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


def import_class(class_path: str):
    module_path, class_name = class_path.rsplit(".", 1)  # split last dot
    module = importlib.import_module(module_path)  # import the module
    cls = getattr(module, class_name)  # get the class
    return cls


class GraphSolver(LightningModule):
    def __init__(
        self,
        model_class_path: str,
        model_init_args: dict,
        loss: torch.nn.Module = None,
        unrolling_steps: int = 48,
    ):
        super().__init__()
        self.model = import_class(model_class_path)(**model_init_args)
        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,
        boundary: torch.Tensor,
        boundary_mask: torch.Tensor,
        edge_index: torch.Tensor,
        edge_attr: torch.Tensor,
        unrolling_steps: int = None,
    ):
        return self.model(
            x,
            c,
            boundary,
            boundary_mask,
            edge_index,
            edge_attr,
            unrolling_steps,
        )

    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)
        y_pred = self(
            x,
            c,
            batch.boundary_values,
            batch.boundary_mask,
            edge_index=edge_index,
            edge_attr=edge_attr,
            unrolling_steps=self.unrolling_steps,
        )
        loss = self.loss(y_pred, y)
        boundary_loss = self.loss(
            y_pred[batch.boundary_mask], y[batch.boundary_mask]
        )
        self._log_loss(loss, batch, "train")
        self._log_loss(boundary_loss, batch, "train_boundary")
        return loss

    def validation_step(self, batch: Batch, _):
        x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
        y_pred = self(
            x,
            c,
            batch.boundary_values,
            batch.boundary_mask,
            edge_index=edge_index,
            edge_attr=edge_attr,
            unrolling_steps=self.unrolling_steps,
        )
        loss = self.loss(y_pred, y)
        boundary_loss = self.loss(
            y_pred[batch.boundary_mask], y[batch.boundary_mask]
        )
        self._log_loss(loss, batch, "val")
        self._log_loss(boundary_loss, batch, "val_boundary")
        return loss

    def test_step(self, batch: Batch, _):
        x, y, c, edge_index, edge_attr = self._preprocess_batch(batch)
        y_pred = self.model(
            x,
            c,
            batch.boundary_values,
            batch.boundary_mask,
            edge_index=edge_index,
            edge_attr=edge_attr,
            unrolling_steps=self.unrolling_steps,
            batch=batch.batch,
            pos=batch.pos,
            plot_results=True,
        )
        loss = self._compute_loss(y_pred, y)
        self._log_loss(loss, batch, "test")
        return loss

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

    def _impose_bc(self, x: torch.Tensor, data: Batch):
        x[data.boundary_mask] = data.boundary_values
        return x