






"""simple docstring"""






import numpy as np

import datasets


UpperCAmelCase_     :				Any         =				"""
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""

UpperCAmelCase_     :				str         =				"""\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
"""

UpperCAmelCase_     :				List[str]         =				"""
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {'mahalanobis': array([0.5])}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION				)
class 							lowerCAmelCase__       (    datasets.Metric				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''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 _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.array(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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
          SCREAMING_SNAKE_CASE_   :						Dict							=	X - np.mean(lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.cov(reference_distribution.T)
          try:
              SCREAMING_SNAKE_CASE_   :						str							=	np.linalg.inv(lowercase_)
          except np.linalg.LinAlgError:
              SCREAMING_SNAKE_CASE_   :						Any							=	np.linalg.pinv(lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.dot(lowercase_		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.dot(lowercase_		,			X_minus_mu.T).diagonal()

          return {"mahalanobis": mahal_dist}










"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	UNetaDModel(
              block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
          return model



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_   :						int							=	ScoreSdeVeScheduler()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Dict							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_		,			return_dict=lowercase_)[
              0
          ]

          SCREAMING_SNAKE_CASE_   :						Dict							=	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_   :						str							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''google/ncsnpp-church-256'''
          SCREAMING_SNAKE_CASE_   :						str							=	UNetaDModel.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVeScheduler.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Any							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


