from __future__ import annotations

import threading
from functools import cached_property
from pathlib import Path
from types import SimpleNamespace
from typing import Any

import numpy as np


class Miner:

    REPO_SENTINEL = "config.json"
    SETTINGS_FILE = "vocence_config.yaml"
    WARMUP_TIMEOUT = 180.0

    def __init__(self, path_hf_repo: Path) -> None:
        self.root = Path(path_hf_repo).resolve()
        if not (self.root / self.REPO_SENTINEL).is_file():
            raise FileNotFoundError(f"{self.REPO_SENTINEL} not present in {self.root}")
        _ = self.settings
        _ = self.model

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

    @cached_property
    def settings(self) -> SimpleNamespace:
        raw = self._load_yaml(self.root / self.SETTINGS_FILE)
        rt = raw.get("runtime") or {}
        gen = raw.get("generation") or {}
        lim = raw.get("limits") or {}
        return SimpleNamespace(
            language=str(lim.get("default_language") or rt.get("default_language") or "English"),
            sample_rate=int(gen.get("sample_rate", 24000)),
            max_instruction_chars=int(lim.get("max_instruction_chars", 600)),
            max_text_chars=int(lim.get("max_text_chars", 2000)),
            prefer_cuda=str(rt.get("device_preference", "cuda")).lower() == "cuda",
            prefer_bf16=str(rt.get("dtype", "bfloat16")).lower() == "bfloat16",
            prefer_flash=bool(rt.get("use_flash_attention_2", False)),
        )

    @cached_property
    def model(self) -> Any:
        return self._instantiate_engine()

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

        def _trial() -> None:
            try:
                self.generate_wav(instruction="Neutral voice.", text="Warming up.")
                outcome["done"] = True
            except Exception as exc:
                outcome["err"] = repr(exc)

        worker = threading.Thread(target=_trial, daemon=True)
        worker.start()
        worker.join(timeout=self.WARMUP_TIMEOUT)
        if not outcome["done"]:
            raise RuntimeError(
                f"warmup did not complete within {self.WARMUP_TIMEOUT}s: {outcome['err'] or 'no completion signal'}"
            )

    def generate_wav(self, instruction: str, text: str) -> tuple[np.ndarray, int]:
        s = self.settings
        prompt = instruction[: s.max_instruction_chars] if s.max_instruction_chars > 0 else instruction
        body = text[: s.max_text_chars] if s.max_text_chars > 0 else text
        wavs, sample_rate = self.model.generate_voice_design(
            text=body,
            instruct=prompt,
            language=s.language,
        )
        if not wavs or wavs[0] is None:
            raise ValueError("qwen3-tts produced no audio")
        wave = np.asarray(wavs[0], dtype=np.float32)
        if wave.ndim > 1:
            wave = wave.mean(axis=1)
        return wave, int(sample_rate)

    def _instantiate_engine(self) -> Any:
        import torch
        from qwen_tts import Qwen3TTSModel

        s = self.settings
        cuda_ready = bool(torch.cuda.is_available())
        device_map = "cuda:0" if (s.prefer_cuda and cuda_ready) else "cpu"
        torch_dtype = torch.bfloat16 if (s.prefer_bf16 and cuda_ready) else torch.float32
        attempts = ("flash_attention_2", "sdpa") if s.prefer_flash else ("sdpa",)

        last_failure: BaseException | None = None
        for attn in attempts:
            try:
                engine = Qwen3TTSModel.from_pretrained(
                    pretrained_model_name_or_path=str(self.root),
                    device_map=device_map,
                    dtype=torch_dtype,
                    attn_implementation=attn,
                )
                dtype_tag = "bf16" if torch_dtype is torch.bfloat16 else "fp32"
                print(f"[Miner] qwen3-tts ready :: device={device_map} dtype={dtype_tag} attn={attn}")
                return engine
            except Exception as exc:
                last_failure = exc
        raise RuntimeError(f"qwen3-tts failed to load :: {last_failure!r}")

    @staticmethod
    def _load_yaml(path: Path) -> dict[str, Any]:
        if not path.is_file():
            return {}
        from yaml import safe_load
        with path.open("r", encoding="utf-8") as fh:
            return safe_load(fh) or {}