

'''simple docstring'''
import unittest

import numpy as np


def        _lowerCAmelCase					(						_UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     np.ndarray | None = None							, )      ->			np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.shape(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =np.shape(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =np.shape(_UpperCamelCase				)

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

     _SCREAMING_SNAKE_CASE							    =pseudo_inv
     if a_inv is None:
          try:
               _SCREAMING_SNAKE_CASE							    =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 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	Dict )	->    None:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							    =schur_complement(_a							,					_a							,					_a )

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

       _SCREAMING_SNAKE_CASE							    =np.linalg.det(_a )
       _SCREAMING_SNAKE_CASE							    =np.linalg.det(_a )
       _SCREAMING_SNAKE_CASE							    =np.linalg.det(_a )

       self.assertAlmostEqual(_a							,					det_a * det_s )





  def 						A  (		self      :	str )	->    None:





       '''simple docstring'''






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

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





  def 						A  (		self      :	List[Any] )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
       _SCREAMING_SNAKE_CASE							    =np.array([[0, 3], [3, 0], [2, 3]] )
       _SCREAMING_SNAKE_CASE							    =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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase :      List[str]					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}







class 							A__    (       A__     ,   A__  ):
  A__		 =				'convnextv2'





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=3							,					_a      :	Any=4							,					_a      :	int=4							,					_a      :	Union[str, Any]=None							,					_a      :	List[str]=None							,					_a      :	Optional[Any]="gelu"							,					_a      :	Any=0.02							,					_a      :	Any=1e-12							,					_a      :	Tuple=0.0							,					_a      :	int=224							,					_a      :	Any=None							,					_a      :	Optional[int]=None							,					**_a      :	List[str]							,					)	->    Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a )

       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =patch_size
       _SCREAMING_SNAKE_CASE							    =num_stages
       _SCREAMING_SNAKE_CASE							    =[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       _SCREAMING_SNAKE_CASE							    =[3, 3, 9, 3] if depths is None else depths
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =layer_norm_eps
       _SCREAMING_SNAKE_CASE							    =drop_path_rate
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =['stem'] + [f"stage{idx}" for idx in range(1							,					len(self.depths ) + 1 )]
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =get_aligned_output_features_output_indices(
           out_features=_a							,					out_indices=_a							,					stage_names=self.stage_names )
