


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)







class  lowerCamelCase__  (			lowerCAmelCase):





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	)	->  List[Any]:
      super().__init__()

      self.register_modules(vae=UpperCAmelCase						,	text_encoder=UpperCAmelCase						,	tokenizer=UpperCAmelCase						,	unet=UpperCAmelCase						,	scheduler=UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase			)	->  int:
      with torch.no_grad():
       _lowercase				 =self.vae.encode(tfms.ToTensor()(UpperCAmelCase			).unsqueeze(0			).to(self.device			) * 2 - 1			)
       _lowercase				 =0.1_8215 * latent.latent_dist.sample()
      return latent





     def 	__A   (self						,	UpperCAmelCase			)	->  List[str]:
      _lowercase				 =(1 / 0.1_8215) * latent
      with torch.no_grad():
       _lowercase				 =self.vae.decode(UpperCAmelCase			).sample
      _lowercase				 =(img / 2 + 0.5).clamp(0						,	1			)
      _lowercase				 =img.detach().cpu().permute(0						,	2						,	3						,	1			).numpy()
      _lowercase				 =(img * 2_5_5).round().astype('''uint8'''			)
      return Image.fromarray(img[0]			)





     def 	__A   (self						,	UpperCAmelCase			)	->  Tuple:
      _lowercase				 =self.tokenizer(
          UpperCAmelCase						,	padding='''max_length'''						,	max_length=self.tokenizer.model_max_length						,	truncation=UpperCAmelCase						,	return_tensors='''pt'''						,	)

      _lowercase				 =self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

      _lowercase				 =self.tokenizer(
          ''''''						,	padding='''max_length'''						,	max_length=self.tokenizer.model_max_length						,	truncation=UpperCAmelCase						,	return_tensors='''pt'''						,	)

      _lowercase				 =self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

      return torch.cat([uncond_embedding, text_embedding]			)





     def __call__(self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase = 0.3						,	UpperCAmelCase = 0.6						,	UpperCAmelCase = 0.5						,	UpperCAmelCase = 4_2						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = 7.5						,	)	->  Image.Image:
      _lowercase				 =steps - int(kmin * steps			)
      _lowercase				 =steps - int(kmax * steps			)

      _lowercase				 =self.prep_text(UpperCAmelCase			)

      self.scheduler.set_timesteps(UpperCAmelCase			)

      _lowercase ,       _lowercase				 =img.size
      _lowercase				 =self.encode(UpperCAmelCase			)

      torch.manual_seed(UpperCAmelCase			)
      _lowercase				 =torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)						,	).to(self.device			)

      _lowercase				 =self.scheduler.add_noise(
          UpperCAmelCase						,	UpperCAmelCase						,	timesteps=self.scheduler.timesteps[tmax]						,	)

      _lowercase				 =torch.cat([latents] * 2			)

      _lowercase				 =self.scheduler.scale_model_input(UpperCAmelCase						,	self.scheduler.timesteps[tmax]			)

      with torch.no_grad():
       _lowercase				 =self.unet(
           UpperCAmelCase						,	self.scheduler.timesteps[tmax]						,	encoder_hidden_states=UpperCAmelCase						,	).sample

      _lowercase ,       _lowercase				 =pred.chunk(2			)
      _lowercase				 =pred_uncond + guidance_scale * (pred_text - pred_uncond)

      _lowercase				 =self.scheduler.step(UpperCAmelCase						,	self.scheduler.timesteps[tmax]						,	UpperCAmelCase			).prev_sample

      for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
       if i > tmax:
        if i < tmin:  # layout generation phase
         _lowercase				 =self.scheduler.add_noise(
             UpperCAmelCase						,	UpperCAmelCase						,	timesteps=UpperCAmelCase						,	)

         _lowercase				 =(mix_factor * latents) + (
             1 - mix_factor
         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
         _lowercase				 =torch.cat([input] * 2			)

        else:  # content generation phase
         _lowercase				 =torch.cat([latents] * 2			)

        _lowercase				 =self.scheduler.scale_model_input(UpperCAmelCase						,	UpperCAmelCase			)

        with torch.no_grad():
         _lowercase				 =self.unet(
             UpperCAmelCase						,	UpperCAmelCase						,	encoder_hidden_states=UpperCAmelCase						,	).sample

        _lowercase ,       _lowercase				 =pred.chunk(2			)
        _lowercase				 =pred_uncond + guidance_scale * (pred_text - pred_uncond)

        _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

      return self.decode(UpperCAmelCase			)





import math
def 							UpperCAmelCase_   (				__snake_case		)	->     int:







 """simple docstring"""


 return sum(int(__snake_case		) for digit in str(__snake_case		)		)
def 							UpperCAmelCase_   (				__snake_case = 30		)	->     int:







 """simple docstring"""


 _lowercase				 =[]
 for digit in range(2	,		100		):
  for power in range(2	,		100		):
   _lowercase				 =int(math.pow(__snake_case	,		__snake_case		)		)
   if digit == digit_sum(__snake_case		):
    digit_to_powers.append(__snake_case		)

 digit_to_powers.sort()
 return digit_to_powers[n - 1]


if __name__ == "__main__":
  print(solution())

