
'''simple docstring'''







import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


lowercase    :							Union[str, Any]      =					"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

lowercase    :							str      =					"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

lowercase    :							Any      =					"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,   _KWARGS_DESCRIPTION						)
class 						__UpperCAmelCase      (					datasets.Metric						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return datasets.MetricInfo(
								    description=_DESCRIPTION    ,   citation=_CITATION    ,   inputs_description=_KWARGS_DESCRIPTION    ,   features=datasets.Features(
								        {
								            'predictions': datasets.Value('string'    ,   id='sequence'  ),
								            'references': datasets.Value('string'    ,   id='sequence'  ),
								        }  )    ,   codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']    ,   reference_urls=[
								        'https://en.wikipedia.org/wiki/ROUGE_(metric)',
								        'https://github.com/google-research/google-research/tree/master/rouge',
								    ]    ,   )




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






								if rouge_types is None:
									_snake_case   						=		['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

								_snake_case   						=		rouge_scorer.RougeScorer(rouge_types=lowerCAmelCase_    ,   use_stemmer=lowerCAmelCase_  )
								if use_aggregator:
									_snake_case   						=		scoring.BootstrapAggregator()
								else:
									_snake_case   						=		[]

								for ref, pred in zip(lowerCAmelCase_    ,   lowerCAmelCase_  ):
									_snake_case   						=		scorer.score(lowerCAmelCase_    ,   lowerCAmelCase_  )
									if use_aggregator:
										aggregator.add_scores(lowerCAmelCase_  )
									else:
										scores.append(lowerCAmelCase_  )

								if use_aggregator:
									_snake_case   						=		aggregator.aggregate()
								else:
									_snake_case   						=		{}
									for key in scores[0]:
										_snake_case   						=		[score[key] for score in scores]

								return result



'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	str:
	_snake_case   						=		1
	_snake_case   						=		2
	while i * i <= n:
		_snake_case   						=		0
		while n % i == 0:
			n //= i
			multiplicity += 1
		n_divisors *= multiplicity + 1
		i += 1
	if n > 1:
		n_divisors *= 2
	return n_divisors







def 				SCREAMING_SNAKE_CASE__	(   )       ->	List[str]:
	_snake_case   						=		1
	_snake_case   						=		1

	while True:
		i += 1
		t_num += i

		if count_divisors(__A	) > 500:
			break

	return t_num


if __name__ == "__main__":
					print(solution())

