# coding=utf-8 # Copyright 2019 Hugging Face inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest from transformers import AlbertTokenizer, AlbertTokenizerFast from transformers.testing_utils import require_sentencepiece, require_tokenizers, slow from .test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/spiece.model") @require_sentencepiece @require_tokenizers class AlbertTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = AlbertTokenizer rust_tokenizer_class = AlbertTokenizerFast test_rust_tokenizer = True test_sentencepiece = True test_sentencepiece_ignore_case = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = AlbertTokenizer(SAMPLE_VOCAB) tokenizer.save_pretrained(self.tmpdirname) def get_input_output_texts(self, tokenizer): input_text = "this is a test" output_text = "this is a test" return input_text, output_text def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: return tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer() sequence = "I was born in 92000, and this is falsé." tokens = tokenizer.tokenize(sequence) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) ids = tokenizer.encode(sequence, add_special_tokens=False) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) rust_tokenizer = self.get_rust_tokenizer() ids = tokenizer.encode(sequence) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) def test_full_tokenizer(self): tokenizer = AlbertTokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁this", "▁is", "▁a", "▁test"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [48, 25, 21, 1289]) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."] ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual(ids, [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "", "."], ) def test_sequence_builders(self): tokenizer = AlbertTokenizer(SAMPLE_VOCAB) text = tokenizer.encode("sequence builders") text_2 = tokenizer.encode("multi-sequence build") encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [ tokenizer.sep_token_id ] @slow def test_tokenizer_integration(self): tokenizer_classes = [self.tokenizer_class] if self.test_rust_tokenizer: tokenizer_classes.append(self.rust_tokenizer_class) for tokenizer_class in tokenizer_classes: tokenizer = tokenizer_class.from_pretrained("albert-base-v2") sequences = [ "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations", "ALBERT incorporates two parameter reduction techniques", "The first one is a factorized embedding parameterization. By decomposing the large vocabulary embedding matrix into two small matrices, we separate the size of the hidden layers from the size of vocabulary embedding.", # noqa: E231 ] encoding = tokenizer(sequences, padding=True) decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]] # fmt: off expected_encoding = { 'input_ids': [ [2, 2953, 45, 21, 13, 10601, 11502, 26, 1119, 8, 8542, 3762, 69, 2477, 16, 816, 18667, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231 [2, 2953, 13760, 81, 18906, 5895, 4212, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231 [2, 14, 64, 53, 25, 21, 3932, 1333, 11911, 69, 3258, 18906, 1829, 9, 34, 121, 960, 14717, 14, 370, 18630, 11911, 69, 3258, 8187, 77, 81, 284, 24849, 15, 95, 1725, 14, 1072, 16, 14, 3689, 9124, 37, 14, 1072, 16, 18630, 11911, 69, 3258, 9, 3]], # noqa: E231 'token_type_ids': [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # noqa: E231 'attention_mask': [ [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231 [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # noqa: E231 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] # noqa: E231 ] } expected_decoded_sequence = [ "albert: a lite bert for self-supervised learning of language representations", 'albert incorporates two parameter reduction techniques', 'the first one is a factorized embedding parameterization. by decomposing the large vocabulary embedding matrix into two small matrices, we separate the size of the hidden layers from the size of vocabulary embedding.' # noqa: E231 ] # fmt: on self.assertDictEqual(encoding.data, expected_encoding) for expected, decoded in zip(expected_decoded_sequence, decoded_sequences): self.assertEqual(expected, decoded)