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

class ResNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResNetBlock, self).__init__()
        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=7, stride=stride, padding=3, bias=False)
        self.bn1 = nn.BatchNorm1d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=7, stride=1, padding=3, bias=False)
        self.bn2 = nn.BatchNorm1d(out_channels)
        self.downsample = downsample

    def forward(self, x):
        identity = x
        if self.downsample is not None:
            identity = self.downsample(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += identity
        out = self.relu(out)
        return out

class ResNet1d(nn.Module):
    """
    ResNet-1D for ECG Classification.
    Adapted from 'Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline' (Wang et al. 2017)
    """
    def __init__(self, num_classes=5):
        super(ResNet1d, self).__init__()
        
        self.inplanes = 64
        # Initial: 12 leads -> 64 channels
        self.conv1 = nn.Conv1d(12, 64, kernel_size=15, stride=2, padding=7, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)

        # Layers
        self.layer1 = self._make_layer(64, 2, stride=1)
        self.layer2 = self._make_layer(128, 2, stride=2)
        self.layer3 = self._make_layer(256, 2, stride=2)
        self.layer4 = self._make_layer(512, 2, stride=2)

        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes:
            downsample = nn.Sequential(
                nn.Conv1d(self.inplanes, planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(planes),
            )

        layers = []
        layers.append(ResNetBlock(self.inplanes, planes, stride, downsample))
        self.inplanes = planes
        for _ in range(1, blocks):
            layers.append(ResNetBlock(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

if __name__ == "__main__":
    # Test
    model = ResNet1d(num_classes=5)
    dummy = torch.randn(2, 12, 5000)
    out = model(dummy)
    print(f"Input: {dummy.shape}")
    print(f"Output: {out.shape}")