"""CacheMW: 响应缓存中间件;key 公式在此(算法一份),后端是笨 KV。

ARCH §7.5: key = sha256(canonical_json({model, messages_digest, namespace,
salt}));多模态 part 先摘要再 hash(防 Video-Tree 整段 base64 进 hash 的
开销);namespace 必填防跨项目/租户毒化;只缓存阶梯通过的成功响应
(StructuredMW 在内层,能返回即已通过)。读写失败静默降级(铁律)。
"""

from __future__ import annotations

import asyncio
import dataclasses
import hashlib
import json
import uuid
from typing import TYPE_CHECKING, Any

from loguru import logger

from polygateway.types import ChatRequest, LLMResponse

if TYPE_CHECKING:
    from polygateway.ports import CacheBackend, CallNext, StructuredOutputStrategy

_KEY_PREFIX = "pgw:cache:"
_RESPONSE_FIELDS = {f.name for f in dataclasses.fields(LLMResponse)}


def digest_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
    """多模态 content part 先各自 sha256 摘要再参与序列化;文本原文参与。

    与遥测落库共用同一函数(ARCH §7.8),保证缓存 key 与遥测口径一致。
    """
    digested = []
    for msg in messages:
        content = msg.get("content")
        if isinstance(content, list):
            parts = [_digest_part(part) for part in content]
            digested.append({**msg, "content": parts})
        else:
            digested.append(msg)
    return digested


def _digest_part(part: Any) -> Any:
    if isinstance(part, dict) and part.get("type") == "image_url":
        url = str(part.get("image_url", {}).get("url", ""))
        return {"type": "image_url", "sha256": hashlib.sha256(url.encode()).hexdigest()}
    return part


def build_cache_key(
    model_fingerprint: str, messages: list[dict[str, Any]], namespace: str, salt: str | None
) -> str:
    """缓存 key 公式;salt 仅非 None 时参与(VT 旧键语义: 不传 salt 键形不变)。"""
    key_obj: dict[str, Any] = {
        "model": model_fingerprint,
        "messages": digest_messages(messages),
        "namespace": namespace,
    }
    if salt is not None:
        key_obj["salt"] = salt
    payload = json.dumps(key_obj, sort_keys=True, ensure_ascii=False)
    return _KEY_PREFIX + hashlib.sha256(payload.encode("utf-8")).hexdigest()


class CacheMW:
    """洋葱第二层(遥测内、结构化外)。

    model_fingerprint 由装配层从源列表计算(多源 scope = 排序去重的 model
    名合集);源集合变化 → key 变化 → 一次性冷启动,换取跨源集合零毒化。
    """

    def __init__(
        self,
        *,
        backend: CacheBackend,
        model_fingerprint: str,
        default_namespace: str,
        ttl_s: int,
        strategy: StructuredOutputStrategy | None = None,
    ) -> None:
        if not default_namespace.strip():
            raise ValueError("缓存 namespace 不能为空(防跨项目/租户毒化)")
        if ttl_s <= 0:
            raise ValueError("缓存 TTL 必须 > 0(禁止永不过期)")
        self._backend = backend
        self._fingerprint = model_fingerprint
        self._namespace = default_namespace
        self._ttl_s = ttl_s
        self._strategy = strategy

    async def __call__(self, request: ChatRequest, call_next: CallNext) -> LLMResponse:
        namespace = request.cache_namespace or self._namespace
        key = build_cache_key(self._fingerprint, request.messages, namespace, request.cache_salt)
        cached = await self._safe_get(key)
        if cached is not None:
            hit = self._rehydrate(cached, request)
            if hit is not None:
                return hit
        response = await call_next(request)
        await self._safe_set(key, self._serialize(response))
        return response

    # —— 命中路径 ——

    def _rehydrate(self, raw: str, request: ChatRequest) -> LLMResponse | None:
        """反序列化 + 按本次调用的 structured 档零网络重建;失败按未命中。"""
        try:
            data = json.loads(raw)
            fields = {k: v for k, v in data.items() if k in _RESPONSE_FIELDS}
            structured_data = self._rebuild_structured(fields.get("content", ""), request)
            fields.update(
                cache_hit=True,
                latency_ms=0,
                ttft_ms=None,
                max_inter_token_ms=None,
                call_id=str(uuid.uuid4()),
                structured_data=structured_data,
            )
            return LLMResponse(**fields)
        except Exception as exc:
            logger.warning("缓存命中重建失败,按未命中回源: {}", exc)
            return None

    def _rebuild_structured(self, content: str, request: ChatRequest) -> Any | None:
        """对缓存 content 重跑阶梯②③(schema 变更后旧缓存自动重校验,设计 §2.1)。"""
        if request.structured is None:
            return None
        if self._strategy is None:
            raise ValueError("structured 调用需要装配 strategy 才能命中重建")
        parsed = self._strategy.parse(content)
        if request.structured == "json":
            return parsed
        return request.structured.model_validate(parsed)

    # —— 写路径与降级 ——

    def _serialize(self, response: LLMResponse) -> str:
        data = dataclasses.asdict(response)
        data.pop("structured_data", None)  # pydantic 实例不可 JSON 往返(设计 §2.1)
        return json.dumps(data, ensure_ascii=False)

    async def _safe_get(self, key: str) -> str | None:
        try:
            return await self._backend.get(key)
        except asyncio.CancelledError:
            raise
        except Exception as exc:
            logger.warning("缓存读取失败,降级为未命中: {}", exc)
            return None

    async def _safe_set(self, key: str, value: str) -> None:
        try:
            await self._backend.set(key, value, self._ttl_s)
        except asyncio.CancelledError:
            raise
        except Exception as exc:
            logger.warning("缓存写入失败,跳过缓存: {}", exc)