import torch
import torch.nn as nn
from torchvision import models
from torchvision.models import ResNet18_Weights


class SimpleCNN(nn.Module):
    """
    A minimalist CNN model as a baseline.
    Consists of two convolutional layers followed by a fully connected layer.
    """

    def __init__(self, num_classes=6):
        super(SimpleCNN, self).__init__()
        # First Convolutional Block: Takes 3 channels (RGB) as input and
        # outputs 16
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)  # 224 -> 112

        # Second Convolutional Block
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)  # 112 -> 56

        # Adaptive Pooling ensures the output is always 7x7, regardless of
        # input size
        self.adaptive_pool = nn.AdaptiveAvgPool2d((7, 7))

        # Classification Layer
        self.fc = nn.Linear(32 * 7 * 7, num_classes)

    def forward(self, x):
        """
        Defines the forward pass of the data through the network.
        """
        x = self.pool1(self.relu1(self.conv1(x)))
        x = self.pool2(self.relu2(self.conv2(x)))
        x = self.adaptive_pool(x)
        x = torch.flatten(x, 1)  # Flatten for the linear layer
        x = self.fc(x)
        return x


class DeepCNN(nn.Module):
    """
    A deeper CNN model with Batch Normalization and Dropout for regularization.
    Better suited for more complex image features.
    """

    def __init__(self, num_classes=6):
        super(DeepCNN, self).__init__()

        # Block 1
        self.layer1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),  # 112
        )

        # Block 2
        self.layer2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),  # 56
        )

        # Block 3
        self.layer3 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),  # 28
        )

        self.adaptive_pool = nn.AdaptiveAvgPool2d((7, 7))

        # Classifier with Dropout to prevent overfitting
        self.classifier = nn.Sequential(
            nn.Linear(128 * 7 * 7, 512), nn.ReLU(), nn.Dropout(0.5), nn.Linear(512, num_classes)
        )

    def forward(self, x):
        """
        Forward pass through the sequential layers.
        """
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.adaptive_pool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x


class ResNet18Transfer(nn.Module):
    """
    Transfer Learning model based on ResNet18.
    Allows loading pretrained weights and freezing the backbone.
    """

    def __init__(self, num_classes=6, pretrained=True, freeze_backbone=False):
        super(ResNet18Transfer, self).__init__()

        # Load the ResNet18 model
        weights = ResNet18_Weights.DEFAULT if pretrained else None
        self.backbone = models.resnet18(weights=weights)

        # Freeze the backbone if requested
        if freeze_backbone:
            for param in self.backbone.parameters():
                param.requires_grad = False

        # Adjust the final fully connected layer (fc)
        # ResNet18 fc has 512 input features by default
        in_features = self.backbone.fc.in_features
        self.backbone.fc = nn.Linear(in_features, num_classes)

    def forward(self, x):
        """
        Uses the ResNet backbone for feature extraction and classification.
        """
        return self.backbone(x)