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| # coding=utf-8 | |
| # 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 transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| class BertweetTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = BertweetTokenizer | |
| test_rust_tokenizer = False | |
| def setUp(self): | |
| super().setUp() | |
| # 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>"] | |
| self.special_tokens_map = {"unk_token": "<unk>"} | |
| 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"]) | |
| with open(self.vocab_file, "w", encoding="utf-8") as fp: | |
| for token in vocab_tokens: | |
| fp.write(f"{token} {vocab_tokens[token]}\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 BertweetTokenizer.from_pretrained(self.tmpdirname, **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) | |