


'''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[int]:
														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  )			->				Dict:
														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_  )}




import math





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * power_factor





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






