code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils... | 664 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : ... | 664 | 1 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 664 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__magic_na... | 664 | 1 |
'''simple docstring'''
import random
def __snake_case ( lowerCamelCase_ : list , lowerCamelCase_ : int ):
'''simple docstring'''
__magic_name__ , __magic_name__ , __magic_name__ = [], [], []
for element in data:
if element < pivot:
... | 664 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCa... | 664 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__magic_name__ : List[str] =loggi... | 664 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tr... | 664 | 1 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_tor... | 664 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[int] ):
'''simple docstring'''
if not nums:
return 0
__magic_name__ = nums[0]
__magic_name__ = 0
for num in nums[1:]:
__magic_name__ ,... | 664 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingf... | 664 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 1 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __snake_case ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optiona... | 664 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : str , lowerCamelCase_ : str ):
'''simple docstring'''
__magic_name__ = get_failure_array(lowerCamelCase_ )
# 2) Step through text searching for pattern
... | 664 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : Tuple ={'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTO... | 664 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase_... | 664 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 664 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threaded... | 664 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder,... | 664 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : Any ): # noqa: E741
'''simple docstring'''
__magic_name__ = len(lowerCamelCase_ )
__magic_name__ = 0
__magic_name__ = [0] * n
__magic_name__ = [False] * n
__magic_name__ ... | 664 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__magic_name__ = str(bin(lowerCamelCase_ ) )[... | 664 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transfor... | 664 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingf... | 664 | 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
#
# Un... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Union[str, Any] ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
... | 664 | 1 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tr... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Optional[Any] ={
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CON... | 664 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
UpperCAmelCase__ : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Mod... | 664 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__magic_name__ : str ={
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Ima... | 664 | 1 |
'''simple docstring'''
import string
import numpy
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , lowerCamelCase_ )
class UpperCamelCase_ :
... | 664 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't ... | 664 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[int] , lowerCamelCase_ : int ):
'''simple docstring'''
if len(lowerCamelCase_ ) < k or k < 0:
raise ValueError("Invalid Input" )
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__magic_name__ : List[str] ='\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplificati... | 664 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : int =logging.get_logger(__name__)
__magic_name__ : List[Any] ={}
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : in... | 664 | 1 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : int , _lowerCamelCase : Dict=None , _lowerCamelCase : Optional[Any]=None ) -> List[str]:... | 664 |
'''simple docstring'''
__magic_name__ : Dict =8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __snake_case ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
... | 664 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 664 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__magic_name__ : List[Any] =logging.getLogger(__name__)
class UpperCamelCase_ ( A ):
"""simple docst... | 664 | 1 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__magic_na... | 664 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : ... | 664 | 1 |
'''simple docstring'''
__magic_name__ : Tuple ={'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__magic_name__ : Union[str, Any] =['a', 'b', 'c', 'd', 'e']
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : Tuple , lowerCamelCase_ : Optiona... | 664 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__magic_na... | 664 | 1 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transform... | 664 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCa... | 664 | 1 |
'''simple docstring'''
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,
)
__magic_name__ : List[Any] =pytest.mark.integration
@pytest.mark.parametrize("pat... | 664 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tr... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : Optional[Any] ={'processing_layoutxlm': ['LayoutX... | 664 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 | 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 U... | 664 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : bool = True , lowerCamelCase_ : float = math.inf , lowerCamelCase_ : float = -math.in... | 664 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def __snake_case ( lowerCamelCase_ : list ):
'''simple docstring'''
if not postfix_notation:
return 0
__magic_name__ = {"+", "-", "*", "/"}
__magic_name__ = []
... | 664 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Any ):
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , lowerCamelCas... | 664 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 | 1 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__magic_name__ : Dict =''
__magic_name__ : int =''
__magic_name__ : str =''
__magic_name__ : List[str] =''
def __snake_case ( lowerCamelCase_ : str ):
'''simple docstring'''
__magic_name... | 664 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase_... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : str ={'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 664 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threaded... | 664 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__magic_na... | 664 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 664 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOut... | 664 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__magic_name__ = str(bin(lowerCamelCase_ ) )[... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
UpperCAmelCase__ : int
UpperCAmelCase__ : TreeNode | None = None
... | 664 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingf... | 664 | 1 |
'''simple docstring'''
__magic_name__ : List[str] ={
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'hu... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Union[str, Any] ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
... | 664 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__magic_name__ : Optional[Any] =logging.get_logge... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Optional[Any] ={
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CON... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__magic_name__ : Union[str, Any] =TypeVar('T')
__magic_name__ : str =TypeVar('U')
class UpperCamelCase_ ( Generic[T, U] ):
"""simple docstring"""
... | 664 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__magic_name__ : str ={
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Ima... | 664 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, req... | 664 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, ... | 664 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[int] , lowerCamelCase_ : int ):
'''simple docstring'''
if len(lowerCamelCase_ ) < k or k < 0:
raise ValueError("Invalid Input" )
__magic_name__ ... | 664 | 1 |
'''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 sklearn.preprocessing... | 664 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : int =logging.get_logger(__name__)
__magic_name__ : List[Any] ={}
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : in... | 664 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 |
'''simple docstring'''
__magic_name__ : Dict =8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __snake_case ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
... | 664 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...u... | 664 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__magic_name__ : List[Any] =logging.getLogger(__name__)
class UpperCamelCase_ ( A ):
"""simple docst... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : list[list] ):
'''simple docstring'''
__magic_name__ = current_set.copy()
for row_index, row in enumerate(lowerCamelCase_ ):
__magic_name__ = row[0]
for column_index, column in enumera... | 664 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : ... | 664 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCamelCase_ ( A ):
"""simple docstring"""
def __init__( self : List[Any] , _lowerCamelCase : ... | 664 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__magic_na... | 664 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ : Any =logging.get_logger(__name__)
__magic_name__ : List[Any] ={
'vocab_file': 'vocab.json',
'merg... | 664 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCa... | 664 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ : Any =logging.get_logger(__name_... | 664 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tr... | 664 | 1 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 664 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : str =logging.get_logger(__name__)
__magic_name__ : List[Any] ... | 664 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 664 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 1 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __snake_case ( ):
'''simple docstring'''
__magic_name__ = ArgumentParser(
description=(... | 664 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 664 | 1 |
'''simple docstring'''
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int ) -> Any:
__magic_name__ = 0
__magic_name__ = 0
__magic_name__ = {}
def __A ( self : Optiona... | 664 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 | 1 |
'''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 ImageProcessingSavingTestMixin, prepare... | 664 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase_... | 664 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, ... | 664 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threaded... | 664 | 1 |
'''simple docstring'''
__magic_name__ : Tuple ='\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__magic_name__ : int =[{'type': 'code', 'content': INSTALL_CONTENT}]
__ma... | 664 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 664 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 664 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__magic_name__ = str(bin(lowerCamelCase_ ) )[... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : str ):
'''simple docstring'''
__magic_name__ = 1
__magic_name__ = 2
while i * i <= n:
__magic_name__ = 0
while n % i == 0:
n //= i
multiplicity += 1
... | 664 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingf... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[int] , lowerCamelCase_ : int ):
'''simple docstring'''
if len(lowerCamelCase_ ) < k or k < 0:
raise ValueError("Invalid Input" )
__magic_name__ ... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Union[str, Any] ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
... | 664 | 1 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Optional[Any] ={
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CON... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int = 1000 ):
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 664 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__magic_name__ : str ={
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Ima... | 664 | 1 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:... | 664 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 | 1 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
fr... | 664 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[int] , lowerCamelCase_ : int ):
'''simple docstring'''
if len(lowerCamelCase_ ) < k or k < 0:
raise ValueError("Invalid Input" )
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __snake_case ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : str , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Any , lowerCamelCase_ : Optional[int] ):
'''si... | 664 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : int =logging.get_logger(__name__)
__magic_name__ : List[Any] ={}
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : in... | 664 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_det... | 664 |
'''simple docstring'''
__magic_name__ : Dict =8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __snake_case ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
... | 664 | 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_logging
... | 664 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__magic_name__ : List[Any] =logging.getLogger(__name__)
class UpperCamelCase_ ( A ):
"""simple docst... | 664 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[Any] =logging.get_logger(__name__)
__magic_name__ : List[Any] ={
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uc... | 664 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : ... | 664 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPas... | 664 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__magic_na... | 664 | 1 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disa... | 664 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCa... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__magic_name__ : Optional[Any] =numpy.array([0, 0])
__magic_name__ : List[str] =numpy.array([0.5, 0.8_6_6_0_2_5_4])
__magic_name__ : str =numpy.arr... | 664 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tr... | 664 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Confi... | 664 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : ... | 664 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : List[Any] ={
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Autofor... | 664 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __snake_case ( lowerCamelCase_ : Any ):
'''simple docstring'''
__magic_name__ = [
"encoder.version",
... | 664 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 664 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimu... | 664 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image... | 664 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase_... | 664 | 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_mvp import Mv... | 664 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threaded... | 664 | 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 UpperCamelCase_ ( unittest.TestCase ):
"""simple docstring"""
def ... | 664 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 664 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : str =logging.get_logger(__name__)
__magic_name__ : Tuple ={
'camembert-base': 'https:/... | 664 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__magic_name__ = str(bin(lowerCamelCase_ ) )[... | 664 | 1 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingf... | 664 | 1 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Union[str, Any] ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
return "\n".join(
F'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(mul... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Optional[Any] ={
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CON... | 664 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 664 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__magic_name__ : str ={
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Ima... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int ):
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
__magic_name__ = [True] * (num + 1)
__magic_name__ = 2
while p * p <= num:
i... | 664 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 | 1 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[int] , lowerCamelCase_ : int ):
'''simple docstring'''
if len(lowerCamelCase_ ) < k or k < 0:
raise ValueError("Invalid Input" )
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepende... | 664 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : int =logging.get_logger(__name__)
__magic_name__ : List[Any] ={}
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : in... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class UpperCamelCase_ ( A ):
"""simple docstring"""
def __lt__( self : Any , _lowerCamelCase ... | 664 |
'''simple docstring'''
__magic_name__ : Dict =8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __snake_case ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__magic_name__ : List[Any] ={'UserAgent': UserAgent().random}
def __snake_case ( lowerCamelCase_ : Tuple ):
'''simple docstrin... | 664 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__magic_name__ : List[Any] =logging.getLogger(__name__)
class UpperCamelCase_ ( A ):
"""simple docst... | 664 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 664 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : ... | 664 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ : List[str] =logging.get_logger(__name__)
__magic_name__ : Tuple ={
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Attention-Network/van-base/blob/mai... | 664 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__magic_na... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
__magic_name__ = len(bin(lowerCamelCase_ )[3:] )
__magic_name__ = bin(abs(lowerCam... | 664 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCa... | 664 | 1 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from ... | 664 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tr... | 664 | 1 |
'''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
__magic_name__ : Any =datasets.utils... | 664 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 | 1 |
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_... | 664 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : List[str] =logging.get_logger(__name__)
__magic_name__ : str ={
'bert-base-uncased': '... | 664 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 1 |
'''simple docstring'''
from torch import nn
class UpperCamelCase_ ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] ) -> Union[str, Any]:
... | 664 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Dict ={
'configuration_blenderbot': [
'BLENDERBOT_... | 664 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__magic_name__ : Dict =''
__magic_name__ : List[Any] =''
__magic_name__ : Dict =''
__magic_name__ : Tuple =1 # (0 is vertical, 1 is horizontal)
def __snake_case ( ):
... | 664 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase_... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__magic_name__ = str(bin(lowerCamelCase_ ) )[... | 664 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threaded... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Union[str, Any] ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
... | 664 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 664 | 1 |
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