




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCamelCase__						=		False


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

					def       lowercase_				(							self   ):







						'''simple docstring'''




						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'   )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__lowercase   )
						pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						Optional[Any]				  =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )

						lowerCamelCase_				:						int				  =				torch.manual_seed(0   )
						lowerCamelCase_				:						List[Any]				  =				pipe(
						    prompt='first prompt'					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			guidance_scale=7.5					,			num_inference_steps=2					,			output_type='numpy'					,			).images

						with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(__lowercase   )
							lowerCamelCase_				:						Dict				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained(__lowercase   )

						pipe.to(__lowercase   )
						pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						Optional[Any]				  =				generator.manual_seed(0   )
						lowerCamelCase_				:						List[Any]				  =				pipe(
						    prompt='first prompt'					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			guidance_scale=7.5					,			num_inference_steps=2					,			output_type='numpy'					,			).images

						assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'   )
						pipe.remove_unused_weights()
						pipe.to(__lowercase   )
						pipe.set_progress_bar_config(disable=__lowercase   )

						lowerCamelCase_				:						Union[str, Any]				  =				'cyberpunk 2077'
						lowerCamelCase_				:						Optional[Any]				  =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )
						lowerCamelCase_				:						str				  =				torch.manual_seed(0   )
						lowerCamelCase_				:						Dict				  =				pipe(
						    prompt=__lowercase					,			image=__lowercase					,			text_to_image_strength=0.75					,			generator=__lowercase					,			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[str]				  =				np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]   )

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






def    snake_case   (				A		,  A     )				->				int:




	'''simple docstring'''


	if a < b:
		return gcd(A		,  A     )
	if a % b == 0:
		return b
	return gcd(A		,  a % b     )






def    snake_case   (				A		,  A		,  A     )				->				int:




	'''simple docstring'''


	if y == 0:
		return 1
	lowerCamelCase_				:						int				  =				power(A		,  y // 2		,  A     ) % mod
	lowerCamelCase_				:						List[Any]				  =				(temp * temp) % mod
	if y % 2 == 1:
		lowerCamelCase_				:						str				  =				(temp * x) % mod
	return temp






def    snake_case   (				A     )				->				bool:




	'''simple docstring'''


	lowerCamelCase_				:						Union[str, Any]				  =				2
	while b < n:
		if gcd(A		,  A     ) == 1 and power(A		,  n - 1		,  A     ) != 1:
			return False
		b += 1
	return True


if __name__ == "__main__":
							UpperCamelCase__						=		int(input('Enter number: ').strip())
							if is_carmichael_number(number):
														print(F'{number} is a Carmichael Number.')
							else:
														print(F'{number} is not a Carmichael Number.')
