

"""simple docstring"""





import datasets

from .evaluate import evaluate


_a       =							'\\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'

_a       =							'\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'

_a       =							'\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 					_lowerCAmelCase  (	datasets.Metric   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[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 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			int	):
    __lowercase								=						{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
    __lowercase								=						[
        {
            "paragraphs": [
                {
                    "qas": [
                        {
                            "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                            "id": ref["id"],
                        }
                        for ref in references
                    ]
                }
            ]
        }
    ]
    __lowercase								=						evaluate(dataset=UpperCAmelCase__,					predictions=UpperCAmelCase__	)
    return score









def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  list:



    """simple docstring"""






    return [
        txt[:a] + txt[a].upper() + txt[a + 1 :]
        for a in range(len(_lowerCAmelCase		)		)
        if txt[a].isalpha()
    ]


if __name__ == "__main__":
     __import__("""doctest""").testmod()


