






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


snake_case     					=		"""\
@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},
}
"""

snake_case     					=		"""\
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.
"""

snake_case     					=		"""
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 			SCREAMING_SNAKE_CASE      (      datasets.Metric       ):
     '''simple docstring'''


     def 					_A							(					self							:						str   ):
       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							:						Any       ,			UpperCAmelCase_							:						Dict   ):
       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							:						List[str]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Union[str, Any]=0.9       ,			UpperCAmelCase_							:						Optional[int]=3       ,			UpperCAmelCase_							:						str=0.5   ):
       if NLTK_VERSION >= version.Version("3.6.5"   ):
         SCREAMING_SNAKE_CASE							:   Dict						    =     [
             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							:   List[Any]						    =     [
             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_   )}










'''simple docstring'''
from math import ceil


def       _lowercase							(					UpperCamelCase__							:       int = 1001      ):
 __A	:		int	  =      1

 for i in range(1,					int(ceil(n / 2.0      )      )      ):
  __A	:		Any	  =      2 * i + 1
  __A	:		List[str]	  =      2 * i
  __A	:		str	  =      total + 4 * odd**2 - 6 * even

 return total


if __name__ == "__main__":
 import sys

 if len(sys.argv) == 1:
  print(solution())
 else:
  try:
   UpperCAmelCase_			:  int										=   int(sys.argv[1])
   print(solution(n))
  except ValueError:
   print('Invalid entry - please enter a number')






