code: 
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  snake_case				:	Any							   =						"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"
  
  snake_case				:	List[str]							   =						"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"
  
  
  snake_case				:	Any							   =						"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Tuple   ,     _snake_case   :						Any   ,     _snake_case   :						Dict   ,     _snake_case   :						Dict     )       ->	Optional[Any]:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   __magic_name__	:	Tuple			     =  [rgram for rgrams in rgramslist for rgram in rgrams]
   __magic_name__	:	Any			     =  Counter(_snake_case     )
  
   __magic_name__	:	List[Any]			     =  Counter(_snake_case     )
   __magic_name__	:	Union[str, Any]			     =  Counter()
   for sgram, scount in sgramcounter.items():
    __magic_name__	:	Optional[Any]			     =  scount * numref
  
   __magic_name__	:	Tuple			     =  Counter(_snake_case     )
   __magic_name__	:	List[Any]			     =  Counter()
   for cgram, ccount in cgramcounter.items():
    __magic_name__	:	Any			     =  ccount * numref
  
   # KEEP
   __magic_name__	:	List[str]			     =  sgramcounter_rep & cgramcounter_rep
   __magic_name__	:	Any			     =  keepgramcounter_rep & rgramcounter
   __magic_name__	:	int			     =  sgramcounter_rep & rgramcounter
  
   __magic_name__	:	Optional[Any]			     =  0
   __magic_name__	:	Tuple			     =  0
   for keepgram in keepgramcountergood_rep:
    keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
    # Fix an alleged bug [2] in the keep score computation.
    # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
    keeptmpscorea += keepgramcountergood_rep[keepgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   #      a target exactly.
   __magic_name__	:	List[Any]			     =  1
   __magic_name__	:	List[str]			     =  1
   if len(_snake_case     ) > 0:
    __magic_name__	:	Union[str, Any]			     =  keeptmpscorea / len(_snake_case     )
   if len(_snake_case     ) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    __magic_name__	:	Optional[Any]			     =  keeptmpscorea / sum(keepgramcounterall_rep.values()     )
   __magic_name__	:	str			     =  0
   if keepscore_precision > 0 or keepscore_recall > 0:
    __magic_name__	:	Dict			     =  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
   # DELETION
   __magic_name__	:	str			     =  sgramcounter_rep - cgramcounter_rep
   __magic_name__	:	Optional[int]			     =  delgramcounter_rep - rgramcounter
   __magic_name__	:	str			     =  sgramcounter_rep - rgramcounter
   __magic_name__	:	str			     =  0
   __magic_name__	:	List[Any]			     =  0
   for delgram in delgramcountergood_rep:
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   __magic_name__	:	Any			     =  1
   if len(_snake_case     ) > 0:
    __magic_name__	:	str			     =  deltmpscorea / len(_snake_case     )
  
   # ADDITION
   __magic_name__	:	List[Any]			     =  set(_snake_case     ) - set(_snake_case     )
   __magic_name__	:	Tuple			     =  set(_snake_case     ) & set(_snake_case     )
   __magic_name__	:	int			     =  set(_snake_case     ) - set(_snake_case     )
  
   __magic_name__	:	Any			     =  0
   for addgram in addgramcountergood:
    addtmpscore += 1
  
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   __magic_name__	:	List[Any]			     =  1
   __magic_name__	:	List[Any]			     =  1
   if len(_snake_case     ) > 0:
    __magic_name__	:	List[str]			     =  addtmpscore / len(_snake_case     )
   if len(_snake_case     ) > 0:
    __magic_name__	:	List[Any]			     =  addtmpscore / len(_snake_case     )
   __magic_name__	:	Union[str, Any]			     =  0
   if addscore_precision > 0 or addscore_recall > 0:
    __magic_name__	:	Any			     =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
   return (keepscore, delscore_precision, addscore)
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						Optional[Any]   ,     _snake_case   :						str     )       ->	List[Any]:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   __magic_name__	:	List[Any]			     =  len(_snake_case     )
  
   __magic_name__	:	Dict			     =  ssent.split(" "     )
   __magic_name__	:	int			     =  csent.split(" "     )
   __magic_name__	:	Any			     =  []
   __magic_name__	:	Tuple			     =  []
   __magic_name__	:	Union[str, Any]			     =  []
   __magic_name__	:	Tuple			     =  []
   __magic_name__	:	Optional[Any]			     =  []
   __magic_name__	:	str			     =  []
  
   __magic_name__	:	Dict			     =  []
   __magic_name__	:	Optional[Any]			     =  []
   __magic_name__	:	Dict			     =  []
   __magic_name__	:	Optional[Any]			     =  []
   for rsent in rsents:
    __magic_name__	:	Optional[Any]			     =  rsent.split(" "     )
    __magic_name__	:	Union[str, Any]			     =  []
    __magic_name__	:	List[str]			     =  []
    __magic_name__	:	Optional[int]			     =  []
    ragramslist.append(_snake_case     )
    for i in range(0   ,     len(_snake_case     ) - 1     ):
     if i < len(_snake_case     ) - 1:
      __magic_name__	:	str			     =  ragrams[i] + " " + ragrams[i + 1]
      ragrams.append(_snake_case     )
     if i < len(_snake_case     ) - 2:
      __magic_name__	:	Any			     =  ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
      ragrams.append(_snake_case     )
     if i < len(_snake_case     ) - 3:
      __magic_name__	:	Union[str, Any]			     =  ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
      ragrams.append(_snake_case     )
    ragramslist.append(_snake_case     )
    ragramslist.append(_snake_case     )
    ragramslist.append(_snake_case     )
  
   for i in range(0   ,     len(_snake_case     ) - 1     ):
    if i < len(_snake_case     ) - 1:
     __magic_name__	:	Any			     =  sagrams[i] + " " + sagrams[i + 1]
     sagrams.append(_snake_case     )
    if i < len(_snake_case     ) - 2:
     __magic_name__	:	Union[str, Any]			     =  sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
     sagrams.append(_snake_case     )
    if i < len(_snake_case     ) - 3:
     __magic_name__	:	List[Any]			     =  sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
     sagrams.append(_snake_case     )
  
   for i in range(0   ,     len(_snake_case     ) - 1     ):
    if i < len(_snake_case     ) - 1:
     __magic_name__	:	List[str]			     =  cagrams[i] + " " + cagrams[i + 1]
     cagrams.append(_snake_case     )
    if i < len(_snake_case     ) - 2:
     __magic_name__	:	Union[str, Any]			     =  cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
     cagrams.append(_snake_case     )
    if i < len(_snake_case     ) - 3:
     __magic_name__	:	List[Any]			     =  cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
     cagrams.append(_snake_case     )
  
   (__magic_name__)	:	Optional[Any]			     =  SARIngram(_snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )
   (__magic_name__)	:	List[str]			     =  SARIngram(_snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )
   (__magic_name__)	:	List[str]			     =  SARIngram(_snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )
   (__magic_name__)	:	int			     =  SARIngram(_snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )
   __magic_name__	:	Any			     =  sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
   __magic_name__	:	int			     =  sum([delascore, delascore, delascore, delascore]     ) / 4
   __magic_name__	:	str			     =  sum([addascore, addascore, addascore, addascore]     ) / 4
   __magic_name__	:	Any			     =  (avgkeepscore + avgdelscore + avgaddscore) / 3
   return finalscore
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Any   ,     _snake_case   :						bool = True   ,     _snake_case   :						str = "13a"   ,     _snake_case   :						bool = True     )       ->	List[Any]:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   if lowercase:
    __magic_name__	:	Dict			     =  sentence.lower()
  
   if tokenizer in ["13a", "intl"]:
    if version.parse(sacrebleu.__version__     ).major >= 2:
     __magic_name__	:	int			     =  sacrebleu.metrics.bleu._get_tokenizer(_snake_case     )()(_snake_case     )
    else:
     __magic_name__	:	Union[str, Any]			     =  sacrebleu.TOKENIZERS[tokenizer]()(_snake_case     )
   elif tokenizer == "moses":
    __magic_name__	:	Dict			     =  sacremoses.MosesTokenizer().tokenize(_snake_case   ,     return_str=_snake_case   ,     escape=_snake_case     )
   elif tokenizer == "penn":
    __magic_name__	:	int			     =  sacremoses.MosesTokenizer().penn_tokenize(_snake_case   ,     return_str=_snake_case     )
   else:
    __magic_name__	:	Dict			     =  sentence
  
   if not return_str:
    __magic_name__	:	Dict			     =  normalized_sent.split()
  
   return normalized_sent
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,		_KWARGS_DESCRIPTION       )
  class    _snake_case      (  datasets.Metric       ):
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      return datasets.MetricInfo(
          description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
              {
                  "sources": datasets.Value("string"		,     id="sequence"	),
                  "predictions": datasets.Value("string"		,     id="sequence"	),
                  "references": datasets.Sequence(datasets.Value("string"		,     id="sequence"	)		,     id="references"	),
              }	)		,     codebase_urls=[
              "https://github.com/cocoxu/simplification/blob/master/SARI.py",
              "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
          ]		,     reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]		,     )
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a	):
      if not (len(_a	) == len(_a	) == len(_a	)):
       raise ValueError("Sources length must match predictions and references lengths."	)
      __magic_name__	:	Dict			     =  0
      for src, pred, refs in zip(_a		,     _a		,     _a	):
       sari_score += SARIsent(normalize(_a	)		,     normalize(_a	)		,     [normalize(_a	) for sent in refs]	)
      __magic_name__	:	str			     =  sari_score / len(_a	)
      return {"sari": 100 * sari_score}
  
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)
  