text: import logging
  import torch
  import pprint
  import re
  import os
  
  from tqdm import tqdm
  
  import util.tool
  
  from util.tool import Batch
  
  class Model(torch.nn.Module):
      def __init__(self, args, DatasetTool, inputs):
          super().__init__()
          self.args = args
          self.optimizer = None
          self.DatasetTool = DatasetTool
      
      @property
      def device(self):
          if self.args.train.gpu:
              return torch.device('cuda')
          else:
              return torch.device('cpu')
  
      def forward(self, batch):
          raise NotImplementedError
  
      def set_optimizer(self):
          all_params = set(self.parameters())
          params = [{"params": list(all_params), "lr": self.args.lr.default}]
          self.optimizer = torch.optim.Adam(params)
  
      def get_pred(self, out):
          raise NotImplementedError
  
      def get_max_train(self, dataset):
          if self.args.dataset.part:
              max_train = min(self.args.dataset.part, len(dataset))
          else:
              max_train = len(dataset)
          return max_train
  
      def run_test(self, dataset):
          self.eval()
          all_out = []
          for batch in tqdm(Batch.to_list(dataset, self.args.train.batch)[0 : self.get_max_train(dataset)]):
              loss, out = self.forward(batch)
              all_out += self.get_pred(out)
          return self.DatasetTool.evaluate(all_out, dataset, self.args), all_out
  
      def run_batches(self, dataset, epoch):
          all_loss = 0
          all_size = 0
          iteration = 0
          for batch in tqdm(Batch.to_list(dataset, self.args.train.batch)[0 : self.get_max_train(dataset)]):
              loss, _ = self.forward(batch)
              self.zero_grad()
              loss.backward()
              self.optimizer.step()
              all_loss += loss.item()
              iteration += 1
              if self.args.train.iter_save is not None:
                  if iteration % self.args.train.iter_save == 0:
                      if self.args.train.max_save > 0:
                          self.save('epoch={epoch},iter={iter}'.format(epoch = epoch, iter = iteration))
                          self.clear_saves()
              all_size += len(batch)
          return all_loss / all_size, iteration
  
      def update_best(self, best, summary, epoch):
          stop_key = 'eval_dev_{}'.format(self.args.train.stop)
          train_key = 'eval_train_{}'.format(self.args.train.stop)
          if self.args.train.not_eval or (best.get(stop_key, 0) <= summary[stop_key] and self.args.train.stopmin is None) or (best.get(stop_key, summary[stop_key]) >= summary[stop_key] and self.args.train.stopmin is not None):
              if self.args.train.not_eval:
                  best.update(summary)
                  if self.args.train.max_save > 0:
                      self.save('epoch={epoch}'.format(epoch = epoch))
                      self.clear_saves()
              else:
                  best_dev = '{:f}'.format(summary[stop_key])
                  best_train = '{:f}'.format(summary[train_key])
                  best.update(summary)
                  if self.args.train.max_save > 0:
                      self.save('epoch={epoch},train_{key}={train},dev_{key}={dev}'.format(epoch = epoch, train = best_train, dev = best_dev, key = self.args.train.stop))
                      self.clear_saves()
          return best
  
      def get_summary(self, epoch, iteration):
          return {"epoch": epoch, "iteration": iteration}
  
      def update_summary(self, summary, tmp_summary):
          pass
  
      def run_train(self, train, dev, test):
          self.set_optimizer()
          iteration = 0
          best = {}
          for epoch in range(self.args.train.epoch):
              self.train()
              logging.info("Starting training epoch {}".format(epoch))
              summary = self.get_summary(epoch, iteration)
              loss, iter = self.run_batches(train, epoch)
              iteration += iter
              summary.update({"loss": loss})
              if not self.args.train.not_eval:
                  for set_name, dataset in {"train": train, "dev": dev, "test": test}.items():
                      tmp_summary, pred = self.run_test(dataset)
                      self.DatasetTool.record(pred, dataset, set_name, self.args)
                      summary.update({"eval_{}_{}".format(set_name, k): v for k, v in tmp_summary.items()})
              best = self.update_best(best, summary, epoch)
              logging.info(pprint.pformat(best))
              logging.info(pprint.pformat(summary))
  
      def run_eval(self, train, dev, test):
          logging.info("Starting evaluation")
          summary = {}
          for set_name, dataset in {"train": train, "dev": dev, "test": test}.items():
              tmp_summary, pred = self.run_test(dataset)
              self.DatasetTool.record(pred, dataset, set_name, self.args)
              summary.update({"eval_{}_{}".format(set_name, k): v for k, v in tmp_summary.items()})
          logging.info(pprint.pformat(summary))
             
  
      def start(self, inputs):
          train, dev, test = inputs
          if self.args.model.resume is not None:
              self.load(self.args.model.resume)
          if not self.args.model.test:
              self.run_train(train, dev, test)
          if self.args.model.resume is not None:
              self.run_eval(train, dev, test)
  
      def load(self, file):
          logging.info("Loading model from {}".format(file))
          state = torch.load(file)
          model_state = state["model"]
          self.load_state_dict(model_state)
  
      def save(self, name):
          file = "{}/{}.pkl".format(self.args.dir.output, name)
          if not os.path.exists(self.args.dir.output):
              os.makedirs(self.args.dir.output)
          logging.info("Saving model to {}".format(name))
          state = {
              "model": self.state_dict()
          }
          torch.save(state, file)
  
      def get_saves(self):
          files = [f for f in os.listdir(self.args.dir.output) if f.endswith('.pkl')]
          scores = []
          for name in files:
              re_str = r'dev_{}=([0-9\.]+)'.format(self.args.train.stop)
              dev_acc = re.findall(re_str, name)
              if dev_acc:
                  score = float(dev_acc[0].strip('.'))
                  scores.append((score, os.path.join(self.args.dir.output, name)))
          scores.sort(key=lambda tup: tup[0], reverse=True)
          return scores
  
      def clear_saves(self):
          scores_and_files = self.get_saves()
          if len(scores_and_files) > self.args.train.max_save:
              for score, name in scores_and_files[self.args.train.max_save : ]:
                  os.remove(name)