import tempfile
from argparse import Namespace
from pathlib import Path

import gradio as gr
import soundfile as sf
import torch
from matcha.cli import (MATCHA_URLS, VOCODER_URLS, assert_model_downloaded,
                        get_device, load_matcha, load_vocoder, process_text,
                        to_waveform)
from matcha.utils.utils import get_user_data_dir, plot_tensor

LOCATION = Path(get_user_data_dir())

args = Namespace(
    cpu=False,
    model="matcha_ljspeech",
    vocoder="hifigan_T2_v1",
    spk=0,
)


MATCHA_TTS_LOC = lambda x: LOCATION / f"{x}.ckpt"  # noqa: E731
VOCODER_LOC = lambda x: LOCATION / f"{x}"  # noqa: E731
LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png"
RADIO_OPTIONS = {
    "Multi Speaker (VCTK)": {
        "model": "matcha_vctk",
        "vocoder": "hifigan_univ_v1",
    },
    "Single Speaker (LJ Speech)": {
        "model": "matcha_ljspeech",
        "vocoder": "hifigan_T2_v1",
    },
}

# Ensure all the required models are downloaded
assert_model_downloaded(MATCHA_TTS_LOC("matcha_ljspeech"), MATCHA_URLS["matcha_ljspeech"])
assert_model_downloaded(VOCODER_LOC("hifigan_T2_v1"), VOCODER_URLS["hifigan_T2_v1"])
assert_model_downloaded(MATCHA_TTS_LOC("matcha_vctk"), MATCHA_URLS["matcha_vctk"])
assert_model_downloaded(VOCODER_LOC("hifigan_univ_v1"), VOCODER_URLS["hifigan_univ_v1"])

# get device
device = get_device(args)

# Load default models
matcha_ljspeech = load_matcha(args.model, MATCHA_TTS_LOC(args.model), device)
hifigan_T2_v1, hifigan_T2_v1_denoiser = load_vocoder(args.vocoder, VOCODER_LOC(args.vocoder), device)

matcha_vctk = load_matcha("matcha_vctk", MATCHA_TTS_LOC("matcha_vctk"), device)
hifigan_univ_v1, hifigan_univ_v1_denoiser = load_vocoder("hifigan_univ_v1", VOCODER_LOC("hifigan_univ_v1"), device)



def load_model_ui(model_type, textbox):
    model_name = RADIO_OPTIONS[model_type]["model"]

    if model_name == "matcha_ljspeech":
        spk_slider = gr.update(visible=False, value=-1)
        single_speaker_examples = gr.update(visible=True)
        multi_speaker_examples = gr.update(visible=False)
        length_scale = gr.update(value=0.95)
    else:
        spk_slider = gr.update(visible=True, value=0)
        single_speaker_examples = gr.update(visible=False)
        multi_speaker_examples = gr.update(visible=True)
        length_scale = gr.update(value=0.85)

    return textbox, gr.update(interactive=True), spk_slider, single_speaker_examples, multi_speaker_examples, length_scale


@torch.inference_mode()
def process_text_gradio(text):
    output = process_text(1, text, device)
    return output["x_phones"][1::2], output["x"], output["x_lengths"]


@torch.inference_mode()
def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale, spk):
    spk = torch.tensor([spk], device=device, dtype=torch.long) if spk >= 0 else None
    
    if spk is None:
        output = matcha_ljspeech.synthesise(
            text,
            text_length,
            n_timesteps=n_timesteps,
            temperature=temperature,
            spks=None,
            length_scale=length_scale,
        )
        output["waveform"] = to_waveform(output["mel"], hifigan_T2_v1, hifigan_T2_v1_denoiser)
    else:
        output = matcha_vctk.synthesise(
            text,
            text_length,
            n_timesteps=n_timesteps,
            temperature=temperature,
            spks=spk,
            length_scale=length_scale,
        )
        output["waveform"] = to_waveform(output["mel"], hifigan_univ_v1, hifigan_univ_v1_denoiser)

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        sf.write(fp.name, output["waveform"], 22050, "PCM_24")

    return fp.name, plot_tensor(output["mel"].squeeze().cpu().numpy())


def multispeaker_example_cacher(text, n_timesteps, mel_temp, length_scale, spk):
    phones, text, text_lengths = process_text_gradio(text)
    audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)
    return phones, audio, mel_spectrogram


def ljspeech_example_cacher(text, n_timesteps, mel_temp, length_scale, spk=-1):
    phones, text, text_lengths = process_text_gradio(text)
    audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)
    return phones, audio, mel_spectrogram


description = """# 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
### [Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/)
We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up ODE-based speech synthesis. Our method:


* Is probabilistic
* Has compact memory footprint
* Sounds highly natural
* Is very fast to synthesise from


Check out audio examples below and read our [arXiv preprint for more details](https://arxiv.org/abs/2309.03199).
Code is available in our [GitHub repository](https://github.com/shivammehta25/Matcha-TTS), along with pre-trained models.

Cached examples are available at the bottom of the page.

Note: Synthesis speed may be slower than in our paper due to I/O latency and because this instance runs on CPUs.
"""

with gr.Blocks(title="🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching") as demo:
    processed_text = gr.State(value=None)
    processed_text_len = gr.State(value=None)

    with gr.Box():
        with gr.Row():
            gr.Markdown(description, scale=3)
            with gr.Column():
                gr.Image(LOGO_URL, label="Matcha-TTS logo", height=50, width=50, scale=1, show_label=False)
                html = '<br><iframe width="560" height="315" src="https://www.youtube.com/embed/xmvJkz3bqw0?si=jN7ILyDsbPwJCGoa" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>'
                gr.HTML(html)

    with gr.Box():
        radio_options = list(RADIO_OPTIONS.keys())
        model_type = gr.Radio(
            radio_options, value=radio_options[0], label="Choose a Model", interactive=True, container=False
        )

        with gr.Row():
            gr.Markdown("# Text Input")
        with gr.Row():
            text = gr.Textbox(value="", lines=2, label="Text to synthesise", scale=3)
            spk_slider = gr.Slider(
                minimum=0, maximum=107, step=1, value=args.spk, label="Speaker ID", interactive=True, scale=1
            )

        with gr.Row():
            gr.Markdown("### Hyper parameters")
        with gr.Row():
            n_timesteps = gr.Slider(
                label="Number of ODE steps",
                minimum=1,
                maximum=100,
                step=1,
                value=10,
                interactive=True,
            )
            length_scale = gr.Slider(
                label="Length scale (Speaking rate)",
                minimum=0.5,
                maximum=1.5,
                step=0.05,
                value=0.85,
                interactive=True,
            )
            mel_temp = gr.Slider(
                label="Sampling temperature",
                minimum=0.00,
                maximum=2.001,
                step=0.16675,
                value=0.667,
                interactive=True,
            )

            synth_btn = gr.Button("Synthesise")

    with gr.Box():
        with gr.Row():
            gr.Markdown("### Phonetised text")
            phonetised_text = gr.Textbox(interactive=False, scale=10, label="Phonetised text")

    with gr.Box():
        with gr.Row():
            mel_spectrogram = gr.Image(interactive=False, label="mel spectrogram")

            # with gr.Row():
            audio = gr.Audio(interactive=False, label="Audio")

    with gr.Row(visible=False) as example_row_lj_speech:
        examples = gr.Examples(  # pylint: disable=unused-variable
            examples=[
                [
                    "We propose Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up O D E-based speech synthesis.",
                    50,
                    0.677,
                    0.95,
                ],
                [
                    "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.",
                    2,
                    0.677,
                    0.95,
                ],
                [
                    "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.",
                    4,
                    0.677,
                    0.95,
                ],
                [
                    "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.",
                    10,
                    0.677,
                    0.95,
                ],
                [
                    "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.",
                    50,
                    0.677,
                    0.95,
                ],
                [
                    "The narrative of these events is based largely on the recollections of the participants.",
                    10,
                    0.677,
                    0.95,
                ],
                [
                    "The jury did not believe him, and the verdict was for the defendants.",
                    10,
                    0.677,
                    0.95,
                ],
            ],
            fn=ljspeech_example_cacher,
            inputs=[text, n_timesteps, mel_temp, length_scale],
            outputs=[phonetised_text, audio, mel_spectrogram],
            cache_examples=True,
        )

    with gr.Row() as example_row_multispeaker:
        multi_speaker_examples = gr.Examples(  # pylint: disable=unused-variable
            examples=[
                [
                    "Hello everyone! I am speaker 0 and I am here to tell you that Matcha-TTS is amazing!",
                    10,
                    0.677,
                    0.85,
                    0,
                ],
                [
                    "Hello everyone! I am speaker 16 and I am here to tell you that Matcha-TTS is amazing!",
                    10,
                    0.677,
                    0.85,
                    16,
                ],
                [
                    "Hello everyone! I am speaker 44 and I am here to tell you that Matcha-TTS is amazing!",
                    50,
                    0.677,
                    0.85,
                    44,
                ],
                [
                    "Hello everyone! I am speaker 45 and I am here to tell you that Matcha-TTS is amazing!",
                    50,
                    0.677,
                    0.85,
                    45,
                ],
                [
                    "Hello everyone! I am speaker 58 and I am here to tell you that Matcha-TTS is amazing!",
                    4,
                    0.677,
                    0.85,
                    58,
                ],
            ],
            fn=multispeaker_example_cacher,
            inputs=[text, n_timesteps, mel_temp, length_scale, spk_slider],
            outputs=[phonetised_text, audio, mel_spectrogram],
            cache_examples=True,
            label="Multi Speaker Examples",
        )
    
    model_type.change(lambda x: gr.update(interactive=False), inputs=[synth_btn], outputs=[synth_btn]).then(
        load_model_ui,
        inputs=[model_type, text],
        outputs=[text, synth_btn, spk_slider, example_row_lj_speech, example_row_multispeaker, length_scale],
    )

    synth_btn.click(
        fn=process_text_gradio,
        inputs=[
            text,
        ],
        outputs=[phonetised_text, processed_text, processed_text_len],
        api_name="matcha_tts",
        queue=True,
    ).then(
        fn=synthesise_mel,
        inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale, spk_slider],
        outputs=[audio, mel_spectrogram],
    )

    demo.queue(concurrency_count=5).launch()