import torch
import torch.nn as nn

class ResNeXtBottleneck(nn.Module):
    expansion = 2

    def __init__(self, inplanes, planes, stride=1, downsample=None, base_width=2, cardinality=32):
        super(ResNeXtBottleneck, self).__init__()
        D = int(planes * (base_width / 64.)) * cardinality  # = 1024 for planes=1024
        C = cardinality

        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)

        self.conv2 = nn.Conv2d(planes, D, kernel_size=3, stride=stride,
                               padding=1, groups=C, bias=False)
        self.bn2 = nn.BatchNorm2d(D)

        self.conv3 = nn.Conv2d(D, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)

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

    def forward(self, x):
        identity = x

        out = self.relu(self.bn1(self.conv1(x)))
        out = self.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))

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

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


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

        self.model = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),

            self._make_layer(ResNeXtBottleneck, 128, 3, stride=1),   # 128*2 = 256
            self._make_layer(ResNeXtBottleneck, 256, 4, stride=2),   # 256*2 = 512
            self._make_layer(ResNeXtBottleneck, 512, 6, stride=2),   # 512*2 = 1024
            self._make_layer(ResNeXtBottleneck, 1024, 3, stride=2)   # 1024*2 = 2048
        )

        self.lstm = nn.LSTM(input_size=2048, hidden_size=2048, batch_first=True, bias=True)
        self.linear1 = nn.Linear(2048, 2)

    def _make_layer(self, block, planes, blocks, stride=1):
        inplanes = self._get_inplanes()
        downsample = None
        if stride != 1 or inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _get_inplanes(self):
        if not hasattr(self, '_inplanes'):
            self._inplanes = 64
        return self._inplanes

    def forward(self, x):
        if x.ndim == 4:
            # Single image input: (batch, channels, height, width)
            x = self.model(x)
            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
            x = x.view(x.size(0), -1)
            return self.linear1(x)  # No LSTM for images
        elif x.ndim == 5:
            # Video input: (batch, frames, channels, height, width)
            batch, frames, c, h, w = x.shape
            x = x.view(-1, c, h, w)
            x = self.model(x)
            x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
            x = x.view(batch, frames, -1)
            x, _ = self.lstm(x)
            x = x[:, -1, :]
            return self.linear1(x)
        else:
            raise ValueError(f"Unexpected input shape: {x.shape}")