from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def   __A		(			):



 """simple docstring"""


 __a       ,     __a       =  9, 14  # noqa: F841
 __a       =  [
     [0, 1, 4],
     [0, 7, 8],
     [1, 2, 8],
     [7, 8, 7],
     [7, 6, 1],
     [2, 8, 2],
     [8, 6, 6],
     [2, 3, 7],
     [2, 5, 4],
     [6, 5, 2],
     [3, 5, 14],
     [3, 4, 9],
     [5, 4, 10],
     [1, 7, 11],
 ]

 __a       =  defaultdict(_A					)
 for nodea, nodea, cost in edges:
  adjancency[nodea].append([nodea, cost]					)
  adjancency[nodea].append([nodea, cost]					)

 __a       =  mst(_A					)

 __a       =  [
     [7, 6, 1],
     [2, 8, 2],
     [6, 5, 2],
     [0, 1, 4],
     [2, 5, 4],
     [2, 3, 7],
     [0, 7, 8],
     [3, 4, 9],
 ]

 for answer in expected:
  __a       =  tuple(answer[:2]					)
  __a       =  tuple(edge[::-1]					)
  assert edge in result or reverse in result



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


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
  from .pipeline_unclip import UnCLIPPipeline
  from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
  from .text_proj import UnCLIPTextProjModel


