text: import collections
  import logging
  import os
  from shutil import copyfile
  
  import numpy as np
  import torch
  
  from torch import optim
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  from tqdm import tqdm
  
  from data_loader import (
      AmericanNationalCorpusDataset,
      ObliterateLetters,
      ToTensor,
  )
  from src.discriminator_net import DiscriminatorNet
  from src.eval import measure_accuracy
  from src.generator_net import GeneratorNet
  from src.penalties import compute_gradient_penalty
  
  
  class BatchSampler:
      def __init__(self, data_loader):
          self.data_loader = data_loader
          self.iter = iter(data_loader)
  
      def sample_batch(self):
          try:
              return next(self.iter)
          except StopIteration:
              self.iter = iter(self.data_loader)
              return next(self.iter)
  
  
  def train(config, save_dir, device='cpu'):
      noisy_phrases = AmericanNationalCorpusDataset(
          config,
          transform_raw_phrase=ObliterateLetters(
              obliterate_ratio=config['replace_with_noise_probability']
          ),
          transform_sample_dict=ToTensor()
      )
      real_phrases = AmericanNationalCorpusDataset(
          config,
          transform_raw_phrase=None,
          transform_sample_dict=ToTensor()
      )
  
      logging.info(f'Dataset_size: {len(noisy_phrases)}')
  
      noisy_data_loader = DataLoader(
          noisy_phrases,
          batch_size=config['batch_size'],
          num_workers=config['num_workers'],
          shuffle=True,
          drop_last=True,
      )
      real_data_loader = DataLoader(
          real_phrases,
          batch_size=config['batch_size'],
          num_workers=config['num_workers'],
          shuffle=True,
          drop_last=True,
      )
  
      test_noisy_data_loader = DataLoader(
          noisy_phrases,
          batch_size=config['batch_size'],
          num_workers=config['num_workers'],
          shuffle=False,
          drop_last=True
      )
  
      test_real_data_loader = DataLoader(
          real_phrases,
          batch_size=config['batch_size'],
          num_workers=config['num_workers'],
          shuffle=False,
          drop_last=True
      )
  
      generator = GeneratorNet(config).to(device)
      discriminator = DiscriminatorNet(config).to(device)
  
      def show_params(model: GeneratorNet):
          for k,v in  model.state_dict().items():
              print(f'{k:<30} {str(tuple(v.shape)):<16} {v.mean().item():.6f} {v.std().item():.6f}')
      print('Generator')
      show_params(generator)
      print('Discriminator')
      show_params(discriminator)
      # exit(0)
      optimizer_gen = optim.Adam(
          generator.parameters(),
          lr=config['gen_learning_rate'],
          betas=(0.5, 0.9),
      )
      optimizer_dis = optim.Adam(
          discriminator.parameters(),
          lr=config['dis_learning_rate'],
          betas=(0.5, 0.9),
      )
  
      if save_dir is not None:
          writer = SummaryWriter(save_dir)
          copyfile('config.yaml', os.path.join(save_dir, 'config.yaml'))
  
      noisy_sampler = BatchSampler(noisy_data_loader)
      real_sampler = BatchSampler(real_data_loader)
  
      logs = collections.defaultdict(list)
      for step in range(1, config['steps'] + 1):
          for _ in range(config['dis_iter']):
              #### Discriminator_training
              discriminator.zero_grad()
              noisy_batch = noisy_sampler.sample_batch()
              real_batch = real_sampler.sample_batch()
  
              gen_input = noisy_batch['concat_phrase'].to(device)
              real_sample = real_batch['raw_phrase'].float().to(device)
              fake_sample = generator.forward(gen_input, temperature=0.9)
  
              fake_sample_pred = discriminator.forward(
                  fake_sample
              )
              real_sample_pred = discriminator.forward(real_sample)
  
              gradient_penalty = compute_gradient_penalty(
                  discriminator,
                  real_sample,
                  fake_sample
              )
  
              fake_score = fake_sample_pred.mean()
              real_score = real_sample_pred.mean()
  
              dis_loss = (
                  fake_score - real_score
                  + config['gradient_penalty_ratio'] * gradient_penalty
              )
  
              dis_loss.backward()
              optimizer_dis.step()
  
              logs['gradient_penalty'].append(gradient_penalty.item())
              logs['real_score'].append(real_score.item())
              logs['fake_score'].append(fake_score.item())
              logs['dis_loss'].append(dis_loss.item())
  
          for _ in range(config['gen_iter']):
              #### Generator traning
              generator.zero_grad()
              noisy_batch = noisy_sampler.sample_batch()
              # real_batch = real_sampler.sample_batch()
              gen_input = noisy_batch['concat_phrase'].to(device)
              # real_sample = real_batch['raw_phrase'].float().to(device)
              # real_sample_pred = discriminator.forward(real_sample)
              # real_score = real_sample_pred.mean()
  
              fake_sample = generator.forward(gen_input, temperature=0.9)
              fake_sample_pred = discriminator.forward(
                  fake_sample
              )
              fake_score = fake_sample_pred.mean()
              gen_loss = - fake_score
              gen_loss.backward()
              optimizer_gen.step()
  
              logs['real_score'].append(real_score.item())
              logs['fake_score'].append(fake_score.item())
              logs['gen_loss'].append(gen_loss.item())
  
          # Traning supervision. Saving scalars for tensorboard
          if step % config['print_step'] == 0:
              ###################################
              # Injected Prinitng
              ###################################
              print('#' * 80)
              p_fake = fake_sample.argmax(axis=-1)
              p_real = noisy_batch['raw_phrase'].argmax(axis=-1)
  
              def decode(xs):
                  return ''.join(
                      "_"
                      if x == ord('\0') or x <= 31 or x > 126
                      else chr(x)
                          for x in xs
                  )
  
              for (rl, fk) in zip(p_real[:5], p_fake[:5]):
                  print(decode(rl))
                  print(decode(fk))
              print('#' * 80)
              step_fer = (np.array(p_fake.cpu()) == np.array(p_real)).mean()
              print(f'Acc: {step_fer:.2}')
              ###################################
              # Injected Prinitng
              ###################################
              for k in logs.keys():
                  logs[k] = np.array(logs[k]).mean()
              logging.info(
                  f'Step:{step:5d} gen_loss:{logs["gen_loss"]:.3f} '
                  f'dis_loss:{logs["dis_loss"]:.3f}'
              )
  
              def assign_label(x):
                  if 'score' in x:
                      return f'scores/{x}'
                  if 'loss' in x:
                      return f'loss/{x}'
                  return x
  
              if save_dir is not None:
                  for k, v in logs.items():
                      writer.add_scalar(
                          assign_label(k),
                          v,
                          global_step=step,
                      )
              logs.clear()
          if (
              step % config['eval_step'] == 0
              or step == config['steps']
          ):
              acc = measure_accuracy(
                  generator,
                  test_real_data_loader,
                  test_noisy_data_loader,
                  device
              )
              logging.info(f'EvalStep:{step:5d} Accuracy: {acc:.2f}')
              if save_dir is not None:
                  writer.add_scalar(
                      'accuracy/accuracy',
                      acc,
                      global_step=step
                  )
                  # Model saving
                  torch.save(
                      generator.state_dict(),
                      os.path.join(save_dir, f'epoch_{step}_generator.pt')
                  )
                  torch.save(
                      discriminator.state_dict(),
                      os.path.join(save_dir,
                                   f'epoch_{step}_discriminator.pt')
                  )
      if save_dir is not None:
          writer.close()
  