"""MPI parallel sliced contraction using pre-sliced tree."""
import time, pickle, os
import numpy as np
from mpi4py import MPI

NQUBITS, NLAYERS, NCORES = 25, 10, 48

comm = MPI.COMM_WORLD
rank, size = comm.Get_rank(), comm.Get_size()

os.environ['OMP_NUM_THREADS'] = str(NCORES)
os.environ['MKL_NUM_THREADS'] = str(NCORES)

import torch
import qibo, quimb as qu
from qibotn.observables import build_random_circuit

torch.set_num_threads(NCORES)

circuit = build_random_circuit(NQUBITS, NLAYERS)
qibo.set_backend("qibotn", platform="quimb")
backend = qibo.get_backend()
backend.configure_tn_simulation(ansatz="tn")
qc = backend._qibo_circuit_to_quimb(circuit, backend.circuit_ansatz)
tn = qc.local_expectation(qu.pauli('x') & qu.pauli('z'), (0, 1), rehearse='tn')

if rank == 0:
    with open(f"data/tree_q{NQUBITS}_l{NLAYERS}_sliced.pkl", 'rb') as f:
        tree = pickle.load(f)
else:
    tree = None
tree = comm.bcast(tree, root=0)

arrays = [torch.from_numpy(np.asarray(t._data)) for t in tn.tensors]
n_slices = tree.multiplicity

if rank == 0:
    print(f"Slices: {n_slices}, Ranks: {size}, "
          f"Peak: {tree.max_size() * 16 / 1e9:.2f} GB, "
          f"Threads/rank: {NCORES}, Backend: torch")

t0 = time.time()
result = None
for i in range(rank, n_slices, size):
    val = tree.contract_slice(arrays, i, backend='torch')
    val_np = val.cpu().numpy().reshape(-1)
    result = val_np if result is None else result + val_np

if result is None:
    result = np.zeros(1, dtype=np.complex128)

total = np.zeros_like(result) if rank == 0 else None
comm.Reduce(result, total, root=0)

if rank == 0:
    print(f"Contract: {time.time() - t0:.4f}s  Expectation: {0.5 * total[0].real:.10f}")