


'''simple docstring'''
import numpy as np

import datasets


UpperCAmelCase_ :     List[str]								=      '\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_ :     str								=      '\\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_ :     str								=      '\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				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """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  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







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

  return {"mahalanobis": mahal_dist}










'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Optional[int]       =			UNetaDModel(
      sample_size=(32, 64)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('AttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'AttnUpBlock2D')      ,      )
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Dict       =			UNetaDConditionModel(
      sample_size=(64, 32)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')      ,      cross_attention_dim=10      ,      )
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Union[str, Any]       =			AutoencoderKL(
      sample_size=(128, 64)      ,      in_channels=1      ,      out_channels=1      ,      latent_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')      ,      up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')      ,      )
  __A  :		Union[str, Any]       =			UNetaDModel(
      sample_size=(64, 32)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('AttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'AttnUpBlock2D')      ,      )
  return vqvae, unet







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
  __A  :		int       =			Mel(
      x_res=self.dummy_unet.config.sample_size[1]      ,      y_res=self.dummy_unet.config.sample_size[0]      ,      )

  __A  :		Tuple       =			DDPMScheduler()
  __A  :		Optional[int]       =			AudioDiffusionPipeline(vqvae=__lowercase      ,      unet=self.dummy_unet      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Tuple       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Tuple       =			pipe(generator=__lowercase      ,      steps=4						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		Dict       =			output.images[0]

  __A  :		Optional[Any]       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[int]       =			pipe(generator=__lowercase      ,      steps=4      ,      return_dict=__lowercase						)
  __A  :		int       =			output[0][0]

  assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
  assert (
      image.height == self.dummy_unet.config.sample_size[0]
      and image.width == self.dummy_unet.config.sample_size[1]
  )
  __A  :		Optional[int]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[Any]       =			np.frombuffer(image_from_tuple.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0
  assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() == 0

  __A  :		Optional[int]       =			Mel(
      x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]      ,      y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]      ,      )

  __A  :		Optional[Any]       =			DDIMScheduler()
  __A  :		List[Any]       =			self.dummy_vqvae_and_unet
  __A  :		int       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=dummy_vqvae_and_unet[1]      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			np.random.uniform(-1      ,      1      ,      ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
  __A  :		Dict       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		List[Any]       =			pipe(raw_audio=__lowercase      ,      generator=__lowercase      ,      start_step=5      ,      steps=10						)
  __A  :		Tuple       =			output.images[0]

  assert (
      image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
      and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
  )
  __A  :		int       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[str]       =			np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0

  __A  :		Any       =			self.dummy_unet_condition
  __A  :		Optional[int]       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=__lowercase      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Union[str, Any]       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			torch.rand((1, 1, 10)						)
  __A  :		Any       =			pipe(generator=__lowercase      ,      encoding=__lowercase						)
  __A  :		Union[str, Any]       =			output.images[0]
  __A  :		List[Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		int       =			np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0


@slow
@require_torch_gpu
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			torch_device

  __A  :		Union[str, Any]       =			DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Any       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[Any]       =			pipe(generator=__lowercase						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		List[Any]       =			output.images[0]

  assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
  assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
  __A  :		Union[str, Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0






