import numpy as np


def meta_learner(old_params, grad_loss, delta_loss, additional_metrics):
    """
    Simulated meta learner function that computes an update step direction.
    This placeholder function returns a combination of the gradient information 
    and the change in loss, optionally scaled by a provided metric.
    
    Parameters:
        old_params (np.array): The current parameters.
        grad_loss (np.array): The gradient of the loss with respect to parameters.
        delta_loss (float): Change in loss value between iterations.
        additional_metrics (dict): Additional metrics that might affect the update.
        
    Returns:
        np.array: Computed update step.
    """
    # As a placeholder, perform a simple weighted sum.
    # In a learning scenario, this would come from a trained meta learner model.
    update_direction = grad_loss + 0.5 * delta_loss
    # Optionally modify update with additional metrics if provided
    if 'scaling' in additional_metrics:
        update_direction *= additional_metrics['scaling']
    return update_direction


def advanced_meta_update(old_params, grad_loss, delta_loss, additional_metrics, alpha=0.1):
    """
    Computes an advanced meta update of parameters using an adaptive update rule.
    The update rule is defined as:
        new_params = old_params + alpha * tanh(meta_learner(old_params, grad_loss, delta_loss, additional_metrics))
    This method adapts the update based on gradient information, change in loss, and additional metrics,
    scaling the update with tanh to clip extreme values.
    
    Parameters:
        old_params (np.array): The current parameters of the model.
        grad_loss (np.array): The gradient of the loss with respect to parameters.
        delta_loss (float): The change in loss value between successive iterations.
        additional_metrics (dict): A dictionary of additional performance metrics influencing the update.
        alpha (float): The learning rate or scaling factor for the update step.
        
    Returns:
        np.array: The updated parameters.
    """
    # Compute the meta learner output
    meta_update = meta_learner(old_params, grad_loss, delta_loss, additional_metrics)
    
    # Compute the clipped update using the hyperbolic tangent function to stabilize updates
    clipped_update = np.tanh(meta_update)
    
    # Debug note: alpha scales the clipped update. Adjust alpha to control update aggressiveness.
    new_params = old_params + alpha * clipped_update
    
    # Extensive inline commentary for development and future tuning:
    # - old_params: expected as a numpy array for elementwise operations.
    # - grad_loss: ideally obtained from backpropagation or similar gradient computations.
    # - delta_loss: calculated as the difference between current and previous loss values.
    # - additional_metrics: can include keys like 'scaling' to modulate the update magnitude.
    return new_params


# Example Usage: (for testing purposes)
if __name__ == "__main__":
    # Example parameter update scenario
    parameters = np.array([1.0, 2.0, 3.0])
    gradient = np.array([-0.1, -0.2, -0.3])
    delta_loss = 0.05  # sample change in loss
    metrics = {'scaling': 1.2}  # an arbitrary scaling metric

    updated_params = advanced_meta_update(parameters, gradient, delta_loss, metrics, alpha=0.1)
    print("Old Parameters:", parameters)
    print("Updated Parameters:", updated_params) 