



from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_snake_case  :   Optional[int]						   =  TypeVar('T')



class     _UpperCAmelCase   (   Generic[T]   ):

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     data
     __lowerCAmelCase							     =     None






  def __str__(    self			:		Tuple					)							->						str:
     return f"""{self.data}"""





class     _UpperCAmelCase   (   Generic[T]   ):

  """simple docstring"""


  def __init__(    self			:		Dict					)							->						None:
     __lowerCAmelCase							     =     None






  def __iter__(    self			:		List[str]					)							->						Iterator[T]:
     __lowerCAmelCase							     =     self.top
     while node:
        yield node.data
        __lowerCAmelCase							     =     node.next






  def __str__(    self			:		Union[str, Any]					)							->						str:
     return "->".join([str(a__					) for item in self]					)






  def __len__(    self			:		List[str]					)							->						int:
     return len(tuple(iter(self					)					)					)






  def     lowercase		(    self			:		List[Any]					)							->						bool:
     return self.top is None






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str]					)							->						None:
     __lowerCAmelCase							     =     Node(a__					)
     if not self.is_empty():
        __lowerCAmelCase							     =     self.top
     __lowerCAmelCase							     =     node






  def     lowercase		(    self			:		str					)							->						T:




     if self.is_empty():
        raise IndexError('pop from empty stack'					)
     assert isinstance(self.top ,  a__					)
     __lowerCAmelCase							     =     self.top
     __lowerCAmelCase							     =     self.top.next
     return pop_node.data






  def     lowercase		(    self			:		Optional[Any]					)							->						T:




     if self.is_empty():
        raise IndexError('peek from empty stack'					)

     assert self.top is not None
     return self.top.data






  def     lowercase		(    self			:		Union[str, Any]					)							->						None:
     __lowerCAmelCase							     =     None


if __name__ == "__main__":
     from doctest import testmod

     testmod()








from typing import TYPE_CHECKING

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


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[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

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

