import torch
import torch.nn as nn
from layers.Pyraformer_EncDec import Encoder

from layers.Tem_Agg_Block import Tem_Agg_Block,series_decomp

class Model(nn.Module):
    def __init__(self, configs):
        super(Model, self).__init__()
        self.seq_len = configs.seq_len
        self.pred_len = configs.pred_len
        self.viper=configs.viper
        
        # Series decomposition block from Autoformer
        self.decompsition = series_decomp(configs.moving_avg)

        self.Linear_Trend = nn.Linear(self.seq_len, self.pred_len)
        
        self.tem_agg_layer=Tem_Agg_Block(configs.top_k,configs.seq_len)

        self.Linear_Trend.weight = nn.Parameter(
                (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]))
        self.backbone=Pyraformer(configs)
        
    def encoder(self, x,x_mark_enc, x_dec, x_mark_dec, mask=None):
        # Normalization from Non-stationary Transformer
        means = x.mean(1, keepdim=True).detach()
        x = x - means
        stdev = torch.sqrt(
                    torch.var(x, dim=1, keepdim=True, unbiased=False) + 1e-5)
        x /= stdev
        seasonal_init, trend_init = self.decompsition(x)
        
        seasonal_output = self.tem_agg_layer(seasonal_init)
        seasonal_output=self.backbone(seasonal_output,x_mark_enc, x_dec, x_mark_dec, mask=None)
        trend_output = self.Linear_Trend(trend_init.permute(0, 2, 1)).permute(0,2,1)
        
        pred = seasonal_output + trend_output
        pred=pred
        pred = pred * \
                (stdev[:, 0, :].unsqueeze(1).repeat(
                            1, self.pred_len, 1))
        pred = pred + \
                (means[:, 0, :].unsqueeze(1).repeat(
                                1, self.pred_len, 1))
        return pred[:, -self.pred_len:, :]  # [B, L, D]

    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
        if self.viper:
            dec_out = self.encoder(x_enc,x_mark_enc, x_dec, x_mark_dec, mask=None)
        else: dec_out = self.backbone(x_enc,x_mark_enc, x_dec, x_mark_dec, mask=None)
        return dec_out[:, -self.pred_len:, :]  # [B, L, D]
        
class Pyraformer(nn.Module):
    """ 
    Pyraformer: Pyramidal attention to reduce complexity
    Paper link: https://openreview.net/pdf?id=0EXmFzUn5I
    """

    def __init__(self, configs, window_size=[4,4], inner_size=5):
        """
        window_size: list, the downsample window size in pyramidal attention.
        inner_size: int, the size of neighbour attention
        """
        super(Pyraformer,self).__init__()
        self.task_name = configs.task_name
        self.pred_len = configs.pred_len
        self.d_model = configs.d_model


        if self.task_name == 'short_term_forecast':
            window_size = [2,2]
        self.encoder = Encoder(configs, window_size, inner_size)

        if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
            self.projection = nn.Linear(
                (len(window_size)+1)*self.d_model, self.pred_len * configs.enc_in)
        elif self.task_name == 'imputation' or self.task_name == 'anomaly_detection':
            self.projection = nn.Linear(
                (len(window_size)+1)*self.d_model, configs.enc_in, bias=True)
        elif self.task_name == 'classification':
            self.act = torch.nn.functional.gelu
            self.dropout = nn.Dropout(configs.dropout)
            self.projection = nn.Linear(
                (len(window_size)+1)*self.d_model * configs.seq_len, configs.num_class)

    def long_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
        enc_out = self.encoder(x_enc, x_mark_enc)[:, -1, :]
        dec_out = self.projection(enc_out).view(
            enc_out.size(0), self.pred_len, -1)
        return dec_out
    
    def short_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
        # Normalization
        mean_enc = x_enc.mean(1, keepdim=True).detach()  # B x 1 x E
        x_enc = x_enc - mean_enc
        std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach()  # B x 1 x E
        x_enc = x_enc / std_enc

        enc_out = self.encoder(x_enc, x_mark_enc)[:, -1, :]
        dec_out = self.projection(enc_out).view(
            enc_out.size(0), self.pred_len, -1)
        
        dec_out = dec_out * std_enc + mean_enc
        return dec_out

    def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
        enc_out = self.encoder(x_enc, x_mark_enc)
        dec_out = self.projection(enc_out)
        return dec_out

    def anomaly_detection(self, x_enc, x_mark_enc):
        enc_out = self.encoder(x_enc, x_mark_enc)
        dec_out = self.projection(enc_out)
        return dec_out

    def classification(self, x_enc, x_mark_enc):
        # enc
        enc_out = self.encoder(x_enc, x_mark_enc=None)

        # Output
        # the output transformer encoder/decoder embeddings don't include non-linearity
        output = self.act(enc_out)
        output = self.dropout(output)
        # zero-out padding embeddings
        output = output * x_mark_enc.unsqueeze(-1)
        # (batch_size, seq_length * d_model)
        output = output.reshape(output.shape[0], -1)
        output = self.projection(output)  # (batch_size, num_classes)

        return output

    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
        if self.task_name == 'long_term_forecast':
            dec_out = self.long_forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
            return dec_out[:, -self.pred_len:, :]  # [B, L, D]
        if self.task_name == 'short_term_forecast':
            dec_out = self.short_forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
            return dec_out[:, -self.pred_len:, :]  # [B, L, D]
        if self.task_name == 'imputation':
            dec_out = self.imputation(
                x_enc, x_mark_enc, x_dec, x_mark_dec, mask)
            return dec_out  # [B, L, D]
        if self.task_name == 'anomaly_detection':
            dec_out = self.anomaly_detection(x_enc, x_mark_enc)
            return dec_out  # [B, L, D]
        if self.task_name == 'classification':
            dec_out = self.classification(x_enc, x_mark_enc)
            return dec_out  # [B, N]
        return None
