code: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				data
            _a          =				None
  
  
  
  
     def __str__(		self							:Any       ):
            return f'{self.data}'
  
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Any       ):
            _a          =				None
            _a          =				None
  
  
  
  
     def __iter__(		self							:List[str]       ):
            _a          =				self.front
            while node:
                   yield node.data
                   _a          =				node.next
  
  
  
  
     def __len__(		self							:List[str]       ):
            return len(tuple(iter(self       )       )       )
  
  
  
  
     def __str__(		self							:Union[str, Any]       ):
            return " <- ".join(str(UpperCamelCase__       ) for item in self       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            return len(self       ) == 0
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
            _a          =				Node(UpperCamelCase__       )
            if self.is_empty():
                   _a          =				_a          =				node
            else:
                   assert isinstance(self.rear				,  UpperCamelCase__       )
                   _a          =				node
                   _a          =				node
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            if self.is_empty():
                   raise IndexError("dequeue from empty queue"       )
            assert isinstance(self.front				,  UpperCamelCase__       )
            _a          =				self.front
            _a          =				self.front.next
            if self.front is None:
                   _a          =				None
            return node.data
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				_a          =				None
  
  
  if __name__ == "__main__":
     from doctest import testmod
  
     testmod()
  
style_context: 
  
  
  """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 __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
            super().__init__()
  
            self.register_modules(vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
            with torch.no_grad():
                   _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                   _a          =				0.18215 * latent.latent_dist.sample()
            return latent
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				(1 / 0.18215) * latent
            with torch.no_grad():
                   _a          =				self.vae.decode(UpperCamelCase__       ).sample
            _a          =				(img / 2 + 0.5).clamp(0				,  1       )
            _a          =				img.detach().cpu().permute(0				,  2				,  3				,  1       ).numpy()
            _a          =				(img * 255).round().astype("uint8"       )
            return Image.fromarray(img[0]       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
            _a          =				self.tokenizer(
                UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  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=UpperCamelCase__				,  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							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
            _a          =				steps - int(kmin * steps       )
            _a          =				steps - int(kmax * steps       )
  
            _a          =				self.prep_text(UpperCamelCase__       )
  
            self.scheduler.set_timesteps(UpperCamelCase__       )
  
            _a   , _a          =				img.size
            _a          =				self.encode(UpperCamelCase__       )
  
            torch.manual_seed(UpperCamelCase__       )
            _a          =				torch.randn(
                (1, self.unet.config.in_channels, height // 8, width // 8)				,  ).to(self.device       )
  
            _a          =				self.scheduler.add_noise(
                UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )
  
            _a          =				torch.cat([latents] * 2       )
  
            _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )
  
            with torch.no_grad():
                   _a          =				self.unet(
                       UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  encoder_hidden_states=UpperCamelCase__				,  ).sample
  
            _a   , _a          =				pred.chunk(2       )
            _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
            _a          =				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
                                 _a          =				self.scheduler.add_noise(
                                     UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )
  
                                 _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(UpperCamelCase__				,  UpperCamelCase__       )
  
                          with torch.no_grad():
                                 _a          =				self.unet(
                                     UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample
  
                          _a   , _a          =				pred.chunk(2       )
                          _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
                          _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample
  
            return self.decode(UpperCamelCase__       )
  