text: import numpy as np
  import fasttext
  
  from pure_fasttext import FastText
  
  K = [1, 2, 3]
  THRESHOLD = [0.0, 0.5]
  DATA_LOC = "data/supervised.txt"
  SENTENCE = [
      "this is text",
      "this is more text",
      "this   bananas text" "here is  text with    some   space  ",
      "   text's are fun with this is text" " this IS Text TEXT ",
  ]
  
  
  def test_supervised_predict():
      model = fasttext.train_supervised(DATA_LOC)
      model_ = FastText(model)
      for k in K:
          for threshold in THRESHOLD:
              for sentence in SENTENCE:
                  wv = model.predict(sentence, k=k, threshold=threshold)
                  wv_ = model_.predict(sentence, k=k, threshold=threshold)
                  assert wv[0] == wv_[0]
                  assert np.allclose(wv[1], wv_[1], atol=1e-4)
  
  
  def test_supervised_sentence():
      model = fasttext.train_supervised(DATA_LOC)
      model_ = FastText(model)
      for sentence in SENTENCE:
          wv = model.get_sentence_vector(sentence).tolist()
          wv_ = model_.get_sentence_vector(sentence)
          assert np.allclose(wv, wv_)
  