import lightning as L
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import ReduceLROnPlateau

from losses import calculate_separation_loss, preservation_loss  # noqa: F401
from utils import PURE_HSV, PURE_RGB


class ColorTransformerModel(L.LightningModule):
    def __init__(
        self,
        transform: str = "relu",
        width: int = 128,
        depth: int = 1,
        bias: bool = False,
    ):
        super().__init__()
        self.save_hyperparameters()
        if self.hparams.transform.lower() == "tanh":
            t = nn.Tanh
        elif self.hparams.transform.lower() == "relu":
            t = nn.ReLU

        w = self.hparams.width
        d = self.hparams.depth
        bias = self.hparams.bias
        midlayers = [nn.Linear(w, w, bias=bias), t()] * d
        self.network = nn.Sequential(
            nn.Linear(3, w, bias=bias),
            t(),
            *midlayers,
            nn.Linear(w, 3, bias=bias),
            t(),
            nn.Linear(3, 1, bias=bias),
        )

    def forward(self, x):
        x = self.network(x)
        # Circular mapping
        # x = (torch.sin(x) + 1) / 2
        x = torch.sigmoid(x)
        return x

    def training_step(self, batch, batch_idx):
        inputs, labels = batch  # x are the RGB inputs, labels are the strings
        outputs = self.forward(inputs)
        # s_loss = calculate_separation_loss(model=self)
        # preserve distance to pure R, G, B. this acts kind of like labeled data.
        s_loss = preservation_loss(
            inputs,
            outputs,
            target_inputs=PURE_RGB,
            target_outputs=PURE_HSV,
        )
        p_loss = preservation_loss(
            inputs,
            outputs,
        )
        alpha = self.hparams.alpha
        loss = p_loss + alpha * s_loss
        self.log("hp_metric", loss)
        self.log("p_loss", p_loss)
        self.log("s_loss", s_loss)
        return loss

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(
            self.parameters(),
            lr=self.hparams.learning_rate,
        )
        lr_scheduler = ReduceLROnPlateau(
            optimizer, mode="min", factor=0.05, patience=5, cooldown=10, verbose=True
        )
        return {
            "optimizer": optimizer,
            "lr_scheduler": {
                "scheduler": lr_scheduler,
                "monitor": "hp_metric",  # Specify the metric to monitor
            },
        }