

from __future__ import annotations

lowercase_   							=  list[list[int]]

# assigning initial values to the grid
lowercase_   							=  [
    [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
lowercase_   							=  [
    [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:





      """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 (     A__      :  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 (     A__      :  Matrix	)       -> Matrix | None:





      """simple docstring"""



      if location := find_empty_location(A__	):
            _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(A__ ,  A__ ,  A__ ,  A__	):
                  _lowercase      			=digit

                  if sudoku(A__	) is not None:
                        return grid

                  _lowercase      			=0

      return None
def 	a (     A__      :  Matrix	)       -> None:





      """simple docstring"""



      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' + '=' * 2_0)
    print_solution(example_grid)
    print('\nExample grid solution:')
    lowercase_   							=  sudoku(example_grid)
    if solution is not None:
      print_solution(solution)
    else:
      print('Cannot find a solution.')





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


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """timesformer"""




  def __init__(							self				,  lowerCAmelCase=224				,  lowerCAmelCase=16				,  lowerCAmelCase=3				,  lowerCAmelCase=8				,  lowerCAmelCase=768				,  lowerCAmelCase=12				,  lowerCAmelCase=12				,  lowerCAmelCase=3_072				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.02				,  lowerCAmelCase=1e-6				,  lowerCAmelCase=True				,  lowerCAmelCase="divided_space_time"				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Any:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

        _lowercase      			=image_size
        _lowercase      			=patch_size
        _lowercase      			=num_channels
        _lowercase      			=num_frames

        _lowercase      			=hidden_size
        _lowercase      			=num_hidden_layers
        _lowercase      			=num_attention_heads
        _lowercase      			=intermediate_size
        _lowercase      			=hidden_act
        _lowercase      			=hidden_dropout_prob
        _lowercase      			=attention_probs_dropout_prob
        _lowercase      			=initializer_range
        _lowercase      			=layer_norm_eps
        _lowercase      			=qkv_bias

        _lowercase      			=attention_type
        _lowercase      			=drop_path_rate


