


'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowerCAmelCase						=					"""\
@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},
}
"""

lowerCAmelCase						=					"""\
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.
"""

lowerCAmelCase						=					"""
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   )->      int:
            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			,     _UpperCamelCase   )->      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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=0.9			,     _UpperCamelCase=3			,     _UpperCamelCase=0.5   )->      Dict:
            if NLTK_VERSION >= version.Version('3.6.5'   ):
                 _A				=							[
                     meteor_score.single_meteor_score(
                         word_tokenize(_UpperCamelCase   )			,     word_tokenize(_UpperCamelCase   )			,     alpha=_UpperCamelCase			,     beta=_UpperCamelCase			,     gamma=_UpperCamelCase   )
                     for ref, pred in zip(_UpperCamelCase			,     _UpperCamelCase   )
                 ]
            else:
                 _A				=							[
                     meteor_score.single_meteor_score(_UpperCamelCase			,     _UpperCamelCase			,     alpha=_UpperCamelCase			,     beta=_UpperCamelCase			,     gamma=_UpperCamelCase   )
                     for ref, pred in zip(_UpperCamelCase			,     _UpperCamelCase   )
                 ]

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









'''simple docstring'''
class        lowerCAmelCase_  :






       def __init__( self			,     _UpperCamelCase   )->      None:
            _A				=							size
            _A				=							[0] * size
            _A				=							[0] * size






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase   )->      int:
            return index | (index + 1)






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase   )->      int:
            return (index & (index + 1)) - 1






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      None:
            _A				=							value
            while index < self.size:
                 _A				=							self.get_prev(_UpperCamelCase   ) + 1
                 if current_left_border == index:
                      _A				=							value
                 else:
                      _A				=							max(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
                 _A				=							self.get_next(_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      int:
            right -= 1  # Because of right is exclusive
            _A				=							0
            while left <= right:
                 _A				=							self.get_prev(_UpperCamelCase   )
                 if left <= current_left:
                      _A				=							max(_UpperCamelCase			,     self.tree[right]   )
                      _A				=							current_left
                 else:
                      _A				=							max(_UpperCamelCase			,     self.arr[right]   )
                      right -= 1
            return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()





