# Copyright 2022 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 os import re import shutil import tempfile import unittest from parameterized import parameterized from transformers import ( AddedToken, LayoutLMv3TokenizerFast, is_mlx_available, is_torch_available, logging, ) from transformers.models.layoutlmv3.tokenization_layoutlmv3 import VOCAB_FILES_NAMES, LayoutLMv3Tokenizer from transformers.testing_utils import ( require_pandas, require_tokenizers, slow, ) from transformers.tokenization_utils_base import PreTrainedTokenizerBase from ...test_tokenization_common import ( SMALL_TRAINING_CORPUS, TokenizerTesterMixin, ) logger = logging.get_logger(__name__) @require_tokenizers @require_pandas class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "microsoft/layoutlmv3-base" tokenizer_class = LayoutLMv3Tokenizer rust_tokenizer_class = LayoutLMv3TokenizerFast test_rust_tokenizer = True # determined by the tokenization algorithm and the way it's decoded by the fast tokenizers space_between_special_tokens = False test_seq2seq = False from_pretrained_kwargs = {"cls_token": ""} def get_words_and_boxes(self): words = ["lower", "newer"] boxes = [[423, 237, 440, 251], [427, 272, 441, 287]] return words, boxes def test_integration(self): """Integration test with hardcoded expectations for LayoutLMv3.""" input_words = ["a", "weirdly", "test", "hello", "my", "name", "is", "bob"] input_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], ] expected_tokens = [ "Ġa", "Ġweird", "ly", "Ġtest", "Ġhello", "Ġmy", "Ġname", "Ġis", "Ġbob", ] expected_ids = [10, 7735, 352, 1296, 20760, 127, 766, 16, 22401] expected_tokens_from_ids = ['Ġa', 'Ġweird', 'ly', 'Ġtest', 'Ġhello', 'Ġmy', 'Ġname', 'Ġis', 'Ġbob'] # fmt: skip expected_decoded_text = " a weirdly test hello my name is bob" tokenizer = self.tokenizer_class.from_pretrained("microsoft/layoutlmv3-base", cls_token="") # Flatten tokens per word tokens = [] for word in input_words: tokens.extend(tokenizer.tokenize(word)) self.assertListEqual(tokens, expected_tokens) ids = tokenizer.encode(input_words, boxes=input_boxes, add_special_tokens=False) self.assertListEqual(ids, expected_ids) roundtrip_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual(roundtrip_tokens, expected_tokens_from_ids) decoded_text = tokenizer.decode(ids, clean_up_tokenization_spaces=False) self.assertEqual(decoded_text, expected_decoded_text) def test_integration_from_extractor(self): """Integration test using pretokenized words and boxes as if coming from an extractor.""" input_words = ["a", "weirdly", "test", "hello", "my", "name", "is", "bob"] input_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], ] expected_tokens = [ "Ġa", "Ġweird", "ly", "Ġtest", "Ġhello", "Ġmy", "Ġname", "Ġis", "Ġbob", ] expected_ids = [10, 7735, 352, 1296, 20760, 127, 766, 16, 22401] expected_tokens_from_ids = ['Ġa', 'Ġweird', 'ly', 'Ġtest', 'Ġhello', 'Ġmy', 'Ġname', 'Ġis', 'Ġbob'] # fmt: skip expected_decoded_text = " a weirdly test hello my name is bob" tokenizer = self.tokenizer_class.from_pretrained("microsoft/layoutlmv3-base", cls_token="") tokens = [] for word in input_words: tokens.extend(tokenizer.tokenize(word)) self.assertListEqual(tokens, expected_tokens) ids = tokenizer.encode(input_words, boxes=input_boxes, add_special_tokens=False) self.assertListEqual(ids, expected_ids) roundtrip_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual(roundtrip_tokens, expected_tokens_from_ids) decoded_text = tokenizer.decode(ids, clean_up_tokenization_spaces=False) self.assertEqual(decoded_text, expected_decoded_text) def get_words_and_boxes_batch(self): words = [["lower", "newer"], ["new", "low"]] boxes = [ [[423, 237, 440, 251], [427, 272, 441, 287]], [[961, 885, 992, 912], [256, 38, 330, 58]], ] return words, boxes def get_question_words_and_boxes(self): question = "what's his name?" words = ["lower", "newer"] boxes = [[423, 237, 440, 251], [427, 272, 441, 287]] return question, words, boxes def get_question_words_and_boxes_batch(self): questions = ["what's his name?", "how is he called?"] words = [["lower", "newer"], ["newer", "lower"]] boxes = [ [[423, 237, 440, 251], [427, 272, 441, 287]], [[256, 38, 330, 58], [256, 38, 330, 58]], ] 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() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges_list = [("\u0120", "l"), ("\u0120l", "o"), ("\u0120lo", "w"), ("e", "r")] cls.special_tokens_map = {"unk_token": ""} # Create tokenizer and save it (which will create all necessary files including tokenizer.json) tokenizer = cls.tokenizer_class(vocab=vocab_tokens, merges=merges_list, **cls.special_tokens_map) tokenizer.save_pretrained(cls.tmpdirname) cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) cls.merges_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) @classmethod def get_tokenizer(cls, pretrained_name=None, **kwargs): kwargs.update(cls.special_tokens_map) pretrained_name = pretrained_name or cls.tmpdirname return cls.tokenizer_class.from_pretrained(pretrained_name, **kwargs) @classmethod def get_rust_tokenizer(cls, pretrained_name=None, **kwargs): kwargs.update(cls.special_tokens_map) pretrained_name = pretrained_name or cls.tmpdirname return LayoutLMv3TokenizerFast.from_pretrained(pretrained_name, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower newer" return input_text, output_text 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 @unittest.skip(reason="Chat template tests don't play well with table/layout models.") def test_chat_template_batched(self): pass @unittest.skip(reason="LayoutLMv3 requires pre-tokenized words with boxes.") def test_bos_token_with_add_bos_token_false(self): pass @unittest.skip(reason="LayoutLMv3 requires pre-tokenized words with boxes.") def test_bos_token_with_add_bos_token_true(self): pass @unittest.skip(reason="LayoutLMv3 requires pre-tokenized words with boxes.") def test_encode_basic_padding(self): pass @unittest.skip(reason="LayoutLMv3 requires pre-tokenized words with boxes.") def test_pad_token_initialization(self): pass def test_full_tokenizer(self): tokenizer = self.get_tokenizer() text = "lower newer" bpe_tokens = ["Ġlow", "er", "Ġ", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text) # , add_prefix_space=True) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/layoutlmv3-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_add_special_tokens(self): tokenizers: list[LayoutLMv3Tokenizer] = 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[LayoutLMv3Tokenizer] = 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()]) @unittest.skip(reason="Not implemented") def test_right_and_left_truncation(self): pass @unittest.skip(reason="Not implemented") def test_split_special_tokens(self): pass @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 = " lower newer" 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_rust_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_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) 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"]) 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) 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_layoutlmv3_truncation_integration_test(self): words, boxes = self.get_words_and_boxes() tokenizer = LayoutLMv3Tokenizer.from_pretrained("microsoft/layoutlmv3-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, additional_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_rust_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. 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) 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_rust_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 = list(PreTrainedTokenizerBase.SPECIAL_TOKENS_ATTRIBUTES) if "additional_special_tokens" in special_tokens_list: special_tokens_list.remove("additional_special_tokens") 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 for special_token in tokenizer.all_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.all_special_tokens, f"'{special_token}' should be in {new_tokenizer.all_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.all_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.all_special_tokens}' as an AddedToken with the same parameters as " f"'{special_token}' in the list {tokenizer.all_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.all_special_tokens, f"'{special_token}' should be in {new_tokenizer.all_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.all_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) @unittest.skip(reason="LayoutLMv3Tokenizer no longer has separate slow tokenizer with prepare_for_model") def test_prepare_for_model(self): pass 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 = ["HuggingFace", "is", "solving", "NLP", "one", "commit", "at", "a", "time"] boxes = [[i, i, i, i] for i in range(len(words))] 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_batched = [ ["HuggingFace", "is", "solving", "NLP", "one", "commit", "at", "a", "time"], ["Very", "tiny", "input"], ] boxes_batched = [[[i, i, i, i] for i in range(len(words_item))] for words_item in words_batched] tokens = tokenizer.batch_encode_plus( words_batched, boxes=boxes_batched, 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) @unittest.skip(reason="TO DO: overwrite this very extensive test.") def test_alignment_methods(self): pass def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5): toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))] toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks)) toks = list( filter( lambda t: [t[0]] == tokenizer.encode(t[1].split(" "), boxes=len(t[1]) * [[1, 1, 1, 1]], add_special_tokens=False), toks, ) ) if max_length is not None and len(toks) > max_length: toks = toks[:max_length] if min_length is not None and len(toks) < min_length and len(toks) > 0: while len(toks) < min_length: toks = toks + toks # toks_str = [t[1] for t in toks] toks_ids = [t[0] for t in toks] # Ensure consistency output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False) if " " not in output_txt and len(toks_ids) > 1: output_txt = ( tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False) + " " + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False) ) if with_prefix_space: output_txt = " " + output_txt words = output_txt.split(" ") boxes = [[i, i, i, i] for i in range(len(words))] output_ids = tokenizer.encode(words, boxes=boxes, add_special_tokens=False) return words, boxes, output_ids def test_added_token_with_space_before(self): tokenizer_s = self.get_tokenizer() tokenizer_f = self.get_rust_tokenizer() tokens_to_add = ["AAA", "bbb"] words_with_space = [f" {token}" for token in tokens_to_add + list(tokenizer_s.added_tokens_encoder.keys())] words_without_space = tokens_to_add + list(tokenizer_s.added_tokens_encoder.keys()) boxes = [[i, i, i, i] for i in range(len(words_with_space))] tokens_to_add_formatted = [ AddedToken(token, rstrip=True, lstrip=True, single_word=False) for token in tokens_to_add ] tokenizer_s.add_tokens(tokens_to_add_formatted) tokenizer_f.add_tokens(tokens_to_add_formatted) ids_s = tokenizer_s(words_with_space, boxes=boxes).input_ids ids_f = tokenizer_f(words_with_space, boxes=boxes).input_ids tokens_s = tokenizer_s.convert_ids_to_tokens(ids_s) tokens_f = tokenizer_f.convert_ids_to_tokens(ids_f) ids_s = tokenizer_s(words_without_space, boxes=boxes).input_ids ids_f = tokenizer_f(words_without_space, boxes=boxes).input_ids tokens_s = tokenizer_s.convert_ids_to_tokens(ids_s) tokens_f = tokenizer_f.convert_ids_to_tokens(ids_f) self.assertEqual(tokens_s, tokens_f) def test_maximum_encoding_length_pair_input(self): tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Build a sequence from our model's vocabulary stride = 2 seq_0, boxes_0, ids = self.get_clean_sequence(tokenizer, max_length=20) question_0 = " ".join(map(str, seq_0)) if len(ids) <= 2 + stride: seq_0 = (seq_0 + " ") * (2 + stride) ids = None seq0_tokens = tokenizer(seq_0, boxes=boxes_0, add_special_tokens=False) seq0_input_ids = seq0_tokens["input_ids"] self.assertGreater(len(seq0_input_ids), 2 + stride) question_1 = "This is another sentence to be encoded." seq_1 = ["what", "a", "weird", "test", "weirdly", "weird"] boxes_1 = [[i, i, i, i] for i in range(1, len(seq_1) + 1)] seq1_tokens = tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False) if abs(len(seq0_input_ids) - len(seq1_tokens["input_ids"])) <= 2: seq1_tokens_input_ids = seq1_tokens["input_ids"] + seq1_tokens["input_ids"] seq_1 = tokenizer.decode(seq1_tokens_input_ids, clean_up_tokenization_spaces=False) seq_1 = seq_1.split(" ") boxes_1 = [[i, i, i, i] for i in range(1, len(seq_1) + 1)] seq1_tokens = tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False) seq1_input_ids = seq1_tokens["input_ids"] self.assertGreater(len(seq1_input_ids), 2 + stride) smallest = seq1_input_ids if len(seq0_input_ids) > len(seq1_input_ids) else seq0_input_ids # We are not using the special tokens - a bit too hard to test all the tokenizers with this # TODO try this again later sequence = tokenizer( question_0, seq_1, boxes=boxes_1, add_special_tokens=False ) # , add_prefix_space=False) # Test with max model input length model_max_length = tokenizer.model_max_length self.assertEqual(model_max_length, 100) seq_2 = seq_0 * model_max_length question_2 = " ".join(map(str, seq_2)) boxes_2 = boxes_0 * model_max_length self.assertGreater(len(seq_2), model_max_length) sequence1 = tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False) total_length1 = len(sequence1["input_ids"]) sequence2 = tokenizer(question_2, seq_1, boxes=boxes_1, add_special_tokens=False) total_length2 = len(sequence2["input_ids"]) self.assertLess(total_length1, model_max_length, "Issue with the testing sequence, please update it.") self.assertGreater( total_length2, model_max_length, "Issue with the testing sequence, please update it." ) # Simple padding_strategies = ( [False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False] ) for padding_state in padding_strategies: with self.subTest(f"{tokenizer.__class__.__name__} Padding: {padding_state}"): for truncation_state in [True, "longest_first", "only_first"]: with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"): output = tokenizer( question_2, seq_1, boxes=boxes_1, padding=padding_state, truncation=truncation_state, ) self.assertEqual(len(output["input_ids"]), model_max_length) self.assertEqual(len(output["bbox"]), model_max_length) output = tokenizer( [question_2], [seq_1], boxes=[boxes_1], padding=padding_state, truncation=truncation_state, ) self.assertEqual(len(output["input_ids"][0]), model_max_length) self.assertEqual(len(output["bbox"][0]), model_max_length) # Simple output = tokenizer( question_1, seq_2, boxes=boxes_2, padding=padding_state, truncation="only_second" ) self.assertEqual(len(output["input_ids"]), model_max_length) self.assertEqual(len(output["bbox"]), model_max_length) output = tokenizer( [question_1], [seq_2], boxes=[boxes_2], padding=padding_state, truncation="only_second" ) self.assertEqual(len(output["input_ids"][0]), model_max_length) self.assertEqual(len(output["bbox"][0]), model_max_length) # Simple with no truncation # Reset warnings tokenizer.deprecation_warnings = {} with self.assertLogs("transformers", level="WARNING") as cm: output = tokenizer( question_1, seq_2, boxes=boxes_2, padding=padding_state, truncation=False ) self.assertNotEqual(len(output["input_ids"]), model_max_length) self.assertNotEqual(len(output["bbox"]), model_max_length) self.assertEqual(len(cm.records), 1) self.assertTrue( cm.records[0].message.startswith( "Token indices sequence length is longer than the specified maximum sequence length" " for this model" ) ) tokenizer.deprecation_warnings = {} with self.assertLogs("transformers", level="WARNING") as cm: output = tokenizer( [question_1], [seq_2], boxes=[boxes_2], padding=padding_state, truncation=False ) self.assertNotEqual(len(output["input_ids"][0]), model_max_length) self.assertNotEqual(len(output["bbox"][0]), model_max_length) self.assertEqual(len(cm.records), 1) self.assertTrue( cm.records[0].message.startswith( "Token indices sequence length is longer than the specified maximum sequence length" " for this model" ) ) # Check the order of Sequence of input ids, overflowing tokens and bbox sequence with truncation truncated_first_sequence = ( tokenizer(seq_0, boxes=boxes_0, add_special_tokens=False)["input_ids"][:-2] + tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False)["input_ids"] ) truncated_second_sequence = ( tokenizer(seq_0, boxes=boxes_0, add_special_tokens=False)["input_ids"] + tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False)["input_ids"][:-2] ) truncated_longest_sequence = ( truncated_first_sequence if len(seq0_input_ids) > len(seq1_input_ids) else truncated_second_sequence ) overflow_first_sequence = ( tokenizer(seq_0, boxes=boxes_0, add_special_tokens=False)["input_ids"][-(2 + stride) :] + tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False)["input_ids"] ) overflow_second_sequence = ( tokenizer(seq_0, boxes=boxes_0, add_special_tokens=False)["input_ids"] + tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False)["input_ids"][-(2 + stride) :] ) overflow_longest_sequence = ( overflow_first_sequence if len(seq0_input_ids) > len(seq1_input_ids) else overflow_second_sequence ) bbox_first = [[0, 0, 0, 0]] * (len(seq0_input_ids) - 2) bbox_first_sequence = bbox_first + tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False)["bbox"] overflowing_token_bbox_first_sequence_slow = [[0, 0, 0, 0]] * (2 + stride) overflowing_token_bbox_first_sequence_fast = [[0, 0, 0, 0]] * (2 + stride) + tokenizer( seq_1, boxes=boxes_1, add_special_tokens=False )["bbox"] bbox_second = [[0, 0, 0, 0]] * len(seq0_input_ids) bbox_second_sequence = ( bbox_second + tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False)["bbox"][:-2] ) overflowing_token_bbox_second_sequence_slow = tokenizer( seq_1, boxes=boxes_1, add_special_tokens=False )["bbox"][-(2 + stride) :] overflowing_token_bbox_second_sequence_fast = [[0, 0, 0, 0]] * len(seq0_input_ids) + tokenizer( seq_1, boxes=boxes_1, add_special_tokens=False )["bbox"][-(2 + stride) :] bbox_longest_sequence = ( bbox_first_sequence if len(seq0_tokens) > len(seq1_tokens) else bbox_second_sequence ) overflowing_token_bbox_longest_sequence_fast = ( overflowing_token_bbox_first_sequence_fast if len(seq0_tokens) > len(seq1_tokens) else overflowing_token_bbox_second_sequence_fast ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, LayoutLMv3TokenizerFast): information = tokenizer( question_0, seq_1, boxes=boxes_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation="longest_first", return_overflowing_tokens=True, # add_prefix_space=False, ) truncated_sequence = information["input_ids"][0] overflowing_tokens = information["input_ids"][1] bbox = information["bbox"][0] overflowing_bbox = information["bbox"][1] self.assertEqual(len(information["input_ids"]), 2) self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_longest_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest)) self.assertEqual(overflowing_tokens, overflow_longest_sequence) self.assertEqual(bbox, bbox_longest_sequence) self.assertEqual(len(overflowing_bbox), 2 + stride + len(smallest)) self.assertEqual(overflowing_bbox, overflowing_token_bbox_longest_sequence_fast) else: # No overflowing tokens when using 'longest' in python tokenizers with self.assertRaises(ValueError) as context: information = tokenizer( question_0, seq_1, boxes=boxes_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation="longest_first", return_overflowing_tokens=True, # add_prefix_space=False, ) self.assertTrue( context.exception.args[0].startswith( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, LayoutLMv3TokenizerFast): information = tokenizer( question_0, seq_1, boxes=boxes_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation=True, return_overflowing_tokens=True, # add_prefix_space=False, ) truncated_sequence = information["input_ids"][0] overflowing_tokens = information["input_ids"][1] bbox = information["bbox"][0] overflowing_bbox = information["bbox"][1] self.assertEqual(len(information["input_ids"]), 2) self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_longest_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest)) self.assertEqual(overflowing_tokens, overflow_longest_sequence) self.assertEqual(bbox, bbox_longest_sequence) self.assertEqual(overflowing_bbox, overflowing_token_bbox_longest_sequence_fast) else: # No overflowing tokens when using 'longest' in python tokenizers with self.assertRaises(ValueError) as context: information = tokenizer( question_0, seq_1, boxes=boxes_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation=True, return_overflowing_tokens=True, # add_prefix_space=False, ) self.assertTrue( context.exception.args[0].startswith( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) ) information_first_truncated = tokenizer( question_0, seq_1, boxes=boxes_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation="only_first", return_overflowing_tokens=True, # add_prefix_space=False, ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, LayoutLMv3TokenizerFast): truncated_sequence = information_first_truncated["input_ids"][0] overflowing_tokens = information_first_truncated["input_ids"][1] bbox = information_first_truncated["bbox"][0] overflowing_bbox = information_first_truncated["bbox"][0] self.assertEqual(len(information_first_truncated["input_ids"]), 2) self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_first_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq1_input_ids)) self.assertEqual(overflowing_tokens, overflow_first_sequence) self.assertEqual(bbox, bbox_first_sequence) self.assertEqual(overflowing_bbox, overflowing_token_bbox_first_sequence_fast) else: truncated_sequence = information_first_truncated["input_ids"] overflowing_tokens = information_first_truncated["overflowing_tokens"] overflowing_bbox = information_first_truncated["overflowing_token_boxes"] bbox = information_first_truncated["bbox"] self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_first_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride) self.assertEqual(overflowing_tokens, seq0_input_ids[-(2 + stride) :]) self.assertEqual(bbox, bbox_first_sequence) self.assertEqual(overflowing_bbox, overflowing_token_bbox_first_sequence_slow) information_second_truncated = tokenizer( question_0, seq_1, boxes=boxes_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation="only_second", return_overflowing_tokens=True, # add_prefix_space=False, ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, LayoutLMv3TokenizerFast): truncated_sequence = information_second_truncated["input_ids"][0] overflowing_tokens = information_second_truncated["input_ids"][1] bbox = information_second_truncated["bbox"][0] overflowing_bbox = information_second_truncated["bbox"][1] self.assertEqual(len(information_second_truncated["input_ids"]), 2) self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_second_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq0_input_ids)) self.assertEqual(overflowing_tokens, overflow_second_sequence) self.assertEqual(bbox, bbox_second_sequence) self.assertEqual(overflowing_bbox, overflowing_token_bbox_second_sequence_fast) else: truncated_sequence = information_second_truncated["input_ids"] overflowing_tokens = information_second_truncated["overflowing_tokens"] bbox = information_second_truncated["bbox"] overflowing_bbox = information_second_truncated["overflowing_token_boxes"] self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_second_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride) self.assertEqual(overflowing_tokens, seq1_input_ids[-(2 + stride) :]) self.assertEqual(bbox, bbox_second_sequence) self.assertEqual(overflowing_bbox, overflowing_token_bbox_second_sequence_slow) def test_maximum_encoding_length_single_input(self): tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): seq_0, boxes_0, ids = self.get_clean_sequence(tokenizer, max_length=20) sequence = tokenizer(seq_0, boxes=boxes_0, add_special_tokens=False) total_length = len(sequence["input_ids"]) self.assertGreater( total_length, 4, "Issue with the testing sequence, please update it, it's too short" ) # Test with max model input length model_max_length = tokenizer.model_max_length self.assertEqual(model_max_length, 100) seq_1 = seq_0 * model_max_length boxes_1 = boxes_0 * model_max_length sequence1 = tokenizer(seq_1, boxes=boxes_1, add_special_tokens=False) total_length1 = len(sequence1["input_ids"]) self.assertGreater( total_length1, model_max_length, "Issue with the testing sequence, please update it, it's too short", ) # Simple padding_strategies = ( [False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False] ) for padding_state in padding_strategies: with self.subTest(f"Padding: {padding_state}"): for truncation_state in [True, "longest_first", "only_first"]: with self.subTest(f"Truncation: {truncation_state}"): output = tokenizer( seq_1, boxes=boxes_1, padding=padding_state, truncation=truncation_state, ) self.assertEqual(len(output["input_ids"]), model_max_length) self.assertEqual(len(output["bbox"]), model_max_length) output = tokenizer( [seq_1], boxes=[boxes_1], padding=padding_state, truncation=truncation_state, ) self.assertEqual(len(output["input_ids"][0]), model_max_length) self.assertEqual(len(output["bbox"][0]), model_max_length) # Simple with no truncation # Reset warnings tokenizer.deprecation_warnings = {} with self.assertLogs("transformers", level="WARNING") as cm: output = tokenizer(seq_1, boxes=boxes_1, padding=padding_state, truncation=False) self.assertNotEqual(len(output["input_ids"]), model_max_length) self.assertNotEqual(len(output["bbox"]), model_max_length) self.assertEqual(len(cm.records), 1) self.assertTrue( cm.records[0].message.startswith( "Token indices sequence length is longer than the specified maximum sequence length" " for this model" ) ) tokenizer.deprecation_warnings = {} with self.assertLogs("transformers", level="WARNING") as cm: output = tokenizer([seq_1], boxes=[boxes_1], padding=padding_state, truncation=False) self.assertNotEqual(len(output["input_ids"][0]), model_max_length) self.assertNotEqual(len(output["bbox"][0]), model_max_length) self.assertEqual(len(cm.records), 1) self.assertTrue( cm.records[0].message.startswith( "Token indices sequence length is longer than the specified maximum sequence length" " for this model" ) ) # Check the order of Sequence of input ids, overflowing tokens and bbox sequence with truncation stride = 2 information = tokenizer( seq_0, boxes=boxes_0, max_length=total_length - 2, add_special_tokens=False, stride=stride, truncation=True, return_overflowing_tokens=True, # add_prefix_space=False, ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, LayoutLMv3TokenizerFast): truncated_sequence = information["input_ids"][0] overflowing_tokens = information["input_ids"][1] # bbox = information["bbox"][0] # overflowing_bbox = information["bbox"][1] self.assertEqual(len(information["input_ids"]), 2) self.assertEqual(len(truncated_sequence), total_length - 2) self.assertEqual(truncated_sequence, sequence["input_ids"][:-2]) self.assertEqual(len(overflowing_tokens), 2 + stride) self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :]) # self.assertEqual(bbox, sequence["bbox"][:-2]) # self.assertEqual(overflowing_bbox, sequence["bbox"][-(2 + stride) :]) else: truncated_sequence = information["input_ids"] overflowing_tokens = information["overflowing_tokens"] # bbox = information["bbox"] # overflowing_bbox = information["overflowing_token_boxes"] self.assertEqual(len(truncated_sequence), total_length - 2) self.assertEqual(truncated_sequence, sequence["input_ids"][:-2]) self.assertEqual(len(overflowing_tokens), 2 + stride) self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :]) # self.assertEqual(bbox, sequence["bbox"][:-2]) # self.assertEqual(overflowing_bbox, sequence["bbox"][-(2 + stride) :]) @unittest.skip(reason="LayoutLMv3 tokenizer requires boxes besides sequences.") def test_pretokenized_inputs(self): pass @unittest.skip(reason="LayoutLMv3 tokenizer always expects pretokenized inputs.") def test_compare_pretokenized_inputs(self): pass @unittest.skip(reason="LayoutLMv3 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", "0000000000000000"] boxes = [[1000, 1000, 1000, 1000] for _ in range(len(words))] word_labels = [0, 1, 2] # test slow tokenizer tokenizer_p = LayoutLMv3Tokenizer.from_pretrained("microsoft/layoutlmv3-base", add_visual_labels=False) encoding = tokenizer_p(words, boxes=boxes, word_labels=word_labels) self.assertListEqual(encoding.labels, [-100, 0, 1, -100, 2, -100, -100]) tokenizer_p = LayoutLMv3Tokenizer.from_pretrained( "microsoft/layoutlmv3-base", only_label_first_subword=False, add_visual_labels=False, ) encoding = tokenizer_p(words, boxes=boxes, word_labels=word_labels) self.assertListEqual(encoding.labels, [-100, 0, 1, 1, 2, 2, -100]) # test fast tokenizer tokenizer_r = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base", add_visual_labels=False) encoding = tokenizer_r(words, boxes=boxes, word_labels=word_labels) self.assertListEqual(encoding.labels, [-100, 0, 1, -100, 2, -100, -100]) tokenizer_r = LayoutLMv3TokenizerFast.from_pretrained( "microsoft/layoutlmv3-base", only_label_first_subword=False, add_visual_labels=False, ) encoding = tokenizer_r(words, boxes=boxes, word_labels=word_labels) self.assertListEqual(encoding.labels, [-100, 0, 1, 1, 2, 2, -100]) def test_layoutlmv3_integration_test(self): tokenizer_p = LayoutLMv3Tokenizer.from_pretrained("microsoft/layoutlmv3-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, 795, 13964, 2, 1, 1, 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], [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], [0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) # CASE 1: batched words, boxes = self.get_words_and_boxes_batch() expected_results = {'input_ids': [[0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 92, 614, 2, 1, 1, 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], [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], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [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], [0, 0, 0, 0], [0, 0, 0, 0]]], 'attention_mask': [[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) # CASE 2: not batched words, boxes = self.get_words_and_boxes() word_labels = [1, 2] expected_results = {'input_ids': [0, 795, 13964, 2, 1, 1, 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], [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], [0, 0, 0, 0], [0, 0, 0, 0]], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) # # CASE 2: batched words, boxes = self.get_words_and_boxes_batch() word_labels = [[1, 2], [2, 46]] expected_results = {'input_ids': [[0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 92, 614, 2, 1, 1, 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], [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], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [961, 885, 992, 912], [256, 38, 330, 58], [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], [0, 0, 0, 0], [0, 0, 0, 0]]], 'labels': [[-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, 46, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) # # CASE 3: not batched question, words, boxes = self.get_question_words_and_boxes() expected_results = {'input_ids': [0, 99, 18, 39, 766, 116, 2, 2, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], '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], [0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [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, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(question, words, boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) # # CASE 3: batched questions, words, boxes = self.get_question_words_and_boxes_batch() expected_results = {'input_ids': [[0, 99, 18, 39, 766, 116, 2, 2, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 141, 16, 37, 373, 116, 2, 2, 13964, 795, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '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], [0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [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], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [256, 38, 330, 58], [256, 38, 330, 58], [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, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(questions, words, boxes, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) @unittest.skip(reason="Doesn't support returning Numpy arrays") def test_np_encode_plus_sent_to_model(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)