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

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Semeru Lab 7

python3.10.4/Lib/calendar.py

def monthdays2calendar(self, year, month): days = list(self.itermonthdays2(year, month)) r

add python 3.10.4 for windows

monthdays2calendar

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

calendar.py

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

Python

def monthdays2calendar(self, year, month): days = list(self.itermonthdays2(year, month)) return [ days[i:i+7] for i in range(0, len(days), 7) ]

python/ray/serve/drivers.py

async def predict_with_route(self, route_path, *args, **kwargs): if route_path not in self.dags: raise RayServeExc

[Serve] Support Multiple DAG Entrypoints in DAGDriver (#26573)

predict_with_route

410fe1b5ec9e798d6e7ffbb5844e258d08e323b3

ray

drivers.py

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

Python

async def predict_with_route(self, route_path, *args, **kwargs): if route_path not in self.dags: raise RayServeException(f"{route_path} does not exist in dags routes") return await self.dags[route_path].remote(*args, **kwargs)

python/ray/tune/experiment/trial.py

def should_recover(self): return ( self.num_failures < self.max_failures or self.max_failures < 0 or ( self.num_failures == self.max_failures and self.num_restore_failures < int(os.environ.get("TUNE_RESTORE_RETRY_NUM", 0)) )

[tune] Add retry logic for restoring trials. (#29086) This is an advanced setting. Consider the following scenario: Due to scheduling glitches, sometimes a restoring trial may be scheduled onto a dying node. By setting this env var to a positive number, the trial can be restored several times and hopefully one of the times it will not be put on a dying node. This retry behavior won't increment the per trial failure number, which is compared against max_failures. Signed-off-by: xwjiang2010 <[email protected]> Signed-off-by: xwjiang2010 <[email protected]>

should_recover

f1882f90cf2d91f5d802b7dffd41db5e306d6e6c

ray

trial.py

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

Python

def should_recover(self): return ( self.num_failures < self.max_failures or self.max_failures < 0 or ( self.num_failures == self.max_failures and self.num_restore_failures < int(os.environ.get("TUNE_RESTORE_RETRY_NUM", 0)) ) )

wagtail/core/tests/test_locale_model.py

def test_change_root_page_locale_on_locale_deletion(self): # change 'real' pages first Page.objects.filter(depth__gt=1).update( locale=Locale.objects.get(language_code="fr") ) self.assertEqual(Page.get_first_root_node().locale.language_code, "en") Locale.objects.get(language_code="en").delete() self.assertEqual(Page.get_first_root_node().locale.language_code, "fr")

Reformat with black

test_change_root_page_locale_on_locale_deletion

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_locale_model.py

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

Python

def test_change_root_page_locale_on_locale_deletion(self): # change 'real' pages first Page.objects.filter(depth__gt=1).update( locale=Locale.objects.get(language_code="fr") ) self.assertEqual(Page.get_first_root_node().locale.language_code, "en") Locale.objects.get(language_code="en").delete() self.assertEqual(Page.get_first_root_node().locale.language_code, "fr")

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

def _proc_pax(self, tarfile): # Read the header information. buf = tarfile.fileobj.read(self._block(self.size)) # A pax header stores supplemental information for either # the following file (extended) or all following files # (global). if self.type == XGLTYPE: pax_headers = tarfile.pax_headers else: pax_headers = tarfile.pax_headers.copy() # Check if the pax header contains a hdrcharset field. This tells us # the encoding of the path, linkpath, uname and gname fields. Normally, # these fields are UTF-8 encoded but since POSIX.1-2008 tar # implementations are allowed to store them as raw binary strings if # the translation to UTF-8 fails. match = re.search(br"\d+ hdrcharset=([^\n]+)\n", buf) if match is not None: pax_headers["hdrcharset"] = match.group(1).decode("utf8") # For the time being, we don't care about anything other than "BINARY". # The only other value that is currently allowed by the standard is # "ISO-IR 10646 2000 UTF-8" in other words UTF-8. hdrcharset = pax_headers.get("hdrcharset") if hdrcharset == "BINARY": encoding = tarfile.encoding else: encoding = "utf8" # Parse pax header information. A record looks like that: # "%d %s=%s\n" % (length, keyword, value). length is the size # of the complete record including the length field itself and # the newline. keyword and value are both UTF-8 encoded strings. regex = re.compile(br"(\d+) ([^=]+)=") pos = 0 while True: match = regex.match(buf, pos) if not match: break length, keyword = match.groups() length = int(length) value = buf[match.end(2)

Vendor in pip 22.1.2

_proc_pax

c69d55f7c82d5ae2cce542bcfb98d043ca4836a0

pipenv

tarfile.py

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

Python

def _proc_pax(self, tarfile): # Read the header information. buf = tarfile.fileobj.read(self._block(self.size)) # A pax header stores supplemental information for either # the following file (extended) or all following files # (global). if self.type == XGLTYPE: pax_headers = tarfile.pax_headers else: pax_headers = tarfile.pax_headers.copy() # Check if the pax header contains a hdrcharset field. This tells us # the encoding of the path, linkpath, uname and gname fields. Normally, # these fields are UTF-8 encoded but since POSIX.1-2008 tar # implementations are allowed to store them as raw binary strings if # the translation to UTF-8 fails. match = re.search(br"\d+ hdrcharset=([^\n]+)\n", buf) if match is not None: pax_headers["hdrcharset"] = match.group(1).decode("utf8") # For the time being, we don't care about anything other than "BINARY". # The only other value that is currently allowed by the standard is # "ISO-IR 10646 2000 UTF-8" in other words UTF-8. hdrcharset = pax_headers.get("hdrcharset") if hdrcharset == "BINARY": encoding = tarfile.encoding else: encoding = "utf8" # Parse pax header information. A record looks like that: # "%d %s=%s\n" % (length, keyword, value). length is the size # of the complete record including the length field itself and # the newline. keyword and value are both UTF-8 encoded strings. regex = re.compile(br"(\d+) ([^=]+)=") pos = 0 while True: match = regex.match(buf, pos) if not match: break length, keyword = match.groups() length = int(length) value = buf[match.end(2) + 1:match.start(1) + length - 1] # Normally, we could just use "utf8" as the encoding and "strict" # as the error handler, but we better not take the risk. For # example, GNU tar <= 1.23 is known to store filenames it cannot # translate to UTF-8 as raw strings (unfortunately without a # hdrcharset=BINARY header). # We first try the strict standard encoding, and if that fails we # fall back on the user's encoding and error handler. keyword = self._decode_pax_field(keyword, "utf8", "utf8", tarfile.errors) if keyword in PAX_NAME_FIELDS: value = self._decode_pax_field(value, encoding, tarfile.encoding, tarfile.errors) else: value = self._decode_pax_field(value, "utf8", "utf8", tarfile.errors) pax_headers[keyword] = value pos += length # Fetch the next header. try: next = self.fromtarfile(tarfile) except HeaderError: raise SubsequentHeaderError("missing or bad subsequent header") # Process GNU sparse information. if "GNU.sparse.map" in pax_headers: # GNU extended sparse format version 0.1. self._proc_gnusparse_01(next, pax_headers) elif "GNU.sparse.size" in pax_headers: # GNU extended sparse format version 0.0. self._proc_gnusparse_00(next, pax_headers, buf) elif pax_headers.get("GNU.sparse.major") == "1" and pax_headers.get("GNU.sparse.minor") == "0": # GNU extended sparse format version 1.0. self._proc_gnusparse_10(next, pax_headers, tarfile) if self.type in (XHDTYPE, SOLARIS_XHDTYPE): # Patch the TarInfo object with the extended header info. next._apply_pax_info(pax_headers, tarfile.encoding, tarfile.errors) next.offset = self.offset if "size" in pax_headers: # If the extended header replaces the size field, # we need to recalculate the offset where the next # header starts. offset = next.offset_data if next.isreg() or next.type not in SUPPORTED_TYPES: offset += next._block(next.size) tarfile.offset = offset return next

ppocr/modeling/backbones/rec_efficientb3_pren.py

def get_global_params(): GlobalParams = namedtuple('GlobalParams', [ 'drop_connect_rate', 'width_coefficient', 'depth_coefficient', 'depth_divisor', 'image_size' ]) global_params = GlobalParams( drop_connect_rate=0.3, width_coefficient=1.2, depth_coefficient=1.4, depth_divisor=8,

[Feature] Add PREN Scene Text Recognition Model(Accepted in CVPR2021) (#5563) * [Feature] add PREN scene text recognition model * [Patch] Optimize yml File * [Patch] Save Label/Pred Preprocess Time Cost * [BugFix] Modify Shape Conversion to Fit for Inference Model Exportion * [Patch] ? * [Patch] ? * 啥情况...

get_global_params

6e607a0fa1cefbf0388dac86c84debf4781cec48

PaddleOCR

rec_efficientb3_pren.py

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

Python

def get_global_params(): GlobalParams = namedtuple('GlobalParams', [ 'drop_connect_rate', 'width_coefficient', 'depth_coefficient', 'depth_divisor', 'image_size' ]) global_params = GlobalParams( drop_connect_rate=0.3, width_coefficient=1.2, depth_coefficient=1.4, depth_divisor=8, image_size=64) return global_params

lib/matplotlib/path.py

def _create_closed(cls, vertices): v = _to_unmasked_float_array(vertices)

Add a helper to generate closed paths. Instead of having to manually append an unused vertex that corresponds to the CLOSEPATH code, add a _make_closed helper (private for now) which does that for us.

_create_closed

e994b58e49bcd98334b220d74540005f62af918d

matplotlib

path.py

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

Python

def _create_closed(cls, vertices): v = _to_unmasked_float_array(vertices) return cls(np.concatenate([v, v[:1]]), closed=True)

@pytest.mark.parametrize("loss", ALL_LOSSES) @pytest.mark.parametrize("sample_weight", [None, "range"]) @pytest.mark.parametrize("dtype", (np.float32, np.float64)) @pytest.mark.parametrize("order", ("C", "F"))

sklearn/_loss/tests/test_loss.py

def test_predict_proba(loss, global_random_seed): n_samples = 20 y_true, raw_prediction = random_y_true_raw_prediction( loss=loss, n_samples=n_samples, y_bound=(-100, 100), raw_bound=(-5, 5), seed=global_random_seed, ) if hasattr(loss, "predict_proba"): proba = loss.predict_proba(raw_prediction) assert proba.shape == (n_samples, loss.n_classes) assert np.sum(proba, axis=1) == approx(1, rel=1e-11)

TST ensure that sklearn/_loss/tests/test_loss.py is seed insensitive (#22847) Co-authored-by: Christian Lorentzen <[email protected]>

test_predict_proba

751c5cd05ff545c20ad0b09ac491c07f31e4cd56

scikit-learn

test_loss.py

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

Python

def test_predict_proba(loss, global_random_seed): n_samples = 20 y_true, raw_prediction = random_y_true_raw_prediction( loss=loss, n_samples=n_samples, y_bound=(-100, 100), raw_bound=(-5, 5), seed=global_random_seed, ) if hasattr(loss, "predict_proba"): proba = loss.predict_proba(raw_prediction) assert proba.shape == (n_samples, loss.n_classes) assert np.sum(proba, axis=1) == approx(1, rel=1e-11) if hasattr(loss, "gradient_proba"): for grad, proba in ( (None, None), (None, np.empty_like(raw_prediction)), (np.empty_like(raw_prediction), None), (np.empty_like(raw_prediction), np.empty_like(raw_prediction)), ): grad, proba = loss.gradient_proba( y_true=y_true, raw_prediction=raw_prediction, sample_weight=None, gradient_out=grad, proba_out=proba, ) assert proba.shape == (n_samples, loss.n_classes) assert np.sum(proba, axis=1) == approx(1, rel=1e-11) assert_allclose( grad, loss.gradient( y_true=y_true, raw_prediction=raw_prediction, sample_weight=None, gradient_out=None, ), ) @pytest.mark.parametrize("loss", ALL_LOSSES) @pytest.mark.parametrize("sample_weight", [None, "range"]) @pytest.mark.parametrize("dtype", (np.float32, np.float64)) @pytest.mark.parametrize("order", ("C", "F"))

erpnext/buying/doctype/supplier_scorecard_variable/supplier_scorecard_variable.py

def get_late_shipments(scorecard): return get

style: format code with black

get_late_shipments

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

supplier_scorecard_variable.py

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

Python

def get_late_shipments(scorecard): return get_total_shipments(scorecard) - get_on_time_shipments(scorecard)

ludwig/models/base.py

def eval_loss(self, targets, predictions): eval_loss = 0 for of_name, of_obj in self.outp

feat: Added model type GBM (LightGBM tree learner), as an alternative to ECD (#2027)

eval_loss

aa0c63bf2ed825eb3ca8eff8a002d5ccbe395173

ludwig

base.py

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

Python

def eval_loss(self, targets, predictions): eval_loss = 0 for of_name, of_obj in self.output_features.items(): of_eval_loss = of_obj.eval_loss(targets[of_name], predictions[of_name]) eval_loss += of_obj.loss["weight"] * of_eval_loss additional_loss = 0 additional_losses = self.losses() if additional_losses: additional_loss = torch.sum(torch.stack(additional_losses)) # other losses return eval_loss, additional_loss

modin/db_conn.py

def partition_query(self, query, limit, offset): return ( ( f"SELECT * FROM ({query}) AS _ ORDER BY(SELECT NULL)" + f" OFFSET {offset} ROWS FETCH NEXT {limit} ROWS ONLY" ) if self._dialect_is_microsoft_sql() else f"SELECT * FROM

FEAT-#979: Enable reading from SQL server. (#4279) Co-authored-by: eavidan <[email protected]> Co-authored-by: Devin Petersohn <[email protected]> Signed-off-by: mvashishtha <[email protected]>

partition_query

2d40797b2b700d81d4db4a4cd023d563edf6431f

modin

db_conn.py

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

Python

def partition_query(self, query, limit, offset): return ( ( f"SELECT * FROM ({query}) AS _ ORDER BY(SELECT NULL)" + f" OFFSET {offset} ROWS FETCH NEXT {limit} ROWS ONLY" ) if self._dialect_is_microsoft_sql() else f"SELECT * FROM ({query}) LIMIT {limit} OFFSET {offset}" )

tests/admin_filters/tests.py

def test_parameter_ends_with__in__or__isnull(self): # When it ends with '__in' ----------------------------------------- modeladmin = DecadeFilterBookAdminParameterEndsWith__In(Book, site) request = self.request_factory.get("/", {"decade__in": "the 90s"}) request.user = self.alfred changelist = modeladmin.get_changelist_instance(request) # Make sure the correct queryset is returned queryset = changelist.get_queryset(request) self.assertEqual(list(queryset), [self.bio_book]) # Make sure the correct choice is selected filterspec = changelist.get_filters(request)[0][0] self.assertEqual(filterspec.title, "publication decade") choices = list(filterspec.choices(changelist)) self.assertEqual(choices[2]["display"], "the 1990's") self.assertIs(choices[2]["selected"], True) self.assertEqual(choices[2]["query_string"], "?decade__in=the+90s") # When it ends with '__isnull' ----------

Refs #33476 -- Reformatted code with Black.

test_parameter_ends_with__in__or__isnull

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

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

Python

def test_parameter_ends_with__in__or__isnull(self): # When it ends with '__in' ----------------------------------------- modeladmin = DecadeFilterBookAdminParameterEndsWith__In(Book, site) request = self.request_factory.get("/", {"decade__in": "the 90s"}) request.user = self.alfred changelist = modeladmin.get_changelist_instance(request) # Make sure the correct queryset is returned queryset = changelist.get_queryset(request) self.assertEqual(list(queryset), [self.bio_book]) # Make sure the correct choice is selected filterspec = changelist.get_filters(request)[0][0] self.assertEqual(filterspec.title, "publication decade") choices = list(filterspec.choices(changelist)) self.assertEqual(choices[2]["display"], "the 1990's") self.assertIs(choices[2]["selected"], True) self.assertEqual(choices[2]["query_string"], "?decade__in=the+90s") # When it ends with '__isnull' --------------------------------------- modeladmin = DecadeFilterBookAdminParameterEndsWith__Isnull(Book, site) request = self.request_factory.get("/", {"decade__isnull": "the 90s"}) request.user = self.alfred changelist = modeladmin.get_changelist_instance(request) # Make sure the correct queryset is returned queryset = changelist.get_queryset(request) self.assertEqual(list(queryset), [self.bio_book]) # Make sure the correct choice is selected filterspec = changelist.get_filters(request)[0][0] self.assertEqual(filterspec.title, "publication decade") choices = list(filterspec.choices(changelist)) self.assertEqual(choices[2]["display"], "the 1990's") self.assertIs(choices[2]["selected"], True) self.assertEqual(choices[2]["query_string"], "?decade__isnull=the+90s")

python3.10.4/Lib/ipaddress.py

def v4_int_to_packed(address): try: return address.to_bytes(4, 'big') except OverflowError: raise ValueError("Address negative or too large for I

add python 3.10.4 for windows

v4_int_to_packed

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

ipaddress.py

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

Python

def v4_int_to_packed(address): try: return address.to_bytes(4, 'big') except OverflowError: raise ValueError("Address negative or too large for IPv4")

tests/handlers/test_directory.py

def test_delete_alias_not_allowed(self) -> None: self._create_alias(self.admin_user) self.get_failure( self.handler.delete_association( create_requester(self.test_us

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

test_delete_alias_not_allowed

5dd949bee6158a8b651db9f2ae417a62c8184bfd

synapse

test_directory.py

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

Python

def test_delete_alias_not_allowed(self) -> None: self._create_alias(self.admin_user) self.get_failure( self.handler.delete_association( create_requester(self.test_user), self.room_alias ), synapse.api.errors.AuthError, )

sympy/integrals/trigonometry.py

def trigintegrate(f, x, conds='piecewise'): pat, a, n, m = _pat_sincos(x) f = f.rewrite('sincos') M = f.match(pat) if M is None: return n, m = M[n], M[m] if n.is_zero and m.is_zero: return x zz = x if n.is_zero else S.Zero a = M[a] if n.is_odd or m.is_odd: u = _u n_, m_ = n.is_odd, m.is_odd # take smallest n or m -- to choose simplest substitution if n_ and m_: # Make sure to choose the positive one # otherwise an incorrect integral can occur. if n < 0 and m > 0: m_ = True n_ = False elif m < 0 and n > 0: n_ = True m_ = False # Both are negative so choose the smallest n or m # in absolute value for simplest substitution. elif (n < 0 and m < 0): n_ = n > m m_ = not (n > m) # Both n and m are odd and positive else: n_ = (n < m) # NB: careful here, one of the m_ = not (n < m) # conditions *must* be true # n m u=C (n-1)/2 m # S(x) * C(x) dx --> -(1-u^2) * u du if n_: ff = -(1 - u**2)**((n - 1)/2) * u**m uu = cos(a*x) # n m u=S n (m-1)/2 # S(x) * C(x) dx --> u * (1-u^2) du elif m_: ff = u**n * (1 - u**2)**((m - 1)/2) uu = sin(a*x) fi = integrate(ff, u) # XXX cyclic deps fx = fi.subs(u, uu) if conds == 'piecewise': return Piecewise((fx / a, Ne(a, 0)), (zz, True)) return fx / a # n & m are both even # # 2k 2m 2l 2l # we transform S (x) * C (x) into terms with only S (x) or C (x) # # example: # 100 4 100 2 2 100 4 2 # S (x) * C (x) = S (x) * (1-S (x)) = S (x) * (1 + S (x) - 2*S (x)) # # 104 102 100 # = S (x) - 2*S (x) + S (x) # 2k # then S is integrated with recursive formula # take largest n or m -- to choose simplest substitution n_ = (Abs(n) > Abs(m)) m_ = (Abs(m) > Abs(n)) res = S.Zero if n_: # 2k 2 k i 2i # C = (1 - S ) = sum(i, (-) * B(k, i) * S ) if m > 0: for i in range(0, m//2 + 1): res += (S.NegativeOne**i * binomial(m//2, i) * _sin_pow_integrate(n + 2*i, x)) elif m == 0: res = _sin_pow_integrate(n, x) else: # m < 0 , |n| > |m| # / # | # | m n # | cos (x) sin (x) dx = # | # | #/ # / # | # -1 m+1 n-1 n - 1 | m+2 n-2 # ________ cos (x) sin (x) + _______ | cos (x) sin (x) dx # | # m + 1 m + 1 | # / res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) + Rational(n - 1, m + 1) * trigintegrate(cos(x)**(m + 2)*sin(x)**(n - 2), x)) elif m_: # 2k 2 k i 2i # S = (1 - C ) = sum(i, (-) * B(k, i) * C ) if n > 0: # / / # | | # | m n | -m n # | cos (x)*sin (x) dx or | cos (x) * sin (x) dx # | | # / / # # |m| > |n| ; m, n >0 ; m, n belong to Z - {0} # n

Improve loop performance

trigintegrate

e94a7b45d7b033ccbd57395dca28b654f875c54c

sympy

trigonometry.py

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

Python

def trigintegrate(f, x, conds='piecewise'): pat, a, n, m = _pat_sincos(x) f = f.rewrite('sincos') M = f.match(pat) if M is None: return n, m = M[n], M[m] if n.is_zero and m.is_zero: return x zz = x if n.is_zero else S.Zero a = M[a] if n.is_odd or m.is_odd: u = _u n_, m_ = n.is_odd, m.is_odd # take smallest n or m -- to choose simplest substitution if n_ and m_: # Make sure to choose the positive one # otherwise an incorrect integral can occur. if n < 0 and m > 0: m_ = True n_ = False elif m < 0 and n > 0: n_ = True m_ = False # Both are negative so choose the smallest n or m # in absolute value for simplest substitution. elif (n < 0 and m < 0): n_ = n > m m_ = not (n > m) # Both n and m are odd and positive else: n_ = (n < m) # NB: careful here, one of the m_ = not (n < m) # conditions *must* be true # n m u=C (n-1)/2 m # S(x) * C(x) dx --> -(1-u^2) * u du if n_: ff = -(1 - u**2)**((n - 1)/2) * u**m uu = cos(a*x) # n m u=S n (m-1)/2 # S(x) * C(x) dx --> u * (1-u^2) du elif m_: ff = u**n * (1 - u**2)**((m - 1)/2) uu = sin(a*x) fi = integrate(ff, u) # XXX cyclic deps fx = fi.subs(u, uu) if conds == 'piecewise': return Piecewise((fx / a, Ne(a, 0)), (zz, True)) return fx / a # n & m are both even # # 2k 2m 2l 2l # we transform S (x) * C (x) into terms with only S (x) or C (x) # # example: # 100 4 100 2 2 100 4 2 # S (x) * C (x) = S (x) * (1-S (x)) = S (x) * (1 + S (x) - 2*S (x)) # # 104 102 100 # = S (x) - 2*S (x) + S (x) # 2k # then S is integrated with recursive formula # take largest n or m -- to choose simplest substitution n_ = (Abs(n) > Abs(m)) m_ = (Abs(m) > Abs(n)) res = S.Zero if n_: # 2k 2 k i 2i # C = (1 - S ) = sum(i, (-) * B(k, i) * S ) if m > 0: for i in range(0, m//2 + 1): res += (S.NegativeOne**i * binomial(m//2, i) * _sin_pow_integrate(n + 2*i, x)) elif m == 0: res = _sin_pow_integrate(n, x) else: # m < 0 , |n| > |m| # / # | # | m n # | cos (x) sin (x) dx = # | # | #/ # / # | # -1 m+1 n-1 n - 1 | m+2 n-2 # ________ cos (x) sin (x) + _______ | cos (x) sin (x) dx # | # m + 1 m + 1 | # / res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) + Rational(n - 1, m + 1) * trigintegrate(cos(x)**(m + 2)*sin(x)**(n - 2), x)) elif m_: # 2k 2 k i 2i # S = (1 - C ) = sum(i, (-) * B(k, i) * C ) if n > 0: # / / # | | # | m n | -m n # | cos (x)*sin (x) dx or | cos (x) * sin (x) dx # | | # / / # # |m| > |n| ; m, n >0 ; m, n belong to Z - {0} # n 2 # sin (x) term is expanded here in terms of cos (x), # and then integrated. # for i in range(0, n//2 + 1): res += (S.NegativeOne**i * binomial(n//2, i) * _cos_pow_integrate(m + 2*i, x)) elif n == 0: # / # | # | 1 # | _ _ _ # | m # | cos (x) # / # res = _cos_pow_integrate(m, x) else: # n < 0 , |m| > |n| # / # | # | m n # | cos (x) sin (x) dx = # | # | #/ # / # | # 1 m-1 n+1 m - 1 | m-2 n+2 # _______ cos (x) sin (x) + _______ | cos (x) sin (x) dx # | # n + 1 n + 1 | # / res = (Rational(1, n + 1) * cos(x)**(m - 1)*sin(x)**(n + 1) + Rational(m - 1, n + 1) * trigintegrate(cos(x)**(m - 2)*sin(x)**(n + 2), x)) else: if m == n: ##Substitute sin(2x)/2 for sin(x)cos(x) and then Integrate. res = integrate((sin(2*x)*S.Half)**m, x) elif (m == -n): if n < 0: # Same as the scheme described above. # the function argument to integrate in the end will # be 1, this cannot be integrated by trigintegrate. # Hence use sympy.integrals.integrate. res = (Rational(1, n + 1) * cos(x)**(m - 1) * sin(x)**(n + 1) + Rational(m - 1, n + 1) * integrate(cos(x)**(m - 2) * sin(x)**(n + 2), x)) else: res = (Rational(-1, m + 1) * cos(x)**(m + 1) * sin(x)**(n - 1) + Rational(n - 1, m + 1) * integrate(cos(x)**(m + 2)*sin(x)**(n - 2), x)) if conds == 'piecewise': return Piecewise((res.subs(x, a*x) / a, Ne(a, 0)), (zz, True)) return res.subs(x, a*x) / a

PyInstaller/utils/hooks/win32.py

def get_pywin32_module_file_attribute(module_name): from PyInstaller.utils.win32 import winutils module = w

hookutils: win32: port to PyInstaller.isolated framework

get_pywin32_module_file_attribute

b87832b35dc1866c81ecaf7e502afe48a4e50a82

pyinstaller

win32.py

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

Python

def get_pywin32_module_file_attribute(module_name): from PyInstaller.utils.win32 import winutils module = winutils.import_pywin32_module(module_name) return module.__file__

src/sentry/pipeline/base.py

def render_warning(self, message): context = {"error": message} return render_to_response("sentry/pipeline-provider-error.html", context, self.request)

ref(py): Split up large file (#32862) Co-authored-by: getsantry[bot] <66042841+getsantry[bot]@users.noreply.github.com>

render_warning

d246d2b6d3e014270941209e54f2f12e09ad9a81

sentry

base.py

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

Python

def render_warning(self, message): context = {"error": message} return render_to_response("sentry/pipeline-provider-error.html", context, self.request)

tests/rest/client/test_rooms.py

def test_context_filter_not_labels(self) -> None: event_id = self._send_labelled_messages_in_room() channel = self.make_request( "GET", "/rooms/%s/context/%s?filter=%s" % (self.room_id, event_id, json.dumps(self.FILTER_NOT_LABELS)), access_token=self.tok, ) self.assertEqual(channel.code, 200, channel.result) events_before = channel.json_body["events_before"] self.assertEqual( len(events_before), 1, [event["content"] for event in events_before] ) self.assertEqual( events_before[0]["content"]["body"], "without label", events_before[0] ) events_after = channel.json_body["events_after"] self.assertEqual( len(events_after), 2, [event["content"] for event in events_after] ) self.assertEqual( events_after[0]["content"]["body"], "with wrong label", events_after[0] ) self.assertEqual( events_after[1]["content"]["body"], "with two wrong labels", events_after[1] )

Add type hints to `tests/rest/client` (#12108) * Add type hints to `tests/rest/client` * newsfile * fix imports * add `test_account.py` * Remove one type hint in `test_report_event.py` * change `on_create_room` to `async` * update new functions in `test_third_party_rules.py` * Add `test_filter.py` * add `test_rooms.py` * change to `assertEquals` to `assertEqual` * lint

test_context_filter_not_labels

2ffaf30803f93273a4d8a65c9e6c3110c8433488

synapse

test_rooms.py

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

Python

def test_context_filter_not_labels(self) -> None: event_id = self._send_labelled_messages_in_room() channel = self.make_request( "GET", "/rooms/%s/context/%s?filter=%s" % (self.room_id, event_id, json.dumps(self.FILTER_NOT_LABELS)), access_token=self.tok, ) self.assertEqual(channel.code, 200, channel.result) events_before = channel.json_body["events_before"] self.assertEqual( len(events_before), 1, [event["content"] for event in events_before] ) self.assertEqual( events_before[0]["content"]["body"], "without label", events_before[0] ) events_after = channel.json_body["events_after"] self.assertEqual( len(events_after), 2, [event["content"] for event in events_after] ) self.assertEqual( events_after[0]["content"]["body"], "with wrong label", events_after[0] ) self.assertEqual( events_after[1]["content"]["body"], "with two wrong labels", events_after[1] )

networkx/algorithms/isomorphism/vf2pp_helpers/feasibility.py

def _consistent_PT(u, v, graph_params, state_params): G1, G2 = graph_params.G1, graph_params.G2 mapping, reverse_mapping = state_params.mapping, state_params.reverse_mapping for neighbor in G1[u]: if neighbor in mapping: if G1.number_of_edges(u, neighbor) != G2.number_of_edges( v, mapping[neighbor] ): return False for neighbor in G2[v]:

Preliminary VF2++ Implementation (#5788) * Preliminary implementation of the candidate node pair ordering of VF2++ * Removed unused lines of code * Added todos * Added demo and pseudocode for VF2++ * Pointed out a problem with the pseudocode * Initialisation of the VF2++ basis structure * Initialise the GraphMatcher * Remove useless changes * Check labels for the node ordering + demo * Code to verify the ordering * Implement the ISO feasibility check * Implement the IND feasibility * Create State class * Fixed Dan's code for the ordering * Preliminary form of the node ordering * Add visualisation * Use list comprehension for the Ti computation * Remove function * Create Unit Tests * Add labels check + update unit tests * Add pre-computation of G-labels * Remove todo * First implementation of the candidate selection * Initial version of candidate selection * Remove unnecessary files * Merge candidate selection cases into one * Create a function to incrementally update Ti and Ti_out * Unit Test for the Ti updating * Implement the Ti/Ti_out restoring * Finish the restoring of Ti and create unit test * Update test file names * Uncommented test section * Replace redundant loop with for-any * Create unit test for candidate selection using the same label for all nodes * Create unit test for candidate selection using different labels for the nodes * Update feasibility tests without the use of the state class * Create more unit tests for the feasibility checking * Provide explanation for the unit tests * First successful test of the complete ISO VF2++ algorithm (except from the buggy ordering) * Fix bug: when popping a node to climb up the DFS tree we need the previous node ordering (containing the node that we just popped) * Create a separate file for the VF2++ ISO algorithm * Delete file * Remove redundant iteration and memory use * Demo for different labels * Add benchmark for the incremental Ti updating * Remove unnecessary class * Fix bug with the ordering WOOOHOOOOO * Unit tests for the node ordering * Add unit tests for the VF2++ ISO * Fix ordering * Probablly fix logic error in ordering * Reformatted with black * Test precommit * Test precommit * Test pre commit * Testing pre commit * Update networkx/algorithms/isomorphism/tests/VF2++/test_vf2pp.py Co-authored-by: Ross Barnowski <[email protected]> * Add unit tests for vf2++ * Added vf2++ unit test * Added precheck for VF2++ * Add unit tests for the precheck * Updated the benchmarking * Updated the benchmark * Apply hooks * Add documentation for the ordering * Add documentation for the candidate selection * Added documentation for the feasibility * Added documentation for vf2++ * Separate functions for ISO feasibility * Refine unit tests * Apply hooks * Force reformat all files * Remove redundant return statements from VF2__ * Apply hooks * Apply hooks * Format * Minor changes * Add unit tests * Adjusted benchmark * Fix benchmark * Isort * Isort benchmark * Apply optimization in the candidate selection * Track matched node with pointer * Adjust benchmark * Restructure in VF2 function * Make VF2++ EXTREMELY PRETTY * Removed sorting in feasibility rules * Get rid of visited set, check mapping instead * Update networkx/algorithms/isomorphism/tests/VF2++/test_vf2pp.py Co-authored-by: Dan Schult <[email protected]> * Made color assignement deterministic in VF2++ unit tests * Add keyword argument in unit tests * Hoepfully fix pipeline errors * Add vf2++ unit tests for multigraphs * Add Unit tests for Feasibility * Add unit tests for feasibility on multi graphs * Finalize feasibility tests for multigraph settings * Update documentation * Remove list comprehension and boost performance * Add unit tests for both graphs and multi graphs, using same labels * Isort * Optimized precheck * Replace loop with any * Optimize multigraph chceck * Transfer except statement * Check order consistency * Cache degrees and labels from the beginning * Delete benchmark to create new * Fix precheck bug * Adjust unit tests * Add benchmark for perofmance comparison between VF2 and VF2++ * Fix Ti computing tests * Hopefully fix isort * Add benchmark for the candidate selection methods * Rename modules: lower case, remove + * Refactor VF2++ arguments * Adjust VF2++ to work with multiple node labels * Add unit tests for multiple labels * Adjust for different number of labels per node * Finish arguments of VF2++ * Add user functions * Exported the two vf2++ functions * Added underscore prefix to private functions and fixed tests * Update networkx/algorithms/isomorphism/vf2pp.py Co-authored-by: Dan Schult <[email protected]> * Update networkx/algorithms/isomorphism/demo.py Co-authored-by: Dan Schult <[email protected]> * Update networkx/algorithms/isomorphism/vf2pp.py Co-authored-by: Dan Schult <[email protected]> * Apply suggested changes * Refactor rst files * Rm unnecessary toctree from isomorphism page. * Autodoc vf2pp module + public functions. * Rm dedicated vf2pp reference article. * Rm extra vf2pp listing from autosummaries. * Add summary of three functions to module docstring. * Make sure docstrings match their functions. * Refactor everything * Format code * Add unit test * Inline process level function in node ordering * Perform intersection first rather than last * Update networkx/algorithms/isomorphism/vf2pp_helpers/candidates.py Co-authored-by: Dan Schult <[email protected]> * Replace return statement with multiple operations and make it more readable * Update networkx/algorithms/isomorphism/vf2pp_helpers/feasibility.py Co-authored-by: Dan Schult <[email protected]> * Fix multigraph bug in update_Tinout * Abstract the argmax function * Add unit test for first case of candidate selection * Create unit test for all candidate selection cases * Remove re-definition of namedtuple parameters * Update doc/reference/algorithms/isomorphism.rst Co-authored-by: Ross Barnowski <[email protected]> * Update networkx/algorithms/__init__.py Co-authored-by: Ross Barnowski <[email protected]> * Delete benchmark file * Add demo file * Create util file containing the helper functions, common across all unit tests * Fix CI/CD * Make unit tests for Ti updating specific * Remove util functions from vf2pp tests * Remove utils functions from multivf2pp tests * Remove utils functions from candidate tests * Remove utils functions from ordering checks * Remove utils functions from Ti tests * Add example in docstring * Remove unused utils functions * Separate initialization of vf2pp * Inline functions and add new abstract function for pushing to stack * Inline push to stack * Add commentsa * Separate precheck functions * Replace method with existing networkx function * Include label initialization inside parameter initializer function * Rename Tiout to Titilde * Update networkx/algorithms/isomorphism/tests/vf2pp/test_Ti_computing.py Co-authored-by: Ross Barnowski <[email protected]> * Use canonical setitem for dictionary insertions * Update networkx/algorithms/isomorphism/tests/vf2pp/test_precheck.py Co-authored-by: Ross Barnowski <[email protected]> * Remove variable assignement * Merge unit tests of vf2pp for graphs and multigraphs into the same file * Update networkx/algorithms/isomorphism/vf2pp.py Co-authored-by: Ross Barnowski <[email protected]> * Update networkx/algorithms/isomorphism/vf2pp.py Co-authored-by: Ross Barnowski <[email protected]> * Update networkx/algorithms/isomorphism/vf2pp.py Co-authored-by: Ross Barnowski <[email protected]> * Change variable name * Update networkx/algorithms/isomorphism/vf2pp.py Co-authored-by: Ross Barnowski <[email protected]> * Re-write ordering unit tests * Rename vf2pp solver * Update networkx/algorithms/isomorphism/vf2pp_helpers/feasibility.py Co-authored-by: Dan Schult <[email protected]> * Replace abstractified argmax function with two loops for readability * Apply final changes * Fix mistake * Update ref guide to reflect new fn names. * Update docstrings * Fix line length in module docstring * Copy updated parameter section to all 3 public fns. * Add Yields section to all_isomorphisms fn. Co-authored-by: Ross Barnowski <[email protected]> Co-authored-by: Dan Schult <[email protected]>

_consistent_PT

bffcd74649fb95a57fb834846eb3c7d9693c55b8

networkx

feasibility.py

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

Python

def _consistent_PT(u, v, graph_params, state_params): G1, G2 = graph_params.G1, graph_params.G2 mapping, reverse_mapping = state_params.mapping, state_params.reverse_mapping for neighbor in G1[u]: if neighbor in mapping: if G1.number_of_edges(u, neighbor) != G2.number_of_edges( v, mapping[neighbor] ): return False for neighbor in G2[v]: if neighbor in reverse_mapping: if G1.number_of_edges(u, reverse_mapping[neighbor]) != G2.number_of_edges( v, neighbor ): return False return True

erpnext/stock/report/stock_balance/stock_balance.py

def get_variant_values_for(items): attribute_map = {} for attr in frappe.db.sql( % ", ".join(["%s"] * len(items)), tuple(items), as_dict=1, ): attribute_map.setdefault(attr["parent"], {}) attribute_map[attr["parent"]].

style: format code with black

get_variant_values_for

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

stock_balance.py

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

Python

def get_variant_values_for(items): attribute_map = {} for attr in frappe.db.sql( % ", ".join(["%s"] * len(items)), tuple(items), as_dict=1, ): attribute_map.setdefault(attr["parent"], {}) attribute_map[attr["parent"]].update({attr["attribute"]: attr["attribute_value"]}) return attribute_map

wagtail/snippets/tests/test_locking.py

def test_edit_post_locked_by_self(self): # Lock the snippet self.lock_snippet(self.user) # Try to edit the snippet response = self.client.post( self.get_url("edit"), {"text": "Edited while locked"}, follow=True, )

Add tests for locking snippets

test_edit_post_locked_by_self

10dbbddaf35607e4257f50dd960520a1268dd225

wagtail

test_locking.py

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

Python

def test_edit_post_locked_by_self(self): # Lock the snippet self.lock_snippet(self.user) # Try to edit the snippet response = self.client.post( self.get_url("edit"), {"text": "Edited while locked"}, follow=True, ) self.refresh_snippet() # Should not show error message self.assertNotContains( response, f"The {self.model_name} could not be saved as it is locked", ) # Check that the snippet is still locked self.assertTrue(self.snippet.locked) # Check that the snippet is edited self.assertEqual(self.snippet.text, "Edited while locked")

ludwig/models/base.py

def update_metrics(self, targets, predictions):

feat: Added model type GBM (LightGBM tree learner), as an alternative to ECD (#2027)

update_metrics

aa0c63bf2ed825eb3ca8eff8a002d5ccbe395173

ludwig

base.py

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

Python

def update_metrics(self, targets, predictions): for of_name, of_obj in self.output_features.items(): of_obj.update_metrics(targets[of_name], predictions[of_name]) eval_loss, additional_losses = self.eval_loss(targets, predictions) self.eval_loss_metric.update(eval_loss) self.eval_additional_losses_metrics.update(additional_losses)

lib/matplotlib/axis.py

def tick_right(self): label = True if 'label1On' in self._m

DOC: More cleanup axes -> Axes

tick_right

f156db08eee54d285ab0fb4e031e48d078ba6aa3

matplotlib

axis.py

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

Python

def tick_right(self): label = True if 'label1On' in self._major_tick_kw: label = (self._major_tick_kw['label1On'] or self._major_tick_kw['label2On']) self.set_ticks_position('right') # if labels were turned off before this was called # leave them off self.set_tick_params(which='both', labelright=label)

tests/unit/mainwindow/test_messageview.py

def test_show_message_twice(view, info1, info2, count): view.show_message(info1) view.show_message(info2) assert len(view._messages) == count

Only replace the exact same message If we have a error message followed by an info message with the same text, they should both be shown, not replaced automatically.

test_show_message_twice

676e01677183825d19107d3b2fbf1bb2c0684ede

qutebrowser

test_messageview.py

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

Python

def test_show_message_twice(view, info1, info2, count): view.show_message(info1) view.show_message(info2) assert len(view._messages) == count

mmdet/models/task_modules/assigners/iou2d_calculator.py

def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False): bboxes1 = get_box_tensor(bboxes1) bboxes2 = get_box_tensor(bboxes2) assert bboxes1.size(-1) in [0, 4, 5] assert bboxes2.size(-1) in [0, 4, 5] if bboxes2.size(-1) == 5: bboxes2 = bboxes2[..., :4] if bboxes1.size(-1) == 5: bboxes1 = bboxes1[..., :4] if self.dtype == 'fp16': # change tensor type to save cpu and cuda memory and keep speed bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype) bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype) overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) if not overlaps.is_cuda and overlaps.dtype == torch.float16: # resume cpu float32

[Refactor] Refactor anchor head and base head with boxlist (#8625) * Refactor anchor head * Update * Update * Update * Add a series of boxes tools * Fix box type to support n x box_dim boxes * revert box type changes * Add docstring * refactor retina_head * Update * Update * Fix comments * modify docstring of coder and ioucalculator * Replace with_boxlist with use_box_type

__call__

d915740fa8228cf57741b27d9e5d66e358456b8e

mmdetection

iou2d_calculator.py

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

Python

def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False): bboxes1 = get_box_tensor(bboxes1) bboxes2 = get_box_tensor(bboxes2) assert bboxes1.size(-1) in [0, 4, 5] assert bboxes2.size(-1) in [0, 4, 5] if bboxes2.size(-1) == 5: bboxes2 = bboxes2[..., :4] if bboxes1.size(-1) == 5: bboxes1 = bboxes1[..., :4] if self.dtype == 'fp16': # change tensor type to save cpu and cuda memory and keep speed bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype) bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype) overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) if not overlaps.is_cuda and overlaps.dtype == torch.float16: # resume cpu float32 overlaps = overlaps.float() return overlaps return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)

tools/sort/sort_methods_aligned.py

def binning(self) -> List[List[str]]: return self._binning_linear_threshold(multiplier=100)

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

binning

98d01760e469fd2108eed8d0b0a1ba6297c3177c

faceswap

sort_methods_aligned.py

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

Python

def binning(self) -> List[List[str]]: return self._binning_linear_threshold(multiplier=100)

salt/transport/base.py

def connect(self, publish_port, connect_callback=None, disconnect_callback=None): raise NotImpleme

Add NotImplimentedError to stubs

connect

8683fed190f0ac807ab3f87e0e66808f7dbc130c

salt

base.py

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

Python

def connect(self, publish_port, connect_callback=None, disconnect_callback=None): raise NotImplementedError

sympy/combinatorics/free_groups.py

def contains(self, g): if not isinstance(g, FreeGroupElement): return False elif self != g.group: return False else: ret

Updated import locations

contains

498015021131af4dbb07eb110e5badaba8250c7b

sympy

free_groups.py

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

Python

def contains(self, g): if not isinstance(g, FreeGroupElement): return False elif self != g.group: return False else: return True

erpnext/loan_management/doctype/loan_security_unpledge/loan_security_unpledge.py

def get_pledged_security_qty(loan): current_pledges = {} unpledges = frappe._dict( frappe.db.sql( , (loan), ) ) pledges = frappe._dict( frappe.db.sql( , (loan), ) ) for security, qt

style: format code with black

get_pledged_security_qty

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

loan_security_unpledge.py

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

Python

def get_pledged_security_qty(loan): current_pledges = {} unpledges = frappe._dict( frappe.db.sql( , (loan), ) ) pledges = frappe._dict( frappe.db.sql( , (loan), ) ) for security, qty in pledges.items(): current_pledges.setdefault(security, qty) current_pledges[security] -= unpledges.get(security, 0.0) return current_pledges

django/db/models/sql/compiler.py

def get_select(self): select = [] klass_info = None annotations = {} select_idx = 0 for alias, (sql, params) in self.query.extra_select.items(): annotations[alias] = select_idx select.append((RawSQL(sql, params), alias)) select_idx += 1 assert not (self.query.select and self.query.default_cols) if self.query.default_cols: cols = self.get_default_columns() else: # self.query.select is a special case. These columns never go to # any model. cols = self.query.select if cols: select_list = [] for col in cols: select_list.append(select_idx) select.append((col, None)) select_idx += 1 klass_info = { "model": self.query.model, "select_fields": select_list, } for alias, annotation in self.query.annotation_select.items(): annotations[alias] = select_idx select.appe

Refs #33476 -- Reformatted code with Black.

get_select

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

compiler.py

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

Python

def get_select(self): select = [] klass_info = None annotations = {} select_idx = 0 for alias, (sql, params) in self.query.extra_select.items(): annotations[alias] = select_idx select.append((RawSQL(sql, params), alias)) select_idx += 1 assert not (self.query.select and self.query.default_cols) if self.query.default_cols: cols = self.get_default_columns() else: # self.query.select is a special case. These columns never go to # any model. cols = self.query.select if cols: select_list = [] for col in cols: select_list.append(select_idx) select.append((col, None)) select_idx += 1 klass_info = { "model": self.query.model, "select_fields": select_list, } for alias, annotation in self.query.annotation_select.items(): annotations[alias] = select_idx select.append((annotation, alias)) select_idx += 1 if self.query.select_related: related_klass_infos = self.get_related_selections(select) klass_info["related_klass_infos"] = related_klass_infos

PyInstaller/depend/imphookapi.py

def set_module_collection_mode(self, name, mode): if name is None: name = self.__name__ if mode is None: self._module_collection_mode.pop(name) else:

building & hooks: implement module collection mode setting Implement a mechanism for controlling the collection mode of modules and packages, with granularity ranging from top-level packages to individual sub-modules. Therefore, the hooks can now specify whether the hooked package should be collected as byte-compiled .pyc modules into embedded PYZ archive (the default behavior), or as source .py files collected as external data files (without corresponding modules in the PYZ archive). The latter option should let us avoid unnecessary .pyc module collection when the source files are required by the code, or work around the situations where having a .pyc module in PYZ archive causes issues due to FrozenImporter's incompatibility with sys.path manipulation that some packages attempt to perform. This feature adds a new optional global hook variable, called `module_collection_mode`. The value can be either a string ("py" or "pyc") or a dictionary of module names and setting strings. In the case of a string, the setting affects the hooked module or a package, and is applied recursively to all sub-packages and sub-modules, unless another hook overrides it. The dictionary setting allows a hook to specify different settings for the package and it subpackages, or even different settings for other packages. A corresponding `set_module_collection_mode` method has been added to the `hook_api` object for adjusting the collection mode from within the `hook()` function. The `Analysis` object can now also be passed a dictionary via an optional `module_collection_mode` argument; the corresponding settings are applied last, which allows advanced users to both supplement and override the settings made by the hooks.

set_module_collection_mode

5b2ab7067ba954bd7950a79ed31e5ee177ff3f43

pyinstaller

imphookapi.py

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

Python

def set_module_collection_mode(self, name, mode): if name is None: name = self.__name__ if mode is None: self._module_collection_mode.pop(name) else: self._module_collection_mode[name] = mode

python3.10.4/Lib/distutils/file_util.py

def write_file (filename, contents): f = open(filename, "w") try: for line in contents: f.writ

add python 3.10.4 for windows

write_file

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

file_util.py

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

Python

def write_file (filename, contents): f = open(filename, "w") try: for line in contents: f.write(line + "\n") finally: f.close()

tests/orion/schemas/test_core.py

async def test_flow_run_policy_is_backwards_compatible(self): empty_new_policy = schemas.core.FlowRunPolicy() # should not raise an error self.OldFlowRu

Revert breaking schema change (#6521) * Revert breaking schema change * Add new retry properties on policies; deprecate prior ones * Add tests for schema compat * Use root_validator to populate properties from deprecated

test_flow_run_policy_is_backwards_compatible

82c78fe8b65117dc5fe89365acb62e7aa902f8ba

prefect

test_core.py

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

Python

async def test_flow_run_policy_is_backwards_compatible(self): empty_new_policy = schemas.core.FlowRunPolicy() # should not raise an error self.OldFlowRunPolicy(**empty_new_policy.dict())

tests/css/test_help_text.py

def test_help_text_examples_are_contextualized(): rendered_inline = render(spacing_invalid_value("padding", "inline")) assert "widget.styles.padding" in rendered_inline rendered_css = render(spacing_invalid_value("padding", "css")) assert "padding:" in rendered_css

Testing for help text

test_help_text_examples_are_contextualized

91783b7c1e06a45e93fd89dbdb6aa3d1a9c2e990

textual

test_help_text.py

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

Python

def test_help_text_examples_are_contextualized(): rendered_inline = render(spacing_invalid_value("padding", "inline")) assert "widget.styles.padding" in rendered_inline rendered_css = render(spacing_invalid_value("padding", "css")) assert "padding:" in rendered_css

@pytest.mark.parametrize("patch", [True, False]) @pytest.mark.parametrize("connection_strategy", ["eager", "lazy"])

test/mitmproxy/proxy/layers/test_modes.py

def test_reverse_proxy(tctx, keep_host_header): server = Placeholder(Server) tctx.options.mode = "reverse:http://localhost:8000" tctx.options.connection_strategy = "lazy" tctx.options.keep_host_header = keep_host_header assert ( Playbook(modes.ReverseProxy(tctx), hooks=False) >> DataReceived( tctx.client, b"GET /foo HTTP/1.1\r\n" b"Host: example.com\r\n\r\n" ) << NextLayerHook(Placeholder(NextLayer)) >> reply_next_layer(lambda ctx: http.HttpLayer(ctx, HTTPMode.transparent)) << OpenConnection(server) >> reply(None) << SendData( server, b"GET /foo HTTP/1.1\r\n" b"Host: " + (b"example.com" if keep_host_header else b"localhost:8000") + b"\r\n\r\n", ) >> DataReceived(server, b"HTTP/1.1 200 OK\r\nContent-Length: 0\r\n\r\n") << SendData(tctx.client, b"HTTP/1.1 200 OK\r\nContent-Length: 0\r\n\r\n") ) assert server().address == ("localhost", 8000) @pytest.mark.parametrize("patch", [True, False]) @pytest.mark.parametrize("connection_strategy", ["eager", "lazy"])

make it black!

test_reverse_proxy

b3587b52b25077f68116b9852b041d33e7fc6601

mitmproxy

test_modes.py

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

Python

def test_reverse_proxy(tctx, keep_host_header): server = Placeholder(Server) tctx.options.mode = "reverse:http://localhost:8000" tctx.options.connection_strategy = "lazy" tctx.options.keep_host_header = keep_host_header assert ( Playbook(modes.ReverseProxy(tctx), hooks=False) >> DataReceived( tctx.client, b"GET /foo HTTP/1.1\r\n" b"Host: example.com\r\n\r\n" ) << NextLayerHook(Placeholder(NextLayer)) >> reply_next_layer(lambda ctx: http.HttpLayer(ctx, HTTPMode.transparent)) << OpenConnection(server) >> reply(None) << SendData( server, b"GET /foo HTTP/1.1\r\n" b"Host: " + (b"example.com" if keep_host_header else b"localhost:8000") + b"\r\n\r\n", ) >> DataReceived(server, b"HTTP/1.1 200 OK\r\nContent-Length: 0\r\n\r\n") << SendData(tctx.client, b"HTTP/1.1 200 OK\r\nContent-Length: 0\r\n\r\n") ) assert server().address == ("localhost", 8000) @pytest.mark.parametrize("patch", [True, False]) @pytest.mark.parametrize("connection_strategy", ["eager", "lazy"])

django/contrib/admin/checks.py

def _check_filter_horizontal(self, obj): if not isinstance(obj.filter_horizontal, (list, tuple)): return must_be( "a list or tuple", option="filter_horizontal", obj=obj, id="admin.E018" ) else: return list( chain.from_iterable( self._check_filter_item( obj, field_name, "filter_horizontal[%d]" % index

Refs #33476 -- Reformatted code with Black.

_check_filter_horizontal

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

checks.py

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

Python

def _check_filter_horizontal(self, obj): if not isinstance(obj.filter_horizontal, (list, tuple)): return must_be( "a list or tuple", option="filter_horizontal", obj=obj, id="admin.E018" ) else: return list( chain.from_iterable( self._check_filter_item( obj, field_name, "filter_horizontal[%d]" % index ) for index, field_name in enumerate(obj.filter_horizontal) ) )

ppocr/data/imaug/fce_aug.py

def poly_intersection(poly_det, poly_gt): assert isinstance(poly_det, plg.Polygon) assert isinstance(poly_gt, plg.Polygon) poly_inter = poly_det & poly_gt if len(poly_inter) == 0: return 0, poly_in

add fcenet

poly_intersection

9f62b610dea6161627200ed85d92e19b1923279a

PaddleOCR

fce_aug.py

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

Python

def poly_intersection(poly_det, poly_gt): assert isinstance(poly_det, plg.Polygon) assert isinstance(poly_gt, plg.Polygon) poly_inter = poly_det & poly_gt if len(poly_inter) == 0: return 0, poly_inter return poly_inter.area(), poly_inter

mitmproxy/dns.py

def size(self) -> int: return sum(len(x.data) for x in [*self.answers, *self.authorities, *self.additionals])

[dns] first commit

size

8c700ec6e45fc69379eec230da1bd840854ac20e

mitmproxy

dns.py

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

Python

def size(self) -> int: return sum(len(x.data) for x in [*self.answers, *self.authorities, *self.additionals])

dashboard/modules/snapshot/snapshot_head.py

async def get_job_submission_info(self): jobs = {} fetched_jobs = await self._job_info_client.get_all_jobs() for ( job_submission_id, job_info, ) in fetched_jobs.items(): if job_info is not None: entry = { "jo

Convert job_manager to be async (#27123) Updates jobs api Updates snapshot api Updates state api Increases jobs api version to 2 Signed-off-by: Alan Guo [email protected] Why are these changes needed? follow-up for #25902 (comment)

get_job_submission_info

326b5bd1acc6d3d00ab0546e4ae45da6bed501f7

ray

snapshot_head.py

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

Python

async def get_job_submission_info(self): jobs = {} fetched_jobs = await self._job_info_client.get_all_jobs() for ( job_submission_id, job_info, ) in fetched_jobs.items(): if job_info is not None: entry = { "job_submission_id": job_submission_id, "status": job_info.status, "message": job_info.message, "error_type": job_info.error_type, "start_time": job_info.start_time, "end_time": job_info.end_time, "metadata": job_info.metadata, "runtime_env": job_info.runtime_env, "entrypoint": job_info.entrypoint, } jobs[job_submission_id] = entry return jobs

sympy/ntheory/qs.py

def _gen_sieve_array(M, factor_base): sieve_array =

Remove abbreviations in documentation

_gen_sieve_array

65be461082dda54c8748922f9c29a19af1279fe1

sympy

qs.py

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

Python

def _gen_sieve_array(M, factor_base): sieve_array = [0]*(2*M + 1) for factor in factor_base: if factor.soln1 is None: #The prime does not divides a continue for idx in range((M + factor.soln1) % factor.prime, 2*M, factor.prime): sieve_array[idx] += factor.log_p if factor.prime == 2: continue #if prime is 2 then sieve only with soln_1_p for idx in range((M + factor.soln2) % factor.prime, 2*M, factor.prime): sieve_array[idx] += factor.log_p return sieve_array

setup.py

def rescue_docarray(): try: import docarray as docarray __docarray_version__ = docarray.__version__ except AttributeError: # Being here means docarray is not installed correctly, attempt to reinstall it # as recommended by pip https://pip.pypa.io/en/latest/user_guide/#using-pip-from-your-program import subprocess subprocess.check_call( [sys.executable, '-m', 'pip', 'uninstall', '--yes', 'docarray']

fix: rescue docarray in setup (#4203)

rescue_docarray

1f2c86359246e00eae7cba081d9e952cb64c9aea

jina

setup.py

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

Python

def rescue_docarray(): try: import docarray as docarray __docarray_version__ = docarray.__version__ except AttributeError: # Being here means docarray is not installed correctly, attempt to reinstall it # as recommended by pip https://pip.pypa.io/en/latest/user_guide/#using-pip-from-your-program import subprocess subprocess.check_call( [sys.executable, '-m', 'pip', 'uninstall', '--yes', 'docarray'] ) subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'docarray'])

sklearn/kernel_approximation.py

def transform(self, X): msg = ( "%(name)s is not fitted. Call f

DOC Fix docstring for AdditiveChi2Sampler (#22138)

transform

ff85a34c95a9d8de13805be55f1a72f1b7ee2a42

scikit-learn

kernel_approximation.py

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

Python

def transform(self, X): msg = ( "%(name)s is not fitted. Call fit to set the parameters before" " calling transform" ) check_is_fitted(self, msg=msg) X = self._validate_data(X, accept_sparse="csr", reset=False) check_non_negative(X, "X in AdditiveChi2Sampler.transform") sparse = sp.issparse(X) # zeroth component # 1/cosh = sech # cosh(0) = 1.0 transf = self._transform_sparse if sparse else self._transform_dense return transf(X)

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

def get_repository_root(cls, location): # type: (str) -> Optional[str] if cls.is_repository_directory(location): return location return None

upd; format

get_repository_root

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

versioncontrol.py

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

Python

def get_repository_root(cls, location): # type: (str) -> Optional[str] if cls.is_repository_directory(location): return location return None

airbyte-integrations/connectors/source-hubspot/unit_tests/test_client.py

def test_it_should_not_read_quotes_stream_if_it_does_not_exist_in_client(oauth_config, configured_catalog): source = SourceHubspot() all_records = list(source.read(logger, config=oauth_config, catalog=configured_catalog, state=None)) records = [record for record in all_records if reco

Source Hubspot: fix "quotes" key error exception (#10055) * check if stream exists in source * check if stream exists in source, added comment * test skipping reading quotes stream * format code * airbyte-cdk version * added __init__.py to unit_tests * fix importing airbyte models * bump the version * update spec and def yamls Co-authored-by: auganbay <[email protected]>

test_it_should_not_read_quotes_stream_if_it_does_not_exist_in_client

b22efc03a18c5545c12cf8a0462dea7505aec410

airbyte

test_client.py

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

Python

def test_it_should_not_read_quotes_stream_if_it_does_not_exist_in_client(oauth_config, configured_catalog): source = SourceHubspot() all_records = list(source.read(logger, config=oauth_config, catalog=configured_catalog, state=None)) records = [record for record in all_records if record.type == Type.RECORD] assert not records

python/ray/_private/utils.py

def get_conda_env_dir(env_name): conda_prefix = os.environ.get("CONDA_PREFIX") if conda_prefix is None: # The caller is neither in a conda env or in (base) env. This is rare # because by default, new terminals start in (base), but we can still # support this case. conda_exe = os.environ.get("CONDA_EXE") if conda_exe is None: raise ValueError( "Cannot find environment variables set by conda. " "Please verify conda is installed." ) # Example: CONDA_EXE=$HOME/anaconda3/bin/python # Strip out /bin/python by going up two parent directories. conda_prefix = str(Path(conda_exe).parent.parent) # There are two cases: # 1. We are in a conda (base) env: CONDA_DEFAULT_ENV=base and # CONDA_PREFIX=$HOME/anaconda3 # 2. We are in a user-created conda env: CONDA_DEFAULT_ENV=$env_name and # CONDA_PREFIX=$HOME/anaconda3/envs/$current_env_name if os.environ.get("CONDA_DEFAULT_ENV") == "base": # Caller's curent environment is (base). # Not recommended by conda, but we can still support it. if env_name == "base": # Desired environment is (base), located at e.g. $HOME/anaconda3 env_dir = conda_prefix else: # Des

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

get_conda_env_dir

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

utils.py

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

Python

def get_conda_env_dir(env_name): conda_prefix = os.environ.get("CONDA_PREFIX") if conda_prefix is None: # The caller is neither in a conda env or in (base) env. This is rare # because by default, new terminals start in (base), but we can still # support this case. conda_exe = os.environ.get("CONDA_EXE") if conda_exe is None: raise ValueError( "Cannot find environment variables set by conda. " "Please verify conda is installed." ) # Example: CONDA_EXE=$HOME/anaconda3/bin/python # Strip out /bin/python by going up two parent directories. conda_prefix = str(Path(conda_exe).parent.parent) # There are two cases: # 1. We are in a conda (base) env: CONDA_DEFAULT_ENV=base and # CONDA_PREFIX=$HOME/anaconda3 # 2. We are in a user-created conda env: CONDA_DEFAULT_ENV=$env_name and # CONDA_PREFIX=$HOME/anaconda3/envs/$current_env_name if os.environ.get("CONDA_DEFAULT_ENV") == "base": # Caller's curent environment is (base). # Not recommended by conda, but we can still support it. if env_name == "base": # Desired environment is (base), located at e.g. $HOME/anaconda3 env_dir = conda_prefix else: # Desired environment is user-created, e.g. # $HOME/anaconda3/envs/$env_name env_dir = os.path.join(conda_prefix, "envs", env_name) else: # Now `conda_prefix` should be something like # $HOME/anaconda3/envs/$current_env_name # We want to replace the last component with the desired env name. conda_envs_dir = os.path.split(conda_prefix)[0] env_dir = os.path.join(conda_envs_dir, env_name) if not os.path.isdir(env_dir): raise ValueError( "conda env " + env_name + " not found in conda envs directory. Run `conda env list` to " + "verify the name is correct." ) return env_dir

tests/components/homekit_controller/test_switch.py

async def test_switch_change_outlet_state(hass, utcnow): helper = await setup_test_component(hass, create_switch_service) await hass.services.async_call( "switch", "turn_on", {"entity_id": "switch.testdevice"}, blocking=True ) helper.async_assert_service_values( ServicesTypes.OUTLET, { CharacteristicsTypes.ON: 1, }, ) await hass.services.async_call( "switch", "turn_off", {"entity_id": "switch.testdevice"}, blocking=True ) helper.async_assert_service_values( ServicesTypes.OUTLET, {

Improve homekit_controller tests (#65266)

test_switch_change_outlet_state

58b8c30221a6f6e5acbbe98b7e3298b03fb741f5

core

test_switch.py

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

Python

async def test_switch_change_outlet_state(hass, utcnow): helper = await setup_test_component(hass, create_switch_service) await hass.services.async_call( "switch", "turn_on", {"entity_id": "switch.testdevice"}, blocking=True ) helper.async_assert_service_values( ServicesTypes.OUTLET, { CharacteristicsTypes.ON: 1, }, ) await hass.services.async_call( "switch", "turn_off", {"entity_id": "switch.testdevice"}, blocking=True ) helper.async_assert_service_values( ServicesTypes.OUTLET, { CharacteristicsTypes.ON: 0, }, )

lib/matplotlib/axis.py

def _update_label_position(self, renderer): if not self._autolabelpos: return # get bounding boxes for this axis and any siblings # that have been set by `fig.align_xlabels()` bboxes, bboxes2 = self._get_tick_boxes_siblings(renderer=renderer) x, y = self.label.get_position() if self.label_position == 'bottom': try: spine = self.axes.spines['bottom'] spinebbox = spine.get_window_extent() except KeyError: # use axes if spine doesn't

FIX: use window_extent instead

_update_label_position

c0a78bdff86d7b02b8a23d373b64c72297f935d5

matplotlib

axis.py

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

Python

def _update_label_position(self, renderer): if not self._autolabelpos: return # get bounding boxes for this axis and any siblings # that have been set by `fig.align_xlabels()` bboxes, bboxes2 = self._get_tick_boxes_siblings(renderer=renderer) x, y = self.label.get_position() if self.label_position == 'bottom': try: spine = self.axes.spines['bottom'] spinebbox = spine.get_window_extent() except KeyError: # use axes if spine doesn't exist spinebbox = self.axes.bbox bbox = mtransforms.Bbox.union(bboxes + [spinebbox]) bottom = bbox.y0 self.label.set_position( (x, bottom - self.labelpad * self.figure.dpi / 72) ) else: try: spine = self.axes.spines['top'] spinebbox = spine.get_window_extent() except KeyError: # use axes if spine doesn't exist spinebbox = self.axes.bbox bbox = mtransforms.Bbox.union(bboxes2 + [spinebbox]) top = bbox.y1 self.label.set_position( (x, top + self.labelpad * self.figure.dpi / 72) )

ppocr/postprocess/fce_postprocess.py

def poly_union(poly_det, poly_gt): assert isinstance(poly_det, plg.Polygon) assert isinstance(poly_gt, plg.Polygon) area_det = poly_det.area() area_gt = poly_gt.area() area_inters, _ = poly_intersection(poly_det, poly_gt) return area_det + area_gt - area_inters

add fcenet

poly_union

9f62b610dea6161627200ed85d92e19b1923279a

PaddleOCR

fce_postprocess.py

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

Python

def poly_union(poly_det, poly_gt): assert isinstance(poly_det, plg.Polygon) assert isinstance(poly_gt, plg.Polygon) area_det = poly_det.area() area_gt = poly_gt.area() area_inters, _ = poly_intersection(poly_det, poly_gt) return area_det + area_gt - area_inters

tests/config/test_tls.py

def test_whitelist_idna_result(self) -> None: config: JsonDict = { "federation_certificate_verification_whitelist": [ "example.com", "*.xn--eckwd4c7c.xn--zckzah", ] } t = TestConfig() t.tls.read_

Add missing type hints to tests.config. (#14681)

test_whitelist_idna_result

3aeca2588b79111a48a6083c88efc4d68a2cea19

synapse

test_tls.py

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

Python

def test_whitelist_idna_result(self) -> None: config: JsonDict = { "federation_certificate_verification_whitelist": [ "example.com", "*.xn--eckwd4c7c.xn--zckzah", ] } t = TestConfig() t.tls.read_config(config, config_dir_path="", data_dir_path="") cf = FederationPolicyForHTTPS(cast(HomeServerConfig, t)) # Not in the whitelist opts = cf.get_options(b"notexample.com") assert isinstance(opts, SSLClientConnectionCreator) self.assertTrue(opts._verifier._verify_certs) # Caught by the wildcard opts = cf.get_options(idna.encode("テスト.ドメイン.テスト")) assert isinstance(opts, SSLClientConnectionCreator) self.assertFalse(opts._verifier._verify_certs)

mmdet/models/dense_heads/dense_test_mixins.py

def simple_test_rpn(self, x, img_metas): rpn_outs = self(x) proposal_list = self.get_results(*rpn_outs, img_metas=img_metas) r

[Refactor] Refactor dense head outputs to InstanceResults.

simple_test_rpn

9a3bf7660e6ced54672741095f96df07919f9ba7

mmdetection

dense_test_mixins.py

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

Python

def simple_test_rpn(self, x, img_metas): rpn_outs = self(x) proposal_list = self.get_results(*rpn_outs, img_metas=img_metas) return proposal_list

lib/matplotlib/axis.py

def _get_tick_boxes_siblings(self, renderer): # Get the Grouper keeping track of x or y label groups for this figure. axis_names = [ name for name, axis in self.axes._get_axis_map().items() if name in self.figure._align_label_groups and axis is self] if len(axis_names) != 1: return

DOC: More cleanup axes -> Axes

_get_tick_boxes_siblings

f156db08eee54d285ab0fb4e031e48d078ba6aa3

matplotlib

axis.py

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

Python

def _get_tick_boxes_siblings(self, renderer): # Get the Grouper keeping track of x or y label groups for this figure. axis_names = [ name for name, axis in self.axes._get_axis_map().items() if name in self.figure._align_label_groups and axis is self] if len(axis_names) != 1: return [], [] axis_name, = axis_names grouper = self.figure._align_label_groups[axis_name] bboxes = [] bboxes2 = [] # If we want to align labels from other Axes: for ax in grouper.get_siblings(self.axes): axis = getattr(ax, f"{axis_name}axis") ticks_to_draw = axis._update_ticks() tlb, tlb2 = axis._get_ticklabel_bboxes(ticks_to_draw, renderer) bboxes.extend(tlb) bboxes2.extend(tlb2) return bboxes, bboxes2

keras/utils/generic_utils.py

def default(method): method._is_de

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

default

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

generic_utils.py

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

Python

def default(method): method._is_default = True # pylint: disable=protected-access return method

lib/tests/streamlit/camera_input_test.py

def test_help_tooltip(self): st.camera_input("the label", help="help_label") c = self.get_delta_from_queue().new_element.camera_input self.assert

Feature/camera image input (#4038) * Camera_input widget Co-authored-by: willhuang1997 <[email protected]> Co-authored-by: Henrikh Kantuni <[email protected]> Co-authored-by: William Huang <[email protected]> Co-authored-by: Vincent Donato <[email protected]>

test_help_tooltip

33855278eaf8599b2bec1ddefa5eebb592e55e25

streamlit

camera_input_test.py

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

Python

def test_help_tooltip(self): st.camera_input("the label", help="help_label") c = self.get_delta_from_queue().new_element.camera_input self.assertEqual(c.help, "help_label")

tests/components/nest/test_camera_sdm.py

async def test_multiple_event_images(hass, auth): subscriber = await async_setup_camera(hass, DEVICE_TRAITS, auth=auth) assert len(hass.states.async_all()) == 1 assert hass.states.get("camera.my_camera") event_timestamp = utcnow() await subscriber.async_receive_event( make_motion_event(event_session_id="event-session-1", timestamp=event_timestamp) ) await hass.async_block_till_done() auth.responses = [ # Fake response from API that returns url image aiohttp.web.json_response(GENERATE_IMAGE_URL_RESPONSE), # Fake response for the image content fetch aiohttp.web.Response(body=IMAGE_BYTES_FROM_EVENT), # Image is refetched after being cleared by expiration alarm aiohttp.web.json_response(GENERATE_IMAGE_URL_RESPONSE), aiohttp.web.Response(body=b"updated image bytes"), ] image = await async_get_image(hass) assert image.content == IMAGE_BYTES_FROM_EVENT next_event_timestamp = event_timestamp + datetime.timedelta(seconds=25) await subscriber.async_receive_ev

Delete nest event image fetching and use same APIs as media player (#62789)

test_multiple_event_images

4203e1b0640e16fbda55672c5be089431face880

core

test_camera_sdm.py

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

Python

async def test_multiple_event_images(hass, auth): subscriber = await async_setup_camera(hass, DEVICE_TRAITS, auth=auth) assert len(hass.states.async_all()) == 1 assert hass.states.get("camera.my_camera") event_timestamp = utcnow() await subscriber.async_receive_event( make_motion_event(event_session_id="event-session-1", timestamp=event_timestamp) ) await hass.async_block_till_done() auth.responses = [ # Fake response from API that returns url image aiohttp.web.json_response(GENERATE_IMAGE_URL_RESPONSE), # Fake response for the image content fetch aiohttp.web.Response(body=IMAGE_BYTES_FROM_EVENT), # Image is refetched after being cleared by expiration alarm aiohttp.web.json_response(GENERATE_IMAGE_URL_RESPONSE), aiohttp.web.Response(body=b"updated image bytes"), ] image = await async_get_image(hass) assert image.content == IMAGE_BYTES_FROM_EVENT next_event_timestamp = event_timestamp + datetime.timedelta(seconds=25) await subscriber.async_receive_event( make_motion_event( event_id="updated-event-id", event_session_id="event-session-2", timestamp=next_event_timestamp, ) ) await hass.async_block_till_done() image = await async_get_image(hass) assert image.content == b"updated image bytes"

tests/rest/admin/test_room.py

def test_delete_same_room_twice(self) -> None: body = {"new_room_user_id": self.admin_user} # first call to delete room # and do not wait for finish the task first_channel = self.make_request( "DELETE", self.url.encode("ascii"), content=body, access_token=self.admin_user_tok, await_result=False, ) # second call to delete room second_channel = self.make_request( "DELETE", self.url.encode("ascii"), content=body, access_token=s

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

test_delete_same_room_twice

c97042f7eef3748e17c90e48a4122389a89c4735

synapse

test_room.py

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

Python

def test_delete_same_room_twice(self) -> None: body = {"new_room_user_id": self.admin_user} # first call to delete room # and do not wait for finish the task first_channel = self.make_request( "DELETE", self.url.encode("ascii"), content=body, access_token=self.admin_user_tok, await_result=False, ) # second call to delete room second_channel = self.make_request( "DELETE", self.url.encode("ascii"), content=body, access_token=self.admin_user_tok, ) self.assertEqual( HTTPStatus.BAD_REQUEST, second_channel.code, msg=second_channel.json_body ) self.assertEqual(Codes.UNKNOWN, second_channel.json_body["errcode"]) self.assertEqual( f"History purge already in progress for {self.room_id}", second_channel.json_body["error"], ) # get result of first call first_channel.await_result() self.assertEqual(200, first_channel.code, msg=first_channel.json_body) self.assertIn("delete_id", first_channel.json_body) # check status after finish the task self._test_result( first_channel.json_body["delete_id"], self.other_user, expect_new_room=True, )

homeassistant/components/august/__init__.py

async def _async_refresh_device_detail_by_ids(self, device_ids_list): for device_id in device_ids_list: try: await self._async_refresh_device_detail_by_id(device_id) except asyncio.TimeoutError: _LOGGER.warning( "Timed out calling august api during refresh of device: %s", device_id, ) except (ClientResponseError, CannotConnect) as err: _LOGGER.warning( "Error from august api during refres

Improve reliability of august setup with recent api changes (#65314)

_async_refresh_device_detail_by_ids

fab9c4aa20b4c2549691d0aa5066798a0259e803

core

__init__.py

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

Python

async def _async_refresh_device_detail_by_ids(self, device_ids_list): for device_id in device_ids_list: try: await self._async_refresh_device_detail_by_id(device_id) except asyncio.TimeoutError: _LOGGER.warning( "Timed out calling august api during refresh of device: %s", device_id, ) except (ClientResponseError, CannotConnect) as err: _LOGGER.warning( "Error from august api during refresh of device: %s", device_id, exc_info=err, )

src/transformers/models/yolos/feature_extraction_yolos.py

def masks_to_boxes(masks): if masks.size == 0: return np.zeros((0, 4)) h, w = masks.shape[-2:] y = np.arange(0, h, dtype=np.float32) x = np.arange(0, w, dtype=np.float32) # see https://github.com/pytorch/pytorch/issues/50276 y, x = np.meshgrid(y, x, indexing="ij") x_mask = masks * np.expand_dims(x, axis=0) x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1) x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool))) x_min = x.filled(fill_value=1e8) x_min = x_min.reshape(x_min.shape[0], -1).min(-1) y_mask = masks * np.expand_dims(y, axis=0) y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1) y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool))) y_min = y.filled(fill_value=1e8) y_min = y_min.reshape(y_min.shape[0], -1).min(-1)

Add YOLOS (#16848) * First draft * Add YolosForObjectDetection * Make forward pass work * Add mid position embeddings * Add interpolation of position encodings * Add expected values * Add YOLOS to tests * Add integration test * Support tiny model as well * Support all models in conversion script * Remove mid_pe_size attribute * Make more tests pass * Add model to README and fix config * Add copied from statements * Rename base_model_prefix to vit * Add missing YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP * Apply suggestions from code review * Apply more suggestions from code review * Convert remaining checkpoints * Improve docstrings * Add YolosFeatureExtractor * Add feature extractor to docs * Add corresponding tests * Fix style * Fix docs * Apply suggestion from code review * Fix bad rebase * Fix some more bad rebase * Fix missing character * Improve docs and variable names Co-authored-by: Niels Rogge <[email protected]>

masks_to_boxes

1ac698744c4dbdf1495d303246d08ffacdf4f5b8

transformers

feature_extraction_yolos.py

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

Python

def masks_to_boxes(masks): if masks.size == 0: return np.zeros((0, 4)) h, w = masks.shape[-2:] y = np.arange(0, h, dtype=np.float32) x = np.arange(0, w, dtype=np.float32) # see https://github.com/pytorch/pytorch/issues/50276 y, x = np.meshgrid(y, x, indexing="ij") x_mask = masks * np.expand_dims(x, axis=0) x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1) x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool))) x_min = x.filled(fill_value=1e8) x_min = x_min.reshape(x_min.shape[0], -1).min(-1) y_mask = masks * np.expand_dims(y, axis=0) y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1) y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool))) y_min = y.filled(fill_value=1e8) y_min = y_min.reshape(y_min.shape[0], -1).min(-1) return np.stack([x_min, y_min, x_max, y_max], 1) # Copied from transformers.models.detr.feature_extraction_detr.rgb_to_id

@_wraps(np.roots, lax_description="""\ Unlike the numpy version of this function, the JAX version returns the roots in a complex array regardless of the values of the roots. Additionally, the jax version of this function adds the ``strip_zeros`` function which must be set to False for the function to be compatible with JIT and other JAX transformations. With ``strip_zeros=False``, if your coefficients have leading zeros, the roots will be padded with NaN values: >>> coeffs = jnp.array([0, 1, 2]) # The default behavior matches numpy and strips leading zeros: >>> jnp.roots(coeffs) DeviceArray([-2.+0.j], dtype=complex64) # With strip_zeros=False, extra roots are set to NaN: >>> jnp.roots(coeffs, strip_zeros=False) DeviceArray([-2. +0.j, nan+nanj], dtype=complex64) """, extra_params=""" strip_zeros : bool, default=True If set to True, then leading zeros in the coefficients will be stripped, similar to :func:`numpy.roots`. If set to False, leading zeros will not be stripped, and undefined roots will be represented by NaN values in the function output. ``strip_zeros`` must be set to ``False`` for the function to be compatible with :func:`jax.jit` and other JAX transformations. """)

jax/_src/numpy/polynomial.py

def _roots_with_zeros(p, num_leading_zeros): # Avoid lapack errors when p is all zero p = _where(len(p) == num_leading_zeros, 1.0, p) # Roll any leading zeros to the end & compute the roots roots = _roots_no_zeros(roll(p, -num_leading_zeros)) # Sort zero roots to the end. roots = lax.sort_key_val(roots == 0, roots)[1] # Set roots associated with num_leading_zeros to NaN

jnp.roots: better support for computation under JIT

_roots_with_zeros

f6476f7a03f8390627c1a8e2a2ec8702d8a320e5

jax

polynomial.py

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

Python

def _roots_with_zeros(p, num_leading_zeros): # Avoid lapack errors when p is all zero p = _where(len(p) == num_leading_zeros, 1.0, p) # Roll any leading zeros to the end & compute the roots roots = _roots_no_zeros(roll(p, -num_leading_zeros)) # Sort zero roots to the end. roots = lax.sort_key_val(roots == 0, roots)[1] # Set roots associated with num_leading_zeros to NaN return _where(arange(roots.size) < roots.size - num_leading_zeros, roots, complex(np.nan, np.nan)) @_wraps(np.roots, lax_description=, extra_params=)

jina/orchestrate/flow/base.py

def port_monitoring(self) -> int: if GATEWAY_NAME in self._deployment_nodes: return self[GATEWAY_NAME].args.port_monitoring else: return self._common_kwargs.get( 'port_monitoring', __default_port_monitoring__ )

feat: expose prometheus metrics (#4526)

port_monitoring

8dc2999a588c46deca60b3f0d5c1b6278a6e165c

jina

base.py

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

Python

def port_monitoring(self) -> int: if GATEWAY_NAME in self._deployment_nodes: return self[GATEWAY_NAME].args.port_monitoring else: return self._common_kwargs.get( 'port_monitoring', __default_port_monitoring__ )

django/utils/module_loading.py

def autodiscover_modules(*args, **kwargs): from django.apps import apps register_to = kwargs.get("register_to") for app_config in apps.get_app_configs(): for module_to_search in args: # Attempt to import the app's module. try: if register_to: before_import_registry = copy.copy(register_to._registry) import_module("%s.%s" % (app_config.name, module_to_search)) except Exception: # Reset the registry to the state before the last import # as this import will have to reoccur on the next request and # this could raise NotRegistered and AlreadyRegistered # exceptions (see #8245). if register_to: register_to._registry = before_import_registry # Decide whether to bubble up this error. If the app just # doesn't have the module in

Refs #33476 -- Reformatted code with Black.

autodiscover_modules

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

module_loading.py

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

Python

def autodiscover_modules(*args, **kwargs): from django.apps import apps register_to = kwargs.get("register_to") for app_config in apps.get_app_configs(): for module_to_search in args: # Attempt to import the app's module. try: if register_to: before_import_registry = copy.copy(register_to._registry) import_module("%s.%s" % (app_config.name, module_to_search)) except Exception: # Reset the registry to the state before the last import # as this import will have to reoccur on the next request and # this could raise NotRegistered and AlreadyRegistered # exceptions (see #8245). if register_to: register_to._registry = before_import_registry # Decide whether to bubble up this error. If the app just # doesn't have the module in question, we can ignore the error # attempting to import it, otherwise we want it to bubble up. if module_has_submodule(app_config.module, module_to_search): raise

python3.10.4/Lib/asyncio/tasks.py

def ensure_future(coro_or_future, *, loop=None): return _ensure_future(coro_or_future, loop=loop)

add python 3.10.4 for windows

ensure_future

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

tasks.py

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

Python

def ensure_future(coro_or_future, *, loop=None): return _ensure_future(coro_or_future, loop=loop)

src/PIL/Image.py

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

Allow rawmode None to return the palette in the current mode

getpalette

6be87277f71948bc7e4b945c46660cac3e5ce919

Pillow

Image.py

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

Python

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))

jina/types/request/data.py

def last_executor(self): if len(self.proto_wo

feat: pass `docs_map` to Executor (#5366)

last_executor

ad96553b064b9c17d626f6fcb78e4a45987be2c3

jina

data.py

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

Python

def last_executor(self): if len(self.proto_wo_data.routes) > 0: return self.proto_wo_data.routes[-1].executor

keras/tests/temporal_sample_weights_correctness_test.py

def custom_generator_multi_io_temporal(self, sample_weights=None): batch_size = 3 num_samples = 3 iteration = 0 while True: batch_index = iteration * batch_size % num_samples iteration += 1 start = batch_index end = start + batch_size x = [self.x[start:end], self.x[start:end]] y = [self.y1[start:end], self.y2[start:end]] if sample_weights: sw = tf.nest.map_structure( lambda w: w[start:end], sample_weights ) else:

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

custom_generator_multi_io_temporal

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

temporal_sample_weights_correctness_test.py

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

Python

def custom_generator_multi_io_temporal(self, sample_weights=None): batch_size = 3 num_samples = 3 iteration = 0 while True: batch_index = iteration * batch_size % num_samples iteration += 1 start = batch_index end = start + batch_size x = [self.x[start:end], self.x[start:end]] y = [self.y1[start:end], self.y2[start:end]] if sample_weights: sw = tf.nest.map_structure( lambda w: w[start:end], sample_weights ) else: sw = None yield x, y, sw

test/lib/ansible_test/_util/controller/sanity/validate-modules/validate_modules/main.py

def _just_docs(self): try: for child in self.ast.body: if not isinstance(child, as

ansible-test - Allow docstring in docs-only module

_just_docs

5b3557f8ba5c176eb7d2de21b3a4da3dcab3bada

ansible

main.py

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

Python

def _just_docs(self): try: for child in self.ast.body: if not isinstance(child, ast.Assign): # allow string constant expressions (these are docstrings) if isinstance(child, ast.Expr) and isinstance(child.value, ast.Constant) and isinstance(child.value.value, str): continue # allowed from __future__ imports if isinstance(child, ast.ImportFrom) and child.module == '__future__': for future_import in child.names: if future_import.name not in self.ACCEPTLIST_FUTURE_IMPORTS: break else: continue return False return True except AttributeError: return False

zerver/openapi/openapi.py

def response_validator(self) -> RequestValidator: self.check_reload() assert s

openapi: Use openapi_core ResponseValidator to validate responses. Signed-off-by: Anders Kaseorg <[email protected]>

response_validator

031f4596ab1737a237b1c099e792fe627a937ff7

zulip

openapi.py

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

Python

def response_validator(self) -> RequestValidator: self.check_reload() assert self._response_validator is not None return self._response_validator

@_wraps(np.polyfit, lax_description=_POLYFIT_DOC) @partial(jit, static_argnames=('deg', 'rcond', 'full', 'cov'))

jax/_src/numpy/polynomial.py

def roots(p, *, strip_zeros=True): # ported from https://github.com/numpy/numpy/blob/v1.17.0/numpy/lib/polynomial.py#L168-L251 p = atleast_1d(p) if p.ndim != 1: raise Valu

lax_numpy: move poly functions into numpy.polynomial

roots

603bb3c5ca288674579211e64fa47c6b2b0fb7a6

jax

polynomial.py

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

Python

def roots(p, *, strip_zeros=True): # ported from https://github.com/numpy/numpy/blob/v1.17.0/numpy/lib/polynomial.py#L168-L251 p = atleast_1d(p) if p.ndim != 1: raise ValueError("Input must be a rank-1 array.") # strip_zeros=False is unsafe because leading zeros aren't removed if not strip_zeros: if p.size > 1: return _roots_no_zeros(p) else: return array([]) if all(p == 0): return array([]) # factor out trivial roots start, end = _nonzero_range(p) # number of trailing zeros = number of roots at 0 trailing_zeros = p.size - end # strip leading and trailing zeros p = p[start:end] if p.size < 2: return zeros(trailing_zeros, p.dtype) else: roots = _roots_no_zeros(p) # combine roots and zero roots roots = hstack((roots, zeros(trailing_zeros, p.dtype))) return roots _POLYFIT_DOC = @_wraps(np.polyfit, lax_description=_POLYFIT_DOC) @partial(jit, static_argnames=('deg', 'rcond', 'full', 'cov'))

python/ray/tune/trainable.py

def _storage_path(self, local_path): rel_local_path =

[tune] only sync up and sync down checkpoint folder for cloud checkpoint. (#21658) By default, ~/ray_results/exp_name/trial_name/checkpoint_name. Instead of the whole trial checkpoint (~/ray_results/exp_name/trial_name/) directory. Stuff like progress.csv, result.json, params.pkl, params.json, events.out etc are coming from driver process. This could also enable us to de-couple sync up and delete - they don't have to wait for each other to finish.

_storage_path

0abcd5eea529fc84c4398620f2808087e4d8c6b6

ray

trainable.py

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

Python

def _storage_path(self, local_path): rel_local_path = os.path.relpath(local_path, self.logdir) return os.path.join(self.remote_checkpoint_dir, rel_local_path)

reconstruction/ostec/external/stylegan2/dnnlib/submission/run_context.py

def get_time_since_last_update(self) -> float: return time.time() - self.last_upda

initialize ostec

get_time_since_last_update

7375ee364e0df2a417f92593e09557f1b2a3575a

insightface

run_context.py

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

Python

def get_time_since_last_update(self) -> float: return time.time() - self.last_update_time

reconstruction/ostec/external/graphonomy/FaceHairMask/deeplab_xception.py

def train_fixbn(self, mode=True, freeze_bn=True, freeze_bn_affine=False): r super(DeepLabv3_plus, self).train(mode) if freeze_bn: print("Freezing Mean/Var of BatchNorm2D.") if freeze_bn_affine: print("Freezing Weight/Bias of BatchNorm2D.") if freeze_bn: for m in self.xception_features.modules(): if isinstance(m, nn.BatchNorm2d): m.eval() if freeze_bn_affine: m.weight.requires_grad = False m.bias.requires_grad = False # for m in self.aspp1.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.aspp2.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.aspp3.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.aspp4.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.global_avg_pool.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.

Graphonomy Face/Hair Segmentation added

train_fixbn

2e5d23ee0e7fc1fdd7ad2e615fd651655aeb0f5b

insightface

deeplab_xception.py

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

Python

def train_fixbn(self, mode=True, freeze_bn=True, freeze_bn_affine=False): r super(DeepLabv3_plus, self).train(mode) if freeze_bn: print("Freezing Mean/Var of BatchNorm2D.") if freeze_bn_affine: print("Freezing Weight/Bias of BatchNorm2D.") if freeze_bn: for m in self.xception_features.modules(): if isinstance(m, nn.BatchNorm2d): m.eval() if freeze_bn_affine: m.weight.requires_grad = False m.bias.requires_grad = False # for m in self.aspp1.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.aspp2.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.aspp3.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.aspp4.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.global_avg_pool.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.concat_projection_bn1.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False # for m in self.feature_projection_bn1.modules(): # if isinstance(m, nn.BatchNorm2d): # m.eval() # if freeze_bn_affine: # m.weight.requires_grad = False # m.bias.requires_grad = False

tests/test_models/test_backbones/test_resnet.py

def assert_params_all_zeros(module) -> bool: weight_data = module.weight.data is_weight_zero = weight_data.allclose( weight_data.new_zeros(weight_data.size())) if hasattr(module, 'bias') and module.bias is not None: bias_data = module.bias.data is_bias_zero = bias_data.allclose( bias_data.new_zeros(bias_data.size())) else: is_bias_zero = True ret

[Fix] Fix UT and remove delete mmcv ops. (#8623) * Remove get_root_logger * Fix UT * Update

assert_params_all_zeros

73a12e6508d4ba0331b84b1313027a511ba26fe3

mmdetection

test_resnet.py

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

Python

def assert_params_all_zeros(module) -> bool: weight_data = module.weight.data is_weight_zero = weight_data.allclose( weight_data.new_zeros(weight_data.size())) if hasattr(module, 'bias') and module.bias is not None: bias_data = module.bias.data is_bias_zero = bias_data.allclose( bias_data.new_zeros(bias_data.size())) else: is_bias_zero = True return is_weight_zero and is_bias_zero

scapy/contrib/automotive/scanner/executor.py

def cleanup_state(self): # type: () -> None for f in self.cleanup_functions: if not callable(f): continue try: if not f(self.socket, self.configuration): log_automotive.info(

Add Automotive Logger for all debug outputs of the automotive layer

cleanup_state

495b21f2867e48286767085c8cf2918e4092e9dc

scapy

executor.py

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

Python

def cleanup_state(self): # type: () -> None for f in self.cleanup_functions: if not callable(f): continue try: if not f(self.socket, self.configuration): log_automotive.info( "Cleanup function %s failed", repr(f)) except (OSError, ValueError, Scapy_Exception) as e: log_automotive.critical("Exception during cleanup: %s", e) self.cleanup_functions = list()

wagtail/contrib/modeladmin/tests/test_simple_modeladmin.py

def test_model_with_two_tabbed_panels_only(self): Publisher.settings_panels = [FieldPanel("name")] Publisher.promote_panels = [FieldPanel("headquartered_in")] warning_1 = checks.Warning( "Publisher.promo

Reformat with black

test_model_with_two_tabbed_panels_only

d10f15e55806c6944827d801cd9c2d53f5da4186

wagtail

test_simple_modeladmin.py

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

Python

def test_model_with_two_tabbed_panels_only(self): Publisher.settings_panels = [FieldPanel("name")] Publisher.promote_panels = [FieldPanel("headquartered_in")] warning_1 = checks.Warning( "Publisher.promote_panels will have no effect on modeladmin editing", hint=, obj=Publisher, id="wagtailadmin.W002", ) warning_2 = checks.Warning( "Publisher.settings_panels will have no effect on modeladmin editing", hint=, obj=Publisher, id="wagtailadmin.W002", ) checks_results = self.get_checks_result() self.assertIn(warning_1, checks_results) self.assertIn(warning_2, checks_results) # clean up for future checks delattr(Publisher, "settings_panels") delattr(Publisher, "promote_panels")

keras/layers/serialization.py

def get_builtin_layer(class_name): if not hasattr(LOCAL, "ALL_OBJECTS"): populate_deserializable_objects() return L

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

get_builtin_layer

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

serialization.py

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

Python

def get_builtin_layer(class_name): if not hasattr(LOCAL, "ALL_OBJECTS"): populate_deserializable_objects() return LOCAL.ALL_OBJECTS.get(class_name)

sympy/combinatorics/perm_groups.py

def abelian_invariants(self): if self.is_trivial: return [] gns = self.generators inv = [] G = self H = G.derived_subgroup() Hgens = H.generators for p in primefactors(G.order()): ranks = [] while True: pows = [] for g in gns:

Updated import locations

abelian_invariants

498015021131af4dbb07eb110e5badaba8250c7b

sympy

perm_groups.py

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

Python

def abelian_invariants(self): if self.is_trivial: return [] gns = self.generators inv = [] G = self H = G.derived_subgroup() Hgens = H.generators for p in primefactors(G.order()): ranks = [] while True: pows = [] for g in gns: elm = g**p if not H.contains(elm): pows.append(elm) K = PermutationGroup(Hgens + pows) if pows else H r = G.order()//K.order() G = K gns = pows if r == 1: break ranks.append(multiplicity(p, r)) if ranks: pows = [1]*ranks[0] for i in ranks: for j in range(0, i): pows[j] = pows[j]*p inv.extend(pows) inv.sort() return inv

keras/engine/functional_utils.py

def clone_graph_nodes(inputs, outputs): nodes_to_clone = find_nodes_by_inputs_and_outputs(inputs, outputs) cloned_inputs = [] cloned_outputs = [] # We not only need to create copies of Nodes (mimic the calls), also need to # clone keras_tensors to avoid the override of _keras_history attached on the # keras_tensor. The following dict is used to track any keras tensor we cloned # The key is the string ID of the original keras tensor, and value is the # cloned keras_tensor instance. kt_id_mapping = {} for kt_input in tf.nest.flatten(inputs): if kt_input.

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

clone_graph_nodes

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

functional_utils.py

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

Python

def clone_graph_nodes(inputs, outputs): nodes_to_clone = find_nodes_by_inputs_and_outputs(inputs, outputs) cloned_inputs = [] cloned_outputs = [] # We not only need to create copies of Nodes (mimic the calls), also need to # clone keras_tensors to avoid the override of _keras_history attached on the # keras_tensor. The following dict is used to track any keras tensor we cloned # The key is the string ID of the original keras tensor, and value is the # cloned keras_tensor instance. kt_id_mapping = {} for kt_input in tf.nest.flatten(inputs): if kt_input.node.is_input: # For any existing keras_tensor from tf.keras.Input, we leave them as is. cloned_inputs.append(kt_input) kt_id_mapping[id(kt_input)] = kt_input else: # We need to create a new tf.keras.Input for any intermediate keras_tensor cpy = _clone_keras_tensor(kt_input) cloned_input = input_layer_module.Input(tensor=cpy) cloned_inputs.append(cloned_input) kt_id_mapping[id(kt_input)] = cloned_input cloned_inputs = tf.nest.pack_sequence_as(inputs, cloned_inputs) for kt_output in tf.nest.flatten(outputs): cpy = _clone_keras_tensor(kt_output) # We reuse the _keras_history here, which contains the old information. It # is used in the Node constructor to check if the tensor "is_keras_tensor()" # The history will be override by the Node constructor anyway for the # corresponding layer output anyway. cpy._keras_history = ( kt_output._keras_history ) # pylint: disable=protected-access cloned_outputs.append(cpy) kt_id_mapping[id(kt_output)] = cpy cloned_outputs = tf.nest.pack_sequence_as(outputs, cloned_outputs) for node in nodes_to_clone: # Clone any keras_tensors to avoid override of _keras_history # Or reuse an existing keras_tensor if it has already been cloned. output_copy = clone_keras_tensors(node.output_tensors, kt_id_mapping) call_args_copy = clone_keras_tensors(node.call_args, kt_id_mapping) call_kwargs_copy = clone_keras_tensors(node.call_kwargs, kt_id_mapping) # Creating new nodes based on the existing node information. # Node wires itself to inbound and outbound layers. # The Node constructor actually updates this layer's self._inbound_nodes, # sets _keras_history on the outputs, and adds itself to the # `_outbound_nodes` of the layers that produced the inputs to this # layer call. node_module.Node( node.layer, call_args=call_args_copy, call_kwargs=call_kwargs_copy, outputs=output_copy, ) return cloned_inputs, cloned_outputs

sympy/physics/mechanics/rigidbody.py

def parallel_axis(self, point, frame=None): # circular import issue from sympy.physics.mechanics.functions import inertia_of_point_mass if frame is None: frame = self.frame return self.central_inertia.express(frame) + inertia_of_point_mass( sel

Add optional frame argument to parallel axis method

parallel_axis

801e149d69d5f88919a735f8b55b6024f97c6950

sympy

rigidbody.py

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

Python

def parallel_axis(self, point, frame=None): # circular import issue from sympy.physics.mechanics.functions import inertia_of_point_mass if frame is None: frame = self.frame return self.central_inertia.express(frame) + inertia_of_point_mass( self.mass, self.masscenter.pos_from(point), frame)

sklearn/utils/tests/test_class_weight.py

def test_class_weight_does_not_contains_more_classses(): tree = DecisionTreeClassifier(class_weight={0: 1, 1: 10, 2: 20}) # Does not raise tree.fit([[0, 0, 1], [1, 0, 1], [1, 2, 0]], [0, 0, 1])

FIX Support extra class_weights in compute_class_weight (#22595)

test_class_weight_does_not_contains_more_classses

3605c140af992b6ac52f04f1689c58509cc0b5b2

scikit-learn

test_class_weight.py

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

Python

def test_class_weight_does_not_contains_more_classses(): tree = DecisionTreeClassifier(class_weight={0: 1, 1: 10, 2: 20}) # Does not raise tree.fit([[0, 0, 1], [1, 0, 1], [1, 2, 0]], [0, 0, 1])

test/lib/ansible_test/_internal/cli/argparsing/parsers.py

def parse(self, state): # type: (ParserState) -> str if state.mode == ParserMode.PARSE: path = AnyParser().

ansible-test - Code cleanup and refactoring. (#77169) * Remove unnecessary PyCharm ignores. * Ignore intentional undefined attribute usage. * Add missing type hints. Fix existing type hints. * Fix docstrings and comments. * Use function to register completion handler. * Pass strings to display functions. * Fix CompositeAction handling of dest argument. * Use consistent types in expressions/assignments. * Use custom function to keep linters happy. * Add missing raise for custom exception. * Clean up key/value type handling in cloud plugins. * Use dataclass instead of dict for results. * Add custom type_guard function to check lists. * Ignore return type that can't be checked (yet). * Avoid changing types on local variables.

parse

a06fa496d3f837cca3c437ab6e9858525633d147

ansible

parsers.py

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

Python

def parse(self, state): # type: (ParserState) -> str if state.mode == ParserMode.PARSE: path = AnyParser().parse(state) if not os.path.isfile(path): raise ParserError(f'Not a file: {path}') else: path = '' with state.delimit(PATH_DELIMITER, required=False) as boundary: # type: ParserBoundary while boundary.ready: directory = path or '.' try: with os.scandir(directory) as scan: # type: t.Iterator[os.DirEntry] choices = [f'{item.name}{PATH_DELIMITER}' if item.is_dir() else item.name for item in scan] except OSError: choices = [] if not path: choices.append(PATH_DELIMITER) # allow absolute paths choices.append('../') # suggest relative paths part = RelativePathNameParser(choices).parse(state) path += f'{part}{boundary.match or ""}' return path

src/textual/view.py

def layout(self) -> Layout: # self.log("I", self._inline_styles) # self.log("C", self._css_styles) # self.log("S", self.styles) assert self.s

implement inline styles

layout

c90cdd4ec8a10c689fee83a6a71e025393dcb38d

textual

view.py

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

Python

def layout(self) -> Layout: # self.log("I", self._inline_styles) # self.log("C", self._css_styles) # self.log("S", self.styles) assert self.styles.layout return self.styles.layout # @layout.setter # def layout(self, new_value: Layout) -> None: # # self.styles.layout = new_value

salt/transport/rabbitmq.py

def timeout_message(self, message): future = self.send_future_map.pop(message, None) # In a race condition the message might have been sent by the time # we're timing it out. Make sure the future is not None if future is not None: del self.send_timeout_map[message] if future.attempts < future.tries: future.attempts += 1 log.info( "SaltReqTimeoutError, retrying. (%s/%s)", future.attempts, future.tries, ) self.send( message, timeout=future.timeout, tries=future.tries,

Start to add base class defs

timeout_message

ab4803984bce4a4de7cc10910e7310c4babf557e

salt

rabbitmq.py

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

Python

def timeout_message(self, message): future = self.send_future_map.pop(message, None) # In a race condition the message might have been sent by the time # we're timing it out. Make sure the future is not None if future is not None: del self.send_timeout_map[message] if future.attempts < future.tries: future.attempts += 1 log.info( "SaltReqTimeoutError, retrying. (%s/%s)", future.attempts, future.tries, ) self.send( message, timeout=future.timeout, tries=future.tries, future=future, ) else: future.set_exception(SaltReqTimeoutError("Message timed out"))

airbyte-integrations/connectors/source-freshdesk/unit_tests/test_300_page.py

def test_not_all_records(self, requests_mock, authenticator, config, responses): expected_output = [ {"id": 1, "updated_at": "2018-01-02T00:00:00Z"}, {"id": 2, "updated_at": "2018-02-02T00:00:00Z"}, {"id": 2, "updated_at": "2018-02-02T00:00:00Z"}, # duplicate {"id": 3, "updated_at": "2018-03-02T00:00:00Z"}, {"id": 3, "updated_at": "2018-03-02T00:00:00Z"}, # duplicate {"id": 4, "updated_at": "2019-01-03T00:00:00Z"}, {"id": 4, "updated_at": "2019-01-03T00:00:00Z"}, # duplicate {"id": 5, "updated_at": "2019-02-03T00:00:00Z"}, {"id": 5, "updated_at": "2019-02-03T00:00:00Z"}, # duplicate {"id": 6, "updated_at": "2019-03-03T00:00:00Z"}, ] # INT value of page number where the switch state should be triggered. # in this test case values from: 1 - 4, assuming we want to switch state on this page. ticket_paginate_limit = 2 # This parameter mocks the "per_page" parameter in the API Ca

🎉 Source Freshdesk: Migrated to latest CDK (#12334)

test_not_all_records

9d1cd42ff9f3118e2312ea9c94ad647f1baaad73

airbyte

test_300_page.py

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

Python

def test_not_all_records(self, requests_mock, authenticator, config, responses): expected_output = [ {"id": 1, "updated_at": "2018-01-02T00:00:00Z"}, {"id": 2, "updated_at": "2018-02-02T00:00:00Z"}, {"id": 2, "updated_at": "2018-02-02T00:00:00Z"}, # duplicate {"id": 3, "updated_at": "2018-03-02T00:00:00Z"}, {"id": 3, "updated_at": "2018-03-02T00:00:00Z"}, # duplicate {"id": 4, "updated_at": "2019-01-03T00:00:00Z"}, {"id": 4, "updated_at": "2019-01-03T00:00:00Z"}, # duplicate {"id": 5, "updated_at": "2019-02-03T00:00:00Z"}, {"id": 5, "updated_at": "2019-02-03T00:00:00Z"}, # duplicate {"id": 6, "updated_at": "2019-03-03T00:00:00Z"}, ] # INT value of page number where the switch state should be triggered. # in this test case values from: 1 - 4, assuming we want to switch state on this page. ticket_paginate_limit = 2 # This parameter mocks the "per_page" parameter in the API Call result_return_limit = 1 # Create test_stream instance. test_stream = Tickets(authenticator=authenticator, config=config) test_stream.ticket_paginate_limit = ticket_paginate_limit test_stream.result_return_limit = result_return_limit # Mocking Request for response in responses: requests_mock.register_uri( "GET", response["url"], json=response["json"], headers=response.get("headers", {}), ) records = list(test_stream.read_records(sync_mode=SyncMode.full_refresh)) # We're expecting 6 records to return from the tickets_stream assert records == expected_output

django/core/management/commands/test.py

def run_from_argv(self, argv): s

Refs #33476 -- Reformatted code with Black.

run_from_argv

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

test.py

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

Python

def run_from_argv(self, argv): self.test_runner = get_command_line_option(argv, "--testrunner") super().run_from_argv(argv)

tests/sentry/integrations/slack/notifications/test_new_processing_issues.py

def test_new_processing_issue(self, mock_func): notification = NewProcessingIssuesActivityNotification( Activity( project=self.project, user=self.user, type=ActivityType.NEW_PROCESSING_ISSUES, data={ "issues": get_issues_data(), "reprocessing_active": True, },

fix(slack): Fix broken url formatting (#36976) Fix the URL format, it should be `<url|text>`.

test_new_processing_issue

e4c6ad69c22692e2999baa26d8bf8f44947cd1c1

sentry

test_new_processing_issues.py

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

Python

def test_new_processing_issue(self, mock_func): notification = NewProcessingIssuesActivityNotification( Activity( project=self.project, user=self.user, type=ActivityType.NEW_PROCESSING_ISSUES, data={ "issues": get_issues_data(), "reprocessing_active": True, }, ) ) with self.tasks(): notification.send() attachment, text = get_attachment() assert ( text == f"Processing issues on <http://testserver/settings/{self.organization.slug}/projects/{self.project.slug}/processing-issues/|{self.project.slug}>" ) assert ( attachment["text"] == f"Some events failed to process in your project {self.project.slug}" ) assert ( attachment["footer"] == f"{self.project.slug} | <http://testserver/settings/account/notifications/workflow/?referrer=new_processing_issues_activity-slack-user|Notification Settings>" )

d2l/torch.py

def load_array(data_arrays, batch_size, is_train=True): dataset = data.TensorDataset(*data_arrays)

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

load_array

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

d2l-zh

torch.py

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

Python

def load_array(data_arrays, batch_size, is_train=True): dataset = data.TensorDataset(*data_arrays) return data.DataLoader(dataset, batch_size, shuffle=is_train)

pipenv/patched/pip/_vendor/requests/models.py

def is_permanent_redirect(self):

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

is_permanent_redirect

cd5a9683be69c86c8f3adcd13385a9bc5db198ec

pipenv

models.py

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

Python

def is_permanent_redirect(self): return "location" in self.headers and self.status_code in ( codes.moved_permanently, codes.permanent_redirect, )

keras/utils/generic_utils.py

def _estimate_step_duration(self, current, now): if current: # there are a few special scenarios here: # 1) somebody is calling the progress bar without ever supplying # step 1 # 2) somebody is calling the p

resolve line-too-long in utils

_estimate_step_duration

80ee2fa4e1db2dda14370110830db82be3eb97b7

keras

generic_utils.py

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

Python

def _estimate_step_duration(self, current, now): if current: # there are a few special scenarios here: # 1) somebody is calling the progress bar without ever supplying # step 1 # 2) somebody is calling the progress bar and supplies step one # multiple times, e.g. as part of a finalizing call # in these cases, we just fall back to the simple calculation if self._time_after_first_step is not None and current > 1: time_per_unit = (now - self._time_after_first_step) / ( current - 1 ) else: time_per_unit = (now - self._start) / current if current == 1: self._time_after_first_step = now return time_per_unit else: return 0

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

def _propagate_index_objs(self, axis=None): self._filter_empties() if axis is None or axis == 0: cum_row_lengths = np.cumsum([0] + self._row_lengths) if axis is None or axis == 1: cum_col_widths = np.cumsum([0] + self._column

FEAT-#3111: Ensure relabeling Modin Frame does not lose partition shape (#3662) Co-authored-by: Devin Petersohn <[email protected]> Signed-off-by: Naren Krishna <[email protected]>

_propagate_index_objs

3c740dbfcdd69ddc3ab45a42be996e5c61104342

modin

dataframe.py

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

Python

def _propagate_index_objs(self, axis=None): self._filter_empties() if axis is None or axis == 0: cum_row_lengths = np.cumsum([0] + self._row_lengths) if axis is None or axis == 1: cum_col_widths = np.cumsum([0] + self._column_widths) if axis is None:

tests/handlers/test_e2e_keys.py

def test_query_devices_remote_no_sync(self) -> None: remote_user_id = "@test:other" local_user_id = "@test:test" remote_master_key = "85T7JXPFBAySB/jwby4S3lBPTqY3+Zg53nYuGmu1ggY" remote_self_signing_key = "QeIiFEjluPBtI7WQdG365QKZcFs9kqmHir6RBD0//nQ" self.hs.get_federation_client().query_client_keys

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

test_query_devices_remote_no_sync

5dd949bee6158a8b651db9f2ae417a62c8184bfd

synapse

test_e2e_keys.py

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

Python

def test_query_devices_remote_no_sync(self) -> None: remote_user_id = "@test:other" local_user_id = "@test:test" remote_master_key = "85T7JXPFBAySB/jwby4S3lBPTqY3+Zg53nYuGmu1ggY" remote_self_signing_key = "QeIiFEjluPBtI7WQdG365QKZcFs9kqmHir6RBD0//nQ" self.hs.get_federation_client().query_client_keys = mock.Mock( return_value=defer.succeed( { "device_keys": {remote_user_id: {}}, "master_keys": { remote_user_id: { "user_id": remote_user_id, "usage": ["master"], "keys": {"ed25519:" + remote_master_key: remote_master_key}, }, }, "self_signing_keys": { remote_user_id: { "user_id": remote_user_id, "usage": ["self_signing"], "keys": { "ed25519:" + remote_self_signing_key: remote_self_signing_key }, } }, } ) ) e2e_handler = self.hs.get_e2e_keys_handler() query_result = self.get_success( e2e_handler.query_devices( { "device_keys": {remote_user_id: []}, }, timeout=10, from_user_id=local_user_id, from_device_id="some_device_id", ) ) self.assertEqual(query_result["failures"], {}) self.assertEqual( query_result["master_keys"], { remote_user_id: { "user_id": remote_user_id, "usage": ["master"], "keys": {"ed25519:" + remote_master_key: remote_master_key}, }, }, ) self.assertEqual( query_result["self_signing_keys"], { remote_user_id: { "user_id": remote_user_id, "usage": ["self_signing"], "keys": { "ed25519:" + remote_self_signing_key: remote_self_signing_key }, } }, )

sympy/core/mul.py

def _matches_get_other_nodes(dictionary, nodes, node_ind): ind_node = nodes[node_ind] return [ind for ind in dictionary if nodes[ind] == ind_node]

Code cleanup

_matches_get_other_nodes

9d58006fc0a23afcba38f641c9472917c436428a

sympy

mul.py

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

Python

def _matches_get_other_nodes(dictionary, nodes, node_ind): ind_node = nodes[node_ind] return [ind for ind in dictionary if nodes[ind] == ind_node]

src/transformers/processing_utils.py

def save_pretrained(self, save_directory): for attribute_name in self.attributes: attribute = getattr(self, attribute_name) # Include the processor class in the attribute config so this processor can then be reloaded with the # `AutoProcessor` API. if hasattr(attribute, "_set_processor_class"):

PoC for a ProcessorMixin class (#15549) * PoC for a ProcessorMixin class * Documentation * Apply suggestions from code review Co-authored-by: NielsRogge <[email protected]> Co-authored-by: Suraj Patil <[email protected]> Co-authored-by: Patrick von Platen <[email protected]> * Roll out to other processors * Add base feature extractor class in init * Use args and kwargs Co-authored-by: NielsRogge <[email protected]> Co-authored-by: Suraj Patil <[email protected]> Co-authored-by: Patrick von Platen <[email protected]>

save_pretrained

b5c6fdecf0cab6ffe22bee2ca5b8474afba0d813

transformers

processing_utils.py

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

Python

def save_pretrained(self, save_directory): for attribute_name in self.attributes: attribute = getattr(self, attribute_name) # Include the processor class in the attribute config so this processor can then be reloaded with the # `AutoProcessor` API. if hasattr(attribute, "_set_processor_class"): attribute._set_processor_class(self.__class__.__name__) attribute.save_pretrained(save_directory)

saleor/permission/models.py

def _user_has_module_perms(user, app_label): for backend in auth.get_backends(): if not hasattr(backend, "has_module_perms"): continue try: if backend.has_module_perms(user, app_label

Move PermissionsMixin from django auth

_user_has_module_perms

d5ef58653803075849a6a13177e7a6e604aa2f60

saleor

models.py

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

Python

def _user_has_module_perms(user, app_label): for backend in auth.get_backends(): if not hasattr(backend, "has_module_perms"): continue try: if backend.has_module_perms(user, app_label): return True except PermissionDenied: return False return False

@frappe.whitelist()

erpnext/stock/doctype/delivery_trip/delivery_trip.py

def get_default_address(out, name): shipping_addresses = frappe.db.sql( , (name), as_dict=1, ) if shipping_addresses: for out.shipping_address in shipping_addresses: if out.shipping_address.is_shipping_address: return out.shipping_address out.shipping_address = shipping_addresses[0] return out.shipping_address @frappe.whitelis

style: format code with black

get_default_address

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

delivery_trip.py

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

Python

def get_default_address(out, name): shipping_addresses = frappe.db.sql( , (name), as_dict=1, ) if shipping_addresses: for out.shipping_address in shipping_addresses: if out.shipping_address.is_shipping_address: return out.shipping_address out.shipping_address = shipping_addresses[0] return out.shipping_address @frappe.whitelist()

rllib/utils/__init__.py

def force_list(elements=None, to_tuple=False): ctor = list if to_tuple is True: ctor = tuple return ctor() if elements is None else ctor(elements) \ if type(el

[RLlib] Make `policies_to_train` more flexible via callable option. (#20735)