


'''simple docstring'''


import datasets


lowerCamelCase__    	=     '\\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'

lowerCamelCase__    	=     '\\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'

lowerCamelCase__    	=     '\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  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return (preds == labels).mean()






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




			def 							lowerCAmelCase__    (		self  :     Any )   ->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 							lowerCAmelCase__    (		self  :     Dict	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Optional[Any] )   ->str:



					'''simple docstring'''







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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__    	=     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'InstructBlipQFormerModel',
						    'InstructBlipPreTrainedModel',
						    'InstructBlipForConditionalGeneration',
						    'InstructBlipVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



