

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline





def   a   (    A__			:     Optional[int]  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =[0] * len(A__  )
      _lowercase			 =[]
      _lowercase			 =[]
      _lowercase			 =0

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

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

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

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


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







topological_sort(graph)


