


'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


__A   :    Any											= logging.get_logger(__name__)






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(  self       :    str       ,		*lowerCamelCase       :    Optional[int]       ,		**lowerCamelCase       :    str						)  ->    None:
      warnings.warn(
          """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use SegformerImageProcessor instead."""       ,		lowerCamelCase       ,		)
      super().__init__(*lowerCamelCase       ,		**lowerCamelCase						)







'''simple docstring'''






from __future__ import annotations

import numpy as np







def 							UpperCamelCase_     (       A__   :  np.ndarray   ):
  '''simple docstring'''





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

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()



