code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        if "model" in orig_key:
              lowercase			:   Any					   =		orig_key.replace('''model.''',    ''''''     )
        if "norm1" in orig_key:
              lowercase			:   List[str]					   =		orig_key.replace('''norm1''',    '''attention.output.LayerNorm'''     )
        if "norm2" in orig_key:
              lowercase			:   str					   =		orig_key.replace('''norm2''',    '''output.LayerNorm'''     )
        if "norm" in orig_key:
              lowercase			:   Any					   =		orig_key.replace('''norm''',    '''LayerNorm'''     )
        if "transformer" in orig_key:
              lowercase			:   Any					   =		orig_key.split('''.'''     )[0].split('''_'''     )[-1]
              lowercase			:   str					   =		orig_key.replace(f"""transformer_{layer_num}""",    f"""encoder.layer.{layer_num}"""     )
        if "mha.attn" in orig_key:
              lowercase			:   List[Any]					   =		orig_key.replace('''mha.attn''',    '''attention.self'''     )
        if "mha" in orig_key:
              lowercase			:   int					   =		orig_key.replace('''mha''',    '''attention'''     )
        if "W_q" in orig_key:
              lowercase			:   Union[str, Any]					   =		orig_key.replace('''W_q''',    '''self.query'''     )
        if "W_k" in orig_key:
              lowercase			:   str					   =		orig_key.replace('''W_k''',    '''self.key'''     )
        if "W_v" in orig_key:
              lowercase			:   Dict					   =		orig_key.replace('''W_v''',    '''self.value'''     )
        if "ff.0" in orig_key:
              lowercase			:   Tuple					   =		orig_key.replace('''ff.0''',    '''intermediate.dense'''     )
        if "ff.2" in orig_key:
              lowercase			:   Optional[int]					   =		orig_key.replace('''ff.2''',    '''output.dense'''     )
        if "ff" in orig_key:
              lowercase			:   str					   =		orig_key.replace('''ff''',    '''output.dense'''     )
        if "mlm_class" in orig_key:
              lowercase			:   Optional[Any]					   =		orig_key.replace('''mlm.mlm_class''',    '''cls.predictions.decoder'''     )
        if "mlm" in orig_key:
              lowercase			:   Tuple					   =		orig_key.replace('''mlm''',    '''cls.predictions.transform'''     )
        if "backbone.backbone.encoders" in orig_key:
              lowercase			:   str					   =		orig_key.replace('''backbone.backbone.encoders''',    '''encoder.layer'''     )
        if "cls" not in orig_key:
              lowercase			:   List[Any]					   =		'''mra.''' + orig_key
  
        return orig_key
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for key in orig_state_dict.copy().keys():
              lowercase			:   Dict					   =		orig_state_dict.pop(_UpperCamelCase     )
  
              if ("pooler" in key) or ("sen_class" in key):
                    continue
              else:
                    lowercase			:   List[Any]					   =		val
  
        lowercase			:   Union[str, Any]					   =		orig_state_dict['''cls.predictions.decoder.bias''']
        lowercase			:   List[Any]					   =		torch.arange(_UpperCamelCase     ).expand((1, -1)     ) + 2
  
        return orig_state_dict
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->int:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[int]					   =		torch.load(_UpperCamelCase,    map_location='''cpu'''     )['''model_state_dict''']
        lowercase			:   Union[str, Any]					   =		MraConfig.from_json_file(_UpperCamelCase     )
        lowercase			:   List[str]					   =		MraForMaskedLM(_UpperCamelCase     )
  
        lowercase			:   List[str]					   =		convert_checkpoint_helper(config.max_position_embeddings,    _UpperCamelCase     )
  
        print(model.load_state_dict(_UpperCamelCase     )     )
        model.eval()
        model.save_pretrained(_UpperCamelCase     )
  
        print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
  
  
  if __name__ == "__main__":
    __a   				=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The json file for Mra model config.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __a   				=      parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
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}''')
  
  