"""大模型回退层：Mock 与 OpenAI 兼容 API 两种后端。"""
from __future__ import annotations

import asyncio
from typing import Dict, Optional

from .models import ExpertResponse


class FallbackProvider:
    name: str = "fallback"

    async def generate(self, query: str) -> ExpertResponse:
        raise NotImplementedError


class MockFallback(FallbackProvider):
    """确定性 mock 大模型：标识为 fallback，便于测试升级路径。"""

    def __init__(self, model: str = "mock-large"):
        self.model = model
        self.name = f"fallback-{model}"

    async def generate(self, query: str) -> ExpertResponse:
        await asyncio.sleep(0.002)
        body = (
            f"（大模型回退）「{query}」\n\n"
            "这是一条来自大模型回退路径的完整回答。\n"
            "要点：\n"
            "1. 对复杂/跨域任务给出综合推理\n"
            "2. 补充领域专家未覆盖的上下文\n"
            "3. 给出可执行的后续建议\n"
        )
        return ExpertResponse(
            text=body,
            model_used=self.model,
            latency_ms=2.0,
            tokens=120,
            cost_est=2.0 * 120 / 1_000_000,
        )


class APIFallback(FallbackProvider):
    """OpenAI 兼容大模型 API（如 DeepSeek / OpenAI / 本地 vLLM）。"""

    def __init__(self, model: str, base_url: str, api_key: str):
        self.model = model
        self.base_url = base_url.rstrip("/")
        self.api_key = api_key
        self.name = f"fallback-{model}"
        self._client = None

    def _get_client(self):
        if self._client is None:
            import httpx
            self._client = httpx.AsyncClient(timeout=90.0)
        return self._client

    async def generate(self, query: str) -> ExpertResponse:
        client = self._get_client()
        resp = await client.post(
            f"{self.base_url}/chat/completions",
            headers={"Authorization": f"Bearer {self.api_key}"},
            json={
                "model": self.model,
                "messages": [{"role": "user", "content": query}],
                "temperature": 0.3,
                "max_tokens": 2048,
            },
        )
        resp.raise_for_status()
        data = resp.json()
        body = data["choices"][0]["message"]["content"]
        usage = data.get("usage", {})
        tokens = usage.get("completion_tokens", int(len(body) / 2.2))
        return ExpertResponse(
            text=body,
            model_used=self.model,
            latency_ms=0.0,
            tokens=tokens,
            cost_est=2.0 * tokens / 1_000_000,
        )


def build_fallback(cfg: Dict) -> FallbackProvider:
    """cfg 为 fallback 段配置。"""
    ftype = cfg.get("type", "mock")
    model = cfg.get("model", "deepseek-chat")
    if ftype == "mock":
        return MockFallback(model=model)
    if ftype == "api":
        import os
        api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY"))
        if not api_key:
            raise RuntimeError(
                f"APIFallback 缺少 API Key：请设置环境变量 {cfg.get('api_key_env')} 或配置 api_key"
            )
        return APIFallback(model, cfg.get("base_url", "https://api.deepseek.com/v1"), api_key)
    raise ValueError(f"未知 fallback 类型: {ftype}（支持 mock | api）")