"""GovernedLLMClient — 四层治理栈统一 LLM 调用网关。

组合熔断器、Redis 缓存、指数退避重试、流式 SSE 消费 + 三层活性看门狗，
并在每次调用后自动写入遥测。provider 差异处理覆盖 DeepSeek（reasoning_content）
和 Qwen（<think> 标签剥离）。

治理栈五层（ARCHITECTURE.md §5）:
  1. 硬超时 — asyncio.wait_for + StreamLivenessTimeout
  2. 指数退避重试 — max_retries + base_delay + max_delay
  3. 熔断器 — CircuitBreaker（连续 N 失败开路，冷却后半开探针）
  4. Redis 响应缓存 — content-addressed hash(model + messages)
  5. 遥测 — TelemetryRecorder（每次调用必录）
"""

from __future__ import annotations

import asyncio
import json
import re
import time
from typing import TYPE_CHECKING, Any
from uuid import uuid4

import httpx
from loguru import logger

from adapters.streaming import StreamLivenessTimeout, stream_with_liveness_timeouts
from core.types import LLMResponse

if TYPE_CHECKING:
    from collections.abc import AsyncIterator

    from adapters.breaker import CircuitBreaker


class CircuitOpenError(Exception):
    """熔断器已开启，拒绝调用。"""


class _SseAnomaly(Exception):  # noqa: N818 — 参考 CHSAnalyzer2 命名
    """SSE 协议异常（malformed_json 等）。"""

    def __init__(self, kind: str) -> None:
        self.kind = kind
        super().__init__(f"SSE 协议异常: {kind}")


# ── SSE 解析（模块级纯函数） ────────────────────────────────────────


def _sse_data_payload(raw: str) -> str | None:
    """提取一行 SSE 的 data 段；ping/空行/非 data 行返回 None。

    参数:
        raw: 原始 SSE 行文本。

    返回:
        data 段 payload 字符串，或 None（应被跳过的行）。
    """
    line = raw.strip()
    if not line or line.startswith(":") or not line.startswith("data:"):
        return None
    return line[len("data:") :].strip()


def _sse_delta(chunk: dict, usage_sink: dict) -> tuple[bool, str] | None:
    """旁路收集 usage 并提取一帧增量 token。

    返回 (True, content) 或 (False, reasoning_content)，无增量则 None。
    优先 content（正式输出）；否则取 reasoning_content（thinking 模型的思考 token）。

    参数:
        chunk: 一帧解析后的 SSE JSON。
        usage_sink: 旁路字典；含 usage 的帧写入 usage_sink["usage"]。

    返回:
        (is_content, text) 或 None。
    """
    if chunk.get("usage"):
        usage_sink["usage"] = chunk["usage"]
    choices = chunk.get("choices") or []
    if not choices:
        return None
    delta = choices[0].get("delta") or {}
    content = delta.get("content")
    if content:
        return (True, content)
    reasoning = delta.get("reasoning_content")
    if reasoning:
        return (False, reasoning)
    return None


async def _iter_sse_deltas(
    lines: AsyncIterator[str], usage_sink: dict
) -> AsyncIterator[tuple[bool, str]]:
    """OpenAI 兼容 SSE 行流 → 逐帧产出 (是否 content, 文本)。

    usage 旁路写入 usage_sink。内部消化 ping/空行/[DONE]/usage-only 帧。

    参数:
        lines: 底层 SSE 文本行异步迭代器。
        usage_sink: 旁路字典。

    产出:
        (is_content, text)。

    异常:
        _SseAnomaly: data 段 JSON 解析失败。
    """
    async for raw in lines:
        data = _sse_data_payload(raw)
        if data is None:
            continue
        if data == "[DONE]":
            usage_sink["done"] = True
            return
        try:
            chunk = json.loads(data)
        except json.JSONDecodeError as exc:
            raise _SseAnomaly("malformed_json") from exc
        delta = _sse_delta(chunk, usage_sink)
        if delta is not None:
            yield delta


# ── Provider 差异处理 ────────────────────────────────────────────────


def _build_thinking_body(provider: str) -> dict:
    """构造 thinking/reasoning 启用参数（provider 差异处理）。

    参数:
        provider: provider 标识（如 "deepseek"、"qwen" 等）。

    返回:
        合并进请求体的字典片段。
    """
    provider_lower = provider.lower()
    if "deepseek" in provider_lower:
        return {"thinking": {"type": "enabled"}}
    if "qwen" in provider_lower:
        return {"enable_thinking": True}
    return {}


_THINK_PATTERN = re.compile(r"<think>(.*?)</think>", re.DOTALL)


def _strip_qwen_thinking(content: str) -> tuple[str, str]:
    """剥离 Qwen 模型 <think>...</think> 标签，拆分为 (正文, 思考内容)。

    参数:
        content: 含有可能的 <think> 标签的模型原始输出。

    返回:
        (stripped_content, thinking_text)。无 think 标签时 thinking 为空字符串。
    """
    match = _THINK_PATTERN.search(content)
    if match is None:
        return content, ""
    thinking = match.group(1).strip()
    stripped = _THINK_PATTERN.sub("", content).strip()
    return stripped, thinking


# ── 瞬时错误判定 ─────────────────────────────────────────────────────

_TRANSIENT_STATUS_CODES = frozenset({429, 500, 502, 503, 504})
_FATAL_STATUS_CODES = frozenset({401, 403})


def _is_transient_error(exc: Exception) -> bool:
    """判断异常是否为瞬时可重试错误。

    参数:
        exc: 捕获的异常。

    返回:
        True 表示可重试，False 表示不可重试。
    """
    if isinstance(exc, (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout)):
        return True
    if isinstance(exc, httpx.HTTPStatusError):
        return exc.response.status_code in _TRANSIENT_STATUS_CODES
    return isinstance(exc, (StreamLivenessTimeout, _SseAnomaly))


def _is_fatal_auth_error(exc: Exception) -> bool:
    """判断异常是否为不可恢复的认证/授权错误。

    参数:
        exc: 捕获的异常。

    返回:
        True 表示应立即 force_open 熔断器。
    """
    if isinstance(exc, httpx.HTTPStatusError):
        return exc.response.status_code in _FATAL_STATUS_CODES
    return False


# ── GovernedLLMClient ────────────────────────────────────────────────


class GovernedLLMClient:
    """四层治理栈统一 LLM 调用网关，实现 LLMProvider Protocol。

    构造函数接收所有组件（依赖注入），不从全局状态获取任何配置。

    参数:
        model: 模型名称。
        base_url: API 基础 URL（如 http://localhost:8000/v1）。
        api_key: API 密钥。
        provider: provider 标识（如 "deepseek"、"qwen"）。
        thinking: 是否启用 thinking/reasoning 模式。
        breaker: 熔断器实例。
        cache: Redis 响应缓存实例。
        telemetry: 遥测记录器实例。
        timeout_s: 总超时秒数。
        ttft_timeout_s: 首 token 超时秒数。
        inter_token_timeout_s: token 间超时秒数。
        max_retries: 最大重试次数。
        retry_base_delay_s: 重试基础延迟秒数。
        retry_max_delay_s: 重试最大延迟秒数。
    """

    def __init__(
        self,
        *,
        model: str,
        base_url: str,
        api_key: str,
        provider: str,
        thinking: bool,
        breaker: CircuitBreaker,
        cache: Any,
        telemetry: Any,
        timeout_s: float,
        ttft_timeout_s: float,
        inter_token_timeout_s: float,
        max_retries: int,
        retry_base_delay_s: float,
        retry_max_delay_s: float,
    ) -> None:
        self._model = model
        self._base_url = base_url.rstrip("/")
        self._api_key = api_key
        self._provider = provider
        self._thinking = thinking
        self._breaker = breaker
        self._cache = cache
        self._telemetry = telemetry
        self._timeout_s = timeout_s
        self._ttft_timeout_s = ttft_timeout_s
        self._inter_token_timeout_s = inter_token_timeout_s
        self._max_retries = max_retries
        self._retry_base_delay_s = retry_base_delay_s
        self._retry_max_delay_s = retry_max_delay_s
        self._http = httpx.AsyncClient(
            base_url=self._base_url,
            headers={
                "Authorization": f"Bearer {self._api_key}",
                "Content-Type": "application/json",
            },
            timeout=httpx.Timeout(timeout_s),
        )

    async def chat(
        self,
        messages: list[dict[str, Any]],
        *,
        session_id: str | None = None,
        parent_call_id: str | None = None,
    ) -> LLMResponse:
        """发起 LLM 调用，经四层治理栈：熔断 → 缓存 → 重试+流式 → 遥测。

        参数:
            messages: OpenAI 格式消息列表。
            session_id: 会话 ID（传递到遥测）。
            parent_call_id: 父调用 ID（传递到遥测）。

        返回:
            LLMResponse 统一响应。

        异常:
            CircuitOpenError: 熔断器开路。
            httpx.HTTPStatusError: 不可恢复的 HTTP 错误（401/403）。
        """
        started = time.monotonic()
        call_id = str(uuid4())

        # ① 熔断器检查
        if self._breaker.is_open(self._provider, time.monotonic()):
            raise CircuitOpenError(f"熔断器已开启，拒绝调用 provider={self._provider}")

        # ② 缓存查询
        cached = await self._cache.get(self._model, messages)
        if cached is not None:
            response = LLMResponse(
                content=cached.content,
                thinking=cached.thinking,
                model=cached.model,
                provider=cached.provider,
                prompt_tokens=cached.prompt_tokens,
                completion_tokens=cached.completion_tokens,
                latency_ms=0,
                ttft_ms=None,
                max_inter_token_ms=None,
                cache_hit=True,
                call_id=call_id,
            )
            await self._telemetry.record_llm_call(
                call_id=call_id,
                parent_call_id=parent_call_id,
                session_id=session_id,
                model_name=self._model,
                provider=self._provider,
                messages=json.dumps(messages, ensure_ascii=False),
                response=response.content,
                thinking=response.thinking,
                prompt_tokens=response.prompt_tokens,
                completion_tokens=response.completion_tokens,
                latency_ms=0,
                ttft_ms=None,
                max_inter_token_ms=None,
                cache_hit=True,
                error=None,
            )
            return response

        # ③ 重试循环 + 流式消费
        last_exc: Exception | None = None
        for attempt in range(self._max_retries):
            attempt_start = time.monotonic()
            try:
                sse_lines = await self._stream_request(messages)
                content, thinking_text, ttft_ms, max_itoken_ms, usage = await self._consume_stream(
                    sse_lines
                )

                # 熔断器记成功
                self._breaker.record_success(self._provider)

                # provider 差异：Qwen think 标签剥离
                if "qwen" in self._provider.lower() and "<think>" in content:
                    content, qwen_thinking = _strip_qwen_thinking(content)
                    if qwen_thinking:
                        thinking_text = qwen_thinking

                latency_ms = int((time.monotonic() - started) * 1000)
                prompt_tokens = usage.get("prompt_tokens", 0)
                completion_tokens = usage.get("completion_tokens", 0)

                response = LLMResponse(
                    content=content,
                    thinking=thinking_text,
                    model=self._model,
                    provider=self._provider,
                    prompt_tokens=prompt_tokens,
                    completion_tokens=completion_tokens,
                    latency_ms=latency_ms,
                    ttft_ms=ttft_ms,
                    max_inter_token_ms=max_itoken_ms,
                    cache_hit=False,
                    call_id=call_id,
                )

                # ④ 写缓存
                await self._cache.set(self._model, messages, response)

                # ⑤ 遥测
                await self._telemetry.record_llm_call(
                    call_id=call_id,
                    parent_call_id=parent_call_id,
                    session_id=session_id,
                    model_name=self._model,
                    provider=self._provider,
                    messages=json.dumps(messages, ensure_ascii=False),
                    response=content,
                    thinking=thinking_text,
                    prompt_tokens=prompt_tokens,
                    completion_tokens=completion_tokens,
                    latency_ms=latency_ms,
                    ttft_ms=ttft_ms,
                    max_inter_token_ms=max_itoken_ms,
                    cache_hit=False,
                    error=None,
                )

                return response

            except Exception as exc:
                last_exc = exc
                attempt_latency = int((time.monotonic() - attempt_start) * 1000)

                # 致命错误（401/403）→ force_open + 遥测 + 立即抛出
                if _is_fatal_auth_error(exc):
                    self._breaker.force_open(self._provider, time.monotonic())
                    await self._telemetry.record_llm_call(
                        call_id=call_id,
                        parent_call_id=parent_call_id,
                        session_id=session_id,
                        model_name=self._model,
                        provider=self._provider,
                        messages=json.dumps(messages, ensure_ascii=False),
                        response="",
                        thinking="",
                        prompt_tokens=0,
                        completion_tokens=0,
                        latency_ms=attempt_latency,
                        ttft_ms=None,
                        max_inter_token_ms=None,
                        cache_hit=False,
                        error=str(exc),
                    )
                    raise

                # 瞬时错误 → 记失败 + 遥测 + 退避重试
                if _is_transient_error(exc):
                    self._breaker.record_failure(self._provider, time.monotonic())
                    await self._telemetry.record_llm_call(
                        call_id=str(uuid4()),  # 每次重试用新 call_id
                        parent_call_id=parent_call_id,
                        session_id=session_id,
                        model_name=self._model,
                        provider=self._provider,
                        messages=json.dumps(messages, ensure_ascii=False),
                        response="",
                        thinking="",
                        prompt_tokens=0,
                        completion_tokens=0,
                        latency_ms=attempt_latency,
                        ttft_ms=None,
                        max_inter_token_ms=None,
                        cache_hit=False,
                        error=str(exc),
                    )
                    logger.warning(
                        "llm.call.transient_error",
                        extra={
                            "provider": self._provider,
                            "attempt": attempt + 1,
                            "max_retries": self._max_retries,
                            "error": str(exc),
                        },
                    )
                    if attempt < self._max_retries - 1:
                        delay = min(
                            self._retry_base_delay_s * (2**attempt),
                            self._retry_max_delay_s,
                        )
                        if delay > 0:
                            await asyncio.sleep(delay)
                    continue

                # 非瞬时、非致命 → 记遥测后直接抛出
                await self._telemetry.record_llm_call(
                    call_id=call_id,
                    parent_call_id=parent_call_id,
                    session_id=session_id,
                    model_name=self._model,
                    provider=self._provider,
                    messages=json.dumps(messages, ensure_ascii=False),
                    response="",
                    thinking="",
                    prompt_tokens=0,
                    completion_tokens=0,
                    latency_ms=attempt_latency,
                    ttft_ms=None,
                    max_inter_token_ms=None,
                    cache_hit=False,
                    error=str(exc),
                )
                raise

        # 所有重试耗尽
        assert last_exc is not None
        raise last_exc

    async def _stream_request(self, messages: list[dict[str, Any]]) -> list[str]:
        """发起流式 HTTP 请求并收集全部 SSE 行。

        此方法为主要的 HTTP 交互点，测试通过 patch 替换以注入假 SSE 行。

        参数:
            messages: OpenAI 格式消息列表。

        返回:
            SSE 文本行列表。

        异常:
            httpx.HTTPStatusError: HTTP 状态码错误。
            httpx.ConnectError: 连接错误。
        """
        payload: dict[str, Any] = {
            "model": self._model,
            "messages": messages,
            "stream": True,
            "stream_options": {"include_usage": True},
        }
        if self._thinking:
            payload.update(_build_thinking_body(self._provider))

        collected_lines: list[str] = []
        async with self._http.stream("POST", "/chat/completions", json=payload) as resp:
            if resp.status_code >= 400:
                await resp.aread()
                resp.raise_for_status()
            async for line in resp.aiter_lines():
                collected_lines.append(line)
        return collected_lines

    async def _consume_stream(
        self, sse_lines: list[str]
    ) -> tuple[str, str, float | None, float | None, dict]:
        """消费 SSE 行序列，累积 content/thinking，测量 TTFT 和 max_inter_token_ms。

        参数:
            sse_lines: SSE 文本行列表。

        返回:
            (content, thinking, ttft_ms, max_inter_token_ms, usage_dict)。
        """
        usage_sink: dict = {}

        async def _lines_iter() -> AsyncIterator[str]:
            for line in sse_lines:
                yield line

        raw_deltas = _iter_sse_deltas(_lines_iter(), usage_sink)
        guarded = stream_with_liveness_timeouts(
            raw_deltas,
            ttft_s=self._ttft_timeout_s,
            inter_token_s=self._inter_token_timeout_s,
            total_s=self._timeout_s,
        )

        content_parts: list[str] = []
        thinking_parts: list[str] = []
        ttft_ms: float | None = None
        max_inter_token_ms: float | None = None
        stream_start = time.monotonic()
        last_token_time = stream_start

        async for is_content, text in guarded:
            now = time.monotonic()
            if ttft_ms is None:
                ttft_ms = (now - stream_start) * 1000
            else:
                gap_ms = (now - last_token_time) * 1000
                if max_inter_token_ms is None or gap_ms > max_inter_token_ms:
                    max_inter_token_ms = gap_ms
            last_token_time = now

            if is_content:
                content_parts.append(text)
            else:
                thinking_parts.append(text)

        content = "".join(content_parts)
        thinking = "".join(thinking_parts)
        usage = usage_sink.get("usage", {})
        return content, thinking, ttft_ms, max_inter_token_ms, usage

    async def close(self) -> None:
        """关闭底层 HTTP 客户端。"""
        await self._http.aclose()