




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


UpperCamelCase								=					'\\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'

UpperCamelCase								=					'\\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'

UpperCamelCase								=					'\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 			__lowerCamelCase	(   datasets.Metric   ):




    """simple docstring"""

    def        a				(	self					:					List[str]       )				->	List[str]:
         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        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					Any=False       )				->	Optional[Any]:




         if rouge_types is None:
              lowerCAmelCase__          =			["rouge1", "rouge2", "rougeL", "rougeLsum"]

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

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

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

         return result






def 							_A       (						lowerCAmelCase_					: int = 1000      ):
     """simple docstring"""

     return sum(2 * a * ((a - 1) // 2) for a in range(3       ,    n + 1      )      )


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