





'''simple docstring'''







from __future__ import annotations



class 							__SCREAMING_SNAKE_CASE			:
      '''simple docstring'''



      def __init__(							self    : List[str]   ,UpperCamelCase    : int  )		->  None:
           _lowercase							:      Union[str, Any]  						=							vertices
           _lowercase							:      int  						=							[[0] * vertices for _ in range(UpperCamelCase  )]

      def   _lowerCamelCase		(							self    : Dict   ,UpperCamelCase    : list[int]  )		->  None:
           print('Vertex \t Distance from Source'  )
           for vertex in range(self.vertices  ):
                print(UpperCamelCase   ,'\t\t'   ,distances_from_source[vertex]  )

      def   _lowerCamelCase		(							self    : Union[str, Any]   ,UpperCamelCase    : list[int]   ,UpperCamelCase    : list[bool]  )		->  int:
           _lowercase							:      Any  						=							1e7
           _lowercase							:      List[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:
                     _lowercase							:      Optional[int]  						=							distances_from_source[vertex]
                     _lowercase							:      Dict  						=							vertex
           return min_index



      def   _lowerCamelCase		(							self    : List[str]   ,UpperCamelCase    : int  )		->  None:
           _lowercase							:      Optional[Any]  						=							[int(1e7  )] * self.vertices  # distances from the source
           _lowercase							:      Optional[Any]  						=							0
           _lowercase							:      Tuple  						=							[False] * self.vertices

           for _ in range(self.vertices  ):
                _lowercase							:      Any  						=							self.minimum_distance(UpperCamelCase   ,UpperCamelCase  )
                _lowercase							:      List[Any]  						=							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]
                     ):
                          _lowercase							:      Any  						=							distances[u] + self.graph[u][v]

           self.print_solution(UpperCamelCase  )


if __name__ == "__main__":
    A						    =							Graph(9)
    A						    =							[
        [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)






'''simple docstring'''







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available



@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      Union[List[PIL.Image.Image], np.ndarray]
      lowerCAmelCase__       :      Optional[List[bool]]


if is_transformers_available() and is_torch_available():
    from .modeling_roberta_series import RobertaSeriesModelWithTransformation
    from .pipeline_alt_diffusion import AltDiffusionPipeline
    from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline