#!/usr/bin/env python3

import numpy as np
import random
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import transpile
from qiskit_aer import AerSimulator
from qiskit.visualization import plot_histogram
import os
import logging
import time

# Import the quantum-AI interface
from quantum_ai_interface import QuantumMeasurementResult, CircuitMetadata, UncertaintyMetrics

# Assuming SimulationWrapper is the intended class from quantum_wrapper
from quantum_finance.backend.quantum_wrapper import SimulationWrapper # Corrected import

def create_basic_circuit(num_qubits=2):
    """Create a basic Bell state circuit using Qiskit.
    
    Args:
        num_qubits: Number of qubits for the circuit (default: 2)
        
    Returns:
        QuantumCircuit: A Qiskit quantum circuit for a Bell state
    """
    # Create a quantum circuit with specified qubits and classical bits
    qr = QuantumRegister(num_qubits, 'q')
    cr = ClassicalRegister(num_qubits, 'c')
    circuit = QuantumCircuit(qr, cr)
    
    # Apply Hadamard to the first qubit
    circuit.h(qr[0])
    
    # Apply CNOT between first and second qubits if we have at least 2 qubits
    if num_qubits >= 2:
        circuit.cx(qr[0], qr[1])
    
    # Apply additional operations for circuits with more than 2 qubits
    if num_qubits > 2:
        for i in range(1, num_qubits-1):
            circuit.cx(qr[0], qr[i+1])
    
    # Add measurement if needed
    # circuit.measure(qr, cr)
    
    return circuit

def quantum_fourier_transform(n_qubits):
    """Create a Quantum Fourier Transform circuit.
    
    Args:
        n_qubits: Number of qubits in the circuit
        
    Returns:
        QuantumCircuit: A Qiskit quantum circuit for QFT
    """
    # Create a quantum circuit with n_qubits
    circuit = QuantumCircuit(n_qubits)
    
    # Initialize with a superposition state
    for i in range(n_qubits):
        circuit.h(i)
    
    # Add some phase shifts to make the QFT output more interesting
    for i in range(n_qubits):
        circuit.t(i)
    
    # Implement QFT
    for i in range(n_qubits):
        circuit.h(i)
        for j in range(i + 1, n_qubits):
            # Phase rotation gates with diminishing angles
            circuit.cp(np.pi / (2 ** (j - i)), i, j)
    
    # Reverse the order of qubits
    for i in range(n_qubits // 2):
        circuit.swap(i, n_qubits - i - 1)
    
    # Circuit for measurement will be added later
    
    return circuit

def run_simulation(circuit, shots=1024, simulation_method='statevector'):
    """Run a quantum circuit simulation and return results in our standardized format.
    
    Args:
        circuit: Qiskit QuantumCircuit
        shots: Number of simulation shots
        simulation_method: Simulation backend to use
        
    Returns:
        QuantumMeasurementResult object
    """
    try:
        start_time = time.time() # Define start_time

        # Use the imported SimulationWrapper
        wrapper = SimulationWrapper()

        # Prepare circuit_data for the wrapper
        # Assuming circuit object has n_qubits attribute or similar
        # Need to confirm how to represent the circuit for the wrapper's simulate_circuit method
        # Placeholder: We might need to serialize the circuit or pass specific properties
        circuit_data = {
            "n_qubits": circuit.num_qubits, 
            # "gates": serialize_circuit(circuit) # Hypothetical serialization
            # If the wrapper needs the qiskit circuit object, we might need to adjust the wrapper
            # For now, passing a basic dict
        }
        
        # Ensure circuit has measurements if the simulation requires counts
        # The SimulationWrapper's simulate_circuit currently returns statevector
        # We need a way to get counts if that's what the rest of the code expects
        if not circuit.num_clbits:
             circuit.measure_all()

        # Run simulation using the wrapper. 
        # NOTE: The current SimulationWrapper.simulate_circuit returns a state vector,
        # not counts. This will likely cause issues downstream. 
        # We need to adjust either this function or the wrapper later.
        # For now, we call simulate_circuit and assign the result to 'results'.
        # We cannot directly get 'counts' from the state vector without measurement simulation.
        # Let's assume results is the state vector for now.
        results = wrapper.simulate_circuit(circuit_data)

        # Placeholder for counts - this needs proper implementation
        # based on how simulation results (statevector) should be converted to counts.
        # Maybe run a separate measurement simulation?
        counts = {"0" * circuit.num_qubits: shots} # Temporary placeholder counts

        # Log simulation details
        end_time = time.time()
        duration = end_time - start_time
        
        # Extract circuit metadata
        gate_counts = {}
        for instruction, _, _ in circuit.data:
            gate_name = instruction.name
            if gate_name in gate_counts:
                gate_counts[gate_name] += 1
            else:
                gate_counts[gate_name] = 1
        
        # Create circuit metadata
        metadata = CircuitMetadata(
            num_qubits=circuit.num_qubits,
            circuit_depth=circuit.depth(),
            gate_counts=gate_counts,
            simulation_method=simulation_method
        )
        
        # Create uncertainty metrics
        # Shot noise scales with 1/sqrt(shots)
        shot_noise = 1.0 / np.sqrt(shots)
        
        # Gate error is a simplified estimate based on circuit depth
        # In a real system, this would come from device calibration data
        gate_error = 0.001 * circuit.depth() * circuit.num_qubits
        
        uncertainty = UncertaintyMetrics(
            shot_noise=shot_noise,
            gate_error_estimate=gate_error,
            standard_error=shot_noise,  # Simplified approximation
            confidence_interval=(0.0, 1.0),  # Default placeholder
        )
        
        # Create the measurement result
        measurement_result = QuantumMeasurementResult(
            counts=counts,
            metadata=metadata,
            uncertainty=uncertainty,
            shots=shots
        )
        
        # Update uncertainty metrics based on the counts
        measurement_result.uncertainty.estimate_from_counts(counts, shots)
        
        return measurement_result
    except Exception as e:
        # Re-raise the exception to propagate it
        raise

def create_dynamic_circuit(n_qubits, depth, seed=None):
    """Create a dynamic quantum circuit with parameterized gates.
    
    Args:
        n_qubits: Number of qubits
        depth: Circuit depth parameter
        seed: Random seed for reproducibility
        
    Returns:
        QuantumCircuit: A parameterized quantum circuit
    """
    if seed is not None:
        random.seed(seed)
        np.random.seed(seed)
    
    circuit = QuantumCircuit(n_qubits)
    
    # Create a layered circuit architecture
    for d in range(depth):
        # Add a layer of Hadamard gates on alternating qubits based on layer index
        for i in range(n_qubits):
            if (i + d) % 2 == 0:
                circuit.h(i)
        
        # Add a layer of rotational gates with random parameters
        for i in range(n_qubits):
            theta = random.uniform(0, 2 * np.pi)
            if i % 3 == 0:
                circuit.rx(theta, i)
            elif i % 3 == 1:
                circuit.ry(theta, i)
            else:
                circuit.rz(theta, i)
        
        # Add entangling gates
        for i in range(n_qubits - 1):
            if d % 2 == 0:  # Alternate between gate types
                circuit.cx(i, i + 1)
            else:
                circuit.cz(i, i + 1)
    
    return circuit

def create_bell_state():
    """Creates a Bell state quantum circuit."""
    qc = QuantumCircuit(2, 2)
    qc.h(0)
    qc.cx(0, 1)
    qc.measure_all()
    return qc

def create_ghz_circuit(num_qubits):
    """Creates a GHZ state circuit."""
    qc = QuantumCircuit(num_qubits, num_qubits)
    qc.h(0)
    for i in range(num_qubits - 1):
        qc.cx(i, i + 1)
    qc.measure_all()
    return qc

def create_w_state_circuit(num_qubits):
    """Creates a W state circuit."""
    qc = QuantumCircuit(num_qubits, num_qubits)
    
    # Initialize first qubit to |1⟩
    qc.x(0)
    
    # Apply rotations and CNOTs to create W state
    for i in range(1, num_qubits):
        angle = 2 * np.arccos(np.sqrt(1/(num_qubits-i+1)))
        qc.ry(angle, i)
        for j in range(i):
            qc.cx(j, i)
    
    qc.measure_all()
    return qc

def create_random_circuit(num_qubits, depth=3, seed=None):
    """Creates a random quantum circuit."""
    if seed is not None:
        random.seed(seed)
        np.random.seed(seed)
    
    qc = QuantumCircuit(num_qubits, num_qubits)
    
    # Available gates
    single_qubit_gates = [
        lambda circ, q: circ.h(q),
        lambda circ, q: circ.x(q),
        lambda circ, q: circ.y(q),
        lambda circ, q: circ.z(q),
        lambda circ, q: circ.s(q),
        lambda circ, q: circ.t(q),
        lambda circ, q: circ.rx(np.random.uniform(0, 2*np.pi), q),
        lambda circ, q: circ.ry(np.random.uniform(0, 2*np.pi), q),
        lambda circ, q: circ.rz(np.random.uniform(0, 2*np.pi), q),
    ]
    
    two_qubit_gates = [
        lambda circ, q1, q2: circ.cx(q1, q2),
        lambda circ, q1, q2: circ.cz(q1, q2),
        lambda circ, q1, q2: circ.swap(q1, q2),
    ]
    
    # Apply random gates for the specified depth
    for _ in range(depth):
        # Apply single-qubit gates
        for i in range(num_qubits):
            gate = random.choice(single_qubit_gates)
            gate(qc, i)
        
        # Apply two-qubit gates to random pairs
        for _ in range(num_qubits // 2):
            i, j = random.sample(range(num_qubits), 2)
            gate = random.choice(two_qubit_gates)
            gate(qc, i, j)
    
    qc.measure_all()
    return qc

if __name__ == "__main__":
    # Test the simulation
    bell_circuit = create_basic_circuit()
    result = run_simulation(bell_circuit, shots=2000)
    print(f"Bell state counts: {result.counts}")
    print(f"Uncertainty metrics: shot_noise={result.uncertainty.shot_noise:.6f}")
    
    # Test the QFT circuit
    qft_circuit = quantum_fourier_transform(3)
    qft_circuit.measure_all()
    qft_result = run_simulation(qft_circuit, shots=2000)
    print(f"QFT counts: {qft_result.counts}")