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semeru

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

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Semeru Lab 7

refresh_access_token

def refresh_access_token(self) -> Tuple[str, int]: token, expires_at = super().refresh_access_token() expires_in = pendulum.parse(expires_at) - pendulum.now() return token, expires_in.seconds

6b502d8c326bcde791128f5711a21cc03bde19f3

source.py

🎉 square: added oauth support (#6842) * fixed test which check incorrect cred config * Added oauth2 authentication * Added oauth creds * fixed formatting * added oauth2 spec section, added missing type hints * Added java part of Square OAuth * fixed checkstyle * removed commented code * added support for old format of spec.json files, updated change logs docs * renamed spec property 'authentication' to default 'credentials'. fixed changes in java part * recovered empty files * updated OAuthImplementationFactory.java * fixed issue with autheticator for sub streams, added config catalog with all streams, updated docs * use advanced_auth * added advanced_auth * moved scopes to private property * updated source version * Revert "updated source version" This reverts commit ce3d06165c4bbbe1592e22203d6b6c545deec9a9. * updated source version * added new version for airbyte index Co-authored-by: ievgeniit <[email protected]>

airbyte

airbyte-integrations/connectors/source-square/source_square/source.py

Python

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

start_object

def start_object(self, obj): if not hasattr(obj, "_meta"): raise base.SerializationError( "Non-model object (%s) encountered during serialization" % type(obj) ) self.indent(1) attrs = {"model": str(obj._meta)} if not self.use_natural_primary_keys or not hasattr(obj, "natural_key"): obj_pk = obj.pk if obj_pk is not None: attrs["pk"] = str(obj_pk) self.xml.startElement("object", attrs)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

xml_serializer.py

Refs #33476 -- Reformatted code with Black.

django

django/core/serializers/xml_serializer.py

Python

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

test_syncer_callback_dead_node_log_error

def test_syncer_callback_dead_node_log_error(caplog, ray_start_2_cpus, temp_data_dirs): caplog.set_level(logging.ERROR, logger="ray.tune.syncer") tmp_source, tmp_target = temp_data_dirs syncer_callback = TestSyncerCallback( sync_period=0, local_logdir_override=tmp_target, ) trial1 = MockTrial(trial_id="a", logdir=tmp_source, on_dead_node=True) syncer_callback.on_trial_result(iteration=1, trials=[], trial=trial1, result={}) assert ( "An error occurred when trying to get the node ip where this trial is running" in caplog.text )

fc9f8e458c4dad7a51e0d781917b1a003cb55cd7

test_syncer_callback.py

[Tune] Catch SyncerCallback failure with dead node (#29438) ### Context This issue was uncovered by this long running test: `long_running_distributed_pytorch_pbt_failure`. This test randomly kills nodes via `FailureInjectorCallback`, and the test failure happens when: 1. A trial result comes in and is processed 2. The node this trial is running on is requested to be killed by the failure injector 3. The driver's syncer callback runs on the on_trial_result event 4. The node dies 5. The driver is in the middle of syncing, trying to access the node ip, which errors ### What's in this PR? 1. Gracefully handle this race condition by catching the error thrown by the sync operation on a dead node 2. Log an error to the user 3. Adds a test for this sync with dead node scenario Signed-off-by: Justin Yu <[email protected]>

ray

python/ray/tune/tests/test_syncer_callback.py

Python

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

test_array_vs_scalar_strict

def test_array_vs_scalar_strict(self): a = np.array([1., 1., 1.]) b = 1. with pytest.raises(AssertionError): assert_array_equal(a, b, strict=True)

cafec60a5e28af98fb8798049edd7942720d2d74

test_utils.py

ENH: Add strict parameter to assert_array_equal. (#21595) Fixes #9542 Co-authored-by: Bas van Beek <[email protected]>

numpy

numpy/testing/tests/test_utils.py

Python

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

__getitem__

def __getitem__(self, key): if key != -1: raise NotImplementedError("Support only `-1` (for `reset_parameters`).") return self.out[key]

b617a87ee40ab384767a27335313c2c65ee094ec

subsampling.py

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

MockingBird

ppg_extractor/encoder/subsampling.py

Python

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

__mul__

def __mul__(self, other): if other is Ellipsis: other = (0, None) elif isinstance(other, tuple) and other[:1] == (Ellipsis,): other = ((0, ) + other[1:] + (None,))[:2] if isinstance(other, int): minElements, optElements = other, 0 elif isinstance(other, tuple): other = tuple(o if o is not Ellipsis else None for o in other) other = (other + (None, None))[:2] if other[0] is None: other = (0, other[1]) if isinstance(other[0], int) and other[1] is None: if other[0] == 0: return ZeroOrMore(self) if other[0] == 1: return OneOrMore(self) else: return self * other[0] + ZeroOrMore(self) elif isinstance(other[0], int) and isinstance(other[1], int): minElements, optElements = other optElements -= minElements else: raise TypeError("cannot multiply 'ParserElement' and ('%s', '%s') objects", type(other[0]), type(other[1])) else: raise TypeError("cannot multiply 'ParserElement' and '%s' objects", type(other)) if minElements < 0: raise ValueError("cannot multiply ParserElement by negative value") if optElements < 0: raise ValueError("second tuple value must be greater or equal to first tuple value") if minElements == optElements == 0: raise ValueError("cannot multiply ParserElement by 0 or (0, 0)") if optElements:

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

pyparsing.py

upd; format

transferlearning

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

Python

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

_calc_impute

def _calc_impute(self, dist_pot_donors, n_neighbors, fit_X_col, mask_fit_X_col): # Get donors donors_idx = np.argpartition(dist_pot_donors, n_neighbors - 1, axis=1)[ :, :n_neighbors ] # Get weight matrix from distance matrix donors_dist = dist_pot_donors[ np.arange(donors_idx.shape[0])[:, None], donors_idx ] weight_matrix = _get_weights(donors_dist, self.weights) # fill nans with zeros if weight_matrix is not None: weight_matrix[np.isnan(weight_matrix)] = 0.0 # Retrieve donor values and calculate kNN average donors = fit_X_col.take(donors_idx) donors_mask = mask_fit_X_col.take(donors_idx) donors = np.ma.array(donors, mask=donors_mask) return np.ma.average(donors, axis=1, weights=weight_matrix).data

2e3abc2e32eefbfea78f15bcc767ca9bb4911568

_knn.py

MAINT Fix some typos (#23251)

scikit-learn

sklearn/impute/_knn.py

Python

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

add_row

def add_row(self, *args): NumRows = len(self.Rows) # number of existing rows is our row number CurrentRowNumber = NumRows # this row's number CurrentRow = [] # start with a blank row and build up # ------------------------- Add the elements to a row ------------------------- # for i, element in enumerate(args): # Loop through list of elements and add them to the row if type(element) == list: popup_error_with_traceback('Error creating Tab layout', 'Layout has a LIST instead of an ELEMENT', 'This means you have a badly placed ]', 'The offensive list is:', element, 'This list will be stripped from your layout') continue elif callable(element) and not isinstance(element, Element): popup_error_with_traceback('Error creating Tab layout', 'Layout has a FUNCTION instead of an ELEMENT', 'This likely means you are missing () from your layout', 'The offensive list is:', element, 'This item will be stripped from your layout') continue if element.ParentContainer is not None: warnings.warn( '*** YOU ARE ATTEMPTING TO RESUSE AN ELEMENT IN YOUR LAYOUT! Once placed in a layout, an element cannot be used in another layout. ***', UserWarning) popup_error_with_traceback('Error creating Tab layout', 'The layout specified has already been used', 'You MUST start witha "clean", unused layout every time you create a window', 'The offensive Element = ', element, 'and has a key = ', element.Key, 'This item will be stripped from your layout', 'Hint - try printing your layout and matching the IDs "print(layout)"') continue element.Position = (CurrentRowNumber, i) element.ParentContainer = self CurrentRow.append(element) if element.Key is not None: self.UseDictionary = True # ------------------------- Append the row to list of Rows ------------------------- # self.Rows.append(CurrentRow)

85d664925ad7042896b76b32b518d778aae024e1

PySimpleGUI.py

Changed all Tab errors to the nicer traceback error popup. Removed Output Element from the Pack function (that makes the change as real as it gets)

PySimpleGUI

PySimpleGUI.py

Python

https://github.com/PySimpleGUI/PySimpleGUI.git

build_system_components

def build_system_components(device_type, os_id, navigator_id): if os_id == "win": platform_version = randomizer.choice(OS_PLATFORM["win"]) cpu = randomizer.choice(OS_CPU["win"]) if cpu: platform = f"{platform_version}; {cpu}" else: platform = platform_version res = { "platform_version": platform_version, "platform": platform, "ua_platform": platform, "oscpu": platform, } elif os_id == "linux": cpu = randomizer.choice(OS_CPU["linux"]) platform_version = randomizer.choice(OS_PLATFORM["linux"]) platform = f"{platform_version} {cpu}" res = { "platform_version": platform_version, "platform": platform, "ua_platform": platform, "oscpu": "Linux %s" % cpu, } elif os_id == "mac": cpu = randomizer.choice(OS_CPU["mac"]) platform_version = randomizer.choice(OS_PLATFORM["mac"]) platform = platform_version if navigator_id == "chrome": platform = fix_chrome_mac_platform(platform) res = { "platform_version": platform_version, "platform": "MacIntel", "ua_platform": platform, "oscpu": "Intel Mac OS X %s" % platform.split(" ")[-1], } elif os_id == "android": assert navigator_id in ("firefox", "chrome") assert device_type in ("smartphone", "tablet") platform_version = randomizer.choice(OS_PLATFORM["android"]) if navigator_id == "firefox": if device_type == "smartphone": ua_platform = "%s; Mobile" % platform_version elif device_type == "tablet": ua_platform = "%s; Tablet" % platform_version elif navigator_id == "chrome": device_id = randomizer.choice(SMARTPHONE_DEV_IDS) ua_platform = f"Linux; {platform_version}; {device_id}" oscpu = "Linux %s" % randomizer.choice(OS_CPU["android"]) res = { "platform_version": platform_version, "ua_platform": ua_platform, "platform": oscpu, "oscpu": oscpu, } return res

ab4de1dd70fba866930150e440a03e461a6ca6a8

base.py

Create a packaged app bundle with Pyinstaller (#1525) * Add dashboard widget assets * Add ipywidgets and ipyflex to project * Add currencies dashboard notebook * Update docs and docstrings * Add pyinstaller to project deps * Add pyinstaller artifacts to gitignore * Fix linter errors in terminal.py * Update cspell hook and action with a pyinstaller specific word * Add pyinstaller specfile and artifacts * Add splashscreen image * Add app icon * adding splash screen support to terminal.spec and terminal.py * Restore the conda env build files * Sync deps * Add border to the splashscreen image * Clean up terminal launcher * Add support for default feature flags in packages apps * Fix types and linting * Add splashscreen management to app bootup * Check prediction feature flag when entering crypto/pred * Update pyinstaller spec file * fix .spec file to work for splash and icon - removed the ".." * Allows to export when using installer (#1568) * fix export for packaged apps * fix filename * Git : replace commit_hash when it is set in config_terminal * Add update of the git commit hash in gtff default during build * Add packaged app name and feature flag to logs * Add platform specific icon assignment * Add macOS build assets * Add tensorflow to hidden imports * Move LOGGING_COMMIT_HASH to gtff * Adding files/folders needed to .spec and pyinstaller folder. This will make certain commands work again. * Linting * Workflow : ignore ./build/pyinstaller from codespell * Workflow : exclude ./build/pyinstaller from flake8 * Poetry + Workflow : add types-six * Pyinstaller : remove property_cached, user_agent and vaderSentiment * Revert "Pyinstaller : remove property_cached, user_agent and vaderSentiment" This reverts commit dbb3e2b81086f97819ebd21457148c7160a4d703. * Clean up local paths in specfile * Validate deps have correct Jinja version (they do) * Fix logging commit hash to be set correctly for the logger to see it Co-authored-by: Andrew <[email protected]> Co-authored-by: didierlopes.eth <[email protected]> Co-authored-by: Chavithra PARANA <[email protected]>

OpenBBTerminal

build/pyinstaller/user_agent/base.py

Python

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

update_pea_args

def update_pea_args(self): if isinstance(self.args, Dict): # This is used when a Pod is created in a remote context, where peas & their connections are already given. self.peas_args = self.args else: self.peas_args = self._parse_args(self.args) if self.is_sandbox: host, port = HubIO.deploy_public_sandbox(getattr(self.args, 'uses', '')) self.first_pea_args.host = host self.first_pea_args.port_in = port self.peas_args['head'].host = host self.peas_args['head'].port_in = port

eea04c36350e86b3b0f16217cd37e630bfb81b57

__init__.py

feat: support jinahub+sandbox (#4130)

jina

jina/peapods/pods/__init__.py

Python

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

voice_conversion

def voice_conversion(self, y, y_lengths, speaker_cond_src, speaker_cond_tgt): assert self.num_speakers > 0, "num_speakers have to be larger than 0." # speaker embedding if self.args.use_speaker_embedding and not self.args.use_d_vector_file: g_src = self.emb_g(speaker_cond_src).unsqueeze(-1) g_tgt = self.emb_g(speaker_cond_tgt).unsqueeze(-1) elif not self.args.use_speaker_embedding and self.args.use_d_vector_file: g_src = F.normalize(speaker_cond_src).unsqueeze(-1) g_tgt = F.normalize(speaker_cond_tgt).unsqueeze(-1) else: raise RuntimeError(" [!] Voice conversion is only supported on multi-speaker models.") z, _, _, y_mask = self.posterior_encoder(y.transpose(1, 2), y_lengths, g=g_src) z_p = self.flow(z, y_mask, g=g_src) z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) o_hat = self.waveform_decoder(z_hat * y_mask, g=g_tgt) return o_hat, y_mask, (z, z_p, z_hat)

dbe9da7f15544b83043f481a99e5bcb23e002dc9

vits.py

Add Voice conversion inference support (#1337) * Add support for voice conversion inference * Cache d_vectors_by_speaker for fast inference using a bigger speakers.json * Rebase bug fix * Use the average d-vector for inference

TTS

TTS/tts/models/vits.py

Python

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

upgrade

def upgrade(): conn = op.get_bind() if conn.dialect.name == 'sqlite': op.execute('PRAGMA foreign_keys=OFF') with op.batch_alter_table('ab_view_menu', schema=None) as batch_op: batch_op.create_unique_constraint(batch_op.f('ab_view_menu_name_uq'), ['name']) op.execute('PRAGMA foreign_keys=ON') elif conn.dialect.name == 'mysql': with op.batch_alter_table('ab_register_user', schema=None) as batch_op: batch_op.alter_column('username', existing_type=sa.String(256), nullable=False) batch_op.alter_column('email', existing_type=sa.String(256), nullable=False) with op.batch_alter_table('ab_user', schema=None) as batch_op: batch_op.alter_column('username', existing_type=sa.String(256), nullable=False) batch_op.alter_column('email', existing_type=sa.String(256), nullable=False) elif conn.dialect.name == 'mssql': with op.batch_alter_table('ab_register_user') as batch_op: # Drop the unique constraint on username and email constraints = get_mssql_table_constraints(conn, 'ab_register_user') for k, _ in constraints.get('UNIQUE').items(): batch_op.drop_constraint(k, type_='unique') batch_op.alter_column('username', existing_type=sa.String(256), nullable=False) batch_op.create_unique_constraint(None, ['username']) batch_op.alter_column('email', existing_type=sa.String(256), nullable=False) with op.batch_alter_table('ab_user') as batch_op: # Drop the unique constraint on username and email constraints = get_mssql_table_constraints(conn, 'ab_user') for k, _ in constraints.get('UNIQUE').items(): batch_op.drop_constraint(k, type_='unique') batch_op.alter_column('username', existing_type=sa.String(256), nullable=False) batch_op.create_unique_constraint(None, ['username']) batch_op.alter_column('email', existing_type=sa.String(256), nullable=False) batch_op.create_unique_constraint(None, ['email'])

2f5a567977e1219cab16c2548825a1b9eba07ab3

0106_909884dea523_update_migration_for_fab_tables_to_add_missing_constraints.py

Use Airflow.Base.metadata in FAB models (#22353) Since FAB models are now in airflow, it makes sense to monitor changes in them. Therefore we use Airflow.models.base.Base.metadata for FAB models

airflow

airflow/migrations/versions/0106_909884dea523_update_migration_for_fab_tables_to_add_missing_constraints.py

Python

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

_iter_all_mapped_downstreams

def _iter_all_mapped_downstreams(self) -> Iterator["MappedOperator"]: from airflow.models.mappedoperator import MappedOperator from airflow.utils.task_group import TaskGroup

197cff3194e855b9207c3c0da8ae093a0d5dda55

taskmixin.py

Ensure TaskMap only checks "relevant" dependencies (#23053) When looking for "mapped dependants" of a task, we only want a task if it not only is a direct downstream of the task, but also it actually "uses" the task's pushed XCom for task mapping. So we need to peek into the mapped downstream task's expansion kwargs, and only count it as a mapped dependant if the upstream is referenced there.

airflow

airflow/models/taskmixin.py

Python

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

_rescale_dataset_split_sizes

def _rescale_dataset_split_sizes(left_size,right_size,total_length): left_size_type = type(left_size) right_size_type = type(right_size) if ((left_size is not None and left_size_type not in [int,float]) and (right_size is not None and right_size_type not in [int,float])): raise TypeError('Invalid `left_size` and `right_size` Types. ' 'Expected: integer or float or None. ' f' Received: {left_size_type} and {right_size_type}') if left_size is not None and left_size_type not in [int,float]: raise TypeError(f'Invalid `left_size` Type. Received: {left_size_type}. ' ' Expected: int or float or None') if right_size is not None and right_size_type not in [int,float]: raise TypeError(f'Invalid `right_size` Type. Received: {right_size_type}.' ' Expected: int or float or None') if left_size == 0 and right_size == 0: raise ValueError('Invalid `left_size` and `right_size` values. ' 'You must specify either `left_size` or `right_size` with ' f'value greater than 0 and less than {total_length} ' 'or a float within range [0,1] to split the dataset' f'Received: `left_size`={left_size}, ' f'`right_size`={right_size}') if (left_size_type == int and (left_size <= 0 or left_size>= total_length) or left_size_type == float and (left_size <= 0 or left_size>= 1) ): raise ValueError('`left_size` should be either a positive integer ' f'and smaller than {total_length} or a float ' 'within the range `[0, 1]`. Received: left_size=' f'{left_size}') if (right_size_type == int and (right_size <= 0 or right_size>= total_length) or right_size_type == float and (right_size <= 0 or right_size>= 1)): raise ValueError('`right_size` should be either a positive integer ' f'and smaller than {total_length} or ' 'a float within the range `[0, 1]`. Received: right_size=' f'{right_size}') if right_size_type == left_size_type == float and right_size + left_size > 1: raise ValueError('sum of `left_size` and `right_size`' ' should be within `[0,1]`.' f'Received: {right_size + left_size} ,' 'reduce the `left_size` or `right_size`') if left_size_type == float: left_size = round(left_size*total_length) elif left_size_type == int: left_size = float(left_size) if right_size_type == float: right_size = round(right_size*total_length) elif right_size_type == int: right_size = float(right_size) if left_size is None: left_size = total_length - right_size elif right_size is None: right_size = total_length - left_size if left_size + right_size > total_length: raise ValueError('The sum of `left_size` and `right_size`' f' should be smaller than the samples {total_length} ' ' reduce `left_size` or `right_size` ' ) for split,side in [(left_size,'left'),(right_size,'right')]: if split == 0: raise ValueError(f'with dataset of length={total_length} ' '`left_size`={left_size} and `right_size`={right_size}, ' f'resulting {side} dataset split will be empty. ' 'Adjust any of the aforementioned parameters') left_size,right_size = int(left_size) ,int(right_size) return left_size,right_size

56b75d030cd9c3e3fde0ddf1f908436e6a5be3d6

dataset_utils.py

adds test case for fractional size with tuple of numpy arrays in different shape

keras

keras/utils/dataset_utils.py

Python

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

best_checkpoint_path

def best_checkpoint_path(self) -> Optional[Path]: return self.checkpoint_manager.best_checkpoint_path

dc7ed086a5038775e378b32cb31fb4a79f418dd9

trainer.py

[AIR] More checkpoint configurability, `Result` extension (#25943) This PR: * Allows the user to set `keep_checkpoints_num` and `checkpoint_score_attr` in `RunConfig` using the `CheckpointStrategy` dataclass * Adds two new fields to the `Result` object - `best_checkpoints` - a list of saved best checkpoints as determined by `CheckpointingConfig`.

ray

python/ray/train/trainer.py

Python

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

clone

def clone(self, **kw): newpolicy = self.__class__.__new__(self.__class__) for attr, value in self.__dict__.items(): object.__setattr__(newpolicy, attr, value) for attr, value in kw.items(): if not hasattr(self, attr): raise TypeError( "{!r} is an invalid keyword argument for {}".format( attr, self.__class__.__name__)) object.__setattr__(newpolicy, attr, value) return newpolicy

8198943edd73a363c266633e1aa5b2a9e9c9f526

_policybase.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/email/_policybase.py

Python

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

call_deploy

def call_deploy(cls, fname, col_partitions, **kwargs): return np.array( [ cls.deploy( cls.parse, num_returns=NPartitions.get() + 2, fname=fname, columns=cols, num_splits=NPartitions.get(), **kwargs, ) for cols in col_partitions ] ).T

97769988a6f19e4b76f34238c97bf159ee7626a5

column_store_dispatcher.py

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]>

modin

modin/core/io/column_stores/column_store_dispatcher.py

Python

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

test_nowrapfunc

def test_nowrapfunc(capfd, hello_world_f90, monkeypatch): ipath = Path(hello_world_f90) mname = "blah" monkeypatch.setattr(sys, "argv", f'f2py -m {mname} {ipath} --no-wrap-functions'.split()) with util.switchdir(ipath.parent): f2pycli() out, _ = capfd.readouterr() assert r"Fortran 77 wrappers are saved to" not in out

729ad4f92420231e2a7009b3223c6c7620b8b808

test_f2py2e.py

TST: Initialize f2py2e tests of the F2PY CLI (#20668) Increases F2PY coverage by around 15 percent. For the CLI itself it covers the major features (around 70 percent), with the exception of mostly numpy.distutils stuff. More importantly, sets the groundwork for #20056, in that passing the same testsuite should indicate feature parity.

numpy

numpy/f2py/tests/test_f2py2e.py

Python

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

get_queryset

def get_queryset(self, request): queryset = SavedFilter.objects.all() user = request.user if user.is_superuser: return queryset if user.is_anonymous: return queryset.filter(shared=True) return queryset.filter( Q(shared=True) | Q(user=user) )

484efdaf75f267a43f9321b938fda1bc967b9e53

views.py

Closes #9623: Implement saved filters (#10801) * Initial work on saved filters * Return only enabled/shared filters * Add tests * Clean up filtering of usable SavedFilters

netbox

netbox/extras/views.py

Python

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

to_bag

def to_bag(self, index=False, format="tuple"): from .io import to_bag return to_bag(self, index, format)

8a6f6a7b95762df4e44bc4d82ce33a7c388a0676

core.py

Move Bag.map_partitions to Blockwise (#8646) 1. Adds `format="frame"` option to `dataframe.io.to_bag` (effectively returning a zero-copy view of the same dask graph, that no-longer tracks meta/divisions) 2. Revises `Bag.map_partitions` to use `blockwise` (and to support the `token=` option) 3. Modifies the ACA code path to use a `Bag.map_partitions` for any blockwise operations where partitions may loose "dataframe-like" properties. This represents an alternative to using `map_partitions` incorrectly in ACA. It is also an alternative to the low-level `blockwise` API.

dask

dask/dataframe/core.py

Python

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

accuracy

def accuracy(self, Y_hat, Y, averaged=True): Y_hat = d2l.reshape(Y_hat, (-1, Y_hat.shape[-1])) preds = d2l.astype(d2l.argmax(Y_hat, axis=1), Y.dtype) compare = d2l.astype(preds == d2l.reshape(Y, -1), d2l.float32) return d2l.reduce_mean(compare) if averaged else compare

19aba1f059efad45e1466d47954b2cf54d45b106

mxnet.py

simplify d2l lib

d2l-en

d2l/mxnet.py

Python

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

_reset_replica_iterator

def _reset_replica_iterator(self): replicas = list(self.in_flight_queries.keys()) random.shuffle(replicas) self.replica_iterator = itertools.cycle(replicas)

545c51609f0f55b41cf99cec95a9c21bee6846de

router.py

[Serve] ServeHandle detects ActorError and drop replicas from target group (#26685)

ray

python/ray/serve/_private/router.py

Python

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

test_connection

def test_connection(self): status, message = False, '' try: if self.get_first("select 1 from dual"): status = True message = 'Connection successfully tested' except Exception as e: status = False message = str(e) return status, message

900bad1c67654252196bb095a2a150a23ae5fc9a

oracle.py

Fix oracle test connection (#21699)

airflow

airflow/providers/oracle/hooks/oracle.py

Python

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

task_instance_link

def task_instance_link(attr): dag_id = attr.get('dag_id') task_id = attr.get('task_id') execution_date = attr.get('dag_run.execution_date') or attr.get('execution_date') or timezone.utcnow() url = url_for( 'Airflow.task', dag_id=dag_id, task_id=task_id, execution_date=execution_date.isoformat(), map_index=attr.get('map_index', -1), ) url_root = url_for( 'Airflow.graph', dag_id=dag_id, root=task_id, execution_date=execution_date.isoformat() ) return Markup( ).format(url=url, task_id=task_id, url_root=url_root)

3182ba2f402656bfc7d7777f1678161ec5a9cf79

utils.py

Add map_index support to all task instance-related views (#22272) Co-authored-by: Ash Berlin-Taylor <[email protected]> Co-authored-by: Brent Bovenzi <[email protected]>

airflow

airflow/www/utils.py

Python

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

downgrade

def downgrade(): op.create_table("__airflow_tmp_xcom", *_get_old_xcom_columns()) xcom = Table("xcom", metadata, *_get_new_xcom_columns()) query = select( [ xcom.c.key, xcom.c.value, xcom.c.timestamp, xcom.c.task_id, xcom.c.dag_id, dagrun.c.execution_date, ], ).select_from( xcom.join( dagrun, xcom.c.dag_id == dagrun.c.dag_id, xcom.c.run_id == dagrun.c.run_id, ), ) op.execute(f"INSERT INTO __airflow_tmp_xcom {query.selectable.compile(op.get_bind())}") op.drop_table("xcom") op.rename_table("__airflow_tmp_xcom", "xcom") op.create_primary_key("xcom_pkey", "xcom", ["dag_id", "task_id", "execution_date", "key"])

0ebd6428e6b484790bfbbe1b8687ef4e6cae10e9

c306b5b5ae4a_switch_xcom_table_to_use_run_id.py

Switch XCom implementation to use run_id (#20975)

airflow

airflow/migrations/versions/c306b5b5ae4a_switch_xcom_table_to_use_run_id.py

Python

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

remap_palette

def remap_palette(self, dest_map, source_palette=None): from . import ImagePalette if self.mode not in ("L", "P"): raise ValueError("illegal image mode") if source_palette is None: if self.mode == "P": self.load() source_palette = self.im.getpalette("RGB")[:768] else: # L-mode source_palette = bytearray(i // 3 for i in range(768)) palette_bytes = b"" new_positions = [0] * 256 # pick only the used colors from the palette for i, oldPosition in enumerate(dest_map): palette_bytes += source_palette[oldPosition * 3 : oldPosition * 3 + 3] new_positions[oldPosition] = i # replace the palette color id of all pixel with the new id # Palette images are [0..255], mapped through a 1 or 3 # byte/color map. We need to remap the whole image # from palette 1 to palette 2. New_positions is # an array of indexes into palette 1. Palette 2 is # palette 1 with any holes removed. # We're going to leverage the convert mechanism to use the # C code to remap the image from palette 1 to palette 2, # by forcing the source image into 'L' mode and adding a # mapping 'L' mode palette, then converting back to 'L' # sans palette thus converting the image bytes, then # assigning the optimized RGB palette. # perf reference, 9500x4000 gif, w/~135 colors # 14 sec prepatch, 1 sec postpatch with optimization forced. mapping_palette = bytearray(new_positions) m_im = self.copy() m_im.mode = "P" m_im.palette = ImagePalette.ImagePalette("RGB", palette=mapping_palette * 3) # possibly set palette dirty, then # m_im.putpalette(mapping_palette, 'L') # converts to 'P' # or just force it. # UNDONE -- this is part of the general issue with palettes m_im.im.putpalette("RGB;L", m_im.palette.tobytes()) m_im = m_im.convert("L") # Internally, we require 768 bytes for a palette. new_palette_bytes = palette_bytes + (768 - len(palette_bytes)) * b"\x00" m_im.putpalette(new_palette_bytes) m_im.palette = ImagePalette.ImagePalette("RGB", palette=palette_bytes) if "transparency" in self.info: m_im.info["transparency"] = new_positions[self.info["transparency"]] return m_im

46a80d144a16836af304a7aaa8e620962d91ac23

Image.py

Update transparency when remapping the palette

Pillow

src/PIL/Image.py

Python

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

_update_tasks_counters_and_is_labeled

def _update_tasks_counters_and_is_labeled(self, queryset, from_scratch=True): queryset = make_queryset_from_iterable(queryset) objs = self._update_tasks_counters(queryset, from_scratch) bulk_update_stats_project_tasks(queryset, self) return objs

aa36f4f70f7a6290f74059a6e13fd89dfd3e6ef8

models.py

fix: DEV-3798: Improve performance for _rearrange_overlap_cohort (#3271) * fix: DEV-3798: Improve performance for _rearrange_overlap_cohort

label-studio

label_studio/projects/models.py

Python

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

async_enable

async def async_enable(self) -> None: if self._is_enabled: return self._is_enabled = True # HomeAssistant is starting up if self.hass.state != CoreState.not_running: self._async_detach_triggers = await self._async_attach_triggers(False) self.async_write_ha_state() return

5e338d21665cb04f66fcebd9376cdda389c30c01

__init__.py

Improve type hints in automation (#78368) * Improve type hints in automation * Apply suggestion * Apply suggestion * Apply suggestion * Add Protocol for IfAction * Use ConfigType for IfAction * Rename variable

core

homeassistant/components/automation/__init__.py

Python

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

test_calibration_without_sample_weight_base_estimator

def test_calibration_without_sample_weight_base_estimator(data): X, y = data sample_weight = np.ones_like(y)

effdd6e215c67f2ae8ed1e378ea1661e936059a4

test_calibration.py

API Rename base_estimator in CalibratedClassifierCV (#22054) Co-authored-by: Kevin Roice <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]>

scikit-learn

sklearn/tests/test_calibration.py

Python

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

_load_dependencies

def _load_dependencies(self): deps = [] for role_include in self._metadata.dependencies: r = Role.load(role_include, play=self._play, parent_role=self) deps.append(r) return deps # other functions

1998521e2d5b89bc53d00639bad178330ebb98df

__init__.py

Always create new role (#78661) Don't use role cache for determining whether to create a new instance of role

ansible

lib/ansible/playbook/role/__init__.py

Python

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

test_timesteps_unit

def test_timesteps_unit(self): self.batch_id = 0 batch_size = 5 buffer_size = 15 buffer = ReplayBuffer(capacity=buffer_size) # Test add/sample self._add_data_to_buffer(buffer, batch_size=batch_size, num_batches=1) self._add_data_to_buffer(buffer, batch_size=batch_size, num_batches=2) # Sampling from it now should yield our first batch 1/3 of the time num_sampled_dict = {_id: 0 for _id in range(self.batch_id)} num_samples = 200 for i in range(num_samples): _id = buffer.sample(1)["batch_id"][0] num_sampled_dict[_id] += 1 assert np.allclose( np.array(list(num_sampled_dict.values())) / num_samples, len(num_sampled_dict) * [1 / 3], atol=0.1, ) # Test set/get state state = buffer.get_state() other_buffer = ReplayBuffer(capacity=buffer_size) self._add_data_to_buffer(other_buffer, 1) other_buffer.set_state(state) assert other_buffer._storage == buffer._storage assert other_buffer._next_idx == buffer._next_idx assert other_buffer._num_timesteps_added == buffer._num_timesteps_added assert ( other_buffer._num_timesteps_added_wrap == buffer._num_timesteps_added_wrap ) assert other_buffer._num_timesteps_sampled == buffer._num_timesteps_sampled assert other_buffer._eviction_started == buffer._eviction_started assert other_buffer._est_size_bytes == buffer._est_size_bytes assert len(other_buffer) == len(other_buffer)

e57ce7efd6ea1d0e4f6942fcf6f526287340e63d

test_replay_buffer.py

[RLlib] Replay Buffer API and Training Iteration Fn for DQN. (#23420)

ray

rllib/utils/replay_buffers/tests/test_replay_buffer.py

Python

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

_unschedule_refresh

def _unschedule_refresh(self) -> None: if self._unsub_refresh: self._unsub_refresh() self._unsub_refresh = None

8910d265d6cf15fed4e6e98b4344031019c1016d

update_coordinator.py

Keep track of a context for each listener (#72702) * Remove async_remove_listener This avoids the ambuigity as to what happens if same callback is added multiple times. * Keep track of a context for each listener This allow a update coordinator to adapt what data to request on update from the backing service based on which entities are enabled. * Clone list before calling callbacks The callbacks can end up unregistering and modifying the dict while iterating. * Only yield actual values * Add a test for update context * Factor out iteration of _listeners to helper * Verify context is passed to coordinator * Switch to Any as type instead of object * Remove function which use was dropped earliers The use was removed in 8bee25c938a123f0da7569b4e2753598d478b900

core

homeassistant/helpers/update_coordinator.py

Python

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

split_sections

def split_sections(s): section = None content = [] for line in yield_lines(s): if line.startswith("["): if line.endswith("]"): if section or content: yield section, content section = line[1:-1].strip() content = [] else: raise ValueError("Invalid section heading", line) else: content.append(line) # wrap up last segment yield section, content

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

__init__.py

upd; format

transferlearning

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

Python

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

close

def close(self) -> None: if self.is_wrapped: assert isinstance(self.handle, TextIOWrapper) self.handle.flush() self.handle.detach() self.created_handles.remove(self.handle) for handle in self.created_handles: handle.close() self.created_handles = [] self.is_wrapped = False

96ba36ddcbedda4bfe4e70ea0261a3194724f768

common.py

BUG: do not suppress errors when closing file handles (#47165)

pandas

pandas/io/common.py

Python

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

robots

def robots(self): return send_from_directory(get_airflow_app().static_folder, 'robots.txt')

e2f19505bf3622935480e80bee55bf5b6d80097b

views.py

Upgrade FAB to 4.1.1 (#24399) * Upgrade FAB to 4.1.1 The Flask Application Builder have been updated recently to support a number of newer dependencies. This PR is the attempt to migrate FAB to newer version. This includes: * update setup.py and setup.cfg upper and lower bounds to account for proper version of dependencies that FAB < 4.0.0 was blocking from upgrade * added typed Flask application retrieval with a custom application fields available for MyPy typing checks. * fix typing to account for typing hints added in multiple upgraded libraries optional values and content of request returned as Mapping * switch to PyJWT 2.* by using non-deprecated "required" claim as list rather than separate fields * add possibiliyt to install providers without constraints so that we could avoid errors on conflicting constraints when upgrade-to-newer-dependencies is used * add pre-commit to check that 2.4+ only get_airflow_app is not used in providers * avoid Bad Request in case the request sent to Flask 2.0 is not JSon content type * switch imports of internal classes to direct packages where classes are available rather than from "airflow.models" to satisfy MyPY * synchronize changes of FAB Security Manager 4.1.1 with our copy of the Security Manager. * add error handling for a few "None" cases detected by MyPY * corrected test cases that were broken by immutability of Flask 2 objects and better escaping done by Flask 2 * updated test cases to account for redirection to "path" rather than full URL by Flask2 Fixes: #22397 * fixup! Upgrade FAB to 4.1.1

airflow

airflow/www/views.py

Python

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

geom_output

def geom_output(func, argtypes, offset=None): # Setting the argument types func.argtypes = argtypes if not offset: # When a geometry pointer is directly returned. func.restype = c_void_p func.errcheck = check_geom else: # Error code returned, geometry is returned by-reference. func.restype = c_int

9c19aff7c7561e3a82978a272ecdaad40dda5c00

generation.py

Refs #33476 -- Reformatted code with Black.

django

django/contrib/gis/gdal/prototypes/generation.py

Python

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

query

def query(self, expr, **kwargs): return self.map_partitions(M.query, expr, **kwargs)

e30471041ab9be7126a14f412fd17a1e2df8a7f5

core.py

Update `DataFrame.query` docstring (#8890)

dask

dask/dataframe/core.py

Python

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

_check_gpu_tensors

def _check_gpu_tensors(tensors): if not tensors or not isinstance(tensors, list): raise RuntimeError("'tensors' must be a nonempty list.") if len(tensors) > nccl_util.get_num_gpus(): raise RuntimeError( "Tensor list cannot be larger than the number" "of available GPUs. Got {} > {}.".format( len(tensors), nccl_util.get_num_gpus() ) ) t0 = tensors[0] dt = nccl_util.get_nccl_tensor_dtype(t0) s = nccl_util.get_tensor_shape(t0) d = nccl_util.get_tensor_device(t0) for i, t in enumerate(tensors): if i == 0: continue # We need to check the following: # (1) tensor is cuda (already checked during API) # (2) tensor dtype # (3) tensor shape match # (4) each tensor is on a different GPU dtype = nccl_util.get_nccl_tensor_dtype(t) if dt != dtype: raise RuntimeError( "Tensors must have identical dtype. Got: '{}'.".format(dtype) ) shape = nccl_util.get_tensor_shape(t) if s != shape: raise RuntimeError( "Tensor must have identical shape. Got: '{}'.".format(shape) ) device = nccl_util.get_tensor_device(t) if device == d: raise RuntimeError("Tensor must be on distinct GPUs.")

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

nccl_collective_group.py

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

ray

python/ray/util/collective/collective_group/nccl_collective_group.py

Python

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

test_map_xcom_arg

def test_map_xcom_arg(): with DAG("test-dag", start_date=DEFAULT_DATE): task1 = BaseOperator(task_id="op1") mapped = MockOperator.partial(task_id='task_2').expand(arg2=XComArg(task1)) finish = MockOperator(task_id="finish") mapped >> finish assert task1.downstream_list == [mapped]

70b41e46b46e65c0446a40ab91624cb2291a5039

test_mappedoperator.py

Move MappedOperator tests to mirror code location (#23884) At some point during the development of AIP-42 we moved the code for MappedOperator out of baseoperator.py to mappedoperator.py, but we didn't move the tests at the same time

airflow

tests/models/test_mappedoperator.py

Python

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

testClusterAutoscaling

def testClusterAutoscaling(self): self.cluster.update_config( { "provider": {"head_resources": {"CPU": 4, "GPU": 0}}, } ) self.cluster.start() self.cluster.connect(client=True, timeout=120) self.assertGreater(ray.cluster_resources().get("CPU", 0), 0) # Trigger autoscaling pg = ray.util.placement_group([{"CPU": 1, "GPU": 1}] * 2) timeout = time.monotonic() + 120 while ray.cluster_resources().get("GPU", 0) < 2: if time.monotonic() > timeout: raise RuntimeError("Autoscaling failed or too slow.") time.sleep(1) # Schedule task with resources self.assertEquals( 5, ray.get( remote_task.options( num_cpus=1, num_gpus=1, scheduling_strategy=PlacementGroupSchedulingStrategy( placement_group=pg ), ).remote(5) ), ) print("Autoscaling worked") ray.util.remove_placement_group(pg) time.sleep(2) # Give some time so nodes.json is updated self.cluster.kill_node(num=2) print("Killed GPU node.") pg = ray.util.placement_group([{"CPU": 1, "GPU": 1}] * 2) table = ray.util.placement_group_table(pg) assert table["state"] == "PENDING" timeout = time.monotonic() + 180 while table["state"] != "CREATED": if time.monotonic() > timeout: raise RuntimeError("Re-starting killed node failed or too slow.") time.sleep(1) table = ray.util.placement_group_table(pg) print("Node was restarted.")

57cdbb1769a9c32972ba0ec9e7e857eeea961869

test_multinode_sync.py

Migrate the deprecated placement_group option to PlacementGroupSchedulingStrategy (#28437) placement_group option is deprecated, use PlacementGroupSchedulingStrategy instead.

ray

python/ray/tune/tests/test_multinode_sync.py

Python

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

string_width_in_pixels

def string_width_in_pixels(cls, font, string): # if no windows have been created (there is no hidden master root to rely on) then temporarily make a window so the measurement can happen if Window.NumOpenWindows == 0: root = tk.Tk() else: root = None size = 0 try: size = tkinter.font.Font(font=font).measure(string) # string's width except Exception as e: _error_popup_with_traceback('Exception retrieving string width in pixels', e) if root is not None: root.destroy() return size

acaae54a1ade24b2e55f7274ae4db747160a38db

PySimpleGUI.py

Enable Text class methods to be called prior to any windows being created: string_width_in_pixels, char_height_in_pixels, char_width_in_pixels. Removed destruction of hidden master root from popup_get_file & popup_get_folder (was old code)

PySimpleGUI

PySimpleGUI.py

Python

https://github.com/PySimpleGUI/PySimpleGUI.git

_should_use_osx_framework_prefix

def _should_use_osx_framework_prefix() -> bool: return ( "osx_framework_library" in _AVAILABLE_SCHEMES and not running_under_virtualenv() and is_osx_framework() )

7e33fcae4384563b4c927fd44318c29dd524a097

_sysconfig.py

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

pipenv

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

Python

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

set_focus

def set_focus(self, force=False): if not self._widget_was_created(): # if widget hasn't been created yet, then don't allow return try: if force: self.Widget.focus_force() else: self.Widget.focus_set() except Exception as e: _error_popup_with_traceback("Exception blocking focus. Check your element's Widget", e)

9b814f003b0685757d76ce56ee9c98eae114d346

PySimpleGUI.py

Added key and widget Element properties, new focus methods Element.get_next_focus, Element.get_previous_focus. New Window method Window.widget_to_element

PySimpleGUI

PySimpleGUI.py

Python

https://github.com/PySimpleGUI/PySimpleGUI.git

announce

def announce(): current_version, _, _ = parse_version_from_module() tag_name = f"v{current_version}" click.echo( f ) if "rc" in tag_name: click.echo( ) else: click.echo( )

12d1f82db213603972d60be3f46f6a36c3c2330f

release.py

Generate announcement links in release script (#12242)

synapse

scripts-dev/release.py

Python

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

workflow_in_progress

def workflow_in_progress(self): if not getattr(settings, "WAGTAIL_WORKFLOW_ENABLED", True): return False # `_current_workflow_states` may be populated by `prefetch_workflow_states` on `PageQuerySet` as a # performance optimisation if hasattr(self, "_current_workflow_states"): for state in self._current_workflow_states: if state.status == WorkflowState.STATUS_IN_PROGRESS: return True return False return WorkflowState.objects.filter( page=self, status=WorkflowState.STATUS_IN_PROGRESS ).exists()

d10f15e55806c6944827d801cd9c2d53f5da4186

__init__.py

Reformat with black

wagtail

wagtail/core/models/__init__.py

Python

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

numpy_to_pil

def numpy_to_pil(images): if images.ndim == 3: images = images[None, ...] images = (images * 255).round().astype("uint8") pil_images = [Image.fromarray(image) for image in images] return pil_images

1b42732ced07861b810f77ecf3fc8ce63ce465e8

pipeline_utils.py

PIL-ify the pipeline outputs (#111)

diffusers

src/diffusers/pipeline_utils.py

Python

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

make_authors_file_lines

def make_authors_file_lines(git_people): # define new lines for the file header = filldedent().lstrip() header_extra = "There are a total of %d authors." % len(git_people) lines = header.splitlines() lines.append('') lines.append(header_extra) lines.append('') lines.extend(git_people) return lines

e8bf22b0eb76ecb6aec12dd45549649c490e1354

mailmap_check.py

mailmap documents python2 I believe this to be useful for at least two reasons. Current documentation either state to use `python bin/mailmap_check.py` or just `bin/mailmap_check.py` which itself calles `python`. In both case, on ubuntu this will run python2. Worse, instead of printing "This script requires Python 3.8 or newer" it prints an error message that python don't know what to do with the f-string. If the f-string is removed, that it does not know pathlib. So, I ensure that the script still work as before on 3.8 and prints a more useful message on python2. I admit I'm not fan of removing a f-string, however, since there was a single f-string, I assume it was relatively acceptable. I suppose most sympy contributor are at ease with those subtilities of python2/3. However, this will at least be useful for people, like me, who only wanted to contribute to improving documentation and not have to deal with complexity of system administration.

sympy

bin/mailmap_check.py

Python

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

avatar_url

def avatar_url(user, size=50, gravatar_only=False): if ( not gravatar_only and hasattr(user, "wagtail_userprofile") and user.wagtail_userprofile.avatar ): return user.wagtail_userprofile.avatar.url if hasattr(user, "email"): gravatar_url = get_gravatar_url(user.email, size=size) if gravatar_url is not None: return gravatar_url return versioned_static_func("wagtailadmin/images/default-user-avatar.png") @register.simple_tag

d10f15e55806c6944827d801cd9c2d53f5da4186

@register.simple_tag

wagtailadmin_tags.py

Reformat with black

wagtail

wagtail/admin/templatetags/wagtailadmin_tags.py

Python

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

test_failing_open

def test_failing_open(self, tmp_path): qf = QFile(str(tmp_path)) dev = qtutils.PyQIODevice(qf) with pytest.raises(qtutils.QtOSError) as excinfo: dev.open(QIODevice.OpenModeFlag.WriteOnly) assert excinfo.value.qt_errno == QFileDevice.FileError.OpenError assert dev.closed

0877fb0d78635692e481c8bde224fac5ad0dd430

test_qtutils.py

Run scripts/dev/rewrite_enums.py

qutebrowser

tests/unit/utils/test_qtutils.py

Python

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

deep_deconstruct

def deep_deconstruct(self, obj): if isinstance(obj, list): return [self.deep_deconstruct(value) for value in obj] elif isinstance(obj, tuple): return tuple(self.deep_deconstruct(value) for value in obj) elif isinstance(obj, dict): return {key: self.deep_deconstruct(value) for key, value in obj.items()} elif isinstance(obj, functools.partial): return ( obj.func, self.deep_deconstruct(obj.args), self.deep_deconstruct(obj.keywords), ) elif isinstance(obj, COMPILED_REGEX_TYPE): return RegexObject(obj) elif isinstance(obj, type): # If this is a type that implements 'deconstruct' as an instance method, # avoid treating this as being deconstructible itself - see #22951 return obj elif hasattr(obj, "deconstruct"): deconstructed = obj.deconstruct() if isinstance(obj, models.Field): # we have a field which also returns a name deconstructed = deconstructed[1:] path, args, kwargs = deconstructed return ( path, [self.deep_deconstruct(value) for value in args], {key: self.deep_deconstruct(value) for key, value in kwargs.items()}, ) else: return obj

9c19aff7c7561e3a82978a272ecdaad40dda5c00

autodetector.py

Refs #33476 -- Reformatted code with Black.

django

django/db/migrations/autodetector.py

Python

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

test_reordering_concurrently

def test_reordering_concurrently(dummy_attribute, assert_num_queries): qs = SortedModel.objects attribute = dummy_attribute entries = list( qs.bulk_create( [ SortedModel(attribute=attribute, slug="1", name="1", sort_order=0), SortedModel(attribute=attribute, slug="2", name="2", sort_order=1), ] ) ) operations = {entries[0].pk: +1} with assert_num_queries(2) as ctx: perform_reordering(qs, operations) assert ctx[0]["sql"] == ( 'SELECT "attribute_attributevalue"."id", ' '"attribute_attributevalue"."sort_order" ' 'FROM "attribute_attributevalue" ' "ORDER BY " '"attribute_attributevalue"."sort_order" ASC NULLS LAST, ' '"attribute_attributevalue"."id" ASC FOR UPDATE' ) assert ctx[1]["sql"] == ( 'UPDATE "attribute_attributevalue" ' 'SET "sort_order" = ' f'(CASE WHEN ("attribute_attributevalue"."id" = {entries[0].pk}) ' f'THEN 1 WHEN ("attribute_attributevalue"."id" = {entries[1].pk}) ' "THEN 0 ELSE NULL END)::integer " 'WHERE "attribute_attributevalue"."id" ' f"IN ({entries[0].pk}, {entries[1].pk})" )

9e2ea5b2f647f7d19c3a54347ccdcbf787f2ca0b

test_core_reordering.py

Upgrade Saleor to Django version 4.0 (#10518) Lots of typing fixes uncovered while I attempted to upgrade to the latest version of django-stubs. Didn't pull it here as it has a bug that causes it to report problems related to its own type cloning code.

saleor

saleor/graphql/core/tests/test_core_reordering.py

Python

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

enhanced_current_hue

def enhanced_current_hue(self) -> int | None: return self.cluster.get("enhanced_current_hue")

04c6b9c51963418ffebddc7753939700fbea7e42

lighting.py

ZHA light entity cleanup (#75573) * use base class attributes * initial hue and saturation support * spec is 65536 not 65535 * fixes * enhanced current hue * fix comparison * clean up * fix channel test * oops * report enhanced current hue

core

homeassistant/components/zha/core/channels/lighting.py

Python

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

test_stream_slices_with_state

def test_stream_slices_with_state(self, api, async_manager_mock, start_date): end_date = start_date + duration(days=10) cursor_value = start_date + duration(days=5) state = {AdsInsights.cursor_field: cursor_value.date().isoformat()} stream = AdsInsights(api=api, start_date=start_date, end_date=end_date) async_manager_mock.completed_jobs.return_value = [1, 2, 3] slices = list(stream.stream_slices(stream_state=state, sync_mode=SyncMode.incremental)) assert slices == [{"insight_job": 1}, {"insight_job": 2}, {"insight_job": 3}] async_manager_mock.assert_called_once() args, kwargs = async_manager_mock.call_args generated_jobs = list(kwargs["jobs"]) assert len(generated_jobs) == (end_date - cursor_value).days assert generated_jobs[0].interval.start == cursor_value.date() + duration(days=1) assert generated_jobs[1].interval.start == cursor_value.date() + duration(days=2)

a3aae8017a0a40ff2006e2567f71dccb04c997a5

test_base_insight_streams.py

🎉 🎉 Source FB Marketing: performance and reliability fixes (#9805) * Facebook Marketing performance improvement * add comments and little refactoring * fix integration tests with the new config * improve job status handling, limit concurrency to 10 * fix campaign jobs, refactor manager * big refactoring of async jobs, support random order of slices * update source _read_incremental to hook new state logic * fix issues with timeout * remove debugging and clean up, improve retry logic * merge changes from #8234 * fix call super _read_increment * generalize batch execution, add use_batch flag * improve coverage, do some refactoring of spec * update test, remove overrides of source * add split by AdSet * add smaller insights * fix end_date < start_date case * add account_id to PK * add notes * fix new streams * fix reversed incremental stream * update spec.json for SAT * upgrade CDK and bump version Co-authored-by: Dmytro Rezchykov <[email protected]> Co-authored-by: Eugene Kulak <[email protected]>

airbyte

airbyte-integrations/connectors/source-facebook-marketing/unit_tests/test_base_insight_streams.py

Python

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

detect_rnet

def detect_rnet(self, images, rectangle_batch, height, width): ret = [] # TODO: batching for idx, rectangles in enumerate(rectangle_batch): if not rectangles: ret.append([]) continue image = images[idx] crop_number = 0 predict_24_batch = [] for rect in rectangles: crop_img = image[int(rect[1]):int(rect[3]), int(rect[0]):int(rect[2])] scale_img = cv2.resize(crop_img, (24, 24)) predict_24_batch.append(scale_img) crop_number += 1 predict_24_batch = np.array(predict_24_batch) output = self.rnet.predict(predict_24_batch, batch_size=128) cls_prob = output[0] cls_prob = np.array(cls_prob) roi_prob = output[1] roi_prob = np.array(roi_prob) ret.append(filter_face_24net( cls_prob, roi_prob, rectangles, width, height, self.threshold[1] )) return ret

aa39234538a8f83e6aa2b60b8275a570e8876ac2

mtcnn.py

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

faceswap

plugins/extract/detect/mtcnn.py

Python

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

_update_selection_poly

def _update_selection_poly(self, vmin, vmax): # The vertices are positioned # 1 ------ 2 # | | # 0, 4 ---- 3 verts = self.poly.xy if self.orientation == "vertical": verts[0] = verts[4] = .25, vmin verts[1] = .25, vmax verts[2] = .75, vmax verts[3] = .75, vmin else: verts[0] = verts[4] = vmin, .25 verts[1] = vmin, .75 verts[2] = vmax, .75 verts[3] = vmax, .25

bed236261a74045a0e4a85fc930bc0ad0da786bb

widgets.py

Fix RangeSlider for same init values Closes #22686. Co-authored-by: Nickolaos Giannatos <[email protected]>

matplotlib

lib/matplotlib/widgets.py

Python

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

clear_region_to_control_producer

def clear_region_to_control_producer(): global _publisher if _publisher: _publisher.close() _publisher = None

941184cd24186324fd9f7f304b7f713041834726

producer.py

chore(hybrid-cloud): AuditLogEntry is a control silo model now (#39890) In the control silo, creating an audit log entry writes to the db directly, whilst in region silo mode creating an audit log entry will instead push to a new kafka producer that consumes into the control silo asynchronously.

sentry

src/sentry/region_to_control/producer.py

Python

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

find_loader

def find_loader(self, fullname): warnings.warn("PathEntryFinder.find_loader() is deprecated since Python " "3.4 in favor of PathEntryFinder.find_spec() " "(available since 3.4)", DeprecationWarning, stacklevel=2) if not hasattr(self, 'find_spec'): return None, [] found = self.find_spec(fullname) if found is not None: if not found.submodule_search_locations: portions = [] else: portions = found.submodule_search_locations return found.loader, portions else: return None, [] find_module = _bootstrap_external._find_module_shim

8198943edd73a363c266633e1aa5b2a9e9c9f526

abc.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/importlib/abc.py

Python

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

_api_all

def _api_all(self): response.content_type = 'application/json; charset=utf-8' if self.args.debug: fname = os.path.join(tempfile.gettempdir(), 'glances-debug.json') try: with open(fname) as f: return f.read() except IOError: logger.debug("Debug file (%s) not found" % fname) # Update the stat self.__update__() try: # Get the JSON value of the stat ID statval = json_dumps(self.stats.getAllAsDict()) except Exception as e: abort(404, "Cannot get stats (%s)" % str(e)) return statval

a9ee2aa09c00b6adc396c666d6e970e2b58918e6

glances_bottle.py

Replace json by ujson #2201

glances

glances/outputs/glances_bottle.py

Python

https://github.com/nicolargo/glances.git

pretrained_cfg_for_features

def pretrained_cfg_for_features(pretrained_cfg): pretrained_cfg = deepcopy(pretrained_cfg) # remove default pretrained cfg fields that don't have much relevance for feature backbone to_remove = ('num_classes', 'crop_pct', 'classifier', 'global_pool') # add default final pool size? for tr in to_remove: pretrained_cfg.pop(tr, None) return pretrained_cfg # def overlay_external_pretrained_cfg(pretrained_cfg, kwargs): # # external_pretrained_cfg = kwargs.pop('external_pretrained_cfg', None) # if external_pretrained_cfg: # pretrained_cfg.pop('url', None) # url should come from external cfg # pretrained_cfg.pop('hf_hub', None) # hf hub id should come from external cfg # pretrained_cfg.update(external_pretrained_cfg)

abc9ba254430ef971ea3dbd12f2b4f1969da55be

helpers.py

Transitioning default_cfg -> pretrained_cfg. Improving handling of pretrained_cfg source (HF-Hub, files, timm config, etc). Checkpoint handling tweaks.

pytorch-image-models

timm/models/helpers.py

Python

https://github.com/huggingface/pytorch-image-models.git

test_get_cert_serial_valid

def test_get_cert_serial_valid(certutil, cert_file): serial = certutil.get_cert_serial(str(cert_file)) assert serial == "5be1cc5d51b78dbd49a0b7c00d44806d"

a8d2d1e1397cdc79b2c5f1ad7f6e3b729dcf8857

test_win_certutil.py

Add tests, fix state module

salt

tests/pytests/functional/modules/test_win_certutil.py

Python

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

check_string

def check_string(result, func, cargs): if not result: raise GEOSException( 'Error encountered checking string return value in GEOS C function "%s".' % func.__name__ ) # Getting the string value at the pointer address. s = string_at(result) # Freeing the memory allocated within GEOS free(result) return s

9c19aff7c7561e3a82978a272ecdaad40dda5c00

errcheck.py

Refs #33476 -- Reformatted code with Black.

django

django/contrib/gis/geos/prototypes/errcheck.py

Python

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

set_data

def set_data(self, x, y, A): x = np.array(x, np.float32) y = np.array(y, np.float32) A = cbook.safe_masked_invalid(A, copy=True) if not (x.ndim == y.ndim == 1 and A.shape[0:2] == y.shape + x.shape): raise TypeError("Axes don't match array shape") if A.ndim not in [2, 3]: raise TypeError("Can only plot 2D or 3D data") if A.ndim == 3 and A.shape[2] not in [1, 3, 4]: raise TypeError("3D arrays must have three (RGB) " "or four (RGBA) color components") if A.ndim == 3 and A.shape[2] == 1: A = A.squeeze(axis=-1) self._A = A self._Ax = x self._Ay = y self._imcache = None self.stale = True

f16da868d016363c4cd734b2abd6535230b094df

image.py

[Doc] Fix ndarray-links for arguments

matplotlib

lib/matplotlib/image.py

Python

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

tf_shard_checkpoint

def tf_shard_checkpoint(weights, max_shard_size="10GB"): max_shard_size = convert_file_size_to_int(max_shard_size) sharded_state_dicts = [] current_block = [] current_block_size = 0 total_size = 0 for item in weights: weight_size = item.numpy().size * dtype_byte_size(item.dtype) # If this weight is going to tip up over the maximal size, we split. if current_block_size + weight_size > max_shard_size: sharded_state_dicts.append(current_block) current_block = [] current_block_size = 0 current_block.append(item) current_block_size += weight_size total_size += weight_size # Add the last block sharded_state_dicts.append(current_block) # If we only have one shard, we return it if len(sharded_state_dicts) == 1: return {TF2_WEIGHTS_NAME: sharded_state_dicts[0]}, None # Otherwise, let's build the index weight_map = {} shards = {} for idx, shard in enumerate(sharded_state_dicts): shard_file = TF2_WEIGHTS_NAME.replace(".h5", f"-{idx+1:05d}-of-{len(sharded_state_dicts):05d}.h5") shards[shard_file] = shard for weight in shard: weight_name = weight.name weight_map[weight_name] = shard_file # Add the metadata metadata = {"total_size": total_size} index = {"metadata": metadata, "weight_map": weight_map} return shards, index

7cced021fa8ddc59f0f77384300760d34545394e

modeling_tf_utils.py

TF Sharded (#17713) * initial commit * update modeeling tf utils * quality * clean and update args * update * remove potential bug * code quality * update * update max shard * update tests for sharding from pretrained * fix remaining test * make style * h5py if tf available * update and fix test * fix test * style * modified push to hub to support shard for TF * quick fix * update code * merge branch main and style * Apply suggestions from code review Co-authored-by: Joao Gante <[email protected]> Co-authored-by: Patrick von Platen <[email protected]> * update based on reviews * update doc * update and style * Apply suggestions from code review Co-authored-by: Sylvain Gugger <[email protected]> * Update based on reviews * fix typo * style Co-authored-by: Joao Gante <[email protected]> Co-authored-by: Patrick von Platen <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]>

transformers

src/transformers/modeling_tf_utils.py

Python

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

start

def start(self): if self._build_level.value < FlowBuildLevel.GRAPH.value: self.build(copy_flow=False) # set env only before the Deployment get started if self.args.env: for k, v in self.args.env.items(): os.environ[k] = str(v) for k, v in self: if not v.external: self.enter_context(v) self._wait_until_all_ready() self._build_level = FlowBuildLevel.RUNNING return self

13edc16d806fb5d77a6849551178ccc75937f25f

base.py

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

jina

jina/orchestrate/flow/base.py

Python

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

get_sal_struct

def get_sal_struct(company, currency, salary_slip_based_on_timesheet, condition): return frappe.db.sql_list( .format( condition=condition ), { "company": company, "currency": currency, "salary_slip_based_on_timesheet": salary_slip_based_on_timesheet, }, )

494bd9ef78313436f0424b918f200dab8fc7c20b

payroll_entry.py

style: format code with black

erpnext

erpnext/payroll/doctype/payroll_entry/payroll_entry.py

Python

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

test_track_task_functions

async def test_track_task_functions(event_loop): hass = ha.HomeAssistant() try: assert hass._track_task hass.async_stop_track_tasks() assert not hass._track_task hass.async_track_tasks() assert hass._track_task finally: await hass.async_stop()

c576a68d336bc91fd82c299d9b3e5dfdc1c14960

test_core.py

Upgrade pytest-aiohttp (#82475) * Upgrade pytest-aiohttp * Make sure executors, tasks and timers are closed Some test will trigger warnings on garbage collect, these warnings spills over into next test. Some test trigger tasks that raise errors on shutdown, these spill over into next test. This is to mimic older pytest-aiohttp and it's behaviour on test cleanup. Discussions on similar changes for pytest-aiohttp are here: https://github.com/pytest-dev/pytest-asyncio/pull/309 * Replace loop with event_loop * Make sure time is frozen for tests * Make sure the ConditionType is not async /home-assistant/homeassistant/helpers/template.py:2082: RuntimeWarning: coroutine 'AsyncMockMixin._execute_mock_call' was never awaited def wrapper(*args, **kwargs): Enable tracemalloc to get traceback where the object was allocated. See https://docs.pytest.org/en/stable/how-to/capture-warnings.html#resource-warnings for more info. * Increase litejet press tests with a factor 10 The times are simulated anyway, and we can't stop the normal event from occuring. * Use async handlers for aiohttp tests/components/motioneye/test_camera.py::test_get_still_image_from_camera tests/components/motioneye/test_camera.py::test_get_still_image_from_camera tests/components/motioneye/test_camera.py::test_get_stream_from_camera tests/components/motioneye/test_camera.py::test_get_stream_from_camera tests/components/motioneye/test_camera.py::test_camera_option_stream_url_template tests/components/motioneye/test_camera.py::test_camera_option_stream_url_template /Users/joakim/src/hass/home-assistant/venv/lib/python3.9/site-packages/aiohttp/web_urldispatcher.py:189: DeprecationWarning: Bare functions are deprecated, use async ones warnings.warn( * Switch to freezegun in modbus tests The tests allowed clock to tick in between steps * Make sure skybell object are fully mocked Old tests would trigger attempts to post to could services: ``` DEBUG:aioskybell:HTTP post https://cloud.myskybell.com/api/v3/login/ Request with headers: {'content-type': 'application/json', 'accept': '*/*', 'x-skybell-app-id': 'd2b542c7-a7e4-4e1e-b77d-2b76911c7c46', 'x-skybell-client-id': '1f36a3c0-6dee-4997-a6db-4e1c67338e57'} ``` * Fix sorting that broke after rebase

core

tests/test_core.py

Python

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

async_test_still

async def async_test_still(hass, info) -> tuple[dict[str, str], str | None]: fmt = None if not (url := info.get(CONF_STILL_IMAGE_URL)): return {}, None if not isinstance(url, template_helper.Template) and url: url = cv.template(url) url.hass = hass try: url = url.async_render(parse_result=False) except TemplateError as err: _LOGGER.error("Error parsing template %s: %s", url, err) return {CONF_STILL_IMAGE_URL: "template_error"}, None verify_ssl = info.get(CONF_VERIFY_SSL) auth = generate_auth(info) try: async_client = get_async_client(hass, verify_ssl=verify_ssl)

c1a2be72fc8b76b55cfde1823c5688100e397369

async def async_test_still(hass, info) -> tuple[dict[str, str], str | None]: """Verify that the still image is valid before we create an entity.""" fmt = None if not (url := info.get(CONF_STILL_IMAGE_URL)): return {}, None if not isinstance(url, template_helper.Template) and url: url = cv.template(url) url.hass = hass try: url = url.async_render(parse_result=False) except TemplateError as err: _LOGGER.error("Error parsing template %s: %s", url, err) return {CONF_STILL_IMAGE_URL: "template_error"}, None verify_ssl = info.get(CONF_VERIFY_SSL) auth = generate_auth(info) try: async_client = get_async_client(hass, verify_ssl=verify_ssl)

config_flow.py

Generic IP Camera configflow 2 (#52360) Co-authored-by: J. Nick Koston <[email protected]>

core

homeassistant/components/generic/config_flow.py

Python

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

split_list

def split_list(v, n): k, m = divmod(len(v), n) return (v[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n))

f60f0e8fc697d98d9f3a4b9ea851329321e64be9

captum_ray.py

Adds Ray implementation of IntegratedGradientsExplainer that distributes across cluster resources (#2697)

ludwig

ludwig/explain/captum_ray.py

Python

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

test_bad_inputs

def test_bad_inputs() -> None: chain = FakeChain() with pytest.raises(ValueError): chain({"foobar": "baz"})

18aeb720126a68201c7e3b5a617139c27c779496

test_base.py

initial commit

langchain

tests/unit_tests/chains/test_base.py

Python

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

prefix

def prefix(self) -> str: if self.collection: return self.collection.prefix return ''

3eb0485dd92c88cc92152d3656d94492db44b183

__init__.py

ansible-test - Use more native type hints. (#78435) * ansible-test - Use more native type hints. Simple search and replace to switch from comments to native type hints for return types of functions with no arguments. * ansible-test - Use more native type hints. Conversion of simple single-line function annotation type comments to native type hints. * ansible-test - Use more native type hints. Conversion of single-line function annotation type comments with default values to native type hints. * ansible-test - Use more native type hints. Manual conversion of type annotation comments for functions which have pylint directives.

ansible

test/lib/ansible_test/_internal/provider/layout/__init__.py

Python

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

object_inspect_mime

def object_inspect_mime(self, oname, detail_level=0, omit_sections=()): with self.builtin_trap: info = self._object_find(oname) if info.found: docformat = sphinxify(self.object_inspect(oname)) if self.sphinxify_docstring else None return self.inspector._get_info( info.obj, oname, info=info, detail_level=detail_level, formatter=docformat, omit_sections=omit_sections, ) else: raise KeyError(oname) #------------------------------------------------------------------------- # Things related to history management #-------------------------------------------------------------------------

d55a692f46402f397ab38e6c4c9fb6423a85b54f

interactiveshell.py

Update sphinxify usage

ipython

IPython/core/interactiveshell.py

Python

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

get_admin_urls_for_registration

def get_admin_urls_for_registration(self): urls = ( re_path( self.url_helper.get_action_url_pattern("index"), self.index_view, name=self.url_helper.get_action_url_name("index"), ), re_path( self.url_helper.get_action_url_pattern("create"), self.create_view, name=self.url_helper.get_action_url_name("create"), ), re_path( self.url_helper.get_action_url_pattern("edit"), self.edit_view, name=self.url_helper.get_action_url_name("edit"), ), re_path( self.url_helper.get_action_url_pattern("delete"), self.delete_view, name=self.url_helper.get_action_url_name("delete"), ), ) if self.inspect_view_enabled: urls = urls + ( re_path( self.url_helper.get_action_url_pattern("inspect"), self.inspect_view, name=self.url_helper.get_action_url_name("inspect"), ), ) if self.history_view_enabled: urls = urls + ( re_path( self.url_helper.get_action_url_pattern("history"), self.history_view, name=self.url_helper.get_action_url_name("history"), ), ) if self.is_pagemodel: urls = urls + ( re_path( self.url_helper.get_action_url_pattern("choose_parent"), self.choose_parent_view, name=self.url_helper.get_action_url_name("choose_parent"), ), ) return urls

d10f15e55806c6944827d801cd9c2d53f5da4186

options.py

Reformat with black

wagtail

wagtail/contrib/modeladmin/options.py

Python

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

async_added_to_hass

async def async_added_to_hass(self) -> None: self._table.add_listener(self.async_write_ha_state)

0c767bd0d37a41af37728b1d8b4eae8dceb7e188

media_player.py

Improve entity type hints [s] (part 1/2) (#77881)

core

homeassistant/components/sisyphus/media_player.py

Python

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

unified_job_template_table

def unified_job_template_table(since, full_path, **kwargs): unified_job_template_query = return _copy_table(table='unified_job_template', query=unified_job_template_query, path=full_path) @register('workflow_job_node_table', '1.0', format='csv', description=_('Data on workflow runs'), expensive=four_hour_slicing)

17756f0e725fb3a87862ac8234a5974c67b0f6e2

@register('workflow_job_node_table', '1.0', format='csv', description=_('Data on workflow runs'), expensive=four_hour_slicing)

collectors.py

Add job execution environment image to analytics data (#11835) * Add job execution environment image to analytics data * Add EE image to UJT analytics data * Bump the unified job templates table

awx

awx/main/analytics/collectors.py

Python

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

get_base_rev_args

def get_base_rev_args(rev): # type: (str) -> List[str] raise NotImplementedError

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

versioncontrol.py

upd; format

transferlearning

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

Python

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

stored_mask

def stored_mask(self) -> np.ndarray: assert self._mask is not None dims = (self.stored_size, self.stored_size, 1) mask = np.frombuffer(decompress(self._mask), dtype="uint8").reshape(dims) logger.trace("stored mask shape: %s", mask.shape) # type: ignore return mask

5e73437be47f2410439a3c6716de96354e6a0c94

detected_face.py

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

faceswap

lib/align/detected_face.py

Python

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

test_smaller_request_deduplicated

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

546b9c9e648f5e2b25bb7c8350570787ff9befae

test_state_store.py

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

synapse

tests/storage/databases/test_state_store.py

Python

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

testSuccessiveHalving

def testSuccessiveHalving(self): stats = self.default_statistics() sched, mock_runner = self.schedulerSetup(stats["max_trials"]) big_bracket = sched._state["bracket"] cur_units = stats[str(stats["s_max"])]["r"] # The last bracket will downscale 4 times for x in range(stats["brack_count"] - 1): trials = big_bracket.current_trials() current_length = len(trials) for trl in trials: mock_runner._launch_trial(trl) # Provides results from 0 to 8 in order, keeping last one running for i, trl in enumerate(trials): action = sched.on_trial_result(mock_runner, trl, result(cur_units, i)) if i < current_length - 1: self.assertEqual(action, TrialScheduler.PAUSE) mock_runner.process_action(trl, action) self.assertEqual(action, TrialScheduler.CONTINUE) new_length = len(big_bracket.current_trials()) self.assertEqual(new_length, self.downscale(current_length, sched)) cur_units = int(cur_units * sched._eta) self.assertEqual(len(big_bracket.current_trials()), 1)

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

test_trial_scheduler.py

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

ray

python/ray/tune/tests/test_trial_scheduler.py

Python

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

test_authorization_header

def test_authorization_header(self) -> None: # test a "normal" Authorization header self.assertEqual( _parse_auth_header( b'X-Matrix origin=foo,key="ed25519:1",sig="sig",destination="bar"' ), ("foo", "ed25519:1", "sig", "bar"), ) # test an Authorization with extra spaces, upper-case names, and escaped # characters self.assertEqual( _parse_auth_header( b'X-Matrix ORIGIN=foo,KEY="ed25\\519:1",SIG="sig",destination="bar"' ), ("foo", "ed25519:1", "sig", "bar"), ) self.assertEqual( _parse_auth_header( b'X-Matrix origin=foo,key="ed25519:1",sig="sig",destination="bar",extra_field=ignored' ), ("foo", "ed25519:1", "sig", "bar"), )

8afb7b55d0527f8c6af7690b162ebaabe9b5d9f5

test__base.py

Make handling of federation Authorization header (more) compliant with RFC7230 (#12774) The main differences are: - values with delimiters (such as colons) should be quoted, so always quote the origin, since it could contain a colon followed by a port number - should allow more than one space after "X-Matrix" - quoted values with backslash-escaped characters should be unescaped - names should be case insensitive

synapse

tests/federation/transport/server/test__base.py

Python

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

find_free_port

def find_free_port(self) -> int: from ray.air._internal.util import find_free_port return find_free_port()

862d10c162421706f77f73428429379a8b22fc38

rollout_worker.py

[AIR] Remove ML code from `ray.util` (#27005) Removes all ML related code from `ray.util` Removes: - `ray.util.xgboost` - `ray.util.lightgbm` - `ray.util.horovod` - `ray.util.ray_lightning` Moves `ray.util.ml_utils` to other locations Closes #23900 Signed-off-by: Amog Kamsetty <[email protected]> Signed-off-by: Kai Fricke <[email protected]> Co-authored-by: Kai Fricke <[email protected]>

ray

rllib/evaluation/rollout_worker.py

Python

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

test_relative_json

def test_relative_json(self): # this should work regardless of where th current working directory is. with tempfile.TemporaryDirectory() as tmp_dir: cwdir = os.getcwd() os.chdir(tmp_dir) unzipped_paths = _unzip_if_needed( [str(Path(self.relative_path) / "large.json")], "json" ) self.assertEqual( os.path.realpath(str(Path(unzipped_paths[0]).absolute())), os.path.realpath( str( Path(__file__).parent.parent.parent / self.relative_path / "large.json" ) ), ) assert all([Path(fpath).exists() for fpath in unzipped_paths]) os.chdir(cwdir)

569fe0109629048d08e1d9e023f7769f10bd2244

test_dataset_reader.py

[RLlib] improved unittests for dataset_reader and fixed bugs (#26458)

ray

rllib/offline/tests/test_dataset_reader.py

Python

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

pub_connect

def pub_connect(self): if self.pub_sock: self.pub_close() ctx = zmq.Context.instance() self._sock_data.sock = ctx.socket(zmq.PUSH) self.pub_sock.setsockopt(zmq.LINGER, -1) if self.opts.get("ipc_mode", "") == "tcp": pull_uri = "tcp://127.0.0.1:{}".format( self.opts.get("tcp_master_publish_pull", 4514) ) else: pull_uri = "ipc://{}".format( os.path.join(self.opts["sock_dir"], "publish_pull.ipc") ) log.debug("Connecting to pub server: %s", pull_uri) self.pub_sock.connect(pull_uri) return self._sock_data.sock

d4e6111086ff713eb6609dc6c98cec98aded2564

zeromq.py

Refactor into transports and channels

salt

salt/transport/zeromq.py

Python

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

x_forwarded_ip

def x_forwarded_ip(request): ip_address_list = request.headers.get('X-Forwarded-For') if ip_address_list: ip_address_list = ip_address_list.split(',') return ip_address_list[0]

9e8eb17497edf8f40d68f7dcb53b1b2c3576313c

request_ip_resolvers.py

Feat/django4 support (#7268) * feat: update requirements for the deps to install for django 4 * fix: django utils http deprecations * fix: signals deprecation from Django * fix: lang key deprecation for session * wip: middleware deprecation fixes * feat: add django 4 to the ci mix * fix: 3.6 is deprecated * fix: use the same name as existing convention * wip: fix the toolbar * fix: more tests * fix: issue with thread local in Django Django uses thread-locals internally to track the currently active language for the request. Python implements thread-local data through the threading.local class, but as of Django 3.x, multiple requests can be handled in a single thread and so thread-locals will no longer be unique to a single request. Django therefore provides asgiref.Local as a drop-in replacement. Authored-by: Vinit Kumar <[email protected]> Signed-off-by: Vinit Kumar <[email protected]> * fix: add correct version of package deps * revert: old style middlewares * fix: current user middleware issues * fix: django 4.0 is 3.8+ only * fix: issue with middlewares upgrade to the new convention * fix: port the middleware to new convention Authored-by: Vinit Kumar <[email protected]> Signed-off-by: Vinit Kumar <[email protected]> Co-authored-by: Mark Walker <[email protected]> * fix: isort linting issues * feat: port the middleware to the new format * Move django upper limit from 4 to 5 Co-authored-by: Mark Walker <[email protected]> Co-authored-by: Mark Walker <[email protected]>

django-cms

cms/utils/request_ip_resolvers.py

Python

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

test_basic

def test_basic(self, ray_start_regular_shared): # "simple" contains three 32x32 RGB images. ds = ray.data.read_images("example://image-datasets/simple") assert ds.schema().names == [TENSOR_COLUMN_NAME] column_type = ds.schema().types[0] assert isinstance(column_type, ArrowTensorType) assert all(array.shape == (32, 32, 3) for array in ds.take())

fe3c2294f08fd27867d77d0e3dc3ebfeba0d6d05

test_dataset_image.py

[AIR - Datasets] Add experimental `read_images` (#29177) Users can't discover ImageFolderDatasource. This PR adds a more-discoverable way to read images. Signed-off-by: Balaji Veeramani <[email protected]> Co-authored-by: Balaji Veeramani <[email protected]>

ray

python/ray/data/tests/test_dataset_image.py

Python

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

inplace_increment

def inplace_increment(x, val, f=None): return _cur_framework(x, f=f).inplace_increment(x, val)

ec8341197ccdd240a346a95c2a434e5ef9f9ef72

general.py

moved all inplace methods from gradients submodule to general submodule, as inplace ops are also relevant for non-Variable tensors.

ivy

ivy/core/general.py

Python

https://github.com/unifyai/ivy.git

func_dump

def func_dump(func): if os.name == "nt": raw_code = marshal.dumps(func.__code__).replace(b"\\", b"/") code = codecs.encode(raw_code, "base64").decode("ascii") else: raw_code = marshal.dumps(func.__code__) code = codecs.encode(raw_code, "base64").decode("ascii") defaults = func.__defaults__ if func.__closure__: closure = tuple(c.cell_contents for c in func.__closure__) else: closure = None return code, defaults, closure

84afc5193d38057e2e2badf9c889ea87d80d8fbf

generic_utils.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/utils/generic_utils.py

Python

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

_set_state

def _set_state(self, state): for child_attr, child_obj in self.__dict__.items(): # TODO(rchao): Retrieve non-variable states from the dict as well. # TODO(rchao): Give a warning for mismatches. if isinstance(child_obj, tf.Variable): child_obj.assign(state[child_attr]) elif saving_lib.is_container(child_obj): for k, contained_obj in enumerate(child_obj): if isinstance(contained_obj, tf.Variable): # Handling the case where `child_obj` is a list/tuple. contained_obj.assign(state[f"{child_attr}-{k}"]) elif isinstance(child_obj, dict) and isinstance( child_obj[contained_obj], tf.Variable ): # Handling the case where `child_obj` is a dict. child_obj[contained_obj].assign( state[f"{child_attr}-{contained_obj}"] )

ba5086fa31d24a9f61b46d4a844311b58dea7ff1

base_layer.py

Keras saving: A prototype of config-based (idempotent) saving and loading, with simple model state restoration added. It's done via the archive provided by `zipfile` package. Preliminary for review and the APIs and implementation are subject to changes. PiperOrigin-RevId: 470784761

keras

keras/engine/base_layer.py

Python

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

test_deepspeed_multigpu_single_file

def test_deepspeed_multigpu_single_file(tmpdir): model = BoringModel() checkpoint_path = os.path.join(tmpdir, "model.pt") trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True) trainer.fit(model) trainer.save_checkpoint(checkpoint_path) trainer = Trainer( default_root_dir=tmpdir, strategy=DeepSpeedStrategy(stage=3), gpus=1, fast_dev_run=True, precision=16 ) strategy = trainer.strategy assert isinstance(strategy, DeepSpeedStrategy) assert not strategy.load_full_weights with pytest.raises(MisconfigurationException, match="DeepSpeed was unable to load the checkpoint."): trainer.test(model, ckpt_path=checkpoint_path) trainer = Trainer( default_root_dir=tmpdir, strategy=DeepSpeedStrategy(stage=3, load_full_weights=True), gpus=1, fast_dev_run=True, precision=16, ) strategy = trainer.strategy assert isinstance(strategy, DeepSpeedStrategy) assert strategy.load_full_weights trainer.test(model, ckpt_path=checkpoint_path)

650c710efacd633fa283955145342bb64063c883

test_deepspeed_strategy.py

Rename training plugin test files & names to strategy (#11303)

lightning

tests/strategies/test_deepspeed_strategy.py

Python

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

get_model_modules

def get_model_modules(): _ignore_modules = [ "modeling_auto", "modeling_encoder_decoder", "modeling_marian", "modeling_mmbt", "modeling_outputs", "modeling_retribert", "modeling_utils", "modeling_flax_auto", "modeling_flax_encoder_decoder", "modeling_flax_utils", "modeling_speech_encoder_decoder", "modeling_flax_vision_encoder_decoder", "modeling_transfo_xl_utilities", "modeling_tf_auto", "modeling_tf_encoder_decoder", "modeling_tf_outputs", "modeling_tf_pytorch_utils", "modeling_tf_utils", "modeling_tf_transfo_xl_utilities", "modeling_tf_vision_encoder_decoder", "modeling_vision_encoder_decoder", ] modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them if not model.startswith("__"): model_module = getattr(transformers.models, model) for submodule in dir(model_module): if submodule.startswith("modeling") and submodule not in _ignore_modules: modeling_module = getattr(model_module, submodule) if inspect.ismodule(modeling_module): modules.append(modeling_module) return modules

b67fd797bec56b59e1cd3ad54fa2783f7d7b7cbc

check_repo.py

Add TFVisionEncoderDecoderModel (#14148) * Start the work on TFVisionEncoderDecoderModel * Expose TFVisionEncoderDecoderModel * fix import * Add modeling_tf_vision_encoder_decoder to _ignore_modules in get_model_modules() * reorder * Apply the fix for checkpoint loading as in #14016 * remove attention_mask + fix VISION_DUMMY_INPUTS * A minimal change to make TF generate() work for vision models as encoder in encoder-decoder setting * fix wrong condition: shape_list(input_ids) == 2 * add tests * use personal TFViTModel checkpoint (for now) * Add equivalence tests + projection layer * style * make sure projection layer can run * Add examples * Apply suggestions from code review Co-authored-by: Sylvain Gugger <[email protected]> * Clean comments (need to work on TODOs for PyTorch models) * Remove TF -> PT in check_pt_tf_equivalence for TFVisionEncoderDecoderModel * fixes * Revert changes in PT code. * Update tests/test_modeling_tf_vision_encoder_decoder.py Co-authored-by: Patrick von Platen <[email protected]> * Add test_inference_coco_en for TF test * fix quality * fix name * build doc * add main_input_name * Fix ckpt name in test * fix diff between master and this PR * fix doc * fix style and quality * fix missing doc * fix labels handling * Delete auto.rst * Add the changes done in #14016 * fix prefix * Apply suggestions from code review Co-authored-by: Sylvain Gugger <[email protected]> * make style Co-authored-by: ydshieh <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]> Co-authored-by: Patrick von Platen <[email protected]>

transformers

utils/check_repo.py

Python

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

_stop_wallet

async def _stop_wallet(self): if self.service is not None: self.service._close() peers_close_task: Optional[asyncio.Task] = await self.service._await_closed() if peers_close_task is not None: await peers_close_task ########################################################################################## # Key management ##########################################################################################

89f15f591cc3cc3e8ae40e95ffc802f7f2561ece

wallet_rpc_api.py

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]>

chia-blockchain

chia/rpc/wallet_rpc_api.py

Python

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

should_autoscale

def should_autoscale(self) -> bool: return self._target_state.info.autoscaling_policy is not None

1fd2913abdcf376edd148692bfeb5962a6e1c478

deployment_state.py

[serve] Refactor checkpointing to write ahead target state (#26797)

ray

python/ray/serve/deployment_state.py

Python

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

assign_proto

def assign_proto(proto, name, val): is_repeated_field = hasattr(getattr(proto, name), 'extend') if is_repeated_field and not isinstance(val, list): val = [val] if isinstance(val, list): if isinstance(val[0], dict): for item in val: proto_item = getattr(proto, name).add() for k, v in six.iteritems(item): assign_proto(proto_item, k, v) else: getattr(proto, name).extend(val) elif isinstance(val, dict): for k, v in six.iteritems(val): assign_proto(getattr(proto, name), k, v) else: setattr(proto, name, val)

cc4d0564756ca067516f71718a3d135996525909

net_spec.py

Balanced joint maximum mean discrepancy for deep transfer learning

transferlearning

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

Python

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

readlines

def readlines(self, sizehint=None, keepends=True): data = self.read() return data.splitlines(keepends)

8198943edd73a363c266633e1aa5b2a9e9c9f526

codecs.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/codecs.py

Python

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

arc_cosine

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

4885331509eeffe50f42d76b234996467b06170f

template.py

Fail template functions when no default specified (#71687)

core

homeassistant/helpers/template.py

Python

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

heawood_graph

def heawood_graph(create_using=None): G = LCF_graph(14, [5, -5], 7, create_using) G.name = "Heawood Graph" return G

dec723f072eb997a497a159dbe8674cd39999ee9

small.py

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

networkx

networkx/generators/small.py

Python

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

testRelativeLogdirWithNestedDir

def testRelativeLogdirWithNestedDir(self): local_dir_path = Path("/tmp/test_rel") if local_dir_path.exists(): local_dir = tempfile.mkdtemp(prefix=str(local_dir_path) + "_") else: local_dir = str(local_dir_path) tune.run( "rel_logdir", config={"a": tune.randint(0, 10)}, local_dir=local_dir, # Create a nested experiment directory. name="exp_dir/deep_exp_dir", ) # Copy the folder local_dir_moved = local_dir + "_moved" shutil.copytree(local_dir + "/exp_dir", local_dir_moved) # Load the trials. with self.assertRaises(ValueError): analysis = tune.ExperimentAnalysis(local_dir) # Using the subdir should work, however. analysis = tune.ExperimentAnalysis(local_dir + "/exp_dir") analysis_moved = tune.ExperimentAnalysis(local_dir_moved) configs = analysis.get_all_configs() configs_moved = analysis_moved.get_all_configs() config = configs[next(iter(configs))] config_moved = configs_moved[next(iter(configs_moved))] # Check, if the trial attributes can be loaded. self.assertEqual(len(configs), 1) self.assertEqual(len(configs_moved), 1) # Check, if the two configs are equal. self.assertDictEqual(config, config_moved) metric = "metric1" mode = "max" analysis_df = analysis.dataframe(metric, mode) analysis_moved_df = analysis_moved.dataframe(metric, mode) self.assertEqual(analysis_df.shape[0], 1) self.assertEqual(analysis_moved_df.shape[0], 1) # Drop the `logdir` column as this should be different # between the two trials. analysis_df.drop(columns="logdir", inplace=True) analysis_moved_df.drop(columns="logdir", inplace=True) self.assertEqual(analysis_df, analysis_moved_df) # Remove the files and directories. if shutil.rmtree.avoids_symlink_attacks: if local_dir_path.exists(): shutil.rmtree(local_dir) shutil.rmtree(local_dir_moved)

2a5d322e705df080e9254c9c9a3e187c1ea41c4e

test_trial_relative_logdir.py

[tune] Relative logdir paths in trials for ExperimentAnalysis in remote buckets (#25063) When running an experiment for example in the cloud and syncing to a bucket the logdir path in the trials will be changed when working with the checkpoints in the bucket. There are some workarounds, but the easier solution is to also add a rel_logdir containing the relative path to the trials/checkpoints that can handle any changes in the location of experiment results. As discussed with @Yard1 and @krfricke Co-authored-by: Antoni Baum <[email protected]> Co-authored-by: Kai Fricke <[email protected]>

ray

python/ray/tune/tests/test_trial_relative_logdir.py

Python

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

test_add_remove_actors

def test_add_remove_actors(self): workers = [] manager = AsyncRequestsManager( workers, max_remote_requests_in_flight_per_worker=2 ) if not ( ( len(manager._all_workers) == len(manager._remote_requests_in_flight) == len(manager._pending_to_actor) == len(manager._pending_remotes) == 0 ) ): raise ValueError("We should have no workers in this case.") assert not manager.call(lambda w: w.task()), ( "Task shouldn't have been " "launched since there are no " "workers in the manager." ) worker = RemoteRLlibActor.remote(sleep_time=0.1) manager.add_workers(worker) manager.call(lambda w: w.task()) if not ( len(manager._remote_requests_in_flight[worker]) == len(manager._pending_to_actor) == len(manager._all_workers) == len(manager._pending_remotes) == 1 ): raise ValueError("We should have 1 worker and 1 pending request") time.sleep(3) manager.get_ready() # test worker removal for i in range(2): manager.call(lambda w: w.task()) assert len(manager._pending_remotes) == i + 1 manager.remove_workers(worker) if not ((len(manager._all_workers) == 0)): raise ValueError("We should have no workers that we can schedule tasks to") if not ( (len(manager._pending_remotes) == 2 and len(manager._pending_to_actor) == 2) ): raise ValueError( "We should still have 2 pending requests in flight from the worker" ) time.sleep(3) result = manager.get_ready() if not ( len(result) == 1 and len(result[worker]) == 2 and len(manager._pending_remotes) == 0 and len(manager._pending_to_actor) == 0 ): raise ValueError( "We should have 2 ready results from the worker and no pending requests" )

eaed256d6863c529b8ada42514f7fba12d146f22

test_async_requests_manager.py

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

ray

rllib/execution/tests/test_async_requests_manager.py

Python

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

get_config

def get_config(self): json_word_counts = json.dumps(self.word_counts) json_word_docs = json.dumps(self.word_docs) json_index_docs = json.dumps(self.index_docs) json_word_index = json.dumps(self.word_index) json_index_word = json.dumps(self.index_word) return { "num_words": self.num_words, "filters": self.filters, "lower": self.lower, "split": self.split, "char_level": self.char_level, "oov_token": self.oov_token, "document_count": self.document_count, "word_counts": json_word_counts, "word_docs": json_word_docs, "index_docs": json_index_docs, "index_word": json_index_word, "word_index": json_word_index, }

84afc5193d38057e2e2badf9c889ea87d80d8fbf

text.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/preprocessing/text.py

Python

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

send_direct

async def send_direct(self, channel, data): # We iterate over the channels to get reference to the websocket object # so we can disconnect incase of failure await channel.send(data)

6f5478cc029bc146e3980affa61dd7956c5cb416

channel.py

DataFrame transmission, strategy follower logic