"""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__					       =	'\\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__					       =	'\\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__					       =	'\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    __snake_case       (     datasets.Metric				):



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''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    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			lowercase=True    ,			lowercase=False)	->			Union[str, Any]:





        '''simple docstring'''





        if rouge_types is None:
          a__:     Union[str, Any]										=	['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

        a__:     Any										=	rouge_scorer.RougeScorer(rouge_types=lowercase    ,			use_stemmer=lowercase)
        if use_aggregator:
          a__:     Dict										=	scoring.BootstrapAggregator()
        else:
          a__:     Optional[Any]										=	[]

        for ref, pred in zip(lowercase    ,			lowercase):
          a__:     List[str]										=	scorer.score(lowercase    ,			lowercase)
          if use_aggregator:
            aggregator.add_scores(lowercase)
          else:
            scores.append(lowercase)

        if use_aggregator:
          a__:     List[str]										=	aggregator.aggregate()
        else:
          a__:     Any										=	{}
          for key in scores[0]:
            a__:     Optional[Any]										=	[score[key] for score in scores]

        return result



"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase__					       =	TypeVar('T')

lowercase__					       =	Union[List[T], Tuple[T, ...]]
lowercase__					       =	Union[T, List[T], Dict[str, T]]
lowercase__					       =	Union[str, bytes, os.PathLike]


