



"""simple docstring"""
SCREAMING_SNAKE_CASE_				= {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      list[str]:
		a_     :      Any 						=     set()
		# keep track of all the paths to be checked
		a_     :      Optional[Any] 						=     [[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				a_     :      List[str] 						=     queue.pop(0	)
				# get the last node from the path
				a_     :      str 						=     path[-1]
				if node not in explored:
						a_     :      int 						=     graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								a_     :      int 						=     list(SCREAMING_SNAKE_CASE__	)
								new_path.append(SCREAMING_SNAKE_CASE__	)
								queue.append(SCREAMING_SNAKE_CASE__	)
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(SCREAMING_SNAKE_CASE__	)

    # in case there's no path between the 2 nodes
		return []



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		a_     :      int 						=     [start]
		a_     :      Optional[Any] 						=     set(SCREAMING_SNAKE_CASE__	)
		# Keep tab on distances from `start` node.
		a_     :      Optional[int] 						=     {start: 0, target: -1}
		while queue:
				a_     :      Union[str, Any] 						=     queue.pop(0	)
				if node == target:
						a_     :      Optional[int] 						=     (
						    dist[node] if dist[target] == -1 else min(dist[target], dist[node]	)
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(SCREAMING_SNAKE_CASE__	)
								queue.append(SCREAMING_SNAKE_CASE__	)
								a_     :      Any 						=     dist[node] + 1
		return dist[target]


if __name__ == "__main__":
					print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
					print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4




"""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