

"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class       snake_case__  ( unittest.TestCase     ):







			@property
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								torch.manual_seed(0  )
								UpperCAmelCase			:   List[Any]      =				UNetaDModel(
								    block_out_channels=(32, 64)			,  layers_per_block=2			,  sample_size=32			,  in_channels=3			,  out_channels=3			,  down_block_types=("DownBlock2D", "AttnDownBlock2D")			,  up_block_types=("AttnUpBlock2D", "UpBlock2D")			,  )
								return model







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.dummy_uncond_unet
								UpperCAmelCase			:   Tuple      =				KarrasVeScheduler()

								UpperCAmelCase			:   Optional[int]      =				KarrasVePipeline(unet=lowercase			,  scheduler=lowercase  )
								pipe.to(lowercase  )
								pipe.set_progress_bar_config(disable=lowercase  )

								UpperCAmelCase			:   Any      =				torch.manual_seed(0  )
								UpperCAmelCase			:   List[str]      =				pipe(num_inference_steps=2			,  generator=lowercase			,  output_type="numpy"  ).images

								UpperCAmelCase			:   Optional[int]      =				torch.manual_seed(0  )
								UpperCAmelCase			:   Optional[int]      =				pipe(num_inference_steps=2			,  generator=lowercase			,  output_type="numpy"			,  return_dict=lowercase  )[0]

								UpperCAmelCase			:   int      =				image[0, -3:, -3:, -1]
								UpperCAmelCase			:   Optional[int]      =				image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase			:   Union[str, Any]      =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2







@slow
@require_torch
class       snake_case__  ( unittest.TestCase     ):







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				"google/ncsnpp-celebahq-256"
								UpperCAmelCase			:   Dict      =				UNetaDModel.from_pretrained(lowercase  )
								UpperCAmelCase			:   int      =				KarrasVeScheduler()

								UpperCAmelCase			:   Tuple      =				KarrasVePipeline(unet=lowercase			,  scheduler=lowercase  )
								pipe.to(lowercase  )
								pipe.set_progress_bar_config(disable=lowercase  )

								UpperCAmelCase			:   List[str]      =				torch.manual_seed(0  )
								UpperCAmelCase			:   List[str]      =				pipe(num_inference_steps=20			,  generator=lowercase			,  output_type="numpy"  ).images

								UpperCAmelCase			:   Any      =				image[0, -3:, -3:, -1]
								assert image.shape == (1, 2_56, 2_56, 3)
								UpperCAmelCase			:   Optional[int]      =				np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2





"""simple docstring"""






from __future__ import annotations

snake_case_   :   str        =       list[list[int]]

# assigning initial values to the grid
snake_case_   :   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
snake_case_   :   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   lowercase_	(    _lowercase       :		Matrix							,      _lowercase       :		int							,      _lowercase       :		int							,      _lowercase       :		int	):




					'''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   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					for i in range(9	):
										for j in range(9	):
															if grid[i][j] == 0:
																				return i, j
					return None



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					if location := find_empty_location(_lowercase	):
										UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				location
					else:
										# If the location is ``None``, then the grid is solved.
										return grid

					for digit in range(1							,      10	):
										if is_safe(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
															UpperCAmelCase			:   int      =				digit

															if sudoku(_lowercase	) is not None:
																				return grid

															UpperCAmelCase			:   Optional[Any]      =				0

					return None



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					for row in grid:
										for cell in row:
															print(_lowercase							,      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:""")
		snake_case_   :   Dict        =       sudoku(example_grid)
		if solution is not None:
			print_solution(solution)
		else:
			print("""Cannot find a solution.""")


