





'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	int:
						return 1 if digit in (0, 1) else (digit * factorial(digit - 1   ))





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict   ) ->	bool:
						UpperCAmelCase_			:      Optional[Any]     						=  0
						UpperCAmelCase_			:      Dict     						=  number
						while duplicate > 0:
												UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  divmod(a__,			10   )
												fact_sum += factorial(a__   )
						return fact_sum == number


if __name__ == "__main__":
					print("Program to check whether a number is a Krisnamurthy Number or not.")
					snake_case_     :       Dict						=  int(input("Enter number: ").strip())
					print(
					    f'''{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number.'''
					)












'''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





