import torch.nn as nn
from transformer import *

class Transformer(nn.Module):
    def __init__(self, src_pad_idx, trg_pad_idx, enc_voc_size, dec_voc_size, d_model, n_head, max_len,
                 ffn_hidden, n_layers, drop_prob, learnable_pos_emb=True):
        super().__init__()
        self.src_pad_idx = src_pad_idx
        self.trg_pad_idx = trg_pad_idx
        self.encoder = Encoder(d_model=d_model,
                               n_head=n_head,
                               max_len=max_len,
                               ffn_hidden=ffn_hidden,
                               enc_voc_size=enc_voc_size,
                               drop_prob=drop_prob,
                               n_layers=n_layers,
                               padding_idx=src_pad_idx,
                               learnable_pos_emb=learnable_pos_emb)

        self.decoder = nn.Linear(d_model, dec_voc_size)

    def get_device(self):
        return next(self.parameters()).device

    def forward(self, src):
        device = self.get_device()
        src_mask = self.make_pad_mask(src, src, self.src_pad_idx, self.src_pad_idx).to(device)
        enc_src = self.encoder(src, src_mask)
        output = self.decoder(enc_src)
        return output

    def make_pad_mask(self, q, k, q_pad_idx, k_pad_idx):
        len_q, len_k = q.size(1), k.size(1)
        # batch_size x 1 x 1 x len_k
        k = k.ne(k_pad_idx).unsqueeze(1).unsqueeze(2)
        # batch_size x 1 x len_q x len_k
        k = k.repeat(1, 1, len_q, 1)
        # batch_size x 1 x len_q x 1
        q = q.ne(q_pad_idx).unsqueeze(1).unsqueeze(3)
        # batch_size x 1 x len_q x len_k
        q = q.repeat(1, 1, 1, len_k)
        mask = k & q
        return mask

    def make_no_peak_mask(self, q, k):
        len_q, len_k = q.size(1), k.size(1)
        # len_q x len_k
        mask = torch.tril(torch.ones(len_q, len_k)).type(torch.BoolTensor)
        return mask