import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from ..cross_models.attn import FullAttention, AttentionLayer, TwoStageAttentionLayer
from math import ceil

class SegMerging(nn.Module):
    '''
    Segment Merging Layer.
    The adjacent `win_size' segments in each dimension will be merged into one segment to
    get representation of a coarser scale
    we set win_size = 2 in our paper  在每个维度中，相邻的 win_size 个段（segments）将被合并为一个段，以获得更粗粒度的表示。
    '''
    def __init__(self, d_model, win_size, norm_layer=nn.LayerNorm):
        super().__init__()
        self.d_model = d_model
        self.win_size = win_size
        self.linear_trans = nn.Linear(win_size * d_model, d_model) # 输入维度是 win_size * d_model，输出维度是 d_model
        self.norm = norm_layer(win_size * d_model) # 指定维度上层归一化

    def forward(self, x):
        """
        x: B, ts_d, L, d_model
        """
        batch_size, ts_d, seg_num, d_model = x.shape
        pad_num = seg_num % self.win_size
        if pad_num != 0: 
            pad_num = self.win_size - pad_num
            x = torch.cat((x, x[:, :, -pad_num:, :]), dim = -2) # L维度进行拼接，满足整除条件

        seg_to_merge = []
        for i in range(self.win_size):
            seg_to_merge.append(x[:, :, i::self.win_size, :])  # 第三维度拆分为2个矩阵
        x = torch.cat(seg_to_merge, -1)  # [B, ts_d, seg_num/win_size, win_size*d_model] # 保留第三维度，将数据都拼接在第四维度

        x = self.norm(x)
        x = self.linear_trans(x)                              # 线性，数据量减半

        return x

class scale_block(nn.Module):
    '''
    We can use one segment merging layer followed by multiple TSA layers in each scale
    the parameter `depth' determines the number of TSA layers used in each scale
    We set depth = 1 in the paper
    '''# # win_size = 1 or 2， seg_num = 28， depth = 1, factor = 10
    def __init__(self, win_size, d_model, n_heads, d_ff, depth, dropout, \
                    seg_num = 10, factor=10):
        super(scale_block, self).__init__()

        if (win_size > 1):
            self.merge_layer = SegMerging(d_model, win_size, nn.LayerNorm)
        else:
            self.merge_layer = None
        
        self.encode_layers = nn.ModuleList()

        for i in range(depth):
            self.encode_layers.append(TwoStageAttentionLayer(seg_num, factor, d_model, n_heads, \
                                                        d_ff, dropout))
    
    def forward(self, x):
        _, ts_dim, _, _ = x.shape

        if self.merge_layer is not None: # 如果不是第一层，则需要Segment Merging Layer.进行数据降维
            x = self.merge_layer(x)
        
        for layer in self.encode_layers:
            x = layer(x)        
        
        return x

class Encoder(nn.Module):
    '''
    The Encoder of Crossformer.
    e_blocks,   3
    win_size,   2
    d_model,    256
    n_heads,    4  
    d_ff,       512
    block_depth, 1
    dropout,     0.2
    in_seg_num = 10,  16
    factor=10  ,10
    '''
    def __init__(self, e_blocks, win_size, d_model, n_heads, d_ff, block_depth, dropout,
                in_seg_num = 10, factor=10):
        super(Encoder, self).__init__()
        self.encode_blocks = nn.ModuleList()

        self.encode_blocks.append(scale_block(1, d_model, n_heads, d_ff, block_depth, dropout,\
                                            in_seg_num, factor))
        for i in range(1, e_blocks):
            self.encode_blocks.append(scale_block(win_size, d_model, n_heads, d_ff, block_depth, dropout,\
                                            ceil(in_seg_num/win_size**i), factor))

    def forward(self, x):  # x [32,7,28,256]
        encode_x = []
        encode_x.append(x)
        
        for block in self.encode_blocks: # self.encode_blocks  总共三层 
            x = block(x)
            encode_x.append(x)

        return encode_x