


"""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


__magic_name__						: 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'

__magic_name__						: 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'

__magic_name__						: 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 			__snake_case   (datasets.Metric  ):




							def   __a   (     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   __a   (     self:    Optional[int]     ,							A_:    Any     ,							A_:    List[str]     ,							A_:    str=None     ,							A_:    Optional[Any]=True     ,							A_:    Tuple=False   ):
										if rouge_types is None:
													__lowerCamelCase          =      ["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

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

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

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

										return result








"""simple docstring"""




from collections.abc import Callable





def       a_ (	lowercase__ :Callable[[float], float],			lowercase__ :float,			lowercase__ :float     ):
			__lowerCamelCase          =      a
			__lowerCamelCase          =      b
			if function(lowercase__     ) == 0:  # one of the a or b is a root for the function
						return a
			elif function(lowercase__     ) == 0:
						return b
			elif (
			    function(lowercase__     ) * function(lowercase__     ) > 0
			):  # if none of these are root and they are both positive or negative,
						# then this algorithm can't find the root
						raise ValueError("""could not find root in given interval."""     )
			else:
						__lowerCamelCase          =      start + (end - start) / 2.0
						while abs(start - mid     ) > 10**-7:  # until precisely equals to 10^-7
									if function(lowercase__     ) == 0:
												return mid
									elif function(lowercase__     ) * function(lowercase__     ) < 0:
												__lowerCamelCase          =      mid
									else:
												__lowerCamelCase          =      mid
									__lowerCamelCase          =      start + (end - start) / 2.0
						return mid





def       a_ (	lowercase__ :float     ):
			return x**3 - 2 * x - 5


if __name__ == "__main__":
					print(bisection(f, 1, 1_0_0_0))

					import doctest

					doctest.testmod()




