text: # Copyright The PyTorch Lightning team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import pickle
  from argparse import ArgumentParser
  from unittest.mock import MagicMock
  from typing import Optional
  
  import pytest
  import torch
  from torch.utils.data import DataLoader, random_split
  
  from pytorch_lightning import LightningDataModule, Trainer, seed_everything
  from tests.base import EvalModelTemplate
  from tests.base.datasets import TrialMNIST
  from tests.base.datamodules import TrialMNISTDataModule
  from tests.base.develop_utils import reset_seed
  from pytorch_lightning.utilities.model_utils import is_overridden
  from pytorch_lightning.accelerators.gpu_accelerator import GPUAccelerator
  from pytorch_lightning.callbacks import ModelCheckpoint
  
  
  def test_can_prepare_data(tmpdir):
  
      dm = TrialMNISTDataModule()
      trainer = Trainer()
      trainer.datamodule = dm
  
      # 1 no DM
      # prepare_data_per_node = True
      # local rank = 0   (True)
      trainer.prepare_data_per_node = True
      trainer.local_rank = 0
      assert trainer.data_connector.can_prepare_data()
  
      # local rank = 1   (False)
      trainer.local_rank = 1
      assert not trainer.data_connector.can_prepare_data()
  
      # prepare_data_per_node = False (prepare across all nodes)
      # global rank = 0   (True)
      trainer.prepare_data_per_node = False
      trainer.node_rank = 0
      trainer.local_rank = 0
      assert trainer.data_connector.can_prepare_data()
  
      # global rank = 1   (False)
      trainer.node_rank = 1
      trainer.local_rank = 0
      assert not trainer.data_connector.can_prepare_data()
      trainer.node_rank = 0
      trainer.local_rank = 1
      assert not trainer.data_connector.can_prepare_data()
  
      # 2 dm
      # prepar per node = True
      # local rank = 0 (True)
      trainer.prepare_data_per_node = True
      trainer.local_rank = 0
  
      # is_overridden prepare data = True
      # has been called
      # False
      dm._has_prepared_data = True
      assert not trainer.data_connector.can_prepare_data()
  
      # has not been called
      # True
      dm._has_prepared_data = False
      assert trainer.data_connector.can_prepare_data()
  
      # is_overridden prepare data = False
      # True
      dm.prepare_data = None
      assert trainer.data_connector.can_prepare_data()
  
  
  def test_hooks_no_recursion_error(tmpdir):
      # hooks were appended in cascade every tine a new data module was instantiated leading to a recursion error.
      # See https://github.com/PyTorchLightning/pytorch-lightning/issues/3652
      class DummyDM(LightningDataModule):
          def setup(self, *args, **kwargs):
              pass
  
          def prepare_data(self, *args, **kwargs):
              pass
  
      for i in range(1005):
          dm = DummyDM()
          dm.setup()
          dm.prepare_data()
  
  
  def test_base_datamodule(tmpdir):
      dm = TrialMNISTDataModule()
      dm.prepare_data()
      dm.setup()
  
  
  def test_base_datamodule_with_verbose_setup(tmpdir):
      dm = TrialMNISTDataModule()
      dm.prepare_data()
      dm.setup('fit')
      dm.setup('test')
  
  
  def test_data_hooks_called(tmpdir):
      dm = TrialMNISTDataModule()
      assert dm.has_prepared_data is False
      assert dm.has_setup_fit is False
      assert dm.has_setup_test is False
  
      dm.prepare_data()
      assert dm.has_prepared_data is True
      assert dm.has_setup_fit is False
      assert dm.has_setup_test is False
  
      dm.setup()
      assert dm.has_prepared_data is True
      assert dm.has_setup_fit is True
      assert dm.has_setup_test is True
  
  
  def test_data_hooks_called_verbose(tmpdir):
      dm = TrialMNISTDataModule()
      assert dm.has_prepared_data is False
      assert dm.has_setup_fit is False
      assert dm.has_setup_test is False
  
      dm.prepare_data()
      assert dm.has_prepared_data is True
      assert dm.has_setup_fit is False
      assert dm.has_setup_test is False
  
      dm.setup('fit')
      assert dm.has_prepared_data is True
      assert dm.has_setup_fit is True
      assert dm.has_setup_test is False
  
      dm.setup('test')
      assert dm.has_prepared_data is True
      assert dm.has_setup_fit is True
      assert dm.has_setup_test is True
  
  
  def test_data_hooks_called_with_stage_kwarg(tmpdir):
      dm = TrialMNISTDataModule()
      dm.prepare_data()
      assert dm.has_prepared_data is True
  
      dm.setup(stage='fit')
      assert dm.has_setup_fit is True
      assert dm.has_setup_test is False
  
      dm.setup(stage='test')
      assert dm.has_setup_fit is True
      assert dm.has_setup_test is True
  
  
  def test_dm_add_argparse_args(tmpdir):
      parser = ArgumentParser()
      parser = TrialMNISTDataModule.add_argparse_args(parser)
      args = parser.parse_args(['--data_dir', './my_data'])
      assert args.data_dir == './my_data'
  
  
  def test_dm_init_from_argparse_args(tmpdir):
      parser = ArgumentParser()
      parser = TrialMNISTDataModule.add_argparse_args(parser)
      args = parser.parse_args(['--data_dir', './my_data'])
      dm = TrialMNISTDataModule.from_argparse_args(args)
      dm.prepare_data()
      dm.setup()
  
  
  def test_dm_pickle_after_init(tmpdir):
      dm = TrialMNISTDataModule()
      pickle.dumps(dm)
  
  
  def test_train_loop_only(tmpdir):
      dm = TrialMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
      model.validation_step = None
      model.validation_step_end = None
      model.validation_epoch_end = None
      model.test_step = None
      model.test_step_end = None
      model.test_epoch_end = None
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=3,
          weights_summary=None,
      )
  
      # fit model
      result = trainer.fit(model, dm)
      assert result == 1
      assert trainer.logger_connector.callback_metrics['loss'] < 0.6
  
  
  def test_train_val_loop_only(tmpdir):
      reset_seed()
  
      dm = TrialMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
      model.validation_step = None
      model.validation_step_end = None
      model.validation_epoch_end = None
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=3,
          weights_summary=None,
      )
  
      # fit model
      result = trainer.fit(model, dm)
      assert result == 1
      assert trainer.logger_connector.callback_metrics['loss'] < 0.6
  
  
  def test_dm_checkpoint_save(tmpdir):
      reset_seed()
  
      dm = TrialMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=3,
          weights_summary=None,
          callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on')],
      )
  
      # fit model
      result = trainer.fit(model, dm)
      checkpoint_path = list(trainer.checkpoint_callback.best_k_models.keys())[0]
      checkpoint = torch.load(checkpoint_path)
      assert dm.__class__.__name__ in checkpoint
      assert checkpoint[dm.__class__.__name__] == dm.__class__.__name__
  
  
  def test_test_loop_only(tmpdir):
      reset_seed()
  
      dm = TrialMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=3,
          weights_summary=None,
      )
      trainer.test(model, datamodule=dm)
  
  
  def test_full_loop(tmpdir):
      reset_seed()
  
      dm = TrialMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=3,
          weights_summary=None,
          deterministic=True,
      )
  
      # fit model
      result = trainer.fit(model, dm)
      assert result == 1
  
      # test
      result = trainer.test(datamodule=dm)
      result = result[0]
      assert result['test_acc'] > 0.8
  
  
  def test_trainer_attached_to_dm(tmpdir):
      reset_seed()
  
      dm = TrialMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=3,
          weights_summary=None,
          deterministic=True,
      )
  
      # fit model
      result = trainer.fit(model, dm)
      assert result == 1
      assert dm.trainer is not None
  
      # test
      result = trainer.test(datamodule=dm)
      result = result[0]
      assert dm.trainer is not None
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="test requires multi-GPU machine")
  def test_full_loop_single_gpu(tmpdir):
      reset_seed()
  
      dm = TrialMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=3,
          weights_summary=None,
          gpus=1,
          deterministic=True,
      )
  
      # fit model
      result = trainer.fit(model, dm)
      assert result == 1
  
      # test
      result = trainer.test(datamodule=dm)
      result = result[0]
      assert result['test_acc'] > 0.8
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
  def test_full_loop_dp(tmpdir):
      reset_seed()
  
      dm = TrialMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=3,
          weights_summary=None,
          accelerator='dp',
          gpus=2,
          deterministic=True,
      )
  
      # fit model
      result = trainer.fit(model, dm)
      assert result == 1
  
      # test
      result = trainer.test(datamodule=dm)
      result = result[0]
      assert result['test_acc'] > 0.8
  
  
  @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="test requires multi-GPU machine")
  def test_dm_transfer_batch_to_device(tmpdir):
      class CustomBatch:
  
          def __init__(self, data):
              self.samples = data[0]
              self.targets = data[1]
  
      class CurrentTestDM(LightningDataModule):
  
          hook_called = False
  
          def transfer_batch_to_device(self, data, device):
              self.hook_called = True
              if isinstance(data, CustomBatch):
                  data.samples = data.samples.to(device)
                  data.targets = data.targets.to(device)
              else:
                  data = super().transfer_batch_to_device(data, device)
              return data
  
      model = EvalModelTemplate()
      dm = CurrentTestDM()
      batch = CustomBatch((torch.zeros(5, 28), torch.ones(5, 1, dtype=torch.long)))
  
      trainer = Trainer(gpus=1)
      # running .fit() would require us to implement custom data loaders, we mock the model reference instead
      trainer.get_model = MagicMock(return_value=model)
      if is_overridden('transfer_batch_to_device', dm):
          model.transfer_batch_to_device = dm.transfer_batch_to_device
  
      trainer.accelerator_backend = GPUAccelerator(trainer)
      batch_gpu = trainer.accelerator_backend.batch_to_device(batch, torch.device('cuda:0'))
      expected = torch.device('cuda', 0)
      assert dm.hook_called
      assert batch_gpu.samples.device == batch_gpu.targets.device == expected
  
  
  class CustomMNISTDataModule(LightningDataModule):
  
      def __init__(self, data_dir: str = "./"):
          super().__init__()
          self.data_dir = data_dir
          self._epochs_called_for = []
  
      def prepare_data(self):
          TrialMNIST(self.data_dir, train=True, download=True)
  
      def setup(self, stage: Optional[str] = None):
  
          mnist_full = TrialMNIST(
              root=self.data_dir, train=True, num_samples=64, download=True
          )
          self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64])
          self.dims = self.mnist_train[0][0].shape
  
      def train_dataloader(self):
          assert self.trainer.current_epoch not in self._epochs_called_for
          self._epochs_called_for.append(self.trainer.current_epoch)
  
          return DataLoader(self.mnist_train, batch_size=4)
  
  
  def test_dm_reload_dataloaders_every_epoch(tmpdir):
      """Test datamodule, where trainer argument
      reload_dataloaders_every_epoch is set to True/False"""
  
      dm = CustomMNISTDataModule(tmpdir)
  
      model = EvalModelTemplate()
      model.validation_step = None
      model.validation_step_end = None
      model.validation_epoch_end = None
      model.test_step = None
      model.test_step_end = None
      model.test_epoch_end = None
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=2,
          limit_train_batches=0.01,
          reload_dataloaders_every_epoch=True,
      )
      trainer.fit(model, dm)
  