






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


__lowerCamelCase	:		str										=   '''\
@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	:		Optional[Any]										=   '''
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  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       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				,			a   ):
       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				,			a				,			a				,			a=0.9				,			a=3				,			a=0.5   ):
       if NLTK_VERSION >= version.Version("""3.6.5"""   ):
        snake_case__  :    int							       =[
            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:
        snake_case__  :    Optional[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__   )}













def 			A__							(      _a  :					Any		,  _a  :					Optional[Any]		,  _a  :					int		,  _a  :					Any	):





 '''simple docstring'''







 if height >= 1:
  move_tower(height - 1		,  _a		,  _a		,  _a	)
  move_disk(_a		,  _a	)
  move_tower(height - 1		,  _a		,  _a		,  _a	)







def 			A__							(      _a  :					Optional[int]		,  _a  :					str	):





 '''simple docstring'''







 print("""moving disk from"""		,  _a		,  """to"""		,  _a	)







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Dict							       =int(input("""Height of hanoi: """	).strip()	)
 move_tower(_a		,  """A"""		,  """B"""		,  """C"""	)


if __name__ == "__main__":
       main()





