# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # 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 paddlenlp.transformers import SPIECE_UNDERLINE, MBart50Tokenizer from paddlenlp.transformers.mbart.modeling import shift_tokens_right from ...testing_utils import get_tests_dir, nested_simplify from ..test_tokenizer_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") EN_CODE = 250004 RO_CODE = 250020 class MBart50TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = MBart50Tokenizer test_sentencepiece = True test_offsets = False def setUp(self): super().setUp() # 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(self.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, # fmt: off [ 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: on ) 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, # fmt: off [ 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: on ) class MBart50OneToManyIntegrationTest(unittest.TestCase): checkpoint_name = "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_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) def test_seq2seq_max_target_length(self): batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pd") targets = self.tokenizer(self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pd") 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) def test_tokenizer_translation(self): inputs = self.tokenizer._build_translation_inputs( "A test", return_tensors="pd", 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, }, )