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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>
91 lines
3.6 KiB
Python
91 lines
3.6 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 tempfile
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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.testing_utils import _torch_available
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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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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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FRAMEWORK = "pt" if _torch_available else "tf"
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class MarianTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = MarianTokenizer
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test_rust_tokenizer = False
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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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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_seq2seq_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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save_dir = tempfile.mkdtemp()
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en_de_tokenizer.save_pretrained(save_dir)
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contents = [x.name for x in Path(save_dir).glob("*")]
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self.assertIn("source.spm", contents)
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MarianTokenizer.from_pretrained(save_dir)
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def test_outputs_not_longer_than_maxlen(self):
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tok = self.get_tokenizer()
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batch = tok.prepare_seq2seq_batch(["I am a small frog" * 1000, "I am a small frog"], return_tensors=FRAMEWORK)
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual(batch.input_ids.shape, (2, 512))
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def test_outputs_can_be_shorter(self):
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tok = self.get_tokenizer()
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batch_smaller = tok.prepare_seq2seq_batch(["I am a tiny frog", "I am a small frog"], return_tensors=FRAMEWORK)
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self.assertIsInstance(batch_smaller, BatchEncoding)
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self.assertEqual(batch_smaller.input_ids.shape, (2, 10))
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