

import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_lowerCamelCase    :       Union[str, Any]        =							version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
     from nltk import word_tokenize


_lowerCamelCase    :       Tuple        =							"\\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"

_lowerCamelCase    :       List[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"

_lowerCamelCase    :       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  __UpperCAmelCase					(	datasets.Metric						):



   def 	__magic_name__   (							self							:					List[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 	__magic_name__   (							self							:					int,		__A							:					List[str]						):
     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 	__magic_name__   (							self							:					List[str],		__A							:					List[Any],		__A							:					Tuple,		__A							:					List[str]=0.9,		__A							:					Union[str, Any]=3,		__A							:					Tuple=0.5						):
     if NLTK_VERSION >= version.Version('''3.6.5'''						):
       UpperCAmelCase	:							List[Any]						=			[
           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:
       UpperCAmelCase	:							Union[str, 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						)}








from math import log

from scipy.constants import Boltzmann, physical_constants

_lowerCamelCase    :       Tuple        =							3_0_0  # TEMPERATURE (unit = K)

def       a__					(		UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			)  ->						float:

  if donor_conc <= 0:
    raise ValueError('''Donor concentration should be positive'''      )
  elif acceptor_conc <= 0:
    raise ValueError('''Acceptor concentration should be positive'''      )
  elif intrinsic_conc <= 0:
    raise ValueError('''Intrinsic concentration should be positive'''      )
  elif donor_conc <= intrinsic_conc:
    raise ValueError(
        '''Donor concentration should be greater than intrinsic concentration'''      )
  elif acceptor_conc <= intrinsic_conc:
    raise ValueError(
        '''Acceptor concentration should be greater than intrinsic concentration'''      )
  else:
    return (
        Boltzmann
        * T
        * log((donor_conc * acceptor_conc) / intrinsic_conc**2      )
        / physical_constants["electron volt"][0]
    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





