code: 
  
  
  """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				,					__A				,					__A				,					__A				,					__A				,					__A				,					)      ->  List[str]:
       super().__init__()
  
       self.register_modules(vae=__A				,					text_encoder=__A				,					tokenizer=__A				,					unet=__A				,					scheduler=__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  str:
       with torch.no_grad():
           a					=self.vae.encode(tfms.ToTensor()(__A		).unsqueeze(0		).to(self.device		) * 2 - 1		)
           a					=0.18_215 * latent.latent_dist.sample()
       return latent
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Union[str, Any]:
       a					=(1 / 0.18_215) * latent
       with torch.no_grad():
           a					=self.vae.decode(__A		).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				,					__A		)      ->  Dict:
       a					=self.tokenizer(
           __A				,					padding='''max_length'''				,					max_length=self.tokenizer.model_max_length				,					truncation=__A				,					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=__A				,					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				,					__A				,					__A				,					__A = 0.3				,					__A = 0.6				,					__A = 0.5				,					__A = 42				,					__A = 50				,					__A = 7.5				,					)      ->  Image.Image:
       a					=steps - int(kmin * steps		)
       a					=steps - int(kmax * steps		)
  
       a					=self.prep_text(__A		)
  
       self.scheduler.set_timesteps(__A		)
  
       a	,   a					=img.size
       a					=self.encode(__A		)
  
       torch.manual_seed(__A		)
       a					=torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)				,					).to(self.device		)
  
       a					=self.scheduler.add_noise(
           __A				,					__A				,					timesteps=self.scheduler.timesteps[tmax]				,					)
  
       a					=torch.cat([latents] * 2		)
  
       a					=self.scheduler.scale_model_input(__A				,					self.scheduler.timesteps[tmax]		)
  
       with torch.no_grad():
           a					=self.unet(
               __A				,					self.scheduler.timesteps[tmax]				,					encoder_hidden_states=__A				,					).sample
  
       a	,   a					=pred.chunk(2		)
       a					=pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
       a					=self.scheduler.step(__A				,					self.scheduler.timesteps[tmax]				,					__A		).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(
                       __A				,					__A				,					timesteps=__A				,					)
  
                   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(__A				,					__A		)
  
               with torch.no_grad():
                   a					=self.unet(
                       __A				,					__A				,					encoder_hidden_states=__A				,					).sample
  
               a	,   a					=pred.chunk(2		)
               a					=pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
               a					=self.scheduler.step(__A				,					__A				,					__A		).prev_sample
  
       return self.decode(__A		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"hybrid-clip"
   __lowerCAmelCase				      =						True
  
  
   def __init__(	self				,					__A=512				,					**__A		)      ->  Any:
       super().__init__(**__A		)
  
       if "text_config" not in kwargs:
           raise ValueError('''`text_config` can not be `None`.'''		)
  
       if "vision_config" not in kwargs:
           raise ValueError('''`vision_config` can not be `None`.'''		)
  
       a					=kwargs.pop('''text_config'''		)
       a					=kwargs.pop('''vision_config'''		)
  
       a					=text_config.pop('''model_type'''		)
       a					=vision_config.pop('''model_type'''		)
  
       from transformers import AutoConfig
  
       a					=AutoConfig.for_model(__A				,					**__A		)
  
       if vision_model_type == "clip":
           a					=AutoConfig.for_model(__A				,					**__A		).vision_config
       elif vision_model_type == "clip_vision_model":
           from transformers import CLIPVisionConfig
  
           a					=CLIPVisionConfig(**__A		)
       else:
           a					=AutoConfig.for_model(__A				,					**__A		)
  
       a					=projection_dim
       a					=1.0
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					**__A		)      ->  Tuple:
       return cls(text_config=text_config.to_dict()				,					vision_config=vision_config.to_dict()				,					**__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=copy.deepcopy(self.__dict__		)
       a					=self.text_config.to_dict()
       a					=self.vision_config.to_dict()
       a					=self.__class__.model_type
       return output