




"""simple docstring"""


import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


A_      =    '''\
@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},
}
'''

A_      =    '''\
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.
'''

A_      =    '''
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			):







 '''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:		Tuple,				a_:		int		):




   '''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:		str,				a_:		Optional[Any],				a_:		Tuple,				a_:		List[str]=0.9,				a_:		List[str]=3,				a_:		Any=0.5		):




   '''simple docstring'''



   if NLTK_VERSION >= version.Version("""3.6.5"""		):
     _snake_case							:		str								=  [
         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:
     _snake_case							:		Optional[Any]								=  [
         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_		)}








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  []
  _snake_case							:		Dict								=  set({"""(""", """[""", """{"""}   )
  _snake_case							:		Union[str, Any]								=  set({""")""", """]""", """}"""}   )
  _snake_case							:		Tuple								=  {"""{""": """}""", """[""": """]""", """(""": """)"""}

  for i in range(len(snake_case__   )   ):
    if s[i] in open_brackets:
      stack.append(s[i]   )

    elif s[i] in closed_brackets and (
        len(snake_case__   ) == 0 or (len(snake_case__   ) > 0 and open_to_closed[stack.pop()] != s[i])
    ):
      return False

  return len(snake_case__   ) == 0


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Any								=  input("""Enter sequence of brackets: """   )
  if is_balanced(snake_case__   ):
    print(snake_case__		,       """is balanced"""   )
  else:
    print(snake_case__		,       """is not balanced"""   )


if __name__ == "__main__":
     main()


