old_contents: # -*- coding: utf-8 -*-
  from __future__ import unicode_literals
  from collections import Counter
  import logging
  import unittest
  
  import numpy as np
  from numpy.testing import assert_allclose
  from torchtext import vocab
  
  
  logging.basicConfig(format="%(asctime)s - %(levelname)s "
                      "- %(name)s - %(message)s",
                      level=logging.INFO)
  
  
  class TestVocab(unittest.TestCase):
      def test_vocab_basic(self):
          c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2})
          v = vocab.Vocab(c, min_freq=3, specials=['<pad>', '<bos>'])
  
          self.assertEqual(v.itos, ['<unk>', '<pad>', '<bos>',
                                    'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'])
  
      def test_vocab_download_vectors(self):
          c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2})
          # Build a vocab and get vectors twice to test caching.
          for i in range(2):
              v = vocab.Vocab(c, min_freq=3, specials=['<pad>', '<bos>'],
                              vectors='fasttext.simple.300d')
  
              self.assertEqual(v.itos, ['<unk>', '<pad>', '<bos>',
                                        'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'])
              vectors = v.vectors.numpy()
  
              # The first 5 entries in each vector.
              expected_glove_twitter = {
                  'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645],
                  'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165],
              }
  
              for word in expected_glove_twitter:
                  assert_allclose(vectors[v.stoi[word], :5], expected_glove_twitter[word])
  
              assert_allclose(vectors[v.stoi['<unk>']], np.zeros(300))
  
new_contents: # -*- coding: utf-8 -*-
  from __future__ import unicode_literals
  from collections import Counter
  import logging
  import unittest
  
  import numpy as np
  from numpy.testing import assert_allclose
  from torchtext import vocab
  
  
  logging.basicConfig(format="%(asctime)s - %(levelname)s "
                      "- %(name)s - %(message)s",
                      level=logging.INFO)
  
  
  class TestVocab(unittest.TestCase):
      def test_vocab_basic(self):
          c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2})
          v = vocab.Vocab(c, min_freq=3, specials=['<pad>', '<bos>'])
  
          self.assertEqual(v.itos, ['<unk>', '<pad>', '<bos>',
                                    'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'])
  
      def test_vocab_download_vectors(self):
          c = Counter({'hello': 4, 'world': 3, 'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T': 5, 'freq_too_low': 2})
          # Build a vocab and get vectors twice to test caching.
          for i in range(2):
              v = vocab.Vocab(c, min_freq=3, specials=['<pad>', '<bos>'],
                              vectors='fasttext.simple.300d')
  
              self.assertEqual(v.itos, ['<unk>', '<pad>', '<bos>',
                                        'ᑌᑎIᑕOᗪᕮ_Tᕮ᙭T', 'hello', 'world'])
              vectors = v.vectors.numpy()
  
              # The first 5 entries in each vector.
              expected_fasttext_simple_en = {
                  'hello': [0.39567, 0.21454, -0.035389, -0.24299, -0.095645],
                  'world': [0.10444, -0.10858, 0.27212, 0.13299, -0.33165],
              }
  
              for word in expected_fasttext_simple_en:
                  assert_allclose(vectors[v.stoi[word], :5],
                                  expected_fasttext_simple_en[word])
  
              assert_allclose(vectors[v.stoi['<unk>']], np.zeros(300))
  
subject: Change expected vectors variable name for fasttext vs twitter