content: from typing import List, Tuple
  from scipy import interpolate
  import numpy as np
  import torch
  import matplotlib.pyplot as plt
  from IPython.display import clear_output
  import abc
  
  
  class GuideModel(torch.nn.Module, abc.ABC):
      def __init__(self) -> None:
          super().__init__()
  
      @abc.abstractmethod
      def preprocess(self, x_img):
          pass
  
      @abc.abstractmethod
      def compute_loss(self, inp):
          pass
  
  
  class Guider(torch.nn.Module):
      def __init__(self, sampler, guide_model, scale=1.0, verbose=False):
          """Apply classifier guidance
  
          Specify a guidance scale as either a scalar
          Or a schedule as a list of tuples t = 0->1 and scale, e.g.
          [(0, 10), (0.5, 20), (1, 50)]
          """
          super().__init__()
          self.sampler = sampler
          self.index = 0
          self.show = verbose
          self.guide_model = guide_model
          self.history = []
  
          if isinstance(scale, (Tuple, List)):
              times = np.array([x[0] for x in scale])
              values = np.array([x[1] for x in scale])
              self.scale_schedule = {"times": times, "values": values}
          else:
              self.scale_schedule = float(scale)
  
          self.ddim_timesteps = sampler.ddim_timesteps
          self.ddpm_num_timesteps = sampler.ddpm_num_timesteps
  
  
      def get_scales(self):
          if isinstance(self.scale_schedule, float):
              return len(self.ddim_timesteps)*[self.scale_schedule]
  
          interpolater = interpolate.interp1d(self.scale_schedule["times"], self.scale_schedule["values"])
          fractional_steps = np.array(self.ddim_timesteps)/self.ddpm_num_timesteps
          return interpolater(fractional_steps)
  
      def modify_score(self, model, e_t, x, t, c):
  
          # TODO look up index by t
          scale = self.get_scales()[self.index]
  
          if (scale == 0):
              return e_t
  
          sqrt_1ma = self.sampler.ddim_sqrt_one_minus_alphas[self.index].to(x.device)
          with torch.enable_grad():
              x_in = x.detach().requires_grad_(True)
              pred_x0 = model.predict_start_from_noise(x_in, t=t, noise=e_t)
              x_img = model.first_stage_model.decode((1/0.18215)*pred_x0)
  
              inp = self.guide_model.preprocess(x_img)
              loss = self.guide_model.compute_loss(inp)
              grads = torch.autograd.grad(loss.sum(), x_in)[0]
              correction = grads * scale
  
              if self.show:
                  clear_output(wait=True)
                  print(loss.item(), scale, correction.abs().max().item(), e_t.abs().max().item())
                  self.history.append([loss.item(), scale, correction.min().item(), correction.max().item()])
                  plt.imshow((inp[0].detach().permute(1,2,0).clamp(-1,1).cpu()+1)/2)
                  plt.axis('off')
                  plt.show()
                  plt.imshow(correction[0][0].detach().cpu())
                  plt.axis('off')
                  plt.show()
  
  
          e_t_mod = e_t - sqrt_1ma*correction
          if self.show:
              fig, axs = plt.subplots(1, 3)
              axs[0].imshow(e_t[0][0].detach().cpu(), vmin=-2, vmax=+2)
              axs[1].imshow(e_t_mod[0][0].detach().cpu(), vmin=-2, vmax=+2)
              axs[2].imshow(correction[0][0].detach().cpu(), vmin=-2, vmax=+2)
              plt.show()
          self.index += 1
          return e_t_mod