import torch
import torch.nn as nn

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()

        # 1x1 Convolution
        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(out_channels)

        # 3x3 Convolution
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(out_channels)

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

    def forward(self, x):
        out = torch.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = torch.relu(out)
        return out

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1):
        super(Bottleneck, self).__init__()

        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm1d(out_channels)

        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(out_channels)

        self.conv3 = nn.Conv1d(out_channels, self.expansion * out_channels, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm1d(self.expansion * out_channels)

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

    def forward(self, x):
        out = torch.relu(self.bn1(self.conv1(x)))
        out = torch.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = torch.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_channels = 64

        self.conv1 = nn.Conv1d(12, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512 * block.expansion, num_classes)
        
        self.avgpool = nn.AdaptiveAvgPool1d((1))

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

    def forward(self, x):
        out = torch.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        # out = self.avgpool(out)
        # out = out.view(out.size(0), -1)
        # out = self.linear(out)
        return out

def ResNet18():
    return ResNet(BasicBlock, [2, 2, 2, 2])

def ResNet34():
    return ResNet(BasicBlock, [3, 4, 6, 3])

def ResNet50():
    return ResNet(Bottleneck, [3, 4, 6, 3])

def ResNet101():
    # Here we would usually use a different block type, Bottleneck, which contains three convolution layers
    # For simplicity, we're going to use BasicBlock here
    return ResNet(Bottleneck, [3, 4, 23, 3])

def ResNet152():
    return ResNet(Bottleneck, [3, 8, 36, 3])