# Copyright 2020 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 transformers import BatchEncoding, MBartTokenizer, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, ) 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 MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "facebook/mbart-large-en-ro" tokenizer_class = MBartTokenizer integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁', '生活的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '', '▁hi', '', '▁there', '▁The', '▁following', '▁string', '▁should', '▁be', '▁properly', '▁en', 'code', 'd', ':', '▁Hello', '.', '▁But', '▁ir', 'd', '▁and', '▁ปี', '▁ir', 'd', '▁ด', '▁Hey', '▁how', '▁are', '▁you', '▁doing'] # fmt: skip integration_expected_token_ids = [3293, 83, 10, 3034, 6, 82803, 87, 509, 103122, 23, 483, 13821, 4, 136, 903, 83, 84047, 446, 5, 6, 62668, 5364, 245875, 354, 2673, 35378, 2673, 35378, 35378, 0, 1274, 0, 2685, 581, 25632, 79315, 5608, 186, 155965, 22, 40899, 71, 12, 35378, 5, 4966, 193, 71, 136, 10249, 193, 71, 48229, 28240, 3642, 621, 398, 20594] # fmt: skip expected_tokens_from_ids = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁', '生活的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '', '▁hi', '', '▁there', '▁The', '▁following', '▁string', '▁should', '▁be', '▁properly', '▁en', 'code', 'd', ':', '▁Hello', '.', '▁But', '▁ir', 'd', '▁and', '▁ปี', '▁ir', 'd', '▁ด', '▁Hey', '▁how', '▁are', '▁you', '▁doing'] # fmt: skip integration_expected_decoded_text = "This is a test 😊 I was born in 92000, and this is falsé. 生活的真谛是 Hi Hello Hi Hello Hello hi there The following string should be properly encoded: Hello. But ird and ปี ird ด Hey how are you doing" @require_torch @require_sentencepiece @require_tokenizers 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 = MBartTokenizer.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) def test_enro_tokenizer_batch_encode_plus(self): ids = self.tokenizer(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(src_text, max_length=desired_max_length, truncation=True).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) @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][-2:].tolist() == [2, EN_CODE] assert batch.decoder_input_ids[1][0].tolist() == RO_CODE assert batch.decoder_input_ids[1][-1] == 2 assert batch.labels[1][-2:].tolist() == [2, RO_CODE] @require_torch def test_enro_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, -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_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), { # A, test, EOS, en_XX "input_ids": [[62, 3034, 2, 250004]], "attention_mask": [[1, 1, 1, 1]], # ar_AR "forced_bos_token_id": 250001, }, )