From 4e10b8e88c553cced0e0b09ef916c0413677ec32 Mon Sep 17 00:00:00 2001
From: xunyoyo <33387866+xunyoyo@users.noreply.github.com>
Date: Wed, 12 Nov 2025 21:22:34 +0800
Subject: [PATCH] Refactor text processor tests to use unittest
---
tests/input/test_text_processor.py | 618 +++++++++++++++++++++++++----
1 file changed, 543 insertions(+), 75 deletions(-)
diff --git a/tests/input/test_text_processor.py b/tests/input/test_text_processor.py
index 794d81895d7..ad429e917db 100644
--- a/tests/input/test_text_processor.py
+++ b/tests/input/test_text_processor.py
@@ -1,89 +1,557 @@
+import importlib
+import importlib.util
+import sys
+import types
import unittest
-from unittest.mock import MagicMock, patch
+from pathlib import Path
+from types import SimpleNamespace
+from unittest import mock
-from fastdeploy.engine.request import Request
-from fastdeploy.input.text_processor import DataProcessor
+import numpy as np
-class TestDataProcessorProcess(unittest.TestCase):
- def setUp(self):
- # 创建 DataProcessor 实例的模拟对象
- with patch.object(DataProcessor, "__init__", return_value=None) as mock_init:
- self.processor = DataProcessor("model_path")
- mock_init.side_effect = lambda *args, **kwargs: print(f"__init__ called with {args}, {kwargs}")
-
- # 设置必要的属性
- self.processor.tokenizer = MagicMock()
- self.processor.tokenizer.eos_token_id = 1
- self.processor.decode_status = {}
- self.processor.reasoning_end_dict = {}
- self.processor.tool_parser_dict = {}
- self.processor.generation_config = MagicMock()
- self.processor.eos_token_ids = [1]
- self.processor.reasoning_parser = MagicMock()
-
- def mock_messages2ids(request, **kwargs):
- if "chat_template" in kwargs:
- return [1]
+class DummyTokenizer:
+ bos_token = ""
+ cls_token = ""
+ sep_token = ""
+ eos_token = ""
+ mask_token = ""
+ chat_template = "dummy"
+
+ def __init__(self):
+ self.pad_token_id = 1
+ self.eos_token_id = 2
+ self.eos_token = 2
+ self.vocab_size = 256
+ self.bos_token_id = self._convert_token_to_id(self.bos_token)
+ self.cls_token_id = self._convert_token_to_id(self.cls_token)
+ self.sep_token_id = self._convert_token_to_id(self.sep_token)
+ self.mask_token_id = self._convert_token_to_id(self.mask_token)
+
+ def _convert_token_to_id(self, token):
+ return len(str(token))
+
+ def __call__(self, text, **kwargs):
+ if isinstance(text, list):
+ values = [self._value(item) for item in text]
+ else:
+ values = [self._value(text)]
+ max_length = kwargs.get("max_length")
+ if max_length is not None:
+ values = values[:max_length]
+ return {"input_ids": np.array([values], dtype=np.int64)}
+
+ def _value(self, item):
+ if isinstance(item, str):
+ return len(item)
+ return int(item)
+
+ def tokenize(self, text):
+ if isinstance(text, str):
+ return [text]
+ return [str(text)]
+
+ def convert_tokens_to_ids(self, tokens):
+ return [self._value(token) for token in tokens]
+
+ def decode(self, token_ids, **kwargs):
+ return " ".join(str(t) for t in token_ids)
+
+ def decode_token(self, token_ids, prefix_offset, read_offset):
+ start = read_offset
+ delta_tokens = token_ids[start:]
+ delta = "".join(str(t) for t in delta_tokens)
+ prefix_offset += len(token_ids)
+ read_offset += len(delta_tokens)
+ return delta, prefix_offset, read_offset
+
+ def batch_decode(self, batch, **kwargs):
+ return [self.decode(seq) for seq in batch]
+
+ def apply_chat_template(self, request, **kwargs):
+ if isinstance(request, dict):
+ system = request.get("system")
+ messages = request.get("messages", [])
+ else:
+ system = getattr(request, "system", None)
+ messages = getattr(request, "messages", [])
+ parts = [system] if system else []
+ parts.extend(msg.get("content", "") for msg in messages)
+ return " ".join(part for part in parts if part)
+
+
+class DummyLlamaTokenizer(DummyTokenizer):
+ pass
+
+
+class DummyAutoTokenizer:
+ @classmethod
+ def from_pretrained(cls, *args, **kwargs):
+ return DummyTokenizer()
+
+
+class DummyHFTokenizer:
+ @classmethod
+ def from_pretrained(cls, *args, **kwargs):
+ return DummyTokenizer()
+
+
+def _import_text_processor(use_hf_tokenizer=False):
+ repo_root = Path(__file__).resolve().parents[2]
+
+ dummy_logger = SimpleNamespace(
+ info=lambda *args, **kwargs: None,
+ warning=lambda *args, **kwargs: None,
+ debug=lambda *args, **kwargs: None,
+ )
+
+ utils_module = types.ModuleType("fastdeploy.utils")
+ utils_module.data_processor_logger = dummy_logger
+
+ envs_module = types.ModuleType("fastdeploy.envs")
+ envs_module.FD_USE_HF_TOKENIZER = use_hf_tokenizer
+
+ fastdeploy_module = types.ModuleType("fastdeploy")
+ fastdeploy_module.__path__ = [str(repo_root / "fastdeploy")]
+ fastdeploy_module.utils = utils_module
+ fastdeploy_module.envs = envs_module
+
+ generation_module = types.ModuleType("paddleformers.generation")
+
+ class DummyGenerationConfig:
+ def __init__(self):
+ self.top_p = 0.8
+ self.temperature = 0.9
+ self.repetition_penalty = 1.1
+ self.frequency_penalty = 0.2
+ self.presence_penalty = 0.1
+
+ @classmethod
+ def from_pretrained(cls, *args, **kwargs):
+ return cls()
+
+ generation_module.GenerationConfig = DummyGenerationConfig
+
+ transformers_module = types.ModuleType("paddleformers.transformers")
+ transformers_module.AutoTokenizer = DummyAutoTokenizer
+ transformers_module.LlamaTokenizer = DummyLlamaTokenizer
+ transformers_module.Llama3Tokenizer = DummyLlamaTokenizer
+
+ hf_transformers_module = types.ModuleType("transformers")
+ hf_transformers_module.AutoTokenizer = DummyHFTokenizer
+
+ llm_utils_module = types.ModuleType("paddleformers.trl.llm_utils")
+ llm_utils_module.get_eos_token_id = lambda tokenizer, config: [tokenizer.eos_token_id]
+
+ injected_modules = {
+ "fastdeploy": fastdeploy_module,
+ "fastdeploy.utils": utils_module,
+ "fastdeploy.envs": envs_module,
+ "paddleformers.generation": generation_module,
+ "paddleformers.transformers": transformers_module,
+ "transformers": hf_transformers_module,
+ "paddleformers.trl.llm_utils": llm_utils_module,
+ }
+
+ previous_modules = {}
+ for name, module in injected_modules.items():
+ previous_modules[name] = sys.modules.get(name)
+ sys.modules[name] = module
+
+ try:
+ text_processor_module = importlib.import_module("fastdeploy.input.text_processor")
+ importlib.reload(text_processor_module)
+ except Exception:
+ for name, original in previous_modules.items():
+ if original is None:
+ sys.modules.pop(name, None)
+ else:
+ sys.modules[name] = original
+ raise
+
+ def cleanup():
+ sys.modules.pop("fastdeploy.input.text_processor", None)
+ for name, module in injected_modules.items():
+ original = previous_modules[name]
+ if original is None:
+ sys.modules.pop(name, None)
else:
- return [0]
-
- def mock_apply_default_parameters(request):
- return request
-
- self.processor.messages2ids = mock_messages2ids
- self.processor._apply_default_parameters = mock_apply_default_parameters
-
- def test_process_request(self):
- request = Request.from_dict(
- {
- "request_id": "123",
- "messages": [{"role": "user", "content": "Hello!"}],
- "eos_token_ids": [1],
- "temperature": 1,
- "top_p": 1,
- }
+ sys.modules[name] = original
+
+ return text_processor_module, cleanup
+
+
+class DummyRequest:
+ def __init__(self, **kwargs):
+ self.request_id = kwargs.get("request_id", "req")
+ self.prompt = kwargs.get("prompt")
+ self.prompt_token_ids = kwargs.get("prompt_token_ids")
+ self.messages = kwargs.get("messages")
+ self.eos_token_ids = kwargs.get("eos_token_ids")
+ self.chat_template = kwargs.get("chat_template")
+ self.enable_thinking = kwargs.get("enable_thinking")
+ self.history = kwargs.get("history")
+ self.tools = kwargs.get("tools")
+ self.system = kwargs.get("system")
+ self.sampling_params = SimpleNamespace(
+ top_p=kwargs.get("top_p"),
+ temperature=kwargs.get("temperature"),
+ repetition_penalty=kwargs.get("repetition_penalty"),
+ frequency_penalty=kwargs.get("frequency_penalty"),
+ presence_penalty=kwargs.get("presence_penalty"),
+ stop=kwargs.get("stop"),
+ stop_token_ids=kwargs.get("stop_token_ids"),
+ stop_seqs_len=kwargs.get("stop_seqs_len"),
+ bad_words=kwargs.get("bad_words"),
+ bad_words_token_ids=kwargs.get("bad_words_token_ids"),
+ max_tokens=kwargs.get("max_tokens"),
)
- chat_template_kwargs = {"chat_template": "Hello!"}
- result = self.processor.process_request(request, 100, chat_template_kwargs=chat_template_kwargs)
- self.assertEqual(result.prompt_token_ids, [1])
-
- def test_process_request_dict(self):
- request_dict = {
- "messages": [{"role": "user", "content": "Hello!"}],
- "chat_template_kwargs": {"chat_template": "Hello!"},
- "eos_token_ids": [1],
- "temperature": 1,
- "top_p": 1,
+
+ def get(self, key, default=None):
+ if hasattr(self, key) and getattr(self, key) is not None:
+ return getattr(self, key)
+ return getattr(self.sampling_params, key, default)
+
+ def set(self, key, value):
+ if hasattr(self.sampling_params, key):
+ setattr(self.sampling_params, key, value)
+ else:
+ setattr(self, key, value)
+
+ def to_dict(self):
+ return {
+ "request_id": self.request_id,
+ "messages": self.messages,
+ "prompt": self.prompt,
+ "system": self.system,
+ "history": self.history,
+ "tools": self.tools,
+ "chat_template": self.chat_template,
+ "enable_thinking": self.enable_thinking,
}
- result = self.processor.process_request_dict(request_dict, 100)
- self.assertEqual(result["prompt_token_ids"], [1])
- def test_process_response_dict_normal(self):
- self.processor.tokenizer.decode_token = MagicMock(return_value=("Mock decoded text", 0, 0))
- self.processor.reasoning_parser.extract_reasoning_content = MagicMock(
- return_value=("Mock reasoning content", "Mock final text")
+ def __getitem__(self, key):
+ return self.get(key)
+
+ def __setitem__(self, key, value):
+ self.set(key, value)
+
+
+class DataProcessorTestCase(unittest.TestCase):
+ def setUp(self):
+ module, cleanup = _import_text_processor()
+ self.text_processor_module = module
+ self.addCleanup(cleanup)
+ self.processor = self.text_processor_module.DataProcessor("stub-model")
+
+ def test_base_data_processor_contract(self):
+ text_processor_module = self.text_processor_module
+
+ class MinimalProcessor(text_processor_module.BaseDataProcessor):
+ def __init__(self):
+ self.generation_config = SimpleNamespace(
+ top_p=0.5,
+ temperature=0.6,
+ repetition_penalty=1.1,
+ frequency_penalty=0.2,
+ presence_penalty=0.3,
+ )
+ super().__init__()
+
+ def _load_tokenizer(self):
+ return DummyTokenizer()
+
+ def process_request(self, request, **kwargs):
+ return super().process_request(request, **kwargs)
+
+ def process_response(self, response_dict):
+ return super().process_response(response_dict)
+
+ processor = MinimalProcessor()
+ defaults = processor._apply_default_parameters({})
+ self.assertAlmostEqual(defaults["top_p"], 0.5)
+ with self.assertRaises(NotImplementedError):
+ processor.process_request({}, max_model_len=None)
+ with self.assertRaises(NotImplementedError):
+ processor.process_response({})
+ with self.assertRaises(NotImplementedError):
+ processor.text2ids("text")
+ with self.assertRaises(NotImplementedError):
+ processor.messages2ids([])
+ with self.assertRaises(NotImplementedError):
+ processor.ids2tokens([1], "task")
+
+ def test_process_request_dict_prompt_defaults(self):
+ request = {"prompt": "hi", "temperature": 0, "top_p": 0, "stop": ["stop"]}
+ processed = self.processor.process_request_dict(request, max_model_len=5)
+
+ self.assertEqual(processed["prompt_token_ids"], [2])
+ self.assertEqual(processed["stop_token_ids"], [[4]])
+ self.assertEqual(processed["stop_seqs_len"], [1])
+ self.assertEqual(processed["temperature"], 1)
+ self.assertAlmostEqual(processed["top_p"], 1e-5)
+ self.assertEqual(processed["max_tokens"], 4)
+
+ def test_process_request_dict_messages_template(self):
+ request = {
+ "request_id": "chat",
+ "messages": [{"role": "user", "content": "hello"}],
+ "chat_template_kwargs": {"system": "system prompt"},
+ }
+ processed = self.processor.process_request_dict(request, max_model_len=6)
+
+ self.assertEqual(processed["prompt_token_ids"], [len("system prompt hello")])
+ self.assertEqual(processed["system"], "system prompt")
+ self.assertTrue(processed["enable_thinking"])
+ self.assertEqual(processed["prompt_tokens"], "system prompt hello")
+
+ def test_process_request_object_handles_sequences(self):
+ request = DummyRequest(
+ prompt=[1, 2, 3, 4, 5, 6],
+ stop=["stop"],
+ bad_words=["zz"],
+ temperature=0,
+ top_p=0,
)
- mock_tokens = ["mock", "reasoning", "tokens"]
- self.processor.tokenizer.tokenize = MagicMock(return_value=mock_tokens)
- self.processor.tool_parser_obj = None
- response_dict = {
- "request_id": "request-id_0",
- "outputs": {
- "token_ids": [2, 3, 4, 5, 1],
- "text": "Hello",
- "top_logprobs": [{"a": 0.1}, {"b": 0.2}, {"c": 0.3}],
- },
- "finish_reason": "stop",
+ processed = self.processor.process_request(request, max_model_len=5)
+
+ self.assertEqual(processed.prompt_token_ids, [1, 2, 3, 4])
+ self.assertEqual(processed.sampling_params.max_tokens, 1)
+ self.assertEqual(processed.sampling_params.stop_token_ids, [[4]])
+ self.assertEqual(set(processed.sampling_params.bad_words_token_ids), {2, 3})
+ self.assertEqual(processed.sampling_params.temperature, 1)
+ self.assertAlmostEqual(processed.sampling_params.top_p, 1e-5)
+
+ def test_process_request_requires_prompt_or_messages(self):
+ request = DummyRequest(prompt=None, messages=None, prompt_token_ids=None)
+ with self.assertRaisesRegex(ValueError, "should have `input_ids`, `text` or `messages`"):
+ self.processor.process_request(request, max_model_len=5)
+
+ def test_process_request_dict_rejects_bad_kwargs(self):
+ request = {
+ "messages": [{"role": "user", "content": "hi"}],
+ "chat_template_kwargs": "invalid",
+ }
+ with self.assertRaisesRegex(ValueError, "chat_template_kwargs must be a dict"):
+ self.processor.process_request_dict(request)
+
+ def test_ids2tokens_and_clear_request_status(self):
+ delta, _, _ = self.processor.ids2tokens([3], "task-1")
+ self.assertEqual(delta, "3")
+ delta, _, _ = self.processor.ids2tokens([4], "task-1")
+ self.assertEqual(delta, "4")
+
+ combined = self.processor.clear_request_status("task-1")
+ self.assertEqual(combined, "34")
+ self.assertNotIn("task-1", self.processor.decode_status)
+
+ def test_clear_request_status_hf_branch(self):
+ module, cleanup = _import_text_processor(use_hf_tokenizer=True)
+ self.addCleanup(cleanup)
+ processor = module.DataProcessor("stub-model")
+ processor.decode_status = {"task": [[], [], "transcript"]}
+
+ self.assertEqual(processor.clear_request_status("task"), "transcript")
+ self.assertNotIn("task", processor.decode_status)
+
+ def test_data_processor_init_handles_missing_generation_config(self):
+ with mock.patch.object(
+ self.text_processor_module.GenerationConfig,
+ "from_pretrained",
+ side_effect=OSError("missing"),
+ ):
+ processor = self.text_processor_module.DataProcessor("stub-model")
+ self.assertIsNone(processor.generation_config)
+
+ def test_process_response_with_reasoning_and_tools(self):
+ processor = self.processor
+
+ class DummyReasoning:
+ def __init__(self, tokenizer):
+ self.tokenizer = tokenizer
+
+ def extract_reasoning_content(self, full_text, response_dict):
+ return "think", f"{full_text}!"
+
+ class DummyToolParser:
+ def __init__(self, tokenizer):
+ self.tokenizer = tokenizer
+
+ def extract_tool_calls(self, full_text, response_dict):
+ return SimpleNamespace(tools_called=True, tool_calls=["tool"], content="tool-only")
+
+ processor.reasoning_parser = DummyReasoning(processor.tokenizer)
+ processor.tool_parser_obj = DummyToolParser
+
+ response = SimpleNamespace(
+ request_id="resp",
+ outputs=SimpleNamespace(token_ids=[1, processor.tokenizer.eos_token_id]),
+ )
+
+ processed = processor.process_response(response)
+ self.assertEqual(processed.outputs.text, "tool-only")
+ self.assertEqual(processed.outputs.reasoning_content, "think")
+ self.assertEqual(processed.outputs.tool_calls, ["tool"])
+
+ def test_process_response_streaming_clears_state(self):
+ processor = self.processor
+ req_id = "stream"
+ processor.decode_status[req_id] = [0, 0, [], ""]
+ response = {"finished": True, "request_id": req_id, "outputs": {"token_ids": [7]}}
+
+ result = processor.process_response_dict_streaming(response, enable_thinking=False)
+ self.assertEqual(result["outputs"]["text"], "7")
+ self.assertNotIn(req_id, processor.decode_status)
+
+ def test_process_response_dict_normal_with_reasoning(self):
+ processor = self.processor
+
+ class DummyReasoning:
+ def __init__(self, tokenizer):
+ self.tokenizer = tokenizer
+
+ def extract_reasoning_content(self, full_text, response_dict):
+ return "because", full_text + "!"
+
+ class DummyToolParser:
+ def __init__(self, tokenizer):
+ self.tokenizer = tokenizer
+
+ def extract_tool_calls(self, full_text, response_dict):
+ return SimpleNamespace(tools_called=True, tool_calls=["tool"], content="tool-text")
+
+ processor.reasoning_parser = DummyReasoning(processor.tokenizer)
+ processor.tool_parser_obj = DummyToolParser
+
+ response = {
"finished": True,
+ "request_id": "normal",
+ "outputs": {"token_ids": [7, processor.tokenizer.eos_token_id]},
}
- kwargs = {"enable_thinking": True}
- with patch("fastdeploy.input.text_processor.data_processor_logger"):
- result = self.processor.process_response_dict_normal(response_dict, **kwargs)
- self.assertEqual(result["outputs"]["reasoning_content"], "Mock reasoning content")
- self.assertEqual(result["outputs"]["reasoning_token_num"], len(mock_tokens))
- self.assertEqual(result["outputs"]["text"], "Mock final text")
- self.assertIn("completion_tokens", result["outputs"])
+
+ result = processor.process_response_dict_normal(response, enable_thinking=True)
+ self.assertEqual(result["outputs"]["completion_tokens"], "7")
+ self.assertEqual(result["outputs"]["text"], "tool-text")
+ self.assertEqual(result["outputs"]["reasoning_content"], "because")
+ self.assertEqual(result["outputs"]["reasoning_token_num"], 1)
+
+ def test_process_response_dict_dispatch(self):
+ processor = self.processor
+ calls = {}
+
+ def fake_stream(response_dict, **kwargs):
+ calls["stream"] = kwargs
+ return "stream"
+
+ def fake_normal(response_dict, **kwargs):
+ calls["normal"] = kwargs
+ return "normal"
+
+ original_stream = processor.process_response_dict_streaming
+ original_normal = processor.process_response_dict_normal
+ processor.process_response_dict_streaming = fake_stream
+ processor.process_response_dict_normal = fake_normal
+ self.addCleanup(lambda: setattr(processor, "process_response_dict_streaming", original_stream))
+ self.addCleanup(lambda: setattr(processor, "process_response_dict_normal", original_normal))
+
+ response = {"outputs": {}, "finished": False, "request_id": "req"}
+ self.assertEqual(processor.process_response_dict(response), "stream")
+ self.assertTrue(calls["stream"]["enable_thinking"])
+ self.assertEqual(
+ processor.process_response_dict(response, stream=False, enable_thinking=None),
+ "normal",
+ )
+ self.assertTrue(calls["normal"]["enable_thinking"])
+
+ def test_update_stop_seq_excludes_eos(self):
+ stop_seqs, stop_len = self.processor.update_stop_seq(
+ ["stop", self.processor.tokenizer.eos_token_id]
+ )
+ self.assertEqual(stop_seqs, [[4]])
+ self.assertEqual(stop_len, [1])
+
+ def test_pad_batch_data_left_padding(self):
+ padded, lengths = self.processor.pad_batch_data(
+ [[1], [2, 3]],
+ pad_id=-1,
+ return_seq_len=True,
+ return_array=False,
+ pad_style="left",
+ )
+ self.assertEqual(padded, [[-1, 1], [2, 3]])
+ self.assertEqual(lengths, [1, 2])
+
+ def test_pad_batch_data_empty_returns_array(self):
+ padded, lengths = self.processor.pad_batch_data([], return_seq_len=True)
+ self.assertEqual(padded.shape, (1, 0))
+ self.assertEqual(lengths.shape, (0,))
+
+ def test_get_pad_id_prefers_eos_when_missing(self):
+ processor = self.text_processor_module.DataProcessor("stub-model")
+ llama_tokenizer = DummyLlamaTokenizer()
+ llama_tokenizer.pad_token_id = None
+ llama_tokenizer.eos_token = 99
+ processor.tokenizer = llama_tokenizer
+
+ self.assertEqual(processor.get_pad_id(), 99)
+
+ def test_load_tokenizer_hf_branch(self):
+ module, cleanup = _import_text_processor(use_hf_tokenizer=True)
+ self.addCleanup(cleanup)
+ processor = module.DataProcessor("stub-model")
+ self.assertIsInstance(processor.tokenizer, DummyTokenizer)
+
+ def test_text2ids_hf_branch(self):
+ module, cleanup = _import_text_processor(use_hf_tokenizer=True)
+ self.addCleanup(cleanup)
+ processor = module.DataProcessor("stub-model")
+ ids = processor.text2ids("hi", max_model_len=5)
+ self.assertEqual(ids.tolist(), [2, 0, 0, 0, 0][: len(ids)])
+
+ def test_process_logprob_response(self):
+ self.assertEqual(self.processor.process_logprob_response([1, 2]), "1 2")
+
+ def test_process_request_dict_uses_existing_ids(self):
+ request = {"prompt_token_ids": [1, 2, 3], "max_tokens": 5}
+ processed = self.processor.process_request_dict(request, max_model_len=6)
+ self.assertEqual(processed["prompt_token_ids"], [1, 2, 3])
+ self.assertEqual(processed["max_tokens"], 5)
+
+ def test_process_request_dict_requires_chat_template(self):
+ original_template = self.processor.tokenizer.chat_template
+ self.processor.tokenizer.chat_template = None
+ self.addCleanup(lambda: setattr(self.processor.tokenizer, "chat_template", original_template))
+ with self.assertRaisesRegex(ValueError, "chat_template"):
+ self.processor.process_request_dict({"messages": [{"role": "user", "content": "hi"}]})
+
+ def test_update_bad_words_with_warnings(self):
+ processor = self.processor
+
+ def custom_tokenize(text):
+ base = text.strip()
+ if base == "combo":
+ return ["co", "mbo"]
+ if base == "oversize":
+ return [base]
+ return [base]
+
+ def custom_convert(tokens):
+ if tokens == ["co", "mbo"]:
+ return [1, 2]
+ if tokens == ["oversize"]:
+ return [processor.tokenizer.vocab_size + 1]
+ return [len(tokens[0])]
+
+ original_tokenize = processor.tokenizer.tokenize
+ original_convert = processor.tokenizer.convert_tokens_to_ids
+ processor.tokenizer.tokenize = custom_tokenize
+ processor.tokenizer.convert_tokens_to_ids = custom_convert
+ self.addCleanup(lambda: setattr(processor.tokenizer, "tokenize", original_tokenize))
+ self.addCleanup(
+ lambda: setattr(processor.tokenizer, "convert_tokens_to_ids", original_convert)
+ )
+
+ self.assertEqual(processor.update_bad_words(["combo", "oversize"], []), [])
if __name__ == "__main__":