code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
lowerCamelCase_ : Any = {}
lowerCamelCase_... | 364 |
import colorsys
from PIL import Image # type: ignore
def UpperCamelCase ( snake_case__ : float ,snake_case__ : float ,snake_case__ : int ):
'''simple docstring'''
__snake_case :List[Any] = x
__snake_case :str ... | 455 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_ful... | 719 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_conf... | 549 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE_ = False
class low... | 597 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2... | 597 | 1 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common imp... | 694 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)... | 694 | 1 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : Optional[int] ,_snake_case : Tuple ,_snake_case : Tuple ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(_snake_case ,n -... | 267 |
'''simple docstring'''
def lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1_000 - i ,-1_000 - i ,-1 ) ) for i in range(1_000 )]
SCREAMING_SNAKE_CASE__ = generate_large_matrix()
SCREAMING_SNAKE_CASE__ = (
... | 267 | 1 |
'''simple docstring'''
def a ( __a ) -> str:
'''simple docstring'''
UpperCamelCase__ :Any = []
UpperCamelCase__ :Dict = []
UpperCamelCase__ :List[str] = {
'''^''': 3,
'''*''': 2,
'''/'... | 280 |
'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
__snake_case = logging.getLogger()
def a ( __a ) ... | 280 | 1 |
import argparse
import datetime
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
lowerCamelCase__ : Tuple = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Satur... | 295 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.ut... | 295 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, 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... | 483 |
'''simple docstring'''
def __lowercase (_lowercase, _lowercase, _lowercase, _lowercase ) -> Union[str, Any]:
"""simple docstring"""
# Return True if there is node that has not iterated.
__lowerCamelCase : Optional[Any] = [False] * len(_lowercase... | 483 | 1 |
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():
from transforme... | 6 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .datac... | 386 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( a_ : int ):
__a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def SCREAMING_SNAKE_CASE ( a_ : int = 100 ):
__a = 1
__a = 2
for i ... | 712 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( a_ : int ):
__a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def SCREAMING_SNAKE_CASE ( a_ : int = 100 ):
__a = 1
__a = 2
for i ... | 490 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
raise OptionalDepe... | 604 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def lowercase (_lowerCAmelCase ):
def decorator(_lowerCAmelCase ):
__lowerCAmelCase = getattr(_lowerCAmelCase , """handle_key""" , [] )
handle += [key]
... | 465 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sql... | 139 |
import baseaa
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8" ) )
def lowerCamelCase__ ( _A ):
'''simple docstring'''
return baseaa.baadecode(_A ).decod... | 139 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
... | 369 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
_SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
snake_case_ = "m... | 369 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
lowercase_ = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINE... | 456 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'''
... | 456 | 1 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCamelCase = logging.getLogger(__name__)
lowerCamelCase ... | 474 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _UpperCamelCase :
'''simple docstring'''
_A : int
_A : TreeNode | None = None
_A : TreeNode | None ... | 578 | 0 |
'''simple docstring'''
import math
def lowercase__ ( __lowercase : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# N... | 709 |
'''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, prep... | 434 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a : str = logging.get_logger(__name__)
a : Optional[Any] = {"""vocab_file""": """vocab.json"... | 613 | def snake_case__ ( lowercase , lowercase ):
lowerCAmelCase_: list[list[str]] = [[] for _ in range(lowercase )]
lowerCAmelCase_: Optional[Any] = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1 or len(lowercase ... | 613 | 1 |
import os
from datetime import datetime as dt
from github import Github
__lowerCamelCase : Optional[Any] = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
... | 25 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between che... | 25 | 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/LICENSE-2.0
#
... | 314 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_t... | 314 | 1 |
_snake_case = {}
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not faile... | 611 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""... | 611 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimens... | 400 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCamelCase_( snake_case : List[str] , snake_case : str ):
'''simple docstring'''
snake_case_ = int(snake_case )
assert noofclusters < len(sn... | 400 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 525 | import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A_ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
_SCREAMING_SNAKE_CASE = [("""size""", ctypes.c_int), ("""visible""... | 525 | 1 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
lowerCamelCase = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
lowerCamelCase = """
Ar... | 82 |
'''simple docstring'''
a : Dict = range(2, 20 + 1)
a : Optional[int] = [10**k for k in range(ks[-1] + 1)]
a : dict[int, dict[int, list[list[int]]]] = {}
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : D... | 69 | 0 |
'''simple docstring'''
import math
class _lowerCamelCase :
'''simple docstring'''
def snake_case__ ( self , __lowercase , __lowercase ):
"""simple docstring"""
__A : List[str] = 0.0
__A : Optional[int] = 0.0
for i in range(len(__... | 540 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=loggi... | 540 | 1 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must b... | 89 |
from collections.abc import Callable
class __UpperCamelCase :
def __init__( self : Dict , lowerCAmelCase : Callable | None = None ):
'''simple docstring'''
UpperCAmelCase_ = []
# Stores indexes of each item for supporting updates and deletion.
U... | 162 | 0 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCAmelCase: Tuple = TypeVar("""KEY""")
UpperCAmelCase: Any = TypeVar("""VAL""")
@dataclass(frozen=_snake_case , slots... | 721 |
"""simple docstring"""
import math
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 600 | 0 |
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase = " " )-> Optional[Any]:
"""simple docstring"""
lowercase = []
lowercase = 0
for index, char in enumerate(UpperCAmelCase ):
if char == separator:
split_words... | 604 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
_SCREAMING_SNAKE_CASE : Optional[Any] = datasets.utils.logging.get_logger(__name... | 400 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class... | 718 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""s... | 62 | 0 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_h... | 163 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 163 | 1 |
# 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
#
# Unless required by ... | 711 | import random
from .binary_exp_mod import bin_exp_mod
def __lowerCamelCase ( __a : List[Any] , __a : Optional[Any]=1_000 ) -> Union[str, Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
_lowercase =n - 1
_lowercase =0
whil... | 594 | 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... | 553 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# 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_docst... | 387 | 0 |
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
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = '''▁'''
UpperCame... | 322 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, D... | 322 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@requ... | 179 |
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: float , UpperCamelCase__: float ):
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(UpperCamelCase__ ) * abs(UpperCamelCase__ )
if __name__ == "__main__":
import d... | 6 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
A: str = get_tests_dir("fixtures/test_sentencepiece_with... | 359 |
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( Upp... | 359 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Dict = ('''dense.weight''', '''attention.self.query''', '''att... | 696 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Tuple = prime_factors(lowercase__ )
if is_square_free(lowercase__ ):
return -1 if len(lowercase__ ) % 2 else 1
... | 696 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowe... | 306 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import... | 306 | 1 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _lowerCamelCase ( UpperCAmelCase_ : str, UpperCAmelCase_ : complex, UpperCAmelCase_ : str = "x", UpperCAmelCase_ : float = 10**-10, UpperCAme... | 104 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDepen... | 104 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INP... | 704 |
from __future__ import annotations
def lowerCamelCase_ ( _a : int | float | str , _a : int | float | str ):
'''simple docstring'''
if nth_term == "":
return [""]
UpperCAmelCase_ : Tuple = int(_a )
UpperCAmelCase_ : Opti... | 322 | 0 |
"""simple docstring"""
from math import factorial
def a ( __snake_case : int, __snake_case : int ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(lowercase__ ) // (f... | 608 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A ( lowercase__ : Optional[int] ) -> Optional[Any]:
UpperCamelCase__ :Union[str, Any] = {}
UpperCamelCase... | 45 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelin... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 588 | 0 |
"""simple docstring"""
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
a ... | 7 |
"""simple docstring"""
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
__SCREAMING_SNAKE_CASE = logging.get_logger(... | 357 | 0 |
import re
def _lowerCAmelCase (_lowerCAmelCase):
if len(re.findall("[ATCG]" , _lowerCAmelCase)) != len(_lowerCAmelCase):
raise ValueError("Invalid Strand")
return dna.translate(dna.maketrans("ATCG" , "TAGC"))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hu... | 504 | 0 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class _lowerCAmelCase :
'''simple docstring'''
def lowercase (self , UpperCAmelCase ) -> List[Any]:
... | 585 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__lowerCAmelCase = logging.getLogger(__name__)
def __SCREAMING_SNAKE_CASE ( ):
_snake_case = argparse.ArgumentParser(
description=""... | 585 | 1 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _A (lowerCAmelCase__ :Dict ) -> Any:
'''simple docstring'''
_a = FileLock(str(tmpdir / 'foo.lock' ) )
_a ... | 709 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.... | 532 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_... | 86 |
import math
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase , __lowerCAmelCase ):
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise Val... | 276 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
... | 717 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if i >... | 45 | 0 |
'''simple docstring'''
import re
def __A ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = re.compile(R"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(UpperCamelCase_ ... | 211 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__lowerCamelCase = Lock()
def a__ ( UpperCamelCase_ : str, UpperCamelCase_ : Any, UpperCamelCase_ :... | 467 | 0 |
"""simple docstring"""
import argparse
import os
# New Code #
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_war... | 63 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __magic_name__ ( _lowerCamelCase : int , _lowerCamelCase : Optional[Any]=False ):
__a : Dict = Omeg... | 63 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_t... | 489 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class UpperCamelCase__ ( __lowerCAmelCase ):
lowerCAmelCase__ : Any = "bert-generation"
def __init__( self : Union[str, Any] , lowerCamelCase : Optiona... | 489 | 1 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
Skip... | 702 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None ) -> None:
if start is None:
UpperCAmelCase__ : List[Any] = 0
if end is None:
Up... | 312 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=snake_case_ ):
__UpperCAmelCase : Union[str, Any] = ['''speech''']
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> ... | 178 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowerCAmelCase ( lowercase : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or nu... | 178 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Optional[Any] = ['''speech''']
def __init__( self, ... | 508 |
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers... | 508 | 1 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : int = 4_0_0_0_0_0_0 ):
'''simple docstring'''
lowerCAmelCase : int = [0, 1]
lowerCAmelCase : Union[str, Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[... | 645 |
"""simple docstring"""
from random import randint, random
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : ... | 645 | 1 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_co... | 719 |
"""simple docstring"""
from manim import *
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
def __UpperCAmelCase ( self : int ):
lowerCamelCase__ = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase__ = Rectangle(he... | 258 | 0 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Optional[int] = logging.get_logger(__name__)
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> Di... | 336 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 336 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 503 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _snake_case ( SCREAMING_SNAKE_CASE ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
_lowerCAmelCase : int ... | 503 | 1 |
def _UpperCAmelCase ( a : Any , a : Optional[Any] ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 654 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 200 ) -> int:
lowerCamelCase__ : Dict = [1, 2, 5, 10, 20, 50, 100, 200]
lowerCamelCase__ : Union[str, Any] = [0] * (pence + 1)
lowerCamelCase__ : List[str] = 1 # base case: 1 way to make 0 ... | 295 | 0 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_we... | 363 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_UpperCamelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Union[tf.Tensor, n... | 363 | 1 |
'''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_atte... | 48 |
'''simple docstring'''
import sys
from collections import defaultdict
class A :
def __init__( self : Any ):
"""simple docstring"""
lowerCAmelCase__ = []
def __SCREAMING_SNAKE_CASE ( self : List[str] , __magic_name__ : ... | 48 | 1 |
"""simple docstring"""
from __future__ import annotations
a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C''']... | 705 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
try... | 505 | 0 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {name: get... | 365 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datase... | 657 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
_UpperCamelCase : str = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
_UpperCamelCase : Any ... | 705 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case (A_ :int = 3 ):
'''simple docstring'''
if isinstance(A_ , A_ ):
raise TypeError('number of qubits must ... | 118 | 0 |
import os
import sys
import unittest
_lowerCamelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_du... | 352 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : Dict = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Any = typing.Union[np.floataa, int, float] # noqa: U... | 352 | 1 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def lowerCAmelCase ( ... | 704 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from t... | 612 | 0 |
"""simple docstring"""
a_ : int = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def UpperCAmelCase ( A__: float , A__: float , A__: float ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. Enter positive value.' )... | 594 | '''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, Attn... | 370 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 720 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__A : Tuple = TypeVar("T")
__A : Optional[Any] = TypeVar("U")
class __snake_case ( Generic... | 398 | 0 |
'''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 ...test_mode... | 676 |
'''simple docstring'''
from collections.abc import Sequence
def a_ ( __snake_case : Sequence[float] , __snake_case : float ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(__snake_case ) )
def a_ ( __snake_case : Se... | 676 | 1 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
A = logging.getLogger(__name__)
if is_torch_tpu_available(ch... | 708 |
'''simple docstring'''
A = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A = ['a', 'b', 'c', 'd', 'e']
def UpperCAmelCase ( UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Any , UpperCA... | 449 | 0 |
'''simple docstring'''
def _snake_case ( A ) -> float:
return 10 - x * x
def _snake_case ( A , A ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(A ) * equation(A ) >= 0:
ra... | 90 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return " ".join(
''.join(word[::-1] ) if len(_snake_case ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
... | 7 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _A (__a ) ->... | 176 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase_ : int , lowercase_ : List[str] , lowercase_ : L... | 176 | 1 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = ""
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise Exception("join() accepts onl... | 82 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = len(lowerCAmelCase__ )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCAmelCase__ ):
if arr[i] > arr[j]:
... | 82 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 701 |
lowerCamelCase : str = '''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowerCamelCase : Tuple = [{'''type''': '''code''', '''content''': INSTALL_... | 290 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 188 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logge... | 354 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __lowercase ( ):
'''simple docstring'''
print('''Making key files...''' )
make_key_files('''rsa''' , ... | 717 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.t... | 485 | 0 |
from math import pow
def UpperCAmelCase__ ( __magic_name__ : int , __magic_name__ : int , __magic_name__ : int , __magic_name__ : int , __magic_name__ : int , ):
'''simple docstring'''
if current_sum == needed_sum:
# If the sum of the powers is eq... | 348 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __magic_name__ ( ... | 348 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'''junnyu/roformer... | 420 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def __snake_case ( lowercase : Sequence[float] , lowercase : int , lowercase : int )... | 420 | 1 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
lowerCAmelCase_ = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
lowerCAmelCase_ ... | 338 | """simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from... | 338 | 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 AddedToken, PreTrainedTokenizer
from ...utils import logging
_A : Any = logging.get_logger(__name__)
_A ... | 716 | '''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class _lowercase :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
_SCREAMING_SNAKE_CASE : torch.Tensor # ... | 330 | 0 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_v... | 498 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ ... | 616 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__UpperCAmelCase = logging.get_logger(__name__)
@... | 503 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
'tokeniz... | 503 | 1 |
import os
def _a ( lowercase__ : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) as input_file:
SCREAMING_SNAKE_CASE__ : List[str] = [
[int(lowercase__ ) for elem... | 85 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE... | 85 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
A__ : Optional[Any] = 42
A__ : List[Any]... | 706 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
SCREAMING_SNAKE_CASE__ = r'''
Args:
input_ids (`jnp.ndarray` of shape `(batch_... | 52 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def __snake_case ( _lowercase = "AAPL" ):
"""simple docstring"""
UpperCamelCase = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
UpperCamelCase = BeautifulSoup(reque... | 34 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock imp... | 34 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers im... | 586 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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_tensor, random_attention... | 586 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"YituTech/conv-bert-base": "htt... | 631 |
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE__ = "examples/"
SCREAMING_SNAKE_CASE__ = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\... | 631 | 1 |
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 Accelerator
fr... | 73 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_... | 73 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available()... | 518 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConf... | 518 | 1 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase: int = logging.get_logger(__name__)
def lowerCamelCase__ ( _A , _A , _A ):
a ... | 702 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCAmelCase: Optional[Any] = logging.get_logger(__name__)
class a__( lowerCamelCase__ ):
def __init__( self : Union[str, An... | 195 | 0 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class _snake_case :
def __init__( self ) -> None:
'''simple docstring'''
snake_case_ = [2, 1, 2, -1]
snake_case_ = [1, 2, 3, 4]
... | 400 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart i... | 400 | 1 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import re... | 332 |
def a__ (__lowercase :Tuple ) -> Optional[Any]:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
_A : List[str] = len(__lowercase )
_A : Optional[Any] ... | 332 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE:
def __init__( self ,SCREAMING_SNAKE_CASE__ ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE... | 498 |
"""simple docstring"""
import sys
def __lowerCamelCase ( a_ : List[Any] ) -> str:
__SCREAMING_SNAKE_CASE :str = len(a_ )
__SCREAMING_SNAKE_CASE :List[Any] = [[0 for x in range(a_ )] for x in range(a_ )]
__SCREAMING_SNAKE_... | 498 | 1 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_a : List[str]= logging.get_logger(__name__)
_a : Tuple= {"vocab_file": "v... | 716 | """simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.t... | 192 | 0 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__UpperCAmelCase : Optional[int] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
... | 584 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Dict = {
'microsoft/wavlm-base': 'https://huggingface.co... | 584 | 1 |
"""simple docstring"""
def A_ ( ) -> Tuple:
a : Tuple = []
a : List[str] = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
a : Optional[int] = ''.join(UpperCAmelCas... | 509 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord ... | 509 | 1 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeniz... | 111 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
A : List[Any] = False
try:
A : ... | 128 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 275 | """simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("""--dump_path""", defau... | 275 | 1 |
from math import factorial
def _a ( lowerCAmelCase , lowerCAmelCase )-> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError('Please enter ... | 360 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE: List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE: Optional[Any] = {
'''google/u... | 360 | 1 |
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 accelerate import Ac... | 720 |
from __future__ import annotations
from collections.abc import Iterator
class UpperCamelCase:
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : int ) -> None:
'''simple docstring'''
__snake_case = value
... | 473 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested... | 109 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_mem... | 503 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertC... | 226 |
from __future__ import annotations
lowerCamelCase__ = 8.9_88e9 # units = N * m^s * C^-2
def A(__a: float , __a: float , __a: float , __a: float ):
lowerCAmelCase_ = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
rai... | 226 | 1 |
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