mirror of
https://github.com/huggingface/transformers.git
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9aeacb58ba
* [WIP] SP tokenizers * fixing tests for T5 * WIP tokenizers * serialization * update T5 * WIP T5 tokenization * slow to fast conversion script * Refactoring to move tokenzier implementations inside transformers * Adding gpt - refactoring - quality * WIP adding several tokenizers to the fast world * WIP Roberta - moving implementations * update to dev4 switch file loading to in-memory loading * Updating and fixing * advancing on the tokenizers - updating do_lower_case * style and quality * moving forward with tokenizers conversion and tests * MBart, T5 * dumping the fast version of transformer XL * Adding to autotokenizers + style/quality * update init and space_between_special_tokens * style and quality * bump up tokenizers version * add protobuf * fix pickle Bert JP with Mecab * fix newly added tokenizers * style and quality * fix bert japanese * fix funnel * limite tokenizer warning to one occurence * clean up file * fix new tokenizers * fast tokenizers deep tests * WIP adding all the special fast tests on the new fast tokenizers * quick fix * adding more fast tokenizers in the fast tests * all tokenizers in fast version tested * Adding BertGenerationFast * bump up setup.py for CI * remove BertGenerationFast (too early) * bump up tokenizers version * Clean old docstrings * Typo * Update following Lysandre comments Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>
270 lines
11 KiB
Python
270 lines
11 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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 os
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import pickle
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import unittest
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from transformers.testing_utils import custom_tokenizers
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from transformers.tokenization_bert import WordpieceTokenizer
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from transformers.tokenization_bert_japanese import (
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VOCAB_FILES_NAMES,
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BertJapaneseTokenizer,
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CharacterTokenizer,
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MecabTokenizer,
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)
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from .test_tokenization_common import TokenizerTesterMixin
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@custom_tokenizers
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class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = BertJapaneseTokenizer
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space_between_special_tokens = True
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def setUp(self):
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super().setUp()
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vocab_tokens = [
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"[UNK]",
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"[CLS]",
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"[SEP]",
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"こんにちは",
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"こん",
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"にちは",
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"ばんは",
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"##こん",
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"##にちは",
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"##ばんは",
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"世界",
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"##世界",
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"、",
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"##、",
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"。",
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"##。",
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]
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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def get_input_output_texts(self, tokenizer):
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input_text = "こんにちは、世界。 \nこんばんは、世界。"
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output_text = "こんにちは 、 世界 。 こんばんは 、 世界 。"
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return input_text, output_text
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def get_clean_sequence(self, tokenizer):
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input_text, output_text = self.get_input_output_texts(tokenizer)
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ids = tokenizer.encode(output_text, add_special_tokens=False)
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text = tokenizer.decode(ids, clean_up_tokenization_spaces=False)
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return text, ids
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def test_pretokenized_inputs(self):
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pass # TODO add if relevant
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def test_maximum_encoding_length_pair_input(self):
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pass # TODO add if relevant
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def test_maximum_encoding_length_single_input(self):
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pass # TODO add if relevant
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def test_full_tokenizer(self):
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tokenizer = self.tokenizer_class(self.vocab_file)
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tokens = tokenizer.tokenize("こんにちは、世界。\nこんばんは、世界。")
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self.assertListEqual(tokens, ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [3, 12, 10, 14, 4, 9, 12, 10, 14])
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def test_pickle_mecab_tokenizer(self):
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tokenizer = self.tokenizer_class(self.vocab_file, word_tokenizer_type="mecab")
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self.assertIsNotNone(tokenizer)
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text = "こんにちは、世界。\nこんばんは、世界。"
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tokens = tokenizer.tokenize(text)
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self.assertListEqual(tokens, ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [3, 12, 10, 14, 4, 9, 12, 10, 14])
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filename = os.path.join(self.tmpdirname, "tokenizer.bin")
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with open(filename, "wb") as handle:
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pickle.dump(tokenizer, handle)
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with open(filename, "rb") as handle:
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tokenizer_new = pickle.load(handle)
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tokens_loaded = tokenizer_new.tokenize(text)
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self.assertListEqual(tokens, tokens_loaded)
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def test_mecab_tokenizer_ipadic(self):
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tokenizer = MecabTokenizer(mecab_dic="ipadic")
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self.assertListEqual(
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tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
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["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"],
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)
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def test_mecab_tokenizer_unidic_lite(self):
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try:
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tokenizer = MecabTokenizer(mecab_dic="unidic_lite")
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except ModuleNotFoundError:
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return
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self.assertListEqual(
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tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
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["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"],
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)
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def test_mecab_tokenizer_unidic(self):
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try:
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tokenizer = MecabTokenizer(mecab_dic="unidic")
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except ModuleNotFoundError:
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return
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self.assertListEqual(
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tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
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["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"],
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)
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def test_mecab_tokenizer_lower(self):
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tokenizer = MecabTokenizer(do_lower_case=True, mecab_dic="ipadic")
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self.assertListEqual(
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tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
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["アップルストア", "で", "iphone", "8", "が", "発売", "さ", "れ", "た", "。"],
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)
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def test_mecab_tokenizer_with_option(self):
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try:
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tokenizer = MecabTokenizer(
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do_lower_case=True, normalize_text=False, mecab_option="-d /usr/local/lib/mecab/dic/jumandic"
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)
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except RuntimeError:
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# if dict doesn't exist in the system, previous code raises this error.
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return
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self.assertListEqual(
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tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
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["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れた", "\u3000", "。"],
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)
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def test_mecab_tokenizer_no_normalize(self):
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tokenizer = MecabTokenizer(normalize_text=False, mecab_dic="ipadic")
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self.assertListEqual(
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tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 "),
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["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れ", "た", " ", "。"],
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)
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def test_wordpiece_tokenizer(self):
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは" "ばんは", "##こん", "##にちは", "##ばんは"]
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vocab = {}
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for (i, token) in enumerate(vocab_tokens):
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vocab[token] = i
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tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
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self.assertListEqual(tokenizer.tokenize(""), [])
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self.assertListEqual(tokenizer.tokenize("こんにちは"), ["こんにちは"])
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self.assertListEqual(tokenizer.tokenize("こんばんは"), ["こん", "##ばんは"])
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self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは"), ["こん", "##ばんは", "[UNK]", "こんにちは"])
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese")
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text = tokenizer.encode("ありがとう。", add_special_tokens=False)
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text_2 = tokenizer.encode("どういたしまして。", add_special_tokens=False)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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# 2 is for "[CLS]", 3 is for "[SEP]"
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assert encoded_sentence == [2] + text + [3]
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assert encoded_pair == [2] + text + [3] + text_2 + [3]
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@custom_tokenizers
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class BertJapaneseCharacterTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = BertJapaneseTokenizer
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def setUp(self):
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super().setUp()
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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def get_tokenizer(self, **kwargs):
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return BertJapaneseTokenizer.from_pretrained(self.tmpdirname, subword_tokenizer_type="character", **kwargs)
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def get_input_output_texts(self, tokenizer):
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input_text = "こんにちは、世界。 \nこんばんは、世界。"
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output_text = "こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
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return input_text, output_text
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def test_pretokenized_inputs(self):
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pass # TODO add if relevant
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def test_maximum_encoding_length_pair_input(self):
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pass # TODO add if relevant
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def test_maximum_encoding_length_single_input(self):
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pass # TODO add if relevant
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def test_full_tokenizer(self):
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tokenizer = self.tokenizer_class(self.vocab_file, subword_tokenizer_type="character")
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tokens = tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。")
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self.assertListEqual(
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tokens, ["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"]
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)
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens), [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12]
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)
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def test_character_tokenizer(self):
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界" "、", "。"]
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vocab = {}
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for (i, token) in enumerate(vocab_tokens):
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vocab[token] = i
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tokenizer = CharacterTokenizer(vocab=vocab, unk_token="[UNK]")
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self.assertListEqual(tokenizer.tokenize(""), [])
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self.assertListEqual(tokenizer.tokenize("こんにちは"), ["こ", "ん", "に", "ち", "は"])
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self.assertListEqual(tokenizer.tokenize("こんにちほ"), ["こ", "ん", "に", "ち", "[UNK]"])
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char")
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text = tokenizer.encode("ありがとう。", add_special_tokens=False)
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text_2 = tokenizer.encode("どういたしまして。", add_special_tokens=False)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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# 2 is for "[CLS]", 3 is for "[SEP]"
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assert encoded_sentence == [2] + text + [3]
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assert encoded_pair == [2] + text + [3] + text_2 + [3]
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