clock/generate_clock.py

import json
import random
import itertools

# 设置随机种子
SEED = 42
random.seed(SEED)

# 1. 生成所有可能的数字组合 (1-12) 的排列（排除重复）
digits = list(range(1, 13))
combinations = list(itertools.permutations(digits, 2))  # 共 132 组
random.shuffle(combinations)  # 打乱顺序


# 计算结果的函数
def calculate_result(num1, num2, op):
    num1, num2 = int(num1), int(num2)
    if op == "+":
        return num1 + num2
    elif op == "-":
        return num1 - num2
    elif op == "x":
        return num1 * num2


# 2. 选取不同的组
support_combinations = combinations[:16]  # 16 组，用于 support.json
training_shots_combinations = combinations[16:32]  # 16 组，用于 training_data 的 shots
remaining_combinations = combinations[32:]  # 剩余 100 组（但可生成 300 个 samples）

# 3. 生成 query 数据 (query.json)
operators = ["+", "-", "x"]
all_remaining_samples = [(num1, num2, op) for num1, num2 in remaining_combinations for op in operators]
query_samples = random.sample(all_remaining_samples, 100)  # 直接选 100 个样本
query_data = []
for num1, num2, op in query_samples:
    query_data.append({
        "id": f"clock_query_{num1}_{num2}_{op}",
        "image": [f"clock/img/clock_{num1}_{num2}.png"],
        "question": "What is the mathematical result of the blue number and the red number?",
        "answer": calculate_result(num1, num2, op),
        "operator": op
    })

with open("query.json", "w") as f:
    json.dump(query_data, f, ensure_ascii=False, indent=4)

# 4. 生成 support 数据 (support.json)
support_data = []
for num1, num2 in support_combinations:
    support_data.append({
        "id": f"clock_support_{num1}_{num2}",
        "image": [f"clock/img/clock_{num1}_{num2}.png"],
        "question": "What is the mathematical result of the blue number and the red number?",
        "answer": [calculate_result(num1, num2, op) for op in operators]
    })

with open("support.json", "w") as f:
    json.dump(support_data, f, ensure_ascii=False, indent=4)

# 5. 生成 processed_training_data_ck.json
remaining_training_samples = list(set(all_remaining_samples) - set(query_samples))  # 剩余 200 个
selected_training_queries = random.sample(remaining_training_samples, 50)  # 选 50 组作为训练 query

training_data = []
for num1, num2, op in selected_training_queries:
    # 选 4 个 shots，确保运算符一致
    selected_shots = random.sample(training_shots_combinations, 4)

    user_messages = [
        {"type": "text", "text": "Learn from the demos and give only the answer to the final question."}
    ]

    for shot_num1, shot_num2 in selected_shots:
        user_messages.append({
            "type": "image",
            "image": f"clock/img/clock_{shot_num1}_{shot_num2}.png"
        })
        user_messages.append({
            "type": "text",
            "text": f"Question: What is the mathematical result of the blue number and the red number? Answer: {calculate_result(shot_num1, shot_num2, op)}"
        })

    # 添加 query
    user_messages.append({
        "type": "image",
        "image": f"clock/img/clock_{num1}_{num2}.png"
    })
    user_messages.append({
        "type": "text",
        "text": "Question: What is the mathematical result of the blue number and the red number? Answer: "
    })

    assistant_messages = [
        {"type": "text", "text": str(calculate_result(num1, num2, op))}
    ]

    training_data.append({
        "id": f"clock_query_{num1}_{num2}_{op}",
        "messages": [
            {"role": "user", "content": user_messages},
            {"role": "assistant", "content": assistant_messages}
        ]
    })

with open("processed_training_data_ck.json", "w") as f:
    json.dump(training_data, f, ensure_ascii=False, indent=4)

print("所有数据已成功生成！")