# 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 DebertaV2Tokenizer from transformers.testing_utils import require_sentencepiece, require_tokenizers 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 DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = DebertaV2Tokenizer rust_tokenizer_class = None test_rust_tokenizer = False test_sentencepiece = True test_sentencepiece_ignore_case = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = DebertaV2Tokenizer(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 = DebertaV2Tokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁", "[UNK]", "his", "▁is", "▁a", "▁test"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [13, 1, 4398, 25, 21, 1289]) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") # fmt: off self.assertListEqual( tokens, ["▁", "[UNK]", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "[UNK]", "."], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual(ids, [13, 1, 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, ["▁", "", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "", "."], ) # fmt: on def test_sequence_builders(self): tokenizer = DebertaV2Tokenizer(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 ] 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("microsoft/deberta-xlarge-v2") sequences = [ [ "DeBERTa: Decoding-enhanced BERT with Disentangled Attention", "DeBERTa: Decoding-enhanced BERT with Disentangled Attention", ], [ "Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.", "DeBERTa: Decoding-enhanced BERT with Disentangled Attention", ], [ "In this paper we propose a new model architecture DeBERTa", "DeBERTa: Decoding-enhanced BERT with Disentangled Attention", ], ] 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': [ [1, 1804, 69418, 191, 43, 117056, 18, 44596, 448, 37132, 19, 8655, 10625, 69860, 21149, 2, 1804, 69418, 191, 43, 117056, 18, 44596, 448, 37132, 19, 8655, 10625, 69860, 21149, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 9755, 1944, 11, 1053, 18, 16899, 12730, 1072, 1506, 45, 2497, 2510, 5, 610, 9, 127, 699, 1072, 2101, 36, 99388, 53, 2930, 4, 2, 1804, 69418, 191, 43, 117056, 18, 44596, 448, 37132, 19, 8655, 10625, 69860, 21149, 2], [1, 84, 32, 778, 42, 9441, 10, 94, 735, 3372, 1804, 69418, 191, 2, 1804, 69418, 191, 43, 117056, 18, 44596, 448, 37132, 19, 8655, 10625, 69860, 21149, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [ [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 1, 1, 1, 1, 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] ] } expected_decoded_sequences = [ 'DeBERTa: Decoding-enhanced BERT with Disentangled Attention DeBERTa: Decoding-enhanced BERT with Disentangled Attention', 'Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. DeBERTa: Decoding-enhanced BERT with Disentangled Attention', 'In this paper we propose a new model architecture DeBERTa DeBERTa: Decoding-enhanced BERT with Disentangled Attention' ] # fmt: on self.assertDictEqual(encoding.data, expected_encoding) for expected, decoded in zip(expected_decoded_sequences, decoded_sequences): self.assertEqual(expected, decoded)