import logging
import torch
from typing import Optional, Tuple

logger = logging.getLogger(__name__)

chatterbox_model = None
_sample_rate = 24000
_is_turbo = False

# Cache: voice file path → prepared conditionals object.
# prepare_conditionals loads audio, runs s3tokenizer + voice encoder, and
# builds mel embeddings — expensive work that only depends on the reference
# audio, not the text. Cache it so multi-chunk requests pay the cost once.
_cond_cache: dict = {}


def _test_cuda() -> bool:
    try:
        if torch.cuda.is_available():
            torch.zeros(1).cuda()
            return True
    except Exception:
        pass
    return False


def detect_device() -> str:
    return "cuda" if _test_cuda() else "cpu"


def load_model() -> bool:
    global chatterbox_model, _sample_rate, _is_turbo

    from config import get_model_repo_id, get_device_override

    device = get_device_override() or detect_device()
    repo_id = get_model_repo_id()

    logger.info(f"Loading model '{repo_id}' on device '{device}'")

    try:
        if "turbo" in repo_id.lower():
            from chatterbox.tts_turbo import ChatterboxTurboTTS
            chatterbox_model = ChatterboxTurboTTS.from_pretrained(device)
            _is_turbo = True
        else:
            from chatterbox.tts import ChatterboxTTS
            chatterbox_model = ChatterboxTTS.from_pretrained(device)
            _is_turbo = False

        _sample_rate = 24000
        logger.info("Model loaded successfully")
        return True
    except Exception:
        logger.exception("Failed to load model")
        return False


def prepare_voice(audio_prompt_path: str) -> None:
    """
    Pre-compute and cache the voice conditionals for a reference audio file.
    Calling this once avoids repeating the s3tokenizer + voice encoder work
    on every synthesis chunk that uses the same voice.
    """
    if chatterbox_model is None:
        return
    if audio_prompt_path in _cond_cache:
        return
    if not _is_turbo:
        return  # only turbo exposes prepare_conditionals

    logger.info(f"Preparing voice conditionals for '{audio_prompt_path}'")
    with torch.inference_mode():
        chatterbox_model.prepare_conditionals(audio_prompt_path)
    _cond_cache[audio_prompt_path] = chatterbox_model.conds
    logger.info("Voice conditionals cached")


def get_sample_rate() -> int:
    return _sample_rate


def synthesize(
    text: str,
    audio_prompt_path: Optional[str] = None,
    exaggeration: float = 0.5,
    cfg_weight: float = 0.5,
    temperature: float = 0.8,
    seed: int = 0,
) -> Tuple[torch.Tensor, int]:
    if chatterbox_model is None:
        raise RuntimeError("Model not loaded. Call load_model() first.")

    if seed > 0:
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)

    # Restore cached conditionals so generate() skips prepare_conditionals.
    if audio_prompt_path and _is_turbo:
        if audio_prompt_path not in _cond_cache:
            prepare_voice(audio_prompt_path)
        chatterbox_model.conds = _cond_cache[audio_prompt_path]

    kwargs: dict = {}
    # Don't pass audio_prompt_path — conds are already set above.
    # For non-turbo models there's no cache, pass path as normal.
    if audio_prompt_path and not _is_turbo:
        kwargs["audio_prompt_path"] = audio_prompt_path

    if _is_turbo:
        kwargs["temperature"] = temperature
    else:
        kwargs["exaggeration"] = exaggeration
        kwargs["cfg_weight"] = cfg_weight

    with torch.inference_mode():
        with torch.amp.autocast(device_type="cuda", dtype=torch.float16):
            wav = chatterbox_model.generate(text=text, **kwargs)

    if torch.cuda.is_available():
        torch.cuda.synchronize()
        torch.cuda.empty_cache()

    return wav, _sample_rate