"""
Notes
-----
This module contains the functions for audiobook_gen that handle text-to-speech generation.
The functions take in the preprocessed text and invoke the Silero package to generate audio tensors.
"""
import logging

import torch
from stqdm import stqdm

from src import output, config


def load_model():
    """
    Load Silero package containg the model information
    for the language and speaker set in config.py
    and converts it to the set device.

    Parameters
    ----------
    None

    Returns
    -------
    model : torch.package

    """
    from silero import silero_tts

    model, _ = silero_tts(language=config.LANGUAGE, speaker=config.MODEL_ID)
    model.to(config.DEVICE)
    return model


def generate_audio(corpus, title, model, speaker):
    """
    For each section within the corpus, calls predict() function to generate audio tensors
    and then calls write_audio() to output the tensors to audio files.

    Parameters
    ----------
    corpus : array_like
        list of list of strings,
        body of tokenized text from which audio is generated

    title : str
        title of document, used to name output files

    model : torch.package
        torch package containing model for language and speaker specified

    speaker : str
        identifier of selected speaker for audio generation

    Returns
    -------
    None

    """
    for section in stqdm(corpus, desc="Sections in document:"):
        section_index = f'part{corpus.index(section):03}'
        audio_list, sample_path = predict(section, section_index, title, model, speaker)
        output.write_audio(audio_list, sample_path)


def predict(text_section, section_index, title, model, speaker):
    """
    Applies Silero TTS engine for each token within the corpus section,
    appending it to the output tensor array, and creates file path for output.

    Parameters
    ----------
    text_section : array_like
        list of strings,
        body of tokenized text from which audio is generated

    section_index : int
        index of current section within corpus

    title : str
        title of document, used to name output files

    model : torch.package
        torch package containing model for language and speaker specified

    speaker : str
        identifier of selected speaker for audio generation

    Returns
    -------
    audio_list : torch.tensor
        pytorch tensor containing generated audio

    sample_path : str
        file name and path for outputting tensor to audio file

    """
    audio_list = []
    for sentence in stqdm(text_section, desc="Sentences in section:"):
        audio = model.apply_tts(text=sentence, speaker=speaker, sample_rate=config.SAMPLE_RATE)
        if len(audio) > 0 and isinstance(audio, torch.Tensor):
            audio_list.append(audio)
            logging.info(f'Tensor generated for sentence: \n {sentence}')
        else:
            logging.info(f'Tensor for sentence is not valid: \n {sentence}')

    sample_path = config.output_path / f'{title}_{section_index}.wav'
    return audio_list, sample_path