import sys
import io, os, stat
import subprocess
import random
from zipfile import ZipFile
import uuid
import time
import torch
import torchaudio

#download for mecab
os.system('python -m unidic download')

# By using XTTS you agree to CPML license https://coqui.ai/cpml
os.environ["COQUI_TOS_AGREED"] = "1"

# langid is used to detect language for longer text
# Most users expect text to be their own language, there is checkbox to disable it
import langid
import base64
import csv
from io import StringIO
import datetime
import re

import gradio as gr
from scipy.io.wavfile import write
from pydub import AudioSegment

from TTS.api import TTS
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts

HF_TOKEN = os.environ.get("HF_TOKEN")

from huggingface_hub import HfApi, hf_hub_download

# will use api to restart space on a unrecoverable error
api = HfApi(token=HF_TOKEN)
repo_id = "tartuNLP/XTTS-v2-multi"

# Use never ffmpeg binary for Ubuntu20 to use denoising for microphone input
print("Export newer ffmpeg binary for denoise filter")

ZipFile("ffmpeg.zip").extractall()
print("Make ffmpeg binary executable")
st = os.stat("ffmpeg")
os.chmod("ffmpeg", st.st_mode | stat.S_IEXEC)

# This will trigger downloading model
print("Downloading Coqui XTTS V2")

model_folder = "model/"
config_file = "config.json"
if not os.path.exists(model_folder + config_file):
    hf_hub_download(
        repo_id=repo_id,
        filename=config_file,
        local_dir=model_folder,
    )
model_path = "model.pth"
if not os.path.exists(model_folder + model_path):
    hf_hub_download(
        repo_id=repo_id,
        filename=model_path,
        local_dir=model_folder,
    )
vocab_path = "vocab.json"
if not os.path.exists(model_folder + vocab_path):
    hf_hub_download(
        repo_id=repo_id,
        filename=vocab_path,
        local_dir=model_folder,
    )
print("XTTS downloaded")

config = XttsConfig()
config.load_json(os.path.join(model_folder, config_file))

model = Xtts.init_from_config(config)
model.load_checkpoint(
    config,
    checkpoint_path=os.path.join(model_folder, model_path),
    vocab_path=os.path.join(model_folder, vocab_path),
    eval=True,
    use_deepspeed=False,
)

# This is for debugging purposes only
DEVICE_ASSERT_DETECTED = 0
DEVICE_ASSERT_PROMPT = None
DEVICE_ASSERT_LANG = None

supported_languages = config.languages

def predict(
    prompt,
    language,
    speaker_wav,
    voice_cleanup,
    agree,
):
    if agree == True:
        if language not in supported_languages:
            gr.Warning(
                f"Language you put {language} in is not in is not in our Supported Languages, please choose from dropdown"
            )

            return (
                None,
                None,
                None,
                None,
            )

        language_predicted = langid.classify(prompt)[
            0
        ].strip()  # strip need as there is space at end!

        # tts expects chinese as zh-cn
        if language_predicted == "zh":
            # we use zh-cn
            language_predicted = "zh-cn"

        print(f"Detected language:{language_predicted}, Chosen language:{language}")

        # Filtering for microphone input, as it has BG noise, maybe silence in beginning and end
        # This is fast filtering not perfect

        # Apply all on demand
        lowpassfilter = denoise = trim = loudness = True

        if lowpassfilter:
            lowpass_highpass = "lowpass=8000,highpass=75,"
        else:
            lowpass_highpass = ""

        if trim:
            # better to remove silence in beginning and end for microphone
            trim_silence = "areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,"
        else:
            trim_silence = ""

        if voice_cleanup:
            try:
                out_filename = (
                    speaker_wav + str(uuid.uuid4()) + ".wav"
                )  # ffmpeg to know output format

                # we will use newer ffmpeg as that has afftn denoise filter
                shell_command = f"ffmpeg -y -i {speaker_wav} -af {lowpass_highpass}{trim_silence} {out_filename}".split(
                    " "
                )

                command_result = subprocess.run(
                    [item for item in shell_command],
                    capture_output=False,
                    text=True,
                    check=True,
                )
                speaker_wav = out_filename
                print("Filtered microphone input")
            except subprocess.CalledProcessError:
                # There was an error - command exited with non-zero code
                print("Error: failed filtering, use original microphone input")
        else:
            speaker_wav = speaker_wav

        if len(prompt) < 2:
            gr.Warning("Please give a longer prompt text")
            return (
                None,
                None,
                None,
                None,
            )
        if len(prompt) > 200:
            gr.Warning(
                "Text length limited to 200 characters for this demo, please try shorter text. You can clone this space and edit code for your own usage"
            )
            return (
                None,
                None,
                None,
                None,
            )
        global DEVICE_ASSERT_DETECTED
        if DEVICE_ASSERT_DETECTED:
            global DEVICE_ASSERT_PROMPT
            global DEVICE_ASSERT_LANG
            # It will likely never come here as we restart space on first unrecoverable error now
            print(
                f"Unrecoverable exception caused by language:{DEVICE_ASSERT_LANG} prompt:{DEVICE_ASSERT_PROMPT}"
            )

            # HF Space specific.. This error is unrecoverable need to restart space
            space = api.get_space_runtime(repo_id=repo_id)
            if space.stage!="BUILDING":
                api.restart_space(repo_id=repo_id)
            else:
                print("TRIED TO RESTART but space is building")

        try:
            metrics_text = ""
            t_latent = time.time()

            # note diffusion_conditioning not used on hifigan (default mode), it will be empty but need to pass it to model.inference
            try:
                (gpt_cond_latent, speaker_embedding) = model.get_conditioning_latents(
                    audio_path=speaker_wav,
                    gpt_cond_len=model.config.gpt_cond_len,
                    gpt_cond_chunk_len=model.config.gpt_cond_chunk_len,
                    max_ref_length=model.config.max_ref_len,
                    sound_norm_refs=model.config.sound_norm_refs,
                )
            except Exception as e:
                print("Speaker encoding error", str(e))
                gr.Warning(
                    "It appears something wrong with reference, did you unmute your microphone?"
                )
                return (
                    None,
                    None,
                    None,
                    None,
                )

            latent_calculation_time = time.time() - t_latent
            # metrics_text=f"Embedding calculation time: {latent_calculation_time:.2f} seconds\n"

            # temporary comma fix
            prompt= re.sub("([^\x00-\x7F]|\w)(\.|\。|\?)",r"\1 \2\2",prompt)

            wav_chunks = []
            ## Direct mode
            
            '''
            print("I: Generating new audio...")
            t0 = time.time()
            out = model.inference(
                prompt,
                language,
                gpt_cond_latent,
                speaker_embedding,
                temperature=model.config.temperature,
                length_penalty=model.config.length_penalty,
                repetition_penalty=model.config.repetition_penalty,
                top_k=model.config.top_k,
                top_p=model.config.top_p,
            )
            inference_time = time.time() - t0
            print(f"I: Time to generate audio: {round(inference_time*1000)} milliseconds")
            metrics_text+=f"Time to generate audio: {round(inference_time*1000)} milliseconds\n"
            real_time_factor= (time.time() - t0) / out['wav'].shape[-1] * 24000
            print(f"Real-time factor (RTF): {real_time_factor}")
            metrics_text+=f"Real-time factor (RTF): {real_time_factor:.2f}\n"
            torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
            '''

            
            print("I: Generating new audio in streaming mode...")
            t0 = time.time()
            chunks = model.inference_stream(
                prompt,
                language,
                gpt_cond_latent,
                speaker_embedding,
                repetition_penalty=7.0,
                temperature=0.85,
            )

            first_chunk = True
            for i, chunk in enumerate(chunks):
                if first_chunk:
                    first_chunk_time = time.time() - t0
                    metrics_text += f"Latency to first audio chunk: {round(first_chunk_time*1000)} milliseconds\n"
                    first_chunk = False
                wav_chunks.append(chunk)
                print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
            inference_time = time.time() - t0
            print(
                f"I: Time to generate audio: {round(inference_time*1000)} milliseconds"
            )
            #metrics_text += (
            #    f"Time to generate audio: {round(inference_time*1000)} milliseconds\n"
            #)

            wav = torch.cat(wav_chunks, dim=0)
            print(wav.shape)
            real_time_factor = (time.time() - t0) / wav.shape[0] * 24000
            print(f"Real-time factor (RTF): {real_time_factor}")
            metrics_text += f"Real-time factor (RTF): {real_time_factor:.2f}\n"

            torchaudio.save("output.wav", wav.squeeze().unsqueeze(0).cpu(), 24000)
            

        except RuntimeError as e:
            if "device-side assert" in str(e):
                # cannot do anything on cuda device side error, need tor estart
                print(
                    f"Exit due to: Unrecoverable exception caused by language:{language} prompt:{prompt}",
                    flush=True,
                )
                gr.Warning("Unhandled Exception encounter, please retry in a minute")
                print("Cuda device-assert Runtime encountered need restart")
                if not DEVICE_ASSERT_DETECTED:
                    DEVICE_ASSERT_DETECTED = 1
                    DEVICE_ASSERT_PROMPT = prompt
                    DEVICE_ASSERT_LANG = language

                # just before restarting save what caused the issue so we can handle it in future
                # Uploading Error data only happens for unrecovarable error
                error_time = datetime.datetime.now().strftime("%d-%m-%Y-%H:%M:%S")
                error_data = [
                    error_time,
                    prompt,
                    language,
                    speaker_wav,
                    voice_cleanup,
                    agree,
                ]
                error_data = [str(e) if type(e) != str else e for e in error_data]
                print(error_data)
                print(speaker_wav)

                '''
                write_io = StringIO()
                csv.writer(write_io).writerows([error_data])
                csv_upload = write_io.getvalue().encode()

                filename = error_time + "_" + str(uuid.uuid4()) + ".csv"
                print("Writing error csv")
                error_api = HfApi()
                error_api.upload_file(
                    path_or_fileobj=csv_upload,
                    path_in_repo=filename,
                    repo_id="coqui/xtts-flagged-dataset",
                    repo_type="dataset",
                )

                # speaker_wav
                print("Writing error reference audio")
                speaker_filename = (
                    error_time + "_reference_" + str(uuid.uuid4()) + ".wav"
                )
                error_api = HfApi()
                error_api.upload_file(
                    path_or_fileobj=speaker_wav,
                    path_in_repo=speaker_filename,
                    repo_id="coqui/xtts-flagged-dataset",
                    repo_type="dataset",
                )
                '''

                # HF Space specific.. This error is unrecoverable need to restart space
                space = api.get_space_runtime(repo_id=repo_id)
                if space.stage!="BUILDING":
                    api.restart_space(repo_id=repo_id)
                else:
                    print("TRIED TO RESTART but space is building")
                    
            else:
                if "Failed to decode" in str(e):
                    print("Speaker encoding error", str(e))
                    gr.Warning(
                        "It appears something wrong with reference, did you unmute your microphone?"
                    )
                else:
                    print("RuntimeError: non device-side assert error:", str(e))
                    gr.Warning("Something unexpected happened please retry again.")
                return (
                    None,
                    None,
                    None,
                    None,
                )
        return (
            "output.wav",
            metrics_text,
            speaker_wav,
        )
    else:
        gr.Warning("Please accept the Terms & Condition!")
        return (
            None,
            None,
            None,
            None,
        )


title = "Coqui🐸 XTTSv2-est"

description = """

<br/>

This demo is running a fine-tuned XTTS model. <a href="https://huggingface.co/coqui/XTTS-v2">XTTS</a> is a multilingual text-to-speech and voice-cloning model. This demo features zero-shot voice cloning.

Supported languages: Finnish: fi, English: en, Estonian: et, German: de, Russian: ru

<br/>
"""


article = """
<div style='margin:20px auto;'>
<p>By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml</p>
<p>We collect data only for error cases for improvement.</p>
</div>
"""
examples = [
    [
        "Once when I was six years old I saw a magnificent picture",
        "en",
        "examples/female.wav",
        False,
        False,
        True,
    ],
    [
        "Als ich sechs war, sah ich einmal ein wunderbares Bild",
        "de",
        "examples/female.wav",
        False,
        False,
        True,
    ],
    [
        "Kunagi, kui olin kuueaastane, nägin ma ühte imelist pilti",
        "et",
        "examples/female.wav",
        False,
        False,
        True,
    ],
    [
        "Когда мне было шесть лет, я увидел однажды удивительную картинку",
        "ru",
        "examples/female.wav",
        False,
        False,
        True,
    ],
    [
        "Kerran ollessani kuusivuotias näin mahtavan kuvan",
        "fi",
        "examples/female.wav",
        False,
        False,
        True,
    ],
]



with gr.Blocks(analytics_enabled=False) as demo:
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                """
                ## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
                """
            )
        with gr.Column():
            # placeholder to align the image
            pass

    with gr.Row():
        with gr.Column():
            gr.Markdown(description)

    with gr.Row():
        with gr.Column():
            input_text_gr = gr.Textbox(
                lines=2,
                label="Text Prompt",
                info="One or two sentences at a time is better. Up to 200 text characters.",
                value="Tere, olen sinu hääle kloon. Ürita mulle lindistada võimalikult hea kvaliteediga klipp, et oskaksin su kõnet paremini jäljendada.",
            )
            language_gr = gr.Dropdown(
                label="Language",
                info="Select an output language for the synthesised speech",
                choices=[
                    "de",
                    "fi",
                    "en",
                    "et",
                    "ru"
                ],
                multiselect=False,
                value="et",
                interactive=True,
            )
            ref_gr = gr.Audio(
                label="Reference Audio",
                sources=["microphone", "upload"],
                #info="Click on the ✎ button to upload your own target speaker audio",
                type="filepath",
                value="examples/female.wav",
            )
            clean_ref_gr = gr.Checkbox(
                label="Cleanup Reference Voice",
                value=False,
                info="This check can improve output if your microphone or reference voice is noisy",
            )
            tos_gr = gr.Checkbox(
                label="Agree",
                value=False,
                info="I agree to the terms of the CPML: https://coqui.ai/cpml",
            )

            tts_button = gr.Button("Send", elem_id="send-btn", visible=True)


        with gr.Column():
            audio_gr = gr.Audio(label="Synthesised Audio", autoplay=True)
            out_text_gr = gr.Text(label="Metrics")
            ref_audio_gr = gr.Audio(label="Reference Audio Used")

    with gr.Row():
        gr.Examples(examples,
                    label="Examples",
                    inputs=[input_text_gr, language_gr, ref_gr, clean_ref_gr, tos_gr],
                    outputs=[audio_gr, out_text_gr, ref_audio_gr],
                    fn=predict,
                    cache_examples=False,)

    tts_button.click(predict, [input_text_gr, language_gr, ref_gr, clean_ref_gr, tos_gr], outputs=[audio_gr, out_text_gr, ref_audio_gr])

if __name__ == "__main__":
    demo.queue()  
    demo.launch(debug=True, show_api=True)