



'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:list[tuple[int, int, int]]			)		->						list[tuple[int, int, int]]:
				snake_case__      :   int      =    sorted(UpperCamelCase__   ,				key=lambda UpperCamelCase__			: edge[2]			)

				snake_case__      :   List[Any]      =    list(range(UpperCamelCase__			)			)

				def find_parent(UpperCamelCase__				:Tuple			):
								if i != parent[i]:
												snake_case__      :   Tuple      =    find_parent(parent[i]			)
								return parent[i]

				snake_case__      :   Dict      =    0
				snake_case__      :   str      =    []

				for edge in edges:
								snake_case__      :   int      =    find_parent(edge[0]			)
								snake_case__      :   Union[str, Any]      =    find_parent(edge[1]			)
								if parent_a != parent_b:
												minimum_spanning_tree_cost += edge[2]
												minimum_spanning_tree.append(UpperCamelCase__			)
												snake_case__      :   List[Any]      =    parent_b

				return minimum_spanning_tree


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

				for _ in range(num_edges):
								_lowercase		,	_lowercase		,	_lowercase       :						int						=(int(x) for x in input().strip().split())
								edges.append((nodea, nodea, cost))

				kruskal(num_nodes, edges)








"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							["""BartTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       List[Any]							   =							[
			    """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """BartForCausalLM""",
			    """BartForConditionalGeneration""",
			    """BartForQuestionAnswering""",
			    """BartForSequenceClassification""",
			    """BartModel""",
			    """BartPretrainedModel""",
			    """PretrainedBartModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Any							   =							[
			    """TFBartForConditionalGeneration""",
			    """TFBartForSequenceClassification""",
			    """TFBartModel""",
			    """TFBartPretrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       str							   =							[
			    """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

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





