






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__				   =		logging.get_logger(__name__)






class 		lowercase_					(	lowercase ,  lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''maskformer-swin'''

      __snake_case       =  {
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						Tuple=224 ,					__UpperCAmelCase      :						Tuple=4 ,					__UpperCAmelCase      :						int=3 ,					__UpperCAmelCase      :						Optional[Any]=96 ,					__UpperCAmelCase      :						Tuple=[2, 2, 6, 2] ,					__UpperCAmelCase      :						Optional[Any]=[3, 6, 12, 24] ,					__UpperCAmelCase      :						Dict=7 ,					__UpperCAmelCase      :						Tuple=4.0 ,					__UpperCAmelCase      :						Tuple=True ,					__UpperCAmelCase      :						Tuple=0.0 ,					__UpperCAmelCase      :						Optional[Any]=0.0 ,					__UpperCAmelCase      :						Union[str, Any]=0.1 ,					__UpperCAmelCase      :						str="gelu" ,					__UpperCAmelCase      :						Tuple=False ,					__UpperCAmelCase      :						List[str]=0.02 ,					__UpperCAmelCase      :						List[Any]=1e-5 ,					__UpperCAmelCase      :						int=None ,					__UpperCAmelCase      :						str=None ,					**__UpperCAmelCase      :						str ,					)    ->List[str]:




       """simple docstring"""


       super().__init__(**__UpperCAmelCase  )

       a		  =      image_size
       a		  =      patch_size
       a		  =      num_channels
       a		  =      embed_dim
       a		  =      depths
       a		  =      len(__UpperCAmelCase  )
       a		  =      num_heads
       a		  =      window_size
       a		  =      mlp_ratio
       a		  =      qkv_bias
       a		  =      hidden_dropout_prob
       a		  =      attention_probs_dropout_prob
       a		  =      drop_path_rate
       a		  =      hidden_act
       a		  =      use_absolute_embeddings
       a		  =      layer_norm_eps
       a		  =      initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       a		  =      int(embed_dim * 2 ** (len(__UpperCAmelCase  ) - 1)  )
       a		  =      ['''stem'''] + [F"""stage{idx}""" for idx in range(1 ,					len(__UpperCAmelCase  ) + 1  )]
       a  ,     a		  =      get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase ,					out_indices=__UpperCAmelCase ,					stage_names=self.stage_names  )










from __future__ import annotations

UpperCAmelCase__				   =		list[list[int]]

# assigning initial values to the grid
UpperCAmelCase__				   =		[
    [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
UpperCAmelCase__				   =		[
    [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    (      a :Matrix	,							a :int	,							a :int	,							a :int )							->			bool:
 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    (      a :Matrix )							->			tuple[int, int] | None:
 for i in range(9 ):
  for j in range(9 ):
   if grid[i][j] == 0:
    return i, j
 return None
def 						_a    (      a :Matrix )							->			Matrix | None:
 if location := find_empty_location(a ):
  a  ,     a		  =      location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

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

   if sudoku(a ) is not None:
    return grid

   a		  =      0

 return None
def 						_a    (      a :Matrix )							->			None:
 for row in grid:
  for cell in row:
   print(a	,							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:")
          UpperCAmelCase__				   =		sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("Cannot find a solution.")


