'''simple docstring'''

import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__a: Union[str, Any]          =   version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
  from nltk import word_tokenize


__a: Optional[Any]          =   """\
@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},
}
"""

__a: Tuple          =   """\
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.
"""

__a: str          =   """
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 			UpperCAmelCase  (						datasets.Metric					):





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  str:
        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 _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        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 _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=0.9	,   __lowerCAmelCase=3	,   __lowerCAmelCase=0.5 )  ->  int:
        if NLTK_VERSION >= version.Version('''3.6.5''' ):
         lowercase__				:    Optional[int]  =					[
             meteor_score.single_meteor_score(
                 word_tokenize(__lowerCAmelCase )	,   word_tokenize(__lowerCAmelCase )	,   alpha=__lowerCAmelCase	,   beta=__lowerCAmelCase	,   gamma=__lowerCAmelCase )
             for ref, pred in zip(__lowerCAmelCase	,   __lowerCAmelCase )
         ]
        else:
         lowercase__				:    List[Any]  =					[
             meteor_score.single_meteor_score(__lowerCAmelCase	,   __lowerCAmelCase	,   alpha=__lowerCAmelCase	,   beta=__lowerCAmelCase	,   gamma=__lowerCAmelCase )
             for ref, pred in zip(__lowerCAmelCase	,   __lowerCAmelCase )
         ]

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


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    List[str]  =					0
 while num > 0:
  digit_sum += num % 10
  num //= 10
 return digit_sum
def 			__UpperCamelCase       (   UpperCAmelCase = 100     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Optional[int]  =					2

 for i in range(2		,     max_n + 1     ):
  lowercase__				:    List[str]  =					pre_numerator
  lowercase__				:    Optional[Any]  =					2 * i // 3 if i % 3 == 0 else 1
  lowercase__				:    Union[str, Any]  =					cur_numerator
  lowercase__				:    int  =					e_cont * pre_numerator + temp

 return sum_digits(UpperCAmelCase     )


if __name__ == "__main__":
  print(F'{solution() = }')

