code: 
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  
  
  
  @require_tokenizers
  class    _snake_case      (  A__       ,		unittest.TestCase       ):
     UpperCamelCase__					   =					BartTokenizer
     UpperCamelCase__					   =					BartTokenizerFast
     UpperCamelCase__					   =					True
     UpperCamelCase__					   =					filter_roberta_detectors
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      super().setUp()
      __magic_name__	:	Tuple			     =  [
          "l",
          "o",
          "w",
          "e",
          "r",
          "s",
          "t",
          "i",
          "d",
          "n",
          "\u0120",
          "\u0120l",
          "\u0120n",
          "\u0120lo",
          "\u0120low",
          "er",
          "\u0120lowest",
          "\u0120newer",
          "\u0120wider",
          "<unk>",
      ]
      __magic_name__	:	List[str]			     =  dict(zip(_lowerCAmelCase		,     range(len(_lowerCAmelCase	)	)	)	)
      __magic_name__	:	int			     =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
      __magic_name__	:	List[Any]			     =  {"unk_token": "<unk>"}
  
      __magic_name__	:	Optional[int]			     =  os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["vocab_file"]	)
      __magic_name__	:	List[Any]			     =  os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["merges_file"]	)
      with open(self.vocab_file		,     "w"		,     encoding="utf-8"	) as fp:
       fp.write(json.dumps(_lowerCAmelCase	) + "\n"	)
      with open(self.merges_file		,     "w"		,     encoding="utf-8"	) as fp:
       fp.write("\n".join(_lowerCAmelCase	)	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     **_a	):
      kwargs.update(self.special_tokens_map	)
      return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **_lowerCAmelCase	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     **_a	):
      kwargs.update(self.special_tokens_map	)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,     **_lowerCAmelCase	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
      return "lower newer", "lower newer"
  
  
  
  
  
  
  
     @cached_property
     def     SCREAMING_SNAKE_CASE					(       self	):
      return BartTokenizer.from_pretrained("facebook/bart-large"	)
  
  
  
  
  
  
  
     @cached_property
     def     SCREAMING_SNAKE_CASE					(       self	):
      return BartTokenizerFast.from_pretrained("facebook/bart-large"	)
  
  
  
  
  
  
  
     @require_torch
     def     SCREAMING_SNAKE_CASE					(       self	):
      __magic_name__	:	Dict			     =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
      __magic_name__	:	List[str]			     =  [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
  
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__	:	Optional[int]			     =  tokenizer(_lowerCAmelCase		,     max_length=len(_lowerCAmelCase	)		,     padding=_lowerCAmelCase		,     return_tensors="pt"	)
       self.assertIsInstance(_lowerCAmelCase		,     _lowerCAmelCase	)
  
       self.assertEqual((2, 9)		,     batch.input_ids.shape	)
       self.assertEqual((2, 9)		,     batch.attention_mask.shape	)
       __magic_name__	:	int			     =  batch.input_ids.tolist()[0]
       self.assertListEqual(_lowerCAmelCase		,     _lowerCAmelCase	)
       # Test that special tokens are reset
  
  
  
  
  
  
  
     @require_torch
     def     SCREAMING_SNAKE_CASE					(       self	):
      __magic_name__	:	Tuple			     =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__	:	Tuple			     =  tokenizer(_lowerCAmelCase		,     padding=_lowerCAmelCase		,     return_tensors="pt"	)
       # check if input_ids are returned and no labels
       self.assertIn("input_ids"		,     _lowerCAmelCase	)
       self.assertIn("attention_mask"		,     _lowerCAmelCase	)
       self.assertNotIn("labels"		,     _lowerCAmelCase	)
       self.assertNotIn("decoder_attention_mask"		,     _lowerCAmelCase	)
  
  
  
  
  
  
  
     @require_torch
     def     SCREAMING_SNAKE_CASE					(       self	):
      __magic_name__	:	Dict			     =  [
          "Summary of the text.",
          "Another summary.",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__	:	List[str]			     =  tokenizer(text_target=_lowerCAmelCase		,     max_length=32		,     padding="max_length"		,     return_tensors="pt"	)
       self.assertEqual(32		,     targets["input_ids"].shape[1]	)
  
  
  
  
  
  
  
     @require_torch
     def     SCREAMING_SNAKE_CASE					(       self	):
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__	:	List[str]			     =  tokenizer(
           ["I am a small frog" * 1_024, "I am a small frog"]		,     padding=_lowerCAmelCase		,     truncation=_lowerCAmelCase		,     return_tensors="pt"	)
       self.assertIsInstance(_lowerCAmelCase		,     _lowerCAmelCase	)
       self.assertEqual(batch.input_ids.shape		,     (2, 1_024)	)
  
  
  
  
  
  
  
     @require_torch
     def     SCREAMING_SNAKE_CASE					(       self	):
      __magic_name__	:	Optional[Any]			     =  ["A long paragraph for summarization."]
      __magic_name__	:	int			     =  [
          "Summary of the text.",
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __magic_name__	:	Any			     =  tokenizer(_lowerCAmelCase		,     return_tensors="pt"	)
       __magic_name__	:	Dict			     =  tokenizer(text_target=_lowerCAmelCase		,     return_tensors="pt"	)
       __magic_name__	:	Dict			     =  inputs["input_ids"]
       __magic_name__	:	int			     =  targets["input_ids"]
       self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
       self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
       self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
       self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      pass
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
        __magic_name__	:	Union[str, Any]			     =  self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase		,     **_lowerCAmelCase	)
        __magic_name__	:	List[str]			     =  self.tokenizer_class.from_pretrained(_lowerCAmelCase		,     **_lowerCAmelCase	)
        __magic_name__	:	Union[str, Any]			     =  "A, <mask> AllenNLP sentence."
        __magic_name__	:	Any			     =  tokenizer_r.encode_plus(_lowerCAmelCase		,     add_special_tokens=_lowerCAmelCase		,     return_token_type_ids=_lowerCAmelCase	)
        __magic_name__	:	Optional[int]			     =  tokenizer_p.encode_plus(_lowerCAmelCase		,     add_special_tokens=_lowerCAmelCase		,     return_token_type_ids=_lowerCAmelCase	)
  
        # token_type_ids should put 0 everywhere
        self.assertEqual(sum(tokens_r["token_type_ids"]	)		,     sum(tokens_p["token_type_ids"]	)	)
  
        # attention_mask should put 1 everywhere, so sum over length should be 1
        self.assertEqual(
            sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)		,     sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)		,     )
  
        __magic_name__	:	Optional[int]			     =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
        __magic_name__	:	Optional[int]			     =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)
  
        # Rust correctly handles the space before the mask while python doesnt
        self.assertSequenceEqual(tokens_p["input_ids"]		,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
        self.assertSequenceEqual(tokens_r["input_ids"]		,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
  
        self.assertSequenceEqual(
            _lowerCAmelCase		,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
        self.assertSequenceEqual(
            _lowerCAmelCase		,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						list[tuple[int, int, int]]     )       ->	list[tuple[int, int, int]]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Union[str, Any]			     =  sorted(_snake_case   ,     key=lambda _snake_case     : edge[2]     )
  
  	__magic_name__	:	Union[str, Any]			     =  list(range(_snake_case     )     )
  
  	def find_parent(_snake_case   :						Dict     ):
  		if i != parent[i]:
  			__magic_name__	:	Any			     =  find_parent(parent[i]     )
  		return parent[i]
  
  	__magic_name__	:	List[str]			     =  0
  	__magic_name__	:	int			     =  []
  
  	for edge in edges:
  		__magic_name__	:	Any			     =  find_parent(edge[0]     )
  		__magic_name__	:	int			     =  find_parent(edge[1]     )
  		if parent_a != parent_b:
  			minimum_spanning_tree_cost += edge[2]
  			minimum_spanning_tree.append(_snake_case     )
  			__magic_name__	:	Optional[int]			     =  parent_b
  
  	return minimum_spanning_tree
  
  
  if __name__ == "__main__":  # pragma: no cover
  				snake_case   ,snake_case				:	int							   =						list(map(int, input().strip().split()))
  				snake_case				:	int							   =						[]
  
  				for _ in range(num_edges):
  								snake_case   ,snake_case   ,snake_case				:	Dict							   =						(int(x) for x in input().strip().split())
  								edges.append((nodea, nodea, cost))
  
  				kruskal(num_nodes, edges)
  