

'''simple docstring'''





import gc
import tempfile
import unittest

import numpy as np
import torch

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


UpperCamelCase_    						=       False





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):
				pass





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







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		,					torch_dtype=torch.floataa   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

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

										lowercase :							Dict    		=torch.manual_seed(0   )
										lowercase :							Union[str, Any]    		=pipe.dual_guided(
										    prompt='''first prompt'''		,					image=UpperCAmelCase__		,					text_to_image_strength=0.75		,					generator=UpperCAmelCase__		,					guidance_scale=7.5		,					num_inference_steps=2		,					output_type='''numpy'''		,					).images

										with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(UpperCAmelCase__   )
																lowercase :							Union[str, Any]    		=VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase__		,					torch_dtype=torch.floataa   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							List[Any]    		=generator.manual_seed(0   )
										lowercase :							Optional[Any]    		=pipe.dual_guided(
										    prompt='''first prompt'''		,					image=UpperCAmelCase__		,					text_to_image_strength=0.75		,					generator=UpperCAmelCase__		,					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  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Any    		=VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		,					torch_dtype=torch.floataa   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							int    		='''cyberpunk 2077'''
										lowercase :							int    		=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''   )
										lowercase :							Union[str, Any]    		=torch.manual_seed(0   )
										lowercase :							Optional[Any]    		=pipe.dual_guided(
										    prompt=UpperCAmelCase__		,					image=UpperCAmelCase__		,					text_to_image_strength=0.75		,					generator=UpperCAmelCase__		,					guidance_scale=7.5		,					num_inference_steps=50		,					output_type='''numpy'''		,					).images

										lowercase :							List[Any]    		=image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										lowercase :							int    		=np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]   )

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

										lowercase :							List[str]    		='''A painting of a squirrel eating a burger '''
										lowercase :							str    		=torch.manual_seed(0   )
										lowercase :							Any    		=pipe.text_to_image(
										    prompt=UpperCAmelCase__		,					generator=UpperCAmelCase__		,					guidance_scale=7.5		,					num_inference_steps=50		,					output_type='''numpy'''   ).images

										lowercase :							str    		=image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										lowercase :							Optional[Any]    		=np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]   )

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

										lowercase :							Dict    		=pipe.image_variation(UpperCAmelCase__		,					generator=UpperCAmelCase__		,					output_type='''numpy'''   ).images

										lowercase :							int    		=image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										lowercase :							Optional[int]    		=np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]   )

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








'''simple docstring'''





def 				_lowerCAmelCase			(   __magic_name__						:     int = 600851475143						)				->  int:

						try:
												lowercase :							Any    		=int(__magic_name__						)
						except (TypeError, ValueError):
												raise TypeError('''Parameter n must be int or castable to int.'''						)
						if n <= 0:
												raise ValueError('''Parameter n must be greater than or equal to one.'''						)
						lowercase :							Optional[Any]    		=2
						lowercase :							Dict    		=0
						if n == 2:
												return 2
						while n > 2:
												while n % i != 0:
																		i += 1
												lowercase :							Union[str, Any]    		=i
												while n % i == 0:
																		lowercase :							Optional[int]    		=n // i
												i += 1
						return int(__magic_name__						)


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





