'''Module: training_loop.py

This module implements a training loop that integrates the LSTM-based meta optimizer with the dynamic circuit generator.
It simulates a training process by iteratively updating circuit parameters via the integrated optimizer and evaluating the circuit.

Assumptions:
- The integrated optimizer module (integrated_optimizer.py) is available in the backend folder.
- A dummy evaluation function is used to simulate circuit performance.
'''

# If running as a script, insert parent directory in sys.path to allow absolute imports
if __name__ == '__main__' and __package__ is None:
    import sys
    from os import path
    sys.path.insert(0, path.abspath(path.join(path.dirname(__file__), '..')))
    __package__ = 'backend'

import time
import numpy as np
import logging
logging.basicConfig(filename='training_loop.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Import the integrated optimizer's helper function
from quantum_finance.backend.integrated_optimizer import run_integration


def evaluate_circuit(circuit):
    """
    Dummy function to evaluate the performance of a quantum circuit.
    In a real scenario, this function would execute the circuit simulation and compute loss metrics.
    :param circuit: The quantum circuit object returned by the dynamic circuit generator.
    :return: A simulated loss value (float).
    """
    # For demonstration purposes, return a random loss between 0 and 1
    return np.random.random()


def training_loop(num_qubits: int, num_layers: int, initial_params: list, num_epochs: int = 10, alpha: float = 0.1):
    """
    Runs a training loop for the quantum circuit optimization.

    At each epoch:
    - The integrated optimizer updates the circuit parameters and generates a new circuit.
    - The circuit is evaluated using a dummy loss function.
    - The circuit parameters are adjusted based on the loss (dummy update for demonstration).
    
    :param num_qubits: Number of qubits in the circuit.
    :param num_layers: Number of layers in the circuit.
    :param initial_params: Initial list of circuit parameters.
    :param num_epochs: Number of training epochs.
    :param alpha: Learning rate for the dynamic parameter update.
    """
    params = initial_params
    print("Starting training loop...")
    logging.info("Starting training loop...")
    
    for epoch in range(num_epochs):
        print(f"\nEpoch {epoch + 1}/{num_epochs}")
        logging.info(f"Epoch {epoch + 1}/{num_epochs}")
        
        # Generate an optimized circuit using the integrated optimizer
        circuit = run_integration(num_qubits, num_layers, params)
        
        # Evaluate the circuit performance (dummy evaluation)
        loss = evaluate_circuit(circuit)
        print(f"Loss: {loss:.4f}")
        logging.info(f"Loss: {loss:.4f}")
        
        # --- Begin dynamic parameter update ---
        old_params = params.copy()  # capture current parameters
        new_params = [p * (1 - alpha * loss) for p in params]  # update using dynamic learning rate
        # Compute update magnitude as the L2 norm of differences
        update_magnitude = sum((new - old)**2 for new, old in zip(new_params, old_params))**0.5
        print(f"Update magnitude (L2 norm): {update_magnitude:.6f}")
        logging.info(f"Update magnitude (L2 norm): {update_magnitude:.6f}")
        
        # Adjust learning rate alpha based on update magnitude thresholds
        if update_magnitude < 0.001:
            alpha *= 1.1
            print(f"Update magnitude too low; increasing learning rate to {alpha:.6f}")
            logging.info(f"Update magnitude too low; increasing learning rate to {alpha:.6f}")
        elif update_magnitude > 0.1:
            alpha *= 0.9
            print(f"Update magnitude too high; decreasing learning rate to {alpha:.6f}")
            logging.info(f"Update magnitude too high; decreasing learning rate to {alpha:.6f}")
        else:
            print(f"Learning rate remains at {alpha:.6f}")
            logging.info(f"Learning rate remains at {alpha:.6f}")
        
        # Update parameters with the newly computed values
        params = new_params
        print("Updated training parameters:", params)
        logging.info("Updated training parameters: " + str(params))
        # --- End dynamic parameter update ---
        
        # Simulate training time delay
        time.sleep(1)
    
    print("\nTraining complete.")
    logging.info("Training complete.")


if __name__ == '__main__':
    # Example configuration: 5 qubits, 3 layers, with initial parameters for training
    num_qubits = 5
    num_layers = 3
    # Initial parameters: one parameter per gate (for demonstration, we use 0.1 for each position)
    initial_params = [0.1] * (num_qubits * num_layers)  
    training_loop(num_qubits, num_layers, initial_params, num_epochs=5) 