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476ba679dd
* feature for tokenizer without slow/legacy version * format * modify common test * add tests * add PreTrainedTokenizerFast to AutoTokenizer * format * change tokenizer common test in order to be able to run test without a slow version * update tokenizer fast test in order to use `rust_tokenizer_class` attribute instead of `tokenizer_class` * add autokenizer test * replace `if self.tokenizer_class is not None` with ` if self.tokenizer_class is None` * remove obsolete change in comment * Update src/transformers/tokenization_utils_base.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/tokenization_utils_fast.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * change `get_main_tokenizer` into `get_tokenizers` * clarify `get_tokenizers` method * homogenize with `test_slow_tokenizer` and `test_rust_tokenizer` * add `test_rust_tokenizer = False` to tokenizer which don't define a fast version * `test_rust_tokenizer = False` for BertJapaneseTokenizer * `test_rust_tokenizer = False` for BertJapaneseCharacterTokenizationTest Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
209 lines
8.9 KiB
Python
209 lines
8.9 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import unittest
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from transformers import CLIPTokenizer, CLIPTokenizerFast
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from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
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from transformers.testing_utils import require_tokenizers
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from .test_tokenization_common import TokenizerTesterMixin
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@require_tokenizers
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class CLIPTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = CLIPTokenizer
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rust_tokenizer_class = CLIPTokenizerFast
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test_rust_tokenizer = False
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from_pretrained_kwargs = {"add_prefix_space": True}
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test_seq2seq = False
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def setUp(self):
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super().setUp()
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# fmt: off
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vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|endoftext|>"]
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# fmt: on
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
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self.special_tokens_map = {"unk_token": "<unk>"}
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_rust_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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def get_input_output_texts(self, tokenizer):
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input_text = "lower newer"
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output_text = "lower newer "
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = CLIPTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
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text = "lower newer"
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bpe_tokens = ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
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tokens = tokenizer.tokenize(text, add_prefix_space=True)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + [tokenizer.unk_token]
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input_bpe_tokens = [10, 2, 12, 9, 3, 2, 12, 16]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_rust_and_python_full_tokenizers(self):
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
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sequence = "lower newer"
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# Testing tokenization
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tokens = tokenizer.tokenize(sequence, add_prefix_space=True)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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# Testing conversion to ids without special tokens
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ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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# Testing conversion to ids with special tokens
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rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
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ids = tokenizer.encode(sequence, add_prefix_space=True)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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# Testing the unknown token
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input_tokens = tokens + [rust_tokenizer.unk_token]
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input_bpe_tokens = [10, 2, 12, 9, 3, 2, 12, 16]
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self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_pretokenized_inputs(self, *args, **kwargs):
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# It's very difficult to mix/test pretokenization with byte-level
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# And get both CLIP and Roberta to work at the same time (mostly an issue of adding a space before the string)
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pass
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def test_padding(self, max_length=15):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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# Simple input
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s = "This is a simple input"
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s2 = ["This is a simple input 1", "This is a simple input 2"]
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p = ("This is a simple input", "This is a pair")
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p2 = [
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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]
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# Simple input tests
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self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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s2,
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max_length=max_length,
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padding="max_length",
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)
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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p2,
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max_length=max_length,
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padding="max_length",
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)
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def test_add_tokens_tokenizer(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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# We usually have added tokens from the start in tests because our vocab fixtures are
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# smaller than the original vocabs - let's not assert this
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# self.assertEqual(vocab_size, all_size)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
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added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
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vocab_size_3 = tokenizer.vocab_size
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all_size_3 = len(tokenizer)
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self.assertNotEqual(vocab_size_3, 0)
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self.assertEqual(vocab_size, vocab_size_3)
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self.assertEqual(added_toks_2, len(new_toks_2))
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self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
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tokens = tokenizer.encode(
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">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
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)
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self.assertGreaterEqual(len(tokens), 6)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[0], tokens[1])
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokens[-3])
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self.assertEqual(tokens[0], tokenizer.eos_token_id)
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# padding is very hacky in CLIPTokenizer, pad_token_id is always 0
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# so skip this check
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# self.assertEqual(tokens[-2], tokenizer.pad_token_id)
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