# 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 os

import pytest

from pytorch_lightning import Trainer
from tests.helpers.advanced_models import BasicGAN, ParityModuleMNIST, ParityModuleRNN
from tests.helpers.boring_model import BoringModel
from tests.helpers.datamodules import ClassifDataModule, RegressDataModule
from tests.helpers.simple_models import ClassificationModel, RegressionModel


@pytest.mark.parametrize(
    "data_class,model_class",
    [
        (None, BoringModel),
        (None, BasicGAN),
        (None, ParityModuleRNN),
        (None, ParityModuleMNIST),
        (ClassifDataModule, ClassificationModel),
        (RegressDataModule, RegressionModel),
    ],
)
def test_models(tmpdir, data_class, model_class):
    """Test simple models."""
    dm = data_class() if data_class else data_class
    model = model_class()
    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)

    trainer.fit(model, datamodule=dm)

    if dm is not None:
        trainer.test(model, datamodule=dm)

    model.to_torchscript()
    if data_class:
        model.to_onnx(os.path.join(tmpdir, "my-model.onnx"), input_sample=dm.sample)