content: import pathlib
  
  import pytest
  import fasttext
  
  from whatlies.language import FasttextLanguage
  
  text_path = str(pathlib.Path(__file__).parent.parent.absolute() / "data" / "foobar.txt")
  model1 = fasttext.train_unsupervised(
      text_path, model="cbow", dim=20, epoch=20, min_count=1
  )
  model2 = fasttext.train_unsupervised(text_path, model="skipgram", dim=10, min_count=1)
  
  
  def test_load_in_model1():
      lang = FasttextLanguage(model1)
      assert lang["dog"].vector.shape[0] == 20
  
  
  def test_load_in_model2():
      lang = FasttextLanguage(model2)
      assert lang["dog"].vector.shape[0] == 10
  
  
  def test_retreive_similar_len():
      assert len(FasttextLanguage(model1).score_similar("cat", 20)) == 20
      assert len(FasttextLanguage(model2).score_similar("cat", 10)) == 10
      assert len(FasttextLanguage(model1).score_similar("cat", 1000)) == 91
      assert len(FasttextLanguage(model2).score_similar("cat", 1000)) == 91
  
  
  def test_raise_warning():
      with pytest.warns(UserWarning):
          FasttextLanguage(model1).score_similar("cat", 1000)
  