






from typing import TYPE_CHECKING

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


a		:       Any			     = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       Any			     = ["FNetTokenizer"]

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       str			     = [
           "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FNetForMaskedLM",
           "FNetForMultipleChoice",
           "FNetForNextSentencePrediction",
           "FNetForPreTraining",
           "FNetForQuestionAnswering",
           "FNetForSequenceClassification",
           "FNetForTokenClassification",
           "FNetLayer",
           "FNetModel",
           "FNetPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_fnet import FNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_fnet_fast import FNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_fnet import (
                  FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FNetForMaskedLM,
                  FNetForMultipleChoice,
                  FNetForNextSentencePrediction,
                  FNetForPreTraining,
                  FNetForQuestionAnswering,
                  FNetForSequenceClassification,
                  FNetForTokenClassification,
                  FNetLayer,
                  FNetModel,
                  FNetPreTrainedModel,
              )


else:
       import sys

       a		:       int			     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










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     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			Any		,      __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					:			List[str]		,      )							->      Tuple:
        __UpperCAmelCase							:	Any           =  path_or_paths
        __UpperCAmelCase							:	Dict           =  split if split or isinstance(__lowercase		,      __lowercase					) else """train"""
        __UpperCAmelCase							:	Optional[int]           =  features
        __UpperCAmelCase							:	str           =  cache_dir
        __UpperCAmelCase							:	str           =  keep_in_memory
        __UpperCAmelCase							:	Optional[int]           =  streaming
        __UpperCAmelCase							:	Dict           =  num_proc
        __UpperCAmelCase							:	Tuple           =  kwargs




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


class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			List[str]		,      __lowercase					:			Optional[Features] = None		,      __lowercase					:			str = None		,      __lowercase					:			bool = False		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[int] = None		,      **__lowercase					:			Optional[Any]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	Optional[Any]           =  features
        __UpperCAmelCase							:	str           =  cache_dir
        __UpperCAmelCase							:	Optional[int]           =  keep_in_memory
        __UpperCAmelCase							:	Dict           =  streaming
        __UpperCAmelCase							:	Optional[Any]           =  num_proc
        __UpperCAmelCase							:	Union[str, Any]           =  kwargs




      @abstractmethod
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Union[Dataset, IterableDataset]:
        pass


