wehub-resource-sync
274 lines
11 KiB
Python
274 lines
11 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2023 The Fairseq Authors, Microsoft Research, and the HuggingFace Inc. 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 AddedToken, SpeechT5Tokenizer
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from ..test_utils import get_tests_dir
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
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from ..test_tokenizer_common import TokenizerTesterMixin
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SPIECE_UNDERLINE = "▁"
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class SpeechT5TokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = SpeechT5Tokenizer
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test_rust_tokenizer = False
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test_sentencepiece = True
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def test_pretokenized_inputs(self, *args, **kwargs):
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pass
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def test_tokenizers_common_ids_setters(self, *args, **kwargs):
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pass
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def test_mask_output(self):
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pass
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def test_offsets_mapping(self):
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pass
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def test_offsets_mapping_with_unk(self):
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pass
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def test_special_tokens_mask(self):
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pass
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def test_special_tokens_mask_input_pairs(self):
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pass
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def test_right_and_left_padding(self):
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pass
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def test_encode_decode_with_spaces(self):
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# TODO Fix decode in tokenizer.
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pass
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def test_add_special_tokens(self):
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pass
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def test_padding_to_multiple_of(self):
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pass
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def test_batch_encode_plus_batch_sequence_length(self):
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# Tests that all encoded values have the correct size
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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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sequences = [
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"Testing batch encode plus",
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"Testing batch encode plus with different sequence lengths",
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"Testing batch encode plus with different sequence lengths correctly pads",
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]
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encoded_sequences = [tokenizer.encode(sequence) for sequence in sequences]
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encoded_sequences_batch = tokenizer.batch_encode(sequences, padding=False)
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self.assertListEqual(
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encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
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)
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maximum_length = len(
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max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len)
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)
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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, sequences)
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encoded_sequences_padded = [
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tokenizer.encode(sequence, max_length=maximum_length, padding="max_length")
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for sequence in sequences
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]
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encoded_sequences_batch_padded = tokenizer.batch_encode(sequences, padding=True)
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self.assertListEqual(
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encoded_sequences_padded,
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self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded),
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)
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# check 'longest' is unsensitive to a max length
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encoded_sequences_batch_padded_1 = tokenizer.batch_encode(sequences, padding=True)
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encoded_sequences_batch_padded_2 = tokenizer.batch_encode(
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sequences, max_length=maximum_length + 10, padding="longest"
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)
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for key in encoded_sequences_batch_padded_1.keys():
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self.assertListEqual(
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encoded_sequences_batch_padded_1[key],
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encoded_sequences_batch_padded_2[key],
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)
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# check 'no_padding' is unsensitive to a max length
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encoded_sequences_batch_padded_1 = tokenizer.batch_encode(sequences, padding=False)
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encoded_sequences_batch_padded_2 = tokenizer.batch_encode(
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sequences, max_length=maximum_length + 10, padding=False
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)
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for key in encoded_sequences_batch_padded_1.keys():
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self.assertListEqual(
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encoded_sequences_batch_padded_1[key],
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encoded_sequences_batch_padded_2[key],
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)
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def test_consecutive_unk_string(self):
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tokenizer = self.get_tokenizer(add_bos_token=False)
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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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add_special_tokens=False,
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runcation=True,
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return_offsets_mapping=True,
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)
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self.assertEqual(len(encoding["input_ids"]), 2)
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self.assertEqual(len(encoding["offset_mapping"]), 2)
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = SpeechT5Tokenizer(SAMPLE_VOCAB)
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mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
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tokenizer.mask_token = mask_token
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tokenizer.add_special_tokens({"mask_token": mask_token})
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tokenizer.add_tokens(["<ctc_blank>"])
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tokenizer.save_pretrained(self.tmpdirname)
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def get_input_output_texts(self, tokenizer):
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input_text = "this is a test"
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output_text = "this is a test"
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return input_text, output_text
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def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5):
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input_text, output_text = self.get_input_output_texts(tokenizer)
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ids = tokenizer.encode(output_text, add_special_tokens=False)
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text = tokenizer.decode(ids["input_ids"], clean_up_tokenization_spaces=False)
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return text, ids["input_ids"]
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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 = "<pad>"
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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[0], "<s>")
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self.assertEqual(vocab_keys[1], "<pad>")
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self.assertEqual(vocab_keys[-4], "œ")
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self.assertEqual(vocab_keys[-2], "<mask>")
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self.assertEqual(vocab_keys[-1], "<ctc_blank>")
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self.assertEqual(len(vocab_keys), 81)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 79)
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def test_add_tokens_tokenizer(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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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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# We usually have added tokens from the start in tests because our vocab fixtures are
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# smaller than the original vocabs - let's not assert this
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# self.assertEqual(vocab_size, all_size)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)[
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"input_ids"
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]
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
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new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
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added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
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vocab_size_3 = tokenizer.vocab_size
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all_size_3 = len(tokenizer)
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self.assertNotEqual(vocab_size_3, 0)
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self.assertEqual(vocab_size, vocab_size_3)
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self.assertEqual(added_toks_2, len(new_toks_2))
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self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
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tokens = tokenizer.encode(
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">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
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)["input_ids"]
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self.assertGreaterEqual(len(tokens), 6)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[0], tokens[1])
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self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-3], tokens[-4])
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self.assertEqual(tokens[0], tokenizer.eos_token_id)
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self.assertEqual(tokens[-3], tokenizer.pad_token_id)
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def test_pickle_subword_regularization_tokenizer(self):
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pass
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def test_subword_regularization_tokenizer(self):
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pass
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def test_full_tokenizer(self):
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tokenizer = self.get_tokenizer()
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tokens = tokenizer.tokenize("This is a test")
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# fmt: off
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self.assertListEqual(tokens, [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't'])
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# fmt: on
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens),
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[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6],
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)
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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# fmt: off
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[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']
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# fmt: on
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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# fmt: off
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self.assertListEqual(ids, [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26])
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# fmt: on
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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# fmt: off
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[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']
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# fmt: on
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)
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