
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


__lowerCamelCase							=       """\
@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							=       """\
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							=       """
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   UpperCAmelCase		(						datasets.Metric ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	List[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 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					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 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Any    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					str=0.9    ,						snake_case__		:					int=3    ,						snake_case__		:					List[Any]=0.5 )		->	Optional[Any]:


          '''simple docstring'''


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

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









def    snake_case_     (  snake_case			,							snake_case     )  ->				str:
       if number < 0 or shift_amount < 0:
              raise ValueError('both inputs must be positive integers'     )

       lowercase__:   str  			=   str(bin(snake_case     )     )
       binary_number += "0" * shift_amount
       return binary_number




def    snake_case_     (  snake_case			,							snake_case     )  ->				str:
       if number < 0 or shift_amount < 0:
              raise ValueError('both inputs must be positive integers'     )

       lowercase__:   Optional[Any]  			=   str(bin(snake_case     )     )[2:]
       if shift_amount >= len(snake_case     ):
              return "0b0"
       lowercase__:   Optional[int]  			=   binary_number[: len(snake_case     ) - shift_amount]
       return "0b" + shifted_binary_number




def    snake_case_     (  snake_case			,							snake_case     )  ->				str:
       if number >= 0:  # Get binary representation of positive number
              lowercase__:   Union[str, Any]  			=   '0' + str(bin(snake_case     )     ).strip('-'     )[2:]
       else:  # Get binary (2's complement) representation of negative number
              lowercase__:   Dict  			=   len(bin(snake_case     )[3:]     )  # Find 2's complement of number
              lowercase__:   int  			=   bin(abs(snake_case     ) - (1 << binary_number_length)     )[3:]
              lowercase__:   Any  			=   (
                  '1' + '0' * (binary_number_length - len(snake_case     )) + binary_number
              )

       if shift_amount >= len(snake_case     ):
              return "0b" + binary_number[0] * len(snake_case     )
       return (
           "0b"
           + binary_number[0] * shift_amount
           + binary_number[: len(snake_case     ) - shift_amount]
       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


