code: 
  
  
  '''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  :      Optional[Any]      		=				collections.namedtuple(
      """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
  )
  
  
  
  
  
  def 							a_       (						lowercase    )			->CheckpointState:
  
  
  
  
  
  
      '''simple docstring'''
  
      print(F'Reading saved model from {model_file}'    )
      __lowerCamelCase		:      Optional[int]											=					torch.load(snake_case_							,						map_location=lambda lowercase							,						lowercase    : default_restore_location(snake_case_							,						"cpu"    )    )
      return CheckpointState(**snake_case_    )
  
  
  
  
  
  class _snake_case   :
         """simple docstring"""
  
  
  
  
  
         def __init__(   self			,				__A	)     ->Union[str, Any]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             __lowerCamelCase		:      Dict											=					src_file
  
  
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             raise NotImplementedError
  
  
  
  
  
  
  
         @staticmethod
         def 		__SCREAMING_SNAKE_CASE(   __A			,				*__A			,				**__A	)     ->"DPRState":
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             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 _snake_case   ( UpperCamelCase_   ):
         """simple docstring"""
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             __lowerCamelCase		:      Optional[int]											=					DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"	)[0]	)	)
             print(f'Loading DPR biencoder from {self.src_file}'	)
             __lowerCamelCase		:      Optional[int]											=					load_states_from_checkpoint(self.src_file	)
             __lowerCamelCase		:      List[str]											=					model.ctx_encoder, '''ctx_model.'''
             # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
             __lowerCamelCase		:      Optional[int]											=					{'''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		:      int											=					key[len(__UpperCamelCase	) :]
                     if not key.startswith("encode_proj."	):
                         __lowerCamelCase		:      Any											=					'''bert_model.''' + key
                     __lowerCamelCase		:      List[str]											=					value
             encoder.load_state_dict(__UpperCamelCase	)
             return model
  
  
  
  
  
  class _snake_case   ( UpperCamelCase_   ):
         """simple docstring"""
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             __lowerCamelCase		:      Union[str, Any]											=					DPRQuestionEncoder(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		:      Optional[int]											=					model.question_encoder, '''question_model.'''
             # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
             __lowerCamelCase		:      Optional[Any]											=					{'''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		:      Tuple											=					'''bert_model.''' + key
                     __lowerCamelCase		:      str											=					value
             encoder.load_state_dict(__UpperCamelCase	)
             return model
  
  
  
  
  
  class _snake_case   ( UpperCamelCase_   ):
         """simple docstring"""
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             __lowerCamelCase		:      Optional[int]											=					DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"	)[0]	)	)
             print(f'Loading DPR reader from {self.src_file}'	)
             __lowerCamelCase		:      Dict											=					load_states_from_checkpoint(self.src_file	)
             # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
             __lowerCamelCase		:      Optional[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"	):
                     __lowerCamelCase		:      List[Any]											=					'''encoder.bert_model.''' + key[len("encoder."	) :]
                 __lowerCamelCase		:      Optional[int]											=					value
             model.span_predictor.load_state_dict(__UpperCamelCase	)
             return model
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Optional[int]:
  
  
  
  
  
  
      '''simple docstring'''
  
      __lowerCamelCase		:      Optional[int]											=					Path(snake_case_    )
      dest_dir.mkdir(exist_ok=snake_case_    )
  
      __lowerCamelCase		:      Tuple											=					DPRState.from_type(snake_case_							,						src_file=snake_case_    )
      __lowerCamelCase		:      str											=					dpr_state.load_dpr_model()
      model.save_pretrained(snake_case_    )
      model.from_pretrained(snake_case_    )  # sanity check
  
  
  if __name__ == "__main__":
      __lowercase  :      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.""")
      __lowercase  :      Tuple      		=				parser.parse_args()
  
      __lowercase  :      Optional[Any]      		=				Path(args.src)
      __lowercase  :      Optional[int]      		=				f"converted-{src_file.name}" if args.dest is None else args.dest
      __lowercase  :      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)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  # tests directory-specific settings - this file is run automatically
  # by pytest before any tests are run
  
  import sys
  import warnings
  from os.path import abspath, dirname, join
  
  
  # allow having multiple repository checkouts and not needing to remember to rerun
  # 'pip install -e .[dev]' when switching between checkouts and running tests.
  __lowercase  :      Tuple      		=				abspath(join(dirname(dirname(dirname(__file__))), """src"""))
  sys.path.insert(1, git_repo_path)
  
  
  # silence FutureWarning warnings in tests since often we can't act on them until
  # they become normal warnings - i.e. the tests still need to test the current functionality
  warnings.simplefilter(action="""ignore""", category=FutureWarning)
  
  
  
  
  
  def 							a_       (						lowercase    )			->Any:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				from diffusers.utils.testing_utils import pytest_addoption_shared
  
  				pytest_addoption_shared(lowercase    )
  
  
  
  
  
  def 							a_       (						lowercase    )			->str:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				from diffusers.utils.testing_utils import pytest_terminal_summary_main
  
  				__lowerCamelCase		:      Union[str, Any]											=					terminalreporter.config.getoption("--make-reports"    )
  				if make_reports:
  								pytest_terminal_summary_main(lowercase							,						id=lowercase    )
  
  
  
  
  
  