"""ResNet models for CIFAR datasets"""
import torch
import torch.nn as nn
import torch.nn.functional as F

from ...registry.decorators import model


class BasicBlock(nn.Module):
    """Basic Block for ResNet18/34"""
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    """ResNet architecture for CIFAR (32x32 images)"""

    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64

        # Initial conv layer (for CIFAR: kernel=3, stride=1, no maxpool)
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)

        # ResNet layers
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)

        # Final classifier
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = torch.flatten(out, 1)
        out = self.fc(out)
        return out


@model(
    name='resnet18',
    description='ResNet18 for CIFAR-10/100 (32x32 images)',
    task='classification',
    version='1.0'
)
class ResNet18_CIFAR(ResNet):
    """ResNet18 for CIFAR datasets

    Adapted for 32x32 images (CIFAR-10/100):
    - Uses kernel=3, stride=1 in conv1 (no stride=2)
    - No initial maxpool layer
    - 4 residual layers: [2, 2, 2, 2] blocks

    Args:
        num_classes: Number of output classes (10 for CIFAR-10, 100 for CIFAR-100)
    """

    def __init__(self, num_classes=10):
        super(ResNet18_CIFAR, self).__init__(
            block=BasicBlock,
            num_blocks=[2, 2, 2, 2],
            num_classes=num_classes
        )


@model(
    name='resnet34',
    description='ResNet34 for CIFAR-10/100 (32x32 images)',
    task='classification',
    version='1.0'
)
class ResNet34_CIFAR(ResNet):
    """ResNet34 for CIFAR datasets

    Args:
        num_classes: Number of output classes
    """

    def __init__(self, num_classes=10):
        super(ResNet34_CIFAR, self).__init__(
            block=BasicBlock,
            num_blocks=[3, 4, 6, 3],
            num_classes=num_classes
        )
