# 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 unittest from transformers import ( AddedToken, BatchEncoding, SeamlessM4TTokenizer, is_torch_available, ) from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, ) from transformers.tokenization_utils_sentencepiece import SentencePieceExtractor 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 SMALL_TRAINING_CORPUS = [ ["This is the first sentence.", "This is the second one."], ["This sentence (contains #) over symbols and numbers 12 3.", "But not this one."], ] @require_sentencepiece @require_tokenizers class SeamlessM4TTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "facebook/hf-seamless-m4t-medium" tokenizer_class = SeamlessM4TTokenizer test_rust_tokenizer = True integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁生活', '的', '真', '', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '', '▁hi', '', 'th', 'ere', '▁The', '▁following', '▁string', '▁should', '▁be', '▁properly', '▁enc', 'od', 'ed', ':', '▁Hello', '.', '▁But', '▁ir', 'd', '▁and', '▁ปี', '▁ir', 'd', '▁ด', '▁Hey', '▁how', '▁are', '▁you', '▁doing'] # fmt: skip integration_expected_token_ids = [9680, 248, 9, 7356, 248059, 253515, 117, 1398, 79519, 108, 855, 45299, 248079, 540, 3423, 248, 52428, 248132, 248075, 182892, 248506, 249573, 1, 249221, 2867, 94124, 2867, 94124, 94124, 2, 435, 2, 419, 275, 1617, 45893, 191422, 12516, 280, 242514, 12025, 129, 76, 248144, 94124, 248075, 9062, 528, 248072, 540, 99681, 528, 248072, 34744, 27426, 11657, 2442, 1259, 34512] # 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', '', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '', '▁hi', '', 'th', 'ere', '▁The', '▁following', '▁string', '▁should', '▁be', '▁properly', '▁enc', 'od', 'ed', ':', '▁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. 生活的真是 Hi Hello Hi Hello Hello hithere The following string should be properly encoded: Hello. But ird and ปี ird ด Hey how are you doing" def test_batch_encode_plus_batch_sequence_length(self): # Override the parent test because SeamlessM4T uses padding=True by default # Tests that all encoded values have the correct size tokenizer = self.get_tokenizer(do_lower_case=False) sequences = [ "Testing batch encode plus", "Testing batch encode plus with different sequence lengths", "Testing batch encode plus with different sequence lengths correctly pads", ] # For SeamlessM4T, encode with explicit padding=False for individual sequences too encoded_sequences = [tokenizer(sequence, padding=False) for sequence in sequences] encoded_sequences_batch = tokenizer(sequences, padding=False) self.assertListEqual(encoded_sequences, self.convert_batch_to_list_format(encoded_sequences_batch)) def test_padding_to_multiple_of(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): if tokenizer.pad_token is None: self.skipTest(reason="No padding token.") else: empty_tokens = tokenizer("", padding=True, pad_to_multiple_of=8) normal_tokens = tokenizer("This is a sample input", padding=True, pad_to_multiple_of=8) for key, value in empty_tokens.items(): self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") for key, value in normal_tokens.items(): self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") # default to padding=True so need to precise which padding is called normal_tokens = tokenizer("This", pad_to_multiple_of=8, padding=False) for key, value in normal_tokens.items(): self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") # Should also work with truncation normal_tokens = tokenizer("This", padding=True, truncation=True, pad_to_multiple_of=8) for key, value in normal_tokens.items(): self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") # truncation to something which is not a multiple of pad_to_multiple_of raises an error self.assertRaises( ValueError, tokenizer.__call__, "This", padding=True, truncation=True, max_length=12, pad_to_multiple_of=8, ) @require_torch def test_prepare_seq2seq_batch(self): if not self.test_seq2seq: self.skipTest(reason="test_seq2seq is set to False") 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", tgt_lang="ron", pad_to_multiple_of=None, ) except NotImplementedError: self.skipTest(reason="Encountered NotImplementedError when calling prepare_seq2seq_batch") self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.labels.shape[1], 10) # TODO: not working for tgt_text # max_target_length will default to max_length if not specified batch = tokenizer.prepare_seq2seq_batch( src_texts=src_text, tgt_texts=tgt_text, max_length=4, return_tensors="pt", pad_to_multiple_of=None, ) self.assertEqual(batch.input_ids.shape[1], 4) self.assertEqual(batch.labels.shape[1], 4) batch_encoder_only = tokenizer.prepare_seq2seq_batch( src_texts=src_text, max_length=4, max_target_length=10, return_tensors="pt", pad_to_multiple_of=None, ) self.assertEqual(batch_encoder_only.input_ids.shape[1], 4) self.assertEqual(batch_encoder_only.attention_mask.shape[1], 4) self.assertNotIn("decoder_input_ids", batch_encoder_only) # Copied from tests.models.nllb.test_tokenization_nllb.NllbTokenizationTest.test_special_tokens_initialization 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.get_tokenizer(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) def test_training_new_tokenizer(self): # This feature only exists for fast tokenizers if not self.test_rust_tokenizer: self.skipTest(reason="test_rust_tokenizer is set to False") tokenizer = self.get_tokenizer() new_tokenizer = tokenizer.train_new_from_iterator(SMALL_TRAINING_CORPUS, 100) # Test we can use the new tokenizer with something not seen during training inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."]) self.assertEqual(len(inputs["input_ids"]), 2) decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True) expected_result = "This is the first sentence" if tokenizer.backend_tokenizer.normalizer is not None: expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result) self.assertEqual(expected_result, decoded_input) # We check that the parameters of the tokenizer remained the same # Check we have the same number of added_tokens for both pair and non-pair inputs. # make sure it has the same prefix tokens first new_tokenizer.tgt_lang = tokenizer.tgt_lang tokenizer.tgt_lang = tokenizer.tgt_lang self.assertEqual(tokenizer.num_special_tokens_to_add(False), new_tokenizer.num_special_tokens_to_add(False)) self.assertEqual(tokenizer.num_special_tokens_to_add(True), new_tokenizer.num_special_tokens_to_add(True)) # Check we have the correct max_length for both pair and non-pair inputs. self.assertEqual(tokenizer.max_len_single_sentence, new_tokenizer.max_len_single_sentence) self.assertEqual(tokenizer.max_len_sentences_pair, new_tokenizer.max_len_sentences_pair) # Assert the set of special tokens match as we didn't ask to change them self.assertSequenceEqual( tokenizer.all_special_tokens, new_tokenizer.all_special_tokens, ) self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map) @require_torch @require_sentencepiece @require_tokenizers class SeamlessM4TDistilledIntegrationTest(unittest.TestCase): checkpoint_name = "facebook/hf-seamless-m4t-medium" 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, 3] # fmt: skip @classmethod def setUpClass(cls): cls.tokenizer: SeamlessM4TTokenizer = SeamlessM4TTokenizer.from_pretrained( cls.checkpoint_name, src_lang="eng", tgt_lang="ron" ) # cls.pad_token_id = 1 return cls def setUp(self): # Some tests may change to source mode and not reset self.tokenizer.set_tgt_lang_special_tokens(self.tokenizer.tgt_lang) def test_int_remove_extra_whitespaces(self): # make sure the extra spaces are eaten. Since the sample vocab does not have # `______`. sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute is set to False input_ids = self.tokenizer.encode(" . Hello") self.assertEqual(input_ids, [3, 256145, 81, 94124, 3]) tokens = self.tokenizer.tokenize(" . Hello") self.assertEqual(tokens, ["▁.", "▁Hello"]) # `'▁'` is also a whitespace input_ids = self.tokenizer.encode("▁He is not") self.assertEqual(input_ids, [3, 256145, 1808, 248, 2294, 3]) tokens = self.tokenizer.tokenize("▁He is not") self.assertEqual(tokens, ["▁He", "▁is", "▁not"]) # no extra space added input_ids = self.tokenizer.encode("▁He is not ▁He") self.assertEqual(input_ids, [3, 256145, 1808, 248, 2294, 2, 1808, 3]) tokens = self.tokenizer.tokenize("▁He is not ▁He") self.assertEqual(tokens, ["▁He", "▁is", "▁not", "", "▁He"]) # spaces are eaten by spm + our strip # make sure that the output after the extra id is the same as if # extra_id was not there input_ids = self.tokenizer.encode("▁He is not ▁He") self.assertEqual(input_ids, [3, 256145, 1808, 248, 2294, 1808, 3]) tokens = self.tokenizer.tokenize("▁He is not ▁He") self.assertEqual(tokens, ["▁He", "▁is", "▁not", "▁He"]) # spaces are eaten by spm even if not start def test_language_codes(self): self.assertEqual(self.tokenizer.convert_tokens_to_ids("__ace_Latn__"), 256002) self.assertEqual(self.tokenizer.convert_tokens_to_ids("__shn__"), 256152) self.assertEqual(self.tokenizer.convert_tokens_to_ids("__eng__"), 256047) self.assertEqual(self.tokenizer.convert_tokens_to_ids("__fra__"), 256057) self.assertEqual(self.tokenizer.convert_tokens_to_ids("__quy__"), 256144) def test_tokenizer_tgt_lang(self): ids = self.tokenizer(self.src_text, src_lang="fra").input_ids[0] self.assertListEqual(self.expected_src_tokens[1:], ids[1 : len(self.expected_src_tokens)]) self.assertEqual(256057, ids[0]) rest_ids = ids[len(self.expected_src_tokens) :] self.assertListEqual([0] * len(rest_ids), rest_ids) ids = self.tokenizer(self.src_text, src_lang="__shn__").input_ids[0] self.assertListEqual(self.expected_src_tokens[1:], ids[1 : len(self.expected_src_tokens)]) self.assertEqual(256152, ids[0]) # Copied from tests.models.nllb.test_tokenization_nllb.NllbDistilledIntegrationTest.test_enro_tokenizer_decode_ignores_language_codes def test_enro_tokenizer_decode_ignores_language_codes(self): self.assertIn(RO_CODE, self.tokenizer.all_special_ids) generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] # fmt: skip 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], 3) self.assertEqual(ids[0], EN_CODE) self.assertEqual(len(ids), desired_max_length) @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), pad_to_multiple_of=None, return_tensors="pt", ) batch["decoder_input_ids"] = shift_tokens_right( batch["labels"], self.tokenizer.pad_token_id, self.tokenizer.convert_tokens_to_ids("__ron__") ) 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", pad_to_multiple_of=None ) 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.convert_tokens_to_ids(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", tgt_lang="fra" ) self.assertEqual( nested_simplify(inputs), { # A, test, EOS, en_XX "input_ids": [[256047, 70, 7356, 3]], "attention_mask": [[1, 1, 1, 1]], # ar_AR "forced_bos_token_id": 256057, }, ) @require_sentencepiece @require_tokenizers class CommonSpmIntegrationTests(unittest.TestCase): """ A class that regroups important test to make sure that we properly handle the special tokens. """ @classmethod def setUpClass(cls): extractor = SentencePieceExtractor(SAMPLE_VOCAB) _, vocab_scores, merges = extractor.extract() tokenizer = SeamlessM4TTokenizer( vocab=vocab_scores, merges=merges, ) tokenizer.add_special_tokens({"additional_special_tokens": [AddedToken("", rstrip=False, lstrip=False)]}) cls.tokenizer = tokenizer return cls def setUp(self): self.tokenizer.set_tgt_lang_special_tokens(self.tokenizer.tgt_lang) def test_add_dummy_prefix(self): # make sure `'▁'` is prepended properly input_ids = self.tokenizer.encode(". Hello") self.assertEqual(input_ids, [3, 1, 8, 5, 157, 87, 21, 3]) tokens = self.tokenizer.tokenize(". Hello") self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"]) tokens = self.tokenizer.tokenize("") self.assertEqual(tokens, []) tokens = self.tokenizer.tokenize(" ") self.assertEqual(tokens, []) tokens = self.tokenizer.tokenize("▁") self.assertEqual(tokens, []) def test_character_after_special_token(self): # Make sure that `tokenizer.tokenize` is similar to # adding the equivalent special token to the vocab input_ids = self.tokenizer.encode("Hey I") self.assertEqual(input_ids, [3, 1, 157, 31, 2, 101, 3]) tokens = self.tokenizer.tokenize("I") self.assertEqual(tokens, ["", "I"]) input_ids = self.tokenizer.encode("Hello, ,") self.assertEqual(input_ids, [3, 1, 157, 87, 21, 4, 2, 4, 3]) tokens = self.tokenizer.tokenize("Hello, ,") self.assertEqual(tokens, ["▁He", "ll", "o", ",", "", ","]) def test_special_tokens_strip(self): input_ids = self.tokenizer.encode(" ,") self.assertEqual(input_ids, [3, 1, 2, 8, 4, 3]) tokens = self.tokenizer.tokenize(" ,") # spaces are eaten by rstrip / lstrip + normalizer self.assertEqual(tokens, ["", "▁", ","]) input_ids = self.tokenizer.encode("No He") self.assertEqual(input_ids, [3, 1, 285, 2, 157, 3]) tokens = self.tokenizer.tokenize("No ▁He") self.assertEqual(tokens, ["▁No", "", "▁He"]) # spaces are eaten by rstrip / lstrip