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

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





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :int   ,     lowercase      :Optional[NestedDataStructureLike[PathLike]] = None   ,     lowercase      :Optional[NamedSplit] = None   ,     lowercase      :Optional[Features] = None   ,     lowercase      :str = None   ,     lowercase      :bool = False   ,     lowercase      :bool = False   ,     lowercase      :Optional[int] = None   ,     **lowercase      :str   ,     )		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= path_or_paths
				SCREAMING_SNAKE_CASE     						= split if split or isinstance(lowercase   ,     lowercase			) else '''train'''
				SCREAMING_SNAKE_CASE     						= features
				SCREAMING_SNAKE_CASE     						= cache_dir
				SCREAMING_SNAKE_CASE     						= keep_in_memory
				SCREAMING_SNAKE_CASE     						= streaming
				SCREAMING_SNAKE_CASE     						= num_proc
				SCREAMING_SNAKE_CASE     						= kwargs







	@abstractmethod
	def  snake_case__     (     self      :Optional[int]			)		->      Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:







				"""simple docstring"""






				pass





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :int   ,     lowercase      :Optional[Features] = None   ,     lowercase      :str = None   ,     lowercase      :bool = False   ,     lowercase      :bool = False   ,     lowercase      :Optional[int] = None   ,     **lowercase      :List[Any]   ,     )		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= features
				SCREAMING_SNAKE_CASE     						= cache_dir
				SCREAMING_SNAKE_CASE     						= keep_in_memory
				SCREAMING_SNAKE_CASE     						= streaming
				SCREAMING_SNAKE_CASE     						= num_proc
				SCREAMING_SNAKE_CASE     						= kwargs







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







				"""simple docstring"""






				pass

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase					   =       {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['DebertaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'DebertaForMaskedLM',
							    'DebertaForQuestionAnswering',
							    'DebertaForSequenceClassification',
							    'DebertaForTokenClassification',
							    'DebertaModel',
							    'DebertaPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFDebertaForMaskedLM',
							    'TFDebertaForQuestionAnswering',
							    'TFDebertaForSequenceClassification',
							    'TFDebertaForTokenClassification',
							    'TFDebertaModel',
							    'TFDebertaPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)