"""
SeasonPatch = PatchTST (CI) + ChannelGraphMixer + Linear prediction head
支持两种编码器：
- Transformer 编码器路径：PatchTST + GraphMixer + Head
- Mamba2 编码器路径：Mamba2Encoder（不使用mixer），直接用最后得到的 d_model 走 Head
"""
import torch
import torch.nn as nn
from layers.TSTEncoder import TSTiEncoder
from layers.GraphMixer import HierarchicalGraphMixer
from layers.MambaSeries import Mamba2Encoder


class SeasonPatch(nn.Module):
    def __init__(self,
                 c_in: int,
                 seq_len: int,
                 pred_len: int,
                 patch_len: int,
                 stride: int,
                 k_graph: int = 8,
                 d_model: int = 128,
                 n_layers: int = 3,
                 n_heads: int = 16,
                 # Mamba2 相关可选超参
                 d_state: int = 64,
                 d_conv: int = 4,
                 expand: int = 2,
                 headdim: int = 64):
        super().__init__()
        
        # Store patch parameters
        self.patch_len = patch_len  # patch 长度
        self.stride = stride        # patch 步幅

        # Calculate patch number
        patch_num = (seq_len - patch_len) // stride + 1  # patch_num: int

        # Transformer (PatchTST) 编码器（channel independent）
        self.encoder = TSTiEncoder(
            c_in=c_in, 
            patch_num=patch_num, 
            patch_len=patch_len,
            d_model=d_model,
            n_layers=n_layers,
            n_heads=n_heads
        )

        # Mamba2 编码器（channel independent），返回 [B, C, d_model]
        self.mamba_encoder = Mamba2Encoder(
            c_in=c_in,
            patch_num=patch_num,
            patch_len=patch_len,
            d_model=d_model,
            d_state=d_state,
            d_conv=d_conv,
            expand=expand,
            headdim=headdim,
        )

        # Cross-channel mixer（仅 Transformer 路径使用）
        self.mixer = HierarchicalGraphMixer(c_in, dim=d_model, k=k_graph)

        # Prediction head（Transformer 路径用到，输入维度为 patch_num * d_model）
        self.head_tr = nn.Sequential(
            nn.Linear(patch_num * d_model, patch_num * d_model),
            nn.SiLU(),
            nn.Linear(patch_num * d_model, pred_len)
        )

        # Prediction head（Mamba2 路径用到，输入维度为 d_model）
        self.head_mamba = nn.Sequential(
            nn.Linear(d_model, d_model),
            nn.SiLU(),
            nn.Linear(d_model, pred_len)
        )

    def forward(self, x, encoder="Transformer"):
        # x: [B, L, C]
        x = x.permute(0, 2, 1)  # x: [B, C, L]

        # Patch the input
        x_patch = x.unfold(-1, self.patch_len, self.stride)  # x_patch: [B, C, patch_num, patch_len]

        if encoder == "Transformer":
            # Encode patches (PatchTST)
            z = self.encoder(x_patch)  # z: [B, C, d_model, patch_num]

            # [B, C, d_model, patch_num] -> [B, C, patch_num, d_model]
            B, C, D, N = z.shape  # B: batch, C: channels, D: d_model, N: patch_num
            z = z.permute(0, 1, 3, 2)  # z: [B, C, patch_num, d_model]

            # Cross-channel mixing
            z_mix = self.mixer(z)  # z_mix: [B, C, patch_num, d_model]

            # Flatten and predict
            z_mix = z_mix.view(B, C, N * D)  # z_mix: [B, C, patch_num * d_model]
            y_pred = self.head_tr(z_mix)  # y_pred: [B, C, pred_len]

        elif encoder == "Mamba2":
            # 使用 Mamba2 编码器（不使用 mixer）
            z_last = self.mamba_encoder(x_patch)  # z_last: [B, C, d_model]（仅最后一个时间步）
            y_pred = self.head_mamba(z_last)      # y_pred: [B, C, pred_len]

        else:
            raise ValueError(f"Unsupported encoder type: {encoder}. Use 'Transformer' or 'Mamba2'.")

        return y_pred  # [B, C, pred_len]