code: 
  """simple docstring"""
  
  
  
  
  
  
  
  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
  
  
  SCREAMING_SNAKE_CASE_     				=   collections.namedtuple(
      'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
  )
  def 			__UpperCAmelCase		(		a_			:		str				)     ->       Union[str, Any]:
     print(F'''Reading saved model from {model_file}'''				)
     _UpperCAmelCase   	=					torch.load(_A    ,  map_location=lambda a_    ,  a_				: default_restore_location(_A    ,  "cpu"				)				)
     return CheckpointState(**_A				)
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					src_file
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            raise NotImplementedError
  
  
  
  
  
  
  
         @staticmethod
         def    _snake_case			(					lowerCamelCase_							,*lowerCamelCase_							,**lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if comp_type.startswith("c"		):
               return DPRContextEncoderState(*lowercase_							,**lowercase_		)
            if comp_type.startswith("q"		):
               return DPRQuestionEncoderState(*lowercase_							,**lowercase_		)
            if comp_type.startswith("r"		):
               return DPRReaderState(*lowercase_							,**lowercase_		)
            else:
               raise ValueError("Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'."		)
  
  
  
  class     __a			(					lowerCamelCase__			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"		)[0]		)		)
            print(f'''Loading DPR biencoder from {self.src_file}'''		)
            _UpperCAmelCase   	=					load_states_from_checkpoint(self.src_file		)
            _UpperCAmelCase	,							_UpperCAmelCase   	=					model.ctx_encoder, "ctx_model."
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _UpperCAmelCase   	=					{"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(lowercase_		):
                  _UpperCAmelCase   	=					key[len(lowercase_		) :]
                  if not key.startswith("encode_proj."		):
                     _UpperCAmelCase   	=					"bert_model." + key
                  _UpperCAmelCase   	=					value
            encoder.load_state_dict(lowercase_		)
            return model
  
  
  
  class     __a			(					lowerCamelCase__			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"		)[0]		)		)
            print(f'''Loading DPR biencoder from {self.src_file}'''		)
            _UpperCAmelCase   	=					load_states_from_checkpoint(self.src_file		)
            _UpperCAmelCase	,							_UpperCAmelCase   	=					model.question_encoder, "question_model."
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _UpperCAmelCase   	=					{"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(lowercase_		):
                  _UpperCAmelCase   	=					key[len(lowercase_		) :]
                  if not key.startswith("encode_proj."		):
                     _UpperCAmelCase   	=					"bert_model." + key
                  _UpperCAmelCase   	=					value
            encoder.load_state_dict(lowercase_		)
            return model
  
  
  
  class     __a			(					lowerCamelCase__			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"		)[0]		)		)
            print(f'''Loading DPR reader from {self.src_file}'''		)
            _UpperCAmelCase   	=					load_states_from_checkpoint(self.src_file		)
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            _UpperCAmelCase   	=					{
                "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"		):
                  _UpperCAmelCase   	=					"encoder.bert_model." + key[len("encoder."		) :]
               _UpperCAmelCase   	=					value
            model.span_predictor.load_state_dict(lowercase_		)
            return model
  
  
  
  def 			__UpperCAmelCase		(		a_			:		str    ,  a_			:		Path    ,  a_			:		Path				)     ->       List[str]:
     _UpperCAmelCase   	=					Path(_A				)
     dest_dir.mkdir(exist_ok=_A				)
  
     _UpperCAmelCase   	=					DPRState.from_type(_A    ,  src_file=_A				)
     _UpperCAmelCase   	=					dpr_state.load_dpr_model()
     model.save_pretrained(_A				)
     model.from_pretrained(_A				)  # sanity check
  
  
  if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     				=   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.')
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  
    SCREAMING_SNAKE_CASE_     				=   Path(args.src)
    SCREAMING_SNAKE_CASE_     				=   f'''converted-{src_file.name}''' if args.dest is None else args.dest
    SCREAMING_SNAKE_CASE_     				=   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: 
  """simple docstring"""
  
  
  
  
  
  
  
  import warnings
  
  from .generation import FlaxGenerationMixin
  
  
  
  
  
  
  class     __a			(					lowerCAmelCase_			):
         # warning at import time
         warnings.warn(
             'Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '
             'be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'				,	lowerCAmelCase_				,	)
  
  