Datasets:

semeru

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

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Semeru Lab 7

def set_name_with_model(self, model): _, table_name = split_identifier(model._meta.db_table) column_names = [ model._meta.get_field(field_name).column for field_name, order in self.fields_orders ] column_names_with_order = [ (("-%s" if order else "%s") % column_name) for column_name, (field_name, order) in zip( column_names, self.fields_orders ) ] # The length of the parts of the name is based on the default max # length of 30 characters. hash_data = [table_name] + column_names_with_order + [self.suffix] self.name = "%s_%s_%s" % ( table_name[:11], column_names[0][:7], "%s_%s" % (names_digest(*hash_data, length=6), self.suffix), ) if len(self.name) > self.max_name_length: raise ValueError( "Index too long for multiple database support. Is self.suffix " "longer than 3 characters?" ) if self.name[0] == "_" or self.name[0].isdigit(): self.name = "D%s" % self.name[1:]

django/db/models/indexes.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

indexes.py

set_name_with_model

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

Refs #33476 -- Reformatted code with Black.

def test_sparse_matrix(): tpot_obj = TPOTClassifier( random_state=42, population_size=1, offspring_size=2, generations=1, verbosity=0, config_dict='TPOT light' ) assert_raises(ValueError, tpot_obj.fit, sparse_features, sparse_target)

tests/tpot_tests.py

tpot

Python

388616b6247ca4ea8de4e2f340d6206aee523541

tpot_tests.py

test_sparse_matrix

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

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

def check_requirements(self, reqs): # type: (Iterable[str]) -> Tuple[Set[Tuple[str, str]], Set[str]] missing = set() conflicting = set() if reqs: ws = WorkingSet(self._lib_dirs) for req in reqs: try: if ws.find(Requirement.parse(req)) is None: missing.add(req) except VersionConflict as e: conflicting.add((str(e.args[0].as_requirement()), str(e.args[1]))) return conflicting, missing

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

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

build_env.py

check_requirements

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

upd; format

def unpack_iterator_input(iterator): try: next_element = iterator.get_next() except tf.errors.OutOfRangeError: raise RuntimeError( "Your dataset iterator ran out of data; " "Make sure that your dataset can generate " "required number of samples." ) if isinstance(next_element, (list, tuple)): if len(next_element) not in [2, 3]: raise ValueError( "Please provide model inputs as a list or tuple of 2 or 3 " "elements: (input, target) or (input, target, sample_weights) " "Received %s" % next_element ) if len(next_element) == 2: x, y = next_element weights = None else: x, y, weights = next_element else: x = next_element y = None weights = None return x, y, weights

keras/engine/training_utils_v1.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

training_utils_v1.py

unpack_iterator_input

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def test_missing_required_field(self): cf3 = CustomField(type=CustomFieldTypeChoices.TYPE_TEXT, name='baz', required=True) cf3.save() cf3.content_types.set([ContentType.objects.get_for_model(Site)]) site = Site(name='Test Site', slug='test-site') # Set custom field data with a required field omitted site.custom_field_data['foo'] = 'abc' with self.assertRaises(ValidationError): site.clean() site.custom_field_data['baz'] = 'def' site.clean()

netbox/extras/tests/test_customfields.py

netbox

Python

ea6d86e6c4bb6037465410db6205a7471bc81a6c

test_customfields.py

test_missing_required_field

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

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

def get_annotations(obj, *, globals=None, locals=None, eval_str=False): if isinstance(obj, type): # class obj_dict = getattr(obj, '__dict__', None) if obj_dict and hasattr(obj_dict, 'get'): ann = obj_dict.get('__annotations__', None) if isinstance(ann, types.GetSetDescriptorType): ann = None else: ann = None obj_globals = None module_name = getattr(obj, '__module__', None) if module_name: module = sys.modules.get(module_name, None) if module: obj_globals = getattr(module, '__dict__', None) obj_locals = dict(vars(obj)) unwrap = obj elif isinstance(obj, types.ModuleType): # module ann = getattr(obj, '__annotations__', None) obj_globals = getattr(obj, '__dict__') obj_locals = None unwrap = None elif callable(obj): # this includes types.Function, types.BuiltinFunctionType, # types.BuiltinMethodType, functools.partial, functools.singledispatch, # "class funclike" from Lib/test/test_inspect... on and on it goes. ann = getattr(obj, '__annotations__', None) obj_globals = getattr(obj, '__globals__', None) obj_locals = None unwrap = obj else: raise TypeError(f"{obj!r} is not a module, class, or callable.") if ann is None: return {} if not isinstance(ann, dict): raise ValueError(f"{obj!r}.__annotations__ is neither a dict nor None") if not ann: return {} if not eval_str: return dict(ann) if unwrap is not None: while True: if hasattr(unwrap, '__wrapped__'): unwrap = unwrap.__wrapped__ continue if isinstance(unwrap, functools.partial): unwrap = unwrap.func continue break if hasattr(unwrap, "__globals__"): obj_globals = unwrap.__globals__ if globals is None: globals = obj_globals if locals is None: locals = obj_locals return_value = {key: value if not isinstance(value, str) else eval(value, globals, locals) for key, value in ann.items() } return return_value # ----------------------------------------------------------- type-checking

python3.10.4/Lib/inspect.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

inspect.py

get_annotations

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

add python 3.10.4 for windows

def load_weights(self, filepath, by_name=False, skip_mismatch=False): if backend.is_tpu_strategy(self._distribution_strategy): if self._distribution_strategy.extended.steps_per_run > 1 and ( not saving_utils.is_hdf5_filepath(filepath) ): raise ValueError( "Load weights is not yet supported with TPUStrategy " "with steps_per_run greater than 1." ) return super().load_weights( filepath, by_name=by_name, skip_mismatch=skip_mismatch )

keras/engine/training_v1.py

keras

Python

e69dd22bc51b28b9f311c81abed92dfe46e82960

training_v1.py

load_weights

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

Add ability to do partial reloading of v3 models. PiperOrigin-RevId: 493123409

def get_mode_of_payments(invoice_list): mode_of_payments = {} if invoice_list: inv_mop = frappe.db.sql( % ", ".join(["%s"] * len(invoice_list)), tuple(invoice_list), as_dict=1, ) for d in inv_mop: mode_of_payments.setdefault(d.parent, []).append(d.mode_of_payment) return mode_of_payments

erpnext/accounts/report/sales_register/sales_register.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

sales_register.py

get_mode_of_payments

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

style: format code with black

def assert_configs_are_equivalent_besides_ids(config1, config2): assert config1["mode"] == config2["mode"], "Modes are different" assert config1["theme"] == config2["theme"], "Themes are different" assert len(config1["components"]) == len( config2["components"] ), "# of components are different" mapping = {} for c1, c2 in zip(config1["components"], config2["components"]): c1, c2 = deepcopy(c1), deepcopy(c2) mapping[c1["id"]] = c2["id"] c1.pop("id") c2.pop("id") assert c1 == c2, "{} does not match {}".format(c1, c2)

gradio/utils.py

gradio

Python

dc6175a21d7ccf922d53379df5a35111bd1093fd

utils.py

assert_configs_are_equivalent_besides_ids

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

tabbed-interface-rewritten (#958)

def execute_list_collection(self, artifacts_manager=None): if artifacts_manager is not None: artifacts_manager.require_build_metadata = False output_format = context.CLIARGS['output_format'] collections_search_paths = set(context.CLIARGS['collections_path']) collection_name = context.CLIARGS['collection'] default_collections_path = AnsibleCollectionConfig.collection_paths collections_in_paths = {} warnings = [] path_found = False collection_found = False for path in collections_search_paths: collection_path = GalaxyCLI._resolve_path(path) if not os.path.exists(path): if path in default_collections_path: # don't warn for missing default paths continue warnings.append("- the configured path {0} does not exist.".format(collection_path)) continue if not os.path.isdir(collection_path): warnings.append("- the configured path {0}, exists, but it is not a directory.".format(collection_path)) continue path_found = True if collection_name: # list a specific collection validate_collection_name(collection_name) namespace, collection = collection_name.split('.') collection_path = validate_collection_path(collection_path) b_collection_path = to_bytes(os.path.join(collection_path, namespace, collection), errors='surrogate_or_strict') if not os.path.exists(b_collection_path): warnings.append("- unable to find {0} in collection paths".format(collection_name)) continue if not os.path.isdir(collection_path): warnings.append("- the configured path {0}, exists, but it is not a directory.".format(collection_path)) continue collection_found = True try: collection = Requirement.from_dir_path_as_unknown( b_collection_path, artifacts_manager, ) except ValueError as val_err: six.raise_from(AnsibleError(val_err), val_err) if output_format in {'yaml', 'json'}: collections_in_paths[collection_path] = { collection.fqcn: {'version': collection.ver} } continue fqcn_width, version_width = _get_collection_widths([collection]) _display_header(collection_path, 'Collection', 'Version', fqcn_width, version_width) _display_collection(collection, fqcn_width, version_width) else: # list all collections collection_path = validate_collection_path(path) if os.path.isdir(collection_path): display.vvv("Searching {0} for collections".format(collection_path)) collections = list(find_existing_collections( collection_path, artifacts_manager, )) else: # There was no 'ansible_collections/' directory in the path, so there # or no collections here. display.vvv("No 'ansible_collections' directory found at {0}".format(collection_path)) continue if not collections: display.vvv("No collections found at {0}".format(collection_path)) continue if output_format in {'yaml', 'json'}: collections_in_paths[collection_path] = { collection.fqcn: {'version': collection.ver} for collection in collections } continue # Display header fqcn_width, version_width = _get_collection_widths(collections) _display_header(collection_path, 'Collection', 'Version', fqcn_width, version_width) # Sort collections by the namespace and name for collection in sorted(collections, key=to_text): _display_collection(collection, fqcn_width, version_width) # Do not warn if the specific collection was found in any of the search paths if collection_found and collection_name: warnings = [] for w in warnings: display.warning(w) if not path_found: raise AnsibleOptionsError("- None of the provided paths were usable. Please specify a valid path with --{0}s-path".format(context.CLIARGS['type'])) if output_format == 'json': display.display(json.dumps(collections_in_paths)) elif output_format == 'yaml': display.display(yaml_dump(collections_in_paths)) return 0

lib/ansible/cli/galaxy.py

ansible

Python

05608b20e8f875d51866a184f8c579fe60498e05

galaxy.py

execute_list_collection

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

Fix listing collections that are missing the metadata required by build (#76596) * Rethread pr/70185 through the dependency resolver Hang optional metadata toggle on the ConcreteArtifactsManager instead of threading it through whole list codepath Don't error while listing collections if a collection's metadata is missing keys required for building a collection. Give an informative warning if metadata has been badly formatted. Co-authored-by: Sam Doran <[email protected]>

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

packages/python/plotly/plotly/graph_objs/_layout.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_layout.py

piecolorway

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

switch to black .22

def _generate_object_paths(object_graph_def): paths = {0: "root"} nodes_to_visit = [0] while nodes_to_visit: current_node = nodes_to_visit.pop() current_path = paths[current_node] for reference in object_graph_def.nodes[current_node].children: if reference.node_id in paths: continue paths[reference.node_id] = "{}.{}".format( current_path, reference.local_name ) nodes_to_visit.append(reference.node_id) return paths

keras/saving/saved_model/load.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

load.py

_generate_object_paths

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

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

packages/python/plotly/plotly/graph_objs/_box.py

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_box.py

notchwidth

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

switch to black .22

def python_slice_replace(funcstr): # parse the input parameters pattern = 'torch\.slice\((.*)\)' parameter_str = re.findall(pattern, funcstr) parameters = re.split(',', parameter_str[0]) target_tensor = parameters[0] dim = int(parameters[1]) dim_str = ','.join([':']*(dim) + [':'.join(parameters[2:])]) print('%s[%s]' % (target_tensor, dim_str)) new_str = funcstr.replace( 'torch.slice(%s)' % parameter_str[0], '%s[%s]' % (target_tensor, dim_str)) return new_str

nni/compression/pytorch/utils/utils.py

nni

Python

97d067e614243f06ed1f8e2d389512977fff8828

utils.py

python_slice_replace

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

Speedup enhancement (#4925)

def forward(self, input, encoder_state, embedded_input=None, incr_state=None): encoder_output, encoder_mask = encoder_state if input is not None: seq_len = input.size(1) positions = input.new(seq_len).long() else: seq_len = embedded_input.size(1) positions = embedded_input.new(seq_len).long() positions = torch.arange(seq_len, out=positions).unsqueeze(0) if incr_state is not None: # We're doing incremental decoding, so select only the most recent position if input is not None: input = input[:, -1:] if embedded_input is not None: embedded_input = embedded_input[:, -1:, :] if positions is not None: positions = positions[:, -1:] else: incr_state = {} if embedded_input is not None: tensor = embedded_input # No need to copy because we only reassign below else: tensor = self.embeddings(input) if self.embeddings_scale: tensor = tensor * np.sqrt(self.dim) if self.variant == 'xlm': tensor = self.norm_embeddings(tensor) if positions.max().item() > self.n_positions: warn_once( 'You are inputting a sequence of {x} length, but only have ' '--n-positions {y}. Set --truncate or increase --n-positions'.format( x=positions.max().item(), y=self.n_positions ) ) tensor = tensor + self.position_embeddings(positions).expand_as(tensor) if self.variant == 'bart': tensor = self.norm_embeddings(tensor) tensor = self.dropout(tensor) # --dropout new_incr_state = {} if getattr(self.layers, 'is_model_parallel', False): tensor, new_incr_state = self._apply_model_parallel( tensor, encoder_output, encoder_mask, incr_state=incr_state ) else: for idx, layer in enumerate(self.layers): tensor, new_incr_state[idx] = layer( x=tensor, encoder_output=encoder_output, encoder_mask=encoder_mask, incr_state=incr_state.get(idx), ) if self.variant == 'prelayernorm': tensor = self.norm_embeddings(tensor) return tensor, new_incr_state

projects/style_gen/modules.py

ParlAI

Python

ecdfbd0bb2ab76876e9fd3817d4502c3938a2ade

modules.py

forward

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

Decoder-Only Transformer (#4329) * quick and dirty decoder-only implementation * fix decoder_only incremental decoding * remove unused code, add some comments, propogate func signature change * consolidate code in decoder.py * unify encoder_state * export PassThroughEncoder * add missing build_ functions * defaults in TransformerDecoderLayer __init__ * comments, consolidating more logic, simplified forward_layers args * resize token embeddings and unit test * attempt to suppress some unused import warnings * padded_tensor fp16 friendly * autoformat * decoder_only -> decoder * more documentation * update name in test * add missing dict args * more argument massaging * update TestBartDistillation::test_narrow_distillation_losses numbers * update TestTransformerDistillation::test_narrow_distillation_losses numbers * fix _pad_tensor in seeker Co-authored-by: klshuster <[email protected]>

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

lib/matplotlib/pyplot.py

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

matplotlib

Python

2d918ba09155810194bb4ba136369082ad46c8c8

pyplot.py

xkcd

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

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

def post_validate(self, templar): # save the omit value for later checking omit_value = templar.available_variables.get('omit') for (name, attribute) in self.fattributes.items(): if attribute.static: value = getattr(self, name) # we don't template 'vars' but allow template as values for later use if name not in ('vars',) and templar.is_template(value): display.warning('"%s" is not templatable, but we found: %s, ' 'it will not be templated and will be used "as is".' % (name, value)) continue if getattr(self, name) is None: if not attribute.required: continue else: raise AnsibleParserError("the field '%s' is required but was not set" % name) elif not attribute.always_post_validate and self.__class__.__name__ not in ('Task', 'Handler', 'PlayContext'): # Intermediate objects like Play() won't have their fields validated by # default, as their values are often inherited by other objects and validated # later, so we don't want them to fail out early continue try: # Run the post-validator if present. These methods are responsible for # using the given templar to template the values, if required. method = getattr(self, '_post_validate_%s' % name, None) if method: value = method(attribute, getattr(self, name), templar) elif attribute.isa == 'class': value = getattr(self, name) else: # if the attribute contains a variable, template it now value = templar.template(getattr(self, name)) # if this evaluated to the omit value, set the value back to # the default specified in the FieldAttribute and move on if omit_value is not None and value == omit_value: if callable(attribute.default): setattr(self, name, attribute.default()) else: setattr(self, name, attribute.default) continue # and make sure the attribute is of the type it should be if value is not None: value = self.get_validated_value(name, attribute, value, templar) # and assign the massaged value back to the attribute field setattr(self, name, value) except (TypeError, ValueError) as e: value = getattr(self, name) raise AnsibleParserError("the field '%s' has an invalid value (%s), and could not be converted to an %s." "The error was: %s" % (name, value, attribute.isa, e), obj=self.get_ds(), orig_exc=e) except (AnsibleUndefinedVariable, UndefinedError) as e: if templar._fail_on_undefined_errors and name != 'name': if name == 'args': msg = "The task includes an option with an undefined variable. The error was: %s" % (to_native(e)) else: msg = "The field '%s' has an invalid value, which includes an undefined variable. The error was: %s" % (name, to_native(e)) raise AnsibleParserError(msg, obj=self.get_ds(), orig_exc=e) self._finalized = True

lib/ansible/playbook/base.py

ansible

Python

43153c58310d02223f2cb0964f4255ba1ac4ed53

base.py

post_validate

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

`FieldAttribute`s as descriptors (#73908)

def wait_for_nodes(self, timeout=30): start_time = time.time() while time.time() - start_time < timeout: clients = self.global_state.node_table() live_clients = [client for client in clients if client["Alive"]] expected = len(self.list_all_nodes()) if len(live_clients) == expected: logger.debug("All nodes registered as expected.") return else: logger.debug( f"{len(live_clients)} nodes are currently registered, " f"but we are expecting {expected}" ) time.sleep(0.1) raise TimeoutError("Timed out while waiting for nodes to join.")

python/ray/cluster_utils.py

ray

Python

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

cluster_utils.py

wait_for_nodes

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

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

def rotate_point(pin, r): if isinstance(r, tuple): # if r is of the form (vector, angle) q = Quaternion.from_axis_angle(r[0], r[1]) else: # if r is a quaternion q = r.normalize() pout = q * Quaternion(0, pin[0], pin[1], pin[2]) * conjugate(q) return (pout.b, pout.c, pout.d)

sympy/algebras/quaternion.py

sympy

Python

498015021131af4dbb07eb110e5badaba8250c7b

quaternion.py

rotate_point

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

Updated import locations

def vstack(tup, *, dtype=None, casting="same_kind"): if not overrides.ARRAY_FUNCTION_ENABLED: # raise warning if necessary _arrays_for_stack_dispatcher(tup, stacklevel=2) arrs = atleast_2d(*tup) if not isinstance(arrs, list): arrs = [arrs] return _nx.concatenate(arrs, 0, dtype=dtype, casting=casting) @array_function_dispatch(_vhstack_dispatcher)

numpy/core/shape_base.py

@array_function_dispatch(_vhstack_dispatcher)

numpy

Python

126046f84449fffeb0c75ae88657ce6b90236eee

shape_base.py

vstack

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

ENH: adding casting option to numpy.stack. (#21627) np.concatenate and np.stack are similar methods, but only np.concatenate has the casting option. This PR puts the casting option into the np.stack method to control what kind of data casting may occur Closes gh-20959 * ENH: adding casting option to numpy.stack. See #20959 * ENH: adding dtype option to numpy.stack. See #20959 * REV: removing auto-generated file loops_modulo.dispatch.c See numpy#20959 * REV: removing auto-generated file loops_modulo.dispatch.c See numpy#20959 * REV: removing inserted newlines See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * DOC: inserting versionadded info in dtype and casting parameters. See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * TST: writing tests to stack method with dtype and casting options See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * DOC: adding upcoming_change file for new options casting and dtype in method stack. See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * REV: reverting lint errors. See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * DOC: inserting hstack and vstack methods in upcoming changes See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * ENH: adding dtype and casting keyword arguments to numpy.vstack and numpy.hstack. See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * TST: writing tests to vstack and hstack methods with dtype and casting keyword arguments. See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * REV: reverting the 'out' option type in stack method. See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> * REV: Reverting out type changes in overload of shape_base.pyi file. See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: jhonatancunha <[email protected]> Co-authored-by: patriarka <[email protected]> * DOC: correcting some english erros in upcoming_changes file. See numpy#20959 Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: alescrocaro <[email protected]> Co-authored-by: JessePires <[email protected]> Co-authored-by: patriarka <[email protected]>

def _determine_setitem_axis(self, row_lookup, col_lookup, row_scalar, col_scalar): if self.df.shape == (1, 1): return None if not (row_scalar ^ col_scalar) else 1 if row_scalar else 0

modin/pandas/indexing.py

modin

Python

0d9d14e6669be3dd6bb3b72222dbe6a6dffe1bee

indexing.py

_determine_setitem_axis

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

FIX-#3860: Fix single row Series assignment. (#3894) Signed-off-by: mvashishtha <[email protected]>

def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1): assert isinstance(factor, int) and factor >= 1 # Setup filter kernel. if kernel is None: kernel = [1] * factor # setup kernel kernel = torch.tensor(kernel, dtype=torch.float32) if kernel.ndim == 1: kernel = torch.outer(kernel, kernel) kernel /= torch.sum(kernel) kernel = kernel * (gain * (factor**2)) if self.use_conv: convH = weight.shape[2] convW = weight.shape[3] inC = weight.shape[1] pad_value = (kernel.shape[0] - factor) - (convW - 1) stride = (factor, factor) # Determine data dimensions. output_shape = ( (hidden_states.shape[2] - 1) * factor + convH, (hidden_states.shape[3] - 1) * factor + convW, ) output_padding = ( output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH, output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW, ) assert output_padding[0] >= 0 and output_padding[1] >= 0 num_groups = hidden_states.shape[1] // inC # Transpose weights. weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW)) weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4) weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW)) inverse_conv = F.conv_transpose2d( hidden_states, weight, stride=stride, output_padding=output_padding, padding=0 ) output = upfirdn2d_native( inverse_conv, torch.tensor(kernel, device=inverse_conv.device), pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1), ) else: pad_value = kernel.shape[0] - factor output = upfirdn2d_native( hidden_states, torch.tensor(kernel, device=hidden_states.device), up=factor, pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2), ) return output

src/diffusers/models/resnet.py

diffusers

Python

a73f8b725105b12a60a9b22918bda68f8b6d26c3

resnet.py

_upsample_2d

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

Clean up resnet.py file (#780) * clean up resnet.py * make style and quality * minor formatting

def validate_thread_sharing(self): if not (self.allow_thread_sharing or self._thread_ident == _thread.get_ident()): raise DatabaseError( "DatabaseWrapper objects created in a " "thread can only be used in that same thread. The object " "with alias '%s' was created in thread id %s and this is " "thread id %s." % (self.alias, self._thread_ident, _thread.get_ident()) ) # ##### Miscellaneous #####

django/db/backends/base/base.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

base.py

validate_thread_sharing

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

Refs #33476 -- Reformatted code with Black.

def process_response(self, request, response): # If the given URL is "Not Found", then check if we should redirect to # a path with a slash appended. if response.status_code == 404 and self.should_redirect_with_slash(request): return self.response_redirect_class(self.get_full_path_with_slash(request)) # Add the Content-Length header to non-streaming responses if not # already set. if not response.streaming and not response.has_header("Content-Length"): response.headers["Content-Length"] = str(len(response.content)) return response

django/middleware/common.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

common.py

process_response

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

Refs #33476 -- Reformatted code with Black.

def test_duplicate_statistics_handle_integrity_error(hass_recorder, caplog): hass = hass_recorder() wait_recording_done(hass) period1 = dt_util.as_utc(dt_util.parse_datetime("2021-09-01 00:00:00")) period2 = dt_util.as_utc(dt_util.parse_datetime("2021-09-30 23:00:00")) external_energy_metadata_1 = { "has_mean": False, "has_sum": True, "name": "Total imported energy", "source": "test", "state_unit_of_measurement": "kWh", "statistic_id": "test:total_energy_import_tariff_1", "unit_of_measurement": "kWh", } external_energy_statistics_1 = [ { "start": period1, "last_reset": None, "state": 3, "sum": 5, }, ] external_energy_statistics_2 = [ { "start": period2, "last_reset": None, "state": 3, "sum": 6, } ] with patch.object( statistics, "_statistics_exists", return_value=False ), patch.object( statistics, "_insert_statistics", wraps=statistics._insert_statistics ) as insert_statistics_mock: async_add_external_statistics( hass, external_energy_metadata_1, external_energy_statistics_1 ) async_add_external_statistics( hass, external_energy_metadata_1, external_energy_statistics_1 ) async_add_external_statistics( hass, external_energy_metadata_1, external_energy_statistics_2 ) wait_recording_done(hass) assert insert_statistics_mock.call_count == 3 with session_scope(hass=hass) as session: tmp = session.query(recorder.db_schema.Statistics).all() assert len(tmp) == 2 assert "Blocked attempt to insert duplicated statistic rows" in caplog.text

tests/components/recorder/test_statistics.py

core

Python

dd20a7ea62fc003748c5f0cf99be25c69c9b5a05

test_statistics.py

test_duplicate_statistics_handle_integrity_error

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

Display statistics in the source's unit (#78031)

def save_model(model, filepath, weights_format="h5", use_zip=True): if not filepath.endswith(".keras"): raise ValueError( "Invalid filename: expected a `.keras` extension. " f"Received: filepath={filepath}" ) if weights_format == "h5" and h5py is None: raise ImportError( "h5py must be installed in order to save a model in hdf5 format." ) if not model.built: warnings.warn( "You are saving a model that has not yet been built. " "It might not contain any weights yet. " "Consider building the model first by calling it " "on some data.", stacklevel=2, ) saving_v3_enabled_value = getattr(_SAVING_V3_ENABLED, "value", False) _SAVING_V3_ENABLED.value = True serialized_model_dict = serialize_keras_object(model) config_json = json.dumps(serialized_model_dict) # TODO(fchollet): consider saving dependencies list / versions in metadata. metadata_json = json.dumps( { "keras_version": keras.__version__, "date_saved": datetime.datetime.now().strftime("%Y-%m-%d@%H:%M:%S"), } ) if use_zip: # Use a temporary directory for the storing files prior to zipping. write_path = _get_temp_dir() else: tf.io.gfile.makedirs(filepath) write_path = filepath try: # Write files locally before zipping. with open(tf.io.gfile.join(write_path, _METADATA_FILENAME), "w") as f: f.write(metadata_json) with open(tf.io.gfile.join(write_path, _CONFIG_FILENAME), "w") as f: f.write(config_json) weights_path = tf.io.gfile.join(write_path, _VARS_FNAME) assets_path = tf.io.gfile.join(write_path, _ASSETS_DIRNAME) if weights_format == "h5": weights_store = H5IOStore(weights_path, mode="w") elif weights_format == "npz": weights_store = NpzIOStore(weights_path, mode="w") else: raise ValueError( "Unknown `weights_format`. Expected 'h5' or 'npz'. " f"Received: {weights_format}" ) _save_state( model, weights_handler=weights_store, assets_handler=DiskIOStore(assets_path), inner_path="", visited_trackables=set(), ) weights_store.close() if use_zip: # Zip local files into an archive. with zipfile.ZipFile(filepath, "w") as zipfile_to_save: _write_to_zip_recursively(zipfile_to_save, write_path, "") except Exception as e: raise e finally: _SAVING_V3_ENABLED.value = saving_v3_enabled_value if use_zip and tf.io.gfile.exists(write_path): # Remove the directory temporarily used. tf.io.gfile.rmtree(write_path)

keras/saving/experimental/saving_lib.py

keras

Python

0b393d4049afd187ed7d24ea70177a72cf4a3ce2

saving_lib.py

save_model

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

New saving: add npz support and make zipping optional. We should decide which store to go with by default. h5 is faster, but only marginally so. Zip has no speed impact (the long saving/loading time was due to the breakdown into many files/dirs previously). But it has a temporary disk space impact. Note: Using h5 without zipping will not work with GCS (due to H5 using its own file pointer). This issue could be worked around via special casing. All other combinations work with GCS. Saving time for NASNetLarge: - Legacy h5: 2.8s - New h5 + zip: 2.6s - New h5 + no zip: 2.5s - New npz + zip: 3.2s - New npz + no zip: 3.0s - Legacy savedmodel: 142.2s (!) Loading times are similar across the board (nozip is a bit faster). PiperOrigin-RevId: 481705383

def cleanup(self) -> None: if self.remove_config: os.remove(self.config_path)

test/lib/ansible_test/_internal/commands/integration/cloud/__init__.py

ansible

Python

3eb0485dd92c88cc92152d3656d94492db44b183

__init__.py

cleanup

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

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

def notna(obj): # noqa: PR01, RT01, D200 if isinstance(obj, BasePandasDataset): return obj.notna() else: return pandas.notna(obj) notnull = notna @_inherit_docstrings(pandas.merge, apilink="pandas.merge")

modin/pandas/general.py

@_inherit_docstrings(pandas.merge, apilink="pandas.merge")

modin

Python

2809f7c12384e2615390bcb76f2e7f0d88a3fffa

general.py

notna

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

DOCS-#4336: Reformat general utilities docstrings (#4338) Co-authored-by: Yaroslav Igoshev <[email protected]> Signed-off-by: Alexander Myskov <[email protected]>

def sample(self) -> SampleBatchType: if self.fake_sampler and self.last_batch is not None: return self.last_batch elif self.input_reader is None: raise ValueError( "RolloutWorker has no `input_reader` object! " "Cannot call `sample()`. You can try setting " "`create_env_on_driver` to True." ) if log_once("sample_start"): logger.info( "Generating sample batch of size {}".format( self.rollout_fragment_length ) ) batches = [self.input_reader.next()] steps_so_far = ( batches[0].count if self.count_steps_by == "env_steps" else batches[0].agent_steps() ) # In truncate_episodes mode, never pull more than 1 batch per env. # This avoids over-running the target batch size. if self.batch_mode == "truncate_episodes": max_batches = self.num_envs else: max_batches = float("inf") while steps_so_far < self.rollout_fragment_length and ( len(batches) < max_batches or self.policy_config.get("offline_sampling") ): batch = self.input_reader.next() steps_so_far += ( batch.count if self.count_steps_by == "env_steps" else batch.agent_steps() ) batches.append(batch) batch = concat_samples(batches) self.callbacks.on_sample_end(worker=self, samples=batch) # Always do writes prior to compression for consistency and to allow # for better compression inside the writer. self.output_writer.write(batch) if log_once("sample_end"): logger.info("Completed sample batch:\n\n{}\n".format(summarize(batch))) if self.compress_observations: batch.compress(bulk=self.compress_observations == "bulk") if self.fake_sampler: self.last_batch = batch return batch

rllib/evaluation/rollout_worker.py

ray

Python

d6b6dc560dd2920bf8ee1e4de01c4ec25fdab555

rollout_worker.py

sample

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

[AIR] Address multiple warnings in AIR examples (#28800) These changes address warnings and errors in several of the AIR examples. They're all small changes, so I've batched them together in this PR: - Fixed `concant_samples` deprecation warning, which appears in `rollout_worker.py` - Fixed call to `randrange` which passed floating point argument; this will generate errors for python>=3.11 - Removed another use of the `object_store_memory` in instantiating an actor - Replaced a number of strings (docstrings and regexes) which had escape sequences with string literals - Fixed an issue that broke the `tfx_tabular_train_to_serve` example where data that was being sent to the server was not being encoded as json correctly by `requests`. - Fixed a deprecated import of `ray.rllib.agents.marwil` in the `rl_offline_example`. Signed-off-by: pdmurray <[email protected]>

def benchmark_custom_training_mnist_bs_256(self): batch_size = 256 run_iters = 5 train_dataset = self.train_dataset.shuffle(buffer_size=1024).batch( batch_size ) # Instantiate a loss function. loss_fn = tf.keras.losses.CategoricalCrossentropy( reduction=tf.keras.losses.Reduction.NONE ) # Instantiate an optimizer to train the model. optimizer = tf.keras.optimizers.Adam() model = self._build_model() metrics, wall_time = self.measure_performance( model, train_dataset, loss_fn, optimizer, batch_size, run_iters, self.epochs, ) extras = benchmark_util.get_keras_examples_metadata( "conv", batch_size, ".keras.ctl_graph" ) self.report_benchmark( iters=run_iters, wall_time=wall_time, metrics=metrics, extras=extras )

keras/benchmarks/keras_examples_benchmarks/mnist_conv_custom_training_benchmark_test.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

mnist_conv_custom_training_benchmark_test.py

benchmark_custom_training_mnist_bs_256

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def match_submerged_margins(layoutgrids, fig): for sfig in fig.subfigs: match_submerged_margins(layoutgrids, sfig) axs = [a for a in fig.get_axes() if a.get_subplotspec() is not None and a.get_in_layout()] for ax1 in axs: ss1 = ax1.get_subplotspec() if ss1.get_gridspec() not in layoutgrids: axs.remove(ax1) continue lg1 = layoutgrids[ss1.get_gridspec()] # interior columns: if len(ss1.colspan) > 1: maxsubl = np.max( lg1.margin_vals['left'][ss1.colspan[1:]] + lg1.margin_vals['leftcb'][ss1.colspan[1:]] ) maxsubr = np.max( lg1.margin_vals['right'][ss1.colspan[:-1]] + lg1.margin_vals['rightcb'][ss1.colspan[:-1]] ) for ax2 in axs: ss2 = ax2.get_subplotspec() lg2 = layoutgrids[ss2.get_gridspec()] if lg2 is not None and len(ss2.colspan) > 1: maxsubl2 = np.max( lg2.margin_vals['left'][ss2.colspan[1:]] + lg2.margin_vals['leftcb'][ss2.colspan[1:]]) if maxsubl2 > maxsubl: maxsubl = maxsubl2 maxsubr2 = np.max( lg2.margin_vals['right'][ss2.colspan[:-1]] + lg2.margin_vals['rightcb'][ss2.colspan[:-1]]) if maxsubr2 > maxsubr: maxsubr = maxsubr2 for i in ss1.colspan[1:]: lg1.edit_margin_min('left', maxsubl, cell=i) for i in ss1.colspan[:-1]: lg1.edit_margin_min('right', maxsubr, cell=i) # interior rows: if len(ss1.rowspan) > 1: maxsubt = np.max( lg1.margin_vals['top'][ss1.rowspan[1:]] + lg1.margin_vals['topcb'][ss1.rowspan[1:]] ) maxsubb = np.max( lg1.margin_vals['bottom'][ss1.rowspan[:-1]] + lg1.margin_vals['bottomcb'][ss1.rowspan[:-1]] ) for ax2 in axs: ss2 = ax2.get_subplotspec() lg2 = layoutgrids[ss2.get_gridspec()] if lg2 is not None: if len(ss2.rowspan) > 1: maxsubt = np.max([np.max( lg2.margin_vals['top'][ss2.rowspan[1:]] + lg2.margin_vals['topcb'][ss2.rowspan[1:]] ), maxsubt]) maxsubb = np.max([np.max( lg2.margin_vals['bottom'][ss2.rowspan[:-1]] + lg2.margin_vals['bottomcb'][ss2.rowspan[:-1]] ), maxsubb]) for i in ss1.rowspan[1:]: lg1.edit_margin_min('top', maxsubt, cell=i) for i in ss1.rowspan[:-1]: lg1.edit_margin_min('bottom', maxsubb, cell=i)

lib/matplotlib/_constrained_layout.py

matplotlib

Python

c73f4c455514cf5422d27bf38c93250de8316b21

_constrained_layout.py

match_submerged_margins

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

Merge SubplotBase into AxesBase.

def get_returned_qty_map(delivery_note): returned_qty_map = frappe._dict( frappe.db.sql( , delivery_note, ) ) return returned_qty_map @frappe.whitelist()

erpnext/stock/doctype/delivery_note/delivery_note.py

@frappe.whitelist()

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

delivery_note.py

get_returned_qty_map

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

style: format code with black

def set_dash_capstyle(self, s): cs = CapStyle(s) if self._dashcapstyle != cs: self.stale = True self._dashcapstyle = cs

lib/matplotlib/lines.py

matplotlib

Python

b24acb7772e0534f4bcdb0b5b492d9d94954dd91

lines.py

set_dash_capstyle

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

DOC: Document default cap styles - remove '(default)' from cap style demo as this is only true for Line2D and the default rcParameters - document default cap styles for Line2D and Patch in their cap style setters - document default cap style for GraphicsContextBase in the same way as it's already done for joinstyle

def app_get_relative_path(requests_pathname, path): if requests_pathname == "/" and path == "": return "/" if requests_pathname != "/" and path == "": return requests_pathname if not path.startswith("/"): raise exceptions.UnsupportedRelativePath( f ) return "/".join([requests_pathname.rstrip("/"), path.lstrip("/")])

dash/_get_paths.py

dash

Python

c3c84b9ecf16bcc61ed80ec39d511af92fe07f2c

_get_paths.py

app_get_relative_path

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

f-strings everywhere! fffff

def translate_exprs_to_base(exprs, base): new_exprs = dict(exprs) frames = set() for k, v in new_exprs.items(): v.collect_frames(frames) frames.discard(base) while len(frames) > 0: mapper = InputMapper() new_frames = set() for frame in frames: frame_base = frame._op.input[0] if frame_base != base: new_frames.add(frame_base) assert isinstance(frame._op, TransformNode) mapper.add_mapper(frame, TransformMapper(frame._op)) for k, v in new_exprs.items(): new_expr = new_exprs[k].translate_input(mapper) new_expr.collect_frames(new_frames) new_exprs[k] = new_expr new_frames.discard(base) frames = new_frames res = OrderedDict() for col in exprs.keys(): res[col] = new_exprs[col] return res

modin/experimental/core/execution/native/implementations/hdk_on_native/df_algebra.py

modin

Python

e5b1888cd932909e49194d58035da34b210b91c4

df_algebra.py

translate_exprs_to_base

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

FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <[email protected]> Signed-off-by: Andrey Pavlenko <[email protected]>

def get_http_proxy_names(self) -> bytes: if self.http_state is None: return None from ray.serve.generated.serve_pb2 import ActorNameList actor_name_list = ActorNameList( names=self.http_state.get_http_proxy_names().values() ) return actor_name_list.SerializeToString()

python/ray/serve/controller.py

ray

Python

65d0c0aa48be8f9f7faae857d3ab71444997755a

controller.py

get_http_proxy_names

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

[Serve] add alpha gRPC support (#28175)

def decode_crop_and_flip_tf_record_batch(tf_record_batch):

release/air_tests/air_benchmarks/mlperf-train/resnet50_ray_air.py

ray

Python

02f911ce78137cb63ecb685a8ef8e56dcb60062c

resnet50_ray_air.py

decode_crop_and_flip_tf_record_batch

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

Benchmarking Ray Data bulk ingest as input file size changes. (#29296) This PR adds a benchmark which takes work from https://github.com/anyscale/air-benchmarks and makes it run as a release test. Full metrics are stored in Databricks. Signed-off-by: Cade Daniel <[email protected]>

def all_checks(ctx, connectors=None): # type: ignore[no-untyped-def] tasks = ( black, flake, isort, mypy, coverage, ) for task_ in tasks: try: task_(ctx, connectors=connectors) except Exit as e: if e.code: raise @task(help={"connectors": _arg_help_connectors, "write": "Write changes into the files (runs 'black' without '--check' option)"})

airbyte-integrations/connectors/tasks.py

@task(help={"connectors": _arg_help_connectors, "write": "Write changes into the files (runs 'black' without '--check' option)"})

airbyte

Python

e3e05d79655bc0b2b4c3fbc0c05b8d90fce6dcd8

tasks.py

all_checks

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

update code-checkers config (#9707) fix `all-checks` command Signed-off-by: Sergei Solonitcyn <[email protected]>

def parseString(self, instring, parseAll=False): ParserElement.resetCache() if not self.streamlined: self.streamline() # ~ self.saveAsList = True for e in self.ignoreExprs: e.streamline() if not self.keepTabs: instring = instring.expandtabs() try: loc, tokens = self._parse(instring, 0) if parseAll: loc = self.preParse(instring, loc) se = Empty() + StringEnd() se._parse(instring, loc) except ParseBaseException as exc: if ParserElement.verbose_stacktrace: raise else: # catch and re-raise exception from here, clearing out pyparsing internal stack trace if getattr(exc, '__traceback__', None) is not None: exc.__traceback__ = self._trim_traceback(exc.__traceback__) raise exc else: return tokens

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

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

pyparsing.py

parseString

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

upd; format

def _create_input_dict_and_dummy_batch(self, view_requirements, existing_inputs): input_dict = {} for view_col, view_req in view_requirements.items(): # Point state_in to the already existing self._state_inputs. mo = re.match("state_in_(\d+)", view_col) if mo is not None: input_dict[view_col] = self._state_inputs[int(mo.group(1))] # State-outs (no placeholders needed). elif view_col.startswith("state_out_"): continue # Skip action dist inputs placeholder (do later). elif view_col == SampleBatch.ACTION_DIST_INPUTS: continue # This is a tower: Input placeholders already exist. elif view_col in existing_inputs: input_dict[view_col] = existing_inputs[view_col] # All others. else: time_axis = not isinstance(view_req.shift, int) if view_req.used_for_training: # Create a +time-axis placeholder if the shift is not an # int (range or list of ints). # Do not flatten actions if action flattening disabled. if self.config.get("_disable_action_flattening") and view_col in [ SampleBatch.ACTIONS, SampleBatch.PREV_ACTIONS, ]: flatten = False # Do not flatten observations if no preprocessor API used. elif ( view_col in [SampleBatch.OBS, SampleBatch.NEXT_OBS] and self.config["_disable_preprocessor_api"] ): flatten = False # Flatten everything else. else: flatten = True input_dict[view_col] = get_placeholder( space=view_req.space, name=view_col, time_axis=time_axis, flatten=flatten, ) dummy_batch = self._get_dummy_batch_from_view_requirements(batch_size=32) return SampleBatch(input_dict, seq_lens=self._seq_lens), dummy_batch

rllib/policy/dynamic_tf_policy_v2.py

ray

Python

bc3a1d35cf6e9a5fd7eef908a8e76aefb80ce6a9

dynamic_tf_policy_v2.py

_create_input_dict_and_dummy_batch

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

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

def lock(remote=None):

salt/fileserver/hgfs.py

salt

Python

06aeefffad82d8f5db43b4429aeae87bad735acf

hgfs.py

lock

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

Don't leak sub-processes Signed-off-by: Pedro Algarvio <[email protected]>

def log_img_scale(img_scale, shape_order='hw', skip_square=False): if shape_order == 'hw': height, width = img_scale elif shape_order == 'wh': width, height = img_scale else: raise ValueError(f'Invalid shape_order {shape_order}.') if skip_square and (height == width): return False logger = get_root_logger() caller = get_caller_name() logger.info(f'image shape: height={height}, width={width} in {caller}') return True

mmdet/utils/logger.py

mmdetection

Python

8d7da432af02a52bc5330b30997984335d0930a4

logger.py

log_img_scale

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

Update YOLOX log for non square input (#7235)

def get_category(cls): if hasattr(cls, 'category'): return cls.category return cls.model._meta.app_config.verbose_name

netbox/netbox/search/__init__.py

netbox

Python

ffce5d968d8a77c97852999b6ef916e80c1de55f

__init__.py

get_category

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

8927 plugin search (#10489) * #7016 base search classes * 7016 add search indexes * 7016 add search indexes * 7016 add search indexes * 7016 add search indexes * 7016 add search indexes * 7016 add search indexes * 8927 refactor search * 8927 refactor search * 8927 refactor search * 8927 refactor search * 8927 get search choices working * 8927 cleanup - optimize * 8927 use backend search function * 8927 fix for plugin search * 8927 add docs * Move search app to a module under netbox/ * Utilize global registry to register model search classes * Build search form options from registry * Determine search categories from model app by default * Enable dynamic search registration for plugins * Update docs & improve plugin support * Clean up search backend class * Docs for #8927 Co-authored-by: jeremystretch <[email protected]>

def _get_animated_artists(self): return tuple(a for ax_ in self.ax.get_figure().get_axes() for a in ax_.get_children() if a.get_animated() and a not in self.artists)

lib/matplotlib/widgets.py

matplotlib

Python

7603999becc00d39f7616ed20e426e776144cda1

widgets.py

_get_animated_artists

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

Improve docstring and add comments

def fit(self, X, y=None): subset_df = pd.read_csv(self.subset_list, header=0, index_col=0) if isinstance(self.sel_subset, int): self.sel_subset_name = subset_df.index[self.sel_subset] elif isinstance(self.sel_subset, str): self.sel_subset_name = self.sel_subset else: # list or tuple self.sel_subset_name = [] for s in self.sel_subset: if isinstance(s, int): self.sel_subset_name.append(subset_df.index[s]) else: self.sel_subset_name.append(s) sel_features = subset_df.loc[self.sel_subset_name, 'Features'] if not isinstance(sel_features, str): sel_features = ";".join(sel_features.tolist()) sel_uniq_features = set(sel_features.split(';')) if isinstance(X, pd.DataFrame): # use columns' names self.feature_names = list(X.columns.values) self.feat_list = sorted(list(set(sel_uniq_features).intersection(set(self.feature_names)))) self.feat_list_idx = [list(X.columns).index(feat_name) for feat_name in self.feat_list] elif isinstance(X, np.ndarray): # use index self.feature_names = list(range(X.shape[1])) sel_uniq_features = [int(val) for val in sel_uniq_features] self.feat_list = sorted(list(set(sel_uniq_features).intersection(set(self.feature_names)))) self.feat_list_idx = self.feat_list if not len(self.feat_list): raise ValueError('No feature is found on the subset list!') return self

tpot/builtins/feature_set_selector.py

tpot

Python

388616b6247ca4ea8de4e2f340d6206aee523541

feature_set_selector.py

fit

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

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

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

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

plotly.py

Python

43e3a4011080911901176aab919c0ecf5046ddd3

_modebar.py

removesrc

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

switch to black .22

def __len__(self) -> int: return len(self._valid_keys)

rllib/policy/policy_map.py

ray

Python

ed3f3c08225c7049a96a290b586c67c37e2d0bc0

policy_map.py

__len__

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

[RLlib] PolicyMap LRU cache enhancements: Swap out policies (instead of GC'ing and recreating) + use Ray object store (instead of file system). (#29513)

def test_copy_keep_live_false_not_emits_signal(self): homepage = Page.objects.get(url_path="/home/") signal_fired = False

wagtail/core/tests/test_page_model.py

wagtail

Python

d10f15e55806c6944827d801cd9c2d53f5da4186

test_page_model.py

test_copy_keep_live_false_not_emits_signal

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

Reformat with black

def SqueezeAndExciteBlock(filters_in, se_filters, name=None): if name is None: name = str(backend.get_uid("squeeze_and_excite"))

keras/applications/regnet.py

keras

Python

84afc5193d38057e2e2badf9c889ea87d80d8fbf

regnet.py

SqueezeAndExciteBlock

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

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

def _sanitize_column(self, value) -> ArrayLike: self._ensure_valid_index(value) # We should never get here with DataFrame value if isinstance(value, Series): return _reindex_for_setitem(value, self.index) elif isinstance(value, dict): return _reindex_for_setitem(Series(value), self.index) if is_list_like(value): com.require_length_match(value, self.index) return sanitize_array(value, self.index, copy=True, allow_2d=True)

pandas/core/frame.py

pandas

Python

cd2b8196c519b58c82cccdb8472d86c1c58f9511

frame.py

_sanitize_column

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

BUG: DataFrame.loc not aligning dict when setting to a column (#47361) * BUG: DataFrame.loc not aligning dict when setting to a column * Add partial case * Use is_dict_like * Revert "Use is_dict_like" This reverts commit d2708512b751c6470690fdff0abc9c99404dc002.

def get_all_items(date_range, company, field, limit=None): if field in ("available_stock_qty", "available_stock_value"): select_field = "sum(actual_qty)" if field == "available_stock_qty" else "sum(stock_value)" return frappe.db.get_all( "Bin", fields=["item_code as name", "{0} as value".format(select_field)], group_by="item_code", order_by="value desc", limit=limit, ) else: if field == "total_sales_amount": select_field = "sum(order_item.base_net_amount)" select_doctype = "Sales Order" elif field == "total_purchase_amount": select_field = "sum(order_item.base_net_amount)" select_doctype = "Purchase Order" elif field == "total_qty_sold": select_field = "sum(order_item.stock_qty)" select_doctype = "Sales Order" elif field == "total_qty_purchased": select_field = "sum(order_item.stock_qty)" select_doctype = "Purchase Order" date_condition = get_date_condition(date_range, "sales_order.transaction_date") return frappe.db.sql( .format( select_field, select_doctype, date_condition ), (company, cint(limit)), as_dict=1, ) # nosec @frappe.whitelist()

erpnext/startup/leaderboard.py

@frappe.whitelist()

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

leaderboard.py

get_all_items

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

style: format code with black

def _get_reconciled_name_object(self, other) -> MultiIndex: names = self._maybe_match_names(other) if self.names != names: # error: Cannot determine type of "rename" return self.rename(names) # type: ignore[has-type] return self

pandas/core/indexes/multi.py

pandas

Python

5c66e65d7b9fef47ccb585ce2fd0b3ea18dc82ea

multi.py

_get_reconciled_name_object

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

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

def is_equilateral(self): return not has_variety(s.length for s in self.sides)

sympy/geometry/polygon.py

sympy

Python

498015021131af4dbb07eb110e5badaba8250c7b

polygon.py

is_equilateral

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

Updated import locations

def makeport(self): sock = socket.create_server(("", 0), family=self.af, backlog=1) port = sock.getsockname()[1] # Get proper port host = self.sock.getsockname()[0] # Get proper host if self.af == socket.AF_INET: resp = self.sendport(host, port) else: resp = self.sendeprt(host, port) if self.timeout is not _GLOBAL_DEFAULT_TIMEOUT: sock.settimeout(self.timeout) return sock

python3.10.4/Lib/ftplib.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

ftplib.py

makeport

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

add python 3.10.4 for windows

def test_in1d_mixed_boolean(self, kind): for dtype in np.typecodes["AllInteger"]: a = np.array([True, False, False], dtype=bool) b = np.array([1, 1, 1, 1], dtype=dtype) expected = np.array([True, False, False], dtype=bool) assert_array_equal(in1d(a, b, kind=kind), expected) a, b = b, a expected = np.array([True, True, True, True], dtype=bool) assert_array_equal(in1d(a, b, kind=kind), expected)

numpy/lib/tests/test_arraysetops.py

numpy

Python

f9bed20bffd88bce06dbc8be200179edfe7580a4

test_arraysetops.py

test_in1d_mixed_boolean

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

BUG: Fix numpy.isin for timedelta dtype (#21860) This PR fixes the issue discussed on #12065 and #21843 where 'timedelta64' was noted to be a subtype of numpy.integer. This in principle should detect any cases where int(np.min(ar2)) fails. This PR also adds unittests for these. * TST: Create in1d test for timedelta input * MAINT: fix in1d for timedelta input * TST: in1d raise ValueError for timedelta input * MAINT: Clean up type checking for isin kind="table" * TST: Add test for mixed boolean/integer in1d * MAINT: Increase readability of in1d type checking * STY: Apply small code style tweaks This is probably really mainly my personal opinion... Co-authored-by: Sebastian Berg <[email protected]>

def strip(self, text): return re.sub(self.puncs_regular_exp, " ", text).strip()

TTS/tts/utils/text/punctuation.py

TTS

Python

8d85af84cd5f1748f979fddcbc4aab1449f61ecb

punctuation.py

strip

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

Implement Punctuation class

def dispatch_line(self, frame): if self.stop_here(frame) or self.break_here(frame): self.user_line(frame) if self.quitting: raise BdbQuit return self.trace_dispatch

python3.10.4/Lib/bdb.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

bdb.py

dispatch_line

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

add python 3.10.4 for windows

def articles_from_same_day_2(self): from django.db import connection with connection.cursor() as cursor: cursor.execute( , [connection.ops.adapt_datefield_value(self.pub_date), self.id], ) return [self.__class__(*row) for row in cursor.fetchall()]

tests/custom_methods/models.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

models.py

articles_from_same_day_2

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

Refs #33476 -- Reformatted code with Black.

def async_create_post_interval_update_cb(self) -> None: self._post_interval_update_cb_canceller = async_call_later( self.hass, get_unavailability_interval(self.ping_interval), self.async_post_interval_update, )

homeassistant/components/sia/sia_entity_base.py

core

Python

af4e37339a39badd5596e8bc9ba86d6c1994aa1b

sia_entity_base.py

async_create_post_interval_update_cb

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

Add Connectivity sensor to SIA (#64305) * implemented connectivity sensor * further cleanup off update code * cleanup and tighter behaviour for attributes * added seperate connectivity class to binary sensor * callbacks and keys * redid name and unique_id logic, non-breaking result * using entry more in inits * Fix import * fix ping_interval in sia_entity_base * added ping_interval default to next * fixed next Co-authored-by: Martin Hjelmare <[email protected]>

def test_rate_reached_perf_issue(self): snooze = GroupSnooze.objects.create(group=self.perf_group, count=10, window=24 * 60) for i in range(0, 10): self.store_transaction( environment=None, project_id=self.project.id, user_id=str(i), groups=[self.perf_group], ) assert not snooze.is_valid(test_rates=True)

tests/sentry/models/test_groupsnooze.py

sentry

Python

d745edbd591063f2c3241cd1960c361834058823

test_groupsnooze.py

test_rate_reached_perf_issue

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

ref(perf issues): Enable ignore in a time period (#39120) Enable ignoring a performance issue in a time period e.g. ignore this until it happens 10x / hr or ignore until 10 users experience it in an hour.

def test_lazy_load_get_prep_value(self): with self.assertNumQueries(1): instance = self.model.objects.get(pk=self.with_image.pk) # Expect a single UPDATE to update the model, without any additional # SELECT related to the image block that has not been accessed. with self.assertNumQueries(1): instance.save()

wagtail/tests/test_streamfield.py

wagtail

Python

dcae64c255f2fe97f658b1a3f438d3644b197661

test_streamfield.py

test_lazy_load_get_prep_value

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

Allow `StreamField` to use `JSONField` internal type via `use_json_field` kwarg Add system check for use_json_field in StreamField Change system check level to Warning Add use_json_field argument to StreamField in test models Use RemovedInWagtail219Warning instead of a system check Handle unpacked values in to_python when use_json_field is True Duplicate models and tests for JSONField-based StreamField Add basic tests for JSONField-based StreamField Add json_field property in StreamField to unify JSONField usage Add docs Don't use destructuring for kwargs in deconstruct Add versionchanged note to StreamField reference

def gen_quad_from_poly(self, poly): point_num = poly.shape[0] min_area_quad = np.zeros((4, 2), dtype=np.float32) rect = cv2.minAreaRect(poly.astype( np.int32)) # (center (x,y), (width, height), angle of rotation) box = np.array(cv2.boxPoints(rect)) first_point_idx = 0 min_dist = 1e4 for i in range(4): dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \ np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \ np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \ np.linalg.norm(box[(i + 3) % 4] - poly[-1]) if dist < min_dist: min_dist = dist first_point_idx = i for i in range(4): min_area_quad[i] = box[(first_point_idx + i) % 4] bbox_new = min_area_quad.tolist() bbox = [] for box in bbox_new: box = list(map(int, box)) bbox.append(box) return bbox

PPOCRLabel/PPOCRLabel.py

PaddleOCR

Python

3c6d551207f8eb5d0916404a7eca5e641887047d

PPOCRLabel.py

gen_quad_from_poly

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

Support multipoint labeling Support multipoint labeling

def all_pairs_node_connectivity(G, nbunch=None, cutoff=None): if nbunch is None: nbunch = G else: nbunch = set(nbunch) directed = G.is_directed() if directed: iter_func = itertools.permutations else: iter_func = itertools.combinations all_pairs = {n: {} for n in nbunch} for u, v in iter_func(nbunch, 2): k = local_node_connectivity(G, u, v, cutoff=cutoff) all_pairs[u][v] = k if not directed: all_pairs[v][u] = k return all_pairs

networkx/algorithms/approximation/connectivity.py

networkx

Python

eae1accf4a4eb8b767e82a45f9aca18785301670

connectivity.py

all_pairs_node_connectivity

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

Added an example in all_pairs_node_connectivity (#6126) * add missing reference in all_node_cuts flow_func parameter * added example to all_pairs_node_connectivity * Update networkx/algorithms/approximation/connectivity.py Added suggestion Co-authored-by: Ross Barnowski <[email protected]> * Update networkx/algorithms/approximation/connectivity.py added pprint Co-authored-by: Ross Barnowski <[email protected]> * Update networkx/algorithms/connectivity/kcutsets.py fix linking Co-authored-by: Ross Barnowski <[email protected]> * solved style problems Co-authored-by: Ross Barnowski <[email protected]>

def require_torch_multi_gpu(test_case): if not is_torch_available(): return unittest.skip("test requires PyTorch")(test_case) import torch return unittest.skipUnless(torch.cuda.device_count() > 1, "test requires multiple GPUs")(test_case)

src/transformers/testing_utils.py

transformers

Python

57e6464ac9a31156f1c93e59107323e6ec01309e

testing_utils.py

require_torch_multi_gpu

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

Update all require decorators to use skipUnless when possible (#16999)

def send_gstin_reminder(party_type, party): frappe.has_permission(party_type, throw=True) email = _send_gstin_reminder(party_type, party) if email: frappe.msgprint(_("Reminder to update GSTIN Sent"), title="Reminder sent", indicator="green")

erpnext/regional/doctype/gst_settings/gst_settings.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

gst_settings.py

send_gstin_reminder

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

style: format code with black

async def test_midnight_turnover_before_midnight_outside_period(hass): config = { "binary_sensor": [ {"platform": "tod", "name": "Night", "after": "22:00", "before": "5:00"} ] } await async_setup_component(hass, "binary_sensor", config) await hass.async_block_till_done() state = hass.states.get("binary_sensor.night") assert state.state == STATE_OFF @freeze_time("2019-01-10 10:00:00-08:00")

tests/components/tod/test_binary_sensor.py

@freeze_time("2019-01-10 10:00:00-08:00")

core

Python

23a630e0bcbd2aec6a598a19ebaf2929eba97e5b

test_binary_sensor.py

test_midnight_turnover_before_midnight_outside_period

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

Update Times of the Day tests to use freezegun (#68327)

def turn_off(self) -> None: if self.state != MediaPlayerState.OFF: self._device.send_key("POWER") self._attr_state = MediaPlayerState.OFF

homeassistant/components/panasonic_bluray/media_player.py

core

Python

9ecbcd2d8fedc4f0a59f231d1f221157d4cfa359

media_player.py

turn_off

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

Use _attr_state in panasonic bluray media player (#82834)

def test_personal_message(self) -> None: user_profile = self.example_user("hamlet") self.login_user(user_profile) othello = self.example_user("othello") result = self.client_post( "/json/messages", { "type": "private", "content": "Test message", "to": orjson.dumps([othello.email]).decode(), }, ) self.assert_json_success(result) message_id = orjson.loads(result.content)["id"] recent_conversations = get_recent_private_conversations(user_profile) self.assert_length(recent_conversations, 1) recent_conversation = list(recent_conversations.values())[0] recipient_id = list(recent_conversations.keys())[0] self.assertEqual(set(recent_conversation["user_ids"]), {othello.id}) self.assertEqual(recent_conversation["max_message_id"], message_id) # Now send a message to yourself and see how that interacts with the data structure result = self.client_post( "/json/messages", { "type": "private", "content": "Test message", "to": orjson.dumps([user_profile.email]).decode(), }, ) self.assert_json_success(result) self_message_id = orjson.loads(result.content)["id"] recent_conversations = get_recent_private_conversations(user_profile) self.assert_length(recent_conversations, 2) recent_conversation = recent_conversations[recipient_id] self.assertEqual(set(recent_conversation["user_ids"]), {othello.id}) self.assertEqual(recent_conversation["max_message_id"], message_id) # Now verify we have the appropriate self-pm data structure del recent_conversations[recipient_id] recent_conversation = list(recent_conversations.values())[0] recipient_id = list(recent_conversations.keys())[0] self.assertEqual(set(recent_conversation["user_ids"]), set()) self.assertEqual(recent_conversation["max_message_id"], self_message_id)

zerver/tests/test_message_send.py

zulip

Python

bd9a1dc9710293e36d2d47d970d7afb95100c2e6

test_message_send.py

test_personal_message

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

tests: Consistently JSON-encode ‘to’ parameter Although our POST /messages handler accepts the ‘to’ parameter with or without JSON encoding, there are two problems with passing it as an unencoded string. Firstly, you’d fail to send a message to a stream named ‘true’ or ‘false’ or ‘null’ or ‘2022’, as the JSON interpretation is prioritized over the plain string interpretation. Secondly, and more importantly for our tests, it violates our OpenAPI schema, which requires the parameter to be JSON-encoded. This is because OpenAPI has no concept of a parameter that’s “optionally JSON-encoded”, nor should it: such a parameter cannot be unambiguously decoded for the reason above. Our version of openapi-core doesn’t currently detect this schema violation, but after the next upgrade it will. Signed-off-by: Anders Kaseorg <[email protected]>

def external_ray_cluster_activity_hook4(): raise Exception("Error in external cluster activity hook")

python/ray/_private/test_utils.py

ray

Python

56716a1c1b6f9aae3967b910a799bb6af9f2c5d9

test_utils.py

external_ray_cluster_activity_hook4

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

[dashboard] Add `RAY_CLUSTER_ACTIVITY_HOOK` to `/api/component_activities` (#26297) Add external hook to /api/component_activities endpoint in dashboard snapshot router Change is_active field of RayActivityResponse to take an enum RayActivityStatus instead of bool. This is a backward incompatible change, but should be ok because [dashboard] Add component_activities API #25996 wasn't included in any branch cuts. RayActivityResponse now supports informing when there was an error getting the activity observation and the reason.

def get_item_warehouse_projected_qty(items_to_consider): item_warehouse_projected_qty = {} for item_code, warehouse, projected_qty in frappe.db.sql( .format( ", ".join(["%s"] * len(items_to_consider)) ), items_to_consider, ): if item_code not in item_warehouse_projected_qty: item_warehouse_projected_qty.setdefault(item_code, {}) if warehouse not in item_warehouse_projected_qty.get(item_code): item_warehouse_projected_qty[item_code][warehouse] = flt(projected_qty) warehouse_doc = frappe.get_doc("Warehouse", warehouse) while warehouse_doc.parent_warehouse: if not item_warehouse_projected_qty.get(item_code, {}).get(warehouse_doc.parent_warehouse): item_warehouse_projected_qty.setdefault(item_code, {})[warehouse_doc.parent_warehouse] = flt( projected_qty ) else: item_warehouse_projected_qty[item_code][warehouse_doc.parent_warehouse] += flt(projected_qty) warehouse_doc = frappe.get_doc("Warehouse", warehouse_doc.parent_warehouse) return item_warehouse_projected_qty

erpnext/stock/reorder_item.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

reorder_item.py

get_item_warehouse_projected_qty

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

style: format code with black

def affine_matrix(self) -> np.ndarray: assert self._affine_matrix is not None return self._affine_matrix

lib/align/detected_face.py

faceswap

Python

5e73437be47f2410439a3c6716de96354e6a0c94

detected_face.py

affine_matrix

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

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

def predict(self, X): if self.weights == "uniform": # In that case, we do not need the distances to perform # the weighting so we do not compute them. neigh_ind = self.kneighbors(X, return_distance=False) neigh_dist = None else: neigh_dist, neigh_ind = self.kneighbors(X) weights = _get_weights(neigh_dist, self.weights) _y = self._y if _y.ndim == 1: _y = _y.reshape((-1, 1)) if weights is None: y_pred = np.mean(_y[neigh_ind], axis=1) else: y_pred = np.empty((X.shape[0], _y.shape[1]), dtype=np.float64) denom = np.sum(weights, axis=1) for j in range(_y.shape[1]): num = np.sum(_y[neigh_ind, j] * weights, axis=1) y_pred[:, j] = num / denom if self._y.ndim == 1: y_pred = y_pred.ravel() return y_pred

sklearn/neighbors/_regression.py

scikit-learn

Python

fb082b223dc9f1dd327f48dc9b830ee382d6f661

_regression.py

predict

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

MAINT Do not compute distances for uniform weighting (#22280)

def handle_m2m_field(self, obj, field): raise NotImplementedError( "subclasses of Serializer must provide a handle_m2m_field() method" )

django/core/serializers/base.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

base.py

handle_m2m_field

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

Refs #33476 -- Reformatted code with Black.

def get_user_timezone() -> str: filename = os.path.join( os.path.dirname(os.path.abspath(__file__)), "timezone.openbb", ) if os.path.isfile(filename): with open(filename) as f: return f.read() return ""

openbb_terminal/helper_funcs.py

OpenBBTerminal

Python

b71abcfbf4d7e8ac1855522aff0378e13c8b5362

helper_funcs.py

get_user_timezone

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

Updating some names (#1575) * quick econ fix * black * keys and feature flags * terminal name :eyes: * some more replacements * some more replacements * edit pyproject * gst -> openbb * add example portfolios back to git * Update api from gst * sorry. skipping some tests * another round of names * another round of test edits * Missed some .gst refs and update timezone * water mark stuff * Fixing Names in terminal.spec and name of GTFF_DEFAULTS to OBBFF_DEFAULTS * fix more GST to OpenBB Terminal * Logging : merge conflicts with main * Revert wrong files Co-authored-by: Andrew <[email protected]> Co-authored-by: DidierRLopes <[email protected]> Co-authored-by: Chavithra PARANA <[email protected]>

def require_sqlalchemy(test_case): try: import sqlalchemy # noqa except ImportError: test_case = unittest.skip("test requires sqlalchemy")(test_case) return test_case

tests/utils.py

datasets

Python

d7dfbc83d68e87ba002c5eb2555f7a932e59038a

utils.py

require_sqlalchemy

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

Add ability to read-write to SQL databases. (#4928) * Add ability to read-write to SQL databases. * Fix issue where pandas<1.4.0 doesn't return the number of rows * Fix issue where connections were not closed properly * Apply suggestions from code review Co-authored-by: Quentin Lhoest <[email protected]> * Change according to reviews * Change according to reviews * Inherit from AbstractDatasetInputStream in SqlDatasetReader * Revert typing in SQLDatasetReader as we do not support Connexion * Align API with Pandas/Daskk * Update tests * Update docs * Update some more tests * Missing comma * Small docs fix * Style * Update src/datasets/arrow_dataset.py Co-authored-by: Quentin Lhoest <[email protected]> * Update src/datasets/packaged_modules/sql/sql.py Co-authored-by: Quentin Lhoest <[email protected]> * Address some comments * Address the rest * Improve tests * sqlalchemy required tip Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: mariosasko <[email protected]>

def make_tempfile(name): open(name, 'w', encoding='utf-8').close() try: yield finally: os.unlink(name)

IPython/testing/tools.py

ipython

Python

23276ac4770f380ce1d5808950dd412a35594af1

tools.py

make_tempfile

https://github.com/ipython/ipython.git

Fix EncodingWarning on Python 3.10

def findall(dir=os.curdir): files = _find_all_simple(dir) if dir == os.curdir: make_rel = functools.partial(os.path.relpath, start=dir) files = map(make_rel, files) return list(files)

python3.10.4/Lib/distutils/filelist.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

filelist.py

findall

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

add python 3.10.4 for windows

async def test_ip_ban_manager_never_started(hass, aiohttp_client, caplog): app = web.Application() app["hass"] = hass setup_bans(hass, app, 5) set_real_ip = mock_real_ip(app) with patch( "homeassistant.components.http.ban.load_yaml_config_file", side_effect=FileNotFoundError, ): client = await aiohttp_client(app) # Mock the manager never being started del app[KEY_BAN_MANAGER] set_real_ip("4.3.2.1") resp = await client.get("/") assert resp.status == HTTPStatus.NOT_FOUND assert "IP Ban middleware loaded but banned IPs not loaded" in caplog.text @pytest.mark.parametrize( "remote_addr, bans, status", list( zip( BANNED_IPS_WITH_SUPERVISOR, [1, 1, 0], [HTTPStatus.FORBIDDEN, HTTPStatus.FORBIDDEN, HTTPStatus.UNAUTHORIZED], ) ), )

tests/components/http/test_ban.py

@pytest.mark.parametrize( "remote_addr, bans, status", list( zip( BANNED_IPS_WITH_SUPERVISOR, [1, 1, 0], [HTTPStatus.FORBIDDEN, HTTPStatus.FORBIDDEN, HTTPStatus.UNAUTHORIZED], ) ), )

core

Python

0c29b68cf82c777ec6fd70ce38911f1d1e39d26a

test_ban.py

test_ip_ban_manager_never_started

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

Switch linear search to a dict lookup for ip bans (#74482)

def remove_field(self, model, field): # Special-case implicit M2M tables if field.many_to_many and field.remote_field.through._meta.auto_created: return self.delete_model(field.remote_field.through) # It might not actually have a column behind it if field.db_parameters(connection=self.connection)["type"] is None: return # Drop any FK constraints, MySQL requires explicit deletion if field.remote_field: fk_names = self._constraint_names(model, [field.column], foreign_key=True) for fk_name in fk_names: self.execute(self._delete_fk_sql(model, fk_name)) # Delete the column sql = self.sql_delete_column % { "table": self.quote_name(model._meta.db_table), "column": self.quote_name(field.column), } self.execute(sql) # Reset connection if required if self.connection.features.connection_persists_old_columns: self.connection.close() # Remove all deferred statements referencing the deleted column. for sql in list(self.deferred_sql): if isinstance(sql, Statement) and sql.references_column( model._meta.db_table, field.column ): self.deferred_sql.remove(sql)

django/db/backends/base/schema.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

schema.py

remove_field

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

Refs #33476 -- Reformatted code with Black.

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

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

salt

Python

3c7e1ec1f08abd7cd1ba78ad7880acb6ba6fdce7

test_req_channel.py

test_basic

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

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

def test_upgrade_allow_downgrades(uninstall_var, upgrade_var): with patch("salt.utils.pkg.clear_rtag", MagicMock()): with patch( "salt.modules.aptpkg.list_pkgs", MagicMock(return_value=uninstall_var) ): mock_cmd = MagicMock(return_value={"retcode": 0, "stdout": upgrade_var}) patch_kwargs = { "__salt__": { "config.get": MagicMock(return_value=True), "cmd.run_all": mock_cmd, }, } with patch.multiple(aptpkg, **patch_kwargs): aptpkg.upgrade() args_matching = [ True for args in patch_kwargs["__salt__"]["cmd.run_all"].call_args[0] if "--allow-downgrades" in args ] # Here we shouldn't see the parameter and args_matching should be empty. assert any(args_matching) is False aptpkg.upgrade(allow_downgrades=True) args_matching = [ True for args in patch_kwargs["__salt__"]["cmd.run_all"].call_args[0] if "--allow-downgrades" in args ] # --allow-downgrades should be in the args list and we should have at least on True in the list. assert any(args_matching) is True

tests/pytests/unit/modules/test_aptpkg.py

salt

Python

9e1ca8b5b9e7006fea28f473711917755cf5a262

test_aptpkg.py

test_upgrade_allow_downgrades

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

Add --allow-downgrades capability for apt upgrade

def srs(self): # TODO: Is caching really necessary here? Is complexity worth it? if hasattr(self, "_srs"): # Returning a clone of the cached SpatialReference object. return self._srs.clone() else: # Attempting to cache a SpatialReference object. # Trying to get from WKT first. try: self._srs = gdal.SpatialReference(self.wkt) return self.srs except Exception as e: msg = e try: self._srs = gdal.SpatialReference(self.proj4text) return self.srs except Exception as e: msg = e raise Exception( "Could not get OSR SpatialReference from WKT: %s\nError:\n%s" % (self.wkt, msg) )

django/contrib/gis/db/backends/base/models.py

django

Python

9c19aff7c7561e3a82978a272ecdaad40dda5c00

models.py

srs

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

Refs #33476 -- Reformatted code with Black.

def set_package(fxn):

python3.10.4/Lib/importlib/util.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

util.py

set_package

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

add python 3.10.4 for windows

def get_subsidiary_companies(company): lft, rgt = frappe.get_cached_value("Company", company, ["lft", "rgt"]) return frappe.db.sql_list( .format( lft, rgt ) )

erpnext/accounts/report/consolidated_financial_statement/consolidated_financial_statement.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

consolidated_financial_statement.py

get_subsidiary_companies

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

style: format code with black

def from_dict(cls, other, name=None):

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

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

pyparsing.py

from_dict

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

upd; format

def create_view(self, request): kwargs = {"model_admin": self} view_class = self.create_view_class return view_class.as_view(**kwargs)(request)

wagtail/contrib/modeladmin/options.py

wagtail

Python

d10f15e55806c6944827d801cd9c2d53f5da4186

options.py

create_view

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

Reformat with black

def get_viewname(model, action=None): viewname = f'{model._meta.app_label}:{model._meta.model_name}' # Determine whether this is a plugin view and adjust the namespace appropriately if isinstance(model._meta.app_config, PluginConfig): viewname = f'plugins:{viewname}' # Append the action, if any if action: viewname = f'{viewname}_{action}' return viewname

netbox/utilities/utils.py

netbox

Python

10e6ae20949028171fdfcc50fde78bf289f41d5f

utils.py

get_viewname

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

Introduce get_viewname() as a standard utility

def plus(self, a): a = _convert_other(a, raiseit=True) return a.__pos__(context=self)

python3.10.4/Lib/_pydecimal.py

XX-Net

Python

8198943edd73a363c266633e1aa5b2a9e9c9f526

_pydecimal.py

plus

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

add python 3.10.4 for windows

def get_tables(self) -> Response: q = f"SHOW TABLES FROM {self.connection_data['database']}" result = self.native_query(q) df = result.data_frame result.data_frame = df.rename(columns={df.columns[0]: 'table_name'}) return result

mindsdb/integrations/handlers/clickhouse_handler/clickhouse_handler.py

mindsdb

Python

149ae900c62910a480f8af70daa98362d513a350

clickhouse_handler.py

get_tables

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

CH handler implementation

def uses_before_args(self) -> Namespace: return self.pod_args['uses_before']

jina/orchestrate/deployments/__init__.py

jina

Python

13edc16d806fb5d77a6849551178ccc75937f25f

__init__.py

uses_before_args

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

refactor: rename pod to deployment (#4230) * refactor: rename pod to deployment * style: fix overload and cli autocomplete * fix: undo daemon mistake * refactor: leftover cleanup * fix: more test fixes * fix: more fixes * fix: more fixes * fix: more fixes * fix: more tests * fix: fix more tests * refactor: fix more tests * refactor: more tests fixes * refactor: rename pea to pod * refactor: adjust docs * refactor: complete pea renaming * refactor: more fixes * fix: pea_type in k8s yamls * fix: adjust pod args name * refactor: rename peapods parser folder * fix: da init Co-authored-by: Jina Dev Bot <[email protected]>

def user_config_dir(appname=None, appauthor=None, version=None, roaming=False): r if system in ["win32", "darwin"]: path = user_data_dir(appname, appauthor, None, roaming) else: path = os.getenv('XDG_CONFIG_HOME', os.path.expanduser("~/.config")) if appname: path = os.path.join(path, appname) if appname and version: path = os.path.join(path, version) return path # for the discussion regarding site_config_dir locations # see <https://github.com/pypa/pip/issues/1733>

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

transferlearning

Python

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

appdirs.py

user_config_dir

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

upd; format

def test_page_token_expired_retry_fails(mock_ads_client, test_config): stream_slice = {"start_date": "2021-01-01", "end_date": "2021-01-15"} google_api = MockGoogleAdsFails(credentials=test_config["credentials"], customer_id=test_config["customer_id"]) incremental_stream_config = dict( api=google_api, conversion_window_days=test_config["conversion_window_days"], start_date=test_config["start_date"], time_zone="local", end_date="2021-04-04", ) stream = ClickView(**incremental_stream_config) stream.get_query = Mock() stream.get_query.return_value = "query" with pytest.raises(GoogleAdsException): list(stream.read_records(sync_mode=SyncMode.incremental, cursor_field=["segments.date"], stream_slice=stream_slice)) stream.get_query.assert_called_with({"start_date": "2021-01-03", "end_date": "2021-01-15"}) assert stream.get_query.call_count == 2

airbyte-integrations/connectors/source-google-ads/unit_tests/test_streams.py

airbyte

Python

359fcd801128239b39297828d39821f631ce00c0

test_streams.py

test_page_token_expired_retry_fails

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

Source Google Ads: handle page token expired exception (#9812) * dynamic date range * raise exception if exites the cycle without error * if range days is 1 already do not retry * added unit tests * added comments * added comments * common mock classes are moved to common module * change read_records * refactored get_date_params * handle corner case * added parse_dates function * added test_streams * check mock calls * fix unit tests for chunk date range refactoring * removed commented codes * remove commented line * refactor test_streams * refactor CustomQuery.get_query * remove TODO * deleted unused json * format * fix chunk_date_range * added docstring * set range_days to 15 for ShoppingPerformanceReport * refactor chunk_date_range * format code 2 * call parent read_records method * add return type in get_date_params * change e to exception * set start_date as end_date * log page token has expired * bump version * updated spec and def yaml Co-authored-by: auganbay <[email protected]>

def get_favorite_articles_by_page_view(): return frappe.db.sql( , as_dict=True, )

erpnext/www/support/index.py

erpnext

Python

494bd9ef78313436f0424b918f200dab8fc7c20b

index.py

get_favorite_articles_by_page_view

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

style: format code with black

def test_previous_pods_ignored_for_reattached(self): k = KubernetesPodOperator( namespace="default", image="ubuntu:16.04", name="test", task_id="task", ) self.run_pod(k) k.client.list_namespaced_pod.assert_called_once() _, kwargs = k.client.list_namespaced_pod.call_args assert 'already_checked!=True' in kwargs['label_selector']

tests/providers/cncf/kubernetes/operators/test_kubernetes_pod.py

airflow

Python

60eb9e106f5915398eafd6aa339ec710c102dc09

test_kubernetes_pod.py

test_previous_pods_ignored_for_reattached

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

Use KubernetesHook to create api client in KubernetesPodOperator (#20578) Add support for k8s hook in KPO; use it always (even when no conn id); continue to consider the core k8s settings that KPO already takes into account but emit deprecation warning about them. KPO historically takes into account a few settings from core airflow cfg (e.g. verify ssl, tcp keepalive, context, config file, and in_cluster). So to use the hook to generate the client, somehow the hook has to take these settings into account. But we don't want the hook to consider these settings in general. So we read them in KPO and if necessary patch the hook and warn.

def tight_layout(self, *, pad=1.08, h_pad=None, w_pad=None, rect=None): from contextlib import nullcontext from ._tight_layout import ( get_subplotspec_list, get_tight_layout_figure) subplotspec_list = get_subplotspec_list(self.axes) if None in subplotspec_list: _api.warn_external("This figure includes Axes that are not " "compatible with tight_layout, so results " "might be incorrect.") renderer = _get_renderer(self) with getattr(renderer, "_draw_disabled", nullcontext)(): kwargs = get_tight_layout_figure( self, self.axes, subplotspec_list, renderer, pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect) if kwargs: self.subplots_adjust(**kwargs)

lib/matplotlib/figure.py

matplotlib

Python

2d8bd625813e4c513bbe8bedddca45da368bca9b

figure.py

tight_layout

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

Recreated deprecated files and changed references

def test_load_files_allowed_extensions(tmp_path, allowed_extensions): d = tmp_path / "sub" d.mkdir() files = ("file1.txt", "file2.json", "file3.json", "file4.md") paths = [d / f for f in files] for p in paths: p.touch() res = load_files(tmp_path, allowed_extensions=allowed_extensions) assert set([str(p) for p in paths if p.suffix in allowed_extensions]) == set( res.filenames ) @pytest.mark.parametrize( "filename, expected_n_samples, expected_n_features, expected_target_names", [ ("wine_data.csv", 178, 13, ["class_0", "class_1", "class_2"]), ("iris.csv", 150, 4, ["setosa", "versicolor", "virginica"]), ("breast_cancer.csv", 569, 30, ["malignant", "benign"]), ], )

sklearn/datasets/tests/test_base.py

@pytest.mark.parametrize( "filename, expected_n_samples, expected_n_features, expected_target_names", [ ("wine_data.csv", 178, 13, ["class_0", "class_1", "class_2"]), ("iris.csv", 150, 4, ["setosa", "versicolor", "virginica"]), ("breast_cancer.csv", 569, 30, ["malignant", "benign"]), ], )

scikit-learn

Python

fc72ebe61c833f227560bd9d0dcf88cdda6c6adb

test_base.py

test_load_files_allowed_extensions

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

ENH Adds file extension selection to load_files (#22498) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Tony <[email protected]> Co-authored-by: Kazim <[email protected]> Co-authored-by: Tony Attalla <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]>

def test_rolling_non_monotonic(method, expected): # Based on an example found in computation.rst use_expanding = [True, False, True, False, True, True, True, True] df = DataFrame({"values": np.arange(len(use_expanding)) ** 2})

pandas/tests/window/test_rolling.py

pandas

Python

6caefb19f4d7c05451fafca182c6eb39fe9901ed

test_rolling.py

test_rolling_non_monotonic

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

ENH: Rolling window with step size (GH-15354) (#45765)

def test_remember_collapsed(self): # Sidebar should not be collapsed self.client.cookies["wagtail_sidebar_collapsed"] = "0" response = self.client.get(reverse("wagtailadmin_home")) self.assertNotContains(response, "sidebar-collapsed") # Sidebar should be collapsed self.client.cookies["wagtail_sidebar_collapsed"] = "1" response = self.client.get(reverse("wagtailadmin_home")) self.assertContains(response, "sidebar-collapsed")

wagtail/admin/tests/test_menu.py

wagtail

Python

d10f15e55806c6944827d801cd9c2d53f5da4186

test_menu.py

test_remember_collapsed

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

Reformat with black

def _get_index_and_columns(df): return len(df.index), len(df.columns) @ray.remote(num_returns=4)

modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py

@ray.remote(num_returns=4)

modin

Python