1957 lines
98 KiB
Python
1957 lines
98 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import shutil
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import tempfile
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import unittest
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from typing import List
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from transformers import (
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AddedToken,
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LayoutXLMTokenizerFast,
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SpecialTokensMixin,
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is_tf_available,
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is_torch_available,
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logging,
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)
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from transformers.models.layoutxlm.tokenization_layoutxlm import LayoutXLMTokenizer
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from transformers.testing_utils import (
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get_tests_dir,
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is_pt_tf_cross_test,
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require_pandas,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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slow,
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)
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from ...test_tokenization_common import (
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SMALL_TRAINING_CORPUS,
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TokenizerTesterMixin,
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filter_non_english,
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merge_model_tokenizer_mappings,
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)
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logger = logging.get_logger(__name__)
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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@require_sentencepiece
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@require_tokenizers
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@require_pandas
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class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "FacebookAI/xlm-roberta-base"
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tokenizer_class = LayoutXLMTokenizer
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rust_tokenizer_class = LayoutXLMTokenizerFast
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test_rust_tokenizer = True
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from_pretrained_filter = filter_non_english
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test_seq2seq = False
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test_sentencepiece = True
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maxDiff = None
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def get_words_and_boxes(self):
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words = ["a", "weirdly", "test"]
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boxes = [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]]
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return words, boxes
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def get_words_and_boxes_batch(self):
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words = [["a", "weirdly", "test"], ["hello", "my", "name", "is", "bob"]]
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boxes = [
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[[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]],
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[[961, 885, 992, 912], [256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69]],
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]
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return words, boxes
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def get_question_words_and_boxes(self):
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question = "what's his name?"
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words = ["a", "weirdly", "test"]
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boxes = [[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]]
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return question, words, boxes
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def get_question_words_and_boxes_batch(self):
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questions = ["what's his name?", "how is he called?"]
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words = [["a", "weirdly", "test"], ["what", "a", "laif", "gastn"]]
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boxes = [
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[[423, 237, 440, 251], [427, 272, 441, 287], [419, 115, 437, 129]],
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[[256, 38, 330, 58], [256, 38, 330, 58], [336, 42, 353, 57], [34, 42, 66, 69]],
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]
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return questions, words, boxes
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = LayoutXLMTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.save_pretrained(self.tmpdirname)
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def get_input_output_texts(self, tokenizer):
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input_text = "UNwant\u00E9d,running"
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output_text = "unwanted, running"
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return input_text, output_text
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@unittest.skip("Chat template tests don't play well with table/layout models.")
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def test_chat_template_batched(self):
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pass
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# override test in `test_tokenization_common.py` because of the required input format of the `__call__`` method of
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# this tokenizer
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def test_save_sentencepiece_tokenizer(self) -> None:
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if not self.test_sentencepiece or not self.test_slow_tokenizer:
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return
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# We want to verify that we will be able to save the tokenizer even if the original files that were used to
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# build the tokenizer have been deleted in the meantime.
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words, boxes = self.get_words_and_boxes()
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tokenizer_slow_1 = self.get_tokenizer()
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encoding_tokenizer_slow_1 = tokenizer_slow_1(
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words,
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boxes=boxes,
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)
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tmpdirname_1 = tempfile.mkdtemp()
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tmpdirname_2 = tempfile.mkdtemp()
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tokenizer_slow_1.save_pretrained(tmpdirname_1)
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tokenizer_slow_2 = self.tokenizer_class.from_pretrained(tmpdirname_1)
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encoding_tokenizer_slow_2 = tokenizer_slow_2(
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words,
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boxes=boxes,
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)
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shutil.rmtree(tmpdirname_1)
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tokenizer_slow_2.save_pretrained(tmpdirname_2)
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tokenizer_slow_3 = self.tokenizer_class.from_pretrained(tmpdirname_2)
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encoding_tokenizer_slow_3 = tokenizer_slow_3(
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words,
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boxes=boxes,
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)
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shutil.rmtree(tmpdirname_2)
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self.assertEqual(encoding_tokenizer_slow_1, encoding_tokenizer_slow_2)
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self.assertEqual(encoding_tokenizer_slow_1, encoding_tokenizer_slow_3)
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def test_split_special_tokens(self):
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tokenizer = self.tokenizer_class.from_pretrained("microsoft/layoutxlm-base")
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_, _, boxes = self.get_question_words_and_boxes()
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special_token = "[SPECIAL_TOKEN]"
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tokenizer.add_special_tokens({"additional_special_tokens": [special_token]})
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encoded_special_token = tokenizer.tokenize(special_token, boxes=boxes, add_special_tokens=False)
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self.assertEqual(len(encoded_special_token), 1)
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encoded_split_special_token = tokenizer.tokenize(
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special_token, add_special_tokens=False, split_special_tokens=True, boxes=boxes
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)
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self.assertTrue(len(encoded_split_special_token) > 1)
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@slow
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_class.from_pretrained("microsoft/layoutxlm-base")
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question, words, boxes = self.get_question_words_and_boxes()
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text = tokenizer.encode(
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question.split(),
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boxes=[tokenizer.pad_token_box for _ in range(len(question.split()))],
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add_special_tokens=False,
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)
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text_2 = tokenizer.encode(words, boxes=boxes, add_special_tokens=False)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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assert encoded_pair == [0] + text + [2] + [2] + text_2 + [2]
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def test_offsets_with_special_characters(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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words, boxes = self.get_words_and_boxes()
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words[1] = tokenizer_r.mask_token
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tokens = tokenizer_r.encode_plus(
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words,
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boxes=boxes,
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return_attention_mask=False,
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return_token_type_ids=False,
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return_offsets_mapping=True,
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add_special_tokens=True,
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)
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expected_results = [
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((0, 0), tokenizer_r.cls_token),
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((0, 1), "▁a"),
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((0, 6), tokenizer_r.mask_token),
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((0, 4), "▁test"),
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((0, 0), tokenizer_r.sep_token),
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]
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self.assertEqual(
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[e[1] for e in expected_results], tokenizer_r.convert_ids_to_tokens(tokens["input_ids"])
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)
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self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"])
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def test_add_special_tokens(self):
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tokenizers: List[LayoutXLMTokenizer] = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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special_token = "[SPECIAL_TOKEN]"
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special_token_box = [1000, 1000, 1000, 1000]
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tokenizer.add_special_tokens({"cls_token": special_token})
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encoded_special_token = tokenizer.encode(
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[special_token], boxes=[special_token_box], add_special_tokens=False
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)
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self.assertEqual(len(encoded_special_token), 1)
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decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True)
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self.assertTrue(special_token not in decoded)
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def test_add_tokens_tokenizer(self):
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tokenizers: List[LayoutXLMTokenizer] = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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# We usually have added tokens from the start in tests because our vocab fixtures are
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# smaller than the original vocabs - let's not assert this
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# self.assertEqual(vocab_size, all_size)
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new_toks = ["aaaaa", "bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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words = "aaaaa bbbbbb low cccccccccdddddddd l".split()
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boxes = [[1000, 1000, 1000, 1000] for _ in range(len(words))]
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tokens = tokenizer.encode(words, boxes=boxes, add_special_tokens=False)
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
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added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
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vocab_size_3 = tokenizer.vocab_size
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all_size_3 = len(tokenizer)
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self.assertNotEqual(vocab_size_3, 0)
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self.assertEqual(vocab_size, vocab_size_3)
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self.assertEqual(added_toks_2, len(new_toks_2))
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self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
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words = ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l".split()
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boxes = [[1000, 1000, 1000, 1000] for _ in range(len(words))]
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tokens = tokenizer.encode(
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words,
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boxes=boxes,
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add_special_tokens=False,
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)
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self.assertGreaterEqual(len(tokens), 6)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[0], tokens[1])
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokens[-3])
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self.assertEqual(tokens[0], tokenizer.eos_token_id)
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self.assertEqual(tokens[-2], tokenizer.pad_token_id)
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@require_tokenizers
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def test_encode_decode_with_spaces(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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words, boxes = self.get_words_and_boxes()
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new_toks = [AddedToken("[ABC]", normalized=False), AddedToken("[DEF]", normalized=False)]
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tokenizer.add_tokens(new_toks)
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input = "[ABC][DEF][ABC][DEF]"
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if self.space_between_special_tokens:
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output = "[ABC] [DEF] [ABC] [DEF]"
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else:
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output = input
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encoded = tokenizer.encode(input.split(), boxes=boxes, add_special_tokens=False)
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decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
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self.assertIn(decoded, [output, output.lower()])
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def test_encode_plus_with_padding(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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words, boxes = self.get_words_and_boxes()
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# check correct behaviour if no pad_token_id exists and add it eventually
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self._check_no_pad_token_padding(tokenizer, words)
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padding_size = 10
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padding_idx = tokenizer.pad_token_id
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encoded_sequence = tokenizer.encode_plus(words, boxes=boxes, return_special_tokens_mask=True)
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input_ids = encoded_sequence["input_ids"]
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special_tokens_mask = encoded_sequence["special_tokens_mask"]
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sequence_length = len(input_ids)
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# Test 'longest' and 'no_padding' don't do anything
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tokenizer.padding_side = "right"
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not_padded_sequence = tokenizer.encode_plus(
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words,
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boxes=boxes,
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padding=False,
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return_special_tokens_mask=True,
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)
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not_padded_input_ids = not_padded_sequence["input_ids"]
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not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
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not_padded_sequence_length = len(not_padded_input_ids)
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self.assertTrue(sequence_length == not_padded_sequence_length)
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self.assertTrue(input_ids == not_padded_input_ids)
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self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask)
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not_padded_sequence = tokenizer.encode_plus(
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words,
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boxes=boxes,
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padding=False,
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return_special_tokens_mask=True,
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)
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not_padded_input_ids = not_padded_sequence["input_ids"]
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not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
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not_padded_sequence_length = len(not_padded_input_ids)
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self.assertTrue(sequence_length == not_padded_sequence_length)
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self.assertTrue(input_ids == not_padded_input_ids)
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self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask)
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# Test right padding
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tokenizer.padding_side = "right"
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right_padded_sequence = tokenizer.encode_plus(
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words,
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boxes=boxes,
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max_length=sequence_length + padding_size,
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padding="max_length",
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return_special_tokens_mask=True,
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)
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right_padded_input_ids = right_padded_sequence["input_ids"]
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right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"]
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right_padded_sequence_length = len(right_padded_input_ids)
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self.assertTrue(sequence_length + padding_size == right_padded_sequence_length)
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self.assertTrue(input_ids + [padding_idx] * padding_size == right_padded_input_ids)
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self.assertTrue(special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask)
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# Test left padding
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tokenizer.padding_side = "left"
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left_padded_sequence = tokenizer.encode_plus(
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words,
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boxes=boxes,
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max_length=sequence_length + padding_size,
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padding="max_length",
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return_special_tokens_mask=True,
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)
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left_padded_input_ids = left_padded_sequence["input_ids"]
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left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
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left_padded_sequence_length = len(left_padded_input_ids)
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self.assertTrue(sequence_length + padding_size == left_padded_sequence_length)
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self.assertTrue([padding_idx] * padding_size + input_ids == left_padded_input_ids)
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self.assertTrue([1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask)
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if "token_type_ids" in tokenizer.model_input_names:
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token_type_ids = encoded_sequence["token_type_ids"]
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left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
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right_padded_token_type_ids = right_padded_sequence["token_type_ids"]
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assert token_type_ids + [0] * padding_size == right_padded_token_type_ids
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assert [0] * padding_size + token_type_ids == left_padded_token_type_ids
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if "attention_mask" in tokenizer.model_input_names:
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attention_mask = encoded_sequence["attention_mask"]
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right_padded_attention_mask = right_padded_sequence["attention_mask"]
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left_padded_attention_mask = left_padded_sequence["attention_mask"]
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self.assertTrue(attention_mask + [0] * padding_size == right_padded_attention_mask)
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self.assertTrue([0] * padding_size + attention_mask == left_padded_attention_mask)
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def test_internal_consistency(self):
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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words, boxes = self.get_words_and_boxes()
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tokens = []
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for word in words:
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tokens.extend(tokenizer.tokenize(word))
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ids = tokenizer.convert_tokens_to_ids(tokens)
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ids_2 = tokenizer.encode(words, boxes=boxes, add_special_tokens=False)
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self.assertListEqual(ids, ids_2)
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tokens_2 = tokenizer.convert_ids_to_tokens(ids)
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self.assertNotEqual(len(tokens_2), 0)
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text_2 = tokenizer.decode(ids)
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self.assertIsInstance(text_2, str)
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output_text = "a weirdly test"
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self.assertEqual(text_2, output_text)
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def test_mask_output(self):
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tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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words, boxes = self.get_words_and_boxes()
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if (
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tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
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and "token_type_ids" in tokenizer.model_input_names
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):
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information = tokenizer.encode_plus(words, boxes=boxes, add_special_tokens=True)
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sequences, mask = information["input_ids"], information["token_type_ids"]
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self.assertEqual(len(sequences), len(mask))
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def test_number_of_added_tokens(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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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_to_max_length(self):
|
|
"""We keep this test for backward compatibility but it should be removed when `pad_to_max_length` will be deprecated"""
|
|
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()
|
|
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
|
|
|
|
# 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)
|
|
# FIXME: the next line should be padding(max_length) to avoid warning
|
|
padded_sequence = tokenizer.encode(
|
|
words, boxes=boxes, max_length=sequence_length + padding_size, pad_to_max_length=True
|
|
)
|
|
padded_sequence_length = len(padded_sequence)
|
|
assert sequence_length + padding_size == padded_sequence_length
|
|
assert encoded_sequence + [padding_idx] * padding_size == padded_sequence
|
|
|
|
# Check that nothing is done when a maximum length is not specified
|
|
encoded_sequence = tokenizer.encode(words, boxes=boxes)
|
|
sequence_length = len(encoded_sequence)
|
|
|
|
tokenizer.padding_side = "right"
|
|
padded_sequence_right = tokenizer.encode(words, boxes=boxes, pad_to_max_length=True)
|
|
padded_sequence_right_length = len(padded_sequence_right)
|
|
assert sequence_length == padded_sequence_right_length
|
|
assert encoded_sequence == padded_sequence_right
|
|
|
|
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.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
tokenizer_p = self.tokenizer_class.from_pretrained(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, pad_to_max_length=True)
|
|
input_p = tokenizer_p.encode(words, boxes=boxes, max_length=max_length, pad_to_max_length=True)
|
|
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
|
|
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, pad_to_max_length=True
|
|
)
|
|
input_p = tokenizer_p.encode(
|
|
question, words, boxes=boxes, max_length=max_length, pad_to_max_length=True
|
|
)
|
|
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
|
|
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, pad_to_max_length=True)
|
|
input_p = tokenizer_p.encode_plus(words, boxes=boxes, max_length=max_length, pad_to_max_length=True)
|
|
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, 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, pad_to_max_length=True
|
|
)
|
|
input_p = tokenizer_p.encode_plus(
|
|
question, words, boxes=boxes, max_length=max_length, pad_to_max_length=True
|
|
)
|
|
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, 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,
|
|
pad_to_max_length=True,
|
|
)
|
|
input_p = tokenizer_p.batch_encode_plus(
|
|
words,
|
|
boxes=boxes,
|
|
max_length=max_length,
|
|
pad_to_max_length=True,
|
|
)
|
|
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="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_padding_warning_message_fast_tokenizer(self):
|
|
if not self.test_rust_tokenizer:
|
|
return
|
|
|
|
words, boxes = self.get_words_and_boxes_batch()
|
|
|
|
tokenizer_fast = self.get_rust_tokenizer()
|
|
|
|
encoding_fast = tokenizer_fast(
|
|
words,
|
|
boxes=boxes,
|
|
)
|
|
|
|
with self.assertLogs("transformers", level="WARNING") as cm:
|
|
tokenizer_fast.pad(encoding_fast)
|
|
self.assertEqual(len(cm.records), 1)
|
|
self.assertIn(
|
|
"Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to"
|
|
" encode the text followed by a call to the `pad` method to get a padded encoding.",
|
|
cm.records[0].message,
|
|
)
|
|
|
|
if not self.test_slow_tokenizer:
|
|
return
|
|
|
|
tokenizer_slow = self.get_tokenizer()
|
|
|
|
encoding_slow = tokenizer_slow(
|
|
words,
|
|
boxes=boxes,
|
|
)
|
|
|
|
with self.assertLogs(level="WARNING") as cm:
|
|
# We want to assert there are no warnings, but the 'assertLogs' method does not support that.
|
|
# Therefore, we are adding a dummy warning, and then we will assert it is the only warning.
|
|
logger.warning("Dummy warning")
|
|
tokenizer_slow.pad(encoding_slow)
|
|
self.assertEqual(len(cm.records), 1)
|
|
self.assertIn(
|
|
"Dummy warning",
|
|
cm.records[0].message,
|
|
)
|
|
|
|
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.keys():
|
|
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.keys():
|
|
self.assertListEqual(
|
|
encoded_sequences_batch_padded_1[key],
|
|
encoded_sequences_batch_padded_2[key],
|
|
)
|
|
|
|
@unittest.skip("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("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):
|
|
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
|
|
return
|
|
|
|
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
|
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
|
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
|
|
# Input tokens id
|
|
words, boxes = self.get_words_and_boxes()
|
|
input_simple = tokenizer_p.encode(words, boxes=boxes, add_special_tokens=False)
|
|
input_pair = tokenizer_p.encode(words, boxes=boxes, add_special_tokens=False)
|
|
|
|
# Generate output
|
|
output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
|
|
output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
|
|
self.assertEqual(output_p, output_r)
|
|
|
|
# Generate pair output
|
|
output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
|
|
output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
|
|
self.assertEqual(output_p, output_r)
|
|
|
|
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("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:
|
|
return
|
|
|
|
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
|
|
config = config_class()
|
|
|
|
if config.is_encoder_decoder or config.pad_token_id is None:
|
|
return
|
|
|
|
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:
|
|
return
|
|
|
|
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
|
|
return
|
|
|
|
tokenizer = self.get_tokenizer()
|
|
rust_tokenizer = self.get_rust_tokenizer()
|
|
|
|
words, boxes = self.get_words_and_boxes()
|
|
|
|
ids = tokenizer.encode(words, boxes=boxes, add_special_tokens=False)
|
|
rust_ids = rust_tokenizer.encode(words, boxes=boxes, add_special_tokens=False)
|
|
self.assertListEqual(ids, rust_ids)
|
|
|
|
ids = tokenizer.encode(words, boxes=boxes, add_special_tokens=True)
|
|
rust_ids = rust_tokenizer.encode(words, boxes=boxes, add_special_tokens=True)
|
|
self.assertListEqual(ids, rust_ids)
|
|
|
|
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
|
|
return
|
|
|
|
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
|
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
|
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
|
|
words, boxes = self.get_words_and_boxes()
|
|
|
|
# Ensure basic input match
|
|
input_p = tokenizer_p.encode_plus(words, boxes=boxes)
|
|
input_r = tokenizer_r.encode_plus(words, boxes=boxes)
|
|
|
|
for key in filter(
|
|
lambda x: x in ["input_ids", "token_type_ids", "attention_mask", "bbox"], input_p.keys()
|
|
):
|
|
self.assertSequenceEqual(input_p[key], input_r[key])
|
|
|
|
input_pairs_p = tokenizer_p.encode_plus(words, boxes=boxes)
|
|
input_pairs_r = tokenizer_r.encode_plus(words, boxes=boxes)
|
|
|
|
for key in filter(
|
|
lambda x: x in ["input_ids", "token_type_ids", "attention_mask", "bbox"], input_p.keys()
|
|
):
|
|
self.assertSequenceEqual(input_pairs_p[key], input_pairs_r[key])
|
|
|
|
words = ["hello" for _ in range(1000)]
|
|
boxes = [[1000, 1000, 1000, 1000] for _ in range(1000)]
|
|
|
|
# Ensure truncation match
|
|
input_p = tokenizer_p.encode_plus(words, boxes=boxes, max_length=512, truncation=True)
|
|
input_r = tokenizer_r.encode_plus(words, boxes=boxes, max_length=512, truncation=True)
|
|
|
|
for key in filter(
|
|
lambda x: x in ["input_ids", "token_type_ids", "attention_mask", "bbox"], input_p.keys()
|
|
):
|
|
self.assertSequenceEqual(input_p[key], input_r[key])
|
|
|
|
# Ensure truncation with stride match
|
|
input_p = tokenizer_p.encode_plus(
|
|
words, boxes=boxes, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
|
|
)
|
|
input_r = tokenizer_r.encode_plus(
|
|
words, boxes=boxes, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
|
|
)
|
|
|
|
for key in filter(
|
|
lambda x: x in ["input_ids", "token_type_ids", "attention_mask", "bbox"], input_p.keys()
|
|
):
|
|
self.assertSequenceEqual(input_p[key], input_r[key][0])
|
|
|
|
def test_embeded_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
|
|
return
|
|
|
|
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
|
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
|
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
words, boxes = self.get_words_and_boxes()
|
|
tokens_r = tokenizer_r.encode_plus(
|
|
words,
|
|
boxes=boxes,
|
|
add_special_tokens=True,
|
|
)
|
|
tokens_p = tokenizer_p.encode_plus(
|
|
words,
|
|
boxes=boxes,
|
|
add_special_tokens=True,
|
|
)
|
|
|
|
for key in tokens_p.keys():
|
|
self.assertEqual(tokens_r[key], tokens_p[key])
|
|
|
|
if "token_type_ids" in tokens_r:
|
|
self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
|
|
|
|
tokens_r = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
|
|
tokens_p = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
|
|
self.assertSequenceEqual(tokens_r, tokens_p)
|
|
|
|
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.keys():
|
|
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.keys():
|
|
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)
|
|
|
|
@is_pt_tf_cross_test
|
|
def test_batch_encode_plus_tensors(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_batch()
|
|
|
|
# A Tensor cannot be build by sequences which are not the same size
|
|
self.assertRaises(ValueError, tokenizer.batch_encode_plus, words, boxes=boxes, return_tensors="pt")
|
|
self.assertRaises(ValueError, tokenizer.batch_encode_plus, words, boxes=boxes, return_tensors="tf")
|
|
|
|
if tokenizer.pad_token_id is None:
|
|
self.assertRaises(
|
|
ValueError,
|
|
tokenizer.batch_encode_plus,
|
|
words,
|
|
boxes=boxes,
|
|
padding=True,
|
|
return_tensors="pt",
|
|
)
|
|
self.assertRaises(
|
|
ValueError,
|
|
tokenizer.batch_encode_plus,
|
|
words,
|
|
boxes=boxes,
|
|
padding="longest",
|
|
return_tensors="tf",
|
|
)
|
|
else:
|
|
pytorch_tensor = tokenizer.batch_encode_plus(words, boxes=boxes, padding=True, return_tensors="pt")
|
|
tensorflow_tensor = tokenizer.batch_encode_plus(
|
|
words, boxes=boxes, padding="longest", return_tensors="tf"
|
|
)
|
|
encoded_sequences = tokenizer.batch_encode_plus(words, boxes=boxes, padding=True)
|
|
|
|
for key in encoded_sequences.keys():
|
|
pytorch_value = pytorch_tensor[key].tolist()
|
|
tensorflow_value = tensorflow_tensor[key].numpy().tolist()
|
|
encoded_value = encoded_sequences[key]
|
|
|
|
self.assertEqual(pytorch_value, tensorflow_value, encoded_value)
|
|
|
|
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("<special>", lstrip=True)]
|
|
|
|
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
|
|
pretrained_name, additional_special_tokens=added_tokens, **kwargs
|
|
)
|
|
words = "Hey this is a <special> 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(
|
|
["<special>"], boxes=[1000, 1000, 1000, 1000], add_special_tokens=False
|
|
)[0]
|
|
|
|
self.assertTrue(special_token_id in r_output)
|
|
|
|
if self.test_slow_tokenizer:
|
|
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
|
|
pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
|
|
)
|
|
tokenizer_p = self.tokenizer_class.from_pretrained(
|
|
pretrained_name, additional_special_tokens=added_tokens, **kwargs
|
|
)
|
|
|
|
words = "Hey this is a <special> token".split()
|
|
boxes = [[1000, 1000, 1000, 1000] for _ in range(len(words))]
|
|
|
|
p_output = tokenizer_p.encode(words, boxes=boxes)
|
|
cr_output = tokenizer_cr.encode(words, boxes=boxes)
|
|
|
|
self.assertEqual(p_output, r_output)
|
|
self.assertEqual(cr_output, r_output)
|
|
self.assertTrue(special_token_id in p_output)
|
|
self.assertTrue(special_token_id in cr_output)
|
|
|
|
def test_training_new_tokenizer(self):
|
|
# This feature only exists for fast tokenizers
|
|
if not self.test_rust_tokenizer:
|
|
return
|
|
|
|
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_extended,
|
|
new_tokenizer.all_special_tokens_extended,
|
|
)
|
|
|
|
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:
|
|
return
|
|
|
|
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>"}
|
|
)
|
|
cls_id = new_tokenizer.get_vocab()["<cls>"]
|
|
self.assertEqual(new_tokenizer.cls_token, "<cls>")
|
|
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 = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
|
|
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, f"_{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, f"_{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_extended:
|
|
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_extended,
|
|
f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
|
|
)
|
|
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_extended:
|
|
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_extended}' as an AddedToken with the same parameters as "
|
|
f"'{special_token}' in the list {tokenizer.all_special_tokens_extended}",
|
|
)
|
|
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_extended,
|
|
f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
|
|
)
|
|
|
|
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_extended)
|
|
|
|
# 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__ == "LayoutXLMTokenizerFast":
|
|
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
|
|
return
|
|
|
|
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
|
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
|
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
|
|
pad_token_id = tokenizer_p.pad_token_id
|
|
|
|
words, boxes = self.get_words_and_boxes_batch()
|
|
|
|
input_r = tokenizer_r.batch_encode_plus(words, boxes=boxes)
|
|
input_p = tokenizer_r.batch_encode_plus(words, boxes=boxes)
|
|
|
|
# rename encoded batch to "inputs"
|
|
input_r["inputs"] = input_r[tokenizer_r.model_input_names[0]]
|
|
del input_r[tokenizer_r.model_input_names[0]]
|
|
|
|
input_p["inputs"] = input_p[tokenizer_p.model_input_names[0]]
|
|
del input_p[tokenizer_p.model_input_names[0]]
|
|
|
|
# Renaming `input_ids` to `inputs`
|
|
tokenizer_r.model_input_names = ["inputs"] + tokenizer_r.model_input_names[1:]
|
|
tokenizer_p.model_input_names = ["inputs"] + tokenizer_p.model_input_names[1:]
|
|
|
|
input_r = tokenizer_r.pad(input_r, padding="longest")
|
|
input_p = tokenizer_r.pad(input_p, padding="longest")
|
|
|
|
max_length = len(input_p["inputs"][0])
|
|
self.assert_batch_padded_input_match(
|
|
input_r, input_p, max_length, pad_token_id, model_main_input_name="inputs"
|
|
)
|
|
|
|
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__})"):
|
|
if is_torch_available():
|
|
returned_tensor = "pt"
|
|
elif is_tf_available():
|
|
returned_tensor = "tf"
|
|
else:
|
|
returned_tensor = "jax"
|
|
|
|
# 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
|
|
return
|
|
|
|
self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-layoutxlm", {})
|
|
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
|
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
|
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
|
|
tmpdirname2 = tempfile.mkdtemp()
|
|
|
|
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
|
|
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
|
|
|
|
# Checks it save with the same files + the tokenizer.json file for the fast one
|
|
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
|
|
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
|
|
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
|
|
|
|
# Checks everything loads correctly in the same way
|
|
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
|
|
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
|
|
|
|
# Check special tokens are set accordingly on Rust and Python
|
|
for key in tokenizer_pp.special_tokens_map:
|
|
self.assertTrue(hasattr(tokenizer_rp, key))
|
|
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
|
|
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
|
|
|
|
shutil.rmtree(tmpdirname2)
|
|
|
|
# Save tokenizer rust, legacy_format=True
|
|
tmpdirname2 = tempfile.mkdtemp()
|
|
|
|
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
|
|
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
|
|
|
|
# Checks it save with the same files
|
|
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
|
|
|
|
# Checks everything loads correctly in the same way
|
|
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
|
|
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
|
|
|
|
# Check special tokens are set accordingly on Rust and Python
|
|
for key in tokenizer_pp.special_tokens_map:
|
|
self.assertTrue(hasattr(tokenizer_rp, key))
|
|
|
|
shutil.rmtree(tmpdirname2)
|
|
|
|
# Save tokenizer rust, legacy_format=False
|
|
tmpdirname2 = tempfile.mkdtemp()
|
|
|
|
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
|
|
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
|
|
|
|
# Checks it saved the tokenizer.json file
|
|
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
|
|
|
|
# Checks everything loads correctly in the same way
|
|
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
|
|
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
|
|
|
|
# Check special tokens are set accordingly on Rust and Python
|
|
for key in tokenizer_pp.special_tokens_map:
|
|
self.assertTrue(hasattr(tokenizer_rp, key))
|
|
|
|
shutil.rmtree(tmpdirname2)
|
|
|
|
@unittest.skip("TO DO: overwrite this very extensive test.")
|
|
def test_alignement_methods(self):
|
|
pass
|
|
|
|
@unittest.skip("layoutxlm tokenizer requires boxes besides sequences.")
|
|
def test_maximum_encoding_length_pair_input(self):
|
|
pass
|
|
|
|
@unittest.skip("layoutxlm tokenizer requires boxes besides sequences.")
|
|
def test_maximum_encoding_length_single_input(self):
|
|
pass
|
|
|
|
@unittest.skip("layoutxlm tokenizer requires boxes besides sequences.")
|
|
def test_pretokenized_inputs(self):
|
|
pass
|
|
|
|
@unittest.skip("layoutxlm tokenizer always expects pretokenized inputs.")
|
|
def test_compare_pretokenized_inputs(self):
|
|
pass
|
|
|
|
@unittest.skip("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 slow tokenizer
|
|
tokenizer_p = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")
|
|
encoding = tokenizer_p(words, boxes=boxes, word_labels=word_labels)
|
|
self.assertListEqual(encoding.labels, [-100, 0, -100, 1, -100, -100])
|
|
|
|
tokenizer_p = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base", only_label_first_subword=False)
|
|
encoding = tokenizer_p(words, boxes=boxes, word_labels=word_labels)
|
|
self.assertListEqual(encoding.labels, [-100, 0, 0, 1, 1, -100])
|
|
|
|
# test fast tokenizer
|
|
tokenizer_r = LayoutXLMTokenizerFast.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_p = LayoutXLMTokenizer.from_pretrained("microsoft/layoutxlm-base")
|
|
tokenizer_r = LayoutXLMTokenizerFast.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_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20)
|
|
encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20)
|
|
self.assertDictEqual(dict(encoding_p), expected_results)
|
|
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_p = tokenizer_p(words, boxes=boxes, padding="max_length", max_length=20)
|
|
encoding_r = tokenizer_r(words, boxes=boxes, padding="max_length", max_length=20)
|
|
self.assertDictEqual(dict(encoding_p), expected_results)
|
|
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_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20)
|
|
encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20)
|
|
self.assertDictEqual(dict(encoding_p), expected_results)
|
|
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_p = tokenizer_p(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20)
|
|
encoding_r = tokenizer_r(words, boxes=boxes, word_labels=word_labels, padding="max_length", max_length=20)
|
|
self.assertDictEqual(dict(encoding_p), expected_results)
|
|
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_p = tokenizer_p(question, words, boxes, padding="max_length", max_length=20)
|
|
encoding_r = tokenizer_r(question, words, boxes, padding="max_length", max_length=20)
|
|
self.assertDictEqual(dict(encoding_p), expected_results)
|
|
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_p = tokenizer_p(questions, words, boxes, padding="max_length", max_length=20)
|
|
encoding_r = tokenizer_r(questions, words, boxes, padding="max_length", max_length=20)
|
|
self.assertDictEqual(dict(encoding_p), expected_results)
|
|
self.assertDictEqual(dict(encoding_r), expected_results)
|
|
|
|
@unittest.skip("Doesn't support another framework than PyTorch")
|
|
def test_np_encode_plus_sent_to_model(self):
|
|
pass
|
|
|
|
@unittest.skip("Doesn't use SentencePiece")
|
|
def test_sentencepiece_tokenize_and_convert_tokens_to_string(self):
|
|
pass
|
|
|
|
@unittest.skip("Doesn't use SentencePiece")
|
|
def test_sentencepiece_tokenize_and_decode(self):
|
|
pass
|
|
|
|
@unittest.skip("Chat is not supported")
|
|
def test_chat_template(self):
|
|
pass
|