




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowerCamelCase       :	Optional[int]				  =     "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowerCamelCase       :	str				  =     "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowerCamelCase       :	Tuple				  =     "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,   _KWARGS_DESCRIPTION     )
class 						A(		datasets.Metric     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]						)     ->			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        a__				(       self      :						int		,							A_      :						Union[str, Any]						)     ->			Tuple:

       """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        a__				(       self      :						Dict		,							A_      :						Optional[Any]		,							A_      :						Any		,							A_      :						Tuple=0.9		,							A_      :						List[Any]=3		,							A_      :						Optional[Any]=0.5						)     ->			Optional[int]:

       """simple docstring"""






       if NLTK_VERSION >= version.Version('3.6.5'						):
             lowerCamelCase_	  =					[
                 meteor_score.single_meteor_score(
                     word_tokenize(A_						)		,							word_tokenize(A_						)		,							alpha=A_		,							beta=A_		,							gamma=A_						)
                 for ref, pred in zip(A_		,							A_						)
             ]
       else:
             lowerCamelCase_	  =					[
                 meteor_score.single_meteor_score(A_		,							A_		,							alpha=A_		,							beta=A_		,							gamma=A_						)
                 for ref, pred in zip(A_		,							A_						)
             ]

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









def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					0
      for i in range(1			,							10_01  ):
            total += i**i
      return str(lowercase  )[-10:]


if __name__ == "__main__":
 print(solution())



