from pennylane import numpy as np
import pennylane as qml
import os
from multiprocessing import Queue

os.environ["OMP_NUM_THREADS"] = '16'

def get_sctructure_from_xyz_path(path: str):
    return qml.qchem.read_structure("methane.xyz")

def run_vqe(q: Queue, symbols, coordinates, active_electrons, active_orbitals, max_iterations, conv_tol, step_size):
    molecule = qml.qchem.Molecule(symbols, coordinates, load_data=True)
    H, qubits = qml.qchem.molecular_hamiltonian(molecule, active_electrons=active_electrons,
        active_orbitals=active_orbitals)
    dev = qml.device("lightning.qubit", wires=qubits)



    singles, doubles = qml.qchem.excitations(active_electrons, qubits)
    params =  np.array(np.zeros(len(singles) + len(doubles)), requires_grad=True)
    @qml.qnode(dev)
    def circuit(param, wires):
            # Map excitations to the wires the UCCSD circuit will act on
        s_wires, d_wires = qml.qchem.excitations_to_wires(singles, doubles)
        qml.UCCSD(param, wires, s_wires=s_wires, d_wires=d_wires, init_state=qml.qchem.hf_state(active_electrons, qubits))
        return qml.expval(H)

    def cost_fn(param):
        return circuit(param, wires=range(qubits))
    
    opt = qml.GradientDescentOptimizer(stepsize=step_size)
    
    for n in range(max_iterations):
        # Take step
        params, prev_energy = opt.step_and_cost(cost_fn, params)

        energy = cost_fn(params)

        # Calculate difference between new and old energies
        conv = np.abs(energy - prev_energy)

        q.put([n, energy,params])

        if conv <= conv_tol:
            break