
'''simple docstring'''





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


SCREAMING_SNAKE_CASE     =       """\
@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},
}
"""

SCREAMING_SNAKE_CASE     =       """\
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.
"""

SCREAMING_SNAKE_CASE     =       """
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 							UpperCAmelCase_							(    datasets.Metric				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Dict ):



   '''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 							UpperCamelCase  (					self		:  Dict			,				snake_case__		:  Union[str, Any] ):



   '''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 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  Optional[Any]			,				snake_case__		:  Tuple			,				snake_case__		:  int=0.9			,				snake_case__		:  Tuple=3			,				snake_case__		:  Optional[Any]=0.5 ):



   '''simple docstring'''




   if NLTK_VERSION >= version.Version("3.6.5" ):
     UpperCAmelCase__      :					List[Any]				       =							[
         meteor_score.single_meteor_score(
             word_tokenize(snake_case__ )			,				word_tokenize(snake_case__ )			,				alpha=snake_case__			,				beta=snake_case__			,				gamma=snake_case__ )
         for ref, pred in zip(snake_case__			,				snake_case__ )
     ]
   else:
     UpperCAmelCase__      :					Union[str, Any]				       =							[
         meteor_score.single_meteor_score(snake_case__			,				snake_case__			,				alpha=snake_case__			,				beta=snake_case__			,				gamma=snake_case__ )
         for ref, pred in zip(snake_case__			,				snake_case__ )
     ]

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





'''simple docstring'''





import math







def snake_case_     (					lowercase__		):

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5   ,       int(math.sqrt(lowercase__		) + 1		)   ,       6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def snake_case_     (					lowercase__ = 0.1		):
  UpperCAmelCase__      :					Dict				       =							3
  UpperCAmelCase__      :					Tuple				       =							3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1   ,       (j + 2) * (j + 2)   ,       j + 1		):
      primes += is_prime(lowercase__		)
    j += 2
  return j


if __name__ == "__main__":
      import doctest

      doctest.testmod()



