import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader


# Bottleneck block (same as before)
class Bottleneck(nn.Module):
 expansion = 4

 def __init__(self, in_channels, out_channels, stride=1, downsample=None):
  super(Bottleneck, self).__init__()
  self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
  self.bn1 = nn.BatchNorm2d(out_channels)

  self.conv2 = nn.Conv2d(
   out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False
  )
  self.bn2 = nn.BatchNorm2d(out_channels)

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

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

 def forward(self, x):
  identity = x

  out = self.conv1(x)
  out = self.bn1(out)
  out = self.relu(out)

  out = self.conv2(out)
  out = self.bn2(out)
  out = self.relu(out)

  out = self.conv3(out)
  out = self.bn3(out)

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

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


# ResNet tailored for CIFAR (no initial 7x7 stride-2 conv + no maxpool)
class ResNetCIFAR(nn.Module):
 def __init__(self, block, layers, num_classes=100):
  super(ResNetCIFAR, self).__init__()
  self.in_channels = 64

  # Adjusted first conv for CIFAR (3x3, stride=1)
  self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
  self.bn1 = nn.BatchNorm2d(64)
  self.relu = nn.ReLU(inplace=True)

  # NOTE: we do NOT use the 7x7 stride-2 conv or the 3x3 maxpool used for ImageNet
  # Stage layers
  self.layer1 = self._make_layer(block, 64, layers[0], stride=1)
  self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
  self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
  self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

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

  # Weight initialization
  for m in self.modules():
   if isinstance(m, nn.Conv2d):
    nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
   elif isinstance(m, nn.BatchNorm2d):
    nn.init.constant_(m.weight, 1)
    nn.init.constant_(m.bias, 0)

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

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

  return nn.Sequential(*layers)

 def forward(self, x):
  x = self.conv1(x)
  x = self.bn1(x)
  x = self.relu(x)
  # no maxpool

  x = self.layer1(x)
  x = self.layer2(x)
  x = self.layer3(x)
  x = self.layer4(x)

  x = self.avgpool(x)
  x = torch.flatten(x, 1)
  x = self.fc(x)
  return x


def get_model(num_classes=100):
 return ResNetCIFAR(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)