


import unittest

import numpy as np






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase = None					,  )					-> np.ndarray:


     """simple docstring"""


     __UpperCamelCase =			np.shape(_lowercase )
     __UpperCamelCase =			np.shape(_lowercase )
     __UpperCamelCase =			np.shape(_lowercase )

     if shape_a[0] != shape_b[0]:
          __UpperCamelCase =			(
              '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]:
          __UpperCamelCase =			(
              '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 )

     __UpperCamelCase =			pseudo_inv
     if a_inv is None:
          try:
               __UpperCamelCase =			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 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
            __UpperCamelCase =			np.array([[0, 3], [3, 0], [2, 3]]					)
            __UpperCamelCase =			np.array([[2, 1], [6, 3]]					)

            __UpperCamelCase =			schur_complement(A_,A_,A_					)

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

            __UpperCamelCase =			np.linalg.det(A_					)
            __UpperCamelCase =			np.linalg.det(A_					)
            __UpperCamelCase =			np.linalg.det(A_					)

            self.assertAlmostEqual(A_,det_a * det_s					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
            __UpperCamelCase =			np.array([[0, 3], [3, 0], [2, 3]]					)
            __UpperCamelCase =			np.array([[2, 1], [6, 3]]					)

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







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
            __UpperCamelCase =			np.array([[0, 3], [3, 0], [2, 3]]					)
            __UpperCamelCase =			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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "donut-swin"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  **_lowerCAmelCase      ,  )     ->				Optional[Any]:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)


