


'''simple docstring'''






def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Optional[int]=None							):







   '''simple docstring'''




   _UpperCAmelCase       :       Dict							   =(path or []) + [u]
   for v in graph[u]:
      if visited_edge[u][v] is False:
         _UpperCAmelCase       :       Optional[Any]							   =True, True
         _UpperCAmelCase       :       Optional[Any]							   =dfs(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
   return path





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							Optional[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =0
   _UpperCAmelCase       :       Optional[int]							   =-1
   for i in range(__lowerCamelCase							):
      if i not in graph.keys():
         continue
      if len(graph[i]							) % 2 == 1:
         odd_degree_nodes += 1
         _UpperCAmelCase       :       Union[str, Any]							   =i
   if odd_degree_nodes == 0:
      return 1, odd_node
   if odd_degree_nodes == 2:
      return 2, odd_node
   return 3, odd_node





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]	,   __lowerCamelCase	:							Optional[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       Union[str, Any]							   =[[False for _ in range(max_node + 1							)] for _ in range(max_node + 1							)]
   _UpperCAmelCase       :       Tuple							   =check_circuit_or_path(__lowerCamelCase	,   __lowerCamelCase							)
   if check == 3:
      print('graph is not Eulerian'							)
      print('no path'							)
      return
   _UpperCAmelCase       :       Optional[Any]							   =1
   if check == 2:
      _UpperCAmelCase       :       int							   =odd_node
      print('graph has a Euler path'							)
   if check == 1:
      print('graph has a Euler cycle'							)
   _UpperCAmelCase       :       Optional[int]							   =dfs(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)
   print(__lowerCamelCase							)





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       List[str]							   ={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
   _UpperCAmelCase       :       Tuple							   ={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
   _UpperCAmelCase       :       List[str]							   ={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
   _UpperCAmelCase       :       Any							   ={1: [2, 3], 2: [1, 3], 3: [1, 2]}
   _UpperCAmelCase       :       Tuple							   ={
       1: [],
       2: []
       # all degree is zero
   }
   _UpperCAmelCase       :       Optional[Any]							   =1_0
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)
   check_euler(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
  main()





'''simple docstring'''






import datasets

from .evaluate import evaluate


lowercase							='\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

lowercase							='\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

lowercase							='\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'






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






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          return datasets.MetricInfo(
              description=_DESCRIPTION  ,							citation=_CITATION  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(
                  {
                      'predictions': {'id': datasets.Value('string'), 'prediction_text': datasets.Value('string')},
                      'references': {
                          'id': datasets.Value('string'),
                          'answers': datasets.features.Sequence(
                              {
                                  'text': datasets.Value('string'),
                                  'answer_start': datasets.Value('int32'),
                              }),
                      },
                  })  ,							codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']  ,							reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']  ,							)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   ={prediction['id']: prediction['prediction_text'] for prediction in predictions}
          _UpperCAmelCase       :       Optional[int]							   =[
              {
                  'paragraphs': [
                      {
                          'qas': [
                              {
                                  'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                                  'id': ref['id'],
                              }
                              for ref in references
                          ]
                      }
                  ]
              }
          ]
          _UpperCAmelCase       :       List[Any]							   =evaluate(dataset=snake_case  ,							predictions=snake_case)
          return score

