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semeru

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

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Semeru Lab 7

tests/storage/test_event_federation.py

def _setup_room_for_insertion_backfill_tests(self) -> _BackfillSetupInfo: room_id = "!backfill-room-test:some-host" depth_map: Dict[str, int] = {

Only try to backfill event if we haven't tried before recently (#13635) Only try to backfill event if we haven't tried before recently (exponential backoff). No need to keep trying the same backfill point that fails over and over. Fix https://github.com/matrix-org/synapse/issues/13622 Fix https://github.com/matrix-org/synapse/issues/8451 Follow-up to https://github.com/matrix-org/synapse/pull/13589 Part of https://github.com/matrix-org/synapse/issues/13356

_setup_room_for_insertion_backfill_tests

ac1a31740b6d0dfda4d57a25762aaddfde981caf

synapse

test_event_federation.py

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

Python

def _setup_room_for_insertion_backfill_tests(self) -> _BackfillSetupInfo: room_id = "!backfill-room-test:some-host" depth_map: Dict[str, int] = { "1": 1, "2": 2, "insertion_eventA": 3, "3": 4, "insertion_eventB": 5, "4": 6, "5": 7, }

python/ray/serve/experimental/gradio_visualize_graph.py

def postprocessing(data): if type_to_string(type(data)) == "torch.Tensor": try: import torch from torchvision import transforms # By default Torch tensors are displayed as images. To display them as JSON, # the user can simply convert them to numpy arrays. transformer = transforms.ToPILImage() return transformer(torch.squeeze(data)) except ModuleNotFoundError: logger.warning( "Module `torchvision` isn't installed,

[serve] Add additional features to DAG visualization with Gradio (#28246)

postprocessing

203253321d34543aa25483803ebc21e3903679b6

ray

gradio_visualize_graph.py

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

Python

def postprocessing(data): if type_to_string(type(data)) == "torch.Tensor": try: import torch from torchvision import transforms # By default Torch tensors are displayed as images. To display them as JSON, # the user can simply convert them to numpy arrays. transformer = transforms.ToPILImage() return transformer(torch.squeeze(data)) except ModuleNotFoundError: logger.warning( "Module `torchvision` isn't installed, unable to process torch tensor." ) return data return data

erpnext/manufacturing/doctype/work_order/test_work_order.py

def get_scrap_item_details(bom_no): scrap_items = {} for item in frappe.db.sql( , bom_no, as_dict=1, ): scrap_items[item.item_code] = item.stock_qty return scrap_items

style: format code with black

get_scrap_item_details

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

test_work_order.py

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

Python

def get_scrap_item_details(bom_no): scrap_items = {} for item in frappe.db.sql( , bom_no, as_dict=1, ): scrap_items[item.item_code] = item.stock_qty return scrap_items

airflow/models/dag.py

def get_is_active(self, session=NEW_SESSION) -> Optional[None]:

API: Fix deprecation warning due to using query.value (#22775) When using sqlalchemy 1.4, there's a deprecation warning at the task logging: SADeprecationWarning: Query.value() is deprecated and will be removed in a future release. Please use Query.with_entities() in combination with Query.scalar() (deprecated since: 1.4) This PR fixes it

get_is_active

921ccedf7f90f15e8d18c27a77b29d232be3c8cb

airflow

dag.py

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

Python

def get_is_active(self, session=NEW_SESSION) -> Optional[None]: return session.query(DagModel.is_active).filter(DagModel.dag_id == self.dag_id).scalar()

mkdocs/structure/pages.py

def is_homepage(self) -> bool: return self.is_top_level and self.is_index and self.file.url in ('.', './', 'index.html') previous_page: Optional[Page] next_page: Optional[Page] parent: Optional[Section] children: None = None is_section: bool = False is_page: bool = True is_link: bool =

Relative links end with slash even for homepage links (#3022) Fixes #3015

is_homepage

32359f3e93f5ca7778b9f7c3d6d92f49a629c84c

mkdocs

pages.py

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

Python

def is_homepage(self) -> bool: return self.is_top_level and self.is_index and self.file.url in ('.', './', 'index.html') previous_page: Optional[Page] next_page: Optional[Page] parent: Optional[Section] children: None = None is_section: bool = False is_page: bool = True is_link: bool = False

lib/gui/utils.py

def _filetypes(self): all_files = ("All files", "*.*") filetypes = dict( default=(all_files,), alignments=[("Faceswap Alignments", "*.fsa"), all_files], config_project=[("Faceswap Project files", "*.fsw"), all_files], config_task=[("Faceswap Task files", "*.fst"), all_files], config_all=[("Faceswap Project and Task files", "*.fst *.fsw"), all_files], csv=[("Comma separated values", "*.csv"), all_files], image=[("Bitmap", "*.bmp"), ("JPG", "*.jpeg *.jpg"), ("PNG", "*.png"), ("TIFF", "*.tif *.tiff"), all_files], ini=[("Faceswap config files", "*.ini"), all_files],

Update code to support Tensorflow versions up to 2.8 (#1213) * Update maximum tf version in setup + requirements * - bump max version of tf version in launcher - standardise tf version check * update keras get_custom_objects for tf>2.6 * bugfix: force black text in GUI file dialogs (linux) * dssim loss - Move to stock tf.ssim function * Update optimizer imports for compatibility * fix logging for tf2.8 * Fix GUI graphing for TF2.8 * update tests * bump requirements.txt versions * Remove limit on nvidia-ml-py * Graphing bugfixes - Prevent live graph from displaying if data not yet available * bugfix: Live graph. Collect loss labels correctly * fix: live graph - swallow inconsistent loss errors * Bugfix: Prevent live graph from clearing during training * Fix graphing for AMD

_filetypes

c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf

faceswap

utils.py

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

Python

def _filetypes(self): all_files = ("All files", "*.*") filetypes = dict( default=(all_files,), alignments=[("Faceswap Alignments", "*.fsa"), all_files], config_project=[("Faceswap Project files", "*.fsw"), all_files], config_task=[("Faceswap Task files", "*.fst"), all_files], config_all=[("Faceswap Project and Task files", "*.fst *.fsw"), all_files], csv=[("Comma separated values", "*.csv"), all_files], image=[("Bitmap", "*.bmp"), ("JPG", "*.jpeg *.jpg"), ("PNG", "*.png"), ("TIFF", "*.tif *.tiff"), all_files], ini=[("Faceswap config files", "*.ini"), all_files], json=[("JSON file", "*.json"), all_files], model=[("Keras model files", "*.h5"), all_files], state=[("State files", "*.json"), all_files], log=[("Log files", "*.log"), all_files], video=[("Audio Video Interleave", "*.avi"), ("Flash Video", "*.flv"), ("Matroska", "*.mkv"), ("MOV", "*.mov"), ("MP4", "*.mp4"), ("MPEG", "*.mpeg *.mpg *.ts *.vob"), ("WebM", "*.webm"), ("Windows Media Video", "*.wmv"), all_files]) # Add in multi-select options and upper case extensions for Linux for key in filetypes: if platform.system() == "Linux": filetypes[key] = [item if item[0] == "All files" else (item[0], f"{item[1]} {item[1].upper()}") for item in filetypes[key]] if len(filetypes[key]) > 2: multi = [f"{key.title()} Files"] multi.append(" ".join([ftype[1] for ftype in filetypes[key] if ftype[0] != "All files"])) filetypes[key].insert(0, tuple(multi)) return filetypes

python3.10.4/Lib/asyncio/futures.py

def result(self): if self._state == _CANCELLED: exc = self._make_cancelled_error() raise exc if self._state != _FINISHED: raise exceptions.InvalidStateError('Result is not ready.')

add python 3.10.4 for windows

result

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

futures.py

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

Python

def result(self): if self._state == _CANCELLED: exc = self._make_cancelled_error() raise exc if self._state != _FINISHED: raise exceptions.InvalidStateError('Result is not ready.') self.__log_traceback = False if self._exception is not None: raise self._exception return self._result

src/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py

def _set_fsspec_for_multiprocess() -> None: fsspec.asyn.iothread[0] = None fsspec.asyn.

Support DataLoader with num_workers > 0 in streaming mode (#4375) * make TorchIterableDataset work in parallel - make it picklable - paralellize over the shards when num_workers is passed * start writing some tests * fix streaming extension and fsspec issues in subprocesses * fix some tests * fix more tests * fix import * fix and add tests * fix patch (handle successive patches and builtins) * revert unnecessary change to enriched_web_blg * style * use open locally to fix win permission errors * keep file opened in read_csv * fix compression for read_csv * consistency of read_csv: don't infer compression for file-like objects * stringify Path objects * comments + raise error if sharding is ambiguous * minor * Update src/datasets/iterable_dataset.py Co-authored-by: Mario Šaško <mariosasko777@gmail.com> Co-authored-by: Mario Šaško <mariosasko777@gmail.com>

_set_fsspec_for_multiprocess

ab7d3045ac9154e9c1c2602d0869130defdc6dc7

datasets

torch_iterable_dataset.py

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

Python

def _set_fsspec_for_multiprocess() -> None: fsspec.asyn.iothread[0] = None fsspec.asyn.loop[0] = None

sklearn/utils/extmath.py

def stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08): out = np.cumsum(arr, axis=axis, dtype=np.float64) expected = np.sum(arr, axis=axis, dtype=np.float64) if not np.all( np.isclose(

DOC ensure sklearn/utils/extmath/stable_cumsum passes numpydoc (#24348)

stable_cumsum

45756377c748d84aa52f66950b8d9eeefc31456c

scikit-learn

extmath.py

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

Python

def stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08): out = np.cumsum(arr, axis=axis, dtype=np.float64) expected = np.sum(arr, axis=axis, dtype=np.float64) if not np.all( np.isclose( out.take(-1, axis=axis), expected, rtol=rtol, atol=atol, equal_nan=True ) ): warnings.warn( "cumsum was found to be unstable: " "its last element does not correspond to sum", RuntimeWarning, ) return out

airflow/www/views.py

def confirm(self): args = request.args dag_id = args.get('dag_id') task_id = args.get('task_id') dag_run_id = args.get('dag_run_id') state = args.get('state') origin = args.get('origin') if 'map_index' not in args: map_indexes: Optional[List[int]] = None else: map_indexes = args.getlist('map_index', type=int) upstream = to_boolean(args.get('upstream')) downstream = to_boolean(args.get('downstream')) future = to_boolean(args.get('future')) past = to_boolean(args.get('past')) origin = origin or url_for('Airflow.index') dag = get_airflow_app().dag_bag.get_dag(dag_id) if not dag: msg = f'DAG {dag_id} not found' return redirect_or_json(origin, msg, status='error', status_code=404) try: task = dag.get_task(task_id) except airflow.exceptions.TaskNotFound: msg = f"Task {task_id} not found" return redirect_or_json(origin, msg, status='error', status_code=404) task.dag = dag if state not in ( 'success', 'failed', ): msg = f"Invalid state {state}, must be either 'success' or 'failed'" return redirect_or_json(origin, msg

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

confirm

e2f19505bf3622935480e80bee55bf5b6d80097b

airflow

views.py

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

Python

def confirm(self): args = request.args dag_id = args.get('dag_id') task_id = args.get('task_id') dag_run_id = args.get('dag_run_id') state = args.get('state') origin = args.get('origin') if 'map_index' not in args: map_indexes: Optional[List[int]] = None else: map_indexes = args.getlist('map_index', type=int) upstream = to_boolean(args.get('upstream')) downstream = to_boolean(args.get('downstream')) future = to_boolean(args.get('future')) past = to_boolean(args.get('past')) origin = origin or url_for('Airflow.index') dag = get_airflow_app().dag_bag.get_dag(dag_id) if not dag: msg = f'DAG {dag_id} not found' return redirect_or_json(origin, msg, status='error', status_code=404) try: task = dag.get_task(task_id) except airflow.exceptions.TaskNotFound: msg = f"Task {task_id} not found" return redirect_or_json(origin, msg, status='error', status_code=404) task.dag = dag if state not in ( 'success', 'failed', ): msg = f"Invalid state {state}, must be either 'success' or 'failed'" return redirect_or_json(origin, msg, status='error', status_code=400) latest_execution_date = dag.get_latest_execution_date() if not latest_execution_date: msg = f"Cannot mark tasks as {state}, seem that dag {dag_id} has never run" return redirect_or_json(origin, msg, status='error', status_code=400) if map_indexes is None: tasks: Union[List[Operator], List[Tuple[Operator, int]]] = [task] else: tasks = [(task, map_index) for map_index in map_indexes] to_be_altered = set_state( tasks=tasks, run_id=dag_run_id, upstream=upstream, downstream=downstream, future=future, past=past, state=state, commit=False, ) if request.headers.get('Accept') == 'application/json': details = [str(t) for t in to_be_altered] return htmlsafe_json_dumps(details, separators=(',', ':')) details = "\n".join(str(t) for t in to_be_altered) response = self.render_template( "airflow/confirm.html", endpoint=url_for(f'Airflow.{state}'), message=f"Task instances you are about to mark as {state}:", details=details, ) return response

nni/retiarii/oneshot/pytorch/base_lightning.py

def resample(self) -> Dict[str, Any]:

Valuechoice oneshot lightning (#4602)

resample

14d2966b9e91ae16dcc39de8f41017a75cec8ff9

nni

base_lightning.py

https://github.com/microsoft/nni.git

Python

def resample(self) -> Dict[str, Any]: result = {} for module in self.nas_modules: result.update(module.resample(memo=result)) return result

spacy/tests/doc/test_json_doc_conversion.py

def test_json_to_doc_attribute_consistency(doc): doc_json = doc.to_json() doc_json["tokens"][1].pop("morph") with pytest.raises(ValueError): Doc(doc.vocab).from_js

Add Doc.from_json() (#10688) * Implement Doc.from_json: rough draft. * Implement Doc.from_json: first draft with tests. * Implement Doc.from_json: added documentation on website for Doc.to_json(), Doc.from_json(). * Implement Doc.from_json: formatting changes. * Implement Doc.to_json(): reverting unrelated formatting changes. * Implement Doc.to_json(): fixing entity and span conversion. Moving fixture and doc <-> json conversion tests into single file. * Implement Doc.from_json(): replaced entity/span converters with doc.char_span() calls. * Implement Doc.from_json(): handling sentence boundaries in spans. * Implementing Doc.from_json(): added parser-free sentence boundaries transfer. * Implementing Doc.from_json(): added parser-free sentence boundaries transfer. * Implementing Doc.from_json(): incorporated various PR feedback. * Renaming fixture for document without dependencies. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implementing Doc.from_json(): using two sent_starts instead of one. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implementing Doc.from_json(): doc_without_dependency_parser() -> doc_without_deps. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implementing Doc.from_json(): incorporating various PR feedback. Rebased on latest master. * Implementing Doc.from_json(): refactored Doc.from_json() to work with annotation IDs instead of their string representations. * Implement Doc.from_json(): reverting unwanted formatting/rebasing changes. * Implement Doc.from_json(): added check for char_span() calculation for entities. * Update spacy/tokens/doc.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): minor refactoring, additional check for token attribute consistency with corresponding test. * Implement Doc.from_json(): removed redundancy in annotation type key naming. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): Simplifying setting annotation values. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement doc.from_json(): renaming annot_types to token_attrs. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): adjustments for renaming of annot_types to token_attrs. * Implement Doc.from_json(): removing default categories. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): simplifying lexeme initialization. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): simplifying lexeme initialization. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): refactoring to only have keys for present annotations. * Implement Doc.from_json(): fix check for tokens' HEAD attributes. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): refactoring Doc.from_json(). * Implement Doc.from_json(): fixing span_group retrieval. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): fixing span retrieval. * Implement Doc.from_json(): added schema for Doc JSON format. Minor refactoring in Doc.from_json(). * Implement Doc.from_json(): added comment regarding Token and Span extension support. * Implement Doc.from_json(): renaming inconsistent_props to partial_attrs.. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): adjusting error message. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): extending E1038 message. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): added params to E1038 raises. * Implement Doc.from_json(): combined attribute collection with partial attributes check. * Implement Doc.from_json(): added optional schema validation. * Implement Doc.from_json(): fixed optional fields in schema, tests. * Implement Doc.from_json(): removed redundant None check for DEP. * Implement Doc.from_json(): added passing of schema validatoin message to E1037.. * Implement Doc.from_json(): removing redundant error E1040. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): changing message for E1037. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): adjusted website docs and docstring of Doc.from_json(). * Update spacy/tests/doc/test_json_doc_conversion.py * Implement Doc.from_json(): docstring update. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): docstring update. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): website docs update. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): docstring formatting. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): docstring formatting. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): fixing Doc reference in website docs. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): reformatted website/docs/api/doc.md. * Implement Doc.from_json(): bumped IDs of new errors to avoid merge conflicts. * Implement Doc.from_json(): fixing bug in tests. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Implement Doc.from_json(): fix setting of sentence starts for docs without DEP. * Implement Doc.from_json(): add check for valid char spans when manually setting sentence boundaries. Refactor sentence boundary setting slightly. Move error message for lack of support for partial token annotations to errors.py. * Implement Doc.from_json(): simplify token sentence start manipulation. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Combine related error messages * Update spacy/tests/doc/test_json_doc_conversion.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>

test_json_to_doc_attribute_consistency

8387ce4c01db48d92ac5638e18316c0f1fc8861e

spaCy

test_json_doc_conversion.py

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

Python

def test_json_to_doc_attribute_consistency(doc): doc_json = doc.to_json() doc_json["tokens"][1].pop("morph") with pytest.raises(ValueError): Doc(doc.vocab).from_json(doc_json)

tests/admin_views/tests.py

def test_index_css_classes(self):

Refs #33476 -- Reformatted code with Black.

test_index_css_classes

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_index_css_classes(self): # General index page response = self.client.get(reverse("admin:index")) self.assertContains(response, '<div class="app-admin_views module') self.assertContains(response, '<tr class="model-actor">') self.assertContains(response, '<tr class="model-album">') # App index page response = self.client.get(reverse("admin:app_list", args=("admin_views",))) self.assertContains(response, '<div class="app-admin_views module') self.assertContains(response, '<tr class="model-actor">') self.assertContains(response, '<tr class="model-album">')

tests/admin_views/test_actions.py

def test_action_column_class(self): response = self.client.get(reverse("admin:admin_views_subscriber_changelist")) self.assertIsNotNone(response.context["act

Refs #33476 -- Reformatted code with Black.

test_action_column_class

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

test_actions.py

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

Python

def test_action_column_class(self): response = self.client.get(reverse("admin:admin_views_subscriber_changelist")) self.assertIsNotNone(response.context["action_form"]) self.assertContains(response, "action-checkbox-column")

awx/conf/settings.py

def hashkey(cls, *args, **kwargs): return cachetools.keys.hashkey(f"<{cls.__name__}>", *args, **kwargs)

add lock to cachetools usage * We observed daphne giving tracebacks when accessing logging settings. Originally, configure tower in tower settings was no a suspect because daphne is not multi-process. We've had issues with configure tower in tower settings and multi-process before. We later learned that Daphne is multi-threaded. Configure tower in tower was back to being a suspect. We constructed a minimal reproducer to show that multiple threads accessing settings can cause the same traceback that we saw in daphne. See https://gist.github.com/chrismeyersfsu/7aa4bdcf76e435efd617cb078c64d413 for that recreator. These fixes stop the recreation.

hashkey

21972c91dd2b52cd206bf71ea038ab0e1f478b32

awx

settings.py

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

Python

def hashkey(cls, *args, **kwargs): return cachetools.keys.hashkey(f"<{cls.__name__}>", *args, **kwargs)

nni/experiment/config/base.py

def load(cls, path): with open(path) as yaml_file: data = yaml.safe_load(yaml_file) if not isinstance(data, dict): raise TypeError(f'Conent of config file {path} is not a dict/object') utils.set_base_path(Path(path).parent) config = cls(**data) utils.uns

Some string changes around experiment module (#4442)

load

3f6a8274a97bf003b5eadc05faa324162b7f4123

nni

base.py

https://github.com/microsoft/nni.git

Python

def load(cls, path): with open(path) as yaml_file: data = yaml.safe_load(yaml_file) if not isinstance(data, dict): raise TypeError(f'Conent of config file {path} is not a dict/object') utils.set_base_path(Path(path).parent) config = cls(**data) utils.unset_base_path() return config

@frappe.whitelist()

erpnext/hr/doctype/leave_allocation/leave_allocation.py

def get_leave_allocation_for_period(employee, leave_type, from_date, to_date): leave_allocated = 0 leave_allocations = frappe.db.sql( , {"from_date": from_date, "to_date": to_date, "employee": employee, "leave_type": leave_type}, as_dict=1, ) if leave_allocations: for leave_alloc in leave_allocations: leave_allocated += leave_alloc.total_leaves_allocated return leave_allocated @frappe.whitelist()

style: format code with black

get_leave_allocation_for_period

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

leave_allocation.py

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

Python

def get_leave_allocation_for_period(employee, leave_type, from_date, to_date): leave_allocated = 0 leave_allocations = frappe.db.sql( , {"from_date": from_date, "to_date": to_date, "employee": employee, "leave_type": leave_type}, as_dict=1, ) if leave_allocations: for leave_alloc in leave_allocations: leave_allocated += leave_alloc.total_leaves_allocated return leave_allocated @frappe.whitelist()

tests/admin_utils/test_logentry.py

def test_proxy_model_content_type_is_used_for_log_entries(self): proxy_content_type = ContentType.objects.get_for_model( ArticleProxy, for_concrete_model=False ) post_data = { "site": self.site.pk, "title": "Foo", "hist": "Bar", "created_0": "2015-12-25", "created_1": "00:00", } changelist_url = reverse("admin:admin_utils_articleproxy_changelist") # add proxy_add_url = reverse("admin:admin_utils_articleproxy_add") response = self.client.post(proxy_add_url, post_data) self.assertRedirects(response, changelist_url) proxy_addition_log = LogEntry.objects.latest("id") self.assertEqual(proxy_addition_log.action_flag, ADDITION) self.assertEqual(proxy_addition_log.content_type, proxy_content_type) # change article_id = proxy_addition_log.object_id proxy_change_url = reverse( "admin:admin_utils_articleproxy_change", args=(article_id,) ) post_data["title"] = "New" response = self.client.post(proxy_change_url, post_data) self.assertRedirects(response, changelist_url) proxy_change_log = LogEntry.objects.latest("id") self.assertEqual(proxy_change_log.action_flag, CHANGE) self.assertEqual(proxy_change_log.content_type, proxy_content_type) # delete proxy_delete_url = reverse( "admin:admin_utils_articleproxy_delete", args=(arti

Refs #33476 -- Reformatted code with Black.

test_proxy_model_content_type_is_used_for_log_entries

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

test_logentry.py

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

Python

def test_proxy_model_content_type_is_used_for_log_entries(self): proxy_content_type = ContentType.objects.get_for_model( ArticleProxy, for_concrete_model=False ) post_data = { "site": self.site.pk, "title": "Foo", "hist": "Bar", "created_0": "2015-12-25", "created_1": "00:00", } changelist_url = reverse("admin:admin_utils_articleproxy_changelist") # add proxy_add_url = reverse("admin:admin_utils_articleproxy_add") response = self.client.post(proxy_add_url, post_data) self.assertRedirects(response, changelist_url) proxy_addition_log = LogEntry.objects.latest("id") self.assertEqual(proxy_addition_log.action_flag, ADDITION) self.assertEqual(proxy_addition_log.content_type, proxy_content_type) # change article_id = proxy_addition_log.object_id proxy_change_url = reverse( "admin:admin_utils_articleproxy_change", args=(article_id,) ) post_data["title"] = "New" response = self.client.post(proxy_change_url, post_data) self.assertRedirects(response, changelist_url) proxy_change_log = LogEntry.objects.latest("id") self.assertEqual(proxy_change_log.action_flag, CHANGE) self.assertEqual(proxy_change_log.content_type, proxy_content_type) # delete proxy_delete_url = reverse( "admin:admin_utils_articleproxy_delete", args=(article_id,) ) response = self.client.post(proxy_delete_url, {"post": "yes"}) self.assertRedirects(response, changelist_url) proxy_delete_log = LogEntry.objects.latest("id") self.assertEqual(proxy_delete_log.action_flag, DELETION) self.assertEqual(proxy_delete_log.content_type, proxy_content_type)

dask/utils.py

def typename(typ, short=False) -> str: if not isinstance(typ, type): return typename(type(typ)) try: if not typ.__module__ or typ.__module__ == "builtins": return typ.

Add mild typing to common utils functions (#8848)

typename

261bf174931580230717abca93fe172e166cc1e8

dask

utils.py

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

Python

def typename(typ, short=False) -> str: if not isinstance(typ, type): return typename(type(typ)) try: if not typ.__module__ or typ.__module__ == "builtins": return typ.__name__ else: if short: module, *_ = typ.__module__.split(".") else: module = typ.__module__ return module + "." + typ.__name__ except AttributeError: return str(typ)

django/core/management/__init__.py

def fetch_command(self, subcommand): # Get commands outside of try block to prevent swallowing exceptions commands = get_commands() try: app_name = commands[subcommand] except KeyError: if os.environ.get("DJANGO_SETTINGS_MODULE"): # If `subcommand` is missing due to misconfigured settings, the # following line will retrigger an ImproperlyConfigured exception # (get_commands() swallows the original one) so the user is # informed about it. settings.INSTALLED_APPS elif not settings.configured: sys.stderr.write("No Django settings specified.\n") possible_matches = get_close_matches(subcommand, commands) sys.stderr.write("Unknown command: %r" % subcommand) if possible_matches: sys.stderr.write(". Did you mean %s?" % possible_matches[0]) sys.stderr.write("\nType '%s help' for usage.\n" % self

Refs #33476 -- Reformatted code with Black.

fetch_command

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

__init__.py

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

Python

def fetch_command(self, subcommand): # Get commands outside of try block to prevent swallowing exceptions commands = get_commands() try: app_name = commands[subcommand] except KeyError: if os.environ.get("DJANGO_SETTINGS_MODULE"): # If `subcommand` is missing due to misconfigured settings, the # following line will retrigger an ImproperlyConfigured exception # (get_commands() swallows the original one) so the user is # informed about it. settings.INSTALLED_APPS elif not settings.configured: sys.stderr.write("No Django settings specified.\n") possible_matches = get_close_matches(subcommand, commands) sys.stderr.write("Unknown command: %r" % subcommand) if possible_matches: sys.stderr.write(". Did you mean %s?" % possible_matches[0]) sys.stderr.write("\nType '%s help' for usage.\n" % self.prog_name) sys.exit(1) if isinstance(app_name, BaseCommand): # If the command is already loaded, use it directly. klass = app_name else: klass = load_command_class(app_name, subcommand) return klass

label_studio/core/storage.py

def url(self, name): name = self._normalize_name(clean_name(name)) blob = self.bucket.blob(name) blob_params = self.get_object_parameters(name) no_signed_url = ( blob_params.get('acl', self.default_acl) == 'publicRead' or not self.querystring_auth) if not self.custom_endpoint and no_signed_url: return blob.public_url elif no_signed_url: out = '{storage_base_url}/{quoted_name}'.format( storage_base_url=self.custom_endpoint, quoted_name=_quote(name, safe=b"/~"), ) return out elif not self.custom_endpoint: out2 = blob.generate_signed_url( expiration=self.expiration, version="v4", **self._get_signing_kwargs() ) return out2 else: out3 = blob.generate_signed_url( bucket_bound_hostname=self.custom_endpoint, expir

fix: DEV-3911: Move persistent storages to OS (#3377) * fix: DEV-3911: Move persistent storages to OS * Fix * Add deps * Back header * Move DownloadStorageData handler * Update all urls json * Fix import * add nginx config * Fix GSC storage Co-authored-by: Sergei Ivashchenko <triklozoid@gmail.com> Co-authored-by: Sergey Zhuk <sergey.zhuk@heartex.com>

url

92314e4a9c431c407533e4a064481acf3c5983ab

label-studio

storage.py

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

Python

def url(self, name): name = self._normalize_name(clean_name(name)) blob = self.bucket.blob(name) blob_params = self.get_object_parameters(name) no_signed_url = ( blob_params.get('acl', self.default_acl) == 'publicRead' or not self.querystring_auth) if not self.custom_endpoint and no_signed_url: return blob.public_url elif no_signed_url: out = '{storage_base_url}/{quoted_name}'.format( storage_base_url=self.custom_endpoint, quoted_name=_quote(name, safe=b"/~"), ) return out elif not self.custom_endpoint: out2 = blob.generate_signed_url( expiration=self.expiration, version="v4", **self._get_signing_kwargs() ) return out2 else: out3 = blob.generate_signed_url( bucket_bound_hostname=self.custom_endpoint, expiration=self.expiration, version="v4", **self._get_signing_kwargs() ) return out3

sklearn/utils/validation.py

def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all): if isclass(estimator): raise TypeError("{} is a class, not an instance.".format(estimator)) if msg is None: msg = ( "This %(name)s instance is not fitted yet. Call 'fit' with " "appropriate arguments before using this estimator." ) if not hasattr(estimator, "fit"): raise TypeError("%s is not an estimator instance." % (estimator)) if attributes is not None: if not isinstance(attributes, (list, tuple)): attributes = [attributes] fitted = all_or_any([hasattr(estimator, attr) for attr in attributes]) elif hasattr(estimator, "__sklearn_is_fitted__"): fitted = estimator.__sklearn_is_fitted__() else: fitted = [ v for v in vars(estimator) if v.endswith("_") and not v.startswith("__") ] if not fitted: raise NotFittedError(msg % {"name": type(estimator).__name__})

DOC Ensures that check_is_fitted passes numpydoc validation (#24454)

check_is_fitted

b850a9417d4777931e2894fd8155b73dc87973b9

scikit-learn

validation.py

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

Python

def check_is_fitted(estimator, attributes=None, *, msg=None, all_or_any=all): if isclass(estimator): raise TypeError("{} is a class, not an instance.".format(estimator)) if msg is None: msg = ( "This %(name)s instance is not fitted yet. Call 'fit' with " "appropriate arguments before using this estimator." ) if not hasattr(estimator, "fit"): raise TypeError("%s is not an estimator instance." % (estimator)) if attributes is not None: if not isinstance(attributes, (list, tuple)): attributes = [attributes] fitted = all_or_any([hasattr(estimator, attr) for attr in attributes]) elif hasattr(estimator, "__sklearn_is_fitted__"): fitted = estimator.__sklearn_is_fitted__() else: fitted = [ v for v in vars(estimator) if v.endswith("_") and not v.startswith("__") ] if not fitted: raise NotFittedError(msg % {"name": type(estimator).__name__})

models/vision/glide/modeling_glide.py

def p_mean_variance(self, model, x, t, transformer_out, clip_denoised=True, model_kwargs=None): if model_kwargs is None: model_kwargs = {} B, C = x.shape[:2] assert t.shape == (B,) model_output = model(x, t, transformer_out) assert model_output.shape == (B, C * 2, *x.shape[2:]) model_output, model_var_values = torch.split(model_output, C, dim=1) min_log = _extract_into_tensor(self.noise_scheduler.posterior_log_variance_clipped, t, x.shape) max_log = _extract_into_tensor(np.log(self.noise_scheduler.betas), t, x.shape) # The model_var_values is [-1, 1] for [min_var, max_var]. frac = (model_var_values + 1) / 2 model_log_variance = frac * max_log + (1 - frac) * min_log model_variance = torch.exp(model_log_variance) pred_xstart = self._predict_xstart_from_eps(x

Classifier-free guidance scheduler + GLIDe pipeline

p_mean_variance

1e21f061601dda0aa9740e88bfce68bf4aac4acd

diffusers

modeling_glide.py

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

Python

def p_mean_variance(self, model, x, t, transformer_out, clip_denoised=True, model_kwargs=None): if model_kwargs is None: model_kwargs = {} B, C = x.shape[:2] assert t.shape == (B,) model_output = model(x, t, transformer_out) assert model_output.shape == (B, C * 2, *x.shape[2:]) model_output, model_var_values = torch.split(model_output, C, dim=1) min_log = _extract_into_tensor(self.noise_scheduler.posterior_log_variance_clipped, t, x.shape) max_log = _extract_into_tensor(np.log(self.noise_scheduler.betas), t, x.shape) # The model_var_values is [-1, 1] for [min_var, max_var]. frac = (model_var_values + 1) / 2 model_log_variance = frac * max_log + (1 - frac) * min_log model_variance = torch.exp(model_log_variance) pred_xstart = self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output) if clip_denoised: pred_xstart = pred_xstart.clamp(-1, 1) model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t) assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape return model_mean, model_variance, model_log_variance, pred_xstart

sklearn/utils/tests/test_estimator_html_repr.py

def test_invalid_parameters_in_stacking(): stacker = StackingClassifier(estimators

FIX Show a HTML repr for meta-estimatosr with invalid parameters (#24015) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

test_invalid_parameters_in_stacking

84c6421a9067de7d1b54b7a6d8e21ce38e1f0eca

scikit-learn

test_estimator_html_repr.py

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

Python

def test_invalid_parameters_in_stacking(): stacker = StackingClassifier(estimators=[]) html_output = estimator_html_repr(stacker) assert html.escape(str(stacker)) in html_output

erpnext/regional/south_africa/setup.py

def add_permissions(): for doctype in ("South Africa VAT Settings", "South Africa VAT Account"): add_permission(doctype, "All", 0) for role in ("Accounts Manager", "Accounts User", "System Manager"): add_permission(doctype, role, 0) update_permission_property(doctype, role, 0, "write", 1) update_permission_property(doctype, role, 0, "create", 1) if not frappe.db.get_value("Custom Role",

style: format code with black

add_permissions

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

setup.py

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

Python

def add_permissions(): for doctype in ("South Africa VAT Settings", "South Africa VAT Account"): add_permission(doctype, "All", 0) for role in ("Accounts Manager", "Accounts User", "System Manager"): add_permission(doctype, role, 0) update_permission_property(doctype, role, 0, "write", 1) update_permission_property(doctype, role, 0, "create", 1) if not frappe.db.get_value("Custom Role", dict(report="VAT Audit Report")): frappe.get_doc( dict( doctype="Custom Role", report="VAT Audit Report", roles=[dict(role="Accounts User"), dict(role="Accounts Manager"), dict(role="Auditor")], ) ).insert()

django/contrib/admin/options.py

def has_delete_permission(self, request, obj=None): opts = self.opts codename = get_permission_codename("delete", opts) retu

Refs #33476 -- Reformatted code with Black.

has_delete_permission

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

options.py

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

Python

def has_delete_permission(self, request, obj=None): opts = self.opts codename = get_permission_codename("delete", opts) return request.user.has_perm("%s.%s" % (opts.app_label, codename))

lib/mpl_toolkits/mplot3d/art3d.py

def set_3d_properties(self, zs=0, zdir='z'): xs = self.get_xdata() ys = self.get_ydata() zs = cbook._to_unmasked_float_array(zs).ravel() zs = np.broadcast_to(zs, len(xs)) self._ve

Improve mpl_toolkit documentation

set_3d_properties

df6f95703b60348e01603f98a439b133da2938a0

matplotlib

art3d.py

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

Python

def set_3d_properties(self, zs=0, zdir='z'): xs = self.get_xdata() ys = self.get_ydata() zs = cbook._to_unmasked_float_array(zs).ravel() zs = np.broadcast_to(zs, len(xs)) self._verts3d = juggle_axes(xs, ys, zs, zdir) self.stale = True

lib/gui/project.py

def clear_tasks(self): logger.debug("Clearing stored tasks") self._tasks =

Typing - lib.gui.display_command

clear_tasks

dab823a3eb7a5257cb1e0818ee10ed234d3de97f

faceswap

project.py

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

Python

def clear_tasks(self): logger.debug("Clearing stored tasks") self._tasks = {}

erpnext/hr/doctype/leave_application/leave_application.py

def add_department_leaves(events, start, end, employee, company): department = frappe.db.get_value("Emplo

style: format code with black

add_department_leaves

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

leave_application.py

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

Python

def add_department_leaves(events, start, end, employee, company): department = frappe.db.get_value("Employee", employee, "department") if not department: return # department leaves department_employees = frappe.db.sql_list( , (department, company), ) filter_conditions = ' and employee in ("%s")' % '", "'.join(department_employees) add_leaves(events, start, end, filter_conditions=filter_conditions)

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

def is_file(self, follow_links=None): if follow_links is None: follow_links = True node_stat = self._stat if follow_links else self._lstat

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

is_file

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

util.py

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

Python

def is_file(self, follow_links=None): if follow_links is None: follow_links = True node_stat = self._stat if follow_links else self._lstat return stat.S_ISREG(node_stat.st_mode)

tests/admin_inlines/tests.py

def test_tabular_model_form_meta_readonly_field(self): response = self.client.get(reverse("admin:admin_inlines_someparentmodel_add")) self.assertCont

Refs #33476 -- Reformatted code with Black.

test_tabular_model_form_meta_readonly_field

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_tabular_model_form_meta_readonly_field(self): response = self.client.get(reverse("admin:admin_inlines_someparentmodel_add")) self.assertContains( response, '<img src="/static/admin/img/icon-unknown.svg" ' 'class="help help-tooltip" width="10" height="10" ' 'alt="(Help text from ModelForm.Meta)" ' 'title="Help text from ModelForm.Meta">', ) self.assertContains(response, "Label from ModelForm.Meta")

wagtail/snippets/tests/test_locking.py

def test_edit_get_unlocked_no_lock_permission(self): # Use edit permission only self.set_permissions(["change"]) # Get the edit page response = self.client.get(self.get_url("edit")) html = response.content.decode() lock_url = self.get_url("lock") # Should not show lock message self.assertNotContains( response, "<b>'I&#x27;m a lockable snippet!' was locked</b>", ) # Should show unlocked information in the side panel self.assertContains( response, f"Anyone can edit this {self.model_name}.", ) # Should not show info to lock the object in the side panel self.assertNotContains( response, "Lock it to prevent others from editing.", ) # Should show Save action menu item self.assertContains( response, f"<em>{self.save_button_label}</em>", html=True, ) # Should not show Locked action menu item self.assertTagInHTML( '<button type="submit" disabled>Locked</button>',

Add tests for locking snippets

test_edit_get_unlocked_no_lock_permission

10dbbddaf35607e4257f50dd960520a1268dd225

wagtail

test_locking.py

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

Python

def test_edit_get_unlocked_no_lock_permission(self): # Use edit permission only self.set_permissions(["change"]) # Get the edit page response = self.client.get(self.get_url("edit")) html = response.content.decode() lock_url = self.get_url("lock") # Should not show lock message self.assertNotContains( response, "<b>'I&#x27;m a lockable snippet!' was locked</b>", ) # Should show unlocked information in the side panel self.assertContains( response, f"Anyone can edit this {self.model_name}.", ) # Should not show info to lock the object in the side panel self.assertNotContains( response, "Lock it to prevent others from editing.", ) # Should show Save action menu item self.assertContains( response, f"<em>{self.save_button_label}</em>", html=True, ) # Should not show Locked action menu item self.assertTagInHTML( '<button type="submit" disabled>Locked</button>', html, count=0, allow_extra_attrs=True, ) # Should not show the lock button self.assertTagInHTML( f'<button type="button" data-url="{lock_url}" data-action-lock-unlock>Lock</button>', html, count=0, allow_extra_attrs=True, )

src/sentry/models/counter.py

def increment_project_counter(project, delta=1, using="default"): if delta <= 0: raise ValueError("There is only one way, and that's up.") sample_rate = options.get("store.projectcounter-modern-upsert-sample-rate") modern_upsert = sample_rate and random.random() <= sample_rate # To prevent the statement_timeout leaking into the session we need to use # set local which can be used only within a transaction with transaction.atomic(using=using): cur = connections[using].cursor() try: statement_timeout = None if settings.SENTRY_PROJECT_COUNTER_STATEMENT_TIMEOUT: # WARNING: This is not a proper fix and should be removed once # we have better way of generating next_short_id. cur.execute("show statement_timeout") statement_timeout = cur.fetchone()[0] cur.execute( "set local statement_timeout = %s", [settings.SENTRY_PROJECT_COUNTER_STATEMENT_TIMEOUT], ) if modern_upsert: # Our postgres wrapper thing does not allow for named arguments cur.execute( "insert into sentry_projectcounter (project_id, value) " "values (%s, %s) " "on conflict (project_id) do update " "set value = sentry_projectcounter.value + %s "

fix(counter): Fix minor linting violation (#37392)

increment_project_counter

7f0e298ca45cd41f0e6df3968a6c0c2923a7b831

sentry

counter.py

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

Python

def increment_project_counter(project, delta=1, using="default"): if delta <= 0: raise ValueError("There is only one way, and that's up.") sample_rate = options.get("store.projectcounter-modern-upsert-sample-rate") modern_upsert = sample_rate and random.random() <= sample_rate # To prevent the statement_timeout leaking into the session we need to use # set local which can be used only within a transaction with transaction.atomic(using=using): cur = connections[using].cursor() try: statement_timeout = None if settings.SENTRY_PROJECT_COUNTER_STATEMENT_TIMEOUT: # WARNING: This is not a proper fix and should be removed once # we have better way of generating next_short_id. cur.execute("show statement_timeout") statement_timeout = cur.fetchone()[0] cur.execute( "set local statement_timeout = %s", [settings.SENTRY_PROJECT_COUNTER_STATEMENT_TIMEOUT], ) if modern_upsert: # Our postgres wrapper thing does not allow for named arguments cur.execute( "insert into sentry_projectcounter (project_id, value) " "values (%s, %s) " "on conflict (project_id) do update " "set value = sentry_projectcounter.value + %s " "returning value", [project.id, delta, delta], ) else: cur.execute( "select sentry_increment_project_counter(%s, %s)", [project.id, delta], ) project_counter = cur.fetchone()[0] if statement_timeout is not None: cur.execute( "set local statement_timeout = %s", [statement_timeout], ) return project_counter finally: cur.close() # this must be idempotent because it seems to execute twice # (at least during test runs)

tests/storage/test_id_generators.py

def test_multiple_gen_nexts_closed_in_different_order(self) -> None:

Reintroduce #14376, with bugfix for monoliths (#14468) * Add tests for StreamIdGenerator * Drive-by: annotate all defs * Revert "Revert "Remove slaved id tracker (#14376)" (#14463)" This reverts commit d63814fd736fed5d3d45ff3af5e6d3bfae50c439, which in turn reverted 36097e88c4da51fce6556a58c49bd675f4cf20ab. This restores the latter. * Fix StreamIdGenerator not handling unpersisted IDs Spotted by @erikjohnston. Closes #14456. * Changelog Co-authored-by: Nick Mills-Barrett <nick@fizzadar.com> Co-authored-by: Erik Johnston <erik@matrix.org>

test_multiple_gen_nexts_closed_in_different_order

115f0eb2334b13665e5c112bd87f95ea393c9047

synapse

test_id_generators.py

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

Python

def test_multiple_gen_nexts_closed_in_different_order(self) -> None: id_gen = self._create_id_generator()

src/prefect/cli/deployment.py

def str_presenter(dumper, data): if len(data.splitlines()) > 1: # check for multiline string return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|") return dumper.represent_scalar("tag:yaml.org,2002:str", data) yaml.add_representer(str, str_presen

Working YAML generation with lots of bells and whistles

str_presenter

36d9870433a22fff3944fa07f8e2feeb1b622bd9

prefect

deployment.py

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

Python

def str_presenter(dumper, data): if len(data.splitlines()) > 1: # check for multiline string return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|") return dumper.represent_scalar("tag:yaml.org,2002:str", data) yaml.add_representer(str, str_presenter) yaml.representer.SafeRepresenter.add_representer(str, str_presenter) deployment_app = PrefectTyper( name="deployment", help="Commands for working with deployments." ) app.add_typer(deployment_app)

saleor/core/auth_backend.py

def _get_permissions(self, user_obj, obj, from_name): if not user_obj.is_active or user_obj.is_anonymous or obj is not None: return set() perm_cache_name = "_effective_permissions_cache" if not getattr(user_obj, perm_cache_name, None): perms = getattr(self, f"_get_{from_name}_permissions")(user_obj) perms = perms.values_list("content_type__app_label", "codename").order_by() setattr(user_obj, perm_cache_name, {f"{c

Replace Interpolation With Fstring (#11016) * Replace Interpolation With Fstring * Fix out of bound lines. * Revert to lazy formatting for log messages. Also fix failing flake8. * Fix minor code smells and typo. * Make street_address to one line. * Fix test cases. * Fix lints.

_get_permissions

92a0c6c9f4324aa8f65a9b3e3a319604660a92a8

saleor

auth_backend.py

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

Python

def _get_permissions(self, user_obj, obj, from_name): if not user_obj.is_active or user_obj.is_anonymous or obj is not None: return set() perm_cache_name = "_effective_permissions_cache" if not getattr(user_obj, perm_cache_name, None): perms = getattr(self, f"_get_{from_name}_permissions")(user_obj) perms = perms.values_list("content_type__app_label", "codename").order_by() setattr(user_obj, perm_cache_name, {f"{ct}.{name}" for ct, name in perms}) return getattr(user_obj, perm_cache_name)

keras/saving/saved_model/saved_model_test.py

def test_trainable_layers(self): mo

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

test_trainable_layers

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

saved_model_test.py

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

Python

def test_trainable_layers(self): model = model = self._get_model() # Set the last layer to *not* be trainable. model.layers[-1].trainable = False self._train_model(model, use_dataset=True) loaded = self._save_and_load(model) self._test_evaluation(model, loaded) self.assertFalse(model.layers[-1].trainable) self.assertFalse(loaded.layers[-1].trainable)

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

def _reorder_labels(self, row_positions=None, col_positions=None): if row_positions is not None: ordered_rows = self._partition_mgr_cls.map_axis_partitions( 0, self._partitions, lambda df: df.iloc[row_positions] ) row_idx = self.index[row_positions] else: ordered_rows = self._partitions row_idx = self.index if col_positions is not None: ordered_cols = self._partition_mgr_cls.map_a

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

_reorder_labels

58bbcc37477866d19c8b092a0e1974a4f0baa586

modin

dataframe.py

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

Python

def _reorder_labels(self, row_positions=None, col_positions=None): if row_positions is not None: ordered_rows = self._partition_mgr_cls.map_axis_partitions( 0, self._partitions, lambda df: df.iloc[row_positions] ) row_idx = self.index[row_positions] else: ordered_rows = self._partitions row_idx = self.index if col_positions is not None: ordered_cols = self._partition_mgr_cls.map_axis_partitions( 1, ordered_rows, lambda df: df.iloc[:, col_positions] ) col_idx = self.columns[col_positions] else: ordered_cols = ordered_rows col_idx = self.columns return self.__constructor__(ordered_cols, row_idx, col_idx)

erpnext/assets/doctype/asset_capitalization/test_asset_capitalization.py

def get_actual_sle_dict(name): sles = frappe.db.sql( , name, as_dict=1, ) sle_dict = {} for d in sles: sle_dict[(d.item_code, d.warehouse)] = { "actual_qty": d.actual_qty,

feat: Asset Capitalization - manual selection of entry type - GLE cleanup with smaller functions - GLE considering periodical inventory - test cases

get_actual_sle_dict

58d430fe3ee62e93ad8d16a08bb42156a25b7d41

erpnext

test_asset_capitalization.py

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

Python

def get_actual_sle_dict(name): sles = frappe.db.sql( , name, as_dict=1, ) sle_dict = {} for d in sles: sle_dict[(d.item_code, d.warehouse)] = { "actual_qty": d.actual_qty, "stock_value_difference": d.stock_value_difference, } return sle_dict

homeassistant/config_entries.py

async def _async_process_on_unload(self) -> None:

Track tasks adding entities (#73828) * Track tasks adding entities * Update homeassistant/config_entries.py * fix cast tests Co-authored-by: J. Nick Koston <nick@koston.org>

_async_process_on_unload

00810235c92b492a966c6021021d49360ffb3cdd

core

config_entries.py

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

Python

async def _async_process_on_unload(self) -> None: if self._on_unload is not None: while self._on_unload: self._on_unload.pop()() while self._pending_tasks: pending = [task for task in self._pending_tasks if not task.done()] self._pending_tasks.clear() if pending: await asyncio.gather(*pending)

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

def apply_func(partition, func, *args, **kwargs):

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

apply_func

d6d503ac7c3028d871c34d9e99e925ddb0746df6

modin

partition.py

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

Python

def apply_func(partition, func, *args, **kwargs): result = func(partition, *args, **kwargs) return result, get_ip()

salt/client/mixins.py

def _proc_function_remote(self, *, fun, low, user, tag, jid, daemonize=True): if daemonize and not salt.utils.platform.is_windows(): # Shutdown the multiprocessing before daemonizing salt.log.setup.shutdown_multiprocessing_logging() salt.utils.process.daemonize()

Implement ``__getstate__`` and ``__setstate__`` instead of using ``classmethod`` Signed-off-by: Pedro Algarvio <palgarvio@vmware.com>

_proc_function_remote

c78f1ee4f49df35ab04e921a45de0878716d8bf5

salt

mixins.py

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

Python

def _proc_function_remote(self, *, fun, low, user, tag, jid, daemonize=True): if daemonize and not salt.utils.platform.is_windows(): # Shutdown the multiprocessing before daemonizing salt.log.setup.shutdown_multiprocessing_logging() salt.utils.process.daemonize() # Reconfigure multiprocessing logging after daemonizing salt.log.setup.setup_multiprocessing_logging() # pack a few things into low low["__jid__"] = jid low["__user__"] = user low["__tag__"] = tag try: return self.cmd_sync(low) except salt.exceptions.EauthAuthenticationError as exc: log.error(exc)

dask/array/utils.py

def array_safe(a, like, **kwargs):

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

array_safe

cccb9d8d8e33a891396b1275c2448c352ef40c27

dask

utils.py

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

Python

def array_safe(a, like, **kwargs): from dask.array.routines import array return _array_like_safe(np.array, array, a, like, **kwargs)

mindsdb/integrations/handlers/elasticsearch_handler/elasticsearch_handler.py

def get_tables(self) -> StatusResponse: query = result = self.native_query(query) df = result.data_frame df = df.drop(['type', 'type'], axis=1) result.data_frame = df.rename(columns={'name': 'table_name'}) return result

implemented the get_tables() and get_columns() methods

get_tables

c8accc16e3c56d0e7d2a0b63c63a956849da57da

mindsdb

elasticsearch_handler.py

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

Python

def get_tables(self) -> StatusResponse: query = result = self.native_query(query) df = result.data_frame df = df.drop(['type', 'type'], axis=1) result.data_frame = df.rename(columns={'name': 'table_name'}) return result

d2l/jax.py

def accuracy(self, params, X, Y, averaged=True): Y_hat = self.apply(params, X) Y_hat = d2l.reshap

JAX: Add section classification.md (#2293)

accuracy

f348aecdade3cdec4f93b72da548c7394ecb42ce

d2l-en

jax.py

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

Python

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

cps/tasks/metadata_backup.py

def open_metadata(self, book, custom_columns): if config.config_use_google_drive: if not gdriveutils.is_gdrive_ready(): raise Exception('Google Drive is configured but not ready') web_content_link = gdriveutils.get_metadata_backup_via_gdrive(book.path) if not web_content_link: raise Exception('Google Drive cover url not found') stream = None try: stream = urlopen(web_content_link) except Exception as ex: # Bubble exception to calling function self.log.debug('Error reading metadata

Backup metadata 3rd step

open_metadata

26be5ee2372b08c2f906661283a12e84d6c181f8

calibre-web

metadata_backup.py

https://github.com/janeczku/calibre-web.git

Python

def open_metadata(self, book, custom_columns): if config.config_use_google_drive: if not gdriveutils.is_gdrive_ready(): raise Exception('Google Drive is configured but not ready') web_content_link = gdriveutils.get_metadata_backup_via_gdrive(book.path) if not web_content_link: raise Exception('Google Drive cover url not found') stream = None try: stream = urlopen(web_content_link) except Exception as ex: # Bubble exception to calling function self.log.debug('Error reading metadata.opf: ' + str(ex)) # ToDo Check whats going on raise ex finally: if stream is not None: stream.close() else: # ToDo: Handle book folder not found or not readable book_metadata_filepath = os.path.join(config.config_calibre_dir, book.path, 'metadata.opf') #if not os.path.isfile(book_metadata_filepath): self.create_new_metadata_backup(book, custom_columns, book_metadata_filepath) # else:

@pytest.mark.skipif(not can_import_module("tkinter"), reason="tkinter cannot be imported.")

tests/functional/test_libraries.py

def test_gevent_monkey(pyi_builder): pyi_builder.test_source() # The tkinter

tests: gevent tests: remove no-op excludes The `gevent` tests seem to be attempting to exclude several packages. As per comment in 416e1a0e83bf5a4924cc50d2befa2bb622b55107, this was introduced in an attempt to break the following Windows-specific import chain: setuptools.msvc -> numpy -> numpy.testing -> pytest -> pygments -> PIL -> PIL.ImageQt -> PySide2. However, nowadays we already break that chain in two places: our setuptools.msvc hook excludes numpy, and our numpy hook excludes pytest. More importantly, `excludes` is not a valid keyword argument for the `pyi_builder.test_source` (anymore?), and is quietly swallowed by the `**kwargs`. So those exclude lists achieve nothing, except confusing people who look at existing code to find a way to exclude packages in a test. (As a side note, the tests that do use `excludes` keyword argument are passing it to the modulegraph's functions, not the `pyi_builder` fixture ones.)

test_gevent_monkey

93ad16d5c970f70f843a5eda8b177f681743005b

pyinstaller

test_libraries.py

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

Python

def test_gevent_monkey(pyi_builder): pyi_builder.test_source() # The tkinter module may be available for import, but not actually importable due to missing shared libraries. # Therefore, we need to use `can_import_module`-based skip decorator instead of `@importorskip`. @pytest.mark.skipif(not can_import_module("tkinter"), reason="tkinter cannot be imported.")

.venv/lib/python3.8/site-packages/pip/_internal/utils/misc.py

def split_auth_netloc_from_url(url): # type: (str) -> Tuple[str, str, Tuple[str,

upd; format

split_auth_netloc_from_url

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

misc.py

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

Python

def split_auth_netloc_from_url(url): # type: (str) -> Tuple[str, str, Tuple[str, str]] url_without_auth, (netloc, auth) = _transform_url(url, _get_netloc) return url_without_auth, netloc, auth

tests/test_text_mobject.py

def test_non_str_color(): text = Text("test_color_inheritance", color=Color("blue")) markup_text = MarkupText("test_color_inheritance", color=Color("blue"))

:class:`~.MathTex`, :class:`~.Tex`, :class:`~.Text` and :class:`~.MarkupText` inherit color from their parent mobjects. (#2467) * comment out color-related things from tex_mob * add change to svg_mobject * MarkupText handles colour internally * MarkupText handles colour internally * make coordinate_system.py colour agnostic * get_line_from_axis_to_point * add typings for SingleStringMathTex * add typings for MathTex * make internal methods internal * black + isort * fix typo * black + isort * fix typo * revert internalizing change * Revert "Merge branch 'mathtexx' of https://github.com/hydrobeam/manim into mathtexx" This reverts commit 6be3c3981440fd5cfee54e5d9f24b30e1ba991e9, reversing changes made to 2b30b446ae4004efb06adbb646f54e9ef269bc61. * remove accidental import * do it in a less bad way * WIP: Text2setting causing problems * allow tex_mobject.py to inherit colour * allow tex_mobject.py to inherit colour * add tests * remove undeedde imports + formatting * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix warnings from pre-commit hooks * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix some tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove other color_inheritance test * [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 * fix typo * accomodate the color->attribute PR * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix tests and doc build add a check for None when inheriting colour in `coordinate_systems.py`, and turn written tests into graphical tests * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Comment out `Text` color inheritance test. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Set font for text_color_inheritance test * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Small change to retrigger docs build * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

test_non_str_color

2275ec5916de0ad3bedbc276da09fc3bfbae4d5e

manim

test_text_mobject.py

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

Python

def test_non_str_color(): text = Text("test_color_inheritance", color=Color("blue")) markup_text = MarkupText("test_color_inheritance", color=Color("blue"))

tests/components/zha/test_config_flow.py

async def test_strategy_no_network_settings(pick_radio, mock_app, hass): mock_app.load_network_info = MagicMock(side_effect=NetworkNotFormed()) result, port = await pick_radio(RadioType.ezsp) assert ( config_flow.FORMATION_REUSE_SETTINGS not in result["data_schema"].schema["next_step_id"].container )

ZHA backup/restore config flow (#77044)

test_strategy_no_network_settings

f78b39bdbfbe151e8bab72610b6fe03afc8c0747

core

test_config_flow.py

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

Python

async def test_strategy_no_network_settings(pick_radio, mock_app, hass): mock_app.load_network_info = MagicMock(side_effect=NetworkNotFormed()) result, port = await pick_radio(RadioType.ezsp) assert ( config_flow.FORMATION_REUSE_SETTINGS not in result["data_schema"].schema["next_step_id"].container )

lib/matplotlib/mlab.py

def detrend(x, key=None, axis=None): if key is None or key in ['constant', 'mean', 'default']: return detrend(x, key=detrend_mean, axis=axis) elif key == 'linear': return detrend(x, key=detrend_linear, axis=axis) elif key == 'none': return detrend(x, key=detrend_none, axis=axis) elif callable(key):

Improve mlab documentation (and example)

detrend

17b3c44f67f779e7d103381878f08c548c2c8495

matplotlib

mlab.py

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

Python

def detrend(x, key=None, axis=None): if key is None or key in ['constant', 'mean', 'default']: return detrend(x, key=detrend_mean, axis=axis) elif key == 'linear': return detrend(x, key=detrend_linear, axis=axis) elif key == 'none': return detrend(x, key=detrend_none, axis=axis) elif callable(key): x = np.asarray(x) if axis is not None and axis + 1 > x.ndim: raise ValueError(f'axis(={axis}) out of bounds') if (axis is None and x.ndim == 0) or (not axis and x.ndim == 1): return key(x) # try to use the 'axis' argument if the function supports it, # otherwise use apply_along_axis to do it try: return key(x, axis=axis) except TypeError: return np.apply_along_axis(key, axis=axis, arr=x) else: raise ValueError( f"Unknown value for key: {key!r}, must be one of: 'default', " f"'constant', 'mean', 'linear', or a function")

@register.filter(is_safe=True) @stringfilter

wagtail/admin/templatetags/wagtailadmin_tags.py

def has_unrendered_errors(bound_field): return bound_field.errors and not hasattr( bound_field.field.widget, "render_with_errors" )

Reformat with black

has_unrendered_errors

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

wagtailadmin_tags.py

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

Python

def has_unrendered_errors(bound_field): return bound_field.errors and not hasattr( bound_field.field.widget, "render_with_errors" ) @register.filter(is_safe=True) @stringfilter

cms/tests/test_admin.py

def test_raw_id_threshold_page_permission_inline_admin(self): with self.settings(CMS_RAW_ID_USERS=1): with self.assertNumQueries(1): self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, []) # Create users to check if threshold is honored self._get_guys() with self.settings(CMS_RAW_ID_USERS=False): with self.assertNumQueries(0): self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, []) with self.settings(CMS_RAW_ID_USERS=True): with sel

perf: Don't count users when CMS_RAW_ID_USERS=True (#7414) * perf: Don't count users when CMS_RAW_ID_USERS=True When using CMS_RAW_ID_USERS=True on a Postgres database with many users, counting the users is slow and will always yield the same result. Only count users when using an integer value as a threshold and reuse the same logic for both PagePermissionInlineAdmin and GlobalPagePermissionAdmin. * Ensure that only integer settings of CMS_RAW_ID_USERS are compared to the number of users * Add documentation for the CMS_RAW_ID_USER=True setting * fix isort for added tests * Fix: in python this is always True: isinstance(False, int) Co-authored-by: Pankrat <lhaehne@gmail.com>

test_raw_id_threshold_page_permission_inline_admin

7ca1b613d8573dff70e45dd54229b0032c3e8ca7

django-cms

test_admin.py

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

Python

def test_raw_id_threshold_page_permission_inline_admin(self): with self.settings(CMS_RAW_ID_USERS=1): with self.assertNumQueries(1): self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, []) # Create users to check if threshold is honored self._get_guys() with self.settings(CMS_RAW_ID_USERS=False): with self.assertNumQueries(0): self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, []) with self.settings(CMS_RAW_ID_USERS=True): with self.assertNumQueries(0): self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, ['user']) with self.settings(CMS_RAW_ID_USERS=1): with self.assertNumQueries(1): self.assertEqual(PagePermissionInlineAdmin.raw_id_fields, ['user'])

python3.10.4/Lib/bdb.py

def get_breaks(self, filename, lineno): filename = self

add python 3.10.4 for windows

get_breaks

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

bdb.py

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

Python

def get_breaks(self, filename, lineno): filename = self.canonic(filename) return filename in self.breaks and \ lineno in self.breaks[filename] and \ Breakpoint.bplist[filename, lineno] or []

tests/storage/test_registration.py

def test_override(self) -> None: self.get_success( self.store.register_user( self.user_id, self.pwhash, approved=True, ) ) user = self.get_success(self.store.get_user_by_id(self.user_id)) self.assertIsNotNone(user) assert user is not None self.assertEqual(user["approved"], 1) approved = self.get_success(s

Allow admins to require a manual approval process before new accounts can be used (using MSC3866) (#13556)

test_override

be76cd8200b18f3c68b895f85ac7ef5b0ddc2466

synapse

test_registration.py

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

Python

def test_override(self) -> None: self.get_success( self.store.register_user( self.user_id, self.pwhash, approved=True, ) ) user = self.get_success(self.store.get_user_by_id(self.user_id)) self.assertIsNotNone(user) assert user is not None self.assertEqual(user["approved"], 1) approved = self.get_success(self.store.is_user_approved(self.user_id)) self.assertTrue(approved)

d2l/mxnet.py

def tokenize(lines, token='word'): if token == 'word': return [line.spl

[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 <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * 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 <haizhou.sun@smartmore.com> * 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 <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * 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 <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Correct a translation error. (#1091) * Correct a translation error. * Update chapter_computer-vision/image-augmentation.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update aws.md (#1121) * Update aws.md * Update chapter_appendix-tools-for-deep-learning/aws.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * 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 <22279212+astonzhang@users.noreply.github.com> * 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 <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> * 重复语句 (#1188) Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * 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 <22279212+astonzhang@users.noreply.github.com> * 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 <22279212+astonzhang@users.noreply.github.com> * 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 <haizhou.sun@smartmore.com> Co-authored-by: zhou201505013 <39976863+zhou201505013@users.noreply.github.com> Co-authored-by: Xinwei Liu <xinzone@outlook.com> Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: hugo_han <57249629+HugoHann@users.noreply.github.com> Co-authored-by: gyro永不抽风 <1247006353@qq.com> Co-authored-by: CanChengZheng <zcc550169544@163.com> Co-authored-by: linlin <jajupmochi@gmail.com> Co-authored-by: iuk <liukun0104@gmail.com> Co-authored-by: yoos <49556860+liyunlongaaa@users.noreply.github.com> Co-authored-by: Mr. Justice Lawrence John Wargrave <65226618+RUCWargrave@users.noreply.github.com> Co-authored-by: Chiyuan Fu <fuchiyuan2019@outlook.com> Co-authored-by: Sunhuashan <48636870+Sunhuashan@users.noreply.github.com> Co-authored-by: Haiker Sun <haizhou.uestc2011@gmail.com> Co-authored-by: Ming Liu <akira.liu@njnu.edu.cn> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: silenceZheng66 <13754430639@163.com> Co-authored-by: Wenchao Yan <56541797+YWonchall@users.noreply.github.com> Co-authored-by: Kiki2049 <55939997+Kiki2049@users.noreply.github.com> Co-authored-by: Krahets <krahets@163.com> Co-authored-by: friedmainfunction <73703265+friedmainfunction@users.noreply.github.com> Co-authored-by: Jameson <miraclecome@gmail.com> Co-authored-by: P. Yao <12227516+YaoPengCN@users.noreply.github.com> Co-authored-by: Yulv-git <34329208+Yulv-git@users.noreply.github.com> Co-authored-by: Liu,Xiao <45966993+liuxiao916@users.noreply.github.com> Co-authored-by: YIN, Gang <1246410+yingang@users.noreply.github.com> Co-authored-by: Joe-HZ <58297431+Joe-HZ@users.noreply.github.com> Co-authored-by: lybloveyou <102609904+lybloveyou@users.noreply.github.com> Co-authored-by: VigourJiang <jiangfuqiang154@163.com> Co-authored-by: zxhd863943427 <74853597+zxhd863943427@users.noreply.github.com> Co-authored-by: LYF <27893441+liyufan@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> Co-authored-by: xiaotinghe <xiaotih@amazon.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Holly-Max <60691735+Holly-Max@users.noreply.github.com> Co-authored-by: HinGwenWoong <peterhuang0323@qq.com> Co-authored-by: Shuai Zhang <cheungdaven@gmail.com>

tokenize

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

d2l-zh

mxnet.py

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

Python

def tokenize(lines, token='word'): if token == 'word': return [line.split() for line in lines] elif token == 'char': return [list(line) for line in lines] else: print('ERROR: unknown token type: ' + token)

@frappe.whitelist()

erpnext/selling/doctype/sales_order/sales_order.py

def make_purchase_order_for_default_supplier(source_name, selected_items=None, target_doc=None): if not selected_items: return if isinstance(selected_items, str): selected_items = json.loads(selected_items) def set_missing_values(source, target): target.supplier = supplier target.apply_discount_on = "" target.additional_discount_percentage = 0.0 target.discount_amount = 0.0 target.inter_company_order_reference = "" default_price_list = frappe.get_value("Supplier", supplier, "default_price_list") if default_price_list: target.buying_price_list = default_price_list if any(item.delivered_by_supplier == 1 for item in source.items): if source.shipping_address_name: target.shipping_address = source.shipping_address_name target.shipping_address_display = source.shipping_address else: target.shipping_address = source.customer_address target.shipping_address_display = source.address_display target.customer_contact_person = source.contact_person target.customer_contact_display = source.contact_display target.customer_contact_mobile = source.contact_mobile target.customer_contact_email = source.contact_email else: target.customer = "" target.customer_name = "" target.run_method("set_missing_values") target.run_method("calculate_taxes_and_totals") def update_item(source, target, source_parent): target.schedule_date = source.delivery_date target.qty = flt(source.qty) - (flt(source.ordered_qty) / flt(source.conversion_factor)) target.stock_qty = flt(source.stock_qty) - flt(source.ordered_qty) target.project = source_parent.project suppliers = [item.get("supplier") for item in selected_items if item.get("supplier")] suppliers = list(dict.fromkeys(suppliers)) # remove duplicates while preserving order items_to_map = [item.get("item_code") for item in selected_items if item.get("item_code")] items_to_map = list(set(items_to_map)) if not suppliers: frappe.throw( _("Please set a Supplier against the Items to be considered in the Purchase Order.") ) purchase_orders = [] for supplier in suppliers: doc = get_mapped_doc( "Sales Order", source_name, { "Sales Order": { "doctype": "Purchase Order", "field_no_map": [ "address_display", "contact_display", "contact_mobile", "contact_email", "contact_person", "taxes_and_charges", "shipping_address", "terms", ], "validation": {"docstatus": ["=", 1]}, }, "Sales Order Item": { "doctype": "Purchase Order Item", "field_map": [ ["name", "sales_order_item"

style: format code with black

make_purchase_order_for_default_supplier

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

sales_order.py

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

Python

def make_purchase_order_for_default_supplier(source_name, selected_items=None, target_doc=None): if not selected_items: return if isinstance(selected_items, str): selected_items = json.loads(selected_items) def set_missing_values(source, target): target.supplier = supplier target.apply_discount_on = "" target.additional_discount_percentage = 0.0 target.discount_amount = 0.0 target.inter_company_order_reference = "" default_price_list = frappe.get_value("Supplier", supplier, "default_price_list") if default_price_list: target.buying_price_list = default_price_list if any(item.delivered_by_supplier == 1 for item in source.items): if source.shipping_address_name: target.shipping_address = source.shipping_address_name target.shipping_address_display = source.shipping_address else: target.shipping_address = source.customer_address target.shipping_address_display = source.address_display target.customer_contact_person = source.contact_person target.customer_contact_display = source.contact_display target.customer_contact_mobile = source.contact_mobile target.customer_contact_email = source.contact_email else: target.customer = "" target.customer_name = "" target.run_method("set_missing_values") target.run_method("calculate_taxes_and_totals") def update_item(source, target, source_parent): target.schedule_date = source.delivery_date target.qty = flt(source.qty) - (flt(source.ordered_qty) / flt(source.conversion_factor)) target.stock_qty = flt(source.stock_qty) - flt(source.ordered_qty) target.project = source_parent.project suppliers = [item.get("supplier") for item in selected_items if item.get("supplier")] suppliers = list(dict.fromkeys(suppliers)) # remove duplicates while preserving order items_to_map = [item.get("item_code") for item in selected_items if item.get("item_code")] items_to_map = list(set(items_to_map)) if not suppliers: frappe.throw( _("Please set a Supplier against the Items to be considered in the Purchase Order.") ) purchase_orders = [] for supplier in suppliers: doc = get_mapped_doc( "Sales Order", source_name, { "Sales Order": { "doctype": "Purchase Order", "field_no_map": [ "address_display", "contact_display", "contact_mobile", "contact_email", "contact_person", "taxes_and_charges", "shipping_address", "terms", ], "validation": {"docstatus": ["=", 1]}, }, "Sales Order Item": { "doctype": "Purchase Order Item", "field_map": [ ["name", "sales_order_item"], ["parent", "sales_order"], ["stock_uom", "stock_uom"], ["uom", "uom"], ["conversion_factor", "conversion_factor"], ["delivery_date", "schedule_date"], ], "field_no_map": [ "rate", "price_list_rate", "item_tax_template", "discount_percentage", "discount_amount", "pricing_rules", ], "postprocess": update_item, "condition": lambda doc: doc.ordered_qty < doc.stock_qty and doc.supplier == supplier and doc.item_code in items_to_map, }, }, target_doc, set_missing_values, ) doc.insert() frappe.db.commit() purchase_orders.append(doc) return purchase_orders @frappe.whitelist()

django/db/migrations/utils.py

def resolve_relation(model, app_label=None, model_name=None): if isinstance(model, str): if model == RECURSIVE_RELATIONSHIP_CONSTANT:

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

resolve_relation

c5cd8783825b5f6384417dac5f3889b4210b7d08

django

utils.py

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

Python

def resolve_relation(model, app_label=None, model_name=None): if isinstance(model, str): if model == RECURSIVE_RELATIONSHIP_CONSTANT: if app_label is None or model_name is None: raise TypeError( 'app_label and model_name must be provided to resolve ' 'recursive relationships.' ) return app_label, model_name if '.' in model: app_label, model_name = model.split('.', 1) return app_label, model_name.lower() if app_label is None: raise TypeError( 'app_label must be provided to resolve unscoped model relationships.' ) return app_label, model.lower() return model._meta.app_label, model._meta.model_name

src/transformers/modeling_tf_utils.py

def load_tf_weights(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None): missing_layers = [] unexpected_layers = [] mismatched_layers = [] # Read the H5 file with h5py.File(resolved_archive_file, "r") as sharded_checkpoint_file: # Retrieve the name of each layer from the H5 file saved_h5_model_layers_name = set( hdf5_format.load_attributes_from_hdf5_group(sharded_checkpoint_file, "layer_names") ) # Find the missing layers from the high level list of layers missing_layers = list(set([layer.name for layer in model.layers]) - saved_h5_model_layers_name) # Find the unexpected layers from the high level list of layers unexpected_layers = list(saved_h5_model_layers_name - set([layer.name for layer in model.layers])) saved_weight_names_set = set() symbolic_weights_names = set() weight_value_tuples = [] # Compute missing and unexpected sub layers # Store the weights in list of tuples that looks like [(weight_object, value_of_weight),...] for layer in model.layers: # if layer_name from the H5 file belongs to the layers from the instantiated model if layer.name in saved_h5_model_layers_name: # Get the H5 layer object from its name h5_layer_object = sharded_checkpoint_file[layer.name] # Get all the weights as a list from the layer object symbolic_weights = layer.trainable_weights + layer.non_trainable_weights saved_weights = {} # Create a dict from the H5 saved model that looks like {"weight_name": weight_value} # And a set with only the names for weight_name in

TF Sharded (#17713) * initial commit * update modeeling tf utils * quality * clean and update args * update * remove potential bug * code quality * update * update max shard * update tests for sharding from pretrained * fix remaining test * make style * h5py if tf available * update and fix test * fix test * style * modified push to hub to support shard for TF * quick fix * update code * merge branch main and style * Apply suggestions from code review Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * update based on reviews * update doc * update and style * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update based on reviews * fix typo * style Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

load_tf_weights

7cced021fa8ddc59f0f77384300760d34545394e

transformers

modeling_tf_utils.py

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

Python

def load_tf_weights(model, resolved_archive_file, ignore_mismatched_sizes=False, _prefix=None): missing_layers = [] unexpected_layers = [] mismatched_layers = [] # Read the H5 file with h5py.File(resolved_archive_file, "r") as sharded_checkpoint_file: # Retrieve the name of each layer from the H5 file saved_h5_model_layers_name = set( hdf5_format.load_attributes_from_hdf5_group(sharded_checkpoint_file, "layer_names") ) # Find the missing layers from the high level list of layers missing_layers = list(set([layer.name for layer in model.layers]) - saved_h5_model_layers_name) # Find the unexpected layers from the high level list of layers unexpected_layers = list(saved_h5_model_layers_name - set([layer.name for layer in model.layers])) saved_weight_names_set = set() symbolic_weights_names = set() weight_value_tuples = [] # Compute missing and unexpected sub layers # Store the weights in list of tuples that looks like [(weight_object, value_of_weight),...] for layer in model.layers: # if layer_name from the H5 file belongs to the layers from the instantiated model if layer.name in saved_h5_model_layers_name: # Get the H5 layer object from its name h5_layer_object = sharded_checkpoint_file[layer.name] # Get all the weights as a list from the layer object symbolic_weights = layer.trainable_weights + layer.non_trainable_weights saved_weights = {} # Create a dict from the H5 saved model that looks like {"weight_name": weight_value} # And a set with only the names for weight_name in hdf5_format.load_attributes_from_hdf5_group(h5_layer_object, "weight_names"): # TF names always start with the model name so we ignore it name = "/".join(weight_name.split("/")[1:]) if _prefix is not None: name = _prefix + "/" + name saved_weights[name] = np.asarray(h5_layer_object[weight_name]) # Add the updated name to the final list for computing missing/unexpected values saved_weight_names_set.add(name) # Loop over each weights from the instantiated model and compare with the weights from the H5 file for symbolic_weight in symbolic_weights: # TF names always start with the model name so we ignore it if _prefix is not None: delimeter = len(_prefix.split("/")) symbolic_weight_name = "/".join( symbolic_weight.name.split("/")[:delimeter] + symbolic_weight.name.split("/")[delimeter + 1 :] ) else: symbolic_weight_name = "/".join(symbolic_weight.name.split("/")[1:]) # here we check if the current weight is among the weights from the H5 file # If yes, get the weight_value of the corresponding weight from the H5 file # If not, make the value to None saved_weight_value = saved_weights.get(symbolic_weight_name, None) # Add the updated name to the final list for computing missing/unexpected values symbolic_weights_names.add(symbolic_weight_name) # If the current weight is found if saved_weight_value is not None: # Check if the shape of the current weight and the one from the H5 file are different if K.int_shape(symbolic_weight) != saved_weight_value.shape: # If yes we reshape the weight from the H5 file accordingly to the current weight # If the two shapes are not compatible we raise an issue try: array = np.reshape(saved_weight_value, K.int_shape(symbolic_weight)) except ValueError as e: if ignore_mismatched_sizes: mismatched_layers.append( (symbolic_weight_name, saved_weight_value.shape, K.int_shape(symbolic_weight)) ) continue else: raise e else: array = saved_weight_value # We create the tuple that will be loaded and add it to the final list weight_value_tuples.append((symbolic_weight, array)) # Load all the weights K.batch_set_value(weight_value_tuples) # Compute the missing and unexpected layers missing_layers.extend(list(symbolic_weights_names - saved_weight_names_set)) unexpected_layers.extend(list(saved_weight_names_set - symbolic_weights_names)) return missing_layers, unexpected_layers, mismatched_layers

django/forms/boundfield.py

def css_classes(self, extra_classes=None): if hasattr(extra_classes, "split"): extra_classes = extra_classes.split() extra_classes = set(extra_classes or []) if self.errors and h

Refs #33476 -- Reformatted code with Black.

css_classes

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

boundfield.py

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

Python

def css_classes(self, extra_classes=None): if hasattr(extra_classes, "split"): extra_classes = extra_classes.split() extra_classes = set(extra_classes or []) if self.errors and hasattr(self.form, "error_css_class"): extra_classes.add(self.form.error_css_class) if self.field.required and hasattr(self.form, "required_css_class"): extra_classes.add(self.form.required_css_class) return " ".join(extra_classes)

lib/gpu_stats/nvidia.py

def _get_free_vram(self) -> List[float]: vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024) for handle in self._handles] self._log("debug", f"GPU VRAM free: {vram}") return vram

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

_get_free_vram

bdbbad4d310fb606b6f412aa81e9f57ccd994e97

faceswap

nvidia.py

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

Python

def _get_free_vram(self) -> List[float]: vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024) for handle in self._handles] self._log("debug", f"GPU VRAM free: {vram}") return vram

@frappe.whitelist()

erpnext/projects/doctype/task/task.py

def get_project(doctype, txt, searchfield, start, page_len, filters): from erpnext.controllers.queries imp

style: format code with black

get_project

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

task.py

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

Python

def get_project(doctype, txt, searchfield, start, page_len, filters): from erpnext.controllers.queries import get_match_cond meta = frappe.get_meta(doctype) searchfields = meta.get_search_fields() search_columns = ", " + ", ".join(searchfields) if searchfields else "" search_cond = " or " + " or ".join(field + " like %(txt)s" for field in searchfields) return frappe.db.sql( .format( search_columns=search_columns, search_condition=search_cond ), { "key": searchfield, "txt": "%" + txt + "%", "mcond": get_match_cond(doctype), "start": start, "page_len": page_len, }, ) @frappe.whitelist()

keras/optimizers/optimizer_experimental/nadam.py

def build(self, var_list): super().build(var_list) if getattr(self, "_built", False): return self._built = True self._momentums = [] self._velocities = [] self._u_product = tf.Variable(1.0, dtype=var_list[0].dtype) # Keep a counter on how many times of _u_product has been

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

build

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

nadam.py

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

Python

def build(self, var_list): super().build(var_list) if getattr(self, "_built", False): return self._built = True self._momentums = [] self._velocities = [] self._u_product = tf.Variable(1.0, dtype=var_list[0].dtype) # Keep a counter on how many times of _u_product has been computed to # avoid duplicated computations. self._u_product_counter = 1 for var in var_list: self._momentums.append( self.add_variable_from_reference( model_variable=var, variable_name="m" ) ) self._velocities.append( self.add_variable_from_reference( model_variable=var, variable_name="v" ) )

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

def list_distinfo_files(self, absolute=False): record_path = os.path.join(self.path, 'installed-files.txt') if os.path.exists(record_path): skip = True with codecs.open(record_path, 'r', encoding='utf-8') as f: for line in f: line = line.strip() if line == './': skip = False continue if not skip: p = os.path.normpath(os.path.join(self.path, line)) if p.startswith(self.path): if absolute: yield p

upd; format

list_distinfo_files

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

database.py

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

Python

def list_distinfo_files(self, absolute=False): record_path = os.path.join(self.path, 'installed-files.txt') if os.path.exists(record_path): skip = True with codecs.open(record_path, 'r', encoding='utf-8') as f: for line in f: line = line.strip() if line == './': skip = False continue if not skip: p = os.path.normpath(os.path.join(self.path, line)) if p.startswith(self.path): if absolute: yield p else: yield line

jina/parsers/orchestrate/base.py

def mixin_scalable_deployment_parser(parser): gp = mixin_base_deployment_parser(parser, title='Scalable Deployment') gp.add_argument( '--polling', type=str, default=PollingType.ANY.name, help=, ) gp.add_argument( '--shards', type=int, default=1, help='The number of shards in the deployment running at the same time. For more details check ' 'https://docs.jina.ai/fundamentals/flow/create-flow/#complex-flow-topologies', ) gp.add_argument( '--replicas', type=int, default=1, help='The number of

refactor: remove unnecessary parser args (#5328) * refactor: refactor deployment mixin and remove polling and shards for gateway * chore: rename executor to pod and move native and array type to worker args * refactor: make exit-on-exceptions just a worker arg * style: fix overload and cli autocomplete * chore: apply suggestion * chore: move native parameter to deployment group * fix: fix pod init * style: fix overload and cli autocomplete * fix: fix shards and replicas in deployment * chore: disable gpu and volumes for gateway * style: fix overload and cli autocomplete * fix: volume and gpus are optional for container pods Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>

mixin_scalable_deployment_parser

bd8003508da0b35713361484f5801ebc818bd0c3

jina

base.py

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

Python

def mixin_scalable_deployment_parser(parser): gp = mixin_base_deployment_parser(parser, title='Scalable Deployment') gp.add_argument( '--polling', type=str, default=PollingType.ANY.name, help=, ) gp.add_argument( '--shards', type=int, default=1, help='The number of shards in the deployment running at the same time. For more details check ' 'https://docs.jina.ai/fundamentals/flow/create-flow/#complex-flow-topologies', ) gp.add_argument( '--replicas', type=int, default=1, help='The number of replicas in the deployment', ) gp.add_argument( '--native', action='store_true', default=False, help='If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.', )

wagtail/admin/tests/test_privacy.py

def test_explorer_list_private(self): response = self.client.get( reverse("wagtailadmin_explore", args=(self.private_page.id,)) )

Reformat with black

test_explorer_list_private

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_privacy.py

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

Python

def test_explorer_list_private(self): response = self.client.get( reverse("wagtailadmin_explore", args=(self.private_page.id,)) ) # Check the response self.assertEqual(response.status_code, 200) # Must have one privacy icon (next to the private child page) self.assertContains( response, '<span class="indicator privacy-indicator icon icon-no-view"', count=1, )

pipenv/patched/notpip/_vendor/distlib/_backport/tarfile.py

def next(self): self._check("ra") if self.firstmember is not None: m = self.firstmember self.firstmember = None return m # Read the next block. self.fileobj.seek(self.offset) tarinfo = None while True: try: tarinfo = self.tarinfo.fromtarfile(self) except EOFHeaderError as e: if self.ignore_zeros: self._dbg(2, "0x%X: %s" % (self.offset, e)) self.offset += BLOCKSIZE continue except InvalidHeaderError as e: if self.ignore_zeros: self._dbg(2, "0x%X: %s" % (self.offset, e)) self.offset += BLOCKSIZE continue elif self.offset == 0: raise ReadError(str(e)) except EmptyHeaderError: if self.offset == 0: raise ReadError("empty file") except TruncatedHeaderError as e: if self.offset == 0:

Vendor in pip 22.1.2

next

c69d55f7c82d5ae2cce542bcfb98d043ca4836a0

pipenv

tarfile.py

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

Python

def next(self): self._check("ra") if self.firstmember is not None: m = self.firstmember self.firstmember = None return m # Read the next block. self.fileobj.seek(self.offset) tarinfo = None while True: try: tarinfo = self.tarinfo.fromtarfile(self) except EOFHeaderError as e: if self.ignore_zeros: self._dbg(2, "0x%X: %s" % (self.offset, e)) self.offset += BLOCKSIZE continue except InvalidHeaderError as e: if self.ignore_zeros: self._dbg(2, "0x%X: %s" % (self.offset, e)) self.offset += BLOCKSIZE continue elif self.offset == 0: raise ReadError(str(e)) except EmptyHeaderError: if self.offset == 0: raise ReadError("empty file") except TruncatedHeaderError as e: if self.offset == 0: raise ReadError(str(e)) except SubsequentHeaderError as e: raise ReadError(str(e)) break if tarinfo is not None: self.members.append(tarinfo) else: self._loaded = True return tarinfo #-------------------------------------------------------------------------- # Little helper methods:

sklearn/linear_model/_logistic.py

def predict_proba(self, X): check_is_fitted(self) ovr = self.multi_class in ["ovr", "warn"] or ( self.multi_class == "auto" and ( self.classes_.size <= 2 or self.solver in ("liblinear", "newton-cholesky") ) ) if ovr: return super()._predict_proba_lr(X) else: decision =

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

predict_proba

bb080aa690364d84d11232c73dc8db2f0dde3578

scikit-learn

_logistic.py

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

Python

def predict_proba(self, X): check_is_fitted(self) ovr = self.multi_class in ["ovr", "warn"] or ( self.multi_class == "auto" and ( self.classes_.size <= 2 or self.solver in ("liblinear", "newton-cholesky") ) ) if ovr: return super()._predict_proba_lr(X) else: decision = self.decision_function(X) if decision.ndim == 1: # Workaround for multi_class="multinomial" and binary outcomes # which requires softmax prediction with only a 1D decision. decision_2d = np.c_[-decision, decision] else: decision_2d = decision return softmax(decision_2d, copy=False)

@keras_export("keras.activations.softplus") @tf.__internal__.dispatch.add_dispatch_support

keras/activations.py

def selu(x): return tf.nn.selu(x) @keras_export("keras.activations.softplus") @tf

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

selu

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

activations.py

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

Python

def selu(x): return tf.nn.selu(x) @keras_export("keras.activations.softplus") @tf.__internal__.dispatch.add_dispatch_support

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

def _find_safe_escape_tokens(self, text): for i, t, v in self._filter_to( self.lang.get_tokens_unprocessed(text), lambda t: t in Token.Comment or t in Token.String ): if t is None: for i2, t2, v

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

_find_safe_escape_tokens

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

latex.py

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

Python

def _find_safe_escape_tokens(self, text): for i, t, v in self._filter_to( self.lang.get_tokens_unprocessed(text), lambda t: t in Token.Comment or t in Token.String ): if t is None: for i2, t2, v2 in self._find_escape_tokens(v): yield i + i2, t2, v2 else: yield i, None, v

tests/end2end/features/test_downloads_bdd.py

def set_up_fileselector(quteproc, py_proc, kind, files, output_type): cmd, args = py_proc(r) args += files.split(' ') if output_type == "a temporary file": args += ['--file={}'] fileselect_cmd = json.dumps([cmd, *args]) quteproc.set_setting('fileselect.handler', 'external') quteproc.set_setting(f'fileselect.{kind}.command', fileselect_cmd)

test(downloads) wip test for external fileselect

set_up_fileselector

36563450763868f12a2481ca636efccb2c7a43cc

qutebrowser

test_downloads_bdd.py

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

Python

def set_up_fileselector(quteproc, py_proc, kind, files, output_type): cmd, args = py_proc(r) args += files.split(' ') if output_type == "a temporary file": args += ['--file={}'] fileselect_cmd = json.dumps([cmd, *args]) quteproc.set_setting('fileselect.handler', 'external') quteproc.set_setting(f'fileselect.{kind}.command', fileselect_cmd)

tests/test_task_runners.py

def task_runner(request): if not hasattr(r

Add service marks to task runner tests

task_runner

dc0f9feb764c72620a68ca139eb56e43f6e5f068

prefect

test_task_runners.py

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

Python

def task_runner(request): if not hasattr(request.param, "_pytestfixturefunction"): raise TypeError("Received invalid `task_runner` parameter. Expected fixture.") yield request.getfixturevalue(request.param.__name__)

zerver/tests/test_push_notifications.py

def test_get_apns_context(self) -> None: import zerver.lib.push_notifications zerver.lib.push_notifications.get_apns_context.cache_clear() try: with self.settings(APNS_CERT_FILE="/foo.pem"), mock.patch("aioapns.APNs") as mock_apns: apns_context = get_apns_context() assert apns_context is not None try: self.assertEqual(mock_apns.return_value, apns_context.apns) finally: apns_context.loop.close() finally:

test_push_notifications: Close event loops. Fixes “ResourceWarning: unclosed event loop <_UnixSelectorEventLoop running=False closed=False debug=False>”. Signed-off-by: Anders Kaseorg <anders@zulip.com>

test_get_apns_context

9e70a47f93ad422cadc9d26c656cc8c02e08805e

zulip

test_push_notifications.py

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

Python

def test_get_apns_context(self) -> None: import zerver.lib.push_notifications zerver.lib.push_notifications.get_apns_context.cache_clear() try: with self.settings(APNS_CERT_FILE="/foo.pem"), mock.patch("aioapns.APNs") as mock_apns: apns_context = get_apns_context() assert apns_context is not None try: self.assertEqual(mock_apns.return_value, apns_context.apns) finally: apns_context.loop.close() finally: # Reset the cache for `get_apns_context` so that we don't # leak changes to the rest of the world. zerver.lib.push_notifications.get_apns_context.cache_clear()

scripts/convert.py

def _validate(self) -> None: if (self._args.writer == "ffmpeg" and not self._images.is_video and self._args.reference_video is None): raise FaceswapError("Output as video selected, but using frames as input. You must " "provide a reference video ('-ref', '--reference-video').") if (self._args.on_the_fly and self._args.mask_type not in ("none", "extended", "components")): logger.warning("You have selected an incompatible mask type ('%s') for On-The-Fly " "conversion. Switching to 'extended'", self._args.mask_type) self._args.mask_type = "extended" if (not self._args.on_the_fly and self._args.mask_type not in ("none", "predicted") and not self._alignments.mask_is_valid(self._args.mask_type)): msg = (f"You have selected the Mask Type `{self._args.mask_type}` but at least one " "face does not have this mask stored in the A

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

_validate

1022651eb8a7741014f5d2ec7cbfe882120dfa5f

faceswap

convert.py

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

Python

def _validate(self) -> None: if (self._args.writer == "ffmpeg" and not self._images.is_video and self._args.reference_video is None): raise FaceswapError("Output as video selected, but using frames as input. You must " "provide a reference video ('-ref', '--reference-video').") if (self._args.on_the_fly and self._args.mask_type not in ("none", "extended", "components")): logger.warning("You have selected an incompatible mask type ('%s') for On-The-Fly " "conversion. Switching to 'extended'", self._args.mask_type) self._args.mask_type = "extended" if (not self._args.on_the_fly and self._args.mask_type not in ("none", "predicted") and not self._alignments.mask_is_valid(self._args.mask_type)): msg = (f"You have selected the Mask Type `{self._args.mask_type}` but at least one " "face does not have this mask stored in the Alignments File.\nYou should " "generate the required masks with the Mask Tool or set the Mask Type option to " "an existing Mask Type.\nA summary of existing masks is as follows:\nTotal " f"faces: {self._alignments.faces_count}, " f"Masks: {self._alignments.mask_summary}") raise FaceswapError(msg) if self._args.mask_type == "predicted" and not self._predictor.has_predicted_mask: available_masks = [k for k, v in self._alignments.mask_summary.items() if k != "none" and v == self._alignments.faces_count] if not available_masks: msg = ("Predicted Mask selected, but the model was not trained with a mask and no " "masks are stored in the Alignments File.\nYou should generate the " "required masks with the Mask Tool or set the Mask Type to `none`.") raise FaceswapError(msg) mask_type = available_masks[0] logger.warning("Predicted Mask selected, but the model was not trained with a " "mask. Selecting first available mask: '%s'", mask_type) self._args.mask_type = mask_type

src/sentry/api/base.py

def get_authenticators(self) -> List[BaseAuthentication]:

ref(hybrid-cloud): Additional test annotations: auth_index (#42425) Extends the hybrid cloud auth service to be usable in many more places ( TY @corps) Annotate 30+ more api endpoint tests Co-authored-by: Mike Ihbe <mike.ihbe@sentry.io> Co-authored-by: Zachary Collins <zachary.collins@sentry.io> Co-authored-by: Zach Collins <recursive.cookie.jar@gmail.com>

get_authenticators

17644550024d6a2eb01356ee48ec0d3ef95c043d

sentry

base.py

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

Python

def get_authenticators(self) -> List[BaseAuthentication]: # TODO: Increase test coverage and get this working for monolith mode. if SiloMode.get_current_mode() == SiloMode.MONOLITH: return super().get_authenticators() last_api_authenticator = ApiAuthentication([]) result: List[BaseAuthentication] = [] for authenticator_cls in self.authentication_classes: auth_type = ApiAuthenticatorType.from_authenticator(authenticator_cls) if auth_type: last_api_authenticator.types.append(auth_type) else: if last_api_authenticator.types: result.append(last_api_authenticator) last_api_authenticator = ApiAuthentication([]) result.append(authenticator_cls()) if last_api_authenticator.types: result.append(last_api_authenticator) return result

networkx/algorithms/tests/test_lowest_common_ancestors.py

def test_naive_lowest_common_ancestor2(self): G = nx.DiGraph() G.add_edge(0, 1) G.add_edge(2, 0) G.add_edge(2, 3) G.add_edge(4, 0) G.add_edge(5, 2) assert naive_lca(G, 1, 3) == 2

Naive lowest common ancestor implementation (#5736) * Add naive lca methods * Naive algorithm implementation for LCA * Modify naive lca functions * Correct parameters of nx.ancestors * Update lowest_common_ancestors.py * Parametrize tests * Apply suggestions from code review Co-authored-by: Dan Schult <dschult@colgate.edu> * Yield instead of append * Tests for naive lca * Correct test cases for naive lca algorithms * Apply suggestions from code review Co-authored-by: Mridul Seth <mail@mriduls.com> * Fix function name -when calling * Make requested changes * Inlining _get_a_lowest_common_ancestor Co-authored-by: dtuncturk <dilaramemis@sabanciuniv.edu> Co-authored-by: Dan Schult <dschult@colgate.edu> Co-authored-by: Mridul Seth <mail@mriduls.com>

test_naive_lowest_common_ancestor2

b2f91c34a23058dd70b41784af0d87890216026a

networkx

test_lowest_common_ancestors.py

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

Python

def test_naive_lowest_common_ancestor2(self): G = nx.DiGraph() G.add_edge(0, 1) G.add_edge(2, 0) G.add_edge(2, 3) G.add_edge(4, 0) G.add_edge(5, 2) assert naive_lca(G, 1, 3) == 2

django/db/migrations/utils.py

def get_references(state, model_tuple, field_tuple=()): for state_model_tuple, model_state in state.models.items(): for name, field in model_state.fields.items(): reference = field_references( state_model_tuple, field, model_tuple, *field_tuple ) if reference: yie

Refs #33476 -- Reformatted code with Black.

get_references

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

utils.py

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

Python

def get_references(state, model_tuple, field_tuple=()): for state_model_tuple, model_state in state.models.items(): for name, field in model_state.fields.items(): reference = field_references( state_model_tuple, field, model_tuple, *field_tuple ) if reference: yield model_state, name, field, reference

tests/components/alexa/test_smart_home.py

async def test_media_player_eq_bands_not_supported(hass): device = ( "media_player.test_bands", "on", { "friendly_name": "Test media player", "supported_features": SUPPORT_SELECT_SOUND_MODE, "sound_mode": "tv", "sound_mode_list": ["movie", "music", "night", "sport", "tv", "rocknroll"], }, ) await discovery_test(device, hass) context = Context() # Test for SetBands Error request = get_new_request( "Alexa.EqualizerController", "SetBands", "media_player#test_bands" ) request["directive"]["payload"] = {"bands": [{"name": "BASS", "value": -2}]} msg = await smart_home.async_handle_message( hass, get_default_config(hass), request, context ) assert "event" in msg msg = msg["event"] assert msg["header"]["name"] == "ErrorResponse" assert msg["header"]["namespace"] == "Alexa" assert msg["payload"]["type"] == "INVALID_DIRECTIVE" # Test for AdjustBands Error request = get_new_request( "Alexa.EqualizerController", "AdjustBands", "media_player#test_bands" ) request["directive"]["payload"] = { "bands": [{"name": "BASS", "levelDelta": 3, "levelDirection": "UP"}] } msg = await smart_home.async_handle_message( hass, get_default_config(hass), request, context ) assert "event" in msg msg = msg["event"] assert msg["header"]["name"] == "ErrorResponse" assert msg["header"]["namespace"] == "Alexa" assert msg["payload"]["type"] == "INVALID_DIRECTIVE" # Test for ResetBands Error request = get_new_request( "Alexa.EqualizerController", "ResetBands", "media_player#test_bands" ) request["directive"]["payload"] = { "bands": [{"name": "BASS", "levelDelta": 3, "levelDirection": "UP"}] } msg = await smart_home.async_handle_message( hass, get_default_config(hass), request, context ) assert "event" in msg msg = msg["event"] assert msg["header"]["name"] == "ErrorResponse" assert msg["header"]["namespace"] == "Alexa" assert msg["payload"]["type"] == "INVALID_DIRECTIVE"

Exclude hidden entities from alexa (#68555)

test_media_player_eq_bands_not_supported

dc8e87a6f70439f9830d93d03c53d6ff098a4861

core

test_smart_home.py

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

Python

async def test_media_player_eq_bands_not_supported(hass): device = ( "media_player.test_bands", "on", { "friendly_name": "Test media player", "supported_features": SUPPORT_SELECT_SOUND_MODE, "sound_mode": "tv", "sound_mode_list": ["movie", "music", "night", "sport", "tv", "rocknroll"], }, ) await discovery_test(device, hass) context = Context() # Test for SetBands Error request = get_new_request( "Alexa.EqualizerController", "SetBands", "media_player#test_bands" ) request["directive"]["payload"] = {"bands": [{"name": "BASS", "value": -2}]} msg = await smart_home.async_handle_message( hass, get_default_config(hass), request, context ) assert "event" in msg msg = msg["event"] assert msg["header"]["name"] == "ErrorResponse" assert msg["header"]["namespace"] == "Alexa" assert msg["payload"]["type"] == "INVALID_DIRECTIVE" # Test for AdjustBands Error request = get_new_request( "Alexa.EqualizerController", "AdjustBands", "media_player#test_bands" ) request["directive"]["payload"] = { "bands": [{"name": "BASS", "levelDelta": 3, "levelDirection": "UP"}] } msg = await smart_home.async_handle_message( hass, get_default_config(hass), request, context ) assert "event" in msg msg = msg["event"] assert msg["header"]["name"] == "ErrorResponse" assert msg["header"]["namespace"] == "Alexa" assert msg["payload"]["type"] == "INVALID_DIRECTIVE" # Test for ResetBands Error request = get_new_request( "Alexa.EqualizerController", "ResetBands", "media_player#test_bands" ) request["directive"]["payload"] = { "bands": [{"name": "BASS", "levelDelta": 3, "levelDirection": "UP"}] } msg = await smart_home.async_handle_message( hass, get_default_config(hass), request, context ) assert "event" in msg msg = msg["event"] assert msg["header"]["name"] == "ErrorResponse" assert msg["header"]["namespace"] == "Alexa" assert msg["payload"]["type"] == "INVALID_DIRECTIVE"

@derived_from(np.linalg)

dask/array/linalg.py

def lstsq(a, b): q, r = qr(a) x = solve_triangular(r, q.T.conj().dot(b)) residuals = b - a.dot(x) residuals = abs(residuals**2).sum(axis=0, keepdims=b.ndim == 1) token = tokenize(a, b) # r must be a triangular with single block # rank rname = "lstsq-rank-" + token rdsk = {(rname,): (np.linalg.matrix_rank, (r.name, 0, 0))} graph = HighLevelGraph.from_collections(rname, rdsk, dependencies=[r]) # rank must be an integer rank = Array(graph, rname, shape=(), chunks=(), dtype=int) # singular sname = "lstsq-singular-" + token rt = r.T.conj() sdsk = { (sname, 0): ( _reverse,

Update `pre-commit` version (#8691)

lstsq

510bbc380531cbf56a409f1ae68e6fd84a9599e6

dask

linalg.py

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

Python

def lstsq(a, b): q, r = qr(a) x = solve_triangular(r, q.T.conj().dot(b)) residuals = b - a.dot(x) residuals = abs(residuals**2).sum(axis=0, keepdims=b.ndim == 1) token = tokenize(a, b) # r must be a triangular with single block # rank rname = "lstsq-rank-" + token rdsk = {(rname,): (np.linalg.matrix_rank, (r.name, 0, 0))} graph = HighLevelGraph.from_collections(rname, rdsk, dependencies=[r]) # rank must be an integer rank = Array(graph, rname, shape=(), chunks=(), dtype=int) # singular sname = "lstsq-singular-" + token rt = r.T.conj() sdsk = { (sname, 0): ( _reverse, (np.sqrt, (np.linalg.eigvalsh, (np.dot, (rt.name, 0, 0), (r.name, 0, 0)))), ) } graph = HighLevelGraph.from_collections(sname, sdsk, dependencies=[rt, r]) meta = meta_from_array(residuals, 1) s = Array(graph, sname, shape=(r.shape[0],), chunks=r.shape[0], meta=meta) return x, residuals, rank, s @derived_from(np.linalg)

test/lib/ansible_test/_internal/docker_util.py

def docker_environment(): # type: () -> t.Dict[str, str] env = common_environment() env.update(dict((key, os.environ[key]

Support podman-remote in ansible-test (#75753)

docker_environment

7cb581ed2cb1d4591d094df37a40c9155ea446da

ansible

docker_util.py

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

Python

def docker_environment(): # type: () -> t.Dict[str, str] env = common_environment() env.update(dict((key, os.environ[key]) for key in os.environ if key.startswith('DOCKER_') or key.startswith('CONTAINER_'))) return env

lib/matplotlib/tests/test_colorbar.py

def test_remove_from_figure(use_gridspec): fig, ax = plt.subplots() sc = ax.scatter([1, 2], [3, 4])

warning when scatter plot color settings discarded (#23516) * Warning when scatter plot color settings discarded * Update lib/matplotlib/axes/_axes.py Co-authored-by: Tim Hoffmann <2836374+timhoffm@users.noreply.github.com> * Wrapped 23516-MS.rst lines at 80 characters * Fixed tests to look for proper warning message * Update doc/api/next_api_changes/behavior/23516-MS.rst Co-authored-by: Elliott Sales de Andrade <quantum.analyst@gmail.com> Co-authored-by: Tim Hoffmann <2836374+timhoffm@users.noreply.github.com> Co-authored-by: Elliott Sales de Andrade <quantum.analyst@gmail.com>

test_remove_from_figure

5d3124dbc826a019bb55b4229312a033912331ff

matplotlib

test_colorbar.py

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

Python

def test_remove_from_figure(use_gridspec): fig, ax = plt.subplots() sc = ax.scatter([1, 2], [3, 4]) sc.set_array(np.array([5, 6])) pre_position = ax.get_position() cb = fig.colorbar(sc, use_gridspec=use_gridspec) fig.subplots_adjust() cb.remove() fig.subplots_adjust() post_position = ax.get_position() assert (pre_position.get_points() == post_position.get_points()).all()

sympy/multipledispatch/utils.py

def groupby(func, seq): d = {} for item in seq: key = func(item) if key not in d: d[key] = []

Code cleanup

groupby

9d58006fc0a23afcba38f641c9472917c436428a

sympy

utils.py

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

Python

def groupby(func, seq): d = {} for item in seq: key = func(item) if key not in d: d[key] = [] d[key].append(item) return d

python/ray/data/dataset.py

def input_files(self) -> List[str]: metadata = self._plan.execute().get_metadata() files = set() for m in metadata: for f in m.input_files: file

Lay the groundwork for lazy dataset optimization (no behavior changes) (#22233) This PR refactors Dataset execution to enable lazy mode in the future, which can reduce memory usage in large-scale ingest pipelines. There should be no behavior changes in this PR. Many of the optimizations are also punted for future work.

input_files

35a157948efa7ba1adf1d1507c2af1d6d84a7db7

ray

dataset.py

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

Python

def input_files(self) -> List[str]: metadata = self._plan.execute().get_metadata() files = set() for m in metadata: for f in m.input_files: files.add(f) return list(files)

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

def get_nccl_reduce_op(reduce_op): if reduce_op not in NCCL_REDUCE_OP_MAP:

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

get_nccl_reduce_op

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

nccl_util.py

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

Python

def get_nccl_reduce_op(reduce_op): if reduce_op not in NCCL_REDUCE_OP_MAP: raise RuntimeError("NCCL does not support reduce op: '{}'.".format(reduce_op)) return NCCL_REDUCE_OP_MAP[reduce_op]

lib/align/detected_face.py

def aligned(self) -> AlignedFace: assert self._aligned is not None return self._aligned

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

aligned

5e73437be47f2410439a3c6716de96354e6a0c94

faceswap

detected_face.py

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

Python

def aligned(self) -> AlignedFace: assert self._aligned is not None return self._aligned

airflow/migrations/versions/64a7d6477aae_fix_description_field_in_connection_to_.py

def upgrade(): conn = op.get_bind() if conn.dialect.name == "sqlite": # in sqlite TEXT and STRING column types are the same

Autogenerate migration reference doc (#21601) * document airflow version in each alembic migration module and use this to autogen the doc * update each migration module to have the same description used in migration ref (so it can be used in autogen)

upgrade

69f6f9e01b6df76c3c8fa266d460324163957887

airflow

64a7d6477aae_fix_description_field_in_connection_to_.py

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

Python

def upgrade(): conn = op.get_bind() if conn.dialect.name == "sqlite": # in sqlite TEXT and STRING column types are the same return if conn.dialect.name == "mysql": op.alter_column( 'connection', 'description', existing_type=sa.String(length=5000), type_=sa.Text(length=5000), existing_nullable=True, ) else: # postgres does not allow size modifier for text type op.alter_column('connection', 'description', existing_type=sa.String(length=5000), type_=sa.Text())

code/deep/BJMMD/caffe/python/caffe/test/test_net.py

def test_memory(self): params = sum(map(list, six.itervalues(self.net.params)), []) blobs = self.net.blobs.values() del self.net # now sum every

Balanced joint maximum mean discrepancy for deep transfer learning

test_memory

cc4d0564756ca067516f71718a3d135996525909

transferlearning

test_net.py

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

Python

def test_memory(self): params = sum(map(list, six.itervalues(self.net.params)), []) blobs = self.net.blobs.values() del self.net # now sum everything (forcing all memory to be read) total = 0 for p in params: total += p.data.sum() + p.diff.sum() for bl in blobs: total += bl.data.sum() + bl.diff.sum()

tests/test_flows.py

async def test_timeout_stops_execution_in_sync_subflows(self, tmp_path): canary_file = tmp_path / "canary"

Ensure flows are called in an interruptible thread (PrefectHQ/orion#2174) * Ensure flows are called in an interruptible thread * Set higher runtime limit in `test_timeout_stops_execution_in_sync_subflows`

test_timeout_stops_execution_in_sync_subflows

336eca7839fccbcbdb77179f352f926da8b1fa15

prefect

test_flows.py

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

Python

async def test_timeout_stops_execution_in_sync_subflows(self, tmp_path): canary_file = tmp_path / "canary"

@frappe.whitelist() @frappe.validate_and_sanitize_search_inputs

erpnext/controllers/queries.py

def get_income_account(doctype, txt, searchfield, start, page_len, filters): from erpnext.controllers.queries import get_match_cond # income account can be any Credit account, # but can also be a Asset account with account_type='Income Account' in special circumstances. # Hence the first condition is an "OR" if not filter

style: format code with black

get_income_account

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

queries.py

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

Python

def get_income_account(doctype, txt, searchfield, start, page_len, filters): from erpnext.controllers.queries import get_match_cond # income account can be any Credit account, # but can also be a Asset account with account_type='Income Account' in special circumstances. # Hence the first condition is an "OR" if not filters: filters = {} condition = "" if filters.get("company"): condition += "and tabAccount.company = %(company)s" return frappe.db.sql( .format( condition=condition, match_condition=get_match_cond(doctype), key=searchfield ), {"txt": "%" + txt + "%", "company": filters.get("company", "")}, ) @frappe.whitelist() @frappe.validate_and_sanitize_search_inputs

scapy/arch/windows/__init__.py

def win_find_exe(filename, installsubdir=None, env="ProgramFiles"): # type: (str, Optional[Any],

[Hinty] Core typing: windows (#3684) * Core typing: windows Co-authored-by: Pierre <pierre@droids-corp.org>

win_find_exe

a2b7a28faff1db058dd22ce097a268e0ad5d1d33

scapy

__init__.py

https://github.com/secdev/scapy.git

Python

def win_find_exe(filename, installsubdir=None, env="ProgramFiles"): # type: (str, Optional[Any], str) -> str fns = [filename] if filename.endswith(".exe") else [filename + ".exe", filename] # noqa: E501 for fn in fns: try: if installsubdir is None: path = _where(fn) else: path = _where(fn, dirs=[os.path.join(os.environ[env], installsubdir)]) # noqa: E501 except IOError: path = None else: break return path or ""

wagtail/core/tests/test_page_model.py

def test_custom_page_queryset(self): self.assertIs(type(CustomManagerPage.objects.all()), CustomPageQuerySet)

Reformat with black

test_custom_page_queryset

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_page_model.py

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

Python

def test_custom_page_queryset(self): self.assertIs(type(CustomManagerPage.objects.all()), CustomPageQuerySet) self.assertIs(type(CustomManagerPage.objects.about_spam()), CustomPageQuerySet) self.assertIs( type(CustomManagerPage.objects.all().about_spam()), CustomPageQuerySet ) self.assertIs( type(CustomManagerPage.objects.about_spam().all()), CustomPageQuerySet )

pipenv/patched/notpip/_vendor/distro.py

def distro_release_info(self): # type: () -> Dict[s

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

distro_release_info

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

distro.py

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

Python

def distro_release_info(self): # type: () -> Dict[str, str] return self._distro_release_info

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

def user_documents_dir(self) -> str: return os.path.normpath(get

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

user_documents_dir

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

windows.py

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

Python

def user_documents_dir(self) -> str: return os.path.normpath(get_win_folder("CSIDL_PERSONAL"))

python/ray/data/_internal/push_based_shuffle.py

def round_robin_reduce_idx_iterator(self): idx = 0 round_idx = 0 while idx < self.output_num_blocks: for merge_idx in range(self.num_merge_tasks_per_round): if merge_idx < self._partitions_with_extra_task: reduce_idx = merge_idx * (self.merge_partition_size + 1) partition_size = self.merge_partition_size + 1 else: reduce_idx = self

[dataset] Pipeline task submission during reduce stage in push-based shuffle (#25795) Reduce stage in push-based shuffle fails to complete at 100k output partitions or more. This is likely because of driver or raylet load from having too many tasks in flight at once. We can fix this from ray core too, but for now, this PR adds pipelining for the reduce stage, to limit the total number of reduce tasks in flight at the same time. This is currently set to 2 * available parallelism in the cluster. We have to pick which reduce tasks to submit carefully since these are pinned to specific nodes. The PR does this by assigning tasks round-robin according to the corresponding merge task (which get spread throughout the cluster). In addition, this PR refactors the map, merge, and reduce stages to use a common pipelined iterator pattern, since they all have a similar pattern of submitting a round of tasks at a time, then waiting for a previous round to finish before submitting more. Related issue number Closes #25412.

round_robin_reduce_idx_iterator

93aae48b80db80f7e9f922eaabedead0d15ee01c

ray

push_based_shuffle.py

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

Python

def round_robin_reduce_idx_iterator(self): idx = 0 round_idx = 0 while idx < self.output_num_blocks: for merge_idx in range(self.num_merge_tasks_per_round): if merge_idx < self._partitions_with_extra_task: reduce_idx = merge_idx * (self.merge_partition_size + 1) partition_size = self.merge_partition_size + 1 else: reduce_idx = self._partitions_with_extra_task * ( self.merge_partition_size + 1 ) merge_idx -= self._partitions_with_extra_task reduce_idx += merge_idx * self.merge_partition_size partition_size = self.merge_partition_size if round_idx >= partition_size: continue reduce_idx += round_idx yield reduce_idx idx += 1 round_idx += 1

modin/pandas/groupby.py

def _check_index_name(self, result): if self._by is not None: # pandas does not n

FIX-#3197: do not pass lambdas to the backend in GroupBy (#3373) Signed-off-by: Dmitry Chigarev <dmitry.chigarev@intel.com>

_check_index_name

1e65a4afd191cf61ba05b80545d23f9b88962f41

modin

groupby.py

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