






import unittest

import numpy as np



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





    """simple docstring"""

    __A         =	np.shape(__lowercase    )
    __A         =	np.shape(__lowercase    )
    __A         =	np.shape(__lowercase    )

    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(__lowercase    )

    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(__lowercase    )

    __A         =	pseudo_inv
    if a_inv is None:
        try:
            __A         =	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       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __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(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)

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

         __A         =	np.linalg.det(UpperCamelCase_				)
         __A         =	np.linalg.det(UpperCamelCase_				)
         __A         =	np.linalg.det(UpperCamelCase_				)

         self.assertAlmostEqual(UpperCamelCase_ ,	det_a * det_s				)




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __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(UpperCamelCase_				):
             schur_complement(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __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(UpperCamelCase_				):
             schur_complement(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()












from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

         if text is not None:
             __A         =	self.tokenizer(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if text is not None and images is not None:
             __A         =	image_features.pixel_values
             return encoding
         elif text is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**UpperCamelCase_				) ,	tensor_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return ["input_ids", "attention_mask", "pixel_values"]




