import torch
import torch.nn as nn
from einops import rearrange, reduce, repeat


class DampingLayer(nn.Module):

    def __init__(self, pred_len, nhead, dropout=0.1, output_attention=False):
        super().__init__()
        self.pred_len = pred_len
        self.nhead = nhead
        self.output_attention = output_attention
        self._damping_factor = nn.Parameter(torch.randn(1, nhead))
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = repeat(x, 'b 1 d -> b t d', t=self.pred_len)
        b, t, d = x.shape

        powers = torch.arange(self.pred_len).to(self._damping_factor.device) + 1
        powers = powers.view(self.pred_len, 1)
        damping_factors = self.damping_factor ** powers
        damping_factors = damping_factors.cumsum(dim=0)
        x = x.view(b, t, self.nhead, -1)
        x = self.dropout(x) * damping_factors.unsqueeze(-1)
        x = x.view(b, t, d)
        if self.output_attention:
            return x, damping_factors
        return x, None

    @property
    def damping_factor(self):
        return torch.sigmoid(self._damping_factor)


class DecoderLayer(nn.Module):

    def __init__(self, d_model, nhead, c_out, pred_len, dropout=0.1, output_attention=False):
        super().__init__()
        self.d_model = d_model
        self.nhead = nhead
        self.c_out = c_out
        self.pred_len = pred_len
        self.output_attention = output_attention

        self.growth_damping = DampingLayer(pred_len, nhead, dropout=dropout, output_attention=output_attention)
        self.dropout1 = nn.Dropout(dropout)

    def forward(self, growth, season):
        growth_horizon, growth_damping = self.growth_damping(growth[:, -1:])
        growth_horizon = self.dropout1(growth_horizon)

        seasonal_horizon = season[:, -self.pred_len:]

        if self.output_attention:
            return growth_horizon, seasonal_horizon, growth_damping
        return growth_horizon, seasonal_horizon, None


class Decoder(nn.Module):

    def __init__(self, layers):
        super().__init__()
        self.d_model = layers[0].d_model
        self.c_out = layers[0].c_out
        self.pred_len = layers[0].pred_len
        self.nhead = layers[0].nhead

        self.layers = nn.ModuleList(layers)
        self.pred = nn.Linear(self.d_model, self.c_out)

    def forward(self, growths, seasons):
        growth_repr = []
        season_repr = []
        growth_dampings = []

        for idx, layer in enumerate(self.layers):
            growth_horizon, season_horizon, growth_damping = layer(growths[idx], seasons[idx])
            growth_repr.append(growth_horizon)
            season_repr.append(season_horizon)
            growth_dampings.append(growth_damping)
        growth_repr = sum(growth_repr)
        season_repr = sum(season_repr)
        return self.pred(growth_repr), self.pred(season_repr), growth_dampings