95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
# coding=utf-8
|
|
# Copyright 2020 Huggingface
|
|
#
|
|
# 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 tempfile
|
|
import unittest
|
|
from pathlib import Path
|
|
from shutil import copyfile
|
|
|
|
from transformers import BatchEncoding, MarianTokenizer
|
|
from transformers.testing_utils import _sentencepiece_available, _torch_available, require_sentencepiece
|
|
|
|
|
|
if _sentencepiece_available:
|
|
from transformers.tokenization_marian import save_json, vocab_files_names
|
|
|
|
from .test_tokenization_common import TokenizerTesterMixin
|
|
|
|
|
|
SAMPLE_SP = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
|
|
|
|
mock_tokenizer_config = {"target_lang": "fi", "source_lang": "en"}
|
|
zh_code = ">>zh<<"
|
|
ORG_NAME = "Helsinki-NLP/"
|
|
FRAMEWORK = "pt" if _torch_available else "tf"
|
|
|
|
|
|
@require_sentencepiece
|
|
class MarianTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
|
|
|
tokenizer_class = MarianTokenizer
|
|
test_rust_tokenizer = False
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
vocab = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
|
|
vocab_tokens = dict(zip(vocab, range(len(vocab))))
|
|
save_dir = Path(self.tmpdirname)
|
|
save_json(vocab_tokens, save_dir / vocab_files_names["vocab"])
|
|
save_json(mock_tokenizer_config, save_dir / vocab_files_names["tokenizer_config_file"])
|
|
if not (save_dir / vocab_files_names["source_spm"]).exists():
|
|
copyfile(SAMPLE_SP, save_dir / vocab_files_names["source_spm"])
|
|
copyfile(SAMPLE_SP, save_dir / vocab_files_names["target_spm"])
|
|
|
|
tokenizer = MarianTokenizer.from_pretrained(self.tmpdirname)
|
|
tokenizer.save_pretrained(self.tmpdirname)
|
|
|
|
def get_tokenizer(self, **kwargs) -> MarianTokenizer:
|
|
return MarianTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
|
|
|
def get_input_output_texts(self, tokenizer):
|
|
return (
|
|
"This is a test",
|
|
"This is a test",
|
|
)
|
|
|
|
def test_tokenizer_equivalence_en_de(self):
|
|
en_de_tokenizer = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de")
|
|
batch = en_de_tokenizer.prepare_seq2seq_batch(["I am a small frog"], return_tensors=None)
|
|
self.assertIsInstance(batch, BatchEncoding)
|
|
expected = [38, 121, 14, 697, 38848, 0]
|
|
self.assertListEqual(expected, batch.input_ids[0])
|
|
|
|
save_dir = tempfile.mkdtemp()
|
|
en_de_tokenizer.save_pretrained(save_dir)
|
|
contents = [x.name for x in Path(save_dir).glob("*")]
|
|
self.assertIn("source.spm", contents)
|
|
MarianTokenizer.from_pretrained(save_dir)
|
|
|
|
def test_outputs_not_longer_than_maxlen(self):
|
|
tok = self.get_tokenizer()
|
|
|
|
batch = tok.prepare_seq2seq_batch(["I am a small frog" * 1000, "I am a small frog"], return_tensors=FRAMEWORK)
|
|
self.assertIsInstance(batch, BatchEncoding)
|
|
self.assertEqual(batch.input_ids.shape, (2, 512))
|
|
|
|
def test_outputs_can_be_shorter(self):
|
|
tok = self.get_tokenizer()
|
|
batch_smaller = tok.prepare_seq2seq_batch(["I am a tiny frog", "I am a small frog"], return_tensors=FRAMEWORK)
|
|
self.assertIsInstance(batch_smaller, BatchEncoding)
|
|
self.assertEqual(batch_smaller.input_ids.shape, (2, 10))
|