"""Pydantic schemas for API requests and responses."""

from typing import Optional, List, Any, Dict, TYPE_CHECKING, Union
from datetime import datetime
from pydantic import BaseModel, Field, model_validator
from enum import Enum

if TYPE_CHECKING:
    from th_agenter.schemas.permission import RoleResponse

class MessageRole(str, Enum):
    """消息角色枚举"""
    USER = "user"
    ASSISTANT = "assistant"
    SYSTEM = "system"

class MessageType(str, Enum):
    """消息类型枚举"""
    TEXT = "text"
    IMAGE = "image"
    FILE = "file"
    AUDIO = "audio"

# Base schemas
class BaseResponse(BaseModel):
    """基础响应模型"""
    id: int
    created_at: datetime
    updated_at: datetime

    class Config:
        from_attributes = True


# User schemas
class UserBase(BaseModel):
    """用户基础模型"""
    username: str = Field(..., min_length=3, max_length=50)
    email: str = Field(..., max_length=100)
    full_name: Optional[str] = Field(None, max_length=100)
    bio: Optional[str] = None
    avatar_url: Optional[str] = None


class UserCreate(UserBase):
    """用户创建模型"""
    password: str = Field(..., min_length=6)


class UserUpdate(BaseModel):
    """用户更新模型"""
    username: Optional[str] = Field(None, min_length=3, max_length=50)
    email: Optional[str] = Field(None, max_length=100)
    full_name: Optional[str] = Field(None, max_length=100)
    bio: Optional[str] = None
    avatar_url: Optional[str] = None
    password: Optional[str] = Field(None, min_length=6)
    is_active: Optional[bool] = None
    department_id: Optional[int] = None


class UserResponse(BaseResponse, UserBase):
    """用户响应模型"""
    is_active: bool
    department_id: Optional[int] = None
    roles: Optional[List['RoleResponse']] = Field(default=[], description="用户角色列表")
    permissions: Optional[List[Dict[str, Any]]] = Field(default=[], description="用户权限列表")
    is_superuser: Optional[bool] = Field(default=False, description="是否为超级管理员")
    
    @classmethod
    def from_orm(cls, obj):
        """从ORM对象创建响应对象，安全处理关系属性（同步版本）."""
        # 获取基本字段
        data = {
            'id': obj.id,
            'username': obj.username,
            'email': obj.email,
            'full_name': obj.full_name,
            'is_active': obj.is_active,
            'department_id': obj.department_id,
            'created_at': obj.created_at,
            'updated_at': obj.updated_at,
            'created_by': obj.created_by,
            'updated_by': obj.updated_by,
        }
        
        # 安全处理roles关系 - 仅使用已加载的关系，不尝试刷新
        try:
            if hasattr(obj, 'roles'):
                try:
                    from th_agenter.schemas.permission import RoleResponse
                    # 仅访问已加载的角色，不触发新查询
                    data['roles'] = [RoleResponse.from_orm(role) for role in obj.roles if role.is_active]
                except Exception:
                    # 如果访问roles失败（DetachedInstanceError或延迟加载错误），使用空列表
                    data['roles'] = []
            else:
                data['roles'] = []
        except Exception:
            data['roles'] = []
        
        # 安全处理权限信息 - 仅使用已加载的关系，不尝试刷新
        try:
            permissions = set()
            if hasattr(obj, 'roles'):
                try:
                    for role in obj.roles:
                        if role.is_active:
                            try:
                                for perm in role.permissions:
                                    if perm.is_active:
                                        permissions.add((perm.code, perm.name))
                            except Exception:
                                # 权限加载失败，跳过
                                continue
                except Exception:
                    # 角色加载失败，跳过
                    pass
            data['permissions'] = [{'code': code, 'name': name} for code, name in permissions]
        except Exception:
            data['permissions'] = []
        
        # 添加is_superuser字段
        try:
            # 检查是否有is_admin属性或is_superuser属性
            if hasattr(obj, 'is_admin'):
                data['is_superuser'] = obj.is_admin
            elif hasattr(obj, 'is_superuser'):
                if callable(obj.is_superuser):
                    try:
                        data['is_superuser'] = obj.is_superuser()
                    except Exception:
                        data['is_superuser'] = False
                else:
                    data['is_superuser'] = obj.is_superuser
            else:
                data['is_superuser'] = False
        except Exception:
            data['is_superuser'] = False
        
        return cls(**data)
        
    @classmethod
    async def from_orm_async(cls, obj):
        """从ORM对象创建响应对象，安全处理关系属性（异步版本）."""
        # 获取基本字段
        data = {
            'id': obj.id,
            'username': obj.username,
            'email': obj.email,
            'full_name': obj.full_name,
            'is_active': obj.is_active,
            'department_id': obj.department_id,
            'created_at': obj.created_at,
            'updated_at': obj.updated_at,
            'created_by': obj.created_by,
            'updated_by': obj.updated_by,
        }
        
        # 安全处理roles关系
        try:
            from sqlalchemy.orm import object_session
            from sqlalchemy.ext.asyncio import AsyncSession
            
            session = object_session(obj)
            roles_loaded = []
            
            if hasattr(obj, 'roles'):
                # 根据会话类型加载角色
                if session and isinstance(session, AsyncSession):
                    # 异步会话，使用await刷新
                    await session.refresh(obj, ['roles'])
                    roles_loaded = obj.roles if obj.roles is not None else []
                else:
                    # 同步会话或无会话，直接访问
                    try:
                        roles_loaded = obj.roles if obj.roles is not None else []
                    except Exception:
                        roles_loaded = []
            else:
                roles_loaded = []
                
            from th_agenter.schemas.permission import RoleResponse
            data['roles'] = [RoleResponse.from_orm(role) for role in roles_loaded]
        except Exception as e:
            # 如果访问roles失败，使用空列表
            data['roles'] = []
        
        # 添加权限信息
        try:
            # 获取数据库会话
            from sqlalchemy.orm import object_session
            session = object_session(obj)
            
            is_super_admin = False
            if hasattr(obj, 'has_role'):
                if callable(obj.has_role):
                    # 检查has_role是否为异步方法
                    import inspect
                    if inspect.iscoroutinefunction(obj.has_role):
                        is_super_admin = await obj.has_role('SUPER_ADMIN')
                    else:
                        is_super_admin = obj.has_role('SUPER_ADMIN')
            
            if is_super_admin:
                # 超级管理员拥有所有权限
                if session:
                    from th_agenter.models.permission import Permission
                    if isinstance(session, AsyncSession):
                        from sqlalchemy import select
                        all_permissions = await session.execute(select(Permission).filter(Permission.is_active == True))
                        all_permissions = all_permissions.scalars().all()
                    else:
                        all_permissions = session.query(Permission).filter(Permission.is_active == True).all()
                    data['permissions'] = [{'code': perm.code, 'name': perm.name} for perm in all_permissions]
                else:
                    data['permissions'] = [{'code': '*', 'name': '所有权限'}]
            else:
                # 从角色获取权限
                permissions = set()
                # 使用已加载的角色，避免再次访问关系
                for role in roles_loaded:
                    if role.is_active:
                        # 同样处理role.permissions关系
                        role_perms = []
                        if hasattr(role, 'permissions'):
                            try:
                                if session and isinstance(session, AsyncSession):
                                    await session.refresh(role, ['permissions'])
                                    role_perms = role.permissions if role.permissions is not None else []
                                else:
                                    role_perms = role.permissions if role.permissions is not None else []
                            except Exception:
                                role_perms = []
                        
                        for perm in role_perms:
                            if perm.is_active:
                                permissions.add((perm.code, perm.name))
                
                data['permissions'] = [{'code': code, 'name': name} for code, name in permissions]
        except Exception as e:
            # 如果访问权限失败，使用空列表
            data['permissions'] = []
        
        # 添加is_superuser字段
        try:
            # 检查是否有is_admin属性或is_superuser属性
            if hasattr(obj, 'is_admin'):
                data['is_superuser'] = obj.is_admin
            elif hasattr(obj, 'is_superuser'):
                if callable(obj.is_superuser):
                    import inspect
                    if inspect.iscoroutinefunction(obj.is_superuser):
                        data['is_superuser'] = await obj.is_superuser()
                    else:
                        data['is_superuser'] = obj.is_superuser()
                else:
                    data['is_superuser'] = obj.is_superuser
            else:
                data['is_superuser'] = False
        except Exception:
            data['is_superuser'] = False
        
        return cls(**data)


# Authentication schemas
class LoginRequest(BaseModel):
    """登录请求模型，兼容前端多余字段（如 selectAccount、captcha、username）"""
    email: str = Field(..., max_length=100)
    password: str = Field(..., min_length=6)

    model_config = {"extra": "ignore"}


class Token(BaseModel):
    """访问令牌响应模型"""
    access_token: str
    token_type: str
    expires_in: int


# Conversation schemas
class ConversationBase(BaseModel):
    """对话基础模型"""
    title: str = Field(..., min_length=1, max_length=200)
    system_prompt: Optional[str] = None
    model_name: str = Field(default="gpt-3.5-turbo", max_length=100)
    temperature: str = Field(default="0.7", max_length=10)
    max_tokens: int = Field(default=2048, ge=1, le=8192)
    knowledge_base_id: Optional[int] = None


class ConversationCreate(ConversationBase):
    """对话创建模型"""
    pass


class ConversationUpdate(BaseModel):
    """对话更新模型"""
    title: Optional[str] = Field(None, min_length=1, max_length=200)
    system_prompt: Optional[str] = None
    model_name: Optional[str] = Field(None, max_length=100)
    temperature: Optional[str] = Field(None, max_length=10)
    max_tokens: Optional[int] = Field(None, ge=1, le=8192)
    is_archived: Optional[bool] = None


class ConversationResponse(BaseResponse, ConversationBase):
    """对话响应模型"""
    user_id: int
    is_archived: bool
    message_count: int = 0
    last_message_at: Optional[datetime] = None
    messages: Optional[List["MessageResponse"]] = None


# Message schemas
class MessageBase(BaseModel):
    """消息基础模型"""
    content: str = Field(..., min_length=1)
    role: MessageRole
    message_type: MessageType = MessageType.TEXT
    metadata: Optional[Dict[str, Any]] = Field(None, alias="message_metadata")


class MessageCreate(MessageBase):
    """消息创建模型"""
    conversation_id: int


class MessageResponse(BaseResponse, MessageBase):
    """消息响应模型"""
    conversation_id: int
    context_documents: Optional[List[Dict[str, Any]]] = None
    prompt_tokens: Optional[int] = None
    completion_tokens: Optional[int] = None
    total_tokens: Optional[int] = None
    
    class Config:
        from_attributes = True
        populate_by_name = True


# Chat schemas
class ChatRequest(BaseModel):
    """聊天请求模型"""
    message: str = Field(..., min_length=1, max_length=10000)
    stream: bool = Field(default=False)
    use_knowledge_base: bool = Field(default=False)
    knowledge_base_id: Optional[int] = Field(default=None, description="Knowledge base ID for RAG mode")
    use_agent: bool = Field(default=False, description="Enable agent mode with tool calling capabilities")
    use_langgraph: bool = Field(default=False, description="Enable LangGraph agent mode with advanced tool calling")
    temperature: Optional[float] = Field(default=0.7, ge=0.0, le=2.0)
    max_tokens: Optional[int] = Field(default=2048, ge=1, le=8192)


class ChatResponse(BaseModel):
    """聊天响应模型"""
    user_message: MessageResponse
    assistant_message: MessageResponse
    total_tokens: Optional[int] = None
    model_used: str


class AgentChatRequest(BaseModel):
    """agentChat 请求：AI大模型、提示词、关联知识库"""
    model_id: int = Field(..., ge=1, description="AI大模型配置ID")
    prompt: Optional[str] = Field(default=None, max_length=20000, description="提示词，与 message 二选一")
    message: Optional[str] = Field(default=None, max_length=20000, description="提示词（与 prompt 等价，二选一）")
    knowledge_base_id: Optional[int] = Field(default=None, ge=1, description="关联知识库ID（单个），与 knowledge_base_ids 二选一")
    knowledge_base_ids: Optional[List[Union[int, str]]] = Field(default=None, description="关联知识库ID列表，如 [1, 2] 或 ['3']")
    top_k: int = Field(default=5, ge=1, le=20, description="知识库检索返回条数")
    temperature: Optional[float] = Field(default=None, ge=0.0, le=2.0)
    max_tokens: Optional[int] = Field(default=None, ge=1, le=32768)

    @model_validator(mode="after")
    def require_prompt_or_message(self):
        if not ((self.prompt or "").strip() or (self.message or "").strip()):
            raise ValueError("prompt 或 message 至少提供一个")
        return self


class AgentChatResponse(BaseModel):
    """agentChat 响应"""
    response: str = Field(..., description="模型输出结果")
    model_id: int = Field(..., description="使用的大模型配置ID")
    model_name: str = Field(..., description="使用的大模型名称")
    knowledge_base_id: Optional[int] = Field(default=None, description="关联的知识库ID（若使用）")
    knowledge_base_used: bool = Field(default=False, description="是否使用了知识库RAG")
    references: Optional[List[Dict[str, Any]]] = Field(default=None, description="引用的知识库片段（若使用RAG）")


class StreamChunk(BaseModel):
    """流式响应块模型"""
    content: str
    role: MessageRole = MessageRole.ASSISTANT
    finish_reason: Optional[str] = None
    tokens_used: Optional[int] = None


# Knowledge Base schemas
class KnowledgeBaseBase(BaseModel):
    """知识库基础模型"""
    name: str = Field(..., min_length=1, max_length=100)
    description: Optional[str] = None
    embedding_model: str = Field(default="sentence-transformers/all-MiniLM-L6-v2")
    chunk_size: int = Field(default=1000, ge=100, le=5000)
    chunk_overlap: int = Field(default=200, ge=0, le=1000)


class KnowledgeBaseCreate(KnowledgeBaseBase):
    """知识库创建模型"""
    pass


class KnowledgeBaseUpdate(BaseModel):
    """知识库更新模型"""
    name: Optional[str] = Field(None, min_length=1, max_length=100)
    description: Optional[str] = None
    embedding_model: Optional[str] = None
    chunk_size: Optional[int] = Field(None, ge=100, le=5000)
    chunk_overlap: Optional[int] = Field(None, ge=0, le=1000)
    is_active: Optional[bool] = None


class KnowledgeBaseResponse(BaseResponse, KnowledgeBaseBase):
    """知识库响应模型"""
    is_active: bool
    vector_db_type: str
    collection_name: Optional[str]
    document_count: int = 0
    active_document_count: int = 0


# Document schemas
class DocumentBase(BaseModel):
    """文档基础模型"""
    filename: str
    original_filename: str
    file_type: str
    file_size: int


class DocumentUpload(BaseModel):
    """文档上传模型"""
    knowledge_base_id: int
    process_immediately: bool = Field(default=True)


class DocumentResponse(BaseResponse, DocumentBase):
    """文档响应模型"""
    knowledge_base_id: int
    file_path: str
    mime_type: Optional[str]
    is_processed: bool
    processing_error: Optional[str]
    chunk_count: int = 0
    embedding_model: Optional[str]
    file_size_mb: float


class DocumentListResponse(BaseModel):
    """文档列表响应模型"""
    documents: List[DocumentResponse]
    total: int
    page: int
    page_size: int


class DocumentProcessingStatus(BaseModel):
    """文档处理状态模型"""
    document_id: int
    status: str  # 'pending', 'processing', 'completed', 'failed'
    progress: float = Field(default=0.0, ge=0.0, le=100.0)
    error_message: Optional[str] = None
    chunks_created: int = 0
    estimated_time_remaining: Optional[int] = None  # seconds


# Error schemas
# Document chunk schemas
class DocumentChunk(BaseModel):
    """文档分块模型"""
    id: str
    content: str
    metadata: Dict[str, Any] = Field(default_factory=dict)
    page_number: Optional[int] = None
    chunk_index: int
    start_char: Optional[int] = None
    end_char: Optional[int] = None
    vector_id: Optional[str] = None


class DocumentChunksResponse(BaseModel):
    """文档分块响应模型"""
    document_id: int
    document_name: str
    total_chunks: int
    chunks: List[DocumentChunk]


class ErrorResponse(BaseModel):
    """错误响应模型"""
    error: str
    detail: Optional[str] = None
    code: Optional[str] = None

# 通用返回结构
class NormalResponse(BaseModel):
    """通用返回模型"""
    success: bool
    message: str
    data: Optional[Dict[str, Any]] = None

class ExcelPreviewRequest(BaseModel):
    """Excel预览请求模型"""
    file_id: str
    page: int = 1
    page_size: int = 20

class FileListResponse(BaseModel):
    """文件列表响应模型"""
    success: bool
    message: str
    data: Optional[Dict[str, Any]] = None


# 解决前向引用问题
def rebuild_models():
    """重建模型以解决前向引用问题."""
    try:
        from th_agenter.schemas.permission import RoleResponse
        UserResponse.model_rebuild()
    except ImportError:
        # 如果无法导入RoleResponse，跳过重建
        pass


# 在模块加载时尝试重建模型
rebuild_models()