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 json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 27 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/co... | 27 | 1 |
"""simple docstring"""
import math
import os
import sys
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = """"""
try:
with open(snake_case_ ,"""rb""" ) as binary_file:
UpperCamelCase ... | 27 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if n == 1 or not isinstance(snake_case_ ,snake_case_ ):
return 0
elif n == 2:
return 1
else:
UpperCamelCase : Optional[Any] = [0, 1]
... | 27 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_fi... | 27 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import re... | 27 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, Auto... | 27 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extractio... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] ... | 27 | 1 |
"""simple docstring"""
__A : Dict = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
"""simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 27 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 27 | 1 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_lo... | 27 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : flo... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : str = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 27 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 27 | 1 |
"""simple docstring"""
__A : dict[tuple[int, int, int], int] = {}
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further priz... | 27 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for... | 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[Any] ,snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
... | 27 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : str = (1 + 2_4 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( snake_case_ : int = 5_0_0_0 ):
'''simple docstrin... | 27 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:... | 27 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import... | 27 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase ... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def A_ ( snake_case_ : list ):
'''simple docstring'''
if not postfix_notation:
return 0
UpperCamelCase : Dict = {"""+""", """-""", """*""", """/"""}
Uppe... | 27 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):... | 27 | 1 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def A_ ( ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Dict = 9, 1_4 # noqa: F841
UpperCamelCase : Uni... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict ... | 27 | 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__":
__A : Optional[Any] = pd.read_csv('''sample_data.csv''', hea... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
... | 27 | 1 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageProce... | 27 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline... | 27 | 1 |
"""simple docstring"""
from collections import defaultdict
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = total # total no of tasks (N)
# DP table will have a dimen... | 27 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
... | 27 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart im... | 27 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : Lis... | 27 | 1 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__A : Optional[Any] = 2048
__A : List[Any] = 4096
__A : str = 42
__A : Optional[int] = os.environ.pop('''PROCESS_TRAIN''', '''false''')
__A : Li... | 27 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.... | 27 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow... | 27 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 27 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 27 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCRE... | 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
while b:
UpperCamelCase , UpperCamelCase : Optional[int] = b, a % b
return a
def A_ ( snake_case_ : int ... | 27 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/co... | 27 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Tuple = ['image_processor', 'tokenizer']
lowercase : Union[str, Any] = ... | 27 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 27 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetune... | 27 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_fi... | 27 | 1 |
"""simple docstring"""
import math
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Dict = input("""Enter message: """ )
UpperCamelCase : Optional[int] = int(input(f'Enter key [2-{len(snake_case_ ) - 1}]: ' ) )
UpperC... | 27 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, Auto... | 27 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] ... | 27 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__A : List[Any] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__A : Tuple = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'... | 27 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__A : List[Any] = [8, 5, 9, 7]
__A : Union[str, Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__A : str = [
[... | 27 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : flo... | 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : str ):
'''simple docstring'''
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Em... | 27 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 27 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 27 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for... | 27 | 1 |
"""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 logging
__A : Any = logging.get_logger(__na... | 27 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 27 | 1 |
"""simple docstring"""
import requests
__A : List[Any] = '''''' # <-- Put your OpenWeatherMap appid here!
__A : Tuple = '''https://api.openweathermap.org/data/2.5/'''
def A_ ( snake_case_ : str = "Chicago" ,snake_case_ : str = APPID ):
... | 27 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:... | 27 | 1 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__A : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase :
lowercase : List[str] = None
... | 27 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase ... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : list[Any] = []
UpperCamelCase : int = 0
UpperCamelCase ... | 27 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):... | 27 | 1 |
"""simple docstring"""
import json
import sys
def A_ ( snake_case_ : List[Any] ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : Optional[int] = json.lo... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict ... | 27 | 1 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nn... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
... | 27 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 27 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline... | 27 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 27 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
... | 27 | 1 |
"""simple docstring"""
import numpy as np
def A_ ( snake_case_ : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : Lis... | 27 | 1 |
"""simple docstring"""
import argparse
import struct
import unittest
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = data
# Initialize hash values
UpperCamelCase : Union[str, Any] =... | 27 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : List[str] = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_AR... | 27 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 27 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transforme... | 27 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCRE... | 27 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_fla... | 27 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/co... | 27 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMulti... | 27 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A : Dict = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_AR... | 27 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_fi... | 27 | 1 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
... | 27 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, Auto... | 27 | 1 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def A_ ( snake_case_ : Tuple ,snake_case_ : Union[str, Any] ,snake_case_ : str ... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] ... | 27 | 1 |
"""simple docstring"""
import cva
import numpy as np
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if k in (0.04, 0.06):
UpperCamelCase : Tuple = k
UpperCamelCase : Any ... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
... | 27 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase :
lowercase : int
lowercase : int
class lowerCamelCase :
d... | 27 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : flo... | 27 | 1 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig... | 27 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 27 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_t... | 27 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for... | 27 | 1 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
__A : Tuple = False
try:
__A... | 27 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 27 | 1 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
... | 27 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:... | 27 | 1 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A : Tuple = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplif... | 27 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase ... | 27 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__A : Opt... | 27 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):... | 27 | 1 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgu... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict ... | 27 | 1 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
f... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
... | 27 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowerCamelCase :
lowercase : str = field(
m... | 27 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline... | 27 | 1 |
"""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 floats_list
@req... | 27 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
... | 27 | 1 |
"""simple docstring"""
from string import ascii_uppercase
__A : str = {str(ord(c) - 55): c for c in ascii_uppercase}
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
if isinstance(snake_case_ ,snake_case_ ):
... | 27 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : Lis... | 27 | 1 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import tran... | 27 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTester... | 27 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square(snake_case_ : int ,snake_case_ : int ) -> int:
# BASE CASE
... | 27 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCRE... | 27 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=_UpperCAmelCase ):
lowercase : List[str] = ['flax', 'transformers']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
... | 27 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/co... | 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : int = 2_0_0_0_0_0_0 ):
'''simple docstring'''
UpperCamelCase : Optional[int] = [0 for i in range(n + 1 )]
UpperCamelCase : Dict = 1
UpperCamelCase : Tuple = ... | 27 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 27 | 1 |
"""simple docstring"""
import os
import sys
import unittest
__A : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get... | 27 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_fi... | 27 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__A : List[Any] = logging.get_logger(__name__)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , *SCREAMING_SNAKE_CASE_ ... | 27 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, Auto... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] ... | 27 | 1 |
"""simple docstring"""
import math
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : str = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(snake_case_ )
def A_ ( sn... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
"""simple docstring"""
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,
StableDiff... | 27 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 27 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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
#... | 27 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : flo... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Dict = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2Struc... | 27 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : List[str] = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_A... | 27 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Optional[Any] = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_t... | 27 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[str] = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeT... | 27 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:... | 27 | 1 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase ... | 27 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase ... | 27 | 1 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokeni... | 27 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.util... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict ... | 27 | 1 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
... | 27 | 1 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
fro... | 27 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline... | 27 | 1 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def A_ ( snake_case_ : List[Any] ,snake_case_ : Any ,snake_case_ : Optional[Any] ,snake_case_ : Dict=None ):
'''simple docstring'''
UpperCamelCase : str = ... | 27 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
... | 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : list[list[int]] ,snake_case_ : int ,snake_case_ : int ,snake_case_ : set ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : int = len(snake_case_ ), len(... | 27 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : Lis... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( snake_case_ : list[int | str] ):
'''simple docstring'''
create_state_space_tree(snake_case_ ,[] ,0 ,[0 for i in range(len(snake_case_ ) )] )
def A_ ( snake_case_ : ... | 27 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.... | 27 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/co... | 27 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 27 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils ... | 27 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCRE... | 27 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[str] = {
'''googl... | 27 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/co... | 27 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test impor... | 27 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.... | 27 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_fi... | 27 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Any = {
'''google/umt5-small''': '''https:/... | 27 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, Auto... | 27 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase ... | 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] ... | 27 | 1 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
"""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, random_... | 27 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_uti... | 27 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for... | 27 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : flo... | 27 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class lowerCamelCase :
lowercase : List[str]
lowercase ... | 27 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 27 | 1 |
"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__A : Any = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses ... | 27 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for... | 27 | 1 |
"""simple docstring"""
import random
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : List[Any] = num - 1
UpperCamelCase : str = 0
while s % 2 == 0:
UpperCamelCase : List[str] = ... | 27 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 27 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipeline... | 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:... | 27 | 0 |
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