





import datasets


snake_case_				=			'\\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'

snake_case_				=			'\\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'

snake_case_				=			'\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__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[Any]       )      ->				Dict:
   return (preds == labels).mean()







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


      def    a (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    a (self       :      Any				,			a__       :      List[str]				,			a__       :      Dict      ):





         """simple docstring"""



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







"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					tempfile.mkdtemp()

							# fmt: off
							lowerCamelCase			      =					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
							# fmt: on
							lowerCamelCase			      =					os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES["""vocab_file"""] )
							with open(self.vocab_file		, """w"""		, encoding="""utf-8""" ) as vocab_writer:
										vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

							lowerCamelCase			      =					{
							    """do_resize""": True,
							    """size""": {"""height""": 18, """width""": 18},
							    """do_normalize""": True,
							    """image_mean""": [0.5, 0.5, 0.5],
							    """image_std""": [0.5, 0.5, 0.5],
							}
							lowerCamelCase			      =					os.path.join(self.tmpdirname		, _a )
							with open(self.image_processor_file		, """w"""		, encoding="""utf-8""" ) as fp:
										json.dump(_a		, _a )





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							return BertTokenizer.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							return ViTImageProcessor.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							shutil.rmtree(self.tmpdirname )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					[np.random.randint(255		, size=(3, 30, 400)		, dtype=np.uinta )]

							lowerCamelCase			      =					[Image.fromarray(np.moveaxis(_a		, 0		, -1 ) ) for x in image_inputs]

							return image_inputs





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_tokenizer()
							lowerCamelCase			      =					self.get_image_processor()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							processor.save_pretrained(self.tmpdirname )
							lowerCamelCase			      =					VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

							self.assertEqual(processor.tokenizer.get_vocab()		, tokenizer.get_vocab() )
							self.assertIsInstance(processor.tokenizer		, (BertTokenizer, BertTokenizerFast) )

							self.assertEqual(processor.image_processor.to_json_string()		, image_processor.to_json_string() )
							self.assertIsInstance(processor.image_processor		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					VisionTextDualEncoderProcessor(
							    tokenizer=self.get_tokenizer()		, image_processor=self.get_image_processor() )
							processor.save_pretrained(self.tmpdirname )

							lowerCamelCase			      =					self.get_tokenizer(bos_token="""(BOS)"""		, eos_token="""(EOS)""" )
							lowerCamelCase			      =					self.get_image_processor(do_normalize=_a		, padding_value=1.0 )

							lowerCamelCase			      =					VisionTextDualEncoderProcessor.from_pretrained(
							    self.tmpdirname		, bos_token="""(BOS)"""		, eos_token="""(EOS)"""		, do_normalize=_a		, padding_value=1.0 )

							self.assertEqual(processor.tokenizer.get_vocab()		, tokenizer_add_kwargs.get_vocab() )
							self.assertIsInstance(processor.tokenizer		, (BertTokenizer, BertTokenizerFast) )

							self.assertEqual(processor.image_processor.to_json_string()		, image_processor_add_kwargs.to_json_string() )
							self.assertIsInstance(processor.image_processor		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					image_processor(_a		, return_tensors="""np""" )
							lowerCamelCase			      =					processor(images=_a		, return_tensors="""np""" )

							for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()		, input_processor[key].sum()		, delta=1e-2 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""

							lowerCamelCase			      =					processor(text=_a )

							lowerCamelCase			      =					tokenizer(_a )

							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]		, encoded_processor[key] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""
							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					processor(text=_a		, images=_a )

							self.assertListEqual(list(inputs.keys() )		, ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )

							# test if it raises when no input is passed
							with self.assertRaises(_a ):
										processor()





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCamelCase			      =					processor.batch_decode(_a )
							lowerCamelCase			      =					tokenizer.batch_decode(_a )

							self.assertListEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""
							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					processor(text=_a		, images=_a )

							self.assertListEqual(list(inputs.keys() )		, processor.model_input_names )



