



"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCamelCase      	=						{
    """configuration_mobilebert""": [
        """MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileBertConfig""",
        """MobileBertOnnxConfig""",
    ],
    """tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      	=						["""MobileBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      	=						[
      """MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MobileBertForMaskedLM""",
      """MobileBertForMultipleChoice""",
      """MobileBertForNextSentencePrediction""",
      """MobileBertForPreTraining""",
      """MobileBertForQuestionAnswering""",
      """MobileBertForSequenceClassification""",
      """MobileBertForTokenClassification""",
      """MobileBertLayer""",
      """MobileBertModel""",
      """MobileBertPreTrainedModel""",
      """load_tf_weights_in_mobilebert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      	=						[
      """TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFMobileBertForMaskedLM""",
      """TFMobileBertForMultipleChoice""",
      """TFMobileBertForNextSentencePrediction""",
      """TFMobileBertForPreTraining""",
      """TFMobileBertForQuestionAnswering""",
      """TFMobileBertForSequenceClassification""",
      """TFMobileBertForTokenClassification""",
      """TFMobileBertMainLayer""",
      """TFMobileBertModel""",
      """TFMobileBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  lowerCamelCase      	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









"""simple docstring"""





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

lowerCamelCase       	=						TypeVar("""_T""")
class 				lowercase__       (   Generic[_T]				):







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  Iterable[_T] | None = None   )			->						None:


             '''simple docstring'''




             UpperCAmelCase_      			=							list(iterable or []   )
             UpperCAmelCase_      			=							[]



       def __len__(							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             return len(self._stacka   ) + len(self._stacka   )



       def __repr__(							self      :  Optional[Any]   )			->						str:


             '''simple docstring'''




             return F"""Queue({tuple(self._stacka[::-1] + self._stacka   )})"""



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  _T   )			->						None:


             '''simple docstring'''




             self._stacka.append(_UpperCAmelCase   )







       def 					lowercase__    (							self      :  Dict   )			->						_T:


             '''simple docstring'''




             UpperCAmelCase_      			=							self._stacka.pop
             UpperCAmelCase_      			=							self._stacka.append

             if not self._stacka:
                   while self._stacka:
                         stacka_append(stacka_pop()   )

             if not self._stacka:
                   raise IndexError("Queue is empty"   )
             return self._stacka.pop()


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

  testmod()




