





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


_a						=   "\\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"

_a						=   "\\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"

_a						=   "\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       __A					(					datasets.Metric				):

			'''simple docstring'''







			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:
					lowerCamelCase__              =	['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

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

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

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

				return result










from __future__ import annotations





def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None							)       ->   None:







	'''simple docstring'''







	if start is None:
		lowerCamelCase__              =	0

	if end is None:
		lowerCamelCase__              =	len(__snake_case							) - 1

	if start >= end:
		return

	lowerCamelCase__              =	(start + end) // 2

	slowsort(__snake_case ,__snake_case ,__snake_case							)
	slowsort(__snake_case ,mid + 1 ,__snake_case							)

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

	slowsort(__snake_case ,__snake_case ,end - 1							)


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

			testmod()



