# -*- coding: utf-8 -*-
"""PostgreSQL/pgvector storage for registered speaker embeddings."""

from __future__ import annotations

import logging
from typing import Optional

import numpy as np

from app.core.config import settings

logger = logging.getLogger(__name__)

try:
    import asyncpg
except ImportError:  # pragma: no cover - runtime dependency is installed in Docker images.
    asyncpg = None  # type: ignore[assignment]


class PgSpeakerStorage:
    """Small asyncpg wrapper shared by speaker registration and ASR matching."""

    def __init__(self) -> None:
        self.pool: Optional[asyncpg.Pool] = None

    @property
    def is_enabled(self) -> bool:
        return bool(settings.SPEAKER_DB_ENABLED)

    @property
    def is_connected(self) -> bool:
        return self.pool is not None

    async def _create_database_if_not_exists(self) -> None:
        system_config = {
            "user": settings.DB_USER,
            "password": settings.DB_PASSWORD,
            "database": "postgres",
            "host": settings.DB_HOST,
            "port": settings.DB_PORT,
        }
        connection: Optional[asyncpg.Connection] = None
        try:
            if asyncpg is None:
                raise RuntimeError("asyncpg is not installed")
            connection = await asyncpg.connect(**system_config)
            exists = await connection.fetchval(
                "SELECT 1 FROM pg_database WHERE datname = $1",
                settings.DB_NAME,
            )
            if not exists:
                await connection.execute(f'CREATE DATABASE "{settings.DB_NAME}"')
        except Exception as exc:
            logger.warning("尝试自动创建数据库失败，将继续连接业务库: %s", exc)
        finally:
            if connection is not None:
                await connection.close()

    async def connect(self) -> None:
        if not self.is_enabled:
            logger.info("声纹数据库未启用，跳过 PostgreSQL 连接")
            return
        if asyncpg is None:
            raise RuntimeError("asyncpg is not installed")
        if self.pool is not None:
            return

        await self._create_database_if_not_exists()
        self.pool = await asyncpg.create_pool(
            user=settings.DB_USER,
            password=settings.DB_PASSWORD,
            database=settings.DB_NAME,
            host=settings.DB_HOST,
            port=settings.DB_PORT,
            min_size=1,
            max_size=settings.DB_POOL_MAX_SIZE,
        )
        async with self.pool.acquire() as connection:
            await connection.execute("CREATE EXTENSION IF NOT EXISTS vector;")
            await connection.execute(
                """
                CREATE TABLE IF NOT EXISTS speakers (
                    id SERIAL PRIMARY KEY,
                    name TEXT NOT NULL UNIQUE,
                    user_id TEXT,
                    embedding vector(192),
                    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                );
                """
            )
            await connection.execute(
                """
                DO $$
                BEGIN
                    IF NOT EXISTS (
                        SELECT 1 FROM information_schema.columns
                        WHERE table_name='speakers' AND column_name='user_id'
                    ) THEN
                        ALTER TABLE speakers ADD COLUMN user_id TEXT;
                    END IF;
                END $$;
                """
            )
            await connection.execute(
                """
                DO $$
                BEGIN
                    IF NOT EXISTS (
                        SELECT 1
                        FROM pg_class c
                        JOIN pg_namespace n ON n.oid = c.relnamespace
                        WHERE c.relname = 'speakers_embedding_idx'
                    ) THEN
                        CREATE INDEX speakers_embedding_idx
                        ON speakers USING hnsw (embedding vector_cosine_ops)
                        WITH (m = 16, ef_construction = 64);
                    END IF;
                END $$;
                """
            )
        logger.info(
            "声纹数据库已连接: %s:%s/%s",
            settings.DB_HOST,
            settings.DB_PORT,
            settings.DB_NAME,
        )

    async def close(self) -> None:
        if self.pool is not None:
            await self.pool.close()
            self.pool = None

    def _require_pool(self) -> asyncpg.Pool:
        if self.pool is None:
            raise RuntimeError("Speaker database pool is not initialized")
        return self.pool

    @staticmethod
    def _embedding_text(embedding: np.ndarray) -> str:
        return str(np.asarray(embedding, dtype=np.float32).flatten().tolist())

    async def save_speaker(
        self,
        name: str,
        embedding: np.ndarray,
        user_id: Optional[str] = None,
    ) -> dict[str, Optional[str]]:
        pool = self._require_pool()
        async with pool.acquire() as connection:
            row = await connection.fetchrow(
                """
                INSERT INTO speakers (name, embedding, user_id)
                VALUES ($1, $2, $3)
                ON CONFLICT (name) DO UPDATE
                SET embedding = EXCLUDED.embedding, user_id = EXCLUDED.user_id
                RETURNING id, name, user_id;
                """,
                name,
                self._embedding_text(embedding),
                user_id,
            )
        return {
            "id": str(row["id"]) if row is not None else None,
            "name": row["name"] if row is not None else name,
            "user_id": row["user_id"] if row is not None else user_id,
        }

    async def identify_speaker(
        self,
        embedding: np.ndarray,
        threshold: float,
    ) -> dict[str, Optional[str]]:
        pool = self._require_pool()
        distance_threshold = 1.0 - float(threshold)
        async with pool.acquire() as connection:
            row = await connection.fetchrow(
                """
                SELECT id, name, user_id, (embedding <=> $1::vector) AS distance
                FROM speakers
                ORDER BY embedding <=> $1::vector
                LIMIT 1;
                """,
                self._embedding_text(embedding),
            )
        if row is not None and float(row["distance"]) < distance_threshold:
            return {
                "id": str(row["id"]),
                "name": row["name"],
                "user_id": row["user_id"],
            }
        return {"id": None, "name": None, "user_id": None}

    async def list_speakers(self) -> list[dict[str, Optional[str]]]:
        pool = self._require_pool()
        async with pool.acquire() as connection:
            rows = await connection.fetch(
                "SELECT id, name, user_id, created_at FROM speakers ORDER BY id ASC"
            )
        return [
            {
                "id": str(row["id"]),
                "name": row["name"],
                "user_id": row["user_id"],
                "created_at": row["created_at"].isoformat()
                if row["created_at"]
                else None,
            }
            for row in rows
        ]

    async def delete_speaker(self, speaker_id: int) -> bool:
        pool = self._require_pool()
        async with pool.acquire() as connection:
            result = await connection.execute(
                "DELETE FROM speakers WHERE id = $1",
                speaker_id,
            )
        return result != "DELETE 0"


pg_speaker_db = PgSpeakerStorage()