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tests/tokenization/test_tokenization_utils.py

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+# Copyright 2018 HuggingFace Inc..
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+ruff: isort: skip_file
+"""
+
+import os
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers import (
+    AutoTokenizer,
+    BatchEncoding,
+    BertTokenizer,
+    LlamaTokenizer,
+    PreTrainedTokenizerFast,
+    PythonBackend,
+    TensorType,
+    TokenSpan,
+    is_tokenizers_available,
+)
+from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
+from transformers.testing_utils import (
+    CaptureStderr,
+    require_sentencepiece,
+    require_tokenizers,
+    require_torch,
+    slow,
+)
+
+
+if is_tokenizers_available():
+    import tokenizers
+    from tokenizers import AddedToken, Tokenizer
+    from tokenizers.models import WordPiece
+
+
+class TokenizerUtilsTest(unittest.TestCase):
+    def check_tokenizer_from_pretrained(self, tokenizer_class):
+        # max_model_input_sizes is a legacy attribute that may not exist on all tokenizers
+        if not hasattr(tokenizer_class, "max_model_input_sizes"):
+            return
+
+        s3_models = list(tokenizer_class.max_model_input_sizes.keys())
+        for model_name in s3_models[:1]:
+            tokenizer = tokenizer_class.from_pretrained(model_name)
+            self.assertIsNotNone(tokenizer)
+            self.assertIsInstance(tokenizer, tokenizer_class)
+            self.assertIsInstance(tokenizer, PythonBackend)
+
+            for special_tok in tokenizer.all_special_tokens:
+                self.assertIsInstance(special_tok, str)
+                special_tok_id = tokenizer.convert_tokens_to_ids(special_tok)
+                self.assertIsInstance(special_tok_id, int)
+
+    @slow
+    def test_pretrained_tokenizers(self):
+        self.check_tokenizer_from_pretrained(GPT2Tokenizer)
+
+    def test_tensor_type_from_str(self):
+        self.assertEqual(TensorType("pt"), TensorType.PYTORCH)
+        self.assertEqual(TensorType("np"), TensorType.NUMPY)
+
+    @require_tokenizers
+    def test_batch_encoding_word_to_tokens(self):
+        tokenizer_r = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
+        encoded = tokenizer_r(["Test", "\xad", "test"], is_split_into_words=True)
+
+        self.assertEqual(encoded.word_to_tokens(0), TokenSpan(start=1, end=2))
+        self.assertEqual(encoded.word_to_tokens(1), None)
+        self.assertEqual(encoded.word_to_tokens(2), TokenSpan(start=2, end=3))
+
+    def test_batch_encoding_with_labels(self):
+        batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
+        tensor_batch = batch.convert_to_tensors(tensor_type="np")
+        self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
+        self.assertEqual(tensor_batch["labels"].shape, (2,))
+        # test converting the converted
+        with CaptureStderr() as cs:
+            tensor_batch = batch.convert_to_tensors(tensor_type="np")
+        self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
+
+        batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
+        tensor_batch = batch.convert_to_tensors(tensor_type="np", prepend_batch_axis=True)
+        self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
+        self.assertEqual(tensor_batch["labels"].shape, (1,))
+
+    @require_torch
+    def test_batch_encoding_with_labels_pt(self):
+        batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
+        tensor_batch = batch.convert_to_tensors(tensor_type="pt")
+        self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
+        self.assertEqual(tensor_batch["labels"].shape, (2,))
+        # test converting the converted
+        with CaptureStderr() as cs:
+            tensor_batch = batch.convert_to_tensors(tensor_type="pt")
+        self.assertFalse(len(cs.err), msg=f"should have no warning, but got {cs.err}")
+
+        batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
+        tensor_batch = batch.convert_to_tensors(tensor_type="pt", prepend_batch_axis=True)
+        self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
+        self.assertEqual(tensor_batch["labels"].shape, (1,))
+
+    def test_padding_accepts_tensors(self):
+        features = [{"input_ids": np.array([0, 1, 2])}, {"input_ids": np.array([0, 1, 2, 3])}]
+        tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
+
+        batch = tokenizer.pad(features, padding=True)
+        self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
+        self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
+        batch = tokenizer.pad(features, padding=True, return_tensors="np")
+        self.assertTrue(isinstance(batch["input_ids"], np.ndarray))
+        self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
+
+    @require_tokenizers
+    def test_decoding_single_token(self):
+        for tokenizer_class in [BertTokenizer, BertTokenizer]:
+            with self.subTest(f"{tokenizer_class}"):
+                tokenizer = tokenizer_class.from_pretrained("google-bert/bert-base-cased")
+
+                token_id = 2300
+                decoded_flat = tokenizer.decode(token_id)
+                decoded_list = tokenizer.decode([token_id])
+
+                self.assertEqual(decoded_flat, "Force")
+                self.assertEqual(decoded_list, "Force")
+
+                token_id = 0
+                decoded_flat = tokenizer.decode(token_id)
+                decoded_list = tokenizer.decode([token_id])
+
+                self.assertEqual(decoded_flat, "[PAD]")
+                self.assertEqual(decoded_list, "[PAD]")
+
+                last_item_id = tokenizer.vocab_size - 1
+                decoded_flat = tokenizer.decode(last_item_id)
+                decoded_list = tokenizer.decode([last_item_id])
+
+                self.assertEqual(decoded_flat, "##：")
+                self.assertEqual(decoded_list, "##：")
+
+    def test_extra_special_tokens_multimodal(self):
+        attribute_special_tokens_list = [
+            "bos_token",
+            "eos_token",
+            "unk_token",
+            "sep_token",
+            "pad_token",
+            "cls_token",
+            "mask_token",
+        ]
+        llama_tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b")
+        llama_tokenizer._set_model_specific_special_tokens(
+            {
+                "boi_token": "<image_start>",
+                "eoi_token": "<image_end>",
+                "image_token": "<image>",
+            }
+        )
+        multimodal_special_tokens_list = attribute_special_tokens_list + ["boi_token", "eoi_token", "image_token"]
+        self.assertListEqual(llama_tokenizer.SPECIAL_TOKENS_ATTRIBUTES, multimodal_special_tokens_list)
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            llama_tokenizer.save_pretrained(tmpdirname)
+
+            # load back and check we have extra special tokens set
+            loaded_tokenizer = LlamaTokenizer.from_pretrained(tmpdirname)
+            multimodal_special_tokens_list = attribute_special_tokens_list + ["boi_token", "eoi_token", "image_token"]
+            self.assertListEqual(loaded_tokenizer.SPECIAL_TOKENS_ATTRIBUTES, multimodal_special_tokens_list)
+
+            # We set an image_token_id before, so we can get an "image_token" as str that matches the id
+            self.assertTrue(loaded_tokenizer.image_token == "<image>")
+            self.assertTrue(loaded_tokenizer.image_token_id == loaded_tokenizer.convert_tokens_to_ids("<image>"))
+
+        # save one more time and make sure the image token can get loaded back
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            loaded_tokenizer.save_pretrained(tmpdirname)
+            loaded_tokenizer_with_extra_tokens = LlamaTokenizer.from_pretrained(tmpdirname)
+            self.assertTrue(loaded_tokenizer_with_extra_tokens.image_token == "<image>")
+
+        # test that we can also indicate extra tokens during load time
+        extra_special_tokens = {
+            "boi_token": "<image_start>",
+            "eoi_token": "<image_end>",
+            "image_token": "<image>",
+        }
+        tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", extra_special_tokens=extra_special_tokens)
+        self.assertTrue(tokenizer.image_token == "<image>")
+        self.assertTrue(tokenizer.image_token_id == loaded_tokenizer.convert_tokens_to_ids("<image>"))
+
+    @require_tokenizers
+    def test_decoding_skip_special_tokens(self):
+        for tokenizer_class in [BertTokenizer, BertTokenizer]:
+            with self.subTest(f"{tokenizer_class}"):
+                tokenizer = tokenizer_class.from_pretrained("google-bert/bert-base-cased")
+                tokenizer.add_tokens(["ஐ"], special_tokens=True)
+
+                # test special token with other tokens, skip the special tokens
+                sentence = "This is a beautiful flower ஐ"
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "This is a beautiful flower")
+
+                # test special token with other tokens, do not skip the special tokens
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[CLS] This is a beautiful flower ஐ [SEP]")
+
+                # test special token stand alone, skip the special tokens
+                sentence = "ஐ"
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "")
+
+                # test special token stand alone, do not skip the special tokens
+                ids = tokenizer(sentence)["input_ids"]
+                decoded_sent = tokenizer.decode(ids, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[CLS] ஐ [SEP]")
+
+                # test single special token alone, skip
+                pad_id = 0
+                decoded_sent = tokenizer.decode(pad_id, skip_special_tokens=True)
+                self.assertEqual(decoded_sent, "")
+
+                # test single special token alone, do not skip
+                decoded_sent = tokenizer.decode(pad_id, skip_special_tokens=False)
+                self.assertEqual(decoded_sent, "[PAD]")
+
+    @require_torch
+    def test_padding_accepts_tensors_pt(self):
+        import torch
+
+        features = [{"input_ids": torch.tensor([0, 1, 2])}, {"input_ids": torch.tensor([0, 1, 2, 3])}]
+        tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
+
+        batch = tokenizer.pad(features, padding=True)
+        self.assertTrue(isinstance(batch["input_ids"], torch.Tensor))
+        self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
+        batch = tokenizer.pad(features, padding=True, return_tensors="pt")
+        self.assertTrue(isinstance(batch["input_ids"], torch.Tensor))
+        self.assertEqual(batch["input_ids"].tolist(), [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]])
+
+    @require_tokenizers
+    def test_instantiation_from_tokenizers(self):
+        bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
+        PreTrainedTokenizerFast(tokenizer_object=bert_tokenizer)
+
+    @require_tokenizers
+    def test_instantiation_from_tokenizers_json_file(self):
+        bert_tokenizer = Tokenizer(WordPiece(unk_token="[UNK]"))
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            bert_tokenizer.save(os.path.join(tmpdirname, "tokenizer.json"))
+            PreTrainedTokenizerFast(tokenizer_file=os.path.join(tmpdirname, "tokenizer.json"))
+
+    def test_len_tokenizer(self):
+        for tokenizer_class in [BertTokenizer, BertTokenizer]:
+            with self.subTest(f"{tokenizer_class}"):
+                tokenizer = tokenizer_class.from_pretrained("bert-base-uncased")
+                added_tokens_size = len(tokenizer.added_tokens_decoder)
+                self.assertEqual(len(tokenizer), tokenizer.vocab_size)
+
+                tokenizer.add_tokens(["<test_token>"])
+                self.assertEqual(len(tokenizer), tokenizer.vocab_size + 1)
+                self.assertEqual(len(tokenizer.added_tokens_decoder), added_tokens_size + 1)
+                self.assertEqual(len(tokenizer.added_tokens_encoder), added_tokens_size + 1)
+
+    @require_sentencepiece
+    def test_sentencepiece_cohabitation(self):
+        from sentencepiece import sentencepiece_model_pb2 as _original_protobuf  # noqa: F401
+
+        from transformers.convert_slow_tokenizer import import_protobuf  # noqa: F401
+
+        # Now this will try to import sentencepiece_model_pb2_new.py. This should not fail even if the protobuf
+        # was already imported.
+        import_protobuf()
+
+    def test_training_new_tokenizer_edge_cases(self):
+        _tokenizer = Tokenizer(tokenizers.models.BPE(vocab={"a": 1, "b": 2, "ab": 3}, merges=[("a", "b")]))
+        _tokenizer.pre_tokenizer = None
+
+        tokenizer = PreTrainedTokenizerFast(tokenizer_object=_tokenizer)
+        toy_text_iterator = ("a" for _ in range(1000))
+        tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
+
+        _tokenizer.normalizer = None
+        tokenizer = PreTrainedTokenizerFast(tokenizer_object=_tokenizer)
+        toy_text_iterator = ("a" for _ in range(1000))
+        tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
+
+        _tokenizer.post_processor = None
+        tokenizer = PreTrainedTokenizerFast(tokenizer_object=_tokenizer)
+        toy_text_iterator = ("a" for _ in range(1000))
+        tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
+
+    def test_encode_message(self):
+        tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
+        conversation = [
+            {"role": "system", "content": "You are a helpful assistant"},
+            {"role": "user", "content": "Hey there, how are you?"},
+            {"role": "assistant", "content": "Thank you for asking, I am doing well"},
+            {"role": "user", "content": "What's the weather like today?"},
+            {"role": "assistant", "content": "Today the weather is nice"},
+        ]
+
+        # First, test the default case, where we encode the whole conversation at once
+        whole_conversation_tokens = tokenizer.apply_chat_template(conversation, tokenize=True, return_dict=False)
+
+        # Now, test the message-by-message encoding
+        tokens = []
+        for i, message in enumerate(conversation):
+            tokens += tokenizer.encode_message_with_chat_template(message, conversation_history=conversation[:i])
+
+        self.assertEqual(whole_conversation_tokens, tokens)
+
+    def test_encode_message_raises_on_add_generation_prompt(self):
+        tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
+        conversation = [
+            {"role": "system", "content": "You are a helpful assistant"},
+            {"role": "user", "content": "Hey there, how are you?"},
+        ]
+        with self.assertRaises(ValueError):
+            tokenizer.encode_message_with_chat_template(conversation[0], add_generation_prompt=True)
+
+    @require_tokenizers
+    def test_special_tokens_overwrite(self):
+        text_with_nonspecial_tokens = "there are 2 cats"  # '2' is originally special
+
+        tokenizer = LlamaTokenizer.from_pretrained("hf-internal-testing/Ernie4_5_Tokenizer")
+        # Overwrite special tokens 0-9 to non-special
+        tokenizer.add_tokens([AddedToken(f"{i}", normalized=False, special=False) for i in range(10)])
+        self.assertTrue(
+            tokenizer.decode(tokenizer.encode(text_with_nonspecial_tokens), skip_special_tokens=True)
+            == text_with_nonspecial_tokens
+        )
+
+        # Checking if this carries over even after saving and relaoding
+        tokenizer.save_pretrained("/tmp/ernie_tokenizer")
+        new_tokenizer = AutoTokenizer.from_pretrained("/tmp/ernie_tokenizer")
+        self.assertTrue(
+            new_tokenizer.decode(new_tokenizer.encode(text_with_nonspecial_tokens), skip_special_tokens=True)
+            == text_with_nonspecial_tokens
+        )
+
+    def test_import_protobuf_decode_error_without_protobuf(self):
+        from unittest.mock import patch
+
+        from transformers.tokenization_utils_base import import_protobuf_decode_error
+
+        with patch("transformers.tokenization_utils_base.is_protobuf_available", return_value=False):
+            result = import_protobuf_decode_error()
+            self.assertEqual(result, ())
+
+    def test_import_protobuf_decode_error_does_not_mask_exceptions(self):
+        from unittest.mock import patch
+
+        from transformers.tokenization_utils_base import import_protobuf_decode_error
+
+        with patch("transformers.tokenization_utils_base.is_protobuf_available", return_value=False):
+            with self.assertRaises(ValueError):
+                try:
+                    raise ValueError("real error")
+                except import_protobuf_decode_error():
+                    pass