"""Negotiation Arena — Vendor inference driver (V3).

Runs all 3 tasks against the (running) HTTP server using a Llama-3.3-70B
Vendor agent. The Vendor produces a Theory-of-Mind prediction alongside
each action; the server grades the prediction and may run a 3-judge
Tribunal at episode end. This script reads those signals back and prints
them as optional fields on the existing log lines.

Output format (V2-compatible; new fields are optional):
    [START] task=<task_id> env=negotiation-arena model=<MODEL_NAME>
    [STEP] step=<n> action=<json>[ tom_acc=<0.00>] reward=<0.00> done=<true|false> error=<msg|null>
    [END] success=<true|false> steps=<n> score=<0.000>[ tribunal=<0.000>] rewards=<r1,r2,...,rn>

The ``tom_acc=`` field appears only when the server returned a graded ToM
prediction for that turn. The ``tribunal=`` field appears only when the
server's terminal observation included a tribunal breakdown.
"""
from __future__ import annotations

import json
import os
import sys
from typing import Any, Dict, List, Optional

from client import NegotiationArenaEnv
from evaluation.llm_vendor import LLMVendor


# ---------------------------------------------------------------------------
# Environment knobs (V2-compatible — same names, same defaults)
# ---------------------------------------------------------------------------

API_BASE_URL = os.environ.get("API_BASE_URL", "https://router.huggingface.co/v1")
MODEL_NAME = os.environ.get("MODEL_NAME", "meta-llama/Llama-3.3-70B-Instruct")
HF_TOKEN = os.environ.get("HF_TOKEN", "")
SPACE_URL = os.environ.get("SPACE_URL", "http://localhost:8000")
EVAL_MODE = os.environ.get("EVAL_MODE", "false").lower() == "true"

TASKS = ["simple_saas", "gdpr_dpa", "enterprise_partnership"]


# ---------------------------------------------------------------------------
# Logging helpers
# ---------------------------------------------------------------------------
# log_start / log_step / log_end keep their V2 signatures and produce identical
# output when no new V3 fields are passed. New optional kwargs add the fields.

def log_start(task_id: str) -> None:
    print(
        f"[START] task={task_id} env=negotiation-arena model={MODEL_NAME}",
        flush=True,
    )


def log_step(
    step: int,
    action: Dict[str, Any],
    reward: float,
    done: bool,
    error: Optional[str],
    tom_acc: Optional[float] = None,
) -> None:
    """Print a [STEP] line.

    When ``tom_acc`` is provided, an additional ``tom_acc=X.XX`` field is
    inserted between ``action=...`` and ``reward=...``. Otherwise the line
    is byte-for-byte identical to V2.
    """
    err = "null" if not error else error
    action_json = json.dumps(_compact_action(action), separators=(",", ":"))
    tom_field = f" tom_acc={float(tom_acc):.2f}" if tom_acc is not None else ""
    print(
        f"[STEP] step={step} action={action_json}{tom_field} "
        f"reward={reward:.2f} done={'true' if done else 'false'} error={err}",
        flush=True,
    )


def log_end(
    success: bool,
    steps: int,
    score: float,
    rewards: List[float],
    tribunal_score: Optional[float] = None,
) -> None:
    """Print an [END] line.

    When ``tribunal_score`` is provided, a ``tribunal=X.XXX`` field is inserted
    between ``score=...`` and ``rewards=...``.
    """
    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
    tribunal_field = (
        f" tribunal={float(tribunal_score):.3f}" if tribunal_score is not None else ""
    )
    print(
        f"[END] success={'true' if success else 'false'} steps={steps} "
        f"score={score:.3f}{tribunal_field} rewards={rewards_str}",
        flush=True,
    )


def _compact_action(action: Dict[str, Any]) -> Dict[str, Any]:
    """Trim large fields from the logged action."""
    out: Dict[str, Any] = {}
    for k in ("action_type", "issue_name", "new_value"):
        if action.get(k) is not None:
            out[k] = action[k]
    if action.get("proposed_terms"):
        out["proposed_terms"] = action["proposed_terms"]
    if action.get("reasoning"):
        out["reasoning"] = str(action["reasoning"])[:120]
    return out


# ---------------------------------------------------------------------------
# Per-task driver
# ---------------------------------------------------------------------------


def run_task(env: NegotiationArenaEnv, vendor: LLMVendor, task_id: str) -> bool:
    log_start(task_id)

    rewards: List[float] = []
    tribunal_score: Optional[float] = None
    success = False
    step_count = 0
    final_score = 0.0

    obs = env.reset(task_id=task_id, eval_mode=EVAL_MODE, client_strategy="balanced")
    max_steps = int(obs.get("max_turns", 30)) + 2  # safety margin

    while True:
        if obs.get("done"):
            break
        if step_count >= max_steps:
            break

        # V3: Vendor produces (tom_prediction, action) in one LLM call.
        tom_pred, action = vendor.act_with_tom(obs, turn=step_count + 1)

        # Stamp the boilerplate fields the server expects.
        action.setdefault("episode_id", obs.get("episode_id", ""))
        action.setdefault("turn_number", int(obs.get("turn_number", 0)) + 1)
        action.setdefault("agent_role", "vendor")

        # Attach the ToM prediction (server treats this as optional — V2-compatible).
        if tom_pred:
            action["tom_prediction"] = tom_pred

        try:
            obs = env.step(action)
            error = None
        except Exception as e:  # noqa: BLE001
            error = str(e)
            log_step(step_count + 1, action, 0.0, True, error)
            break

        step_count += 1
        reward = float(obs.get("reward") or 0.0)
        rewards.append(reward)

        # ToM accuracy is in the observation when the server graded a prediction
        # this turn. Field is None on V2 servers or when no prediction was sent.
        tom_acc: Optional[float] = None
        feedback = obs.get("tom_grader_feedback")
        if isinstance(feedback, dict) and "composite" in feedback:
            try:
                tom_acc = float(feedback["composite"])
            except (TypeError, ValueError):
                tom_acc = None

        log_step(
            step_count,
            action,
            reward,
            bool(obs.get("done")),
            obs.get("error_message"),
            tom_acc=tom_acc,
        )

        if obs.get("done"):
            # Tribunal breakdown is only present on the terminal observation,
            # and only when the server's tribunal feature is enabled and an
            # API key is configured server-side.
            breakdown = obs.get("tribunal_breakdown")
            if isinstance(breakdown, dict) and "trimmed_mean" in breakdown:
                try:
                    tribunal_score = float(breakdown["trimmed_mean"])
                except (TypeError, ValueError):
                    tribunal_score = None
            break

    if rewards:
        # Score = average per-turn reward (final entry is the composite if terminal).
        final_score = sum(rewards) / len(rewards)
    success = bool(obs.get("done")) and bool(obs.get("final_deal_struck", False))
    log_end(success, step_count, final_score, rewards, tribunal_score=tribunal_score)
    return success


def main() -> int:
    vendor = LLMVendor(
        api_base_url=API_BASE_URL,
        model_name=MODEL_NAME,
        api_key=HF_TOKEN,
        temperature=0.2,
        max_tokens=800,
    )
    env = NegotiationArenaEnv(base_url=SPACE_URL)
    try:
        try:
            env.health()
        except Exception as e:  # noqa: BLE001
            print(f"[FATAL] cannot reach server at {SPACE_URL}: {e}", flush=True)
            return 1

        for task_id in TASKS:
            try:
                run_task(env, vendor, task_id)
            except Exception as e:  # noqa: BLE001
                print(f"[ERROR] task {task_id} crashed: {e}", flush=True)
    finally:
        env.close()
    return 0


if __name__ == "__main__":
    sys.exit(main())