Datasets:

semeru

/

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

like 0

Follow

Semeru Lab 7

keras/layers/reshaping/reshape.py

def _fix_unknown_dimension(self, input_shape, output_shape): output_shape = list(output_shape) msg = ( "total size of new array must be unchanged, " "input_shape = {}, output_shape = {}".format( input_shape, output_shape ) ) known, unknown = 1, None for index, dim in enumerate(output_shape): if dim < 0: if unknown is None: unknown = index else: raise ValueError( f"There must be at most one unknown dimension in " f"output_shape. Received: output_shape={output_shape}." ) else: known *= dim original = np.prod(input_shape, dtype=int) if unknown is not None: if known == 0 or original % known != 0: raise ValueError(msg) output_shape[unknown] = original // known elif original != known: raise ValueError(msg) return output_

reduce layers line-too-long

_fix_unknown_dimension

8401e08334d4b1f102a6ee9479738bacfee0600c

keras

reshape.py

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

Python

def _fix_unknown_dimension(self, input_shape, output_shape): output_shape = list(output_shape) msg = ( "total size of new array must be unchanged, " "input_shape = {}, output_shape = {}".format( input_shape, output_shape ) ) known, unknown = 1, None for index, dim in enumerate(output_shape): if dim < 0: if unknown is None: unknown = index else: raise ValueError( f"There must be at most one unknown dimension in " f"output_shape. Received: output_shape={output_shape}." ) else: known *= dim original = np.prod(input_shape, dtype=int) if unknown is not None: if known == 0 or original % known != 0: raise ValueError(msg) output_shape[unknown] = original // known elif original != known: raise ValueError(msg) return output_shape

numpy/lib/tests/test_loadtxt.py

def test_converter_with_unicode_dtype(): txt = StringIO('abc,def\nrst,xyz') conv = bytes.upper res = np.loadtxt( txt, dtype=np.dtype("U3"), converters=conv, delimiter=",") expected = np.array([['ABC', 'DEF'], ['RST', 'XYZ']]) assert_equal(res, expec

TST,STY: Add small additional tests for converters/usecols Also fix style a bit to silence linter (hopefully), removes some black style, but I am not too opinionated about that :)

test_converter_with_unicode_dtype

1e6b72b42292e62c1c86e4f77e30324e43aaa218

numpy

test_loadtxt.py

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

Python

def test_converter_with_unicode_dtype(): txt = StringIO('abc,def\nrst,xyz') conv = bytes.upper res = np.loadtxt( txt, dtype=np.dtype("U3"), converters=conv, delimiter=",") expected = np.array([['ABC', 'DEF'], ['RST', 'XYZ']]) assert_equal(res, expected)

src/prefect/orion/database/alembic_commands.py

def alembic_stamp(revision): # lazy import for performance import alembic.command alemb

code review revisions pt3

alembic_stamp

36e7e0838aeaffc9492b330297e4905f3ab4b11f

prefect

alembic_commands.py

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

Python

def alembic_stamp(revision): # lazy import for performance import alembic.command alembic.command.stamp(alembic_config(), revision=revision)

jax/_src/pjit.py

def unflatten_superdims(assignment): def check(cond): if cond: return raise NotImplementedError("Failed to convert OpSharding into a ShardingSpec. " "Please open a bug report!") flat_assignment = np.asarray(assignment, dtype=np.int64) check(flat_assignment[0] == 0) dims = [] while flat_assignment.size > 1: stride = flat_assignment[1] for i in range(len(flat_assignment)): if flat_assignment[i] != i * stride: break else: # After this loop i should point to an "element after the sequence", so # we have t

Move `pjit.py` to `jax/_src` in preparation for merging the `jit` and `pjit` frontend APIs PiperOrigin-RevId: 495944279

unflatten_superdims

4b587fa1f0049db5366fd04812ab940d80a71a22

jax

pjit.py

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

Python

def unflatten_superdims(assignment): def check(cond): if cond: return raise NotImplementedError("Failed to convert OpSharding into a ShardingSpec. " "Please open a bug report!") flat_assignment = np.asarray(assignment, dtype=np.int64) check(flat_assignment[0] == 0) dims = [] while flat_assignment.size > 1: stride = flat_assignment[1] for i in range(len(flat_assignment)): if flat_assignment[i] != i * stride: break else: # After this loop i should point to an "element after the sequence", so # we have to increment it if the whole array is a strided sequence. i += 1 size = i dims.append((size, stride)) assert size > 1 # Ensure progress flat_assignment = flat_assignment[::size] return dims

tests/sentry/event_manager/test_event_manager.py

def test_perf_issue_no_associate_error_event(self): self.project.update_option("sentry:performance_issue_creation_rate", 1.0) with mock.patch("sentry_sdk.tracing.Span.containing_transaction"), self.feature( { "projects:performance-suspect-spans-ingestion": True, "organizations:performance-issues-ingest": True, } ): manager = EventManager(make_event()) manager.normalize() event = manager.save(self.project.id) assert len(event.groups) == 1 # sneakily make the group type wrong group = event.groups[0] group.type = GroupType.PERFORMANCE_N_PLUS_ONE.value group.save() manager = EventManager(make_event()) manager.normalize() event = manager.

chore(perf issues): Check group type before adding event (#39171) Ensure the group type matches the kind of event before association, e.g. don't put an error event on a performance issue and vice versa.

test_perf_issue_no_associate_error_event

bbd7137b3d379744265f46564d5878490046dd3b

sentry

test_event_manager.py

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

Python

def test_perf_issue_no_associate_error_event(self): self.project.update_option("sentry:performance_issue_creation_rate", 1.0) with mock.patch("sentry_sdk.tracing.Span.containing_transaction"), self.feature( { "projects:performance-suspect-spans-ingestion": True, "organizations:performance-issues-ingest": True, } ): manager = EventManager(make_event()) manager.normalize() event = manager.save(self.project.id) assert len(event.groups) == 1 # sneakily make the group type wrong group = event.groups[0] group.type = GroupType.PERFORMANCE_N_PLUS_ONE.value group.save() manager = EventManager(make_event()) manager.normalize() event = manager.save(self.project.id) assert len(event.groups) == 0

python/ray/tests/test_autoscaler.py

def testNodeTerminatedDuringUpdate(self): cluster_config = copy.deepcopy(MOCK_DEFAULT_CONFIG) cluster_config["available_node_types"]["ray.worker.default"]["min_workers"] = 2 cluster_config["worker_start_ray_commands"] = ["ray_start_cmd"] # Don't need the extra node type or a docker config. cluster_config["head_node_type"] = ["ray.worker.default"] del cluster_config["available_node_types"]["ray.head.default"] del cluster_config["docker"] config_path = self.write_config(cluster_config) self.provider = MockProvider() runner = MockProcessRunner() lm = LoadMetrics() mock_metrics = Mock(spec=AutoscalerPrometheusMetrics()) autoscaler = MockAutoscaler( config_path, lm, MockNodeInfoStub(), max_failures=0, process_runner=runner, update_interval_s=0, prom_metrics=mock_metrics, ) # Scale up to two up-to-date workers autoscaler.update() self.waitForNodes(2) self.provider.finish_starting_nodes() autoscaler.update() self.waitForNodes(2, tag_filters={TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE}) # Mark both nodes as unhealthy for _ in range(5): if autoscaler.updaters: time.sleep(0.05) autoscaler.update() lm.last_heartbeat_time_by_ip["172.0.0.0"] = 0 lm.last_hear

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

testNodeTerminatedDuringUpdate

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

test_autoscaler.py

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

Python

def testNodeTerminatedDuringUpdate(self): cluster_config = copy.deepcopy(MOCK_DEFAULT_CONFIG) cluster_config["available_node_types"]["ray.worker.default"]["min_workers"] = 2 cluster_config["worker_start_ray_commands"] = ["ray_start_cmd"] # Don't need the extra node type or a docker config. cluster_config["head_node_type"] = ["ray.worker.default"] del cluster_config["available_node_types"]["ray.head.default"] del cluster_config["docker"] config_path = self.write_config(cluster_config) self.provider = MockProvider() runner = MockProcessRunner() lm = LoadMetrics() mock_metrics = Mock(spec=AutoscalerPrometheusMetrics()) autoscaler = MockAutoscaler( config_path, lm, MockNodeInfoStub(), max_failures=0, process_runner=runner, update_interval_s=0, prom_metrics=mock_metrics, ) # Scale up to two up-to-date workers autoscaler.update() self.waitForNodes(2) self.provider.finish_starting_nodes() autoscaler.update() self.waitForNodes(2, tag_filters={TAG_RAY_NODE_STATUS: STATUS_UP_TO_DATE}) # Mark both nodes as unhealthy for _ in range(5): if autoscaler.updaters: time.sleep(0.05) autoscaler.update() lm.last_heartbeat_time_by_ip["172.0.0.0"] = 0 lm.last_heartbeat_time_by_ip["172.0.0.1"] = 0 # Expect both updates to be successful, no nodes in updating state assert mock_metrics.successful_updates.inc.call_count == 2 assert mock_metrics.worker_update_time.observe.call_count == 2 mock_metrics.updating_nodes.set.assert_called_with(0) assert not autoscaler.updaters # Set up process runner to terminate worker 0 during missed heartbeat # recovery and also cause the updater to fail.

openbb_terminal/cryptocurrency/overview/overview_controller.py

def call_cr(self, other_args): parser = argparse.ArgumentParser( prog="cr", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=, ) parser.add_argument( "-t", "--type", dest="type", type=str, help="Select interest rate type", default="supply", choices=["borrow", "supply"], ) parser.add_argument( "-c", "--cryptocurrrencies", dest="cryptos", type=loanscan_model.check_valid_coin, help=f, default="BTC,ETH,USDT,USDC", ) parser.add_argument( "-p", "--platforms", dest="platforms", type=loanscan_model.check_valid_platform, help=f, default="BlockFi,Ledn,SwissBorg,Youhodler", ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-t") ns_parser = parse_known_args_and_warn( parser, other_args, EXPORT_ON

Replaces coingecko deprecated commands (#1650) * removes cgproducts and cgplatforms and replaces with cr * add ignore word * added .openbb script * reverted crypto change * doc * failing tests * trying chart and fixed minh issues * Create barh * Fix ticker labels * fix test * loanscan mock * defi test * defi test * Fix defi test Co-authored-by: Minh Hoang <nminh.hoang1023@gmail.com> Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com>

call_cr

670402396e7e25e95bd6497affb143565d9bd4ea

OpenBBTerminal

overview_controller.py

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

Python

def call_cr(self, other_args): parser = argparse.ArgumentParser( prog="cr", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=, ) parser.add_argument( "-t", "--type", dest="type", type=str, help="Select interest rate type", default="supply", choices=["borrow", "supply"], ) parser.add_argument( "-c", "--cryptocurrrencies", dest="cryptos", type=loanscan_model.check_valid_coin, help=f, default="BTC,ETH,USDT,USDC", ) parser.add_argument( "-p", "--platforms", dest="platforms", type=loanscan_model.check_valid_platform, help=f, default="BlockFi,Ledn,SwissBorg,Youhodler", ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-t") ns_parser = parse_known_args_and_warn( parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED, limit=10 ) if ns_parser: loanscan_view.display_crypto_rates( rate_type=ns_parser.type, cryptos=ns_parser.cryptos, platforms=ns_parser.platforms, limit=ns_parser.limit, export=ns_parser.export, )

nni/retiarii/oneshot/pytorch/base_lightning.py

def export(self): result = {} for na

Lightning implementation for retiarii oneshot nas (#4479)

export

8b2eb425274cdb4537fbce4a315aec12a378d6db

nni

base_lightning.py

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

Python

def export(self): result = {} for name, module in self.nas_modules: if name not in result: result[name] = module.export() return result

tpot/base.py

def clean_pipeline_string(self, individual): dirty_string = str(individual) # There are many parameter prefixes in the pipeline strings, used solely for # making the terminal name unique, eg. LinearSVC__. parameter_prefixes = [ (m.start(), m.end()) for m in re.finditer(", [\w]+__", dirty_string) ] # We handle them in reverse so we do not mess up indices pretty = dirty_string for (start, end) in reversed(parameter_prefixes

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

clean_pipeline_string

388616b6247ca4ea8de4e2f340d6206aee523541

tpot

base.py

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

Python

def clean_pipeline_string(self, individual): dirty_string = str(individual) # There are many parameter prefixes in the pipeline strings, used solely for # making the terminal name unique, eg. LinearSVC__. parameter_prefixes = [ (m.start(), m.end()) for m in re.finditer(", [\w]+__", dirty_string) ] # We handle them in reverse so we do not mess up indices pretty = dirty_string for (start, end) in reversed(parameter_prefixes): pretty = pretty[: start + 2] + pretty[end:] return pretty

sympy/polys/matrices/linsolve.py

def _lin_eq2dict(a, symset): if a in symset: return S.Zero, {a: S.One} elif a.is_Add: terms_list = defaultdict(list) coeff_list = [] for ai in a.args: ci, ti = _lin_eq2dict(ai, symset) coeff_list.append(ci) for mij, cij in ti.items(): terms_list[mij].append(cij) coeff = Add(*coeff_list) terms = {sym: Add(*coeffs) for sym, coeffs in t

Revert "solve changes"

_lin_eq2dict

5534ff6796b8d515192576f771af8488a838775c

sympy

linsolve.py

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

Python

def _lin_eq2dict(a, symset): if a in symset: return S.Zero, {a: S.One} elif a.is_Add: terms_list = defaultdict(list) coeff_list = [] for ai in a.args: ci, ti = _lin_eq2dict(ai, symset) coeff_list.append(ci) for mij, cij in ti.items(): terms_list[mij].append(cij) coeff = Add(*coeff_list) terms = {sym: Add(*coeffs) for sym, coeffs in terms_list.items()} return coeff, terms elif a.is_Mul: terms = terms_coeff = None coeff_list = [] for ai in a.args: ci, ti = _lin_eq2dict(ai, symset) if not ti: coeff_list.append(ci) elif terms is None: terms = ti terms_coeff = ci else: raise PolyNonlinearError coeff = Mul(*coeff_list) if terms is None: return coeff, {} else: terms = {sym: coeff * c for sym, c in terms.items()} return coeff * terms_coeff, terms elif a.is_Equality: return _lin_eq2dict(a.lhs - a.rhs, symset) elif not a.has_free(*symset): return a, {} else: raise PolyNonlinearError

python/ray/autoscaler/_private/_azure/node_provider.py

def internal_ip(self, node_id): ip = ( self._get_cached_node(node_id=node_id)["internal_ip"] or self._get_node(node_id=node_id)["internal_ip"] ) return ip

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

internal_ip

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

node_provider.py

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

Python

def internal_ip(self, node_id): ip = ( self._get_cached_node(node_id=node_id)["internal_ip"] or self._get_node(node_id=node_id)["internal_ip"] ) return ip

PyInstaller/archive/writers.py

def _write_file(self, source, dest, type, compress=False): start = self.lib.tell() length = os.stat(source).st_size with open(source, 'rb') as f:

Fix file handle leaks. This is mostly a refactoring of CArchiveWriter().add() which has gotten somewhat tangled trying to apply various file modifications whilst simultaneously juggling file streaming and optional zip compression. Since the modifications are all done on Python source/byte code files which are small, split the archive packing into two helpers, one which streams big files and a more malleable one which writes small files from memory without streaming, both of which handle the bookkeeping job of updating the TOC. This fixes a file handle leak. Additionally take advantage of Python's builtin, but suppressed by default, file handle leakage detection; any such leakages under a pytest run will now fail the test. This requires a few other leakage fixes throughout the test suite to make it pass.

_write_file

9541ad638f73c1442c35ea870ad9c6e4f8cd9b62

pyinstaller

writers.py

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

Python

def _write_file(self, source, dest, type, compress=False): start = self.lib.tell() length = os.stat(source).st_size with open(source, 'rb') as f: if compress: buffer = bytearray(16 * 1024) compressor = zlib.compressobj(self.LEVEL) while 1: read = f.readinto(buffer) if not read: break self.lib.write(compressor.compress(buffer[:read])) self.lib.write(compressor.flush()) else: shutil.copyfileobj(f, self.lib) self.toc.add(start, self.lib.tell() - start, length, int(compress), type, dest)

networkx/linalg/graphmatrix.py

def incidence_matrix(G, nodelist=None, edgelist=None, oriented=False, weight=None): import scipy as sp import scipy.sparse # call as sp.sparse if nodelist is None: nodelist = list(G) if edgelist is None: if G.is_multigraph(): edgelist = list(G.edges(keys=True)) else: edgelist = list(G.edges()) A = sp.sparse.lil_array((len(nodelist), len(edgelist))) node_index = {node: i for i, node in enumerate(nodelist)} for ei, e in enumerate(edgelist): (u, v) = e[:2] if u == v: continue # self loops give zero column try: ui = node_index[u] vi = node_index[v] except KeyError as err: raise nx.NetworkXError( f"node {u} or {v} in edgelist but not in nodelist" ) from err if weight is None: wt = 1 else: if G.is_multigraph(): ekey = e[2] wt = G[u][v][ekey].get(weight, 1) else: wt = G[u][v].get(weight, 1) if oriented: A[ui, ei] = -wt A[vi, ei] = wt else: A[ui, ei] = wt A[vi, ei] = wt import warnings warnings.warn( "incidence

Use scipy.sparse array datastructure (#5139) * Step 1: use sparse arrays in nx.to_scipy_sparse_matrix. Seems like a reasonable place to start. nx.to_scipy_sparse_matrix is one of the primary interfaces to scipy.sparse from within NetworkX. * 1: Use np.outer instead of mult col/row vectors Fix two instances in modularitymatrix where a new 2D array was being created via an outer product of two \"vectors\". In the matrix case, this was a row vector \* a column vector. In the array case this can be disambiguated by being explicit with np.outer. * Update _transition_matrix in laplacianmatrix module - A few instances of matrix multiplication operator - Add np.newaxis + transpose to get shape right for broadcasting - Explicitly convert e.g. sp.sparse.spdiags to a csr_array. * Update directed_combinitorial_laplacian w/ sparse array. - Wrap spdiags in csr_array and update matmul operators. * Rm matrix-specific code from lgc and hmn modules - Replace .A call with appropriate array semantics - wrap sparse.diags in csr_array. * Change hits to use sparse array semantics. - Replace * with @ - Remove superfluous calls to flatten. * Update sparse matrix usage in layout module. - Simplify lil.getrowview call - Wrap spdiags in csr_array. * lil_matrix -> lil_array in graphmatrix.py. * WIP: Start working on algebraic connectivity module. * Incorporate auth mat varname feedback. * Revert 1D slice and comment for 1D sparse future. * Add TODOs: rm csr_array wrapper around spdiags etc. * WIP: cleanup algebraicconn: tracemin_fiedler. * Typo. * Finish reviewing algebraicconnectivity. * Convert bethe_hessian matrix to use sparse arrays. * WIP: update laplacian. Update undirected laplacian functions. * WIP: laplacian - add comment about _transition_matrix return types. * Finish laplacianmatrix review. * Update attrmatrix. * Switch to official laplacian function. * Update pagerank to use sparse array. * Switch bipartite matrix to sparse arrays. * Check from_scipy_sparse_matrix works with arrays. Modifies test suite. * Apply changes from review. * Fix failing docstring tests. * Fix missing axis for in-place multiplication. * Use scipy==1.8rc2 * Use matrix multiplication * Fix PyPy CI * [MRG] Create plot_subgraphs.py example (#5165) * Create plot_subgraphs.py https://github.com/networkx/networkx/issues/4220 * Update plot_subgraphs.py black * Update plot_subgraphs.py lint plus font_size * Update plot_subgraphs.py added more plots * Update plot_subgraphs.py removed plots from the unit test and added comments * Update plot_subgraphs.py lint * Update plot_subgraphs.py typos fixed * Update plot_subgraphs.py added nodes to the plot of the edges removed that was commented out for whatever reason * Update plot_subgraphs.py revert the latest commit - the line was commented out for a reason - it's broken * Update plot_subgraphs.py fixed node color issue * Update plot_subgraphs.py format fix * Update plot_subgraphs.py forgot to draw the nodes... now fixed * Fix sphinx warnings about heading length. * Update examples/algorithms/plot_subgraphs.py * Update examples/algorithms/plot_subgraphs.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> Co-authored-by: Dan Schult <dschult@colgate.edu> * Add traveling salesman problem to example gallery (#4874) Adds an example of the using Christofides to solve the TSP problem to the example galery. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() (#5037) * Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() * Resolved Requested Changes * Revert changes to degree docstrings. * Update comments in example. * Apply wording to edges method in all graph classes. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> * Compatibility updates from testing with numpy/scipy/pytest rc's (#5226) * Rm deprecated scipy subpkg access. * Use recwarn fixture in place of deprecated pytest pattern. * Rm unnecessary try/except from tests. * Replace internal `close` fn with `math.isclose`. (#5224) * Replace internal close fn with math.isclose. * Fix lines in docstring examples. * Fix Python 3.10 deprecation warning w/ int div. (#5231) * Touchups and suggestions for subgraph gallery example (#5225) * Simplify construction of G with edges rm'd * Rm unused graph attribute. * Shorten categorization by node type. * Simplify node coloring. * Simplify isomorphism check. * Rm unit test. * Rm redundant plotting of each subgraph. * Use new package name (#5234) * Allowing None edges in weight function of bidirectional Dijkstra (#5232) * added following feature also to bidirectional dijkstra: The weight function can be used to hide edges by returning None. * changed syntax for better readability and code duplicate avoidance Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de> * Add an FAQ about assigning issues. (#5182) * Add FAQ about assigning issues. * Add note about linking issues from new PRs. * Update dev deps (#5243) * Update minor doc issues with tex notation (#5244) * Add FutureWarnings to fns that return sparse matrices - biadjacency_matrix. - bethe_hessian_matrix. - incidence_matrix. - laplacian functions. - modularity_matrix functions. - adjacency_matrix. * Add to_scipy_sparse_array and use it everywhere. Add a new conversion function to preserve array semantics internally while not altering behavior for users. Also adds FutureWarning to to_scipy_sparse_matrix. * Add from_scipy_sparse_array. Supercedes from_scipy_sparse_matrix. * Handle deprecations in separate PR. * Fix docstring examples. Co-authored-by: Mridul Seth <mail@mriduls.com> Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com> Co-authored-by: Andrew Knyazev <andrew.knyazev@ucdenver.edu> Co-authored-by: Dan Schult <dschult@colgate.edu> Co-authored-by: eskountis <56514439+eskountis@users.noreply.github.com> Co-authored-by: Anutosh Bhat <87052487+anutosh491@users.noreply.github.com> Co-authored-by: NikHoh <nikhoh@web.de> Co-authored-by: Hohmann, Nikolas <nikolas.hohmann@tu-darmstadt.de> Co-authored-by: Sultan Orazbayev <contact@econpoint.com> Co-authored-by: Mridul Seth <mail@mriduls.com>

incidence_matrix

5dfd57af2a141a013ae3753e160180b82bec9469

networkx

graphmatrix.py

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

Python

def incidence_matrix(G, nodelist=None, edgelist=None, oriented=False, weight=None): import scipy as sp import scipy.sparse # call as sp.sparse if nodelist is None: nodelist = list(G) if edgelist is None: if G.is_multigraph(): edgelist = list(G.edges(keys=True)) else: edgelist = list(G.edges()) A = sp.sparse.lil_array((len(nodelist), len(edgelist))) node_index = {node: i for i, node in enumerate(nodelist)} for ei, e in enumerate(edgelist): (u, v) = e[:2] if u == v: continue # self loops give zero column try: ui = node_index[u] vi = node_index[v] except KeyError as err: raise nx.NetworkXError( f"node {u} or {v} in edgelist but not in nodelist" ) from err if weight is None: wt = 1 else: if G.is_multigraph(): ekey = e[2] wt = G[u][v][ekey].get(weight, 1) else: wt = G[u][v].get(weight, 1) if oriented: A[ui, ei] = -wt A[vi, ei] = wt else: A[ui, ei] = wt A[vi, ei] = wt import warnings warnings.warn( "incidence_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.", FutureWarning, stacklevel=2, ) # TODO: Rm sp.sparse.csc_matrix in Networkx 3.0 return A.asformat("csc")

keras/feature_column/sequence_feature_column.py

def call(self, features, training=None): if not isinstance(features, dict): raise ValueError( "We expected a dictionary here. Instead we got: ", features ) if training is None: training = backend.learning_phase() transformation_cache = ( tf.__internal__.feature_column.FeatureTransformationCache(features) ) output_tensors = [] sequence_lengths = [] for column in self._feature_columns: with backend.name_scope(column.name): try: ( dense_tensor, sequence_length, ) = column.get_sequence_dense_tensor( transformation_cache, self._state_manager, training=training, ) except TypeError: ( dense_tensor, sequence_length, ) = column.get_sequence_dense_tensor( transformation_cache, self._state_manager ) # Flattens the final dimension to produce a 3D Tensor. output_tensors.append( self._process_dense_tensor(column, dense_tensor) ) sequence_lengths.append(sequence_length) # Check and process sequence lengths. kfc._verify

resolve line-too-long in feature_column

call

6fafb567af4e4d9f42974d0b6c55b18bc03e17eb

keras

sequence_feature_column.py

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

Python

def call(self, features, training=None): if not isinstance(features, dict): raise ValueError( "We expected a dictionary here. Instead we got: ", features ) if training is None: training = backend.learning_phase() transformation_cache = ( tf.__internal__.feature_column.FeatureTransformationCache(features) ) output_tensors = [] sequence_lengths = [] for column in self._feature_columns: with backend.name_scope(column.name): try: ( dense_tensor, sequence_length, ) = column.get_sequence_dense_tensor( transformation_cache, self._state_manager, training=training, ) except TypeError: ( dense_tensor, sequence_length, ) = column.get_sequence_dense_tensor( transformation_cache, self._state_manager ) # Flattens the final dimension to produce a 3D Tensor. output_tensors.append( self._process_dense_tensor(column, dense_tensor) ) sequence_lengths.append(sequence_length) # Check and process sequence lengths. kfc._verify_static_batch_size_equality( sequence_lengths, self._feature_columns ) sequence_length = _assert_all_equal_and_return(sequence_lengths) return self._verify_and_concat_tensors(output_tensors), sequence_length

python3.10.4/Lib/asyncio/sslproto.py

def feed_eof(self): self._incoming.write

add python 3.10.4 for windows

feed_eof

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

sslproto.py

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

Python

def feed_eof(self): self._incoming.write_eof() ssldata, appdata = self.feed_ssldata(b'') assert appdata == [] or appdata == [b'']

src/textual/app.py

def screen(self) -> Screen: try: return

lots of docstrings

screen

b22436933acc0d7440ec300f971a249bd6105a5b

textual

app.py

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

Python

def screen(self) -> Screen: try: return self._screen_stack[-1] except IndexError: raise ScreenStackError("No screens on stack") from None

tests/apps/tests.py

def test_empty_dunder_path_no_dunder_file(self): with self.assertRaises(ImproperlyConfigured): AppConfig("label", Stub(__path__=[]

Refs #33476 -- Reformatted code with Black.

test_empty_dunder_path_no_dunder_file

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_empty_dunder_path_no_dunder_file(self): with self.assertRaises(ImproperlyConfigured): AppConfig("label", Stub(__path__=[]))

d2l/tensorflow.py

def train_epoch_ch3(net, train_iter, loss, updater): # Sum of training loss, sum of training accuracy, no. of examples metric = Accumulator(3) for X, y

[PaddlePaddle] Merge master into Paddle branch (#1186) * change 15.2 title in chinese version (#1109) change title ’15.2. 情感分析：使用递归神经网络‘ to ’15.2. 情感分析：使用循环神经网络‘ * 修改部分语义表述 (#1105) * Update r0.17.5 (#1120) * Bump versions in installation * 94行typo: （“bert.mall”）->（“bert.small”） (#1129) * line 313: "bert.mall" -> "bert.small" (#1130) * fix: update language as native reader (#1114) * Fix the translation of "stride" (#1115) * Update index.md (#1118) 修改部分语义表述 * Update self-attention-and-positional-encoding.md (#1133) 依照本书的翻译习惯，将pooling翻译成汇聚 * maybe a comment false (#1149) * maybe a little false * maybe a little false * A minor bug in the rcnn section (Chinese edition) (#1148) * Update bert.md (#1137) 一个笔误 # 假设batch_size=2，num_pred_positions=3 # 那么batch_idx应该是np.repeat( [0,1], 3 ) = [0,0,0,1,1,1] * Update calculus.md (#1135) * fix typo in git documentation (#1106) * fix: Update the Chinese translation in lr-scheduler.md (#1136) * Update lr-scheduler.md * Update chapter_optimization/lr-scheduler.md Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * fix translation for kaggle-house-price.md (#1107) * fix translation for kaggle-house-price.md * fix translation for kaggle-house-price.md Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com> * Update weight-decay.md (#1150) * Update weight-decay.md 关于“k多选d”这一部分，中文读者使用排列组合的方式可能更容易理解 关于“给定k个变量，阶数的个数为...”这句话是有歧义的，不是很像中国话，应该是说“阶数为d的项的个数为...”。 并增加了一句对“因此即使是阶数上的微小变化，比如从$2$到$3$，也会显著增加我们模型的复杂性。”的解释 解释为何会增加复杂性以及为何需要细粒度工具。 * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/weight-decay.md yep Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Fix a spelling error (#1161) * Update gru.md (#1152) The key distinction between vanilla RNNs and GRUs is that the latter support gating of the hidden state. 翻译错误 * Unify the function naming (#1113) Unify naming of the function 'init_xavier()'. * Update mlp-concise.md (#1166) * Update mlp-concise.md 语句不通顺 * Update environment.md 语序异常 * Update config.ini * fix the imprecise description (#1168) Co-authored-by: yuande <yuande> * fix typo in chapter_natural-language-processing-pretraining/glove.md (#1175) * Fix some typos. (#1163) * Update batch-norm.md (#1170) fixing typos u->x in article * Update linear-regression.md (#1090) We invoke Stuart Russell and Peter Norvig who, in their classic AI text book Artificial Intelligence: A Modern Approach :cite:Russell.Norvig.2016, pointed out that 原译文把who也直接翻译出来了。 * Update mlp.md (#1117) * Update mlp.md 修改部分语义表述 * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Update chapter_multilayer-perceptrons/mlp.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> * Correct a translation error. (#1091) * Correct a translation error. * Update chapter_computer-vision/image-augmentation.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update aws.md (#1121) * Update aws.md * Update chapter_appendix-tools-for-deep-learning/aws.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update image-augmentation.md (#1093) * Update anchor.md (#1088) fix a minor issue in code * Update anchor.md * Update image-augmentation.md * fix typo and improve translation in chapter_linear-networks\softmax-regression.md (#1087) * Avoid `torch.meshgrid` user warning (#1174) Avoids the following user warning: ```python ~/anaconda3/envs/torch/lib/python3.10/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] ``` * bump to 2.0.0-beta1 * Update sequence.md * bump beta1 on readme * Add latex code block background to config * BLD: Bump python support version 3.9 (#1183) * BLD: Bump python support version 3.9 * Remove clear and manually downgrade protobuf 4.21.4 to 3.19.4 * BLD: Bump torch and tensorflow * Update Jenkinsfile * Update chapter_installation/index.md * Update chapter_installation/index.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Update config.ini * Update INFO.md * Update INFO.md * Drop mint to show code in pdf, use Inconsolata font, apply code cell color (#1187) * resolve the conflicts * revise from publisher (#1089) * revise from publisher * d2l api * post_latex * revise from publisher * revise ch11 * Delete d2l-Copy1.bib * clear cache * rm d2lbook clear * debug anchor * keep original d2l doc Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> * 重复语句 (#1188) Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Improve expression for chapter_preliminaries/pandas.md (#1184) * Update pandas.md * Improve expression * Improve expression * Update chapter_preliminaries/pandas.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Improce expression for chapter_preliminaries/linear-algebra.md (#1185) * Improce expression * Improve code comments * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md * Update chapter_preliminaries/linear-algebra.md Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> * Fix multibox_detection bugs * Update d2l to 0.17.5 version * restore older version * Upgrade pandas * change to python3.8 * Test warning log * relocate warning log * test logs filtering * Update gru.md * Add DeprecationWarning filter * Test warning log * Update attention mechanisms & computational performance * Update multilayer perceptron& linear & convolution networks & computer vision * Update recurrent&optimition&nlp pretraining & nlp applications * ignore warnings * Update index.md * Update linear networks * Update multilayer perceptrons&deep learning computation * Update preliminaries * Check and Add warning filter * Update kaggle-cifar10.md * Update object-detection-dataset.md * Update ssd.md fcn.md * Update hybridize.md * Update hybridize.md Signed-off-by: sunhaizhou <haizhou.sun@smartmore.com> Co-authored-by: zhou201505013 <39976863+zhou201505013@users.noreply.github.com> Co-authored-by: Xinwei Liu <xinzone@outlook.com> Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com> Co-authored-by: Aston Zhang <22279212+astonzhang@users.noreply.github.com> Co-authored-by: hugo_han <57249629+HugoHann@users.noreply.github.com> Co-authored-by: gyro永不抽风 <1247006353@qq.com> Co-authored-by: CanChengZheng <zcc550169544@163.com> Co-authored-by: linlin <jajupmochi@gmail.com> Co-authored-by: iuk <liukun0104@gmail.com> Co-authored-by: yoos <49556860+liyunlongaaa@users.noreply.github.com> Co-authored-by: Mr. Justice Lawrence John Wargrave <65226618+RUCWargrave@users.noreply.github.com> Co-authored-by: Chiyuan Fu <fuchiyuan2019@outlook.com> Co-authored-by: Sunhuashan <48636870+Sunhuashan@users.noreply.github.com> Co-authored-by: Haiker Sun <haizhou.uestc2011@gmail.com> Co-authored-by: Ming Liu <akira.liu@njnu.edu.cn> Co-authored-by: goldmermaid <goldpiggy@berkeley.edu> Co-authored-by: silenceZheng66 <13754430639@163.com> Co-authored-by: Wenchao Yan <56541797+YWonchall@users.noreply.github.com> Co-authored-by: Kiki2049 <55939997+Kiki2049@users.noreply.github.com> Co-authored-by: Krahets <krahets@163.com> Co-authored-by: friedmainfunction <73703265+friedmainfunction@users.noreply.github.com> Co-authored-by: Jameson <miraclecome@gmail.com> Co-authored-by: P. Yao <12227516+YaoPengCN@users.noreply.github.com> Co-authored-by: Yulv-git <34329208+Yulv-git@users.noreply.github.com> Co-authored-by: Liu,Xiao <45966993+liuxiao916@users.noreply.github.com> Co-authored-by: YIN, Gang <1246410+yingang@users.noreply.github.com> Co-authored-by: Joe-HZ <58297431+Joe-HZ@users.noreply.github.com> Co-authored-by: lybloveyou <102609904+lybloveyou@users.noreply.github.com> Co-authored-by: VigourJiang <jiangfuqiang154@163.com> Co-authored-by: zxhd863943427 <74853597+zxhd863943427@users.noreply.github.com> Co-authored-by: LYF <27893441+liyufan@users.noreply.github.com> Co-authored-by: Aston Zhang <asv325@gmail.com> Co-authored-by: xiaotinghe <xiaotih@amazon.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-12-66.us-west-2.compute.internal> Co-authored-by: Holly-Max <60691735+Holly-Max@users.noreply.github.com> Co-authored-by: HinGwenWoong <peterhuang0323@qq.com> Co-authored-by: Shuai Zhang <cheungdaven@gmail.com>

train_epoch_ch3

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

d2l-zh

tensorflow.py

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

Python

def train_epoch_ch3(net, train_iter, loss, updater): # Sum of training loss, sum of training accuracy, no. of examples metric = Accumulator(3) for X, y in train_iter: # Compute gradients and update parameters with tf.GradientTape() as tape: y_hat = net(X) # Keras implementations for loss takes (labels, predictions) # instead of (predictions, labels) that users might implement # in this book, e.g. `cross_entropy` that we implemented above if isinstance(loss, tf.keras.losses.Loss): l = loss(y, y_hat) else: l = loss(y_hat, y) if isinstance(updater, tf.keras.optimizers.Optimizer): params = net.trainable_variables grads = tape.gradient(l, params) updater.apply_gradients(zip(grads, params)) else: updater(X.shape[0], tape.gradient(l, updater.params)) # Keras loss by default returns the average loss in a batch l_sum = l * float(tf.size(y)) if isinstance( loss, tf.keras.losses.Loss) else tf.reduce_sum(l) metric.add(l_sum, accuracy(y_hat, y), tf.size(y)) # Return training loss and training accuracy return metric[0] / metric[2], metric[1] / metric[2]

sklearn/utils/discovery.py

def all_estimators(type_filter=None): # lazy import to avoid circular imports from sklearn.base from . import IS_PYPY from ._testing import ignore_warnings

MNT numpydoc validation for Displays (#21469) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

all_estimators

b22f7fa552c03aa7f6b9b4d661470d0173f8db5d

scikit-learn

discovery.py

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

Python

def all_estimators(type_filter=None): # lazy import to avoid circular imports from sklearn.base from . import IS_PYPY from ._testing import ignore_warnings from ..base import ( BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin, ClusterMixin, )

@image_comparison(['legend_various_labels'], remove_text=True)

lib/matplotlib/tests/test_legend.py

def test_legend_auto5(): fig, axs = plt.subplots(ncols=2, figsize=(9.6, 4.8)) leg_bboxes = [] for ax, loc in zip(axs.flat, ("center", "best")): # An Ellipse patch at the top, a U-shaped Polygon patch at the # bottom and a ring-like Wedge patch: the correct placement of # the legend should be in the center. for _patch in [ mpatches.Ellipse( xy=(0.5, 0.9), width=0.8, height=0.2, fc="C1"), mpatches.Polygon(np.array([ [0, 1], [0, 0], [1, 0], [1, 1], [0.9, 1.0], [0.9, 0.1], [0.1, 0.1], [0.1, 1.0], [0.1, 1.0]]), fc="C1"), mpatches.Wedge((0.5, 0.5), 0.5, 0, 360, width=0.05, fc="C0") ]: ax.add_patch(_patch) ax.plot([0.1, 0.9], [0.9, 0.9], label="A segment") # sthg to label leg = ax.legend(loc=loc) fig.canvas.draw() leg_bboxes.append( leg.get_window_extent().transformed(ax.transAxes.inverted())) assert_allclose(leg_bboxes[1].bounds, leg_bboxes[0].bounds) @image_comparison(['legend_various_labels'], remove_t

ENH: rely on non-rectangular patch paths rather than bboxes for legend auto-placing (fix #9580) (#9598) * use path rather than bbox for non rectangular patches * Add tests * Add a short breadcrumb note in api_changes

test_legend_auto5

d8bb1a52316c38434e526412c27d9c4b01960084

matplotlib

test_legend.py

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

Python

def test_legend_auto5(): fig, axs = plt.subplots(ncols=2, figsize=(9.6, 4.8)) leg_bboxes = [] for ax, loc in zip(axs.flat, ("center", "best")): # An Ellipse patch at the top, a U-shaped Polygon patch at the # bottom and a ring-like Wedge patch: the correct placement of # the legend should be in the center. for _patch in [ mpatches.Ellipse( xy=(0.5, 0.9), width=0.8, height=0.2, fc="C1"), mpatches.Polygon(np.array([ [0, 1], [0, 0], [1, 0], [1, 1], [0.9, 1.0], [0.9, 0.1], [0.1, 0.1], [0.1, 1.0], [0.1, 1.0]]), fc="C1"), mpatches.Wedge((0.5, 0.5), 0.5, 0, 360, width=0.05, fc="C0") ]: ax.add_patch(_patch) ax.plot([0.1, 0.9], [0.9, 0.9], label="A segment") # sthg to label leg = ax.legend(loc=loc) fig.canvas.draw() leg_bboxes.append( leg.get_window_extent().transformed(ax.transAxes.inverted())) assert_allclose(leg_bboxes[1].bounds, leg_bboxes[0].bounds) @image_comparison(['legend_various_labels'], remove_text=True)

src/textual/widgets/text_input.py

def _toggle_cursor_visible(self): if time.monotonic() - self._last_keypress_time > self.cursor

Conditional blinking

_toggle_cursor_visible

d8179c70dc06e06b2f445fdfb47fb7012d4cb2ed

textual

text_input.py

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

Python

def _toggle_cursor_visible(self): if time.monotonic() - self._last_keypress_time > self.cursor_blink_period: self._cursor_blink_visible = not self._cursor_blink_visible self.refresh()

src/sentry/search/events/filter.py

def parse_semver(version, operator) -> Optional[SemverFilter]: (operator, negated) = handle_operator_negation(operator) try: operator = OPERATOR_TO_DJANGO[operator] except KeyError: raise InvalidSearchQuery("Invalid operation 'IN' for semantic version filter.") version = version if "@" in version else f"{SEMVER_FAKE_PACKAGE}@{version}" parsed = parse_release_relay(version) parsed_version = parsed.get("version_parsed") if parsed_version: # Convert `pre` to always be a string prerelease = parsed_version["pre"] if parsed_version["pre"] else "" semver_filter = SemverFilter( operator, [ parsed_version["major"], parsed_version["minor"], parsed_version["patch"], parsed_version["revision"], 0 if prerelease else 1, prerelease, ], negated=negated, ) if parsed["package"] and parsed["package"] != SEMVER_FAKE_PACKAGE: semver_filter.package = parsed["package"] return semver_filter else: # Try to parse as a wildcard match package, version = version.split("@", 1) version_parts = [] if version: for part in version.split(".", 3): if part in SEMVER_WILDCARDS: break try: # We assume all ints for a wildcard match - not handling prerelease as # part of these version_parts.append(int(part)) except ValueError: raise InvalidSearchQuery(INVALID_SEMVER_MESSAGE) package = package if package and package != SEMVER_FAKE_PACKAGE else None return SemverFilter("exact", version_parts, package, negated) key_convers

fix(events-search): Return helpful error message on semver filter (#33785) 'IN' type queries currently raise an unhandled KeyError, raising an InvalidSearchQuery instead.

parse_semver

4ffb52489e662029a08169351cd997d525977e88

sentry

filter.py

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

Python

def parse_semver(version, operator) -> Optional[SemverFilter]: (operator, negated) = handle_operator_negation(operator) try: operator = OPERATOR_TO_DJANGO[operator] except KeyError: raise InvalidSearchQuery("Invalid operation 'IN' for semantic version filter.") version = version if "@" in version else f"{SEMVER_FAKE_PACKAGE}@{version}" parsed = parse_release_relay(version) parsed_version = parsed.get("version_parsed") if parsed_version: # Convert `pre` to always be a string prerelease = parsed_version["pre"] if parsed_version["pre"] else "" semver_filter = SemverFilter( operator, [ parsed_version["major"], parsed_version["minor"], parsed_version["patch"], parsed_version["revision"], 0 if prerelease else 1, prerelease, ], negated=negated, ) if parsed["package"] and parsed["package"] != SEMVER_FAKE_PACKAGE: semver_filter.package = parsed["package"] return semver_filter else: # Try to parse as a wildcard match package, version = version.split("@", 1) version_parts = [] if version: for part in version.split(".", 3): if part in SEMVER_WILDCARDS: break try: # We assume all ints for a wildcard match - not handling prerelease as # part of these version_parts.append(int(part)) except ValueError: raise InvalidSearchQuery(INVALID_SEMVER_MESSAGE) package = package if package and package != SEMVER_FAKE_PACKAGE else None return SemverFilter("exact", version_parts, package, negated) key_conversion_map: Mapping[ str, Callable[[SearchFilter, str, Mapping[str, Union[int, str, datetime]]], Optional[Sequence[any]]], ] = { "environment": _environment_filter_converter, "message": _message_filter_converter, TRANSACTION_STATUS_ALIAS: _transaction_status_filter_converter, "issue.id": _issue_id_filter_converter, USER_DISPLAY_ALIAS: _user_display_filter_converter, ERROR_UNHANDLED_ALIAS: _error_unhandled_filter_converter, "error.handled": _error_handled_filter_converter, TEAM_KEY_TRANSACTION_ALIAS: _team_key_transaction_filter_converter, RELEASE_STAGE_ALIAS: _release_stage_filter_converter, SEMVER_ALIAS: _semver_filter_converter, SEMVER_PACKAGE_ALIAS: _semver_package_filter_converter, SEMVER_BUILD_ALIAS: _semver_build_filter_converter, }

wagtail/admin/tests/test_account_management.py

def test_not_logged_in_gives_403_to_ajax_requests(self): # Get dashboard response = self.client.get( reverse("wagtailadmin_home"), HTTP_X_REQUESTED_WITH="XMLHttpRequest" ) # AJAX requests should be given a 403 error instead of being redirected self.assertEqual(response.status_code, 403)

Reformat with black

test_not_logged_in_gives_403_to_ajax_requests

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_account_management.py

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

Python

def test_not_logged_in_gives_403_to_ajax_requests(self): # Get dashboard response = self.client.get( reverse("wagtailadmin_home"), HTTP_X_REQUESTED_WITH="XMLHttpRequest" ) # AJAX requests should be given a 403 error instead of being redirected self.assertEqual(response.status_code, 403)

parlai/utils/bpe.py

def bytes_to_unicode(self) -> Dict[int, str]: bs: List[int] = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs: List[int] = bs[:]

autoformat (#4378)

bytes_to_unicode

81f722d29045a7a5841d0931a082ded1d1f13863

ParlAI

bpe.py

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

Python

def bytes_to_unicode(self) -> Dict[int, str]: bs: List[int] = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs: List[int] = bs[:] n = 0 for b in range(2 ** 8): if b not in bs: bs.append(b) cs.append(2 ** 8 + n) n += 1 str_cs: List[str] = [chr(n) for n in cs] return dict(zip(bs, str_cs))

tests/components/caldav/test_calendar.py

async def test_get_events_custom_calendars(hass, calendar, get_api_events): config = dict(CALDAV_CONFIG) config["custom_calendars"] = [ {"name": "Private", "calendar": "Private", "search": "This is a normal event"} ] assert await async_setup_component(hass, "calendar", {"calend

Local calendar integration (#79601)

test_get_events_custom_calendars

532ab12a48b6832180599088250fc23446a45d1e

core

test_calendar.py

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

Python

async def test_get_events_custom_calendars(hass, calendar, get_api_events): config = dict(CALDAV_CONFIG) config["custom_calendars"] = [ {"name": "Private", "calendar": "Private", "search": "This is a normal event"} ] assert await async_setup_component(hass, "calendar", {"calendar": config}) await hass.async_block_till_done() events = await get_api_events("calendar.private_private") assert events == [ { "end": {"dateTime": "2017-11-27T10:00:00-08:00"}, "start": {"dateTime": "2017-11-27T09:00:00-08:00"}, "summary": "This is a normal event", "location": "Hamburg", "description": "Surprisingly rainy", "uid": None, "recurrence_id": None, "rrule": None, } ]

nni/mutable/symbol.py

def leaf_symbols(self) -> Iterable[Symbol]: for arg in self.arguments: if isinstanc

Mutable V3 (Stage 2) - Symbolic execution engine (#5195)

leaf_symbols

8f454f3bf29e2c3cd0d359231a46edd8ee768d42

nni

symbol.py

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

Python

def leaf_symbols(self) -> Iterable[Symbol]: for arg in self.arguments: if isinstance(arg, SymbolicExpression): yield from arg.leaf_symbols()

@eval_app.command()

rllib/evaluate.py

def append_step(self, obs, action, next_obs, reward, terminated, truncated, info): if self._outfile: if self._save_info: self._current_rollout.append( [obs, action, next_obs, reward, terminated, truncated, info] ) else: self._current_rollout.append( [obs, action,

[RLlib] gymnasium support (new `Env.reset()/step()/seed()/render()` APIs). (#28369)

append_step

8e680c483ce326cefc62e44f68ab1a6948b1c3d2

ray

evaluate.py

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

Python

def append_step(self, obs, action, next_obs, reward, terminated, truncated, info): if self._outfile: if self._save_info: self._current_rollout.append( [obs, action, next_obs, reward, terminated, truncated, info] ) else: self._current_rollout.append( [obs, action, next_obs, reward, terminated, truncated] ) self._total_steps += 1 @eval_app.command()

tests/prefetch_related/tests.py

def test_nested_prefetch_is_not_overwritten_by_related_object(self): queryset = House.objects.only('name').prefetch_related( Prefetch('rooms', queryset=Room.objects.prefetch_related( Prefetch('house', queryset=House.objects.only('address')), )), ) with self.assertNumQueries(3): house = queryset.first() self.assertIs(

Fixed #32511 -- Corrected handling prefetched nested reverse relationships. When prefetching a set of child objects related to a set of parent objects, we usually want to populate the relationship back from the child to the parent to avoid a query when accessing that relationship attribute. However, there's an edge case where the child queryset itself specifies a prefetch back to the parent. In that case, we want to use the prefetched relationship rather than populating the reverse relationship from the parent.

test_nested_prefetch_is_not_overwritten_by_related_object

f5233dce309543c826224be9dfa9c9f4f855f73c

django

tests.py

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

Python

def test_nested_prefetch_is_not_overwritten_by_related_object(self): queryset = House.objects.only('name').prefetch_related( Prefetch('rooms', queryset=Room.objects.prefetch_related( Prefetch('house', queryset=House.objects.only('address')), )), ) with self.assertNumQueries(3): house = queryset.first() self.assertIs(Room.house.is_cached(self.room), True) with self.assertNumQueries(0): house.rooms.first().house.address

sympy/combinatorics/permutations.py

def apply(self, i): r i = _sympify(i) if i.is_integer is False: raise NotImplementedError("{} should be an integer.".format(i)) n = self.size if (i < 0) == True or (i >= n) == True: raise NotImplementedError( "{} should be an integer between 0 and {}".format(i, n-1)) if i.is_Integer: return Integer(self._array_form[i]) retur

Fix various typos Found via `codespell -q 3 -L aboves,aline,ans,aother,arithmetics,assum,atleast,braket,clen,declar,declars,dorder,dum,enew,fo,fro,inout,iself,ist,ket,lamda,lightyear,lightyears,nd,numer,numers,orderd,ot,pring,rcall,rever,ro,ser,siz,splitted,sring,supercedes,te,tht,unequality,upto,vas,versin,whet`

apply

24f1e7730119fe958cc8e28411f790c9a5ec04eb

sympy

permutations.py

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

Python

def apply(self, i): r i = _sympify(i) if i.is_integer is False: raise NotImplementedError("{} should be an integer.".format(i)) n = self.size if (i < 0) == True or (i >= n) == True: raise NotImplementedError( "{} should be an integer between 0 and {}".format(i, n-1)) if i.is_Integer: return Integer(self._array_form[i]) return AppliedPermutation(self, i)

lib/matplotlib/collections.py

def _convert_mesh_to_triangles(self, coordinates): if isinstance(coordinates, np.ma.MaskedArray): p = coordinates.data else: p = coordinates p_a = p[:-1, :-1] p_b = p[:-1, 1:] p_c = p[1:, 1:] p_d = p[1:, :-1] p_center = (p_a + p_

Deprecate draw_gouraud_triangle (#23824) * Deprecate draw_gouraud_triangle * DOC: minor rewording Co-authored-by: Elliott Sales de Andrade <quantum.analyst@gmail.com> Co-authored-by: Thomas A Caswell <tcaswell@gmail.com> Co-authored-by: Elliott Sales de Andrade <quantum.analyst@gmail.com>

_convert_mesh_to_triangles

4a5d09cba5f4a20e14553cebd8f70c1f34d20d35

matplotlib

collections.py

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

Python

def _convert_mesh_to_triangles(self, coordinates): if isinstance(coordinates, np.ma.MaskedArray): p = coordinates.data else: p = coordinates p_a = p[:-1, :-1] p_b = p[:-1, 1:] p_c = p[1:, 1:] p_d = p[1:, :-1] p_center = (p_a + p_b + p_c + p_d) / 4.0 triangles = np.concatenate([ p_a, p_b, p_center, p_b, p_c, p_center, p_c, p_d, p_center, p_d, p_a, p_center, ], axis=2).reshape((-1, 3, 2)) c = self.get_facecolor().reshape((*coordinates.shape[:2], 4)) c_a = c[:-1, :-1] c_b = c[:-1, 1:] c_c = c[1:, 1:] c_d = c[1:, :-1] c_center = (c_a + c_b + c_c + c_d) / 4.0 colors = np.concatenate([ c_a, c_b, c_center, c_b, c_c, c_center, c_c, c_d, c_center, c_d, c_a, c_center, ], axis=2).reshape((-1, 3, 4)) return triangles, colors

pandas/core/series.py

def count(self, level=None): if level is None: return notna(self._values).sum().astype("int64") else: warnings.warn( "Using the level keyword in DataFrame and Series aggregations is " "deprecated and will be removed in a future version. Use groupby " "instead. ser.count(level=1) should use ser.groupby(level=1).count().", FutureWarning, stacklevel=find_stack_level(), ) if not isinstance(self.index, MultiIndex): raise ValueError("Series.count level is only valid with a MultiIndex") index = self.index assert isinstance(index, MultiIndex) # for mypy if isinstance(level, str): level = index._get_level_number(level) lev = index.levels[level] level_codes = np.array(index.codes[level], subok=False, copy=True) mask = level_codes == -1 if mask.any(): level_codes[mask] = cnt = len(lev) lev = lev.insert(cnt, lev._na_value) obs = level_codes[notna(self._values)]

TYP: Ignore numpy related issues (#45244)

count

d603d43df2057ecdf74010d9dadc735e37f8f7b5

pandas

series.py

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

Python

def count(self, level=None): if level is None: return notna(self._values).sum().astype("int64") else: warnings.warn( "Using the level keyword in DataFrame and Series aggregations is " "deprecated and will be removed in a future version. Use groupby " "instead. ser.count(level=1) should use ser.groupby(level=1).count().", FutureWarning, stacklevel=find_stack_level(), ) if not isinstance(self.index, MultiIndex): raise ValueError("Series.count level is only valid with a MultiIndex") index = self.index assert isinstance(index, MultiIndex) # for mypy if isinstance(level, str): level = index._get_level_number(level) lev = index.levels[level] level_codes = np.array(index.codes[level], subok=False, copy=True) mask = level_codes == -1 if mask.any(): level_codes[mask] = cnt = len(lev) lev = lev.insert(cnt, lev._na_value) obs = level_codes[notna(self._values)] # Argument "minlength" to "bincount" has incompatible type "Optional[int]"; # expected "SupportsIndex" [arg-type] out = np.bincount(obs, minlength=len(lev) or None) # type: ignore[arg-type] return self._constructor(out, index=lev, dtype="int64").__finalize__( self, method="count" )

python3.10.4/Lib/_pydecimal.py

def __format__(self, specifier, context=None, _localeconv=None): # Note: PEP 3101 says that if the type is not present then # there should be at least one digit after the decimal point. # We take the liberty of ignoring this requirement for # Decimal---it's presumably there to make sure that # format(float, '') behaves similarly to str(float). if context is None: context = getcontext() spec = _parse_format_specifier(specifier, _localeconv=_localeconv) # special values don't care about the type or precision if self._is_special: sign = _format_sign(self._sign, spec) body = str(self.copy_abs()) if spec['type'] == '%': body += '%' return _format_align(sign, body, spec) # a type of None defaults to 'g' or 'G', depending on context if spec['type'] is None: spec['type'] = ['g', 'G'][context.capitals] # if type is '%', adjust exponent of self accordingly if spec['type'] == '%': self = _dec_from_triple(self._sign, self._int, self._exp+2) # round if necessary, taking rounding mode from the context rounding = context.rounding precision = spec['precision'] if precision is not None: if spec['type'] in 'eE': self = self._round(precision+1, rounding) elif spec['type'] in 'fF%': self = self._rescale(-precision, rounding) elif spec['type'] in 'gG' and len(self._int) > precision: self = self._round(precision, rounding) # special case: zeros with a positive exponent can't be # represented in fixed point; rescale them to 0e0. if not self and self._exp > 0 and spec['type'] in 'fF%': self = self._rescale(0, rounding) # figure out placement of the decimal point leftdigits = self._exp + len(self._int) if spec['type'] in 'eE': if not self and precision is not None: dotplace = 1 - precision else: dotplace = 1 elif spec['type'] in 'fF%': dotplace = leftdigits elif spec['type'] in 'gG': if self._exp <= 0 and leftdigits > -6: dotplace = leftdigits else: dotplace =

add python 3.10.4 for windows

__format__

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

_pydecimal.py

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

Python

def __format__(self, specifier, context=None, _localeconv=None): # Note: PEP 3101 says that if the type is not present then # there should be at least one digit after the decimal point. # We take the liberty of ignoring this requirement for # Decimal---it's presumably there to make sure that # format(float, '') behaves similarly to str(float). if context is None: context = getcontext() spec = _parse_format_specifier(specifier, _localeconv=_localeconv) # special values don't care about the type or precision if self._is_special: sign = _format_sign(self._sign, spec) body = str(self.copy_abs()) if spec['type'] == '%': body += '%' return _format_align(sign, body, spec) # a type of None defaults to 'g' or 'G', depending on context if spec['type'] is None: spec['type'] = ['g', 'G'][context.capitals] # if type is '%', adjust exponent of self accordingly if spec['type'] == '%': self = _dec_from_triple(self._sign, self._int, self._exp+2) # round if necessary, taking rounding mode from the context rounding = context.rounding precision = spec['precision'] if precision is not None: if spec['type'] in 'eE': self = self._round(precision+1, rounding) elif spec['type'] in 'fF%': self = self._rescale(-precision, rounding) elif spec['type'] in 'gG' and len(self._int) > precision: self = self._round(precision, rounding) # special case: zeros with a positive exponent can't be # represented in fixed point; rescale them to 0e0. if not self and self._exp > 0 and spec['type'] in 'fF%': self = self._rescale(0, rounding) # figure out placement of the decimal point leftdigits = self._exp + len(self._int) if spec['type'] in 'eE': if not self and precision is not None: dotplace = 1 - precision else: dotplace = 1 elif spec['type'] in 'fF%': dotplace = leftdigits elif spec['type'] in 'gG': if self._exp <= 0 and leftdigits > -6: dotplace = leftdigits else: dotplace = 1 # find digits before and after decimal point, and get exponent if dotplace < 0: intpart = '0' fracpart = '0'*(-dotplace) + self._int elif dotplace > len(self._int): intpart = self._int + '0'*(dotplace-len(self._int)) fracpart = '' else: intpart = self._int[:dotplace] or '0' fracpart = self._int[dotplace:] exp = leftdigits-dotplace # done with the decimal-specific stuff; hand over the rest # of the formatting to the _format_number function return _format_number(self._sign, intpart, fracpart, exp, spec)

sklearn/tests/test_base.py

def test_feature_names_in(): pd = pytest.importorskip("pandas") iris = datasets.load_iris() X_np = iris.data df = pd.DataFrame(X_np, columns=iris.feature_names)

TST Better info when checking for no warnings in tests (#22362)

test_feature_names_in

9f85c9d44965b764f40169ef2917e5f7a798684f

scikit-learn

test_base.py

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

Python

def test_feature_names_in(): pd = pytest.importorskip("pandas") iris = datasets.load_iris() X_np = iris.data df = pd.DataFrame(X_np, columns=iris.feature_names)

pytorch_lightning/trainer/connectors/accelerator_connector.py

def check_interactive_compatibility(self): from pytorch_lightning.utilities import _IS_INTERACTIVE if _IS_INTERACTIVE and self._strategy_type is not None and not self._strategy_type.is_interactive_compatible(): raise MisconfigurationException( f"`Trainer(strategy={self._strategy_type.value!r})` or" f" `Trainer(accelerator={self._strategy_type.value!r})` is not compatible with an interactive" " environment. Run your code as a script, or choose one of the compatible backends:" f" {', '.join(_StrategyType.interactive_compatible_types())}." " In case you are spaw

Rename `_distrib_type` to `_strategy_type` (#11328) Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

check_interactive_compatibility

e15579a4f32ee3c08318a466583f4a0a8517d654

lightning

accelerator_connector.py

https://github.com/Lightning-AI/lightning.git

Python

def check_interactive_compatibility(self): from pytorch_lightning.utilities import _IS_INTERACTIVE if _IS_INTERACTIVE and self._strategy_type is not None and not self._strategy_type.is_interactive_compatible(): raise MisconfigurationException( f"`Trainer(strategy={self._strategy_type.value!r})` or" f" `Trainer(accelerator={self._strategy_type.value!r})` is not compatible with an interactive" " environment. Run your code as a script, or choose one of the compatible backends:" f" {', '.join(_StrategyType.interactive_compatible_types())}." " In case you are spawning processes yourself, make sure to include the Trainer" " creation inside the worker function." )

PySimpleGUI.py

def theme_global(new_theme=None): if new_theme is not None: if new_theme not in theme_list(): popup_error_with_traceback('Cannot use custom themes with theme_global call', 'Your request to use theme {} cannot be performed.'.format(new_theme), 'The PySimpleGUI Global User Se

Better error checking/reporting in theme_global. NEW THEME DarkGrey15

theme_global

dfad2e3b7671b7128895c8a0e29fff38d7efe6e9

PySimpleGUI

PySimpleGUI.py

https://github.com/PySimpleGUI/PySimpleGUI.git

Python

def theme_global(new_theme=None): if new_theme is not None: if new_theme not in theme_list(): popup_error_with_traceback('Cannot use custom themes with theme_global call', 'Your request to use theme {} cannot be performed.'.format(new_theme), 'The PySimpleGUI Global User Settings are meant for PySimpleGUI standard items, not user config items', 'You can use any of the many built-in themes instead or use your own UserSettings file to store your custom theme') return pysimplegui_user_settings.get('-theme-', CURRENT_LOOK_AND_FEEL) pysimplegui_user_settings.set('-theme-', new_theme) theme(new_theme) return new_theme else: return pysimplegui_user_settings.get('-theme-', CURRENT_LOOK_AND_FEEL)

packages/python/chart-studio/chart_studio/plotly/chunked_requests/chunked_request.py

def close(self):

switch to black .22

close

43e3a4011080911901176aab919c0ecf5046ddd3

plotly.py

chunked_request.py

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

Python

def close(self): self._reset_retries() self._closed = True # Chunked-encoded posts are terminated with '0\r\n\r\n' # For some reason, either Python or node.js seems to # require an extra \r\n. try: self._conn.send("\r\n0\r\n\r\n".encode("utf-8")) except http_client.socket.error: # In case the socket has already been closed return "" return self._getresponse()

salt/modules/runit.py

def _get_svc_path(name="*", status=None): # This is the core routine to work with services, called by many # other functions of this module. # # The name of a service is the "apparent" folder's name that contains its # "run" script. If its "folder" is a symlink, the service is an "alias" of # the targeted service. if not SERVICE_DIR: raise CommandExecutionError("Could not find service directory.") # path list of enabled services as /AVAIL_SVR_DIRS/$service, # taking care of any service aliases (do not use os.path.realpath()).

fix runit module failing on non-symlinked service

_get_svc_path

5bf2904e7ac79d438ce03a673aa9a5c99f4e8e0f

salt

runit.py

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

Python

def _get_svc_path(name="*", status=None): # This is the core routine to work with services, called by many # other functions of this module. # # The name of a service is the "apparent" folder's name that contains its # "run" script. If its "folder" is a symlink, the service is an "alias" of # the targeted service. if not SERVICE_DIR: raise CommandExecutionError("Could not find service directory.") # path list of enabled services as /AVAIL_SVR_DIRS/$service, # taking care of any service aliases (do not use os.path.realpath()). ena = set() for el in glob.glob(os.path.join(SERVICE_DIR, name)): if _is_svc(el): if os.path.islink(el): ena.add(os.readlink(el)) else: ena.add(el) log.trace("found enabled service path: %s", el) if status == "ENABLED": return sorted(ena) # path list of available services as /AVAIL_SVR_DIRS/$service ava = set() for d in AVAIL_SVR_DIRS: for el in glob.glob(os.path.join(d, name)): if _is_svc(el): ava.add(el) log.trace("found available service path: %s", el) if status == "DISABLED": # service available but not enabled ret = ava.difference(ena) else: # default: return available services ret = ava.union(ena) return sorted(ret)

@RunIf(min_gpus=2)

tests/checkpointing/test_torch_saving.py

def test_model_torch_save_ddp_cpu(tmpdir): model = BoringModel() num_epochs = 1 trainer = Trainer( default_root_dir=tmpdir, max_epochs=num_epochs, strategy="ddp_spawn", accelerator="cpu", devices=2, logger=False ) temp_path = os.path.jo

Update `tests/checkpointing/*.py` to use `devices` instead of `gpus` or `ipus` (#11408) Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

test_model_torch_save_ddp_cpu

d2d284fd6e3e8f53e9a44ab233771850af1e4dab

lightning

test_torch_saving.py

https://github.com/Lightning-AI/lightning.git

Python

def test_model_torch_save_ddp_cpu(tmpdir): model = BoringModel() num_epochs = 1 trainer = Trainer( default_root_dir=tmpdir, max_epochs=num_epochs, strategy="ddp_spawn", accelerator="cpu", devices=2, logger=False ) temp_path = os.path.join(tmpdir, "temp.pt") trainer.fit(model) # Ensure these do not fail torch.save(trainer.model, temp_path) torch.save(trainer, temp_path) @RunIf(min_gpus=2)

src/transformers/onnx/config.py

def default_batch_size(self) -> int: # Using 2 avoid ONNX making assumption about single sample batch return On

Add ONNX export for ViT (#15658) * Add ONNX support for ViT * Refactor to use generic preprocessor * Add vision dep to tests * Extend ONNX slow tests to ViT * Add dummy image generator * Use model_type to determine modality * Add deprecation warnings for tokenizer argument * Add warning when overwriting the preprocessor * Add optional args to docstrings * Add minimum PyTorch version to OnnxConfig * Refactor OnnxConfig class variables from CONSTANT_NAME to snake_case * Add reasonable value for default atol Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

default_batch_size

50dd314d939a86f3a81e19af01459f449fbaeeca

transformers

config.py

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

Python

def default_batch_size(self) -> int: # Using 2 avoid ONNX making assumption about single sample batch return OnnxConfig.default_fixed_batch

nuitka/utils/Signing.py

def addMacOSCodeSignature(filenames): # Weak signing. identity = getMacOSSigningIdentity() command = [ "codesign", "-s", identity, "--force", "--deep", "--preserve-metadata=entitlements", ] assert type(filenames) is not str command.extend(filenam

macOS: Add support for specifying signing identity and access to protected resources.

addMacOSCodeSignature

51ca460bd8c382cc165cbb1325e7cb65895d1a0b

Nuitka

Signing.py

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

Python

def addMacOSCodeSignature(filenames): # Weak signing. identity = getMacOSSigningIdentity() command = [ "codesign", "-s", identity, "--force", "--deep", "--preserve-metadata=entitlements", ] assert type(filenames) is not str command.extend(filenames) with withMadeWritableFileMode(filenames): executeToolChecked( logger=postprocessing_logger, command=command, absence_message=macos_codesign_usage, stderr_filter=_filterSigntoolErrorOutput, )

rest_framework/views.py

def exception_handler(exc, context): if isinstance(exc, Http

Preserve exception messages for wrapped Django exceptions (#8051) * Preserve messages for wrapped Django exceptions * Fix the test * Update test_generics.py * Update test_generics.py Co-authored-by: Tom Christie <tom@tomchristie.com>

exception_handler

56946fac8f29aa44ce84391f138d63c4c8a2a285

django-rest-framework

views.py

https://github.com/encode/django-rest-framework.git

Python

def exception_handler(exc, context): if isinstance(exc, Http404): exc = exceptions.NotFound(*(exc.args)) elif isinstance(exc, PermissionDenied): exc = exceptions.PermissionDenied(*(exc.args)) if isinstance(exc, exceptions.APIException): headers = {} if getattr(exc, 'auth_header', None): headers['WWW-Authenticate'] = exc.auth_header if getattr(exc, 'wait', None): headers['Retry-After'] = '%d' % exc.wait if isinstance(exc.detail, (list, dict)): data = exc.detail else: data = {'detail': exc.detail} set_rollback() return Response(data, status=exc.status_code, headers=headers) return None

recommenders/models/sar/sar_singlenode.py

def compute_cooccurrence_matrix(self, df): u

Remove drop_duplicates() from SAR method fix #1464 (#1588) * Remove drop_duplicates() from SAR method fix #1464 * flake is complaining * Typos * Define self.unity_user_affinity inside __init__() * Remove drop_duplicates() from SAR method * Remove duplicates in testing data * Remove duplicates in test data for recommend_k_items * Allow duplicates in score data Co-authored-by: miguelgfierro <miguelgfierro@users.noreply.github.com> Co-authored-by: Andreas Argyriou <anargyri@users.noreply.github.com> Co-authored-by: Simon Zhao <43029286+simonzhaoms@users.noreply.github.com>

compute_cooccurrence_matrix

96b5053fa688bec79a729f9ea238e5f916bced01

recommenders

sar_singlenode.py

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

Python

def compute_cooccurrence_matrix(self, df): user_item_hits = sparse.coo_matrix( (np.repeat(1, df.shape[0]), (df[self.col_user_id], df[self.col_item_id])), shape=(self.n_users, self.n_items), ).tocsr() item_cooccurrence = user_item_hits.transpose().dot(user_item_hits) item_cooccurrence = item_cooccurrence.multiply( item_cooccurrence >= self.threshold ) return item_cooccurrence.astype(df[self.col_rating].dtype)

pipenv/patched/notpip/_vendor/pyparsing/helpers.py

def _makeTags(tagStr, xml, suppress_LT=Suppress("<"), suppress_GT=Suppress(">")): if isinstance(tagStr, str_type): resname = tagStr tagStr = Keyword(tagStr, caseless=not xml) else: resname = tagStr.name tagAttrName = Word(alphas, alphanums + "_-:") if xml: tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes) openTag = ( suppress_LT + tagStr("tag") + Dict(ZeroOrMore(Group(tagAttrName + Suppress("=") + tagAttrValue))) + Opt("/", default=[False])("empty").set_parse_action( lambda s, l, t: t[0] == "/" ) + suppress_GT ) else: tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word( printables, exclude_chars=">" ) openTag = ( suppress_LT + tagStr("tag") + Dict( ZeroOrMore( Group( tagAttrName.set_parse_action(lambda t: t[0].lower()) + Opt(Suppress("=") + tagAttrValue) ) ) ) + Opt("/", default=[False])("empty").set_parse_action( lambda s, l, t: t[0] == "/" ) + suppress_GT ) closeTag = Combine(Literal("</") + tagStr + ">", adjacent=False) openTag.set_name("<%s>" % resname) # add start<tagname> results name in parse action now that ungrouped names are not reported at two levels openTag.add_parse_action( lambda t: t.__setitem__( "start" + "".join(resname.replace(":", " ").title().split()), t.copy() )

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

_makeTags

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

helpers.py

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

Python

def _makeTags(tagStr, xml, suppress_LT=Suppress("<"), suppress_GT=Suppress(">")): if isinstance(tagStr, str_type): resname = tagStr tagStr = Keyword(tagStr, caseless=not xml) else: resname = tagStr.name tagAttrName = Word(alphas, alphanums + "_-:") if xml: tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes) openTag = ( suppress_LT + tagStr("tag") + Dict(ZeroOrMore(Group(tagAttrName + Suppress("=") + tagAttrValue))) + Opt("/", default=[False])("empty").set_parse_action( lambda s, l, t: t[0] == "/" ) + suppress_GT ) else: tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word( printables, exclude_chars=">" ) openTag = ( suppress_LT + tagStr("tag") + Dict( ZeroOrMore( Group( tagAttrName.set_parse_action(lambda t: t[0].lower()) + Opt(Suppress("=") + tagAttrValue) ) ) ) + Opt("/", default=[False])("empty").set_parse_action( lambda s, l, t: t[0] == "/" ) + suppress_GT ) closeTag = Combine(Literal("</") + tagStr + ">", adjacent=False) openTag.set_name("<%s>" % resname) # add start<tagname> results name in parse action now that ungrouped names are not reported at two levels openTag.add_parse_action( lambda t: t.__setitem__( "start" + "".join(resname.replace(":", " ").title().split()), t.copy() ) ) closeTag = closeTag( "end" + "".join(resname.replace(":", " ").title().split()) ).set_name("</%s>" % resname) openTag.tag = resname closeTag.tag = resname openTag.tag_body = SkipTo(closeTag()) return openTag, closeTag

keras/datasets/cifar100.py

def load_data(label_mode="fine"): if label_mode not in ["fine", "coarse"]: raise ValueError( '`label_mode` must be one of `"fine"`, `"coarse"`. ' f"Received: label_mode=

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

load_data

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

cifar100.py

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

Python

def load_data(label_mode="fine"): if label_mode not in ["fine", "coarse"]: raise ValueError( '`label_mode` must be one of `"fine"`, `"coarse"`. ' f"Received: label_mode={label_mode}." ) dirname = "cifar-100-python" origin = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz" path = get_file( dirname, origin=origin, untar=True, file_hash="85cd44d02ba6437773c5bbd22e183051d648de2e7d6b014e1ef29b855ba677a7", ) fpath = os.path.join(path, "train") x_train, y_train = load_batch(fpath, label_key=label_mode + "_labels") fpath = os.path.join(path, "test") x_test, y_test = load_batch(fpath, label_key=label_mode + "_labels") y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if backend.image_data_format() == "channels_last": x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) return (x_train, y_train), (x_test, y_test)

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

def find_airflow_sources_root() -> Path: default_airflow_sources_root = Path.cwd() # Try to find airflow sources in current working dir airflow_sources_root = search_upwards_for_airflow_sources_root(Path.cwd()) if not airflow_sources_root: # Or if it fails, find it in parents of the directory where the ./breeze.py is. airflow_sources_root = search_upwards_for_airflow_sources_root(Path(__file__).resolve().parent) if airflow_sources_root: os.chdir(airflow_sources_root) return Path(airflow_sources_root) else: console.print( f"\n[bright_yellow]Could not find Airflow sources location. " f"Assuming {default_airflow_sources_root}" ) os.chdir(default_airflow_sources_root) return Path(default_airflow_sources_root) AIRFLOW_SOURCES_ROOT = find_airflow_sources_root() BUILD_CACHE_DIR = AIRFLOW_SOURCES_ROOT / '.build' FILES_DIR = AIRFLOW

Prepare Breeze2 for prime time :) (#22713) This is a review and clean-up for all the parameters and commands for Breeze2 in order to prepare it for being used by the contribugors. There are various small fixes here and there, removal of duplicated code, refactoring and moving code around as well as cleanup and review all the parameters used for all implemented commands. The parameters, default values and their behaviours were updated to match "new" life of Breeze rather than old one. Some improvements are made to the autocomplete and click help messages printed. Full list of choices is always displayed, parameters are groups according to their target audience, and they were sorted according to importance and frequency of use. Various messages have been colourised according to their meaning - warnings as yellow, errors as red and informational messages as bright_blue. The `dry-run` option has been added to just show what would have been run without actually running some potentially "write" commands (read commands are still executed) so that you can easily verify and manually copy and execute the commands with option to modify them before. The `dry_run` and `verbose` options are now used for all commands. The "main" command now runs "shell" by default similarly as the original Breeze. All "shortcut" parameters have been standardized - i.e common options (verbose/dry run/help) have one and all common flags that are likely to be used often have an assigned shortcute. The "stop" and "cleanup" command have been added as they are necessary for average user to complete the regular usage cycle. Documentation for all the important methods have been updated.

find_airflow_sources_root

4ffd4f09532fceb67675fce4c1f5cd383eff992e

airflow

path_utils.py

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

Python

def find_airflow_sources_root() -> Path: default_airflow_sources_root = Path.cwd() # Try to find airflow sources in current working dir airflow_sources_root = search_upwards_for_airflow_sources_root(Path.cwd()) if not airflow_sources_root: # Or if it fails, find it in parents of the directory where the ./breeze.py is. airflow_sources_root = search_upwards_for_airflow_sources_root(Path(__file__).resolve().parent) if airflow_sources_root: os.chdir(airflow_sources_root) return Path(airflow_sources_root) else: console.print( f"\n[bright_yellow]Could not find Airflow sources location. " f"Assuming {default_airflow_sources_root}" ) os.chdir(default_airflow_sources_root) return Path(default_airflow_sources_root) AIRFLOW_SOURCES_ROOT = find_airflow_sources_root() BUILD_CACHE_DIR = AIRFLOW_SOURCES_ROOT / '.build' FILES_DIR = AIRFLOW_SOURCES_ROOT / 'files' MSSQL_DATA_VOLUME = AIRFLOW_SOURCES_ROOT / 'tmp_mssql_volume' MYPY_CACHE_DIR = AIRFLOW_SOURCES_ROOT / '.mypy_cache' LOGS_DIR = AIRFLOW_SOURCES_ROOT / 'logs' DIST_DIR = AIRFLOW_SOURCES_ROOT / 'dist' SCRIPTS_CI_DIR = AIRFLOW_SOURCES_ROOT / 'scripts' / 'ci' DOCKER_CONTEXT_DIR = AIRFLOW_SOURCES_ROOT / 'docker-context-files' CACHE_TMP_FILE_DIR = tempfile.TemporaryDirectory() OUTPUT_LOG = Path(CACHE_TMP_FILE_DIR.name, 'out.log')

qutebrowser/browser/network/pac.py

def _parse_proxy_entry(proxy_str): config = [c.strip() for c in proxy_str.split(' ') if c] if not config: raise ParseProxyError("Empty proxy entry") if config[0] == "DIRECT": if len(config) != 1: raise ParseProxyError("Invalid number of parameters for " + "DIRECT") return QNetworkProxy(QNetworkProxy.ProxyType.NoProxy) elif config[0] == "PROXY": if len(config) != 2: raise ParseProxyError("Invalid number of parameters for PROXY") host, port = PACResolver._parse_proxy_host(config[1]) return QNetworkProxy(QNetworkProxy.ProxyType.HttpProxy, host, port) elif config[0] in ["SOCKS", "SOCKS5"]: if len(config) != 2: raise ParseProxyError("Invalid number of parameters for SOCKS") host, port = PACResol

Run scripts/dev/rewrite_enums.py

_parse_proxy_entry

0877fb0d78635692e481c8bde224fac5ad0dd430

qutebrowser

pac.py

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

Python

def _parse_proxy_entry(proxy_str): config = [c.strip() for c in proxy_str.split(' ') if c] if not config: raise ParseProxyError("Empty proxy entry") if config[0] == "DIRECT": if len(config) != 1: raise ParseProxyError("Invalid number of parameters for " + "DIRECT") return QNetworkProxy(QNetworkProxy.ProxyType.NoProxy) elif config[0] == "PROXY": if len(config) != 2: raise ParseProxyError("Invalid number of parameters for PROXY") host, port = PACResolver._parse_proxy_host(config[1]) return QNetworkProxy(QNetworkProxy.ProxyType.HttpProxy, host, port) elif config[0] in ["SOCKS", "SOCKS5"]: if len(config) != 2: raise ParseProxyError("Invalid number of parameters for SOCKS") host, port = PACResolver._parse_proxy_host(config[1]) return QNetworkProxy(QNetworkProxy.ProxyType.Socks5Proxy, host, port) else: err = "Unknown proxy type: {}" raise ParseProxyError(err.format(config[0]))

numpy/f2py/tests/test_f2py2e.py

def test_gen_pyf(capfd, hello_world_f90, monkeypatch): ipath = Path(hello_world_f90) opath = Path(hello_world_f90).stem + ".pyf" monkeypatch.setattr(sys,

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

test_gen_pyf

729ad4f92420231e2a7009b3223c6c7620b8b808

numpy

test_f2py2e.py

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

Python

def test_gen_pyf(capfd, hello_world_f90, monkeypatch): ipath = Path(hello_world_f90) opath = Path(hello_world_f90).stem + ".pyf" monkeypatch.setattr(sys, "argv", f'f2py -h {opath} {ipath}'.split()) with util.switchdir(ipath.parent): f2pycli() # Generate wrappers out, _ = capfd.readouterr() assert "Saving signatures to file" in out assert Path(f'{opath}').exists()

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

def _dictionary(self): # type: () -> Dict[str, Any] # NOTE: Dictionaries are not populated if not loaded. So, conditionals # are not needed here. retval = {} for variant in OVERRIDE_ORDER:

upd; format

_dictionary

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

configuration.py

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

Python

def _dictionary(self): # type: () -> Dict[str, Any] # NOTE: Dictionaries are not populated if not loaded. So, conditionals # are not needed here. retval = {} for variant in OVERRIDE_ORDER: retval.update(self._config[variant]) return retval

django/db/models/query.py

def using(self, alias): return RawQuerySet( self.raw_query, model=self.model, query=self.query.chain(using=alias), params=self.params, translations=self.translations, using=alias, )

Refs #33476 -- Reformatted code with Black.

using

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

query.py

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

Python

def using(self, alias): return RawQuerySet( self.raw_query, model=self.model, query=self.query.chain(using=alias), params=self.params, translations=self.translations, using=alias, )

keras/optimizers/optimizer_v2/optimizer_v2.py

def get_weights(self): params = self.we

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

get_weights

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

optimizer_v2.py

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

Python

def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer.

mmdet/core/bbox/match_costs/match_cost.py

def _focal_loss_cost(self, cls_pred, gt_labels): cls_pred = cls_pred.sigmoid() neg_cost = -(1 - cls_pred + self.eps).log() * ( 1 - self.alpha) * cls_pred.pow(self.gamma) pos_cost = -(cls_pred + sel

[Feature] Add Maskformer to mmdet (#7212) * first commit * add README * move model description from config to readme add description for binary_input add description for dice loss add a independent panoptic gt processing function add a independent panoptic gt processing function remove compatibility of pretrain in maskformer * update comments in maskformer_head * update docs format

_focal_loss_cost

cac356380d505bf15587f07c0529218cc36b9652

mmdetection

match_cost.py

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

Python

def _focal_loss_cost(self, cls_pred, gt_labels): cls_pred = cls_pred.sigmoid() neg_cost = -(1 - cls_pred + self.eps).log() * ( 1 - self.alpha) * cls_pred.pow(self.gamma) pos_cost = -(cls_pred + self.eps).log() * self.alpha * ( 1 - cls_pred).pow(self.gamma) cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels] return cls_cost * self.weight

django/db/backends/base/operations.py

def date_extract_sql(self, lookup_type, field_name): raise NotImplementedError( "subclasses of BaseDatabaseOperations may require a date_extract_sq

Refs #33476 -- Reformatted code with Black.

date_extract_sql

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

operations.py

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

Python

def date_extract_sql(self, lookup_type, field_name): raise NotImplementedError( "subclasses of BaseDatabaseOperations may require a date_extract_sql() method" )

tests/rest/client/test_rooms.py

def test_threepid_invite_spamcheck(self) -> None: # Mock a few functions to prevent the test from failing due to failing to talk to # a remote IS. We keep the mock for make_and_store_3pid_invite around so we # can check its call_count later on during the test. make_invite_mock = Mock(return_value=make_awaitable(0)) self.hs.get_room_member_handler()._make_and_store_3pid_invite = make_invite_mock self.hs.get_identity_handler().lookup_3pid = Mock( return_value=make_awaitable(None), ) # Add a mock to the spamchecker callbacks for user_may_send_3pid_invite. Make it # allow everything for now. # `spec` argument is needed for this function mock to have `__qualname__`, which # is needed for `Measure` metrics buried in SpamChecker. mock = Mock( return_value=make_awaitable(synapse.module_api.NOT_SPAM), spec=lambda *x: None, ) self.hs.get_spam_checker()._user_may_send_3pid_invite_callbacks.append(mock) # Send a 3PID invite into the room and check that it succeeded. email_to_invite = "teresa@example.com" channel = self.make_request( method="POST", path="/rooms/" + self.room_id + "/invite", content={ "id_server": "example.com", "id_access_token": "sometoken", "medium": "email", "address":

Uniformize spam-checker API, part 4: port other spam-checker callbacks to return `Union[Allow, Codes]`. (#12857) Co-authored-by: Brendan Abolivier <babolivier@matrix.org>

test_threepid_invite_spamcheck

a164a46038b0e51142781619db0e6dec8e0c2aaa

synapse

test_rooms.py

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

Python

def test_threepid_invite_spamcheck(self) -> None: # Mock a few functions to prevent the test from failing due to failing to talk to # a remote IS. We keep the mock for make_and_store_3pid_invite around so we # can check its call_count later on during the test. make_invite_mock = Mock(return_value=make_awaitable(0)) self.hs.get_room_member_handler()._make_and_store_3pid_invite = make_invite_mock self.hs.get_identity_handler().lookup_3pid = Mock( return_value=make_awaitable(None), ) # Add a mock to the spamchecker callbacks for user_may_send_3pid_invite. Make it # allow everything for now. # `spec` argument is needed for this function mock to have `__qualname__`, which # is needed for `Measure` metrics buried in SpamChecker. mock = Mock( return_value=make_awaitable(synapse.module_api.NOT_SPAM), spec=lambda *x: None, ) self.hs.get_spam_checker()._user_may_send_3pid_invite_callbacks.append(mock) # Send a 3PID invite into the room and check that it succeeded. email_to_invite = "teresa@example.com" channel = self.make_request( method="POST", path="/rooms/" + self.room_id + "/invite", content={ "id_server": "example.com", "id_access_token": "sometoken", "medium": "email", "address": email_to_invite, }, access_token=self.tok, ) self.assertEqual(channel.code, 200) # Check that the callback was called with the right params. mock.assert_called_with(self.user_id, "email", email_to_invite, self.room_id) # Check that the call to send the invite was made. make_invite_mock.assert_called_once() # Now change the return value of the callback to deny any invite and test that # we can't send the invite. mock.return_value = make_awaitable(Codes.CONSENT_NOT_GIVEN) channel = self.make_request( method="POST", path="/rooms/" + self.room_id + "/invite", content={ "id_server": "example.com", "id_access_token": "sometoken", "medium": "email", "address": email_to_invite, }, access_token=self.tok, ) self.assertEqual(channel.code, 403) # Also check that it stopped before calling _make_and_store_3pid_invite. make_invite_mock.assert_called_once()

@pytest.mark.parametrize( "constraint", [ _ArrayLikes, _Callables, _InstancesOf, _NoneConstraint, _RandomStates, _SparseMatrices, ], )

sklearn/utils/tests/test_param_validation.py

def test_generate_invalid_param_val_all_valid(constraints): with pytest.raises(NotImplementedError): generate_invalid_param_val(constraints[0], constraints=constraints) @pytest.mark.parametrize( "constraint", [ _ArrayLikes, _Callables, _InstancesOf, _NoneConstraint, _RandomStates,

FIX Param validation: fix generating invalid param when 2 interval constraints (#23513) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

test_generate_invalid_param_val_all_valid

02cbe01e67165d7d38e5e441cfccd6b57b2207b6

scikit-learn

test_param_validation.py

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

Python

def test_generate_invalid_param_val_all_valid(constraints): with pytest.raises(NotImplementedError): generate_invalid_param_val(constraints[0], constraints=constraints) @pytest.mark.parametrize( "constraint", [ _ArrayLikes, _Callables, _InstancesOf, _NoneConstraint, _RandomStates, _SparseMatrices, ], )

freqtrade/freqai/data_drawer.py

def load_drawer_from_disk(self): exists = Path(se

rehaul of backend data management - increasing performance by holding history in memory, reducing load on the ratelimit by only pinging exchange once per candle. Improve code readability.

load_drawer_from_disk

16b4a5b71ff140f5de31e5d5572f1f193457cf6b

freqtrade

data_drawer.py

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

Python

def load_drawer_from_disk(self): exists = Path(self.full_path / str('pair_dictionary.json')).resolve().exists() if exists: with open(self.full_path / str('pair_dictionary.json'), "r") as fp: self.pair_dict = json.load(fp) elif not self.follow_mode: logger.info("Could not find existing datadrawer, starting from scratch") else: logger.warning(f'Follower could not find pair_dictionary at {self.full_path} ' 'sending null values back to strategy') return exists

python3.10.4/Lib/ipaddress.py

def address_exclude(self, other): if not self._version == other._version: raise TypeError("%s and %s are not of the same version" % ( self, other)) if not isinstance(other, _BaseNetwork):

add python 3.10.4 for windows

address_exclude

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

ipaddress.py

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

Python

def address_exclude(self, other): if not self._version == other._version: raise TypeError("%s and %s are not of the same version" % ( self, other)) if not isinstance(other, _BaseNetwork): raise TypeError("%s is not a network object" % other) if not other.subnet_of(self): raise ValueError('%s not contained in %s' % (other, self)) if other == self: return # Make sure we're comparing the network of other. other = other.__class__('%s/%s' % (other.network_address, other.prefixlen)) s1, s2 = self.subnets() while s1 != other and s2 != other: if other.subnet_of(s1): yield s2 s1, s2 = s1.subnets() elif other.subnet_of(s2): yield s1 s1, s2 = s2.subnets() else: # If we got here, there's a bug somewhere. raise AssertionError('Error performing exclusion: ' 's1: %s s2: %s other: %s' % (s1, s2, other)) if s1 == other: yield s2 elif s2 == other: yield s1 else: # If we got here, there's a bug somewhere. raise AssertionError('Error performing exclusion: ' 's1: %s s2: %s other: %s' % (s1, s2, other))

recommenders/utils/python_utils.py

def mutual_information(cooccurrence): with np.errstate(invalid="ignore", divide="ignore"): result = np.log2(cooccurrence.shape[0] * lift(cooccurrence)) return np.array(result)

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

mutual_information

1d7341e93d1f03387699fb3c6ae0b6c0e464296f

recommenders

python_utils.py

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

Python

def mutual_information(cooccurrence): with np.errstate(invalid="ignore", divide="ignore"): result = np.log2(cooccurrence.shape[0] * lift(cooccurrence)) return np.array(result)

pipenv/patched/notpip/_vendor/typing_extensions.py

def _is_dunder(name): return len(name) > 4 and name.startswith('__') and name.endswith('__

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

_is_dunder

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

typing_extensions.py

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

Python

def _is_dunder(name): return len(name) > 4 and name.startswith('__') and name.endswith('__') # Prior to Python 3.7 types did not have `copy_with`. A lot of the equality # checks, argument expansion etc. are done on the _subs_tre. As a result we # can't provide a get_type_hints function that strips out annotations.

sklearn/neighbors/_kde.py

def fit(self, X, y=None, sample_weight=None): algorithm = self._choose_algorithm(self.algorithm, self.metric) if isinstance(self.bandwidth, str): methods_supported = ("scott", "silvermann") if self.bandwidth not in methods_supported: raise ValueError( "When `bandwidth` is a string, it should be one of: " f"{', '.join(methods_supported)}. Got {self.bandwidth!r} instead." ) if self.bandwidth == "scott": self.bandwidth_ = X.shape[0] ** (-1 / (X.shape[1] + 4)) elif self.bandwidth == "silvermann": self.bandwidth_ = (X.shape[0] * (X.shape[1] + 2) / 4) ** ( -1 / (X.shape[1] + 4) ) else: check_scalar( self.bandwidth, "bandwidth", target_type=numbers.Real, min_val=0, include_boundaries="neither", ) self.bandwidth_ = self.bandwidth if self.kernel not in VALID_KERNELS: raise ValueError("invalid kernel: '{0}'".format(self.kernel)) X = self._validate_data(X, order="C", dtype=DTYP

FEA Added Kernel Density bandwidth estimation and test (#22993) Co-authored-by: STOJANOVIC Jovan <jovan.stojanovic@inria.fr> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

fit

dedaa8f25f136e954941d15151bbbc88150789fc

scikit-learn

_kde.py

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

Python

def fit(self, X, y=None, sample_weight=None): algorithm = self._choose_algorithm(self.algorithm, self.metric) if isinstance(self.bandwidth, str): methods_supported = ("scott", "silvermann") if self.bandwidth not in methods_supported: raise ValueError( "When `bandwidth` is a string, it should be one of: " f"{', '.join(methods_supported)}. Got {self.bandwidth!r} instead." ) if self.bandwidth == "scott": self.bandwidth_ = X.shape[0] ** (-1 / (X.shape[1] + 4)) elif self.bandwidth == "silvermann": self.bandwidth_ = (X.shape[0] * (X.shape[1] + 2) / 4) ** ( -1 / (X.shape[1] + 4) ) else: check_scalar( self.bandwidth, "bandwidth", target_type=numbers.Real, min_val=0, include_boundaries="neither", ) self.bandwidth_ = self.bandwidth if self.kernel not in VALID_KERNELS: raise ValueError("invalid kernel: '{0}'".format(self.kernel)) X = self._validate_data(X, order="C", dtype=DTYPE) if sample_weight is not None: sample_weight = _check_sample_weight( sample_weight, X, DTYPE, only_non_negative=True ) kwargs = self.metric_params if kwargs is None: kwargs = {} self.tree_ = TREE_DICT[algorithm]( X, metric=self.metric, leaf_size=self.leaf_size, sample_weight=sample_weight, **kwargs, ) return self

src/PIL/ImageMorph.py

def get_on_pixels(self, image):

Improve exception traceback readability

get_on_pixels

2ae55ccbdad9c842929fb238ea1eb81d1f999024

Pillow

ImageMorph.py

https://github.com/python-pillow/Pillow.git

Python

def get_on_pixels(self, image): if image.mode != "L": msg = "Image mode must be L" raise ValueError(msg) return _imagingmorph.get_on_pixels(image.im.id)

sympy/parsing/ast_parser.py

def visit_Num(self, node): if isinstance(node.n, int): return fix_missing_locations(Call(func=Name('Integer', Load(

Inserted the `visit_Num` function back in. This was required to keep SymPy compatible with Python 3.7.

visit_Num

e95d725680aab772037848628471a31f03a13901

sympy

ast_parser.py

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

Python

def visit_Num(self, node): if isinstance(node.n, int): return fix_missing_locations(Call(func=Name('Integer', Load()), args=[node], keywords=[])) elif isinstance(node.n, float): return fix_missing_locations(Call(func=Name('Float', Load()), args=[node], keywords=[])) return node

python3.10.4/Lib/cgitb.py

def enable(display=1, logdir=None, context=5, format="html"): sys.excepthook = Hook(display=display, logdir=logdir, context=context, format=format

add python 3.10.4 for windows

enable

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

cgitb.py

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

Python

def enable(display=1, logdir=None, context=5, format="html"): sys.excepthook = Hook(display=display, logdir=logdir, context=context, format=format)

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

def parse_list_header(value): result = [] for item in

upd; format

parse_list_header

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

utils.py

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

Python

def parse_list_header(value): result = [] for item in _parse_list_header(value): if item[:1] == item[-1:] == '"': item = unquote_header_value(item[1:-1]) result.append(item) return result # From mitsuhiko/werkzeug (used with permission).

homeassistant/components/apple_tv/media_player.py

def media_series_title(self) -> str | None: if self._playing and self._is_feature_available(FeatureName.SeriesName): return self

Improve type hints in apple_tv media player (#77940)

media_series_title

5276d849ec497ccd0cecf3cb6a8dacae4fa6f845

core

media_player.py

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

Python

def media_series_title(self) -> str | None: if self._playing and self._is_feature_available(FeatureName.SeriesName): return self._playing.series_name return None

mkdocs/commands/build.py

def _build_template(name, template, files, config, nav): # Run `pre_template` plugin events. template = config['plugins'].run_event( 'pre_template', template, template_name=name, config=config ) if utils.is_error_template(name): # Force absolute URLs in the nav of error pages and account for the # possibility that the docs root might be different than the server root. # See https://github.com/mkdocs/mkdocs/issues/77. # However, if site_url is not set, assume the docs root and server root # are the same. See https://github.com/mkdocs/mkdocs/issues/1598. base_url = urlsplit(config['site_url'] or '/').path else: base_url = utils.get_relative_url('.', name) context = get_contex

Format code with `black -l100 --skip-string-normalization`

_build_template

dca7cbb43fcd6ea7c677c98ba585395b070d387b

mkdocs

build.py

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

Python

def _build_template(name, template, files, config, nav): # Run `pre_template` plugin events. template = config['plugins'].run_event( 'pre_template', template, template_name=name, config=config ) if utils.is_error_template(name): # Force absolute URLs in the nav of error pages and account for the # possibility that the docs root might be different than the server root. # See https://github.com/mkdocs/mkdocs/issues/77. # However, if site_url is not set, assume the docs root and server root # are the same. See https://github.com/mkdocs/mkdocs/issues/1598. base_url = urlsplit(config['site_url'] or '/').path else: base_url = utils.get_relative_url('.', name) context = get_context(nav, files, config, base_url=base_url) # Run `template_context` plugin events. context = config['plugins'].run_event( 'template_context', context, template_name=name, config=config ) output = template.render(context) # Run `post_template` plugin events. output = config['plugins'].run_event('post_template', output, template_name=name, config=config) return output

sympy/core/basic.py

def matches(self, expr, repl_dict=None, old=False): expr = sympify(expr) if not isinstance(expr, self.__class__): return None if repl_dict is None: repl_dict = {} else: repl_dict = repl_dict.copy() if self == expr:

Code cleanup

matches

9d58006fc0a23afcba38f641c9472917c436428a

sympy

basic.py

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

Python

def matches(self, expr, repl_dict=None, old=False): expr = sympify(expr) if not isinstance(expr, self.__class__): return None if repl_dict is None: repl_dict = {} else: repl_dict = repl_dict.copy() if self == expr: return repl_dict if len(self.args) != len(expr.args): return None d = repl_dict # already a copy for arg, other_arg in zip(self.args, expr.args): if arg == other_arg: continue if arg.is_Relational: try: d = arg.xreplace(d).matches(other_arg, d, old=old) except TypeError: # Should be InvalidComparisonError when introduced d = None else: d = arg.xreplace(d).matches(other_arg, d, old=old) if d is None: return None return d

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

def _create_placement_group(self, num_workers): pg = get_current_placement_group() if pg is None: bundle = {"CPU": self._num_cpus_per_worker, "GPU": int(self._use_gpu)} bundles = [bundle] * num_workers pg = ray.util.placement_group(bundles, strategy="SPREAD") logger.debug("Waiting for placement group to start.") ready, _ = ray.wait([pg.ready()], timeout=SGD_PLACEMENT_GROUP_TIMEOUT_S) if ready: logger.debug("Placement group has started.")

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

_create_placement_group

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

worker_group.py

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

Python

def _create_placement_group(self, num_workers): pg = get_current_placement_group() if pg is None: bundle = {"CPU": self._num_cpus_per_worker, "GPU": int(self._use_gpu)} bundles = [bundle] * num_workers pg = ray.util.placement_group(bundles, strategy="SPREAD") logger.debug("Waiting for placement group to start.") ready, _ = ray.wait([pg.ready()], timeout=SGD_PLACEMENT_GROUP_TIMEOUT_S) if ready: logger.debug("Placement group has started.") else: raise TimeoutError( "Placement group creation timed out. Make sure " "your cluster either has enough resources or use " "an autoscaling cluster. Current resources " "available: {}, resources requested by the " "placement group: {}".format( ray.available_resources(), pg.bundle_specs ) ) self._worker_placement_group = pg

erpnext/stock/report/warehouse_wise_item_balance_age_and_value/warehouse_wise_item_balance_age_and_value.py

def get_warehouse_list(filters): from frappe.core.doctype.user_permission.user_permission import get_permitted_documents condition = "" user_permitted_warehouse = get_permitted_documents("Warehouse") value = () if user_permitted_warehouse: condition = "and name in %s" value = set(

style: format code with black

get_warehouse_list

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

warehouse_wise_item_balance_age_and_value.py

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

Python

def get_warehouse_list(filters): from frappe.core.doctype.user_permission.user_permission import get_permitted_documents condition = "" user_permitted_warehouse = get_permitted_documents("Warehouse") value = () if user_permitted_warehouse: condition = "and name in %s" value = set(user_permitted_warehouse) elif not user_permitted_warehouse and filters.get("warehouse"): condition = "and name = %s" value = filters.get("warehouse") return frappe.db.sql( .format( condition=condition ), value, as_dict=1, )

airflow/models/mappedoperator.py

def unmap(self) -> "BaseOperator": dag = self.dag if not dag:

More explicit mapped argument validation (#21933) * More explicit mapped argument validation Instead of always using MagicMock to validate mapped arguments, this implements a more sophisticated protocol that allows an operator to implement a 'validate_mapped_arguments' to provide custom validation logic. If an operator just wants to use __init__ for validation, however, they can set a flag 'mapped_arguments_validated_by_init' to get the behavior easily. (This does *not* use MagicMock, however, since any custom validation logic should be able to handle those on its own). The 'validate_mapped_arguments' flag is currently only set on PythonOperator. It can likely be used on a lot more operators down the road. * Add flag to distinguish a validation-only init There's just too much magic during a task's initialization that tries to add it into the dependency graph. This flag is needed to work around all that, I think.

unmap

b65e52205a7045eb08d471289b85abda587442b7

airflow

mappedoperator.py

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

Python

def unmap(self) -> "BaseOperator": dag = self.dag if not dag: raise RuntimeError("Cannot unmap a task without a DAG") dag._remove_task(self.task_id) if isinstance(self.operator_class, str): raise RuntimeError("Cannot unmap a deserialized operator") return self.operator_class(**self._get_unmap_kwargs())

src/accelerate/test_utils/testing.py

def require_tensorflow(test_case): if not is_tensorflow_available(): return unittest.skip(

Add logging capabilities (#293) Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> - Added experiment tracking API, and support for Weights and Biases, TensorBoard, and CometML + Tests - Added `tensorflow` to a new dependency list to be used during tests - Added three new functions in `Accelerator` to interact with the API

require_tensorflow

5668270de74a09e5bff15891054f73ddbb1176ac

accelerate

testing.py

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

Python

def require_tensorflow(test_case): if not is_tensorflow_available(): return unittest.skip("test requires TensorFlow")(test_case) else: return test_case

python3.10.4/Lib/concurrent/futures/_base.py

def as_completed(fs, timeout=None): if timeout is not None: end_time = timeout + time.monotonic() fs = set(fs) total_futures = len(fs) with _AcquireFutures(fs): finished = set( f for f in fs if f._state in [CANCELLED_AND_NOTIFIED, FINISHED]) pending = fs - finished waiter = _create_and_install_waiters(fs, _AS_COMPLETED) finished = list(finished) try: yield from _yield_finished_futures(finished, waiter, ref_collect=(fs,)) while pending: if timeout is None: wait_timeout = None else: wait_timeout = end_time - time.monotonic() if wait_timeout < 0: raise TimeoutError( '%d (of %d) futures unfinished' % ( len(pending), total_futures)) waiter.event.wait(wait_timeout) with waiter.lock: finished = waiter.finished_futures waiter.finished_futures = [] wai

add python 3.10.4 for windows

as_completed

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

_base.py

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

Python

def as_completed(fs, timeout=None): if timeout is not None: end_time = timeout + time.monotonic() fs = set(fs) total_futures = len(fs) with _AcquireFutures(fs): finished = set( f for f in fs if f._state in [CANCELLED_AND_NOTIFIED, FINISHED]) pending = fs - finished waiter = _create_and_install_waiters(fs, _AS_COMPLETED) finished = list(finished) try: yield from _yield_finished_futures(finished, waiter, ref_collect=(fs,)) while pending: if timeout is None: wait_timeout = None else: wait_timeout = end_time - time.monotonic() if wait_timeout < 0: raise TimeoutError( '%d (of %d) futures unfinished' % ( len(pending), total_futures)) waiter.event.wait(wait_timeout) with waiter.lock: finished = waiter.finished_futures waiter.finished_futures = [] waiter.event.clear() # reverse to keep finishing order finished.reverse() yield from _yield_finished_futures(finished, waiter, ref_collect=(fs, pending)) finally: # Remove waiter from unfinished futures for f in fs: with f._condition: f._waiters.remove(waiter) DoneAndNotDoneFutures = collections.namedtuple( 'DoneAndNotDoneFutures', 'done not_done')

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

def pack_env_dict(self) -> Dict[str, Any]: env_info = {"window_size": self.CONV_WIDTH, "reward_kwargs": self.reward_params, "config": self.config, "live": self.live} if self.data_provider: env_info["fee"] = self.data_pr

use a dictionary to make code more readable

pack_env_dict

7b4abd5ef50f3c6f84c6604fc1f79ff4b92c2575

freqtrade

BaseReinforcementLearningModel.py

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

Python

def pack_env_dict(self) -> Dict[str, Any]: env_info = {"window_size": self.CONV_WIDTH, "reward_kwargs": self.reward_params, "config": self.config, "live": self.live} if self.data_provider: env_info["fee"] = self.data_provider._exchange \ .get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore return env_info

rllib/evaluation/rollout_worker.py

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

[RLlib] Agents to algos: DQN w/o Apex and R2D2, DDPG/TD3, SAC, SlateQ, QMIX, PG, Bandits (#24896)

sample

3815e52a61b6afe44b883d7d745fa00b599f66ca

ray

rollout_worker.py

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

Python

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

tests/admin_changelist/tests.py

def test_no_duplicates_for_non_unique_related_object_in_list_filter(self): parent = Parent.objects.create(name="Mary") # Two children with the same name Child.objects.create(parent=parent, name="Daniel") Child.objects.create(parent=parent, name="Daniel") m = ParentAdmin(Parent, custom_site)

Refs #33476 -- Reformatted code with Black.

test_no_duplicates_for_non_unique_related_object_in_list_filter

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_no_duplicates_for_non_unique_related_object_in_list_filter(self): parent = Parent.objects.create(name="Mary") # Two children with the same name Child.objects.create(parent=parent, name="Daniel") Child.objects.create(parent=parent, name="Daniel") m = ParentAdmin(Parent, custom_site) request = self.factory.get("/parent/", data={"child__name": "Daniel"}) request.user = self.superuser cl = m.get_changelist_instance(request) # Exists() is applied. self.assertEqual(cl.queryset.count(), 1) # Queryset must be deletable. self.assertIs(cl.queryset.query.distinct, False) cl.queryset.delete() self.assertEqual(cl.queryset.count(), 0)

saleor/plugins/webhook/utils.py

def get_current_tax_app() -> Optional[App]: return ( App.objects.order_by("pk") .for_event_type(WebhookEventSyncType.CHECKOUT_CALCULATE_TAXES)

Add support for calculating taxes in Saleor Apps (#9526) * Squash previouse PR in taxes by Sync webhooks * Adjust incoming communication form tax app in order calculation * Change return type for base_checkout_total to Money * Fix cratign order lines for checkout lines * Remove not needed args * Fix order discount recalculation * Fix order discounts events amount calculation * Fix order calculation tests * Use base price in checkout line serializer * Use base shipping price in checkout tax payload * Use base total in checkout tax payload * Tax app interface should recive tax rate as decimal * Tax app interface should recive tax rate as decimal * Clear migrations * Add descriptions to webhook events enums * Update changelog * Drop not required changes from plugin interface * Fix review remarks

get_current_tax_app

3e06a6462559498c6ad09c0591e648a7943ac0c6

saleor

utils.py

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

Python

def get_current_tax_app() -> Optional[App]: return ( App.objects.order_by("pk") .for_event_type(WebhookEventSyncType.CHECKOUT_CALCULATE_TAXES) .for_event_type(WebhookEventSyncType.ORDER_CALCULATE_TAXES) .last() )

tests/util/caches/test_deferred_cache.py

def test_callbacks(self) -> None: cache: DeferredCache[str, int] = DeferredCache("test") callbacks = set() # start with an entry, with a callba

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

test_callbacks

4ae967cf6308e80b03da749f0cbaed36988e235e

synapse

test_deferred_cache.py

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

Python

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

django/core/management/sql.py

def sql_flush(style, connection, reset_sequences=True, allow_cascade=False): tables = connection.in

Refs #33476 -- Reformatted code with Black.

sql_flush

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

sql.py

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

Python

def sql_flush(style, connection, reset_sequences=True, allow_cascade=False): tables = connection.introspection.django_table_names( only_existing=True, include_views=False ) return connection.ops.sql_flush( style, tables, reset_sequences=reset_sequences, allow_cascade=allow_cascade, )

src/PIL/ImageFilter.py

def generate(cls, size, callback, channels=3, target_mode=None): size_1d, size_2d, size_3d = cls._check_size(size) if channels not in (3, 4): raise ValueError("Only 3 or 4 output channels are supported") table = [0] * (size_1d * size_2d * size_3d * channels) idx_out = 0 for b in range(size_3d)

Variable in function should be snake_case

generate

d3c9a6504e84f87379554b6b671a1fb6c66a449e

Pillow

ImageFilter.py

https://github.com/python-pillow/Pillow.git

Python

def generate(cls, size, callback, channels=3, target_mode=None): size_1d, size_2d, size_3d = cls._check_size(size) if channels not in (3, 4): raise ValueError("Only 3 or 4 output channels are supported") table = [0] * (size_1d * size_2d * size_3d * channels) idx_out = 0 for b in range(size_3d): for g in range(size_2d): for r in range(size_1d): table[idx_out : idx_out + channels] = callback( r / (size_1d - 1), g / (size_2d - 1), b / (size_3d - 1) ) idx_out += channels return cls( (size_1d, size_2d, size_3d), table, channels=channels, target_mode=target_mode, _copy_table=False, )

django/db/backends/base/operations.py

def regex_lookup(self, lookup_type): raise NotImplementedError( "subclasses of BaseDatabaseOperations may require a regex_lookup() method"

Refs #33476 -- Reformatted code with Black.

regex_lookup

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

operations.py

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

Python

def regex_lookup(self, lookup_type): raise NotImplementedError( "subclasses of BaseDatabaseOperations may require a regex_lookup() method" )

erpnext/telephony/doctype/call_log/call_log.py

def link_existing_conversations(doc, state): if doc.doctype != "Contact": return try: numbers = [d.phone for d in doc.phone_nos] for number in numbers: number = strip_number(number) if not number: continue logs = frappe.db.sql_list( , dict(phone_number="%{}".format(number), docname=doc.name, doctype=doc.doctype), ) for log in logs: call_log = frappe.get_doc("Call Log", log) call_log.add_link(link_

style: format code with black

link_existing_conversations

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

call_log.py

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

Python

def link_existing_conversations(doc, state): if doc.doctype != "Contact": return try: numbers = [d.phone for d in doc.phone_nos] for number in numbers: number = strip_number(number) if not number: continue logs = frappe.db.sql_list( , dict(phone_number="%{}".format(number), docname=doc.name, doctype=doc.doctype), ) for log in logs: call_log = frappe.get_doc("Call Log", log) call_log.add_link(link_type=doc.doctype, link_name=doc.name) call_log.save(ignore_permissions=True) frappe.db.commit() except Exception: frappe.log_error(title=_("Error during caller information update"))

homeassistant/components/media_player/__init__.py

async def async_volume_up(self) -> None: if hasattr(self, "volume_up"): await

Update mypy to 0.990 (#81783) * Update mypy to 0.990 * Remove type ignore - overriding attr with property (13475) * Remove type ignores - hasattr (13544) * Adjust type ignore - assignment (13549) * New error code - type-abstract (13785) * Disable annotation-unchecked (13851)

async_volume_up

0c8eeaa6436b04ba6da46bccab8b11523f314d9b

core

__init__.py

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

Python

async def async_volume_up(self) -> None: if hasattr(self, "volume_up"): await self.hass.async_add_executor_job(self.volume_up) return if ( self.volume_level is not None and self.volume_level < 1 and self.supported_features & MediaPlayerEntityFeature.VOLUME_SET ): await self.async_set_volume_level(min(1, self.volume_level + 0.1))

pandas/io/pytables.py

def infer_axes(self) -> bool: s = self.storable if s is None: return False self.get_attrs() return True

TYP: some return annotations in pytables.py (#47512)

infer_axes

7d2f9b8d59908fbf57c6453bc41891efbfe981a6

pandas

pytables.py

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

Python

def infer_axes(self) -> bool: s = self.storable if s is None: return False self.get_attrs() return True

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

def _validate_set_axis(self, new_labels, old_labels): new_labels = ensure_index(new_labels) old_len = len(old_labels) new_len = len(new_labels) if old_len != new_len: raise ValueError( f"Length mismatch: Expected axis has {old_len} elements, " + "new values have {new_len} elements" ) return new_labels

REFACTOR-#3900: add flake8-no-implicit-concat plugin and refactor flake8 error codes (#3901) Signed-off-by: jeffreykennethli <jkli@ponder.io>

_validate_set_axis

e5e9634357e60925a5a70e56a1d4882d269f533a

modin

dataframe.py

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

Python

def _validate_set_axis(self, new_labels, old_labels): new_labels = ensure_index(new_labels) old_len = len(old_labels) new_len = len(new_labels) if old_len != new_len: raise ValueError( f"Length mismatch: Expected axis has {old_len} elements, " + "new values have {new_len} elements" ) return new_labels

src/sentry/snuba/discover.py

def transform_data(result, translated_columns, query_builder) -> EventsResponse: final_result: EventsResponse = {"data": result["data"], "meta": result["meta"]} for col in final_result["meta"]: # Translate back column names that were converted to snuba format col["name"] = translated_columns.get(col["name"], col["name"])

feat(discover): Only transform when ordering project (#39468) - This updates the querybuilder with a orderby resolver so we can implement more custom orderbys(orderbies?) in the future - This changes the project field to just select the project_id only, which results in needing a new post-processing capability to the querybuilder - This is done via the `value_resolver_map` and the `meta_resolver_map` - Removed the snuba_filter param from transform_results since we no longer use it - Removes the old discover 1 query since it shouldn't be supported and no longer is being used - Needed to update ds code too since it relied on the old project behaviour but doesn't use `discover.query`

transform_data

bf416f7ad23d7537a84c9727cfe1c0a7effd27bb

sentry

discover.py

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

Python

def transform_data(result, translated_columns, query_builder) -> EventsResponse: final_result: EventsResponse = {"data": result["data"], "meta": result["meta"]} for col in final_result["meta"]: # Translate back column names that were converted to snuba format col["name"] = translated_columns.get(col["name"], col["name"])

sympy/plotting/plot.py

def plot_contour(*args, show=True, **kwargs): args = list(map(sympify, args)) plot_expr = check_arguments(args, 1, 2) series = [ContourSeries(*arg) for arg

Improve documentation

plot_contour

eb20cbe9b89917786a10d50b785b4f21230f04be

sympy

plot.py

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

Python

def plot_contour(*args, show=True, **kwargs): args = list(map(sympify, args)) plot_expr = check_arguments(args, 1, 2) series = [ContourSeries(*arg) for arg in plot_expr] plot_contours = Plot(*series, **kwargs) if len(plot_expr[0].free_symbols) > 2: raise ValueError('Contour Plot cannot Plot for more than two variables.') if show: plot_contours.show() return plot_contours

python3.10.4/Lib/bz2.py

def peek(self, n=0): self._check_can_read() # Relies on the u

add python 3.10.4 for windows

peek

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

bz2.py

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

Python

def peek(self, n=0): self._check_can_read() # Relies on the undocumented fact that BufferedReader.peek() # always returns at least one byte (except at EOF), independent # of the value of n return self._buffer.peek(n)

lib/training/cache.py

def cache_full(self) -> bool: if self._cache_

Data Augmentation update (#1263) - lib.detected_face - Subclass Masks for Landmark based masks - Add training mask propery + methods to DetectedFace - lib.training_training - subclass TrainingDataGenerator for training and preview data - Split cache into own module - Reduce thread count to 1 to prevent image corruption + data re-use - Process on largest model input/output size rather than stored image size - Size and crop masks during caching stage - Implement ring buffer for data flow - Fix preview reload bug - augmentation - typing - switch color aug order - better initialization - Fix warp + landmark warp to correctly apply at different image scales - Slightly improved warp caching - Don't store whether image is_preview. Handle all data as training images implicitly - plugins.trainer: Typing and fixes to work with trainingdata refactor

cache_full

2beceffad9b15c1fd78f06b9b272563321c5a41e

faceswap

cache.py

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

Python

def cache_full(self) -> bool: if self._cache_info["cache_full"]: return self._cache_info["cache_full"] with self._lock: return self._cache_info["cache_full"]

netbox/netbox/views/generic/base.py

def get_queryset(self, request): if self.queryset is None: raise ImproperlyConfigured( f"{self.__class__.__name__} does not define a queryset. Set queryset on the class or " f"override its get_queryset() method."

Closes #10739: Introduce get_queryset() method on generic views

get_queryset

b2e2e3be35f3922ecee945b97279c50725c0b7fa

netbox

base.py

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

Python

def get_queryset(self, request): if self.queryset is None: raise ImproperlyConfigured( f"{self.__class__.__name__} does not define a queryset. Set queryset on the class or " f"override its get_queryset() method." ) return self.queryset.all()

paddlenlp/taskflow/knowledge_mining.py

def _preprocess(self, inputs): inputs = self._check_input_text(inputs) self._max_cls_len = 5 num_workers = self.kwargs[ 'num_workers'] if 'num_workers' in self.kwargs else 0 lazy_load = self.kwargs[ 'lazy_load'] if 'lazy_load' in self.kwargs else False # Prompt template: input_text + "是" + "[MASK]" * cls_seq_length prompt_template = ["是"] + ["[MASK]"] * self._max_cls_len

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>

_preprocess

621357338437ee420eabbbf5ab19065bc85e73a5

PaddleNLP

knowledge_mining.py

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

Python

def _preprocess(self, inputs): inputs = self._check_input_text(inputs) self._max_cls_len = 5 num_workers = self.kwargs[ 'num_workers'] if 'num_workers' in self.kwargs else 0 lazy_load = self.kwargs[ 'lazy_load'] if 'lazy_load' in self.kwargs else False # Prompt template: input_text + "是" + "[MASK]" * cls_seq_length prompt_template = ["是"] + ["[MASK]"] * self._max_cls_len

salt/modules/consul.py

def session_destroy(consul_url=None, token=None, session=None, **kwargs): ret = {} if not consul_url: consul_url = _get_config() if not consul_url: log.error("No Consul URL found.") ret["me

[merge jam] Master port 49261 - consul modules (#58101) * add consul states and acl function present/absent * add consul to states doc index * refact/fix consul states * fix doc, fix states * fix name parameter for acl_changes * fixing pylint errors * small changes after review by @rallytime * fix header count * Update consul.py * fix acl_exists description, fix when both id and name are missing * Adding some tests for consul module and consul state module. Some additional fixes in the consul module. * Fixing tests. * Fixing failing tests on Windows. * Adding changelog. * Adding some tests for consul module and consul state module. Some additional fixes in the consul module. * moving tests to pytest. * manual black changes. * One more manual black change. * fixing formatting. Adding versionadded for state module. Co-authored-by: Rémi Jouannet <remi.jouannet@outscale.com> Co-authored-by: Mike Place <mp@saltstack.com> Co-authored-by: Daniel Wozniak <dwozniak@saltstack.com> Co-authored-by: Wayne Werner <wwerner@vmware.com>

session_destroy

fb825aa760fa0585a2c8fdafc6e62be8aec8cecf

salt

consul.py

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

Python

def session_destroy(consul_url=None, token=None, session=None, **kwargs): ret = {} if not consul_url: consul_url = _get_config() if not consul_url: log.error("No Consul URL found.") ret["message"] = "No Consul URL found." ret["res"] = False return ret if not session: raise SaltInvocationError('Required argument "session" is missing.') query_params = {} if "dc" in kwargs: query_params["dc"] = kwargs["dc"] function = "session/destroy/{}".format(session) res = _query( consul_url=consul_url, function=function, token=token, method="PUT", query_params=query_params, ) if res["res"]: ret["res"] = True ret["message"] = "Destroyed Session {}.".format(session) else: ret["res"] = False ret["message"] = "Unable to destroy session {}.".format(session) return ret

saleor/graphql/product/mutations/collection/collection_update.py

def post_save_action(cls, info, instance, cleaned_input): manager = load_plugin_manager(info.context

Split product types and mutations (#11259) * Split product types file * Split product/mutations/products.py file

post_save_action

74d1c8d8504dbdd339865ff97ca4ac9bd30a8faf

saleor

collection_update.py

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

Python

def post_save_action(cls, info, instance, cleaned_input): manager = load_plugin_manager(info.context) cls.call_event(manager.collection_updated, instance)

keras/saving/saving_utils.py

def _deserialize_metric(metric_config): from keras import ( metrics as metrics_module, ) # pylint:disable=g-import-not-at-top if metric_config in ["accuracy", "acc", "crossentropy", "ce"]: # Do not deserialize accuracy and cross-entropy strings as we have special # case handling for these

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

_deserialize_metric

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

saving_utils.py

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

Python

def _deserialize_metric(metric_config): from keras import ( metrics as metrics_module, ) # pylint:disable=g-import-not-at-top if metric_config in ["accuracy", "acc", "crossentropy", "ce"]: # Do not deserialize accuracy and cross-entropy strings as we have special # case handling for these in compile, based on model output shape. return metric_config return metrics_module.deserialize(metric_config)

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

keras/activations.py

def softsign(x): return tf.math.softsign(x) @keras_export("keras.activations.swish") @tf.__internal__.dispatch.add_dispatch_support

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

softsign

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

activations.py

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

Python

def softsign(x): return tf.math.softsign(x) @keras_export("keras.activations.swish") @tf.__internal__.dispatch.add_dispatch_support

freqtrade/freqai/data_kitchen.py

def compute_inlier_metric(self) -> None: import scipy.stats as ss nmb_previous_points = self.data['InlierMetric_nmb_points'] weibull_percentile = self.data['InlierMetric_weib_perc'] train_ft_df = self.data_dictionary['train_features'] train_ft_df_reindexed = train_ft_df.reindex( index=np.flip(train_ft_df.index) ) pairwise = pd.DataFrame( np.triu( pairwise_distances(train_ft_df_reindexed, n_jobs=self.thread_count) ), columns=train_ft_df_reindexed.index, index=train_ft_df_reindexed.index ) pairwise = pairwise.round(5) column_labels = [ '{}{}'.format('d', i) for i in range(1, nmb_previous_points+1) ] distances = pd.DataFrame( columns=column_labels, index=train_ft_df.index ) for index in train_ft_df.index[nmb_previous_points]: current_row = pairwise.loc[[index]] current_row_no_zeros = current_row.loc[ :, (current_row!=0).any(axis=0) ] distances.loc[[index]] = current_row_no_zeros.iloc[ :, :nmb_previous_points ] distances = distances.replace([np.inf, -np.inf], np.nan) drop_index = pd.isnull(distances).any(1) distances = distances[drop_index==0] inliers = pd.DataFrame(index=distances.index) for key in distances.keys(): current_distances = distances[key].dropna() fit_params = ss.weibull_min.fit(current_distances) cutoff = ss.weibull_min.ppf(weibull_percentile, *fit_params) is_inlier = np.where( current_distances<=cutoff, 1, 0 ) df_inlier = pd.DataFrame( {key+'_IsInlier':is_inlier}, index=distances.index ) inliers = pd.concat( [inliers, df_inlier], axis=1 ) self.data_dictionary['train_features'] = pd.DataFrame( data=inliers.sum(axis=1)/nmb_previous_points, col

Add inlier metric computation

compute_inlier_metric

d3cb211283ced68d082cfdbdac12f3d2ab90d63b

freqtrade

data_kitchen.py

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

Python

def compute_inlier_metric(self) -> None: import scipy.stats as ss nmb_previous_points = self.data['InlierMetric_nmb_points'] weibull_percentile = self.data['InlierMetric_weib_perc'] train_ft_df = self.data_dictionary['train_features'] train_ft_df_reindexed = train_ft_df.reindex( index=np.flip(train_ft_df.index) ) pairwise = pd.DataFrame( np.triu( pairwise_distances(train_ft_df_reindexed, n_jobs=self.thread_count) ), columns=train_ft_df_reindexed.index, index=train_ft_df_reindexed.index ) pairwise = pairwise.round(5) column_labels = [ '{}{}'.format('d', i) for i in range(1, nmb_previous_points+1) ] distances = pd.DataFrame( columns=column_labels, index=train_ft_df.index ) for index in train_ft_df.index[nmb_previous_points]: current_row = pairwise.loc[[index]] current_row_no_zeros = current_row.loc[ :, (current_row!=0).any(axis=0) ] distances.loc[[index]] = current_row_no_zeros.iloc[ :, :nmb_previous_points ] distances = distances.replace([np.inf, -np.inf], np.nan) drop_index = pd.isnull(distances).any(1) distances = distances[drop_index==0] inliers = pd.DataFrame(index=distances.index) for key in distances.keys(): current_distances = distances[key].dropna() fit_params = ss.weibull_min.fit(current_distances) cutoff = ss.weibull_min.ppf(weibull_percentile, *fit_params) is_inlier = np.where( current_distances<=cutoff, 1, 0 ) df_inlier = pd.DataFrame( {key+'_IsInlier':is_inlier}, index=distances.index ) inliers = pd.concat( [inliers, df_inlier], axis=1 ) self.data_dictionary['train_features'] = pd.DataFrame( data=inliers.sum(axis=1)/nmb_previous_points, columns=['inlier_metric'], index = train_ft_df.index ) percent_outliers = np.round( 100*(1-self.data_dictionary['iniler_metric'].sum()/ len(train_ft_df.index)), 2 ) logger.info('{percent_outliers}%% of data points were identified as outliers') return None

python3.10.4/Lib/ast.py

def items_view(self, traverser, items): if len(items) == 1: traverser(items[0]) self.write(",") else: self.interleave(lambda: self.write(", "), tra

add python 3.10.4 for windows

items_view

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

ast.py

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