import concurrent.futures
import threading

import torch.nn as nn
import torch.nn.functional as F
import torch
from mamba_ssm import Mamba
from layers.SelfAttention_Family import FullAttention, AttentionLayer

import torch
import torch.nn as nn

class FeedForwardNetwork(nn.Module):
    def __init__(self, nvars, dmodel, dff, drop=0.1):
        super(FeedForwardNetwork, self).__init__()
        

        self.ffn1pw1 = nn.Conv1d(in_channels=nvars * dmodel, out_channels=nvars * dff, 
                                 kernel_size=1, stride=1, padding=0, dilation=1, groups=nvars)
        

        self.ffn1act = nn.GELU()
        

        self.ffn1pw2 = nn.Conv1d(in_channels=nvars * dff, out_channels=nvars * dmodel, 
                                 kernel_size=1, stride=1, padding=0, dilation=1, groups=nvars)

        self.ffn1drop1 = nn.Dropout(drop)
        self.ffn1drop2 = nn.Dropout(drop)

    def forward(self, x):
        x = self.ffn1drop1(self.ffn1pw1(x))  
        x = self.ffn1act(x)                  
        x = self.ffn1drop2(self.ffn1pw2(x)) 
    
        return x


class EncoderLayer(nn.Module):
    def __init__(self, attention, attention_r, d_model, d_ff=None, dropout=0.1, activation="relu"):
        super(EncoderLayer, self).__init__()
        d_ff = d_ff or 4 * d_model
        self.attention = attention
        self.attention_r = attention_r

        self.ffn1 = FeedForwardNetwork(nvars=1, dmodel=d_model, dff=d_ff)
        self.ffn2 = FeedForwardNetwork(nvars=1, dmodel=d_ff, dff=d_model)                                    
        self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
        self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        self.activation = F.relu if activation == "relu" else F.gelu
        self.man = Mamba(
            d_model=11,  # Model dimension d_model
            d_state=16,  # SSM state expansion factor
            d_conv=2,  # Local convolution width
            expand=1,  # Block expansion factor)
        )
        self.man2 = Mamba(
            d_model=11,  # Model dimension d_model
            d_state=16,  # SSM state expansion factor
            d_conv=2,  # Local convolution width
            expand=1,  # Block expansion factor)
        )
        self.a = AttentionLayer(
                        FullAttention(False, 2, attention_dropout=0.1,
                                      output_attention=True), 11,1)
    def forward(self, x, attn_mask=None, tau=None, delta=None):

        x = x.permute(0, 2, 1)
        x = self.ffn1(x)
        x = self.ffn2(x) 
        x = x.permute(0, 2, 1)
        new_x = self.attention(x) + self.attention_r(x.flip(dims=[1])).flip(dims=[1])
        attn = 1

        x = x + new_x #torch.Size([32, 11, 256])
        # print(x.shape)
        y = x = self.norm1(x)

        # print(y.shape)
        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
        y = self.dropout(self.conv2(y).transpose(-1, 1))
        
        return self.norm2(x + y), attn


class Encoder(nn.Module):
    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
        super(Encoder, self).__init__()
        self.attn_layers = nn.ModuleList(attn_layers)
        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
        self.norm = norm_layer

    def forward(self, x, attn_mask=None, tau=None, delta=None):
        # x [B, L, D]
        # print(x.shape)
        attns = []
        if self.conv_layers is not None:
            for i, (attn_layer, conv_layer) in enumerate(zip(self.attn_layers, self.conv_layers)):
                delta = delta if i == 0 else None
                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
                x = conv_layer(x)
                attns.append(attn)
            x, attn = self.attn_layers[-1](x, tau=tau, delta=None)
            attns.append(attn)
        else:
            for attn_layer in self.attn_layers:
                x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
                
                attns.append(attn)

        if self.norm is not None:
            x = self.norm(x)

        return x, attns