





import numpy as np

import datasets


a__:      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'

a__:      Optional[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'

a__:      Any									=		'\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     SCREAMING_SNAKE_CASE__ (						datasets.Metric      ):



 def 				UpperCamelCase			(							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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        # convert to numpy arrays
        A__											=						np.array(__lowerCamelCase )
        A__											=						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
        A__											=						X - np.mean(__lowerCamelCase )
        A__											=						np.cov(reference_distribution.T )
        try:
               A__											=						np.linalg.inv(__lowerCamelCase )
        except np.linalg.LinAlgError:
               A__											=						np.linalg.pinv(__lowerCamelCase )
        A__											=						np.dot(__lowerCamelCase,__lowerCamelCase )
        A__											=						np.dot(__lowerCamelCase,X_minus_mu.T ).diagonal()

        return {"mahalanobis": mahal_dist}












from ..utils import DummyObject, requires_backends




class     SCREAMING_SNAKE_CASE__ (						metaclass=UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	['''keras_nlp''']



 def __init__(							self,*__lowerCamelCase,**__lowerCamelCase ):
        requires_backends(self,['''keras_nlp'''] )





