
"""simple docstring"""






from __future__ import annotations

__SCREAMING_SNAKE_CASE							=     list[list[int]]

# assigning initial values to the grid
__SCREAMING_SNAKE_CASE							=     [
    [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
__SCREAMING_SNAKE_CASE							=     [
    [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_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase	):
 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_  (				__lowercase	):
 for i in range(9	):
  for j in range(9	):
   if grid[i][j] == 0:
    return i, j
 return None




def       A_  (				__lowercase	):
 if location := find_empty_location(__lowerCAmelCase	):
  UpperCamelCase_	,     UpperCamelCase_     :					Union[str, Any]   	=location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

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

   if sudoku(__lowerCAmelCase	) is not None:
    return grid

   UpperCamelCase_     :					Tuple   	=0

 return None




def       A_  (				__lowercase	):
 for row in grid:
  for cell in row:
   print(__lowerCAmelCase						,	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:')
  __SCREAMING_SNAKE_CASE							=     sudoku(example_grid)
  if solution is not None:
   print_solution(solution)
  else:
   print('Cannot find a solution.')






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BioGptForCausalLM""",
          """BioGptForTokenClassification""",
          """BioGptForSequenceClassification""",
          """BioGptModel""",
          """BioGptPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

