text: import logging as log
  import os
  
  import torch
  
  import tests.utils as tutils
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.testing import LightningTestModel
  
  
  def test_running_test_pretrained_model_ddp(tmpdir):
      """Verify `test()` on pretrained model."""
      if not tutils.can_run_gpu_test():
          return
  
      tutils.reset_seed()
      tutils.set_random_master_port()
  
      hparams = tutils.get_hparams()
      model = LightningTestModel(hparams)
  
      # exp file to get meta
      logger = tutils.get_test_tube_logger(tmpdir, False)
  
      # exp file to get weights
      checkpoint = tutils.init_checkpoint_callback(logger)
  
      trainer_options = dict(
          show_progress_bar=False,
          max_epochs=1,
          train_percent_check=0.4,
          val_percent_check=0.2,
          checkpoint_callback=checkpoint,
          logger=logger,
          gpus=[0, 1],
          distributed_backend='ddp'
      )
  
      # fit model
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model)
  
      log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
  
      # correct result and ok accuracy
      assert result == 1, 'training failed to complete'
      pretrained_model = tutils.load_model(logger,
                                           trainer.checkpoint_callback.filepath,
                                           module_class=LightningTestModel)
  
      # run test set
      new_trainer = Trainer(**trainer_options)
      new_trainer.test(pretrained_model)
  
      for dataloader in model.test_dataloader():
          tutils.run_prediction(dataloader, pretrained_model)
  
  
  def test_running_test_pretrained_model(tmpdir):
      """Verify test() on pretrained model."""
      tutils.reset_seed()
  
      hparams = tutils.get_hparams()
      model = LightningTestModel(hparams)
  
      # logger file to get meta
      logger = tutils.get_test_tube_logger(tmpdir, False)
  
      # logger file to get weights
      checkpoint = tutils.init_checkpoint_callback(logger)
  
      trainer_options = dict(
          show_progress_bar=False,
          max_epochs=4,
          train_percent_check=0.4,
          val_percent_check=0.2,
          checkpoint_callback=checkpoint,
          logger=logger
      )
  
      # fit model
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model)
  
      # correct result and ok accuracy
      assert result == 1, 'training failed to complete'
      pretrained_model = tutils.load_model(
          logger, trainer.checkpoint_callback.filepath, module_class=LightningTestModel
      )
  
      new_trainer = Trainer(**trainer_options)
      new_trainer.test(pretrained_model)
  
      # test we have good test accuracy
      tutils.assert_ok_model_acc(new_trainer)
  
  
  def test_load_model_from_checkpoint(tmpdir):
      """Verify test() on pretrained model."""
      tutils.reset_seed()
  
      hparams = tutils.get_hparams()
      model = LightningTestModel(hparams)
  
      trainer_options = dict(
          show_progress_bar=False,
          max_epochs=2,
          train_percent_check=0.4,
          val_percent_check=0.2,
          checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1),
          logger=False,
          default_save_path=tmpdir,
      )
  
      # fit model
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model)
  
      # correct result and ok accuracy
      assert result == 1, 'training failed to complete'
  
      # load last checkpoint
      last_checkpoint = os.path.join(trainer.checkpoint_callback.filepath, "_ckpt_epoch_1.ckpt")
      if not os.path.isfile(last_checkpoint):
          last_checkpoint = os.path.join(trainer.checkpoint_callback.filepath, "_ckpt_epoch_0.ckpt")
      pretrained_model = LightningTestModel.load_from_checkpoint(last_checkpoint)
  
      # test that hparams loaded correctly
      for k, v in vars(hparams).items():
          assert getattr(pretrained_model.hparams, k) == v
  
      new_trainer = Trainer(**trainer_options)
      new_trainer.test(pretrained_model)
  
      # test we have good test accuracy
      tutils.assert_ok_model_acc(new_trainer)
  
  
  def test_running_test_pretrained_model_dp(tmpdir):
      """Verify test() on pretrained model."""
      tutils.reset_seed()
  
      if not tutils.can_run_gpu_test():
          return
  
      hparams = tutils.get_hparams()
      model = LightningTestModel(hparams)
  
      # logger file to get meta
      logger = tutils.get_test_tube_logger(tmpdir, False)
  
      # logger file to get weights
      checkpoint = tutils.init_checkpoint_callback(logger)
  
      trainer_options = dict(
          show_progress_bar=True,
          max_epochs=4,
          train_percent_check=0.4,
          val_percent_check=0.2,
          checkpoint_callback=checkpoint,
          logger=logger,
          gpus=[0, 1],
          distributed_backend='dp'
      )
  
      # fit model
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model)
  
      # correct result and ok accuracy
      assert result == 1, 'training failed to complete'
      pretrained_model = tutils.load_model(logger,
                                           trainer.checkpoint_callback.filepath,
                                           module_class=LightningTestModel)
  
      new_trainer = Trainer(**trainer_options)
      new_trainer.test(pretrained_model)
  
      # test we have good test accuracy
      tutils.assert_ok_model_acc(new_trainer)
  
  
  def test_dp_resume(tmpdir):
      """Make sure DP continues training correctly."""
      if not tutils.can_run_gpu_test():
          return
  
      tutils.reset_seed()
  
      hparams = tutils.get_hparams()
      model = LightningTestModel(hparams)
  
      trainer_options = dict(
          show_progress_bar=True,
          max_epochs=2,
          gpus=2,
          distributed_backend='dp',
      )
  
      # get logger
      logger = tutils.get_test_tube_logger(tmpdir, debug=False)
  
      # exp file to get weights
      # logger file to get weights
      checkpoint = tutils.init_checkpoint_callback(logger)
  
      # add these to the trainer options
      trainer_options['logger'] = logger
      trainer_options['checkpoint_callback'] = checkpoint
  
      # fit model
      trainer = Trainer(**trainer_options)
      trainer.is_slurm_managing_tasks = True
      result = trainer.fit(model)
  
      # track epoch before saving
      real_global_epoch = trainer.current_epoch
  
      # correct result and ok accuracy
      assert result == 1, 'amp + dp model failed to complete'
  
      # ---------------------------
      # HPC LOAD/SAVE
      # ---------------------------
      # save
      trainer.hpc_save(tmpdir, logger)
  
      # init new trainer
      new_logger = tutils.get_test_tube_logger(tmpdir, version=logger.version)
      trainer_options['logger'] = new_logger
      trainer_options['checkpoint_callback'] = ModelCheckpoint(tmpdir)
      trainer_options['train_percent_check'] = 0.2
      trainer_options['val_percent_check'] = 0.2
      trainer_options['max_epochs'] = 1
      new_trainer = Trainer(**trainer_options)
  
      # set the epoch start hook so we can predict before the model does the full training
      def assert_good_acc():
          assert new_trainer.current_epoch == real_global_epoch and new_trainer.current_epoch > 0
  
          # if model and state loaded correctly, predictions will be good even though we
          # haven't trained with the new loaded model
          dp_model = new_trainer.model
          dp_model.eval()
  
          dataloader = trainer.get_train_dataloader()
          tutils.run_prediction(dataloader, dp_model, dp=True)
  
      # new model
      model = LightningTestModel(hparams)
      model.on_train_start = assert_good_acc
  
      # fit new model which should load hpc weights
      new_trainer.fit(model)
  
      # test freeze on gpu
      model.freeze()
      model.unfreeze()
  
  
  def test_cpu_restore_training(tmpdir):
      """Verify continue training session on CPU."""
      tutils.reset_seed()
  
      hparams = tutils.get_hparams()
      model = LightningTestModel(hparams)
  
      # logger file to get meta
      test_logger_version = 10
      logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
  
      trainer_options = dict(
          max_epochs=8,
          val_check_interval=0.50,
          val_percent_check=0.2,
          train_percent_check=0.2,
          logger=logger,
          checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1)
      )
  
      # fit model
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model)
      real_global_epoch = trainer.current_epoch
  
      # traning complete
      assert result == 1, 'amp + ddp model failed to complete'
  
      # wipe-out trainer and model
      # retrain with not much data... this simulates picking training back up after slurm
      # we want to see if the weights come back correctly
      new_logger = tutils.get_test_tube_logger(tmpdir, False, version=test_logger_version)
      trainer_options = dict(
          max_epochs=2,
          val_check_interval=0.50,
          val_percent_check=0.2,
          train_percent_check=0.2,
          logger=new_logger,
          checkpoint_callback=ModelCheckpoint(tmpdir),
      )
      trainer = Trainer(**trainer_options)
      model = LightningTestModel(hparams)
  
      # set the epoch start hook so we can predict before the model does the full training
      def assert_good_acc():
          assert trainer.current_epoch == real_global_epoch
          assert trainer.current_epoch >= 0
  
          # if model and state loaded correctly, predictions will be good even though we
          # haven't trained with the new loaded model
          trainer.model.eval()
          for dataloader in trainer.get_val_dataloaders():
              tutils.run_prediction(dataloader, trainer.model)
  
      model.on_train_start = assert_good_acc
  
      # by calling fit again, we trigger training, loading weights from the cluster
      # and our hook to predict using current model before any more weight updates
      trainer.fit(model)
  
  
  def test_model_saving_loading(tmpdir):
      """Tests use case where trainer saves the model, and user loads it from tags independently."""
      tutils.reset_seed()
  
      hparams = tutils.get_hparams()
      model = LightningTestModel(hparams)
  
      # logger file to get meta
      logger = tutils.get_test_tube_logger(tmpdir, False)
  
      trainer_options = dict(
          max_epochs=1,
          logger=logger,
          checkpoint_callback=ModelCheckpoint(tmpdir)
      )
  
      # fit model
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model)
  
      # traning complete
      assert result == 1, 'amp + ddp model failed to complete'
  
      # make a prediction
      for dataloader in model.test_dataloader():
          for batch in dataloader:
              break
  
      x, y = batch
      x = x.view(x.size(0), -1)
  
      # generate preds before saving model
      model.eval()
      pred_before_saving = model(x)
  
      # save model
      new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
      trainer.save_checkpoint(new_weights_path)
  
      # load new model
      tags_path = tutils.get_data_path(logger, path_dir=tmpdir)
      tags_path = os.path.join(tags_path, 'meta_tags.csv')
      model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path,
                                                     tags_csv=tags_path)
      model_2.eval()
  
      # make prediction
      # assert that both predictions are the same
      new_pred = model_2(x)
      assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
  
  # if __name__ == '__main__':
  #     pytest.main([__file__])
  