


'''simple docstring'''


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


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				'swin2sr'

     lowerCamelCase__       						=				{
         'hidden_size': 'embed_dim',
         'num_attention_heads': 'num_heads',
         'num_hidden_layers': 'num_layers',
     }


     def __init__(   self			,     lowerCamelCase=64			,     lowerCamelCase=1			,     lowerCamelCase=3			,     lowerCamelCase=180			,     lowerCamelCase=[6, 6, 6, 6, 6, 6]			,     lowerCamelCase=[6, 6, 6, 6, 6, 6]			,     lowerCamelCase=8			,     lowerCamelCase=2.0			,     lowerCamelCase=True			,     lowerCamelCase=0.0			,     lowerCamelCase=0.0			,     lowerCamelCase=0.1			,     lowerCamelCase="gelu"			,     lowerCamelCase=False			,     lowerCamelCase=0.02			,     lowerCamelCase=1E-5			,     lowerCamelCase=2			,     lowerCamelCase=1.0			,     lowerCamelCase="1conv"			,     lowerCamelCase="pixelshuffle"			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A	:       int      =							image_size
          __A	:       Dict      =							patch_size
          __A	:       Union[str, Any]      =							num_channels
          __A	:       Optional[int]      =							embed_dim
          __A	:       Optional[Any]      =							depths
          __A	:       List[Any]      =							len(lowerCamelCase		)
          __A	:       str      =							num_heads
          __A	:       str      =							window_size
          __A	:       Tuple      =							mlp_ratio
          __A	:       Tuple      =							qkv_bias
          __A	:       Optional[Any]      =							hidden_dropout_prob
          __A	:       Optional[int]      =							attention_probs_dropout_prob
          __A	:       Any      =							drop_path_rate
          __A	:       int      =							hidden_act
          __A	:       Union[str, Any]      =							use_absolute_embeddings
          __A	:       str      =							layer_norm_eps
          __A	:       Optional[Any]      =							initializer_range
          __A	:       List[str]      =							upscale
          __A	:       List[str]      =							img_range
          __A	:       Optional[int]      =							resi_connection
          __A	:       Optional[Any]      =							upsampler





'''simple docstring'''


import unittest

import numpy as np


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = None       ,					):




     '''simple docstring'''





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

     if shape_a[0] != shape_b[0]:
          __A	:       Tuple      =							(
              "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(SCREAMING_SNAKE_CASE							)

     if shape_b[1] != shape_c[1]:
          __A	:       Optional[Any]      =							(
              "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(SCREAMING_SNAKE_CASE							)

     __A	:       Tuple      =							pseudo_inv
     if a_inv is None:
          try:
               __A	:       Optional[Any]      =							np.linalg.inv(SCREAMING_SNAKE_CASE							)
          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 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       Tuple      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       Tuple      =							np.array([[2, 1], [6, 3]]		)

          __A	:       Optional[Any]      =							schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)

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

          __A	:       int      =							np.linalg.det(lowerCamelCase		)
          __A	:       List[str]      =							np.linalg.det(lowerCamelCase		)
          __A	:       Any      =							np.linalg.det(lowerCamelCase		)

          self.assertAlmostEqual(lowerCamelCase			,     det_a * det_s		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       List[Any]      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       List[str]      =							np.array([[2, 1], [6, 3]]		)

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       Union[str, Any]      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       Optional[Any]      =							np.array([[2, 1, 3], [6, 3, 5]]		)

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()

