"""Vocence engine for the merged Qwen3-TTS VoiceDesign checkpoint.

The Vocence Chutes wrapper instantiates ``Miner`` with the on-disk path of the HF
snapshot and then drives it through the contract:

    Miner(path_hf_repo: Path)
    warmup() -> None
    generate_wav(instruction: str, text: str) -> tuple[np.ndarray, int]

All weights, the audio codec, and the tokenizer ship together in the snapshot —
nothing is fetched at runtime.
"""
from __future__ import annotations

import dataclasses
import threading
from pathlib import Path
from typing import Any

import numpy as np


_REPO_REQUIRED_FILE = "config.json"
_RUNTIME_CONFIG_FILE = "vocence_config.yaml"


@dataclasses.dataclass
class _RuntimeOpts:
    """Subset of vocence_config.yaml that the engine actually consumes."""

    language: str = "English"
    sample_rate: int = 24000
    max_instruction_chars: int = 600
    max_text_chars: int = 2000
    device_pref: str = "cuda"
    dtype_pref: str = "bfloat16"
    flash_attention_2: bool = False

    @classmethod
    def from_repo(cls, repo: Path) -> "_RuntimeOpts":
        cfg_path = repo / _RUNTIME_CONFIG_FILE
        if not cfg_path.is_file():
            return cls()
        from yaml import safe_load

        with cfg_path.open("r", encoding="utf-8") as fh:
            data = safe_load(fh) or {}
        runtime = data.get("runtime") or {}
        generation = data.get("generation") or {}
        limits = data.get("limits") or {}
        return cls(
            language=str(limits.get("default_language") or runtime.get("default_language") or "English"),
            sample_rate=int(generation.get("sample_rate", 24000)),
            max_instruction_chars=int(limits.get("max_instruction_chars", 600)),
            max_text_chars=int(limits.get("max_text_chars", 2000)),
            device_pref=str(runtime.get("device_preference", "cuda")).lower(),
            dtype_pref=str(runtime.get("dtype", "bfloat16")).lower(),
            flash_attention_2=bool(runtime.get("use_flash_attention_2", False)),
        )


class Miner:
    """Loads merged Qwen3-TTS weights from the snapshot and serves the Vocence API."""

    WARMUP_BUDGET_S = 180.0

    def __init__(self, path_hf_repo: Path) -> None:
        self.repo = Path(path_hf_repo).resolve()
        if not (self.repo / _REPO_REQUIRED_FILE).is_file():
            raise FileNotFoundError(
                f"Snapshot incomplete: {self.repo / _REPO_REQUIRED_FILE} not found"
            )
        self.opts = _RuntimeOpts.from_repo(self.repo)
        self.model = self._build_model()

    def __repr__(self) -> str:
        return f"<Miner repo={self.repo.name} language={self.opts.language!r}>"

    # ------------------------------------------------------------------ #
    # Vocence contract                                                    #
    # ------------------------------------------------------------------ #

    def warmup(self) -> None:
        outcome: dict[str, Any] = {"ok": False, "err": None}

        def _heat() -> None:
            try:
                self.generate_wav(instruction="Calm neutral delivery.", text="Warmup.")
                outcome["ok"] = True
            except Exception as exc:  # noqa: BLE001 — surface to host
                outcome["err"] = repr(exc)

        worker = threading.Thread(target=_heat, daemon=True)
        worker.start()
        worker.join(timeout=self.WARMUP_BUDGET_S)
        if not outcome["ok"]:
            raise RuntimeError(f"Miner warmup did not complete: {outcome['err'] or 'timeout'}")

    def generate_wav(self, instruction: str, text: str) -> tuple[np.ndarray, int]:
        prompt = self._truncate(instruction, self.opts.max_instruction_chars)
        body = self._truncate(text, self.opts.max_text_chars)

        wavs, sample_rate = self.model.generate_voice_design(
            text=body,
            instruct=prompt,
            language=self.opts.language,
        )
        if not wavs or wavs[0] is None:
            raise ValueError("Qwen3-TTS returned no audio")

        wave = self._coerce_mono_float32(wavs[0])
        return wave, int(sample_rate)

    # ------------------------------------------------------------------ #
    # Internal                                                            #
    # ------------------------------------------------------------------ #

    @staticmethod
    def _truncate(value: str, limit: int) -> str:
        return value[:limit] if limit and limit > 0 else value

    @staticmethod
    def _coerce_mono_float32(arr: Any) -> np.ndarray:
        wave = np.asarray(arr, dtype=np.float32)
        if wave.ndim > 1:
            wave = wave.mean(axis=1)
        return wave

    def _build_model(self):
        import torch
        from qwen_tts import Qwen3TTSModel

        cuda_available = bool(torch.cuda.is_available())
        device_map = "cuda:0" if (self.opts.device_pref == "cuda" and cuda_available) else "cpu"
        torch_dtype = (
            torch.bfloat16
            if (self.opts.dtype_pref == "bfloat16" and cuda_available)
            else torch.float32
        )

        attempt_order = ("flash_attention_2", "sdpa") if self.opts.flash_attention_2 else ("sdpa",)
        last_error: BaseException | None = None
        for attn in attempt_order:
            try:
                model = Qwen3TTSModel.from_pretrained(
                    pretrained_model_name_or_path=str(self.repo),
                    device_map=device_map,
                    dtype=torch_dtype,
                    attn_implementation=attn,
                )
                print(
                    f"[Miner] Qwen3-TTS ready on {device_map} "
                    f"(dtype={self.opts.dtype_pref}, attn={attn})"
                )
                return model
            except Exception as exc:  # noqa: BLE001 — try next attn variant
                last_error = exc
        raise RuntimeError(f"Qwen3-TTS failed to load: {last_error!r}")