456 lines
20 KiB
Python
456 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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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 json
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import os
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import tempfile
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import unittest
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from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, SiglipTokenizer
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from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
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from transformers.utils import cached_property, is_tf_available, is_torch_available
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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if is_torch_available():
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FRAMEWORK = "pt"
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elif is_tf_available():
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FRAMEWORK = "tf"
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else:
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FRAMEWORK = "jax"
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@require_sentencepiece
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@require_tokenizers
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class SiglipTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "google/siglip-base-patch16-224"
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tokenizer_class = SiglipTokenizer
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test_rust_tokenizer = False
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test_sentencepiece = True
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test_sentencepiece_ignore_case = True
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# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.setUp with T5->Siglip
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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 = SiglipTokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_convert_token_and_id with T5->Siglip
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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[0], "<unk>")
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self.assertEqual(vocab_keys[1], "<s>")
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def test_full_tokenizer(self):
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tokenizer = SiglipTokenizer(SAMPLE_VOCAB)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁this", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [66, 46, 10, 170, 382])
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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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[
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SPIECE_UNDERLINE,
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"i",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(ids, [7, 23, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 12, 66, 46, 72, 80, 6, 0])
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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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[
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SPIECE_UNDERLINE,
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"i",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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],
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)
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@cached_property
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def siglip_tokenizer(self):
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return SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224")
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# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.get_tokenizer with T5->Siglip
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def get_tokenizer(self, **kwargs) -> SiglipTokenizer:
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return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
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# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_rust_and_python_full_tokenizers with T5->Siglip
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def test_rust_and_python_full_tokenizers(self):
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer()
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sequence = "I was born in 92000, and this is falsé."
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tokens = tokenizer.tokenize(sequence)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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rust_tokenizer = self.get_rust_tokenizer()
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ids = tokenizer.encode(sequence)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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def test_eos_treatment(self):
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tokenizer = self.siglip_tokenizer
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batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
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batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
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self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])
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def test_prepare_batch(self):
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tokenizer = self.siglip_tokenizer
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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expected_src_tokens = [262, 266, 476, 8532, 270, 4460, 3949, 1682, tokenizer.eos_token_id]
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batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
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self.assertIsInstance(batch, BatchEncoding)
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if FRAMEWORK != "jax":
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result = list(batch.input_ids.numpy()[0])
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else:
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result = list(batch.input_ids.tolist()[0])
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self.assertListEqual(expected_src_tokens, result)
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self.assertEqual((2, 9), batch.input_ids.shape)
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def test_empty_target_text(self):
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tokenizer = self.siglip_tokenizer
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
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# check if input_ids are returned and no decoder_input_ids
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self.assertIn("input_ids", batch)
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self.assertNotIn("decoder_input_ids", batch)
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self.assertNotIn("decoder_attention_mask", batch)
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def test_max_length(self):
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tokenizer = self.siglip_tokenizer
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tgt_text = ["Summary of the text.", "Another summary."]
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targets = tokenizer(
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text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
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)
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self.assertEqual(32, targets["input_ids"].shape[1])
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def test_eos_in_input(self):
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tokenizer = self.siglip_tokenizer
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src_text = ["A long paragraph for summarization. </s>"]
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tgt_text = ["Summary of the text. </s>"]
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expected_src_tokens = [262, 266, 476, 8532, 270, 4460, 3949, 1682, 1]
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expected_tgt_tokens = [6254, 267, 260, 1443, 1]
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batch = tokenizer(src_text, text_target=tgt_text)
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self.assertEqual(expected_src_tokens, batch["input_ids"][0])
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self.assertEqual(expected_tgt_tokens, batch["labels"][0])
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@unittest.skip(reason="SiglipTokenizer strips the punctuation")
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def test_subword_regularization_tokenizer(self):
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pass
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@unittest.skip(reason="SiglipTokenizer strips the punctuation")
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def test_pickle_subword_regularization_tokenizer(self):
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pass
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# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_special_tokens_initialization with T5->Siglip
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def test_special_tokens_initialization(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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added_tokens = [f"<extra_id_{i}>" for i in range(100)] + [AddedToken("<special>", lstrip=True)]
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
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)
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tokenizer_p = self.tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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p_output = tokenizer_p.encode("Hey this is a <special> token")
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r_output = tokenizer_r.encode("Hey this is a <special> token")
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cr_output = tokenizer_cr.encode("Hey this is a <special> token")
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special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
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self.assertEqual(p_output, r_output)
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self.assertEqual(cr_output, r_output)
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self.assertTrue(special_token_id in p_output)
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self.assertTrue(special_token_id in r_output)
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self.assertTrue(special_token_id in cr_output)
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# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_special_tokens_initialization_with_non_empty_additional_special_tokens with T5->Siglip
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def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
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tokenizer_list = []
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if self.test_slow_tokenizer:
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tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
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if self.test_rust_tokenizer:
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tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
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for tokenizer_class, tokenizer_utils in tokenizer_list:
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with tempfile.TemporaryDirectory() as tmp_dir:
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tokenizer_utils.save_pretrained(tmp_dir)
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with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
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special_tokens_map = json.load(json_file)
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with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
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tokenizer_config = json.load(json_file)
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added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)]
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special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
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"an_additional_special_token"
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]
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tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
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"an_additional_special_token"
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]
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with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
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json.dump(special_tokens_map, outfile)
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with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
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json.dump(tokenizer_config, outfile)
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# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
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# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
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# "special_tokens_map.json" files
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tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
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tmp_dir,
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)
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self.assertIn(
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"an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
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)
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# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # BySiglipTokenization no vocab
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self.assertEqual(
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["an_additional_special_token"],
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tokenizer_without_change_in_init.convert_ids_to_tokens(
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tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
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),
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)
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# Now we test that we can change the value of additional_special_tokens in the from_pretrained
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new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
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tokenizer = tokenizer_class.from_pretrained(
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tmp_dir,
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additional_special_tokens=new_added_tokens,
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)
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self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
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self.assertEqual(
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["a_new_additional_special_token"],
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tokenizer.convert_ids_to_tokens(
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tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
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),
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)
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def test_sentencepiece_tokenize_and_convert_tokens_to_string(self):
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"""Test ``_tokenize`` and ``convert_tokens_to_string``."""
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if not self.test_sentencepiece:
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return
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tokenizer = self.get_tokenizer()
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text = "This is text to test the tokenizer."
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if self.test_sentencepiece_ignore_case:
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text = text.lower()
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tokens = tokenizer.tokenize(text)
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self.assertTrue(len(tokens) > 0)
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# check if converting back to original text works
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reverse_text = tokenizer.convert_tokens_to_string(tokens)
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if self.test_sentencepiece_ignore_case:
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reverse_text = reverse_text.lower()
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expected_text = "this is text to test the tokenizer"
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self.assertEqual(reverse_text, expected_text)
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special_tokens = tokenizer.all_special_tokens
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special_tokens_string = tokenizer.convert_tokens_to_string(special_tokens)
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for special_token in special_tokens:
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self.assertIn(special_token, special_tokens_string)
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if self.test_rust_tokenizer:
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rust_tokenizer = self.get_rust_tokenizer()
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special_tokens_string_rust = rust_tokenizer.convert_tokens_to_string(special_tokens)
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self.assertEqual(special_tokens_string, special_tokens_string_rust)
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@slow
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def test_tokenizer_integration(self):
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tokenizer = SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224")
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# fmt: off
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texts = [
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'the real mountain view',
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'Zürich',
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'San Francisco',
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'a picture of a laptop with the lockscreen on, a cup of cappucino, salt and pepper grinders. The view through the window reveals lake Zürich and the Alps in the background of the city.',
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]
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expected_input_ids = [
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[260, 638, 3293, 870, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[262, 761, 5879, 5345, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[262, 264, 452, 20563, 15949, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[262, 266, 1357, 267, 262, 266, 4429, 275, 260, 3940, 6360, 277, 262, 266, 3064, 267, 3549, 388, 16538, 296, 298, 2617, 263, 4869, 14998, 264, 260, 870, 393, 260, 1710, 7958, 4324, 262, 761, 5879, 5345, 263, 260, 1518, 388, 264, 268, 260, 1970, 267, 260, 741, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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]
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# fmt: on
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for text, expected in zip(texts, expected_input_ids):
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input_ids = tokenizer(text, padding="max_length").input_ids
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self.assertListEqual(input_ids, expected)
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def test_some_edge_cases(self):
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tokenizer = SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224", legacy=False)
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sp_tokens = tokenizer.sp_model.encode("</s>>", out_type=str)
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self.assertEqual(sp_tokens, ["</", "s", ">", ">"])
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tokens = tokenizer.tokenize("</s>>")
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self.assertNotEqual(sp_tokens, tokens)
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self.assertEqual(tokens, ["</s>"])
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tokens = tokenizer.tokenize("")
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self.assertEqual(tokens, [])
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self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str))
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tokens = tokenizer.tokenize(" ")
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self.assertEqual(tokens, [])
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self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str))
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tokens = tokenizer.tokenize("▁")
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self.assertEqual(tokens, [])
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self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
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tokens = tokenizer.tokenize(" ▁")
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self.assertEqual(tokens, [])
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self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
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@require_sentencepiece
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@require_tokenizers
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class CommonSpmIntegrationTests(unittest.TestCase):
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"""
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A class that regroups important test to make sure that we properly handle the special tokens.
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"""
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@classmethod
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def setUpClass(cls):
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tokenizer = SiglipTokenizer(SAMPLE_VOCAB, extra_ids=0, legacy=False)
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tokenizer.add_special_tokens(
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{"additional_special_tokens": [AddedToken("<extra_id_0>", rstrip=False, lstrip=False)]}
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)
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cls.tokenizer = tokenizer
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def test_add_dummy_prefix(self):
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# make sure `'▁'` is prepended, and outputs match sp_model's
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# `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute
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input_ids = self.tokenizer.encode(". Hello", add_special_tokens=False)
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self.assertEqual(input_ids, [37, 86, 20])
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self.assertEqual(input_ids, [37, 86, 20])
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tokens = self.tokenizer.tokenize(". Hello")
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self.assertEqual(tokens, ["▁he", "ll", "o"])
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|
|
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tokens = self.tokenizer.tokenize("")
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self.assertEqual(tokens, [])
|
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self.assertEqual(tokens, self.tokenizer.sp_model.encode("", out_type=str))
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|
|
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tokens = self.tokenizer.tokenize(" ")
|
|
self.assertEqual(tokens, [])
|
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self.assertEqual(tokens, self.tokenizer.sp_model.encode(" ", out_type=str))
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|
|
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tokens = self.tokenizer.tokenize("▁")
|
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self.assertEqual(tokens, [])
|
|
self.assertEqual(tokens, self.tokenizer.sp_model.encode("▁", out_type=str))
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|
|
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def test_remove_extra_whitespaces(self):
|
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# make sure the extra spaces are eaten
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|
# sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute
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input_ids = self.tokenizer.encode(" . Hello", add_special_tokens=False)
|
|
self.assertEqual(input_ids, [37, 86, 20])
|
|
self.assertEqual(input_ids, [37, 86, 20])
|
|
tokens = self.tokenizer.tokenize(" . Hello")
|
|
self.assertEqual(tokens, ["▁he", "ll", "o"])
|
|
|
|
# `'▁'` is also a whitespace
|
|
input_ids = self.tokenizer.encode("▁He is not")
|
|
self.assertEqual(input_ids, [37, 46, 44, 2])
|
|
tokens = self.tokenizer.tokenize("▁He is not")
|
|
self.assertEqual(tokens, ["▁he", "▁is", "▁not"]) # no extra space added
|
|
|
|
input_ids = self.tokenizer.encode("▁He is not ▁He")
|
|
self.assertEqual(input_ids, [37, 46, 44, 37, 2])
|
|
tokens = self.tokenizer.tokenize("▁He is not ▁He")
|
|
self.assertEqual(tokens, ["▁he", "▁is", "▁not", "▁he"]) # spaces are eaten by spm even if not start
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