



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class    _A	(     unittest.TestCase   ):



       @property
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    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 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    self.dummy_uncond_unet
           __a							=    PNDMScheduler()

           __a							=    PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE)
           pndm.to(__SCREAMING_SNAKE_CASE)
           pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    torch.manual_seed(0)
           __a							=    pndm(generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=20					,		output_type='''numpy''').images

           __a							=    torch.manual_seed(0)
           __a							=    pndm(generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=20					,		output_type='''numpy'''					,		return_dict=__SCREAMING_SNAKE_CASE)[0]

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

           assert image.shape == (1, 32, 32, 3)
           __a							=    np.array([1.0, 1.0, 0.0, 1.0, 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    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    """google/ddpm-cifar10-32"""

           __a							=    UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE)
           __a							=    PNDMScheduler()

           __a							=    PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE)
           pndm.to(__SCREAMING_SNAKE_CASE)
           pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
           __a							=    torch.manual_seed(0)
           __a							=    pndm(generator=__SCREAMING_SNAKE_CASE					,		output_type='''numpy''').images

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

           assert image.shape == (1, 32, 32, 3)
           __a							=    np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25])

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










def 					__snake_case     (      _UpperCAmelCase    ):
    if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ):
        raise TypeError('''Input value must be an \'int\' type'''    )

    __a							=    0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
 import doctest

 doctest.testmod()





