




'''simple docstring'''






from typing import TYPE_CHECKING

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


lowercase	:      List[str]				=       {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      Tuple				=       ['''BartTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      int				=       [
          '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BartForCausalLM''',
          '''BartForConditionalGeneration''',
          '''BartForQuestionAnswering''',
          '''BartForSequenceClassification''',
          '''BartModel''',
          '''BartPretrainedModel''',
          '''PretrainedBartModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''TFBartForConditionalGeneration''',
          '''TFBartForSequenceClassification''',
          '''TFBartModel''',
          '''TFBartPretrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      Tuple				=       [
          '''FlaxBartDecoderPreTrainedModel''',
          '''FlaxBartForCausalLM''',
          '''FlaxBartForConditionalGeneration''',
          '''FlaxBartForQuestionAnswering''',
          '''FlaxBartForSequenceClassification''',
          '''FlaxBartModel''',
          '''FlaxBartPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
      from .tokenization_bart import BartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bart_fast import BartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bart import (
                BART_PRETRAINED_MODEL_ARCHIVE_LIST,
                BartForCausalLM,
                BartForConditionalGeneration,
                BartForQuestionAnswering,
                BartForSequenceClassification,
                BartModel,
                BartPretrainedModel,
                PretrainedBartModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bart import (
                TFBartForConditionalGeneration,
                TFBartForSequenceClassification,
                TFBartModel,
                TFBartPretrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bart import (
                FlaxBartDecoderPreTrainedModel,
                FlaxBartForCausalLM,
                FlaxBartForConditionalGeneration,
                FlaxBartForQuestionAnswering,
                FlaxBartForSequenceClassification,
                FlaxBartModel,
                FlaxBartPreTrainedModel,
            )

else:
      import sys

      lowercase	:      Optional[int]				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''simple docstring'''






def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->list[tuple[int, int, int]]:






       '''simple docstring'''




       lowercase  :		Dict						=      sorted(lowerCamelCase   ,				key=lambda lowerCamelCase      : edge[2]      )

       lowercase  :		str						=      list(range(lowerCamelCase      )      )

       def find_parent(lowerCamelCase      ):
              if i != parent[i]:
                     lowercase  :		Optional[int]						=      find_parent(parent[i]      )
              return parent[i]

       lowercase  :		Union[str, Any]						=      0
       lowercase  :		Any						=      []

       for edge in edges:
              lowercase  :		Optional[Any]						=      find_parent(edge[0]      )
              lowercase  :		Any						=      find_parent(edge[1]      )
              if parent_a != parent_b:
                     minimum_spanning_tree_cost += edge[2]
                     minimum_spanning_tree.append(lowerCamelCase      )
                     lowercase  :		Optional[int]						=      parent_b

       return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
      lowercase	,	lowercase	:      List[Any]				=       list(map(int, input().strip().split()))
      lowercase	:      List[Any]				=       []

      for _ in range(num_edges):
            lowercase	,	lowercase	,	lowercase	:      Optional[Any]				=       (int(x) for x in input().strip().split())
            edges.append((nodea, nodea, cost))

      kruskal(num_nodes, edges)




