import torch
import lightning as L
import torchmetrics


class LightningModel(L.LightningModule):
    def __init__(self, model, learning_rate, cosine_t_max, mode):
        super().__init__()

        self.learning_rate = learning_rate
        self.cosine_t_max = cosine_t_max
        self.model = model
        self.example_input_array = torch.Tensor(1, 3, 32, 32)
        self.mode = mode

        self.save_hyperparameters(ignore=["model"])

        self.train_acc = torchmetrics.Accuracy(task="multiclass", num_classes=10)
        self.val_acc = torchmetrics.Accuracy(task="multiclass", num_classes=10)
        self.test_acc = torchmetrics.Accuracy(task="multiclass", num_classes=10)

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

    def _shared_step(self, batch):
        features, true_labels = batch
        logits = self(features)

        loss = F.cross_entropy(logits, true_labels)
        predicted_labels = torch.argmax(logits, dim=1)
        return loss, true_labels, predicted_labels

    def training_step(self, batch, batch_idx):
        loss, true_labels, predicted_labels = self._shared_step(batch)

        self.log("train_loss", loss)
        self.train_acc(predicted_labels, true_labels)
        self.log(
            "train_acc", self.train_acc, prog_bar=True, on_epoch=True, on_step=False
        )
        return loss

    def validation_step(self, batch, batch_idx):
        loss, true_labels, predicted_labels = self._shared_step(batch)

        self.log("val_loss", loss, prog_bar=True)
        self.val_acc(predicted_labels, true_labels)
        self.log("val_acc", self.val_acc, prog_bar=True)

    def test_step(self, batch, batch_idx):
        loss, true_labels, predicted_labels = self._shared_step(batch)
        self.test_acc(predicted_labels, true_labels)
        self.log("test_acc", self.test_acc)

    def configure_optimizers(self):
        opt = torch.optim.SGD(self.parameters(), lr=self.learning_rate)
        if self.mode == 'lrfind':
            return opt
        else:
            sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=self.cosine_t_max) # New!

            return {
                "optimizer": opt,
                "lr_scheduler": {
                    "scheduler": sch,
                    "monitor": "train_loss",
                    "interval": "step", # step means "batch" here, default: epoch
                    "frequency": 1, # default
                },
            }