"""
Attack CNN Model Architectures
"""

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


class StandardCNN(nn.Module):
    """
    Standard CNN Model (Original)
    Architecture: 3 Conv blocks with BatchNorm + 3 FC layers
    Parameters: ~817K
    """
    
    def __init__(self, num_classes=10, dropout_rate=0.5):
        super(StandardCNN, self).__init__()
        
        # First convolutional block
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # Second convolutional block
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # Third convolutional block
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # Calculate the flattened size after convolutions
        self.flattened_size = 128 * 3 * 3  # 28x28 -> 14x14 -> 7x7 -> 3x3
        
        # Fully connected layers
        self.fc1 = nn.Linear(self.flattened_size, 512)
        self.dropout1 = nn.Dropout(dropout_rate)
        self.fc2 = nn.Linear(512, 256)
        self.dropout2 = nn.Dropout(dropout_rate)
        self.fc3 = nn.Linear(256, num_classes)
        
    def forward(self, x, return_logits=False):
        # Conv block 1
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.pool1(x)

        # Conv block 2
        x = self.conv2(x)
        x = self.bn2(x)
        x = F.relu(x)
        x = self.pool2(x)

        # Conv block 3
        x = self.conv3(x)
        x = self.bn3(x)
        x = F.relu(x)
        x = self.pool3(x)

        # Flatten
        x = x.view(x.size(0), -1)

        # FC layers
        x = F.relu(self.fc1(x))
        x = self.dropout1(x)
        x = F.relu(self.fc2(x))
        x = self.dropout2(x)

        logits = self.fc3(x)

        if return_logits:
            return logits
        return F.softmax(logits, dim=1)


class LighterCNN(nn.Module):
    """
    Lighter CNN Model
    Architecture: 3 Conv blocks with fewer filters + Global Average Pooling
    Parameters: ~94K
    """
    
    def __init__(self, num_classes=10, dropout_rate=0.5):
        super(LighterCNN, self).__init__()
        
        self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
        self.bn1   = nn.BatchNorm2d(32)
        self.pool1 = nn.MaxPool2d(2,2)

        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.bn2   = nn.BatchNorm2d(64)
        self.pool2 = nn.MaxPool2d(2,2)

        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.bn3   = nn.BatchNorm2d(128)
        self.pool3 = nn.MaxPool2d(2,2)  # 28->14->7->3

        self.gap = nn.AdaptiveAvgPool2d(1)   # (B,128,1,1)
        self.fc  = nn.Linear(128, num_classes)

    def forward(self, x, return_logits=False):
        x = self.pool1(F.relu(self.bn1(self.conv1(x))))
        x = self.pool2(F.relu(self.bn2(self.conv2(x))))
        x = self.pool3(F.relu(self.bn3(self.conv3(x))))
        x = self.gap(x).view(x.size(0), -1)  # (B,128)
        logits = self.fc(x)
        return logits if return_logits else F.softmax(logits, dim=1)


class DepthwiseSeparableConv(nn.Module):
    def __init__(self, in_ch, out_ch, stride=1):
        super(DepthwiseSeparableConv, self).__init__()
        self.dw = nn.Conv2d(in_ch, in_ch, 3, stride=stride, padding=1,
                            groups=in_ch, bias=False)           # depthwise
        self.pw = nn.Conv2d(in_ch, out_ch, 1, bias=False)        # pointwise
        self.bn = nn.BatchNorm2d(out_ch)
    def forward(self, x):
        x = self.dw(x)
        x = self.pw(x)
        return F.relu(self.bn(x), inplace=True)


class DepthwiseCNN(nn.Module):
    """
    Depthwise Separable CNN
    Ultra-efficient using Depthwise Separable Convolutions
    Parameters: ~1.4K
    """
    
    def __init__(self, num_classes=10, dropout_rate=0.5):
        super(DepthwiseCNN, self).__init__()
        
        # Stem: 1 -> 8, reduce size with stride=2 (28->14)
        self.stem = nn.Sequential(
            nn.Conv2d(1, 8, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(8),
            nn.ReLU(inplace=True),
        )
        
        # DS blocks
        self.ds1 = DepthwiseSeparableConv(8, 16, stride=1)
        self.ds2 = DepthwiseSeparableConv(16, 32, stride=2)  # 14->7

        self.gap = nn.AdaptiveAvgPool2d(1)
        self.fc  = nn.Linear(32, num_classes)

    def forward(self, x, return_logits=False):
        x = self.stem(x)     # B, 8, 14, 14
        x = self.ds1(x)      # B,16,14,14
        x = self.ds2(x)      # B,32, 7, 7
        x = self.gap(x).flatten(1)   # B,32
        logits = self.fc(x)          # B,10
        return logits if return_logits else F.softmax(logits, dim=1)