# -*- coding: utf-8 -*-
# @Author: Weisen Pan

import logging
import torch
import torch.nn as nn

__all__ = ['ResNet', 'resnet110']

def apply_3x3_convolution(in_channels, out_channels, stride=1, groups=1, dilation=1):
    """3x3 Convolution with padding."""
    return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)

def apply_1x1_convolution(in_channels, out_channels, stride=1):
    """1x1 Convolution."""
    return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        """Basic Block used in ResNet. Consists of two 3x3 convolutions."""
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        
        self.conv1 = apply_3x3_convolution(in_channels, out_channels, stride)
        self.bn1 = norm_layer(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = apply_3x3_convolution(out_channels, out_channels)
        self.bn2 = norm_layer(out_channels)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        """Defines the forward pass through the block."""
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)

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

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

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        """Bottleneck block used in ResNet. Has three layers: 1x1, 3x3, and 1x1 convolutions."""
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(out_channels * (base_width / 64.)) * groups
        
        self.conv1 = apply_1x1_convolution(in_channels, width)
        self.bn1 = norm_layer(width)
        self.conv2 = apply_3x3_convolution(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = apply_1x1_convolution(width, out_channels * self.expansion)
        self.bn3 = norm_layer(out_channels * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        """Defines the forward pass through the bottleneck block."""
        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

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10, zero_init_residual=False, groups=1,
                 width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, KD=False):
        """Defines the ResNet architecture."""
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 16
        self.dilation = 1
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None or a 3-element tuple.")

        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._create_model_layer(block, 16, layers[0])
        self.layer2 = self._create_model_layer(block, 32, layers[1], stride=2)
        self.layer3 = self._create_model_layer(block, 64, layers[2], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(64 * block.expansion, num_classes)
        self.KD = KD

        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)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _create_model_layer(self, block, planes, blocks, stride=1, dilate=False):
        """Creates a layer in ResNet using the specified block type."""
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation

        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                apply_1x1_convolution(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion

        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def forward(self, x):
        """Defines the forward pass of the ResNet model."""
        x = self.layer1(x)  # Output: B x 16 x 32 x 32
        x = self.layer2(x)  # Output: B x 32 x 16 x 16
        x = self.layer3(x)  # Output: B x 64 x 8 x 8

        x = self.avgpool(x)  # Output: B x 64 x 1 x 1
        x_f = x.view(x.size(0), -1)  # Flatten: B x 64
        x = self.fc(x_f)  # Output: B x num_classes
        return x

def resnet56_server(num_classes, models_pretrained=False, path=None, **kwargs):
    """
    Constructs a ResNet-110 model.

    Args:
        num_classes (int): Number of output classes.
        models_pretrained (bool): If True, returns a model pre-trained on ImageNet.
        path (str): Path to the pre-trained model.
    """
    logging.info("Loading model with path: " + str(path))
    model = ResNet(Bottleneck, [6, 6, 6], num_classes=num_classes, **kwargs)
    
    if models_pretrained:
        checkpoint = torch.load(path)
        state_dict = checkpoint['state_dict']
        new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
        model.load_state_dict(new_state_dict)
    
    return model