text: from torch.utils.data import Dataset
  from typing import List
  from ..readers.InputExample import InputExample
  import numpy as np
  import nltk
  from nltk.tokenize.treebank import TreebankWordDetokenizer
  
  class DenoisingAutoEncoderDataset(Dataset):
      """
      The DenoisingAutoEncoderDataset returns InputExamples in the format: texts=[noise_fn(sentence), sentence]
      It is used in combination with the DenoisingAutoEncoderLoss: Here, a decoder tries to re-construct the
      sentence without noise.
  
      :param sentences: A list of sentences
      :param noise_fn: A noise function: Given a string, it returns a string with noise, e.g. deleted words
      """
      def __init__(self, sentences: List[str], noise_fn=lambda s: DenoisingAutoEncoderDataset.delete(s)):
          self.sentences = sentences
          self.noise_fn = noise_fn
  
  
      def __getitem__(self, item):
          sent = self.sentences[item]
          return InputExample(texts=[self.noise_fn(sent), sent])
  
  
      def __len__(self):
          return len(self.sentences)
  
      # Deletion noise.
      @staticmethod
      def delete(text, del_ratio=0.6):
          words = nltk.word_tokenize(text)
          n = len(words)
          if n == 0:
              return text
  
          keep_or_not = np.random.rand(n) > del_ratio
          if sum(keep_or_not) == 0:
              keep_or_not[np.random.choice(n)] = True # guarantee that at least one word remains
          words_processed = TreebankWordDetokenizer().detokenize(np.array(words)[keep_or_not])
          return words_processed