#!/usr/bin/env python3
"""
Benchmarking Simulation for Meta Optimizer
-------------------------------------------
This script runs multiple simulation experiments by varying hyperparameters such as
learning rate and noise standard deviation. It logs key metrics including convergence
epoch, final loss, and total training time for each experiment.

Extensive comments are provided to track assumptions and to serve as hooks for future
upgrades (e.g., advanced meta-learning methods like MAML/Reptile or quantum-inspired techniques).

Author: Quantum Pioneers
Date: 2023-10-XX
"""

import numpy as np
import logging
import time

# Import the simulation function from our enhanced simulation module
# Ensure that the simulation directory is a Python package (contains __init__.py) or adjust the import accordingly.
from enhanced_simulation import simulate_training

# Set up logging configuration specific for benchmarking
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")


def run_benchmarks():
    """
    Runs a series of training simulations with varying learning rates and noise standard deviations.

    Returns:
        list: A list of experiments with hyperparameters and corresponding metrics.
    """
    # Define experiment hyperparameters: list of learning rates and noise standard deviations
    learning_rates = [0.05, 0.1, 0.2]
    noise_stds = [0.05, 0.1, 0.2]
    epochs = 100  # Fixed number of epochs for each experiment

    experiments = []  # List to store benchmark results for each experiment

    np.random.seed(42)  # Ensures reproducibility across experiments

    # Iterate over combinations of learning rate and noise standard deviation
    for lr in learning_rates:
        for ns in noise_stds:
            logging.info(f"Starting simulation for learning_rate={lr}, noise_std={ns}")
            initial_params = np.random.randn(10)  # Initialize parameters randomly for each run
            metrics = simulate_training(initial_params, learning_rate=lr, noise_std=ns, epochs=epochs)
            # Store the hyperparameters and the resulting metrics
            experiments.append({
                'learning_rate': lr,
                'noise_std': ns,
                'metrics': metrics
            })
            logging.info(f"Completed simulation for learning_rate={lr}, noise_std={ns}")
    
    return experiments


def main():
    """
    Main function to run all benchmark experiments and summarize the results.
    """
    start_time = time.time()
    experiments = run_benchmarks()
    total_time = time.time() - start_time
    logging.info("Benchmarking complete. Summary of experiments:")

    for exp in experiments:
        lr = exp['learning_rate']
        ns = exp['noise_std']
        metrics = exp['metrics']
        logging.info(f"Learning Rate: {lr}, Noise Std: {ns} -> Convergence Epoch: {metrics['convergence_epoch']}, Final Loss: {metrics['final_loss']:.4f}, Total Time: {metrics['total_time_sec']:.2f} sec")
    
    logging.info(f"Total benchmarking runtime: {total_time:.2f} sec")


if __name__ == "__main__":
    main() 