text: import re
  import torch
  from string import printable, punctuation
  from tqdm import tqdm
  import warnings
  
  
  class Normalizer():
      def __init__(self,
                   device='cpu',
                   jit_model='./normalizer/jit_s2s.pt'):
          super(Normalizer, self).__init__()
  
          self.device = torch.device(device)
  
          self.init_vocabs()
  
          self.model = torch.jit.load(jit_model, map_location=device)
          self.model.eval()
          self.max_len = 150
  
      def init_vocabs(self):
          # Initializes source and target vocabularies
  
          # vocabs
          rus_letters = 'абвгдеёжзийклмнопрстуфхцчшщъыьэюяАБВГДЕЁЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯ'
          spec_symbols = '¼³№¾⅞½⅔⅓⅛⅜²'
          # numbers + eng + punctuation + space + rus
          self.src_vocab = {token: i+5 for i, token in enumerate(printable[:-5] + rus_letters + '«»—' + spec_symbols)}
          # punctuation + space + rus
          self.tgt_vocab = {token: i+5 for i, token in enumerate(punctuation + rus_letters + ' ' + '«»—')}
  
          unk = '#UNK#'
          pad = '#PAD#'
          sos = '#SOS#'
          eos = '#EOS#'
          tfo = '#TFO#'
          for i, token in enumerate([unk, pad, sos, eos, tfo]):
              self.src_vocab[token] = i
              self.tgt_vocab[token] = i
  
          for i, token_name in enumerate(['unk', 'pad', 'sos', 'eos', 'tfo']):
              setattr(self, '{}_index'.format(token_name), i)
  
          inv_src_vocab = {v: k for k, v in self.src_vocab.items()}
          self.src2tgt = {src_i: self.tgt_vocab.get(src_symb, -1) for src_i, src_symb in inv_src_vocab.items()}
  
      def keep_unknown(self, string):
          reg = re.compile(r'[^{}]+'.format(''.join(self.src_vocab.keys())))
          unk_list = re.findall(reg, string)
  
          unk_ids = [range(m.start()+1, m.end()) for m in re.finditer(reg, string) if m.end() - m.start() > 1]
          flat_unk_ids = [i for sublist in unk_ids for i in sublist]
  
          upd_string = ''.join([s for i, s in enumerate(string) if i not in flat_unk_ids])
          return upd_string, unk_list
  
      def _norm_string(self, string):
          # Normalizes chunk
  
          if len(string) == 0:
              return string
          string, unk_list = self.keep_unknown(string)
  
          token_src_list = [self.src_vocab.get(s, self.unk_index) for s in list(string)]
          src = token_src_list + [self.eos_index] + [self.pad_index]
  
          src2tgt = [self.src2tgt[s] for s in src]
          src2tgt = torch.LongTensor(src2tgt).to(self.device)
  
          src = torch.LongTensor(src).unsqueeze(0).to(self.device)
          out = self.model(src, src2tgt)
          pred_words = self.decode_words(out, unk_list)
          if len(pred_words) > 199:
              warnings.warn("Sentence {} is too long".format(string), Warning)
          return pred_words
  
      def norm_text(self, text):
          # Normalizes text
  
          # Splits sentences to small chunks with weighted length <= max_len:
          # * weighted length - estimated length of normalized sentence
          #
          # 1. Full text is splitted by "ending" symbols (\n\t?!.) to sentences;
          # 2. Long sentences additionally splitted to chunks: by spaces or just dividing too long words
  
          splitters = '\n\t?!'
          parts = [p for p in re.split(r'({})'.format('|\\'.join(splitters)), text) if p != '']
          norm_parts = []
          for part in tqdm(parts):
              if part in splitters:
                  norm_parts.append(part)
              else:
                  weighted_string = [7 if symb.isdigit() else 1 for symb in part]
                  weighted_len = sum(weighted_string)
                  if sum(weighted_string) <= self.max_len:
                      norm_parts.append(self._norm_string(part))
                  else:
                      spaces = [m.start() for m in re.finditer(' ', part)]
                      start_point = 0
                      end_point = 0
                      curr_point = 0
  
                      while start_point < len(part):
                          if curr_point in spaces:
                              if sum(weighted_string[start_point:curr_point]) < self.max_len:
                                  end_point = curr_point+1
                              else:
                                  norm_parts.append(self._norm_string(part[start_point:end_point]))
                                  start_point = end_point
  
                          elif sum(weighted_string[end_point:curr_point]) >= self.max_len:
                              if end_point > start_point:
                                  norm_parts.append(self._norm_string(part[start_point:end_point]))
                                  start_point = end_point
                              end_point = curr_point - 1
                              norm_parts.append(self._norm_string(part[start_point:end_point]))
                              start_point = end_point
                          elif curr_point == len(part):
                              norm_parts.append(self._norm_string(part[start_point:]))
                              start_point = len(part)
  
                          curr_point += 1
          return ''.join(norm_parts)
  
      def decode_words(self, pred, unk_list=[]):
          pred = pred.cpu().numpy()
          pred_words = "".join(self.lookup_words(x=pred,
                                                 vocab={i: w for w, i in self.tgt_vocab.items()},
                                                 unk_list=unk_list))
          return pred_words
  
      def lookup_words(self, x, vocab, unk_list=[]):
          result = []
          for i in x:
              if i == self.unk_index:
                  if len(unk_list) > 0:
                      result.append(unk_list.pop(0))
                  else:
                      continue
              else:
                  result.append(vocab[i])
          return [str(t) for t in result]
  