
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a														=		{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMRobertaXLForCausalLM""",
				    """XLMRobertaXLForMaskedLM""",
				    """XLMRobertaXLForMultipleChoice""",
				    """XLMRobertaXLForQuestionAnswering""",
				    """XLMRobertaXLForSequenceClassification""",
				    """XLMRobertaXLForTokenClassification""",
				    """XLMRobertaXLModel""",
				    """XLMRobertaXLPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_xlm_roberta_xl import (
				    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    XLMRobertaXLConfig,
				    XLMRobertaXLOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_roberta_xl import (
								    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMRobertaXLForCausalLM,
								    XLMRobertaXLForMaskedLM,
								    XLMRobertaXLForMultipleChoice,
								    XLMRobertaXLForQuestionAnswering,
								    XLMRobertaXLForSequenceClassification,
								    XLMRobertaXLForTokenClassification,
								    XLMRobertaXLModel,
								    XLMRobertaXLPreTrainedModel,
								)

else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure)








"""simple docstring"""





import datasets


_a														=		"""\
@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\",
}
"""

_a														=		"""\
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).
"""

_a														=		"""
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       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		str:


					"""simple docstring"""





					return (preds == labels).mean()






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



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''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 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       Dict:






							'''simple docstring'''
							return {"accuracy": simple_accuracy(__a				,		__a)}






