


'''simple docstring'''





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__     			=							'''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

UpperCAmelCase__     			=							'''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


UpperCAmelCase__     			=							'''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,							_KWARGS_DESCRIPTION)
class    lowerCamelCase__     ( datasets.Metric):





     def   __A      (self )  ->     Optional[int]:
      return datasets.MetricInfo(
          description=_DESCRIPTION	,  citation=_CITATION	,  inputs_description=_KWARGS_DESCRIPTION	,  features=datasets.Features(
              {
                  '''predictions''': datasets.Value('''string''' ),
                  '''references''': datasets.Value('''string''' ),
              } )	,  homepage='''https://github.com/moussaKam/FrugalScore'''	,  )





     def   __A      (self	,  UpperCAmelCase )  ->     Dict:






      if self.config_name == "default":
       _lowercase							   ='''moussaKam/frugalscore_tiny_bert-base_bert-score'''
      else:
       _lowercase							   =self.config_name
      _lowercase							   =AutoModelForSequenceClassification.from_pretrained(lowercase_ )
      _lowercase							   =AutoTokenizer.from_pretrained(lowercase_ )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=3_2	,  UpperCAmelCase=1_2_8	,  UpperCAmelCase=None	,  )  ->     Union[str, Any]:
      assert len(lowercase_ ) == len(
          lowercase_ ), "predictions and references should have the same number of sentences."
      if device is not None:
       assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
      else:
       _lowercase							   ='''gpu''' if torch.cuda.is_available() else '''cpu'''
      _lowercase							   =TrainingArguments(
          '''trainer'''	,  fpaa=(device == '''gpu''')	,  per_device_eval_batch_size=lowercase_	,  report_to='''all'''	,  no_cuda=(device == '''cpu''')	,  log_level='''warning'''	,  )
      _lowercase							   ={'''sentence1''': predictions, '''sentence2''': references}
      _lowercase							   =datasets.Dataset.from_dict(lowercase_ )

      def tokenize_function(UpperCAmelCase ):
       return self.tokenizer(
           data['''sentence1''']	,  data['''sentence2''']	,  max_length=lowercase_	,  truncation=lowercase_	,  padding=lowercase_ )

      _lowercase							   =raw_datasets.map(lowercase_	,  batched=lowercase_ )
      tokenized_datasets.remove_columns(['''sentence1''', '''sentence2'''] )
      _lowercase							   =Trainer(self.model	,  lowercase_	,  tokenizer=self.tokenizer )
      _lowercase							   =trainer.predict(lowercase_ )
      return {"scores": list(predictions.predictions.squeeze(-1 ) )}





from collections import deque
def        UpperCAmelCase_      (							__snake_case		)  ->    Any:







 """simple docstring"""


 return input(__snake_case		).strip().split(''' '''		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    dict[int, list[int]]:







 """simple docstring"""


 _lowercase							   ={}
 for i in range(__snake_case		):
  _lowercase							   =[]

 for e in range(__snake_case		):
  _lowercase   ,					_lowercase							   =(int(__snake_case		) for i in _input(F"Edge {e + 1}: <node1> <node2> "		))
  graph[x].append(__snake_case		)
 return graph
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    dict[int, list[int]]:







 """simple docstring"""


 _lowercase							   ={}
 for i in range(__snake_case		):
  _lowercase							   =[]

 for e in range(__snake_case		):
  _lowercase   ,					_lowercase							   =(int(__snake_case		) for i in _input(F"Edge {e + 1}: <node1> <node2> "		))
  graph[x].append(__snake_case		)
  graph[y].append(__snake_case		)
 return graph
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    dict[int, list[tuple[int, int]]]:







 """simple docstring"""


 _lowercase							   ={}
 for i in range(__snake_case		):
  _lowercase							   =[]

 for e in range(__snake_case		):
  _lowercase   ,					_lowercase   ,					_lowercase							   =(int(__snake_case		) for i in _input(F"Edge {e + 1}: <node1> <node2> <weight> "		))
  graph[x].append((y, w)		)
  graph[y].append((x, w)		)
 return graph


if __name__ == "__main__":
  UpperCAmelCase__			,UpperCAmelCase__     			=							(int(i) for i in _input('''Number of nodes and edges: '''))

  UpperCAmelCase__     			=							int(
      _input(
          '''Press 1 or 2 or 3 \n'''
          '''1. Unweighted directed \n'''
          '''2. Unweighted undirected \n'''
          '''3. Weighted undirected \n'''
      )[0]
  )

  UpperCAmelCase__     			=							{
      1: initialize_unweighted_directed_graph,
      2: initialize_unweighted_undirected_graph,
      3: initialize_weighted_undirected_graph,
  }[graph_choice](n, m)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Tuple:







 """simple docstring"""


 _lowercase   ,					_lowercase							   ={s}, [s]
 print(__snake_case		)
 while _s:
  _lowercase							   =0
  for i in g[_s[-1]]:
   if i not in vis:
    _s.append(__snake_case		)
    vis.add(__snake_case		)
    _lowercase							   =1
    print(__snake_case		)
    break
  if not flag:
   _s.pop()
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    int:







 """simple docstring"""


 _lowercase   ,					_lowercase							   ={s}, deque([s]		)
 print(__snake_case		)
 while q:
  _lowercase							   =q.popleft()
  for v in g[u]:
   if v not in vis:
    vis.add(__snake_case		)
    q.append(__snake_case		)
    print(__snake_case		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase   ,					_lowercase   ,					_lowercase							   ={s: 0}, set(), {s: 0}
 while True:
  if len(__snake_case		) == len(__snake_case		) - 1:
   break
  _lowercase							   =100000
  for i in dist:
   if i not in known and dist[i] < mini:
    _lowercase							   =dist[i]
    _lowercase							   =i
  known.add(__snake_case		)
  for v in g[u]:
   if v[0] not in known and dist[u] + v[1] < dist.get(v[0]  ,		100000		):
    _lowercase							   =dist[u] + v[1]
    _lowercase							   =u
 for i in dist:
  if i != s:
   print(dist[i]		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case=None  ,		__snake_case=None		)  ->    Tuple:







 """simple docstring"""


 if q is None:
  _lowercase							   =[1]
 if ind is None:
  _lowercase							   =[0] * (len(__snake_case		) + 1)  # SInce oth Index is ignored
  for u in g:
   for v in g[u]:
    ind[v] += 1
  _lowercase							   =deque()
  for i in g:
   if ind[i] == 0:
    q.append(__snake_case		)
 if len(__snake_case		) == 0:
  return
 _lowercase							   =q.popleft()
 print(__snake_case		)
 for w in g[v]:
  ind[w] -= 1
  if ind[w] == 0:
   q.append(__snake_case		)
 topo(__snake_case  ,		__snake_case  ,		__snake_case		)
def        UpperCAmelCase_      (							)  ->    Tuple:







 """simple docstring"""


 _lowercase							   =input().strip()
 _lowercase							   =[]
 for _ in range(__snake_case		):
  a.append(map(__snake_case  ,		input().strip().split()		)		)
 return a, n
def        UpperCAmelCase_      (							__snake_case		)  ->    Any:







 """simple docstring"""


 ((_lowercase)   ,					(_lowercase))							   =a_and_n
 _lowercase							   =list(__snake_case		)
 _lowercase							   =[[0] * n for i in range(__snake_case		)]
 for k in range(__snake_case		):
  for i in range(__snake_case		):
   for j in range(__snake_case		):
    if dist[i][j] > dist[i][k] + dist[k][j]:
     _lowercase							   =dist[i][k] + dist[k][j]
     _lowercase							   =k
 print(__snake_case		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Optional[Any]:







 """simple docstring"""


 _lowercase   ,					_lowercase   ,					_lowercase							   ={s: 0}, set(), {s: 0}
 while True:
  if len(__snake_case		) == len(__snake_case		) - 1:
   break
  _lowercase							   =100000
  for i in dist:
   if i not in known and dist[i] < mini:
    _lowercase							   =dist[i]
    _lowercase							   =i
  known.add(__snake_case		)
  for v in g[u]:
   if v[0] not in known and v[1] < dist.get(v[0]  ,		100000		):
    _lowercase							   =v[1]
    _lowercase							   =u
 return dist
def        UpperCAmelCase_      (							)  ->    Dict:







 """simple docstring"""


 _lowercase   ,					_lowercase							   =map(__snake_case  ,		input().split(''' '''		)		)
 _lowercase							   =[]
 for _ in range(__snake_case		):
  edges.append(map(__snake_case  ,		input().split(''' '''		)		)		)
 return edges, n
def        UpperCAmelCase_      (							__snake_case		)  ->    str:







 """simple docstring"""


 ((_lowercase)   ,					(_lowercase))							   =e_and_n
 e.sort(reverse=__snake_case  ,		key=lambda __snake_case		: x[2]		)
 _lowercase							   =[{i} for i in range(1  ,		n + 1		)]
 while True:
  if len(__snake_case		) == 1:
   break
  print(__snake_case		)
  _lowercase							   =e.pop()
  for i in range(len(__snake_case		)		):
   if x[0] in s[i]:
    break
  for j in range(len(__snake_case		)		):
   if x[1] in s[j]:
    if i == j:
     break
    s[j].update(s[i]		)
    s.pop(__snake_case		)
    break



def        UpperCAmelCase_      (							__snake_case		)  ->    Tuple:







 """simple docstring"""


 _lowercase							   =[]
 for node in graph:
  if not graph[node]:
   isolated.append(__snake_case		)
 return isolated

