"""
路由与初始化模块
包含状态初始化节点和条件路由函数

三层统一循环防护：
1. 全局步数硬上限（reasoning_step > max_steps）
2. 路由模式检测（A→B→A→B 交替循环）
3. 状态停滞检测（连续相同动作）
"""

from datetime import datetime

from backend.app.core.intent import get_route_by_reasoning, ReasoningAction
from ...main_graph.state import MainGraphState
from backend.app.logger import info


# ========== 初始化状态节点 ==========
def init_state_node(state: MainGraphState) -> MainGraphState:
    """初始化状态节点：在流程开始时设置初始值"""
    state.current_phase = "initializing"
    state.reasoning_step = 0
    state.start_time = datetime.now().isoformat()

    if not state.user_query and state.messages:
        last_msg = state.messages[-1]
        state.user_query = getattr(last_msg, "content", str(last_msg))

    return state


# ========== 条件路由函数 ==========
def route_by_reasoning(state: MainGraphState) -> str:
    """
    根据推理结果决定下一步路由，带三层统一循环防护

    核心逻辑：
    1. DIRECT_RESPONSE → 直接返回 llm_call
    2. 子图完成/已有结果 → 直接返回 llm_call
    3. 步数超限 → 直接返回 llm_call
    4. 其他 → 正常路由
    """
    # 获取历史动作
    previous_actions = [h.get("action") for h in state.reasoning_history]

    info(f"[条件路由] step={state.reasoning_step}, phase={state.current_phase}, history={previous_actions}")

    # ========== 获取推理结果 ==========
    reasoning_result = state.debug_info.get("reasoning_result")
    latest_action = reasoning_result.action.name if reasoning_result else None

    # ========== 核心检查：DIRECT_RESPONSE 优先 ==========
    # 从 reasoning_result 检查（最新）
    if latest_action == "DIRECT_RESPONSE":
        info(f"[条件路由] 推理结果为 DIRECT_RESPONSE，直接去 llm_call")
        return "llm_call"

    # 备用：从历史记录检查
    if previous_actions and previous_actions[-1] == "DIRECT_RESPONSE":
        info(f"[条件路由] 历史记录最新动作为 DIRECT_RESPONSE，直接去 llm_call")
        return "llm_call"

    # ========== 子图完成/已有结果 ==========
    if "subgraph_completed" in previous_actions or state.final_result:
        info("[条件路由] 子图已完成或已有结果，直接终止")
        return "llm_call"

    # ========== 步数超限 ==========
    if state.reasoning_step > state.max_steps:
        info(f"[条件路由] 步数超限 ({state.reasoning_step}/{state.max_steps})，强制终止")
        return "llm_call"

    # ========== 特殊阶段快速通道 ==========
    if state.current_phase in ("max_steps_exceeded", "finalizing", "done"):
        return "llm_call"
    if state.current_phase == "error_handling" or state.current_error:
        return "handle_error"

    # ========== 无推理结果，默认终止 ==========
    if not reasoning_result:
        info("[条件路由] 无推理结果，默认去 llm_call")
        return "llm_call"

    # ========== 计算目标路由 ==========
    route = get_route_by_reasoning(reasoning_result)

    route_mapping = {
        "direct_response": "llm_call",
        "retrieve_rag": "rag_retrieve",
        "re_retrieve_rag": "rag_retrieve",
        "web_search": "web_search",
        "clarify": "llm_call",
        "call_tool": "llm_call",
        "contact": "contact_subgraph",
        "dictionary": "dictionary_subgraph",
        "news_analysis": "news_analysis_subgraph",
    }
    target = route_mapping.get(route, "llm_call")

    # ========== RAG 次数硬限制 ==========
    rag_attempts = getattr(state, 'rag_attempts', 0)
    if target == "rag_retrieve" and rag_attempts >= 2:
        info(f"[条件路由] RAG已尝试{rag_attempts}次，强制走联网搜索")
        target = "web_search"

    # ========== 循环防护检测 ==========
    # 1. 路由模式检测（A→B→A→B 交替）
    if len(previous_actions) >= 4:
        if (previous_actions[-4] == previous_actions[-2]
                and previous_actions[-3] == previous_actions[-1]
                and previous_actions[-2] != previous_actions[-1]):
            info(f"[条件路由] 检测到路由循环: {previous_actions[-4:]}，强制终止")
            return "llm_call"

    # 2. 状态停滞检测（连续相同动作 TODO：本来应该是2）
    if len(previous_actions) >= 3 and previous_actions[-1] == previous_actions[-2] and previous_actions[-2] == previous_actions[-3]:
        info(f"[条件路由] 连续相同动作 '{previous_actions[-1]}'，强制终止")
        return "llm_call"

    # ========== 智能优化 ==========
    if target == "rag_retrieve" and (state.rag_docs or state.rag_context):
        info("[条件路由] RAG 结果已存在，跳过检索")
        return "llm_call"

    info(f"[条件路由] 动作={latest_action}, 目标={target}")
    return target


# ========== 完成阶段条件路由函数 ==========

def should_summarize(state: MainGraphState) -> str:
    """
    检查是否需要总结对话（对话足够长时）

    Args:
        state: 当前图状态

    Returns:
        "summarize" 或 "finalize"
    """
    if state.turns_since_last_summary >= 5:  # 每5轮对话总结一次
        return "summarize"
    else:
        return "finalize"