import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
import numpy as np

from math import sqrt

class FullAttention(nn.Module):
    '''
    The Attention operation
    '''
    def __init__(self, scale=None, attention_dropout=0.1):
        super(FullAttention, self).__init__()
        self.scale = scale  # attention计算时，缩放，公式中的根号下dk
        self.dropout = nn.Dropout(attention_dropout)
        
    def forward(self, queries, keys, values):
        B, L, H, E = queries.shape
        _, S, _, D = values.shape
        scale = self.scale or 1./sqrt(E)
        # qkv的计算
        scores = torch.einsum("blhe,bshe->bhls", queries, keys) # 查询序列长度L， 键序列S
        A = self.dropout(torch.softmax(scale * scores, dim=-1)) # 这里训练的时候就drop呀
        V = torch.einsum("bhls,bshd->blhd", A, values)
        
        return V.contiguous()      # 将v放在连续内存中


class AttentionLayer(nn.Module):
    '''
    The Multi-head Self-Attention (MSA) Layer
    '''
    def __init__(self, d_model, n_heads, d_keys=None, d_values=None, dropout = 0.1):
        super(AttentionLayer, self).__init__()

        d_keys = d_keys or (d_model//n_heads)  
        d_values = d_values or (d_model//n_heads)

        self.inner_attention = FullAttention(scale=None, attention_dropout = dropout)  # 全链接层
        self.query_projection = nn.Linear(d_model, d_keys * n_heads)    # q
        self.key_projection = nn.Linear(d_model, d_keys * n_heads)      # k
        self.value_projection = nn.Linear(d_model, d_values * n_heads)  # v
        self.out_projection = nn.Linear(d_values * n_heads, d_model)    # 输出层
        self.n_heads = n_heads

    def forward(self, queries, keys, values): # 原始输入作为qkv
        B, L, _ = queries.shape
        _, S, _ = keys.shape
        H = self.n_heads
        # 获取qkv矩阵
        queries = self.query_projection(queries).view(B, L, H, -1) # queries（224,28,256） -> (224,28,256) -> (224,28,4,64)
        keys = self.key_projection(keys).view(B, S, H, -1)
        values = self.value_projection(values).view(B, S, H, -1)

        out = self.inner_attention(
            queries,
            keys,
            values,
        )

        out = out.view(B, L, -1)  # （224,28,256）

        return self.out_projection(out)

class TwoStageAttentionLayer(nn.Module):
    '''
    The Two Stage Attention (TSA) Layer
    input/output shape: [batch_size, Data_dim(D), Seg_num(L), d_model]
    '''
    def __init__(self, seg_num, factor, d_model, n_heads, d_ff = None, dropout=0.1):
        super(TwoStageAttentionLayer, self).__init__()
        d_ff = d_ff or 4*d_model # 如果变量 d_ff 的值为 None 或等价于布尔值 False（例如未显式指定），则将 d_ff 设置为 4 * d_model。 如果 d_ff 已经有定义（即非 None 且布尔值为 True），那么 d_ff 保持原值
        self.time_attention = AttentionLayer(d_model, n_heads, dropout = dropout) # 用于时间维度上的注意力计算，捕捉时间序列数据中的时间依赖性
        self.dim_sender = AttentionLayer(d_model, n_heads, dropout = dropout)   #  在注意力机制中，dim_sender 是发送方特征的维度，用于生成查询（Query）或键（Key）
        self.dim_receiver = AttentionLayer(d_model, n_heads, dropout = dropout) # 在注意力机制中，dim_receiver 是接收方特征的维度，用于生成值（Value）或接收注意力权重。
        self.router = nn.Parameter(torch.randn(seg_num, factor, d_model))       # (28,10,256)在复杂注意力机制中，router 帮助模型动态分配注意力资源，提升对不同特征的关注能力。
        # nn.Parameter 是一个特殊的张量类型，用于定义可以被优化器（如 SGD、Adam 等）更新的模型参数
        self.dropout = nn.Dropout(dropout)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.norm4 = nn.LayerNorm(d_model)

        self.MLP1 = nn.Sequential(nn.Linear(d_model, d_ff),  #FFN模块；
                                nn.GELU(),
                                nn.Linear(d_ff, d_model))
        self.MLP2 = nn.Sequential(nn.Linear(d_model, d_ff),
                                nn.GELU(),
                                nn.Linear(d_ff, d_model))

    def forward(self, x):
        #Cross Time Stage: Directly apply MSA to each dimension
        batch = x.shape[0]
        time_in = rearrange(x, 'b ts_d seg_num d_model -> (b ts_d) seg_num d_model')  # 224,28,256  , 其中  224 = 32* 7 
        time_enc = self.time_attention(
            time_in, time_in, time_in
        )
        dim_in = time_in + self.dropout(time_enc) # 残差
        dim_in = self.norm1(dim_in)               # 层归一化
        dim_in = dim_in + self.dropout(self.MLP1(dim_in)) # FNN + 残差
        dim_in = self.norm2(dim_in)               # 层归一化

        #Cross Dimension Stage: use a small set of learnable vectors to aggregate and distribute messages to build the D-to-D connection
        dim_send = rearrange(dim_in, '(b ts_d) seg_num d_model -> (b seg_num) ts_d d_model', b = batch) # (224,28,256) -> (896,7,256) 这样的重排使得每个批次的每个分段（segment）可以独立处理。通过将批量和分段数量结合在一起，后续的操作可以更方便地进行并行计算和消息传递。
        batch_router = repeat(self.router, 'seg_num factor d_model -> (repeat seg_num) factor d_model', repeat = batch)  # (28,10,256) => (896,10,256) 28*32 = 896 , 通过重复路由器参数，确保每个批次都使用相同的路由器设置。这种方式可以保持模型的一致性，并减少模型参数的数量，同时允许每个批次的数据使用相同的学习策略。
        dim_buffer = self.dim_sender(batch_router, dim_send, dim_send) # 通过路由器将输入数据（dim_send）进行处理，生成一个中间的缓冲区（dim_buffer）。
        dim_receive = self.dim_receiver(dim_send, dim_buffer, dim_buffer) # 接收来自 dim_sender 的消息，并结合原始输入（dim_send）和缓冲区（dim_buffer）来生成接收的输出（dim_receive）
        dim_enc = dim_send + self.dropout(dim_receive) # 使用残差连接（dim_send + self.dropout(dim_receive)）来结合原始输入和接收的消息。这种方法有助于缓解深度网络中的梯度消失问题，并保持信息流动
        dim_enc = self.norm3(dim_enc) # 来标准化输出，进一步稳定训练过程
        dim_enc = dim_enc + self.dropout(self.MLP2(dim_enc)) # 通过多层感知机（MLP）对编码后的信息进行进一步的变换和学习。再次使用残差连接将 MLP 的输出与 dim_enc 结合
        dim_enc = self.norm4(dim_enc)

        final_out = rearrange(dim_enc, '(b seg_num) ts_d d_model -> b ts_d seg_num d_model', b = batch)

        return final_out