


class    A_   :







 """simple docstring"""





 class    A_   :







  """simple docstring"""





  _UpperCamelCase				: Any      =					'''_prev''', '''_data''', '''_next'''
  def __init__(			self					: Optional[Any]       ,    _a					: str       ,    _a					: List[Any]       ,    _a					: Optional[Any]				)    ->			Tuple:
       UpperCAmelCase						:				Optional[int]		  =			link_p
       UpperCAmelCase						:				int		  =			element
       UpperCAmelCase						:				Union[str, Any]		  =			link_n

  def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Any:
       return (
           f' Prev -> {self._prev is not None}, Next -> {self._next is not None}'
       )
 def __init__(			self					: Optional[Any]				)    ->			Optional[Any]:
      UpperCAmelCase						:				Union[str, Any]		  =			self._Node(_a       ,    _a       ,    _a				)
      UpperCAmelCase						:				Tuple		  =			self._Node(_a       ,    _a       ,    _a				)
      UpperCAmelCase						:				Union[str, Any]		  =			self._trailer
      UpperCAmelCase						:				List[str]		  =			self._header
      UpperCAmelCase						:				int		  =			0
 def __len__(			self					: Any				)    ->			Any:
      return self._size
 def   _UpperCamelCase    (			self					: int				)    ->			Tuple:
      return self.__len__() == 0
 def   _UpperCamelCase    (			self					: str       ,    _a					: List[Any]       ,    _a					: List[str]       ,    _a					: Any				)    ->			str:
      # Create new_node by setting it's prev.link -> header
      # setting it's next.link -> trailer
      UpperCAmelCase						:				Tuple		  =			self._Node(_a       ,    _a       ,    _a				)
      UpperCAmelCase						:				Optional[Any]		  =			new_node
      UpperCAmelCase						:				Union[str, Any]		  =			new_node
      self._size += 1
      return self

 def   _UpperCamelCase    (			self					: int       ,    _a					: Dict				)    ->			Optional[int]:
      UpperCAmelCase						:				Tuple		  =			node._prev
      UpperCAmelCase						:				Tuple		  =			node._next

      UpperCAmelCase						:				Dict		  =			successor
      UpperCAmelCase						:				Tuple		  =			predecessor
      self._size -= 1
      UpperCAmelCase						:				Optional[Any]		  =			node._data
      UpperCAmelCase						:				Any		  =			None
      del node
      return temp




class    A_   ( _DoublyLinkedBase     ):







 """simple docstring"""





 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			Union[str, Any]:
      if self.is_empty():
           raise Exception("List is empty"				)
      return self._header._next._data
 def   _UpperCamelCase    (			self					: Any				)    ->			int:
      if self.is_empty():
           raise Exception("List is empty"				)
      return self._trailer._prev._data
 def   _UpperCamelCase    (			self					: Tuple       ,    _a					: Optional[int]				)    ->			Tuple:
      return self._insert(self._header       ,    _a       ,    self._header._next				)
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: Dict				)    ->			List[str]:
      return self._insert(self._trailer._prev       ,    _a       ,    self._trailer				)
 def   _UpperCamelCase    (			self					: List[Any]				)    ->			Optional[int]:
      if self.is_empty():
           raise IndexError("remove_first from empty list"				)
      return self._delete(self._header._next				)

 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			List[Any]:
      if self.is_empty():
           raise IndexError("remove_first from empty list"				)
      return self._delete(self._trailer._prev				)








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,
)


_snake_case   = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5Tokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5TokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "T5EncoderModel",
          "T5ForConditionalGeneration",
          "T5Model",
          "T5PreTrainedModel",
          "load_tf_weights_in_t5",
          "T5ForQuestionAnswering",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFT5EncoderModel",
          "TFT5ForConditionalGeneration",
          "TFT5Model",
          "TFT5PreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "FlaxT5EncoderModel",
          "FlaxT5ForConditionalGeneration",
          "FlaxT5Model",
          "FlaxT5PreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta import TaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta_fast import TaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ta import (
                T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TaEncoderModel,
                TaForConditionalGeneration,
                TaForQuestionAnswering,
                TaModel,
                TaPreTrainedModel,
                load_tf_weights_in_ta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_ta import (
                TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTaEncoderModel,
                TFTaForConditionalGeneration,
                TFTaModel,
                TFTaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_ta import (
                FlaxTaEncoderModel,
                FlaxTaForConditionalGeneration,
                FlaxTaModel,
                FlaxTaPreTrainedModel,
            )


else:
      import sys

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



