"""Unified training utilities for neural and graph models."""

from __future__ import annotations

import math
import os
from typing import Any, Dict, List, Optional, Tuple, Type

import numpy as np
import torch
import torch.nn as nn
from sklearn.metrics import mean_absolute_error, r2_score
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR, ReduceLROnPlateau
from torch_geometric.loader import DataLoader

from .models import GATModel, HybridModel
from .neural_models import DescriptorNN, FingerprintNN


class NeuralNetworkTrainer:
    """Utility helper to train descriptor or fingerprint networks with scaling."""

    def __init__(self, model: nn.Module, device: torch.device, lr: float, weight_decay: float) -> None:
        self.model = model.to(device)
        self.device = device
        self.base_lr = lr
        self.optimizer = AdamW(
            self.model.parameters(), lr=lr, weight_decay=weight_decay, betas=(0.9, 0.999)
        )
        self.criterion = nn.SmoothL1Loss(beta=1.0)
        self.scheduler: CosineAnnealingLR | None = None

    def train_fold(
        self,
        train_features: np.ndarray,
        train_targets: np.ndarray,
        val_features: np.ndarray,
        val_targets: np.ndarray,
        epochs: int,
        batch_size: int,
        patience: int,
        gradient_clip: float,
        warmup_epochs: int = 10,
        *,
        train_lab_indices: Optional[np.ndarray] = None,
        val_lab_indices: Optional[np.ndarray] = None,
        verbose: bool = True,
    ) -> Tuple[Dict[str, float], np.ndarray]:
        device = self.device

        # Target scaling for stability during training
        y_mean = float(train_targets.mean())
        y_std_raw = float(train_targets.std())
        y_std = y_std_raw if y_std_raw > 1e-6 else 1.0

        train_y_scaled = (train_targets - y_mean) / y_std
        val_y_scaled = (val_targets - y_mean) / y_std

        train_features_tensor = torch.tensor(train_features, dtype=torch.float32)
        train_targets_tensor = torch.tensor(train_y_scaled, dtype=torch.float32)
        val_features_tensor = torch.tensor(val_features, dtype=torch.float32)
        val_targets_tensor = torch.tensor(val_y_scaled, dtype=torch.float32)

        if train_lab_indices is not None:
            train_lab_np = np.asarray(train_lab_indices).reshape(-1)
            train_lab_tensor = torch.tensor(train_lab_np, dtype=torch.long)
            train_dataset = torch.utils.data.TensorDataset(
                train_features_tensor,
                train_lab_tensor,
                train_targets_tensor,
            )
        else:
            train_dataset = torch.utils.data.TensorDataset(
                train_features_tensor,
                train_targets_tensor,
            )

        labs_in_use = train_lab_indices is not None

        val_lab_tensor = (
            torch.tensor(np.asarray(val_lab_indices).reshape(-1), dtype=torch.long)
            if val_lab_indices is not None
            else None
        )

        train_loader = torch.utils.data.DataLoader(
            train_dataset, batch_size=batch_size, shuffle=True, drop_last=False
        )

        if epochs <= warmup_epochs:
            warmup_epochs = max(0, epochs - 1)

        self.scheduler = CosineAnnealingLR(
            self.optimizer,
            T_max=max(1, epochs - warmup_epochs),
            eta_min=self.base_lr * 0.05,
        )

        best_state: Dict[str, Any] | None = None
        best_mae = float("inf")
        patience_counter = 0

        for epoch in range(epochs):
            self.model.train()
            epoch_losses: List[float] = []

            for batch in train_loader:
                if labs_in_use:
                    batch_x, batch_lab, batch_y = batch
                    batch_lab = batch_lab.to(device)
                else:
                    batch_x, batch_y = batch
                    batch_lab = None

                batch_x = batch_x.to(device)
                batch_y = batch_y.to(device)

                self.optimizer.zero_grad(set_to_none=True)
                preds = (
                    self.model(batch_x, batch_lab)
                    if batch_lab is not None
                    else self.model(batch_x)
                )
                loss = self.criterion(preds, batch_y)
                loss.backward()
                if gradient_clip:
                    torch.nn.utils.clip_grad_norm_(self.model.parameters(), gradient_clip)
                self.optimizer.step()
                epoch_losses.append(loss.item())

            # Warmup before cosine decay
            if epoch >= warmup_epochs and self.scheduler is not None:
                self.scheduler.step()

            # Evaluation in original scale
            self.model.eval()
            with torch.no_grad():
                val_preds_scaled = (
                    self.model(val_features_tensor.to(device), val_lab_tensor.to(device))
                    if val_lab_tensor is not None
                    else self.model(val_features_tensor.to(device))
                ).cpu()
            val_preds = val_preds_scaled.numpy() * y_std + y_mean
            val_targets_unscaled = val_targets

            current_mae = mean_absolute_error(val_targets_unscaled, val_preds)
            current_r2 = r2_score(val_targets_unscaled, val_preds)

            improved = current_mae + 1e-5 < best_mae
            if improved:
                best_mae = current_mae
                patience_counter = 0
                best_state = {
                    "model": self.model.state_dict(),
                    "y_mean": y_mean,
                    "y_std": y_std,
                    "epoch": epoch + 1,
                    "mae": current_mae,
                    "r2": current_r2,
                }
            else:
                patience_counter += 1

            if verbose and ((epoch + 1) % 50 == 0 or improved):
                lr = self.optimizer.param_groups[0]["lr"]
                print(
                    f"      Epoch {epoch+1}: TrainLoss={np.mean(epoch_losses):.4f} "
                    f"ValMAE={current_mae:.4f} ValR²={current_r2:.4f} LR={lr:.2e}"
                    f"{' *' if improved else ''}"
                )

            if patience_counter >= patience:
                if verbose:
                    best_epoch = best_state["epoch"] if best_state else "N/A"
                    print(f"      Early stopping at epoch {epoch+1} (best epoch={best_epoch})")
                break

        if best_state is None:
            raise RuntimeError("Training failed to record a best state.")

        # Restore best weights
        self.model.load_state_dict(best_state["model"])
        self.model.target_mean = best_state["y_mean"]
        self.model.target_std = best_state["y_std"]

        with torch.no_grad():
            final_preds_scaled = (
                self.model(val_features_tensor.to(device), val_lab_tensor.to(device))
                if val_lab_tensor is not None
                else self.model(val_features_tensor.to(device))
            ).cpu().numpy()

        final_preds = final_preds_scaled * self.model.target_std + self.model.target_mean

        metrics = {
            "r2": best_state["r2"],
            "mae": best_state["mae"],
            "best_epoch": best_state["epoch"],
        }

        return metrics, final_preds

    def save(self, path: str) -> None:
        torch.save(
            {
                "model_state": self.model.state_dict(),
                "target_mean": self.model.target_mean,
                "target_std": self.model.target_std,
            },
            path,
        )

    def predict(self, features: np.ndarray, lab_indices: Optional[np.ndarray] = None) -> np.ndarray:
        self.model.eval()
        with torch.no_grad():
            feature_tensor = torch.tensor(features, dtype=torch.float32)
            if lab_indices is not None:
                lab_tensor = torch.tensor(np.asarray(lab_indices).reshape(-1), dtype=torch.long)
                preds = self.model(
                    feature_tensor.to(self.device),
                    lab_tensor.to(self.device),
                ).cpu().numpy()
            else:
                preds = self.model(feature_tensor.to(self.device)).cpu().numpy()
        return preds * self.model.target_std + self.model.target_mean


def _ensure_dir(path: str) -> None:
    os.makedirs(path, exist_ok=True)


def train_descriptor_nn_fold(
    *,
    train_features: np.ndarray,
    val_features: np.ndarray,
    train_targets: np.ndarray,
    val_targets: np.ndarray,
    fold_idx: int,
    device: torch.device,
    config: Dict[str, Any],
    training_config: Dict[str, Any],
    save_dir: str = "oof_models",
    train_lab_indices: Optional[np.ndarray] = None,
    val_lab_indices: Optional[np.ndarray] = None,
) -> Tuple[DescriptorNN, np.ndarray, Dict[str, float]]:
    """Train Descriptor Neural Network for one CV fold."""

    print(f"    Training Descriptor NN (Fold {fold_idx})...")

    model = DescriptorNN(**config)
    trainer = NeuralNetworkTrainer(
        model=model,
        device=device,
        lr=training_config["lr"],
        weight_decay=training_config["weight_decay"],
    )

    metrics, val_predictions = trainer.train_fold(
        train_features=train_features,
        train_targets=train_targets,
        val_features=val_features,
        val_targets=val_targets,
        epochs=training_config["epochs"],
        batch_size=training_config["batch_size"],
        patience=training_config["patience"],
        gradient_clip=training_config["gradient_clip"],
        warmup_epochs=training_config.get("warmup_epochs", 10),
        train_lab_indices=train_lab_indices,
        val_lab_indices=val_lab_indices,
        verbose=True,
    )

    _ensure_dir(save_dir)
    save_path = os.path.join(save_dir, f"desc_nn_fold_{fold_idx}.pt")
    trainer.save(save_path)

    print(
        f"      Descriptor NN Fold {fold_idx}: R² = {metrics['r2']:.4f}, "
        f"MAE = {metrics['mae']:.4f}, Best Epoch = {metrics['best_epoch']}"
    )

    return model, val_predictions, metrics


def train_fingerprint_nn_fold(
    *,
    train_fingerprints: np.ndarray,
    val_fingerprints: np.ndarray,
    train_targets: np.ndarray,
    val_targets: np.ndarray,
    fold_idx: int,
    device: torch.device,
    config: Dict[str, Any],
    training_config: Dict[str, Any],
    save_dir: str = "oof_models",
    train_lab_indices: Optional[np.ndarray] = None,
    val_lab_indices: Optional[np.ndarray] = None,
) -> Tuple[FingerprintNN, np.ndarray, Dict[str, float]]:
    """Train Fingerprint Neural Network for one CV fold."""

    print(f"    Training Fingerprint NN (Fold {fold_idx})...")

    model = FingerprintNN(**config)
    trainer = NeuralNetworkTrainer(
        model=model,
        device=device,
        lr=training_config["lr"],
        weight_decay=training_config["weight_decay"],
    )

    metrics, val_predictions = trainer.train_fold(
        train_features=train_fingerprints,
        train_targets=train_targets,
        val_features=val_fingerprints,
        val_targets=val_targets,
        epochs=training_config["epochs"],
        batch_size=training_config["batch_size"],
        patience=training_config["patience"],
        gradient_clip=training_config["gradient_clip"],
        warmup_epochs=training_config.get("warmup_epochs", 10),
        train_lab_indices=train_lab_indices,
        val_lab_indices=val_lab_indices,
        verbose=True,
    )

    _ensure_dir(save_dir)
    save_path = os.path.join(save_dir, f"fp_nn_fold_{fold_idx}.pt")
    trainer.save(save_path)

    print(
        f"      Fingerprint NN Fold {fold_idx}: R² = {metrics['r2']:.4f}, "
        f"MAE = {metrics['mae']:.4f}, Best Epoch = {metrics['best_epoch']}"
    )

    return model, val_predictions, metrics


def train_gnn_fold(
    *,
    fold_train_graphs,
    fold_val_graphs,
    fold_train_lab,
    fold_val_lab,
    fold_train_targets,
    fold_val_targets,
    fold_idx: int,
    device: torch.device,
    config: Dict[str, Any],
    training_config: Dict[str, Any],
    save_dir: str = "oof_models",
    verbose_interval: int = 100,
):
    """Train a single GNN fold with warmup + cosine scheduling."""

    _ensure_dir(save_dir)

    target_mean = float(np.mean(fold_train_targets))
    target_std_raw = float(np.std(fold_train_targets))
    target_std = target_std_raw if target_std_raw > 1e-6 else 1.0

    scaled_train_targets = (fold_train_targets - target_mean) / target_std
    scaled_val_targets = (fold_val_targets - target_mean) / target_std

    for i, graph in enumerate(fold_train_graphs):
        graph.lab_feature = torch.tensor([fold_train_lab[i]], dtype=torch.long)
        graph.y = torch.tensor([scaled_train_targets[i]], dtype=torch.float32)

    for i, graph in enumerate(fold_val_graphs):
        graph.lab_feature = torch.tensor([fold_val_lab[i]], dtype=torch.long)
        graph.y = torch.tensor([scaled_val_targets[i]], dtype=torch.float32)

    model = GATModel(**config).to(device)

    lr = training_config.get("lr", 3e-4)
    weight_decay = training_config.get("weight_decay", 5e-6)
    optimizer = AdamW(
        model.parameters(),
        lr=lr,
        weight_decay=weight_decay,
        betas=training_config.get("betas", (0.9, 0.999)),
    )

    criterion = nn.MSELoss()

    epochs = training_config.get("epochs", 800)
    warmup_epochs = training_config.get("warmup_epochs", 0)
    min_lr = training_config.get("min_lr", 1e-6)
    factor = training_config.get("factor", 0.7)

    def lr_lambda(epoch: int) -> float:
        if warmup_epochs > 0 and epoch < warmup_epochs:
            return float(epoch + 1) / warmup_epochs
        total_decay_epochs = max(1, epochs - warmup_epochs)
        progress = max(0.0, epoch - warmup_epochs) / total_decay_epochs
        return 0.5 * (1.0 + math.cos(math.pi * progress))

    scheduler = LambdaLR(optimizer, lr_lambda)
    plateau_scheduler = ReduceLROnPlateau(
        optimizer,
        patience=training_config.get("plateau_patience", 30),
        factor=factor,
        min_lr=min_lr,
    )

    batch_size = training_config.get("batch_size", 32)
    gradient_clip = training_config.get("gradient_clip", 1.0)

    train_loader = DataLoader(
        fold_train_graphs,
        batch_size=batch_size,
        shuffle=True,
        drop_last=True,
    )
    val_loader = DataLoader(
        fold_val_graphs,
        batch_size=batch_size,
        shuffle=False,
    )

    best_val_r2 = -float("inf")
    patience = training_config.get("patience", 100)
    patience_counter = 0
    checkpoint_path = os.path.join(save_dir, f"gnn_fold_{fold_idx}.pt")

    for epoch in range(epochs):
        model.train()
        train_loss = 0.0
        train_batches = 0

        for batch in train_loader:
            batch = batch.to(device)
            optimizer.zero_grad()
            pred = model(
                batch.x,
                batch.edge_index,
                batch.batch,
                batch.lab_feature,
                getattr(batch, "edge_attr", None),
            )
            loss = criterion(pred, batch.y)
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=gradient_clip)
            optimizer.step()

            train_loss += loss.item()
            train_batches += 1

        if train_batches > 0:
            train_loss /= train_batches

        model.eval()
        val_preds_scaled: List[float] = []
        val_targets_scaled: List[float] = []

        with torch.no_grad():
            for batch in val_loader:
                batch = batch.to(device)
                pred = model(
                    batch.x,
                    batch.edge_index,
                    batch.batch,
                    batch.lab_feature,
                    getattr(batch, "edge_attr", None),
                )
                preds_np = pred.cpu().numpy()
                targets_np = batch.y.cpu().numpy()
                val_preds_scaled.extend(preds_np.tolist())
                val_targets_scaled.extend(targets_np.tolist())

        val_preds = np.asarray(val_preds_scaled, dtype=np.float32) * target_std + target_mean
        val_targets = np.asarray(val_targets_scaled, dtype=np.float32) * target_std + target_mean

        val_r2 = r2_score(val_targets, val_preds)
        val_mae = mean_absolute_error(val_targets, val_preds)

        scheduler.step()
        plateau_scheduler.step(train_loss)

        if val_r2 > best_val_r2:
            best_val_r2 = val_r2
            patience_counter = 0
            torch.save(
                {
                    "model_state": model.state_dict(),
                    "target_mean": target_mean,
                    "target_std": target_std,
                },
                checkpoint_path,
            )
        else:
            patience_counter += 1

        if verbose_interval and (epoch + 1) % verbose_interval == 0:
            current_lr = optimizer.param_groups[0]["lr"]
            print(
                f"      Epoch {epoch + 1}: Train Loss={train_loss:.4f}, "
                f"Val R²={val_r2:.4f}, MAE={val_mae:.4f}, LR={current_lr:.2e}"
            )

        if patience_counter >= patience:
            break

    if os.path.exists(checkpoint_path):
        checkpoint = torch.load(checkpoint_path, map_location=device)
        if isinstance(checkpoint, dict) and "model_state" in checkpoint:
            model.load_state_dict(checkpoint["model_state"])
            target_mean = float(checkpoint.get("target_mean", target_mean))
            target_std = float(checkpoint.get("target_std", target_std))
        else:
            model.load_state_dict(checkpoint)
        if target_std == 0:
            target_std = 1.0

    model.eval()
    final_val_preds_scaled: List[float] = []
    with torch.no_grad():
        for batch in val_loader:
            batch = batch.to(device)
            pred = model(
                batch.x,
                batch.edge_index,
                batch.batch,
                batch.lab_feature,
                getattr(batch, "edge_attr", None),
            )
            final_val_preds_scaled.extend(pred.cpu().numpy().tolist())

    final_val_preds = np.asarray(final_val_preds_scaled, dtype=np.float32) * target_std + target_mean
    final_r2 = r2_score(fold_val_targets, final_val_preds)
    final_mae = mean_absolute_error(fold_val_targets, final_val_preds)

    setattr(model, "target_mean", float(target_mean))
    setattr(model, "target_std", float(target_std))

    print(f"    GNN Fold {fold_idx}: R² = {final_r2:.4f}, MAE = {final_mae:.4f}")

    return model, final_val_preds.astype(np.float32), {"r2": final_r2, "mae": final_mae}


__all__ = [
    "NeuralNetworkTrainer",
    "train_descriptor_nn_fold",
    "train_fingerprint_nn_fold",
    "train_gnn_fold",
    "train_hybrid_model_fold",
]


def _prepare_hybrid_graphs(
    graphs,
    lab_indices: np.ndarray,
    targets: np.ndarray,
    descriptors: np.ndarray,
) -> None:
    for i, graph in enumerate(graphs):
        graph.lab_feature = torch.tensor([lab_indices[i]], dtype=torch.long)
        graph.y = torch.tensor([targets[i]], dtype=torch.float32)
        descriptor_tensor = torch.tensor(descriptors[i], dtype=torch.float32)
        if descriptor_tensor.dim() == 1:
            descriptor_tensor = descriptor_tensor.unsqueeze(0)
        graph.descriptors = descriptor_tensor


def train_hybrid_model_fold(
    *,
    model_name: str,
    graph_model_class: Type[nn.Module],
    config: Dict[str, Any],
    training_config: Dict[str, Any],
    fold_train_graphs,
    fold_val_graphs,
    fold_train_lab,
    fold_val_lab,
    fold_train_targets,
    fold_val_targets,
    fold_train_descriptors,
    fold_val_descriptors,
    fold_idx: int,
    device: torch.device,
    save_dir: str = "hybrid_models",
    verbose_interval: int = 50,
):
    """Train a Hybrid GNN model (graph + descriptors) for one CV fold."""

    _ensure_dir(save_dir)

    target_mean = float(np.mean(fold_train_targets))
    target_std_raw = float(np.std(fold_train_targets))
    target_std = target_std_raw if target_std_raw > 1e-6 else 1.0

    scaled_train_targets = (fold_train_targets - target_mean) / target_std
    scaled_val_targets = (fold_val_targets - target_mean) / target_std

    fold_train_descriptors = np.asarray(fold_train_descriptors, dtype=np.float32)
    fold_val_descriptors = np.asarray(fold_val_descriptors, dtype=np.float32)

    _prepare_hybrid_graphs(
        fold_train_graphs,
        fold_train_lab,
        scaled_train_targets,
        fold_train_descriptors,
    )
    _prepare_hybrid_graphs(
        fold_val_graphs,
        fold_val_lab,
        scaled_val_targets,
        fold_val_descriptors,
    )

    graph_model_kwargs = dict(config.get("graph_model_kwargs", {}))
    graph_feature_dim = config.get("graph_feature_dim")

    model = HybridModel(
        graph_model_class=graph_model_class,
        descriptor_dim=fold_train_descriptors.shape[1],
        graph_model_kwargs=graph_model_kwargs,
        graph_feature_dim=graph_feature_dim,
        descriptor_hidden_dims=config.get("descriptor_hidden_dims"),
        final_hidden_dims=config.get("final_hidden_dims"),
        dropout=config.get("dropout", 0.2),
        use_batch_norm=config.get("use_batch_norm", True),
        output_dim=config.get("output_dim", 1),
    ).to(device)

    lr = training_config.get("lr", 3e-4)
    weight_decay = training_config.get("weight_decay", 1e-5)
    optimizer = AdamW(
        model.parameters(),
        lr=lr,
        weight_decay=weight_decay,
        betas=training_config.get("betas", (0.9, 0.999)),
    )

    criterion = nn.MSELoss()

    epochs = training_config.get("epochs", 400)
    patience = training_config.get("patience", 80)
    gradient_clip = training_config.get("gradient_clip", 1.0)

    plateau_scheduler = ReduceLROnPlateau(
        optimizer,
        patience=training_config.get("plateau_patience", 30),
        factor=training_config.get("factor", 0.7),
        min_lr=training_config.get("min_lr", 1e-6),
    )

    batch_size = training_config.get("batch_size", 32)
    train_loader = DataLoader(
        fold_train_graphs,
        batch_size=batch_size,
        shuffle=True,
        drop_last=len(fold_train_graphs) > batch_size,
    )
    val_loader = DataLoader(
        fold_val_graphs,
        batch_size=batch_size,
        shuffle=False,
    )

    best_state: Dict[str, Any] | None = None
    best_r2 = -float("inf")
    best_mae = float("inf")
    patience_counter = 0

    for epoch in range(epochs):
        model.train()
        cumulative_loss = 0.0
        batch_count = 0

        for batch in train_loader:
            batch = batch.to(device)
            optimizer.zero_grad()

            lab_tensor = batch.lab_feature.squeeze(-1) if batch.lab_feature.dim() > 1 else batch.lab_feature
            descriptors_tensor = batch.descriptors.reshape(batch.num_graphs, -1)
            preds = model(
                batch.x,
                batch.edge_index,
                batch.batch,
                lab_tensor,
                descriptors_tensor,
                getattr(batch, "edge_attr", None),
            )
            target_tensor = batch.y.view(-1)
            loss = criterion(preds, target_tensor)
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=gradient_clip)
            optimizer.step()

            cumulative_loss += loss.item()
            batch_count += 1

        avg_train_loss = cumulative_loss / max(batch_count, 1)

        model.eval()
        val_preds: List[float] = []
        val_targets_list: List[float] = []
        val_loss = 0.0
        val_batches = 0

        with torch.no_grad():
            for batch in val_loader:
                batch = batch.to(device)
                lab_tensor = batch.lab_feature.squeeze(-1) if batch.lab_feature.dim() > 1 else batch.lab_feature
                descriptors_tensor = batch.descriptors.reshape(batch.num_graphs, -1)
                preds = model(
                    batch.x,
                    batch.edge_index,
                    batch.batch,
                    lab_tensor,
                    descriptors_tensor,
                    getattr(batch, "edge_attr", None),
                )
                target_tensor = batch.y.view(-1)
                val_loss += criterion(preds, target_tensor).item()
                val_batches += 1
                preds_np = preds.cpu().numpy()
                targets_np = target_tensor.cpu().numpy()
                val_preds.extend((preds_np * target_std + target_mean).tolist())
                val_targets_list.extend((targets_np * target_std + target_mean).tolist())

        avg_val_loss = val_loss / max(val_batches, 1)
        plateau_scheduler.step(avg_val_loss)

        current_r2 = r2_score(val_targets_list, val_preds)
        current_mae = mean_absolute_error(val_targets_list, val_preds)

        improved = current_r2 > best_r2 + 1e-5
        if improved:
            best_r2 = current_r2
            best_mae = current_mae
            patience_counter = 0
            best_state = {
                "state_dict": model.state_dict(),
                "epoch": epoch + 1,
                "train_loss": avg_train_loss,
            }
            torch.save(
                {
                    "model_state": best_state["state_dict"],
                    "config": config,
                    "training_config": training_config,
                    "target_mean": target_mean,
                    "target_std": target_std,
                },
                os.path.join(save_dir, f"{model_name}_fold_{fold_idx}.pt"),
            )
        else:
            patience_counter += 1

        if verbose_interval and (epoch + 1) % verbose_interval == 0:
            current_lr = optimizer.param_groups[0]["lr"]
            print(
                f"      [{model_name}] Epoch {epoch+1}: TrainLoss={avg_train_loss:.4f} "
                f"ValLoss={avg_val_loss:.4f} ValR²={current_r2:.4f} ValMAE={current_mae:.4f} LR={current_lr:.2e}"
                f"{' *' if improved else ''}"
            )

        if patience_counter >= patience:
            break

    if best_state is None:
        raise RuntimeError(f"{model_name} fold {fold_idx} failed to improve during training.")

    model.load_state_dict(best_state["state_dict"])
    model.eval()

    final_val_preds: List[float] = []
    with torch.no_grad():
        for batch in val_loader:
            batch = batch.to(device)
            lab_tensor = batch.lab_feature.squeeze(-1) if batch.lab_feature.dim() > 1 else batch.lab_feature
            descriptors_tensor = batch.descriptors.reshape(batch.num_graphs, -1)
            preds = model(
                batch.x,
                batch.edge_index,
                batch.batch,
                lab_tensor,
                descriptors_tensor,
                getattr(batch, "edge_attr", None),
            )
            preds_np = preds.cpu().numpy()
            final_val_preds.extend((preds_np * target_std + target_mean).tolist())

    final_val_preds_array = np.asarray(final_val_preds, dtype=np.float32)

    metrics = {"r2": best_r2, "mae": best_mae, "best_epoch": best_state["epoch"]}

    setattr(model, "target_mean", float(target_mean))
    setattr(model, "target_std", float(target_std))

    print(
        f"    {model_name} Fold {fold_idx}: R² = {best_r2:.4f}, MAE = {best_mae:.4f}, "
        f"Best Epoch = {best_state['epoch']}"
    )

    return model, final_val_preds_array, metrics