





'''simple docstring'''






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    BioGptTokenizer
					_SCREAMING_SNAKE_CASE							   =    False


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





											'''simple docstring'''





											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											_snake_case:   Optional[Any]  					=		[
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    'w</w>',
											    'r</w>',
											    't</w>',
											    'lo',
											    'low',
											    'er</w>',
											    'low</w>',
											    'lowest</w>',
											    'newer</w>',
											    'wider</w>',
											    '<unk>',
											]
											_snake_case:   int  					=		dict(zip(__snake_case       ,     range(len(__snake_case       )       )       )       )
											_snake_case:   Optional[Any]  					=		['l o 123', 'lo w 1456', 'e r</w> 1789', '']

											_snake_case:   Dict  					=		os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['vocab_file']       )
											_snake_case:   List[str]  					=		os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file       ,     'w'       ) as fp:
																	fp.write(json.dumps(__snake_case       )       )
											with open(self.merges_file       ,     'w'       ) as fp:
																	fp.write('\n'.join(__snake_case       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		'lower newer'
											_snake_case:   List[Any]  					=		'lower newer'
											return input_text, output_text


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		BioGptTokenizer(self.vocab_file       ,     self.merges_file       )

											_snake_case:   int  					=		'lower'
											_snake_case:   Tuple  					=		['low', 'er</w>']
											_snake_case:   str  					=		tokenizer.tokenize(__snake_case       )
											self.assertListEqual(__snake_case       ,     __snake_case       )

											_snake_case:   Union[str, Any]  					=		tokens + ['<unk>']
											_snake_case:   Tuple  					=		[14, 15, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case       )       ,     __snake_case       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		BioGptTokenizer.from_pretrained('microsoft/biogpt'       )

											_snake_case:   Tuple  					=		tokenizer.encode('sequence builders'       ,     add_special_tokens=__snake_case       )
											_snake_case:   Union[str, Any]  					=		tokenizer.encode('multi-sequence build'       ,     add_special_tokens=__snake_case       )

											_snake_case:   Union[str, Any]  					=		tokenizer.build_inputs_with_special_tokens(__snake_case       )
											_snake_case:   Any  					=		tokenizer.build_inputs_with_special_tokens(__snake_case       ,     __snake_case       )

											self.assertTrue(encoded_sentence == [2] + text       )
											self.assertTrue(encoded_pair == [2] + text + [2] + text_a       )












'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


A   : Dict             =			'\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'

A   : int             =			'\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        -0.25\n'

A   : 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 			lowerCamelCase					(						datasets.Metric  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											return datasets.MetricInfo(
											    description=_DESCRIPTION       ,     citation=_CITATION       ,     inputs_description=_KWARGS_DESCRIPTION       ,     features=datasets.Features(
											        {
											            'predictions': datasets.Value('int32'       ),
											            'references': datasets.Value('int32'       ),
											        }       )       ,     reference_urls=[
											        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
											    ]       ,     )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  int       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Union[str, Any]=None       ):





											'''simple docstring'''





											return {
											    "matthews_correlation": float(matthews_corrcoef(__snake_case       ,     __snake_case       ,     sample_weight=__snake_case       )       ),
											}





