# Copyright 2022 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 transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, ) from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): from transformers.models.plbart.modeling_plbart import shift_tokens_right EN_CODE = 50003 PYTHON_CODE = 50002 @require_sentencepiece @require_tokenizers class PLBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "uclanlp/plbart-base" tokenizer_class = PLBartTokenizer test_tokenizer_from_extractor = False test_sentencepiece = True @classmethod def setUpClass(cls): super().setUpClass() tokenizer = PLBartTokenizer(SAMPLE_VOCAB, language_codes="base") tokenizer.save_pretrained(cls.tmpdirname) # Integration test data - expected outputs for the default input string integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁b', 'orn', '▁in', '▁92', '000,', '▁and', '▁this', '▁is', '▁f', 'als', 'é', '.', '▁', '生', '活', '的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '', '▁hi', '', '▁there', '▁The', '▁following', '▁string', '▁should', '▁be', '▁properly', '▁encoded', ':', '▁Hello', '.', '▁But', '▁ir', 'd', '▁and', '▁', 'ป', 'ี', '▁ir', 'd', '▁', 'ด', '▁Hey', '▁how', '▁are', '▁you', '▁doing'] # fmt: skip integration_expected_token_ids = [670, 96, 14, 242, 33438, 39172, 34, 880, 56, 6309, 55, 26431, 9478, 135, 143, 96, 33, 3875, 33537, 33455, 33438, 33859, 34721, 33590, 34984, 3, 33720, 9434, 4536, 9434, 4536, 4536, 0, 8039, 0, 656, 418, 987, 625, 595, 229, 3914, 5158, 33475, 4536, 33455, 1160, 4042, 33448, 135, 33438, 34693, 34205, 4042, 33448, 33438, 34410, 22256, 654, 395, 144, 1777] # fmt: skip expected_tokens_from_ids = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁b', 'orn', '▁in', '▁92', '000,', '▁and', '▁this', '▁is', '▁f', 'als', 'é', '.', '▁', '生', '活', '的', '真', '', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '', '▁hi', '', '▁there', '▁The', '▁following', '▁string', '▁should', '▁be', '▁properly', '▁encoded', ':', '▁Hello', '.', '▁But', '▁ir', 'd', '▁and', '▁', 'ป', 'ี', '▁ir', 'd', '▁', 'ด', '▁Hey', '▁how', '▁are', '▁you', '▁doing'] # fmt: skip integration_expected_decoded_text = "This is a test 😊 I was born in 92000, and this is falsé. 生活的真是 Hi Hello Hi Hello Hello hi there The following string should be properly encoded: Hello. But ird and ปี ird ด Hey how are you doing" def test_full_base_tokenizer(self): tokenizer = PLBartTokenizer(vocab_file=SAMPLE_VOCAB, language_codes="base") tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, [ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4] ], ) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "", ".", ], ) end = tokenizer.vocab_size language_tokens = [tokenizer.convert_ids_to_tokens(x) for x in range(end - 4, end)] self.assertListEqual(language_tokens, ["__java__", "__python__", "__en_XX__", ""]) code = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go" input_ids = tokenizer(code).input_ids self.assertEqual( tokenizer.decode(input_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False), code, ) def test_full_multi_tokenizer(self): tokenizer = PLBartTokenizer(vocab_file=SAMPLE_VOCAB, language_codes="multi") tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, [ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4] ], ) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "", ".", ], ) end = tokenizer.vocab_size language_tokens = [tokenizer.convert_ids_to_tokens(x) for x in range(end - 7, end)] self.assertListEqual( language_tokens, ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"] ) code = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go" input_ids = tokenizer(code).input_ids self.assertEqual( tokenizer.decode(input_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False), code, ) @require_torch @require_sentencepiece @require_tokenizers class PLBartPythonEnIntegrationTest(unittest.TestCase): checkpoint_name = "uclanlp/plbart-python-en_XX" src_text = [ "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])", "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])", ] tgt_text = [ "Returns the maximum value of a b c.", "Sums the values of a b c.", ] expected_src_tokens = [ 134, 5452, 33460, 33441, 33463, 33465, 33463, 33449, 988, 20, 33456, 19, 33456, 771, 39, 4258, 889, 3318, 33441, 33463, 33465, 33463, 33449, 2471, 2, PYTHON_CODE, ] @classmethod def setUpClass(cls): cls.tokenizer: PLBartTokenizer = PLBartTokenizer.from_pretrained( cls.checkpoint_name, language_codes="base", src_lang="python", tgt_lang="en_XX" ) cls.pad_token_id = 1 return cls def check_language_codes(self): self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"], 50001) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"], 50002) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"], 50003) def test_python_en_tokenizer_batch_encode_plus(self): ids = self.tokenizer(self.src_text).input_ids[0] self.assertListEqual(self.expected_src_tokens, ids) def test_python_en_tokenizer_decode_ignores_language_codes(self): self.assertIn(PYTHON_CODE, self.tokenizer.all_special_ids) generated_ids = [EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2] result = self.tokenizer.decode(generated_ids, skip_special_tokens=True) expected_english = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True) self.assertEqual(result, expected_english) self.assertNotIn(self.tokenizer.eos_token, result) def test_python_en_tokenizer_truncation(self): src_text = ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20] self.assertIsInstance(src_text[0], str) desired_max_length = 10 ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0] self.assertEqual(ids[-2], 2) self.assertEqual(ids[-1], PYTHON_CODE) self.assertEqual(len(ids), desired_max_length) def test_mask_token(self): self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["", "__java__"]), [50004, 50001]) def test_special_tokens_unaffacted_by_save_load(self): tmpdirname = tempfile.mkdtemp() original_special_tokens = self.tokenizer.fairseq_tokens_to_ids self.tokenizer.save_pretrained(tmpdirname) new_tok = PLBartTokenizer.from_pretrained(tmpdirname) self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens) @require_torch def test_batch_fairseq_parity(self): 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) # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4 self.assertEqual(batch.input_ids[1][-2:].tolist(), [2, PYTHON_CODE]) self.assertEqual(batch.decoder_input_ids[1][0], EN_CODE) self.assertEqual(batch.decoder_input_ids[1][-1], 2) self.assertEqual(batch.labels[1][-2:].tolist(), [2, EN_CODE]) @require_torch def test_python_en_tokenizer_prepare_batch(self): batch = self.tokenizer( self.src_text, text_target=self.tgt_text, padding=True, truncation=True, max_length=len(self.expected_src_tokens), return_tensors="pt", ) batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id) self.assertIsInstance(batch, BatchEncoding) self.assertEqual((2, 26), batch.input_ids.shape) self.assertEqual((2, 26), batch.attention_mask.shape) result = batch.input_ids.tolist()[0] self.assertListEqual(self.expected_src_tokens, result) self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS # Test that special tokens are reset self.assertEqual(self.tokenizer.prefix_tokens, []) self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, PYTHON_CODE]) def test_seq2seq_max_length(self): batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt") targets = self.tokenizer( text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt" ) labels = targets["input_ids"] batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id) self.assertEqual(batch.input_ids.shape[1], 3) self.assertEqual(batch.decoder_input_ids.shape[1], 10) @require_torch def test_tokenizer_translation(self): inputs = self.tokenizer._build_translation_inputs( "A test", return_tensors="pt", src_lang="en_XX", tgt_lang="java" ) self.assertEqual( nested_simplify(inputs), { # A, test, EOS, en_XX "input_ids": [[150, 242, 2, 50003]], "attention_mask": [[1, 1, 1, 1]], # java "forced_bos_token_id": 50001, }, )