"""
ONNX-based TTS Gradio Application for Japanese
PyTorch-free implementation using ONNX Runtime
"""

import glob
import os
import tempfile
from time import perf_counter
from typing import Optional

import gradio as gr
import numpy as np
import onnxruntime as ort
import pyopenjtalk
import soundfile as sf

try:
    import spaces
except ImportError:
    class spaces:
        @staticmethod
        def GPU(func):
            return func


# ============================================================================
# Configuration
# ============================================================================

# Get script directory
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
MODELS_DIR = os.path.join(SCRIPT_DIR, "models")
DEFAULT_MODEL = "g003_ep5709.onnx"
MODEL_PATH = os.getenv("MODEL_PATH", os.path.join(MODELS_DIR, DEFAULT_MODEL))
VOCODER_PATH = os.getenv("VOCODER_PATH", None)
USE_GPU = os.getenv("USE_GPU", "false").lower() == "true"
SAMPLE_RATE = 22050
DEBUG = os.getenv("DEBUG", "false").lower() == "true"


def get_available_models():
    """Get list of available ONNX models from models directory"""
    if not os.path.exists(MODELS_DIR):
        return [DEFAULT_MODEL]

    models = glob.glob(os.path.join(MODELS_DIR, "*.onnx"))
    model_names = [os.path.basename(m) for m in models]

    if not model_names:
        return [DEFAULT_MODEL]

    return sorted(model_names)

# ============================================================================
# Text Processing (PyTorch-free)
# ============================================================================

# Load symbols from matcha
_pad = "_"
_punctuation = ';:,.!?¡¿—…"«»"" '
_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"

symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
_symbol_to_id = {s: i for i, s in enumerate(symbols)}


def text_to_sequence(text):
    """Convert text to sequence of IDs"""
    sequence = []
    for symbol in text:
        if symbol in _symbol_to_id:
            sequence.append(_symbol_to_id[symbol])
        else:
            sequence.append(0)  # Unknown symbol
    return sequence


def intersperse(sequence, token):
    """Intersperse token between elements of sequence"""
    result = [token] * (len(sequence) * 2 + 1)
    result[1::2] = sequence
    return result


def process_japanese_text(text: str):
    """Process Japanese text to phoneme sequence"""
    if not text.strip():
        raise ValueError("Text cannot be empty")

    # Phonemize using pyopenjtalk
    phonemes = pyopenjtalk.g2p(text, kana=False)
    phonemes = phonemes.replace(" ", "")
    phonemes = phonemes.replace("pau", " ")

    if DEBUG:
        print(f"Input: {text}")
        print(f"Phonemes: {phonemes}")

    # Text to sequence
    sequence = text_to_sequence(phonemes)

    # Intersperse with padding
    sequence = intersperse(sequence, 0)

    # Convert to numpy
    x = np.array(sequence, dtype=np.int64)[np.newaxis, :]
    x_lengths = np.array([x.shape[-1]], dtype=np.int64)

    return x, x_lengths


# ============================================================================
# ONNX Model Manager
# ============================================================================

class ONNXModelManager:
    """Manages ONNX model loading and inference"""

    def __init__(self, model_path: str, vocoder_path: Optional[str] = None, use_gpu: bool = False):
        self.model_path = model_path
        self.vocoder_path = vocoder_path
        self.use_gpu = use_gpu

        # Select execution providers
        if use_gpu:
            self.providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
        else:
            self.providers = ["CPUExecutionProvider"]

        self.model = None
        self.vocoder = None
        self.is_multi_speaker = False
        self.has_vocoder_embedded = False

        self._load_model()

    def _load_model(self):
        """Load ONNX model(s)"""
        if DEBUG:
            print(f"Loading model from {self.model_path} with providers {self.providers}")
        self.model = ort.InferenceSession(self.model_path, providers=self.providers)

        model_inputs = self.model.get_inputs()
        model_outputs = list(self.model.get_outputs())

        self.is_multi_speaker = len(model_inputs) == 4
        self.has_vocoder_embedded = model_outputs[0].name == "wav"

        if DEBUG:
            print(f"Model loaded: multi_speaker={self.is_multi_speaker}, "
                  f"vocoder_embedded={self.has_vocoder_embedded}")

        # Load external vocoder if needed
        if not self.has_vocoder_embedded and self.vocoder_path:
            if DEBUG:
                print(f"Loading external vocoder from {self.vocoder_path}")
            self.vocoder = ort.InferenceSession(self.vocoder_path, providers=self.providers)

    def synthesize(
        self,
        x: np.ndarray,
        x_lengths: np.ndarray,
        scales: np.ndarray,
        spks: Optional[np.ndarray] = None
    ):
        """Run ONNX inference"""
        inputs = {
            "x": x,
            "x_lengths": x_lengths,
            "scales": scales,
        }

        if self.is_multi_speaker and spks is not None:
            inputs["spks"] = spks

        # Run Matcha inference
        outputs = self.model.run(None, inputs)

        if self.has_vocoder_embedded:
            # End-to-end: model outputs waveform directly
            return outputs[0], outputs[1]  # wav, wav_lengths
        else:
            # Model outputs mel spectrogram
            mels, mel_lengths = outputs[0], outputs[1]

            if self.vocoder is not None:
                # Run external vocoder
                vocoder_inputs = {self.vocoder.get_inputs()[0].name: mels}
                wavs = self.vocoder.run(None, vocoder_inputs)[0]
                wavs = wavs.squeeze(1)
                wav_lengths = mel_lengths * 256
                return wavs, wav_lengths
            else:
                # No vocoder available, return mel
                return mels, mel_lengths


# Initialize model managers (one per model)
model_managers = {}
current_model = None


def get_model_manager(model_name: str) -> ONNXModelManager:
    """Get or create model manager for specified model"""
    global model_managers, current_model

    model_path = os.path.join(MODELS_DIR, model_name)

    if model_name not in model_managers:
        if DEBUG:
            print(f"Loading new model: {model_name}")
        model_managers[model_name] = ONNXModelManager(
            model_path=model_path,
            vocoder_path=VOCODER_PATH,
            use_gpu=USE_GPU
        )

    current_model = model_name
    return model_managers[model_name]


# Pre-load all available models
if DEBUG:
    print("Pre-loading all models for ZeroGPU...")
for model_name in get_available_models():
    get_model_manager(model_name)
if DEBUG:
    print("All models loaded.")

# ============================================================================
# Gradio Interface Functions
# ============================================================================

@spaces.GPU
def synthesise(
    text: str,
    model_name: str,
    speaker_id: int,
    temperature: float,
    speaking_rate: float,
):
    """
    Synthesize speech from Japanese text

    Args:
        text: Japanese text input
        model_name: Model filename
        speaker_id: Speaker ID (for multi-speaker models)
        temperature: Sampling temperature
        speaking_rate: Speaking rate multiplier

    Returns:
        Tuple of (audio_path, phonemes_text)
    """
    t0 = perf_counter()

    try:
        # Get model manager
        manager = get_model_manager(model_name)

        # Process text
        x, x_lengths = process_japanese_text(text)

        # Prepare scales
        scales = np.array([temperature, speaking_rate], dtype=np.float32)

        # Prepare speaker ID
        spks = None
        if manager.is_multi_speaker and speaker_id >= 0:
            spks = np.array([speaker_id], dtype=np.int64)

        # Run inference
        outputs, output_lengths = manager.synthesize(x, x_lengths, scales, spks)

        # Extract single result
        audio = outputs[0][:output_lengths[0]]
        inference_time = perf_counter() - t0

        # Calculate RTF
        audio_duration_sec = len(audio) / SAMPLE_RATE
        rtf = inference_time / audio_duration_sec

        if DEBUG:
            print(f"Inference time: {inference_time:.3f}s, "
                  f"Audio duration: {audio_duration_sec:.3f}s, "
                  f"RTF: {rtf:.3f}")

        # Save to temporary file
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
            sf.write(fp.name, audio, SAMPLE_RATE, "PCM_24")
            audio_path = fp.name

        # Get phonemes for display
        phonemes = pyopenjtalk.g2p(text, kana=False)
        phonemes = phonemes.replace(" ", "")
        phonemes = phonemes.replace("pau", " ")

        info = f"Model: {model_name}\n"
        info += f"Speaker ID: {speaker_id if manager.is_multi_speaker else 'N/A (Single speaker)'}\n"
        info += f"Phonemes: {phonemes}\n"
        info += f"RTF: {rtf:.3f}"

        return audio_path, info

    except Exception as e:
        print(f"Error: {e}")
        raise


# ============================================================================
# Gradio Application
# ============================================================================

def create_gradio_interface():
    """Create Gradio interface"""

    # Get available models
    available_models = get_available_models()

    # Load speaker images
    imgs_dir = os.path.join(SCRIPT_DIR, "imgs")
    speaker_images = []
    if os.path.exists(imgs_dir):
        # Sort by numerical filename (0.webp, 1.webp, ...)
        image_files = sorted(glob.glob(os.path.join(imgs_dir, "*.webp")),
                           key=lambda x: int(os.path.splitext(os.path.basename(x))[0]))
        speaker_images = [(img, f"Speaker {os.path.splitext(os.path.basename(img))[0]}") for img in image_files]

    with gr.Blocks(
        title="AI Gaming Voice",
    ) as demo:
        gr.Markdown(
            """
            # AI Gaming Voice - 🍵 Matcha-TTS ONNX (Japanese) / 日本語

            ### 6 Voices - 140MB or 42MB(Qint8 but slow)
            Japanese Text-to-Speech.(Half-width alphanumeric characters are not supported. Please correct/fix it.)
            日本語音声合成です。(半角・英数字は未対応・直してください。)
            """
        )

        with gr.Row():
            with gr.Column():
                # Model Selection
                model_dropdown = gr.Dropdown(
                    label="モデル / Model",
                    choices=available_models,
                    value=DEFAULT_MODEL if DEFAULT_MODEL in available_models else available_models[0],
                    interactive=True
                )

                text_input = gr.Textbox(
                    label="日本語テキスト / Japanese Text",
                    value="こんにちは、世界！",
                    lines=3,
                    placeholder="日本語のテキストを入力してください..."
                )

                # Speaker Selection Gallery
                if speaker_images:
                    gr.Markdown("### 話者選択 / Select Speaker")
                    speaker_gallery = gr.Gallery(
                        value=speaker_images,
                        label="話者 / Speakers",
                        show_label=False,
                        columns=6,
                        rows=1,
                        height=160,
                        allow_preview=False,
                        interactive=False,
                        object_fit="cover",
                        elem_id="speaker_gallery"
                    )

                # Speaker ID
                speaker_id = gr.Number(
                    label="Speaker ID (スピーカーID)",
                    value=0,
                    minimum=0,
                    maximum=99,
                    precision=0,
                    info="上の画像をタップするか、数値を入力してください"
                )

                with gr.Row():
                    temperature = gr.Slider(
                        label="Temperature (温度)",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.01,
                        value=0.667,
                        info="サンプリングのランダム性"
                    )

                    speaking_rate = gr.Slider(
                        label="Speaking Rate (話速)",
                        minimum=0.1,
                        maximum=5.0,
                        step=0.1,
                        value=1.0,
                        info="1.0 = 標準速度"
                    )

                with gr.Row():
                    synthesise_btn = gr.Button(
                        "🎵 音声生成 / Synthesize",
                        variant="primary",
                        size="lg"
                    )
                    clear_btn = gr.Button(
                        "クリア / Clear",
                        variant="secondary"
                    )

            with gr.Column():
                audio_output = gr.Audio(
                    label="生成音声 / Generated Audio",
                    type="filepath"
                )

                info_output = gr.Textbox(
                    label="情報 / Information",
                    lines=5,
                    interactive=False
                )

        # Examples
        gr.Examples(
            examples=[
                ["こんにちは、世界！", "g003_ep5709.onnx", 0, 0.667, 1.0],
                ["エイアイゲーミングボイス", "g003_ep5709.onnx", 0, 0.667, 0.8],
                ["わたくしの名前はストラよ", "g003_ep5709.onnx", 0, 0.667, 1.0],
                ["わたしの名前はシムですよ", "g003_ep5709.onnx", 1, 0.667, 1.0],
                ["わたしはナラともうします", "g003_ep5709.onnx", 2, 0.667, 1.0],
                ["わたし、ロールプリンよ!", "g003_ep5709.onnx", 3, 0.667, 1.0],
                ["僕の名前はショーンだよ", "g003_ep5709.onnx", 4, 0.667, 1.0],
                ["私の名前はありません", "g003_ep5709.onnx", 5, 0.667, 1.0],
            ],
            inputs=[text_input, model_dropdown, speaker_id, temperature, speaking_rate],
            label="例文 / Examples"
        )

        # Event handlers

        # Gallery click handler
        if speaker_images:
            def on_gallery_select(evt: gr.SelectData):
                return evt.index

            speaker_gallery.select(
                fn=on_gallery_select,
                inputs=None,
                outputs=speaker_id
            ).then(
                fn=synthesise,
                inputs=[text_input, model_dropdown, speaker_id, temperature, speaking_rate],
                outputs=[audio_output, info_output]
            )

        synthesise_btn.click(
            fn=synthesise,
            inputs=[text_input, model_dropdown, speaker_id, temperature, speaking_rate],
            outputs=[audio_output, info_output]
        )

        clear_btn.click(
            fn=lambda: (None, None, ""),
            outputs=[audio_output, info_output]
        )

        gr.Markdown(
            """
            ---
            ### ℹ️ Information / 情報

            - **Model / モデル**: Matcha-TTS (ONNX)
            - **Inference / 推論**: ONNX Runtime
            - **Phonemizer / 音素化**: `pyopenjtalk`
            - **ZeroGPU**: Optimized for fast startup & inference / 高速起動・推論に最適化

            ### 🗣️ Speaker Selection / 話者選択
            - **Click Image / 画像クリック**: Selects speaker & generates audio / 話者を選択して音声を生成
            - **Speaker ID**: Manual input also supported / 手動入力も可能

            ### FAQ
            **Why AI Gaming Voice?**
            - I have a plan to support another ONNX models.

            **Model Difference**
            - **qint8**: 1/3 size but slow.

            **How to create my voice**
            - [Github](https://github.com/akjava/Matcha-TTS-Japanese) - I'll update here.

            **Model**
            - [Huggingface:matcha-tts_ja_100speakers_group003f-CL-V2](https://huggingface.co/Akjava/matcha-tts_ja_100speakers_group003f-CL-V2)


             **Who are they?**
             - [Youtube:4 of them are member of AI Gaming Circle](https://www.youtube.com/@ai-gaming-circle)
             """

        )

    return demo


# ============================================================================
# Main
# ============================================================================

if __name__ == "__main__":
    demo = create_gradio_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True
    )