# Copyright 2021 The HuggingFace Inc. team. # # 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 inspect import shutil import tempfile import unittest from parameterized import parameterized from transformers import ( AddedToken, LayoutXLMTokenizer, PreTrainedTokenizerBase, is_mlx_available, is_torch_available, logging, ) from transformers.testing_utils import ( get_tests_dir, require_pandas, require_sentencepiece, require_tokenizers, require_torch, slow, ) from transformers.tokenization_utils_sentencepiece import SentencePieceExtractor from ...test_tokenization_common import ( TokenizerTesterMixin, filter_non_english, merge_model_tokenizer_mappings, ) from ...test_tokenizers_backend_mixin import SMALL_TRAINING_CORPUS logger = logging.get_logger(__name__) SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") @require_sentencepiece @require_tokenizers @require_pandas class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "FacebookAI/xlm-roberta-base" tokenizer_class = LayoutXLMTokenizer rust_tokenizer_class = LayoutXLMTokenizer test_rust_tokenizer = True test_slow_tokenizer = False from_pretrained_filter = filter_non_english test_seq2seq = False test_sentencepiece = True maxDiff = None def get_words_and_boxes(self): words = ["a", "weirdly", "test"] boxes = [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]] return words, boxes def get_words_and_boxes_batch(self): words = [["a", "weirdly", "test"], ["hello", "my", "name", "is", "bob"]] boxes = [ [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]], [[961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69]], ] return words, boxes def get_question_words_and_boxes(self): question = "what's his name?" words = ["a", "weirdly", "test"] boxes = [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]] return question, words, boxes def get_question_words_and_boxes_batch(self): questions = ["what's his name?", "how is he called?"] words = [["a", "weirdly", "test"], ["what", "a", "laif", "gastn"]] boxes = [ [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]], [[256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69]], ] return questions, words, boxes def get_empty_words_and_boxes(self): words = ["test", "empty", ""] boxes = [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]] return words, boxes def get_empty_words_and_boxes_batch(self): words = [["test", "empty", ""], ["one", "more", "empty", ""]] boxes = [ [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]], [[961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57]], ] return words, boxes def get_empty_question_words_and_boxes(self): question = "" words = ["test", "empty", ""] boxes = [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]] return question, words, boxes def get_empty_question_words_and_boxes_batch(self): questions = ["what's his name?", ""] words = [["test", "empty", ""], ["one", "more", "empty", ""]] boxes = [ [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]], [[961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57]], ] return questions, words, boxes @classmethod def setUpClass(cls): super().setUpClass() # Extract vocab from SentencePiece model extractor = SentencePieceExtractor(SAMPLE_VOCAB) vocab_ids, vocab_scores, merges = extractor.extract() # Create tokenizer from vocab tokenizer = LayoutXLMTokenizer(vocab=vocab_scores) tokenizer.save_pretrained(cls.tmpdirname) def convert_batch_encode_plus_format_to_encode_plus(self, batch_encode_plus_sequences): """Helper method to convert batch_encode_plus output to list of encode_plus outputs""" # Get the batch size first_key = list(batch_encode_plus_sequences.keys())[0] batch_size = len(batch_encode_plus_sequences[first_key]) # Convert to list of dicts encode_plus_sequences = [] for i in range(batch_size): single_sequence = {} for key, value in batch_encode_plus_sequences.items(): if key != "encodings": # Skip the encodings attribute single_sequence[key] = value[i] encode_plus_sequences.append(single_sequence) return encode_plus_sequences def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00e9d,running" output_text = "unwanted, running" return input_text, output_text @unittest.skip(reason="Chat template tests don't play well with table/layout models.") def test_chat_template_batched(self): pass def test_bos_token_with_add_bos_token_true(self): # LayoutXLM requires pretokenized input with boxes tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words = ["hello", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # LayoutXLM doesn't use add_bos_token, it uses post_processor # Just verify it can encode without error encoded = tokenizer.encode(words, boxes=boxes) self.assertIsInstance(encoded, list) def test_bos_token_with_add_bos_token_false(self): # LayoutXLM requires pretokenized input with boxes tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words = ["hello", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] # LayoutXLM doesn't use add_bos_token, it uses post_processor # Just verify it can encode without error encoded = tokenizer.encode(words, boxes=boxes) self.assertIsInstance(encoded, list) def test_pad_token_initialization(self): """Test that passing pad_token when creating a tokenizer works correctly.""" # LayoutXLM requires pretokenized input with boxes tokenizer = self.get_tokenizer(pad_token="[PAD]") # Verify the pad_token was set correctly self.assertEqual(tokenizer.pad_token, "[PAD]") self.assertIsNotNone(tokenizer.pad_token_id) # Test with two sequences of different lengths to trigger padding seq_0 = ["Test", "this", "method"] seq_1 = ["With", "these", "inputs", "and", "some", "extra"] boxes_0 = [[1, 2, 3, 4] for _ in seq_0] boxes_1 = [[1, 2, 3, 4] for _ in seq_1] # Test padding works with the custom pad_token output_with_padding = tokenizer( [seq_0, seq_1], boxes=[boxes_0, boxes_1], padding=True, ) # Check padding was applied correctly self.assertEqual(len(output_with_padding["input_ids"][0]), len(output_with_padding["input_ids"][1])) def test_encode_basic_padding(self): """Test basic left/right padding behavior using encode() method with max_length strategy.""" tokenizer = self.get_tokenizer(do_lower_case=False) # LayoutXLM requires pretokenized input with boxes words = ["Sequence"] boxes = [[1, 2, 3, 4]] padding_size = 10 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, words) padding_idx = tokenizer.pad_token_id # Test right padding encoded_sequence = tokenizer.encode(words, boxes=boxes) sequence_length = len(encoded_sequence) padded_sequence = tokenizer.encode( words, boxes=boxes, max_length=sequence_length + padding_size, padding="max_length" ) padded_sequence_length = len(padded_sequence) assert sequence_length + padding_size == padded_sequence_length assert encoded_sequence + [padding_idx] * padding_size == padded_sequence # Test left padding tokenizer.padding_side = "left" encoded_sequence = tokenizer.encode(words, boxes=boxes) sequence_length = len(encoded_sequence) padded_sequence = tokenizer.encode( words, boxes=boxes, max_length=sequence_length + padding_size, padding="max_length" ) padded_sequence_length = len(padded_sequence) assert sequence_length + padding_size == padded_sequence_length assert [padding_idx] * padding_size + encoded_sequence == padded_sequence # override test in `test_tokenization_common.py` because of the required input format of the `__call__`` method of # this tokenizer def test_save_sentencepiece_tokenizer(self) -> None: if not self.test_sentencepiece: self.skipTest(reason="test_sentencepiece is set to False") # We want to verify that we will be able to save the tokenizer even if the original files that were used to # build the tokenizer have been deleted in the meantime. words, boxes = self.get_words_and_boxes() tokenizer_1 = self.get_tokenizer() encoding_tokenizer_1 = tokenizer_1( words, boxes=boxes, ) tmpdirname_1 = tempfile.mkdtemp() tmpdirname_2 = tempfile.mkdtemp() tokenizer_1.save_pretrained(tmpdirname_1) tokenizer_2 = self.tokenizer_class.from_pretrained(tmpdirname_1) encoding_tokenizer_2 = tokenizer_2( words, boxes=boxes, ) shutil.rmtree(tmpdirname_1) tokenizer_2.save_pretrained(tmpdirname_2) tokenizer_3 = self.tokenizer_class.from_pretrained(tmpdirname_2) encoding_tokenizer_3 = tokenizer_3( words, boxes=boxes, ) shutil.rmtree(tmpdirname_2) self.assertEqual(encoding_tokenizer_1, encoding_tokenizer_2) self.assertEqual(encoding_tokenizer_1, encoding_tokenizer_3) def test_split_special_tokens(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: special_token = "" special_sentence = f"Hey this is a {special_token} token" with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.get_tokenizer( pretrained_name, extra_special_tokens=[special_token], split_special_tokens=True, **kwargs ) # For LayoutXLM, tokenize works with strings (not requiring boxes) r_tokens_output = tokenizer_r.tokenize(special_sentence) self.assertTrue(special_token not in r_tokens_output) r_tokens_output_unsplit = tokenizer_r.tokenize(special_sentence, split_special_tokens=False) self.assertTrue(special_token in r_tokens_output_unsplit) tmpdirname = tempfile.mkdtemp() tokenizer_r.save_pretrained(tmpdirname) fast_from_saved = self.tokenizer_class.from_pretrained(tmpdirname) output_tokens_reloaded_split = fast_from_saved.tokenize(special_sentence) self.assertTrue(special_token not in output_tokens_reloaded_split) output_tokens_reloaded_unsplit = fast_from_saved.tokenize(special_sentence, split_special_tokens=False) self.assertTrue(special_token in output_tokens_reloaded_unsplit) @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/layoutxlm-base") question, words, boxes = self.get_question_words_and_boxes() text = tokenizer.encode( question.split(), boxes=[tokenizer.pad_token_box for _ in range(len(question.split()))], add_special_tokens=False, ) text_2 = tokenizer.encode(words, boxes=boxes, add_special_tokens=False) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_pair == [0] + text + [2] + [2] + text_2 + [2] def test_offsets_with_special_characters(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.get_tokenizer(pretrained_name, **kwargs) words, boxes = self.get_words_and_boxes() words[1] = tokenizer_r.mask_token tokens = tokenizer_r.encode_plus( words, boxes=boxes, return_attention_mask=False, return_token_type_ids=False, return_offsets_mapping=True, add_special_tokens=True, ) expected_results = [ ((0, 0), tokenizer_r.cls_token), ((0, 1), "▁a"), ((0, 6), tokenizer_r.mask_token), ((0, 4), "▁test"), ((0, 0), tokenizer_r.sep_token), ] self.assertEqual( [e[1] for e in expected_results], tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]) ) self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"]) def test_add_special_tokens(self): tokenizers: list[LayoutXLMTokenizer] = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): special_token = "[SPECIAL_TOKEN]" special_token_box = [1000, 1000, 1000, 1000] tokenizer.add_special_tokens({"cls_token": special_token}) encoded_special_token = tokenizer.encode( [special_token], boxes=[special_token_box], add_special_tokens=False ) self.assertEqual(len(encoded_special_token), 1) decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True) self.assertTrue(special_token not in decoded) def test_add_tokens_tokenizer(self): tokenizers: list[LayoutXLMTokenizer] = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): vocab_size = tokenizer.vocab_size all_size = len(tokenizer) self.assertNotEqual(vocab_size, 0) # We usually have added tokens from the start in tests because our vocab fixtures are # smaller than the original vocabs - let's not assert this # self.assertEqual(vocab_size, all_size) new_toks = ["aaaaa", "bbbbbb", "cccccccccdddddddd"] added_toks = tokenizer.add_tokens(new_toks) vocab_size_2 = tokenizer.vocab_size all_size_2 = len(tokenizer) self.assertNotEqual(vocab_size_2, 0) self.assertEqual(vocab_size, vocab_size_2) self.assertEqual(added_toks, len(new_toks)) self.assertEqual(all_size_2, all_size + len(new_toks)) words = "aaaaa bbbbbb low cccccccccdddddddd l".split() boxes = [[1000, 1000, 1000, 1000] for _ in range(len(words))] tokens = tokenizer.encode(words, boxes=boxes, add_special_tokens=False) self.assertGreaterEqual(len(tokens), 4) self.assertGreater(tokens[0], tokenizer.vocab_size - 1) self.assertGreater(tokens[-2], tokenizer.vocab_size - 1) new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"} added_toks_2 = tokenizer.add_special_tokens(new_toks_2) vocab_size_3 = tokenizer.vocab_size all_size_3 = len(tokenizer) self.assertNotEqual(vocab_size_3, 0) self.assertEqual(vocab_size, vocab_size_3) self.assertEqual(added_toks_2, len(new_toks_2)) self.assertEqual(all_size_3, all_size_2 + len(new_toks_2)) words = ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l".split() boxes = [[1000, 1000, 1000, 1000] for _ in range(len(words))] tokens = tokenizer.encode( words, boxes=boxes, add_special_tokens=False, ) self.assertGreaterEqual(len(tokens), 6) self.assertGreater(tokens[0], tokenizer.vocab_size - 1) self.assertGreater(tokens[0], tokens[1]) self.assertGreater(tokens[-2], tokenizer.vocab_size - 1) self.assertGreater(tokens[-2], tokens[-3]) self.assertEqual(tokens[0], tokenizer.eos_token_id) self.assertEqual(tokens[-2], tokenizer.pad_token_id) @require_tokenizers def test_encode_decode_with_spaces(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes() new_toks = [AddedToken("[ABC]", normalized=False), AddedToken("[DEF]", normalized=False)] tokenizer.add_tokens(new_toks) input = "[ABC][DEF][ABC][DEF]" if self.space_between_special_tokens: output = "[ABC] [DEF] [ABC] [DEF]" else: output = input encoded = tokenizer.encode(input.split(), boxes=boxes, add_special_tokens=False) decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens) self.assertIn(decoded, [output, output.lower()]) @parameterized.expand([(True,), (False,)]) def test_encode_plus_with_padding(self, use_padding_as_call_kwarg: bool): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes() # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, words) padding_size = 10 padding_idx = tokenizer.pad_token_id encoded_sequence = tokenizer.encode_plus(words, boxes=boxes, return_special_tokens_mask=True) input_ids = encoded_sequence["input_ids"] special_tokens_mask = encoded_sequence["special_tokens_mask"] sequence_length = len(input_ids) # Test 'longest' and 'no_padding' don't do anything tokenizer.padding_side = "right" not_padded_sequence = tokenizer.encode_plus( words, boxes=boxes, padding=False, return_special_tokens_mask=True, ) not_padded_input_ids = not_padded_sequence["input_ids"] not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"] not_padded_sequence_length = len(not_padded_input_ids) self.assertTrue(sequence_length == not_padded_sequence_length) self.assertTrue(input_ids == not_padded_input_ids) self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask) not_padded_sequence = tokenizer.encode_plus( words, boxes=boxes, padding=False, return_special_tokens_mask=True, ) not_padded_input_ids = not_padded_sequence["input_ids"] not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"] not_padded_sequence_length = len(not_padded_input_ids) self.assertTrue(sequence_length == not_padded_sequence_length) self.assertTrue(input_ids == not_padded_input_ids) self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask) # Test right padding tokenizer_kwargs_right = { "max_length": sequence_length + padding_size, "padding": "max_length", "return_special_tokens_mask": True, } if not use_padding_as_call_kwarg: tokenizer.padding_side = "right" else: tokenizer_kwargs_right["padding_side"] = "right" right_padded_sequence = tokenizer.encode_plus(words, boxes=boxes, **tokenizer_kwargs_right) right_padded_input_ids = right_padded_sequence["input_ids"] right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"] right_padded_sequence_length = len(right_padded_input_ids) self.assertTrue(sequence_length + padding_size == right_padded_sequence_length) self.assertTrue(input_ids + [padding_idx] * padding_size == right_padded_input_ids) self.assertTrue(special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask) # Test left padding tokenizer_kwargs_left = { "max_length": sequence_length + padding_size, "padding": "max_length", "return_special_tokens_mask": True, } if not use_padding_as_call_kwarg: tokenizer.padding_side = "left" else: tokenizer_kwargs_left["padding_side"] = "left" left_padded_sequence = tokenizer.encode_plus(words, boxes=boxes, **tokenizer_kwargs_left) left_padded_input_ids = left_padded_sequence["input_ids"] left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"] left_padded_sequence_length = len(left_padded_input_ids) self.assertTrue(sequence_length + padding_size == left_padded_sequence_length) self.assertTrue([padding_idx] * padding_size + input_ids == left_padded_input_ids) self.assertTrue([1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask) if "token_type_ids" in tokenizer.model_input_names: token_type_ids = encoded_sequence["token_type_ids"] left_padded_token_type_ids = left_padded_sequence["token_type_ids"] right_padded_token_type_ids = right_padded_sequence["token_type_ids"] assert token_type_ids + [0] * padding_size == right_padded_token_type_ids assert [0] * padding_size + token_type_ids == left_padded_token_type_ids if "attention_mask" in tokenizer.model_input_names: attention_mask = encoded_sequence["attention_mask"] right_padded_attention_mask = right_padded_sequence["attention_mask"] left_padded_attention_mask = left_padded_sequence["attention_mask"] self.assertTrue(attention_mask + [0] * padding_size == right_padded_attention_mask) self.assertTrue([0] * padding_size + attention_mask == left_padded_attention_mask) def test_internal_consistency(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes() tokens = [] for word in words: tokens.extend(tokenizer.tokenize(word)) ids = tokenizer.convert_tokens_to_ids(tokens) ids_2 = tokenizer.encode(words, boxes=boxes, add_special_tokens=False) self.assertListEqual(ids, ids_2) tokens_2 = tokenizer.convert_ids_to_tokens(ids) self.assertNotEqual(len(tokens_2), 0) text_2 = tokenizer.decode(ids) self.assertIsInstance(text_2, str) output_text = "a weirdly test" self.assertEqual(text_2, output_text) def test_mask_output(self): tokenizers = self.get_tokenizers(fast=False, do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes() if ( tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer" and "token_type_ids" in tokenizer.model_input_names ): information = tokenizer.encode_plus(words, boxes=boxes, add_special_tokens=True) sequences, mask = information["input_ids"], information["token_type_ids"] self.assertEqual(len(sequences), len(mask)) def test_number_of_added_tokens(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # test 1: single sequence words, boxes = self.get_words_and_boxes() sequences = tokenizer.encode(words, boxes=boxes, add_special_tokens=False) attached_sequences = tokenizer.encode(words, boxes=boxes, add_special_tokens=True) # Method is implemented (e.g. not GPT-2) if len(attached_sequences) != 2: self.assertEqual( tokenizer.num_special_tokens_to_add(pair=False), len(attached_sequences) - len(sequences) ) # test 2: two sequences question, words, boxes = self.get_question_words_and_boxes() sequences = tokenizer.encode(question, words, boxes=boxes, add_special_tokens=False) attached_sequences = tokenizer.encode(question, words, boxes=boxes, add_special_tokens=True) # Method is implemented (e.g. not GPT-2) if len(attached_sequences) != 2: self.assertEqual( tokenizer.num_special_tokens_to_add(pair=True), len(attached_sequences) - len(sequences) ) def test_padding(self, max_length=50): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.get_tokenizer(pretrained_name, **kwargs) tokenizer_p = self.get_tokenizer(pretrained_name, **kwargs) self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id) pad_token_id = tokenizer_p.pad_token_id # Encode - Simple input words, boxes = self.get_words_and_boxes() input_r = tokenizer_r.encode(words, boxes=boxes, max_length=max_length, padding="max_length") input_p = tokenizer_p.encode(words, boxes=boxes, max_length=max_length, padding="max_length") self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.encode(words, boxes=boxes, padding="longest") input_p = tokenizer_p.encode(words, boxes=boxes, padding=True) self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id) # Encode - Pair input question, words, boxes = self.get_question_words_and_boxes() input_r = tokenizer_r.encode(question, words, boxes=boxes, max_length=max_length, padding="max_length") input_p = tokenizer_p.encode(question, words, boxes=boxes, max_length=max_length, padding="max_length") self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.encode(question, words, boxes=boxes, padding=True) input_p = tokenizer_p.encode(question, words, boxes=boxes, padding="longest") self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id) # Encode_plus - Simple input words, boxes = self.get_words_and_boxes() input_r = tokenizer_r.encode_plus(words, boxes=boxes, max_length=max_length, padding="max_length") input_p = tokenizer_p.encode_plus(words, boxes=boxes, max_length=max_length, padding="max_length") self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) input_r = tokenizer_r.encode_plus(words, boxes=boxes, padding="longest") input_p = tokenizer_p.encode_plus(words, boxes=boxes, padding=True) self.assert_padded_input_match( input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id ) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) # Encode_plus - Pair input question, words, boxes = self.get_question_words_and_boxes() input_r = tokenizer_r.encode_plus( question, words, boxes=boxes, max_length=max_length, padding="max_length" ) input_p = tokenizer_p.encode_plus( question, words, boxes=boxes, max_length=max_length, padding="max_length" ) self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) input_r = tokenizer_r.encode_plus(question, words, boxes=boxes, padding="longest") input_p = tokenizer_p.encode_plus(question, words, boxes=boxes, padding=True) self.assert_padded_input_match( input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id ) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) # Batch_encode_plus - Simple input words, boxes = self.get_words_and_boxes_batch() input_r = tokenizer_r.batch_encode_plus( words, boxes=boxes, max_length=max_length, padding="max_length", ) input_p = tokenizer_p.batch_encode_plus( words, boxes=boxes, max_length=max_length, padding="max_length", ) self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.batch_encode_plus( words, boxes=boxes, max_length=max_length, padding="longest", ) input_p = tokenizer_p.batch_encode_plus( words, boxes=boxes, max_length=max_length, padding=True, ) self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) input_r = tokenizer_r.batch_encode_plus(words, boxes=boxes, padding="longest") input_p = tokenizer_p.batch_encode_plus(words, boxes=boxes, padding=True) self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) # Batch_encode_plus - Pair input questions, words, boxes = self.get_question_words_and_boxes_batch() input_r = tokenizer_r.batch_encode_plus( list(zip(questions, words)), is_pair=True, boxes=boxes, max_length=max_length, truncation=True, padding="max_length", ) input_p = tokenizer_p.batch_encode_plus( list(zip(questions, words)), is_pair=True, boxes=boxes, max_length=max_length, truncation=True, padding="max_length", ) self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.batch_encode_plus( list(zip(questions, words)), is_pair=True, boxes=boxes, padding=True, ) input_p = tokenizer_p.batch_encode_plus( list(zip(questions, words)), is_pair=True, boxes=boxes, padding="longest", ) self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) # Using pad on single examples after tokenization words, boxes = self.get_words_and_boxes() input_r = tokenizer_r.encode_plus(words, boxes=boxes) input_r = tokenizer_r.pad(input_r) input_p = tokenizer_r.encode_plus(words, boxes=boxes) input_p = tokenizer_r.pad(input_p) self.assert_padded_input_match( input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id ) # Using pad on single examples after tokenization input_r = tokenizer_r.encode_plus(words, boxes=boxes) input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length") input_p = tokenizer_r.encode_plus(words, boxes=boxes) input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length") self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) # Using pad after tokenization words, boxes = self.get_words_and_boxes_batch() input_r = tokenizer_r.batch_encode_plus( words, boxes=boxes, ) input_r = tokenizer_r.pad(input_r) input_p = tokenizer_r.batch_encode_plus( words, boxes=boxes, ) input_p = tokenizer_r.pad(input_p) self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) # Using pad after tokenization words, boxes = self.get_words_and_boxes_batch() input_r = tokenizer_r.batch_encode_plus( words, boxes=boxes, ) input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length") input_p = tokenizer_r.batch_encode_plus( words, boxes=boxes, ) input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length") self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) def test_call(self): # Tests that all call wrap to encode_plus and batch_encode_plus tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Test not batched words, boxes = self.get_words_and_boxes() encoded_sequences_1 = tokenizer.encode_plus(words, boxes=boxes) encoded_sequences_2 = tokenizer(words, boxes=boxes) self.assertEqual(encoded_sequences_1, encoded_sequences_2) # Test not batched pairs question, words, boxes = self.get_question_words_and_boxes() encoded_sequences_1 = tokenizer.encode_plus(words, boxes=boxes) encoded_sequences_2 = tokenizer(words, boxes=boxes) self.assertEqual(encoded_sequences_1, encoded_sequences_2) # Test batched words, boxes = self.get_words_and_boxes_batch() encoded_sequences_1 = tokenizer.batch_encode_plus(words, is_pair=False, boxes=boxes) encoded_sequences_2 = tokenizer(words, boxes=boxes) self.assertEqual(encoded_sequences_1, encoded_sequences_2) def test_batch_encode_plus_batch_sequence_length(self): # Tests that all encoded values have the correct size tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes_batch() encoded_sequences = [ tokenizer.encode_plus(words_example, boxes=boxes_example) for words_example, boxes_example in zip(words, boxes) ] encoded_sequences_batch = tokenizer.batch_encode_plus(words, is_pair=False, boxes=boxes, padding=False) self.assertListEqual( encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch) ) maximum_length = len( max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len) ) # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, words) encoded_sequences_padded = [ tokenizer.encode_plus( words_example, boxes=boxes_example, max_length=maximum_length, padding="max_length" ) for words_example, boxes_example in zip(words, boxes) ] encoded_sequences_batch_padded = tokenizer.batch_encode_plus( words, is_pair=False, boxes=boxes, padding=True ) self.assertListEqual( encoded_sequences_padded, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded), ) # check 'longest' is unsensitive to a max length encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus( words, is_pair=False, boxes=boxes, padding=True ) encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus( words, is_pair=False, boxes=boxes, max_length=maximum_length + 10, padding="longest" ) for key in encoded_sequences_batch_padded_1: self.assertListEqual( encoded_sequences_batch_padded_1[key], encoded_sequences_batch_padded_2[key], ) # check 'no_padding' is unsensitive to a max length encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus( words, is_pair=False, boxes=boxes, padding=False ) encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus( words, is_pair=False, boxes=boxes, max_length=maximum_length + 10, padding=False ) for key in encoded_sequences_batch_padded_1: self.assertListEqual( encoded_sequences_batch_padded_1[key], encoded_sequences_batch_padded_2[key], ) @unittest.skip(reason="batch_encode_plus does not handle overflowing tokens.") def test_batch_encode_plus_overflowing_tokens(self): pass def test_batch_encode_plus_padding(self): # Test that padded sequences are equivalent between batch_encode_plus and encode_plus # Right padding tests tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes_batch() max_length = 100 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, words) encoded_sequences = [ tokenizer.encode_plus( words_example, boxes=boxes_example, max_length=max_length, padding="max_length" ) for words_example, boxes_example in zip(words, boxes) ] encoded_sequences_batch = tokenizer.batch_encode_plus( words, is_pair=False, boxes=boxes, max_length=max_length, padding="max_length" ) self.assertListEqual( encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch) ) # Left padding tests tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): tokenizer.padding_side = "left" words, boxes = self.get_words_and_boxes_batch() max_length = 100 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, words) encoded_sequences = [ tokenizer.encode_plus( words_example, boxes=boxes_example, max_length=max_length, padding="max_length" ) for words_example, boxes_example in zip(words, boxes) ] encoded_sequences_batch = tokenizer.batch_encode_plus( words, is_pair=False, boxes=boxes, max_length=max_length, padding="max_length" ) self.assertListEqual( encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(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: words, boxes = self.get_words_and_boxes() # empty_tokens = tokenizer([""], [[]], padding=True, pad_to_multiple_of=8) normal_tokens = tokenizer(words, boxes=boxes, 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") normal_tokens = tokenizer(words, boxes=boxes, pad_to_multiple_of=8) 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(words, boxes=boxes, 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__, words, boxes=boxes, padding=True, truncation=True, max_length=12, pad_to_multiple_of=8, ) def test_tokenizer_slow_store_full_signature(self): signature = inspect.signature(self.tokenizer_class.__init__) tokenizer = self.get_tokenizer() for parameter_name, parameter in signature.parameters.items(): if parameter.default != inspect.Parameter.empty: self.assertIn(parameter_name, tokenizer.init_kwargs) def test_build_inputs_with_special_tokens(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.get_tokenizer(pretrained_name, **kwargs) # Input tokens id words, boxes = self.get_words_and_boxes() input_simple = tokenizer_r.encode(words, boxes=boxes, add_special_tokens=False) input_pair = tokenizer_r.encode(words, boxes=boxes, add_special_tokens=False) # Generate output _ = tokenizer_r.build_inputs_with_special_tokens(input_simple) # Generate pair output _ = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair) def test_special_tokens_mask_input_pairs(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes() encoded_sequence = tokenizer.encode(words, boxes=boxes, add_special_tokens=False) encoded_sequence_dict = tokenizer.encode_plus( words, boxes=boxes, add_special_tokens=True, return_special_tokens_mask=True, # add_prefix_space=False, ) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special)) filtered_sequence = [ (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special) ] filtered_sequence = [x for x in filtered_sequence if x is not None] self.assertEqual(encoded_sequence, filtered_sequence) def test_special_tokens_mask(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes() # Testing single inputs encoded_sequence = tokenizer.encode(words, boxes=boxes, add_special_tokens=False) encoded_sequence_dict = tokenizer.encode_plus( words, boxes=boxes, add_special_tokens=True, return_special_tokens_mask=True ) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special)) filtered_sequence = [x for i, x in enumerate(encoded_sequence_w_special) if not special_tokens_mask[i]] self.assertEqual(encoded_sequence, filtered_sequence) def test_save_and_load_tokenizer(self): # safety check on max_len default value so we are sure the test works tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): self.assertNotEqual(tokenizer.model_max_length, 42) # Now let's start the test tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Isolate this from the other tests because we save additional tokens/etc words, boxes = self.get_words_and_boxes() tmpdirname = tempfile.mkdtemp() before_tokens = tokenizer.encode(words, boxes=boxes, add_special_tokens=False) before_vocab = tokenizer.get_vocab() tokenizer.save_pretrained(tmpdirname) after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname) after_tokens = after_tokenizer.encode(words, boxes=boxes, add_special_tokens=False) after_vocab = after_tokenizer.get_vocab() self.assertListEqual(before_tokens, after_tokens) self.assertDictEqual(before_vocab, after_vocab) shutil.rmtree(tmpdirname) @unittest.skip(reason="Not implemented") def test_right_and_left_truncation(self): pass def test_right_and_left_padding(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes() sequence = "Sequence" padding_size = 10 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, sequence) padding_idx = tokenizer.pad_token_id # RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True tokenizer.padding_side = "right" encoded_sequence = tokenizer.encode(words, boxes=boxes) sequence_length = len(encoded_sequence) padded_sequence = tokenizer.encode( words, boxes=boxes, max_length=sequence_length + padding_size, padding="max_length" ) padded_sequence_length = len(padded_sequence) assert sequence_length + padding_size == padded_sequence_length assert encoded_sequence + [padding_idx] * padding_size == padded_sequence # LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True tokenizer.padding_side = "left" encoded_sequence = tokenizer.encode(words, boxes=boxes) sequence_length = len(encoded_sequence) padded_sequence = tokenizer.encode( words, boxes=boxes, max_length=sequence_length + padding_size, padding="max_length" ) padded_sequence_length = len(padded_sequence) assert sequence_length + padding_size == padded_sequence_length assert [padding_idx] * padding_size + encoded_sequence == padded_sequence # RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding' encoded_sequence = tokenizer.encode(words, boxes=boxes) sequence_length = len(encoded_sequence) tokenizer.padding_side = "right" padded_sequence_right = tokenizer.encode(words, boxes=boxes, padding=True) padded_sequence_right_length = len(padded_sequence_right) assert sequence_length == padded_sequence_right_length assert encoded_sequence == padded_sequence_right tokenizer.padding_side = "left" padded_sequence_left = tokenizer.encode(words, boxes=boxes, padding="longest") padded_sequence_left_length = len(padded_sequence_left) assert sequence_length == padded_sequence_left_length assert encoded_sequence == padded_sequence_left tokenizer.padding_side = "right" padded_sequence_right = tokenizer.encode(words, boxes=boxes) padded_sequence_right_length = len(padded_sequence_right) assert sequence_length == padded_sequence_right_length assert encoded_sequence == padded_sequence_right tokenizer.padding_side = "left" padded_sequence_left = tokenizer.encode(words, boxes=boxes, padding=False) padded_sequence_left_length = len(padded_sequence_left) assert sequence_length == padded_sequence_left_length assert encoded_sequence == padded_sequence_left def test_token_type_ids(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # test 1: single sequence words, boxes = self.get_words_and_boxes() output = tokenizer(words, boxes=boxes, return_token_type_ids=True) # Assert that the token type IDs have the same length as the input IDs self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"])) # Assert that the token type IDs have the same length as the attention mask self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"])) self.assertIn(0, output["token_type_ids"]) self.assertNotIn(1, output["token_type_ids"]) # test 2: two sequences (question + words) question, words, boxes = self.get_question_words_and_boxes() output = tokenizer(question, words, boxes, return_token_type_ids=True) # Assert that the token type IDs have the same length as the input IDs self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"])) # Assert that the token type IDs have the same length as the attention mask self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"])) self.assertIn(0, output["token_type_ids"]) self.assertNotIn(1, output["token_type_ids"]) def test_offsets_mapping(self): 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) text = ["a", "wonderful", "test"] boxes = [[1, 8, 12, 20] for _ in range(len(text))] # No pair tokens_with_offsets = tokenizer_r.encode_plus( text, boxes=boxes, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True, ) added_tokens = tokenizer_r.num_special_tokens_to_add(False) offsets = tokens_with_offsets["offset_mapping"] # Assert there is the same number of tokens and offsets self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"])) # Assert there is online added_tokens special_tokens self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens) # Pairs text = "what's his name" pair = ["a", "wonderful", "test"] boxes = [[1, 8, 12, 20] for _ in range(len(pair))] tokens_with_offsets = tokenizer_r.encode_plus( text, pair, boxes=boxes, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True, ) added_tokens = tokenizer_r.num_special_tokens_to_add(True) offsets = tokens_with_offsets["offset_mapping"] # Assert there is the same number of tokens and offsets self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"])) # Assert there is online added_tokens special_tokens self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens) @require_torch @slow def test_torch_encode_plus_sent_to_model(self): import torch from transformers import MODEL_MAPPING, TOKENIZER_MAPPING MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING) tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING: self.skipTest(f"{tokenizer.__class__} is not in the MODEL_TOKENIZER_MAPPING") config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__] config = config_class() if config.is_encoder_decoder or config.pad_token_id is None: self.skipTest(reason="Model is an encoder-decoder or has no pad token id set.") model = model_class(config) # Make sure the model contains at least the full vocabulary size in its embedding matrix is_using_common_embeddings = hasattr(model.get_input_embeddings(), "weight") assert ( (model.get_input_embeddings().weight.shape[0] >= len(tokenizer)) if is_using_common_embeddings else True ) # Build sequence words, boxes = self.get_words_and_boxes() encoded_sequence = tokenizer.encode_plus(words, boxes=boxes, return_tensors="pt") batch_encoded_sequence = tokenizer.batch_encode_plus( [words, words], [boxes, boxes], return_tensors="pt" ) # This should not fail with torch.no_grad(): # saves some time model(**encoded_sequence) model(**batch_encoded_sequence) def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: self.skipTest(reason="test_rust_tokenizer is set to False") if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions self.skipTest(reason="test_slow_tokenizer is set to False") def test_tokenization_python_rust_equals(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions self.skipTest(reason="test_slow_tokenizer is set to False") def test_embedded_special_tokens(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions self.skipTest(reason="test_slow_tokenizer is set to False") def test_compare_add_special_tokens(self): 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) simple_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=False) words, boxes = self.get_words_and_boxes() # tokenize() no_special_tokens = tokenizer_r.tokenize(" ".join(words), add_special_tokens=False) with_special_tokens = tokenizer_r.tokenize(" ".join(words), add_special_tokens=True) self.assertEqual(len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add) # encode() no_special_tokens = tokenizer_r.encode(words, boxes=boxes, add_special_tokens=False) with_special_tokens = tokenizer_r.encode(words, boxes=boxes, add_special_tokens=True) self.assertEqual(len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add) # encode_plus() no_special_tokens = tokenizer_r.encode_plus(words, boxes=boxes, add_special_tokens=False) with_special_tokens = tokenizer_r.encode_plus(words, boxes=boxes, add_special_tokens=True) for key in no_special_tokens: self.assertEqual( len(no_special_tokens[key]), len(with_special_tokens[key]) - simple_num_special_tokens_to_add, ) # # batch_encode_plus words, boxes = self.get_words_and_boxes_batch() no_special_tokens = tokenizer_r.batch_encode_plus(words, boxes=boxes, add_special_tokens=False) with_special_tokens = tokenizer_r.batch_encode_plus(words, boxes=boxes, add_special_tokens=True) for key in no_special_tokens: for i_no, i_with in zip(no_special_tokens[key], with_special_tokens[key]): self.assertEqual(len(i_no), len(i_with) - simple_num_special_tokens_to_add) @slow def test_layoutxlm_truncation_integration_test(self): words, boxes = self.get_words_and_boxes() tokenizer = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base", model_max_length=512) for i in range(12, 512): new_encoded_inputs = tokenizer.encode(words, boxes=boxes, max_length=i, truncation=True) # Ensure that the input IDs are less than the max length defined. self.assertLessEqual(len(new_encoded_inputs), i) tokenizer.model_max_length = 20 new_encoded_inputs = tokenizer.encode(words, boxes=boxes, truncation=True) dropped_encoded_inputs = tokenizer.encode(words, boxes=boxes, truncation=True) # Ensure that the input IDs are still truncated when no max_length is specified self.assertListEqual(new_encoded_inputs, dropped_encoded_inputs) self.assertLessEqual(len(new_encoded_inputs), 20) def test_sequence_ids(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: if not tokenizer.is_fast: continue with self.subTest(f"{tokenizer.__class__.__name__}"): seq_0 = "Test this method." seq_1 = ["With", "these", "inputs."] boxes = [[1000, 1000, 1000, 1000] for _ in range(len(seq_1))] # We want to have sequence 0 and sequence 1 are tagged # respectively with 0 and 1 token_ids # (regardless of whether the model use token type ids) # We use this assumption in the QA pipeline among other place output = tokenizer(seq_0.split(), boxes=boxes) self.assertIn(0, output.sequence_ids()) output = tokenizer(seq_0, seq_1, boxes=boxes) self.assertIn(0, output.sequence_ids()) self.assertIn(1, output.sequence_ids()) if tokenizer.num_special_tokens_to_add(pair=True): self.assertIn(None, output.sequence_ids()) 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, extra_special_tokens=added_tokens, **kwargs ) words = "Hey this is a token".split() boxes = [[1000, 1000, 1000, 1000] for _ in range(len(words))] r_output = tokenizer_r.encode(words, boxes=boxes) special_token_id = tokenizer_r.encode( [""], boxes=[1000, 1000, 1000, 1000], 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 text = [["this", "is", "the"], ["how", "are", "you"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8], [1, 3, 4, 8]], [[5, 6, 7, 8], [4, 5, 6, 7], [3, 9, 2, 7]]] inputs = new_tokenizer(text, boxes=boxes) 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" 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. 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. # max_len_single_sentence = model_max_length - num_special_tokens_to_add(pair=False) self.assertEqual( tokenizer.model_max_length - tokenizer.num_special_tokens_to_add(pair=False), new_tokenizer.model_max_length - new_tokenizer.num_special_tokens_to_add(pair=False), ) # max_len_sentences_pair = model_max_length - num_special_tokens_to_add(pair=True) self.assertEqual( tokenizer.model_max_length - tokenizer.num_special_tokens_to_add(pair=True), new_tokenizer.model_max_length - new_tokenizer.num_special_tokens_to_add(pair=True), ) # Assert the set of special tokens match as we didn't ask to change them self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map) def test_training_new_tokenizer_with_special_tokens_change(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() # Test with a special tokens map class_signature = inspect.signature(tokenizer.__class__) if "cls_token" in class_signature.parameters: new_tokenizer = tokenizer.train_new_from_iterator( SMALL_TRAINING_CORPUS, 100, special_tokens_map={tokenizer.cls_token: ""} ) cls_id = new_tokenizer.get_vocab()[""] self.assertEqual(new_tokenizer.cls_token, "") self.assertEqual(new_tokenizer.cls_token_id, cls_id) # Create a new mapping from the special tokens defined in the original tokenizer special_tokens_list = PreTrainedTokenizerBase.SPECIAL_TOKENS_ATTRIBUTES.copy() special_tokens_map = {} for token in special_tokens_list: # Get the private one to avoid unnecessary warnings. if getattr(tokenizer, token) is not None: special_token = getattr(tokenizer, token) special_tokens_map[special_token] = f"{special_token}a" # Train new tokenizer new_tokenizer = tokenizer.train_new_from_iterator( SMALL_TRAINING_CORPUS, 100, special_tokens_map=special_tokens_map ) # Check the changes for token in special_tokens_list: # Get the private one to avoid unnecessary warnings. if getattr(tokenizer, token) is None: continue special_token = getattr(tokenizer, token) if special_token in special_tokens_map: new_special_token = getattr(new_tokenizer, token) self.assertEqual(special_tokens_map[special_token], new_special_token) new_id = new_tokenizer.get_vocab()[new_special_token] self.assertEqual(getattr(new_tokenizer, f"{token}_id"), new_id) # Check if the AddedToken / string format has been kept tokenizer_special_tokens = [ tok for value in tokenizer._special_tokens_map.values() if value for tok in (value if isinstance(value, (list, tuple)) else [value]) ] new_tokenizer_special_tokens = [ tok for value in new_tokenizer._special_tokens_map.values() if value for tok in (value if isinstance(value, (list, tuple)) else [value]) ] for special_token in tokenizer_special_tokens: if isinstance(special_token, AddedToken) and special_token.content not in special_tokens_map: # The special token must appear identically in the list of the new tokenizer. self.assertTrue( special_token in new_tokenizer_special_tokens, f"'{special_token}' should be in {new_tokenizer_special_tokens}", ) elif isinstance(special_token, AddedToken): # The special token must appear in the list of the new tokenizer as an object of type AddedToken with # the same parameters as the old AddedToken except the content that the user has requested to change. special_token_str = special_token.content new_special_token_str = special_tokens_map[special_token_str] find = False for candidate in new_tokenizer_special_tokens: if ( isinstance(candidate, AddedToken) and candidate.content == new_special_token_str and candidate.lstrip == special_token.lstrip and candidate.rstrip == special_token.rstrip and candidate.normalized == special_token.normalized and candidate.single_word == special_token.single_word ): find = True break self.assertTrue( find, f"'{new_special_token_str}' doesn't appear in the list " f"'{new_tokenizer_special_tokens}' as an AddedToken with the same parameters as " f"'{special_token}' in the list {tokenizer_special_tokens}", ) elif special_token not in special_tokens_map: # The special token must appear identically in the list of the new tokenizer. self.assertTrue( special_token in new_tokenizer_special_tokens, f"'{special_token}' should be in {new_tokenizer_special_tokens}", ) else: # The special token must appear in the list of the new tokenizer as an object of type string. self.assertTrue(special_tokens_map[special_token] in new_tokenizer_special_tokens) # Test we can use the new tokenizer with something not seen during training words = [["this", "is"], ["hello", "🤗"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[1, 2, 3, 4], [5, 6, 7, 8]]] inputs = new_tokenizer(words, boxes=boxes) self.assertEqual(len(inputs["input_ids"]), 2) decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True) expected_result = "this is" if tokenizer.backend_tokenizer.normalizer is not None: expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result) self.assertEqual(expected_result, decoded_input) def test_prepare_for_model(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: # only test prepare_for_model for the slow tokenizer if tokenizer.__class__.__name__ == "LayoutXLMTokenizer": continue with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_words_and_boxes() prepared_input_dict = tokenizer.prepare_for_model(words, boxes=boxes, add_special_tokens=True) input_dict = tokenizer.encode_plus(words, boxes=boxes, add_special_tokens=True) self.assertEqual(input_dict, prepared_input_dict) def test_padding_different_model_input_name(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions self.skipTest(reason="test_slow_tokenizer is set to False") def test_batch_encode_dynamic_overflowing(self): """ When calling batch_encode with multiple sequences, it can return different number of overflowing encoding for each sequence: [ Sequence 1: [Encoding 1, Encoding 2], Sequence 2: [Encoding 1], Sequence 3: [Encoding 1, Encoding 2, ... Encoding N] ] This needs to be padded so that it can represented as a tensor """ for tokenizer, pretrained_name, kwargs in self.tokenizers_list: tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"): returned_tensor = "pt" # Single example words, boxes = self.get_words_and_boxes() tokens = tokenizer.encode_plus( words, boxes=boxes, max_length=6, padding=True, truncation=True, return_tensors=returned_tensor, return_overflowing_tokens=True, ) for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()): if key != "bbox": self.assertEqual(len(tokens[key].shape), 2) else: self.assertEqual(len(tokens[key].shape), 3) # Batch of examples # For these 2 examples, 3 training examples will be created words, boxes = self.get_words_and_boxes_batch() tokens = tokenizer.batch_encode_plus( words, boxes=boxes, max_length=6, padding=True, truncation="only_first", return_tensors=returned_tensor, return_overflowing_tokens=True, ) for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()): if key != "bbox": self.assertEqual(len(tokens[key].shape), 2) self.assertEqual(tokens[key].shape[-1], 6) else: self.assertEqual(len(tokens[key].shape), 3) self.assertEqual(tokens[key].shape[-1], 4) # overwrite from test_tokenization_common to speed up test def test_save_pretrained(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions self.skipTest(reason="test_slow_tokenizer is set to False") @unittest.skip(reason="TO DO: overwrite this very extensive test.") def test_alignment_methods(self): pass @unittest.skip(reason="layoutxlm tokenizer requires boxes besides sequences.") def test_maximum_encoding_length_pair_input(self): pass @unittest.skip(reason="layoutxlm tokenizer requires boxes besides sequences.") def test_maximum_encoding_length_single_input(self): pass @unittest.skip(reason="layoutxlm tokenizer requires boxes besides sequences.") def test_pretokenized_inputs(self): pass @unittest.skip(reason="layoutxlm tokenizer always expects pretokenized inputs.") def test_compare_pretokenized_inputs(self): pass @unittest.skip(reason="layoutxlm fast tokenizer does not support prepare_for_model") def test_compare_prepare_for_model(self): pass @slow def test_only_label_first_subword(self): words = ["hello", "niels"] boxes = [[1000, 1000, 1000, 1000] for _ in range(len(words))] word_labels = [0, 1] # test fast tokenizer tokenizer_r = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base") encoding = tokenizer_r(words, boxes=boxes, word_labels=word_labels) self.assertListEqual(encoding.labels, [-100, 0, -100, 1, -100, -100]) tokenizer_r = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base", only_label_first_subword=False) encoding = tokenizer_r(words, boxes=boxes, word_labels=word_labels) self.assertListEqual(encoding.labels, [-100, 0, 0, 1, 1, -100]) @slow def test_layoutxlm_integration_test(self): tokenizer_r = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base") # There are 3 cases: # CASE 1: document image classification (training + inference), document image token classification (inference), # in which case only words and normalized bounding boxes are provided to the tokenizer # CASE 2: document image token classification (training), # in which case one also provides word labels to the tokenizer # CASE 3: document image visual question answering (inference), # in which case one also provides a question to the tokenizer # We need to test all 3 cases both on batched and non-batched inputs. # CASE 1: not batched words, boxes = self.get_words_and_boxes() expected_results = {'input_ids': [0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [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]], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 1: batched words, boxes = self.get_words_and_boxes_batch() expected_results = {'input_ids': [[0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 33600, 31, 759, 9351, 83, 21895, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [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], [961, 885, 992, 912], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [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]]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 2: not batched words, boxes = self.get_words_and_boxes() word_labels = [1, 2, 3] expected_results = {'input_ids': [0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [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]], 'labels': [-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 2: batched words, boxes = self.get_words_and_boxes_batch() word_labels = [[1, 2, 3], [2, 46, 17, 22, 3]] expected_results = {'input_ids': [[0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 33600, 31, 759, 9351, 83, 21895, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [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], [961, 885, 992, 912], [961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [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]]], 'labels': [[-100, 1, 2, -100, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, 46, 17, 22, 3, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 3: not batched question, words, boxes = self.get_question_words_and_boxes() expected_results = {'input_ids': [0, 2367, 25, 7, 1919, 9351, 32, 2, 2, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], 'bbox': [[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], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]} # fmt: skip encoding_r = tokenizer_r(question, words, boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 3: batched questions, words, boxes = self.get_question_words_and_boxes_batch() expected_results = {'input_ids': [[0, 2367, 25, 7, 1919, 9351, 32, 2, 2, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1], [0, 3642, 83, 764, 35839, 32, 2, 2, 2367, 10, 21, 3190, 53496, 19, 2, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]], 'bbox': [[[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], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [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], [1000, 1000, 1000, 1000], [1000, 1000, 1000, 1000], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [336, 42, 353, 57], [34, 42, 66, 69], [34, 42, 66, 69], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]} # fmt: skip encoding_r = tokenizer_r(questions, words, boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_r), expected_results) @unittest.skip(reason="Doesn't support returning Numpy arrays") def test_np_encode_plus_sent_to_model(self): pass @unittest.skip(reason="Doesn't use SentencePiece") def test_sentencepiece_tokenize_and_convert_tokens_to_string(self): pass @unittest.skip(reason="Doesn't use SentencePiece") def test_sentencepiece_tokenize_and_decode(self): pass @unittest.skip(reason="Chat is not supported") def test_chat_template(self): pass @unittest.skip("Chat is not supported") def test_chat_template_return_assistant_tokens_mask(self): pass @unittest.skip("Chat is not supported") def test_chat_template_return_assistant_tokens_mask_truncated(self): pass def test_empty_input_string(self): tokenizer_return_type = [] output_tensor_type = [] if is_torch_available(): import numpy as np import torch tokenizer_return_type.append("pt") output_tensor_type.append(torch.int64) tokenizer_return_type.append("np") output_tensor_type.append(np.int64) if is_mlx_available(): import mlx.core as mx tokenizer_return_type.append("mlx") output_tensor_type.append(mx.int32) if len(tokenizer_return_type) == 0: self.skipTest(reason="No expected framework from PT or MLX found") tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): words, boxes = self.get_empty_words_and_boxes() for return_type, target_type in zip(tokenizer_return_type, output_tensor_type): output = tokenizer(words, boxes=boxes, return_tensors=return_type) self.assertEqual(output.input_ids.dtype, target_type) question, words, boxes = self.get_empty_question_words_and_boxes() for return_type, target_type in zip(tokenizer_return_type, output_tensor_type): output = tokenizer(words, boxes=boxes, return_tensors=return_type) self.assertEqual(output.input_ids.dtype, target_type) words, boxes = self.get_empty_words_and_boxes_batch() for return_type, target_type in zip(tokenizer_return_type, output_tensor_type): output = tokenizer(words, boxes=boxes, padding=True, return_tensors=return_type) self.assertEqual(output.input_ids.dtype, target_type) question, words, boxes = self.get_empty_question_words_and_boxes_batch() for return_type, target_type in zip(tokenizer_return_type, output_tensor_type): output = tokenizer(words, boxes=boxes, padding=True, return_tensors=return_type) self.assertEqual(output.input_ids.dtype, target_type)