
'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]




'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int   ,     snake_case_				:	list[tuple[int, int, int]]							)       ->	list[tuple[int, int, int]]:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		sorted(snake_case_   ,     key=lambda snake_case_							: edge[2]							)

 SCREAMING_SNAKE_CASE  :  str								=		list(range(snake_case_							)							)

 def find_parent(snake_case_				:	Union[str, Any]							):
  if i != parent[i]:
   SCREAMING_SNAKE_CASE  :  int								=		find_parent(parent[i]							)
  return parent[i]

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

 for edge in edges:
  SCREAMING_SNAKE_CASE  :  Dict								=		find_parent(edge[0]							)
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		find_parent(edge[1]							)
  if parent_a != parent_b:
   minimum_spanning_tree_cost += edge[2]
   minimum_spanning_tree.append(snake_case_							)
   SCREAMING_SNAKE_CASE  :  Tuple								=		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)


