import re
def check_standalization(model_response, prompt_style, type):
    if any (match in type for match in ['NIE','NDE','ETT','CDE','ATE']):
        if model_response.startswith(("{\"answer\":")) and model_response.endswith(("}")):
            return 0
        else:
            return 1
    elif any (match in type for match in ['PN','PS']):
        if model_response.startswith(("{\"prob\":")) and model_response.endswith(("}")):
            return 0
        else:
            return 1
    
def check_empty(model_response):
    if model_response == "":
        return 1
    else:
        return 0

def check_repetition(model_response):
    if any(response in model_response for response in ["input info: imagine a self-contained","provide the calculation result to four decimal places", "输入信息：设想一个", "请根据上述信息，给出计算结果（答案保留四位小数）"]):
        return 1
    else:
        return 0

def contains_chinese(text):
    chinese_pattern = re.compile(r'[\u4e00-\u9fff]+')
    result = 1 if chinese_pattern.search(text) is not None else 0

    return result

def contains_english(text):
    english_pattern = re.compile(r'[A-Za-z]{7,}') # Taking into account 'fake' and 'random' modes, and considering that the shortest occurrence of English characters in an 'answer' is of length 6, therefore detecting lengths of 7 or more.
    result = 1 if english_pattern.search(text) is not None else 0

    return result

def check_abnormality(preds):
    affect_num = sum(1 for pred in preds if pred == 0.1234) # 0.1234 is the example value in prompt for probability computation
    affected = affect_num / len(preds)
    abnormalities = "All Yes" if affected == 1 else \
                    "All No" if all(pred == 0 for pred in preds) else 0
    return abnormalities