


from __future__ import annotations

class   lowerCamelCase						:

       def __init__(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   vertices
        snake_case :			str							=   [[0] * vertices for _ in range(a__       )]

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        print('''Vertex \t Distance from Source'''       )
        for vertex in range(self.vertices       ):
         print(a__						,    '''\t\t'''						,    distances_from_source[vertex]       )

       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			str							=   1e7
        snake_case :			Union[str, Any]							=   0

        # Search not nearest vertex not in the shortest path tree
        for vertex in range(self.vertices       ):
         if distances_from_source[vertex] < minimum and visited[vertex] is False:
          snake_case :			Tuple							=   distances_from_source[vertex]
          snake_case :			Optional[Any]							=   vertex
        return min_index



       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			int							=   [int(1e7       )] * self.vertices  # distances from the source
        snake_case :			int							=   0
        snake_case :			List[str]							=   [False] * self.vertices

        for _ in range(self.vertices       ):
         snake_case :			Dict							=   self.minimum_distance(a__						,    a__       )
         snake_case :			Optional[int]							=   True

         # Update dist value of the adjacent vertices
         # of the picked vertex only if the current
         # distance is greater than new distance and
         # the vertex in not in the shortest path tree
         for v in range(self.vertices       ):
          if (
              self.graph[u][v] > 0
              and visited[v] is False
              and distances[v] > distances[u] + self.graph[u][v]
          ):
           snake_case :			Dict							=   distances[u] + self.graph[u][v]

        self.print_solution(a__       )


if __name__ == "__main__":
  a					:				Dict       =      Graph(9)
  a					:				str       =      [
      [0, 4, 0, 0, 0, 0, 0, 8, 0],
      [4, 0, 8, 0, 0, 0, 0, 11, 0],
      [0, 8, 0, 7, 0, 4, 0, 0, 2],
      [0, 0, 7, 0, 9, 14, 0, 0, 0],
      [0, 0, 0, 9, 0, 10, 0, 0, 0],
      [0, 0, 4, 14, 10, 0, 2, 0, 0],
      [0, 0, 0, 0, 0, 2, 0, 1, 6],
      [8, 11, 0, 0, 0, 0, 1, 0, 7],
      [0, 0, 2, 0, 0, 0, 6, 7, 0],
  ]
  graph.dijkstra(0)










import gc
import tempfile
import unittest

import numpy as np
import torch

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


a					:				Optional[int]       =      False

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Union[str, Any]							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )

        snake_case :			str							=   torch.manual_seed(0       )
        snake_case :			Tuple							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__       )
         snake_case :			str							=   VersatileDiffusionDualGuidedPipeline.from_pretrained(a__       )

        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Optional[Any]							=   generator.manual_seed(0       )
        snake_case :			List[str]							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			int							=   '''cyberpunk 2077'''
        snake_case :			Dict							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )
        snake_case :			Optional[int]							=   torch.manual_seed(0       )
        snake_case :			Union[str, Any]							=   pipe(
            prompt=a__						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=50						,    output_type='''numpy'''						,    ).images

        snake_case :			str							=   image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case :			int							=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]       )

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






