def        lowerCAmelCase_			(					__A,  __A,  __A,  __A							)					->    List[str]:







      '''simple docstring'''


      UpperCAmelCase__								=  [False] * len(__A							)
      UpperCAmelCase__								=  []
      queue.append(__A							)
      UpperCAmelCase__								=  True

      while queue:
            UpperCAmelCase__								=  queue.pop(0							)
            for ind in range(len(graph[u]							)							):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(__A							)
                        UpperCAmelCase__								=  True
                        UpperCAmelCase__								=  u

      return visited[t]




def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  [-1] * (len(__A							))
      UpperCAmelCase__								=  0
      while bfs(__A,  __A,  __A,  __A							):
            UpperCAmelCase__								=  float("Inf"							)
            UpperCAmelCase__								=  sink

            while s != source:
                  # Find the minimum value in select path
                  UpperCAmelCase__								=  min(__A,  graph[parent[s]][s]							)
                  UpperCAmelCase__								=  parent[s]

            max_flow += path_flow
            UpperCAmelCase__								=  sink

            while v != source:
                  UpperCAmelCase__								=  parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  UpperCAmelCase__								=  parent[v]
      return max_flow


UpperCamelCase__    							= [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCamelCase__							,							UpperCamelCase__    							= 0, 5







print(ford_fulkerson(graph, source, sink))




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,
      )



