import logging
import torch
import numpy as np
from pathlib import Path
from typing import Dict, List, Any

from forecasting_model.utils.config_model import MainConfig
from forecasting_model.data_processing import (
    load_raw_data,
    TimeSeriesCrossValidationSplitter,
    prepare_fold_data_and_loaders
)

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def load_config(config_path: Path) -> MainConfig:
    """
    Load and validate configuration from YAML file.
    """
    # TODO: Implement config loading
    pass

def set_seeds(seed: int = 42) -> None:
    """
    Set random seeds for reproducibility.
    """
    # TODO: Implement seed setting
    pass

def determine_device(config: MainConfig) -> torch.device:
    """
    Determine the device to use for training.
    """
    # TODO: Implement device determination
    pass

def aggregate_cv_metrics(all_fold_metrics: List[Dict[str, float]]) -> Dict[str, Dict[str, float]]:
    """
    Calculate mean and standard deviation of metrics across folds.
    """
    # TODO: Implement metric aggregation
    pass

def main():
    # Load configuration
    config = load_config(Path("config.yaml"))
    
    # Set random seeds
    set_seeds()
    
    # Determine device
    device = determine_device(config)
    
    # Load raw data
    df = load_raw_data(config.data)
    
    # Initialize CV splitter
    cv_splitter = TimeSeriesCrossValidationSplitter(config.cross_validation, len(df))
    
    # Initialize list to store fold metrics
    all_fold_metrics = []
    
    # Cross-validation loop
    for fold_num, (train_idx, val_idx, test_idx) in enumerate(cv_splitter.split(), 1):
        logger.info(f"Starting fold {fold_num}")
        
        # Prepare data loaders
        train_loader, val_loader, test_loader, target_scaler, input_size = prepare_fold_data_and_loaders(
            df, train_idx, val_idx, test_idx,
            config.features, config.training, config.evaluation
        )
        
        # Update model config with input size
        config.model.input_size = input_size
        
        # Initialize model
        model = LSTMForecastModel(config.model).to(device)
        
        # Initialize loss function
        loss_fn = torch.nn.MSELoss() if config.training.loss_function == "MSE" else torch.nn.L1Loss()
        
        # Initialize scheduler if configured
        scheduler = None
        if config.training.scheduler_step_size is not None:
            # TODO: Initialize scheduler
            pass
        
        # Initialize trainer
        trainer = Trainer(
            model, train_loader, val_loader, loss_fn, device,
            config.training, scheduler, target_scaler
        )
        
        # Train model
        trainer.train()
        
        # Evaluate on test set
        fold_metrics = evaluate_fold(
            model, test_loader, loss_fn, device,
            target_scaler, config.evaluation, fold_num
        )
        
        all_fold_metrics.append(fold_metrics)
        
        # Optional: Clear GPU memory
        if device.type == "cuda":
            torch.cuda.empty_cache()
    
    # Aggregate metrics
    aggregated_metrics = aggregate_cv_metrics(all_fold_metrics)
    
    # Log final results
    logger.info("Cross-validation results:")
    for metric, stats in aggregated_metrics.items():
        logger.info(f"{metric}: {stats['mean']:.4f} ± {stats['std']:.4f}")

if __name__ == "__main__":
    main()