

'''simple docstring'''






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     NestedDataStructureLike[PathLike]					,lowercase__   :     Optional[NamedSplit] = None					,lowercase__   :     Optional[Features] = None					,lowercase__   :     str = None					,lowercase__   :     bool = False					,lowercase__   :     bool = False					,lowercase__   :     Optional[int] = None					,**lowercase__   :     Optional[int]					,):
   super().__init__(
       lowercase__					,split=lowercase__					,features=lowercase__					,cache_dir=lowercase__					,keep_in_memory=lowercase__					,streaming=lowercase__					,num_proc=lowercase__					,**lowercase__					,)
   __lowercase							  = path_or_paths if isinstance(lowercase__					,lowercase__			) else {self.split: path_or_paths}
   __lowercase							  = Text(
       cache_dir=lowercase__					,data_files=lowercase__					,features=lowercase__					,**lowercase__					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   # Build iterable dataset
   if self.streaming:
    __lowercase							  = self.builder.as_streaming_dataset(split=self.split			)
   # Build regular (map-style) dataset
   else:
    __lowercase							  = None
    __lowercase							  = None
    __lowercase							  = None
    __lowercase							  = None

    self.builder.download_and_prepare(
        download_config=lowercase__					,download_mode=lowercase__					,verification_mode=lowercase__					,base_path=lowercase__					,num_proc=self.num_proc					,)
    __lowercase							  = self.builder.as_dataset(
        split=self.split					,verification_mode=lowercase__					,in_memory=self.keep_in_memory			)
   return dataset







from typing import TYPE_CHECKING

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


snake_case_ : int    		=       {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : int    		=       ["DebertaTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaForMaskedLM",
        "DebertaForQuestionAnswering",
        "DebertaForSequenceClassification",
        "DebertaForTokenClassification",
        "DebertaModel",
        "DebertaPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "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

    snake_case_ : Dict    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






