# 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 pytorch_lightning import Trainer
from tests.helpers.boring_model import BoringModel, RandomDataset


def test_overfit_multiple_val_loaders(tmpdir):
    """Tests that only training_step can be used."""

    class TestModel(BoringModel):
        def validation_step(self, batch, batch_idx, dataloader_idx):
            output = self.layer(batch[0])
            loss = self.loss(batch, output)
            return {"x": loss}

        def validation_epoch_end(self, outputs) -> None:
            pass

        def val_dataloader(self):
            dl1 = torch.utils.data.DataLoader(RandomDataset(32, 64))
            dl2 = torch.utils.data.DataLoader(RandomDataset(32, 64))
            return [dl1, dl2]

    model = TestModel()

    trainer = Trainer(
        default_root_dir=tmpdir, max_epochs=2, overfit_batches=1, log_every_n_steps=1, weights_summary=None
    )

    trainer.fit(model)


@pytest.mark.parametrize("overfit", [1, 2, 0.1, 0.25, 1.0])
def test_overfit_basic(tmpdir, overfit):
    """Tests that only training_step can be used."""

    model = BoringModel()

    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, overfit_batches=overfit, weights_summary=None)

    trainer.fit(model)