
'''simple docstring'''







import datasets


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

_lowercase			:  Any   							=	"\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"

_lowercase			:  str   							=	"\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    'accuracy': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric(\"xnli\")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n"
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						return (preds == labels).mean()






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






					def      _snake_case   (       self		):


											"""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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase			:  Tuple   							=	{"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  str   							=	[
							    "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTMSNModel",
							    "ViTMSNForImageClassification",
							    "ViTMSNPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_msn import (
														    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMSNForImageClassification,
														    ViTMSNModel,
														    ViTMSNPreTrainedModel,
														)

else:
							import sys

							_lowercase			:  int   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



