



'''simple docstring'''





from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __init__(   self						:							Union[str, Any]      ,     __a						:							Optional[NestedDataStructureLike[PathLike]] = None      ,     __a						:							Optional[NamedSplit] = None      ,     __a						:							Optional[Features] = None      ,     __a						:							str = None      ,     __a						:							bool = False      ,     __a						:							bool = False      ,     __a						:							Optional[int] = None      ,     **__a						:							Dict      ,     )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    path_or_paths
       __snake_case :   Dict													=    split if split or isinstance(__a      ,     __a  ) else 'train'
       __snake_case :   Optional[Any]													=    features
       __snake_case :   Optional[int]													=    cache_dir
       __snake_case :   Optional[int]													=    keep_in_memory
       __snake_case :   Tuple													=    streaming
       __snake_case :   Union[str, Any]													=    num_proc
       __snake_case :   Union[str, Any]													=    kwargs





 @abstractmethod
 def A_   (   self						:							Any  )      ->   Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:







       '''simple docstring'''
       pass



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __init__(   self						:							Any      ,     __a						:							Optional[Features] = None      ,     __a						:							str = None      ,     __a						:							bool = False      ,     __a						:							bool = False      ,     __a						:							Optional[int] = None      ,     **__a						:							str      ,     )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    features
       __snake_case :   List[str]													=    cache_dir
       __snake_case :   Optional[Any]													=    keep_in_memory
       __snake_case :   Dict													=    streaming
       __snake_case :   List[Any]													=    num_proc
       __snake_case :   Optional[int]													=    kwargs





 @abstractmethod
 def A_   (   self						:							Optional[Any]  )      ->   Union[Dataset, IterableDataset]:







       '''simple docstring'''
       pass








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[Any]           =					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Union[str, Any]           =					[
        '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LukeForEntityClassification''',
        '''LukeForEntityPairClassification''',
        '''LukeForEntitySpanClassification''',
        '''LukeForMultipleChoice''',
        '''LukeForQuestionAnswering''',
        '''LukeForSequenceClassification''',
        '''LukeForTokenClassification''',
        '''LukeForMaskedLM''',
        '''LukeModel''',
        '''LukePreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    A__    :   List[str]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



