"""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


__lowerCAmelCase     :				List[str]    				=    "\\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     :				Tuple    				=    "\\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     :				Union[str, 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      ):




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							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     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  List[Any]	,    snake_case__    :  Tuple	,    snake_case__    :  str=None	,    snake_case__    :  int=True	,    snake_case__    :  Any=False  ):
							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





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return abs(lowerCamelCase__  ) if a == 0 else greatest_common_divisor(b % a      ,	lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				lowerCAmelCase__ ,       lowerCAmelCase__   = y, x % y
		return abs(lowerCamelCase__  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		try:
				lowerCAmelCase__   = input("""Enter two integers separated by comma (,): """  ).split(""","""  )
				lowerCAmelCase__   = int(nums[0]  )
				lowerCAmelCase__   = int(nums[1]  )
				print(
				    f"""greatest_common_divisor({num_a}, {num_a}) = """
				    f"""{greatest_common_divisor(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
				print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
		except (IndexError, UnboundLocalError, ValueError):
				print("""Wrong input"""  )


if __name__ == "__main__":
			main()




