

import datasets

from .evaluate import evaluate


A_	:			Tuple  	=      '\\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_	:			Union[str, Any]  	=      '\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_	:			List[Any]  	=      '\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    _a						(datasets.Metric    ):


					'''simple docstring'''






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






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



