code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
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_torch, require_... | 304 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokeni... | 304 | 1 |
"""simple docstring"""
from string import ascii_lowercase, ascii_uppercase
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
if not sentence:
return ""
lowercase_ = dict(zip(__lowerCAmelCase , __lowerCAmelCase ) ... | 353 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : Union[str, Any] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
rai... | 313 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __A ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
UpperCamelCase__ = [True] * (num + 1)
UpperCamelCase__ = 2
... | 80 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
a__ : Tuple = {'UserAgent': UserAgent().random}
def _UpperCamelCase ( __A ) -> dict:
'''simple docstr... | 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : str = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not ... | 265 |
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
lowerCamelCase__ : List[str] = 0
while number:... | 265 | 1 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__snake_case = "sshleifer/bart-tiny-random"
__snake_ca... | 259 |
"""simple docstring"""
def _lowercase ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
... | 132 | 0 |
from __future__ import annotations
def __A ( _lowercase , _lowercase = None , _lowercase = None , _lowercase = False , ):
'''simple docstring'''
_A = cipher_alphabet or [chr(_lowercase ) for i in range(97 , 1_23 )]
# If the argument is... | 352 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: List[Any] , *__A: Union[str, An... | 75 | 0 |
import itertools
import math
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
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
# All prime... | 186 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class _lo... | 186 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversatio... | 43 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _... | 43 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 131 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 131 | 1 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class _a :
def __init__( self : Any, lowerCAmelCase__ : int=None, lowerCAmelCase__ : Union[str, Any]=None ) -> int:
'''simple docstring'''
... | 128 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` in... | 128 | 1 |
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 = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-robert... | 36 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
... | 163 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepie... | 370 |
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Dict:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase , int(b / 2 ) ) * actual_power(UpperCAmelCase , int(b / 2 ) )
else:
return a *... | 265 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CL... | 24 | """simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
... | 289 | 0 |
import os
import sys
import unittest
__lowerCamelCase : Tuple = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_m... | 140 |
__lowerCamelCase : Tuple = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def A_ ( _lowerCAmelCase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
UpperCamelCase ... | 140 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
Dist... | 326 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 10 | 0 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLi... | 115 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_:Optional[int] = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""... | 115 | 1 |
'''simple docstring'''
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = []
UpperCamelCase__ :int = 1
while len(__a ) < 1e6:
constant.append(str(__a ) )
i += 1
UpperCamelCase__ :U... | 97 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformer... | 97 | 1 |
from __future__ import annotations
def UpperCamelCase ( _A : list[int] )-> bool:
"""simple docstring"""
return len(set(_A ) ) == len(_A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 198 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLi... | 198 | 1 |
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 import patch
... | 193 | import operator
def lowercase_ ( _lowerCamelCase : list , _lowerCamelCase : bool = False , _lowerCamelCase : list | None = None):
lowercase__ : int = operator.lt if reverse else operator.gt
lowercase__ : str = solution or []
if ... | 87 | 0 |
import operator as op
UpperCamelCase_ = "scaler.pt"
UpperCamelCase_ = "pytorch_model"
UpperCamelCase_ = "random_states"
UpperCamelCase_ = "optimizer"
UpperCamelCase_ = "scheduler"
UpperCamelCase_ = "pytorch_model.bin"
UpperCamelCase_ = "... | 344 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class a_ ( _snake_case ):
... | 344 | 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
lowercase : Optional[int] = logging.get_logger(__name__)
lowerca... | 42 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__... | 341 | 0 |
import torch
from torch import nn
class lowerCamelCase__ ( nn.Module):
'''simple docstring'''
def __init__( self :Dict , a :Union[str, Any] , a :List[str] , a :Tuple , a :List[Any] , a :List[Any]=1 , a :Any=Fa... | 151 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowercase : List[str] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : ... | 151 | 1 |
from __future__ import annotations
def lowercase ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ) -> Dict:
if len(a_ ) < k or k < 0:
raise ValueError("""Invalid Input""" )
_snake_case : int = sum(array[:k] )
for i in range(len(a_ ) - ... | 317 | '''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
... | 145 | 0 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.ve... | 311 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : int = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/... | 311 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_prope... | 202 | def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1_000 ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = 1
__UpperCamelCase :Any = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
__UpperCamelCase :list[i... | 43 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from... | 365 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
i... | 145 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
__snake_case : Union[str, Any] =version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
__... | 129 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__snake_case : Any =2_0_4_8
__snake_case : Union[str, Any] =4_0_9_6
__snake_case : Optional[Any] =4_2
__snake_case : Dict =os.environ.pop('PROCESS_TRAIN', 'false')
__snake_case : List[str] ... | 129 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uclanlp/visualbert-vqa-pre': 'ht... | 144 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _A ( UpperCamelCase_ : str, UpperCamelCase_ : str, **UpperCamelCase_ : Optional[int]) -> Tuple:
'''simple docstring'''
__lowercase = AutoCon... | 144 | 1 |
'''simple docstring'''
A__ : Optional[int] ='''
# 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
'''
A__ : int =[{'''type''': '''code''', '''c... | 70 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thu... | 281 | 0 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def UpperCAmelCase ... | 205 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_A = logging.get_logger('transformers.models.speecht5')
def UpperCAmelCase ( a_, a_, a_ ):
... | 205 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models... | 19 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 19 | 1 |
def lowerCAmelCase_ ( UpperCamelCase_ = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCamelCase_ = set()
# Replace all the whitespace in our sentence
UpperCamelCase_ = input_str.replace(" " , "" )
for alpha in... | 328 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCAmelCase_ ( U... | 328 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers impor... | 215 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers... | 215 | 1 |
"""simple docstring"""
def UpperCamelCase ( _A = 1000 ):
"""simple docstring"""
__magic_name__ : Dict = 1, 1
__magic_name__ : Dict = 2
while True:
__magic_name__ : Tuple = 0
... | 367 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before t... | 138 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
),
... | 34 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hug... | 354 |
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 ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWit... | 295 | 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 lowerCAmelCase ( __Uppe... | 266 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case ( ctypes.Structure ):
'''simple docstring'''
A_ : List[str] = [("... | 266 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_blenderbot_small""": [
"""BLENDERBO... | 140 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before to... | 140 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase )
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstrin... | 108 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowercase__ = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
... | 151 | 0 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : int ):
"""simple docstring"""
lowerCamelCase__ : str =generate_pascal_triangle(__lowerCamelCase )
for row_idx in range(__lowerCamelCase ):
# Print left spaces
for _ in range(num_rows - row_id... | 272 |
"""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
_lowercase : Tuple = False
class __SCREAMING_SNAKE_CASE ... | 272 | 1 |
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def a__ ( __SCREAMING_SNAKE_CASE ) -> Any:
__lowerCAmelCase: O... | 217 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case ( __snake... | 217 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester... | 55 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common i... | 55 | 1 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClass... | 159 |
"""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_funnel import FunnelTokenizer
__UpperCamelCase = logging.ge... | 113 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torc... | 109 |
from collections import defaultdict
from math import ceil, sqrt
def __lowercase ( __lowerCAmelCase : int = 1_0_0_0_0_0_0 , __lowerCAmelCase : int = 1_0 ):
a__ = defaultdict(__lowerCAmelCase )
for outer_width in range(3 , (t_limit // 4) + 2... | 109 | 1 |
"""simple docstring"""
A: Tuple = 0 # The first color of the flag.
A: Any = 1 # The second color of the flag.
A: List[Any] = 2 # The third color of the flag.
A: Optional[int] = (red, white, blue)
def _snake_case ( UpperCamelCase : list ):
if no... | 109 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, tor... | 104 | 0 |
__SCREAMING_SNAKE_CASE = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import Strea... | 256 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase_ ( _A ):
'''simp... | 256 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.c... | 91 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def ... | 91 | 1 |
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
snake_case : str = "▁"
snake_case : int = {"vocab_file": "spiece.model"}
snake_case ... | 41 |
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
snake_case : Dict = datasets.utils.logging.get_logger(__name__)
@dataclass
class _snake_case ( data... | 41 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
clas... | 39 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.mode... | 92 | 0 |
"""simple docstring"""
import pytest
lowercase__ = '__dummy_dataset1__'
lowercase__ = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_UR... | 361 | """simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowercase__ = None
try:
import msvcrt
except ImportError:
lowercase__ = None
try:
import fcntl
except ImportError:
lowercase__ = None
# Backward compatibility
# --------------... | 203 | 0 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sen... | 308 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabl... | 308 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import loa... | 92 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distr... | 92 | 1 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if not isinstance(snake_case , snake_case ):
_lowerCAmelCase = F'Input value of [number={number}] must be an integer'
raise TypeError(snake_case )
if number < 0:
return False
... | 82 |
"""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, pre... | 106 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_A = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggingface.co... | 352 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCamelCase ( tf.keras.layers.Layer ):
'''simple docstri... | 166 | 0 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate... | 102 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_siz... | 176 | 0 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__snake_case =Lock()
def a_ ( lowerCamelCase : str , lowerCamelCase : List[str] , lowerCamelCase... | 351 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester... | 55 | 0 |
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,
SkipBatchSampler,
S... | 228 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 1 |
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.testing_utils import require_torch, require_vis... | 357 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelera... | 189 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : str = ['''image_processor''', '''tokenizer''']
__lowercase ... | 100 |
def _UpperCAmelCase (UpperCamelCase__ : str , UpperCamelCase__ : bool = False ):
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_A : Optional[Any] = f"Expected string as input, found {type(UpperCamelCase__ )}"
... | 11 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_UpperCamelC... | 310 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueE... | 310 | 1 |
_A = 65_521
def lowerCamelCase__ ( a__ : str ) -> int:
UpperCamelCase_ = 1
UpperCamelCase_ = 0
for plain_chr in plain_text:
UpperCamelCase_ = (a + ord(a__ )) % MOD_ADLER
UpperCamelCase_ = ... | 122 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase__ ( ) -> tuple[list[int], int]:
UpperCamelCase_ = [randint(-1000 , 1000 ) for i in range(10 )]
UpperCamelCase_ = ra... | 122 | 1 |
from __future__ import annotations
import math
lowercase : List[Any] = """2020.9.26"""
lowercase : List[Any] = """xcodz-dot, cclaus, dhruvmanila"""
def A_ ( A__ , A__ , A__ , A__ , A__ ) -> tuple[float, float]:
if not all(isinstan... | 355 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase : str = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncod... | 225 | 0 |
"""simple docstring"""
import numpy
class UpperCamelCase_ :
def __init__( self : Optional[int] , lowerCAmelCase_ : numpy.ndarray , lowerCAmelCase_ : numpy.ndarray ) -> None:
UpperCAmelCase_ : Any = input_array
# Random initial weights are... | 268 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, log... | 268 | 1 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __A ( a_ :Dict) -> Dict:
__a : Any = FileLock(str(tmpdir / '''foo.lock'''))
__a : Any = FileLock(str(tmpdir / '... | 188 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A = TypeVar('''T''')
class __lowercase ( Generic[T] ):
'''simple docstring'''
__low... | 188 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : Tuple = ["image_processor", "tokenizer"]
lowerCamelCase__ : Tuple = "ChineseCLIPImageProces... | 77 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCamelCase_ (UpperCamelCase__ : Optional[Any] , Uppe... | 263 | 0 |
import heapq
import sys
import numpy as np
snake_case = tuple[int, int]
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[Any] ):
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Optional[int] = set()
... | 319 |
from math import sqrt
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for i in range(1 , int(sqrt(lowercase ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase ):
total += i + n // i
elif i ==... | 319 | 1 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _A ( UpperCamelCase_ : Dict, UpperCamelCase_ : Dict=(), UpperCamelCase_ : ... | 17 |
from __future__ import annotations
def _lowerCamelCase( lowercase__ , lowercase__ ) -> Any:
'''simple docstring'''
if len(lowercase__ ) <= 1 or n <= 1:
return
insert_next(lowercase__ , n - 1 )
rec_insertion_sort(lowercase__ , n - 1 )
def _lowerCamelCase( lo... | 295 | 0 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from... | 14 |
# Copyright 2021 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
#
# U... | 14 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
),
# See al... | 177 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin impor... | 216 | 0 |
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 impor... | 7 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __snake_case ... | 7 | 1 |
import inspect
import re
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_docstrings.py
lowerCamelCase : str = "src/transformers"
# This is t... | 204 |
from typing import Any
def lowerCAmelCase_ ( __a , __a , __a , __a , __a , ) -> list:
"""simple docstring"""
_validation(
__a , __a , __a , __a , __a , )
# Creates data structures and fill initial step
lowerCamelCase__: dict ={}... | 10 | 0 |
"""simple docstring"""
from math import factorial
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 20 )-> int:
_lowerCamelCase = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
_lowerCamelCase = n // 2
return i... | 80 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
A_ : List[Any] =argparse.ArgumentParser()
parser.add_argument(
"""--checkpoi... | 80 | 1 |
'''simple docstring'''
import functools
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if not isinstance(UpperCamelCase , UpperCamelCase ) or not all(isinstance(UpperCamelCase , UpperCamelCase ) for day in days ... | 37 | import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied f... | 65 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from tra... | 101 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
... | 101 | 1 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
__lowerCAmelCase : List[str] ="\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
__lo... | 197 |
"""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
if is_... | 238 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switc... | 158 |
# 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 diffusers.configuration_utils import Con... | 158 | 1 |
"""simple docstring"""
from manim import *
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __UpperCAmelCase ( self ):
__a = Rectangle(height=0.5 , width=0.5 )
__a = Rectangle(height... | 45 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tra... | 95 | 0 |
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 TokenizerTesterMixin
@require_tokenizers
class... | 273 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _SCREAMING_SNAKE_CASE ( __SCREAMIN... | 273 | 1 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_see... | 148 |
"""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 = "src/transformers"
# This is to make sure the trans... | 148 | 1 |
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a__ ( _... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase = logging.get_... | 67 | 0 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
def __lowerCAmelCase ( UpperCamelCase__ ) -> Any:
__lowe... | 67 | '''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAmelCase =log... | 67 | 1 |
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> bool:
'''simple docstring'''
lowerCAmelCase : Any = first_str.lower().strip()
lowerCAmelCase : List[str] = second_str.lower().strip()
# Rem... | 323 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 323 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_UpperCamelCase = TypeVar('''KEY''')
_UpperCamelCase = TypeVar('''VAL''')
@dataclass(frozen=a , slots=a )
class _lowerCamelCase ( Generic[KEY, VAL... | 326 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCAmelCase__( lowercase : Dict , lowercase : bool = True , lowercase : float = math.inf , lowercase : float = -math.inf , lowercase : float = math.in... | 326 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 212 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase ) -> str:
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was passed t... | 212 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'R... | 180 | import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case ( snake_case__ :int , snake_case__ :List[str] , snake_case__ :Union[str, Any]) -> str:
... | 180 | 1 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def lowerCamelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : List[str] , _UpperCamelCase : Optional[int] , _UpperCamelCase : str ) -> str:
'''simple docstring'''
... | 368 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCamelCase__ ( A ):
"""simple docstring"""
def _... | 320 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
... | 117 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def _a ( lowerCamelCase: np.ndarray , lowerCamelCase: np.ndarray , lowerCamelCase: np.ndarray , lowerCamelCase: int , lowerCamelCase: int ) -> np.ndarray:
... | 117 | 1 |
"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowerCAmelCase : Tuple = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname i... | 298 |
"""simple docstring"""
import numpy as np
import datasets
_lowerCAmelCase : Optional[int] = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the... | 298 | 1 |
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
... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
UpperCAmelCase =4_2
UpperCAmelCase ... | 355 |
'''simple docstring'''
from string import ascii_uppercase
lowercase ={str(ord(c) - 55): c for c in ascii_uppercase}
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
'''simple docstring'''
if isinstance(__lowerCamelCase , __lo... | 242 | 0 |
import copy
import re
class __snake_case :
lowerCAmelCase_ = "hp"
lowerCAmelCase_ = {}
lowerCAmelCase_ = None
@classmethod
def __a ( cls : Tuple , _lowercase : int , _lowercase : O... | 219 | def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
SCREAMING_SNAKE_CASE__ = str(bin... | 219 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __lowerCamelCase ( _lowercase ) -> ... | 367 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : ... | 338 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'''hustvl/yolos-small''': '''https://hug... | 337 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , SCREAMING_SNAKE_CASE__ ):
super... | 337 | 1 |
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
if num < 0:
return False
__A = num
__A = 0
while num > 0:
__A = rev_num * 1_0 + (num % 1_0)
num //= 1_0
return num_copy == rev_num
i... | 124 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...ut... | 124 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class A_ :
def __init__( self : int , snake_case_ : str ):
_UpperCAmelCase = data
_UpperCAmelCase = [0X67_45_23_01, 0... | 22 |
from __future__ import annotations
from math import pi
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and on... | 114 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils imp... | 289 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
lowercase__ : Any = TypeVar("""_T""")
class UpperCamelCase__ ( Generic[_T] ):
"""simple docstring"""
def __init__( self ... | 289 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Any = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW mod... | 118 | def a__ ( __UpperCamelCase ):
if not head:
return True
# split the list to two parts
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = head.next, head
while fast and fast.next:
SCREAMING_SNAKE_CASE_ = fast.next.next
SCREAM... | 118 | 1 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :float , snake_case_ :float ):
return round(float(moles / volume ) * nfactor )
def lowercase__ ( snake_case_ :float , snake_case_ :float , snake_case_ :float ):
return round(float(... | 370 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _Up... | 86 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
'''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARC... | 322 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils imp... | 300 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (__lowercase ):
lowerCamelCase__ = '''Speech2TextFeatureExtractor'''
lowerCamelCase__ = '''Speech2TextTokenizer'''
def __init__( self ... | 358 |
import re
from filelock import FileLock
try:
import nltk
lowerCamelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCamelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def A(__a: str ):
... | 22 | 0 |
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