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79 lines
3.2 KiB
Python
79 lines
3.2 KiB
Python
# Copyright 2021 HuggingFace Inc. team.
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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 tempfile
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import unittest
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from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
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from transformers.testing_utils import get_tests_dir
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
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class BartphoTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "vinai/bartpho-syllable"
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tokenizer_class = BartphoTokenizer
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test_rust_tokenizer = False
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test_sentencepiece = True
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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cls.special_tokens_map = {"unk_token": "<unk>"}
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@classmethod
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def get_tokenizer(cls, pretrained_name=None, **kwargs):
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"""Create a fresh tokenizer for each test instead of loading from saved."""
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kwargs.update(cls.special_tokens_map)
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# Create a temporary directory for this tokenizer
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tmpdir = tempfile.mkdtemp()
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vocab = ["▁This", "▁is", "▁a", "▁t", "est"]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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monolingual_vocab_file = os.path.join(tmpdir, VOCAB_FILES_NAMES["monolingual_vocab_file"])
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with open(monolingual_vocab_file, "w", encoding="utf-8") as fp:
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fp.writelines(f"{token} {vocab_tokens[token]}\n" for token in vocab_tokens)
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return BartphoTokenizer(SAMPLE_VOCAB, monolingual_vocab_file, **kwargs)
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def get_input_output_texts(self, tokenizer):
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input_text = "This is a là test"
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output_text = "This is a<unk><unk> test"
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return input_text, output_text
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def test_full_tokenizer(self):
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vocab = ["▁This", "▁is", "▁a", "▁t", "est"]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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special_tokens_map = {"unk_token": "<unk>"}
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with tempfile.TemporaryDirectory() as tmpdir:
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monolingual_vocab_file = os.path.join(tmpdir, VOCAB_FILES_NAMES["monolingual_vocab_file"])
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with open(monolingual_vocab_file, "w", encoding="utf-8") as fp:
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fp.writelines(f"{token} {vocab_tokens[token]}\n" for token in vocab_tokens)
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tokenizer = BartphoTokenizer(SAMPLE_VOCAB, monolingual_vocab_file, **special_tokens_map)
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text = "This is a là test"
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bpe_tokens = "▁This ▁is ▁a ▁l à ▁t est".split()
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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 = [4, 5, 6, 3, 3, 7, 8, 3]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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