'''Module: integrated_optimizer.py

This module integrates the LSTM-based meta optimizer (LSTMMetaOptimizer) with the 
dynamic circuit generator. It is responsible for:
- Capturing the historical parameters of the circuit.
- Running the meta optimizer to generate update recommendations.
- Applying these recommendations to the dynamic circuit generator to build an optimized circuit.

Assumptions:
- The dynamic circuit generator is available in 'backend/quantum_simulation.py' with a function 'build_quantum_circuit'.
- The LSTM-based meta optimizer is defined in 'backend/meta_optimizer.py' as 'LSTMMetaOptimizer'.
'''

import numpy as np
import torch

# Import the dynamic circuit generator
from quantum_finance.backend.quantum_simulation import build_quantum_circuit

# Import the LSTM-based meta optimizer
from quantum_finance.backend.meta_optimizer import LSTMMetaOptimizer  # Ensure this module exists or implement a stub


class IntegratedOptimizer:
    def __init__(self, num_qubits: int, num_layers: int, circuit_params: list):
        """
        Initialize the integrated optimizer.
        :param num_qubits: Number of qubits for the circuit.
        :param num_layers: Number of layers in the circuit.
        :param circuit_params: List of initial rotation/parameter values for the circuit.
        """
        self.num_qubits = num_qubits
        self.num_layers = num_layers
        self.circuit_params = circuit_params  # Initial parameters of the circuit
        
        # Instantiate the LSTM-based meta optimizer
        self.meta_optimizer = LSTMMetaOptimizer()

    def capture_parameter_history(self) -> np.ndarray:
        """
        Captures the historical parameter data.
        In a real scenario, this could capture data over time; for now, we assume the history
        is the current state of parameters.
        :return: A numpy array representation of current parameters.
        """
        history = np.array(self.circuit_params)
        # [NOTE] Future versions should extend this to capture temporal changes.
        return history

    def update_parameters(self) -> list:
        """
        Uses the meta optimizer to update the circuit parameters based on history.
        :return: Updated circuit parameters as a list.
        """
        history = self.capture_parameter_history()
        # The meta optimizer processes the history to generate update recommendations.
        updated_params = self.meta_optimizer.optimize(history)
        print("Updated circuit parameters:", updated_params)
        # Update internal circuit parameters
        self.circuit_params = updated_params.tolist()
        return self.circuit_params

    def apply_optimized_circuit(self):
        """
        Applies the updated parameters to build a new circuit using the dynamic circuit generator.
        :return: The optimized circuit object.
        """
        updated_params = self.update_parameters()
        circuit = build_quantum_circuit(
            num_qubits=self.num_qubits,
            num_layers=self.num_layers,
            rotation_params=updated_params
        )
        return circuit


def run_integration(num_qubits: int, num_layers: int, current_params: list):
    """
    A helper function to run the integrated optimization process.
    :param num_qubits: Number of qubits.
    :param num_layers: Number of layers.
    :param current_params: Initial circuit parameters.
    :return: Circuit constructed using the updated parameters.
    """
    optimizer = IntegratedOptimizer(num_qubits, num_layers, current_params)
    new_circuit = optimizer.apply_optimized_circuit()
    return new_circuit


if __name__ == "__main__":
    # Example usage: This block will run when the module is executed directly.
    # [NOTE] Ensure that the length of initial_params matches the expected circuit parameter count.
    initial_params = [0.1] * (5 * 3)  # Example: 5 qubits, 3 layers.
    circuit = run_integration(num_qubits=5, num_layers=3, current_params=initial_params)
    print("Circuit built with updated parameters.") 