code: 
  
  
  
  
  
  
  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						(					A__ ):
  
  
  
  
  
  
  
   def __init__(	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     AutoencoderKL							,     SCREAMING_SNAKE_CASE      :     CLIPTextModel							,     SCREAMING_SNAKE_CASE      :     CLIPTokenizer							,     SCREAMING_SNAKE_CASE      :     UNetaDConditionModel							,     SCREAMING_SNAKE_CASE      :     Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().__init__()
  
        self.register_modules(vae=SCREAMING_SNAKE_CASE							,     text_encoder=SCREAMING_SNAKE_CASE							,     tokenizer=SCREAMING_SNAKE_CASE							,     unet=SCREAMING_SNAKE_CASE							,     scheduler=SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict							,     SCREAMING_SNAKE_CASE      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        with torch.no_grad():
             UpperCamelCase__    :     Any        = self.vae.encode(tfms.ToTensor()(SCREAMING_SNAKE_CASE		).unsqueeze(0		).to(self.device		) * 2 - 1		)
             UpperCamelCase__    :     Tuple        = 0.1_8_2_1_5 * latent.latent_dist.sample()
        return latent
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any							,     SCREAMING_SNAKE_CASE      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        = (1 / 0.1_8_2_1_5) * latent
        with torch.no_grad():
             UpperCamelCase__    :     Tuple        = self.vae.decode(SCREAMING_SNAKE_CASE		).sample
        UpperCamelCase__    :     List[Any]        = (img / 2 + 0.5).clamp(0							,     1		)
        UpperCamelCase__    :     Optional[Any]        = img.detach().cpu().permute(0							,     2							,     3							,     1		).numpy()
        UpperCamelCase__    :     Tuple        = (img * 2_55).round().astype("uint8"		)
        return Image.fromarray(img[0]		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        = self.tokenizer(
            SCREAMING_SNAKE_CASE							,     padding="max_length"							,     max_length=self.tokenizer.model_max_length							,     truncation=SCREAMING_SNAKE_CASE							,     return_tensors="pt"							,     )
  
        UpperCamelCase__    :     List[Any]        = self.text_encoder(text_input.input_ids.to(self.device		)		)[0]
  
        UpperCamelCase__    :     Union[str, Any]        = self.tokenizer(
            ""							,     padding="max_length"							,     max_length=self.tokenizer.model_max_length							,     truncation=SCREAMING_SNAKE_CASE							,     return_tensors="pt"							,     )
  
        UpperCamelCase__    :     Dict        = self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
  
        return torch.cat([uncond_embedding, text_embedding]		)
  
  
  
  
  
  
  
   def __call__(	self      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Image.Image							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     float = 0.3							,     SCREAMING_SNAKE_CASE      :     float = 0.6							,     SCREAMING_SNAKE_CASE      :     float = 0.5							,     SCREAMING_SNAKE_CASE      :     int = 42							,     SCREAMING_SNAKE_CASE      :     int = 50							,     SCREAMING_SNAKE_CASE      :     float = 7.5							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        = steps - int(kmin * steps		)
        UpperCamelCase__    :     Optional[int]        = steps - int(kmax * steps		)
  
        UpperCamelCase__    :     List[str]        = self.prep_text(SCREAMING_SNAKE_CASE		)
  
        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Optional[int]        = img.size
        UpperCamelCase__    :     str        = self.encode(SCREAMING_SNAKE_CASE		)
  
        torch.manual_seed(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        = torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)							,     ).to(self.device		)
  
        UpperCamelCase__    :     List[Any]        = self.scheduler.add_noise(
            SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     timesteps=self.scheduler.timesteps[tmax]							,     )
  
        UpperCamelCase__    :     Tuple        = torch.cat([latents] * 2		)
  
        UpperCamelCase__    :     Optional[int]        = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]		)
  
        with torch.no_grad():
             UpperCamelCase__    :     str        = self.unet(
                 SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]							,     encoder_hidden_states=SCREAMING_SNAKE_CASE							,     ).sample
  
        UpperCamelCase__    :     int        = pred.chunk(2		)
        UpperCamelCase__    :     Optional[Any]        = pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
        UpperCamelCase__    :     List[str]        = self.scheduler.step(SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]							,     SCREAMING_SNAKE_CASE		).prev_sample
  
        for i, t in enumerate(tqdm(self.scheduler.timesteps		)		):
             if i > tmax:
                  if i < tmin:  # layout generation phase
                       UpperCamelCase__    :     Any        = self.scheduler.add_noise(
                           SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     timesteps=SCREAMING_SNAKE_CASE							,     )
  
                       UpperCamelCase__    :     List[Any]        = (mix_factor * latents) + (
                           1 - mix_factor
                       ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                       UpperCamelCase__    :     Optional[int]        = torch.cat([input] * 2		)
  
                  else:  # content generation phase
                       UpperCamelCase__    :     Dict        = torch.cat([latents] * 2		)
  
                  UpperCamelCase__    :     Union[str, Any]        = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
                  with torch.no_grad():
                       UpperCamelCase__    :     Optional[int]        = self.unet(
                           SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     encoder_hidden_states=SCREAMING_SNAKE_CASE							,     ).sample
  
                  UpperCamelCase__    :     Optional[int]        = pred.chunk(2		)
                  UpperCamelCase__    :     Optional[Any]        = pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
                  UpperCamelCase__    :     List[str]        = self.scheduler.step(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		).prev_sample
  
        return self.decode(SCREAMING_SNAKE_CASE		)
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			list[int]:
       if not list_of_ints:
            return []
  
       if min(__lowerCAmelCase    ) < 0:
            raise ValueError("All numbers must be positive"    )
  
       UpperCamelCase__    :     Dict        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
       return _msd_radix_sort(__lowerCAmelCase      ,					__lowerCAmelCase    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			list[int]:
       if bit_position == 0 or len(__lowerCAmelCase    ) in [0, 1]:
            return list_of_ints
  
       UpperCamelCase__    :     Union[str, Any]        =						[]
       UpperCamelCase__    :     Dict        =						[]
       # Split numbers based on bit at bit_position from the right
       for number in list_of_ints:
            if (number >> (bit_position - 1)) & 1:
                 # number has a one at bit bit_position
                 ones.append(__lowerCAmelCase    )
            else:
                 # number has a zero at bit bit_position
                 zeros.append(__lowerCAmelCase    )
  
      # recursively split both lists further
       UpperCamelCase__    :     str        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )
       UpperCamelCase__    :     Optional[Any]        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )
  
       # recombine lists
       UpperCamelCase__    :     Optional[int]        =						zeros
       res.extend(__lowerCAmelCase    )
  
       return res
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Dict:
       UpperCamelCase__    :     Optional[int]        =						len(__lowerCAmelCase    )
       if not list_of_ints or length == 1:
            return
  
       if min(__lowerCAmelCase    ) < 0:
            raise ValueError("All numbers must be positive"    )
  
       UpperCamelCase__    :     int        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
       _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					0      ,					__lowerCAmelCase    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[str]:
       if bit_position == 0 or end_index - begin_index <= 1:
            return
  
       bit_position -= 1
  
       UpperCamelCase__    :     Any        =						begin_index
       UpperCamelCase__    :     List[str]        =						end_index - 1
       while i <= j:
            UpperCamelCase__    :     Tuple        =						False
            if not (list_of_ints[i] >> bit_position) & 1:
                 # found zero at the beginning
                 i += 1
                 UpperCamelCase__    :     Any        =						True
            if (list_of_ints[j] >> bit_position) & 1:
                 # found one at the end
                 j -= 1
                 UpperCamelCase__    :     Dict        =						True
  
            if changed:
                 continue
  
            UpperCamelCase__       ,  UpperCamelCase__    :     Any        =						list_of_ints[j], list_of_ints[i]
            j -= 1
            if j != i:
                 i += 1
  
       _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
       _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()