import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from typing import Optional, Dict, Any
from ..utils.config_model import TrainingConfig

class Trainer:
    def __init__(
        self,
        model: nn.Module,
        train_loader: DataLoader,
        val_loader: DataLoader,
        loss_fn: nn.Module,
        device: torch.device,
        config: TrainingConfig,
        scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
        target_scaler: Optional[Any] = None
    ):
        self.model = model
        self.train_loader = train_loader
        self.val_loader = val_loader
        self.loss_fn = loss_fn
        self.device = device
        self.config = config
        self.scheduler = scheduler
        self.target_scaler = target_scaler
        
        # TODO: Initialize optimizer (Adam)
        # TODO: Initialize early stopping if configured

    def train_epoch(self) -> Dict[str, float]:
        """
        Train for one epoch.
        """
        # TODO: Implement training loop for one epoch
        pass

    def evaluate(self, loader: DataLoader) -> Dict[str, float]:
        """
        Evaluate model on given data loader.
        """
        # TODO: Implement evaluation with metrics on original scale
        pass

    def train(self) -> Dict[str, Any]:
        """
        Main training loop with validation and early stopping.
        """
        # TODO: Implement full training loop with validation
        pass