inference.py

"""
Inference Script — Meta_com OpenEnv Agent
=========================================
MANDATORY
- Before submitting, ensure the following variables are defined in your environment configuration:
    API_BASE_URL   The API endpoint for the LLM.
    MODEL_NAME     The model identifier to use for inference.
    HF_TOKEN       Your Hugging Face / API key.
    LOCAL_IMAGE_NAME The name of the local image to use for the environment if you are using from_docker_image()
                     method

- Defaults are set only for API_BASE_URL and MODEL_NAME
    (and should reflect your active inference setup):
    API_BASE_URL = os.getenv("API_BASE_URL", "<your-active-endpoint>")
    MODEL_NAME = os.getenv("MODEL_NAME", "<your-active-model>")

- The inference script must be named `inference.py` and placed in the root directory of the project
- Participants must use OpenAI Client for all LLM calls using above variables

STDOUT FORMAT
- The script must emit exactly three line types to stdout, in this order:

    [START] task=<task_name> env=<benchmark> model=<model_name>
    [STEP]  step=<n> action=<action_str> reward=<0.00> done=<true|false> error=<msg|null>
    [END]   success=<true|false> steps=<n> score=<score> rewards=<r1,r2,...,rn>

TASK REQUIREMENTS
- Must run at least 3 tasks with graders.
- Each task score must be strictly between 0 and 1 (not 0.0 and not 1.0).
"""

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

from openai import OpenAI

from my_env_v4 import MyEnvV4Action, MyEnvV4Env

# ---------------------------------------------------------------------------
# Environment configuration (OpenEnv-compliant variable names)
# ---------------------------------------------------------------------------
API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct")
HF_TOKEN = os.getenv("HF_TOKEN")

# Optional — if you use from_docker_image():
LOCAL_IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME")

BENCHMARK = os.getenv("MY_ENV_V4_BENCHMARK", "my_env_v4")

# ---------------------------------------------------------------------------
# Task registry — at least 3 tasks required by OpenEnv Phase 2 validation
# ---------------------------------------------------------------------------
TASKS: List[Dict] = [
    {
        "id": "git_conflict_trivial",
        "max_steps": 4,
        "temperature": 0.5,
        "system_prompt": (
            "You are resolving a trivial Git merge conflict. "
            "The conflict involves simple whitespace or formatting differences. "
            "Reply with exactly one corrected code block — no quotes, no prefixes."
        ),
    },
    {
        "id": "git_conflict_multifile",
        "max_steps": 6,
        "temperature": 0.7,
        "system_prompt": (
            "You are resolving a multi-file Git merge conflict. "
            "Two branches modified different parts of an API. Reconcile both changes. "
            "Reply with exactly one corrected code block — no quotes, no prefixes."
        ),
    },
    {
        "id": "git_conflict_semantic",
        "max_steps": 8,
        "temperature": 0.8,
        "system_prompt": (
            "You are resolving a deep semantic Git merge conflict. "
            "Two branches implement competing logic for the same feature. "
            "Synthesize both intents into a single coherent implementation. "
            "Reply with exactly one corrected code block — no quotes, no prefixes."
        ),
    },
]

MAX_TOKENS = 150

# Score boundary constants — OpenEnv requires scores strictly in (0, 1)
SCORE_FLOOR = 0.001
SCORE_CEIL = 0.999


# ---------------------------------------------------------------------------
# Structured logging helpers (stdout format required by OpenEnv)
# ---------------------------------------------------------------------------
def log_start(task: str, env: str, model: str) -> None:
    print(f"[START] task={task} env={env} model={model}", flush=True)


def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]) -> None:
    error_val = error if error else "null"
    done_val = str(done).lower()
    print(
        f"[STEP] step={step} action={action} reward={reward:.2f} done={done_val} error={error_val}",
        flush=True,
    )


def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None:
    rewards_str = ",".join(f"{r:.2f}" for r in rewards)
    print(
        f"[END] success={str(success).lower()} steps={steps} score={score:.4f} rewards={rewards_str}",
        flush=True,
    )


# ---------------------------------------------------------------------------
# Agent interaction helpers
# ---------------------------------------------------------------------------
def build_user_prompt(step: int, last_echoed: str, last_reward: float, history: List[str]) -> str:
    history_block = "\n".join(history[-4:]) if history else "None"
    return textwrap.dedent(
        f"""
        Step: {step}
        Last echoed message: {last_echoed!r}
        Last reward: {last_reward:.2f}
        Previous steps:
        {history_block}
        Send your next message.
        """
    ).strip()


def get_model_message(
    client: OpenAI,
    system_prompt: str,
    step: int,
    last_echoed: str,
    last_reward: float,
    history: List[str],
    temperature: float,
) -> str:
    user_prompt = build_user_prompt(step, last_echoed, last_reward, history)

    # Fallback heuristic when no real API key is available (build/test phase)
    if HF_TOKEN is None:
        return f"Conflict resolution patch for step {step}"

    try:
        completion = client.chat.completions.create(
            model=MODEL_NAME,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt},
            ],
            temperature=temperature,
            max_tokens=MAX_TOKENS,
            stream=False,
        )
        text = (completion.choices[0].message.content or "").strip()
        return text if text else "hello"
    except Exception as exc:
        print(f"[DEBUG] Model request failed: {exc}", flush=True)
        return "hello"


def clamp_score(raw: float) -> float:
    """Clamp a raw score to the open interval (0, 1) as required by OpenEnv."""
    return min(max(raw, SCORE_FLOOR), SCORE_CEIL)


# ---------------------------------------------------------------------------
# Single-task episode runner
# ---------------------------------------------------------------------------
async def run_task(client: OpenAI, env: MyEnvV4Env, task: Dict) -> None:
    """Run one complete agent episode for the given task definition."""
    task_id = task["id"]
    max_steps = task["max_steps"]
    temperature = task["temperature"]
    system_prompt = task["system_prompt"]

    # Max possible reward for normalization
    max_reward_per_step = MAX_TOKENS * 0.1
    max_total_reward = max_steps * max_reward_per_step

    history: List[str] = []
    rewards: List[float] = []
    steps_taken = 0
    score = 0.0
    success = False

    log_start(task=task_id, env=BENCHMARK, model=MODEL_NAME)

    try:
        result = await env.reset(task_id=task_id)
        last_echoed = result.observation.echoed_message
        last_reward = 0.0

        for step in range(1, max_steps + 1):
            if result.done:
                break

            message = get_model_message(
                client, system_prompt, step, last_echoed, last_reward, history, temperature
            )

            result = await env.step(MyEnvV4Action(message=message))
            obs = result.observation

            reward = result.reward or 0.0
            done = result.done
            error = None

            rewards.append(reward)
            steps_taken = step
            last_echoed = obs.echoed_message
            last_reward = reward

            log_step(step=step, action=message, reward=reward, done=done, error=error)

            history.append(f"Step {step}: {message!r} -> reward {reward:+.2f}")

            if done:
                break

        raw_score = sum(rewards) / max_total_reward if max_total_reward > 0 else 0.5
        score = clamp_score(raw_score)
        success = score >= 0.1

    except Exception as exc:
        print(f"[DEBUG] Task {task_id} error: {exc}", flush=True)
        # Even on error, emit a valid clamped score so the grader accepts it
        score = clamp_score(0.0)
        success = False

    finally:
        log_end(success=success, steps=steps_taken, score=score, rewards=rewards)


# ---------------------------------------------------------------------------
# Main entrypoint — runs ALL registered tasks sequentially
# ---------------------------------------------------------------------------
async def main() -> None:
    api_key_to_use = HF_TOKEN if HF_TOKEN else "fake-key"
    client = OpenAI(base_url=API_BASE_URL, api_key=api_key_to_use)

    env = await MyEnvV4Env.from_docker_image(LOCAL_IMAGE_NAME)

    try:
        for task in TASKS:
            await run_task(client, env, task)
    finally:
        try:
            await env.close()
        except Exception as e:
            print(f"[DEBUG] env.close() error (container cleanup): {e}", flush=True)


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