




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class   a__			(       unittest.TestCase  ):





						def       __UpperCamelCase     (							self      :	Tuple)						->       Tuple:




											"""simple docstring"""







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





						@property
						def       __UpperCamelCase     (							self      :	str)						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=1
											_lowerCAmelCase:Optional[int]									=3
											_lowerCAmelCase:int									=(32, 32)

											_lowerCAmelCase:int									=floats_tensor((batch_size, num_channels) + sizes     ,rng=random.Random(0)).to(a__)
											return image





						@property
						def       __UpperCamelCase     (							self      :	Any)						->       Any:




											"""simple docstring"""







											torch.manual_seed(0)
											_lowerCAmelCase:str									=UNetaDConditionModel(
											    block_out_channels=(32, 32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=7     ,out_channels=4     ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')     ,up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,cross_attention_dim=32     ,attention_head_dim=8     ,use_linear_projection=a__     ,only_cross_attention=(True, True, False)     ,num_class_embeds=100     ,)
											return model





						@property
						def       __UpperCamelCase     (							self      :	int)						->       Any:




											"""simple docstring"""







											torch.manual_seed(0)
											_lowerCAmelCase:Optional[Any]									=AutoencoderKL(
											    block_out_channels=[32, 32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,latent_channels=4     ,)
											return model





						@property
						def       __UpperCamelCase     (							self      :	List[Any])						->       Optional[Any]:




											"""simple docstring"""







											torch.manual_seed(0)
											_lowerCAmelCase:str									=CLIPTextConfig(
											    bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1000     ,hidden_act='''gelu'''     ,projection_dim=512     ,)
											return CLIPTextModel(a__)





						def       __UpperCamelCase     (							self      :	int)						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									='''cpu'''  # ensure determinism for the device-dependent torch.Generator
											_lowerCAmelCase:Union[str, Any]									=self.dummy_cond_unet_upscale
											_lowerCAmelCase:int									=DDPMScheduler()
											_lowerCAmelCase:Tuple									=DDIMScheduler(prediction_type='''v_prediction''')
											_lowerCAmelCase:Union[str, Any]									=self.dummy_vae
											_lowerCAmelCase:List[str]									=self.dummy_text_encoder
											_lowerCAmelCase:List[Any]									=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

											_lowerCAmelCase:List[str]									=self.dummy_image.cpu().permute(0     ,2     ,3     ,1)[0]
											_lowerCAmelCase:str									=Image.fromarray(np.uinta(a__)).convert('''RGB''').resize((64, 64))

											# make sure here that pndm scheduler skips prk
											_lowerCAmelCase:Tuple									=StableDiffusionUpscalePipeline(
											    unet=a__     ,low_res_scheduler=a__     ,scheduler=a__     ,vae=a__     ,text_encoder=a__     ,tokenizer=a__     ,max_noise_level=350     ,)
											_lowerCAmelCase:List[str]									=sd_pipe.to(a__)
											sd_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:Union[str, Any]									='''A painting of a squirrel eating a burger'''
											_lowerCAmelCase:Union[str, Any]									=torch.Generator(device=a__).manual_seed(0)
											_lowerCAmelCase:str									=sd_pipe(
											    [prompt]     ,image=a__     ,generator=a__     ,guidance_scale=6.0     ,noise_level=20     ,num_inference_steps=2     ,output_type='''np'''     ,)

											_lowerCAmelCase:Optional[int]									=output.images

											_lowerCAmelCase:Union[str, Any]									=torch.Generator(device=a__).manual_seed(0)
											_lowerCAmelCase:List[str]									=sd_pipe(
											    [prompt]     ,image=a__     ,generator=a__     ,guidance_scale=6.0     ,noise_level=20     ,num_inference_steps=2     ,output_type='''np'''     ,return_dict=a__     ,)[0]

											_lowerCAmelCase:int									=image[0, -3:, -3:, -1]
											_lowerCAmelCase:Tuple									=image_from_tuple[0, -3:, -3:, -1]

											_lowerCAmelCase:List[Any]									=low_res_image.size[0] * 4
											assert image.shape == (1, expected_height_width, expected_height_width, 3)
											_lowerCAmelCase:Any									=np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

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





						def       __UpperCamelCase     (							self      :	Any)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Dict									='''cpu'''  # ensure determinism for the device-dependent torch.Generator
											_lowerCAmelCase:str									=self.dummy_cond_unet_upscale
											_lowerCAmelCase:Optional[Any]									=DDPMScheduler()
											_lowerCAmelCase:List[str]									=DDIMScheduler(prediction_type='''v_prediction''')
											_lowerCAmelCase:Optional[Any]									=self.dummy_vae
											_lowerCAmelCase:Union[str, Any]									=self.dummy_text_encoder
											_lowerCAmelCase:Optional[Any]									=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

											_lowerCAmelCase:Optional[Any]									=self.dummy_image.cpu().permute(0     ,2     ,3     ,1)[0]
											_lowerCAmelCase:Union[str, Any]									=Image.fromarray(np.uinta(a__)).convert('''RGB''').resize((64, 64))

											# make sure here that pndm scheduler skips prk
											_lowerCAmelCase:int									=StableDiffusionUpscalePipeline(
											    unet=a__     ,low_res_scheduler=a__     ,scheduler=a__     ,vae=a__     ,text_encoder=a__     ,tokenizer=a__     ,max_noise_level=350     ,)
											_lowerCAmelCase:int									=sd_pipe.to(a__)
											sd_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:int									='''A painting of a squirrel eating a burger'''
											_lowerCAmelCase:List[str]									=sd_pipe(
											    2 * [prompt]     ,image=2 * [low_res_image]     ,guidance_scale=6.0     ,noise_level=20     ,num_inference_steps=2     ,output_type='''np'''     ,)
											_lowerCAmelCase:Dict									=output.images
											assert image.shape[0] == 2

											_lowerCAmelCase:List[Any]									=torch.Generator(device=a__).manual_seed(0)
											_lowerCAmelCase:str									=sd_pipe(
											    [prompt]     ,image=a__     ,generator=a__     ,num_images_per_prompt=2     ,guidance_scale=6.0     ,noise_level=20     ,num_inference_steps=2     ,output_type='''np'''     ,)
											_lowerCAmelCase:Optional[Any]									=output.images
											assert image.shape[0] == 2







						@unittest.skipIf(torch_device != '''cuda'''     ,'''This test requires a GPU''')
						def       __UpperCamelCase     (							self      :	Tuple)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:str									=self.dummy_cond_unet_upscale
											_lowerCAmelCase:str									=DDPMScheduler()
											_lowerCAmelCase:str									=DDIMScheduler(prediction_type='''v_prediction''')
											_lowerCAmelCase:str									=self.dummy_vae
											_lowerCAmelCase:List[str]									=self.dummy_text_encoder
											_lowerCAmelCase:Tuple									=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

											_lowerCAmelCase:Optional[Any]									=self.dummy_image.cpu().permute(0     ,2     ,3     ,1)[0]
											_lowerCAmelCase:Optional[int]									=Image.fromarray(np.uinta(a__)).convert('''RGB''').resize((64, 64))

											# put models in fp16, except vae as it overflows in fp16
											_lowerCAmelCase:List[str]									=unet.half()
											_lowerCAmelCase:Dict									=text_encoder.half()

											# make sure here that pndm scheduler skips prk
											_lowerCAmelCase:Union[str, Any]									=StableDiffusionUpscalePipeline(
											    unet=a__     ,low_res_scheduler=a__     ,scheduler=a__     ,vae=a__     ,text_encoder=a__     ,tokenizer=a__     ,max_noise_level=350     ,)
											_lowerCAmelCase:Any									=sd_pipe.to(a__)
											sd_pipe.set_progress_bar_config(disable=a__)

											_lowerCAmelCase:List[Any]									='''A painting of a squirrel eating a burger'''
											_lowerCAmelCase:List[str]									=torch.manual_seed(0)
											_lowerCAmelCase:List[str]									=sd_pipe(
											    [prompt]     ,image=a__     ,generator=a__     ,num_inference_steps=2     ,output_type='''np'''     ,).images

											_lowerCAmelCase:Union[str, Any]									=low_res_image.size[0] * 4
											assert image.shape == (1, expected_height_width, expected_height_width, 3)




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





						def       __UpperCamelCase     (							self      :	str)						->       int:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Tuple)						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/sd2-upscale/low_res_cat.png''')
											_lowerCAmelCase:str									=load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
											    '''/upsampled_cat.npy''')

											_lowerCAmelCase:Optional[Any]									='''stabilityai/stable-diffusion-x4-upscaler'''
											_lowerCAmelCase:int									=StableDiffusionUpscalePipeline.from_pretrained(a__)
											pipe.to(a__)
											pipe.set_progress_bar_config(disable=a__)
											pipe.enable_attention_slicing()

											_lowerCAmelCase:str									='''a cat sitting on a park bench'''

											_lowerCAmelCase:Any									=torch.manual_seed(0)
											_lowerCAmelCase:Tuple									=pipe(
											    prompt=a__     ,image=a__     ,generator=a__     ,output_type='''np'''     ,)
											_lowerCAmelCase:Dict									=output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image).max() < 1E-3





						def       __UpperCamelCase     (							self      :	Tuple)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:int									=load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/sd2-upscale/low_res_cat.png''')
											_lowerCAmelCase:int									=load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
											    '''/upsampled_cat_fp16.npy''')

											_lowerCAmelCase:Any									='''stabilityai/stable-diffusion-x4-upscaler'''
											_lowerCAmelCase:int									=StableDiffusionUpscalePipeline.from_pretrained(
											    a__     ,torch_dtype=torch.floataa     ,)
											pipe.to(a__)
											pipe.set_progress_bar_config(disable=a__)
											pipe.enable_attention_slicing()

											_lowerCAmelCase:int									='''a cat sitting on a park bench'''

											_lowerCAmelCase:int									=torch.manual_seed(0)
											_lowerCAmelCase:int									=pipe(
											    prompt=a__     ,image=a__     ,generator=a__     ,output_type='''np'''     ,)
											_lowerCAmelCase:List[Any]									=output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image).max() < 5E-1







						def       __UpperCamelCase     (							self      :	str)						->       List[str]:




											"""simple docstring"""







											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											_lowerCAmelCase:Union[str, Any]									=load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/sd2-upscale/low_res_cat.png''')

											_lowerCAmelCase:Any									='''stabilityai/stable-diffusion-x4-upscaler'''
											_lowerCAmelCase:Tuple									=StableDiffusionUpscalePipeline.from_pretrained(
											    a__     ,torch_dtype=torch.floataa     ,)
											pipe.to(a__)
											pipe.set_progress_bar_config(disable=a__)
											pipe.enable_attention_slicing(1)
											pipe.enable_sequential_cpu_offload()

											_lowerCAmelCase:List[str]									='''a cat sitting on a park bench'''

											_lowerCAmelCase:Optional[Any]									=torch.manual_seed(0)
											_lowerCAmelCase:int									=pipe(
											    prompt=a__     ,image=a__     ,generator=a__     ,num_inference_steps=5     ,output_type='''np'''     ,)

											_lowerCAmelCase:Optional[Any]									=torch.cuda.max_memory_allocated()
											# make sure that less than 2.9 GB is allocated
											assert mem_bytes < 2.9 * 10**9












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return x if y == 0 else greatest_common_divisor(snake_case      ,       x % y    )





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return (x * y) // greatest_common_divisor(snake_case      ,       snake_case    )





def 							UpperCAmelCase		(		snake_case			:					int = 20    ):
					_lowerCAmelCase:List[Any]									=  1
					for i in range(1      ,       n + 1    ):
										_lowerCAmelCase:List[str]									=  lcm(snake_case      ,       snake_case    )
					return g


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






