import shap
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
import numpy as np
import matplotlib.pyplot as plt

# Custom BasicBlock to avoid in-place operations
class CustomBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None):
        super(CustomBasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = norm_layer(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x.clone()

        out = self.conv1(x)
        out = self.bn1(out)
        out = F.relu(out.clone(), inplace=False)

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

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

        out = out.clone() + identity  # Clone before addition to avoid in-place modification
        out = F.relu(out.clone(), inplace=False)

        return out

# Custom ResNet using CustomBasicBlock
class CustomResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(CustomResNet, self).__init__()
        self.inplanes = 64
        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=False)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        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)

        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, planes, blocks, stride=1):
        norm_layer = nn.BatchNorm2d
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, groups=1, base_width=64, dilation=1, norm_layer=norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=1, base_width=64, dilation=1, norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x.clone())  # Clone to avoid in-place operation
        x = self.maxpool(x)

        x = self.layer1(x.clone())  # Clone to avoid in-place operation
        x = self.layer2(x.clone())  # Clone to avoid in-place operation
        x = self.layer3(x.clone())  # Clone to avoid in-place operation
        x = self.layer4(x.clone())  # Clone to avoid in-place operation

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x.clone())  # Clone to avoid in-place operation

        return x

# Initialize the custom model with pre-trained weights
model = CustomResNet(CustomBasicBlock, [2, 2, 2, 2])
state_dict = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1).state_dict()
model.load_state_dict(state_dict)
model.eval()

# Initialize SHAP explainer with the custom model
explainer = shap.DeepExplainer(model, torch.randn(1, 3, 224, 224))

# Generate SHAP values for an input image
sample_image = torch.randn(1, 3, 224, 224)
shap_values = explainer.shap_values(sample_image, check_additivity=False)

# Convert SHAP values and sample image to numpy for SHAP visualization
shap_values_class_0 = shap_values[0][0]  # Extract SHAP values for the first class
sample_image_np = sample_image.squeeze().permute(1, 2, 0).detach().numpy()

# Normalize sample image and SHAP values to range [0, 1] for visualization
sample_image_np = np.clip(sample_image_np, 0, 1)
shap_min, shap_max = shap_values_class_0.min(), shap_values_class_0.max()
shap_values_class_0 = (shap_values_class_0 - shap_min) / (shap_max - shap_min)

# Ensure both `sample_image_np` and `shap_values_class_0` are NumPy arrays with correct shapes for image_plot
sample_image_np = np.array([sample_image_np])  # Add batch dimension for SHAP
shap_values_class_0 = np.array([shap_values_class_0])  # Add batch dimension for SHAP

# Visualize SHAP values for the first class
shap.image_plot(shap_values_class_0, sample_image_np)