



"""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


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

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict		)		->  CheckpointState:

  """simple docstring"""




  print(f"Reading saved model from {model_file}"		)
  lowerCAmelCase_  :							Union[str, Any]								=					torch.load(lowerCAmelCase__       ,  map_location=lambda lowerCAmelCase__       ,  lowerCAmelCase__		: default_restore_location(lowerCAmelCase__       ,  'cpu'		)		)
  return CheckpointState(**lowerCAmelCase__		)







class  UpperCamelCase__  :




      """simple docstring"""




      def __init__( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: Path			):
        lowerCAmelCase_  :							List[str]								=					src_file



      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        raise NotImplementedError





      @staticmethod
      def 	SCREAMING_SNAKE_CASE__					( SCREAMING_SNAKE_CASE_							: str       ,     *SCREAMING_SNAKE_CASE_							: Optional[int]       ,     **SCREAMING_SNAKE_CASE_							: int			):






        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__  (      __lowerCAmelCase    ):




      """simple docstring"""




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







class  UpperCamelCase__  (      __lowerCAmelCase    ):




      """simple docstring"""




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







class  UpperCamelCase__  (      __lowerCAmelCase    ):




      """simple docstring"""




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




def 							UpperCamelCase_       (       lowerCAmelCase__ :							Tuple       ,  lowerCAmelCase__ :							str       ,  lowerCAmelCase__ :							int		)		->  int:

  """simple docstring"""




  lowerCAmelCase_  :							Optional[Any]								=					Path(lowerCAmelCase__		)
  dest_dir.mkdir(exist_ok=lowerCAmelCase__		)

  lowerCAmelCase_  :							List[str]								=					DPRState.from_type(lowerCAmelCase__       ,  src_file=lowerCAmelCase__		)
  lowerCAmelCase_  :							str								=					dpr_state.load_dpr_model()
  model.save_pretrained(lowerCAmelCase__		)
  model.from_pretrained(lowerCAmelCase__		)  # sanity check


if __name__ == "__main__":
     lowercase__						:							Dict        =      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.""")
     lowercase__						:							Optional[Any]        =      parser.parse_args()

     lowercase__						:							List[str]        =      Path(args.src)
     lowercase__						:							str        =      f'converted-{src_file.name}' if args.dest is None else args.dest
     lowercase__						:							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)











'''simple docstring'''

import unittest
import warnings

from datasets.utils import experimental




@experimental
def    snake_case__     (  )			->       Union[str, Any]:





			'''simple docstring'''






			return "success"





class __magic_name__(						unittest.TestCase      ):






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									with warnings.catch_warnings(record=__UpperCamelCase       ) as w:
												warnings.simplefilter("""always"""       )
												self.assertEqual(dummy_function()					,   """success"""       )
									self.assertEqual(len(__UpperCamelCase       )					,   1       )