import torch
import argparse

def apply_snr_weight(loss, timesteps, noise_scheduler, gamma): 
  alphas_cumprod = noise_scheduler.alphas_cumprod
  sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
  sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
  alpha = sqrt_alphas_cumprod
  sigma = sqrt_one_minus_alphas_cumprod
  all_snr = (alpha / sigma) ** 2
  snr = torch.stack([all_snr[t] for t in timesteps])
  gamma_over_snr = torch.div(torch.ones_like(snr)*gamma,snr)
  snr_weight = torch.minimum(gamma_over_snr,torch.ones_like(gamma_over_snr)).float() #from paper
  loss = loss * snr_weight
  return loss

def add_custom_train_arguments(parser: argparse.ArgumentParser):
  parser.add_argument("--min_snr_gamma", type=float, default=None, help="gamma for reducing the weight of high loss timesteps. Lower numbers have stronger effect. 5 is recommended by paper. / 低いタイムステップでの高いlossに対して重みを減らすためのgamma値、低いほど効果が強く、論文では5が推奨")