


import datasets

from .evaluate import evaluate


SCREAMING_SNAKE_CASE							    =							"""\
@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}
}
"""

SCREAMING_SNAKE_CASE							    =							"""
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.
"""

SCREAMING_SNAKE_CASE							    =							"""
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     __UpperCAmelCase							(       datasets.Metric							):


     """simple docstring"""


     def 	snake_case_  (	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 	snake_case_  (	self  ,       __A  ,       __A    ):
        __a					=  {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
        __a					=  [
            {
                """paragraphs""": [
                    {
                        """qas""": [
                            {
                                """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                                """id""": ref["""id"""],
                            }
                            for ref in references
                        ]
                    }
                ]
            }
        ]
        __a					=  evaluate(dataset=__lowerCAmelCase  ,       predictions=__lowerCAmelCase    )
        return score




"""simple docstring"""






def      UpperCAmelCase__			(							lowerCAmelCase__	:int = 1_0**9						)					->       int:
      '''simple docstring'''


      lowercase								=      1
      lowercase								=      2

      lowercase								=      0
      lowercase								=      0
      lowercase								=      0
      while perimeter <= max_perimeter:
            perimeters_sum += perimeter

            prev_value += 2 * value
            value += prev_value

            lowercase								=      2 * value + 2 if i % 2 == 0 else 2 * value - 2
            i += 1

      return perimeters_sum


if __name__ == "__main__":
       print(F"""{solution() = }""")



