



"""simple docstring"""







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst

def 				UpperCamelCase_    (    )						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= 9, 14  # noqa: F841
  lowerCAmelCase_   :				Optional[Any]								= [
      [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],
  ]

  lowerCAmelCase_   :				List[Any]								= defaultdict(_lowerCAmelCase						)
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]						)
    adjancency[nodea].append([nodea, cost]						)

  lowerCAmelCase_   :				Optional[int]								= mst(_lowerCAmelCase						)

  lowerCAmelCase_   :				Dict								= [
      [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:
    lowerCAmelCase_   :				List[Any]								= tuple(answer[:2]						)
    lowerCAmelCase_   :				Dict								= tuple(edge[::-1]						)
    assert edge in result or reverse in result










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase											=						{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['SpeechEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






