# 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 json import os import unittest from paddle.utils import try_import from paddlenlp.transformers import BlenderbotTokenizer from ..test_tokenizer_common import TokenizerTesterMixin VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", } class TestTokenizationBlenderbot(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = BlenderbotTokenizer test_rust_tokenizer = False test_offsets = False def setUp(self): super().setUp() vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "", "", "", "", "", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120low er", ""] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) self.special_tokens_map = { "bos_token": "", "eos_token": "", "cls_token": "", "sep_token": "", "unk_token": "", "pad_token": "", "mask_token": "", } with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5): toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))] # filter the english only character re = try_import("regex") if self.only_english_character: toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks)) toks = list( filter( lambda t: t[0] == tokenizer.encode(t[1], return_token_type_ids=None, add_special_tokens=False)["input_ids"][ 1 ], # first is add_prefix toks, ) ) if max_length is not None and len(toks) > max_length: toks = toks[:max_length] if min_length is not None and len(toks) < min_length and len(toks) > 0: while len(toks) < min_length: toks = toks + toks toks_ids = [t[0] for t in toks] # Ensure consistency output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False) if " " not in output_txt and len(toks_ids) > 1: output_txt = ( tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False) + " " + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False) ) if with_prefix_space: output_txt = " " + output_txt output_ids = tokenizer.encode(output_txt, return_token_type_ids=None, add_special_tokens=False)["input_ids"] return output_txt, output_ids def test_add_special_tokens(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): input_text, ids = self.get_clean_sequence(tokenizer) special_token = "[SPECIAL_TOKEN]" tokenizer.add_special_tokens({"cls_token": special_token}) encoded_special_token = tokenizer.encode( special_token, return_token_type_ids=None, add_special_tokens=False )["input_ids"] self.assertEqual(len(encoded_special_token), 1) text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False) encoded = tokenizer.encode(text, return_token_type_ids=None, add_special_tokens=False)["input_ids"] print(text) input_encoded = tokenizer.encode(input_text, return_token_type_ids=None, add_special_tokens=False)[ "input_ids" ] special_token_id = tokenizer.encode( special_token, return_token_type_ids=None, add_special_tokens=False )["input_ids"] # Each encoding adds a space token at the beginning of the sentence self.assertNotEqual(encoded, input_encoded + special_token_id) decoded = tokenizer.decode(encoded, skip_special_tokens=True) self.assertTrue(special_token not in decoded) def test_internal_consistency(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): input_text, output_text = self.get_input_output_texts(tokenizer) tokens = tokenizer.tokenize(input_text) ids = tokenizer.convert_tokens_to_ids(tokens) ids_2 = tokenizer.encode(input_text, return_token_type_ids=None, add_special_tokens=False)["input_ids"] self.assertListEqual(ids, ids_2) tokens_2 = tokenizer.convert_ids_to_tokens(ids) self.assertNotEqual(len(tokens_2), 0) text_2 = tokenizer.decode(ids) self.assertIsInstance(text_2, str) # Each encoding adds a space token self.assertEqual(text_2, " " + output_text) 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__}"): sequence_0 = "Encode this." # Testing single inputs encoded_sequence = tokenizer.encode(sequence_0, return_token_type_ids=None, add_special_tokens=False)[ "input_ids" ] encoded_sequence_dict = tokenizer.encode( sequence_0, add_special_tokens=True, return_special_tokens_mask=True # , add_prefix_space=False ) # Each encoding adds a space token at the beginning of the sentence encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = [0] + 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]] # Each encoding adds a eos token at the end of the sentence self.assertEqual(encoded_sequence, filtered_sequence[:-1]) def test_padding_to_multiple_of(self): # token_type_ids is shorter than input_ids 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: empty_tokens = tokenizer("", padding=True, pad_to_multiple_of=8) normal_tokens = tokenizer("This is a sample input", padding=True, pad_to_multiple_of=8) self.assertEqual(len(empty_tokens["input_ids"]) % 8, 0, "BatchEncoding is not multiple of 8") self.assertEqual(len(normal_tokens["input_ids"]) % 8, 0, "BatchEncoding is not multiple of 8") normal_tokens = tokenizer("This", pad_to_multiple_of=8) for key, value in normal_tokens.items(): self.assertNotEqual(len(value) % 8, 0, "BatchEncoding is not multiple of 8") # Should also work with truncation normal_tokens = tokenizer("This", padding=True, truncation=True, pad_to_multiple_of=8) self.assertEqual(len(normal_tokens["input_ids"]) % 8, 0, "BatchEncoding 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__, "This", padding=True, truncation=True, max_length=12, pad_to_multiple_of=8, ) def test_consecutive_unk_string(self): tokenizers = self.get_tokenizers(fast=True, do_lower_case=True) for tokenizer in tokenizers: tokens = [tokenizer.unk_token for _ in range(2)] string = tokenizer.convert_tokens_to_string(tokens) encoding = tokenizer( text=string, runcation=True, return_offsets_mapping=True, ) self.assertEqual(len(encoding["input_ids"]), 4) # offset_mapping is shorter than input_ids self.assertEqual(len(encoding["offset_mapping"]), 4 - 1) def test_add_tokens_tokenizer(self): 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.assertGreater(tokens[0], tokenizer.vocab_size - 1) self.assertGreater(tokens[-2], tokenizer.vocab_size - 1) new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"} added_toks_2 = tokenizer.add_special_tokens(new_toks_2) vocab_size_3 = tokenizer.vocab_size all_size_3 = len(tokenizer) self.assertNotEqual(vocab_size_3, 0) self.assertEqual(vocab_size, vocab_size_3) self.assertEqual(added_toks_2, len(new_toks_2)) self.assertEqual(all_size_3, all_size_2 + len(new_toks_2)) tokens = tokenizer.encode( ">>>>|||<||<<|<< aaaaa bbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", return_token_type_ids=None, add_special_tokens=False, )["input_ids"] self.assertGreaterEqual(len(tokens), 6) self.assertGreater(tokens[0], tokenizer.vocab_size - 1) self.assertGreater(tokens[0], tokens[1]) self.assertGreater(tokens[-2], tokenizer.vocab_size - 1) self.assertGreater(tokens[-2], tokens[-3]) self.assertEqual(tokens[0], tokenizer.eos_token_id) self.assertEqual(tokens[-2], tokenizer.pad_token_id) def test_special_tokens_mask_input_pairs(self): # token_ids_1 Will be ignored self.skipTest("token_ids_1 Will be ignored") def test_number_of_added_tokens(self): # token_ids_1 Will be ignored self.skipTest("token_ids_1 Will be ignored") def test_mask_output(self): # token_ids_1 Will be ignored self.skipTest("token_ids_1 Will be ignored") def test_maximum_encoding_length_pair_input(self): # token_ids_1 Will be ignored self.skipTest("token_ids_1 Will be ignored")