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

"""
Configuration Models
配置数据模型 - 定义所有算法组件的配置结构
"""

from typing import Dict, List, Any, Optional, Union
from pydantic import BaseModel, Field, validator
from pathlib import Path
import os


class BaseConfig(BaseModel):
    """基础配置类"""
    
    class Config:
        extra = "allow"  # 允许额外字段
        validate_assignment = True


class TaskModelConfig(BaseModel):
    """任务级别的模型配置"""
    temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
    top_p: Optional[float] = Field(None, ge=0.0, le=1.0)
    presence_penalty: Optional[float] = Field(None, ge=-2.0, le=2.0)
    frequency_penalty: Optional[float] = Field(None, ge=-2.0, le=2.0)
    max_tokens: Optional[int] = Field(None, gt=0)
    enable_stream: Optional[bool] = None
    
    def merge_with_default(self, default_config: "AIModelConfig") -> Dict[str, Any]:
        """与默认配置合并，任务配置优先"""
        result = {
            "temperature": self.temperature if self.temperature is not None else default_config.temperature,
            "top_p": self.top_p if self.top_p is not None else default_config.top_p,
            "presence_penalty": self.presence_penalty if self.presence_penalty is not None else default_config.presence_penalty,
            "frequency_penalty": self.frequency_penalty if self.frequency_penalty is not None else default_config.frequency_penalty,
            "max_tokens": self.max_tokens if self.max_tokens is not None else default_config.max_tokens,
            "enable_stream": self.enable_stream if self.enable_stream is not None else default_config.enable_stream,
        }
        return {k: v for k, v in result.items() if v is not None}


class AIModelConfig(BaseConfig):
    """AI模型配置 - 改进版，支持任务级别参数"""
    
    # 基础配置
    model: str = "qwq-plus"
    api_url: str = ""
    api_key: str = ""
    timeout: int = Field(60, gt=0)
    max_retries: int = Field(3, ge=0)
    
    # 默认模型参数
    temperature: float = Field(0.7, ge=0.0, le=2.0)
    top_p: float = Field(0.5, ge=0.0, le=1.0)
    presence_penalty: float = Field(1.2, ge=-2.0, le=2.0)
    frequency_penalty: float = Field(0.0, ge=-2.0, le=2.0)
    max_tokens: Optional[int] = Field(None, gt=0)
    
    # 流式输出配置
    enable_stream: bool = True
    stream_chunk_size: int = 1024
    
    # 任务级别的模型参数配置
    task_configs: Dict[str, TaskModelConfig] = Field(default_factory=dict)
    
    def get_task_config(self, task_name: str) -> Dict[str, Any]:
        """
        获取特定任务的模型配置
        
        Args:
            task_name: 任务名称 (topic_generation, content_generation, content_judging, poster_generation)
            
        Returns:
            该任务的模型参数字典
        """
        if task_name in self.task_configs:
            return self.task_configs[task_name].merge_with_default(self)
        else:
            # 返回默认配置
            return {
                "temperature": self.temperature,
                "top_p": self.top_p,
                "presence_penalty": self.presence_penalty,
                "frequency_penalty": self.frequency_penalty,
                "max_tokens": self.max_tokens,
                "enable_stream": self.enable_stream,
            }
    
    def set_task_config(self, task_name: str, **params) -> None:
        """
        设置特定任务的模型参数
        
        Args:
            task_name: 任务名称
            **params: 模型参数
        """
        self.task_configs[task_name] = TaskModelConfig(**params)


class PromptConfig(BaseConfig):
    """提示词配置 - 解决硬编码的提示词路径问题"""
    
    # 内容生成提示词
    topic_generation: Dict[str, str] = Field(default_factory=dict)
    content_generation: Dict[str, str] = Field(default_factory=dict)
    content_judging: Dict[str, str] = Field(default_factory=dict)
    poster_generation: Dict[str, str] = Field(default_factory=dict)
    
    # 提示词模板路径 (支持自定义)
    template_directory: Optional[str] = None
    
    @validator("*", pre=True)
    def validate_prompts(cls, v):
        """验证提示词配置"""
        if isinstance(v, dict):
            # 确保包含system和user两个键
            if "system" not in v or "user" not in v:
                return {
                    "system": v.get("system", ""),
                    "user": v.get("user", "")
                }
        return v


class ContentGenerationConfig(BaseConfig):
    """内容生成配置"""
    
    # 主题生成配置
    topic_count: int = Field(5, ge=1, le=20)
    topic_date_range: Optional[str] = None
    topic_style: str = "default"
    
    # 内容生成配置
    content_length: str = Field("medium", regex="^(short|medium|long)$")
    content_style: str = "default"
    enable_auto_judge: bool = True
    
    # 审核配置
    judge_enabled: bool = True
    judge_threshold: float = Field(0.7, ge=0.0, le=1.0)
    
    # 参考资料配置
    refer_sampling_rate: float = Field(0.5, ge=0.0, le=1.0)
    enable_refer_content: bool = True


class PosterGenerationConfig(BaseConfig):
    """海报生成配置 - 解决硬编码的模板和资源问题"""
    
    # 尺寸配置
    default_size: List[int] = Field(default_factory=lambda: [900, 1200])
    supported_sizes: List[List[int]] = Field(
        default_factory=lambda: [[900, 1200], [1080, 1080], [750, 1334]]
    )
    
    # 模板配置
    default_template: str = "vibrant"
    available_templates: List[str] = Field(
        default_factory=lambda: ["vibrant", "business", "collage"]
    )
    template_selection_mode: str = Field("manual", regex="^(manual|random|auto)$")
    
    # 字体配置 - 解决硬编码字体路径
    font_configs: Dict[str, Dict[str, Any]] = Field(default_factory=dict)
    default_font_family: str = "default"
    
    # 颜色主题配置 - 从硬编码中提取
    color_themes: Dict[str, Dict[str, List[List[int]]]] = Field(default_factory=dict)
    default_color_theme: str = "ocean_deep"
    
    # 效果配置
    glass_effect: Dict[str, Any] = Field(default_factory=dict)
    text_effects: Dict[str, Any] = Field(default_factory=dict)
    
    # 输出配置
    output_format: str = Field("PNG", regex="^(PNG|JPEG|WEBP)$")
    output_quality: int = Field(95, ge=1, le=100)
    generate_thumbnail: bool = True
    thumbnail_size: List[int] = Field(default_factory=lambda: [300, 400])


class DocumentProcessingConfig(BaseConfig):
    """文档处理配置"""
    
    # 支持的文件类型
    supported_extensions: List[str] = Field(
        default_factory=lambda: [".txt", ".md", ".pdf", ".docx", ".doc", ".xlsx", ".xls"]
    )
    
    # 提取配置
    max_file_size: int = Field(50 * 1024 * 1024)  # 50MB
    encoding: str = "utf-8"
    extract_images: bool = True
    extract_tables: bool = True
    
    # 处理配置
    enable_content_cleaning: bool = True
    remove_extra_whitespace: bool = True
    normalize_text: bool = True
    
    # 爬虫相关配置
    enable_web_scraping: bool = True
    scraping_config: Dict[str, Any] = Field(default_factory=dict)


class OutputConfig(BaseConfig):
    """输出配置 - 解决硬编码的文件名和路径问题"""
    
    # 基础路径配置
    base_output_directory: str = "result"
    enable_timestamped_folders: bool = True
    folder_name_pattern: str = "run_{timestamp}"
    
    # 文件命名配置
    file_naming: Dict[str, str] = Field(default_factory=lambda: {
        "topics": "topics.json",
        "content": "article.json", 
        "poster": "poster.png",
        "raw_response": "{stage}_raw_response.txt",
        "system_prompt": "{stage}_system_prompt.txt",
        "user_prompt": "{stage}_user_prompt.txt"
    })
    
    # 保存格式配置
    save_raw_responses: bool = True
    save_prompts: bool = True
    save_metadata: bool = True
    
    # 压缩和备份
    enable_compression: bool = False
    backup_count: int = 5


class ResourceConfig(BaseConfig):
    """资源配置 - 管理外部资源路径，包括static文件"""
    
    # 资源基础路径
    resource_base_directory: Optional[str] = None
    
    # 字体资源
    font_directory: Optional[str] = None
    font_files: Dict[str, str] = Field(default_factory=dict)
    
    # 提示词模板目录
    prompt_template_directory: Optional[str] = None
    
    # 图片资源
    image_assets_directory: Optional[str] = None
    default_background_images: List[str] = Field(default_factory=list)
    
    # 静态文件配置 - 解决爬虫模块依赖
    static_files_directory: Optional[str] = None
    required_js_files: Dict[str, str] = Field(default_factory=lambda: {
        "xhs_xs_xsc_56": "xhs_xs_xsc_56.js",
        "xhs_xray": "xhs_xray.js",
        "xhs_creator_xs": "xhs_creator_xs.js",
        "xhs_xray_pack1": "xhs_xray_pack1.js",
        "xhs_xray_pack2": "xhs_xray_pack2.js"
    })

    def get_js_file_path(self, js_name: str) -> Optional[str]:
        """
        解析JavaScript文件路径

        Args:
            js_name: JS文件标识符

        Returns:
            完整的文件路径，如果找不到返回None
        """
        if js_name not in self.required_js_files:
            return None

        filename = self.required_js_files[js_name]
        
        if self.static_files_directory:
            js_path = Path(self.resolve_path(self.static_files_directory)) / filename
            if js_path.exists():
                return str(js_path)
        
        # 搜索可能的路径
        search_paths = [
            Path("static") / filename,
            Path("../static") / filename,
            Path("../../static") / filename,
            Path(f"../{self.static_files_directory or 'static'}") / filename if self.static_files_directory else None
        ]
        
        for path in search_paths:
            if path and path.exists():
                return str(path.resolve())
        
        return None

    def resolve_path(self, relative_path: str) -> str:
        """
        解析相对路径为绝对路径

        Args:
            relative_path: 相对路径

        Returns:
            解析后的路径
        """
        if not relative_path:
            return ""
        
        path = Path(relative_path)
        
        # 如果已经是绝对路径，直接返回
        if path.is_absolute():
            return str(path)
        
        # 如果有resource_base_directory，基于它解析
        if self.resource_base_directory:
            base_path = Path(self.resource_base_directory)
            resolved = base_path / path
            return str(resolved)
        
        # 否则基于当前目录解析
        return str(path.resolve())

    def find_resource_directory(self, resource_type: str) -> Optional[Path]:
        """
        查找资源目录

        Args:
            resource_type: 资源类型 (fonts, prompts, images, static)

        Returns:
            找到的目录路径，如果找不到返回None
        """
        # 根据资源类型确定目录名
        dir_mapping = {
            "fonts": self.font_directory,
            "prompts": self.prompt_template_directory,
            "images": self.image_assets_directory,
            "static": self.static_files_directory
        }
        
        relative_dir = dir_mapping.get(resource_type)
        if not relative_dir:
            return None
        
        # 构建搜索路径列表
        search_paths = []
        
        # 如果有基础目录，优先搜索
        if self.resource_base_directory:
            base_path = Path(self.resource_base_directory)
            search_paths.append(base_path / relative_dir)
        
        # 添加相对路径搜索
        search_paths.extend([
            Path(relative_dir),
            Path("..") / relative_dir,
            Path("../..") / relative_dir,
            Path("../../..") / relative_dir
        ])
        
        # 特殊搜索：针对外部项目的resource目录
        if resource_type == "prompts":
            search_paths.extend([
                Path("resource/prompt"),
                Path("../resource/prompt"),
                Path("../../resource/prompt"),
                Path("../../../resource/prompt")
            ])
        elif resource_type == "static":
            search_paths.extend([
                Path("static"),
                Path("../static"),
                Path("../../static"),
                Path("../../../static")
            ])
        
        # 搜索存在的目录
        for path in search_paths:
            if path.exists() and path.is_dir():
                return path.resolve()
        
        return None
    
    def validate_js_files(self) -> Dict[str, bool]:
        """
        验证所有必需的JS文件是否存在
        
        Returns:
            文件名->是否存在的字典
        """
        result = {}
        for js_name in self.required_js_files:
            result[js_name] = self.get_js_file_path(js_name) is not None
        return result 


class WebCrawlingConfig(BaseModel):
    """网页爬虫配置"""
    
    # 请求配置
    request_interval: float = Field(default=1.0, ge=0.1, description="请求间隔（秒）")
    max_requests: int = Field(default=100, ge=0, description="最大请求数，0表示无限制")
    request_timeout: float = Field(default=30.0, ge=1.0, description="请求超时时间")
    
    # 浏览器配置
    user_agent: str = Field(
        default="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
        description="用户代理字符串"
    )
    
    # 代理配置
    enable_proxy: bool = Field(default=False, description="是否启用代理")
    proxy_config: Optional[Dict[str, str]] = Field(default=None, description="代理配置")
    
    # 错误处理
    max_retries: int = Field(default=3, ge=0, description="最大重试次数")
    retry_delay: float = Field(default=2.0, ge=0.1, description="重试延迟")
    
    # 内容过滤
    min_content_length: int = Field(default=50, ge=0, description="最小内容长度")
    max_content_length: int = Field(default=10000, ge=100, description="最大内容长度")
    
    # 小红书特定配置
    xhs_search_config: Dict[str, Any] = Field(
        default_factory=lambda: {
            "default_sort_type": 0,  # 默认排序类型
            "default_note_type": 0,  # 默认笔记类型
            "enable_image_download": True,
            "enable_video_download": False,
            "search_result_limit": 50
        },
        description="小红书搜索配置"
    )


class KeywordAnalysisConfig(BaseModel):
    """关键词分析配置"""
    
    # 基础配置
    max_keywords: int = Field(default=20, ge=1, le=100, description="最大关键词数量")
    min_keyword_length: int = Field(default=2, ge=1, description="最小关键词长度")
    max_keyword_length: int = Field(default=10, ge=2, description="最大关键词长度")
    
    # AI分析配置
    enable_ai_analysis: bool = Field(default=True, description="是否启用AI分析")
    ai_analysis_threshold: int = Field(default=100, ge=0, description="启用AI分析的最小内容长度")
    
    # 评分配置
    frequency_weight: float = Field(default=0.4, ge=0.0, le=1.0, description="频率权重")
    position_weight: float = Field(default=0.3, ge=0.0, le=1.0, description="位置权重")
    type_weight: float = Field(default=0.3, ge=0.0, le=1.0, description="类型权重")
    
    # 分类配置
    enable_categorization: bool = Field(default=True, description="是否启用关键词分类")
    category_threshold: int = Field(default=2, ge=1, description="分类阈值")
    
    # 搜索建议配置
    max_suggestions: int = Field(default=20, ge=1, description="最大搜索建议数量")
    enable_combination_suggestions: bool = Field(default=True, description="是否启用组合建议")


class ContentAnalysisConfig(BaseModel):
    """内容分析配置"""
    
    # 质量评分配置
    content_length_weight: float = Field(default=0.25, ge=0.0, le=1.0, description="内容长度权重")
    title_quality_weight: float = Field(default=0.20, ge=0.0, le=1.0, description="标题质量权重")
    media_richness_weight: float = Field(default=0.20, ge=0.0, le=1.0, description="媒体丰富度权重")
    structure_weight: float = Field(default=0.20, ge=0.0, le=1.0, description="结构权重")
    tag_completeness_weight: float = Field(default=0.15, ge=0.0, le=1.0, description="标签完整性权重")
    
    # 互动评分配置
    likes_weight: float = Field(default=0.40, ge=0.0, le=1.0, description="点赞权重")
    comments_weight: float = Field(default=0.35, ge=0.0, le=1.0, description="评论权重")
    shares_weight: float = Field(default=0.25, ge=0.0, le=1.0, description="分享权重")
    
    # 分析配置
    enable_sentiment_analysis: bool = Field(default=True, description="是否启用情感分析")
    enable_theme_extraction: bool = Field(default=True, description="是否启用主题提取")
    enable_readability_analysis: bool = Field(default=True, description="是否启用可读性分析")
    
    # 主题提取配置
    max_themes: int = Field(default=10, ge=1, description="最大主题数量")
    theme_frequency_threshold: int = Field(default=2, ge=1, description="主题频率阈值")