





"""simple docstring"""





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class    _lowerCAmelCase  :
       """simple docstring"""




       __magic_name__	:Dict						    =   42
       __magic_name__	:int						    =   42






class    _lowerCAmelCase  :
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				[[] for _ in range(lowerCAmelCase_ )]
         lowerCAmelCase__     :str											=				size






       def __getitem__(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return iter(self._graph[vertex] )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self._size






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         if weight not in (0, 1):
           raise ValueError('Edge weight must be either 0 or 1.' )

         if to_vertex < 0 or to_vertex >= self.size:
           raise ValueError('Vertex indexes must be in [0; size).' )

         self._graph[from_vertex].append(Edge(lowerCAmelCase_       ,					lowerCAmelCase_ ) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				deque([start_vertex] )
         lowerCAmelCase__     :Union[str, Any]											=				[None] * self.size
         lowerCAmelCase__     :int											=				0

         while queue:
           lowerCAmelCase__     :Any											=				queue.popleft()
           lowerCAmelCase__     :Dict											=				distances[current_vertex]
           if current_distance is None:
             continue

           for edge in self[current_vertex]:
             lowerCAmelCase__     :Any											=				current_distance + edge.weight
             lowerCAmelCase__     :int											=				distances[edge.destination_vertex]
             if (
                 isinstance(lowerCAmelCase_       ,					lowerCAmelCase_ )
                 and new_distance >= dest_vertex_distance
             ):
               continue
             lowerCAmelCase__     :Optional[int]											=				new_distance
             if edge.weight == 0:
               queue.appendleft(edge.destination_vertex )
             else:
               queue.append(edge.destination_vertex )

         if distances[finish_vertex] is None:
           raise ValueError('No path from start_vertex to finish_vertex.' )

         return distances[finish_vertex]


if __name__ == "__main__":
  import doctest

  doctest.testmod()






'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

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







							def   __lowerCAmelCase     (    self			:    int   )     ->				Any:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]   )

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







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_dpmpp_2m'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=7.5				,     num_inference_steps=15				,     output_type='''np'''				,     use_karras_sigmas=lowerCAmelCase_				,     )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array(
														    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]   )

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

