# Copyright 2021 Tencent
# SPDX-License-Identifier: BSD-3-Clause

import torch
import torch.nn as nn
import torch.nn.functional as F

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

    def forward(self, x, y, z, w):
        x = F.upsample(x, size=16)
        x = F.upsample(x, scale_factor=2, mode='nearest')
        x = F.upsample(x, size=(20), mode='nearest')
        x = F.upsample(x, scale_factor=(4), mode='nearest')
        x = F.upsample(x, size=16, mode='linear')
        x = F.upsample(x, scale_factor=2, mode='linear')
        x = F.upsample(x, size=(24), mode='linear', align_corners=True)
        x = F.upsample(x, scale_factor=(3), mode='linear', align_corners=True)

        y = F.upsample(y, size=16)
        y = F.upsample(y, scale_factor=2, mode='nearest')
        y = F.upsample(y, size=(20,20), mode='nearest')
        y = F.upsample(y, scale_factor=(4,4), mode='nearest')
        y = F.upsample(y, size=(16,24), mode='nearest')
        y = F.upsample(y, scale_factor=(2,3), mode='nearest')
        y = F.upsample(y, size=16, mode='bilinear')
        y = F.upsample(y, scale_factor=2, mode='bilinear')
        y = F.upsample(y, size=(20,20), mode='bilinear', align_corners=False)
        y = F.upsample(y, scale_factor=(4,4), mode='bilinear', align_corners=False)
        y = F.upsample(y, size=(16,24), mode='bilinear', align_corners=True)
        y = F.upsample(y, scale_factor=(2,3), mode='bilinear', align_corners=True)
        y = F.upsample(y, size=16, mode='bicubic')
        y = F.upsample(y, scale_factor=2, mode='bicubic')
        y = F.upsample(y, size=(20,20), mode='bicubic', align_corners=False)
        y = F.upsample(y, scale_factor=(4,4), mode='bicubic', align_corners=False)
        y = F.upsample(y, size=(16,24), mode='bicubic', align_corners=True)
        y = F.upsample(y, scale_factor=(2,3), mode='bicubic', align_corners=True)

        z = F.upsample(z, size=16)
        z = F.upsample(z, scale_factor=2, mode='nearest')
        z = F.upsample(z, size=(20,20,20), mode='nearest')
        z = F.upsample(z, scale_factor=(4,4,4), mode='nearest')
        z = F.upsample(z, size=(16,24,20), mode='nearest')
        z = F.upsample(z, scale_factor=(2,3,4), mode='nearest')
        z = F.upsample(z, size=16, mode='trilinear')
        z = F.upsample(z, scale_factor=2, mode='trilinear')
        z = F.upsample(z, size=(20,20,20), mode='trilinear', align_corners=False)
        z = F.upsample(z, scale_factor=(4,4,4), mode='trilinear', align_corners=False)
        z = F.upsample(z, size=(16,24,20), mode='trilinear', align_corners=True)
        z = F.upsample(z, scale_factor=(2,3,4), mode='trilinear', align_corners=True)

        w = F.upsample(w, scale_factor=(1.499,1.499), mode='nearest')

        return x, y, z, w

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

    torch.manual_seed(0)
    x = torch.rand(1, 3, 32)
    y = torch.rand(1, 3, 32, 32)
    z = torch.rand(1, 3, 32, 32, 32)
    w = torch.rand(1, 8, 12, 12)

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

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

    # torchscript to pnnx
    import os
    os.system("../src/pnnx test_F_upsample.pt inputshape=[1,3,32],[1,3,32,32],[1,3,32,32,32],[1,8,12,12]")

    # pnnx inference
    import test_F_upsample_pnnx
    b = test_F_upsample_pnnx.test_inference()

    for a0, b0 in zip(a, b):
        if not torch.equal(a0, b0):
            return False
    return True

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