

def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					len(__UpperCamelCase      )
 UpperCAmelCase__					:  List[Any]					   =					len(matrix[0]      )
 UpperCAmelCase__					:  Any					   =					min(__UpperCamelCase							,    __UpperCamelCase      )

 for row in range(__UpperCamelCase      ):
  # 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							,    __UpperCamelCase      ):
    UpperCAmelCase__					:  int					   =					matrix[col][row] / matrix[row][row]
    for i in range(__UpperCamelCase							,    __UpperCamelCase      ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   UpperCAmelCase__					:  Union[str, Any]					   =					True
   for i in range(row + 1							,    __UpperCamelCase      ):
    if matrix[i][row] != 0:
     UpperCAmelCase__					:  str					   =					matrix[i], matrix[row]
     UpperCAmelCase__					:  int					   =					False
     break
   if reduce:
    rank -= 1
    for i in range(__UpperCamelCase      ):
     UpperCAmelCase__					:  Optional[int]					   =					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()








"""simple docstring"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





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

 return orig_key
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__					:  Optional[Any]					   =					orig_state_dict.pop(__UpperCamelCase      )

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

 UpperCAmelCase__					:  Dict					   =					orig_state_dict["""cls.predictions.decoder.bias"""]
 UpperCAmelCase__					:  Optional[Any]					   =					torch.arange(__UpperCamelCase      ).expand((1, -1)      ) + 2

 return orig_state_dict
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					torch.load(__UpperCamelCase							,    map_location="""cpu"""      )["""model_state_dict"""]
 UpperCAmelCase__					:  List[str]					   =					YosoConfig.from_json_file(__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					YosoForMaskedLM(__UpperCamelCase      )

 UpperCAmelCase__					:  Optional[int]					   =					convert_checkpoint_helper(config.max_position_embeddings							,    __UpperCamelCase      )

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

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


if __name__ == "__main__":
      __UpperCAmelCase =				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.'
      )
      __UpperCAmelCase =				parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





