"""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


_A   					=    '\\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   					=    '\\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   					=    '\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 			lowerCAmelCase_					(						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 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				List[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Tuple=None   , _snake_case			:				Dict=True   , _snake_case			:				int=False		)							->       Any:
									if rouge_types is None:
													SCREAMING_SNAKE_CASE__		  =				["rouge1", "rouge2", "rougeL", "rougeLsum"]

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

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

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

									return result





"""simple docstring"""






from math import pi




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
						print(arc_length(9_0, 1_0))




