#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
工作流执行任务 - 供定时任务调度器调用
"""
import logging

logger = logging.getLogger(__name__)


async def execute_workflow_task(
    job_code: str = None,
    workflow_code: str = None,
    inputs: dict = None,
    **kwargs
):
    """
    执行工作流任务

    在定时任务中调用已发布的数据处理/自动化工作流。

    Args:
        job_code: 任务编码（由调度器自动传入）
        workflow_code: 要执行的工作流编码（必填）
        inputs: 工作流输入变量
        **kwargs: 其他参数（包含 task_logger）

    Returns:
        dict: 执行结果
    """
    if not workflow_code:
        raise ValueError("workflow_code 参数不能为空，请在 task_kwargs 中配置")

    inputs = inputs or {}
    inputs['job_code'] = job_code

    from app.database import AsyncSessionLocal
    from ai_platform.services.workflow_service import AIWorkflowService
    from ai_platform.models.workflow import AIWorkflow
    from sqlalchemy import select
    from scheduler.task_utils import TaskLoggerWrapper

    log = TaskLoggerWrapper(job_code, kwargs.get('task_logger'))
    await log.info(f"开始执行工作流: {workflow_code}")

    async with AsyncSessionLocal() as db:
        result = await db.execute(
            select(AIWorkflow).where(
                AIWorkflow.code == workflow_code,
                AIWorkflow.is_deleted == False
            )
        )
        workflow = result.scalar_one_or_none()

        if not workflow:
            raise ValueError(f"工作流不存在: {workflow_code}")

        if workflow.workflow_type not in ('data_process', 'automation'):
            raise ValueError(
                f"工作流类型不支持定时执行: {workflow.workflow_type}，"
                f"仅支持 data_process 和 automation 类型"
            )

        if workflow.status != 'published':
            raise ValueError(f"工作流未发布: {workflow_code}")

        await log.info(
            f"工作流类型: {workflow.workflow_type}, "
            f"工作流名称: {workflow.name}, "
            f"输入变量: {list(inputs.keys())}"
        )

        service = AIWorkflowService(db)
        run = await service.run_workflow(
            workflow_id=str(workflow.id),
            inputs=inputs,
            trigger_type='api',
        )

        await log.info(
            f"工作流执行完成: status={run.status}, "
            f"elapsed_time={run.elapsed_time}ms, "
            f"total_steps={run.total_steps}"
        )

        if run.status == 'failed':
            raise RuntimeError(f"工作流执行失败: {run.error_message}")

        return {
            'run_id': str(run.id),
            'workflow_code': workflow_code,
            'workflow_name': workflow.name,
            'status': run.status,
            'elapsed_time': run.elapsed_time,
            'total_steps': run.total_steps,
            'total_tokens': run.total_tokens,
            'outputs': run.outputs,
        }