





'''simple docstring'''

import datasets

from .evaluate import evaluate


lowercase_					= """\
@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}
}
"""

lowercase_					= """
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.
"""

lowercase_					= """
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 					a_     (				datasets.Metric       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   int:
        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 			snake_case_(		self ,		A ,		A       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
        _SCREAMING_SNAKE_CASE									=			[
            {
                """paragraphs""": [
                    {
                        """qas""": [
                            {
                                """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                                """id""": ref["""id"""],
                            }
                            for ref in references
                        ]
                    }
                ]
            }
        ]
        _SCREAMING_SNAKE_CASE									=			evaluate(dataset=A ,		predictions=A       )
        return score







'''simple docstring'''




class 		UpperCAmelCase_      :





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      list[int]    )				->  None:
        lowerCAmelCase     =			len(UpperCAmelCase__    )
        lowerCAmelCase     =			[0] * len_array

        if len_array > 0:
           lowerCAmelCase     =			array[0]

        for i in range(1    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			self.prefix_sum[i - 1] + array[i]





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  int:
        if start == 0:
           return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      int    )				->  bool:
        lowerCAmelCase     =			{0}
        for sum_item in self.prefix_sum:
           if sum_item - target_sum in sums:
              return True

           sums.add(UpperCAmelCase__    )

        return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()





