


"""simple docstring"""

import unittest

import numpy as np


def lowerCAmelCase_      (				UpperCamelCase__	: np.ndarray				,   UpperCamelCase__	: np.ndarray				,   UpperCamelCase__	: np.ndarray				,   UpperCamelCase__	: np.ndarray | None = None				,   ):






 """simple docstring"""







 __lowercase									=	np.shape(UpperCamelCase__ )
 __lowercase									=	np.shape(UpperCamelCase__ )
 __lowercase									=	np.shape(UpperCamelCase__ )

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

 if shape_b[1] != shape_c[1]:
  __lowercase									=	(
      '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(UpperCamelCase__ )

 __lowercase									=	pseudo_inv
 if a_inv is None:
  try:
   __lowercase									=	np.linalg.inv(UpperCamelCase__ )
  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 SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


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

       __lowercase									=	schur_complement(_lowercase      ,       _lowercase      ,       _lowercase							)

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

       __lowercase									=	np.linalg.det(_lowercase							)
       __lowercase									=	np.linalg.det(_lowercase							)
       __lowercase									=	np.linalg.det(_lowercase							)

       self.assertAlmostEqual(_lowercase      ,       det_a * det_s							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


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

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






      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
       __lowercase									=	np.array([[0, 3], [3, 0], [2, 3]]							)
       __lowercase									=	np.array([[2, 1, 3], [6, 3, 5]]							)

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


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       unittest.main()




"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}





class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		"""swinv2"""

      a :     Any											=		{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(		self	:	List[str]      ,       A_	:	Any=2_2_4      ,       A_	:	int=4      ,       A_	:	Optional[int]=3      ,       A_	:	List[Any]=9_6      ,       A_	:	List[Any]=[2, 2, 6, 2]      ,       A_	:	List[str]=[3, 6, 1_2, 2_4]      ,       A_	:	Union[str, Any]=7      ,       A_	:	int=4.0      ,       A_	:	List[str]=True      ,       A_	:	str=0.0      ,       A_	:	Any=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	int=False      ,       A_	:	List[Any]=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Tuple=3_2      ,       **A_	:	int      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

       __lowercase									=	image_size
       __lowercase									=	patch_size
       __lowercase									=	num_channels
       __lowercase									=	embed_dim
       __lowercase									=	depths
       __lowercase									=	len(A_							)
       __lowercase									=	num_heads
       __lowercase									=	window_size
       __lowercase									=	mlp_ratio
       __lowercase									=	qkv_bias
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	drop_path_rate
       __lowercase									=	hidden_act
       __lowercase									=	use_absolute_embeddings
       __lowercase									=	layer_norm_eps
       __lowercase									=	initializer_range
       __lowercase									=	encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       __lowercase									=	int(embed_dim * 2 ** (len(A_							) - 1)							)
       __lowercase									=	(0, 0, 0, 0)
