

"""simple docstring"""





from __future__ import annotations

_a							=							list[list[int]]

# assigning initial values to the grid
_a							=							[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
_a							=							[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]




def   _A  (  UpperCamelCase_					:	Matrix,      UpperCamelCase_					:	int,      UpperCamelCase_					:	int,      UpperCamelCase_					:	int)     ->						bool:
   '''simple docstring'''


   for i in range(9):
      if grid[row][i] == n or grid[i][column] == n:
         return False

   for i in range(3):
      for j in range(3):
         if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
            return False

   return True




def   _A  (  UpperCamelCase_					:	Matrix)     ->						tuple[int, int] | None:
   '''simple docstring'''


   for i in range(9):
      for j in range(9):
         if grid[i][j] == 0:
            return i, j
   return None




def   _A  (  UpperCamelCase_					:	Matrix)     ->						Matrix | None:
   '''simple docstring'''


   if location := find_empty_location(UpperCamelCase_):
      __lowercase		,__lowercase								=						location
   else:
      # If the location is ``None``, then the grid is solved.
      return grid

   for digit in range(1,      10):
      if is_safe(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_):
         __lowercase								=						digit

         if sudoku(UpperCamelCase_) is not None:
            return grid

         __lowercase								=						0

   return None




def   _A  (  UpperCamelCase_					:	Matrix)     ->						None:
   '''simple docstring'''


   for row in grid:
      for cell in row:
         print(UpperCamelCase_,      end=" ")
      print()


if __name__ == "__main__":
   # make a copy of grid so that you can compare with the unmodified grid
   for example_grid in (initial_grid, no_solution):
      print('\nExample grid:\n' + '=' * 20)
      print_solution(example_grid)
      print('\nExample grid solution:')
      _a							=							sudoku(example_grid)
      if solution is not None:
         print_solution(solution)
      else:
         print('Cannot find a solution.')







"""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




