code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSToken... | 100 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 0 |
import os
def a__ ( ):
SCREAMING_SNAKE_CASE_ : List[str] = os.path.dirname(os.path.realpath(A__ ) )
SCREAMING_SNAKE_CASE_ : int = os.path.join(A__, 'triangle.txt' )
with open(A__ ) as f:
SCREAMING_SNAKE_CASE_ : Any = ... | 101 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 0 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
UpperCamelCase : int = tf.convert_to_tensor(SCREAMING_SNAKE_CASE )
UpperCamelCase : Any = 0.5 * (1.0 + tf.... | 102 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
Wav... | 103 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase = typing.Union[np.floataa, int, fl... | 104 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 0 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.... | 105 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case :Dict =logging.get_logger(__name__)
__snake_case :Any ={'vo... | 106 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 0 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # ... | 107 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 0 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__a: Any = logging.getLogg... | 108 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the r... | 122 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class __a ( _snake_case ):
__Upper... | 109 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_job... | 578 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 0 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionMod... | 436 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 0 |
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> List[str]:
if not (isinstance(__a , __a ) and isinstance(__a , __a )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
_UpperCAmelCase = le... | 108 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 0 |
def lowercase_ (A : int = 1_0**9 ):
snake_case__ : int = 1
snake_case__ : List[str] = 2
snake_case__ : Any = 0
snake_case__ : int = 0
snake_case__ : Union[str, Any] = ... | 478 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 0 |
def _UpperCAmelCase ( UpperCAmelCase : list[int] ):
"""simple docstring"""
__lowerCamelCase : Optional[int] = len(__a )
for i in range(__a ):
for j in range(i + 1 , __a ):
if numbers[j] < numbers[i]:
... | 519 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ : Dict = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG... | 313 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 0 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 234 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 0 |
from functools import lru_cache
@lru_cache
def UpperCamelCase ( _A ):
"""simple docstring"""
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":... | 324 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 0 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffu... | 449 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common impo... | 468 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
return 1 if input_a == input_a else 0
def _UpperCamelCase ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 407 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : List[Any] = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/m... | 578 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Any = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 436 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__a: List[str] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7... | 108 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 0 |
from sklearn.metrics import mean_squared_error
import datasets
a_ :Optional[int] = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 478 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : List[Any] = {
'configuration_blenderbot_small': [
... | 519 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 0 |
from numpy import exp, pi, sqrt
def A ( snake_case__ : List[str] , snake_case__ : float = 0.0 , snake_case__ : float = 1.0 ) -> Optional[Any]:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
... | 313 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__SCREAMING_... | 234 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 0 |
from __future__ import annotations
def UpperCamelCase ( _A, _A, _A ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = list(range(len(__a ) ) )
__magic_name__ : Optional[int] = [v / w for v, w in zip(__a, __a... | 324 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__lowercase):
__magic_name__ = ["""flax""", """transformers"""]
def __init__( self : List[Any] , *_lowercase : List[str] , **_lowercase ... | 449 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 0 |
# using dfs for finding eulerian path traversal
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None ) -> Dict:
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
a ... | 468 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the r... | 122 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A ={
'configuration_electra': ['ELECTRA_PRETRAINE... | 407 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers... | 578 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 0 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _UpperCamelCase ( UpperCamelCase__ ... | 436 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 0 |
def _SCREAMING_SNAKE_CASE ( ) -> List[str]:
_UpperCAmelCase = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(__a )[-1_0:]
if __name__ == "__main__":
print(solution()) | 108 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 0 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils im... | 478 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 0 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 519 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 0 |
import argparse
from collections import defaultdict
import yaml
UpperCAmelCase__ : Optional[Any] = "docs/source/en/_toctree.yml"
def A ( snake_case__ : int ) -> Optional[Any]:
'''simple docstring'''
__snake_case = defaultdict(__a )
_... | 313 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 0 |
def a (_lowerCAmelCase , _lowerCAmelCase ):
_enforce_args(__a , __a )
if n == 0:
return 0
SCREAMING_SNAKE_CASE_ = float('''-inf''' )
for i in range(1 , n + 1 ):
SCREAMING_SNAKE_CASE_ = max(
... | 234 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__magic_name__: Tuple = datasets.utils.logging.get_logger(__name__)
class snake_case__ ( folder_based_builder.FolderBasedBuilderConfig ):
low... | 324 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: int = 2_0_0_0_0_0_0 ):
"""simple docstring"""
snake_case : List[Any] = [0 for i in range(n + 1 )]
snake_case : Tuple = 1
snake_case : Tuple ... | 449 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 0 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self :Dict ):
'''simple docstr... | 468 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 0 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : int = Omeg... | 407 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: int ):
__SCREAMING_SNAKE_CASE : Tuple = int(__a )
if n_element < 1:
__SCREAMING_SNAKE_CASE : int = ValueError("""a should be a positive number""" )
raise my_error
__SCREAMIN... | 578 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _snake_case :
'''simple docstring'''
def __init__( self: int , __UpperCamelCase: Optional[int] = 6 ) -> None:
__mag... | 436 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Any: # This function is recursive
_UpperCAmelCase = len(__a )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_len... | 108 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case__ ( __lowercase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (KDPMaDiscreteScheduler,)
_SCREAMING... | 478 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipelin... | 519 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 0 |
import copy
import random
from transformers import CLIPTokenizer
class __lowercase ( __lowercase ):
def __init__( self , *lowercase_ , **lowercase_) -> int:
super().__init__(*_a , **_a)
__snake_case ... | 313 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 0 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __magic_name__ ( __lowercase , __lowercase):
''... | 234 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 0 |
from __future__ import annotations
def UpperCamelCase ( _A ):
"""simple docstring"""
if len(__a ) == 0:
return array
__magic_name__ ,__magic_name__ : Any = min(__a ), max(__a )
# Compute the variables
__magic_name__ ... | 324 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
snake_case : List[st... | 449 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 0 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
... | 468 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the r... | 122 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ = 1_0 , UpperCamelCase__ = 2_2 ):
UpperCAmelCase__ : Optional[Any] = range(1 , __a )
UpperCAmelCase__ : Any = range(1 , __a )
return sum(
1 for power in powers for base ... | 407 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_cha... | 578 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : Union[str, Any] = TypeVar("T")
_SCREAMING_SNAKE_CASE : Dict = TypeVar("U")
class _snake... | 436 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 ap... | 108 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 0 |
def lowercase_ (A : int = 1_0_0_0_0_0_0 ):
snake_case__ : Union[str, Any] = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , __a ):
... | 478 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 0 |
from math import asin, atan, cos, radians, sin, sqrt, tan
__UpperCamelCase : Tuple = 6378137.0
__UpperCamelCase : Any = 6356752.314245
__UpperCamelCase : Tuple = 6378137
def _UpperCAmelCase ( UpperCAmelCase : float , UpperCAmelCase : f... | 519 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 0 |
from itertools import permutations
def A ( snake_case__ : tuple ) -> List[str]:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__snake_case = [7, 1... | 313 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
class __magic_name__ ( __lowercase):
'''simple docstring'''
def __init__( self: Optional[int] ... | 234 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCamelCase ( _A ):
"""simple docstring"""
if not isinstance(__a, __a ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise... | 324 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: int = 5_0 ):
"""simple docstring"""
snake_case : Optional[int] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5... | 449 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 0 |
def __A ( __lowerCamelCase ) -> Union[str, Any]:
if not isinstance(__a , __a ):
raise TypeError("""only integers accepted as input""" )
else:
a = str(abs(__a ) )
a = [list(__a ) for char in range(len(__a ) ... | 468 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 0 |
'''simple docstring'''
from math import factorial
def _UpperCamelCase ( UpperCamelCase__ = 1_0_0 ):
return sum(map(__a , str(factorial(__a ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 407 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 0 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
fr... | 578 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup,... | 436 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( ... | 108 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 0 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class snake_case__ :
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 42... | 478 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 0 |
from collections import namedtuple
__UpperCamelCase : int = namedtuple('from_to', 'from_ to')
__UpperCamelCase : Union[str, Any] = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.00_454, 264.172),
'c... | 519 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_... | 313 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 0 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoIm... | 234 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 0 |
from collections.abc import Generator
def UpperCamelCase ( ):
"""simple docstring"""
__magic_name__ ,__magic_name__ : Any = 0, 1
while True:
__magic_name__ ,__magic_name__ : Dict = b, a + b
yield b
def UpperCamelCase ... | 324 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 0 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa:... | 449 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 0 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerC... | 468 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the r... | 122 | 0 |
'''simple docstring'''
from math import factorial
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k o... | 407 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any] ):
"""simple docstr... | 578 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if not isinstance(__a , __a ):
__magic_name__ : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise Ty... | 436 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_... | 108 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowerCamelCase__ = 4
lowerCamelCase__ = 3
class __magic_name__ (__lowercase ):
pass... | 122 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ :List[str] = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 478 |
import torch
from torch import nn
class __magic_name__ (nn.Module ):
def __init__( self , _a , _a , _a , _a , _a=1 , _a=False ) -> str:
super().__init__()
lowerCAmelCase_ = n_token
lowerCAmelCase_ = d_embed
lowerCAme... | 122 | 0 |
__UpperCamelCase : Tuple = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/... | 519 |
from collections import defaultdict
class __magic_name__ :
def __init__( self , _a , _a ) -> Tuple:
lowerCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
lowerCAm... | 122 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterM... | 313 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 0 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.... | 234 |
def A(__a: int = 50 ):
lowerCAmelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length... | 122 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__magic_name__: Optional[int] = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try... | 324 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 0 |
"""simple docstring"""
import numpy as np
from PIL import Image
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: np.ndarray , lowerCamelCase_: int , lowerCamelCase_: int ):
"""simple docstring"""
snake_case : int = np.array(_... | 449 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDepende... | 122 | 0 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__UpperCamelCase : Optional[int] = logging.ge... | 468 |
from math import factorial
def A(__a: int , __a: int ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n... | 122 | 0 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packag... | 407 |
from string import ascii_uppercase
lowerCamelCase__ = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__ = dict(enumerate(ascii_uppercase))
def A(__a: str , __a: str ):
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = ... | 122 | 0 |
'''simple docstring'''
from collections import defaultdict
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : ... | 578 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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/lic... | 436 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 0 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a: Union[str, Any] = logging.get_logger(__name__)
__a: int = {
... | 108 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'... | 122 | 0 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor,... | 478 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 122 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : str = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
... | 519 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ... | 122 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusio... | 313 |
from __future__ import annotations
def A(__a: list[int] , __a: list[int] , __a: int ):
lowerCAmelCase_ = list(range(len(__a ) ) )
lowerCAmelCase_ = [v / w for v, w in zip(__a , __a )]
index.sort(key=lambda __a : ratio[i] , ... | 122 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
P... | 234 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 122 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
... | 324 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 122 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
A = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n aut... | 449 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 122 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : ... | 468 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the r... | 122 | 0 |
'''simple docstring'''
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__A =[
'kernels/rwkv/wkv_cuda.cu',
'kernels/rwkv/wkv_op.cpp',
'kernels/deformable_detr/ms_deform_attn.h',
'kernels/deformable_detr/cuda/ms_deform_im... | 407 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 122 | 0 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed... | 578 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transf... | 122 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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/lic... | 436 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 0 |
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