





import datasets

from .evaluate import evaluate


_lowerCamelCase 					=      '\\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'

_lowerCamelCase 					=      '\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'

_lowerCamelCase 					=      '\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 	UpperCamelCase_							(  datasets.Metric     ):


							def   _snake_case	(				self     :Optional[Any]		) -> Optional[Any]:



									"""simple docstring"""



									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     :Dict				,					__A     :str				,					__A     :List[Any]		) -> Dict:



									"""simple docstring"""



									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=lowerCamelCase__				,					predictions=lowerCamelCase__		)
									return score


from __future__ import annotations







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None   ):
      '''simple docstring'''







      if start is None:
            A_							       =		0

      if end is None:
            A_							       =		len(SCREAMING_SNAKE_CASE   ) - 1

      if start >= end:
            return

      A_							       =		(start + end) // 2

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
      slowsort(SCREAMING_SNAKE_CASE		,				mid + 1		,				SCREAMING_SNAKE_CASE   )

      if sequence[end] < sequence[mid]:
            A_       ,A_							       =		sequence[mid], sequence[end]

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				end - 1   )


if __name__ == "__main__":
      from doctest import testmod

      testmod()





