wehub-resource-sync
279 lines
14 KiB
Python
279 lines
14 KiB
Python
# coding=utf-8
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# Copyright (c) 2022 PaddlePaddle Authors. 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 tempfile
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import unittest
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from typing import List
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from paddlenlp.transformers import PretrainedTokenizer, UNIMOTokenizer
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from paddlenlp.transformers.tokenizer_utils_base import PretrainedTokenizerBase
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from ...testing_utils import get_tests_dir
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SAMPLE_VOCAB = get_tests_dir("fixtures/vocab.zh.unimo.txt")
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class UNIMOTokenizationTest(unittest.TestCase):
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tokenizer_class = UNIMOTokenizer
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test_sentencepiece = True
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from_pretrained_vocab_key = "vocab_file"
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test_seq2seq = False
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test_offsets = False
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space_between_special_tokens = False
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from_pretrained_kwargs = None
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from_pretrained_filter = None
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test_sentencepiece_ignore_case = False
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def setUp(self):
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super().setUp()
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tokenizers_list = [
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(
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self.tokenizer_class,
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pretrained_name,
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self.from_pretrained_kwargs if self.from_pretrained_kwargs is not None else {},
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)
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for pretrained_name in self.tokenizer_class.pretrained_resource_files_map[
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self.from_pretrained_vocab_key
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].keys()
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if self.from_pretrained_filter is None
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or (self.from_pretrained_filter is not None and self.from_pretrained_filter(pretrained_name))
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]
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self.tokenizers_list = tokenizers_list[:1]
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with open(f"{get_tests_dir()}/sample_text.txt", encoding="utf-8") as f_data:
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self._data = f_data.read().replace("\n\n", "\n").strip()
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self.tmpdirname = tempfile.mkdtemp()
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tokenizer = UNIMOTokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def get_tokenizers(self, **kwargs) -> List[PretrainedTokenizerBase]:
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return [self.get_tokenizer(**kwargs)]
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def get_tokenizer(self, **kwargs) -> PretrainedTokenizer:
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return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
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def test_get_vocab(self):
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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vocab_dict = tokenizer.get_vocab()
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self.assertIsInstance(vocab_dict, dict)
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self.assertGreaterEqual(len(tokenizer), len(vocab_dict))
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vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
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self.assertEqual(len(vocab), len(tokenizer))
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tokenizer.add_tokens(["asdfasdfasdfasdf"])
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vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
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self.assertEqual(len(vocab), len(tokenizer))
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def test_right_and_left_padding(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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sequence = "Sequence"
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padding_size = 10
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# check correct behaviour if no pad_token_id exists and add it eventually
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self._check_no_pad_token_padding(tokenizer, sequence)
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padding_idx = tokenizer.pad_token_id
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# RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
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tokenizer.padding_side = "right"
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encoded_sequence = tokenizer.encode(sequence)["input_ids"]
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sequence_length = len(encoded_sequence)
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padded_sequence = tokenizer.encode(
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sequence, max_length=sequence_length + padding_size, padding="max_length"
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)["input_ids"]
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padded_sequence_length = len(padded_sequence)
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self.assertEqual(sequence_length + padding_size, padded_sequence_length)
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self.assertEqual(encoded_sequence + [padding_idx] * padding_size, padded_sequence)
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# LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
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tokenizer.padding_side = "left"
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encoded_sequence = tokenizer.encode(sequence)["input_ids"]
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sequence_length = len(encoded_sequence)
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padded_sequence = tokenizer.encode(
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sequence, max_length=sequence_length + padding_size, padding="max_length"
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)["input_ids"]
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padded_sequence_length = len(padded_sequence)
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self.assertEqual(sequence_length + padding_size, padded_sequence_length)
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self.assertEqual([padding_idx] * padding_size + encoded_sequence, padded_sequence)
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# RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding'
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encoded_sequence = tokenizer.encode(sequence)["input_ids"]
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sequence_length = len(encoded_sequence)
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tokenizer.padding_side = "right"
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padded_sequence_right = tokenizer.encode(sequence, padding=True)["input_ids"]
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padded_sequence_right_length = len(padded_sequence_right)
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self.assertEqual(sequence_length, padded_sequence_right_length)
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self.assertEqual(encoded_sequence, padded_sequence_right)
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tokenizer.padding_side = "left"
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padded_sequence_left = tokenizer.encode(sequence, padding="longest")["input_ids"]
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padded_sequence_left_length = len(padded_sequence_left)
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self.assertEqual(sequence_length, padded_sequence_left_length)
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self.assertEqual(encoded_sequence, padded_sequence_left)
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tokenizer.padding_side = "right"
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padded_sequence_right = tokenizer.encode(sequence)["input_ids"]
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padded_sequence_right_length = len(padded_sequence_right)
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self.assertEqual(sequence_length, padded_sequence_right_length)
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self.assertEqual(encoded_sequence, padded_sequence_right)
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tokenizer.padding_side = "left"
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padded_sequence_left = tokenizer.encode(sequence, padding=False)["input_ids"]
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padded_sequence_left_length = len(padded_sequence_left)
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self.assertEqual(sequence_length, padded_sequence_left_length)
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self.assertEqual(encoded_sequence, padded_sequence_left)
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def test_right_and_left_truncation(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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sequence = "This is a test sequence"
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# RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
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truncation_size = 3
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tokenizer.truncation_side = "right"
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encoded_sequence = tokenizer.encode(sequence, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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sequence_length = len(encoded_sequence)
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# Remove EOS/BOS tokens
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truncated_sequence = tokenizer.encode(
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sequence,
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max_length=sequence_length - truncation_size,
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truncation=True,
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return_token_type_ids=None,
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add_special_tokens=False,
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)["input_ids"]
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truncated_sequence_length = len(truncated_sequence)
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self.assertEqual(sequence_length, truncated_sequence_length + truncation_size)
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self.assertEqual(encoded_sequence[:-truncation_size], truncated_sequence)
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# LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the truncation flag set to True
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tokenizer.truncation_side = "left"
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sequence_length = len(encoded_sequence)
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truncated_sequence = tokenizer.encode(
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sequence,
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max_length=sequence_length - truncation_size,
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truncation=True,
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return_token_type_ids=None,
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add_special_tokens=False,
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)["input_ids"]
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truncated_sequence_length = len(truncated_sequence)
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self.assertEqual(sequence_length, truncated_sequence_length + truncation_size)
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self.assertEqual(encoded_sequence[truncation_size:], truncated_sequence)
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# RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_truncation'
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sequence_length = len(encoded_sequence)
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tokenizer.truncation_side = "right"
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truncated_sequence_right = tokenizer.encode(
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sequence, truncation=True, return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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truncated_sequence_right_length = len(truncated_sequence_right)
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self.assertEqual(sequence_length, truncated_sequence_right_length)
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self.assertEqual(encoded_sequence, truncated_sequence_right)
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tokenizer.truncation_side = "left"
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truncated_sequence_left = tokenizer.encode(
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sequence, truncation="longest_first", return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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truncated_sequence_left_length = len(truncated_sequence_left)
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self.assertEqual(sequence_length, truncated_sequence_left_length)
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self.assertEqual(encoded_sequence, truncated_sequence_left)
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tokenizer.truncation_side = "right"
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truncated_sequence_right = tokenizer.encode(
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sequence, return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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truncated_sequence_right_length = len(truncated_sequence_right)
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self.assertEqual(sequence_length, truncated_sequence_right_length)
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self.assertEqual(encoded_sequence, truncated_sequence_right)
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tokenizer.truncation_side = "left"
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truncated_sequence_left = tokenizer.encode(
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sequence, truncation=False, return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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truncated_sequence_left_length = len(truncated_sequence_left)
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self.assertEqual(sequence_length, truncated_sequence_left_length)
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self.assertEqual(encoded_sequence, truncated_sequence_left)
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def test_padding_to_max_length(self):
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"""We keep this test for backward compatibility but it should be remove when `pad_to_max_seq_len` is deprecated."""
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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sequence = "Sequence"
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padding_size = 10
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# check correct behaviour if no pad_token_id exists and add it eventually
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self._check_no_pad_token_padding(tokenizer, sequence)
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padding_idx = tokenizer.pad_token_id
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# Check that it correctly pads when a maximum length is specified along with the padding flag set to True
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tokenizer.padding_side = "right"
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encoded_sequence = tokenizer.encode(sequence)["input_ids"]
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sequence_length = len(encoded_sequence)
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# FIXME: the next line should be padding(max_length) to avoid warning
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padded_sequence = tokenizer.encode(
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sequence, max_length=sequence_length + padding_size, pad_to_max_seq_len=True
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)["input_ids"]
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padded_sequence_length = len(padded_sequence)
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self.assertEqual(sequence_length + padding_size, padded_sequence_length)
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self.assertEqual(encoded_sequence + [padding_idx] * padding_size, padded_sequence)
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# Check that nothing is done when a maximum length is not specified
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encoded_sequence = tokenizer.encode(sequence)["input_ids"]
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sequence_length = len(encoded_sequence)
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tokenizer.padding_side = "right"
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padded_sequence_right = tokenizer.encode(sequence, pad_to_max_seq_len=True)["input_ids"]
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padded_sequence_right_length = len(padded_sequence_right)
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self.assertEqual(sequence_length, padded_sequence_right_length)
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self.assertEqual(encoded_sequence, padded_sequence_right)
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def _check_no_pad_token_padding(self, tokenizer, sequences):
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# if tokenizer does not have pad_token_id, an error should be thrown
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if tokenizer.pad_token_id is None:
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with self.assertRaises(ValueError):
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if isinstance(sequences, list):
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tokenizer.batch_encode(sequences, padding="longest")
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else:
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tokenizer.encode(sequences, padding=True)
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# add pad_token_id to pass subsequent tests
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tokenizer.add_special_tokens({"pad_token": "<PAD>"})
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def test_convert_tokens_to_string_format(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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with self.subTest(f"{tokenizer.__class__.__name__}"):
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tokens = ["今天", "天气"]
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string = tokenizer.convert_tokens_to_string(tokens)
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self.assertIsInstance(string, str)
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