


"""simple docstring"""


import unittest

import numpy as np


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase = None       ,					):

			'''simple docstring'''




			__lowerCAmelCase							       =     np.shape(_UpperCamelCase )
			__lowerCAmelCase							       =     np.shape(_UpperCamelCase )
			__lowerCAmelCase							       =     np.shape(_UpperCamelCase )

			if shape_a[0] != shape_b[0]:
						__lowerCAmelCase							       =     (
						    "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(_UpperCamelCase )

			if shape_b[1] != shape_c[1]:
						__lowerCAmelCase							       =     (
						    "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(_UpperCamelCase )

			__lowerCAmelCase							       =     pseudo_inv
			if a_inv is None:
						try:
									__lowerCAmelCase							       =     np.linalg.inv(_UpperCamelCase )
						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      snake_case							(	self      ):
										__lowerCAmelCase							       =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]      )
										__lowerCAmelCase							       =     np.array([[0, 3], [3, 0], [2, 3]]      )
										__lowerCAmelCase							       =     np.array([[2, 1], [6, 3]]      )

										__lowerCAmelCase							       =     schur_complement(__a       ,  __a       ,  __a      )

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

										__lowerCAmelCase							       =     np.linalg.det(__a      )
										__lowerCAmelCase							       =     np.linalg.det(__a      )
										__lowerCAmelCase							       =     np.linalg.det(__a      )

										self.assertAlmostEqual(__a       ,  det_a * det_s      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]      )
										__lowerCAmelCase							       =     np.array([[0, 3], [3, 0], [2, 3]]      )
										__lowerCAmelCase							       =     np.array([[2, 1], [6, 3]]      )

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



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]      )
										__lowerCAmelCase							       =     np.array([[0, 3], [3, 0], [2, 3]]      )
										__lowerCAmelCase							       =     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()








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

      _UpperCamelCase 							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

