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 |
|---|---|---|---|---|
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 85 |
"""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.get_logge... | 617 | 0 |
from collections import defaultdict
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : List[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : List... | 683 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils impor... | 683 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''shi-labs/dinat-mini-... | 684 |
import unittest
from transformers import BertGenerationConfig, 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 ModelTesterM... | 493 | 0 |
import math
def __magic_name__ ( lowercase_ , lowercase_ ) -> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
... | 414 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a : Union[str, Any] = logging.get_logger(__name__)
def __magic_name__ ( lowercase_ ) -> ... | 414 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class A_ ( __UpperCAmelCase ):
'''simple docstring'''
... | 138 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | 567 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase :
def __init__( self , lowercase__):
__UpperCAmelCase : List[str] = str(id_)
__UpperCAmelCase : str = None
__UpperCAmelCase : Any = ... | 720 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCAmelCase = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file... | 675 | 0 |
"""simple docstring"""
import argparse
A_ = """docs/source/_static/js/custom.js"""
def lowercase ( lowerCAmelCase__ ):
with open(lowerCAmelCase__ ,encoding='''utf-8''' ,newline='''\n''' ) as f:
lowerCamelCase_ = f.readlines()
lowerCamelCase_ = 0
# First... | 29 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : List[str] = {
"asapp/sew-tiny-100k": "https://huggingface.co/as... | 567 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case_ ( __A , unittest.TestCase ):
... | 253 |
class snake_case_ :
'''simple docstring'''
def __init__( self : str ) -> Optional[int]:
lowerCamelCase_ : Optional[Any] = ""
lowerCamelCase_ : Dict = ""
lowerCamelCase_ : Union[str, Any] = []
... | 253 | 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 Accelerator, Di... | 35 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFM... | 351 | 0 |
UpperCAmelCase_ : str = [
"""Audio""",
"""Array2D""",
"""Array3D""",
"""Array4D""",
"""Array5D""",
"""ClassLabel""",
"""Features""",
"""Sequence""",
"""Value""",
"""Image""",
"""Translation""",
"""TranslationVariableLanguages""",
]
from .audio impo... | 706 |
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_dimension
from ...tokenization_ut... | 440 | 0 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.uti... | 513 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewToke... | 513 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
UpperCAmelCase_ : Tuple = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow(... | 712 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
UpperCAmelCase_ : Optional[int] = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention... | 367 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 267 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Dict = 1
__lowerCamelCase : str = 2
while i * i <= n:
__lowerCamelCase : int = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
... | 669 | 0 |
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_torch_available():
impo... | 392 |
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
@require_sentencepiece
@require_tokenizers
@require_torc... | 392 | 1 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"{price_plus_tax(100, 0.25) = }")
print(F"{price_plus_tax(125.50, 0.05) = }")
| 533 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_ba... | 533 | 1 |
import math
from collections.abc import Callable
def __snake_case ( _UpperCAmelCase : List[str], _UpperCAmelCase : str, _UpperCAmelCase : Tuple):
UpperCamelCase = xa
UpperCamelCase = xa
while True:
if x_n == x_na or function(_Upp... | 719 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils impo... | 350 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
__lowerCAmelCase : Tuple = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Che... | 58 |
"""simple docstring"""
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 589 | 0 |
"""simple docstring"""
import random
from typing import Any
def lowercase__ ( snake_case_ :list ):
for _ in range(len(snake_case_ ) ):
__UpperCAmelCase = random.randint(0 , len(snake_case_ ) - 1 )
__UpperCAmelCase = random.randint(0 , l... | 720 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_tes... | 397 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class lowercase__ :
def __init__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None) -> Tuple:
# Input as list
_lowerCamelCase : Any = list(poly_a or [0]... | 88 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation... | 88 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'facebook/convnextv2-tiny-1k-224': 'htt... | 371 |
import re
import string
import numpy as np
import datasets
_UpperCAmelCase = '\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'
_UpperCAmelCase = '\nArgs:\n predictions: List of p... | 371 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ ={
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig... | 415 | import os
import jsonlines
import numpy as np
from tqdm import tqdm
__magic_name__ =2048
__magic_name__ =4096
__magic_name__ =42
__magic_name__ =os.environ.pop('''PROCESS_TRAIN''', '''false''')
__magic_name__ ={'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': ... | 415 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a ( __UpperCamelCase ):
a_ : int = int(number**0.5 )
return number == sq * sq
def _a ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ... | 478 |
from heapq import heappop, heappush
import numpy as np
def _a ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
a_ , a_ : Any = grid.shape
a_ : Dict = [-1, 1, 0, 0]
a_ : List[Any] = [0, 0, -... | 478 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = list(SCREAMING_SNAKE_CASE )
lowercase__ = list(SCREAMING_SNAKE_CASE )
l... | 43 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 599 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_... | 244 |
"""simple docstring"""
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 CLIP... | 244 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"pro... | 393 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 488 | 0 |
import copy
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 ..auto import CONFIG_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
_UpperCa... | 583 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise ... | 583 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__UpperCAmelCase : Optional[Any] = get_logger(__name__)
class _snake_case ( enum.Enum ):
_A = 'all_checks'
_A =... | 241 |
from __future__ import annotations
def lowercase_ ( __snake_case : list[int] ) -> int:
'''simple docstring'''
if not nums:
return 0
snake_case__ :Union[str, Any] = nums[0]
snake_case__ :List[Any] = 0
for num i... | 241 | 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... | 700 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
A_ = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models f... | 384 | 0 |
from __future__ import annotations
snake_case : Optional[int] = []
def lowerCAmelCase_ ( _snake_case : list[list[int]] , _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
for i in range(len(_snake_case ) ):
if board[row][i] == 1:... | 124 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler... | 124 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 378 |
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import ... | 378 | 1 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinCo... | 49 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchau... | 519 | 0 |
def __lowerCAmelCase ( snake_case : list[int] ) -> list[list[int]]:
__lowerCamelCase: Optional[Any] = []
if len(snake_case ) == 1:
return [nums.copy()]
for _ in range(len(snake_case ) ):
__lowerCamelCase: Tuple = nums.pop(0 )
__lowerCamelCase: ... | 189 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 189 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowerCAmelCase_ = TypeVar('T')
lowerCAmelCase_ = Union[List[T], Tuple[T, ...]]
lowerCAmelCase_ = Union[T, List[T], Dict[str, T]]
lowerCAmelCase_ = Union[str, bytes, os.PathLike]
| 560 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : int
lowerCAmelCase : TreeNode | None = ... | 560 | 1 |
'''simple docstring'''
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
... | 703 |
'''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_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_... | 665 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 4 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : Optional[int]=2 ... | 304 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_... | 311 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase__: List[str] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]... | 311 | 1 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCamelCase_ (__A ):
__magic_... | 95 |
"""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 lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Any , SCREA... | 179 | 0 |
def _A( UpperCamelCase__ : Dict ) -> int:
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError('''only integers accepted as input''' )
else:
__lowercase = str(abs(__UpperCamelCase ... | 708 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging... | 362 | 0 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def A ( snake_case__ , snake_case__ , snake_case__=10_24 , snake_case__=10_24 , sna... | 196 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 0 |
from __future__ import annotations
from collections.abc import Callable
_lowerCamelCase : Optional[Any] = list[list[float | int]]
def A__ ( __A : Optional[Any] , __A : Optional[Any] ) ->Matrix:
__A =len(SCREAMING_SNAKE_CASE_ )
__A =[[0 for _ in r... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all Bio... | 516 | 0 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thr... | 289 | '''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ ) -> float:
"""simple docstring"""
__UpperCAmelCase : Any = 0.00
__UpperCAmelCase : Union[str, Any] = 0
for resistor... | 168 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
def __init__( self , *a , **a ... | 709 | """simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 645 | 0 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=snake_case__):
"""simple docstring"""
a__ : Tuple = ["note_seq"]
def __init__( self : Union[str, Any] , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : List[... | 593 |
def __lowerCAmelCase ( __lowerCamelCase : int ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMIN... | 104 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise... | 104 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 39 |
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` instead.'''
)
| 17 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import Tokeniz... | 702 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ... | 66 | 0 |
import os
def lowerCAmelCase__ ( ):
with open(os.path.dirname(_a ) + "/grid.txt" ) as f:
snake_case_ : Any = [] # noqa: E741
for _ in range(20 ):
l.append([int(_a ) for x in f.readline().split()] )
snake_case_ : Union[str, Any] ... | 568 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : str = {
'''vocab_file''': '''vocab.txt''',... | 568 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType... | 664 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
try:... | 664 | 1 |
from statistics import mean, stdev
def __a ( lowerCAmelCase_ : list ,lowerCAmelCase_ : int = 3 ) -> list:
'''simple docstring'''
UpperCAmelCase_= min(lowerCAmelCase_ )
UpperCAmelCase_= max(lowerCAmelCase_ )
# normalize data
return ... | 593 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int ) -> Dict:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase , int(b / 2 ) ) * actual_power(lowerCAmelCase , int(b / 2 ) )
else:
return a * actual_power(low... | 300 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, 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_... | 54 |
import operator
def lowerCAmelCase_ ( snake_case_,snake_case_ = False,snake_case_ = None ):
_A : str = operator.lt if reverse else operator.gt
_A : Optional[Any] = solution or []
if not arr:
return solution
_A : Dict = [arr.po... | 54 | 1 |
import numpy as np
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(snake_case__ )[0] == np.shape(snake_case__ )[1]
# Ensure proper... | 193 |
from bisect import bisect
from itertools import accumulate
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ = sorted(zip(snake_case__ , snake_case__ ) , ... | 193 | 1 |
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
SCREAMING_SNAKE_CASE_ ... | 719 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class a ( UpperCAmelCase ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'... | 467 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 4_0_0_0_0_0_0 ):
_A = []
_A , _A = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__snake_case )
_A , _A = b, a + b
return sum(__s... | 107 |
from __future__ import annotations
import requests
UpperCamelCase_ = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories c... | 625 | 0 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def A_ ( UpperCAmelCase__ , UpperCAmelCase__=False ) -> List[str]:
a : Tuple = OmegaConf.load(UpperCAmelCase__ )... | 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 unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCa... | 104 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__na... | 104 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diff... | 558 | """simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase (... | 558 | 1 |
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
a_ = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
a_ = re.compile(r"""([a-z\d])([A-Z])""")
a_ = re.compile(r"""(?<!_)_(?!_)""")
a_ = re.compile(r"""(_{2,})""")
a_ = r"""^\w+(\.\w+)*$"""
... | 437 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a_ = logging.get_logger(__name__)
class A_(SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self , *A , **A ... | 437 | 1 |
"""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_... | 712 |
"""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_warmup, set_seed
... | 282 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Any ):
"""simple docstri... | 419 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.uti... | 189 | 0 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def a_ ( lowerCamelCase : Tuple ):
lowerCAmelCase = [
'decoder.version',
'decoder.out... | 513 |
'''simple docstring'''
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__snake_case ="""scheduler_config.json"""
class UpperCAmelCase_ ( __lowercase... | 513 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCAmelCase__ ( __snake_case ) -> Any:
# A local function to see if a dot lands in the circle.
def is_in_circle(__snake_case , __snake_case ) -> bool:
_A... | 317 |
from __future__ import annotations
def UpperCAmelCase__ ( __snake_case , __snake_case ) -> bool:
_A = get_failure_array(__snake_case )
# 2) Step through text searching for pattern
_A , _A = 0, 0 # index into text, pattern
while i < len(__snake... | 317 | 1 |
from collections.abc import Sequence
from queue import Queue
class _lowerCamelCase:
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Li... | 711 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 354 | 0 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring''... | 7 |
"""simple docstring"""
class lowercase_ :
'''simple docstring'''
def __init__( self : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = None
_A = None
_A = graph... | 7 | 1 |
def snake_case__ ( _snake_case : list ):
"""simple docstring"""
if len(UpperCamelCase__ ) <= 1:
return [tuple(UpperCamelCase__ )]
UpperCamelCase__ = []
def generate(_snake_case : int , _snake_case : list ):
... | 720 | """simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
... | 304 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPE... | 593 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 446 | 0 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
imp... | 721 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from ... | 317 | 0 |
import os
def UpperCamelCase ( ) -> Dict:
'''simple docstring'''
lowercase_ :int = os.path.dirname(os.path.realpath(_a ) )
lowercase_ :Any = os.path.join(_a , '''triangle.txt''' )
with open(_a ) as f:... | 257 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
fr... | 257 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring""... | 408 |
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
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
... | 408 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
loggin... | 343 |
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_SIZE
from datas... | 343 | 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
_A = logging.get_logger(__name__)
_A = {"vocab_file": "spiece.model"}
... | 294 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase ( A_ ... | 294 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""t5-small""": """https://huggingface.co/t5... | 178 |
"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoMo... | 178 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : List[Any] = {
"""configuration_blenderbot"... | 706 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
'''simpl... | 265 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 34 |
from functools import reduce
_A = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504452445231617318564030987111217223831... | 290 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : int = {
'''configuration_whisper''': ['''WHISPER_... | 700 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelerato... | 357 | 0 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import... | 50 |
from __future__ import annotations
from typing import Any
class _A :
def __init__( self : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : float = 0 ):
"""simple docstring"""
__UpperCamelCase , __UpperCamelCa... | 269 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : str= datasets.load_iris()
_a : List[Any]= np.array(data["data"])
_a : Optional[Any]= np.array(data["target"])
_a : ... | 710 | """simple docstring"""
import math
def __UpperCAmelCase ( UpperCAmelCase_ : list , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
__snake_case : List[str] = len(UpperCAmelCase_ )
__snake_case : ... | 192 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not i... | 43 |
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 = {
"facebook/deit-base-distilled-patch16-22... | 475 | 0 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name_... | 701 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAm... | 667 | 0 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _A ( A ,A ,A ,A=5 ) -> List[str]:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("<mask... | 372 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _A ( A ) -> int:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Uni... | 372 | 1 |
def _a ( lowerCamelCase__ ) -> Optional[Any]:
lowerCamelCase_ : List[str] = []
if len(lowerCamelCase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowerCamelCase__ ) ):
lowerCamelCase_ : List[str] = nums.pop(0 )
... | 702 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCamelCase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
UpperCamelCase = None
def _a ( ) -> Tuple:
lowerCamelCase_ : Optional[int] = ar... | 144 | 0 |
"""simple docstring"""
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_schedu... | 104 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
_UpperCAmelCase : int = [8, 5, 9, 7]
_UpperCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_UpperCAmelCase : Union[str, Any] = [
[3, 2, 1, 4... | 72 | 0 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowercase__ :Optional[int] = 2_9979_2458
# Symbols
lowercase__ , lowercase__ , lowercase__ , lowercase__ :Dict = symbols("ct x y z")
def UpperCamelCase ( ... | 633 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[... | 633 | 1 |
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 import HeunDiscreteScheduler
fro... | 130 |
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 transformers import BitConfig... | 130 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import ... | 561 |
from collections import defaultdict
class a_ :
def __init__( self :Tuple , _lowercase :List[str] , _lowercase :List[Any]) -> List[Any]:
UpperCAmelCase_ = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
... | 561 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having mul... | 250 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A__: str = logging.get_logger(__name__)
A__: Union[str, Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/res... | 380 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowerCamelCase =typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowerCamelCase =typing.Union[np.floataa, int, float] # noqa: UP007
def SCREAMING_SNAKE_CASE_ ( U... | 462 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase ={
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertCon... | 462 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase : int = {
"configurat... | 261 |
'''simple docstring'''
class snake_case :
"""simple docstring"""
def __init__( self ) -> None:
"""simple docstring"""
snake_case__ : dict[str, TrieNode] = {} # Mapping from char to TrieNode
snake_case__ : int = False
d... | 261 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __UpperCAmelCase :
def __init__( self: str , UpperC... | 569 |
import math
from numpy import inf
from scipy.integrate import quad
def __lowerCamelCase ( snake_case__ ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(snake_case__ ,0 ,sna... | 569 | 1 |
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, prepare_im... | 387 |
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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBer... | 92 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
a_ = ["small", "medium", "large"]
a_ = "lm_head.decoder.weight"
a_ = "lm_head.weight"
def UpperCamelCase_ ( __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_C... | 92 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
UpperCAmelCase__ : List[str] = {
"google/umt5-small... | 48 |
import math
def UpperCamelCase ( _A, _A ):
"""simple docstring"""
__magic_name__ : Optional[int] = len(_A )
__magic_name__ : Tuple = int(math.floor(math.sqrt(_A ) ) )
__magic_name__ : Optional[int] = 0
w... | 324 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __snake_case ( unitt... | 398 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( ... | 398 | 1 |
"""simple docstring"""
from collections.abc import Callable
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : float = a
__lowercase : float = b
if function(__UpperCamelCase ) == 0: ... | 76 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> Tuple:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__UpperCamelCase , n - 1 , __UpperCamelCas... | 301 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
... | 386 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily... | 386 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_back... | 532 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
SCREAMING_SNAKE_CASE_ : Optional[Any] = namedtuple(
'''_TestCommandArgs'''... | 375 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCamelCase = logging.get_logger(__name__)
def _a ( lowerCamelCase__=None , lowerCamelCase__=None ) -> int:... | 710 |
import warnings
from .generation import TFGenerationMixin
class lowerCamelCase__ ( UpperCAmelCase ):
# warning at import time
warnings.warn(
'Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '
'be removed in T... | 144 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
a_ ... | 25 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 0 |
from ..utils import DummyObject, requires_backends
class lowercase__ (metaclass=lowercase__ ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : List[str] , *__a ... | 715 |
import colorsys
from PIL import Image # type: ignore
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
snake_case__ : List[Any] = x
snake_case__ : int = y
for step in range(Uppe... | 127 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class _lowerCamelCase :
'''simple docstring'''
def __init__( self : Tuple , _A : list[str] ) -> Optional[Any]:
__magic_name__ : list[dict] = []
self.adlist.append(... | 561 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase ( lowerCAmelCase : int ... | 561 | 1 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=... | 310 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def A_ ( _lowercase, _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Any = s.rsplit(_lowercase, ... | 310 | 1 |
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