File size: 3,430 Bytes
326a7fe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
#!/usr/bin/env python3
# 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.
"""Tests for Blenderbot Tokenizers, including common tests for BlenderbotSmallTokenizer."""

import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.testing_utils import require_jinja
from transformers.utils import cached_property


class Blenderbot3BTokenizerTests(unittest.TestCase):
    @cached_property
    def tokenizer_3b(self):
        return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")

    @cached_property
    def rust_tokenizer_3b(self):
        return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B")

    def test_encode_decode_cycle(self):
        tok = self.tokenizer_3b
        src_text = " I am a small frog."
        encoded = tok([src_text], padding=False, truncation=False)["input_ids"]
        decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        assert src_text == decoded

    def test_encode_decode_cycle_rust_tokenizer(self):
        tok = self.rust_tokenizer_3b
        src_text = " I am a small frog."
        encoded = tok([src_text], padding=False, truncation=False)["input_ids"]
        decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        assert src_text == decoded

    def test_3B_tokenization_same_as_parlai(self):
        assert self.tokenizer_3b.add_prefix_space
        assert self.tokenizer_3b([" Sam", "Sam"]).input_ids == [[5502, 2], [5502, 2]]

    def test_3B_tokenization_same_as_parlai_rust_tokenizer(self):
        assert self.rust_tokenizer_3b.add_prefix_space
        assert self.rust_tokenizer_3b([" Sam", "Sam"]).input_ids == [[5502, 2], [5502, 2]]

    @require_jinja
    def test_tokenization_for_chat(self):
        tok = self.tokenizer_3b
        test_chats = [
            [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}],
            [
                {"role": "system", "content": "You are a helpful chatbot."},
                {"role": "user", "content": "Hello!"},
                {"role": "assistant", "content": "Nice to meet you."},
            ],
            [{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}],
        ]
        tokenized_chats = [tok.apply_chat_template(test_chat) for test_chat in test_chats]
        expected_tokens = [
            [553, 366, 265, 4792, 3879, 73, 311, 21, 228, 228, 6950, 8, 2],
            [553, 366, 265, 4792, 3879, 73, 311, 21, 228, 228, 6950, 8, 228, 3490, 287, 2273, 304, 21, 2],
            [3490, 287, 2273, 304, 21, 228, 228, 6950, 8, 2],
        ]
        for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens):
            self.assertListEqual(tokenized_chat, expected_tokens)