



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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowercase: Tuple      			=		logging.get_logger(__name__)


if is_vision_available():
				import PIL



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							["pixel_values"]




	def __init__(self		,       lowerCamelCase_ = True		,       lowerCamelCase_ = None		,       lowerCamelCase_ = PILImageResampling.BICUBIC		,       lowerCamelCase_ = True		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 1 / 255		,       lowerCamelCase_ = True		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						super().__init__(**lowerCamelCase_		)
						a					     =   size if size is not None else {"shortest_edge": 224}
						a					     =   get_size_dict(lowerCamelCase_		,       default_to_square=lowerCamelCase_		)
						a					     =   crop_size if crop_size is not None else {"height": 224, "width": 224}
						a					     =   get_size_dict(lowerCamelCase_		,       default_to_square=lowerCamelCase_		,       param_name="crop_size"		)

						a					     =   do_resize
						a					     =   size
						a					     =   resample
						a					     =   do_center_crop
						a					     =   crop_size
						a					     =   do_rescale
						a					     =   rescale_factor
						a					     =   do_normalize
						a					     =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						a					     =   image_std if image_std is not None else OPENAI_CLIP_STD
						a					     =   do_convert_rgb




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = PILImageResampling.BICUBIC		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   get_size_dict(lowerCamelCase_		,       default_to_square=lowerCamelCase_		)
						if "shortest_edge" not in size:
											raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
						a					     =   get_resize_output_image_size(lowerCamelCase_		,       size=size["shortest_edge"]		,       default_to_square=lowerCamelCase_		)
						return resize(lowerCamelCase_		,       size=lowerCamelCase_		,       resample=lowerCamelCase_		,       data_format=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   get_size_dict(lowerCamelCase_		)
						if "height" not in size or "width" not in size:
											raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
						return center_crop(lowerCamelCase_		,       size=(size["height"], size["width"])		,       data_format=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = ChannelDimension.FIRST		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   do_resize if do_resize is not None else self.do_resize
						a					     =   size if size is not None else self.size
						a					     =   get_size_dict(lowerCamelCase_		,       param_name="size"		,       default_to_square=lowerCamelCase_		)
						a					     =   resample if resample is not None else self.resample
						a					     =   do_center_crop if do_center_crop is not None else self.do_center_crop
						a					     =   crop_size if crop_size is not None else self.crop_size
						a					     =   get_size_dict(lowerCamelCase_		,       param_name="crop_size"		,       default_to_square=lowerCamelCase_		)
						a					     =   do_rescale if do_rescale is not None else self.do_rescale
						a					     =   rescale_factor if rescale_factor is not None else self.rescale_factor
						a					     =   do_normalize if do_normalize is not None else self.do_normalize
						a					     =   image_mean if image_mean is not None else self.image_mean
						a					     =   image_std if image_std is not None else self.image_std
						a					     =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						a					     =   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_resize and size is None:
											raise ValueError("Size must be specified if do_resize is True."		)

						if do_center_crop and crop_size is None:
											raise ValueError("Crop size 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."		)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
											a					     =   [convert_to_rgb(lowerCamelCase_		) for image in images]

						# All transformations expect numpy arrays.
						a					     =   [to_numpy_array(lowerCamelCase_		) for image in images]

						if do_resize:
											a					     =   [self.resize(image=lowerCamelCase_		,       size=lowerCamelCase_		,       resample=lowerCamelCase_		) for image in images]

						if do_center_crop:
											a					     =   [self.center_crop(image=lowerCamelCase_		,       size=lowerCamelCase_		) for image in images]

						if do_rescale:
											a					     =   [self.rescale(image=lowerCamelCase_		,       scale=lowerCamelCase_		) for image in images]

						if do_normalize:
											a					     =   [self.normalize(image=lowerCamelCase_		,       mean=lowerCamelCase_		,       std=lowerCamelCase_		) for image in images]

						a					     =   [to_channel_dimension_format(lowerCamelCase_		,       lowerCamelCase_		) for image in images]

						a					     =   {"pixel_values": images}
						return BatchFeature(data=lowerCamelCase_		,       tensor_type=lowerCamelCase_		)





from __future__ import annotations

_lowercase: Tuple      			=		list[list[int]]

# assigning initial values to the grid
_lowercase: Matrix      			=		[
    [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: Matrix      			=		[
    [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       ):
										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:
					"""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:")
								_lowercase: List[str]      			=		sudoku(example_grid)
								if solution is not None:
												print_solution(solution)
								else:
												print("Cannot find a solution.")
