# 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.
from pytorch_lightning import Trainer
from pytorch_lightning.plugins import DDPPlugin
from tests.helpers import BoringModel
from tests.helpers.runif import RunIf


class CustomParallelPlugin(DDPPlugin):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        # Set to None so it will be overwritten by the accelerator connector.
        self.sync_batchnorm = None


@RunIf(skip_windows=True)
def test_sync_batchnorm_set(tmpdir):
    """Tests if sync_batchnorm is automatically set for custom plugin."""
    model = BoringModel()
    plugin = CustomParallelPlugin()
    assert plugin.sync_batchnorm is None
    trainer = Trainer(
        max_epochs=1,
        plugins=[plugin],
        default_root_dir=tmpdir,
        sync_batchnorm=True,
    )
    trainer.fit(model)
    assert plugin.sync_batchnorm is True