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 |
|---|---|---|---|---|
from timeit import timeit
def lowerCamelCase__ (__lowerCamelCase ):
if number < 0:
raise ValueError("the value of input must not be negative" )
_SCREAMING_SNAKE_CASE : str = 0
while number:
number &= number - 1
result += 1
return... | 363 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : typing.Counter[int] = Counter()
for base in range(1, max_perimeter + 1 ):
for perpendicular in range(__lower... | 325 | 0 |
from __future__ import annotations
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self , __lowerCamelCase = 0 ) -> int:
_SCREAMING_SNAKE_CASE : int = key
def UpperCamelCase_ ( self , __lower... | 364 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
# ===== initialization =====
_SCREAMING_SNAKE_CASE : List[Any] = Mock(... | 325 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
... | 365 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import ... | 325 | 0 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.te... | 366 |
from maths.prime_check import is_prime
def lowerCamelCase__ (__lowerCamelCase ):
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__l... | 325 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
def UpperCamelCase_ ( self , __lowerCamelCase ) -> List[Any]:
wit... | 367 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ (__lowerCamelCase ):
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class lowerCAmelCase__( __lowercase ):
'''simp... | 325 | 0 |
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than... | 368 |
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_common import Config... | 325 | 0 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCamelCase__ =TypeVar('T')
class lowerCAmelCase__( Generic[T] ):
'''simple docs... | 369 |
from math import isqrt, loga
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[Any] = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, __lowerCamelCase, __lo... | 325 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 370 |
from math import factorial
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Pleas... | 325 | 0 |
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = generate_pascal_triangle(__lowerCamelCase )
for row_idx in range(__lowerCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " )
... | 371 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowerCAmelCase__( __lowercase , __lowercase ... | 325 | 0 |
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 import TokenizerTesterMixin
@require_t... | 350 |
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Tuple = [0 for i in range(r + 1 )]
# nc0 = 1
_SCREAMING_SNAKE_CASE : Optional[int] = 1
for i in range(1, n + 1 ):
# to compute current row from pre... | 325 | 0 |
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase = " " ):
_SCREAMING_SNAKE_CASE : Dict = []
_SCREAMING_SNAKE_CASE : List[Any] = 0
for index, char in enumerate(__lowerCamelCase ):
if char == separator:
split_words.app... | 351 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
UpperCamelCase__ =logging.getLogger(__name__)
class lowerCAmelCase__( __lowercase ):
'''simple docstri... | 325 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
... | 352 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class lowerCAmelCase__( __lowercase ... | 325 | 0 |
from __future__ import annotations
def lowerCamelCase__ (__lowerCamelCase = 4 ):
_SCREAMING_SNAKE_CASE : List[str] = abs(__lowerCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__lowerCamelCase )] for y in range(__lowerCamelCase )]
def lower... | 353 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ... | 325 | 0 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, ... | 354 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase__ =logging.get_logger(__name__)
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
def __init__( self , *__lowerCamelCase ... | 325 | 0 |
def lowerCamelCase__ (__lowerCamelCase = 1000 ):
_SCREAMING_SNAKE_CASE : Any = -1
_SCREAMING_SNAKE_CASE : List[str] = 0
for a in range(1, n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_SCREAMING_SN... | 355 |
import numpy as np
import datasets
UpperCamelCase__ ='\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P.... | 325 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ ={
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_... | 356 |
from __future__ import annotations
import math
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__lowerCamelCase ) == 0:
... | 325 | 0 |
def lowerCamelCase__ (__lowerCamelCase = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3, n + 1 ) )
if __name__ == "__main__":
print(solution()) | 357 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase__ ='src/di... | 325 | 0 |
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_attention_mask
from ...... | 358 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 325 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : List[str] = [randint(-1000, 1000 ) for i in range(10 )]
_SCREAMING_SNAKE_CASE : Union[str... | 359 |
from timeit import timeit
def lowerCamelCase__ (__lowerCamelCase ):
if number < 0:
raise ValueError("the value of input must not be negative" )
_SCREAMING_SNAKE_CASE : str = 0
while number:
number &= number - 1
result += 1
return... | 325 | 0 |
def lowerCamelCase__ (__lowerCamelCase ):
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
raise ValueError("check_bouncy() accepts only integer arguments" )
_SCREAMING_SNAKE_CASE : List[Any] = str(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str... | 360 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ ={
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftFormerOnnxConfig',... | 325 | 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/LICENS... | 361 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCamelCase__ =np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (... | 325 | 0 |
UpperCamelCase__ ={
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : ... | 362 |
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 ..pipeline_utils import AudioPipelineOutp... | 325 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ ={
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
'convert_... | 363 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : typing.Counter[int] = Counter()
for base in range(1, max_perimeter + 1 ):
for perpendicular in range(__lower... | 325 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forwar... | 364 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
# ===== initialization =====
_SCREAMING_SNAKE_CASE : List[Any] = Mock(... | 325 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : typing.Counter[int] = Counter()
for base in range(1, max_perimeter + 1 ):
for perpendicular in range(__lower... | 365 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import ... | 325 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ =datasets.logging.get_logger(__name__)
UpperCamelCase__ ='\\n@InProceedings{moosavi2019minimum,\n author = { N... | 366 |
from maths.prime_check import is_prime
def lowerCamelCase__ (__lowerCamelCase ):
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__l... | 325 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/... | 367 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ (__lowerCamelCase ):
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class lowerCAmelCase__( __lowercase ):
'''simp... | 325 | 0 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 368 |
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_common import Config... | 325 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 369 |
from math import isqrt, loga
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[Any] = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, __lowerCamelCase, __lo... | 325 | 0 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say tha... | 370 |
from math import factorial
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Pleas... | 325 | 0 |
from __future__ import annotations
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
if len(__lowerCamelCase ) == 0:
return False
_SCREAMING_SNAKE_CASE : List[Any] = len(__lowerCamelCase ) // 2
if a_list[midpoint] == item:
return ... | 371 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowerCAmelCase__( __lowercase , __lowercase ... | 325 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class _a :
"""simple docstring"""
def __init__( self : Any , __UpperCamelCase : Union[str, Any]=None , __UpperCamelCase : str=None )->Tuple:
# Input ... | 326 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
__A : ... | 326 | 1 |
"""simple docstring"""
import requests
__A : int = "YOUR API KEY"
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = giphy_api_key ):
'''simple docstring'''
_UpperCAmelCase = '''+'''.jo... | 326 |
"""simple docstring"""
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... | 326 | 1 |
"""simple docstring"""
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while num > 0:
_UpperCAmelCase = num % 8
... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
rais... | 326 | 1 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _a :
"""simple docstring"""
UpperCamelCase__ = None
def lowercase__ ( self : int )->List[str]:
_UpperC... | 326 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A : Tuple = logging.getLogger()
@unittest.skip("... | 326 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(_SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int , ... | 326 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ... | 326 | 1 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_confi... | 326 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = int(number**0.5 )
return n... | 326 | 1 |
"""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 _a ( ... | 326 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 326 | 1 |
"""simple docstring"""
import string
import numpy
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _SCREAMING_SNAKE_CASE )
... | 326 |
"""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/LI... | 326 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowercase ( _SCREAMING_SNAKE_CASE : An... | 326 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Traine... | 326 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "... | 326 | 1 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def lowercase ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[... | 326 |
"""simple docstring"""
class _a :
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : list[int] )->None:
_UpperCAmelCase = len(__UpperCamelCase )
_UpperCAmelCase = [0] * len_array
... | 326 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int = 1000 ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = 1, 1
_UpperCAmelCase = 2
while True:
_UpperCAmelCase = 0
... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[int] = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 326 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class _a :
"""simple docstring"""
def __init__( self : List[str] , __UpperCamelCase : list[str] )->int:
_UpperCAmelCase = []
self.... | 326 |
"""simple docstring"""
__A : Tuple = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__A : U... | 326 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[str] ... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_funnel": ["FUNNEL_PRETRAI... | 326 | 1 |
"""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_squeezebert import SqueezeBertTokenizer
__A : str = log... | 326 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A : Union[str, Any] = "src/transformers"
# This is ... | 326 | 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/LI... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
_UpperCAmelCase = gray_code_... | 326 | 1 |
"""simple docstring"""
import datasets
__A : List[str] = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 326 |
"""simple docstring"""
import math
def lowercase ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 0 , _SCREAMING_SNAKE_CASE : int = 0 ):
'''simple docstring'''
_UpperCAmelCase = end or len(_SCREAMING_SNA... | 326 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "... | 326 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = np.shape(_SCREAMING_SNAKE_CASE )
if... | 326 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : Optional[int] = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioC... | 326 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( lowerCAmelCase , unittest.TestCase):
"""simple docstring... | 326 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)... | 326 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class _a ( logging.LoggerAdapter):
"""simple docstring"""
@staticmethod
def lowercase__ ( __UpperCamelCase : Optional[Any] )->List[Any]:
_UpperCAmelCase = ... | 326 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int = 100_0000 ):
'''simple docstring'''
_UpperCAmelCase = limit + 1
_UpperCAmelCase = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
... | 326 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
res... | 326 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[False] * (required_sum + 1)... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : List[Any] = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConf... | 326 | 1 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A : ... | 326 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
__A : ... | 326 | 1 |
"""simple docstring"""
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesC... | 326 |
"""simple docstring"""
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... | 326 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exce... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
rais... | 326 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : List[Any] = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConf... | 326 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A : Tuple = logging.getLogger()
@unittest.skip("... | 326 | 1 |
"""simple docstring"""
import argparse
import datetime
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday'''... | 326 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ... | 326 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exce... | 326 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = int(number**0.5 )
return n... | 326 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from t... | 326 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 326 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : list[str] | None = None ):
'''simple docstring'''
_UpperCAmelCase = word_bank or []
# create a t... | 326 |
"""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/LI... | 326 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tor... | 326 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Traine... | 326 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__A : List[str] = TypeVar("KEY")
__A : List[Any] = TypeVar("VAL")
@dataclass(frozen=lowerCAmelCase , slots=lower... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "... | 326 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTes... | 326 |
"""simple docstring"""
class _a :
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : list[int] )->None:
_UpperCAmelCase = len(__UpperCamelCase )
_UpperCAmelCase = [0] * len_array
... | 326 | 1 |
"""simple docstring"""
from __future__ import annotations
__A : Union[str, Any] = list[list[int]]
# assigning initial values to the grid
__A : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, ... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[int] = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 326 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 326 |
"""simple docstring"""
__A : Tuple = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__A : U... | 326 | 1 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_funnel": ["FUNNEL_PRETRAI... | 326 | 1 |
"""simple docstring"""
from datetime import datetime
import requests
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
_... | 326 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A : Union[str, Any] = "src/transformers"
# This is ... | 326 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers impor... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
_UpperCAmelCase = gray_code_... | 326 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int = 50 ):
'''simple docstring'''
_UpperCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile... | 326 |
"""simple docstring"""
import math
def lowercase ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 0 , _SCREAMING_SNAKE_CASE : int = 0 ):
'''simple docstring'''
_UpperCAmelCase = end or len(_SCREAMING_SNA... | 326 | 1 |
"""simple docstring"""
class _a ( lowerCAmelCase):
"""simple docstring"""
pass
class _a ( lowerCAmelCase):
"""simple docstring"""
pass
class _a :
"""simple docstring"""
def __init__( self : Tuple )->Optional[int]:
_Up... | 326 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = np.shape(_SCREAMING_SNAKE_CASE )
if... | 326 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : Dict = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPCon... | 326 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( lowerCAmelCase , unittest.TestCase):
"""simple docstring... | 326 | 1 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class _a :
"""simple docstring"""
def __init__( self : Union[str, Any] , __UpperCamelCase : list[tuple[float, float]] )->str:
_UpperCAme... | 326 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class _a ( logging.LoggerAdapter):
"""simple docstring"""
@staticmethod
def lowercase__ ( __UpperCamelCase : Optional[Any] )->List[Any]:
_UpperCAmelCase = ... | 326 | 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... | 326 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
res... | 326 | 1 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
__A : Optional[int] = ""
__A : Union[str, Any] = ""
__A : Any = ""
__A : List[str] = ""
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : List[Any] = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConf... | 326 | 1 |
"""simple docstring"""
import functools
def lowercase ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not all(isins... | 326 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
__A : ... | 326 | 1 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Traine... | 326 |
"""simple docstring"""
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... | 326 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__A : Optional[Any] = ""
__A : Tuple = ""
__A : Union[str, Any] = ""
__A : List[str] = 1 # (0 is vertical, 1 is horizon... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
rais... | 326 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A :... | 326 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A : Tuple = logging.getLogger()
@unittest.skip("... | 326 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.util... | 326 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ... | 326 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : str = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONF... | 326 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase = int(number**0.5 )
return n... | 326 | 1 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _a ( unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = JukeboxTokenizer
UpperCamelCase__ = {
"""artist""": """Z... | 326 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 326 | 1 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tra... | 326 |
"""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/LI... | 326 | 1 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 326 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Traine... | 326 | 1 |
"""simple docstring"""
__A : List[Any] = 256
# Modulus to hash a string
__A : int = 1000003
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase ... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "... | 326 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text im... | 326 |
"""simple docstring"""
class _a :
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : list[int] )->None:
_UpperCAmelCase = len(__UpperCamelCase )
_UpperCAmelCase = [0] * len_array
... | 326 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int = 100 ):
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = 0
_UpperCAmelCase = n + 1 # maximum limit
for a in range(2 , ... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[int] = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 326 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , ):
'''simple docstring'''
if (electron_conc, hole_conc, ... | 326 |
"""simple docstring"""
__A : Tuple = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
__A : U... | 326 | 1 |
"""simple docstring"""
class _a :
"""simple docstring"""
def __init__( self : Tuple , __UpperCamelCase : list[int] )->None:
_UpperCAmelCase = len(__UpperCamelCase )
_UpperCAmelCase = [0] * len_array
... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_funnel": ["FUNNEL_PRETRAI... | 326 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTest... | 326 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A : Union[str, Any] = "src/transformers"
# This is ... | 326 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaPro... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
_UpperCAmelCase = gray_code_... | 326 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : tuple[int, int] , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = position
_Up... | 326 |
"""simple docstring"""
import math
def lowercase ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 0 , _SCREAMING_SNAKE_CASE : int = 0 ):
'''simple docstring'''
_UpperCAmelCase = end or len(_SCREAMING_SNA... | 326 | 1 |
"""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 trans... | 326 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = np.shape(_SCREAMING_SNAKE_CASE )
if... | 326 | 1 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Optional[Any] = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/d... | 326 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( lowerCAmelCase , unittest.TestCase):
"""simple docstring... | 326 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[Any] = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
t... | 326 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class _a ( logging.LoggerAdapter):
"""simple docstring"""
@staticmethod
def lowercase__ ( __UpperCamelCase : Optional[Any] )->List[Any]:
_UpperCAmelCase = ... | 326 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
... | 326 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
res... | 326 | 1 |
"""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_convbert import ConvBertTokenizer
__A : Any = logging.g... | 326 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : List[Any] = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConf... | 326 | 1 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 326 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
__A : ... | 326 | 1 |
"""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 ... | 326 |
"""simple docstring"""
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... | 326 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLogg... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
rais... | 326 | 1 |
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