


import random


def      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		float							,			SCREAMING_SNAKE_CASE_      :		bool = False       ):






       """simple docstring"""





       UpperCamelCase    :	dict   	=		{i: [] for i in range(SCREAMING_SNAKE_CASE_       )}

       # if probability is greater or equal than 1, then generate a complete graph
       if probability >= 1:
              return complete_graph(SCREAMING_SNAKE_CASE_       )
       # if probability is lower or equal than 0, then return a graph without edges
       if probability <= 0:
              return graph

       # for each couple of nodes, add an edge from u to v
       # if the number randomly generated is greater than probability probability
       for i in range(SCREAMING_SNAKE_CASE_       ):
              for j in range(i + 1							,			SCREAMING_SNAKE_CASE_       ):
                     if random.random() < probability:
                            graph[i].append(SCREAMING_SNAKE_CASE_       )
                            if not directed:
                                   # if the graph is undirected, add an edge in from j to i, either
                                   graph[j].append(SCREAMING_SNAKE_CASE_       )
       return graph


def      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       return {
           i: [j for j in range(SCREAMING_SNAKE_CASE_       ) if i != j] for i in range(SCREAMING_SNAKE_CASE_       )
       }


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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,
  )

