# 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.
"""Runs either `.fit()` or `.test()` on a single node across multiple gpus."""
import os
from argparse import ArgumentParser

import torch

from pytorch_lightning import seed_everything, Trainer
from tests.helpers.datamodules import ClassifDataModule
from tests.helpers.simple_models import ClassificationModel


def main():
    seed_everything(4321)

    parser = ArgumentParser(add_help=False)
    parser = Trainer.add_argparse_args(parser)
    parser.add_argument("--trainer_method", default="fit")
    parser.add_argument("--tmpdir")
    parser.add_argument("--workdir")
    parser.set_defaults(gpus=2)
    parser.set_defaults(strategy="ddp")
    args = parser.parse_args()

    dm = ClassifDataModule()
    model = ClassificationModel()
    trainer = Trainer.from_argparse_args(args)

    if args.trainer_method == "fit":
        trainer.fit(model, datamodule=dm)
        result = None
    elif args.trainer_method == "test":
        result = trainer.test(model, datamodule=dm)
    elif args.trainer_method == "fit_test":
        trainer.fit(model, datamodule=dm)
        result = trainer.test(model, datamodule=dm)
    else:
        raise ValueError(f"Unsupported: {args.trainer_method}")

    result_ext = {"status": "complete", "method": args.trainer_method, "result": result}
    file_path = os.path.join(args.tmpdir, "ddp.result")
    torch.save(result_ext, file_path)


if __name__ == "__main__":
    main()
