# Copyright 2024 Tencent
# SPDX-License-Identifier: BSD-3-Clause

import torch
import torch.nn as nn
import torch.nn.functional as F
from packaging import version

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, x, y, z, w):
        x = torch.view_as_complex(x)
        y = torch.view_as_complex(y)
        z = torch.view_as_complex(z)
        w = torch.view_as_complex(w)
        out0 = torch.istft(x, n_fft=64, window=torch.hann_window(44), win_length=44, center=True, normalized=True, return_complex=False)
        if version.parse(torch.__version__) < version.parse('1.13'):
            # https://github.com/pytorch/pytorch/commit/4906a956181fb337e99860fa3e6071473c0e30e6
            out1 = torch.istft(y, n_fft=128, center=True, onesided=True, return_complex=False)
        else:
            out1 = torch.istft(y, n_fft=128, center=False, onesided=True, return_complex=False)
        out2 = torch.istft(z, n_fft=512, window=torch.hamming_window(256), win_length=256, hop_length=128, center=True, onesided=True, return_complex=False)
        if version.parse(torch.__version__) < version.parse('1.13'):
            # https://github.com/pytorch/pytorch/commit/4906a956181fb337e99860fa3e6071473c0e30e6
            out3 = torch.istft(w, n_fft=512, center=True, onesided=False, return_complex=True)
        else:
            out3 = torch.istft(w, n_fft=512, center=False, onesided=False, return_complex=True)
        out3 = torch.view_as_real(out3)
        return out0, out1, out2, out3

def test():
    net = Model()
    net.eval()

    torch.manual_seed(0)
    x = torch.rand(33, 161, 2)
    y = torch.rand(65, 77, 2)
    z = torch.rand(257, 8, 2)
    w = torch.rand(512, 4, 2)

    a = net(x, y, z, w)

    # export torchscript
    mod = torch.jit.trace(net, (x, y, z, w))
    mod.save("test_torch_istft.pt")

    # torchscript to pnnx
    import os
    os.system("../../src/pnnx test_torch_istft.pt inputshape=[33,161,2],[65,77,2],[257,8,2],[512,4,2]")

    # ncnn inference
    import test_torch_istft_ncnn
    b = test_torch_istft_ncnn.test_inference()

    for a0, b0 in zip(a, b):
        if not torch.allclose(a0, b0, 1e-3, 1e-3):
            return False
    return True

if __name__ == "__main__":
    if test():
        exit(0)
    else:
        exit(1)
