code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCamelCase_ (UpperCamelCase__ : str ):
_UpperCAmelCase : Optional[int] = [
'''encoder.version''',
'''decoder.version... | 358 |
"""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/licenses/LICENSE-2.0
#
# U... | 68 | 0 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCAmelCase :Optional[Any] = 300 # TEMPERATURE (unit = K)
def lowerCamelCase_ (UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float , ):
if... | 359 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and a... | 68 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 360 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase_ ():
_UpperCAmelCase : Optional[int] = [randint(-1000 , 1000 ) for i in range(10 )]
_UpperCAme... | 68 | 0 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : str ):
_UpperCAmelCase : Tuple = int(UpperCamelCase__ )
# Initialize Result
_UpperCAmelCase : Optional[Any] = []
# Traverse through all de... | 361 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=a ):
'''simple docstring'''
a__ =['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A , **A ) -> int:
requires_ba... | 68 | 0 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_lowerCAmelCase :Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase :... | 362 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _UpperCAmelCase ( unitt... | 68 | 0 |
"""simple docstring"""
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
_lowerCAmelCase :List[Any] = 'Create a default config file for Accelerate ... | 363 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax... | 68 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_toke... | 364 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 68 | 0 |
"""simple docstring"""
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**2... | 365 |
"""simple docstring"""
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 _UpperCAmelCase :
'''simple docstring'''
a__ ... | 68 | 0 |
"""simple docstring"""
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_lowerCAmelCase :List[Any] = collections.namedtuple('_... | 366 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _UpperCAmelCase ( unittest.TestCase )... | 68 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and a... | 367 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_UpperCAmelCase : str = {}
def __lo... | 68 | 0 |
from math import ceil
def lowerCamelCase_ (UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict ):
_UpperCAmelCase : Dict = list(range(0 , UpperCamelCase__ ) )
_UpperCAmelCase : Optional[Any] = [item for sublist in list(devi... | 368 |
"""simple docstring"""
from itertools import count
def lowerCamelCase_ (UpperCamelCase__ : int = 50 ):
_UpperCAmelCase : Tuple = [1] * min_block_length
for n in count(UpperCamelCase__ ):
fill_count_functions.append(1 )
for block_length in... | 68 | 0 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int ):
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase_ ():
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_g... | 369 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_lowerCAmelCase :int = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 68 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
... | 370 |
"""simple docstring"""
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.... | 68 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def lowerCamelCase_ (UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Any ):
_UpperCAmelCase : Union[str, Any] = {
'''en''': '''Machine learning is great, i... | 371 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase_ ():
_UpperCAmelCase : int = HfArgumentParser(UpperCamelCase__ )
_UpperCAmelCase : Optional[Any] = parser.parse_args... | 68 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL... | 350 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( a ,unittest.TestCase ):
'''simple docstring'''... | 68 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
U... | 351 |
"""simple docstring"""
import qiskit
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int ):
_UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
_UpperCAmelCase... | 68 | 0 |
"""simple docstring"""
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.... | 352 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''WhisperFeatureExtractor'''
a__ ='''WhisperTokenizer'''
def __init__( self , A , A ) -> Any:
super().__i... | 68 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'xlm-mlm-... | 353 |
"""simple docstring"""
_lowerCAmelCase :List[str] = 0 # The first color of the flag.
_lowerCAmelCase :Optional[Any] = 1 # The second color of the flag.
_lowerCAmelCase :List[Any] = 2 # The third color of the flag.
_lowerCAmelCase :Any = (red, white, blue)
def lowerCamelCase_ ... | 68 | 0 |
"""simple docstring"""
_lowerCAmelCase :Any = tuple[float, float, float]
_lowerCAmelCase :Optional[Any] = tuple[float, float, float]
def lowerCamelCase_ (UpperCamelCase__ : Pointad , UpperCamelCase__ : Pointad ):
_UpperCAmelCase : str = end_pointa[0] - ... | 354 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowerCAmelCase :int = get_logger(__name__)
class _UpperCAmelCase ( enum.Enum ):
'''simple docstring'''
a__ ='''a... | 68 | 0 |
"""simple docstring"""
_lowerCAmelCase :List[str] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperC... | 355 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from .... | 68 | 0 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neu... | 356 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'xlm-mlm-... | 68 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCAmelCase :int = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['T... | 357 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 / sqrt(2 ) ):
_UpperCAmelCase : str = tau * fr... | 68 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase :str = logging.get_logger(__name__)
_lowerCAmelCase :Optional[int] = {
'google/bigbird-roberta-base': 'ht... | 358 |
"""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/licenses/LICENSE-2.0
#
# U... | 68 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_com... | 359 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and a... | 68 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def lowerCamelCase_ (UpperCamelCase__ : Dict ):
_UpperCAmelCase : str = SwinConfig(image_size=192 ... | 360 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase_ ():
_UpperCAmelCase : Optional[int] = [randint(-1000 , 1000 ) for i in range(10 )]
_UpperCAme... | 68 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_toke... | 361 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=a ):
'''simple docstring'''
a__ =['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A , **A ) -> int:
requires_ba... | 68 | 0 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ..... | 362 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _UpperCAmelCase ( unitt... | 68 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase :List[Any] = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAIN... | 363 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax... | 68 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 364 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 68 | 0 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_lowerCAmelCase :Optional[int] = None
try:
import msvcrt
except ImportError:
_lowerCAmelCase :List[str] = None
try:
import fcntl
except ImportError:
_lowerCAm... | 365 |
"""simple docstring"""
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 _UpperCAmelCase :
'''simple docstring'''
a__ ... | 68 | 0 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Any ):
if index == r:
for j in range(UpperCamel... | 366 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _UpperCAmelCase ( unittest.TestCase )... | 68 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_avai... | 367 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_UpperCAmelCase : str = {}
def __lo... | 68 | 0 |
def lowerCamelCase_ (UpperCamelCase__ : Optional[int]=2_8123 ):
_UpperCAmelCase : Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 368 |
"""simple docstring"""
from itertools import count
def lowerCamelCase_ (UpperCamelCase__ : int = 50 ):
_UpperCAmelCase : Tuple = [1] * min_block_length
for n in count(UpperCamelCase__ ):
fill_count_functions.append(1 )
for block_length in... | 68 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :str = logging.get_logger(__name__)
_lowerCAmelCase :Tuple = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-96... | 369 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_lowerCAmelCase :int = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 68 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import ... | 370 |
"""simple docstring"""
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.... | 68 | 0 |
"""simple docstring"""
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , A , A , A ) -> int:
_UpperCAmelCase : str = None
_UpperCAmelCase : List[Any] = None
_UpperCAmelCase : O... | 371 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase_ ():
_UpperCAmelCase : int = HfArgumentParser(UpperCamelCase__ )
_UpperCAmelCase : Optional[Any] = parser.parse_args... | 68 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@... | 350 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( a ,unittest.TestCase ):
'''simple docstring'''... | 68 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow,... | 351 |
"""simple docstring"""
import qiskit
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int ):
_UpperCAmelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
_UpperCAmelCase... | 68 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_lowerCAmelCase :int = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 352 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''WhisperFeatureExtractor'''
a__ ='''WhisperTokenizer'''
def __init__( self , A , A ) -> Any:
super().__i... | 68 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :Tuple = logging.get_logger(__name__)
_lowerCAmelCase :str = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
... | 353 |
"""simple docstring"""
_lowerCAmelCase :List[str] = 0 # The first color of the flag.
_lowerCAmelCase :Optional[Any] = 1 # The second color of the flag.
_lowerCAmelCase :List[Any] = 2 # The third color of the flag.
_lowerCAmelCase :Any = (red, white, blue)
def lowerCamelCase_ ... | 68 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCAmelCase :Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_lowerCAme... | 354 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowerCAmelCase :int = get_logger(__name__)
class _UpperCAmelCase ( enum.Enum ):
'''simple docstring'''
a__ ='''a... | 68 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :Dict = logging.get_logger(__name__)
_lowerCAmelCase :int = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN ... | 355 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from .... | 68 | 0 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowerCAmelCase :int = get_logger(__name__)
class _UpperCAmelCase ( enum.Enum ):
'''simple docstring'''
a__ ='''a... | 356 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'xlm-mlm-... | 68 | 0 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase_ (UpperCamelCase__ : Dict , UpperCamelCase__ : Any=1 ):
if n_shave_prefix_segments >= 0:
return ".".joi... | 357 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 1 / sqrt(2 ) ):
_UpperCAmelCase : str = tau * fr... | 68 | 0 |
def lowerCamelCase_ (UpperCamelCase__ : list[int] ):
_UpperCAmelCase : Tuple = len(UpperCamelCase__ )
for i in range(UpperCamelCase__ ):
for j in range(i + 1 , UpperCamelCase__ ):
if numbers[j] < numbers[i]:
_UpperC... | 358 |
"""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/licenses/LICENSE-2.0
#
# U... | 68 | 0 |
"""simple docstring"""
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
... | 359 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and a... | 68 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( a ):
... | 360 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase_ ():
_UpperCAmelCase : Optional[int] = [randint(-1000 , 1000 ) for i in range(10 )]
_UpperCAme... | 68 | 0 |
"""simple docstring"""
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> None:
_UpperCAmelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
_UpperCAmelCase : List[str] = False
... | 361 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=a ):
'''simple docstring'''
a__ =['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A , **A ) -> int:
requires_ba... | 68 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase :List[str] = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP... | 362 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _UpperCAmelCase ( unitt... | 68 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
_lowerCAmelCase :List[str] = 'examples/'
_lowerCAmelCase :Union[str, Any] = {
'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile... | 363 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax... | 68 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
... | 364 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 68 | 0 |
"""simple docstring"""
from PIL import Image
def lowerCamelCase_ (UpperCamelCase__ : Image ):
_UpperCAmelCase : int = image.size
_UpperCAmelCase : List[Any] = 0
_UpperCAmelCase : int = image.load()
for i in range(UpperCame... | 365 |
"""simple docstring"""
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 _UpperCAmelCase :
'''simple docstring'''
a__ ... | 68 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
... | 366 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _UpperCAmelCase ( unittest.TestCase )... | 68 | 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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OP... | 367 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_UpperCAmelCase : str = {}
def __lo... | 68 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_UpperCAmelCase : str = {}
def __lowerCAmelCase ( self... | 368 |
"""simple docstring"""
from itertools import count
def lowerCamelCase_ (UpperCamelCase__ : int = 50 ):
_UpperCAmelCase : Tuple = [1] * min_block_length
for n in count(UpperCamelCase__ ):
fill_count_functions.append(1 )
for block_length in... | 68 | 0 |
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
a__ =4_2
a__ =jnp.floataa
def __lowerCAmelCase ( self ) -> List[Any]:
_UpperCAmelCase ... | 369 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_lowerCAmelCase :int = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 68 | 0 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , A=None ) -> List[Any]:
_UpperCAmelCase : Dict = data
_UpperCAmelCase : Optional[int] = None
... | 370 |
"""simple docstring"""
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.... | 68 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :Tuple = logging.get_logger(__name__)
_lowerCAmelCase :List[Any] = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class _U... | 371 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase_ ():
_UpperCAmelCase : int = HfArgumentParser(UpperCamelCase__ )
_UpperCAmelCase : Optional[Any] = parser.parse_args... | 68 | 0 |
"""simple docstring"""
import sys
from collections import defaultdict
class a :
"""simple docstring"""
def __init__( self: Union[str, Any] ):
"""simple docstring"""
A__ = []
def UpperCamelCase ( sel... | 69 |
"""simple docstring"""
import sys
from collections import defaultdict
class a :
"""simple docstring"""
def __init__( self: Union[str, Any] ):
"""simple docstring"""
A__ = []
def UpperCamelCase ( sel... | 69 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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... | 69 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configur... | 69 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNE... | 69 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
SCREAMING_SNAKE_CASE_ : int = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
"""simple docstring"""
def ... | 69 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
fro... | 69 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is... | 69 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
SCREAMING_S... | 69 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE_ : List[str] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n a... | 69 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Optional[Any] = {
'asapp/sew-tiny-100k'... | 69 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 50 ):
A__ = [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 +... | 69 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'microsoft/markuplm-base': 'https://huggingface.co... | 69 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Tuple = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
... | 69 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class a :
"""simple docstring"""
def __init__( self: List[str] , UpperCamelCase: list[str] ):
"""simple docstring"""
A__ = ... | 69 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.pro... | 69 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding... | 69 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.... | 69 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
SCREAMING_SNAKE_CASE_ : Union[str, Any] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The ... | 69 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
SCREAMING_SNAKE_CASE_ : Tuple = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
"""simple docstring"""
def __in... | 69 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE_ : Union[str... | 69 |
"""simple docstring"""
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
SCREAMING_SNAKE_CASE_... | 69 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _snake_case ( UpperCAmelCase_ : Union[str, Any] ):
A__ = [
"""encoder.version""",
"... | 69 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class a ( _lowerCamelC... | 69 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available... | 69 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_a... | 69 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Optional[Any] = logging... | 69 |
"""simple docstring"""
from typing import Any
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: Any ):
"""simple docstring"""
A__ = data
A__ = None
... | 69 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_a... | 69 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch... | 69 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = range(2, 2_0 + 1)
SCREAMING_SNAKE_CASE_ : Dict = [1_0**k for k in range(ks[-1] + 1)]
SCREAMING_SNAKE_CASE_ : dict[int, dict[int, list[list[int]]]] = {}
def _snake_case ( Uppe... | 69 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impo... | 69 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : list[int] ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_... | 69 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def _snake_case ( UpperCAmelCase_ : int="ro" , UpperCAmelCase_ : Optional[int]="en" , UpperCAmelCase_ : List[Any]="wmt16" , UpperCAmelCase_ : str=None ):
... | 69 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 100 ):
A__ = (n * (n + 1) // 2) ** 2
A__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
... | 69 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE_ : Union[str... | 69 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int ):
A__ = [[0 for _ in range(UpperCAmelCase_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
A__ = 1
for n in range(m + 1 ):
for k in ... | 69 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : str = {
'funnel-transformer/small': 'https://huggingface.co/funnel-trans... | 69 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _snake_case ( UpperCAmelCase_ :... | 69 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL i... | 69 | 1 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class a ( _lowerCamelCase ):
"""simple docstring"""
@require_torch
def... | 69 |
"""simple docstring"""
import argparse
SCREAMING_SNAKE_CASE_ : Any = 'docs/source/_static/js/custom.js'
def _snake_case ( UpperCAmelCase_ : List[Any] ):
with open(UpperCAmelCase_ , encoding="""utf-8""" , newline="""\n""" ) as f:
... | 69 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
SCREAMING_SNAKE_CASE_ : Optional[int] = datasets.utils.logging.get_... | 69 |
"""simple docstring"""
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,
LMSD... | 69 | 1 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..p... | 69 |
"""simple docstring"""
import sys
from collections import defaultdict
class a :
"""simple docstring"""
def __init__( self: Union[str, Any] ):
"""simple docstring"""
A__ = []
def UpperCamelCase ( sel... | 69 | 1 |
"""simple docstring"""
from math import sqrt
def _snake_case ( UpperCAmelCase_ : int = 100_0000 ):
A__ = 0
A__ = 0
A__ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_sh... | 69 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configur... | 69 | 1 |
"""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/licenses/LICE... | 69 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
SCREAMING_SNAKE_CASE_ : int = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
"""simple docstring"""
def ... | 69 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -... | 69 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is... | 69 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : bool = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
... | 69 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE_ : List[str] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n a... | 69 | 1 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _snake_case ( UpperCA... | 69 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 50 ):
A__ = [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 +... | 69 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ):
A__ = HfArgumentParser(UpperCAmelCase_ )
A__ = parser.parse_args_into_dataclasses()[0]
A__ ... | 69 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Tuple = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
... | 69 | 1 |
"""simple docstring"""
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 ...t... | 69 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.pro... | 69 | 1 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCAmelCase_ : int ):
if number != int(UpperCAmelCase_ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the ... | 69 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.... | 69 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
SCREAMING_SNAKE_CASE_ : Optional[Any] = logging.get_logger(__name__)
SCREA... | 69 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
SCREAMING_SNAKE_CASE_ : Tuple = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
"""simple docstring"""
def __in... | 69 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _snake_case ( ):
A__ = {}
A__ = 2
while True:
A__ = factor_map.pop(UpperCAmelCase_ , UpperCAmelCase_ ... | 69 |
"""simple docstring"""
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
SCREAMING_SNAKE_CASE_... | 69 | 1 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUE... | 69 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class a ( _lowerCamelC... | 69 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
SCREAMING_SNAKE_CASE_ : str = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
SCREAMING_SNAKE_CASE_ ... | 69 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_a... | 69 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
SCREAMING_SNAKE_CASE_ : int = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _snake_case ( UpperCAmelCase_ : dict[int, list[int]] , ... | 69 |
"""simple docstring"""
from typing import Any
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: Any ):
"""simple docstring"""
A__ = data
A__ = None
... | 69 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_ : List[Any] = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig'... | 69 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch... | 69 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common i... | 69 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impo... | 69 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizati... | 69 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def _snake_case ( UpperCAmelCase_ : int="ro" , UpperCAmelCase_ : Optional[int]="en" , UpperCAmelCase_ : List[Any]="wmt16" , UpperCAmelCase_ : str=None ):
... | 69 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -... | 69 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE_ : Union[str... | 69 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
SCREAMING_SNAKE_CASE_ : Dict = 5_0_0_0_0
SCREAMING_SNAKE_CASE_ : Optional[Any] = 5_0_0_0
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAK... | 69 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : str = {
'funnel-transformer/small': 'https://huggingface.co/funnel-trans... | 69 | 1 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class a ( _lowerCamelCase ):
"""simpl... | 69 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL i... | 69 | 1 |
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