






"""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]=	"""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

A__					: Tuple=	"""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

A__					: List[Any]=	"""
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    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))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,	_KWARGS_DESCRIPTION  )
class    __lowerCamelCase    (  datasets.Metric  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_=None ,				snake_case_=True ,				snake_case_=False      )		->       Optional[Any]:
       if rouge_types is None:
           UpperCamelCase__         =    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

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

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

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

       return result












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	list[int]:
    """simple docstring"""

    UpperCamelCase__         =    len(SCREAMING_SNAKE_CASE					)
    for i in range(SCREAMING_SNAKE_CASE					):
        for j in range(i + 1 ,	SCREAMING_SNAKE_CASE					):
            if numbers[j] < numbers[i]:
                UpperCamelCase__       ,						UpperCamelCase__         =    numbers[j], numbers[i]
    return numbers


if __name__ == "__main__":
  A__					: Union[str, Any]=	input("""Enter numbers separated by a comma:\n""").strip()
  A__					: List[Any]=	[int(item) for item in user_input.split(""",""")]
  print(exchange_sort(unsorted))




