





'''simple docstring'''







import math





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	str:
						UpperCAmelCase_			:      Dict     						=  0
						UpperCAmelCase_			:      Union[str, Any]     						=  0
						while num > 0:
												UpperCAmelCase_			:      Any     						=  num % 8
												UpperCAmelCase_			:      Tuple     						=  octal + (remainder * math.floor(math.pow(10,			SCREAMING_SNAKE_CASE__   )   ))
												counter += 1
												UpperCAmelCase_			:      Any     						=  math.floor(num / 8   )  # basically /= 8 without remainder if any
												# This formatting removes trailing '.0' from `octal`.
						return F"""0o{int(SCREAMING_SNAKE_CASE__   )}"""





def        lowerCamelCase_      (  ) ->	None:
						print('''\n2 in octal is:'''   )
						print(decimal_to_octal(2   )   )  # = 2
						print('''\n8 in octal is:'''   )
						print(decimal_to_octal(8   )   )  # = 10
						print('''\n65 in octal is:'''   )
						print(decimal_to_octal(65   )   )  # = 101
						print('''\n216 in octal is:'''   )
						print(decimal_to_octal(216   )   )  # = 330
						print('''\n512 in octal is:'''   )
						print(decimal_to_octal(512   )   )  # = 1000
						print('''\n'''   )


if __name__ == "__main__":
					main()












'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, 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							:     Dict   )					->       str:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      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     UpperCAmelCase__	(							self							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.dummy_uncond_unet
													UpperCAmelCase_			:      Dict     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Union[str, Any]     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[int]     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      str     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''       ,     return_dict=__magic_name__   )[0]

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

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_			:      Dict     						=  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 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  '''google/ncsnpp-celebahq-256'''
													UpperCAmelCase_			:      List[str]     						=  UNetaDModel.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Any     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe(num_inference_steps=20       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Any     						=  image[0, -3:, -3:, -1]
													assert image.shape == (1, 2_56, 2_56, 3)
													UpperCAmelCase_			:      Optional[Any]     						=  np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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





