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# Copyright 2021 The HuggingFace Team. All rights reserved. | |
# | |
# 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 M2M100Tokenizer, is_torch_available | |
from transformers.testing_utils import ( | |
get_tests_dir, | |
nested_simplify, | |
require_sentencepiece, | |
require_tokenizers, | |
require_torch, | |
slow, | |
) | |
from transformers.utils import is_sentencepiece_available | |
if is_sentencepiece_available(): | |
from transformers.models.m2m_100.tokenization_m2m_100 import VOCAB_FILES_NAMES, save_json | |
from ...test_tokenization_common import TokenizerTesterMixin | |
if is_sentencepiece_available(): | |
SAMPLE_SP = get_tests_dir("fixtures/test_sentencepiece.model") | |
if is_torch_available(): | |
from transformers.models.m2m_100.modeling_m2m_100 import shift_tokens_right | |
EN_CODE = 128022 | |
FR_CODE = 128028 | |
class M2M100TokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
tokenizer_class = M2M100Tokenizer | |
test_rust_tokenizer = False | |
test_seq2seq = False | |
test_sentencepiece = True | |
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_file"]) | |
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists(): | |
copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"]) | |
tokenizer = M2M100Tokenizer.from_pretrained(self.tmpdirname) | |
tokenizer.save_pretrained(self.tmpdirname) | |
def get_tokenizer(self, **kwargs): | |
return M2M100Tokenizer.from_pretrained(self.tmpdirname, **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 = "</s>" | |
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): | |
tokenizer = self.get_tokenizer() | |
vocab_keys = list(tokenizer.get_vocab().keys()) | |
self.assertEqual(vocab_keys[0], "</s>") | |
self.assertEqual(vocab_keys[1], "<unk>") | |
self.assertEqual(vocab_keys[-1], "<s>") | |
self.assertEqual(len(vocab_keys), tokenizer.vocab_size + len(tokenizer.get_added_vocab())) | |
def test_pretrained_model_lists(self): | |
pass | |
def test_full_tokenizer(self): | |
tokenizer = self.get_tokenizer() | |
tokens = tokenizer.tokenize("This is a test") | |
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) | |
self.assertListEqual( | |
tokenizer.convert_tokens_to_ids(tokens), | |
[2, 3, 4, 5, 6], | |
) | |
back_tokens = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]) | |
self.assertListEqual(back_tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) | |
text = tokenizer.convert_tokens_to_string(tokens) | |
self.assertEqual(text, "This is a test") | |
def test_tokenizer_integration(self): | |
# fmt: off | |
expected_encoding = {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], '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, 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], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 | |
# fmt: on | |
self.tokenizer_integration_test_util( | |
expected_encoding=expected_encoding, | |
model_name="facebook/m2m100_418M", | |
revision="c168bae485c864188cf9aa0e4108b0b6934dc91e", | |
) | |
class M2M100TokenizerIntegrationTest(unittest.TestCase): | |
checkpoint_name = "facebook/m2m100_418M" | |
src_text = [ | |
"In my opinion, there are two levels of response from the French government.", | |
"NSA Affair Emphasizes Complete Lack of Debate on Intelligence", | |
] | |
tgt_text = [ | |
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.", | |
"L'affaire NSA souligne l'absence totale de débat sur le renseignement", | |
] | |
# fmt: off | |
expected_src_tokens = [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2] | |
# fmt: on | |
def setUpClass(cls): | |
cls.tokenizer: M2M100Tokenizer = M2M100Tokenizer.from_pretrained( | |
cls.checkpoint_name, src_lang="en", tgt_lang="fr" | |
) | |
cls.pad_token_id = 1 | |
return cls | |
def check_language_codes(self): | |
self.assertEqual(self.tokenizer.get_lang_id("ar"), 128006) | |
self.assertEqual(self.tokenizer.get_lang_id("en"), 128022) | |
self.assertEqual(self.tokenizer.get_lang_id("ro"), 128076) | |
self.assertEqual(self.tokenizer.get_lang_id("mr"), 128063) | |
def test_get_vocab(self): | |
vocab = self.tokenizer.get_vocab() | |
self.assertEqual(len(vocab), self.tokenizer.vocab_size) | |
self.assertEqual(vocab["<unk>"], 3) | |
self.assertIn(self.tokenizer.get_lang_token("en"), vocab) | |
def test_tokenizer_batch_encode_plus(self): | |
self.tokenizer.src_lang = "en" | |
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0] | |
self.assertListEqual(self.expected_src_tokens, ids) | |
def test_tokenizer_decode_ignores_language_codes(self): | |
self.assertIn(FR_CODE, self.tokenizer.all_special_ids) | |
# fmt: off | |
generated_ids = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2] | |
# fmt: on | |
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) | |
expected_french = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) | |
self.assertEqual(result, expected_french) | |
self.assertNotIn(self.tokenizer.eos_token, result) | |
def test_special_tokens_unaffacted_by_save_load(self): | |
tmpdirname = tempfile.mkdtemp() | |
original_special_tokens = self.tokenizer.lang_token_to_id | |
self.tokenizer.save_pretrained(tmpdirname) | |
new_tok = M2M100Tokenizer.from_pretrained(tmpdirname) | |
self.assertDictEqual(new_tok.lang_token_to_id, original_special_tokens) | |
def test_batch_fairseq_parity(self): | |
self.tokenizer.src_lang = "en" | |
self.tokenizer.tgt_lang = "fr" | |
batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt") | |
batch["decoder_input_ids"] = shift_tokens_right( | |
batch["labels"], self.tokenizer.pad_token_id, self.tokenizer.eos_token_id | |
) | |
for k in batch: | |
batch[k] = batch[k].tolist() | |
# batch = {k: v.tolist() for k,v in batch.items()} | |
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4 | |
# batch.decoder_inputs_ids[0][0] == | |
assert batch.input_ids[1][0] == EN_CODE | |
assert batch.input_ids[1][-1] == 2 | |
assert batch.labels[1][0] == FR_CODE | |
assert batch.labels[1][-1] == 2 | |
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE] | |
def test_src_lang_setter(self): | |
self.tokenizer.src_lang = "mr" | |
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("mr")]) | |
self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) | |
self.tokenizer.src_lang = "zh" | |
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("zh")]) | |
self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) | |
def test_tokenizer_target_mode(self): | |
self.tokenizer.tgt_lang = "mr" | |
self.tokenizer._switch_to_target_mode() | |
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("mr")]) | |
self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) | |
self.tokenizer._switch_to_input_mode() | |
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)]) | |
self.tokenizer.tgt_lang = "zh" | |
self.tokenizer._switch_to_target_mode() | |
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("zh")]) | |
self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id]) | |
self.tokenizer._switch_to_input_mode() | |
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)]) | |
def test_tokenizer_translation(self): | |
inputs = self.tokenizer._build_translation_inputs("A test", return_tensors="pt", src_lang="en", tgt_lang="ar") | |
self.assertEqual( | |
nested_simplify(inputs), | |
{ | |
# en_XX, A, test, EOS | |
"input_ids": [[128022, 58, 4183, 2]], | |
"attention_mask": [[1, 1, 1, 1]], | |
# ar_AR | |
"forced_bos_token_id": 128006, | |
}, | |
) | |