



"""simple docstring"""




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 	a__		(    SCREAMING_SNAKE_CASE__  ):


   def __init__(				self   :				Any,  lowerCAmelCase   :				AutoencoderKL,  lowerCAmelCase   :				CLIPTextModel,  lowerCAmelCase   :				CLIPTokenizer,  lowerCAmelCase   :				UNetaDConditionModel,  lowerCAmelCase   :				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],  )     ->							List[str]:
         super().__init__()

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


   def 							lowercase					(				self   :				Tuple,  lowerCAmelCase   :				Optional[Any]      )     ->							Any:
         with torch.no_grad():
               lowercase	:   Any											= self.vae.encode(tfms.ToTensor()(lowerCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
               lowercase	:   Optional[Any]											= 0.1_8215 * latent.latent_dist.sample()
         return latent


   def 							lowercase					(				self   :				List[str],  lowerCAmelCase   :				Union[str, Any]      )     ->							List[Any]:
         lowercase	:   List[Any]											= (1 / 0.1_8215) * latent
         with torch.no_grad():
               lowercase	:   str											= self.vae.decode(lowerCAmelCase      ).sample
         lowercase	:   Any											= (img / 2 + 0.5).clamp(0,  1      )
         lowercase	:   Optional[Any]											= img.detach().cpu().permute(0,  2,  3,  1      ).numpy()
         lowercase	:   Union[str, Any]											= (img * 255).round().astype('uint8'      )
         return Image.fromarray(img[0]      )


   def 							lowercase					(				self   :				Union[str, Any],  lowerCAmelCase   :				Dict      )     ->							Union[str, Any]:
         lowercase	:   Tuple											= self.tokenizer(
             lowerCAmelCase,  padding='max_length',  max_length=self.tokenizer.model_max_length,  truncation=lowerCAmelCase,  return_tensors='pt',  )

         lowercase	:   str											= self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

         lowercase	:   str											= self.tokenizer(
             '',  padding='max_length',  max_length=self.tokenizer.model_max_length,  truncation=lowerCAmelCase,  return_tensors='pt',  )

         lowercase	:   str											= self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

         return torch.cat([uncond_embedding, text_embedding]      )






   def __call__(				self   :				Optional[int],  lowerCAmelCase   :				Image.Image,  lowerCAmelCase   :				str,  lowerCAmelCase   :				float = 0.3,  lowerCAmelCase   :				float = 0.6,  lowerCAmelCase   :				float = 0.5,  lowerCAmelCase   :				int = 42,  lowerCAmelCase   :				int = 50,  lowerCAmelCase   :				float = 7.5,  )     ->							Image.Image:
         lowercase	:   List[str]											= steps - int(kmin * steps      )
         lowercase	:   int											= steps - int(kmax * steps      )

         lowercase	:   List[Any]											= self.prep_text(lowerCAmelCase      )

         self.scheduler.set_timesteps(lowerCAmelCase      )

         lowercase						,     lowercase	:   Tuple											= img.size
         lowercase	:   Union[str, Any]											= self.encode(lowerCAmelCase      )

         torch.manual_seed(lowerCAmelCase      )
         lowercase	:   Dict											= torch.randn(
             (1, self.unet.config.in_channels, height // 8, width // 8),  ).to(self.device      )

         lowercase	:   List[Any]											= self.scheduler.add_noise(
             lowerCAmelCase,  lowerCAmelCase,  timesteps=self.scheduler.timesteps[tmax],  )

         lowercase	:   Optional[int]											= torch.cat([latents] * 2      )

         lowercase	:   Any											= self.scheduler.scale_model_input(lowerCAmelCase,  self.scheduler.timesteps[tmax]      )

         with torch.no_grad():
               lowercase	:   str											= self.unet(
                   lowerCAmelCase,  self.scheduler.timesteps[tmax],  encoder_hidden_states=lowerCAmelCase,  ).sample

         lowercase						,     lowercase	:   str											= pred.chunk(2      )
         lowercase	:   Tuple											= pred_uncond + guidance_scale * (pred_text - pred_uncond)

         lowercase	:   Optional[Any]											= self.scheduler.step(lowerCAmelCase,  self.scheduler.timesteps[tmax],  lowerCAmelCase      ).prev_sample

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

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

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

                     lowercase	:   Tuple											= self.scheduler.scale_model_input(lowerCAmelCase,  lowerCAmelCase      )

                     with torch.no_grad():
                           lowercase	:   int											= self.unet(
                               lowerCAmelCase,  lowerCAmelCase,  encoder_hidden_states=lowerCAmelCase,  ).sample

                     lowercase						,     lowercase	:   Optional[int]											= pred.chunk(2      )
                     lowercase	:   Optional[int]											= pred_uncond + guidance_scale * (pred_text - pred_uncond)

                     lowercase	:   List[Any]											= self.scheduler.step(lowerCAmelCase,  lowerCAmelCase,  lowerCAmelCase      ).prev_sample

         return self.decode(lowerCAmelCase      )





"""simple docstring"""




def 	lowercase__  (      )  ->      Any:






      '''simple docstring'''


      lowercase	:   Dict											= 1

      while True:
            if (
                sorted(str(_UpperCAmelCase					)					)
                == sorted(str(2 * i					)					)
                == sorted(str(3 * i					)					)
                == sorted(str(4 * i					)					)
                == sorted(str(5 * i					)					)
                == sorted(str(6 * i					)					)
            ):
                  return i

            i += 1


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