#!/usr/bin/env python3
"""
Enhanced Simulation for Meta Optimizer Training
------------------------------------------------
This simulation enhances the integration environment by introducing:
- Gradient noise injection: adding configurable Gaussian noise to gradients.
- A more complex loss function: combining non-linear (sin^2 component) and quadratic terms.
- Benchmarking: logging metrics like convergence speed, loss history, and update norms.
- Hooks for future advanced meta-learning updates (e.g., MAML/Reptile) and quantum-inspired optimization techniques.

Extensive comments are provided to track changes and document the evolution of our simulation.

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

import numpy as np
import time
import logging
from meta_learner import meta_update

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


def complex_loss(params):
    """
    Computes a more complex loss that simulates realistic training signals.

    Loss function:
      L = sum(sin(params)^2) + sum(params^2)
      This mix introduces nonlinearity and quadratic behavior to better model complex losses.

    Args:
        params (np.array): Parameter vector.

    Returns:
        float: Computed loss value.
    """
    return np.sum(np.sin(params)**2) + np.sum(params**2)


def compute_gradient(params, loss_fn):
    """
    Numerically approximates the gradient of the loss function using finite differences.
    (This is a simple approximation for simulation purposes.)

    Args:
        params (np.array): Current parameters.
        loss_fn (function): Function to compute loss.

    Returns:
        np.array: Approximated gradient.
    """
    grad = np.zeros_like(params)
    epsilon = 1e-5
    base_loss = loss_fn(params)
    for i in range(len(params)):
        perturbed = np.copy(params)
        perturbed[i] += epsilon
        grad[i] = (loss_fn(perturbed) - base_loss) / epsilon
    return grad


def add_noise(gradient, noise_std=0.1):
    """
    Injects Gaussian noise into the provided gradient to simulate real-world noise.

    Args:
        gradient (np.array): The original gradient.
        noise_std (float): Standard deviation for the Gaussian noise.

    Returns:
        np.array: Noisy gradient.
    """
    noise = np.random.normal(0, noise_std, size=gradient.shape)
    return gradient + noise


def simulate_training(initial_params, learning_rate=0.1, noise_std=0.1, epochs=100):
    """
    Simulates the training of the meta optimizer with noise in gradient calculations and an advanced loss function.

    Tracks and logs benchmark metrics such as convergence speed, update norms, and loss history.

    Args:
        initial_params (np.array): Starting parameters.
        learning_rate (float): Scaling factor for parameter updates.
        noise_std (float): Standard deviation for the gradient noise.
        epochs (int): Number of training iterations.
        
    Returns:
        dict: Benchmark metrics including loss history and convergence details.
    """
    params = np.copy(initial_params)
    loss_history = []
    start_time = time.time()

    logging.info("Starting training simulation with enhanced settings.")
    for epoch in range(epochs):
        # Calculate current loss using the complex loss function
        loss = complex_loss(params)
        loss_history.append(loss)

        # Approximate gradient via finite differences
        grad = compute_gradient(params, complex_loss)
        # Introduce noise to the gradient to simulate estimation uncertainties
        noisy_grad = add_noise(grad, noise_std=noise_std)

        # New update rule using meta_update for adaptive updates
        update = meta_update(noisy_grad, learning_rate)
        params -= update  # gradient descent update

        # Log key benchmarks at regular intervals for monitoring performance
        if (epoch + 1) % 10 == 0 or epoch == 0:
            logging.info(f"Epoch {epoch+1}/{epochs} - Loss: {loss:.4f} - Update Norm: {np.linalg.norm(update):.4f}")
    
    total_time = time.time() - start_time
    logging.info("Training simulation completed.")
    logging.info(f"Total training time: {total_time:.2f} seconds")

    # Benchmark: Determine the epoch where loss first drops below an arbitrary threshold (e.g., 0.5)
    convergence_epoch = next((i+1 for i, l in enumerate(loss_history) if l < 0.5), None)
    if convergence_epoch:
        logging.info(f"Convergence achieved at epoch {convergence_epoch}")
    else:
        logging.info("Convergence not achieved within the given epochs")

    metrics = {
        "loss_history": loss_history,
        "total_time_sec": total_time,
        "convergence_epoch": convergence_epoch,
        "final_loss": loss_history[-1]
    }
    return metrics


def main():
    """
    Main function to run the enhanced training simulation.

    Demonstrates the simulation with default parameters and logs benchmark metrics.
    """
    np.random.seed(42)  # Ensures reproducibility of noise effects
    initial_params = np.random.randn(10)  # Initialize parameters randomly
    metrics = simulate_training(initial_params, learning_rate=0.1, noise_std=0.1, epochs=100)
    logging.info("Final Benchmark Metrics:")
    for key, value in metrics.items():
        logging.info(f"{key}: {value}")


if __name__ == "__main__":
    main() 