
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  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	@property
	def   __snake_case				(			self		):
					A__  :	int         =							1
					A__  :	List[Any]         =							3
					A__  :	Dict         =							(32, 32)

					A__  :	List[Any]         =							floats_tensor((batch_size, num_channels) + sizes				,       rng=random.Random(0		)		).to(UpperCamelCase__		)
					return image





	@property
	def   __snake_case				(			self		):
					torch.manual_seed(0		)
					A__  :	Optional[int]         =							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=UpperCamelCase__				,       only_cross_attention=(True, True, False)				,       num_class_embeds=100				,       )
					return model





	@property
	def   __snake_case				(			self		):
					torch.manual_seed(0		)
					A__  :	int         =							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   __snake_case				(			self		):
					torch.manual_seed(0		)
					A__  :	Dict         =							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(UpperCamelCase__		)





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

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

					# make sure here that pndm scheduler skips prk
					A__  :	Dict         =							StableDiffusionUpscalePipeline(
					    unet=UpperCamelCase__				,       low_res_scheduler=UpperCamelCase__				,       scheduler=UpperCamelCase__				,       vae=UpperCamelCase__				,       text_encoder=UpperCamelCase__				,       tokenizer=UpperCamelCase__				,       max_noise_level=350				,       )
					A__  :	List[Any]         =							sd_pipe.to(UpperCamelCase__		)
					sd_pipe.set_progress_bar_config(disable=UpperCamelCase__		)

					A__  :	Dict         =							'''A painting of a squirrel eating a burger'''
					A__  :	int         =							torch.Generator(device=UpperCamelCase__		).manual_seed(0		)
					A__  :	Optional[int]         =							sd_pipe(
					    [prompt]				,       image=UpperCamelCase__				,       generator=UpperCamelCase__				,       guidance_scale=6.0				,       noise_level=20				,       num_inference_steps=2				,       output_type='''np'''				,       )

					A__  :	str         =							output.images

					A__  :	Optional[Any]         =							torch.Generator(device=UpperCamelCase__		).manual_seed(0		)
					A__  :	Optional[int]         =							sd_pipe(
					    [prompt]				,       image=UpperCamelCase__				,       generator=UpperCamelCase__				,       guidance_scale=6.0				,       noise_level=20				,       num_inference_steps=2				,       output_type='''np'''				,       return_dict=UpperCamelCase__				,       )[0]

					A__  :	Union[str, Any]         =							image[0, -3:, -3:, -1]
					A__  :	Tuple         =							image_from_tuple[0, -3:, -3:, -1]

					A__  :	str         =							low_res_image.size[0] * 4
					assert image.shape == (1, expected_height_width, expected_height_width, 3)
					A__  :	int         =							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   __snake_case				(			self		):
					A__  :	Optional[Any]         =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					A__  :	Union[str, Any]         =							self.dummy_cond_unet_upscale
					A__  :	Union[str, Any]         =							DDPMScheduler()
					A__  :	Optional[int]         =							DDIMScheduler(prediction_type='''v_prediction'''		)
					A__  :	List[str]         =							self.dummy_vae
					A__  :	Any         =							self.dummy_text_encoder
					A__  :	List[str]         =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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

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

					A__  :	str         =							'''A painting of a squirrel eating a burger'''
					A__  :	Union[str, 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__  :	List[str]         =							output.images
					assert image.shape[0] == 2

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





	@unittest.skipIf(torch_device != '''cuda'''				,       '''This test requires a GPU'''		)
	def   __snake_case				(			self		):
					A__  :	List[str]         =							self.dummy_cond_unet_upscale
					A__  :	Union[str, Any]         =							DDPMScheduler()
					A__  :	Tuple         =							DDIMScheduler(prediction_type='''v_prediction'''		)
					A__  :	Optional[Any]         =							self.dummy_vae
					A__  :	Optional[int]         =							self.dummy_text_encoder
					A__  :	List[str]         =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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

					# put models in fp16, except vae as it overflows in fp16
					A__  :	int         =							unet.half()
					A__  :	str         =							text_encoder.half()

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

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

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







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







	'''simple docstring'''





	def   __snake_case				(			self		):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	def   __snake_case				(			self		):
					A__  :	int         =							load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-upscale/low_res_cat.png'''		)
					A__  :	int         =							load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
					    '''/upsampled_cat.npy'''		)

					A__  :	Union[str, Any]         =							'''stabilityai/stable-diffusion-x4-upscaler'''
					A__  :	Any         =							StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase__		)
					pipe.to(UpperCamelCase__		)
					pipe.set_progress_bar_config(disable=UpperCamelCase__		)
					pipe.enable_attention_slicing()

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

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

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





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-upscale/low_res_cat.png'''		)
					A__  :	Tuple         =							load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
					    '''/upsampled_cat_fp16.npy'''		)

					A__  :	Tuple         =							'''stabilityai/stable-diffusion-x4-upscaler'''
					A__  :	str         =							StableDiffusionUpscalePipeline.from_pretrained(
					    UpperCamelCase__				,       torch_dtype=torch.floataa				,       )
					pipe.to(UpperCamelCase__		)
					pipe.set_progress_bar_config(disable=UpperCamelCase__		)
					pipe.enable_attention_slicing()

					A__  :	List[Any]         =							'''a cat sitting on a park bench'''

					A__  :	int         =							torch.manual_seed(0		)
					A__  :	Dict         =							pipe(
					    prompt=UpperCamelCase__				,       image=UpperCamelCase__				,       generator=UpperCamelCase__				,       output_type='''np'''				,       )
					A__  :	List[str]         =							output.images[0]

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





	def   __snake_case				(			self		):
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

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

					A__  :	Optional[int]         =							'''stabilityai/stable-diffusion-x4-upscaler'''
					A__  :	Dict         =							StableDiffusionUpscalePipeline.from_pretrained(
					    UpperCamelCase__				,       torch_dtype=torch.floataa				,       )
					pipe.to(UpperCamelCase__		)
					pipe.set_progress_bar_config(disable=UpperCamelCase__		)
					pipe.enable_attention_slicing(1		)
					pipe.enable_sequential_cpu_offload()

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

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

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



from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


if __name__ == "__main__":
					# Load image
					with Image.open('image_data/lena.jpg') as img:
										# Change brightness to 100
										_SCREAMING_SNAKE_CASE    :      Dict          =			change_brightness(img, 1_0_0)
										brigt_img.save('image_data/lena_brightness.png', format='png')