


def   _lowercase(					__a				:						int     ):
							a__      =len(__a     )
							a__      =sum(__a     )

							a__      =[[False for x in range(s + 1     )] for y in range(n + 1     )]

							for i in range(1    ,     n + 1     ):
														a__      =True

							for i in range(1    ,     s + 1     ):
														a__      =False

							for i in range(1    ,     n + 1     ):
														for j in range(1    ,     s + 1     ):
																					a__      =dp[i][j - 1]

																					if arr[i - 1] <= j:
																												a__      =dp[i][j] or dp[i - 1][j - arr[i - 1]]

							for j in range(int(s / 2     )    ,     -1    ,     -1     ):
														if dp[n][j] is True:
																					a__      =s - 2 * j
																					break

							return diff





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


_lowerCAmelCase:						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'

_lowerCAmelCase:						Dict   				=       '\\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'

_lowerCAmelCase:						List[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       lowercase_					(datasets.Metric   ):





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=False)  -> Any:
											if rouge_types is None:
																		a__      =['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

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

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

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

											return result

