






import datasets


A__	:			Optional[Any]			   = '\\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'

A__	:			str			   = '\\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'

A__	:			Optional[Any]			   = '\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   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				return (preds == labels).mean()




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

				"""simple docstring"""




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''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 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Optional[int]  ):

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












import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       CTRLTokenizer
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowercase__    =			['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
								lowercase__    =			dict(zip(lowerCamelCase,  range(len(lowerCamelCase  )  )  )  )
								lowercase__    =			['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
								lowercase__    =			{'''unk_token''': '''<unk>'''}

								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']  )
								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write(json.dumps(lowerCamelCase  ) + '''\n'''  )
								with open(self.merges_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write('''\n'''.join(lowerCamelCase  )  )




				def 				lowercase__   (     self						:    Union[str, Any],  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								kwargs.update(self.special_tokens_map  )
								return CTRLTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt react readapt apt'''
								return input_text, output_text




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			CTRLTokenizer(self.vocab_file,  self.merges_file,  **self.special_tokens_map  )
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
								lowercase__    =			tokenizer.tokenize(lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			tokens + [tokenizer.unk_token]

								lowercase__    =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase  ),  lowerCamelCase  )




