# 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 torch

from pytorch_lightning import Trainer
from pytorch_lightning.plugins import DDPSpawnPlugin
from tests.helpers.boring_model import BoringDataModule, BoringModel
from tests.helpers.runif import RunIf


class BoringModelDDPCPU(BoringModel):
    def on_train_start(self) -> None:
        # make sure that the model is on CPU when training
        assert self.device == torch.device("cpu")


class BoringCallbackDDPSpawnModel(BoringModel):
    def __init__(self, name: str, val: float):
        super().__init__()
        self.name = name
        self.val = val

    def validation_step(self, batch, batch_idx):
        self.log(self.name, self.val)
        return super().validation_step(batch, batch_idx)

    def add_to_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
        queue.put("test_val")
        return super().add_to_queue(queue)

    def get_from_queue(self, queue: torch.multiprocessing.SimpleQueue) -> None:
        self.test_val = queue.get()
        return super().get_from_queue(queue)


@RunIf(skip_windows=True)
def test_ddp_cpu():
    """Tests if device is set correctely when training for DDPSpawnPlugin."""
    trainer = Trainer(num_processes=2, fast_dev_run=True)
    # assert training type plugin attributes for device setting

    assert isinstance(trainer.training_type_plugin, DDPSpawnPlugin)
    assert not trainer.training_type_plugin.on_gpu
    assert not trainer.training_type_plugin.on_tpu
    assert trainer.training_type_plugin.root_device == torch.device("cpu")

    model = BoringModelDDPCPU()

    trainer.fit(model)


@RunIf(min_gpus=2)
def test_ddp_spawn_extra_parameters(tmpdir):
    """Tests if device is set correctely when training for DDPSpawnPlugin."""
    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=2, accelerator="ddp_spawn")

    assert isinstance(trainer.training_type_plugin, DDPSpawnPlugin)
    assert trainer.training_type_plugin.on_gpu
    assert trainer.training_type_plugin.root_device == torch.device("cuda:0")

    val: float = 1.0
    val_name: str = "val_acc"
    model = BoringCallbackDDPSpawnModel(val_name, val)
    dm = BoringDataModule()

    trainer.fit(model, datamodule=dm)
    assert trainer.callback_metrics[val_name] == torch.tensor(val)
    assert model.test_val == "test_val"