

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def        __lowercase   (  _A      )  ->    int:
      if "model" in orig_key:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = orig_key.replace("""model."""    ,     """"""      )
      if "norm1" in orig_key:
            SCREAMING_SNAKE_CASE			:   List[str]	  = orig_key.replace("""norm1"""    ,     """attention.output.LayerNorm"""      )
      if "norm2" in orig_key:
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = orig_key.replace("""norm2"""    ,     """output.LayerNorm"""      )
      if "norm" in orig_key:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = orig_key.replace("""norm"""    ,     """LayerNorm"""      )
      if "transformer" in orig_key:
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = orig_key.split("""."""      )[0].split("""_"""      )[-1]
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = orig_key.replace(F"transformer_{layer_num}"    ,     F"encoder.layer.{layer_num}"      )
      if "mha.attn" in orig_key:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = orig_key.replace("""mha.attn"""    ,     """attention.self"""      )
      if "mha" in orig_key:
            SCREAMING_SNAKE_CASE			:   Dict	  = orig_key.replace("""mha"""    ,     """attention"""      )
      if "W_q" in orig_key:
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = orig_key.replace("""W_q"""    ,     """self.query"""      )
      if "W_k" in orig_key:
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = orig_key.replace("""W_k"""    ,     """self.key"""      )
      if "W_v" in orig_key:
            SCREAMING_SNAKE_CASE			:   str	  = orig_key.replace("""W_v"""    ,     """self.value"""      )
      if "ff.0" in orig_key:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = orig_key.replace("""ff.0"""    ,     """intermediate.dense"""      )
      if "ff.2" in orig_key:
            SCREAMING_SNAKE_CASE			:   Tuple	  = orig_key.replace("""ff.2"""    ,     """output.dense"""      )
      if "ff" in orig_key:
            SCREAMING_SNAKE_CASE			:   List[str]	  = orig_key.replace("""ff"""    ,     """output.dense"""      )
      if "mlm_class" in orig_key:
            SCREAMING_SNAKE_CASE			:   List[Any]	  = orig_key.replace("""mlm.mlm_class"""    ,     """cls.predictions.decoder"""      )
      if "mlm" in orig_key:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = orig_key.replace("""mlm"""    ,     """cls.predictions.transform"""      )
      if "backbone.backbone.encoders" in orig_key:
            SCREAMING_SNAKE_CASE			:   str	  = orig_key.replace("""backbone.backbone.encoders"""    ,     """encoder.layer"""      )
      if "cls" not in orig_key:
            SCREAMING_SNAKE_CASE			:   Dict	  = """mra.""" + orig_key

      return orig_key






def        __lowercase   (  _A    ,     _A      )  ->    Dict:
      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = orig_state_dict.pop(_A      )

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

      SCREAMING_SNAKE_CASE			:   str	  = orig_state_dict["""cls.predictions.decoder.bias"""]
      SCREAMING_SNAKE_CASE			:   List[str]	  = torch.arange(_A      ).expand((1, -1)      ) + 2

      return orig_state_dict






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Any:
      SCREAMING_SNAKE_CASE			:   Any	  = torch.load(_A    ,     map_location="""cpu"""      )["""model_state_dict"""]
      SCREAMING_SNAKE_CASE			:   Tuple	  = MraConfig.from_json_file(_A      )
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = MraForMaskedLM(_A      )

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 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__":
  UpperCAmelCase__			:  Union[str, Any]					     =     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__			:  Tuple					     =     parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







import itertools






def        __lowercase   (  _A = 1000000      )  ->    int:
      SCREAMING_SNAKE_CASE			:   Tuple	  = [1]

      for i in itertools.count(len(_A      )      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
            for j in itertools.count(1      ):
                  SCREAMING_SNAKE_CASE			:   str	  = -1 if j % 2 == 0 else +1
                  SCREAMING_SNAKE_CASE			:   Any	  = (j * j * 3 - j) // 2
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number
                  index += j
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number

            if item == 0:
                  return i
            partitions.append(_A      )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")




