



'''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


UpperCamelCase__    :     Dict			  =   collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)



def     UpperCAmelCase     (    a_		)   ->       CheckpointState:



   """simple docstring"""






   print(F"Reading saved model from {model_file}"		)
   A_  :		Tuple						=					torch.load(a_      ,		map_location=lambda a_      ,		a_		: default_restore_location(a_      ,		"""cpu"""		)		)
   return CheckpointState(**a_		)


class 							_lowerCAmelCase	:


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase  )    ->    Union[str, Any]:
    A_  :		int						=					src_file







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    raise NotImplementedError







 @staticmethod
 def       UpperCAmelCase_       (      _lowerCamelCase					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    "DPRState":
    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 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

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


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

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


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

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



def     UpperCAmelCase     (    a_      ,		a_      ,		a_		)   ->       Dict:



   """simple docstring"""






   A_  :		Tuple						=					Path(a_		)
   dest_dir.mkdir(exist_ok=a_		)

   A_  :		Optional[Any]						=					DPRState.from_type(a_      ,		src_file=a_		)
   A_  :		Optional[Any]						=					dpr_state.load_dpr_model()
   model.save_pretrained(a_		)
   model.from_pretrained(a_		)  # sanity check


if __name__ == "__main__":
  UpperCamelCase__    :     int			  =   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__    :     Dict			  =   parser.parse_args()

  UpperCamelCase__    :     Dict			  =   Path(args.src)
  UpperCamelCase__    :     Tuple			  =   f'converted-{src_file.name}' if args.dest is None else args.dest
  UpperCamelCase__    :     Optional[Any]			  =   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'''





def     UpperCAmelCase     (    a_      ,		a_		)   ->       set:



   """simple docstring"""






   assert isinstance(a_      ,		a_		), F"The input value of [set_a={set_a}] is not a set"
   assert isinstance(a_      ,		a_		), F"The input value of [set_b={set_b}] is not a set"

   return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
  from doctest import testmod

  testmod()
