




"""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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    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 	lowercase_				(			__UpperCAmelCase				)						->    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 	lowercase_				(			__UpperCAmelCase				)						->    Matrix | None:
      if location := find_empty_location(__UpperCAmelCase				):
            lowerCAmelCase__ ,   lowerCAmelCase__      :			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(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				):
                  lowerCAmelCase__      :			str   =	digit

                  if sudoku(__UpperCAmelCase				) is not None:
                        return grid

                  lowerCAmelCase__      :			Optional[int]   =	0

      return None



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      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""" + """=""" * 2_0)
    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"""


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      # Initialise PyTorch model
      lowerCAmelCase__      :			int   =	TaConfig.from_json_file(__UpperCAmelCase				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			Optional[int]   =	TaForConditionalGeneration(__UpperCAmelCase				)

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

      # Save pytorch-model
      print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
      model.save_pretrained(__UpperCAmelCase				)


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _A													=      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





