


'''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 	a__       (							datasets.Metric						):





		'''simple docstring'''






		def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Optional[Any]:
									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 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_=0.9      ,					lowerCamelCase_=3      ,					lowerCamelCase_=0.5     ) ->							Optional[Any]:
									if NLTK_VERSION >= version.Version('''3.6.5'''     ):
																lowerCAmelCase__      =			[
																    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:
																lowerCAmelCase__      =			[
																    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       _snake_case     (  A )		->					bool:
							if not isinstance(A				,					A ):
														raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
							if len(A ) == 0:
														raise ValueError('''Input list must be a non empty list''' )
							if len(A ) == 1:
														return True
							lowerCAmelCase__      =			series[1] - series[0]
							for index in range(len(A ) - 1 ):
														if series[index + 1] - series[index] != common_diff:
																					return False
							return True


def       _snake_case     (  A )		->					float:
							if not isinstance(A				,					A ):
														raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
							if len(A ) == 0:
														raise ValueError('''Input list must be a non empty list''' )
							lowerCAmelCase__      =			0
							for val in series:
														answer += val
							return answer / len(A )


if __name__ == "__main__":
		import doctest

		doctest.testmod()