# coding: utf-8
import torch
from torch import nn
from utils.text.symbols import symbols
from layers.tacotron import Prenet, Encoder, Decoder, CBHG


class Tacotron(nn.Module):
    def __init__(self, embedding_dim=256, linear_dim=1025, mel_dim=80,
                 r=5, padding_idx=None):
        super(Tacotron, self).__init__()
        self.r = r
        self.mel_dim = mel_dim
        self.linear_dim = linear_dim
        self.embedding = nn.Embedding(len(symbols), embedding_dim,
                                      padding_idx=padding_idx)
        print(" | > Number of characters : {}".format(len(symbols)))
        self.embedding.weight.data.normal_(0, 0.3)
        self.encoder = Encoder(embedding_dim)
        self.decoder = Decoder(256, mel_dim, r)
        self.postnet = CBHG(mel_dim, K=8, projections=[256, mel_dim])
        self.last_linear = nn.Linear(mel_dim * 2, linear_dim)

    def forward(self, characters, mel_specs=None):
        B = characters.size(0)
        inputs = self.embedding(characters)
        # batch x time x dim
        encoder_outputs = self.encoder(inputs)
        # batch x time x dim*r
        mel_outputs, alignments, stop_tokens = self.decoder(
            encoder_outputs, mel_specs)
        # Reshape
        # batch x time x dim
        mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
        linear_outputs = self.postnet(mel_outputs)
        linear_outputs = self.last_linear(linear_outputs)
        return mel_outputs, linear_outputs, alignments, stop_tokens