


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







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

     return orig_key







def 	A (		A__		,		A__			):
     for key in orig_state_dict.copy().keys():
          UpperCAmelCase    :					Any			=      orig_state_dict.pop(A__			)

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

     UpperCAmelCase    :					str			=      orig_state_dict["cls.predictions.decoder.bias"]
     UpperCAmelCase    :					Optional[Any]			=      torch.arange(A__			).expand((1, -1)			) + 2

     return orig_state_dict







def 	A (		A__		,		A__		,		A__			):
     UpperCAmelCase    :					List[Any]			=      torch.load(A__		,		map_location="cpu"			)["model_state_dict"]
     UpperCAmelCase    :					str			=      MraConfig.from_json_file(A__			)
     UpperCAmelCase    :					str			=      MraForMaskedLM(A__			)

     UpperCAmelCase    :					List[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__":
      _snake_case				=			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."
      )
      _snake_case				=			parser.parse_args()
      convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					):
										_snake_case							:       Optional[Any]												= F"""Input value of [number={number}] must be an integer"""
										raise TypeError(UpperCAmelCase					)
					if number < 1:
										raise ValueError("Input must be a positive integer"					)
					return number * (2 * number - 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()