





'''simple docstring'''







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     UpperCAmelCase__	(							self							:     List[Any]   )					->       Optional[Any]:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      int     						=  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     UpperCAmelCase__	(							self							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  self.dummy_uncond_unet
													UpperCAmelCase_			:      Union[str, Any]     						=  PNDMScheduler()

													UpperCAmelCase_			:      Optional[int]     						=  PNDMPipeline(unet=lowerCamelCase_       ,     scheduler=lowerCamelCase_   )
													pndm.to(lowerCamelCase_   )
													pndm.set_progress_bar_config(disable=lowerCamelCase_   )

													UpperCAmelCase_			:      Union[str, Any]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Dict     						=  pndm(generator=lowerCamelCase_       ,     num_inference_steps=20       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      List[Any]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Union[str, Any]     						=  pndm(generator=lowerCamelCase_       ,     num_inference_steps=20       ,     output_type='''numpy'''       ,     return_dict=lowerCamelCase_   )[0]

													UpperCAmelCase_			:      str     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      int     						=  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_			:      Optional[int]     						=  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     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  '''google/ddpm-cifar10-32'''

													UpperCAmelCase_			:      Optional[int]     						=  UNetaDModel.from_pretrained(lowerCamelCase_   )
													UpperCAmelCase_			:      Tuple     						=  PNDMScheduler()

													UpperCAmelCase_			:      str     						=  PNDMPipeline(unet=lowerCamelCase_       ,     scheduler=lowerCamelCase_   )
													pndm.to(lowerCamelCase_   )
													pndm.set_progress_bar_config(disable=lowerCamelCase_   )
													UpperCAmelCase_			:      Optional[Any]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Tuple     						=  pndm(generator=lowerCamelCase_       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Optional[Any]     						=  image[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_			:      Any     						=  np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]   )

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












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int = 10,			SCREAMING_SNAKE_CASE__      :			int = 22   ) ->	int:
						UpperCAmelCase_			:      Optional[int]     						=  range(1,			SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      List[Any]     						=  range(1,			SCREAMING_SNAKE_CASE__   )
						return sum(
						    1 for power in powers for base in bases if len(str(base**power   )   ) == power   )


if __name__ == "__main__":
					print(f'''{solution(10, 22) = }''')





