"""
PMFlow 人员能力图谱 - Pydantic 请求/响应模型

包含 SkillTag、UserSkillBinding、UserWorkload 及人员推荐相关模型。
"""

from datetime import datetime
from typing import List, Optional
from uuid import UUID

from pydantic import BaseModel, Field


# ============================================================================
# SkillTag 模型
# ============================================================================


class SkillTagCreate(BaseModel):
    """创建技能标签请求"""

    name: str = Field(..., min_length=1, max_length=100, description="技能名称")
    slug: str = Field(..., min_length=1, max_length=80, description="URL 安全标识")
    category: Optional[str] = Field(None, max_length=50, description="分类")
    description: Optional[str] = Field(None, description="描述")
    sort_order: int = Field(default=0, ge=0, description="排序序号")


class SkillTagUpdate(BaseModel):
    """更新技能标签请求（系统标签不可更新）"""

    name: Optional[str] = Field(None, min_length=1, max_length=100)
    category: Optional[str] = Field(None, max_length=50)
    description: Optional[str] = None
    sort_order: Optional[int] = Field(None, ge=0)


class SkillTagResponse(BaseModel):
    """技能标签响应"""

    id: str
    tenant_id: int | str
    name: str
    slug: str
    category: Optional[str] = None
    description: Optional[str] = None
    is_system: bool
    sort_order: int
    created_at: str
    updated_at: str

    class Config:
        from_attributes = True


# ============================================================================
# UserSkillBinding 模型
# ============================================================================


class UserSkillBindingCreate(BaseModel):
    """创建用户技能绑定请求"""

    user_id: UUID
    skill_tag_id: UUID
    proficiency: int = Field(default=50, ge=0, le=100, description="掌握程度 0-100")
    level: str = Field(
        default="intermediate",
        description="熟练等级: beginner, intermediate, advanced, expert",
    )
    years_experience: Optional[float] = Field(None, ge=0, description="从业年限")


class UserSkillBindingUpdate(BaseModel):
    """更新用户技能绑定请求"""

    proficiency: Optional[int] = Field(None, ge=0, le=100)
    level: Optional[str] = None
    years_experience: Optional[float] = Field(None, ge=0)


class UserSkillBindingBatchUpdate(BaseModel):
    """批量更新用户技能绑定请求"""

    user_id: Optional[UUID] = None  # 可选，由路径提供时省略
    bindings: List[dict] = Field(
        ...,
        description="绑定列表，每项含 skill_tag_id, proficiency, level, years_experience",
    )


class UserSkillBindingResponse(BaseModel):
    """用户技能绑定响应"""

    id: str
    user_id: str
    skill_tag_id: str
    skill_name: str
    proficiency: int
    level: str
    years_experience: Optional[float] = None
    quality_score: Optional[float] = None  # 0-100 质量分
    efficiency_score: Optional[float] = None  # 0-100 效率分
    last_used_at: Optional[str] = None
    created_at: str
    updated_at: str

    class Config:
        from_attributes = True


# ============================================================================
# UserWorkload 模型
# ============================================================================


class UserWorkloadUpdate(BaseModel):
    """更新用户工作量请求（通常由系统自动调用）"""

    user_id: UUID
    total_tasks: Optional[int] = Field(None, ge=0)
    completed_tasks: Optional[int] = Field(None, ge=0)
    in_progress_tasks: Optional[int] = Field(None, ge=0)
    estimated_hours: Optional[float] = Field(None, ge=0)
    completed_hours: Optional[float] = Field(None, ge=0)


class UserWorkloadResponse(BaseModel):
    """用户工作量响应"""

    id: str
    user_id: str
    user_name: str
    total_tasks: int
    completed_tasks: int
    in_progress_tasks: int
    estimated_hours: float
    completed_hours: float
    workload_rate: float
    period_start: str
    period_end: str
    updated_at: str

    class Config:
        from_attributes = True


# ============================================================================
# 人员推荐模型
# ============================================================================


class PersonnelRecommendationItem(BaseModel):
    """人员推荐单项"""

    user_id: str
    user_name: str
    match_score: float = Field(..., ge=0, le=100, description="匹配度评分")
    reason: str = Field(..., description="推荐理由")
    skill_match_count: int = Field(..., ge=0)
    avg_proficiency: float = Field(..., ge=0, le=100)
    workload_rate: float = Field(..., ge=0, le=100)
    recommended_role: Optional[str] = None


class PersonnelRecommendationResponse(BaseModel):
    """人员推荐响应"""

    task_id: Optional[str] = None
    task_description: Optional[str] = None
    required_skills: List[str] = []
    recommendations: List[PersonnelRecommendationItem] = []
    total_count: int = 0


# ============================================================================
# 技能历史任务
# ============================================================================


class SkillTaskHistoryItem(BaseModel):
    """用户某技能下的历史任务项"""

    id: str
    title: str
    description: Optional[str] = None
    status: Optional[str] = None
    priority: Optional[str] = None
    estimated_hours: Optional[float] = None
    project_id: Optional[str] = None
    truth_node_id: Optional[str] = None
    updated_at: Optional[str] = None


class SkillTaskHistoryResponse(BaseModel):
    """技能历史任务列表响应"""

    items: List[SkillTaskHistoryItem] = []
    total: int = 0