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149 lines
9.2 KiB
Python
149 lines
9.2 KiB
Python
# Copyright 2019 Hugging Face inc.
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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 unittest
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from transformers import DebertaTokenizer
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from ...test_tokenization_common import TokenizerTesterMixin
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class DebertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = ["microsoft/deberta-base"]
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tokenizer_class = DebertaTokenizer
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integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'Ġ', 'Ġ', 'ĠHello', 'ĊĊ', 'Ġ', 'Ċ', 'Ġ', 'Ġ', 'Ċ', 'ĠHello', 'Ċ', '<', 's', '>', 'Ċ', 'hi', '<', 's', '>', 'there', 'Ċ', 'The', 'Ġfollowing', 'Ġstring', 'Ġshould', 'Ġbe', 'Ġproperly', 'Ġencoded', ':', 'ĠHello', '.', 'Ċ', 'But', 'Ġ', 'ird', 'Ġand', 'Ġ', 'à¸', 'Ľ', 'à¸', 'µ', 'Ġ', 'Ġ', 'Ġ', 'ird', 'Ġ', 'Ġ', 'Ġ', 'à¸', 'Ķ', 'Ċ', 'Hey', 'Ġhow', 'Ġare', 'Ġyou', 'Ġdoing'] # fmt: skip
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integration_expected_token_ids = [713, 16, 10, 1296, 17841, 27969, 50118, 100, 21, 2421, 11, 8403, 151, 6, 8, 42, 16, 22461, 1140, 4, 50118, 48998, 37127, 20024, 2023, 44574, 49122, 4333, 36484, 7487, 3726, 48569, 50118, 30086, 1437, 20920, 50118, 30086, 1437, 1437, 20920, 50140, 1437, 50118, 1437, 1437, 50118, 20920, 50118, 41552, 29, 15698, 50118, 3592, 41552, 29, 15698, 8585, 50118, 133, 511, 6755, 197, 28, 5083, 45320, 35, 20920, 4, 50118, 1708, 1437, 8602, 8, 1437, 24107, 3726, 24107, 8906, 1437, 1437, 1437, 8602, 1437, 1437, 1437, 24107, 10674, 50118, 13368, 141, 32, 47, 608] # fmt: skip
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expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'Ġ', 'Ġ', 'ĠHello', 'ĊĊ', 'Ġ', 'Ċ', 'Ġ', 'Ġ', 'Ċ', 'ĠHello', 'Ċ', '<', 's', '>', 'Ċ', 'hi', '<', 's', '>', 'there', 'Ċ', 'The', 'Ġfollowing', 'Ġstring', 'Ġshould', 'Ġbe', 'Ġproperly', 'Ġencoded', ':', 'ĠHello', '.', 'Ċ', 'But', 'Ġ', 'ird', 'Ġand', 'Ġ', 'à¸', 'Ľ', 'à¸', 'µ', 'Ġ', 'Ġ', 'Ġ', 'ird', 'Ġ', 'Ġ', 'Ġ', 'à¸', 'Ķ', 'Ċ', 'Hey', 'Ġhow', 'Ġare', 'Ġyou', 'Ġdoing'] # fmt: skip
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integration_expected_decoded_text = "This is a test 😊\nI was born in 92000, and this is falsé.\n生活的真谛是\nHi Hello\nHi Hello\n\n \n \n Hello\n<s>\nhi<s>there\nThe following string should be properly encoded: Hello.\nBut ird and ปี ird ด\nHey how are you doing"
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# @classmethod
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# def setUpClass(cls):
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# super().setUpClass()
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# # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
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# vocab = [
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# "l",
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# "o",
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# "w",
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# "e",
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# "r",
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# "s",
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# "t",
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# "i",
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# "d",
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# "n",
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# "\u0120",
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# "\u0120l",
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# "\u0120n",
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# "\u0120lo",
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# "\u0120low",
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# "er",
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# "\u0120lowest",
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# "\u0120newer",
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# "\u0120wider",
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# "[UNK]",
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# ]
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# vocab_tokens = dict(zip(vocab, range(len(vocab))))
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# # merges as list of tuples, matching what load_merges returns
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# merges = [("\u0120", "l"), ("\u0120l", "o"), ("\u0120lo", "w"), ("e", "r")]
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# cls.special_tokens_map = {"unk_token": "[UNK]"}
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# cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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# cls.merges_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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# with open(cls.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(cls.merges_file, "w", encoding="utf-8") as fp:
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# # Write merges file in the standard format
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# fp.write("#version: 0.2\n")
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# fp.write("\n".join([f"{a} {b}" for a, b in merges]))
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# tokenizer = DebertaTokenizer(vocab=vocab_tokens, merges=merges)
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# tokenizer.save_pretrained(cls.tmpdirname)
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# cls.tokenizers = [tokenizer]
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# @classmethod
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# def get_tokenizer(cls, pretrained_name=None, **kwargs):
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# kwargs.update(cls.special_tokens_map)
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# pretrained_name = pretrained_name or cls.tmpdirname
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# return cls.tokenizer_class.from_pretrained(pretrained_name, **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 = self.get_tokenizer()
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# text = "lower newer"
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# bpe_tokens = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
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# tokens = tokenizer.tokenize(text)
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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 = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
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# self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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# def test_tokenizer_integration(self):
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# tokenizer_classes = [self.tokenizer_class]
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# if self.test_rust_tokenizer:
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# tokenizer_classes.append(self.rust_tokenizer_class)
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# for tokenizer_class in tokenizer_classes:
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# tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-base")
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# sequences = [
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# "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
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# "ALBERT incorporates two parameter reduction techniques",
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# "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
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# " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
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# " vocabulary embedding.",
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# ]
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# encoding = tokenizer(sequences, padding=True)
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# decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]]
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# # fmt: off
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# expected_encoding = {
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# 'input_ids': [
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# [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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# [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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# [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
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# ],
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# 'token_type_ids': [
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# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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# ],
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# 'attention_mask': [
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# [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],
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# [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],
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# [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
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# ]
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# }
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# # fmt: on
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# expected_decoded_sequence = [
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# "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
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# "ALBERT incorporates two parameter reduction techniques",
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# "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
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# " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
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# " vocabulary embedding.",
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# ]
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# # self.assertDictEqual(encoding.data, expected_encoding)
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# for expected, decoded in zip(expected_decoded_sequence, decoded_sequences):
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# self.assertEqual(expected, decoded)
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