



'''simple docstring'''




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase							:Optional[int]       = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
						from nltk import word_tokenize


lowerCamelCase							:str       = '''\
@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							: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.
'''

lowerCamelCase							:Any       = '''
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    _a     (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    _a     (self	,				lowercase							):
				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    _a     (self	,				lowercase	,				lowercase	,				lowercase=0.9	,				lowercase=3	,				lowercase=0.5							):
				if NLTK_VERSION >= version.Version("""3.6.5"""							):
							A_     :						Any     =					[
							    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_     :						int     =					[
							    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							)}




'''simple docstring'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()