import numpy as np
import torch
from torch.utils.data import DataLoader
from typing import Dict, Any, Optional
from utils.config_model import EvaluationConfig

def calculate_mae(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculate Mean Absolute Error.
    """
    # TODO: Implement MAE calculation
    pass

def calculate_rmse(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculate Root Mean Squared Error.
    """
    # TODO: Implement RMSE calculation
    pass

def plot_predictions_vs_actual(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title_suffix: str,
    filename: str,
    max_points: Optional[int] = None
) -> None:
    """
    Create line plot of predictions vs actual values.
    """
    # TODO: Implement prediction vs actual plot
    pass

def plot_scatter_predictions(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    title_suffix: str,
    filename: str
) -> None:
    """
    Create scatter plot of predictions vs actual values.
    """
    # TODO: Implement scatter plot
    pass

def plot_residuals_time(
    residuals: np.ndarray,
    title_suffix: str,
    filename: str,
    max_points: Optional[int] = None
) -> None:
    """
    Create plot of residuals over time.
    """
    # TODO: Implement residuals time plot
    pass

def plot_residuals_distribution(
    residuals: np.ndarray,
    title_suffix: str,
    filename: str
) -> None:
    """
    Create histogram/KDE of residuals.
    """
    # TODO: Implement residuals distribution plot
    pass

def evaluate_fold(
    model: torch.nn.Module,
    test_loader: DataLoader,
    loss_fn: torch.nn.Module,
    device: torch.device,
    target_scaler: Any,
    eval_config: EvaluationConfig,
    fold_num: int
) -> Dict[str, float]:
    """
    Evaluate model on test set and generate plots.
    """
    # TODO: Implement full evaluation pipeline
    pass