

'''simple docstring'''


from __future__ import annotations

lowerCAmelCase__										= list[list[int]]

# assigning initial values to the grid
lowerCAmelCase__										= [
    [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
lowerCAmelCase__										= [
    [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 						SCREAMING_SNAKE_CASE(  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 						SCREAMING_SNAKE_CASE(  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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Matrix | None:
	if location := find_empty_location(UpperCamelCase  ):
		UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					location
	else:
		# If the location is ``None``, then the grid is solved.
		return grid

	for digit in range(1   ,1_0  ):
		if is_safe(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  ):
			UpperCAmelCase_		:      List[Any]       	=					digit

			if sudoku(UpperCamelCase  ) is not None:
				return grid

			UpperCAmelCase_		:      Dict       	=					0

	return None







def 						SCREAMING_SNAKE_CASE(  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" + "=" * 20)
												print_solution(example_grid)
												print("\nExample grid solution:")
												lowerCAmelCase__										= sudoku(example_grid)
												if solution is not None:
																		print_solution(solution)
												else:
																		print("Cannot find a solution.")









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						parser.add_argument(
						    "--txt2img_unclip",
						    default="kakaobrain/karlo-v1-alpha",
						    type=str,
						    required=False,
						    help="The pretrained txt2img unclip.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)






