import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class 	_lowerCamelCase  (     unittest.TestCase				):

      """simple docstring"""

      @property
      def  snake_case		(       self   :	Optional[Any]			):
          torch.manual_seed(0			)
          __UpperCamelCase											=  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  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  '''cpu'''
          __UpperCamelCase											=  self.dummy_uncond_unet
          __UpperCamelCase											=  DDPMScheduler()

          __UpperCamelCase											=  DDPMPipeline(unet=__SCREAMING_SNAKE_CASE	,    scheduler=__SCREAMING_SNAKE_CASE			)
          ddpm.to(__SCREAMING_SNAKE_CASE			)
          ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

          __UpperCamelCase											=  torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(0			)
          __UpperCamelCase											=  ddpm(generator=__SCREAMING_SNAKE_CASE	,    num_inference_steps=2	,    output_type='''numpy'''			).images

          __UpperCamelCase											=  torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(0			)
          __UpperCamelCase											=  ddpm(generator=__SCREAMING_SNAKE_CASE	,    num_inference_steps=2	,    output_type='''numpy'''	,    return_dict=__SCREAMING_SNAKE_CASE			)[0]

          __UpperCamelCase											=  image[0, -3:, -3:, -1]
          __UpperCamelCase											=  image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCamelCase											=  np.array(
              [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]			)

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







      def  snake_case		(       self   :	str			):
          __UpperCamelCase											=  self.dummy_uncond_unet
          __UpperCamelCase											=  DDPMScheduler(prediction_type='''sample'''			)

          __UpperCamelCase											=  DDPMPipeline(unet=__SCREAMING_SNAKE_CASE	,    scheduler=__SCREAMING_SNAKE_CASE			)
          ddpm.to(__SCREAMING_SNAKE_CASE			)
          ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

          __UpperCamelCase											=  torch.manual_seed(0			)
          __UpperCamelCase											=  ddpm(generator=__SCREAMING_SNAKE_CASE	,    num_inference_steps=2	,    output_type='''numpy'''			).images

          __UpperCamelCase											=  torch.manual_seed(0			)
          __UpperCamelCase											=  ddpm(generator=__SCREAMING_SNAKE_CASE	,    num_inference_steps=2	,    output_type='''numpy'''			)[0]

          __UpperCamelCase											=  image[0, -3:, -3:, -1]
          __UpperCamelCase											=  image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCamelCase											=  1E-2 if torch_device != '''mps''' else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance







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

      """simple docstring"""

      def  snake_case		(       self   :	Optional[int]			):
          __UpperCamelCase											=  '''google/ddpm-cifar10-32'''

          __UpperCamelCase											=  UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
          __UpperCamelCase											=  DDPMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE			)

          __UpperCamelCase											=  DDPMPipeline(unet=__SCREAMING_SNAKE_CASE	,    scheduler=__SCREAMING_SNAKE_CASE			)
          ddpm.to(__SCREAMING_SNAKE_CASE			)
          ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

          __UpperCamelCase											=  torch.manual_seed(0			)
          __UpperCamelCase											=  ddpm(generator=__SCREAMING_SNAKE_CASE	,    output_type='''numpy'''			).images

          __UpperCamelCase											=  image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCamelCase											=  np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]			)
          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2












def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		list:

    """simple docstring"""


    __UpperCamelCase											=  len(lowercase_ )

    # Set the gap value to be decreased by a factor of 1.3
    # after each iteration
    __UpperCamelCase											=  1.3

    # Continue sorting until the gap is 1
    while gap > 1:
        # Decrease the gap value
        __UpperCamelCase											=  int(gap / shrink )

        # Sort the elements using insertion sort
        for i in range(lowercase_   ,       len(lowercase_ ) ):
            __UpperCamelCase											=  collection[i]
            __UpperCamelCase											=  i
            while j >= gap and collection[j - gap] > temp:
                __UpperCamelCase											=  collection[j - gap]
                j -= gap
            __UpperCamelCase											=  temp

    return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()






