


from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class    __magic_name__						:


     def __init__(   self				:  str				,						lowerCamelCase__				:  Any      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= data
      UpperCamelCase__       :     Node | None 							= None


     def __str__(   self				:  int      )	->   str:





      '''simple docstring'''






      return F"{self.data}"



class    __magic_name__						:


     def __init__(   self				:  Optional[int]      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     Node | None 							= None
      UpperCamelCase__       :     Node | None 							= None


     def __iter__(   self				:  Optional[Any]      )	->   Iterator[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= self.front
      while node:
       yield node.data
       UpperCamelCase__       :     Dict 							= node.next


     def __len__(   self				:  Any      )	->   int:





      '''simple docstring'''






      return len(tuple(iter(self      )      )      )


     def __str__(   self				:  Union[str, Any]      )	->   str:





      '''simple docstring'''






      return " <- ".join(str(lowerCamelCase__      ) for item in self      )


     def   UpperCAmelCase__						(   self				:  List[str]      )	->   bool:





      '''simple docstring'''






      return len(self      ) == 0


     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  Any      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     str 							= Node(lowerCamelCase__      )
      if self.is_empty():
       UpperCamelCase__       :     List[str] 							= node
      else:
       assert isinstance(self.rear				,						lowerCamelCase__      )
       UpperCamelCase__       :     str 							= node
       UpperCamelCase__       :     Optional[Any] 							= node


     def   UpperCAmelCase__						(   self				:  Any      )	->   Any:





      '''simple docstring'''






      if self.is_empty():
       raise IndexError('''dequeue from empty queue'''      )
      assert isinstance(self.front				,						lowerCamelCase__      )
      UpperCamelCase__       :     Optional[int] 							= self.front
      UpperCamelCase__       :     Dict 							= self.front.next
      if self.front is None:
       UpperCamelCase__       :     Any 							= None
      return node.data


     def   UpperCAmelCase__						(   self				:  Optional[int]      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= None


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

       testmod()






from typing import TYPE_CHECKING

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


__UpperCamelCase							:							Dict    							=					{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Any    							=					[
           "DPRContextEncoderTokenizerFast",
           "DPRQuestionEncoderTokenizerFast",
           "DPRReaderTokenizerFast",
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							str    							=					[
           "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPRContextEncoder",
           "DPRPretrainedContextEncoder",
           "DPRPreTrainedModel",
           "DPRPretrainedQuestionEncoder",
           "DPRPretrainedReader",
           "DPRQuestionEncoder",
           "DPRReader",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							int    							=					[
           "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFDPRContextEncoder",
           "TFDPRPretrainedContextEncoder",
           "TFDPRPretrainedQuestionEncoder",
           "TFDPRPretrainedReader",
           "TFDPRQuestionEncoder",
           "TFDPRReader",
       ]


if TYPE_CHECKING:
       from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
       from .tokenization_dpr import (
           DPRContextEncoderTokenizer,
           DPRQuestionEncoderTokenizer,
           DPRReaderOutput,
           DPRReaderTokenizer,
       )

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_dpr_fast import (
                  DPRContextEncoderTokenizerFast,
                  DPRQuestionEncoderTokenizerFast,
                  DPRReaderTokenizerFast,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dpr import (
                  DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPRContextEncoder,
                  DPRPretrainedContextEncoder,
                  DPRPreTrainedModel,
                  DPRPretrainedQuestionEncoder,
                  DPRPretrainedReader,
                  DPRQuestionEncoder,
                  DPRReader,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_dpr import (
                  TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDPRContextEncoder,
                  TFDPRPretrainedContextEncoder,
                  TFDPRPretrainedQuestionEncoder,
                  TFDPRPretrainedReader,
                  TFDPRQuestionEncoder,
                  TFDPRReader,
              )

else:
       import sys

       __UpperCamelCase							:							Dict    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


