"""
    The completion for Mean-opinion Network(MoNet)
"""
import torch
import torch.nn as nn
import timm

from timm.models.vision_transformer import Block
from einops import rearrange


class Attention_Block(nn.Module):
    def __init__(self, dim, drop=0.1):
        super().__init__()
        self.c_q = nn.Linear(dim, dim)
        self.c_k = nn.Linear(dim, dim)
        self.c_v = nn.Linear(dim, dim)
        self.norm_fact = dim ** -0.5
        self.softmax = nn.Softmax(dim=-1)
        self.proj_drop = nn.Dropout(drop)

    def forward(self, x):
        _x = x
        B, C, N = x.shape
        q = self.c_q(x)
        k = self.c_k(x)
        v = self.c_v(x)

        attn = q @ k.transpose(-2, -1) * self.norm_fact
        attn = self.softmax(attn)
        x = (attn @ v).transpose(1, 2).reshape(B, C, N)
        x = self.proj_drop(x)
        x = x + _x
        return x


class Self_Attention(nn.Module):
    """ Self attention Layer"""

    def __init__(self, in_dim):
        super(Self_Attention, self).__init__()

        self.qConv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)
        self.kConv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)
        self.vConv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.gamma = nn.Parameter(torch.zeros(1))

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, inFeature):
        bs, C, w, h = inFeature.size()

        proj_query = self.qConv(inFeature).view(bs, -1, w * h).permute(0, 2, 1)
        proj_key = self.kConv(inFeature).view(bs, -1, w * h)
        energy = torch.bmm(proj_query, proj_key)
        attention = self.softmax(energy)
        proj_value = self.vConv(inFeature).view(bs, -1, w * h)

        out = torch.bmm(proj_value, attention.permute(0, 2, 1))
        out = out.view(bs, C, w, h)

        out = self.gamma * out + inFeature

        return out


class MAL(nn.Module):
    """
        Multi-view Attention Learning (MAL) module
    """

    def __init__(self, in_dim=768, feature_num=4, feature_size=28):
        super().__init__()

        self.channel_attention = Attention_Block(in_dim * feature_num)  # Channel-wise self attention
        self.feature_attention = Attention_Block(feature_size ** 2 * feature_num)  # Pixel-wise self attention

        # Self attention module for each input feature
        self.attention_module = nn.ModuleList()
        for _ in range(feature_num):
            self.attention_module.append(Self_Attention(in_dim))

        self.feature_num = feature_num
        self.in_dim = in_dim

    def forward(self, features):
        feature = torch.tensor([]).cuda()
        for index, _ in enumerate(features):
            feature = torch.cat((feature, self.attention_module[index](features[index]).unsqueeze(0)), dim=0)
        features = feature

        input_tensor = rearrange(features, 'n b c w h -> b (n c) (w h)')  # bs, 768 * feature_num, 28 * 28
        bs, _, _ = input_tensor.shape  # [2, 3072, 784]

        in_feature = rearrange(input_tensor, 'b (w c) h -> b w (c h)', w=self.in_dim, c=self.feature_num)  # bs, 768, 28 * 28 * feature_num
        feature_weight_sum = self.feature_attention(in_feature)  # bs, 768, 768

        in_channel = input_tensor.permute(0, 2, 1)  # bs, 28 * 28, 768 * feature_num
        channel_weight_sum = self.channel_attention(in_channel)  # bs, 28 * 28, 28 * 28

        weight_sum_res = (rearrange(feature_weight_sum, 'b w (c h) -> b (w c) h', w=self.in_dim,
                                    c=self.feature_num) + channel_weight_sum.permute(0, 2, 1)) / 2  # [2, 3072, 784]

        weight_sum_res = torch.mean(weight_sum_res.view(bs, self.feature_num, self.in_dim, -1), dim=1)

        return weight_sum_res  # bs, 768, 28 * 28


class SaveOutput:
    def __init__(self):
        self.outputs = []

    def __call__(self, module, module_in, module_out):
        self.outputs.append(module_out)

    def clear(self):
        self.outputs = []


class MoNet(nn.Module):
    def __init__(self, config, patch_size=8, drop=0.1, dim_mlp=768, img_size=224):
        super().__init__()
        self.img_size = img_size
        self.input_size = img_size // patch_size
        self.dim_mlp = dim_mlp

        self.vit = timm.create_model(config.backbone, pretrained=False)
        self.save_output = SaveOutput()

        # Register Hooks
        hook_handles = []
        for layer in self.vit.modules():
            if isinstance(layer, Block):
                handle = layer.register_forward_hook(self.save_output)
                hook_handles.append(handle)

        self.MALs = nn.ModuleList()
        for _ in range(config.mal_num):
            self.MALs.append(MAL())

        # Image Quality Score Regression
        self.fusion_wam = MAL(feature_num=config.mal_num)
        self.block = Block(dim_mlp, 12)
        self.cnn = nn.Sequential(
            nn.Conv2d(dim_mlp, 256, 5),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.AvgPool2d((2, 2)),
            nn.Conv2d(256, 128, 3),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.AvgPool2d((2, 2)),
            nn.Conv2d(128, 128, 3),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.AvgPool2d((3, 3)),
        )
        self.fc_score = nn.Sequential(
            nn.Linear(128, 128 // 2),
            nn.ReLU(),
            nn.Dropout(drop),
            nn.Linear(128 // 2, 1),
            nn.Sigmoid()
        )

    def extract_feature(self, save_output, block_index=[2, 5, 8, 11]):
        x1 = save_output.outputs[block_index[0]][:, 1:]
        x2 = save_output.outputs[block_index[1]][:, 1:]
        x3 = save_output.outputs[block_index[2]][:, 1:]
        x4 = save_output.outputs[block_index[3]][:, 1:]
        x = torch.cat((x1, x2, x3, x4), dim=2)
        return x

    def forward(self, x):
        # Multi-level Feature From Different Transformer Blocks
        _x = self.vit(x)
        x = self.extract_feature(self.save_output)  # bs, 28 * 28, 768 * 4
        self.save_output.outputs.clear()

        x = x.permute(0, 2, 1)  # bs, 768 * 4, 28 * 28
        x = rearrange(x, 'b (d n) (w h) -> b d n w h', d=4, n=self.dim_mlp, w=self.input_size, h=self.input_size)  # bs, 4, 768, 28, 28
        x = x.permute(1, 0, 2, 3, 4)  # bs, 4, 768, 28 * 28

        # Different Opinion Features (DOF)
        DOF = torch.tensor([]).cuda()
        for index, _ in enumerate(self.MALs):
            DOF = torch.cat((DOF, self.MALs[index](x).unsqueeze(0)), dim=0)
        DOF = rearrange(DOF, 'n c d (w h) -> n c d w h', w=self.input_size, h=self.input_size)  # 3, bs, 768, 28, 28

        # Image Quality Score Regression
        wam = self.fusion_wam(DOF).permute(0, 2, 1)  # bs, 28 * 28 768
        wam = self.block(wam).permute(0, 2, 1)
        wam = rearrange(wam, 'c d (w h) -> c d w h', w=self.input_size, h=self.input_size)
        score = self.cnn(wam).squeeze(-1).squeeze(-1)
        score = self.fc_score(score).view(-1)

        return score


if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument('--seed', dest='seed', type=int, default=3407)
    parser.add_argument('--gpu_id', dest='gpu_id', type=str, default='0')

    # model related
    parser.add_argument('--backbone', dest='backbone', type=str, default='vit_base_patch8_224',
                        help='The backbone for MoNet.')
    parser.add_argument('--mal_num', dest='mal_num', type=int, default=3, help='The number of the MAL modules.')

    # data related
    parser.add_argument('--dataset', dest='dataset', type=str, default='livec',
                        help='Support datasets: livec|koniq10k|bid|spaq')
    parser.add_argument('--train_patch_num', dest='train_patch_num', type=int, default=5,
                        help='Number of sample patches from training image')
    parser.add_argument('--test_patch_num', dest='test_patch_num', type=int, default=25,
                        help='Number of sample patches from testing image')
    parser.add_argument('--patch_size', dest='patch_size', type=int, default=224,
                        help='Crop size for training & testing image patches')

    # training related
    parser.add_argument('--lr', dest='lr', type=float, default=1e-5, help='Learning rate')
    parser.add_argument('--weight_decay', dest='weight_decay', type=float, default=1e-5, help='Weight decay')
    parser.add_argument('--batch_size', dest='batch_size', type=int, default=11, help='Batch size')
    parser.add_argument('--epochs', dest='epochs', type=int, default=50, help='Epochs for training')
    parser.add_argument('--T_max', dest='T_max', type=int, default=50, help='Hyper-parameter for CosineAnnealingLR')
    parser.add_argument('--eta_min', dest='eta_min', type=int, default=0, help='Hyper-parameter for CosineAnnealingLR')

    parser.add_argument('--save_path', dest='save_path', type=str, default='./training_for_IQA',
                        help='The path where the model and logs will be saved.')

    config = parser.parse_args()

    # torch.autograd.set_detect_anomaly(True)
    # with torch.autograd.detect_anomaly():
    in_tensor = torch.zeros((2, 3, 224, 224), dtype=torch.float).cuda()
    model = MoNet(config).cuda()
    res = model(in_tensor)

    print('{} : {} [M]'.format('#Params', sum(map(lambda x: x.numel(), model.parameters())) / 10 ** 6))

    # label = torch.tensor([1, 2], dtype=torch.float).cuda()
    # loss = torch.nn.L1Loss().cuda()
    #
    # res = model(in_tensor)
    # # loss = loss_func()
    # l = loss(label, res)
    # print(l)
    # l.backward()