import torch
import torch.nn as nn
from torch.nn import functional as F
from torchvision import transforms
from utils import data_augment


# Artifact Detector (Extract artifact features using VAE)
class ArtifactDetectorProGAN(torch.nn.Module):
    def __init__(self, dim_artifact=512, num_classes=1):
        super(ArtifactDetectorProGAN, self).__init__()
        # Load the artifact encoder based on NPR
        self.artifact_encoder = ResNet(Bottleneck, [3, 4, 6, 3])

        # Classifier
        self.fc = torch.nn.Linear(dim_artifact, num_classes)

        # Normalization
        self.mean = [0.485, 0.456, 0.406]
        self.std = [0.229, 0.224, 0.225]

        # Resolution
        self.loadSize = 256
        self.cropSize = 224

        # Data augmentation
        self.blur_prob = 0.0
        self.blur_sig = [0.0, 3.0]
        self.jpg_prob = 0.0
        self.jpg_method = ['cv2', 'pil']
        self.jpg_qual = list(range(70, 96))

        # Define the augmentation configuration
        self.aug_config = {
            "blur_prob": self.blur_prob,
            "blur_sig": self.blur_sig,
            "jpg_prob": self.jpg_prob,
            "jpg_method": self.jpg_method,
            "jpg_qual": self.jpg_qual,
        }

        # Pre-processing
        crop_func = transforms.RandomCrop(self.cropSize)
        flip_func = transforms.RandomHorizontalFlip()
        rz_func = transforms.Resize(self.loadSize)
        aug_func = transforms.Lambda(lambda x: data_augment(x, self.aug_config))

        self.train_transform = transforms.Compose([
            aug_func,
            rz_func,
            crop_func,
            flip_func,
            transforms.ToTensor(),
            transforms.Normalize(mean=self.mean, std=self.std),
        ])

        self.test_transform = transforms.Compose([
            rz_func,
            crop_func,
            transforms.ToTensor(),
            transforms.Normalize(mean=self.mean, std=self.std),
        ])

    def forward(self, x, return_feat=False):
        feat = self.artifact_encoder(x)
        out = self.fc(feat)
        if return_feat:
            return feat, out
        return out

    def save_weights(self, weights_path):
        save_params = {k: v.cpu() for k, v in self.state_dict().items()}
        torch.save(save_params, weights_path)

    def load_weights(self, weights_path):
        weights = torch.load(weights_path)
        self.load_state_dict(weights)


# Define the artifact encoder (based on NPR)
def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    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 ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1):
        super(ResNet, self).__init__()

        self.unfoldSize = 2
        self.unfoldIndex = 0
        assert self.unfoldSize > 1
        assert -1 < self.unfoldIndex and self.unfoldIndex < self.unfoldSize*self.unfoldSize
        self.inplanes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        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.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc1 = nn.Linear(512, 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):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def interpolate(self, img, factor):
        return F.interpolate(
            F.interpolate(img, 
                          scale_factor=factor,
                          mode='nearest',
                          recompute_scale_factor=True),
            scale_factor=1 / factor,
            mode='nearest',
            recompute_scale_factor=True)

    def forward(self, x):
        artifact = x - self.interpolate(x, 0.5)

        x = self.conv1(artifact * 2.0 / 3.0)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

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

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)

        return x