import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


# Attention Model: TriangularCausalMask, ProbMask, FullAttention, ProbAttention, AttentionLayer
class TriangularCausalMask:
    def __init__(self, B, L):
        mask_shape = [B, 1, L, L]
        with torch.no_grad():
            self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool).cuda(), diagonal=1)

    @property
    def mask(self):
        return self._mask


class ProbMask:
    def __init__(self, B, H, L, index, scores):
        _mask = torch.ones(L, scores.shape[-1], dtype=torch.bool).cuda().triu(1)
        _mask_ex = _mask[None, None, :].expand(B, H, L, scores.shape[-1])
        indicator = _mask_ex[torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :]
        self._mask = indicator.view(scores.shape)

    @property
    def mask(self):
        return self._mask


class FullAttention(nn.Module):
    def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
        super(FullAttention, self).__init__()
        self.scale = scale
        self.mask_flag = mask_flag
        self.output_attention = output_attention
        self.dropout = nn.Dropout(attention_dropout)

    def forward(self, queries, keys, values, attn_mask):
        B, L, H, E = queries.shape
        _, S, _, D = values.shape
        scale = self.scale or 1.0 / math.sqrt(E)

        scores = torch.einsum("blhe,bshe->bhls", queries, keys)
        if self.mask_flag:
            if attn_mask is None:
                attn_mask = TriangularCausalMask(B, L)

            scores.masked_fill_(attn_mask.mask, -np.inf)

        A = self.dropout(torch.softmax(scale * scores, dim=-1))
        V = torch.einsum("bhls,bshd->blhd", A, values)

        if self.output_attention:
            return V.contiguous(), A
        else:
            return V.contiguous(), None


class ProbAttention(nn.Module):
    def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
        super(ProbAttention, self).__init__()
        self.factor = factor
        self.scale = scale
        self.mask_flag = mask_flag
        self.output_attention = output_attention
        self.dropout = nn.Dropout(attention_dropout)

    def _prob_QK(self, Q, K, sample_k, n_top):  # n_top: c*ln(L_q)
        # Q [B, H, L, D]
        B, H, L_K, E = K.shape
        _, _, L_Q, _ = Q.shape

        # calculate the sampled Q_K
        K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
        index_sample = torch.randint(L_K, (L_Q, sample_k))  # real U = U_part(factor*ln(L_k))*L_q
        K_sample = K_expand[:, :, torch.arange(L_Q).unsqueeze(1), index_sample, :]
        Q_K_sample = torch.matmul(Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze(-2)

        # find the Top_k query with sparisty measurement
        M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
        M_top = M.topk(n_top, sorted=False)[1]

        # use the reduced Q to calculate Q_K
        Q_reduce = Q[torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], M_top, :]  # factor*ln(L_q)
        Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1))  # factor*ln(L_q)*L_k

        return Q_K, M_top

    def _get_initial_context(self, V, L_Q):
        B, H, L_V, D = V.shape
        if not self.mask_flag:
            # V_sum = V.sum(dim=-2)
            V_sum = V.mean(dim=-2)
            contex = V_sum.unsqueeze(-2).expand(B, H, L_Q, V_sum.shape[-1]).clone()
        else:  # use mask
            assert L_Q == L_V  # requires that L_Q == L_V, i.e. for self-attention only
            contex = V.cumsum(dim=-2)
        return contex

    def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
        B, H, L_V, D = V.shape

        if self.mask_flag:
            attn_mask = ProbMask(B, H, L_Q, index, scores)
            scores.masked_fill_(attn_mask.mask, -np.inf)

        attn = torch.softmax(scores, dim=-1)  # nn.Softmax(dim=-1)(scores)

        context_in[torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :] = torch.matmul(
            attn, V
        ).type_as(context_in)
        if self.output_attention:
            attns = (torch.ones([B, H, L_V, L_V]) / L_V).type_as(attn)
            attns[torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :] = attn
            return context_in, attns
        else:
            return context_in, None

    def forward(self, queries, keys, values, attn_mask):
        B, L_Q, H, D = queries.shape
        _, L_K, _, _ = keys.shape

        queries = queries.transpose(2, 1)
        keys = keys.transpose(2, 1)
        values = values.transpose(2, 1)

        U_part = self.factor * np.ceil(np.log(L_K)).astype("int").item()  # c*ln(L_k)
        u = self.factor * np.ceil(np.log(L_Q)).astype("int").item()  # c*ln(L_q)

        U_part = U_part if U_part < L_K else L_K
        u = u if u < L_Q else L_Q

        scores_top, index = self._prob_QK(queries, keys, sample_k=U_part, n_top=u)

        # add scale factor
        scale = self.scale or 1.0 / math.sqrt(D)
        if scale is not None:
            scores_top = scores_top * scale
        # get the context
        context = self._get_initial_context(values, L_Q)
        # update the context with selected top_k queries
        context, attn = self._update_context(context, values, scores_top, index, L_Q, attn_mask)

        return context.transpose(2, 1).contiguous(), attn


class AttentionLayer(nn.Module):
    def __init__(self, attention, d_model, n_heads, d_keys=None, d_values=None, mix=False):
        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 = attention
        self.query_projection = nn.Linear(d_model, d_keys * n_heads)
        self.key_projection = nn.Linear(d_model, d_keys * n_heads)
        self.value_projection = nn.Linear(d_model, d_values * n_heads)
        self.out_projection = nn.Linear(d_values * n_heads, d_model)
        self.n_heads = n_heads
        self.mix = mix

    def forward(self, queries, keys, values, attn_mask):
        B, L, _ = queries.shape
        _, S, _ = keys.shape
        H = self.n_heads

        queries = self.query_projection(queries).view(B, L, H, -1)
        keys = self.key_projection(keys).view(B, S, H, -1)
        values = self.value_projection(values).view(B, S, H, -1)

        out, attn = self.inner_attention(queries, keys, values, attn_mask)
        if self.mix:
            out = out.transpose(2, 1).contiguous()
        out = out.view(B, L, -1)

        return self.out_projection(out), attn


# Embedding Model: PositionalEmbedding, TokenEmbedding, FixedEmbedding, TemporalEmbedding, TimeFeatureEmbedding, DataEmbedding


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 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"
        )
        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 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__()

        minute_size = 4
        hour_size = 24
        weekday_size = 7
        day_size = 32
        month_size = 13

        Embed = FixedEmbedding if embed_type == "fixed" else nn.Embedding
        if freq == "t":
            self.minute_embed = Embed(minute_size, d_model)
        self.hour_embed = Embed(hour_size, d_model)
        self.weekday_embed = Embed(weekday_size, d_model)
        self.day_embed = Embed(day_size, d_model)
        self.month_embed = Embed(month_size, d_model)

    def forward(self, x):
        x = x.long()

        minute_x = self.minute_embed(x[:, :, 4]) if hasattr(self, "minute_embed") else 0.0
        hour_x = self.hour_embed(x[:, :, 3])
        weekday_x = self.weekday_embed(x[:, :, 2])
        day_x = self.day_embed(x[:, :, 1])
        month_x = self.month_embed(x[:, :, 0])

        return hour_x + weekday_x + day_x + month_x + minute_x


class TimeFeatureEmbedding(nn.Module):
    def __init__(self, d_model, embed_type="timeF", freq="h"):
        super(TimeFeatureEmbedding, self).__init__()

        freq_map = {"h": 4, "t": 5, "s": 6, "m": 1, "a": 1, "w": 2, "d": 3, "b": 3}
        d_inp = freq_map[freq]
        self.embed = nn.Linear(d_inp, d_model)

    def forward(self, x):
        return self.embed(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)
        if embed_type != "timeF":
            self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
        else:
            self.temporal_embedding = TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)

        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, x_mark):
        x = self.value_embedding(x) + self.position_embedding(x) + self.temporal_embedding(x_mark)

        return self.dropout(x)


# Encoder Layer: ConvLayer, EncoderLayer, Encoder, EncoderStack


class ConvLayer(nn.Module):
    def __init__(self, c_in):
        super(ConvLayer, self).__init__()
        padding = 1 if torch.__version__ >= "1.5.0" else 2
        self.downConv = nn.Conv1d(
            in_channels=c_in, out_channels=c_in, kernel_size=3, padding=padding, padding_mode="circular"
        )
        self.norm = nn.BatchNorm1d(c_in)
        self.activation = nn.ELU()
        self.maxPool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        x = self.downConv(x.permute(0, 2, 1))
        x = self.norm(x)
        x = self.activation(x)
        x = self.maxPool(x)
        x = x.transpose(1, 2)
        return x


class EncoderLayer(nn.Module):
    def __init__(self, attention, 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.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

    def forward(self, x, attn_mask=None):
        # x [B, L, D]
        # x = x + self.dropout(self.attention(
        #     x, x, x,
        #     attn_mask = attn_mask
        # ))
        new_x, attn = self.attention(x, x, x, attn_mask=attn_mask)
        x = x + self.dropout(new_x)

        y = x = self.norm1(x)
        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):
        # x [B, L, D]
        attns = []
        if self.conv_layers is not None:
            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
                x, attn = attn_layer(x, attn_mask=attn_mask)
                x = conv_layer(x)
                attns.append(attn)
            x, attn = self.attn_layers[-1](x, attn_mask=attn_mask)
            attns.append(attn)
        else:
            for attn_layer in self.attn_layers:
                x, attn = attn_layer(x, attn_mask=attn_mask)
                attns.append(attn)

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

        return x, attns


class EncoderStack(nn.Module):
    def __init__(self, encoders, inp_lens):
        super(EncoderStack, self).__init__()
        self.encoders = nn.ModuleList(encoders)
        self.inp_lens = inp_lens

    def forward(self, x, attn_mask=None):
        # x [B, L, D]
        x_stack = []
        attns = []
        for i_len, encoder in zip(self.inp_lens, self.encoders):
            inp_len = x.shape[1] // (2 ** i_len)
            x_s, attn = encoder(x[:, -inp_len:, :])
            x_stack.append(x_s)
            attns.append(attn)
        x_stack = torch.cat(x_stack, -2)

        return x_stack, attns


# Decoder Layer: DecoderLayer, Decoder


class DecoderLayer(nn.Module):
    def __init__(self, self_attention, cross_attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
        super(DecoderLayer, self).__init__()
        d_ff = d_ff or 4 * d_model
        self.self_attention = self_attention
        self.cross_attention = cross_attention
        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.norm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)
        self.activation = F.relu if activation == "relu" else F.gelu

    def forward(self, x, cross, x_mask=None, cross_mask=None):
        x = x + self.dropout(self.self_attention(x, x, x, attn_mask=x_mask)[0])
        x = self.norm1(x)

        x = x + self.dropout(self.cross_attention(x, cross, cross, attn_mask=cross_mask)[0])

        y = x = self.norm2(x)
        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
        y = self.dropout(self.conv2(y).transpose(-1, 1))

        return self.norm3(x + y)


class Decoder(nn.Module):
    def __init__(self, layers, norm_layer=None):
        super(Decoder, self).__init__()
        self.layers = nn.ModuleList(layers)
        self.norm = norm_layer

    def forward(self, x, cross, x_mask=None, cross_mask=None):
        for layer in self.layers:
            x = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)

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

        return x


class Informer(nn.Module):
    """
    Implementation of AAAI 2021 best paper "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting"

    Args:
        enc_in (int): encoder input size
        dec_in (int): decoder input size
        c_out (int): output size
        seq_len (int): input sequence length of Informer encode
        label_len (int): start token length of Informer decoder
        out_len (int): prediction sequence length
        factor (int): probsparse attn factor (default is 5)
        d_model (int): dimension of model (default is 512)
        n_heads (int): num of heads (default is 8)
        e_layers (int): num of encoder layers (default is 3)
        d_layers (int): num of decoder layers (default is 2)
        d_ff (int): dimension of fcn (default is 512)
        dropout (float): dropout (default is 0.0)
        attn (string): attention used in encoder, options:[prob, full] (default is prob)
        embed (string): time features encoding, options:[timeF, fixed, learned] (default is fixed)
        freq (string): freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h (default is h)
        activation (string): activation (default is gelu)
        output_attention (bool): whether to output attention in ecoder (default is False)
        distil (bool): whether to use distilling in encoder, using this argument means not using distilling (default is True)
        mix (bool): whether to use mix attention in generative decode (default is True)

    Predefined models:

        informer_univariate:
            enc_in = 1
            dec_in = 1
            c_out = 1
            seq_len = 720
            label_len = 168
            out_len = 24
            e_layers = 2
            d_layers = 1
            d_ff = 2048
            dropout = 0.05
            embed = 'timeF'

        informer_multivariate:
            enc_in = 7
            dec_in = 7
            c_out = 7
            seq_len = 48
            label_len = 48
            out_len = 24
            e_layers = 2
            d_layers = 1
            d_ff = 2048
            dropout = 0.05
            embed = 'timeF'

    Example:

    .. code-block:: python

        x, x_mask = torch.zeros(32, 720, 1).cuda(), torch.zeros(32, 720, 4).cuda()
        y, y_mask = torch.zeros(32, 168, 1).cuda(), torch.zeros(32, 168, 4).cuda()

        model = hfai.models.informer_univariate().cuda()
        # or
        # model = hfai.models.Informer(
        #     enc_in=1,
        #     dec_in=1,
        #     c_out=1,
        #     seq_len=720,
        #     label_len=168,
        #     out_len=24,
        #     e_layers=2,
        #     d_layers=1,
        #     d_ff=2048,
        #     dropout=0.05,
        #     embed='timeF',
        # )

        # forward
        logits = model(x, x_mask, y, y_mask)

    """

    def __init__(
        self,
        enc_in,
        dec_in,
        c_out,
        seq_len,
        label_len,
        out_len,
        factor=5,
        d_model=512,
        n_heads=8,
        e_layers=3,
        d_layers=2,
        d_ff=512,
        dropout=0.0,
        attn="prob",
        embed="fixed",
        freq="h",
        activation="gelu",
        output_attention=False,
        distil=True,
        mix=True,
    ):
        super(Informer, self).__init__()
        self.seq_len = seq_len
        self.label_len = label_len
        self.pred_len = out_len
        self.attn = attn
        self.output_attention = output_attention

        # Encoding
        self.enc_embedding = DataEmbedding(enc_in, d_model, embed, freq, dropout)
        self.dec_embedding = DataEmbedding(dec_in, d_model, embed, freq, dropout)
        # Attention
        Attn = ProbAttention if attn == "prob" else FullAttention
        # Encoder
        self.encoder = Encoder(
            [
                EncoderLayer(
                    AttentionLayer(
                        Attn(False, factor, attention_dropout=dropout, output_attention=output_attention),
                        d_model,
                        n_heads,
                        mix=False,
                    ),
                    d_model,
                    d_ff,
                    dropout=dropout,
                    activation=activation,
                )
                for l in range(e_layers)
            ],
            [ConvLayer(d_model) for l in range(e_layers - 1)] if distil else None,
            norm_layer=torch.nn.LayerNorm(d_model),
        )
        # Decoder
        self.decoder = Decoder(
            [
                DecoderLayer(
                    AttentionLayer(
                        Attn(True, factor, attention_dropout=dropout, output_attention=False), d_model, n_heads, mix=mix
                    ),
                    AttentionLayer(
                        FullAttention(False, factor, attention_dropout=dropout, output_attention=False),
                        d_model,
                        n_heads,
                        mix=False,
                    ),
                    d_model,
                    d_ff,
                    dropout=dropout,
                    activation=activation,
                )
                for l in range(d_layers)
            ],
            norm_layer=torch.nn.LayerNorm(d_model),
        )
        # self.end_conv1 = nn.Conv1d(in_channels=label_len+out_len, out_channels=out_len, kernel_size=1, bias=True)
        # self.end_conv2 = nn.Conv1d(in_channels=d_model, out_channels=c_out, kernel_size=1, bias=True)
        self.projection = nn.Linear(d_model, c_out, bias=True)

    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None):
        """
        Args:
            x_enc (Tensor): input time series, size ``[B, L, D]``
            x_mark_enc (Tensor): input time-series features, size ``[B, L, D]``
            x_dec (Tensor): target time series, size ``[B, L, D]``
            x_mark_dec (Tensor): target time-series features, size ``[B, L, D]``
            enc_self_mask (Tensor): encode mask, size ``[B, D]``
            dec_self_mask (Tensor): decode mask, size ``[B, D]``
            dec_enc_mask (Tensor): encode-decode mask, size ``[B, D]``

        Returns:
            pred (Tensor): prediction, size ``[B, L, D]``
        """

        enc_out = self.enc_embedding(x_enc, x_mark_enc)
        enc_out, attns = self.encoder(enc_out, attn_mask=enc_self_mask)

        dec_out = self.dec_embedding(x_dec, x_mark_dec)
        dec_out = self.decoder(dec_out, enc_out, x_mask=dec_self_mask, cross_mask=dec_enc_mask)
        dec_out = self.projection(dec_out)

        # dec_out = self.end_conv1(dec_out)
        # dec_out = self.end_conv2(dec_out.transpose(2,1)).transpose(1,2)
        if self.output_attention:
            return dec_out[:, -self.pred_len :, :], attns
        else:
            return dec_out[:, -self.pred_len :, :]


class InformerStack(nn.Module):
    """
    Implementation of AAAI 2021 best paper "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting"

    Args:
        enc_in (int): encoder input size
        dec_in (int): decoder input size
        c_out (int): output size
        seq_len (int): input sequence length of Informer encode
        label_len (int): start token length of Informer decoder
        out_len (int): prediction sequence length
        factor (int): probsparse attn factor (default is 5)
        d_model (int): dimension of model (default is 512)
        n_heads (int): num of heads (default is 8)
        e_layers (list): num of stack encoder layers (default is [3,2,1])
        d_layers (int): num of decoder layers (default is 2)
        d_ff (int): dimension of fcn (default is 512)
        dropout (float): dropout (default is 0.0)
        attn (string): attention used in encoder, options:[prob, full] (default is prob)
        embed (string): time features encoding, options:[timeF, fixed, learned] (default is fixed)
        freq (string): freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h (default is h)
        activation (string): activation (default is gelu)
        output_attention (bool): whether to output attention in ecoder (default is False)
        distil (bool): whether to use distilling in encoder, using this argument means not using distilling (default is True)
        mix (bool): whether to use mix attention in generative decode (default is True)

    Predefined models:

        informer_stack_univariate:
            enc_in = 1
            dec_in = 1
            c_out = 1
            seq_len = 720
            label_len = 168
            out_len = 24
            e_layers = 2
            d_layers = 1
            d_ff = 2048
            dropout = 0.05
            embed = 'timeF'

        informer_stack_multivariate:
            enc_in = 7
            dec_in = 7
            c_out = 7
            seq_len = 48
            label_len = 48
            out_len = 24
            e_layers = 2
            d_layers = 1
            d_ff = 2048
            dropout = 0.05
            embed = 'timeF'

    Example:

    .. code-block:: python

        x, x_mask = torch.zeros(32, 720, 1).cuda(), torch.zeros(32, 720, 4).cuda()
        y, y_mask = torch.zeros(32, 168, 1).cuda(), torch.zeros(32, 168, 4).cuda()

        model = hfai.models.informer_stack_univariate().cuda()
        # or
        # model = hfai.models.InformerStack(
        #     enc_in=1,
        #     dec_in=1,
        #     c_out=1,
        #     seq_len=720,
        #     label_len=168,
        #     out_len=24,
        #     e_layers=2,
        #     d_layers=1,
        #     d_ff=2048,
        #     dropout=0.05,
        #     embed='timeF',
        # )

        # forward
        logits = model(x, x_mask, y, y_mask)

    """

    def __init__(
        self,
        enc_in,
        dec_in,
        c_out,
        seq_len,
        label_len,
        out_len,
        factor=5,
        d_model=512,
        n_heads=8,
        e_layers=[3, 2, 1],
        d_layers=2,
        d_ff=512,
        dropout=0.0,
        attn="prob",
        embed="fixed",
        freq="h",
        activation="gelu",
        output_attention=False,
        distil=True,
        mix=True,
    ):
        super(InformerStack, self).__init__()
        self.seq_len = seq_len
        self.label_len = label_len
        self.pred_len = out_len
        self.attn = attn
        self.output_attention = output_attention

        # Encoding
        self.enc_embedding = DataEmbedding(enc_in, d_model, embed, freq, dropout)
        self.dec_embedding = DataEmbedding(dec_in, d_model, embed, freq, dropout)
        # Attention
        Attn = ProbAttention if attn == "prob" else FullAttention
        # Encoder

        inp_lens = list(range(len(e_layers)))  # [0,1,2,...] you can customize here
        encoders = [
            Encoder(
                [
                    EncoderLayer(
                        AttentionLayer(
                            Attn(False, factor, attention_dropout=dropout, output_attention=output_attention),
                            d_model,
                            n_heads,
                            mix=False,
                        ),
                        d_model,
                        d_ff,
                        dropout=dropout,
                        activation=activation,
                    )
                    for l in range(el)
                ],
                [ConvLayer(d_model) for l in range(el - 1)] if distil else None,
                norm_layer=torch.nn.LayerNorm(d_model),
            )
            for el in e_layers
        ]
        self.encoder = EncoderStack(encoders, inp_lens)
        # Decoder
        self.decoder = Decoder(
            [
                DecoderLayer(
                    AttentionLayer(
                        Attn(True, factor, attention_dropout=dropout, output_attention=False), d_model, n_heads, mix=mix
                    ),
                    AttentionLayer(
                        FullAttention(False, factor, attention_dropout=dropout, output_attention=False),
                        d_model,
                        n_heads,
                        mix=False,
                    ),
                    d_model,
                    d_ff,
                    dropout=dropout,
                    activation=activation,
                )
                for l in range(d_layers)
            ],
            norm_layer=torch.nn.LayerNorm(d_model),
        )
        # self.end_conv1 = nn.Conv1d(in_channels=label_len+out_len, out_channels=out_len, kernel_size=1, bias=True)
        # self.end_conv2 = nn.Conv1d(in_channels=d_model, out_channels=c_out, kernel_size=1, bias=True)
        self.projection = nn.Linear(d_model, c_out, bias=True)

    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, enc_self_mask=None, dec_self_mask=None, dec_enc_mask=None):
        """
        Args:
            x_enc (Tensor): input time series, size ``[B, L, D]``
            x_mark_enc (Tensor): input time-series features, size ``[B, L, D]``
            x_dec (Tensor): target time series, size ``[B, L, D]``
            x_mark_dec (Tensor): target time-series features, size ``[B, L, D]``
            enc_self_mask (Tensor): encode mask, size ``[B, D]``
            dec_self_mask (Tensor): decode mask, size ``[B, D]``
            dec_enc_mask (Tensor): encode-decode mask, size ``[B, D]``

        Returns:
            pred (Tensor): prediction, size ``[B, L, D]``
        """

        enc_out = self.enc_embedding(x_enc, x_mark_enc)
        enc_out, attns = self.encoder(enc_out, attn_mask=enc_self_mask)

        dec_out = self.dec_embedding(x_dec, x_mark_dec)
        dec_out = self.decoder(dec_out, enc_out, x_mask=dec_self_mask, cross_mask=dec_enc_mask)
        dec_out = self.projection(dec_out)

        # dec_out = self.end_conv1(dec_out)
        # dec_out = self.end_conv2(dec_out.transpose(2,1)).transpose(1,2)
        if self.output_attention:
            return dec_out[:, -self.pred_len :, :], attns
        else:
            return dec_out[:, -self.pred_len :, :]  # [B, L, D]