import asyncio
import time
import logging
from typing import Optional
import gc
import os

_proc = None
try:
    import psutil  # type: ignore
    _proc = psutil.Process(os.getpid())
except Exception:
    psutil = None  # type: ignore

def _rss_mb() -> float:
    """Return current process RSS in MB, or -1.0 if unavailable."""
    global _proc
    try:
        if _proc is None and psutil is not None:
            _proc = psutil.Process(os.getpid())
        if _proc is not None:
            return _proc.memory_info().rss / (1024 * 1024)
    except Exception:
        return -1.0
    return -1.0

try:
    import torch  # Optional; used for cache cleanup metrics
except Exception:  # pragma: no cover - torch may not be present in some envs
    torch = None  # type: ignore

from app import config
from app.services.tts_service import TTSService

logger = logging.getLogger(__name__)


class ModelManager:
    _instance: Optional["ModelManager"] = None

    def __init__(self):
        self._service: Optional[TTSService] = None
        self._last_used: float = time.time()
        self._active: int = 0
        self._lock = asyncio.Lock()
        self._counter_lock = asyncio.Lock()

    @classmethod
    def instance(cls) -> "ModelManager":
        if not cls._instance:
            cls._instance = cls()
        return cls._instance

    async def _ensure_service(self) -> None:
        if self._service is None:
            # Use configured device, default is handled by TTSService itself
            device = getattr(config, "DEVICE", "auto")
            # TTSService presently expects explicit device like "mps"/"cpu"/"cuda"; map "auto" to "mps" on Mac otherwise cpu
            if device == "auto":
                try:
                    import torch
                    if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
                        device = "mps"
                    elif torch.cuda.is_available():
                        device = "cuda"
                    else:
                        device = "cpu"
                except Exception:
                    device = "cpu"
            self._service = TTSService(device=device)

    async def load(self) -> None:
        async with self._lock:
            await self._ensure_service()
            if self._service and self._service.model is None:
                before_mb = _rss_mb()
                logger.info(
                    "Loading TTS model (device=%s)... (rss_before=%.1f MB)",
                    self._service.device,
                    before_mb,
                )
                self._service.load_model()
                after_mb = _rss_mb()
                if after_mb >= 0 and before_mb >= 0:
                    logger.info(
                        "TTS model loaded (rss_after=%.1f MB, delta=%.1f MB)",
                        after_mb,
                        after_mb - before_mb,
                    )
                self._last_used = time.time()

    async def unload(self) -> None:
        async with self._lock:
            if not self._service:
                return
            if self._active > 0:
                logger.debug("Skip unload: %d active operations", self._active)
                return
            if self._service.model is not None:
                before_mb = _rss_mb()
                logger.info(
                    "Unloading idle TTS model... (rss_before=%.1f MB, active=%d)",
                    before_mb,
                    self._active,
                )
                self._service.unload_model()
                # Drop the service instance as well to release any lingering refs
                self._service = None
                # Force GC and attempt allocator cache cleanup
                try:
                    gc.collect()
                finally:
                    if torch is not None:
                        try:
                            if hasattr(torch, "cuda") and torch.cuda.is_available():
                                torch.cuda.empty_cache()
                        except Exception:
                            logger.debug("cuda.empty_cache() failed", exc_info=True)
                        try:
                            # MPS empty_cache may exist depending on torch version
                            mps = getattr(torch, "mps", None)
                            if mps is not None and hasattr(mps, "empty_cache"):
                                mps.empty_cache()
                        except Exception:
                            logger.debug("mps.empty_cache() failed", exc_info=True)
                after_mb = _rss_mb()
                if after_mb >= 0 and before_mb >= 0:
                    logger.info(
                        "Idle unload complete (rss_after=%.1f MB, delta=%.1f MB)",
                        after_mb,
                        after_mb - before_mb,
                    )
                self._last_used = time.time()

    async def get_service(self) -> TTSService:
        if not self._service or self._service.model is None:
            await self.load()
        self._last_used = time.time()
        return self._service  # type: ignore[return-value]

    async def _inc(self) -> None:
        async with self._counter_lock:
            self._active += 1

    async def _dec(self) -> None:
        async with self._counter_lock:
            self._active = max(0, self._active - 1)
            self._last_used = time.time()

    def last_used(self) -> float:
        return self._last_used

    def is_loaded(self) -> bool:
        return bool(self._service and self._service.model is not None)

    def active(self) -> int:
        return self._active

    def using(self):
        manager = self

        class _Ctx:
            async def __aenter__(self):
                await manager._inc()
                return manager

            async def __aexit__(self, exc_type, exc, tb):
                await manager._dec()

        return _Ctx()