Datasets:

semeru

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code-text-galeras-code-summarization-3k-deduped

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Semeru Lab 7

spinner

def spinner(text="In progress..."): import streamlit.legacy_caching.caching as legacy_caching import streamlit.caching as caching from streamlit.elements.utils import clean_text from streamlit.proto.Spinner_pb2 import Spinner as SpinnerProto # @st.cache optionally uses spinner for long-running computations. # Normally, streamlit warns the user when they call st functions # from within an @st.cache'd function. But we do *not* want to show # these warnings for spinner's message, so we create and mutate this # message delta within the "suppress_cached_st_function_warning" # context. with legacy_caching.suppress_cached_st_function_warning(): with caching.suppress_cached_st_function_warning(): message = empty() try: # Set the message 0.1 seconds in the future to avoid annoying # flickering if this spinner runs too quickly. DELAY_SECS = 0.1 display_message = True display_message_lock = _threading.Lock()

704eab3478cf69847825b23dabf15813a8ac9fa2

def spinner(text="In progress..."): """Temporarily displays a message while executing a block of code. Parameters ---------- text : str A message to display while executing that block Example ------- >>> with st.spinner('Wait for it...'): >>> time.sleep(5) >>> st.success('Done!') """ import streamlit.legacy_caching.caching as legacy_caching import streamlit.caching as caching from streamlit.elements.utils import clean_text from streamlit.proto.Spinner_pb2 import Spinner as SpinnerProto # @st.cache optionally uses spinner for long-running computations. # Normally, streamlit warns the user when they call st functions # from within an @st.cache'd function. But we do *not* want to show # these warnings for spinner's message, so we create and mutate this # message delta within the "suppress_cached_st_function_warning" # context. with legacy_caching.suppress_cached_st_function_warning(): with caching.suppress_cached_st_function_warning(): message = empty() try: # Set the message 0.1 seconds in the future to avoid annoying # flickering if this spinner runs too quickly. DELAY_SECS = 0.1 display_message = True display_message_lock = _threading.Lock()

__init__.py

Rename and refactor `Report` machinery (#4141) This refactor renames (almost) everything related to the outdated "report" concept with more precise concepts that we use throughout our code, primarily "script run", "session", and "app".

streamlit

lib/streamlit/__init__.py

Python

https://github.com/streamlit/streamlit.git

process_queue

def process_queue(self): if not self.queue: logger.debug(f"No queued changes; aborting") return logger.debug(f"Processing {len(self.queue)} queued changes") # Iterate through the in-memory queue, creating Change instances changes = [] for key, change in self.queue.items(): logger.debug(f' {key}: {change}') object_type, pk = key action, data = change changes.append(StagedChange( branch=self.branch, action=action, object_type=object_type, object_id=pk, data=data )) # Save all Change instances to the database StagedChange.objects.bulk_create(changes) # # Signal handlers #

a5308ea28e851a4ddb65a4e7ca2297b641e5891f

staging.py

Closes #10851: New staging mechanism (#10890) * WIP * Convert checkout() context manager to a class * Misc cleanup * Drop unique constraint from Change model * Extend staging tests * Misc cleanup * Incorporate M2M changes * Don't cancel wipe out creation records when an object is deleted * Rename Change to StagedChange * Add documentation for change staging

netbox

netbox/netbox/staging.py

Python

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

save

def save(self, commit=True): if self.errors: raise ValueError( "The %s could not be %s because the data didn't validate." % ( self.instance._meta.object_name, "created" if self.instance._state.adding else "changed", ) ) if commit: # If committing, save the instance and the m2m data immediately. self.instance.save() self._save_m2m() else: # If not committing, add a method to the form to allow deferred # saving of m2m data. self.save_m2m = self._save_m2m return self.instance save.alters_data = True

9c19aff7c7561e3a82978a272ecdaad40dda5c00

models.py

Refs #33476 -- Reformatted code with Black.

django

django/forms/models.py

Python

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

solve

def solve(self, parameters=None, limit=None): self.pre_solve(parameters) coeff = self.coeff var = self.free_symbols if 1 in coeff: # negate coeff[] because input is of the form: ax + by + c == 0 # but is used as: ax + by == -c c = -coeff[1] else: c = 0 result = DiophantineSolutionSet(var, parameters=self.parameters) params = result.parameters if len(var) == 1: q, r = divmod(c, coeff[var[0]]) if not r: result.add((q,)) return result else: return result A = [coeff[v] for v in var] B = [] if len(var) > 2: B.append(igcd(A[-2], A[-1])) A[-2] = A[-2] // B[0] A[-1] = A[-1] // B[0] for i in range(len(A) - 3, 0, -1): gcd = igcd(B[0], A[i]) B[0] = B[0] // gcd A[i] = A[i] // gcd B.insert(0, gcd) B.append(A[-1]) solutions = [] for Ai, Bi in zip(A, B): tot_x, tot_y = [], [] for j, arg in enumerate(Add.make_args(c)): if arg.is_Integer: # example: 5 -> k = 5 k, p = arg, S.One pnew = params[0] else: # arg is a Mul or Symbol # example: 3*t_1 -> k = 3 # example: t_0 -> k = 1 k, p = arg.as_coeff_Mul() pnew = params[params.index(p) + 1] sol = sol_x, sol_y = base_solution_linear(k, Ai, Bi, pnew) if p is S.One: if None in sol: return result else: # convert a + b*pnew -> a*p + b*pnew if isinstance(sol_x, Add): sol_x = sol_x.args[0]*p + sol_x.args[1] if isinstance(sol_y, Add): sol_y = sol_y.args[0]*p + sol_y.args[1] tot_x.append(sol_x) tot_y.append(sol_y) solutions.append(Add(*tot_x)) c = Add(*tot_y) solutions.append(c) result.add(solutions) return result

bd9f607176c58dfba01e27c05c2b7d49ff97c901

diophantine.py

Improve loop performance in solvers

sympy

sympy/solvers/diophantine/diophantine.py

Python

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

disable_bracketed_paste

def disable_bracketed_paste(self) -> None: self.console.file.write("\x1b[?2004l") self.console.file.flush()

fe151a7f25cfd7f1134ebafbddc7eeade1c18ccb

driver.py

Support for bracketed paste mode (#567) * Detecting bracketed paste, sending paste events * Bracketed pasting support in TextInput * Restore debugging conditional * Handle pasting of text in text-input, improve scrolling * Fix ordering of handling in parser for bracketed pastes * Docstrings * Add docstrings

textual

src/textual/driver.py

Python

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

get_training_arguments

def get_training_arguments(self) -> transformers.training_args.TrainingArguments: with self.as_directory() as checkpoint_path: training_args_path = os.path.join(checkpoint_path, TRAINING_ARGS_NAME) if os.path.exists(training_args_path): with open(training_args_path, "rb") as f: training_args = torch.load(f, map_location="cpu") else: training_args = None return training_args

ac1d21027da8a8c002cc7c28b8d1dc89c0d72fcf

huggingface_checkpoint.py

[AIR] Add framework-specific checkpoints (#26777)

ray

python/ray/train/huggingface/huggingface_checkpoint.py

Python

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

all_bursa

def all_bursa(): path = os.path.join(os.path.dirname(__file__), "data/bursa_open_hours.json") bursa = pd.read_json(path) # , orient="index") return bursa

33a041e5bf93ce93ab1a19adbc5ed74c2f1eb337

bursa_model.py

Trading hours stock feature (#1697)

OpenBBTerminal

openbb_terminal/stocks/tradinghours/bursa_model.py

Python

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

get_git_changeset

def get_git_changeset(): # Repository may not be found if __file__ is undefined, e.g. in a frozen # module. if "__file__" not in globals(): return None repo_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) git_log = subprocess.run( "git log --pretty=format:%ct --quiet -1 HEAD", capture_output=True, shell=True, cwd=repo_dir, text=True, ) timestamp = git_log.stdout tz = datetime.timezone.utc try: timestamp = datetime.datetime.fromtimestamp(int(timestamp), tz=tz) except ValueError: return None return timestamp.strftime("%Y%m%d%H%M%S") version_component_re = _lazy_re_compile(r"(\d+|[a-z]+|\.)")

9c19aff7c7561e3a82978a272ecdaad40dda5c00

version.py

Refs #33476 -- Reformatted code with Black.

django

django/utils/version.py

Python

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

tournament_matrix

def tournament_matrix(G): r A = nx.adjacency_matrix(G) return A - A.T @not_implemented_for("undirected") @not_implemented_for("multigraph")

8a325d26aa7fdd3a72580c4720fa97f971bbefcb

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

tournament.py

Use scipy.sparse array datastructure (#6037) * Use scipy.sparse array datastructure * Add reminder to rm wrapper when scipy adds creation fns. * Rm mention of np matrix from code comment. * Update networkx/algorithms/bipartite/matrix.py Co-authored-by: Stefan van der Walt <[email protected]> Co-authored-by: Ross Barnowski <[email protected]> Co-authored-by: Stefan van der Walt <[email protected]>

networkx

networkx/algorithms/tournament.py

Python

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

model_scaling

def model_scaling(layer_setting, arch_setting): # scale width new_layer_setting = copy.deepcopy(layer_setting) for layer_cfg in new_layer_setting: for block_cfg in layer_cfg: block_cfg[1] = make_divisible(block_cfg[1] * arch_setting[0], 8) # scale depth split_layer_setting = [new_layer_setting[0]] for layer_cfg in new_layer_setting[1:-1]: tmp_index = [0] for i in range(len(layer_cfg) - 1): if layer_cfg[i + 1][1] != layer_cfg[i][1]: tmp_index.append(i + 1) tmp_index.append(len(layer_cfg)) for i in range(len(tmp_index) - 1): split_layer_setting.append(layer_cfg[tmp_index[i]:tmp_index[i + 1]]) split_layer_setting.append(new_layer_setting[-1]) num_of_layers = [len(layer_cfg) for layer_cfg in split_layer_setting[1:-1]] new_layers = [ int(math.ceil(arch_setting[1] * num)) for num in num_of_layers ] merge_layer_setting = [split_layer_setting[0]] for i, layer_cfg in enumerate(split_layer_setting[1:-1]): if new_layers[i] <= num_of_layers[i]: tmp_layer_cfg = layer_cfg[:new_layers[i]] else: tmp_layer_cfg = copy.deepcopy(layer_cfg) + [layer_cfg[-1]] * ( new_layers[i] - num_of_layers[i]) if tmp_layer_cfg[0][3] == 1 and i != 0: merge_layer_setting[-1] += tmp_layer_cfg.copy() else: merge_layer_setting.append(tmp_layer_cfg.copy()) merge_layer_setting.append(split_layer_setting[-1]) return merge_layer_setting @BACKBONES.register_module()

3f0f2a059743593fd07b629c261b609bd9a767e6

@BACKBONES.register_module()

efficientnet.py

[Feature] Support efficientnet in mmdetection. (#7514) * Initial implementation * Add missing import * Add MemoryEfficientSwishImplementation. Add docstrings * Add efficientnet2mmdet tool * Add config folder * Flake8 * Flake8 * Flake8 * Fix config * Requested changes * docformatter * Update train config from https://github.com/google/automl/blob/master/efficientdet * Run pre-commit * Fix schedule * Set by_epoch=False in scheduler * Train 80 epochs * Remove duplicated arg * Update README.md * efficient3 efficient0 * efficientNet imports * efficientNet * config edit path for eff3 and dropout for eff0 * efficientnet review2 * fix model_converter location and drop path * fix model converter and efficientnet import * register memoryefficietnswish * eff0, eff3 * fix flake8 yapf isort * same padding in tensorflow and edit drop path rate * fix init of utils * Align mmdet utils with mmcls * Align mmdet.models.utils with mmcls * Use mmcls efficientnet backbone * Update * Update * Update metafile Co-authored-by: David de la Iglesia Castro <[email protected]> Co-authored-by: David de la Iglesia Castro <[email protected]> Co-authored-by: jiangyitong <[email protected]> Co-authored-by: jiangyitong <[email protected]>

mmdetection

mmdet/models/backbones/efficientnet.py

Python

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

polyfit

def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): _check_arraylike("polyfit", x, y) deg = core.concrete_or_error(int, deg, "deg must be int") order = deg + 1 # check arguments if deg < 0: raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") if y.ndim < 1 or y.ndim > 2: raise TypeError("expected 1D or 2D array for y") if x.shape[0] != y.shape[0]: raise TypeError("expected x and y to have same length") # set rcond if rcond is None: rcond = len(x) * finfo(x.dtype).eps rcond = core.concrete_or_error(float, rcond, "rcond must be float") # set up least squares equation for powers of x lhs = vander(x, order) rhs = y # apply weighting if w is not None: _check_arraylike("polyfit", w) w, = _promote_dtypes_inexact(w) if w.ndim != 1: raise TypeError("expected a 1-d array for weights") if w.shape[0] != y.shape[0]: raise TypeError("expected w and y to have the same length") lhs *= w[:, np.newaxis] if rhs.ndim == 2: rhs *= w[:, np.newaxis] else: rhs *= w # scale lhs to improve condition number and solve scale = sqrt((lhs*lhs).sum(axis=0)) lhs /= scale[np.newaxis,:] c, resids, rank, s = linalg.lstsq(lhs, rhs, rcond) c = (c.T/scale).T # broadcast scale coefficients if full: return c, resids, rank, s, rcond elif cov: Vbase = linalg.inv(dot(lhs.T, lhs)) Vbase /= outer(scale, scale) if cov == "unscaled": fac = 1 else: if len(x) <= order: raise ValueError("the number of data points must exceed order " "to scale the covariance matrix") fac = resids / (len(x) - order) fac = fac[0] #making np.array() of shape (1,) to int if y.ndim == 1: return c, Vbase * fac else: return c, Vbase[:, :, np.newaxis] * fac else: return c _POLY_DOC = @_wraps(np.poly, lax_description=_POLY_DOC) @jit

603bb3c5ca288674579211e64fa47c6b2b0fb7a6

@_wraps(np.poly, lax_description=_POLY_DOC) @jit

polynomial.py

lax_numpy: move poly functions into numpy.polynomial

jax

jax/_src/numpy/polynomial.py

Python

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

key

def key(self) -> TaskInstanceKey: return TaskInstanceKey(self.dag_id, self.task_id, self.run_id, self.try_number, self.map_index)

6fc6edf6af7f676bfa54ff3a2e6e6d2edb938f2e

taskinstance.py

Make `airflow dags test` be able to execute Mapped Tasks (#21210) * Make `airflow dags test` be able to execute Mapped Tasks In order to do this there were two steps required: - The BackfillJob needs to know about mapped tasks, both to expand them, and in order to update it's TI tracking - The DebugExecutor needed to "unmap" the mapped task to get the real operator back I was testing this with the following dag: ``` from airflow import DAG from airflow.decorators import task from airflow.operators.python import PythonOperator import pendulum @task def make_list(): return list(map(lambda a: f'echo "{a!r}"', [1, 2, {'a': 'b'}])) def consumer(*args): print(repr(args)) with DAG(dag_id='maptest', start_date=pendulum.DateTime(2022, 1, 18)) as dag: PythonOperator(task_id='consumer', python_callable=consumer).map(op_args=make_list()) ``` It can't "unmap" decorated operators successfully yet, so we're using old-school PythonOperator We also just pass the whole value to the operator, not just the current mapping value(s) * Always have a `task_group` property on DAGNodes And since TaskGroup is a DAGNode, we don't need to store parent group directly anymore -- it'll already be stored * Add "integation" tests for running mapped tasks via BackfillJob * Only show "Map Index" in Backfill report when relevant Co-authored-by: Tzu-ping Chung <[email protected]>

airflow

airflow/models/taskinstance.py

Python

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

_check_guts_toc_mtime

def _check_guts_toc_mtime(attr_name, old_toc, new_toc, last_build): for dest_name, src_name, typecode in old_toc: if misc.mtime(src_name) > last_build: logger.info("Building because %s changed", src_name) return True return False

f5925fa56f713e78ab5723de2a58195ca346847f

utils.py

building: cleanup remove pyc argument from _check_guts_toc_mtime The only place where we use `_check_guts_toc_mtime` with `pyc` argument enabled is when checking the `Analysis.pure` TOC, and the source names of those entries already point to source .py files. So shortening the filenames by one character results in checking for non-existant .p files. Even if an entry happened to point to a .pyc file, it is highly unlikely that there would be an adjacent .py file available, because under contemporary python 3 versions, that would hide the .pyc file from the loader.

pyinstaller

PyInstaller/building/utils.py

Python

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

fit

def fit(self, X, y=None): X = self._validate_data( X, accept_sparse=["csr", "csc"], dtype=[np.float64, np.float32] ) n_samples, n_features = X.shape if self.n_components == "auto": self.n_components_ = johnson_lindenstrauss_min_dim( n_samples=n_samples, eps=self.eps ) if self.n_components_ <= 0: raise ValueError( "eps=%f and n_samples=%d lead to a target dimension of " "%d which is invalid" % (self.eps, n_samples, self.n_components_) ) elif self.n_components_ > n_features: raise ValueError( "eps=%f and n_samples=%d lead to a target dimension of " "%d which is larger than the original space with " "n_features=%d" % (self.eps, n_samples, self.n_components_, n_features) ) else: if self.n_components <= 0: raise ValueError( "n_components must be greater than 0, got %s" % self.n_components ) elif self.n_components > n_features: warnings.warn( "The number of components is higher than the number of" " features: n_features < n_components (%s < %s)." "The dimensionality of the problem will not be reduced." % (n_features, self.n_components), DataDimensionalityWarning, ) self.n_components_ = self.n_components # Generate a projection matrix of size [n_components, n_features] self.components_ = self._make_random_matrix( self.n_components_, n_features ).astype(X.dtype, copy=False) # Check contract assert self.components_.shape == (self.n_components_, n_features), ( "An error has occurred the self.components_ matrix has " " not the proper shape." ) return self

8b6b519caf3b3b9602958a859b4d3a7eb1d9eadd

random_projection.py

ENH Preserving dtype for np.float32 in RandomProjection (#22114) Co-authored-by: takoika <> Co-authored-by: Thomas J. Fan <[email protected]>

scikit-learn

sklearn/random_projection.py

Python

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

bisectors

def bisectors(self): # use lines containing sides so containment check during # intersection calculation can be avoided, thus reducing # the processing time for calculating the bisectors s = [Line(l) for l in self.sides] v = self.vertices c = self.incenter l1 = Segment(v[0], Line(v[0], c).intersection(s[1])[0]) l2 = Segment(v[1], Line(v[1], c).intersection(s[2])[0]) l3 = Segment(v[2], Line(v[2], c).intersection(s[0])[0]) return {v[0]: l1, v[1]: l2, v[2]: l3}

498015021131af4dbb07eb110e5badaba8250c7b

polygon.py

Updated import locations

sympy

sympy/geometry/polygon.py

Python

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

get_success_response

def get_success_response(self, *args, **params): status_code = params.pop("status_code", None) if status_code and status_code >= 400: raise Exception("status_code must be < 400") method = params.pop("method", self.method).lower() response = self.get_response(*args, method=method, **params) if status_code: assert_status_code(response, status_code) elif method == "get": assert_status_code(response, status.HTTP_200_OK) # TODO(mgaeta): Add the other methods. # elif method == "post": # assert_status_code(response, status.HTTP_201_CREATED) elif method == "put": assert_status_code(response, status.HTTP_200_OK) elif method == "delete": assert_status_code(response, status.HTTP_204_NO_CONTENT) else: # TODO(mgaeta): Add other methods. assert_status_code(response, 200, 300) return response

a68089d62f514557ec38e3744593e20af484e5e2

cases.py

ref(tests): Infer `status_code` from `method` (#34825)

sentry

src/sentry/testutils/cases.py

Python

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

starmap

def starmap(self, func, iterable, chunksize=None): return self._map_async( func, iterable, chunksize=chunksize, unpack_args=True ).get()

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

pool.py

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

ray

python/ray/util/multiprocessing/pool.py

Python

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

_get_char_x

def _get_char_x(self, linelength): return linelength + self.image_pad + self.line_number_width

f3166e673fe8d40277b804d35d77dcdb760fc3b3

img.py

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

pipenv

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

Python

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

test_evaluate_word_analogies

def test_evaluate_word_analogies(self): model = word2vec.Word2Vec(LeeCorpus()) score, sections = model.wv.evaluate_word_analogies(datapath('questions-words.txt')) score_cosmul, sections_cosmul = model.wv.evaluate_word_analogies( datapath('questions-words.txt'), similarity_function='most_similar_cosmul' ) self.assertEqual(score, score_cosmul) self.assertEqual(sections, sections_cosmul) self.assertGreaterEqual(score, 0.0) self.assertLessEqual(score, 1.0) self.assertGreater(len(sections), 0) # Check that dict contains the right keys first_section = sections[0] self.assertIn('section', first_section) self.assertIn('correct', first_section) self.assertIn('incorrect', first_section)

ac3bbcdf87b263f79d5e19cce173e6c709a15f9d

test_word2vec.py

streamlining most_similar_cosmul and evaluate_word_analogies (#2656) * streamlining most_similar_cosmul * Fix PR requested changes and add unit test * fix merge artifacts Co-authored-by: n3hrox <[email protected]> Co-authored-by: Michael Penkov <[email protected]>

gensim

gensim/test/test_word2vec.py

Python

https://github.com/RaRe-Technologies/gensim.git

get_default_locale

def get_default_locale(self): parent = self.get_parent() if parent is not None: return ( parent.specific_class.objects.defer() .select_related("locale") .get(id=parent.id) .locale ) return super().get_default_locale()

d10f15e55806c6944827d801cd9c2d53f5da4186

__init__.py

Reformat with black

wagtail

wagtail/core/models/__init__.py

Python

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

_wait_for_data

async def _wait_for_data(self, func_name): # StreamReader uses a future to link the protocol feed_data() method # to a read coroutine. Running two read coroutines at the same time # would have an unexpected behaviour. It would not possible to know # which coroutine would get the next data. if self._waiter is not None: raise RuntimeError( f'{func_name}() called while another coroutine is ' f'already waiting for incoming data') assert not self._eof, '_wait_for_data after EOF' # Waiting for data while paused will make deadlock, so prevent it. # This is essential for readexactly(n) for case when n > self._limit. if self._paused: self._paused = False self._transport.resume_reading() self._waiter = self._loop.create_future() try: await self._waiter finally: self._waiter = None

8198943edd73a363c266633e1aa5b2a9e9c9f526

streams.py

add python 3.10.4 for windows

XX-Net

python3.10.4/Lib/asyncio/streams.py

Python

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

chi2

def chi2(X, y): # XXX: we might want to do some of the following in logspace instead for # numerical stability. X = check_array(X, accept_sparse="csr") if np.any((X.data if issparse(X) else X) < 0): raise ValueError("Input X must be non-negative.") # Use a sparse representation for Y by default to reduce memory usage when # y has many unique classes. Y = LabelBinarizer(sparse_output=True).fit_transform(y) if Y.shape[1] == 1: Y = Y.toarray() Y = np.append(1 - Y, Y, axis=1) observed = safe_sparse_dot(Y.T, X) # n_classes * n_features if issparse(observed): # convert back to a dense array before calling _chisquare # XXX: could _chisquare be reimplement to accept sparse matrices for # cases where both n_classes and n_features are large (and X is # sparse)? observed = observed.toarray() feature_count = X.sum(axis=0).reshape(1, -1) class_prob = Y.mean(axis=0).reshape(1, -1) expected = np.dot(class_prob.T, feature_count) return _chisquare(observed, expected)

432778464cbffc8ca675c1df786c31f8c23fc62c

_univariate_selection.py

[MRG] chi2: reduce memory footprint (#21837) * added sparse_output=True to LabelBinarizer in chi2 * added changelog entry * Update sklearn/feature_selection/_univariate_selection.py Co-authored-by: Olivier Grisel <[email protected]> * Update sklearn/feature_selection/_univariate_selection.py Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Wagner, Louis <[email protected]> Co-authored-by: Olivier Grisel <[email protected]>

scikit-learn

sklearn/feature_selection/_univariate_selection.py

Python

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

equals

def equals(self, word1, word2): if self.reduce(word1*word2**-1) == self.identity: return True elif self._rewriting_system.is_confluent: return False return None

65be461082dda54c8748922f9c29a19af1279fe1

fp_groups.py

Remove abbreviations in documentation

sympy

sympy/combinatorics/fp_groups.py

Python

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

now

def now(parser, token): bits = token.split_contents() asvar = None if len(bits) == 4 and bits[-2] == "as": asvar = bits[-1] bits = bits[:-2] if len(bits) != 2: raise TemplateSyntaxError("'now' statement takes one argument") format_string = bits[1][1:-1] return NowNode(format_string, asvar) @register.tag

9c19aff7c7561e3a82978a272ecdaad40dda5c00

@register.tag

defaulttags.py

Refs #33476 -- Reformatted code with Black.

django

django/template/defaulttags.py

Python

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

exradii

def exradii(self): side = self.sides a = side[0].length b = side[1].length c = side[2].length s = (a+b+c)/2 area = self.area exradii = {self.sides[0]: simplify(area/(s-a)), self.sides[1]: simplify(area/(s-b)), self.sides[2]: simplify(area/(s-c))} return exradii

498015021131af4dbb07eb110e5badaba8250c7b

polygon.py

Updated import locations

sympy

sympy/geometry/polygon.py

Python

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

col_swap

def col_swap(self, i, j): for k in range(0, self.rows): self[k, i], self[k, j] = self[k, j], self[k, i]

59d22b6bb7287613d598611027f640d068ca5748

repmatrix.py

Moved imports to higher level

sympy

sympy/matrices/repmatrix.py

Python

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

renderable

def renderable(self) -> RenderableType: renderable = self.get_renderable() return Screen(renderable) if self._alt_screen else renderable

f3166e673fe8d40277b804d35d77dcdb760fc3b3

live.py

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

pipenv

pipenv/patched/notpip/_vendor/rich/live.py

Python

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

get_base_snippet_action_menu_items

def get_base_snippet_action_menu_items(model): menu_items = [ SaveMenuItem(order=0), DeleteMenuItem(order=10), ] for hook in hooks.get_hooks("register_snippet_action_menu_item"): action_menu_item = hook(model) if action_menu_item: menu_items.append(action_menu_item) return menu_items

d10f15e55806c6944827d801cd9c2d53f5da4186

action_menu.py

Reformat with black

wagtail

wagtail/snippets/action_menu.py

Python

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

has_key

def has_key(self, key, version=None): return ( self.get(key, self._missing_key, version=version) is not self._missing_key )

9c19aff7c7561e3a82978a272ecdaad40dda5c00

base.py

Refs #33476 -- Reformatted code with Black.

django

django/core/cache/backends/base.py

Python

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

_read_all_pages

def _read_all_pages(self, endpoint): internal_data = [] while True: resp = self._session.get(endpoint) if resp.status_code == 200: internal_data += resp.json() if "next" in resp.links: endpoint = resp.links["next"]["url"] else: logger.debug("Exiting pagination loop") break else: logger.warning(f"Request to {endpoint} return HTTP {resp.status_code}") break return internal_data

0fdd3d56f43c8442a0c9ecd3cad07a88137ff7de

cleanup-tags.py

Changes the cleanup images workflow so it uses a OAuth token with the correct scope (GITHUB_TOKEN is not enough). Also prevents running if the token is not defined and generally does commenting/cleanups"

paperless-ngx

.github/scripts/cleanup-tags.py

Python

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

_build_template

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

dca7cbb43fcd6ea7c677c98ba585395b070d387b

build.py

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

mkdocs

mkdocs/commands/build.py

Python

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

get_build_datetime

def get_build_datetime() -> datetime: source_date_epoch = os.environ.get('SOURCE_DATE_EPOCH') if source_date_epoch is None: return datetime.now(timezone.utc) return datetime.fromtimestamp(int(source_date_epoch), timezone.utc)

df3739d51903ab56771ac071a05b5aa9cdf9e129

__init__.py

Add a lot more type annotations, fix new type warnings (#2970) (including some behavior changes, assumed to be no-op) This is based on auto-generated annotations from "monkeytype".

mkdocs

mkdocs/utils/__init__.py

Python

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

test_read_video_from_file_audio_resampling

def test_read_video_from_file_audio_resampling(self, test_video, samples): # video related width, height, min_dimension, max_dimension = 0, 0, 0, 0 video_start_pts, video_end_pts = 0, -1 video_timebase_num, video_timebase_den = 0, 1 # audio related channels = 0 audio_start_pts, audio_end_pts = 0, -1 audio_timebase_num, audio_timebase_den = 0, 1 full_path = os.path.join(VIDEO_DIR, test_video) tv_result = torch.ops.video_reader.read_video_from_file( full_path, SEEK_FRAME_MARGIN, 0, # getPtsOnly 1, # readVideoStream width, height, min_dimension, max_dimension, video_start_pts, video_end_pts, video_timebase_num, video_timebase_den, 1, # readAudioStream samples, channels, audio_start_pts, audio_end_pts, audio_timebase_num, audio_timebase_den, ) ( vframes, vframe_pts, vtimebase, vfps, vduration, aframes, aframe_pts, atimebase, asample_rate, aduration, ) = tv_result if aframes.numel() > 0: assert samples == asample_rate.item() assert 1 == aframes.size(1) # when audio stream is found duration = float(aframe_pts[-1]) * float(atimebase[0]) / float(atimebase[1]) assert aframes.size(0) == approx(int(duration * asample_rate.item()), abs=0.1 * asample_rate.item())

c50d48845f7b1ca86d6a3b7f37a59be0ae11e36b

test_video_reader.py

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

vision

test/test_video_reader.py

Python

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

get_data

def get_data(filters): data = [] if erpnext.get_region() == "India": employee_pan_dict = frappe._dict( frappe.db.sql() ) component_types = frappe.db.sql( ) component_types = [comp_type[0] for comp_type in component_types] if not len(component_types): return [] conditions = get_conditions(filters) entry = frappe.db.sql( % (conditions, ", ".join(["%s"] * len(component_types))), tuple(component_types), as_dict=1, ) for d in entry: employee = { "employee": d.employee, "employee_name": d.employee_name, "it_comp": d.salary_component, "posting_date": d.posting_date, # "pan_number": employee_pan_dict.get(d.employee), "it_amount": d.amount, "gross_pay": d.gross_pay, } if erpnext.get_region() == "India": employee["pan_number"] = employee_pan_dict.get(d.employee) data.append(employee) return data

494bd9ef78313436f0424b918f200dab8fc7c20b

income_tax_deductions.py

style: format code with black

erpnext

erpnext/payroll/report/income_tax_deductions/income_tax_deductions.py

Python

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

smart_str

def smart_str(s, encoding="utf-8", strings_only=False, errors="strict"): if isinstance(s, Promise): # The input is the result of a gettext_lazy() call. return s return force_str(s, encoding, strings_only, errors) _PROTECTED_TYPES = ( type(None), int, float, Decimal, datetime.datetime, datetime.date, datetime.time, )

9c19aff7c7561e3a82978a272ecdaad40dda5c00

encoding.py

Refs #33476 -- Reformatted code with Black.

django

django/utils/encoding.py

Python

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

mayDisableConsoleWindow

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

613c31d98f20bdd9a4e5884c99826a06a3328438

Options.py

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

Nuitka

nuitka/Options.py

Python

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

test_as_ignores_mau

def test_as_ignores_mau(self): # Create and sync so that the MAU counts get updated token1 = self.create_user("kermit1") self.do_sync_for_user(token1) token2 = self.create_user("kermit2") self.do_sync_for_user(token2) # check we're testing what we think we are: there should be two active users self.assertEqual(self.get_success(self.store.get_monthly_active_count()), 2) # We've created and activated two users, we shouldn't be able to # register new users with self.assertRaises(SynapseError) as cm: self.create_user("kermit3") e = cm.exception self.assertEqual(e.code, 403) self.assertEqual(e.errcode, Codes.RESOURCE_LIMIT_EXCEEDED) # Cheekily add an application service that we use to register a new user # with. as_token = "foobartoken" self.store.services_cache.append( ApplicationService( token=as_token, id="SomeASID", sender="@as_sender:test", namespaces={"users": [{"regex": "@as_*", "exclusive": True}]}, ) ) self.create_user("as_kermit4", token=as_token, appservice=True)

7bc08f320147a1d80371eb13258328c88073fad0

test_mau.py

Remove remaining bits of groups code. (#12936) * Update worker docs to remove group endpoints. * Removes an unused parameter to `ApplicationService`. * Break dependency between media repo and groups. * Avoid copying `m.room.related_groups` state events during room upgrades.

synapse

tests/test_mau.py

Python

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

get_region_to_control_producer

def get_region_to_control_producer() -> KafkaProducer: global _publisher if _publisher is None: config = settings.KAFKA_TOPICS.get(settings.KAFKA_REGION_TO_CONTROL) _publisher = KafkaProducer( kafka_config.get_kafka_producer_cluster_options(config["cluster"]) )

941184cd24186324fd9f7f304b7f713041834726

producer.py

chore(hybrid-cloud): AuditLogEntry is a control silo model now (#39890) In the control silo, creating an audit log entry writes to the db directly, whilst in region silo mode creating an audit log entry will instead push to a new kafka producer that consumes into the control silo asynchronously.

sentry

src/sentry/region_to_control/producer.py

Python

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

get_subplot

def get_subplot(self, row, col, secondary_y=False): from plotly._subplots import _get_grid_subplot return _get_grid_subplot(self, row, col, secondary_y) # Child property operations # -------------------------

5dc67fa7a7314cab97d4c96a30fdf4c5661c9039

basedatatypes.py

fix subplot imports

plotly.py

packages/python/plotly/plotly/basedatatypes.py

Python

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

_linear_eq_to_dict

def _linear_eq_to_dict(eqs, syms): coeffs = [] ind = [] symset = set(syms) for i, e in enumerate(eqs): c, d = _lin_eq2dict(e, symset) coeffs.append(d) ind.append(c) return coeffs, ind

e0aaa724190c49f2725bb7880eddd13ce4fef4b7

linsolve.py

more efficient coefficient extraction

sympy

sympy/polys/matrices/linsolve.py

Python

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

vector_reset

def vector_reset(self): self.cur_obs = [e.reset() for e in self.envs] self._timesteps = [0 for _ in range(self.num_envs)] return self.cur_obs

b52a81b3de6f4b7015c6694049d094f2964e1c96

model_vector_env.py

[RLlib] Preparation for gymnasium/gym0.26 upgrade: Deprecate `horizon` and `soft_horizon` settings. (#30583)

ray

rllib/env/wrappers/model_vector_env.py

Python

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

cache_frame

def cache_frame(self, filename, image) -> None: frame_no = int(re.search(self.re_search, filename).group()) self.cache[frame_no] = image logger.trace("Added to cache. Frame no: %s", frame_no) logger.trace("Current cache: %s", sorted(self.cache.keys()))

60291d49c4da1cd260fbc0b04aa6a312eedfefbb

_base.py

ffmpeg writer: Create new filename if output pre-exists

faceswap

plugins/convert/writer/_base.py

Python

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

allow_regional

def allow_regional(fn): def caller(*args, **kwargs): overrides = frappe.get_hooks("regional_overrides", {}).get(get_region()) function_path = f"{inspect.getmodule(fn).__name__}.{fn.__name__}" if not overrides or function_path not in overrides: return fn(*args, **kwargs) # Priority given to last installed app return frappe.get_attr(overrides[function_path][-1])(*args, **kwargs) return caller

b68a99675d12a1ffbda538ee07a2020ba66fb3cc

__init__.py

fix: allow `regional_overrides` hook to be set in subsequent apps

erpnext

erpnext/__init__.py

Python

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

getchannel

def getchannel(self, channel): self.load() if isinstance(channel, str): try: channel = self.getbands().index(channel) except ValueError as e: msg = f'The image has no channel "{channel}"' raise ValueError(msg) from e return self._new(self.im.getband(channel))

2ae55ccbdad9c842929fb238ea1eb81d1f999024

Image.py

Improve exception traceback readability

Pillow

src/PIL/Image.py

Python

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

_update_legacy_config

def _update_legacy_config(self) -> bool: logger.debug("Checking for legacy state file update") priors = ["dssim_loss", "mask_type", "mask_type", "l2_reg_term"] new_items = ["loss_function", "learn_mask", "mask_type", "loss_function_2"] updated = False for old, new in zip(priors, new_items): if old not in self._config: logger.debug("Legacy item '%s' not in config. Skipping update", old) continue # dssim_loss > loss_function if old == "dssim_loss": self._config[new] = "ssim" if self._config[old] else "mae" del self._config[old] updated = True logger.info("Updated config from legacy dssim format. New config loss " "function: '%s'", self._config[new]) continue # Add learn mask option and set to True if model has "penalized_mask_loss" specified if old == "mask_type" and new == "learn_mask" and new not in self._config: self._config[new] = self._config["mask_type"] is not None updated = True logger.info("Added new 'learn_mask' config item for this model. Value set to: %s", self._config[new]) continue # Replace removed masks with most similar equivalent if old == "mask_type" and new == "mask_type" and self._config[old] in ("facehull", "dfl_full"): old_mask = self._config[old] self._config[new] = "components" updated = True logger.info("Updated 'mask_type' from '%s' to '%s' for this model", old_mask, self._config[new]) # Replace l2_reg_term with the correct loss_2_function and update the value of # loss_2_weight if old == "l2_reg_term": self._config[new] = "mse" self._config["loss_weight_2"] = self._config[old] del self._config[old] updated = True logger.info("Updated config from legacy 'l2_reg_term' to 'loss_function_2'") logger.debug("State file updated for legacy config: %s", updated) return updated

94c3dcff7ebd02a5a5758f33a3eb2bfc66282117

model.py

Training updates - Add multiple selected loss functions - Unlock loss as a model configuration - Phaze-A remove encoder scaling max xap

faceswap

plugins/train/model/_base/model.py

Python

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

get_memos

def get_memos(self) -> Dict[bytes32, List[bytes]]: memos: Dict[bytes32, List[bytes]] = {} for coin_spend in self.coin_spends: result = Program.from_bytes(bytes(coin_spend.puzzle_reveal)).run( Program.from_bytes(bytes(coin_spend.solution)) ) for condition in result.as_python(): if condition[0] == ConditionOpcode.CREATE_COIN and len(condition) >= 4: # If only 3 elements (opcode + 2 args), there is no memo, this is ph, amount coin_added = Coin(coin_spend.coin.name(), bytes32(condition[1]), int_from_bytes(condition[2])) if type(condition[3]) != list: # If it's not a list, it's not the correct format continue memos[coin_added.name()] = condition[3] return memos # Note that `coin_spends` used to have the bad name `coin_solutions`. # Some API still expects this name. For now, we accept both names. # # TODO: continue this deprecation. Eventually, all code below here should be removed. # 1. set `exclude_modern_keys` to `False` (and manually set to `True` where necessary) # 2. set `include_legacy_keys` to `False` (and manually set to `False` where necessary) # 3. remove all references to `include_legacy_keys=True` # 4. remove all code below this point

89f15f591cc3cc3e8ae40e95ffc802f7f2561ece

spend_bundle.py

Merge standalone wallet into main (#9793) * wallet changes from pac * cat changes * pool tests * pooling tests passing * offers * lint * mempool_mode * black * linting * workflow files * flake8 * more cleanup * renamed * remove obsolete test, don't cast announcement * memos are not only bytes32 * trade renames * fix rpcs, block_record * wallet rpc, recompile settlement clvm * key derivation * clvm tests * lgtm issues and wallet peers * stash * rename * mypy linting * flake8 * bad initializer * flaky tests * Make CAT wallets only create on verified hints (#9651) * fix clvm tests * return to log lvl warn * check puzzle unhardened * public key, not bytes. api caching change * precommit changes * remove unused import * mypy ci file, tests * ensure balance before creating a tx * Remove CAT logic from full node test (#9741) * Add confirmations and sleeps for wallet (#9742) * use pool executor * rever merge mistakes/cleanup * Fix trade test flakiness (#9751) * remove precommit * older version of black * lint only in super linter * Make announcements in RPC be objects instead of bytes (#9752) * Make announcements in RPC be objects instead of bytes * Lint * misc hint'ish cleanup (#9753) * misc hint'ish cleanup * unremove some ci bits * Use main cached_bls.py * Fix bad merge in main_pac (#9774) * Fix bad merge at 71da0487b9cd5564453ec24b76f1ac773c272b75 * Remove unused ignores * more unused ignores * Fix bad merge at 3b143e705057d6c14e2fb3e00078aceff0552d7e * One more byte32.from_hexstr * Remove obsolete test * remove commented out * remove duplicate payment object * remove long sync * remove unused test, noise * memos type * bytes32 * make it clear it's a single state at a time * copy over asset ids from pacr * file endl linter * Update chia/server/ws_connection.py Co-authored-by: dustinface <[email protected]> Co-authored-by: Matt Hauff <[email protected]> Co-authored-by: Kyle Altendorf <[email protected]> Co-authored-by: dustinface <[email protected]>

chia-blockchain

chia/types/spend_bundle.py

Python

https://github.com/Chia-Network/chia-blockchain.git

test_learning_curve_display_default_usage

def test_learning_curve_display_default_usage(pyplot, data): X, y = data estimator = DecisionTreeClassifier(random_state=0) train_sizes = [0.3, 0.6, 0.9] display = LearningCurveDisplay.from_estimator( estimator, X, y, train_sizes=train_sizes ) import matplotlib as mpl assert display.errorbar_ is None assert isinstance(display.lines_, list) for line in display.lines_: assert isinstance(line, mpl.lines.Line2D) assert isinstance(display.fill_between_, list) for fill in display.fill_between_: assert isinstance(fill, mpl.collections.PolyCollection) assert fill.get_alpha() == 0.5 assert display.score_name == "Score" assert display.ax_.get_xlabel() == "Number of samples in the training set" assert display.ax_.get_ylabel() == "Score" _, legend_labels = display.ax_.get_legend_handles_labels() assert legend_labels == ["Testing metric"] train_sizes_abs, train_scores, test_scores = learning_curve( estimator, X, y, train_sizes=train_sizes ) assert_array_equal(display.train_sizes, train_sizes_abs) assert_allclose(display.train_scores, train_scores) assert_allclose(display.test_scores, test_scores)

758fe0d9c72ba343097003e7992c9239e58bfc63

test_plot.py

FEA add LearningCurveDisplay to show plot learning curve (#24084) Co-authored-by: jeremie du boisberranger <[email protected]> Co-authored-by: Arturo Amor <[email protected]>

scikit-learn

sklearn/model_selection/tests/test_plot.py

Python

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

test_climate_find_valid_targets

async def test_climate_find_valid_targets(): valid_targets = [10, 16, 17, 18, 19, 20] assert _find_valid_target_temp(7, valid_targets) == 10 assert _find_valid_target_temp(10, valid_targets) == 10 assert _find_valid_target_temp(11, valid_targets) == 16 assert _find_valid_target_temp(15, valid_targets) == 16 assert _find_valid_target_temp(16, valid_targets) == 16 assert _find_valid_target_temp(18.5, valid_targets) == 19 assert _find_valid_target_temp(20, valid_targets) == 20 assert _find_valid_target_temp(25, valid_targets) == 20

5ee2f4f438f8acb119308738639169138b15662c

test_climate.py

Sensibo Set temperature improvement (#72992)

core

tests/components/sensibo/test_climate.py

Python

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

test_send_push_single_worker

def test_send_push_single_worker(self): http_client_mock = Mock(spec_set=["post_json_get_json"]) http_client_mock.post_json_get_json.side_effect = ( lambda *_, **__: defer.succeed({}) ) self.make_worker_hs( "synapse.app.generic_worker", {"worker_name": "pusher1", "pusher_instances": ["pusher1"]}, proxied_blacklisted_http_client=http_client_mock, ) event_id = self._create_pusher_and_send_msg("user") # Advance time a bit, so the pusher will register something has happened self.pump() http_client_mock.post_json_get_json.assert_called_once() self.assertEqual( http_client_mock.post_json_get_json.call_args[0][0], "https://push.example.com/_matrix/push/v1/notify", ) self.assertEqual( event_id, http_client_mock.post_json_get_json.call_args[0][1]["notification"][ "event_id" ], )

854a6884d81c95297bf93badcddc00a4cab93418

test_pusher_shard.py

Modernize unit tests configuration settings for workers. (#14568) Use the newer foo_instances configuration instead of the deprecated flags to enable specific features (e.g. start_pushers).

synapse

tests/replication/test_pusher_shard.py

Python

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

test_load_existing_stream

def test_load_existing_stream(self) -> None: self._insert_rows("foobar1", "first", 3) self._insert_rows("foobar2", "second", 3) self._insert_rows("foobar2", "second", 1, update_stream_table=False) first_id_gen = self._create_id_generator("first", writers=["first", "second"]) second_id_gen = self._create_id_generator("second", writers=["first", "second"]) # The first ID gen will notice that it can advance its token to 7 as it # has no in progress writes... self.assertEqual(first_id_gen.get_positions(), {"first": 7, "second": 6}) self.assertEqual(first_id_gen.get_current_token_for_writer("first"), 7) self.assertEqual(first_id_gen.get_current_token_for_writer("second"), 6) self.assertEqual(first_id_gen.get_persisted_upto_position(), 7) # ... but the second ID gen doesn't know that. self.assertEqual(second_id_gen.get_positions(), {"first": 3, "second": 7}) self.assertEqual(second_id_gen.get_current_token_for_writer("first"), 3) self.assertEqual(second_id_gen.get_current_token_for_writer("second"), 7) self.assertEqual(first_id_gen.get_persisted_upto_position(), 7)

9d21ecf7ceab55bc19c4457b8b07401b0b1623a7

test_id_generators.py

Add type hints to tests files. (#12256)

synapse

tests/storage/test_id_generators.py

Python

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

test_notification_preferences_panel_reduced_for_non_moderators

def test_notification_preferences_panel_reduced_for_non_moderators(self): response = self.client.get(reverse("wagtailadmin_account")) # Find notifications panel through context notifications_panel = None for panelset in response.context["panels_by_tab"].values(): for panel in panelset: if panel.name == "notifications": notifications_panel = panel break notifications_form = notifications_panel.get_form() self.assertIn("approved_notifications", notifications_form.fields.keys()) self.assertIn("rejected_notifications", notifications_form.fields.keys()) self.assertNotIn("submitted_notifications", notifications_form.fields.keys()) self.assertIn( "updated_comments_notifications", notifications_form.fields.keys() )

d10f15e55806c6944827d801cd9c2d53f5da4186

test_account_management.py

Reformat with black

wagtail

wagtail/admin/tests/test_account_management.py

Python

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

as_dict

def as_dict(self) -> Dict[Text, Any]: serializable_graph_schema: Dict[Text, Dict[Text, Any]] = {"nodes": {}} for node_name, node in self.nodes.items(): serializable = dataclasses.asdict(node) # Classes are not JSON serializable (surprise) serializable["uses"] = f"{node.uses.__module__}.{node.uses.__name__}" serializable_graph_schema["nodes"][node_name] = serializable return serializable_graph_schema

9fc462da870f69f9976be3bc081675844b9f64c2

graph.py

fix type annotation in rasa.engine

rasa

rasa/engine/graph.py

Python

https://github.com/RasaHQ/rasa.git

page_identity

def page_identity(self, response, request_json=None): request_path = response.request.path_url if request_path == '/migrations_notran/': raise exc.IsMigrating('You have been redirected to the migration-in-progress page.') request_method = response.request.method.lower() self.last_elapsed = response.elapsed if isinstance(request_json, dict) and 'ds' in request_json: ds = request_json.ds else: ds = None data = self.extract_data(response) exc_str = "%s (%s) received" % (http.responses[response.status_code], response.status_code) exception = exception_from_status_code(response.status_code) if exception: raise exception(exc_str, data) if response.status_code in (http.OK, http.CREATED, http.ACCEPTED): # Not all JSON responses include a URL. Grab it from the request # object, if needed. if 'url' in data: endpoint = data['url'] else: endpoint = request_path data = objectify_response_json(response) if request_method in ('get', 'patch', 'put'): # Update existing resource and return it if are_same_endpoint(self.endpoint, request_path): self.json = data self.r = response return self registered_type = get_registered_page(request_path, request_method) return registered_type(self.connection, endpoint=endpoint, json=data, last_elapsed=response.elapsed, r=response, ds=ds) elif response.status_code == http.FORBIDDEN: if is_license_invalid(response): raise exc.LicenseInvalid(exc_str, data) elif is_license_exceeded(response): raise exc.LicenseExceeded(exc_str, data) else: raise exc.Forbidden(exc_str, data) elif response.status_code == http.BAD_REQUEST: if is_license_invalid(response): raise exc.LicenseInvalid(exc_str, data) if is_duplicate_error(response): raise exc.Duplicate(exc_str, data) else: raise exc.BadRequest(exc_str, data) else: raise exc.Unknown(exc_str, data)

68a44529b6b77d2d43d7099b654560bfd8bbf518

page.py

Register pages for the Instance peers and install bundle endpoints This includes exposing a new interface for Page objects, Page.bytes, to return the full bytestring contents of the response.

awx

awxkit/awxkit/api/pages/page.py

Python

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

call_categories

def call_categories(self, other_args): parser = argparse.ArgumentParser( prog="categories", add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=, ) parser.add_argument( "-l", "--limit", dest="limit", type=check_positive, help="display N number of records", default=15, ) parser.add_argument( "-s", "--sortby", dest="sortby", type=str, help="Sort by given column. Default: market_cap_desc", default=pycoingecko_model.SORT_VALUES[0], choices=pycoingecko_model.SORT_VALUES, ) parser.add_argument( "--pie", action="store_true", help="Flag to show pie chart", dest="pie", default=False, ) ns_parser = self.parse_known_args_and_warn( parser, other_args, EXPORT_ONLY_RAW_DATA_ALLOWED ) if ns_parser: pycoingecko_view.display_categories( limit=ns_parser.limit, export=ns_parser.export, sortby=ns_parser.sortby, pie=ns_parser.pie, ) # TODO: solve sort (similar to losers from discovery)

09f753da1c2a2f03c41fe6a3ca2eb79f6ea58995

overview_controller.py

More Fixes to Crypto + key sort (#3244) * fix #3095 - autocomplete and command working + key sort * fix #3056 * fix [Bug] bugs #3048 * fix [Bug] bug #3017 * sort -> sortby, not ascend, tests * fix my goof ups Co-authored-by: james <[email protected]>

OpenBBTerminal

openbb_terminal/cryptocurrency/overview/overview_controller.py

Python

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

listify_tensors

def listify_tensors(x): if tf.is_tensor(x): x = x.numpy() if isinstance(x, np.ndarray): x = x.tolist() return x

84afc5193d38057e2e2badf9c889ea87d80d8fbf

preprocessing_utils.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/layers/preprocessing/preprocessing_utils.py

Python

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

test_key_query_cancellation

def test_key_query_cancellation(self) -> None: self.register_user("alice", "wonderland") alice_token = self.login("alice", "wonderland") bob = self.register_user("bob", "uncle") channel = make_request_with_cancellation_test( "test_key_query_cancellation", self.reactor, self.site, "POST", "/_matrix/client/r0/keys/query", { "device_keys": { # Empty list means we request keys for all bob's devices bob: [], }, }, token=alice_token, ) self.assertEqual(200, channel.code, msg=channel.result["body"]) self.assertIn(bob, channel.json_body["device_keys"])

d3d9ca156e323fe194b1bcb1af1628f65a2f3c1c

test_keys.py

Cancel the processing of key query requests when they time out. (#13680)

synapse

tests/rest/client/test_keys.py

Python

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

trigsimp

def trigsimp(expr, inverse=False, **opts): from sympy.simplify.fu import fu expr = sympify(expr) _eval_trigsimp = getattr(expr, '_eval_trigsimp', None) if _eval_trigsimp is not None: return _eval_trigsimp(**opts) old = opts.pop('old', False) if not old: opts.pop('deep', None) opts.pop('recursive', None) method = opts.pop('method', 'matching') else: method = 'old'

5fc97f8ef40cbc9363c7f7e0ff25f12c45a2203e

trigsimp.py

implemented inverse option for trigsimp

sympy

sympy/simplify/trigsimp.py

Python

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

db_supports_json

def db_supports_json(self): return not conf.get("database", "sql_alchemy_conn").startswith("mssql")

d8889da29ccfcbecd2c89b9e8e278c480767d678

sqlalchemy.py

Move the database configuration to a new section (#22284) Co-authored-by: gitstart-airflow <[email protected]> Co-authored-by: GitStart <[email protected]> Co-authored-by: Egbosi Kelechi <[email protected]>

airflow

airflow/utils/sqlalchemy.py

Python

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

coord_map_from_to

def coord_map_from_to(top_from, top_to): # We need to find a common ancestor of top_from and top_to. # We'll assume that all ancestors are equivalent here (otherwise the graph # is an inconsistent state (which we could improve this to check for)). # For now use a brute-force algorithm.

cc4d0564756ca067516f71718a3d135996525909

coord_map.py

Balanced joint maximum mean discrepancy for deep transfer learning

transferlearning

code/deep/BJMMD/caffe/python/caffe/coord_map.py

Python

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

plot_feature_importance

def plot_feature_importance(model, feature_names, pair, train_dir, count_max=50) -> None: try: import plotly.graph_objects as go from plotly.subplots import make_subplots except ImportError: logger.exception("Module plotly not found \n Please install using `pip3 install plotly`") exit(1) from freqtrade.plot.plotting import store_plot_file # Gather feature importance from model if "catboost.core" in str(model.__class__): feature_importance = model.get_feature_importance() elif "lightgbm.sklearn" in str(model.__class__): feature_importance = model.feature_importances_ else: raise NotImplementedError(f"Cannot extract feature importance for {model.__class__}") # Data preparation fi_df = pd.DataFrame({ "feature_names": np.array(feature_names), "feature_importance": np.array(feature_importance) }) fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1] fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1] # Plotting

86aa875bc9d5edeba04f908fe45b011e52045c83

utils.py

plot features as html instead of png

freqtrade

freqtrade/freqai/utils.py

Python

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

get_objects

async def get_objects(self) -> dict: replies = await asyncio.gather( *[ self._client.get_object_info(node_id, timeout=DEFAULT_RPC_TIMEOUT) for node_id in self._client.get_all_registered_raylet_ids() ] ) worker_stats = [] for reply in replies: for core_worker_stat in reply.core_workers_stats: # NOTE: Set preserving_proto_field_name=False here because # `construct_memory_table` requires a dictionary that has # modified protobuf name # (e.g., workerId instead of worker_id) as a key. worker_stats.append( self._message_to_dict( message=core_worker_stat, fields_to_decode=["object_id"], preserving_proto_field_name=False, ) ) result = {} memory_table = memory_utils.construct_memory_table(worker_stats) for entry in memory_table.table: data = entry.as_dict() # `construct_memory_table` returns object_ref field which is indeed # object_id. We do transformation here. # TODO(sang): Refactor `construct_memory_table`. data["object_id"] = data["object_ref"] del data["object_ref"] data = filter_fields(data, ObjectState) result[data["object_id"]] = data return result

30ab5458a7e4ba2351d5e1beef8c8797b5946493

state_aggregator.py

[State Observability] Tasks and Objects API (#23912) This PR implements ray list tasks and ray list objects APIs. NOTE: You can ignore the merge conflict for now. It is because the first PR was reverted. There's a fix PR open now.

ray

dashboard/state_aggregator.py

Python

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

exec_

def exec_(_code_, _globs_=None, _locs_=None): if _globs_ is None: frame = sys._getframe(1) _globs_ = frame.f_globals if _locs_ is None: _locs_ = frame.f_locals del frame elif _locs_ is None: _locs_ = _globs_ exec() exec_() if sys.version_info[:2] > (3,): exec_() else:

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

six.py

upd; format

transferlearning

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

Python

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

test_create_single_object_with_values

def test_create_single_object_with_values(self): data = { 'name': 'Site 3', 'slug': 'site-3', 'custom_fields': { 'text_field': 'bar', 'longtext_field': 'blah blah blah', 'number_field': 456, 'boolean_field': True, 'date_field': '2020-01-02', 'url_field': 'http://example.com/2', 'json_field': '{"foo": 1, "bar": 2}', 'choice_field': 'Bar', 'object_field': VLAN.objects.get(vid=2).pk, }, } url = reverse('dcim-api:site-list') self.add_permissions('dcim.add_site') response = self.client.post(url, data, format='json', **self.header) self.assertHttpStatus(response, status.HTTP_201_CREATED) # Validate response data response_cf = response.data['custom_fields'] data_cf = data['custom_fields'] self.assertEqual(response_cf['text_field'], data_cf['text_field']) self.assertEqual(response_cf['longtext_field'], data_cf['longtext_field']) self.assertEqual(response_cf['number_field'], data_cf['number_field']) self.assertEqual(response_cf['boolean_field'], data_cf['boolean_field']) self.assertEqual(response_cf['date_field'], data_cf['date_field']) self.assertEqual(response_cf['url_field'], data_cf['url_field']) self.assertEqual(response_cf['json_field'], data_cf['json_field']) self.assertEqual(response_cf['choice_field'], data_cf['choice_field']) self.assertEqual(response_cf['object_field']['id'], data_cf['object_field']) # Validate database data site = Site.objects.get(pk=response.data['id']) self.assertEqual(site.custom_field_data['text_field'], data_cf['text_field']) self.assertEqual(site.custom_field_data['longtext_field'], data_cf['longtext_field']) self.assertEqual(site.custom_field_data['number_field'], data_cf['number_field']) self.assertEqual(site.custom_field_data['boolean_field'], data_cf['boolean_field']) self.assertEqual(str(site.custom_field_data['date_field']), data_cf['date_field']) self.assertEqual(site.custom_field_data['url_field'], data_cf['url_field']) self.assertEqual(site.custom_field_data['json_field'], data_cf['json_field']) self.assertEqual(site.custom_field_data['choice_field'], data_cf['choice_field']) self.assertEqual(site.custom_field_data['object_field'], data_cf['object_field'])

fa1e28e860c4bdb3e585a968bd248a2ac666e1f6

test_customfields.py

Initial work on #7006

netbox

netbox/extras/tests/test_customfields.py

Python

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

shift

def shift(self, periods=1, freq=None, axis=0, fill_value=None, meta=no_default): if meta is no_default: with raise_on_meta_error("groupby.shift()", udf=False): meta_kwargs = _extract_meta( { "periods": periods, "freq": freq, "axis": axis, "fill_value": fill_value, }, nonempty=True, ) meta = self._meta_nonempty.shift(**meta_kwargs) msg = ( "`meta` is not specified, inferred from partial data. " "Please provide `meta` if the result is unexpected.\n" " Before: .shift(1)\n" " After: .shift(1, meta={'x': 'f8', 'y': 'f8'}) for dataframe result\n" " or: .shift(1, meta=('x', 'f8')) for series result" ) warnings.warn(msg, stacklevel=2) meta = make_meta(meta, parent_meta=self._meta.obj) # Validate self.by if isinstance(self.by, list) and any( isinstance(item, Series) for item in self.by ): raise NotImplementedError( "groupby-shift with a multiple Series is currently not supported" ) df = self.obj should_shuffle = not (df.known_divisions and df._contains_index_name(self.by)) if should_shuffle: df2, by = self._shuffle(meta) else: df2 = df by = self.by # Perform embarrassingly parallel groupby-shift result = map_partitions( _groupby_slice_shift, df2, by, self._slice, periods=periods, freq=freq, axis=axis, fill_value=fill_value, token="groupby-shift", group_keys=self.group_keys, meta=meta, **self.observed, **self.dropna, ) return result

336aac39ee8a616ac2645e532392123ae1bfddd1

groupby.py

Add groupby shift method (#8522) Implements the shift `method` following the `transform` and `apply` methods.

dask

dask/dataframe/groupby.py

Python

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

nice_decrease

def nice_decrease(self, pid): p = psutil.Process(pid) try: p.nice(p.nice() - 1) logger.info('Set nice level of process {} to {} (higher the priority)'.format(pid, p.nice())) except psutil.AccessDenied: logger.warning( 'Can not decrease (higher the priority) the nice level of process {} (access denied)'.format(pid) )

917f01a8306055b21437deac35333dddd1210e39

processes.py

Update formater in the Makefile with flake8 and autopep8/autoflake

glances

glances/processes.py

Python

https://github.com/nicolargo/glances.git

_get_device_count

def _get_device_count(self): if self._is_plaidml: self._device_count = self._plaid.device_count elif IS_MACOS: self._device_count = metal.get_device_count() else: try: self._device_count = pynvml.nvmlDeviceGetCount() except pynvml.NVMLError: self._device_count = 0 self._log("debug", "GPU Device count: {}".format(self._device_count))

444762114c1b1ad2e72c871e825373bd74880aba

gpu_stats.py

Initial somewhat working version

faceswap

lib/gpu_stats.py

Python

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

_handle_analyzed_df_message

def _handle_analyzed_df_message(self, type, data): key, value = data["key"], data["value"] pair, timeframe, candle_type = key # Skip any pairs that we don't have in the pairlist? # leader_pairlist = self._freqtrade.pairlists._whitelist # if pair not in leader_pairlist: # return dataframe = json_to_dataframe(value) if self._config.get('external_signal', {}).get('remove_signals_analyzed_df', False): dataframe = remove_entry_exit_signals(dataframe) logger.debug(f"Handling analyzed dataframe for {pair}") logger.debug(dataframe.tail()) # Add the dataframe to the dataprovider dataprovider = self._freqtrade.dataprovider dataprovider.add_external_df(pair, timeframe, dataframe, candle_type)

2b5f0678772bea0abaf4abe93efc55de43ea3e0e

rpc.py

Refactoring, minor improvements, data provider improvements

freqtrade

freqtrade/rpc/rpc.py

Python

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

pdf

def pdf(self, x, *args, **kwds): # override base class version to correct # location for S1 parameterization if self._parameterization() == "S0": return super().pdf(x, *args, **kwds) elif self._parameterization() == "S1": (alpha, beta), delta, gamma = self._parse_args(*args, **kwds) if np.all(np.reshape(alpha, (1, -1))[0, :] != 1): return super().pdf(x, *args, **kwds) else: # correct location for this parameterisation x = np.reshape(x, (1, -1))[0, :] x, alpha, beta = np.broadcast_arrays(x, alpha, beta) data_in = np.dstack((x, alpha, beta))[0] data_out = np.empty(shape=(len(data_in), 1)) # group data in unique arrays of alpha, beta pairs uniq_param_pairs = np.unique(data_in[:, 1:], axis=0) for pair in uniq_param_pairs: _alpha, _beta = pair _delta = ( delta + 2 * _beta * gamma * np.log(gamma) / np.pi if _alpha == 1.0 else delta ) data_mask = np.all(data_in[:, 1:] == pair, axis=-1) _x = data_in[data_mask, 0] data_out[data_mask] = ( super() .pdf(_x, _alpha, _beta, loc=_delta, scale=gamma) .reshape(len(_x), 1) ) output = data_out.T[0] if output.shape == (1,): return output[0] return output

3a3727c022a361a0bc8a519ebc60e7de8124a5d9

__init__.py

DOC: stats: add levy_stable pdf/cdf/rvs docstring

scipy

scipy/stats/_levy_stable/__init__.py

Python

https://github.com/scipy/scipy.git

project_columns

def project_columns(self, columns): if columns == self.columns: return self func = copy.deepcopy(self) func._columns = columns return func

b946406a30cd12cd6989df3440011a734441a200

core.py

Add from_map function to Dask-DataFrame (#8911)

dask

dask/dataframe/io/orc/core.py

Python

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

replace_embedding

def replace_embedding(embedding, masks): # currently we donnot support replace the embedding layer # because we donnot have the corressponding pruner return embedding

97d067e614243f06ed1f8e2d389512977fff8828

compress_modules.py

Speedup enhancement (#4925)

nni

nni/compression/pytorch/speedup/compress_modules.py

Python

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

get_edit_handler

def get_edit_handler(cls): if hasattr(cls, "edit_handler"): edit_handler = cls.edit_handler else: # construct a TabbedInterface made up of content_panels, promote_panels # and settings_panels, skipping any which are empty tabs = [] if cls.content_panels: tabs.append(ObjectList(cls.content_panels, heading=gettext_lazy("Content"))) if cls.promote_panels: tabs.append(ObjectList(cls.promote_panels, heading=gettext_lazy("Promote"))) if cls.settings_panels: tabs.append( ObjectList( cls.settings_panels, heading=gettext_lazy("Settings"), classname="settings", ) ) edit_handler = TabbedInterface(tabs, base_form_class=cls.base_form_class) return edit_handler.bind_to_model(cls) Page.get_edit_handler = get_edit_handler @receiver(setting_changed)

470d39e1fe86084f729997f7c4e13f551e7e8c73

@receiver(setting_changed)

panels.py

Split out bind_to(model) into a separate bind_to_model method

wagtail

wagtail/admin/panels.py

Python

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

descendants_at_distance

def descendants_at_distance(G, source, distance): if source not in G: raise nx.NetworkXError(f"The node {source} is not in the graph.") bfs_generator = nx.bfs_layers(G, source) for i, layer in enumerate(bfs_generator): if i == distance: return set(layer) return set()

4a019f04d0e304ecd2f28b15d854e1282e03461d

breadth_first_search.py

Adds ```nx.bfs_layers``` method (#5879) * reformatted the files * reformatted the files * added final changes * changed descendants_at_distance * fixed comment in bfs_layers * fixed comment in bfs_layers

networkx

networkx/algorithms/traversal/breadth_first_search.py

Python

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

test_unknown_device

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

c97042f7eef3748e17c90e48a4122389a89c4735

test_device.py

Use literals in place of `HTTPStatus` constants in tests (#13469)

synapse

tests/rest/admin/test_device.py

Python

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

test_get_cache_path

def test_get_cache_path(setup): assert get_cache_path() == Path(setup.directory, ".spotdl", ".spotipy")

fa2ad657482aca9dc628e6d7062b8badf2706bb6

test_config.py

v4 init

spotify-downloader

tests/utils/test_config.py

Python

https://github.com/spotDL/spotify-downloader.git

_get_cluster_uid

def _get_cluster_uid(self) -> str: # Default to an environment variable env_cluster_uid = os.environ.get("PREFECT_KUBERNETES_CLUSTER_UID") if env_cluster_uid: return env_cluster_uid # Read the UID from the cluster namespace with self.get_client() as client: namespace = client.read_namespace("kube-system") cluster_uid = namespace.metadata.uid return cluster_uid

9ab65f6480a31ba022d9846fdfbfca1d17da8164

kubernetes.py

Add `PREFECT_KUBERNETES_CLUSTER_UID` to allow bypass of `kube-system` namespace read (#7864) Co-authored-by: Peyton <[email protected]>

prefect

src/prefect/infrastructure/kubernetes.py

Python

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

get_default_mesh

def get_default_mesh(self): return self._default_mesh LayoutMap.get.__doc__ = LayoutMap.__getitem__.__doc__ @keras_export("keras.dtensor.experimental.layout_map_scope", v1=[]) @contextlib.contextmanager

84afc5193d38057e2e2badf9c889ea87d80d8fbf

@keras_export("keras.dtensor.experimental.layout_map_scope", v1=[]) @contextlib.contextmanager

layout_map.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/dtensor/layout_map.py

Python

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

state

def state(self) -> MediaPlayerState: if self._tv.on and (self._tv.powerstate == "On" or self._tv.powerstate is None): return MediaPlayerState.ON return MediaPlayerState.OFF

52b5e1779f1ed6e5005dc0bdff4137040d7216fb

media_player.py

Use new media player enums [p] (#78058)

core

homeassistant/components/philips_js/media_player.py

Python

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

to_json

def to_json(self, **kwargs): config = self.get_config() tokenizer_config = { "class_name": self.__class__.__name__, "config": config, } return json.dumps(tokenizer_config, **kwargs) @keras_export("keras.preprocessing.text.tokenizer_from_json")

84afc5193d38057e2e2badf9c889ea87d80d8fbf

@keras_export("keras.preprocessing.text.tokenizer_from_json")

text.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/preprocessing/text.py

Python

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

test_delete_view

def test_delete_view(self): delete_dict = {"post": "yes"} delete_url = reverse("admin:admin_views_article_delete", args=(self.a1.pk,)) # add user should not be able to delete articles self.client.force_login(self.adduser) response = self.client.get(delete_url) self.assertEqual(response.status_code, 403) post = self.client.post(delete_url, delete_dict) self.assertEqual(post.status_code, 403) self.assertEqual(Article.objects.count(), 3) self.client.logout() # view user should not be able to delete articles self.client.force_login(self.viewuser) response = self.client.get(delete_url) self.assertEqual(response.status_code, 403) post = self.client.post(delete_url, delete_dict) self.assertEqual(post.status_code, 403) self.assertEqual(Article.objects.count(), 3) self.client.logout() # Delete user can delete self.client.force_login(self.deleteuser) response = self.client.get( reverse("admin:admin_views_section_delete", args=(self.s1.pk,)) ) self.assertContains(response, "<h2>Summary</h2>") self.assertContains(response, "<li>Articles: 3</li>") # test response contains link to related Article self.assertContains(response, "admin_views/article/%s/" % self.a1.pk) response = self.client.get(delete_url) self.assertContains(response, "admin_views/article/%s/" % self.a1.pk) self.assertContains(response, "<h2>Summary</h2>") self.assertContains(response, "<li>Articles: 1</li>") post = self.client.post(delete_url, delete_dict) self.assertRedirects(post, self.index_url) self.assertEqual(Article.objects.count(), 2) self.assertEqual(len(mail.outbox), 1) self.assertEqual(mail.outbox[0].subject, "Greetings from a deleted object") article_ct = ContentType.objects.get_for_model(Article) logged = LogEntry.objects.get(content_type=article_ct, action_flag=DELETION) self.assertEqual(logged.object_id, str(self.a1.pk))

9c19aff7c7561e3a82978a272ecdaad40dda5c00

tests.py

Refs #33476 -- Reformatted code with Black.

django

tests/admin_views/tests.py

Python

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

tasks_from_url

def tasks_from_url(file_upload_ids, project, request, url): # process URL with tasks ctx = ssl.create_default_context() ctx.check_hostname = False ctx.verify_mode = ssl.CERT_NONE try: filename = url.rsplit('/', 1)[-1] with urlopen(url, context=ctx) as file: # nosec # check size meta = file.info() file.size = int(meta.get("Content-Length")) file.urlopen = True check_file_sizes_and_number({url: file}) file_content = file.read() if isinstance(file_content, str): file_content = file_content.encode() file_upload = create_file_upload(request, project, SimpleUploadedFile(filename, file_content)) file_upload_ids.append(file_upload.id) tasks, found_formats, data_keys = FileUpload.load_tasks_from_uploaded_files(project, file_upload_ids) except ValidationError as e: raise e except Exception as e: raise ValidationError(str(e)) return data_keys, found_formats, tasks, file_upload_ids

d8d6a0554bfd263f8ce12ff3ce5a69986edd9bc0

uploader.py

fix: DEV-2361: Fix bandit check in LabelStudio Opensource (#2379)

label-studio

label_studio/data_import/uploader.py

Python

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

_multi_worker_concat

def _multi_worker_concat(v, strategy): replicas = strategy.gather(v, axis=0) # v might not have the same shape on different replicas if _is_per_replica_instance(v): shapes = tf.concat( [ tf.expand_dims(tf.shape(single_value)[0], axis=0) for single_value in v.values ], axis=0, ) all_shapes = strategy.gather(shapes, axis=0) else: # v is a tensor. This may happen when, say, we have 2x1 multi-worker. all_shapes = strategy.gather( tf.expand_dims(tf.shape(v)[0], axis=0), axis=0 ) replicas = tf.split( replicas, num_or_size_splits=all_shapes, num=strategy.num_replicas_in_sync, ) ordered_replicas = [] num_replicas_per_worker = len(strategy.extended.worker_devices) for replica_id in range(num_replicas_per_worker): ordered_replicas += replicas[replica_id::num_replicas_per_worker] return concat(ordered_replicas)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

training.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/engine/training.py

Python

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

shape

def shape(source, kind=None): return FunctionCall( 'shape', [_printable(source)] + ([_printable(kind)] if kind else []) )

498015021131af4dbb07eb110e5badaba8250c7b

fnodes.py

Updated import locations

sympy

sympy/codegen/fnodes.py

Python

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

test_interactive_annotating_with_drafts

def test_interactive_annotating_with_drafts(business_client, configured_project): # create project with predefined task set ml_backend = configured_project.ml_backends.first() ml_backend.is_interactive = True ml_backend.save() users = list(User.objects.all()) task = configured_project.tasks.first() AnnotationDraft.objects.create(task=task, user=users[0], result={}, lead_time=1) AnnotationDraft.objects.create(task=task, user=users[1], result={}, lead_time=2) # run prediction with requests_mock.Mocker(real_http=True) as m: m.register_uri('POST', f'{ml_backend.url}/predict', json={'results': [{'x': 'x'}]}, status_code=200) r = business_client.post( f'/api/ml/{ml_backend.pk}/interactive-annotating', data=json.dumps( { 'task': task.id, 'context': {'y': 'y'}, } ), content_type="application/json", ) r.status_code = 200 result = r.json() assert 'data' in result assert 'x' in result['data'] assert result['data']['x'] == 'x' history = [req for req in m.request_history if 'predict' in req.path][0] assert history.text js = json.loads(history.text) assert len(js['tasks'][0]['drafts']) == 1

4ec4614e5e8b74795ecf8620e414f0340c6b94ef

test_predictions.py

fix: DEV-2138: In interactive prediction only current user's draft should be sent (#2233) * fix: DEV-2138: In interactive prediction only current user's draft should be sent * Add test to check drafts in interactive prediction Co-authored-by: hlomzik <[email protected]>

label-studio

label_studio/tests/test_predictions.py

Python

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

_parse_distro_release_file

def _parse_distro_release_file(self, filepath): # type: (str) -> Dict[str, str] try: with open(filepath) as fp: # Only parse the first line. For instance, on SLES there # are multiple lines. We don't want them... return self._parse_distro_release_content(fp.readline()) except (OSError, IOError): # Ignore not being able to read a specific, seemingly version # related file. # See https://github.com/python-distro/distro/issues/162 return {}

f3166e673fe8d40277b804d35d77dcdb760fc3b3

distro.py

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

pipenv

pipenv/patched/notpip/_vendor/distro.py

Python

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

save_hyperparameters

def save_hyperparameters(self, ignore=[]): frame = inspect.currentframe().f_back _, _, _, local_vars = inspect.getargvalues(frame) self.hparams = {k:v for k, v in local_vars.items() if k not in set(ignore+['self']) and not k.startswith('_')} for k, v in self.hparams.items(): setattr(self, k, v)

19aba1f059efad45e1466d47954b2cf54d45b106

mxnet.py

simplify d2l lib

d2l-en

d2l/mxnet.py

Python

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

test_str_structvalue

def test_str_structvalue(self): block = SectionBlock() value = block.to_python({"title": "Hello", "body": "<i>italic</i> world"}) result = str(value) self.assertNotIn("<h1>", result) # The expected rendering should correspond to the native representation of an OrderedDict: # "StructValue([('title', u'Hello'), ('body', <wagtail.core.rich_text.RichText object at 0xb12d5eed>)])" # - give or take some quoting differences between Python versions self.assertIn("StructValue", result) self.assertIn("title", result) self.assertIn("Hello", result)

d10f15e55806c6944827d801cd9c2d53f5da4186

test_blocks.py

Reformat with black

wagtail

wagtail/core/tests/test_blocks.py

Python

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

revert

def revert(self): if self._backup: self.set_state(self._backup) self._backup = None

b3587b52b25077f68116b9852b041d33e7fc6601

flow.py

make it black!

mitmproxy

mitmproxy/flow.py

Python

https://github.com/mitmproxy/mitmproxy.git

test_fetch_period_api_with_no_timestamp

async def test_fetch_period_api_with_no_timestamp(recorder_mock, hass, hass_client): await async_setup_component(hass, "history", {}) client = await hass_client() response = await client.get("/api/history/period") assert response.status == HTTPStatus.OK

31a787558fd312331b55e5c2c4b33341fc3601fc

test_init.py

Ensure recorder test fixture is setup before hass fixture (#80528) * Ensure recorder test fixture is setup before hass fixture * Adjust more tests

core

tests/components/history/test_init.py

Python

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

test_print_info_with_numpy

def test_print_info_with_numpy(self, do_validation): model = keras.models.Sequential( [keras.layers.Dense(1, input_shape=(2,))] ) model.compile(loss="mse", optimizer="sgd") dataset = np.arange(200).reshape(100, 2) if do_validation: val_data = ( np.arange(100).reshape(50, 2), np.arange(50).reshape(50, 1), ) else: val_data = None mock_stdout = io.StringIO() with tf.compat.v1.test.mock.patch.object(sys, "stdout", mock_stdout): model.fit( dataset, batch_size=10, epochs=2, validation_data=val_data ) self.assertIn("Train on 100 samples", mock_stdout.getvalue()) if do_validation: self.assertIn(", validate on 50 samples", mock_stdout.getvalue())

84afc5193d38057e2e2badf9c889ea87d80d8fbf

training_arrays_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

keras/engine/training_arrays_test.py

Python

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

q_mean_variance

def q_mean_variance(self, x_start, t): mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) return mean, variance, log_variance

ca86da3a30c4e080d4db8c25fca73de843663cb4

ddpm.py

release more models

stablediffusion

ldm/models/diffusion/ddpm.py

Python

https://github.com/Stability-AI/stablediffusion.git

getpalette

def getpalette(self, rawmode="RGB"): self.load() try: mode = self.im.getpalettemode() except ValueError: return None # no palette if rawmode is None: rawmode = mode return list(self.im.getpalette(mode, rawmode))

6be87277f71948bc7e4b945c46660cac3e5ce919

Image.py

Allow rawmode None to return the palette in the current mode

Pillow

src/PIL/Image.py

Python

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

test_get_mutable_invalid_value

def test_get_mutable_invalid_value(self, conf): option = 'keyhint.blacklist' obj = conf.get_mutable_obj(option) assert obj == [] obj.append(42) with pytest.raises(configexc.ValidationError): conf.update_mutables() obj = conf.get_mutable_obj(option) assert obj == []

8eecf3af83fc9a4e465744a83e86856fe1c6df10

test_config.py

config: Discard prior mutables before applying If we only clear existing mutables *after* applying, we get into an inconsistent state if there was an error in one of the config values: The improper value lingers around in self._mutables, and then gets returned when get_mutable_obj() (or update_mutables()) gets called the next time. Reproducer: qutebrowser --debug --temp-basedir \ ':config-dict-add content.javascript.log_message.levels example.org bla' \ ':later 1000 config-dict-add content.javascript.log_message.levels example.org bla' Results in: ERROR: Invalid value 'bla' - expected a value of type list but got str. ERROR: example.org already exists in content.javascript.log_message - use --replace to overwrite! Fixes the second part of #7343. nb: As before, the mutable updating actually gets interrupted by a failing update, instead of it e.g. collecting all errors but carrying on. With this change, the remaining updates will thus also be discarded, but that does not seem to be a problem with how mutables are currently used. Ideally, we should get rid of the mutable handling entirely anyways, at least for qutebrowser internal code - see #4344.

qutebrowser

tests/unit/config/test_config.py

Python

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

keras_model_summary

def keras_model_summary(name, data, step=None): summary_metadata = tf.compat.v1.SummaryMetadata() # Hard coding a plugin name. Please refer to go/tb-plugin-name-hardcode for # the rationale. summary_metadata.plugin_data.plugin_name = "graph_keras_model" # version number = 1 summary_metadata.plugin_data.content = b"1" try: json_string = data.to_json() except Exception as exc: # An exception should not break a model code. logging.warning( "Model failed to serialize as JSON. Ignoring... %s", exc ) return False with tf.summary.experimental.summary_scope( name, "graph_keras_model", [data, step] ) as (tag, _): with tf.device("cpu:0"): tensor = tf.constant(json_string, dtype=tf.string) return tf.summary.write( tag=tag, tensor=tensor, step=step, metadata=summary_metadata ) @keras_export("keras.callbacks.TensorBoard", v1=[])

3613c3defc39c236fb1592c4f7ba1a9cc887343a

@keras_export("keras.callbacks.TensorBoard", v1=[])

callbacks.py

Remove pylint comments. PiperOrigin-RevId: 452353044

keras

keras/callbacks.py

Python

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

_update_counters

def _update_counters(self, ti_status, session=None): tis_to_be_scheduled = [] refreshed_tis = [] TI = TaskInstance filter_for_tis = TI.filter_for_tis(list(ti_status.running.values())) if filter_for_tis is not None: refreshed_tis = session.query(TI).filter(filter_for_tis).all() for ti in refreshed_tis: # Here we remake the key by subtracting 1 to match in memory information reduced_key = ti.key.reduced if ti.state == TaskInstanceState.SUCCESS: ti_status.succeeded.add(reduced_key) self.log.debug("Task instance %s succeeded. Don't rerun.", ti) ti_status.running.pop(reduced_key) continue if ti.state == TaskInstanceState.SKIPPED: ti_status.skipped.add(reduced_key) self.log.debug("Task instance %s skipped. Don't rerun.", ti) ti_status.running.pop(reduced_key) continue if ti.state == TaskInstanceState.FAILED: self.log.error("Task instance %s failed", ti) ti_status.failed.add(reduced_key) ti_status.running.pop(reduced_key) continue # special case: if the task needs to run again put it back if ti.state == TaskInstanceState.UP_FOR_RETRY: self.log.warning("Task instance %s is up for retry", ti) ti_status.running.pop(reduced_key) ti_status.to_run[ti.key] = ti # special case: if the task needs to be rescheduled put it back elif ti.state == TaskInstanceState.UP_FOR_RESCHEDULE: self.log.warning("Task instance %s is up for reschedule", ti) # During handling of reschedule state in ti._handle_reschedule, try number is reduced # by one, so we should not use reduced_key to avoid key error ti_status.running.pop(ti.key) ti_status.to_run[ti.key] = ti # special case: The state of the task can be set to NONE by the task itself # when it reaches concurrency limits. It could also happen when the state # is changed externally, e.g. by clearing tasks from the ui. We need to cover # for that as otherwise those tasks would fall outside of the scope of # the backfill suddenly. elif ti.state == State.NONE: self.log.warning( "FIXME: task instance %s state was set to none externally or " "reaching concurrency limits. Re-adding task to queue.", ti, ) tis_to_be_scheduled.append(ti) ti_status.running.pop(reduced_key) ti_status.to_run[ti.key] = ti # Batch schedule of task instances if tis_to_be_scheduled: filter_for_tis = TI.filter_for_tis(tis_to_be_scheduled) session.query(TI).filter(filter_for_tis).update( values={TI.state: TaskInstanceState.SCHEDULED}, synchronize_session=False ) session.flush()

6fc6edf6af7f676bfa54ff3a2e6e6d2edb938f2e

backfill_job.py

Make `airflow dags test` be able to execute Mapped Tasks (#21210) * Make `airflow dags test` be able to execute Mapped Tasks In order to do this there were two steps required: - The BackfillJob needs to know about mapped tasks, both to expand them, and in order to update it's TI tracking - The DebugExecutor needed to "unmap" the mapped task to get the real operator back I was testing this with the following dag: ``` from airflow import DAG from airflow.decorators import task from airflow.operators.python import PythonOperator import pendulum @task def make_list(): return list(map(lambda a: f'echo "{a!r}"', [1, 2, {'a': 'b'}])) def consumer(*args): print(repr(args)) with DAG(dag_id='maptest', start_date=pendulum.DateTime(2022, 1, 18)) as dag: PythonOperator(task_id='consumer', python_callable=consumer).map(op_args=make_list()) ``` It can't "unmap" decorated operators successfully yet, so we're using old-school PythonOperator We also just pass the whole value to the operator, not just the current mapping value(s) * Always have a `task_group` property on DAGNodes And since TaskGroup is a DAGNode, we don't need to store parent group directly anymore -- it'll already be stored * Add "integation" tests for running mapped tasks via BackfillJob * Only show "Map Index" in Backfill report when relevant Co-authored-by: Tzu-ping Chung <[email protected]>

airflow

airflow/jobs/backfill_job.py

Python

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

user_config_dir

def user_config_dir(self) -> str: return self._append_app_name_and_version(os.path.expanduser("~/Library/Preferences/"))

f3166e673fe8d40277b804d35d77dcdb760fc3b3

macos.py

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

pipenv

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

Python

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

future_sle_exists

def future_sle_exists(args, sl_entries=None): key = (args.voucher_type, args.voucher_no) if validate_future_sle_not_exists(args, key, sl_entries): return False elif get_cached_data(args, key): return True if not sl_entries: sl_entries = get_sle_entries_against_voucher(args) if not sl_entries: return or_conditions = get_conditions_to_validate_future_sle(sl_entries) data = frappe.db.sql( .format( " or ".join(or_conditions) ), args, as_dict=1, ) for d in data: frappe.local.future_sle[key][(d.item_code, d.warehouse)] = d.total_row return len(data)

494bd9ef78313436f0424b918f200dab8fc7c20b

stock_controller.py

style: format code with black

erpnext

erpnext/controllers/stock_controller.py

Python

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

normalize_histogram_results

def normalize_histogram_results(fields, histogram_params, results): # zerofill and rename the columns while making sure to adjust for precision bucket_maps = {field: {} for field in fields} # Only one row in metrics result data = results["data"][0] for field in fields: histogram_column = f"histogram({field})" histogram_alias = get_function_alias(histogram_column) bucket_maps[field] = {start: height for start, end, height in data[histogram_alias]} new_data = {field: [] for field in fields} for i in range(histogram_params.num_buckets): bucket = histogram_params.start_offset + histogram_params.bucket_size * i for field in fields: row = { "bin": bucket, "count": bucket_maps[field].get(bucket, 0), } # make sure to adjust for the precision if necessary if histogram_params.multiplier > 1: row["bin"] /= float(histogram_params.multiplier) new_data[field].append(row) return new_data

6307cf52c4c7f185f9023c6279e565dd7812c202

metrics_performance.py

feat(mep): Adding histogram support to metrics enhanced perf (#34462) - This uses the metrics dataset to supply histogram data in the same format discover expects - Outlier is currently based on p25 and p75, may change to using tags later

sentry

src/sentry/snuba/metrics_performance.py

Python

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

inplace_swap_column

def inplace_swap_column(X, m, n): if m < 0: m += X.shape[1] if n < 0: n += X.shape[1] if isinstance(X, sp.csc_matrix): inplace_swap_row_csr(X, m, n) elif isinstance(X, sp.csr_matrix): inplace_swap_row_csc(X, m, n) else: _raise_typeerror(X)

a2c4d8b1f4471f52a4fcf1026f495e637a472568

sparsefuncs.py

DOC Ensures that inplace_swap_column passes numpydoc validation (#23476) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: harshit5674 <[email protected]>

scikit-learn

sklearn/utils/sparsefuncs.py

Python

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

map

def map(self, mapper): mapped = self._values.map(mapper) return Index(mapped, name=self.name)

521259299f7829da667ba39302ec77acedde9e5e

category.py

DOC: Improve doc summaries in series.rst (#45237)

pandas

pandas/core/indexes/category.py

Python