# Copyright 2023 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 json import os import unittest from transformers import ClvpTokenizer from transformers.testing_utils import slow from ...test_tokenization_common import TokenizerTesterMixin class ClvpTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "susnato/clvp_dev" tokenizer_class = ClvpTokenizer test_rust_tokenizer = False from_pretrained_kwargs = {"add_prefix_space": True} test_seq2seq = False test_sentencepiece_ignore_case = True @classmethod def setUpClass(cls): super().setUpClass() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "", "<|endoftext|>", "[SPACE]", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] cls.special_tokens_map = {"unk_token": ""} cls.vocab_file = os.path.join(cls.tmpdirname, "vocab.json") cls.merges_file = os.path.join(cls.tmpdirname, "merges.txt") with open(cls.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(cls.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) # Remove files from parent class loading from hub to avoid conflicts for filename in ["added_tokens.json", "special_tokens_map.json", "tokenizer_config.json"]: filepath = os.path.join(cls.tmpdirname, filename) if os.path.exists(filepath): os.remove(filepath) # Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.get_tokenizer with GPT2->Clvp @classmethod def get_tokenizer(cls, pretrained_name=None, **kwargs): kwargs.update(cls.special_tokens_map) pretrained_name = pretrained_name or cls.tmpdirname return ClvpTokenizer.from_pretrained(pretrained_name, **kwargs) # Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.get_input_output_texts def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower[SPACE]newer" # [SPACE] tokens preserved when clean_up_tokenization_spaces=False (default) return input_text, output_text # Copied from transformers.tests.models.layoutxlm.test_tokenization_layoutxlm.LayoutXLMTokenizationTest.test_add_special_tokens def test_add_special_tokens(self): tokenizers: list[ClvpTokenizer] = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): special_token = "[SPECIAL_TOKEN]" special_token_box = [1000, 1000, 1000, 1000] tokenizer.add_special_tokens({"cls_token": special_token}) encoded_special_token = tokenizer.encode( [special_token], boxes=[special_token_box], add_special_tokens=False ) self.assertEqual(len(encoded_special_token), 1) decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True) self.assertTrue(special_token not in decoded) # Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.test_rust_and_python_full_tokenizers def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: self.skipTest(reason="test_rust_tokenizer is set to False") tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True) sequence = "lower newer" # Testing tokenization tokens = tokenizer.tokenize(sequence, add_prefix_space=True) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) # Testing conversion to ids without special tokens ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) # Testing conversion to ids with special tokens rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True) ids = tokenizer.encode(sequence, add_prefix_space=True) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) # Testing the unknown token input_tokens = tokens + [rust_tokenizer.unk_token] input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) # Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.test_padding def test_padding(self, max_length=15): if not self.test_rust_tokenizer: self.skipTest(reason="test_rust_tokenizer is set to False") for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs) # Simple input s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, padding="max_length", ) # Pair input self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, padding="max_length", ) # Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.test_padding_if_pad_token_set_slow def test_padding_if_pad_token_set_slow(self): tokenizer = ClvpTokenizer.from_pretrained(self.tmpdirname, pad_token="") # Simple input s = "This is a simple input" s2 = ["This is a simple input looooooooong", "This is a simple input"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] pad_token_id = tokenizer.pad_token_id out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np") out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np") out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np") out_p2 = tokenizer(p2, padding=True, truncate=True, return_tensors="np") # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1], 30) self.assertTrue(pad_token_id in out_s["input_ids"]) self.assertTrue(0 in out_s["attention_mask"]) # s2 # test automatic padding self.assertEqual(out_s2["input_ids"].shape[-1], 33) # long slice doesn't have padding self.assertFalse(pad_token_id in out_s2["input_ids"][0]) self.assertFalse(0 in out_s2["attention_mask"][0]) # short slice does have padding self.assertTrue(pad_token_id in out_s2["input_ids"][1]) self.assertTrue(0 in out_s2["attention_mask"][1]) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1], 60) self.assertTrue(pad_token_id in out_p["input_ids"]) self.assertTrue(0 in out_p["attention_mask"]) # p2 # test automatic padding pair self.assertEqual(out_p2["input_ids"].shape[-1], 52) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_p2["input_ids"][0]) self.assertFalse(0 in out_p2["attention_mask"][0]) # short slice pair does have padding self.assertTrue(pad_token_id in out_p2["input_ids"][1]) self.assertTrue(0 in out_p2["attention_mask"][1]) # Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.test_special_tokens_mask_input_pairs_and_bos_token def test_special_tokens_mask_input_pairs_and_bos_token(self): # TODO: change to self.get_tokenizers() when the fast version is implemented tokenizers = [self.get_tokenizer(do_lower_case=False, add_bos_token=True)] for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): sequence_0 = "Encode this." sequence_1 = "This one too please." encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False) encoded_sequence_dict = tokenizer( sequence_0, sequence_1, add_special_tokens=True, return_special_tokens_mask=True, ) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special)) filtered_sequence = [ (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special) ] filtered_sequence = [x for x in filtered_sequence if x is not None] self.assertEqual(encoded_sequence, filtered_sequence) def test_token_type_ids(self): tokenizer = self.get_tokenizer() seq_0 = "Test this method." # We want to have sequence 0 and sequence 1 are tagged # respectively with 0 and 1 token_ids # (regardless of whether the model use token type ids) # We use this assumption in the QA pipeline among other place output = tokenizer(seq_0, return_token_type_ids=True, add_special_tokens=True) self.assertIn(0, output["token_type_ids"]) def test_full_tokenizer(self): tokenizer = ClvpTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map) text = "lower newer" bpe_tokens = ["l", "o", "w", "er", "[SPACE]", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text, add_prefix_space=False) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [0, 1, 2, 15, 21, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) @slow def test_outputs_with_numbers(self): text = "hello and this is an example text and I have $1000. my lucky number is 12345." tokenizer = ClvpTokenizer.from_pretrained("susnato/clvp_dev") # fmt: off EXPECTED_OUTPUT = [62, 84, 28, 2, 53, 2,147, 2, 54, 2, 43, 2, 169, 122, 29, 64, 2, 136, 37, 33, 2, 53, 2, 22, 2, 148, 2, 110, 2, 40, 206, 53, 2, 134, 84, 59, 32, 9, 2, 125, 2, 25, 34, 197, 38, 2, 27, 231, 15, 44, 2, 54, 2, 33, 100, 25, 76, 2, 40, 206, 53, 7, 2, 40, 46, 18, 2, 21, 97, 17, 219, 2, 87, 210, 8, 19, 22, 76, 9, ] # fmt: on self.assertListEqual(tokenizer.encode(text, add_special_tokens=False), EXPECTED_OUTPUT) @slow def test_tokenizer_integration(self): sequences = [ "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides " "general-purpose architectures (BERT, RoBERTa, XLM, DistilBert, XLNet...) for Natural " "Language Understanding (NLU) and Natural Language Generation (NLG) with over multiple pretrained " "models and deep interoperability between Jax, PyTorch and TensorFlow.", "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly " "conditioning on both left and right context in all layers.", "The quick brown fox jumps over the lazy dog.", ] # fmt: off expected_encoding = {'input_ids': [[144, 43, 32, 87, 26, 173, 2, 5, 87, 26, 44, 70, 2, 209, 27, 2, 55, 2, 29, 38, 51, 31, 71, 8, 144, 43, 32, 87, 26, 173, 2, 53, 2, 29, 38, 51, 31, 71, 8, 29, 46, 144, 137, 49, 8, 15, 44, 33, 6, 2, 187, 35, 83, 61, 2, 20, 50, 44, 56, 8, 29, 121, 139, 66, 2, 59, 71, 60, 18, 16, 33, 34, 175, 2, 5, 15, 44, 33, 7, 2, 89, 15, 44, 33, 14, 7, 2, 37, 25, 26, 7, 2, 17, 54, 78, 25, 15, 44, 33, 7, 2, 37, 25, 111, 33, 9, 9, 9, 6, 2, 87, 2, 27, 48, 121, 56, 2, 25, 43, 20, 34, 14, 112, 2, 97, 234, 63, 53, 52, 2, 5, 27, 25, 34, 6, 2, 53, 2, 27, 48, 121, 56, 2, 25, 43, 20, 34, 14, 112, 2, 20, 50, 44, 158, 2, 5, 27, 25, 20, 6, 2, 103, 2, 253, 2, 26, 167, 78, 29, 64, 2, 29, 46, 144, 137, 49, 2, 115, 126, 25, 32, 2, 53, 2, 126, 18, 29, 2, 41, 114, 161, 44, 109, 151, 240, 2, 67, 33, 100, 50, 2, 23, 14, 37, 7, 2, 29, 38, 51, 31, 71, 2, 53, 2, 33, 50, 32, 57, 19, 25, 69, 9], [ 15, 44, 33, 2, 54, 2, 17, 61, 22, 20, 27, 49, 2, 51, 2, 29, 46, 8, 144, 137, 2, 126, 18, 29, 2, 15, 83, 22, 46, 16, 181, 56, 2, 46, 29, 175, 86, 158, 32, 2, 154, 2, 97, 25, 14, 67, 25, 49, 2, 136, 37, 33, 2, 185, 2, 23, 28, 41, 33, 70, 2, 135, 17, 60, 107, 52, 2, 47, 2, 165, 40, 2, 64, 19, 33, 2, 53, 2, 101, 104, 2, 135, 136, 37, 33, 2, 41, 2, 108, 2, 25, 88, 173, 9, 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, 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], [ 42, 2, 194, 91, 24, 2, 243, 190, 2, 182, 37, 2, 23, 231, 29, 32, 2, 253, 2, 42, 2, 25, 14, 39, 38, 2, 134, 20, 9, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # noqa: E501 '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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # noqa: E501 } # fmt: on self.tokenizer_integration_test_util( sequences=sequences, expected_encoding=expected_encoding, model_name="susnato/clvp_dev", padding=True )