text: import tensorflow as tf
  from herpetologist import check_type
  from malaya.text.function import (
      transformer_textcleaning,
      split_into_sentences,
      upperfirst,
  )
  from malaya.text.rouge import (
      filter_rouge,
      postprocessing_summarization,
      find_lapor_and_remove,
  )
  from typing import List
  import re
  
  
  def cleaning(string):
      return re.sub(r'[ ]+', ' ', string).strip()
  
  
  def remove_repeat_fullstop(string):
      return ' '.join([k.strip() for k in string.split('.') if len(k.strip())])
  
  
  class T5:
      def __init__(self, X, decode, sess, pred):
          self._X = X
          self._decode = decode
          self._sess = sess
          self._pred = pred
  
      def _predict(self, string):
          if self._pred:
              r = self._pred([string])[0].decode('utf-8')
          else:
              r = self._sess.run(self._decode, feed_dict = {self._X: [string]})[
                  0
              ].decode('utf-8')
          return r
  
  
  class SUMMARIZATION(T5):
      def __init__(self, X, decode, sess, pred):
          T5.__init__(self, X = X, decode = decode, sess = sess, pred = pred)
  
      def _summarize(self, string, mode, postprocess, **kwargs):
          summary = upperfirst(self._predict(f'{mode}: {cleaning(string)}'))
          if postprocess:
              summary = filter_rouge(string, summary, **kwargs)
              summary = postprocessing_summarization(summary)
              summary = find_lapor_and_remove(string, summary)
          return summary
  
      @check_type
      def summarize(
          self,
          string: str,
          mode: str = 'ringkasan',
          postprocess: bool = True,
          **kwargs,
      ):
          """
          Summarize a string. Decoder is beam decoder with beam width size 1, alpha 0.5 .
  
          Parameters
          ----------
          string: str
          mode: str
              mode for summarization. Allowed values:
  
              * ``'ringkasan'`` - summarization for long sentence, eg, news summarization.
              * ``'tajuk'`` - title summarization for long sentence, eg, news title.
          postprocess: bool, optional (default=True)
              If True, will filter sentence generated using ROUGE score and removed international news publisher.
  
          Returns
          -------
          result: str
          """
          mode = mode.lower()
          if mode not in ['ringkasan', 'tajuk']:
              raise ValueError('mode only supports [`ringkasan`, `tajuk`]')
  
          results = self._summarize(string, mode, postprocess, **kwargs)
  
          return results
  
  
  class GENERATOR(T5):
      def __init__(self, X, decode, sess, pred):
          T5.__init__(self, X = X, decode = decode, sess = sess, pred = pred)
  
      @check_type
      def generate(self, strings: List[str]):
          """
          generate a long text given a isi penting. Decoder is beam decoder with beam width size 1, alpha 0.5 .
  
          Parameters
          ----------
          strings: List[str]
  
          Returns
          -------
          result: str
          """
  
          points = [
              f'{no + 1}. {remove_repeat_fullstop(string)}.'
              for no, string in enumerate(strings)
          ]
          points = ' '.join(points)
          points = f'karangan: {points}'
          return upperfirst(self._predict(cleaning(points)))
  
  
  class PARAPHRASE(T5):
      def __init__(self, X, decode, sess, pred):
          T5.__init__(self, X = X, decode = decode, sess = sess, pred = pred)
  
      def _paraphrase(self, string):
  
          string = f'parafrasa: {cleaning(string)}'
          return upperfirst(self._predict(string))
  
      @check_type
      def paraphrase(self, string: str, split_fullstop: bool = True):
          """
          paraphrase a string. Decoder is beam decoder with beam width size 1, alpha 0.5 .
  
          Parameters
          ----------
          string: str
          split_fullstop: bool, (default=True)
              if True, will generate paraphrase for each strings splitted by fullstop.
  
          Returns
          -------
          result: str
          """
  
          if split_fullstop:
  
              splitted_fullstop = split_into_sentences(string)
  
              results = []
              for splitted in splitted_fullstop:
                  if len(splitted.split()) < 4:
                      results.append(splitted)
                  else:
                      results.append(self._paraphrase(splitted))
              return ' '.join(results)
  
          else:
              return self._paraphrase(string)
  