# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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 concurrent.futures
import json
import os
import shutil
import tempfile
import unittest
from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers
from ..test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class PreTrainedTokenizationFastTest(TokenizerTesterMixin, unittest.TestCase):
rust_tokenizer_class = PreTrainedTokenizerFast
test_slow_tokenizer = False
test_rust_tokenizer = True
from_pretrained_vocab_key = "tokenizer_file"
def setUp(self):
self.test_rust_tokenizer = False # because we don't have pretrained_vocab_files_map
super().setUp()
self.test_rust_tokenizer = True
model_paths = ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
self.bytelevel_bpe_model_name = "SaulLu/dummy-tokenizer-bytelevel-bpe"
# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
self.tokenizers_list = [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]
tokenizer = PreTrainedTokenizerFast.from_pretrained(model_paths[0])
tokenizer.save_pretrained(self.tmpdirname)
def test_tokenizer_mismatch_warning(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
def test_pretrained_model_lists(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
def test_prepare_for_model(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
def test_rust_tokenizer_signature(self):
# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
pass
def test_training_new_tokenizer(self):
tmpdirname_orig = self.tmpdirname
# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
try:
self.tmpdirname = tempfile.mkdtemp()
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer.save_pretrained(self.tmpdirname)
super().test_training_new_tokenizer()
finally:
# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
# is restored
shutil.rmtree(self.tmpdirname)
self.tmpdirname = tmpdirname_orig
def test_training_new_tokenizer_with_special_tokens_change(self):
tmpdirname_orig = self.tmpdirname
# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
try:
self.tmpdirname = tempfile.mkdtemp()
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer.save_pretrained(self.tmpdirname)
super().test_training_new_tokenizer_with_special_tokens_change()
finally:
# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
# is restored
shutil.rmtree(self.tmpdirname)
self.tmpdirname = tmpdirname_orig
def test_training_new_tokenizer_with_bytelevel(self):
tokenizer = self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name)
toy_text_iterator = ("a" for _ in range(1000))
new_tokenizer = tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
encoding_ids = new_tokenizer.encode("a🤗")
self.assertEqual(encoding_ids, [64, 172, 253, 97, 245])
@require_tokenizers
class TokenizerVersioningTest(unittest.TestCase):
def test_local_versioning(self):
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
json_tokenizer["model"]["vocab"]["huggingface"] = len(tokenizer)
with tempfile.TemporaryDirectory() as tmp_dir:
# Hack to save this in the tokenizer_config.json
tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.4.0.0.json"]
tokenizer.save_pretrained(tmp_dir)
json.dump(json_tokenizer, open(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), "w"))
# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)
self.assertEqual(len(new_tokenizer), len(tokenizer) + 1)
json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str())
self.assertIn("huggingface", json_tokenizer["model"]["vocab"])
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
shutil.move(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), os.path.join(tmp_dir, "tokenizer.42.0.0.json"))
tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.42.0.0.json"]
tokenizer.save_pretrained(tmp_dir)
new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)
self.assertEqual(len(new_tokenizer), len(tokenizer))
json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str())
self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"])
def test_repo_versioning(self):
# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
repo = "hf-internal-testing/test-two-tokenizers"
# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
tokenizer = AutoTokenizer.from_pretrained(repo)
self.assertEqual(len(tokenizer), 28997)
json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
self.assertIn("huggingface", json_tokenizer["model"]["vocab"])
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
old_transformers.tokenization_utils_base.__version__ = "3.0.0"
old_tokenizer = old_transformers.models.auto.AutoTokenizer.from_pretrained(repo)
self.assertEqual(len(old_tokenizer), 28996)
json_tokenizer = json.loads(old_tokenizer._tokenizer.to_str())
self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"])
@require_tokenizers
class ReduceMutableBorrowTests(unittest.TestCase):
def test_async_share_tokenizer(self):
# See https://github.com/huggingface/transformers/pull/12550
# and https://github.com/huggingface/tokenizers/issues/537
tokenizer = PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel")
text = "The Matrix is a 1999 science fiction action film."
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.fetch, tokenizer, text) for i in range(10)]
return_value = [future.result() for future in futures]
self.assertEqual(return_value, [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10)])
def fetch(self, tokenizer, text):
return tokenizer.encode(text, truncation="longest_first", padding="longest")