code: 
  
  import argparse
  import collections
  from pathlib import Path
  
  import torch
  from torch.serialization import default_restore_location
  
  from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
  
  
  __A   :     List[str]      =			collections.namedtuple(
      '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
  )
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> CheckpointState:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      print(f"Reading saved model from {model_file}"				)
      lowerCAmelCase				:			int    			=      torch.load(_UpperCAmelCase,      map_location=lambda _UpperCAmelCase,      _UpperCAmelCase				: default_restore_location(_UpperCAmelCase,      'cpu'				)				)
      return CheckpointState(**_UpperCAmelCase				)
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    int    ,				UpperCAmelCase_			:    Path    ):
          lowerCAmelCase				:			str    			=      src_file
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          raise NotImplementedError
  
  
  
  
  
  
      @staticmethod
      def 	lowercase__ (	UpperCAmelCase_			:    str    ,				*UpperCAmelCase_			:    Any    ,				**UpperCAmelCase_			:    Optional[Any]    ):
          if comp_type.startswith('c'    ):
              return DPRContextEncoderState(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
          if comp_type.startswith('q'    ):
              return DPRQuestionEncoderState(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
          if comp_type.startswith('r'    ):
              return DPRReaderState(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
          else:
              raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'    )
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase				:			Optional[Any]    			=      DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'    )[0]    )    )
          print(f"Loading DPR biencoder from {self.src_file}"    )
          lowerCAmelCase				:			Any    			=      load_states_from_checkpoint(self.src_file    )
          lowerCAmelCase			,  lowerCAmelCase				:			Any    			=      model.ctx_encoder, 'ctx_model.'
          # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
          lowerCAmelCase				:			Union[str, Any]    			=      {'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
          for key, value in saved_state.model_dict.items():
              if key.startswith(UpperCAmelCase_    ):
                  lowerCAmelCase				:			List[Any]    			=      key[len(UpperCAmelCase_    ) :]
                  if not key.startswith('encode_proj.'    ):
                      lowerCAmelCase				:			str    			=      'bert_model.' + key
                  lowerCAmelCase				:			Union[str, Any]    			=      value
          encoder.load_state_dict(UpperCAmelCase_    )
          return model
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			List[str]    			=      DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'    )[0]    )    )
          print(f"Loading DPR biencoder from {self.src_file}"    )
          lowerCAmelCase				:			Optional[Any]    			=      load_states_from_checkpoint(self.src_file    )
          lowerCAmelCase			,  lowerCAmelCase				:			List[Any]    			=      model.question_encoder, 'question_model.'
          # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
          lowerCAmelCase				:			Optional[int]    			=      {'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
          for key, value in saved_state.model_dict.items():
              if key.startswith(UpperCAmelCase_    ):
                  lowerCAmelCase				:			int    			=      key[len(UpperCAmelCase_    ) :]
                  if not key.startswith('encode_proj.'    ):
                      lowerCAmelCase				:			Tuple    			=      'bert_model.' + key
                  lowerCAmelCase				:			Any    			=      value
          encoder.load_state_dict(UpperCAmelCase_    )
          return model
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase				:			Any    			=      DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'    )[0]    )    )
          print(f"Loading DPR reader from {self.src_file}"    )
          lowerCAmelCase				:			Dict    			=      load_states_from_checkpoint(self.src_file    )
          # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
          lowerCAmelCase				:			Optional[Any]    			=      {
              'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
          }
          for key, value in saved_state.model_dict.items():
              if key.startswith('encoder.'    ) and not key.startswith('encoder.encode_proj'    ):
                  lowerCAmelCase				:			Tuple    			=      'encoder.bert_model.' + key[len('encoder.'    ) :]
              lowerCAmelCase				:			Any    			=      value
          model.span_predictor.load_state_dict(UpperCAmelCase_    )
          return model
  
  
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			str    			=      Path(_UpperCAmelCase				)
      dest_dir.mkdir(exist_ok=_UpperCAmelCase				)
  
      lowerCAmelCase				:			Any    			=      DPRState.from_type(_UpperCAmelCase,      src_file=_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      dpr_state.load_dpr_model()
      model.save_pretrained(_UpperCAmelCase				)
      model.from_pretrained(_UpperCAmelCase				)  # sanity check
  
  
  if __name__ == "__main__":
   __A   :     Optional[Any]      =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
   )
   parser.add_argument(
       '''--src''',
       type=str,
       help=(
           '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
           ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
           ''' \'retriever\' checkpoints.'''
       ),
   )
   parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
   __A   :     Optional[int]      =			parser.parse_args()
  
   __A   :     Any      =			Path(args.src)
   __A   :     Optional[Any]      =			F'converted-{src_file.name}' if args.dest is None else args.dest
   __A   :     Dict      =			Path(dest_dir)
   assert src_file.exists()
   assert (
       args.type is not None
   ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
   convert(args.type, src_file, dest_dir)
  
  
  
  
  
  
style_context: 
  
  __A   :     List[Any]      =			'''
  # Installation de Transformers
  ! pip install transformers datasets
  # Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
  # ! pip install git+https://github.com/huggingface/transformers.git
  '''
  
  __A   :     Dict      =			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
  __A   :     Tuple      =			{
      '''{processor_class}''': '''FakeProcessorClass''',
      '''{model_class}''': '''FakeModelClass''',
      '''{object_class}''': '''FakeObjectClass''',
  }
  
  
  
  
  
  