"""
模型训练业务编排（移植自模型服务 projects/backend 的 training_service）

- preset 参数预设（quick / standard / high）
- train_type → stage 映射（sft/dpo/cpt/cot）
- 训练任务的启动 / 暂停 / 恢复 / 取消
- compute_gateway 状态轮询线程（当任务派发到算力时自动同步状态）
"""
from __future__ import annotations

import asyncio
import threading
import time
from typing import Any

from app.core.config import get_settings
from app.db.platform_store import get_platform_store
from app.modules.fine_tune import runner

PRESETS: dict[str, dict[str, Any]] = {
    "quick": {"learning_rate": "5e-5", "n_epochs": 1, "batch_size": 4, "lora_rank": 8},
    "standard": {"learning_rate": "2e-5", "n_epochs": 3, "batch_size": 8, "lora_rank": 16},
    "high": {"learning_rate": "1e-5", "n_epochs": 5, "batch_size": 4, "lora_rank": 32},
}

# ── compute sync 轮询线程 ──────────────────────────────────────────────
_sync_thread: threading.Thread | None = None
_sync_thread_stop = threading.Event()


def _compute_sync_loop() -> None:
    """后台线程：周期性轮询算力节点，同步训练任务状态/日志/指标。"""
    interval = get_settings().compute_poll_interval_seconds or 3
    while not _sync_thread_stop.is_set():
        try:
            store = get_platform_store()
            running = store.running_compute_tasks()
            if running:
                asyncio.run(_poll_once())
        except Exception:  # noqa: BLE001 - keep polling loop alive
            pass
        time.sleep(interval)


async def _poll_once() -> None:
    from app.modules.compute_gateway.sync import poll_compute_jobs_once
    await poll_compute_jobs_once()


def start_compute_sync_worker() -> None:
    """启动后台轮询线程（幂等，多次调用安全）。"""
    global _sync_thread
    if _sync_thread is not None and _sync_thread.is_alive():
        return
    _sync_thread_stop.clear()
    _sync_thread = threading.Thread(target=_compute_sync_loop, daemon=True)
    _sync_thread.start()


def stop_compute_sync_worker() -> None:
    """停止后台轮询线程。"""
    _sync_thread_stop.set()


# ── preset / config ────────────────────────────────────────────────────
def apply_presets(payload: dict[str, Any]) -> dict[str, Any]:
    """根据 preset 字段补全缺失的超参；preset=custom 时不覆盖。"""
    payload = dict(payload)
    preset = payload.get("preset", "standard")
    if preset in PRESETS and payload.get("preset") != "custom":
        for key, value in PRESETS[preset].items():
            payload.setdefault(key, value)
    return payload


def build_training_config(payload: dict[str, Any]) -> dict[str, Any]:
    """把前端创建/启动载荷标准化为执行器可消费的 config。"""
    payload = apply_presets(dict(payload))
    gpus = payload.get("gpus") or [0]
    return {
        "name": payload.get("name", ""),
        "description": payload.get("description", ""),
        "train_type": payload.get("train_type", "SFT"),
        "train_method": payload.get("train_method", "lora"),
        "template": payload.get("template", "qwen"),
        "base_model": payload.get("base_model", "") or payload.get("base_model_id", ""),
        "train_dataset_id": payload.get("train_dataset_id", ""),
        "eval_dataset_id": payload.get("eval_dataset_id", ""),
        "auto_merge": bool(payload.get("auto_merge", False)),
        "output_model_name": payload.get("output_model_name", ""),
        "gpus": gpus,
        "num_gpus": payload.get("num_gpus", len(gpus)),
        "batch_size": payload.get("batch_size", 2),
        "learning_rate": payload.get("learning_rate", 0.0002),
        "n_epochs": payload.get("n_epochs", 3),
        "save_steps": payload.get("save_steps", 50),
        "lr_scheduler_type": payload.get("lr_scheduler_type", "cosine"),
        "max_length": payload.get("max_length", 2048),
        "warmup_ratio": payload.get("warmup_ratio", 0.03),
        "weight_decay": payload.get("weight_decay", 0.01),
        "lora_rank": payload.get("lora_rank", 8),
        "lora_alpha": payload.get("lora_alpha", 16),
        "lora_dropout": payload.get("lora_dropout", 0.05),
        "resume_from": payload.get("resume_from"),
    }


def launch_training(task_id: str) -> None:
    """在后台线程启动真实训练（本机 subprocess fallback，当算力节点不可用时使用）。

    架构原则：GPU 计算应派发到算力服务进程执行。
    当 platform_store.start_task 检测到在线算力节点时，会走 _dispatch_to_compute 派发路径；
    仅当无可用算力节点且非 simulator 模式时，降级到本机 runner（违反 §1.1，待移除）。
    """
    threading.Thread(target=runner.run_training, args=(task_id,), daemon=True).start()


def pause(task_id: str) -> bool:
    return runner.pause(task_id)


def resume(task_id: str) -> bool:
    return runner.resume(task_id)


def cancel(task_id: str) -> bool:
    return runner.cancel(task_id)