


'''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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     1
														lowerCAmelCase__	       =     3
														lowerCAmelCase__	       =     (32, 32)

														lowerCAmelCase__	       =     floats_tensor((batch_size, num_channels) + sizes ,   rng=random.Random(0       )       ).to(_A       )
														return image







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														torch.manual_seed(0       )
														lowerCAmelCase__	       =     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=1_00 ,   )
														return model







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														torch.manual_seed(0       )
														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														torch.manual_seed(0       )
														lowerCAmelCase__	       =     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=10_00 ,   hidden_act='''gelu''' ,   projection_dim=5_12 ,   )
														return CLIPTextModel(_A       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     'cpu'  # ensure determinism for the device-dependent torch.Generator
														lowerCAmelCase__	       =     self.dummy_cond_unet_upscale
														lowerCAmelCase__	       =     DDPMScheduler()
														lowerCAmelCase__	       =     DDIMScheduler(prediction_type='''v_prediction'''       )
														lowerCAmelCase__	       =     self.dummy_vae
														lowerCAmelCase__	       =     self.dummy_text_encoder
														lowerCAmelCase__	       =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

														lowerCAmelCase__	       =     self.dummy_image.cpu().permute(0 ,   2 ,   3 ,   1       )[0]
														lowerCAmelCase__	       =     Image.fromarray(np.uinta(_A       )       ).convert('''RGB'''       ).resize((64, 64)       )

														# make sure here that pndm scheduler skips prk
														lowerCAmelCase__	       =     StableDiffusionUpscalePipeline(
														    unet=_A ,   low_res_scheduler=_A ,   scheduler=_A ,   vae=_A ,   text_encoder=_A ,   tokenizer=_A ,   max_noise_level=3_50 ,   )
														lowerCAmelCase__	       =     sd_pipe.to(_A       )
														sd_pipe.set_progress_bar_config(disable=_A       )

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

														lowerCAmelCase__	       =     output.images

														lowerCAmelCase__	       =     torch.Generator(device=_A       ).manual_seed(0       )
														lowerCAmelCase__	       =     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__	       =     image[0, -3:, -3:, -1]
														lowerCAmelCase__	       =     image_from_tuple[0, -3:, -3:, -1]

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

														lowerCAmelCase__	       =     self.dummy_image.cpu().permute(0 ,   2 ,   3 ,   1       )[0]
														lowerCAmelCase__	       =     Image.fromarray(np.uinta(_A       )       ).convert('''RGB'''       ).resize((64, 64)       )

														# make sure here that pndm scheduler skips prk
														lowerCAmelCase__	       =     StableDiffusionUpscalePipeline(
														    unet=_A ,   low_res_scheduler=_A ,   scheduler=_A ,   vae=_A ,   text_encoder=_A ,   tokenizer=_A ,   max_noise_level=3_50 ,   )
														lowerCAmelCase__	       =     sd_pipe.to(_A       )
														sd_pipe.set_progress_bar_config(disable=_A       )

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

														lowerCAmelCase__	       =     torch.Generator(device=_A       ).manual_seed(0       )
														lowerCAmelCase__	       =     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__	       =     output.images
														assert image.shape[0] == 2







							@unittest.skipIf(torch_device != '''cuda''' ,   '''This test requires a GPU'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     self.dummy_cond_unet_upscale
														lowerCAmelCase__	       =     DDPMScheduler()
														lowerCAmelCase__	       =     DDIMScheduler(prediction_type='''v_prediction'''       )
														lowerCAmelCase__	       =     self.dummy_vae
														lowerCAmelCase__	       =     self.dummy_text_encoder
														lowerCAmelCase__	       =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

														lowerCAmelCase__	       =     self.dummy_image.cpu().permute(0 ,   2 ,   3 ,   1       )[0]
														lowerCAmelCase__	       =     Image.fromarray(np.uinta(_A       )       ).convert('''RGB'''       ).resize((64, 64)       )

														# put models in fp16, except vae as it overflows in fp16
														lowerCAmelCase__	       =     unet.half()
														lowerCAmelCase__	       =     text_encoder.half()

														# make sure here that pndm scheduler skips prk
														lowerCAmelCase__	       =     StableDiffusionUpscalePipeline(
														    unet=_A ,   low_res_scheduler=_A ,   scheduler=_A ,   vae=_A ,   text_encoder=_A ,   tokenizer=_A ,   max_noise_level=3_50 ,   )
														lowerCAmelCase__	       =     sd_pipe.to(_A       )
														sd_pipe.set_progress_bar_config(disable=_A       )

														lowerCAmelCase__	       =     'A painting of a squirrel eating a burger'
														lowerCAmelCase__	       =     torch.manual_seed(0       )
														lowerCAmelCase__	       =     sd_pipe(
														    [prompt] ,   image=_A ,   generator=_A ,   num_inference_steps=2 ,   output_type='''np''' ,   ).images

														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







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

														lowerCAmelCase__	       =     'stabilityai/stable-diffusion-x4-upscaler'
														lowerCAmelCase__	       =     StableDiffusionUpscalePipeline.from_pretrained(_A       )
														pipe.to(_A       )
														pipe.set_progress_bar_config(disable=_A       )
														pipe.enable_attention_slicing()

														lowerCAmelCase__	       =     'a cat sitting on a park bench'

														lowerCAmelCase__	       =     torch.manual_seed(0       )
														lowerCAmelCase__	       =     pipe(
														    prompt=_A ,   image=_A ,   generator=_A ,   output_type='''np''' ,   )
														lowerCAmelCase__	       =     output.images[0]

														assert image.shape == (5_12, 5_12, 3)
														assert np.abs(expected_image - image       ).max() < 1e-3







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/sd2-upscale/low_res_cat.png'''       )
														lowerCAmelCase__	       =     load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
														    '''/upsampled_cat_fp16.npy'''       )

														lowerCAmelCase__	       =     'stabilityai/stable-diffusion-x4-upscaler'
														lowerCAmelCase__	       =     StableDiffusionUpscalePipeline.from_pretrained(
														    _A ,   torch_dtype=torch.floataa ,   )
														pipe.to(_A       )
														pipe.set_progress_bar_config(disable=_A       )
														pipe.enable_attention_slicing()

														lowerCAmelCase__	       =     'a cat sitting on a park bench'

														lowerCAmelCase__	       =     torch.manual_seed(0       )
														lowerCAmelCase__	       =     pipe(
														    prompt=_A ,   image=_A ,   generator=_A ,   output_type='''np''' ,   )
														lowerCAmelCase__	       =     output.images[0]

														assert image.shape == (5_12, 5_12, 3)
														assert np.abs(expected_image - image       ).max() < 5e-1







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

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

														lowerCAmelCase__	       =     'stabilityai/stable-diffusion-x4-upscaler'
														lowerCAmelCase__	       =     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__	       =     'a cat sitting on a park bench'

														lowerCAmelCase__	       =     torch.manual_seed(0       )
														lowerCAmelCase__	       =     pipe(
														    prompt=_A ,   image=_A ,   generator=_A ,   num_inference_steps=5 ,   output_type='''np''' ,   )

														lowerCAmelCase__	       =     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  _snake_case       (					A = 10			,   A = 22	)						->   int:
							lowerCAmelCase__	       =     range(1			,   A	)
							lowerCAmelCase__	       =     range(1			,   A	)
							return sum(
							    1 for power in powers for base in bases if len(str(base**power	)	) == power	)


if __name__ == "__main__":
		print(f"""{solution(10, 22) = }""")