"""
Agentic orchestration loop for the VL model.

Calls `InferenceClient.chat_completion` with the three agent tools defined in
`agent/tools.py`.  The loop continues as long as the model emits tool calls.
It stops (and returns a final string) when:
  - The model calls `final_output`  -> return the `answer` argument
  - The model calls `abort`         -> return "Aborted: {reason}"
  - The model returns plain content with no tool calls
  - `max_tool_rounds` is reached    -> return the last assistant content
"""

from __future__ import annotations

import json
from typing import Any

from huggingface_hub import InferenceClient

from .tools import AGENT_TOOLS, dispatch_tool

DEFAULT_MAX_TOOL_ROUNDS = 10

# Qwen3 "Thinking" models emit reasoning between these tags; the user-facing
# answer follows the closing tag. See vLLM Qwen3ReasoningParser.
_QWEN_THINK_START = "<think>"
_QWEN_THINK_END = "</think>"


def strip_qwen_thinking(text: str) -> str:
    """Drop Qwen3-Thinking chain-of-thought; keep only the final answer segment."""
    if not text or not isinstance(text, str):
        return text
    s = text
    before, found, after = s.partition(_QWEN_THINK_START)
    s = after if found else before
    # Use the *last* closing tag: some replies contain multiple `</think>`
    # segments; only the final segment is meant for the user.
    if _QWEN_THINK_END in s:
        return s.rsplit(_QWEN_THINK_END, 1)[-1].lstrip()
    return s


def run_agent(
    messages: list[dict[str, Any]],
    client: InferenceClient,
    model: str,
    max_tokens: int = 512,
    temperature: float = 0.7,
    top_p: float = 0.95,
    max_tool_rounds: int = DEFAULT_MAX_TOOL_ROUNDS,
) -> str:
    """
    Run the agentic loop and return the final answer as a string.

    Parameters
    ----------
    messages:
        Full conversation so far, including the system message, all history,
        and the latest user message (which may contain an image as a multimodal
        content list).
    client:
        An authenticated `InferenceClient` instance.
    model:
        HF model ID to use for inference.
    max_tokens:
        Maximum tokens per completion call.
    temperature:
        Sampling temperature.
    top_p:
        Nucleus sampling top-p.
    max_tool_rounds:
        Hard cap on how many tool-calling rounds are allowed before the loop
        gives up and returns whatever the model last said.
    """
    messages = list(messages)  # work on a local copy

    for _round in range(max_tool_rounds + 1):
        response = client.chat_completion(
            messages=messages,
            model=model,
            tools=AGENT_TOOLS,
            tool_choice="auto",
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            stream=False,
        )

        choice = response.choices[0]
        msg = choice.message

        tool_calls = getattr(msg, "tool_calls", None) or []

        # ------------------------------------------------------------------ #
        # No tool calls → plain text answer, we're done                       #
        # ------------------------------------------------------------------ #
        if not tool_calls:
            return _visible_assistant_text(msg)

        # ------------------------------------------------------------------ #
        # There are tool calls → check for terminal tools first               #
        # ------------------------------------------------------------------ #
        for tc in tool_calls:
            fn = tc.function
            name = fn.name
            raw_args = fn.arguments or "{}"
            args = raw_args if isinstance(raw_args, dict) else _safe_parse(raw_args)

            if name == "final_output":
                return strip_qwen_thinking(
                    args.get("answer", "") or _visible_assistant_text(msg)
                )

            if name == "abort":
                reason = args.get("reason", "")
                return f"Aborted: {reason}" if reason else "Task aborted."

        # ------------------------------------------------------------------ #
        # Non-terminal tool calls → execute each one and feed results back    #
        # ------------------------------------------------------------------ #
        messages.append(_assistant_tool_call_message(msg))

        for tc in tool_calls:
            fn = tc.function
            name = fn.name
            raw_args = fn.arguments or "{}"
            args = raw_args if isinstance(raw_args, dict) else _safe_parse(raw_args)

            result = dispatch_tool(name, args)

            messages.append(
                {
                    "role": "tool",
                    "content": result,
                    "tool_call_id": tc.id,
                }
            )

    # Max rounds exhausted — return the last assistant content if any
    last_assistant = next(
        (m["content"] for m in reversed(messages) if m.get("role") == "assistant"),
        "I was unable to complete the task within the allowed number of steps.",
    )
    return strip_qwen_thinking(
        last_assistant
        or "I was unable to complete the task within the allowed number of steps."
    )


# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------

def _visible_assistant_text(msg: Any) -> str:
    """
    User-visible assistant string from `message.content` only. The Inference API
    may return chain-of-thought in a separate `reasoning` field; we never merge
    that into the UI. Strip any `</think>`...`</think>` blocks left in `content`.
    """
    raw = getattr(msg, "content", None) or ""
    return strip_qwen_thinking(raw)


def _safe_parse(raw: str) -> dict:
    """Parse a JSON string into a dict, returning {} on failure."""
    try:
        return json.loads(raw)
    except (json.JSONDecodeError, TypeError):
        return {}


def _assistant_tool_call_message(msg: Any) -> dict:
    """
    Re-serialise the assistant message that contains tool_calls into the plain
    dict format expected when appended back to `messages`.
    """
    tool_calls_payload = []
    for tc in msg.tool_calls or []:
        fn = tc.function
        tool_calls_payload.append(
            {
                "id": tc.id,
                "type": "function",
                "function": {
                    "name": fn.name,
                    "arguments": (
                        fn.arguments
                        if isinstance(fn.arguments, str)
                        else json.dumps(fn.arguments)
                    ),
                },
            }
        )

    return {
        "role": "assistant",
        # Drop thinking from prior step so follow-up completions are not primed
        # to dump huge `</think>` blocks into `content` on later turns.
        "content": strip_qwen_thinking(msg.content or ""),
        "tool_calls": tool_calls_payload,
    }