


'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def 							UpperCamelCase_     (       A__   :  Tuple   ):
  '''simple docstring'''





  if "model" in orig_key:
    lowerCAmelCase_     : str     =  orig_key.replace("""model."""				,       """"""   )
  if "norm1" in orig_key:
    lowerCAmelCase_     : Any     =  orig_key.replace("""norm1"""				,       """attention.output.LayerNorm"""   )
  if "norm2" in orig_key:
    lowerCAmelCase_     : str     =  orig_key.replace("""norm2"""				,       """output.LayerNorm"""   )
  if "norm" in orig_key:
    lowerCAmelCase_     : Union[str, Any]     =  orig_key.replace("""norm"""				,       """LayerNorm"""   )
  if "transformer" in orig_key:
    lowerCAmelCase_     : Optional[int]     =  orig_key.split("""."""   )[0].split("""_"""   )[-1]
    lowerCAmelCase_     : List[str]     =  orig_key.replace(f'transformer_{layer_num}'				,       f'encoder.layer.{layer_num}'   )
  if "mha.attn" in orig_key:
    lowerCAmelCase_     : Any     =  orig_key.replace("""mha.attn"""				,       """attention.self"""   )
  if "mha" in orig_key:
    lowerCAmelCase_     : Optional[Any]     =  orig_key.replace("""mha"""				,       """attention"""   )
  if "W_q" in orig_key:
    lowerCAmelCase_     : Any     =  orig_key.replace("""W_q"""				,       """self.query"""   )
  if "W_k" in orig_key:
    lowerCAmelCase_     : Dict     =  orig_key.replace("""W_k"""				,       """self.key"""   )
  if "W_v" in orig_key:
    lowerCAmelCase_     : List[Any]     =  orig_key.replace("""W_v"""				,       """self.value"""   )
  if "ff1" in orig_key:
    lowerCAmelCase_     : Union[str, Any]     =  orig_key.replace("""ff1"""				,       """intermediate.dense"""   )
  if "ff2" in orig_key:
    lowerCAmelCase_     : int     =  orig_key.replace("""ff2"""				,       """output.dense"""   )
  if "ff" in orig_key:
    lowerCAmelCase_     : Tuple     =  orig_key.replace("""ff"""				,       """output.dense"""   )
  if "mlm_class" in orig_key:
    lowerCAmelCase_     : List[Any]     =  orig_key.replace("""mlm.mlm_class"""				,       """cls.predictions.decoder"""   )
  if "mlm" in orig_key:
    lowerCAmelCase_     : Union[str, Any]     =  orig_key.replace("""mlm"""				,       """cls.predictions.transform"""   )
  if "cls" not in orig_key:
    lowerCAmelCase_     : Optional[int]     =  """yoso.""" + orig_key

  return orig_key







def 							UpperCamelCase_     (       A__   :  Optional[Any]				,       A__   :  Dict   ):
  '''simple docstring'''





  for key in orig_state_dict.copy().keys():
    lowerCAmelCase_     : List[Any]     =  orig_state_dict.pop(A__   )

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCAmelCase_     : Union[str, Any]     =  val

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

  return orig_state_dict







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  int				,       A__   :  Dict   ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  torch.load(A__				,       map_location="""cpu"""   )["""model_state_dict"""]
  lowerCAmelCase_     : Dict     =  YosoConfig.from_json_file(A__   )
  lowerCAmelCase_     : str     =  YosoForMaskedLM(A__   )

  lowerCAmelCase_     : List[str]     =  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__":
  __A   :    List[Any]											= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The json file for YOSO model config.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  __A   :    Any											= parser.parse_args()
  convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







'''simple docstring'''






def 							UpperCamelCase_     (       A__   :  list[list[int | float]]   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[str]     =  len(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  len(matrix[0]   )
  lowerCAmelCase_     : Optional[int]     =  min(A__				,       A__   )

  for row in range(A__   ):
    # Check if diagonal element is not zero
    if matrix[row][row] != 0:
      # Eliminate all the elements below the diagonal
      for col in range(row + 1				,       A__   ):
        lowerCAmelCase_     : int     =  matrix[col][row] / matrix[row][row]
        for i in range(A__				,       A__   ):
          matrix[col][i] -= multiplier * matrix[row][i]
    else:
      # Find a non-zero diagonal element to swap rows
      lowerCAmelCase_     : int     =  True
      for i in range(row + 1				,       A__   ):
        if matrix[i][row] != 0:
          lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  matrix[i], matrix[row]
          lowerCAmelCase_     : Dict     =  False
          break
      if reduce:
        rank -= 1
        for i in range(A__   ):
          lowerCAmelCase_     : List[Any]     =  matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
      row -= 1

  return rank


if __name__ == "__main__":
  import doctest

  doctest.testmod()



