
"""simple docstring"""


import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


_lowercase				 =					'''\
@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},
}
'''

_lowercase				 =					'''\
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.
'''

_lowercase				 =					'''
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 					lowerCAmelCase_					(   datasets.Metric     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[int]:
							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  :   Tuple	,A_  :   List[Any]   )  -> Tuple:
							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  :   Any	,A_  :   Any	,A_  :   Tuple	,A_  :   Union[str, Any]=0.9	,A_  :   List[str]=3	,A_  :   Dict=0.5   )  -> Optional[Any]:
							if NLTK_VERSION >= version.Version('3.6.5'   ):
								A     = [
								    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:
								A     = [
								    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_   )}

"""simple docstring"""


from torch import nn






def    _snake_case					(		snake_case__ :  Union[str, Any]  ):
	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(F'Unsupported activation function: {act_fn}'  )