#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
配置模型
定义了项目中所有配置的数据类模型
使用pydantic进行数据验证和序列化
"""

from typing import Dict, Any, List, Optional, Type, TypeVar, Union, ClassVar
from pydantic import BaseModel, Field, model_validator, ConfigDict

T = TypeVar('T', bound='BaseConfig')

class BaseConfig(BaseModel):
    """可从字典更新的配置基类"""
    
    model_config = ConfigDict(
        extra="allow",  # 允许额外字段
        arbitrary_types_allowed=True  # 允许任意类型
    )
    
    def update(self, new_data: Dict[str, Any]) -> None:
        """从字典递归更新配置"""
        for key, value in new_data.items():
            if hasattr(self, key):
                current_attr = getattr(self, key)
                # 检查当前属性是否为配置基类实例，并且新值是字典
                if isinstance(current_attr, BaseConfig) and isinstance(value, dict):
                    # 递归更新嵌套的配置对象
                    current_attr.update(value)
                else:
                    # 否则，直接设置值
                    setattr(self, key, value)
    
    def to_dict(self) -> Dict[str, Any]:
        """将配置转换为字典"""
        return self.model_dump()


class ReferItem(BaseConfig):
    """单个Refer项"""
    path: str = ""
    sampling_rate: float = 1.0
    step: str = ""  # 可选值: "topic", "content", "judge"，表示在哪个阶段使用


class ReferConfig(BaseConfig):
    """Refer配置，现在是一个列表"""
    refer_list: List[ReferItem] = Field(default_factory=list)
    
    def update(self, new_data: Dict[str, Any]) -> None:
        """特殊处理refer_list的更新"""
        if 'refer_list' in new_data and isinstance(new_data['refer_list'], list):
            # 将字典列表转换为ReferItem对象列表
            new_list = []
            for item in new_data['refer_list']:
                if isinstance(item, dict):
                    new_list.append(ReferItem(**item))
                elif isinstance(item, ReferItem):
                    new_list.append(item)
            self.refer_list = new_list
            
            # 移除已处理的refer_list，避免重复处理
            new_data_copy = new_data.copy()
            new_data_copy.pop('refer_list')
            super().update(new_data_copy)
        else:
            super().update(new_data)


class PathConfig(BaseConfig):
    """单个路径配置"""
    path: str = ""


class PathListConfig(BaseConfig):
    """路径列表配置"""
    paths: List[str] = Field(default_factory=list)


class SamplingPathListConfig(BaseConfig):
    """带采样率的路径列表配置"""
    sampling_rate: float = 1.0
    paths: List[str] = Field(default_factory=list)


class OutputConfig(BaseConfig):
    """输出配置"""
    base_dir: str = "result"
    image_dir: str = "images"
    topic_dir: str = "topics"
    content_dir: str = "contents"


class ResourceConfig(BaseConfig):
    """资源配置"""
    resource_dirs: List[str] = Field(default_factory=list)
    style: PathListConfig = Field(default_factory=PathListConfig)
    demand: PathListConfig = Field(default_factory=PathListConfig)
    object: PathListConfig = Field(default_factory=PathListConfig)
    product: PathListConfig = Field(default_factory=PathListConfig)
    refer: ReferConfig = Field(default_factory=ReferConfig)
    image: PathListConfig = Field(default_factory=PathListConfig)
    output_dir: OutputConfig = Field(default_factory=OutputConfig)


class SystemConfig(BaseConfig):
    """系统配置"""
    debug: bool = False
    log_level: str = "INFO"
    parallel_processing: bool = True
    max_workers: int = 4


class TopicConfig(BaseConfig):
    """选题配置"""
    date: str = ""
    num: int = 5
    variants: int = 1


class GenerateTopicConfig(BaseConfig):
    """主题生成配置"""
    topic_system_prompt: str = "resource/prompt/generateTopics/system.txt"
    topic_user_prompt: str = "resource/prompt/generateTopics/user.txt"
    model: Dict[str, Any] = Field(default_factory=dict)
    topic: TopicConfig = Field(default_factory=TopicConfig)


class GenerateContentConfig(BaseConfig):
    """内容生成配置"""
    content_system_prompt: str = "resource/prompt/generateContent/system.txt"
    content_user_prompt: str = "resource/prompt/generateContent/user.txt"
    judger_system_prompt: str = "resource/prompt/judgeContent/system.txt"
    judger_user_prompt: str = "resource/prompt/judgeContent/user.txt"
    enable_content_judge: bool = True
    refer_sampling_rate: float = 1.0
    model: Dict[str, Any] = Field(default_factory=dict)
    judger_model: Dict[str, Any] = Field(default_factory=dict)


class AIModelConfig(BaseConfig):
    """AI模型配置"""
    model: str = "qwq-plus"
    api_url: str = ""
    api_key: str = ""
    temperature: float = 0.7
    top_p: float = 0.5
    presence_penalty: float = 1.2
    timeout: int = 60
    max_retries: int = 3
    topic_system_prompt: str = "resource/prompt/generateTopics/system.txt"
    topic_user_prompt: str = "resource/prompt/generateTopics/user.txt"
    refer_sampling_rate: float = Field(0.5, ge=0.0, le=1.0)


class PosterConfig(BaseConfig):
    """海报生成配置"""
    target_size: List[int] = Field(default_factory=lambda: [900, 1200])
    additional_images_enabled: bool = True
    template_selection: str = "random"  # random, business, vibrant, original
    available_templates: List[str] = Field(default_factory=lambda: ["original", "business", "vibrant"])
    poster_system_prompt: str = "resource/prompt/generatePoster/system.txt"
    poster_user_prompt: str = "resource/prompt/generatePoster/user.txt"


class ContentConfig(BaseConfig):
    """内容生成配置"""
    enable_content_judge: bool = True
    num: int = 5
    variants_per_topic: int = 1
    max_title_length: int = 30
    max_content_length: int = 500