





"""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 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     1
     __A    :						Any						 =     3
     __A    :						List[str]						 =     (32, 32)

     __A    :						List[str]						 =     floats_tensor((batch_size, num_channels) + sizes					,						rng=random.Random(0  )  ).to(__lowerCamelCase  )
     return image

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						List[Any]						 =     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=__lowerCamelCase					,						only_cross_attention=(True, True, False)					,						num_class_embeds=100					,						)
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						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  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Tuple						 =     CLIPTextConfig(
         bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1e-0_5					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						hidden_act='''gelu'''					,						projection_dim=512					,						)
     return CLIPTextModel(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

     __A    :						Tuple						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						Any						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # make sure here that pndm scheduler skips prk
     __A    :						Dict						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						str						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

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

     __A    :						Union[str, Any]						 =     output.images

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

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

     __A    :						Dict						 =     low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     __A    :						str						 =     np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]  )

     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  ):



     '''simple docstring'''




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

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

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

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

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







 @unittest.skipIf(torch_device != '''cuda'''					,						'''This test requires a GPU'''  )
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

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

     # put models in fp16, except vae as it overflows in fp16
     __A    :						Union[str, Any]						 =     unet.half()
     __A    :						Optional[int]						 =     text_encoder.half()

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

     __A    :						Union[str, Any]						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Optional[Any]						 =     torch.manual_seed(0  )
     __A    :						Tuple						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=2					,						output_type='''np'''					,						).images

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






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





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						Dict						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat.npy'''  )

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

     __A    :						Union[str, Any]						 =     '''a cat sitting on a park bench'''

     __A    :						Union[str, Any]						 =     torch.manual_seed(0  )
     __A    :						Optional[Any]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						List[str]						 =     output.images[0]

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						List[Any]						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat_fp16.npy'''  )

     __A    :						Optional[int]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Optional[int]						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing()

     __A    :						Dict						 =     '''a cat sitting on a park bench'''

     __A    :						Any						 =     torch.manual_seed(0  )
     __A    :						Optional[int]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						Any						 =     output.images[0]

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







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

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

     __A    :						List[str]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Dict						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing(1  )
     pipe.enable_sequential_cpu_offload()

     __A    :						Tuple						 =     '''a cat sitting on a park bench'''

     __A    :						Tuple						 =     torch.manual_seed(0  )
     __A    :						List[str]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=5					,						output_type='''np'''					,						)

     __A    :						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"""



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 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     1
     __A    :						Any						 =     3
     __A    :						List[str]						 =     (32, 32)

     __A    :						List[str]						 =     floats_tensor((batch_size, num_channels) + sizes					,						rng=random.Random(0  )  ).to(__lowerCamelCase  )
     return image

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						List[Any]						 =     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=__lowerCamelCase					,						only_cross_attention=(True, True, False)					,						num_class_embeds=100					,						)
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						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  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Tuple						 =     CLIPTextConfig(
         bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1e-0_5					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						hidden_act='''gelu'''					,						projection_dim=512					,						)
     return CLIPTextModel(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

     __A    :						Tuple						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						Any						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # make sure here that pndm scheduler skips prk
     __A    :						Dict						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						str						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

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

     __A    :						Union[str, Any]						 =     output.images

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

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

     __A    :						Dict						 =     low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     __A    :						str						 =     np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]  )

     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  ):



     '''simple docstring'''




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

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

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

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

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







 @unittest.skipIf(torch_device != '''cuda'''					,						'''This test requires a GPU'''  )
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

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

     # put models in fp16, except vae as it overflows in fp16
     __A    :						Union[str, Any]						 =     unet.half()
     __A    :						Optional[int]						 =     text_encoder.half()

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

     __A    :						Union[str, Any]						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Optional[Any]						 =     torch.manual_seed(0  )
     __A    :						Tuple						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=2					,						output_type='''np'''					,						).images

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






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





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						Dict						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat.npy'''  )

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

     __A    :						Union[str, Any]						 =     '''a cat sitting on a park bench'''

     __A    :						Union[str, Any]						 =     torch.manual_seed(0  )
     __A    :						Optional[Any]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						List[str]						 =     output.images[0]

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						List[Any]						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat_fp16.npy'''  )

     __A    :						Optional[int]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Optional[int]						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing()

     __A    :						Dict						 =     '''a cat sitting on a park bench'''

     __A    :						Any						 =     torch.manual_seed(0  )
     __A    :						Optional[int]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						Any						 =     output.images[0]

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







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

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

     __A    :						List[str]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Dict						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing(1  )
     pipe.enable_sequential_cpu_offload()

     __A    :						Tuple						 =     '''a cat sitting on a park bench'''

     __A    :						Tuple						 =     torch.manual_seed(0  )
     __A    :						List[str]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=5					,						output_type='''np'''					,						)

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




