
"""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__   :					Optional[int]							=   '\\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__   :					Union[str, 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__   :					Optional[int]							=   '\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 			__magic_name__       (       datasets.Metric ):
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         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 							lowercase_							(     self ,				A_ ,				A_ ,				A_=None ,				A_=True ,				A_=False       )     ->	Optional[int]:





         """simple docstring"""




         if rouge_types is None:
           _lowercase:			str       =     ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

         _lowercase:			int       =     rouge_scorer.RougeScorer(rouge_types=A_ ,				use_stemmer=A_       )
         if use_aggregator:
           _lowercase:			Tuple       =     scoring.BootstrapAggregator()
         else:
           _lowercase:			List[Any]       =     []

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

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

         return result


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  if len(_UpperCamelCase						) == 1:
    return True
  _lowercase:			Any       =     series[1] - series[0]
  for index in range(len(_UpperCamelCase						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  _lowercase:			Tuple       =     0
  for val in series:
    answer += val
  return answer / len(_UpperCamelCase						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
