






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_     :				Optional[Any]         =				{
    """configuration_blenderbot_small""": [
        """BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlenderbotSmallConfig""",
        """BlenderbotSmallOnnxConfig""",
    ],
    """tokenization_blenderbot_small""": ["""BlenderbotSmallTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				Any         =				["""BlenderbotSmallTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				List[str]         =				[
      """BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BlenderbotSmallForCausalLM""",
      """BlenderbotSmallForConditionalGeneration""",
      """BlenderbotSmallModel""",
      """BlenderbotSmallPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				Optional[int]         =				[
      """TFBlenderbotSmallForConditionalGeneration""",
      """TFBlenderbotSmallModel""",
      """TFBlenderbotSmallPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				str         =				[
      """FlaxBlenderbotSmallForConditionalGeneration""",
      """FlaxBlenderbotSmallModel""",
      """FlaxBlenderbotSmallPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_blenderbot_small import (
      BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlenderbotSmallConfig,
      BlenderbotSmallOnnxConfig,
  )
  from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blenderbot_small import (
        BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlenderbotSmallForCausalLM,
        BlenderbotSmallForConditionalGeneration,
        BlenderbotSmallModel,
        BlenderbotSmallPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blenderbot_small import (
        TFBlenderbotSmallForConditionalGeneration,
        TFBlenderbotSmallModel,
        TFBlenderbotSmallPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_blenderbot_small import (
        FlaxBlenderbotSmallForConditionalGeneration,
        FlaxBlenderbotSmallModel,
        FlaxBlenderbotSmallPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_     :				Tuple         =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       _A    ()				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	9, 14  # noqa: F841
    SCREAMING_SNAKE_CASE_   :						Tuple							=	[
        [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],
    ]

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

    SCREAMING_SNAKE_CASE_   :						Any							=	mst(__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[
        [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:
        SCREAMING_SNAKE_CASE_   :						Dict							=	tuple(answer[:2]							)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tuple(edge[::-1]							)
        assert edge in result or reverse in result


