"""Провайдер TTS на Piper — полностью офлайн-фолбэк.

Используется, если edge-tts недоступен (сервис Microsoft ломался в прошлом,
а интернет у отца может пропасть). Piper работает локально на CPU.

Установка и голос (однократно):
    pip install piper-tts
    python -m piper.download_voices ru_RU-dmitri-medium --download-dir models/piper

Пайпер медленнее edge-tts и голоса проще, но работает без сети.
API: PiperVoice.load(path); voice.synthesize(text) -> чанки AudioChunk
(audio_int16_bytes, sample_rate, sample_width, sample_channels).
"""
from __future__ import annotations

import time
from pathlib import Path
from typing import List, Optional

import numpy as np

from .base import SynthResult, TtsProvider
from .text_split import split_sentences


class PiperTtsProvider(TtsProvider):
    name = "piper"

    def __init__(
        self,
        model_path: Union[str, Path],
        length_scale: float = 1.15,          # 1.0 = обычная скорость; 1.15 ≈ −15%
        noise_scale: float = 0.667,
        noise_w_scale: float = 0.8,
        sentence_pause_sec: float = 0.35,
    ) -> None:
        from piper import PiperVoice, SynthesisConfig  # ленивый импорт

        path = Path(model_path)
        if not path.is_file():
            raise FileNotFoundError(
                f"Модель Piper не найдена: {path}\n"
                f"Скачай голос: python -m piper.download_voices ru_RU-dmitri-medium "
                f"--download-dir {path.parent}"
            )
        self._voice = PiperVoice.load(str(path))
        self._config = SynthesisConfig(
            length_scale=length_scale,
            noise_scale=noise_scale,
            noise_w_scale=noise_w_scale,
        )
        self._pause = float(sentence_pause_sec)

    def synthesize(self, text: str) -> SynthResult:
        t0 = time.perf_counter()
        sentences = split_sentences(text) or [text]

        pieces: List[np.ndarray] = []
        samplerate: Optional[int] = None

        for i, sentence in enumerate(sentences):
            chunks = list(self._voice.synthesize(sentence, syn_config=self._config))
            for ch in chunks:
                if samplerate is None:
                    samplerate = ch.sample_rate
                pcm = np.frombuffer(ch.audio_int16_bytes, dtype=np.int16)
                pieces.append(pcm.astype(np.float32) / 32768.0)
            if i < len(sentences) - 1 and self._pause > 0 and samplerate:
                pieces.append(np.zeros(int(self._pause * samplerate), dtype=np.float32))

        audio = np.concatenate(pieces) if pieces else np.zeros(0, dtype=np.float32)
        return SynthResult(
            text=text,
            audio=audio,
            samplerate=samplerate or 22050,
            generation_sec=time.perf_counter() - t0,
            provider=self.name,
            sentence_count=len(sentences),
        )