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SpatialParseback
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from spacy.tokens import Span
from spacy.tokens import Doc
from spacy.tokens import Token
import regex_spatial
from spacy.language import Language
import re
from utils import llm_ent_extract

id =""
rse_id = "rse_id"
def set_extension():
     Span.set_extension(rse_id, default = "",force = True)
     Doc.set_extension(rse_id, default = "",force = True)
     Token.set_extension(rse_id, default = "",force = True)
     
def get_level1(doc, sentence, ent):
    return find_ent_by_regex(doc, sentence, ent, regex_spatial.get_level1_regex())

def get_level2(doc, sentence, ent):
  return find_ent_by_regex(doc, sentence, ent, regex_spatial.get_level2_regex()) 

def get_level3(doc, sentence, ent):
  return find_ent_by_regex(doc, sentence, ent, regex_spatial.get_level3_regex())


def find_ent_by_regex(doc, sentence, ent, regex):
  global id

  if id == "":
      id = ent.text
  for match in re.finditer(regex, doc.text):
        start, end = match.span()
        if(start>= sentence.start_char and start<= sentence.end_char):
          span = doc.char_span(start, end)
          if span is not None:
            id = span.text +"_"+ id
            if(start > ent.end_char):
              ent.end_char = end
            else:
              ent.start_char = start         

          return ent

  return ent


def update_entities(doc, entity_texts, replace=True):
    """
    根据给定的文本内容标注实体，并直接修改 doc.ents。

    :param doc: spaCy 解析后的 Doc 对象
    :param entity_texts: 字典，键是要标注的实体文本，值是对应的实体类别
    :param replace: 布尔值，True 则替换现有实体，False 则保留现有实体并添加新的
    """
    new_ents = list(doc.ents) if not replace else []  # 如果 replace=False，保留已有实体

    for ent_text, ent_label in entity_texts.items():
        start = doc.text.find(ent_text)  # 在全文中查找文本位置
        if start != -1:
            start_token = len(doc.text[:start].split())  # 计算起始 token 索引
            end_token = start_token + len(ent_text.split())  # 计算结束 token 索引

            if start_token < len(doc) and end_token <= len(doc):  # 确保索引不越界
                new_ent = Span(doc, start_token, end_token, label=ent_label)
                new_ents.append(new_ent)

    doc.set_ents(new_ents)  # 更新 doc.ents


def get_relative_entity(doc, sentence, ent):
  global id

  id = ""
  rel_entity = get_level1(doc, sentence, ent)
  # print(1111 ,rel_entity)
  rel_entity = get_level2(doc, sentence, rel_entity)
  # print(2222 ,rel_entity)
  rel_entity = get_level3(doc, sentence, rel_entity)
  # print(3333 ,rel_entity)

  if("_" in id):
    rel_entity = doc.char_span(rel_entity.start_char, rel_entity.end_char, "RSE")
    rel_entity._.rse_id = id

    # print(id, 'idid')
    # print(rel_entity._.rse_id, '._._')

    return rel_entity
  rel_entity = doc.char_span(ent.start_char, ent.end_char, ent.label_)
  rel_entity._.rse_id = id
  # print(4444 ,rel_entity)
  return rel_entity

@Language.component("spatial_pipeline")
def get_spatial_ent(doc):
  set_extension()
  new_ents = []
  # ents = [ent for ent in doc.ents if ent.label_ == "GPE" or ent.label_ == "LOC"]        # 筛选出ase


  # LLM 输出
  # GPE = '[###Pyrmont###, ###Glebe###]'                       # LLM 输出的实体
  GPE = llm_ent_extract.extract_GPE(doc.text)                       # LLM 输出的实体
  print(doc.text, 'llmin')
  print(GPE, 'llout')

  GPE = llm_ent_extract.extract(GPE, 'GPE')
  print(GPE, 'llmout2')
  update_entities(doc, GPE, True)
  ents = doc.ents
  print(ents, 'eee')
  # print(doc, 'ddd')
  # print(ents, 'ddd')
  # GPE = llm_ent_extract.extract(llm_ent_extract.extract_GPE(doc.text), 'gpe')
  # update_entities(doc, GPE)
  # LLM 输出完毕


  # print(doc.ents, 111)
  # print(doc.ents[2], 222)
  # print(type(doc.ents[2]), 222)
  # print(doc.ents[2].label_, 333)
  # print('----------')
  # doc.ents[2] = 'pp'
  # print(doc.ents[2], 111)
  # print(doc.ents[2].label_, 222)
  # print(type(doc.ents), 333)
  end = None
  for ent in ents:

    if ent.end != len(doc):
        next_token = doc[ent.end]
        if end is not None:
          start = end
        else:
          start = ent.sent.start
        if next_token.text.lower() in regex_spatial.get_keywords():
          end = next_token.i
        else:
          end = ent.end

    else:
        start = ent.sent.start
        end = ent.end

    # print(doc, '//',start, '//', end, 999888)
    # print(doc[start],'//', doc[end])
    # print(ents, 999)


    rsi_ent = get_relative_entity(doc,Span(doc, start, end), ent)
    # print(doc.ents[0]._.rse_id, '._._2')


    # print(rsi_ent.text, rsi_ent.label_, rsi_ent._.rse_id)
    new_ents.append(rsi_ent)

  doc.ents = new_ents
  return doc

# def update_doc_ents(doc, new_dict):
#     """
#     更新 doc.ents, 将新的实体文本和标签添加到 doc 中。
#
#     参数:
#     - doc: spaCy 的 Doc 对象
#     - new_dict: 一个字典，键是实体文本，值是标签
#     """
#     modified_ents = []
#
#     # 遍历字典中的实体文本和标签
#     for ent_text, label in new_dict.items():
#         # 将实体文本拆分成单词
#         ent_words = ent_text.split()
#
#         # 遍历 doc 中的 token 来查找第一个单词
#         start = None
#         for i in range(len(doc)):
#             # 如果当前 token 和实体的第一个单词匹配，确定 start
#             if doc[i].text == ent_words[0]:
#                 start = i
#                 # 然后检查后续的单词是否都匹配
#                 end = start + len(ent_words)  # 计算 end 为 start + 单词数
#                 if all(doc[start + j].text == ent_words[j] for j in range(len(ent_words))):
#                     # 创建 Span 对象
#                     new_ent = Span(doc, start, end, label=label)
#                     modified_ents.append(new_ent)
#                     break  # 找到匹配后跳出循环
#
#     # 使用 doc.set_ents() 更新 doc.ents
#     doc.set_ents(modified_ents)
#
#
# # def llm_extract(doc, model):
#
# def split_doc_into_sentences(doc):
#     """
#     将 doc 的文本按句子分割，并返回每个句子的字符串列表。
#     """
#     sentence_list = [sent.text.strip() for sent in doc.sents]
#     return sentence_list
#
#
# @Language.component("spatial_pipeline")
# def get_spatial_ent(doc):
#
#     set_extension()
#
#     split_sent = split_doc_into_sentences(doc)
#     for i in range(len(split_sent)):
#         gpe_dict = llm_ent_extract.extract_GPE(split_sent[i])
#         loc_dict = llm_ent_extract.extract_LOC(split_sent[i])
#         new_dict = gpe_dict|loc_dict
#
#
#     print(gpe_dict, '111')
#     print(loc_dict)
#     print(new_dict)
#     # new_dict = {'pp': 'ORG', 'France': 'GPE', 'Paris': 'GPE'}
#
#
#     # 调用新的函数更新 doc 的实体
#     update_doc_ents(doc, new_dict)
#
#     # 继续处理 doc.ents
#     ents = [ent for ent in doc.ents if ent.label_ == "GPE" or ent.label_ == "LOC"]
#     print(ents[1].label_)
#
#     end = None
#     new_ents = []
#
#     for ent in ents:
#         if ent.end != len(doc):
#             next_token = doc[ent.end + 1]
#             if end is not None:
#                 start = end
#             else:
#                 start = ent.sent.start
#             if next_token.text.lower() in regex_spatial.get_keywords():
#                 end = next_token.i
#             else:
#                 end = ent.end
#         else:
#             start = ent.sent.start
#             end = ent.end
#
#         # 调用 get_relative_entity 来获得新的实体信息
#         rsi_ent = get_relative_entity(doc, Span(doc, start, end), ent)
#
#         # 将处理后的实体添加到新的实体列表中
#         new_ents.append(rsi_ent)
#
#     doc.ents = new_ents  # 更新 doc.ents
#     print(new_ents, '111222')
#
#     return doc