



"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]		     =int(__lowerCamelCase     )
			if n_element < 1:
						lowerCamelCase__		:		Optional[Any]		     =ValueError('''a should be a positive number'''     )
						raise my_error

			lowerCamelCase__		:		Tuple		     =[1]
			lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		int		     =(0, 0, 0)
			lowerCamelCase__		:		Union[str, Any]		     =1
			while index < n_element:
						while hamming_list[i] * 2 <= hamming_list[-1]:
									i += 1
						while hamming_list[j] * 3 <= hamming_list[-1]:
									j += 1
						while hamming_list[k] * 5 <= hamming_list[-1]:
									k += 1
						hamming_list.append(
						    min(hamming_list[i] * 2							,		hamming_list[j] * 3							,		hamming_list[k] * 5     )     )
						index += 1
			return hamming_list


if __name__ == "__main__":
		_lowercase			:			Union[str, Any]										=						input("Enter the last number (nth term) of the Hamming Number Series: ")
		print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
		_lowercase			:			Any										=						hamming(int(n))
		print("-----------------------------------------------------")
		print(f'The list with nth numbers is: {hamming_numbers}')
		print("-----------------------------------------------------")









"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowercase			:			Tuple										=						False

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					pass





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


					'''simple docstring'''





					def 		snake_case	(	self							:  List[Any]					)-> Dict:
								lowerCamelCase__		:		str		     =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
								pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		int		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''					)
								lowerCamelCase__		:		Dict		     =torch.manual_seed(0					)
								lowerCamelCase__		:		str		     =pipe(
								    image=lowerCamelCase,  generator=lowerCamelCase,  guidance_scale=7.5,  num_inference_steps=50,  output_type='''numpy''',  ).images

								lowerCamelCase__		:		Dict		     =image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__		:		List[Any]		     =np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]					)

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




