


'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


__snake_case											=					logging.get_logger(__name__)
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    ['pixel_values']




		def __init__(		self   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = 0.9   ,       UpperCamelCase_ = PILImageResampling.BICUBIC   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = 1 / 255   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			size if size is not None else {'''shortest_edge''': 224}
							UpperCamelCase__       :Any							 =			get_size_dict(UpperCamelCase_   ,       default_to_square=UpperCamelCase_							)
							UpperCamelCase__       :str							 =			crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							UpperCamelCase__       :Tuple							 =			get_size_dict(UpperCamelCase_   ,       param_name='''crop_size'''							)

							UpperCamelCase__       :Optional[int]							 =			do_resize
							UpperCamelCase__       :Tuple							 =			size
							UpperCamelCase__       :int							 =			crop_pct
							UpperCamelCase__       :str							 =			resample
							UpperCamelCase__       :Dict							 =			do_center_crop
							UpperCamelCase__       :int							 =			crop_size
							UpperCamelCase__       :Optional[int]							 =			do_rescale
							UpperCamelCase__       :Any							 =			rescale_factor
							UpperCamelCase__       :str							 =			do_normalize
							UpperCamelCase__       :Any							 =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							UpperCamelCase__       :Optional[Any]							 =			image_std if image_std is not None else IMAGENET_DEFAULT_STD




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = PILImageResampling.BICUBIC   ,       UpperCamelCase_ = None   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			get_size_dict(UpperCamelCase_   ,       default_to_square=UpperCamelCase_							)
							if "shortest_edge" not in size and ("height" not in size or "width" not in size):
												raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
							if crop_pct is not None:
												if "shortest_edge" in size:
																	UpperCamelCase__       :Dict							 =			int(size['''shortest_edge'''] / crop_pct							)
												elif "height" in size and "width" in size:
																	if size["height"] == size["width"]:
																						UpperCamelCase__       :int							 =			int(size['''height'''] / crop_pct							)
																	else:
																						UpperCamelCase__       :Tuple							 =			(int(size['''height'''] / crop_pct							), int(size['''width'''] / crop_pct							))
												else:
																	raise ValueError('''Invalid size for resize: {}'''.format(UpperCamelCase_							)							)

												UpperCamelCase__       :Union[str, Any]							 =			get_resize_output_image_size(UpperCamelCase_   ,       size=UpperCamelCase_   ,       default_to_square=UpperCamelCase_							)
							else:
												if "shortest_edge" in size:
																	UpperCamelCase__       :str							 =			get_resize_output_image_size(UpperCamelCase_   ,       size=size['''shortest_edge''']   ,       default_to_square=UpperCamelCase_							)
												elif "height" in size and "width" in size:
																	UpperCamelCase__       :int							 =			(size['''height'''], size['''width'''])
												else:
																	raise ValueError('''Invalid size for resize: {}'''.format(UpperCamelCase_							)							)

							return resize(UpperCamelCase_   ,       size=UpperCamelCase_   ,       resample=UpperCamelCase_   ,       data_format=UpperCamelCase_   ,       **UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			get_size_dict(UpperCamelCase_							)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''							)
							return center_crop(UpperCamelCase_   ,       size=(size['''height'''], size['''width'''])   ,       data_format=UpperCamelCase_   ,       **UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							return rescale(UpperCamelCase_   ,       scale=UpperCamelCase_   ,       data_format=UpperCamelCase_   ,       **UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							return normalize(UpperCamelCase_   ,       mean=UpperCamelCase_   ,       std=UpperCamelCase_   ,       data_format=UpperCamelCase_   ,       **UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = ChannelDimension.FIRST   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__       :Optional[Any]							 =			crop_pct if crop_pct is not None else self.crop_pct
							UpperCamelCase__       :Tuple							 =			resample if resample is not None else self.resample
							UpperCamelCase__       :Union[str, Any]							 =			do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase__       :Optional[int]							 =			do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__       :Any							 =			rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__       :int							 =			do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__       :List[str]							 =			image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__       :Optional[int]							 =			image_std if image_std is not None else self.image_std

							UpperCamelCase__       :Tuple							 =			size if size is not None else self.size
							UpperCamelCase__       :Optional[int]							 =			get_size_dict(UpperCamelCase_   ,       default_to_square=UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			crop_size if crop_size is not None else self.crop_size
							UpperCamelCase__       :List[str]							 =			get_size_dict(UpperCamelCase_   ,       param_name='''crop_size'''							)

							UpperCamelCase__       :Dict							 =			make_list_of_images(UpperCamelCase_							)

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

							if do_resize and size is None or resample is None:
												raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

							if do_center_crop and crop_pct is None:
												raise ValueError('''Crop_pct must be specified if do_center_crop is True.'''							)

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

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

							# All transformations expect numpy arrays.
							UpperCamelCase__       :Dict							 =			[to_numpy_array(UpperCamelCase_							) for image in images]

							if do_resize:
												UpperCamelCase__       :Optional[Any]							 =			[self.resize(image=UpperCamelCase_   ,       size=UpperCamelCase_   ,       crop_pct=UpperCamelCase_   ,       resample=UpperCamelCase_							) for image in images]

							if do_center_crop:
												UpperCamelCase__       :Union[str, Any]							 =			[self.center_crop(image=UpperCamelCase_   ,       size=UpperCamelCase_							) for image in images]

							if do_rescale:
												UpperCamelCase__       :Tuple							 =			[self.rescale(image=UpperCamelCase_   ,       scale=UpperCamelCase_							) for image in images]

							if do_normalize:
												UpperCamelCase__       :Union[str, Any]							 =			[self.normalize(image=UpperCamelCase_   ,       mean=UpperCamelCase_   ,       std=UpperCamelCase_							) for image in images]

							UpperCamelCase__       :Tuple							 =			[to_channel_dimension_format(UpperCamelCase_   ,       UpperCamelCase_							) for image in images]

							UpperCamelCase__       :List[str]							 =			{'''pixel_values''': images}
							return BatchFeature(data=UpperCamelCase_   ,       tensor_type=UpperCamelCase_							)



'''simple docstring'''
from __future__ import annotations

__snake_case											=					list[list[int]]

# assigning initial values to the grid
__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
__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				(      __a			, __a			, __a			, __a       )  ->     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       )  ->     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 | None:



					'''simple docstring'''






					if location := find_empty_location(__a       ):
										UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			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       ):
															UpperCamelCase__       :Tuple							 =			digit

															if sudoku(__a       ) is not None:
																				return grid

															UpperCamelCase__       :Union[str, Any]							 =			0

					return None


def       a				(      __a       )  ->     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''' + '''=''' * 20)
				print_solution(example_grid)
				print('''\nExample grid solution:''')
				__snake_case											=					sudoku(example_grid)
				if solution is not None:
						print_solution(solution)
				else:
						print('''Cannot find a solution.''')