


import unittest

import numpy as np





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[str]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     List[Any] = None							,					) ->   np.ndarray:





	"""simple docstring"""







	A								=				np.shape(__lowerCAmelCase						)
	A								=				np.shape(__lowerCAmelCase						)
	A								=				np.shape(__lowerCAmelCase						)

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

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

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

					A								=				schur_complement(_a				,   _a				,   _a   )

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

					A								=				np.linalg.det(_a   )
					A								=				np.linalg.det(_a   )
					A								=				np.linalg.det(_a   )

					self.assertAlmostEqual(_a				,   det_a * det_s   )

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

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



