# 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 tempfile import unittest from pathlib import Path from shutil import copyfile from transformers import BatchEncoding, MarianTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow from transformers.utils import is_sentencepiece_available if is_sentencepiece_available(): from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_SP = get_tests_dir("fixtures/test_sentencepiece.model") mock_tokenizer_config = {"target_lang": "fi", "source_lang": "en"} zh_code = ">>zh<<" ORG_NAME = "Helsinki-NLP/" @require_sentencepiece class MarianTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "Helsinki-NLP/opus-mt-en-de" tokenizer_class = MarianTokenizer test_rust_tokenizer = False test_sentencepiece = True @classmethod def setUpClass(cls): super().setUpClass() vocab = ["", "", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", ""] vocab_tokens = dict(zip(vocab, range(len(vocab)))) save_dir = Path(cls.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(cls.tmpdirname) tokenizer.save_pretrained(cls.tmpdirname) @classmethod def get_tokenizer(cls, pretrained_name=None, **kwargs) -> MarianTokenizer: pretrained_name = pretrained_name or cls.tmpdirname return MarianTokenizer.from_pretrained(pretrained_name, **kwargs) def get_input_output_texts(self, tokenizer): return ( "This is a test", "This is a test", ) def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "" token_id = 0 self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "") self.assertEqual(vocab_keys[1], "") self.assertEqual(vocab_keys[-1], "") self.assertEqual(len(vocab_keys), 9) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 9) def test_tokenizer_equivalence_en_de(self): en_de_tokenizer = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de") batch = en_de_tokenizer(["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( ["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors="pt" ) 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(["I am a tiny frog", "I am a small frog"], padding=True, return_tensors="pt") self.assertIsInstance(batch_smaller, BatchEncoding) self.assertEqual(batch_smaller.input_ids.shape, (2, 6)) @slow def test_tokenizer_integration(self): expected_encoding = {'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="Helsinki-NLP/opus-mt-en-de", revision="1a8c2263da11e68e50938f97e10cd57820bd504c", decode_kwargs={"use_source_tokenizer": True}, ) def test_tokenizer_integration_separate_vocabs(self): tokenizer = MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs") source_text = "Tämä on testi" target_text = "This is a test" expected_src_ids = [76, 7, 2047, 2] expected_target_ids = [69, 12, 11, 940, 2] src_ids = tokenizer(source_text).input_ids self.assertListEqual(src_ids, expected_src_ids) target_ids = tokenizer(text_target=target_text).input_ids self.assertListEqual(target_ids, expected_target_ids) decoded = tokenizer.decode(target_ids, skip_special_tokens=True) self.assertEqual(decoded, target_text) def test_tokenizer_decode(self): tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-es") source_text = "Hello World" ids = tokenizer(source_text)["input_ids"] output_text = tokenizer.decode(ids, skip_special_tokens=True) self.assertEqual(source_text, output_text) def test_internal_consistency(self): self.skipTest("TODO: failing for v5")