text: from src.Rus_summarizer.download_tools import download_MlSBERT
  import pandas as pd
  from nltk.corpus import stopwords
  stop_words = stopwords.words('english') + stopwords.words('russian')
  import src.preprocessing_tools as pt
  from itertools import combinations
  from nltk import word_tokenize
  import networkx as nx
  import math
  from sentence_transformers import SentenceTransformer
  from sklearn.cluster import KMeans
  from sklearn.metrics import silhouette_score
  
  
  class MixedSummarizer():
      """
      Class used to represent Mixed summarizer (TextRank + MlSBERT_KMeans)
  
      Attributes
      ----------
      sent_textrank_num: int
          base number of sentences by textrank
      model: SBERT
          MultlingualSBERT model
  
      Methods
      -------
  
      tokenize(text, min_sent_len=pt.min_sent_len)
          tokenizes text into sentences
      encode_sentences(self, sentences, additional_args)
          extracts features from sentences
      cluster_sentences(sentence_embeddings, max_count=10)
          clusterizes sentences
      _get_result_summary(clusters, sentences, sentence_embeddings) - private
          creates summary by sentences and clusters
      similarity(s1, s2)
          calculates similarity between 2 sentences
      get_sentences_textrank(sentences)
          get the most significant sentences by textrank
      get_sentences_kmeans(sentences, base_sent_count)
          get sentences of summary by kmeans
      generate_summary(text, base_sent_cout=7, min_sent_len=pt.min_sent_len)
          generates summary of text
      """
      sent_textrank_num = 3
  
      def __init__(self):
          super().__init__()
          download_MlSBERT("../models")
          self.model = SentenceTransformer("../models/multilingual_SBERT")
  
      def tokenize(self, text, min_sent_len=pt.min_sent_len):
          return pt.tokenize(text, min_sent_len)
  
      def encode_sentences(self, sentences, additional_args=None):
          return self.model.encode(sentences)
  
      def cluster_sentences(self, sentence_embeddings, max_count=10):
          """cluster sentences"""
          max_score = -1
          best_clustering = None
          for count_sentences in range(4, max_count + 1):
              clustering = KMeans(n_clusters=count_sentences, random_state=0).fit(sentence_embeddings)
              cur_score = silhouette_score(sentence_embeddings, clustering.labels_)
              if cur_score > max_score:
                  best_clustering = clustering
                  max_score = cur_score
          return best_clustering
  
      def _get_result_summary(self, clusters, sentences, sentence_embeddings):
          """form summary from clusters"""
          sent_dist = clusters.transform(sentence_embeddings).min(axis=1)
          df = pd.DataFrame({'sentences': sentences, 'dist': sent_dist, 'label': clusters.labels_})
          grouped_df = df.groupby('label')
          sentence_ids = grouped_df.apply(lambda df: df.dist.idxmin())
          return list(sentence_ids)
  
      def similarity(self, s1, s2):
          """
          calculates similarity between 2 sentences
  
          Parameters
          ----------
          s1 : set
              set of words in sentence 1
          s2 : set
              set of words in sentence 2
          """
          if not len(s1) or not len(s2):
              return 0.0
          return len(s1.intersection(s2)) / (math.log(len(s1) + 1) + math.log(len(s2) + 1))
  
      def get_sentences_textrank(self, sentences):
          """
          get the most significant sentences by textrank
  
          Parameters
          ----------
          sentences : list
              list of sentences
          """
          words = []
          for sent in sentences:
              cur_words = set()
              for word in word_tokenize(sent, language='russian'):
                  cur_words.add(pt.lemmatize_word(word, True))
              words.append(cur_words)
  
          pairs = combinations(range(len(sentences)), 2)
          scores = [(i, j, self.similarity(words[i], words[j])) for i, j in pairs]
          scores = filter(lambda x: x[2], scores)
  
          g = nx.Graph()
          g.add_weighted_edges_from(scores)
          pr = nx.pagerank(g)
  
          return sorted((i for i, s in enumerate(sentences) if i in pr),
                        key=lambda x: pr[x], reverse=True)
  
      def get_sentences_kmeans(self, sentences, base_sent_count):
          sentence_embeddings = self.encode_sentences(sentences)
          max_count = min(base_sent_count, len(sentences) - 1)
          clusters_kmeans_adjusted = self.cluster_sentences(sentence_embeddings,
                                                            max_count=max(max_count, len(sentences) // 30))
          if clusters_kmeans_adjusted is None:
              return []
          return self._get_result_summary(clusters_kmeans_adjusted, sentences, sentence_embeddings)
  
      def generate_summary(self, text, base_sent_count=7, min_sent_len=pt.min_sent_len, additional_args=None):
          """
          generate summary of the text
  
          Parameters
          ----------
          text : str
              input text
          base_sent_cout: int
              count of sentences in summary
          min_sent_len: int
              minimum length of sentence to remain
          """
          sentences = self.tokenize(text, min_sent_len)
          if len(sentences) <= 4:
              return text
          sentence_ids_kmeans = self.get_sentences_kmeans(sentences, base_sent_count-1)
          tr_num = max(self.sent_textrank_num, base_sent_count - len(sentence_ids_kmeans))
          sentence_ids_tr = self.get_sentences_textrank(sentences)
  
          sentence_ids = set(sentence_ids_kmeans + sentence_ids_tr[:tr_num])
          sentence_ids = sorted(sentence_ids)
          res = []
          for sent_num in sentence_ids:
              res.append(sentences[sent_num])
          return ' '.join(res)
  
  
  
  
  
  