diff --git a/paddlenlp/transformers/roformerv2/modeling.py b/paddlenlp/transformers/roformerv2/modeling.py
index 857afe2802e1..f41f3354e548 100644
--- a/paddlenlp/transformers/roformerv2/modeling.py
+++ b/paddlenlp/transformers/roformerv2/modeling.py
@@ -520,6 +520,12 @@ def forward(self,
 
         return outputs
 
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, embedding: nn.Embedding):
+        self.embeddings.word_embeddings = embedding
+
 
 class RoFormerv2ForQuestionAnswering(RoFormerv2PretrainedModel):
     """
diff --git a/paddlenlp/transformers/roformerv2/tokenizer.py b/paddlenlp/transformers/roformerv2/tokenizer.py
index a62153bc9040..a96ce67cfe5a 100644
--- a/paddlenlp/transformers/roformerv2/tokenizer.py
+++ b/paddlenlp/transformers/roformerv2/tokenizer.py
@@ -100,6 +100,13 @@ class RoFormerv2Tokenizer(PretrainedTokenizer):
             "do_lower_case": True
         },
     }
+
+    # TODO(wj-Mcat): to be confirmed
+    max_model_input_sizes = {
+        "roformer_v2_chinese_char_small": 1024,
+        "roformer_v2_chinese_char_base": 1024,
+        "roformer_v2_chinese_char_large": 1024,
+    }
     padding_side = "right"
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 
diff --git a/tests/transformers/roformerv2/__init__.py b/tests/transformers/roformerv2/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/transformers/roformerv2/test_modeling.py b/tests/transformers/roformerv2/test_modeling.py
new file mode 100644
index 000000000000..3d043d0c0794
--- /dev/null
+++ b/tests/transformers/roformerv2/test_modeling.py
@@ -0,0 +1,348 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+import unittest
+from typing import Optional, Tuple
+from dataclasses import dataclass, fields, Field
+
+import paddle
+from paddlenlp.transformers import (
+    RoFormerv2Model,
+    RoFormerv2ForMaskedLM,
+    RoFormerv2PretrainedModel,
+    RoFormerv2ForSequenceClassification,
+    RoFormerv2ForTokenClassification,
+    RoFormerv2ForQuestionAnswering,
+    RoFormerv2ForMultipleChoice,
+)
+
+from ..test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
+from ...testing_utils import slow
+
+
+@dataclass
+class RoFormerv2ModelTestModelConfig:
+    """RoFormerv2Model model config which keep consist with pretrained_init_configuration sub fields
+    """
+    vocab_size: int = 200
+    hidden_size: int = 36
+    num_hidden_layers: int = 6
+    num_attention_heads: int = 6
+    intermediate_size: int = 20
+    hidden_act: str = "relu"
+    hidden_dropout_prob: float = 0.1
+    attention_probs_dropout_prob: float = 0.1
+    max_position_embeddings: int = 20
+    type_vocab_size: int = 2
+    pad_token_id: int = 0
+    rotary_value: bool = False
+    use_bias: bool = False
+
+    @property
+    def model_kwargs(self) -> dict:
+        """get the model kwargs configuration to init the model"""
+        model_config_fields: Tuple[Field,
+                                   ...] = fields(RoFormerv2ModelTestModelConfig)
+        return {
+            field.name: getattr(self, field.name)
+            for field in model_config_fields
+        }
+
+
+@dataclass
+class RoFormerv2ModelTestConfig(RoFormerv2ModelTestModelConfig):
+    """train config under unittest code"""
+    batch_size: int = 2
+    seq_length: int = 7
+    is_training: bool = False
+    use_input_mask: bool = False
+    use_token_type_ids: bool = True
+
+    # used for sequence classification
+    num_classes: int = 3
+    num_choices: int = 3
+
+
+class RoFormerv2ModelTester:
+
+    def __init__(
+        self,
+        parent,
+        config: Optional[RoFormerv2ModelTestConfig] = None,
+    ):
+        self.parent = parent
+        self.config: RoFormerv2ModelTestConfig = config or RoFormerv2ModelTestConfig(
+        )
+
+        self.is_training = self.config.is_training
+        self.num_classes = self.config.num_classes
+        self.num_choices = self.config.num_choices
+
+    def prepare_config_and_inputs(self):
+        config = self.config
+        input_ids = ids_tensor([config.batch_size, config.seq_length],
+                               config.vocab_size)
+
+        input_mask = None
+        if self.config.use_input_mask:
+            input_mask = random_attention_mask(
+                [config.batch_size, config.seq_length])
+
+        token_type_ids = None
+        if self.config.use_token_type_ids:
+            token_type_ids = ids_tensor([config.batch_size, config.seq_length],
+                                        config.type_vocab_size)
+
+        config = self.get_config()
+        return config, input_ids, token_type_ids, input_mask
+
+    def get_config(self) -> dict:
+        return self.config.model_kwargs
+
+    def create_and_check_model(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2Model(**config)
+        model.eval()
+        result = model(input_ids,
+                       attention_mask=input_mask,
+                       token_type_ids=token_type_ids,
+                       output_hidden_states=True)
+        result = model(input_ids,
+                       token_type_ids=token_type_ids,
+                       output_hidden_states=True)
+        result = model(input_ids, output_hidden_states=True)
+        self.parent.assertEqual(result[0].shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.hidden_size
+        ])
+        self.parent.assertEqual(result[1].shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.hidden_size
+        ])
+
+    def create_and_check_for_multiple_choice(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2ForMultipleChoice(RoFormerv2Model(**config),
+                                            num_choices=self.config.num_choices)
+        model.eval()
+        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(
+            [-1, self.config.num_choices, -1])
+
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids.unsqueeze(1).expand(
+                [-1, self.config.num_choices, -1])
+
+        if input_mask is not None:
+            input_mask = input_mask.unsqueeze(1).expand(
+                [-1, self.config.num_choices, -1])
+
+        result = model(
+            multiple_choice_inputs_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+        )
+        self.parent.assertEqual(
+            result.shape, [self.config.batch_size, self.config.num_choices])
+
+    def create_and_check_for_masked_lm(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2ForMaskedLM(RoFormerv2Model(**config))
+        model.eval()
+        result = model(input_ids,
+                       attention_mask=input_mask,
+                       token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.vocab_size
+        ])
+
+    def create_and_check_for_sequence_classification(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2ForSequenceClassification(
+            RoFormerv2Model(**config), num_classes=self.config.num_classes)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+        )
+        self.parent.assertEqual(
+            result.shape, [self.config.batch_size, self.config.num_classes])
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+        ) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "attention_mask": input_mask
+        }
+        return config, inputs_dict
+
+    def create_and_check_for_question_answering(self, config, input_ids,
+                                                token_type_ids, input_mask):
+        model = RoFormerv2ForQuestionAnswering(RoFormerv2Model(**config))
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+        )
+        self.parent.assertEqual(
+            result[0].shape, [self.config.batch_size, self.config.seq_length])
+        self.parent.assertEqual(
+            result[1].shape, [self.config.batch_size, self.config.seq_length])
+
+    def create_and_check_for_token_classification(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2ForTokenClassification(RoFormerv2Model(**config),
+                                                 num_classes=self.num_classes)
+        model.eval()
+        result = model(input_ids,
+                       attention_mask=input_mask,
+                       token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.num_classes
+        ])
+
+
+class RoFormerv2ModelTest(ModelTesterMixin, unittest.TestCase):
+    base_model_class = RoFormerv2Model
+
+    all_model_classes = (
+        RoFormerv2ForMaskedLM,
+        RoFormerv2ForSequenceClassification,
+        RoFormerv2ForTokenClassification,
+        RoFormerv2ForQuestionAnswering,
+        RoFormerv2ForMultipleChoice,
+    )
+
+    def setUp(self):
+        self.model_tester = RoFormerv2ModelTester(self)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_multiple_choice(
+            *config_and_inputs)
+
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_question_answering(
+            *config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(
+            *config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(
+            *config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in list(
+                RoFormerv2PretrainedModel.pretrained_init_configuration)[:1]:
+            model = RoFormerv2Model.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+class RoFormerv2ModelIntegrationTest(unittest.TestCase):
+
+    @slow
+    def test_inference_no_attention(self):
+        model = RoFormerv2Model.from_pretrained(
+            "roformer_v2_chinese_char_small")
+        model.eval()
+        input_ids = paddle.to_tensor(
+            [[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
+        with paddle.no_grad():
+            output = model(input_ids, output_hidden_states=True)[0]
+        expected_shape = [1, 11, 384]
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = paddle.to_tensor(
+            [[[0.75068903, 0.13977423, 0.07971212],
+              [0.08614583, 0.21606587, -1.08551681],
+              [0.98021960, -0.85751861, -1.42552316]]])
+
+        self.assertTrue(
+            paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
+
+    @slow
+    def test_inference_with_attention(self):
+        model = RoFormerv2Model.from_pretrained(
+            "roformer_v2_chinese_char_small")
+        model.eval()
+        input_ids = paddle.to_tensor(
+            [[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
+        attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
+        with paddle.no_grad():
+            output = model(input_ids,
+                           attention_mask=attention_mask,
+                           output_hidden_states=True)[0]
+        expected_shape = [1, 11, 384]
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = paddle.to_tensor(
+            [[[0.75068903, 0.13977423, 0.07971212],
+              [0.08614583, 0.21606587, -1.08551681],
+              [0.98021960, -0.85751861, -1.42552316]]])
+        self.assertTrue(
+            paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/transformers/roformerv2/test_tokenizer.py b/tests/transformers/roformerv2/test_tokenizer.py
new file mode 100644
index 000000000000..e9d129ae33e2
--- /dev/null
+++ b/tests/transformers/roformerv2/test_tokenizer.py
@@ -0,0 +1,261 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+import os
+import unittest
+
+from paddlenlp.data.vocab import Vocab
+
+from paddlenlp.transformers.roformerv2.tokenizer import (BasicTokenizer,
+                                                         RoFormerv2Tokenizer,
+                                                         WordpieceTokenizer)
+
+from tests.testing_utils import slow
+from tests.transformers.test_tokenizer_common import TokenizerTesterMixin, filter_non_english
+
+
+class RoFormerv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+
+    tokenizer_class = RoFormerv2Tokenizer
+    space_between_special_tokens = True
+    from_pretrained_filter = filter_non_english
+    test_seq2seq = True
+
+    def setUp(self):
+        self.from_pretrained_kwargs = {"do_lower_case": False}
+
+        super().setUp()
+        vocab_tokens = [
+            "[UNK]",
+            "[CLS]",
+            "[SEP]",
+            "[PAD]",
+            "[MASK]",
+            "want",
+            "##want",
+            "##ed",
+            "wa",
+            "un",
+            "runn",
+            "##ing",
+            ",",
+            "low",
+            "lowest",
+        ]
+
+        self.vocab_file = os.path.join(
+            self.tmpdirname,
+            RoFormerv2Tokenizer.resource_files_names["vocab_file"])
+        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+
+        self.vocab = Vocab.from_dict(
+            {token: index
+             for index, token in enumerate(vocab_tokens)},
+            unk_token='[UNK]',
+            pad_token='[PAD]',
+            bos_token='[CLS]',
+            eos_token='[SEP]',
+        )
+
+    def get_input_output_texts(self, tokenizer):
+        input_text = "UNwant\u00E9d,running"
+        output_text = "unwanted, running"
+        return input_text, output_text
+
+    def test_full_tokenizer(self):
+        tokenizer = self.tokenizer_class(self.vocab_file)
+
+        tokens = tokenizer.tokenize("UNwant\u00E9d,running")
+        self.assertListEqual(tokens,
+                             ["un", "##want", "##ed", ",", "runn", "##ing"])
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
+                             [9, 6, 7, 12, 10, 11])
+
+    def test_chinese(self):
+        tokenizer = BasicTokenizer()
+
+        self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"),
+                             ["ah", "\u535A", "\u63A8", "zz"])
+
+    def test_basic_tokenizer_lower(self):
+        tokenizer = BasicTokenizer(do_lower_case=True)
+
+        self.assertListEqual(tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "),
+                             ["hello", "!", "how", "are", "you", "?"])
+        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
+
+    def test_basic_tokenizer_lower_strip_accents_false(self):
+        tokenizer = BasicTokenizer(do_lower_case=True, strip_accents=False)
+
+        self.assertListEqual(tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),
+                             ["hällo", "!", "how", "are", "you", "?"])
+        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["h\u00E9llo"])
+
+    def test_basic_tokenizer_lower_strip_accents_true(self):
+        tokenizer = BasicTokenizer(do_lower_case=True)
+
+        self.assertListEqual(tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),
+                             ["hallo", "!", "how", "are", "you", "?"])
+        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
+
+    def test_basic_tokenizer_lower_strip_accents_default(self):
+        tokenizer = BasicTokenizer(do_lower_case=True)
+
+        self.assertListEqual(tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),
+                             ["hallo", "!", "how", "are", "you", "?"])
+        self.assertListEqual(tokenizer.tokenize("H\u00E9llo"), ["hello"])
+
+    def test_basic_tokenizer_no_lower(self):
+        tokenizer = BasicTokenizer(do_lower_case=False)
+
+        self.assertListEqual(tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "),
+                             ["HeLLo", "!", "how", "Are", "yoU", "?"])
+
+    def test_basic_tokenizer_no_lower_strip_accents_false(self):
+        tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=False)
+
+        self.assertListEqual(tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),
+                             ["HäLLo", "!", "how", "Are", "yoU", "?"])
+
+    def test_basic_tokenizer_no_lower_strip_accents_true(self):
+        tokenizer = BasicTokenizer(do_lower_case=False, strip_accents=True)
+
+        self.assertListEqual(tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "),
+                             ["HaLLo", "!", "how", "Are", "yoU", "?"])
+
+    def test_basic_tokenizer_respects_never_split_tokens(self):
+        tokenizer = BasicTokenizer(do_lower_case=False, never_split=["[UNK]"])
+
+        self.assertListEqual(
+            tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"),
+            ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"])
+
+    def test_wordpiece_tokenizer(self):
+        vocab_tokens = [
+            "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un",
+            "runn", "##ing"
+        ]
+
+        vocab = {}
+        for (i, token) in enumerate(vocab_tokens):
+            vocab[token] = i
+        tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
+
+        self.assertListEqual(tokenizer.tokenize(""), [])
+
+        self.assertListEqual(tokenizer.tokenize("unwanted running"),
+                             ["un", "##want", "##ed", "runn", "##ing"])
+
+        self.assertListEqual(tokenizer.tokenize("unwantedX running"),
+                             ["[UNK]", "runn", "##ing"])
+
+    def test_clean_text(self):
+        tokenizer = self.get_tokenizer()
+
+        # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
+        self.assertListEqual(
+            [tokenizer.tokenize(t) for t in ["Test", "\xad", "test"]],
+            [["[UNK]"], [], ["[UNK]"]])
+
+    # @slow
+    def test_sequence_builders(self):
+        tokenizer = self.tokenizer_class.from_pretrained(
+            "roformer-chinese-small")
+
+        text = tokenizer.encode("sequence builders",
+                                return_token_type_ids=None,
+                                add_special_tokens=False)["input_ids"]
+        text_2 = tokenizer.encode("multi-sequence build",
+                                  return_token_type_ids=None,
+                                  add_special_tokens=False)["input_ids"]
+
+        encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
+        encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
+
+        assert encoded_sentence == [101] + text + [102]
+        assert encoded_pair == [101] + text + [102] + text_2 + [102]
+
+    def test_offsets_with_special_characters(self):
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(
+                    f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+                tokenizer = self.tokenizer_class.from_pretrained(
+                    pretrained_name, **kwargs)
+
+                # sentence = f"testing with {tokenizer.mask_token} simple sentence"
+                sentence = f"a simple {tokenizer.mask_token} allennlp sentence."
+                tokens = tokenizer.encode(
+                    sentence,
+                    return_attention_mask=False,
+                    return_token_type_ids=False,
+                    return_offsets_mapping=True,
+                    add_special_tokens=True,
+                )
+                expected_results = [
+                    ((0, 0), tokenizer.cls_token),
+                    ((0, 1), "a"),
+                    ((2, 8), "simple"),
+                    ((9, 15), tokenizer.mask_token),
+                    ((16, 21), "allen"),
+                    ((21, 23), "##nl"),
+                    ((23, 24), "##p"),
+                    ((25, 33), "sentence"),
+                    ((33, 34), "."),
+                    ((0, 0), tokenizer.sep_token),
+                ]
+
+                self.assertEqual([e[1] for e in expected_results],
+                                 tokenizer.convert_ids_to_tokens(
+                                     tokens["input_ids"]))
+                self.assertEqual([e[0] for e in expected_results],
+                                 tokens["offset_mapping"])
+
+    def test_change_tokenize_chinese_chars(self):
+        list_of_commun_chinese_char = ["的", "人", "有"]
+        text_with_chinese_char = "".join(list_of_commun_chinese_char)
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(
+                    f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+
+                kwargs["tokenize_chinese_chars"] = True
+                tokenizer = self.tokenizer_class.from_pretrained(
+                    pretrained_name, **kwargs)
+
+                ids_without_spe_char_p = tokenizer.encode(
+                    text_with_chinese_char,
+                    return_token_type_ids=None,
+                    add_special_tokens=False)["input_ids"]
+
+                tokens_without_spe_char_p = tokenizer.convert_ids_to_tokens(
+                    ids_without_spe_char_p)
+
+                # it is expected that each Chinese character is not preceded by "##"
+                self.assertListEqual(tokens_without_spe_char_p,
+                                     list_of_commun_chinese_char)
+                '''
+                kwargs["tokenize_chinese_chars"] = False
+                tokenizer = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+
+                ids_without_spe_char_p = tokenizer.encode(text_with_chinese_char, return_token_type_ids=None,add_special_tokens=False)["input_ids"]
+
+                tokens_without_spe_char_p = tokenizer.convert_ids_to_tokens(ids_without_spe_char_p)
+
+                # it is expected that only the first Chinese character is not preceded by "##".
+                expected_tokens = [
+                    f"##{token}" if idx != 0 else token for idx, token in enumerate(list_of_commun_chinese_char)
+                ]
+                self.assertListEqual(tokens_without_spe_char_p, expected_tokens)
+                '''