Datasets:

semeru

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text-code-galeras-code-generation-from-docstring-3k-deduped

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Semeru Lab 7

def minzoom(self): return self["minzoom"]

packages/python/plotly/plotly/graph_objs/layout/mapbox/_layer.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_layer.py

minzoom

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

switch to black .22

def telescopic(L, R, limits): (i, a, b) = limits if L.is_Add or R.is_Add: return None # We want to solve(L.subs(i, i + m) + R, m) # First we try a simple match since this does things that # solve doesn't do, e.g. solve(f(k+m)-f(k), m) fails k = Wild("k") sol = (-R).match(L.subs(i, i + k)) s = None if sol and k in sol: s = sol[k] if not (s.is_Integer and L.subs(i, i + s) == -R): # sometimes match fail(f(x+2).match(-f(x+k))->{k: -2 - 2x})) s = None # But there are things that match doesn't do that solve # can do, e.g. determine that 1/(x + m) = 1/(1 - x) when m = 1 if s is None: m = Dummy('m') try: from sympy.solvers.solvers import solve sol = solve(L.subs(i, i + m) + R, m) or [] except NotImplementedError: return None sol = [si for si in sol if si.is_Integer and (L.subs(i, i + si) + R).expand().is_zero] if len(sol) != 1: return None s = sol[0] if s < 0: return telescopic_direct(R, L, abs(s), (i, a, b)) elif s > 0: return telescopic_direct(L, R, s, (i, a, b))

sympy/concrete/summations.py

sympy

Python

f757f3daae6e11ea0cfb7dadc133274d8d74315f

summations.py

telescopic

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

Reordered imports 2

def startarrowsize(self): return self["startarrowsize"]

packages/python/plotly/plotly/graph_objs/layout/_annotation.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_annotation.py

startarrowsize

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

switch to black .22

def get_dependencies(self, candidate): # type: (Candidate) -> list[Candidate] r # FIXME: If there's several galaxy servers set, there may be a # FIXME: situation when the metadata of the same collection # FIXME: differs. So how do we resolve this case? Priority? # FIXME: Taking into account a pinned hash? Exploding on # FIXME: any differences? # NOTE: The underlying implmentation currently uses first found req_map = self._api_proxy.get_collection_dependencies(candidate) # NOTE: This guard expression MUST perform an early exit only # NOTE: after the `get_collection_dependencies()` call because # NOTE: internally it polulates the artifact URL of the candidate, # NOTE: its SHA hash and the Galaxy API token. These are still # NOTE: necessary with `--no-deps` because even with the disabled # NOTE: dependency resolution the outer layer will still need to # NOTE: know how to download and validate the artifact. # # NOTE: Virtual candidates should always return dependencies # NOTE: because they are ephemeral and non-installable. if not self._with_deps and not candidate.is_virtual: return [] return [ self._make_req_from_dict({'name': dep_name, 'version': dep_req}) for dep_name, dep_req in req_map.items() ]

lib/ansible/galaxy/dependency_resolution/providers.py

ansible

Python

8b2e6285650ec42ec4a19075a8567047e8304ba2

providers.py

get_dependencies

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

galaxy - Clean up type hints and imports.

def active_loop_name(self) -> Optional[Text]: if not self.active_loop or self.active_loop.name == SHOULD_NOT_BE_SET: return None return self.active_loop.name

rasa/shared/core/trackers.py

rasa

Python

e798bf049f036a5865c14d4343ed8a833864aabe

trackers.py

active_loop_name

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

convert TrackerActiveLoop to a dataclass

def get_lr(self) -> List: return [self.config.lr_disc, self.config.lr_gen]

TTS/tts/models/vits.py

TTS

Python

00c7600103ee34ac50506af88f1b34b713f849e7

vits.py

get_lr

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

Update Vits model API

def _convert_args_to_cli(vargs): args = ['cleanup'] for option in ('exclude_strings', 'remove_images'): if vargs.get(option): args.append('--{}={}'.format(option.replace('_', '-'), ' '.join(vargs.get(option)))) for option in ('file_pattern', 'image_prune', 'process_isolation_executable', 'grace_period'): if vargs.get(option) is True: args.append('--{}'.format(option.replace('_', '-'))) elif vargs.get(option) not in (None, ''): args.append('--{}={}'.format(option.replace('_', '-'), vargs.get(option))) return args

awx/main/tasks/receptor.py

awx

Python

a4a3ba65d736045733cb49430d7076b73aec23bb

receptor.py

_convert_args_to_cli

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

Refactored tasks.py to a package --- Added 3 new sub-package : awx.main.tasks.system , awx.main.tasks.jobs , awx.main.tasks.receptor --- Modified the functional tests and unit tests accordingly

def get_normal_vector(self) -> np.ndarray: p0, p1, p2 = self.tip.get_start_anchors()[:3] return normalize(np.cross(p2 - p1, p1 - p0))

manim/mobject/geometry/line.py

manim

Python

e040bcacd38378386749db18aeba575b93f4ebca

line.py

get_normal_vector

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

Improved structure of the :mod:`.mobject` module (#2476) * group graphing and update its references * group text and update its references * group opengl and update its references * group three_d and update its references * group geometry and update (most) references * move some chaning.py + updater files into animation * refactor arc.py * refactor line.py * refactor polygram.py * refactor tips.py * black + isort * import new files in __init__.py * refactor places where geometry was used * black + isort again * remove unused imports * update reference.rst * add descriptions to files * fix circular imports * forgot ArrowTip * fix tests * fix doctests * satisfy mypy? * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix ALL merge conflicts * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * one VMobject import slipped through * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * re-add imports to `manim/opengl/__init__.py` * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix reference manual * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * ignore unknown directive type * fix arrow tip imports in docstrings Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Benjamin Hackl <devel@benjamin-hackl.at>

def get_loan_wise_pledges(filters): loan_wise_unpledges = {} current_pledges = {} conditions = "" if filters.get("company"): conditions = "AND company = %(company)s" unpledges = frappe.db.sql( .format( conditions=conditions ), filters, as_dict=1, ) for unpledge in unpledges: loan_wise_unpledges.setdefault((unpledge.loan, unpledge.loan_security), unpledge.qty) pledges = frappe.db.sql( .format( conditions=conditions ), filters, as_dict=1, ) for security in pledges: current_pledges.setdefault((security.loan, security.loan_security), security.qty) current_pledges[(security.loan, security.loan_security)] -= loan_wise_unpledges.get( (security.loan, security.loan_security), 0.0 ) return current_pledges

erpnext/loan_management/report/loan_interest_report/loan_interest_report.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

loan_interest_report.py

get_loan_wise_pledges

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

style: format code with black

def _check_multi_class(multi_class, solver, n_classes): if multi_class == "auto": if solver in ("liblinear", "newton-cholesky"): multi_class = "ovr" elif n_classes > 2: multi_class = "multinomial" else: multi_class = "ovr" if multi_class == "multinomial" and solver in ("liblinear", "newton-cholesky"): raise ValueError("Solver %s does not support a multinomial backend." % solver) return multi_class

sklearn/linear_model/_logistic.py

scikit-learn

Python

bb080aa690364d84d11232c73dc8db2f0dde3578

_logistic.py

_check_multi_class

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

ENH add newton-cholesky solver to LogisticRegression (#24767)

def get_data(filters, mode_of_payments): data = [] conditions = get_conditions(filters) entry = frappe.db.sql( % (conditions), as_dict=1, ) branch_wise_entries, gross_pay = prepare_data(entry) branches = frappe.db.sql_list( % (conditions) ) total_row = {"total": 0, "branch": "Total"} for branch in branches: total = 0 row = {"branch": branch} for mode in mode_of_payments: if branch_wise_entries.get(branch).get(mode): row[mode] = branch_wise_entries.get(branch).get(mode) total += branch_wise_entries.get(branch).get(mode) row["total"] = total data.append(row) total_row = get_total_based_on_mode_of_payment(data, mode_of_payments) total_deductions = gross_pay - total_row.get("total") report_summary = [] if data: data.append(total_row) data.append({}) data.append({"branch": "<b>Total Gross Pay</b>", mode_of_payments[0]: gross_pay}) data.append({"branch": "<b>Total Deductions</b>", mode_of_payments[0]: total_deductions}) data.append({"branch": "<b>Total Net Pay</b>", mode_of_payments[0]: total_row.get("total")}) currency = erpnext.get_company_currency(filters.company) report_summary = get_report_summary( gross_pay, total_deductions, total_row.get("total"), currency ) return data, total_row, report_summary

erpnext/payroll/report/salary_payments_based_on_payment_mode/salary_payments_based_on_payment_mode.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

salary_payments_based_on_payment_mode.py

get_data

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

style: format code with black

def attribute_rule(allowed_attrs):

wagtail/core/whitelist.py

wagtail

Python

d10f15e55806c6944827d801cd9c2d53f5da4186

whitelist.py

attribute_rule

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

Reformat with black

def get_region_to_control_producer(self) -> KafkaProducer: if self._publisher is None: config = settings.KAFKA_TOPICS.get(settings.KAFKA_REGION_TO_CONTROL) self._publisher = KafkaProducer( kafka_config.get_kafka_producer_cluster_options(config["cluster"]) )

src/sentry/region_to_control/producer.py

sentry

Python

fe07466a1449a5ae60526528ce7bf9399b59b47d

producer.py

get_region_to_control_producer

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

chore(hybrid-cloud): Extract region to control silo into service abstraction (#40353) 1. Use the `silo_mode_delegator` to make the silo conditional sensitive logic of region to control processing like other services that need to be conditional based on deployment. 2. Leverage the lifecycle management offered by the `DelegatedBySiloMode` to stop arroyo kafka producer between tests or after test failures (rather than requiring explicit test fixture clean up, it's now 'implicit' to the lifecycle of the mocks introduced at the top level). 3. Add default mocks for the region to control kafka producer so that most tests do not require kafka running (also improves performance significantly). There is still the integration test that uses the real producer. 4. *Attempt* to fix ModuleDeadlock error with more granular importing. I could not reproduce this issue locally, unfortunately, so this is a best effort attempt to reduce any circular import possibilities. Co-authored-by: getsantry[bot] <66042841+getsantry[bot]@users.noreply.github.com>

def dict_from_cookiejar(cj): cookie_dict = {} for cookie in cj: cookie_dict[cookie.name] = cookie.value return cookie_dict

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

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

utils.py

dict_from_cookiejar

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

upd; format

def galois_group(T, max_tries=30, randomize=False): r from sympy.combinatorics.named_groups import CyclicGroup gg = { 3: _galois_group_degree_3, 4: _galois_group_degree_4, 5: _galois_group_degree_5, } max_supported = max(gg.keys()) n = T.degree() if n > max_supported: raise ValueError(f"Only polynomials up to degree {max_supported} are supported.") if n < 1: raise ValueError("Constant polynomial has no Galois group.") if n < 3: return (CyclicGroup(n), n == 1) return gg[n](T, max_tries=max_tries, randomize=randomize)

sympy/polys/numberfields/galoisgroups.py

sympy

Python

d3c0fc825c4a80904a1fb9a2092137c3d9e0c3fe

galoisgroups.py

galois_group

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

Add a `galois_group()` function

def seek(self, pos, whence=SEEK_SET): if isinstance(pos, float): raise TypeError('an integer is required') self._checkClosed() return os.lseek(self._fd, pos, whence)

python3.10.4/Lib/_pyio.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

_pyio.py

seek

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

add python 3.10.4 for windows

def deploy_dask_func(deployer, axis, f_to_deploy, f_args, f_kwargs, *args, **kwargs): result = deployer(axis, f_to_deploy, f_args, f_kwargs, *args, **kwargs) ip = get_ip() if isinstance(result, pandas.DataFrame): return result, len(result), len(result.columns), ip elif all(isinstance(r, pandas.DataFrame) for r in result): return [i for r in result for i in [r, len(r), len(r.columns), ip]] else: return [i for r in result for i in [r, None, None, ip]]

modin/core/execution/dask/implementations/pandas_on_dask/partitioning/virtual_partition.py

modin

Python

d6d503ac7c3028d871c34d9e99e925ddb0746df6

virtual_partition.py

deploy_dask_func

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

FIX-#4597: Refactor Partition handling of func, args, kwargs (#4715) Co-authored-by: Iaroslav Igoshev <Poolliver868@mail.ru> Signed-off-by: Jonathan Shi <jhshi@ponder.io>

def get_used_airflow_sources() -> Path: current_sources = search_upwards_for_airflow_sources_root(Path.cwd()) if current_sources is None: current_sources = get_installation_airflow_sources() if current_sources is None: warn_non_editable() sys.exit(1) return current_sources @lru_cache(maxsize=None)

dev/breeze/src/airflow_breeze/utils/path_utils.py

@lru_cache(maxsize=None)

airflow

Python

bca849b4586c7446438f959b62903da4b997b9ea

path_utils.py

get_used_airflow_sources

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

Switch to `pipx` as the only installation Breeze2 method (#22740) Switching Breeze2 to only use `pipx` for installation of Breeze2 due to problems it might cause for autocompletion if entrypoint is not avaiable on PATH.

def require_spacy_model(model):

tests/utils.py

datasets

Python

0d9c12ad5155c6d505e70813a07c0aecd7120405

utils.py

require_spacy_model

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

Make torch.Tensor and spacy models cacheable (#5191) * Make torch.Tensor and spacy models cacheable * Use newest models * Address comments * Small optim

def tutte_polynomial(G): r import sympy x = sympy.Symbol("x") y = sympy.Symbol("y") stack = deque() stack.append(nx.MultiGraph(G)) polynomial = 0 while stack: G = stack.pop() bridges = set(nx.bridges(G)) e = None for i in G.edges: if (i[0], i[1]) not in bridges and i[0] != i[1]: e = i break if not e: loops = list(nx.selfloop_edges(G, keys=True)) polynomial += x ** len(bridges) * y ** len(loops) else: # deletion-contraction C = nx.contracted_edge(G, e, self_loops=True) C.remove_edge(e[0], e[0]) G.remove_edge(*e) stack.append(G) stack.append(C) return sympy.simplify(polynomial)

networkx/algorithms/polynomials.py

networkx

Python

f11068c0115ede0c7b631f771c10be7efd0b950b

polynomials.py

tutte_polynomial

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

Add Tutte polynomial (#5265) Add a new polynomial module to algorithms for characteristic polynomials. Adds the Tutte polynomial, which is computed and ultimate represented as a sympy expression. Co-authored-by: Dan Schult <dschult@colgate.edu> Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

def idxmax(self, **kwargs): # noqa: PR02 return DataFrameDefault.register(pandas.DataFrame.idxmax)(self, **kwargs)

modin/core/storage_formats/base/query_compiler.py

modin

Python

57e29bc5d82348006c5170ef9ac0a9eedcd9acf9

query_compiler.py

idxmax

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

REFACTOR-#4513: Fix spelling mistakes in docs and docstrings (#4514) Co-authored-by: Rehan Sohail Durrani <rdurrani@berkeley.edu> Signed-off-by: jeffreykennethli <jkli@ponder.io>

def get_mapped_pr_records(): return frappe._dict( frappe.db.sql( ) )

erpnext/buying/report/procurement_tracker/procurement_tracker.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

procurement_tracker.py

get_mapped_pr_records

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

style: format code with black

def calc_mean_std(feat, eps=1e-5): size = feat.size() assert len(size) == 4, 'The input feature should be 4D tensor.' b, c = size[:2] feat_var = feat.view(b, c, -1).var(dim=2) + eps feat_std = feat_var.sqrt().view(b, c, 1, 1) feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) return feat_mean, feat_std

modules/codeformer/codeformer_arch.py

stable-diffusion-webui

Python

6a9b33c848281cb02f38764e4f91ef767f5e3edd

codeformer_arch.py

calc_mean_std

https://github.com/AUTOMATIC1111/stable-diffusion-webui.git

codeformer support

def _lg_directed(G, create_using=None): L = nx.empty_graph(0, create_using, default=G.__class__) # Create a graph specific edge function. get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges for from_node in get_edges(): # from_node is: (u,v) or (u,v,key) L.add_node(from_node) for to_node in get_edges(from_node[1]): L.add_edge(from_node, to_node) return L

networkx/generators/line.py

networkx

Python

e308b80f17264b89acf8defe185c71c6656d5105

line.py

_lg_directed

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

MAINT: Remove unnecessary helper functions, use inbuilt methods for line graph generator (#5327) * MAINT: Remove unnecessary helper functions, use inbuilt methods * Use multigraph key to create node, add tests for multi(di)graphs

def id_for_label(self, id_, index="0"): if id_ and self.add_id_index: id_ = "%s_%s" % (id_, index) return id_

django/forms/widgets.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

widgets.py

id_for_label

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

Refs #33476 -- Reformatted code with Black.

def validate_snuba() -> None: if not settings.DEBUG: return has_all_snuba_required_backends = ( settings.SENTRY_SEARCH in ( "sentry.search.snuba.EventsDatasetSnubaSearchBackend", "sentry.utils.services.ServiceDelegator", ) and settings.SENTRY_TAGSTORE == "sentry.tagstore.snuba.SnubaTagStorage" and # TODO(mattrobenolt): Remove ServiceDelegator check settings.SENTRY_TSDB in ("sentry.tsdb.redissnuba.RedisSnubaTSDB", "sentry.utils.services.ServiceDelegator") ) eventstream_is_snuba = ( settings.SENTRY_EVENTSTREAM == "sentry.eventstream.snuba.SnubaEventStream" or settings.SENTRY_EVENTSTREAM == "sentry.eventstream.kafka.KafkaEventStream" ) # All good here, it doesn't matter what else is going on if has_all_snuba_required_backends and eventstream_is_snuba: return from sentry.features import requires_snuba as snuba_features snuba_enabled_features = set() for feature in snuba_features: if settings.SENTRY_FEATURES.get(feature, False): snuba_enabled_features.add(feature) if snuba_enabled_features and not eventstream_is_snuba: from .importer import ConfigurationError show_big_error( % "\n".join(snuba_enabled_features) ) raise ConfigurationError("Cannot continue without Snuba configured.") if not eventstream_is_snuba: from .importer import ConfigurationError show_big_error( % ( settings.SENTRY_SEARCH, settings.SENTRY_TAGSTORE, settings.SENTRY_TSDB, settings.SENTRY_EVENTSTREAM, ) ) raise ConfigurationError("Cannot continue without Snuba configured correctly.") if eventstream_is_snuba and not has_all_snuba_required_backends: show_big_error( % ( settings.SENTRY_SEARCH, settings.SENTRY_TAGSTORE, settings.SENTRY_TSDB, settings.SENTRY_EVENTSTREAM, ) )

src/sentry/runner/initializer.py

sentry

Python

2f6716c264bbd916c2773edb8b75cf2e9b26c51b

initializer.py

validate_snuba

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

ref: type devserver startup (#38598) I noticed `sentry devserver 127.0.0.1` produced this error and decided to prevent it using typing: ```console $ sentry devserver 127.0.0.1 INFO:The Sentry runner will report development issues to Sentry.io. Use SENTRY_DEVENV_NO_REPORT to avoid reporting issues. 16:33:40 [WARNING] sentry.utils.geo: settings.GEOIP_PATH_MMDB not configured. /Users/armenzg/code/sentry/src/sentry/runner/initializer.py:571: DeprecatedSettingWarning: The SENTRY_URL_PREFIX setting is deprecated. Please use SENTRY_OPTIONS['system.url-prefix'] instead. warnings.warn(DeprecatedSettingWarning(old, "SENTRY_OPTIONS['%s']" % new)) 16:33:41 [INFO] sentry.plugins.github: apps-not-configured 16:33:41 [INFO] sentry.runner: We have reported the error below to Sentry /Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/sentry_sdk/worker.py:123: ResourceWarning: unclosed <ssl.SSLSocket fd=6, family=AddressFamily.AF_INET, type=SocketKind.SOCK_STREAM, proto=0, laddr=('192.168.0.14', 58764), raddr=('34.120.195.249', 443)> callback = self._queue.get() ResourceWarning: Enable tracemalloc to get the object allocation traceback Traceback (most recent call last): File "/Users/armenzg/code/sentry/.venv/bin/sentry", line 33, in <module> sys.exit(load_entry_point('sentry', 'console_scripts', 'sentry')()) File "/Users/armenzg/code/sentry/src/sentry/runner/__init__.py", line 186, in main raise e File "/Users/armenzg/code/sentry/src/sentry/runner/__init__.py", line 178, in main func(**kwargs) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/core.py", line 1128, in __call__ return self.main(*args, **kwargs) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/core.py", line 1053, in main rv = self.invoke(ctx) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/core.py", line 1659, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/core.py", line 1395, in invoke return ctx.invoke(self.callback, **ctx.params) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/core.py", line 754, in invoke return __callback(*args, **kwargs) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/decorators.py", line 26, in new_func return f(get_current_context(), *args, **kwargs) File "/Users/armenzg/code/sentry/src/sentry/runner/decorators.py", line 69, in inner return ctx.invoke(f, *args, **kwargs) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/core.py", line 754, in invoke return __callback(*args, **kwargs) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/decorators.py", line 26, in new_func return f(get_current_context(), *args, **kwargs) File "/Users/armenzg/code/sentry/src/sentry/runner/decorators.py", line 29, in inner return ctx.invoke(f, *args, **kwargs) File "/Users/armenzg/code/sentry/.venv/lib/python3.8/site-packages/click/core.py", line 754, in invoke return __callback(*args, **kwargs) File "/Users/armenzg/code/sentry/src/sentry/runner/commands/devserver.py", line 215, in devserver port = port + 1 TypeError: unsupported operand type(s) for +: 'NoneType' and 'int ```

def flatten(self) -> Union["FeatureType", Dict[str, "FeatureType"]]: from .features import Value return ( self if self.decode else { "bytes": Value("binary"), "path": Value("string"), } )

src/datasets/features/image.py

datasets

Python

3804442bb7cfcb9d52044d92688115cfdc69c2da

image.py

flatten

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

Fix flatten of complex feature types (#3723) * Flatten Translation and TranslationVariableLanguages * Add tests * Style * Flatten for decodable features * Fix flatten for non-dict types * Add test * Descriptive message in flatten for Audio feature * Small refactor * Add flatten to features * Update table_flatten * Revert changes in Dataset.flatten_/flatten * Apply Quentin's suggestions from code review Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> * Improve table_flatten docstring * Fix tests * Add nested test * Minor fix * Remove comment Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>

def dot(self, other): from dask.array.routines import tensordot return tensordot(self, other, axes=((self.ndim - 1,), (other.ndim - 2,)))

dask/array/core.py

dask

Python

2820bae493a49cb1d0a6e376985c5473b8f04fa8

core.py

dot

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

Don't include docs in ``Array`` methods, just refer to module docs (#9244) Co-authored-by: James Bourbeau <jrbourbeau@users.noreply.github.com>

def sort(self) -> None: raise NotImplementedError()

tools/sort/sort_methods.py

faceswap

Python

98d01760e469fd2108eed8d0b0a1ba6297c3177c

sort_methods.py

sort

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

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

def losses(self): collected_losses = [] for layer in self._flatten_layers(): # If any eager losses are present, we assume the model to be part of # an eager training loop (either a custom one or the one used when # `run_eagerly=True`) and so we always return just the eager losses. if layer._eager_losses: # Filter placeholder losses that may have been added by revived # layers. (see base_layer_utils for details). if ( layer._eager_losses[0] is not base_layer_utils.REVIVED_LOSS_PLACEHOLDER ): collected_losses.extend(layer._eager_losses) else: collected_losses.extend(layer._losses) for regularizer in layer._callable_losses: loss_tensor = regularizer() if loss_tensor is not None: collected_losses.append(loss_tensor) return collected_losses

keras/engine/base_layer.py

keras

Python

fa6d9107a498f7c2403ff28c7b389a1a0c5cc083

base_layer.py

losses

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

reduct too long lines

def store_rendered_templates(store, signal, sender, template, context, **kwargs): store.setdefault("templates", []).append(template) if "context" not in store: store["context"] = ContextList() store["context"].append(copy(context))

django/test/client.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

client.py

store_rendered_templates

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

Refs #33476 -- Reformatted code with Black.

def test_unicode_idval(self) -> None: values = [ ("", r""), ("ascii", r"ascii"), ("ação", r"a\xe7\xe3o"), ("josé@blah.com", r"jos\xe9@blah.com"), ( r"δοκ.ιμή@παράδειγμα.δοκιμή", r"\u03b4\u03bf\u03ba.\u03b9\u03bc\u03ae@\u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3" r"\u03bc\u03b1.\u03b4\u03bf\u03ba\u03b9\u03bc\u03ae", ), ] for val, expected in values: assert ( IdMaker([], [], None, None, None, None)._idval(val, "a", 6) == expected )

testing/python/metafunc.py

pytest

Python

b21b008118fc8cf65b4bcd9b059f1cd704e05c68

metafunc.py

test_unicode_idval

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

Refactor idmaker functions into class IdMaker This commit only refactors, it does not change or add functionality yet. Public API is retained. Reason or refactoring: User provided parameter IDs (e.g. Metafunc.parametrize(ids=...)) had so far only been used to calculate a unique test ID for each test invocation. That test ID was a joined string where each parameter contributed some partial ID. We're soon going to reuse functionality to generate parameter keys for reorder_items and FixtureDef cache. We will be interested in the partial IDs, and only if they originate from explicit user information. Refactoring makes logic and data accessible for reuse, and increases cohesion in general.

def _has_webengine(self) -> bool: try: import qutebrowser.qt.webenginewidgets # pylint: disable=unused-import except ImportError: return False return True

qutebrowser/config/configfiles.py

qutebrowser

Python

218f490484066660dd4e899da600b252f7edd468

configfiles.py

_has_webengine

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

Warn on QtWebEngine downgrade and Qt 5 -> 6 upgrade

def tag_resource(self, resource_ids, tags, resource_type="instance"): request = TagResourcesRequest() request.set_Tags(tags) request.set_ResourceType(resource_type) request.set_ResourceIds(resource_ids) response = self._send_request(request) if response is not None: logging.info("instance %s create tag successfully.", resource_ids) else: logging.error("instance %s create tag failed.", resource_ids)

python/ray/autoscaler/_private/aliyun/utils.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

utils.py

tag_resource

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

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

async def async_close_cover(self, **kwargs): await mqtt.async_publish( self.hass, self._config.get(CONF_COMMAND_TOPIC), self._config[CONF_PAYLOAD_CLOSE], self._config[CONF_QOS], self._config[CONF_RETAIN], self._config[CONF_ENCODING], ) if self._optimistic: # Optimistically assume that cover has changed state. self._state = STATE_CLOSED if self._config.get(CONF_GET_POSITION_TOPIC): self._position = self.find_percentage_in_range( self._config[CONF_POSITION_CLOSED], COVER_PAYLOAD ) self.async_write_ha_state()

homeassistant/components/mqtt/cover.py

core

Python

d0c4f0fec4216e4193da716001b5e13e1e3f2106

cover.py

async_close_cover

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

Add mqtt encoding support for publishing (#62739) * encoding support for mqtt publishing - todo tests * signature allows None values for qos and retain * common test for mqtt publishing encoding * better test with command templates * more tests * fix tests alarm control panel+tests light basic * tests light json and template * add tests vacuum and fix tests light_template

def links(self): header = self.headers.get("link") resolved_links = {} if header: links = parse_header_links(header) for link in links: key = link.get("rel") or link.get("url") resolved_links[key] = link return resolved_links

pipenv/patched/pip/_vendor/requests/models.py

pipenv

Python

cd5a9683be69c86c8f3adcd13385a9bc5db198ec

models.py

links

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

Rename notpip to pip. Vendor in pip-22.2.1 and latest requirementslib and vistir.

def text(self, body): text_proto = TextProto() text_proto.body = clean_text(body) return self.dg._enqueue("text", text_proto)

lib/streamlit/elements/text.py

streamlit

Python

72703b38029f9358a0ec7ca5ed875a6b438ece19

text.py

text

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

Replace static apps with live Cloud apps (#4317) Co-authored-by: kajarenc <kajarenc@gmail.com>

def baseratio(self): return self["baseratio"]

packages/python/plotly/plotly/graph_objs/_funnelarea.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_funnelarea.py

baseratio

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

switch to black .22

def test_sum_distinct_aggregate(self): authors = Author.objects.filter(book__in=[self.b5, self.b6]) self.assertEqual(authors.count(), 3) distinct_authors = authors.distinct() self.assertEqual(distinct_authors.count(), 2) # Selected author ages are 57 and 46 age_sum = distinct_authors.aggregate(Sum("age")) self.assertEqual(age_sum["age__sum"], 103)

tests/aggregation/tests.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

tests.py

test_sum_distinct_aggregate

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

Refs #33476 -- Reformatted code with Black.

def test_episodes_unit(self): self.batch_id = 0

rllib/utils/replay_buffers/tests/test_reservoir_buffer.py

ray

Python

acf2bf9b2fa9f6cac8c599ec1eea6a9d5249905f

test_reservoir_buffer.py

test_episodes_unit

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

[RLlib] Get rid of all these deprecation warnings. (#27085)

def test_presubmit_shortcircuit(ray_start_1_cpu):

python/ray/util/dask/tests/test_dask_callback.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

test_dask_callback.py

test_presubmit_shortcircuit

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

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

def _check_prepopulated_fields_value(self, obj, val, label): if not isinstance(val, (list, tuple)): return must_be("a list or tuple", option=label, obj=obj, id="admin.E029") else: return list( chain.from_iterable( self._check_prepopulated_fields_value_item( obj, subfield_name, "%s[%r]" % (label, index) ) for index, subfield_name in enumerate(val) ) )

django/contrib/admin/checks.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

checks.py

_check_prepopulated_fields_value

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

Refs #33476 -- Reformatted code with Black.

def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): r requires_backends(cls, "pyctcdecode") from pyctcdecode import BeamSearchDecoderCTC feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs) tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs) if os.path.isdir(pretrained_model_name_or_path): decoder = BeamSearchDecoderCTC.load_from_dir(pretrained_model_name_or_path) else: # BeamSearchDecoderCTC has no auto class kwargs.pop("_from_auto", None) # make sure that only relevant filenames are downloaded language_model_filenames = os.path.join(BeamSearchDecoderCTC._LANGUAGE_MODEL_SERIALIZED_DIRECTORY, "*") alphabet_filename = BeamSearchDecoderCTC._ALPHABET_SERIALIZED_FILENAME allow_regex = [language_model_filenames, alphabet_filename] decoder = BeamSearchDecoderCTC.load_from_hf_hub( pretrained_model_name_or_path, allow_regex=allow_regex, **kwargs ) # set language model attributes for attribute in ["alpha", "beta", "unk_score_offset", "score_boundary"]: value = kwargs.pop(attribute, None) if value is not None: cls._set_language_model_attribute(decoder, attribute, value) # make sure that decoder's alphabet and tokenizer's vocab match in content missing_decoder_tokens = cls.get_missing_alphabet_tokens(decoder, tokenizer) if len(missing_decoder_tokens) > 0: raise ValueError( f"The tokens {missing_decoder_tokens} are defined in the tokenizer's " "vocabulary, but not in the decoder's alphabet. " f"Make sure to include {missing_decoder_tokens} in the decoder's alphabet." ) return cls(feature_extractor=feature_extractor, tokenizer=tokenizer, decoder=decoder)

src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py

transformers

Python

efb35a4107478f7d2ebcf56572c0967e68536e15

processing_wav2vec2_with_lm.py

from_pretrained

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

[Wav2Vec2ProcessorWithLM] improve decoder downlaod (#15040)

async def follower_loop(self): try: await self._connect_to_leaders() except Exception as e: logger.error("Exception occurred in follower loop: ") logger.exception(e)

freqtrade/rpc/replicate/__init__.py

freqtrade

Python

9f6bba40af1a407f190a89f5c0c8b4e3f528ba46

__init__.py

follower_loop

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

initial concept for replicate, basic leader and follower logic

def coverage_ratio(self) -> float: return self._coverage_ratio

scripts/convert.py

faceswap

Python

1022651eb8a7741014f5d2ec7cbfe882120dfa5f

convert.py

coverage_ratio

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

Bugfix: convert - Gif Writer - Fix non-launch error on Gif Writer - convert plugins - linting - convert/fs_media/preview/queue_manager - typing - Change convert items from dict to Dataclass

def test_standard_get_document_model_string(self): del settings.WAGTAILDOCS_DOCUMENT_MODEL self.assertEqual(get_document_model_string(), "wagtaildocs.Document")

wagtail/documents/tests/test_models.py

wagtail

Python

d10f15e55806c6944827d801cd9c2d53f5da4186

test_models.py

test_standard_get_document_model_string

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

Reformat with black

def get_denominations() -> Dict[DENOMINATION, float]: return { "Trillions": 1_000_000_000_000, "Billions": 1_000_000_000, "Millions": 1_000_000, "Thousands": 1_000, "Units": 1, }

openbb_terminal/helpers_denomination.py

OpenBBTerminal

Python

07c08df84e2af99be4ee32ab276128cafb9e7986

helpers_denomination.py

get_denominations

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

Bug/2583 (#2671) * #2583 [CT] Add and use denomination helper * #2583 [CT] Fix Yahoo Finance denomination * #2583 [CT] Fix typings for dict * #2583 [CT] Add YF model get financials tests * #2583 [CT] Fix stubbed currency * #2583 [CT] Add test coverage for denomination helpers * #2583 [CT] Fix YF view not exporting raw data Co-authored-by: DidierRLopes <dro.lopes@campus.fct.unl.pt> Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com>

def update_parent_account_names(accounts): name_to_account_map = {} for d in accounts: if d.account_number: account_name = d.account_number + " - " + d.account_name else: account_name = d.account_name name_to_account_map[d.name] = account_name for account in accounts: if account.parent_account: account["parent_account_name"] = name_to_account_map.get(account.parent_account) return accounts

erpnext/accounts/report/consolidated_financial_statement/consolidated_financial_statement.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

consolidated_financial_statement.py

update_parent_account_names

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

style: format code with black

def test_cluster_interrupt_searcher(start_connected_cluster, tmpdir, searcher): cluster = start_connected_cluster dirpath = str(tmpdir) local_checkpoint_dir = os.path.join(dirpath, "experiment") from ray.tune import register_trainable register_trainable("trainable", MyTrainableClass)

python/ray/tune/tests/test_cluster_searcher.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

test_cluster_searcher.py

test_cluster_interrupt_searcher

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

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

def __invert__(self): return NotAny(self)

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

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

pyparsing.py

__invert__

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

upd; format

def solidity(self): return self["solidity"]

packages/python/plotly/plotly/graph_objs/bar/marker/_pattern.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_pattern.py

solidity

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

switch to black .22

def extract_data(self, response): try: data = response.json() except ValueError as e: # If there was no json to parse data = {} if response.text or response.status_code not in (200, 202, 204): text = response.text if len(text) > 1024: text = text[:1024] + '... <<< Truncated >>> ...' log.debug("Unable to parse JSON response ({0.status_code}): {1} - '{2}'".format(response, e, text)) return data

awxkit/awxkit/api/pages/page.py

awx

Python

68a44529b6b77d2d43d7099b654560bfd8bbf518

page.py

extract_data

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

Register pages for the Instance peers and install bundle endpoints This includes exposing a new interface for Page objects, Page.bytes, to return the full bytestring contents of the response.

def add_to_apply_calls(self, func, *args, **kwargs): return PandasOnPythonDataframePartition( self._data.copy(), call_queue=self.call_queue + [(func, args, kwargs)], )

modin/core/execution/python/implementations/pandas_on_python/partitioning/partition.py

modin

Python

4ec7f6347903f9133c65ebc5b6e0e15553b98577

partition.py

add_to_apply_calls

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

REFACTOR-#4530: Standardize access to physical data in partitions (#4563) Signed-off-by: Alexey Prutskov <lehaprutskov@gmail.com>

def callback_data(self) -> JSONData: return json.loads(self.data["callback_id"])

src/sentry/integrations/slack/requests/action.py

sentry

Python

10fbaf4b856f85879611d50b714fa47eb4a358c3

action.py

callback_data

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

ref: add src/sentry/utils/json.py to mypy.ini (#41133) first commit I sorted some of the mypy files (separated out to make the diff of the second commit easier to follow)

def test_overlap_first(business_client, setup_before_upload, show_overlap_first): c = business_client config = dict( title='test_overlap_first', is_published=True, maximum_annotations=1, show_overlap_first=show_overlap_first, sampling="Uniform sampling", label_config= ) project = make_project(config, business_client.user) annotation_result = json.dumps([{ 'from_name': 'text_class', 'to_name': 'text', 'type': 'choices', 'value': {'choices': ['class_A']} }]) num_tasks = 1000 overlap_cohort_percentage = 1 # set up tasks overlap setup_after_upload = True if setup_before_upload: r = c.patch( f'/api/projects/{project.id}/', data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}), content_type='application/json' ) assert r.status_code == 200 setup_after_upload = False # create tasks tasks = [] for i in range(num_tasks): tasks.append({'data': {'text': f'this is {str(i)}'}}) r = business_client.post( f'/api/projects/{project.id}/tasks/bulk/', data=json.dumps(tasks), content_type='application/json') assert r.status_code == 201 if setup_after_upload: r = c.patch( f'/api/projects/{project.id}/', data=json.dumps({'maximum_annotations': 2, 'overlap_cohort_percentage': overlap_cohort_percentage}), content_type='application/json' ) assert r.status_code == 200 expected_tasks_with_overlap = int(overlap_cohort_percentage / 100. * num_tasks) assert Task.objects.filter(Q(project_id=project.id) & Q(overlap__gt=1)).count() == expected_tasks_with_overlap

label_studio/tests/test_next_task.py

label-studio

Python

35125cca12ba1e8703c4284894e4e2db44ce7009

test_next_task.py

test_overlap_first

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

fix: DEV-1348: Fix _rearrange_overlap_cohort filter condition for overlap bulk update with concurrent import (#1844) * [fix] Rearrange overlap depending in annotations count * Fix next task test for not random overlap assignment * Delete unused method * Rename rearrange method to have back compatibility * Refactor to Q_finished_annotations from tasks.models * Fix filter for tasks with max annotations * Change filter for tasks with max annotations * Change project stats recalculation condition * Fix rearrange during import from storage * Change _rearrange_overlap_cohort filter condition * Switching to bulk_update in _rearrange_overlap_cohort * Stylize code * Add is_labeled on import * Fix tests * Fix tests * Fix tests more Co-authored-by: nik <nik@heartex.net> Co-authored-by: Sergei Ivashchenko <triklozoid@gmail.com> Co-authored-by: niklub <lubimov.nicolas@gmail.com> Co-authored-by: Max Tkachenko <makseq@gmail.com>

def get_variant(template, args=None, variant=None, manufacturer=None, manufacturer_part_no=None): item_template = frappe.get_doc("Item", template) if item_template.variant_based_on == "Manufacturer" and manufacturer: return make_variant_based_on_manufacturer(item_template, manufacturer, manufacturer_part_no) else: if isinstance(args, str): args = json.loads(args) if not args: frappe.throw(_("Please specify at least one attribute in the Attributes table")) return find_variant(template, args, variant)

erpnext/controllers/item_variant.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

item_variant.py

get_variant

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

style: format code with black

def replaceHTMLEntity(t): return _htmlEntityMap.get(t.entity) # it's easy to get these comment structures wrong - they're very common, so may as well make them available cStyleComment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + '*/').setName("C style comment") "Comment of the form ``/* ... */``" htmlComment = Regex(r"<!--[\s\S]*?-->").setName("HTML comment") "Comment of the form ``<!-- ... -->``" restOfLine = Regex(r".*").leaveWhitespace().setName("rest of line") dblSlashComment = Regex(r"//(?:\\\n|[^\n])*").setName("// comment") "Comment of the form ``// ... (to end of line)``" cppStyleComment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + '*/' | dblSlashComment).setName("C++ style comment") "Comment of either form :class:`cStyleComment` or :class:`dblSlashComment`" javaStyleComment = cppStyleComment "Same as :class:`cppStyleComment`" pythonStyleComment = Regex(r"#.*").setName("Python style comment") "Comment of the form ``# ... (to end of line)``" _commasepitem = Combine(OneOrMore(Word(printables, excludeChars=',') + Optional(Word(" \t") + ~Literal(",") + ~LineEnd()))).streamline().setName("commaItem") commaSeparatedList = delimitedList(Optional(quotedString.copy() | _commasepitem, default="")).setName("commaSeparatedList") # some other useful expressions - using lower-case class name since we are really using this as a namespace

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

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

pyparsing.py

replaceHTMLEntity

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

upd; format

def test_reading_jsonl_dataset_should_be_successful(tasks_base_path): dataset = JsonlDataset(tasks_base_path / "jsonl/train.jsonl") assert len(dataset.sentences) == 5 assert dataset.sentences[0].to_tagged_string() == "This is New <B-LOC> Berlin <I-LOC>" assert dataset.sentences[1].to_tagged_string() == "This is New <B-LOC> Berlin <I-LOC> ." assert dataset.sentences[2].to_tagged_string() == "This is New <B-LOC> Berlin <I-LOC> . <I-LOC>" assert ( dataset.sentences[3].to_tagged_string() == "EU <B-ORG> rejects German <B-MISC> call to boycott British <B-MISC> lamb <I-MISC> ." )

tests/test_datasets.py

flair

Python

a3120b5179f51308d4c0c1f4865873debb566bbd

test_datasets.py

test_reading_jsonl_dataset_should_be_successful

https://github.com/flairNLP/flair.git

refactor: :recycle: make label_type configurable for Jsonl corpora

def rands(nchars) -> str: return "".join(np.random.choice(RANDS_CHARS, nchars))

pandas/_testing/_random.py

pandas

Python

f538568afc2c76c2d738d32e3544cf9fe6742960

_random.py

rands

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

TYP: misc return type annotations (#47558)

def test_get_action(self): action_name = "delete_selected" self.assertEqual(self.site.get_action(action_name), delete_selected) self.site.disable_action(action_name) self.assertEqual(self.site.get_action(action_name), delete_selected)

tests/admin_views/test_adminsite.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

test_adminsite.py

test_get_action

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

Refs #33476 -- Reformatted code with Black.

def arrowsize(self): return self["arrowsize"]

packages/python/plotly/plotly/graph_objs/layout/_annotation.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_annotation.py

arrowsize

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

switch to black .22

def get_basic_details(args, item, overwrite_warehouse=True): if not item: item = frappe.get_doc("Item", args.get("item_code")) if item.variant_of: item.update_template_tables() item_defaults = get_item_defaults(item.name, args.company) item_group_defaults = get_item_group_defaults(item.name, args.company) brand_defaults = get_brand_defaults(item.name, args.company) defaults = frappe._dict( { "item_defaults": item_defaults, "item_group_defaults": item_group_defaults, "brand_defaults": brand_defaults, } ) warehouse = get_item_warehouse(item, args, overwrite_warehouse, defaults) if args.get("doctype") == "Material Request" and not args.get("material_request_type"): args["material_request_type"] = frappe.db.get_value( "Material Request", args.get("name"), "material_request_type", cache=True ) expense_account = None if args.get("doctype") == "Purchase Invoice" and item.is_fixed_asset: from erpnext.assets.doctype.asset_category.asset_category import get_asset_category_account expense_account = get_asset_category_account( fieldname="fixed_asset_account", item=args.item_code, company=args.company ) # Set the UOM to the Default Sales UOM or Default Purchase UOM if configured in the Item Master if not args.get("uom"): if args.get("doctype") in sales_doctypes: args.uom = item.sales_uom if item.sales_uom else item.stock_uom elif (args.get("doctype") in ["Purchase Order", "Purchase Receipt", "Purchase Invoice"]) or ( args.get("doctype") == "Material Request" and args.get("material_request_type") == "Purchase" ): args.uom = item.purchase_uom if item.purchase_uom else item.stock_uom else: args.uom = item.stock_uom if args.get("batch_no") and item.name != frappe.get_cached_value( "Batch", args.get("batch_no"), "item" ): args["batch_no"] = "" out = frappe._dict( { "item_code": item.name, "item_name": item.item_name, "description": cstr(item.description).strip(), "image": cstr(item.image).strip(), "warehouse": warehouse, "income_account": get_default_income_account( args, item_defaults, item_group_defaults, brand_defaults ), "expense_account": expense_account or get_default_expense_account(args, item_defaults, item_group_defaults, brand_defaults), "discount_account": get_default_discount_account(args, item_defaults), "cost_center": get_default_cost_center( args, item_defaults, item_group_defaults, brand_defaults ), "has_serial_no": item.has_serial_no, "has_batch_no": item.has_batch_no, "batch_no": args.get("batch_no"), "uom": args.uom, "min_order_qty": flt(item.min_order_qty) if args.doctype == "Material Request" else "", "qty": flt(args.qty) or 1.0, "stock_qty": flt(args.qty) or 1.0, "price_list_rate": 0.0, "base_price_list_rate": 0.0, "rate": 0.0, "base_rate": 0.0, "amount": 0.0, "base_amount": 0.0, "net_rate": 0.0, "net_amount": 0.0, "discount_percentage": 0.0, "discount_amount": 0.0, "supplier": get_default_supplier(args, item_defaults, item_group_defaults, brand_defaults), "update_stock": args.get("update_stock") if args.get("doctype") in ["Sales Invoice", "Purchase Invoice"] else 0, "delivered_by_supplier": item.delivered_by_supplier if args.get("doctype") in ["Sales Order", "Sales Invoice"] else 0, "is_fixed_asset": item.is_fixed_asset, "last_purchase_rate": item.last_purchase_rate if args.get("doctype") in ["Purchase Order"] else 0, "transaction_date": args.get("transaction_date"), "against_blanket_order": args.get("against_blanket_order"), "bom_no": item.get("default_bom"), "weight_per_unit": args.get("weight_per_unit") or item.get("weight_per_unit"), "weight_uom": args.get("weight_uom") or item.get("weight_uom"), "grant_commission": item.get("grant_commission"), } ) if item.get("enable_deferred_revenue") or item.get("enable_deferred_expense"): out.update(calculate_service_end_date(args, item)) # calculate conversion factor if item.stock_uom == args.uom: out.conversion_factor = 1.0 else: out.conversion_factor = args.conversion_factor or get_conversion_factor(item.name, args.uom).get( "conversion_factor" ) args.conversion_factor = out.conversion_factor out.stock_qty = out.qty * out.conversion_factor args.stock_qty = out.stock_qty # calculate last purchase rate if args.get("doctype") in purchase_doctypes: from erpnext.buying.doctype.purchase_order.purchase_order import item_last_purchase_rate out.last_purchase_rate = item_last_purchase_rate( args.name, args.conversion_rate, item.name, out.conversion_factor ) # if default specified in item is for another company, fetch from company for d in [ ["Account", "income_account", "default_income_account"], ["Account", "expense_account", "default_expense_account"], ["Cost Center", "cost_center", "cost_center"], ["Warehouse", "warehouse", ""], ]: if not out[d[1]]: out[d[1]] = frappe.get_cached_value("Company", args.company, d[2]) if d[2] else None for fieldname in ("item_name", "item_group", "brand", "stock_uom"): out[fieldname] = item.get(fieldname) if args.get("manufacturer"): part_no = get_item_manufacturer_part_no(args.get("item_code"), args.get("manufacturer")) if part_no: out["manufacturer_part_no"] = part_no else: out["manufacturer_part_no"] = None out["manufacturer"] = None else: data = frappe.get_value( "Item", item.name, ["default_item_manufacturer", "default_manufacturer_part_no"], as_dict=1 ) if data: out.update( { "manufacturer": data.default_item_manufacturer, "manufacturer_part_no": data.default_manufacturer_part_no, } ) child_doctype = args.doctype + " Item" meta = frappe.get_meta(child_doctype) if meta.get_field("barcode"): update_barcode_value(out) if out.get("weight_per_unit"): out["total_weight"] = out.weight_per_unit * out.stock_qty return out

erpnext/stock/get_item_details.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

get_item_details.py

get_basic_details

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

style: format code with black

def outer_size(self) -> Size: return self._size

src/textual/widget.py

textual

Python

0ba3ffb1718bdd01a5136fd1bc30e8ed58e6a47c

widget.py

outer_size

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

size properties

def test_retina_sepbn_head_loss(self): s = 256 img_metas = [{ 'img_shape': (s, s, 3), 'pad_shape': (s, s, 3), 'scale_factor': 1, }] cfg = Config( dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.4, min_pos_iou=0, ignore_iof_thr=-1), sampler=dict(type='PseudoSampler' ), # Focal loss should use PseudoSampler allowed_border=-1, pos_weight=-1, debug=False)) anchor_head = RetinaSepBNHead( num_classes=4, num_ins=5, in_channels=1, train_cfg=cfg) # Anchor head expects a multiple levels of features per image feats = [] for i in range(len(anchor_head.prior_generator.strides)): feats.append( torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))) cls_scores, bbox_preds = anchor_head.forward(tuple(feats)) # Test that empty ground truth encourages the network to # predict background gt_instances = InstanceData() gt_instances.bboxes = torch.empty((0, 4)) gt_instances.labels = torch.LongTensor([]) empty_gt_losses = anchor_head.loss_by_feat(cls_scores, bbox_preds, [gt_instances], img_metas) # When there is no truth, the cls loss should be nonzero but # there should be no box loss. empty_cls_loss = sum(empty_gt_losses['loss_cls']) empty_box_loss = sum(empty_gt_losses['loss_bbox']) self.assertGreater(empty_cls_loss.item(), 0, 'cls loss should be non-zero') self.assertEqual( empty_box_loss.item(), 0, 'there should be no box loss when there are no true boxes') # When truth is non-empty then both cls and box loss # should be nonzero for random inputs gt_instances = InstanceData() gt_instances.bboxes = torch.Tensor( [[23.6667, 23.8757, 238.6326, 151.8874]]) gt_instances.labels = torch.LongTensor([2]) one_gt_losses = anchor_head.loss_by_feat(cls_scores, bbox_preds, [gt_instances], img_metas) onegt_cls_loss = sum(one_gt_losses['loss_cls']) onegt_box_loss = sum(one_gt_losses['loss_bbox']) self.assertGreater(onegt_cls_loss.item(), 0, 'cls loss should be non-zero') self.assertGreater(onegt_box_loss.item(), 0, 'box loss should be non-zero')

tests/test_models/test_dense_heads/test_retina_sepBN_head.py

mmdetection

Python

665b55f6768dd0c2c32f8e73cd3069eddc1677b0

test_retina_sepBN_head.py

test_retina_sepbn_head_loss

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

[Refactor] Refactor NAS-FPN and anchor-free

def transpose(self) -> Tuple[int, int]: if self.transpose_method is not None: # Safety: `transpose` takes an int rather than e.g. an IntEnum. # self.transpose_method is set above to be a value in # EXIF_TRANSPOSE_MAPPINGS, and that only contains correct values. with self.image: self.image = self.image.transpose(self.transpose_method) # type: ignore[arg-type] self.width, self.height = self.image.size self.transpose_method = None # We don't need EXIF any more self.image.info["exif"] = None return self.image.size

synapse/rest/media/v1/thumbnailer.py

synapse

Python

5949ab86f8db0ef3dac2063e42210030f17786fb

thumbnailer.py

transpose

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

Fix potential thumbnail memory leaks. (#12932)

def test_get_command_line(self): mock_context = MagicMock() mock_context.parent.command_path = "streamlit" with patch("click.get_current_context", return_value=mock_context): with patch("click.get_os_args", return_value=["os_arg1", "os_arg2"]): result = cli._get_command_line_as_string() self.assertEqual("streamlit os_arg1 os_arg2", result)

lib/tests/streamlit/cli_test.py

streamlit

Python

5f39da13c0c551533a6d313dd0e2f6f9f0f9a5ac

cli_test.py

test_get_command_line

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

Get rid of preheated script runs (#4259) * Get rid of preheated script runs When a streamlit server is first started, we currently trigger a run of the script defining an app and save the resulting deltas so that the very first page load of an app can be more or less instantaneous. This optimization is currently not too helpful given how streamlit is used in practice today (it was originally added to make long-running jobs started via `streamlit run` feel fast, but people generally don't use streamlit to kick off long-running computations). Furthermore, we'll soon be adding some features that won't play nicely with the optimization. In particular, the upcoming `st.user` feature interacts with script preheats weirdly as the information required to populate `st.user` doesn't exist in a preheat run. Given complications that will arise in the near-future as well as the fact that the optimization itself is a vestigial one, it seems like it's time to remove preheated script runs. * Rework cli_smoke_tests to no longer rely on script preheats * Try making tests less timing sensitive * Revert an unintended change in an e2e test script * Replace `%` usage with an f-string

def get_positions_from_labels(self, row_loc, col_loc): from modin.pandas.indexing import ( is_boolean_array, is_list_like, is_range_like, boolean_mask_to_numeric, ) lookups = [] for axis, axis_loc in enumerate((row_loc, col_loc)): if is_scalar(axis_loc): axis_loc = np.array([axis_loc]) if isinstance(axis_loc, slice) or is_range_like(axis_loc): if isinstance(axis_loc, slice) and axis_loc == slice(None): axis_lookup = axis_loc else: axis_labels = self.get_axis(axis) # `slice_indexer` returns a fully-defined numeric slice for a non-fully-defined labels-based slice axis_lookup = axis_labels.slice_indexer( axis_loc.start, axis_loc.stop, axis_loc.step ) # Converting negative indices to their actual positions: axis_lookup = pandas.RangeIndex( start=( axis_lookup.start if axis_lookup.start >= 0 else axis_lookup.start + len(axis_labels) ), stop=( axis_lookup.stop if axis_lookup.stop >= 0 else axis_lookup.stop + len(axis_labels) ), step=axis_lookup.step, ) elif self.has_multiindex(axis): # `Index.get_locs` raises an IndexError by itself if missing labels were provided, # we don't have to do missing-check for the received `axis_lookup`. if isinstance(axis_loc, pandas.MultiIndex): axis_lookup = self.get_axis(axis).get_indexer_for(axis_loc) else: axis_lookup = self.get_axis(axis).get_locs(axis_loc) elif is_boolean_array(axis_loc): axis_lookup = boolean_mask_to_numeric(axis_loc) else: axis_labels = self.get_axis(axis) if is_list_like(axis_loc) and not isinstance( axis_loc, (np.ndarray, pandas.Index) ): # `Index.get_indexer_for` works much faster with numpy arrays than with python lists, # so although we lose some time here on converting to numpy, `Index.get_indexer_for` # speedup covers the loss that we gain here. axis_loc = np.array(axis_loc, dtype=axis_labels.dtype) axis_lookup = axis_labels.get_indexer_for(axis_loc) # `Index.get_indexer_for` sets -1 value for missing labels, we have to verify whether # there are any -1 in the received indexer to raise a KeyError here. missing_mask = axis_lookup == -1 if missing_mask.any(): missing_labels = ( axis_loc[missing_mask] if is_list_like(axis_loc) # If `axis_loc` is not a list-like then we can't select certain # labels that are missing and so printing the whole indexer else axis_loc ) raise KeyError(missing_labels) if isinstance(axis_lookup, pandas.Index) and not is_range_like(axis_lookup): axis_lookup = axis_lookup.values lookups.append(axis_lookup) return lookups

modin/core/storage_formats/base/query_compiler.py

modin

Python

dc7abf04518230d102bb5272c5ebf9fe20092338

query_compiler.py

get_positions_from_labels

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

REFACTOR-#5202: Pass loc arguments to query compiler. (#5305) Some Modin implementations may prefer to take rows and columns by label rather than by position. Signed-off-by: mvashishtha <mahesh@ponder.io>

def test_get_stored_cert_serials(certutil, populate_store): serials = certutil.get_stored_cert_serials("TrustedPublisher") assert "5be1cc5d51b78dbd49a0b7c00d44806d" in serials

tests/pytests/functional/modules/test_win_certutil.py

salt

Python

a8d2d1e1397cdc79b2c5f1ad7f6e3b729dcf8857

test_win_certutil.py

test_get_stored_cert_serials

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

Add tests, fix state module

def _generate_sparse6_bytes(G, nodes, header): n = len(G) if n >= 2**36: raise ValueError( "sparse6 is only defined if number of nodes is less " "than 2 ** 36" ) if header: yield b">>sparse6<<" yield b":" for d in n_to_data(n): yield str.encode(chr(d + 63)) k = 1 while 1 << k < n: k += 1

networkx/readwrite/sparse6.py

networkx

Python

f6755ffa00211b523c6c0bec5398bc6c3c43c8b1

sparse6.py

_generate_sparse6_bytes

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

Update black (#5438) * CI: sync up black dev requirements version with precommit * Run black Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com>

def load_linnerud(*, return_X_y=False, as_frame=False): data_filename = "linnerud_exercise.csv" target_filename = "linnerud_physiological.csv" # Read header and data with _open_text(DATA_MODULE, data_filename) as f: header_exercise = f.readline().split() f.seek(0) # reset file obj data_exercise = np.loadtxt(f, skiprows=1) with _open_text(DATA_MODULE, target_filename) as f: header_physiological = f.readline().split() f.seek(0) # reset file obj data_physiological = np.loadtxt(f, skiprows=1) fdescr = load_descr("linnerud.rst") frame = None if as_frame: (frame, data_exercise, data_physiological) = _convert_data_dataframe( "load_linnerud", data_exercise, data_physiological, header_exercise, header_physiological, ) if return_X_y: return data_exercise, data_physiological return Bunch( data=data_exercise, feature_names=header_exercise, target=data_physiological, target_names=header_physiological, frame=frame, DESCR=fdescr, data_filename=data_filename, target_filename=target_filename, data_module=DATA_MODULE, )

sklearn/datasets/_base.py

scikit-learn

Python

f2c78fe8c5cf2576f8351238c55dace23fb1d691

_base.py

load_linnerud

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

MAINT handle deprecations from `importlib.resources` (#25157) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

def train_epoch(self, iterator, info=None, num_steps=None, epoch_idx=0):

python/ray/util/sgd/torch/training_operator.py

"""Runs one standard training pass over the training dataloader. Bythis method will iterate over the givencall ``self.train_batch`` over each batch. Ifscheduler_step_freqis set, this default method will also step the scheduler accordingly. You do not need to call ``train_batch`` in this method if you plan to implement a custom optimization/training routine here. You may find ``ray.util.sgd.utils.AverageMeterCollection`` useful when overriding this method. See example below: .. code-block:: pythonthis default method will also step the scheduler accordingly. You do not need to calltrain_batchthisyou plan to implement a custom optimization/training routine here. You may find ``ray.util.sgd.utils.AverageMeterCollection`` useful when overriding this method. See example below: .. code-block::training routine here. You may find ``ray.util.sgd.utils.AverageMeterCollection`` useful when overriding this method. See exampleroutine here

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

training_operator.py

train_epoch

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

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

def solve_linear_system_LU(matrix, syms): if matrix.rows != matrix.cols - 1: raise ValueError("Rows should be equal to columns - 1") A = matrix[:matrix.rows, :matrix.rows] b = matrix[:, matrix.cols - 1:] soln = A.LUsolve(b) solutions = {} for i in range(soln.rows): solutions[syms[i]] = soln[i, 0] return solutions

sympy/solvers/solvers.py

sympy

Python

59d22b6bb7287613d598611027f640d068ca5748

solvers.py

solve_linear_system_LU

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

Moved imports to higher level

def args2(self): return self["args2"]

packages/python/plotly/plotly/graph_objs/layout/updatemenu/_button.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_button.py

args2

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

switch to black .22

def test_suppresses_second_cancellation(self): deferred: "Deferred[str]" = Deferred() wrapper_deferred = delay_cancellation(deferred) # Cancel the new `Deferred`, twice. wrapper_deferred.cancel() wrapper_deferred.cancel() self.assertNoResult(wrapper_deferred) self.assertFalse( deferred.called, "Original `Deferred` was unexpectedly cancelled" ) # Now make the original `Deferred` fail. # The `Failure` must be consumed, otherwise unwanted tracebacks will be printed # in logs. deferred.errback(ValueError("abc")) self.assertIsNone(deferred.result, "`Failure` was not consumed") # Now that the original `Deferred` has failed, we should get a `CancelledError`. self.failureResultOf(wrapper_deferred, CancelledError)

tests/util/test_async_helpers.py

synapse

Python

90b2327066d2343faa86c464a182b6f3c4422ecd

test_async_helpers.py

test_suppresses_second_cancellation

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

Add `delay_cancellation` utility function (#12180) `delay_cancellation` behaves like `stop_cancellation`, except it delays `CancelledError`s until the original `Deferred` resolves. This is handy for unifying cleanup paths and ensuring that uncancelled coroutines don't use finished logcontexts. Signed-off-by: Sean Quah <seanq@element.io>

def do_extends(parser, token): bits = token.split_contents() if len(bits) != 2: raise TemplateSyntaxError("'%s' takes one argument" % bits[0]) bits[1] = construct_relative_path(parser.origin.template_name, bits[1]) parent_name = parser.compile_filter(bits[1]) nodelist = parser.parse() if nodelist.get_nodes_by_type(ExtendsNode): raise TemplateSyntaxError( "'%s' cannot appear more than once in the same template" % bits[0] ) return ExtendsNode(nodelist, parent_name) @register.tag("include")

django/template/loader_tags.py

@register.tag("include")

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

loader_tags.py

do_extends

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

Refs #33476 -- Reformatted code with Black.

def get_temp_export_dir(timestamped_export_dir): (dirname, basename) = os.path.split(timestamped_export_dir) if isinstance(basename, bytes): str_name = basename.decode("utf-8") else: str_name = str(basename) temp_export_dir = tf.io.gfile.join( tf.compat.as_bytes(dirname), tf.compat.as_bytes("temp-{}".format(str_name)), ) return temp_export_dir

keras/saving/utils_v1/export_utils.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

export_utils.py

get_temp_export_dir

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def test_callbacks(self) -> None: cache: DeferredCache[str, int] = DeferredCache("test") callbacks = set() # start with an entry, with a callback cache.prefill("k1", 10, callback=lambda: callbacks.add("prefill")) # now replace that entry with a pending result origin_d: "defer.Deferred[int]" = defer.Deferred() set_d = cache.set("k1", origin_d, callback=lambda: callbacks.add("set")) # ... and also make a get request get_d = cache.get("k1", callback=lambda: callbacks.add("get")) # we don't expect the invalidation callback for the original value to have # been called yet, even though get() will now return a different result. # I'm not sure if that is by design or not. self.assertEqual(callbacks, set()) # now fire off all the deferreds origin_d.callback(20) self.assertEqual(self.successResultOf(set_d), 20) self.assertEqual(self.successResultOf(get_d), 20) # now the original invalidation callback should have been called, but none of # the others self.assertEqual(callbacks, {"prefill"}) callbacks.clear() # another update should invalidate both the previous results cache.prefill("k1", 30) self.assertEqual(callbacks, {"set", "get"})

tests/util/caches/test_deferred_cache.py

synapse

Python

4ae967cf6308e80b03da749f0cbaed36988e235e

test_deferred_cache.py

test_callbacks

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

Add missing type hints to test.util.caches (#14529)

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

haystack/pipelines/base.py

haystack

Python

f6e3a639063887f9f5b27f574a04c7fe602b3185

base.py

components

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

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

def set_xcomargs_dependencies(self) -> None: from airflow.models.xcom_arg import XComArg for field in self.template_fields: if hasattr(self, field): arg = getattr(self, field) XComArg.apply_upstream_relationship(self, arg)

airflow/models/baseoperator.py

airflow

Python

10f5db863e387c0fd7369cf521d624b6df77a65d

baseoperator.py

set_xcomargs_dependencies

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

Set dependencies in MappedOperator via XComArgs (#20931) Co-authored-by: Kaxil Naik <kaxilnaik@gmail.com> Co-authored-by: Ephraim Anierobi <splendidzigy24@gmail.com>

def _hyab(self, y_true, y_pred): delta = y_true - y_pred root = K.sqrt(K.clip(K.pow(delta[..., 0:1], 2), self._epsilon, None)) delta_norm = frobenius_norm(delta[..., 1:3]) return root + delta_norm

lib/model/loss/perceptual_loss_plaid.py

faceswap

Python

582c2ce40c11ef235dd3f9100f70e1e2832f8dd3

perceptual_loss_plaid.py

_hyab

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

Add Flip Loss Function - Add Flip for AMD and TF - Split Perceptual Loss functions to own modules - Fix allowed input shape for models - Allow GUI tooltip to display at higher width

def test_valid_full_refresh_read_no_slices(mocker): stream_output = [{"k1": "v1"}, {"k2": "v2"}] s1 = MockStream([({"sync_mode": SyncMode.full_refresh}, stream_output)], name="s1") s2 = MockStream([({"sync_mode": SyncMode.full_refresh}, stream_output)], name="s2") mocker.patch.object(MockStream, "get_json_schema", return_value={}) src = MockSource(streams=[s1, s2]) catalog = ConfiguredAirbyteCatalog( streams=[_configured_stream(s1, SyncMode.full_refresh), _configured_stream(s2, SyncMode.full_refresh)] ) expected = _as_records("s1", stream_output) + _as_records("s2", stream_output) messages = _fix_emitted_at(list(src.read(logger, {}, catalog))) assert expected == messages

airbyte-cdk/python/unit_tests/sources/test_abstract_source.py

airbyte

Python

f83eca58eaf2129d21b5796a301732ab22675130

test_abstract_source.py

test_valid_full_refresh_read_no_slices

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

CDK: Fix typing errors (#9037) * fix typing, drop AirbyteLogger * format * bump the version * use logger instead of fixture logger Co-authored-by: Eugene Kulak <kulak.eugene@gmail.com> Co-authored-by: auganbay <auganenu@gmail.com>

def depth_first_search(self): if self.isSolvable() == False: return (None, None) closed = list() q = list() q.append(Node(state=self.state, depth=0)) while q: node = q.pop() if node.isGoalState(): return (node.moves, len(closed)) if node.state not in closed: closed.append(node.state) for action in node.getAvailableActions(): q.append(node.getResultFromAction(action)) return (None, None)

Eight_Puzzle_Solver/eight_puzzle.py

Python

Python

f0af0c43340763724f139fa68aa1e5a9ffe458b4

eight_puzzle.py

depth_first_search

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

refactor: clean code Signed-off-by: slowy07 <slowy.arfy@gmail.com>

def update(self) -> bool: try: # Add or remove DeploymentReplica instances in self._replicas. # This should be the only place we adjust total number of replicas # we manage. running_replicas_changed = self._scale_deployment_replicas() # Check the state of existing replicas and transition if necessary. running_replicas_changed |= self._check_and_update_replicas() if running_replicas_changed: self._notify_running_replicas_changed() deleted = self._check_curr_status() except Exception: self._curr_status_info = DeploymentStatusInfo( name=self._name, status=DeploymentStatus.UNHEALTHY, message="Failed to update deployment:" f"\n{traceback.format_exc()}", ) deleted = False return deleted

python/ray/serve/_private/deployment_state.py

ray

Python

65d0c0aa48be8f9f7faae857d3ab71444997755a

deployment_state.py

update

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

[Serve] add alpha gRPC support (#28175)

def _finished_processing(self) -> None: assert self.logcontext is not None assert self.finish_time is not None usage = self.logcontext.get_resource_usage() if self._processing_finished_time is None: # we completed the request without anything calling processing() self._processing_finished_time = time.time() # the time between receiving the request and the request handler finishing processing_time = self._processing_finished_time - self.start_time # the time between the request handler finishing and the response being sent # to the client (nb may be negative) response_send_time = self.finish_time - self._processing_finished_time user_agent = get_request_user_agent(self, "-") # int(self.code) looks redundant, because self.code is already an int. # But self.code might be an HTTPStatus (which inherits from int)---which has # a different string representation. So ensure we really have an integer. code = str(int(self.code)) if not self.finished: # we didn't send the full response before we gave up (presumably because # the connection dropped) code += "!" log_level = logging.INFO if self._should_log_request() else logging.DEBUG # If this is a request where the target user doesn't match the user who # authenticated (e.g. and admin is puppetting a user) then we log both. requester, authenticated_entity = self.get_authenticated_entity() if authenticated_entity: requester = f"{authenticated_entity}|{requester}" self.synapse_site.access_logger.log( log_level, "%s - %s - {%s}" " Processed request: %.3fsec/%.3fsec (%.3fsec, %.3fsec) (%.3fsec/%.3fsec/%d)" ' %sB %s "%s %s %s" "%s" [%d dbevts]', self.getClientIP(), self.synapse_site.site_tag, requester, processing_time, response_send_time, usage.ru_utime, usage.ru_stime, usage.db_sched_duration_sec, usage.db_txn_duration_sec, int(usage.db_txn_count), self.sentLength, code, self.get_method(), self.get_redacted_uri(), self.clientproto.decode("ascii", errors="replace"), user_agent, usage.evt_db_fetch_count, ) # complete the opentracing span, if any. if self._opentracing_span: self._opentracing_span.finish() try: self.request_metrics.stop(self.finish_time, self.code, self.sentLength) except Exception as e: logger.warning("Failed to stop metrics: %r", e)

synapse/http/site.py

synapse

Python

d8df8e6c1432d25ea1c0310a5f2dc48d1688345f

site.py

_finished_processing

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

Don't print HTTPStatus.* in "Processed..." logs (#11827) * Don't print HTTPStatus.* in "Processed..." logs Fixes #11812. See also #7118 and https://github.com/matrix-org/synapse/pull/7188#r401719326 in particular. Co-authored-by: Brendan Abolivier <babolivier@matrix.org>

def test_submit_with_logs_instant_job(self, ray_start_stop): cmd = "echo hello" stdout, _ = _run_cmd(f"ray job submit -- bash -c '{cmd}'") assert "hello" in stdout

dashboard/modules/job/tests/test_cli_integration.py

ray

Python

813e1a857d5dfc060b3b6cb846157fdca425e6b0

test_cli_integration.py

test_submit_with_logs_instant_job

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

Revert "Revert "[Job Submission][refactor 5/N] Remove the head node dependency on the `Raylet` process"" (#29008) Reverts #28931 and fixes the tests that were made flaky by that PR. Fix address="auto" in cpp job test (fixed by @Catch-Bull ) Fix len_new_owner_port flakiness in test_sdk(fixed by @Catch-Bull ) Fix int conversion flakiness Additionally, this PR updates the log tailing behavior from the previous PR to return logs instantly when the job exits, to match the current behavior on master, including the case where the runtime env fails to set up. (In the previous PR, there was a timeout for waiting for the supervisor actor to start, so if the runtime env failed to set up instantly,ray job submit would still wait for the entire 60s timeout before closing the log stream and returning.) Finally, this PR updates the default scheduling behavior from the previous PR to make jobs run on the head node by default (configurable via the environment variable RAY_JOB_ALLOW_DRIVERS_ON_HEAD_NODE.). This is to avoid making a breaking behavior change unless absolutely necessary. We can update this default in the future after more discussion. In this PR, the head node id is passed to the agent via internal KV. This is a workaround for the fact that there is no way to retrieve the head node id from within Ray (#29607)

def serialize_model_as_bytecode(model): # Note: we don't use a RAM path for this because zipfile cannot write # to such paths. temp_dir = tempfile.mkdtemp() try: filepath = os.path.join(temp_dir, "model.keras") saving_lib.save_model(model, filepath) with open(filepath, "rb") as f: data = f.read() except Exception as e: raise e else: return data finally: tf.io.gfile.rmtree(temp_dir)

keras/saving/pickle_utils.py

keras

Python

2ed044d06d0ae552477672aa8b778f8edafb52f1

pickle_utils.py

serialize_model_as_bytecode

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

Use new saving logic for pickling. This is somewhat cleaner since it restores the exact same model (no usage of traces). It may however be less convenient since it requires get_config() to be implemented and the use of a custom_object_scope. PiperOrigin-RevId: 474146108

def get_value_data_from_instance(self, instance): return { "id": instance.pk, "edit_url": AdminURLFinder().get_edit_url(instance), }

wagtail/admin/widgets/chooser.py

wagtail

Python

39f7886a6f8ee98db7e73ce33d94c06139f35bd8

chooser.py

get_value_data_from_instance

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

Split out common logic from get_value_data

def copy_files(from_dir, to_dir): if from_dir.exists(): shutil.copytree(from_dir, to_dir, dirs_exist_ok=True) dirs_list = [ SETTINGS_DIRECTORY, USER_DATA_DIRECTORY, USER_DATA_DIRECTORY / "styles", CUSTOM_IMPORTS_DIRECTORY, CUSTOM_IMPORTS_DIRECTORY / "econometrics", ] dirs_files = [USER_ENV_FILE, REPOSITORY_ENV_FILE] create_paths(dirs_list) create_files(dirs_files) copy_files(REPOSITORY_DIRECTORY / "custom_imports", CUSTOM_IMPORTS_DIRECTORY)

openbb_terminal/core/config/paths_helper.py

OpenBBTerminal

Python

c4658b63a936ad219625d30dcbd12a1aa798af09

paths_helper.py

copy_files

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

Add path for custom_imports outside the terminal (#2567) * add log path * add test to check if log file is in correct dir * env path * black * mypy fix * add styles folder and styles from repo * add timezone as env variable * fix changes with main * fix test * flake8 * fix linting * fix linting * changes * custom changes * add custom_imports outside terminal * black * black terminal * fix test * fix merge and remove styles/user * some stylistic changes and remove move_files * flake8 * merge main * merge move and make into paths_helper Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com>

def build_query_compiler(cls, path, columns, index_columns, **kwargs): col_partitions, column_widths = cls.build_columns(columns) partition_ids = cls.call_deploy(path, col_partitions, **kwargs) index, sync_index = cls.build_index(path, partition_ids, index_columns) remote_parts = cls.build_partition(partition_ids, column_widths) if len(partition_ids) > 0: row_lengths = [part.length() for part in remote_parts.T[0]] else: row_lengths = None frame = cls.frame_cls( remote_parts, index, columns, row_lengths=row_lengths, column_widths=column_widths, dtypes=None, ) if sync_index: frame.synchronize_labels(axis=0) return cls.query_compiler_cls(frame)

modin/core/io/column_stores/parquet_dispatcher.py

modin

Python

8864bc197974da6d8cda2de2f35ca31d561be1cc

parquet_dispatcher.py

build_query_compiler

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

PERF-#4305: Parallelize `read_parquet` over row groups (#4700) Co-authored-by: mvashishtha <mahesh@ponder.io>

def packing(self): return self["packing"]

packages/python/plotly/plotly/graph_objs/treemap/_tiling.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_tiling.py

packing

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

switch to black .22

def kg_to_pounds(n): return float(n) * 2.204623 @register.filter("startswith")

netbox/utilities/templatetags/helpers.py

@register.filter("startswith")

netbox

Python

87fd09ca8b5a0d3ec692e241351e1bbc4ac298a7

helpers.py

kg_to_pounds

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

Cleanup for #9654

def set_weights(self, weights): if not getattr(self, "_built", False): raise ValueError( "You are calling `set_weights()` on an optimizer that has not " "yet been built. Please call " "`optimizer.build(trainable_variables)` to create the " "optimizer weights before calling `set_weights()`." ) for variable, weight in zip(self._variables, weights): if variable.shape != weight.shape: raise ValueError( f"Optimizer variable {self._var_key(variable)} has shape " f"{str(variable.shape)} not compatible with provided " f"weight shape {str(weight.shape)}." ) variable.assign(weight)

keras/optimizers/optimizer_experimental/optimizer.py

keras

Python

571d8786df580d6daa5c57c77b5b15a125631c8f

optimizer.py

set_weights

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

Add method `set_weights` for optimizer backward compatibility. Remove @doc_controls.do_not_generate_docs for `variables()` method because optimizer is no longer a `tf.Module`. PiperOrigin-RevId: 474149115

def test_subdag_pools(self): dag = DAG('parent', default_args=default_args) subdag = DAG('parent.child', default_args=default_args) session = airflow.settings.Session() pool_1 = airflow.models.Pool(pool='test_pool_1', slots=1) pool_10 = airflow.models.Pool(pool='test_pool_10', slots=10) session.add(pool_1) session.add(pool_10) session.commit() EmptyOperator(task_id='dummy', dag=subdag, pool='test_pool_1') with pytest.raises(AirflowException): SubDagOperator(task_id='child', dag=dag, subdag=subdag, pool='test_pool_1') # recreate dag because failed subdagoperator was already added dag = DAG('parent', default_args=default_args) SubDagOperator(task_id='child', dag=dag, subdag=subdag, pool='test_pool_10') session.delete(pool_1) session.delete(pool_10) session.commit()

tests/operators/test_subdag_operator.py

airflow

Python

49e336ae0302b386a2f47269a6d13988382d975f

test_subdag_operator.py

test_subdag_pools

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

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

def __call__(self, results): assert 'mix_results' in results num_images = len(results['mix_results']) assert num_images == 1, \ f'CopyPaste only supports processing 2 images, got {num_images}' if self.selected: selected_results = self._select_object(results['mix_results'][0]) else: selected_results = results['mix_results'][0] return self._copy_paste(results, selected_results)

mmdet/datasets/pipelines/transforms.py

mmdetection

Python

9a166a380229d2aaf5986fa1ff303a941865961a

transforms.py

__call__

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

[Feature] Support simple copy paste with some configs. (#7501) * Testing pre-commit hooks * Added base code in transforms * Added Simple Copy Paste working version * Added checks to simple copy paste * refactor simplecopypaste and provide some configs * remove lvis-api in .gitignore * refactor simplecopypaste and use resize/flip/pad in load_pipeline * pre-commit * add README.md for simplecopypaste * add some unit tests * rename some variables * add a blend_fn * add some unit tests * add some comments * delete blend_fn * simplify some commits Co-authored-by: Sudarshan Kamath <sudarshan.kamath97@gmail.com>

def safe_quote_currency(self) -> str: try: return self.stake_currency or self.pair.split('/')[1].split(':')[0] except IndexError: return ''

freqtrade/persistence/models.py

freqtrade

Python

8e98a2ff9f4fabf81bf5a4f4e1f772f5c4a091ec

models.py

safe_quote_currency

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

api - provide assset_currency via API

def _make_twin_axes(self, *args, **kwargs): if 'sharex' in kwargs and 'sharey' in kwargs: # The following line is added in v2.2 to avoid breaking Seaborn, # which currently uses this internal API. if kwargs["sharex"] is not self and kwargs["sharey"] is not self: raise ValueError("Twinned Axes may share only one axis") ss = self.get_subplotspec() if ss: twin = self.figure.add_subplot(ss, *args, **kwargs) else: twin = self.figure.add_axes( self.get_position(True), *args, **kwargs, axes_locator=_TransformedBoundsLocator( [0, 0, 1, 1], self.transAxes)) self.set_adjustable('datalim') twin.set_adjustable('datalim') self._twinned_axes.join(self, twin) return twin

lib/matplotlib/axes/_base.py

matplotlib

Python

c73f4c455514cf5422d27bf38c93250de8316b21

_base.py

_make_twin_axes

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

Merge SubplotBase into AxesBase.

def setup_awaitable_errors() -> Callable[[], None]: warnings.simplefilter("error", RuntimeWarning) # unraisablehook was added in Python 3.8. if not hasattr(sys, "unraisablehook"): return lambda: None # State shared between unraisablehook and check_for_unraisable_exceptions. unraisable_exceptions = [] orig_unraisablehook = sys.unraisablehook

tests/test_utils/__init__.py

synapse

Python

646324437543c096e737777c81b4fe4b45c3e1a7

__init__.py

setup_awaitable_errors

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

Remove unused `# type: ignore`s (#12531) Over time we've begun to use newer versions of mypy, typeshed, stub packages---and of course we've improved our own annotations. This makes some type ignore comments no longer necessary. I have removed them. There was one exception: a module that imports `select.epoll`. The ignore is redundant on Linux, but I've kept it ignored for those of us who work on the source tree using not-Linux. (#11771) I'm more interested in the config line which enforces this. I want unused ignores to be reported, because I think it's useful feedback when annotating to know when you've fixed a problem you had to previously ignore. * Installing extras before typechecking Lacking an easy way to install all extras generically, let's bite the bullet and make install the hand-maintained `all` extra before typechecking. Now that https://github.com/matrix-org/backend-meta/pull/6 is merged to the release/v1 branch.

def required_columns(self) -> List[str]: return []

ludwig/data/split.py

ludwig

Python

d85269cd60734790a65c11673bfdd98516b62b6c

split.py

required_columns

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

Use clearer error messages in ludwig serving, and enable serving to work with configs that have stratified splitting on target columns. (#2740) * Use clearer serving error messages, and enable serving to work with configs that have stratified splitting on target columns. * Adjust warning message

def get_local_ip_address() -> str: try: ip_address = requests.get( "https://checkip.amazonaws.com/", timeout=3 ).text.strip() except (requests.ConnectionError, requests.exceptions.ReadTimeout): ip_address = "No internet connection" return ip_address

gradio/utils.py

gradio

Python

51824608865b66ab04b018f55055124edbe603f3

utils.py

get_local_ip_address

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

Patching `test_get_ip` attempt 2 (#2810) * ip-patch-2 * formatting * patch 2

def path(self): self._deprecate("path") return self._path

pandas/io/excel/_base.py

pandas

Python