import tempfile import unittest from transformers import AutoTokenizer, BatchEncoding, MBartTokenizer, is_torch_available from transformers.testing_utils import require_torch from .test_tokenization_common import TokenizerTesterMixin from .test_tokenization_xlm_roberta import SAMPLE_VOCAB, SPIECE_UNDERLINE if is_torch_available(): from transformers.modeling_bart import shift_tokens_right EN_CODE = 250004 RO_CODE = 250020 class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = MBartTokenizer def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True) tokenizer.save_pretrained(self.tmpdirname) def test_full_tokenizer(self): tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, [ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4] # ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^ ], ) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "", ".", ], ) @require_torch class MBartEnroIntegrationTest(unittest.TestCase): checkpoint_name = "facebook/mbart-large-en-ro" src_text = [ " UN Chief Says There Is No Military Solution in Syria", """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""", ] tgt_text = [ "Şeful ONU declară că nu există o soluţie militară în Siria", 'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.', ] expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE] @classmethod def setUpClass(cls): cls.tokenizer: MBartTokenizer = AutoTokenizer.from_pretrained(cls.checkpoint_name) cls.pad_token_id = 1 return cls def check_language_codes(self): self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020) def test_enro_tokenizer_batch_encode_plus(self): ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0] self.assertListEqual(self.expected_src_tokens, ids) def test_enro_tokenizer_decode_ignores_language_codes(self): self.assertIn(RO_CODE, self.tokenizer.all_special_ids) generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2] result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) self.assertEqual(result, expected_romanian) self.assertNotIn(self.tokenizer.eos_token, result) def test_enro_tokenizer_truncation(self): src_text = ["this is gunna be a long sentence " * 20] assert isinstance(src_text[0], str) desired_max_length = 10 ids = self.tokenizer.prepare_seq2seq_batch( src_text, return_tensors=None, max_length=desired_max_length, ).input_ids[0] self.assertEqual(ids[-2], 2) self.assertEqual(ids[-1], EN_CODE) self.assertEqual(len(ids), desired_max_length) def test_mask_token(self): self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["", "ar_AR"]), [250026, 250001]) def test_special_tokens_unaffacted_by_save_load(self): tmpdirname = tempfile.mkdtemp() original_special_tokens = self.tokenizer.fairseq_tokens_to_ids self.tokenizer.save_pretrained(tmpdirname) new_tok = MBartTokenizer.from_pretrained(tmpdirname) self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens) # prepare_seq2seq_batch tests below @require_torch def test_batch_fairseq_parity(self): batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch( self.src_text, tgt_texts=self.tgt_text, return_tensors="pt" ) batch["decoder_input_ids"] = shift_tokens_right(batch.labels, self.tokenizer.pad_token_id) for k in batch: batch[k] = batch[k].tolist() # batch = {k: v.tolist() for k,v in batch.items()} # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4 # batch.decoder_inputs_ids[0][0] == assert batch.input_ids[1][-2:] == [2, EN_CODE] assert batch.decoder_input_ids[1][0] == RO_CODE assert batch.decoder_input_ids[1][-1] == 2 assert batch.labels[1][-2:] == [2, RO_CODE] @require_torch def test_enro_tokenizer_prepare_seq2seq_batch(self): batch = self.tokenizer.prepare_seq2seq_batch( self.src_text, tgt_texts=self.tgt_text, max_length=len(self.expected_src_tokens), ) batch["decoder_input_ids"] = shift_tokens_right(batch.labels, self.tokenizer.pad_token_id) self.assertIsInstance(batch, BatchEncoding) self.assertEqual((2, 14), batch.input_ids.shape) self.assertEqual((2, 14), batch.attention_mask.shape) result = batch.input_ids.tolist()[0] self.assertListEqual(self.expected_src_tokens, result) self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS # Test that special tokens are reset self.assertEqual(self.tokenizer.prefix_tokens, []) self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, EN_CODE]) def test_seq2seq_max_target_length(self): batch = self.tokenizer.prepare_seq2seq_batch( self.src_text, tgt_texts=self.tgt_text, max_length=3, max_target_length=10 ) batch["decoder_input_ids"] = shift_tokens_right(batch.labels, self.tokenizer.pad_token_id) self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.decoder_input_ids.shape[1], 10) # max_target_length will default to max_length if not specified batch = self.tokenizer.prepare_seq2seq_batch(self.src_text, tgt_texts=self.tgt_text, max_length=3) batch["decoder_input_ids"] = shift_tokens_right(batch.labels, self.tokenizer.pad_token_id) self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.decoder_input_ids.shape[1], 3)