






lowerCamelCase     :							Optional[int]          ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[str]:
     UpperCamelCase__      :       Optional[Any]	    =  set()
     # keep track of all the paths to be checked
     UpperCamelCase__      :       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
          UpperCamelCase__      :       int	    =  queue.pop(0      )
          # get the last node from the path
          UpperCamelCase__      :       Dict	    =  path[-1]
          if node not in explored:
               UpperCamelCase__      :       Tuple	    =  graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    UpperCamelCase__      :       List[str]	    =  list(__lowerCAmelCase      )
                    new_path.append(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(__lowerCAmelCase      )

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







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







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase     :							Optional[Any]    =False





class       __a  (   unittest.TestCase	):
 pass







@nightly
@require_torch_gpu
class       __a  (   unittest.TestCase	):







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)

      UpperCamelCase__      :       Dict	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       str	    =  pipe.dual_guided(
          prompt="first prompt"							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=2							,       output_type="numpy"							,       ).images

      with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Tuple	    =  VersatileDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       int	    =  generator.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  pipe.dual_guided(
          prompt="first prompt"							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=2							,       output_type="numpy"							,       ).images

      assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Tuple	    =  "cyberpunk 2077"
      UpperCamelCase__      :       Any	    =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__      :       int	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Any	    =  pipe.dual_guided(
          prompt=SCREAMING_SNAKE_CASE							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=50							,       output_type="numpy"							,       ).images

      UpperCamelCase__      :       Tuple	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Optional[int]	    =  np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]						)

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

      UpperCamelCase__      :       List[str]	    =  "A painting of a squirrel eating a burger "
      UpperCamelCase__      :       List[Any]	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  pipe.text_to_image(
          prompt=SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=50							,       output_type="numpy"						).images

      UpperCamelCase__      :       Union[str, Any]	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Dict	    =  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]						)

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

      UpperCamelCase__      :       Any	    =  pipe.image_variation(SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       output_type="numpy"						).images

      UpperCamelCase__      :       Optional[Any]	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       int	    =  np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]						)

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