content: """SAMPLING ONLY."""
  
  import torch
  import numpy as np
  from tqdm import tqdm
  from functools import partial
  
  from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
  from ldm.models.diffusion.sampling_util import norm_thresholding
  
  
  class PLMSSampler(object):
      def __init__(self, model, schedule="linear", **kwargs):
          super().__init__()
          self.model = model
          self.ddpm_num_timesteps = model.num_timesteps
          self.schedule = schedule
  
      def register_buffer(self, name, attr):
          if type(attr) == torch.Tensor:
              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):
          if ddim_eta != 0:
              raise ValueError('ddim_eta must be 0 for PLMS')
          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.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
          self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
  
          # 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.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
          self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
          self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
          self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
  
          # 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.register_buffer('ddim_alphas', ddim_alphas)
          self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
          self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
          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)
  
      @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 PLMS sampling is {size}')
  
          samples, intermediates = self.plms_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 plms_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):
          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]
  
          intermediates = {'x_inter': [img], 'pred_x0': [img]}
          time_range = list(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 PLMS Sampling with {total_steps} timesteps")
  
          iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
          old_eps = []
  
          for i, step in enumerate(iterator):
              index = total_steps - i - 1
              ts = torch.full((b,), step, device=device, dtype=torch.long)
              ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], 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_plms(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,
                                        old_eps=old_eps, t_next=ts_next,
                                        dynamic_threshold=dynamic_threshold)
              img, pred_x0, e_t = outs
              old_eps.append(e_t)
              if len(old_eps) >= 4:
                  old_eps.pop(0)
              if callback: callback(i)
              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_plms(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, old_eps=None, t_next=None,
                        dynamic_threshold=None):
          b, *_, device = *x.shape, x.device
  
          def get_model_output(x, t):
              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)
  
              return e_t
  
          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
  
          def get_x_prev_and_pred_x0(e_t, index):
              # 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
  
          e_t = get_model_output(x, t)
          if len(old_eps) == 0:
              # Pseudo Improved Euler (2nd order)
              x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
              e_t_next = get_model_output(x_prev, t_next)
              e_t_prime = (e_t + e_t_next) / 2
          elif len(old_eps) == 1:
              # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
              e_t_prime = (3 * e_t - old_eps[-1]) / 2
          elif len(old_eps) == 2:
              # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
              e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
          elif len(old_eps) >= 3:
              # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
              e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
  
          x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
  
          return x_prev, pred_x0, e_t
  