# Copyright 2022 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, AddedToken, BatchEncoding, NllbTokenizer, NllbTokenizerFast, 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.m2m_100.modeling_m2m_100 import shift_tokens_right EN_CODE = 256047 RO_CODE = 256145 @require_sentencepiece @require_tokenizers class NllbTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = NllbTokenizer rust_tokenizer_class = NllbTokenizerFast test_rust_tokenizer = True test_sentencepiece = True from_pretrained_kwargs = {} def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = NllbTokenizer(SAMPLE_VOCAB, keep_accents=True) tokenizer.save_pretrained(self.tmpdirname) def test_full_tokenizer(self): tokenizer = NllbTokenizer(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] ], ) 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", "", ".", ], ) # overwrite from test_tokenization_common to speed up test def test_save_pretrained(self): self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {}) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) tokenizer_p = self.tokenizer_class.from_pretrained(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)) 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 def test_prepare_seq2seq_batch(self): if not self.test_seq2seq: return tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Longer text that will definitely require truncation. 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.", ] try: batch = tokenizer.prepare_seq2seq_batch( src_texts=src_text, tgt_texts=tgt_text, max_length=3, max_target_length=10, return_tensors="pt", src_lang="eng_Latn", tgt_lang="ron_Latn", ) except NotImplementedError: return self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.labels.shape[1], 10) # max_target_length will default to max_length if not specified batch = tokenizer.prepare_seq2seq_batch( src_text, tgt_texts=tgt_text, max_length=3, return_tensors="pt" ) self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.labels.shape[1], 3) batch_encoder_only = tokenizer.prepare_seq2seq_batch( src_texts=src_text, max_length=3, max_target_length=10, return_tensors="pt" ) self.assertEqual(batch_encoder_only.input_ids.shape[1], 3) self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3) self.assertNotIn("decoder_input_ids", batch_encoder_only) @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.") def test_save_slow_from_fast_and_reload_fast(self): pass def test_special_tokens_initialization(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): added_tokens = [AddedToken("", lstrip=True)] tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) r_output = tokenizer_r.encode("Hey this is a token") special_token_id = tokenizer_r.encode("", add_special_tokens=False)[0] self.assertTrue(special_token_id in r_output) if self.test_slow_tokenizer: tokenizer_cr = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs, # , from_slow=True <- unfortunately too slow to convert ) tokenizer_p = self.tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) p_output = tokenizer_p.encode("Hey this is a token") cr_output = tokenizer_cr.encode("Hey this is a token") self.assertEqual(p_output, r_output) self.assertEqual(cr_output, r_output) self.assertTrue(special_token_id in p_output) self.assertTrue(special_token_id in cr_output) @unittest.skip("Need to fix this after #26538") def test_training_new_tokenizer(self): pass @require_torch @require_sentencepiece @require_tokenizers class NllbDistilledIntegrationTest(unittest.TestCase): checkpoint_name = "facebook/nllb-200-distilled-600M" 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 = [ 256047, 16297, 134408, 8165, 248066, 14734, 950, 1135, 105721, 3573, 83, 27352, 108, 49486, 2, ] @classmethod def setUpClass(cls): cls.tokenizer: NllbTokenizer = NllbTokenizer.from_pretrained( cls.checkpoint_name, src_lang="eng_Latn", tgt_lang="ron_Latn" ) cls.pad_token_id = 1 return cls def test_language_codes(self): self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"], 256001) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"], 256002) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"], 256057) 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) # fmt: off generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] # fmt: on 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[-1], 2) self.assertEqual(ids[0], EN_CODE) self.assertEqual(len(ids), desired_max_length) def test_mask_token(self): self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["", "ar_AR"]), [256203, 3]) 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 = NllbTokenizer.from_pretrained(tmpdirname) self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens) @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.tokenizer.lang_code_to_id["ron_Latn"] ) self.assertIsInstance(batch, BatchEncoding) self.assertEqual((2, 15), batch.input_ids.shape) self.assertEqual((2, 15), batch.attention_mask.shape) result = batch.input_ids.tolist()[0] self.assertListEqual(self.expected_src_tokens, result) self.assertEqual(RO_CODE, batch.decoder_input_ids[0, 0]) # EOS # 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_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, decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang], ) 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="eng_Latn", tgt_lang="fra_Latn" ) self.assertEqual( nested_simplify(inputs), { # A, test, EOS, en_XX "input_ids": [[256047, 70, 7356, 2]], "attention_mask": [[1, 1, 1, 1]], # ar_AR "forced_bos_token_id": 256057, }, ) @require_torch def test_legacy_behaviour(self): self.tokenizer.legacy_behaviour = True inputs = self.tokenizer( "UN Chief says there is no military solution in Syria", src_lang="eng_Latn", tgt_lang="fra_Latn" ) self.assertEqual( inputs.input_ids, [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] ) self.tokenizer.legacy_behaviour = False inputs = self.tokenizer( "UN Chief says there is no military solution in Syria", src_lang="eng_Latn", tgt_lang="fra_Latn" ) self.assertEqual( inputs.input_ids, [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )