


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 					lowerCAmelCase_    (       unittest.TestCase):




						def  _snake_case					(	self						:   Any						)      ->Dict:






									"""simple docstring"""






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




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Optional[Any]:






									"""simple docstring"""






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

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




						@property
						def  _snake_case					(	self						:   Optional[Any]						)      ->Tuple:






									"""simple docstring"""






									torch.manual_seed(0						)
									a__ :List[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=lowercase__   ,     only_cross_attention=(True, True, False)   ,     num_class_embeds=100   ,     )
									return model




						@property
						def  _snake_case					(	self						:   Tuple						)      ->List[str]:






									"""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  _snake_case					(	self						:   int						)      ->List[str]:






									"""simple docstring"""






									torch.manual_seed(0						)
									a__ :Union[str, Any]						  =    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(lowercase__						)




						def  _snake_case					(	self						:   List[Any]						)      ->Any:






									"""simple docstring"""






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

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

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

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

									a__ :Dict						  =    output.images

									a__ :Optional[int]						  =    torch.Generator(device=lowercase__						).manual_seed(0						)
									a__ :List[str]						  =    sd_pipe(
									    [prompt]   ,     image=lowercase__   ,     generator=lowercase__   ,     guidance_scale=6.0   ,     noise_level=20   ,     num_inference_steps=2   ,     output_type="np"   ,     return_dict=lowercase__   ,     )[0]

									a__ :Optional[Any]						  =    image[0, -3:, -3:, -1]
									a__ :Union[str, Any]						  =    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__ :int						  =    np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]						)

									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						:   List[Any]						)      ->str:






									"""simple docstring"""






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

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

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

									a__ :str						  =    '''A painting of a squirrel eating a burger'''
									a__ :Optional[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__ :Any						  =    output.images
									assert image.shape[0] == 2

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






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






									"""simple docstring"""






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

									a__ :Dict						  =    self.dummy_image.cpu().permute(0   ,     2   ,     3   ,     1						)[0]
									a__ :Dict						  =    Image.fromarray(np.uinta(lowercase__						)						).convert("RGB"						).resize((64, 64)						)

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

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

									a__ :List[Any]						  =    '''A painting of a squirrel eating a burger'''
									a__ :int						  =    torch.manual_seed(0						)
									a__ :Tuple						  =    sd_pipe(
									    [prompt]   ,     image=lowercase__   ,     generator=lowercase__   ,     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 					lowerCAmelCase_    (       unittest.TestCase):




						def  _snake_case					(	self						:   Optional[Any]						)      ->Any:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Any						)      ->List[str]:






									"""simple docstring"""






									a__ :Optional[Any]						  =    load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-upscale/low_res_cat.png"						)
									a__ :Optional[Any]						  =    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__ :Tuple						  =    StableDiffusionUpscalePipeline.from_pretrained(lowercase__						)
									pipe.to(lowercase__						)
									pipe.set_progress_bar_config(disable=lowercase__						)
									pipe.enable_attention_slicing()

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

									a__ :Any						  =    torch.manual_seed(0						)
									a__ :Tuple						  =    pipe(
									    prompt=lowercase__   ,     image=lowercase__   ,     generator=lowercase__   ,     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						:   List[Any]						)      ->str:






									"""simple docstring"""






									a__ :str						  =    load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-upscale/low_res_cat.png"						)
									a__ :Optional[Any]						  =    load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
									    "/upsampled_cat_fp16.npy"						)

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

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

									a__ :List[str]						  =    torch.manual_seed(0						)
									a__ :str						  =    pipe(
									    prompt=lowercase__   ,     image=lowercase__   ,     generator=lowercase__   ,     output_type="np"   ,     )
									a__ :Optional[int]						  =    output.images[0]

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






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






									"""simple docstring"""






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

									a__ :List[Any]						  =    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__ :Union[str, Any]						  =    StableDiffusionUpscalePipeline.from_pretrained(
									    lowercase__   ,     torch_dtype=torch.floataa   ,     )
									pipe.to(lowercase__						)
									pipe.set_progress_bar_config(disable=lowercase__						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

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

									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :List[str]						  =    pipe(
									    prompt=lowercase__   ,     image=lowercase__   ,     generator=lowercase__   ,     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






def 				lowerCamelCase__	(      a	:   int = 100				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    n * (n + 1) * (2 * n + 1) / 6
			a__ :Tuple						  =    (n * (n + 1) / 2) ** 2
			return int(square_of_sum - sum_of_squares				)


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


