# -*- coding: utf-8 -*-
"""分句与文本清洗。

按 ARCHITECTURE.md Phase 4 拆分建议，从 ``interface/Preprocessing.py`` 迁出。
类型：PREPROCESS。

原位置：``interface/Preprocessing.py`` 中以下函数：
    - ``segment`` — HTML 文本清洗与标点规范化
    - ``get_preprocessed_sentences`` — 分句与大纲解析

``interface/Preprocessing.py`` 仍 re-export 以上全部名称，老 import 不受影响。
"""
from __future__ import absolute_import

import re
import time

from BiddingKG.dl.common.nerUtils import getTokens
from BiddingKG.dl.interface.Entitys import Sentences, Outline

__all__ = [
    "segment",
    "get_preprocessed_sentences",
]


def segment(soup,final=True):
    # print("==")
    # print(soup)
    # print("====")
    #segList = ["tr","div","h1", "h2", "h3", "h4", "h5", "h6", "header"]
    subspaceList = ["td","span","p"] # 20250723去掉 a 标签，角色会被补充a标签
    if soup.name in subspaceList:
        #判断有值叶子节点数
        _count = 0
        for child in soup.find_all(recursive=True):
            if child.get_text().strip()!="" and len(child.find_all())==0:
                _count += 1
        if _count<=1:
            text = soup.get_text()
            # 2020/11/24 大网站规则添加
            if 'title' in soup.attrs:
                if '...' in soup.get_text() and soup.get_text().strip()[:-3] in soup.attrs['title']:
                    text = soup.attrs['title']

            _list = []
            for x in re.split("\s+",text):
                if x.strip()!="":
                    _list.append(len(x))
            if len(_list)>0:
                _minLength = min(_list)
                if _minLength>2:
                    _substr = "，"
                else:
                    _substr = ""
            else:
                _substr = ""

            text = text.replace("\r\n","，").replace("\n","，")
            text = re.sub("\s+",_substr,text)
            # text = re.sub("\s+","##space##",text)
            return text
    segList = ["title"]
    commaList = ["div","br","td","p","li","h1","h2","h3","h4","h5","h6"]
    #commaList = []
    spaceList = ["span"]
    tbodies = soup.find_all('tbody')
    if len(tbodies) == 0:
        tbodies = soup.find_all('table')
    # 递归遍历所有节点,插入符号
    for child in soup.find_all(recursive=True):
        # print(child.name,child.get_text())
        if child.name in segList:
            child.insert_after("。")
        if child.name in commaList:
            if child.name in ["p", "div"] and re.search('(排名：[\d]+|排名第[一二三四五\d])$', child.get_text().strip()):
                child.insert_after("；")
            else:
                child.insert_after("，")
            # if child.name != "td" and re.match('[（(][一二三四五六七八九十]+[)）]|[一二三四五六七八九十]+\s*、', child.get_text().strip()): # 大纲前面用句号分割 20240930 注销，修复529501491关键词 三、中标供应商：（一）单位名称被分句
            #     child.insert_before("。")
        # if child.name == 'div' and 'class' in child.attrs:
        #     # 添加附件"attachment"标识
        #     if "richTextFetch" in child['class']:
        #         child.insert_before("##attachment##")
                # print(child.parent)
        # if child.name in subspaceList:
        #     child.insert_before("#subs"+str(child.name)+"#")
        #     child.insert_after("#sube"+str(child.name)+"#")
        # if child.name in spaceList:
        #     child.insert_after(" ")
        # if child.name in subspaceList and len(child.get_text()) > 5 and re.search('\w$', child.get_text()): # 20250718 补充逗号避免642261504 分隔不了 联系邮箱：2034758276@qq.com中治交通建设集团有限公司北京分公司2025年6月25日， 20250801 注释掉 653089421 等多篇公告公司被span拆分
        #     child.insert_after("，")
    text = str(soup.get_text())
    #替换英文冒号为中文冒号
    text = re.sub("(?<=[\u4e00-\u9fa5]):|:(?=[\u4e00-\u9fa5])","：",text)
    #替换为中文逗号
    text = re.sub("(?<=[\u4e00-\u9fa5]),|,(?=[\u4e00-\u9fa5])","，",text)
    #替换为中文分号
    text = re.sub("(?<=[\u4e00-\u9fa5]);|;(?=[\u4e00-\u9fa5])","；",text)
    # 感叹号替换为中文句号
    text = re.sub("(?<=[\u4e00-\u9fa5])[!！]|[!！](?=[\u4e00-\u9fa5])","。",text)
    #替换格式未识别的问号为" " ,update:2021/7/20
    text = re.sub("[？\?]{2,}|\n"," ",text)


    #替换"""为"“",否则导入deepdive出错
    # text = text.replace('"',"“").replace("\r","").replace("\n","，")
    text = text.replace('"',"“").replace("\r","").replace("\n","").replace("\\n","") #2022/1/4修复 非分段\n 替换为逗号造成 公司拆分 span \n南航\n上海\n分公司
    text = re.sub("(&nbsp)+"," ",text) # 空白符替换
    # print('==1',text)
    # text = re.sub("\s{4,}","，",text)
    # 解决公告中的" "空格替换问题
    if re.search("\s{4,}",text):
        _text = ""
        for _sent in re.split("。+",text):
            for _sent2 in re.split('，+',_sent):
                for _sent3 in re.split("：+",_sent2):
                    pre_t = ''
                    for _t in re.split("\s{4,}",_sent3):
                        if len(_t)<3 or len(pre_t)<3 or re.search('[^\w\s]$', pre_t):  # 20240726 前文小于3字或以符合结尾的不加 避免乱加逗号 例：2）    申请人的资格要求
                            _text += _t
                        else:
                            _text += "，"+_t
                        pre_t = _t
                    _text += "："
                _text = _text[:-1]
                _text += "，"
            _text = _text[:-1]
            _text += "。"
        _text = _text[:-1]
        text = _text
    # print('==2',text)
    #替换标点

    #替换连续的标点

    if final:
        text = re.sub("##space##"," ",text)

    punc_pattern = "(?P<del>[。，；：:,\s]+)"

    list_punc = re.findall(punc_pattern,text)
    list_punc.sort(key=lambda x:len(x),reverse=True)
    for punc_del in list_punc:
        if len(punc_del)>1:
            if len(punc_del.strip())>0:
                if "：" in punc_del.strip():
                    if "。" in punc_del.strip():
                        text = re.sub(punc_del, "：。", text)
                    else:
                        text = re.sub(punc_del,"：",text)
                else:
                    text = re.sub(punc_del,punc_del.strip()[0],text)   #2021/12/09 修正由于某些标签后插入符号把原来符号替换
            else:
                text = re.sub(punc_del," ",text) # 多个空字符替换为一个空格（防止时间类连接），后面还有对空格处理

    #将连续的中文句号替换为一个
    # text_split = text.split("。")
    # text_split = [x for x in text_split if len(x)>0]
    # text = "。".join(text_split)
    text = re.sub('。+', '。', text).lstrip('。') # 20240703 修复上面的方法造成文末句号丢失问题。

    # #删除标签中的所有空格
    # for subs in subspaceList:
    #     patten = "#subs"+str(subs)+"#(.*?)#sube"+str(subs)+"#"
    #     while(True):
    #         oneMatch = re.search(re.compile(patten),text)
    #         if oneMatch is not None:
    #             _match = oneMatch.group(1)
    #             text = text.replace("#subs"+str(subs)+"#"+_match+"#sube"+str(subs)+"#",_match)
    #         else:
    #             break

    # text过大报错
    LOOP_LEN = 10000
    LOOP_BEGIN = 0
    _text = ""



    if len(text)<10000000:
        while(LOOP_BEGIN<len(text)):
            _text += re.sub("）",")",re.sub("（","(",re.sub("\s(?!\d{1,2}[:：]\d{2}|\d{1,2}[点时])","",text[LOOP_BEGIN:LOOP_BEGIN+LOOP_LEN])))
            LOOP_BEGIN += LOOP_LEN
        text = _text
    # 附件标识前修改为句号，避免正文和附件内容混合在一起
    text = re.sub("[^。](?=##attachment##)","。",text)
    text = re.sub("[^。](?=##attachment_begin##)","。",text)
    text = re.sub("[^。](?=##attachment_end##)","。",text)
    text = re.sub("##attachment_begin##。","##attachment_begin##",text)
    text = re.sub("##attachment_end##。","##attachment_end##",text)

    return text


def get_preprocessed_sentences(list_articles,useselffool=True,cost_time=dict()):
    '''

    :param list_articles: 经过预处理的article text
    :return: list_sentences
    '''
    list_sentences = []
    list_outlines = []
    for article in list_articles:
        list_sentences_temp = []
        list_entitys_temp = []
        doc_id = article.id
        _send_doc_id = article.doc_id
        _title = article.title
        #表格处理
        key_preprocess = "tableToText"
        start_time = time.time()
        article_processed = article.content

        if len(_title)<100 and _title not in article_processed: # 把标题放到正文
            article_processed = _title + '，' + article_processed   # 2023/01/06 标题正文加逗号分割，预防标题后面是产品，正文开头是公司实体，实体识别把产品和公司作为整个角色实体

        attachment_begin_index = -1

        if key_preprocess not in cost_time:
            cost_time[key_preprocess] = 0
        cost_time[key_preprocess] += time.time()-start_time

        #nlp处理
        outline_list = [] # 20240906 修复下面条件不成立时，后面 list_outlines.append(outline_list) 名称未定义报错
        if article_processed is not None and len(article_processed)!=0:
            split_patten = "。"
            sentences = []
            _begin = 0
            sentences_set = set()
            for _iter in re.finditer(split_patten,article_processed):
                _sen = article_processed[_begin:_iter.span()[1]]
                # if len(_sen)>0 and _sen not in sentences_set: # 去重导致内容丢失
                if len(_sen)>0 and (len(sentences)>0 and _sen != sentences[-1] or len(sentences)==0): # 2024/07/25 改为顺序去重
                    # 标识在附件里的句子
                    if re.search("##attachment##",_sen):
                        attachment_begin_index = len(sentences)
                        # _sen = re.sub("##attachment##","",_sen)
                    sentences.append(_sen)
                    sentences_set.add(_sen)
                _begin = _iter.span()[1]
            _sen = article_processed[_begin:]
            if re.search("##attachment##", _sen):
                # _sen = re.sub("##attachment##", "", _sen)
                attachment_begin_index = len(sentences)
            # if len(_sen)>0 and _sen not in sentences_set:
            if len(_sen)>0 and (len(sentences)>0 and _sen != sentences[-1] or len(sentences)==0):  # 2024/07/25 改为顺序去重
                sentences.append(_sen)
                sentences_set.add(_sen)
            # 解析outline大纲分段
            outline_list = []
            if re.search("##split##",article.content):
                temp_sentences = []
                last_sentence_index = (-1,-1)
                outline_index = 0
                for sentence_index in range(len(sentences)):
                    sentence_text = sentences[sentence_index]
                    for _ in re.findall("##split##", sentence_text):
                        _match = re.search("##split##", sentence_text)
                        if last_sentence_index[0] > -1:
                            sentence_begin_index,wordOffset_begin = last_sentence_index
                            sentence_end_index = sentence_index
                            wordOffset_end = _match.start()
                            if sentence_begin_index<attachment_begin_index and sentence_end_index>=attachment_begin_index:
                                outline_list.append(Outline(doc_id,outline_index,'',sentence_begin_index,attachment_begin_index-1,wordOffset_begin,len(sentences[attachment_begin_index-1])))
                            else:
                                outline_list.append(Outline(doc_id,outline_index,'',sentence_begin_index,sentence_end_index,wordOffset_begin,wordOffset_end))
                            outline_index += 1
                        sentence_text = re.sub("##split##，?", "", sentence_text,count=1)
                        last_sentence_index = (sentence_index,_match.start())
                    temp_sentences.append(sentence_text)
                if attachment_begin_index>-1 and last_sentence_index[0]<attachment_begin_index:
                    outline_list.append(Outline(doc_id,outline_index,'',last_sentence_index[0],attachment_begin_index-1,last_sentence_index[1],len(temp_sentences[attachment_begin_index-1])))
                else:
                    outline_list.append(Outline(doc_id,outline_index,'',last_sentence_index[0],len(sentences)-1,last_sentence_index[1],len(temp_sentences[-1])))
                sentences = temp_sentences
            #解析outline的outline_text内容
            for _outline in outline_list:
                if _outline.sentence_begin_index==_outline.sentence_end_index:
                    _text = sentences[_outline.sentence_begin_index][_outline.wordOffset_begin:_outline.wordOffset_end]
                else:
                    _text = ""
                    for idx in range(_outline.sentence_begin_index,_outline.sentence_end_index+1):
                        if idx==_outline.sentence_begin_index:
                            _text += sentences[idx][_outline.wordOffset_begin:]
                        elif idx==_outline.sentence_end_index:
                            _text += sentences[idx][:_outline.wordOffset_end]
                        else:
                            _text += sentences[idx]
                _outline.outline_text = _text
                _outline_summary = re.split("[:：，]",_text,1)[0]
                if len(_outline_summary)<30:
                    _outline.outline_summary = _outline_summary
                # print(_outline.outline_index,_outline.outline_text)

            article.content = "".join(sentences)
            # sentences.append(article_processed[_begin:])
            article.content = re.sub('[，。\s]+。', '。', article.content) # 处理连续标点

            lemmas = []
            doc_offsets = []
            dep_types = []
            dep_tokens = []

            time1 = time.time()

            '''
            tokens_all = fool.cut(sentences)
            #pos_all = fool.LEXICAL_ANALYSER.pos(tokens_all)
            #ner_tag_all = fool.LEXICAL_ANALYSER.ner_labels(sentences,tokens_all)
            ner_entitys_all = fool.ner(sentences)
            '''
            #限流执行
            key_nerToken = "nerToken"
            start_time = time.time()
            # tokens_all = getTokens(sentences,useselffool=useselffool)
            sentences = [_sen for _sen in sentences if _sen]
            tokens_all = getTokens([re.sub("##attachment_begin##|##attachment_end##","",_sen) for _sen in sentences],useselffool=useselffool)
            if key_nerToken not in cost_time:
                cost_time[key_nerToken] = 0
            cost_time[key_nerToken] += round(time.time()-start_time,2)

            in_attachment = False
            for sentence_index in range(len(sentences)):
                sentence_text = sentences[sentence_index]
                if re.search("##attachment_begin##",sentence_text):
                    in_attachment = True
                    sentence_text = re.sub("##attachment_begin##","",sentence_text)
                if re.search("##attachment_end##",sentence_text):
                    in_attachment = False
                    sentence_text = re.sub("##attachment_end##", "", sentence_text)
                if sentence_index >= attachment_begin_index and attachment_begin_index!=-1:
                    in_attachment = True
                tokens = tokens_all[sentence_index]

                #pos_tag = pos_all[sentence_index]
                pos_tag = ""

                ner_entitys = ""

                list_sentences_temp.append(Sentences(doc_id=doc_id,sentence_index=sentence_index,sentence_text=sentence_text,tokens=tokens,pos_tags=pos_tag,ner_tags=ner_entitys,in_attachment=in_attachment))
        if len(list_sentences_temp)==0:
            list_sentences_temp.append(Sentences(doc_id=doc_id,sentence_index=0,sentence_text="sentence_text",tokens=[],pos_tags=[],ner_tags=""))
        list_sentences.append(list_sentences_temp)
        list_outlines.append(outline_list)
        article.content = re.sub("##attachment_begin##|##attachment_end##", "", article.content)
    return list_sentences,list_outlines