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36434220fc
* Use tokenizers pre-tokenized pipeline * failing pretrokenized test * Fix is_pretokenized in python * add pretokenized tests * style and quality * better tests for batched pretokenized inputs * tokenizers clean up - new padding_strategy - split the files * [HUGE] refactoring tokenizers - padding - truncation - tests * style and quality * bump up requied tokenizers version to 0.8.0-rc1 * switched padding/truncation API - simpler better backward compat * updating tests for custom tokenizers * style and quality - tests on pad * fix QA pipeline * fix backward compatibility for max_length only * style and quality * Various cleans up - add verbose * fix tests * update docstrings * Fix tests * Docs reformatted * __call__ method documented Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
# coding=utf-8
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# Copyright 2020 Huggingface
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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 unittest
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from pathlib import Path
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from shutil import copyfile
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from transformers.tokenization_marian import MarianTokenizer, save_json, vocab_files_names
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from transformers.tokenization_utils import BatchEncoding
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from .test_tokenization_common import TokenizerTesterMixin
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from .utils import slow
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SAMPLE_SP = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
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mock_tokenizer_config = {"target_lang": "fi", "source_lang": "en"}
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zh_code = ">>zh<<"
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ORG_NAME = "Helsinki-NLP/"
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class MarianTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = MarianTokenizer
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def setUp(self):
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super().setUp()
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vocab = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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save_dir = Path(self.tmpdirname)
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save_json(vocab_tokens, save_dir / vocab_files_names["vocab"])
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save_json(mock_tokenizer_config, save_dir / vocab_files_names["tokenizer_config_file"])
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if not (save_dir / vocab_files_names["source_spm"]).exists():
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copyfile(SAMPLE_SP, save_dir / vocab_files_names["source_spm"])
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copyfile(SAMPLE_SP, save_dir / vocab_files_names["target_spm"])
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tokenizer = MarianTokenizer.from_pretrained(self.tmpdirname)
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tokenizer.save_pretrained(self.tmpdirname)
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def get_tokenizer(self, **kwargs) -> MarianTokenizer:
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return MarianTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_input_output_texts(self, tokenizer):
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return (
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"This is a test",
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"This is a test",
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)
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@slow
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def test_tokenizer_equivalence_en_de(self):
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en_de_tokenizer = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de")
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batch = en_de_tokenizer.prepare_translation_batch(["I am a small frog"], return_tensors=None)
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self.assertIsInstance(batch, BatchEncoding)
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expected = [38, 121, 14, 697, 38848, 0]
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self.assertListEqual(expected, batch.input_ids[0])
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