


'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case							      =     {
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     ['''WhisperTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''WhisperForConditionalGeneration''',
		    '''WhisperModel''',
		    '''WhisperPreTrainedModel''',
		    '''WhisperForAudioClassification''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFWhisperForConditionalGeneration''',
		    '''TFWhisperModel''',
		    '''TFWhisperPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''FlaxWhisperForConditionalGeneration''',
		    '''FlaxWhisperModel''',
		    '''FlaxWhisperPreTrainedModel''',
		    '''FlaxWhisperForAudioClassification''',
		]


if TYPE_CHECKING:
		from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
		from .feature_extraction_whisper import WhisperFeatureExtractor
		from .processing_whisper import WhisperProcessor
		from .tokenization_whisper import WhisperTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_whisper_fast import WhisperTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_whisper import (
				    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    WhisperForAudioClassification,
				    WhisperForConditionalGeneration,
				    WhisperModel,
				    WhisperPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_whisper import (
				    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFWhisperForConditionalGeneration,
				    TFWhisperModel,
				    TFWhisperPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_whisper import (
				    FlaxWhisperForAudioClassification,
				    FlaxWhisperForConditionalGeneration,
				    FlaxWhisperModel,
				    FlaxWhisperPreTrainedModel,
				)

else:
		import sys

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

import sys
from collections import defaultdict







class        A_   :




	'''simple docstring'''


	def __init__(       self       ):
		lowercase       =    []





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		return self.node_position[vertex]





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    pos





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		if start > size // 2 - 1:
			return
		else:
			if 2 * start + 2 >= size:
				lowercase       =    2 * start + 1
			else:
				if heap[2 * start + 1] < heap[2 * start + 2]:
					lowercase       =    2 * start + 1
				else:
					lowercase       =    2 * start + 2
			if heap[smallest_child] < heap[start]:
				lowercase  ,					lowercase       =    heap[smallest_child], positions[smallest_child]
				lowercase  ,					lowercase       =    (
				    heap[start],
				    positions[start],
				)
				lowercase  ,					lowercase       =    temp, tempa

				lowercase       =    self.get_position(positions[smallest_child]       )
				self.set_position(
				    positions[smallest_child]		,    self.get_position(positions[start]       )       )
				self.set_position(positions[start]		,    snake_case       )

				self.top_to_bottom(snake_case		,    snake_case		,    snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    position[index]

		while index != 0:
			lowercase       =    int((index - 2) / 2       ) if index % 2 == 0 else int((index - 1) / 2       )

			if val < heap[parent]:
				lowercase       =    heap[parent]
				lowercase       =    position[parent]
				self.set_position(position[parent]		,    snake_case       )
			else:
				lowercase       =    val
				lowercase       =    temp
				self.set_position(snake_case		,    snake_case       )
				break
			lowercase       =    parent
		else:
			lowercase       =    val
			lowercase       =    temp
			self.set_position(snake_case		,    0       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    len(snake_case       ) // 2 - 1
		for i in range(snake_case		,    -1		,    -1       ):
			self.top_to_bottom(snake_case		,    snake_case		,    len(snake_case       )		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    positions[0]
		lowercase       =    sys.maxsize
		self.top_to_bottom(snake_case		,    0		,    len(snake_case       )		,    snake_case       )
		return temp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    Heap()

	lowercase       =    [0] * len(__SCREAMING_SNAKE_CASE							)
	lowercase       =    [-1] * len(__SCREAMING_SNAKE_CASE							)  # Neighboring Tree Vertex of selected vertex
	# Minimum Distance of explored vertex with neighboring vertex of partial tree
	# formed in graph
	lowercase       =    []  # Heap of Distance of vertices from their neighboring vertex
	lowercase       =    []

	for vertex in range(len(__SCREAMING_SNAKE_CASE							)							):
		distance_tv.append(sys.maxsize							)
		positions.append(__SCREAMING_SNAKE_CASE							)
		heap.node_position.append(__SCREAMING_SNAKE_CASE							)

	lowercase       =    []
	lowercase       =    1
	lowercase       =    sys.maxsize
	for neighbor, distance in adjacency_list[0]:
		lowercase       =    0
		lowercase       =    distance
	heap.heapify(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	for _ in range(1 ,							len(__SCREAMING_SNAKE_CASE							)							):
		lowercase       =    heap.delete_minimum(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		if visited[vertex] == 0:
			tree_edges.append((nbr_tv[vertex], vertex)							)
			lowercase       =    1
			for neighbor, distance in adjacency_list[vertex]:
				if (
				    visited[neighbor] == 0
				    and distance < distance_tv[heap.get_position(__SCREAMING_SNAKE_CASE							)]
				):
					lowercase       =    distance
					heap.bottom_to_top(
					    __SCREAMING_SNAKE_CASE ,							heap.get_position(__SCREAMING_SNAKE_CASE							) ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
					lowercase       =    vertex
	return tree_edges


if __name__ == "__main__":  # pragma: no cover
	# < --------- Prims Algorithm --------- >
	UpperCAmelCase	      = int(input('''Enter number of edges: ''').strip())
	UpperCAmelCase	      = defaultdict(list)
	for _ in range(edges_number):
		UpperCAmelCase	      = [int(x) for x in input().strip().split()]
		adjacency_list[edge[0]].append([edge[1], edge[2]])
		adjacency_list[edge[1]].append([edge[0], edge[2]])
	print(prisms_algorithm(adjacency_list))

