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

class ChannelGraphMixer(nn.Module):
    """
    在 PatchTST 的通道独立输出上做一次可学习的稀疏跨通道交互.
    输入  z_list : 长度=M 的 list, 每个元素形状 [B, D] (单通道表示)
    输出  list, 形状同输入
    """
    def __init__(self, n_channel: int, dim: int, k: int = 5, tau: float = 0.2):
        super().__init__()
        self.k = k
        self.tau = tau
        self.dim = dim
        self.A = nn.Parameter(torch.zeros(n_channel, n_channel))     # 可学习邻接
        self.mix = nn.Linear(dim, dim, bias=False)                  # 通道映射
        # 通道注意力过滤 CAF
        self.se = nn.Sequential(
            nn.Linear(dim, dim // 4, bias=False),
            nn.ReLU(),
            nn.Linear(dim // 4, 1, bias=False),
            nn.Sigmoid()
        )

    # -------- util: 生成 row-wise top-k 稀疏邻接 -------------
    def _row_sparse(self, logits: torch.Tensor) -> torch.Tensor:
        """
        logits: [M,M].   返回一个稀疏矩阵, 每行只保留 top-k, 其余为 0
        """
        # Straight-through Gumbel–Softmax
        g = -torch.empty_like(logits).exponential_().log()
        y = (logits + g) / self.tau
        probs = F.softmax(y, dim=-1)                     # 可导
        topk_val, _ = torch.topk(probs, self.k, dim=-1)
        thr = topk_val[..., -1].unsqueeze(-1)            # 每行阈值
        sparse = torch.where(probs >= thr, probs, torch.zeros_like(probs))
        return sparse.detach() + probs - probs.detach()  # ST-estimator

    # --------------------------------------------------------
    def forward(self, z_list):
        M = len(z_list)
        B = z_list[0].shape[0]
        Z = torch.stack(z_list, dim=1)          # [B,M,D]
        alpha = self.se(Z).squeeze(-1)          # [B,M] 通道重要性

        A_sparse = self._row_sparse(self.A)     # [M,M]

        out = []
        for i in range(M):
            # 汇聚来自其余通道的表示
            agg = 0
            for j in range(M):
                if A_sparse[i, j] != 0:
                    agg = agg + alpha[:, j:j+1] * A_sparse[i, j] * self.mix(z_list[j])
            out.append(z_list[i] + agg)         # 残差
        return out


class HierarchicalGraphMixer(nn.Module):
    """
    分层图混合器，同时考虑宏观通道关系和微观 Patch 级别注意力。
    输入  z : 形状为 [B, C, N, D] 的张量
    输出  z_out : 形状同输入
    """
    def __init__(self, n_channel: int, dim: int, k: int = 5, tau: float = 0.2):
        super().__init__()
        self.k = k
        self.tau = tau
        
        # Level 1: Channel Graph
        self.A = nn.Parameter(torch.zeros(n_channel, n_channel))
        self.se = nn.Sequential(
            nn.Linear(dim, dim // 4, bias=False), nn.ReLU(),
            nn.Linear(dim // 4, 1, bias=False), nn.Sigmoid()
        )
        
        # Level 2: Patch Cross-Attention
        self.q_proj = nn.Linear(dim, dim)
        self.k_proj = nn.Linear(dim, dim)
        self.v_proj = nn.Linear(dim, dim)
        self.out_proj = nn.Linear(dim, dim)
        self.norm = nn.LayerNorm(dim)

    def _row_sparse(self, logits: torch.Tensor) -> torch.Tensor:
        #  (Gumbel-Softmax 函数，无需改变) 
        g = -torch.empty_like(logits).exponential_().log()
        y = (logits + g) / self.tau
        probs = F.softmax(y, dim=-1)
        topk_val, _ = torch.topk(probs, self.k, dim=-1)
        thr = topk_val[..., -1].unsqueeze(-1)
        sparse = torch.where(probs >= thr, probs, torch.zeros_like(probs))
        return sparse.detach() + probs - probs.detach()

    def forward(self, z):
        # z 的形状: [B, C, N, D]
        B, C, N, D = z.shape

        # --- Level 1: 计算宏观权重 ---
        A_sparse = self._row_sparse(self.A) # 通道连接稀疏图 A_sparse: [C, C]

        # --- Level 2: 跨通道 Patch 交互 ---
        out_z = torch.zeros_like(z)
        for i in range(C):  # 遍历每个目标通道 i
            target_z = z[:, i, :, :]  # [B, N, D]
            
            # 准备聚合来自其他通道的 patch 级别上下文
            aggregated_context = torch.zeros_like(target_z)
            
            for j in range(C):  # 遍历每个源通道 j
                if A_sparse[i, j] != 0:
                    source_z = z[:, j, :, :] # [B, N, D]

                    # --- 执行交叉注意力 ---
                    Q = self.q_proj(target_z)      # Query 来自目标通道 i
                    K = self.k_proj(source_z)      # Key 来自源通道 j
                    V = self.v_proj(source_z)      # Value 来自源通道 j
                    
                    attn_scores = torch.bmm(Q, K.transpose(1, 2)) / math.sqrt(D)
                    attn_probs = F.softmax(attn_scores, dim=-1) # [B, N, N]
                    
                    context = torch.bmm(attn_probs, V) # [B, N, D], 从 j 聚合到 i 的上下文
                    
                    # 宏观权重2 (通道连接强度): A_sparse[i, j] -> 标量
                    
                    
                    # 加权上下文
                    weighted_context =  A_sparse[i, j] * context
                    aggregated_context = aggregated_context + weighted_context

            # 将聚合后的上下文通过输出层，并与原始目标表示相加（残差连接）
            # LayerNorm 增加稳定性
            out_z[:, i, :, :] = self.norm(target_z + self.out_proj(aggregated_context))
            
        return out_z