

'''simple docstring'''


import datasets


lowerCAmelCase__						:       Any										=						"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

lowerCAmelCase__						:       int										=						"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

lowerCAmelCase__						:       Optional[Any]										=						"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""




def 	_a       (				__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		return (preds == labels).mean()





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION       )
class     a			(				datasets.Metric       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								return datasets.MetricInfo(
								    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''  ),
								            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''  ),
								        }  )			,			codebase_urls=[]			,			reference_urls=[]			,			format='''numpy'''			,			)







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								return {"accuracy": simple_accuracy(snake_case_			,			snake_case_  )}






'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)



