

"""simple docstring"""




import numpy as np

import datasets


_lowercase   :	Tuple												=						'\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   :	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'

_lowercase   :	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      _UpperCAmelCase   (							datasets.Metric       ):



	def        a  (  self     :       int						):
		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     :       List[Any]			,    _lowercase     :       Dict			,    _lowercase     :       int						):
		# convert to numpy arrays
		__UpperCAmelCase      		= np.array(_lowercase						)
		__UpperCAmelCase      		= np.array(_lowercase						)

		# 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
		__UpperCAmelCase      		= X - np.mean(_lowercase						)
		__UpperCAmelCase      		= np.cov(reference_distribution.T						)
		try:
			__UpperCAmelCase      		= np.linalg.inv(_lowercase						)
		except np.linalg.LinAlgError:
			__UpperCAmelCase      		= np.linalg.pinv(_lowercase						)
		__UpperCAmelCase      		= np.dot(_lowercase			,    _lowercase						)
		__UpperCAmelCase      		= np.dot(_lowercase			,    X_minus_mu.T						).diagonal()

		return {"mahalanobis": mahal_dist}


import warnings

from .generation import TFGenerationMixin







class 							lowerCAmelCase__   ( __lowercase						):
						# warning at import time
						warnings.warn(
						    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
						    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,						__lowercase		,						)



