
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


A__   :		Optional[Any] 		=version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
     from nltk import word_tokenize


A__   :		Any 		='\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

A__   :		Any 		='\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

A__   :		Optional[int] 		='\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION			)
class  __A				( datasets.Metric			):
 def 		lowercase_(   self			:    List[str]       ):



   """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			:    int				,   lowerCamelCase			:    Optional[Any]       ):



   """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				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Optional[Any]=0.9				,   lowerCamelCase			:    Dict=3				,   lowerCamelCase			:    Union[str, Any]=0.5       ):



   """simple docstring"""





   if NLTK_VERSION >= version.Version("""3.6.5"""       ):
     __A		:     List[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:
     __A		:     Optional[int]												=		[
         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       )}





'''simple docstring'''
A__   :		int 		=8.3_1_4_4_6_2  # Unit - J mol-1 K-1







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or volume < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or pressure < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()



