





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   __snake_case				(			_a   ):






    '''simple docstring'''





    def __init__(   self						:		Any			,						snake_case						:		AutoencoderKL			,						snake_case						:		CLIPTextModel			,						snake_case						:		CLIPTokenizer			,						snake_case						:		UNetaDConditionModel			,						snake_case						:		Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,						):

      """simple docstring"""





      super().__init__()

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





    def 	A		(   self						:		Tuple			,						snake_case						:		Any							):

      """simple docstring"""





      with torch.no_grad():
        __lowerCAmelCase			=				self.vae.encode(tfms.ToTensor()(snake_case							).unsqueeze(0							).to(self.device							) * 2 - 1							)
        __lowerCAmelCase			=				0.1_8215 * latent.latent_dist.sample()
      return latent





    def 	A		(   self						:		Any			,						snake_case						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				(1 / 0.1_8215) * latent
      with torch.no_grad():
        __lowerCAmelCase			=				self.vae.decode(snake_case							).sample
      __lowerCAmelCase			=				(img / 2 + 0.5).clamp(0			,						1							)
      __lowerCAmelCase			=				img.detach().cpu().permute(0			,						2			,						3			,						1							).numpy()
      __lowerCAmelCase			=				(img * 255).round().astype('''uint8'''							)
      return Image.fromarray(img[0]							)





    def 	A		(   self						:		Union[str, Any]			,						snake_case						:		Union[str, Any]							):

      """simple docstring"""





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

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

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

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

      return torch.cat([uncond_embedding, text_embedding]							)





    def __call__(   self						:		str			,						snake_case						:		Image.Image			,						snake_case						:		str			,						snake_case						:		float = 0.3			,						snake_case						:		float = 0.6			,						snake_case						:		float = 0.5			,						snake_case						:		int = 42			,						snake_case						:		int = 50			,						snake_case						:		float = 7.5			,						):

      """simple docstring"""





      __lowerCAmelCase			=				steps - int(kmin * steps							)
      __lowerCAmelCase			=				steps - int(kmax * steps							)

      __lowerCAmelCase			=				self.prep_text(snake_case							)

      self.scheduler.set_timesteps(snake_case							)

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

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

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

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

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

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

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

      __lowerCAmelCase			=				self.scheduler.step(snake_case			,						self.scheduler.timesteps[tmax]			,						snake_case							).prev_sample

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

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

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

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

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

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

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

      return self.decode(snake_case							)











def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		list):



  '''simple docstring'''






  if len(__lowercase) <= 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()
