from __future__ import annotations

import json
import random
from datetime import datetime, timedelta
from pathlib import Path


ROOT = Path(__file__).resolve().parent
random.seed(42)

skills = [
    "签到",
    "引导",
    "登记",
    "促销",
    "地推",
    "导购",
    "会展接待",
    "分拣",
    "打包",
    "客服",
    "电话邀约",
    "安装",
    "配送",
    "仓储",
    "陈列",
    "数据录入",
    "物料搬运",
    "收银",
    "盘点",
    "直播协助",
    "短视频拍摄",
    "面销",
    "海报派发",
    "问卷访问",
    "现场执行",
    "活动控场",
    "礼仪接待",
    "样品派发",
    "售后支持",
    "装卸",
    "拣货",
    "骑手配送",
    "司机协助",
    "设备调试",
    "展台搭建",
    "线上客服",
    "社群运营",
    "线索收集",
    "POS操作",
    "报表整理",
]

extra_skills = [f"扩展技能{i:02d}" for i in range(1, 61)]
skills = (skills + extra_skills)[:100]

categories = [
    "促销",
    "地推",
    "导购",
    "会展",
    "分拣",
    "客服",
    "安装",
    "配送",
    "仓储",
    "活动执行",
    "礼仪",
    "数据录入",
    "盘点",
    "装卸",
    "直播协助",
    "社群运营",
    "收银",
    "拣货",
    "线下推广",
    "电话销售",
    "问卷调研",
    "样品派发",
    "售后服务",
    "展台搭建",
    "酒店服务",
    "商超导购",
    "会场服务",
    "物流协助",
    "活动控场",
    "物料执行",
]

regions = [
    {"city": "深圳", "region": "南山"},
    {"city": "深圳", "region": "福田"},
    {"city": "深圳", "region": "宝安"},
    {"city": "深圳", "region": "龙岗"},
    {"city": "深圳", "region": "罗湖"},
    {"city": "广州", "region": "天河"},
    {"city": "广州", "region": "海珠"},
    {"city": "广州", "region": "番禺"},
    {"city": "广州", "region": "白云"},
    {"city": "广州", "region": "越秀"},
    {"city": "上海", "region": "浦东"},
    {"city": "上海", "region": "徐汇"},
    {"city": "上海", "region": "静安"},
    {"city": "上海", "region": "闵行"},
    {"city": "上海", "region": "杨浦"},
    {"city": "杭州", "region": "西湖"},
    {"city": "杭州", "region": "滨江"},
    {"city": "杭州", "region": "余杭"},
    {"city": "成都", "region": "高新"},
    {"city": "成都", "region": "武侯"},
]

time_tags = ["weekend", "weekday_am", "weekday_pm", "anytime"]
experience_tags = ["商场", "会展", "活动执行", "物流", "零售", "校园推广", "客服中心", "展台", "仓库", "快消"]
work_modes = ["排班制", "兼职", "临时工", "项目制"]


def region_label(item: dict) -> str:
    return f"{item['city']}{item['region']}"


jobs = []
for index in range(1, 101):
    region = random.choice(regions)
    category = random.choice(categories)
    job_skills = random.sample(skills[:40], k=random.randint(3, 5))
    start_at = datetime(2026, 4, 1, 9, 0, 0) + timedelta(days=random.randint(0, 20), hours=random.choice([0, 4, 8]))
    tags = list(set(random.sample(experience_tags, k=2) + [random.choice(["女生优先", "有经验优先", "可连做优先", "沟通好"]) ]))
    salary_amount = random.choice([120, 150, 180, 200, 220, 260, 300])
    jobs.append(
        {
            "job_id": f"job_{index:03d}",
            "title": f"{category}兼职{index:03d}",
            "category": category,
            "description": f"{start_at.month}月{start_at.day}日{region['city']}{region['region']}需要{random.randint(1,4)}名{category}兼职，负责{'、'.join(job_skills[:3])}，{random.randint(4,8)}小时，{salary_amount}元/天。",
            "skills": job_skills,
            "city": region["city"],
            "region": region["region"],
            "location_detail": f"{region_label(region)}核心商圈点位{random.randint(1, 20)}号",
            "start_time": start_at.isoformat() + "+08:00",
            "duration_hours": random.randint(4, 8),
            "headcount": random.randint(1, 4),
            "salary": {"type": "daily", "amount": salary_amount, "currency": "CNY"},
            "work_mode": random.choice(work_modes),
            "tags": tags,
            "confidence": round(random.uniform(0.82, 0.96), 2),
        }
    )


workers = []
family_names = "赵钱孙李周吴郑王冯陈褚卫蒋沈韩杨朱秦尤许何吕施张孔曹严华金魏陶姜"
given_names = ["伟", "芳", "娜", "敏", "静", "磊", "洋", "勇", "艳", "杰", "娟", "涛", "明", "超", "秀英", "霞"]
for index in range(1, 301):
    primary_region = random.choice(regions)
    extra_region = random.choice([r for r in regions if r["city"] == primary_region["city"] and r["region"] != primary_region["region"]])
    worker_skills = random.sample(skills[:40], k=random.randint(3, 6))
    name = random.choice(list(family_names)) + random.choice(given_names)
    availability = random.sample(time_tags, k=random.randint(1, 2))
    exp_tags = list(set(random.sample(experience_tags, k=2) + [random.choice(categories[:10])]))
    workers.append(
        {
            "worker_id": f"worker_{index:03d}",
            "name": name,
            "description": f"我做过{'、'.join(exp_tags[:2])}相关兼职，擅长{'、'.join(worker_skills[:3])}，平时{ '和'.join(availability) }都能接单，{primary_region['region']}和{extra_region['region']}都方便。",
            "skills": [{"name": item, "score": round(random.uniform(0.62, 0.94), 2)} for item in worker_skills],
            "cities": [primary_region["city"]],
            "regions": [primary_region["region"], extra_region["region"]],
            "availability": availability,
            "experience_tags": exp_tags,
            "reliability_score": round(random.uniform(0.65, 0.95), 2),
            "profile_completion": round(random.uniform(0.55, 0.98), 2),
            "confidence": round(random.uniform(0.8, 0.96), 2),
        }
    )


(ROOT / "skills.json").write_text(json.dumps(skills, ensure_ascii=False, indent=2), encoding="utf-8")
(ROOT / "categories.json").write_text(json.dumps(categories, ensure_ascii=False, indent=2), encoding="utf-8")
(ROOT / "regions.json").write_text(json.dumps(regions, ensure_ascii=False, indent=2), encoding="utf-8")
(ROOT / "jobs.json").write_text(json.dumps(jobs, ensure_ascii=False, indent=2), encoding="utf-8")
(ROOT / "workers.json").write_text(json.dumps(workers, ensure_ascii=False, indent=2), encoding="utf-8")