from __future__ import annotations

import asyncio
import json
import os
import subprocess
from contextlib import asynccontextmanager
from dataclasses import dataclass
from pathlib import Path

from openai import OpenAI
from workflow_arena import WorkflowArenaAction, WorkflowArenaEnv
from workflow_arena.models import (
    DifficultyPreset,
    WorkflowActionType,
    WorkflowArenaObservation,
    WorkflowTaskView,
)

BENCHMARK = "WorkflowArena"
PRESETS = [
    DifficultyPreset.EASY,
    DifficultyPreset.MEDIUM,
    DifficultyPreset.HARD,
]
PROJECT_DIR = Path(__file__).resolve().parent
IMAGE_NAME = "workflow-arena-inference:latest"
DOCKERFILE_PATH = PROJECT_DIR / "server" / "Dockerfile"
API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
MODEL_NAME = os.getenv("MODEL_NAME", "qwen/qwen3.5-9b")
HF_TOKEN = os.getenv("HF_TOKEN")
LOCAL_IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME")
DEFAULT_BASE_URL = os.getenv("WORKFLOW_ARENA_BASE_URL", "http://localhost:8000")
TEMPERATURE = 0.0
MAX_STEPS = 256

SYSTEM_PROMPT = (
    "You are scheduling a dependency-constrained workflow on limited workers. "
    "Respond with compact JSON only. "
    'Valid formats: {"action_type":"wait","task_ids":[]} or '
    '{"action_type":"dispatch","task_ids":["task_01","task_02"]}. '
    "Only dispatch task ids that appear in ready_tasks for the current observation. "
    "Never exceed free_workers. "
    'If free_workers is 0 and running_tasks is non-empty, respond with {"action_type":"wait","task_ids":[]}. '
    "If your previous action was invalid, use validation_error to correct it while still reasoning from the current observation. "
    "Never repeat a previously dispatched task unless it still appears in ready_tasks."
)


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: str | None
) -> 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"{reward:.2f}" for reward in rewards)
    print(
        f"[END] success={str(success).lower()} steps={steps} score={score:.3f} rewards={rewards_str}",
        flush=True,
    )


def log_warning(message: str) -> None:
    print(f"[WARN] {message}", flush=True)


def compact_task(task: WorkflowTaskView) -> dict[str, object]:
    return {
        "task_id": task.task_id,
        "duration": task.duration,
        "priority": task.priority,
        "deadline": task.deadline,
        "criticality": task.criticality,
        "slack": task.slack,
        "downstream_count": task.downstream_count,
        "dependencies": task.dependencies,
        "attempt_count": task.attempt_count,
    }


def make_user_prompt(observation: WorkflowArenaObservation) -> str:
    must_wait = observation.free_workers == 0 and bool(observation.running_tasks)
    return json.dumps(
        {
            "instruction": observation.instruction,
            "current_time": observation.current_time,
            "effective_workers": observation.effective_workers,
            "degraded_workers": observation.degraded_workers,
            "free_workers": observation.free_workers,
            "time_budget": observation.time_budget,
            "time_remaining": observation.time_remaining,
            "must_wait": must_wait,
            "ready_tasks": [compact_task(task) for task in observation.ready_tasks],
            "running_tasks": [compact_task(task) for task in observation.running_tasks],
            "progress": observation.progress.model_dump(mode="json"),
            "reward_breakdown": observation.last_reward_breakdown.model_dump(
                mode="json"
            ),
            "note": observation.note,
            "validation_error": observation.validation_error,
            "recent_failure_events": [
                event.model_dump(mode="json")
                for event in observation.recent_failure_events
            ],
            "last_action": observation.received_action,
        },
        separators=(",", ":"),
    )


def heuristic_action(observation: WorkflowArenaObservation) -> WorkflowArenaAction:
    if observation.free_workers <= 0 and observation.running_tasks:
        return WorkflowArenaAction(action_type=WorkflowActionType.WAIT, task_ids=[])

    if not observation.ready_tasks or observation.free_workers <= 0:
        return WorkflowArenaAction(action_type=WorkflowActionType.WAIT, task_ids=[])

    time_remaining = observation.time_remaining
    ranked = sorted(
        observation.ready_tasks,
        key=lambda task: (
            time_remaining is not None and task.duration > time_remaining,
            max(0, task.duration - time_remaining) if time_remaining is not None else 0,
            task.deadline if task.deadline is not None else 10**9,
            -(task.criticality or 0.0),
            -task.priority,
            task.duration,
            task.task_id,
        ),
    )
    selected = [task.task_id for task in ranked[: observation.free_workers]]
    return WorkflowArenaAction(
        action_type=WorkflowActionType.DISPATCH,
        task_ids=selected,
    )


def parse_action(
    text: str, observation: WorkflowArenaObservation
) -> WorkflowArenaAction:
    text = text.strip()
    if not text:
        raise ValueError("Model response did not include JSON action")
    payload = json.loads(text)
    return WorkflowArenaAction.model_validate(payload)


def get_model_action(
    client: OpenAI,
    model_name: str,
    observation: WorkflowArenaObservation,
) -> WorkflowArenaAction:
    prompt = make_user_prompt(observation)
    completion = client.chat.completions.create(
        model=model_name,
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": prompt},
        ],
        temperature=TEMPERATURE,
        max_tokens=120,
    )
    text = (completion.choices[0].message.content or "").strip()
    return parse_action(text, observation)


def action_to_log_string(action: WorkflowArenaAction) -> str:
    payload = action.model_dump(mode="json")
    if payload.get("metadata") == {}:
        payload.pop("metadata", None)
    return json.dumps(payload, separators=(",", ":"))


def resolve_model_client() -> tuple[OpenAI | None, str]:
    api_key = (
        os.getenv("API_KEY")
        or HF_TOKEN
        or os.getenv("OPENAI_API_KEY")
    )
    missing = []

    if not api_key:
        missing.append("API_KEY or HF_TOKEN")

    if missing:
        log_warning(
            "Missing model configuration ("
            + ", ".join(missing)
            + "). Falling back to heuristic policy."
        )
        return None, "heuristic"

    try:
        return OpenAI(base_url=API_BASE_URL, api_key=api_key), MODEL_NAME
    except Exception as exc:  # pragma: no cover - defensive initialization fallback
        log_warning(
            f"Failed to initialize model client: {exc}. Falling back to heuristic policy."
        )
        return None, "heuristic"


def compute_score(observation: WorkflowArenaObservation) -> float:
    score = observation.benchmark_score
    if score is None:
        score = observation.success_metrics.benchmark_score
    return max(0.0, min(1.0, float(score or 0.0)))


def is_success(observation: WorkflowArenaObservation) -> bool:
    return bool(
        observation.done
        and observation.success_metrics.makespan is not None
        and observation.termination_reason is None
    )


@dataclass
class EpisodeResult:
    success: bool
    steps: int
    score: float
    rewards: list[float]


def ensure_local_image() -> None:
    local_image_name = LOCAL_IMAGE_NAME or IMAGE_NAME
    try:
        inspect_result = subprocess.run(
            ["docker", "image", "inspect", local_image_name],
            cwd=PROJECT_DIR,
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL,
            check=False,
        )
    except OSError as exc:
        raise RuntimeError(f"Failed to execute docker: {exc}") from exc

    if inspect_result.returncode == 0:
        return

    try:
        build_result = subprocess.run(
            ["docker", "build", "-t", local_image_name, "-f", str(DOCKERFILE_PATH), "."],
            cwd=PROJECT_DIR,
            capture_output=True,
            text=True,
            check=False,
        )
    except OSError as exc:
        raise RuntimeError(f"Failed to execute docker build: {exc}") from exc

    if build_result.returncode != 0:
        raise RuntimeError(
            "Failed to build Docker image for inference.\n"
            f"Command: docker build -t {local_image_name} -f {DOCKERFILE_PATH} .\n"
            f"Exit code: {build_result.returncode}\n"
            f"Stdout: {build_result.stdout}\n"
            f"Stderr: {build_result.stderr}"
        )


@asynccontextmanager
async def managed_env():
    try:
        async with WorkflowArenaEnv(base_url=DEFAULT_BASE_URL) as env:
            yield env
            return
    except Exception as exc:
        log_warning(
            f"Failed to connect to environment at {DEFAULT_BASE_URL}: {exc}. "
            "Trying local Docker fallback."
        )

    ensure_local_image()
    env = await WorkflowArenaEnv.from_docker_image(LOCAL_IMAGE_NAME or IMAGE_NAME)
    try:
        yield env
    finally:
        try:
            await env.close()
        except Exception as exc:  # pragma: no cover - teardown failures should not fail inference
            log_warning(f"Failed to close Docker environment cleanly: {exc}")


async def run_episode(
    env,
    client: OpenAI | None,
    model_name: str,
    preset: DifficultyPreset,
    seed: int,
) -> EpisodeResult:
    rewards: list[float] = []
    steps_taken = 0
    success = False
    score = 0.0

    log_start(task=preset.value, env=BENCHMARK, model=model_name)

    try:
        result = await env.reset(
            seed=seed,
            preset=preset.value,
        )
    except Exception as exc:  # pragma: no cover - env availability failures are external
        log_warning(f"Failed to reset preset={preset.value}: {exc}")
        log_end(success=False, steps=steps_taken, score=score, rewards=rewards)
        return EpisodeResult(
            success=success, steps=steps_taken, score=score, rewards=rewards
        )

    observation = result.observation

    while not observation.done and steps_taken < MAX_STEPS:
        try:
            if client is None:
                action = heuristic_action(observation)
            else:
                action = get_model_action(client, model_name, observation)
        except (
            Exception
        ):  # pragma: no cover - network/model failures are expected sometimes
            action = heuristic_action(observation)

        try:
            result = await env.step(action)
        except Exception as exc:  # pragma: no cover - preserve log format and continue safely
            fallback_action = heuristic_action(observation)
            if fallback_action != action:
                log_warning(
                    f"Step failed for preset={preset.value} with model action: {exc}. "
                    "Retrying with heuristic action."
                )
            action = fallback_action
            try:
                result = await env.step(action)
            except Exception as retry_exc:
                log_warning(
                    f"Step failed for preset={preset.value} even with heuristic action: {retry_exc}"
                )
                break

        observation = result.observation
        reward = float(result.reward or 0.0)
        rewards.append(reward)
        steps_taken += 1
        log_step(
            step=steps_taken,
            action=action_to_log_string(action),
            reward=reward,
            done=bool(result.done),
            error=observation.validation_error,
        )

    success = is_success(observation)
    score = compute_score(observation) if observation.done else 0.0
    log_end(success=success, steps=steps_taken, score=score, rewards=rewards)

    return EpisodeResult(
        success=success, steps=steps_taken, score=score, rewards=rewards
    )


async def main() -> None:
    client, model_name = resolve_model_client()

    async with managed_env() as env:
        for index, preset in enumerate(PRESETS):
            await run_episode(
                env=env,
                client=client,
                model_name=model_name,
                preset=preset,
                seed=100 + index,
            )


if __name__ == "__main__":
    try:
        asyncio.run(main())
    except Exception as exc:  # pragma: no cover - final safeguard for validator stability
        log_warning(f"Fatal inference error: {exc}")