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 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformer... | 552 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def UpperCAmelCase ( _lowercase : str = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
de... | 552 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
class UpperCamelCase__ ( Generic[T] ):
'''simple docstring'''
def __init__( self, snake_case... | 706 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCAmelCase_ = """."""
if __name__ == "__main__":
UpperCAmelCase_ = os.path.join(REPO_PATH, """utils/docum... | 436 | 0 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCamelCase ( *SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
if not isinstance(__UpperCamelCase , __UpperCamelCase )... | 163 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 9 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor... | 249 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase_ ( UpperCamelCase_ = "AAPL" ):
_a : List[str] = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_a : Any = BeautifulSoup(requests.get(UpperCamelCase_ ).text , '''html.parser''' ... | 249 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRoberta... | 276 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from trans... | 276 | 1 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAM... | 706 | import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCamelCase_ : Dict = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] = {name: getattr(transformers, name... | 246 | 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 transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils im... | 95 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffuse... | 659 | 0 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_earl... | 706 |
import fire
from utils import calculate_rouge, save_json
def __lowerCamelCase ( A__ : Union[str, Any] , A__ : Optional[int] , A__ : Dict=None , **A__ : Dict ) -> str:
lowerCamelCase_ : Union[str, Any] = [x.strip() for x in open(A__ ).readlines()]
lowerCamelCase_ : ... | 171 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOut... | 558 | import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPT... | 558 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIE... | 370 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandin... | 370 | 1 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _lowerCAmelCase ( __magic_name__ : BertModel , __magic_name__ : str , __magic_name__ : str ) -> List[str]:
... | 92 |
def _A ( __snake_case :int , __snake_case :float , __snake_case :float ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def _A ( __snake_case :float , __snake_case :float , __snake_case :float ) -> float... | 693 | 0 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __snake_case ( SCREAMING_SNAKE_CASE: int ):
"""simple docstring"""
_lowerCAmelCase = prime_factors(SCREAMING_SNAKE_CASE ... | 711 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __snake_case ( SCREAMING_SNAKE_CASE: O... | 491 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolF... | 573 |
"""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_pipeli... | 573 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCAmelCase = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Sc... | 71 | 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 lowerCAmelCase_ ( enum.Enum ):
U... | 71 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowerCAmelCase: List[Any] ="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company th... | 607 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __snake_case ( ) -> None:
print("""Making key files...""" )
make_key_files("""rsa""" ... | 607 | 1 |
"""simple docstring"""
UpperCamelCase__ = '''Input must be a string of 8 numbers plus letter'''
UpperCamelCase__ = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def UpperCAmelCase ( snake_case : Optional[Any] ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAM... | 701 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCAmelCase ( ):
_lowerCAmelCase:Optional[int] = ArgumentParser(
description=(
... | 439 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def a__ ( _SCREAMING_... | 71 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase_ ( lowerCamelCase ):
a__ = ['''image_processor''', '''tokenizer''']
a__ = '''ChineseCLIPImageProcessor'''
a__ = ... | 0 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.... | 703 | """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_schedule_with_warmup, set_seed
from... | 668 | 0 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
... | 207 |
'''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... | 207 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impor... | 707 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str, lowerCamelCase : int )-> None:
lowerCamelCase__ : str =value
... | 625 | 0 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
__A : Optional[int] = 'src/transformers'
# Matches is_xxx_available()
__A : Union[str, Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
... | 575 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under g... | 575 | 1 |
'''simple docstring'''
def lowercase__ ( __lowercase : bytes ) -> str:
"""simple docstring"""
return "".join([hex(__lowercase )[2:].zfill(2 ).upper() for byte in list(__lowercase )] )
def lowercase__ ( __lowercase : str ) -> bytes:
... | 717 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_avai... | 434 | 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 import BatchFeatu... | 346 | '''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@requi... | 251 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIV... | 85 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : List[str] = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
... | 85 | 1 |
import argparse
SCREAMING_SNAKE_CASE :List[str] = '''docs/source/_static/js/custom.js'''
def _lowerCAmelCase ( lowerCAmelCase_ :str )->List[str]:
'''simple docstring'''
with open(lowerCAmelCase_ , encoding="utf-8" , newline="\n" ) as f:
s... | 283 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
# See all Donut ... | 678 | 0 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A = 5_0_0_0_0_0
A ,A = os.path.split(__file__)
A = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration... | 234 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 234 | 1 |
def UpperCAmelCase__( __UpperCAmelCase : str ):
return "".join(chr(ord(__UpperCAmelCase ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 576 | import math
import unittest
from transformers import BioGptConfig, 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 ModelTeste... | 576 | 1 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( UpperCamelCase__ : int ) -> bool:
lowerCamelCase : Dict = str(UpperCamelCase__ )
return len(UpperCamelCase__ ) == 9 and set(UpperCamelCase__ ) == set("""123456789""" )
... | 42 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImga... | 42 | 1 |
"""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 t... | 532 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_roformer": ["ROFORMER_PRET... | 532 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_m... | 76 |
import collections
import importlib.util
import os
import re
from pathlib import Path
__lowerCAmelCase : int = 'src/transformers'
# Matches is_xxx_available()
__lowerCAmelCase : Optional[int] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xx... | 76 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
... | 42 |
def A__ (snake_case : int ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : Optional[Any] = ["""note_seq"""]
def __init__( self , *lowerCAmelCase , **lowerCAmel... | 104 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/co... | 104 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase_ :
lowerCamelCase_ = 42
lowerCamelCase_ = None
lowerCamelCase_ = None
_lowerCamelCase ... | 6 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowerCamelCase = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 6 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCAmelCase ( ):
raise RuntimeError("CUDA out of memory." )
class _UpperCamelCase ... | 290 |
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_channel_dimension_format,
)... | 290 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCAmelCase_ ( ) -> Optional[Any]:
__lowercase : List[Any] = {
'''repo_name''': ['''test_repo1''', '''test_repo2'''... | 509 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 509 | 1 |
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 lowerCamelCase ( unittest.TestCase ):
@require_torch
def snake... | 249 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common... | 249 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self , ... | 406 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=5 ):
'''simple docstring'''
assert masked_input.count("<mask>" ) == 1
_... | 500 | 0 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """filename with blanks.cs... | 710 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( lowercase_ : List[str] , lowercase_ : Optional[int] ):
lowercase = int(lowercase_ )
assert noofclusters < len(lowercas... | 653 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : Union[str, Any] = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_t... | 53 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ):
UpperCamelCase :list[list[int]] = []
UpperCamelCase :list[int] = []
UpperCamelCase :List[str] = 0
UpperCamelCase ... | 658 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : int = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_AR... | 705 |
__magic_name__ : List[str] = tuple[float, float, float]
__magic_name__ : Optional[int] = tuple[float, float, float]
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> Vectorad:
"""simple docstring"""
UpperCamelC... | 410 | 0 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(lowerCamelCase_ , n - 1 , lowerCamelCase_ ) * a) % mod
else:
_lowercase ... | 89 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def __A ( a_ : Callable[[int | float], int | float] , a_ : int | float , a_ : int | float , a_ : int = 1_00 , )-> float:
'''simple docstring'''
SCREAMIN... | 698 | 0 |
"""simple docstring"""
import argparse
import os
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_task_guides.py
a : Optional[int] = '''src/t... | 705 |
"""simple docstring"""
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
... | 31 | 0 |
from __future__ import annotations
import math
def lowerCamelCase_ ( _lowercase ) -> bool:
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 no... | 520 |
'''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[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkp... | 640 | 0 |
"""simple docstring"""
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a_ = abspath(join(dirname(d... | 717 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Con... | 387 |
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 lowerCamelCas... | 387 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__SCREAMING_SNAKE_CASE : Optional[int] = 10
def snake_case_ ( lowercase__ : int , lowercase_... | 149 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attentio... | 149 | 1 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _UpperCamelCase ( UpperCamelCase__ ):
"""simple docstring... | 436 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = False ):
"""simple docstring""... | 436 | 1 |
"""simple docstring"""
from math import pi
def __lowercase ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 112 |
"""simple docstring"""
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.hugg... | 112 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase : List[str] ={
"configuration_speech_to_text": ["SPEECH_TO_TEXT_P... | 305 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( A__ , A__ ) -> list[tuple[int, int]]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = position
UpperCamelCase =... | 430 | 0 |
import numpy as np
def a_ ( _A , _A , _A , _A , _A ) -> Optional[Any]:
"""simple docstring"""
snake_case__ = int(np.ceil((x_end - xa) / h ) )
snake_case__ = np.zeros((n + 1,) )
snake_case__ = ... | 717 |
from collections.abc import Sequence
def a_ ( _A , _A ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(_A ) )
def a_ ( _A , _A ) -> float:
"""simple docstring"""
snake_case__ ... | 372 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 113 |
def lowerCAmelCase__ ( _a : str ):
snake_case_ : List[Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCAmelCase__ ( _a : str ):
snake_case_ ... | 568 | 0 |
"""simple docstring"""
import numpy as np
class _UpperCAmelCase :
def __init__( self : List[str] , _lowercase : Dict=None , _lowercase : str=None , _lowercase : str=None , _lowercase : List[str]=None , _lowercase : Opt... | 719 |
"""simple docstring"""
import unittest
from transformers import DonutProcessor
_lowercase : List[str] = 'naver-clova-ix/donut-base'
class _UpperCAmelCase ( unittest.TestCase ):
def a ( self : Union[str, Any] ):
__UpperCAmelCase ... | 397 | 0 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats... | 363 | """simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.js... | 363 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCAmelCase : List[str] ={"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
t... | 504 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hu... | 504 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Dict = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if ... | 23 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_... | 439 | 0 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from dat... | 706 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 254 | 0 |
"""simple docstring"""
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def snak... | 516 | """simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
... | 516 | 1 |
'''simple docstring'''
def UpperCAmelCase ( A : int , A : int ):
while second != 0:
SCREAMING_SNAKE_CASE : List[str] = first & second
first ^= second
SCREAMING_SNAKE_CASE : Any = c << ... | 464 |
'''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 lowerCamelCa... | 464 | 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 ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_S... | 539 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
... | 539 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =[
'''encoder.version''',
'''decoder.vers... | 712 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorSt... | 271 | 0 |
from __future__ import annotations
import math
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> bool:
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 eve... | 66 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCAmelCase = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It ... | 642 | 0 |
'''simple docstring'''
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 PaddingStrate... | 496 | '''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCamelCase( _A : Optional[Any] ):
'''simple doc... | 496 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _UpperCAmelCase ( nn.Module ... | 183 |
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = []
for data in source_data:
for i, el in enumerate(lowerCamelCase_ ):
if len(lowerCamelCase_ ) < i + 1:
data_lists.append([] )
data_lists[i]... | 183 | 1 |
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 .tokenization_albert ... | 311 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = len(SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : Optional[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each... | 311 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCAmelCase ( __a : Any ) -> Dict:
"""simple... | 14 | '''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when... | 494 | 0 |
from random import randint, random
def __lowerCamelCase ( A__ : int , A__ : int , A__ : int , A__ : bool = False , A__ : bool = False , A__ : int = 5 , ) -> list:
lowerCamelCase_ : int = [[-1] * number_of_cells] # Create a highway without any car
lowerCamelCase_ : ... | 721 |
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self : int ) ->None:
lowerCamelCase_ : dict[str, TrieNode] = {} # Mapping from char to TrieNode
lowerCamelCase_ : str = False
def _lowerCAmelCase ... | 171 | 0 |
'''simple docstring'''
def UpperCAmelCase ( A : bytes ):
return "".join([hex(A )[2:].zfill(2 ).upper() for byte in list(A )] )
def UpperCAmelCase ( A : str ):
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/r... | 527 |
'''simple docstring'''
from math import pow
def UpperCAmelCase ( A : int , A : int , A : int , A : int , A : int , ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a so... | 527 | 1 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class A ( _a ):
def __init__( self : Any , lowerCAmelC... | 377 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 377 | 1 |
def A__ ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int ) -> list[str]:
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doc... | 32 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def UpperCamelCase_( ) -> Any:
_lowercase... | 89 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_) -> str: # noqa: E741
while r - l > 1:
UpperCamelCase__ : List[str] = ... | 700 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ = "cpu" , lowerCamelCase_ = None) -> None:
UpperCamelCase__ : List[Any] ... | 6 | 0 |
UpperCamelCase_ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCamelCase_ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( UpperCamelCase: dict[int, list[int]] , UpperCamelCase: int , UpperCamelCase: list[bool] ):
"""simple ... | 611 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"andreasmadsen/efficient_mlm_m0.40": (
... | 611 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase (... | 458 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : Any = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}... | 458 | 1 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import i... | 89 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE : str = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available()... | 89 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 719 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[str] = {"vocab_file": "vocab.json"}
Uppe... | 48 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 48 | 1 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARC... | 612 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCAmelCase ( UpperCamelCase_: Dict ) -> Any:
'''simple docstring'''
_a = os.path.jo... | 612 | 1 |
'''simple docstring'''
from __future__ import annotations
_a : Optional[int] = list[tuple[int, int]]
_a : Union[str, Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0... | 689 |
'''simple docstring'''
import string
from math import logaa
def a_ ( UpperCamelCase_ , UpperCamelCase_ ):
A_ = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
A_ = document_without_punctuation.split(... | 452 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
g... | 185 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 185 | 1 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCamelCase__:
def __init__( self , __UpperCAmelCase ):
"""simple docstring"""
... | 566 |
'''simple docstring'''
from typing import Any
def A__ ( A_ ) -> list[Any]:
if not input_list:
return []
_lowercase = [input_list.count(A_ ) for value in input_list]
_lowercase = max(A_ ) # Gets the maximum count in the input list.
# Gets values of modes
... | 497 | 0 |
def _lowercase ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(U... | 708 |
def _lowercase ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert (
isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
if numb... | 181 | 0 |
'''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 load_image
... | 152 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqCo... | 41 | 0 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require... | 147 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCamelCase_ ( lowerCamelCase : ndarray ) -> float:
"""simple docstring"""
return np.dot(lowerCamelCase , lowerCamelCas... | 147 | 1 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_check... | 93 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditi... | 93 | 1 |
"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A_ (__a , __a , __a , __a ):
A_ = Big... | 705 |
"""simple docstring"""
import baseaa
def A_ (__a ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def A_ (__a ):
'''simple docstring'''
return baseaa.aaadecode(__a ).decode("utf-8" )
if __name__ == "__main__":
i... | 482 | 0 |
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__snake_case ):
A__ : Tuple = ['note_seq']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> str:
requires_backends(self , ['note_seq'] )
... | 559 | from __future__ import annotations
def lowerCAmelCase( __lowerCamelCase ):
if len(__lowerCamelCase ) == 0:
return array
__a , __a = min(__lowerCamelCase ), max(__lowerCamelCase )
# Compute the variables
__a = _max - _min + 1
__a , __a ... | 559 | 1 |
"""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
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ ... | 702 |
"""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__ = {
'''configuration_blenderbot''': [
... | 544 | 0 |
import argparse
from collections import defaultdict
def lowerCAmelCase_ ( __a , __a , __a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__: Union[str, Any] =F"""{file}_{class_name}_{test_name}"""
d... | 59 |
from typing import List
import numpy as np
def lowerCamelCase_ ( lowerCAmelCase__ : dict ) -> int:
'''simple docstring'''
A = {key: len(lowerCAmelCase__ ) for key, value in gen_kwargs.items() if isinstance(lowerCAmelCase__ , lowerCAmelCase__ )}
... | 106 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVeca... | 704 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case__ = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
snake_case__ ... | 372 | 0 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 1000 ):
'''simple docstring'''
_lowerCAmelCase = 1
_lowerCAmelCase = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE_ , digit... | 18 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 102 | 0 |
import argparse
import os
import re
lowercase__ = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
lowercase__ = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase__ = re.compile(R'''^\s*"([^"]+)":''')
... | 701 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import... | 420 | 0 |
def __lowerCAmelCase ( __snake_case ):
def merge(__snake_case , __snake_case ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yi... | 367 |
from __future__ import annotations
import os
from typing import Any
import requests
lowerCamelCase : Tuple = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowerCamelCase : int = BASE_URL + '... | 367 | 1 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
_snake_case : Optional[Any] = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\... | 421 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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():
impor... | 421 | 1 |
import argparse
from collections import defaultdict
import yaml
a__ = """docs/source/en/_toctree.yml"""
def _UpperCAmelCase ( a : str ):
snake_case__ = defaultdict(a )
for doc in model_doc:
counts[doc["local"]] += 1
snake_case__ = ... | 654 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : int = (IPNDMScheduler,)
_lowercase : int = (('''num_inference_steps''', 50... | 654 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : int = 50 ) -> int:
A_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start in range(row_length - tile_length + 1 )... | 703 | '''simple docstring'''
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_tokeniza... | 174 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms impo... | 541 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_UpperCamelCase : Tuple = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAIN... | 541 | 1 |
'''simple docstring'''
__snake_case : Dict = range(2, 20 + 1)
__snake_case : Any = [10**k for k in range(ks[-1] + 1)]
__snake_case : dict[int, dict[int, list[list[int]]]] = {}
def _lowercase ( lowerCamelCase__ : int, lowerCamelCase__ : Optional[Any], l... | 718 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet imp... | 691 | 0 |
from __future__ import annotations
from math import pow, sqrt
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any ) -> Union[str, Any]:
if (resistance, reactance, impedance).count(0 ) != 1:
... | 443 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _lowerCAmelCase ( A__ ):
lowercase__ = SwinConfig(image_size=192 )
if "base" in model_name:
lowercase__ = 6
... | 622 | 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 PreTrainedTokenizer
from ...utils import logging
UpperCamelCase ="▁"
UpperCamelCase ={"vocab_file": "spiece.model"}
Upp... | 543 |
'''simple docstring'''
import warnings
from .generation import TFGenerationMixin
class A ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
warnings.warn(
'''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
'''be... | 543 | 1 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _lowercase ( __A ,__A ,__A ):
'''simple docstring'''
__UpperCamelCase = np.array(UpperCAmelCase_ )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array is not ... | 601 |
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... | 648 | 0 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A_ ( _lowerCAmelCase : str = "laptop" ):
"""simple docstring"""
_lowerCamelCase : Dict = F'https://www.amazon.in/lapt... | 711 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 0 |
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