






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase     :							Any    =version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
	from nltk import word_tokenize


lowerCamelCase     :							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},
}
'''

lowerCamelCase     :							List[str]    ='''\
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     :							List[str]    ='''
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	):







	def  __lowercase (		self :       Union[str, Any]						):


						'''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 :       List[Any]							,       SCREAMING_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  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       Any							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Tuple=0.9							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Tuple=0.5						):


						'''simple docstring'''






						if NLTK_VERSION >= version.Version("3.6.5"						):
											UpperCamelCase__      :       Dict	    =  [
											    meteor_score.single_meteor_score(
											        word_tokenize(SCREAMING_SNAKE_CASE						)							,       word_tokenize(SCREAMING_SNAKE_CASE						)							,       alpha=SCREAMING_SNAKE_CASE							,       beta=SCREAMING_SNAKE_CASE							,       gamma=SCREAMING_SNAKE_CASE						)
											    for ref, pred in zip(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
											]
						else:
											UpperCamelCase__      :       Optional[int]	    =  [
											    meteor_score.single_meteor_score(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       alpha=SCREAMING_SNAKE_CASE							,       beta=SCREAMING_SNAKE_CASE							,       gamma=SCREAMING_SNAKE_CASE						)
											    for ref, pred in zip(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
											]

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     assert isinstance(__lowerCAmelCase ,   __lowerCAmelCase      ), f'The input value of [n={number}] is not an integer'

     if number == 1:
          return 2
     elif number < 1:
          UpperCamelCase__      :       List[Any]	    =  f'The input value of [n={number}] has to be > 0'
          raise ValueError(__lowerCAmelCase      )
     else:
          UpperCamelCase__      :       Optional[Any]	    =  sylvester(number - 1      )
          UpperCamelCase__      :       str	    =  num - 1
          UpperCamelCase__      :       int	    =  num
          return lower * upper + 1


if __name__ == "__main__":
 print(F"""The 8th number in Sylvester's sequence: {sylvester(8)}""")