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import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable


# This function creates a 3x3 convolution with optional stride.
def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)


# A helper to initialize model parameters in a Xavier style.
def conv_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.xavier_uniform_(m.weight, gain=np.sqrt(2))
        init.constant_(m.bias, 0)
    elif classname.find('BatchNorm') != -1:
        init.constant_(m.weight, 1)
        init.constant_(m.bias, 0)


# This is one "wide block" used in WideResNet.
# Typically, it's a residual block with dropout.
class WideBasic(nn.Module):
    def __init__(self, in_planes, planes, dropout_rate, stride=1, *args, **kwargs):
        # Notice that there's a duplicated super() call below, might need a fix.
        # "super(wide_basic, self).__init__()" was probably intended
        super(WideBasic, self).__init__()  # <-- "wide_basic" might be a leftover name
        super().__init__(*args, **kwargs)

        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)
        self.dropout = nn.Dropout(p=dropout_rate)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)

        # Shortcut for dimension/stride mismatch.
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != planes:
            self.shortcut = nn.Sequential(nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True), )

    def forward(self, x):
        # Classic WRN sequence: BN -> ReLU -> Conv -> Dropout -> BN -> ReLU -> Conv
        out = self.dropout(self.conv1(F.relu(self.bn1(x))))
        out = self.conv2(F.relu(self.bn2(out)))
        # Add skip connection from input x
        out += self.shortcut(x)

        return out


# Full WideResNet model that uses multiple WideBasic blocks.
class WideResNet(nn.Module):
    def __init__(self, depth, widen_factor, dropout_rate, num_classes, *args, **kwargs):
        # Similar duplication of super calls here:
        super(WideResNet, self).__init__()  # Might be leftover from earlier naming
        super().__init__(*args, **kwargs)

        self.in_planes = 16

        # According to the paper, wide-resnet depth is 6n + 4.
        assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4'
        n = (depth - 4) / 6
        k = widen_factor

        print('| Wide-Resnet %dx%d' % (depth, k))
        nStages = [16, 16 * k, 32 * k, 64 * k]

        # Initial conv layer
        self.conv1 = conv3x3(3, nStages[0])

        # First group of blocks
        self.layer1 = self._wide_layer(WideBasic, nStages[1], n, dropout_rate, stride=1)
        self.layer2 = self._wide_layer(WideBasic, nStages[2], n, dropout_rate, stride=2)
        self.layer3 = self._wide_layer(WideBasic, nStages[3], n, dropout_rate, stride=2)

        # Final BN + linear classifier
        self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9)
        self.linear = nn.Linear(nStages[3], num_classes)

    def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
        # Each "layer" is a sequence of wide_basic blocks, controlling strides
        strides = [stride] + [1] * (int(num_blocks) - 1)
        layers = []

        for stride in strides:
            layers.append(block(self.in_planes, planes, dropout_rate, stride))
            self.in_planes = planes

        return nn.Sequential(*layers)

    def forward(self, x):
        # Standard WRN forward pass:
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)

        out = F.relu(self.bn1(out))
        # Global average pool, typical for wide resnet
        out = F.avg_pool2d(out, 8)
        # Flatten
        out = out.view(out.size(0), -1)
        # Final linear layer
        out = self.linear(out)

        return out

    def forward_features(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)

        out = F.relu(self.bn1(out))
        # Global average pool, but we don't flatten it yet for feature extraction
        out = F.avg_pool2d(out, 8)
        return out