






import datasets

from .evaluate import evaluate


__lowerCamelCase	:		List[Any]										=   """\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

__lowerCamelCase	:		Tuple										=   """
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

__lowerCamelCase	:		List[str]										=   """
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    Dict							       ={prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
       snake_case__  :    Optional[Any]							       =[
           {
               """paragraphs""": [
                   {
                       """qas""": [
                           {
                               """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                               """id""": ref["""id"""],
                           }
                           for ref in references
                       ]
                   }
               ]
           }
       ]
       snake_case__  :    Optional[Any]							       =evaluate(dataset=a				,			predictions=a   )
       return score













def 			A__							(      _a  :					Any		,  _a  :					Optional[Any]		,  _a  :					int		,  _a  :					Any	):





 '''simple docstring'''







 if height >= 1:
  move_tower(height - 1		,  _a		,  _a		,  _a	)
  move_disk(_a		,  _a	)
  move_tower(height - 1		,  _a		,  _a		,  _a	)







def 			A__							(      _a  :					Optional[int]		,  _a  :					str	):





 '''simple docstring'''







 print("""moving disk from"""		,  _a		,  """to"""		,  _a	)







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Dict							       =int(input("""Height of hanoi: """	).strip()	)
 move_tower(_a		,  """A"""		,  """B"""		,  """C"""	)


if __name__ == "__main__":
       main()





