"""
===================================
@Auther：WenZ
@Company: BooWay
@project：booway_dm
===================================
"""

# import spacy
# import zh_core_web_sm, zh_core_web_md, zh_core_web_lg, zh_core_web_trf
#
# # nlp_sm = zh_core_web_sm.load()
# # nlp_md = zh_core_web_md.load()
# # nlp_lg = zh_core_web_lg.load()
# nlp_trf = zh_core_web_trf.load()
#
# polite_words = {"你好", "您好", "请", "请问", "谢谢", "不客气", "麻烦", "打扰", "拜托", "辛苦", "劳驾"}

# 停用词清理
def stop_word_processing(input_str, nlp_stytle, polite_words):
    doc = nlp_stytle(input_str)

    # 去除停用词
    filtered_tokens = [
        token.text for token in doc
        if not token.is_stop and not token.is_punct and not token.is_space and token.text not in polite_words]

    return ''.join(filtered_tokens)


# 后缀名检测
def extract_names_from_json(file_path):
    import json

    with open(file_path, 'r', encoding='utf-8') as file:
        data = json.load(file)

    # 确保数据是一个列表
    if isinstance(data, list):
        names = [item.get("name") for item in data if "name" in item]
        return names
    else:
        raise ValueError("JSON 文件的格式应为包含对象的列表")


def judge_define_suffix(input_str):
    suffix_file_path = "../data/booway_knowledge_base/keywords_kg/suffix_keywords.json"
    suffix_fields = extract_names_from_json(suffix_file_path)
    suffix_fields.extend(['gec5', 'bczc2', 'xzwb2', 'BPQ', 'BPY'])

    import re

    # 构建正则表达式模式，匹配大小写不敏感且前面可能带有.
    # 去掉 \b 以允许字段是其他字符串的一部分
    pattern = r'(?:\.?)(' + '|'.join(re.escape(field) for field in suffix_fields) + r')'

    # 使用 re.IGNORECASE 标志来忽略大小写
    if re.search(pattern, input_str, re.IGNORECASE):
        return True
    else:
        return False

def match_suffix(input_str):
    import re

    # 修改正则表达式，匹配连续字母组合或字母+数字组合
    segments = re.findall(r'[A-Za-z]+(?:[0-9]+)?', input_str)

    # 过滤条件：必须是包含字母的组合，且可以包含字母或字母+数字
    matches = [seg for seg in segments if any(c.isalpha() for c in seg)]

    return matches[0] if matches else '未知'

def retrieve_relevant_software(suffix_name):
    import json
    suffix_file_path = "../data/booway_knowledge_base/keywords_kg/suffix_keywords.json"

    with open(suffix_file_path, 'r', encoding='utf-8') as file:
        data = json.load(file)

    suffix_name_lower = suffix_name.lower()

    for item in data:
        item_name = item.get('name', '').lower()
        if item_name == suffix_name_lower:
            return item.get('description', {}).get('software_name', 0)

    return 0



def get_keywords(input_str):
    import re
    matches = re.findall(r'【(.*?)】', input_str)

    # 获取第二个【】里的内容（索引为1）
    if len(matches) >= 2:
        second = matches[1]
        return second
    else:
        return None

def get_keywords_v2(input_str):
    import re
    match = re.search(r'】(.*)', input_str)

    if match:
        output = match.group(1)
        return output
    else:
        return None

def get_keywords_v3(input_str):
    import re
    match = re.findall(r'【(.*?)】', input_str)

    if match:
        output = match[1:]
        return output
    else:
        return None


def normalize_text(text: str, synonym_dict: dict) -> str:
    import re
    # 构建同义词到主词的映射表（扁平化）
    flat_synonyms = {}
    for main_word, synonyms in synonym_dict.items():
        for syn in synonyms:
            flat_synonyms[syn] = main_word

    # 按长度从大到小排序，避免短词覆盖长词（如“下载”和“下载下来”）
    sorted_synonyms = sorted(flat_synonyms.keys(), key=len, reverse=True)

    # 逐个替换
    for syn in sorted_synonyms:
        # 用正则确保是整词匹配，但保留灵活性（可处理“...费费率...”这种词连着的情况）
        text = re.sub(re.escape(syn), flat_synonyms[syn], text)

    return text