import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence


class TextEncoderBiGRUCo(nn.Module):
    def __init__(self, word_size: int, pos_size: int, hidden_size: int, output_size: int) -> None:
        super(TextEncoderBiGRUCo, self).__init__()

        self.pos_emb = nn.Linear(pos_size, word_size)
        self.input_emb = nn.Linear(word_size, hidden_size)
        self.gru = nn.GRU(
            hidden_size, hidden_size, batch_first=True, bidirectional=True
        )
        self.output_net = nn.Sequential(
            nn.Linear(hidden_size * 2, hidden_size),
            nn.LayerNorm(hidden_size),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(hidden_size, output_size),
        )

        self.hidden_size = hidden_size
        self.hidden = nn.Parameter(
            torch.randn((2, 1, self.hidden_size), requires_grad=True)
        )

    def forward(self, word_embs: torch.Tensor, pos_onehot: torch.Tensor,
                cap_lens: torch.Tensor) -> torch.Tensor:
        num_samples = word_embs.shape[0]

        pos_embs = self.pos_emb(pos_onehot)
        inputs = word_embs + pos_embs
        input_embs = self.input_emb(inputs)
        hidden = self.hidden.repeat(1, num_samples, 1)

        cap_lens = cap_lens.data.tolist()
        emb = pack_padded_sequence(input_embs, cap_lens, batch_first=True)

        gru_seq, gru_last = self.gru(emb, hidden)

        gru_last = torch.cat([gru_last[0], gru_last[1]], dim=-1)

        return self.output_net(gru_last)