text: import glob
  import logging as log
  import os
  import pickle
  
  import cloudpickle
  import pytest
  import torch
  
  import tests.base.utils as tutils
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint
  from tests.base import EvalModelTemplate
  
  
  @pytest.mark.spawn
  @pytest.mark.parametrize("backend", ['dp', 'ddp'])
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  def test_running_test_pretrained_model_distrib(tmpdir, backend):
      """Verify `test()` on pretrained model."""
      tutils.set_random_master_port()
  
      model = EvalModelTemplate()
  
      # exp file to get meta
      logger = tutils.get_default_logger(tmpdir)
  
      # exp file to get weights
      checkpoint = tutils.init_checkpoint_callback(logger)
  
      trainer_options = dict(
          progress_bar_refresh_rate=0,
          max_epochs=2,
          train_percent_check=0.4,
          val_percent_check=0.2,
          checkpoint_callback=checkpoint,
          logger=logger,
          gpus=[0, 1],
          distributed_backend=backend,
      )
  
      # 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.dirpath,
                                           module_class=EvalModelTemplate)
  
      # run test set
      new_trainer = Trainer(**trainer_options)
      new_trainer.test(pretrained_model)
  
      # test we have good test accuracy
      tutils.assert_ok_model_acc(new_trainer)
  
      dataloaders = model.test_dataloader()
      if not isinstance(dataloaders, list):
          dataloaders = [dataloaders]
  
      for dataloader in dataloaders:
          tutils.run_prediction(dataloader, pretrained_model)
  
  
  def test_running_test_pretrained_model_cpu(tmpdir):
      """Verify test() on pretrained model."""
      model = EvalModelTemplate()
  
      # logger file to get meta
      logger = tutils.get_default_logger(tmpdir)
  
      # logger file to get weights
      checkpoint = tutils.init_checkpoint_callback(logger)
  
      trainer_options = dict(
          progress_bar_refresh_rate=0,
          max_epochs=3,
          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.dirpath, module_class=EvalModelTemplate
      )
  
      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."""
      hparams = EvalModelTemplate.get_default_hparams()
      model = EvalModelTemplate(**hparams)
  
      trainer_options = dict(
          progress_bar_refresh_rate=0,
          max_epochs=2,
          train_percent_check=0.4,
          val_percent_check=0.2,
          checkpoint_callback=ModelCheckpoint(tmpdir, save_top_k=-1),
          default_root_dir=tmpdir,
      )
  
      # fit model
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model)
      trainer.test(ckpt_path=None)
  
      # correct result and ok accuracy
      assert result == 1, 'training failed to complete'
  
      # load last checkpoint
      last_checkpoint = sorted(glob.glob(os.path.join(trainer.checkpoint_callback.dirpath, "*.ckpt")))[-1]
      pretrained_model = EvalModelTemplate.load_from_checkpoint(last_checkpoint)
  
      # test that hparams loaded correctly
      for k, v in hparams.items():
          assert getattr(pretrained_model, k) == v
  
      # assert weights are the same
      for (old_name, old_p), (new_name, new_p) in zip(model.named_parameters(), pretrained_model.named_parameters()):
          assert torch.all(torch.eq(old_p, new_p)), 'loaded weights are not the same as the saved weights'
  
      new_trainer = Trainer(**trainer_options)
      new_trainer.test(pretrained_model)
  
      # test we have good test accuracy
      tutils.assert_ok_model_acc(new_trainer)
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  def test_dp_resume(tmpdir):
      """Make sure DP continues training correctly."""
      hparams = EvalModelTemplate.get_default_hparams()
      model = EvalModelTemplate(**hparams)
  
      trainer_options = dict(
          max_epochs=1,
          gpus=2,
          distributed_backend='dp',
      )
  
      # get logger
      logger = tutils.get_default_logger(tmpdir)
  
      # 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. Increment since we finished the current epoch, don't want to rerun
      real_global_epoch = trainer.current_epoch + 1
  
      # 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_default_logger(tmpdir, version=logger.version)
      trainer_options['logger'] = new_logger
      trainer_options['checkpoint_callback'] = ModelCheckpoint(tmpdir)
      trainer_options['train_percent_check'] = 0.5
      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.train_dataloader
          tutils.run_prediction(dataloader, dp_model, dp=True)
  
      # new model
      model = EvalModelTemplate(**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_model_saving_loading(tmpdir):
      """Tests use case where trainer saves the model, and user loads it from tags independently."""
      model = EvalModelTemplate()
  
      # logger file to get meta
      logger = tutils.get_default_logger(tmpdir)
  
      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
      dataloaders = model.test_dataloader()
      if not isinstance(dataloaders, list):
          dataloaders = [dataloaders]
  
      for dataloader in dataloaders:
          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
      hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
      hparams_path = os.path.join(hparams_path, 'hparams.yaml')
      model_2 = EvalModelTemplate.load_from_checkpoint(
          checkpoint_path=new_weights_path,
          hparams_file=hparams_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
  
  
  def test_model_pickle(tmpdir):
      model = EvalModelTemplate()
      pickle.dumps(model)
      cloudpickle.dumps(model)
  