


__UpperCAmelCase : Optional[Any] 			=				{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}


def      a	(							SCREAMING_SNAKE_CASE_      :		dict							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		set()
       # keep track of all the paths to be checked
       UpperCamelCase    :	str   	=		[[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
              UpperCamelCase    :	Optional[Any]   	=		queue.pop(0       )
              # get the last node from the path
              UpperCamelCase    :	List[Any]   	=		path[-1]
              if node not in explored:
                     UpperCamelCase    :	int   	=		graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            UpperCamelCase    :	Union[str, Any]   	=		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      a	(							SCREAMING_SNAKE_CASE_      :		dict							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       UpperCamelCase    :	Tuple   	=		[start]
       UpperCamelCase    :	List[str]   	=		set(SCREAMING_SNAKE_CASE_       )
       # Keep tab on distances from `start` node.
       UpperCamelCase    :	Tuple   	=		{start: 0, target: -1}
       while queue:
              UpperCamelCase    :	List[Any]   	=		queue.pop(0       )
              if node == target:
                     UpperCamelCase    :	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_       )
                            UpperCamelCase    :	List[str]   	=		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





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


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		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

