"""Process-wide thread-safe LRU cache for speaker extraction artifacts.

Keyed by ``(model_type, speaker_name, created_at)`` so each upload generation
has its own slot. Access via :func:`get_speaker_cache`.
"""

from __future__ import annotations

import json
import os
import threading
from collections import OrderedDict
from collections.abc import Callable
from pathlib import Path
from typing import Any

import torch
from vllm.logger import init_logger

logger = init_logger(__name__)

_MAX_BYTES = 512 * 1024**2  # 512 MiB
_CUSTOM_VOICE_MANIFEST = "custom_voice_manifest.json"
_CUSTOM_VOICE_SCHEMA_VERSION = 1

_SINGLETON: SpeakerEmbeddingCache | None = None
_SINGLETON_LOCK = threading.Lock()


def _estimate_tensor_bytes(obj: object) -> int:
    if isinstance(obj, torch.Tensor):
        return obj.numel() * obj.element_size()
    if isinstance(obj, dict):
        return sum(_estimate_tensor_bytes(v) for v in obj.values())
    if isinstance(obj, (list, tuple)):
        return sum(_estimate_tensor_bytes(item) for item in obj)
    return 0


def _as_voice_map(voices: object) -> dict[str, dict[str, Any]]:
    if not isinstance(voices, dict):
        return {}
    result: dict[str, dict[str, Any]] = {}
    for name, info in voices.items():
        entry = dict(info) if isinstance(info, dict) else {}
        entry.setdefault("name", str(name))
        result[str(name)] = entry
    return result


def _load_custom_voice_manifest(custom_voice_dir: str | os.PathLike[str] | None) -> dict[str, Any] | None:
    if not custom_voice_dir:
        return None
    manifest_path = Path(custom_voice_dir).expanduser() / _CUSTOM_VOICE_MANIFEST
    if not manifest_path.exists():
        return None
    try:
        return json.loads(manifest_path.read_text(encoding="utf-8"))
    except (OSError, json.JSONDecodeError) as exc:
        logger.warning("Failed to load custom voice manifest %s: %s", manifest_path, exc)
        return None


def iter_custom_voice_profiles(
    custom_voice_dir: str | os.PathLike[str] | None,
    *,
    expected_model_type: str | None = None,
) -> list[dict[str, Any]]:
    manifest = _load_custom_voice_manifest(custom_voice_dir)
    if manifest is None:
        return []

    manifest_model_type = manifest.get("model_type")
    if expected_model_type and manifest_model_type and manifest_model_type != expected_model_type:
        logger.warning(
            "Skipping custom voice manifest for model_type=%s while loading %s",
            manifest_model_type,
            expected_model_type,
        )
        return []

    root = Path(custom_voice_dir).expanduser().resolve()  # type: ignore[arg-type]
    profiles: list[dict[str, Any]] = []
    for raw_name, raw_info in _as_voice_map(manifest.get("voices", {})).items():
        name = str(raw_info.get("name") or raw_name).strip()
        if not name:
            continue
        lower = name.lower()
        info = dict(raw_info)
        for stale_key in ("model_revision", "audio_codec_version", "audio_codec_config_hash"):
            info.pop(stale_key, None)
        filename = info.get("file") or info.get("filename") or f"{lower}.safetensors"
        file_path = (root / str(filename)).resolve()
        if not file_path.is_relative_to(root):
            logger.warning("Skipping custom voice %s: file path escapes custom_voice_dir", name)
            continue
        info.update(
            {
                "name": name,
                "voice_name_lower": lower,
                "file": str(filename),
                "file_path": str(file_path),
                "custom_voice_dir": str(root),
                "model_type": manifest_model_type or expected_model_type,
                "schema_version": manifest.get("schema_version", _CUSTOM_VOICE_SCHEMA_VERSION),
            }
        )
        profiles.append(info)
    return profiles


def _load_profile_tensors(profile: dict[str, Any]) -> dict[str, torch.Tensor] | None:
    try:
        from safetensors.torch import load_file
    except ImportError:
        logger.warning("safetensors is required to load custom voice profiles")
        return None

    file_path = profile.get("file_path")
    if not file_path:
        return None
    try:
        return load_file(str(file_path))
    except Exception as exc:
        logger.warning("Failed to load custom voice profile %s: %s", file_path, exc)
        return None


def _first_dim(tensor: torch.Tensor | None) -> int:
    if tensor is None or tensor.ndim == 0:
        return 0
    return int(tensor.shape[0])


def validate_qwen3_tts_profile(
    profile: dict[str, Any],
    tensors: dict[str, torch.Tensor],
    *,
    expected_embedding_dim: int | None,
) -> str | None:
    # Successful validation also normalizes/augments `profile` for serving metadata.
    speaker_embedding = tensors.get("speaker_embedding")
    if not isinstance(speaker_embedding, torch.Tensor):
        return "missing required tensor `speaker_embedding`"

    embedding_dim = int(speaker_embedding.reshape(-1).numel())
    if expected_embedding_dim and embedding_dim != expected_embedding_dim:
        return f"speaker_embedding dim={embedding_dim}, expected={expected_embedding_dim}"

    mode = str(profile.get("mode") or "xvec").lower()
    if mode not in ("xvec", "icl"):
        return f"invalid mode={mode!r}; expected 'xvec' or 'icl'"
    profile["mode"] = mode
    profile["embedding_dim"] = embedding_dim

    if mode == "icl":
        ref_code = tensors.get("ref_code")
        if not isinstance(ref_code, torch.Tensor):
            return "mode='icl' requires tensor `ref_code`"
        ref_code_length = _first_dim(ref_code)
        if ref_code_length <= 0:
            return "mode='icl' requires non-empty tensor `ref_code`"
        profile["ref_code_length"] = ref_code_length

    return None


def validate_voxcpm2_profile(profile: dict[str, Any], tensors: dict[str, torch.Tensor]) -> str | None:
    # Successful validation also normalizes/augments `profile` for serving metadata.
    ref_audio_feat = tensors.get("ref_audio_feat")
    audio_feat = tensors.get("audio_feat")
    has_ref_audio_feat = isinstance(ref_audio_feat, torch.Tensor)
    has_audio_feat = isinstance(audio_feat, torch.Tensor)
    if not has_ref_audio_feat and not has_audio_feat:
        return "missing required tensor `ref_audio_feat` or `audio_feat`"

    mode = str(profile.get("mode") or "").lower()
    if not mode:
        if has_ref_audio_feat and has_audio_feat:
            mode = "ref_continuation"
        elif has_ref_audio_feat:
            mode = "reference"
        else:
            mode = "continuation"
    if mode not in ("reference", "continuation", "ref_continuation"):
        return f"invalid mode={mode!r}; expected 'reference', 'continuation', or 'ref_continuation'"
    if mode in ("reference", "ref_continuation") and not has_ref_audio_feat:
        return f"mode={mode!r} requires tensor `ref_audio_feat`"
    if mode in ("continuation", "ref_continuation") and not has_audio_feat:
        return f"mode={mode!r} requires tensor `audio_feat`"

    ref_audio_feat_len = _first_dim(ref_audio_feat)
    audio_feat_len = _first_dim(audio_feat)
    if mode in ("reference", "ref_continuation") and ref_audio_feat_len <= 0:
        return f"mode={mode!r} requires non-empty tensor `ref_audio_feat`"
    if mode in ("continuation", "ref_continuation") and audio_feat_len <= 0:
        return f"mode={mode!r} requires non-empty tensor `audio_feat`"

    profile["mode"] = mode
    if has_ref_audio_feat:
        profile["ref_audio_feat_len"] = ref_audio_feat_len
    if has_audio_feat:
        profile["audio_feat_len"] = audio_feat_len
    return None


def load_validated_profile_tensors(
    profile: dict[str, Any],
    *,
    expected_model_type: str,
    validate_profile: Callable[[dict[str, Any], dict[str, torch.Tensor]], str | None],
) -> dict[str, torch.Tensor] | None:
    tensors = _load_profile_tensors(profile)
    error = None
    if tensors is None:
        error = "safetensors file could not be loaded"
    elif str(profile.get("model_type") or expected_model_type or "") != expected_model_type:
        error = f"model_type={profile.get('model_type')!r}, expected={expected_model_type!r}"
    else:
        error = validate_profile(profile, tensors)

    if error is not None:
        logger.warning("Skipping custom %s voice %s: %s", expected_model_type, profile.get("name"), error)
        return None
    return tensors


class SpeakerEmbeddingCache:
    """Thread-safe in-memory LRU cache for speaker extraction artifacts."""

    def __init__(self, *, max_bytes: int = _MAX_BYTES):
        self._cache: OrderedDict[tuple[str, str, int], dict[str, Any]] = OrderedDict()
        self._sizes: dict[tuple[str, str, int], int] = {}
        self._total_bytes = 0
        self._lock = threading.Lock()
        self._hits = 0
        self._misses = 0
        self._max_bytes = max_bytes
        logger.info("Speaker cache ready (max_bytes=%d)", self._max_bytes)

    @staticmethod
    def make_cache_key(speaker_name: str, model_type: str, created_at: int = 0) -> tuple[str, str, int]:
        """Build a cache key. ``created_at=0`` for built-in speakers (no upload).

        Names are normalized (stripped + lowercased) so delete/clear paths that
        normalize to lowercase match entries put with mixed-case names.
        """
        if not speaker_name or not speaker_name.strip():
            raise ValueError("speaker_name is required")
        if not model_type:
            raise ValueError("model_type is required")
        return (model_type, speaker_name.strip().lower(), int(created_at))

    def get(self, key: tuple[str, str, int]) -> dict[str, Any] | None:
        with self._lock:
            if key in self._cache:
                self._cache.move_to_end(key)
                self._hits += 1
                return self._cache[key]
            self._misses += 1
            return None

    def put(self, key: tuple[str, str, int], artifacts: dict[str, Any]) -> None:
        with self._lock:
            self._insert_locked(key, artifacts)

    def _insert_locked(self, key: tuple[str, str, int], artifacts: dict[str, Any]) -> None:
        size = _estimate_tensor_bytes(artifacts)
        if size > self._max_bytes:
            logger.warning("Speaker cache skip: entry %s size=%dB exceeds max_bytes=%dB", key, size, self._max_bytes)
            return
        if key in self._cache:
            self._total_bytes -= self._sizes.pop(key, 0)
            del self._cache[key]
        self._cache[key] = artifacts
        self._sizes[key] = size
        self._total_bytes += size
        self._cache.move_to_end(key)
        while self._cache and self._total_bytes > self._max_bytes:
            evict_key, _ = self._cache.popitem(last=False)
            self._total_bytes -= self._sizes.pop(evict_key, 0)
            logger.debug("Speaker cache EVICT: key=%s", evict_key)

    def clear(self, speaker_name: str | None = None) -> int:
        """Remove entries. With a name, drops matches across model types and generations."""
        with self._lock:
            if speaker_name is None:
                removed = len(self._cache)
                self._cache.clear()
                self._sizes.clear()
                self._total_bytes = 0
                self._hits = 0
                self._misses = 0
                return removed

            if not speaker_name or not speaker_name.strip():
                raise ValueError("speaker_name cannot be an empty string")
            normalized = speaker_name.strip().lower()
            removed = 0
            for k in list(self._cache.keys()):
                if isinstance(k, tuple) and len(k) >= 2 and k[1] == normalized:
                    self._total_bytes -= self._sizes.pop(k, 0)
                    del self._cache[k]
                    removed += 1
            return removed

    def memory_bytes(self) -> int:
        with self._lock:
            return self._total_bytes

    def stats(self) -> dict[str, Any]:
        with self._lock:
            return {
                "entries": len(self._cache),
                "memory_bytes": self._total_bytes,
                "max_bytes": self._max_bytes,
                "memory_mb": round(self._total_bytes / (1024 * 1024), 2),
                "hits": self._hits,
                "misses": self._misses,
            }


def get_speaker_cache() -> SpeakerEmbeddingCache:
    """Return the process-wide speaker cache singleton."""
    global _SINGLETON
    if _SINGLETON is None:
        with _SINGLETON_LOCK:
            if _SINGLETON is None:
                _SINGLETON = SpeakerEmbeddingCache()
    return _SINGLETON