
import datasets

from .evaluate import evaluate


lowerCamelCase_  :	List[str] 				=					"""\
@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_  :	Optional[Any] 				=					"""
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_  :	Optional[Any] 				=					"""
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  lowerCAmelCase__		(    self	:    Any ):
							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	:    Optional[Any]  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    Optional[int] ):
							UpperCamelCase_:							int		=  {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
							UpperCamelCase_:							Dict		=  [
							    {
							        """paragraphs""": [
							            {
							                """qas""": [
							                    {
							                        """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
							                        """id""": ref["""id"""],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							UpperCamelCase_:							Tuple		=  evaluate(dataset=_A  ,		predictions=_A )
							return score








def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		float:
				if mass < 0:
								raise ValueError("""The mass of a body cannot be negative"""  )
				return 0.5 * mass * abs(lowerCamelCase  ) * abs(lowerCamelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






