code: import os
  
  import pytest
  import torch
  
  import tests.base.utils as tutils
  from pytorch_lightning import Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.core import memory
  from pytorch_lightning.trainer.distrib_parts import (
      parse_gpu_ids,
      determine_root_gpu_device,
  )
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  from tests.base import LightningTestModel
  
  PRETEND_N_OF_GPUS = 16
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  def test_multi_gpu_model_ddp2(tmpdir):
      """Make sure DDP2 works."""
  
      tutils.reset_seed()
      tutils.set_random_master_port()
  
      model, hparams = tutils.get_default_model()
      trainer_options = dict(
          default_save_path=tmpdir,
          show_progress_bar=True,
          max_epochs=1,
          train_percent_check=0.4,
          val_percent_check=0.2,
          gpus=2,
          weights_summary=None,
          distributed_backend='ddp2'
      )
  
      tutils.run_model_test(trainer_options, model)
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  def test_multi_gpu_model_ddp(tmpdir):
      """Make sure DDP works."""
  
      tutils.reset_seed()
      tutils.set_random_master_port()
  
      model, hparams = tutils.get_default_model()
      trainer_options = dict(
          default_save_path=tmpdir,
          show_progress_bar=False,
          max_epochs=1,
          train_percent_check=0.4,
          val_percent_check=0.2,
          gpus=[0, 1],
          distributed_backend='ddp'
      )
  
      tutils.run_model_test(trainer_options, model)
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
      """Make sure DDP works with dataloaders passed to fit()"""
  
      tutils.reset_seed()
      tutils.set_random_master_port()
  
      model, hparams = tutils.get_default_model()
      trainer_options = dict(default_save_path=tmpdir,
                             show_progress_bar=False,
                             max_epochs=1,
                             train_percent_check=0.4,
                             val_percent_check=0.2,
                             gpus=[0, 1],
                             distributed_backend='ddp')
  
      fit_options = dict(train_dataloader=model.train_dataloader(),
                         val_dataloaders=model.val_dataloader())
  
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model, **fit_options)
      assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
  
  
  def test_optimizer_return_options():
      tutils.reset_seed()
  
      trainer = Trainer()
      model, hparams = tutils.get_default_model()
  
      # single optimizer
      opt_a = torch.optim.Adam(model.parameters(), lr=0.002)
      opt_b = torch.optim.SGD(model.parameters(), lr=0.002)
      scheduler_a = torch.optim.lr_scheduler.StepLR(opt_a, 10)
      scheduler_b = torch.optim.lr_scheduler.StepLR(opt_b, 10)
  
      # single optimizer
      optim, lr_sched, freq = trainer.init_optimizers(opt_a)
      assert len(optim) == 1 and len(lr_sched) == 0 and len(freq) == 0
  
      # opt tuple
      opts = (opt_a, opt_b)
      optim, lr_sched, freq = trainer.init_optimizers(opts)
      assert len(optim) == 2 and optim[0] == opts[0] and optim[1] == opts[1]
      assert len(lr_sched) == 0 and len(freq) == 0
  
      # opt list
      opts = [opt_a, opt_b]
      optim, lr_sched, freq = trainer.init_optimizers(opts)
      assert len(optim) == 2 and optim[0] == opts[0] and optim[1] == opts[1]
      assert len(lr_sched) == 0 and len(freq) == 0
  
      # opt tuple of 2 lists
      opts = ([opt_a], [scheduler_a])
      optim, lr_sched, freq = trainer.init_optimizers(opts)
      assert len(optim) == 1 and len(lr_sched) == 1 and len(freq) == 0
      assert optim[0] == opts[0][0]
      assert lr_sched[0] == dict(scheduler=scheduler_a, interval='epoch',
                                 frequency=1, reduce_on_plateau=False, monitor='val_loss')
  
      # opt single dictionary
      opts = {"optimizer": opt_a, "lr_scheduler": scheduler_a}
      optim, lr_sched, freq = trainer.init_optimizers(opts)
      assert len(optim) == 1 and len(lr_sched) == 1 and len(freq) == 0
      assert optim[0] == opt_a
      assert lr_sched[0] == dict(scheduler=scheduler_a, interval='epoch',
                                 frequency=1, reduce_on_plateau=False, monitor='val_loss')
  
      # opt multiple dictionaries with frequencies
      opts = (
          {"optimizer": opt_a, "lr_scheduler": scheduler_a, "frequency": 1},
          {"optimizer": opt_b, "lr_scheduler": scheduler_b, "frequency": 5},
      )
      optim, lr_sched, freq = trainer.init_optimizers(opts)
      assert len(optim) == 2 and len(lr_sched) == 2 and len(freq) == 2
      assert optim[0] == opt_a
      assert lr_sched[0] == dict(scheduler=scheduler_a, interval='epoch',
                                 frequency=1, reduce_on_plateau=False, monitor='val_loss')
      assert freq == [1, 5]
  
  
  def test_cpu_slurm_save_load(tmpdir):
      """Verify model save/load/checkpoint on CPU."""
      tutils.reset_seed()
  
      hparams = tutils.get_default_hparams()
      model = LightningTestModel(hparams)
  
      # logger file to get meta
      logger = tutils.get_default_testtube_logger(tmpdir, False)
      version = logger.version
  
      trainer_options = dict(
          max_epochs=1,
          logger=logger,
          checkpoint_callback=ModelCheckpoint(tmpdir)
      )
  
      # fit model
      trainer = Trainer(**trainer_options)
      result = trainer.fit(model)
      real_global_step = trainer.global_step
  
      # traning complete
      assert result == 1, 'amp + ddp model failed to complete'
  
      # predict with trained model before saving
      # 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)
  
      model.eval()
      pred_before_saving = model(x)
  
      # test HPC saving
      # simulate snapshot on slurm
      saved_filepath = trainer.hpc_save(tmpdir, logger)
      assert os.path.exists(saved_filepath)
  
      # new logger file to get meta
      logger = tutils.get_default_testtube_logger(tmpdir, False, version=version)
  
      trainer_options = dict(
          max_epochs=1,
          logger=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_pred_same():
          assert trainer.global_step == real_global_step and trainer.global_step > 0
  
          # predict with loaded model to make sure answers are the same
          trainer.model.eval()
          new_pred = trainer.model(x)
          assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
  
      model.on_epoch_start = assert_pred_same
  
      # 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)
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  def test_multi_gpu_none_backend(tmpdir):
      """Make sure when using multiple GPUs the user can't use `distributed_backend = None`."""
      tutils.reset_seed()
  
      model, hparams = tutils.get_default_model()
      trainer_options = dict(
          default_save_path=tmpdir,
          show_progress_bar=False,
          max_epochs=1,
          train_percent_check=0.1,
          val_percent_check=0.1,
          gpus='-1'
      )
  
      with pytest.warns(UserWarning):
          tutils.run_model_test(trainer_options, model)
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  def test_multi_gpu_model_dp(tmpdir):
      """Make sure DP works."""
      tutils.reset_seed()
  
      model, hparams = tutils.get_default_model()
      trainer_options = dict(
          default_save_path=tmpdir,
          show_progress_bar=False,
          distributed_backend='dp',
          max_epochs=1,
          train_percent_check=0.1,
          val_percent_check=0.1,
          gpus='-1'
      )
  
      tutils.run_model_test(trainer_options, model)
  
      # test memory helper functions
      memory.get_memory_profile('min_max')
  
  
  @pytest.fixture
  def mocked_device_count(monkeypatch):
      def device_count():
          return PRETEND_N_OF_GPUS
  
      monkeypatch.setattr(torch.cuda, 'device_count', device_count)
  
  
  @pytest.fixture
  def mocked_device_count_0(monkeypatch):
      def device_count():
          return 0
  
      monkeypatch.setattr(torch.cuda, 'device_count', device_count)
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize(["gpus", "expected_num_gpus", "distributed_backend"], [
      pytest.param(None, 0, None, id="None - expect 0 gpu to use."),
      pytest.param(0, 0, None, id="Oth gpu, expect 1 gpu to use."),
      pytest.param(1, 1, None, id="1st gpu, expect 1 gpu to use."),
      pytest.param(-1, PRETEND_N_OF_GPUS, "ddp", id="-1 - use all gpus"),
      pytest.param('-1', PRETEND_N_OF_GPUS, "ddp", id="'-1' - use all gpus"),
      pytest.param(3, 3, "ddp", id="3rd gpu - 1 gpu to use (backend:ddp)")
  ])
  def test_trainer_gpu_parse(mocked_device_count, gpus, expected_num_gpus, distributed_backend):
      assert Trainer(gpus=gpus, distributed_backend=distributed_backend).num_gpus == expected_num_gpus
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize(["gpus", "expected_num_gpus", "distributed_backend"], [
      pytest.param(None, 0, None, id="None - expect 0 gpu to use."),
      pytest.param(None, 0, "ddp", id="None - expect 0 gpu to use."),
  ])
  def test_trainer_num_gpu_0(mocked_device_count_0, gpus, expected_num_gpus, distributed_backend):
      assert Trainer(gpus=gpus, distributed_backend=distributed_backend).num_gpus == expected_num_gpus
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize(['gpus', 'expected_root_gpu', "distributed_backend"], [
      pytest.param(None, None, "ddp", id="None is None"),
      pytest.param(0, None, "ddp", id="O gpus, expect gpu root device to be None."),
      pytest.param(1, 0, "ddp", id="1 gpu, expect gpu root device to be 0."),
      pytest.param(-1, 0, "ddp", id="-1 - use all gpus, expect gpu root device to be 0."),
      pytest.param('-1', 0, "ddp", id="'-1' - use all gpus, expect gpu root device to be 0."),
      pytest.param(3, 0, "ddp", id="3 gpus, expect gpu root device to be 0.(backend:ddp)")
  ])
  def test_root_gpu_property(mocked_device_count, gpus, expected_root_gpu, distributed_backend):
      assert Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu == expected_root_gpu
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize([
      'gpus', 'expected_root_gpu', "distributed_backend"], [
      pytest.param(None, None, None, id="None is None"),
      pytest.param(None, None, "ddp", id="None is None"),
      pytest.param(0, None, "ddp", id="None is None"),
  ])
  def test_root_gpu_property_0_passing(
          mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
      assert Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu == expected_root_gpu
  
  
  # Asking for a gpu when non are available will result in a MisconfigurationException
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize([
      'gpus', 'expected_root_gpu', "distributed_backend"], [
      pytest.param(1, None, "ddp"),
      pytest.param(3, None, "ddp"),
      pytest.param(3, None, "ddp"),
      pytest.param([1, 2], None, "ddp"),
      pytest.param([0, 1], None, "ddp"),
      pytest.param(-1, None, "ddp"),
      pytest.param('-1', None, "ddp")
  ])
  def test_root_gpu_property_0_raising(
          mocked_device_count_0, gpus, expected_root_gpu, distributed_backend):
      with pytest.raises(MisconfigurationException):
          Trainer(gpus=gpus, distributed_backend=distributed_backend).root_gpu
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize(['gpus', 'expected_root_gpu'], [
      pytest.param(None, None, id="No gpus, expect gpu root device to be None"),
      pytest.param([0], 0, id="Oth gpu, expect gpu root device to be 0."),
      pytest.param([1], 1, id="1st gpu, expect gpu root device to be 1."),
      pytest.param([3], 3, id="3rd gpu, expect gpu root device to be 3."),
      pytest.param([1, 2], 1, id="[1, 2] gpus, expect gpu root device to be 1."),
  ])
  def test_determine_root_gpu_device(gpus, expected_root_gpu):
      assert determine_root_gpu_device(gpus) == expected_root_gpu
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize(['gpus', 'expected_gpu_ids'], [
      pytest.param(None, None),
      pytest.param(0, None),
      pytest.param(1, [0]),
      pytest.param(3, [0, 1, 2]),
      pytest.param(-1, list(range(PRETEND_N_OF_GPUS)), id="-1 - use all gpus"),
      pytest.param([0], [0]),
      pytest.param([1, 3], [1, 3]),
      pytest.param('0', [0]),
      pytest.param('3', [3]),
      pytest.param('1, 3', [1, 3]),
      pytest.param('-1', list(range(PRETEND_N_OF_GPUS)), id="'-1' - use all gpus"),
  ])
  def test_parse_gpu_ids(mocked_device_count, gpus, expected_gpu_ids):
      assert parse_gpu_ids(gpus) == expected_gpu_ids
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize(['gpus'], [
      pytest.param(0.1),
      pytest.param(-2),
      pytest.param(False),
      pytest.param([]),
      pytest.param([-1]),
      pytest.param([None]),
      pytest.param(['0']),
      pytest.param((0, 1)),
  ])
  def test_parse_gpu_fail_on_unsupported_inputs(mocked_device_count, gpus):
      with pytest.raises(MisconfigurationException):
          parse_gpu_ids(gpus)
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize("gpus", [''])
  def test_parse_gpu_fail_on_empty_string(mocked_device_count, gpus):
      # This currently results in a ValueError instead of MisconfigurationException
      with pytest.raises(ValueError):
          parse_gpu_ids(gpus)
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize("gpus", [[1, 2, 19], -1, '-1'])
  def test_parse_gpu_fail_on_non_existant_id(mocked_device_count_0, gpus):
      with pytest.raises(MisconfigurationException):
          parse_gpu_ids(gpus)
  
  
  @pytest.mark.gpus_param_tests
  def test_parse_gpu_fail_on_non_existant_id_2(mocked_device_count):
      with pytest.raises(MisconfigurationException):
          parse_gpu_ids([1, 2, 19])
  
  
  @pytest.mark.gpus_param_tests
  @pytest.mark.parametrize("gpus", [-1, '-1'])
  def test_parse_gpu_returns_None_when_no_devices_are_available(mocked_device_count_0, gpus):
      with pytest.raises(MisconfigurationException):
          parse_gpu_ids(gpus)
  
  
  # if __name__ == '__main__':
  #     pytest.main([__file__])
  
apis: torch.optim.lr_scheduler.StepLR torch.eq torch.cuda.device_count