

import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


UpperCAmelCase__ :     Tuple				    =    """\
@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},
}
"""

UpperCAmelCase__ :     List[Any]				    =    """\
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.
"""

UpperCAmelCase__ :     int				    =    """
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       ):







  """simple docstring"""




  def  _lowercase      (			self    : Tuple		)					->Tuple:
        """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    : str				,				UpperCAmelCase__    : Tuple		)					->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  _lowercase      (			self    : int				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Any=0.9				,				UpperCAmelCase__    : Tuple=3				,				UpperCAmelCase__    : Optional[Any]=0.5		)					->Optional[Any]:
        """simple docstring"""







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

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





def 					__lowercase      (     _A						,    _A			)     ->						int:
      return int((input_a, input_a).count(1			) != 0			)






def 					__lowercase      (     )     ->						None:
      assert or_gate(0						,    0			) == 0
      assert or_gate(0						,    1			) == 1
      assert or_gate(1						,    0			) == 1
      assert or_gate(1						,    1			) == 1


if __name__ == "__main__":
  print(or_gate(0, 1))
  print(or_gate(1, 0))
  print(or_gate(0, 0))
  print(or_gate(1, 1))




