






"""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


UpperCAmelCase_   : 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\",
}
"""

UpperCAmelCase_   : str   			= """\
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
"""

UpperCAmelCase_   : Optional[int]   			= """
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 ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''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 _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Optional[int]						,	lowercase_     :  str=None						,	lowercase_     :  int=True						,	lowercase_     :  Tuple=False):




          '''simple docstring'''


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

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

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

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

          return result










"""simple docstring"""






from math import factorial



def 					_A			(__a = 20							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
    # 2, 3,...
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		n // 2

    return int(factorial(__a							) / (factorial(__a							) * factorial(n - k							))							)


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution(20))
  else:
    try:
      UpperCAmelCase_   : List[str]   			= int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("""Invalid entry - please enter a number.""")


