146 lines
5.0 KiB
Python
146 lines
5.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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测试配置管理器和pydantic模型的兼容性
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"""
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import os
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import json
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import tempfile
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from pathlib import Path
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from core.config.manager import ConfigManager
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from core.config.models import (
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BaseConfig, AIModelConfig, SystemConfig, GenerateTopicConfig, ResourceConfig,
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GenerateContentConfig, PosterConfig, ContentConfig, ReferItem, ReferConfig
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)
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def test_config_creation():
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"""测试创建配置实例"""
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print("测试创建配置实例...")
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# 创建各种配置实例
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ai_config = AIModelConfig(model="test-model", api_key="test-key")
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print(f"AI配置: {ai_config.model}, {ai_config.api_key}")
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# 测试嵌套配置
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resource_config = ResourceConfig(
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resource_dirs=["resource"],
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refer=ReferConfig(
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refer_list=[
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ReferItem(path="path1", sampling_rate=0.5, step="topic"),
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ReferItem(path="path2", sampling_rate=0.8, step="content")
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]
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)
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)
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# 验证嵌套配置
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print(f"资源目录: {resource_config.resource_dirs}")
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print(f"Refer项数量: {len(resource_config.refer.refer_list)}")
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print(f"第一个Refer项: {resource_config.refer.refer_list[0].path}, {resource_config.refer.refer_list[0].sampling_rate}")
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# 测试序列化
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config_dict = resource_config.to_dict()
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print(f"序列化后的字典: {json.dumps(config_dict, indent=2)}")
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return True
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def test_config_manager():
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"""测试配置管理器"""
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print("\n测试配置管理器...")
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# 创建临时目录
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with tempfile.TemporaryDirectory() as temp_dir:
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# 创建测试配置文件
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ai_config_path = Path(temp_dir) / "ai_model.json"
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ai_config = {
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"model": "test-model",
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"api_key": "test-key",
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"temperature": 0.8
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}
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with open(ai_config_path, "w") as f:
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json.dump(ai_config, f)
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# 创建嵌套配置文件
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resource_config_path = Path(temp_dir) / "resource.json"
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resource_config = {
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"resource_dirs": ["test_resource"],
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"refer": {
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"refer_list": [
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{"path": "test_path", "sampling_rate": 0.7, "step": "judge"}
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]
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}
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}
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with open(resource_config_path, "w") as f:
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json.dump(resource_config, f)
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# 初始化配置管理器
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config_manager = ConfigManager()
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config_manager.load_from_directory(temp_dir)
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# 获取并验证AI配置
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ai_model_config = config_manager.get_config("ai_model", AIModelConfig)
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print(f"加载的AI配置: {ai_model_config.model}, {ai_model_config.api_key}, {ai_model_config.temperature}")
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assert ai_model_config.model == "test-model"
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assert ai_model_config.api_key == "test-key"
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assert ai_model_config.temperature == 0.8
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# 获取并验证资源配置
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resource_config = config_manager.get_config("resource", ResourceConfig)
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print(f"加载的资源配置: {resource_config.resource_dirs}")
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print(f"加载的Refer项: {resource_config.refer.refer_list[0].path}, {resource_config.refer.refer_list[0].sampling_rate}")
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assert resource_config.resource_dirs == ["test_resource"]
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assert len(resource_config.refer.refer_list) == 1
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assert resource_config.refer.refer_list[0].path == "test_path"
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assert resource_config.refer.refer_list[0].sampling_rate == 0.7
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# 测试更新配置
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ai_model_config.update({"model": "updated-model"})
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print(f"更新后的AI配置: {ai_model_config.model}")
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assert ai_model_config.model == "updated-model"
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# 测试保存配置
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config_manager.save_config("ai_model")
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# 重新加载并验证
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with open(ai_config_path, "r") as f:
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saved_config = json.load(f)
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print(f"保存的配置: {saved_config['model']}")
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assert saved_config["model"] == "updated-model"
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return True
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def test_config_type_conversion():
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"""测试配置类型转换"""
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print("\n测试配置类型转换...")
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# 创建配置管理器
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config_manager = ConfigManager()
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# 注册一个SystemConfig
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config_manager.register_config("test_config", SystemConfig)
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# 尝试以不同类型获取
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system_config = config_manager.get_config("test_config", SystemConfig)
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print(f"获取为SystemConfig: {type(system_config).__name__}")
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# 尝试转换类型
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try:
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content_config = config_manager.get_config("test_config", ContentConfig)
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print(f"成功转换为ContentConfig: {type(content_config).__name__}")
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except TypeError as e:
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print(f"类型转换失败，符合预期: {e}")
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return True
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if __name__ == "__main__":
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print("开始测试pydantic配置模型和ConfigManager...")
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test_config_creation()
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test_config_manager()
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test_config_type_conversion()
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print("\n所有测试完成!") |