# import chromadb
# from chromadb import Documents, EmbeddingFunction, Embeddings
# from transformers import AutoModel
# import json
# from numpy.linalg import norm
# import sqlite3
# import urllib
# from django.conf import settings


# # this module act as a singleton class

# class JinaAIEmbeddingFunction(EmbeddingFunction):
#     def __init__(self, model):
#         super().__init__()
#         self.model = model

#     def __call__(self, input: Documents) -> Embeddings:
#         embeddings = self.model.encode(input)
#         return embeddings.tolist()

# # instance of embedding_model
# embedding_model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en',
#                                                 trust_remote_code=True,
#                                                 cache_dir='models')

# # instance of JinaAIEmbeddingFunction
# ef = JinaAIEmbeddingFunction(embedding_model)

# # list of topics
# topic_descriptions = json.load(open("topic_descriptions.txt")) 
# topics = list(dict.keys(topic_descriptions))
# embeddings = [embedding_model.encode(topic_descriptions[key]) for key in topic_descriptions]
# cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))

# def choose_topic(summary):
#     embed = embedding_model.encode(summary)
#     topic = ""
#     max_sim = 0.
#     for i,key in enumerate(topics):
#         sim = cos_sim(embed,embeddings[i])
#         if sim > max_sim:
#             topic = key
#             max_sim = sim
#     return topic

# def authors_list_to_str(authors):
#    """input a list of authors, return a string represent authors"""
#    text = ""
#    for author in authors:
#       text+=author+", "
#    return text[:-3]

# def authors_str_to_list(string):
#     """input a string of authors, return a list of authors"""
#     authors = []
#     list_auth = string.split("and")
#     for author in list_auth:
#         if author != "et al.":
#             authors.append(author.strip())
#     return authors

# def chunk_texts(text, max_char=400):
#   """
#   Chunk a long text into several chunks, with each chunk about 300-400 characters long,
#   but make sure no word is cut in half.
#   Args:
#       text: The long text to be chunked.
#       max_char: The maximum number of characters per chunk (default: 400).
#   Returns:
#       A list of chunks.
#   """
#   chunks = []
#   current_chunk = ""
#   words = text.split()
#   for word in words:
#     if len(current_chunk) + len(word) + 1 >= max_char:
#         chunks.append(current_chunk)
#         current_chunk = " "
#     else:
#       current_chunk += " " + word
#   chunks.append(current_chunk.strip())
#   return chunks

# def trimming(txt):
#     start = txt.find("{")
#     end = txt.rfind("}")
#     return txt[start:end+1].replace("\n"," ")

# # crawl data

# def extract_tag(txt,tagname):
#     return txt[txt.find("<"+tagname+">")+len(tagname)+2:txt.find("</"+tagname+">")]

# def get_record(extract):
#     id = extract_tag(extract,"id")
#     updated = extract_tag(extract,"updated")
#     published = extract_tag(extract,"published")
#     title = extract_tag(extract,"title").replace("\n ","").strip()
#     summary = extract_tag(extract,"summary").replace("\n","").strip()
#     authors = []
#     while extract.find("<author>")!=-1:
#         author = extract_tag(extract,"name")
#         extract = extract[extract.find("</author>")+9:]
#         authors.append(author)
#     pattern = '<link title="pdf" href="'
#     link_start = extract.find('<link title="pdf" href="')
#     link = extract[link_start+len(pattern):extract.find("rel=",link_start)-2]
#     return [id, updated, published, title, authors, link, summary]

# def crawl_exact_paper(title,author,max_results=3):
#     authors = authors_list_to_str(author)
#     records = []
#     url = 'http://export.arxiv.org/api/query?search_query=ti:{title}+AND+au:{author}&max_results={max_results}'.format(title=title,author=authors,max_results=max_results)
#     url = url.replace(" ","%20")
#     try:
#         arxiv_page = urllib.request.urlopen(url,timeout=100).read()
#         xml = str(arxiv_page,encoding="utf-8") 
#         while xml.find("<entry>") != -1:
#             extract = xml[xml.find("<entry>")+7:xml.find("</entry>")]
#             xml = xml[xml.find("</entry>")+8:]
#             extract = get_record(extract)
#             topic = choose_topic(extract[6])
#             records.append([topic,*extract])
#         return records
#     except Exception as e:
#         return "Error: "+str(e)

# def crawl_arxiv(keyword_list, max_results=100):
#     baseurl = 'http://export.arxiv.org/api/query?search_query='
#     records = []
#     for i,keyword in enumerate(keyword_list):
#         if i ==0:
#             url = baseurl + 'all:' + keyword
#         else:
#             url = url + '+OR+' + 'all:' + keyword
#     url = url+ '&max_results=' + str(max_results)
#     url = url.replace(' ', '%20')
#     try:
#         arxiv_page = urllib.request.urlopen(url,timeout=100).read()
#         xml = str(arxiv_page,encoding="utf-8") 
#         while xml.find("<entry>") != -1:
#             extract = xml[xml.find("<entry>")+7:xml.find("</entry>")]
#             xml = xml[xml.find("</entry>")+8:]
#             extract = get_record(extract)
#             topic = choose_topic(extract[6])
#             records.append([topic,*extract])
#         return records
#     except Exception as e:
#         return "Error: "+str(e)

# class ArxivSQL:
#     def __init__(self, table="arxivsql", name="db.sqlite3"):
#         self.con = sqlite3.connect(name)
#         self.cur = self.con.cursor()
#         self.table = table
    
#     def query(self, title="", author=[]):
#         if len(title)>0:
#             query_title = 'title like "%{}%"'.format(title)
#         else:
#             query_title = "True"
#         if len(author)>0:
#             query_author = 'authors like '
#             for auth in author:
#                 query_author += "'%{}%' or ".format(auth)
#             query_author = query_author[:-4]
#         else:
#             query_author = "True"
#         query = "select * from {} where {} and {}".format(self.table,query_title,query_author)
#         result = self.cur.execute(query)
#         return result.fetchall()

#     def query_id(self, ids=[]):
#         try:
#             if len(ids) == 0:
#                 return None
#             query = "select * from {} where id in (".format(self.table)
#             for id in ids:
#                 query+="'"+id+"',"
#             query = query[:-1] + ")"
#             result = self.cur.execute(query)
#             return result.fetchall()
#         except Exception as e:
#             print(e)
#             print("Error query: ",query)
    
#     def add(self, crawl_records):
#         """
#         Add crawl_records (list) obtained from arxiv_crawlers
#         A record is a list of 8 columns: 
#         [topic, id, updated, published, title, author, link, summary]
#         Return the final length of the database table
#         """
#         results = ""
#         for record in crawl_records:
#             try:
#                 query = """insert into arxivsql values("{}","{}","{}","{}","{}","{}","{}")""".format(
#                     record[1][21:],
#                     record[0],
#                     record[4].replace('"',"'"),
#                     authors_list_to_str(record[5]),
#                     record[2][:10],
#                     record[3][:10],
#                     record[6]
#                 )
#                 self.cur.execute(query)
#                 self.con.commit()
#             except Exception as e:
#                 result+=str(e)
#                 result+="\n" + query + "\n"
#             finally:
#                 return results
            
# # instance of ArxivSQL
# sqldb = ArxivSQL()

# class ArxivChroma:
#     """
#     Create an interface to arxivdb, which only support query and addition.
#     This interface do not support edition and deletion procedures. 
#     """
#     def __init__(self, table="arxiv_records", name="arxivdb/"):
#         self.client = chromadb.PersistentClient(name)
#         self.model = embedding_model
#         self.collection = self.client.get_or_create_collection(table,
#                                                                embedding_function=JinaAIEmbeddingFunction(
#                                                                    model = self.model
#                                                                ))
    
#     def query_relevant(self, keywords, query_texts, n_results=3):
#         """
#         Perform a query using a list of keywords (str),
#         or using a relavant string
#         """
#         contains = []
#         for keyword in keywords:
#             contains.append({"$contains":keyword.lower()})
#         return self.collection.query(
#             query_texts=query_texts,
#             where_document={
#                 "$or":contains
#             },
#             n_results=n_results,
#         )
    
#     def query_exact(self, id):
#         ids = ["{}_{}".format(id,j) for j in range(0,10)]
#         return self.collection.get(ids=ids)

#     def add(self, crawl_records):
#         """
#         Add crawl_records (list) obtained from arxiv_crawlers
#         A record is a list of 8 columns: 
#         [topic, id, updated, published, title, author, link, summary]
#         Return the final length of the database table
#         """
#         for record in crawl_records:
#                 embed_text = """
#                 Topic: {},
#                 Title: {},
#                 Summary: {}
#             """.format(record[0],record[4],record[7])
#                 chunks = chunk_texts(embed_text)
#                 ids = [record[1][21:]+"_"+str(j) for j in range(len(chunks))]
#                 paper_ids = [{"paper_id":record[1][21:]} for _ in range(len(chunks))]
#                 self.collection.add(
#                     documents = chunks,
#                     metadatas=paper_ids,
#                     ids = ids
#                 )
#         return self.collection.count()

# # instance of ArxivChroma
# db = ArxivChroma()