code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_c... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 1 | 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() and is_transformers_version('>=', '4.25.0')):
r... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 1 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import H... | 370 |
"""simple docstring"""
__UpperCAmelCase = 2_56
# Modulus to hash a string
__UpperCAmelCase = 1_00_00_03
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple ... | 1 | 0 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iter... | 371 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 1 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from trans... | 350 |
"""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_... | 1 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
return [
{"col_1": 3, "col_2": "a... | 351 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[... | 1 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__UpperCAmelCase = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}... | 352 |
"""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/lice... | 1 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}... | 353 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCA... | 1 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _SCREAMING_SNAKE_CASE :
UpperCAmelCase_ :Optional[Union[str, Path]] = None
UpperCAmelCase_ :bool = False... | 354 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : s... | 1 | 0 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
__UpperCAmelCase = TypeVar('T')
__UpperCAmelCase = Union[List[T], Tuple[T, ...]]
__UpperCAmelCase = Union[T, List[T], Dict[str, T]]
__UpperCAmelCase = Union[str, bytes, os.PathLike... | 355 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.ber... | 1 | 0 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
... | 356 |
"""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 im... | 1 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
'configuration_electra':... | 357 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from s... | 1 | 0 |
__UpperCAmelCase = 'Tobias Carryer'
from time import time
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A , __A , __A=int(time() ) ) -> Union[str, Any]: # noqa: B008
lowerCAmelCase_ :Tuple = multiplier
l... | 358 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
... | 1 | 0 |
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def _snake_case ( lowercase__ : int = 1_0_0 )... | 359 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 1 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : int ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = [[0 for _ in range(lowercase__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCAmelCase_ :List[str] ... | 360 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _snake_case ( lowercase__ : str = "laptop" ) -> DataFrame:
'''simple docstring'''
lowerCAmelCase_ :Dict ... | 1 | 0 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
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 Image... | 361 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import flo... | 1 | 0 |
"""simple docstring"""
import os
import string
import sys
__UpperCAmelCase = 1 << 8
__UpperCAmelCase = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'lef... | 362 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line ... | 1 | 0 |
"""simple docstring"""
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
f... | 363 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 1 | 0 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""]... | 364 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + ... | 1 | 0 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_... | 365 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextMode... | 1 | 0 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 366 |
"""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 ...model... | 1 | 0 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class _SCREAMING_SNAKE_CASE :
def __init__( self ) -> str:
lowerCAmelCase_ :Optional[int] = [2, 1, 2, -1]
lowerCAmelCase_ :List[str] = [1, 2, 3, 4]
de... | 367 |
"""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
__UpperCA... | 1 | 0 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _SCREAMING_SNAKE_CASE ( pl.LightningModule ):
def __init__( self , ... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 1 | 0 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ : int ) -> list:
'''simple docstring'''
lowerCAmelCase_ :Any = [True] * n
lowerCAmelCase_ :Any = False
lowerCAmelCase_ :Dict = False
low... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 1 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , **__A ) -> None:
... | 370 |
"""simple docstring"""
__UpperCAmelCase = 2_56
# Modulus to hash a string
__UpperCAmelCase = 1_00_00_03
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple ... | 1 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class _SCREAMING_SNAKE_CASE :
def __init__( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = {}
def _... | 371 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 1 | 0 |
"""simple docstring"""
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.testi... | 350 |
"""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_... | 1 | 0 |
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
__UpperCAmelCase = ... | 351 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[... | 1 | 0 |
"""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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def ... | 352 |
"""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/lice... | 1 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 353 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCA... | 1 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple = len(lowercase__ )
lowerCAmelCase_ :Dict = len(lowercase__ )
lowerCAmelCase_ :Opti... | 354 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : s... | 1 | 0 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__UpperCAmelCase = '%20'.join(argv[1:]) if len(argv) > 1 else quote(st... | 355 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.ber... | 1 | 0 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__UpperCAmelCase = pytest.mark.integration
@pytest.mark.parametrize("""path""" ... | 356 |
"""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 im... | 1 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=a_ ):
UpperCAmelCase_ :List[Any] = ["sentencepiece"]
def __init__( self , *__A , **__A ) -> List[str]:
re... | 357 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from s... | 1 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_availabl... | 358 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
... | 1 | 0 |
import operator as op
__UpperCAmelCase = "scaler.pt"
__UpperCAmelCase = "pytorch_model"
__UpperCAmelCase = "random_states"
__UpperCAmelCase = "optimizer"
__UpperCAmelCase = "scheduler"
__UpperCAmelCase = "pytorch_model.bin"
__UpperCAmelCase = "pytorch_model.... | 359 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 1 | 0 |
"""simple docstring"""
import argparse
import copy
def _snake_case ( lowercase__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = {}
with open(_a ) as f:
for line in f:
if line... | 360 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _snake_case ( lowercase__ : str = "laptop" ) -> DataFrame:
'''simple docstring'''
lowerCAmelCase_ :Dict ... | 1 | 0 |
"""simple docstring"""
import os
import numpy
import onnx
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : List[str] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :str = a.name
lowerCAmelCase_ :Tuple ... | 361 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import flo... | 1 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSav... | 362 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line ... | 1 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Dict = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def _snake_case ( lower... | 363 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 1 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( lowercase__ : int ) -> list[int]:
'''simple docstring'''
if num <= 0:
lowerCAmelCase_ :Dict = f"""{num}: Invalid input, please enter a positiv... | 364 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + ... | 1 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCAmelCase = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
try:
if n... | 365 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextMode... | 1 | 0 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVis... | 366 |
"""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 ...model... | 1 | 0 |
"""simple docstring"""
from __future__ import annotations
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A ) -> Union[str, Any]:
lowerCAmelCase_ :Optional[Any] = text, pattern
lowerCAmelCase_ :Any = len(__A ), len(__A )
... | 367 |
"""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
__UpperCA... | 1 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ : list[int] ) -> Tuple:
'''simple docstring'''
return len(set(__UpperCamelCase ) ) == len(__UpperCamelCase )
if __name__ == "__main__":
import doctest
... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 1 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 1 | 0 |
"""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 _SCREAMING_SNAKE_... | 370 |
"""simple docstring"""
__UpperCAmelCase = 2_56
# Modulus to hash a string
__UpperCAmelCase = 1_00_00_03
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple ... | 1 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : str , lowercase__ : Union[str, Any] ) -> Any:
'''simple docstring'''
return 1 if input_a == input_a else 0
def _snake_case ( ) -> Any:
'''simple docstring'''
... | 371 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 1 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__UpperCAmelCase = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={W... | 350 |
"""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_... | 1 | 0 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__UpperCAmelCase = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
'U':... | 351 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[... | 1 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar('T')
class _SCREAMING_SNAKE_CASE ( Generic[T] ):
def __init__( self , __A ) -> ... | 352 |
"""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/lice... | 1 | 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 = logging.get_logger(__name__)
__UpperCA... | 353 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCA... | 1 | 0 |
"""simple docstring"""
from math import ceil
def _snake_case ( lowercase__ : Optional[Any] = 1_0_0_1 ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
low... | 354 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : s... | 1 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://h... | 355 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.ber... | 1 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , **__A ) -> None:
w... | 356 |
"""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 im... | 1 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__UpperCAmelCase ... | 357 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from s... | 1 | 0 |
from typing import Any
import numpy as np
def _snake_case ( lowercase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
return np.array_equal(lowerCAmelCase__ , matrix.conjugate().T )
def _snake_case ( lowercase__ : Optional[An... | 358 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
... | 1 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__a )
class _SCREAMING_SNAKE_CASE ( __a ):
# `task` is not a ClassVar since we want it to be part of the `asdict`... | 359 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 1 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : int ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
... | 360 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _snake_case ( lowercase__ : str = "laptop" ) -> DataFrame:
'''simple docstring'''
lowerCAmelCase_ :Dict ... | 1 | 0 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__UpperCAmelCase = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
header... | 361 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import flo... | 1 | 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_CO... | 362 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line ... | 1 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _SCREAMING_... | 363 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 1 | 0 |
"""simple docstring"""
__UpperCAmelCase = 'Tobias Carryer'
from time import time
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A , __A , __A=int(time() ) ) -> Any: # noqa: B008
lowerCAmelCase_ :Union[str, Any... | 364 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + ... | 1 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copie... | 365 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextMode... | 1 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARA... | 366 |
"""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 ...model... | 1 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 367 |
"""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
__UpperCA... | 1 | 0 |
"""simple docstring"""
import string
def _snake_case ( lowercase__ : Optional[Any] ) -> None:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
lowerCAmelCase_ :int = ''
for symbol in message:
... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 1 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : Any ) -> list:
'''simple docstring'''
lowerCAmelCase_ :List[str] = [0] * len(__SCREAMING_SNAKE_CASE )
for i in range(1 , len(__SCREAMING_SNAKE_CASE ) ):
# use last results... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 1 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCAmelCase = datasets.u... | 370 |
"""simple docstring"""
__UpperCAmelCase = 2_56
# Modulus to hash a string
__UpperCAmelCase = 1_00_00_03
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple ... | 1 | 0 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested... | 371 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 1 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/con... | 350 |
"""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_... | 1 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _SCREAMING_SNAKE_CASE ( unitt... | 351 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[... | 1 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Dict = 0
while num > 0:
digit_sum += num % 1_0
num //= 1_0
return digit_sum
def _snake_case ( lower... | 352 |
"""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/lice... | 1 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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... | 353 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCA... | 1 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCA... | 354 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : s... | 1 | 0 |
"""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_availab... | 355 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.ber... | 1 | 0 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__UpperCAmelCase = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for ... | 356 |
"""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 im... | 1 | 0 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _SCREAMING_SNAKE_CASE ( unittest.TestCase , A__ ):
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAm... | 357 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from s... | 1 | 0 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization... | 358 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
... | 1 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__UpperCAmelCase = get_tests... | 359 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 1 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import flo... | 360 |
"""simple docstring"""
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _snake_case ( lowercase__ : str = "laptop" ) -> DataFrame:
'''simple docstring'''
lowerCAmelCase_ :Dict ... | 1 | 0 |
"""simple docstring"""
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,
MobileNetVaImage... | 361 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import flo... | 1 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils imp... | 362 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line ... | 1 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ..... | 363 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 1 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 364 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 5_0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + ... | 1 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _snake_case ( ... | 365 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextMode... | 1 | 0 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 366 |
"""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 ...model... | 1 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[str] = (UnCLIPScheduler,)
def __lowerCAmelCase ( self , **__A ) -> ... | 367 |
"""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
__UpperCA... | 1 | 0 |
"""simple docstring"""
import math
def _snake_case ( ) -> None:
'''simple docstring'''
lowerCAmelCase_ :Tuple = input("""Enter message: """ )
lowerCAmelCase_ :Optional[Any] = int(input(f"""Enter key [2-{len(lowercase__ ) - 1... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 1 | 0 |
"""simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelO... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 1 | 0 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : bool = False ) -> list[float]:
'''simple... | 370 |
"""simple docstring"""
__UpperCAmelCase = 2_56
# Modulus to hash a string
__UpperCAmelCase = 1_00_00_03
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ :Tuple ... | 1 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,... | 371 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 1 | 0 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingSt... | 350 |
"""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_... | 1 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils impor... | 351 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> Union[... | 1 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__UpperCAmelCase = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_bl... | 352 |
"""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/lice... | 1 | 0 |
"""simple docstring"""
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,
)
fr... | 353 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCA... | 1 | 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 _snake_case ( lowercase__ : List[str] , lowercase__ : Any , lo... | 354 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : s... | 1 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.d... | 355 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.ber... | 1 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
... | 356 |
"""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 im... | 1 | 0 |
"""simple docstring"""
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.... | 357 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from s... | 1 | 0 |
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