


import unittest

import numpy as np

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : np.ndarray | None = None      ,					):
     __UpperCamelCase   							=np.shape(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=np.shape(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=np.shape(SCREAMING_SNAKE_CASE__    )

     if shape_a[0] != shape_b[0]:
          __UpperCamelCase   							=(
              'Expected the same number of rows for A and B. '
              F'Instead found A of size {shape_a} and B of size {shape_b}'
          )
          raise ValueError(SCREAMING_SNAKE_CASE__    )

     if shape_b[1] != shape_c[1]:
          __UpperCamelCase   							=(
              'Expected the same number of columns for B and C. '
              F'Instead found B of size {shape_b} and C of size {shape_c}'
          )
          raise ValueError(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=pseudo_inv
     if a_inv is None:
          try:
               __UpperCamelCase   							=np.linalg.inv(SCREAMING_SNAKE_CASE__    )
          except np.linalg.LinAlgError:
               raise ValueError(
                   'Input matrix A is not invertible. Cannot compute Schur complement.'    )

     return mat_c - mat_b.T @ a_inv @ mat_b






class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	None:
         __UpperCamelCase   							=np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
         __UpperCamelCase   							=np.array([[0, 3], [3, 0], [2, 3]]   )
         __UpperCamelCase   							=np.array([[2, 1], [6, 3]]   )

         __UpperCamelCase   							=schur_complement(A_ ,       A_ ,       A_   )

         __UpperCamelCase   							=np.block([[a, b], [b.T, c]]   )

         __UpperCamelCase   							=np.linalg.det(A_   )
         __UpperCamelCase   							=np.linalg.det(A_   )
         __UpperCamelCase   							=np.linalg.det(A_   )

         self.assertAlmostEqual(A_ ,       det_a * det_s   )






    def _a (      self   )  ->	None:
         __UpperCamelCase   							=np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
         __UpperCamelCase   							=np.array([[0, 3], [3, 0], [2, 3]]   )
         __UpperCamelCase   							=np.array([[2, 1], [6, 3]]   )

         with self.assertRaises(A_   ):
              schur_complement(A_ ,       A_ ,       A_   )






    def _a (      self   )  ->	None:
         __UpperCamelCase   							=np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
         __UpperCamelCase   							=np.array([[0, 3], [3, 0], [2, 3]]   )
         __UpperCamelCase   							=np.array([[2, 1, 3], [6, 3, 5]]   )

         with self.assertRaises(A_   ):
              schur_complement(A_ ,       A_ ,       A_   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()










import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       *A_ ,       **A_   )  ->	None:
         warnings.warn(
             'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
             ' Please use ImageGPTImageProcessor instead.' ,       A_ ,       )
         super().__init__(*A_ ,       **A_   )





