import time
import random
import string
from framework.core.logger import get_logger
from framework.business.quantization_page import QuantizationPage

logger = get_logger("QuantizationScenario")

def _create_and_submit_task(quant_page: QuantizationPage, task_name: str):
    """内部辅助函数：执行单次完整的任务创建流"""
    # 1. 进入任务创建流程 
    quant_page.navigate_to()
    quant_page.click_create_task()
    
    # 2. 输入名称
    quant_page.input_task_name(task_name)
    
    # 3. 选择编译模式 
    quant_page.select_mode_fast_eval()
    
    # 4. 选择镜像
    quant_page.select_image("dcloud_ai_toolchain_ubuntu_22_s100_cpu-2")
    
    # 5. 选取模型文件
    quant_page.upload_model_file("model", "result.onnx")
    
    # 6. 输入 march 值 
    quant_page.input_march_param("nash-e")
    
    # 7. 提交表单
    quant_page.click_final_submit()


def run_quantization_lifecycle(quant_page: QuantizationPage):
    """量化任务全生命周期流程"""
    logger.info("开始执行量化工具自动化流程...")
    
    # ================= 阶段 1：手动终止链路 =================
    random_str1 = ''.join(random.choices(string.ascii_uppercase + string.digits, k=8))
    task_1 = f"AutoQuant_{random_str1}"
    logger.info(f"--- 阶段一: 测试任务手动终止与删除 [{task_1}] ---")
    
    _create_and_submit_task(quant_page, task_1)
    
    # 1. 查日志
    quant_page.open_and_close_logs(task_1)
    # 2. 停止
    quant_page.stop_task(task_1)
    # 3. 删除
    quant_page.delete_task(task_1)
    
    
    # ================= 阶段 2：完整成功链路 =================
    random_str2 = ''.join(random.choices(string.ascii_uppercase + string.digits, k=8))
    task_2 = f"AutoQuant_{random_str2}"
    logger.info(f"--- 阶段二: 测试任务完整成功链路 [{task_2}] ---")
    
    _create_and_submit_task(quant_page, task_2)

    # 1. 查日志
    quant_page.open_and_close_logs(task_2)
    
    # 监控状态流转为终态 (完成或失败)
    quant_page.wait_for_task_finished(task_2)
    
    # + 成功后的新增操作: 报告查看与全量下载拦截
    quant_page.open_and_close_report(task_2)
    quant_page.download_all_results(task_2)
    
    logger.info("✅ 量化工具全生命周期(异常截断+成功收尾) 双链路完整验证通过！")