from exec_eval import eval_exec_match
import argparse

def evaluate(gold, predict, db_dir,  plug_value, keep_distinct, progress_bar_for_each_datapoint):

    scores = []
    with open(gold) as f:
        glist = []
        for l in f.readlines():
            glist.append(l)

    with open(predict) as f:
        plist = []
        for l in f.readlines():
            plist.append(l)

    assert len(plist) == len(glist), "number of sessions must equal"

    for idx,(p, g) in enumerate(zip(plist, glist)):
        # print(p)
        print("序号：",idx)
        
        p_str = p
        # p_str = p_str.replace("value", "1")
        g_str = g
        exec_score = eval_exec_match(db=db_dir, p_str=p_str, g_str=g_str, plug_value=plug_value,
                                        keep_distinct=keep_distinct, progress_bar_for_each_datapoint=progress_bar_for_each_datapoint)
        
        scores.append(exec_score)
    return sum(scores),len(scores)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    gold = "/home/zhangxj/WorkFile/LCA-GPT/LLM-SQL/data/tb_process/gold_no_err.txt"
    pred = "/home/zhangxj/WorkFile/LCA-GPT/LLM-SQL/data/tb_process/pred_no_err.txt"
    db_dir = "/home/zhangxj/WorkFile/LCA-GPT/LLM-SQL/data/tb_process/tb_process.db"

    parser.add_argument('--gold', dest='gold', type=str, help="the path to the gold queries",default=gold)
    parser.add_argument('--pred', dest='pred', type=str, help="the path to the predicted queries",default=pred)
    parser.add_argument('--db', dest='db', type=str, help="the directory that contains all the databases and test suites",default=db_dir)
    # parser.add_argument('--table', dest='table', type=str, help="the tables.json schema file")
    parser.add_argument('--plug_value', default=False, action='store_true',
                        help='whether to plug in the gold value into the predicted query; suitable if your model does not predict values.')
    parser.add_argument('--keep_distinct', default=False, action='store_true',
                        help='whether to keep distinct keyword during evaluation. default is false.')
    parser.add_argument('--progress_bar_for_each_datapoint', default=False, action='store_true',
                        help='whether to print progress bar of running test inputs for each datapoint')
    args = parser.parse_args()

    sum,len = evaluate(args.gold, args.pred, args.db,  args.plug_value, args.keep_distinct, args.progress_bar_for_each_datapoint)
    print(sum/len,f"({sum}/{len})")