





'''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


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


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		print(F"Reading saved model from {model_file}"  )
		__UpperCAmelCase	:							int     =     torch.load(_UpperCAmelCase,       map_location=lambda _UpperCAmelCase,       _UpperCAmelCase  : default_restore_location(_UpperCAmelCase,       "cpu"  )  )
		return CheckpointState(**_UpperCAmelCase  )


class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      str		,	UpperCAmelCase_       :      Path		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     src_file
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							raise NotImplementedError






					@staticmethod
					def 		lowerCamelCase_   (   UpperCAmelCase_       :      str		,	*UpperCAmelCase_       :      Union[str, Any]		,	**UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""






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






class      SCREAMING_SNAKE_CASE__      (   __lowerCAmelCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







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






class      SCREAMING_SNAKE_CASE__      (   __lowerCAmelCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      Any		):







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






class      SCREAMING_SNAKE_CASE__      (   __lowerCAmelCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      int		):







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






def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							Any     =     Path(_UpperCAmelCase  )
		dest_dir.mkdir(exist_ok=_UpperCAmelCase  )

		__UpperCAmelCase	:							str     =     DPRState.from_type(_UpperCAmelCase,       src_file=_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     dpr_state.load_dpr_model()
		model.save_pretrained(_UpperCAmelCase  )
		model.from_pretrained(_UpperCAmelCase  )  # sanity check


if __name__ == "__main__":
			lowerCAmelCase__      :   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.")
			lowerCAmelCase__      :   Union[str, Any]      	=			parser.parse_args()

			lowerCAmelCase__      :   Optional[int]      	=			Path(args.src)
			lowerCAmelCase__      :   Dict      	=			f"converted-{src_file.name}" if args.dest is None else args.dest
			lowerCAmelCase__      :   str      	=			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)








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


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

def       _a	(					SCREAMING_SNAKE_CASE						:					str  ):







 """simple docstring"""



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

class    __magic_name__						:


     def __init__(   self				:  str				,						lowerCamelCase__				:  Path      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__       :     List[str] 							= src_file


     def   UpperCAmelCase__						(   self				:  Dict      )	->   int:





      '''simple docstring'''






      raise NotImplementedError


     @staticmethod
     def   UpperCAmelCase__						(   lowerCamelCase__				:  str				,						*lowerCamelCase__				:  Union[str, Any]				,						**lowerCamelCase__				:  Optional[int]      )	->   "DPRState":





      '''simple docstring'''






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



class    __magic_name__						(  __lowerCAmelCase):


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





      '''simple docstring'''






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



class    __magic_name__						(  __lowerCAmelCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   Dict:





      '''simple docstring'''






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



class    __magic_name__						(  __lowerCAmelCase):


     def   UpperCAmelCase__						(   self				:  int      )	->   Any:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''      )[0]      )      )
      print(F"Loading DPR reader from {self.src_file}"      )
      UpperCamelCase__       :     List[str] 							= load_states_from_checkpoint(self.src_file      )
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      UpperCamelCase__       :     Tuple 							= {
          '''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__       :     List[str] 							= '''encoder.bert_model.''' + key[len('''encoder.'''      ) :]
       UpperCamelCase__       :     List[Any] 							= value
      model.span_predictor.load_state_dict(lowerCamelCase__      )
      return model



def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					Path     ,			SCREAMING_SNAKE_CASE						:					Path  ):







 """simple docstring"""



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

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


if __name__ == "__main__":
       __UpperCamelCase							:							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.")
       __UpperCamelCase							:							Union[str, Any]    							=					parser.parse_args()

       __UpperCamelCase							:							Optional[int]    							=					Path(args.src)
       __UpperCamelCase							:							Dict    							=					f"converted-{src_file.name}" if args.dest is None else args.dest
       __UpperCamelCase							:							str    							=					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)


