import numpy as np
import logging

# Configure logging for diagnostics and debugging
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

"""
Meta Optimizer Module
---------------------
This module implements an adaptive meta optimizer leveraging gradient-based and meta-learning approaches.
Based on the refinement plan in docs/meta_optimizer_refinement_plan.md, this module outlines a strategy to update parameters dynamically.

High-Level Algorithm:
    new_params = old_params + α * tanh(MetaLearner(old_params, ∇loss, Δloss, additional_metrics))

For now, a dummy MetaLearner is implemented that returns a weighted combination of inputs,
serving as a placeholder for a more sophisticated model (e.g., LSTM or feed-forward network).

Extensive inline comments and logging are provided to facilitate diagnostics and further development.
"""

def meta_learner(old_params, grad_loss, delta_loss, additional_metrics):
    """Compute an update based on gradients and delta loss.

    For simplicity, the update is computed as:
       update = grad_loss * 0.1 + delta_loss * 0.05
    """
    update = grad_loss * 0.1 + delta_loss * 0.05
    return update


def adaptive_meta_update(old_params, grad_loss, delta_loss, additional_metrics, alpha):
    """Compute the adaptive meta update by calling meta_learner and then applying tanh clipping.

    The new parameters are calculated as:
         new_params = old_params + alpha * tanh(update)
    """
    update = meta_learner(old_params, grad_loss, delta_loss, additional_metrics)
    clipped_update = np.tanh(update)
    new_params = old_params + alpha * clipped_update
    return new_params


def gradient_adjustment(gradient_norm, threshold):
    """Compute an adjustment factor based on the gradient norm.

    Returns 1.5 if the gradient norm exceeds the threshold, otherwise returns 1.0.
    """
    return 1.5 if gradient_norm > threshold else 1.0


def adjustment_based_on_variance(gradient_variance):
    """Compute learning rate adjustment based on gradient variance.

    For demonstration, the adjustment is computed as: 0.5 + (1.0 - gradient_variance).
    """
    return 0.5 + (1.0 - gradient_variance)


# Example usage for testing and demonstration
if __name__ == "__main__":
    # Define a sample parameter vector (e.g., weights of a model)
    old_params = np.array([0.5, -0.3, 0.8])
    
    # Dummy gradient of loss (could be computed via backpropagation in a real scenario)
    grad_loss = np.array([0.2, -0.1, 0.05])
    
    # Dummy change in loss from the previous iteration
    delta_loss = -0.05
    
    # Additional metrics for diagnostics (e.g., current loss, gradient norms, etc.)
    additional_metrics = {
        'loss': 0.25,
        'gradient_norm': np.linalg.norm(grad_loss)
    }
    
    logging.info("Starting adaptive meta update demonstration.")
    
    # Apply the adaptive meta update rule
    new_params = adaptive_meta_update(old_params, grad_loss, delta_loss, additional_metrics, alpha=0.1)
    logging.info("Resulting new parameters: %s", new_params)

    # Note: This is a preliminary implementation. Future iterations will incorporate advanced meta-learning and quantum-inspired techniques as outlined in the refinement plan. 