"""
TODO:
    + [x] Load Configuration
    + [ ] Checking
    + [ ] Better saving directory
"""
import numpy as np
from pathlib import Path
import jiwer
import pdb
import torch.nn as nn
import torch
import torchaudio
from transformers import pipeline
from time import process_time, time
from pathlib import Path

# local import
import sys
from espnet2.bin.tts_inference import Text2Speech

# pdb.set_trace()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

sys.path.append("src")

import gradio as gr

# ASR part

audio_files = [
    str(x)
    for x in sorted(
        Path(
            "/home/kevingeng/Disk2/laronix/laronix_automos/data/20230103_video"
        ).glob("**/*wav")
    )
]
# audio_files = [str(x) for x in sorted(Path("./data/Patient_sil_trim_16k_normed_5_snr_40/Rainbow").glob("**/*wav"))]
transcriber = pipeline(
    "automatic-speech-recognition",
    model="KevinGeng/PAL_John_128_train_dev_test_seed_1",
)
# transcriber = pipeline("automatic-speech-recognition", model="KevinGeng/PAL_John_128_p326_300_train_dev_test_seed_1")
# 【Female】kan-bayashi ljspeech parallel wavegan
# tts_model = Text2Speech.from_pretrained("espnet/kan-bayashi_ljspeech_vits")
# 【Male】fastspeech2-en-200_speaker-cv4, hifigan vocoder
# pdb.set_trace()
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface

# @title English multi-speaker pretrained model { run: "auto" }
lang = "English"
tag = "kan-bayashi/libritts_xvector_vits"
# vits needs no
vocoder_tag = "parallel_wavegan/vctk_parallel_wavegan.v1.long"  # @param ["none", "parallel_wavegan/vctk_parallel_wavegan.v1.long", "parallel_wavegan/vctk_multi_band_melgan.v2", "parallel_wavegan/vctk_style_melgan.v1", "parallel_wavegan/vctk_hifigan.v1", "parallel_wavegan/libritts_parallel_wavegan.v1.long", "parallel_wavegan/libritts_multi_band_melgan.v2", "parallel_wavegan/libritts_hifigan.v1", "parallel_wavegan/libritts_style_melgan.v1"] {type:"string"}
from espnet2.bin.tts_inference import Text2Speech
from espnet2.utils.types import str_or_none

text2speech = Text2Speech.from_pretrained(
    model_tag=str_or_none(tag),
    vocoder_tag=str_or_none(vocoder_tag),
    device="cuda",
    use_att_constraint=False,
    backward_window=1,
    forward_window=3,
    speed_control_alpha=1.0,
)

import glob
import os
import numpy as np
import kaldiio

# Get model directory path
from espnet_model_zoo.downloader import ModelDownloader

d = ModelDownloader()
model_dir = os.path.dirname(d.download_and_unpack(tag)["train_config"])

# Speaker x-vector selection

xvector_ark = [
    p
    for p in glob.glob(
        f"{model_dir}/../../dump/**/spk_xvector.ark", recursive=True
    )
    if "tr" in p
][0]
xvectors = {k: v for k, v in kaldiio.load_ark(xvector_ark)}
spks = list(xvectors.keys())

male_spks = {
    "M1": "2300_131720",
    "M2": "1320_122612",
}
    # "M3": "1188_133604",
    # "M4": "61_70970",
female_spks = {"F1": "2961_961", "F2": "8463_287645", }
# "F3": "121_121726"
spks = dict(male_spks, **female_spks)
spk_names = sorted(spks.keys())


## 20230224 Mousa: No reference,
def ASRTTS(audio_file, spk_name, ref_text=""):
    spk = spks[spk_name]
    spembs = xvectors[spk]
    if ref_text == "":
        reg_text = transcriber(audio_file)["text"]
    else:
        reg_text = ref_text

    speech, sr = torchaudio.load(
        audio_file, channels_first=True
    )  # Mono channel
    wav_tensor_spembs = text2speech(
        text=reg_text, speech=speech, spembs=spembs
    )["wav"]
    wav_numpy = wav_tensor_spembs.unsqueeze(1).to("cpu")
    sample_rate = 22050
    save_id = (
        "./wav/" + Path(audio_file).stem + "_" + spk_name + "_spkembs.wav"
    )
    torchaudio.save(
        save_id,
        src=wav_tensor_spembs.unsqueeze(0).to("cpu"),
        sample_rate=22050,
    )

    return save_id, reg_text


def ASRTTS_clean(audio_file, spk_name):
    spk = spks[spk_name]
    spembs = xvectors[spk]

    reg_text = transcriber(audio_file)["text"]

    speech, sr = torchaudio.load(
        audio_file, channels_first=True
    )  # Mono channel
    wav_tensor_spembs = text2speech(
        text=reg_text, speech=speech, spembs=spembs
    )["wav"]
    wav_numpy = wav_tensor_spembs.unsqueeze(1).to("cpu")
    sample_rate = 22050
    save_id = (
        "./wav/" + Path(audio_file).stem + "_" + spk_name + "_spkembs.wav"
    )
    torchaudio.save(
        save_id,
        src=wav_tensor_spembs.unsqueeze(0).to("cpu"),
        sample_rate=22050,
    )
    return save_id


# def ref_reg_callback(audio_file, spk_name, ref_text):
#     reg_text = ref_text
#     return audio_file, spk_name, reg_text

reference_textbox = gr.Textbox(
    value="",
    placeholder="Input reference here",
    label="Reference",
)

recognization_textbox = gr.Textbox(
    value="",
    placeholder="Output recognization here",
    label="recognization_textbox",
)

speaker_option = gr.Radio(choices=spk_names, label="Speaker")
# speaker_profiles = {
#     "Male_1": "speaker_icons/male1.png",
#     "Male_2": "speaker_icons/male2.png",
#     "Female_1": "speaker_icons/female1.png",
#     "Female_2": "speaker_icons/female2.png",
# }

# speaker_option = gr.Image(label="Choose your speaker profile",
#                                  image_mode="RGB",
#                                  options=speaker_profiles
#                                  )

input_audio = gr.Audio(
    source="upload", type="filepath", label="Audio_to_Evaluate"
)
output_audio = gr.Audio(
    source="upload", file="filepath", label="Synthesized Audio"
)
examples = [
    ["./samples/001.wav", "M1", ""],
    ["./samples/002.wav", "M2", ""],
    ["./samples/003.wav", "F1", ""],
    ["./samples/004.wav", "F2", ""],
]


def change_audiobox(choice):
    if choice == "upload":
        input_audio = gr.Audio.update(source="upload", visible=True)
    elif choice == "microphone":
        input_audio = gr.Audio.update(source="microphone", visible=True)
    else:
        input_audio = gr.Audio.update(visible=False)
    return input_audio


def show_icon(choice):
    if choice == "M1":
        spk_icon = gr.Image.update(value="speaker_icons/male1.png", visible=True)
    elif choice == "M2":
        spk_icon = gr.Image.update(value="speaker_icons/male2.png", visible=True)
    elif choice == "F1":
        spk_icon = gr.Image.update(value="speaker_icons/female1.png", visible=True)
    elif choice == "F2":
        spk_icon = gr.Image.update(value="speaker_icons/female2.png", visible=True)
    return spk_icon


with gr.Blocks(
    analytics_enabled=False,
    css=".gradio-container {background-color: #78BD91}",
) as demo:
    with gr.Column():
        input_format = gr.Radio(
            choices=["upload", "microphone"], label="Choose your input format"
        )
        input_audio = gr.Audio(
            source="upload",
            type="filepath",
            label="Input Audio",
            interactive=True,
            visible=False,
        )
        input_format.change(
            fn=change_audiobox, inputs=input_format, outputs=input_audio
        )

        speaker_option = gr.Radio(choices=spk_names, value="M1", label="Choose your target speaker")
        spk_icon = gr.Image(value="speaker_icons/male1.png",
                            type="filepath",
                            image_mode="RGB",
                            source="upload",
                            shape=[50, 50],
                            interactive=True,
                            visible=True)
        speaker_option.change(
            fn=show_icon, inputs=speaker_option, outputs=spk_icon
        )

    b2 = gr.Button("Convert")

    output_audio = gr.Audio(
        source="upload", file="filepath", label="Synthesized Audio"
    )
    b2.click(
        ASRTTS_clean,
        inputs=[input_audio, speaker_option],
        outputs=output_audio,
    )

demo.launch(share=True)