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| # coding=utf-8 | |
| # Copyright 2022 The OpenBMB Team and The 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.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer | |
| from transformers.testing_utils import require_jieba, tooslow | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| class CPMAntTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = CpmAntTokenizer | |
| test_rust_tokenizer = False | |
| def setUp(self): | |
| super().setUp() | |
| vocab_tokens = [ | |
| "<d>", | |
| "</d>", | |
| "<s>", | |
| "</s>", | |
| "</_>", | |
| "<unk>", | |
| "<pad>", | |
| "</n>", | |
| "我", | |
| "是", | |
| "C", | |
| "P", | |
| "M", | |
| "A", | |
| "n", | |
| "t", | |
| ] | |
| self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) | |
| with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: | |
| vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) | |
| def test_pre_tokenization(self): | |
| tokenizer = CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b") | |
| texts = "今天天气真好!" | |
| jieba_tokens = ["今天", "天气", "真", "好", "!"] | |
| tokens = tokenizer.tokenize(texts) | |
| self.assertListEqual(tokens, jieba_tokens) | |
| normalized_text = "今天天气真好!" | |
| input_tokens = [tokenizer.bos_token] + tokens | |
| input_jieba_tokens = [6, 9802, 14962, 2082, 831, 244] | |
| self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_jieba_tokens) | |
| reconstructed_text = tokenizer.decode(input_jieba_tokens) | |
| self.assertEqual(reconstructed_text, normalized_text) | |