

"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array




def   _A  (  UpperCamelCase_					:	list[list[float]])     ->						list[list[float]]:
   '''simple docstring'''


   __lowercase								=						Decimal

   # Check if the provided matrix has 2 rows and 2 columns
   # since this implementation only works for 2x2 matrices
   if len(UpperCamelCase_) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
      # Calculate the determinant of the matrix
      __lowercase								=						float(
          d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
      if determinant == 0:
         raise ValueError("This matrix has no inverse.")

      # Creates a copy of the matrix with swapped positions of the elements
      __lowercase								=						[[0.0, 0.0], [0.0, 0.0]]
      __lowercase		,__lowercase								=						matrix[1][1], matrix[0][0]
      __lowercase		,__lowercase								=						-matrix[1][0], -matrix[0][1]

      # Calculate the inverse of the matrix
      return [
          [(float(d(UpperCamelCase_)) / determinant) or 0.0 for n in row] for row in swapped_matrix
      ]
   elif (
       len(UpperCamelCase_) == 3
       and len(matrix[0]) == 3
       and len(matrix[1]) == 3
       and len(matrix[2]) == 3
   ):
      # Calculate the determinant of the matrix using Sarrus rule
      __lowercase								=						float(
          (
              (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
              + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
              + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
          )
          - (
              (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
              + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
              + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
          ))
      if determinant == 0:
         raise ValueError("This matrix has no inverse.")

      # Creating cofactor matrix
      __lowercase								=						[
          [d(0.0), d(0.0), d(0.0)],
          [d(0.0), d(0.0), d(0.0)],
          [d(0.0), d(0.0), d(0.0)],
      ]
      __lowercase								=						(d(matrix[1][1]) * d(matrix[2][2])) - (
          d(matrix[1][2]) * d(matrix[2][1])
      )
      __lowercase								=						-(
          (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
      )
      __lowercase								=						(d(matrix[1][0]) * d(matrix[2][1])) - (
          d(matrix[1][1]) * d(matrix[2][0])
      )
      __lowercase								=						-(
          (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
      )
      __lowercase								=						(d(matrix[0][0]) * d(matrix[2][2])) - (
          d(matrix[0][2]) * d(matrix[2][0])
      )
      __lowercase								=						-(
          (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
      )
      __lowercase								=						(d(matrix[0][1]) * d(matrix[1][2])) - (
          d(matrix[0][2]) * d(matrix[1][1])
      )
      __lowercase								=						-(
          (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
      )
      __lowercase								=						(d(matrix[0][0]) * d(matrix[1][1])) - (
          d(matrix[0][1]) * d(matrix[1][0])
      )

      # Transpose the cofactor matrix (Adjoint matrix)
      __lowercase								=						array(UpperCamelCase_)
      for i in range(3):
         for j in range(3):
            __lowercase								=						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
      __lowercase								=						array(UpperCamelCase_)
      for i in range(3):
         for j in range(3):
            inverse_matrix[i][j] /= d(UpperCamelCase_)

        # Calculate the inverse of the matrix
      return [[float(d(UpperCamelCase_)) or 0.0 for n in row] for row in inverse_matrix]
   raise ValueError("Please provide a matrix of size 2x2 or 3x3.")








"""simple docstring"""





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


_a							=							logging.get_logger(__name__)

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




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : List[Any]       		=     "swin2sr"

 __UpperCAmelCase     : List[Any]       		=     {
     "hidden_size": "embed_dim",
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }


 def __init__(      self							:      Any, UpperCAmelCase__							:      Dict=6_4, UpperCAmelCase__							:      List[Any]=1, UpperCAmelCase__							:      Dict=3, UpperCAmelCase__							:      Optional[Any]=1_8_0, UpperCAmelCase__							:      Any=[6, 6, 6, 6, 6, 6], UpperCAmelCase__							:      Dict=[6, 6, 6, 6, 6, 6], UpperCAmelCase__							:      Tuple=8, UpperCAmelCase__							:      Optional[int]=2.0, UpperCAmelCase__							:      List[str]=True, UpperCAmelCase__							:      Tuple=0.0, UpperCAmelCase__							:      Optional[Any]=0.0, UpperCAmelCase__							:      List[str]=0.1, UpperCAmelCase__							:      Dict="gelu", UpperCAmelCase__							:      Dict=False, UpperCAmelCase__							:      Dict=0.02, UpperCAmelCase__							:      Tuple=1E-5, UpperCAmelCase__							:      str=2, UpperCAmelCase__							:      str=1.0, UpperCAmelCase__							:      Optional[int]="1conv", UpperCAmelCase__							:      Dict="pixelshuffle", **UpperCAmelCase__							:      List[Any], ):
    super().__init__(**UpperCAmelCase__     )

    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						embed_dim
    __lowercase								=						depths
    __lowercase								=						len(UpperCAmelCase__     )
    __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								=						upscale
    __lowercase								=						img_range
    __lowercase								=						resi_connection
    __lowercase								=						upsampler




