code: 
  
  
  
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import is_torch_available, is_transformers_available
  
  
  if is_transformers_available() and is_torch_available():
       from .image_encoder import PaintByExampleImageEncoder
       from .pipeline_paint_by_example import PaintByExamplePipeline
  
  
style_context: 
  
  
  
  
  
  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				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)       ->   Optional[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__()
  
          self.register_modules(vae=UpperCAmelCase				,						text_encoder=UpperCAmelCase				,						tokenizer=UpperCAmelCase				,						unet=UpperCAmelCase				,						scheduler=UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          with torch.no_grad():
            __snake_case   :				Tuple        =       self.vae.encode(tfms.ToTensor()(UpperCAmelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
            __snake_case   :				Optional[int]        =       0.18_215 * latent.latent_dist.sample()
          return latent
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[Any]        =       (1 / 0.18_215) * latent
          with torch.no_grad():
            __snake_case   :				Any        =       self.vae.decode(UpperCAmelCase   ).sample
          __snake_case   :				Optional[int]        =       (img / 2 + 0.5).clamp(0				,						1   )
          __snake_case   :				Tuple        =       img.detach().cpu().permute(0				,						2				,						3				,						1   ).numpy()
          __snake_case   :				Dict        =       (img * 255).round().astype("uint8"   )
          return Image.fromarray(img[0]   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				int        =       self.tokenizer(
              UpperCAmelCase				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)
  
          __snake_case   :				List[Any]        =       self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
  
          __snake_case   :				Union[str, Any]        =       self.tokenizer(
              ""				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)
  
          __snake_case   :				int        =       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 = 42				,						UpperCAmelCase = 50				,						UpperCAmelCase = 7.5				,						)       ->   Image.Image:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       steps - int(kmin * steps   )
          __snake_case   :				Optional[int]        =       steps - int(kmax * steps   )
  
          __snake_case   :				Tuple        =       self.prep_text(UpperCAmelCase   )
  
          self.scheduler.set_timesteps(UpperCAmelCase   )
  
          __snake_case    ,   __snake_case   :				Optional[Any]        =       img.size
          __snake_case   :				List[Any]        =       self.encode(UpperCAmelCase   )
  
          torch.manual_seed(UpperCAmelCase   )
          __snake_case   :				str        =       torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)				,						).to(self.device   )
  
          __snake_case   :				int        =       self.scheduler.add_noise(
              UpperCAmelCase				,						UpperCAmelCase				,						timesteps=self.scheduler.timesteps[tmax]				,						)
  
          __snake_case   :				Optional[Any]        =       torch.cat([latents] * 2   )
  
          __snake_case   :				Union[str, Any]        =       self.scheduler.scale_model_input(UpperCAmelCase				,						self.scheduler.timesteps[tmax]   )
  
          with torch.no_grad():
            __snake_case   :				int        =       self.unet(
                UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						encoder_hidden_states=UpperCAmelCase				,						).sample
  
          __snake_case    ,   __snake_case   :				Union[str, Any]        =       pred.chunk(2   )
          __snake_case   :				Optional[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
          __snake_case   :				int        =       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
                __snake_case   :				int        =       self.scheduler.add_noise(
                    UpperCAmelCase				,						UpperCAmelCase				,						timesteps=UpperCAmelCase				,						)
  
                __snake_case   :				str        =       (mix_factor * latents) + (
                    1 - mix_factor
                ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                __snake_case   :				List[Any]        =       torch.cat([input] * 2   )
  
              else:  # content generation phase
                __snake_case   :				str        =       torch.cat([latents] * 2   )
  
              __snake_case   :				Tuple        =       self.scheduler.scale_model_input(UpperCAmelCase				,						UpperCAmelCase   )
  
              with torch.no_grad():
                __snake_case   :				List[str]        =       self.unet(
                    UpperCAmelCase				,						UpperCAmelCase				,						encoder_hidden_states=UpperCAmelCase				,						).sample
  
              __snake_case    ,   __snake_case   :				str        =       pred.chunk(2   )
              __snake_case   :				List[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
              __snake_case   :				List[Any]        =       self.scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ).prev_sample
  
          return self.decode(UpperCAmelCase   )
  
  