


from __future__ import annotations


def      a	(							SCREAMING_SNAKE_CASE_      :		list[float]							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       print(F"""Vertex\tShortest Distance from vertex {src}"""       )
       for i, d in enumerate(SCREAMING_SNAKE_CASE_       ):
              print(F"""{i}\t\t{d}"""       )


def      a	(							SCREAMING_SNAKE_CASE_      :		list[dict[str, int]]							,			SCREAMING_SNAKE_CASE_      :		list[float]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       for j in range(SCREAMING_SNAKE_CASE_       ):
              UpperCamelCase    :	Optional[int]   	=		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
              if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
                     return True
       return False


def      a	(							SCREAMING_SNAKE_CASE_      :		list[dict[str, int]]							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		[float('''inf'''       )] * vertex_count
       UpperCamelCase    :	Tuple   	=		0.0

       for _ in range(vertex_count - 1       ):
              for j in range(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	str   	=		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

                     if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
                            UpperCamelCase    :	str   	=		distance[u] + w

       UpperCamelCase    :	str   	=		check_negative_cycle(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       if negative_cycle_exists:
              raise Exception('''Negative cycle found'''       )

       return distance


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __UpperCAmelCase : Optional[Any] 			=				int(input("Enter number of vertices: ").strip())
  __UpperCAmelCase : Any 			=				int(input("Enter number of edges: ").strip())

  __UpperCAmelCase : list[dict[str, int]] 			=				[{} for _ in range(E)]

  for i in range(E):
    print("Edge ", i + 1)
    __UpperCAmelCase : str 			=				(
        int(x)
        for x in input("Enter source, destination, weight: ").strip().split(" ")
    )
    __UpperCAmelCase : List[str] 			=				{"src": src, "dst": dest, "weight": weight}

  __UpperCAmelCase : Optional[int] 			=				int(input("\nEnter shortest path source:").strip())
  __UpperCAmelCase : List[str] 			=				bellman_ford(graph, V, E, source)
  print_distance(shortest_distance, 0)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )

