docs/transformers/tests/models/bertweet/test_tokenization_bertweet.py

# Copyright 2018 Salesforce and 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.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible


class BertweetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    from_pretrained_id = "vinai/bertweet-base"
    tokenizer_class = BertweetTokenizer
    test_rust_tokenizer = False

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

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        vocab = ["I", "m", "V@@", "R@@", "r", "e@@"]
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "a m</w>"]
        cls.special_tokens_map = {"unk_token": "<unk>"}

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

    @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 BertweetTokenizer.from_pretrained(pretrained_name, **kwargs)

    def get_input_output_texts(self, tokenizer):
        input_text = "I am VinAI Research"
        output_text = "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
        return input_text, output_text

    def test_full_tokenizer(self):
        tokenizer = BertweetTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
        text = "I am VinAI Research"
        bpe_tokens = "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
        tokens = tokenizer.tokenize(text)
        self.assertListEqual(tokens, bpe_tokens)

        input_tokens = tokens + [tokenizer.unk_token]

        input_bpe_tokens = [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)