# -*- coding: utf-8 -*-
# Copyright 2020 Minh Nguyen (@dathudeptrai)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tacotron-2 Config object."""


from tensorflow_tts.configs import BaseConfig
from tensorflow_tts.processor.ljspeech import LJSPEECH_SYMBOLS as lj_symbols
from tensorflow_tts.processor.kss import KSS_SYMBOLS as kss_symbols
from tensorflow_tts.processor.baker import BAKER_SYMBOLS as bk_symbols
from tensorflow_tts.processor.libritts import LIBRITTS_SYMBOLS as lbri_symbols


class Tacotron2Config(BaseConfig):
    """Initialize Tacotron-2 Config."""

    def __init__(
        self,
        dataset="ljspeech",
        vocab_size=len(lj_symbols),
        embedding_hidden_size=512,
        initializer_range=0.02,
        layer_norm_eps=1e-6,
        embedding_dropout_prob=0.1,
        n_speakers=5,
        n_conv_encoder=3,
        encoder_conv_filters=512,
        encoder_conv_kernel_sizes=5,
        encoder_conv_activation="mish",
        encoder_conv_dropout_rate=0.5,
        encoder_lstm_units=256,
        reduction_factor=5,
        n_prenet_layers=2,
        prenet_units=256,
        prenet_activation="mish",
        prenet_dropout_rate=0.5,
        n_lstm_decoder=1,
        decoder_lstm_units=1024,
        attention_type="lsa",
        attention_dim=128,
        attention_filters=32,
        attention_kernel=31,
        n_mels=80,
        n_conv_postnet=5,
        postnet_conv_filters=512,
        postnet_conv_kernel_sizes=5,
        postnet_dropout_rate=0.1,
    ):
        """Init parameters for Tacotron-2 model."""
        if dataset == "ljspeech":
            self.vocab_size = vocab_size
        elif dataset == "kss":
            self.vocab_size = len(kss_symbols)
        elif dataset == "baker":
            self.vocab_size = len(bk_symbols)
        elif dataset == "libritts":
            self.vocab_size = len(lbri_symbols)
        else:
            raise ValueError("No such dataset: {}".format(dataset))
        self.embedding_hidden_size = embedding_hidden_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.embedding_dropout_prob = embedding_dropout_prob
        self.n_speakers = n_speakers
        self.n_conv_encoder = n_conv_encoder
        self.encoder_conv_filters = encoder_conv_filters
        self.encoder_conv_kernel_sizes = encoder_conv_kernel_sizes
        self.encoder_conv_activation = encoder_conv_activation
        self.encoder_conv_dropout_rate = encoder_conv_dropout_rate
        self.encoder_lstm_units = encoder_lstm_units

        # decoder param
        self.reduction_factor = reduction_factor
        self.n_prenet_layers = n_prenet_layers
        self.prenet_units = prenet_units
        self.prenet_activation = prenet_activation
        self.prenet_dropout_rate = prenet_dropout_rate
        self.n_lstm_decoder = n_lstm_decoder
        self.decoder_lstm_units = decoder_lstm_units
        self.attention_type = attention_type
        self.attention_dim = attention_dim
        self.attention_filters = attention_filters
        self.attention_kernel = attention_kernel
        self.n_mels = n_mels

        # postnet
        self.n_conv_postnet = n_conv_postnet
        self.postnet_conv_filters = postnet_conv_filters
        self.postnet_conv_kernel_sizes = postnet_conv_kernel_sizes
        self.postnet_dropout_rate = postnet_dropout_rate
