# 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 tempfile import unittest from pathlib import Path from shutil import copyfile from transformers import M2M100Tokenizer, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, slow, ) from transformers.utils import is_sentencepiece_available if is_sentencepiece_available(): from transformers.models.m2m_100.tokenization_m2m_100 import VOCAB_FILES_NAMES, save_json from ...test_tokenization_common import TokenizerTesterMixin if is_sentencepiece_available(): SAMPLE_SP = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): from transformers.models.m2m_100.modeling_m2m_100 import shift_tokens_right EN_CODE = 128022 FR_CODE = 128028 @require_sentencepiece class M2M100TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = M2M100Tokenizer test_rust_tokenizer = False test_seq2seq = False test_sentencepiece = True def setUp(self): super().setUp() vocab = ["", "", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", ""] vocab_tokens = dict(zip(vocab, range(len(vocab)))) save_dir = Path(self.tmpdirname) save_json(vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab_file"]) if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists(): copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"]) tokenizer = M2M100Tokenizer.from_pretrained(self.tmpdirname) tokenizer.save_pretrained(self.tmpdirname) def get_tokenizer(self, **kwargs): return M2M100Tokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): return ( "This is a test", "This is a test", ) 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): tokenizer = self.get_tokenizer() vocab_keys = list(tokenizer.get_vocab().keys()) self.assertEqual(vocab_keys[0], "") self.assertEqual(vocab_keys[1], "") self.assertEqual(vocab_keys[-1], "") self.assertEqual(len(vocab_keys), tokenizer.vocab_size + len(tokenizer.get_added_vocab())) @unittest.skip("Skip this test while all models are still to be uploaded.") def test_pretrained_model_lists(self): pass def test_full_tokenizer(self): tokenizer = self.get_tokenizer() tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [2, 3, 4, 5, 6], ) back_tokens = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]) self.assertListEqual(back_tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, "This is a test") @slow def test_tokenizer_integration(self): # fmt: off expected_encoding = {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 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, 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, 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], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="facebook/m2m100_418M", revision="c168bae485c864188cf9aa0e4108b0b6934dc91e", ) @require_torch @require_sentencepiece @require_tokenizers class M2M100TokenizerIntegrationTest(unittest.TestCase): checkpoint_name = "facebook/m2m100_418M" src_text = [ "In my opinion, there are two levels of response from the French government.", "NSA Affair Emphasizes Complete Lack of Debate on Intelligence", ] tgt_text = [ "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.", "L'affaire NSA souligne l'absence totale de débat sur le renseignement", ] # fmt: off expected_src_tokens = [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2] # fmt: on @classmethod def setUpClass(cls): cls.tokenizer: M2M100Tokenizer = M2M100Tokenizer.from_pretrained( cls.checkpoint_name, src_lang="en", tgt_lang="fr" ) cls.pad_token_id = 1 return cls def check_language_codes(self): self.assertEqual(self.tokenizer.get_lang_id("ar"), 128006) self.assertEqual(self.tokenizer.get_lang_id("en"), 128022) self.assertEqual(self.tokenizer.get_lang_id("ro"), 128076) self.assertEqual(self.tokenizer.get_lang_id("mr"), 128063) def test_get_vocab(self): vocab = self.tokenizer.get_vocab() self.assertEqual(len(vocab), self.tokenizer.vocab_size) self.assertEqual(vocab[""], 3) self.assertIn(self.tokenizer.get_lang_token("en"), vocab) def test_tokenizer_batch_encode_plus(self): self.tokenizer.src_lang = "en" 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(FR_CODE, self.tokenizer.all_special_ids) # fmt: off generated_ids = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2] # fmt: on result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) expected_french = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) self.assertEqual(result, expected_french) self.assertNotIn(self.tokenizer.eos_token, result) def test_special_tokens_unaffacted_by_save_load(self): tmpdirname = tempfile.mkdtemp() original_special_tokens = self.tokenizer.lang_token_to_id self.tokenizer.save_pretrained(tmpdirname) new_tok = M2M100Tokenizer.from_pretrained(tmpdirname) self.assertDictEqual(new_tok.lang_token_to_id, original_special_tokens) @require_torch def test_batch_fairseq_parity(self): self.tokenizer.src_lang = "en" self.tokenizer.tgt_lang = "fr" 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, self.tokenizer.eos_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][0] == EN_CODE assert batch.input_ids[1][-1] == 2 assert batch.labels[1][0] == FR_CODE assert batch.labels[1][-1] == 2 assert batch.decoder_input_ids[1][:2] == [2, FR_CODE] @require_torch def test_src_lang_setter(self): self.tokenizer.src_lang = "mr" self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("mr")]) self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) self.tokenizer.src_lang = "zh" self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("zh")]) self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) @require_torch def test_tokenizer_target_mode(self): self.tokenizer.tgt_lang = "mr" self.tokenizer._switch_to_target_mode() self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("mr")]) self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) self.tokenizer._switch_to_input_mode() self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)]) self.tokenizer.tgt_lang = "zh" self.tokenizer._switch_to_target_mode() self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("zh")]) self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) self.tokenizer._switch_to_input_mode() self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)]) @require_torch def test_tokenizer_translation(self): inputs = self.tokenizer._build_translation_inputs("A test", return_tensors="pt", src_lang="en", tgt_lang="ar") self.assertEqual( nested_simplify(inputs), { # en_XX, A, test, EOS "input_ids": [[128022, 58, 4183, 2]], "attention_mask": [[1, 1, 1, 1]], # ar_AR "forced_bos_token_id": 128006, }, )