


import unittest

import numpy as np




def       __lowercase						(    _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase = None,     )     ->np.ndarray:







      """simple docstring"""

      lowercase      : List[str]        =		np.shape(_lowercase  )
      lowercase      : List[Any]        =		np.shape(_lowercase  )
      lowercase      : Union[str, Any]        =		np.shape(_lowercase  )

      if shape_a[0] != shape_b[0]:
            lowercase      : List[Any]        =		(
                '''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(_lowercase  )

      if shape_b[1] != shape_c[1]:
            lowercase      : Dict        =		(
                '''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(_lowercase  )

      lowercase      : Tuple        =		pseudo_inv
      if a_inv is None:
            try:
                  lowercase      : Union[str, Any]        =		np.linalg.inv(_lowercase  )
            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 					__SCREAMING_SNAKE_CASE (      unittest.TestCase ):


      def 							__lowerCamelCase      (				self		):
            lowercase      : Any        =		np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
            lowercase      : Union[str, Any]        =		np.array([[0, 3], [3, 0], [2, 3]]		)
            lowercase      : List[str]        =		np.array([[2, 1], [6, 3]]		)

            lowercase      : Any        =		schur_complement(A_				,    A_				,    A_		)

            lowercase      : List[Any]        =		np.block([[a, b], [b.T, c]]		)

            lowercase      : Union[str, Any]        =		np.linalg.det(A_		)
            lowercase      : Optional[int]        =		np.linalg.det(A_		)
            lowercase      : Union[str, Any]        =		np.linalg.det(A_		)

            self.assertAlmostEqual(A_				,    det_a * det_s		)


      def 							__lowerCamelCase      (				self		):
            lowercase      : str        =		np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
            lowercase      : Any        =		np.array([[0, 3], [3, 0], [2, 3]]		)
            lowercase      : List[str]        =		np.array([[2, 1], [6, 3]]		)

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






      def 							__lowerCamelCase      (				self		):
            lowercase      : List[Any]        =		np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
            lowercase      : List[Any]        =		np.array([[0, 3], [3, 0], [2, 3]]		)
            lowercase      : Union[str, Any]        =		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_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

    __snake_case   							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


