# Copyright 2021 Google AI and HuggingFace Inc. team. # # 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 shutil import tempfile import unittest from functools import cached_property from transformers import BatchEncoding, CanineTokenizer from transformers.testing_utils import require_tokenizers, require_torch from transformers.tokenization_python import AddedToken from ...test_tokenization_common import TokenizerTesterMixin class CanineTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "nielsr/canine-s" tokenizer_class = CanineTokenizer test_slow_tokenizer = True test_rust_tokenizer = False @classmethod def setUpClass(cls): super().setUpClass() tokenizer = CanineTokenizer() tokenizer.save_pretrained(cls.tmpdirname) @cached_property def canine_tokenizer(self): return CanineTokenizer.from_pretrained("google/canine-s") @classmethod def get_tokenizer(cls, pretrained_name=None, **kwargs) -> CanineTokenizer: pretrained_name = pretrained_name or cls.tmpdirname tokenizer = cls.tokenizer_class.from_pretrained(pretrained_name, **kwargs) tokenizer._unicode_vocab_size = 1024 return tokenizer @require_torch def test_prepare_batch_integration(self): tokenizer = self.canine_tokenizer src_text = ["Life is like a box of chocolates.", "You never know what you're gonna get."] expected_src_tokens = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0] # fmt: skip batch = tokenizer(src_text, padding=True, return_tensors="pt") self.assertIsInstance(batch, BatchEncoding) result = list(batch.input_ids.numpy()[0]) self.assertListEqual(expected_src_tokens, result) self.assertEqual((2, 39), batch.input_ids.shape) self.assertEqual((2, 39), batch.attention_mask.shape) @require_torch def test_encoding_keys(self): tokenizer = self.canine_tokenizer src_text = ["Once there was a man.", "He wrote a test in HuggingFace Transformers."] batch = tokenizer(src_text, padding=True, return_tensors="pt") # check if input_ids, attention_mask and token_type_ids are returned self.assertIn("input_ids", batch) self.assertIn("attention_mask", batch) self.assertIn("token_type_ids", batch) @require_torch def test_max_length_integration(self): tokenizer = self.canine_tokenizer tgt_text = [ "What's the weater?", "It's about 25 degrees.", ] targets = tokenizer( text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors="pt" ) self.assertEqual(32, targets["input_ids"].shape[1]) # cannot use default save_and_load_tokenizer test method because tokenizer has no vocab def test_save_and_load_tokenizer(self): # safety check on max_len default value so we are sure the test works tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): self.assertNotEqual(tokenizer.model_max_length, 42) # Now let's start the test tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Isolate this from the other tests because we save additional tokens/etc tmpdirname = tempfile.mkdtemp() sample_text = " He is very happy, UNwant\u00e9d,running" before_tokens = tokenizer.encode(sample_text, add_special_tokens=False) tokenizer.save_pretrained(tmpdirname) after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname) after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False) self.assertListEqual(before_tokens, after_tokens) shutil.rmtree(tmpdirname) tokenizers = self.get_tokenizers(model_max_length=42) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Isolate this from the other tests because we save additional tokens/etc tmpdirname = tempfile.mkdtemp() sample_text = " He is very happy, UNwant\u00e9d,running" extra_special_tokens = tokenizer.extra_special_tokens # We can add a new special token for Canine as follows: new_extra_special_token = chr(0xE007) extra_special_tokens.append(new_extra_special_token) tokenizer.add_special_tokens( {"extra_special_tokens": extra_special_tokens}, replace_extra_special_tokens=False ) before_tokens = tokenizer.encode(sample_text, add_special_tokens=False) tokenizer.save_pretrained(tmpdirname) after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname) after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False) self.assertListEqual(before_tokens, after_tokens) self.assertIn(new_extra_special_token, after_tokenizer.extra_special_tokens) self.assertEqual(after_tokenizer.model_max_length, 42) tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43) self.assertEqual(tokenizer.model_max_length, 43) shutil.rmtree(tmpdirname) def test_add_special_tokens(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): input_text, ids = self.get_clean_sequence(tokenizer) # a special token for Canine can be defined as follows: SPECIAL_TOKEN = 0xE005 special_token = chr(SPECIAL_TOKEN) tokenizer.add_special_tokens({"cls_token": special_token}) encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False) self.assertEqual(len(encoded_special_token), 1) text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False) encoded = tokenizer.encode(text, add_special_tokens=False) input_encoded = tokenizer.encode(input_text, add_special_tokens=False) special_token_id = tokenizer.encode(special_token, add_special_tokens=False) self.assertEqual(encoded, input_encoded + special_token_id) decoded = tokenizer.decode(encoded, skip_special_tokens=True) self.assertTrue(special_token not in decoded) def test_tokenize_special_tokens(self): tokenizers = self.get_tokenizers(do_lower_case=True) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): SPECIAL_TOKEN_1 = chr(0xE005) SPECIAL_TOKEN_2 = chr(0xE006) tokenizer.add_tokens([SPECIAL_TOKEN_1], special_tokens=True) tokenizer.add_special_tokens({"extra_special_tokens": [SPECIAL_TOKEN_2]}) token_1 = tokenizer.tokenize(SPECIAL_TOKEN_1) token_2 = tokenizer.tokenize(SPECIAL_TOKEN_2) self.assertEqual(len(token_1), 1) self.assertEqual(len(token_2), 1) self.assertEqual(token_1[0], SPECIAL_TOKEN_1) self.assertEqual(token_2[0], SPECIAL_TOKEN_2) @require_tokenizers def test_added_token_serializable(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # a special token for Canine can be defined as follows: NEW_TOKEN = 0xE006 new_token = chr(NEW_TOKEN) new_token = AddedToken(new_token, lstrip=True) tokenizer.add_special_tokens({"extra_special_tokens": [new_token]}) with tempfile.TemporaryDirectory() as tmp_dir_name: tokenizer.save_pretrained(tmp_dir_name) tokenizer.from_pretrained(tmp_dir_name) @require_tokenizers def test_encode_decode_with_spaces(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): input = "hello world" if self.space_between_special_tokens: output = "[CLS] hello world [SEP]" else: output = input encoded = tokenizer.encode(input, add_special_tokens=False) decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens) self.assertIn(decoded, [output, output.lower()]) # cannot use default `test_tokenizers_common_ids_setters` method because tokenizer has no vocab def test_tokenizers_common_ids_setters(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): attributes_list = [ "bos_token", "eos_token", "unk_token", "sep_token", "pad_token", "cls_token", "mask_token", ] token_to_test_setters = "a" token_id_to_test_setters = ord(token_to_test_setters) for attr in attributes_list: setattr(tokenizer, attr + "_id", None) self.assertEqual(getattr(tokenizer, attr), None) self.assertEqual(getattr(tokenizer, attr + "_id"), None) setattr(tokenizer, attr + "_id", token_id_to_test_setters) self.assertEqual(getattr(tokenizer, attr), token_to_test_setters) self.assertEqual(getattr(tokenizer, attr + "_id"), token_id_to_test_setters) setattr(tokenizer, "extra_special_tokens_ids", []) self.assertListEqual(getattr(tokenizer, "extra_special_tokens"), []) self.assertListEqual(getattr(tokenizer, "extra_special_tokens_ids"), []) additional_special_token_id = 0xE006 additional_special_token = chr(additional_special_token_id) setattr(tokenizer, "extra_special_tokens_ids", [additional_special_token_id]) self.assertListEqual(getattr(tokenizer, "extra_special_tokens"), [additional_special_token]) self.assertListEqual(getattr(tokenizer, "extra_special_tokens_ids"), [additional_special_token_id]) @unittest.skip(reason="tokenizer has a fixed vocab_size (namely all possible unicode code points)") def test_add_tokens_tokenizer(self): pass # CanineTokenizer does not support do_lower_case = True, as each character has its own Unicode code point # ("b" and "B" for example have different Unicode code points) @unittest.skip(reason="CanineTokenizer does not support do_lower_case = True") def test_added_tokens_do_lower_case(self): pass @unittest.skip(reason="CanineModel does not support the get_input_embeddings nor the get_vocab method") def test_np_encode_plus_sent_to_model(self): pass @unittest.skip(reason="CanineModel does not support the get_input_embeddings nor the get_vocab method") def test_torch_encode_plus_sent_to_model(self): pass @unittest.skip(reason="CanineTokenizer does not have vocabulary") def test_get_vocab(self): pass @unittest.skip(reason="inputs cannot be pretokenized since ids depend on whole input string") def test_pretokenized_inputs(self): pass @unittest.skip(reason="CanineTokenizer does not have vocabulary") def test_conversion_reversible(self): pass