import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
@require_tokenizers
class     snake_case	(			unittest.TestCase    ):




					"""simple docstring"""

					snake_case__       =			EsmTokenizer







					def 		__lowerCAmelCase						(    self       :     Optional[Any]      ):
									super().setUp()
									UpperCAmelCase__					=							tempfile.mkdtemp()
									# fmt: off
									UpperCAmelCase__					=							["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
									# fmt: on
									UpperCAmelCase__					=							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]      )      )







					def 		__lowerCAmelCase						(    self       :     Dict   ,**lowerCamelCase__       :     Tuple      ):
									return [self.get_tokenizer(**snake_case_      )]







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,**lowerCamelCase__       :     List[str]      ):
									return self.tokenizer_class.from_pretrained(self.tmpdirname   ,**snake_case_      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							self.tokenizer_class(self.vocab_file      )

									UpperCAmelCase__					=							tokenizer.tokenize('LAGVS'      )
									self.assertListEqual(snake_case_   ,['L', 'A', 'G', 'V', 'S']      )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_      )   ,[4, 5, 6, 7, 8]      )







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.tokenizer_class(self.vocab_file      )

									UpperCAmelCase__					=							"""LAGVS"""
									self.assertListEqual(tokenizer.encode(snake_case_      )   ,[0, 4, 5, 6, 7, 8, 2]      )







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							self.tokenizer_class(self.vocab_file      )

									UpperCAmelCase__					=							["""LAGVS""", """WCB"""]
									UpperCAmelCase__					=							tokenizer(snake_case_   ,padding=snake_case_      )["""input_ids"""]

									self.assertListEqual(snake_case_   ,[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]      )







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.tokenizer_class(self.vocab_file      )

									UpperCAmelCase__					=							["""LAGVS""", """WCB"""]
									UpperCAmelCase__					=							tokenizer(snake_case_   ,padding=snake_case_      )["""input_ids"""]

									self.assertListEqual(snake_case_   ,[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							self.get_tokenizers(fast=snake_case_      )
									for tokenizer in tokenizers:
													with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
																	UpperCAmelCase__					=							"""<unk>"""
																	UpperCAmelCase__					=							"""<mask>"""

																	UpperCAmelCase__					=							tokenizer.tokenize(snake_case_      )
																	UpperCAmelCase__					=							tokenizer.tokenize(snake_case_      )

																	self.assertEqual(len(snake_case_      )   ,1      )
																	self.assertEqual(len(snake_case_      )   ,1      )
																	self.assertEqual(token_a[0]   ,snake_case_      )
																	self.assertEqual(token_a[0]   ,snake_case_      )




"""simple docstring"""






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase__  :  List[str]											=     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


lowerCAmelCase__  :  List[str]											=     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


lowerCAmelCase__  :  Dict											=     '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,   _KWARGS_DESCRIPTION    )
class     snake_case	(			datasets.Metric    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
									        {
									            'predictions': datasets.Sequence(datasets.Value('int32'      )      ),
									            'references': datasets.Sequence(datasets.Value('int32'      )      ),
									        }
									        if self.config_name == 'multilabel'
									        else {
									            'predictions': datasets.Value('int32'      ),
									            'references': datasets.Value('int32'      ),
									        }      )   ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']   ,)







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     Tuple=None      ):
									return {
									    "accuracy": float(
									        accuracy_score(lowerCamelCase__   ,lowerCamelCase__   ,normalize=lowerCamelCase__   ,sample_weight=lowerCamelCase__      )      )
									}



