






import os
import sys
import unittest


A__      :     List[Any]       =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


A__      :     List[Any]       =					os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
A__      :     str       =					os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     get_test_to_tester_mapping(lowerCamelCase    )
								lowercase__  					=     get_test_to_tester_mapping(lowerCamelCase    )

								lowercase__  					=     {'''BertModelTest''': '''BertModelTester'''}

								lowercase__  					=     {
								    '''BlipModelTest''': '''BlipModelTester''',
								    '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
								    '''BlipTextModelTest''': '''BlipTextModelTester''',
								    '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
								    '''BlipVQAModelTest''': '''BlipVQAModelTester''',
								    '''BlipVisionModelTest''': '''BlipVisionModelTester''',
								}

								self.assertEqual(get_test_info.to_json(lowerCamelCase    ),    lowerCamelCase    )
								self.assertEqual(get_test_info.to_json(lowerCamelCase    ),    lowerCamelCase    )




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     get_model_to_test_mapping(lowerCamelCase    )
								lowercase__  					=     get_model_to_test_mapping(lowerCamelCase    )

								lowercase__  					=     {
								    '''BertForMaskedLM''': ['''BertModelTest'''],
								    '''BertForMultipleChoice''': ['''BertModelTest'''],
								    '''BertForNextSentencePrediction''': ['''BertModelTest'''],
								    '''BertForPreTraining''': ['''BertModelTest'''],
								    '''BertForQuestionAnswering''': ['''BertModelTest'''],
								    '''BertForSequenceClassification''': ['''BertModelTest'''],
								    '''BertForTokenClassification''': ['''BertModelTest'''],
								    '''BertLMHeadModel''': ['''BertModelTest'''],
								    '''BertModel''': ['''BertModelTest'''],
								}

								lowercase__  					=     {
								    '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
								    '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
								    '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
								    '''BlipModel''': ['''BlipModelTest'''],
								    '''BlipTextModel''': ['''BlipTextModelTest'''],
								    '''BlipVisionModel''': ['''BlipVisionModelTest'''],
								}

								self.assertEqual(get_test_info.to_json(lowerCamelCase    ),    lowerCamelCase    )
								self.assertEqual(get_test_info.to_json(lowerCamelCase    ),    lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     get_model_to_tester_mapping(lowerCamelCase    )
								lowercase__  					=     get_model_to_tester_mapping(lowerCamelCase    )

								lowercase__  					=     {
								    '''BertForMaskedLM''': ['''BertModelTester'''],
								    '''BertForMultipleChoice''': ['''BertModelTester'''],
								    '''BertForNextSentencePrediction''': ['''BertModelTester'''],
								    '''BertForPreTraining''': ['''BertModelTester'''],
								    '''BertForQuestionAnswering''': ['''BertModelTester'''],
								    '''BertForSequenceClassification''': ['''BertModelTester'''],
								    '''BertForTokenClassification''': ['''BertModelTester'''],
								    '''BertLMHeadModel''': ['''BertModelTester'''],
								    '''BertModel''': ['''BertModelTester'''],
								}

								lowercase__  					=     {
								    '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
								    '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
								    '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
								    '''BlipModel''': ['''BlipModelTester'''],
								    '''BlipTextModel''': ['''BlipTextModelTester'''],
								    '''BlipVisionModel''': ['''BlipVisionModelTester'''],
								}

								self.assertEqual(get_test_info.to_json(lowerCamelCase    ),    lowerCamelCase    )
								self.assertEqual(get_test_info.to_json(lowerCamelCase    ),    lowerCamelCase    )












import numpy as np

import datasets


A__      :     int       =					'\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'

A__      :     Optional[int]       =					'\\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'

A__      :     Optional[int]       =					'\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  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								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 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								# convert to numpy arrays
								lowercase__  					=     np.array(lowerCamelCase    )
								lowercase__  					=     np.array(lowerCamelCase    )

								# 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(lowerCamelCase    )
								lowercase__  					=     np.cov(reference_distribution.T    )
								try:
												lowercase__  					=     np.linalg.inv(lowerCamelCase    )
								except np.linalg.LinAlgError:
												lowercase__  					=     np.linalg.pinv(lowerCamelCase    )
								lowercase__  					=     np.dot(lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     np.dot(lowerCamelCase,    X_minus_mu.T    ).diagonal()

								return {"mahalanobis": mahal_dist}




