import torch
from torch import nn
from torch.nn import functional as F

from ..util import mask_upper_triangular

class Transformer(nn.Module):
    """
    A single transformer, masking nan or 0
    """
    def __init__(self, x_dim, y_dim, attention_dropout=0, nhead=8, nlayers=8, hidden_size=32, nan_value=0, min_std=0.01):
        super().__init__()
        self._min_std = min_std
        self.nan_value = nan_value
        enc_x_dim = x_dim + y_dim

        self.enc_emb = nn.Linear(enc_x_dim, hidden_size)
        encoder_norm = nn.LayerNorm(hidden_size)
        layer_enc = nn.TransformerEncoderLayer(
            d_model=hidden_size,
            dim_feedforward=hidden_size*8,
            dropout=attention_dropout,
            nhead=nhead,
            # activation
        )
        self.encoder = nn.TransformerEncoder(
            layer_enc, num_layers=nlayers, norm=encoder_norm
        )
        self.mean = nn.Linear(hidden_size, y_dim)
        self.std = nn.Linear(hidden_size, y_dim)

    def forward(self, past_x, past_y, future_x, future_y=None):
        device = next(self.parameters()).device
        B, S, _ = future_x.shape
        future_y_fake = past_y[:, -1:, :].repeat(1, S, 1).to(device)
        # future_y_fake = (
        #     torch.ones(past_y.shape[0], future_x.shape[1], past_y.shape[2]).float().to(device) * past_y[:, -1].repeat(B, S, 1)
        # )
        context = torch.cat([past_x, past_y], -1).detach()
        target = torch.cat([future_x, future_y_fake], -1).detach()
        x = torch.cat([context, target * 1], 1).detach()

        x = self.enc_emb(x).permute(1, 0, 2)

        S, B, _ = x.shape
        mask = mask_upper_triangular(S, device)
        
        outputs = self.encoder(x, mask=mask#, src_key_padding_mask=x_key_padding_mask
        ).permute(
            1, 0, 2
        )

        # Seems to help a little, especially with extrapolating out of bounds
        steps = past_y.shape[1]
        mean = self.mean(outputs)[:, steps:, :]
        log_sigma = self.std(outputs)[:, steps:, :]
        
        sigma = self._min_std + (1 - self._min_std) * F.softplus(log_sigma)
        return torch.distributions.Normal(mean, sigma), {}