# Tencent is pleased to support the open source community by making ncnn available.
#
# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software distributed
# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
# CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.

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__()

        self.pool_0 = nn.MaxPool1d(kernel_size=3)
        self.pool_1 = nn.MaxPool1d(kernel_size=4, stride=2, padding=2, dilation=1)
        self.pool_2 = nn.MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=1, return_indices=False, ceil_mode=False)
        self.pool_3 = nn.MaxPool1d(kernel_size=5, stride=2, padding=2, dilation=1, return_indices=False, ceil_mode=True)
        self.pool_4 = nn.MaxPool1d(kernel_size=3, stride=1, padding=1, dilation=2, return_indices=False, ceil_mode=False)
        self.pool_5 = nn.MaxPool1d(kernel_size=2, stride=1, padding=0, dilation=1, return_indices=True, ceil_mode=True)
        self.pool_6 = nn.MaxPool1d(kernel_size=5, stride=1, padding=2, dilation=1, return_indices=True, ceil_mode=False)

    def forward(self, x, y):
        x = self.pool_0(x)
        x = self.pool_1(x)
        x = self.pool_2(x)
        x = self.pool_3(x)
        x = self.pool_4(x)
        x, tx = self.pool_5(x)
        x, indices = self.pool_6(x)

        y = self.pool_0(y)
        y = self.pool_1(y)
        y = self.pool_2(y)
        y = self.pool_3(y)
        y = self.pool_4(y)
        if version.parse(torch.__version__) < version.parse('1.10'):
            y = y.unsqueeze(0)
        y, ty = self.pool_5(y)

        return x, indices, y

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

    torch.manual_seed(0)
    x = torch.rand(1, 12, 64)
    y = torch.rand(12, 64)

    a = net(x, y)

    # export torchscript
    mod = torch.jit.trace(net, (x, y))
    mod.save("test_nn_MaxPool1d.pt")

    # torchscript to pnnx
    import os
    os.system("../src/pnnx test_nn_MaxPool1d.pt inputshape=[1,12,64],[12,64]")

    # pnnx inference
    import test_nn_MaxPool1d_pnnx
    b = test_nn_MaxPool1d_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)
