


import datasets

from .evaluate import evaluate


A	:		List[str]    =       '\\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	:		Tuple    =       '\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	:		str    =       '\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   lowerCamelCase__					(self   :						Any		)						->       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   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							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





A	:		Union[str, Any]    =       [
    (1_0_0_0, 'M'),
    (9_0_0, 'CM'),
    (5_0_0, 'D'),
    (4_0_0, 'CD'),
    (1_0_0, 'C'),
    (9_0, 'XC'),
    (5_0, 'L'),
    (4_0, 'XL'),
    (1_0, 'X'),
    (9, 'IX'),
    (5, 'V'),
    (4, 'IV'),
    (1, 'I'),
]






def 			UpperCamelCase (     __magic_name__					:							str					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
		lowercase__					      =		0
		lowercase__					      =		0
		while place < len(__magic_name__					):
				if (place + 1 < len(__magic_name__					)) and (vals[roman[place]] < vals[roman[place + 1]]):
						total += vals[roman[place + 1]] - vals[roman[place]]
						place += 2
				else:
						total += vals[roman[place]]
						place += 1
		return total






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		[]
		for arabic, roman in ROMAN:
				((lowercase__)      ,     (lowercase__))					      =		divmod(__magic_name__   ,   __magic_name__					)
				result.append(roman * factor					)
				if number == 0:
						break
		return "".join(__magic_name__					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

