content: """SAMPLING ONLY."""
  
  import torch
  import numpy as np
  from tqdm import tqdm
  from functools import partial
  from einops import rearrange
  
  from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
  from ldm.models.diffusion.sampling_util import renorm_thresholding, norm_thresholding, spatial_norm_thresholding
  
  
  class DDIMSampler(object):
      def __init__(self, model, schedule="linear", **kwargs):
          super().__init__()
          self.model = model
          self.ddpm_num_timesteps = model.num_timesteps
          self.schedule = schedule
          self.device = model.device
  
      def to(self, device):
          """Same as to in torch module
          Don't really underestand why this isn't a module in the first place"""
          for k, v in self.__dict__.items():
              if isinstance(v, torch.Tensor):
                  new_v = getattr(self, k).to(device)
                  setattr(self, k, new_v)
  
  
      def register_buffer(self, name, attr, device=None):
          if type(attr) == torch.Tensor:
              attr = attr.to(device)
              # if attr.device != torch.device("cuda"):
              #     attr = attr.to(torch.device("cuda"))
          setattr(self, name, attr)
  
      def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
          self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
                                                    num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
          alphas_cumprod = self.model.alphas_cumprod
          assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
          to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
  
          self.register_buffer('betas', to_torch(self.model.betas), self.device)
          self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod), self.device)
          self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev), self.device)
  
          # calculations for diffusion q(x_t | x_{t-1}) and others
          self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())), self.device)
          self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())), self.device)
          self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())), self.device)
          self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())), self.device)
          self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)), self.device)
  
          # ddim sampling parameters
          ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
                                                                                     ddim_timesteps=self.ddim_timesteps,
                                                                                     eta=ddim_eta,verbose=verbose)
          self.register_buffer('ddim_sigmas', ddim_sigmas, self.device)
          self.register_buffer('ddim_alphas', ddim_alphas, self.device)
          self.register_buffer('ddim_alphas_prev', ddim_alphas_prev, self.device)
          self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas), self.device)
          sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
              (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
                          1 - self.alphas_cumprod / self.alphas_cumprod_prev))
          self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps, self.device)
  
      @torch.no_grad()
      def sample(self,
                 S,
                 batch_size,
                 shape,
                 conditioning=None,
                 callback=None,
                 normals_sequence=None,
                 img_callback=None,
                 quantize_x0=False,
                 eta=0.,
                 mask=None,
                 x0=None,
                 temperature=1.,
                 noise_dropout=0.,
                 score_corrector=None,
                 corrector_kwargs=None,
                 verbose=True,
                 x_T=None,
                 log_every_t=100,
                 unconditional_guidance_scale=1.,
                 unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
                 dynamic_threshold=None,
                 **kwargs
                 ):
          if conditioning is not None:
              if isinstance(conditioning, dict):
                  ctmp = conditioning[list(conditioning.keys())[0]]
                  while isinstance(ctmp, list): ctmp = ctmp[0]
                  cbs = ctmp.shape[0]
                  if cbs != batch_size:
                      print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
  
              else:
                  if conditioning.shape[0] != batch_size:
                      print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
  
          self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
          # sampling
          C, H, W = shape
          size = (batch_size, C, H, W)
          print(f'Data shape for DDIM sampling is {size}, eta {eta}')
  
          samples, intermediates = self.ddim_sampling(conditioning, size,
                                                      callback=callback,
                                                      img_callback=img_callback,
                                                      quantize_denoised=quantize_x0,
                                                      mask=mask, x0=x0,
                                                      ddim_use_original_steps=False,
                                                      noise_dropout=noise_dropout,
                                                      temperature=temperature,
                                                      score_corrector=score_corrector,
                                                      corrector_kwargs=corrector_kwargs,
                                                      x_T=x_T,
                                                      log_every_t=log_every_t,
                                                      unconditional_guidance_scale=unconditional_guidance_scale,
                                                      unconditional_conditioning=unconditional_conditioning,
                                                      dynamic_threshold=dynamic_threshold,
                                                      )
          return samples, intermediates
  
      @torch.no_grad()
      def ddim_sampling(self, cond, shape,
                        x_T=None, ddim_use_original_steps=False,
                        callback=None, timesteps=None, quantize_denoised=False,
                        mask=None, x0=None, img_callback=None, log_every_t=100,
                        temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                        unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
                        t_start=-1):
          device = self.model.betas.device
          b = shape[0]
          if x_T is None:
              img = torch.randn(shape, device=device)
          else:
              img = x_T
  
          if timesteps is None:
              timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
          elif timesteps is not None and not ddim_use_original_steps:
              subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
              timesteps = self.ddim_timesteps[:subset_end]
  
          timesteps = timesteps[:t_start]
  
          intermediates = {'x_inter': [img], 'pred_x0': [img]}
          time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
          total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
          print(f"Running DDIM Sampling with {total_steps} timesteps")
  
          iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
  
          for i, step in enumerate(iterator):
              index = total_steps - i - 1
              ts = torch.full((b,), step, device=device, dtype=torch.long)
  
              if mask is not None:
                  assert x0 is not None
                  img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
                  img = img_orig * mask + (1. - mask) * img
  
              outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
                                        quantize_denoised=quantize_denoised, temperature=temperature,
                                        noise_dropout=noise_dropout, score_corrector=score_corrector,
                                        corrector_kwargs=corrector_kwargs,
                                        unconditional_guidance_scale=unconditional_guidance_scale,
                                        unconditional_conditioning=unconditional_conditioning,
                                        dynamic_threshold=dynamic_threshold)
              img, pred_x0 = outs
              if callback:
                  img = callback(i, img, pred_x0)
              if img_callback: img_callback(pred_x0, i)
  
              if index % log_every_t == 0 or index == total_steps - 1:
                  intermediates['x_inter'].append(img)
                  intermediates['pred_x0'].append(pred_x0)
  
          return img, intermediates
  
      @torch.no_grad()
      def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                        temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                        unconditional_guidance_scale=1., unconditional_conditioning=None,
                        dynamic_threshold=None):
          b, *_, device = *x.shape, x.device
  
          if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
              e_t = self.model.apply_model(x, t, c)
          else:
              x_in = torch.cat([x] * 2)
              t_in = torch.cat([t] * 2)
              if isinstance(c, dict):
                  assert isinstance(unconditional_conditioning, dict)
                  c_in = dict()
                  for k in c:
                      if isinstance(c[k], list):
                          c_in[k] = [torch.cat([
                              unconditional_conditioning[k][i],
                              c[k][i]]) for i in range(len(c[k]))]
                      else:
                          c_in[k] = torch.cat([
                                  unconditional_conditioning[k],
                                  c[k]])
              else:
                  c_in = torch.cat([unconditional_conditioning, c])
              e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
              e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
  
          if score_corrector is not None:
              assert self.model.parameterization == "eps"
              e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
  
          alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
          alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
          sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
          sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
          # select parameters corresponding to the currently considered timestep
          a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
          a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
          sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
          sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
  
          # current prediction for x_0
          pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
          if quantize_denoised:
              pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
  
          if dynamic_threshold is not None:
              pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
  
          # direction pointing to x_t
          dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
          noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
          if noise_dropout > 0.:
              noise = torch.nn.functional.dropout(noise, p=noise_dropout)
          x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
          return x_prev, pred_x0
  
      @torch.no_grad()
      def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
                 unconditional_guidance_scale=1.0, unconditional_conditioning=None):
          num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
  
          assert t_enc <= num_reference_steps
          num_steps = t_enc
  
          if use_original_steps:
              alphas_next = self.alphas_cumprod[:num_steps]
              alphas = self.alphas_cumprod_prev[:num_steps]
          else:
              alphas_next = self.ddim_alphas[:num_steps]
              alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
  
          x_next = x0
          intermediates = []
          inter_steps = []
          for i in tqdm(range(num_steps), desc='Encoding Image'):
              t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
              if unconditional_guidance_scale == 1.:
                  noise_pred = self.model.apply_model(x_next, t, c)
              else:
                  assert unconditional_conditioning is not None
                  e_t_uncond, noise_pred = torch.chunk(
                      self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
                                             torch.cat((unconditional_conditioning, c))), 2)
                  noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
  
              xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
              weighted_noise_pred = alphas_next[i].sqrt() * (
                      (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
              x_next = xt_weighted + weighted_noise_pred
              if return_intermediates and i % (
                      num_steps // return_intermediates) == 0 and i < num_steps - 1:
                  intermediates.append(x_next)
                  inter_steps.append(i)
              elif return_intermediates and i >= num_steps - 2:
                  intermediates.append(x_next)
                  inter_steps.append(i)
  
          out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
          if return_intermediates:
              out.update({'intermediates': intermediates})
          return x_next, out
  
      @torch.no_grad()
      def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
          # fast, but does not allow for exact reconstruction
          # t serves as an index to gather the correct alphas
          if use_original_steps:
              sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
              sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
          else:
              sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
              sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
  
          if noise is None:
              noise = torch.randn_like(x0)
          return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
                  extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
  
      @torch.no_grad()
      def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
                 use_original_steps=False):
  
          timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
          timesteps = timesteps[:t_start]
  
          time_range = np.flip(timesteps)
          total_steps = timesteps.shape[0]
          print(f"Running DDIM Sampling with {total_steps} timesteps")
  
          iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
          x_dec = x_latent
          for i, step in enumerate(iterator):
              index = total_steps - i - 1
              ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
              x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
                                            unconditional_guidance_scale=unconditional_guidance_scale,
                                            unconditional_conditioning=unconditional_conditioning)
          return x_dec