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[CI] 【Hackathon 9th Sprint No.17】NO.17 功能模块单测补充 (#5054)
* Refactor text processor tests to use unittest * Add helpers for text processor tests
This commit is contained in:
xunyoyo
@@ -1,89 +1,569 @@
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"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import importlib
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import importlib.util
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import sys
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import types
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import unittest
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from unittest.mock import MagicMock, patch
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from pathlib import Path
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from types import SimpleNamespace
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from unittest import mock
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from fastdeploy.engine.request import Request
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from fastdeploy.input.text_processor import DataProcessor
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import numpy as np
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class TestDataProcessorProcess(unittest.TestCase):
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def setUp(self):
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# 创建 DataProcessor 实例的模拟对象
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with patch.object(DataProcessor, "__init__", return_value=None) as mock_init:
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self.processor = DataProcessor("model_path")
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mock_init.side_effect = lambda *args, **kwargs: print(f"__init__ called with {args}, {kwargs}")
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class DummyTokenizer:
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bos_token = "<s>"
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cls_token = "<cls>"
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sep_token = "</s>"
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eos_token = "</eos>"
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mask_token = "<mask>"
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chat_template = "dummy"
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# 设置必要的属性
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self.processor.tokenizer = MagicMock()
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self.processor.tokenizer.eos_token_id = 1
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self.processor.decode_status = {}
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self.processor.reasoning_end_dict = {}
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self.processor.tool_parser_dict = {}
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self.processor.generation_config = MagicMock()
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self.processor.eos_token_ids = [1]
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self.processor.reasoning_parser = MagicMock()
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def __init__(self):
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self.pad_token_id = 1
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self.eos_token_id = 2
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self.eos_token = 2
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self.vocab_size = 256
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self.bos_token_id = self._convert_token_to_id(self.bos_token)
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self.cls_token_id = self._convert_token_to_id(self.cls_token)
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self.sep_token_id = self._convert_token_to_id(self.sep_token)
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self.mask_token_id = self._convert_token_to_id(self.mask_token)
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def mock_messages2ids(request, **kwargs):
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if "chat_template" in kwargs:
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return [1]
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def _convert_token_to_id(self, token):
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return len(str(token))
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def __call__(self, text, **kwargs):
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if isinstance(text, list):
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values = [self._value(item) for item in text]
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else:
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values = [self._value(text)]
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max_length = kwargs.get("max_length")
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if max_length is not None:
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values = values[:max_length]
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return {"input_ids": np.array([values], dtype=np.int64)}
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def _value(self, item):
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if isinstance(item, str):
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return len(item)
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return int(item)
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def tokenize(self, text):
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if isinstance(text, str):
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return [text]
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return [str(text)]
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def convert_tokens_to_ids(self, tokens):
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return [self._value(token) for token in tokens]
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def decode(self, token_ids, **kwargs):
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return " ".join(str(t) for t in token_ids)
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def decode_token(self, token_ids, prefix_offset, read_offset):
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start = read_offset
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delta_tokens = token_ids[start:]
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delta = "".join(str(t) for t in delta_tokens)
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prefix_offset += len(token_ids)
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read_offset += len(delta_tokens)
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return delta, prefix_offset, read_offset
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def batch_decode(self, batch, **kwargs):
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return [self.decode(seq) for seq in batch]
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def apply_chat_template(self, request, **kwargs):
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if isinstance(request, dict):
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system = request.get("system")
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messages = request.get("messages", [])
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else:
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system = getattr(request, "system", None)
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messages = getattr(request, "messages", [])
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parts = [system] if system else []
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parts.extend(msg.get("content", "") for msg in messages)
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return " ".join(part for part in parts if part)
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class DummyLlamaTokenizer(DummyTokenizer):
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pass
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class DummyAutoTokenizer:
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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return DummyTokenizer()
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class DummyHFTokenizer:
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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return DummyTokenizer()
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def _create_dummy_modules():
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"""Create all dummy modules needed for testing fastdeploy.input.text_processor."""
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repo_root = Path(__file__).resolve().parents[2]
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dummy_logger = SimpleNamespace(
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info=lambda *args, **kwargs: None,
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warning=lambda *args, **kwargs: None,
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debug=lambda *args, **kwargs: None,
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)
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utils_module = types.ModuleType("fastdeploy.utils")
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utils_module.data_processor_logger = dummy_logger
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envs_module = types.ModuleType("fastdeploy.envs")
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envs_module.FD_USE_HF_TOKENIZER = False
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generation_module = types.ModuleType("paddleformers.generation")
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class DummyGenerationConfig:
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def __init__(self):
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self.top_p = 0.8
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self.temperature = 0.9
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self.repetition_penalty = 1.1
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self.frequency_penalty = 0.2
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self.presence_penalty = 0.1
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@classmethod
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def from_pretrained(cls, *args, **kwargs):
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return cls()
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generation_module.GenerationConfig = DummyGenerationConfig
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transformers_module = types.ModuleType("paddleformers.transformers")
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transformers_module.AutoTokenizer = DummyAutoTokenizer
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transformers_module.LlamaTokenizer = DummyLlamaTokenizer
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transformers_module.Llama3Tokenizer = DummyLlamaTokenizer
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hf_transformers_module = types.ModuleType("transformers")
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hf_transformers_module.AutoTokenizer = DummyHFTokenizer
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llm_utils_module = types.ModuleType("paddleformers.trl.llm_utils")
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llm_utils_module.get_eos_token_id = lambda tokenizer, config: [tokenizer.eos_token_id]
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fastdeploy_module = types.ModuleType("fastdeploy")
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fastdeploy_module.__path__ = [str(repo_root / "fastdeploy")]
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fastdeploy_module.utils = utils_module
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fastdeploy_module.envs = envs_module
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return {
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"fastdeploy": fastdeploy_module,
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"fastdeploy.utils": utils_module,
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"fastdeploy.envs": envs_module,
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"paddleformers.generation": generation_module,
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"paddleformers.transformers": transformers_module,
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"transformers": hf_transformers_module,
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"paddleformers.trl.llm_utils": llm_utils_module,
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}
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def _import_text_processor(use_hf_tokenizer=False):
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modules = _create_dummy_modules()
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modules["fastdeploy.envs"].FD_USE_HF_TOKENIZER = use_hf_tokenizer
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previous_modules = {}
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for name, module in modules.items():
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previous_modules[name] = sys.modules.get(name)
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sys.modules[name] = module
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try:
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text_processor_module = importlib.import_module("fastdeploy.input.text_processor")
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importlib.reload(text_processor_module)
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except Exception:
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for name, original in previous_modules.items():
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if original is None:
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sys.modules.pop(name, None)
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else:
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return [0]
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sys.modules[name] = original
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raise
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def mock_apply_default_parameters(request):
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return request
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def cleanup():
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sys.modules.pop("fastdeploy.input.text_processor", None)
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for name, original in previous_modules.items():
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if original is None:
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sys.modules.pop(name, None)
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else:
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sys.modules[name] = original
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self.processor.messages2ids = mock_messages2ids
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self.processor._apply_default_parameters = mock_apply_default_parameters
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return text_processor_module, cleanup
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def test_process_request(self):
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request = Request.from_dict(
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{
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"request_id": "123",
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"messages": [{"role": "user", "content": "Hello!"}],
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"eos_token_ids": [1],
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"temperature": 1,
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"top_p": 1,
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}
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class DummyRequest:
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def __init__(self, **kwargs):
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self.request_id = kwargs.get("request_id", "req")
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self.prompt = kwargs.get("prompt")
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self.prompt_token_ids = kwargs.get("prompt_token_ids")
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self.messages = kwargs.get("messages")
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self.eos_token_ids = kwargs.get("eos_token_ids")
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self.chat_template = kwargs.get("chat_template")
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self.enable_thinking = kwargs.get("enable_thinking")
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self.history = kwargs.get("history")
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self.tools = kwargs.get("tools")
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self.system = kwargs.get("system")
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self.sampling_params = SimpleNamespace(
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top_p=kwargs.get("top_p"),
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temperature=kwargs.get("temperature"),
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repetition_penalty=kwargs.get("repetition_penalty"),
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frequency_penalty=kwargs.get("frequency_penalty"),
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presence_penalty=kwargs.get("presence_penalty"),
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stop=kwargs.get("stop"),
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stop_token_ids=kwargs.get("stop_token_ids"),
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stop_seqs_len=kwargs.get("stop_seqs_len"),
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bad_words=kwargs.get("bad_words"),
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bad_words_token_ids=kwargs.get("bad_words_token_ids"),
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max_tokens=kwargs.get("max_tokens"),
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)
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chat_template_kwargs = {"chat_template": "Hello!"}
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result = self.processor.process_request(request, 100, chat_template_kwargs=chat_template_kwargs)
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self.assertEqual(result.prompt_token_ids, [1])
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def test_process_request_dict(self):
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request_dict = {
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"messages": [{"role": "user", "content": "Hello!"}],
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"chat_template_kwargs": {"chat_template": "Hello!"},
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"eos_token_ids": [1],
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"temperature": 1,
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"top_p": 1,
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def get(self, key, default=None):
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if hasattr(self, key) and getattr(self, key) is not None:
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return getattr(self, key)
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return getattr(self.sampling_params, key, default)
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def set(self, key, value):
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if hasattr(self.sampling_params, key):
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setattr(self.sampling_params, key, value)
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else:
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setattr(self, key, value)
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def to_dict(self):
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return {
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"request_id": self.request_id,
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"messages": self.messages,
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"prompt": self.prompt,
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"system": self.system,
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"history": self.history,
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"tools": self.tools,
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"chat_template": self.chat_template,
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"enable_thinking": self.enable_thinking,
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}
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result = self.processor.process_request_dict(request_dict, 100)
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self.assertEqual(result["prompt_token_ids"], [1])
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def test_process_response_dict_normal(self):
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self.processor.tokenizer.decode_token = MagicMock(return_value=("Mock decoded text", 0, 0))
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self.processor.reasoning_parser.extract_reasoning_content = MagicMock(
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return_value=("Mock reasoning content", "Mock final text")
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def __getitem__(self, key):
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return self.get(key)
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def __setitem__(self, key, value):
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self.set(key, value)
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class DataProcessorTestCase(unittest.TestCase):
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@staticmethod
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def create_dummy_reasoning(tokenizer, reasoning_content="think"):
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class DummyReasoning:
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def __init__(self, tokenizer):
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self.tokenizer = tokenizer
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def extract_reasoning_content(self, full_text, response_dict):
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return reasoning_content, f"{full_text}!"
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return DummyReasoning(tokenizer)
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@staticmethod
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def create_dummy_tool_parser(tokenizer, content="tool-text"):
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class DummyToolParser:
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def __init__(self, tokenizer):
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self.tokenizer = tokenizer
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def extract_tool_calls(self, full_text, response_dict):
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return SimpleNamespace(tools_called=True, tool_calls=["tool"], content=content)
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return DummyToolParser
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def setUp(self):
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module, cleanup = _import_text_processor()
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self.text_processor_module = module
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self.addCleanup(cleanup)
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self.processor = self.text_processor_module.DataProcessor("stub-model")
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def test_base_data_processor_contract(self):
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text_processor_module = self.text_processor_module
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class MinimalProcessor(text_processor_module.BaseDataProcessor):
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def __init__(self):
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self.generation_config = SimpleNamespace(
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top_p=0.5,
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temperature=0.6,
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repetition_penalty=1.1,
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frequency_penalty=0.2,
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presence_penalty=0.3,
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)
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super().__init__()
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def _load_tokenizer(self):
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return DummyTokenizer()
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def process_request(self, request, **kwargs):
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return super().process_request(request, **kwargs)
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def process_response(self, response_dict):
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return super().process_response(response_dict)
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processor = MinimalProcessor()
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defaults = processor._apply_default_parameters({})
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self.assertAlmostEqual(defaults["top_p"], 0.5)
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with self.assertRaises(NotImplementedError):
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processor.process_request({}, max_model_len=None)
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with self.assertRaises(NotImplementedError):
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processor.process_response({})
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with self.assertRaises(NotImplementedError):
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processor.text2ids("text")
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with self.assertRaises(NotImplementedError):
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processor.messages2ids([])
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with self.assertRaises(NotImplementedError):
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processor.ids2tokens([1], "task")
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def test_process_request_dict_prompt_defaults(self):
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request = {"prompt": "hi", "temperature": 0, "top_p": 0, "stop": ["stop"]}
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processed = self.processor.process_request_dict(request, max_model_len=5)
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self.assertEqual(processed["prompt_token_ids"], [2])
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self.assertEqual(processed["stop_token_ids"], [[4]])
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self.assertEqual(processed["stop_seqs_len"], [1])
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self.assertEqual(processed["temperature"], 1)
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self.assertAlmostEqual(processed["top_p"], 1e-5)
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self.assertEqual(processed["max_tokens"], 4)
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def test_process_request_dict_messages_template(self):
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request = {
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"request_id": "chat",
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"messages": [{"role": "user", "content": "hello"}],
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"chat_template_kwargs": {"system": "system prompt"},
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}
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processed = self.processor.process_request_dict(request, max_model_len=6)
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self.assertEqual(processed["prompt_token_ids"], [len("system prompt hello")])
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self.assertEqual(processed["system"], "system prompt")
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self.assertTrue(processed["enable_thinking"])
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self.assertEqual(processed["prompt_tokens"], "system prompt hello")
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def test_process_request_object_handles_sequences(self):
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request = DummyRequest(
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prompt=[1, 2, 3, 4, 5, 6],
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stop=["stop"],
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bad_words=["zz"],
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temperature=0,
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top_p=0,
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)
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mock_tokens = ["mock", "reasoning", "tokens"]
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self.processor.tokenizer.tokenize = MagicMock(return_value=mock_tokens)
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self.processor.tool_parser_obj = None
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response_dict = {
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"request_id": "request-id_0",
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"outputs": {
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"token_ids": [2, 3, 4, 5, 1],
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"text": "Hello",
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"top_logprobs": [{"a": 0.1}, {"b": 0.2}, {"c": 0.3}],
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},
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"finish_reason": "stop",
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processed = self.processor.process_request(request, max_model_len=5)
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self.assertEqual(processed.prompt_token_ids, [1, 2, 3, 4])
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self.assertEqual(processed.sampling_params.max_tokens, 1)
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self.assertEqual(processed.sampling_params.stop_token_ids, [[4]])
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self.assertEqual(set(processed.sampling_params.bad_words_token_ids), {2, 3})
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self.assertEqual(processed.sampling_params.temperature, 1)
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self.assertAlmostEqual(processed.sampling_params.top_p, 1e-5)
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def test_process_request_requires_prompt_or_messages(self):
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request = DummyRequest(prompt=None, messages=None, prompt_token_ids=None)
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with self.assertRaisesRegex(ValueError, "should have `input_ids`, `text` or `messages`"):
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self.processor.process_request(request, max_model_len=5)
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def test_process_request_dict_rejects_bad_kwargs(self):
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request = {
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"messages": [{"role": "user", "content": "hi"}],
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"chat_template_kwargs": "invalid",
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}
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with self.assertRaisesRegex(ValueError, "chat_template_kwargs must be a dict"):
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self.processor.process_request_dict(request)
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def test_ids2tokens_and_clear_request_status(self):
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delta, _, _ = self.processor.ids2tokens([3], "task-1")
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self.assertEqual(delta, "3")
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delta, _, _ = self.processor.ids2tokens([4], "task-1")
|
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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
|
||||
|
||||
processor.reasoning_parser = self.create_dummy_reasoning(processor.tokenizer)
|
||||
processor.tool_parser_obj = self.create_dummy_tool_parser(processor.tokenizer, content="tool-only")
|
||||
|
||||
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
|
||||
|
||||
processor.reasoning_parser = self.create_dummy_reasoning(processor.tokenizer, reasoning_content="because")
|
||||
processor.tool_parser_obj = self.create_dummy_tool_parser(processor.tokenizer, content="tool-text")
|
||||
|
||||
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__":
|
||||
|
||||
Reference in New Issue
Block a user