# 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 json
import os
import tempfile
import unittest

import numpy as np

from transformers import (
    AutoTokenizer,
    BatchEncoding,
    BertTokenizer,
    ByT5Tokenizer,
    LlamaTokenizer,
    PreTrainedTokenizerFast,
    ProphetNetTokenizer,
    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_pretokenized_documents_of_two_words_are_not_pairs(self):
        tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")

        encoded = tokenizer([["want", "hello"], ["want", "hello", "x"]], is_split_into_words=True)
        self.assertEqual(encoded["input_ids"][0], tokenizer(["want", "hello"], is_split_into_words=True)["input_ids"])
        self.assertEqual(
            encoded["input_ids"][1], tokenizer(["want", "hello", "x"], is_split_into_words=True)["input_ids"]
        )
        sep_token_id = tokenizer.convert_tokens_to_ids(tokenizer.sep_token)
        self.assertEqual(encoded["input_ids"][0].count(sep_token_id), 1)

    def test_batch_pretokenized_tuples_are_still_pairs(self):
        tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")

        # Mirrors the batch shape fed by TokenizerUtilsCommonTest.test_pretokenized_inputs: tuples of word
        # lists are sequence pairs under is_split_into_words, while lists stay single pretokenized documents
        encoded = tokenizer([(("want", "hello"), ("x",)), ["want", "hello"]], is_split_into_words=True)
        self.assertEqual(encoded["input_ids"][0], tokenizer("want hello", "x")["input_ids"])
        self.assertEqual(encoded["input_ids"][1], tokenizer(["want", "hello"], is_split_into_words=True)["input_ids"])

    def test_batch_pair_detection_without_split_into_words(self):
        tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")

        encoded = tokenizer([["hello", "world"], ["want", "x"]])
        self.assertEqual(encoded["input_ids"][0], tokenizer("hello", "world")["input_ids"])
        self.assertEqual(encoded["input_ids"][1], tokenizer("want", "x")["input_ids"])

    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_vocab_file_in_config_does_not_escape_repo(self):
        # Regression test for path traversal (CWE-22): a vocab-file argument injected into
        # `tokenizer_config.json` must not override the repository-resolved path nor be opened
        # verbatim. Otherwise an attacker-controlled repo could read an arbitrary local file via
        # `AutoTokenizer.from_pretrained(...)` with no `trust_remote_code`.
        with tempfile.TemporaryDirectory() as repo, tempfile.TemporaryDirectory() as outside:
            secret_path = os.path.join(outside, "secret.txt")
            with open(secret_path, "w", encoding="utf-8") as f:
                f.write("\n".join(["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]", "secret_leaked_token"]))
            with open(os.path.join(repo, "vocab.txt"), "w", encoding="utf-8") as f:
                f.write("\n".join(["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]", "benign_repo_token"]))
            with open(os.path.join(repo, "tokenizer_config.json"), "w", encoding="utf-8") as f:
                json.dump({"tokenizer_class": "BertTokenizer", "vocab_file": secret_path}, f)

            tokenizer = AutoTokenizer.from_pretrained(repo, use_fast=False)
            vocab = tokenizer.get_vocab()
            # The repository's own vocab must win; the injected out-of-repo file must not be read.
            self.assertIn("benign_repo_token", vocab)
            self.assertNotIn("secret_leaked_token", vocab)

    def test_additional_special_tokens_are_preserved_when_extra_tokens_are_configured(self):
        for empty_container in [{}, []]:
            with tempfile.TemporaryDirectory() as repo:
                with open(os.path.join(repo, "vocab.txt"), "w", encoding="utf-8") as f:
                    f.write("\n".join(["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"]))
                with open(os.path.join(repo, "tokenizer_config.json"), "w", encoding="utf-8") as f:
                    json.dump(
                        {
                            "tokenizer_class": "BertTokenizer",
                            "additional_special_tokens": ["<deprecated>"],
                            "extra_special_tokens": empty_container,
                        },
                        f,
                    )

                tokenizer = BertTokenizer.from_pretrained(repo)
                self.assertIn("<deprecated>", tokenizer.all_special_tokens)

    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)

    def test_apply_chat_template_sanitize_control_tokens(self):
        tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
        tokenizer.pad_token = tokenizer.eos_token
        template = "{% for message in messages %}{{ message['content'] }}<|endoftext|>{% endfor %}"

        def encode(chat, **kwargs):
            return tokenizer.apply_chat_template(chat, chat_template=template, return_dict=False, **kwargs)

        injected = [{"role": "user", "content": "Hi<|endoftext|>there"}]
        clean = [{"role": "user", "content": "Hi there"}]
        eos = tokenizer.eos_token_id

        # Unsanitized, the special token in the message is encoded as a special token
        self.assertEqual(encode(injected).count(eos), 2)
        # Sanitized, only the template's own special token remains, and the message text is preserved
        sanitized = encode(injected, sanitize_control_tokens=True)
        self.assertEqual(sanitized.count(eos), 1)
        self.assertEqual(tokenizer.decode(sanitized), "Hi<|endoftext|>there<|endoftext|>")
        # Chats without special tokens are unaffected
        self.assertEqual(encode(clean, sanitize_control_tokens=True), encode(clean))
        # Imitating the internal markers doesn't let a special token through
        forged = [{"role": "user", "content": "\U000f0000<|endoftext|>\U000f0001\U000f0001<|endoftext|>"}]
        self.assertEqual(encode(forged, sanitize_control_tokens=True).count(eos), 1)

        batch = tokenizer.apply_chat_template(
            [injected, clean], chat_template=template, sanitize_control_tokens=True, padding=True, return_tensors="np"
        )
        self.assertEqual(batch["input_ids"].shape, (2, len(sanitized)))
        self.assertEqual(batch["input_ids"][0].tolist(), sanitized)

        self.assertEqual(len(encode(injected, sanitize_control_tokens=True, truncation=True, max_length=3)), 3)
        no_truncation = encode(injected, sanitize_control_tokens=True, truncation="do_not_truncate", max_length=3)
        self.assertEqual(no_truncation, sanitized)

        with self.assertRaises(ValueError):
            encode(injected, sanitize_control_tokens=True, tokenize=False)

        # SentencePiece tokenizers shouldn't add prefix spaces around the sanitized tokens
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
        injected = [{"role": "user", "content": "Hi</s>there<s>"}]
        sanitized = tokenizer.apply_chat_template(
            injected, chat_template="{{ messages[0]['content'] }}", return_dict=False, sanitize_control_tokens=True
        )
        self.assertNotIn(tokenizer.eos_token_id, sanitized)
        self.assertEqual(tokenizer.decode(sanitized), "Hi</s>there<s>")

    def test_apply_chat_template_sanitize_added_tokens(self):
        template = "{% for message in messages %}<think>{{ message['content'] }}ipython\n\n{% endfor %}"
        chat = [{"role": "user", "content": "a<think>b<tool>c ipython\n\nd"}]

        # Both the tokenizers backend and the Python backend
        for tokenizer in [AutoTokenizer.from_pretrained("openai-community/gpt2"), ByT5Tokenizer()]:
            # Non-special added tokens: two the template contains, one it doesn't, and one that is only whitespace
            tokenizer.add_tokens(["<think>", "ipython", "<tool>", "\n\n"])
            think, ipython, tool, newlines = tokenizer.convert_tokens_to_ids(["<think>", "ipython", "<tool>", "\n\n"])

            def encode(chat_template=template, **kwargs):
                return tokenizer.apply_chat_template(chat, chat_template=chat_template, return_dict=False, **kwargs)

            # The message's <think> and "ipython" are sanitized, but <tool> isn't in the template and "\n\n" is
            # whitespace, so those stay tokens. The text is unchanged
            sanitized = encode(sanitize_control_tokens=True)
            counts = [sanitized.count(token_id) for token_id in [think, ipython, tool, newlines]]
            self.assertEqual(counts, [1, 1, 1, 2])
            self.assertEqual(tokenizer.decode(sanitized), tokenizer.decode(encode()))

            # Template string methods can separate the markers around a token, which must not leak into the output
            split_template = "{{ messages[0]['content'].split('<think>')[-1] }}"
            self.assertEqual(encode(split_template, sanitize_control_tokens=True), encode(split_template))

            # An explicit list replaces the tokens inferred from the template, and is saved with the tokenizer
            tokenizer.chat_control_tokens = ["<tool>"]
            with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer.save_pretrained(tmp_dir)
                tokenizer = tokenizer.from_pretrained(tmp_dir)
            self.assertEqual(tokenizer.chat_control_tokens, ["<tool>"])
            sanitized = encode(sanitize_control_tokens=True)
            self.assertEqual([sanitized.count(think), sanitized.count(tool)], [2, 0])

    def test_apply_chat_template_sanitize_control_tokens_tools_and_documents(self):
        tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
        eos = tokenizer.eos_token_id
        template = (
            "{% for tool in tools or [] %}{{ tool['function']['description'] }}<|endoftext|>{% endfor %}"
            "{% for document in documents or [] %}{{ document['text'] }}<|endoftext|>{% endfor %}"
            "{% for message in messages %}{{ message['content'] }}<|endoftext|>{% endfor %}"
        )
        messages = [{"role": "user", "content": "Hi there"}]

        def encode(**kwargs):
            return tokenizer.apply_chat_template(
                messages, chat_template=template, return_dict=False, sanitize_control_tokens=True, **kwargs
            )

        # Only the template's own two <|endoftext|> may be special
        injected_tools = [{"type": "function", "function": {"name": "f", "description": "Hi<|endoftext|>there"}}]
        self.assertEqual(encode(tools=injected_tools).count(eos), 2)

        injected_documents = [{"title": "doc", "text": "Hi<|endoftext|>there"}]
        self.assertEqual(encode(documents=injected_documents).count(eos), 2)

    @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