import torch
import torch.nn as nn
import torchvision.models as models

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

class ResNet50(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet50, self).__init__()
        
        self.in_channels = 64
        
        # Initial layers
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        # Residual layers
        self.layer1 = self._make_layer(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 6, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)
        
        # Classification head
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * Bottleneck.expansion, num_classes)
        
        # Weight initialization
        self._initialize_weights()

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

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

        return nn.Sequential(*layers)

    def _initialize_weights(self):
        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 forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        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 create_model(num_classes, pretrained=False):
    """
    Create a ResNet-50 model
    Args:
        num_classes: Number of output classes
        pretrained: Whether to use pretrained weights from ImageNet
    """
    # Load model with or without pretrained weights
    model = models.resnet50(pretrained=pretrained)
    
    # Modify the final layer for our number of classes
    num_ftrs = model.fc.in_features
    model.fc = nn.Linear(num_ftrs, num_classes)
    
    return model