





from typing import TYPE_CHECKING

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


lowercase      :       Dict									=  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       List[str]									=  ["""XLNetTokenizer"""]

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       Optional[int]									=  [
        """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """XLNetForMultipleChoice""",
        """XLNetForQuestionAnswering""",
        """XLNetForQuestionAnsweringSimple""",
        """XLNetForSequenceClassification""",
        """XLNetForTokenClassification""",
        """XLNetLMHeadModel""",
        """XLNetModel""",
        """XLNetPreTrainedModel""",
        """load_tf_weights_in_xlnet""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       int									=  [
        """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFXLNetForMultipleChoice""",
        """TFXLNetForQuestionAnsweringSimple""",
        """TFXLNetForSequenceClassification""",
        """TFXLNetForTokenClassification""",
        """TFXLNetLMHeadModel""",
        """TFXLNetMainLayer""",
        """TFXLNetModel""",
        """TFXLNetPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xlnet import XLNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xlnet_fast import XLNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlnet import (
            XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLNetForMultipleChoice,
            XLNetForQuestionAnswering,
            XLNetForQuestionAnsweringSimple,
            XLNetForSequenceClassification,
            XLNetForTokenClassification,
            XLNetLMHeadModel,
            XLNetModel,
            XLNetPreTrainedModel,
            load_tf_weights_in_xlnet,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlnet import (
            TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLNetForMultipleChoice,
            TFXLNetForQuestionAnsweringSimple,
            TFXLNetForSequenceClassification,
            TFXLNetForTokenClassification,
            TFXLNetLMHeadModel,
            TFXLNetMainLayer,
            TFXLNetModel,
            TFXLNetPreTrainedModel,
        )

else:
    import sys

    lowercase      :       List[Any]									=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









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

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

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

   def __repr__(					self)			->						str:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				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()


