mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-23 06:20:22 +06:00
80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
# coding=utf-8
|
|
# Copyright 2018 The Google AI Language Team Authors.
|
|
#
|
|
# 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.
|
|
from __future__ import absolute_import, division, print_function, unicode_literals
|
|
|
|
import os
|
|
import unittest
|
|
import json
|
|
import shutil
|
|
import pytest
|
|
|
|
from pytorch_pretrained_bert.tokenization_openai import OpenAIGPTTokenizer, PRETRAINED_VOCAB_ARCHIVE_MAP
|
|
|
|
|
|
class OpenAIGPTTokenizationTest(unittest.TestCase):
|
|
|
|
def test_full_tokenizer(self):
|
|
""" Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
|
|
vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
|
|
"w</w>", "r</w>", "t</w>",
|
|
"lo", "low", "er</w>",
|
|
"low</w>", "lowest</w>", "newer</w>", "wider</w>"]
|
|
vocab_tokens = dict(zip(vocab, range(len(vocab))))
|
|
merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
|
|
with open("/tmp/openai_tokenizer_vocab_test.json", "w") as fp:
|
|
fp.write(json.dumps(vocab_tokens))
|
|
vocab_file = fp.name
|
|
with open("/tmp/openai_tokenizer_merges_test.txt", "w") as fp:
|
|
fp.write("\n".join(merges))
|
|
merges_file = fp.name
|
|
|
|
tokenizer = OpenAIGPTTokenizer(vocab_file, merges_file, special_tokens=["<unk>", "<pad>"])
|
|
os.remove(vocab_file)
|
|
os.remove(merges_file)
|
|
|
|
text = "lower"
|
|
bpe_tokens = ["low", "er</w>"]
|
|
tokens = tokenizer.tokenize(text)
|
|
self.assertListEqual(tokens, bpe_tokens)
|
|
|
|
input_tokens = tokens + ["<unk>"]
|
|
input_bpe_tokens = [14, 15, 20]
|
|
self.assertListEqual(
|
|
tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
|
|
|
|
vocab_file, merges_file, special_tokens_file = tokenizer.save_vocabulary(vocab_path="/tmp/")
|
|
tokenizer_2 = OpenAIGPTTokenizer.from_pretrained("/tmp/")
|
|
os.remove(vocab_file)
|
|
os.remove(merges_file)
|
|
os.remove(special_tokens_file)
|
|
|
|
self.assertListEqual(
|
|
[tokenizer.encoder, tokenizer.decoder, tokenizer.bpe_ranks,
|
|
tokenizer.special_tokens, tokenizer.special_tokens_decoder],
|
|
[tokenizer_2.encoder, tokenizer_2.decoder, tokenizer_2.bpe_ranks,
|
|
tokenizer_2.special_tokens, tokenizer_2.special_tokens_decoder])
|
|
|
|
@pytest.mark.slow
|
|
def test_tokenizer_from_pretrained(self):
|
|
cache_dir = "/tmp/pytorch_pretrained_bert_test/"
|
|
for model_name in list(PRETRAINED_VOCAB_ARCHIVE_MAP.keys())[:1]:
|
|
tokenizer = OpenAIGPTTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
|
|
shutil.rmtree(cache_dir)
|
|
self.assertIsNotNone(tokenizer)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|