"""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				:		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'

_a				:		Any	   =       '\\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				:		List[str]	   =       '\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    __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:
         _lowerCAmelCase						:	List[Any]        =					["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

       _lowerCAmelCase						:	Optional[Any]        =					rouge_scorer.RougeScorer(rouge_types=a__    ,					use_stemmer=a__  )
       if use_aggregator:
         _lowerCAmelCase						:	Union[str, Any]        =					scoring.BootstrapAggregator()
       else:
         _lowerCAmelCase						:	str        =					[]

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

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

       return result





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]     )


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

     testmod()




