

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


def 			__UpperCamelCase    (lowerCAmelCase					:  Optional[int]     )     ->  CheckpointState:
      print(f'''Reading saved model from {model_file}'''     )
      A    =				torch.load(snake_case_,    map_location=lambda lowerCAmelCase,    lowerCAmelCase     : default_restore_location(snake_case_,    'cpu'     )     )
      return CheckpointState(**snake_case_     )



class        _UpperCAmelCase :
  '''simple docstring'''







  def __init__( self	: Tuple		,						UpperCamelCase__	: Any ):
        A    =				src_file







  def 	UpperCamelCase       ( self	: Tuple ):
        raise NotImplementedError







  @staticmethod
  def 	UpperCamelCase       ( UpperCamelCase__	: str		,						*UpperCamelCase__	: Union[str, Any]		,						**UpperCamelCase__	: List[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        _UpperCAmelCase (   UpperCamelCase_			):
  '''simple docstring'''







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



class        _UpperCAmelCase (   UpperCamelCase_			):
  '''simple docstring'''







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



class        _UpperCAmelCase (   UpperCamelCase_			):
  '''simple docstring'''







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


def 			__UpperCamelCase    (lowerCAmelCase					:  Optional[Any],    lowerCAmelCase					:  Dict,    lowerCAmelCase					:  Optional[int]     )     ->  Optional[int]:
      A    =				Path(snake_case_     )
      dest_dir.mkdir(exist_ok=snake_case_     )

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


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

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






from ..utils import requires_backends


_UpperCAmelCase    	=			None



class        _UpperCAmelCase :
  '''simple docstring'''







  def __init__( self	: List[str]		,						*UpperCamelCase__	: str		,						**UpperCamelCase__	: Any ):
        requires_backends(self		,						['detectron2'] )







  @classmethod
  def 	UpperCamelCase       ( cls	: Dict		,						*UpperCamelCase__	: Tuple		,						**UpperCamelCase__	: List[Any] ):
        requires_backends(cls		,						['detectron2'] )



