"""
Text2SQL 共享启动逻辑：环境检查、Schema 加载、Orchestrator 构造。

供 `main` CLI 与 `api_server` 复用，避免 API 层依赖 CLI 入口模块。
"""

from __future__ import annotations

import logging
import os
import sys
from pathlib import Path
from typing import Any, Optional

from dotenv import load_dotenv

logger = logging.getLogger(__name__)


def _repo_root() -> Path:
    """仓库根目录（含 data/、.env、api_server.py 的目录）。"""
    return Path(__file__).resolve().parent.parent


def load_project_env() -> None:
    """加载项目根目录 .env，供后续 os.getenv 使用。"""
    load_dotenv(_repo_root() / ".env")


def resolve_sql_dialect(name: str) -> str:
    """CLI / 配置中的方言别名统一为 sqlglot 方言名（SQL Server -> tsql）。"""
    n = (name or "sqlserver").lower().strip()
    if n in ("sqlserver", "mssql"):
        return "tsql"
    return n


def setup_environment() -> bool:
    """环境检查"""
    load_project_env()
    ms_key = os.getenv("MODELSCOPE_API_KEY", "").strip()
    oa_key = os.getenv("OPENAI_API_KEY", "").strip()
    oa_key_ok = oa_key and not (
        oa_key.startswith("http://") or oa_key.startswith("https://")
    )
    if ms_key:
        pass  # ModelScope：BASE_URL / MODEL 有默认值，仅需 KEY
    elif oa_key_ok:
        if not os.getenv("OPENAI_EMBEDDING_MODEL", "").strip():
            logger.warning("未设置 OPENAI_EMBEDDING_MODEL（远程 Embedding 模型名）")
            return False
    else:
        if not os.getenv("DASHSCOPE_API_KEY", "").strip():
            logger.warning(
                "未设置 MODELSCOPE_API_KEY、"
                "OPENAI_API_KEY+OPENAI_EMBEDDING_MODEL 或 DASHSCOPE_API_KEY"
            )
            return False
        base = (
            os.getenv("DASHSCOPE_BASE_URL") or os.getenv("DASHSCOPE_base_url", "")
        ).strip()
        if not base:
            logger.warning("未设置 DASHSCOPE_BASE_URL（或 DASHSCOPE_base_url）")
            return False
        if not os.getenv("DASHSCOPE_MODEL", "").strip():
            logger.warning("未设置 DASHSCOPE_MODEL")
            return False

    # 检查Schema文件（支持相对路径和绝对路径）
    schema_path_str = os.getenv(
        "SCHEMA_PATH", "./data/schemas/G3SB_MCDataDictionary_table_structure.json"
    )
    schema_path = Path(schema_path_str)

    # 如果是相对路径，尝试从多个位置查找
    if not schema_path.is_absolute():
        # 尝试1: PyInstaller 临时目录（单文件模式）
        if getattr(sys, "frozen", False) and hasattr(sys, "_MEIPASS"):
            meipass_schema = Path(sys._MEIPASS) / schema_path_str
            if meipass_schema.exists():
                schema_path = meipass_schema

        # 尝试2: 当前工作目录
        if not schema_path.exists():
            schema_path = Path.cwd() / schema_path_str

        # 尝试3: 仓库根目录（本文件位于 backend/）
        if not schema_path.exists():
            script_dir = Path(__file__).resolve().parent.parent
            schema_path = script_dir / schema_path_str

        # 尝试4: 可执行文件所在目录
        if not schema_path.exists() and getattr(sys, "frozen", False):
            exe_dir = Path(sys.executable).parent
            schema_path = exe_dir / schema_path_str

    if not schema_path.exists():
        logger.warning(f"Schema文件不存在: {schema_path}")
        logger.info("请将G3SB Schema文件放置在 ./data/schemas/ 目录")
        return False

    # 检查 LLM Key（DeepSeek / OpenAI 可切换）
    llm_sc = (os.getenv("LLM_SERVICE_CODE") or "").strip().lower()
    if llm_sc and llm_sc not in ("deepseek", "openai"):
        logger.warning("未知 LLM_SERVICE_CODE=%r（仅支持 deepseek/openai）", llm_sc)
        return False
    if llm_sc == "openai":
        if not (os.getenv("OPENAI_API_KEY") or "").strip():
            logger.warning("LLM_SERVICE_CODE=openai 但 OPENAI_API_KEY 未设置")
            return False
    else:
        if not (os.getenv("DEEPSEEK_API_KEY") or "").strip():
            logger.warning("环境变量 DEEPSEEK_API_KEY 未设置（默认 LLM_SERVICE_CODE=deepseek）")
            logger.info("请在 .env 文件中配置，或 export DEEPSEEK_API_KEY=your_key")
            return False

    logger.info(f"[OK] 环境检查通过")
    logger.info(f"  - Schema: {schema_path}")
    logger.info(
        "  - LLM: %s",
        (llm_sc or "deepseek(auto)"),
    )

    return True


def _default_g3sb_meta_path(structure_path: str) -> Optional[str]:
    """若存在与 table_structure 同名的 table_meta 文件则返回其路径。"""
    p = Path(structure_path)
    if "table_structure" not in p.name:
        return None
    cand = p.parent / p.name.replace("table_structure", "table_meta")
    return str(cand) if cand.is_file() else None


def load_schema(schema_path: str, schema_meta_path: Optional[str] = None):
    """加载 Schema；G3SB structure JSON 会自动尝试配对 table_meta（可用 --schema-meta 指定）。"""
    from schema.manager import SchemaManager

    meta = (
        schema_meta_path
        if schema_meta_path is not None
        else _default_g3sb_meta_path(schema_path)
    )
    logger.info(f"加载Schema: {schema_path}")
    if meta:
        logger.info(f"  表注释(meta): {meta}")

    schema_mgr = SchemaManager.load_from_json(
        schema_path, g3sb_meta_path=meta
    )

    stats = schema_mgr.get_statistics()
    logger.info(
        f"[OK] Schema加载完成: {stats['database']}, "
        f"共{stats['total_tables']}张表, {stats['total_columns']}个字段"
    )

    return schema_mgr


def create_orchestrator(schema_mgr: Any, args: Any):
    """创建编排器"""
    from agents.orchestrator import Text2SQLOrchestrator
    from llm.router import create_llm_client, resolve_llm_service_code
    from llm.deepseek_client import DeepSeekConfig

    translate_en = os.getenv("TRANSLATE_EN_TO_ZH", "true").strip().lower() not in (
        "0",
        "false",
        "no",
        "off",
    )
    if getattr(args, "no_translate_en", False):
        translate_en = False

    sc = resolve_llm_service_code()
    if sc == "deepseek":
        api_key = (args.api_key or os.getenv("DEEPSEEK_API_KEY") or "").strip()
        if not api_key:
            raise ValueError(
                "未配置 DeepSeek API Key：请在 .env 中设置 DEEPSEEK_API_KEY，"
                "或使用命令行参数 --api-key"
            )
        base_url = (os.getenv("DEEPSEEK_BASE_URL") or "https://api.deepseek.com").strip()
        cfg = DeepSeekConfig(
            api_key=api_key,
            base_url=base_url,
            model_name=args.model or "deepseek-chat",
            temperature=args.temperature,
            max_tokens=args.max_tokens,
        )
        llm_client = create_llm_client("deepseek", **cfg.__dict__)
    else:
        # openai：完全由 OPENAI_* 决定；同时沿用 temperature/max_tokens 作为默认值覆盖
        llm_client = create_llm_client(
            "openai",
            temperature=args.temperature,
            max_tokens=args.max_tokens,
        )

    orchestrator = Text2SQLOrchestrator(
        schema_manager=schema_mgr,
        llm_client=llm_client,
        vector_db_path=args.vector_db,
        max_retry=args.max_retry,
        use_vector_search=not args.no_vector_search,
        # Few-shot配置
        fewshot_enabled=not args.no_fewshot,
        fewshot_top_k=args.fewshot_top_k,
        fewshot_min_rating=args.fewshot_min_rating,
        translate_english_to_zh=translate_en,
    )

    return orchestrator