

"""simple docstring"""
class 			lowerCamelCase  :


  '''simple docstring'''







  class 			lowerCamelCase  :


    '''simple docstring'''







    SCREAMING_SNAKE_CASE							=     "_prev", "_data", "_next"



    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):


          """simple docstring"""

          UpperCAmelCase__ :       List[str]						=						link_p
          UpperCAmelCase__ :       str						=						element
          UpperCAmelCase__ :       int						=						link_n




    def    _a			(self  ):


          """simple docstring"""

          return (
              F""" Prev -> {self._prev is not None}, Next -> {self._next is not None}"""
          )




  def __init__(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Union[str, Any]						=						self._Node(SCREAMING_SNAKE_CASE_       ,					SCREAMING_SNAKE_CASE_       ,					SCREAMING_SNAKE_CASE_  )
        UpperCAmelCase__ :       Optional[Any]						=						self._Node(SCREAMING_SNAKE_CASE_       ,					SCREAMING_SNAKE_CASE_       ,					SCREAMING_SNAKE_CASE_  )
        UpperCAmelCase__ :       Any						=						self._trailer
        UpperCAmelCase__ :       Union[str, Any]						=						self._header
        UpperCAmelCase__ :       List[Any]						=						0



  def __len__(self  ):


        """simple docstring"""

        return self._size



  def    _a			(self  ):


        """simple docstring"""

        return self.__len__() == 0



  def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						self._Node(SCREAMING_SNAKE_CASE_       ,					SCREAMING_SNAKE_CASE_       ,					SCREAMING_SNAKE_CASE_  )
        UpperCAmelCase__ :       int						=						new_node
        UpperCAmelCase__ :       str						=						new_node
        self._size += 1
        return self




  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						node._prev
        UpperCAmelCase__ :       List[Any]						=						node._next

        UpperCAmelCase__ :       List[str]						=						successor
        UpperCAmelCase__ :       Any						=						predecessor
        self._size -= 1
        UpperCAmelCase__ :       Dict						=						node._data
        UpperCAmelCase__ :       str						=						None
        del node
        return temp




class 			lowerCamelCase  (					_DoublyLinkedBase      ):


  '''simple docstring'''







  def    _a			(self  ):


        """simple docstring"""

        if self.is_empty():
              raise Exception("""List is empty"""  )
        return self._header._next._data



  def    _a			(self  ):


        """simple docstring"""

        if self.is_empty():
              raise Exception("""List is empty"""  )
        return self._trailer._prev._data



  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        return self._insert(self._header       ,					SCREAMING_SNAKE_CASE_       ,					self._header._next  )



  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        return self._insert(self._trailer._prev       ,					SCREAMING_SNAKE_CASE_       ,					self._trailer  )



  def    _a			(self  ):


        """simple docstring"""

        if self.is_empty():
              raise IndexError("""remove_first from empty list"""  )
        return self._delete(self._header._next  )




  def    _a			(self  ):


        """simple docstring"""

        if self.is_empty():
              raise IndexError("""remove_first from empty list"""  )
        return self._delete(self._trailer._prev  )




"""simple docstring"""
from typing import TYPE_CHECKING

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


_A						=     {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""GPT2TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPT2DoubleHeadsModel""",
        """GPT2ForQuestionAnswering""",
        """GPT2ForSequenceClassification""",
        """GPT2ForTokenClassification""",
        """GPT2LMHeadModel""",
        """GPT2Model""",
        """GPT2PreTrainedModel""",
        """load_tf_weights_in_gpt2""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFGPT2DoubleHeadsModel""",
        """TFGPT2ForSequenceClassification""",
        """TFGPT2LMHeadModel""",
        """TFGPT2MainLayer""",
        """TFGPT2Model""",
        """TFGPT2PreTrainedModel""",
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""TFGPT2Tokenizer"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
    from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
    from .tokenization_gpta import GPTaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_fast import GPTaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_tf import TFGPTaTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
    import sys

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

