code: 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 SCREAMING_SNAKE_CASE				(     lowercase_:	str				):
         print(F"Reading saved model from {model_file}"				)
         _UpperCamelCase     :       str  						=  torch.load(lowercase_		,						map_location=lambda lowercase_		,						lowercase_				: default_restore_location(lowercase_		,						'cpu'				)				)
         return CheckpointState(**lowercase_				)
  
  
  
  
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Optional[Any]       ,			lowerCamelCase       :		Path  ) ->							Tuple:
          _UpperCamelCase     :       Any  						=  src_file
  
   def 					__snake_case					(			self       :		Tuple  ) ->							Optional[int]:
          raise NotImplementedError
  
   @staticmethod
   def 					__snake_case					(			lowerCamelCase       :		str       ,			*lowerCamelCase       :		List[Any]       ,			**lowerCamelCase       :		Optional[Any]  ) ->							"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 					__snake_case					(			self       :		Union[str, Any]  ) ->							Optional[Any]:
          _UpperCamelCase     :       List[str]  						=  DPRContextEncoder(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.ctx_encoder, 'ctx_model.'
          # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
          _UpperCamelCase     :       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(lowerCamelCase  ):
                        _UpperCamelCase     :       Dict  						=  key[len(lowerCamelCase  ) :]
                        if not key.startswith('encode_proj.'  ):
                               _UpperCamelCase     :       int  						=  'bert_model.' + key
                        _UpperCamelCase     :       int  						=  value
          encoder.load_state_dict(lowerCamelCase  )
          return model
  
  
  
  
  
  
  
  class    __lowerCAmelCase  (       a_						):
  
  
  
   '''simple docstring'''
  
   def 					__snake_case					(			self       :		List[Any]  ) ->							Optional[int]:
          _UpperCamelCase     :       Tuple  						=  DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'  )[0]  )  )
          print(f"Loading DPR biencoder from {self.src_file}"  )
          _UpperCamelCase     :       Tuple  						=  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[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(lowerCamelCase  ):
                        _UpperCamelCase     :       Optional[Any]  						=  key[len(lowerCamelCase  ) :]
                        if not key.startswith('encode_proj.'  ):
                               _UpperCamelCase     :       List[str]  						=  'bert_model.' + key
                        _UpperCamelCase     :       Optional[int]  						=  value
          encoder.load_state_dict(lowerCamelCase  )
          return model
  
  
  
  
  
  
  
  class    __lowerCAmelCase  (       a_						):
  
  
  
   '''simple docstring'''
  
   def 					__snake_case					(			self       :		Optional[int]  ) ->							Optional[Any]:
          _UpperCamelCase     :       Optional[int]  						=  DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'  )[0]  )  )
          print(f"Loading DPR reader from {self.src_file}"  )
          _UpperCamelCase     :       Optional[int]  						=  load_states_from_checkpoint(self.src_file  )
          # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
          _UpperCamelCase     :       int  						=  {
              '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     :       str  						=  'encoder.bert_model.' + key[len('encoder.'  ) :]
                 _UpperCamelCase     :       List[str]  						=  value
          model.span_predictor.load_state_dict(lowerCamelCase  )
          return model
  
  
  
  
  
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Path		,						lowercase_:	Path				):
         _UpperCamelCase     :       List[str]  						=  Path(lowercase_				)
         dest_dir.mkdir(exist_ok=lowercase_				)
  
         _UpperCamelCase     :       List[Any]  						=  DPRState.from_type(lowercase_		,						src_file=lowercase_				)
         _UpperCamelCase     :       Dict  						=  dpr_state.load_dpr_model()
         model.save_pretrained(lowercase_				)
         model.from_pretrained(lowercase_				)  # 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)
  
  
  
  
style_context: from .chunk_utils import chunk_layer
  from .data_transforms import make_atomaa_masks
  from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
  from .loss import compute_predicted_aligned_error, compute_tm
  from .protein import Protein as OFProtein
  from .protein import to_pdb
  from .rigid_utils import Rigid, Rotation
  from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims
  
  
  
  