"""Bedtime narration: Kokoro-82M (Apache 2.0) — an 82M-parameter TTS that fits the
hackathon's small-model spirit perfectly.

Voices: zh / en / fr supported; German has no Kokoro voice -> returns None and the
UI degrades gracefully to text-only for German stories.
"""

from __future__ import annotations

import os
from typing import Optional

import numpy as np

MOCK = os.environ.get("STORY_MOCK") == "1"

SAMPLE_RATE = 24_000

# language -> (kokoro lang_code, voice)
VOICES = {
    "zh": ("z", "zf_xiaobei"),
    "en": ("a", "af_heart"),
    "fr": ("f", "ff_siwis"),
}

_pipelines: dict[str, object] = {}


def _get_pipeline(lang_code: str):
    if lang_code not in _pipelines:
        from kokoro import KPipeline

        # device='cpu' is required on ZeroGPU: Kokoro's CUDA autodetect calls
        # torch._C._cuda_init outside a @spaces.GPU scope, which ZeroGPU forbids.
        # 82M params synthesize comfortably on CPU anyway.
        _pipelines[lang_code] = KPipeline(
            lang_code=lang_code, repo_id="hexgrad/Kokoro-82M", device="cpu"
        )
    return _pipelines[lang_code]


def synthesize(text: str, language: str) -> Optional[tuple[int, np.ndarray]]:
    """Returns (sample_rate, waveform) for gr.Audio, or None when unsupported (de)."""
    if language not in VOICES:
        return None
    if MOCK:
        return SAMPLE_RATE, np.zeros(SAMPLE_RATE // 2, dtype=np.float32)

    lang_code, voice = VOICES[language]
    pipeline = _get_pipeline(lang_code)
    chunks: list[np.ndarray] = []
    pause = np.zeros(int(SAMPLE_RATE * 0.45), dtype=np.float32)  # gentle bedtime pause
    for _, _, audio in pipeline(text, voice=voice):
        wav = audio.numpy() if hasattr(audio, "numpy") else np.asarray(audio)
        chunks.append(wav.astype(np.float32))
        chunks.append(pause)
    if not chunks:
        return None
    return SAMPLE_RATE, np.concatenate(chunks)