import os

import torch
from torch.utils.data import DataLoader, Dataset
from pytorch_lightning import LightningModule, Trainer


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 BoringModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

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

    def training_step(self, batch, batch_idx):
        loss = self(batch).sum()

        for param_group in self.optimizers().optimizer.param_groups:
            lr = param_group['lr']
        self.log('lr', lr, prog_bar=True, on_step=True, on_epoch=False)
        return {"loss": loss}

    def configure_optimizers(self):
        opt = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        scheduler = torch.optim.lr_scheduler.StepLR(opt, 1)
        return {"optimizer": opt, 'lr_scheduler': {"scheduler": scheduler,
                                                   "interval": "step",
                                                   "frequency": 10}}


def run():
    train_data = DataLoader(RandomDataset(32, 32), batch_size=8)

    model = BoringModel()
    trainer = Trainer(
        accelerator='cpu',
        default_root_dir=os.getcwd(),
        num_sanity_val_steps=0,
        max_epochs=-1,
        max_steps=30,
        log_every_n_steps=1
    )
    trainer.fit(model, train_dataloaders=train_data)


if __name__ == "__main__":
    run()