


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_   :				Union[str, Any]						      = logging.get_logger(__name__)
class 				SCREAMING_SNAKE_CASE__						(       __lowercase    ):
      snake_case__       :  Optional[Any]       =		['''pixel_values''']
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : Dict=PILImageResampling.BILINEAR  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							**SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							)	->    None:
             a_      :	Optional[Any]							=						do_resize
             a_      :	str							=						do_rescale
             a_      :	List[str]							=						size_divisor
             a_      :	str							=						resample
             super().__init__(**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : np.ndarray  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Optional[ChannelDimension] = None  ,							**SCREAMING_SNAKE_CASE__       : List[str]  )	->    np.ndarray:
             a_      :	int							=						get_image_size(SCREAMING_SNAKE_CASE__  )
             # Rounds the height and width down to the closest multiple of size_divisor
             a_      :	Dict							=						height // size_divisor * size_divisor
             a_      :	str							=						width // size_divisor * size_divisor
             a_      :	str							=						resize(SCREAMING_SNAKE_CASE__  ,							(new_h, new_w)  ,							resample=SCREAMING_SNAKE_CASE__  ,							data_format=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
             return image
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : np.ndarray  ,							SCREAMING_SNAKE_CASE__       : float  ,							SCREAMING_SNAKE_CASE__       : Optional[ChannelDimension] = None  ,							**SCREAMING_SNAKE_CASE__       : Union[str, Any]  )	->    np.ndarray:
             return rescale(image=SCREAMING_SNAKE_CASE__  ,							scale=SCREAMING_SNAKE_CASE__  ,							data_format=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : Tuple=None  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[TensorType, str]] = None  ,							SCREAMING_SNAKE_CASE__       : ChannelDimension = ChannelDimension.FIRST  ,							**SCREAMING_SNAKE_CASE__       : Dict  ,							)	->    BatchFeature:
             a_      :	Union[str, Any]							=						do_resize if do_resize is not None else self.do_resize
             a_      :	Optional[int]							=						do_rescale if do_rescale is not None else self.do_rescale
             a_      :	Dict							=						size_divisor if size_divisor is not None else self.size_divisor
             a_      :	List[str]							=						resample if resample is not None else self.resample

             if do_resize and size_divisor is None:
                    raise ValueError('size_divisor is required for resizing'  )

             a_      :	int							=						make_list_of_images(SCREAMING_SNAKE_CASE__  )

             if not valid_images(SCREAMING_SNAKE_CASE__  ):
                    raise ValueError('Invalid image(s)'  )

             # All transformations expect numpy arrays.
             a_      :	Tuple							=						[to_numpy_array(SCREAMING_SNAKE_CASE__  ) for img in images]

             if do_resize:
                    a_      :	Optional[int]							=						[self.resize(SCREAMING_SNAKE_CASE__  ,							size_divisor=SCREAMING_SNAKE_CASE__  ,							resample=SCREAMING_SNAKE_CASE__  ) for image in images]

             if do_rescale:
                    a_      :	List[str]							=						[self.rescale(SCREAMING_SNAKE_CASE__  ,							scale=1 / 2_5_5  ) for image in images]

             a_      :	Optional[Any]							=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) for image in images]

             a_      :	Optional[int]							=						{"pixel_values": images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__  ,							tensor_type=SCREAMING_SNAKE_CASE__  )






import unittest

import numpy as np







def  SCREAMING_SNAKE_CASE_		(    __A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray | None = None						,					)			->	np.ndarray:

       """simple docstring"""

       a_      :	List[str]							=						np.shape(__A       )
       a_      :	Any							=						np.shape(__A       )
       a_      :	List[Any]							=						np.shape(__A       )

       if shape_a[0] != shape_b[0]:
              a_      :	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(__A       )

       if shape_b[1] != shape_c[1]:
              a_      :	int							=						(
                  '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(__A       )

       a_      :	Any							=						pseudo_inv
       if a_inv is None:
              try:
                     a_      :	List[str]							=						np.linalg.inv(__A       )
              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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    None:
             a_      :	str							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Any							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1], [6, 3]]  )

             a_      :	int							=						schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.block([[a, b], [b.T, c]]  )

             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )

             self.assertAlmostEqual(SCREAMING_SNAKE_CASE__  ,							det_a * det_s  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    None:
             a_      :	Optional[int]							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	str							=						np.array([[2, 1], [6, 3]]  )

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

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    None:
             a_      :	Tuple							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1, 3], [6, 3, 5]]  )

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()


