# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, BatchEncoding, MBart50Tokenizer, MBart50TokenizerFast, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, slow, ) from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): from transformers.models.mbart.modeling_mbart import shift_tokens_right EN_CODE = 250004 RO_CODE = 250020 @require_sentencepiece @require_tokenizers class MBart50TokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "facebook/mbart-large-50-one-to-many-mmt" tokenizer_class = MBart50Tokenizer rust_tokenizer_class = MBart50TokenizerFast test_rust_tokenizer = True test_sentencepiece = True @classmethod def setUpClass(cls): super().setUpClass() # We have a SentencePiece fixture for testing tokenizer = MBart50Tokenizer(SAMPLE_VOCAB, src_lang="en_XX", tgt_lang="ro_RO", keep_accents=True) tokenizer.save_pretrained(cls.tmpdirname) def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "" token_id = 0 self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "") self.assertEqual(vocab_keys[1], "") self.assertEqual(vocab_keys[-1], "") self.assertEqual(len(vocab_keys), 1_054) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 1_054) def test_full_tokenizer(self): tokenizer = MBart50Tokenizer(SAMPLE_VOCAB, src_lang="en_XX", tgt_lang="ro_RO", 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", "."]) # fmt: skip 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] ], ) 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", "", "."],) # fmt: skip @slow def test_tokenizer_integration(self): expected_encoding = {'input_ids': [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="facebook/mbart-large-50", revision="d3913889c59cd5c9e456b269c376325eabad57e2", ) # overwrite from test_tokenization_common to speed up test def test_save_pretrained(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions self.skipTest(reason="test_slow_tokenizer is set to False") self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {}) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs) tokenizer_p = self.get_tokenizer(pretrained_name, **kwargs) tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it save with the same files + the tokenizer.json file for the fast one self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files)) tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f) self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key)) # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id")) shutil.rmtree(tmpdirname2) # Save tokenizer rust, legacy_format=True tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it save with the same files self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) # Save tokenizer rust, legacy_format=False tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it saved the tokenizer.json file self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files)) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) @require_torch @require_sentencepiece @require_tokenizers class MBart50OneToManyIntegrationTest(unittest.TestCase): checkpoint_name = "facebook/mbart-large-50-one-to-many-mmt" 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 = [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2] @classmethod def setUpClass(cls): cls.tokenizer: MBart50Tokenizer = MBart50Tokenizer.from_pretrained( cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO" ) 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) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"], 250038) def test_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_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_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(src_text, max_length=desired_max_length, truncation=True).input_ids[0] self.assertEqual(ids[0], EN_CODE) self.assertEqual(ids[-1], 2) self.assertEqual(len(ids), desired_max_length) def test_mask_token(self): self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["", "ar_AR"]), [250053, 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 = MBart50Tokenizer.from_pretrained(tmpdirname) self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens) @require_torch def test_batch_fairseq_parity(self): batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt") batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id) # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4 assert batch.input_ids[1][0] == EN_CODE assert batch.input_ids[1][-1] == 2 assert batch.labels[1][0] == RO_CODE assert batch.labels[1][-1] == 2 assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE] @require_torch def test_tokenizer_prepare_batch(self): batch = self.tokenizer( self.src_text, text_target=self.tgt_text, padding=True, truncation=True, max_length=len(self.expected_src_tokens), return_tensors="pt", ) 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, 0]) # decoder_start_token_id # Test that special tokens are reset self.assertEqual(self.tokenizer.prefix_tokens, [EN_CODE]) self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) def test_seq2seq_max_target_length(self): batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt") targets = self.tokenizer( text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt" ) labels = targets["input_ids"] batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id) self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.decoder_input_ids.shape[1], 10) @require_torch def test_tokenizer_translation(self): inputs = self.tokenizer._build_translation_inputs( "A test", return_tensors="pt", src_lang="en_XX", tgt_lang="ar_AR" ) self.assertEqual( nested_simplify(inputs), { # en_XX, A, test, EOS "input_ids": [[250004, 62, 3034, 2]], "attention_mask": [[1, 1, 1, 1]], # ar_AR "forced_bos_token_id": 250001, }, )