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 |
|---|---|---|---|---|
"""simple docstring"""
from math import pow
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ) -> Optional[int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed... | 370 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
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... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_... | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if no... | 56 | 0 |
"""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/LICENSE-2.0
... | 350 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .... | 351 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __a (tf.keras.optimizers.schedules.LearningRateSchedule):
... | 56 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import ... | 352 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase )... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at... | 353 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = arr.split(""",""" )
def _a ( self ) -> Any:
"... | 56 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
... | 354 |
"""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 import ModelTeste... | 56 | 0 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict ... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 56 | 0 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_availab... | 356 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a :List[Any] =... | 56 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def _lowercase ( __lowerCAmelCase ) -> Tuple:
@wraps(__lowerCAmelCase )
def _inner_fn(*__lowerCAmelCase , **__lowerCAmelCase ):
warnings.warn(
... | 357 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParse... | 56 | 0 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 / sqrt(2 ) ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] = tau * frequen... | 358 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a :Optional[int] = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> List[int]:
if isinstance(__lowerCAmelCase , np.nd... | 56 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxConfig... | 359 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 56 | 0 |
"""simple docstring"""
a :List[str] = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
a :Optional[Any] = frozenset(["prompt",... | 360 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a (metaclass=UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *_a , **_a ) ... | 56 | 0 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a :Dict = "sshleifer/bart-tiny-random"
... | 361 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [0] * len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Any = [1] * len(__lowerCAmelCase ... | 56 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __a (_A):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = ["""image_processor""", """tokenizer"""]
_SCREAM... | 362 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :Union[str, Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve... | 56 | 0 |
"""simple docstring"""
import random
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = a[left_index]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = left_index + 1
... | 363 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number | (1 << position)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number & ~(1 << position)
def _lowercase ( __lower... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase = 50 ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Dict = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(r... | 364 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _lowerca... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
if depth < 0:
raise ValueError("""Depth cannot be less... | 365 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
a :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0... | 56 | 0 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List,... | 366 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
SCREAMING_SNAKE_CASE__ : int = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCAmelCase ... | 367 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise Exception("""Rate of interest mus... | 56 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 368 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __a (UpperCamelCas... | 56 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_o... | 369 |
"""simple docstring"""
from math import loga
def _lowercase ( __lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""In... | 56 | 0 |
"""simple docstring"""
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
a :Any = logging.get_logger(__name__)
a :Optional[int] = {
"micro... | 370 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
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... | 56 | 0 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import dis... | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if no... | 56 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}
class __a (__SCREAMING_SNAKE... | 350 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :Unio... | 351 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __a (tf.keras.optimizers.schedules.LearningRateSchedule):
... | 56 | 0 |
"""simple docstring"""
from collections import deque
def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = len(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ : Any = deque()
SCREAMING... | 352 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase )... | 56 | 0 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
a :Optional[int] = pytest.mark.integration
@pytest.mark.parametrize("""path""... | 353 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = arr.split(""",""" )
def _a ( self ) -> Any:
"... | 56 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :int = logging.get_logger(__name__)
a :str = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __a (UpperCamelCase_):
... | 354 |
"""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 import ModelTeste... | 56 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __a (_lowerCamelCase):
'''simple docstring'''
def __init__( self ) -> Tuple:
"""simple docstring"""
self.test()
def _a ( self ) -... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 56 | 0 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = name
SCREAMING_SNAKE_CASE__ : List[str] ... | 356 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a :List[Any] =... | 56 | 0 |
"""simple docstring"""
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_i... | 357 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParse... | 56 | 0 |
"""simple docstring"""
import os
def _lowercase ( ) -> str:
SCREAMING_SNAKE_CASE__ : List[Any] = os.path.join(os.path.dirname(_lowercase ) , """num.txt""" )
with open(_lowercase ) as file_hand:
return str(sum(int(_lowercase ) for line ... | 358 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a :Optional[int] = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> List[int]:
if isinstance(__lowerCAmelCase , np.nd... | 56 | 0 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCas... | 359 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 56 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a :Dict = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftForme... | 360 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a (metaclass=UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *_a , **_a ) ... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0] * no_of_processes
SCREAMING_SNAKE_CASE__ : O... | 361 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [0] * len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Any = [1] * len(__lowerCAmelCase ... | 56 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Union[str, Any] = logging.get_logger(__name__)
a :Optional[int] = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json'''... | 362 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :Union[str, Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve... | 56 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a :List[str] = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise Opti... | 363 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number | (1 << position)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number & ~(1 << position)
def _lowercase ( __lower... | 56 | 0 |
"""simple docstring"""
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 Au... | 364 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _lowerca... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise Exception("""Rate of interest mus... | 365 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
a :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0... | 56 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any = [
'''encoder.version''',
''... | 366 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
SCREAMING_SNAKE_CASE__ : int = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
... | 56 | 0 |
"""simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __a :
'''simple docstring'''
def _a ( self , _a ) -> int:
"""simple docstring"""
... | 367 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise Exception("""Rate of interest mus... | 56 | 0 |
"""simple docstring"""
from math import ceil
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : int = list(range(0 , __UpperCamelCase ) )
SCREAMING_SNAKE_CASE__ : List[Any] = [item for sublist in li... | 368 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __a (UpperCamelCas... | 56 | 0 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import comput... | 369 |
"""simple docstring"""
from math import loga
def _lowercase ( __lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""In... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
return abs(a__ ) if a == 0 else greatest_common_divisor(b % a , a__ )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
while y:... | 370 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
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... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase = 6008_5147_5143 ) -> int:
try:
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(__a )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" ... | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if no... | 56 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusion... | 350 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 | 0 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 351 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __a (tf.keras.optimizers.schedules.LearningRateSchedule):
... | 56 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = ["image_processor", "tokenizer"]
_SCREAMING_SN... | 352 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase )... | 56 | 0 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
a :List[str] = logging.ge... | 353 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = arr.split(""",""" )
def _a ( self ) -> Any:
"... | 56 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
... | 354 |
"""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 import ModelTeste... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" ... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 56 | 0 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Generator[tuple[str, ...], None, None]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] ... | 356 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a :List[Any] =... | 56 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowercase ( __... | 357 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParse... | 56 | 0 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : int = math.sqrt(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = 1 / (sig... | 358 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a :Optional[int] = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> List[int]:
if isinstance(__lowerCAmelCase , np.nd... | 56 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __a (__a , unittest.TestCase):
... | 359 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 56 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggin... | 360 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a (metaclass=UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *_a , **_a ) ... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
a :Dict = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
a :Dict = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def _lowercase ( __lowerCAmelCase ) -> list[float]:
SCREAMING_SNAKE_CASE__ ... | 361 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [0] * len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Any = [1] * len(__lowerCAmelCase ... | 56 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _lowercase ( __lowerCAmelCase = "https://www.worldometers.info/coronavirus" ) -> dict:
SCREAMING_SNAKE_CASE__ : Optional[Any] = BeautifulSoup(requests.get(UpperCAmelCase__ ).text , ... | 362 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :Union[str, Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Any:
if n_term == "":
return []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
for temp in range(int(SCREAMING_SNAKE_CASE_ ) ):
series.append(F'''1/{temp + 1}''' if series e... | 363 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number | (1 << position)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number & ~(1 << position)
def _lowercase ( __lower... | 56 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.util... | 364 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _lowerca... | 56 | 0 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accele... | 365 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
a :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0... | 56 | 0 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
a :List[Any] = {
# 1536-bit
5: {
"prime": int(
... | 366 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
SCREAMING_SNAKE_CASE__ : int = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase = 1000 ) -> int:
SCREAMING_SNAKE_CASE__ : List[Any] = 3
SCREAMING_SNAKE_CASE__ : List[str] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15... | 367 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise Exception("""Rate of interest mus... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import float... | 368 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __a (UpperCamelCas... | 56 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__snake_case)
class __a (__snake_case):
'''simple docstring'''
# `task` is ... | 369 |
"""simple docstring"""
from math import loga
def _lowercase ( __lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""In... | 56 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a :Union[str, Any] = logging.getLogger(__name__)... | 370 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
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... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available,... | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if no... | 56 | 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, set_seed
from accelerat... | 350 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 | 0 |
import itertools
import os
import re
a :Any = re.compile(r"([A-Z]+)([A-Z][a-z])")
a :Optional[int] = re.compile(r"([a-z\d])([A-Z])")
a :str = re.compile(r"(?<!_)_(?!_)")
a :Dict = re.compile(r"(_{2,})")
a :int = r"^\w+(\.\w+)*$"
a :Dict = r"<>:/\|?*"
def _lowercase ... | 351 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __a (tf.keras.optimizers.schedules.LearningRateSchedule):
... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
a :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0... | 352 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase )... | 56 | 0 |
"""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 import ModelTeste... | 353 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = arr.split(""",""" )
def _a ( self ) -> Any:
"... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> list[int]: # This function is recursive
SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(__lowerCAmelCase )
# If the array contains only one element, we return it (it's ... | 354 |
"""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 import ModelTeste... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 56 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepar... | 356 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a :List[Any] =... | 56 | 0 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class ... | 357 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParse... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
return base * power(__lowerCAmelCase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("Raise base to the power of exponent using recursion...")
a :Optional... | 358 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a :Optional[int] = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> List[int]:
if isinstance(__lowerCAmelCase , np.nd... | 56 | 0 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __a :
'''simple docstring'''
def __init__( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str]... | 359 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 56 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
a :List[Any] = logging.get_logger(__name__)
... | 360 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a (metaclass=UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *_a , **_a ) ... | 56 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a :Optional[int] = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_emb... | 361 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [0] * len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Any = [1] * len(__lowerCAmelCase ... | 56 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import t... | 362 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :Union[str, Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve... | 56 | 0 |
"""simple docstring"""
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_su... | 363 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number | (1 << position)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number & ~(1 << position)
def _lowercase ( __lower... | 56 | 0 |
"""simple docstring"""
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = """openai/whisper-base"""
_SCREAMING_SNAKE_CASE :int = (
... | 364 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _lowerca... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : str = str(__lowerCAmelCase )
return n == n[::-1]
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> Optional[Any]:
... | 365 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
a :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0... | 56 | 0 |
"""simple docstring"""
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 366 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
SCREAMING_SNAKE_CASE__ : int = {
"""^""": 3,
"""*""": 2,
"""/""": 2,
... | 56 | 0 |
"""simple docstring"""
import re
import subprocess
import sys
a :str = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
a :int = (
subprocess.check_output(f'git diff --diff-filter=d --name-only {fork_point_sha}'.split()).decode("utf-8").split()
)
a :int = "|".j... | 367 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise Exception("""Rate of interest mus... | 56 | 0 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParse... | 368 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __a (UpperCamelCas... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> set:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE__ : List[str] = get_edges(__lowerCAmelCase )
# While there are still elements... | 369 |
"""simple docstring"""
from math import loga
def _lowercase ( __lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""In... | 56 | 0 |
"""simple docstring"""
from random import randint, random
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = False , __lowerCAmelCase = False , __lowerCAmelCase = 5 , ) -> list:
SCREAMING_SNAKE_CASE__ : Any ... | 370 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
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... | 56 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
fro... | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if no... | 56 | 0 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
a = "src/transformers"
# Matches is_xxx_available()
a = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
a = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a ... | 350 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 | 0 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
a :List[Any] = Mapping[str, np.ndarray]
a :str = Mapping[str, Any] # Is a nested dict.
a :Optional[Any] = 0.01
@data... | 351 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __a (tf.keras.optimizers.schedules.LearningRateSchedule):
... | 56 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_com... | 352 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase )... | 56 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize... | 353 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = arr.split(""",""" )
def _a ( self ) -> Any:
"... | 56 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import j... | 354 |
"""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 import ModelTeste... | 56 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__lowerCAmelCase , n - 1 , __lowerCAmelCase ) * a) % mod
... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 56 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils... | 356 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a :List[Any] =... | 56 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a :Optional[int] = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> List[int]:
if isinstance(__lowerCAmelCase , np.nd... | 357 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParse... | 56 | 0 |
"""simple docstring"""
from __future__ import annotations
class __a :
'''simple docstring'''
def __init__( self , _a=None ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = data
SCREAMING_SNAKE_CASE__ : O... | 358 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a :Optional[int] = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> List[int]:
if isinstance(__lowerCAmelCase , np.nd... | 56 | 0 |
"""simple docstring"""
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
... | 359 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 56 | 0 |
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