



"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		a_						,				a_     :      List[Any] 						=     len(SCREAMING_SNAKE_CASE__	), len(grid[0]	)
		if (
		    min(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) < 0
		    or row == row_length
		    or col == col_length
		    or (row, col) in visit
		    or grid[row][col] == 1
		):
				return 0
		if row == row_length - 1 and col == col_length - 1:
				return 1

		visit.add((row, col)	)

		a_     :      Dict 						=     0
		count += depth_first_search(SCREAMING_SNAKE_CASE__, row + 1, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		count += depth_first_search(SCREAMING_SNAKE_CASE__, row - 1, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		count += depth_first_search(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, col + 1, SCREAMING_SNAKE_CASE__	)
		count += depth_first_search(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, col - 1, SCREAMING_SNAKE_CASE__	)

		visit.remove((row, col)	)
		return count


if __name__ == "__main__":
					import doctest

					doctest.testmod()




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




@slow
@require_torch_gpu
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

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