"""
SQL Agent OpenEnv — Baseline Inference Script
==============================================

Runs a baseline LLM agent against all 3 tasks of the SQL Agent OpenEnv environment.

Environment variables (required):
  API_BASE_URL   — OpenAI-compatible base URL  (default: https://router.huggingface.co/v1)
  MODEL_NAME     — Model identifier            (default: Qwen/Qwen2.5-72B-Instruct)
  HF_TOKEN       — Hugging Face / API key

STDOUT format (strictly enforced):
  [START] task=<task_id> env=sql-agent-openenv model=<model>
  [STEP]  step=<n> action=<action> reward=<0.00> done=<true|false> error=<msg|null>
  [END]   success=<true|false> steps=<n> score=<0.000> rewards=<r1,r2,...>
"""

from __future__ import annotations

import asyncio
import os
import sys
import textwrap
from typing import List, Optional

# ── Path setup (inference.py lives at repo root; backend is a subdirectory) ──
_BACKEND = os.path.join(os.path.dirname(os.path.abspath(__file__)), "backend")
if _BACKEND not in sys.path:
    sys.path.insert(0, _BACKEND)

from openai import OpenAI  # noqa: E402

from env.sql_env import SQLAgentEnv, Action, Observation  # noqa: E402

# ── Config ────────────────────────────────────────────────────────────────────

API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY") or os.getenv("OPENAI_API_KEY", "")
API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct")
BENCHMARK = "sql-agent-openenv"

TASKS = ["simple_queries", "join_queries", "complex_queries"]
MAX_STEPS = 5
TEMPERATURE = 0.2
MAX_TOKENS = 50

REPAIR_ACTIONS = [
    "rewrite_full",
    "fix_column",
    "fix_table",
    "add_groupby",
    "rewrite_cte",
    "fix_syntax",
    "change_dialect",
    "relax_filter",
]

SYSTEM_PROMPT = textwrap.dedent("""
    You are an expert SQL agent interacting with a SQL repair environment.

    At each step you receive a natural language question, a database schema,
    and optionally the last SQL attempt + error message.

    Your job: pick ONE repair action from the list below that is most likely
    to fix the SQL error on the next attempt.

    Available actions:
      generate       — write fresh SQL from scratch (use on first attempt)
      rewrite_full   — completely rewrite the query from scratch
      fix_column     — fix wrong column name references
      fix_table      — fix wrong table name references
      add_groupby    — add or fix GROUP BY / aggregation clauses
      rewrite_cte    — restructure subqueries or CTEs
      fix_syntax     — fix syntax errors (brackets, commas, keywords)
      change_dialect — convert to SQLite-compatible functions
      relax_filter   — broaden or remove overly strict WHERE conditions

    Reply with ONLY the action name. No explanation. No punctuation.
    Example: fix_column
""").strip()


# ── Logging ───────────────────────────────────────────────────────────────────

# Hard bounds: every score/reward we ever emit is clamped to this closed range.
# 0.05 margin guarantees that :.2f and :.3f formatting never produces
# "0.00", "0.000", "1.00", or "1.000" (all of which parse as exactly 0.0 / 1.0).
_MIN_SCORE = 0.05
_MAX_SCORE = 0.95


def _safe_score(x) -> float:
    """Coerce anything (None, NaN, str, bool, int, float) to a float strictly in (0, 1)."""
    try:
        if x is None:
            return _MIN_SCORE
        if isinstance(x, bool):
            return _MAX_SCORE if x else _MIN_SCORE
        v = float(x)
        if v != v:  # NaN
            return _MIN_SCORE
        if v == float("inf"):
            return _MAX_SCORE
        if v == float("-inf"):
            return _MIN_SCORE
    except (TypeError, ValueError):
        return _MIN_SCORE
    return max(_MIN_SCORE, min(_MAX_SCORE, v))


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


def log_step(step: int, action: str, reward, done: bool, error: Optional[str]) -> None:
    r = _safe_score(reward)
    error_val = (error or "null")
    if hasattr(error_val, "replace"):
        error_val = error_val.replace("\n", " ").strip() or "null"
    done_val = str(bool(done)).lower()
    print(
        f"[STEP] step={int(step)} action={action or 'noop'} reward={r:.2f} "
        f"done={done_val} error={error_val}",
        flush=True,
    )


def log_end(success: bool, steps: int, score, rewards: List) -> None:
    s = _safe_score(score)
    safe_rewards = [_safe_score(r) for r in (rewards or [])]
    if not safe_rewards:
        safe_rewards = [_MIN_SCORE]
    rewards_str = ",".join(f"{r:.2f}" for r in safe_rewards)
    print(
        f"[END] success={str(bool(success)).lower()} steps={int(steps)} "
        f"score={s:.3f} rewards={rewards_str}",
        flush=True,
    )


# ── LLM helper ────────────────────────────────────────────────────────────────

def pick_action(
    client: OpenAI,
    obs: Observation,
    step: int,
) -> str:
    """Ask the LLM to pick a repair action given the current observation."""
    if step == 1 or obs.current_sql is None:
        return "generate"

    user_msg = textwrap.dedent(f"""
        Question: {obs.question}

        Current SQL (failed):
        {obs.current_sql}

        Error: {obs.error_message or "unknown"}
        Error class: {obs.error_class or "unknown"}
        Attempt number: {obs.attempt_number} of {obs.max_attempts}

        Which repair action should I use next?
    """).strip()

    try:
        completion = client.chat.completions.create(
            model=MODEL_NAME,
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": user_msg},
            ],
            temperature=TEMPERATURE,
            max_tokens=MAX_TOKENS,
        )
        raw = (completion.choices[0].message.content or "").strip().lower()
        # Normalise to valid action name
        for action in REPAIR_ACTIONS:
            if action in raw:
                return action
        return "rewrite_full"
    except Exception as exc:
        print(f"[DEBUG] LLM call failed: {exc}", flush=True)
        return "rewrite_full"


# ── Single-episode runner ─────────────────────────────────────────────────────

async def run_episode(
    env: SQLAgentEnv,
    client: OpenAI,
    task_id: str,
) -> None:
    """Run one full episode for a task, emitting structured stdout logs."""
    log_start(task=task_id, model=MODEL_NAME)

    rewards: List[float] = []
    steps_taken = 0
    score = _MIN_SCORE
    success = False
    last_error: Optional[str] = None

    try:
        try:
            obs = env.reset(task_id)
        except Exception as exc:
            log_step(step=1, action="reset", reward=_MIN_SCORE, done=True, error=str(exc))
            rewards.append(_MIN_SCORE)
            steps_taken = 1
            return

        for step in range(1, MAX_STEPS + 1):
            try:
                action_name = pick_action(client, obs, step)
            except Exception:
                action_name = "generate"
            action = Action(repair_action=action_name)

            try:
                obs, reward_info = await env.step(action)
            except Exception as exc:
                log_step(step=step, action=action_name, reward=_MIN_SCORE, done=True, error=str(exc))
                rewards.append(_MIN_SCORE)
                steps_taken = step
                break

            reward = _safe_score(getattr(reward_info, "value", None))
            done = bool(getattr(reward_info, "done", False))
            last_error = getattr(obs, "error_message", None)
            success = bool(getattr(reward_info, "success", False))

            rewards.append(reward)
            steps_taken = step

            log_step(
                step=step,
                action=action_name,
                reward=reward,
                done=done,
                error=last_error,
            )

            if done:
                break

        denom = max(len(rewards), 1)
        avg = sum(rewards) / denom if rewards else _MIN_SCORE
        score = _safe_score(avg)

    except Exception as exc:
        # Catch-all so we always emit a valid [END] line
        log_step(step=steps_taken or 1, action="error", reward=_MIN_SCORE, done=True, error=str(exc))
        if not rewards:
            rewards.append(_MIN_SCORE)
        score = _MIN_SCORE
    finally:
        log_end(
            success=success,
            steps=max(int(steps_taken), 1),
            score=score,
            rewards=rewards,
        )


# ── Main ──────────────────────────────────────────────────────────────────────

async def main() -> None:
    try:
        client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
        env = SQLAgentEnv()
    except Exception as exc:
        # Environment couldn't init — still emit a valid [START]/[STEP]/[END] per task
        for task_id in TASKS:
            log_start(task=task_id, model=MODEL_NAME)
            log_step(step=1, action="init_error", reward=_MIN_SCORE, done=True, error=str(exc))
            log_end(success=False, steps=1, score=_MIN_SCORE, rewards=[_MIN_SCORE])
            print("", flush=True)
        return

    for task_id in TASKS:
        try:
            await run_episode(env, client, task_id)
        except Exception as exc:
            # run_episode already has its own catch-all, but guard against anything leaking
            log_end(success=False, steps=1, score=_MIN_SCORE, rewards=[_MIN_SCORE])
            print(f"[DEBUG] run_episode({task_id}) crashed: {exc}", flush=True)
        print("", flush=True)


if __name__ == "__main__":
    asyncio.run(main())