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
  
  
  __a   				=      collections.namedtuple(
      '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
  )
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->CheckpointState:
  
  
  
  
  
  
  
        """simple docstring"""
  
        print(f"""Reading saved model from {model_file}"""     )
        lowercase			:   int					   =		torch.load(_UpperCAmelCase,    map_location=lambda _UpperCamelCase,    _UpperCamelCase     : default_restore_location(_UpperCAmelCase,    '''cpu'''     )     )
        return CheckpointState(**_UpperCAmelCase     )
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   str					   =		src_file
  
  
        def        __lowerCamelCase			(							self		):
              raise NotImplementedError
  
  
  
  
  
  
        @staticmethod
        def        __lowerCamelCase			(							SCREAMING_SNAKE_CASE__							,						*SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		):
              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 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   Optional[Any]					   =		DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''		)[0]		)		)
              print(f"""Loading DPR biencoder from {self.src_file}"""		)
              lowercase			:   Any					   =		load_states_from_checkpoint(self.src_file		)
              lowercase			:   Any					   =		model.ctx_encoder, 'ctx_model.'
              # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
              lowercase			:   Union[str, Any]					   =		{'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_		):
                          lowercase			:   List[Any]					   =		key[len(UpperCAmelCase_		) :]
                          if not key.startswith('''encode_proj.'''		):
                                lowercase			:   str					   =		'bert_model.' + key
                          lowercase			:   Union[str, Any]					   =		value
              encoder.load_state_dict(UpperCAmelCase_		)
              return model
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   List[str]					   =		DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''		)[0]		)		)
              print(f"""Loading DPR biencoder from {self.src_file}"""		)
              lowercase			:   Optional[Any]					   =		load_states_from_checkpoint(self.src_file		)
              lowercase			:   List[Any]					   =		model.question_encoder, 'question_model.'
              # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
              lowercase			:   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(UpperCAmelCase_		):
                          lowercase			:   int					   =		key[len(UpperCAmelCase_		) :]
                          if not key.startswith('''encode_proj.'''		):
                                lowercase			:   Tuple					   =		'bert_model.' + key
                          lowercase			:   Any					   =		value
              encoder.load_state_dict(UpperCAmelCase_		)
              return model
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   Any					   =		DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''		)[0]		)		)
              print(f"""Loading DPR reader from {self.src_file}"""		)
              lowercase			:   Dict					   =		load_states_from_checkpoint(self.src_file		)
              # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
              lowercase			:   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'''		):
                          lowercase			:   Tuple					   =		'encoder.bert_model.' + key[len('''encoder.'''		) :]
                    lowercase			:   Any					   =		value
              model.span_predictor.load_state_dict(UpperCAmelCase_		)
              return model
  
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		Path(_UpperCAmelCase     )
        dest_dir.mkdir(exist_ok=_UpperCAmelCase     )
  
        lowercase			:   Any					   =		DPRState.from_type(_UpperCAmelCase,    src_file=_UpperCAmelCase     )
        lowercase			:   List[Any]					   =		dpr_state.load_dpr_model()
        model.save_pretrained(_UpperCAmelCase     )
        model.from_pretrained(_UpperCAmelCase     )  # sanity check
  
  
  if __name__ == "__main__":
    __a   				=      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.''')
    __a   				=      parser.parse_args()
  
    __a   				=      Path(args.src)
    __a   				=      F'''converted-{src_file.name}''' if args.dest is None else args.dest
    __a   				=      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: 
  
  
  import math
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		0
        if n % 2 == 0:
              count += 1
              while n % 2 == 0:
                    lowercase			:   Any					   =		int(n / 2     )
      # the n input value must be odd so that
      # we can skip one element (ie i += 2)
  
        lowercase			:   Optional[int]					   =		3
  
        while i <= int(math.sqrt(_UpperCamelCase     )     ):
              if n % i == 0:
                    count += 1
                    while n % i == 0:
                          lowercase			:   Optional[Any]					   =		int(n / i     )
              lowercase			:   str					   =		i + 2
  
        # this condition checks the prime
        # number n is greater than 2
  
        if n > 2:
              count += 1
        return count
  
  
  if __name__ == "__main__":
    __a   				=      51_24_21_83
    print(F'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
    print(F'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')
  
  