docs/transformers/tests/models/bartpho/test_tokenization_bartpho.py

# Copyright 2021 HuggingFace Inc. team.
# 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 functools import lru_cache

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible


SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe.model")


class BartphoTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
    from_pretrained_id = "vinai/bartpho-syllable"
    tokenizer_class = BartphoTokenizer
    test_rust_tokenizer = False
    test_sentencepiece = True

    @classmethod
    def setUpClass(cls):
        super().setUpClass()

        vocab = ["▁This", "▁is", "▁a", "▁t", "est"]
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        cls.special_tokens_map = {"unk_token": "<unk>"}

        cls.monolingual_vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["monolingual_vocab_file"])
        with open(cls.monolingual_vocab_file, "w", encoding="utf-8") as fp:
            for token in vocab_tokens:
                fp.write(f"{token} {vocab_tokens[token]}\n")

        tokenizer = BartphoTokenizer(SAMPLE_VOCAB, cls.monolingual_vocab_file, **cls.special_tokens_map)
        tokenizer.save_pretrained(cls.tmpdirname)

    @classmethod
    @use_cache_if_possible
    @lru_cache(maxsize=64)
    def get_tokenizer(cls, pretrained_name=None, **kwargs):
        kwargs.update(cls.special_tokens_map)
        pretrained_name = pretrained_name or cls.tmpdirname
        return BartphoTokenizer.from_pretrained(pretrained_name, **kwargs)

    def get_input_output_texts(self, tokenizer):
        input_text = "This is a là test"
        output_text = "This is a<unk><unk> test"
        return input_text, output_text

    def test_full_tokenizer(self):
        tokenizer = BartphoTokenizer(SAMPLE_VOCAB, self.monolingual_vocab_file, **self.special_tokens_map)
        text = "This is a là test"
        bpe_tokens = "▁This ▁is ▁a ▁l à ▁t est".split()
        tokens = tokenizer.tokenize(text)
        self.assertListEqual(tokens, bpe_tokens)

        input_tokens = tokens + [tokenizer.unk_token]
        input_bpe_tokens = [4, 5, 6, 3, 3, 7, 8, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)