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Semeru Lab 7

_extra_input_signature_def

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

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

def _extra_input_signature_def(self): feed_dict = self.extra_compute_action_feed_dict() return { k.name: tf1.saved_model.utils.build_tensor_info(k) for k in feed_dict.keys() }

tf_policy.py

Python

rllib/policy/tf_policy.py

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

test_summary_correctly_updated

fix: 17track package summary status is not updated when there are no more packages in that summary (#64421) * 17track package status is not updated when there are no packages * 17track package status is not updated when there are no packages * 17track package status is not updated when there are no packages

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

async def test_summary_correctly_updated(hass): package = Package( tracking_number="456", destination_country=206, friendly_name="friendly name 1", info_text="info text 1", location="location 1", timestamp="2020-08-10 10:32", origin_country=206, package_type=2, status=30, ) ProfileMock.package_list = [package] await _setup_seventeentrack(hass, summary_data=DEFAULT_SUMMARY) assert len(hass.states.async_entity_ids()) == 8 for state in hass.states.async_all(): if state.entity_id == "sensor.seventeentrack_package_456": break assert state.state == "0" assert ( len( hass.states.get( "sensor.seventeentrack_packages_ready_to_be_picked_up" ).attributes["packages"] ) == 1 ) ProfileMock.package_list = [] ProfileMock.summary_data = NEW_SUMMARY_DATA await _goto_future(hass) assert len(hass.states.async_entity_ids()) == 7 for state in hass.states.async_all(): assert state.state == "1" assert ( hass.states.get( "sensor.seventeentrack_packages_ready_to_be_picked_up" ).attributes["packages"] is None )

test_sensor.py

Python

tests/components/seventeentrack/test_sensor.py

6176bb954c4aa68b33c9db487dbb5712059f4b38

core

acquire

Add model parallel for FasterGPT. (#1755) * Add model parallel for FasterGPT. * Make GPT model parallel runable * Make FT model parallel optional. * Fix _write_setup_file when kwargs is not empty. * Fix ext_utils.load * Add file lock for model parallel. * Fix model_parallel.flag in CMakeLists.txt. * Use a separated lib for model parallel. * Make from_pretrained get partial model. * Make model parallel support layer group in python. * Fix fit_partial_model when model having keys state not including. Add api docs for model parallel. * Fix the default world_size when mpi is not available. * Add demo for GPT model parallel. * Fix default global ft_para_conf. * Fix GPTModel state_dict wrapper for layer parallel. * Set seed for tensor parallel. * Fix location of gpt.h in cmake. * Fix seed in gpt.h * Fix NV FT GPT embedding. * Add more cases in gpt_mp_sample.py * Fix seed in ker_curand_setupLauncher. Put build dir of FG in PPNLP_HOME with digest of current path. * Refine gpt_mp_sample.py

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

def acquire(self): start_time = time.time() while True: try: self.fd = os.open(self.lock_file_path, os.O_CREAT | os.O_EXCL | os.O_RDWR) self.is_locked = True # moved to ensure tag only when locked break except OSError as e: if e.errno != errno.EEXIST: raise if self.timeout is None: raise FileLockException("Could not acquire lock on {}". format(self.lock_file_path)) if self.timeout > 0 and (time.time() - start_time ) >= self.timeout: raise FileLockException("Timeout occured.") time.sleep(self.delay)

file_lock.py

Python

paddlenlp/utils/file_lock.py

c541f4ba1fcab8304c7ac4efdce3d63a2e478176

PaddleNLP

render_pep440

BLD: use nonvendor versioneer (#49924) * BLD: remove vendored versioneer * run vis * move config to pyproject.toml * add versioneer to deps * run pyupgrade * fix isort and pylint * fix ci * fix env

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

def render_pep440(pieces): if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += f"{pieces['distance']}.g{pieces['short']}" if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = f"0+untagged.{pieces['distance']}.g{pieces['short']}" if pieces["dirty"]: rendered += ".dirty" return rendered

_version.py

Python

pandas/_version.py

e2df99823758210fb2b7c4aba39e23f3445f7cd3

pandas

workflow_logging_context

[Workflow]Make workflow logs publish to the correct driver. (#24089) All workflow tasks are executed as remote functions that submitted from WorkflowManagmentActor. WorkflowManagmentActor is a detached long-running actor whose owner is the first driver in the cluster that runs the very first workflow execution. Therefore, for new drivers that run workflows, the loggings won't be properly published back to the driver because loggings are saved and published based on job_id and the job_id is always the first driver's job_id as the ownership goes like: first_driver -> WorkflowManagmentActor -> workflow executions using remote functions. To solve this, during workflow execution, we pass the actual driver's job_id along with execution, and re-configure the logging files on each worker that runs the remote functions. Notice that we need to do this in multiple places as a workflow task is executed with more than one remote functions that are running in different workers.

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

def workflow_logging_context(job_id) -> None: node = ray.worker._global_node original_out_file, original_err_file = node.get_log_file_handles( get_worker_log_file_name("WORKER") ) out_file, err_file = node.get_log_file_handles( get_worker_log_file_name("WORKER", job_id) ) try: configure_log_file(out_file, err_file) yield finally: configure_log_file(original_out_file, original_err_file)

workflow_context.py

Python

python/ray/workflow/workflow_context.py

e8fc66af348f2afd2b578fe1c6776cc88ea82499

ray

verify_dataset_shuffled

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

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

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

training_utils_v1.py

Python

keras/engine/training_utils_v1.py

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

get

Refactor generic views; add plugins dev documentation

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

def get(self, request): model = self.queryset.model content_type = ContentType.objects.get_for_model(model) if self.filterset: self.queryset = self.filterset(request.GET, self.queryset).qs # Compile a dictionary indicating which permissions are available to the current user for this model permissions = {} for action in ('add', 'change', 'delete', 'view'): perm_name = get_permission_for_model(model, action) permissions[action] = request.user.has_perm(perm_name) if 'export' in request.GET: # Export the current table view if request.GET['export'] == 'table': table = self.get_table(request, permissions) columns = [name for name, _ in table.selected_columns] return self.export_table(table, columns) # Render an ExportTemplate elif request.GET['export']: template = get_object_or_404(ExportTemplate, content_type=content_type, name=request.GET['export']) return self.export_template(template, request) # Check for YAML export support on the model elif hasattr(model, 'to_yaml'): response = HttpResponse(self.export_yaml(), content_type='text/yaml') filename = 'netbox_{}.yaml'.format(self.queryset.model._meta.verbose_name_plural) response['Content-Disposition'] = 'attachment; filename="{}"'.format(filename) return response # Fall back to default table/YAML export else: table = self.get_table(request, permissions) return self.export_table(table) # Render the objects table table = self.get_table(request, permissions) configure_table(table, request) # If this is an HTMX request, return only the rendered table HTML if is_htmx(request): return render(request, 'htmx/table.html', { 'table': table, }) context = { 'content_type': content_type, 'table': table, 'permissions': permissions, 'action_buttons': self.action_buttons, 'filter_form': self.filterset_form(request.GET, label_suffix='') if self.filterset_form else None, } context.update(self.get_extra_context(request)) return render(request, self.template_name, context) # # Export methods #

bulk_views.py

Python

netbox/netbox/views/generic/bulk_views.py

54834c47f8870e7faabcd847c3270da0bd3d2884

netbox

get_project_ids

Add 3 Token Terminal commands (#2447) * add crypto/ov/fun * add tokenterminal to dependencies * update website content * add to main.yml * fix tests * add tests * Update _index.md * Update _index.md * fix tests * fix test * List hint added * improve code based on Jose input * fix tests * requirements for token terminal * add source and fix source bug * some improvements * colors bars * fix dependencies * update kaleido version * update setuptools for pkg_resources * replace pkg_resources by importlib_metadata * Added fixes * Fixed tests * fix stuff for Josecas Co-authored-by: Colin Delahunty <[email protected]> Co-authored-by: colin99d <[email protected]>

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

def get_project_ids() -> List[str]: return [project["project_id"] for project in PROJECTS_DATA] @log_start_end(log=logger)

@log_start_end(log=logger)

tokenterminal_model.py

Python

openbb_terminal/cryptocurrency/due_diligence/tokenterminal_model.py

7979b1fc071a1c3e7463044bea617d7305b4a17e

OpenBBTerminal

test_delete_performance_issue

feat(perf issues): Prevent deleting and merging (#38479) * Prevent deleting, discarding, and merging in single and bulk operations for performance issues.

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

def test_delete_performance_issue(self): self.login_as(user=self.user) group = self.create_group(type=GroupType.PERFORMANCE_SLOW_SPAN.value) GroupHash.objects.create(project=group.project, hash="x" * 32, group=group) url = f"/api/0/issues/{group.id}/" response = self.client.delete(url, format="json") assert response.status_code == 400, response.content # Ensure it's still there assert Group.objects.filter(id=group.id).exists() assert GroupHash.objects.filter(group_id=group.id).exists()

test_group_details.py

Python

tests/sentry/api/endpoints/test_group_details.py

dfe1d3442af1535cc2d4f8a511ee5733b3887572

sentry

dynamic_counter

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

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

def dynamic_counter(self): raise NotImplementedError

loss_scale_optimizer.py

Python

keras/mixed_precision/loss_scale_optimizer.py

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

test_missing_tones_dict

Add EntityFeature enum to Siren (#69585) Co-authored-by: Franck Nijhof <[email protected]>

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

async def test_missing_tones_dict(hass): siren = MockSirenEntity(SirenEntityFeature.TONES, {1: "a", 2: "b"}) siren.hass = hass with pytest.raises(ValueError): process_turn_on_params(siren, {"tone": 3})

test_init.py

Python

tests/components/siren/test_init.py

a61ac3ddc6d65522dfa1eb599adf73420a9267dc

core

get_matching_frame_actions

fix(grouping): Exception matcher with no frames (#38994) We used to pass `-1` as a frame index for exception matchers, which worked by accident because `-1` is a valid list index in Python, except when the list of frames was empty. Replace `-1` by `None` and make sure we do not attempt to access the list of frames in the exception matcher, by giving it its own `matches_frame` override. Fixes SENTRY-VWW

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

def get_matching_frame_actions(self, frames, platform, exception_data=None, cache=None): if not self.matchers: return [] # 1 - Check if exception matchers match for m in self._exception_matchers: if not m.matches_frame(frames, None, platform, exception_data, cache): return [] rv = [] # 2 - Check if frame matchers match for idx, frame in enumerate(frames): if all( m.matches_frame(frames, idx, platform, exception_data, cache) for m in self._other_matchers ): for action in self.actions: rv.append((idx, action)) return rv

__init__.py

Python

src/sentry/grouping/enhancer/__init__.py

686675f81bf9402bc9b671e61ea0481b0c5c3468

sentry

eye

BUG: lib: Allow type uint64 for eye() arguments. Closes gh-9982. (Plus a few small PEP 8 fixes.)

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

def eye(N, M=None, k=0, dtype=float, order='C', *, like=None): if like is not None: return _eye_with_like(N, M=M, k=k, dtype=dtype, order=order, like=like) if M is None: M = N m = zeros((N, M), dtype=dtype, order=order) if k >= M: return m # Ensure M and k are integers, so we don't get any surprise casting # results in the expressions `M-k` and `M+1` used below. This avoids # a problem with inputs with type (for example) np.uint64. M = operator.index(M) k = operator.index(k) if k >= 0: i = k else: i = (-k) * M m[:M-k].flat[i::M+1] = 1 return m _eye_with_like = array_function_dispatch( _eye_dispatcher )(eye)

twodim_base.py

Python

numpy/lib/twodim_base.py

f9355942f6ef7c5d27691c4571096234efb67a2b

numpy

_validate_target_and_loss

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

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

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

training.py

Python

keras/engine/training.py

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

func_load

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

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

def func_load(code, defaults=None, closure=None, globs=None): if isinstance(code, (tuple, list)): # unpack previous dump code, defaults, closure = code if isinstance(defaults, list): defaults = tuple(defaults)

generic_utils.py

Python

keras/utils/generic_utils.py

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

float_range

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

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

def float_range(value): try: value = float(value) except Exception: raise argparse.ArgumentTypeError('Invalid float value: \'{}\''.format(value)) if value < 0.0 or value > 1.0: raise argparse.ArgumentTypeError('Invalid float value: \'{}\''.format(value)) return value

driver.py

Python

tpot/driver.py

388616b6247ca4ea8de4e2f340d6206aee523541

tpot

config_for_enable_caching_device

Reorganize RNN layers, cells and wrappers into smaller logically organized files hosted under an `rnn` directory. PiperOrigin-RevId: 428841673

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

def config_for_enable_caching_device(rnn_cell): default_enable_caching_device = tf.compat.v1.executing_eagerly_outside_functions( ) if rnn_cell._enable_caching_device != default_enable_caching_device: return {'enable_caching_device': rnn_cell._enable_caching_device} return {}

rnn_utils.py

Python

keras/layers/rnn/rnn_utils.py

01c906c4178db5ae03b7eb2d298a052c952a0667

keras

test_update_hvac_mode

Use climate enums in gree (#70655) * Use climate enums in gree * Adjust tests

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

async def test_update_hvac_mode(hass, discovery, device, mock_now, hvac_mode): device().power = hvac_mode != HVACMode.OFF device().mode = HVAC_MODES_REVERSE.get(hvac_mode) await async_setup_gree(hass) state = hass.states.get(ENTITY_ID) assert state is not None assert state.state == hvac_mode @pytest.mark.parametrize( "fan_mode", (FAN_AUTO, FAN_LOW, FAN_MEDIUM_LOW, FAN_MEDIUM, FAN_MEDIUM_HIGH, FAN_HIGH), )

@pytest.mark.parametrize( "fan_mode", (FAN_AUTO, FAN_LOW, FAN_MEDIUM_LOW, FAN_MEDIUM, FAN_MEDIUM_HIGH, FAN_HIGH), )

test_climate.py

Python

tests/components/gree/test_climate.py

23c5bd97793af4eed9806a237593b482f8e1b932

core

retry

ansible-test - Fix subprocess management. (#77638) * Run code-smell sanity tests in UTF-8 Mode. * Update subprocess use in sanity test programs. * Use raw_command instead of run_command with always=True set. * Add more capture=True usage. * Don't expose stdin to subprocesses. * Capture more output. Warn on retry. * Add more captures. * Capture coverage cli output. * Capture windows and network host checks. * Be explicit about interactive usage. * Use a shell for non-captured, non-interactive subprocesses. * Add integration test to assert no TTY. * Add unit test to assert no TTY. * Require blocking stdin/stdout/stderr. * Use subprocess.run in ansible-core sanity tests. * Remove unused arg. * Be explicit with subprocess.run check=False. * Add changelog.

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

def retry(func, ex_type=SubprocessError, sleep=10, attempts=10, warn=True): for dummy in range(1, attempts): try: return func() except ex_type as ex: if warn: display.warning(str(ex)) time.sleep(sleep) return func()

util.py

Python

test/lib/ansible_test/_internal/util.py

62d03c8e752ee35057031a91d7028e0a2e5d43e4

ansible

event_processing_finished

Split TaskManager into - DependencyManager spawns dependencies if necessary - WorkflowManager processes running workflows to see if a new job is ready to spawn - TaskManager starts tasks if unblocked and has execution capacity

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

def event_processing_finished(self): if self.status in ACTIVE_STATES: return False # tally of events is only available at end of run try: event_qs = self.get_event_queryset() except NotImplementedError: return True # Model without events, such as WFJT return self.emitted_events == event_qs.count()

unified_jobs.py

Python

awx/main/models/unified_jobs.py

431b9370dfbbbcb64dee0b4ebc8af7df12740d08

awx

TypeAlias

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

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

def TypeAlias(self, parameters): raise TypeError(f"{self} is not subscriptable") # 3.7-3.8 elif sys.version_info[:2] >= (3, 7):

elif sys.version_info[:2] >= (3, 7):sys

typing_extensions.py

Python

pipenv/patched/notpip/_vendor/typing_extensions.py

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

_load_theme_config

Remove spaces at the ends of docstrings, normalize quotes

https://github.com/mkdocs/mkdocs.git

def _load_theme_config(self, name): theme_dir = utils.get_theme_dir(name) self.dirs.append(theme_dir) try: file_path = os.path.join(theme_dir, 'mkdocs_theme.yml') with open(file_path, 'rb') as f: theme_config = utils.yaml_load(f) if theme_config is None: theme_config = {} except OSError as e: log.debug(e) raise ValidationError( f"The theme '{name}' does not appear to have a configuration file. " f"Please upgrade to a current version of the theme." ) log.debug(f"Loaded theme configuration for '{name}' from '{file_path}': {theme_config}") parent_theme = theme_config.pop('extends', None) if parent_theme: themes = utils.get_theme_names() if parent_theme not in themes: raise ValidationError( f"The theme '{name}' inherits from '{parent_theme}', which does not appear to be installed. " f"The available installed themes are: {', '.join(themes)}" ) self._load_theme_config(parent_theme) self.static_templates.update(theme_config.pop('static_templates', [])) self._vars.update(theme_config)

theme.py

Python

mkdocs/theme.py

e7f07cc82ab2be920ab426ba07456d8b2592714d

mkdocs

test_default_args

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

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

def test_default_args(): execution_date = pendulum.parse("20201109") with DAG( dag_id='example_task_group_default_args', start_date=execution_date, default_args={ "owner": "dag", }, ): with TaskGroup("group1", default_args={"owner": "group"}): task_1 = EmptyOperator(task_id='task_1') task_2 = EmptyOperator(task_id='task_2', owner='task') task_3 = EmptyOperator(task_id='task_3', default_args={"owner": "task"}) assert task_1.owner == 'group' assert task_2.owner == 'task' assert task_3.owner == 'task'

test_task_group.py

Python

tests/utils/test_task_group.py

49e336ae0302b386a2f47269a6d13988382d975f

airflow

trackable_children

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

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

def trackable_children(self, serialization_cache): if not utils.should_save_traces(): return {} children = self.objects_to_serialize(serialization_cache) children.update(self.functions_to_serialize(serialization_cache)) return children

base_serialization.py

Python

keras/saving/saved_model/base_serialization.py

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

copy

Constrained Beam Search [without disjunctive decoding] (#15416) * added classes to get started with constrained beam search * in progress, think i can directly force tokens now but not yet with the round robin * think now i have total control, now need to code the bank selection * technically works as desired, need to optimize and fix design choices leading to undersirable outputs * complete PR #1 without disjunctive decoding * removed incorrect tests * Delete k.txt * Delete test.py * Delete test.sh * revert changes to test scripts * genutils * full implementation with testing, no disjunctive yet * shifted docs * passing all tests realistically ran locally * removing accidentally included print statements * fixed source of error in initial PR test * fixing the get_device() vs device trap * fixed documentation docstrings about constrained_beam_search * fixed tests having failing for Speech2TextModel's floating point inputs * fix cuda long tensor * added examples and testing for them and founx & fixed a bug in beam_search and constrained_beam_search * deleted accidentally added test halting code with assert False * code reformat * Update tests/test_generation_utils.py Co-authored-by: Patrick von Platen <[email protected]> * Update tests/test_generation_utils.py Co-authored-by: Patrick von Platen <[email protected]> * Update tests/test_generation_utils.py Co-authored-by: Patrick von Platen <[email protected]> * Update tests/test_generation_utils.py Co-authored-by: Patrick von Platen <[email protected]> * Update tests/test_generation_utils.py * fixing based on comments on PR * took out the testing code that should but work fails without the beam search moditification ; style changes * fixing comments issues * docstrings for ConstraintListState * typo in PhrsalConstraint docstring * docstrings improvements Co-authored-by: Patrick von Platen <[email protected]>

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

def copy(self, stateful=False): raise NotImplementedError( f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )

generation_beam_constraints.py

Python

src/transformers/generation_beam_constraints.py

2b5603f6ac58f0cd3b2116c01d6b9f62575248b2

transformers

test_settings_no_request_no_use_default

Add generic settings to compliment site-specific settings (#8327)

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

def test_settings_no_request_no_use_default(self): context = {} # Without a request in the context, and without use_default_site, this # should bail with an error template = '{{ settings("tests.testsitesetting").title }}' with self.assertRaises(RuntimeError): self.render(template, context, request_context=False)

test_templates.py

Python

wagtail/contrib/settings/tests/site_specific/test_templates.py

d967eccef28ce47f60d26be1c28f2d83a25f40b0

wagtail

render_warning

ref(py): Split up large file (#32862) Co-authored-by: getsantry[bot] <66042841+getsantry[bot]@users.noreply.github.com>

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

def render_warning(self, message): context = {"error": message} return render_to_response("sentry/pipeline-provider-error.html", context, self.request)

base.py

Python

src/sentry/pipeline/base.py

d246d2b6d3e014270941209e54f2f12e09ad9a81

sentry

get_page_from_request

fix: Prefer titles matching request language (#7144) * prefer titles matching request language * add comments on use of annotate * fix wayward imports * Add changelog entry Co-authored-by: Vinit Kumar <[email protected]> Co-authored-by: Mark Walker <[email protected]>

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

def get_page_from_request(request, use_path=None, clean_path=None): from cms.utils.page_permissions import user_can_view_page_draft if not bool(use_path) and hasattr(request, '_current_page_cache'): # The following is set by CurrentPageMiddleware return request._current_page_cache if clean_path is None: clean_path = not bool(use_path) draft = use_draft(request) preview = 'preview' in request.GET path = request.path_info if use_path is None else use_path if clean_path: pages_root = reverse("pages-root") if path.startswith(pages_root): path = path[len(pages_root):] # strip any final slash if path.endswith("/"): path = path[:-1] site = get_current_site() request_language_code = getattr(request, "LANGUAGE_CODE", None) page = get_page_from_path( site, path, preview, draft, language_code=request_language_code ) if draft and page and not user_can_view_page_draft(request.user, page): page = get_page_from_path( site, path, preview, draft=False, language_code=request_language_code ) # For public pages, check if any parent is hidden due to published dates # In this case the selected page is not reachable if page and not draft: now = timezone.now() unpublished_ancestors = ( page .get_ancestor_pages() .filter( Q(publication_date__gt=now) | Q(publication_end_date__lt=now), ) ) if unpublished_ancestors.exists(): page = None return page

page.py

Python

cms/utils/page.py

06c9a85df486581f152dbf11bbf40a1c6c5e6cd3

django-cms

__call__

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

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

def __call__(self, checkpoint): if not self.runner: return if checkpoint.storage == Checkpoint.PERSISTENT and checkpoint.value: checkpoint_path = checkpoint.value logger.debug( "Trial %s: Deleting checkpoint %s", self.trial_id, checkpoint_path ) # TODO(ujvl): Batch remote deletes. # We first delete the remote checkpoint. If it is on the same # node as the driver, it will also remove the local copy. ray.get(self.runner.delete_checkpoint.remote(checkpoint_path)) # Delete local copy, if any exists. if os.path.exists(checkpoint_path): try: checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path) shutil.rmtree(checkpoint_dir) except FileNotFoundError: logger.debug("Local checkpoint dir not found during deletion.")

trial.py

Python

python/ray/tune/trial.py

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

test_filename_rubout

Add :rl-rubout and :rl-filename-rubout Closes #4561

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

def test_filename_rubout(os_sep, monkeypatch, lineedit, text, deleted, rest): monkeypatch.setattr(os, "sep", os_sep) _validate_deletion(lineedit, readlinecommands.rl_filename_rubout, [], text, deleted, rest) @pytest.mark.parametrize('text, deleted, rest', [ pytest.param('test foobar| delete', ' delete', 'test foobar|', marks=fixme), ('test foobar| delete', ' ', 'test foobar|delete'), # wrong pytest.param('test foo|delete bar', 'delete', 'test foo| bar', marks=fixme), ('test foo|delete bar', 'delete ', 'test foo|bar'), # wrong pytest.param('test foo<bar> delete', ' delete', 'test foobar|', marks=fixme), ('test foo<bar>delete', 'bardelete', 'test foo|'), # wrong ])

@pytest.mark.parametrize('text, deleted, rest', [ pytest.param('test foobar| delete', ' delete', 'test foobar|', marks=fixme), ('test foobar| delete', ' ', 'test foobar|delete'), # wrong pytest.param('test foo|delete bar', 'delete', 'test foo| bar', marks=fixme), ('test foo|delete bar', 'delete ', 'test foo|bar'), # wrong pytest.param('test foo<bar> delete', ' delete', 'test foobar|', marks=fixme), ('test foo<bar>delete', 'bardelete', 'test foo|'), # wrong ])

test_readlinecommands.py

Python

tests/unit/components/test_readlinecommands.py

ab65c542a0551abf105eeb58803cd08bd040753b

qutebrowser

_map_attributes

Downgrade exception on mapping list length mismatch to warning (#2856) * Downgrade exception on mapping list length mismatch to warning * Lint * Fix pairplot test * Set stacklevel to report warning in user code

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

def _map_attributes(self, arg, levels, defaults, attr): if arg is True: lookup_table = dict(zip(levels, defaults)) elif isinstance(arg, dict): missing = set(levels) - set(arg) if missing: err = f"These `{attr}` levels are missing values: {missing}" raise ValueError(err) lookup_table = arg elif isinstance(arg, Sequence): arg = self._check_list_length(levels, arg, attr) lookup_table = dict(zip(levels, arg)) elif arg: err = f"This `{attr}` argument was not understood: {arg}" raise ValueError(err) else: lookup_table = {} return lookup_table # =========================================================================== #

_oldcore.py

Python

seaborn/_oldcore.py

563e96d3be1eaee8db8dfbccf7eed1f1c66dfd31

seaborn

process_new_events

Clean up accessing dispatcher helpers via hass (#72014) Clean up accessing ditpatcher helpers via hass

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

def process_new_events(self, new_values_dict) -> None: self.async_set_available_state(True) # Process any stateless events (via device_triggers) async_fire_triggers(self, new_values_dict) for (aid, cid), value in new_values_dict.items(): accessory = self.current_state.setdefault(aid, {}) accessory[cid] = value # self.current_state will be replaced by entity_map in a future PR # For now we update both self.entity_map.process_changes(new_values_dict) async_dispatcher_send(self.hass, self.signal_state_updated)

connection.py

Python

homeassistant/components/homekit_controller/connection.py

c8f700c80319cef81a9a817c1b9111887ea98b1a

core

get_dependencies

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

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

def get_dependencies(ireq, sources=None, parent=None): # type: (Union[InstallRequirement, InstallationCandidate], Optional[List[Dict[S, Union[S, bool]]]], Optional[AbstractDependency]) -> Set[S, ...] if not isinstance(ireq, shims.InstallRequirement): name = getattr(ireq, "project_name", getattr(ireq, "project", ireq.name)) version = getattr(ireq, "version", None) if not version: ireq = shims.InstallRequirement.from_line("{0}".format(name)) else: ireq = shims.InstallRequirement.from_line("{0}=={1}".format(name, version)) pip_options = get_pip_options(sources=sources) getters = [ get_dependencies_from_cache, get_dependencies_from_wheel_cache, get_dependencies_from_json, functools.partial(get_dependencies_from_index, pip_options=pip_options), ] for getter in getters: deps = getter(ireq) if deps is not None: return deps raise RuntimeError("failed to get dependencies for {}".format(ireq))

dependencies.py

Python

pipenv/vendor/requirementslib/models/dependencies.py

cd5a9683be69c86c8f3adcd13385a9bc5db198ec

pipenv

odd_ext

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

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

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

signal.py

Python

jax/_src/scipy/signal.py

e085370ec4137cf0f73c5163cb664bc4e1c46082

jax

create_position_ids_from_input_ids

Add XGLM models (#14876) * add xglm * update vocab size * fix model name * style and tokenizer * typo * no mask token * fix pos embed compute * fix args * fix tokenizer * fix positions * fix tokenization * style and dic fixes * fix imports * add fast tokenizer * update names * add pt tests * fix tokenizer * fix typo * fix tokenizer import * fix fast tokenizer * fix tokenizer * fix converter * add tokenizer test * update checkpoint names * fix tokenizer tests * fix slow tests * add copied from comments * rst -> mdx * flax model * update flax tests * quality * style * doc * update index and readme * fix copies * fix doc * update toctrr * fix indent * minor fixes * fix config doc * don't save embed_pos weights * Apply suggestions from code review Co-authored-by: Sylvain Gugger <[email protected]> Co-authored-by: Patrick von Platen <[email protected]> * address Sylvains commnets, few doc fixes * fix check_repo * align order of arguments * fix copies * fix labels * remove unnecessary mapping * fix saving tokenizer Co-authored-by: Sylvain Gugger <[email protected]> Co-authored-by: Patrick von Platen <[email protected]>

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

def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding with M2M100->XGLM

modeling_xglm.py

Python

src/transformers/models/xglm/modeling_xglm.py

d25e25ee2b63ebfcd099deb689a5a7272574a10f

transformers

_build_tabs

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

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

def _build_tabs(self) -> None: logger.debug("Build Tabs") for section in self.config_tools.sections: tab = ttk.Notebook(self) self._tabs[section] = {"tab": tab} self.add(tab, text=section.replace("_", " ").title())

preview.py

Python

tools/preview/preview.py

1022651eb8a7741014f5d2ec7cbfe882120dfa5f

faceswap

token_expiry

Refresh google calendar tokens with invalid expiration times (#69679) * Refresh google calendar tokens with invalid expiration times * Update tests/components/google/conftest.py Co-authored-by: Martin Hjelmare <[email protected]> * Remove unnecessary async methods in functions being touched already Co-authored-by: Martin Hjelmare <[email protected]>

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

def token_expiry() -> datetime.datetime: return utcnow() + datetime.timedelta(days=7) @pytest.fixture

@pytest.fixture

conftest.py

Python

tests/components/google/conftest.py

06d2aeec6b153a104b275c73068cf05a7b5c0c6b

core

start

Implement previous backtest result reuse when config and strategy did not change.

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

def start(self) -> None: data: Dict[str, Any] = {} data, timerange = self.load_bt_data() self.load_bt_data_detail() logger.info("Dataload complete. Calculating indicators") run_ids = { strategy.get_strategy_name(): get_strategy_run_id(strategy) for strategy in self.strategylist } # Load previous result that will be updated incrementally. if self.config.get('timerange', '-').endswith('-'): self.config['no_backtest_cache'] = True logger.warning('Backtest result caching disabled due to use of open-ended timerange.') if not self.config.get('no_backtest_cache', False): self.results = find_existing_backtest_stats( self.config['user_data_dir'] / 'backtest_results', run_ids) for strat in self.strategylist: if self.results and strat.get_strategy_name() in self.results['strategy']: # When previous result hash matches - reuse that result and skip backtesting. logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}') continue min_date, max_date = self.backtest_one_strategy(strat, data, timerange) # Update old results with new ones. if len(self.all_results) > 0: results = generate_backtest_stats( data, self.all_results, min_date=min_date, max_date=max_date) if self.results: self.results['metadata'].update(results['metadata']) self.results['strategy'].update(results['strategy']) self.results['strategy_comparison'].extend(results['strategy_comparison']) else: self.results = results if self.config.get('export', 'none') == 'trades': store_backtest_stats(self.config['exportfilename'], self.results) # Results may be mixed up now. Sort them so they follow --strategy-list order. if 'strategy_list' in self.config and len(self.results) > 0: self.results['strategy_comparison'] = sorted( self.results['strategy_comparison'], key=lambda c: self.config['strategy_list'].index(c['key'])) self.results['strategy'] = dict( sorted(self.results['strategy'].items(), key=lambda kv: self.config['strategy_list'].index(kv[0]))) if len(self.strategylist) > 0: # Show backtest results show_backtest_results(self.config, self.results)

backtesting.py

Python

freqtrade/optimize/backtesting.py

16861db653ec8166f73fc8480894f186a137e7bd

freqtrade

evaluate_accuracy

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

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

def evaluate_accuracy(net, data_iter): metric = Accumulator(2) # No. of correct predictions, no. of predictions for X, y in data_iter: metric.add(accuracy(net(X), y), d2l.size(y)) return metric[0] / metric[1]

mxnet.py

Python

d2l/mxnet.py

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

d2l-zh

async_terminate_apps

Add Button platform to Bravia TV (#78093) * Add Button platform to Bravia TV * Add button.py to coveragerc * improve callable type

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

async def async_terminate_apps(self) -> None: await self.client.terminate_apps()

coordinator.py

Python

homeassistant/components/braviatv/coordinator.py

ab4c1ebfd6ab79abfc4e214853f71afba2380099

core

extra_compute_grad_fetches

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

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

def extra_compute_grad_fetches(self) -> Dict[str, Any]: return {LEARNER_STATS_KEY: {}} # e.g, stats, td error, etc.

torch_policy_v2.py

Python

rllib/policy/torch_policy_v2.py

bc3a1d35cf6e9a5fd7eef908a8e76aefb80ce6a9

ray

test_join_rules_public

EventAuthTestCase: build events for the right room version In practice, when we run the auth rules, all of the events have the right room version. Let's stop building Room V1 events for these tests and use the right version.

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

def test_join_rules_public(self): creator = "@creator:example.com" pleb = "@joiner:example.com" auth_events = { ("m.room.create", ""): _create_event(RoomVersions.V6, creator), ("m.room.member", creator): _join_event(RoomVersions.V6, creator), ("m.room.join_rules", ""): _join_rules_event( RoomVersions.V6, creator, "public" ), } # Check join. event_auth.check_auth_rules_for_event( RoomVersions.V6, _join_event(RoomVersions.V6, pleb), auth_events.values(), ) # A user cannot be force-joined to a room. with self.assertRaises(AuthError): event_auth.check_auth_rules_for_event( RoomVersions.V6, _member_event(RoomVersions.V6, pleb, "join", sender=creator), auth_events.values(), ) # Banned should be rejected. auth_events[("m.room.member", pleb)] = _member_event( RoomVersions.V6, pleb, "ban" ) with self.assertRaises(AuthError): event_auth.check_auth_rules_for_event( RoomVersions.V6, _join_event(RoomVersions.V6, pleb), auth_events.values(), ) # A user who left can re-join. auth_events[("m.room.member", pleb)] = _member_event( RoomVersions.V6, pleb, "leave" ) event_auth.check_auth_rules_for_event( RoomVersions.V6, _join_event(RoomVersions.V6, pleb), auth_events.values(), ) # A user can send a join if they're in the room. auth_events[("m.room.member", pleb)] = _member_event( RoomVersions.V6, pleb, "join" ) event_auth.check_auth_rules_for_event( RoomVersions.V6, _join_event(RoomVersions.V6, pleb), auth_events.values(), ) # A user can accept an invite. auth_events[("m.room.member", pleb)] = _member_event( RoomVersions.V6, pleb, "invite", sender=creator ) event_auth.check_auth_rules_for_event( RoomVersions.V6, _join_event(RoomVersions.V6, pleb), auth_events.values(), )

test_event_auth.py

Python

tests/test_event_auth.py

2959184a42398277ff916206235b844a8f7be5d7

synapse

check_toname_in_config_by_regex

fix: DEV-1462: Fix changing label config for repeater tag (#2725) * fix: DEV-1462: Fix changing label config for repeater tag with created annotations

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

def check_toname_in_config_by_regex(config_string, to_name, control_type=None): c = parse_config(config_string) if control_type: check_list = [control_type] else: check_list = list(c.keys()) for control in check_list: item = c[control].get('regex', {}) for to_name_item in c[control]['to_name']: expression = to_name_item for key in item: expression = expression.replace(key, item[key]) pattern = re.compile(expression) full_match = pattern.fullmatch(to_name) if full_match: return True return False

label_config.py

Python

label_studio/core/label_config.py

583b3cb3b03a36a30b3ce9fe96eb4fb28548a070

label-studio

ask_multi_load

Core updates - Change loss loading mechanism - Autosize tooltips based on content size - Random linting + code modernisation

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

def ask_multi_load(filepath, filetypes): filenames = FileHandler("filename_multi", filetypes).return_file if filenames: final_names = " ".join(f"\"{fname}\"" for fname in filenames) logger.debug(final_names) filepath.set(final_names)

control_helper.py

Python

lib/gui/control_helper.py

bad5025aea1adb9126580e14e064e6c99089243d

faceswap

tokenize

Black preview (#17217) * Black preview * Fixup too! * Fix check copies * Use the same version as the CI * Bump black

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

def tokenize(self, x): if isinstance(x, list) and all([isinstance(_x, list) for _x in x]): d = None for l in x: t = self.tokenizer( l, padding="max_length", max_length=384, truncation=True, return_tensors="pt", ) t["sizes"] = torch.tensor([len(l)]) if d is not None: for k in d.keys(): d[k] = torch.cat((d[k], t[k]), 0) else: d = t d["start_token_id"] = torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]")) d["end_token_id"] = torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]")) elif isinstance(x, list) and all([isinstance(_x, str) for _x in x]): d = self.tokenizer( x, padding="max_length", max_length=384, truncation=True, return_tensors="pt", ) else: raise Exception( "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of" " strings` for supports are supported." ) return d

tokenizer_utils.py

Python

examples/research_projects/fsner/src/fsner/tokenizer_utils.py

afe5d42d8d1d80af911ed980c2936bfe887078f6

transformers

run

end_play: end the current play only (#76674) Fixes #76672

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

def run(self): result = 0 entrylist = [] entry = {} try: # preload become/connection/shell to set config defs cached list(connection_loader.all(class_only=True)) list(shell_loader.all(class_only=True)) list(become_loader.all(class_only=True)) for playbook in self._playbooks: # deal with FQCN resource = _get_collection_playbook_path(playbook) if resource is not None: playbook_path = resource[1] playbook_collection = resource[2] else: playbook_path = playbook # not fqcn, but might still be colleciotn playbook playbook_collection = _get_collection_name_from_path(playbook) if playbook_collection: display.warning("running playbook inside collection {0}".format(playbook_collection)) AnsibleCollectionConfig.default_collection = playbook_collection else: AnsibleCollectionConfig.default_collection = None pb = Playbook.load(playbook_path, variable_manager=self._variable_manager, loader=self._loader) # FIXME: move out of inventory self._inventory.set_playbook_basedir(os.path.realpath(os.path.dirname(playbook_path))) if self._tqm is None: # we are doing a listing entry = {'playbook': playbook_path} entry['plays'] = [] else: # make sure the tqm has callbacks loaded self._tqm.load_callbacks() self._tqm.send_callback('v2_playbook_on_start', pb) i = 1 plays = pb.get_plays() display.vv(u'%d plays in %s' % (len(plays), to_text(playbook_path))) for play in plays: if play._included_path is not None: self._loader.set_basedir(play._included_path) else: self._loader.set_basedir(pb._basedir) # clear any filters which may have been applied to the inventory self._inventory.remove_restriction() # Allow variables to be used in vars_prompt fields. all_vars = self._variable_manager.get_vars(play=play) templar = Templar(loader=self._loader, variables=all_vars) setattr(play, 'vars_prompt', templar.template(play.vars_prompt)) # FIXME: this should be a play 'sub object' like loop_control if play.vars_prompt: for var in play.vars_prompt: vname = var['name'] prompt = var.get("prompt", vname) default = var.get("default", None) private = boolean(var.get("private", True)) confirm = boolean(var.get("confirm", False)) encrypt = var.get("encrypt", None) salt_size = var.get("salt_size", None) salt = var.get("salt", None) unsafe = var.get("unsafe", None) if vname not in self._variable_manager.extra_vars: if self._tqm: self._tqm.send_callback('v2_playbook_on_vars_prompt', vname, private, prompt, encrypt, confirm, salt_size, salt, default, unsafe) play.vars[vname] = display.do_var_prompt(vname, private, prompt, encrypt, confirm, salt_size, salt, default, unsafe) else: # we are either in --list-<option> or syntax check play.vars[vname] = default # Post validate so any play level variables are templated all_vars = self._variable_manager.get_vars(play=play) templar = Templar(loader=self._loader, variables=all_vars) play.post_validate(templar) if context.CLIARGS['syntax']: continue if self._tqm is None: # we are just doing a listing entry['plays'].append(play) else: self._tqm._unreachable_hosts.update(self._unreachable_hosts) previously_failed = len(self._tqm._failed_hosts) previously_unreachable = len(self._tqm._unreachable_hosts) break_play = False # we are actually running plays batches = self._get_serialized_batches(play) if len(batches) == 0: self._tqm.send_callback('v2_playbook_on_play_start', play) self._tqm.send_callback('v2_playbook_on_no_hosts_matched') for batch in batches: # restrict the inventory to the hosts in the serialized batch self._inventory.restrict_to_hosts(batch) # and run it... try: result = self._tqm.run(play=play) except AnsibleEndPlay as e: result = e.result break # break the play if the result equals the special return code if result & self._tqm.RUN_FAILED_BREAK_PLAY != 0: result = self._tqm.RUN_FAILED_HOSTS break_play = True # check the number of failures here, to see if they're above the maximum # failure percentage allowed, or if any errors are fatal. If either of those # conditions are met, we break out, otherwise we only break out if the entire # batch failed failed_hosts_count = len(self._tqm._failed_hosts) + len(self._tqm._unreachable_hosts) - \ (previously_failed + previously_unreachable) if len(batch) == failed_hosts_count: break_play = True break # update the previous counts so they don't accumulate incorrectly # over multiple serial batches previously_failed += len(self._tqm._failed_hosts) - previously_failed previously_unreachable += len(self._tqm._unreachable_hosts) - previously_unreachable # save the unreachable hosts from this batch self._unreachable_hosts.update(self._tqm._unreachable_hosts) if break_play: break i = i + 1 # per play if entry: entrylist.append(entry) # per playbook # send the stats callback for this playbook if self._tqm is not None: if C.RETRY_FILES_ENABLED: retries = set(self._tqm._failed_hosts.keys()) retries.update(self._tqm._unreachable_hosts.keys()) retries = sorted(retries) if len(retries) > 0: if C.RETRY_FILES_SAVE_PATH: basedir = C.RETRY_FILES_SAVE_PATH elif playbook_path: basedir = os.path.dirname(os.path.abspath(playbook_path)) else: basedir = '~/' (retry_name, _) = os.path.splitext(os.path.basename(playbook_path)) filename = os.path.join(basedir, "%s.retry" % retry_name) if self._generate_retry_inventory(filename, retries): display.display("\tto retry, use: --limit @%s\n" % filename) self._tqm.send_callback('v2_playbook_on_stats', self._tqm._stats) # if the last result wasn't zero, break out of the playbook file name loop if result != 0: break if entrylist: return entrylist finally: if self._tqm is not None: self._tqm.cleanup() if self._loader: self._loader.cleanup_all_tmp_files() if context.CLIARGS['syntax']: display.display("No issues encountered") return result if context.CLIARGS['start_at_task'] and not self._tqm._start_at_done: display.error( "No matching task \"%s\" found." " Note: --start-at-task can only follow static includes." % context.CLIARGS['start_at_task'] ) return result

playbook_executor.py

Python

lib/ansible/executor/playbook_executor.py

f78deccec2d4b5447f32d4fc67eaa549f479ccaa

ansible

_coo_matmat

[sparse] change call signature of coo primitive wrappers

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

def _coo_matmat(data, row, col, B, *, spinfo, transpose=False): return coo_matmat_p.bind(data, row, col, B, spinfo=spinfo, transpose=transpose) @coo_matmat_p.def_impl

@coo_matmat_p.def_impl

coo.py

Python

jax/experimental/sparse/coo.py

424536dcf421a8dd4b6dfd7ddffc066fec7661c7

jax

test_symlink_exists_file

Add some funtional tests Add functional tests for the following: - file.readlink - file.replace - file.symlink Remove unit tests for file.replace as they are duplicated in the added functional test

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

def test_symlink_exists_file(file, source): with pytest.helpers.temp_file("symlink.txt", contents="Source content") as target: with pytest.raises(CommandExecutionError) as exc: file.symlink(source, target) assert "Existing path is not a symlink:" in exc.value.message

test_symlink.py

Python

tests/pytests/functional/modules/file/test_symlink.py

a35b29b2651bf33c5d5b45e64bc7765ffde4aff4

salt

nargs_error

Factor out error generation for function calls with wrong nargs. ... matching the wording for standard functions. Note that nargs_error returns the exception without raising it itself to make the control flow clearer on the caller side.

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

def nargs_error(name, takes, given): return TypeError(f"{name}() takes {takes} positional arguments but " f"{given} were given")

__init__.py

Python

lib/matplotlib/_api/__init__.py

973e475ef85524c5e9cef0638c90ca9a159935e4

matplotlib

conv_shape_tuple

lax.conv_general_dilated: validate negative paddings

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

def conv_shape_tuple(lhs_shape, rhs_shape, strides, pads, batch_group_count=1): if isinstance(pads, str): pads = lax.padtype_to_pads(lhs_shape[2:], rhs_shape[2:], strides, pads) if len(pads) != len(lhs_shape) - 2: msg = "Wrong number of explicit pads for convolution: expected {}, got {}." raise TypeError(msg.format(len(lhs_shape) - 2, len(pads))) lhs_padded = np.add(lhs_shape[2:], np.sum(np.array(pads).reshape(-1, 2), axis=1)) if np.any(lhs_padded < 0): raise ValueError("Negative padding is larger than the size of the corresponding dimension: " f"got padding={pads} for lhs_shape[2:]={lhs_shape[2:]}") out_space = core.stride_shape(lhs_padded, rhs_shape[2:], strides) out_space = np.maximum(0, out_space) if batch_group_count > 1: assert lhs_shape[0] % batch_group_count == 0 out_shape_0 = lhs_shape[0] // batch_group_count else: out_shape_0 = lhs_shape[0] out_shape = (out_shape_0, rhs_shape[0]) return tuple(out_shape + tuple(out_space))

convolution.py

Python

jax/_src/lax/convolution.py

489596c0e268bf37a7f6c2cb86822f38d24eecc9

jax

test_widget_remove_order

Add a unit test for removal ordering via DOMQuery.remove

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

async def test_widget_remove_order(): removals: list[str] = []

test_widget_removing.py

Python

tests/test_widget_removing.py

d3e7f5ad994a92ae1734caea8bb66cfb043fcfc4

textual

nested_simplify

Replace dict/BatchEncoding instance checks by Mapping (#17014) * Replace dict/BatchEncoding instance checks by Mapping * Typo

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

def nested_simplify(obj, decimals=3): import numpy as np if isinstance(obj, list): return [nested_simplify(item, decimals) for item in obj] elif isinstance(obj, np.ndarray): return nested_simplify(obj.tolist()) elif isinstance(obj, Mapping): return {nested_simplify(k, decimals): nested_simplify(v, decimals) for k, v in obj.items()} elif isinstance(obj, (str, int, np.int64)): return obj elif obj is None: return obj elif is_torch_available() and isinstance(obj, torch.Tensor): return nested_simplify(obj.tolist(), decimals) elif is_tf_available() and tf.is_tensor(obj): return nested_simplify(obj.numpy().tolist()) elif isinstance(obj, float): return round(obj, decimals) elif isinstance(obj, (np.int32, np.float32)): return nested_simplify(obj.item(), decimals) else: raise Exception(f"Not supported: {type(obj)}")

testing_utils.py

Python

src/transformers/testing_utils.py

18df440709f1b19d1c5617c0d987c5ff8fd0915d

transformers

guess_lexer_for_filename

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

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

def guess_lexer_for_filename(_fn, _text, **options): fn = basename(_fn) primary = {} matching_lexers = set() for lexer in _iter_lexerclasses(): for filename in lexer.filenames: if _fn_matches(fn, filename): matching_lexers.add(lexer) primary[lexer] = True for filename in lexer.alias_filenames: if _fn_matches(fn, filename): matching_lexers.add(lexer) primary[lexer] = False if not matching_lexers: raise ClassNotFound('no lexer for filename %r found' % fn) if len(matching_lexers) == 1: return matching_lexers.pop()(**options) result = [] for lexer in matching_lexers: rv = lexer.analyse_text(_text) if rv == 1.0: return lexer(**options) result.append((rv, lexer))

__init__.py

Python

pipenv/patched/notpip/_vendor/pygments/lexers/__init__.py

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

test_issue4674

Refactor KB for easier customization (#11268) * Add implementation of batching + backwards compatibility fixes. Tests indicate issue with batch disambiguation for custom singular entity lookups. * Fix tests. Add distinction w.r.t. batch size. * Remove redundant and add new comments. * Adjust comments. Fix variable naming in EL prediction. * Fix mypy errors. * Remove KB entity type config option. Change return types of candidate retrieval functions to Iterable from Iterator. Fix various other issues. * Update spacy/pipeline/entity_linker.py Co-authored-by: Paul O'Leary McCann <[email protected]> * Update spacy/pipeline/entity_linker.py Co-authored-by: Paul O'Leary McCann <[email protected]> * Update spacy/kb_base.pyx Co-authored-by: Paul O'Leary McCann <[email protected]> * Update spacy/kb_base.pyx Co-authored-by: Paul O'Leary McCann <[email protected]> * Update spacy/pipeline/entity_linker.py Co-authored-by: Paul O'Leary McCann <[email protected]> * Add error messages to NotImplementedErrors. Remove redundant comment. * Fix imports. * Remove redundant comments. * Rename KnowledgeBase to InMemoryLookupKB and BaseKnowledgeBase to KnowledgeBase. * Fix tests. * Update spacy/errors.py Co-authored-by: Sofie Van Landeghem <[email protected]> * Move KB into subdirectory. * Adjust imports after KB move to dedicated subdirectory. * Fix config imports. * Move Candidate + retrieval functions to separate module. Fix other, small issues. * Fix docstrings and error message w.r.t. class names. Fix typing for candidate retrieval functions. * Update spacy/kb/kb_in_memory.pyx Co-authored-by: Sofie Van Landeghem <[email protected]> * Update spacy/ml/models/entity_linker.py Co-authored-by: Sofie Van Landeghem <[email protected]> * Fix typing. * Change typing of mentions to be Span instead of Union[Span, str]. * Update docs. * Update EntityLinker and _architecture docs. * Update website/docs/api/entitylinker.md Co-authored-by: Paul O'Leary McCann <[email protected]> * Adjust message for E1046. * Re-add section for Candidate in kb.md, add reference to dedicated page. * Update docs and docstrings. * Re-add section + reference for KnowledgeBase.get_alias_candidates() in docs. * Update spacy/kb/candidate.pyx * Update spacy/kb/kb_in_memory.pyx * Update spacy/pipeline/legacy/entity_linker.py * Remove canididate.md. Remove mistakenly added config snippet in entity_linker.py. Co-authored-by: Paul O'Leary McCann <[email protected]> Co-authored-by: Sofie Van Landeghem <[email protected]>

https://github.com/explosion/spaCy.git

def test_issue4674(): nlp = English() kb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3) vector1 = [0.9, 1.1, 1.01] vector2 = [1.8, 2.25, 2.01] with pytest.warns(UserWarning): kb.set_entities( entity_list=["Q1", "Q1"], freq_list=[32, 111], vector_list=[vector1, vector2], ) assert kb.get_size_entities() == 1 # dumping to file & loading back in with make_tempdir() as d: dir_path = ensure_path(d) if not dir_path.exists(): dir_path.mkdir() file_path = dir_path / "kb" kb.to_disk(str(file_path)) kb2 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3) kb2.from_disk(str(file_path)) assert kb2.get_size_entities() == 1 @pytest.mark.issue(6730)

@pytest.mark.issue(6730)

test_entity_linker.py

Python

spacy/tests/pipeline/test_entity_linker.py

1f23c615d7a7326ca5a38a7d768b8b70caaa0e17

spaCy

postprocess

update yolov3_darknet53_vehicles (#1957) * update yolov3_darknet53_vehicles * update gpu config * update * add clean func * update save inference model

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

def postprocess(paths, images, data_out, score_thresh, label_names, output_dir, handle_id, visualization=True): results = data_out.copy_to_cpu() lod = data_out.lod()[0] check_dir(output_dir) if paths: assert type(paths) is list, "type(paths) is not list." if handle_id < len(paths): unhandled_paths = paths[handle_id:] unhandled_paths_num = len(unhandled_paths) else: unhandled_paths_num = 0 if images is not None: if handle_id < len(images): unhandled_paths = None unhandled_paths_num = len(images) - handle_id else: unhandled_paths_num = 0 output = list() for index in range(len(lod) - 1): output_i = {'data': []} if unhandled_paths and index < unhandled_paths_num: org_img_path = unhandled_paths[index] org_img = Image.open(org_img_path) else: org_img = images[index - unhandled_paths_num] org_img = org_img.astype(np.uint8) org_img = Image.fromarray(org_img[:, :, ::-1]) if visualization: org_img_path = get_save_image_name(org_img, output_dir, 'image_numpy_{}'.format((handle_id + index))) org_img.save(org_img_path) org_img_height = org_img.height org_img_width = org_img.width result_i = results[lod[index]:lod[index + 1]] for row in result_i: if len(row) != 6: continue if row[1] < score_thresh: continue category_id = int(row[0]) confidence = row[1] bbox = row[2:] dt = {} dt['label'] = label_names[category_id] dt['confidence'] = float(confidence) dt['left'], dt['top'], dt['right'], dt['bottom'] = clip_bbox(bbox, org_img_width, org_img_height) output_i['data'].append(dt) output.append(output_i) if visualization: output_i['save_path'] = draw_bounding_box_on_image(org_img_path, output_i['data'], output_dir) return output

processor.py

Python

modules/image/object_detection/yolov3_darknet53_vehicles/processor.py

7a847a39b1da6e6867031f52f713d92391b9729d

PaddleHub

test_error_opset_import_mismatch

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

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

def test_error_opset_import_mismatch(self) -> None: m1, m2 = _load_model(m1_def), _load_model(m2_def) m1 = helper.make_model(m1.graph, producer_name='test', opset_imports=[helper.make_opsetid("", 10)]) m2 = helper.make_model(m2.graph, producer_name='test', opset_imports=[helper.make_opsetid("", 15)]) io_map = [("B00", "B01"), ("B10", "B11"), ("B20", "B21")] self.assertRaises(ValueError, compose.merge_models, m1, m2, io_map) # Converting to the same Operator set version, should work m1 = version_converter.convert_version(m1, 15) m3 = compose.merge_models(m1, m2, io_map=io_map) checker.check_model(m3)

compose_test.py

Python

onnx/test/compose_test.py

83fa57c74edfd13ddac9548b8a12f9e3e2ed05bd

onnx

test_load_corrupt_file

Updates the classifier to catch warnings from scikit-learn and rebuild the model file when this happens

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

def test_load_corrupt_file(self, patched_pickle_load): # First load is the schema version patched_pickle_load.side_effect = [DocumentClassifier.FORMAT_VERSION, OSError()] with self.assertRaises(ClassifierModelCorruptError): self.classifier.load()

test_classifier.py

Python

src/documents/tests/test_classifier.py

77fbbe95ffb965525136982846f50e3ad8244de9

paperless-ngx

ipv4_enabled

Fully type certbot-nginx module (#9124) * Work in progress * Fix type * Work in progress * Work in progress * Work in progress * Work in progress * Work in progress * Oups. * Fix typing in UnspacedList * Fix logic * Finish typing * List certbot-nginx as fully typed in tox * Fix lint * Fix checks * Organize imports * Fix typing for Python 3.6 * Fix checks * Fix lint * Update certbot-nginx/certbot_nginx/_internal/configurator.py Co-authored-by: alexzorin <[email protected]> * Update certbot-nginx/certbot_nginx/_internal/configurator.py Co-authored-by: alexzorin <[email protected]> * Fix signature of deploy_cert regarding the installer interface * Update certbot-nginx/certbot_nginx/_internal/obj.py Co-authored-by: alexzorin <[email protected]> * Fix types * Update certbot-nginx/certbot_nginx/_internal/parser.py Co-authored-by: alexzorin <[email protected]> * Precise type * Precise _coerce possible inputs/outputs * Fix type * Update certbot-nginx/certbot_nginx/_internal/http_01.py Co-authored-by: ohemorange <[email protected]> * Fix type * Remove an undesirable implementation. * Fix type Co-authored-by: alexzorin <[email protected]> Co-authored-by: ohemorange <[email protected]>

https://github.com/certbot/certbot.git

def ipv4_enabled(self) -> bool: if not self.addrs: return True for a in self.addrs: if not a.ipv6: return True return False

obj.py

Python

certbot-nginx/certbot_nginx/_internal/obj.py

16aad35d31a887dab157f9d4f5e0fe9218d06064

certbot

test_basic

Update black (#5438) * CI: sync up black dev requirements version with precommit * Run black Co-authored-by: Jarrod Millman <[email protected]>

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

def test_basic(self): trees = [(nx.full_rary_tree(2, 2**2 - 1), 0) for i in range(2)] actual = nx.join(trees) expected = nx.full_rary_tree(2, 2**3 - 1) assert nx.is_isomorphic(actual, expected)

test_operations.py

Python

networkx/algorithms/tree/tests/test_operations.py

f6755ffa00211b523c6c0bec5398bc6c3c43c8b1

networkx

format_target_temperature

Fix #69952: Daikin AC Temperature jumps after being set (#70326)

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

def format_target_temperature(target_temperature): return str(round(float(target_temperature), 1)).rstrip("0").rstrip(".")

climate.py

Python

homeassistant/components/daikin/climate.py

b0ed42a5a58976ebe82b5bbbb60c499648a1718b

core

test_not_collapsed_with_legacy

Update Wagtail test cases to match slim sidebar capabilities and implementation details

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

def test_not_collapsed_with_legacy(self): # Sidebar should not be collapsed because the feature flag is not enabled self.client.cookies['wagtail_sidebar_collapsed'] = '1' response = self.client.get(reverse('wagtailadmin_home')) self.assertNotContains(response, 'sidebar-collapsed')

test_menu.py

Python

wagtail/admin/tests/test_menu.py

18c4d7c81356dbd5c4503db2ea24b21492512317

wagtail

test_pool_slots_property

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

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

def test_pool_slots_property(self): with pytest.raises(ValueError, match="pool slots .* cannot be less than 1"): dag = models.DAG(dag_id='test_run_pooling_task') EmptyOperator( task_id='test_run_pooling_task_op', dag=dag, pool='test_pool', pool_slots=0, )

test_taskinstance.py

Python

tests/models/test_taskinstance.py

49e336ae0302b386a2f47269a6d13988382d975f

airflow

putmask

BUG: setting pd.NA into Series casts to object (#45431)

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

def putmask(self, mask, new) -> list[Block]: orig_mask = mask values = cast(np.ndarray, self.values) mask, noop = validate_putmask(values.T, mask) assert not isinstance(new, (ABCIndex, ABCSeries, ABCDataFrame)) if new is lib.no_default: new = self.fill_value new = self._standardize_fill_value(new) if self._can_hold_element(new): putmask_without_repeat(values.T, mask, new) return [self] elif np_version_under1p20 and infer_dtype_from(new)[0].kind in ["m", "M"]: # using putmask with object dtype will incorrectly cast to object # Having excluded self._can_hold_element, we know we cannot operate # in-place, so we are safe using `where` return self.where(new, ~mask) elif noop: return [self] elif self.ndim == 1 or self.shape[0] == 1: # no need to split columns if not is_list_like(new): # putmask_smart can't save us the need to cast return self.coerce_to_target_dtype(new).putmask(mask, new) # This differs from # `self.coerce_to_target_dtype(new).putmask(mask, new)` # because putmask_smart will check if new[mask] may be held # by our dtype. nv = putmask_smart(values.T, mask, new).T return [self.make_block(nv)] else: is_array = isinstance(new, np.ndarray) res_blocks = [] nbs = self._split() for i, nb in enumerate(nbs): n = new if is_array: # we have a different value per-column n = new[:, i : i + 1] submask = orig_mask[:, i : i + 1] rbs = nb.putmask(submask, n) res_blocks.extend(rbs) return res_blocks

blocks.py

Python

pandas/core/internals/blocks.py

3510b1fd2a9cf752638f4af751bdeb33496db766

pandas

xkcd

Simplify impl. of functions optionally used as context managers. We can actually just put the "exit" logic into an ExitStack callback. If the return value is never `__enter__`'d via a "with" statement, it is never `__exit__`'d either.

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

def xkcd(scale=1, length=100, randomness=2): # This cannot be implemented in terms of contextmanager() or rc_context() # because this needs to work as a non-contextmanager too. if rcParams['text.usetex']: raise RuntimeError( "xkcd mode is not compatible with text.usetex = True") stack = ExitStack() stack.callback(dict.update, rcParams, rcParams.copy()) from matplotlib import patheffects rcParams.update({ 'font.family': ['xkcd', 'xkcd Script', 'Humor Sans', 'Comic Neue', 'Comic Sans MS'], 'font.size': 14.0, 'path.sketch': (scale, length, randomness), 'path.effects': [ patheffects.withStroke(linewidth=4, foreground="w")], 'axes.linewidth': 1.5, 'lines.linewidth': 2.0, 'figure.facecolor': 'white', 'grid.linewidth': 0.0, 'axes.grid': False, 'axes.unicode_minus': False, 'axes.edgecolor': 'black', 'xtick.major.size': 8, 'xtick.major.width': 3, 'ytick.major.size': 8, 'ytick.major.width': 3, }) return stack ## Figures ## @_api.make_keyword_only("3.6", "facecolor")

@_api.make_keyword_only("3.6", "facecolor")

pyplot.py

Python

lib/matplotlib/pyplot.py

2d918ba09155810194bb4ba136369082ad46c8c8

matplotlib

test_use_nullable_dtypes

Add support for `use_nullable_dtypes` to `dd.read_parquet` (#9617)

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

def test_use_nullable_dtypes(tmp_path, engine): df = pd.DataFrame( { "a": pd.Series([1, 2, pd.NA, 3, 4], dtype="Int64"), "b": pd.Series([True, pd.NA, False, True, False], dtype="boolean"), "c": pd.Series([0.1, 0.2, 0.3, pd.NA, 0.4], dtype="Float64"), "d": pd.Series(["a", "b", "c", "d", pd.NA], dtype="string"), } ) ddf = dd.from_pandas(df, npartitions=2)

test_parquet.py

Python

dask/dataframe/io/tests/test_parquet.py

b1e468e8645baee30992fbfa84250d816ac1098a

dask

update_positions

Cleanup AnnotationBbox. Inline _update_position_xybox into update_positions. Avoid unpacking x,y pairs where unnecessary. Don't bother copying arrowprops, as we don't actually modify it. Reuse mutation scale for both patch and arrow. Clarify the doc for frameon. Various small extra cleanups.

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

def update_positions(self, renderer): x, y = self.xybox if isinstance(self.boxcoords, tuple): xcoord, ycoord = self.boxcoords x1, y1 = self._get_xy(renderer, x, y, xcoord) x2, y2 = self._get_xy(renderer, x, y, ycoord) ox0, oy0 = x1, y2 else: ox0, oy0 = self._get_xy(renderer, x, y, self.boxcoords) w, h, xd, yd = self.offsetbox.get_extent(renderer) fw, fh = self._box_alignment self.offsetbox.set_offset((ox0 - fw * w + xd, oy0 - fh * h + yd)) bbox = self.offsetbox.get_window_extent(renderer) self.patch.set_bounds(bbox.bounds) mutation_scale = renderer.points_to_pixels(self.get_fontsize()) self.patch.set_mutation_scale(mutation_scale) if self.arrowprops: # Use FancyArrowPatch if self.arrowprops has "arrowstyle" key. # Adjust the starting point of the arrow relative to the textbox. # TODO: Rotation needs to be accounted. arrow_begin = bbox.p0 + bbox.size * self._arrow_relpos arrow_end = self._get_position_xy(renderer) # The arrow (from arrow_begin to arrow_end) will be first clipped # by patchA and patchB, then shrunk by shrinkA and shrinkB (in # points). If patch A is not set, self.bbox_patch is used. self.arrow_patch.set_positions(arrow_begin, arrow_end) if "mutation_scale" in self.arrowprops: mutation_scale = renderer.points_to_pixels( self.arrowprops["mutation_scale"]) # Else, use fontsize-based mutation_scale defined above. self.arrow_patch.set_mutation_scale(mutation_scale) patchA = self.arrowprops.get("patchA", self.patch) self.arrow_patch.set_patchA(patchA)

offsetbox.py

Python

lib/matplotlib/offsetbox.py

924d7c7f9900d8839e66616791121237101e7b57

matplotlib

test_component_functions

detect all types of null default value (#1685) * detect all types of null default value * fix test * address review comments

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

def test_component_functions(self): radio_input = gr.Radio(["a", "b", "c"]) self.assertEqual(radio_input.preprocess("c"), "c") self.assertEqual(radio_input.preprocess_example("a"), "a") self.assertEqual(radio_input.serialize("a", True), "a") with tempfile.TemporaryDirectory() as tmpdirname: to_save = radio_input.save_flagged(tmpdirname, "radio_input", "a", None) self.assertEqual(to_save, "a") restored = radio_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, "a") self.assertIsInstance(radio_input.generate_sample(), str) radio_input = gr.Radio( choices=["a", "b", "c"], default="a", label="Pick Your One Input" ) self.assertEqual( radio_input.get_config(), { "choices": ["a", "b", "c"], "value": None, "name": "radio", "show_label": True, "label": "Pick Your One Input", "style": {}, "elem_id": None, "visible": True, "interactive": None, }, ) with self.assertRaises(ValueError): wrong_type = gr.Radio(["a", "b"], type="unknown") wrong_type.preprocess(0)

test_components.py

Python

test/test_components.py

a2b84199d88f84fd2dc515e092e79380ed7cef50

gradio

stop_submobject_movement

Upgraded typehints (#2429) * Future Annotations * Delete template_twitter_post.py * [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 * Apply suggestions from code review * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed broken RTD Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

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

def stop_submobject_movement(self) -> VectorField: self.remove_updater(self.submob_movement_updater) self.submob_movement_updater = None return self

vector_field.py

Python

manim/mobject/vector_field.py

daf23c9d1031b12d9c119b8f6b7e60727d7f9242

manim

_extend_upper

FIX: Handle inverted colorbar axes with extensions This fixes the colorbar extensions to use the proper color when the long axis is inverted.

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

def _extend_upper(self): minmax = "min" if self._long_axis().get_inverted() else "max" return self.extend in ('both', minmax)

colorbar.py

Python

lib/matplotlib/colorbar.py

ec374f5148631e4d392ed7e6d4c454d163a62f21

matplotlib

fit

MAINT Use _validate_params in SparsePCA and MiniBatchSparsePCA (#23710) Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: jeremiedbb <[email protected]>

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

def fit(self, X, y=None): self._validate_params() random_state = check_random_state(self.random_state) X = self._validate_data(X) self.mean_ = X.mean(axis=0) X = X - self.mean_ if self.n_components is None: n_components = X.shape[1] else: n_components = self.n_components return self._fit(X, n_components, random_state)

_sparse_pca.py

Python

sklearn/decomposition/_sparse_pca.py

db6123fe40400828918037f3fae949bfcc4d9d05

scikit-learn

get_repo

feat(github): Log Github integration errors (#39993) We are flying blind without this. This helps the debugging of issues.

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

def get_repo(self, integration, organization, event): try: project_id = event["project"]["id"] except KeyError: logger.info( "gitlab.webhook.missing-projectid", extra={"integration_id": integration.id} ) logger.exception("Missing project ID.") raise Http404() external_id = "{}:{}".format(integration.metadata["instance"], project_id) try: repo = Repository.objects.get( organization_id=organization.id, provider=PROVIDER_NAME, external_id=external_id ) except Repository.DoesNotExist: return None return repo

webhooks.py

Python

src/sentry/integrations/gitlab/webhooks.py

746c20250f419a227bed0d174791e9c9b75daa13

sentry

handle_subscription_metrics_logger

refs(metric_alerts): Consolidate `QueryDatasets` and `Dataset` (#36894) This refactor pr removes `QueryDatasets` and just uses `Dataset` everywhere. `QueryDatasets` existed before `Dataset`, but `Dataset` is now more widely used and is more up to date. The values here are the same, `Dataset` just supports a few more datasets. We already make sure that only datasets that are valid for alerts can be passed to the alert rules api, so this won't allow people to attempt to create alerts on datasets that don't support them.

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

def handle_subscription_metrics_logger(subscription_update, subscription): from sentry.incidents.subscription_processor import SubscriptionProcessor try: if subscription.snuba_query.dataset == Dataset.Metrics.value: processor = SubscriptionProcessor(subscription) # XXX: Temporary hack so that we can extract these values without raising an exception processor.reset_trigger_counts = lambda *arg, **kwargs: None aggregation_value = processor.get_aggregation_value(subscription_update) logger.info( "handle_subscription_metrics_logger.message", extra={ "subscription_id": subscription.id, "dataset": subscription.snuba_query.dataset, "snuba_subscription_id": subscription.subscription_id, "result": subscription_update, "aggregation_value": aggregation_value, }, ) except Exception: logger.exception("Failed to log subscription results") @register_subscriber(INCIDENTS_SNUBA_SUBSCRIPTION_TYPE)

@register_subscriber(INCIDENTS_SNUBA_SUBSCRIPTION_TYPE)

tasks.py

Python

src/sentry/incidents/tasks.py

e1482001662b446c7c2be7c9daa19cba562c615c

sentry

_set_locations

Add typing to certbot.apache (#9071) * Add typing to certbot.apache Co-authored-by: Adrien Ferrand <[email protected]>

https://github.com/certbot/certbot.git

def _set_locations(self) -> Dict[str, str]: default: str = self.loc["root"] temp: str = os.path.join(self.root, "ports.conf") if os.path.isfile(temp): listen = temp name = temp else: listen = default name = default return {"default": default, "listen": listen, "name": name}

parser.py

Python

certbot-apache/certbot_apache/_internal/parser.py

7d9e9a49005de7961e84d2a7c608db57dbab3046

certbot

test_placement_group_parent

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

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

def test_placement_group_parent(ray_4_node_4_cpu, placement_group_capture_child_tasks): num_workers = 2 bundle = {"CPU": 1} bundles = [bundle.copy() for _ in range(num_workers + 1)] placement_group = ray.util.placement_group(bundles)

test_backend.py

Python

python/ray/train/tests/test_backend.py

57cdbb1769a9c32972ba0ec9e7e857eeea961869

ray

async_will_remove_from_hass

Fix cover, light, select, sensor, switch type hints in zha (#73770) * Fix zha sensor type hints * Fix zha entity type hints * Fix switch type hints * Fix light type hints * Fix cover type hints * Fix select type hints

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

async def async_will_remove_from_hass(self) -> None: assert self._cancel_refresh_handle self._cancel_refresh_handle() await super().async_will_remove_from_hass()

light.py

Python

homeassistant/components/zha/light.py

243905ae3e10f21c9bc8cbde565532e1b7b9112f

core

test_find_executable_task_instances_negative_open_pool_slots

Pools with negative open slots should not block other pools (#23143)

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

def test_find_executable_task_instances_negative_open_pool_slots(self, dag_maker): set_default_pool_slots(0) self.scheduler_job = SchedulerJob(subdir=os.devnull) session = settings.Session() pool1 = Pool(pool='pool1', slots=1) pool2 = Pool(pool='pool2', slots=1) session.add(pool1) session.add(pool2) dag_id = 'SchedulerJobTest.test_find_executable_task_instances_negative_open_pool_slots' with dag_maker(dag_id=dag_id): op1 = EmptyOperator(task_id='op1', pool='pool1') op2 = EmptyOperator(task_id='op2', pool='pool2', pool_slots=2) dr1 = dag_maker.create_dagrun(run_type=DagRunType.SCHEDULED) ti1 = dr1.get_task_instance(op1.task_id, session) ti2 = dr1.get_task_instance(op2.task_id, session) ti1.state = State.SCHEDULED ti2.state = State.RUNNING session.flush() res = self.scheduler_job._executable_task_instances_to_queued(max_tis=1, session=session) assert 1 == len(res) assert res[0].key == ti1.key session.rollback()

test_scheduler_job.py

Python

tests/jobs/test_scheduler_job.py

7132be2f11db24161940f57613874b4af86369c7

airflow

staged_predict_proba

DOC Fix incorrect heading underline length in docstrings (#22278)

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

def staged_predict_proba(self, X): for raw_predictions in self._staged_raw_predict(X): yield self._loss.predict_proba(raw_predictions)

gradient_boosting.py

Python

sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py

24106c2149683efeb642c8c1317152d7fe5be162

scikit-learn

provide_gcp_credential_file_as_context

Ensure @contextmanager decorates generator func (#23103)

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

def provide_gcp_credential_file_as_context(self) -> Generator[Optional[str], None, None]: key_path: Optional[str] = self._get_field('key_path', None) keyfile_dict: Optional[str] = self._get_field('keyfile_dict', None) if key_path and keyfile_dict: raise AirflowException( "The `keyfile_dict` and `key_path` fields are mutually exclusive. " "Please provide only one value." ) elif key_path: if key_path.endswith('.p12'): raise AirflowException('Legacy P12 key file are not supported, use a JSON key file.') with patch_environ({CREDENTIALS: key_path}): yield key_path elif keyfile_dict: with tempfile.NamedTemporaryFile(mode='w+t') as conf_file: conf_file.write(keyfile_dict) conf_file.flush() with patch_environ({CREDENTIALS: conf_file.name}): yield conf_file.name else: # We will use the default service account credentials. yield None

base_google.py

Python

airflow/providers/google/common/hooks/base_google.py

e58985598f202395098e15b686aec33645a906ff

airflow

test_migration_1_1_to_1_4

Add serial_number to device registry entries (#77713)

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

async def test_migration_1_1_to_1_4(hass, hass_storage): hass_storage[device_registry.STORAGE_KEY] = { "version": 1, "minor_version": 1, "data": { "devices": [ { "config_entries": ["1234"], "connections": [["Zigbee", "01.23.45.67.89"]], "entry_type": "service", "id": "abcdefghijklm", "identifiers": [["serial", "12:34:56:AB:CD:EF"]], "manufacturer": "manufacturer", "model": "model", "name": "name", "sw_version": "version", }, # Invalid entry type { "config_entries": [None], "connections": [], "entry_type": "INVALID_VALUE", "id": "invalid-entry-type", "identifiers": [["serial", "mock-id-invalid-entry"]], "manufacturer": None, "model": None, "name": None, "sw_version": None, }, ], "deleted_devices": [ { "config_entries": ["123456"], "connections": [], "entry_type": "service", "id": "deletedid", "identifiers": [["serial", "12:34:56:AB:CD:FF"]], "manufacturer": "manufacturer", "model": "model", "name": "name", "sw_version": "version", } ], }, } await device_registry.async_load(hass) registry = device_registry.async_get(hass) # Test data was loaded entry = registry.async_get_or_create( config_entry_id="1234", connections={("Zigbee", "01.23.45.67.89")}, identifiers={("serial", "12:34:56:AB:CD:EF")}, ) assert entry.id == "abcdefghijklm" # Update to trigger a store entry = registry.async_get_or_create( config_entry_id="1234", connections={("Zigbee", "01.23.45.67.89")}, identifiers={("serial", "12:34:56:AB:CD:EF")}, sw_version="new_version", ) assert entry.id == "abcdefghijklm" # Check we store migrated data await flush_store(registry._store) assert hass_storage[device_registry.STORAGE_KEY] == { "version": device_registry.STORAGE_VERSION_MAJOR, "minor_version": device_registry.STORAGE_VERSION_MINOR, "key": device_registry.STORAGE_KEY, "data": { "devices": [ { "area_id": None, "config_entries": ["1234"], "configuration_url": None, "connections": [["Zigbee", "01.23.45.67.89"]], "disabled_by": None, "entry_type": "service", "hw_version": None, "id": "abcdefghijklm", "identifiers": [["serial", "12:34:56:AB:CD:EF"]], "manufacturer": "manufacturer", "model": "model", "name": "name", "name_by_user": None, "serial_number": None, "sw_version": "new_version", "via_device_id": None, }, { "area_id": None, "config_entries": [None], "configuration_url": None, "connections": [], "disabled_by": None, "entry_type": None, "hw_version": None, "id": "invalid-entry-type", "identifiers": [["serial", "mock-id-invalid-entry"]], "manufacturer": None, "model": None, "name_by_user": None, "name": None, "serial_number": None, "sw_version": None, "via_device_id": None, }, ], "deleted_devices": [ { "config_entries": ["123456"], "connections": [], "id": "deletedid", "identifiers": [["serial", "12:34:56:AB:CD:FF"]], "orphaned_timestamp": None, } ], }, } @pytest.mark.parametrize("load_registries", [False])

@pytest.mark.parametrize("load_registries", [False])

test_device_registry.py

Python

tests/helpers/test_device_registry.py

cba3b6ad944408b9ffd906f4da5e5f5fd615b174

core

test_thread_edit_latest_event

Misc. clean-ups to the relations code (#12519) * Corrects some typos / copy & paste errors in tests. * Clarifies docstrings. * Removes an unnecessary method.

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

def test_thread_edit_latest_event(self) -> None: # Create a thread and edit the last event. channel = self._send_relation( RelationTypes.THREAD, "m.room.message", content={"msgtype": "m.text", "body": "A threaded reply!"}, ) threaded_event_id = channel.json_body["event_id"] new_body = {"msgtype": "m.text", "body": "I've been edited!"} channel = self._send_relation( RelationTypes.REPLACE, "m.room.message", content={"msgtype": "m.text", "body": "foo", "m.new_content": new_body}, parent_id=threaded_event_id, ) # Fetch the thread root, to get the bundled aggregation for the thread. relations_dict = self._get_bundled_aggregations() # We expect that the edit message appears in the thread summary in the # unsigned relations section. self.assertIn(RelationTypes.THREAD, relations_dict) thread_summary = relations_dict[RelationTypes.THREAD] self.assertIn("latest_event", thread_summary) latest_event_in_thread = thread_summary["latest_event"] self.assertEqual(latest_event_in_thread["content"]["body"], "I've been edited!")

test_relations.py

Python

tests/rest/client/test_relations.py

185da8f0f2db8e4d502a904942cbd8a6840e27c8

synapse

_split

Add H&M fashion recommendation dataset (#2708) * allow individual file downloads from kaggle * pipe download_filenames to kaggle download fn * add dataset config for H&M Fashion Recommendations * add custom loader * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * use local backend instead of mock * add docstring for sample * fix titanic test * move negative_sample to ludwig.data * do not negative sample in loader Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

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

def _split(df): splitter = get_splitter("datetime", column="year_month", probabilities=(0.7, 0.2, 0.1)) if not isinstance(df, pd.DataFrame): df = df.compute() train_dfs, val_dfs, test_dfs = [], [], [] for customer_id in df["customer_id"].unique(): # Split per customer_id to ensure that interactions for a customer are across all splits train_df, val_df, test_df = splitter.split(df[df["customer_id"] == customer_id], backend=LocalBackend()) train_dfs.append(train_df) val_dfs.append(val_df) test_dfs.append(test_df) return pd.concat(train_dfs), pd.concat(val_dfs), pd.concat(test_dfs)

hm_fashion_recommendations.py

Python

ludwig/datasets/loaders/hm_fashion_recommendations.py

abfdc05018cc4dec5a2fed20ad09e94f1749fca9

ludwig

_get_next_trial

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

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

def _get_next_trial(self): no_trials_unfinished = True no_trials_pending = True for trial in self._live_trials: if not trial.is_finished(): no_trials_unfinished = False if trial.status == Trial.PENDING: no_trials_pending = False if not no_trials_unfinished and not no_trials_pending: break wait_for_trial = no_trials_unfinished and not self._search_alg.is_finished() # Only fetch a new trial if we have no pending trial if wait_for_trial or no_trials_pending: self._update_trial_queue(blocking=wait_for_trial) with warn_if_slow("choose_trial_to_run"): trial = self._scheduler_alg.choose_trial_to_run(self) if trial: logger.debug("Running trial {}".format(trial)) return trial

trial_runner.py

Python

python/ray/tune/trial_runner.py

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

test__build_query

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

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

def test__build_query(self, table_name, date_add_kwargs, expected_to_delete, external_trigger): base_date = pendulum.DateTime(2022, 1, 1, tzinfo=pendulum.timezone('UTC')) create_tis( base_date=base_date, num_tis=10, external_trigger=external_trigger, ) with create_session() as session: clean_before_date = base_date.add(**date_add_kwargs) query = _build_query( **config_dict[table_name].__dict__, clean_before_timestamp=clean_before_date, session=session, ) assert len(query.all()) == expected_to_delete

test_db_cleanup.py

Python

tests/utils/test_db_cleanup.py

95bd6b71cc9f5da377e272707f7b68000d980939

airflow

test_build_dir

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

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

def test_build_dir(capfd, hello_world_f90, monkeypatch): ipath = Path(hello_world_f90) mname = "blah" odir = "tttmp" monkeypatch.setattr(sys, "argv", f'f2py -m {mname} {ipath} --build-dir {odir}'.split()) with util.switchdir(ipath.parent): f2pycli() out, _ = capfd.readouterr() assert f"Wrote C/API module \"{mname}\"" in out

test_f2py2e.py

Python

numpy/f2py/tests/test_f2py2e.py

729ad4f92420231e2a7009b3223c6c7620b8b808

numpy

help_test_reload_with_config

Do not depend MQTT CI tests on debug logs (#84783) * Do not depend MQTT CI tests on debug logs * Leave Clean up expire as debug message

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

async def help_test_reload_with_config(hass, caplog, tmp_path, config): new_yaml_config_file = tmp_path / "configuration.yaml" new_yaml_config = yaml.dump(config) new_yaml_config_file.write_text(new_yaml_config) assert new_yaml_config_file.read_text() == new_yaml_config with patch.object(hass_config, "YAML_CONFIG_FILE", new_yaml_config_file): await hass.services.async_call( "mqtt", SERVICE_RELOAD, {}, blocking=True, ) await hass.async_block_till_done()

test_common.py

Python

tests/components/mqtt/test_common.py

ee66ffc8deaa6d383becc60c0418f63a7cfa4dc9

core

get_loc

DEPR: Remove method and tolerance in Index.get_loc, bump xarray (#49630) * DEPR: Remove method and tolerance in Index.get_loc * note xarray bump * Fix tests * Fix refactor in period * Lighter parameterization * xfail xarray test * Just use get_indexer

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

def get_loc(self, key): self._check_indexing_error(key)

multi.py

Python

pandas/core/indexes/multi.py

bc987e708b9856f5d5c8cf3096e1e2bcf23e1121

pandas

test_probe_video_from_memory

Improve test_video_reader (#5498) * Improve test_video_reader * Fix linter error

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

def test_probe_video_from_memory(self, test_video, config): _, video_tensor = _get_video_tensor(VIDEO_DIR, test_video) probe_result = torch.ops.video_reader.probe_video_from_memory(video_tensor) self.check_probe_result(probe_result, config)

test_video_reader.py

Python

test/test_video_reader.py

c50d48845f7b1ca86d6a3b7f37a59be0ae11e36b

vision

__lazymodule_import

Prevent LazyLoader from modifying nltk.__dict__ Allows pytest --doctest-modules nltk to be executed

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

def __lazymodule_import(self): # Load and register module local_name = self.__lazymodule_name # e.g. "toolbox" full_name = self.__name__ # e.g. "nltk.toolbox" if self.__lazymodule_loaded: return self.__lazymodule_locals[local_name] if _debug: print("LazyModule: Loading module %r" % full_name) self.__lazymodule_locals[local_name] = module = __import__( full_name, self.__lazymodule_locals, self.__lazymodule_globals, "*" ) # Fill namespace with all symbols from original module to # provide faster access. self.__dict__.update(module.__dict__) # Set import flag self.__dict__["__lazymodule_loaded"] = 1 if _debug: print("LazyModule: Module %r loaded" % full_name) return module

lazyimport.py

Python

nltk/lazyimport.py

0fbbff998ed4b91b2f640a5193161642d98898cd

nltk

print_table

Balanced joint maximum mean discrepancy for deep transfer learning

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

def print_table(table, max_width): max_widths = [max_width] * len(table[0]) column_widths = [max(printed_len(row[j]) + 1 for row in table) for j in range(len(table[0]))] column_widths = [min(w, max_w) for w, max_w in zip(column_widths, max_widths)] for row in table: row_str = '' right_col = 0 for cell, width in zip(row, column_widths): right_col += width row_str += cell + ' ' row_str += ' ' * max(right_col - printed_len(row_str), 0) print row_str

summarize.py

Python

code/deep/BJMMD/caffe/tools/extra/summarize.py

cc4d0564756ca067516f71718a3d135996525909

transferlearning

vehicle_name

Refactor volvooncall to (mostly) use DataUpdateCoordinator (#75885) Co-authored-by: Martin Hjelmare <[email protected]>

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

def vehicle_name(self, vehicle): if vehicle.registration_number and vehicle.registration_number != "UNKNOWN": return vehicle.registration_number if vehicle.vin: return vehicle.vin return "Volvo"

__init__.py

Python

homeassistant/components/volvooncall/__init__.py

b5a6ee3c567aa50633ef47d342af685fb75e5219

core

fill_predictions

add freqao backend machinery, user interface, documentation

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

def fill_predictions(self, len_dataframe): filler = np.zeros(len_dataframe -len(self.predictions)) # startup_candle_count self.predictions = np.append(filler,self.predictions) self.do_predict = np.append(filler,self.do_predict) self.target_mean = np.append(filler,self.target_mean) self.target_std = np.append(filler,self.target_std) return

data_handler.py

Python

freqtrade/freqai/data_handler.py

fc837c4daa27a18ff0e86128f4d52089b88fa5fb

freqtrade

test_get_dynamic_sampling_default_biases

fix(dyn-sampling): Backend code clean up (#42001) We are consolidating server-side-sampling and dynamic-sampling flags into only dynamic-sampling. The flag is being controlled by plan

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

def test_get_dynamic_sampling_default_biases(self): with Feature( { self.new_ds_flag: True, } ): response = self.get_success_response( self.organization.slug, self.project.slug, method="get" ) assert response.data["dynamicSamplingBiases"] == DEFAULT_BIASES

test_project_details.py

Python

tests/sentry/api/endpoints/test_project_details.py

6fc6106b6a57149a5bae3c0f4677349cfbae1155

sentry

save

Add serialization support to FeatureSpace. PiperOrigin-RevId: 496914744

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

def save(self, filepath): saving_lib.save_model(self, filepath)

feature_space.py

Python

keras/utils/feature_space.py

799f70761eeb8155dc25c6afce8c1d22b38367b0

keras

detect_rnet

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

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

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

mtcnn.py

Python

plugins/extract/detect/mtcnn.py

aa39234538a8f83e6aa2b60b8275a570e8876ac2

faceswap

extract_data

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.

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

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

page.py

Python

awxkit/awxkit/api/pages/page.py

68a44529b6b77d2d43d7099b654560bfd8bbf518

awx

_isolated_tk_test

TST: Remove numpy cpu disabling from some subprocess tests This removes the NPY_DISABLE_CPU_FEATURES flag from the sphinx and tk tests as they emit warnings on CI which leads to failure from the subprocess. These don't need to be disabled on these tests, so remove them from the environment variables that are passed in.

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

def _isolated_tk_test(success_count, func=None): if func is None: return functools.partial(_isolated_tk_test, success_count) # Remove decorators. source = re.search(r"(?ms)^def .*", inspect.getsource(func)).group(0)

test_backend_tk.py

Python

lib/matplotlib/tests/test_backend_tk.py

d6f68757c234d33f341adc9e3bd65053094cf748

matplotlib

test_is_html_renderer

Improve asserts in wagtail. These improvements were based on flake8-assertive, which compiled an extensive list of patterns to replace with more precise assertions. This should make the error messages better in case of failures.

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

def test_is_html_renderer(self): # TableBlock with default table_options block1 = TableBlock() self.assertIs(block1.is_html_renderer(), False) # TableBlock with altered table_options new_options = self.default_table_options.copy() new_options['renderer'] = 'html' block2 = TableBlock(table_options=new_options) self.assertIs(block2.is_html_renderer(), True)

tests.py

Python

wagtail/contrib/table_block/tests.py

a0ef2477a68f2deb83cdc9a0bb709cb644be028b

wagtail

top_k_categorical_matches

making util methods for all the categorical accuracies

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

def top_k_categorical_matches(y_true, y_pred, k=5): y_true = tf.math.argmax(y_true, axis=-1) return sparse_top_k_categorical_matches(y_true, y_pred, k=k)

metrics_utils.py

Python

keras/utils/metrics_utils.py

33f395aeceaad95910ce6f0931621bb82d3e967c

keras

basic_sequential

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

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

def basic_sequential(): model = keras.Sequential( [ keras.layers.Dense(3, activation="relu", input_shape=(3,)), keras.layers.Dense(2, activation="softmax"), ] ) return ModelFn(model, (None, 3), (None, 2))

model_architectures.py

Python

keras/tests/model_architectures.py

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

env_dict

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

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

def env_dict(self) -> t.Dict[str, str]: return dict((item[0], item[1]) for item in [e.split('=', 1) for e in self.env])

docker_util.py

Python

test/lib/ansible_test/_internal/docker_util.py

3eb0485dd92c88cc92152d3656d94492db44b183

ansible