
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def 				lowerCamelCase__				(						a__  :  Dict     )		->	Dict:
       if "model" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""model."""	,      """"""     )
       if "norm1" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""norm1"""	,      """attention.output.LayerNorm"""     )
       if "norm2" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""norm2"""	,      """output.LayerNorm"""     )
       if "norm" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""norm"""	,      """LayerNorm"""     )
       if "transformer" in orig_key:
              UpperCamelCase_							       =       orig_key.split("""."""     )[0].split("""_"""     )[-1]
              UpperCamelCase_							       =       orig_key.replace(f'''transformer_{layer_num}'''	,      f'''encoder.layer.{layer_num}'''     )
       if "mha.attn" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""mha.attn"""	,      """attention.self"""     )
       if "mha" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""mha"""	,      """attention"""     )
       if "W_q" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""W_q"""	,      """self.query"""     )
       if "W_k" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""W_k"""	,      """self.key"""     )
       if "W_v" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""W_v"""	,      """self.value"""     )
       if "ff.0" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""ff.0"""	,      """intermediate.dense"""     )
       if "ff.2" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""ff.2"""	,      """output.dense"""     )
       if "ff" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""ff"""	,      """output.dense"""     )
       if "mlm_class" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""mlm.mlm_class"""	,      """cls.predictions.decoder"""     )
       if "mlm" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""mlm"""	,      """cls.predictions.transform"""     )
       if "backbone.backbone.encoders" in orig_key:
              UpperCamelCase_							       =       orig_key.replace("""backbone.backbone.encoders"""	,      """encoder.layer"""     )
       if "cls" not in orig_key:
              UpperCamelCase_							       =       """mra.""" + orig_key

       return orig_key




def 				lowerCamelCase__				(						a__  :  Union[str, Any]	,      a__  :  List[Any]     )		->	List[str]:
       for key in orig_state_dict.copy().keys():
              UpperCamelCase_							       =       orig_state_dict.pop(a__     )

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     UpperCamelCase_							       =       val

       UpperCamelCase_							       =       orig_state_dict["""cls.predictions.decoder.bias"""]
       UpperCamelCase_							       =       torch.arange(a__     ).expand((1, -1)     ) + 2

       return orig_state_dict




def 				lowerCamelCase__				(						a__  :  Optional[int]	,      a__  :  str	,      a__  :  Optional[int]     )		->	Optional[int]:
       UpperCamelCase_							       =       torch.load(a__	,      map_location="""cpu"""     )["""model_state_dict"""]
       UpperCamelCase_							       =       MraConfig.from_json_file(a__     )
       UpperCamelCase_							       =       MraForMaskedLM(a__     )

       UpperCamelCase_							       =       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__":
      _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)





def 				lowerCamelCase__				(						a__  :  float	,      a__  :  float	,      a__  :  float     )		->	float:
       if mass < 0:
              raise ValueError("""The mass of the body cannot be negative"""     )
       if radius <= 0:
              raise ValueError("""The radius is always a positive non zero integer"""     )
       return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



