

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_       : int         =			'\\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_       : 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'

A_       : Optional[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     _a						(datasets.Metric				):


					'''simple docstring'''






					def   __A			( self			):
						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    ,       A__    ,       A__    ,       A__=None    ,       A__=True    ,       A__=False			):
						if rouge_types is None:
							A__							:    Optional[int]        =		["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

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

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

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

						return result






def 						UpperCamelCase					(lowercase_: int = 10					)     ->      str:
	if not isinstance(lowercase_    ,      lowercase_					) or n < 0:
		raise ValueError("""Invalid input"""					)
	A__							:    List[str]        =		10**n
	A__							:    Any        =		28433 * (pow(2    ,      7830457    ,      lowercase_					)) + 1
	return str(number % modulus					)


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

	testmod()
	print(f'''{solution(10) = }''')



