"""主 agent loop: ReAct 风格,LLM ↔ Tool 反复直到无 tool_call。

loop 不直接 print —— 进度通过 sink.emit(event) 上抛。Sink 决定怎么呈现
(本地 console / SSE / 日志)。事件类型见 core/sinks.py 头部说明。
"""
from __future__ import annotations

import json
import time
from typing import Any, Callable, Dict, Optional, Tuple

from .capabilities import ModelCapabilities
from .llm import LLM
from .session import Session


_CANCELLED_TOOL_PLACEHOLDER = "[cancelled by user]"


def _extract_usage(usage: Any) -> Tuple[int, int]:
    """从 litellm response.usage 提 (prompt_tokens, completion_tokens)。"""
    if not usage:
        return 0, 0
    if hasattr(usage, "model_dump"):
        usage = usage.model_dump()
    elif hasattr(usage, "dict"):
        usage = usage.dict()
    if isinstance(usage, dict):
        return int(usage.get("prompt_tokens") or 0), int(usage.get("completion_tokens") or 0)
    return 0, 0


class AgentLoop:
    def __init__(
        self,
        llm: LLM,
        tools: Dict[str, Any],
        session: Session,
        capabilities: ModelCapabilities,
        sink: Optional[Any] = None,
        max_iterations: Optional[int] = None,
        cancel_check: Optional[Callable[[], bool]] = None,
    ) -> None:
        self.llm = llm
        self.tools = tools
        self.session = session
        self.caps = capabilities
        self.max_iterations = max_iterations or capabilities.max_iterations
        self.sink = sink
        # 协作式 cancel:web 层注入 `lambda: broker.is_cancelled(run_id)`;
        # CLI 路径不设(None → 永不 cancel)。LLM 调用本身是 litellm 同步阻塞、不可中断,
        # check 点放在每轮 LLM 前、tool_calls 之间;一次 LLM call 最坏卡几十秒。
        self.cancel_check = cancel_check

    def _emit(self, event: dict) -> None:
        if self.sink is not None:
            self.sink.emit(event)

    def _is_cancelled(self) -> bool:
        return bool(self.cancel_check and self.cancel_check())

    def _fill_cancelled_tool_results(self, remaining: list) -> None:
        """给未执行的 tool_call 补 cancelled tool result,保 LiteLLM 协议完整。
        每个 assistant tool_call 必须有对应的 tool message,否则 resume 时 LLM 报错。
        """
        for tc in remaining:
            self.session.append({
                "role": "tool",
                "tool_call_id": tc.id,
                "content": _CANCELLED_TOOL_PLACEHOLDER,
            })

    def run(self, user_message: str) -> str:
        self.session.append({"role": "user", "content": user_message})

        for _ in range(self.max_iterations):
            if self._is_cancelled():
                self._emit({"type": "cancelled"})
                return "[cancelled]"

            self._emit({"type": "llm_start"})
            start = time.monotonic()
            response = self.llm.chat(
                messages=self.session.messages,
                tools=[t.schema for t in self.tools.values()],
                reasoning_effort=self.caps.default_reasoning_effort or None,
            )
            elapsed = time.monotonic() - start
            msg = response.choices[0].message
            self.session.append(msg)

            pt, ct = _extract_usage(getattr(response, "usage", None))
            self._emit({
                "type": "llm_end",
                "prompt_tokens": pt,
                "completion_tokens": ct,
                "elapsed": elapsed,
            })

            tool_calls = getattr(msg, "tool_calls", None) or []
            content = getattr(msg, "content", None)
            if content:
                self._emit({"type": "text", "content": content})

            if not tool_calls:
                self._emit({"type": "done"})
                return content or ""

            for i, tc in enumerate(tool_calls):
                if self._is_cancelled():
                    self._fill_cancelled_tool_results(tool_calls[i:])
                    self._emit({"type": "cancelled"})
                    return "[cancelled]"
                result = self._execute_tool_call(tc)
                self.session.append(
                    {
                        "role": "tool",
                        "tool_call_id": tc.id,
                        "content": result,
                    }
                )

        self._emit({"type": "done"})
        return "[reached max iterations]"

    def _execute_tool_call(self, tc: Any) -> str:
        name = tc.function.name
        raw_args = tc.function.arguments or "{}"
        try:
            args = json.loads(raw_args)
        except json.JSONDecodeError as e:
            return f"[Error] invalid JSON arguments for {name}: {e}"

        args_preview = json.dumps(args, ensure_ascii=False)
        if len(args_preview) > 200:
            args_preview = args_preview[:200] + "..."
        self._emit({
            "type": "tool_call",
            "name": name,
            "args": args,
            "args_preview": args_preview,
        })

        tool = self.tools.get(name)
        if tool is None:
            err = f"[Error] unknown tool: {name}"
            self._emit({"type": "tool_result", "name": name, "result": err,
                        "preview": err, "truncated": False})
            return err

        try:
            result = tool.execute(**args)
        except TypeError as e:
            err = f"[Error] bad arguments to {name}: {e}"
            self._emit({"type": "tool_result", "name": name, "result": err,
                        "preview": err, "truncated": False})
            return err
        except Exception as e:
            err = f"[Error executing {name}] {type(e).__name__}: {e}"
            self._emit({"type": "tool_result", "name": name, "result": err,
                        "preview": err, "truncated": False})
            return err

        if not isinstance(result, str):
            result = str(result)

        # 控制返回给模型的 tool 结果体量,避免炸 context
        MAX_LEN = 16_000
        truncated = False
        if len(result) > MAX_LEN:
            result = result[:MAX_LEN] + f"\n[... truncated, {len(result) - MAX_LEN} chars ...]"
            truncated = True

        preview = result if len(result) < 400 else result[:400] + "..."
        self._emit({
            "type": "tool_result",
            "name": name,
            "result": result,
            "preview": preview,
            "truncated": truncated,
        })
        return result