Datasets:

semeru

/

Galeras-3k_deduped

like 0

rllib/__init__.py

[RLlib] Deprecate (delete) `contrib` folder. (#30992)

ray

Python

__init__.py

def _register_all(): from ray.rllib.algorithms.registry import ALGORITHMS, _get_algorithm_class for key, get_trainable_class_and_config in ALGORITHMS.items(): register_trainable(key, get_trainable_class_and_config()[0]) for key in ["__fake", "__sigmoid_fake_data", "__parameter_tuning"]: register_trainable(key, _get_algorithm_class(key)) _setup_logger() usage_lib.record_library_usage("rllib") __all__ = [ "Policy", "TFPolicy", "TorchPolicy", "RolloutWorker", "SampleBatch", "BaseEnv", "MultiAgentEnv", "VectorEnv", "ExternalEnv", ]

64d744b4750b749cede563b04c5d32396470a236

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

def _register_all(): from ray.rllib.algorithms.registry import ALGORITHMS, _get_algorithm_class for key, get_trainable_class_and_config in ALGORITHMS.items(): register_trainable(key, get_trainable_class_and_config()[0]) for key in ["__fake", "__sigmoid_fake_data", "__parameter_tuning"]: register_trainable(key, _get_algorithm_class(key)) _setup_logger() usage_lib.record_library_usage("rllib") __all__ = [ "Policy", "TFPolic

_register_all

jax/_src/numpy/ufuncs.py

lax_numpy.py: factor ufuncs into their own private submodule Re-lands part of #9724 PiperOrigin-RevId: 434629548

jax

Python

ufuncs.py

def _sinc_maclaurin(k, x): # compute the kth derivative of x -> sin(x)/x evaluated at zero (since we # compute the monomial term in the jvp rule) if k % 2: return lax.full_like(x, 0) else: return lax.full_like(x, (-1) ** (k // 2) / (k + 1)) @_sinc_maclaurin.defjvp

6355fac8822bced4bfa657187a7284477f373c52

@_sinc_maclaurin.defjvp

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

def _sinc_maclaurin(k, x): # compute the kth derivative of x -> sin(x)/x evaluated at zero (since we # compute the monomial term in the jvp rule) if k % 2: return lax.full_like(x, 0) else: return lax.full_like(

_sinc_maclaurin

d2l/torch.py

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

d2l-zh

Python

torch.py

def evaluate_loss(net, data_iter, loss): metric = d2l.Accumulator(2) # Sum of losses, no. of examples for X, y in data_iter: out = net(X) y = d2l.reshape(y, out.shape) l = loss(out, y) metric.add(d2l.reduce_sum(l), d2l.size(l)) return metric[0] / metric[1] DATA_HUB = dict() DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'

b64b41d8c1ac23c43f7a4e3f9f6339d6f0012ab2

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

def evaluate_loss(net, data_iter, loss): metric = d2l.Accumulator(2) # Sum of losses, no. of examples for X, y in data_iter: out = net(X) y = d2l.reshape(y, out.shape) l = loss(out, y) metric.add(d2l.reduce_sum(l), d2l.size(l)) return metric[0] / metric[1] DATA_HUB = dict() DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'

evaluate_loss

sympy/tensor/tensor.py

Update the various tensor deprecations

sympy

Python

tensor.py

def __iter__(self): deprecate_data() with ignore_warnings(SymPyDeprecationWarning): return self.data.__iter__()

cba899d4137b0b65f6850120ee42cd4fcd4f9dbf

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

def __iter__(self): deprecate_data() with ignore_warnings(SymPyDeprecationWarning

__iter__

modules/image/Image_editing/super_resolution/swinir_l_real_sr_x4/test.py

add swinir_l_real_sr_x4 (#2076) * git add swinir_l_real_sr_x4 * fix typo * fix typo Co-authored-by: chenjian <chenjian26@baidu.com>

PaddleHub

Python

test.py

def test_real_sr4(self): self.assertRaises(Exception, self.module.real_sr, image=['tests/test.jpg'])

2e373966a7fd3119c205350fb14d0b7bfe74185d

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

def test_real_sr4(self):

test_real_sr4

pandas/core/groupby/groupby.py

BUG: numeric_only with axis=1 in DataFrame.corrwith and DataFrameGroupBy.cummin/max (#47724) * BUG: DataFrame.corrwith and DataFrameGroupBy.cummin/cummax with numeric_only=True * test improvements

pandas

Python

groupby.py

def cummax(self, axis=0, numeric_only=False, **kwargs) -> NDFrameT: skipna = kwargs.get("skipna", True) if axis != 0: f = lambda x: np.maximum.accumulate(x, axis) numeric_only_bool = self._resolve_numeric_only("cummax", numeric_only, axis) obj = self._selected_obj if numeric_only_bool: obj = obj._get_numeric_data() return self._python_apply_general(f, obj, is_transform=True) return self._cython_transform( "cummax", numeric_only=numeric_only, skipna=skipna )

ad7dcef6f0dbdbb14240dd13db51f4d8892ad808

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

def cummax(self, axis=0, numeric_only=False, **kwargs) -> NDFrameT: skipna = kwargs.get("skipna", True) if axis != 0: f = lambda x: np.maximum.accumulate(x, axis) numeric_only_bool = self.

cummax

jax/tools/colab_tpu.py

Update values for release 0.4.1 PiperOrigin-RevId: 494889744

jax

Python

colab_tpu.py

def setup_tpu(tpu_driver_version='tpu_driver_20221212'): global TPU_DRIVER_MODE if not TPU_DRIVER_MODE: colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0] url = f'http://{colab_tpu_addr}:8475/requestversion/{tpu_driver_version}' requests.post(url) TPU_DRIVER_MODE = 1 # The following is required to use TPU Driver as JAX's backend. config.FLAGS.jax_xla_backend = "tpu_driver" config.FLAGS.jax_backend_target = "grpc://" + os.environ['COLAB_TPU_ADDR'] # TODO(skyewm): Remove this after SPMD is supported for colab tpu. config.update('jax_array', False)

c4d590b1b640cc9fcfdbe91bf3fe34c47bcde917

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

def setup_tpu(tpu_driver_version='tpu_driver_20221212'): global TPU_DRIVER_MODE if not TPU_DRIVER_MODE: colab_tpu_addr = os.environ['COLAB_TPU_ADDR'].split(':')[0] url = f'http://{colab_tpu_addr}:8475/requestversion/{tpu_driver_version}' requests.post(url) TPU_DRIVER_MODE = 1 # The following is required to use TPU Driver as JAX's backend. config.FLAGS.jax_xla_backend = "tpu_driver" config.FLAGS.jax_backend_target = "grpc://" + os.environ['COLAB_TPU_ADDR'] # TODO(skyewm): Remove this after SPMD is supported for colab tpu. config.

setup_tpu

dashboard/modules/job/tests/test_cli_integration.py

[Job submission] Add `list_jobs` API (#22679) Adds an API to the REST server, the SDK, and the CLI for listing all jobs that have been submitted, along with their information. Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>

ray

Python

test_cli_integration.py

def test_list(self, ray_start_stop): _run_cmd("ray job submit --job-id='hello_id' -- echo hello") runtime_env = {"env_vars": {"TEST": "123"}} _run_cmd( "ray job submit --job-id='hi_id' " f"--runtime-env-json='{json.dumps(runtime_env)}' -- echo hi" ) stdout, _ = _run_cmd("ray job list") assert "JobInfo" in stdout assert "123" in stdout assert "hello_id" in stdout assert "hi_id" in stdout

1752f17c6d6fceac3d7902d3220a756b8424b7da

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

def test_list(self, ray_start_stop): _run_cmd("ray job submit --job-id='hello_id' -- echo hello") runtime_env = {"env_vars": {"TEST": "123"}} _run_cmd( "ray job submit --job-id='hi_id' " f"--runtime-env-json='{json.dumps(runtime_env)}' -- echo hi" ) stdout, _ = _run_

test_list

python3.10.4/Lib/datetime.py

add python 3.10.4 for windows

XX-Net

Python

datetime.py

def isoformat(self, timespec='auto'): s = _format_time(self._hour, self._minute, self._second, self._microsecond, timespec) tz = self._tzstr() if tz: s += tz return s __str__ = isoformat

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def isoformat(self, timespec='auto'): s = _format_time(self._hour, self._minute, self._second,

isoformat

pandas/tests/reshape/concat/test_index.py

TST: add validation checks on levels keyword from pd.concat (#46654)

pandas

Python

test_index.py

def test_concat_with_duplicated_levels(self): # keyword levels should be unique df1 = DataFrame({"A": [1]}, index=["x"]) df2 = DataFrame({"A": [1]}, index=["y"]) msg = r"Level values not unique: \['x', 'y', 'y'\]" with pytest.raises(ValueError, match=msg): concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])

361021b56f3159afb71d690fac3a1f3b381b0da6

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

def test_concat_with_duplicated_levels(self): # keyword levels should be unique df1 = DataFrame({"A": [1]}, index=["x"]) df2 = DataFrame({"A": [1]}, index=["y"]) msg = r"Level values not unique: \['x', 'y', 'y'\]" with pytest.raises(ValueError, match=msg): concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"

test_concat_with_duplicated_levels

wagtail/search/index.py

Reformat with black

wagtail

Python

index.py

def class_is_indexed(cls): return ( issubclass(cls, Indexed) and issubclass(cls, models.Model) and not cls._meta.abstract )

d10f15e55806c6944827d801cd9c2d53f5da4186

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

def class_is_indexed(cls): return ( issubclass(cls,

class_is_indexed

python3.10.4/Lib/encodings/bz2_codec.py

add python 3.10.4 for windows

XX-Net

Python

bz2_codec.py

def bz2_encode(input, errors='strict'): assert errors == 'strict' return (bz2.compress(input), len(input))

8198943edd73a363c266633e1aa5b2a9e9c9f526

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

def bz2_encode(input, errors='strict'): assert errors == 's

bz2_encode

tests/snuba/api/endpoints/test_organization_events_v2.py

fix(discover): Equation change and meta conflict tests (#34889) - This fixes this test which broke cause the meta changed in one PR, and the equation format in another

sentry

Python

test_organization_events_v2.py

def test_equation_simple(self): event_data = load_data("transaction", timestamp=before_now(minutes=1)) event_data["breakdowns"]["span_ops"]["ops.http"]["value"] = 1500 self.store_event(data=event_data, project_id=self.project.id) query = { "field": ["spans.http", "equation|spans.http / 3"], "project": [self.project.id], "query": "event.type:transaction", } response = self.do_request( query, { "organizations:discover-basic": True, }, ) assert response.status_code == 200, response.content assert len(response.data["data"]) == 1 assert ( response.data["data"][0]["equation|spans.http / 3"] == event_data["breakdowns"]["span_ops"]["ops.http"]["value"] / 3 ) assert response.data["meta"]["fields"]["equation|spans.http / 3"] == "number"

3a1d4f5105f9b01e70efa92af651107399e76f99

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

def test_equation_simple(self): event_data =

test_equation_simple

sklearn/linear_model/tests/test_sgd.py

MNT ensure creation of dataset is deterministic in SGD (#19716) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

scikit-learn

Python

test_sgd.py

def test_sgd_random_state(Estimator, global_random_seed): # Train the same model on the same data without converging and check that we # get reproducible results by fixing the random seed. if Estimator == linear_model.SGDRegressor: X, y = datasets.make_regression(random_state=global_random_seed) else: X, y = datasets.make_classification(random_state=global_random_seed) # Fitting twice a model with the same hyper-parameters on the same training # set with the same seed leads to the same results deterministically. est = Estimator(random_state=global_random_seed, max_iter=1) with pytest.warns(ConvergenceWarning): coef_same_seed_a = est.fit(X, y).coef_ assert est.n_iter_ == 1 est = Estimator(random_state=global_random_seed, max_iter=1) with pytest.warns(ConvergenceWarning): coef_same_seed_b = est.fit(X, y).coef_ assert est.n_iter_ == 1 assert_allclose(coef_same_seed_a, coef_same_seed_b) # Fitting twice a model with the same hyper-parameters on the same training # set but with different random seed leads to different results after one # epoch because of the random shuffling of the dataset. est = Estimator(random_state=global_random_seed + 1, max_iter=1) with pytest.warns(ConvergenceWarning): coef_other_seed = est.fit(X, y).coef_ assert est.n_iter_ == 1 assert np.abs(coef_same_seed_a - coef_other_seed).max() > 1.0

b4da3b406379b241bf5e81d0f60bbcddd424625b

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

def test_sgd_random_state(Estimator, global_random_seed): # Train the same model on the same data without converging and check that we # get reproducible results by fixing the random seed. if Estimator == linear_model.SGDR

test_sgd_random_state

src/prefect/logging/loggers.py

Move logging into separate modules at 'prefect.logging'

prefect

Python

loggers.py

def process(self, msg, kwargs): kwargs["extra"] = {**self.extra, **(kwargs.get("extra") or {})} return (msg, kwargs) @lru_cache()

08e580acf95963a2579971eb0ff4514233b5e7ea

@lru_cache()

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

def process(self, msg, kwargs): kwargs["extra"] = {**self.extra, **(kwargs.get("extra") or {})} return (msg, kwargs) @lru_cache()

process

tests/unit/test_yamlparser.py

feat: allow passing custom gateway in Flow (#5189)

jina

Python

test_yamlparser.py

def test_load_gateway_override_with(): with Gateway.load_config( 'yaml/test-custom-gateway.yml', uses_with={'arg1': 'arg1', 'arg2': 'arg2', 'arg3': 'arg3'}, ) as gateway: assert gateway.__class__.__name__ == 'DummyGateway' assert gateway.arg1 == 'arg1' assert gateway.arg2 == 'arg2' assert gateway.arg3 == 'arg3'

cdaf7f87ececf9e13b517379ca183b17f0d7b007

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

def test_load_gateway_override_with(): with Gateway.load_config( 'yaml/test-custom-gateway.yml',

test_load_gateway_override_with

pandas/core/base.py

TYP: Autotyping (#48191) * annotate-magics * annotate-imprecise-magics * none-return * scalar-return * pyi files * ignore vendored file * manual changes * ignore pyright in pickle_compat (these errors would be legit if the current __new__ methods were called but I think these pickle tests call older __new__ methods which allowed providing multiple positional arguments) * run autotyping in pre-commit * remove final and expand safe (and add annotate-imprecise-magics)

pandas

Python

base.py

def __iter__(self) -> Iterator: # We are explicitly making element iterators. if not isinstance(self._values, np.ndarray): # Check type instead of dtype to catch DTA/TDA return iter(self._values) else: return map(self._values.item, range(self._values.size))

54347fe684e0f7844bf407b1fb958a5269646825

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

def __iter__(self) -> Iterator: # We are explicitly making element iterators. if not isinstance(self._values, np.ndarray): # Check type instead of dtype to catch DTA/TDA return

__iter__

pandas/tests/window/test_base_indexer.py

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

pandas

Python

test_base_indexer.py

def test_rolling_forward_window(constructor, func, np_func, expected, np_kwargs, step): # GH 32865 values = np.arange(10.0) values[5] = 100.0 indexer = FixedForwardWindowIndexer(window_size=3) match = "Forward-looking windows can't have center=True" with pytest.raises(ValueError, match=match): rolling = constructor(values).rolling(window=indexer, center=True) getattr(rolling, func)() match = "Forward-looking windows don't support setting the closed argument" with pytest.raises(ValueError, match=match): rolling = constructor(values).rolling(window=indexer, closed="right") getattr(rolling, func)() rolling = constructor(values).rolling(window=indexer, min_periods=2, step=step) result = getattr(rolling, func)() # Check that the function output matches the explicitly provided array expected = constructor(expected)[::step] tm.assert_equal(result, expected) # Check that the rolling function output matches applying an alternative # function to the rolling window object expected2 = constructor(rolling.apply(lambda x: np_func(x, **np_kwargs))) tm.assert_equal(result, expected2) # Check that the function output matches applying an alternative function # if min_periods isn't specified # GH 39604: After count-min_periods deprecation, apply(lambda x: len(x)) # is equivalent to count after setting min_periods=0 min_periods = 0 if func == "count" else None rolling3 = constructor(values).rolling(window=indexer, min_periods=min_periods) result3 = getattr(rolling3, func)() expected3 = constructor(rolling3.apply(lambda x: np_func(x, **np_kwargs))) tm.assert_equal(result3, expected3) @pytest.mark.parametrize("constructor", [Series, DataFrame])

6caefb19f4d7c05451fafca182c6eb39fe9901ed

@pytest.mark.parametrize("constructor", [Series, DataFrame])

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

def test_rolling_forward_window(constructor, func, np_func, expected, np_kwargs, step): # GH 32865 values = np.arange(10.0) values[5] = 100.0 indexer = FixedForwardWindowIndexer(window_size=3) match = "Forward-looking windows can't have center=True" with pytest.raises(ValueError, match=match): rolling = constructor(values).rolling(window=indexer, center=True) getattr(rolling, func)() match = "Forward-looking windows don't support setting the closed argument" with pytest.raises(ValueError, match=match): rolling = constructor(values).rolling(window=indexer, closed="right") getattr(rolling, func)() rolling = constructor(values).rolling(window=indexer, min_periods=2, step=step) result = getattr(rolling, func)() # Check that the function output matches the explicitly provided array expected = constructor(expected)[::step] tm.assert_equal(result, expected) # Check that the rolling function output matches applying an alternative # function to the rolling window object expected2 = constructor(rolling.apply(lambda x: np_func(x, **np_kwargs))) tm.assert_equal(result, expected2) # Check that the function output matches applying an alternative function # if min_periods isn't specified # GH 39604: After count-min_periods deprecation, apply(lambda x: len(x)) # is

test_rolling_forward_window

python/ccxt/async_support/okx.py

1.72.35 [ci skip]

ccxt

Python

okx.py

def set_sandbox_mode(self, enable): super(okx, self).set_sandbox_mode(enable) if enable: self.headers['x-simulated-trading'] = '1' elif 'x-simulated-trading' in self.headers: self.headers = self.omit(self.headers, 'x-simulated-trading')

50ff6d21431b2f87bc0d7a7c671c34b52d01ef99

https://github.com/ccxt/ccxt.git

def set_sandbox_mode(self, enable): super(okx, self).set_sandbox_mode(enable) if enable: self.headers['x-simulated-trading'] = '1' elif 'x-simulated-trading' in self.headers: self.heade

set_sandbox_mode

netbox/dcim/models/cables.py

Fixes #10579: Mark cable traces terminating to a provider network as complete

netbox

Python

cables.py

def from_origin(cls, terminations): from circuits.models import CircuitTermination if not terminations: return None # Ensure all originating terminations are attached to the same link if len(terminations) > 1: assert all(t.link == terminations[0].link for t in terminations[1:]) path = [] position_stack = [] is_complete = False is_active = True is_split = False while terminations: # Terminations must all be of the same type assert all(isinstance(t, type(terminations[0])) for t in terminations[1:]) # Check for a split path (e.g. rear port fanning out to multiple front ports with # different cables attached) if len(set(t.link for t in terminations)) > 1: is_split = True break # Step 1: Record the near-end termination object(s) path.append([ object_to_path_node(t) for t in terminations ]) # Step 2: Determine the attached link (Cable or WirelessLink), if any link = terminations[0].link if link is None and len(path) == 1: # If this is the start of the path and no link exists, return None return None elif link is None: # Otherwise, halt the trace if no link exists break assert type(link) in (Cable, WirelessLink) # Step 3: Record the link and update path status if not "connected" path.append([object_to_path_node(link)]) if hasattr(link, 'status') and link.status != LinkStatusChoices.STATUS_CONNECTED: is_active = False # Step 4: Determine the far-end terminations if isinstance(link, Cable): termination_type = ContentType.objects.get_for_model(terminations[0]) local_cable_terminations = CableTermination.objects.filter( termination_type=termination_type, termination_id__in=[t.pk for t in terminations] ) # Terminations must all belong to same end of Cable local_cable_end = local_cable_terminations[0].cable_end assert all(ct.cable_end == local_cable_end for ct in local_cable_terminations[1:]) remote_cable_terminations = CableTermination.objects.filter( cable=link, cable_end='A' if local_cable_end == 'B' else 'B' ) remote_terminations = [ct.termination for ct in remote_cable_terminations] else: # WirelessLink remote_terminations = [link.interface_b] if link.interface_a is terminations[0] else [link.interface_a] # Step 5: Record the far-end termination object(s) path.append([ object_to_path_node(t) for t in remote_terminations ]) # Step 6: Determine the "next hop" terminations, if applicable if not remote_terminations: break if isinstance(remote_terminations[0], FrontPort): # Follow FrontPorts to their corresponding RearPorts rear_ports = RearPort.objects.filter( pk__in=[t.rear_port_id for t in remote_terminations] ) if len(rear_ports) > 1: assert all(rp.positions == 1 for rp in rear_ports) elif rear_ports[0].positions > 1: position_stack.append([fp.rear_port_position for fp in remote_terminations]) terminations = rear_ports elif isinstance(remote_terminations[0], RearPort): if len(remote_terminations) > 1 or remote_terminations[0].positions == 1: front_ports = FrontPort.objects.filter( rear_port_id__in=[rp.pk for rp in remote_terminations], rear_port_position=1 ) elif position_stack: front_ports = FrontPort.objects.filter( rear_port_id=remote_terminations[0].pk, rear_port_position__in=position_stack.pop() ) else: # No position indicated: path has split, so we stop at the RearPorts is_split = True break terminations = front_ports elif isinstance(remote_terminations[0], CircuitTermination): # Follow a CircuitTermination to its corresponding CircuitTermination (A to Z or vice versa) term_side = remote_terminations[0].term_side assert all(ct.term_side == term_side for ct in remote_terminations[1:]) circuit_termination = CircuitTermination.objects.filter( circuit=remote_terminations[0].circuit, term_side='Z' if term_side == 'A' else 'A' ).first() if circuit_termination is None: break elif circuit_termination.provider_network: # Circuit terminates to a ProviderNetwork path.extend([ [object_to_path_node(circuit_termination)], [object_to_path_node(circuit_termination.provider_network)], ]) is_complete = True break elif circuit_termination.site and not circuit_termination.cable: # Circuit terminates to a Site path.extend([ [object_to_path_node(circuit_termination)], [object_to_path_node(circuit_termination.site)], ]) break terminations = [circuit_termination] # Anything else marks the end of the path else: is_complete = True break return cls( path=path, is_complete=is_complete, is_active=is_active, is_split=is_split )

bd29d1581461f1b97cf0bcdaa10752d89e3ac0ae

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

def from_origin(cls, terminations): from circuits.models import CircuitTermination if not terminations: return None # Ensure all originating terminations are attached to the same link if len(terminations) > 1: assert all(t.link == terminations[0].link for t in terminations[1:]) path = [] position_stack = [] is_complete = False is_active = True is_split = False while terminations: # Terminations must all be of the same type assert all(isinstance(t, type(terminations[0])) for t in terminations[1:]) # Check for a split path (e.g. rear port fanning out to multiple front ports with # different cables attached) if len(set(t.link for t in terminations)) > 1: is_split = True break # Step 1: Record the near-end termination object(s) path.append([ object_to_path_node(t) for t in terminations ]) # Step 2: Determine the attached link (Cable or WirelessLink), if any link = terminations[0].link if link is None and len(path) == 1: # If this is the start of the path and no link exists, return None return None elif link is None: # Otherwise, halt the trace if no link exists break assert type(link) in (Cable, WirelessLink) # Step 3: Record the link and update path status if not "connected" path.append([object_to_path_node(link)]) if hasattr(link, 'status') and link.status != LinkStatusChoices.STATUS_CONNECTED: is_active = False # Step 4: Determine the far-end terminations if isinstance(link, Cable): termination_type = ContentType.objects.get_for_model(terminations[0]) local_cable_terminations = CableTermination.objects.filter( termination_type=termination_type, termination_id__in=[t.pk for t in terminations] ) # Terminations must all belong to same end of Cable local_cable_end = local_cable_terminations[0].cable_end assert all(ct.cable_end == local_cable_end for ct in local_cable_terminations[1:]) remote_cable_terminations = CableTermination.objects.filter( cable=link, cable_end='A' if local_cable_end == 'B' else 'B' ) remote_terminations = [ct.termination for ct in remote_cable_terminations] else: # WirelessLink remote_terminations = [link.interface_b] if link.interface_a is terminations[0] else [link.interface_a] # Step 5: Record the far-end termination object(s) path.append([ object_to_path_node(t) for t in remote_terminations ]) # Step 6: Determine the "next hop" terminations, if applicable if not remote_terminations: break if isinstance(remote_terminations[0], FrontPort): # Follow FrontPorts to their corresponding RearPorts rear_ports = RearPort.objects.filter( pk__in=[t.rear_port_id for t in remote_terminations] ) if len(rear_ports) > 1: assert all(rp.positions == 1 for rp in rear_ports) elif rear_ports[0].positions > 1: position_stack.append([fp.rear_port_position for fp in remote_terminations]) terminations = rear_ports elif isinstance(remote_terminations[0], RearPort): if len(remote_terminations) > 1 or remote_terminations[0].positions == 1: front_ports = FrontPort.objects.filter( rear_port_id__in=[rp.pk for rp in remote_terminations], rear_port_position=1 ) elif position_stack: front_ports = FrontPort.objects.filter( rear_port_id=remote_terminations[0].pk, rear_port_position__in=position_stack.pop() ) else: # No position indicated: path has split, so we stop at the RearPor

from_origin

erpnext/setup/setup_wizard/operations/install_fixtures.py

style: format code with black

erpnext

Python

install_fixtures.py

def add_uom_data(): # add UOMs uoms = json.loads( open(frappe.get_app_path("erpnext", "setup", "setup_wizard", "data", "uom_data.json")).read() ) for d in uoms: if not frappe.db.exists("UOM", _(d.get("uom_name"))): uom_doc = frappe.get_doc( { "doctype": "UOM", "uom_name": _(d.get("uom_name")), "name": _(d.get("uom_name")), "must_be_whole_number": d.get("must_be_whole_number"), "enabled": 1, } ).db_insert() # bootstrap uom conversion factors uom_conversions = json.loads( open( frappe.get_app_path("erpnext", "setup", "setup_wizard", "data", "uom_conversion_data.json") ).read() ) for d in uom_conversions: if not frappe.db.exists("UOM Category", _(d.get("category"))): frappe.get_doc({"doctype": "UOM Category", "category_name": _(d.get("category"))}).db_insert() if not frappe.db.exists( "UOM Conversion Factor", {"from_uom": _(d.get("from_uom")), "to_uom": _(d.get("to_uom"))} ): uom_conversion = frappe.get_doc( { "doctype": "UOM Conversion Factor", "category": _(d.get("category")), "from_uom": _(d.get("from_uom")), "to_uom": _(d.get("to_uom")), "value": d.get("value"), } ).insert(ignore_permissions=True)

494bd9ef78313436f0424b918f200dab8fc7c20b

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

def add_uom_data(): # add UOMs uoms = json.loads( open(frappe.get_app_path("erpnext", "setup", "setup_wizard", "data", "uom_data.json")).read() ) for d in uoms: if not frappe.db.exists("UOM", _(d.get("uom_name"))): uom_doc = frappe.get_doc( { "doctype": "UOM", "uom_name": _(d.get("uom_name")), "name": _(d.get("uom_name")), "must_be_whole_number": d.get("must_be_whole_number"), "enabled": 1, } ).db_insert() # bootstrap uom conversion factors uom_conversions = json.loads( open( frappe.get_app_path("erpnext", "setup", "setup_wizard", "data", "uom_conversion_data.json") ).read() ) for d in uom_conversions: if not frappe.db.exists("UOM Category", _(d.get("category"))): frappe.get_doc({"doctype": "UOM Category", "category_name": _(d.get("category"))}).db_insert() if not f

add_uom_data

tests/cache/tests.py

Refs #33476 -- Reformatted code with Black.

django

Python

tests.py

def test_set_many_invalid_key(self): msg = KEY_ERRORS_WITH_MEMCACHED_MSG % ":1:key with spaces" with self.assertWarnsMessage(CacheKeyWarning, msg): cache.set_many({"key with spaces": "foo"})

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def test_set_many_invalid_key(self): msg = KEY_ERRORS_WITH_MEMCACHED_MSG % ":1:key with spaces" with self.assertWarnsMessage(CacheKeyWarning, msg): cache.set_many({"key with spaces":

test_set_many_invalid_key

utils/check_repo.py

Add `is_torch_available`, `is_flax_available` (#204) * Add is_<framework>_available, refactor import utils * deps * quality

diffusers

Python

check_repo.py

def ignore_undocumented(name): # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # ModelMixins / Encoders / Decoders / Layers / Embeddings / Attention are not documented. if ( name.endswith("ModelMixin") or name.endswith("Decoder") or name.endswith("Encoder") or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") ): return True # Submodules are not documented. if os.path.isdir(os.path.join(PATH_TO_DIFFUSERS, name)) or os.path.isfile( os.path.join(PATH_TO_DIFFUSERS, f"{name}.py") ): return True # All load functions are not documented. if name.startswith("load_tf") or name.startswith("load_pytorch"): return True # is_xxx_available functions are not documented. if name.startswith("is_") and name.endswith("_available"): return True # Deprecated objects are not documented. if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: return True # MMBT model does not really work. if name.startswith("MMBT"): return True if name in SHOULD_HAVE_THEIR_OWN_PAGE: return True return False

df90f0ce989dcccd7ef2fe9ff085da3197b2f2ad

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

def ignore_undocumented(name): # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # ModelMixins / Encoders / Decoders /

ignore_undocumented

homeassistant/components/volumio/media_player.py

Improve entity type hints [v] (#77885)

core

Python

media_player.py

async def async_media_pause(self) -> None: if self._state.get("trackType") == "webradio": await self._volumio.stop() else: await self._volumio.pause()

050cb275ffd51891fa58121643086dad304776a3

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

async def async_media_pause(self) -> None: if self._state.get("trackType") == "webradio": await self._volumio

async_media_pause

tests/providers/microsoft/azure/hooks/test_azure_cosmos.py

(AzureCosmosDBHook) Update to latest Cosmos API (#21514) * Bumping the ms azure cosmos providers to work with the 4.x azure python sdk api Co-authored-by: gatewoodb <ben@everythingisbroken.net>

airflow

Python

test_azure_cosmos.py

def test_delete_database(self, mock_cosmos): hook = AzureCosmosDBHook(azure_cosmos_conn_id='azure_cosmos_test_key_id') hook.delete_database(self.test_database_name) expected_calls = [mock.call().delete_database('test_database_name')] mock_cosmos.assert_any_call(self.test_end_point, {'masterKey': self.test_master_key}) mock_cosmos.assert_has_calls(expected_calls)

3c4524b4ec2b42a8af0a8c7b9d8f1d065b2bfc83

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

def test_delete_database(self, mock_cosmos): hook = AzureCosmosDBHook(azure_cosmos_conn_id='azure_cosmos_test_key_id') hook.delete_database(self.test_database_name) expected_calls = [mock.call().delete_database('test_database_name')] mock_cosmos.assert_any_call(self.test_end_point, {'masterKey': self.test_master_key}) mock_cosmos.assert_has_calls(expected_calls)

test_delete_database

pandas/tests/arrays/sparse/test_arithmetics.py

TST/CLN: organize SparseArray tests (#45693)

pandas

Python

test_arithmetics.py

def test_float_same_index_comparison(self, kind): # when sp_index are the same values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan]) a = SparseArray(values, kind=kind) b = SparseArray(rvalues, kind=kind) self._check_comparison_ops(a, b, values, rvalues) values = np.array([0.0, 1.0, 2.0, 6.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0]) rvalues = np.array([0.0, 2.0, 3.0, 4.0, 0.0, 0.0, 1.0, 3.0, 2.0, 0.0]) a = SparseArray(values, kind=kind, fill_value=0) b = SparseArray(rvalues, kind=kind, fill_value=0) self._check_comparison_ops(a, b, values, rvalues)

5e40ff55ae2a4e2a1eaab0c924e5c369c591523d

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

def test_float_same_index_comparison(self, kind): # when sp_index are the same values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan]) a = SparseArray(values, kind=kind) b = SparseArray(rvalues, kind=kind) self._check_comparison_ops(a, b, values, rvalues) values = np.array([0.0, 1.0, 2.0, 6.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0]) rvalues = np.array([0.0, 2.0, 3.0, 4.0, 0.0, 0.0, 1.0, 3.0, 2.0, 0.0]) a = SparseArray(values, kind=kind, fill_value=0) b = SparseArray(rvalues, kind=kind, fill_value=0) self._check_comparison_ops(a, b, values, rvalues)

test_float_same_index_comparison

mindsdb/api/mongo/responders/coll_stats.py

del model interface

mindsdb

Python

coll_stats.py

def result(self, query, request_env, mindsdb_env, session): db = query['$db'] collection = query['collStats'] scale = query.get('scale') if db != 'mindsdb' or collection == 'predictors' or scale is None: # old behavior # NOTE real answer is huge, i removed most data from it. res = { 'ns': "db.collection", 'size': 1, 'count': 0, 'avgObjSize': 1, 'storageSize': 16384, 'capped': False, 'wiredTiger': { }, 'nindexes': 1, 'indexDetails': { }, 'totalIndexSize': 16384, 'indexSizes': { '_id_': 16384 }, 'ok': 1 } res['ns'] = f"{db}.{collection}" if db == 'mindsdb' and collection == 'predictors': res['count'] = len(mindsdb_env['model_controller'].get_models()) else: ident_parts = [collection] if scale is not None: ident_parts.append(scale) ast_query = Describe(Identifier( parts=ident_parts )) data = run_sql_command(mindsdb_env, ast_query) res = { 'data': data } res['ns'] = f"{db}.{collection}" return res responder = Responce()

6eb408a9973fbc24c973d6524dc34cb9b1e0ee05

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

def result(self, query, request_env, mindsdb_env, session): db = query['$db'] collection = query['collStats'] scale = query.get('scale') if db != 'mindsdb' or collection == 'predictors' or scale is None: # old behavior # NOTE real answer is huge, i removed most da

result

modules/image/Image_editing/colorization/user_guided_colorization/test.py

update user_guided_colorization (#1994) * update user_guided_colorization * add clean func

PaddleHub

Python

test.py

def setUpClass(cls) -> None: img_url = 'https://unsplash.com/photos/1sLIu1XKQrY/download?ixid=MnwxMjA3fDB8MXxhbGx8MTJ8fHx8fHwyfHwxNjYyMzQxNDUx&force=true&w=640' if not os.path.exists('tests'): os.makedirs('tests') response = requests.get(img_url) assert response.status_code == 200, 'Network Error.' with open('tests/test.jpg', 'wb') as f: f.write(response.content) cls.module = hub.Module(name="user_guided_colorization")

0ea0f8e8757c3844a98d74013ae3708836bd6355

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

def setUpClass(cls) -> None: img_url = 'https://unsplash.com/photos/1sLIu1XKQrY/download?ixid=MnwxMjA3fDB8MXxhbGx8MTJ8fHx8fHwyfHwxNjYyMzQxNDUx&force=true&w=640' if not os.path.exists('tests'):

setUpClass

numpy/core/setup_common.py

make MismatchCAPIWarnining into MismatchCAPIError

numpy

Python

setup_common.py

def check_api_version(apiversion, codegen_dir): curapi_hash, api_hash = get_api_versions(apiversion, codegen_dir) # If different hash, it means that the api .txt files in # codegen_dir have been updated without the API version being # updated. Any modification in those .txt files should be reflected # in the api and eventually abi versions. # To compute the checksum of the current API, use numpy/core/cversions.py if not curapi_hash == api_hash: msg = ("API mismatch detected, the C API version " "numbers have to be updated. Current C api version is " f"{apiversion}, with checksum {curapi_hash}, but recorded " f"checksum in core/codegen_dir/cversions.txt is {api_hash}. If " "functions were added in the C API, you have to update " f"C_API_VERSION in {__file__}." ) raise MismatchCAPIError(msg) FUNC_CALL_ARGS = {}

54a7b0b9843e2e89b217eaa38550752bb4754119

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

def check_api_version(apiversion, codegen_dir): curapi_hash, api_hash = get_api_versions(apiversion, codegen_dir) # If different hash, it means that the api .txt files in # codegen_dir have been updated without the API version being # updated. Any modification in those .txt files should be reflected # in the api and eventually abi versions. # To compute the checksum of the current API, use numpy/core/cversions.py if not curapi_hash == api_hash: msg = ("API mismatc

check_api_version

netbox/dcim/tests/test_cablepaths.py

Add cable topology tests

netbox

Python

test_cablepaths.py

def test_214_interface_to_providernetwork_via_circuit(self): interface1 = Interface.objects.create(device=self.device, name='Interface 1') providernetwork = ProviderNetwork.objects.create(name='Provider Network 1', provider=self.circuit.provider) circuittermination1 = CircuitTermination.objects.create(circuit=self.circuit, site=self.site, term_side='A') circuittermination2 = CircuitTermination.objects.create(circuit=self.circuit, provider_network=providernetwork, term_side='Z') # Create cable 1 cable1 = Cable( a_terminations=[interface1], b_terminations=[circuittermination1] ) cable1.save() self.assertPathExists( (interface1, cable1, circuittermination1, circuittermination2, providernetwork), is_active=True ) self.assertEqual(CablePath.objects.count(), 1) # Delete cable 1 cable1.delete() self.assertEqual(CablePath.objects.count(), 0) interface1.refresh_from_db() self.assertPathIsNotSet(interface1)

537383e0713645564ba2949e37dc2cbf41eb3317

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

def test_214_interface_to_providernetwork_via_circuit(self): interface1 = Interface.objects.create(device=self.device, name='Interface 1') providernetwork = ProviderNetwork.objects.create(name='Provider Network 1', provider=self.circuit.provider) circuittermination1 = CircuitTermination.objects.create(circuit=self.circuit, site=self.site, term_side='A') circuittermination2 = CircuitTerminati

test_214_interface_to_providernetwork_via_circuit

tests/handlers/test_receipts.py

Implement changes to MSC2285 (hidden read receipts) (#12168) * Changes hidden read receipts to be a separate receipt type (instead of a field on `m.read`). * Updates the `/receipts` endpoint to accept `m.fully_read`.

synapse

Python

test_receipts.py

def test_filters_out_event_with_only_hidden_receipts_and_ignores_the_rest(self): self._test_filters_hidden( [ { "content": { "$14356419edgd14394fHBLK:matrix.org": { ReceiptTypes.READ_PRIVATE: { "@rikj:jki.re": { "ts": 1436451550453, }, } }, "$1435641916114394fHBLK:matrix.org": { ReceiptTypes.READ: { "@user:jki.re": { "ts": 1436451550453, } } }, }, "room_id": "!jEsUZKDJdhlrceRyVU:example.org", "type": "m.receipt", } ], [ { "content": { "$1435641916114394fHBLK:matrix.org": { ReceiptTypes.READ: { "@user:jki.re": { "ts": 1436451550453, } } } }, "room_id": "!jEsUZKDJdhlrceRyVU:example.org", "type": "m.receipt", } ], )

116a4c8340b729ffde43be33df24d417384cb28b

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

def test_filters_out_event_with_only_hidden_receipts_and_ignores_the_rest(self): self._test_filters_hidden( [ { "content": { "$14356419edgd14394fHBLK:matrix.org": { ReceiptTypes.READ_PRIVATE: { "@rikj:jki.re": { "ts": 1436451550453, }, } }, "$1435641916114394fHBLK:matrix.org": { ReceiptTypes.READ: { "@user:jki.re": { "ts": 1436451550453, } } }, }, "room_id": "!jEsUZKDJdhlrceRyVU:example.org",

test_filters_out_event_with_only_hidden_receipts_and_ignores_the_rest

tests/api_connexion/endpoints/test_dag_endpoint.py

Add more fields to REST API dags/dag_id/details endpoint (#22756) Added more fields to the DAG details endpoint, which is the endpoint for getting DAG `object` details

airflow

Python

test_dag_endpoint.py

def test_should_response_200_for_null_start_date(self): response = self.client.get( f"/api/v1/dags/{self.dag3_id}/details", environ_overrides={'REMOTE_USER': "test"} ) assert response.status_code == 200 last_parsed = response.json["last_parsed"] expected = { "catchup": True, "concurrency": 16, "max_active_tasks": 16, "dag_id": "test_dag3", "dag_run_timeout": None, "default_view": "grid", "description": None, "doc_md": None, "fileloc": __file__, "file_token": FILE_TOKEN, "is_paused": None, "is_active": None, "is_subdag": False, "orientation": "LR", "owners": ['airflow'], "params": {}, "schedule_interval": { "__type": "TimeDelta", "days": 1, "microseconds": 0, "seconds": 0, }, "start_date": None, "tags": [], "timezone": "Timezone('UTC')", "max_active_runs": 16, "pickle_id": None, "end_date": None, 'is_paused_upon_creation': None, 'last_parsed': last_parsed, 'render_template_as_native_obj': False, } assert response.json == expected

34d2dd8853849d00de2e856b1f79cffe4da6d990

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

def test_should_response_200_for_null_start_date(self): response = self.client.get( f"/api/v1/dags/{self.dag3_id}/details", environ_overrides={'REMOTE_USER': "test"} ) assert response.status_code == 200 last_parsed = response.json["last_parsed"] expected = { "catchup": True, "concurrency": 16, "max_active_tasks": 16, "dag_id": "test_dag3", "dag_run_timeout": None, "default_view": "grid", "description": None, "doc_md": None, "fileloc": __file__, "file_token": FILE_TOKEN, "is_paused": None, "is_active": None, "is_subdag": False, "orientation": "LR", "owners": ['airflow'], "params": {}, "schedule_interval": { "__type": "TimeDelta", "days": 1, "microseconds": 0, "seconds": 0, }, "start_date": None, "tags": [], "timez

test_should_response_200_for_null_start_date

keras/applications/resnet_rs.py

KERAS application addition of Resnet-RS model

keras

Python

resnet_rs.py

def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top) preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF, error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC) decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__ DOC = setattr(ResNetRS50, '__doc__', ResNetRS50.__doc__ + DOC) setattr(ResNetRS152, '__doc__', ResNetRS152.__doc__ + DOC) setattr(ResNetRS200, '__doc__', ResNetRS200.__doc__ + DOC) setattr(ResNetRS270, '__doc__', ResNetRS270.__doc__ + DOC) setattr(ResNetRS350, '__doc__', ResNetRS350.__doc__ + DOC) setattr(ResNetRS420, '__doc__', ResNetRS420.__doc__ + DOC)

c223693db91473c9a71c330d4e38a751d149f93c

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

def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top) preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF, error=imagenet_uti

decode_predictions

pandas/tests/generic/test_frame.py

fix pylint bad-super-call (#48896) * fix pylint bad-super-call * fix black pre commit * Update pyproject.toml Co-authored-by: Marco Edward Gorelli <33491632+MarcoGorelli@users.noreply.github.com> * change super() to df.copy() Co-authored-by: Marco Edward Gorelli <33491632+MarcoGorelli@users.noreply.github.com>

pandas

Python

test_frame.py

def test_validate_bool_args(self, value): df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) msg = 'For argument "inplace" expected type bool, received type' with pytest.raises(ValueError, match=msg): df.copy().rename_axis(mapper={"a": "x", "b": "y"}, axis=1, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().drop("a", axis=1, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().fillna(value=0, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().replace(to_replace=1, value=7, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().interpolate(inplace=value) with pytest.raises(ValueError, match=msg): df.copy()._where(cond=df.a > 2, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().mask(cond=df.a > 2, inplace=value)

159a91754159545df743ff89fc51e83d5421993b

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

def test_validate_bool_args(self, value): df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) msg = 'For argument "inplace" expected type bool, received type' with pytest.raises(ValueError, match=msg): df.copy().rename_axis(mapper={"a": "x", "b": "y"}, axis=1, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().drop("a", axis=1, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().fillna(value=0, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().replace(to_replace=1, value=7, inplace=value) with pytest.raises(ValueError, match=msg): df.copy().interpolate(inplace=value)

test_validate_bool_args

pandas/tests/arrays/datetimes/test_constructors.py

⬆️ UPGRADE: Autoupdate pre-commit config (#45752) Co-authored-by: MarcoGorelli <MarcoGorelli@users.noreply.github.com>

pandas

Python

test_constructors.py

def test_from_pandas_array(self): arr = pd.array(np.arange(5, dtype=np.int64)) * 3600 * 10**9 result = DatetimeArray._from_sequence(arr)._with_freq("infer") expected = pd.date_range("1970-01-01", periods=5, freq="H")._data tm.assert_datetime_array_equal(result, expected)

419331c598a097896edae40bc0687e4127f97b6b

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

def test_from_pandas_array(self): arr = pd.array(np.arange(5, dtype=np.int64)) * 3600 * 10**9 result = Da

test_from_pandas_array

pandas/tests/frame/indexing/test_setitem.py

REF: Add Manager.column_setitem to set values into a single column (without intermediate series) (#47074)

pandas

Python

test_setitem.py

def test_setitem_partial_column_inplace(self, consolidate, using_array_manager): # This setting should be in-place, regardless of whether frame is # single-block or multi-block # GH#304 this used to be incorrectly not-inplace, in which case # we needed to ensure _item_cache was cleared. df = DataFrame( {"x": [1.1, 2.1, 3.1, 4.1], "y": [5.1, 6.1, 7.1, 8.1]}, index=[0, 1, 2, 3] ) df.insert(2, "z", np.nan) if not using_array_manager: if consolidate: df._consolidate_inplace() assert len(df._mgr.blocks) == 1 else: assert len(df._mgr.blocks) == 2 zvals = df["z"]._values df.loc[2:, "z"] = 42 expected = Series([np.nan, np.nan, 42, 42], index=df.index, name="z") tm.assert_series_equal(df["z"], expected) # check setting occurred in-place tm.assert_numpy_array_equal(zvals, expected.values) assert np.shares_memory(zvals, df["z"]._values)

b99ec4a9c92e288ace6b63072ffc4c296f8e5dc9

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

def test_setitem_partial_column_inplace(self, consolidate, using_array_manager): # This setting should be in-place, regardless of whether frame is # single-block or multi-block # GH#304 this used to be i

test_setitem_partial_column_inplace

tests/rest/client/test_rooms.py

Remove unnecessary `json.dumps` from tests (#13303)

synapse

Python

test_rooms.py

def test_search_filter_not_labels(self) -> None: request_data = { "search_categories": { "room_events": { "search_term": "label", "filter": self.FILTER_NOT_LABELS, } } } self._send_labelled_messages_in_room() channel = self.make_request( "POST", "/search?access_token=%s" % self.tok, request_data ) results = channel.json_body["search_categories"]["room_events"]["results"] self.assertEqual( len(results), 4, [result["result"]["content"] for result in results], ) self.assertEqual( results[0]["result"]["content"]["body"], "without label", results[0]["result"]["content"]["body"], ) self.assertEqual( results[1]["result"]["content"]["body"], "without label", results[1]["result"]["content"]["body"], ) self.assertEqual( results[2]["result"]["content"]["body"], "with wrong label", results[2]["result"]["content"]["body"], ) self.assertEqual( results[3]["result"]["content"]["body"], "with two wrong labels", results[3]["result"]["content"]["body"], )

efee345b454ac5e6aeb4b4128793be1fbc308b91

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

def test_search_filter_not_labels(self) -> None: request_data = { "search_categories": { "room_events": { "search_term": "label", "filter": self.FILTER_NOT_LABELS, } } } self._send_labelled_messages_in_room() channel = self.make_request( "POST", "/search?access_token=%s" % self.tok, request_data ) results = channel.json_body["search_categories"]["room_events"]["results"] self.assertEqual( len(results), 4, [result["result"]["content"] for result in results], ) self.assertEqual( results[0]["result"]["content"]["body"], "without label", results[0]["result"]["content"]["body"], ) self.assertEqual( results[1]["result"]["content"]["body"], "without label", results[1]["result"]["content"]["body"], ) self.assertEqual( results[2]["result"]["content"]["body"], "with wrong label", results[2]["result"]["content"]["body"], ) self.assertEqual( results[3

test_search_filter_not_labels

ldm/modules/image_degradation/utils_image.py

release more models

stablediffusion

Python

utils_image.py

def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] n_dim = tensor.dim() if n_dim == 4: n_img = len(tensor) img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 3: img_np = tensor.numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 2: img_np = tensor.numpy() else: raise TypeError( 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) if out_type == np.uint8: img_np = (img_np * 255.0).round() # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. return img_np.astype(out_type)

ca86da3a30c4e080d4db8c25fca73de843663cb4

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

def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] n_dim = tensor.dim() if n_dim == 4: n_img = len(tensor) img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 3: img_np = tensor.numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 2: img_np = tensor.numpy() else: raise TypeError( 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) if out_type == np.uint8

tensor2img

openbb_terminal/featflags_controller.py

New path for .env (#2508) * add log path * add test to check if log file is in correct dir * env path * black * mypy fix * linting * add make_paths and change references * terminal change * change constants to paths * change names * black * mypy * mypy * pylint else * add make paths * remove custom user dir name Co-authored-by: Chavithra <chavithra@gmail.com>

OpenBBTerminal

Python

featflags_controller.py

def call_color(self, _): obbff.USE_COLOR = not obbff.USE_COLOR set_key(obbff.USER_ENV_FILE, "OPENBB_USE_COLOR", str(obbff.USE_COLOR)) console.print("")

3d0190e35bae4092f52025377d8604b3a6a17bfa

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

def call_color(self, _): obbff.USE_COLOR = not obbff.USE_COLOR set_key(obbff.USER_ENV_

call_color

tests/generation/test_generation_beam_search.py

Generate: get the correct beam index on eos token (#18851)

transformers

Python

test_generation_beam_search.py

def check_beam_scorer_update(self, input_ids, next_tokens, next_indices, next_scores): # check too many eos tokens beam_scorer = self.prepare_beam_scorer() tokens = next_tokens.clone() tokens[0, :] = self.eos_token_id with self.parent.assertRaises(ValueError): beam_scorer.process(input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id) # check all batches are done beam_scorer = self.prepare_beam_scorer() tokens = next_tokens.clone() tokens[:, : self.num_beams] = self.eos_token_id beam_indices = torch.zeros_like(input_ids) + torch.arange(input_ids.shape[-1], device=input_ids.device) beam_indices = tuple(tuple(b) for b in beam_indices) beam_scorer.process( input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id, beam_indices=beam_indices ) # beam scorer should be done self.parent.assertTrue(beam_scorer.is_done) # check beam_scorer = self.prepare_beam_scorer() tokens = next_tokens.clone() tokens[:, 1] = self.eos_token_id beam_outputs = beam_scorer.process( input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id, beam_indices=beam_indices ) output_scores = beam_outputs["next_beam_scores"] output_tokens = beam_outputs["next_beam_tokens"] output_indices = beam_outputs["next_beam_indices"]

d4dbd7ca59bd50dd034e7995cb36e5efed3d9512

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

def check_beam_scorer_update(self, input_ids, next_tokens, next_indices, next_scores): # check too many eos tokens beam_scorer = self.prepare_beam_scorer() tokens = next_tokens.clone() tokens[0, :] = self.eos_token_id with self.parent.assertRaises(ValueError): beam_scorer.process(input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id) # check all batches are done beam_scorer = self.prepare_beam_scorer() tokens = next_tokens.clone() tokens[:, : self.num_beams] = self.eos_token_id beam_indices = torch.zeros_like(input_ids) + torch.arange(input_ids.shape[-1], device=input_ids.device) beam_indices = tuple(tuple(b) for b in beam_indices) beam_scorer.process( input_ids, next_scores, tokens, next_indices, eos_token_id=self.eos_token_id, beam_indices=beam_indices ) # beam scorer should be done self.parent.assertTrue(beam_scorer.is_don

check_beam_scorer_update

packages/syft/tests/syft/core/tensor/adp/entity_list_test.py

Added capnp to sy.serialize / sy.deserialize interface - Added np.array utf-8 string serialization

PySyft

Python

entity_list_test.py

def test_entity_list_serde() -> None: entities = ["🥒pickles", "madhava", "short", "muchlongername", "a", "🌶"] entity_list = EntityList.from_objs([Entity(name=entity) for entity in entities]) ser = sy.serialize(entity_list, to_bytes=True) de = sy.deserialize(ser, from_bytes=True) de.one_hot_lookup == entity_list.one_hot_lookup assert entity_list == de

530a1aa0eb7f10555b7dcf61c27e3230e019e9c6

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

def test_entity_list_serde() -> None: entities = ["🥒pickles", "madhava", "short", "muchlongername", "a", "🌶"] entity_list = EntityList.from_objs([Entity(name=entity) for entity in entities]) ser = sy.serialize(entity_list, to_bytes=True)

test_entity_list_serde

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

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

pipenv

Python

windows.py

def _pick_get_win_folder() -> Callable[[str], str]: if hasattr(ctypes, "windll"): return get_win_folder_via_ctypes try: import winreg # noqa: F401 except ImportError: return get_win_folder_from_env_vars else: return get_win_folder_from_registry get_win_folder = lru_cache(maxsize=None)(_pick_get_win_folder()) __all__ = [ "Windows", ]

f3166e673fe8d40277b804d35d77dcdb760fc3b3

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

def _pick_get_win_folder() -> Callable[[str], str]: if hasattr(ctypes, "windll"): return get_win_folder_via_ctypes try: import winreg # noqa: F401 except ImportErr

_pick_get_win_folder

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

[rfc][ci] create fake docker-compose cluster environment (#20256) Following #18987 this PR adds a docker-compose based local multi node cluster. The fake multinode docker comprises two parts. The docker_monitor.py script is a watch script calling docker compose up whenever the docker-compose.yaml changes. The node provider creates and updates the docker compose according to the autoscaling requirements. This mode fully supports autoscaling and comes with test utilities to start and connect to docker-compose autoscaling environments. There's also a sample test case showing how this can be used.

ray

Python

node_provider.py

def _save_node_state(self): with open(self._node_state_path, "wt") as f: json.dump(self._nodes, f) # Make sure this is always writeable from inside the containers if not self.in_docker_container: # Only chmod from the outer container os.chmod(self._node_state_path, 0o777)

5a7f6e4fddabd151baf96d64d6c45e5964766653

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

def _save_node_state(self): with open(self._node_state_path, "wt") as f: json.dump(s

_save_node_state

sympy/physics/continuum_mechanics/truss.py

rectified non-alignment of nodes

sympy

Python

truss.py

def _draw_nodes(self, subs_dict): node_markers = [] for node in list(self._node_coordinates): if (type(self._node_coordinates[node][0]) in (Symbol, Quantity)): if self._node_coordinates[node][0] in list(subs_dict): self._node_coordinates[node][0] = subs_dict[self._node_coordinates[node][0]] else: raise ValueError("provided substituted dictionary is not adequate") elif (type(self._node_coordinates[node][0]) == Mul): objects = self._node_coordinates[node][0].as_coeff_Mul() for object in objects: if type(object) in (Symbol, Quantity): if subs_dict==None or object not in list(subs_dict): raise ValueError("provided substituted dictionary is not adequate") else: self._node_coordinates[node][0] /= object self._node_coordinates[node][0] *= subs_dict[object] if (type(self._node_coordinates[node][1]) in (Symbol, Quantity)): if self._node_coordinates[node][1] in list(subs_dict): self._node_coordinates[node][1] = subs_dict[self._node_coordinates[node][1]] else: raise ValueError("provided substituted dictionary is not adequate") elif (type(self._node_coordinates[node][1]) == Mul): objects = self._node_coordinates[node][1].as_coeff_Mul() for object in objects: if type(object) in (Symbol, Quantity): if subs_dict==None or object not in list(subs_dict): raise ValueError("provided substituted dictionary is not adequate") else: self._node_coordinates[node][1] /= object self._node_coordinates[node][1] *= subs_dict[object] for node in list(self._node_coordinates): node_markers.append( { 'args':[[self._node_coordinates[node][0]], [self._node_coordinates[node][1]]], 'marker':'o', 'markersize':5, 'color':'black' } ) return node_markers

86975d1b114689b68dd9f7b953602f318c4497ec

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

def _draw_nodes(self, subs_dict): node_markers = [] for node in list(self._node_coordinates): if (type(self._node_coordinates[node][0]) in (Symbol, Quantity)): if self._node_coordinates[node][0] in list(subs_dict): self._node_coordinates[node][0] = subs_dict[self._node_coordinates[node][0]] else: raise ValueError("provided substituted dictionary is not adequate") elif (type(self._node_coordinates[node][0]) == Mul): objects = self._node_coordinates[node][0].as_coeff_Mul() for object

_draw_nodes

python/ray/tune/syncer.py

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

ray

Python

syncer.py

def sync_up_to_new_location(self, worker_ip): if worker_ip != self.worker_ip: logger.debug("Setting new worker IP to %s", worker_ip) self.set_worker_ip(worker_ip) self.reset() if not self.sync_up(): logger.warning( "Sync up to new location skipped. This should not occur." ) else: logger.warning("Sync attempted to same IP %s.", worker_ip)

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def sync_up_to_new_location(self, worker_ip): if worker_ip != self.worker_ip: logger.debug("Setting new worker IP to %s", worker_ip) self.set_worker

sync_up_to_new_location

wagtail/admin/tests/pages/test_delete_page.py

review fixes

wagtail

Python

test_delete_page.py

def test_confirm_delete_scenario_1(self): # If the number of pages to be deleted are less than # WAGTAILADMIN_UNSAFE_PAGE_DELETION_LIMIT then don't need # for confirmation child_1 = SimplePage(title="child 1", slug="child-1", content="hello") self.child_page.add_child(instance=child_1) child_2 = SimplePage(title="child 2", slug="child-2", content="hello") self.child_page.add_child(instance=child_2) response = self.client.get( reverse("wagtailadmin_pages:delete", args=(self.child_page.id,)) ) self.assertEqual(response.status_code, 200) self.assertNotContains( response, '<input class="w-mb-4" type="text" name="confirm_site_name" id="id_confirm_site_name" required>', ) # deletion should not actually happen on GET self.assertTrue(SimplePage.objects.filter(id=self.child_page.id).exists()) # And admin should be able to delete page without any confirmation response = self.client.post( reverse("wagtailadmin_pages:delete", args=(self.child_page.id,)) ) # Check that page is deleted self.assertFalse(SimplePage.objects.filter(id=self.child_page.id).exists())

84662031294740d59eee60af37e69c3735de1117

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

def test_confirm_delete_scenario_1(self): # If the number of pages to be deleted are less than # WAGTAILADMIN_UNSAFE_PAGE_DELETION_LIMIT then don't need # for confirmation child_1 = SimplePage(title="child 1", slug="child-1", content="hello") self.child_page.add_child(instance=child_1) child_2 = SimplePage(title="child 2", slug="child-2", content="hello") self.child_page.add_child(instance=child_2) response = self.client.get( reverse("wagtailadmin_pages:delete", args=(self.child_page.id,)) ) self.assertEqual(response.status_code, 200) self.assertNotContains( response, '<input class="w-mb-4" type="text" name="confirm_site_name" id="id_confirm_site_name" required>', ) # deletion should not actually happen on GET self.assertTrue(SimplePage.objects.filter(id=self.child_page.id).exists()) # And admin should be able to delete page without any confirmation response = self.client.post( reverse("wagtailadmin_pages:delete", args=(self.child_page.id,))

test_confirm_delete_scenario_1

modules/sd_samplers.py

prevent replacing torch_randn globally (instead replacing k_diffusion.sampling.torch) and add a setting to disable this all

stable-diffusion-webui

Python

sd_samplers.py

def randn_like(self, x): noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None if noise is not None and x.shape == noise.shape: res = noise else: res = torch.randn_like(x) self.sampler_noise_index += 1 return res

87e8b9a2ab3f033e7fdadbb2fe258857915980ac

https://github.com/AUTOMATIC1111/stable-diffusion-webui.git

def randn_like(self, x): noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None if noise is not None and x.shape == noise.shape: res = noise else: res = torch.randn_like(x) self.sampler_noise_index += 1 return res

randn_like

tests/test_evaluation/test_metrics/test_coco_metric.py

[Refactor] refactor dataflow and sync the latest mmengine (#8620) * refactor dataflow * fix docstr * fix commit * fix commit * fix visualizer hook * fix UT * fix UT * fix UT error * fix bug * update to mmengine main * update typehint * replace data preprocess output type to dict * update * fix typehint

mmdetection

Python

test_coco_metric.py

def test_format_only(self): # create dummy data fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json') self._create_dummy_coco_json(fake_json_file) dummy_pred = self._create_dummy_results() with self.assertRaises(AssertionError): CocoMetric( ann_file=fake_json_file, classwise=False, format_only=True, outfile_prefix=None) coco_metric = CocoMetric( ann_file=fake_json_file, metric='bbox', classwise=False, format_only=True, outfile_prefix=f'{self.tmp_dir.name}/test') coco_metric.dataset_meta = dict(CLASSES=['car', 'bicycle']) coco_metric.process( {}, [dict(pred_instances=dummy_pred, img_id=0, ori_shape=(640, 640))]) eval_results = coco_metric.evaluate(size=1) self.assertDictEqual(eval_results, dict()) self.assertTrue(osp.exists(f'{self.tmp_dir.name}/test.bbox.json'))

8405ad9bfce4867f552f2f7a643c9e78a97eb0b6

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

def test_format_only(self): # create dummy data fake_json_file = osp.join(self.tmp_dir.name, 'fake_data.json') self._create_dummy_coco_json(fake_json_file) dummy_pred = self._create_dummy_results() with self.assertRaises(AssertionError): CocoMetric( ann_file=fake_json_file, classwise=False, format_only=True, outfile_prefix=None) coco_metric = CocoMetric( ann_file=fake_json_file, metric='bbox', classwise=False, format_only=True, outfile_prefix=f'{self.tmp_dir.name}/test') coco_metric.dataset_meta = dict(CLASSES=['car', 'bicycle']) coco_metric.process( {}, [dict(pred_instances=dummy_pred, img_id=0, ori_shape=(640, 640))]) eval_results = coco_metric.evaluate(size=1) self.assertDictEqual(eval_results, dict()) self.assertTrue(osp.exists(f'{self.tmp_dir.name}/test.b

test_format_only

tests/freqai/conftest.py

create dedicated minimal freqai test strat

freqtrade

Python

conftest.py

def freqai_conf(default_conf): freqaiconf = deepcopy(default_conf) freqaiconf.update( { "datadir": Path(default_conf["datadir"]), "strategy": "freqai_test_strat", "strategy-path": "freqtrade/tests/strategy/strats", "freqaimodel": "LightGBMPredictionModel", "freqaimodel_path": "freqai/prediction_models", "timerange": "20180110-20180115", "freqai": { "startup_candles": 10000, "purge_old_models": True, "train_period_days": 5, "backtest_period_days": 2, "live_retrain_hours": 0, "expiration_hours": 1, "identifier": "uniqe-id100", "live_trained_timestamp": 0, "feature_parameters": { "include_timeframes": ["5m"], "include_corr_pairlist": ["ADA/BTC", "DASH/BTC"], "label_period_candles": 20, "include_shifted_candles": 1, "DI_threshold": 0.9, "weight_factor": 0.9, "principal_component_analysis": False, "use_SVM_to_remove_outliers": True, "stratify_training_data": 0, "indicator_max_period_candles": 10, "indicator_periods_candles": [10], }, "data_split_parameters": {"test_size": 0.33, "random_state": 1}, "model_training_parameters": {"n_estimators": 100}, }, "config_files": [Path('config_examples', 'config_freqai_futures.example.json')] } ) freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']}) return freqaiconf

c43935e82ad9b627875a61d02a2923ac101b7374

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

def freqai_conf(default_conf): freqaiconf = deepcopy(default_conf) freqaiconf.update( { "datadir": Path(default_conf["datadir"]), "strategy": "freqai_test_strat", "strategy-path": "freqtrade/tests/strategy/strats", "freqaimodel": "LightGBMPre

freqai_conf

pandas/tests/io/parser/dtypes/test_dtypes_basic.py

Add pyarrow support to python engine in read_csv (#50318)

pandas

Python

test_dtypes_basic.py

def test_use_nullable_dtypes_pyarrow_backend(all_parsers, request): # GH#36712 pa = pytest.importorskip("pyarrow") parser = all_parsers engine = parser.engine data = with pd.option_context("mode.nullable_backend", "pyarrow"): if engine == "c": request.node.add_marker( pytest.mark.xfail( raises=NotImplementedError, reason=f"Not implemented with engine={parser.engine}", ) ) result = parser.read_csv( StringIO(data), use_nullable_dtypes=True, parse_dates=["i"] ) expected = DataFrame( { "a": pd.Series([1, 3], dtype="int64[pyarrow]"), "b": pd.Series([2.5, 4.5], dtype="float64[pyarrow]"), "c": pd.Series([True, False], dtype="bool[pyarrow]"), "d": pd.Series(["a", "b"], dtype=pd.ArrowDtype(pa.string())), "e": pd.Series([pd.NA, 6], dtype="int64[pyarrow]"), "f": pd.Series([pd.NA, 7.5], dtype="float64[pyarrow]"), "g": pd.Series([pd.NA, True], dtype="bool[pyarrow]"), "h": pd.Series( [pd.NA if engine == "python" else "", "a"], dtype=pd.ArrowDtype(pa.string()), ), "i": pd.Series([Timestamp("2019-12-31")] * 2), "j": pd.Series([pd.NA, pd.NA], dtype="null[pyarrow]"), } ) tm.assert_frame_equal(result, expected)

b1c5b5d9517da7269163566892ba230ebf14afea

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

def test_use_nullable_dtypes_pyarrow_backend(all_parsers, request): # GH#36712 pa = pytest.importorskip("pyarrow") parser = all_parsers engine = parser.engine data = with pd.option_context("mode.nullable_backend", "pyarrow"): if engine == "c": request.node.add_marker( pytest.mark.xfail( raises=NotImplementedError, reason=f"Not implemented with engine={parser.engine}", ) ) result = parser.read_csv(

test_use_nullable_dtypes_pyarrow_backend

tests/rest/client/test_rooms.py

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

synapse

Python

test_rooms.py

def test_rooms_messages_sent(self) -> None: path = "/rooms/%s/send/m.room.message/mid1" % (urlparse.quote(self.room_id)) content = b'{"body":"test","msgtype":{"type":"a"}}' channel = self.make_request("PUT", path, content) self.assertEqual(400, channel.code, msg=channel.result["body"]) # custom message types content = b'{"body":"test","msgtype":"test.custom.text"}' channel = self.make_request("PUT", path, content) self.assertEqual(200, channel.code, msg=channel.result["body"]) # m.text message type path = "/rooms/%s/send/m.room.message/mid2" % (urlparse.quote(self.room_id)) content = b'{"body":"test2","msgtype":"m.text"}' channel = self.make_request("PUT", path, content) self.assertEqual(200, channel.code, msg=channel.result["body"])

2ffaf30803f93273a4d8a65c9e6c3110c8433488

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

def test_rooms_messages_sent(self) -> None: path = "/rooms/%s/send/m.room.message/mid1" % (urlparse.quote(self.room_id)) content = b'{"body":"test","msgtype":{"type":"a"}}' channel = self.make_request("PUT", p

test_rooms_messages_sent

saleor/webhook/observability/tests/test_payloads.py

Observability reporter (#9803) * Initial commit * Add observability celery beat task * Add observability_reporter_task and observability_send_events * Convert payload to camel case * Add fakeredis to dev dependencies * Add redis buffer tests * Refactor buffer * Update * Optimize buffer * Add tests * Add types-redis to dev dependencies * Refactor * Fix after rebase * Refactor opentracing * Add opentracing to observability tasks * Add more tests * Fix buffer fixtures * Report dropped events * Fix buffer tests * Refactor get_buffer * Refactor unit tests * Set Redis connection client_name * Refactor redis tests * Fix test_get_or_create_connection_pool * Fix JsonTruncText comparison * Add more generate_event_delivery_attempt_payload tests

saleor

Python

test_payloads.py

def test_serialize_gql_operation_result_when_no_operation_data(): bytes_limit = 1024 result = GraphQLOperationResponse() payload, _ = serialize_gql_operation_result(result, bytes_limit) assert payload == GraphQLOperation( name=None, operation_type=None, query=None, result=None, result_invalid=False ) assert len(dump_payload(payload)) <= bytes_limit

7ea7916c65357741c3911e307acb58d547a5e91a

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

def test_serialize_gql_operation_result_when_no_operation_data(): bytes_limit = 1024 result = G

test_serialize_gql_operation_result_when_no_operation_data

tests/test_optimize.py

Use better img2pdf settings where possible while supporting old versions Fixes #894

OCRmyPDF

Python

test_optimize.py

def test_multiple_pngs(resources, outdir): with Path.open(outdir / 'in.pdf', 'wb') as inpdf: img2pdf.convert( fspath(resources / 'baiona_colormapped.png'), fspath(resources / 'baiona_gray.png'), outputstream=inpdf, **IMG2PDF_KWARGS, )

2d0ac4707c6b19614bf56bede0892656cd0e1f0c

https://github.com/ocrmypdf/OCRmyPDF.git

def test_multiple_pngs(resources, outdir): with Path.open(outdir / 'in.pdf', 'wb') as inpdf: img2pdf.co

test_multiple_pngs

wagtail/snippets/tests/test_bulk_actions/test_bulk_delete.py

Fix plural handling for "no permission to delete these snippets" errors `./manage.py compilemessages` does not allow variables to differ between the singular and plural forms - it fails with a format specification for argument 'snippet_type_name', as in 'msgstr[0]', doesn't exist in 'msgid_plural' It's not possible to use the gettext pluralisation mechanism properly here, because we're using Django's verbose_name and verbose_name_plural properties which don't cover the requirements of languages with complex pluralisation rules. Since we can only hope to support English-style (`if n == 1`) pluralisation, use an n==1 test directly (as we have elsewhere in the template) rather than trying to shoehorn this into gettext pluralisation. While we're at it, remove the capitalisation of the snippet name - it makes no sense here (especially when only done for the plural).

wagtail

Python

test_bulk_delete.py

def test_delete_with_limited_permissions(self): self.user.is_superuser = False self.user.user_permissions.add( Permission.objects.get( content_type__app_label="wagtailadmin", codename="access_admin" ) ) self.user.save() response = self.client.get(self.url) self.assertEqual(response.status_code, 200) html = response.content.decode() self.assertInHTML( "<p>You don't have permission to delete these standard snippets</p>", html, ) for snippet in self.test_snippets: self.assertInHTML(f"<li>{snippet.text}</li>", html) response = self.client.post(self.url) # User should be redirected back to the index self.assertEqual(response.status_code, 302) # Documents should not be deleted for snippet in self.test_snippets: self.assertTrue(self.snippet_model.objects.filter(pk=snippet.pk).exists())

e0fd8e1a473d154c7ec154958e8c334db5a39a6d

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

def test_delete_with_limited_permissions(self): self.user.is_superuser = False self.user.user_permissions.add( Permission.objects.get( content_type__app_label="wagtailadmin", codename="access_admin" ) ) self.user.save() response = self.client.get(self.url) self.assertEqual(response.status_code, 200) html = response.content.decode() self.assertInHTML( "<p>You don't have permission to delete these standard snippets</p>", html, ) for snippet in self.test_snippets: self.assertInHTML(f"<li>{snippet.text}</li>", html) response = self.client.post

test_delete_with_limited_permissions

jax/interpreters/pxla.py

Fix Forward. The fix is on the user's end. Original PR: https://github.com/google/jax/pull/12217 Co-authored-by: Matthew Johnson <mattjj@google.com> Co-authored-by: Yash Katariya <yashkatariya@google.com> PiperOrigin-RevId: 472999907

jax

Python

pxla.py

def _pmap_dce_rule(used_outputs, eqn): # just like pe.dce_jaxpr_call_rule, except handles in_axes / out_axes new_jaxpr, used_inputs = pe.dce_jaxpr(eqn.params['call_jaxpr'], used_outputs) _, donated_invars = partition_list(used_inputs, eqn.params['donated_invars']) # TODO(yashkatariya,mattjj): Handle global_arg_shapes here too. _, in_axes = partition_list(used_inputs, eqn.params['in_axes']) _, out_axes = partition_list(used_outputs, eqn.params['out_axes']) new_params = dict(eqn.params, call_jaxpr=new_jaxpr, donated_invars=tuple(donated_invars), in_axes=tuple(in_axes), out_axes=tuple(out_axes)) if not any(used_inputs) and not any(used_outputs) and not new_jaxpr.effects: return used_inputs, None else: new_eqn = pe.new_jaxpr_eqn( [v for v, used in zip(eqn.invars, used_inputs) if used], [v for v, used in zip(eqn.outvars, used_outputs) if used], eqn.primitive, new_params, new_jaxpr.effects, eqn.source_info) return used_inputs, new_eqn # Set param update handlers to update `donated_invars` just like xla_call_p pe.call_param_updaters[xla_pmap_p] = pe.call_param_updaters[xla.xla_call_p] pe.partial_eval_jaxpr_custom_rules[xla_pmap_p] = \ partial(pe.call_partial_eval_custom_rule, 'call_jaxpr', _pmap_partial_eval_custom_params_updater, res_aval=_pmap_partial_eval_custom_res_maker) pe.dce_rules[xla_pmap_p] = _pmap_dce_rule ad.call_param_updaters[xla_pmap_p] = ad.call_param_updaters[xla.xla_call_p] ad.call_transpose_param_updaters[xla_pmap_p] = \ ad.call_transpose_param_updaters[xla.xla_call_p] ad.primitive_transposes[xla_pmap_p] = partial(ad.map_transpose, xla_pmap_p)

7fbf8ec669c03ce0e1014aaf010dabdf5985509f

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

def _pmap_dce_rule(used_outputs, eqn): # just like pe.dce_jaxpr_call_rule, except handles in_axes / out_axes new_jaxpr, used_inputs = pe.dce_jaxpr(eqn.params['call_jaxpr'], used_outputs) _, donated_invars = partition_list(used_inputs, eqn.params['donated_invars']) # TODO(yashkatariya,mattjj): Handle global_arg_shapes here too. _, in_axes = partition_list(used_inputs, eqn.params['in_axes']) _, out_axes = partition_list(used_outputs, eqn.params['out_axes']) new_params = dict(eqn.params, call_jaxpr=new_jaxpr, donated_invars=tuple(donated_invars), in_axes=tuple(in_axes), out_axes=tuple(out_axes)) if not any(used_inputs) and not any(used_outputs) and not new_jaxpr.effects: return used_inputs, None else: new_eqn = pe.new_jaxpr_eqn( [v for v, used in zip(eqn.invars, used_inputs) if used], [v for v, used in zip(eqn.outvars, used_outputs) if used], eqn.primitive, new_params, new_jaxpr.effects, eqn.source_info) return used_inputs, new_eqn # Set param update handlers to update `donated_invars` just like xla_call_p pe.call_param_updaters[xla_pmap_p] = pe.call_param_updaters[xla.xla_call_p] pe.partial_eval_jaxpr_custom_rules[xla_pmap_p] =

_pmap_dce_rule

python/ray/tests/test_batch_node_provider_unit.py

[autoscaler][kuberay] Batching node provider (#29933) Implements the abstract subclass of NodeProvider proposed in https://docs.google.com/document/d/1JyQINBFirZw7YenA_14zize0R3hIII1_fnfQytIXTPo/ The goal is to simplify the autoscaler's interactions with external cluster managers like the KubeRay operator. A follow-up PR will implement KuberayNodeProvider as a subclass of the BatchingNodeProvider added here. Signed-off-by: Dmitri Gekhtman <dmitri.m.gekhtman@gmail.com>

ray

Python

test_batch_node_provider_unit.py

def _add_node(self, node_type, node_kind): new_node_id = str(uuid4()) self._node_data_dict[new_node_id] = NodeData( kind=node_kind, ip=str(uuid4()), status=STATUS_UP_TO_DATE, type=node_type )

c51b0c9a5664e5c6df3d92f9093b56e61b48f514

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

def _add_node(self, node_type, node_kind): new_node_id = str(uuid4()) self._n

_add_node

erpnext/startup/leaderboard.py

style: format code with black

erpnext

Python

leaderboard.py

def get_all_sales_partner(date_range, company, field, limit=None): if field == "total_sales_amount": select_field = "sum(`base_net_total`)" elif field == "total_commission": select_field = "sum(`total_commission`)" filters = {"sales_partner": ["!=", ""], "docstatus": 1, "company": company} if date_range: date_range = frappe.parse_json(date_range) filters["transaction_date"] = ["between", [date_range[0], date_range[1]]] return frappe.get_list( "Sales Order", fields=[ "`sales_partner` as name", "{} as value".format(select_field), ], filters=filters, group_by="sales_partner", order_by="value DESC", limit=limit, ) @frappe.whitelist()

494bd9ef78313436f0424b918f200dab8fc7c20b

@frappe.whitelist()

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

def get_all_sales_partner(date_range, company, field, limit=None): if field == "total_sales_amount": select_field = "sum(`base_net_total`)" elif field == "total_commission": select_field = "sum(`total_commission`)" filters = {"sales_partner": ["!=", ""], "d

get_all_sales_partner

dask/tests/test_distributed.py

Stringify BlockwiseDepDict mapping values when produces_keys=True (#8972)

dask

Python

test_distributed.py

def test_from_delayed_dataframe(c): # Check that Delayed keys in the form of a tuple # are properly serialized in `from_delayed` pd = pytest.importorskip("pandas") dd = pytest.importorskip("dask.dataframe") df = pd.DataFrame({"x": range(20)}) ddf = dd.from_pandas(df, npartitions=2) ddf = dd.from_delayed(ddf.to_delayed()) dd.utils.assert_eq(ddf, df, scheduler=c) @pytest.mark.parametrize("fuse", [True, False])

bbd1d2f16b5ac4784d758252188047b7816c7fa4

@pytest.mark.parametrize("fuse", [True, False])

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

def test_from_delayed_dataframe(c): # Check that Delayed keys in the form of a tuple # are properly serialized in `from_delayed` pd = pytest.importorskip("pandas") dd = pytest.importorskip("d

test_from_delayed_dataframe

tests/util/test_async.py

Don't warn on time.sleep injected by the debugger (#65420)

core

Python

test_async.py

async def test_check_loop_async_custom(caplog): with pytest.raises(RuntimeError), patch( "homeassistant.util.async_.extract_stack", return_value=[ Mock( filename="/home/paulus/homeassistant/core.py", lineno="23", line="do_something()", ), Mock( filename="/home/paulus/config/custom_components/hue/light.py", lineno="23", line="self.light.is_on", ), Mock( filename="/home/paulus/aiohue/lights.py", lineno="2", line="something()", ), ], ): hasync.check_loop(banned_function) assert ( "Detected blocking call inside the event loop. This is causing stability issues. " "Please report issue to the custom component author for hue doing blocking calls " "at custom_components/hue/light.py, line 23: self.light.is_on" in caplog.text )

5a34feb7de440e0df748c9db500facc72a4c2646

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

async def test_check_loop_async_custom(caplog): with pytest.raises(RuntimeError), patch( "homeassistant.util.async_.extract_stack", return_value=[ Mock( filename="/home/paulus/homeassistant/core.py", lineno="23", line="do_something()", ), Mock( filename="/home/paulus/config/custom_compo

test_check_loop_async_custom

freqtrade/optimize/backtesting.py

Remove surplus mark columns, and make fillna on funding rate only

freqtrade

Python

backtesting.py

def load_bt_data_detail(self) -> None: if self.timeframe_detail: self.detail_data = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.timeframe_detail, timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=self.config.get('candle_type_def', CandleType.SPOT) ) else: self.detail_data = {} if self.trading_mode == TradingMode.FUTURES: # Load additional futures data. funding_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.FUNDING_RATE ) # For simplicity, assign to CandleType.Mark (might contian index candles!) mark_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.from_string(self.exchange._ft_has["mark_ohlcv_price"]) ) # Combine data to avoid combining the data per trade. unavailable_pairs = [] for pair in self.pairlists.whitelist: if pair not in self.exchange._leverage_tiers: unavailable_pairs.append(pair) continue if (pair in mark_rates_dict and len(funding_rates_dict[pair]) == 0 and "futures_funding_rate" in self.config): mark_rates_dict[pair]["open_fund"] = self.config.get('futures_funding_rate') mark_rates_dict[pair].rename( columns={'open': 'open_mark', 'close': 'close_mark', 'high': 'high_mark', 'low': 'low_mark', 'volume': 'volume_mark'}, inplace=True) self.futures_data[pair] = mark_rates_dict[pair] else: if "futures_funding_rate" in self.config: self.futures_data[pair] = mark_rates_dict[pair].merge( funding_rates_dict[pair], on='date', how="outer", suffixes=["_mark", "_fund"])['open_fund'].fillna( self.config.get('futures_funding_rate')) else: self.futures_data[pair] = mark_rates_dict[pair].merge( funding_rates_dict[pair], on='date', how="inner", suffixes=["_mark", "_fund"]) if unavailable_pairs: raise OperationalException( f"Pairs {', '.join(unavailable_pairs)} got no leverage tiers available. " "It is therefore impossible to backtest with this pair at the moment.") else: self.futures_data = {}

c499a92f57cccf520f3d6f19941857af87fac5aa

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

def load_bt_data_detail(self) -> None: if self.timeframe_detail: self.detail_data = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.timeframe_detail, timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=self.config.get('candle_type_def', CandleType.SPOT) ) else: self.detail_data = {} if self.trading_mode == TradingMode.FUTURES: # Load additional futures data. funding_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pairlists.whitelist, timeframe=self.exchange._ft_has['mark_ohlcv_timeframe'], timerange=self.timerange, startup_candles=0, fail_without_data=True, data_format=self.config.get('dataformat_ohlcv', 'json'), candle_type=CandleType.FUNDING_RATE ) # For simplicity, assign to CandleType.Mark (might contian index candles!) mark_rates_dict = history.load_data( datadir=self.config['datadir'], pairs=self.pa

load_bt_data_detail

nni/retiarii/oneshot/pytorch/supermodule/_singlepathnas.py

Valuechoice oneshot lightning (#4602)

nni

Python

_singlepathnas.py

def generate_architecture_params(self): self.alpha = {} if self.kernel_size_candidates is not None: # kernel size arch params self.t_kernel = nn.Parameter(torch.rand(len(self.kernel_size_candidates) - 1)) self.alpha['kernel_size'] = self.t_kernel # kernel size mask self.kernel_masks = [] for i in range(0, len(self.kernel_size_candidates) - 1): big_size = self.kernel_size_candidates[i] small_size = self.kernel_size_candidates[i + 1] mask = torch.zeros_like(self.weight) mask[:, :, :big_size[0], :big_size[1]] = 1 # if self.weight.shape = (out, in, 7, 7), big_size = (5, 5) and mask[:, :, :small_size[0], :small_size[1]] = 0 # small_size = (3, 3), mask will look like: self.kernel_masks.append(mask) # 0 0 0 0 0 0 0 mask = torch.zeros_like(self.weight) # 0 1 1 1 1 1 0 mask[:, :, :self.kernel_size_candidates[-1][0], :self.kernel_size_candidates[-1][1]] = 1 # 0 1 0 0 0 1 0 self.kernel_masks.append(mask) # 0 1 0 0 0 1 0 # 0 1 0 0 0 1 0 if self.out_channel_candidates is not None: # 0 1 1 1 1 1 0 # out_channel (or expansion) arch params. we do not consider skip-op here, so we # 0 0 0 0 0 0 0 # only generate ``len(self.kernel_size_candidates) - 1 `` thresholds self.t_expansion = nn.Parameter(torch.rand(len(self.out_channel_candidates) - 1)) self.alpha['out_channels'] = self.t_expansion self.channel_masks = [] for i in range(0, len(self.out_channel_candidates) - 1): big_channel, small_channel = self.out_channel_candidates[i], self.out_channel_candidates[i + 1] mask = torch.zeros_like(self.weight) mask[:big_channel] = 1 mask[:small_channel] = 0 # if self.weight.shape = (32, in, W, H), big_channel = 16 and small_size = 8, mask will look like: # 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 self.channel_masks.append(mask) mask = torch.zeros_like(self.weight) mask[:self.out_channel_candidates[-1]] = 1 self.channel_masks.append(mask)

14d2966b9e91ae16dcc39de8f41017a75cec8ff9

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

def generate_architecture_params(self): self.alpha = {} if self.kernel_size_candidates is not None: # kernel size arch params self.t_kernel = nn.Parameter(torch.rand(len(self.kernel_size_candidates) - 1)) self.alpha['kernel_size'] = self.t_kernel # kernel size mask self.kernel_masks = [] for i in range(0, len(self.kernel_size_candidates) - 1): big_size = self.kernel_size_candidates[i] small_size = self.kernel_size_candidates[i + 1] mask = torch.zeros_like(self.weight) mask[:, :, :big_size[0], :big_size[1]] = 1 # if self.weight.shape = (out, in, 7, 7), big_size = (5, 5) and

generate_architecture_params

homeassistant/components/alarm_control_panel/__init__.py

Adjust type hints for AlarmControlPanelEntityFeature (#82186)

core

Python

__init__.py

def supported_features(self) -> AlarmControlPanelEntityFeature | int: return self._attr_supported_features

f952b74b74443d20c2ed200990e3040fee38aa9d

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

def supported_features(self) -> AlarmControlPanelEntityFeature | int: return sel

supported_features

keras/layers/preprocessing/image_preprocessing_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

image_preprocessing_test.py

def test_random_crop_output_shape(self, expected_height, expected_width): self._run_test(expected_height, expected_width)

84afc5193d38057e2e2badf9c889ea87d80d8fbf

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

def test_random_crop_output_shape(self, expected_height, expected_width): self._run_

test_random_crop_output_shape

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

upd; format

transferlearning

Python

structs.py

def add(self, key): if key in self._vertices: raise ValueError("vertex exists") self._vertices.add(key) self._forwards[key] = set() self._backwards[key] = set()

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def add(self, key): if key in self._

add

airflow/migrations/versions/54bebd308c5f_add_trigger_table_and_task_info.py

Autogenerate migration reference doc (#21601) * document airflow version in each alembic migration module and use this to autogen the doc * update each migration module to have the same description used in migration ref (so it can be used in autogen)

airflow

Python

54bebd308c5f_add_trigger_table_and_task_info.py

def downgrade(): with op.batch_alter_table('task_instance', schema=None) as batch_op: batch_op.drop_constraint('task_instance_trigger_id_fkey', type_='foreignkey') batch_op.drop_index('ti_trigger_id') batch_op.drop_column('trigger_id') batch_op.drop_column('trigger_timeout') batch_op.drop_column('next_method') batch_op.drop_column('next_kwargs') op.drop_table('trigger')

69f6f9e01b6df76c3c8fa266d460324163957887

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

def downgrade(): with op.batch_alter_table('task_instance', schema=None) as batch_op: batch_op.drop_constraint('task_instance_trigger_id_fkey', type_='foreign

downgrade

modin/pandas/resample.py

REFACTOR-#5038: Remove unnecessary `_method` argument from resamplers (#5039) Signed-off-by: Vasily Litvinov <fam1ly.n4me@yandex.ru>

modin

Python

resample.py

def sem(self, *args, **kwargs): return self._dataframe.__constructor__( query_compiler=self._query_compiler.resample_sem( self.resample_kwargs, *args, **kwargs, ) )

c89f8ba6aaa575ed44f381ad838c8e39050bc102

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

def sem(self, *args, **kwargs): return self._dataframe.__constructor__( query_compiler=self._query_compiler.resample_s

sem

src/datasets/packaged_modules/text/text.py

Run pyupgrade for Python 3.6+ (#3560) * Run pyupgrade for Python 3.6+ * Fix lint issues * Revert changes for the datasets code Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com>

datasets

Python

text.py

def _generate_tables(self, files): schema = pa.schema(self.config.features.type if self.config.features is not None else {"text": pa.string()}) for file_idx, file in enumerate(files): batch_idx = 0 with open(file, encoding=self.config.encoding) as f: if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line batch = batch.splitlines(keepends=self.config.keep_linebreaks) pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 elif self.config.sample_by == "paragraph": batch_idx = 0 batch = "" while True: batch += f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line batch = batch.split("\n\n") pa_table = pa.Table.from_arrays( [pa.array([example for example in batch[:-1] if example])], schema=schema ) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 batch = batch[-1] elif self.config.sample_by == "document": text = f.read() pa_table = pa.Table.from_arrays([pa.array([text])], schema=schema) yield file_idx, pa_table

21bfd0d3f5ff3fbfd691600e2c7071a167816cdf

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

def _generate_tables(self, files): schema = pa.schema(self.config.features.type if self.config.features is not None else {"text": pa.string()}) for file_idx, file in enumerate(files): batch_idx = 0 with open(file, encoding=self.config.encoding) as f: if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line batch = batch.splitlines(keepends=self.config.keep_linebreaks) pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 elif self.config.sample_by == "paragraph": batch_idx = 0 batch = "" while True: batch += f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line batch = batch.split("\n\n") pa_table = pa.Table.from_arrays( [pa.array([example for example in batch[:-1] if example])], schema=schema ) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows

_generate_tables

tests/fsdp/test_fsdp.py

enhancements and fixes for FSDP and DeepSpeed (#532) * checkpointing enhancements and fixes for FSDP and DeepSpeed * resolving comments 1. Adding deprecation args and warnings in launcher for FSDP 2. Handling old configs to work with new launcher args wrt FSDP. 3. Reverting changes to public methods in `checkpointing.py` and handling it in `Accelerator` 4. Explicitly writing the defaults of various FSDP options in `dataclasses` for readability. * fixes 1. FSDP wrapped model being added to the `_models`. 2. Not passing the env variables when args are None. * resolving comments * adding FSDP for all the collective operations * adding deepspeed and fsdp tests 1. Removes mrpc datafiles and directly relies on HF datasets as it was throwing `file not found` error when running from within `tests` folder. Updating `moke_dataloaders` as a result. 2. adding `test_performance.py`, `test_memory.py` and `test_checkpointing.py` for multi-gpu FSDP and DeepSpeed tests * reverting `mocked_dataloader` changes * adding FSDP tests * data files revert * excluding fsdp tests from `tests_core` * try 2 * adding time delay to avoid `torchrun` from crashing at times leading which causing flaky behaviour * reducing the time of tests * fixes * fix * fixes and reduce time further * reduce time further and minor fixes * adding a deepspeed basic e2e test for single gpu setup

accelerate

Python

test_fsdp.py

def test_cpu_offload(self): from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload for flag in [True, False]: env = self.dist_env.copy() env["FSDP_OFFLOAD_PARAMS"] = str(flag).lower() with mockenv_context(**env): fsdp_plugin = FullyShardedDataParallelPlugin() self.assertEqual(fsdp_plugin.cpu_offload, CPUOffload(offload_params=flag)) @require_fsdp @require_multi_gpu @slow

0c6bdc2c237ac071be99ac6f93ddfbc8bbcb8441

@require_fsdp @require_multi_gpu @slow

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

def test_cpu_offload(self): from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload for flag in [True, False]: env = self.dist_env.copy() env["FSDP_OFFLOAD_PARAMS"] = str(flag).lower() with mockenv_context(**env): fsdp_plugin = FullyShardedDataParallelPlugin() self.assertEqual(fsdp_plugin.cpu_o

test_cpu_offload

d2l/paddle.py

[Paddle] Add chapter chapter_linear-networks (#1134) * [Paddletest] Add chapter3 chapter_linear-networks * [Paddle] Add chapter chapter_linear-networks * [Paddle] Add chapter chapter_linear-networks * [Paddle] Add chapter3 linear-networks * [Paddle] Add chapter3 linear-networks * [Paddle] Add chapter3 linear-networks * [Paddle] Add chapter3 linear-networks * Convert tenor to to_tensor * [Paddle] Add chapter_preface * Fix get_dataloader_workers mac & windows * Remove redundant list/tuple unpacking * Minor style fixes * sync lib * Add stop gradient explaination * remove blank content * Update softmax-regression-scratch.md * Fix the sgd bugs Co-authored-by: Anirudh Dagar <anirudhdagar6@gmail.com> Co-authored-by: w5688414 <w5688414@gmail.com>

d2l-zh

Python

paddle.py

def sgd(params, lr, batch_size): with paddle.no_grad(): for i,param in enumerate(params): param -= lr * params[i].grad/ batch_size params[i].set_value(param.numpy()) params[i].clear_gradient()

e292b514b8f4873a36c8ca0ba68b19db2ee8ba44

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

def sgd(params, lr, batch_size): with paddle.no_grad(): for i,param in enumerate(params): param -= lr * params[i].grad/ batch_size params[i].set_va

sgd

python/ray/tests/kubernetes_e2e/test_helm.py

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

ray

Python

test_helm.py

def delete_rayclusters(namespace): cmd = f"kubectl -n {namespace} delete rayclusters --all" try: subprocess.check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode() except subprocess.CalledProcessError as e: assert False, "returncode: {}, stdout: {}".format(e.returncode, e.stdout)

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def delete_rayclusters(namespace): cmd = f"kubectl -n {namespace} delete rayclusters --all" try: subprocess.check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode() except subprocess.CalledProcessError as e: assert False, "returncode: {}, stdout: {}".format(e.returncode, e.stdout)

delete_rayclusters

tests/models/test_mappedoperator.py

Move MappedOperator tests to mirror code location (#23884) At some point during the development of AIP-42 we moved the code for MappedOperator out of baseoperator.py to mappedoperator.py, but we didn't move the tests at the same time

airflow

Python

test_mappedoperator.py

def test_map_xcom_arg(): with DAG("test-dag", start_date=DEFAULT_DATE): task1 = BaseOperator(task_id="op1") mapped = MockOperator.partial(task_id='task_2').expand(arg2=XComArg(task1)) finish = MockOperator(task_id="finish") mapped >> finish assert task1.downstream_list == [mapped]

70b41e46b46e65c0446a40ab91624cb2291a5039

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

def test_map_xcom_arg(): with DAG("test-dag", start_date=DEFAULT_DATE): task1 = BaseOperator(task_id="op1") mapped = MockOperator.partial(task_id='task_2').expand(arg2=XComArg(task1)) finish = MockOperator(task_id="finish") mapped >

test_map_xcom_arg

wagtail/admin/views/generic/models.py

Extract generic RevisionsUnscheduleView and make page's unpublish view extend from it

wagtail

Python

models.py

def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context.update( { "object": self.object, "revision": self.revision, "subtitle": self.get_page_subtitle(), "object_display_title": self.get_object_display_title(), "revisions_unschedule_url": self.get_revisions_unschedule_url(), "next_url": self.get_next_url(), } ) return context

ae0603001638e6b03556aef19bdcfa445f9f74c6

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

def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context.update( { "object": self.object, "revision": self.revision, "subtitle": self.get_page_subtitle(), "object_display_title": self.get_object_display_title(), "revisions_unsched

get_context_data

pandas/tests/extension/base/groupby.py

DEPR: groupby numeric_only default (#47025)

pandas

Python

groupby.py

def test_in_numeric_groupby(self, data_for_grouping): df = pd.DataFrame( { "A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping, "C": [1, 1, 1, 1, 1, 1, 1, 1], } ) dtype = data_for_grouping.dtype if is_numeric_dtype(dtype) or dtype.name == "decimal": warn = None else: warn = FutureWarning msg = "The default value of numeric_only" with tm.assert_produces_warning(warn, match=msg): result = df.groupby("A").sum().columns if data_for_grouping.dtype._is_numeric: expected = pd.Index(["B", "C"]) else: expected = pd.Index(["C"]) tm.assert_index_equal(result, expected)

7c054d6a256fd0186befe03acf9e9e86d81668d6

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

def test_in_numeric_groupby(self, data_for_grouping): df = pd.DataFrame( { "A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping, "C": [1, 1, 1, 1, 1, 1, 1, 1], } ) dtype = data_for_grouping.dtype if is_numeric_dtype(dtype) or dtype.name == "decimal": warn = None else: warn = FutureWarning msg = "The default value of numeric_only" with tm.assert_produces_warning(warn, match=msg): result = df.groupby("A").sum().columns if data_for_grouping.dtype._is_numeric: expected = pd.Index(["B", "C"]

test_in_numeric_groupby

networkx/generators/classic.py

Adjust the usage of nodes_or_number decorator (#5599) * recorrect typo in decorators.py * Update tests to show troubles in current code * fix troubles with usage of nodes_or_number * fix typo * remove nodes_or_number where that makes sense * Reinclude nodes_or_numbers and add some tests for nonstandard usage * fix typowq * hopefully final tweaks (no behavior changes * Update test_classic.py Co-authored-by: Jarrod Millman <jarrod.millman@gmail.com>

networkx

Python

classic.py

def lollipop_graph(m, n, create_using=None): m, m_nodes = m M = len(m_nodes) if M < 2: raise NetworkXError("Invalid description: m should indicate at least 2 nodes") n, n_nodes = n if isinstance(m, numbers.Integral) and isinstance(n, numbers.Integral): n_nodes = list(range(M, M + n)) N = len(n_nodes) # the ball G = complete_graph(m_nodes, create_using) if G.is_directed(): raise NetworkXError("Directed Graph not supported") # the stick G.add_nodes_from(n_nodes) if N > 1: G.add_edges_from(pairwise(n_nodes)) if len(G) != M + N: raise NetworkXError("Nodes must be distinct in containers m and n") # connect ball to stick if M > 0 and N > 0: G.add_edge(m_nodes[-1], n_nodes[0]) return G

de1d00f20e0bc14f1cc911b3486e50225a8fa168

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

def lollipop_graph(m, n, create_using=None): m, m_nodes = m M = len(m_nodes) if M < 2: raise NetworkXError("Invalid description: m should indicate a

lollipop_graph

tests/sentry/api/endpoints/test_organization_metric_details.py

Revert "feat(metrics): make indexer more configurable (#35604)" (#35862) This reverts commit 7f60db924ea37f34e0cfe6856777239e2a2ffe13.

sentry

Python

test_organization_metric_details.py

def test_same_entity_multiple_metric_ids(self, mocked_derived_metrics): mocked_derived_metrics.return_value = MOCKED_DERIVED_METRICS_2 org_id = self.project.organization.id metric_id = indexer.record(org_id, "metric_foo_doe") self.store_session( self.build_session( project_id=self.project.id, started=(time.time() // 60) * 60, status="ok", release="foobar@2.0", errors=2, ) ) self._send_buckets( [ { "org_id": org_id, "project_id": self.project.id, "metric_id": metric_id, "timestamp": (time.time() // 60 - 2) * 60, "tags": { resolve_weak(org_id, "release"): indexer.record(org_id, "fooww"), }, "type": "c", "value": 5, "retention_days": 90, }, ], entity="metrics_counters", ) response = self.get_success_response( self.organization.slug, "derived_metric.multiple_metrics", ) assert response.data == { "name": "derived_metric.multiple_metrics", "type": "numeric", "operations": [], "unit": "percentage", "tags": [{"key": "release"}], }

3ffb14a47d868956ef759a0cd837066629676774

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

def test_same_entity_multiple_metric_ids(self, mocked_derived_metrics): mocked_derived_metrics.return_value = MOCKED_DERIVED_METRICS_2 org_id = self.project.organization.id metric_id = indexer.record(org_id, "metric_foo_doe") self.store_session( self.build_session( project_id=self.project.id, started=(time.time() // 60) * 60, status="ok", release="foobar@2.0", errors=2, ) ) self._send_buckets( [ { "org_id": org_id, "project_id": self.project.id, "metric_id": metric_id, "timestamp": (time.time() // 60 - 2) * 60, "tags": { resolve_weak(org_id, "release"): indexer.record(org_id, "fooww"), }, "type": "c", "value": 5, "retention_days": 90, }, ], entity="metrics_counters", ) response = self.get_success_response( self.organization.slug, "derive

test_same_entity_multiple_metric_ids

networkx/drawing/nx_pydot.py

improve docstring for read_doc, see issue #5604 (#5605)

networkx

Python

nx_pydot.py

def read_dot(path): import pydot data = path.read() # List of one or more "pydot.Dot" instances deserialized from this file. P_list = pydot.graph_from_dot_data(data) # Convert only the first such instance into a NetworkX graph. return from_pydot(P_list[0])

4f2b1b854d5934a487b428f252ad6ff9375d74ad

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

def read_dot(path): import pydot data = path.read() # List of one or more "pydot.Dot" instances deserialized from this file. P_list = pydot.graph_from_dot_d

read_dot

python/ray/tune/schedulers/pb2_utils.py

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

ray

Python

pb2_utils.py

def normalize(data, wrt): return (data - np.min(wrt, axis=0)) / ( np.max(wrt, axis=0) - np.min(wrt, axis=0) + 1e-8 )

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

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

def normalize(data, wrt): return (data - np.min(wrt, axis=0)) / ( np.ma

normalize

tests/db_functions/math/test_sign.py

Refs #33476 -- Reformatted code with Black.

django

Python

test_sign.py

def test_transform(self): with register_lookup(DecimalField, Sign): DecimalModel.objects.create(n1=Decimal("5.4"), n2=Decimal("0")) DecimalModel.objects.create(n1=Decimal("-0.1"), n2=Decimal("0")) obj = DecimalModel.objects.filter(n1__sign__lt=0, n2__sign=0).get() self.assertEqual(obj.n1, Decimal("-0.1"))

9c19aff7c7561e3a82978a272ecdaad40dda5c00

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

def test_transform(self): with register_lookup(DecimalField, Sign): DecimalModel.objects.create(n1=Decimal("5.4"), n2=Decimal("0")) DecimalModel.objects.create(n1=Decimal("-0.1"), n2=Decimal("0")) obj = DecimalModel.objects.filter(n1__sign__lt=0

test_transform

tests/test_suggestions.py

[css] Address "did you mean" PR feedback

textual

Python

test_suggestions.py

def test_get_suggestion(word, possible_words, expected_result): assert get_suggestion(word, possible_words) == expected_result @pytest.mark.parametrize( "word, possible_words, count, expected_result", ( ["background", ("background",), 1, ["background"]], ["backgroundu", ("background",), 1, ["background"]], ["bkgrund", ("background",), 1, ["background"]], ["llow", ("background",), 1, []], ["llow", ("background", "yellow"), 1, ["yellow"]], ["yllow", ("background", "yellow", "ellow"), 1, ["yellow"]], ["yllow", ("background", "yellow", "ellow"), 2, ["yellow", "ellow"]], ["yllow", ("background", "yellow", "red"), 2, ["yellow"]], ), )

26f138e69be49f33fe7ff72cebbb51d617a6338f

@pytest.mark.parametrize( "word, possible_words, count, expected_result", ( ["background", ("background",), 1, ["background"]], ["backgroundu", ("background",), 1, ["background"]], ["bkgrund", ("background",), 1, ["background"]], ["llow", ("background",), 1, []], ["llow", ("background", "yellow"), 1, ["yellow"]], ["yllow", ("background", "yellow", "ellow"), 1, ["yellow"]], ["yllow", ("background", "yellow", "ellow"), 2, ["yellow", "ellow"]], ["yllow", ("background", "yellow", "red"), 2, ["yellow"]], ), )

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

def test_get_suggestion(word, possible_words, expected_result): assert get_suggestion(word, possible_words) == expected_result @pytest.mark.parametrize( "word, possible_words, count, expected_result", ( ["background", ("background",), 1, ["background"]], ["backgroundu", ("background",), 1, ["background"]

test_get_suggestion

python/ccxt/binance.py

1.71.93 [ci skip]

ccxt

Python

binance.py

def set_margin_mode(self, marginType, symbol=None, params={}): # # {"code": -4048 , "msg": "Margin type cannot be changed if there exists position."} # # or # # {"code": 200, "msg": "success"} # marginType = marginType.upper() if marginType == 'CROSS': marginType = 'CROSSED' if (marginType != 'ISOLATED') and (marginType != 'CROSSED'): raise BadRequest(self.id + ' marginType must be either isolated or cross') self.load_markets() market = self.market(symbol) method = None if market['linear']: method = 'fapiPrivatePostMarginType' elif market['inverse']: method = 'dapiPrivatePostMarginType' else: raise NotSupported(self.id + ' setMarginMode() supports linear and inverse contracts only') request = { 'symbol': market['id'], 'marginType': marginType, } return getattr(self, method)(self.extend(request, params))

2d2673c42db3abc79e52ec83b050f12ca1a90fc5

https://github.com/ccxt/ccxt.git

def set_margin_mode(self, marginType, symbol=None, params={}): # # {"code": -4048 , "msg": "Margin type cannot be changed if there exists position."} # # or # # {"code": 200, "msg": "success"} # marginType = marginType.upper() if marginType == 'CROSS': marginType = 'CROSSED' if (marginType != 'ISOLATED') and (marginType != 'CROSSED'): raise BadRequest(self.id + ' marginType must be either isolated or cross') self.load_markets() market = self.market(symbol) method = None if market['linear']: method = 'fapiPrivatePostMarginType' elif market['inverse']: method = 'dapiPrivatePostMarginType' else: raise NotSupported(self.id + ' setMarginMode() supports linear and inverse contracts only') request = { 'symbol': market['id'], 'marginType': marginType, } return getat

set_margin_mode

pandas/tests/series/indexing/test_setitem.py

STYLE: fix some consider-using-enumerate pylint warnings (#49214) * STYLE: fix some consider-using-enumerate pylint errors * fixup! STYLE: fix some consider-using-enumerate pylint errors * fixup! fixup! STYLE: fix some consider-using-enumerate pylint errors * fixup! fixup! fixup! STYLE: fix some consider-using-enumerate pylint errors * fixup! fixup! fixup! fixup! STYLE: fix some consider-using-enumerate pylint errors

pandas

Python

test_setitem.py

def test_setitem_scalar_into_readonly_backing_data(): # GH#14359: test that you cannot mutate a read only buffer array = np.zeros(5) array.flags.writeable = False # make the array immutable series = Series(array) for n in series.index: msg = "assignment destination is read-only" with pytest.raises(ValueError, match=msg): series[n] = 1 assert array[n] == 0

93bd1a8ece37657e887808b1492d3715e25e8bd3

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

def test_setitem_scalar_into_readonly_backing_data(): # GH#14359: test that you cannot mutate a read only buffer array = np.zeros(5) array.flags.writeable = False # make the array immutable series = Series(array) for n in series.index: msg = "assignment destination is read-only" with pytest.raises(ValueError, match=msg): series

test_setitem_scalar_into_readonly_backing_data

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

upd; format

transferlearning

Python

urls.py

def get_url_scheme(url): # type: (str) -> Optional[str] if ":" not in url: return None return url.split(":", 1)[0].lower()

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def get_url_scheme(url): # type: (str) -> Optional[str] if ":" not in url: return None

get_url_scheme

python/ccxt/async_support/coinbase.py

1.66.55 [ci skip]

ccxt

Python

coinbase.py

def parse_transaction(self, transaction, market=None): # # fiat deposit # # { # "id": "f34c19f3-b730-5e3d-9f72", # "status": "completed", # "payment_method": { # "id": "a022b31d-f9c7-5043-98f2", # "resource": "payment_method", # "resource_path": "/v2/payment-methods/a022b31d-f9c7-5043-98f2" # }, # "transaction": { # "id": "04ed4113-3732-5b0c-af86-b1d2146977d0", # "resource": "transaction", # "resource_path": "/v2/accounts/91cd2d36-3a91-55b6-a5d4-0124cf105483/transactions/04ed4113-3732-5b0c-af86" # }, # "user_reference": "2VTYTH", # "created_at": "2017-02-09T07:01:18Z", # "updated_at": "2017-02-09T07:01:26Z", # "resource": "deposit", # "resource_path": "/v2/accounts/91cd2d36-3a91-55b6-a5d4-0124cf105483/deposits/f34c19f3-b730-5e3d-9f72", # "committed": True, # "payout_at": "2017-02-12T07:01:17Z", # "instant": False, # "fee": {"amount": "0.00", "currency": "EUR"}, # "amount": {"amount": "114.02", "currency": "EUR"}, # "subtotal": {"amount": "114.02", "currency": "EUR"}, # "hold_until": null, # "hold_days": 0, # "hold_business_days": 0, # "next_step": null # } # # fiat_withdrawal # # { # "id": "cfcc3b4a-eeb6-5e8c-8058", # "status": "completed", # "payment_method": { # "id": "8b94cfa4-f7fd-5a12-a76a", # "resource": "payment_method", # "resource_path": "/v2/payment-methods/8b94cfa4-f7fd-5a12-a76a" # }, # "transaction": { # "id": "fcc2550b-5104-5f83-a444", # "resource": "transaction", # "resource_path": "/v2/accounts/91cd2d36-3a91-55b6-a5d4-0124cf105483/transactions/fcc2550b-5104-5f83-a444" # }, # "user_reference": "MEUGK", # "created_at": "2018-07-26T08:55:12Z", # "updated_at": "2018-07-26T08:58:18Z", # "resource": "withdrawal", # "resource_path": "/v2/accounts/91cd2d36-3a91-55b6-a5d4-0124cf105483/withdrawals/cfcc3b4a-eeb6-5e8c-8058", # "committed": True, # "payout_at": "2018-07-31T08:55:12Z", # "instant": False, # "fee": {"amount": "0.15", "currency": "EUR"}, # "amount": {"amount": "13130.69", "currency": "EUR"}, # "subtotal": {"amount": "13130.84", "currency": "EUR"}, # "idem": "e549dee5-63ed-4e79-8a96", # "next_step": null # } # subtotalObject = self.safe_value(transaction, 'subtotal', {}) feeObject = self.safe_value(transaction, 'fee', {}) id = self.safe_string(transaction, 'id') timestamp = self.parse8601(self.safe_value(transaction, 'created_at')) updated = self.parse8601(self.safe_value(transaction, 'updated_at')) type = self.safe_string(transaction, 'resource') amount = self.safe_number(subtotalObject, 'amount') currencyId = self.safe_string(subtotalObject, 'currency') currency = self.safe_currency_code(currencyId) feeCost = self.safe_number(feeObject, 'amount') feeCurrencyId = self.safe_string(feeObject, 'currency') feeCurrency = self.safe_currency_code(feeCurrencyId) fee = { 'cost': feeCost, 'currency': feeCurrency, } status = self.parse_transaction_status(self.safe_string(transaction, 'status')) if status is None: committed = self.safe_value(transaction, 'committed') status = 'ok' if committed else 'pending' return { 'info': transaction, 'id': id, 'txid': id, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'network': None, 'address': None, 'addressTo': None, 'addressFrom': None, 'tag': None, 'tagTo': None, 'tagFrom': None, 'type': type, 'amount': amount, 'currency': currency, 'status': status, 'updated': updated, 'fee': fee, }

8543cfb54ecfae0f51f4b77a8df7b38aa0626094

https://github.com/ccxt/ccxt.git

def parse_transaction(self, transaction, market=None): # # fiat deposit # # { # "id": "f34c19f3-b730-5e3d-9f72", # "status": "completed", # "payment_method": { # "id": "a022b31d-f9c7-5043-98f2", # "resource": "payment_method", # "resource_path": "/v2/payment-methods/a022b31d-f9c7-5043-98f2" # }, # "transaction": { # "id": "04ed4113-3732-5b0c-af86-b1d2146977d0", # "resource": "transaction", # "resource_path": "/v2/accounts/91cd2d36-3a91-55b6-a5d4-0124cf105483/transactions/04ed4113-3732-5b0c-

parse_transaction

jaxlib/gpu_prng.py

Raise error for unsupported shape polymorphism for custom call and fallback lowering

jax

Python

gpu_prng.py

def _threefry2x32_lowering(prng, platform, keys, data): assert len(keys) == 2, keys assert len(data) == 2, data assert (ir.RankedTensorType(keys[0].type).element_type == ir.IntegerType.get_unsigned(32)), keys[0].type typ = keys[0].type dims = ir.RankedTensorType(typ).shape if any(d < 0 for d in dims): raise NotImplementedError("Shape polymorphism for custom call is not implemented (threefry); b/261671778") for x in itertools.chain(keys, data): assert x.type == typ, (x.type, typ) ndims = len(dims) opaque = prng.threefry2x32_descriptor(_prod(dims)) layout = tuple(range(ndims - 1, -1, -1)) return custom_call( f"{platform}_threefry2x32", [typ, typ], [keys[0], keys[1], data[0], data[1]], backend_config=opaque, operand_layouts=[layout] * 4, result_layouts=[layout] * 2) cuda_threefry2x32 = partial(_threefry2x32_lowering, _cuda_prng, "cu") rocm_threefry2x32 = partial(_threefry2x32_lowering, _hip_prng, "hip")

ac7740513d0b47894d9170af6aaa6b9355fb2059

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

def _threefry2x32_lowering(prng, platform, keys, data): assert len(keys) == 2, keys assert len(data) == 2, data assert (ir.RankedTensorType(keys[0].type).element_type == ir.IntegerType.get_unsigned(32)), keys[0].type typ = keys[0].type dims = ir.RankedTensorType(typ).shape if any(d < 0 for d in dims): raise NotImplementedError("Shape polymorphism for custom call is not implemented (threefry); b/261671778") for x in itertools.chain(keys, data): assert x.type == typ, (x.type, typ) ndims = len(dims) opaque = prng.threefry2x32_descriptor(_prod(dims)) layout = tuple(range(ndims - 1, -1, -1)) return custom_call( f"{platform}_threefry2x32", [typ, typ], [keys[0], keys[1], data[0], data[1]], backend_config=opaque, op

_threefry2x32_lowering

erpnext/hr/report/vehicle_expenses/vehicle_expenses.py

style: format code with black

erpnext

Python

vehicle_expenses.py

def get_period_dates(filters): if filters.filter_based_on == "Fiscal Year" and filters.fiscal_year: fy = frappe.db.get_value( "Fiscal Year", filters.fiscal_year, ["year_start_date", "year_end_date"], as_dict=True ) return fy.year_start_date, fy.year_end_date else: return filters.from_date, filters.to_date

494bd9ef78313436f0424b918f200dab8fc7c20b

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

def get_period_dates(filters): if filters.filter_based_on == "Fiscal Year" and filters.fiscal_year: fy = frappe.db.get_value(

get_period_dates

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

upd; format

transferlearning

Python

collector.py

def _ensure_html_header(response): # type: (Response) -> None content_type = response.headers.get("Content-Type", "") if not content_type.lower().startswith("text/html"): raise _NotHTML(content_type, response.request.method)

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

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

def _ensure_html_header(response): # type: (Response) -> None content_type = response.headers.get("Content-Type", "") if not content_type.lower().startswith("text/html"): raise _NotHTML(content_type, response.request.method)

_ensure_html_header

sympy/core/expr.py

Code cleanup

sympy

Python

expr.py

def _parse_order(cls, order): from sympy.polys.orderings import monomial_key startswith = getattr(order, "startswith", None) if startswith is None: reverse = False else: reverse = startswith('rev-') if reverse: order = order[4:] monom_key = monomial_key(order)

9d58006fc0a23afcba38f641c9472917c436428a

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

def _parse_order(cls, order): from sympy.polys.orderings import monomial_key startswith = getattr(order, "startswith", None) if startswith is None: reverse = False else: reverse = startswith('rev-') if reverse: order = order[4:] monom_key = mono

_parse_order

awx/main/tasks/jobs.py

Replace git shallow clone with shutil.copytree Introduce build_project_dir method the base method will create an empty project dir for workdir Share code between job and inventory tasks with new mixin combine rest of pre_run_hook logic structure to hold lock for entire sync process force sync to run for inventory updates due to UI issues Remove reference to removed scm_last_revision field

awx

Python

jobs.py

def sync_and_copy(self, project, private_data_dir, scm_branch=None): self.acquire_lock(project, self.instance.id) try: original_branch = None project_path = project.get_project_path(check_if_exists=False) if project.scm_type == 'git' and (scm_branch and scm_branch != project.scm_branch): if os.path.exists(project_path): git_repo = git.Repo(project_path) if git_repo.head.is_detached: original_branch = git_repo.head.commit else: original_branch = git_repo.active_branch return self.sync_and_copy_without_lock(project, private_data_dir, scm_branch=scm_branch) finally: # We have made the copy so we can set the tree back to its normal state if original_branch: # for git project syncs, non-default branches can be problems # restore to branch the repo was on before this run try: original_branch.checkout() except Exception: # this could have failed due to dirty tree, but difficult to predict all cases logger.exception(f'Failed to restore project repo to prior state after {self.instance.id}') self.release_lock(project) @task(queue=get_local_queuename)

46be2d9e5b4423f316d6fae4a080d36716622c15

@task(queue=get_local_queuename)

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

def sync_and_copy(self, project, private_data_dir, scm_branch=None): self.acquire_lock(project, self.instance.id) try: original_branch = None project_path = project.get_project_path(check_if_exists=False) if project.scm_type == 'git' and (scm_branch and scm_branch != project.scm_branch): if os.path.exists(project_path): git_repo = git.Repo(project_path) if git_repo.head.is_detached: original_branch = git_repo.head.commit else: original_branch = git_repo.active_branch return self.sync_and_copy_without_lock(project, private_data_dir, scm_branch=scm_branch) finally: # We have made the copy so we can set the tree back to its normal state if original_branch: # for git project syncs, non-default branches can be problems # restore to branch the repo w

sync_and_copy

src/calibre/gui2/preferences/create_custom_column.py

More CreateNewCustomColumn stuff. - Improved documentation - Check column headings for duplicates - Method to return the current column headings as a dict - Improved exception handling

calibre

Python

create_custom_column.py

def current_columns(self): # deepcopy to prevent users from changing it. The new MappingProxyType # isn't enough because only the top-level dict is immutable, not the # items in the dict. return copy.deepcopy(self.custcols)

9a95d8b0c26bdaea17ea9264ab45e8a81b6422f0

https://github.com/kovidgoyal/calibre.git

def current_columns(self): # deepcopy to prevent users from changing it. The new MappingProxyType # isn't enough because only the top

current_columns

tests/orion/models/test_orm.py

Fix some timestamp sensitive tests on windows

prefect

Python

test_orm.py

async def many_task_run_states(flow_run, session, db): # clear all other task runs await session.execute(sa.delete(db.TaskRun)) await session.execute(sa.delete(db.TaskRunState)) for i in range(5): task_run = await models.task_runs.create_task_run( session=session, task_run=schemas.actions.TaskRunCreate( flow_run_id=flow_run.id, task_key="test-task", dynamic_key=str(i), ), ) states = [ db.TaskRunState( task_run_id=task_run.id, **schemas.states.State( type={ 0: schemas.states.StateType.PENDING, 1: schemas.states.StateType.RUNNING, 2: schemas.states.StateType.COMPLETED, }[i], timestamp=pendulum.now("UTC").add(minutes=i), ).dict(), ) for i in range(3) ] task_run.set_state(states[-1]) session.add_all(states) await session.commit()

5c08c3ed69793298f86f1484f149951ee2a0847f

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

async def many_task_run_states(flow_run, session, db): # clear all other task runs await session.execute(sa.delete(db.TaskRun)) await session.execute(sa.delete(db.TaskRunState)) for i in range(5): task_run = await models.task_runs.create_task_run( session=session, task_run=schemas.actions.TaskRunCreate( flow_run_id=flow_run.id, task_key="test-task", dynamic_key=str(i), ), ) states = [ db.TaskRunState( task_run_id=task_run.id, **schemas.states.State( type={ 0: schemas.states.StateType.PENDING, 1: schemas.states.StateType.RUNNING,

many_task_run_states

homeassistant/components/filesize/sensor.py

Fix file size last_updated (#70114) Co-authored-by: J. Nick Koston <nick@koston.org>

core

Python

sensor.py

async def _async_update_data(self) -> dict[str, float | int | datetime]: try: statinfo = os.stat(self._path) except OSError as error: raise UpdateFailed(f"Can not retrieve file statistics {error}") from error size = statinfo.st_size last_updated = datetime.utcfromtimestamp(statinfo.st_mtime).replace( tzinfo=dt_util.UTC ) _LOGGER.debug("size %s, last updated %s", size, last_updated) data: dict[str, int | float | datetime] = { "file": round(size / 1e6, 2), "bytes": size, "last_updated": last_updated, } return data

23446fa1c0a8579ae314151651b6973af600df09

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

async def _async_update_data(self) -> dict[str, float | int | datetime]: try: statinfo = os.stat(self._path) except OSError as error: raise UpdateFailed(f"Can not retrieve file statistics {error}") from error size = statinfo.st_size last_updated = datetime.utcfromtimestamp(statinfo.st_mtime).replace( tzinfo=dt_util.UTC ) _LOGGER.debug("size %s, last updated %s", size, last_updated) data: dict[str, int | float | datetime] = { "file": round(size /

_async_update_data

homeassistant/components/hdmi_cec/media_player.py

Enforce MediaPlayerState in hdmi_cec media player (#78522)

core

Python

media_player.py

def update(self) -> None: device = self._device if device.power_status in [POWER_OFF, 3]: self._attr_state = MediaPlayerState.OFF elif not self.support_pause: if device.power_status in [POWER_ON, 4]: self._attr_state = MediaPlayerState.ON elif device.status == STATUS_PLAY: self._attr_state = MediaPlayerState.PLAYING elif device.status == STATUS_STOP: self._attr_state = MediaPlayerState.IDLE elif device.status == STATUS_STILL: self._attr_state = MediaPlayerState.PAUSED else: _LOGGER.warning("Unknown state: %s", device.status)

b29605060a74c441550708ccf4ace4b697f66ae6

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

def update(self) -> None: device = self._device if device.power_status in [POWER_OFF, 3]: self._attr_state = MediaPlayerState.OFF elif not self.support_pause: if device.power_status in [POWER_ON, 4]: self._attr_state = MediaPlayerState.

update

tests/sentry/utils/suspect_resolutions/test_commit_correlation.py

ref(suspect-resolutions): refactor code around (#37775) * refactor code * fix metric correlation test

sentry

Python

test_commit_correlation.py

def test_get_files_changed_no_shared_files(self): (project, issue, release, repo) = self.setup() Activity.objects.create( project=project, group=issue, type=ActivityType.SET_RESOLVED_IN_COMMIT.value ) release2 = self.create_release() issue2 = self.create_group() commit2 = Commit.objects.create( organization_id=project.organization_id, repository_id=repo.id, key="2" ) ReleaseCommit.objects.create( organization_id=project.organization_id, release=release2, commit=commit2, order=1 ) CommitFileChange.objects.create( organization_id=project.organization_id, commit=commit2, filename=".gitignore" ) GroupRelease.objects.create( project_id=project.id, group_id=issue2.id, release_id=release2.id ) res1 = get_files_changed_in_releases(issue.id, project.id) res2 = get_files_changed_in_releases(issue2.id, project.id) assert res1.files_changed == {".random", ".random2"} assert res2.files_changed == {".gitignore"} assert res1.release_ids == [release.id] assert res2.release_ids == [release2.id] assert not is_issue_commit_correlated(issue.id, issue2.id, project.id).is_correlated

b25bf3d4efa751232673b1e9d2a07ee439994348

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

def test_get_files_changed_no_shared_files(self): (project, issue, release, repo) = self.setup() Activity.objects.create( project=project, group=issue, type=ActivityType.SET_RESOLVED_IN_COMMIT.value ) release2 = self.create_release() issue2 = self.create_group() commit2 = Commit.objects.create( organization_id=project.organization_id, repository_id=repo.id, key="2" ) ReleaseCommit.objects.create( organization_id=project.organization_id, release=release2, commit=commit2, order=1 ) CommitFileChange.objects.create( organization_id=project.organization_id, commit=commit2, filename=".gitignore" ) GroupRelease.objects.create( project_id=project.id, group_id=issue2.id, release_id=release2.id ) res1 = get_files_changed_in_releases(issue.id, project.id) res2 = get_files_changed_in_releases(issue2.id, project.id) assert res1.files_changed == {".random", ".random2"} assert res2.files_changed == {".gitignore"} assert res1.release_ids == [release.id] assert res2.release_ids == [release2.i

test_get_files_changed_no_shared_files

lib/mpl_toolkits/tests/test_axisartist_axislines.py

Expire axes_grid1/axisartist deprecations.

matplotlib

Python

test_axisartist_axislines.py

def test_ParasiteAxesAuxTrans(): # Remove this line when this test image is regenerated. plt.rcParams['pcolormesh.snap'] = False data = np.ones((6, 6)) data[2, 2] = 2 data[0, :] = 0 data[-2, :] = 0 data[:, 0] = 0 data[:, -2] = 0 x = np.arange(6) y = np.arange(6) xx, yy = np.meshgrid(x, y) funcnames = ['pcolor', 'pcolormesh', 'contourf'] fig = plt.figure() for i, name in enumerate(funcnames): ax1 = SubplotHost(fig, 1, 3, i+1) fig.add_subplot(ax1) ax2 = ParasiteAxes(ax1, IdentityTransform()) ax1.parasites.append(ax2) if name.startswith('pcolor'): getattr(ax2, name)(xx, yy, data[:-1, :-1]) else: getattr(ax2, name)(xx, yy, data) ax1.set_xlim((0, 5)) ax1.set_ylim((0, 5)) ax2.contour(xx, yy, data, colors='k')

7749b7b153219738dcf30f0acbad310a2550aa19

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

def test_ParasiteAxesAuxTrans(): # Remove this line when this test image is regenerated. plt.rcParams['pcolormesh.snap'] = False data = np.ones((6, 6)) data[2, 2] = 2 data[0, :] = 0 data[-2, :] = 0 data[:, 0] = 0 data[:, -2] = 0 x = np.arange(6) y = np.arange(6) xx, yy = np.meshgrid(x

test_ParasiteAxesAuxTrans

qutebrowser/mainwindow/tabwidget.py

Run scripts/dev/rewrite_enums.py

qutebrowser

Python

tabwidget.py

def subElementRect(self, sr, opt, widget=None): if sr == QStyle.SubElement.SE_TabBarTabText: layouts = self._tab_layout(opt) if layouts is None: log.misc.warning("Could not get layouts for tab!") return QRect() return layouts.text elif sr in [QStyle.SubElement.SE_TabWidgetTabBar, QStyle.SubElement.SE_TabBarScrollLeftButton]: # Handling SE_TabBarScrollLeftButton so the left scroll button is # aligned properly. Otherwise, empty space will be shown after the # last tab even though the button width is set to 0 # # Need to use super() because we also use super() to render # element in drawControl(); otherwise, we may get bit by # style differences... return super().subElementRect(sr, opt, widget) else: return self._style.subElementRect(sr, opt, widget)

0877fb0d78635692e481c8bde224fac5ad0dd430

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

def subElementRect(self, sr, opt, widget=None): if sr == QStyle.SubElement.SE_TabBarTabText: layouts = self._tab_layout(opt) if layouts is None: log.misc.warning("Could not get layouts for tab!") return QR

subElementRect

lib/matplotlib/tests/test_axes.py

MNT: when clearing an Axes via clear/cla fully detach children Reset the Axes and Figure of the children to None to help break cycles. Closes #6982

matplotlib

Python

test_axes.py

def test_cla_clears_chlidren_axes_and_fig(): fig, ax = plt.subplots() lines = ax.plot([], [], [], []) img = ax.imshow([[1]]) for art in lines + [img]: assert art.axes is ax assert art.figure is fig ax.clear() for art in lines + [img]: assert art.axes is None assert art.figure is None

ffcc8d314c8a47772ba541027f138ee18155d7e6

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

def test_cla_clears_chlidren_axes_and_fig(): fig, ax = plt.subplots()

test_cla_clears_chlidren_axes_and_fig

sympy/crypto/crypto.py

Fix a misspelling

sympy

Python

crypto.py

def rsa_public_key(*args, **kwargs): r return _rsa_key(*args, public=True, private=False, **kwargs)

2110dbe01539e03ef8634deac4c40f895da38daa

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

def rsa_public_key(*args, **kwargs): r return _rsa_key(*args, public=True, private=False, **kwargs)

rsa_public_key

homeassistant/components/zerproc/light.py

Use attributes in zerproc light (#75951)

core

Python

light.py

async def async_update(self) -> None: try: if not self.available: await self._light.connect() state = await self._light.get_state() except pyzerproc.ZerprocException: if self.available: _LOGGER.warning("Unable to connect to %s", self._light.address) self._attr_available = False return if not self.available: _LOGGER.info("Reconnected to %s", self._light.address) self._attr_available = True self._attr_is_on = state.is_on hsv = color_util.color_RGB_to_hsv(*state.color) self._attr_hs_color = hsv[:2] self._attr_brightness = int(round((hsv[2] / 100) * 255))

90458ee200d6d9e6fe7458fec3021d904e365c13

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

async def async_update(self) -> None: try: if not self.available: await self._light.connect() state = await self._light.get_state() except pyzerproc.Zerproc

async_update

yt_dlp/extractor/awaan.py

[cleanup] Use format_field where applicable

yt-dlp

Python

awaan.py

def _parse_video_data(self, video_data, video_id, is_live): title = video_data.get('title_en') or video_data['title_ar'] img = video_data.get('img') return { 'id': video_id, 'title': title, 'description': video_data.get('description_en') or video_data.get('description_ar'), 'thumbnail': format_field(img, template='http://admin.mangomolo.com/analytics/%s'), 'duration': int_or_none(video_data.get('duration')), 'timestamp': parse_iso8601(video_data.get('create_time'), ' '), 'is_live': is_live, 'uploader_id': video_data.get('user_id'), }

e0ddbd02bd1c365b95bb88eaa6e4e0238faf35eb

https://github.com/yt-dlp/yt-dlp.git

def _parse_video_data(self, video_data, video_id, is_live): title = video_data.get('title_en') or video_data['title_ar'] img = video_data.get('img') return { 'id': video_id, 'title': title, 'description': video_data.get('description_en') or video_data.get('description_ar'), 'thumbnail': format_field(img, template='http://admin.mangomolo.com/analytics/%s'), 'duration': int_or_none(video_data.get('duration')), 'timestamp': parse_iso8601(video_data.get('create_time'), ' '), 'is_liv

_parse_video_data

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

Convert selective checks to Breeze Python (#24610) Instead of bash-based, complex logic script to perform PR selective checks we now integrated the whole logic into Breeze Python code. It is now much simplified, when it comes to algorithm. We've implemented simple rule-based decision tree. The rules describing the decision tree are now are now much easier to reason about and they correspond one-to-one with the rules that are implemented in the code in rather straightforward way. The code is much simpler and diagnostics of the selective checks has also been vastly improved: * The rule engine displays status of applying each rule and explains (with yellow warning message what decision was made and why. Informative messages are printed showing the resulting output * List of files impacting the decision are also displayed * The names of "ci file group" and "test type" were aligned * Unit tests covering wide range of cases are added. Each test describes what is the case they demonstrate * `breeze selective-checks` command that is used in CI can also be used locally by just providing commit-ish reference of the commit to check. This way you can very easily debug problems and fix them Fixes: #19971

airflow

Python

selective_checks.py

def upgrade_to_newer_dependencies(self) -> bool: return len( self._matching_files(FileGroupForCi.SETUP_FILES, CI_FILE_GROUP_MATCHES) ) > 0 or self._github_event in [GithubEvents.PUSH, GithubEvents.SCHEDULE]

d7bd72f494e7debec11672eeddf2e6ba5ef75fac

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

def upgrade_to_newer_dependencies(self) -> bool: return len( self._matching_files(FileGroupForCi.SETUP_FILES, CI_FILE_GROUP_MATCHES) ) > 0 or self._github_event in [GithubEvents.PUSH, GithubEvents.SCHEDULE]

upgrade_to_newer_dependencies