code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MA... | 251 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class _... | 251 | 1 |
"""simple docstring"""
from random import randint, random
def UpperCAmelCase ( a_, a_, a_, a_ = False, a_ = False, a_ = 5, ):
'''simple docstring'''
lowerCamelCase : str = [[-1] * number_of_cells] # Create a highway without any car
lower... | 205 |
"""simple docstring"""
def UpperCAmelCase ( ):
'''simple docstring'''
return 1
def UpperCAmelCase ( a_ ):
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCAmelCase ( a_ ):
'''simple docstring'''
... | 205 | 1 |
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 AcceleratorState
from accelerate.test_utils... | 14 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import ... | 286 | 0 |
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> int:
return len(set(snake_case_ ) ) == len(snake_case_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__... | 338 | 0 |
"""simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : str ):
lowercase_ : str = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
lowercase_ : Union[str, Any] = hex_num[0] == '-'... | 213 | """simple docstring"""
__SCREAMING_SNAKE_CASE =[
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloa... | 213 | 1 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase__ : Optional[int] = loggi... | 356 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, pars... | 301 | 0 |
from math import sqrt
def __magic_name__ ( A : int ):
'''simple docstring'''
a = 0
for i in range(1, int(sqrt(A ) + 1 ) ):
if n % i == 0 and i != sqrt(A ):
total += i + n // i
elif i == sqrt(A ):
total += i
return total - n
... | 107 | from torch import nn
class _lowercase ( nn.Module ):
def __init__( self : Any , snake_case : Dict , snake_case : Union[str, Any] ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCamelCase_ : List[Any] = class_size
Upper... | 175 | 0 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
... | 157 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[Any] = {
"""microsoft/unispeech-large-1500h-cv""": (
... | 157 | 1 |
'''simple docstring'''
import random
def snake_case_ ( __SCREAMING_SNAKE_CASE : list , __SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
lowercase_ , lowercase_ , lowercase_ : Optional[int] = [], [], []... | 93 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
if num < 0:
return False
_a = num
_a = 0
while num > 0:
_a = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main... | 63 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
... | 67 |
'''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
if is_torch_available():
... | 67 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 100_0000 ) ->int:
'''simple docstring'''
a : Dict = set(range(3 , _lowercase , 2 ) )
primes.add(2 )
for p in range(3 , _lowercase ... | 105 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
return n == n[::-1]
def _UpperCAmelCase ( snake_case = 1_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase ... | 82 | 0 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
... | 208 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 208 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 78 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.p... | 78 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : Tuple , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[str] , _UpperCamelCase : Tuple , _UpperCamelCase : Optio... | 31 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : list ) -> float:
'''simple docstring'''
UpperCamelCase__ = 0
while len(_UpperCamelCase ) > 1:
UpperCamelCase__ = 0
# Consider two files with minimum cost to be me... | 31 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__UpperCAmelCase = logging.get_logger(__name__)
class ... | 119 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCon... | 119 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerT... | 142 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __a ( _UpperCamelCase: Tuple ) -> Union[str, Any]:
"""simple docstring"""
_snake_case = os.path.j... | 142 | 1 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: Optional[int] = (path or []) + [u]
for v in graph[u]:
if visite... | 290 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any ... | 290 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Optional[Any] = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 361 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase_ ):
'''simple docstring'''
_a = ['torch', 'torchsde']
def __init__( self : Union[str, Any], *lowerCamelCase ... | 272 | 0 |
'''simple docstring'''
from math import ceil
def __magic_name__( lowerCamelCase = 1_0_0_1):
__lowerCAmelCase = 1
for i in range(1, int(ceil(n / 2.0))):
__lowerCAmelCase = 2 * i + 1
__lowerCAmelCase = 2 * i
__lowerCA... | 174 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__A = end or len(a_ )
for i in range(a_ , a_ ):
__A = i
__A = array[i]
while temp_index != start and temp_index_value < array[temp_inde... | 15 | 0 |
def lowerCamelCase__ ( UpperCamelCase__ : int ) -> int:
'''simple docstring'''
_snake_case = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCamelCase__ ( UpperCamelCase__ : ... | 352 |
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCamelCase__ ( UpperCamelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
_snake_case , _snake_case = img.shape[0], img.shape[1]
# converting each pixel's colo... | 295 | 0 |
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_docstrings.py
_lowerCamelCa... | 287 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name_... | 287 | 1 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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.or... | 170 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( lowerCAmelCase_ , lowerCAmelCas... | 170 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from... | 35 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
imp... | 179 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a_ ( _UpperCAmelCase : Option... | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Tuple = logging.get_logger(__name__)
A__ : Optional[int] = {}
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = '''llama'''
A__ = ['''p... | 0 | 1 |
import random
class __A:
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case ) -> tuple[list[int], list[int]]:
'''simple docstring'''
__a = [ord(_snake_case ) for i in text]
__a = []
__a = []
... | 6 | """simple docstring"""
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : int ) ... | 44 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ..... | 369 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
lowerCAmelCase... | 93 | 0 |
'''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
fr... | 162 |
"""simple docstring"""
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 ):
"""sim... | 115 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __lowercase ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase__ : List[Any] ... | 360 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 127 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__ : Union[str, Any] = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
"con... | 185 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAU... | 132 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[str] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
# See all Donut models at https://huggingfa... | 371 |
import requests
A_ : List[Any] = 'YOUR API KEY'
def UpperCamelCase (lowercase_: str , lowercase_: str = giphy_api_key ) -> list:
A__ : Dict = """+""".join(query.split() )
A__ : Optional[int] = f"""https://api.giphy.com/v1/gifs/search?q={format... | 141 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase ( s... | 16 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
lowerCAmelCase = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.c... | 295 | 0 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 61 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProc... | 61 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE = None ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = va... | 321 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE__ = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n ... | 321 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=UpperCAmelCase__ ):
lowerCamelCase : List[Any] =["flax"]
def __init__( self : Union[str, Any] , *a : Optional[int] , **a : Dict ):
"""simple do... | 237 | '''simple docstring'''
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... | 237 | 1 |
import argparse
import copy
def lowerCAmelCase_ ( __a ) -> Dict:
"""simple docstring"""
lowerCamelCase__: Dict ={}
with open(__a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
lowerCamelCase__: str =[]
_lis... | 10 |
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Optional[Any] , UpperCAmelCase_ : int) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Any =n
lowerCamelCase__: Tuple =[None] * self.n
lowerCamelCase__: ... | 10 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
... | 359 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_robert... | 88 | 0 |
lowerCAmelCase__ = [
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ... | 11 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
# See all ViT MAE models at https://huggingf... | 11 | 1 |
'''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_ut... | 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Tuple = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_avai... | 264 | 0 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_colla... | 82 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, requir... | 30 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __lowerCAmelCase ( snake_case__):
... | 365 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( lowerCAmelCase):
_a = (DDIMParallelScheduler,)
_a = (('''eta''', 0.0), ('''num_inference_steps''', 50))
def SCREAMING_SNAKE_CAS... | 158 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str , lowercase : Optional[int] ) -> Dict:
while b:
_a = b, a % b
return a
def _lowerCamelCase ( lowercase : Optional[Any] , lowercase : List[Any] ... | 63 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_mul... | 151 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Time... | 318 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vis... | 318 | 1 |
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,
... | 274 |
from sklearn.metrics import fa_score
import datasets
A : Any = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
A : List[Any] = '''
Args:
predictions (`li... | 274 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowerCAmelCase_ ( *__A ) -> List[Any]:
'''simple docstring'''
if not isinstance(__A, __A ):
UpperCAmelCase__ ... | 143 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 143 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from... | 37 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'token... | 84 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCamelCase_ = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wo... | 239 |
"""simple docstring"""
def __lowerCamelCase ( a_ : int = 10 , a_ : int = 22 ) -> int:
__SCREAMING_SNAKE_CASE :Optional[int] = range(1 , a_ )
__SCREAMING_SNAKE_CASE :List[Any] = range(1 , a_ )
... | 239 | 1 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
d... | 84 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__UpperCAmelCase = 1.054571817e-34 # unit of ℏ : J * s
__UpperCAmelCase = 3e8 # unit of c : m * s^-1
... | 84 | 1 |
def UpperCamelCase_ ( A__ : str , A__ : str ):
'''simple docstring'''
if not (isinstance(A__ , A__ ) and isinstance(A__ , A__ )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
lowerCAmelCase_ : Dict... | 361 |
'''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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTM... | 89 | 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, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_av... | 304 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 100 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : List[str] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert... | 371 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 331 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCame... | 319 |
import sys
def _A ( SCREAMING_SNAKE_CASE__ : List[str] ):
UpperCamelCase :Any = len(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :Any = [[0 for x in range(SCREAMING_SNAKE_CASE__ )] for x in range(SCREAMING_SNAKE_CASE__ )]
UpperCamelCase ... | 259 | 0 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require... | 122 |
def _UpperCamelCase ( UpperCamelCase_ : str ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 122 | 1 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
a__ = logging.get_logger(__name__)
class snake_case ( UpperCAmelCase__ ):
'''simple docstring'... | 317 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 0 |
'''simple docstring'''
from random import randint, random
def _snake_case ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : bool = False , _SCREAMING_SNAKE_CASE ... | 367 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __snake_case( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , A_ , A_ , A_ , A_ , A_=1 , A_=False , **A_ ) -> Optional[int]:
... | 187 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers i... | 248 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__snake_case : Tuple = logging.getLogger()
def ... | 248 | 1 |
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 AcceleratorState
from acc... | 269 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transfor... | 269 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wav... | 104 |
'''simple docstring'''
# Copyright 2022 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
#... | 104 | 1 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_a : List[str] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str,... | 359 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V a... | 46 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
A__ = [True] * (num + 1)
A__ = 2
while p * p <= num:
if primes[p]:
f... | 68 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nes... | 9 | 0 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
return x + 2
class __lo... | 371 |
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.numpy as jnp
from transf... | 279 | 0 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 268 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 1 |
def lowercase_ ( A__ ) -> int:
"""simple docstring"""
snake_case = abs(A__ )
snake_case = 0
while n > 0:
res += n % 10
n //= 10
return res
def lowercase_ ( A__ ) -> int:
"""simple docstr... | 137 |
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 lowerCamelCase ( A_ ):
... | 137 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_SCREAMING_SNAKE_CASE : Union[str, Any... | 85 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def UpperCame... | 85 | 1 |
"""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 _snake_case ( a__ ... | 64 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A__ :
'''simple docstring'''
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = None
SCREAMING_S... | 269 |
'''simple docstring'''
from math import factorial
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int, SCREAMING_SNAKE_CASE__ : int ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number,... | 125 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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 Conf... | 358 | import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFI... | 105 | 0 |
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 Image
from ..image_utils import load_i... | 210 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a : Optional[int] = logging.get_logger(__name__)
__a : Tuple = {
"""facebook/data2vec-text-base""": """https://... | 210 | 1 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_SCREAMING_SNAKE_CASE : int = None
try:
import msvcrt
except ImportError:
_SCREAMING_SNAKE_CASE : Tuple = None
try:
import fcntl
except Impo... | 355 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCAmelCase_ ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
SCREAMING_SNAKE_CASE__ = pars... | 218 | 0 |
def lowercase__ ( __snake_case : List[str] , __snake_case : Any ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowercase__ ( __snake_case : List[Any] , ... | 29 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerCo... | 29 | 1 |
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
'''simple docst... | 358 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_lowercase : int = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCAmelC... | 272 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> set[str]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Dict =set(__lowerCAmelCase ), [start]
while stack:
UpperCAmelCas... | 348 | import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] = (KDPMaDiscreteScheduler,)
__lowerCamelCase : ... | 348 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
l... | 371 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_CO... | 155 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __A ( unittest.TestCase ):
'''simple docstring'''
... | 211 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 211 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
Effi... | 356 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassific... | 308 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""torch""", """torchsde"""]
def __init__( self , *lowercase , ... | 96 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_E... | 281 | 0 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> List[str]:
... | 371 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] ... | 340 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowerCAmelCase__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__ = [ord(letter) for letter in strin... | 104 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaske... | 251 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
'''google/umt5-smal... | 206 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.ut... | 206 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRA... | 16 |
"""simple docstring"""
import os
def __UpperCAmelCase ( ) -> int:
with open(os.path.dirname(__lowerCamelCase ) + '''/p022_names.txt''' ) as file:
lowercase__ : List[Any] = str(file.readlines()[0] )
lowercase__ : Dict = names.replace(... | 16 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
lo... | 251 |
'''simple docstring'''
from ... import PretrainedConfig
lowerCAmelCase : List[str] = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = NEZHA_PRETRAINED_... | 251 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@requir... | 78 |
"""simple docstring"""
class A_ :
"""simple docstring"""
def __init__( self :List[Any] , lowercase_ :int ) -> None:
UpperCAmelCase = size
UpperCAmelCase = [0] * size
UpperCAmelCase ... | 78 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase_ ( a_ ,unittest.TestCase ):
'''simple docstring'''
a__ ... | 350 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def UpperCAmelCase ( _lowerCamelCase , ... | 256 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching b... | 230 |
from torch import nn
class a ( nn.Module ):
def __init__( self :Tuple ,__lowercase :Optional[int] ,__lowercase :int ):
super().__init__()
snake_case__ : Optional[Any] = class_size
snake_case__ : Dict = embed_size
... | 230 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ = 1000000 ):
UpperCAmelCase = limit + 1
UpperCAmelCase = [0] * limit
for first_term in range(1 , lowercase_ ):
for n in range(lowercase_ , lowercase_ , lowercase_ ... | 364 |
"""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 dif... | 181 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowercase_ ( _lowerCamelCase : np.ndarray):
return input_array.reshape((input_array.size, 1))
def lowercase_ ( _lowerCamelCas... | 87 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[int] =logging.get_logger(__name__)
_lowercase : Tuple ={
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-25... | 170 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 199 |
'''simple docstring'''
from timeit import timeit
def _A ( snake_case ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
_lowercase : Union[str, Any] = 0
while number:
number &= number - 1
result += 1
... | 199 | 1 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class lowercase_ :
def __init__( self , a=None , a=None ):
# Input as list
UpperCamelCase__ = list(poly_a or [0] )[:]
UpperCamelCase__ = list(p... | 80 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
def lowercase__ ( snake_case_ :Union[tf.Tensor, np.ndarray] ):
if isins... | 332 | 0 |
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_attenti... | 365 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_snake_case = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}
try:
if not ... | 300 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json... | 177 | """simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__A = False
class UpperCAmelCase (unittest.TestCase )... | 177 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : int = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.j... | 176 |
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
snake_case : Optional[int] = []
snake_case : Tuple = 1
while len(lowercase ) < 1E6:
constant.append(str(lowercase ) )
i += 1
snake_case : int = """""".join(lowercase )
... | 176 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Any = logging.get_logger(__name__)
UpperCAmelCase__ : List[Any] = {
'microsoft/wavlm-base': 'https:... | 25 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowercase_ ( _snake_case ):
... | 25 | 1 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase ... | 139 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCAmelCase = logging.get_logger(__name__)
def snake_case_ (__A : List[str] , __... | 139 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class a (pl.LightningModule ):
"""simple docstring"""
def __init__( self : Any , lowerCamelCase : Tuple ... | 123 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_snake_case : Union[str, Any] = 0
_snake_case : List[str] = [
[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, ... | 123 | 1 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_UpperCamelCase = pd.read_csv('''sample_data.csv''', header=None)
... | 358 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, r... | 16 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def a ( A__ : Any , A__ : Union[str, Any] , A__ : Dict ) -> Any... | 205 |
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 __lowerCAmelCase ... | 205 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 291 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase : list[list[int]] ):
'''simple docstring'''
lowerCamelCase_ = len(lowercase )
# We need to create solution object to save path.
lowerCamelCase_ = [[0 for... | 204 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 10_00 ):
'''simple docstring'''
lowerCamelCase_ = 2**power
lowerCamelCase_ = 0
while n:
lowerCamelCase_ , lowerCamelCase_ = r + n % 10, n // 10
return ... | 204 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase : List[str] = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
""... | 91 |
'''simple docstring'''
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def lowerCamelCase__ ( ):
'''simple docstring'''
print('''Truth Table of NOR Gate:''' ... | 91 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : float , _a : float , _a : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 value... | 34 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : ... | 34 | 1 |
"""simple docstring"""
def lowercase ( a__ : int ) -> int:
assert (
isinstance(a__ , a__ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
_UpperCa... | 369 | """simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils impor... | 54 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.