import torch
import torch.nn as nn
import torch.nn.functional as F

"""
添加空间嵌入
"""


class DynamicGraphConstructor(nn.Module):
    def __init__(self, node_num, embed_dim):
        super().__init__()
        # 自适应邻接参数
        self.nodevec1 = nn.Parameter(torch.randn(node_num, embed_dim), requires_grad=True)
        self.nodevec2 = nn.Parameter(torch.randn(node_num, embed_dim), requires_grad=True)

    def forward(self):
        # 构造动态邻接矩阵
        adj = torch.matmul(self.nodevec1, self.nodevec2.T)  # (N, N)
        adj = F.relu(adj)
        adj = F.softmax(adj, dim=-1)
        return adj


class GraphConvBlock(nn.Module):
    def __init__(self, input_dim, output_dim):
        super().__init__()
        # 线性变换 + 可选残差投影
        self.theta = nn.Linear(input_dim, output_dim)
        self.residual = (input_dim == output_dim)
        if not self.residual:
            self.res_proj = nn.Linear(input_dim, output_dim)

    def forward(self, x, adj):
        # x: (B, N, F_in), adj: (N, N)
        res = x
        x = torch.matmul(adj, x)  # 邻接乘特征
        x = self.theta(x)  # 线性变换
        # 残差连接
        x = x + (res if self.residual else self.res_proj(res))
        return F.relu(x)


class MANBA_Block(nn.Module):
    def __init__(self, input_dim, hidden_dim):
        super().__init__()
        # 空间自注意力 + 前馈网络
        self.attn = nn.MultiheadAttention(embed_dim=input_dim, num_heads=4, batch_first=True)
        self.ffn = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim)
        )
        self.norm1 = nn.LayerNorm(input_dim)
        self.norm2 = nn.LayerNorm(input_dim)

    def forward(self, x):
        # x: (B, N, C)
        res = x
        x_attn, _ = self.attn(x, x, x)
        x = self.norm1(res + x_attn)
        res2 = x
        x_ffn = self.ffn(x)
        x = self.norm2(res2 + x_ffn)
        return x


class SandwichBlock(nn.Module):
    def __init__(self, num_nodes, embed_dim, hidden_dim):
        super().__init__()
        self.manba1 = MANBA_Block(hidden_dim, hidden_dim * 2)
        self.graph_constructor = DynamicGraphConstructor(num_nodes, embed_dim)
        self.gc = GraphConvBlock(hidden_dim, hidden_dim)
        self.manba2 = MANBA_Block(hidden_dim, hidden_dim * 2)

    def forward(self, h):
        # h: (B, N, hidden_dim)
        h1 = self.manba1(h)
        adj = self.graph_constructor()
        h2 = self.gc(h1, adj)
        h3 = self.manba2(h2)
        return h3


class MLP(nn.Module):
    def __init__(self, in_dim, hidden_dims, out_dim, activation=nn.ReLU):
        super().__init__()
        # 多层感知机
        dims = [in_dim] + hidden_dims + [out_dim]
        layers = []
        for i in range(len(dims) - 2):
            layers += [nn.Linear(dims[i], dims[i + 1]), activation()]
        layers += [nn.Linear(dims[-2], dims[-1])]
        self.net = nn.Sequential(*layers)

    def forward(self, x):
        # 对最后一维做线性映射
        return self.net(x)


class EXP(nn.Module):
    def __init__(self, args):
        super().__init__()
        # 训练 & 输出参数
        self.horizon = args['horizon']
        self.output_dim = args['output_dim']
        self.seq_len = args.get('in_len', 12)
        self.hidden_dim = args.get('hidden_dim', 64)
        self.num_nodes = args['num_nodes']
        self.embed_dim = args.get('embed_dim', 16)

        # ==== 时间嵌入 ====
        self.time_slots = args.get('time_slots', 24 * 60 // args.get('time_slot', 5))
        self.time_embedding = nn.Embedding(self.time_slots, self.hidden_dim)
        self.day_embedding = nn.Embedding(7, self.hidden_dim)
        self.node_emb = nn.Parameter(torch.empty(self.num_nodes, self.embed_dim))

        # ==== 空间嵌入 ====
        # 每个节点一个可学习的向量
        self.spatial_embedding = nn.Parameter(
            torch.randn(self.num_nodes, self.hidden_dim),
            requires_grad=True
        )

        # 输入投影：仅对流量序列做 MLP
        self.input_proj = MLP(
            in_dim=self.seq_len,
            hidden_dims=[self.hidden_dim],
            out_dim=self.hidden_dim
        )

        # 两个 SandwichBlock
        self.sandwich1 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)
        self.sandwich2 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)

        # 输出投影
        self.out_proj = MLP(
            in_dim=self.hidden_dim,
            hidden_dims=[2 * self.hidden_dim],
            out_dim=self.horizon * self.output_dim
        )

    def forward(self, x):
        """
        x: (B, T, N, D_total)
           D_total >= 3，其中：
             x[...,0] = 流量 (flow)
             x[...,1] = 当天时间比 (time_in_day，归一化到 [0,1])
             x[...,2] = 星期几 (day_in_week，0–6)
        """
        # 拆分三条序列
        x_flow = x[..., 0]  # (B, T, N)
        x_time = x[..., 1]  # (B, T, N)
        x_day = x[..., 2]  # (B, T, N)

        B, T, N = x_flow.shape
        assert T == self.seq_len, f"序列长度应为 {self.seq_len}，但收到 {T}"

        # 1) MLP 投影流量历史 -> 节点初始特征 h0
        x_flat = x_flow.permute(0, 2, 1).reshape(B * N, T)  # (B*N, T)
        h0 = self.input_proj(x_flat).view(B, N, self.hidden_dim)  # (B, N, hidden_dim)

        # 2) 计算离散时间嵌入
        t_idx = (x_time[:, -1, :] * (self.time_slots - 1)).long()  # (B, N)
        d_idx = x_day[:, -1, :].long()  # (B, N)
        time_emb = self.time_embedding(t_idx)  # (B, N, hidden_dim)
        day_emb = self.day_embedding(d_idx)  # (B, N, hidden_dim)

        # 3) 计算空间嵌入并扩展到 batch 大小
        # node_emb = []
        # node_emb.append(self.node_emb.unsqueeze(0).expand(
        #     B, -1, -1).transpose(1, 2).unsqueeze(-1))
        # spatial_emb = torch.stack(node_emb)
        spatial_emb = self.spatial_embedding.unsqueeze(0).expand(B, N, self.hidden_dim)  # -> (B, N, hidden_dim)

        # 4) 将三种嵌入相加到 h0
        h0 = h0 + time_emb + day_emb + spatial_emb

        # 5) 两层 Sandwich + 残差连接
        h1 = self.sandwich1(h0)
        h1 = h1 + h0
        h2 = self.sandwich2(h1)

        # 6) 输出投影 -> (B, horizon, N, output_dim)
        out = self.out_proj(h2)  # (B, N, horizon*out_dim)
        out = out.view(B, N, self.horizon, self.output_dim)
        out = out.permute(0, 2, 1, 3)  # (B, horizon, N, output_dim)
        return out