





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase				:	List[Any]      						=  version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
    from nltk import word_tokenize


lowercase				:	Union[str, 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},
}
"""

lowercase				:	Optional[Any]      						=  """\
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.
"""

lowercase				:	Dict      						=  """
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 			A__   (							datasets.Metric     ):




   """simple docstring"""



   def 							__lowercase		( self)      ->						Optional[int]:







          '''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/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 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

          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 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase=0.9 ,  lowercase=3 ,  lowercase=0.5)      ->						Optional[Any]:







          '''simple docstring'''

          if NLTK_VERSION >= version.Version('3.6.5'):
                 a__						:     Dict			=    [
                     meteor_score.single_meteor_score(
                         word_tokenize(lowercase) ,  word_tokenize(lowercase) ,  alpha=lowercase ,  beta=lowercase ,  gamma=lowercase)
                     for ref, pred in zip(lowercase ,  lowercase)
                 ]
          else:
                 a__						:     List[Any]			=    [
                     meteor_score.single_meteor_score(lowercase ,  lowercase ,  alpha=lowercase ,  beta=lowercase ,  gamma=lowercase)
                     for ref, pred in zip(lowercase ,  lowercase)
                 ]

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









import re

from filelock import FileLock


try:
    import nltk

    lowercase				:	List[str]      						=  True
except (ImportError, ModuleNotFoundError):
    lowercase				:	Tuple      						=  False

if NLTK_AVAILABLE:
    with FileLock(""".lock""") as lock:
        nltk.download("""punkt""", quiet=True)



def A_     (		A__    )  ->		str:
       re.sub('<n>'		,				''		,				A__    )  # remove pegasus newline char
       assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
       return "\n".join(nltk.sent_tokenize(A__    )    )


