# 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 LightningModule
from torch.utils.data import Dataset
from typing import Optional


class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len


class SimpleModule(LightningModule):
    def __init__(self, epoch_min_loss_override: Optional[int] = None):
        """LightningModule for testing purposes
        Args:
            epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
                validation loss for testing purposes (zero based). If None this is ignored. Defaults to None.
        """
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)
        self.epoch_min_loss_override = epoch_min_loss_override

    def forward(self, x):
        return self.layer(x)

    def loss(self, batch, prediction):
        # An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
        return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))

    def training_step(self, batch, batch_idx):
        output = self.forward(batch)
        loss = self.loss(batch, output)
        return {"output": output, "loss": loss, "checkpoint_on": loss}

    def validation_step(self, batch, batch_idx):
        output = self.forward(batch)
        loss = self.loss(batch, output)
        return {"output": output, "loss": loss, "checkpoint_on": loss}

    def test_step(self, batch, batch_idx):
        output = self.forward(batch)
        loss = self.loss(batch, output)
        return {"output": output, "loss": loss}

    def training_epoch_end(self, outputs) -> None:
        avg_loss = torch.stack([x["loss"] for x in outputs]).mean()
        self.log("avg_loss", avg_loss)

    def validation_epoch_end(self, outputs) -> None:
        avg_val_loss = torch.stack(
            [torch.randn(1, requires_grad=True) for _ in outputs]
        ).mean()
        # For testing purposes allow a nominated epoch to have a low loss
        if self.current_epoch == self.epoch_min_loss_override:
            avg_val_loss -= 1e10

        self.log("avg_val_loss", avg_val_loss)
        self.log("checkpoint_on", avg_val_loss)

    def test_epoch_end(self, outputs) -> None:
        avg_loss = torch.stack(
            [torch.randn(1, requires_grad=True) for _ in outputs]
        ).mean()
        self.log("test_loss", avg_loss)

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
        return [optimizer], [lr_scheduler]

    def train_dataloader(self):
        return torch.utils.data.DataLoader(RandomDataset(32, 64))

    def val_dataloader(self):
        return torch.utils.data.DataLoader(RandomDataset(32, 64))

    def test_dataloader(self):
        return torch.utils.data.DataLoader(RandomDataset(32, 64))