

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Union[str, Any]:



    """simple docstring"""






    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(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		str:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        UpperCamelCase_          =		orig_state_dict.pop(SCREAMING_SNAKE_CASE_       )

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

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

    return orig_state_dict




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






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

    UpperCamelCase_          =		convert_checkpoint_helper(config.max_position_embeddings       ,		SCREAMING_SNAKE_CASE_       )

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:int      =			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."""
 )
 SCREAMING_SNAKE_CASE						:List[str]      =			parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		str )->						int:
  # The longer word should come first
  if len(lowerCAmelCase ) < len(lowerCAmelCase ):
    return levenshtein_distance(lowerCAmelCase			,							lowerCAmelCase )

  if len(lowerCAmelCase ) == 0:
    return len(lowerCAmelCase )

  _snake_case      : Dict														=		list(range(len(lowerCAmelCase ) + 1 ) )

  for i, ca in enumerate(lowerCAmelCase ):
    _snake_case      : Optional[int]														=		[i + 1]

    for j, ca in enumerate(lowerCAmelCase ):
      # Calculate insertions, deletions and substitutions
      _snake_case      : Optional[Any]														=		previous_row[j + 1] + 1
      _snake_case      : Optional[int]														=		current_row[j] + 1
      _snake_case      : Union[str, Any]														=		previous_row[j] + (ca != ca)

      # Get the minimum to append to the current row
      current_row.append(min(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase ) )

    # Store the previous row
    _snake_case      : str														=		current_row

  # Returns the last element (distance)
  return previous_row[-1]


if __name__ == "__main__":
      lowerCAmelCase_        =			input("""Enter the first word:\n""").strip()
      lowerCAmelCase_        =			input("""Enter the second word:\n""").strip()

      lowerCAmelCase_        =			levenshtein_distance(first_word, second_word)
      print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")




