code: 
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			int:
  
  
  
  
  
  
  
       if "model" in orig_key:
            UpperCamelCase__    :     Any        = orig_key.replace("model."      ,					""    )
       if "norm1" in orig_key:
            UpperCamelCase__    :     Optional[Any]        = orig_key.replace("norm1"      ,					"attention.output.LayerNorm"    )
       if "norm2" in orig_key:
            UpperCamelCase__    :     Tuple        = orig_key.replace("norm2"      ,					"output.LayerNorm"    )
       if "norm" in orig_key:
            UpperCamelCase__    :     Tuple        = orig_key.replace("norm"      ,					"LayerNorm"    )
       if "transformer" in orig_key:
            UpperCamelCase__    :     Any        = orig_key.split("."    )[0].split("_"    )[-1]
            UpperCamelCase__    :     Optional[int]        = orig_key.replace(f'transformer_{layer_num}'      ,					f'encoder.layer.{layer_num}'    )
       if "mha.attn" in orig_key:
            UpperCamelCase__    :     List[Any]        = orig_key.replace("mha.attn"      ,					"attention.self"    )
       if "mha" in orig_key:
            UpperCamelCase__    :     Dict        = orig_key.replace("mha"      ,					"attention"    )
       if "W_q" in orig_key:
            UpperCamelCase__    :     Any        = orig_key.replace("W_q"      ,					"self.query"    )
       if "W_k" in orig_key:
            UpperCamelCase__    :     Tuple        = orig_key.replace("W_k"      ,					"self.key"    )
       if "W_v" in orig_key:
            UpperCamelCase__    :     List[str]        = orig_key.replace("W_v"      ,					"self.value"    )
       if "ff.0" in orig_key:
            UpperCamelCase__    :     Tuple        = orig_key.replace("ff.0"      ,					"intermediate.dense"    )
       if "ff.2" in orig_key:
            UpperCamelCase__    :     Optional[int]        = orig_key.replace("ff.2"      ,					"output.dense"    )
       if "ff" in orig_key:
            UpperCamelCase__    :     Tuple        = orig_key.replace("ff"      ,					"output.dense"    )
       if "mlm_class" in orig_key:
            UpperCamelCase__    :     str        = orig_key.replace("mlm.mlm_class"      ,					"cls.predictions.decoder"    )
       if "mlm" in orig_key:
            UpperCamelCase__    :     List[str]        = orig_key.replace("mlm"      ,					"cls.predictions.transform"    )
       if "backbone.backbone.encoders" in orig_key:
            UpperCamelCase__    :     Tuple        = orig_key.replace("backbone.backbone.encoders"      ,					"encoder.layer"    )
       if "cls" not in orig_key:
            UpperCamelCase__    :     Optional[Any]        = "mra." + orig_key
  
       return orig_key
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[Any]:
  
  
  
  
  
  
  
       for key in orig_state_dict.copy().keys():
            UpperCamelCase__    :     Tuple        = orig_state_dict.pop(_A    )
  
            if ("pooler" in key) or ("sen_class" in key):
                 continue
            else:
                 UpperCamelCase__    :     Dict        = val
  
       UpperCamelCase__    :     List[str]        = orig_state_dict["cls.predictions.decoder.bias"]
       UpperCamelCase__    :     Optional[int]        = torch.arange(_A    ).expand((1, -1)    ) + 2
  
       return orig_state_dict
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			str:
       UpperCamelCase__    :     int        = torch.load(_A      ,					map_location="cpu"    )["model_state_dict"]
       UpperCamelCase__    :     Union[str, Any]        = MraConfig.from_json_file(_A    )
       UpperCamelCase__    :     str        = MraForMaskedLM(_A    )
  
       UpperCamelCase__    :     str        = convert_checkpoint_helper(config.max_position_embeddings      ,					_A    )
  
       print(model.load_state_dict(_A    )    )
       model.eval()
       model.save_pretrained(_A    )
  
       print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'    )
  
  
  if __name__ == "__main__":
   lowerCamelCase			:							Dict       =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.'''
   )
   lowerCamelCase			:							List[str]       =parser.parse_args()
   convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			list[int]:
       if not list_of_ints:
            return []
  
       if min(__lowerCAmelCase    ) < 0:
            raise ValueError("All numbers must be positive"    )
  
       UpperCamelCase__    :     Dict        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
       return _msd_radix_sort(__lowerCAmelCase      ,					__lowerCAmelCase    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			list[int]:
       if bit_position == 0 or len(__lowerCAmelCase    ) in [0, 1]:
            return list_of_ints
  
       UpperCamelCase__    :     Union[str, Any]        =						[]
       UpperCamelCase__    :     Dict        =						[]
       # Split numbers based on bit at bit_position from the right
       for number in list_of_ints:
            if (number >> (bit_position - 1)) & 1:
                 # number has a one at bit bit_position
                 ones.append(__lowerCAmelCase    )
            else:
                 # number has a zero at bit bit_position
                 zeros.append(__lowerCAmelCase    )
  
      # recursively split both lists further
       UpperCamelCase__    :     str        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )
       UpperCamelCase__    :     Optional[Any]        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )
  
       # recombine lists
       UpperCamelCase__    :     Optional[int]        =						zeros
       res.extend(__lowerCAmelCase    )
  
       return res
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Dict:
       UpperCamelCase__    :     Optional[int]        =						len(__lowerCAmelCase    )
       if not list_of_ints or length == 1:
            return
  
       if min(__lowerCAmelCase    ) < 0:
            raise ValueError("All numbers must be positive"    )
  
       UpperCamelCase__    :     int        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
       _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					0      ,					__lowerCAmelCase    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[str]:
       if bit_position == 0 or end_index - begin_index <= 1:
            return
  
       bit_position -= 1
  
       UpperCamelCase__    :     Any        =						begin_index
       UpperCamelCase__    :     List[str]        =						end_index - 1
       while i <= j:
            UpperCamelCase__    :     Tuple        =						False
            if not (list_of_ints[i] >> bit_position) & 1:
                 # found zero at the beginning
                 i += 1
                 UpperCamelCase__    :     Any        =						True
            if (list_of_ints[j] >> bit_position) & 1:
                 # found one at the end
                 j -= 1
                 UpperCamelCase__    :     Dict        =						True
  
            if changed:
                 continue
  
            UpperCamelCase__       ,  UpperCamelCase__    :     Any        =						list_of_ints[j], list_of_ints[i]
            j -= 1
            if j != i:
                 i += 1
  
       _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
       _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()