import torch
from lightning import LightningModule
from torch_geometric.data import Batch


class GraphSolver(LightningModule):
    def __init__(
        self,
        model: torch.nn.Module,
        loss: torch.nn.Module = None,
        unrolling_steps: int = 10,
    ):
        super().__init__()
        self.model = model
        self.loss = loss if loss is not None else torch.nn.MSELoss()
        self.unrolling_steps = unrolling_steps

    def forward(
        self,
        x: torch.Tensor,
        c: torch.Tensor,
        edge_index: torch.Tensor,
        edge_attr: torch.Tensor,
    ):
        return self.model(x, c, edge_index, edge_attr)

    def _compute_loss_train(self, x, x_prev, y):
        return self.loss(x, y) + self.loss(x, x_prev)

    def _compute_loss(self, x, y):
        return self.loss(x, y)

    def _preprocess_batch(self, batch: Batch):
        return batch.x, batch.y, batch.c, batch.edge_index, batch.edge_attr

    def _log_loss(self, loss, batch, stage: str):
        self.log(
            f"{stage}_loss",
            loss,
            on_step=False,
            on_epoch=True,
            prog_bar=True,
            batch_size=int(batch.num_graphs),
        )
        return loss

    def training_step(self, batch: Batch, _):
        x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
        loss = 0.0
        for _ in range(self.unrolling_steps):
            x_prev = x.detach()
            x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
            loss += self.loss(x, y)
        self._log_loss(loss, batch, "train")
        return loss

    def validation_step(self, batch: Batch, _):
        x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
        for _ in range(self.unrolling_steps):
            x_prev = x.detach()
            x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
            loss = self.loss(x, x_prev)
            if loss < 1e-5:
                break
        loss = self._compute_loss(x, y)
        self._log_loss(loss, batch, "val")
        return loss

    def test_step(self, batch: Batch, _):
        x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
        for _ in range(self.unrolling_steps):
            x_prev = x.detach()
            x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
        loss = self._compute_loss(x, y)
        self._log_loss(loss, batch, "test")
        return loss

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer