






"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_UpperCamelCase	     =    version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
  from nltk import word_tokenize


_UpperCamelCase	     =    """\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
"""

_UpperCamelCase	     =    """\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
"""

_UpperCamelCase	     =    """
Computes METEOR score of translated segments against one or more 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.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    'meteor': meteor score.
Examples:

    >>> meteor = datasets.load_metric('meteor')
    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]
    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results[\"meteor\"], 4))
    0.6944
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION					)
class    lowerCamelCase__   (			datasets.Metric					):






    def 							_UpperCamelCase				(					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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"""]      ,reference_urls=[
                  """https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score""",
                  """https://en.wikipedia.org/wiki/METEOR""",
              ]      ,)






    def 							_UpperCamelCase				(					self      ,A   ):
          import nltk

          nltk.download("""wordnet"""   )
          if NLTK_VERSION >= version.Version("""3.6.5"""   ):
                nltk.download("""punkt"""   )
          if NLTK_VERSION >= version.Version("""3.6.6"""   ):
                nltk.download("""omw-1.4"""   )






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A=0.9      ,A=3      ,A=0.5   ):




          if NLTK_VERSION >= version.Version("""3.6.5"""   ):
                UpperCAmelCase							     =			[
                    meteor_score.single_meteor_score(
                        word_tokenize(snake_case__   )      ,word_tokenize(snake_case__   )      ,alpha=snake_case__      ,beta=snake_case__      ,gamma=snake_case__   )
                    for ref, pred in zip(snake_case__      ,snake_case__   )
                ]
          else:
                UpperCAmelCase							     =			[
                    meteor_score.single_meteor_score(snake_case__      ,snake_case__      ,alpha=snake_case__      ,beta=snake_case__      ,gamma=snake_case__   )
                    for ref, pred in zip(snake_case__      ,snake_case__   )
                ]

          return {"meteor": np.mean(snake_case__   )}





'''simple docstring'''





def snake_case_     (					lowercase__		):
  return sum(i for i in range(1   ,       number // 2 + 1		) if number % i == 0		) == number


if __name__ == "__main__":
      print("""Program to check whether a number is a Perfect number or not...""")
      SCREAMING_SNAKE_CASE     =       int(input("""Enter number: """).strip())
      print(F'{number} is {"" if perfect(number) else "not "}a Perfect Number.')



