xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

1de81bb477ab8ad84c3bb89808f801c13c0ca84d

244

py

Python

integration/fresnel.py

lemming52/white_knight

9932aeb4b2a2bfa4270a0cd6d39fd054242890cf

[ "MIT" ]

2

2021-02-23T19:25:02.000Z

2021-02-23T19:25:48.000Z

integration/fresnel.py

lemming52/white_knight

9932aeb4b2a2bfa4270a0cd6d39fd054242890cf

[ "MIT" ]

null

integration/fresnel.py

lemming52/white_knight

9932aeb4b2a2bfa4270a0cd6d39fd054242890cf

[ "MIT" ]

1

2021-02-23T19:25:53.000Z

# External Packages import numpy as np # Functions for evaluating the fresnel integrals. def cos_integrand(x, coeff): return np.cos(np.power(x, 2)*coeff) def sin_integrand(x, coeff): return np.sin(np.power(x, 2)*coeff)

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1dfd78dec98a9b953b72f65dd557ac359b5c468b

30

py

Python

scripts/extract_dataset.py

Fedioun/flair

5504bdc6ec4232e3321483c9611bc929c9557175

[ "MIT" ]

null

scripts/extract_dataset.py

Fedioun/flair

5504bdc6ec4232e3321483c9611bc929c9557175

[ "MIT" ]

null

scripts/extract_dataset.py

Fedioun/flair

5504bdc6ec4232e3321483c9611bc929c9557175

[ "MIT" ]

null

def main(): main()

3

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5

3836048cadc3d13946611a6b6c35734490a2e66e

94

py

Python

python/testData/completion/hasattrCompletion/hasattrInConditionalExpression.py

Sajaki/intellij-community

6748af2c40567839d11fd652ec77ba263c074aad

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/completion/hasattrCompletion/hasattrInConditionalExpression.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2022-02-19T09:45:05.000Z

2022-02-27T20:32:55.000Z

python/testData/completion/hasattrCompletion/hasattrInConditionalExpression.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def foo(x): some_var = (x.<caret> if hasattr(x, "foo") else 42) if hasattr(x, "bar") else 42

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5

6985121e4268d20ce7367271b05fed46203f93b5

1,096

py

Python

tools/plane_utils.py

lolrudy/GPV_pose

f326a623b3e45e6edfc1963b068e8e7aaea2bfff

[ "MIT" ]

10

2022-03-16T02:14:56.000Z

2022-03-31T19:01:34.000Z

tools/plane_utils.py

lolrudy/GPV_pose

f326a623b3e45e6edfc1963b068e8e7aaea2bfff

[ "MIT" ]

1

2022-03-18T06:43:16.000Z

2022-03-18T06:56:35.000Z

tools/plane_utils.py

lolrudy/GPV_pose

f326a623b3e45e6edfc1963b068e8e7aaea2bfff

[ "MIT" ]

2

2022-03-19T13:06:28.000Z

2022-03-19T16:08:18.000Z

import torch def get_plane(pc, pc_w): # min least square n = pc.shape[0] A = torch.cat([pc[:, :2], torch.ones([n, 1], device=pc.device)], dim=-1) b = pc[:, 2].view(-1, 1) W = torch.diag(pc_w) WA = torch.mm(W, A) ATWA = torch.mm(A.permute(1, 0), WA) ATWA_1 = torch.inverse(ATWA) Wb = torch.mm(W, b) ATWb = torch.mm(A.permute(1, 0), Wb) X = torch.mm(ATWA_1, ATWb) # return dn dn_up = torch.cat([X[0] * X[2], X[1] * X[2], -X[2]], dim=0), dn_norm = X[0] * X[0] + X[1] * X[1] + 1.0 dn = dn_up[0] / dn_norm normal_n = dn / torch.norm(dn) for_p2plane = X[2] / torch.sqrt(dn_norm) return normal_n, dn, for_p2plane def get_plane_parameter(pc, pc_w): # min least square n = pc.shape[0] A = torch.cat([pc[:, :2], torch.ones([n, 1], device=pc.device)], dim=-1) b = pc[:, 2].view(-1, 1) W = torch.diag(pc_w) WA = torch.mm(W, A) ATWA = torch.mm(A.permute(1, 0), WA) ATWA_1 = torch.inverse(ATWA) Wb = torch.mm(W, b) ATWb = torch.mm(A.permute(1, 0), Wb) X = torch.mm(ATWA_1, ATWb) return X

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698898b86818fab584b81963d400deef9b7fa972

76

py

Python

bandit/backends/__init__.py

russelljk/django-email-bandit

d7f319afb7ee1f663cb93c0f13b3a6c9b3ce6077

[ "BSD-3-Clause" ]

null

bandit/backends/__init__.py

russelljk/django-email-bandit

d7f319afb7ee1f663cb93c0f13b3a6c9b3ce6077

[ "BSD-3-Clause" ]

null

bandit/backends/__init__.py

russelljk/django-email-bandit

d7f319afb7ee1f663cb93c0f13b3a6c9b3ce6077

[ "BSD-3-Clause" ]

null

from bandit.backends.smtp import HijackSMTPBackend as HijackBackend # noqa

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698f1212ea06d52e65226b59e55178e3802e4570

77

py

Python

PythonMusic/__init__.py

Andereoo/PythonMusic

85f33267898c75ae8da5e1ae15cc17f03cdb1896

[ "MIT" ]

null

PythonMusic/__init__.py

Andereoo/PythonMusic

85f33267898c75ae8da5e1ae15cc17f03cdb1896

[ "MIT" ]

null

PythonMusic/__init__.py

Andereoo/PythonMusic

85f33267898c75ae8da5e1ae15cc17f03cdb1896

[ "MIT" ]

null

from PythonMusic.__main__ import custom_sound, wait, note, PythonMusic_help

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69ea19602ba77cfc14fce77372bf9054c051b673

29,121

py

Python

tests/st/numpy_native/test_array_creations.py

kungfu-team/mindspore-bert

71501cf52ae01db9d6a73fb64bcfe68a6509dc32

[ "Apache-2.0" ]

null

tests/st/numpy_native/test_array_creations.py

kungfu-team/mindspore-bert

71501cf52ae01db9d6a73fb64bcfe68a6509dc32

[ "Apache-2.0" ]

null

tests/st/numpy_native/test_array_creations.py

kungfu-team/mindspore-bert

71501cf52ae01db9d6a73fb64bcfe68a6509dc32

[ "Apache-2.0" ]

null

# Copyright 2020-2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """unit tests for numpy array operations""" import pytest import numpy as onp import mindspore.numpy as mnp from .utils import rand_int, rand_bool, match_array, match_res, match_meta, \ match_all_arrays, run_multi_test, to_tensor class Cases(): def __init__(self): self.all_shapes = [ 1, 2, (1,), (2,), (1, 2, 3), [1], [2], [1, 2, 3] ] self.onp_dtypes = [onp.int32, 'int32', int, onp.float32, 'float32', float, onp.uint32, 'uint32', onp.bool_, 'bool', bool] self.mnp_dtypes = [mnp.int32, 'int32', int, mnp.float32, 'float32', float, mnp.uint32, 'uint32', mnp.bool_, 'bool', bool] self.array_sets = [1, 1.1, True, [1, 0, True], [1, 1.0, 2], (1,), [(1, 2, 3), (4, 5, 6)], onp.random.random( # pylint: disable=no-member (100, 100)).astype(onp.float32).tolist(), onp.random.random((100, 100)).astype(onp.bool).tolist()] self.arrs = [ rand_int(2), rand_int(2, 3), rand_int(2, 3, 4), rand_int(2, 3, 4, 5), ] # scalars expanded across the 0th dimension self.scalars = [ rand_int(), rand_int(1), rand_int(1, 1), rand_int(1, 1, 1), ] # arrays of the same size expanded across the 0th dimension self.expanded_arrs = [ rand_int(2, 3), rand_int(1, 2, 3), rand_int(1, 1, 2, 3), rand_int(1, 1, 1, 2, 3), ] # arrays with dimensions of size 1 self.nested_arrs = [ rand_int(1), rand_int(1, 2), rand_int(3, 1, 8), rand_int(1, 3, 9, 1), ] # arrays which can be broadcast self.broadcastables = [ rand_int(5), rand_int(6, 1), rand_int(7, 1, 5), rand_int(8, 1, 6, 1) ] # boolean arrays which can be broadcast self.bool_broadcastables = [ rand_bool(), rand_bool(1), rand_bool(5), rand_bool(6, 1), rand_bool(7, 1, 5), rand_bool(8, 1, 6, 1), ] self.mnp_prototypes = [ mnp.ones((2, 3, 4)), mnp.ones((1, 3, 1, 2, 5)), mnp.ones((2, 7, 1)), [mnp.ones(3), (1, 2, 3), mnp.ones(3), [4, 5, 6]], ([(1, 2), mnp.ones(2)], (mnp.ones(2), [3, 4])), ] self.onp_prototypes = [ onp.ones((2, 3, 4)), onp.ones((1, 3, 1, 2, 5)), onp.ones((2, 7, 1)), [onp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]], ([(1, 2), onp.ones(2)], (onp.ones(2), [3, 4])), ] @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_asarray(): test_case = Cases() for array in test_case.array_sets: # Check for dtype matching actual = onp.asarray(array) expected = mnp.asarray(array).asnumpy() # Since we set float32/int32 as the default dtype in mindspore, we need # to make a conversion between numpy.asarray and mindspore.numpy.asarray if actual.dtype is onp.dtype('float64'): assert expected.dtype == onp.dtype('float32') elif actual.dtype is onp.dtype('int64'): assert expected.dtype == onp.dtype('int32') else: assert actual.dtype == expected.dtype match_array(actual, expected, error=7) for i in range(len(test_case.onp_dtypes)): actual = onp.asarray(array, test_case.onp_dtypes[i]) expected = mnp.asarray(array, test_case.mnp_dtypes[i]).asnumpy() match_array(actual, expected, error=7) # Additional tests for nested tensor mixture mnp_input = [(mnp.ones(3,), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]] onp_input = [(onp.ones(3,), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]] actual = onp.asarray(onp_input) expected = mnp.asarray(mnp_input).asnumpy() match_array(actual, expected, error=7) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_array(): # array's function is very similar to asarray, so we mainly test the # `copy` argument. test_case = Cases() for array in test_case.array_sets: arr1 = mnp.asarray(array) arr2 = mnp.array(arr1, copy=False) arr3 = mnp.array(arr1) arr4 = mnp.asarray(array, dtype='int32') arr5 = mnp.asarray(arr4, dtype=mnp.int32) assert arr1 is arr2 assert arr1 is not arr3 assert arr4 is arr5 # Additional tests for nested tensor/numpy_array mixture mnp_input = [(mnp.ones(3,), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]] onp_input = [(onp.ones(3,), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]] actual = onp.array(onp_input) expected = mnp.array(mnp_input).asnumpy() match_array(actual, expected, error=7) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_asfarray(): test_case = Cases() for array in test_case.array_sets: # Check for dtype matching actual = onp.asfarray(array) expected = mnp.asfarray(array).asnumpy() # Since we set float32/int32 as the default dtype in mindspore, we need # to make a conversion between numpy.asarray and mindspore.numpy.asarray if actual.dtype is onp.dtype('float64'): assert expected.dtype == onp.dtype('float32') else: assert actual.dtype == expected.dtype match_array(actual, expected, error=7) for i in range(len(test_case.onp_dtypes)): actual = onp.asfarray(array, test_case.onp_dtypes[i]) expected = mnp.asfarray(array, test_case.mnp_dtypes[i]).asnumpy() match_array(actual, expected, error=7) # Additional tests for nested tensor/numpy_array mixture mnp_input = [(mnp.ones(3,), mnp.ones(3)), [[1, 1, 1], (1, 1, 1)]] onp_input = [(onp.ones(3,), onp.ones(3)), [[1, 1, 1], (1, 1, 1)]] actual = onp.asfarray(onp_input) expected = mnp.asfarray(mnp_input).asnumpy() match_array(actual, expected, error=7) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_zeros(): test_case = Cases() for shape in test_case.all_shapes: for i in range(len(test_case.onp_dtypes)): actual = onp.zeros(shape, test_case.onp_dtypes[i]) expected = mnp.zeros(shape, test_case.mnp_dtypes[i]).asnumpy() match_array(actual, expected) actual = onp.zeros(shape) expected = mnp.zeros(shape).asnumpy() match_array(actual, expected) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_ones(): test_case = Cases() for shape in test_case.all_shapes: for i in range(len(test_case.onp_dtypes)): actual = onp.ones(shape, test_case.onp_dtypes[i]) expected = mnp.ones(shape, test_case.mnp_dtypes[i]).asnumpy() match_array(actual, expected) actual = onp.ones(shape) expected = mnp.ones(shape).asnumpy() match_array(actual, expected) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_full(): actual = onp.full((2, 2), [1, 2]) expected = mnp.full((2, 2), [1, 2]).asnumpy() match_array(actual, expected) actual = onp.full((2, 3), True) expected = mnp.full((2, 3), True).asnumpy() match_array(actual, expected) actual = onp.full((3, 4, 5), 7.5) expected = mnp.full((3, 4, 5), 7.5).asnumpy() match_array(actual, expected) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_eye(): test_case = Cases() for i in range(len(test_case.onp_dtypes)): for m in range(1, 5): actual = onp.eye(m, dtype=test_case.onp_dtypes[i]) expected = mnp.eye(m, dtype=test_case.mnp_dtypes[i]).asnumpy() match_array(actual, expected) for n in range(1, 5): for k in range(0, 5): actual = onp.eye(m, n, k, dtype=test_case.onp_dtypes[i]) expected = mnp.eye( m, n, k, dtype=test_case.mnp_dtypes[i]).asnumpy() match_array(actual, expected) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_identity(): test_case = Cases() for i in range(len(test_case.onp_dtypes)): for m in range(1, 5): actual = onp.identity(m, dtype=test_case.onp_dtypes[i]) expected = mnp.identity(m, dtype=test_case.mnp_dtypes[i]).asnumpy() match_array(actual, expected) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_arange(): actual = onp.arange(10) expected = mnp.arange(10).asnumpy() match_array(actual, expected) actual = onp.arange(0, 10) expected = mnp.arange(0, 10).asnumpy() match_array(actual, expected) actual = onp.arange(start=10) expected = mnp.arange(start=10).asnumpy() match_array(actual, expected) actual = onp.arange(start=10, step=0.1) expected = mnp.arange(start=10, step=0.1).asnumpy() match_array(actual, expected, error=6) actual = onp.arange(10, step=0.1) expected = mnp.arange(10, step=0.1).asnumpy() match_array(actual, expected, error=6) actual = onp.arange(0.1, 9.9) expected = mnp.arange(0.1, 9.9).asnumpy() match_array(actual, expected, error=6) @pytest.mark.level0 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_linspace(): actual = onp.linspace(2.0, 3.0, dtype=onp.float32) expected = mnp.linspace(2.0, 3.0).asnumpy() match_array(actual, expected, error=6) actual = onp.linspace(2.0, 3.0, num=5, dtype=onp.float32) expected = mnp.linspace(2.0, 3.0, num=5).asnumpy() match_array(actual, expected, error=6) actual = onp.linspace( 2.0, 3.0, num=5, endpoint=False, dtype=onp.float32) expected = mnp.linspace(2.0, 3.0, num=5, endpoint=False).asnumpy() match_array(actual, expected, error=6) actual = onp.linspace(2.0, 3.0, num=5, retstep=True, dtype=onp.float32) expected = mnp.linspace(2.0, 3.0, num=5, retstep=True) match_array(actual[0], expected[0].asnumpy()) assert actual[1] == expected[1].asnumpy() actual = onp.linspace(2.0, [3, 4, 5], num=5, endpoint=False, dtype=onp.float32) expected = mnp.linspace( 2.0, [3, 4, 5], num=5, endpoint=False).asnumpy() match_array(actual, expected, error=6) actual = onp.linspace(2.0, [[3, 4, 5]], num=5, endpoint=False, axis=2) expected = mnp.linspace(2.0, [[3, 4, 5]], num=5, endpoint=False, axis=2).asnumpy() match_array(actual, expected, error=6) start = onp.random.random([2, 1, 4]).astype("float32") stop = onp.random.random([1, 5, 1]).astype("float32") actual = onp.linspace(start, stop, num=20, retstep=True, endpoint=False, dtype=onp.float32) expected = mnp.linspace(to_tensor(start), to_tensor(stop), num=20, retstep=True, endpoint=False) match_array(actual[0], expected[0].asnumpy(), error=6) match_array(actual[1], expected[1].asnumpy(), error=6) actual = onp.linspace(start, stop, num=20, retstep=True, endpoint=False, dtype=onp.int16) expected = mnp.linspace(to_tensor(start), to_tensor(stop), num=20, retstep=True, endpoint=False, dtype=mnp.int16) match_array(actual[0], expected[0].asnumpy(), error=6) match_array(actual[1], expected[1].asnumpy(), error=6) for axis in range(2): actual = onp.linspace(start, stop, num=20, retstep=False, endpoint=False, dtype=onp.float32, axis=axis) expected = mnp.linspace(to_tensor(start), to_tensor(stop), num=20, retstep=False, endpoint=False, dtype=mnp.float32, axis=axis) match_array(actual, expected.asnumpy(), error=6) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_logspace(): actual = onp.logspace(2.0, 3.0, dtype=onp.float32) expected = mnp.logspace(2.0, 3.0).asnumpy() match_array(actual, expected, error=3) actual = onp.logspace(2.0, 3.0, num=5, dtype=onp.float32) expected = mnp.logspace(2.0, 3.0, num=5).asnumpy() match_array(actual, expected, error=3) actual = onp.logspace( 2.0, 3.0, num=5, endpoint=False, dtype=onp.float32) expected = mnp.logspace(2.0, 3.0, num=5, endpoint=False).asnumpy() match_array(actual, expected, error=3) actual = onp.logspace(2.0, [3, 4, 5], num=5, base=2, endpoint=False, dtype=onp.float32) expected = mnp.logspace( 2.0, [3, 4, 5], num=5, base=2, endpoint=False).asnumpy() match_array(actual, expected, error=3) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_empty(): test_case = Cases() for shape in test_case.all_shapes: for mnp_dtype, onp_dtype in zip(test_case.mnp_dtypes, test_case.onp_dtypes): actual = mnp.empty(shape, mnp_dtype).asnumpy() expected = onp.empty(shape, onp_dtype) match_meta(actual, expected) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_empty_like(): test_case = Cases() for mnp_proto, onp_proto in zip(test_case.mnp_prototypes, test_case.onp_prototypes): actual = mnp.empty_like(mnp_proto).asnumpy() expected = onp.empty_like(onp_proto) assert actual.shape == expected.shape for mnp_dtype, onp_dtype in zip(test_case.mnp_dtypes, test_case.onp_dtypes): actual = mnp.empty_like(mnp_proto, dtype=mnp_dtype).asnumpy() expected = onp.empty_like(onp_proto, dtype=onp_dtype) match_meta(actual, expected) def run_x_like(mnp_fn, onp_fn): test_case = Cases() for mnp_proto, onp_proto in zip(test_case.mnp_prototypes, test_case.onp_prototypes): actual = mnp_fn(mnp_proto).asnumpy() expected = onp_fn(onp_proto) match_array(actual, expected) for shape in test_case.all_shapes: actual = mnp_fn(mnp_proto, shape=shape).asnumpy() expected = onp_fn(onp_proto, shape=shape) match_array(actual, expected) for mnp_dtype, onp_dtype in zip(test_case.mnp_dtypes, test_case.onp_dtypes): actual = mnp_fn(mnp_proto, dtype=mnp_dtype).asnumpy() expected = onp_fn(onp_proto, dtype=onp_dtype) match_array(actual, expected) actual = mnp_fn(mnp_proto, dtype=mnp_dtype, shape=shape).asnumpy() expected = onp_fn(onp_proto, dtype=onp_dtype, shape=shape) match_array(actual, expected) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_ones_like(): run_x_like(mnp.ones_like, onp.ones_like) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_zeros_like(): run_x_like(mnp.zeros_like, onp.zeros_like) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_full_like(): test_case = Cases() for mnp_proto, onp_proto in zip(test_case.mnp_prototypes, test_case.onp_prototypes): shape = onp.zeros_like(onp_proto).shape fill_value = rand_int() actual = mnp.full_like(mnp_proto, to_tensor(fill_value)).asnumpy() expected = onp.full_like(onp_proto, fill_value) match_array(actual, expected) for i in range(len(shape) - 1, 0, -1): fill_value = rand_int(*shape[i:]) actual = mnp.full_like(mnp_proto, to_tensor(fill_value)).asnumpy() expected = onp.full_like(onp_proto, fill_value) match_array(actual, expected) fill_value = rand_int(1, *shape[i + 1:]) actual = mnp.full_like(mnp_proto, to_tensor(fill_value)).asnumpy() expected = onp.full_like(onp_proto, fill_value) match_array(actual, expected) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_tri_triu_tril(): x = mnp.ones((16, 32), dtype="bool") match_array(mnp.tril(x).asnumpy(), onp.tril(x.asnumpy())) match_array(mnp.tril(x, -1).asnumpy(), onp.tril(x.asnumpy(), -1)) match_array(mnp.triu(x).asnumpy(), onp.triu(x.asnumpy())) match_array(mnp.triu(x, -1).asnumpy(), onp.triu(x.asnumpy(), -1)) x = mnp.ones((64, 64), dtype="uint8") match_array(mnp.tril(x).asnumpy(), onp.tril(x.asnumpy())) match_array(mnp.tril(x, 25).asnumpy(), onp.tril(x.asnumpy(), 25)) match_array(mnp.triu(x).asnumpy(), onp.triu(x.asnumpy())) match_array(mnp.triu(x, 25).asnumpy(), onp.triu(x.asnumpy(), 25)) match_array(mnp.tri(64, 64).asnumpy(), onp.tri(64, 64)) match_array(mnp.tri(64, 64, -10).asnumpy(), onp.tri(64, 64, -10)) @pytest.mark.level1 @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_nancumsum(): x = rand_int(2, 3, 4, 5) x[0][2][1][3] = onp.nan x[1][0][2][4] = onp.nan x[1][1][1][1] = onp.nan match_res(mnp.nancumsum, onp.nancumsum, x) match_res(mnp.nancumsum, onp.nancumsum, x, axis=-2) match_res(mnp.nancumsum, onp.nancumsum, x, axis=0) match_res(mnp.nancumsum, onp.nancumsum, x, axis=3) def mnp_diagonal(arr): return mnp.diagonal(arr, offset=2, axis1=-1, axis2=0) def onp_diagonal(arr): return onp.diagonal(arr, offset=2, axis1=-1, axis2=0) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_diagonal(): arr = rand_int(3, 5) for i in [-1, 0, 2]: match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=0, axis2=1) match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=1, axis2=0) arr = rand_int(7, 4, 9) for i in [-1, 0, 2]: match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=0, axis2=-1) match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=-2, axis2=2) match_res(mnp.diagonal, onp.diagonal, arr, offset=i, axis1=-1, axis2=-2) def mnp_trace(arr): return mnp.trace(arr, offset=4, axis1=1, axis2=2) def onp_trace(arr): return onp.trace(arr, offset=4, axis1=1, axis2=2) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_trace(): arr = rand_int(3, 5) for i in [-1, 0]: match_res(mnp.trace, onp.trace, arr, offset=i, axis1=0, axis2=1) match_res(mnp.trace, onp.trace, arr, offset=i, axis1=1, axis2=0) arr = rand_int(7, 4, 9) for i in [-1, 0, 2]: match_res(mnp.trace, onp.trace, arr, offset=i, axis1=0, axis2=-1) match_res(mnp.trace, onp.trace, arr, offset=i, axis1=-2, axis2=2) match_res(mnp.trace, onp.trace, arr, offset=i, axis1=-1, axis2=-2) def mnp_meshgrid(*xi): a = mnp.meshgrid(*xi) b = mnp.meshgrid(*xi, sparse=True) c = mnp.meshgrid(*xi, indexing='ij') d = mnp.meshgrid(*xi, sparse=False, indexing='ij') return a, b, c, d def onp_meshgrid(*xi): a = onp.meshgrid(*xi) b = onp.meshgrid(*xi, sparse=True) c = onp.meshgrid(*xi, indexing='ij') d = onp.meshgrid(*xi, sparse=False, indexing='ij') return a, b, c, d @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_meshgrid(): xi = (onp.full(3, 2), onp.full(1, 5), onp.full( (2, 3), 9), onp.full((4, 5, 6), 7)) for i in range(len(xi)): arrs = xi[i:] mnp_arrs = map(to_tensor, arrs) for mnp_res, onp_res in zip(mnp_meshgrid(*mnp_arrs), onp_meshgrid(*arrs)): match_all_arrays(mnp_res, onp_res) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_mgrid(): mnp_res = mnp.mgrid[0:5] onp_res = onp.mgrid[0:5] match_all_arrays(mnp_res, onp_res, error=5) mnp_res = mnp.mgrid[2:30:4j, -10:20:7, 2:5:0.5] onp_res = onp.mgrid[2:30:4j, -10:20:7, 2:5:0.5] match_all_arrays(mnp_res, onp_res, error=5) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_ogrid(): mnp_res = mnp.ogrid[0:5] onp_res = onp.ogrid[0:5] match_all_arrays(mnp_res, onp_res, error=5) mnp_res = mnp.ogrid[2:30:4j, -10:20:7, 2:5:0.5] onp_res = onp.ogrid[2:30:4j, -10:20:7, 2:5:0.5] match_all_arrays(mnp_res, onp_res, error=5) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_diagflat(): arrs = [rand_int(2, 3)] for arr in arrs: for i in [-2, 0, 7]: match_res(mnp.diagflat, onp.diagflat, arr, k=i) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_diag(): arrs = [rand_int(7), rand_int(5, 5), rand_int(3, 8), rand_int(9, 6)] for arr in arrs: for i in [-10, -5, -1, 0, 2, 5, 6, 10]: match_res(mnp.diag, onp.diag, arr, k=i) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_diag_indices(): mnp_res = mnp.diag_indices(5, 7) onp_res = onp.diag_indices(5, 7) match_all_arrays(mnp_res, onp_res) def mnp_ix_(*args): return mnp.ix_(*args) def onp_ix_(*args): return onp.ix_(*args) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_ix_(): arrs = [rand_int(i + 1) for i in range(10)] for i in range(10): test_arrs = arrs[:i + 1] match_res(mnp_ix_, onp_ix_, *test_arrs) def mnp_indices(): a = mnp.indices((2, 3)) b = mnp.indices((2, 3, 4), sparse=True) return a, b def onp_indices(): a = onp.indices((2, 3)) b = onp.indices((2, 3, 4), sparse=True) return a, b def test_indices(): run_multi_test(mnp_indices, onp_indices, ()) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_geomspace(): start = onp.arange(1, 7).reshape(2, 3) end = [1000, 2000, 3000] match_array(mnp.geomspace(1, 256, num=9).asnumpy(), onp.geomspace(1, 256, num=9), error=1) match_array(mnp.geomspace(1, 256, num=8, endpoint=False).asnumpy(), onp.geomspace(1, 256, num=8, endpoint=False), error=1) match_array(mnp.geomspace(to_tensor(start), end, num=4).asnumpy(), onp.geomspace(start, end, num=4), error=1) match_array(mnp.geomspace(to_tensor(start), end, num=4, endpoint=False).asnumpy(), onp.geomspace(start, end, num=4, endpoint=False), error=1) match_array(mnp.geomspace(to_tensor(start), end, num=4, axis=-1).asnumpy(), onp.geomspace(start, end, num=4, axis=-1), error=1) match_array(mnp.geomspace(to_tensor(start), end, num=4, endpoint=False, axis=-1).asnumpy(), onp.geomspace(start, end, num=4, endpoint=False, axis=-1), error=1) start = onp.arange(1, 1 + 2*3*4*5).reshape(2, 3, 4, 5) end = [1000, 2000, 3000, 4000, 5000] for i in range(-5, 5): match_array(mnp.geomspace(to_tensor(start), end, num=4, axis=i).asnumpy(), onp.geomspace(start, end, num=4, axis=i), error=1) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_vander(): arrs = [rand_int(i + 3) for i in range(3)] for i in range(3): mnp_vander = mnp.vander(to_tensor(arrs[i])) onp_vander = onp.vander(arrs[i]) match_all_arrays(mnp_vander, onp_vander, error=1e-4) mnp_vander = mnp.vander(to_tensor(arrs[i]), N=2, increasing=True) onp_vander = onp.vander(arrs[i], N=2, increasing=True) match_all_arrays(mnp_vander, onp_vander, error=1e-4) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_asarray_exception(): with pytest.raises(TypeError): mnp.asarray({1, 2, 3}) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_linspace_exception(): with pytest.raises(TypeError): mnp.linspace(0, 1, num=2.5) @pytest.mark.level1 @pytest.mark.platform_arm_ascend_training @pytest.mark.platform_x86_ascend_training @pytest.mark.platform_x86_gpu_training @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_empty_like_exception(): with pytest.raises(ValueError): mnp.empty_like([[1, 2, 3], [4, 5]])

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38a571922fb7193e479260aa552a5cd7bb096155

114

py

Python

day3/test_package/__init__.py

tdouris1/pynet_test

6c68161a5759c2d82b14dc92fec70fda1aba679c

[ "Apache-2.0" ]

null

day3/test_package/__init__.py

tdouris1/pynet_test

6c68161a5759c2d82b14dc92fec70fda1aba679c

[ "Apache-2.0" ]

null

day3/test_package/__init__.py

tdouris1/pynet_test

6c68161a5759c2d82b14dc92fec70fda1aba679c

[ "Apache-2.0" ]

1

2020-06-03T08:41:10.000Z

from test_package.test1 import my_func from test_package.test2 import my_func2 __all__ = ['my_func', 'my_func2']

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38d6a70cb87c8e2b6a999d7239f94a7970939034

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py

Python

duplicates_parser/duplicates/__init__.py

LilianBoulard/utils

84acd0f24afc976a92aa422bfd8ec5c725800b98

[ "MIT" ]

2

2020-12-15T11:54:58.000Z

2021-01-21T17:34:06.000Z

duplicates_parser/duplicates/__init__.py

Phaide/utils

84acd0f24afc976a92aa422bfd8ec5c725800b98

[ "MIT" ]

null

duplicates_parser/duplicates/__init__.py

Phaide/utils

84acd0f24afc976a92aa422bfd8ec5c725800b98

[ "MIT" ]

null

from .parser import DuplicateParser from .extractor import Extractor from .dashboard import Dashboard

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5

2a0d88802085de00f4ec7be9e5ba21be1c97a13f

530

py

Python

dealbot/buttons.py

whyh/FavourDemo

1b19882fb2e79dee9c3332594bf45c91e7476eaa

[ "Unlicense" ]

1

2020-09-14T12:10:22.000Z

dealbot/buttons.py

whyh/FavourDemo

1b19882fb2e79dee9c3332594bf45c91e7476eaa

[ "Unlicense" ]

4

2021-04-30T20:54:31.000Z

2021-06-02T00:28:04.000Z

dealbot/buttons.py

whyh/FavourDemo

1b19882fb2e79dee9c3332594bf45c91e7476eaa

[ "Unlicense" ]

null

from aiogram.types import InlineKeyboardButton from common import sed, tg import phrases as phr def replenish(lang: str) -> InlineKeyboardButton: return InlineKeyboardButton(phr.REPLENISH(lang), url=tg.BOT_DEEPLINK + sed.encode(**{sed.kv.ACT: sed.kv.ACT_REPL})) def close_deal(lang: str, creator: bool, contributor: bool) -> InlineKeyboardButton: return InlineKeyboardButton(phr.CONFIRM(lang), callback_data=sed.encode(**{sed.kv.ACT: sed.kv.ACT_CLOSE_DEAL, sed.kv.CONTRIBUTOR: contributor, sed.kv.CREATOR: creator}))

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2a17ed45d324b329b727b3376dacf24cf485b3ef

157

py

Python

example/text_output.py

vputz/pyntegrant

6c54c03286b57e883577795440f4d304bfca2506

[ "Apache-2.0" ]

null

example/text_output.py

vputz/pyntegrant

6c54c03286b57e883577795440f4d304bfca2506

[ "Apache-2.0" ]

null

example/text_output.py

vputz/pyntegrant

6c54c03286b57e883577795440f4d304bfca2506

[ "Apache-2.0" ]

null

from pprint import pformat from interfaces import Output class TextOutput(Output): def format_output(self, d: dict) -> str: return pformat(d)

17.444444

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157

5.285714

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null

0

1

0

null

0

1

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5

2a28a20a75291ead3bbcd19f230575ab146edf1c

83

py

Python

openpype/hosts/flame/api/plugin.py

philipluk/OpenPype

103de741de5f71063a9572f0338e879b6361fb31

[ "MIT" ]

44

2019-03-19T04:56:35.000Z

2021-04-23T12:05:08.000Z

openpype/hosts/flame/api/plugin.py

philipluk/OpenPype

103de741de5f71063a9572f0338e879b6361fb31

[ "MIT" ]

655

2020-03-17T15:10:21.000Z

2021-04-23T18:22:52.000Z

openpype/hosts/flame/api/plugin.py

BigRoy/OpenPype

dd0e3657b4e66740f55feddb2693acfd14e2c9ef

[ "MIT" ]

21

2019-03-19T04:56:38.000Z

2021-04-23T09:10:59.000Z

# Creator plugin functions # Publishing plugin functions # Loader plugin functions

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5

2a2a5994acd3bb8bcc7231e2ae957c92474828a9

420

py

Python

Database/Project/login/models.py

ChoKyuWon/SchoolProjects

71a5decefc85ae941ba2d537c4507ba8e615cc34

[ "MIT" ]

null

Database/Project/login/models.py

ChoKyuWon/SchoolProjects

71a5decefc85ae941ba2d537c4507ba8e615cc34

[ "MIT" ]

null

Database/Project/login/models.py

ChoKyuWon/SchoolProjects

71a5decefc85ae941ba2d537c4507ba8e615cc34

[ "MIT" ]

null

from django.db import models class User(models.Model): uid = models.IntegerField(default=0, primary_key=True) user_id = models.IntegerField(default=0) user_passwordhash = models.CharField(max_length=100) user_isadmin = models.BinaryField(default=b'0') user_classroom_id = models.IntegerField(default=0) def __str__(self): return str(self.user_id) # Create your models here.

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420

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null

0

1

0

1

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5

2a6bdde53bd210d14a14d2a03fae66b1645df387

244

py

Python

Framework/KeyboardListener.py

EpicTofuu/Froggers

0395ef801fe11a7881fd32fd570bf3135a4a761f

[ "MIT" ]

1

2020-11-17T04:32:55.000Z

Framework/KeyboardListener.py

EpicTofuu/Froggers

0395ef801fe11a7881fd32fd570bf3135a4a761f

[ "MIT" ]

null

Framework/KeyboardListener.py

EpicTofuu/Froggers

0395ef801fe11a7881fd32fd570bf3135a4a761f

[ "MIT" ]

null

# gets key presses. Only helpful for key down strokes class KeyboardListener: def KeyDown (self, event): pass def get_keydown (oldkeystate, newkeystate, keys): return any ([newkeystate[k] and not oldkeystate[k] for k in keys])

30.5

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null

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1

0

1

0

1

0

5

2a6d03024f5e00d5dca546e48a657e2f58e03241

182

py

Python

bin/iamonds/polyiamonds-12345-trapezoid-1.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/iamonds/polyiamonds-12345-trapezoid-1.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/iamonds/polyiamonds-12345-trapezoid-1.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

1

2022-01-02T16:54:14.000Z

#!/usr/bin/env python # $Id$ """98,807 solutions""" import puzzler from puzzler.puzzles.polyiamonds12345 import Polyiamonds12345Trapezoid1 puzzler.run(Polyiamonds12345Trapezoid1)

18.2

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182

9

72

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null

0

1

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5

2a70865a93c827c819b1cb18d2b9e53e0147e60a

324

py

Python

configs/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/FlowNet512_1.5AugCosyAAEGray_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_13_24Bowl_bop_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

33

2021-12-15T07:11:47.000Z

2022-03-29T08:58:32.000Z

configs/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/FlowNet512_1.5AugCosyAAEGray_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_13_24Bowl_bop_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

3

2021-12-15T11:39:54.000Z

2022-03-29T07:24:23.000Z

configs/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/FlowNet512_1.5AugCosyAAEGray_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_13_24Bowl_bop_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

null

_base_ = ( "./FlowNet512_1.5AugCosyAAEGray_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_Pbr_01_02MasterChefCan_bop_test.py" ) OUTPUT_DIR = "output/deepim/ycbvPbrSO/FlowNet512_1.5AugCosyAAEGray_AggressiveR_ClipGrad_fxfy1_Dtw01_LogDz_PM10_Flat_ycbvPbr_SO/13_24Bowl" DATASETS = dict(TRAIN=("ycbv_024_bowl_train_pbr",))

54

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0

1

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1

0

1

null

0

5

aa841f3cf45dd5c21b74bbfb9c2259f8efaa5d5e

7,114

py

Python

tests/test_aiohttp_path_params.py

iterait/apistrap

e83460fa97f13a95a928971b0d2defe0ac611911

[ "MIT" ]

6

2018-09-06T18:32:48.000Z

2021-05-28T01:03:32.000Z

tests/test_aiohttp_path_params.py

iterait/apistrap

e83460fa97f13a95a928971b0d2defe0ac611911

[ "MIT" ]

53

2018-09-06T16:16:53.000Z

2021-05-19T14:36:58.000Z

tests/test_aiohttp_path_params.py

iterait/apistrap

e83460fa97f13a95a928971b0d2defe0ac611911

[ "MIT" ]

null

import json import pytest from aiohttp import web from aiohttp.web_request import Request from apistrap.aiohttp import AioHTTPApistrap @pytest.fixture(scope="function") def app_without_request_param(): oapi = AioHTTPApistrap() app = web.Application() routes = web.RouteTableDef() oapi.init_app(app) @routes.get('/') async def view(): return web.Response(content_type="application/json", text=json.dumps({ "status": "OK" })) app.add_routes(routes) yield app async def test_aiohttp_spec_no_request_param_spec(app_without_request_param, aiohttp_initialized_client): client = await aiohttp_initialized_client(app_without_request_param) response = await client.get('/spec.json') assert response.status == 200 data = await response.json() assert 'paths' in data assert '/' in data['paths'] response = await client.get('/spec.json') assert response.status == 200 new_data = await response.json() assert new_data == data async def test_aiohttp_spec_no_request_param_invocation(app_without_request_param, aiohttp_initialized_client): client = await aiohttp_initialized_client(app_without_request_param) response = await client.get('/') assert response.status == 200 data = await response.json() assert data == {"status": "OK"} @pytest.fixture(scope="function") def app_with_params_as_args(): oapi = AioHTTPApistrap() app = web.Application() routes = web.RouteTableDef() oapi.init_app(app) @routes.get('/{param_a}/{param_b}') async def view(param_a: str, param_b: int): return web.Response(content_type="application/json", text=json.dumps({ "a": param_a, "b": param_b })) app.add_routes(routes) yield app async def test_aiohttp_path_params_as_args_spec(aiohttp_initialized_client, app_with_params_as_args): client = await aiohttp_initialized_client(app_with_params_as_args) response = await client.get('/spec.json') assert response.status == 200 data = await response.json() parameters = data["paths"]["/{param_a}/{param_b}"]["get"]["parameters"] assert len(parameters) == 2 param_a = next(filter(lambda p: p["name"] == "param_a", parameters), None) param_b = next(filter(lambda p: p["name"] == "param_b", parameters), None) assert param_a == {"in": "path", "name": "param_a", "required": True, "schema": {"type": "string"}} assert param_b == {"in": "path", "name": "param_b", "required": True, "schema": {"type": "integer"}} async def test_aiohttp_path_params_as_args_arg_assignment(aiohttp_initialized_client, app_with_params_as_args): client = await aiohttp_initialized_client(app_with_params_as_args) response = await client.get("/a/42") assert response.status == 200 data = await response.json() assert data == { "a": "a", "b": 42 } @pytest.fixture(scope="function") def app_with_optional_parameter(): oapi = AioHTTPApistrap() app = web.Application() routes = web.RouteTableDef() oapi.init_app(app) @routes.get('/') @routes.get('/{param}') async def view(param: str = "Default"): return web.Response(content_type="application/json", text=json.dumps({ "param": param, })) app.add_routes(routes) yield app async def test_aiohttp_path_param_optional_spec(aiohttp_initialized_client, app_with_optional_parameter): client = await aiohttp_initialized_client(app_with_optional_parameter) response = await client.get("spec.json") assert response.status == 200 data = await response.json() assert len(data["paths"]) == 2 async def test_aiohttp_path_param_optional_invocation(aiohttp_initialized_client, app_with_optional_parameter): client = await aiohttp_initialized_client(app_with_optional_parameter) response = await client.get("/value") assert response.status == 200 data = await response.json() assert data["param"] == "value" async def test_aiohttp_path_param_optional_invocation_default(aiohttp_initialized_client, app_with_optional_parameter): client = await aiohttp_initialized_client(app_with_optional_parameter) response = await client.get("/") assert response.status == 200 data = await response.json() assert data["param"] == "Default" async def test_aiohttp_path_param_multiple_request_parameters(aiohttp_initialized_client): oapi = AioHTTPApistrap() app = web.Application() routes = web.RouteTableDef() oapi.init_app(app) with pytest.raises(TypeError): @routes.get('/') @routes.get('/{param}') async def view(request_1: Request, request_2: Request, param: str = "Default"): return web.Response(content_type="application/json", text=json.dumps({ "param": param, })) app.add_routes(routes) client = await aiohttp_initialized_client(app) await client.get("/spec.json") async def test_aiohttp_path_param_unsupported_parameter(aiohttp_initialized_client): oapi = AioHTTPApistrap() app = web.Application() routes = web.RouteTableDef() oapi.init_app(app) with pytest.raises(TypeError): @routes.get('/{param}') async def view(param: dict): return web.Response(content_type="application/json", text=json.dumps({ "param": param, })) app.add_routes(routes) client = await aiohttp_initialized_client(app) await client.get("/spec.json") @pytest.fixture(scope="function") def app_with_unannotated_parameter(): oapi = AioHTTPApistrap() app = web.Application() routes = web.RouteTableDef() oapi.init_app(app) @routes.get('/{param}') async def view(param): return web.Response(content_type="application/json", text=json.dumps({ "param": param, })) app.add_routes(routes) yield app async def test_aiohttp_path_unannotated_parameter_spec(aiohttp_initialized_client, app_with_unannotated_parameter): client = await aiohttp_initialized_client(app_with_unannotated_parameter) response = await client.get('/spec.json') assert response.status == 200 data = await response.json() parameters = data["paths"]["/{param}"]["get"]["parameters"] assert parameters == [ {"in": "path", "name": "param", "required": True, "schema": {"type": "string"}} ] async def test_aiohttp_path_unannotated_parameter_arg_assignment(aiohttp_initialized_client, app_with_unannotated_parameter): client = await aiohttp_initialized_client(app_with_unannotated_parameter) response = await client.get("/42") assert response.status == 200 data = await response.json() assert data == { "param": "42" } async def test_aiohttp_path_params_invalid_value(aiohttp_initialized_client, app_with_params_as_args): client = await aiohttp_initialized_client(app_with_params_as_args) response = await client.get("/a/foobar") assert response.status == 400

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1

0

1

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null

0

5

2ab96079a3123222dcb751c18b6a701bbb6857cc

212

py

Python

src/forms/__init__.py

nnecklace/webi-shoppi

140d1e6ea8d019aa10ee2104e1bbd2baf0b9aa0f

[ "MIT" ]

null

src/forms/__init__.py

nnecklace/webi-shoppi

140d1e6ea8d019aa10ee2104e1bbd2baf0b9aa0f

[ "MIT" ]

2

2020-06-02T13:55:02.000Z

2020-06-16T17:58:55.000Z

src/forms/__init__.py

nnecklace/webi-shoppi

140d1e6ea8d019aa10ee2104e1bbd2baf0b9aa0f

[ "MIT" ]

null

from .authentication import LoginForm, RegisterForm from .search import SearchForm from .products import ProductForm from .users import UserForm, ChangePasswordForm, BalanceForm from .comments import CommentForm

35.333333

60

0.853774

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212

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true

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0

null

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0

null

0

1

0

5

2ac2926b460fad71cc4d177ac4785a717685f8ec

348

py

Python

waldur_core/monitoring/managers.py

opennode/nodeconductor

d6c17a9592bb6c49c33567542eef8d099605a46a

[ "MIT" ]

23

2015-01-15T13:29:53.000Z

2017-05-04T05:12:24.000Z

waldur_core/monitoring/managers.py

opennode/nodeconductor

d6c17a9592bb6c49c33567542eef8d099605a46a

[ "MIT" ]

null

waldur_core/monitoring/managers.py

opennode/nodeconductor

d6c17a9592bb6c49c33567542eef8d099605a46a

[ "MIT" ]

8

2015-01-11T18:51:47.000Z

2017-06-29T18:53:12.000Z

from django.db import models as django_models from waldur_core.core.managers import GenericKeyMixin class ResourceSlaManager(GenericKeyMixin, django_models.Manager): pass class ResourceItemManager(GenericKeyMixin, django_models.Manager): pass class ResourceSlaStateTransitionManager(GenericKeyMixin, django_models.Manager): pass

21.75

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true

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null

0

1

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1

0

5

2aed056bb066a479a54e413c288e5126a4d1f5dc

33

py

Python

cupy_alias/logic/__init__.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

142

2018-06-07T07:43:10.000Z

2021-10-30T21:06:32.000Z

cupy_alias/logic/__init__.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

282

2018-06-07T08:35:03.000Z

2021-03-31T03:14:32.000Z

cupy_alias/logic/__init__.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

19

2018-06-19T11:07:53.000Z

2021-05-13T20:57:04.000Z

from clpy.logic import * # NOQA

16.5

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4.6

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1

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0.884615

0.121212

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null

0

1

0

1

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5

630052d9f2112b73f5bc04c7356f6192727bcb06

99

py

Python

tests/unit/test_instrument.py

StarostinV/qmap_interpolation

bc432f33a461339e29ff8dfbc73a54fae10d5d34

[ "MIT" ]

null

tests/unit/test_instrument.py

StarostinV/qmap_interpolation

bc432f33a461339e29ff8dfbc73a54fae10d5d34

[ "MIT" ]

null

tests/unit/test_instrument.py

StarostinV/qmap_interpolation

bc432f33a461339e29ff8dfbc73a54fae10d5d34

[ "MIT" ]

null

import pytest def test_instrument(instrument): assert instrument.hot_pixel_threshold is None

16.5

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99

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0.141414

99

5

50

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0.917647

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0.333333

false

0

0.333333

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null

0

1

0

null

0

1

0

1

0

1

0

5

6320b64f9aaab098bc7711582825b97349aebdfb

29

py

Python

src/services/message_management/__init__.py

b1team/trada

22ceaf4d50fe3a38ff402315c029e574773ca9e0

[ "MIT" ]

null

src/services/message_management/__init__.py

b1team/trada

22ceaf4d50fe3a38ff402315c029e574773ca9e0

[ "MIT" ]

1

2021-03-12T15:16:03.000Z

src/services/message_management/__init__.py

b1team/trada

22ceaf4d50fe3a38ff402315c029e574773ca9e0

[ "MIT" ]

null

from .api import send_message

29

0.862069

5

29

4.8

1

0

0.103448

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1

29

0.923077

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1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

2da047e1ebf2d0eb3800443c040c822ad70bd18d

49

py

Python

tkdet/solver/__init__.py

tkhe/tkdetection

54e6c112ef2930e755f457e38449736f5743a9ea

[ "MIT" ]

1

2020-10-09T02:27:13.000Z

tkdet/solver/__init__.py

tkhe/tkdetection

54e6c112ef2930e755f457e38449736f5743a9ea

[ "MIT" ]

null

tkdet/solver/__init__.py

tkhe/tkdetection

54e6c112ef2930e755f457e38449736f5743a9ea

[ "MIT" ]

null

from .build import * from .lr_scheduler import *

16.333333

27

0.755102

7

49

5.142857

0.714286

0

0.163265

49

2

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24.5

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0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

2dc5f270797144470894757dfd7b6d2c8cfa92e6

92

py

Python

dbaas/maintenance/async_jobs/__init__.py

perry-contribs/database-as-a-service

db8fbee3982bbc32abf32aa86f7387de01a243be

[ "BSD-3-Clause" ]

303

2015-01-08T10:35:54.000Z

2022-02-28T08:54:06.000Z

dbaas/maintenance/async_jobs/__init__.py

nouraellm/database-as-a-service

5e655c9347bea991b7218a01549f5e44f161d7be

[ "BSD-3-Clause" ]

124

2015-01-14T12:56:15.000Z

2022-03-22T20:45:11.000Z

dbaas/maintenance/async_jobs/__init__.py

nouraellm/database-as-a-service

5e655c9347bea991b7218a01549f5e44f161d7be

[ "BSD-3-Clause" ]

110

2015-01-02T11:59:48.000Z

2022-02-28T08:54:06.000Z

from .base import * from .restart_database import * from .remove_instance_database import *

23

39

0.804348

12

92

5.916667

0.583333

0.28169

0

0.130435

92

3

40

30.666667

0.8875

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

2dd4db3496199ea8dc43d1819271bdaa264939dd

387

py

Python

api/generateKey.py

JumboCode/South-Florida-Jewish-Academy

02034d083a7311c3c09b86b06517cbf084a2794a

[ "MIT" ]

5

2019-12-11T14:33:11.000Z

2020-08-12T21:06:04.000Z

api/generateKey.py

JumboCode/South-Florida-Jewish-Academy

02034d083a7311c3c09b86b06517cbf084a2794a

[ "MIT" ]

155

2019-09-29T14:42:03.000Z

2022-02-26T18:26:37.000Z

api/generateKey.py

JumboCode/South-Florida-Jewish-Academy

02034d083a7311c3c09b86b06517cbf084a2794a

[ "MIT" ]

4

2019-09-29T16:32:11.000Z

2020-07-23T01:57:06.000Z

import random import string import secrets #generates a unique key as a string of 1 + 15 random letters/numbers def generateKey(): # key = str(1) # for i in range(0,15): # letterOrNum = random.randint(0,1) # if letterOrNum == 0: # key = key + random.choice(string.ascii_letters) # else: # key = key + str(random.randint(0,9)) # print(key) return secrets.token_hex(1024)

24.1875

68

0.682171

60

387

4.366667

0.566667

0.045802

0.10687

0

0.051118

0.191214

387

15

69

25.8

0.785942

0.687339

0

1

0

1

0.2

true

0

0.6

0.2

1

0

null

0

1

0

null

0

1

0

1

0

5

93290c62368395b618b2c95673065f4f79449d52

29,817

py

Python

makahiki/apps/widgets/smartgrid/migrations/0001_initial.py

justinslee/Wai-Not-Makahiki

4b7dd685012ec64758affe0ecee3103596d16aa7

[ "MIT" ]

1

2015-07-22T11:31:20.000Z

makahiki/apps/widgets/smartgrid/migrations/0001_initial.py

justinslee/Wai-Not-Makahiki

4b7dd685012ec64758affe0ecee3103596d16aa7

[ "MIT" ]

null

makahiki/apps/widgets/smartgrid/migrations/0001_initial.py

justinslee/Wai-Not-Makahiki

4b7dd685012ec64758affe0ecee3103596d16aa7

[ "MIT" ]

null

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'Level' db.create_table('smartgrid_level', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=50)), ('priority', self.gf('django.db.models.fields.IntegerField')(default=1)), ('unlock_condition', self.gf('django.db.models.fields.CharField')(max_length=400, null=True, blank=True)), ('unlock_condition_text', self.gf('django.db.models.fields.CharField')(max_length=400, null=True, blank=True)), )) db.send_create_signal('smartgrid', ['Level']) # Adding model 'Category' db.create_table('smartgrid_category', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=255)), ('slug', self.gf('django.db.models.fields.SlugField')(max_length=50, null=True, db_index=True)), ('priority', self.gf('django.db.models.fields.IntegerField')(default=1)), )) db.send_create_signal('smartgrid', ['Category']) # Adding model 'TextPromptQuestion' db.create_table('smartgrid_textpromptquestion', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('action', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.Action'])), ('question', self.gf('django.db.models.fields.TextField')()), ('answer', self.gf('django.db.models.fields.CharField')(max_length=255, null=True, blank=True)), )) db.send_create_signal('smartgrid', ['TextPromptQuestion']) # Adding model 'QuestionChoice' db.create_table('smartgrid_questionchoice', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('question', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.TextPromptQuestion'])), ('action', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.Action'])), ('choice', self.gf('django.db.models.fields.CharField')(max_length=255)), )) db.send_create_signal('smartgrid', ['QuestionChoice']) # Adding model 'ConfirmationCode' db.create_table('smartgrid_confirmationcode', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('action', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.Action'])), ('code', self.gf('django.db.models.fields.CharField')(unique=True, max_length=50, db_index=True)), ('is_active', self.gf('django.db.models.fields.BooleanField')(default=True)), )) db.send_create_signal('smartgrid', ['ConfirmationCode']) # Adding model 'Action' db.create_table('smartgrid_action', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=20)), ('slug', self.gf('django.db.models.fields.SlugField')(max_length=50, db_index=True)), ('title', self.gf('django.db.models.fields.CharField')(max_length=200)), ('image', self.gf('django.db.models.fields.files.ImageField')(max_length=255, null=True, blank=True)), ('video_id', self.gf('django.db.models.fields.CharField')(max_length=200, null=True, blank=True)), ('video_source', self.gf('django.db.models.fields.CharField')(max_length=20, null=True, blank=True)), ('embedded_widget', self.gf('django.db.models.fields.CharField')(max_length=50, null=True, blank=True)), ('description', self.gf('django.db.models.fields.TextField')()), ('type', self.gf('django.db.models.fields.CharField')(max_length=20)), ('level', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.Level'], null=True, blank=True)), ('category', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.Category'], null=True, blank=True)), ('priority', self.gf('django.db.models.fields.IntegerField')(default=1000)), ('pub_date', self.gf('django.db.models.fields.DateField')(default=datetime.date(2012, 6, 8))), ('expire_date', self.gf('django.db.models.fields.DateField')(null=True, blank=True)), ('unlock_condition', self.gf('django.db.models.fields.CharField')(max_length=400, null=True, blank=True)), ('unlock_condition_text', self.gf('django.db.models.fields.CharField')(max_length=400, null=True, blank=True)), ('related_resource', self.gf('django.db.models.fields.CharField')(max_length=20, null=True, blank=True)), ('social_bonus', self.gf('django.db.models.fields.IntegerField')(default=0)), ('is_canopy', self.gf('django.db.models.fields.BooleanField')(default=False)), ('is_group', self.gf('django.db.models.fields.BooleanField')(default=False)), ('point_value', self.gf('django.db.models.fields.IntegerField')(default=0)), )) db.send_create_signal('smartgrid', ['Action']) # Adding model 'Commitment' db.create_table('smartgrid_commitment', ( ('action_ptr', self.gf('django.db.models.fields.related.OneToOneField')(to=orm['smartgrid.Action'], unique=True, primary_key=True)), ('duration', self.gf('django.db.models.fields.IntegerField')(default=5)), )) db.send_create_signal('smartgrid', ['Commitment']) # Adding model 'Activity' db.create_table('smartgrid_activity', ( ('action_ptr', self.gf('django.db.models.fields.related.OneToOneField')(to=orm['smartgrid.Action'], unique=True, primary_key=True)), ('duration', self.gf('django.db.models.fields.IntegerField')()), ('point_range_start', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('point_range_end', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('confirm_type', self.gf('django.db.models.fields.CharField')(default='text', max_length=20)), ('confirm_prompt', self.gf('django.db.models.fields.TextField')(blank=True)), )) db.send_create_signal('smartgrid', ['Activity']) # Adding model 'Event' db.create_table('smartgrid_event', ( ('action_ptr', self.gf('django.db.models.fields.related.OneToOneField')(to=orm['smartgrid.Action'], unique=True, primary_key=True)), ('duration', self.gf('django.db.models.fields.IntegerField')()), ('event_date', self.gf('django.db.models.fields.DateTimeField')(null=True, blank=True)), ('event_location', self.gf('django.db.models.fields.CharField')(max_length=200, null=True, blank=True)), ('event_max_seat', self.gf('django.db.models.fields.IntegerField')(default=1000)), )) db.send_create_signal('smartgrid', ['Event']) # Adding model 'ActionMember' db.create_table('smartgrid_actionmember', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('user', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['auth.User'])), ('action', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.Action'])), ('question', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.TextPromptQuestion'], null=True, blank=True)), ('submission_date', self.gf('django.db.models.fields.DateTimeField')()), ('completion_date', self.gf('django.db.models.fields.DateField')(null=True, blank=True)), ('award_date', self.gf('django.db.models.fields.DateTimeField')(null=True, blank=True)), ('approval_status', self.gf('django.db.models.fields.CharField')(default='pending', max_length=20)), ('social_bonus_awarded', self.gf('django.db.models.fields.BooleanField')(default=False)), ('comment', self.gf('django.db.models.fields.TextField')(blank=True)), ('social_email', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('social_email2', self.gf('django.db.models.fields.TextField')(null=True, blank=True)), ('response', self.gf('django.db.models.fields.TextField')(blank=True)), ('admin_comment', self.gf('django.db.models.fields.TextField')(blank=True)), ('image', self.gf('django.db.models.fields.files.ImageField')(max_length=1024, blank=True)), ('points_awarded', self.gf('django.db.models.fields.IntegerField')(null=True, blank=True)), ('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), ('updated_at', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, null=True, blank=True)), )) db.send_create_signal('smartgrid', ['ActionMember']) # Adding unique constraint on 'ActionMember', fields ['user', 'action', 'submission_date'] db.create_unique('smartgrid_actionmember', ['user_id', 'action_id', 'submission_date']) # Adding model 'EmailReminder' db.create_table('smartgrid_emailreminder', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('user', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['auth.User'])), ('action', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.Action'])), ('send_at', self.gf('django.db.models.fields.DateTimeField')()), ('sent', self.gf('django.db.models.fields.BooleanField')(default=False)), ('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), ('updated_at', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, null=True, blank=True)), ('email_address', self.gf('django.db.models.fields.EmailField')(max_length=75)), )) db.send_create_signal('smartgrid', ['EmailReminder']) # Adding unique constraint on 'EmailReminder', fields ['user', 'action'] db.create_unique('smartgrid_emailreminder', ['user_id', 'action_id']) # Adding model 'TextReminder' db.create_table('smartgrid_textreminder', ( ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('user', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['auth.User'])), ('action', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['smartgrid.Action'])), ('send_at', self.gf('django.db.models.fields.DateTimeField')()), ('sent', self.gf('django.db.models.fields.BooleanField')(default=False)), ('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)), ('updated_at', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, null=True, blank=True)), ('text_number', self.gf('django.contrib.localflavor.us.models.PhoneNumberField')(max_length=20)), ('text_carrier', self.gf('django.db.models.fields.CharField')(max_length=50, null=True, blank=True)), )) db.send_create_signal('smartgrid', ['TextReminder']) # Adding unique constraint on 'TextReminder', fields ['user', 'action'] db.create_unique('smartgrid_textreminder', ['user_id', 'action_id']) def backwards(self, orm): # Removing unique constraint on 'TextReminder', fields ['user', 'action'] db.delete_unique('smartgrid_textreminder', ['user_id', 'action_id']) # Removing unique constraint on 'EmailReminder', fields ['user', 'action'] db.delete_unique('smartgrid_emailreminder', ['user_id', 'action_id']) # Removing unique constraint on 'ActionMember', fields ['user', 'action', 'submission_date'] db.delete_unique('smartgrid_actionmember', ['user_id', 'action_id', 'submission_date']) # Deleting model 'Level' db.delete_table('smartgrid_level') # Deleting model 'Category' db.delete_table('smartgrid_category') # Deleting model 'TextPromptQuestion' db.delete_table('smartgrid_textpromptquestion') # Deleting model 'QuestionChoice' db.delete_table('smartgrid_questionchoice') # Deleting model 'ConfirmationCode' db.delete_table('smartgrid_confirmationcode') # Deleting model 'Action' db.delete_table('smartgrid_action') # Deleting model 'Commitment' db.delete_table('smartgrid_commitment') # Deleting model 'Activity' db.delete_table('smartgrid_activity') # Deleting model 'Event' db.delete_table('smartgrid_event') # Deleting model 'ActionMember' db.delete_table('smartgrid_actionmember') # Deleting model 'EmailReminder' db.delete_table('smartgrid_emailreminder') # Deleting model 'TextReminder' db.delete_table('smartgrid_textreminder') models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2012, 6, 8, 0, 44, 51, 736311)'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2012, 6, 8, 0, 44, 51, 736127)'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'notifications.usernotification': { 'Meta': {'object_name': 'UserNotification'}, 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']", 'null': 'True', 'blank': 'True'}), 'contents': ('django.db.models.fields.TextField', [], {}), 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'display_alert': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'level': ('django.db.models.fields.IntegerField', [], {'default': '20'}), 'object_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'recipient': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}), 'unread': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}) }, 'score_mgr.pointstransaction': { 'Meta': {'ordering': "('-transaction_date',)", 'unique_together': "(('user', 'transaction_date', 'message'),)", 'object_name': 'PointsTransaction'}, 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']", 'null': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'message': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'object_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True'}), 'points': ('django.db.models.fields.IntegerField', [], {}), 'transaction_date': ('django.db.models.fields.DateTimeField', [], {}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) }, 'smartgrid.action': { 'Meta': {'object_name': 'Action'}, 'category': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.Category']", 'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {}), 'embedded_widget': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}), 'expire_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'is_canopy': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_group': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'level': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.Level']", 'null': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '20'}), 'point_value': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'priority': ('django.db.models.fields.IntegerField', [], {'default': '1000'}), 'pub_date': ('django.db.models.fields.DateField', [], {'default': 'datetime.date(2012, 6, 8)'}), 'related_resource': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50', 'db_index': 'True'}), 'social_bonus': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'type': ('django.db.models.fields.CharField', [], {'max_length': '20'}), 'unlock_condition': ('django.db.models.fields.CharField', [], {'max_length': '400', 'null': 'True', 'blank': 'True'}), 'unlock_condition_text': ('django.db.models.fields.CharField', [], {'max_length': '400', 'null': 'True', 'blank': 'True'}), 'users': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.User']", 'through': "orm['smartgrid.ActionMember']", 'symmetrical': 'False'}), 'video_id': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'video_source': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}) }, 'smartgrid.actionmember': { 'Meta': {'unique_together': "(('user', 'action', 'submission_date'),)", 'object_name': 'ActionMember'}, 'action': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.Action']"}), 'admin_comment': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'approval_status': ('django.db.models.fields.CharField', [], {'default': "'pending'", 'max_length': '20'}), 'award_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'comment': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'completion_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '1024', 'blank': 'True'}), 'points_awarded': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'question': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.TextPromptQuestion']", 'null': 'True', 'blank': 'True'}), 'response': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'social_bonus_awarded': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'social_email': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'social_email2': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'submission_date': ('django.db.models.fields.DateTimeField', [], {}), 'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'null': 'True', 'blank': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) }, 'smartgrid.activity': { 'Meta': {'object_name': 'Activity', '_ormbases': ['smartgrid.Action']}, 'action_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['smartgrid.Action']", 'unique': 'True', 'primary_key': 'True'}), 'confirm_prompt': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'confirm_type': ('django.db.models.fields.CharField', [], {'default': "'text'", 'max_length': '20'}), 'duration': ('django.db.models.fields.IntegerField', [], {}), 'point_range_end': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'point_range_start': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}) }, 'smartgrid.category': { 'Meta': {'object_name': 'Category'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'priority': ('django.db.models.fields.IntegerField', [], {'default': '1'}), 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50', 'null': 'True', 'db_index': 'True'}) }, 'smartgrid.commitment': { 'Meta': {'object_name': 'Commitment', '_ormbases': ['smartgrid.Action']}, 'action_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['smartgrid.Action']", 'unique': 'True', 'primary_key': 'True'}), 'duration': ('django.db.models.fields.IntegerField', [], {'default': '5'}) }, 'smartgrid.confirmationcode': { 'Meta': {'object_name': 'ConfirmationCode'}, 'action': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.Action']"}), 'code': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '50', 'db_index': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}) }, 'smartgrid.emailreminder': { 'Meta': {'unique_together': "(('user', 'action'),)", 'object_name': 'EmailReminder'}, 'action': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.Action']"}), 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'email_address': ('django.db.models.fields.EmailField', [], {'max_length': '75'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'send_at': ('django.db.models.fields.DateTimeField', [], {}), 'sent': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'null': 'True', 'blank': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) }, 'smartgrid.event': { 'Meta': {'object_name': 'Event', '_ormbases': ['smartgrid.Action']}, 'action_ptr': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['smartgrid.Action']", 'unique': 'True', 'primary_key': 'True'}), 'duration': ('django.db.models.fields.IntegerField', [], {}), 'event_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'event_location': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'event_max_seat': ('django.db.models.fields.IntegerField', [], {'default': '1000'}) }, 'smartgrid.level': { 'Meta': {'object_name': 'Level'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'priority': ('django.db.models.fields.IntegerField', [], {'default': '1'}), 'unlock_condition': ('django.db.models.fields.CharField', [], {'max_length': '400', 'null': 'True', 'blank': 'True'}), 'unlock_condition_text': ('django.db.models.fields.CharField', [], {'max_length': '400', 'null': 'True', 'blank': 'True'}) }, 'smartgrid.questionchoice': { 'Meta': {'object_name': 'QuestionChoice'}, 'action': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.Action']"}), 'choice': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'question': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.TextPromptQuestion']"}) }, 'smartgrid.textpromptquestion': { 'Meta': {'object_name': 'TextPromptQuestion'}, 'action': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.Action']"}), 'answer': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'question': ('django.db.models.fields.TextField', [], {}) }, 'smartgrid.textreminder': { 'Meta': {'unique_together': "(('user', 'action'),)", 'object_name': 'TextReminder'}, 'action': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['smartgrid.Action']"}), 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'send_at': ('django.db.models.fields.DateTimeField', [], {}), 'sent': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'text_carrier': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}), 'text_number': ('django.contrib.localflavor.us.models.PhoneNumberField', [], {'max_length': '20'}), 'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'null': 'True', 'blank': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) } } complete_apps = ['smartgrid']

72.547445

182

0.597981

3,198

29,817

5.454972

0.063164

0.102264

0.17816

0.254514

0.813872

0.789911

0.778332

0.73104

0.670737

0.588019

0

0.009761

0.182245

29,817

410

183

72.72439

0.705697

0.038334

0

0.280702

0

0.535368

0.333322

0

1

0.005848

false

0.002924

0.011696

0

0.026316

0

null

0

1

0

null

0

5

934acfe844bcf7b5cb0acba9d290acc4e3e9503a

97

py

Python

class9/exercise6/mytest/__init__.py

gleydsonm/pynet_ex

c7095b431d9d0de0e30f65d66a212a950afdb5d6

[ "Apache-2.0" ]

null

class9/exercise6/mytest/__init__.py

gleydsonm/pynet_ex

c7095b431d9d0de0e30f65d66a212a950afdb5d6

[ "Apache-2.0" ]

null

class9/exercise6/mytest/__init__.py

gleydsonm/pynet_ex

c7095b431d9d0de0e30f65d66a212a950afdb5d6

[ "Apache-2.0" ]

null

from world import func1, MyClass, MyChildClass __all__ = ('func1', 'MyClass', 'MyChildClass')

19.4

47

0.721649

10

97

6.6

0.7

0.363636

0.727273

0

0.024096

0.14433

97

4

48

24.25

0.771084

0

0.25

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

937e88169ca913629339d0e3321817d78ab8c3a6

101

py

Python

.scripts/diff.py

GreenBlast/dotfiles

12de7c9e5d8eda0a7f314ed6d19974e7ea549116

[ "MIT" ]

2

2018-08-08T12:39:10.000Z

2019-03-19T13:24:15.000Z

.scripts/diff.py

GreenBlast/dotfiles

12de7c9e5d8eda0a7f314ed6d19974e7ea549116

[ "MIT" ]

null

.scripts/diff.py

GreenBlast/dotfiles

12de7c9e5d8eda0a7f314ed6d19974e7ea549116

[ "MIT" ]

null

#!/usr/bin/python import sys import os os.system('vim -d "%s" "%s"' % (sys.argv[2], sys.argv[5]))

12.625

58

0.584158

19

101

3.105263

0.684211

0.237288

0

0.023256

0.148515

101

7

59

14.428571

0.662791

0.158416

0

0.192771

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

fa986ed6a152d7d89cf1586dcc5279a1a7eeab23

26

py

Python

pycdft/elcoupling/__init__.py

wwwennie/pycdft

3bd6c71d8e0661993af33147225db6396aa5729d

[ "MIT" ]

21

2020-05-23T17:36:55.000Z

2022-01-24T01:26:02.000Z

pycdft/elcoupling/__init__.py

wwwennie/pycdft

3bd6c71d8e0661993af33147225db6396aa5729d

[ "MIT" ]

5

2020-10-28T13:07:12.000Z

2021-12-17T15:53:08.000Z

pycdft/elcoupling/__init__.py

wwwennie/pycdft

3bd6c71d8e0661993af33147225db6396aa5729d

[ "MIT" ]

4

2020-03-14T21:01:11.000Z

2021-05-09T01:37:03.000Z

from .elcoupling import *

13

25

0.769231

3

26

6.666667

1

0

0.153846

26

1

26

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

fafca969cf2663ef2c1da62b9e4563c3318885a7

159

py

Python

asios/kernel/fixup.py

Paolo-Maffei/retro

bf30cec3d82672eb2805392e77f3b21705129fc0

[ "Unlicense" ]

1

2022-02-12T19:35:39.000Z

asios/kernel/fixup.py

Paolo-Maffei/retro

bf30cec3d82672eb2805392e77f3b21705129fc0

[ "Unlicense" ]

null

asios/kernel/fixup.py

Paolo-Maffei/retro

bf30cec3d82672eb2805392e77f3b21705129fc0

[ "Unlicense" ]

null

Import("env") def fixLinkFlag (s): return s[4:] if s.startswith('-Wl,-T') else s env.Replace(LINKFLAGS = [fixLinkFlag(i) for i in env['LINKFLAGS']])

22.714286

67

0.641509

25

159

4.08

0.68

0

0.007576

0.169811

159

6

68

26.5

0.765152

0

0.113208

0

1

0.25

false

0

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

5

878732079fb88bf39d43439fbebda5f3bbaf31e8

137

py

Python

python/testData/quickFixes/RenameElementQuickFixTest/protectedMember_after.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

2

2018-12-29T09:53:39.000Z

2018-12-29T09:53:42.000Z

python/testData/quickFixes/RenameElementQuickFixTest/protectedMember_after.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

null

python/testData/quickFixes/RenameElementQuickFixTest/protectedMember_after.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

1

2020-11-27T10:36:50.000Z

class A: def __init__(self): self.a = 1 def _foo(self): pass a_class = A() a_class._foo() print(a_class.a)

9.133333

23

0.540146

22

137

2.954545

0.409091

0.276923

0.215385

0

0.01087

0.328467

137

14

24

9.785714

0.695652

0

1

0.25

false

0.125

0

0.375

0.125

1

0

null

1

0

null

0

1

0

1

0

5

87f057379dfe6ad11655907ca1ebe6ffd8044984

111

py

Python

banklite/services/base.py

mharrisb1/banklite

d99b224682cc13f914fdc9ba371c0f3789d90a11

[ "MIT" ]

1

2021-09-28T17:34:11.000Z

banklite/services/base.py

mharrisb1/banklite

d99b224682cc13f914fdc9ba371c0f3789d90a11

[ "MIT" ]

null

banklite/services/base.py

mharrisb1/banklite

d99b224682cc13f914fdc9ba371c0f3789d90a11

[ "MIT" ]

null

import sqlite3 class BaseService: def __init__(self, conn: sqlite3.Connection): self.conn = conn

15.857143

49

0.693694

13

111

5.615385

0.692308

0.219178

0

0.023256

0.225225

111

6

50

18.5

0.825581

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

87fbf87762acd039641ddaf0e279649716086f46

102

py

Python

test.py

ecrax/hexo-recover-files

20d09ee035feca54e2c8512b57cf73256a7e1af2

[ "MIT" ]

1

2019-11-05T20:01:16.000Z

test.py

ecrax/hexo-recover-files

20d09ee035feca54e2c8512b57cf73256a7e1af2

[ "MIT" ]

5

2018-12-09T18:34:16.000Z

2020-10-19T13:07:40.000Z

test.py

ecrax/hexo-recover-files

20d09ee035feca54e2c8512b57cf73256a7e1af2

[ "MIT" ]

3

2019-11-05T20:01:27.000Z

2021-09-30T19:47:02.000Z

import convert_html import json convert_html.build_file("Fitting-Polynomial-Regressions-in-Python")

17

67

0.843137

14

102

5.928571

0.785714

0.26506

0

0.068627

102

5

68

20.4

0.873684

0

0.39604

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

355b2f78245bbb3c3fdf65730818a4fbe98d8c7f

74

py

Python

7 kyu/Vowel Count/Vowel Count.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

null

7 kyu/Vowel Count/Vowel Count.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

null

7 kyu/Vowel Count/Vowel Count.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

null

def getCount(inputStr): return sum(inputStr.count(v) for v in 'aeiou')

37

50

0.716216

12

74

4.416667

0.833333

0

0.148649

74

2

50

37

0.84127

0

0.066667

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

35d68d8d6a1ec41494bcaa4e9c609c7ec5144ea3

32,345

py

Python

objective/metadata/datamodel.py

ronaldoussoren/objective.metadata

799408cbe696f954c96d0738bd10ea1d87c3cace

[ "MIT" ]

2

2020-11-15T08:02:53.000Z

2021-11-23T04:00:39.000Z

objective/metadata/datamodel.py

ronaldoussoren/objective.metadata

799408cbe696f954c96d0738bd10ea1d87c3cace

[ "MIT" ]

11

2020-07-25T19:34:23.000Z

2021-05-15T15:59:20.000Z

objective/metadata/datamodel.py

ronaldoussoren/objective.metadata

799408cbe696f954c96d0738bd10ea1d87c3cace

[ "MIT" ]

null

"""" Datamodel for the metadata scanner. This file is currently incomplete. The following needs to be added - Add types and additional fields for exception files - Add helper methods for extracting exception information from the model. - Deal with alternatives (sets of scanned files that have different definitions, for example constants that aren't the same for x86_64 and arm64) """ import json import os from dataclasses import dataclass, field from typing import ( TYPE_CHECKING, Dict, Generic, List, Optional, Set, Tuple, TypedDict, TypeVar, Union, ) from dataclasses_json import Undefined, config, dataclass_json from . import util FILE_TYPE = Union[str, os.PathLike[str]] def bytes_decode(value: Union[None, str, List[str]]) -> Optional[bytes]: if isinstance(value, list): # Backward compatibility with existing metadata exceptions: # [ 32-bit, 64-bit], use only 64-bit value. # # To be removed when PyObjC is converted to the new tooling. value = value[-1] elif value is None: return None return value.encode("ascii") bytes_config = config( encoder=lambda value: None if value is None else value.decode("ascii"), decoder=bytes_decode, ) argdict_config = config( encoder=lambda value: None if value is None else {str(k) for k, v in value.items()}, decoder=lambda value: None if value is None else {int(k) for k, v in value.items()}, # The field_name in existing metadata exception files needs to be changed, # the alternative name will be removed when PyObjC is converted to the new # tooling. field_name="arguments", ) T = TypeVar("T") @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class MergedInfo(Generic[T]): """ At a number of places information will be different between different CPU architectures. This generic class is used to represent those cases. """ # Value on x86_64 architecture x86_64: T # Value on arm64 architecture arm64: T @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class AvailabilityInfo: """Information about API availability""" # API is unavailable for some reason unavailable: Optional[bool] = None # Explict suggestion for unavailable APIs suggestion: Optional[str] = None # macOS version where the API was introduced, # encoded in an integer (format is the same # as the MAC_OS_VERSION_... constants) introduced: Optional[int] = None # macOS version where the API was deprecated, # encoded in an integer. deprecated: Optional[int] = None # Explicit message with a deprecated API. deprecated_message: Optional[str] = None @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class EnumTypeInfo: """Information about a C enum type""" # Type encoding (as used by PyObjC) typestr: bytes = field(metadata=bytes_config) # API Availability availability: Optional[AvailabilityInfo] = None # True if this is a flag enum flags: bool = False # Used to mark APIs as ignored in exception # files. ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class EnumInfo: """Information about individual enum labels""" # Value for this enum label value: Union[int, MergedInfo[int]] # Name of the associated enum type # This is optional for exception data, should # be set otherwise! enum_type: Optional[str] = None # API availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class StructInfo: """Information about a C structure""" # Type encoding (as used by PyObjC) typestr: bytes = field(metadata=bytes_config) # If true the *typestr* is not the same as # what will be seen in the ObjC Runtime. # # Primarily used to mark anonymous structs # where the metadata will contain a # generated name. typestr_special: bool # List of names for the struct fields fieldnames: List[str] # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class ExternInfo: """Information about C global variable (constants)""" # Type encoding (as used by PyObjC) typestr: Union[bytes, MergedInfo[bytes]] = field(metadata=bytes_config) # Name of the type associated with *typestr*, will # by used when there is a better type name than # the default. type_name: Optional[str] = None # If true the value is a magic value. This is used with # _C_ID values where the pointer itself is special and # not a reference to an ObjC object. magic_cookie: bool = False # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False # # Information about callables. The data structure can represent # functions/methods with a callback (function or block) as one # of its arguments, where one of the arguments for the callback # has itself an argument that is a callback. # # The nested callback can not have callback arguments. This is # primarily because the tooling used does not support this without # more code replication, and the current level of nesting is good # enough. # @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class CallbackArgInfo2: """Information about the argument of a callback to a callback""" # Type encoding (as used by PyObjC) typestr: bytes = field(metadata=bytes_config) # Name of the type associated with *typestr*, will # by used when there is a better type name than # the default. type_name: Optional[str] = None # Is *NULL* and acceptable value for the C API? # - True: yes # - False: no # - None: don't know (treated as True by PyObjC) null_accepted: Optional[bool] = None # Used with pointers to _C_ID: If *true* a value # returned through this argument is already retained # (caller has to call -release when it no longer # needds the value). already_retained: Optional[bool] = False # Used with pointers to CoreFoundation objects. # If *true* a value returned through this argument is # already retained # (caller has to call CFRelease when # it no longer needds the value). already_cfretained: Optional[bool] = False @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class CallbackInfo2: """Information about a callback signature for a callback""" # Information about the return value retval: Optional[CallbackArgInfo2] # Information about all arguments args: List[CallbackArgInfo2] # Is this a variadic function? variadic: bool = False # Which of the fixed arguments for a varidic function # is a printf format string (if any) printf_format: Optional[int] = None # Does this variadic function have a number of # arguments of the same time, ending with a NUL value # for the type? null_terminated: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class CallbackArgInfo: """Information about the argument of a callback""" # Type encoding (as used by PyObjC) typestr: bytes = field(metadata=bytes_config) # True if *typestr* is not the same as the value # in the ObjC runtime. typestr_special: bool = False # Name of the type associated with *typestr*, will # by used when there is a better type name than # the default. type_name: Optional[str] = None # Is *NULL* and acceptable value for the C API? # - True: yes # - False: no # - None: don't know (treated as True by PyObjC) null_accepted: Optional[bool] = None # Used with pointers to _C_ID: If *true* a value # returned through this argument is already retained # (caller has to call -release when it no longer # needds the value). already_retained: Optional[bool] = False # Used with pointers to CoreFoundation objects. # If *true* a value returned through this argument is # already retained # (caller has to call CFRelease when # it no longer needds the value). already_cfretained: Optional[bool] = False # Information about the signature for a argument # that is a block or function. callable: Optional[CallbackInfo2] = None @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class CallbackInfo: """Information about a callback signature""" # Information about the return value retval: CallbackArgInfo # Information about all arguments args: List[CallbackArgInfo] = field(metadata=config(field_name="arguments")) # Is this a variadic function? variadic: bool = False # Which of the fixed arguments for a varidic function # is a printf format string (if any) printf_format: Optional[int] = None # Does this variadic function have a number of # arguments of the same time, ending with a NUL value # for the type? null_terminated: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class ArgInfo: """Information about a function/method argument""" # Type encoding (as used by PyObjC) typestr: bytes = field(metadata=bytes_config) # Name of the argument name: Optional[str] = None # True if *typestr* is not the same as the value # in the ObjC runtime. typestr_special: bool = False # Name of the type associated with *typestr*, will # by used when there is a better type name than # the default. type_name: Optional[str] = None # For pointer arguments (_C_PTR) mark if the data is # passed in (_C_IN), out (_C_OUT) or both (_C_INOUT). type_modifier: Optional[bytes] = field(metadata=bytes_config, default=None) # For pointer (_C_PTR) or object (_C_ID) arguments # tell if NULL is an acceptable value for the argument in C. null_accepted: Optional[bool] = None # If true this argument is a printf format string for # variadic callable. printf_format: bool = False # Used with pointers to _C_ID: If *true* a value # returned through this argument is already retained # (caller has to call -release when it no longer # needds the value). already_retained: Optional[bool] = False # Used with pointers to CoreFoundation objects. # If *true* a value returned through this argument is # already retained # (caller has to call CFRelease when # it no longer needds the value). already_cfretained: Optional[bool] = False # Signature information for a block or function argument callable: Optional[CallbackInfo] = None # True if the callable is stored by the called API callable_retained: Optional[bool] = None # If the argument is a C array with the size in another # argument this option describes which argument contains # the size. # - value: The argument containing the size # - (in_value, out_value); The *in_value*-th argument # contains the size of the array before the call, the # *out_value* contains the (effective) size after the call. c_array_length_in_arg: Optional[Union[int, Tuple[int, int]]] = None # If true the argument is a C array (with a length specified by one # of the other options). The effective length of the array on return # is in the return value of the function/method. c_array_length_in_result: bool = False # If true the argument is a C-array for which the # required size is either not known or cannot be described # by the metadata system. c_array_of_variable_length: bool = False # If true the argument is a C-array with the type-appropriated # NUL-value at the end. Python users won't use the delimiter. c_array_delimited_by_null: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class ArgExceptionInfo: """Information about a function/method argument""" # Can there be a shared superclass with ArgInfo? # Type encoding (as used by PyObjC) typestr: Optional[bytes] = field(metadata=bytes_config, default=None) # Name of the argument name: Optional[str] = None # Name of the type associated with *typestr*, will # by used when there is a better type name than # the default. type_name: Optional[str] = None # For pointer arguments (_C_PTR) mark if the data is # passed in (_C_IN), out (_C_OUT) or both (_C_INOUT). type_modifier: Optional[bytes] = field(metadata=bytes_config, default=None) # For pointer (_C_PTR) or object (_C_ID) arguments # tell if NULL is an acceptable value for the argument in C. null_accepted: Optional[bool] = None # If true this argument is a printf format string for # variadic callable. printf_format: bool = False # Used with pointers to _C_ID: If *true* a value # returned through this argument is already retained # (caller has to call -release when it no longer # needds the value). already_retained: Optional[bool] = None # Used with pointers to CoreFoundation objects. # If *true* a value returned through this argument is # already retained # (caller has to call CFRelease when # it no longer needds the value). already_cfretained: Optional[bool] = None # Signature information for a block or function argument callable: Optional[CallbackInfo] = None # True if the callable is stored by the called API callable_retained: Optional[bool] = None # If the argument is a C array with the size in another # argument this option describes which argument contains # the size. # - value: The argument containing the size # - (in_value, out_value); The *in_value*-th argument # contains the size of the array before the call, the # *out_value* contains the (effective) size after the call. c_array_length_in_arg: Optional[Union[int, Tuple[int, int]]] = None # If true the argument is a C array (with a length specified by one # of the other options). The effective length of the array on return # is in the return value of the function/method. c_array_length_in_result: Optional[bool] = None # If true the argument is a C-array for which the # required size is either not known or cannot be described # by the metadata system. c_array_of_variable_length: Optional[bool] = None # If true the argument is a C-array with the type-appropriated # NUL-value at the end. Python users won't use the delimiter. c_array_delimited_by_null: Optional[bool] = None @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class ReturnInfo: """Information about a function/method return value""" # Type encoding (as used by PyObjC) typestr: bytes = field(metadata=bytes_config) # True if *typestr* is not the same as the value # in the ObjC runtime. typestr_special: bool = False # Name of the type associated with *typestr*, will # by used when there is a better type name than # the default. type_name: Optional[str] = None # For pointer (_C_PTR) or object (_C_ID) arguments # tell if NULL is an acceptable value for the argument in C. null_accepted: Optional[bool] = None # Used with pointers to _C_ID: If *true* a value # returned through this argument is already retained # (caller has to call -release when it no longer # needds the value). already_retained: Optional[bool] = False # Used with pointers to CoreFoundation objects. # If *true* a value returned through this argument is # already retained # (caller has to call CFRelease when # it no longer needds the value). already_cfretained: Optional[bool] = False # Signature information for a block or function argument callable: Optional[CallbackInfo] = None # If true the argument is a C-array for which the # required size is either not known or cannot be described # by the metadata system. c_array_of_variable_length: bool = False # If true the argument is a C-array with the type-appropriated # NUL-value at the end. Python users won't use the delimiter. c_array_delimited_by_null: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass(frozen=True) class ReturnExceptionInfo: """Information about a function/method return value""" # Type encoding (as used by PyObjC) typestr: Optional[bytes] = field(metadata=bytes_config, default=None) # For pointer (_C_PTR) or object (_C_ID) arguments # tell if NULL is an acceptable value for the argument in C. null_accepted: Optional[bool] = None # Used with pointers to _C_ID: If *true* a value # returned through this argument is already retained # (caller has to call -release when it no longer # needds the value). already_retained: Optional[bool] = False # Used with pointers to CoreFoundation objects. # If *true* a value returned through this argument is # already retained # (caller has to call CFRelease when # it no longer needds the value). already_cfretained: Optional[bool] = False # Signature information for a block or function argument callable: Optional[CallbackInfo] = None # If true the argument is a C-array for which the # required size is either not known or cannot be described # by the metadata system. c_array_of_variable_length: Optional[bool] = None # If true the argument is a C-array with the type-appropriated # NUL-value at the end. Python users won't use the delimiter. c_array_delimited_by_null: Optional[bool] = None @dataclass_json(undefined=Undefined.RAISE) @dataclass class FunctionInfo: """Information about a function""" # Information about the return value retval: ReturnInfo # Information about all arguments args: List[ArgInfo] # Is this an inline function? inline: bool = False # Is this a variadic method variadic: bool = False @property def is_k_and_r(self): """ K&R style declaration """ return self.variadic and not self.args # Argument index for a variadic function # that is the printf_format (if any) printf_format: Optional[int] = None # API Availability availability: Optional[AvailabilityInfo] = None # To be removed ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass class FunctionExceptionInfo: """Exception data for a function""" # Information about the return value retval: Optional[ReturnExceptionInfo] = None # Information about all arguments args: Optional[Dict[int, ArgExceptionInfo]] = field( default=None, metadata=argdict_config ) # Is this an inline function? inline: bool = False # Is this a variadic method variadic: bool = False # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass class MethodInfo: """Information about a method in an ObjC class or protocol""" # Selector for the method selector: str # Is this a class method? class_method: bool # Information about the return value retval: ReturnInfo # Information about all arguments args: List[ArgInfo] # Used in protocols: is this a required method # (maybe split into two classes?) required: Optional[bool] = None # Is this a variadic method variadic: bool = False # Argument index for a variadic function # that is the printf_format (if any) printf_format: Optional[int] = None # Category that this method is defined in # (*None* for methods in the main class # definition) category: Optional[str] = None # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. # To be removed ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass class MethodExceptionInfo: """Override Information about a method in an ObjC class or protocol""" # Selector for the method selector: str # Is this a class method? class_method: bool # Information about the return value retval: Optional[ReturnExceptionInfo] = None # Information about all arguments args: Optional[Dict[int, ArgExceptionInfo]] = field( default=None, metadata=argdict_config ) # Is this a variadic method variadic: Optional[bool] = None # Argument index for a variadic function # that is the printf_format (if any) printf_format: Optional[int] = None # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass class PropertyInfo: """class property""" # Name of the property name: str # Type encoding (as used by PyObjC) typestr: bytes = field(metadata=bytes_config) # True if *typestr* is not the same as the value # in the ObjC runtime. typestr_special: bool # Name of the type associated with *typestr*, will # by used when there is a better type name than # the default. type_name: Optional[str] = None # Name of getter if it is not default getter: Optional[str] = None # Name of setter if it is not default setter: Optional[str] = None # Property attributes (such as readonline, readwrite) # ... -> should extract custom getter/setter into its # own attribute attributes: Set[str] = field(default_factory=util.sorted_set) # Category that this method is defined in # (*None* for methods in the main class # definition) category: Optional[str] = None # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass class ProtocolInfo: """Objective-C protocol, or deduced informal protocol (category)""" # List of names for protocols this protocol # implements (subclassing for protocols) implements: List[str] # List of methods in this protocol methods: List[MethodInfo] # List of properties in this protocol properties: List[PropertyInfo] # API Availability availability: Optional[AvailabilityInfo] = None @dataclass_json(undefined=Undefined.RAISE) @dataclass class ClassInfo: """Objective-C class""" # Superclass for this class. # This will be *None* in two cases: # - Root classes (NSObject/NSProxy) # - Categories on classes defined in another framework super: Optional[str] # List of names for protocols this protocol # implements (subclassing for protocols) implements: List[str] = field(metadata=config(field_name="protocols")) # List of methods in this protocol methods: List[MethodInfo] # List of properties in this protocol properties: List[PropertyInfo] # API Availability availability: Optional[AvailabilityInfo] = None final: bool = False # Categories defined on this class categories: Set[str] = field(default_factory=util.sorted_set) @dataclass_json(undefined=Undefined.RAISE) @dataclass class ClassExceptionInfo: """Objective-C class""" # Superclass for this class. # This will be *None* in two cases: # - Root classes (NSObject/NSProxy) # - Categories on classes defined in another framework super: Optional[str] # List of names for protocols this protocol # implements (subclassing for protocols) implements: List[str] = field(metadata=config(field_name="protocols")) # List of methods in this protocol methods: List[MethodExceptionInfo] = field(default_factory=list) # List of properties in this protocol properties: List[PropertyInfo] = field(default_factory=list) # API Availability availability: Optional[AvailabilityInfo] = None @dataclass_json(undefined=Undefined.RAISE) @dataclass class CFTypeInfo: """Information about a CoreFoundation type""" # Type encoding (as used by PyObjC) typestr: bytes = field(metadata=bytes_config) # Name of the ...GetTypeID function for this CFType # (can be None when autodetection fails) gettypeid_func: Optional[str] = None # Name of the ObjC class this type is tollfree bridged to. tollfree: Optional[str] = None # Used in exception information # Need to investigate why this is needed, should not happen! opaque: Optional[bool] = None @dataclass_json(undefined=Undefined.RAISE) @dataclass class LiteralInfo: """Named literals other than enums""" # Value of the literal value: Union[None, int, float, str, MergedInfo[Union[None, int, float, str]]] # False if this "str" represents a byte literal unicode: Optional[bool] = False # API Availability availability: Optional[AvailabilityInfo] = None # For exception information: ignore this declaration ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass class AliasInfo: """Name that aliases another name""" # What this name is an alias for alias: str # If this is an alias for an item in a named # enum this field contains that name. enum_type: Optional[str] = None # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass class ExpressionInfo: """A C define that evaluates to a constant expression""" # The expression expression: str # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False @dataclass_json(undefined=Undefined.RAISE) @dataclass class FunctionMacroInfo: """ A C define with parameters that can be evaluated as a Python function """ # String that evaluates to the function definition: str # API Availability availability: Optional[AvailabilityInfo] = None # To be removed ignore: bool = False class FUNCTION_MACRO_UPDATE_DICT(TypedDict, total=False): definition: str availability: AvailabilityInfo @dataclass_json(undefined=Undefined.RAISE) @dataclass class FunctionMacroExceptionInfo: """ A C define with parameters that can be evaluated as a Python function """ # String that evaluates to the function definition: Optional[str] # API Availability availability: Optional[AvailabilityInfo] = None # Used to mark APIs as ignored in exception # files. ignore: bool = False def exception_info(self) -> FUNCTION_MACRO_UPDATE_DICT: result: FUNCTION_MACRO_UPDATE_DICT = {} if self.definition is not None: result["definition"] = self.definition if self.availability is not None: result["availability"] = self.availability return result @dataclass_json(undefined=Undefined.RAISE) @dataclass class FrameworkMetadata: architectures: Set[str] = field(default_factory=util.sorted_set) sdk_version: Optional[str] = None enum_type: Dict[str, EnumTypeInfo] = field(default_factory=dict) enum: Dict[str, EnumInfo] = field(default_factory=dict) structs: Dict[str, StructInfo] = field(default_factory=dict) externs: Dict[str, ExternInfo] = field(default_factory=dict) cftypes: Dict[str, CFTypeInfo] = field(default_factory=dict) literals: Dict[str, LiteralInfo] = field(default_factory=dict) formal_protocols: Dict[str, ProtocolInfo] = field(default_factory=dict) informal_protocols: Dict[str, ProtocolInfo] = field(default_factory=dict) classes: Dict[str, ClassInfo] = field(default_factory=dict) aliases: Dict[str, AliasInfo] = field(default_factory=dict) expressions: Dict[str, ExpressionInfo] = field(default_factory=dict) func_macros: Dict[str, FunctionMacroInfo] = field(default_factory=dict) functions: Dict[str, FunctionInfo] = field(default_factory=dict) @classmethod def from_file(cls, path: FILE_TYPE) -> "FrameworkMetadata": with open(path, "r") as stream: raw_data = stream.read() # Strip optional leading comment block while raw_data.startswith("//"): _, _, raw_data = raw_data.partition("\n") data = json.loads(raw_data) del raw_data return FrameworkMetadata.from_dict(data["definitions"]) def to_file(self, path: FILE_TYPE) -> None: data = self.to_dict() with open(path, "w") as stream: json.dump({"definitions": data}, stream) if TYPE_CHECKING: # Dataclasses_json adds (amongst others) methods from_dict and # to_dict which mypy doesn't know about. # # The declaration below teach the typechecker about these methods. @classmethod def from_dict(cls, value: dict) -> "FrameworkMetadata": ... def to_dict(self) -> dict: ... @dataclass_json @dataclass class ExceptionData: """ Exceptions data This is work in progress, at least some of the dataclasses should have variants for the exception file (required field in regular file, optional in exception file; likewise for the 'ignored' field which is only used in exception data) """ enum_type: Dict[str, EnumTypeInfo] = field(default_factory=dict) enum: Dict[str, EnumInfo] = field(default_factory=dict) # structs: Dict[str, StructInfo] = field(default_factory=dict) externs: Dict[str, ExternInfo] = field(default_factory=dict) # cftypes: Dict[str, CFTypeInfo] = field(default_factory=dict) literals: Dict[str, LiteralInfo] = field(default_factory=dict) # formal_protocols: Dict[str, ProtocolInfo] = field(default_factory=dict) # informal_protocols: Dict[str, ProtocolInfo] = field(default_factory=dict) # classes: Dict[str, ClassExceptionInfo] = field(default_factory=dict) aliases: Dict[str, AliasInfo] = field(default_factory=dict) expressions: Dict[str, ExpressionInfo] = field(default_factory=dict) func_macros: Dict[str, FunctionMacroExceptionInfo] = field(default_factory=dict) functions: Dict[str, FunctionExceptionInfo] = field(default_factory=dict) @classmethod def from_file(cls, path: FILE_TYPE) -> "ExceptionData": with open(path, "r") as stream: raw_data = stream.read() # Strip optional leading comment block while raw_data.startswith("//"): _, _, raw_data = raw_data.partition("\n") data = json.loads(raw_data) del raw_data if "formal_protocols" in data["definitions"]: del data["definitions"]["formal_protocols"] if "informal_protocols" in data["definitions"]: del data["definitions"]["informal_protocols"] if "cftypes" in data["definitions"]: del data["definitions"]["cftypes"] return ExceptionData.from_dict(data["definitions"], infer_missing=True) def to_file(self, path: FILE_TYPE) -> None: data = self.to_dict() with open(path, "w") as stream: json.dump({"definitions": data}, stream) if TYPE_CHECKING: # Dataclasses_json adds (amongst others) methods from_dict and # to_dict which mypy doesn't know about. # # The declaration below teach the typechecker about these methods. @classmethod def from_dict(cls, value: dict, infer_missing: bool = False) -> "ExceptionData": ... def to_dict(self) -> dict: ...

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Python

ckg/report_manager/__init__.py

igg-bioinfo/CKG

7984ae239c5010545b95c9be3201a899682294de

[ "MIT" ]

189

2020-05-11T16:26:39.000Z

2022-03-30T08:24:37.000Z

ckg/report_manager/__init__.py

igg-bioinfo/CKG

7984ae239c5010545b95c9be3201a899682294de

[ "MIT" ]

51

2020-05-12T04:24:28.000Z

2022-03-30T10:51:55.000Z

ckg/report_manager/__init__.py

igg-bioinfo/CKG

7984ae239c5010545b95c9be3201a899682294de

[ "MIT" ]

44

2020-05-13T09:53:21.000Z

2022-03-27T08:43:53.000Z

#This is just an empty directory

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py

Python

tests/test_hilbertcurve.py

binfnstats/hilbertcurve

6599899ee2156fe7833b15447325f330cf5b8113

[ "MIT" ]

129

2019-03-10T22:01:18.000Z

2022-02-24T18:04:28.000Z

tests/test_hilbertcurve.py

binfnstats/hilbertcurve

6599899ee2156fe7833b15447325f330cf5b8113

[ "MIT" ]

12

2019-03-03T18:08:11.000Z

2022-03-13T03:39:25.000Z

tests/test_hilbertcurve.py

galtay/hilbert_curve

6599899ee2156fe7833b15447325f330cf5b8113

[ "MIT" ]

19

2015-11-08T19:48:05.000Z

2018-11-21T14:40:08.000Z

"""Test the functions in hilbert.py""" import pytest import unittest import numpy as np from hilbertcurve.hilbertcurve import HilbertCurve class TestHilbertIntegerToTranspose(unittest.TestCase): """Test hilbert_integer_to_transpose.""" def test_10590(self): """Assert that a 15 bit hilber integer is correctly transposed into a 3-d vector ... ABCDEFGHIJKLMNO 10590 (0b010100101011110) ADGJM X[0] = 0b01101 = 13 BEHKN X[1] = 0b10011 = 19 CFILO X[2] = 0b00110 = 6 """ p = 5 n = 3 hilbert_curve = HilbertCurve(p, n) h = 10590 expected_x = [13, 19, 6] actual_x = hilbert_curve._hilbert_integer_to_transpose(h) self.assertEqual(actual_x, expected_x) class TestTransposeToHilbertInteger(unittest.TestCase): """Test _transpose_to_hilbert_integer.""" def test_13_19_6(self): """Assert that a 15 bit hilber integer is correctly recovered from its transposed 3-d vector ... ABCDEFGHIJKLMNO 10590 (0b010100101011110) ADGJM X[0] = 0b01101 = 13 BEHKN X[1] = 0b10011 = 19 CFILO X[2] = 0b00110 = 6 """ p = 5 n = 3 hilbert_curve = HilbertCurve(p, n) x = [13, 19, 6] expected_h = 10590 actual_h = hilbert_curve._transpose_to_hilbert_integer(x) self.assertEqual(actual_h, expected_h) class TestReversibility(unittest.TestCase): """Test that transpose2axes and axes2transpose are consistent.""" def test_reversibility(self): """Assert points_from_distances and distances_from_points are inverse operations.""" n = 3 p = 5 hilbert_curve = HilbertCurve(p, n) n_h = 2**(n * p) distances = list(range(n_h)) coordinates = hilbert_curve.points_from_distances(distances) distances_check = hilbert_curve.distances_from_points(coordinates) for dist, dist_check in zip(distances, distances_check): self.assertEqual(dist, dist_check) class TestInitIntConversion(unittest.TestCase): """Test __init__ conversion of floating point to integers.""" def test_pt_oh(self): """Assert x.0 goes to x""" n = 3.0 n_int = 3 p = 5 hilbert_curve = HilbertCurve(p, n) self.assertTrue(isinstance(hilbert_curve.n, int)) self.assertEqual(hilbert_curve.n, n_int) n = 3 p_int = 5 p = 5.0 hilbert_curve = HilbertCurve(p, n) self.assertTrue(isinstance(hilbert_curve.p, int)) self.assertEqual(hilbert_curve.p, p_int) def test_pt_one(self): """Assert x.1 raises an error""" n = 3 p = 5.1 with pytest.raises(TypeError): hilbert_curve = HilbertCurve(p, n) n = 3.1 p = 5 with pytest.raises(TypeError): hilbert_curve = HilbertCurve(p, n) class TestInitBounds(unittest.TestCase): """Test __init__ bounds on n and p.""" def test_pt_one(self): """Assert x=0 raises an error""" n = 0 p = 5 with pytest.raises(ValueError): hilbert_curve = HilbertCurve(p, n) n = 3 p = 0 with pytest.raises(ValueError): hilbert_curve = HilbertCurve(p, n) class TestInitUnmodified(unittest.TestCase): """Test distances_from_points does not modify input.""" def test_base(self): """Assert list is unmodified""" n = 4 p = 8 hilbert_curve = HilbertCurve(p, n) x = [[1, 5, 3, 19]] x_in = list(x) h = hilbert_curve.distances_from_points(x_in) self.assertEqual(x, x_in) class TestTypeMatch(unittest.TestCase): """Test match_type kwarg""" def test_points_from_distances_list(self): """Assert list type matching works in points_from_distances""" n = 2 p = 3 hilbert_curve = HilbertCurve(p, n) dists = list(np.arange(hilbert_curve.max_h + 1)) points = hilbert_curve.points_from_distances(dists, match_type=True) target_type = type(dists) self.assertTrue(isinstance(points, target_type)) self.assertTrue( all(isinstance(vec, target_type) for vec in points) ) def test_points_from_distances_tuple(self): """Assert tuple type matching works in points_from_distances""" n = 2 p = 3 hilbert_curve = HilbertCurve(p, n) dists = tuple(np.arange(hilbert_curve.max_h + 1)) points = hilbert_curve.points_from_distances(dists, match_type=True) target_type = type(dists) self.assertTrue(isinstance(points, target_type)) self.assertTrue( all(isinstance(vec, target_type) for vec in points) ) def test_points_from_distances_ndarray(self): """Assert tuple type matching works in points_from_distances""" n = 2 p = 3 hilbert_curve = HilbertCurve(p, n) dists = np.arange(hilbert_curve.max_h + 1) points = hilbert_curve.points_from_distances(dists, match_type=True) target_type = type(dists) self.assertTrue(isinstance(points, target_type)) self.assertTrue( all(isinstance(vec, target_type) for vec in points) ) def test_distances_from_points_list(self): """Assert list type matching works in distances_from_points""" n = 2 p = 3 hilbert_curve = HilbertCurve(p, n) points = [ [0,0], [7,7], ] distances = hilbert_curve.distances_from_points(points, match_type=True) target_type = type(points) self.assertTrue(isinstance(distances, target_type)) def test_distances_from_points_tuple(self): """Assert tuple type matching works in distances_from_points""" n = 2 p = 3 hilbert_curve = HilbertCurve(p, n) points = tuple([ tuple([0,0]), tuple([7,7]), ]) distances = hilbert_curve.distances_from_points(points, match_type=True) target_type = type(points) self.assertTrue(isinstance(distances, target_type)) def test_distances_from_points_ndarray(self): """Assert ndarray type matching works in distances_from_points""" n = 2 p = 3 hilbert_curve = HilbertCurve(p, n) points = np.array([ [0,0], [7,7], ]) distances = hilbert_curve.distances_from_points(points, match_type=True) target_type = type(points) self.assertTrue(isinstance(distances, target_type)) class TestInitIntConversion(unittest.TestCase): """Test __init__ conversion of floating point to integers.""" def test_pt_oh(self): """Assert x.0 goes to x""" n = 3.0 n_int = 3 p = 5 hilbert_curve = HilbertCurve(p, n) self.assertTrue(isinstance(hilbert_curve.n, int)) self.assertEqual(hilbert_curve.n, n_int) n = 3 p_int = 5 p = 5.0 hilbert_curve = HilbertCurve(p, n) self.assertTrue(isinstance(hilbert_curve.p, int)) self.assertEqual(hilbert_curve.p, p_int) def test_pt_one(self): """Assert x.1 raises an error""" n = 3 p = 5.1 with pytest.raises(TypeError): hilbert_curve = HilbertCurve(p, n) n = 3.1 p = 5 with pytest.raises(TypeError): hilbert_curve = HilbertCurve(p, n) class TestInitBounds(unittest.TestCase): """Test __init__ bounds on n and p.""" def test_pt_one(self): """Assert x=0 raises an error""" n = 0 p = 5 with pytest.raises(ValueError): hilbert_curve = HilbertCurve(p, n) n = 3 p = 0 with pytest.raises(ValueError): hilbert_curve = HilbertCurve(p, n) if __name__ == '__main__': unittest.main()

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0.593059

991

8,183

4.684157

0.133199

0.113744

0.118483

0.754201

0.724472

0.717148

0.69776

0

0.038182

0.311866

8,183

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81

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0.786184

0.194305

0

0.700565

0

0.001271

0

0.118644

1

0.090395

false

0

0.022599

0

0.163842

0

null

0

1

0

1

0

1

0

null

0

5

ea2edeaa95f50fa7cad47eb83a8c5f2533e1c2b1

555

py

Python

tests/test_07_end_to_end/conftest.py

primal100/aionetworking

a29cbb022cbae1a4ad1c3d44327e9d0b0c930227

[ "MIT" ]

null

tests/test_07_end_to_end/conftest.py

primal100/aionetworking

a29cbb022cbae1a4ad1c3d44327e9d0b0c930227

[ "MIT" ]

1

2018-12-23T00:50:33.000Z

tests/test_07_end_to_end/conftest.py

primal100/aionetworking

a29cbb022cbae1a4ad1c3d44327e9d0b0c930227

[ "MIT" ]

null

from tests.test_06_senders.conftest import * import pytest from aionetworking.formats.contrib.json import JSONObject @pytest.fixture def echo_exception_response_encoded() -> bytes: return b'{"id": 2, "error": "InvalidRequestError"}' @pytest.fixture def echo_exception_response() -> dict: return {'id': 2, 'error': 'InvalidRequestError'} @pytest.fixture def echo_exception_response_object(echo_exception_response_encoded, echo_exception_response) -> JSONObject: return JSONObject(echo_exception_response_encoded, echo_exception_response)

29.210526

107

0.798198

66

555

6.409091

0.424242

0.21513

0.347518

0.141844

0.621749

0.534279

0.3026

0

0.008032

0.102703

555

18

108

30.833333

0.841365

0

0.25

0

0.120721

0.03964

0

1

0.25

false

0

0.25

0.75

0

null

1

0

null

0

1

0

1

0

5

ea898b485b0efc642f4f48aea338018daf8ca642

39

py

Python

AtC_Gra_Con_001-010/AGC001/C.py

yosho-18/AtCoder

50f6d5c92a01792552c31ac912ce1cd557b06fb0

[ "MIT" ]

null

AtC_Gra_Con_001-010/AGC001/C.py

yosho-18/AtCoder

50f6d5c92a01792552c31ac912ce1cd557b06fb0

[ "MIT" ]

null

AtC_Gra_Con_001-010/AGC001/C.py

yosho-18/AtCoder

50f6d5c92a01792552c31ac912ce1cd557b06fb0

[ "MIT" ]

null

print("Yes" if (True|False) else "No")

19.5

38

0.641026

7

39

3.571429

1

0

0.128205

39

1

39

0.735294

0

0.128205

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

575d82504efcdc7feac7686d860ba8c82a642669

183

py

Python

viberio/types/requests/seen_request.py

vorlov-bash/viberio

8bea3bb4f39f68e9b83f1d1876d293b641ee6a06

[ "MIT" ]

18

2019-02-20T15:24:21.000Z

2022-03-24T12:48:30.000Z

viberio/types/requests/seen_request.py

vorlov-bash/viberio

8bea3bb4f39f68e9b83f1d1876d293b641ee6a06

[ "MIT" ]

null

viberio/types/requests/seen_request.py

vorlov-bash/viberio

8bea3bb4f39f68e9b83f1d1876d293b641ee6a06

[ "MIT" ]

10

2019-04-05T17:09:03.000Z

2021-09-17T17:17:08.000Z

import attr from viberio.types.requests import ViberReqestObject @attr.s class ViberSeenRequest(ViberReqestObject): message_token: int = attr.ib() user_id: str = attr.ib()

18.3

52

0.754098

23

183

5.913043

0.73913

0.088235

0

0.153005

183

9

53

20.333333

0.877419

0

1

0

true

0

0.333333

0

0.833333

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

576a0c3fe5b7d8dc4550313c920a489973592447

3,381

py

Python

test/contrib/train/test_gcmc_dataset.py

hirosassa/redshells

7824381a7d1f042405014b4572a5d5824338fc74

[ "MIT" ]

42

2019-01-02T01:31:39.000Z

2022-01-29T08:56:12.000Z

test/contrib/train/test_gcmc_dataset.py

hirosassa/redshells

7824381a7d1f042405014b4572a5d5824338fc74

[ "MIT" ]

29

2019-03-28T02:33:01.000Z

2021-09-27T00:45:25.000Z

test/contrib/train/test_gcmc_dataset.py

hirosassa/redshells

7824381a7d1f042405014b4572a5d5824338fc74

[ "MIT" ]

17

2019-02-21T03:08:20.000Z

2022-02-17T23:27:48.000Z

import unittest from logging import getLogger import numpy as np from redshells.contrib.model.gcmc_dataset import GcmcDataset from redshells.contrib.model.graph_convolutional_matrix_completion import GcmcGraphDataset logger = getLogger(__name__) class TestGCMCDataset(unittest.TestCase): def test_without_information(self): user_ids = np.array([1, 1, 2, 2, 2]) item_ids = np.array([1, 2, 1, 2, 3]) ratings = np.array([1, 0, 1, 0, 1]) rating_data = GcmcDataset(user_ids=user_ids, item_ids=item_ids, ratings=ratings) test_size = 0.0 dataset = GcmcGraphDataset(rating_data, test_size) data = dataset.train_data() self.assertEqual(user_ids.shape, data['user'].shape) self.assertEqual(item_ids.shape, data['item'].shape) self.assertEqual((ratings.shape[0], 2), data['label'].shape) self.assertEqual(ratings.shape, data['rating'].shape) self.assertEqual(user_ids.shape, data['user_feature_indices'].shape) self.assertEqual(item_ids.shape, data['item_feature_indices'].shape) def test_with_information(self): user_ids = np.array([1, 1, 2, 2, 2]) item_ids = np.array([1, 2, 1, 2, 3]) ratings = np.array([1, 0, 1, 0, 1]) test_size = 0.0 user_features = [{1: np.array([10, 11]), 2: np.array([20, 21])}] item_features = [{1: np.array([10, 11, 12]), 2: np.array([20, 21, 22]), 3: np.array([30, 31, 32])}] dataset = GcmcDataset(user_ids=user_ids, item_ids=item_ids, ratings=ratings, user_features=user_features, item_features=item_features) graph_dataset = GcmcGraphDataset(dataset, test_size) data = graph_dataset.train_data() self.assertEqual(user_ids.shape, data['user'].shape) self.assertEqual(item_ids.shape, data['item'].shape) self.assertEqual((ratings.shape[0], 2), data['label'].shape) self.assertEqual(ratings.shape, data['rating'].shape) self.assertEqual(user_ids.shape, data['user_feature_indices'].shape) self.assertEqual(item_ids.shape, data['item_feature_indices'].shape) def test_with_click_threshold(self): user_ids = np.array([1, 1, 2, 2, 2, 3]) item_ids = np.array([1, 2, 1, 2, 3, 1]) ratings = np.array([1, 0, 1, 0, 1, 0]) test_size = 0.0 user_features = [{1: np.array([10, 11]), 2: np.array([20, 21]), 3: np.array([30, 31])}] item_features = [{1: np.array([10, 11, 12]), 2: np.array([20, 21, 22]), 3: np.array([30, 31, 32])}] dataset = GcmcDataset(user_ids=user_ids, item_ids=item_ids, ratings=ratings, user_features=user_features, item_features=item_features) graph_dataset = GcmcGraphDataset(dataset, test_size, min_user_click_count=3) np.testing.assert_almost_equal([0, 0, 1, 1, 1, 0], graph_dataset._user.indices) np.testing.assert_almost_equal([1, 2, 1, 2, 3, 1], graph_dataset._item.indices) data = graph_dataset.train_data() self.assertEqual(item_ids.shape, graph_dataset._item.indices.shape) self.assertEqual((ratings.shape[0], 2), data['label'].shape) self.assertEqual(ratings.shape, data['rating'].shape) self.assertEqual(user_ids.shape, data['user_feature_indices'].shape) self.assertEqual(item_ids.shape, data['item_feature_indices'].shape) if __name__ == '__main__': unittest.main()

51.227273

142

0.664892

488

3,381

4.393443

0.139344

0.065299

0.130597

0.030784

0.788246

0.752799

0.749534

0.726679

0.717817

0.708955

0

0.050798

0.184857

3,381

65

143

52.015385

0.727141

0

0.553571

0

0.052351

0

0.339286

1

0.053571

false

0

0.089286

0

0.160714

0

null

0

1

0

null

0

5

57a8b542e671291b0e80e91e4b98cc123a9bbdc9

51

py

Python

python/testData/resolve/InnerFuncVar.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/resolve/InnerFuncVar.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/resolve/InnerFuncVar.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def f1(): foo = 1 def f2(): return f<ref>oo

12.75

19

0.509804

10

51

2.6

0.9

0

0.085714

0.313725

51

4

19

12.75

0.657143

0

1

0.5

false

0

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

5

57d7857179f2e84e738fd6577a396d3e3eb97702

61

py

Python

cls2det/utils/__init__.py

Media-Smart/cls2det

2e0eaeec0a74edfe6946ebb6bc1df8a17a8e478a

[ "Apache-2.0" ]

22

2020-06-11T07:00:02.000Z

2020-12-14T15:20:59.000Z

cls2det/utils/__init__.py

Media-Smart/cls2det

2e0eaeec0a74edfe6946ebb6bc1df8a17a8e478a

[ "Apache-2.0" ]

null

cls2det/utils/__init__.py

Media-Smart/cls2det

2e0eaeec0a74edfe6946ebb6bc1df8a17a8e478a

[ "Apache-2.0" ]

4

2020-10-22T05:49:01.000Z

2021-12-18T22:57:54.000Z

from .config import Config from .eval_coco import COCOeval

20.333333

32

0.803279

9

61

5.333333

0.666667

0

0.163934

61

2

33

30.5

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

57e4badd97a2191740da4d29e3b8ae498ff8a1d0

97

py

Python

bayonet/__init__.py

Bayonet-Client/bayonet-python

4d1e44c8f7f1bb85971481bcf319a4ee0604b609

[ "MIT" ]

1

2022-03-28T15:08:47.000Z

bayonet/__init__.py

Bayonet-Client/bayonet-python

4d1e44c8f7f1bb85971481bcf319a4ee0604b609

[ "MIT" ]

null

bayonet/__init__.py

Bayonet-Client/bayonet-python

4d1e44c8f7f1bb85971481bcf319a4ee0604b609

[ "MIT" ]

null

from .bayonet import BayonetClient from .exceptions import BayonetError, InvalidClientSetupError

32.333333

61

0.876289

9

97

9.444444

0.777778

0

0.092784

97

2

62

48.5

0.965909

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

57fc66ae4d38898c681aed3c62c534311b378945

44

py

Python

000403StepPyThin/000403_02_05_Task03_Lists_v02_Stepik_20200223.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

000403StepPyThin/000403_02_05_Task03_Lists_v02_Stepik_20200223.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

000403StepPyThin/000403_02_05_Task03_Lists_v02_Stepik_20200223.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

print(sum(int(i) for i in input().split()))

22

43

0.636364

9

44

3.111111

0.888889

0

0.113636

44

1

44

0.717949

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

17dd56dba05a23d70f3cf46848269359903d5449

215

py

Python

akika_venv/lib/python3.6/site-packages/django_seo_js/templatetags/django_seo_js.py

laetitia123/akikatest

812f26155b6e3d003ac7e48c08c16df406e11086

[ "MIT" ]

183

2015-01-02T09:02:21.000Z

2022-02-24T05:09:08.000Z

akika_venv/lib/python3.6/site-packages/django_seo_js/templatetags/django_seo_js.py

laetitia123/akikatest

812f26155b6e3d003ac7e48c08c16df406e11086

[ "MIT" ]

31

2015-02-03T21:15:53.000Z

2022-03-22T15:07:01.000Z

akika_venv/lib/python3.6/site-packages/django_seo_js/templatetags/django_seo_js.py

laetitia123/akikatest

812f26155b6e3d003ac7e48c08c16df406e11086

[ "MIT" ]

55

2015-02-03T04:00:55.000Z

2022-02-24T05:09:10.000Z

from django import template from django.utils.safestring import mark_safe register = template.Library() @register.simple_tag def seo_js_head(*args): return mark_safe("""<meta name="fragment" content="!">""")

21.5

62

0.748837

29

215

5.37931

0.758621

0.128205

0

0.116279

215

9

63

23.888889

0.821053

0

0.15814

0

1

0.166667

false

0

0.333333

0.166667

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

17ed7bf6db6f082c4acf28a7ed3741a338d07ec8

86

py

Python

phash/__init__.py

vkulikov/python-phash

c61c955c76ec9e987ead86318cff72a5f6a1fbae

[ "CC0-1.0" ]

14

2015-02-23T14:17:24.000Z

2021-12-17T17:48:38.000Z

phash/__init__.py

vkulikov/python-phash

c61c955c76ec9e987ead86318cff72a5f6a1fbae

[ "CC0-1.0" ]

8

2015-07-09T15:07:50.000Z

2020-09-25T10:15:36.000Z

phash/__init__.py

vkulikov/python-phash

c61c955c76ec9e987ead86318cff72a5f6a1fbae

[ "CC0-1.0" ]

15

2015-01-08T19:41:45.000Z

2019-05-23T11:39:11.000Z

# encoding: utf-8 from __future__ import absolute_import from .phash_ctypes import *

17.2

38

0.802326

12

86

5.25

0.75

0

0.013514

0.139535

86

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null

0

1

0

1

0

1

0

5

aa04c5c882ae862c8f69d34dd5b1460ca1800a20

1,274

py

Python

envi/interactive.py

rnui2k/vivisect

b7b00f2d03defef28b4b8c912e3a8016e956c5f7

[ "ECL-2.0", "Apache-2.0" ]

16

2015-12-10T06:18:16.000Z

2021-09-11T21:42:16.000Z

envi/interactive.py

rnui2k/vivisect

b7b00f2d03defef28b4b8c912e3a8016e956c5f7

[ "ECL-2.0", "Apache-2.0" ]

5

2018-10-12T21:07:24.000Z

2018-10-12T21:08:49.000Z

envi/interactive.py

rnui2k/vivisect

b7b00f2d03defef28b4b8c912e3a8016e956c5f7

[ "ECL-2.0", "Apache-2.0" ]

6

2016-03-20T11:15:51.000Z

2021-08-06T07:32:42.000Z

def dbg_interact(lcls, gbls): intro = "Let's interact!" try: import IPython.Shell ipsh = IPython.Shell.IPShell(argv=[''], user_ns=lcls, user_global_ns=gbls) print(intro) ipsh.mainloop() except ImportError as e: try: from IPython.terminal.interactiveshell import TerminalInteractiveShell ipsh = TerminalInteractiveShell() ipsh.user_global_ns.update(gbls) ipsh.user_global_ns.update(lcls) ipsh.autocall = 2 # don't require parenthesis around *everything*. be smart! print(intro) ipsh.mainloop() except ImportError as e: try: from IPython.frontend.terminal.interactiveshell import TerminalInteractiveShell ipsh = TerminalInteractiveShell() ipsh.user_global_ns.update(gbls) ipsh.user_global_ns.update(lcls) ipsh.autocall = 2 # don't require parenthesis around *everything*. be smart! print(intro) ipsh.mainloop() except ImportError as e: print(e) shell = code.InteractiveConsole(gbls) print(intro) shell.interact()

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1,274

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0

null

0

1

0

null

0

5

aa141914ada6cff96aaeff0da123370b82943071

259

py

Python

django_sha2/models.py

fwenzel/django-sha2

f4519bf0cc9b1dd7a7d78394fa4aec4504bc86e9

[ "BSD-3-Clause" ]

9

2015-01-20T23:21:32.000Z

2020-05-03T14:33:12.000Z

django_sha2/models.py

h4ck3rm1k3/django-sha2

f4519bf0cc9b1dd7a7d78394fa4aec4504bc86e9

[ "BSD-3-Clause" ]

1

2016-01-28T10:32:28.000Z

2017-04-12T21:15:47.000Z

django_sha2/models.py

h4ck3rm1k3/django-sha2

f4519bf0cc9b1dd7a7d78394fa4aec4504bc86e9

[ "BSD-3-Clause" ]

3

2015-01-11T22:08:47.000Z

2021-12-06T19:40:29.000Z

"""Make sure django.contrib.auth monkeypatching happens on load.""" from django.conf import settings # If we don't have password hashers, we need to monkey patch the auth module. if not hasattr(settings, 'PASSWORD_HASHERS'): from django_sha2 import auth

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0

null

0

1

0

1

0

5

aa195c2f2bc34413e2c9f3104d6fdeacb926f7b6

70

py

Python

manticore/__init__.py

etcd/manticore

87073d9985c4ca445217b7b135a6af0a51044b21

[ "Apache-2.0" ]

null

manticore/__init__.py

etcd/manticore

87073d9985c4ca445217b7b135a6af0a51044b21

[ "Apache-2.0" ]

null

manticore/__init__.py

etcd/manticore

87073d9985c4ca445217b7b135a6af0a51044b21

[ "Apache-2.0" ]

1

2021-07-12T01:14:03.000Z

from .manticore import Manticore from .utils.helpers import issymbolic

35

37

0.857143

9

70

6.666667

0.666667

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0.1

70

2

37

35

0.952381

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true

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null

0

1

0

1

0

1

0

5

a4c7930242b7ab38d1ef4a3c38f8b64620326022

46

py

Python

cellphonedb/src/exceptions/NoReleasesException.py

BioTuring-Notebooks/CellphoneDB

9ab48d8888f11259bb3030c8d149d7bc7cdaf6e1

[ "MIT" ]

278

2018-10-03T22:12:09.000Z

2022-03-28T15:33:17.000Z

cellphonedb/src/exceptions/NoReleasesException.py

BioTuring-Notebooks/CellphoneDB

9ab48d8888f11259bb3030c8d149d7bc7cdaf6e1

[ "MIT" ]

263

2018-11-16T14:41:31.000Z

2022-03-30T08:38:26.000Z

cellphonedb/src/exceptions/NoReleasesException.py

BioTuring-Notebooks/CellphoneDB

9ab48d8888f11259bb3030c8d149d7bc7cdaf6e1

[ "MIT" ]

106

2018-10-18T15:11:57.000Z

2022-03-14T19:50:27.000Z

class NoReleasesException(Exception): pass

23

37

0.804348

4

46

9.25

1

0

0.130435

46

2

38

23

0.925

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

a4e409bd9fec15fb2ba0d2facec117e18e9e39d8

87

py

Python

lapy/__init__.py

AhmedFaisal95/LaPy

caf84eaf11d7d152cf64fd283dfe1ece65a6ca64

[ "MIT" ]

8

2021-03-19T14:38:06.000Z

2022-02-10T15:45:02.000Z

lapy/__init__.py

AhmedFaisal95/LaPy

caf84eaf11d7d152cf64fd283dfe1ece65a6ca64

[ "MIT" ]

4

2021-03-29T05:45:56.000Z

2022-03-21T13:54:30.000Z

lapy/__init__.py

AhmedFaisal95/LaPy

caf84eaf11d7d152cf64fd283dfe1ece65a6ca64

[ "MIT" ]

3

2021-11-10T08:51:38.000Z

2022-03-21T11:59:58.000Z

from .TriaMesh import TriaMesh from .TetMesh import TetMesh from .Solver import Solver

21.75

30

0.827586

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87

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0.416667

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87

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true

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1

0

1

0

1

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null

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0

null

0

1

0

1

0

1

0

5

3509b3ab904c82d0b43a60e623f3879ee4697a8a

129

py

Python

fedot/core/debug/metrics.py

rozlana-g/FEDOT

a909d6c0ef481cc1cf7a5f10f7b1292d8d2def5c

[ "BSD-3-Clause" ]

358

2020-06-11T09:34:53.000Z

2022-03-31T12:56:22.000Z

fedot/core/debug/metrics.py

rozlana-g/FEDOT

a909d6c0ef481cc1cf7a5f10f7b1292d8d2def5c

[ "BSD-3-Clause" ]

467

2020-06-11T13:49:45.000Z

2022-03-31T14:19:48.000Z

fedot/core/debug/metrics.py

rozlana-g/FEDOT

a909d6c0ef481cc1cf7a5f10f7b1292d8d2def5c

[ "BSD-3-Clause" ]

48

2020-07-13T14:50:45.000Z

2022-03-26T09:37:13.000Z

from random import randint class RandomMetric: @staticmethod def get_value() -> float: return randint(0, 1000)

16.125

31

0.674419

15

129

5.733333

0.933333

0

0.051546

0.248062

129

7

32

18.428571

0.835052

0

1

0.2

true

0

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

5

101c1593b514ccca65296f630e94b70c22f43bd9

231

py

Python

unbalanced_dataset/under_sampling/tests/test_tomek_links.py

kmike/UnbalancedDataset

777f26cee73c04ae2f3d59e43c990cbfd1725b23

[ "MIT" ]

6

2016-06-02T09:27:41.000Z

2021-04-21T06:46:12.000Z

unbalanced_dataset/under_sampling/tests/test_tomek_links.py

kmike/UnbalancedDataset

777f26cee73c04ae2f3d59e43c990cbfd1725b23

[ "MIT" ]

null

unbalanced_dataset/under_sampling/tests/test_tomek_links.py

kmike/UnbalancedDataset

777f26cee73c04ae2f3d59e43c990cbfd1725b23

[ "MIT" ]

1

2018-08-25T03:11:05.000Z

"""Test the module Tomek's links.""" from __future__ import print_function from unbalanced_dataset.under_sampling import TomekLinks def test_tomek_links(): """Test the Tomek links function.""" print('Test Tomek Links')

21

56

0.748918

31

231

5.290323

0.548387

0.182927

0.170732

0

0.151515

231

10

57

23.1

0.836735

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0.25

true

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0

null

0

1

0

1

0

1

0

5

105d2841d52ad4cee04c9a461fa6e857b4f0d1e0

94

py

Python

enthought/envisage/plugin_activator.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/envisage/plugin_activator.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/envisage/plugin_activator.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from envisage.plugin_activator import *

23.5

39

0.851064

12

94

6.166667

0.75

0

0.117021

94

3

40

31.333333

0.891566

0.12766

0

1

0

true

0

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

1086d4970cb973ca918a7eeb7488f9e0559978a0

29

py

Python

widgy/contrib/urlconf_include/__init__.py

IndiumTechnology/django-widgy

3cb5ea0a38b22f7430fe4cfcd12530739d396d70

[ "Apache-2.0" ]

168

2015-01-04T17:22:45.000Z

2022-01-28T09:53:35.000Z

widgy/contrib/urlconf_include/__init__.py

IndiumTechnology/django-widgy

3cb5ea0a38b22f7430fe4cfcd12530739d396d70

[ "Apache-2.0" ]

82

2015-01-09T18:14:32.000Z

2020-10-08T18:13:07.000Z

widgy/contrib/urlconf_include/__init__.py

IndiumTechnology/django-widgy

3cb5ea0a38b22f7430fe4cfcd12530739d396d70

[ "Apache-2.0" ]

61

2015-01-09T17:16:51.000Z

2021-07-03T08:52:27.000Z

from . import signalhandlers

14.5

28

0.827586

3

29

8

1

0

0.137931

29

1

29

0.96

0

1

0

true

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1

0

1

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null

0

1

0

null

0

1

0

1

0

5

52bf512d72fe94f08ab12d7b12b8bd7ff8518152

184

py

Python

DevOps/generate_new.py

Gorialis/vrchat-smarthands

ce6c5a297d6e24fca1c35bdacdcfd7e4817cb165

[ "MIT" ]

2

2021-04-02T18:10:15.000Z

2021-08-09T08:18:00.000Z

DevOps/generate_new.py

Gorialis/vrchat-smarthands

ce6c5a297d6e24fca1c35bdacdcfd7e4817cb165

[ "MIT" ]

null

DevOps/generate_new.py

Gorialis/vrchat-smarthands

ce6c5a297d6e24fca1c35bdacdcfd7e4817cb165

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os git_revision_count = os.getenv("GIT_REV_COUNT") git_revision_hash = os.getenv("GIT_REV_HASH") print(f"{git_revision_count=} {git_revision_hash=}")

20.444444

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0.733696

29

184

4.241379

0.448276

0.357724

0.260163

0.227642

0

0.006024

0.097826

184

8

53

23

0.73494

0.11413

0

0.416149

0.130435

0

1

0

false

0

0.25

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0.25

1

0

null

1

0

1

0

null

0

5

52cd3e252b074f1380f5db09e854dc07760c144a

135

py

Python

test/utils.py

eosW/pyds

20ae4a2f6f102de39e02559357e75bfeb01dfe35

[ "MIT" ]

null

test/utils.py

eosW/pyds

20ae4a2f6f102de39e02559357e75bfeb01dfe35

[ "MIT" ]

null

test/utils.py

eosW/pyds

20ae4a2f6f102de39e02559357e75bfeb01dfe35

[ "MIT" ]

null

def inorder(node): if not node: return yield from inorder(node.left) yield node yield from inorder(node.right)

19.285714

34

0.644444

19

135

4.578947

0.526316

0.37931

0.367816

0.45977

0

0.281481

135

6

35

22.5

0.896907

0

1

0.166667

false

0

0.333333

0

1

0

null

1

0

1

0

null

0

5

5e01c881a17afaf2b218ed81a8d5919dd3915903

28

py

Python

be/transform/art_line.py

lacti/artline

1822a6b8b9946bba0b699728ccfefaa59e3f1aa6

[ "MIT" ]

null

be/transform/art_line.py

lacti/artline

1822a6b8b9946bba0b699728ccfefaa59e3f1aa6

[ "MIT" ]

1

2021-07-13T01:46:41.000Z

be/transform/art_line.py

lacti/artline

1822a6b8b9946bba0b699728ccfefaa59e3f1aa6

[ "MIT" ]

null

class FeatureLoss: pass

9.333333

18

0.714286

3

28

6.666667

1

0

0.25

28

2

19

14

0.952381

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

5e2589eb75208c2d5f9a2fb3fb64472c92a7aa40

5,121

py

Python

code/models/sen_re.py

bflashcp3f/REtology

0645fee683d947ff23dba008bfd5ad2f55b6a912

[ "MIT" ]

1

2021-04-29T19:46:03.000Z

code/models/sen_re.py

bflashcp3f/REtology

0645fee683d947ff23dba008bfd5ad2f55b6a912

[ "MIT" ]

null

code/models/sen_re.py

bflashcp3f/REtology

0645fee683d947ff23dba008bfd5ad2f55b6a912

[ "MIT" ]

null

import torch from constants import * from utils import * from transformers import BertTokenizer, BertModel, BertPreTrainedModel, BertConfig, BertForSequenceClassification from transformers import RobertaTokenizer, RobertaModel, RobertaConfig, RobertaForSequenceClassification from torch import nn import torch.nn.functional as F from torch.nn import CrossEntropyLoss, MSELoss class BertEMES(BertPreTrainedModel): def __init__(self, config): super(BertEMES, self).__init__(config) self.num_labels = config.num_labels self.hidden_size = config.hidden_size self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) # self.classifier = nn.Linear(config.hidden_size, self.config.num_labels) self.classifier = nn.Linear(config.hidden_size * 2, self.config.num_labels) self.init_weights() def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, position_ids=None, head_mask=None, subj_ent_start=None, obj_ent_start=None): outputs = self.bert(input_ids, attention_mask=attention_mask) # outputs = self.bert(input_ids_new, attention_mask=attention_mask_new) sequence_output = outputs[0] pooled_output = outputs[1] # Take the representation for 'SUBJ' token subj_ent_output = torch.cat([a[i].unsqueeze(0) for a, i in zip(sequence_output, subj_ent_start)]) # Take the representation for 'OBJ' token obj_ent_output = torch.cat([a[i].unsqueeze(0) for a, i in zip(sequence_output, obj_ent_start)]) ent_output = torch.cat([subj_ent_output, obj_ent_output], dim=1) bag_output = self.dropout(ent_output) logits = self.classifier(bag_output) outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here if labels is not None: if self.num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits.view(-1), labels.view(-1)) else: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) outputs = (loss,) + outputs return outputs # (loss), logits, (hidden_states), (attentions) class RobertaEMES(BertPreTrainedModel): config_class = RobertaConfig # pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP base_model_prefix = "roberta" def __init__(self, config): super(RobertaEMES, self).__init__(config) self.num_labels = config.num_labels self.hidden_size = config.hidden_size self.roberta = RobertaModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) # self.classifier = nn.Linear(config.hidden_size, self.config.num_labels) self.classifier = nn.Linear(config.hidden_size * 2, self.config.num_labels) self.init_weights() def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, position_ids=None, head_mask=None, subj_ent_start=None, obj_ent_start=None): outputs = self.roberta(input_ids, attention_mask=attention_mask) # outputs = self.bert(input_ids_new, attention_mask=attention_mask_new) sequence_output = outputs[0] pooled_output = outputs[1] # Take the representation for 'SUBJ' token subj_ent_output = torch.cat([a[i].unsqueeze(0) for a, i in zip(sequence_output, subj_ent_start)]) # Take the representation for 'OBJ' token obj_ent_output = torch.cat([a[i].unsqueeze(0) for a, i in zip(sequence_output, obj_ent_start)]) ent_output = torch.cat([subj_ent_output, obj_ent_output], dim=1) bag_output = self.dropout(ent_output) logits = self.classifier(bag_output) outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here if labels is not None: if self.num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits.view(-1), labels.view(-1)) else: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) outputs = (loss,) + outputs # print("outputs: ", outputs) return outputs # (loss), logits, (hidden_states), (attentions) MODELS = {'bert-base-uncased': {'tokenizer': BertTokenizer, 'config': BertConfig, 'emes': BertEMES}, 'SpanBERT/spanbert-base-cased': {'tokenizer': BertTokenizer, 'config': BertConfig, 'emes': BertEMES}, 'roberta-base': {'tokenizer': RobertaTokenizer, 'config': RobertaConfig, 'emes': RobertaEMES} }

39.091603

113

0.628393

598

5,121

5.150502

0.177258

0.035065

0.025325

0.037013

0.778896

0.763312

0.730844

0.699351

0

0.006464

0.274946

5,121

131

114

39.091603

0.823054

0.156415

0

0.707317

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0.028172

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1

0.04878

false

0

0.097561

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0.219512

0

null

0

1

0

1

0

1

0

null

0

5

5e2e156bea2ccddc245b78bc5d88ca9a2549fc21

116

py

Python

fossil/fb/utils.py

Remember-Fossil/Fossil-Server

45b9002a8431fa9377ee3eba23ab01aeb564559a

[ "MIT" ]

null

fossil/fb/utils.py

Remember-Fossil/Fossil-Server

45b9002a8431fa9377ee3eba23ab01aeb564559a

[ "MIT" ]

null

fossil/fb/utils.py

Remember-Fossil/Fossil-Server

45b9002a8431fa9377ee3eba23ab01aeb564559a

[ "MIT" ]

null

# -*- coding: utf-8 -*- def urldecode(res): return dict([data.split('=') for data in res.split('&') if True])

19.333333

69

0.577586

17

116

3.941176

0.823529

0

0.010526

0.181034

116

5

70

23.2

0.694737

0.181034

0

0.021739

0

1

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false

0

0.5

1

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null

0

1

0

null

0

1

0

1

0

5

eaaeb27b171d2a304c9cbaaf9929ffd182fb5023

91

py

Python

library_sql/tool.py

TwinIsland/CTD

88118f2f2a9436ae1f893a94d79d9307dcc993da

[ "Apache-2.0" ]

null

library_sql/tool.py

TwinIsland/CTD

88118f2f2a9436ae1f893a94d79d9307dcc993da

[ "Apache-2.0" ]

null

library_sql/tool.py

TwinIsland/CTD

88118f2f2a9436ae1f893a94d79d9307dcc993da

[ "Apache-2.0" ]

null

import library library = library.library() library.cleanDataBase() library.save_change()

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1

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1

0

null

0

5

d811c3db204845dfc0e6447eec5a348863df40a0

19

py

Python

website/settings/__init__.py

vanderland/vdl_site

43b9ad3e5e7b9a2d35fc3cbb6a378cc8374699f1

[ "MIT" ]

null

website/settings/__init__.py

vanderland/vdl_site

43b9ad3e5e7b9a2d35fc3cbb6a378cc8374699f1

[ "MIT" ]

null

website/settings/__init__.py

vanderland/vdl_site

43b9ad3e5e7b9a2d35fc3cbb6a378cc8374699f1

[ "MIT" ]

null

from .DEVL import *

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5

d8180101eb1386e1435cb2c5cdb3900b1eaa341c

7,043

py

Python

Uwallet/tests/_test_jsonrpc.py

monotone/Ulord-platform

8ec19bbb8845db8f22df925d33b118b22dab0d0b

[ "MIT" ]

28

2018-04-27T08:02:18.000Z

2020-01-14T05:08:34.000Z

Uwallet/tests/_test_jsonrpc.py

monotone/Ulord-platform

8ec19bbb8845db8f22df925d33b118b22dab0d0b

[ "MIT" ]

2

2018-05-16T08:29:20.000Z

2018-06-17T04:51:08.000Z

Uwallet/tests/_test_jsonrpc.py

monotone/Ulord-platform

8ec19bbb8845db8f22df925d33b118b22dab0d0b

[ "MIT" ]

4

2018-05-14T11:43:31.000Z

2018-09-29T09:58:58.000Z

#-*- coding: UTF-8 -*- import time from jsonrpclib import Server def profiler(func): def do_profile(*args, **kw_args): n = func.func_name t0 = time.time() o = func(*args, **kw_args) t = time.time() - t0 print "[profiler] %s %f" % (n, t) return o # return lambda *args, **kw_args: do_profile(func, args, kw_args) return do_profile # server = Server('http://192.168.14.240:8000') server = Server('http://192.168.14.241:8000') @profiler def publish(user, password, claim_name, skip_update_check): """ :return: {u'fee': u'0.000359', u'success': True, u'tx': u'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', u'txid': u'325dbf4ed36ccc50f84118e322b1452aeb0f385d71f5accbd326ecd4df3df121', u'amount': u'0.56999999', u'claim_id': u'50971fe0a5d7751d197ca13e5d6d4e6b9f2090e1', u'nout': 0} """ metadata = { "license": "LBRY Inc", "description": "update test", "language": "en", "title": "What is LBRY?", "author": "Samuel Bryan", "version": "_0_1_0", "nsfw": False, "licenseUrl": "", "preview": "", "thumbnail": "https://s3.amazonaws.com/files.lbry.io/logo.png", "tag": ["action", "sss"] } # sourceHash = "d5169241150022f996fa7cd6a9a1c421937276a3275eb912790bd07ba7aec1fac5fd45431d226b8fb402691e79aeb24b" sourceHash = "QmVcVaHhMeWNNetSLTZArmqaHMpu5ycqntx7mFZaci63VF" contentType = "video/mp4" currency = "UT" amount = 1.56 bid = 0.59 return server.publish(user, password, claim_name, metadata, contentType, sourceHash, currency, amount, bid, None, None, skip_update_check) @profiler def consume(claim_id): """ :return: {u'success': True, u'tx': u'3ecce656dbfeea5b38f385549ac51e550bfa6d70bba9d2042dacdd3c1def662a'} """ user = 'ht' password = '123' return server.consume(user, password, claim_id) @profiler def create(user, password, use_change): """ :return: {u'seed': u'faculty claim ghost cushion helmet sweet solution dirt night bottom gift trophy', u'success': True} """ return server.create(user, password, use_change) @profiler def getbalance(user, password): """ :return: {u'confirmed': u'9999.99965899', u'success': True, u'unconfirmed': 1.33, u'unmatured': 9.2} """ return server.getbalance(user, password) @profiler def pay(receive_user, amount): """ :param send_user: :param password: :return: {u'success': True, u'txid': u'b6b921500444b575b48745e33a8808c692a5bbe3c6ecfae4714c264d81696daf'} """ send_user = 'ht' password = '123' # send_user = 'shu' # password = 'pbkdf2:sha256:50000$oEw0SZX0$f8d9951addfa90213e63bb4553cacc7e3cc8e78d9d59f5e707da1fc09dd4d675' return server.pay(send_user, password, receive_user, amount) @profiler def update_claim(user, password, claim_name, claim_id, txid): metadata = { "license": "LBRY Inc", "description": "What is LBRY? An introduction with Alex Tabarrok", "language": "en", "title": "What is LBRY?", "author": "Samuel Bryan", "version": "_0_1_0", "nsfw": False, "licenseUrl": "", "preview": "", "thumbnail": "https://s3.amazonaws.com/files.lbry.io/logo.png", "tag": ["action", "sss"] } source_hash = "QmVcVaHhMeWNNetSLTZArmqaHMpu5ycqntx7mFZaci63VF" content_type = "video/mp4" currency = "UT" amount = 1.2 bid = 0.57 address = None tx_fee = None nout = 0 return server.update_claim(user, password, claim_name, claim_id, txid, nout, metadata, content_type, source_hash, currency, amount, bid, address, tx_fee) @profiler def delete(user): return server.delete(user) @profiler def mul_test(): def wrap_publish(): print publish(user, password, claim_name, False) def wrap_getbalance(): print getbalance(user, password) def wrap_create(user): print create('test_'+ str(user), 123) from multiprocessing import Process plist=[] for i in range(1): if i % 2 == 0: # p = Process(target=wrap_publish, args=()) p = Process(target=wrap_create, args=(i,)) else: p = Process(target=wrap_create, args=(i,)) p.start() plist.append(p) print 11 for pl in plist: pl.join() if __name__ == '__main__': user = 'hetao' password = '123' # password = 'pbkdf2:sha256:50000$oEw0SZX0$f8d9951addfa90213e63bb4553cacc7e3cc8e78d9d59f5e707da1fc09dd4d675' claim_name = 'fd32773a648611e8bc56f4dsf9c8ab' claim_id = 'e2b29a45d8ee5b39c549a9ff6e7ac667bd18f776' txid = '8aab1a95d08ccf814980d3d10f1af5e959468a1703f8eb3e6063fdbe78651e2b' # print create(user, password, True) # 0.48 # print pay(user, 10) # 0.95 print getbalance(user, password) # 0.14 # print publish(user, password, claim_name, True) # 3.67 # print update_claim(user, password, claim_name, claim_id, txid) # 1.56 amount # print consume(claim_id) # 1.4 # print delete('ht') # ================================================================== cln_password = 'pbkdf2:sha256:50000$wxOHrzn9$bb0569c6e78b5ed621917e28c401499e1e830a86e81febd38b04c6cec49a0460' # print server.listaddresses() # print server.password('shuxudong', '123', 'pbkdf2:sha256:50000$oEw0SZX0$f8d9951addfa90213e63bb4553cacc7e3cc8e78d9d59f5e707da1fc09dd4d675') # print server.is_wallet_exists('5d42b27e581c11e88b12f48e3889c8ab_user1') # print server.pay(user, password, 'hetao1', 1008) # print server.create('cln', cln_password) # print server.pay('cln', cln_password, 'ht', 2) # print getbalance('cln', cln_password) # 0.14

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1

0

1

0

1

0

5

dc1dda7394304c8f667ff1f895674f1aefb2b377

2,061

py

Python

floodsystem/plot.py

cued-ia-computing/flood-hln35-nc580

81ecb652ca932d8e05253982c6efe6befd2bf670

[ "MIT" ]

null

floodsystem/plot.py

cued-ia-computing/flood-hln35-nc580

81ecb652ca932d8e05253982c6efe6befd2bf670

[ "MIT" ]

null

floodsystem/plot.py

cued-ia-computing/flood-hln35-nc580

81ecb652ca932d8e05253982c6efe6befd2bf670

[ "MIT" ]

null

import matplotlib.pyplot as plt import datetime from datetime import timedelta import matplotlib.dates import numpy as np from .station import MonitoringStation from .analysis import polyfit def plot_water_levels(station, dates, levels): if all(isinstance(level,float) for level in levels) is True and len(dates) == len(levels): typical_low = [station.typical_range[0]]*len(dates) typical_high = [station.typical_range[1]]*len(dates) # Plot plt.plot(dates, levels) plt.plot(dates, typical_low, color= 'blue', label = 'typical low') plt.plot(dates, typical_high, color= 'red', label = 'typical high') # Add axis labels, rotate date labels and add plot title plt.xlabel('date') plt.ylabel('water level (m)') plt.xticks(rotation=45) plt.title(station.name) # Display plot plt.tight_layout() # This makes sure plot does not cut off date labels plt.legend() plt.show() else: pass # Define function that plots the water level data and the best-fit polynomial def plot_water_level_with_fit(station, dates, levels, p): if all(isinstance(level,float) for level in levels) is True and len(dates) == len(levels): poly, d0 = polyfit(dates, levels, p) typical_low = [station.typical_range[0]]*len(dates) typical_high = [station.typical_range[1]]*len(dates) # Plot plt.plot(dates, levels, '.') plt.plot(dates, typical_low, color= 'blue', label = 'typical low') plt.plot(dates, typical_high, color= 'red', label = 'typical high') plt.plot(dates, poly(matplotlib.dates.date2num(dates)-d0), color='m') # Add axis labels, rotate date labels and add plot title plt.xlabel('date') plt.ylabel('water level (m)') plt.xticks(rotation=45) plt.title(station.name) # Display plot plt.tight_layout() # This makes sure plot does not cut off date labels plt.legend() plt.show() else: pass

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0

0.219512

0

null

0

1

0

null

0

5

dc476291ec9f8a75c832e296b82b45660b5de1fb

293

py

Python

Toolz/fimap/src/pybing/__init__.py

thezakman/CTF-Toolz

b369246ea6766165cce0852e537fb6a0c970869b

[ "Unlicense" ]

71

2019-02-02T11:38:46.000Z

2022-03-31T14:08:27.000Z

Exploitation/Web-Exploitation/PathTraversal/fimap/src/pybing/__init__.py

bhattsameer/TID3xploits

b57d8bae454081a3883a5684679e2a329e72d6e5

[ "MIT" ]

null

Exploitation/Web-Exploitation/PathTraversal/fimap/src/pybing/__init__.py

bhattsameer/TID3xploits

b57d8bae454081a3883a5684679e2a329e72d6e5

[ "MIT" ]

15

2019-08-07T06:32:04.000Z

2022-03-09T12:48:20.000Z

# This file is part of PyBing (http://pybing.googlecode.com). # # Copyright (C) 2009 JJ Geewax http://geewax.org/ # All rights reserved. # # This software is licensed as described in the file COPYING.txt, # which you should have received as part of this distribution. from bing import Bing

29.3

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0.740614

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293

4.717391

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0.0553

0

0.016461

0.170648

293

9

66

32.555556

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0.870307

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true

0

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0

null

0

1

0

1

0

1

0

5

dc68f4f353acebf9802b1f24bea27b810ccacaaa

21

py

Python

samples/LuceneInAction/lia/analysis/positional/__init__.py

romanchyla/pylucene-trunk

990079ff0c76b972ce5ef2bac9b85334a0a1f27a

[ "Apache-2.0" ]

15

2015-05-21T09:28:01.000Z

2022-03-18T23:41:49.000Z

samples/LuceneInAction/lia/analysis/positional/__init__.py

fnp/pylucene

fb16ac375de5479dec3919a5559cda02c899e387

[ "Apache-2.0" ]

1

2021-09-30T03:59:43.000Z

samples/LuceneInAction/lia/analysis/positional/__init__.py

romanchyla/pylucene-trunk

990079ff0c76b972ce5ef2bac9b85334a0a1f27a

[ "Apache-2.0" ]

13

2015-04-18T23:05:11.000Z

2021-11-29T21:23:26.000Z

# positional package

10.5

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0

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0

1

0

1

0

null

0

1

0

5

dc9b7465c2c6a7fa0faf003130db52c6e600a1a2

90

py

Python

rjax/environments/__init__.py

charlesjsun/rjax

8b56f78b34f593f442db3cc0315a3b6f22191442

[ "MIT" ]

null

rjax/environments/__init__.py

charlesjsun/rjax

8b56f78b34f593f442db3cc0315a3b6f22191442

[ "MIT" ]

null

rjax/environments/__init__.py

charlesjsun/rjax

8b56f78b34f593f442db3cc0315a3b6f22191442

[ "MIT" ]

null

from .episode_monitor import EpisodeMonitor from .single_precision import SinglePrecision

30

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null

0

1

0

1

0

1

0

5

f4de710791b1943c75278d91827b0b25fd5d001c

350

py

Python

framework/oktopus/utils/parser.py

charlee593/oktopus_framework

2835c44c61d1b6170682f014450b8c2203786347

[ "MIT" ]

1

2022-02-21T12:03:17.000Z

framework/oktopus/utils/parser.py

charlee593/oktopus_framework

2835c44c61d1b6170682f014450b8c2203786347

[ "MIT" ]

4

2020-07-02T00:56:32.000Z

2020-07-02T00:56:45.000Z

framework/oktopus/utils/parser.py

charlee593/oktopus_framework

2835c44c61d1b6170682f014450b8c2203786347

[ "MIT" ]

1

2022-02-21T12:03:03.000Z

from ..dataset import parse_objects from ..multicast.network import Node, Link from ..multicast.session import Session def load_sessions(file_path): return parse_objects(file_path, cls=Session) def load_nodes(file_path): return parse_objects(file_path, cls=Node) def load_links(file_path): return parse_objects(file_path, cls=Link)

21.875

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0.782857

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15

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0.858553

0

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false

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0.333333

1

0

null

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1

0

null

0

1

0

1

0

5

52168f186fd44c528191530d5524611f29ac2837

81

py

Python

src/super_batch/__init__.py

Nashavi/batch-config

29d18e8a5c7ac0b69e3c97006ecd4c8f881f269e

[ "MIT" ]

null

src/super_batch/__init__.py

Nashavi/batch-config

29d18e8a5c7ac0b69e3c97006ecd4c8f881f269e

[ "MIT" ]

null

src/super_batch/__init__.py

Nashavi/batch-config

29d18e8a5c7ac0b69e3c97006ecd4c8f881f269e

[ "MIT" ]

null

""" __init__ """ from .client import Client from .BatchConfig import BatchConfig

13.5

36

0.765432

9

81

6.444444

0.555556

0

0.135802

81

5

37

16.2

0.828571

0.098765

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5260321fccf67775a88f45333c5c317a15c8cde8

7,966

py

Python

tests/test_pytorch.py

leonbottou/pymanopt

7d8c46f4513c3746234ba804604694b11db62d0a

[ "BSD-3-Clause" ]

1

2021-09-21T14:28:10.000Z

tests/test_pytorch.py

leonbottou/pymanopt

7d8c46f4513c3746234ba804604694b11db62d0a

[ "BSD-3-Clause" ]

null

tests/test_pytorch.py

leonbottou/pymanopt

7d8c46f4513c3746234ba804604694b11db62d0a

[ "BSD-3-Clause" ]

null

import unittest import numpy.random as rnd import numpy.testing as np_testing import numpy as np import torch from pymanopt.tools.autodiff import PytorchBackend class TestVector(unittest.TestCase): def setUp(self): np.seterr(all='raise') self.cost = lambda X: torch.exp(torch.sum(X.pow(2))) n = self.n = 15 Y = self.Y = rnd.randn(n) A = self.A = rnd.randn(n) # Calculate correct cost and grad... self.correct_cost = np.exp(np.sum(Y ** 2)) self.correct_grad = 2 * Y * np.exp(np.sum(Y ** 2)) # ... and hess # First form hessian matrix H # Convert Y and A into matrices (row vectors) Ymat = np.matrix(Y) Amat = np.matrix(A) diag = np.eye(n) H = np.exp(np.sum(Y ** 2)) * (4 * Ymat.T.dot(Ymat) + 2 * diag) # Then 'left multiply' H by A self.correct_hess = np.squeeze(np.array(Amat.dot(H))) self.backend = PytorchBackend() self.arg = torch.Tensor() def test_compile(self): cost_compiled = self.backend.compile_function(self.cost, self.arg) np_testing.assert_allclose(self.correct_cost, cost_compiled(self.Y)) def test_grad(self): grad = self.backend.compute_gradient(self.cost, self.arg) np_testing.assert_allclose(self.correct_grad, grad(self.Y)) def test_hessian(self): hess = self.backend.compute_hessian(self.cost, self.arg) # Now test hess np_testing.assert_allclose(self.correct_hess, hess(self.Y, self.A)) class TestMatrix(unittest.TestCase): def setUp(self): np.seterr(all='raise') self.cost = lambda X: torch.exp(torch.sum(X.pow(2))) m = self.m = 10 n = self.n = 15 Y = self.Y = rnd.randn(m, n) A = self.A = rnd.randn(m, n) # Calculate correct cost and grad... self.correct_cost = np.exp(np.sum(Y ** 2)) self.correct_grad = 2 * Y * np.exp(np.sum(Y ** 2)) # ... and hess # First form hessian tensor H (4th order) Y1 = Y.reshape(m, n, 1, 1) Y2 = Y.reshape(1, 1, m, n) # Create an m x n x m x n array with diag[i,j,k,l] == 1 iff # (i == k and j == l), this is a 'diagonal' tensor. diag = np.eye(m * n).reshape(m, n, m, n) H = np.exp(np.sum(Y ** 2)) * (4 * Y1 * Y2 + 2 * diag) # Then 'right multiply' H by A Atensor = A.reshape(1, 1, m, n) self.correct_hess = np.sum(H * Atensor, axis=(2, 3)) self.backend = PytorchBackend() self.arg = torch.Tensor() def test_compile(self): cost_compiled = self.backend.compile_function(self.cost, self.arg) np_testing.assert_allclose(self.correct_cost, cost_compiled(self.Y)) def test_grad(self): grad = self.backend.compute_gradient(self.cost, self.arg) np_testing.assert_allclose(self.correct_grad, grad(self.Y)) def test_hessian(self): hess = self.backend.compute_hessian(self.cost, self.arg) # Now test hess np_testing.assert_allclose(self.correct_hess, hess(self.Y, self.A)) class TestTensor3(unittest.TestCase): def setUp(self): np.seterr(all='raise') self.cost = lambda X: torch.exp(torch.sum(X.pow(2))) n1 = self.n1 = 3 n2 = self.n2 = 4 n3 = self.n3 = 5 Y = self.Y = rnd.randn(n1, n2, n3) A = self.A = rnd.randn(n1, n2, n3) # Calculate correct cost and grad... self.correct_cost = np.exp(np.sum(Y ** 2)) self.correct_grad = 2 * Y * np.exp(np.sum(Y ** 2)) # ... and hess # First form hessian tensor H (6th order) Y1 = Y.reshape(n1, n2, n3, 1, 1, 1) Y2 = Y.reshape(1, 1, 1, n1, n2, n3) # Create an n1 x n2 x n3 x n1 x n2 x n3 diagonal tensor diag = np.eye(n1 * n2 * n3).reshape(n1, n2, n3, n1, n2, n3) H = np.exp(np.sum(Y ** 2)) * (4 * Y1 * Y2 + 2 * diag) # Then 'right multiply' H by A Atensor = A.reshape(1, 1, 1, n1, n2, n3) self.correct_hess = np.sum(H * Atensor, axis=(3, 4, 5)) self.backend = PytorchBackend() self.arg = torch.Tensor() def test_compile(self): cost_compiled = self.backend.compile_function(self.cost, self.arg) np_testing.assert_allclose(self.correct_cost, cost_compiled(self.Y)) def test_grad(self): grad = self.backend.compute_gradient(self.cost, self.arg) np_testing.assert_allclose(self.correct_grad, grad(self.Y)) def test_hessian(self): hess = self.backend.compute_hessian(self.cost, self.arg) # Now test hess np_testing.assert_allclose(self.correct_hess, hess(self.Y, self.A)) class TestMixed(unittest.TestCase): # Test autograd on a tuple containing vector, matrix and tensor3. def setUp(self): np.seterr(all='raise') def torchf(x): return (torch.exp(torch.sum(x[0]**2)) + torch.exp(torch.sum(x[1]**2)) + torch.exp(torch.sum(x[2]**2))) def npf(x): return (np.exp(np.sum(x[0]**2)) + np.exp(np.sum(x[1]**2)) + np.exp(np.sum(x[2]**2))) self.cost = torchf n1 = self.n1 = 3 n2 = self.n2 = 4 n3 = self.n3 = 5 n4 = self.n4 = 6 n5 = self.n5 = 7 n6 = self.n6 = 8 self.y = y = (rnd.randn(n1), rnd.randn(n2, n3), rnd.randn(n4, n5, n6)) self.a = a = (rnd.randn(n1), rnd.randn(n2, n3), rnd.randn(n4, n5, n6)) self.correct_cost = npf(y) # CALCULATE CORRECT GRAD g1 = 2 * y[0] * np.exp(np.sum(y[0] ** 2)) g2 = 2 * y[1] * np.exp(np.sum(y[1] ** 2)) g3 = 2 * y[2] * np.exp(np.sum(y[2] ** 2)) self.correct_grad = (g1, g2, g3) # CALCULATE CORRECT HESS # 1. VECTOR Ymat = np.matrix(y[0]) Amat = np.matrix(a[0]) diag = np.eye(n1) H = np.exp(np.sum(y[0] ** 2)) * (4 * Ymat.T.dot(Ymat) + 2 * diag) # Then 'left multiply' H by A h1 = np.array(Amat.dot(H)).flatten() # 2. MATRIX # First form hessian tensor H (4th order) Y1 = y[1].reshape(n2, n3, 1, 1) Y2 = y[1].reshape(1, 1, n2, n3) # Create an m x n x m x n array with diag[i,j,k,l] == 1 iff # (i == k and j == l), this is a 'diagonal' tensor. diag = np.eye(n2 * n3).reshape(n2, n3, n2, n3) H = np.exp(np.sum(y[1] ** 2)) * (4 * Y1 * Y2 + 2 * diag) # Then 'right multiply' H by A Atensor = a[1].reshape(1, 1, n2, n3) h2 = np.sum(H * Atensor, axis=(2, 3)) # 3. Tensor3 # First form hessian tensor H (6th order) Y1 = y[2].reshape(n4, n5, n6, 1, 1, 1) Y2 = y[2].reshape(1, 1, 1, n4, n5, n6) # Create an n1 x n2 x n3 x n1 x n2 x n3 diagonal tensor diag = np.eye(n4 * n5 * n6).reshape(n4, n5, n6, n4, n5, n6) H = np.exp(np.sum(y[2] ** 2)) * (4 * Y1 * Y2 + 2 * diag) # Then 'right multiply' H by A Atensor = a[2].reshape(1, 1, 1, n4, n5, n6) h3 = np.sum(H * Atensor, axis=(3, 4, 5)) self.correct_hess = (h1, h2, h3) self.backend = PytorchBackend() self.arg = torch.Tensor() def test_compile(self): cost_compiled = self.backend.compile_function(self.cost, self.arg) np_testing.assert_allclose(self.correct_cost, cost_compiled(self.y)) def test_grad(self): grad = self.backend.compute_gradient(self.cost, self.arg) for k in range(len(grad(self.y))): np_testing.assert_allclose(self.correct_grad[k], grad(self.y)[k]) def test_hessian(self): hess = self.backend.compute_hessian(self.cost, self.arg) # Now test hess for k in range(len(hess(self.y, self.a))): np_testing.assert_allclose(self.correct_hess[k], hess(self.y, self.a)[k])

31.239216

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0.55423

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7,966

3.458899

0.106145

0.060914

0.029072

0.041532

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0.702123

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0.126761

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0

null

0

1

0

1

0

null

0

5

52643663802bad4266844936b7315d11534ceb83

174

py

Python

cornflow_client/core/instance.py

baobabsoluciones/cornflow-client

f9996f0b841885d26639cb63c8ba6090387de57f

[ "MIT" ]

3

2021-05-12T11:21:26.000Z

2022-02-22T19:23:46.000Z

cornflow_client/core/instance.py

baobabsoluciones/cornflow-client

f9996f0b841885d26639cb63c8ba6090387de57f

[ "MIT" ]

17

2021-03-14T17:09:46.000Z

2022-02-28T19:12:37.000Z

cornflow_client/core/instance.py

baobabsoluciones/cornflow-client

f9996f0b841885d26639cb63c8ba6090387de57f

[ "MIT" ]

2

2020-10-03T20:00:19.000Z

2022-03-24T11:52:22.000Z

from .instance_solution import InstanceSolutionCore from abc import ABC class InstanceCore(InstanceSolutionCore, ABC): """ The instance template. """ pass

15.818182

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0.724138

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174

7.352941

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0.206897

174

10

52

17.4

0.905797

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0

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null

0

null

0

1

0

1

0

5

5289b1731729bfd468d9dfbe0d831465f12f90d8

243

py

Python

feedler/urls.py

pcoder/public-health-ch

cebc4849653560c54238b67814074353ff7c01f3

[ "MIT" ]

2

2020-10-29T16:27:21.000Z

2021-06-07T12:47:46.000Z

feedler/urls.py

pcoder/public-health-ch

cebc4849653560c54238b67814074353ff7c01f3

[ "MIT" ]

11

2017-05-09T10:50:28.000Z

2021-12-15T17:01:23.000Z

feedler/urls.py

pcoder/public-health-ch

cebc4849653560c54238b67814074353ff7c01f3

[ "MIT" ]

4

2017-04-24T13:06:55.000Z

2021-06-04T02:18:32.000Z

from django.conf.urls import include, url from django.conf import settings from django.contrib import admin from django.conf.urls.i18n import i18n_patterns from .api import api_router urlpatterns = [ url(r'^api/v2/', api_router.urls), ]

22.090909

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243

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0.625

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1

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null

0

1

0

1

0

5

8715d0dbd8a1e86ae583871af9880e05cb566fe2

3,774

py

Python

intro/part04-11_first_second_last/test/test_first_second_last_2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

intro/part04-11_first_second_last/test/test_first_second_last_2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

intro/part04-11_first_second_last/test/test_first_second_last_2.py

Hannah-Abi/python-pro-21

2ce32c4bf118054329d19afdf83c50561be1ada8

[ "MIT" ]

null

import unittest from unittest.mock import patch from tmc import points from tmc.utils import load_module, reload_module, get_stdout, check_source from functools import reduce import os exercise = 'src.first_second_last' @points('4.first_second_last') class ETjaVSanaTest(unittest.TestCase): @classmethod def setUpClass(cls): with patch('builtins.input', side_effect=["2"] * 10): cls.module = load_module(exercise, 'en') def test_5_second_word_function_ok(self): for row in ["once upon a time there was a programmer", "happily ever after", "Lorem ipsum dolor sit amet consectetur adipiscing elit", "first second", "please write a program which keeps asking the user for words"]: with patch('builtins.input', side_effect=["2 2"] * 10): reload_module(self.module) output_at_start = get_stdout() from src.first_second_last import second_word try: res = second_word(row) except: self.assertTrue(False, f'Make sure, that function can be called as follows:\nsecond_word("{row}")') output_all = get_stdout().replace(output_at_start, '', 1) expected = row.split(' ')[1] self.assertFalse(res == None, f'Calling second_word("{row}") should return\n{expected}\nnow it does not return anything. Make sure that you use return command in any cases in your function!') self.assertEqual(res, expected, f'Calling second_word("{row}") should return\n{expected}\nnow it returns\n{res}') self.assertFalse(len(output_all)>0, f'Calling second_word("{row}") should not print out anything, but it prints out\n{output_all}\nremove print commands inside function') def test_6_last_word_exists(self): try: from src.first_second_last import last_word except: self.assertTrue(False, f'Your code should contain function named as last_word') try: from src.first_second_last import last_word last_word("once upon a time there was a programmer") except: self.assertTrue(False, f'Make sure, that function can be called as follows:\last_word("once upon a time there was a programmer")') def test_7_last_word_function_ok(self): for row in ["once upon a time there was a programmer", "happily ever after", "Lorem ipsum dolor sit amet consectetur adipiscing elit", "first second", "please write a program which keeps asking the user for words"]: with patch('builtins.input', side_effect=["2 2"] * 10): reload_module(self.module) output_at_start = get_stdout() from src.first_second_last import last_word try: res = last_word(row) except: self.assertTrue(False, f'Make sure, that function can be called as follows:\nlast_word("{row}")') output_all = get_stdout().replace(output_at_start, '', 1) odotettu = row.split(' ')[-1] self.assertFalse(res == None, f'Calling last_word("{row}") should return\n{odotettu}\nnow it does not return anything. Make sure that you use return command in any cases in your function!') self.assertEqual(res, odotettu, f'Calling last_word("{row}") should return\n{odotettu}\nnow it returns\n{res}') self.assertFalse(len(output_all)>0, f'Calling last_word("{row}") should not print out anything, but it prints out\n{output_all}\nremove print commands inside function') if __name__ == '__main__': unittest.main()

55.5

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3,774

4.60835

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0.789905

0.778689

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0.036364

0

null

0

1

0

null

0

5

872711be818b82146685f1ba6c025fbe68c6d4ee

49

py

Python

Code/Mayank.py

mayankparida/CintaKamu

7a0ef976fca8fb01daf443f0cc0276ba7111c0c3

[ "MIT" ]

null

Code/Mayank.py

mayankparida/CintaKamu

7a0ef976fca8fb01daf443f0cc0276ba7111c0c3

[ "MIT" ]

null

Code/Mayank.py

mayankparida/CintaKamu

7a0ef976fca8fb01daf443f0cc0276ba7111c0c3

[ "MIT" ]

null

# Mayank Parida # India print("Aku Cinta Kamu")

16.333333

23

0.693878

7

49

4.857143

1

0

0.183673

49

3

23

16.333333

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1

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null

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null

0

1

0

1

0

5

874f21782d9d52d2f58ab3d407790f047f0692bd

47

py

Python

tf/writing/hebrew.py

ancient-data/text-fabric

c1ccd4a4dc451e94a789f138576576c5d7f13474

[ "MIT" ]

10

2017-10-30T22:38:00.000Z

2018-12-12T06:10:10.000Z

tf/writing/hebrew.py

dirkroorda/text-fabric

c0a49f092ceda3e7bab91fd0f1aa84e2dc029cf4

[ "MIT" ]

37

2017-10-19T12:06:54.000Z

2018-12-13T10:18:23.000Z

tf/writing/hebrew.py

dirkroorda/text-fabric

c0a49f092ceda3e7bab91fd0f1aa84e2dc029cf4

[ "MIT" ]

3

2018-02-28T12:37:21.000Z

2018-06-23T08:32:54.000Z

""" .. include:: ../docs/writing/hebrew.md """

11.75

38

0.553191

5

47

5.2

1

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0.106383

47

3

39

15.666667

0.619048

0.808511

0

null

0

null

0

null

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true

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1

0

1

0

null

0

1

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5

8761d62310dd4238e28323002772232119d7b174

187

py

Python

handlers/user/__init__.py

DataAkbar/jayhunGulshaniBot

963876ef8831e7119386832e8f5a260cd9e4189d

[ "MIT" ]

null

handlers/user/__init__.py

DataAkbar/jayhunGulshaniBot

963876ef8831e7119386832e8f5a260cd9e4189d

[ "MIT" ]

null

handlers/user/__init__.py

DataAkbar/jayhunGulshaniBot

963876ef8831e7119386832e8f5a260cd9e4189d

[ "MIT" ]

null

from .menu import dp from .cart import dp from .wallet import dp from .catalog import dp from .delivery_status import dp from .sos import dp from .create_order import dp __all__ = ['dp']

20.777778

31

0.770053

32

187

4.3125

0.40625

0.405797

0.521739

0

0.165775

187

9

32

20.777778

0.884615

0

0.010638

0

1

0

false

0

0.875

0

0.875

0

null

1

0

1

0

null

0

1

0

1

0

5

876478db338992cb14041cf125dd1388dae96a25

71

py

Python

autokeras/__init__.py

uditjuneja1/autokeras

4770d60f343f3ed0cee689518c3ccefa263402d8

[ "MIT" ]

null

autokeras/__init__.py

uditjuneja1/autokeras

4770d60f343f3ed0cee689518c3ccefa263402d8

[ "MIT" ]

1

2022-02-10T06:00:55.000Z

autokeras/__init__.py

uditjuneja1/autokeras

4770d60f343f3ed0cee689518c3ccefa263402d8

[ "MIT" ]

1

2019-05-17T19:16:26.000Z

from autokeras.image_supervised import ImageClassifier, ImageRegressor

35.5

70

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7

71

9

1

0

0.070423

71

1

71

0.954545

0

1

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0

1

0

1

0

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1

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null

0

1

0

1

0

1

0

5

5e4808858eb12b351fe77d8781119a6c729eead6

41

py

Python

cpt test 2.py

Pyroooooooo/GameBot

4355ee33320450a6d1afe5bb832741761d366aec

[ "MIT" ]

3

2021-03-10T04:23:34.000Z

2022-01-05T08:47:17.000Z

cpt test 2.py

39x/GameBot

230c1f076a9be07d14e6bc6026cfbc565467e74d

[ "MIT" ]

null

cpt test 2.py

39x/GameBot

230c1f076a9be07d14e6bc6026cfbc565467e74d

[ "MIT" ]

4

2021-03-10T04:20:03.000Z

2021-07-12T11:42:46.000Z

import os os.system("node cpt/index.js")

13.666667

30

0.731707

8

41

3.75

0.875

0

0.097561

41

2

31

20.5

0.810811

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0.414634

0

1

0

true

0

0.5

0

0.5

0

1

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null

0

1

0

null

0

1

0

1

0

5

5e9f44126889eaa8f6fc3dee2bded6269561aeaa

61

py

Python

enthought/pyface/split_application_window.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/pyface/split_application_window.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/pyface/split_application_window.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from pyface.split_application_window import *

20.333333

45

0.836066

8

61

6.125

1

0

0.114754

61

2

46

30.5

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0

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0

true

0

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null

0

1

0

null

0

1

0

1

0

1

0

5

5ea53f9ed277b5ca3f470257b54336e64a7040fa

7,503

py

Python

2015/day_01_both.py

jorvis/AdventOfCode

97c42037abc28c1c16cc3e48a30f2689ca950fb5

[ "MIT" ]

null

2015/day_01_both.py

jorvis/AdventOfCode

97c42037abc28c1c16cc3e48a30f2689ca950fb5

[ "MIT" ]

null

2015/day_01_both.py

jorvis/AdventOfCode

97c42037abc28c1c16cc3e48a30f2689ca950fb5

[ "MIT" ]

null

#!/usr/bin/env python3 challenge = '((((()(()(((((((()))(((()((((()())(())()(((()((((((()((()(()(((()(()((())))()((()()())))))))))()((((((())((()))(((((()(((((((((()()))((()(())()((())((()(()))((()))()))()(((((()(((()()))()())((()((((())()())()((((())()(()(()(((()(())(()(())(((((((())()()(((())(()(()(()(())))(()((((())((()))(((()(()()(((((()()(()(((()(((((())()))()((()(()))()((()((((())((((())(()(((())()()(()()()()()(())((((())((())(()()))()((((())))((((()())()((((())((()())((())(())(((((()((((()(((()((((())(()(((()()))()))((((((()((())()())))(((()(()))(()()(()(((()(()))((()()()())((()()()(((())())()())())())((()))(()(()))(((((()(()(())((()(())(())()((((()())()))((((())(())((())())((((()(((())(())((()()((((()((((((()(())()()(()(()()((((()))(())()())()))(())))(())))())()()(())(()))()((()(()(())()()))(()())))))(()))(()()))(())(((((()(()(()()((())()())))))((())())((())(()(())((()))(())(((()((((((((()()()(()))()()(((()))()((()()(())(())())()(()(())))(((((()(())(())(()))))())()))(()))()(()(((((((()((((())))())())())())()((((((((((((((()()((((((()()()())())()())())())(())(())))())((()())((()(()))))))()))))))))))))))))())((())((())()()))))))(((()((()(()()))((())(()()))()()())))(())))))))(()(((())))())()())))()()(())()))()(()))())((()()))))(()))))()))(()()(())))))))()(((()))))()(()))(())())))))()))((()))((()))())(())))))))))((((())()))()))()))())(())()()(())))())))(()())()))((()()(())))(())((((((()(())((()(((()(()()(())))()))))))()))()(()((()))()(()))(()(((())((((())())(())(()))))))))())))))))())())))))())))))()()(((())()(()))))))))())))))(())()()()))()))()))(()(())()()())())))))))())()(()(()))))()()()))))())(()))))()()))))()())))))(((())()()))(()))))))))))()()))))()()()))))(()())())()()())()(()))))()(()))(())))))))(((((())(())())()()))()()))(())))))()(()))))(())(()()))()())()))()))()))()))))())()()))())())))(()))(()))))))())()(((())()))))))))()))()())))())))())))()))))))))))()()))(()()))))))(())()(()))))())(()))))(()))))(()())))))())())()()))))())()))))))))(()))))()))))))()(()())))))))()))())))())))())))())))))))())(()()))))))(()())())))()())()))))))))))))))())))()(())))()))())()()(())(()()))(())))())()())(()(()(()))))())))))))))))())(()))()))()))))(())()())()())))))))))))()()))))))))))))())())))))(()())))))))))))())(())))()))))))))())())(()))()))(())))()))()()(())()))))))()((((())()))())())))))()))()))))((()())()))))())))(())))))))))))))))))()))))()()())()))()()))))())()))((()())))())))(()))(()())))))))()))()))))(())))))))(())))))())()()(()))())()))()()))))())()()))))())()))())))))))(()))))()())()))))))))(()))())))(()))()))))(())()))())())(())())())))))))((((())))))()))()))()())()(())))()))()))()())(()())()()(()())()))))())())))))(()))()))))())(()()(())))))(())()()((())())))))(())(())))))))())))))))))()(())))))))()())())())()(()))))))))(()))))))))())()()))()(()))))))()))))))())))))))(())))()()(())()())))))(((())))()((())()))())))(()()))())(())())))()(((()())))))()(()()())))()()(()()(()()))())()(()()()))())()()))()())(()))))())))))())))(())()()))))(()))))(())(()))(())))))()()))()))))())()))()()(())())))((()))())()))))))()()))))((()(()))))()()))))))())))))())(()((()())))))))))))()())())))()))(()))))))(()))(())()())))(()))))))))())()()()()))))(()())))))))((())))()))(()))(())(())()())()))))))))(())))())))(()))()()))(()()))(()))())))()(())))())((()((()(())))((())))()))))((((())())()())))(())))()))))))())(()()((())))())()(()())))))(()())()))())))))))((())())))))))(()(()))())()()(()()(((()(((()())))))()))))))()(())(()()((()()(())()()))())()())()))()())())())))))))(((())))))))()()))))))(((())()))(()()))(()()))))(()(()()((((())()())((()()))))(()(())))))()((()()()())()()((()((()()))(()))(((()()()))(((())))()(((())()))))))((()(())())))(()())(((((()(()))(()((()))(()())()))))(()(()))()(()))(())(((())(()()))))()()))(((()))))(()()()()))())))((()()()(())()))()))))()()))()))))))((((((()()()))))())((()()(((()))))(()(())(()()())())())))()(((()()))(())((())))(()))(()()()())((())())())(()))))()))()((()(())()(()()(())(()))(())()))(())(()))))(())(())())(()()(()((()()((())))((()))()((())))(((()()()()((((()))(()()))()()()(((())((())())(()()(()()()))()((())(())()))())(((()()(())))()((()()())()())(()(())())(((())(())())((())(())()(((()()))(())))((())(()())())(())((()()()((((((())))((()(((((())()))()))(())(()()))()))(())()()))(())((()()())()()(()))())()((())))()((()()())((((()())((())())())((()((()))()))((())((()()(()((()()(((())(()()))))((()((())()(((())(()((())())((())(()((((((())())()(()())()(())(((())((((((()(())(()((()()()((()()(()()()())))()()(((((()()))()((((((()))()(()(()(()(((()())((()))())()((()))(())))()))()()))())()()))())((((())(()(()))(((((((())(((()(((((()(((()()((((())(((())())))(()()()(()(()))()))((((((()))((()(((()(())((()((((()((((((())(((((())))(((()(()))))(((()(((())()((())(()((()))(((()()(((())((((()(()(((((()))(((()(((((((()(()()()(()(()(()()())(())(((((()(())())()())(()(()(()))()(()()()())(()()(()((()))()((())())()(()))((())(()))()(()))()(((()(()(()((((((()()()()())()(((((()()(((()()()((()(((((()))((((((((()()()(((((()))))))(()()()(())(()))(()()))))(())()))(((((()(((((()()(()(()())(((()))((((()((()(()(()((()(()((())))()(((()((()))((()))(((((((((()((()((()(())))()((((()((()()))((())(((()(((((()()(()(()()((()(()()()(((((((())())()())))))((((()()(()))()))(()((())()(()(((((((((()()(((()(()())(()((()())((())())((((()(((()(((()((((()((()((((()(()((((((())((((((((((((()()(()()((((((((((((((()((()()))()((((((((((((())((((()(()())((()(()(()))()(((((()()(((()()))()())(())((()(((((()((())(((((()((()(((((()))()()((((())()((((())(((((((((()(())(()(())))())(()((())(((())(())(())())(()(()(())()()((()((())()(((()(((((()(())))()(((()((())))((()()()(((()(((()((()(()(())(()((()())(()(()(((()(((((((((())(()((((()()))(()((((()()()()(((()((((((((()(()()((((((()(()()(()((()((((((((((()()(((((((()())(())))(((()()))(((((()((()()())(()()((((())((()((((()))))(())((()(()()(((()(()(((()((((()(((((()))())())(()((())()))(((()())((())((())((((()((()((((((())(()((((()()))((((((())()(()))((()(((())((((((((((()()(((((()(((((()((()()()((((())))(()))()((()(())()()((()((((((((((()((())(())(((((()(()(()()))((((()((((()()((()(((()(((((((((()(()((()((()))((((((()(((())()()((()(((((((()())))()()(()((()((()()(((()(()()()()((((()((())((((()(((((((((()(((()()(((()(()(((()(((()((())()(()((()(()(()(()))()(((()))(()((((()((())((((())((((((())(()))(()((((())((()(()((((((((()()((((((()(()(()()()(())((()((()()(((()(((((((()()((()(((((((()))(((((()(((()(()()()(()(((()((()()((())(()(((((((((()(()((()((((((()()((())()))(((((()((())()())()(((((((((((()))((((()()()()())(()()(()(()()))()))(()))(()(((()()))())(()(()))()()((())(()())()())()(()))()))(()()(()((((((())((()(((((((((((()(())()((()(()((()((()(()((()((((((((((()()())((())()(())))((())()())()(((((()(()())((((()((()(())(()))(((())()((()))(((((())(()))()()(()))(((())((((()((((()(())))(((((((()))))())()())(())((())()(()()((()(()))()(()()(()()((()())((())((()()))((((()))()()))(()()(())()()(((((()(())((()((((()))()))(()())())(((()()(()()))(())))))(()))((())(((((()((((()))()((((()))()((())(((())))(((()())))((()(()()((' floor = 0 position = 1 basement_entered = False for c in challenge: if c == '(': floor += 1 elif c == ')': floor -= 1 else: raise Exception("Error: unrecognized character: {0}".format(c)) if floor < 0 and basement_entered is False: print("INFO: Santa first entered the basement on position {0}".format(position)) basement_entered = True position += 1 print("INFO: Final floor is {0}".format(floor))

312.625

7,014

0.040784

66

7,503

4.590909

0.515152

0.148515

0.046205

0

0.001357

0.017593

7,503

23

7,015

326.217391

0.03975

0.002799

0

0.950942

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0

1

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false

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0.125

0

null

0

1

0

1

null

0

5

5eaf4a9b3d3e763db16e158d2364c82c37b5e125

322

py

Python

Services/registrationWorkerService.py

prodProject/WorkkerAndConsumerServer

95496f026109279c9891e08af46040c7b9487c81

[ "MIT" ]

null

Services/registrationWorkerService.py

prodProject/WorkkerAndConsumerServer

95496f026109279c9891e08af46040c7b9487c81

[ "MIT" ]

null

Services/registrationWorkerService.py

prodProject/WorkkerAndConsumerServer

95496f026109279c9891e08af46040c7b9487c81

[ "MIT" ]

null

from RegisrationModule.registrationWorker import RegistrationWorker class RegistrationWorkerService: m_registrationWorker = RegistrationWorker() def registration(self,registrationRequestPb): self.m_registrationWorker.start(workerPb=registrationRequestPb) return self.m_registrationWorker.done()

32.2

71

0.81677

25

322

10.4

0.6

0.219231

0.176923

0

0.127329

322

9

72

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1

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null

1

0

1

0

null

0

1

0

5

5ed64734b56af8c427299608905c03b1b2c09972

88

py

Python

VSCode_work/chapter2/chapter2_2_2.py

yangyahu-1994/Python-Crash-Course

6f8ef7fe8466d88931a0d3cc423ba5d966663b9d

[ "MIT" ]

12

2020-10-22T14:03:27.000Z

2022-03-28T08:14:22.000Z

VSCode_work/chapter2/chapter2_2_2.py

yangyahu-1994/Python-Crash-Course

6f8ef7fe8466d88931a0d3cc423ba5d966663b9d

[ "MIT" ]

null

VSCode_work/chapter2/chapter2_2_2.py

yangyahu-1994/Python-Crash-Course

6f8ef7fe8466d88931a0d3cc423ba5d966663b9d

[ "MIT" ]

9

2020-12-22T10:22:12.000Z

2022-03-28T08:14:53.000Z

message = "hello python world!" print(message) message = "hello world!" print(message)

14.666667

31

0.727273

11

88

5.818182

0.454545

0.375

0.53125

0

0.136364

88

5

32

17.6

0.842105

0

0.5

0

0.352273

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1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

5

0d80cbf1ddb51f16703490502f66660450b362a4

375

py

Python

nets/base.py

yd8534976/FashionAI-TF

9bb4e84a5d32cb696756d20b34d448a4dd7433dd

[ "MIT" ]

20

2018-07-01T06:31:44.000Z

2021-12-21T10:14:38.000Z

nets/base.py

yd8534976/FashionAI-TF

9bb4e84a5d32cb696756d20b34d448a4dd7433dd

[ "MIT" ]

1

2019-03-20T06:44:28.000Z

2019-03-20T08:16:45.000Z

nets/base.py

yd8534976/FashionAI-TF

9bb4e84a5d32cb696756d20b34d448a4dd7433dd

[ "MIT" ]

3

2018-07-13T14:37:13.000Z

2018-12-28T06:40:58.000Z

class BaseModel(object): def __init__(self): pass def build_inputs(self): raise NotImplementedError def build_inference(self): raise NotImplementedError def build_loss(self): raise NotImplementedError def build_solver(self): raise NotImplementedError def train_op(self): raise NotImplementedError

18.75

33

0.669333

37

375

6.540541

0.432432

0.18595

0.578512

0.512397

0.446281

0

0.274667

375

19

34

19.736842

0.889706

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false

0.076923

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0.538462

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null

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1

0

null

0

1

0

1

0

1

0

5

0dd1f53ecd9b545e07c14c5a4940688d51838472

215

py

Python

index/admin.py

CPLUG/cplug.org-backend

04ed152b0571dcd616afa4ca2c3951660705336f

[ "MIT" ]

null

index/admin.py

CPLUG/cplug.org-backend

04ed152b0571dcd616afa4ca2c3951660705336f

[ "MIT" ]

1

2018-02-25T07:22:54.000Z

index/admin.py

CPLUG/cplug.org-backend

04ed152b0571dcd616afa4ca2c3951660705336f

[ "MIT" ]

1

2018-02-11T08:53:16.000Z

from django.contrib import admin from adminsortable.admin import SortableAdmin from .models import Event, Officer # Register your models here. admin.site.register(Event) admin.site.register(Officer, SortableAdmin)

26.875

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0.827907

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215

6.357143

0.5

0.101124

0.191011

0

0.102326

215

7

46

30.714286

0.92228

0.12093

0

1

0

true

0

0.6

0

0.6

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1

0

null

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0

1

0

null

0

1

0

1

0

5

0dd2b932fbf79c1401af47aabf74be1d39c4b0b4

187

py

Python

map_service/meteo/views.py

berthakim/map-service

881647a4a2627c633094d45519b0cf6b2c7050b9

[ "MIT" ]

null

map_service/meteo/views.py

berthakim/map-service

881647a4a2627c633094d45519b0cf6b2c7050b9

[ "MIT" ]

null

map_service/meteo/views.py

berthakim/map-service

881647a4a2627c633094d45519b0cf6b2c7050b9

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse from django.views.generic import TemplateView def meteo(request): return render(request, 'meteo/meteo.html')

26.714286

46

0.807487

25

187

6.04

0.6

0.198676

0

0.117647

187

6

47

31.166667

0.915152

0

0.085562

0

1

0.2

false

0

0.6

0.2

1

0

1

0

null

0

1

0

null

0

1

0

5

21b38c0fc0286c99e1ff712e33ccd64db436a01e

48

py

Python

slack/signature/__init__.py

TheFRedFox/python-slackclient

909959a1390d8d634263cb1b528f019e35cb88d1

[ "MIT" ]

1

2020-10-21T03:22:06.000Z

slack/signature/__init__.py

TheFRedFox/python-slackclient

909959a1390d8d634263cb1b528f019e35cb88d1

[ "MIT" ]

2

2021-10-05T10:39:19.000Z

2022-01-19T08:41:06.000Z

slack/signature/__init__.py

TheFRedFox/python-slackclient

909959a1390d8d634263cb1b528f019e35cb88d1

[ "MIT" ]

null

from .verifier import SignatureVerifier # noqa

24

47

0.8125

5

48

7.8

1

0

0.145833

48

1

48

0.95122

0.083333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

21b54963bf372bdb57445936a639b7dcb3b635d9

12,751

py

Python

tests/test_tf/test_mnist_tf.py

SymbioticLab/Salus

b2a194e7e4654b51dbd8d8fc1577fb1e9915ca6f

[ "Apache-2.0" ]

104

2019-02-12T20:41:07.000Z

2022-03-07T16:58:47.000Z

tests/test_tf/test_mnist_tf.py

SymbioticLab/Salus

b2a194e7e4654b51dbd8d8fc1577fb1e9915ca6f

[ "Apache-2.0" ]

9

2019-08-24T03:23:21.000Z

2021-06-06T17:59:07.000Z

tests/test_tf/test_mnist_tf.py

SymbioticLab/Salus

b2a194e7e4654b51dbd8d8fc1577fb1e9915ca6f

[ "Apache-2.0" ]

18

2019-03-04T07:45:41.000Z

2021-09-15T22:13:07.000Z

from __future__ import print_function import unittest import numpy as np from datetime import datetime from timeit import default_timer from parameterized import parameterized import tensorflow as tf from . import run_on_rpc_and_gpu, run_on_devices, run_on_sessions, assertAllClose, tfDistributedEndpointOrSkip from .lib import tfhelper from .lib.datasets import fake_data def run_mnist_softmax(sess, batch_size=50): batch_size = tfhelper.batch_size_from_env(batch_size) print('Using batch_size {}'.format(batch_size)) x_image, y_, num_classes = fake_data(batch_size, None, height=28, width=28, depth=1, num_classes=10) y_ = tf.one_hot(y_, num_classes) x = tf.reshape(x_image, [-1, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.matmul(x, W) + b cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(labels=tf.stop_gradient(y_), logits=y)) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) salus_marker = tf.no_op("salus_main_iter") with tfhelper.initialized_scope(sess) as coord: jct = default_timer() speeds = [] losses = [] for i in range(tfhelper.iteration_num_from_env()): if coord.should_stop(): break print("{}: Start running step {}".format(datetime.now(), i)) start_time = default_timer() _, loss_value, _ = sess.run([train_step, cross_entropy, salus_marker]) duration = default_timer() - start_time examples_per_sec = batch_size / duration sec_per_batch = float(duration) speeds.append(sec_per_batch) losses.append(loss_value) fmt_str = '{}: step {}, loss = {:.2f} ({:.1f} examples/sec; {:.3f} sec/batch)' print(fmt_str.format(datetime.now(), i, loss_value, examples_per_sec, sec_per_batch)) jct = default_timer() - jct print('Training time is %.3f sec' % jct) print('Average: %.3f sec/batch' % np.average(speeds)) if len(speeds) > 1: print('First iteration: %.3f sec/batch' % speeds[0]) print('Average excluding first iteration: %.3f sec/batch' % np.average(speeds[1:])) return losses def run_mnist_conv(sess, batch_size=50): def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') batch_size = tfhelper.batch_size_from_env(batch_size) print('Using batch_size {}'.format(batch_size)) x_image, y_, num_classes = fake_data(batch_size, None, height=28, width=28, depth=1, num_classes=10) y_ = tf.one_hot(y_, num_classes) keep_prob = tf.placeholder(tf.float32) W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_fc2 = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(labels=tf.stop_gradient(y_), logits=y_fc2)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) salus_marker = tf.no_op("salus_main_iter") speeds = [] losses = [] with tfhelper.initialized_scope(sess) as coord: jct = default_timer() for i in range(tfhelper.iteration_num_from_env()): if coord.should_stop(): break print("{}: Start running step {}".format(datetime.now(), i)) start_time = default_timer() sess.run([train_step, salus_marker], feed_dict={keep_prob: 0.5}) duration = default_timer() - start_time examples_per_sec = batch_size / duration sec_per_batch = float(duration) speeds.append(sec_per_batch) loss_value = sess.run(cross_entropy, feed_dict={keep_prob: 0.5}) losses.append(loss_value) fmt_str = '{}: step {}, loss = {:.2f} ({:.1f} examples/sec; {:.3f} sec/batch)' print(fmt_str.format(datetime.now(), i, loss_value, examples_per_sec, sec_per_batch)) jct = default_timer() - jct print('Training time is %.3f sec' % jct) print('Average: %.3f sec/batch' % np.average(speeds)) if len(speeds) > 1: print('First iteration: %.3f sec/batch' % speeds[0]) print('Average excluding first iteration: %.3f sec/batch' % np.average(speeds[1:])) return losses def run_mnist_large(sess, batch_size=50): def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') batch_size = tfhelper.batch_size_from_env(batch_size) print('Using batch_size {}'.format(batch_size)) x_image, y_, num_classes = fake_data(batch_size, None, height=28, width=28, depth=1, num_classes=10) y_ = tf.one_hot(y_, num_classes) keep_prob = tf.placeholder(tf.float32) W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) W_conv3 = weight_variable([5, 5, 64, 64]) b_conv3 = bias_variable([64]) h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3) h_pool3 = max_pool_2x2(h_conv3) W_conv4 = weight_variable([5, 5, 64, 128]) b_conv4 = bias_variable([128]) h_conv4 = tf.nn.relu(conv2d(h_pool3, W_conv4) + b_conv4) h_pool4 = max_pool_2x2(h_conv4) h_pool_flat = tf.reshape(h_pool4, [-1, 512]) W_fc1 = weight_variable([512, 1024]) b_fc1 = bias_variable([1024]) h_fc1 = tf.nn.relu(tf.matmul(h_pool_flat, W_fc1) + b_fc1) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc11 = weight_variable([1024, 1024]) b_fc11 = bias_variable([1024]) h_fc11 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc11) + b_fc11) h_fc11_drop = tf.nn.dropout(h_fc11, keep_prob) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_fc2 = tf.matmul(h_fc11_drop, W_fc2) + b_fc2 cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(labels=tf.stop_gradient(y_), logits=y_fc2)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) salus_marker = tf.no_op("salus_main_iter") speeds = [] inbetween = [] last_end_time = 0 losses = [] with tfhelper.initialized_scope(sess) as coord: jct = default_timer() for i in range(tfhelper.iteration_num_from_env()): if coord.should_stop(): break print("{}: Start running step {}".format(datetime.now(), i)) start_time = default_timer() _, loss_value, _ = sess.run([train_step, cross_entropy, salus_marker], feed_dict={keep_prob: 0.5}) end_time = default_timer() if last_end_time > 0: inbetween.append(start_time - last_end_time) last_end_time = end_time duration = end_time - start_time examples_per_sec = batch_size / duration sec_per_batch = float(duration) speeds.append(sec_per_batch) losses.append(loss_value) fmt_str = '{}: step {}, loss = {:.2f} ({:.1f} examples/sec; {:.3f} sec/batch)' print(fmt_str.format(datetime.now(), i, loss_value, examples_per_sec, sec_per_batch)) jct = default_timer() - jct print('Training time is %.3f sec' % jct) print('Average: %.3f sec/batch' % np.average(speeds)) if len(speeds) > 1: print('First iteration: %.3f sec/batch' % speeds[0]) print('Average excluding first iteration: %.3f sec/batch' % np.average(speeds[1:])) return losses class MnistConvBase(unittest.TestCase): def _runner(self, batch_size): return None def _config(self, **kwargs): c = tf.ConfigProto() # c.graph_options.optimizer_options.opt_level = tf.OptimizerOptions.L0 c.allow_soft_placement = True return c @parameterized.expand([(25,), (50,), (100,)]) def test_gpu(self, batch_size): config = self._config(batch_size=batch_size) config.allow_soft_placement = True run_on_devices(self._runner(batch_size), '/device:GPU:0', config=config) @unittest.skip("No need to run on CPU") def test_cpu(self): run_on_devices(self._runner(50), '/device:CPU:0', config=self._config()) @parameterized.expand([(25,), (50,), (100,)]) def test_rpc(self, batch_size): run_on_sessions(self._runner(batch_size), 'zrpc://tcp://127.0.0.1:5501', dev='/device:GPU:0', config=self._config(batch_size=batch_size)) @parameterized.expand([(25,), (50,), (100,)]) def test_distributed(self, batch_size): run_on_sessions(self._runner(batch_size), tfDistributedEndpointOrSkip(), dev='/job:tfworker/device:GPU:0', config=self._config(batch_size=batch_size)) @parameterized.expand([(25,), (50,), (100,)]) def test_correctness(self, batch_size): actual, expected = run_on_rpc_and_gpu(self._runner(batch_size), config=self._config()) assertAllClose(actual, expected, rtol=1e-3) class TestMnistSoftmax(MnistConvBase): def _runner(self, batch_size): return lambda: run_mnist_softmax(tf.get_default_session(), batch_size) class TestMnistConv(MnistConvBase): def _runner(self, batch_size): return lambda: run_mnist_conv(tf.get_default_session(), batch_size) def _config(self, **kwargs): MB = 1024 * 1024 memusages = { 25: (100 * MB - 38 * MB, 38 * MB), 50: (128 * MB - 58 * MB, 58 * MB), 100: (182 * MB - 112 * MB, 112 * MB), } batch_size = kwargs.get('batch_size', 50) config = tf.ConfigProto() config.allow_soft_placement = True config.salus_options.resource_map.temporary['MEMORY:GPU'] = memusages[batch_size][0] config.salus_options.resource_map.persistant['MEMORY:GPU'] = memusages[batch_size][1] config.salus_options.resource_map.temporary['MEMORY:GPU0'] = memusages[batch_size][0] config.salus_options.resource_map.persistant['MEMORY:GPU0'] = memusages[batch_size][1] return config class TestMnistLarge(MnistConvBase): def _runner(self, batch_size): return lambda: run_mnist_large(tf.get_default_session(), batch_size) def _config(self, **kwargs): MB = 1024 * 1024 memusages = { 25: (110 * MB - 61 * MB, 61 * MB), 50: (136 * MB - 86 * MB, 86 * MB), 100: (197 * MB - 137 * MB, 137 * MB), } batch_size = kwargs.get('batch_size', 50) config = tf.ConfigProto() config.allow_soft_placement = True config.salus_options.resource_map.temporary['MEMORY:GPU'] = memusages[batch_size][0] config.salus_options.resource_map.persistant['MEMORY:GPU'] = memusages[batch_size][1] config.salus_options.resource_map.temporary['MEMORY:GPU0'] = memusages[batch_size][0] config.salus_options.resource_map.persistant['MEMORY:GPU0'] = memusages[batch_size][1] return config del MnistConvBase if __name__ == '__main__': unittest.main()

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CodePi/led.py

koushikjay66/NKTR

03acc4ed7f31d00ce9c164828579b7f6cff90d12

[ "Apache-2.0" ]

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2017-04-03T03:46:29.000Z

CodePi/led.py

koushikjay66/NKTR

03acc4ed7f31d00ce9c164828579b7f6cff90d12

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null

CodePi/led.py

koushikjay66/NKTR

03acc4ed7f31d00ce9c164828579b7f6cff90d12

[ "Apache-2.0" ]

2

2018-01-19T16:24:17.000Z

2019-07-28T07:06:23.000Z

import time def setUpChor(): GPIO.setup(11,GPIO.OUT) def chor(): setUpChor() GPIO.output(11, True) time.sleep(2) GPIO.output(11, False) GPIO.cleanup(11)

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notebooks/python_recap/_solutions/python_rehearsal54.py

rprops/Python_DS-WS

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2019-07-23T15:14:03.000Z

2020-11-10T06:12:18.000Z

notebooks/python_recap/_solutions/python_rehearsal54.py

rprops/Python_DS-WS

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rprops/Python_DS-WS

b2fc449a74be0c82863e5fcf1ddbe7d64976d530

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dependencies/utils/get_data.py

narayana1043/pyspark-emr-etl

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2021-03-02T05:07:36.000Z

2021-12-02T12:10:53.000Z

dependencies/utils/get_data.py

narayana1043/pyspark-emr-etl

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dependencies/utils/get_data.py

narayana1043/pyspark-emr-etl

56f397e7d3da09bf1f8818629a695129adbfb9d1

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2021-12-09T12:41:54.000Z

import json import requests from dependencies.utils import conn from dependencies.utils import utils # module level connections es = conn.get_es_conn() countries = utils.get_countries() def get_sample(): return None

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venv/lib/python3.8/site-packages/poetry/installation/operations/install.py

Retraces/UkraineBot

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venv/lib/python3.8/site-packages/poetry/installation/operations/install.py

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2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/poetry/installation/operations/install.py

DesmoSearch/Desmobot

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filterflow/proposal/__init__.py

JTT94/filterflow

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2022-02-05T12:49:36.000Z

filterflow/proposal/__init__.py

JTT94/filterflow

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[ "Apache-2.0" ]

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2021-05-16T22:03:03.000Z

2021-12-15T07:43:10.000Z

filterflow/proposal/__init__.py

JTT94/filterflow

536f71105dc263472f54e80f2ef4ad5e4ee6621f

[ "Apache-2.0" ]

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2020-06-03T14:40:07.000Z

2021-07-27T20:55:58.000Z

from .base import ProposalModelBase from .bootstrap import BootstrapProposalModel from .optimal_proposal import OptimalProposalModel

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oembed/contrib/models.py

EightMedia/djangoembed

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oembed/contrib/models.py

EightMedia/djangoembed

ee325f7375c48405f9c3e7e2c0fa7f5a08fafd48

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2018-03-08T09:45:26.000Z

# ,___, # (6v6) # (_^(_\ # ^^^"^" \\^^^^ # ^^^^^^^^^^^^^

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Python

python/testData/paramInfo/Py3kPastTupleArg.py

truthiswill/intellij-community

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2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/paramInfo/Py3kPastTupleArg.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

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2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/paramInfo/Py3kPastTupleArg.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

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2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def foo(*arg, a=1, b=2): pass foo(<arg1>1, <arg2>2, <arg3>b=10, <arg4>a=20)

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py

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ggnnmols/models/__init__.py

YunjaeChoi/ggnnmols

543c578eddde1a9572a089d646d05a5e3da2c1ff

[ "MIT" ]

4

2020-08-10T12:36:58.000Z

2022-02-20T11:15:20.000Z

ggnnmols/models/__init__.py

YunjaeChoi/ggnnmols

543c578eddde1a9572a089d646d05a5e3da2c1ff

[ "MIT" ]

null

ggnnmols/models/__init__.py

YunjaeChoi/ggnnmols

543c578eddde1a9572a089d646d05a5e3da2c1ff

[ "MIT" ]

2

2020-05-11T09:53:03.000Z

2020-08-10T01:31:35.000Z

from ggnnmols.models.ggnn import GGNN

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src/betterjira.py

jamesbroadhead/betterjira

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null

src/betterjira.py

jamesbroadhead/betterjira

847f1c9c658ca1a2407523bf0d209878e9afb5da

[ "Apache-2.0" ]

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2018-09-19T12:09:16.000Z

2021-06-01T22:57:56.000Z

src/betterjira.py

jamesbroadhead/betterjira

847f1c9c658ca1a2407523bf0d209878e9afb5da

[ "Apache-2.0" ]

null

class BetterJira(object): pass

9

25

0.694444

4

36

6.25

1

0

0.222222

36

3

26

12

0.892857

0

1

0

true

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0

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0

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null

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null

0

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10c51ac249ba4e9fc8474d35946a6d82fee561c6

459

py

Python

numbercrunch.py

TechyTommy3/NumberCrunch

b507f9da80c1786b8ddc21794f741fe7f4d02a91

[ "MIT" ]

null

numbercrunch.py

TechyTommy3/NumberCrunch

b507f9da80c1786b8ddc21794f741fe7f4d02a91

[ "MIT" ]

null

numbercrunch.py

TechyTommy3/NumberCrunch

b507f9da80c1786b8ddc21794f741fe7f4d02a91

[ "MIT" ]

1

2021-09-07T16:41:38.000Z

print("NOTE: NumberCrunch is not a random number generator.") print("NumberCrunch") while 0 == 0: number = input("Enter in a number to crunch: ") print("Crunching...") number = int(number) number2 = (((number * number) * 2) - number + (number * 2)) * number * (number * number) - (number * number - number * (number * number)) - (number * 2) * (number * 2) - (number - 6) * (number * number - number) * (number * 3 - number) print(number2)

57.375

239

0.607843

56

459

4.982143

0.392857

0.55914

0.580645

0.602151

0.286738

0.193548

0

0.027778

0.215686

459

8

240

57.375

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0

0.228261

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false

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1

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1

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null

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5

10c6b61cd942fc1a795e98b155716d4c93e003f3

872

py

Python

common/boto/fps/test/test_verify_signature.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

61

2015-11-10T17:13:46.000Z

2021-08-06T17:58:30.000Z

common/boto/fps/test/test_verify_signature.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

13

2015-11-11T07:49:41.000Z

2021-06-09T03:45:31.000Z

common/boto/fps/test/test_verify_signature.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

18

2015-11-11T04:50:04.000Z

2021-08-20T00:57:11.000Z

from boto.fps.connection import FPSConnection conn = FPSConnection() # example response from the docs params = 'expiry=08%2F2015&signature=ynDukZ9%2FG77uSJVb5YM0cadwHVwYKPMKOO3PNvgADbv6VtymgBxeOWEhED6KGHsGSvSJnMWDN%2FZl639AkRe9Ry%2F7zmn9CmiM%2FZkp1XtshERGTqi2YL10GwQpaH17MQqOX3u1cW4LlyFoLy4celUFBPq1WM2ZJnaNZRJIEY%2FvpeVnCVK8VIPdY3HMxPAkNi5zeF2BbqH%2BL2vAWef6vfHkNcJPlOuOl6jP4E%2B58F24ni%2B9ek%2FQH18O4kw%2FUJ7ZfKwjCCI13%2BcFybpofcKqddq8CuUJj5Ii7Pdw1fje7ktzHeeNhF0r9siWcYmd4JaxTP3NmLJdHFRq2T%2FgsF3vK9m3gw%3D%3D&signatureVersion=2&signatureMethod=RSA-SHA1&certificateUrl=https%3A%2F%2Ffps.sandbox.amazonaws.com%2Fcerts%2F090909%2FPKICert.pem&tokenID=A5BB3HUNAZFJ5CRXIPH72LIODZUNAUZIVP7UB74QNFQDSQ9MN4HPIKISQZWPLJXF&status=SC&callerReference=callerReferenceMultiUse1' endpoint = 'http://vamsik.desktop.amazon.com:8080/ipn.jsp' conn.verify_signature(endpoint, params)

124.571429

665

0.893349

70

872

11.114286

0.885714

0

0.127811

0.030963

872

6

666

145.333333

0.792899

0.034404

0

0.2

0.83693

0.782974

0

1

0

false

0

0.2

0

0.2

0

1

null

0

1

0

1

0

1

null

0

5

10cd5d86ca23d561452f1734af88fb5d09bc0932

134

py

Python

agentk/utils.py

bolaum/agentk

9c616930d3c8748e488fbb9fcabdff3da553f94c

[ "MIT" ]

1

2019-04-18T16:21:39.000Z

agentk/utils.py

bolaum/agentk

9c616930d3c8748e488fbb9fcabdff3da553f94c

[ "MIT" ]

null

agentk/utils.py

bolaum/agentk

9c616930d3c8748e488fbb9fcabdff3da553f94c

[ "MIT" ]

null

def bigint_to_bytes(num, extra_bytes=0): return num.to_bytes(length=((num.bit_length() + 7) // 8) + extra_bytes, byteorder='big')

44.666667

92

0.701493

22

134

4

0.636364

0.159091

0

0.025424

0.119403

134

2

93

67

0.720339

0

0.022388

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5