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

"""
含残差版本
"""


class DynamicGraphConstructor(nn.Module):
    def __init__(self, node_num, embed_dim):
        super().__init__()
        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):
        # (N, D) @ (D, N) -> (N, N)
        adj = torch.matmul(self.nodevec1, self.nodevec2.T)
        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__()
        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, C) / adj: (N, N)
        res = x
        x = torch.matmul(adj, x)  # (B, N, C)
        x = self.theta(x)

        # 残差连接
        if self.residual:
            x = x + res
        else:
            x = x + self.res_proj(res)

        return F.relu(x)


class MANBA_Block(nn.Module):
    def __init__(self, input_dim, hidden_dim):
        super().__init__()
        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, T, 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 EXP(nn.Module):
    def __init__(self, args):
        super().__init__()
        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.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)

        # 动态图构建
        self.graph = DynamicGraphConstructor(self.num_nodes, embed_dim=16)

        # 输入映射层
        self.input_proj = nn.Linear(self.seq_len, self.hidden_dim)

        # 图卷积
        self.gc = GraphConvBlock(self.hidden_dim, self.hidden_dim)

        # MANBA block
        self.manba = MANBA_Block(self.hidden_dim, self.hidden_dim * 2)

        # 输出映射
        self.out_proj = nn.Linear(self.hidden_dim, self.horizon * self.output_dim)

    def forward(self, x):
        # x: (B, T, N, D_total)
        x_time = x[..., 1]  # (B, T, N)
        x_day = x[..., 2]  # (B, T, N)
        x = x[..., 0]  # 只用主通道 (B, T, N)
        B, T, N = x.shape
        assert T == self.seq_len

        # 输入投影 (B, T, N) -> (B, N, T) -> (B*N, T) -> (B*N, H)
        x = x.permute(0, 2, 1).reshape(B * N, T)
        h = self.input_proj(x)  # (B*N, hidden_dim)
        h = h.view(B, N, self.hidden_dim)

        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) inject them into the initial hidden state
        h = h + time_emb + day_emb

        # 动态图构建
        adj = self.graph()  # (N, N)

        # 空间建模：图卷积
        h = self.gc(h, adj)  # (B, N, hidden_dim)

        # 时间建模：MANBA
        h = self.manba(h)  # (B, N, hidden_dim)

        # 输出映射
        out = self.out_proj(h)  # (B, N, horizon * output_dim)
        out = out.view(B, N, self.horizon, self.output_dim).permute(0, 2, 1, 3)
        return out  # (B, horizon, N, output_dim)