




"""simple docstring"""


import numpy as np

import datasets


_A													=      """\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													=      """\\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													=      """\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 _lowerCamelCase    (					datasets.Metric						):






   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						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 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	np.array(_UpperCAmelCase   )
         lowerCAmelCase__      :			List[str]   =	np.array(_UpperCAmelCase   )

         # 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
         lowerCAmelCase__      :			Any   =	X - np.mean(_UpperCAmelCase   )
         lowerCAmelCase__      :			int   =	np.cov(reference_distribution.T   )
         try:
               lowerCAmelCase__      :			str   =	np.linalg.inv(_UpperCAmelCase   )
         except np.linalg.LinAlgError:
               lowerCAmelCase__      :			Tuple   =	np.linalg.pinv(_UpperCAmelCase   )
         lowerCAmelCase__      :			Tuple   =	np.dot(_UpperCAmelCase   ,     _UpperCAmelCase   )
         lowerCAmelCase__      :			List[Any]   =	np.dot(_UpperCAmelCase   ,     X_minus_mu.T   ).diagonal()

         return {"mahalanobis": mahal_dist}











"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Union[str, Any]   ,     *UpperCamelCase       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						None:

         """simple docstring"""
         warnings.warn(
             """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use CLIPImageProcessor instead."""   ,     UpperCamelCase   ,     )
         super().__init__(*UpperCamelCase   ,     **UpperCamelCase   )





