
import numpy as np

import datasets


UpperCAmelCase__		:     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'

UpperCAmelCase__		:     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'

UpperCAmelCase__		:     List[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   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Optional[int]     ):

      """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  __magic_name__				(					self     :							Dict			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      # convert to numpy arrays
      _A:  Union[str, Any]					=       np.array(lowerCAmelCase_     )
      _A:  Any					=       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:  List[Any]					=       X - np.mean(lowerCAmelCase_     )
      _A:  Any					=       np.cov(reference_distribution.T     )
      try:
         _A:  str					=       np.linalg.inv(lowerCAmelCase_     )
      except np.linalg.LinAlgError:
         _A:  str					=       np.linalg.pinv(lowerCAmelCase_     )
      _A:  Optional[int]					=       np.dot(lowerCAmelCase_			,			lowerCAmelCase_     )
      _A:  Dict					=       np.dot(lowerCAmelCase_			,			X_minus_mu.T     ).diagonal()

      return {"mahalanobis": mahal_dist}





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__		:     Any     			=      {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     Union[str, Any]     			=      [
       'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SwinForImageClassification',
       'SwinForMaskedImageModeling',
       'SwinModel',
       'SwinPreTrainedModel',
       'SwinBackbone',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     str     			=      [
       'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFSwinForImageClassification',
       'TFSwinForMaskedImageModeling',
       'TFSwinModel',
       'TFSwinPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase__		:     Tuple     			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



