"""
Monotonic alignment package
"""

import numba
from numpy import zeros, int32, float32
from torch import from_numpy


@numba.jit(
    numba.void(
        numba.int32[:, :, ::1],
        numba.float32[:, :, ::1],
        numba.int32[::1],
        numba.int32[::1],
    ),
    nopython=True,
    nogil=True,
)
def maximum_path_jit(paths, values, t_ys, t_xs):
    b = paths.shape[0]
    max_neg_val = -1e9
    for i in range(int(b)):
        path = paths[i]
        value = values[i]
        t_y = t_ys[i]
        t_x = t_xs[i]

        v_prev = v_cur = 0.0
        index = t_x - 1

        for y in range(t_y):
            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
                if x == y:
                    v_cur = max_neg_val
                else:
                    v_cur = value[y - 1, x]
                if x == 0:
                    if y == 0:
                        v_prev = 0.0
                    else:
                        v_prev = max_neg_val
                else:
                    v_prev = value[y - 1, x - 1]
                value[y, x] += max(v_prev, v_cur)

        for y in range(t_y - 1, -1, -1):
            path[y, index] = 1
            if index != 0 and (
                index == y or value[y - 1, index] < value[y - 1, index - 1]
            ):
                index = index - 1


def maximum_path(neg_cent, mask):
    device = neg_cent.device
    dtype = neg_cent.dtype
    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
    path = zeros(neg_cent.shape, dtype=int32)

    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
    return from_numpy(path).to(device=device, dtype=dtype)