import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.fft
import math

# Modified from: https://github.com/thuml/Time-Series-Library
# Modified by Shourya Bose, shbose@ucsc.edu

class Inception_Block_V1(nn.Module):
    def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
        super(Inception_Block_V1, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.num_kernels = num_kernels
        kernels = []
        for i in range(self.num_kernels):
            kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i))
        self.kernels = nn.ModuleList(kernels)
        if init_weight:
            self._initialize_weights()

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x):
        res_list = []
        for i in range(self.num_kernels):
            res_list.append(self.kernels[i](x))
        res = torch.stack(res_list, dim=-1).mean(-1)
        return res

class PositionalEmbedding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super(PositionalEmbedding, self).__init__()
        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model).float()
        pe.require_grad = False

        position = torch.arange(0, max_len).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2).float()
                    * -(math.log(10000.0) / d_model)).exp()

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)

        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        return self.pe[:, :x.size(1)]

class FixedEmbedding(nn.Module):
    def __init__(self, c_in, d_model):
        super(FixedEmbedding, self).__init__()

        w = torch.zeros(c_in, d_model).float()
        w.require_grad = False

        position = torch.arange(0, c_in).float().unsqueeze(1)
        div_term = (torch.arange(0, d_model, 2).float()
                    * -(math.log(10000.0) / d_model)).exp()

        w[:, 0::2] = torch.sin(position * div_term)
        w[:, 1::2] = torch.cos(position * div_term)

        self.emb = nn.Embedding(c_in, d_model)
        self.emb.weight = nn.Parameter(w, requires_grad=False)

    def forward(self, x):
        return self.emb(x).detach()

class TemporalEmbedding(nn.Module):
    def __init__(self, d_model, embed_type='fixed', freq='h'):
        super(TemporalEmbedding, self).__init__()

        hour_size = 96
        weekday_size = 7

        Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
        self.hour_embed = Embed(hour_size, d_model)
        self.weekday_embed = Embed(weekday_size, d_model)
        
    def forward(self, x):
        x = x.long()
        hour_x = self.hour_embed(x[:, :, 0])
        weekday_x = self.weekday_embed(x[:, :, 1])

        return hour_x + weekday_x

class TokenEmbedding(nn.Module):
    def __init__(self, c_in, d_model):
        super(TokenEmbedding, self).__init__()
        padding = 1 if torch.__version__ >= '1.5.0' else 2
        self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
                                   kernel_size=3, padding=padding, padding_mode='circular', bias=False)
        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_in', nonlinearity='leaky_relu')

    def forward(self, x):
        x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
        return x

class DataEmbedding(nn.Module):
    def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
        super(DataEmbedding, self).__init__()

        self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
        self.position_embedding = PositionalEmbedding(d_model=d_model)
        self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
                                                    freq=freq)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, x_mark):
        if x_mark is None:
            x = self.value_embedding(x) + self.position_embedding(x)
        else:
            x = self.value_embedding(
                x) + self.temporal_embedding(x_mark) + self.position_embedding(x)
        return self.dropout(x)

def FFT_for_Period(x, k=2):
    # [B, T, C]
    xf = torch.fft.rfft(x, dim=1)
    # find period by amplitudes
    frequency_list = abs(xf).mean(0).mean(-1)
    frequency_list[0] = 0
    _, top_list = torch.topk(frequency_list, k)
    top_list = top_list.detach().cpu().numpy()
    period = x.shape[1] // top_list
    return period, abs(xf).mean(-1)[:, top_list]


class TimesBlock(nn.Module):
    def __init__(self, seq_len, pred_len, top_k, d_model, d_ff, num_kernels):
        super(TimesBlock, self).__init__()
        self.seq_len = seq_len
        self.pred_len = pred_len
        self.k = top_k
        # parameter-efficient design
        self.conv = nn.Sequential(
            Inception_Block_V1(d_model, d_ff,
                               num_kernels=num_kernels),
            nn.GELU(),
            Inception_Block_V1(d_ff, d_model,
                               num_kernels=num_kernels)
        )

    def forward(self, x):
        B, T, N = x.size()
        period_list, period_weight = FFT_for_Period(x, self.k)

        res = []
        for i in range(self.k):
            period = period_list[i]
            # padding
            if (self.seq_len + self.pred_len) % period != 0:
                length = (
                                 ((self.seq_len + self.pred_len) // period) + 1) * period
                padding = torch.zeros([x.shape[0], (length - (self.seq_len + self.pred_len)), x.shape[2]]).to(x.device)
                out = torch.cat([x, padding], dim=1)
            else:
                length = (self.seq_len + self.pred_len)
                out = x
            # reshape
            out = out.reshape(B, length // period, period,
                              N).permute(0, 3, 1, 2).contiguous()
            # 2D conv: from 1d Variation to 2d Variation
            out = self.conv(out)
            # reshape back
            out = out.permute(0, 2, 3, 1).reshape(B, -1, N)
            res.append(out[:, :(self.seq_len + self.pred_len), :])
        res = torch.stack(res, dim=-1)
        # adaptive aggregation
        period_weight = F.softmax(period_weight, dim=1)
        period_weight = period_weight.unsqueeze(
            1).unsqueeze(1).repeat(1, T, N, 1)
        res = torch.sum(res * period_weight, -1)
        # residual connection
        res = res + x
        return res


class TimesNet(nn.Module):
    """
    Paper link: https://openreview.net/pdf?id=ju_Uqw384Oq
    """

    def __init__(
        self,
        enc_in,
        dec_in,
        c_out,
        pred_len,
        seq_len,
        output_attention = False,
        data_idx = [0,3,4,5,6,7],
        time_idx = [1,2],
        d_model = 16,
        d_ff = 64,
        e_layers = 2,
        top_k = 5,
        num_kernels = 2,
        dropout = 0.1
    ):
        super(TimesNet, self).__init__()

        self.data_idx = data_idx
        self.time_idx = time_idx
        self.dec_in = dec_in

        self.seq_len = seq_len
        self.pred_len = pred_len
        self.model = nn.ModuleList([TimesBlock(seq_len, pred_len, top_k, d_model, d_ff, num_kernels)
                                    for _ in range(e_layers)])
        self.enc_embedding = DataEmbedding(enc_in, d_model, 'fixed', 'h',
                                           dropout)
        self.layer = e_layers
        self.layer_norm = nn.LayerNorm(d_model)
        self.predict_linear = nn.Linear(
            self.seq_len, self.pred_len + self.seq_len)
        self.projection = nn.Linear(
            d_model, c_out, bias=True)

    def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
        # Normalization from Non-stationary Transformer
        means = x_enc.mean(1, keepdim=True).detach()
        x_enc = x_enc - means
        stdev = torch.sqrt(
            torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
        x_enc /= stdev

        # embedding
        enc_out = self.enc_embedding(x_enc, x_mark_enc)  # [B,T,C]
        enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(
            0, 2, 1)  # align temporal dimension
        # TimesNet
        for i in range(self.layer):
            enc_out = self.layer_norm(self.model[i](enc_out))
        # porject back
        dec_out = self.projection(enc_out)

        # De-Normalization from Non-stationary Transformer
        dec_out = dec_out * \
                  (stdev[:, 0, :].unsqueeze(1).repeat(
                      1, self.pred_len + self.seq_len, 1))
        dec_out = dec_out + \
                  (means[:, 0, :].unsqueeze(1).repeat(
                      1, self.pred_len + self.seq_len, 1))
        return dec_out

    def forward(self, x, fut_time):
        
        x_enc = x[:,:,self.data_idx]
        x_mark_enc = x[:,:,self.time_idx]
        x_dec = torch.zeros((fut_time.shape[0],fut_time.shape[1],self.dec_in),dtype=fut_time.dtype,device=fut_time.device)
        x_mark_dec = fut_time
        
        return self.forecast(x_enc,x_mark_enc,x_dec,x_mark_dec)[:,-1,[0]]