wehub-resource-sync
114 lines
4.8 KiB
Python
114 lines
4.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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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 paddlenlp.transformers import PegasusChineseTokenizer
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from tests.testing_utils import get_tests_dir
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from ..test_tokenizer_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/vocab.zh.pegasus.txt")
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class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = PegasusChineseTokenizer
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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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tokenizer = PegasusChineseTokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def get_tokenizer(self, **kwargs) -> PegasusChineseTokenizer:
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return PegasusChineseTokenizer.from_pretrained("IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese", **kwargs)
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def get_input_output_texts(self, tokenizer):
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return ("这是一个测试。", "这是一个测试。")
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "</s>"
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token_id = 1
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[-4], "<pad>")
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self.assertEqual(vocab_keys[-5], "</s>")
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self.assertEqual(vocab_keys[158], "v")
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self.assertEqual(len(vocab_keys), 50000)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 50000)
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def test_mask_tokens(self):
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tokenizer = self.get_tokenizer()
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# <mask_1> masks whole sentence while <mask_2> masks single word
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raw_input_str = "<mask_1> 为了确保银行决议的 <mask_2> 流动。"
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desired_result = [2, 7569, 26503, 33094, 10328, 3399, 3, 23514, 179, 1]
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ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
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self.assertListEqual(desired_result, ids)
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def test_tokenizer_settings(self):
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tokenizer = self.get_tokenizer()
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# The tracebacks for the following asserts are **better** without messages or self.assertEqual
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assert tokenizer.vocab_size == 50000
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assert tokenizer.pad_token_id == 0
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assert tokenizer.eos_token_id == 1
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assert tokenizer.offset == 100
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assert tokenizer.unk_token_id == tokenizer.offset == 100
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assert tokenizer.unk_token == "<unk>"
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assert tokenizer.model_max_length == 1024
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raw_input_str = "确保银行决议的顺利进行。"
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desired_result = [26503, 33094, 10328, 3399, 5396, 612, 4921, 4503, 179, 1]
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ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
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self.assertListEqual(desired_result, ids)
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assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3], skip_special_tokens=False) == [
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"<pad>",
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"</s>",
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"<mask_1>",
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"<mask_2>",
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]
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def test_seq2seq_truncation(self):
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tokenizer = self.get_tokenizer()
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src_texts = ["这将是一个很长很长的文本。" * 150, "short example"]
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tgt_texts = ["这个不是很长但是超过5个字。", "tiny"]
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batch = tokenizer(text=src_texts, padding=True, truncation=True, return_tensors="pd")
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targets = tokenizer(text=tgt_texts, max_length=5, padding=True, truncation=True, return_tensors="pd")
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assert batch.input_ids.shape == [2, 1024]
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assert batch.attention_mask.shape == [2, 1024]
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assert targets["input_ids"].shape == [2, 5]
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assert len(batch) == 2 # input_ids, attention_mask.
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def test_consecutive_unk_string(self):
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tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
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for tokenizer in tokenizers:
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tokens = [tokenizer.unk_token for _ in range(2)]
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string = tokenizer.convert_tokens_to_string(tokens)
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encoding = tokenizer(
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text=string,
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runcation=True,
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return_offsets_mapping=True,
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)
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# BOS is never used.
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self.assertEqual(len(encoding["input_ids"]), 3)
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self.assertEqual(len(encoding["offset_mapping"]), 3)
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