



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__  (					_A):



 """simple docstring"""

 def __init__(						self				:				Tuple ,	__lowerCAmelCase				:				AutoencoderKL ,	__lowerCAmelCase				:				CLIPTextModel ,	__lowerCAmelCase				:				CLIPTokenizer ,	__lowerCAmelCase				:				UNetaDConditionModel ,	__lowerCAmelCase				:				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler] ,	)      ->  Tuple:
    super().__init__()

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

 def 						snake_case_			(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				Dict			)      ->  Dict:
    with torch.no_grad():
       _A						     =					self.vae.encode(tfms.ToTensor()(__lowerCAmelCase			).unsqueeze(0			).to(self.device			) * 2 - 1			)
       _A						     =					0.1_8215 * latent.latent_dist.sample()
    return latent

 def 						snake_case_			(						self				:				int ,	__lowerCAmelCase				:				Optional[Any]			)      ->  List[str]:
    _A						     =					(1 / 0.1_8215) * latent
    with torch.no_grad():
       _A						     =					self.vae.decode(__lowerCAmelCase			).sample
    _A						     =					(img / 2 + 0.5).clamp(0 ,	1			)
    _A						     =					img.detach().cpu().permute(0 ,	2 ,	3 ,	1			).numpy()
    _A						     =					(img * 2_55).round().astype('''uint8'''			)
    return Image.fromarray(img[0]			)

 def 						snake_case_			(						self				:				Dict ,	__lowerCAmelCase				:				int			)      ->  Optional[Any]:
    _A						     =					self.tokenizer(
        __lowerCAmelCase ,	padding='''max_length''' ,	max_length=self.tokenizer.model_max_length ,	truncation=__lowerCAmelCase ,	return_tensors='''pt''' ,	)

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

    _A						     =					self.tokenizer(
        '''''' ,	padding='''max_length''' ,	max_length=self.tokenizer.model_max_length ,	truncation=__lowerCAmelCase ,	return_tensors='''pt''' ,	)

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

    return torch.cat([uncond_embedding, text_embedding]			)

 def __call__(						self				:				Union[str, Any] ,	__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:
    _A						     =					steps - int(kmin * steps			)
    _A						     =					steps - int(kmax * steps			)

    _A						     =					self.prep_text(__lowerCAmelCase			)

    self.scheduler.set_timesteps(__lowerCAmelCase			)

    _A     ,						_A						     =					img.size
    _A						     =					self.encode(__lowerCAmelCase			)

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

    _A						     =					self.scheduler.add_noise(
        __lowerCAmelCase ,	__lowerCAmelCase ,	timesteps=self.scheduler.timesteps[tmax] ,	)

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

    _A						     =					self.scheduler.scale_model_input(__lowerCAmelCase ,	self.scheduler.timesteps[tmax]			)

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

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

    _A						     =					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
             _A						     =					self.scheduler.add_noise(
                 __lowerCAmelCase ,	__lowerCAmelCase ,	timesteps=__lowerCAmelCase ,	)

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

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

          _A						     =					self.scheduler.scale_model_input(__lowerCAmelCase ,	__lowerCAmelCase			)

          with torch.no_grad():
             _A						     =					self.unet(
                 __lowerCAmelCase ,	__lowerCAmelCase ,	encoder_hidden_states=__lowerCAmelCase ,	).sample

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

          _A						     =					self.scheduler.step(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			).prev_sample

    return self.decode(__lowerCAmelCase			)








def   SCREAMING_SNAKE_CASE_       (  _snake_case  :list							)     ->    list:
   if len(_snake_case							) <= 1:
      return data
   else:
      return [
          *quick_sort([e for e in data[1:] if e <= data[0]]							),
          data[0],
          *quick_sort([e for e in data[1:] if e > data[0]]							),
      ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



