"""
inference.py — Warehouse Fulfillment Agent
==========================================
Mandatory environment variables:
    API_BASE_URL   The API endpoint for the LLM (OpenAI-compatible).
    MODEL_NAME     The model identifier to use for inference.
    HF_TOKEN       Your Hugging Face / API key.
Optional environment variables:
    LOCAL_IMAGE_NAME  Local Docker image name, if needed by the runner.

Uses the OpenAI client for all LLM calls.
Runs all 3 warehouse tasks and prints scores.
Runtime target: < 20 min on vcpu=2, 8 GB RAM.
"""

from __future__ import annotations

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

from openai import OpenAI

from grid_env.env import WarehouseFulfillmentEnv
from grid_env.graders import grade_episode
from grid_env.models import WarehouseObservation, WarehouseState
from grid_env.tasks import TASKS

# ── Environment configuration ────────────────────────────────────────────────

API_BASE_URL: str = os.getenv("API_BASE_URL", "https://api.openai.com/v1")
MODEL_NAME: str = os.getenv("MODEL_NAME", "gpt-4o-mini")
HF_TOKEN: str | None = os.getenv("HF_TOKEN")
LOCAL_IMAGE_NAME: str | None = os.getenv("LOCAL_IMAGE_NAME")

# Multi-seed evaluation: comma-separated list of seeds
EVAL_SEEDS_STR: str = os.getenv("EVAL_SEEDS", "7,42,123,456,789")
EVAL_SEEDS: List[int] = [int(s.strip()) for s in EVAL_SEEDS_STR.split(",")]

# Safety cap so we never exceed the 20-min wall-clock limit.
# Each task has its own max_steps; this is an outer guard.
GLOBAL_MAX_STEPS: int = 60

# ── Validator-parseable stdout blocks ─────────────────────────────────────────

def _stdout_block(tag: str, **fields: Any) -> None:
    """
    Emit a single-line structured block to stdout.

    External validators look for literal `[START]`, `[STEP]`, `[END]` tokens in
    stdout and then parse key=value pairs.
    """
    parts: List[str] = [f"[{tag}]"]
    for k, v in fields.items():
        parts.append(f"{k}={v}")
    print(" ".join(parts), flush=True)


def _reward_value(reward: Any) -> float:
    try:
        return float(getattr(reward, "value"))
    except Exception:  # noqa: BLE001
        try:
            return float(reward)
        except Exception:  # noqa: BLE001
            return 0.0

# ── Prompts ───────────────────────────────────────────────────────────────────

SYSTEM_PROMPT = """\
You control a warehouse fulfillment robot.
You will receive the current environment observation as JSON.
Reply with exactly one JSON object — no markdown, no extra text:
{
  "command": "<one of: turn_left | turn_right | move_forward | scan_bin | pick_item | pack_item | recharge | rest | wait>",
  "rationale": "<one short sentence>"
}

Guidelines:
- Scan the required bin before picking from it.
- Pick the item your order needs and carry it to the pack station.
- Face the pack station before calling pack_item.
- Recharge before battery runs to 0 if needed.
- Avoid invalid actions — every wasted step costs score.

Advanced mechanics (active on harder tasks):
- Obstacles: some cells are impassable. If front_cell says "obstacle", turn to find another route.
- Item weight: items have weight. If an item exceeds your carry capacity, you cannot pick it.
  Heavier items drain more battery while moving.
- Stamina: movement costs stamina. When stamina hits 0, movement costs double battery.
  Use the "rest" action at the rest area to restore stamina.
- Money: packing correct items earns money; wrong packs lose money. Hit the profit target if set.
"""


def _build_user_message(obs: WarehouseObservation, state: WarehouseState) -> str:
    payload = {
        "task_id": obs.task_id,
        "mission": obs.mission,
        "narrative": obs.narrative,
        "agent_position": list(obs.agent_position),
        "heading": obs.heading,
        "front_cell": obs.front_cell,
        "carrying": obs.carrying,
        "battery_level": obs.battery_level,
        "visible_bins": obs.visible_bins,
        "pending_order": [{"sku": p.sku, "remaining": p.remaining} for p in obs.pending_order],
        "packed_order": [{"sku": p.sku, "packed": p.packed} for p in obs.packed_order],
        "progress_ratio": obs.progress_ratio,
        "step_count": state.step_count,
        "max_steps": state.max_steps,
        "recent_actions": state.action_history[-6:],
    }
    return json.dumps(payload, indent=2)


# ── LLM action selection ──────────────────────────────────────────────────────

VALID_COMMANDS = {
    "turn_left", "turn_right", "move_forward",
    "scan_bin", "pick_item", "pack_item", "recharge", "rest", "wait",
}


def pick_action(client: OpenAI, obs: WarehouseObservation, state: WarehouseState) -> str:
    """Call the LLM and return a valid command string."""
    try:
        response = client.chat.completions.create(
            model=MODEL_NAME,
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": _build_user_message(obs, state)},
            ],
            temperature=0.2,
            max_tokens=120,
            response_format={"type": "json_object"},
        )
        content = response.choices[0].message.content or ""
        parsed = json.loads(content)
        command = str(parsed.get("command", "wait")).strip()
        if command not in VALID_COMMANDS:
            print(f"  [warn] Model returned unknown command '{command}', using 'wait'")
            return "wait"
        return command
    except Exception as exc:  # noqa: BLE001
        print(f"  [warn] LLM call failed: {exc} — using 'wait'")
        return "wait"


# ── Task runner ───────────────────────────────────────────────────────────────

def run_task(client: OpenAI, task_id: str, seed: int, verbose: bool = True) -> Dict[str, Any]:
    """Run a single task with a specific seed."""
    if verbose:
        print(f"\n{'='*60}")
        print(f"Task: {task_id} | Seed: {seed}")
        print(f"{'='*60}")

    env = WarehouseFulfillmentEnv(task_id=task_id, seed=seed)
    obs = env.reset(task_id=task_id, seed=seed)

    done = False
    step = 0
    _stdout_block("START", task=task_id, seed=seed)

    while not done and step < GLOBAL_MAX_STEPS:
        state = env.state()
        command = pick_action(client, obs, state)
        obs, reward, done, info = env.step(command)
        step += 1
        _stdout_block(
            "STEP",
            task=task_id,
            seed=seed,
            step=step,
            reward=f"{_reward_value(reward):.6f}",
            command=command,
            done=int(bool(done)),
        )
        if verbose:
            print(
                f"  step {step:>3} | {command:<14} | reward {reward.value:+.3f} "
                f"| progress {obs.progress_ratio:.2f} | battery {obs.battery_level}"
            )

    final_state = env.state()
    score = grade_episode(final_state)
    _stdout_block(
        "END",
        task=task_id,
        seed=seed,
        score=f"{float(score):.6f}",
        steps=final_state.step_count,
        success=int(bool(final_state.success)),
    )
    result = {
        "task_id": task_id,
        "seed": seed,
        "score": round(score, 4),
        "reward": round(final_state.total_reward, 4),
        "steps": final_state.step_count,
        "success": final_state.success,
        "completion_ratio": final_state.completion_ratio,
    }
    if verbose:
        print(
            f"\n  → score={result['score']:.4f}  reward={result['reward']:.4f}  "
            f"steps={result['steps']}  success={result['success']}"
        )
    return result


def run_task_multiseed(client: OpenAI, task_id: str, seeds: List[int]) -> Dict[str, Any]:
    """Run a task across multiple seeds and aggregate results."""
    print(f"\n{'='*60}")
    print(f"Task: {task_id} | Seeds: {seeds}")
    print(f"{'='*60}")
    
    seed_results = []
    for seed in seeds:
        result = run_task(client, task_id, seed, verbose=False)
        seed_results.append(result)
        print(f"  Seed {seed:>3}: score={result['score']:.4f}  steps={result['steps']:>3}  success={result['success']}")
    
    # Aggregate statistics
    scores = [r["score"] for r in seed_results]
    rewards = [r["reward"] for r in seed_results]
    steps = [r["steps"] for r in seed_results]
    successes = [1 if r["success"] else 0 for r in seed_results]
    
    mean_score = sum(scores) / len(scores)
    std_score = (sum((s - mean_score)**2 for s in scores) / len(scores))**0.5
    
    aggregated = {
        "task_id": task_id,
        "score": round(mean_score, 4),
        "score_std": round(std_score, 4),
        "score_min": round(min(scores), 4),
        "score_max": round(max(scores), 4),
        "reward": round(sum(rewards) / len(rewards), 4),
        "steps": round(sum(steps) / len(steps), 1),
        "success_rate": round(sum(successes) / len(successes), 2),
        "num_seeds": len(seeds),
        "seeds": seeds,
        "seed_results": seed_results,
    }
    
    print(f"\n  → Aggregated: score={aggregated['score']:.4f} (±{aggregated['score_std']:.4f})  "
          f"success_rate={aggregated['success_rate']:.2f}")
    
    return aggregated


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

def main() -> None:
    if not HF_TOKEN:
        print("ERROR: HF_TOKEN environment variable is not set.", file=sys.stderr)
        sys.exit(1)
    if not MODEL_NAME:
        print("ERROR: MODEL_NAME environment variable is not set.", file=sys.stderr)
        sys.exit(1)

    print(f"API_BASE_URL : {API_BASE_URL}")
    print(f"MODEL_NAME   : {MODEL_NAME}")
    print(f"LOCAL_IMAGE_NAME : {LOCAL_IMAGE_NAME}")
    print(f"Tasks        : {list(TASKS.keys())}")
    print(f"Eval Seeds   : {EVAL_SEEDS} ({len(EVAL_SEEDS)} seeds)")

    client = OpenAI(base_url=API_BASE_URL, api_key=HF_TOKEN)

    results: List[Dict[str, Any]] = []
    
    if len(EVAL_SEEDS) == 1:
        # Single-seed evaluation (backward compatible)
        for task_id in TASKS:
            result = run_task(client, task_id, EVAL_SEEDS[0], verbose=True)
            results.append(result)
    else:
        # Multi-seed evaluation
        for task_id in TASKS:
            result = run_task_multiseed(client, task_id, EVAL_SEEDS)
            results.append(result)

    # ── Summary report ────────────────────────────────────────────────────────
    print(f"\n{'='*60}")
    print("RESULTS SUMMARY")
    print(f"{'='*60}")
    
    is_multiseed = len(EVAL_SEEDS) > 1
    
    if is_multiseed:
        print(f"{'task_id':<30} {'score (±std)':>15} {'min':>7} {'max':>7} {'success':>8}")
        print("-" * 80)
        for r in results:
            print(
                f"{r['task_id']:<30} {r['score']:>6.4f} (±{r['score_std']:.4f}) "
                f"{r['score_min']:>7.4f} {r['score_max']:>7.4f} {r['success_rate']:>8.2f}"
            )
    else:
        print(f"{'task_id':<30} {'score':>7} {'reward':>8} {'steps':>6} {'success':>8}")
        print("-" * 60)
        for r in results:
            print(
                f"{r['task_id']:<30} {r['score']:>7.4f} {r['reward']:>8.4f} "
                f"{r['steps']:>6} {str(r['success']):>8}"
            )
    
    mean_score = sum(r["score"] for r in results) / len(results)
    print("-" * (80 if is_multiseed else 60))
    print(f"{'mean_score':<30} {mean_score:>7.4f}")

    # Validator-friendly JSON output to stdout
    print("\nJSON_RESULTS:", json.dumps(results, indent=2))

    # Exit non-zero if any score is outside [0, 1] — catches grader bugs
    for r in results:
        if not (0.0 <= r["score"] <= 1.0):
            print(f"ERROR: score out of range for {r['task_id']}: {r['score']}", file=sys.stderr)
            sys.exit(2)

    print("\nAll tasks completed successfully.")


if __name__ == "__main__":
    main()