



'''simple docstring'''


from __future__ import annotations

_lowerCamelCase	:       int      	=	10







def 				__lowerCamelCase				(   A__    )       ->	list[int]:






   """simple docstring"""
   UpperCamelCase					=  1
   UpperCamelCase					=  max(A__    )
   while placement <= max_digit:
      # declare and initialize empty buckets
      UpperCamelCase					=  [[] for _ in range(A__    )]
      # split list_of_ints between the buckets
      for i in list_of_ints:
         UpperCamelCase					=  int((i / placement) % RADIX    )
         buckets[tmp].append(A__    )
      # put each buckets' contents into list_of_ints
      UpperCamelCase					=  0
      for b in range(A__    ):
         for i in buckets[b]:
            UpperCamelCase					=  i
            a += 1
        # move to next
      placement *= RADIX
   return list_of_ints


if __name__ == "__main__":
   import doctest

   doctest.testmod()






'''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	:       Optional[int]      	=	"\\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	:       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  SCREAMING_SNAKE_CASE (  datasets.Metric		):






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """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  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=False ):


       """simple docstring"""







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

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

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

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

       return result

