
"""simple docstring"""




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				**_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     AutoConfig.from_pretrained(_UpperCamelCase ,				**_UpperCamelCase						)
  _lowercase:			Tuple       =     AutoModelForSeqaSeqLM.from_config(_UpperCamelCase						)
  model.save_pretrained(_UpperCamelCase						)
  AutoTokenizer.from_pretrained(_UpperCamelCase						).save_pretrained(_UpperCamelCase						)
  return model


if __name__ == "__main__":
  fire.Fire(save_randomly_initialized_version)


"""simple docstring"""




from __future__ import annotations

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase ,				_lowercase:			List[str]       =     np.shape(_UpperCamelCase						)
  if rows != columns:
    _lowercase:			int       =     (
        '''\'table\' has to be of square shaped array but got a '''
        f'''{rows}x{columns} array:\n{table}'''
    )
    raise ValueError(_UpperCamelCase						)

  _lowercase:			Tuple       =     np.zeros((rows, columns)						)
  _lowercase:			Tuple       =     np.zeros((rows, columns)						)
  for i in range(_UpperCamelCase						):
    for j in range(_UpperCamelCase						):
      _lowercase:			List[Any]       =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase						)						)
      if upper[j][j] == 0:
        raise ArithmeticError('''No LU decomposition exists'''						)
      _lowercase:			Any       =     (table[i][j] - total) / upper[j][j]
    _lowercase:			Any       =     1
    for j in range(_UpperCamelCase ,				_UpperCamelCase						):
      _lowercase:			Optional[int]       =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase						)						)
      _lowercase:			List[str]       =     table[i][j] - total
  return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
