
'''simple docstring'''







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def   lowerCAmelCase(  a__    :  Any      ):







       '''simple docstring'''






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

       return orig_key







def   lowerCAmelCase(  a__    :  Tuple      , a__    :  Optional[int]      ):







       '''simple docstring'''






       for key in orig_state_dict.copy().keys():
              lowerCamelCase__				   =      orig_state_dict.pop(a__      )

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

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

       return orig_state_dict







def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  Dict      , a__    :  int      ):







       '''simple docstring'''






       lowerCamelCase__				   =      torch.load(a__      , map_location="cpu"      )["model_state_dict"]
       lowerCamelCase__				   =      MraConfig.from_json_file(a__      )
       lowerCamelCase__				   =      MraForMaskedLM(a__      )

       lowerCamelCase__				   =      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_      =   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_      =   parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					53_81
      for x in s:
            lowerCamelCase__   	=					((hash_value << 5) + hash_value) + ord(lowerCamelCase    )
      return hash_value & 0Xffff_ffff