Datasets:

semeru

/

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

like 0

saleor/plugins/base_plugin.py

def _clean_configuration_value(cls, item_type, new_value):

Fix plugin configuration (#11278) * Fix updating plugin configuration * Fix failing tax migration

_clean_configuration_value

eac1ae9cf107b8b0189b8b21ff6668c4131c6a00

saleor

base_plugin.py

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

Python

def _clean_configuration_value(cls, item_type, new_value): if ( item_type == ConfigurationTypeField.BOOLEAN and new_value and not isinstance(new_value, bool) ): new_value = new_value.lower() == "true" if item_type == ConfigurationTypeField.OUTPUT: # OUTPUT field is read only. No need to update it return return new_value

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

def coord_map_from_to(top_from, top_to): # We need to find a common ancestor of top_from and top_to. # We'll assume that all ancestors are equivalent here (otherwise the graph # is an inconsistent state (which we could improve this to check for)

Balanced joint maximum mean discrepancy for deep transfer learning

coord_map_from_to

cc4d0564756ca067516f71718a3d135996525909

transferlearning

coord_map.py

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

Python

def coord_map_from_to(top_from, top_to): # We need to find a common ancestor of top_from and top_to. # We'll assume that all ancestors are equivalent here (otherwise the graph # is an inconsistent state (which we could improve this to check for)). # For now use a brute-force algorithm.

erpnext/controllers/stock_controller.py

def get_conditions_to_validate_future_sle(sl_entries): warehouse_items_map = {} for entry in sl_entries: if entry.warehouse not in warehouse_ite

style: format code with black

get_conditions_to_validate_future_sle

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

stock_controller.py

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

Python

def get_conditions_to_validate_future_sle(sl_entries): warehouse_items_map = {} for entry in sl_entries: if entry.warehouse not in warehouse_items_map: warehouse_items_map[entry.warehouse] = set() warehouse_items_map[entry.warehouse].add(entry.item_code) or_conditions = [] for warehouse, items in warehouse_items_map.items(): or_conditions.append( f ) return or_conditions

TTS/tts/datasets/__init__.py

def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01): speakers = [item["speaker_name"] for item in items] is_multi_speaker = len(set(speakers)) > 1 if eval_split_size > 1: eval_split_size = int(eval_split_size) else: if eval_split_max_size: eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size)) else: eval_split_size = int(len(items) * eval_split_size) assert ( eval_split_size > 0 ), " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format( 1 / len(items) ) np.random.seed(

Make style and lint

split_dataset

1425a023fe4bc6bda8578295aeeeb02af78cc082

TTS

__init__.py

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

Python

def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01): speakers = [item["speaker_name"] for item in items] is_multi_speaker = len(set(speakers)) > 1 if eval_split_size > 1: eval_split_size = int(eval_split_size) else: if eval_split_max_size: eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size)) else: eval_split_size = int(len(items) * eval_split_size) assert ( eval_split_size > 0 ), " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format( 1 / len(items) ) np.random.seed(0) np.random.shuffle(items) if is_multi_speaker: items_eval = [] speakers = [item["speaker_name"] for item in items] speaker_counter = Counter(speakers) while len(items_eval) < eval_split_size: item_idx = np.random.randint(0, len(items)) speaker_to_be_removed = items[item_idx]["speaker_name"] if speaker_counter[speaker_to_be_removed] > 1: items_eval.append(items[item_idx]) speaker_counter[speaker_to_be_removed] -= 1 del items[item_idx] return items_eval, items return items[:eval_split_size], items[eval_split_size:]

tests/rest/admin/test_media.py

def test_quarantine_media(self) -> None: media_info = self.get_success(self.store.get_local_media(self.media_id)) assert media_info is not None self.assertFalse(media_info["quarantined_by"]) # quarantining channel = self.make_request( "POST", self.url % ("quarantine", self.server_name, self.media_id), access_token=self.admin_user_tok, ) self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body) self.assertFalse(channel.json_body) media_info = self.get_success(self.store.get_local_media(self.media_id)) assert media_info is not None self.assertTrue(media_info["quarantined_by"]) # remove from quarantine channel = self.make_request( "POST", self.url % ("unquarantine", self.server_name, self.media_id), access_token=self.admin_user_tok, ) self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body) self.assertFalse(channel.json_body) media_info = self.get_success(self.store.get_local_media(self.media_id)) assert media_info is not None self.assertFalse(media_info["quarantined_by"])

Add type hints for `tests/unittest.py`. (#12347) In particular, add type hints for get_success and friends, which are then helpful in a bunch of places.

test_quarantine_media

f0b03186d96305fd44d74a89bf4230beec0c5c31

synapse

test_media.py

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

Python

def test_quarantine_media(self) -> None: media_info = self.get_success(self.store.get_local_media(self.media_id)) assert media_info is not None self.assertFalse(media_info["quarantined_by"]) # quarantining channel = self.make_request( "POST", self.url % ("quarantine", self.server_name, self.media_id), access_token=self.admin_user_tok, ) self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body) self.assertFalse(channel.json_body) media_info = self.get_success(self.store.get_local_media(self.media_id)) assert media_info is not None self.assertTrue(media_info["quarantined_by"]) # remove from quarantine channel = self.make_request( "POST", self.url % ("unquarantine", self.server_name, self.media_id), access_token=self.admin_user_tok, ) self.assertEqual(HTTPStatus.OK, channel.code, msg=channel.json_body) self.assertFalse(channel.json_body) media_info = self.get_success(self.store.get_local_media(self.media_id)) assert media_info is not None self.assertFalse(media_info["quarantined_by"])

networkx/algorithms/community/modularity_max.py

def naive_greedy_modularity_communities(G, resolution=1, weight=None): r # First create one community for each node communities = list(frozenset([u]) for u in G.nodes()) # Track merges merges = [] # Greedily merge communities until no improvement is possible old_modularity = None new_modularity = modularity(G, communities, resolution=resolution, weight=weight) while old_modularity is None or new_modularity > old_modularity: # Save modularity for comparison old_modularity = new_modularity # Find best pair to merge trial_communities = list(communities) to_merge = None for i, u in enumerate(communities): for j, v in enumerate(communities): # Skip i==j and empty communities if j <= i or len(u) == 0 or len(v) == 0: continue # Merge communities u and v trial_communities[j] = u | v trial_communities[i] = frozenset([]) trial_modularity = modularity( G, trial_communities, resolution=resolution, weight=weight ) if trial_modularity >= new_modularity: # Check if strictly better or tie if trial_modularity > new_modularity: # Found new best, save modularity and group indexes new_modularity = trial_modularity to_merge = (i, j, new_modularity - old_modularity) elif to_merge and min(i, j) < min(to_merge[0], to_merge[1]):

Add weights to karate club graph (#5285) Add weights to the karate_club_graph. Modifies `non_randomness` and `naive_greedy_modularity_communities` to accept a `weight` parameter and modifies tests that use the kcg accordingly Co-authored-by: Kevin Berry <kevin.berry@worthix.com> Co-authored-by: Dan Schult <dschult@colgate.edu>

naive_greedy_modularity_communities

290ebce534b84f9db20ec58b98cbb170e65a0ba1

networkx

modularity_max.py

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

Python

def naive_greedy_modularity_communities(G, resolution=1, weight=None): r # First create one community for each node communities = list(frozenset([u]) for u in G.nodes()) # Track merges merges = [] # Greedily merge communities until no improvement is possible old_modularity = None new_modularity = modularity(G, communities, resolution=resolution, weight=weight) while old_modularity is None or new_modularity > old_modularity: # Save modularity for comparison old_modularity = new_modularity # Find best pair to merge trial_communities = list(communities) to_merge = None for i, u in enumerate(communities): for j, v in enumerate(communities): # Skip i==j and empty communities if j <= i or len(u) == 0 or len(v) == 0: continue # Merge communities u and v trial_communities[j] = u | v trial_communities[i] = frozenset([]) trial_modularity = modularity( G, trial_communities, resolution=resolution, weight=weight ) if trial_modularity >= new_modularity: # Check if strictly better or tie if trial_modularity > new_modularity: # Found new best, save modularity and group indexes new_modularity = trial_modularity to_merge = (i, j, new_modularity - old_modularity) elif to_merge and min(i, j) < min(to_merge[0], to_merge[1]): # Break ties by choosing pair with lowest min id new_modularity = trial_modularity to_merge = (i, j, new_modularity - old_modularity) # Un-merge trial_communities[i] = u trial_communities[j] = v if to_merge is not None: # If the best merge improves modularity, use it merges.append(to_merge) i, j, dq = to_merge u, v = communities[i], communities[j] communities[j] = u | v communities[i] = frozenset([]) # Remove empty communities and sort return sorted((c for c in communities if len(c) > 0), key=len, reverse=True) # old name _naive_greedy_modularity_communities = naive_greedy_modularity_communities

airflow/providers/microsoft/psrp/hooks/psrp.py

def invoke(self) -> Generator[PowerShell, None, None]: logger = copy(self.log) logger.setLevel(self._logging_level) local_context = self._conn is None if local_context: self.__enter__() try: assert self._conn is not None ps = PowerShell(self._conn) yield ps ps.begin_invoke() streams = [ ps.output, ps.streams.debug, ps.streams.error, ps.streams.information, ps.streams.progress, ps.streams.verbose, ps.streams.warning, ] offsets = [0 for _ in streams] # We're using polling to make sure output and streams are # handled while the process is running. while ps.state == PSInvocationState.RUNNING: ps.poll_invoke(timeout=self._operation_timeout)

Ensure @contextmanager decorates generator func (#23103)

invoke

e58985598f202395098e15b686aec33645a906ff

airflow

psrp.py

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

Python

def invoke(self) -> Generator[PowerShell, None, None]: logger = copy(self.log) logger.setLevel(self._logging_level) local_context = self._conn is None if local_context: self.__enter__() try: assert self._conn is not None ps = PowerShell(self._conn) yield ps ps.begin_invoke() streams = [ ps.output, ps.streams.debug, ps.streams.error, ps.streams.information, ps.streams.progress, ps.streams.verbose, ps.streams.warning, ] offsets = [0 for _ in streams] # We're using polling to make sure output and streams are # handled while the process is running. while ps.state == PSInvocationState.RUNNING: ps.poll_invoke(timeout=self._operation_timeout) for i, stream in enumerate(streams): offset = offsets[i] while len(stream) > offset: record = stream[offset] # Records received on the output stream during job # status polling are handled via an optional callback, # while the other streams are simply logged. if stream is ps.output: if self._on_output_callback is not None: self._on_output_callback(record) else: self._log_record(logger.log, record) offset += 1 offsets[i] = offset # For good measure, we'll make sure the process has # stopped running in any case. ps.end_invoke() self.log.info("Invocation state: %s", str(PSInvocationState(ps.state))) if ps.streams.error: raise AirflowException("Process had one or more errors") finally: if local_context: self.__exit__(None, None, None)

src/documents/tests/test_task_signals.py

def util_call_before_task_publish_handler(self, headers_to_use, body_to_use): self.assertEqual(PaperlessTask.objects.all().count(), 0) before_task_publish_handler(headers=headers_to_use, body=body_to_use)

Switches task serialization over to pickle format

util_call_before_task_publish_handler

97d6503fefc5737028637c39a2c1f33dd1e12904

paperless-ngx

test_task_signals.py

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

Python

def util_call_before_task_publish_handler(self, headers_to_use, body_to_use): self.assertEqual(PaperlessTask.objects.all().count(), 0) before_task_publish_handler(headers=headers_to_use, body=body_to_use) self.assertEqual(PaperlessTask.objects.all().count(), 1)

wagtail/admin/tests/test_edit_handlers.py

def test_form(self): form = self.EventPageForm(instance=self.event_page) self.assertIn("comments", form.formsets) comments_formset = form.formsets["comments"] self.assertEqual(len(comments_formset.forms), 1) self.asse

Reformat with black

test_form

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_edit_handlers.py

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

Python

def test_form(self): form = self.EventPageForm(instance=self.event_page) self.assertIn("comments", form.formsets) comments_formset = form.formsets["comments"] self.assertEqual(len(comments_formset.forms), 1) self.assertEqual(comments_formset.forms[0].user, self.commenting_user) replies_formset = comments_formset.forms[0].formsets["replies"] self.assertEqual(len(replies_formset.forms), 2) self.assertEqual(replies_formset.forms[0].user, self.commenting_user)

nltk/corpus/reader/wordnet.py

def _doc(self, doc_type, default, lang="eng"): corpus = self._wordnet_corpus_reader if lang not in corpus.langs(): return None elif lang == "eng": return default else: corpus._load_lang_data(lang) of = corpus.ss2of(self) i = corpus.lg_attrs.index(doc_type) if of in corpus._lang_data[lang][i]: return corpus._lang_data[lang][i][

Fix wordnet's all_synsets() function (#3078) * Fix all_synsets() function * Add simple regression tests for #3077 * Add suggestions by @tomaarsen Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>

_doc

3ca43e26efd7d5aa37b3cd79446258d8bfa79561

nltk

wordnet.py

https://github.com/nltk/nltk.git

Python

def _doc(self, doc_type, default, lang="eng"): corpus = self._wordnet_corpus_reader if lang not in corpus.langs(): return None elif lang == "eng": return default else: corpus._load_lang_data(lang) of = corpus.ss2of(self) i = corpus.lg_attrs.index(doc_type) if of in corpus._lang_data[lang][i]: return corpus._lang_data[lang][i][of] else: return None

pandas/core/arrays/categorical.py

def map(self, mapper): new_categories = self.categories.map(mapper) try: return self.from_codes( self._codes.copy(), categories=new_categories, ordered=self.ordered ) except ValueE

DOC: Improve doc summaries in series.rst (#45237)

map

521259299f7829da667ba39302ec77acedde9e5e

pandas

categorical.py

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

Python

def map(self, mapper): new_categories = self.categories.map(mapper) try: return self.from_codes( self._codes.copy(), categories=new_categories, ordered=self.ordered ) except ValueError: # NA values are represented in self._codes with -1 # np.take causes NA values to take final element in new_categories if np.any(self._codes == -1): new_categories = new_categories.insert(len(new_categories), np.nan) return np.take(new_categories, self._codes) __eq__ = _cat_compare_op(operator.eq) __ne__ = _cat_compare_op(operator.ne) __lt__ = _cat_compare_op(operator.lt) __gt__ = _cat_compare_op(operator.gt) __le__ = _cat_compare_op(operator.le) __ge__ = _cat_compare_op(operator.ge) # ------------------------------------------------------------- # Validators; ideally these can be de-duplicated

modin/experimental/core/execution/unidist/implementations/pandas_on_unidist/io/io.py

def to_pickle_distributed(cls, qc, **kwargs): if not ( isinstance(kwargs["filepath_or_buffer"], str) and "*" in kwargs["filepath_or_buffer"] ) or not isinstance(qc, PandasQueryCompiler): warnings.warn("Defaulting to Modin core implementation") return PandasO

FEAT-#5053: Add pandas on unidist execution with MPI backend (#5059) Signed-off-by: Igoshev, Iaroslav <iaroslav.igoshev@intel.com>

to_pickle_distributed

193505fdf0c984743397ba3df56262f30aee13a8

modin

io.py

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

Python

def to_pickle_distributed(cls, qc, **kwargs): if not ( isinstance(kwargs["filepath_or_buffer"], str) and "*" in kwargs["filepath_or_buffer"] ) or not isinstance(qc, PandasQueryCompiler): warnings.warn("Defaulting to Modin core implementation") return PandasOnUnidistIO.to_pickle(qc, **kwargs)

django/db/migrations/loader.py

def project_state(self, nodes=None, at_end=True): return self.graph.make_state( nodes=nodes, at_end=at_end, real_apps=self.unmigrated_apps )

Refs #33476 -- Reformatted code with Black.

project_state

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

loader.py

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

Python

def project_state(self, nodes=None, at_end=True): return self.graph.make_state( nodes=nodes, at_end=at_end, real_apps=self.unmigrated_apps )

research/neo_peq/legacy_frequency_response.py

def center(self, frequency=1000): equal_energy_fr = self.__class__(name='equal_energy', frequency=self.frequency.copy(), raw=self.raw.copy()) equal_energy_fr.interpolate() interpolator = InterpolatedUnivariateSpline(np.log10(equal_energy_fr.frequency), equal_energy_fr.raw, k=1) if type(frequency) in [list, np.ndarray] and len(frequency) > 1: # Use the average of the gain values between the given frequencies as the difference to be subtracted diff = np.mean(equal_energy_fr.raw[np.logical_and( equal_energy_fr.frequency >= frequency[0], equal_energy_fr.frequency <= frequency[1] )]) else: if type(frequency) in [list, np.ndarray]: # List or array with only one element frequency = frequency[0] # Use the gain value a

Added PEQ configs to CLI and function interfaces. Improved default value handling for PEQ parameters and added more predefined configs. Removed legacy PEQ optimization. Fixed readme write. Improved shelf filter initialization. Added plot method to PEQ. Notebook for comparing old and new optimizers. Bug fixes.

center

9120cdffe618c6c2ff16fe6a311b6a1367efdbc8

AutoEq

legacy_frequency_response.py

https://github.com/jaakkopasanen/AutoEq.git

Python

def center(self, frequency=1000): equal_energy_fr = self.__class__(name='equal_energy', frequency=self.frequency.copy(), raw=self.raw.copy()) equal_energy_fr.interpolate() interpolator = InterpolatedUnivariateSpline(np.log10(equal_energy_fr.frequency), equal_energy_fr.raw, k=1) if type(frequency) in [list, np.ndarray] and len(frequency) > 1: # Use the average of the gain values between the given frequencies as the difference to be subtracted diff = np.mean(equal_energy_fr.raw[np.logical_and( equal_energy_fr.frequency >= frequency[0], equal_energy_fr.frequency <= frequency[1] )]) else: if type(frequency) in [list, np.ndarray]: # List or array with only one element frequency = frequency[0] # Use the gain value at the given frequency as the difference to be subtracted diff = interpolator(np.log10(frequency)) self.raw -= diff if len(self.smoothed): self.smoothed -= diff if len(self.error): self.error += diff if len(self.error_smoothed): self.error_smoothed += diff # Everything but raw, smoothed, errors and target is affected by centering, reset them self.reset(raw=False, smoothed=False, error=False, error_smoothed=False, target=False) return -diff

django/utils/cache.py

def _i18n_cache_key_suffix(request, cache_key): if settings.USE_I18N: # first check if LocaleMiddleware or another middleware added # LANGUAGE_CODE to request, then fall back to the active lan

Refs #33476 -- Reformatted code with Black.

_i18n_cache_key_suffix

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

cache.py

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

Python

def _i18n_cache_key_suffix(request, cache_key): if settings.USE_I18N: # first check if LocaleMiddleware or another middleware added # LANGUAGE_CODE to request, then fall back to the active language # which in turn can also fall back to settings.LANGUAGE_CODE cache_key += ".%s" % getattr(request, "LANGUAGE_CODE", get_language()) if settings.USE_TZ: cache_key += ".%s" % get_current_timezone_name() return cache_key

arff_file = BytesIO( textwrap.dedent( """

sklearn/datasets/tests/test_arff_parser.py

def test_pandas_arff_parser_strip_double_quotes(parser_func): pd =

FIX make pandas and liac arff parser quoting behaviour closer (#23497) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Loïc Estève <loic.esteve@ymail.com>

test_pandas_arff_parser_strip_double_quotes

8515b486810e844bc7f5f1a4fb2227405d46871e

scikit-learn

test_arff_parser.py

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

Python

def test_pandas_arff_parser_strip_double_quotes(parser_func): pd = pytest.importorskip("pandas") arff_file = BytesIO( textwrap.dedent(

pipenv/patched/notpip/_vendor/pygments/lexer.py

def get_tokens_unprocessed(self, text=None, context=None): tokendefs = self._tokens if not context: ctx = LexerContext(text, 0) statetokens = tokendefs['root'] else: ctx = context statetokens = tokendefs[ctx.stack[-1]] text = ctx.text while 1: for rexmatch, action, new_state in statetokens: m = rexmatch(text, ctx.pos, ctx.end) if m: if action is not None: if type(action) is _TokenType: yield ctx.pos, action, m.group() ctx.pos = m.end() else: yield from action(self, m, ctx) if not new_state: # altered the state stack? statetokens = tokendefs[ctx.stack[-1]] # CAUTION: callback must set ctx.pos! if new_state is not None: # state transition if isinstance(new_state, tuple): for state in new_state: if state == '#pop': if len(ctx.stack) > 1: ctx.stack.pop() elif state

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

get_tokens_unprocessed

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

lexer.py

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

Python

def get_tokens_unprocessed(self, text=None, context=None): tokendefs = self._tokens if not context: ctx = LexerContext(text, 0) statetokens = tokendefs['root'] else: ctx = context statetokens = tokendefs[ctx.stack[-1]] text = ctx.text while 1: for rexmatch, action, new_state in statetokens: m = rexmatch(text, ctx.pos, ctx.end) if m: if action is not None: if type(action) is _TokenType: yield ctx.pos, action, m.group() ctx.pos = m.end() else: yield from action(self, m, ctx) if not new_state: # altered the state stack? statetokens = tokendefs[ctx.stack[-1]] # CAUTION: callback must set ctx.pos! if new_state is not None: # state transition if isinstance(new_state, tuple): for state in new_state: if state == '#pop': if len(ctx.stack) > 1: ctx.stack.pop() elif state == '#push': ctx.stack.append(ctx.stack[-1]) else: ctx.stack.append(state) elif isinstance(new_state, int): # see RegexLexer for why this check is made if abs(new_state) >= len(ctx.stack): del ctx.state[1:] else: del ctx.stack[new_state:] elif new_state == '#push': ctx.stack.append(ctx.stack[-1]) else: assert False, "wrong state def: %r" % new_state statetokens = tokendefs[ctx.stack[-1]] break else: try: if ctx.pos >= ctx.end: break if text[ctx.pos] == '\n': # at EOL, reset state to "root" ctx.stack = ['root'] statetokens = tokendefs['root'] yield ctx.pos, Text, '\n' ctx.pos += 1 continue yield ctx.pos, Error, text[ctx.pos] ctx.pos += 1 except IndexError: break

rllib/core/rl_module/torch/tests/test_torch_marl_module.py

def get_policy_data_from_agent_data(agent_data, policy_map_fn): policy_data = {} for agent_id, data in agent_data.items(): policy_id = policy_map_fn(agent_id) policy_data.setdefault(policy_id, {}) policy_data[policy_id].setdefault("agent_id", []) if data["obs"].ndim == 1: policy_data[policy_id]["agent_id"].append(agent_id) else: policy_data[policy_id]["agent_id"] += [agent_id] * len(data["obs"]) for k, v in data.items(): policy_data[policy_id].setdefault(k, []) if v.ndim == 1: v = v[None] policy_data[policy_id][k].append(v) for policy_id in policy_data: policy_data[policy_id] = { k: np.concatenate(v) if k != "agent_id" else v for k, v in policy_data[policy_id].items() } return policy_data

[RLlib] MARLModule, RLModule PR 4/N (N=4) (#29449) Signed-off-by: Kourosh Hakhamaneshi <kourosh@anyscale.com>

get_policy_data_from_agent_data

30058267363b8de16b809c987bb1f7d7befad24d

ray

test_torch_marl_module.py

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

Python

def get_policy_data_from_agent_data(agent_data, policy_map_fn): policy_data = {} for agent_id, data in agent_data.items(): policy_id = policy_map_fn(agent_id) policy_data.setdefault(policy_id, {}) policy_data[policy_id].setdefault("agent_id", []) if data["obs"].ndim == 1: policy_data[policy_id]["agent_id"].append(agent_id) else: policy_data[policy_id]["agent_id"] += [agent_id] * len(data["obs"]) for k, v in data.items(): policy_data[policy_id].setdefault(k, []) if v.ndim == 1: v = v[None] policy_data[policy_id][k].append(v) for policy_id in policy_data: policy_data[policy_id] = { k: np.concatenate(v) if k != "agent_id" else v for k, v in policy_data[policy_id].items() } return policy_data

src/textual/widgets/_data_table.py

def _update_dimensions(self) -> None: total_width = sum(column.width for column in self.columns) s

docstring name change

_update_dimensions

c3dcc529b3aa0b168728b3315cfe973218d09685

textual

_data_table.py

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

Python

def _update_dimensions(self) -> None: total_width = sum(column.width for column in self.columns) self.virtual_size = Size( total_width, len(self._y_offsets) + (self.header_height if self.show_header else 0), )

django/contrib/messages/storage/base.py

def _store(self, messages, response, *args, **kwargs): raise NotImplementedError( "subclasses of BaseStorage mu

Refs #33476 -- Reformatted code with Black.

_store

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

base.py

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

Python

def _store(self, messages, response, *args, **kwargs): raise NotImplementedError( "subclasses of BaseStorage must provide a _store() method" )

tests/rest/admin/test_event_reports.py

def test_from_is_negative(self) -> None: channel = self.make_request( "GET", self.url + "?from=-5", access_token=self.admin_user_tok, ) self.assertEqual(400, channel.code, msg=channel.jso

Use literals in place of `HTTPStatus` constants in tests (#13488) * Use literals in place of `HTTPStatus` constants in tests * newsfile * code style * code style

test_from_is_negative

2281427175e4c93a30c39607fb4ac23c2a1f399f

synapse

test_event_reports.py

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

Python

def test_from_is_negative(self) -> None: channel = self.make_request( "GET", self.url + "?from=-5", access_token=self.admin_user_tok, ) self.assertEqual(400, channel.code, msg=channel.json_body) self.assertEqual(Codes.INVALID_PARAM, channel.json_body["errcode"])

tests/test_binding_inheritance.py

async def test_focused_child_widget_no_inherit_empty_bindings_with_movement_bindings_on_screen() -> None:

Add test for focused widget, no inherit, empty BINDINGS Testing the overlap between #1343 and #1351.

test_focused_child_widget_no_inherit_empty_bindings_with_movement_bindings_on_screen

e8c87ced33ccac893121e3cc0fb1097b0d8da035

textual

test_binding_inheritance.py

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

Python

async def test_focused_child_widget_no_inherit_empty_bindings_with_movement_bindings_on_screen() -> None:

python3.10.4/Lib/lib2to3/pgen2/parse.py

def addtoken(self, type, value, context): # Map from token to label ilabel = self.classify(type, value, context) # Loop until the token is shifted; may raise exceptions while True: dfa, state, node = self.stack[-1] states, first = dfa arcs = states[state] # Look for a state with this label for i, newstate in arcs: t, v = self.grammar.labels[i] if ilabel == i: # Look it up in the list of labels assert t < 256 # Shift a token; we're done with it self.shift(type, value, newstate, context) # Pop while we are in an accept-only state state = newstate while states[state] == [(0, state)]: self.pop() if not self.stack: # Done parsing! return True dfa, state, node = self.stack[-1] states, first = dfa # Done with this token return False elif t >= 256: # See if it's a symbol and if we're in its first set itsdfa = self.grammar.dfas[t] itsstates, itsfirst = itsdfa if ilabel in itsfirst: # Push a symbol self.push(t, self.grammar.dfas[t], newstate, context) break # To continue the outer while loop else: if (0, state) in arcs: # An accepting state, pop it and try something else self.pop() if not self.stack: # Done parsing, but another token is input raise ParseError("too much input", type, value, context) else: # No success finding a transition raise ParseError("bad input", type, value, context)

add python 3.10.4 for windows

addtoken

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

parse.py

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

Python

def addtoken(self, type, value, context): # Map from token to label ilabel = self.classify(type, value, context) # Loop until the token is shifted; may raise exceptions while True: dfa, state, node = self.stack[-1] states, first = dfa arcs = states[state] # Look for a state with this label for i, newstate in arcs: t, v = self.grammar.labels[i] if ilabel == i: # Look it up in the list of labels assert t < 256 # Shift a token; we're done with it self.shift(type, value, newstate, context) # Pop while we are in an accept-only state state = newstate while states[state] == [(0, state)]: self.pop() if not self.stack: # Done parsing! return True dfa, state, node = self.stack[-1] states, first = dfa # Done with this token return False elif t >= 256: # See if it's a symbol and if we're in its first set itsdfa = self.grammar.dfas[t] itsstates, itsfirst = itsdfa if ilabel in itsfirst: # Push a symbol self.push(t, self.grammar.dfas[t], newstate, context) break # To continue the outer while loop else: if (0, state) in arcs: # An accepting state, pop it and try something else self.pop() if not self.stack: # Done parsing, but another token is input raise ParseError("too much input", type, value, context) else: # No success finding a transition raise ParseError("bad input", type, value, context)

airflow/settings.py

def import_local_settings(): try: import airflow_local_settings if hasattr(airflow_local_settings, "__all__"): for i in airflow_local_settings.__all__: globals()[i] = getattr(airflow_local_settings, i) else: for k, v in airflow_local_settings.__dict__.items(): if not k.startswith("__"): globals()[k] = v # TODO: Remove once deprecated if "policy" in globals() and "task_policy" not in globals(): warnings.warn( "Using `policy` in airflow_local_settings.py is deprecated. " "Please rename your `policy` to `task_policy`.", DeprecationWarning, stacklevel=2, ) globals()["task_policy"] = globals()["policy"] del globals()["policy"] if not hasattr(task_instance_mutation_hook, 'is_noop'): task_instance_mutation_hook.is_noop = False log.info("Loaded airflow_local_settings from %s .", airflow_local_settings.__file__) except ModuleNotFoundError as e: if e.name == "airflow_local_settings": log.debug("No airflow_local_settings to import.", exc_info=True) else: log.critical( "Failed to import airflow_local_settings due to a trans

Speed up creation of DagRun for large DAGs (5k+ tasks) by 25-130% (#20722) * Speed up creation of DagRun for large DAGs (5k+ tasks) by 15-40% This uses the "bulk" operation API of SQLAlchemy to get a big speed up. Due to the `task_instance_mutation_hook` we still need to keep actual TaskInstance objects around. For postgresql we have enabled to "batch operation helpers"[1] which makes it even faster. The default page sizes are chosen somewhat randomly based on the SQLA docs. To make these options configurable I have added (and used here and in KubeConfig) a new `getjson` option to AirflowConfigParser class. Postgresql is over 77% faster with bulk_save_objects: Before: ``` number_of_tis=1 mean=0.004397215199423954 per=0.004397215199423954 times=[0.009390181003254838, 0.002814065999700688, 0.00284132499655243, 0.0036120269942330196, 0.0033284770033787936] number_of_tis=10 mean=0.008078816600027494 per=0.0008078816600027494 times=[0.011014281000825576, 0.008476420000079088, 0.00741832799394615, 0.006857775995740667, 0.006627278009545989] number_of_tis=50 mean=0.01927847799670417 per=0.00038556955993408336 times=[0.02556803499464877, 0.01935569499619305, 0.01662322599440813, 0.01840184700267855, 0.01644358699559234] number_of_tis=100 mean=0.03301511880126782 per=0.00033015118801267817 times=[0.04117956099798903, 0.030890661000739783, 0.03007458901265636, 0.03125198099587578, 0.03167880199907813] number_of_tis=500 mean=0.15320950179593637 per=0.0003064190035918727 times=[0.20054609200451523, 0.14052859699586406, 0.14509809199080337, 0.1365471329918364, 0.1433275949966628] number_of_tis=1000 mean=0.2929377429973101 per=0.0002929377429973101 times=[0.3517978919990128, 0.2807794280088274, 0.2806490379880415, 0.27710555399244186, 0.27435680299822707] number_of_tis=3000 mean=0.9935687056015012 per=0.00033118956853383374 times=[1.2047388390055858, 0.8248025969951414, 0.8685875020019012, 0.9017027500085533, 1.1680118399963249] number_of_tis=5000 mean=1.5349355740036117 per=0.00030698711480072236 times=[1.8663743910001358, 1.5182018500054255, 1.5446484510030132, 1.3932801040064078, 1.3521730740030762] number_of_tis=10000 mean=3.7448632712010292 per=0.0003744863271201029 times=[4.135914924001554, 3.4411147559876554, 3.526543836007477, 3.7195197630062466, 3.9012230770022143] number_of_tis=15000 mean=6.3099766838044165 per=0.00042066511225362775 times=[6.552250057997298, 6.1369703890086384, 6.8749958210100885, 6.067943914007628, 5.917723236998427] number_of_tis=20000 mean=8.317583500797628 per=0.00041587917503988143 times=[8.720249108009739, 8.0188543760014, 8.328030352990027, 8.398350054994808, 8.122433611992165] ``` When using bulk_save_objects: ``` number_of_tis=20000 mean=4.678154367001843 per=0.00023390771835009216 times=[4.465847548010061, 4.571855771995615, 4.749505186002352, 4.724330568002188, 4.8792327609990025] ``` MySQL is only 10-15% faster (and a lot noisier) Before: ``` number_of_tis=1 mean=0.006164804595755413 per=0.006164804595755413 times=[0.013516580002033152, 0.00427598599344492, 0.004508020996581763, 0.004067091998877004, 0.004456343987840228] number_of_tis=10 mean=0.007822793803643435 per=0.0007822793803643434 times=[0.0081135170039488, 0.00719467100861948, 0.009007985994685441, 0.00758794900320936, 0.007209846007754095] number_of_tis=50 mean=0.020377356800599954 per=0.00040754713601199905 times=[0.02612382399092894, 0.018950315003166907, 0.019109474000288174, 0.018008680999628268, 0.019694490008987486] number_of_tis=100 mean=0.040682651600218375 per=0.00040682651600218374 times=[0.05449078499805182, 0.037430580996442586, 0.039291110006161034, 0.03625023599306587, 0.035950546007370576] number_of_tis=500 mean=0.18646696420037187 per=0.00037293392840074375 times=[0.24278165798750706, 0.17090376401029062, 0.1837275660072919, 0.16893767600413412, 0.1659841569926357] number_of_tis=1000 mean=0.5903461098030676 per=0.0005903461098030675 times=[0.6001852740009781, 0.5642872750031529, 0.686630773008801, 0.5578094649972627, 0.5428177620051429] number_of_tis=3000 mean=1.9076304554007948 per=0.0006358768184669316 times=[2.042052763994434, 2.1137778090051142, 1.7461599689995637, 1.7260139089921722, 1.9101478260126896] number_of_tis=5000 mean=2.9185905692051164 per=0.0005837181138410233 times=[2.9221124830073677, 3.2889883980096783, 2.7569778940087417, 2.973596281008213, 2.651277789991582] number_of_tis=10000 mean=8.880191986600403 per=0.0008880191986600403 times=[7.3548113360011484, 9.13715232499817, 9.568511486999341, 8.80206210000324, 9.538422685000114] number_of_tis=15000 mean=15.426499317999696 per=0.0010284332878666464 times=[14.944712879005237, 15.38737604500784, 15.409629273999599, 15.852925243991194, 15.53785314799461] number_of_tis=20000 mean=20.579332908798825 per=0.0010289666454399414 times=[20.362008597003296, 19.878823954990366, 20.73281196100288, 20.837948996995692, 21.085071034001885] ``` After: ``` number_of_tis=20000 mean=18.36637533060275 per=0.0009183187665301375 times=[17.728908119010157, 18.62269214099797, 18.936747477011522, 17.74613195299753, 18.797396962996572] ``` [1]: https://docs.sqlalchemy.org/en/13/dialects/postgresql.html#psycopg2-batch-mode * Use bulk_insert_mappings for even more speed where possible. It gives us an extra speed up over bulk_save_objects, but we can't use it when the task_instance_mutation_hook does anything, as that hook needs an actual object. So _when_ we know that hook won't do anything we switch in to insert_mappings mode. New speeds (vs baseline, not vs bulk_save_objects) when using bulk_insert_mappings PostgreSQL now 130% faster: ``` number_of_tis=1 mean=0.028053103599813767 per=0.028053103599813767 times=[0.03762496300623752, 0.02637488600157667, 0.025065611000172794, 0.024561002996051684, 0.026639054995030165] number_of_tis=10 mean=0.02647183560184203 per=0.002647183560184203 times=[0.02698062499985099, 0.026417658998980187, 0.027347976007149555, 0.025797458001761697, 0.025815460001467727] number_of_tis=50 mean=0.03149963079486042 per=0.0006299926158972085 times=[0.03810671299288515, 0.03055680700344965, 0.029733988994848914, 0.03016914198815357, 0.02893150299496483] number_of_tis=100 mean=0.033998635396710594 per=0.0003399863539671059 times=[0.0351028829900315, 0.03299884400621522, 0.03358584298985079, 0.03295094799250364, 0.03535465900495183] number_of_tis=500 mean=0.07903424859978259 per=0.00015806849719956516 times=[0.08279920800123364, 0.08588568199775182, 0.07312070899934042, 0.07360191999759991, 0.07976372400298715] number_of_tis=1000 mean=0.12571056479937398 per=0.00012571056479937398 times=[0.12573593499837443, 0.12141938100103289, 0.12616568499652203, 0.12907471299695317, 0.12615711000398733] number_of_tis=3000 mean=0.36025245799683037 per=0.00012008415266561012 times=[0.36071603700111154, 0.3470657339930767, 0.3373015969991684, 0.3337128989951452, 0.42246602299564984] number_of_tis=5000 mean=0.6916533229988999 per=0.00013833066459977998 times=[0.9647149289958179, 0.6451378140045563, 0.5970188640058041, 0.5849326960014878, 0.6664623119868338] number_of_tis=10000 mean=2.071472014003666 per=0.00020714720140036663 times=[2.957865878008306, 1.9388906149979448, 1.766649461002089, 1.8647991580073722, 1.8291549580026185] number_of_tis=15000 mean=2.866650845797267 per=0.00019111005638648446 times=[3.3783503199956613, 2.657773957995232, 2.707275656008278, 2.7875704979960574, 2.802283796991105] number_of_tis=20000 mean=3.5886989389982773 per=0.00017943494694991387 times=[3.969436354993377, 3.436962780993781, 3.9078941010084236, 3.6387251569976797, 2.9904763009981252] ``` MySQL is (only) 27% faster: ``` number_of_tis=1 mean=0.035956257799989545 per=0.035956257799989545 times=[0.03932315899874084, 0.03545605999534018, 0.03535486999317072, 0.034727805003058165, 0.03491939500963781] number_of_tis=10 mean=0.036957260797498746 per=0.0036957260797498745 times=[0.040442515004542656, 0.0379129799985094, 0.03494819799379911, 0.03562593398964964, 0.03585667700099293] number_of_tis=50 mean=0.04745422120031435 per=0.0009490844240062871 times=[0.06965546800347511, 0.04221734800375998, 0.04038520700123627, 0.040363031992455944, 0.04465005100064445] number_of_tis=100 mean=0.0528092162014218 per=0.000528092162014218 times=[0.06113427500531543, 0.04883724599494599, 0.05276876600692049, 0.047688748003565706, 0.05361704599636141] number_of_tis=500 mean=0.16223246100416872 per=0.0003244649220083374 times=[0.24469116200634744, 0.1407806619972689, 0.14792052800476085, 0.14703868801007047, 0.13073126500239596] number_of_tis=1000 mean=0.285728433605982 per=0.00028572843360598197 times=[0.3230128890136257, 0.27035739900020417, 0.3003890450054314, 0.2638379510026425, 0.2710448840080062] number_of_tis=3000 mean=1.1824120475997915 per=0.0003941373491999305 times=[1.3103130240051541, 1.286688863998279, 1.1455156929878285, 1.1072918410063721, 1.062250816001324] number_of_tis=5000 mean=1.9416745471942705 per=0.0003883349094388541 times=[2.3746965279860888, 1.9103765429899795, 2.0542518720030785, 1.7706374429981224, 1.598410349994083] number_of_tis=10000 mean=5.059874459402636 per=0.0005059874459402636 times=[5.431018351999228, 5.262124675995437, 5.174487816999317, 4.423381198008428, 5.008360254010768] number_of_tis=15000 mean=9.717965700797503 per=0.0006478643800531668 times=[7.884617075993447, 9.466949063993525, 10.005758297003922, 10.105231182998978, 11.127272883997648] number_of_tis=20000 mean=16.2008618004038 per=0.00081004309002019 times=[14.645835625007749, 16.304637463006657, 16.255490412993822, 16.830263861003914, 16.968081640006858] ```

import_local_settings

f2039b4c9e15b514661d4facbd710791fe0a2ef4

airflow

settings.py

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

Python

def import_local_settings(): try: import airflow_local_settings if hasattr(airflow_local_settings, "__all__"): for i in airflow_local_settings.__all__: globals()[i] = getattr(airflow_local_settings, i) else: for k, v in airflow_local_settings.__dict__.items(): if not k.startswith("__"): globals()[k] = v # TODO: Remove once deprecated if "policy" in globals() and "task_policy" not in globals(): warnings.warn( "Using `policy` in airflow_local_settings.py is deprecated. " "Please rename your `policy` to `task_policy`.", DeprecationWarning, stacklevel=2, ) globals()["task_policy"] = globals()["policy"] del globals()["policy"] if not hasattr(task_instance_mutation_hook, 'is_noop'): task_instance_mutation_hook.is_noop = False log.info("Loaded airflow_local_settings from %s .", airflow_local_settings.__file__) except ModuleNotFoundError as e: if e.name == "airflow_local_settings": log.debug("No airflow_local_settings to import.", exc_info=True) else: log.critical( "Failed to import airflow_local_settings due to a transitive module not found error.", exc_info=True, ) raise except ImportError: log.critical("Failed to import airflow_local_settings.", exc_info=True) raise

sympy/functions/elementary/complexes.py

def unpolarify(eq, subs=None, exponents_only=False): if isinstance(eq, bool): return eq eq = sympify(eq) if subs is not None: return unpolarify(eq.subs(subs)) changed = True pause = False if exponents_only: pause = True while changed: changed = False res = _unpolarify(eq, exponents_only, pause) if res != eq: changed = True eq = res if isinstance(res, bool): return res # Finally, replacing Exp(0) by 1 is always correct. # So is polar_lift(0) -> 0. return res.subs({exp_pola

Improved documentation formatting

unpolarify

cda8dfe6f45dc5ed394c2f5cda706cd6c729f713

sympy

complexes.py

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

Python

def unpolarify(eq, subs=None, exponents_only=False): if isinstance(eq, bool): return eq eq = sympify(eq) if subs is not None: return unpolarify(eq.subs(subs)) changed = True pause = False if exponents_only: pause = True while changed: changed = False res = _unpolarify(eq, exponents_only, pause) if res != eq: changed = True eq = res if isinstance(res, bool): return res # Finally, replacing Exp(0) by 1 is always correct. # So is polar_lift(0) -> 0. return res.subs({exp_polar(0): 1, polar_lift(0): 0})

plugins/extract/_base.py

def check_and_raise_error(self) -> None: for thread in self._th

Extract: Typing and standardization

check_and_raise_error

765e385177bda9b9e99951492ef33b34b4e4773e

faceswap

_base.py

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

Python

def check_and_raise_error(self) -> None: for thread in self._threads: thread.check_and_raise_error()

homeassistant/components/recorder/pool.py

def recorder_or_dbworker(self) -> bool: thread_name = threading.current_thread().name return bool( thread_name == "Recorder" or thread_name.startswith(DB_WORKER_PREFIX) )

Use a dedicated executor pool for database operations (#68105) Co-authored-by: Erik Montnemery <erik@montnemery.com> Co-authored-by: Franck Nijhof <git@frenck.dev>

recorder_or_dbworker

bc862e97ed68cce8c437327651f85892787e755e

core

pool.py

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

Python

def recorder_or_dbworker(self) -> bool: thread_name = threading.current_thread().name return bool( thread_name == "Recorder" or thread_name.startswith(DB_WORKER_PREFIX) )

scripts/convert_ldm_original_checkpoint_to_diffusers.py

def renew_resnet_paths(old_list, n_shave_prefix_segments=0): mapping = [] for old_item in old_list: new_item = old_item.replace('in_layers.0', 'norm1')

LDM conversion script (#92) Conversion script Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

renew_resnet_paths

87060e6a9c7754b648e621175b4d73161e82906e

diffusers

convert_ldm_original_checkpoint_to_diffusers.py

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

Python

def renew_resnet_paths(old_list, n_shave_prefix_segments=0): mapping = [] for old_item in old_list: new_item = old_item.replace('in_layers.0', 'norm1') new_item = new_item.replace('in_layers.2', 'conv1') new_item = new_item.replace('out_layers.0', 'norm2') new_item = new_item.replace('out_layers.3', 'conv2') new_item = new_item.replace('emb_layers.1', 'time_emb_proj') new_item = new_item.replace('skip_connection', 'conv_shortcut') new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) mapping.append({'old': old_item, 'new': new_item}) return mapping

keras/utils/metrics_utils.py

def _assert_splits_match(nested_splits_lists): error_msg = ( "Inputs must have identical ragged splits. " f"Input received: {nested_splits_lists}" ) for splits_list in nested_splits_lists: if len(splits_list) != len(nested_splits_lists[0]): rais

Remove pylint comments. PiperOrigin-RevId: 452353044

_assert_splits_match

3613c3defc39c236fb1592c4f7ba1a9cc887343a

keras

metrics_utils.py

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

Python

def _assert_splits_match(nested_splits_lists): error_msg = ( "Inputs must have identical ragged splits. " f"Input received: {nested_splits_lists}" ) for splits_list in nested_splits_lists: if len(splits_list) != len(nested_splits_lists[0]): raise ValueError(error_msg) return [ tf.debugging.assert_equal(s1, s2, message=error_msg) for splits_list in nested_splits_lists[1:] for (s1, s2) in zip(nested_splits_lists[0], splits_list) ]

lib/gui/display_command.py

def _iteration_limit_callback(self, *args) -> None: try: limit = self.vars["display_iterations"].get() except tk.TclError:

Typing - lib.gui.display_command

_iteration_limit_callback

dab823a3eb7a5257cb1e0818ee10ed234d3de97f

faceswap

display_command.py

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

Python

def _iteration_limit_callback(self, *args) -> None: try: limit = self.vars["display_iterations"].get() except tk.TclError: # Don't update when there is no value in the variable return logger.debug("Updating graph iteration limit: (new_value: %s, args: %s)", limit, args) for graph in self.subnotebook.children.values(): graph.calcs.set_iterations_limit(limit)

tests/jobs/test_scheduler_job.py

def test_queued_dagruns_stops_creating_when_max_active_is_reached(self, dag_maker): with dag_maker(max_active_runs=10) as dag: EmptyOperator(task_id='mytask') session = settings.Session() self.scheduler_job = SchedulerJob(subdir=os.devnull) self.scheduler_job.executor = MockExecutor() self.scheduler_job.processor_agent = mock.MagicMock() self.scheduler_job.dagbag = dag_maker.dagbag session = settings.Session() orm_dag = session.query(DagModel).get(dag.dag_id) assert orm_dag is no

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

test_queued_dagruns_stops_creating_when_max_active_is_reached

49e336ae0302b386a2f47269a6d13988382d975f

airflow

test_scheduler_job.py

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

Python

def test_queued_dagruns_stops_creating_when_max_active_is_reached(self, dag_maker): with dag_maker(max_active_runs=10) as dag: EmptyOperator(task_id='mytask') session = settings.Session() self.scheduler_job = SchedulerJob(subdir=os.devnull) self.scheduler_job.executor = MockExecutor() self.scheduler_job.processor_agent = mock.MagicMock() self.scheduler_job.dagbag = dag_maker.dagbag session = settings.Session() orm_dag = session.query(DagModel).get(dag.dag_id) assert orm_dag is not None for _ in range(20): self.scheduler_job._create_dag_runs([orm_dag], session) drs = session.query(DagRun).all() assert len(drs) == 10 for dr in drs: dr.state = State.RUNNING session.merge(dr) session.commit() assert session.query(DagRun.state).filter(DagRun.state == State.RUNNING).count() == 10 for _ in range(20): self.scheduler_job._create_dag_runs([orm_dag], session) assert session.query(DagRun).count() == 10 assert session.query(DagRun.state).filter(DagRun.state == State.RUNNING).count() == 10 assert session.query(DagRun.state).filter(DagRun.state == State.QUEUED).count() == 0 assert orm_dag.next_dagrun_create_after is None

tests/test_xterm_parser.py

def test_escape_sequence_resulting_in_multiple_keypresses(parser): events = list(parser.feed("\x1b[2;4~")) assert len(events) == 2 assert events[0].key == "escape"

Backtracking unknown escape sequences, various tests for XTermParser

test_escape_sequence_resulting_in_multiple_keypresses

bfb962bacf274373e5706090cd854b6aa0857270

textual

test_xterm_parser.py

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

Python

def test_escape_sequence_resulting_in_multiple_keypresses(parser): events = list(parser.feed("\x1b[2;4~")) assert len(events) == 2 assert events[0].key == "escape" assert events[1].key == "shift+insert"

django/db/migrations/autodetector.py

def generate_altered_options(self): models_to_check = self.kept_model_keys.union( self.kept_proxy_keys, self.kept_unmanaged_keys, # unmanaged converted to managed self.old_unmanaged_keys & self.new_model_keys, # managed converted to unmanaged self.old_model_keys & self.new_unmanaged_keys, ) for app_label, model_name in sorted(models_to_check): old_model_name = self.renamed_models.get( (app_label, model_name), model_name ) old_model_state = self.from_state.models[app_label, old_model_name] new_model_state = self.to_state.models[app_label, model_name] old_options = { key: value for key, value in old_model_state.options.items() if key in AlterModelOptions.ALTER_OPTION_KEYS } new_options = { key: value for key, value in new_model_state.options.items() if key in AlterModelOptions.ALTER_OPTION_KEYS } if old_options != new_options: self.add_operation( app_label, operations.AlterModelOptions( name=model_name, options=new_options, ),

Refs #33476 -- Reformatted code with Black.

generate_altered_options

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

autodetector.py

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

Python

def generate_altered_options(self): models_to_check = self.kept_model_keys.union( self.kept_proxy_keys, self.kept_unmanaged_keys, # unmanaged converted to managed self.old_unmanaged_keys & self.new_model_keys, # managed converted to unmanaged self.old_model_keys & self.new_unmanaged_keys, ) for app_label, model_name in sorted(models_to_check): old_model_name = self.renamed_models.get( (app_label, model_name), model_name ) old_model_state = self.from_state.models[app_label, old_model_name] new_model_state = self.to_state.models[app_label, model_name] old_options = { key: value for key, value in old_model_state.options.items() if key in AlterModelOptions.ALTER_OPTION_KEYS } new_options = { key: value for key, value in new_model_state.options.items() if key in AlterModelOptions.ALTER_OPTION_KEYS } if old_options != new_options: self.add_operation( app_label, operations.AlterModelOptions( name=model_name, options=new_options, ), )

django/contrib/admin/checks.py

def _check_readonly_fields(self, obj): if obj.readonly_fields == (): return [] elif not isinstance(obj.readonly_fields, (list, tuple)): return must_be( "a list o

Refs #33476 -- Reformatted code with Black.

_check_readonly_fields

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

checks.py

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

Python

def _check_readonly_fields(self, obj): if obj.readonly_fields == (): return [] elif not isinstance(obj.readonly_fields, (list, tuple)): return must_be( "a list or tuple", option="readonly_fields", obj=obj, id="admin.E034" ) else: return list( chain.from_iterable( self._check_readonly_fields_item( obj, field_name, "readonly_fields[%d]" % index ) for index, field_name in enumerate(obj.readonly_fields) ) )

@register.simple_tag(takes_context=True)

wagtail/admin/templatetags/wagtailadmin_tags.py

def querystring(context, **kwargs): request = context["request"] querydict = request.GET.copy() # Can't do querydict.update(kwargs), because QueryDict.update() appends to # the list of values, instead of replacing the values. for key, value in kwar

Reformat with black

querystring

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

wagtailadmin_tags.py

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

Python

def querystring(context, **kwargs): request = context["request"] querydict = request.GET.copy() # Can't do querydict.update(kwargs), because QueryDict.update() appends to # the list of values, instead of replacing the values. for key, value in kwargs.items(): if value is None: # Remove the key if the value is None querydict.pop(key, None) else: # Set the key otherwise querydict[key] = str(value) return "?" + querydict.urlencode() @register.simple_tag(takes_context=True)

rasa/graph_components/validators/finetuning_validator.py

def _get_fingerprint_of_schema_without_irrelevant_keys(self) -> Text: graph_schema = self._execution_context.graph_schema schema_as_dict = graph_schema.as_dict() for node_name, node_dict in schema_as_dict["nodes"].items(): config_copy = copy.deepcopy(node_dict["config"]) config_copy.pop(EPOCHS, None) # ignore default values since they're filled in anyway later and can # end up in configs (or not) in mysterious ways defaults = graph_schema.nodes[node_name].uses.get_default_config() for key, default_value in defaults.items(): if key in config_copy and config_copy[key] == default_value: config_copy.pop(key) node_dict["config"] = config_copy node_dict.pop("eager") node_dict.pop("constructor_name") return rasa.shared.utils.io.deep_container_fingerprint(schema_as_dict)

Update dependencies in 3.0 to align with rasa-sdk (#10667) * align dependencies * use black 21.7b0 * apply black and docstring reformatting * add changelog

_get_fingerprint_of_schema_without_irrelevant_keys

36eb9c9a5fcca2160e54a6cde5076c93db5bd70b

rasa

finetuning_validator.py

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

Python

def _get_fingerprint_of_schema_without_irrelevant_keys(self) -> Text: graph_schema = self._execution_context.graph_schema schema_as_dict = graph_schema.as_dict() for node_name, node_dict in schema_as_dict["nodes"].items(): config_copy = copy.deepcopy(node_dict["config"]) config_copy.pop(EPOCHS, None) # ignore default values since they're filled in anyway later and can # end up in configs (or not) in mysterious ways defaults = graph_schema.nodes[node_name].uses.get_default_config() for key, default_value in defaults.items(): if key in config_copy and config_copy[key] == default_value: config_copy.pop(key) node_dict["config"] = config_copy node_dict.pop("eager") node_dict.pop("constructor_name") return rasa.shared.utils.io.deep_container_fingerprint(schema_as_dict)

PyInstaller/utils/hooks/gi.py

def get_gi_typelibs(module, version): module_info = GiModuleInfo(module, version) return module_info.collect_

hooks: refactor GObject introspection (gi) hooks The modules imported from gi.repository are marked as runtime modules by their corresponding pre-safe-import-module hooks. Therefore, their standard hooks are always loaded and executed, regardless of whether the modue is actually importable or not. In PyInstaller v5, this behavior triggers errors in hooks for GI modules that are not importable, because the new `isolated` framework propagates the errors instead of swallowing them. While these errors could be caught and demoted to warnings to match the old behavior, it would be better hooks checked whether module is importable before doing any processing at all. To that end, we introduce new class, `GiModuleInfo` that, as part of its initialization, allows us to: - perform availability check - obtain data previously returned by `get_gi_typelibs` - obtain data previously returned by `get_gi_libdir` using a single isolated import attempt (instead of one being performed in each of those steps). In addition, if passed `hook_api` as an optional argument, the `GiModuleInfo` can use hook configuration API to override the GI module version to be collected (which allows the standard use pattern to be removed from the hook itself). The old `get_gi_typelibs` and `get_gi_libdir` functions now internally use `GiModuleInfo` to provide backward compatible behavior to (potential) exetnal user. All `gi` hooks are ported to the `GiModuleInfo` and now become no-op if the module is not available. In addition, hooks are cleaned up/refactored so that all processing is performed either in the loading stage ("simple" hooks that do not require access to hook configuration API) or in the `hook()` function (hooks that require access to hook configuration API), but not in the mixture of the two.

get_gi_typelibs

684bfac8adcf254fec5777f212c13eb62181f900

pyinstaller

gi.py

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

Python

def get_gi_typelibs(module, version): module_info = GiModuleInfo(module, version) return module_info.collect_typelib_data()

airflow/www/security.py

def _sync_dag_view_permissions(self, dag_id, access_control): dag

Fix permission issue for dag that has dot in name (#23510) How we determine if a DAG is a subdag in airflow.security.permissions.resource_name_for_dag is not right. If a dag_id contains a dot, the permission is not recorded correctly. The current solution makes a query every time we check for permission for dags that has a dot in the name. Not that I like it but I think it's better than other options I considered such as changing how we name dags for subdag. That's not good in UX. Another option I considered was making a query when parsing, that's not good and it's avoided by passing root_dag to resource_name_for_dag Co-authored-by: Ash Berlin-Taylor <ash_github@firemirror.com> Co-authored-by: Tzu-ping Chung <uranusjr@gmail.com>

_sync_dag_view_permissions

cc35fcaf89eeff3d89e18088c2e68f01f8baad56

airflow

security.py

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

Python

def _sync_dag_view_permissions(self, dag_id, access_control): dag_resource_name = permissions.resource_name_for_dag(dag_id)

pandas/tests/util/test_assert_series_equal.py

def test_series_equal_datetime_values_mismatch(rtol): msg = s1 = Series(pd.date_range("2018-01-01", periods=3, freq="D")) s2 = Series(pd.date_range("2019-02-02", periods=3, freq="D")) with pytest.raises(AssertionError, match=msg): tm.a

ENH: Include column for ea comparison in asserters (#50323) * ENH: Include column for ea comparison in asserters * Add gh ref * Fix test * Add gh ref * Split tests

test_series_equal_datetime_values_mismatch

07b363ea8eee184df30b54bfae9acd04511e1cda

pandas

test_assert_series_equal.py

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

Python

def test_series_equal_datetime_values_mismatch(rtol): msg = s1 = Series(pd.date_range("2018-01-01", periods=3, freq="D")) s2 = Series(pd.date_range("2019-02-02", periods=3, freq="D")) with pytest.raises(AssertionError, match=msg): tm.assert_series_equal(s1, s2, rtol=rtol)

tests/pytests/functional/pillar/test_gpg.py

def test_decrypt_pillar_invalid_renderer(salt_master, grains, pillar_homedir): opts = salt_master.config.copy() opts["decrypt_pillar"] = [{"secrets:vault": "gpg"}] opts["dec

Add tests for gpg decryption failure option Test that: 1. Pillar registers an error when `gpg_decrypt_must_succeed` is `True` and decryption fails 2. The GPG renderer fails silently when `gpg_decrypt_must_succeed` is `False` Also mock `__opts__["gpg_decrypt_must_succeed"]` for gpg renderer unit pytests.

test_decrypt_pillar_invalid_renderer

b856d3225ef1003cbe94499dc8bd82efffabb661

salt

test_gpg.py

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

Python

def test_decrypt_pillar_invalid_renderer(salt_master, grains, pillar_homedir): opts = salt_master.config.copy() opts["decrypt_pillar"] = [{"secrets:vault": "gpg"}] opts["decrypt_pillar_default"] = "foo" opts["decrypt_pillar_renderers"] = ["foo", "bar"] pillar_obj = salt.pillar.Pillar(opts, grains, "test", "base") ret = pillar_obj.compile_pillar() expected = copy.deepcopy(GPG_PILLAR_ENCRYPTED) expected["_errors"] = [ "Failed to decrypt pillar key 'secrets:vault': 'gpg' is not a valid decryption" " renderer. Valid choices are: foo, bar" ] assert ret["_errors"] == expected["_errors"] assert ret["secrets"]["vault"]["foo"] == expected["secrets"]["vault"]["foo"] assert ret["secrets"]["vault"]["bar"] == expected["secrets"]["vault"]["bar"] assert ret["secrets"]["vault"]["baz"] == expected["secrets"]["vault"]["baz"] assert ret["secrets"]["vault"]["qux"] == expected["secrets"]["vault"]["qux"]

@DeveloperAPI

python/ray/tune/trainable/session.py

def get_trial_name(): warnings.warn( _deprecation_msg, DeprecationWarning, ) _session = get_session() if _session: return _session.trial_name

[air] update documentation to use `session.report` (#26051) Update documentation to use `session.report`. Next steps: 1. Update our internal caller to use `session.report`. Most importantly, CheckpointManager and DataParallelTrainer. 2. Update `get_trial_resources` to use PGF notions to incorporate the requirement of ResourceChangingScheduler. @Yard1 3. After 2 is done, change all `tune.get_trial_resources` to `session.get_trial_resources` 4. [internal implementation] remove special checkpoint handling logic from huggingface trainer. Optimize the flow for checkpoint conversion with `session.report`. Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>

get_trial_name

ac831fded416381ad3c7fe2ba135eaa4aaab5879

ray

session.py

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

Python

def get_trial_name(): warnings.warn( _deprecation_msg, DeprecationWarning, ) _session = get_session() if _session: return _session.trial_name @DeveloperAPI

dask/utils.py

def get_scheduler_lock(collection=None, scheduler=None): from dask import multiprocessing from dask.base import get_scheduler actual_get = get_scheduler(collections=[collection], scheduler=scheduler) if actual_get == multiprocessing.get: return multiprocessing.get_context().Manager().

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

get_scheduler_lock

cccb9d8d8e33a891396b1275c2448c352ef40c27

dask

utils.py

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

Python

def get_scheduler_lock(collection=None, scheduler=None): from dask import multiprocessing from dask.base import get_scheduler actual_get = get_scheduler(collections=[collection], scheduler=scheduler) if actual_get == multiprocessing.get: return multiprocessing.get_context().Manager().Lock() return SerializableLock()

keras/engine/training_utils_v1.py

def verify_dataset_shuffled(x): assert isinstance(x, tf.data.Dataset) graph_def = get_dataset_graph_def(x) for node in graph_def.node: if node.op.startswith("ShuffleDataset"): return True # Also check graph_def.library.function for ds.interleave or ds.flat_map for function in graph_def.library.functi

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

verify_dataset_shuffled

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training_utils_v1.py

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

Python

def verify_dataset_shuffled(x): assert isinstance(x, tf.data.Dataset) graph_def = get_dataset_graph_def(x) for node in graph_def.node: if node.op.startswith("ShuffleDataset"): return True # Also check graph_def.library.function for ds.interleave or ds.flat_map for function in graph_def.library.function: for node in function.node_def: if node.op.startswith("ShuffleDataset"): return True logging.warning( "Expected a shuffled dataset but input dataset `x` is " "not shuffled. Please invoke `shuffle()` on input dataset." ) return False

@log_start_end(log=logger)

gamestonk_terminal/cryptocurrency/due_diligence/binance_model.py

def get_binance_available_quotes_for_each_coin() -> dict:

Global plot styles (#1228) * Add default stylesheets * Add terminal style helper class and global style initialization in cfg * Style comments and docstrings * Load rich terminal theme from config file * Add application chart styles to candle charts * Add todos * Remove explicit color setting for some ta charts * Add user styles folder to gitignore * Update default stylesheets * Add matplotlib font manager support * Add matplotlib font manager support * Update docstrings and default style * Update stocks candle chart formatting (return fig to style title) * Style common ta overlap view * Make up and down market colors a part of the style helper * Update stylesheets * Style common ta volume view * Style common ta momentum view * Style common ta trend indicators view * Style common ta volatility view * Style common ta volume view * Style common ta custom indicators view * Fix styling bugs and remove the obvious time x lablel * Style charts in the covid menu * Set legend position to upper left in the mpl stylesheet * Add mpl_rcparams configs for parameters not covered by stylesheets * Remove font configuration files * Update style class utility functions * Implement passing external axes and style utility usage in ema & stoch * Add theme watermark and output helpers * Rename style to theme * Update helper usage in ta/ma and ta/stoch * Update style to theme in sample menus * Style forex (#1305) * Make tight layout optional 'cause mplfinance doesn't support it * Apply global style to the forex menu * Update code layout in oanda view and black * Style common TA (#1315) * Make tight layout optional 'cause mplfinance doesn't support it * Apply global style to the forex menu * Add linewidth to theme for use in mpf's addplots * Add vwap to the stocks notebook api * Update common/ta overlap to follow charting style * Apply style on TerminalStyle init * Enable infrastructure for excluding non-trading days from plots * Update notebook api to include there and resolve bandit warning * Update ta/common/overlap to exclude non-trading days * Enable external ax, style and non-trading days in common/ta/momentum * Enable external ax, style and non-trading days in common/ta/trend * Update vwap to the argument naming convention * Enable external ax, style and non-trading days in common/ta/volatility * Enable external ax, style and non-trading days in common/ta/volume * Enable external ax, style and non-trading days in common/ta/custom * Fix controller tests * Forgot to disable rewriting of the cassettes ... * Fix controller errors that came up because a merge conflict * Fix price label position on fib * Fix line having wrong x values in fib Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com> * Style economy (#1308) * Began converting * Added alphavan_view * Added CNN View * Updated nasdaq view, fixed glitch * Added fred * Refactored URL * Theo's requested changes * Updated docstrings * Updated tests * Fixed pylint * Fixed tests * Theo changes * Econ Fix * Refactor chart style for Crypto context (#1306) * Remove mock for gff * Mock visualize_output helper function * Refactor * Fix plot helper * Update legend loc * Refactor mplfinance candle plot * Fix errors in the helper function * Fix binbook having the wrong call_ function name * Remove hardcoded style params * Resolve kwargs future warning from pandas * Remove warnings import Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> * funds + custom (#1311) * funds + custom * cleanup cleanup everybody everywhere * Fix external axes conditional and a typo Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> * Add external axes mode to covid charts (#1328) * Add portfolio menu plots (#1318) * Portfolio view plots (commenting out report stuff) * PA Menu broken. Commenting out and fix tests * portfolio optimization * comment out commented api line * Add notes on disabling the pa submenu Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> * Plot updates in common BA (#1335) * Add external axes support to common/ba/finbrain * Add external axes support to common/ba/twitter * Add external axes support to common/ba/google * Add external axes support to common/ba/sentimentinvestor * Add sentimentinvestor to the notebooks API * Fix tests * Etf refactor (#1323) * Refactored no ETF * Fixed gtff import * Fixed tests * Fix pie chart style * Refactored etf/candle * Added pylint fix * Fixed tests * Update candle chart layout * Update etf controller test * Remove strange binary file Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> * Expose ETF candle function in the notebooks API * Common BA and Common QA charts update (#1342) * Add external axes support to common/ba/finbrain * Add external axes support to common/ba/twitter * Add external axes support to common/ba/google * Add external axes support to common/ba/sentimentinvestor * Add sentimentinvestor to the notebooks API * Fix tests * Update stylesheet files * Refactor charts for common/qa * Update the forgotten line plot * Update tests * Add missing arg to a docstring * Remove scientific notation * Black imports Co-authored-by: Minh Hoang <nminh.hoang1023@gmail.com> * Options refactor (#1324) * Fixed alphaquery_view * finished options * Fixed pylint * Fixed tests * Fixed tests * Fixed tests * update yfinance * Tradier + Chartexchange * change mocks from gtff to theme.visualize output * tests Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> Co-authored-by: james <jmaslek11@gmail.com> * Refactor Stocks menu (#1325) * Fix backtesting menu * Refactor comparison analysis * Refactor Dark pool shorts * Refactor rest of menu * Fix test * Fix tests failing * Fix tests fail * Fix test failing * Remove record mode=none to record new output * Rewrite test output * Rewrite test outputs * Adding more rewritten test output * Mock plt.show * Mock missing plt.show * Missing @pytest.mark.vcr * Updating tests : common/behavioural_analysis/finbrain * Improve notebooks API coverage for CA and DPS * Silence annoying flake8 warning Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> * Charts update for common/pred (#1344) * Add external axes support to common/ba/finbrain * Add external axes support to common/ba/twitter * Add external axes support to common/ba/google * Add external axes support to common/ba/sentimentinvestor * Add sentimentinvestor to the notebooks API * Fix tests * Update stylesheet files * Refactor charts for common/qa * Update the forgotten line plot * Update tests * Add missing arg to a docstring * Style pred helper and controllers * Update ETS plot * Update plots in KNN and pred helper * Update plot and pretty table for arima * Update plot for common/pred/regression * Refactor mc_view * Fix linting * Fix mypy * Move plot title to the axis level to make more vertical space Co-authored-by: Minh Hoang <nminh.hoang1023@gmail.com> Co-authored-by: jmaslek <jmaslek11@gmail.com> * linter * Update common/ba test data * Change etf candle to match stock candle * try updating sia test Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com> Co-authored-by: jmaslek <jmaslek11@gmail.com> Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com> Co-authored-by: Minh Hoang <nminh.hoang1023@gmail.com> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>

get_binance_available_quotes_for_each_coin

e1b6022b9cf156ffc0697d0d25a5ed2772ea8d68

OpenBBTerminal

binance_model.py

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

Python

def get_binance_available_quotes_for_each_coin() -> dict: trading_pairs = _get_trading_pairs() results = defaultdict(list) for pair in trading_pairs: results[pair["baseAsset"]].append(pair["quoteAsset"]) return results @log_start_end(log=logger)

erpnext/stock/stock_ledger.py

def update_qty_in_future_sle(args, allow_negative_stock=False): datetime_limit_condition = "" qty_shift = args.actual_qty # find difference/shift in qty caused by stock reconciliation if args.voucher_type == "Stock Reconciliation": qty_shift = get_stock_reco_qty_shift(args) # find the next nearest stock reco so that we only recalculate SLEs till that point next_stock_reco_detail = get_next_stock_reco(args) if next_stock_reco_detail: detail = next_stock_reco_detail[0] # add condi

style: format code with black

update_qty_in_future_sle

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

stock_ledger.py

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

Python

def update_qty_in_future_sle(args, allow_negative_stock=False): datetime_limit_condition = "" qty_shift = args.actual_qty # find difference/shift in qty caused by stock reconciliation if args.voucher_type == "Stock Reconciliation": qty_shift = get_stock_reco_qty_shift(args) # find the next nearest stock reco so that we only recalculate SLEs till that point next_stock_reco_detail = get_next_stock_reco(args) if next_stock_reco_detail: detail = next_stock_reco_detail[0] # add condition to update SLEs before this date & time datetime_limit_condition = get_datetime_limit_condition(detail) frappe.db.sql( .format( qty_shift=qty_shift, datetime_limit_condition=datetime_limit_condition ), args, ) validate_negative_qty_in_future_sle(args, allow_negative_stock)

django/templatetags/tz.py

def get_current_timezone_tag(parser, token): # token.split_contents() isn't useful here because this tag doesn't accept variable as arguments a

Refs #33476 -- Refactored problematic code before reformatting by Black. In these cases Black produces unexpected results, e.g. def make_random_password( self, length=10, allowed_chars='abcdefghjkmnpqrstuvwxyz' 'ABCDEFGHJKLMNPQRSTUVWXYZ' '23456789', ): or cursor.execute(""" SELECT ... """, [table name], )

get_current_timezone_tag

c5cd8783825b5f6384417dac5f3889b4210b7d08

django

tz.py

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

Python

def get_current_timezone_tag(parser, token): # token.split_contents() isn't useful here because this tag doesn't accept variable as arguments args = token.contents.split() if len(args) != 3 or args[1] != 'as': raise TemplateSyntaxError( "'get_current_timezone' requires 'as variable' (got %r)" % args ) return GetCurrentTimezoneNode(args[2])

@register_agent("fake_report")

tests/test_train_model.py

def test_save_multiple_world_logs_mutator(self): with testing_utils.tempdir() as tmpdir: log_report = os.path.join(tmpdir, 'world_logs.jsonl') multitask = 'integration_tests:mutators=flatt

Fixes train_model worldlogging for multitask with mutators. (#4414) * Fixes train_model worldlogging for multitask with mutators. * Fix bug in train_model when evaltask doesn't match task.

test_save_multiple_world_logs_mutator

d6773a0b4acf1027dc9b68342a1d84344f1a0d95

ParlAI

test_train_model.py

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

Python

def test_save_multiple_world_logs_mutator(self): with testing_utils.tempdir() as tmpdir: log_report = os.path.join(tmpdir, 'world_logs.jsonl') multitask = 'integration_tests:mutators=flatten,integration_tests:ReverseTeacher:mutator=reverse' valid, test = testing_utils.train_model( { 'task': multitask, 'validation_max_exs': 10, 'model': 'repeat_label', 'short_final_eval': True, 'num_epochs': 1.0, 'world_logs': log_report, } ) for task in multitask.split(','): task_log_report = get_task_world_logs( task, log_report, is_multitask=True ) with PathManager.open(task_log_report) as f: json_lines = f.readlines() assert len(json_lines) == 5 @register_agent("fake_report")

python/ray/tune/tests/test_trial_relative_logdir.py

def testDotsInLogdir(self): local_dir_path = Path("/tmp/test_

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

testDotsInLogdir

2a5d322e705df080e9254c9c9a3e187c1ea41c4e

ray

test_trial_relative_logdir.py

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

Python

def testDotsInLogdir(self): local_dir_path = Path("/tmp/test_rel_dots") local_dir = str(local_dir_path) if local_dir_path.exists(): local_dir = tempfile.mkdtemp(prefix=str(local_dir_path) + "_") trial = Trial(trainable_name="rel_logdir", local_dir=local_dir) with self.assertRaises(ValueError): trial.logdir = "/tmp/test_rel/../dots" with self.assertRaises(ValueError): trial.logdir = local_dir + "/../" if shutil.rmtree.avoids_symlink_attacks: if local_dir_path.exists(): shutil.rmtree(local_dir)

@override_settings(WAGTAILIMAGES_IMAGE_MODEL="tests.CustomImage")

wagtail/images/tests/test_admin_views.py

def test_delete_post(self): # Send request response = self.client.post( reverse("wagtailimages:delete_multiple", args=(self.ima

Reformat with black

test_delete_post

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_admin_views.py

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

Python

def test_delete_post(self): # Send request response = self.client.post( reverse("wagtailimages:delete_multiple", args=(self.image.id,)) ) # Check response self.assertEqual(response.status_code, 200) self.assertEqual(response["Content-Type"], "application/json") # Make sure the image is deleted self.assertFalse(Image.objects.filter(id=self.image.id).exists()) # Check JSON response_json = json.loads(response.content.decode()) self.assertIn("image_id", response_json) self.assertIn("success", response_json) self.assertEqual(response_json["image_id"], self.image.id) self.assertTrue(response_json["success"]) @override_settings(WAGTAILIMAGES_IMAGE_MODEL="tests.CustomImage")

tests/utils/GraphicalUnitTester.py

def _show_diff_helper(self, frame_data, expected_frame_data): import matplotlib.gridspec as gridspec # type: ignore import matplotlib.pyplot as plt gs = gridspec.GridSpec(2, 2) fig = plt.figure() fig.suptitle(f"Test for {str(self.scene

Added MyPy Support (#1972) * MyPy Support * MyPy Hook * Removing MyPy Hook * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Delete __init__.pyi * Delete color.pyi * Update .mypy.ini Co-authored-by: Christopher Besch <christopher.besch@gmx.de> * changes * quick fix * MyPy Hook * MyPy Hook Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christopher Besch <christopher.besch@gmx.de>

_show_diff_helper

c4217731e08470d5a56cf02cf76cae01c03fb78f

manim

GraphicalUnitTester.py

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

Python

def _show_diff_helper(self, frame_data, expected_frame_data): import matplotlib.gridspec as gridspec # type: ignore import matplotlib.pyplot as plt gs = gridspec.GridSpec(2, 2) fig = plt.figure() fig.suptitle(f"Test for {str(self.scene).replace('Test', '')}", fontsize=16) ax = fig.add_subplot(gs[0, 0]) ax.imshow(frame_data) ax.set_title("Generated :") ax = fig.add_subplot(gs[0, 1]) ax.imshow(expected_frame_data) ax.set_title("Expected :") ax = fig.add_subplot(gs[1, :]) diff_im = expected_frame_data.copy() diff_im = np.where( frame_data != np.array([0, 0, 0, 255]), np.array([0, 255, 0, 255], dtype="uint8"), np.array([0, 0, 0, 255], dtype="uint8"), ) # Set any non-black pixels to green np.putmask( diff_im, expected_frame_data != frame_data, np.array([255, 0, 0, 255], dtype="uint8"), ) # Set any different pixels to red ax.imshow(diff_im, interpolation="nearest") ax.set_title("Differences summary : (green = same, red = different)") plt.show() plt.savefig(f"{self.scene}.png")

awx/main/scheduler/task_manager_models.py

def consume_capacity(self, task): if self.is_container_gr

Add max concurrent jobs and max forks per ig The intention of this feature is primarily to provide some notion of max capacity of container groups, but the logic I've left generic. Default is 0, which will be interpereted as no maximum number of jobs or forks. Includes refactor of variable and method names for clarity. instances_by_hostname is an internal attribute of TaskManagerInstances. Clarify when we are expecting the actual TaskManagerInstances object. Unify how we process running tasks and consume capacity. This has the effect that we do less expensive work in after_lock_init and have 1 less loop over all the running tasks. Previously we looped for both building the dependency graph as well as for calculating the starting capacity of all the instances and instance groups. Now we acheive both tasks in the same loop. Because of how this changes the somewhat subtle "do-si-do" of how to initialize the Task Manager models, introduce a wrapper class that tries to take some of that burden off of other areas where we re-use this like in the serializer and the metrics. Also use this wrapper class to handle nicities of how to track capacity consumption on instances and instance groups. Add tests for max_forks and max_concurrent_jobs Fixup tests that use TaskManagerModels to accomodate changes. assign ig before call to consume capacity if we don't do it in that order, then we don't correctly account for the container group jobs we are starting in the middle of the task manager run

consume_capacity

86856f242aec6051c1cace683fe1761c0775babb

awx

task_manager_models.py

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

Python

def consume_capacity(self, task): if self.is_container_group: self.container_group_jobs += 1 self.container_group_consumed_forks += task.task_impact else: raise RuntimeError("We only track capacity for container groups at the instance group level. Otherwise, consume capacity on instances.")

python/ray/util/spark/utils.py

def get_avail_mem_per_ray_worker_node(spark, object_store_memory_per_node): num_cpus_per_spark_task = int( spark.sparkContext.getConf().get("spark.task.cpus", "1") )

Ray on spark implementation (#28771) REP: ray-project/enhancements#14

get_avail_mem_per_ray_worker_node

e76ccee69aaa7583be1a9d81cf7b2aa72cf25647

ray

utils.py

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

Python

def get_avail_mem_per_ray_worker_node(spark, object_store_memory_per_node): num_cpus_per_spark_task = int( spark.sparkContext.getConf().get("spark.task.cpus", "1") )

pipenv/patched/notpip/_internal/metadata/importlib/_dists.py

def _iter_egg_info_dependencies(self) -> Iterable[str]: for entry i

Vendor in pip 22.1.2

_iter_egg_info_dependencies

c69d55f7c82d5ae2cce542bcfb98d043ca4836a0

pipenv

_dists.py

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

Python

def _iter_egg_info_dependencies(self) -> Iterable[str]: for entry in self._iter_requires_txt_entries(): if entry.extra and entry.marker: marker = f'({entry.marker}) and extra == "{safe_extra(entry.extra)}"' elif entry.extra: marker = f'extra == "{safe_extra(entry.extra)}"' elif entry.marker: marker = entry.marker else: marker = "" if marker: yield f"{entry.requirement} ; {marker}" else: yield entry.requirement

python3.10.4/Lib/imp.py

def cache_from_source(path, debug_override=None): with warnings.catch_warnings(): warnings.simplefilter('ignore') return util.cache_from_source(path, debug_override)

add python 3.10.4 for windows

cache_from_source

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

imp.py

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

Python

def cache_from_source(path, debug_override=None): with warnings.catch_warnings(): warnings.simplefilter('ignore') return util.cache_from_source(path, debug_override)

python3.10.4/Lib/http/server.py

def _url_collapse_path(path): # Query componen

add python 3.10.4 for windows

_url_collapse_path

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

server.py

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

Python

def _url_collapse_path(path): # Query component should not be involved. path, _, query = path.partition('?') path = urllib.parse.unquote(path) # Similar to os.path.split(os.path.normpath(path)) but specific to URL # path semantics rather than local operating system semantics. path_parts = path.split('/') head_parts = [] for part in path_parts[:-1]: if part == '..': head_parts.pop() # IndexError if more '..' than prior parts elif part and part != '.': head_parts.append( part ) if path_parts: tail_part = path_parts.pop() if tail_part: if tail_part == '..': head_parts.pop() tail_part = '' elif tail_part == '.': tail_part = '' else: tail_part = '' if query: tail_part = '?'.join((tail_part, query)) splitpath = ('/' + '/'.join(head_parts), tail_part) collapsed_path = "/".join(splitpath) return collapsed_path nobody = None

pandas/core/arrays/sparse/accessor.py

def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels: bool = False): from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo A, rows, columns = sparse_series_to_coo( self._parent, row_levels, column_levels, sort_labels=sort_labels ) return A, rows, columns

TYP: type all arguments with bool default values (#48624) * TYP: type all arguments with bool default values * bool_t * ignore type error in pandas/core/arrays/sparse/accessor.py

to_coo

5c66e65d7b9fef47ccb585ce2fd0b3ea18dc82ea

pandas

accessor.py

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

Python

def to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels: bool = False): from pandas.core.arrays.sparse.scipy_sparse import sparse_series_to_coo A, rows, columns = sparse_series_to_coo( self._parent, row_levels, column_levels, sort_labels=sort_labels ) return A, rows, columns

projects/bb3/agents/r2c2_bb3_agent.py

def _get_memory_heuristic_values(self) -> Dict[str, Union[str, float, bool]]: return { 'ignore_in_session_memories': self.opt.get( 'ignore_in_session_memories_mkm', False ), 'memory_overlap_threshold': self.opt.get('memory_overlap_threshold', 0.0), 'memory_hard_block_for_n_turns': self.opt.get( 'memory_hard_block_for_n_turns', 0

[BB3] Memory Heuristics (#4770) * memory heuristics * small changes * address comments * fix config * reqs

_get_memory_heuristic_values

58b6977a9cb45a91d78aabdc3c5538f873829a9f

ParlAI

r2c2_bb3_agent.py

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

Python

def _get_memory_heuristic_values(self) -> Dict[str, Union[str, float, bool]]: return { 'ignore_in_session_memories': self.opt.get( 'ignore_in_session_memories_mkm', False ), 'memory_overlap_threshold': self.opt.get('memory_overlap_threshold', 0.0), 'memory_hard_block_for_n_turns': self.opt.get( 'memory_hard_block_for_n_turns', 0 ), 'memory_soft_block_decay_factor': self.opt.get( 'memory_soft_block_decay_factor', 0.0 ), }

netbox/extras/templatetags/plugins.py

def plugin_list_buttons(context, model): return _

4751 Enable plugins to inject content within object list views (#10901) * 4751 add plugin buttons to list templates * 4751 add plugin buttons to list templates * 4751 add documentation * 4751 fix object reference * 4751 update docs

plugin_list_buttons

27bf7b4a9add27b4f3f8b0f4fd5dfc4cfe74a65b

netbox

plugins.py

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

Python

def plugin_list_buttons(context, model): return _get_registered_content(model, 'list_buttons', context)

modules/image/text_to_image/disco_diffusion_ernievil_base/vit_b_16x/ernievil2/transformers/droppath.py

def drop_path(self, inputs): # if prob is 0 or eval mode, return original input if self.drop_prob == 0. or not self.training: return inputs

add disco_diffusion_ernievil_base

drop_path

ffcde21305c61d950a9f93e57e6180c9a9665b87

PaddleHub

droppath.py

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

Python

def drop_path(self, inputs): # if prob is 0 or eval mode, return original input if self.drop_prob == 0. or not self.training: return inputs keep_prob = 1 - self.drop_prob keep_prob = paddle.to_tensor(keep_prob, dtype='float32') shape = (inputs.shape[0], ) + (1, ) * (inputs.ndim - 1) # shape=(N, 1, 1, 1) random_tensor = keep_prob + paddle.rand(shape, dtype=inputs.dtype) random_tensor = random_tensor.floor() # mask output = inputs.divide(keep_prob) * random_tensor #divide is to keep same output expectation return output

seaborn/rcmod.py

def set_style(style=None, rc=None): style_object = axes_style

Convert docs to pydata-sphinx-theme and add new material (#2842) * Do basic conversion of site to pydata_sphinx_theme * Remove some pae structure customizations we no longer need * Add some custom CSS * Tweak a few more colors * Remove vestigial div closing tag * Reorganize release notes into hierarchical pages * Rebuild full docs and fix some resulting issues * Make release note doc refs absolute * Convert homepage to use sphinx-design instead of hand-crafted html * Remove original custom css * Simplify header and put archive switcher in footer * Streamline API docs for objects * Play around with templates to fix shrinking content (not perfect yet) * Improve use of horizontal space without sidebars * Various tweaks * Convert tutorial homepage source to native sphinx-design directives * Move intro page into tutorial * More tweaks * Tweak theme colors and footer * Remove reference to navbar version * Note that error bar tutorial demonstrates new features as of v0.12 * Update layout customization for new theme features * Various layout and CSS tweaks * Narrow support guidance to StackOverflow * Run all notebooks * Adapt to new dropdown navbar in pydata theme * Separate tutorial source and outputs * Separate dostring source and outputs * Add scale API template * Update API docs * Fix requirements * Add new objects * Point doc requirements at v0.10 RC for theme

set_style

34662f4be5c364e7518f9c1118c9b362038ee5dd

seaborn

rcmod.py

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

Python

def set_style(style=None, rc=None): style_object = axes_style(style, rc) mpl.rcParams.update(style_object)

src/transformers/models/vilt/feature_extraction_vilt.py

def _resize(self, image, shorter=800, longer=1333, size_divisor=32, resample=Image.BICUBIC): if not isinstance(image, Image.Image): image = self.to_pil_image(image) w, h = image.size min_size = shorter max_size = longer

Add ViLT (#14895) * First commit * Add conversion script * Make conversion script work for base model * More improvements * Update conversion script, works for vqa * Add indexing argument to meshgrid * Make conversion script work for ViltForPreTraining * Add ViltForPreTraining to docs * Fix device issue * Add processor * Add MinMaxResize to feature extractor * Implement call method of ViltProcessor * Fix tests * Add integration test * Add loss calculation for VQA * Improve tests * Improve some more tests * Debug tests * Small improvements * Add support for attention_mask * Remove mask_it * Add pixel_mask * Add tests for ViltFeatureExtractor * Improve tests * Add ViltForNaturalLanguageVisualReasoning * Add ViltForNaturalLanguageVisualReasoning to conversion script * Minor fixes * Add support for image_embeds, update docstrings to markdown * Update docs to markdown * Improve conversion script * Rename ViltForPreTraining to ViltForMaskedLM * Improve conversion script * Convert docstrings to markdown * Fix code example of retrieval model * Properly convert masked language model * Add integration test for nlvr * Fix code quality * Apply suggestions from code review * Add copied from statements * Fix pretrained_config_archive_map * Fix docs * Add model to README * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Apply more suggestions from code review * Make code more readable * Add ViltForNaturalLanguageVisualReasoning to the tests * Rename ViltForVisualQuestionAnswering to ViltForQuestionAnswering * Replace pixel_values_2 by single tensor * Add hidden_states and attentions * Fix one more test * Fix all tests * Update year * Fix rebase issues * Fix another rebase issue * Remove ViltForPreTraining from auto mapping * Rename ViltForImageRetrievalTextRetrieval to ViltForImageAndTextRetrieval * Make it possible to use BertTokenizerFast in the processor * Use BertTokenizerFast by default * Rename ViltForNaturalLanguageVisualReasoning, define custom model output Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

_resize

ac227093e41cecb07c7e0f2fc9a504850907bd06

transformers

feature_extraction_vilt.py

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

Python

def _resize(self, image, shorter=800, longer=1333, size_divisor=32, resample=Image.BICUBIC): if not isinstance(image, Image.Image): image = self.to_pil_image(image) w, h = image.size min_size = shorter max_size = longer scale = min_size / min(w, h) if h < w: newh, neww = min_size, scale * w else: newh, neww = scale * h, min_size if max(newh, neww) > max_size: scale = max_size / max(newh, neww) newh = newh * scale neww = neww * scale newh, neww = int(newh + 0.5), int(neww + 0.5) newh, neww = newh // size_divisor * size_divisor, neww // size_divisor * size_divisor return self.resize(image, size=(neww, newh), resample=resample)

rllib/policy/dynamic_tf_policy_v2.py

def extra_action_out_fn(self) -> Dict[str, TensorType]: extra_action_fetches = super().extra_action_out_fn() extra_action_fetches.update(self._policy_extra_action_fetches) return extra_action_fetches

[RLlib] Introduce new policy base classes. (#24742)

extra_action_out_fn

bc3a1d35cf6e9a5fd7eef908a8e76aefb80ce6a9

ray

dynamic_tf_policy_v2.py

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

Python

def extra_action_out_fn(self) -> Dict[str, TensorType]: extra_action_fetches = super().extra_action_out_fn() extra_action_fetches.update(self._policy_extra_action_fetches) return extra_action_fetches

src/datasets/builder.py

def get_all_exported_dataset_infos(cls) -> dict: dset_infos_file_path = os.path.join(cls.get_imported_module_dir(), config.DATASETDICT_INFOS_FILENAME) if os.path.exists(dset_infos_file_path): return DatasetInfosDict.from_directory(cls.get_imported_module_dir()

Add API code examples for Builder classes (#4313) * 📝 add examples for builder classes * 📝 apply quentin review

get_all_exported_dataset_infos

d1d4f1065fd4ab91b2c8682643dbd12f86d66fcd

datasets

builder.py

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

Python

def get_all_exported_dataset_infos(cls) -> dict: dset_infos_file_path = os.path.join(cls.get_imported_module_dir(), config.DATASETDICT_INFOS_FILENAME) if os.path.exists(dset_infos_file_path): return DatasetInfosDict.from_directory(cls.get_imported_module_dir()) return {}

paddlenlp/trainer/trainer_args.py

def to_sanitized_dict(self) -> Dict[str, Any]: d = self.to_dict() d = { ** d, ** { "train_batch_size": self.train_batch_size, "eval_batch_size": self.eval_batch_size } } valid_types = [bool, int,

[Trainer] Add init version of paddlenlp trainer and apply finetune for ernie-1.0 pretraining. (#1761) * add some datasets for finetune. * support fine tune for all tastks. * add trainer prototype. * init verison for paddlenlp trainer. * refine trainer. * update for some details. * support multi-cards training evaluation. * support load from ckpt. * support for export inference model. * first version of trainer. * seq cls support clue. * trainer support for token classification and question answersing tasks. * fix as reviews. Co-authored-by: Zeyu Chen <chenzeyu01@baidu.com>

to_sanitized_dict

44a290e94d1becd1f09fddc3d873f9e19c9d6919

PaddleNLP

trainer_args.py

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

Python

def to_sanitized_dict(self) -> Dict[str, Any]: d = self.to_dict() d = { ** d, ** { "train_batch_size": self.train_batch_size, "eval_batch_size": self.eval_batch_size } } valid_types = [bool, int, float, str] valid_types.append(paddle.Tensor) return { k: v if type(v) in valid_types else str(v) for k, v in d.items() }

python3.10.4/Lib/bz2.py

def readinto(self, b): self._check_can_read()

add python 3.10.4 for windows

readinto

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

bz2.py

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

Python

def readinto(self, b): self._check_can_read() return self._buffer.readinto(b)

django/db/backends/oracle/base.py

def _output_type_handler(cursor, name, defaultType, length, precision, scale): if defaultType == Database.NUMBER: if scale == -127: if precision == 0: # NUMBER column: decimal-precision floating point. # This will normally be an integer from a sequence, # but it could be a decimal value. outconverter = FormatStylePlaceholderCursor._output_number_converter else: # FLOAT column: binary-precision floating point. # This comes from FloatField columns. outconverter = float elif precision > 0: # NUMBER(p,s) column: decimal-precision fixed point. # This comes from IntegerField and DecimalField columns. outconverter = FormatStylePlaceholderCursor._get_decimal_converter(

Refs #33476 -- Reformatted code with Black.

_output_type_handler

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

base.py

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

Python

def _output_type_handler(cursor, name, defaultType, length, precision, scale): if defaultType == Database.NUMBER: if scale == -127: if precision == 0: # NUMBER column: decimal-precision floating point. # This will normally be an integer from a sequence, # but it could be a decimal value. outconverter = FormatStylePlaceholderCursor._output_number_converter else: # FLOAT column: binary-precision floating point. # This comes from FloatField columns. outconverter = float elif precision > 0: # NUMBER(p,s) column: decimal-precision fixed point. # This comes from IntegerField and DecimalField columns. outconverter = FormatStylePlaceholderCursor._get_decimal_converter( precision, scale ) else: # No type information. This normally comes from a # mathematical expression in the SELECT list. Guess int # or Decimal based on whether it has a decimal point. outconverter = FormatStylePlaceholderCursor._output_number_converter return cursor.var( Database.STRING, size=255, arraysize=cursor.arraysize, outconverter=outconverter, )

src/datasets/features/features.py

def decode_nested_example(schema, obj): # Nested structures: we allow dict, list/tuples, sequences if isinstance(schema, dict): return { k: decode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) } elif isinstance(schema, (list, tuple)): sub_schema = schema[0] if obj is None: return None else: if len(obj) > 0: for first_elmt in obj: if _check_non_null_non_empty_recursive(first_elmt, sub_schema): break if decode_nested_example(sub_schema, first_elmt) != first_elmt: return [decode_nested_example(sub_schema, o) for o in obj] return list(obj) elif isinstance(schema, Sequence): # We allow to reverse list of dict => dict of list for compatiblity with tfds if isinstance(schema.feature, dict): return {k: decode_nested_example([schema.feature[k]

Add Arrow type casting to struct for Image and Audio + Support nested casting (#3575) * add storage cast * implement dict cast for image * factorize extension type creation for audio and image + implement type cast for thos custom types * fix tests * style * [big] allow extension array in nested arrays * docs * style * fix Features pickling * fix some tests * fix more tests * fix more tests * add base extensionarray for pyarrow<6 * add extensionarray for pyarrow<6 * add soundfile to tests requirements * minor * remove not implemented error for complex casting in pyarrow 3 * style * style again * add casting for fixed size lists * add libsndfile1 in the linux CI * style * typo * start adding new tests just to notice the concatenation issue... * [big] remove extension types + move cast_storage to the Image and Audio classes * minor * fix test * style * add more tests to image * add audio tests * support casting from null array * fix field names verifications when casting * docs + tests * use the new table_cast on pyarrow tables * whoops forgot one line * remove unused string handling in Image.decode_example * update tests accordingly

decode_nested_example

6ca96c707502e0689f9b58d94f46d871fa5a3c9c

datasets

features.py

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

Python

def decode_nested_example(schema, obj): # Nested structures: we allow dict, list/tuples, sequences if isinstance(schema, dict): return { k: decode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj) } elif isinstance(schema, (list, tuple)): sub_schema = schema[0] if obj is None: return None else: if len(obj) > 0: for first_elmt in obj: if _check_non_null_non_empty_recursive(first_elmt, sub_schema): break if decode_nested_example(sub_schema, first_elmt) != first_elmt: return [decode_nested_example(sub_schema, o) for o in obj] return list(obj) elif isinstance(schema, Sequence): # We allow to reverse list of dict => dict of list for compatiblity with tfds if isinstance(schema.feature, dict): return {k: decode_nested_example([schema.feature[k]], obj[k]) for k in schema.feature} else: return decode_nested_example([schema.feature], obj) # Object with special decoding: elif isinstance(schema, (Audio, Image)): return schema.decode_example(obj) if obj is not None else None return obj

src/pip/_internal/utils/virtualenv.py

def running_under_virtualenv() -> bool: return _running_und

Name virtualenv<20 as "legacy" Well they are. At least not "regular" anymore.

running_under_virtualenv

5ded5474ac9b323496506e6391e8d8c2c888d7f1

pip

virtualenv.py

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

Python

def running_under_virtualenv() -> bool: return _running_under_venv() or _running_under_legacy_virtualenv()

mmdet/models/dense_heads/base_dense_head.py

def forward_train(self, x, data_samples, proposal_cfg=None, **kwargs): img_metas = [data_sample['meta'] for data_sample in data_samples] outs = self(x) gt_bboxes = [ data_sample.gt_instances.bboxes for data_sample in data_samples ] if hasattr(data_samples[0].gt_instances, 'labels'): gt_labels = [ data_sample.gt_instances.labels

Simplify api of one-stage detector

forward_train

9c5b3331ac8edbfa328922fbab45c382380da540

mmdetection

base_dense_head.py

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

Python

def forward_train(self, x, data_samples, proposal_cfg=None, **kwargs): img_metas = [data_sample['meta'] for data_sample in data_samples] outs = self(x) gt_bboxes = [ data_sample.gt_instances.bboxes for data_sample in data_samples ] if hasattr(data_samples[0].gt_instances, 'labels'): gt_labels = [ data_sample.gt_instances.labels for data_sample in data_samples ] else: # RPN gt_labels = None if hasattr(data_samples[0], 'instances_ignore'): gt_bboxes_ignore = [ data_sample.ignored_instances.bboxes for data_sample in data_samples ] else: gt_bboxes_ignore = None if gt_labels is None: loss_inputs = outs + (gt_bboxes, img_metas) else: loss_inputs = outs + (gt_bboxes, gt_labels, img_metas) losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) if proposal_cfg is None: return losses else: results_list = self.get_results( *outs, img_metas=img_metas, cfg=proposal_cfg) return losses, results_list

netbox/netbox/models/features.py

def cf(self): return { cf.name: cf.deserialize(self.custom_field_data.get(cf.name

Closes #10052: The cf attribute now returns deserialized custom field data

cf

ea6d86e6c4bb6037465410db6205a7471bc81a6c

netbox

features.py

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

Python

def cf(self): return { cf.name: cf.deserialize(self.custom_field_data.get(cf.name)) for cf in self.custom_fields }

networkx/drawing/tests/test_layout.py

def test_arf_layout_negative_a_check(self): G = self.Gs pytest.raises(ValueError, nx.arf_layout, G=G, a=-

Arf layout (#5910) * added arf_layout * reference to docstring and comparison to spring layout * rebase to origin main * black re-format * Left aligned docstring text * Cleaned up computation and update variables to new docstring * Updated naming tests. Added input check on arf_layout parameter `a` * Fixed Linter issues for py38 target * Fixed Linter issues for target p38 * linter issue fixed

test_arf_layout_negative_a_check

88245f69f89dbee75cef67bdf35bbfb986a42d52

networkx

test_layout.py

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

Python

def test_arf_layout_negative_a_check(self): G = self.Gs pytest.raises(ValueError, nx.arf_layout, G=G, a=-1)

tests/components/device_tracker/test_config_entry.py

async def test_connected_device_registered(hass): registry = mock_registry(hass) dispatches = []

Ensure dhcp can still discover new devices from device trackers (#66822) Co-authored-by: Martin Hjelmare <marhje52@gmail.com>

test_connected_device_registered

a18d4c51ff3ab9afd13ee08fe8c65e2f9b77f3b1

core

test_config_entry.py

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

Python

async def test_connected_device_registered(hass): registry = mock_registry(hass) dispatches = []

@RunIf(min_gpus=2, skip_windows=True, fairscale=True)

tests/strategies/test_sharded_strategy.py

def test_ddp_sharded_strategy_checkpoint_multi_gpu(tmpdir): model = BoringModel() trainer = Trainer(gpus=2, strategy="ddp_sharded_spawn", fast_dev_run=True) trainer.fit(model) checkpoint_path = os.path.join(tmpdir, "model.pt") trainer.save_checkpoint(checkpoint_path)

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

test_ddp_sharded_strategy_checkpoint_multi_gpu

650c710efacd633fa283955145342bb64063c883

lightning

test_sharded_strategy.py

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

Python

def test_ddp_sharded_strategy_checkpoint_multi_gpu(tmpdir): model = BoringModel() trainer = Trainer(gpus=2, strategy="ddp_sharded_spawn", fast_dev_run=True) trainer.fit(model) checkpoint_path = os.path.join(tmpdir, "model.pt") trainer.save_checkpoint(checkpoint_path) saved_model = BoringModel.load_from_checkpoint(checkpoint_path) # Assert model parameters are identical after loading for ddp_param, shard_param in zip(model.parameters(), saved_model.parameters()): assert torch.equal(ddp_param.to("cpu"), shard_param) @RunIf(min_gpus=2, skip_windows=True, fairscale=True)

python/ray/serve/controller.py

def autoscale(self) -> None: for deployment_name, ( deployment_info, route_prefix, ) in self.list_deployments().items(): deployment_config = deployment_info.deployment_config autoscaling_policy = depl

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

autoscale

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

controller.py

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

Python

def autoscale(self) -> None: for deployment_name, ( deployment_info, route_prefix, ) in self.list_deployments().items(): deployment_config = deployment_info.deployment_config autoscaling_policy = deployment_info.autoscaling_policy if autoscaling_policy is None: continue replicas = self.deployment_state_manager._deployment_states[ deployment_name ]._replicas running_replicas = replicas.get([ReplicaState.RUNNING]) current_num_ongoing_requests = [] for replica in running_replicas: replica_tag = replica.replica_tag num_ongoing_requests = self.autoscaling_metrics_store.window_average( replica_tag, time.time() - autoscaling_policy.config.look_back_period_s, ) if num_ongoing_requests is not None: current_num_ongoing_requests.append(num_ongoing_requests) if len(current_num_ongoing_requests) == 0: continue new_deployment_config = deployment_config.copy() decision_num_replicas = autoscaling_policy.get_decision_num_replicas( current_num_ongoing_requests=current_num_ongoing_requests, curr_target_num_replicas=deployment_config.num_replicas, ) new_deployment_config.num_replicas = decision_num_replicas new_deployment_info = copy(deployment_info) new_deployment_info.deployment_config = new_deployment_config goal_id, updating = self.deployment_state_manager.deploy( deployment_name, new_deployment_info )

ppdet/utils/checkpoint.py

def match_state_dict(model_state_dict, weight_state_dict):

Add PP-YOLOv3 code (#5281) * [ppyolov3] add ppyolov3 base code * add ppyolov3 s/m/x * modify ema * modify code to convert onnx successfully * support arbitrary shape * update config to use amp default * refine ppyolo_head code * modify reparameter code * refine act layer * adapter pico_head and tood_head code * remove ppyolov3 yaml * fix codestyle Co-authored-by: wangxinxin08 <wangxinxin08@baidu.com>

match_state_dict

ef83ab8a3f7814e9886a7a22c8dcc55f506b6081

PaddleDetection

checkpoint.py

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

Python

def match_state_dict(model_state_dict, weight_state_dict): model_keys = sorted(model_state_dict.keys()) weight_keys = sorted(weight_state_dict.keys())

networkx/algorithms/approximation/connectivity.py

def local_node_connectivity(G, source, target, cutoff=None): if target == source: raise nx.NetworkXError("source and target have to be different nodes.") # Maximum possible node independent paths if G.is_directed(): possible = min(G.out_degree(source), G.in_degree(target)) else: possible = min(G.degree(source), G.degree(target)) K = 0 if not possible: return K if cutoff is None: cutoff = float("inf") exclude = set() for i in range(min(possible, cutoff)): try: path = _bidirectional_shortest_path(G, source, target, exclude)

Minor improvements from general code readthrough (#5414) * Add deprecated directive to reversed docstring. * Add missing dep directives to shpfiles. * Remove defn of INF sentinel. * typo. * str -> comment in forloop. * STY: appropriate casing for var name.

local_node_connectivity

cc1db275efc709cb964ce88abbfa877798d58c10

networkx

connectivity.py

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

Python

def local_node_connectivity(G, source, target, cutoff=None): if target == source: raise nx.NetworkXError("source and target have to be different nodes.") # Maximum possible node independent paths if G.is_directed(): possible = min(G.out_degree(source), G.in_degree(target)) else: possible = min(G.degree(source), G.degree(target)) K = 0 if not possible: return K if cutoff is None: cutoff = float("inf") exclude = set() for i in range(min(possible, cutoff)): try: path = _bidirectional_shortest_path(G, source, target, exclude) exclude.update(set(path)) K += 1 except nx.NetworkXNoPath: break return K

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

def _copartition(self, axis, other, how, sort, force_repartition=False): if isinstance(other, type(self)): other = [other] self_index = self.axes[axis] others_index = [o.axes[axis] for o in other] joined_index, make_reindexer = self._join_index_objects( axis, [self_index] + others_index, how, sort ) frames = [self] + other non_empty_frames_idx = [ i for i, o in enumerate(frames) if o._partitions.size != 0 ] # If all frames are empty if len(non_empty_frames_idx) == 0: return ( self._partitions, [o._partitions for o in other], joined_index, # There are no partition sizes because the resulting dataframe # has no partitions. [], ) base_frame_idx = non_empty_frames_idx[0] other_frames = frames[base_frame_idx + 1 :] # Picking first non-empty frame base_frame = frames[non_empty_frames_idx[0]] base_index = base_frame.axes[axis] # define conditions for reindexing and repartitioning `self` frame do_reindex_base = not base_index.equals(joined_index) do_repartition_base = force_repartition or do_reindex_base # Perform repartitioning and reindexing for `base_frame` if needed. # Also define length of base and fra

PERF-#4493: Use partition size caches more in Modin dataframe. (#4495) Co-authored-by: Devin Petersohn <devin-petersohn@users.noreply.github.com> Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru> Signed-off-by: mvashishtha <mahesh@ponder.io>

_copartition

cca9468648521e9317de1cb69cf8e6b1d5292d21

modin

dataframe.py

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

Python

def _copartition(self, axis, other, how, sort, force_repartition=False): if isinstance(other, type(self)): other = [other] self_index = self.axes[axis] others_index = [o.axes[axis] for o in other] joined_index, make_reindexer = self._join_index_objects( axis, [self_index] + others_index, how, sort ) frames = [self] + other non_empty_frames_idx = [ i for i, o in enumerate(frames) if o._partitions.size != 0 ] # If all frames are empty if len(non_empty_frames_idx) == 0: return ( self._partitions, [o._partitions for o in other], joined_index, # There are no partition sizes because the resulting dataframe # has no partitions. [], ) base_frame_idx = non_empty_frames_idx[0] other_frames = frames[base_frame_idx + 1 :] # Picking first non-empty frame base_frame = frames[non_empty_frames_idx[0]] base_index = base_frame.axes[axis] # define conditions for reindexing and repartitioning `self` frame do_reindex_base = not base_index.equals(joined_index) do_repartition_base = force_repartition or do_reindex_base # Perform repartitioning and reindexing for `base_frame` if needed. # Also define length of base and frames. We will need to know the # lengths for alignment. if do_repartition_base: reindexed_base = base_frame._partition_mgr_cls.map_axis_partitions( axis, base_frame._partitions, make_reindexer(do_reindex_base, base_frame_idx), ) if axis: base_lengths = [obj.width() for obj in reindexed_base[0]] else: base_lengths = [obj.length() for obj in reindexed_base.T[0]] else: reindexed_base = base_frame._partitions base_lengths = self._column_widths if axis else self._row_lengths others_lengths = [o._axes_lengths[axis] for o in other_frames] # define conditions for reindexing and repartitioning `other` frames do_reindex_others = [ not o.axes[axis].equals(joined_index) for o in other_frames ] do_repartition_others = [None] * len(other_frames) for i in range(len(other_frames)): do_repartition_others[i] = ( force_repartition or do_reindex_others[i] or others_lengths[i] != base_lengths ) # perform repartitioning and reindexing for `other_frames` if needed reindexed_other_list = [None] * len(other_frames) for i in range(len(other_frames)): if do_repartition_others[i]: # indices of others frame start from `base_frame_idx` + 1 reindexed_other_list[i] = other_frames[ i ]._partition_mgr_cls.map_axis_partitions( axis, other_frames[i]._partitions, make_reindexer(do_repartition_others[i], base_frame_idx + 1 + i), lengths=base_lengths, ) else: reindexed_other_list[i] = other_frames[i]._partitions reindexed_frames = ( [frames[i]._partitions for i in range(base_frame_idx)] + [reindexed_base] + reindexed_other_list ) return (reindexed_frames[0], reindexed_frames[1:], joined_index, base_lengths)

pandas/core/indexes/multi.py

def size(self) -> int: # override Index.size to avoid materializing _values return len(self) # -------------------------------------------

PERF: MultiIndex.size (#48723) * add MultiIndex.size * whatsnew

size

2fbdd1eb4ef73a470f3db60cbf38a7d9f6c3ffe1

pandas

multi.py

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

Python

def size(self) -> int: # override Index.size to avoid materializing _values return len(self) # -------------------------------------------------------------------- # Levels Methods

modin/experimental/core/execution/native/implementations/omnisci_on_native/dataframe/dataframe.py

def groupby_agg(self, by, axis, agg, groupby_args, **kwargs): # Currently we only expect 'by' to be a projection of the same frame. # If 'by' holds a list of columns/series, then we create such projection # to re-use code. if not isinstance(by, DFAlgQueryCompiler): if is_list_like(by): by_cols = [] by_frames = [] for obj in by: if isinstance(obj, str): by_cols.append(obj) elif hasattr(obj, "_modin_frame"): by_frames.append(obj._modin_frame) else: raise NotImplementedError("unsupported groupby args") by_cols = Index.__new__(Index, data=by_cols, dtype=self

REFACTOR-#2656: Update modin to fit algebra (code only) (#3717) Co-authored-by: Yaroslav Igoshev <Poolliver868@mail.ru> Co-authored-by: Vasily Litvinov <vasilij.n.litvinov@intel.com> Co-authored-by: Alexey Prutskov <alexey.prutskov@intel.com> Co-authored-by: Devin Petersohn <devin-petersohn@users.noreply.github.com> Signed-off-by: Rehan Durrani <rehan@ponder.io>

groupby_agg

58bbcc37477866d19c8b092a0e1974a4f0baa586

modin

dataframe.py

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

Python

def groupby_agg(self, by, axis, agg, groupby_args, **kwargs): # Currently we only expect 'by' to be a projection of the same frame. # If 'by' holds a list of columns/series, then we create such projection # to re-use code. if not isinstance(by, DFAlgQueryCompiler): if is_list_like(by): by_cols = [] by_frames = [] for obj in by: if isinstance(obj, str): by_cols.append(obj) elif hasattr(obj, "_modin_frame"): by_frames.append(obj._modin_frame) else: raise NotImplementedError("unsupported groupby args") by_cols = Index.__new__(Index, data=by_cols, dtype=self.columns.dtype) by_frame = self.mask(col_labels=by_cols) if by_frames: by_frame = by_frame.concat( axis=1, other_modin_frames=by_frames, ignore_index=True ) else: raise NotImplementedError("unsupported groupby args") else: by_frame = by._modin_frame if axis != 0: raise NotImplementedError("groupby is supported for axis = 0 only") base = by_frame._find_common_projections_base(self) if base is None: raise NotImplementedError("unsupported groupby args") if groupby_args["level"] is not None: raise NotImplementedError("levels are not supported for groupby") drop = kwargs.get("drop", True) as_index = groupby_args.get("as_index", True) groupby_cols = by_frame.columns if isinstance(agg, dict): agg_cols = agg.keys() elif not drop: # If 'by' data came from a different frame then 'self-aggregation' # columns are more prioritized. agg_cols = self.columns else: agg_cols = [col for col in self.columns if col not in groupby_cols] # Mimic pandas behaviour: pandas does not allow for aggregation to be empty # in case of multi-column 'by'. if not as_index and len(agg_cols) == 0 and len(groupby_cols) > 1: agg_cols = self.columns # Create new base where all required columns are computed. We don't allow # complex expressions to be a group key or an aggeregate operand. allowed_nodes = (FrameNode, TransformNode) if not isinstance(by_frame._op, allowed_nodes): raise NotImplementedError( "OmniSci doesn't allow complex expression to be a group key. " f"The only allowed frame nodes are: {tuple(o.__name__ for o in allowed_nodes)}, " f"met '{type(by_frame._op).__name__}'." ) col_to_delete_template = "__delete_me_{name}"

src/sentry/snuba/tasks.py

def delete_subscription_from_snuba(query_subscription_id, **kwargs): try: subscription = QuerySubscription.objects.get(id=query_subscription_id) except QuerySubscription.DoesNotExist: metrics.incr("snuba.subscriptions.delete.subscription_does_not_exist") return if subscription.status not in [ QuerySubscription.Status.DELETING.value, QuerySubscription.Status.DISABLED.value, ]: metrics.incr("snuba.subscriptions.delete.incorrect_status") return if subscription.subscription_id is not None: query_dataset = QueryDatasets(subscription.snuba_query.dataset) entity_key = get_entity_key_from_snuba_query( subscription.snuba_query, subscription.project.organization_id, subscription.project_id ) _delete_from_s

feat(mep): Restructure how we determine entity subscription for alerts (#36605) Previously we mapped a specific `EntityKey` to all `EntitySubscription` classes. As part of introducing metric based performance alerts, we want to have the `EntitySubscription` determine the specific entity that the subscription will run on. This allows us to automatically determine the correct entity for metric based alerts without having to duplicate logic that parses aggregates/datasets/etc.

delete_subscription_from_snuba

06885ee7284a274d02a9dc1f6a0348c8edc07184

sentry

tasks.py

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

Python

def delete_subscription_from_snuba(query_subscription_id, **kwargs): try: subscription = QuerySubscription.objects.get(id=query_subscription_id) except QuerySubscription.DoesNotExist: metrics.incr("snuba.subscriptions.delete.subscription_does_not_exist") return if subscription.status not in [ QuerySubscription.Status.DELETING.value, QuerySubscription.Status.DISABLED.value, ]: metrics.incr("snuba.subscriptions.delete.incorrect_status") return if subscription.subscription_id is not None: query_dataset = QueryDatasets(subscription.snuba_query.dataset) entity_key = get_entity_key_from_snuba_query( subscription.snuba_query, subscription.project.organization_id, subscription.project_id ) _delete_from_snuba( query_dataset, subscription.subscription_id, entity_key, ) if subscription.status == QuerySubscription.Status.DELETING.value: subscription.delete() else: subscription.update(subscription_id=None)

@pytest.mark.parametrize('count, expected', [(1, 100), (3, 300), (5, 500), (7, 500)])

tests/unit/mainwindow/test_messageview.py

def test_changing_timer_with_messages_shown(qtbot, view, config_stub): config_stub.val.messages.timeout = 900000 # 15s view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test')) with qtbot.wait_signal(view._clear_timer.timeout): config_stub.val.messages.timeout = 100 @pytest.mark.parametrize('count, expected', [(1, 100), (3, 300), (5, 500), (7, 500)])

Add a MessageInfo data class Preparation for #7246

test_changing_timer_with_messages_shown

5616a99eff34f7074641d1391ed77d6b4b743529

qutebrowser

test_messageview.py

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

Python

def test_changing_timer_with_messages_shown(qtbot, view, config_stub): config_stub.val.messages.timeout = 900000 # 15s view.show_message(message.MessageInfo(usertypes.MessageLevel.info, 'test')) with qtbot.wait_signal(view._clear_timer.timeout): config_stub.val.messages.timeout = 100 @pytest.mark.parametrize('count, expected', [(1, 100), (3, 300), (5, 500), (7, 500)])

erpnext/hr/doctype/interview/interview.py

def get_events(start, end, filters=None): from frappe.desk.calendar import get_event_conditions events = [] event_color = { "Pending": "#fff4f0", "Under Review": "#d3e8fc", "Cleared": "#eaf5ed", "Rejected": "#fce7e7", } conditions = get_event_conditions("Interview", filters) interviews = frappe.db.sql( .format( conditions=conditions ), {"start": start, "end": end}, as_dict=True, update={"allDay": 0}, ) for d in interviews: subject_data = [] for field in ["name", "job_applicant", "interview_round"]: if not d.get(field): continue subject_data.append(d.get(field)) color = event_color.get(d.status) interview_data = { "from": get_datetime("%s %s" % (d.scheduled_on, d.from_time or "00:00:00")), "to": get_datetime("%s %s" % (d.scheduled_on, d.to_time or "00:00:00")), "name": d.name, "subject": "

style: format code with black

get_events

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

interview.py

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

Python

def get_events(start, end, filters=None): from frappe.desk.calendar import get_event_conditions events = [] event_color = { "Pending": "#fff4f0", "Under Review": "#d3e8fc", "Cleared": "#eaf5ed", "Rejected": "#fce7e7", } conditions = get_event_conditions("Interview", filters) interviews = frappe.db.sql( .format( conditions=conditions ), {"start": start, "end": end}, as_dict=True, update={"allDay": 0}, ) for d in interviews: subject_data = [] for field in ["name", "job_applicant", "interview_round"]: if not d.get(field): continue subject_data.append(d.get(field)) color = event_color.get(d.status) interview_data = { "from": get_datetime("%s %s" % (d.scheduled_on, d.from_time or "00:00:00")), "to": get_datetime("%s %s" % (d.scheduled_on, d.to_time or "00:00:00")), "name": d.name, "subject": "\n".join(subject_data), "color": color if color else "#89bcde", } events.append(interview_data) return events

python/ray/tune/tests/test_experiment.py

def testFuncTrainableCheckpointConfigValidation(self): with self.assertRaises(ValueError): Experiment( name="foo", run="f1", # Will point to a wrapped function trainable checkpoint_c

[Tune] Fix CheckpointConfig validation for function trainables (#31255) This fixes an issue where a ValueError wasn't being properly raised when passing in a function trainable and setting `checkpoint_at_end=True` or `checkpoint_frequency > 0`. Previously, the error was only raised for function trainables of the form `def train_func(config, checkpoint_dir):`, which is the old checkpoint dir function API. Signed-off-by: Justin Yu <justinvyu@berkeley.edu>

testFuncTrainableCheckpointConfigValidation

51b56ad0118ed3f4341410e8c75625d1ca8cd757

ray

test_experiment.py

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

Python

def testFuncTrainableCheckpointConfigValidation(self): with self.assertRaises(ValueError): Experiment( name="foo", run="f1", # Will point to a wrapped function trainable checkpoint_config=CheckpointConfig(checkpoint_at_end=True), ) with self.assertRaises(ValueError): Experiment( name="foo", run="f1", checkpoint_config=CheckpointConfig(checkpoint_frequency=1), ) with self.assertRaises(ValueError): Experiment( name="foo", run=lambda config: 1, checkpoint_config=CheckpointConfig(checkpoint_at_end=True), )

reconstruction/ostec/utils/align2stylegan.py

def create_perspective_transform(src, dst, round=False, splat_args=False): try: transform_matrix = create_perspective_transform_matrix(src, dst) error = None except np.linalg.LinAlgError as e: transform_matrix = np.identity(3, dtype=np.float) error = "invalid input quads (%s and %s): %s" %(src, dst, e) error = error.replace("\n", "") to_eval = "def perspective_transform(%s):\n" %( splat_args and "*pt" or "pt", ) to_eval += " res = np.dot(transform_matrix, ((pt[0], ), (pt[1], ), (1, )))\n" to_eval += " res = res / res[2]\n" if round: to_eval += " return (int(round(r

initialize ostec

create_perspective_transform

7375ee364e0df2a417f92593e09557f1b2a3575a

insightface

align2stylegan.py

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

Python

def create_perspective_transform(src, dst, round=False, splat_args=False): try: transform_matrix = create_perspective_transform_matrix(src, dst) error = None except np.linalg.LinAlgError as e: transform_matrix = np.identity(3, dtype=np.float) error = "invalid input quads (%s and %s): %s" %(src, dst, e) error = error.replace("\n", "") to_eval = "def perspective_transform(%s):\n" %( splat_args and "*pt" or "pt", ) to_eval += " res = np.dot(transform_matrix, ((pt[0], ), (pt[1], ), (1, )))\n" to_eval += " res = res / res[2]\n" if round: to_eval += " return (int(round(res[0][0])), int(round(res[1][0])))\n" else: to_eval += " return (res[0][0], res[1][0])\n" locals = { "transform_matrix": transform_matrix, } locals.update(globals()) exec(to_eval,locals,locals) res = locals["perspective_transform"] res.matrix = transform_matrix res.error = error return res

qutebrowser/browser/qtnetworkdownloads.py

def get(self, url, cache=True, **kwargs): if not url.isValid(): urlutils.invalid_url_error(url, "start download") return None req = QNetworkRequest(url) user_agent = websettings.user_agent(url) req.setHeader(QNetworkRequest.KnownHeaders.UserAgentHeader, user_agent) if not cache: req.setAttribute(QNetworkRequest.Attribute.CacheSaveControlAttribute, False) return self.get_request(req, **kw

Run scripts/dev/rewrite_enums.py

get

0877fb0d78635692e481c8bde224fac5ad0dd430

qutebrowser

qtnetworkdownloads.py

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

Python

def get(self, url, cache=True, **kwargs): if not url.isValid(): urlutils.invalid_url_error(url, "start download") return None req = QNetworkRequest(url) user_agent = websettings.user_agent(url) req.setHeader(QNetworkRequest.KnownHeaders.UserAgentHeader, user_agent) if not cache: req.setAttribute(QNetworkRequest.Attribute.CacheSaveControlAttribute, False) return self.get_request(req, **kwargs)

tests/federation/test_federation_server.py

def test_send_join_partial_state(self): joining_user = "@misspiggy:" + self.OTHER_SERVER_NAME join_result = self._make_join(joining_user) join_event_dict = join_result["event"] add_hashes_and_signatures( KNOWN_ROOM_VERSIONS[DEFAULT_ROOM_VERSION], join_event_dict, signature_name=self.OTHER_SERVER_NAME, signing_key=self.OTHER_SERVER_SIGNATURE_KEY, ) channel = self.make_signed_federation_request( "PUT", f"/_matrix/federation/v2/send_join/{self._room_id}/x?org.matrix.msc3706.partial_state=true", content=join_event_dict, ) self.assertEquals(channel.code, 200, channel.json_body) # expect a reduced room state returned_state = [ (ev["type"], ev["state_key"]) for ev in channel.json_body["state"] ] self.assertCountEqual( returned_state, [ ("m.room.create", ""), ("m.room.power_levels", ""), ("m.room.join_rules", ""), ("m.room.history_visibility", ""), ], ) # the auth chain should not include anything already in "state" returned_auth_chain_events = [ (ev["type"], ev["state_key"]) for ev in channel.json_body["auth_chain"] ] self.assertCountEqual( returned_auth

Implement MSC3706: partial state in `/send_join` response (#11967) * Make `get_auth_chain_ids` return a Set It has a set internally, and a set is often useful where it gets used, so let's avoid converting to an intermediate list. * Minor refactors in `on_send_join_request` A little bit of non-functional groundwork * Implement MSC3706: partial state in /send_join response

test_send_join_partial_state

63c46349c41aa967e64a5a4042ef5177f934be47

synapse

test_federation_server.py

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

Python

def test_send_join_partial_state(self): joining_user = "@misspiggy:" + self.OTHER_SERVER_NAME join_result = self._make_join(joining_user) join_event_dict = join_result["event"] add_hashes_and_signatures( KNOWN_ROOM_VERSIONS[DEFAULT_ROOM_VERSION], join_event_dict, signature_name=self.OTHER_SERVER_NAME, signing_key=self.OTHER_SERVER_SIGNATURE_KEY, ) channel = self.make_signed_federation_request( "PUT", f"/_matrix/federation/v2/send_join/{self._room_id}/x?org.matrix.msc3706.partial_state=true", content=join_event_dict, ) self.assertEquals(channel.code, 200, channel.json_body) # expect a reduced room state returned_state = [ (ev["type"], ev["state_key"]) for ev in channel.json_body["state"] ] self.assertCountEqual( returned_state, [ ("m.room.create", ""), ("m.room.power_levels", ""), ("m.room.join_rules", ""), ("m.room.history_visibility", ""), ], ) # the auth chain should not include anything already in "state" returned_auth_chain_events = [ (ev["type"], ev["state_key"]) for ev in channel.json_body["auth_chain"] ] self.assertCountEqual( returned_auth_chain_events, [ ("m.room.member", "@kermit:test"), ], ) # the room should show that the new user is a member r = self.get_success( self.hs.get_state_handler().get_current_state(self._room_id) ) self.assertEqual(r[("m.room.member", joining_user)].membership, "join")

python3.10.4/Lib/asyncio/streams.py

async def drain(self): if self._reader is not None: exc = self._reader.exception() if exc is not None: raise exc if self._transport.is_closing(): # Wait for protocol.connection_lost() call # Raise connection closing error if any, # ConnectionResetError otherwise # Yield to the event loop so connection_lost() may be # called. Without this, _drain_helper() would return # immediately, and code that calls # write(...); await drain() # in a loop would never call connection_lost(), so it # would not see an error when the socket is closed. await sleep(0) await self._

add python 3.10.4 for windows

drain

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

streams.py

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

Python

async def drain(self): if self._reader is not None: exc = self._reader.exception() if exc is not None: raise exc if self._transport.is_closing(): # Wait for protocol.connection_lost() call # Raise connection closing error if any, # ConnectionResetError otherwise # Yield to the event loop so connection_lost() may be # called. Without this, _drain_helper() would return # immediately, and code that calls # write(...); await drain() # in a loop would never call connection_lost(), so it # would not see an error when the socket is closed. await sleep(0) await self._protocol._drain_helper()

celery/canvas.py

def on_chord_header_start(self, chord, **header) -> dict: if not isinstance(chord.tasks, group): chord.tasks = group(c

Canvas Header Stamping (#7384) * Strip down the header-stamping PR to the basics. * Serialize groups. * Add groups to result backend meta data. * Fix spelling mistake. * Revert changes to canvas.py * Revert changes to app/base.py * Add stamping implementation to canvas.py * Send task to AMQP with groups. * Successfully pass single group to result. * _freeze_gid dict merge fixed * First draft of the visitor API. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * OptionsVisitor created * Fixed canvas.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added test for simple test for chord and fixed chord implementation * Changed _IMMUTABLE_OPTIONS * Fixed chord interface * Fixed chord interface * Fixed chord interface * Fixed chord interface * Fixed list order * Fixed tests (stamp test and chord test), fixed order in groups * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed lint and elements * Changed implementation of stamp API and fix lint * Added documentation to Stamping API. Added chord with groups test * Implemented stamping inside replace and added test for an implementation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added test additonal tests for chord, improved coverage * Added test additonal tests for chord, improved coverage * Added test additonal tests for chord, improved coverage * Splitted into subtests * Group stamping rollback * group.id is None fixed * Added integration test * Added integration test * apply_async fixed * Integration test and test_chord fixed * Lint fixed * chord freeze fixed * Minor fixes. * Chain apply_async fixed and tests fixed * lint fixed * Added integration test for chord * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * type -> isinstance * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Redo header stamping (#7341) * _freeze_gid dict merge fixed * OptionsVisitor created * Fixed canvas.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added test for simple test for chord and fixed chord implementation * Changed _IMMUTABLE_OPTIONS * Fixed chord interface * Fixed chord interface * Fixed chord interface * Fixed chord interface * Fixed list order * Fixed tests (stamp test and chord test), fixed order in groups * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed lint and elements * Changed implementation of stamp API and fix lint * Added documentation to Stamping API. Added chord with groups test * Implemented stamping inside replace and added test for an implementation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added test additonal tests for chord, improved coverage * Added test additonal tests for chord, improved coverage * Added test additonal tests for chord, improved coverage * Splitted into subtests * Group stamping rollback * group.id is None fixed * Added integration test * Added integration test * apply_async fixed * Integration test and test_chord fixed * Lint fixed * chord freeze fixed * Minor fixes. * Chain apply_async fixed and tests fixed * lint fixed * Added integration test for chord * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * type -> isinstance * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Omer Katz <omer.katz@omerkatz.com> * Added stamping mechanism * Manual stamping improved * flake8 fixed * Added subtests * Add comma. * Moved groups to stamps * Fixed chord and added test for that * Strip down the header-stamping PR to the basics. * Serialize groups. * Add groups to result backend meta data. * Fix spelling mistake. * Revert changes to canvas.py * Revert changes to app/base.py * Add stamping implementation to canvas.py * Send task to AMQP with groups. * Successfully pass single group to result. * _freeze_gid dict merge fixed * First draft of the visitor API. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * OptionsVisitor created * Fixed canvas.py * Added test for simple test for chord and fixed chord implementation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Changed _IMMUTABLE_OPTIONS * Fixed chord interface * Fixed chord interface * Fixed chord interface * Fixed chord interface * Fixed list order * Fixed tests (stamp test and chord test), fixed order in groups * Fixed lint and elements * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Changed implementation of stamp API and fix lint * Added documentation to Stamping API. Added chord with groups test * Implemented stamping inside replace and added test for an implementation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Added test additonal tests for chord, improved coverage * Added test additonal tests for chord, improved coverage * Added test additonal tests for chord, improved coverage * Splitted into subtests * Group stamping rollback * group.id is None fixed * Added integration test * Added integration test * apply_async fixed * Integration test and test_chord fixed * Lint fixed * chord freeze fixed * Minor fixes. * Chain apply_async fixed and tests fixed * lint fixed * Added integration test for chord * type -> isinstance * Added stamping mechanism * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Manual stamping improved * fail_ci_if_error uncommented * flake8 fixed * Added subtests * Changes * Add comma. * Fixed chord and added test for that * canvas.py fixed * Test chord.py fixed * Fixed stamped_headers * collections import fixed * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * collections import fixed * Update celery/backends/base.py Co-authored-by: Omer Katz <omer.katz@omerkatz.com> * ampq.py fixed * Refrain from using deprecated import path. * Fix test_complex_chain regression. Whenever we stamp a group we need to freeze it first if it wasn't already frozen. Somewhere along the line, the group id changed because we were freezing twice. This commit places the stamping operation after preparing the chain's steps which fixes the problem somehow. We don't know why yet. * Fixed integration tests * Fixed integration tests * Fixed integration tests * Fixed integration tests * Fixed issues with maybe_list. Add documentation * Fixed potential issue with integration tests * Fixed issues with _regen * Fixed issues with _regen * Fixed test_generator issues * Fixed _regen stamping * Fixed _regen stamping * Fixed TimeOut issue * Fixed TimeOut issue * Fixed TimeOut issue * Update docs/userguide/canvas.rst Co-authored-by: Omer Katz <omer.katz@omerkatz.com> * Fixed Couchbase * Better stamping intro * New GroupVisitor example * Adjust documentation. Co-authored-by: Naomi Elstein <naomi.els@omerkatz.com> Co-authored-by: Omer Katz <omer.katz@omerkatz.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Asif Saif Uddin <auvipy@gmail.com> Co-authored-by: Omer Katz <omer.katz@kcg.tech>

on_chord_header_start

1c4ff33bd22cf94e297bd6449a06b5a30c2c1fbc

celery

canvas.py

https://github.com/celery/celery.git

Python

def on_chord_header_start(self, chord, **header) -> dict: if not isinstance(chord.tasks, group): chord.tasks = group(chord.tasks) return self.on_group_start(chord.tasks, **header)

freqtrade/freqai/data_drawer.py

def load_historic_predictions_from_disk(self): exists = self.historic_predictions_path.is_file() if exists: try: with open(self.historic_predictions_path, "rb") as fp: self.historic_predictions = cloudpickle.load(fp) logger.info( f"Found existing historic predictions at {self.full_path}, but beware " "that statistics may be inaccurate if the bot ha

backup historical predictions pickle and load the backup in case of corruption

load_historic_predictions_from_disk

ec76214d023a6c53ffab0af8d43bc5b72b1d66af

freqtrade

data_drawer.py

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

Python

def load_historic_predictions_from_disk(self): exists = self.historic_predictions_path.is_file() if exists: try: with open(self.historic_predictions_path, "rb") as fp: self.historic_predictions = cloudpickle.load(fp) logger.info( f"Found existing historic predictions at {self.full_path}, but beware " "that statistics may be inaccurate if the bot has been offline for " "an extended period of time." ) except EOFError: logger.warning( 'Historical prediction file was corrupted. Trying to load backup file.') with open(self.historic_predictions_bkp_path, "rb") as fp: self.historic_predictions = cloudpickle.load(fp) logger.warning('FreqAI successfully loaded the backup historical predictions file.') elif not self.follow_mode: logger.info("Could not find existing historic_predictions, starting from scratch") else: logger.warning( f"Follower could not find historic predictions at {self.full_path} " "sending null values back to strategy" ) return exists

tests/test_css_parse.py

def test_background(self): css = stylesheet = Stylesheet() stylesheet.parse(css) styles = stylesheet.rules[0].styles

Namespacing parsing tests into classes

test_background

1103844708c7f3a3bd1fc33cae56eb59209ef6c0

textual

test_css_parse.py

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

Python

def test_background(self): css = stylesheet = Stylesheet() stylesheet.parse(css) styles = stylesheet.rules[0].styles assert styles.text_background == Color("red", type=ColorType.STANDARD, number=1)

homeassistant/components/dte_energy_bridge/sensor.py

def update(self) -> None: try: response = requests.get(self._url, timeout=5) except (requests.exceptions.RequestException, ValueError): _LOGGER.warning( "Could not update status for DTE Energy Bridge (%s)", self._attr_name ) return if response.status_code != HTTPStatus.OK: _LOGGER.warning( "Invalid status_code from DTE Energy Bridge: %s (%s)", response.status_code, self._attr_name, ) return response_split = response.text.split() if len(response_split) != 2: _LOGGER.warning( 'Invalid response from DTE Energy Bridge: "%s" (%s)', response.text, self._attr_name, ) return

Improve entity type hints [d] (#77031)

update

bf7239c25db06f1377a895244a906b43242c9963

core

sensor.py

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

Python

def update(self) -> None: try: response = requests.get(self._url, timeout=5) except (requests.exceptions.RequestException, ValueError): _LOGGER.warning( "Could not update status for DTE Energy Bridge (%s)", self._attr_name ) return if response.status_code != HTTPStatus.OK: _LOGGER.warning( "Invalid status_code from DTE Energy Bridge: %s (%s)", response.status_code, self._attr_name, ) return response_split = response.text.split() if len(response_split) != 2: _LOGGER.warning( 'Invalid response from DTE Energy Bridge: "%s" (%s)', response.text, self._attr_name, ) return val = float(response_split[0]) # A workaround for a bug in the DTE energy bridge. # The returned value can randomly be in W or kW. Checking for a # a decimal seems to be a reliable way to determine the units. # Limiting to version 1 because version 2 apparently always returns # values in the format 000000.000 kW, but the scaling is Watts # NOT kWatts if self._version == 1 and "." in response_split[0]: self._attr_native_value = val else: self._attr_native_value = val / 1000

dask/order.py

def ndependencies(dependencies, dependents): num_needed = {} result = {} for k, v in dependencies.items(): num_needed[k] = len(v) if not v: result[k] = 1 num_dependencies = num_needed.copy() current = [] current_pop = current.pop cur

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

ndependencies

cccb9d8d8e33a891396b1275c2448c352ef40c27

dask

order.py

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

Python

def ndependencies(dependencies, dependents): num_needed = {} result = {} for k, v in dependencies.items(): num_needed[k] = len(v) if not v: result[k] = 1 num_dependencies = num_needed.copy() current = [] current_pop = current.pop current_append = current.append for key in result: for parent in dependents[key]: num_needed[parent] -= 1 if not num_needed[parent]: current_append(parent) while current: key = current_pop() result[key] = 1 + sum(result[child] for child in dependencies[key]) for parent in dependents[key]: num_needed[parent] -= 1 if not num_needed[parent]: current_append(parent) return num_dependencies, result

pandas/io/pytables.py

def validate_attr(self, append) -> None: if append: existing_fields = getattr(self.attrs, self.kind_attr, None) if existing_fields is not None and existing_fields != list(self.values): raise ValueError("appended items do not match existing items in table!") existing_dtype = getattr(self.attrs, self.dtype_attr, None) if existing_dtype is not None an

TYP: some return annotations in pytables.py (#47512)

validate_attr

7d2f9b8d59908fbf57c6453bc41891efbfe981a6

pandas

pytables.py

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

Python

def validate_attr(self, append) -> None: if append: existing_fields = getattr(self.attrs, self.kind_attr, None) if existing_fields is not None and existing_fields != list(self.values): raise ValueError("appended items do not match existing items in table!") existing_dtype = getattr(self.attrs, self.dtype_attr, None) if existing_dtype is not None and existing_dtype != self.dtype: raise ValueError( "appended items dtype do not match existing items dtype in table!" )

Final = _FinalForm('Final', doc="""A special typing construct to indicate that a name cannot be re-assigned or overridden in a subclass. For example: MAX_SIZE: Final = 9000 MAX_SIZE += 1 # Error reported by type checker"""A special typing construct to indicate that a name cannot be re-assigned or overridden in a subclass. For example: MAX_SIZE: Final = 9000 MAX_SIZE +=a subclass. For example:

pipenv/patched/notpip/_vendor/typing_extensions.py

def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only single type') return typing._GenericAlias(self, (item,)) Final = _FinalForm('Final', d

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

__getitem__

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

typing_extensions.py

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

Python

def __getitem__(self, parameters): item = typing._type_check(parameters, f'{self._name} accepts only single type') return typing._GenericAlias(self, (item,)) Final = _FinalForm('Final', doc=

keras/utils/tf_utils.py

def type_spec_from_value(value): if is_extension_type(value): return value._type_spec # pylint: disable=protected-access # Get a TensorSpec for array-like data without # converting the data to a Tensor if hasattr(value, "shape") and hasattr(value, "dtype"): return tf.TensorSpec(value.shape, value

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

type_spec_from_value

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

tf_utils.py

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