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 pytest
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.data._utils.collate import default_collate
  
  from pytorch_lightning import Trainer
  from pytorch_lightning.core.lightning import LightningModule
  from pytorch_lightning.loops.closure import Closure
  from pytorch_lightning.trainer.states import RunningStage
  from tests.helpers.boring_model import BoringModel, RandomDataset
  from tests.helpers.deterministic_model import DeterministicModel
  from tests.helpers.utils import no_warning_call
  
  
  def test__training_step__flow_scalar(tmpdir):
      """Tests that only training_step can be used."""
  
      class TestModel(DeterministicModel):
          def training_step(self, batch, batch_idx):
              acc = self.step(batch, batch_idx)
              acc = acc + batch_idx
              self.training_step_called = True
              return acc
  
          def backward(self, loss, optimizer, optimizer_idx):
              return LightningModule.backward(self, loss, optimizer, optimizer_idx)
  
      model = TestModel()
      model.val_dataloader = None
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=2,
          limit_val_batches=2,
          max_epochs=2,
          log_every_n_steps=1,
          weights_summary=None,
      )
      trainer.fit(model)
  
      # make sure correct steps were called
      assert model.training_step_called
      assert not model.training_step_end_called
      assert not model.training_epoch_end_called
  
  
  def test__training_step__tr_step_end__flow_scalar(tmpdir):
      """Tests that only training_step can be used."""
  
      class TestModel(DeterministicModel):
          def training_step(self, batch, batch_idx):
              acc = self.step(batch, batch_idx)
              acc = acc + batch_idx
              self.training_step_called = True
              self.out = acc
              return acc
  
          def training_step_end(self, tr_step_output):
              assert self.out == tr_step_output
              assert self.count_num_graphs({"loss": tr_step_output}) == 1
              self.training_step_end_called = True
              return tr_step_output
  
          def backward(self, loss, optimizer, optimizer_idx):
              return LightningModule.backward(self, loss, optimizer, optimizer_idx)
  
      model = TestModel()
      model.val_dataloader = None
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=2,
          limit_val_batches=2,
          max_epochs=2,
          log_every_n_steps=1,
          weights_summary=None,
      )
      trainer.fit(model)
  
      # make sure correct steps were called
      assert model.training_step_called
      assert model.training_step_end_called
      assert not model.training_epoch_end_called
  
  
  def test__training_step__epoch_end__flow_scalar(tmpdir):
      """Tests that only training_step can be used."""
  
      class TestModel(DeterministicModel):
          def training_step(self, batch, batch_idx):
              acc = self.step(batch, batch_idx)
              acc = acc + batch_idx
  
              self.training_step_called = True
              return acc
  
          def training_epoch_end(self, outputs):
              self.training_epoch_end_called = True
  
              # verify we saw the current num of batches
              assert len(outputs) == 2
  
              for b in outputs:
                  # time = 1
                  assert len(b) == 1
                  assert "loss" in b
                  assert isinstance(b, dict)
  
          def backward(self, loss, optimizer, optimizer_idx):
              return LightningModule.backward(self, loss, optimizer, optimizer_idx)
  
      model = TestModel()
      model.val_dataloader = None
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=2,
          limit_val_batches=2,
          max_epochs=2,
          log_every_n_steps=1,
          weights_summary=None,
      )
      trainer.fit(model)
  
      # make sure correct steps were called
      assert model.training_step_called
      assert not model.training_step_end_called
      assert model.training_epoch_end_called
  
      # assert epoch end metrics were added
      assert len(trainer.callback_metrics) == 0
      assert len(trainer.progress_bar_metrics) == 0
  
      trainer.state.stage = RunningStage.TRAINING
      # make sure training outputs what is expected
      batch_idx, batch = 0, next(iter(model.train_dataloader()))
      out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
      assert out.signal == 0
  
      train_step_out = out.training_step_output
      assert len(train_step_out) == 1
      train_step_out = train_step_out[0][0]
      assert isinstance(train_step_out.minimize, torch.Tensor)
      assert train_step_out.minimize.item() == 171
  
      # make sure the optimizer closure returns the correct things
      opt_closure = trainer.fit_loop.epoch_loop.batch_loop.optimizer_loop._make_closure(
          batch, batch_idx, 0, trainer.optimizers[0], hiddens=None
      )
      opt_closure_result = opt_closure()
      assert opt_closure_result.item() == 171
  
  
  def test__training_step__step_end__epoch_end__flow_scalar(tmpdir):
      """Checks train_step + training_step_end + training_epoch_end (all with scalar return from train_step)."""
  
      class TestModel(DeterministicModel):
          def training_step(self, batch, batch_idx):
              acc = self.step(batch, batch_idx)
              acc = acc + batch_idx
  
              self.training_step_called = True
              return acc
  
          def training_step_end(self, tr_step_output):
              assert isinstance(tr_step_output, torch.Tensor)
              assert self.count_num_graphs({"loss": tr_step_output}) == 1
              self.training_step_end_called = True
              return tr_step_output
  
          def training_epoch_end(self, outputs):
              self.training_epoch_end_called = True
  
              # verify we saw the current num of batches
              assert len(outputs) == 2
  
              for b in outputs:
                  # time = 1
                  assert len(b) == 1
                  assert "loss" in b
                  assert isinstance(b, dict)
  
          def backward(self, loss, optimizer, optimizer_idx):
              return LightningModule.backward(self, loss, optimizer, optimizer_idx)
  
      model = TestModel()
      model.val_dataloader = None
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=2,
          limit_val_batches=2,
          max_epochs=2,
          log_every_n_steps=1,
          weights_summary=None,
      )
      trainer.fit(model)
  
      # make sure correct steps were called
      assert model.training_step_called
      assert model.training_step_end_called
      assert model.training_epoch_end_called
  
      # assert epoch end metrics were added
      assert len(trainer.callback_metrics) == 0
      assert len(trainer.progress_bar_metrics) == 0
  
      trainer.state.stage = RunningStage.TRAINING
      # make sure training outputs what is expected
      batch_idx, batch = 0, next(iter(model.train_dataloader()))
      out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
      assert out.signal == 0
  
      train_step_out = out.training_step_output
      assert len(train_step_out) == 1
      train_step_out = train_step_out[0][0]
      assert isinstance(train_step_out.minimize, torch.Tensor)
      assert train_step_out.minimize.item() == 171
  
      # make sure the optimizer closure returns the correct things
      opt_closure = trainer.fit_loop.epoch_loop.batch_loop.optimizer_loop._make_closure(
          batch, batch_idx, 0, trainer.optimizers[0], hiddens=None
      )
      opt_closure_result = opt_closure()
      assert opt_closure_result.item() == 171
  
  
  def test_train_step_no_return(tmpdir):
      """Tests that only training_step raises a warning when nothing is returned in case of
      automatic_optimization."""
  
      class TestModel(BoringModel):
          def training_step(self, batch, batch_idx):
              self.training_step_called = True
              loss = self.step(batch[0])
              self.log("a", loss, on_step=True, on_epoch=True)
  
          def training_epoch_end(self, outputs) -> None:
              assert len(outputs) == 0
  
          def validation_step(self, batch, batch_idx):
              self.validation_step_called = True
  
          def validation_epoch_end(self, outputs):
              assert len(outputs) == 0
  
      model = TestModel()
      trainer_args = dict(default_root_dir=tmpdir, fast_dev_run=2)
  
      trainer = Trainer(**trainer_args)
  
      Closure.warning_cache.clear()
  
      with pytest.warns(UserWarning, match=r"training_step returned None.*"):
          trainer.fit(model)
  
      assert model.training_step_called
      assert model.validation_step_called
  
      model = TestModel()
      model.automatic_optimization = False
      trainer = Trainer(**trainer_args)
  
      Closure.warning_cache.clear()
  
      with no_warning_call(UserWarning, match=r"training_step returned None.*"):
          trainer.fit(model)
  
  
  def test_training_step_no_return_when_even(tmpdir):
      """Tests correctness when some training steps have been skipped."""
  
      class TestModel(BoringModel):
          def training_step(self, batch, batch_idx):
              self.training_step_called = True
              loss = self.step(batch[0])
              self.log("a", loss, on_step=True, on_epoch=True)
              return loss if batch_idx % 2 else None
  
      model = TestModel()
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=4,
          limit_val_batches=1,
          max_epochs=4,
          weights_summary=None,
          logger=False,
          checkpoint_callback=False,
      )
  
      Closure.warning_cache.clear()
  
      with pytest.warns(UserWarning, match=r".*training_step returned None.*"):
          trainer.fit(model)
  
      trainer.state.stage = RunningStage.TRAINING
  
      # manually check a few batches
      for batch_idx, batch in enumerate(model.train_dataloader()):
          out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
          if not batch_idx % 2:
              assert out.training_step_output == [[]]
          assert out.signal == 0
  
  
  def test_training_step_none_batches(tmpdir):
      """Tests correctness when the train dataloader gives None for some steps."""
  
      class TestModel(BoringModel):
          def __init__(self):
              super().__init__()
  
              self.counter = 0
  
          def collate_none_when_even(self, batch):
              if self.counter % 2 == 0:
                  result = None
              else:
                  result = default_collate(batch)
              self.counter += 1
              return result
  
          def train_dataloader(self):
              return DataLoader(RandomDataset(32, 64), collate_fn=self.collate_none_when_even)
  
      model = TestModel()
      trainer = Trainer(
          default_root_dir=tmpdir,
          limit_train_batches=4,
          limit_val_batches=1,
          max_epochs=4,
          weights_summary=None,
          logger=False,
          checkpoint_callback=False,
      )
  
      with pytest.warns(UserWarning, match=r".*train_dataloader yielded None.*"):
          trainer.fit(model)
  
      trainer.state.stage = RunningStage.TRAINING
  
      # manually check a few batches
      for batch_idx, batch in enumerate(model.train_dataloader()):
          out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
          if not batch_idx % 2:
              assert out.training_step_output == [[]]
          assert out.signal == 0
  