



"""simple docstring"""







from typing import TYPE_CHECKING

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


lowercase__		:				Any          =	{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Union[str, Any]          =	["""XLMRobertaTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Union[str, Any]          =	["""XLMRobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Optional[Any]          =	[
         """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """XLMRobertaForCausalLM""",
         """XLMRobertaForMaskedLM""",
         """XLMRobertaForMultipleChoice""",
         """XLMRobertaForQuestionAnswering""",
         """XLMRobertaForSequenceClassification""",
         """XLMRobertaForTokenClassification""",
         """XLMRobertaModel""",
         """XLMRobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Union[str, Any]          =	[
         """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFXLMRobertaForCausalLM""",
         """TFXLMRobertaForMaskedLM""",
         """TFXLMRobertaForMultipleChoice""",
         """TFXLMRobertaForQuestionAnswering""",
         """TFXLMRobertaForSequenceClassification""",
         """TFXLMRobertaForTokenClassification""",
         """TFXLMRobertaModel""",
         """TFXLMRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				List[str]          =	[
         """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FlaxXLMRobertaForMaskedLM""",
         """FlaxXLMRobertaForCausalLM""",
         """FlaxXLMRobertaForMultipleChoice""",
         """FlaxXLMRobertaForQuestionAnswering""",
         """FlaxXLMRobertaForSequenceClassification""",
         """FlaxXLMRobertaForTokenClassification""",
         """FlaxXLMRobertaModel""",
         """FlaxXLMRobertaPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_xlm_roberta import (
         XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
         XLMRobertaConfig,
         XLMRobertaOnnxConfig,
     )

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_roberta import XLMRobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_roberta import (
              XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMRobertaForCausalLM,
              XLMRobertaForMaskedLM,
              XLMRobertaForMultipleChoice,
              XLMRobertaForQuestionAnswering,
              XLMRobertaForSequenceClassification,
              XLMRobertaForTokenClassification,
              XLMRobertaModel,
              XLMRobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm_roberta import (
              TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMRobertaForCausalLM,
              TFXLMRobertaForMaskedLM,
              TFXLMRobertaForMultipleChoice,
              TFXLMRobertaForQuestionAnswering,
              TFXLMRobertaForSequenceClassification,
              TFXLMRobertaForTokenClassification,
              TFXLMRobertaModel,
              TFXLMRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xlm_roberta import (
              FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxXLMRobertaForCausalLM,
              FlaxXLMRobertaForMaskedLM,
              FlaxXLMRobertaForMultipleChoice,
              FlaxXLMRobertaForQuestionAnswering,
              FlaxXLMRobertaForSequenceClassification,
              FlaxXLMRobertaForTokenClassification,
              FlaxXLMRobertaModel,
              FlaxXLMRobertaPreTrainedModel,
          )

else:
     import sys

     lowercase__		:				Union[str, Any]          =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







'''simple docstring'''







from __future__ import annotations

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

__lowerCAmelCase         =    TypeVar("""T""")







class UpperCAmelCase__	( Generic[T]	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,_a      :     T       ):


     '''simple docstring'''






     _a				:	List[str]			=   data
     _a				:	Node[T] | None			=   None


  def __str__(		self      :     Dict       ):


     '''simple docstring'''






     return F"""{self.data}"""







class UpperCAmelCase__	( Generic[T]	):





  """simple docstring"""

  def __init__(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Node[T] | None			=   None


  def __iter__(		self      :     str       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.top
     while node:
        yield node.data
        _a				:	int			=   node.next


  def __str__(		self      :     str       ):


     '''simple docstring'''






     return "->".join([str(_a       ) for item in self]       )


  def __len__(		self      :     Optional[Any]       ):


     '''simple docstring'''






     return len(tuple(iter(self       )       )       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self.top is None


  def   __lowercase		(		self      :     List[Any]							,_a      :     T       ):


     '''simple docstring'''






     _a				:	int			=   Node(_a       )
     if not self.is_empty():
        _a				:	Optional[Any]			=   self.top
     _a				:	List[str]			=   node


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if self.is_empty():
        raise IndexError('pop from empty stack'       )
     assert isinstance(self.top							,_a       )
     _a				:	List[Any]			=   self.top
     _a				:	int			=   self.top.next
     return pop_node.data


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






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

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


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   None


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

  testmod()



