Datasets:

semeru

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

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Semeru Lab 7

freqtrade/exchange/exchange.py

def reload_markets(self) -> None: # Check whether markets have to be reloaded if (self._last_markets_refresh > 0) and ( self._last_markets_refresh + self.markets_refresh_interval > arrow.utcnow().int_timestamp): return None logger.debug("Performing scheduled market reload..") try: self._markets = self._api.load_markets(reload=True) # Also reload async markets to avoid issues with newly listed pairs self._load_async_markets(reload=True) self._last_markets_refresh = arrow.utcnow().int_timestamp self.f

replaced "leverage" with "tiers"

reload_markets

42e36f44f8a91a79a8ffa14698542f38df39cb50

freqtrade

exchange.py

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

Python

def reload_markets(self) -> None: # Check whether markets have to be reloaded if (self._last_markets_refresh > 0) and ( self._last_markets_refresh + self.markets_refresh_interval > arrow.utcnow().int_timestamp): return None logger.debug("Performing scheduled market reload..") try: self._markets = self._api.load_markets(reload=True) # Also reload async markets to avoid issues with newly listed pairs self._load_async_markets(reload=True) self._last_markets_refresh = arrow.utcnow().int_timestamp self.fill_leverage_tiers() except ccxt.BaseError: logger.exception("Could not reload markets.")

sympy/matrices/common.py

def is_strongly_diagonally_dominant(self): r

Moved imports to higher level

is_strongly_diagonally_dominant

59d22b6bb7287613d598611027f640d068ca5748

sympy

common.py

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

Python

def is_strongly_diagonally_dominant(self): r if not self.is_square: return False rows, cols = self.shape

ludwig/features/binary_feature.py

def create_calibration_module(self, feature) -> torch.nn.Module: if feature.get("calibration"): calibration_cls = calibration.get_calibration_cls(BINARY, "temperature_scaling") return calibration_cls(binary=True) return None

Adds mechanism for calibrating probabilities for category and binary features (#1949) * Started adding files for calibration implementation. * Adds option to return logits and labels in predictor. * Pre-commit fixes * First pass temperature scaling working. * Fixes calibration for categorical feature. * Separate calibrated logits from logits. * Adds option to revert temperature scaling. * Refactoring, move binary prediction logic into calibration class. * Reverted accidental commit to simple_model_training.py * Adds checks and comments. * Fixes matrix scaling, convert pandas series to numpy arrays. * Fixes number of classes for categorical features. * Adds structured calibration result, unit tests. * Make create_calibration_module not abstract, default implementation returns None. * Relax precision requirement for calibration test. * Save weights after calibration, so calibration results are included in save file. * Implemented dirichlet scaling with l2 off-diagonal regularization. * Adds masked_select off_diagonal method. * Change back to matrix scaling. * Updates test expectations to reflect learning rate settings. * Tuned default regularization weight. * Comments. * Set random seed, testing to see if that makes a difference. * Remove checks for exact NLL, ECE values post calibration. * Restored LOGITS to EXCLUDE_PRED_SET, added another option to return logits in batch_predict. * Factor calibration method out of Trainer into Calibrator * Removed horovod argument from calibrator. * Return batch_size if eval_batch_size not specified. * Fix calibration_module docstring. * Updates comment, adds fallback method of calibrating on training set if no validation set available. * Adds calibration registry, replaces if statements for instantiating calibration. * Raise ValueError if unsupported calibration method specified. * Remove calibrate method from Trainer * f string * Use backend to create predictor for calibration. * Moves saving out of calibrator * Fix comment. * Adds ray test of calibration. * Implements collect_logits in ray predictor. * First pass implementation of collect_labels. * Implements collect_logits and collect_labels in ray backend. * Merge predictions and labels in ray backend * Reverts collect_labels, get labels from dataset in calibrate. * Allow overriding EXCLUDE_PRED_SET when getting preds. * Changes 'calibration' config option to binary. * Test both binary and category output features in ray test. * Comments/ * Adds type hints. Co-authored-by: Daniel Treiman <[email protected]>

create_calibration_module

e65f74e87e8e29922f4e9f9d839978ffb2c5b029

ludwig

binary_feature.py

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

Python

def create_calibration_module(self, feature) -> torch.nn.Module: if feature.get("calibration"): calibration_cls = calibration.get_calibration_cls(BINARY, "temperature_scaling") return calibration_cls(binary=True) return None

ppdet/utils/download.py

def _download(url, path, md5sum=None): if not osp.exists(path): os.makedir

fix download.py (#5159)

_download

1dcec15b6600df750d680e97e89117fcb8eb84a0

PaddleDetection

download.py

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

Python

def _download(url, path, md5sum=None): if not osp.exists(path): os.makedirs(path) fname = osp.split(url)[-1] fullname = osp.join(path, fname) retry_cnt = 0 while not (osp.exists(fullname) and _check_exist_file_md5(fullname, md5sum, url)): if retry_cnt < DOWNLOAD_RETRY_LIMIT: retry_cnt += 1 else: raise RuntimeError("Download from {} failed. " "Retry limit reached".format(url)) logger.info("Downloading {} from {}".format(fname, url)) # NOTE: windows path join may incur \, which is invalid in url if sys.platform == "win32": url = url.replace('\\', '/') req = requests.get(url, stream=True) if req.status_code != 200: raise RuntimeError("Downloading from {} failed with code " "{}!".format(url, req.status_code)) # For protecting download interupted, download to # tmp_fullname firstly, move tmp_fullname to fullname # after download finished tmp_fullname = fullname + "_tmp" total_size = req.headers.get('content-length') with open(tmp_fullname, 'wb') as f: if total_size: for chunk in tqdm.tqdm( req.iter_content(chunk_size=1024), total=(int(total_size) + 1023) // 1024, unit='KB'): f.write(chunk) else: for chunk in req.iter_content(chunk_size=1024): if chunk: f.write(chunk) shutil.move(tmp_fullname, fullname) return fullname

django/db/models/options.py

def related_objects(self): all_related_fields = self._get_fields( forward=False, reverse=True, include_hidden=True ) return make_immutable_fields_list( "related_objects", ( obj for obj in

Refs #33476 -- Reformatted code with Black.

related_objects

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

options.py

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

Python

def related_objects(self): all_related_fields = self._get_fields( forward=False, reverse=True, include_hidden=True ) return make_immutable_fields_list( "related_objects", ( obj for obj in all_related_fields if not obj.hidden or obj.field.many_to_many ), )

tests/csrf_tests/tests.py

def test_https_good_referer(self): req = self._get_POST_request_with_token()

Refs #33476 -- Reformatted code with Black.

test_https_good_referer

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_https_good_referer(self): req = self._get_POST_request_with_token() req._is_secure_override = True req.META["HTTP_HOST"] = "www.example.com" req.META["HTTP_REFERER"] = "https://www.example.com/somepage" mw = CsrfViewMiddleware(post_form_view) mw.process_request(req) resp = mw.process_view(req, post_form_view, (), {}) self.assertIsNone(resp)

tests/rest/client/test_retention.py

def test_state_policy(self) -> None: room_id = self.helper.create_room_as(self.user_id, tok=self.token) # Set the maximum lifetime to 35 days so that the first event gets expired but not # the second one. self.helper.send_state( room_id=room_id, event_type=EventTypes.Rete

Add type hints to `tests/rest/client` (#12084)

test_state_policy

1901cb1d4a8b7d9af64493fbd336e9aa2561c20c

synapse

test_retention.py

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

Python

def test_state_policy(self) -> None: room_id = self.helper.create_room_as(self.user_id, tok=self.token) # Set the maximum lifetime to 35 days so that the first event gets expired but not # the second one. self.helper.send_state( room_id=room_id, event_type=EventTypes.Retention, body={"max_lifetime": one_day_ms * 35}, tok=self.token, ) self._test_retention(room_id, expected_code_for_first_event=404)

insertion_sort.py

def insertion_sort(list, n): for i in range(0, n): key = list[i] j = i - 1 # Swap elements witth key iff they are # greater than

refactor: clean code Signed-off-by: slowy07 <[email protected]>

insertion_sort

f0af0c43340763724f139fa68aa1e5a9ffe458b4

Python

insertion_sort.py

https://github.com/geekcomputers/Python.git

Python

def insertion_sort(list, n): for i in range(0, n): key = list[i] j = i - 1 # Swap elements witth key iff they are # greater than key while j >= 0 and list[j] > key: list[j + 1] = list[j] j = j - 1 list[j + 1] = key return list

gradio/component.py

def get_template_context(self): return {"name": self.__class__.__name__.lower(),

Format The Codebase - black formatting - isort formatting

get_template_context

cc0cff893f9d7d472788adc2510c123967b384fe

gradio

component.py

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

Python

def get_template_context(self): return {"name": self.__class__.__name__.lower(), "label": self.label}

mindsdb/integrations/handlers/teradata_handler/teradata_handler.py

def connect(self): if self.is_connected is True: r

feat: add teradata integration

connect

47c5e0ac2d89807f8ff7239d423a3d346bd39a1e

mindsdb

teradata_handler.py

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

Python

def connect(self): if self.is_connected is True: return self.connection connection = teradatasql.connect( **self.connection_data ) self.is_connected = True self.connection = connection return self.connection

kivy/core/window/__init__.py

def unregister_event_manager(self, manager): self.event_managers.remove(manager) for type_id in manager.type_ids: self.event_managers_dict[type_id].remove(manager) m

Feature: EventManagerBase (#7658) * Added EventManagerBase class and event_managers attribute to WindowBase class. * Added on_motion event to Widget class. * Updated post_dispatch_input in EventLoopBase to skip non-touch events. * Using type ids in MouseMotionEventProvider. * Added on_motion method to Widget subclasses. * Updated Widget.on_motion method to dispatch to filtered widgets if 'pos' is not in me.profile. * Changed motion_filter property in Widget to store key to list values. * Updated Widget.on_motion to not dispatch event to children if widget is disabled. * Widget: Using flags to control dispatching in on_motion method. * Widget: Don't dispatch on_motion to children if only self is registered. * Widget: Removed collision on disabled check from on_motion method. * Widget: Added docstrings for motion_filter and related methods. * EventManager: Moved motion event flags to eventmanager/__init__.py module. * ScreenManager: Overrode the on_motion method. * WindowBase: Using attributes event_managers and event_managers_dict. * WindowBase: Added doc for register_event_manager and unregister_event_manager methods. * Widget: Improved default dispatch to stop after the last registered widgets. * EventManagerBase: Added initial docs class and module. * Widget: Added experimental warnings to motion_filter property and to on_motion and (un)register_for_motion_event methods. * WindowBase: Added docs for event_managers and event_managers_dict attributes. * MotionEvent: Added type_id and flags to push_attrs list. * EventManagerBase: Added versionadded tag on all flags. * EventManagerBase: Use dispatch modes instead of flags.

unregister_event_manager

1830123ba3edf7290b7c6cb1c6f406ccf1d0e5d4

kivy

__init__.py

https://github.com/kivy/kivy.git

Python

def unregister_event_manager(self, manager): self.event_managers.remove(manager) for type_id in manager.type_ids: self.event_managers_dict[type_id].remove(manager) manager.stop() manager.window = None

python3.10.4/Lib/functools.py

def _c3_mro(cls, abcs=None): for i, base in enumerate(reversed(cls.__bases__)): if hasattr(base, '__abstractmethods__'): boundary = len(cls.__bases__) - i break # Bases up to the last explicit ABC are considered first. else: boundary = 0 abcs = list(abcs) if abcs else [] explicit_bases = list(cls.__bases__[:boundary]) abstract_bases = [] other_bases = list(cls.__bases__[boundary:]) for base in abcs: if issubclass(cls, base) and not any( issubclass(b, base) for b in cls.__bases__ ): # If *cls* is the class that introduces behaviour described by # an ABC *base*, insert said ABC to its MRO. abstract_bases.append(base) for base in abstract_bases: abcs.remove(base) explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases] abstract_c3_mros = [_c3_mro(base, abcs=abcs) for base in abstract_bases] other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases] return _c3_merge

add python 3.10.4 for windows

_c3_mro

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

functools.py

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

Python

def _c3_mro(cls, abcs=None): for i, base in enumerate(reversed(cls.__bases__)): if hasattr(base, '__abstractmethods__'): boundary = len(cls.__bases__) - i break # Bases up to the last explicit ABC are considered first. else: boundary = 0 abcs = list(abcs) if abcs else [] explicit_bases = list(cls.__bases__[:boundary]) abstract_bases = [] other_bases = list(cls.__bases__[boundary:]) for base in abcs: if issubclass(cls, base) and not any( issubclass(b, base) for b in cls.__bases__ ): # If *cls* is the class that introduces behaviour described by # an ABC *base*, insert said ABC to its MRO. abstract_bases.append(base) for base in abstract_bases: abcs.remove(base) explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases] abstract_c3_mros = [_c3_mro(base, abcs=abcs) for base in abstract_bases] other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases] return _c3_merge( [[cls]] + explicit_c3_mros + abstract_c3_mros + other_c3_mros + [explicit_bases] + [abstract_bases] + [other_bases] )

homeassistant/components/nissan_leaf/__init__.py

async def async_start_charging(self) -> None: awai

Add button to start leaf charge (#62948) Co-authored-by: Bruce Duncan <[email protected]>

async_start_charging

10027b20904b678d8baecbc6e72c5bcc3f4f24b2

core

__init__.py

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

Python

async def async_start_charging(self) -> None: await self.hass.async_add_executor_job(self.leaf.start_charging) self.schedule_update()

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

def test_multiple_server_connections(tctx): server1 = Placeholder(Server) server2 = Placehold

make it black!

test_multiple_server_connections

b3587b52b25077f68116b9852b041d33e7fc6601

mitmproxy

test_http.py

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

Python

def test_multiple_server_connections(tctx): server1 = Placeholder(Server) server2 = Placeholder(Server) playbook = Playbook(http.HttpLayer(tctx, HTTPMode.regular), hooks=False)

onnx/test/compose_test.py

def test_overlapping_output_names(self) -> None: self._test_overlapping_names( outputs0=['o0', 'o1'], outputs1=['o1', 'o2'

Use Python type annotations rather than comments (#3962) * These have been supported since Python 3.5. ONNX doesn't support Python < 3.6, so we can use the annotations. Diffs generated by https://pypi.org/project/com2ann/. Signed-off-by: Gary Miguel <[email protected]> * Remove MYPY conditional logic in gen_proto.py It breaks the type annotations and shouldn't be needed. Signed-off-by: Gary Miguel <[email protected]> * Get rid of MYPY bool from more scripts Signed-off-by: Gary Miguel <[email protected]> * move Descriptors class above where its referenced in type annotation Signed-off-by: Gary Miguel <[email protected]> * fixes Signed-off-by: Gary Miguel <[email protected]> * remove extra blank line Signed-off-by: Gary Miguel <[email protected]> * fix type annotations Signed-off-by: Gary Miguel <[email protected]> * fix type annotation in gen_docs Signed-off-by: Gary Miguel <[email protected]> * fix Operators.md Signed-off-by: Gary Miguel <[email protected]> * fix TestCoverage.md Signed-off-by: Gary Miguel <[email protected]> * fix protoc-gen-mypy.py Signed-off-by: Gary Miguel <[email protected]>

test_overlapping_output_names

83fa57c74edfd13ddac9548b8a12f9e3e2ed05bd

onnx

compose_test.py

https://github.com/onnx/onnx.git

Python

def test_overlapping_output_names(self) -> None: self._test_overlapping_names( outputs0=['o0', 'o1'], outputs1=['o1', 'o2'])

tests/test_settings.py

def test_write_profiles_does_not_include_default(self, temporary_profiles_path): write_profiles({}) assert "profiles.default" not in temporary_profiles_path.read_text()

Tests passing

test_write_profiles_does_not_include_default

1dd7561062328e96594bbf60a6d15f49163c9d87

prefect

test_settings.py

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

Python

def test_write_profiles_does_not_include_default(self, temporary_profiles_path): write_profiles({}) assert "profiles.default" not in temporary_profiles_path.read_text()

sympy/physics/optics/polarization.py

def phase_retarder(theta=0, delta=0): R = Matrix([[cos(theta)**2 + exp(I*delta)*sin(theta)**2, (1-exp(I*delta))*cos(theta)*sin(theta)], [(1-exp(I*delta))*cos(theta)*sin(theta), sin(theta)**2 + exp(I*delta)*cos(theta)**2]]) return R*exp(-I*delta/2)

removed backticks around variable names in docs according to PR review

phase_retarder

ae2baaa0bbcd42792bb2e7887ca61b97abc40463

sympy

polarization.py

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

Python

def phase_retarder(theta=0, delta=0): R = Matrix([[cos(theta)**2 + exp(I*delta)*sin(theta)**2, (1-exp(I*delta))*cos(theta)*sin(theta)], [(1-exp(I*delta))*cos(theta)*sin(theta), sin(theta)**2 + exp(I*delta)*cos(theta)**2]]) return R*exp(-I*delta/2)

nuitka/utils/Execution.py

def wrapCommandForDebuggerForExec(*args): gdb_path = getExecutablePath("gdb") # Windows extra ball, attempt the downloaded one. if isWin32Windows() and gdb_path is None: from nuitka.Options import assumeYesForDownloads mingw64_gcc_path = getCachedDownloadedMinGW64( target_arch=getArchitecture(), assume_yes_for_downloads=assumeYesForDownloads(), ) with withEnvironmentPathAdded("PATH", os.path.dirname(mingw64_gcc_path)): lldb_path = getExecutablePath("lldb") if gdb_path is None and lldb_path is None: lldb_path = getExecutablePath("lldb") if lldb_path is None: general.sysexit("Error, no 'gdb' or 'lldb' binary found in path.") if gdb_path is not None: args = (gdb_path, "gdb", "-ex=run"

Windows: Make running in debugger work with cmd files as well

wrapCommandForDebuggerForExec

98badaaafd4e56529378947358acae489035fa1e

Nuitka

Execution.py

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

Python

def wrapCommandForDebuggerForExec(*args): gdb_path = getExecutablePath("gdb") # Windows extra ball, attempt the downloaded one. if isWin32Windows() and gdb_path is None: from nuitka.Options import assumeYesForDownloads mingw64_gcc_path = getCachedDownloadedMinGW64( target_arch=getArchitecture(), assume_yes_for_downloads=assumeYesForDownloads(), ) with withEnvironmentPathAdded("PATH", os.path.dirname(mingw64_gcc_path)): lldb_path = getExecutablePath("lldb") if gdb_path is None and lldb_path is None: lldb_path = getExecutablePath("lldb") if lldb_path is None: general.sysexit("Error, no 'gdb' or 'lldb' binary found in path.") if gdb_path is not None: args = (gdb_path, "gdb", "-ex=run", "-ex=where", "-ex=quit", "--args") + args else: args = (lldb_path, "lldb", "-o", "run", "-o", "bt", "-o", "quit", "--") + args return args

tests/admin_scripts/tests.py

def test_commands_with_invalid_settings(self): args = ["startproject"] out, err = self.run_django_admin(args, settings_file="bad_settings") self.assertNoOutput(out)

Refs #33476 -- Reformatted code with Black.

test_commands_with_invalid_settings

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_commands_with_invalid_settings(self): args = ["startproject"] out, err = self.run_django_admin(args, settings_file="bad_settings") self.assertNoOutput(out) self.assertOutput(err, "You must provide a project name", regex=True)

python3.10.4/Lib/chunk.py

def read(self, size=-1): if self.closed: raise ValueError("I/O operation on closed file") if self.size_read >= self.chunksize:

add python 3.10.4 for windows

read

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

chunk.py

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

Python

def read(self, size=-1): if self.closed: raise ValueError("I/O operation on closed file") if self.size_read >= self.chunksize: return b'' if size < 0: size = self.chunksize - self.size_read if size > self.chunksize - self.size_read: size = self.chunksize - self.size_read data = self.file.read(size) self.size_read = self.size_read + len(data) if self.size_read == self.chunksize and \ self.align and \ (self.chunksize & 1): dummy = self.file.read(1) self.size_read = self.size_read + len(dummy) return data

dask/array/slicing.py

def slicing_plan(chunks, index): from dask.array.utils import asarray_safe if not is_arraylike(index): index = np.asanyarray(index) cum_chunks = cached_cumsum(chunks) cum_chunks = asarray_safe(cum_chunks, like=index) # this dispactches to the array library chunk_locations = np.searchsorted(cum_chunks, index, side="right") # bu

absolufy-imports - No relative - PEP8 (#8796) Conversation in https://github.com/dask/distributed/issues/5889

slicing_plan

cccb9d8d8e33a891396b1275c2448c352ef40c27

dask

slicing.py

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

Python

def slicing_plan(chunks, index): from dask.array.utils import asarray_safe if not is_arraylike(index): index = np.asanyarray(index) cum_chunks = cached_cumsum(chunks) cum_chunks = asarray_safe(cum_chunks, like=index) # this dispactches to the array library chunk_locations = np.searchsorted(cum_chunks, index, side="right") # but we need chunk_locations as python ints for getitem calls downstream chunk_locations = chunk_locations.tolist() where = np.where(np.diff(chunk_locations))[0] + 1 extra = asarray_safe([0], like=where) c_loc = asarray_safe([len(chunk_locations)], like=where) where = np.concatenate([extra, where, c_loc]) out = [] for i in range(len(where) - 1): sub_index = index[where[i] : where[i + 1]] chunk = chunk_locations[where[i]] if chunk > 0: sub_index = sub_index - cum_chunks[chunk - 1] out.append((chunk, sub_index)) return out

mmdet/models/roi_heads/bbox_heads/double_bbox_head.py

def _add_conv_branch(self) -> None: branch_convs = ModuleList() for i in range(self.num_convs): branch_convs.append( Bottleneck( inplanes=self.conv_out_channels, planes=self.conv_out_channels // 4, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg))

Refactor Double Head, MS, Dynamic, Trident.

_add_conv_branch

cd4e9ed8269b0c767e129169b7268b0ced7e60c9

mmdetection

double_bbox_head.py

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

Python

def _add_conv_branch(self) -> None: branch_convs = ModuleList() for i in range(self.num_convs): branch_convs.append( Bottleneck( inplanes=self.conv_out_channels, planes=self.conv_out_channels // 4, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)) return branch_convs

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

def match_files(patterns, files): all_files = files if isinstance(files, Collection) else list(files) return_files = set() for pattern in patterns: if pattern.include is not None: result_files = pattern.match(all_files) if pattern.include: return

[Bugfix] fix invalid excluding of Black (#24042) - We should use `--force-exclude` when we pass code path explicitly https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html?highlight=--force-exclude#command-line-options - Recover the files in `python/ray/_private/thirdparty` which has been formatted in the PR https://github.com/ray-project/ray/pull/21975 by mistake.

match_files

0e6c042e29cbbe429d81c9c1af3c75c261f00980

ray

util.py

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

Python

def match_files(patterns, files): all_files = files if isinstance(files, Collection) else list(files) return_files = set() for pattern in patterns: if pattern.include is not None: result_files = pattern.match(all_files) if pattern.include: return_files.update(result_files) else: return_files.difference_update(result_files) return return_files

pandas/core/exchange/column.py

def _get_offsets_buffer(self) -> Tuple[PandasBuffer, Any]: if self.dtype[0] == DtypeKind.STRING: # For each string, we need to manually determine the next offset values = self._col.to_numpy() ptr = 0 offsets = np.zeros(shape=(len(values) + 1,), dtype=np.int64) for i, v in enumerate(values): # For missing values (in this case, `np.nan` values) # we don't increment the pointer if isinstance(v, str): b = v.encode(encoding="utf-8") ptr += len(b) offsets[i + 1] = ptr # Convert the offsets to a Pandas "

ENH: Implement DataFrame interchange protocol (#46141)

_get_offsets_buffer

90140f055892a46f473bd26affab88a7f171e394

pandas

column.py

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

Python

def _get_offsets_buffer(self) -> Tuple[PandasBuffer, Any]: if self.dtype[0] == DtypeKind.STRING: # For each string, we need to manually determine the next offset values = self._col.to_numpy() ptr = 0 offsets = np.zeros(shape=(len(values) + 1,), dtype=np.int64) for i, v in enumerate(values): # For missing values (in this case, `np.nan` values) # we don't increment the pointer if isinstance(v, str): b = v.encode(encoding="utf-8") ptr += len(b) offsets[i + 1] = ptr # Convert the offsets to a Pandas "buffer" using # the NumPy array as the backing store buffer = PandasBuffer(offsets) # Assemble the buffer dtype info dtype = ( DtypeKind.INT, 64, ArrowCTypes.INT64, Endianness.NATIVE, ) # note: currently only support native endianness else: raise NoBufferPresent( "This column has a fixed-length dtype so " "it does not have an offsets buffer" ) return buffer, dtype

homeassistant/helpers/update_coordinator.py

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

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

_unschedule_refresh

8910d265d6cf15fed4e6e98b4344031019c1016d

core

update_coordinator.py

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

Python

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

tests/backends/postgresql/tests.py

def test_connect_and_rollback(self): new_connection = connection.copy() try: # Ensure the database default time zone is different than # the time zone in new_connection.settings_dict. We can # get the default time zone by reset & show. with new_connection.cursor() as cursor: cursor.execute("RESET TIMEZONE") cursor.execute("SHOW TIMEZONE") db_default_tz = cursor.fetchone()[0] new_tz = "Europe/Paris" if db_default_tz == "UTC" else "UTC" new_connection.close() # Invalidate timezone name cache, because the setting_changed # handler cannot know about

Refs #33476 -- Reformatted code with Black.

test_connect_and_rollback

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_connect_and_rollback(self): new_connection = connection.copy() try: # Ensure the database default time zone is different than # the time zone in new_connection.settings_dict. We can # get the default time zone by reset & show. with new_connection.cursor() as cursor: cursor.execute("RESET TIMEZONE") cursor.execute("SHOW TIMEZONE") db_default_tz = cursor.fetchone()[0] new_tz = "Europe/Paris" if db_default_tz == "UTC" else "UTC" new_connection.close() # Invalidate timezone name cache, because the setting_changed # handler cannot know about new_connection. del new_connection.timezone_name # Fetch a new connection with the new_tz as default # time zone, run a query and rollback. with self.settings(TIME_ZONE=new_tz): new_connection.set_autocommit(False) new_connection.rollback() # Now let's see if the rollback rolled back the SET TIME ZONE. with new_connection.cursor() as cursor: cursor.execute("SHOW TIMEZONE") tz = cursor.fetchone()[0] self.assertEqual(new_tz, tz) finally: new_connection.close()

python/ray/data/tests/test_dataset_tfrecords.py

def test_readback_tfrecords(ray_start_regular_shared, tmp_path): # The dataset we will write to a .tfrecords file. ds = ray.data.from_items( [ # Row one. { "int_item": 1, "int_list": [2, 2, 3], "float_item": 1.0, "float_list": [2.0, 3.0, 4.0], "bytes_item": b"abc", "bytes_list": [b"abc", b"1234"], }, # Row two. { "int_item": 2, "int_list": [3, 3, 4], "float_item": 2.0, "float_list": [2.0, 2.0, 3.0], "bytes_item": b"def", "bytes_list": [b"def", b"1234"], }, ] ) # Write the TFRecords. ds.write_tfrecords(tmp_path) # Read the TFRecords. readback_ds = ray.data.read_tfrecords(tmp_path) assert ds.take() == readback_ds.take()

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

test_readback_tfrecords

9fab504fe776f96fecf85e12ea006264cbe92f4a

ray

test_dataset_tfrecords.py

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

Python

def test_readback_tfrecords(ray_start_regular_shared, tmp_path): # The dataset we will write to a .tfrecords file. ds = ray.data.from_items( [ # Row one. { "int_item": 1, "int_list": [2, 2, 3], "float_item": 1.0, "float_list": [2.0, 3.0, 4.0], "bytes_item": b"abc", "bytes_list": [b"abc", b"1234"], }, # Row two. { "int_item": 2, "int_list": [3, 3, 4], "float_item": 2.0, "float_list": [2.0, 2.0, 3.0], "bytes_item": b"def", "bytes_list": [b"def", b"1234"], }, ] ) # Write the TFRecords. ds.write_tfrecords(tmp_path) # Read the TFRecords. readback_ds = ray.data.read_tfrecords(tmp_path) assert ds.take() == readback_ds.take()

keras/layers/rnn/legacy_cells.py

def call(self, inputs, state): cur_state_pos = 0 cur_inp = inputs new_states = [] for i, cell in enumerate(self._cells): with tf.compat.v1.variable_scope("cell_%d" % i): if self._state_is_tuple: if not tf.nest.is_nested(state): raise ValueError( "Expected state to be a tuple of length " f"{len(self.state_size)}" f", but received: {state}" ) cur_state = state[i] else: cur_state = tf.slice( state, [0, cur_state_pos], [-1, cell.state_size] )

Add f-string format and lint with flynt on the whole codebase

call

be73ac1a1e25d9abd4d793cba9707098d7adf231

keras

legacy_cells.py

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

Python

def call(self, inputs, state): cur_state_pos = 0 cur_inp = inputs new_states = [] for i, cell in enumerate(self._cells): with tf.compat.v1.variable_scope("cell_%d" % i): if self._state_is_tuple: if not tf.nest.is_nested(state): raise ValueError( "Expected state to be a tuple of length " f"{len(self.state_size)}" f", but received: {state}" ) cur_state = state[i] else: cur_state = tf.slice( state, [0, cur_state_pos], [-1, cell.state_size] ) cur_state_pos += cell.state_size cur_inp, new_state = cell(cur_inp, cur_state) new_states.append(new_state) new_states = ( tuple(new_states) if self._state_is_tuple else tf.concat(new_states, 1) ) return cur_inp, new_states

tests/pytests/functional/transport/server/test_req_channel.py

def test_basic(push_channel): msgs = [ {"foo": "bar"}, {"bar": "baz"}, {"baz": "qux", "list": [1, 2, 3]},

Fix minion unit tests, specifically .../tests/pytests/test_minion.py

test_basic

3c7e1ec1f08abd7cd1ba78ad7880acb6ba6fdce7

salt

test_req_channel.py

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

Python

def test_basic(push_channel): msgs = [ {"foo": "bar"}, {"bar": "baz"}, {"baz": "qux", "list": [1, 2, 3]}, ] for msg in msgs: ret = push_channel.send(msg, timeout=5, tries=1) assert ret["load"] == msg

tests/module_api/test_account_data_manager.py

def test_put_global(self) -> None: self.get_success( self._module_api.account_data_manager.put_global( self.user_id, "test.data", {"wombat": True} ) ) # Request that account data from the normal store; check it's as we expect. self.assertEqual( self.get_success( self._store.get_globa

Add Module API for reading and writing global account data. (#12391)

test_put_global

85ca963c1add5ca12f59238a50dfc63df4846bb7

synapse

test_account_data_manager.py

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

Python

def test_put_global(self) -> None: self.get_success( self._module_api.account_data_manager.put_global( self.user_id, "test.data", {"wombat": True} ) ) # Request that account data from the normal store; check it's as we expect. self.assertEqual( self.get_success( self._store.get_global_account_data_by_type_for_user( self.user_id, "test.data" ) ), {"wombat": True}, )

build_tools/azure/update_environments_and_lock_files.py

def get_conda_environment_content(build_metadata): template = environment.from_string(

CI: move Linux and MacOS Azure builds to conda lock files (#22448) Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]>

get_conda_environment_content

f862129f36786acbae3d9f2d161bbb72d77b87ec

scikit-learn

update_environments_and_lock_files.py

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

Python

def get_conda_environment_content(build_metadata): template = environment.from_string( .strip() ) return template.render(build_metadata=build_metadata)

saleor/thumbnail/utils.py

def preprocess(self, image, image_format): format = self.format or image_format save_kwargs = {"format": format} # Ensuring image is properly rotated if hasattr(image, "_getexif"): exif_datadict = image._getexif() # returns None if no EXIF data if exif_datadict is not None: exif = dict(exif_datadict.items()) orientation = exif.get(self.EXIF_ORIENTATION_KEY, None) if orientation == 3: image = image.transpose(Image.ROTATE_180)

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

preprocess

5d1a36b9aaf408016957db04f86397b2e53c2500

saleor

utils.py

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

Python

def preprocess(self, image, image_format): format = self.format or image_format save_kwargs = {"format": format} # Ensuring image is properly rotated if hasattr(image, "_getexif"): exif_datadict = image._getexif() # returns None if no EXIF data if exif_datadict is not None: exif = dict(exif_datadict.items()) orientation = exif.get(self.EXIF_ORIENTATION_KEY, None) if orientation == 3: image = image.transpose(Image.ROTATE_180) elif orientation == 6: image = image.transpose(Image.ROTATE_270) elif orientation == 8: image = image.transpose(Image.ROTATE_90) # Ensure any embedded ICC profile is preserved save_kwargs["icc_profile"] = image.info.get("icc_profile") if hasattr(self, "preprocess_%s" % format): image, addl_save_kwargs = getattr(self, "preprocess_%s" % format)( image=image ) save_kwargs.update(addl_save_kwargs) return image, save_kwargs

PyInstaller/utils/hooks/__init__.py

def get_package_paths(package): pkg_paths = get_all_package_paths(package) if not pkg_paths: raise ValueError(f"Package '{package}' does not exist or is not a package!") if len(pkg_paths) > 1: logger.warning( "get_package_paths - packa

hookutils: support multiple package paths in collect_* helpers Split the functionality of ``get_package_paths`` into two new helpers, ``get_all_package_paths`` and ``package_base_path``. The former obtains all package paths, while the latter simplifies removal of package-specific sub-path from the full package-path. Implement the old, backwards-compatible ``get_package_paths`` using these helpers; the function now supports namespace packages, but always returns a single package path and its base path. Have ``collect_submodules``, ``collect_dynamic_libs``, and ``collect_data_files`` helpers use the new ``get_all_package_paths`` and extend them to process all returned package paths. This enables proper support for PEP420 namespace packages with multiple package paths.

get_package_paths

e232aaf089d150b085502b97ce0fcf699b45e1b2

pyinstaller

__init__.py

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

Python

def get_package_paths(package): pkg_paths = get_all_package_paths(package) if not pkg_paths: raise ValueError(f"Package '{package}' does not exist or is not a package!") if len(pkg_paths) > 1: logger.warning( "get_package_paths - package %s has multiple paths (%r); returning only first one!", package, pkg_paths ) pkg_dir = pkg_paths[0] pkg_base = package_base_path(pkg_dir, package) return pkg_base, pkg_dir

erpnext/e_commerce/shopping_cart/cart.py

def get_address_territory(address_name): territory = None if address_name: address_fields = frappe.db.get_value("Address", address_name, ["city", "state", "country"]) for value in address_fields: territory = frappe.db.get_value("Territory", value) if territory: break return territory

style: format code with black

get_address_territory

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

cart.py

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

Python

def get_address_territory(address_name): territory = None if address_name: address_fields = frappe.db.get_value("Address", address_name, ["city", "state", "country"]) for value in address_fields: territory = frappe.db.get_value("Territory", value) if territory: break return territory

netbox/dcim/svg.py

def _get_device_coords(self, position, height): x = self.legend_width + RACK_ELEVATION_BORDER_WIDTH y = RACK_ELEVATION_BORDER_WIDTH if self.rack.desc_units: y += int((position - 1) * self.unit_height)

Clean up rack elevation rendering

_get_device_coords

0c915f7de9612c7485da3713cc6d63f368698a5d

netbox

svg.py

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

Python

def _get_device_coords(self, position, height): x = self.legend_width + RACK_ELEVATION_BORDER_WIDTH y = RACK_ELEVATION_BORDER_WIDTH if self.rack.desc_units: y += int((position - 1) * self.unit_height) else: y += int((self.rack.u_height - position + 1) * self.unit_height) - int(height * self.unit_height) return x, y

sympy/core/numbers.py

def igcd(*args): if len(args) < 2: raise TypeError( 'igcd() takes at least 2 arguments (%s given)' % len(args)) args_temp = [abs(as_int(i)) for i in args] if 1 in args_temp: return 1

Improved documentation formatting

igcd

cda8dfe6f45dc5ed394c2f5cda706cd6c729f713

sympy

numbers.py

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

Python

def igcd(*args): if len(args) < 2: raise TypeError( 'igcd() takes at least 2 arguments (%s given)' % len(args)) args_temp = [abs(as_int(i)) for i in args] if 1 in args_temp: return 1 a = args_temp.pop() if HAS_GMPY: # Using gmpy if present to speed up. for b in args_temp: a = gmpy.gcd(a, b) if b else a return as_int(a) for b in args_temp: a = math.gcd(a, b) return a igcd2 = math.gcd

qutebrowser/browser/webengine/webenginetab.py

def _prev_next_cb(self, found, *, going_up, callback): if found: result = browsertab.SearchNavigationResult.found # Check if the match count change is opposite to the search direction if self._old_match.current > 0: if not going_up and self._old_match.current > self.match.current: result = browsertab.SearchNavigationResult.wrapped_bottom elif going_up and self._old_match.current < self.m

search: Split navigation/search callbacks This way, we can move more logic (checking wrapping, etc.) into the API, thus making the commands much more simple and stateless.

_prev_next_cb

e15bda307e42c288b926f578e7bf8c610e4767af

qutebrowser

webenginetab.py

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

Python

def _prev_next_cb(self, found, *, going_up, callback): if found: result = browsertab.SearchNavigationResult.found # Check if the match count change is opposite to the search direction if self._old_match.current > 0: if not going_up and self._old_match.current > self.match.current: result = browsertab.SearchNavigationResult.wrapped_bottom elif going_up and self._old_match.current < self.match.current: result = browsertab.SearchNavigationResult.wrapped_top else: result = browsertab.SearchNavigationResult.not_found callback(result)

nuitka/plugins/standard/TensorflowPlugin.py

def onModuleSourceCode(self, module_name, source_code): if module_name != "tensorflow": return source_code source_lines = source_code.splitlines() found_insert = False for i, l in enumerate(source_lines): if l.startswith("def ") and "_running_from_pip_package():" in l: source_lines.insert(i, "_site_packages_dirs = []") source_lines.insert(i, "from tensorflow.python import keras") found_insert = True break if found_insert is True: self.info("Patched 'running-from-pip' path magic.") else: self.sysexit("Did not find 'running-from-pip' path magic code.")

Plugins: Slight more helpful error message in case tensorflow works

onModuleSourceCode

ab7014c6457b2b65010aea41512ca75d93847c9a

Nuitka

TensorflowPlugin.py

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

Python

def onModuleSourceCode(self, module_name, source_code): if module_name != "tensorflow": return source_code source_lines = source_code.splitlines() found_insert = False for i, l in enumerate(source_lines): if l.startswith("def ") and "_running_from_pip_package():" in l: source_lines.insert(i, "_site_packages_dirs = []") source_lines.insert(i, "from tensorflow.python import keras") found_insert = True break if found_insert is True: self.info("Patched 'running-from-pip' path magic.") else: self.sysexit("Did not find 'running-from-pip' path magic code.") return "\n".join(source_lines)

pandas/core/common.py

def cast_scalar_indexer(val): # assumes lib.is_scalar(val) if lib.is_float(val) and val.is_integer(): raise IndexError( # GH#34193 "Indexing with a float is no lon

DEPR: indexing (#49412) * DEPR: disallow Series.__getitem__ with a single-element list containing slice * DEPR: disallow slicing with positional slicer and .loc * DEPR: disallow positional indexing with float key * move whatsnew * DEPR: disallow multi-dimensional indexing * fix matplotlib tests * update install.rst

cast_scalar_indexer

9820edc174730e11cb423d7869650c13100eb314

pandas

common.py

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

Python

def cast_scalar_indexer(val): # assumes lib.is_scalar(val) if lib.is_float(val) and val.is_integer(): raise IndexError( # GH#34193 "Indexing with a float is no longer supported. Manually convert " "to an integer key instead." ) return val

test/lib/ansible_test/_internal/util.py

def load_plugins(base_type, database): # type: (t.Type[C], t.Dict[str, t.Type[C]]) -> None plugins: t.

ansible-test - Convert more type hints. (#78449) * Simple regex replace of multi-line function arg annotations. * Simple regex replace of multi-line function arg annotations with default values. * Simple regex replace of multi-line function arg return annotations. * Simple regex replace of assignment annotations.

load_plugins

b993b5cd49662f715774c333ce98e2845227ab66

ansible

util.py

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

Python

def load_plugins(base_type, database): # type: (t.Type[C], t.Dict[str, t.Type[C]]) -> None plugins: t.Dict[str, t.Type[C]] = dict((sc.__module__.rsplit('.', 1)[1], sc) for sc in get_subclasses(base_type)) for plugin in plugins: database[plugin] = plugins[plugin]

sympy/solvers/ode/nonhomogeneous.py

def _undetermined_coefficients_match(expr, x, func=None, eq_homogeneous=S.Zero): r a = Wild('a', exclude=[x]) b = W

Fix various typos Found via `codespell -q 3 -L aboves,aline,ans,aother,arithmetics,assum,atleast,braket,clen,declar,declars,dorder,dum,enew,fo,fro,inout,iself,ist,ket,lamda,lightyear,lightyears,nd,numer,numers,orderd,ot,pring,rcall,rever,ro,ser,siz,splitted,sring,supercedes,te,tht,unequality,upto,vas,versin,whet`

_undetermined_coefficients_match

24f1e7730119fe958cc8e28411f790c9a5ec04eb

sympy

nonhomogeneous.py

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

Python

def _undetermined_coefficients_match(expr, x, func=None, eq_homogeneous=S.Zero): r a = Wild('a', exclude=[x]) b = Wild('b', exclude=[x]) expr = powsimp(expr, combine='exp') # exp(x)*exp(2*x + 1) => exp(3*x + 1) retdict = {}

lib/gui/analysis/stats.py

def _remove_raw(self) -> None: if "raw" in self._selections: return logger.debug("Removing Raw Data from output") for key in list(self._stats.keys()): if key.startswith("raw"):

Bugfixes: - Stats graph - Handle NaNs in data - logger - de-elevate matplotlib font messages

_remove_raw

afec52309326304f4323029039e49bfcf928ef43

faceswap

stats.py

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

Python

def _remove_raw(self) -> None: if "raw" in self._selections: return logger.debug("Removing Raw Data from output") for key in list(self._stats.keys()): if key.startswith("raw"): del self._stats[key] logger.debug("Removed Raw Data from output")

keras/applications/efficientnet_weight_update_util.py

def get_keras_blocks(keras_weight_names): # example: 'block1a_dwconv/depthwise_kernel:0' -> 'block1a' keras_blocks = {x.split("_")[0] for x in keras_weight_names if "block" in x} return sorted(keras_blocks)

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

get_keras_blocks

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

efficientnet_weight_update_util.py

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

Python

def get_keras_blocks(keras_weight_names): # example: 'block1a_dwconv/depthwise_kernel:0' -> 'block1a' keras_blocks = {x.split("_")[0] for x in keras_weight_names if "block" in x} return sorted(keras_blocks)

erpnext/accounts/party.py

def get_dashboard_info(party_type, party, loyalty_program=None): current_fiscal_year = get_fiscal_year(nowdate(), as_dict=True) doctype = "Sales Invoice" if party_type == "Customer" else "Purchase Invoice" companies = frappe.get_all( doctype, filters={"docstatus": 1, party_type.lower(): party}, distinct=1, fields=["company"] ) company_wise_info = [] company_wise_grand_total = frappe.get_all( doctype, filters={ "docstatus": 1, party_type.lower(): party, "posting_date": ( "between", [current_fiscal_year.year_start_date, current_fiscal_year.year_end_date], ), }, group_by="company", fields=[ "company", "sum(grand_total) as grand_total", "sum(base_grand_total) as base_grand_total", ], ) loyalty_point_details = [] if party_type == "Customer": loyalty_point_details = frappe._dict( frappe.get_all( "Loyalty Point Entry", filters={ "customer": party, "expiry_d

style: format code with black

get_dashboard_info

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

party.py

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

Python

def get_dashboard_info(party_type, party, loyalty_program=None): current_fiscal_year = get_fiscal_year(nowdate(), as_dict=True) doctype = "Sales Invoice" if party_type == "Customer" else "Purchase Invoice" companies = frappe.get_all( doctype, filters={"docstatus": 1, party_type.lower(): party}, distinct=1, fields=["company"] ) company_wise_info = [] company_wise_grand_total = frappe.get_all( doctype, filters={ "docstatus": 1, party_type.lower(): party, "posting_date": ( "between", [current_fiscal_year.year_start_date, current_fiscal_year.year_end_date], ), }, group_by="company", fields=[ "company", "sum(grand_total) as grand_total", "sum(base_grand_total) as base_grand_total", ], ) loyalty_point_details = [] if party_type == "Customer": loyalty_point_details = frappe._dict( frappe.get_all( "Loyalty Point Entry", filters={ "customer": party, "expiry_date": (">=", getdate()), }, group_by="company", fields=["company", "sum(loyalty_points) as loyalty_points"], as_list=1, ) ) company_wise_billing_this_year = frappe._dict() for d in company_wise_grand_total: company_wise_billing_this_year.setdefault( d.company, {"grand_total": d.grand_total, "base_grand_total": d.base_grand_total} ) company_wise_total_unpaid = frappe._dict( frappe.db.sql( , (party_type, party), ) ) for d in companies: company_default_currency = frappe.db.get_value("Company", d.company, "default_currency") party_account_currency = get_party_account_currency(party_type, party, d.company) if party_account_currency == company_default_currency: billing_this_year = flt( company_wise_billing_this_year.get(d.company, {}).get("base_grand_total") ) else: billing_this_year = flt(company_wise_billing_this_year.get(d.company, {}).get("grand_total")) total_unpaid = flt(company_wise_total_unpaid.get(d.company)) if loyalty_point_details: loyalty_points = loyalty_point_details.get(d.company) info = {} info["billing_this_year"] = flt(billing_this_year) if billing_this_year else 0 info["currency"] = party_account_currency info["total_unpaid"] = flt(total_unpaid) if total_unpaid else 0 info["company"] = d.company if party_type == "Customer" and loyalty_point_details: info["loyalty_points"] = loyalty_points if party_type == "Supplier": info["total_unpaid"] = -1 * info["total_unpaid"] company_wise_info.append(info) return company_wise_info

gamestonk_terminal/portfolio/brokers/robinhood/robinhood_controller.py

def print_help(self): help_text = console.print(text=

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

print_help

82747072c511beb1b2672846ae2ee4aec53eb562

OpenBBTerminal

robinhood_controller.py

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

Python

def print_help(self): help_text = console.print(text=help_text, menu="Portfolio - Brokers - Robinhood")

wagtail/admin/tests/test_edit_handlers.py

def test_page_with_inline_model_with_tabbed_panel_only(self): EventPageSpeaker.settings_panels = [ FieldPanel("first_name"), FieldPanel("last_name"), ] warning = checks.Warning( "EventPageSpeaker.settings_panels will have no effect on InlinePanel model editing", hint=, obj=EventPageSpeaker, id=self.warning_id, ) checks_results = self.get_chec

Reformat with black

test_page_with_inline_model_with_tabbed_panel_only

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_edit_handlers.py

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

Python

def test_page_with_inline_model_with_tabbed_panel_only(self): EventPageSpeaker.settings_panels = [ FieldPanel("first_name"), FieldPanel("last_name"), ] warning = checks.Warning( "EventPageSpeaker.settings_panels will have no effect on InlinePanel model editing", hint=, obj=EventPageSpeaker, id=self.warning_id, ) checks_results = self.get_checks_result() self.assertIn(warning, checks_results) delattr(EventPageSpeaker, "settings_panels")

@dataclass

src/accelerate/utils/dataclasses.py

def to_kwargs(self): default_dict = self.__class__().to_dict() this_dict = self.to_dict()

Refactor utils into its own module (#340) Co-authored-by: Sylvain Gugger <[email protected]>

to_kwargs

02e2ed567be0e6d54b65884265a14873c3a30b2a

accelerate

dataclasses.py

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

Python

def to_kwargs(self): default_dict = self.__class__().to_dict() this_dict = self.to_dict() return {k: v for k, v in this_dict.items() if default_dict[k] != v} @dataclass

src/documents/tasks.py

def update_document_archive_file(document_id): document = Document.objects.get(id=document_id) mime_type = document.mime_type parser_class: Type[DocumentParser] = get_parser_class_for_mime_type(mime_type) if not parser_class: logger.error( f"No parser found for mime type {mime_type}, cannot " f"archive document {document} (ID: {document_id})", ) return parser: DocumentParser = parser_class(logging_group=uuid.uuid4()) try: parser.parse(document.source_path, mime_type, document.get_public_filename()) thumbnail = parser.get_thumbnail( document.source_path, mime_type, document.get_public_filename(), ) if parser.get_archive_path(): with transaction.atomic(): with open(parser.get_archive_path(), "rb") as f: checksum = hashlib.md5(f.read()).hexdigest() # I'm going to save first so that in case the file move # fails, the database is rolled back. # We also don't use save() since that triggers the filehandling # logic, and we don't want that yet (file not yet in place) document.archive_filename = generate_unique_filename(

Implements a better re-do of OCR by making the document archiver function common. Actually creates updated file now

update_document_archive_file

ab761e837c4be4974f699c8c97560a4291a8d298

paperless-ngx

tasks.py

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

Python

def update_document_archive_file(document_id): document = Document.objects.get(id=document_id) mime_type = document.mime_type parser_class: Type[DocumentParser] = get_parser_class_for_mime_type(mime_type) if not parser_class: logger.error( f"No parser found for mime type {mime_type}, cannot " f"archive document {document} (ID: {document_id})", ) return parser: DocumentParser = parser_class(logging_group=uuid.uuid4()) try: parser.parse(document.source_path, mime_type, document.get_public_filename()) thumbnail = parser.get_thumbnail( document.source_path, mime_type, document.get_public_filename(), ) if parser.get_archive_path(): with transaction.atomic(): with open(parser.get_archive_path(), "rb") as f: checksum = hashlib.md5(f.read()).hexdigest() # I'm going to save first so that in case the file move # fails, the database is rolled back. # We also don't use save() since that triggers the filehandling # logic, and we don't want that yet (file not yet in place) document.archive_filename = generate_unique_filename( document, archive_filename=True, ) Document.objects.filter(pk=document.pk).update( archive_checksum=checksum, content=parser.get_text(), archive_filename=document.archive_filename, ) with FileLock(settings.MEDIA_LOCK): create_source_path_directory(document.archive_path) shutil.move(parser.get_archive_path(), document.archive_path) shutil.move(thumbnail, document.thumbnail_path) with index.open_index_writer() as writer: index.update_document(writer, document) except Exception: logger.exception( f"Error while parsing document {document} " f"(ID: {document_id})", ) finally: parser.cleanup()

erpnext/patches/v12_0/move_credit_limit_to_customer_credit_limit.py

def execute(): frappe.reload_doc("Selling", "doctype", "Customer Credit Limit") frappe.reload_doc("Selling", "doctype", "Customer") frappe.reload_doc("Setup", "doctype", "Customer Group

style: format code with black

execute

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

move_credit_limit_to_customer_credit_limit.py

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

Python

def execute(): frappe.reload_doc("Selling", "doctype", "Customer Credit Limit") frappe.reload_doc("Selling", "doctype", "Customer") frappe.reload_doc("Setup", "doctype", "Customer Group") if frappe.db.a_row_exists("Customer Credit Limit"): return move_credit_limit_to_child_table()

scripts/rest_client.py

def forceexit(self, tradeid, ordertype=None, amount=None): return self._post("forceexit", data={ "tradeid": tradeid, "ordertype": ordertype, "amount": amount,

Accept parameters to forceexit

forceexit

82aecc81f393e98b86115e9bdfa46dac1a143fad

freqtrade

rest_client.py

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

Python

def forceexit(self, tradeid, ordertype=None, amount=None): return self._post("forceexit", data={ "tradeid": tradeid, "ordertype": ordertype, "amount": amount, })

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

def _abi3_applies(python_version): # type: (PythonVersion) -> bool return len(python_version) > 1 and tu

upd; format

_abi3_applies

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

tags.py

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

Python

def _abi3_applies(python_version): # type: (PythonVersion) -> bool return len(python_version) > 1 and tuple(python_version) >= (3, 2)

keras/backend.py

def variable(value, dtype=None, name=None, constraint=None): if dtype is None: dtype = floatx() if hasattr(value, "tocoo"): sparse_coo = value.tocoo() indices = np.concatenate( ( np.expand_dims(sparse_coo.row, 1), np.expand_dims(sparse_coo.col, 1), ), 1, ) v = tf.SparseTensor( indices=indices, values=sparse_coo.data, dense_shape=sparse_coo.shape, ) v._keras_shape = sparse_coo.shape return v v = tf.Variable( value, dtype=tf.as_dtype(dtype), name=name, constraint=constraint ) if isinstance(value, np.ndarray):

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

variable

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

backend.py

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

Python

def variable(value, dtype=None, name=None, constraint=None): if dtype is None: dtype = floatx() if hasattr(value, "tocoo"): sparse_coo = value.tocoo() indices = np.concatenate( ( np.expand_dims(sparse_coo.row, 1), np.expand_dims(sparse_coo.col, 1), ), 1, ) v = tf.SparseTensor( indices=indices, values=sparse_coo.data, dense_shape=sparse_coo.shape, ) v._keras_shape = sparse_coo.shape return v v = tf.Variable( value, dtype=tf.as_dtype(dtype), name=name, constraint=constraint ) if isinstance(value, np.ndarray): v._keras_shape = value.shape elif hasattr(value, "shape"): v._keras_shape = int_shape(value) track_variable(v) return v

openbb_terminal/stocks/dark_pool_shorts/ibkr_model.py

def get_cost_to_borrow() -> pd.DataFrame: ftp = ftplib.FTP("ftp3.interactivebrokers.com", "shortstock") flo = BytesIO() ftp.retrbinary("RETR usa.txt", flo.write) flo.seek(0) data = pd.read_csv(flo, sep="|", skiprows=1) data = data[["#SYM", "FEERATE", "AVAILABLE"]] data["AVAILABLE"] = data["AVAILABLE"].replace(">10000000", 10000000) data.fillna(0, inplace=True) data["AVAILABLE"] = data["AVAILABLE"].astype(int) data.sort_values(by=["FEERATE"], ascending=False, inplace=True) data["FEERATE"] = data["FEERATE"].apply(lambda x: str(x) + "%") data.columns = ["Symbol", "Fees", "Available"] return dat

Add cost to borrow of stocks. Data from IBKR (#1663) * add ctb to dps * add test for ctb * reformat using black * fix tests for ctb Co-authored-by: didierlopes.eth <[email protected]> Co-authored-by: jmaslek <[email protected]>

get_cost_to_borrow

73187d9e17a4838fc6ec583bcfcab593e06508cf

OpenBBTerminal

ibkr_model.py

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

Python

def get_cost_to_borrow() -> pd.DataFrame: ftp = ftplib.FTP("ftp3.interactivebrokers.com", "shortstock") flo = BytesIO() ftp.retrbinary("RETR usa.txt", flo.write) flo.seek(0) data = pd.read_csv(flo, sep="|", skiprows=1) data = data[["#SYM", "FEERATE", "AVAILABLE"]] data["AVAILABLE"] = data["AVAILABLE"].replace(">10000000", 10000000) data.fillna(0, inplace=True) data["AVAILABLE"] = data["AVAILABLE"].astype(int) data.sort_values(by=["FEERATE"], ascending=False, inplace=True) data["FEERATE"] = data["FEERATE"].apply(lambda x: str(x) + "%") data.columns = ["Symbol", "Fees", "Available"] return data

airflow/jobs/local_task_job.py

def _enable_task_listeners(): if get_listener_manager()

Add Listener Plugin API that tracks TaskInstance state changes (#20443) This adds new Plugin API - "listeners". It enables plugin authors to write [pluggy hook implementation][1] that will be called on certain formalized extension points. To differentiate between current Airflow extension points, like plugins, and current Airflow hooks, implementations of those hooks are called listeners. The API is ment to be called across all dags, and all operators - in contrast to current on_success_callback, pre_execute and related family which are meant to provide callbacks for particular dag authors, or operator creators. pluggy mechanism enables us to execute multiple, or none, listeners that implement particular extension point, so that users can use multiple listeners seamlessly. In this PR, three such extension points are added. When TaskInstance's state is changed to RUNNING, on_task_instance_running hook is called. On change toSUCCESS on_task_instance_success is called, similarly on FAILED on_task_instance_failed is called. Actual notification mechanism is be implemented using [SQLAlchemy’s events mechanism][2]. This ensures that plugins will get every change of state, regardless of where in the codebase it happened, and not require manual annotation of TI state changes across the codebase. To make sure that this change is not affecting performance, running this mechanism on scheduler is disabled by default. The SQLAlchemy event mechanism is also not affected by default - the event listener is only added if we have any plugin which actually provides any listener. [1]: https://pluggy.readthedocs.io/en/stable/ [2]: https://docs.sqlalchemy.org/en/13/orm/session_events.html#after-flush Signed-off-by: Maciej Obuchowski <[email protected]>

_enable_task_listeners

dba00ce6a32b7f50153887c6974f62985ca8023f

airflow

local_task_job.py

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

Python

def _enable_task_listeners(): if get_listener_manager().has_listeners: register_task_instance_state_events()

python/ray/data/impl/plan.py

def has_computed_output(self) -> bool: return self._snapshot_blocks is not N

[Datasets] [Out-of-Band Serialization: 2/3] Refactor `ExecutionPlan` to maintain complete lineage and eagerly unlink block references. (#23931) This PR refactors ExecutionPlan to maintain complete stage lineage, even for eagerly computed datasets, while ensuring that block references are unlinked as early as possible in order to more eagerly release block memory. This PR is the final precursor to adding the actual out-of-band serialization APIs (PR 3/3). The fully lineage has to be maintained, even for eagerly computed datasets, since the lineage is needed for out-of-band serialization of datasets.

has_computed_output

9ee24530abf1b5e3239869b5257dd7b678337b90

ray

plan.py

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

Python

def has_computed_output(self) -> bool: return self._snapshot_blocks is not None and not self._stages_after_snapshot

erpnext/accounts/report/sales_register/sales_register.py

def get_conditions(filters): conditions = "" accounting_dimensions = get_accounting_dimensions(as_list=False) or [] accounting_dimensions_list = [d.fieldname for d in accounting_dimensions] if filters.get("company"): conditions += " and company=%(company)s" if filters.get("customer") and "customer" not in accounting_dimensions_list: conditions += " and customer = %(customer)s" if filters.get("from_date"): conditions += " and posting_date >= %(from_date)s" if filters.get("to_date"): conditions += " and posting_date <= %(to_date)s" if filters.get("owner"): conditions += " and owner = %(owner)s" def get_sales_invoice_item_field_condition(field, table="Sales Invoice Item") -> str: if not filters.get(field) or field in accounting_dimensions_list: return "" return f conditions += get_sales_invoice_item_field_condition("mode_of_payments", "Sales Invoice Payment") conditions += get_sales_invoice_item_field_condition("cost_center") conditions += get_sales_invoice_item_field_condition("warehouse") conditions += get_sales_invoice_item_field_condition("brand") conditions += get_sales_invoice_item_field_condition("item_group") if accounting_dimensions: common_condition = for dimension in accounting_dimensions: if filters.get(dimension.fieldname): if frappe.get_cached_value("DocType", dimension.document_type, "is_tree"): filters[dimension.fieldname] = get_dimension_with_children( dimension.document_type, filters.get(dimension.fieldname) ) conditions += ( common_condition + "and ifnull(`tabSales Invoice Item`.{0}, '') in %({0})s)".format(dimension.fieldname) ) else: conditions += ( common_condition + "and ifnull(`tabSales Invoice Item`.{0}, '') in %({0})s)".format(dim

fix(Sales Register): incorrect query with dimensions If accounting dimension is also part of the default filters then same query is repeated with incorrect syntax. e.g. `item_group = (child1, child2)` instead of `in` query. fix: don't add default filter if they are part of dimensions to be added.

get_conditions

c3219ebad1cac35afc04cc051c9e215c70cd1e9b

erpnext

sales_register.py

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

Python

def get_conditions(filters): conditions = "" accounting_dimensions = get_accounting_dimensions(as_list=False) or [] accounting_dimensions_list = [d.fieldname for d in accounting_dimensions] if filters.get("company"): conditions += " and company=%(company)s" if filters.get("customer") and "customer" not in accounting_dimensions_list: conditions += " and customer = %(customer)s" if filters.get("from_date"): conditions += " and posting_date >= %(from_date)s" if filters.get("to_date"): conditions += " and posting_date <= %(to_date)s" if filters.get("owner"): conditions += " and owner = %(owner)s" def get_sales_invoice_item_field_condition(field, table="Sales Invoice Item") -> str: if not filters.get(field) or field in accounting_dimensions_list: return "" return f conditions += get_sales_invoice_item_field_condition("mode_of_payments", "Sales Invoice Payment") conditions += get_sales_invoice_item_field_condition("cost_center") conditions += get_sales_invoice_item_field_condition("warehouse") conditions += get_sales_invoice_item_field_condition("brand") conditions += get_sales_invoice_item_field_condition("item_group") if accounting_dimensions: common_condition = for dimension in accounting_dimensions: if filters.get(dimension.fieldname): if frappe.get_cached_value("DocType", dimension.document_type, "is_tree"): filters[dimension.fieldname] = get_dimension_with_children( dimension.document_type, filters.get(dimension.fieldname) ) conditions += ( common_condition + "and ifnull(`tabSales Invoice Item`.{0}, '') in %({0})s)".format(dimension.fieldname) ) else: conditions += ( common_condition + "and ifnull(`tabSales Invoice Item`.{0}, '') in %({0})s)".format(dimension.fieldname) ) return conditions

mlflow/sklearn/utils.py

def _get_classifier_artifacts(fitted_estimator, prefix, X, y_true, sample_weight): import sklearn if

Improve confusion matrix plot (#5273) * update Signed-off-by: Weichen Xu <[email protected]> * fix Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]>

_get_classifier_artifacts

847eb6b22d03f0cffef945996cf835272870435a

mlflow

utils.py

https://github.com/mlflow/mlflow.git

Python

def _get_classifier_artifacts(fitted_estimator, prefix, X, y_true, sample_weight): import sklearn if not _is_plotting_supported(): return []

@py_random_state(3)

networkx/algorithms/swap.py

def double_edge_swap(G, nswap=1, max_tries=100, seed=None): if G.is_directed(): raise nx.NetworkXError( "double_edge_swap() not defined for directed graphs. Use direc

Implement directed edge swap (#5663) * Add tests for directed edge swap * Add directed edge swap algorithm * Allow more swaps in directed tests * Fix errors in swap.py to meet test criteria * Remove TODOs * Update documentation for directed_edge_swap and run black * Fix incosistent spacing * Add references * Added PR to release docs * Fix reference formatting * Improve documentation * An -> A * Update networkx/algorithms/swap.py Co-authored-by: Ross Barnowski <[email protected]> * Add 'Raises' section to documentation * Update tests to use keyword arguments for directed_edge_swap * Fix references to 'triple-edge' swap * Use not_implemented_for decorator for directed_edge_swap * Rename n to tries and remove unnecessary updates * Rename e to msg * Use 'succ' instead of 'out_edges' for getting successors * Update networkx/algorithms/swap.py Co-authored-by: Dan Schult <[email protected]> * Update networkx/algorithms/tests/test_swap.py Co-authored-by: Dan Schult <[email protected]> * Update networkx/algorithms/tests/test_swap.py Co-authored-by: Dan Schult <[email protected]> * Update networkx/algorithms/tests/test_swap.py Co-authored-by: Dan Schult <[email protected]> * Update networkx/algorithms/swap.py Co-authored-by: Dan Schult <[email protected]> * Convert gnp graphs to path graphs for clarity * Use seed when testing directed edge swap * Check node equality sooner * Add directed_edge_swap to documentation Co-authored-by: Ross Barnowski <[email protected]> Co-authored-by: Dan Schult <[email protected]>

double_edge_swap

7d910e7184abd385c929f789b0c935ab143fc932

networkx

swap.py

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

Python

def double_edge_swap(G, nswap=1, max_tries=100, seed=None): if G.is_directed(): raise nx.NetworkXError( "double_edge_swap() not defined for directed graphs. Use directed_edge_swap instead." ) if nswap > max_tries: raise nx.NetworkXError("Number of swaps > number of tries allowed.") if len(G) < 4: raise nx.NetworkXError("Graph has less than four nodes.") # Instead of choosing uniformly at random from a generated edge list, # this algorithm chooses nonuniformly from the set of nodes with # probability weighted by degree. n = 0 swapcount = 0 keys, degrees = zip(*G.degree()) # keys, degree cdf = nx.utils.cumulative_distribution(degrees) # cdf of degree discrete_sequence = nx.utils.discrete_sequence while swapcount < nswap: # if random.random() < 0.5: continue # trick to avoid periodicities? # pick two random edges without creating edge list # choose source node indices from discrete distribution (ui, xi) = discrete_sequence(2, cdistribution=cdf, seed=seed) if ui == xi: continue # same source, skip u = keys[ui] # convert index to label x = keys[xi] # choose target uniformly from neighbors v = seed.choice(list(G[u])) y = seed.choice(list(G[x])) if v == y: continue # same target, skip if (x not in G[u]) and (y not in G[v]): # don't create parallel edges G.add_edge(u, x) G.add_edge(v, y) G.remove_edge(u, v) G.remove_edge(x, y) swapcount += 1 if n >= max_tries: e = ( f"Maximum number of swap attempts ({n}) exceeded " f"before desired swaps achieved ({nswap})." ) raise nx.NetworkXAlgorithmError(e) n += 1 return G @py_random_state(3)

rllib/offline/tests/test_dataset_reader.py

def test_dataset_shard_with_task_parallelization(self): config = { "input": "dataset", "input_config": { "format": "json", "paths": self.dset_path, "parallelism": 10, },

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

test_dataset_shard_with_task_parallelization

569fe0109629048d08e1d9e023f7769f10bd2244

ray

test_dataset_reader.py

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

Python

def test_dataset_shard_with_task_parallelization(self): config = { "input": "dataset", "input_config": { "format": "json", "paths": self.dset_path, "parallelism": 10, }, } NUM_WORKERS = 4 _, shards = get_dataset_and_shards(config, num_workers=NUM_WORKERS) assert len(shards) == NUM_WORKERS + 1 assert shards[0] is None assert all( isinstance(remote_shard, ray.data.Dataset) for remote_shard in shards[1:] )

lib/matplotlib/tests/test_compare_images.py

def test_image_comparison_expect_rms(im1, im2, tol, expect_rms): baseline_dir, result_dir = map(Path, _image_directories(lambda: "dummy")) # Copy both "baseline" a

Add uuid in im1 name

test_image_comparison_expect_rms

f3edc8771b7c292c5539e0e6444746b6ccefec04

matplotlib

test_compare_images.py

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

Python

def test_image_comparison_expect_rms(im1, im2, tol, expect_rms): baseline_dir, result_dir = map(Path, _image_directories(lambda: "dummy")) # Copy both "baseline" and "test" image to result_dir, so that 1) # compare_images writes the diff to result_dir, rather than to the source # tree and 2) the baseline image doesn't appear missing to triage_tests.py. uid = str(uuid.uuid4()) result_im1 = make_test_filename(result_dir / (uid + im1), "expected") shutil.copyfile(baseline_dir / im1, result_im1) result_im2 = result_dir / im1 shutil.copyfile(baseline_dir / im2, result_im2) results = compare_images( result_im1, result_im2, tol=tol, in_decorator=True) if expect_rms is None: assert results is None else: assert results is not None assert results['rms'] == approx(expect_rms, abs=1e-4)

test/jit/test_save_load.py

def test_versioned_symbols_reserialization(self): module_v2 = torch.jit.load(py

Revert D33198155: Bump version number to 7 and compile old operators with old schema Test Plan: revert-hammer Differential Revision: D33198155 (https://github.com/pytorch/pytorch/commit/d35fc409ad84c1a837e7e07ffe3f4e4942538e50) Original commit changeset: 38a1185f9ecb Original Phabricator Diff: D33198155 (https://github.com/pytorch/pytorch/commit/d35fc409ad84c1a837e7e07ffe3f4e4942538e50) fbshipit-source-id: 411aaeb4e047aad9202db50d4d0f2ff35bc51f9d

test_versioned_symbols_reserialization

0ece9a49d7d705b1a0cd4406d4f1c526d720e1f3

pytorch

test_save_load.py

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

Python

def test_versioned_symbols_reserialization(self): module_v2 = torch.jit.load(pytorch_test_dir + "/jit/fixtures/_test_serialization_subcmul_v2.pt") buffer = io.BytesIO() torch.jit.save(module_v2, buffer) buffer.seek(0) module_reserialized = torch.jit.load(buffer) subcmul_nodes = sum("subcmul" in n.kind() for n in module_reserialized.graph.nodes()) self.assertEqual(subcmul_nodes, 0)

tests/css/test_stylesheet.py

def test_stylesheet_apply_takes_final_rule_in_specificity_clash(): css = ".a {background: red; color: lime;} .b {background: blue;}" stylesheet = _make_stylesheet(css) node = DOMNode(classes="a b", id="c") stylesheet.apply(node) assert node.styles.color == Color(0, 255, 0) # color: lime assert node.styles.backg

Add various additional tests around CSS specificity

test_stylesheet_apply_takes_final_rule_in_specificity_clash

4dd0d9fae43583638f34257f97d5749ca4f2c00c

textual

test_stylesheet.py

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

Python

def test_stylesheet_apply_takes_final_rule_in_specificity_clash(): css = ".a {background: red; color: lime;} .b {background: blue;}" stylesheet = _make_stylesheet(css) node = DOMNode(classes="a b", id="c") stylesheet.apply(node) assert node.styles.color == Color(0, 255, 0) # color: lime assert node.styles.background == Color(0, 0, 255) # background: blue

airflow/migrations/versions/0106_909884dea523_update_migration_for_fab_tables_to_add_missing_constraints.py

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

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

upgrade

2f5a567977e1219cab16c2548825a1b9eba07ab3

airflow

0106_909884dea523_update_migration_for_fab_tables_to_add_missing_constraints.py

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

Python

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'])

mindsdb/migrations/versions/2022-02-09_27c5aca9e47e_test.py

def upgrade(): op.drop_table('ai_table') conn = op.get_bind() # views was created with unnamed fk. Therefore need recreate it op.create_table( 'view_tmp', sa.Column('id', sa.Integer(), nullable=False), sa.Column('name', sa.String(), nullable=False), sa.Column('company_id', sa.Integer(), nullable=True), sa.Column('query', sa.String(), nullable=False), sa.Column('integration_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['integration_id'], ['integration.id'], name='fk_integration_id'), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name', 'company_id', name='unique_name_company_id') ) conn.execute(text()) op.drop_table('view') op.rename_table('view_tmp', 'view') op.create_table( 'analysis', sa.Column('id', sa.Integer(), nullable=False), sa.Column('analysis', mindsdb.interfaces.storage.db.Json(), nullable=False), sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.PrimaryKeyConstraint('id') ) with op.batch_alter_table('datasource', schema=None) as batch_op: batch_op.add_column(sa.Column('analysis_id', sa.Integer(), nullable=True)) batch_op.create_foreign_key('fk_analysis_id', 'analysis', ['analysis_id'], ['id']) batch_op.add_column(sa.Column('ds_class', sa.String(), nullable=True)) session = sa.orm.Session(bind=conn) dsatasources = conn.execute('select id, analysis from datasource').fetchall() for row in dsatasources: if row['analysis'] is not None: # NOTE 'returning' is relatively new in sqlite, so better wi

migration

upgrade

2a39e0ab3c81f09a227c50c98a3fb7ee57ec8fac

mindsdb

2022-02-09_27c5aca9e47e_test.py

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

Python

def upgrade(): op.drop_table('ai_table') conn = op.get_bind() # views was created with unnamed fk. Therefore need recreate it op.create_table( 'view_tmp', sa.Column('id', sa.Integer(), nullable=False), sa.Column('name', sa.String(), nullable=False), sa.Column('company_id', sa.Integer(), nullable=True), sa.Column('query', sa.String(), nullable=False), sa.Column('integration_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['integration_id'], ['integration.id'], name='fk_integration_id'), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name', 'company_id', name='unique_name_company_id') ) conn.execute(text()) op.drop_table('view') op.rename_table('view_tmp', 'view') op.create_table( 'analysis', sa.Column('id', sa.Integer(), nullable=False), sa.Column('analysis', mindsdb.interfaces.storage.db.Json(), nullable=False), sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.PrimaryKeyConstraint('id') ) with op.batch_alter_table('datasource', schema=None) as batch_op: batch_op.add_column(sa.Column('analysis_id', sa.Integer(), nullable=True)) batch_op.create_foreign_key('fk_analysis_id', 'analysis', ['analysis_id'], ['id']) batch_op.add_column(sa.Column('ds_class', sa.String(), nullable=True)) session = sa.orm.Session(bind=conn) dsatasources = conn.execute('select id, analysis from datasource').fetchall() for row in dsatasources: if row['analysis'] is not None: # NOTE 'returning' is relatively new in sqlite, so better will be use select after insert. conn.execute( text(), { 'id': row['id'] } ) analysis_id = conn.execute(text()).fetchall() conn.execute( text(), { 'analysis_id': analysis_id[0][0], 'id': row['id'] } ) with op.batch_alter_table('datasource', schema=None) as batch_op: batch_op.drop_column('analysis') op.create_table( 'file', sa.Column('id', sa.Integer(), nullable=False), sa.Column('name', sa.String(), nullable=False), sa.Column('company_id', sa.Integer(), nullable=True), sa.Column('source_file_path', sa.String(), nullable=False), sa.Column('file_path', sa.String(), nullable=False), sa.Column('row_count', sa.Integer(), nullable=False), sa.Column('columns', mindsdb.interfaces.storage.db.Json(), nullable=False), # sa.Column('created_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp()), # ????? # sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp(), server_onupdate=sa.func.current_timestamp()), # ????? erver_default=func.now() # sa.Column('created_at', sa.DateTime(), nullable=True, server_default=datetime.datetime.now), # ????? # sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=datetime.datetime.now, server_onupdate=datetime.datetime.now), # ????? erver_default=func.now() sa.Column('created_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp()), # ????? sa.Column('updated_at', sa.DateTime(), nullable=True, server_default=sa.func.current_timestamp(), server_onupdate=sa.func.current_timestamp()), # ????? erver_default=func.now() sa.Column('analysis_id', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['analysis_id'], ['analysis.id'], name='fk_analysis_id'), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name', 'company_id', name='unique_name_company_id') ) # delete ds where data is none dsatasources = conn.execute(text('select * from datasource')).fetchall() for ds in dsatasources: if ds['data'] is None: conn.execute(text('delete from datasource where id = :id'), {'id': ds['id']}) continue ds_data = json.loads(ds['data']) creation_info = json.loads(ds['creation_info']) datasource_name = ds_data.get('source_type') if datasource_name == 'file': created_at = None if isinstance(ds['created_at'], str): created_at = datetime.datetime.fromisoformat(ds['created_at']) elif isinstance(ds['created_at'], [float, int]): created_at = datetime.fromtimestamp(ds['created_at']) updated_at = None if isinstance(ds['updated_at'], str): updated_at = datetime.datetime.fromisoformat(ds['updated_at']) elif isinstance(ds['updated_at'], [float, int]): updated_at = datetime.fromtimestamp(ds['updated_at']) file = mindsdb.interfaces.storage.db.File( name=ds['name'], company_id=ds['company_id'], source_file_path=ds_data['source'], file_path=creation_info['args'][0], row_count=ds_data['row_count'], columns=ds_data['columns'], created_at=created_at, updated_at=updated_at, analysis_id=ds['analysis_id'] ) session.add(file) conn.execute( text(), { 'datasource_name': datasource_name, 'company_id': ds['company_id'], 'ds_class': creation_info['class'], 'id': ds['id'] } ) session.commit() op.rename_table('datasource', 'dataset') with op.batch_alter_table('dataset', schema=None) as batch_op: batch_op.create_foreign_key('fk_integration_id', 'integration', ['integration_id'], ['id']) # NOTE two different 'batch' is necessary, in other way FK is not creating with op.batch_alter_table('predictor', schema=None) as batch_op: batch_op.alter_column('datasource_id', new_column_name='dataset_id') with op.batch_alter_table('predictor', schema=None) as batch_op: batch_op.create_foreign_key('fk_dataset_id', 'dataset', ['dataset_id'], ['id']) with op.batch_alter_table('predictor', schema=None) as batch_op: batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id']) with op.batch_alter_table('integration', schema=None) as batch_op: batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id']) with op.batch_alter_table('dataset', schema=None) as batch_op: batch_op.create_unique_constraint('unique_name_company_id', ['name', 'company_id'])

sklearn/cluster/_bisect_k_means.py

def _predict_recursive(self, X, sample_weight, cluster_node): if cluster_node.left is None: # This cluster has no subcluster. Labels are just the label of the cluster. return np.full(X.shape[0], cluster_node.label, dtype=np.int32) # Determine if data points belong to the left or right subc

MAINT Remove `x_squared_norms` arg from `k_means_lloyd` signature (#24264) Co-authored-by: Thomas J. Fan <[email protected]>

_predict_recursive

60f16feaadaca28f9a1cc68d2f406201860d27e8

scikit-learn

_bisect_k_means.py

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

Python

def _predict_recursive(self, X, sample_weight, cluster_node): if cluster_node.left is None: # This cluster has no subcluster. Labels are just the label of the cluster. return np.full(X.shape[0], cluster_node.label, dtype=np.int32) # Determine if data points belong to the left or right subcluster centers = np.vstack((cluster_node.left.center, cluster_node.right.center)) if hasattr(self, "_X_mean"): centers += self._X_mean cluster_labels = _labels_inertia_threadpool_limit( X, sample_weight, centers, self._n_threads, return_inertia=False, ) mask = cluster_labels == 0 # Compute the labels for each subset of the data points. labels = np.full(X.shape[0], -1, dtype=np.int32) labels[mask] = self._predict_recursive( X[mask], sample_weight[mask], cluster_node.left ) labels[~mask] = self._predict_recursive( X[~mask], sample_weight[~mask], cluster_node.right ) return labels

TTS/tts/models/vits.py

def inference_voice_conversion(self, reference_wav, speaker_id=None, d_vector=None, reference_speaker_id=None, reference_d_vector=None): # com

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

inference_voice_conversion

dbe9da7f15544b83043f481a99e5bcb23e002dc9

TTS

vits.py

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

Python

def inference_voice_conversion(self, reference_wav, speaker_id=None, d_vector=None, reference_speaker_id=None, reference_d_vector=None): # compute spectrograms y = wav_to_spec(reference_wav, self.config.audio.fft_size, self.config.audio.hop_length, self.config.audio.win_length, center=False).transpose(1, 2) y_lengths = torch.tensor([y.size(-1)]).to(y.device) speaker_cond_src = reference_speaker_id if reference_speaker_id is not None else reference_d_vector speaker_cond_tgt = speaker_id if speaker_id is not None else d_vector # print(y.shape, y_lengths.shape) wav, _, _ = self.voice_conversion(y, y_lengths, speaker_cond_src, speaker_cond_tgt) return wav

tests/test_routers.py

def test_nonconflicting_specified_basename(self): self.router.register(r'notes', NoteViewSet, basename='notes') self.router.register(r'notes_kwduplicate', KWa

raise ImproperlyConfigured exception if `basename` is not unique (#8438) * raise ImproperlyConfigured if basename already exists * rename already_registered function; return True/False * additional basename tests * additional basename tests * Update rest_framework/routers.py Co-authored-by: David Graves <[email protected]> Co-authored-by: Asif Saif Uddin <[email protected]>

test_nonconflicting_specified_basename

48a21aa0eb3a95d32456c2a927eff9552a04231e

django-rest-framework

test_routers.py

https://github.com/encode/django-rest-framework.git

Python

def test_nonconflicting_specified_basename(self): self.router.register(r'notes', NoteViewSet, basename='notes') self.router.register(r'notes_kwduplicate', KWargedNoteViewSet, basename='notes_kwduplicate') self.router.register(r'notes_duplicate', NoteViewSet, basename='notes_duplicate')

@pytest.mark.parametrize("config_count", [(CONFIG_ATTRIBUTES, 1)])

tests/components/group/test_cover.py

async def test_state(hass, setup_comp): state = hass.states.get(COVER_GROUP) # No entity has a valid state -> group state unknown assert state.state == STATE_UNKNOWN assert state.attributes[ATTR_FRIENDLY_NAME] == DEFAULT_NAME assert state.attributes[ATTR_ENTITY_ID] == [ DEMO_COVER, DEMO_COVER_POS, DEMO_COVER_TILT, DEMO_TILT, ] assert ATTR_ASSUMED_STATE not in state.attributes assert state.attributes[ATTR_SUPPORTED_FEATURES] == 0 assert ATTR_CURRENT_POSITION not in state.attributes assert ATTR_CURRENT_TILT_POSITION not in state.attributes # The group state is unknown if all group members are unknown or unavailable. for state_1 in (STATE_UNAVAILABLE, STATE_UNKNOWN): for state_2 in (STATE_UNAVAILABLE, STATE_UNKNOWN): for state_3 in (STATE_UNAVAILABLE, STATE_UNKNOWN): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_UNAVAILABLE, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_UNKNOWN for state_1 in (STATE_UNAVAILABLE, STATE_UNKNOWN): for state_2 in (STATE_UNAVAILABLE, STATE_UNKNOWN): for state_3 in (STATE_UNAVAILABLE, STATE_UNKNOWN): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_UNKNOWN, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_UNKNOWN # At least one member opening -> group opening for state_1 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_OPENING, STATE_UNAVAILABLE, STATE_UNKNOWN, ): for state_2 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_OPENING, STATE_UNAVAILABLE, STATE_UNKNOWN, ): for state_3 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_OPENING, STATE_UNAVAILABLE, STATE_UNKNOWN, ): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_OPENING, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_OPENING # At least one member closing -> group closing for state_1 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN, ): for state_2 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN, ): for state_3 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN, ): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_CLOSING, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_CLOSING # At least one member open -> group open for state_1 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN): for state_2 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN): for state_3 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAIL

Improve group tests (#73630)

test_state

9b8c3e37bbee3dbaa949705c7ae7b29f521988e7

core

test_cover.py

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

Python

async def test_state(hass, setup_comp): state = hass.states.get(COVER_GROUP) # No entity has a valid state -> group state unknown assert state.state == STATE_UNKNOWN assert state.attributes[ATTR_FRIENDLY_NAME] == DEFAULT_NAME assert state.attributes[ATTR_ENTITY_ID] == [ DEMO_COVER, DEMO_COVER_POS, DEMO_COVER_TILT, DEMO_TILT, ] assert ATTR_ASSUMED_STATE not in state.attributes assert state.attributes[ATTR_SUPPORTED_FEATURES] == 0 assert ATTR_CURRENT_POSITION not in state.attributes assert ATTR_CURRENT_TILT_POSITION not in state.attributes # The group state is unknown if all group members are unknown or unavailable. for state_1 in (STATE_UNAVAILABLE, STATE_UNKNOWN): for state_2 in (STATE_UNAVAILABLE, STATE_UNKNOWN): for state_3 in (STATE_UNAVAILABLE, STATE_UNKNOWN): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_UNAVAILABLE, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_UNKNOWN for state_1 in (STATE_UNAVAILABLE, STATE_UNKNOWN): for state_2 in (STATE_UNAVAILABLE, STATE_UNKNOWN): for state_3 in (STATE_UNAVAILABLE, STATE_UNKNOWN): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_UNKNOWN, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_UNKNOWN # At least one member opening -> group opening for state_1 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_OPENING, STATE_UNAVAILABLE, STATE_UNKNOWN, ): for state_2 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_OPENING, STATE_UNAVAILABLE, STATE_UNKNOWN, ): for state_3 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_OPENING, STATE_UNAVAILABLE, STATE_UNKNOWN, ): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_OPENING, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_OPENING # At least one member closing -> group closing for state_1 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN, ): for state_2 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN, ): for state_3 in ( STATE_CLOSED, STATE_CLOSING, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN, ): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_CLOSING, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_CLOSING # At least one member open -> group open for state_1 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN): for state_2 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN): for state_3 in (STATE_CLOSED, STATE_OPEN, STATE_UNAVAILABLE, STATE_UNKNOWN): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_OPEN, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_OPEN # At least one member closed -> group closed for state_1 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN): for state_2 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN): for state_3 in (STATE_CLOSED, STATE_UNAVAILABLE, STATE_UNKNOWN): hass.states.async_set(DEMO_COVER, state_1, {}) hass.states.async_set(DEMO_COVER_POS, state_2, {}) hass.states.async_set(DEMO_COVER_TILT, state_3, {}) hass.states.async_set(DEMO_TILT, STATE_CLOSED, {}) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_CLOSED # All group members removed from the state machine -> unknown hass.states.async_remove(DEMO_COVER) hass.states.async_remove(DEMO_COVER_POS) hass.states.async_remove(DEMO_COVER_TILT) hass.states.async_remove(DEMO_TILT) await hass.async_block_till_done() state = hass.states.get(COVER_GROUP) assert state.state == STATE_UNKNOWN @pytest.mark.parametrize("config_count", [(CONFIG_ATTRIBUTES, 1)])

lib/matplotlib/cbook/__init__.py

def connect(self, signal, func): if self._signals is not None: _api.check_in_list(self._signals, signal=signal) self._func_cid_map.setdefault(signal, {}) proxy = _weak_or_strong_ref(func, self._remove_proxy)

Remove miscellaneous deprecations from 3.5

connect

e199c3b819f66a56f49657de0a9b3fb60c745b94

matplotlib

__init__.py

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

Python

def connect(self, signal, func): if self._signals is not None: _api.check_in_list(self._signals, signal=signal) self._func_cid_map.setdefault(signal, {}) proxy = _weak_or_strong_ref(func, self._remove_proxy) if proxy in self._func_cid_map[signal]: return self._func_cid_map[signal][proxy] cid = next(self._cid_gen) self._func_cid_map[signal][proxy] = cid self.callbacks.setdefault(signal, {}) self.callbacks[signal][cid] = proxy return cid

erpnext/setup/setup_wizard/operations/taxes_setup.py

def get_or_create_account(company_name, account): default_root_type = "Liability" root_type = account.get("root_type", default_root_type) existing_accounts = frappe.get_all( "Account", filters={"company": company_name, "root_type": root_type}, or_filters={ "account_name": account.get("account_name"),

style: format code with black

get_or_create_account

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

taxes_setup.py

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

Python

def get_or_create_account(company_name, account): default_root_type = "Liability" root_type = account.get("root_type", default_root_type) existing_accounts = frappe.get_all( "Account", filters={"company": company_name, "root_type": root_type}, or_filters={ "account_name": account.get("account_name"), "account_number": account.get("account_number"), }, ) if existing_accounts: return frappe.get_doc("Account", existing_accounts[0].name) tax_group = get_or_create_tax_group(company_name, root_type) account["doctype"] = "Account" account["company"] = company_name account["parent_account"] = tax_group account["report_type"] = "Balance Sheet" account["account_type"] = "Tax" account["root_type"] = root_type account["is_group"] = 0 doc = frappe.get_doc(account) doc.flags.ignore_links = True doc.flags.ignore_validate = True doc.insert(ignore_permissions=True, ignore_mandatory=True) return doc

lib/gui/utils.py

def set_default_options(self) -> None: default = self.cli_opts.get_option_values() logger.debug(default) self.

Bugfix: Preview for extract in batch mode

set_default_options

dc18c74eea0c7837a820d27628cb12b0824fa30e

faceswap

utils.py

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

Python

def set_default_options(self) -> None: default = self.cli_opts.get_option_values() logger.debug(default) self._gui_objects.default_options = default self.project.set_default_options()

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

def _format_lines(self, tokensource):

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

_format_lines

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

html.py

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

Python

def _format_lines(self, tokensource): nocls = self.noclasses lsep = self.lineseparator tagsfile = self.tagsfile lspan = '' line = [] for ttype, value in tokensource: try: cspan = self.span_element_openers[ttype] except KeyError: title = ' title="%s"' % '.'.join(ttype) if self.debug_token_types else '' if nocls: css_style = self._get_css_inline_styles(ttype) if css_style: css_style = self.class2style[css_style][0] cspan = '<span style="%s"%s>' % (css_style, title) else: cspan = '' else: css_class = self._get_css_classes(ttype) if css_class: cspan = '<span class="%s"%s>' % (css_class, title) else: cspan = '' self.span_element_openers[ttype] = cspan parts = self._translate_parts(value) if tagsfile and ttype in Token.Name: filename, linenumber = self._lookup_ctag(value) if linenumber: base, filename = os.path.split(filename) if base: base += '/' filename, extension = os.path.splitext(filename) url = self.tagurlformat % {'path': base, 'fname': filename, 'fext': extension} parts[0] = "<a href=\"%s#%s-%d\">%s" % \ (url, self.lineanchors, linenumber, parts[0]) parts[-1] = parts[-1] + "</a>" # for all but the last line for part in parts[:-1]: if line: if lspan != cspan: line.extend(((lspan and '</span>'), cspan, part, (cspan and '</span>'), lsep)) else: # both are the same line.extend((part, (lspan and '</span>'), lsep)) yield 1, ''.join(line) line = [] elif part: yield 1, ''.join((cspan, part, (cspan and '</span>'), lsep)) else: yield 1, lsep # for the last line if line and parts[-1]: if lspan != cspan: line.extend(((lspan and '</span>'), cspan, parts[-1])) lspan = cspan else: line.append(parts[-1]) elif parts[-1]: line = [cspan, parts[-1]] lspan = cspan # else we neither have to open a new span nor set lspan if line: line.extend(((lspan and '</span>'), lsep)) yield 1, ''.join(line)

octavia-cli/octavia_cli/apply/resources.py

def _get_remote_resource(self) -> Optional[Union[SourceRead, DestinationRead, ConnectionRead]]: search_results = self._search().get(f"{self.resource_type}s", []) if len(search_results) > 1: raise DuplicateResourceError("Two or more ressources exist with the same name.") if len(search_results) == 1: return search_results[0] else: return None

🐙 octavia-cli: `apply` connections (#10881)

_get_remote_resource

56bf982cb96f831fe04f5e44a92ee4a669b9e16a

airbyte

resources.py

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

Python

def _get_remote_resource(self) -> Optional[Union[SourceRead, DestinationRead, ConnectionRead]]: search_results = self._search().get(f"{self.resource_type}s", []) if len(search_results) > 1: raise DuplicateResourceError("Two or more ressources exist with the same name.") if len(search_results) == 1: return search_results[0] else: return None

keras/engine/training.py

def _get_compile_args(self, user_metrics=True): self._assert_compile_was_called() # pylint: disable=protected-a

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

_get_compile_args

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training.py

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

Python

def _get_compile_args(self, user_metrics=True): self._assert_compile_was_called() # pylint: disable=protected-access saved_metrics = self.compiled_metrics._user_metrics saved_weighted_metrics = self.compiled_metrics._user_weighted_metrics if not user_metrics: if saved_metrics is not None: saved_metrics = self.compiled_metrics._metrics if saved_weighted_metrics is not None: saved_weighted_metrics = self.compiled_metrics._weighted_metrics compile_args = { "optimizer": self.optimizer, "loss": self.compiled_loss._user_losses, "metrics": saved_metrics, "weighted_metrics": saved_weighted_metrics, "loss_weights": self.compiled_loss._user_loss_weights, } # pylint: enable=protected-access return compile_args

numpy/f2py/tests/test_f2py2e.py

def test_norestexdoc(capfd, hello_world_f90, monkeypatch): ipath = Path(hello_world_f90) mname = "blah" monkeypatch.setattr(sys, "argv",

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.

test_norestexdoc

729ad4f92420231e2a7009b3223c6c7620b8b808

numpy

test_f2py2e.py

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

Python

def test_norestexdoc(capfd, hello_world_f90, monkeypatch): ipath = Path(hello_world_f90) mname = "blah" monkeypatch.setattr(sys, "argv", f'f2py -m {mname} {ipath} --no-rest-doc'.split()) with util.switchdir(ipath.parent): f2pycli() out, _ = capfd.readouterr() assert "ReST Documentation is saved to file" not in out

keras/optimizers/optimizer_v1.py

def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError( "Length of the specified weight list (" + str(len(weights)) + ") does not match the number of weights " "of the optimizer (" + str(len(params)) + ")" ) weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError( "Optimizer weight shape " +

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

set_weights

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

optimizer_v1.py

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

Python

def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError( "Length of the specified weight list (" + str(len(weights)) + ") does not match the number of weights " "of the optimizer (" + str(len(params)) + ")" ) weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError( "Optimizer weight shape " + str(pv.shape) + " not compatible with " "provided weight shape " + str(w.shape) ) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples)

keras/engine/training_utils_v1.py

def extract_tensors_from_dataset(dataset): iterator = get_iterator(dataset) inputs, targets, sample_weight = unpack_iterator_input(iterator) return inputs, targets, sample_weight

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

extract_tensors_from_dataset

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training_utils_v1.py

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

Python

def extract_tensors_from_dataset(dataset): iterator = get_iterator(dataset) inputs, targets, sample_weight = unpack_iterator_input(iterator) return inputs, targets, sample_weight

django/db/backends/base/base.py

def _set_autocommit(self, autocommit): raise NotImplementedError( "subclasses of BaseDatabaseWrapper

Refs #33476 -- Reformatted code with Black.

_set_autocommit

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

base.py

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

Python

def _set_autocommit(self, autocommit): raise NotImplementedError( "subclasses of BaseDatabaseWrapper may require a _set_autocommit() method" ) # ##### Generic transaction management methods #####

wagtail/admin/views/generic/multiple_upload.py

def get_edit_upload_form_context_data(self): edit_form_class = self.get_edit_form_class() return { self.context_upload_name: self.upload_object, "edit_action": reverse( self.edit_upload_url

Reformat with black

get_edit_upload_form_context_data

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

multiple_upload.py

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

Python

def get_edit_upload_form_context_data(self): edit_form_class = self.get_edit_form_class() return { self.context_upload_name: self.upload_object, "edit_action": reverse( self.edit_upload_url_name, args=(self.upload_object.id,) ), "delete_action": reverse( self.delete_upload_url_name, args=(self.upload_object.id,) ), "form": edit_form_class( instance=self.object, prefix="%s-%d" % (self.edit_upload_form_prefix, self.upload_object.id), user=self.request.user, ), }

tests/admin_views/tests.py

def test_overriding_has_module_permission(self): articles = Article._meta.verbose_name_plural.title() sections = Section._meta.verbose_name_plural.title() index_url = reverse("admin7:index") self.client.force_login(self.superuser) response = self.client.get(index_url) self.assertContains(response, sections) self.assertNotContains(response, articles

Refs #33476 -- Reformatted code with Black.

test_overriding_has_module_permission

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_overriding_has_module_permission(self): articles = Article._meta.verbose_name_plural.title() sections = Section._meta.verbose_name_plural.title() index_url = reverse("admin7:index") self.client.force_login(self.superuser) response = self.client.get(index_url) self.assertContains(response, sections) self.assertNotContains(response, articles) self.client.logout() self.client.force_login(self.viewuser) response = self.client.get(index_url) self.assertNotContains(response, "admin_views") self.assertNotContains(response, articles) self.client.logout() self.client.force_login(self.adduser) response = self.client.get(index_url) self.assertNotContains(response, "admin_views") self.assertNotContains(response, articles) self.client.logout() self.client.force_login(self.changeuser) response = self.client.get(index_url) self.assertNotContains(response, "admin_views") self.assertNotContains(response, articles) self.client.logout() self.client.force_login(self.deleteuser) response = self.client.get(index_url) self.assertNotContains(response, articles) # The app list displays Sections but not Articles as the latter has # ModelAdmin.has_module_permission() = False. self.client.force_login(self.superuser) response = self.client.get(reverse("admin7:app_list", args=("admin_views",))) self.assertContains(response, sections) self.assertNotContains(response, articles)

django/forms/forms.py

def non_field_errors(self): return self.errors.get( NON_FIELD_ERRORS, self.error_class(error_class

Refs #33476 -- Reformatted code with Black.

non_field_errors

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

forms.py

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

Python

def non_field_errors(self): return self.errors.get( NON_FIELD_ERRORS, self.error_class(error_class="nonfield", renderer=self.renderer), )

@add_start_docstrings( "The bare VAN model outputting raw features without any specific head on top. Note, VAN does not have an embedding layer.", VAN_START_DOCSTRING, )

src/transformers/models/van/modeling_van.py

def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, V

Visual Attention Network (VAN) (#16027) * encoder works * addded files * norm in stage * convertion script * tests * fix copies * make fix-copies * fixed __init__ * make fix-copies * fix * shapiro test needed * make fix-copie * minor changes * make style + quality * minor refactor conversion script * rebase + tests * removed unused variables * updated doc * toctree * CI * doc * Apply suggestions from code review Co-authored-by: Sylvain Gugger <[email protected]> * resolved conversations * make fixup * config passed to modules * config passed to modules * Apply suggestions from code review Co-authored-by: NielsRogge <[email protected]> * conversations * conversations * copyrights * normal test * tests Co-authored-by: Sylvain Gugger <[email protected]> Co-authored-by: NielsRogge <[email protected]>

_set_gradient_checkpointing

0a057201a96565df29984d716f660fd8d634329a

transformers

modeling_van.py

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

Python

def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, VanModel): module.gradient_checkpointing = value VAN_START_DOCSTRING = r VAN_INPUTS_DOCSTRING = r @add_start_docstrings( "The bare VAN model outputting raw features without any specific head on top. Note, VAN does not have an embedding layer.", VAN_START_DOCSTRING, )

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

def update(self, dest, url, rev_options): # type: (str, HiddenText, RevOptions) -> None raise NotImplementedError

upd; format

update

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

versioncontrol.py

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

Python

def update(self, dest, url, rev_options): # type: (str, HiddenText, RevOptions) -> None raise NotImplementedError

label_studio/webhooks/utils.py

def get_nested_field(value, field): if field == '__self__': return value fields =

fix: DEV-1725: Add ANNOTATIONS_CREATED webhook action to predictions to annotations action (#2052) * fix: DEV-1725: Add ANNOTATIONS_CREATED webhook action to predictions to annotations action * Update predictions_to_annotations.py Co-authored-by: Max Tkachenko <[email protected]>

get_nested_field

b2aa62dc675036f7695c0b09dd509617ba9df90d

label-studio

utils.py

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

Python

def get_nested_field(value, field): if field == '__self__': return value fields = field.split('__') for fld in fields: if isinstance(value, list): value = [getattr(v, fld) for v in value] else: value = getattr(value, fld) return value

tests/css/test_stylesheet.py

def test_stylesheet_many_classes_dont_overrule_id(): css = "#id {color: red;} .a.b.c.d {color: blue;}" stylesheet = _make_stylesheet(css) node = DOMNode(classes="a b c d", id="id") style

Add various additional tests around CSS specificity

test_stylesheet_many_classes_dont_overrule_id

4dd0d9fae43583638f34257f97d5749ca4f2c00c

textual

test_stylesheet.py

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

Python

def test_stylesheet_many_classes_dont_overrule_id(): css = "#id {color: red;} .a.b.c.d {color: blue;}" stylesheet = _make_stylesheet(css) node = DOMNode(classes="a b c d", id="id") stylesheet.apply(node) assert node.styles.color == Color(255, 0, 0)

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

def site_data_dir(self) -> str: return self._append_

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

site_data_dir

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

macos.py

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

Python

def site_data_dir(self) -> str: return self._append_app_name_and_version("/Library/Application Support")

tests/tpot_tests.py

def test_warm_start(): tpot_obj = TPOTClassifier( random_state=42, population_size=1, offspring_size=2, generations=1, verbosity=0, config_dict='TPOT light', warm_start=True) tpot_obj.fit(pretest_X, pretest_y) assert tpot_obj._pop is not None assert tpot_obj._pareto_front is not None first_pop = tpot_obj._pop tpot_obj.random_state = 21 tpot_obj.fit(pretest_X, pret

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

test_warm_start

388616b6247ca4ea8de4e2f340d6206aee523541

tpot

tpot_tests.py

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

Python

def test_warm_start(): tpot_obj = TPOTClassifier( random_state=42, population_size=1, offspring_size=2, generations=1, verbosity=0, config_dict='TPOT light', warm_start=True) tpot_obj.fit(pretest_X, pretest_y) assert tpot_obj._pop is not None assert tpot_obj._pareto_front is not None first_pop = tpot_obj._pop tpot_obj.random_state = 21 tpot_obj.fit(pretest_X, pretest_y) assert tpot_obj._pop == first_pop

d2l/mxnet.py

def load_data_wiki(batch_size, max_len): num_workers = d2l.get_dataloader_workers() data_dir = d2l.download_extract('wikitext-2', 'wikitext-2') paragraphs = _read_wiki(data_dir) train_set = _WikiTextDataset(paragraphs, max_len) train_iter = gluon.data.DataLoader(train_set, batch_size, s

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

load_data_wiki

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

d2l-zh

mxnet.py

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

Python

def load_data_wiki(batch_size, max_len): num_workers = d2l.get_dataloader_workers() data_dir = d2l.download_extract('wikitext-2', 'wikitext-2') paragraphs = _read_wiki(data_dir) train_set = _WikiTextDataset(paragraphs, max_len) train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True, num_workers=num_workers) return train_iter, train_set.vocab

seaborn/_core/plot.py

def save(self, loc, **kwargs) -> Plot: # TODO expose important keyword arguments in our signature? with theme_context(self._theme_with_defaults()): self._plot().save(loc, **kwargs) return self

Add rudimentary themeing support (#2929) * WIP Plot.theme * Add default values for theme to match set_theme() * Depend on matplotib style defaults and update rcParams more selectively * Fix lines test * Improve test coverage

save

762db897b52d16ab2f164d5103df4cc26c1d0503

seaborn

plot.py

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

Python

def save(self, loc, **kwargs) -> Plot: # TODO expose important keyword arguments in our signature? with theme_context(self._theme_with_defaults()): self._plot().save(loc, **kwargs) return self

zerver/tests/test_signup.py

def test_create_realm_no_creation_key(self) -> None: email = "[email protected]" with self.settings(OPEN_REALM_CREATION=False): # Create new realm with the email, but no creation key.

realm_creation: Rework error pages. The previous error page was inadequate for serving the two different scenarios where we show errors in realm_creations, in particular containing a misleading sentence about realm creation being disabled (even in the case where it was actually enabled and the user simply had an expired link).

test_create_realm_no_creation_key

582d5b0aa31ac79a5ee1af95b2e71c4bfc53d5aa

zulip

test_signup.py

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

Python

def test_create_realm_no_creation_key(self) -> None: email = "[email protected]" with self.settings(OPEN_REALM_CREATION=False): # Create new realm with the email, but no creation key. result = self.client_post("/new/", {"email": email}) self.assertEqual(result.status_code, 200) self.assert_in_response("Organization creation link required", result)

lib/gpu_stats/nvidia.py

def _get_device_names(self) -> List[str]: names = [pynvml.nvmlDeviceGetName(handle).d

Refactor lib.gpu_stats (#1218) * inital gpu_stats refactor * Add dummy CPU Backend * Update Sphinx documentation

_get_device_names

bdbbad4d310fb606b6f412aa81e9f57ccd994e97

faceswap

nvidia.py

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

Python

def _get_device_names(self) -> List[str]: names = [pynvml.nvmlDeviceGetName(handle).decode("utf-8") for handle in self._handles] self._log("debug", f"GPU Devices: {names}") return names

tests/push/test_push_rule_evaluator.py

def test_delayed_message(self) -> None: user1 = UserID.from_string(self.user_id1) # Send a message before user2 joins event_id1 = self.create_and_send_event(self.room_id, user1) # Have user2 join the room self.helper.join(self.room_id, self.user_id2, tok=self.tok2) # They start off with no notifications self.assertEqual(self.get_notif_count(self.user_id2), 0) # Send another message tha

Speed up calculating push actions in large rooms (#13973) We move the expensive check of visibility to after calculating push actions, avoiding the expensive check for users who won't get pushed anyway. I think this should have a big impact on rooms with large numbers of local users that have pushed disabled.

test_delayed_message

285b9e9b6c3558718e7d4f513062e277948ac35d

synapse

test_push_rule_evaluator.py

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

Python

def test_delayed_message(self) -> None: user1 = UserID.from_string(self.user_id1) # Send a message before user2 joins event_id1 = self.create_and_send_event(self.room_id, user1) # Have user2 join the room self.helper.join(self.room_id, self.user_id2, tok=self.tok2) # They start off with no notifications self.assertEqual(self.get_notif_count(self.user_id2), 0) # Send another message that references the event before the join to # simulate a "delayed" event self.create_and_send_event(self.room_id, user1, prev_event_ids=[event_id1]) # user2 should not be notified about it, because they can't see it. self.assertEqual(self.get_notif_count(self.user_id2), 0)

@pytest.mark.parametrize("solver", SOLVERS) @pytest.mark.parametrize("fit_intercept", [True, False])

sklearn/linear_model/_glm/tests/test_glm.py

def test_glm_regression_unpenalized_hstacked_X(solver, fit_intercept, glm_dataset): model, X, y, coef, _, _, _ = glm_dataset n_samples, n_features = X.shape alpha = 0 # unpenalized params = dict( alpha=alpha, fit_intercept=fit_intercept, # solver=solver, # only lbfgs available tol=1e-12, max_iter=1000, ) model = clone(model).set_params(**params) if fit_intercept: intercept = coef[-1] coef = coef[:-1] if n_samples > n_features: X = X[:, :-1] # remove intercept X = 0.5 * np.concatenate((X, X), axis=1) else: # To know the minimum norm solution, we keep one intercept column and do # not divide by 2. Later on, we must take special care. X = np.c_[X[:, :-1], X[:, :-1], X[:, -1]] else: intercept = 0 X = 0.5 * np.concatenate((X, X), axis=1) assert np.linalg.matrix_rank(X) <= min(n_samples, n_features) with warnings.catch_warnings(): if fit_intercept and n_samples <= n_features: # XXX: Investigate if the lack of convergence in this case should be # considered a bug or not. warnings.filterwarnings("ignore", category=ConvergenceWarning) model.fit(X, y) if fit_intercept and n_samples <= n_features: # Here we take special care. model_intercept = 2 * model.intercept_ model_coef = 2 * model.coef_[:-1] # exclude the ot

TST tight tests for GLMs (#23619) Co-authored-by: Olivier Grisel <[email protected]>

test_glm_regression_unpenalized_hstacked_X

9d863aba2b6dab9c9cbbcf2f7c3b7a99b6ad168f

scikit-learn

test_glm.py

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

Python

def test_glm_regression_unpenalized_hstacked_X(solver, fit_intercept, glm_dataset): model, X, y, coef, _, _, _ = glm_dataset n_samples, n_features = X.shape alpha = 0 # unpenalized params = dict( alpha=alpha, fit_intercept=fit_intercept, # solver=solver, # only lbfgs available tol=1e-12, max_iter=1000, ) model = clone(model).set_params(**params) if fit_intercept: intercept = coef[-1] coef = coef[:-1] if n_samples > n_features: X = X[:, :-1] # remove intercept X = 0.5 * np.concatenate((X, X), axis=1) else: # To know the minimum norm solution, we keep one intercept column and do # not divide by 2. Later on, we must take special care. X = np.c_[X[:, :-1], X[:, :-1], X[:, -1]] else: intercept = 0 X = 0.5 * np.concatenate((X, X), axis=1) assert np.linalg.matrix_rank(X) <= min(n_samples, n_features) with warnings.catch_warnings(): if fit_intercept and n_samples <= n_features: # XXX: Investigate if the lack of convergence in this case should be # considered a bug or not. warnings.filterwarnings("ignore", category=ConvergenceWarning) model.fit(X, y) if fit_intercept and n_samples <= n_features: # Here we take special care. model_intercept = 2 * model.intercept_ model_coef = 2 * model.coef_[:-1] # exclude the other intercept term. # For minimum norm solution, we would have # assert model.intercept_ == pytest.approx(model.coef_[-1]) else: model_intercept = model.intercept_ model_coef = model.coef_ rtol = 6e-5 if n_samples > n_features: assert model_intercept == pytest.approx(intercept) assert_allclose(model_coef, np.r_[coef, coef], rtol=1e-4) else: # As it is an underdetermined problem, prediction = y. The following shows that # we get a solution, i.e. a (non-unique) minimum of the objective function ... assert_allclose(model.predict(X), y, rtol=1e-6) if fit_intercept: # Same as in test_glm_regression_unpenalized. # But it is not the minimum norm solution. Otherwise the norms would be # equal. norm_solution = np.linalg.norm( 0.5 * np.r_[intercept, intercept, coef, coef] ) norm_model = np.linalg.norm(np.r_[model.intercept_, model.coef_]) assert norm_model > (1 + 1e-12) * norm_solution # For minimum norm solution, we would have # assert model.intercept_ == pytest.approx(model.coef_[-1]) else: assert model_intercept == pytest.approx(intercept) assert_allclose(model_coef, np.r_[coef, coef], rtol=rtol) @pytest.mark.parametrize("solver", SOLVERS) @pytest.mark.parametrize("fit_intercept", [True, False])

tests/storage/test_event_push_actions.py