import torch.nn as nn
from model.MTGNN.layer import *


class gtnet(nn.Module):
    def __init__(self, configs):
        super(gtnet, self).__init__()
        self.gcn_true = configs['gcn_true']  # 是否使用图卷积网络
        self.buildA_true = configs['buildA_true']  # 是否动态构建邻接矩阵
        self.num_nodes = configs['num_nodes']  # 节点数量
        self.device = configs['device']  # 设备（CPU/GPU）
        self.dropout = configs['dropout']  # dropout率
        self.predefined_A = configs.get('predefined_A', None)  # 预定义邻接矩阵
        self.static_feat = configs.get('static_feat', None)  # 静态特征
        self.subgraph_size = configs['subgraph_size']  # 子图大小
        self.node_dim = configs['node_dim']  # 节点嵌入维度
        self.dilation_exponential = configs['dilation_exponential']  # 膨胀卷积指数
        self.conv_channels = configs['conv_channels']  # 卷积通道数
        self.residual_channels = configs['residual_channels']  # 残差通道数
        self.skip_channels = configs['skip_channels']  # 跳跃连接通道数
        self.end_channels = configs['end_channels']  # 输出层通道数
        self.seq_length = configs['seq_len']  # 输入序列长度
        self.in_dim = configs['in_dim']  # 输入特征维度
        self.out_len = configs['out_len']  # 输出序列长度
        self.out_dim = configs['out_dim']  # 输出预测维度
        self.layers = configs['layers']  # 模型层数
        self.propalpha = configs['propalpha']  # 图传播参数alpha
        self.tanhalpha = configs['tanhalpha']  # tanh激活参数alpha
        self.layer_norm_affline = configs['layer_norm_affline']  # 层归一化是否使用affine变换
        self.gcn_depth = configs['gcn_depth']  # 图卷积深度
        self.filter_convs = nn.ModuleList()  # 卷积滤波器列表
        self.gate_convs = nn.ModuleList()  # 门控卷积列表
        self.residual_convs = nn.ModuleList()  # 残差卷积列表
        self.skip_convs = nn.ModuleList()  # 跳跃连接卷积列表
        self.gconv1 = nn.ModuleList()  # 第一层图卷积列表
        self.gconv2 = nn.ModuleList()  # 第二层图卷积列表
        self.norm = nn.ModuleList()  # 归一化层列表
        self.start_conv = nn.Conv2d(in_channels=self.in_dim,
                                    out_channels=self.residual_channels,
                                    kernel_size=(1, 1))
        self.gc = graph_constructor(self.num_nodes, self.subgraph_size, self.node_dim, self.device, alpha=self.tanhalpha, static_feat=self.static_feat)

        kernel_size = 7
        if self.dilation_exponential>1:
            self.receptive_field = int(1+(kernel_size-1)*(self.dilation_exponential**self.layers-1)/(self.dilation_exponential-1))
        else:
            self.receptive_field = self.layers*(kernel_size-1) + 1

        for i in range(1):
            if self.dilation_exponential>1:
                rf_size_i = int(1 + i*(kernel_size-1)*(self.dilation_exponential**self.layers-1)/(self.dilation_exponential-1))
            else:
                rf_size_i = i*self.layers*(kernel_size-1)+1
            new_dilation = 1
            for j in range(1,self.layers+1):
                if self.dilation_exponential > 1:
                    rf_size_j = int(rf_size_i + (kernel_size-1)*(self.dilation_exponential**j-1)/(self.dilation_exponential-1))
                else:
                    rf_size_j = rf_size_i+j*(kernel_size-1)

                self.filter_convs.append(dilated_inception(self.residual_channels, self.conv_channels, dilation_factor=new_dilation))
                self.gate_convs.append(dilated_inception(self.residual_channels, self.conv_channels, dilation_factor=new_dilation))
                self.residual_convs.append(nn.Conv2d(in_channels=self.conv_channels,
                                                    out_channels=self.residual_channels,
                                                 kernel_size=(1, 1)))
                if self.seq_length>self.receptive_field:
                    self.skip_convs.append(nn.Conv2d(in_channels=self.conv_channels,
                                                    out_channels=self.skip_channels,
                                                    kernel_size=(1, self.seq_length-rf_size_j+1)))
                else:
                    self.skip_convs.append(nn.Conv2d(in_channels=self.conv_channels,
                                                    out_channels=self.skip_channels,
                                                    kernel_size=(1, self.receptive_field-rf_size_j+1)))

                if self.gcn_true:
                    self.gconv1.append(mixprop(self.conv_channels, self.residual_channels, self.gcn_depth, self.dropout, self.propalpha))
                    self.gconv2.append(mixprop(self.conv_channels, self.residual_channels, self.gcn_depth, self.dropout, self.propalpha))

                if self.seq_length>self.receptive_field:
                    self.norm.append(LayerNorm((self.residual_channels, self.num_nodes, self.seq_length - rf_size_j + 1),elementwise_affine=self.layer_norm_affline))
                else:
                    self.norm.append(LayerNorm((self.residual_channels, self.num_nodes, self.receptive_field - rf_size_j + 1),elementwise_affine=self.layer_norm_affline))

                new_dilation *= self.dilation_exponential

        self.end_conv_1 = nn.Conv2d(in_channels=self.skip_channels,
                                             out_channels=self.end_channels,
                                             kernel_size=(1,1),
                                             bias=True)
        self.end_conv_2 = nn.Conv2d(in_channels=self.end_channels,
                                             out_channels=self.out_len * self.out_dim,
                                             kernel_size=(1,1),
                                             bias=True)
        if self.seq_length > self.receptive_field:
            self.skip0 = nn.Conv2d(in_channels=self.in_dim, out_channels=self.skip_channels, kernel_size=(1, self.seq_length), bias=True)
            self.skipE = nn.Conv2d(in_channels=self.residual_channels, out_channels=self.skip_channels, kernel_size=(1, self.seq_length-self.receptive_field+1), bias=True)


        else:
            self.skip0 = nn.Conv2d(in_channels=self.in_dim, out_channels=self.skip_channels, kernel_size=(1, self.receptive_field), bias=True)
            self.skipE = nn.Conv2d(in_channels=self.residual_channels, out_channels=self.skip_channels, kernel_size=(1, 1), bias=True)

        self.idx = torch.arange(self.num_nodes).to(self.device)


    def forward(self, input, idx=None):
        input = input[..., :-2] # 去掉周期嵌入
        input = input.transpose(1, 3)
        seq_len = input.size(3)
        assert seq_len==self.seq_length, 'input sequence length not equal to preset sequence length'

        if self.seq_length<self.receptive_field:
            input = nn.functional.pad(input,(self.receptive_field-self.seq_length,0,0,0))

        if self.gcn_true:
            if self.buildA_true:
                if idx is None:
                    adp = self.gc(self.idx)
                else:
                    adp = self.gc(idx)
            else:
                adp = self.predefined_A

        x = self.start_conv(input)
        skip = self.skip0(F.dropout(input, self.dropout, training=self.training))
        for i in range(self.layers):
            residual = x
            filter = self.filter_convs[i](x)
            filter = torch.tanh(filter)
            gate = self.gate_convs[i](x)
            gate = torch.sigmoid(gate)
            x = filter * gate
            x = F.dropout(x, self.dropout, training=self.training)
            s = x
            s = self.skip_convs[i](s)
            skip = s + skip
            if self.gcn_true:
                x = self.gconv1[i](x, adp)+self.gconv2[i](x, adp.transpose(1,0))
            else:
                x = self.residual_convs[i](x)

            x = x + residual[:, :, :, -x.size(3):]
            if idx is None:
                x = self.norm[i](x,self.idx)
            else:
                x = self.norm[i](x,idx)

        skip = self.skipE(x) + skip
        x = F.relu(skip)
        x = F.relu(self.end_conv_1(x))
        x = self.end_conv_2(x) # [b, t*c, n, 1]
        # [b, t*c, n, 1] -> [b,t,c,n] -> [b, t, n, c]
        x = x.reshape(x.size(0), self.out_len, self.out_dim, self.num_nodes)
        x = x.permute(0, 1, 3, 2)
        return x