def    __lowercase     (						lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					[0] * len(lowerCamelCase	)
 UpperCamelCase_					:						int =					[]
 UpperCamelCase_					:						str =					[]
 UpperCamelCase_					:						str =					0

 for values in graph.values():
  for i in values:
   indegree[i] += 1

 for i in range(len(lowerCamelCase	)	):
  if indegree[i] == 0:
   queue.append(lowerCamelCase	)

 while queue:
  UpperCamelCase_					:						str =					queue.pop(0	)
  cnt += 1
  topo.append(lowerCamelCase	)
  for x in graph[vertex]:
   indegree[x] -= 1
   if indegree[x] == 0:
    queue.append(lowerCamelCase	)

 if cnt != len(lowerCamelCase	):
  print('Cycle exists'	)
 else:
  print(lowerCamelCase	)


# Adjacency List of Graph
a_    					=       {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}






topological_sort(graph)











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,
	)
