






from typing import TYPE_CHECKING

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


UpperCAmelCase__			 =	{
    '''auto_factory''': ['''get_values'''],
    '''configuration_auto''': ['''ALL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CONFIG_MAPPING''', '''MODEL_NAMES_MAPPING''', '''AutoConfig'''],
    '''feature_extraction_auto''': ['''FEATURE_EXTRACTOR_MAPPING''', '''AutoFeatureExtractor'''],
    '''image_processing_auto''': ['''IMAGE_PROCESSOR_MAPPING''', '''AutoImageProcessor'''],
    '''processing_auto''': ['''PROCESSOR_MAPPING''', '''AutoProcessor'''],
    '''tokenization_auto''': ['''TOKENIZER_MAPPING''', '''AutoTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
			    '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING''',
			    '''MODEL_FOR_AUDIO_XVECTOR_MAPPING''',
			    '''MODEL_FOR_BACKBONE_MAPPING''',
			    '''MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING''',
			    '''MODEL_FOR_CAUSAL_LM_MAPPING''',
			    '''MODEL_FOR_CTC_MAPPING''',
			    '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
			    '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING''',
			    '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
			    '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING''',
			    '''MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING''',
			    '''MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
			    '''MODEL_FOR_MASKED_LM_MAPPING''',
			    '''MODEL_FOR_MASK_GENERATION_MAPPING''',
			    '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
			    '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
			    '''MODEL_FOR_OBJECT_DETECTION_MAPPING''',
			    '''MODEL_FOR_PRETRAINING_MAPPING''',
			    '''MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
			    '''MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
			    '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
			    '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
			    '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
			    '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
			    '''MODEL_FOR_TEXT_ENCODING_MAPPING''',
			    '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
			    '''MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING''',
			    '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING''',
			    '''MODEL_FOR_VISION_2_SEQ_MAPPING''',
			    '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING''',
			    '''MODEL_MAPPING''',
			    '''MODEL_WITH_LM_HEAD_MAPPING''',
			    '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
			    '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING''',
			    '''AutoModel''',
			    '''AutoBackbone''',
			    '''AutoModelForAudioClassification''',
			    '''AutoModelForAudioFrameClassification''',
			    '''AutoModelForAudioXVector''',
			    '''AutoModelForCausalLM''',
			    '''AutoModelForCTC''',
			    '''AutoModelForDepthEstimation''',
			    '''AutoModelForImageClassification''',
			    '''AutoModelForImageSegmentation''',
			    '''AutoModelForInstanceSegmentation''',
			    '''AutoModelForMaskGeneration''',
			    '''AutoModelForTextEncoding''',
			    '''AutoModelForMaskedImageModeling''',
			    '''AutoModelForMaskedLM''',
			    '''AutoModelForMultipleChoice''',
			    '''AutoModelForNextSentencePrediction''',
			    '''AutoModelForObjectDetection''',
			    '''AutoModelForPreTraining''',
			    '''AutoModelForQuestionAnswering''',
			    '''AutoModelForSemanticSegmentation''',
			    '''AutoModelForSeq2SeqLM''',
			    '''AutoModelForSequenceClassification''',
			    '''AutoModelForSpeechSeq2Seq''',
			    '''AutoModelForTableQuestionAnswering''',
			    '''AutoModelForTokenClassification''',
			    '''AutoModelForUniversalSegmentation''',
			    '''AutoModelForVideoClassification''',
			    '''AutoModelForVision2Seq''',
			    '''AutoModelForVisualQuestionAnswering''',
			    '''AutoModelForDocumentQuestionAnswering''',
			    '''AutoModelWithLMHead''',
			    '''AutoModelForZeroShotImageClassification''',
			    '''AutoModelForZeroShotObjectDetection''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
			    '''TF_MODEL_FOR_CAUSAL_LM_MAPPING''',
			    '''TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
			    '''TF_MODEL_FOR_MASK_GENERATION_MAPPING''',
			    '''TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
			    '''TF_MODEL_FOR_MASKED_LM_MAPPING''',
			    '''TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
			    '''TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
			    '''TF_MODEL_FOR_PRETRAINING_MAPPING''',
			    '''TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
			    '''TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
			    '''TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
			    '''TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
			    '''TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
			    '''TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
			    '''TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
			    '''TF_MODEL_FOR_TEXT_ENCODING_MAPPING''',
			    '''TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
			    '''TF_MODEL_FOR_VISION_2_SEQ_MAPPING''',
			    '''TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
			    '''TF_MODEL_MAPPING''',
			    '''TF_MODEL_WITH_LM_HEAD_MAPPING''',
			    '''TFAutoModel''',
			    '''TFAutoModelForAudioClassification''',
			    '''TFAutoModelForCausalLM''',
			    '''TFAutoModelForImageClassification''',
			    '''TFAutoModelForMaskedImageModeling''',
			    '''TFAutoModelForMaskedLM''',
			    '''TFAutoModelForMaskGeneration''',
			    '''TFAutoModelForMultipleChoice''',
			    '''TFAutoModelForNextSentencePrediction''',
			    '''TFAutoModelForPreTraining''',
			    '''TFAutoModelForDocumentQuestionAnswering''',
			    '''TFAutoModelForQuestionAnswering''',
			    '''TFAutoModelForSemanticSegmentation''',
			    '''TFAutoModelForSeq2SeqLM''',
			    '''TFAutoModelForSequenceClassification''',
			    '''TFAutoModelForSpeechSeq2Seq''',
			    '''TFAutoModelForTableQuestionAnswering''',
			    '''TFAutoModelForTextEncoding''',
			    '''TFAutoModelForTokenClassification''',
			    '''TFAutoModelForVision2Seq''',
			    '''TFAutoModelForZeroShotImageClassification''',
			    '''TFAutoModelWithLMHead''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
			    '''FLAX_MODEL_FOR_CAUSAL_LM_MAPPING''',
			    '''FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
			    '''FLAX_MODEL_FOR_MASKED_LM_MAPPING''',
			    '''FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
			    '''FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
			    '''FLAX_MODEL_FOR_PRETRAINING_MAPPING''',
			    '''FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
			    '''FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
			    '''FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
			    '''FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
			    '''FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
			    '''FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING''',
			    '''FLAX_MODEL_MAPPING''',
			    '''FlaxAutoModel''',
			    '''FlaxAutoModelForCausalLM''',
			    '''FlaxAutoModelForImageClassification''',
			    '''FlaxAutoModelForMaskedLM''',
			    '''FlaxAutoModelForMultipleChoice''',
			    '''FlaxAutoModelForNextSentencePrediction''',
			    '''FlaxAutoModelForPreTraining''',
			    '''FlaxAutoModelForQuestionAnswering''',
			    '''FlaxAutoModelForSeq2SeqLM''',
			    '''FlaxAutoModelForSequenceClassification''',
			    '''FlaxAutoModelForSpeechSeq2Seq''',
			    '''FlaxAutoModelForTokenClassification''',
			    '''FlaxAutoModelForVision2Seq''',
			]


if TYPE_CHECKING:
			from .auto_factory import get_values
			from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
			from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
			from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
			from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
			from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_auto import (
						    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
						    MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
						    MODEL_FOR_AUDIO_XVECTOR_MAPPING,
						    MODEL_FOR_BACKBONE_MAPPING,
						    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
						    MODEL_FOR_CAUSAL_LM_MAPPING,
						    MODEL_FOR_CTC_MAPPING,
						    MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
						    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
						    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
						    MODEL_FOR_MASK_GENERATION_MAPPING,
						    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
						    MODEL_FOR_MASKED_LM_MAPPING,
						    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
						    MODEL_FOR_OBJECT_DETECTION_MAPPING,
						    MODEL_FOR_PRETRAINING_MAPPING,
						    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
						    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
						    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
						    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
						    MODEL_FOR_TEXT_ENCODING_MAPPING,
						    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
						    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
						    MODEL_FOR_VISION_2_SEQ_MAPPING,
						    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
						    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
						    MODEL_MAPPING,
						    MODEL_WITH_LM_HEAD_MAPPING,
						    AutoBackbone,
						    AutoModel,
						    AutoModelForAudioClassification,
						    AutoModelForAudioFrameClassification,
						    AutoModelForAudioXVector,
						    AutoModelForCausalLM,
						    AutoModelForCTC,
						    AutoModelForDepthEstimation,
						    AutoModelForDocumentQuestionAnswering,
						    AutoModelForImageClassification,
						    AutoModelForImageSegmentation,
						    AutoModelForInstanceSegmentation,
						    AutoModelForMaskedImageModeling,
						    AutoModelForMaskedLM,
						    AutoModelForMaskGeneration,
						    AutoModelForMultipleChoice,
						    AutoModelForNextSentencePrediction,
						    AutoModelForObjectDetection,
						    AutoModelForPreTraining,
						    AutoModelForQuestionAnswering,
						    AutoModelForSemanticSegmentation,
						    AutoModelForSeqaSeqLM,
						    AutoModelForSequenceClassification,
						    AutoModelForSpeechSeqaSeq,
						    AutoModelForTableQuestionAnswering,
						    AutoModelForTextEncoding,
						    AutoModelForTokenClassification,
						    AutoModelForUniversalSegmentation,
						    AutoModelForVideoClassification,
						    AutoModelForVisionaSeq,
						    AutoModelForVisualQuestionAnswering,
						    AutoModelForZeroShotImageClassification,
						    AutoModelForZeroShotObjectDetection,
						    AutoModelWithLMHead,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_auto import (
						    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
						    TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
						    TF_MODEL_FOR_MASKED_LM_MAPPING,
						    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
						    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
						    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
						    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
						    TF_MODEL_MAPPING,
						    TF_MODEL_WITH_LM_HEAD_MAPPING,
						    TFAutoModel,
						    TFAutoModelForAudioClassification,
						    TFAutoModelForCausalLM,
						    TFAutoModelForDocumentQuestionAnswering,
						    TFAutoModelForImageClassification,
						    TFAutoModelForMaskedImageModeling,
						    TFAutoModelForMaskedLM,
						    TFAutoModelForMaskGeneration,
						    TFAutoModelForMultipleChoice,
						    TFAutoModelForNextSentencePrediction,
						    TFAutoModelForPreTraining,
						    TFAutoModelForQuestionAnswering,
						    TFAutoModelForSemanticSegmentation,
						    TFAutoModelForSeqaSeqLM,
						    TFAutoModelForSequenceClassification,
						    TFAutoModelForSpeechSeqaSeq,
						    TFAutoModelForTableQuestionAnswering,
						    TFAutoModelForTextEncoding,
						    TFAutoModelForTokenClassification,
						    TFAutoModelForVisionaSeq,
						    TFAutoModelForZeroShotImageClassification,
						    TFAutoModelWithLMHead,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_auto import (
						    FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
						    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
						    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
						    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
						    FLAX_MODEL_FOR_PRETRAINING_MAPPING,
						    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
						    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
						    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
						    FLAX_MODEL_MAPPING,
						    FlaxAutoModel,
						    FlaxAutoModelForCausalLM,
						    FlaxAutoModelForImageClassification,
						    FlaxAutoModelForMaskedLM,
						    FlaxAutoModelForMultipleChoice,
						    FlaxAutoModelForNextSentencePrediction,
						    FlaxAutoModelForPreTraining,
						    FlaxAutoModelForQuestionAnswering,
						    FlaxAutoModelForSeqaSeqLM,
						    FlaxAutoModelForSequenceClassification,
						    FlaxAutoModelForSpeechSeqaSeq,
						    FlaxAutoModelForTokenClassification,
						    FlaxAutoModelForVisionaSeq,
						)

else:
			import sys

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













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



					"""simple docstring"""
					__a				:	str			=				sorted(__A							,					key=lambda __A			: edge[2]			)

					__a				:	int			=				list(range(__A			)			)

					def find_parent(__A			):
										if i != parent[i]:
															__a				:	Optional[Any]			=				find_parent(parent[i]			)
										return parent[i]

					__a				:	Any			=				0
					__a				:	Optional[int]			=				[]

					for edge in edges:
										__a				:	Any			=				find_parent(edge[0]			)
										__a				:	Optional[Any]			=				find_parent(edge[1]			)
										if parent_a != parent_b:
															minimum_spanning_tree_cost += edge[2]
															minimum_spanning_tree.append(__A			)
															__a				:	List[str]			=				parent_b

					return minimum_spanning_tree


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

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

			kruskal(num_nodes, edges)





