






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def 		A_			(		lowercase_	)							->Union[str, Any]:







 """simple docstring"""

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

 return orig_key





def 		A_			(		lowercase_					,       lowercase_	)							->List[Any]:







 """simple docstring"""

 for key in orig_state_dict.copy().keys():
  SCREAMING_SNAKE_CASE							  =						orig_state_dict.pop(lowercase_	)

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

 SCREAMING_SNAKE_CASE							  =						orig_state_dict['cls.predictions.decoder.bias']
 SCREAMING_SNAKE_CASE							  =						torch.arange(lowercase_	).expand((1, -1)	) + 2

 return orig_state_dict





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						torch.load(lowercase_					,       map_location='cpu'	)['model_state_dict']
 SCREAMING_SNAKE_CASE							  =						MraConfig.from_json_file(lowercase_	)
 SCREAMING_SNAKE_CASE							  =						MraForMaskedLM(lowercase_	)

 SCREAMING_SNAKE_CASE							  =						convert_checkpoint_helper(config.max_position_embeddings					,       lowercase_	)

 print(model.load_state_dict(lowercase_	)	)
 model.eval()
 model.save_pretrained(lowercase_	)

 print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''	)


if __name__ == "__main__":
      __UpperCAmelCase      				=							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."
      )
      __UpperCAmelCase      				=							parser.parse_args()
      convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)












def 		A_			(		lowercase_	)							->int:







 """simple docstring"""

 if not isinstance(lowercase_					,       lowercase_	):
  SCREAMING_SNAKE_CASE							  =						f'''Input value of [number={number}] must be an integer'''
  raise TypeError(lowercase_	)
 if number < 1:
  raise ValueError('Input must be a positive integer'	)
 return number * (2 * number - 1)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




