# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # Copyright 2020 The HuggingFace Team. All rights reserved. # # 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 shutil import tempfile import unittest import warnings from paddlenlp.transformers import ProphetNetTokenizer from ..test_tokenizer_common import TokenizerTesterMixin VOCAB_FILES_NAMES = { "vocab_file": "vocab.txt", } class TestTokenizationProphetNet(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = ProphetNetTokenizer test_rust_tokenizer = False test_offsets = False def setUp(self): super().setUp() vocab = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.special_tokens_map = { "unk_token": "[UNK]", "sep_token": "[SEP]", "bos_token": "[SEP]", "eos_token": "[SEP]", "cls_token": "[CLS]", "x_sep_token": "[X_SEP]", "pad_token": "[PAD]", "mask_token": "[MASK]", } self.vocab_file = os.path.join(self.tmpdirname, ProphetNetTokenizer.resource_files_names["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def test_save_and_load_tokenizer(self): warnings.warn("Every addtoken not in vocab is unk_token") 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 tmpdirname = tempfile.mkdtemp() sample_text = " He is very happy, UNwant\u00E9d,running" before_tokens = tokenizer.encode(sample_text, 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(sample_text, add_special_tokens=False) after_vocab = after_tokenizer.get_vocab() self.assertListEqual(before_tokens["input_ids"], after_tokens["input_ids"]) self.assertDictEqual(before_vocab, after_vocab) shutil.rmtree(tmpdirname) def test_add_tokens_tokenizer(self): warnings.warn("Every token not in vocab is unk_token") tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): vocab_size = tokenizer.vocab_size all_size = len(tokenizer) self.assertNotEqual(vocab_size, 0) # We usually have added tokens from the start in tests because our vocab fixtures are # smaller than the original vocabs - let's not assert this # self.assertEqual(vocab_size, all_size) new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"] added_toks = tokenizer.add_tokens(new_toks) vocab_size_2 = tokenizer.vocab_size all_size_2 = len(tokenizer) self.assertNotEqual(vocab_size_2, 0) self.assertEqual(vocab_size, vocab_size_2) self.assertEqual(added_toks, len(new_toks)) self.assertEqual(all_size_2, all_size + len(new_toks)) tokens = tokenizer.encode( "aaaaa bbbbbb low cccccccccdddddddd l", return_token_type_ids=None, add_special_tokens=False )["input_ids"] self.assertGreaterEqual(len(tokens), 4) self.assertEqual(tokens[0], tokenizer.unk_token_id) self.assertEqual(tokens[0], tokenizer.unk_token_id) new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"} added_toks_2 = tokenizer.add_special_tokens(new_toks_2) vocab_size_3 = tokenizer.vocab_size all_size_3 = len(tokenizer) self.assertNotEqual(vocab_size_3, 0) self.assertEqual(vocab_size, vocab_size_3) self.assertEqual(added_toks_2, len(new_toks_2)) self.assertEqual(all_size_3, all_size_2 + len(new_toks_2)) tokens = tokenizer.encode( ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", return_token_type_ids=None, add_special_tokens=False, )["input_ids"] self.assertGreaterEqual(len(tokens), 6) self.assertEqual(tokens[0], tokenizer.unk_token_id) self.assertEqual(tokens[0], tokenizer.eos_token_id) self.assertEqual(tokens[-2], tokenizer.pad_token_id) def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def test_encode_decode_with_spaces(self): self.skipTest("Every token not in vocab is unk_token") def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self): self.skipTest("Every token not in vocab is unk_token") def test_consecutive_unk_string(self): self.skipTest("Every token not in vocab is unk_token") def test_pretokenized_inputs(self): self.skipTest("tokenizer is_split_into_words not implement yet")