


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCAmelCase__      		= logging.get_logger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["pixel_values"]


     def __init__(						self ,						__lowerCamelCase = True ,						__lowerCamelCase = 1 / 2_5_5 ,						__lowerCamelCase = True ,						__lowerCamelCase = 8 ,						**__lowerCamelCase ,						)			->				None:
            super().__init__(**__lowerCamelCase)

            _A      :       List[str]				    = do_rescale
            _A      :       Dict				    = rescale_factor
            _A      :       Any				    = do_pad
            _A      :       Union[str, Any]				    = pad_size


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None ,						**__lowerCamelCase)			->				np.ndarray:
            return rescale(__lowerCamelCase ,						scale=__lowerCamelCase ,						data_format=__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Any:
            _A    ,			_A      :       Optional[int]				    = get_image_size(__lowerCamelCase)
            _A      :       Dict				    = (old_height // size + 1) * size - old_height
            _A      :       str				    = (old_width // size + 1) * size - old_width

            return pad(__lowerCamelCase ,						((0, pad_height), (0, pad_width)) ,						mode="symmetric" ,						data_format=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = ChannelDimension.FIRST ,						**__lowerCamelCase ,						)			->				Union[str, Any]:
            _A      :       int				    = do_rescale if do_rescale is not None else self.do_rescale
            _A      :       List[str]				    = rescale_factor if rescale_factor is not None else self.rescale_factor
            _A      :       Union[str, Any]				    = do_pad if do_pad is not None else self.do_pad
            _A      :       Dict				    = pad_size if pad_size is not None else self.pad_size

            _A      :       Optional[int]				    = make_list_of_images(__lowerCamelCase)

            if not valid_images(__lowerCamelCase):
                   raise ValueError(
                       "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                       "torch.Tensor, tf.Tensor or jax.ndarray.")

            if do_rescale and rescale_factor is None:
                   raise ValueError("Rescale factor must be specified if do_rescale is True.")

            # All transformations expect numpy arrays.
            _A      :       List[Any]				    = [to_numpy_array(__lowerCamelCase) for image in images]

            if do_rescale:
                   _A      :       Optional[int]				    = [self.rescale(image=__lowerCamelCase ,						scale=__lowerCamelCase) for image in images]

            if do_pad:
                   _A      :       str				    = [self.pad(__lowerCamelCase ,						size=__lowerCamelCase) for image in images]

            _A      :       List[Any]				    = [to_channel_dimension_format(__lowerCamelCase ,						__lowerCamelCase) for image in images]

            _A      :       Optional[Any]				    = {"pixel_values": images}
            return BatchFeature(data=__lowerCamelCase ,						tensor_type=__lowerCamelCase)








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.")


