#!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
@author:andrew
@file:SaDetectModel.py
@email:admin@marques22.com
@email:2021022362@m.scnu.edu.cn
@time:2023/09/16
"""
from torch import nn


class BasicBlock_1d(nn.Module):
    expansion = 1

    def __init__(self, input_channel, output_channel, stride=1):
        super(BasicBlock_1d, self).__init__()
        self.left = nn.Sequential(
            nn.Conv1d(in_channels=input_channel, out_channels=output_channel,
                      kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm1d(output_channel),
            nn.ReLU(inplace=True),
            nn.Conv1d(in_channels=output_channel, out_channels=output_channel,
                      kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(output_channel)
        )
        self.right = nn.Sequential()

        if stride != 1 or input_channel != self.expansion * output_channel:
            self.right = nn.Sequential(
                nn.Conv1d(in_channels=input_channel, out_channels=output_channel * self.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(self.expansion * output_channel)
            )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        out = self.left(x)
        residual = self.right(x)
        out += residual
        out = self.relu(out)
        return out


class ResNet_1d(nn.Module):
    def __init__(self, block, number_block, num_classes=2):
        super(ResNet_1d, self).__init__()

        self.in_channel = 64

        self.conv1 = nn.Conv1d(in_channels=1, out_channels=self.in_channel,
                               kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block=block, out_channel=64, num_block=number_block[0], stride=1)
        self.layer2 = self._make_layer(block=block, out_channel=128, num_block=number_block[1], stride=2)
        self.layer3 = self._make_layer(block=block, out_channel=256, num_block=number_block[2], stride=2)
        self.layer4 = self._make_layer(block=block, out_channel=512, num_block=number_block[3], stride=2)
        # self.layer5 = self._make_layer(block=block, out_channel=512, num_block=number_block[4], stride=2)
        self.pool2 = nn.AdaptiveAvgPool1d(1)

        self.features = nn.Sequential(
            # nn.Linear(in_features=1024, out_features=nc),
            nn.Flatten(),
            # nn.Linear(in_features=512 * 23, out_features=512),
            nn.Linear(in_features=512, out_features=num_classes)

            # nn.Softmax()
            # nn.Sigmoid()
        )
        # self.linear = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, out_channel, num_block, stride):
        strides = [stride] + [1] * (num_block - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channel, out_channel, stride))
            self.in_channel = out_channel * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.pool1(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        # x = self.layer5(x)
        x = self.pool2(x)
        x = x.view(x.size(0), -1)

        x = self.features(x)
        return x


class ResNet18_LSTM_1d_v2(ResNet_1d):
    def __init__(self, block, number_block, num_classes, hidden_size, num_layers, bidirectional):
        super(ResNet18_LSTM_1d_v2, self).__init__(
            block=block,
            number_block=number_block,
            num_classes=num_classes
        )
        # self.pool3 = nn.MaxPool1d(4)
        self.lstm = nn.LSTM(input_size=512,
                            hidden_size=hidden_size,
                            num_layers=num_layers,
                            bidirectional=bidirectional,
                            batch_first=True)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.pool1(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = x.transpose(2, 1)
        x, (h_n1, c_n1) = self.lstm(x)
        x = x[:, -1, :]
        x = self.features(x)
        return x


def ResNet18_v2_LSTM():
    return ResNet18_LSTM_1d_v2(BasicBlock_1d, [2, 2, 2, 2],
                               num_classes=2, hidden_size=512, num_layers=2, bidirectional=False)


if __name__ == '__main__':
    # from torchinfo import summary
    # resnet = ResNet18_v2_LSTM().cuda()
    # summary(resnet, (4, 1, 300))
    pass