




'''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__		):




    '''simple docstring'''





    def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )     ->	Union[str, Any]:
       super().__init__()

       self.register_modules(vae=_UpperCAmelCase			,  text_encoder=_UpperCAmelCase			,  tokenizer=_UpperCAmelCase			,  unet=_UpperCAmelCase			,  scheduler=_UpperCAmelCase      )
    def 	a_	(self			,  _UpperCAmelCase      )     ->	List[str]:
       with torch.no_grad():
          __UpperCamelCase				:      Union[str, Any]									=       self.vae.encode(tfms.ToTensor()(_UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
          __UpperCamelCase				:      Optional[Any]									=       0.18_215 * latent.latent_dist.sample()
       return latent
    def 	a_	(self			,  _UpperCAmelCase      )     ->	Dict:
       __UpperCamelCase				:      Optional[int]									=       (1 / 0.18_215) * latent
       with torch.no_grad():
          __UpperCamelCase				:      int									=       self.vae.decode(_UpperCAmelCase      ).sample
       __UpperCamelCase				:      Union[str, Any]									=       (img / 2 + 0.5).clamp(0			,  1      )
       __UpperCamelCase				:      List[str]									=       img.detach().cpu().permute(0			,  2			,  3			,  1      ).numpy()
       __UpperCamelCase				:      Optional[Any]									=       (img * 2_5_5).round().astype("uint8"      )
       return Image.fromarray(img[0]      )
    def 	a_	(self			,  _UpperCAmelCase      )     ->	List[str]:
       __UpperCamelCase				:      Tuple									=       self.tokenizer(
           _UpperCAmelCase			,  padding="max_length"			,  max_length=self.tokenizer.model_max_length			,  truncation=_UpperCAmelCase			,  return_tensors="pt"			,  )

       __UpperCamelCase				:      Optional[int]									=       self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

       __UpperCamelCase				:      Tuple									=       self.tokenizer(
           ""			,  padding="max_length"			,  max_length=self.tokenizer.model_max_length			,  truncation=_UpperCAmelCase			,  return_tensors="pt"			,  )

       __UpperCamelCase				:      Union[str, Any]									=       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:
       __UpperCamelCase				:      List[Any]									=       steps - int(kmin * steps      )
       __UpperCamelCase				:      Dict									=       steps - int(kmax * steps      )

       __UpperCamelCase				:      Any									=       self.prep_text(_UpperCAmelCase      )

       self.scheduler.set_timesteps(_UpperCAmelCase      )

       __UpperCamelCase				:      Dict									=       img.size
       __UpperCamelCase				:      Optional[Any]									=       self.encode(_UpperCAmelCase      )

       torch.manual_seed(_UpperCAmelCase      )
       __UpperCamelCase				:      Optional[Any]									=       torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)			,  ).to(self.device      )

       __UpperCamelCase				:      List[Any]									=       self.scheduler.add_noise(
           _UpperCAmelCase			,  _UpperCAmelCase			,  timesteps=self.scheduler.timesteps[tmax]			,  )

       __UpperCamelCase				:      str									=       torch.cat([latents] * 2      )

       __UpperCamelCase				:      Optional[int]									=       self.scheduler.scale_model_input(_UpperCAmelCase			,  self.scheduler.timesteps[tmax]      )

       with torch.no_grad():
          __UpperCamelCase				:      Optional[int]									=       self.unet(
              _UpperCAmelCase			,  self.scheduler.timesteps[tmax]			,  encoder_hidden_states=_UpperCAmelCase			,  ).sample

       __UpperCamelCase				:      Any									=       pred.chunk(2      )
       __UpperCamelCase				:      List[Any]									=       pred_uncond + guidance_scale * (pred_text - pred_uncond)

       __UpperCamelCase				:      Tuple									=       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
                __UpperCamelCase				:      List[str]									=       self.scheduler.add_noise(
                    _UpperCAmelCase			,  _UpperCAmelCase			,  timesteps=_UpperCAmelCase			,  )

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

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

             __UpperCamelCase				:      Dict									=       self.scheduler.scale_model_input(_UpperCAmelCase			,  _UpperCAmelCase      )

             with torch.no_grad():
                __UpperCamelCase				:      Optional[Any]									=       self.unet(
                    _UpperCAmelCase			,  _UpperCAmelCase			,  encoder_hidden_states=_UpperCAmelCase			,  ).sample

             __UpperCamelCase				:      str									=       pred.chunk(2      )
             __UpperCamelCase				:      List[str]									=       pred_uncond + guidance_scale * (pred_text - pred_uncond)

             __UpperCamelCase				:      int									=       self.scheduler.step(_UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      ).prev_sample

       return self.decode(_UpperCAmelCase      )









'''simple docstring'''






def       __lowerCAmelCase (   snake_case__ = 4_000_000						):
			__UpperCamelCase				:      Tuple									=       1
			__UpperCamelCase				:      int									=       2
			__UpperCamelCase				:      str									=       0
			while j <= n:
						if j % 2 == 0:
									total += j
						__UpperCamelCase		, __UpperCamelCase				:      List[Any]									=       j, i + j

			return total


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



