# coding: utf-8
import torch
from torch import nn
from TTS.layers.tacotron import Encoder, Decoder, PostCBHG
from TTS.utils.generic_utils import sequence_mask
from TTS.layers.gst_layers import GST


class Tacotron(nn.Module):
    def __init__(self,
                 num_chars,
                 num_speakers,
                 r=5,
                 linear_dim=1025,
                 mel_dim=80,
                 memory_size=5,
                 attn_win=False,
                 gst=False,
                 attn_norm="sigmoid",
                 prenet_type="original",
                 prenet_dropout=True,
                 forward_attn=False,
                 trans_agent=False,
                 forward_attn_mask=False,
                 location_attn=True,
                 separate_stopnet=True):
        super(Tacotron, self).__init__()
        self.r = r
        self.mel_dim = mel_dim
        self.linear_dim = linear_dim
        self.gst = gst
        self.num_speakers = num_speakers
        self.embedding = nn.Embedding(num_chars, 256)
        self.embedding.weight.data.normal_(0, 0.3)
        decoder_dim = 512 if num_speakers > 1 else 256
        encoder_dim = 512 if num_speakers > 1 else 256
        proj_speaker_dim = 80 if num_speakers > 1 else 0
        # boilerplate model
        self.encoder = Encoder(encoder_dim)
        self.decoder = Decoder(decoder_dim, mel_dim, r, memory_size, attn_win,
                               attn_norm, prenet_type, prenet_dropout,
                               forward_attn, trans_agent, forward_attn_mask,
                               location_attn, separate_stopnet,
                               proj_speaker_dim)
        self.postnet = PostCBHG(mel_dim)
        self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2,
                                     linear_dim)
        # speaker embedding layers
        if num_speakers > 1:
            self.speaker_embedding = nn.Embedding(num_speakers, 256)
            self.speaker_embedding.weight.data.normal_(0, 0.3)
            self.speaker_project_mel = nn.Sequential(
                nn.Linear(256, proj_speaker_dim), nn.Tanh())
            self.speaker_embeddings = None
            self.speaker_embeddings_projected = None
        # global style token layers
        if self.gst:
            gst_embedding_dim = 256
            self.gst_layer = GST(num_mel=80,
                                 num_heads=4,
                                 num_style_tokens=10,
                                 embedding_dim=gst_embedding_dim)

    def _init_states(self):
        self.speaker_embeddings = None
        self.speaker_embeddings_projected = None

    def compute_speaker_embedding(self, speaker_ids):
        if hasattr(self, "speaker_embedding") and speaker_ids is None:
            raise RuntimeError(
                " [!] Model has speaker embedding layer but speaker_id is not provided"
            )
        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
            self.speaker_embeddings = self._compute_speaker_embedding(
                speaker_ids)
            self.speaker_embeddings_projected = self.speaker_project_mel(
                self.speaker_embeddings).squeeze(1)

    def compute_gst(self, inputs, mel_specs):
        gst_outputs = self.gst_layer(mel_specs)
        inputs = self._add_speaker_embedding(inputs, gst_outputs)
        return inputs

    def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
        B = characters.size(0)
        mask = sequence_mask(text_lengths).to(characters.device)
        inputs = self.embedding(characters)
        self._init_states()
        self.compute_speaker_embedding(speaker_ids)
        if self.num_speakers > 1:
            inputs = self._concat_speaker_embedding(inputs,
                                                    self.speaker_embeddings)
        encoder_outputs = self.encoder(inputs)
        if self.gst:
            encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
        if self.num_speakers > 1:
            encoder_outputs = self._concat_speaker_embedding(
                encoder_outputs, self.speaker_embeddings)
        mel_outputs, alignments, stop_tokens = self.decoder(
            encoder_outputs, mel_specs, mask,
            self.speaker_embeddings_projected)
        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

    def inference(self, characters, speaker_ids=None, style_mel=None):
        B = characters.size(0)
        inputs = self.embedding(characters)
        self._init_states()
        self.compute_speaker_embedding(speaker_ids)
        if self.num_speakers > 1:
            inputs = self._concat_speaker_embedding(inputs,
                                                    self.speaker_embeddings)
        encoder_outputs = self.encoder(inputs)
        if self.gst and style_mel is not None:
            encoder_outputs = self.compute_gst(encoder_outputs, style_mel)
        if self.num_speakers > 1:
            encoder_outputs = self._concat_speaker_embedding(
                encoder_outputs, self.speaker_embeddings)
        mel_outputs, alignments, stop_tokens = self.decoder.inference(
            encoder_outputs, self.speaker_embeddings_projected)
        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

    def _compute_speaker_embedding(self, speaker_ids):
        speaker_embeddings = self.speaker_embedding(speaker_ids)
        return speaker_embeddings.unsqueeze_(1)

    @staticmethod
    def _add_speaker_embedding(outputs, speaker_embeddings):
        speaker_embeddings_ = speaker_embeddings.expand(
            outputs.size(0), outputs.size(1), -1)
        outputs = outputs + speaker_embeddings_
        return outputs

    @staticmethod
    def _concat_speaker_embedding(outputs, speaker_embeddings):
        speaker_embeddings_ = speaker_embeddings.expand(
            outputs.size(0), outputs.size(1), -1)
        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
        return outputs