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semeru

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

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Semeru Lab 7

@serializable @register

ppdet/modeling/losses/probiou_loss.py

def probiou_loss(pred, target, eps=1e-3, mode='l1'): gbboxes1 = gbb_form(pred) gbboxes2 = gbb_form(target) x1, y1, a1_, b1_, c1_ = gbboxes1[:,

add fcosr model (#6765) * add fcosr * fix some problem * add docs for fcosr * modify code * modify focsr reader * finish tensorrt deployment with dynamic shape * modify according to review comment Co-authored-by: wangxinxin08 <>

probiou_loss

92078713cced4f0d9450a6fc80a449fa75fd8c10

PaddleDetection

probiou_loss.py

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

Python

def probiou_loss(pred, target, eps=1e-3, mode='l1'): gbboxes1 = gbb_form(pred) gbboxes2 = gbb_form(target) x1, y1, a1_, b1_, c1_ = gbboxes1[:, 0], gbboxes1[:, 1], gbboxes1[:, 2], gbboxes1[:, 3], gbboxes1[:, 4] x2, y2, a2_, b2_, c2_ = gbboxes2[:, 0], gbboxes2[:, 1], gbboxes2[:, 2], gbboxes2[:, 3], gbboxes2[:, 4] a1, b1, c1 = rotated_form(a1_, b1_, c1_) a2, b2, c2 = rotated_form(a2_, b2_, c2_) t1 = 0.25 * ((a1 + a2) * (paddle.pow(y1 - y2, 2)) + (b1 + b2) * (paddle.pow(x1 - x2, 2))) + \ 0.5 * ((c1+c2)*(x2-x1)*(y1-y2)) t2 = (a1 + a2) * (b1 + b2) - paddle.pow(c1 + c2, 2) t3_ = (a1 * b1 - c1 * c1) * (a2 * b2 - c2 * c2) t3 = 0.5 * paddle.log(t2 / (4 * paddle.sqrt(F.relu(t3_)) + eps)) B_d = (t1 / t2) + t3 # B_d = t1 + t2 + t3 B_d = paddle.clip(B_d, min=eps, max=100.0) l1 = paddle.sqrt(1.0 - paddle.exp(-B_d) + eps) l_i = paddle.pow(l1, 2.0) l2 = -paddle.log(1.0 - l_i + eps) if mode == 'l1': probiou = l1 if mode == 'l2': probiou = l2 return probiou @serializable @register

nuitka/Options.py

def mayDisableConsoleWindow(): # TODO: What about

Standalone: Added support for requiring modes * For wx on macOS, console must be disabled, avoid the trap. * For the PySide2, on macOS the --onefile must be used when the application bundle is built or else signing has issues. * Recommend to use new option --disable-console for PySide2, PySide6 and wx on non-macOS

mayDisableConsoleWindow

613c31d98f20bdd9a4e5884c99826a06a3328438

Nuitka

Options.py

https://github.com/Nuitka/Nuitka.git

Python

def mayDisableConsoleWindow(): # TODO: What about MSYS2? return isWin32Windows() or isMacOS()

mmdet/utils/compat_config.py

def compat_cfg(cfg): cfg = copy.deepcopy(cfg) cfg = compat_imgs_per_gpu(cfg) cfg = compat_loader_args(cfg) cfg = compat_runner_args(cfg) return cf

[Feature] Support set dataloader args in config and and add function to handle config compatibility (#7668) * add cfg_compatibility and support loader args * resolve comments * add unitest * resolve comments * delete all warning

compat_cfg

dc14675f79681b88ce2c5a3ca3c69901b415ffe4

mmdetection

compat_config.py

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

Python

def compat_cfg(cfg): cfg = copy.deepcopy(cfg) cfg = compat_imgs_per_gpu(cfg) cfg = compat_loader_args(cfg) cfg = compat_runner_args(cfg) return cfg

python3.10.4/Lib/inspect.py

def getsourcelines(object): object = unwrap(object) lines, lnum = findsource(object) if istraceback(object): object = object.tb_frame # for module or frame that corresponds to module, return all source lines if (ismodule(object) or

add python 3.10.4 for windows

getsourcelines

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

inspect.py

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

Python

def getsourcelines(object): object = unwrap(object) lines, lnum = findsource(object) if istraceback(object): object = object.tb_frame # for module or frame that corresponds to module, return all source lines if (ismodule(object) or (isframe(object) and object.f_code.co_name == "<module>")): return lines, 0 else: return getblock(lines[lnum:]), lnum + 1

django/db/models/deletion.py

def get_del_batches(self, objs, fields):

Refs #33476 -- Reformatted code with Black.

get_del_batches

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

deletion.py

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

Python

def get_del_batches(self, objs, fields): field_names = [field.name for field in fields] conn_batch_size = max( connections[self.using].ops.bulk_batch_size(field_names, objs), 1 ) if len(objs) > conn_batch_size: return [ objs[i : i + conn_batch_size] for i in range(0, len(objs), conn_batch_size) ] else: return [objs]

ludwig/utils/checkpoint_utils.py

def save(self, global_step): save_path = osp.join(s

Add file lock on training checkpoints to prevent race condition (#1938)

save

44356d2d07370b7044640a068ace95842d5ce98c

ludwig

checkpoint_utils.py

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

Python

def save(self, global_step): save_path = osp.join(self.directory, f"{global_step:09d}.ckpt") self.checkpoint.save(save_path) self.latest_checkpoint = save_path self.queue.put(True)

versioneer.py

def versions_from_file(filename): try: with open(filename) as f: contents = f.read() except OSError: raise NotThisMethod("unable to read _version.py") mo = re.search(r"version_json = # END

add auto tag

versions_from_file

f0194812568c83585ff09488fe7f67df300938cc

rembg

versioneer.py

https://github.com/danielgatis/rembg.git

Python

def versions_from_file(filename): try: with open(filename) as f: contents = f.read() except OSError: raise NotThisMethod("unable to read _version.py") mo = re.search(r"version_json = # END VERSION_JSON", contents, re.M | re.S) if not mo: mo = re.search(r"version_json = # END VERSION_JSON", contents, re.M | re.S) if not mo: raise NotThisMethod("no version_json in _version.py") return json.loads(mo.group(1))

erpnext/accounts/doctype/bank_transaction/bank_transaction.py

def get_total_allocated_amount(payment_entry): return frappe.db.sql( , (payment_entry.p

style: format code with black

get_total_allocated_amount

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

bank_transaction.py

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

Python

def get_total_allocated_amount(payment_entry): return frappe.db.sql( , (payment_entry.payment_document, payment_entry.payment_entry), as_dict=True, )

@not_implemented_for("multigraph") @not_implemented_for("directed")

networkx/algorithms/matching.py

def min_weight_matching(G, maxcardinality=False, weight="weight"): if len(G.edges) == 0: return max_weight_matching(G, maxcardinality, weight) G_edges = G.edges(data=weight, default=1) min_weight = min(w for _, _, w in G_edges) InvG = nx.Graph() edges = ((u, v, 1 / (1 + w - min_weight)) for u, v, w in G_edges) InvG.add_weighted_edges_from(e

Update matching functions for error validation and speed (#4897) * First steps to update matching functions for #4644 Expand tests Change API to raise NetworkXError when matching involves nodes not in G Update is_*_matching to 100+ times faster. * improve matching_dict_to_set and docs for min_weight_matching * fix sphinx error

min_weight_matching

28b3014d68d2b4e40d3e02219770296a827bd55c

networkx

matching.py

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

Python

def min_weight_matching(G, maxcardinality=False, weight="weight"): if len(G.edges) == 0: return max_weight_matching(G, maxcardinality, weight) G_edges = G.edges(data=weight, default=1) min_weight = min(w for _, _, w in G_edges) InvG = nx.Graph() edges = ((u, v, 1 / (1 + w - min_weight)) for u, v, w in G_edges) InvG.add_weighted_edges_from(edges, weight=weight) return max_weight_matching(InvG, maxcardinality, weight) @not_implemented_for("multigraph") @not_implemented_for("directed")

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

def site_config_dir(self) -> str: return self._append_app_name_and_version("/Libr

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

site_config_dir

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

macos.py

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

Python

def site_config_dir(self) -> str: return self._append_app_name_and_version("/Library/Preferences")

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

def _cmp_op(self, other, op_name): lhs_dtype_class = self._get_dtype_cmp_class(self._dtype) rhs_dtype_class = self._get_dtype_cmp_class(other._dtype) res_dtype = get_dtype(bool) # In HDK comparison with NULL always results in NULL, # but in pandas it is True for 'ne' comparison and False # for others. # Also pandas allows 'eq' and 'ne' comparison for value

FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <Poolliver868@mail.ru> Signed-off-by: Andrey Pavlenko <andrey.a.pavlenko@gmail.com>

_cmp_op

e5b1888cd932909e49194d58035da34b210b91c4

modin

expr.py

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

Python

def _cmp_op(self, other, op_name): lhs_dtype_class = self._get_dtype_cmp_class(self._dtype) rhs_dtype_class = self._get_dtype_cmp_class(other._dtype) res_dtype = get_dtype(bool) # In HDK comparison with NULL always results in NULL, # but in pandas it is True for 'ne' comparison and False # for others. # Also pandas allows 'eq' and 'ne' comparison for values # of incompatible types which doesn't work in HDK. if lhs_dtype_class != rhs_dtype_class: if op_name == "eq" or op_name == "ne": return LiteralExpr(op_name == "ne") else: raise TypeError( f"Invalid comparison between {self._dtype} and {other._dtype}" ) else: cmp = OpExpr(self.binary_operations[op_name], [self, other], res_dtype) return build_if_then_else( self.is_null(), LiteralExpr(op_name == "ne"), cmp, res_dtype )

lib/matplotlib/widgets.py

def set_active(self, index): if index not in range(len(self.labels)): raise ValueError(f'Invalid CheckButton index: {index}') if colors.same_color( self._

Use scatter for check boxes instead of Rectangle With the current implementation, the boxes get stretched into rectangles if the aspect ratio is not maintained. To overcome this, the boxes are now created using scatter instead to maintain their shapes.

set_active

723cd86d7d7bdc14a4d3fc0e08c3a01e72d310b6

matplotlib

widgets.py

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

Python

def set_active(self, index): if index not in range(len(self.labels)): raise ValueError(f'Invalid CheckButton index: {index}') if colors.same_color( self._crosses.get_facecolor()[index], colors.to_rgba("none") ): self._crosses.get_facecolor()[index] = colors.to_rgba("k") else: self._crosses.get_facecolor()[index] = colors.to_rgba("none") if hasattr(self, "_rectangles"): for i, p in enumerate(self._rectangles): p.set_facecolor("k" if colors.same_color( p.get_facecolor(), colors.to_rgba("none")) else "none") if self.drawon: self.ax.figure.canvas.draw() if self.eventson: self._observers.process('clicked', self.labels[index].get_text())

homeassistant/components/ebox/sensor.py

async def async_update(self) -> None: await s

Improve entity type hints [e] (#77041)

async_update

3a3f41f3df932368791d3ee3f5fbae5fb3b38bfe

core

sensor.py

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

Python

async def async_update(self) -> None: await self.ebox_data.async_update() if self.entity_description.key in self.ebox_data.data: self._attr_native_value = round( self.ebox_data.data[self.entity_description.key], 2 )

tools/analysis_tools/analyze_results.py

def panoptic_evaluate(self, dataset, results, topk=20): # image to annotations gt_json = dataset.coco.img_ann_map result_files, tmp_dir = dataset.format_results(results) pred_json = mmcv.load(result_files['panoptic'])['annotations'] pred_folder = osp.join(tmp_dir.name, 'panoptic') gt_folder = dataset.seg_prefix pqs = {} prog_bar = mmcv.ProgressBar(len(results)) for i in range(len(results)): data_info = dataset.prepare_train_img(i) image_id = data_info['img_info']['id'] gt_ann = { 'image_id': image_id, 'segments_info': gt_json[image_id], 'file_name': data_info['img_info']['segm_file'] } pred_ann = pred_json[i] pq_stat = pq_compute_single_core( i, [(gt_ann, pred_ann)], gt_folder,

[Feature] Support panoptic segmentation result analysis (#7922) * support analyze panoptic segmentation result * fix lint * update docstring * update docstring * set print_log=False by default * update * fix bug 8035

panoptic_evaluate

f3a451abab8fc89810b317ca0a88ee9fd12cb0c2

mmdetection

analyze_results.py

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

Python

def panoptic_evaluate(self, dataset, results, topk=20): # image to annotations gt_json = dataset.coco.img_ann_map result_files, tmp_dir = dataset.format_results(results) pred_json = mmcv.load(result_files['panoptic'])['annotations'] pred_folder = osp.join(tmp_dir.name, 'panoptic') gt_folder = dataset.seg_prefix pqs = {} prog_bar = mmcv.ProgressBar(len(results)) for i in range(len(results)): data_info = dataset.prepare_train_img(i) image_id = data_info['img_info']['id'] gt_ann = { 'image_id': image_id, 'segments_info': gt_json[image_id], 'file_name': data_info['img_info']['segm_file'] } pred_ann = pred_json[i] pq_stat = pq_compute_single_core( i, [(gt_ann, pred_ann)], gt_folder, pred_folder, dataset.categories, dataset.file_client, print_log=False) pq_results, classwise_results = pq_stat.pq_average( dataset.categories, isthing=None) pqs[i] = pq_results['pq'] prog_bar.update() if tmp_dir is not None: tmp_dir.cleanup() # descending select topk image pqs = list(sorted(pqs.items(), key=lambda kv: kv[1])) good_pqs = pqs[-topk:] bad_pqs = pqs[:topk] return good_pqs, bad_pqs

sklearn/feature_selection/_mutual_info.py

def _compute_mi_cd(c, d, n_neighbors): n_samples = c.shape[0] c = c.reshape((-1, 1)) radius = np.empty(n_samples) label_counts = np.empty(n_samples) k_all = np.empty(n_samples) nn = NearestNeighbors() for label in np.unique(d): mask = d == label count = np.sum(mask) if count > 1: k = min(n_neighbors, count - 1) nn.set_params(n_neighbors=k) nn.fit(c[mask]) r = nn.kneighbors()[0] radius[mask] = np.nextafter(r[:, -1], 0) k_all[mask] = k label_counts[mask] = count # Ignore points with un

CLN Remove unnecessary operation in mutual_info (#24569)

_compute_mi_cd

c22be1defcf3e59ebd79ed3e479ada8ea558f601

scikit-learn

_mutual_info.py

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

Python

def _compute_mi_cd(c, d, n_neighbors): n_samples = c.shape[0] c = c.reshape((-1, 1)) radius = np.empty(n_samples) label_counts = np.empty(n_samples) k_all = np.empty(n_samples) nn = NearestNeighbors() for label in np.unique(d): mask = d == label count = np.sum(mask) if count > 1: k = min(n_neighbors, count - 1) nn.set_params(n_neighbors=k) nn.fit(c[mask]) r = nn.kneighbors()[0] radius[mask] = np.nextafter(r[:, -1], 0) k_all[mask] = k label_counts[mask] = count # Ignore points with unique labels. mask = label_counts > 1 n_samples = np.sum(mask) label_counts = label_counts[mask] k_all = k_all[mask] c = c[mask] radius = radius[mask] kd = KDTree(c) m_all = kd.query_radius(c, radius, count_only=True, return_distance=False) m_all = np.array(m_all) mi = ( digamma(n_samples) + np.mean(digamma(k_all)) - np.mean(digamma(label_counts)) - np.mean(digamma(m_all)) ) return max(0, mi)

django/core/management/base.py

def handle_app_config(self, app_config, **options): raise Not

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

handle_app_config

c5cd8783825b5f6384417dac5f3889b4210b7d08

django

base.py

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

Python

def handle_app_config(self, app_config, **options): raise NotImplementedError( "Subclasses of AppCommand must provide a handle_app_config() method." )

@pytest.mark.parametrize( "max_features", [lambda X: 1, lambda X: X.shape[1], lambda X: min(X.shape[1], 10000)], )

sklearn/feature_selection/tests/test_from_model.py

def test_inferred_max_features_integer(max_features): clf = RandomForestClassifier(n_estimators=5, random_state=0) transformer = SelectFromModel( estimator=clf, max_features=max_features, threshold=-np.inf ) X_trans = transformer.fit_transform(data, y) assert transformer.max_features_ == max_features assert X_trans.shape[1] == transformer.max_features_ @pytest.mark.paramet

ENH Allow `SelectFromModel`'s `max_features` to accept callables (#22356) * Initial implementation * Improved error handling and stability * Added unit tests * Updated test to use `max_features_` instead of `max_features` * Added documentation for new private attribute `max_features_` * Improved error handling for callables * Updated whats_new * Removed incorrect term reference to `max_features` * Removed float case and improved testing * Updated test names to more clearly reflect intention * Added a sample callable in `max_features` description * Improved documentation and streamlined error handling * Updated example to include demonstrate using a callable for max_features * Separated out callable demo into separate example * Removed demo from `max_features` docs (now in example) * Updated changelog * Apply suggestions from code review Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * Trimmed unneeded comments * Updated tests to reflect new error handling * Removed new line at end of docstring * Updated docstring * Fixed example syntax error * Fixed example syntax * Apply suggestions from code review Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * Reverted irrelevant changes * Update sklearn/feature_selection/_from_model.py Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * Fixed error message * Improved test coverage * Minor doc improvement -- added a list for `max_features` type * Update sklearn/feature_selection/_from_model.py Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> * Improved input validation and added test for array-like * Updated doc to use no longer use lambda function * Fixed docstring list * Added missing whitespace for list format in docstring Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>

test_inferred_max_features_integer

db24a30bd3b90a9d55e82e450631de96305744f7

scikit-learn

test_from_model.py

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

Python

def test_inferred_max_features_integer(max_features): clf = RandomForestClassifier(n_estimators=5, random_state=0) transformer = SelectFromModel( estimator=clf, max_features=max_features, threshold=-np.inf ) X_trans = transformer.fit_transform(data, y) assert transformer.max_features_ == max_features assert X_trans.shape[1] == transformer.max_features_ @pytest.mark.parametrize( "max_features", [lambda X: 1, lambda X: X.shape[1], lambda X: min(X.shape[1], 10000)], )

networkx/algorithms/tests/test_lowest_common_ancestors.py

def test_naive_all_pairs_lowest_common_ancestor3(self): all_pairs = product(self.DG.nodes(), self.DG.nodes()) ans = naive_all_pairs_lca(self.DG, pairs=all_pairs) self.assert_lca_dicts_same(dict(ans), self.gold)

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

test_naive_all_pairs_lowest_common_ancestor3

b2f91c34a23058dd70b41784af0d87890216026a

networkx

test_lowest_common_ancestors.py

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

Python

def test_naive_all_pairs_lowest_common_ancestor3(self): all_pairs = product(self.DG.nodes(), self.DG.nodes()) ans = naive_all_pairs_lca(self.DG, pairs=all_pairs) self.assert_lca_dicts_same(dict(ans), self.gold)

django/db/migrations/autodetector.py

def _resolve_dependency(dependency): if dependency[0] != "__setting__": return dependen

Refs #33476 -- Reformatted code with Black.

_resolve_dependency

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

autodetector.py

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

Python

def _resolve_dependency(dependency): if dependency[0] != "__setting__": return dependency, False resolved_app_label, resolved_object_name = getattr( settings, dependency[1] ).split(".") return (resolved_app_label, resolved_object_name.lower()) + dependency[2:], True

@not_implemented_for("undirected") @not_implemented_for("multigraph")

networkx/algorithms/lowest_common_ancestors.py

def lowest_common_ancestor(G, node1, node2, default=None): ans = list(all_pairs_lowest_common_ancestor(G, pairs=[(node1, node2)])) if ans: assert len(ans) == 1 return ans[0][1] else: return default @not_implemented_for("undirected") @not_implemented_for("multigraph")

Add examples to lowest common ancestors algorithms (#5531) * Add examples to lowest common ancestors documentation * Fix output style of examples * Fix output style of example * Update pre-commit * Update networkx/algorithms/lowest_common_ancestors.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Update networkx/algorithms/lowest_common_ancestors.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Indentation fix & pprint dictionary * Update networkx/algorithms/lowest_common_ancestors.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Update networkx/algorithms/lowest_common_ancestors.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Update networkx/algorithms/lowest_common_ancestors.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Move "import pprint" to the example Co-authored-by: dtuncturk <dilaramemis@sabanciuniv.edu> Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

lowest_common_ancestor

abaa68779ccb4cce8d1a5ecade622ab96d01edeb

networkx

lowest_common_ancestors.py

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

Python

def lowest_common_ancestor(G, node1, node2, default=None): ans = list(all_pairs_lowest_common_ancestor(G, pairs=[(node1, node2)])) if ans: assert len(ans) == 1 return ans[0][1] else: return default @not_implemented_for("undirected") @not_implemented_for("multigraph")

erpnext/education/report/program_wise_fee_collection/program_wise_fee_collection.py

def get_data(filters=None): data = [] conditions = get_filter_conditions(filters) fee_details = frappe.db.sql( % (conditions), as_dict=1, ) for entry in fee_details: data.append( { "program": entry.program, "fees_collected": entry.paid_amount, "outstanding_amount": entry.outstanding_amount, "grand_total": entry.grand_total, } ) return data

style: format code with black

get_data

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

program_wise_fee_collection.py

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

Python

def get_data(filters=None): data = [] conditions = get_filter_conditions(filters) fee_details = frappe.db.sql( % (conditions), as_dict=1, ) for entry in fee_details: data.append( { "program": entry.program, "fees_collected": entry.paid_amount, "outstanding_amount": entry.outstanding_amount, "grand_total": entry.grand_total, } ) return data

pipenv/vendor/requirementslib/models/dependencies.py

def get_dependencies_from_json(ireq): if ireq.editable or not is_pinned_requirement(ireq): return # It is technically possible to parse extras out of the JSON API's

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

get_dependencies_from_json

cd5a9683be69c86c8f3adcd13385a9bc5db198ec

pipenv

dependencies.py

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

Python

def get_dependencies_from_json(ireq): if ireq.editable or not is_pinned_requirement(ireq): return # It is technically possible to parse extras out of the JSON API's # requirement format, but it is such a chore let's just use the simple API. if ireq.extras: return session = requests.session() atexit.register(session.close) version = str(ireq.req.specifier).lstrip("=")

pandas/core/frame.py

def assign(self, **kwargs) -> DataFrame: r data = self.copy(deep=None) for k, v in kwargs.items():

ENH/TST: expand copy-on-write to assign() method (#50010)

assign

36dcf519c67a8098572447f7d5a896740fc9c464

pandas

frame.py

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

Python

def assign(self, **kwargs) -> DataFrame: r data = self.copy(deep=None) for k, v in kwargs.items(): data[k] = com.apply_if_callable(v, data) return data

mmdet/models/dense_heads/solo_head.py

def resize_feats(self, feats): out = [] for i in range(len(feats)): if i == 0: out.append( F.interpolate( feats[0], size=feats[i + 1].shape[-2:], mode='bilinear',

[Feature] Support SOLOv2 (#7441) * solov2 init * solov2 r18 lightweight * add model docstrings and reformat the code * add docstrings to model method * add solov2 big model config and correct some errors in the docstring * fix linting issues * refactor code and configs * rename variables according to the convention * add and enhance solov2 logic * add doc strings * update solov2 config files * fix norm_cfg in mask head * minor fix * update configs Co-authored-by: BIGWangYuDong <yudongwang@tju.edu.cn>

resize_feats

d18cdb140ef3cb9ed5fdef6f1a815f5836f1b1ab

mmdetection

solo_head.py

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

Python

def resize_feats(self, feats): out = [] for i in range(len(feats)): if i == 0: out.append( F.interpolate( feats[0], size=feats[i + 1].shape[-2:], mode='bilinear', align_corners=False)) elif i == len(feats) - 1: out.append( F.interpolate( feats[i], size=feats[i - 1].shape[-2:], mode='bilinear', align_corners=False)) else: out.append(feats[i]) return out

erpnext/stock/report/stock_projected_qty/stock_projected_qty.py

def get_bin_list(filters): conditions = [] if filters.item_code: cond

style: format code with black

get_bin_list

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

stock_projected_qty.py

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

Python

def get_bin_list(filters): conditions = [] if filters.item_code: conditions.append("item_code = '%s' " % filters.item_code) if filters.warehouse: warehouse_details = frappe.db.get_value( "Warehouse", filters.warehouse, ["lft", "rgt"], as_dict=1 ) if warehouse_details: conditions.append( " exists (select name from `tabWarehouse` wh \ where wh.lft >= %s and wh.rgt <= %s and bin.warehouse = wh.name)" % (warehouse_details.lft, warehouse_details.rgt) ) bin_list = frappe.db.sql( .format( conditions=" where " + " and ".join(conditions) if conditions else "" ), as_dict=1, ) return bin_list

keras/engine/training.py

def run_eagerly(self): if ( self.dynamic and self._run_eagerly is False ): # pylint:disable=g-bool-id-comparison # TODO(fchollet): consider using py_func to enable this. raise ValueError( "Your model contains layers that can only be " "successfully run in eager execution (layers " "constructed with `dynamic=True`). " "You cannot set `run_eagerly=False`." ) if self._cluster_coordinator and self._run_eagerly: raise ValueError( "When using `Model` with `ParameterServerStrategy`, " "`run_eagerly` is not

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

run_eagerly

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

training.py

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

Python

def run_eagerly(self): if ( self.dynamic and self._run_eagerly is False ): # pylint:disable=g-bool-id-comparison # TODO(fchollet): consider using py_func to enable this. raise ValueError( "Your model contains layers that can only be " "successfully run in eager execution (layers " "constructed with `dynamic=True`). " "You cannot set `run_eagerly=False`." ) if self._cluster_coordinator and self._run_eagerly: raise ValueError( "When using `Model` with `ParameterServerStrategy`, " "`run_eagerly` is not supported." ) # Run eagerly logic, by priority: # (1) Dynamic models must be run eagerly. # (2) Explicitly setting run_eagerly causes a Model to be run eagerly. # (3) Not explicitly setting run_eagerly defaults to TF's global setting. return ( self.dynamic or self._run_eagerly or (tf.config.functions_run_eagerly() and self._run_eagerly is None) )

wagtail/models/__init__.py

def get_preview_context(self, request, *args, **kwargs): return {"object": self, "request": request}

Add docs for PreviewableMixin

get_preview_context

e864b9c4d12ad0edd38283c17c2935e950e73520

wagtail

__init__.py

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

Python

def get_preview_context(self, request, *args, **kwargs): return {"object": self, "request": request}

tests/components/mobile_app/test_sensor.py

async def test_default_disabling_entity(hass, create_registrations, webhook_client): webhook_id = create_registrations[1]["webhook_id"] webhook_url = f"/api/webhook/{webhook_id}" reg_resp = await webhook_client.post( w

Allow mobile app to disable entities by default (#71562)

test_default_disabling_entity

539ce7ff0e9d9bc59cd8f028f245c09f802c89cb

core

test_sensor.py

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

Python

async def test_default_disabling_entity(hass, create_registrations, webhook_client): webhook_id = create_registrations[1]["webhook_id"] webhook_url = f"/api/webhook/{webhook_id}" reg_resp = await webhook_client.post( webhook_url, json={ "type": "register_sensor", "data": { "name": "Battery State", "type": "sensor", "unique_id": "battery_state", "default_disabled": True, }, }, ) assert reg_resp.status == HTTPStatus.CREATED json = await reg_resp.json() assert json == {"success": True} await hass.async_block_till_done() entity = hass.states.get("sensor.test_1_battery_state") assert entity is None assert ( er.async_get(hass).async_get("sensor.test_1_battery_state").disabled_by == er.RegistryEntryDisabler.INTEGRATION )

dask/dataframe/io/parquet/arrow.py

def _create_dd_meta(cls, dataset_info): # Collect necessary information from dataset_info schema = dataset_info["schema"] index = dataset_info["index"] categories = dataset_info["categories"] partition_obj = dataset_info["partitions"] partitions = dataset_info["partition_names"] physical_column_names = dataset_info.get("physical_schema", schema).names columns = None # Set index and column names using # pandas metadata (when available) pandas_metadata = _get_pandas_metadata(schema) if pandas_metadata: ( index_names, column_names, storage_name_mapping, column_index_names, ) = _parse_pandas_metadata(pandas_metadata) if categories is None: categories = [] for col in pandas_metadata["columns"]: if (col["pandas_type"] == "categorical") and ( col["name"] not in categories ): categories.append(col["name"]) else: # No pandas metadata implies no index, unless selected by the user index_names = [] column_names = physical_column_names storage_name_mapping = {k: k for k in column_names} column_index_names = [None] if index is None and index_names: # Pandas metadata has provided the index name for us index = index_names # Ensure that there is no overlap between partition columns # and explicit column storage if partitions: _partitions = [p for p in partitions if p not in physical_column_names] if not _partitions: partitions = [] dataset_info["partitions"] = None dataset_info["partition_keys"] = {} datas

Fix "physical" column bug in pyarrow-based read_parquet (#8775) Starting with pyarrow-5.0, the `pyarrow.dataset` API can now be used to write parquet datasets. Using `pyarrow.dataset.write_dataset` to write partitioned data results in different "pandas metadata" than we get from a Dask-written dataset, because Dask will not include the partitioned column names in this metadata (since they are not "physical" columns), but pyarrow will. This exposed a bug in Dask, where we were conflating "pandas metadata" column names with "physical" column names. This PR adds a small fix to ensure that Dask will only bail on reading partitioned columns if/when the partitioned columns are really "physical" columns.

_create_dd_meta

73acebb3a2066792dea39c78245a6e1a01b2b173

dask

arrow.py

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

Python

def _create_dd_meta(cls, dataset_info): # Collect necessary information from dataset_info schema = dataset_info["schema"] index = dataset_info["index"] categories = dataset_info["categories"] partition_obj = dataset_info["partitions"] partitions = dataset_info["partition_names"] physical_column_names = dataset_info.get("physical_schema", schema).names columns = None # Set index and column names using # pandas metadata (when available) pandas_metadata = _get_pandas_metadata(schema) if pandas_metadata: ( index_names, column_names, storage_name_mapping, column_index_names, ) = _parse_pandas_metadata(pandas_metadata) if categories is None: categories = [] for col in pandas_metadata["columns"]: if (col["pandas_type"] == "categorical") and ( col["name"] not in categories ): categories.append(col["name"]) else: # No pandas metadata implies no index, unless selected by the user index_names = [] column_names = physical_column_names storage_name_mapping = {k: k for k in column_names} column_index_names = [None] if index is None and index_names: # Pandas metadata has provided the index name for us index = index_names # Ensure that there is no overlap between partition columns # and explicit column storage if partitions: _partitions = [p for p in partitions if p not in physical_column_names] if not _partitions: partitions = [] dataset_info["partitions"] = None dataset_info["partition_keys"] = {} dataset_info["partition_names"] = partitions elif len(_partitions) != len(partitions): raise ValueError( "No partition-columns should be written in the \n" "file unless they are ALL written in the file.\n" "physical columns: {} | partitions: {}".format( physical_column_names, partitions ) ) column_names, index_names = _normalize_index_columns( columns, column_names + partitions, index, index_names ) all_columns = index_names + column_names # Check that categories are included in columns if categories and not set(categories).intersection(all_columns): raise ValueError( "categories not in available columns.\n" "categories: {} | columns: {}".format(categories, list(all_columns)) ) dtypes = _get_pyarrow_dtypes(schema, categories) dtypes = {storage_name_mapping.get(k, k): v for k, v in dtypes.items()} index_cols = index or () meta = _meta_from_dtypes(all_columns, dtypes, index_cols, column_index_names) if categories: # Make sure all categories are set to "unknown". # Cannot include index names in the `cols` argument. meta = clear_known_categories( meta, cols=[c for c in categories if c not in meta.index.names] ) if partition_obj: for partition in partition_obj: if isinstance(index, list) and partition.name == index[0]: # Index from directory structure meta.index = pd.CategoricalIndex( [], categories=partition.keys, name=index[0] ) elif partition.name == meta.index.name: # Index created from a categorical column meta.index = pd.CategoricalIndex( [], categories=partition.keys, name=meta.index.name ) elif partition.name in meta.columns: meta[partition.name] = pd.Series( pd.Categorical(categories=partition.keys, values=[]), index=meta.index, ) # Update `dataset_info` and return `meta` dataset_info["index"] = index dataset_info["index_cols"] = index_cols dataset_info["categories"] = categories return meta

airflow/providers/google/cloud/transfers/postgres_to_gcs.py

def convert_type(self, value, schema_type, stringify_dict=True): if isinstance(value, datetime.datetime): iso_format_value = value.isoformat() if value.tzinfo is None: return iso_format_value return pendulum.parse(iso_format_value).float_timestamp if isinstance(val

Fix `PostgresToGCSOperator` does not allow nested JSON (#23063) * Avoid double json.dumps for json data export in PostgresToGCSOperator. * Fix CI

convert_type

766726f2e3a282fcd2662f5dc6e9926dc38a6540

airflow

postgres_to_gcs.py

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

Python

def convert_type(self, value, schema_type, stringify_dict=True): if isinstance(value, datetime.datetime): iso_format_value = value.isoformat() if value.tzinfo is None: return iso_format_value return pendulum.parse(iso_format_value).float_timestamp if isinstance(value, datetime.date): return value.isoformat() if isinstance(value, datetime.time): formatted_time = time.strptime(str(value), "%H:%M:%S") time_delta = datetime.timedelta( hours=formatted_time.tm_hour, minutes=formatted_time.tm_min, seconds=formatted_time.tm_sec ) return str(time_delta) if stringify_dict and isinstance(value, dict): return json.dumps(value) if isinstance(value, Decimal): return float(value) return value

keras/utils/metrics_utils.py

def _filter_top_k(x, k): _, top_k_idx = tf.math.top_k(x, k, sorted=False) top_k_mask = tf.reduce_sum( tf.one_

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

_filter_top_k

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

metrics_utils.py

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

Python

def _filter_top_k(x, k): _, top_k_idx = tf.math.top_k(x, k, sorted=False) top_k_mask = tf.reduce_sum( tf.one_hot(top_k_idx, tf.shape(x)[-1], axis=-1), axis=-2 ) return x * top_k_mask + NEG_INF * (1 - top_k_mask)

tests/tpot_tests.py

def test_pick_two_individuals_eligible_for_crossover_bad(): ind1 = creator.Individual.from_string( 'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)', tpot_obj._pset ) ind2 = creator.Individual.from_string( 'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)', tpot_obj._pset ) ind3 = creator.Individual.from_string( 'GaussianNB(input_matrix)', tpot_obj._pset ) # Ind1 and ind2 are not a pair because they are the same, ind3 shares no primitive pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1, ind2, ind3]) assert pick1 is None and pick2 is None # You can

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

test_pick_two_individuals_eligible_for_crossover_bad

388616b6247ca4ea8de4e2f340d6206aee523541

tpot

tpot_tests.py

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

Python

def test_pick_two_individuals_eligible_for_crossover_bad(): ind1 = creator.Individual.from_string( 'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)', tpot_obj._pset ) ind2 = creator.Individual.from_string( 'BernoulliNB(input_matrix, BernoulliNB__alpha=1.0, BernoulliNB__fit_prior=True)', tpot_obj._pset ) ind3 = creator.Individual.from_string( 'GaussianNB(input_matrix)', tpot_obj._pset ) # Ind1 and ind2 are not a pair because they are the same, ind3 shares no primitive pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1, ind2, ind3]) assert pick1 is None and pick2 is None # You can not do crossover with a population of only 1. pick1, pick2 = pick_two_individuals_eligible_for_crossover([ind1]) assert pick1 is None and pick2 is None # You can not do crossover with a population of 0. pick1, pick2 = pick_two_individuals_eligible_for_crossover([]) assert pick1 is None and pick2 is None

scapy/arch/windows/__init__.py

def get_ips(v6=False): # type: (bool) -> Dict[NetworkInterface, List[

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

get_ips

a2b7a28faff1db058dd22ce097a268e0ad5d1d33

scapy

__init__.py

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

Python

def get_ips(v6=False): # type: (bool) -> Dict[NetworkInterface, List[str]] res = {} for iface in six.itervalues(conf.ifaces): if v6: res[iface] = iface.ips[6] else: res[iface] = iface.ips[4] return res

src/transformers/models/opt/modeling_opt.py

def _set_gradient_checkpointing(self, module, value=False):

Add OPT (#17088) * First version - OPT model * Final changes - putting use cache to False * few changes - remove commented block * few changes - remove unecessary files * fix style issues * few changes - remove a test file - added the logits test * Update src/transformers/models/auto/tokenization_auto.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * add gen tests * few changes - rm mask filling example on docstring * few changes - remove useless args * some changes - more tests should pass now - needs to clean more - documentation still needs to be done * fix code quality * major changes - change attention architecture to BART-like - modify some tests - style fix * rm useless classes - remove opt for: - QA - cond generation - seq classif * Removed autodoc calls to non-existant classes TOkenizers are not implemented * Update src/transformers/__init__.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/__init__.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/auto/modeling_tf_auto.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Replaced OPTTokeniser with GPT2 tokenizer * added GPT2Tokenizer.from_pretrained("patrickvonplaten/opt_gpt2_tokenizer") * Removed OPTTokenizer * make style * Make style replaces ``` ...).unsqueeze(``` by ``` >>>).unsqueeze(``` * make repo consistency * Removed PretrainedOPTModel * fix opt.mdx removed other heads * fix init, removed 3 heads * removed heads * finished cleaning head * removed seauence classif and question answering * removed unused imports * removed useless dummy object for QA, SC and CG * removed tests for removed useless dummy object for QA, SC and CG * Removed head_mask using encoder layers which don't exist * fixed test * fix line * added OPT to toctree * Updated model path with pushed weigths * fix model path * fixed code quality * fixed embeddings and generation tests * update paths * clean comments * removed OPTClassificationHead for sentence classification * renamed hidden layer * renamed num layers to standard num_hidden_layers * num_attention_heads fix * changes for 125m * add first version for 125m * add first version - flax * add new version * causal LM output * replace output type with BaseModelOutputWithPastAndCrossAttentions * revert working config from 150m to 350m * clean * removed decoder input ids * fixed embed dim * more embed_dim issues * make style + removed enc_dec test * update falx model * removed troublesome copy * added is_encoder_decoder=False to config * added set_input emb fuinction to model class * requires torch on embed test * use head mask instead of decoder head mask input param solves a test * 8 test remaining, update * Updated create_and_check_decoder_model_past_large_inputs * Make style * update op tokenizer with condition * make style * See if I can push * some clean up * remove linear head hack * save intermediate * save correct attention * add copied from from bart * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * fix part of the reviewss Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * same changes in naming / conversion * correct mask * more fixes * delete FlaxOPT and TfOPT * clean traces of Flax and Tf * fix mask * fixed positionnal embedding length when past key value is provoded * get 125m, 6.7b to work * Added do_layer_norm * solved mismatch in load dictionnary * clean up preapre opt input dict * fixed past key value as bool * fix previus * fixed return dict False tuple issue * All tests are passing * Make style * Ignore OPTDecoder non tested * make fix-copies * make repo consistency * small fix * removed uselss @torch.no_grad decorator * make styl;e * fix previous opt test * style * make style * added opt documentation * update OPT_PRETRAINED_MODEL_ARCHIVE_LIST * up * more fixes * model & config work * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * added comment on padding hack (+2) * cleaup * review update * docstring for missing arg * Update docs/source/en/model_doc/opt.mdx Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update docs/source/en/model_doc/opt.mdx Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update docs/source/en/model_doc/opt.mdx Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/opt/__init__.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * update pretrained map * update path and tests * make style * styling * make consistency * add gpt2 tok new * more tok fixes * Update src/transformers/models/auto/tokenization_auto.py * Update docs/source/en/model_doc/opt.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update docs/source/en/model_doc/opt.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update docs/source/en/model_doc/opt.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update tests/models/opt/test_modeling_opt.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update based on reviews * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * make style * make tokenizer auto tests pass * apply Lysandre suggestion * finish tests * add some good tokenizer tests * improve docs slighly Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: ArthurZucker <arthur.zucker@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

_set_gradient_checkpointing

b971c769e80fe85fb7dd35c7cf65f3ac97ea6421

transformers

modeling_opt.py

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

Python

def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (OPTDecoder)): module.gradient_checkpointing = value OPT_GENERATION_EXAMPLE = r OPT_INPUTS_DOCSTRING = r

examples/text_to_image/train_text_to_image.py

def to(self, device=None, dtype=None) -> None: r # .to() on the ten

[train_text2image] Fix EMA and make it compatible with deepspeed. (#813) * fix ema * style * add comment about copy * style * quality

to

008b608f1551dbcf521284ed0e7a6722cd02ef07

diffusers

train_text_to_image.py

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

Python

def to(self, device=None, dtype=None) -> None: r # .to() on the tensors handles None correctly self.shadow_params = [ p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device) for p in self.shadow_params ]

openbb_terminal/econometrics/econometrics_model.py

def get_granger_causality(dependent_series, independent_series, lags): granger_set = pd.concat([dependent_series, independent_series], axis=1) granger = grangercausalitytests(granger_set, [lags], verbose=False) return granger

Here we merge all API Refactor related branches (#2236) * Update api.py * Updated forex menu * refactor ycrv command * refactor ycrv command black * refactor ecocal command * Minh changes * Adding space to test pushing * title fix ecocal df * get economic calendar annotation * fix investingcom tests * refactor index command * refactor overview command * give defaults to wsj view function args * rename date args investincom * refacto bigmac command * fix ecocal typo * refactor rtps command * alphavantage gdp * alphavantage gdp per capita * alphavantage cpi * alphavantage tyld * alphavantage inf * refactor macro command * refactor macro command w helpers * refactor treasury command * fix macro on terminal * treasury labels * refactor maturities * update treasury maturities doc strings * refactor get economic calendar finhub * refactor map command api * display map filter choices * route economy api to performance map * route economy api to performance map * display group choices on valuation command * refactor performance and valuation commands * refactor spectrum model and view * add choices to spectrum controller * delete image after view * fix model tests finviz * fix finciz view tests * refactor futures * fix some tests * fix more tests * fix controller test * refactor fred series notes * update fred notes docstring * refacto fred series ids * fix pred and qa when empty datasets * refactor fred * uncomment stuff * refacto get series data * fix some tests * set defaults on args * refactor fred yield curve * black * fix spell and remove ecocal names * fix linting * linting * pylint fix * change dangerous defaults * Working through crypto fixes (#2256) * Working through crypto fixes * Continued adding crypto stuff * Added crypto overview * Added test fixes * Added fixtures * Fixed tests * Fixed charting issue * Removed broken APIs * Final adjustments * Added test fixes * map get groups and get ycrv countries into old api * exposed econdb helper funcs * remove helpers * refactor search indices * linting * refactor arg currency * pylint from currency * Started switching crpyto ascending to ascend * Merging * Portfolio model arguements, params, and docstring * Refactored for etf commands (#2292) * Refactored for etf commands * Fixed tests * Added load command * Fixed menu * Portfolio logic fixes * Added econometrics (#2260) * Added econometrics * Fixed tests * Simplified API * Added test fixes * Added test csv * Allowed examples to be loaded * Fund refactor (#2291) * Fund refactor * Changed fund_name and fund to name * Changed ascending to ascend * Stock menu refactoring for easier API usage (#2194) * Stocks refactoring for easier API usage * Linting * Refactor newly added features * Linting * Fixing tests * Refactor common files used by stocks menu * Fixing flake8 * Fix linting and tests * Linting * Fix flake8 * refactor insider_data * refactor mentions * refactor watchlist * refactor sentiment * refactor sentiment * fix yahoofinance tests * refactor load and candle * refactor get_news and display_news * refactor stocks.ins.act * candle default matplotlib * fix yahoofinance_view tests * fix ark model tests * fix ark view tests * fix business insider model * fix business insider view * refactor csimarket model * fix tests csi market model * update dd controller * fix get suppliers tests * fix dd controller tests * fix finhub tests * fix finviz tests * fix fmp tests * fix marketwatch tests * corrected argument keywords in test_bt_model * corrected argument keywords in test_bt_view * refactor fa controller * refactor marketwatch view * refactor gov controller * fix tests fa av * fix tests elect * fix dcf tests * fix polygon tests * fix fmp tests * fix quiverquant tests * fix yahoofinance fa tests * fix more fa tests * fix insider tests * fix more tests * fix more tests * fix options tests * fix stock gov tests * fix tests test_ba_controller * fix tests for test_finviz_compare_model.py * fixed 2 tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fix final tests * fixed tests * fixed tests * Fix tests * black * forgot to black tests * fixed tests * fixed tests * fixed tests * fixed tests * flakefix * Tests + code : Stocks / Discovery * fix tests * added recorder * fixed tests * fixed tests * black * black * remove unused imports * refactor display raw * sia dicts fix * pylint * linting * remove dangerous default * fix tests * fix beta model test * black * skip screener qa test * change sector path to sectors * update tests readme * fix metric defaults * black * substitute lost ticker * defaults cpic * another round on sia * refactor cramer * reduce default tweets on sentiment * refactor yf hist, corr, volume * arkorders default * refactor income, balance, cashflow * refacto scorr, screener, getfinnhub * refactor stockgrid * ibkr refactor * another round on stockgrid * add dividens end point * refactor discovery endpoints * update docstrings with similar input * refactor messages * refactor ba * refactor regioons * refactor twitter sentiment * refactor hist * refactor regions * give default to timeframe * refactor bunch of defaults and arg names * remove leftover imports * refactor vwap * let tests run * fix tests * fix stock tests * fix stockanalysis tests * flake * MYPY * Made important changes * added fixes * Fixed big issue * Added fixes to tests * fix qa tests * fix tests * fix 1 more test * last stocks failing * fix crypto test Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: montezdesousa <montezdesousa@gmail.com> Co-authored-by: hjoaquim <h.joaquim@campus.fct.unl.pt> Co-authored-by: montezdesousa <79287829+montezdesousa@users.noreply.github.com> Co-authored-by: colin99d <colin99delahunty@gmail.com> * fix portfolio tests * change period to window * update ca docstrings * refactor get_similar_companies func * Fixed * Update CI * Update CI 2 * Update CI 3 * Update dependencies Co-authored-by: colin99d <colin99delahunty@gmail.com> Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com> Co-authored-by: montezdesousa <montezdesousa@gmail.com> Co-authored-by: James Simmons <simmonsj330@gmail.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: montezdesousa <79287829+montezdesousa@users.noreply.github.com> Co-authored-by: northern-64bit <75195383+northern-64bit@users.noreply.github.com> Co-authored-by: hjoaquim <h.joaquim@campus.fct.unl.pt>

get_granger_causality

9e1a58e2dbedec4e4a9f9c2e32ddf091776c606b

OpenBBTerminal

econometrics_model.py

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

Python

def get_granger_causality(dependent_series, independent_series, lags): granger_set = pd.concat([dependent_series, independent_series], axis=1) granger = grangercausalitytests(granger_set, [lags], verbose=False) return granger

django/utils/decorators.py

def _multi_decorate(decorators, method): if hasattr(decorators, "__iter__")

Refs #33476 -- Reformatted code with Black.

_multi_decorate

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

decorators.py

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

Python

def _multi_decorate(decorators, method): if hasattr(decorators, "__iter__"): # Apply a list/tuple of decorators if 'decorators' is one. Decorator # functions are applied so that the call order is the same as the # order in which they appear in the iterable. decorators = decorators[::-1] else: decorators = [decorators]

ppocr/data/imaug/fce_targets.py

def generate_level_targets(self, img_size, text_polys, ignore_polys): h, w = img_size lv_size_divs = self.level_size_divisors lv_proportion_range = self.level_proportion_range lv_text_polys = [[] for i in range(len(lv_size_divs))] lv_ignore_polys = [[] for i in

add fcenet

generate_level_targets

9f62b610dea6161627200ed85d92e19b1923279a

PaddleOCR

fce_targets.py

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

Python

def generate_level_targets(self, img_size, text_polys, ignore_polys): h, w = img_size lv_size_divs = self.level_size_divisors lv_proportion_range = self.level_proportion_range lv_text_polys = [[] for i in range(len(lv_size_divs))] lv_ignore_polys = [[] for i in range(len(lv_size_divs))] level_maps = [] for poly in text_polys: # assert len(poly) == 1 # text_instance = [[poly[i], poly[i + 1]] # for i in range(0, len(poly), 2)] polygon = np.array(poly, dtype=np.int).reshape((1, -1, 2)) _, _, box_w, box_h = cv2.boundingRect(polygon) proportion = max(box_h, box_w) / (h + 1e-8) for ind, proportion_range in enumerate(lv_proportion_range): if proportion_range[0] < proportion < proportion_range[1]: lv_text_polys[ind].append(poly / lv_size_divs[ind]) for ignore_poly in ignore_polys: # assert len(ignore_poly) == 1 # text_instance = [[ignore_poly[i], ignore_poly[i + 1]] # for i in range(0, len(ignore_poly), 2)] polygon = np.array(ignore_poly, dtype=np.int).reshape((1, -1, 2)) _, _, box_w, box_h = cv2.boundingRect(polygon) proportion = max(box_h, box_w) / (h + 1e-8) for ind, proportion_range in enumerate(lv_proportion_range): if proportion_range[0] < proportion < proportion_range[1]: lv_ignore_polys[ind].append(ignore_poly / lv_size_divs[ind]) for ind, size_divisor in enumerate(lv_size_divs): current_level_maps = [] level_img_size = (h // size_divisor, w // size_divisor) text_region = self.generate_text_region_mask( level_img_size, lv_text_polys[ind])[None] current_level_maps.append(text_region) center_region = self.generate_center_region_mask( level_img_size, lv_text_polys[ind])[None] current_level_maps.append(center_region) effective_mask = self.generate_effective_mask( level_img_size, lv_ignore_polys[ind])[None] current_level_maps.append(effective_mask) fourier_real_map, fourier_image_maps = self.generate_fourier_maps( level_img_size, lv_text_polys[ind]) current_level_maps.append(fourier_real_map) current_level_maps.append(fourier_image_maps) level_maps.append(np.concatenate(current_level_maps)) return level_maps

erpnext/stock/stock_balance.py

def get_reserved_qty(item_code, warehouse): reserved_qty = frappe.db.sql( , (item_code, warehouse, item_code, warehouse), ) return flt(

style: format code with black

get_reserved_qty

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

stock_balance.py

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

Python

def get_reserved_qty(item_code, warehouse): reserved_qty = frappe.db.sql( , (item_code, warehouse, item_code, warehouse), ) return flt(reserved_qty[0][0]) if reserved_qty else 0

lib/matplotlib/pyplot.py

def cool(): set_cmap('cool

Cleanup documentation generation for pyplot - remove the awkward `pyplot.plotting()` function, which only served as a namespace to take up the docs for pyplot and output them via `.. autofunction` - Instead generate the same information using `.. autosummary::`. We have to list the desired methods here explicitly. I've added a test that these are the same as previously auto-generated in the `plotting()` docstring. If we change anything in pyplot, we'll be notified through the test failure that we have to adapt the autosummary list. - Removed the docstring generation logic `_setup_pyplot_info_docstrings()`. Apart from generating the `plotting()` docstring, this added docstrings to the pyplot colormap setters. Instead, we now add these docstrings directly via boilerplate.py Co-authored-by: Elliott Sales de Andrade <quantum.analyst@gmail.com>

cool

032316bc6c7798fca6c82de24167c975f237687f

matplotlib

pyplot.py

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

Python

def cool(): set_cmap('cool') # Autogenerated by boilerplate.py. Do not edit as changes will be lost.

scapy/packet.py

def add_parent(self, parent):

Add parent field to Packet (#3607) Co-authored-by: Sergey Matsievskiy <matsievskiy@fastwel.ru>

add_parent

6d7184e8bec5102dfa66bcc10432a30a7e0dcf3a

scapy

packet.py

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

Python

def add_parent(self, parent): # type: (Packet) -> None self.parent = parent

IPython/testing/tools.py

def make_tempfile(name): open(name, 'w', encoding='utf-8

Fix EncodingWarning on Python 3.10

make_tempfile

23276ac4770f380ce1d5808950dd412a35594af1

ipython

tools.py

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

Python

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

pipenv/patched/notpip/_vendor/cachecontrol/controller.py

def update_cached_response(self, request, response): cache_url = self.cache_url(request.url) cached_response = self.serializer.loads(request, self.cache.get(cache_url)) if not cached_response: # we didn't have a cached response return response # Lets update our headers with the headers from the new request: # http://tools.ietf.org/html/draft-ietf-httpbis-p4-conditional-26#section-4.1 # # The server isn't supposed to send headers that would make # the cached body invalid. But... just in case, we'll be sure # to strip out ones we know that might be problmatic due to # typical assumptions. excluded_headers = ["content-length"] cached_response.headers.update( dict( (k, v) for k, v in response.headers.items() if k.lower() not in excluded_headers ) ) # we want a 200 b/c we have content via the cache cached_response.status =

Vendor in pip 22.1.2

update_cached_response

c69d55f7c82d5ae2cce542bcfb98d043ca4836a0

pipenv

controller.py

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

Python

def update_cached_response(self, request, response): cache_url = self.cache_url(request.url) cached_response = self.serializer.loads(request, self.cache.get(cache_url)) if not cached_response: # we didn't have a cached response return response # Lets update our headers with the headers from the new request: # http://tools.ietf.org/html/draft-ietf-httpbis-p4-conditional-26#section-4.1 # # The server isn't supposed to send headers that would make # the cached body invalid. But... just in case, we'll be sure # to strip out ones we know that might be problmatic due to # typical assumptions. excluded_headers = ["content-length"] cached_response.headers.update( dict( (k, v) for k, v in response.headers.items() if k.lower() not in excluded_headers ) ) # we want a 200 b/c we have content via the cache cached_response.status = 200 # update our cache self._cache_set(cache_url, request, cached_response) return cached_response

parsing/dml_csr/loss/lovasz_softmax.py

def binary_xloss(logits, labels, ignore=None): l

Create lovasz_softmax.py

binary_xloss

db307ffb12d6ba1f8eaeeafd29ee6d4a3fd6fa97

insightface

lovasz_softmax.py

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

Python

def binary_xloss(logits, labels, ignore=None): logits, labels = flatten_binary_scores(logits, labels, ignore) loss = StableBCELoss()(logits, Variable(labels.float())) return loss # --------------------------- MULTICLASS LOSSES ---------------------------

build/pyinstaller/user_agent/base.py

def generate_navigator_js(os=None, navigator=None, platform=None, device_type=None): config = generate_navigator( os=os, navigator=navigator, platform=platform, device_type=device_type ) return { "appCodeName": config["app_code_name"], "appName": config["app_name"], "appVersion": config["

Create a packaged app bundle with Pyinstaller (#1525) * Add dashboard widget assets * Add ipywidgets and ipyflex to project * Add currencies dashboard notebook * Update docs and docstrings * Add pyinstaller to project deps * Add pyinstaller artifacts to gitignore * Fix linter errors in terminal.py * Update cspell hook and action with a pyinstaller specific word * Add pyinstaller specfile and artifacts * Add splashscreen image * Add app icon * adding splash screen support to terminal.spec and terminal.py * Restore the conda env build files * Sync deps * Add border to the splashscreen image * Clean up terminal launcher * Add support for default feature flags in packages apps * Fix types and linting * Add splashscreen management to app bootup * Check prediction feature flag when entering crypto/pred * Update pyinstaller spec file * fix .spec file to work for splash and icon - removed the ".." * Allows to export when using installer (#1568) * fix export for packaged apps * fix filename * Git : replace commit_hash when it is set in config_terminal * Add update of the git commit hash in gtff default during build * Add packaged app name and feature flag to logs * Add platform specific icon assignment * Add macOS build assets * Add tensorflow to hidden imports * Move LOGGING_COMMIT_HASH to gtff * Adding files/folders needed to .spec and pyinstaller folder. This will make certain commands work again. * Linting * Workflow : ignore ./build/pyinstaller from codespell * Workflow : exclude ./build/pyinstaller from flake8 * Poetry + Workflow : add types-six * Pyinstaller : remove property_cached, user_agent and vaderSentiment * Revert "Pyinstaller : remove property_cached, user_agent and vaderSentiment" This reverts commit dbb3e2b81086f97819ebd21457148c7160a4d703. * Clean up local paths in specfile * Validate deps have correct Jinja version (they do) * Fix logging commit hash to be set correctly for the logger to see it Co-authored-by: Andrew <andrew.kenreich@gmail.com> Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt> Co-authored-by: Chavithra PARANA <chavithra@gmail.com>

generate_navigator_js

ab4de1dd70fba866930150e440a03e461a6ca6a8

OpenBBTerminal

base.py

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

Python

def generate_navigator_js(os=None, navigator=None, platform=None, device_type=None): config = generate_navigator( os=os, navigator=navigator, platform=platform, device_type=device_type ) return { "appCodeName": config["app_code_name"], "appName": config["app_name"], "appVersion": config["app_version"], "platform": config["platform"], "userAgent": config["user_agent"], "oscpu": config["oscpu"], "product": config["product"], "productSub": config["product_sub"], "vendor": config["vendor"], "vendorSub": config["vendor_sub"], "buildID": config["build_id"], }

modules/image/text_to_image/disco_diffusion_cnclip_vitb16/cn_clip/clip/bert_tokenizer.py

def printable_text(text): # These functions want `str` for both Python2 and Python3, but in one case # it's a Unicode string and in the other it's a byte string.

add disco_diffusion_cnclip_vitb16 module

printable_text

f4d6e64cdc132ae868699a0ba442f4ab1d304a14

PaddleHub

bert_tokenizer.py

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

Python

def printable_text(text): # These functions want `str` for both Python2 and Python3, but in one case # it's a Unicode string and in the other it's a byte string. if six.PY3: if isinstance(text, str): return text elif isinstance(text, bytes): return text.decode("utf-8", "ignore") else: raise ValueError("Unsupported string type: %s" % (type(text))) elif six.PY2: if isinstance(text, str): return text elif isinstance(text, unicode): return text.encode("utf-8") else: raise ValueError("Unsupported string type: %s" % (type(text))) else: raise ValueError("Not running on Python2 or Python 3?")

freqtrade/freqai/prediction_models/RL/RLPrediction_env_v2.py

def is_tradesignal(self, action): # trade signal return not ((action == Actions.Neutral.value and self._position == Positions.Neutral)

callback function and TDQN model added

is_tradesignal

01232e9a1f8e28e3611e38af3816edb026600767

freqtrade

RLPrediction_env_v2.py

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

Python

def is_tradesignal(self, action): # trade signal return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or (action == Actions.Short.value and self._position == Positions.Short) or (action == Actions.Long.value and self._position == Positions.Long))

python3.10.4/Lib/http/server.py

def log_message(self, format, *args): sys.stderr.write("%s - - [%s] %s\n" % (self.address_string(), self.log_date_time_string(),

add python 3.10.4 for windows

log_message

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

server.py

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

Python

def log_message(self, format, *args): sys.stderr.write("%s - - [%s] %s\n" % (self.address_string(), self.log_date_time_string(), format%args))

src/diffusers/utils/logging.py

def warning_advice(self, *args, **kwargs): no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False) if no_advisory_warnings: return self.warning(*args, **kwargs) logging.Logger.warning_advice = warning_advice

changes comments and env vars in `utils/logging` removes mentions of 🤗Transformers with 🤗Diffusers equivalent.

warning_advice

c3cc8eb23c8095217388d350409b454ea396c12b

diffusers

logging.py

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

Python

def warning_advice(self, *args, **kwargs): no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False) if no_advisory_warnings: return self.warning(*args, **kwargs) logging.Logger.warning_advice = warning_advice

sympy/matrices/repmatrix.py

def zip_row_op(self, i, k, f): for j in range(self.cols): self[i, j] = f

Moved imports to higher level

zip_row_op

59d22b6bb7287613d598611027f640d068ca5748

sympy

repmatrix.py

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

Python

def zip_row_op(self, i, k, f): for j in range(self.cols): self[i, j] = f(self[i, j], self[k, j])

sympy/polys/orthopolys.py

def legendre_poly(n, x=None, polys=False): r return named_poly(n, dup_legendre, QQ, "Legendre polynomial", (x,), polys)

Run orthopolys and appellseqs through a common interface Including unifying the two Chebyshev generators into one function. There are also two kinds of Hermite polynomials, and they too share the same recurrence, but the second type He_n(x) (aka the probabilist, reduced or small polynomials) will not be added here.

legendre_poly

d1d46df73ebaad94089847558d00a8b7269f554d

sympy

orthopolys.py

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

Python

def legendre_poly(n, x=None, polys=False): r return named_poly(n, dup_legendre, QQ, "Legendre polynomial", (x,), polys)

paddlenlp/taskflow/knowledge_mining.py

def _concat_short_text_reuslts(self, input_texts, results): long_text_lens = [len(text) for text in input_texts] concat_results = [] single_results = {} count = 0 for text in input_texts: text_len = len(text) while True: if len(single_results) == 0 or len(single_results[ "text"]) < text_len: if len(single_results) == 0: single_results = copy.deepcopy(results[count]) else: single_results["text"] += results[count]["text"] single_results["items"].extend(results[count]["items"]) count += 1 elif len(single_results["text"]) == text_len: concat_results.append(single_results) single_results = {} break else: raise Exception( "The length of input text and raw text is not equal.")

Update neural search readme and Add Paddle Serving Support (#1558) * add recall inference similarity * update examples * updatea readme * update dir name * update neural search readme * update milvus readme * update domain adaptive pretraining readme * fix the mistakes * update readme * add recall Paddle Serving Support * update readme * update readme and format the code * reformat the files * move the files * reformat the code * remove redundant code Co-authored-by: Zeyu Chen <chenzeyu01@baidu.com> Co-authored-by: tianxin <tianxin04@baidu.com>

_concat_short_text_reuslts

621357338437ee420eabbbf5ab19065bc85e73a5

PaddleNLP

knowledge_mining.py

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

Python

def _concat_short_text_reuslts(self, input_texts, results): long_text_lens = [len(text) for text in input_texts] concat_results = [] single_results = {} count = 0 for text in input_texts: text_len = len(text) while True: if len(single_results) == 0 or len(single_results[ "text"]) < text_len: if len(single_results) == 0: single_results = copy.deepcopy(results[count]) else: single_results["text"] += results[count]["text"] single_results["items"].extend(results[count]["items"]) count += 1 elif len(single_results["text"]) == text_len: concat_results.append(single_results) single_results = {} break else: raise Exception( "The length of input text and raw text is not equal.") for result in concat_results: pred_words = result['items'] pred_words = self._reset_offset(pred_words) result['items'] = pred_words return concat_results

ppdet/modeling/heads/s2anet_head.py

def get_pred(self, bboxes, bbox_num, im_shape, scale_factor): origin_shape = paddle.floor(im_shape / scale_factor + 0.5) origin_shape_list = [] scale_factor_list = [] # scale_factor: scale_y, scale_x for i in range(bbox_num.shape[0]): expand_shape = paddle.expand(origin_shape[i:i + 1, :], [b

refactor s2anet (#6604) * refactor s2anet to support batch_size > 1 * fix problem of inference * support batch_size > 1 for training * fix empty results * fix dota eval * fix configs of s2anet_head * modify s2anet_spine_1x to 73 mAP

get_pred

b4727677751081b257c6fa23c3c124ab9e5a32a1

PaddleDetection

s2anet_head.py

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

Python

def get_pred(self, bboxes, bbox_num, im_shape, scale_factor): origin_shape = paddle.floor(im_shape / scale_factor + 0.5) origin_shape_list = [] scale_factor_list = [] # scale_factor: scale_y, scale_x for i in range(bbox_num.shape[0]): expand_shape = paddle.expand(origin_shape[i:i + 1, :], [bbox_num[i], 2]) scale_y, scale_x = scale_factor[i][0], scale_factor[i][1] scale = paddle.concat([ scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x, scale_y ]) expand_scale = paddle.expand(scale, [bbox_num[i], 8]) origin_shape_list.append(expand_shape) scale_factor_list.append(expand_scale) origin_shape_list = paddle.concat(origin_shape_list) scale_factor_list = paddle.concat(scale_factor_list) # bboxes: [N, 10], label, score, bbox pred_label_score = bboxes[:, 0:2] pred_bbox = bboxes[:, 2:] # rescale bbox to original image pred_bbox = pred_bbox.reshape([-1, 8]) scaled_bbox = pred_bbox / scale_factor_list origin_h = origin_shape_list[:, 0] origin_w = origin_shape_list[:, 1] bboxes = scaled_bbox zeros = paddle.zeros_like(origin_h) x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], origin_w - 1), zeros) y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], origin_h - 1), zeros) x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], origin_w - 1), zeros) y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], origin_h - 1), zeros) x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], origin_w - 1), zeros) y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], origin_h - 1), zeros) x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], origin_w - 1), zeros) y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], origin_h - 1), zeros) pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=-1) pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1) return pred_result

erpnext/accounts/doctype/chart_of_accounts_importer/chart_of_accounts_importer.py

def unset_existing_data(company): linked = frappe.db.sql( , as_dict=True, ) # remove accounts data from company update_values = {d.fieldname: "" for d in linked} frappe.db.set_value("Company", company, update_values, update_values) # remove accounts data from various doctypes for doctype in [ "Account", "Party Account", "Mode of Payment Account", "Tax Withholding Account", "Sales Taxes and Charges Template", "Purchase Taxes and Charges Template", ]: frappe.db.sql(

style: format code with black

unset_existing_data

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

chart_of_accounts_importer.py

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

Python

def unset_existing_data(company): linked = frappe.db.sql( , as_dict=True, ) # remove accounts data from company update_values = {d.fieldname: "" for d in linked} frappe.db.set_value("Company", company, update_values, update_values) # remove accounts data from various doctypes for doctype in [ "Account", "Party Account", "Mode of Payment Account", "Tax Withholding Account", "Sales Taxes and Charges Template", "Purchase Taxes and Charges Template", ]: frappe.db.sql( .format(doctype) % (company) # nosec )

mmdet/models/dense_heads/base_dense_head.py

def simple_test(self, feats, batch_img_metas, rescale=False): outs = self.forward(feats) results_list = self.get_results( *outs, batch_img_metas=batch_img_metas, rescale=rescale) return results_list

Modify RetinaNet model interface

simple_test

924c381a78eb70cede198e042ef34e038e05c15a

mmdetection

base_dense_head.py

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

Python

def simple_test(self, feats, batch_img_metas, rescale=False): outs = self.forward(feats) results_list = self.get_results( *outs, batch_img_metas=batch_img_metas, rescale=rescale) return results_list

src/transformers/commands/pt_to_tf.py

def compare_pt_tf_models(pt_model, pt_input, tf_model, tf_input): pt_outputs = pt_model(**pt_input, output_hidden_states=True) tf_outputs = tf_model(**tf_input, output_hidden_states=True) # 1. All output attributes must be the same pt_out_attrs = set(pt_outputs.keys())

CLI: add stricter automatic checks to `pt-to-tf` (#17588) * Stricter pt-to-tf checks; Update docker image for related tests * check all attributes in the output Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

compare_pt_tf_models

78c695eb624bc863ea165b6fb0a8850bfd9fcefa

transformers

pt_to_tf.py

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

Python

def compare_pt_tf_models(pt_model, pt_input, tf_model, tf_input): pt_outputs = pt_model(**pt_input, output_hidden_states=True) tf_outputs = tf_model(**tf_input, output_hidden_states=True) # 1. All output attributes must be the same pt_out_attrs = set(pt_outputs.keys()) tf_out_attrs = set(tf_outputs.keys()) if pt_out_attrs != tf_out_attrs: raise ValueError( f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:" f" {tf_out_attrs})" ) # 2. For each output attribute, ALL values must be the same

@pytest.fixture( params=_get_all_parser_float_precision_combinations()["params"], ids=_get_all_parser_float_precision_combinations()["ids"], )

pandas/tests/io/parser/conftest.py

def _get_all_parser_float_precision_combinations(): params = [] ids = [] for parser, parser_id in zip(_all_parsers, _all_parser_ids): if hasattr(parser, "values"): # Wrapped in pytest.param, get the actual parser back parser = parser.values[0] for precision in parser.float_precision_choices: # Re-wrap in pytest.param for pyarrow mark = pytest.mark.single_cpu if parser.engine == "pyarrow" els

CI: Add single_cpu build (#45995)

_get_all_parser_float_precision_combinations

08104e8c0a80579dfe3e984b32b35ddc94aafa01

pandas

conftest.py

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

Python

def _get_all_parser_float_precision_combinations(): params = [] ids = [] for parser, parser_id in zip(_all_parsers, _all_parser_ids): if hasattr(parser, "values"): # Wrapped in pytest.param, get the actual parser back parser = parser.values[0] for precision in parser.float_precision_choices: # Re-wrap in pytest.param for pyarrow mark = pytest.mark.single_cpu if parser.engine == "pyarrow" else () param = pytest.param((parser(), precision), marks=mark) params.append(param) ids.append(f"{parser_id}-{precision}") return {"params": params, "ids": ids} @pytest.fixture( params=_get_all_parser_float_precision_combinations()["params"], ids=_get_all_parser_float_precision_combinations()["ids"], )

tests/app_loading/tests.py

def test_egg3(self): egg_name = "%s/omelet.egg" % self.egg_dir with extend_sys_path(egg_name):

Refs #33476 -- Reformatted code with Black.

test_egg3

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_egg3(self): egg_name = "%s/omelet.egg" % self.egg_dir with extend_sys_path(egg_name): with self.settings(INSTALLED_APPS=["omelet.app_with_models"]): models_module = apps.get_app_config("app_with_models").models_module self.assertIsNotNone(models_module) del apps.all_models["app_with_models"]

@pytest.mark.parametrize("setting", ["Enabled", "Disabled"])

tests/pytests/functional/modules/win_lgpo/test_audit_settings_module.py

def test_auditing_case_names(lgpo, setting_name, setting, enable_legacy_auditing): lgpo.set_computer_policy(setting_name, setting) result = lgpo.get_policy(setting_name, "machine") assert result == setting @pytest.mark.parametrize("setting", ["Enabled",

Add and update tests

test_auditing_case_names

0e69e2317dfa06f114c6dd63bc70c68fc81d19b1

salt

test_audit_settings_module.py

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

Python

def test_auditing_case_names(lgpo, setting_name, setting, enable_legacy_auditing): lgpo.set_computer_policy(setting_name, setting) result = lgpo.get_policy(setting_name, "machine") assert result == setting @pytest.mark.parametrize("setting", ["Enabled", "Disabled"])

wagtail/images/tests/tests.py

def test_get(self): # Generate signature signature = generate_signature(self.image.id, "fill-800x600") # Get the image response = self.client.get( reverse( "wagtailimages_serve", args=(signature, self.image.id, "fill-800x600") ) ) # Check response self.assertEqual(response.status_code, 200) self.assertTrue(response.streaming) self.asse

Reformat with black

test_get

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

tests.py

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

Python

def test_get(self): # Generate signature signature = generate_signature(self.image.id, "fill-800x600") # Get the image response = self.client.get( reverse( "wagtailimages_serve", args=(signature, self.image.id, "fill-800x600") ) ) # Check response self.assertEqual(response.status_code, 200) self.assertTrue(response.streaming) self.assertEqual(response["Content-Type"], "image/png")

packages/syft/src/syft/lib/python/slice.py

def _object2proto(self) -> Slice_PB: slice_pb

change[syft.lib.python] syft import absolute -> relative

_object2proto

e5bfcd8016813b1d253a72da5c5071b0e0965644

PySyft

slice.py

https://github.com/OpenMined/PySyft.git

Python

def _object2proto(self) -> Slice_PB: slice_pb = Slice_PB() if self.start: slice_pb.start = self.start slice_pb.has_start = True if self.stop: slice_pb.stop = self.stop slice_pb.has_stop = True if self.step: slice_pb.step = self.step slice_pb.has_step = True slice_pb.id.CopyFrom(serialize(obj=self._id)) return slice_pb

plugins/extract/recognition/vgg_face2_keras.py

def __call__(self) -> List[Tuple[int, int]]: logger.info("Sorting face distances. Depending on your dataset this may take some time...") if self._threshold: self._threshold = self._result_linkage[:, 2].max() * self._threshold result_order = self._seriation(self._result_linkage, self._num_pre

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

__call__

98d01760e469fd2108eed8d0b0a1ba6297c3177c

faceswap

vgg_face2_keras.py

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

Python

def __call__(self) -> List[Tuple[int, int]]: logger.info("Sorting face distances. Depending on your dataset this may take some time...") if self._threshold: self._threshold = self._result_linkage[:, 2].max() * self._threshold result_order = self._seriation(self._result_linkage, self._num_predictions, self._num_predictions + self._num_predictions - 2) return result_order

ppdet/modeling/post_process.py

def get_pred(self, bboxes, bbox_num, im_shape, scale_factor): if self.export_eb: # enable rcnn models for edgeboard hw to skip the following postprocess. return bboxes, bboxes, bbox_num if not self.export_onnx: bboxes_list = [] bbox_num_list = [] id_start = 0 fake_bboxes = paddle.to_tensor( np.array( [[0., 0.0, 0.0, 0.0, 1.0, 1.0]], dtype='float32')) fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32')) # add fake bbox when output is empty for each batch for i in range(bbox_num.shape[0]): if bbox_num[i] == 0: bboxes_i = fake_bboxes bbox_num_i = fake_bbox_num else: bboxes_i = bboxes[id_start:id_start + bbox_num[i], :] bbox_num_i = bbox_num[i] id_start += bbox_num[i] bboxes_list.append(bboxes_i) bbox_num_list.append(bbox_num_i) bboxes = paddle.concat(bboxes_list) bbox_num = paddle.concat(bbox_num_list) origin_shape = paddle.floor(im_shape / scale_factor + 0.5) if not self.export_onnx: origin_shape_list = [] scale_factor_list = [] # scale_factor: scale_y, scale_x for i in range(bbox_num.shape[0]): expand_shape = paddle.expand(origin_shape[i:i + 1, :], [bbox_num[i], 2]) scale_y, scale_x = scale_factor[i][0], scale_factor[i][1] scale = paddle.concat([scale_x, scale_y, scale_x, scale_y]) expand_scale = paddle.expand(scale, [bbox_num[i], 4]) origin_shape_list.append(expand_shape) scale_factor_list.append(expand_scale) self.origin_shape_list = paddle.concat(origin_shape_list) scale_factor_list = paddle.concat(scale_factor_list) else: # simplify the computation for bs=1 when exporting onnx scale_y, scale_x = scale_factor[0][0], scale_factor[0][1] scale = paddle.concat( [scale_x, scale_y, scale_x, scale_y]).unsqueeze(0) self.origin_shape_list = paddle.expand(origin_shape, [bbox_num[0], 2]) scale_factor_list = paddle.expand(scale, [bbox_num[0], 4]) # bboxes: [N, 6], label, score, bbox pred_label = bboxes[:, 0:1] pred_score = bboxes[:, 1:2] pred_bbox = bboxes[:, 2:] # rescale bbox to original image scaled_bbox = pred_bbox / scale_factor_list origin_h = self.origin_shape_list[:, 0] origin_w = self.origin_shape_list[:, 1] zeros = paddle.zeros_like(origin_h) # clip bbox to [0, original_size] x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros) y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros) x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros) y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros) pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1) # filter empty bbox keep_mask = nonempty_bbox(pred_bbox, return_mask=True) keep_mask = paddle.unsqueeze(keep_mask, [1]) pred_label = paddle.where(keep_mask, pred_label, paddle.ones_like(pred_label) * -1) pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1) retur

add flag skipping postprocess to support edgeboard hardware (#6719) * add flag skipping postprocess to support edgeboard hardware * add flag skipping postprocess to support edgeboard hardware * add flag skipping postprocess to support edgeboard hardware * add comment for the flag export_eb

get_pred

b41194eaed10a01409451e4d3ea7f8b4812cdd23

PaddleDetection

post_process.py

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

Python

def get_pred(self, bboxes, bbox_num, im_shape, scale_factor): if self.export_eb: # enable rcnn models for edgeboard hw to skip the following postprocess. return bboxes, bboxes, bbox_num if not self.export_onnx: bboxes_list = [] bbox_num_list = [] id_start = 0 fake_bboxes = paddle.to_tensor( np.array( [[0., 0.0, 0.0, 0.0, 1.0, 1.0]], dtype='float32')) fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32')) # add fake bbox when output is empty for each batch for i in range(bbox_num.shape[0]): if bbox_num[i] == 0: bboxes_i = fake_bboxes bbox_num_i = fake_bbox_num else: bboxes_i = bboxes[id_start:id_start + bbox_num[i], :] bbox_num_i = bbox_num[i] id_start += bbox_num[i] bboxes_list.append(bboxes_i) bbox_num_list.append(bbox_num_i) bboxes = paddle.concat(bboxes_list) bbox_num = paddle.concat(bbox_num_list) origin_shape = paddle.floor(im_shape / scale_factor + 0.5) if not self.export_onnx: origin_shape_list = [] scale_factor_list = [] # scale_factor: scale_y, scale_x for i in range(bbox_num.shape[0]): expand_shape = paddle.expand(origin_shape[i:i + 1, :], [bbox_num[i], 2]) scale_y, scale_x = scale_factor[i][0], scale_factor[i][1] scale = paddle.concat([scale_x, scale_y, scale_x, scale_y]) expand_scale = paddle.expand(scale, [bbox_num[i], 4]) origin_shape_list.append(expand_shape) scale_factor_list.append(expand_scale) self.origin_shape_list = paddle.concat(origin_shape_list) scale_factor_list = paddle.concat(scale_factor_list) else: # simplify the computation for bs=1 when exporting onnx scale_y, scale_x = scale_factor[0][0], scale_factor[0][1] scale = paddle.concat( [scale_x, scale_y, scale_x, scale_y]).unsqueeze(0) self.origin_shape_list = paddle.expand(origin_shape, [bbox_num[0], 2]) scale_factor_list = paddle.expand(scale, [bbox_num[0], 4]) # bboxes: [N, 6], label, score, bbox pred_label = bboxes[:, 0:1] pred_score = bboxes[:, 1:2] pred_bbox = bboxes[:, 2:] # rescale bbox to original image scaled_bbox = pred_bbox / scale_factor_list origin_h = self.origin_shape_list[:, 0] origin_w = self.origin_shape_list[:, 1] zeros = paddle.zeros_like(origin_h) # clip bbox to [0, original_size] x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros) y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros) x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros) y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros) pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1) # filter empty bbox keep_mask = nonempty_bbox(pred_bbox, return_mask=True) keep_mask = paddle.unsqueeze(keep_mask, [1]) pred_label = paddle.where(keep_mask, pred_label, paddle.ones_like(pred_label) * -1) pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1) return bboxes, pred_result, bbox_num

homeassistant/components/nexia/entity.py

def _signal_zone_update(self): async_dispatcher_send(self.hass, f"{SIGNAL_ZONE_UPD

Update nexia to use asyncio (#72108)

_signal_zone_update

d8a580a90f8bf3206b31619493f4e653fceb3f4b

core

entity.py

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

Python

def _signal_zone_update(self): async_dispatcher_send(self.hass, f"{SIGNAL_ZONE_UPDATE}-{self._zone.zone_id}")

@pytest.mark.parametrize("y", [([1.0, -2.0, 0.0]), ([0.0, 0.0, 0.0])])

sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py

def test_asymmetric_error(quantile): n_samples = 10_000 rng = np.random.RandomState(42) # take care that X @ coef + intercept > 0 X = np.concatenate( ( np.abs(rng.randn(n_samples)[:, None]), -rng.randint(2, size=(n_samples, 1)), ), a

FEA add quantile HGBT (#21800)

test_asymmetric_error

5ad3421a5b5759ecfaaab93406592d988f5d487f

scikit-learn

test_gradient_boosting.py

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

Python

def test_asymmetric_error(quantile): n_samples = 10_000 rng = np.random.RandomState(42) # take care that X @ coef + intercept > 0 X = np.concatenate( ( np.abs(rng.randn(n_samples)[:, None]), -rng.randint(2, size=(n_samples, 1)), ), axis=1, ) intercept = 1.23 coef = np.array([0.5, -2]) # For an exponential distribution with rate lambda, e.g. exp(-lambda * x), # the quantile at level q is: # quantile(q) = - log(1 - q) / lambda # scale = 1/lambda = -quantile(q) / log(1-q) y = rng.exponential( scale=-(X @ coef + intercept) / np.log(1 - quantile), size=n_samples ) model = HistGradientBoostingRegressor( loss="quantile", quantile=quantile, max_iter=25, random_state=0, max_leaf_nodes=10, ).fit(X, y) assert_allclose(np.mean(model.predict(X) > y), quantile, rtol=1e-2) pinball_loss = PinballLoss(quantile=quantile) loss_true_quantile = pinball_loss(y, X @ coef + intercept) loss_pred_quantile = pinball_loss(y, model.predict(X)) # we are overfitting assert loss_pred_quantile <= loss_true_quantile @pytest.mark.parametrize("y", [([1.0, -2.0, 0.0]), ([0.0, 0.0, 0.0])])

homeassistant/components/gtfs/sensor.py

def update(self) -> None: with self.lock: # Fetch valid stop information once if not self._origin: stops = self._pygtfs.stops_by_id(self.origin) if not stops: self._available = False _LOGGER.warning("Origin stop ID %s not found", self.origin) return self._origin = stops[0] if not self._destination: stops = self._pygtfs.stops_by_id(self.destination) if not stops: self._available = False _LOGGER.warning(

Move attribution to standalone attribute [e-g] (#80513)

update

c717fd19de01fc822d146cc5e353959dfa86d5f7

core

sensor.py

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

Python

def update(self) -> None: with self.lock: # Fetch valid stop information once if not self._origin: stops = self._pygtfs.stops_by_id(self.origin) if not stops: self._available = False _LOGGER.warning("Origin stop ID %s not found", self.origin) return self._origin = stops[0] if not self._destination: stops = self._pygtfs.stops_by_id(self.destination) if not stops: self._available = False _LOGGER.warning( "Destination stop ID %s not found", self.destination ) return self._destination = stops[0] self._available = True # Fetch next departure self._departure = get_next_departure( self._pygtfs, self.origin, self.destination, self._offset, self._include_tomorrow, ) # Define the state as a UTC timestamp with ISO 8601 format if not self._departure: self._state = None else: self._state = self._departure["departure_time"].replace( tzinfo=dt_util.UTC ) # Fetch trip and route details once, unless updated if not self._departure: self._trip = None else: trip_id = self._departure["trip_id"] if not self._trip or self._trip.trip_id != trip_id: _LOGGER.debug("Fetching trip details for %s", trip_id) self._trip = self._pygtfs.trips_by_id(trip_id)[0] route_id = self._departure["route_id"] if not self._route or self._route.route_id != route_id: _LOGGER.debug("Fetching route details for %s", route_id) self._route = self._pygtfs.routes_by_id(route_id)[0] # Fetch agency details exactly once if self._agency is None and self._route: _LOGGER.debug("Fetching agency details for %s", self._route.agency_id) try: self._agency = self._pygtfs.agencies_by_id(self._route.agency_id)[0] except IndexError: _LOGGER.warning( "Agency ID '%s' was not found in agency table, " "you may want to update the routes database table " "to fix this missing reference", self._route.agency_id, ) self._agency = False # Assign attributes, icon and name self.update_attributes() if self._agency: self._attr_attribution = self._agency.agency_name else: self._attr_attribution = None if self._route: self._icon = ICONS.get(self._route.route_type, ICON) else: self._icon = ICON name = ( f"{getattr(self._agency, 'agency_name', DEFAULT_NAME)} " f"{self.origin} to {self.destination} next departure" ) if not self._departure: name = f"{DEFAULT_NAME}" self._name = self._custom_name or name

tests/components/homekit_controller/test_binary_sensor.py

async def test_carbon_monoxide_sensor_read_state(hass, utcnow): helper = await setup_test_component(hass, create_carbon_monoxide_sensor_service) await helper.async_update( ServicesTypes.CARBON_MONOXIDE_SENSOR, {CharacteristicsTypes.CARBON_MONOXIDE_DETECTED: 0}, ) state = await helper.poll_and_get_state() assert state.state == "off" await helper.async_update( ServicesTypes.CARBON_MONOXIDE_SENSOR, {CharacteristicsTypes.CARBON_MONOXIDE_DETECTED: 1}, ) state = await helper.poll_and_get_state() assert state.state == "on" assert state.attributes["d

Fix HomeKit Controller device class for CO Sensors (#69949)

test_carbon_monoxide_sensor_read_state

ad5d7a845b73b6ef09b111597d6c542be4781b07

core

test_binary_sensor.py

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

Python

async def test_carbon_monoxide_sensor_read_state(hass, utcnow): helper = await setup_test_component(hass, create_carbon_monoxide_sensor_service) await helper.async_update( ServicesTypes.CARBON_MONOXIDE_SENSOR, {CharacteristicsTypes.CARBON_MONOXIDE_DETECTED: 0}, ) state = await helper.poll_and_get_state() assert state.state == "off" await helper.async_update( ServicesTypes.CARBON_MONOXIDE_SENSOR, {CharacteristicsTypes.CARBON_MONOXIDE_DETECTED: 1}, ) state = await helper.poll_and_get_state() assert state.state == "on" assert state.attributes["device_class"] == BinarySensorDeviceClass.CO

@dataclass

ludwig/utils/triton_utils.py

def save_config(self) -> TritonArtifact: device = self.device if self.inference_stage != PREDICTOR: device = "cpu" self.config = TritonConfig( self.full_model_name, self.input_features, self.output_features, self.max_batch_size, self.max_queue_delay_microseconds, device, self.model_instance_count, self.inference_stage, ) config_path = os.path.join(self.base_path, "config.pbtxt") with open(config_path, "w") as f: formatted_config = remove_empty_lines(self.config.get_model_conf

Triton ensemble export (#2251)

save_config

ed8d9cf20843744f18593b22fb6a30eaf5f325eb

ludwig

triton_utils.py

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

Python

def save_config(self) -> TritonArtifact: device = self.device if self.inference_stage != PREDICTOR: device = "cpu" self.config = TritonConfig( self.full_model_name, self.input_features, self.output_features, self.max_batch_size, self.max_queue_delay_microseconds, device, self.model_instance_count, self.inference_stage, ) config_path = os.path.join(self.base_path, "config.pbtxt") with open(config_path, "w") as f: formatted_config = remove_empty_lines(self.config.get_model_config()) f.write(formatted_config) config_artifact = TritonArtifact( model_name=self.full_model_name, model_version=self.model_version, platform="pytorch_libtorch", path=config_path, content_type="text/x-protobuf", content_length=os.path.getsize(config_path), ) return config_artifact @dataclass

tests/rest/admin/test_device.py

def test_unknown_device(self) -> None: url = "/_synapse/admin/v2/users/%s/devices/unknown_device" % urllib.parse.quote( self.other_user ) channel = self.make_request( "GET", url, access_token=self.admin_user_tok, ) self.assertEqual(404, channel.code, msg=channel.json_body) self.assertEqual(Codes.NOT_F

Use literals in place of `HTTPStatus` constants in tests (#13479) Replace - `HTTPStatus.NOT_FOUND` - `HTTPStatus.FORBIDDEN` - `HTTPStatus.UNAUTHORIZED` - `HTTPStatus.CONFLICT` - `HTTPStatus.CREATED` Signed-off-by: Dirk Klimpel <dirk@klimpel.org>

test_unknown_device

1595052b2681fb86c1c1b9a6028c1bc0d38a2e4b

synapse

test_device.py

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

Python

def test_unknown_device(self) -> None: url = "/_synapse/admin/v2/users/%s/devices/unknown_device" % urllib.parse.quote( self.other_user ) channel = self.make_request( "GET", url, access_token=self.admin_user_tok, ) self.assertEqual(404, channel.code, msg=channel.json_body) self.assertEqual(Codes.NOT_FOUND, channel.json_body["errcode"]) channel = self.make_request( "PUT", url, access_token=self.admin_user_tok, ) self.assertEqual(200, channel.code, msg=channel.json_body) channel = self.make_request( "DELETE", url, access_token=self.admin_user_tok, ) # Delete unknown device returns status 200 self.assertEqual(200, channel.code, msg=channel.json_body)

examples/research_projects/codeparrot/scripts/human_eval.py

def complete_code(accelerator, model, tokenizer, dataloader, n_tasks, batch_size=20, **gen_kwargs): gen_token_dict = defaultdict(list) # dict of list of generated tokens for step, batch in tqdm(enumerate(dataloader)): with torch.no_grad(): gen_kwargs["stopping_criteria"][0].start_length = batch["ids"].shape[-1] generated_tokens = accelerator.unwrap_model(model).generate( input_ids=batch["ids"][:, : batch["input_len"]], num_return_sequences=batch_size, **gen_kwargs ) # each task is generated batch_size times generated_tasks = batch["task_id"].repeat(batch_size) generated_tokens = accelerator.pad_across_processes( generated_tokens, dim=1, pad_index=tokenizer.pad_token_id ) generated_tokens, generated_tasks = accelerator.gather((generated_tokens, generated_tasks)) generated_tokens = generated_tokens.cpu().numpy() generated_tasks = generated_tasks.cpu().numpy() for task, generated_tokens in zip(generated_tasks, generated_tokens): gen_token_dict[task].append(generated_tokens) code_gens = [[] for _ in range(n_tasks)] for task, generated_tokens in gen_token_dict.items(): for s in generated_tokens: gen_code = tokenizer.decode(s, skip_special_tokens=True, clean_up_tokenization_spaces=True) code_gens[task].append(remove_last_block(gen_code)) return code_gens

Jia multi gpu eval (#16428) * add simple multi gpu complet * add human_eval_multi_gpu * use copy strategy to distribute across gpu, to avoid padding * add doc string * update code style * use task id to arrange output * truncate input to avoid zero pad * Stop the copy mechanism * update style * restore copies to scale better in distributed mode * update style * replace human eval * Apply suggestions from code review 1. Tokenize all input at the same time 2. use attention_mask to get the input length 3. other small fixes Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com> * correct typo and update docstring * update code style * remove num sample division constraint * remove max len calculation * use accelerator.gather once to speed up * use accelerate set_seed; update accelerate version * correct gather bug Co-authored-by: Leandro von Werra <lvwerra@users.noreply.github.com>

complete_code

4868a830db5f19f56712f540979d637368221d50

transformers

human_eval.py

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

Python

def complete_code(accelerator, model, tokenizer, dataloader, n_tasks, batch_size=20, **gen_kwargs): gen_token_dict = defaultdict(list) # dict of list of generated tokens for step, batch in tqdm(enumerate(dataloader)): with torch.no_grad(): gen_kwargs["stopping_criteria"][0].start_length = batch["ids"].shape[-1] generated_tokens = accelerator.unwrap_model(model).generate( input_ids=batch["ids"][:, : batch["input_len"]], num_return_sequences=batch_size, **gen_kwargs ) # each task is generated batch_size times generated_tasks = batch["task_id"].repeat(batch_size) generated_tokens = accelerator.pad_across_processes( generated_tokens, dim=1, pad_index=tokenizer.pad_token_id ) generated_tokens, generated_tasks = accelerator.gather((generated_tokens, generated_tasks)) generated_tokens = generated_tokens.cpu().numpy() generated_tasks = generated_tasks.cpu().numpy() for task, generated_tokens in zip(generated_tasks, generated_tokens): gen_token_dict[task].append(generated_tokens) code_gens = [[] for _ in range(n_tasks)] for task, generated_tokens in gen_token_dict.items(): for s in generated_tokens: gen_code = tokenizer.decode(s, skip_special_tokens=True, clean_up_tokenization_spaces=True) code_gens[task].append(remove_last_block(gen_code)) return code_gens

erpnext/crm/doctype/contract/contract.py

def get_status(start_date, end_date): if not end_date: return "Active" start_date = getdate(start_date) end_date = getdate(end_date) now_date = getdate(nowdate()) return "Active" if start_date <= now_date <= end_date els

style: format code with black

get_status

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

contract.py

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

Python

def get_status(start_date, end_date): if not end_date: return "Active" start_date = getdate(start_date) end_date = getdate(end_date) now_date = getdate(nowdate()) return "Active" if start_date <= now_date <= end_date else "Inactive"

PathPlanning/CubicSpline/cubic_spline_planner.py

def calc_position(self, s): x = self.sx.calc_position(s) y = self.sy.calc_positi

enhance cubic spline path doc (#698) * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cublic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc * enhance cubic spline path doc

calc_position

def289b723e9216830c2a7b2577cb31b55710167

PythonRobotics

cubic_spline_planner.py

https://github.com/AtsushiSakai/PythonRobotics.git

Python

def calc_position(self, s): x = self.sx.calc_position(s) y = self.sy.calc_position(s) return x, y

python3.10.4/Lib/contextlib.py

def push(self, exit): # We use an unbound method rather than a bound method to follow # the standard lookup behaviour for special methods. _cb_type = type(exit) try: exit_method = _cb_type.__exit__ except AttributeError: # Not a context manager, so assume it's a callable. sel

add python 3.10.4 for windows

push

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

contextlib.py

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

Python

def push(self, exit): # We use an unbound method rather than a bound method to follow # the standard lookup behaviour for special methods. _cb_type = type(exit) try: exit_method = _cb_type.__exit__ except AttributeError: # Not a context manager, so assume it's a callable. self._push_exit_callback(exit) else: self._push_cm_exit(exit, exit_method) return exit # Allow use as a decorator.

homeassistant/components/mpd/media_player.py

async def async_media_play(self) -> None: if se

Improve entity type hints [m] (#77816)

async_media_play

6355e682fa4aeb526570597d919ad1fb76755b9a

core

media_player.py

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

Python

async def async_media_play(self) -> None: if self._status["state"] == "pause": await self._client.pause(0) else: await self._client.play()

erpnext/regional/report/datev/datev.py

def download_datev_csv(filters): if isinstance(filters, str): filters = json.loads(filters) validate(filters) company = filters.get("company") fiscal_year = get_fiscal_year(date=filters.get("from_date"), company=company) filters["fiscal_year_start"] = fiscal_year[1] # set chart of accounts used coa = frappe.get_value("Company", company, "chart_of_accounts") filters["skr"] = "04" if "SKR04" in coa else ("03" if "SKR03" in coa else "") datev_settings = frappe.get_doc("DATEV Settings", company) filters["account_number_length"] = datev_settings.account_number_length filters["temporary_against_account_number"] = datev_settings.temporary_against_account_number transactions = get_transactions(filters) account_names = get_account_names(filters) customers = get_customers(filters) suppliers = get_suppliers(filters) zip_name = "{} DATEV.zip".format(frappe.utils.datetime.date.today()) zip_and_download( zip_name, [ { "file_name": "EXTF_Buchungsstapel.csv", "csv_data": get_datev_csv(transactions, filters, csv_class=Transactions), }, { "file_name": "EXTF_Kontenbeschriftungen.csv", "csv_data": get_datev_csv(account_names, filters, csv_class=Accoun

style: format code with black

download_datev_csv

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

datev.py

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

Python

def download_datev_csv(filters): if isinstance(filters, str): filters = json.loads(filters) validate(filters) company = filters.get("company") fiscal_year = get_fiscal_year(date=filters.get("from_date"), company=company) filters["fiscal_year_start"] = fiscal_year[1] # set chart of accounts used coa = frappe.get_value("Company", company, "chart_of_accounts") filters["skr"] = "04" if "SKR04" in coa else ("03" if "SKR03" in coa else "") datev_settings = frappe.get_doc("DATEV Settings", company) filters["account_number_length"] = datev_settings.account_number_length filters["temporary_against_account_number"] = datev_settings.temporary_against_account_number transactions = get_transactions(filters) account_names = get_account_names(filters) customers = get_customers(filters) suppliers = get_suppliers(filters) zip_name = "{} DATEV.zip".format(frappe.utils.datetime.date.today()) zip_and_download( zip_name, [ { "file_name": "EXTF_Buchungsstapel.csv", "csv_data": get_datev_csv(transactions, filters, csv_class=Transactions), }, { "file_name": "EXTF_Kontenbeschriftungen.csv", "csv_data": get_datev_csv(account_names, filters, csv_class=AccountNames), }, { "file_name": "EXTF_Kunden.csv", "csv_data": get_datev_csv(customers, filters, csv_class=DebtorsCreditors), }, { "file_name": "EXTF_Lieferanten.csv", "csv_data": get_datev_csv(suppliers, filters, csv_class=DebtorsCreditors), }, ], )

nltk/parse/util.py

def taggedsent_to_conll(sentence):

Docstring tests (#3050) * fixed pytests * fixed more pytests * fixed more pytest and changed multiline pytest issues fixes for snowball.py and causal.py * fixed pytests (mainly multiline or rounding issues) * fixed treebank pytests, removed test for return_string=True (deprecated) * fixed destructive.py pytests, removed test for return_string=True (deprecated) * fixed pytest (rounding issues) * fixed pytest (initialised missing object) * fixed pytest (formatting issues) * fixed pytest (formatting issues) * fixed pytest (formatting issues) * added pytest +SKIP for deprecated module stanford * updated AUTHORS.md * changed docstring corrections by usage of ELLIPSIS and different roundings * fixed AUTHORS.md to be consistent * Fix framenet doctest formatting with pprint * Change docstring on MultiListBox.__init__ I believe the original typo was misinterpreted and changed to something that was not originally intended. Co-authored-by: Jan Lennartz <jan.lennartz@ing.com> Co-authored-by: Tom Aarsen <37621491+tomaarsen@users.noreply.github.com> Co-authored-by: Tom Aarsen <Cubiegamedev@gmail.com>

taggedsent_to_conll

8a4cf5d94eb94b6427c5d1d7907ba07b119932c5

nltk

util.py

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

Python

def taggedsent_to_conll(sentence): for (i, (word, tag)) in enumerate(sentence, start=1): input_str = [str(i), word, "_", tag, tag, "_", "0", "a", "_", "_"] input_str = "\t".join(input_str) + "\n" yield input_str

django/utils/encoding.py

def get_system_encoding(): try: encoding = locale.getdefaultlocale()[1] or "ascii" codecs.lookup(en

Refs #33476 -- Reformatted code with Black.

get_system_encoding

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

encoding.py

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

Python

def get_system_encoding(): try: encoding = locale.getdefaultlocale()[1] or "ascii" codecs.lookup(encoding) except Exception: encoding = "ascii" return encoding DEFAULT_LOCALE_ENCODING = get_system_encoding()

wagtail/contrib/modeladmin/options.py

def get_menu_item(self): if self.modeladmin_instances: submenu = Menu(items=self.g

Deprecate wagtail.contrib.modeladmin.menus.SubMenu in favour of wagtail.admin.menu.Menu The Menu class was not originally designed to accept menu items at constructor time (instead requiring them to be passed via hooks); ModelAdmin's SubMenu class patched this functionality in, and the documentation for extending admin views piggybacked on this. Add this functionality to the base Menu class so that we don't have this unnecessary dependency on ModelAdmin.

get_menu_item

b8a9a2d319b06fc2318d68d05b5a6cdf85b5b33d

wagtail

options.py

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

Python

def get_menu_item(self): if self.modeladmin_instances: submenu = Menu(items=self.get_submenu_items()) return GroupMenuItem(self, self.get_menu_order(), submenu)

lib/matplotlib/backend_bases.py

def _draw_text_as_path(self, gc, x, y, s, prop, angle, ismath): path, transform = self._get_text_path_transform(

Soft deprecate the textpath module (import from text instead) The textpath module was created in 2009, but the status has been a bit vague with many examples and exisiting code found on the internet importing from text instead. In this PR everything is changed to point at text, although textpath is still available for backwards compatibility.

_draw_text_as_path

9b8a598d00a4fcf9579415586053583ef80a1add

matplotlib

backend_bases.py

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

Python

def _draw_text_as_path(self, gc, x, y, s, prop, angle, ismath): path, transform = self._get_text_path_transform( x, y, s, prop, angle, ismath) color = gc.get_rgb() gc.set_linewidth(0.0) self.draw_path(gc, path, transform, rgbFace=color)

examples/research_projects/lxmert/modeling_frcnn.py

def __call__(self, match_quality_matrix): assert match_quality_matrix.dim() == 2 if match_quality_matrix.numel() == 0: default_matches = match_quality_matrix.new_full((match_quality_matrix.size(1),), 0, dtype=torch.int64) # When no gt boxes exist, we define IOU = 0 and therefore set labels # to `self.labels[0]`, which usually defaults to background class 0 # To choose to ignore instead, # can make labels=[-1,0,-1,1] + set appropriate thresholds default_match_labels = match_quality_matrix.new_full(

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

__call__

afe5d42d8d1d80af911ed980c2936bfe887078f6

transformers

modeling_frcnn.py

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

Python

def __call__(self, match_quality_matrix): assert match_quality_matrix.dim() == 2 if match_quality_matrix.numel() == 0: default_matches = match_quality_matrix.new_full((match_quality_matrix.size(1),), 0, dtype=torch.int64) # When no gt boxes exist, we define IOU = 0 and therefore set labels # to `self.labels[0]`, which usually defaults to background class 0 # To choose to ignore instead, # can make labels=[-1,0,-1,1] + set appropriate thresholds default_match_labels = match_quality_matrix.new_full( (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8 ) return default_matches, default_match_labels assert torch.all(match_quality_matrix >= 0) # match_quality_matrix is M (gt) x N (predicted) # Max over gt elements (dim 0) to find best gt candidate for each prediction matched_vals, matches = match_quality_matrix.max(dim=0) match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8) for l, low, high in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]): low_high = (matched_vals >= low) & (matched_vals < high) match_labels[low_high] = l if self.allow_low_quality_matches: self.set_low_quality_matches_(match_labels, match_quality_matrix) return matches, match_labels

else:

python3.10.4/Lib/inspect.py

def ismemberdescriptor(object): return isinstance(object, types.MemberDescriptorType) else: #

add python 3.10.4 for windows

ismemberdescriptor

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

inspect.py

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

Python

def ismemberdescriptor(object): return isinstance(object, types.MemberDescriptorType) else: # Other implementations

modin/core/dataframe/base/exchange/dataframe_protocol/utils.py

def pandas_dtype_to_arrow_c(dtype) -> str: if isinstance(dtype, pandas.CategoricalDtype): return ArrowCTypes.INT64 elif dtype == np.dtype("O"): return ArrowCTypes.STRING format_str = getattr(ArrowCTypes, dtype.name.upper(), None) if format_str is not None: return format_str if is_datetime64_dtype(dtype): # Selecting the first char of resolution string: # dtype.str -> '<M8[ns]' resolution

FEAT-#4245: Define base interface for dataframe exchange protocol (#4246) Signed-off-by: Igoshev, Yaroslav <yaroslav.igoshev@intel.com> Co-authored-by: Dmitry Chigarev <dmitry.chigarev@intel.com>

pandas_dtype_to_arrow_c

fc539c3d70a40c9d7aabc5c50dd7280aa5e4637e

modin

utils.py

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

Python

def pandas_dtype_to_arrow_c(dtype) -> str: if isinstance(dtype, pandas.CategoricalDtype): return ArrowCTypes.INT64 elif dtype == np.dtype("O"): return ArrowCTypes.STRING format_str = getattr(ArrowCTypes, dtype.name.upper(), None) if format_str is not None: return format_str if is_datetime64_dtype(dtype): # Selecting the first char of resolution string: # dtype.str -> '<M8[ns]' resolution = re.findall(r"\[(.*)\]", dtype.str)[0][:1] return ArrowCTypes.TIMESTAMP.format(resolution=resolution, tz="") raise NotImplementedError( f"Convertion of {dtype} to Arrow C format string is not implemented." )

pandas/core/dtypes/inference.py

def is_file_like(obj) -> bool: if not (hasattr(obj, "read") or hasattr(obj, "write")): return False

Fix some dosctring RT02 error (#50197)

is_file_like

bce995817caf00ab5e82cb4cf1b540f1530cf4ea

pandas

inference.py

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

Python

def is_file_like(obj) -> bool: if not (hasattr(obj, "read") or hasattr(obj, "write")): return False return bool(hasattr(obj, "__iter__"))

octavia-cli/octavia_cli/apply/resources.py

def update(self) -> Union[SourceRead, DestinationRead, ConnectionRead]: return self._create_or_update(self._update_fn, self.update_payload)

🐙 octavia-cli: `apply` connections (#10881)

update

56bf982cb96f831fe04f5e44a92ee4a669b9e16a

airbyte

resources.py

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

Python

def update(self) -> Union[SourceRead, DestinationRead, ConnectionRead]: return self._create_or_update(self._update_fn, self.update_payload)

tests/handlers/test_oidc.py

def test_callback_session(self) -> None: request = Mock(spec=["args", "getCookie", "cookies"]) # Missing cookie request.args = {} request.getCookie.return_value = None self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("missing_session", "No session cookie found") # Missing session parameter request.args = {} request.getCookie.return_value = "session" self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("invalid_request", "State parameter is missing") # Invalid cookie request.args = {} request.args[b"state"] = [b"state"] request.getCookie.return_value = "session" self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("invalid_session") # Mismatching session session = self._generate_oidc_session_token( state="state", nonce="nonce", client_redirect_url="http://client/redirect", ) request.args = {} request.args[b"state"] = [b"mismatching state"] request.getCookie.return_value = session self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("mismatching_session") # Valid session request.args = {} request.args[b"state"] = [b"state"] request.getCookie.return_value = session self.get_success(self.handler.handle_oidc_callback(request)) self.assert

Add type hints to some tests/handlers files. (#12224)

test_callback_session

5dd949bee6158a8b651db9f2ae417a62c8184bfd

synapse

test_oidc.py

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

Python

def test_callback_session(self) -> None: request = Mock(spec=["args", "getCookie", "cookies"]) # Missing cookie request.args = {} request.getCookie.return_value = None self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("missing_session", "No session cookie found") # Missing session parameter request.args = {} request.getCookie.return_value = "session" self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("invalid_request", "State parameter is missing") # Invalid cookie request.args = {} request.args[b"state"] = [b"state"] request.getCookie.return_value = "session" self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("invalid_session") # Mismatching session session = self._generate_oidc_session_token( state="state", nonce="nonce", client_redirect_url="http://client/redirect", ) request.args = {} request.args[b"state"] = [b"mismatching state"] request.getCookie.return_value = session self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("mismatching_session") # Valid session request.args = {} request.args[b"state"] = [b"state"] request.getCookie.return_value = session self.get_success(self.handler.handle_oidc_callback(request)) self.assertRenderedError("invalid_request")

src/prefect/filesystems.py

def _create_repo_url(self) -> str: url_components = urllib.parse.urlparse(self.repository_url) if url_components.scheme == "https" and self.credentials is not None: repo_url = url_components.netloc + url_components.path updated_components = url_components._replace( netloc=f"{self.credentials.get_se

Add private repos

_create_repo_url

bbee097653559003fa0db61ab00f1ff8567eea9a

prefect

filesystems.py

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

Python

def _create_repo_url(self) -> str: url_components = urllib.parse.urlparse(self.repository_url) if url_components.scheme == "https" and self.credentials is not None: repo_url = url_components.netloc + url_components.path updated_components = url_components._replace( netloc=f"{self.credentials.get_secret_value()}@{url_components.netloc}" ) full_url = urllib.parse.urlunparse(updated_components) else: full_url = self.repository_url return full_url

tests/admin_filters/tests.py

def test_lookup_with_dynamic_value(self):

Refs #33476 -- Reformatted code with Black.

test_lookup_with_dynamic_value

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_lookup_with_dynamic_value(self): modeladmin = DepartmentFilterDynamicValueBookAdmin(Book, site)

tests/test_tokenization_common.py

def test_batch_encode_dynamic_overflowing(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"): if is_torch_available(): returned_tensor = "pt" elif is_tf_available(): returned_tensor = "tf" elif is_flax_available(): returned_tensor = "jax" else: return if not tokenizer.pad_token or tokenizer.pad_token_id < 0: return tokens =

Fix custom tokenizers test (#19052) * Fix CI for custom tokenizers * Add nightly tests * Run CI, run! * Fix paths * Typos * Fix test

test_batch_encode_dynamic_overflowing

f7ce4f1ff789c11f129597a1171b5d549d102e09

transformers

test_tokenization_common.py

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

Python

def test_batch_encode_dynamic_overflowing(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"): if is_torch_available(): returned_tensor = "pt" elif is_tf_available(): returned_tensor = "tf" elif is_flax_available(): returned_tensor = "jax" else: return if not tokenizer.pad_token or tokenizer.pad_token_id < 0: return tokens = tokenizer.encode_plus( "HuggingFace is solving NLP one commit at a time", max_length=6, padding=True, truncation=True, return_tensors=returned_tensor, return_overflowing_tokens=True, ) for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()): self.assertEqual(len(tokens[key].shape), 2) # Mono sample tokens = tokenizer.batch_encode_plus( ["HuggingFace is solving NLP one commit at a time"], max_length=6, padding=True, truncation="only_first", return_tensors=returned_tensor, return_overflowing_tokens=True, ) for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()): self.assertEqual(len(tokens[key].shape), 2) self.assertEqual(tokens[key].shape[-1], 6) # Multi sample tokens = tokenizer.batch_encode_plus( ["HuggingFace is solving NLP one commit at a time", "Very tiny input"], max_length=6, padding=True, truncation="only_first", return_tensors=returned_tensor, return_overflowing_tokens=True, ) for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()): self.assertEqual(len(tokens[key].shape), 2) self.assertEqual(tokens[key].shape[-1], 6)

@keras_export("keras.optimizers.get")

keras/optimizers/__init__.py

def deserialize(config, custom_objects=None): # loss_scale_optimizer has a direct dependency of optimizer, import here # rather than top to avoid the cyclic dependency. from keras.mixed_precision import ( loss_scale_optimizer, ) # pylint: disable=g-import-not-at-top all_classes = { "adadelta": adadelta_v2.Adadelta, "adagrad": adagrad_v2.Adagrad, "adam": adam_v2.Adam, "adamax": adamax_v2.Adamax, "experimentaladadelta": adadelta_experimental.Adadelta, "experimentaladagrad": adagrad_experimental.Adagrad, "experimentaladam": adam_experimental.Adam, "experimentalsgd": sgd_experimental.SGD, "nadam": nadam_v2.Nadam, "rmsprop": rmsprop_v2.RMSprop, "sgd": gradient_descent_v2.SGD, "ftrl": ftrl.Ftrl, "lossscaleoptimizer": loss_scale_optimizer.LossScaleOptimizer, "lossscaleoptimizerv3": loss_scale_optimizer.LossScaleOptimizerV3, # LossScaleOptimizerV1 was an old version of LSO that was removed. # Deserializing it turns it into a LossScaleOptimizer "lossscaleoptimizerv1": loss_scale_optimizer.LossScaleOptimizer, } # Make deserialization case-insensitive for built-in optimizers. if config["class_name"].lower() in all_classes: config["class_name"] = config["class_name"].lower() return deserialize_keras_object( config, module_objects=all_classes, custom_objects=custom_objects, printable_module_name="optimizer", ) @keras_export("keras

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

deserialize

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

__init__.py

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

Python

def deserialize(config, custom_objects=None): # loss_scale_optimizer has a direct dependency of optimizer, import here # rather than top to avoid the cyclic dependency. from keras.mixed_precision import ( loss_scale_optimizer, ) # pylint: disable=g-import-not-at-top all_classes = { "adadelta": adadelta_v2.Adadelta, "adagrad": adagrad_v2.Adagrad, "adam": adam_v2.Adam, "adamax": adamax_v2.Adamax, "experimentaladadelta": adadelta_experimental.Adadelta, "experimentaladagrad": adagrad_experimental.Adagrad, "experimentaladam": adam_experimental.Adam, "experimentalsgd": sgd_experimental.SGD, "nadam": nadam_v2.Nadam, "rmsprop": rmsprop_v2.RMSprop, "sgd": gradient_descent_v2.SGD, "ftrl": ftrl.Ftrl, "lossscaleoptimizer": loss_scale_optimizer.LossScaleOptimizer, "lossscaleoptimizerv3": loss_scale_optimizer.LossScaleOptimizerV3, # LossScaleOptimizerV1 was an old version of LSO that was removed. # Deserializing it turns it into a LossScaleOptimizer "lossscaleoptimizerv1": loss_scale_optimizer.LossScaleOptimizer, } # Make deserialization case-insensitive for built-in optimizers. if config["class_name"].lower() in all_classes: config["class_name"] = config["class_name"].lower() return deserialize_keras_object( config, module_objects=all_classes, custom_objects=custom_objects, printable_module_name="optimizer", ) @keras_export("keras.optimizers.get")

sympy/combinatorics/permutations.py

def commutes_with(self, other): a = s

Updated import locations

commutes_with

498015021131af4dbb07eb110e5badaba8250c7b

sympy

permutations.py

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

Python

def commutes_with(self, other): a = self.array_form b = other.array_form return _af_commutes_with(a, b)

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

def get_all_styles(): yield from STYLE_MAP for name, _ in find_plugin_styles(): yield

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

get_all_styles

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

__init__.py

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

Python

def get_all_styles(): yield from STYLE_MAP for name, _ in find_plugin_styles(): yield name

recommenders/utils/python_utils.py

def lift(cooccurrence): diag_rows, diag_cols = _get_row_and_column_matrix(co

Add new item similarity metrics for SAR (#1754) * Add mutual information similarity in SAR * Add lexicographers mutual information similarity for SAR * Add cosine similarity for SAR * Add inclusion index for SAR * Typos * Change SARSingleNode to SAR * Convert item similarity matrix to np.array * Update * Update SAR tests * Remove unused imports * Add explanations for new similarity metrics

lift

1d7341e93d1f03387699fb3c6ae0b6c0e464296f

recommenders

python_utils.py

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

Python

def lift(cooccurrence): diag_rows, diag_cols = _get_row_and_column_matrix(cooccurrence.diagonal()) with np.errstate(invalid="ignore", divide="ignore"): result = cooccurrence / (diag_rows * diag_cols) return np.array(result)

lib/matplotlib/artist.py

def update(self, props): return self._update_props( props, "{cls.__name__!r} object has no property {prop_name!r}")

Clarify error message for bad keyword arguments. `plot([], [], foo=42)` previously emitted ``` 'Line2D' object has no property 'foo' ``` which refers to the Matplotlib-specific concept of "properties". It now instead emits ``` Line2D.set() got an unexpected keyword argument 'foo' ``` which is modeled after the standard error message for unknown keyword arguments. (To maximize backcompat, the implementation goes through a new _internal_update, which does *not* error when the same prop is passed under different aliases. This could be changed later, but is not the goal of this PR.)

update

d69be2554cf6d1ac711bf433b1d6f176e3290d4f

matplotlib

artist.py

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

Python

def update(self, props): return self._update_props( props, "{cls.__name__!r} object has no property {prop_name!r}")

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

def new_workers_size(self): remote_resources = ray.available_resources() max_remote_workers = self._max_workers new_remote_workers = min(remote_resources.get("CPU"

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

new_workers_size

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

worker_group.py

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

Python

def new_workers_size(self): remote_resources = ray.available_resources() max_remote_workers = self._max_workers new_remote_workers = min(remote_resources.get("CPU", 0), max_remote_workers) if self._use_gpu: new_remote_workers = min(remote_resources.get("GPU", 0), new_remote_workers) return new_remote_workers

scapy/arch/windows/__init__.py

def setmonitor(self, enable=True): # type: (bool) -> bool # We must reset the monitor cache if enable: res = self.setmode('monitor') else: res = self.setmode('managed') if not res: log_runtime.error("Npcap WlanHelper returned with an error code !") self.cache_mode = None t

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

setmonitor

a2b7a28faff1db058dd22ce097a268e0ad5d1d33

scapy

__init__.py

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