text: import argparse
  import math
  import random
  import os
  
  import numpy as np
  import torch
  from torch import nn, autograd, optim
  from torch.nn import functional as F
  from torch.utils import data
  import torch.distributed as dist
  from torchvision import transforms, utils
  from tqdm import tqdm
  
  try:
      import wandb
  
  except ImportError:
      wandb = None
  
  
  from dataset import MultiResolutionDataset
  from distributed import (
      get_rank,
      synchronize,
      reduce_loss_dict,
      reduce_sum,
      get_world_size,
  )
  from op import conv2d_gradfix
  from non_leaking import augment, AdaptiveAugment
  
  
  def data_sampler(dataset, shuffle, distributed):
      if distributed:
          return data.distributed.DistributedSampler(dataset, shuffle=shuffle)
  
      if shuffle:
          return data.RandomSampler(dataset)
  
      else:
          return data.SequentialSampler(dataset)
  
  
  def requires_grad(model, flag=True):
      for p in model.parameters():
          p.requires_grad = flag
  
  
  def accumulate(model1, model2, decay=0.999):
      par1 = dict(model1.named_parameters())
      par2 = dict(model2.named_parameters())
  
      for k in par1.keys():
          par1[k].data.mul_(decay).add_(par2[k].data, alpha=1 - decay)
  
  
  def sample_data(loader):
      while True:
          for batch in loader:
              yield batch
  
  
  def d_logistic_loss(real_pred, fake_pred):
      real_loss = F.softplus(-real_pred)
      fake_loss = F.softplus(fake_pred)
  
      return real_loss.mean() + fake_loss.mean()
  
  
  def d_r1_loss(real_pred, real_img):
      with conv2d_gradfix.no_weight_gradients():
          grad_real, = autograd.grad(
              outputs=real_pred.sum(), inputs=real_img, create_graph=True
          )
      grad_penalty = grad_real.pow(2).reshape(grad_real.shape[0], -1).sum(1).mean()
  
      return grad_penalty
  
  
  def g_nonsaturating_loss(fake_pred):
      loss = F.softplus(-fake_pred).mean()
  
      return loss
  
  
  def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01):
      noise = torch.randn_like(fake_img) / math.sqrt(
          fake_img.shape[2] * fake_img.shape[3]
      )
      grad, = autograd.grad(
          outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True
      )
      path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1))
  
      path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length)
  
      path_penalty = (path_lengths - path_mean).pow(2).mean()
  
      return path_penalty, path_mean.detach(), path_lengths
  
  
  def make_noise(batch, latent_dim, n_noise, device):
      if n_noise == 1:
          return torch.randn(batch, latent_dim, device=device)
  
      noises = torch.randn(n_noise, batch, latent_dim, device=device).unbind(0)
  
      return noises
  
  
  def mixing_noise(batch, latent_dim, prob, device):
      if prob > 0 and random.random() < prob:
          return make_noise(batch, latent_dim, 2, device)
  
      else:
          return [make_noise(batch, latent_dim, 1, device)]
  
  
  def set_grad_none(model, targets):
      for n, p in model.named_parameters():
          if n in targets:
              p.grad = None
  
  
  def save(args, loader, device):
  
      for i, real_img in enumerate(loader):
  
          utils.save_image(
              real_img,
              args.root+f"/img_dataset/{str(i).zfill(6)}.png",
              nrow=int(1),
              normalize=True,
              range=(-1, 1),
          )
  
  
  
  
  if __name__ == "__main__":
      device = "cpu"
  
      parser = argparse.ArgumentParser(description="StyleGAN2 trainer")
  
      parser.add_argument("path", type=str, help="path to the lmdb dataset")
      parser.add_argument("--root", type=str, help="root directory of the experiment")
      parser.add_argument("--version", type=str, default="vanilla", help="Whether to use CDC model or not")
      parser.add_argument('--arch', type=str, default='stylegan2', help='model architectures (stylegan2 | swagan)')
      parser.add_argument(
          "--iter", type=int, default=800000, help="total training iterations"
      )
      parser.add_argument("--save_every", default=2000, type=int, help="when to save model checkpoints")
      parser.add_argument(
          "--batch", type=int, default=8, help="batch sizes for each gpus"
      )
      parser.add_argument(
          "--n_sample",
          type=int,
          default=64,
          help="number of the samples generated during training",
      )
      parser.add_argument(
          "--size", type=int, default=256, help="image sizes for the model"
      )
      parser.add_argument(
          "--r1", type=float, default=10, help="weight of the r1 regularization"
      )
      parser.add_argument(
          "--path_regularize",
          type=float,
          default=2,
          help="weight of the path length regularization",
      )
      parser.add_argument(
          "--path_batch_shrink",
          type=int,
          default=2,
          help="batch size reducing factor for the path length regularization (reduce memory consumption)",
      )
      parser.add_argument(
          "--d_reg_every",
          type=int,
          default=16,
          help="interval of the applying r1 regularization",
      )
      parser.add_argument(
          "--g_reg_every",
          type=int,
          default=4,
          help="interval of the applying path length regularization",
      )
      parser.add_argument(
          "--mixing", type=float, default=0.9, help="probability of latent code mixing"
      )
      parser.add_argument(
          "--ckpt",
          type=str,
          default=None,
          help="path to the checkpoints to resume training",
      )
      parser.add_argument("--lr", type=float, default=0.002, help="learning rate")
      parser.add_argument(
          "--channel_multiplier",
          type=int,
          default=2,
          help="channel multiplier factor for the model. config-f = 2, else = 1",
      )
      parser.add_argument(
          "--wandb", action="store_true", help="use weights and biases logging"
      )
      parser.add_argument(
          "--local_rank", type=int, default=0, help="local rank for distributed training"
      )
      parser.add_argument(
          "--augment", action="store_true", help="apply non leaking augmentation"
      )
      parser.add_argument(
          "--augment_p",
          type=float,
          default=0,
          help="probability of applying augmentation. 0 = use adaptive augmentation",
      )
      parser.add_argument(
          "--ada_target",
          type=float,
          default=0.6,
          help="target augmentation probability for adaptive augmentation",
      )
      parser.add_argument(
          "--ada_length",
          type=int,
          default=500 * 1000,
          help="target duraing to reach augmentation probability for adaptive augmentation",
      )
      parser.add_argument(
          "--ada_every",
          type=int,
          default=256,
          help="probability update interval of the adaptive augmentation",
      )
  
      args = parser.parse_args()
  
  
      transform = transforms.Compose(
          [
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True),
          ]
      )
  
      dataset = MultiResolutionDataset(args.root+"/"+args.path, transform, args.size)
      loader = data.DataLoader(
          dataset,
          batch_size=1,
          sampler=data_sampler(dataset, shuffle=True, distributed=False),
          drop_last=False,
      )
  
      if get_rank() == 0 and wandb is not None and args.wandb:
          wandb.init(project="stylegan 2")
  
      save(args, loader, device)
  