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

class ConvEncoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, 3, stride=1, padding=1), nn.ReLU(),
            nn.Conv2d(32, 64, 3, stride=2, padding=1), nn.ReLU(),
            nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.ReLU(),
        )

    def forward(self, x):
        return self.features(x)


class GenConViT(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = ConvEncoder()
        self.classifier = nn.Sequential(
            nn.Linear(128 * 56 * 56, 256),
            nn.ReLU(),
            nn.Linear(256, 2)
        )

    def forward(self, x):
        feat = self.encoder(x)
        feat = feat.view(feat.size(0), -1)
        out = self.classifier(feat)
        return out