

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


_lowerCamelCase							=collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     print(F'Reading saved model from {model_file}'     )
     SCREAMING_SNAKE_CASE 		=torch.load(UpperCamelCase__,       map_location=lambda lowerCAmelCase_,       lowerCAmelCase_     : default_restore_location(UpperCamelCase__,       'cpu'     )     )
     return CheckpointState(**UpperCamelCase__     )



class 						a_			:



      """simple docstring"""







      def __init__( self	:				Optional[int]     ,snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=src_file
      def 			_lowerCAmelCase	( self	:				Optional[int]							):
           raise NotImplementedError


      @staticmethod
      def 			_lowerCAmelCase	( snake_case	:				List[Any]     ,*snake_case	:				Dict     ,**snake_case	:				Tuple							):



           if comp_type.startswith('c'							):
                return DPRContextEncoderState(*A_     ,**A_							)
           if comp_type.startswith('q'							):
                return DPRQuestionEncoderState(*A_     ,**A_							)
           if comp_type.startswith('r'							):
                return DPRReaderState(*A_     ,**A_							)
           else:
                raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'							)







class 						a_			(				__A	):



      """simple docstring"""







      def 			_lowerCAmelCase	( self	:				str							):
           SCREAMING_SNAKE_CASE 		=DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
           print(f'Loading DPR biencoder from {self.src_file}'							)
           SCREAMING_SNAKE_CASE 		=load_states_from_checkpoint(self.src_file							)
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=model.ctx_encoder, 'ctx_model.'
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           SCREAMING_SNAKE_CASE 		={'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(A_							):
                     SCREAMING_SNAKE_CASE 		=key[len(A_							) :]
                     if not key.startswith('encode_proj.'							):
                          SCREAMING_SNAKE_CASE 		='bert_model.' + key
                     SCREAMING_SNAKE_CASE 		=value
           encoder.load_state_dict(A_							)
           return model







class 						a_			(				__A	):



      """simple docstring"""







      def 			_lowerCAmelCase	( self	:				List[str]							):
           SCREAMING_SNAKE_CASE 		=DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
           print(f'Loading DPR biencoder from {self.src_file}'							)
           SCREAMING_SNAKE_CASE 		=load_states_from_checkpoint(self.src_file							)
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=model.question_encoder, 'question_model.'
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           SCREAMING_SNAKE_CASE 		={'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(A_							):
                     SCREAMING_SNAKE_CASE 		=key[len(A_							) :]
                     if not key.startswith('encode_proj.'							):
                          SCREAMING_SNAKE_CASE 		='bert_model.' + key
                     SCREAMING_SNAKE_CASE 		=value
           encoder.load_state_dict(A_							)
           return model







class 						a_			(				__A	):



      """simple docstring"""







      def 			_lowerCAmelCase	( self	:				int							):
           SCREAMING_SNAKE_CASE 		=DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'							)[0]							)							)
           print(f'Loading DPR reader from {self.src_file}'							)
           SCREAMING_SNAKE_CASE 		=load_states_from_checkpoint(self.src_file							)
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           SCREAMING_SNAKE_CASE 		={
               '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'							):
                     SCREAMING_SNAKE_CASE 		='encoder.bert_model.' + key[len('encoder.'							) :]
                SCREAMING_SNAKE_CASE 		=value
           model.span_predictor.load_state_dict(A_							)
           return model







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=Path(UpperCamelCase__     )
     dest_dir.mkdir(exist_ok=UpperCamelCase__     )

     SCREAMING_SNAKE_CASE 		=DPRState.from_type(UpperCamelCase__,       src_file=UpperCamelCase__     )
     SCREAMING_SNAKE_CASE 		=dpr_state.load_dpr_model()
     model.save_pretrained(UpperCamelCase__     )
     model.from_pretrained(UpperCamelCase__     )  # sanity check


if __name__ == "__main__":
 _lowerCamelCase							=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.")
 _lowerCamelCase							=parser.parse_args()

 _lowerCamelCase							=Path(args.src)
 _lowerCamelCase							=f'converted-{src_file.name}' if args.dest is None else args.dest
 _lowerCamelCase							=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)






"""simple docstring"""






from transformers import ProcessorMixin




class    __snake_case(					__A       ):
    _A       = '''AutoFeatureExtractor'''
    _A       = '''AutoTokenizer'''
