * update server with async tracker store calls * await tracker store call in twilio voice * add await in test rasa export * add awaits to test_agent for tracker store * add awaits to tracker store functions in processor tests * refactor exporter tests for async tracker store * use asyncmock from unittest instead of custom * add async in test_rasa_export * fixture update for async tracker store * update marker logic to handle async tracker store * fix mark tracker loader tests for async tracker store * add awaits to server and server tests * add await to dialogue test with tracker store * add await to tracker test * formatting in tracker store * more formatting fixes * more formatting fixes * address formatting changes * change return type and remove awaitable tracker store wrapper in create * make stream_events async * address comments (remove redundant methods in awaitable tracker store + raise exception) * make _run_markers and _run_markers_cli sync to ensure CLI can be run * add warning and test for creating async tracker store from endpoint config * add changelog entry * use TrackerStore instead of "TrackerStore" in typehint Co-authored-by: Joe Juzl <[email protected]> * use TrackerStore instead of "TrackerStore" in typehint Co-authored-by: Joe Juzl <[email protected]> * change user warning to deprecation warning * fix typo in comment * have fallback_tracker_store return in memory tracker store without awaitable wrapper * import async mock from conftest instead of unittest to suport Python 3.7 * delete unused imports in marker_tracker_loader * apply black to modules which failed ci linter * resolve line length linting in tracker_store.py * refer to request.app.ctx.agent object instead of request.app.agent * resolve ci failures from not adding async/await * applied black to reformat three modules failing code quality * correct most docstring linting errors * fix docstring linting errors * fix flake8 line length errors * fix mypy type checking errors * linting corrections after adding type ignores to methods * delete extra periods in docstring Co-authored-by: Joe Juzl <[email protected]>

_get_conversation_ids_to_process

ca316fc80cb490ecf1e2e7261fb7fcef22fccc4a

rasa

exporter.py

https://github.com/RasaHQ/rasa.git

Python

async def _get_conversation_ids_to_process(self) -> Set[Text]: conversation_ids_in_tracker_store = ( await self._get_conversation_ids_in_tracker() ) if not self.requested_conversation_ids: return conversation_ids_in_tracker_store self._validate_all_requested_ids_exist(conversation_ids_in_tracker_store) conversation_ids_to_process = conversation_ids_in_tracker_store & set( self.requested_conversation_ids ) if not conversation_ids_to_process: raise NoEventsToMigrateError( "Could not find an overlap between the requested " "conversation IDs and those found in the tracker store. Exiting." ) return conversation_ids_to_process

keras/engine/training.py

def call(self, inputs, training=None, mask=None): raise NotImplementedError( "Unimplemented `tf.keras.Model.call()`: if you " "intend to create a `Model` with the Functional " "API, please provide `inputs` and

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

call

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training.py

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

Python

def call(self, inputs, training=None, mask=None): raise NotImplementedError( "Unimplemented `tf.keras.Model.call()`: if you " "intend to create a `Model` with the Functional " "API, please provide `inputs` and `outputs` " "arguments. Otherwise, subclass `Model` with an " "overridden `call()` method." )

modin/core/io/text/json_dispatcher.py

def _read(cls, path_or_buf, **kwargs): path_or_buf = cls.get_path_or_buffer(path_or_buf) if isinstance(path_or_buf, str): if not cls.file_exists(path_or_buf): return cls.single_worker_read(path_or_buf, **kwargs) path_or_buf = cls.get_path(path_or_buf) elif not cls.pathlib_or_pypath(path_or_buf): return cls.single_worker_read(path_or_buf, **kwargs) if not kwargs.get("lines", False): return cls.single_worker_read(path_or_buf, **kwargs) with OpenFile(path_or_buf, "rb") as f: columns = pandas.read_json(BytesIO(b"" + f.readline()), lines=True).columns kwargs["columns"] = columns empty_pd_df = pandas.DataFrame(columns=columns) with OpenFile(path_or_buf, "rb", kwargs.get("compression", "in

REFACTOR-#3853: interacting with Dask interface through 'DaskWrapper' class (#3854) Co-authored-by: Devin Petersohn <[email protected]> Co-authored-by: Dmitry Chigarev <[email protected]> Co-authored-by: Yaroslav Igoshev <[email protected]> Signed-off-by: Anatoly Myachev <[email protected]>

_read

97769988a6f19e4b76f34238c97bf159ee7626a5

modin

json_dispatcher.py

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

Python

def _read(cls, path_or_buf, **kwargs): path_or_buf = cls.get_path_or_buffer(path_or_buf) if isinstance(path_or_buf, str): if not cls.file_exists(path_or_buf): return cls.single_worker_read(path_or_buf, **kwargs) path_or_buf = cls.get_path(path_or_buf) elif not cls.pathlib_or_pypath(path_or_buf): return cls.single_worker_read(path_or_buf, **kwargs) if not kwargs.get("lines", False): return cls.single_worker_read(path_or_buf, **kwargs) with OpenFile(path_or_buf, "rb") as f: columns = pandas.read_json(BytesIO(b"" + f.readline()), lines=True).columns kwargs["columns"] = columns empty_pd_df = pandas.DataFrame(columns=columns) with OpenFile(path_or_buf, "rb", kwargs.get("compression", "infer")) as f: partition_ids = [] index_ids = [] dtypes_ids = [] column_widths, num_splits = cls._define_metadata(empty_pd_df, columns) args = {"fname": path_or_buf, "num_splits": num_splits, **kwargs} splits = cls.partitioned_file( f, num_partitions=NPartitions.get(), ) for start, end in splits: args.update({"start": start, "end": end}) partition_id = cls.deploy(cls.parse, num_returns=num_splits + 3, **args) partition_ids.append(partition_id[:-3]) index_ids.append(partition_id[-3]) dtypes_ids.append(partition_id[-2]) # partition_id[-1] contains the columns for each partition, which will be useful # for implementing when `lines=False`. row_lengths = cls.materialize(index_ids) new_index = pandas.RangeIndex(sum(row_lengths)) dtypes = cls.get_dtypes(dtypes_ids) partition_ids = cls.build_partition(partition_ids, row_lengths, column_widths) if isinstance(dtypes, pandas.Series): dtypes.index = columns else: dtypes = pandas.Series(dtypes, index=columns) new_frame = cls.frame_cls( np.array(partition_ids), new_index, columns, row_lengths, column_widths, dtypes=dtypes, ) new_frame.synchronize_labels(axis=0) return cls.query_compiler_cls(new_frame)

manim/scene/three_d_scene.py

def stop_ambient_camera_rotation(self, about="theta"): about: str = about.lower() try: if config.renderer == RendererType.CAIRO: trackers = { "theta": self.camera.theta_tracker, "phi": self.camera.phi_tracker, "gamma": self.camera.gamma_tracker, } x: ValueTracker = trackers[about] x.clear_updaters() self.remove(x) elif config.renderer == RendererType.OPENGL: self.camera.clear_updaters() except Exception: raise ValueError("Invalid ambient rotation angl

Replaced renderer strings with :class:`.RendererType` enum entries (#3017) * remove unused constants * remove deprecated --use_opengl_renderer flag * remove unnecessary workaround with class initialization * add OpenGLMobject.name to get rid of one renderer check * add VMobject.n_points_per_curve property to get rid of more renderer checks * replace renderer string checks with enum check * added mobject.utils module with renderer-dependent class getters * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * ensure that capitalization of passed renderer type is irrelevant * remove unused entries from mobject.utils.__all__ * fixed isort ignore in manim.__init__ * fixed lower-case casting of passed renderer * fixed doctests * more documentation + doctests for mobject.utils * removed incorrect paragraph about ConverToOpenGL metaclass * added docstring for RendererType enum * renderer compatibility section in plugin dev documentation * added mobject.utils to reference manual * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove actual doctest (it ran the compatibility code) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Naveen M K <[email protected]>

stop_ambient_camera_rotation

bd844f46d804c8cad50d06ad20ab5bebaee9987b

manim

three_d_scene.py

https://github.com/ManimCommunity/manim.git

Python

def stop_ambient_camera_rotation(self, about="theta"): about: str = about.lower() try: if config.renderer == RendererType.CAIRO: trackers = { "theta": self.camera.theta_tracker, "phi": self.camera.phi_tracker, "gamma": self.camera.gamma_tracker, } x: ValueTracker = trackers[about] x.clear_updaters() self.remove(x) elif config.renderer == RendererType.OPENGL: self.camera.clear_updaters() except Exception: raise ValueError("Invalid ambient rotation angle.")

tests/rest/client/test_relations.py

def _get_bundled_aggregations(self) -> JsonDict: # Fetch the bundled aggregations of the event. channel = self.make_request( "GET", f"/_matrix/client/unstable/rooms/{self.room}/event/{self.parent_id}", access_token=self.user_token, ) self.assertEquals(200, channel.code, channel.json_body) return channel.json_body["unsigned"].get("m.relations", {})

Refactor relations tests (#12232) * Moves the relation pagination tests to a separate class. * Move the assertion of the response code into the `_send_relation` helper. * Moves some helpers into the base-class.

_get_bundled_aggregations

1da0f79d5455b594f2aa989106a672786f5b990f

synapse

test_relations.py

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

Python

def _get_bundled_aggregations(self) -> JsonDict: # Fetch the bundled aggregations of the event. channel = self.make_request( "GET", f"/_matrix/client/unstable/rooms/{self.room}/event/{self.parent_id}", access_token=self.user_token, ) self.assertEquals(200, channel.code, channel.json_body) return channel.json_body["unsigned"].get("m.relations", {})

chia/types/spend_bundle.py

def get_memos(self) -> Dict[bytes32, List[bytes]]: memos: Dict[bytes32, List[bytes]] = {} for coin_spend in self.coin_spends: result = Program.from_bytes(bytes(coin_spend.puzzle_reveal)).run( Program.from_bytes(bytes(coin_spend.solution)) ) for condition in result.as_python(): if condition[0] == ConditionOpcode.CREATE_COIN and len(condition) >= 4: #

Merge standalone wallet into main (#9793) * wallet changes from pac * cat changes * pool tests * pooling tests passing * offers * lint * mempool_mode * black * linting * workflow files * flake8 * more cleanup * renamed * remove obsolete test, don't cast announcement * memos are not only bytes32 * trade renames * fix rpcs, block_record * wallet rpc, recompile settlement clvm * key derivation * clvm tests * lgtm issues and wallet peers * stash * rename * mypy linting * flake8 * bad initializer * flaky tests * Make CAT wallets only create on verified hints (#9651) * fix clvm tests * return to log lvl warn * check puzzle unhardened * public key, not bytes. api caching change * precommit changes * remove unused import * mypy ci file, tests * ensure balance before creating a tx * Remove CAT logic from full node test (#9741) * Add confirmations and sleeps for wallet (#9742) * use pool executor * rever merge mistakes/cleanup * Fix trade test flakiness (#9751) * remove precommit * older version of black * lint only in super linter * Make announcements in RPC be objects instead of bytes (#9752) * Make announcements in RPC be objects instead of bytes * Lint * misc hint'ish cleanup (#9753) * misc hint'ish cleanup * unremove some ci bits * Use main cached_bls.py * Fix bad merge in main_pac (#9774) * Fix bad merge at 71da0487b9cd5564453ec24b76f1ac773c272b75 * Remove unused ignores * more unused ignores * Fix bad merge at 3b143e705057d6c14e2fb3e00078aceff0552d7e * One more byte32.from_hexstr * Remove obsolete test * remove commented out * remove duplicate payment object * remove long sync * remove unused test, noise * memos type * bytes32 * make it clear it's a single state at a time * copy over asset ids from pacr * file endl linter * Update chia/server/ws_connection.py Co-authored-by: dustinface <[email protected]> Co-authored-by: Matt Hauff <[email protected]> Co-authored-by: Kyle Altendorf <[email protected]> Co-authored-by: dustinface <[email protected]>

get_memos

89f15f591cc3cc3e8ae40e95ffc802f7f2561ece

chia-blockchain

spend_bundle.py

https://github.com/Chia-Network/chia-blockchain.git

Python

def get_memos(self) -> Dict[bytes32, List[bytes]]: memos: Dict[bytes32, List[bytes]] = {} for coin_spend in self.coin_spends: result = Program.from_bytes(bytes(coin_spend.puzzle_reveal)).run( Program.from_bytes(bytes(coin_spend.solution)) ) for condition in result.as_python(): if condition[0] == ConditionOpcode.CREATE_COIN and len(condition) >= 4: # If only 3 elements (opcode + 2 args), there is no memo, this is ph, amount coin_added = Coin(coin_spend.coin.name(), bytes32(condition[1]), int_from_bytes(condition[2])) if type(condition[3]) != list: # If it's not a list, it's not the correct format continue memos[coin_added.name()] = condition[3] return memos # Note that `coin_spends` used to have the bad name `coin_solutions`. # Some API still expects this name. For now, we accept both names. # # TODO: continue this deprecation. Eventually, all code below here should be removed. # 1. set `exclude_modern_keys` to `False` (and manually set to `True` where necessary) # 2. set `include_legacy_keys` to `False` (and manually set to `False` where necessary) # 3. remove all references to `include_legacy_keys=True` # 4. remove all code below this point

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

def _clean_url_path_part(part): # type: (str) -> str # We unquote prior

upd; format

_clean_url_path_part

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

collector.py

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

Python

def _clean_url_path_part(part): # type: (str) -> str # We unquote prior to quoting to make sure nothing is double quoted. return urllib.parse.quote(urllib.parse.unquote(part))

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

def CheckAltTokens(filename, clean_lines, linenum, error): line = clean_lines.elided[linenum] # Avoid preprocessor lines if Match(r'^\s*#', line): return # Last ditch effort to avoid multi-line comments. This will not help # if the comment started before the current line or ended after the # current line, but it catches most of the false positives. At least, # it provides a way to workaround this warning for people who use # multi-line comments in preprocessor macros. # # TODO(unknown): remove this once cpplint has better support for # multi-line comments. if line.find('/*') >= 0 or line.find('*/') >= 0: return for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(lin

Balanced joint maximum mean discrepancy for deep transfer learning

CheckAltTokens

cc4d0564756ca067516f71718a3d135996525909

transferlearning

cpp_lint.py

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

Python

def CheckAltTokens(filename, clean_lines, linenum, error): line = clean_lines.elided[linenum] # Avoid preprocessor lines if Match(r'^\s*#', line): return # Last ditch effort to avoid multi-line comments. This will not help # if the comment started before the current line or ended after the # current line, but it catches most of the false positives. At least, # it provides a way to workaround this warning for people who use # multi-line comments in preprocessor macros. # # TODO(unknown): remove this once cpplint has better support for # multi-line comments. if line.find('/*') >= 0 or line.find('*/') >= 0: return for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line): error(filename, linenum, 'readability/alt_tokens', 2, 'Use operator %s instead of %s' % ( _ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1)))

python/ray/util/joblib/__init__.py

def register_ray(): try: from ray.util.joblib.ray_backend import RayBackend register_parallel_backend("ray", RayBackend) except ImportError: msg = ( "T

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

register_ray

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

__init__.py

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

Python

def register_ray(): try: from ray.util.joblib.ray_backend import RayBackend register_parallel_backend("ray", RayBackend) except ImportError: msg = ( "To use the ray backend you must install ray." "Try running 'pip install ray'." "See https://docs.ray.io/en/master/installation.html" "for more information." ) raise ImportError(msg) __all__ = ["register_ray"]

django/contrib/auth/backends.py

def get_group_permissions(self, user_obj, obj=None): return self._get_permissi

Refs #33476 -- Reformatted code with Black.

get_group_permissions

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

backends.py

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

Python

def get_group_permissions(self, user_obj, obj=None): return self._get_permissions(user_obj, obj, "group")

haystack/document_stores/deepsetcloud.py

def get_evaluation_sets(self) -> List[dict]: return self.evaluation_set_client.get

EvaluationSetClient for deepset cloud to fetch evaluation sets and la… (#2345) * EvaluationSetClient for deepset cloud to fetch evaluation sets and labels for one specific evaluation set * make DeepsetCloudDocumentStore able to fetch uploaded evaluation set names * fix missing renaming of get_evaluation_set_names in DeepsetCloudDocumentStore * update documentation for evaluation set functionality in deepset cloud document store * DeepsetCloudDocumentStore tests for evaluation set functionality * rename index to evaluation_set_name for DeepsetCloudDocumentStore evaluation set functionality * raise DeepsetCloudError when no labels were found for evaluation set * make use of .get_with_auto_paging in EvaluationSetClient * Return result of get_with_auto_paging() as it parses the response already * Make schema import source more specific * fetch all evaluation sets for a workspace in deepset Cloud * Rename evaluation_set_name to label_index * make use of generator functionality for fetching labels * Update Documentation & Code Style * Adjust function input for DeepsetCloudDocumentStore.get_all_labels, adjust tests for it, fix typos, make linter happy * Match error message with pytest.raises * Update Documentation & Code Style * DeepsetCloudDocumentStore.get_labels_count raises DeepsetCloudError when no evaluation set was found to count labels on * remove unneeded import in tests * DeepsetCloudDocumentStore tests, make reponse bodies a string through json.dumps * DeepsetcloudDocumentStore.get_label_count - move raise to return * stringify uuid before json.dump as uuid is not serilizable * DeepsetcloudDocumentStore - adjust response mocking in tests * DeepsetcloudDocumentStore - json dump response body in test * DeepsetCloudDocumentStore introduce label_index, EvaluationSetClient rename label_index to evaluation_set * Update Documentation & Code Style * DeepsetCloudDocumentStore rename evaluation_set to evaluation_set_response as there is a name clash with the input variable * DeepsetCloudDocumentStore - rename missed variable in test * DeepsetCloudDocumentStore - rename missed label_index to index in doc string, rename label_index to evaluation_set in EvaluationSetClient * Update Documentation & Code Style * DeepsetCloudDocumentStore - update docstrings for EvaluationSetClient * DeepsetCloudDocumentStore - fix typo in doc string Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

get_evaluation_sets

a273c3a51dd432bd125e5b35df4be94260a2cdb7

haystack

deepsetcloud.py

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

Python

def get_evaluation_sets(self) -> List[dict]: return self.evaluation_set_client.get_evaluation_sets()

label_studio/data_import/uploader.py

def allowlist_svg(dirty_xml): from lxml.html import clean allow_tags = [ 'xml', 'svg', 'circle', 'ellipse', 'line', 'path', 'polygon', 'polyline', 'rect' ] cleaner = clean.Cleaner( allow_tags=allow_tags,

fix: DEV-2236: Stored XSS via SVG file (#2273) * user uploaded content rendered as plain text or known image only * allow list for svg in progress * allow list for svg basic pass * add error handling * add to file processing re: code review * rm uneeded code * add env var to disable svg cleaning * add test * update env setting * rm lxml string methods * Update uploader.py * Update base.py Co-authored-by: Max Tkachenko <[email protected]>

allowlist_svg

53f6308186aa131946e196b0409f3d732ec9e007

label-studio

uploader.py

https://github.com/heartexlabs/label-studio.git

Python

def allowlist_svg(dirty_xml): from lxml.html import clean allow_tags = [ 'xml', 'svg', 'circle', 'ellipse', 'line', 'path', 'polygon', 'polyline', 'rect' ] cleaner = clean.Cleaner( allow_tags=allow_tags, style=True, links=True, add_nofollow=False, page_structure=True, safe_attrs_only=False, remove_unknown_tags=False) clean_xml = cleaner.clean_html(dirty_xml) return clean_xml

tools/sort/sort_methods.py

def _sort_filelist(self) -> None: for filename, image, alignments in self._iterator(): self.score_image(filename, image, alignments) self.sort() logger.debug("sorted list: %s", [r[0] if isinsta

Overhaul sort: - Standardize image data reading and writing - Optimize loading (just one pass required) - Make all sort groups binnable (to greater or lesser results) - Add sort by pitch - Deprecate multiple options - linting, docs + locales

_sort_filelist

98d01760e469fd2108eed8d0b0a1ba6297c3177c

faceswap

sort_methods.py

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

Python

def _sort_filelist(self) -> None: for filename, image, alignments in self._iterator(): self.score_image(filename, image, alignments) self.sort() logger.debug("sorted list: %s", [r[0] if isinstance(r, (tuple, list)) else r for r in self._result])

keras/optimizers/optimizer_experimental/optimizer.py

def finalize_variable_values(self, var_list): if self.use_ema: # If the optimizer uses EMA, then when finalizing, we replace the model # variable value with its moving average stored inside optimizer. self._overwrite_model_variable

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

finalize_variable_values

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

optimizer.py

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

Python

def finalize_variable_values(self, var_list): if self.use_ema: # If the optimizer uses EMA, then when finalizing, we replace the model # variable value with its moving average stored inside optimizer. self._overwrite_model_variables_with_average_value(var_list)

jina/serve/stream/__init__.py

async def wait_floating_requests_end(self): while self.total_num_floating_tasks_alive > 0:

feat: wait for floating Executor tasks (#5004)

wait_floating_requests_end

1b3edacf531e4e8d29eac4ea73785f8d201255d6

jina

__init__.py

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

Python

async def wait_floating_requests_end(self): while self.total_num_floating_tasks_alive > 0: await asyncio.sleep(0)

torch/functional.py

def _lu_impl(A, pivot=True, get_infos=False, out=None): # type: (Tensor, bool, bool, Any) -> Tuple[Tensor, Tensor, Tensor] r # If get_infos is True, then we don't need to ch

Add linalg.lu_factor (#66933) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66933 This PR exposes `torch.lu` as `torch.linalg.lu_factor` and `torch.linalg.lu_factor_ex`. This PR also adds support for matrices with zero elements both in the size of the matrix and the batch. Note that this function simply returns empty tensors of the correct size in this case. We add a test and an OpInfo for the new function. This PR also adds documentation for this new function in line of the documentation in the rest of `torch.linalg`. Fixes https://github.com/pytorch/pytorch/issues/56590 Fixes https://github.com/pytorch/pytorch/issues/64014 cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano Test Plan: Imported from OSS Reviewed By: gchanan Differential Revision: D32834069 Pulled By: mruberry fbshipit-source-id: 51ef12535fa91d292f419acf83b800b86ee9c7eb

_lu_impl

a35b4b49d2e2a215a64e455101c779ae623b3321

pytorch

functional.py

https://github.com/pytorch/pytorch.git

Python

def _lu_impl(A, pivot=True, get_infos=False, out=None): # type: (Tensor, bool, bool, Any) -> Tuple[Tensor, Tensor, Tensor] r # If get_infos is True, then we don't need to check for errors and vice versa return torch._lu_with_info(A, pivot=pivot, check_errors=(not get_infos)) if TYPE_CHECKING: _ListOrSeq = Sequence[Tensor] else: _ListOrSeq = List[Tensor]

tests/config/test_workers.py

def test_new_configs_appservice_worker(self) -> None: appservice_worker_config = self._make_worker_config( worker_app="synapse.app.generic_worker", worker_name="worker1" ) self.assertTrue( appservice_worker_config._should_this_worker_perform_duty( { "notify_appservices_from_worker": "worker1", }, "notify_appservices", "synapse.app.appservice", "notify_appservices_from_worker", ) ) self.assertFalse( appservice_worker_config._should_this_worker_perform_duty( { "notify_appservices_from_worker"

Add the `notify_appservices_from_worker` configuration option (superseding `notify_appservices`) to allow a generic worker to be designated as the worker to send traffic to Application Services. (#12452)

test_new_configs_appservice_worker

c2d50e9f6c5f7b01cbd8bf1dca36cb8c0e7b007f

synapse

test_workers.py

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

Python

def test_new_configs_appservice_worker(self) -> None: appservice_worker_config = self._make_worker_config( worker_app="synapse.app.generic_worker", worker_name="worker1" ) self.assertTrue( appservice_worker_config._should_this_worker_perform_duty( { "notify_appservices_from_worker": "worker1", }, "notify_appservices", "synapse.app.appservice", "notify_appservices_from_worker", ) ) self.assertFalse( appservice_worker_config._should_this_worker_perform_duty( { "notify_appservices_from_worker": "worker2", }, "notify_appservices", "synapse.app.appservice", "notify_appservices_from_worker", ) )

sympy/physics/hydrogen.py

def E_nl(n, Z=1): n

Remove abbreviations in documentation

E_nl

65be461082dda54c8748922f9c29a19af1279fe1

sympy

hydrogen.py

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

Python

def E_nl(n, Z=1): n, Z = S(n), S(Z) if n.is_integer and (n < 1): raise ValueError("'n' must be positive integer") return -Z**2/(2*n**2)

tests/checkpointing/test_model_checkpoint.py

def test_model_checkpoint_no_extraneous_invocations(tmpdir): model = LogInTwoMethods() num_epochs = 4 model_checkpoint = ModelCheckpointTestInvocations(monitor="early_stop_on", expected_count=num_epochs, save_top_k=-1) trainer = Trainer( strategy="ddp_spawn", accelerator="cpu", devices=2, default_root_dir=tmpdir, callbacks=[model_checkpoint], max_epochs=num_epochs, ) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}"

Update `tests/checkpointing/*.py` to use `devices` instead of `gpus` or `ipus` (#11408) Co-authored-by: Carlos Mocholí <[email protected]>

test_model_checkpoint_no_extraneous_invocations

d2d284fd6e3e8f53e9a44ab233771850af1e4dab

lightning

test_model_checkpoint.py

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

Python

def test_model_checkpoint_no_extraneous_invocations(tmpdir): model = LogInTwoMethods() num_epochs = 4 model_checkpoint = ModelCheckpointTestInvocations(monitor="early_stop_on", expected_count=num_epochs, save_top_k=-1) trainer = Trainer( strategy="ddp_spawn", accelerator="cpu", devices=2, default_root_dir=tmpdir, callbacks=[model_checkpoint], max_epochs=num_epochs, ) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}"

python3.10.4/Lib/importlib/abc.py

def find_module(self, fullname, path): warnings.warn("MetaPathFinder.find_module() is deprecated since Python " "3.4 in favor of MetaPathFinder.find_spec() and is "

add python 3.10.4 for windows

find_module

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

abc.py

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

Python

def find_module(self, fullname, path): warnings.warn("MetaPathFinder.find_module() is deprecated since Python " "3.4 in favor of MetaPathFinder.find_spec() and is " "slated for removal in Python 3.12", DeprecationWarning, stacklevel=2) if not hasattr(self, 'find_spec'): return None found = self.find_spec(fullname, path) return found.loader if found is not None else None

nni/algorithms/hpo/evolution_tuner.py

def _generate_individual(self, parameter_id): pos = -1 for i in range(len(self.population)): if self.population[i].result is None: pos = i break if pos != -1: indiv = copy.deepcopy(self.population[pos]) self.population.pop(pos) else: random.shuffle(self.population) # avoid only 1 individual has result if len(self.population) > 1 and self.population[0].result < self.population[1].result: self.population[0] = self.population[1] # mutation on the worse individual space = json2space(self.searchspace_json, self.population[0].config) is_rand = dict() mutation_pos = space[random.randint(0, len(space)-1)]

[WIP] add doc for evolution (#4575)

_generate_individual

de6662a4a0fbfc557614b6c022edaf8117de7a5a

nni

evolution_tuner.py

https://github.com/microsoft/nni.git

Python

def _generate_individual(self, parameter_id): pos = -1 for i in range(len(self.population)): if self.population[i].result is None: pos = i break if pos != -1: indiv = copy.deepcopy(self.population[pos]) self.population.pop(pos) else: random.shuffle(self.population) # avoid only 1 individual has result if len(self.population) > 1 and self.population[0].result < self.population[1].result: self.population[0] = self.population[1] # mutation on the worse individual space = json2space(self.searchspace_json, self.population[0].config) is_rand = dict() mutation_pos = space[random.randint(0, len(space)-1)] for i in range(len(self.space)): is_rand[self.space[i]] = (self.space[i] == mutation_pos) config = json2parameter( self.searchspace_json, is_rand, self.random_state, self.population[0].config) if len(self.population) > 1: self.population.pop(1) indiv = Individual(config=config) # remove "_index" from config and save params-id self.running_trials[parameter_id] = indiv config = split_index(indiv.config) return config

spotdl/console/web.py

async def connect(self): connection = {"client_id": self.client_id, "websocket": self.websocket} logging.info(f"Connect

fixed docstrings

connect

448bd75fe5de981995446a536963c5bd11e491ec

spotify-downloader

web.py

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

Python

async def connect(self): connection = {"client_id": self.client_id, "websocket": self.websocket} logging.info(f"Connecting WebSocket: {connection}") await self.websocket.accept() WSProgressHandler.instances.append(self)

mmdet/datasets/custom.py

def prepare_test_img(self, idx): img_info = self.data_infos[idx] results = dict(img_info=img_in

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

prepare_test_img

1516986a616fee8bb741d0ab2be40683045efccd

mmdetection

custom.py

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

Python

def prepare_test_img(self, idx): img_info = self.data_infos[idx] results = dict(img_info=img_info) if self.proposals is not None: results['proposals'] = self.proposals[idx] self.pre_pipeline(results) return self.pipeline(results)

@frappe.whitelist()

erpnext/regional/report/provident_fund_deductions/provident_fund_deductions.py

def get_data(filters): data = [] conditions = get_conditions(filters) salary_slips = frappe.db.sql( % (conditions), as_dict=1, ) component_type_dict = frappe._dict( frappe.db.sql( ) ) if not len(component_type_dict): return [] entry = frappe.db.sql( % (conditions, ", ".join(["%s"] * len(component_type_dict))), tuple(component_type_dict.keys()), as_dict=1, ) data_list = prepare_data(entry, component_type_dict) for d in salary_slips: total = 0 if data_list.get(d.name): employee = { "employee": data_list.get(d.name).get("employee"), "employee_name": data_list.get(d.name).get("employee_name"), "pf_account": data_list.get(d.name).get("pf_account"), } if data_list.get(d.name).get("Provident Fund"): employee["pf_amount"] = data_list.get(d.name).get("Provident Fund") total += data_list.get(

style: format code with black

get_data

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

provident_fund_deductions.py

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

Python

def get_data(filters): data = [] conditions = get_conditions(filters) salary_slips = frappe.db.sql( % (conditions), as_dict=1, ) component_type_dict = frappe._dict( frappe.db.sql( ) ) if not len(component_type_dict): return [] entry = frappe.db.sql( % (conditions, ", ".join(["%s"] * len(component_type_dict))), tuple(component_type_dict.keys()), as_dict=1, ) data_list = prepare_data(entry, component_type_dict) for d in salary_slips: total = 0 if data_list.get(d.name): employee = { "employee": data_list.get(d.name).get("employee"), "employee_name": data_list.get(d.name).get("employee_name"), "pf_account": data_list.get(d.name).get("pf_account"), } if data_list.get(d.name).get("Provident Fund"): employee["pf_amount"] = data_list.get(d.name).get("Provident Fund") total += data_list.get(d.name).get("Provident Fund") if data_list.get(d.name).get("Additional Provident Fund"): employee["additional_pf"] = data_list.get(d.name).get("Additional Provident Fund") total += data_list.get(d.name).get("Additional Provident Fund") if data_list.get(d.name).get("Provident Fund Loan"): employee["pf_loan"] = data_list.get(d.name).get("Provident Fund Loan") total += data_list.get(d.name).get("Provident Fund Loan") employee["total"] = total data.append(employee) return data @frappe.whitelist()

homeassistant/components/trace/models.py

def as_dict(self) -> dict[str, Any]: return { "extended_dict": self.a

Move Trace classes to separate module (#78433)

as_dict

219cee2ca9f6cd9eb7e0abcbda6d9540240e20d3

core

models.py

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

Python

def as_dict(self) -> dict[str, Any]: return { "extended_dict": self.as_extended_dict(), "short_dict": self.as_short_dict(), }

@keras_export( "keras.__internal__.optimizers.convert_to_legacy_optimizer", v1=[] )

keras/optimizers/__init__.py

def deserialize(config, custom_objects=None, **kwargs): # loss_scale_optimizer has a direct dependency of optimizer, import here # rather than top to avoid the cyclic

Move new optimizer out of optimizer_experimental/ directory. PiperOrigin-RevId: 488998585

deserialize

5a105aadbdc6fde2c2529280c4789864adbb81c7

keras

__init__.py

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

Python

def deserialize(config, custom_objects=None, **kwargs): # loss_scale_optimizer has a direct dependency of optimizer, import here # rather than top to avoid the cyclic dependency. from keras.mixed_precision import ( loss_scale_optimizer, ) use_legacy_optimizer = kwargs.pop("use_legacy_optimizer", False) if len(config["config"]) > 0: # If the optimizer config is not empty, then we use the value of # `is_legacy_optimizer` to override `use_legacy_optimizer`. If # `is_legacy_optimizer` does not exist in config, it means we are # using the legacy optimzier. use_legacy_optimizer = config["config"].get("is_legacy_optimizer", True) if ( tf.__internal__.tf2.enabled() and tf.executing_eagerly() and not use_legacy_optimizer ): all_classes = { "adadelta": adadelta_experimental.Adadelta, "adagrad": adagrad_experimental.Adagrad, "adam": adam_experimental.Adam, "adamax": adamax_experimental.Adamax, "experimentaladadelta": adadelta_experimental.Adadelta, "experimentaladagrad": adagrad_experimental.Adagrad, "experimentaladam": adam_experimental.Adam, "experimentalsgd": sgd_experimental.SGD, "nadam": nadam_experimental.Nadam, "rmsprop": rmsprop_experimental.RMSprop, "sgd": sgd_experimental.SGD, "ftrl": ftrl_experimental.Ftrl, "lossscaleoptimizer": loss_scale_optimizer.LossScaleOptimizerV3, "lossscaleoptimizerv3": loss_scale_optimizer.LossScaleOptimizerV3, # LossScaleOptimizerV1 was an old version of LSO that was removed. # Deserializing it turns it into a LossScaleOptimizer "lossscaleoptimizerv1": loss_scale_optimizer.LossScaleOptimizer, } else: all_classes = { "adadelta": adadelta_v2.Adadelta, "adagrad": adagrad_v2.Adagrad, "adam": adam_v2.Adam, "adamax": adamax_v2.Adamax, "experimentaladadelta": adadelta_experimental.Adadelta, "experimentaladagrad": adagrad_experimental.Adagrad, "experimentaladam": adam_experimental.Adam, "experimentalsgd": sgd_experimental.SGD, "nadam": nadam_v2.Nadam, "rmsprop": rmsprop_v2.RMSprop, "sgd": gradient_descent_v2.SGD, "ftrl": ftrl_v2.Ftrl, "lossscaleoptimizer": loss_scale_optimizer.LossScaleOptimizer, "lossscaleoptimizerv3": loss_scale_optimizer.LossScaleOptimizerV3, # LossScaleOptimizerV1 was an old version of LSO that was removed. # Deserializing it turns it into a LossScaleOptimizer "lossscaleoptimizerv1": loss_scale_optimizer.LossScaleOptimizer, } # Make deserialization case-insensitive for built-in optimizers. if config["class_name"].lower() in all_classes: config["class_name"] = config["class_name"].lower() return deserialize_keras_object( config, module_objects=all_classes, custom_objects=custom_objects, printable_module_name="optimizer", ) @keras_export( "keras.__internal__.optimizers.convert_to_legacy_optimizer", v1=[] )

python3.10.4/Lib/_pydecimal.py

def multiply(self, a, b): a = _convert_other(a, raiseit=True) r = a.__mul__(b, context=self) if r is NotImplemented: raise TypeError("Unable to convert %s to Decimal" %

add python 3.10.4 for windows

multiply

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

_pydecimal.py

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

Python

def multiply(self, a, b): a = _convert_other(a, raiseit=True) r = a.__mul__(b, context=self) if r is NotImplemented: raise TypeError("Unable to convert %s to Decimal" % b) else: return r

tests/utils/test_db_cleanup.py

def test_run_cleanup_skip_archive(self, cleanup_table_mock, kwargs, should_skip): run_cleanup( clean_before_timestamp=None, table_

Don't rely on current ORM structure for db clean command (#23574) For command DB clean, by not relying on the ORM models, we will be able to use the command even when the metadatabase is not yet upgraded to the version of Airflow you have installed. Additionally we archive all rows before deletion.

test_run_cleanup_skip_archive

95bd6b71cc9f5da377e272707f7b68000d980939

airflow

test_db_cleanup.py

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

Python

def test_run_cleanup_skip_archive(self, cleanup_table_mock, kwargs, should_skip): run_cleanup( clean_before_timestamp=None, table_names=['log'], dry_run=None, verbose=None, confirm=False, **kwargs, ) assert cleanup_table_mock.call_args[1]['skip_archive'] is should_skip

ludwig/datasets/base_dataset.py

def process(self) -> None: if not self.is_downloaded(): self.download()

Add and expand docstrings in base_dataset.py (#1819)

process

d0bcbb2a6e2ab82501fd34ef583329ff2ac22a15

ludwig

base_dataset.py

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

Python

def process(self) -> None: if not self.is_downloaded(): self.download() self.process_downloaded_dataset()

rllib/utils/tests/test_actor_manager.py

def test_healthy_only_works_for_list_of_functions(self): act

[RLlib] Refactor `WorkerSet` on top of `FaultTolerantActorManager`. (#29938) Signed-off-by: Jun Gong <[email protected]>

test_healthy_only_works_for_list_of_functions

e707ce4fb3717e3c05118c57f503dfbd03552ca9

ray

test_actor_manager.py

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

Python

def test_healthy_only_works_for_list_of_functions(self): actors = [Actor.remote(i) for i in range(4)] manager = FaultTolerantActorManager(actors=actors) # Mark first and second actor as unhealthy. manager.set_actor_state(1, False) manager.set_actor_state(2, False)

tests/sentry/integrations/slack/notifications/test_unassigned.py

def test_unassignment(self, mock_func): notification = UnassignedActivityNotification(

fix(notifications): Use `metrics_key` (#34572)

test_unassignment

1730c481f1a8a71446326fa1ff72e10663016385

sentry

test_unassigned.py

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

Python

def test_unassignment(self, mock_func): notification = UnassignedActivityNotification( Activity( project=self.project, group=self.group, user=self.user, type=ActivityType.ASSIGNED, data={"assignee": ""}, ) ) with self.tasks(): notification.send() attachment, text = get_attachment() assert text == f"Issue unassigned by {self.name}" assert attachment["title"] == self.group.title assert ( attachment["footer"] == f"{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=unassigned_activity-slack-user|Notification Settings>" )

gamestonk_terminal/stocks/insider/insider_controller.py

def print_help(self): has_ticker_start = "

Terminal Wide Rich (#1161) * My idea for how we handle Rich moving forward * remove independent consoles * FIxed pylint issues * add a few vars * Switched print to console * More transitions * Changed more prints * Replaced all prints * Fixing tabulate * Finished replace tabulate * Finished removing rich from Tabulate * add Panel around menu * add GST watermark under feature flag * Fixed 46 tests * Delete test_screener[False].yaml * Delete test_screener[True].yaml * Fixed the rest of the tests * add help and source color vars and use rgb * rich on stocks/options * update rich on disc, dps, sia * rich in gov, ins and scr menus * ba and ca menus with rich * Fixed import issue * Fixed some tests * removed termcolor * Removed prettytable * add rich to remaining stocks menus * FIxed linting issue * Added James' changes * Updated dependencies * Add rich to cryptocurrency menu * refactor economy and forex * refactor etf with rich * refactor mfunds * refactor rich rest * not specify style so default color works well on any background * Fixing mypy issues * Updated tests * More test fixes * James' test fixes * Updating tests : stocks/screener - fix cassettes using BR * Updating tests : crypto * Updating tests : disable DEBUG_MODE * Updating tests : stocks/fa/yfinance * minor fixes that escape * Improve the rich table function (that replaces tabulate :D ) * Fixed bad code * delete rogue file + dcf fix + NoConsole * sia mypy * fuck you linter * fuck you linter pt 2 * skip hehe * i hate the black linter * ubuntu mypy attempt * Update : rich_config + gtff * Updating tests : conftest * Updating tests : stocks * Update : rich_config * Updating : rich_config * make panel configurable for Theodore :b * colors update * Merged * Updating : rich_config + feature_flags * Updating : rich_config * Updating tests : stocks * Updating : feature_flags Co-authored-by: DidierRLopes <[email protected]> Co-authored-by: Chavithra PARANA <[email protected]> Co-authored-by: james <[email protected]> Co-authored-by: jose-donato <[email protected]>

print_help

82747072c511beb1b2672846ae2ee4aec53eb562

OpenBBTerminal

insider_controller.py

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

Python

def print_help(self): has_ticker_start = "[unvl]" if not self.ticker else "" has_ticker_end = "[/unvl]" if not self.ticker else "" help_text = f console.print(text=help_text, menu="Stocks - Insider Trading")

@pytest.mark.django_db

tests/sentry/relay/test_config.py

def test_project_config_dynamic_sampling_is_none(default_project): default_project.update_option("sentry:dynamic_sampling", None) with Feature({"organizations:server-side-sampling": True}): cfg = get_project_config(default_project) cfg = cfg.to_dict() dynamic_sampling = get_path(cfg, "config", "dynamicSampling") assert dynamic_sampling is None

feat(dynamic-sampling): Handles updating ProjectConfig with uniform DS rule for v2 [TET-465] (#40268) This PR forces your uniform rule by your plan or respect old logic. If both feature flags are enabled dynamic-sampling-basic flag takes the highest precedence. Original PR https://github.com/getsentry/sentry/pull/40180 was reverted via https://github.com/getsentry/sentry/pull/40266 due to issue of removing incorrect line. Co-authored-by: Joris Bayer <[email protected]>

test_project_config_dynamic_sampling_is_none

e0e2c4ff4248042abda3cc93024930dada416af8

sentry

test_config.py

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

Python

def test_project_config_dynamic_sampling_is_none(default_project): default_project.update_option("sentry:dynamic_sampling", None) with Feature({"organizations:server-side-sampling": True}): cfg = get_project_config(default_project) cfg = cfg.to_dict() dynamic_sampling = get_path(cfg, "config", "dynamicSampling") assert dynamic_sampling is None @pytest.mark.django_db

python3.10.4/Lib/bz2.py

def seek(self, offset, whence=io.SEEK_SET):

add python 3.10.4 for windows

seek

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

bz2.py

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

Python

def seek(self, offset, whence=io.SEEK_SET): self._check_can_seek() return self._buffer.seek(offset, whence)

django/db/backends/base/base.py

def savepoint(self): if not self._savepoint_allowed(): return thread_ident = _thread.get_ident() tid = str(thread_ident).replace("-", "") self.savepoint_state += 1 sid = "s%s_x%d" % (tid, self.savepoint_state) self.validate_thread_sharing() self._savepoint(sid) return sid

Refs #33476 -- Reformatted code with Black.

savepoint

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

base.py

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

Python

def savepoint(self): if not self._savepoint_allowed(): return thread_ident = _thread.get_ident() tid = str(thread_ident).replace("-", "") self.savepoint_state += 1 sid = "s%s_x%d" % (tid, self.savepoint_state) self.validate_thread_sharing() self._savepoint(sid) return sid

erpnext/accounts/report/unpaid_expense_claim/unpaid_expense_claim.py

def get_unclaimed_expese_claims(filters): cond = "1=1" if filters.get("employee"):

style: format code with black

get_unclaimed_expese_claims

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

unpaid_expense_claim.py

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

Python

def get_unclaimed_expese_claims(filters): cond = "1=1" if filters.get("employee"): cond = "ec.employee = %(employee)s" return frappe.db.sql( .format( cond=cond ), filters, as_list=1, )

pipenv/patched/notpip/_vendor/platformdirs/unix.py

def site_config_dir(self) -> str: # XDG default for $XDG_CONFIG_DIRS only first, if multipath is False path = os.environ.get

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

site_config_dir

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

unix.py

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

Python

def site_config_dir(self) -> str: # XDG default for $XDG_CONFIG_DIRS only first, if multipath is False path = os.environ.get("XDG_CONFIG_DIRS", "") if not path.strip(): path = "/etc/xdg" return self._with_multi_path(path)

sklearn/decomposition/_fastica.py

def fit_transform(self, X, y=None): self._validate_params() return self._fit_transform(X, compute_sources=

MAINT Use _validate_params in FastICA (#23711) Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: jeremiedbb <[email protected]>

fit_transform

4cc347d4d0cbbfdcbd353f08842e0668fed78c9f

scikit-learn

_fastica.py

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

Python

def fit_transform(self, X, y=None): self._validate_params() return self._fit_transform(X, compute_sources=True)

src/sentry/api/endpoints/project_dynamic_sampling.py

def __fetch_randomly_sampled_transactions(self, project, query, sample_size, query_time_range): sampling_factor = self.__generate_transactions_sampling_factor( project=project, query=query, sample_size=sample_size, query_time_range=query_time_range, ) builder = QueryBuilder( Dataset.Discover, params={ "start": query_time_range.start_time, "end": query_time_range.end_time, "project_id": [project.id], "organization_id": project.organization.id, }, query=f"{query} event.type:transaction", selected_columns=[ "id", "trace", "random_number() as rand_num", f"modulo(rand_num, {sampling_factor}) as modulo_num", ], equations=[], orderby=None, auto_fields=True, auto_aggregations=True, use_aggregate_conditions=True, functions_acl=["random_number", "modulo"], limit=sample_size, offset=0, equation_config={"auto_add": False}, ) builder.add_conditions([Condition(lhs=Column("modulo_num"), op=Op.EQ, rhs=0)]) snuba_query = builder.get_snql_query().query snuba_query = snuba_query.set_select( snuba_query.select + [ Function( "not", [Function("has", [Column("contexts.key"), TRACE_PARENT_SPAN_CONTEXT

feat(dynamic-sampling): Improve empty transaction breakdown message [TET-338] (#39539) This PR add new attribute parentProjectBreakdown to /api/0/projects/<organization_slug>/<project_slug>/dynamic-sampling/distribution/ api: ``` { "projectBreakdown": null, "sampleSize": 0, "startTimestamp": null, "endTimestamp": null, "parentProjectBreakdown": [ { "projectId": 1, "percentage": 0.9, "project": "sentry" }, { "projectId": 2, "percentage": 0.1, "project": "javascript" } ] } ``` TODO: - [x] Update src/sentry/snuba/referrer.py https://github.com/getsentry/sentry/blob/0fbbf1626f86399b1ca4a2781d66ef96aac69de7/src/sentry/snuba/referrer.py#L208-L210 - [x] Add missing tests Co-authored-by: Andrii Soldatenko <[email protected]> Co-authored-by: ahmedetefy <[email protected]>

__fetch_randomly_sampled_transactions

ceee9dfd8d6fed70d34546e7b46ebb7bf1d49745

sentry

project_dynamic_sampling.py

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

Python

def __fetch_randomly_sampled_transactions(self, project, query, sample_size, query_time_range): sampling_factor = self.__generate_transactions_sampling_factor( project=project, query=query, sample_size=sample_size, query_time_range=query_time_range, ) builder = QueryBuilder( Dataset.Discover, params={ "start": query_time_range.start_time, "end": query_time_range.end_time, "project_id": [project.id], "organization_id": project.organization.id, }, query=f"{query} event.type:transaction", selected_columns=[ "id", "trace", "random_number() as rand_num", f"modulo(rand_num, {sampling_factor}) as modulo_num", ], equations=[], orderby=None, auto_fields=True, auto_aggregations=True, use_aggregate_conditions=True, functions_acl=["random_number", "modulo"], limit=sample_size, offset=0, equation_config={"auto_add": False}, ) builder.add_conditions([Condition(lhs=Column("modulo_num"), op=Op.EQ, rhs=0)]) snuba_query = builder.get_snql_query().query snuba_query = snuba_query.set_select( snuba_query.select + [ Function( "not", [Function("has", [Column("contexts.key"), TRACE_PARENT_SPAN_CONTEXT])], alias="is_root", ) ] ) snuba_query = snuba_query.set_groupby( snuba_query.groupby + [Column("modulo_num"), Column("contexts.key")] ) data = raw_snql_query( SnubaRequest(dataset=Dataset.Discover.value, app_id="default", query=snuba_query), referrer=Referrer.DYNAMIC_SAMPLING_DISTRIBUTION_FETCH_TRANSACTIONS.value, )["data"] return data

@frappe.whitelist() @frappe.validate_and_sanitize_search_inputs

erpnext/controllers/queries.py

def item_query(doctype, txt, searchfield, start, page_len, filters, as_dict=False): conditions = [] if isinstance(filters, str): filters = json.loads(filters) #Get searchfields from meta and use in Item Link field query meta = frappe.get_meta("Item", cached=True) searchfields = meta.get_search_fields() # these are handled separately ignored_search_fields = ("item_name", "description") for ignored_field in ignored_search_fields: if ignored_field in searchfields: searchfields.remove(ignored_field) columns = '' extra_searchfields = [field for field in searchfields if not field in ["name", "item_group", "description", "item_name"]] if extra_searchfields: columns = ", " + ", ".join(extra_searchfields) searchfields = searchfields + [field for field in[searchfield or "name", "item_code", "item_group", "item_name"] if not field in searchfields] searchfields = " or ".join([field + " like %(txt)s" for field in searchfields]) if filters and isinstance(filters, dict): if filters.get('customer') or filters.get('supplier'): party = filters.get('customer') or filters.get('supplier') item_rules_list = frappe.get_all('Party Specific Item', filters = {'party': party}, fields = ['restrict_based_on', 'based_on_value']) filters_dict = {} for rule in item_rules_list: if rule['restrict_based_on'] == 'Item': rule['restrict_based_on'] = 'name' filters_dict[rule.restrict_based_on] = [] for rule in item_rules_list: filters_dict[rule.restrict_based_on].append(rule.based_on_value) for filter in filters_dict: filters[scrub(filter)] = ['in', filters_dict[filter]] if filters.get('customer'): del filters['customer'] else: del filters['supplier'] else: filters.pop('customer', None) filters.pop('supplier', None) description_cond = '' if frappe.db.count('Item', cache=True) < 50000: # scan description only if items are less than 50000 description_cond = 'or tabItem.description LIKE %(txt)s' return frappe.db.sql(.format(

fix: ignore empty customer/supplier in item query (#29610) * fix: dont try to filter by customer/supplier if None * test: item query with emtpy supplier

item_query

41a95e56241ff8f3dceac7285f0bc6b9a43d7a06

erpnext

queries.py

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

Python

def item_query(doctype, txt, searchfield, start, page_len, filters, as_dict=False): conditions = [] if isinstance(filters, str): filters = json.loads(filters) #Get searchfields from meta and use in Item Link field query meta = frappe.get_meta("Item", cached=True) searchfields = meta.get_search_fields() # these are handled separately ignored_search_fields = ("item_name", "description") for ignored_field in ignored_search_fields: if ignored_field in searchfields: searchfields.remove(ignored_field) columns = '' extra_searchfields = [field for field in searchfields if not field in ["name", "item_group", "description", "item_name"]] if extra_searchfields: columns = ", " + ", ".join(extra_searchfields) searchfields = searchfields + [field for field in[searchfield or "name", "item_code", "item_group", "item_name"] if not field in searchfields] searchfields = " or ".join([field + " like %(txt)s" for field in searchfields]) if filters and isinstance(filters, dict): if filters.get('customer') or filters.get('supplier'): party = filters.get('customer') or filters.get('supplier') item_rules_list = frappe.get_all('Party Specific Item', filters = {'party': party}, fields = ['restrict_based_on', 'based_on_value']) filters_dict = {} for rule in item_rules_list: if rule['restrict_based_on'] == 'Item': rule['restrict_based_on'] = 'name' filters_dict[rule.restrict_based_on] = [] for rule in item_rules_list: filters_dict[rule.restrict_based_on].append(rule.based_on_value) for filter in filters_dict: filters[scrub(filter)] = ['in', filters_dict[filter]] if filters.get('customer'): del filters['customer'] else: del filters['supplier'] else: filters.pop('customer', None) filters.pop('supplier', None) description_cond = '' if frappe.db.count('Item', cache=True) < 50000: # scan description only if items are less than 50000 description_cond = 'or tabItem.description LIKE %(txt)s' return frappe.db.sql(.format( columns=columns, scond=searchfields, fcond=get_filters_cond(doctype, filters, conditions).replace('%', '%%'), mcond=get_match_cond(doctype).replace('%', '%%'), description_cond = description_cond), { "today": nowdate(), "txt": "%%%s%%" % txt, "_txt": txt.replace("%", ""), "start": start, "page_len": page_len }, as_dict=as_dict) @frappe.whitelist() @frappe.validate_and_sanitize_search_inputs

dask/array/core.py

def apply_and_enforce(*args, **kwargs): func = kwargs.pop("_func") expected_ndim = kwargs.pop("expected_ndim") out = func(*args, **kwargs) if getattr(out, "ndim", 0) != expected_ndim: out_ndim = getattr(out, "ndim", 0) raise ValueError( f"Dimensio

Add kwarg ``enforce_ndim`` to ``dask.array.map_blocks()`` (#8865)

apply_and_enforce

2b90415b02d3ad1b08362889e0818590ca3133f4

dask

core.py

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

Python

def apply_and_enforce(*args, **kwargs): func = kwargs.pop("_func") expected_ndim = kwargs.pop("expected_ndim") out = func(*args, **kwargs) if getattr(out, "ndim", 0) != expected_ndim: out_ndim = getattr(out, "ndim", 0) raise ValueError( f"Dimension mismatch: expected output of {func} " f"to have dims = {expected_ndim}. Got {out_ndim} instead." ) return out