
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     lowercase		( SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	List[str]	 =BioGptTokenizer
							lowercase_	:	Tuple	 =False




							def 			A__				(    self):
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												lowercase						     =						[
												    '''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>''',
												]
												lowercase						     =						dict(zip(A__       ,range(len(A__))))
												lowercase						     =						['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

												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''') as fp:
																	fp.write(json.dumps(A__))
												with open(self.merges_file       ,'''w''') as fp:
																	fp.write('''\n'''.join(A__))




							def 			A__				(    self       ,A__):
												lowercase						     =						'''lower newer'''
												lowercase						     =						'''lower newer'''
												return input_text, output_text




							def 			A__				(    self):
												lowercase						     =						BioGptTokenizer(self.vocab_file       ,self.merges_file)

												lowercase						     =						'''lower'''
												lowercase						     =						['''low''', '''er</w>''']
												lowercase						     =						tokenizer.tokenize(A__)
												self.assertListEqual(A__       ,A__)

												lowercase						     =						tokens + ['''<unk>''']
												lowercase						     =						[1_4, 1_5, 2_0]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(A__)       ,A__)





							@slow
							def 			A__				(    self):
												lowercase						     =						BioGptTokenizer.from_pretrained('''microsoft/biogpt''')

												lowercase						     =						tokenizer.encode('''sequence builders'''       ,add_special_tokens=A__)
												lowercase						     =						tokenizer.encode('''multi-sequence build'''       ,add_special_tokens=A__)

												lowercase						     =						tokenizer.build_inputs_with_special_tokens(A__)
												lowercase						     =						tokenizer.build_inputs_with_special_tokens(A__       ,A__)

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






import numpy as np

import datasets


lowercase__		:Dict			  =   "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

lowercase__		:List[Any]			  =   "\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

lowercase__		:Dict			  =   "\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"







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




							def 			A__				(    self):
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
												        {
												            '''X''': datasets.Sequence(datasets.Value('''float'''       ,id='''sequence''')       ,id='''X'''),
												        })       ,)





							def 			A__				(    self       ,A__       ,A__):
												# convert to numpy arrays
												lowercase						     =						np.array(A__)
												lowercase						     =						np.array(A__)

												# Assert that arrays are 2D
												if len(X.shape) != 2:
																	raise ValueError('''Expected `X` to be a 2D vector''')
												if len(reference_distribution.shape) != 2:
																	raise ValueError('''Expected `reference_distribution` to be a 2D vector''')
												if reference_distribution.shape[0] < 2:
																	raise ValueError(
																	    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''')

												# Get mahalanobis distance for each prediction
												lowercase						     =						X - np.mean(A__)
												lowercase						     =						np.cov(reference_distribution.T)
												try:
																	lowercase						     =						np.linalg.inv(A__)
												except np.linalg.LinAlgError:
																	lowercase						     =						np.linalg.pinv(A__)
												lowercase						     =						np.dot(A__       ,A__)
												lowercase						     =						np.dot(A__       ,X_minus_mu.T).diagonal()

												return {"mahalanobis": mahal_dist}




