Spaces:
Running
Running
| # 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 json | |
| import os | |
| import unittest | |
| from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| class CTRLTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = CTRLTokenizer | |
| test_rust_tokenizer = False | |
| test_seq2seq = False | |
| def setUp(self): | |
| super().setUp() | |
| # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt | |
| vocab = ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"] | |
| vocab_tokens = dict(zip(vocab, range(len(vocab)))) | |
| merges = ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</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: | |
| fp.write(json.dumps(vocab_tokens) + "\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 CTRLTokenizer.from_pretrained(self.tmpdirname, **kwargs) | |
| def get_input_output_texts(self, tokenizer): | |
| input_text = "adapt react readapt apt" | |
| output_text = "adapt react readapt apt" | |
| return input_text, output_text | |
| def test_full_tokenizer(self): | |
| tokenizer = CTRLTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map) | |
| text = "adapt react readapt apt" | |
| bpe_tokens = "adapt re@@ a@@ c@@ t re@@ adapt apt".split() | |
| tokens = tokenizer.tokenize(text) | |
| self.assertListEqual(tokens, bpe_tokens) | |
| input_tokens = tokens + [tokenizer.unk_token] | |
| input_bpe_tokens = [0, 1, 2, 4, 5, 1, 0, 3, 6] | |
| self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) | |