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   __lowerCamelCase (A__	):
  
  
  
  
  
  
  
         def __init__( self:   Optional[int],A_:   AutoencoderKL,A_:   CLIPTextModel,A_:   CLIPTokenizer,A_:   UNetaDConditionModel,A_:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              super().__init__()
  
              self.register_modules(vae=A_,text_encoder=A_,tokenizer=A_,unet=A_,scheduler=A_				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict,A_:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              with torch.no_grad():
                   __UpperCamelCase   = self.vae.encode(tfms.ToTensor()(A_				).unsqueeze(0				).to(self.device				) * 2 - 1				)
                   __UpperCamelCase   = 0.1_8_2_1_5 * latent.latent_dist.sample()
              return latent
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Any,A_:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = (1 / 0.1_8_2_1_5) * latent
              with torch.no_grad():
                   __UpperCamelCase   = self.vae.decode(A_				).sample
              __UpperCamelCase   = (img / 2 + 0.5).clamp(0,1				)
              __UpperCamelCase   = img.detach().cpu().permute(0,2,3,1				).numpy()
              __UpperCamelCase   = (img * 255).round().astype('uint8'				)
              return Image.fromarray(img[0]				)
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   List[str],A_:   str				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = self.tokenizer(
                  A_,padding='max_length',max_length=self.tokenizer.model_max_length,truncation=A_,return_tensors='pt',)
  
              __UpperCamelCase   = self.text_encoder(text_input.input_ids.to(self.device				)				)[0]
  
              __UpperCamelCase   = self.tokenizer(
                  '',padding='max_length',max_length=self.tokenizer.model_max_length,truncation=A_,return_tensors='pt',)
  
              __UpperCamelCase   = self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
  
              return torch.cat([uncond_embedding, text_embedding]				)
  
  
  
  
  
  
  
         def __call__( self:   List[Any],A_:   Image.Image,A_:   str,A_:   float = 0.3,A_:   float = 0.6,A_:   float = 0.5,A_:   int = 42,A_:   int = 50,A_:   float = 7.5,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = steps - int(kmin * steps				)
              __UpperCamelCase   = steps - int(kmax * steps				)
  
              __UpperCamelCase   = self.prep_text(A_				)
  
              self.scheduler.set_timesteps(A_				)
  
              __UpperCamelCase   = img.size
              __UpperCamelCase   = self.encode(A_				)
  
              torch.manual_seed(A_				)
              __UpperCamelCase   = torch.randn(
                  (1, self.unet.config.in_channels, height // 8, width // 8),).to(self.device				)
  
              __UpperCamelCase   = self.scheduler.add_noise(
                  A_,A_,timesteps=self.scheduler.timesteps[tmax],)
  
              __UpperCamelCase   = torch.cat([latents] * 2				)
  
              __UpperCamelCase   = self.scheduler.scale_model_input(A_,self.scheduler.timesteps[tmax]				)
  
              with torch.no_grad():
                   __UpperCamelCase   = self.unet(
                       A_,self.scheduler.timesteps[tmax],encoder_hidden_states=A_,).sample
  
              __UpperCamelCase   = pred.chunk(2				)
              __UpperCamelCase   = pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
              __UpperCamelCase   = 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
                             __UpperCamelCase   = self.scheduler.add_noise(
                                 A_,A_,timesteps=A_,)
  
                             __UpperCamelCase   = (mix_factor * latents) + (
                                 1 - mix_factor
                             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                             __UpperCamelCase   = torch.cat([input] * 2				)
  
                        else:  # content generation phase
                             __UpperCamelCase   = torch.cat([latents] * 2				)
  
                        __UpperCamelCase   = self.scheduler.scale_model_input(A_,A_				)
  
                        with torch.no_grad():
                             __UpperCamelCase   = self.unet(
                                 A_,A_,encoder_hidden_states=A_,).sample
  
                        __UpperCamelCase   = pred.chunk(2				)
                        __UpperCamelCase   = pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
                        __UpperCamelCase   = self.scheduler.step(A_,A_,A_				).prev_sample
  
              return self.decode(A_				)
  
style_context: 
  
  
  import warnings
  from collections.abc import Callable
  from typing import Any
  
  import numpy
  from matplotlib import pyplot
  
  __snake_case             =	0.25 + 0.0j
  __snake_case             =	-0.4 + 0.6j
  __snake_case             =	-0.1 + 0.6_51j
  __snake_case             =	-2.0
  __snake_case             =	5_6
  __snake_case             =	2.0
  __snake_case             =	6_6_6
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   )				->   numpy.ndarray:
  
  
       """simple docstring"""
  
  
       return numpy.exp(_lowercase   ) + c_parameter
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   )				->   numpy.ndarray:
  
  
       """simple docstring"""
  
  
       return z_values * z_values + c_parameter
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   )				->   numpy.ndarray:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = numpy.linspace(-window_size				,     _lowercase				,     _lowercase   )
       __UpperCamelCase   = x.reshape((nb_pixels, 1)   )
       __UpperCamelCase   = numpy.linspace(-window_size				,     _lowercase				,     _lowercase   )
       __UpperCamelCase   = y.reshape((1, nb_pixels)   )
       return x + 1.0J * y
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase = None				,     )				->   numpy.ndarray:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = z_a.astype('complex64'   )
       for _ in range(_lowercase   ):
            __UpperCamelCase   = eval_function(_lowercase				,     _lowercase   )
            if infinity is not None:
                 numpy.nan_to_num(_lowercase				,     copy=_lowercase				,     nan=_lowercase   )
                 __UpperCamelCase   = infinity
       return z_n
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     )				->   None:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = (abs(_lowercase   )).transpose()
       __UpperCamelCase   = abs_z_final[::-1, :]
       pyplot.matshow(abs_z_final < escape_radius   )
       pyplot.title(f'''Julia set of ${function_label}$, $c={function_params}$'''   )
       pyplot.show()
  
  
  
  
  
  
  def 		_A		(		)				->   None:
  
  
       """simple docstring"""
  
  
       warnings.filterwarnings(
           'ignore'				,     category=_lowercase				,     message='overflow encountered in multiply'   )
       warnings.filterwarnings(
           'ignore'				,     category=_lowercase				,     message='invalid value encountered in multiply'				,     )
       warnings.filterwarnings(
           'ignore'				,     category=_lowercase				,     message='overflow encountered in absolute'   )
       warnings.filterwarnings(
           'ignore'				,     category=_lowercase				,     message='overflow encountered in exp'   )
  
  
  if __name__ == "__main__":
      __snake_case             =	prepare_grid(window_size, nb_pixels)
  
      ignore_overflow_warnings()  # See file header for explanations
  
      __snake_case             =	2_4
      __snake_case             =	2 * abs(c_cauliflower) + 1
      __snake_case             =	iterate_function(
          eval_quadratic_polynomial,
          c_cauliflower,
          nb_iterations,
          z_a,
          infinity=1.1 * escape_radius,
      )
      show_results('''z^2+c''', c_cauliflower, escape_radius, z_final)
  
      __snake_case             =	6_4
      __snake_case             =	2 * abs(c_polynomial_a) + 1
      __snake_case             =	iterate_function(
          eval_quadratic_polynomial,
          c_polynomial_a,
          nb_iterations,
          z_a,
          infinity=1.1 * escape_radius,
      )
      show_results('''z^2+c''', c_polynomial_a, escape_radius, z_final)
  
      __snake_case             =	1_6_1
      __snake_case             =	2 * abs(c_polynomial_a) + 1
      __snake_case             =	iterate_function(
          eval_quadratic_polynomial,
          c_polynomial_a,
          nb_iterations,
          z_a,
          infinity=1.1 * escape_radius,
      )
      show_results('''z^2+c''', c_polynomial_a, escape_radius, z_final)
  
      __snake_case             =	1_2
      __snake_case             =	10_000.0
      __snake_case             =	iterate_function(
          eval_exponential,
          c_exponential,
          nb_iterations,
          z_a + 2,
          infinity=1.0e10,
      )
      show_results('''e^z+c''', c_exponential, escape_radius, z_final)
  
  
  