from __future__ import annotations

from datetime import datetime, timedelta, timezone
from enum import Enum

from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator


class SalaryType(str, Enum):
    daily = "daily"
    hourly = "hourly"
    monthly = "monthly"
    task = "task"


class SourceType(str, Enum):
    job_to_worker = "job_to_worker"
    worker_to_job = "worker_to_job"


class Salary(BaseModel):
    type: SalaryType = Field(default=SalaryType.daily, description="薪资类型：daily/hourly/monthly/task")
    amount: float = Field(default=0, description="薪资金额")
    currency: str = Field(default="CNY", description="货币类型，默认 CNY")


class SkillScore(BaseModel):
    name: str = Field(description="技能名称")
    score: float = Field(ge=0, le=1, description="技能熟练度，范围 0~1")


class JobCard(BaseModel):
    job_id: str = Field(description="岗位唯一 ID")
    title: str = Field(description="岗位标题")
    category: str = Field(description="岗位类别")
    description: str = Field(description="岗位描述")
    skills: list[str] = Field(default_factory=list, description="岗位技能要求列表")
    city: str = Field(description="城市")
    region: str = Field(description="区域")
    location_detail: str = Field(description="详细地点描述")
    start_time: datetime = Field(description="岗位开始时间，ISO-8601")
    duration_hours: float = Field(gt=0, description="工时（小时），必须大于 0")
    headcount: int = Field(gt=0, description="招聘人数，必须大于 0")
    salary: Salary = Field(description="薪资信息")
    work_mode: str = Field(description="工作模式，如兼职、全职、活动")
    tags: list[str] = Field(default_factory=list, description="业务标签列表")
    confidence: float = Field(ge=0, le=1, description="数据置信度，范围 0~1")

    @field_validator("start_time", mode="after")
    @classmethod
    def normalize_start_time(cls, value: datetime) -> datetime:
        shanghai_tz = timezone(timedelta(hours=8))
        if value.tzinfo is None:
            value = value.replace(tzinfo=shanghai_tz)
        else:
            value = value.astimezone(shanghai_tz)
        return value.replace(second=0, microsecond=0)


class WorkerCard(BaseModel):
    worker_id: str = Field(description="工人唯一 ID")
    name: str = Field(description="工人姓名或昵称")
    description: str = Field(description="工人自我描述")
    skills: list[SkillScore] = Field(default_factory=list, description="技能及熟练度列表")
    cities: list[str] = Field(default_factory=list, description="可接单城市列表")
    regions: list[str] = Field(default_factory=list, description="可接单区域列表")
    availability: list[str] = Field(default_factory=list, description="可上岗时间描述")
    experience_tags: list[str] = Field(default_factory=list, description="经验标签列表")
    reliability_score: float = Field(ge=0, le=1, description="履约可靠性分，范围 0~1")
    profile_completion: float = Field(ge=0, le=1, description="档案完善度，范围 0~1")
    confidence: float = Field(ge=0, le=1, description="数据置信度，范围 0~1")


class MatchBreakdown(BaseModel):
    skill_score: float = Field(ge=0, le=1, description="技能匹配分，范围 0~1")
    region_score: float = Field(ge=0, le=1, description="地域匹配分，范围 0~1")
    time_score: float = Field(ge=0, le=1, description="时间匹配分，范围 0~1")
    experience_score: float = Field(ge=0, le=1, description="经验匹配分，范围 0~1")
    reliability_score: float = Field(ge=0, le=1, description="可靠性匹配分，范围 0~1")


class MatchResult(BaseModel):
    match_id: str = Field(description="匹配记录 ID")
    source_type: SourceType = Field(description="匹配方向：job_to_worker 或 worker_to_job")
    source_id: str = Field(description="源实体 ID")
    target_id: str = Field(description="目标实体 ID")
    match_score: float = Field(ge=0, le=1, description="综合匹配分，范围 0~1")
    breakdown: MatchBreakdown = Field(description="多维打分拆解")
    reasons: list[str] = Field(default_factory=list, min_length=3, description="匹配理由，至少 3 条")


class ExtractTextRequest(BaseModel):
    text: str = Field(min_length=5, description="待抽取的自然语言文本，最少 5 个字符")
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
                "text": "明天下午南山会展中心需要2个签到协助，5小时，150/人，女生优先",
            }
        }
    )


class IngestJobRequest(BaseModel):
    job: JobCard = Field(description="岗位卡片对象")


class IngestWorkerRequest(BaseModel):
    worker: WorkerCard = Field(description="工人卡片对象")


class MatchWorkersRequest(BaseModel):
    job_id: str | None = Field(default=None, description="岗位 ID（与 job 二选一）")
    job: JobCard | None = Field(default=None, description="内联岗位对象（与 job_id 二选一）")
    top_n: int = Field(default=10, ge=1, le=50, description="返回条数，范围 1~50")

    @model_validator(mode="after")
    def validate_source(self) -> "MatchWorkersRequest":
        if not self.job_id and not self.job:
            raise ValueError("job_id 或 job 至少需要提供一个")
        return self


class MatchJobsRequest(BaseModel):
    worker_id: str | None = Field(default=None, description="工人 ID（与 worker 二选一）")
    worker: WorkerCard | None = Field(default=None, description="内联工人对象（与 worker_id 二选一）")
    top_n: int = Field(default=10, ge=1, le=50, description="返回条数，范围 1~50")

    @model_validator(mode="after")
    def validate_source(self) -> "MatchJobsRequest":
        if not self.worker_id and not self.worker:
            raise ValueError("worker_id 或 worker 至少需要提供一个")
        return self


class ExtractResponse(BaseModel):
    success: bool = Field(description="抽取是否成功")
    data: JobCard | WorkerCard | None = Field(default=None, description="抽取结果对象，可能为空")
    errors: list[str] = Field(default_factory=list, description="错误信息列表")
    missing_fields: list[str] = Field(default_factory=list, description="缺失字段列表")


class BootstrapResponse(BaseModel):
    jobs: int = Field(description="导入岗位数量")
    workers: int = Field(description="导入工人数量")
    skills: int = Field(description="技能词条数量")
    categories: int = Field(description="类目数量")
    regions: int = Field(description="区域数量")


class HealthStatus(BaseModel):
    service: str = Field(description="服务状态，通常为 ok")
    database: str = Field(description="数据库状态：ok 或 error")
    rag: str = Field(description="RAG 组件状态：ok 或 error")
    timestamp: datetime = Field(description="服务端当前时间")


class ListResponse(BaseModel):
    items: list[dict] = Field(description="列表项")
    total: int = Field(description="总数")


class MatchResponse(BaseModel):
    items: list[MatchResult] = Field(description="匹配结果列表")


class ExplainResponse(BaseModel):
    match: MatchResult = Field(description="单条匹配结果详情")


class MatchFeedbackRequest(BaseModel):
    match_id: str = Field(description="匹配记录 ID")
    accepted: bool = Field(description="反馈是否接受该推荐")


class MatchWeightResponse(BaseModel):
    weights: dict[str, float] = Field(description="当前生效的排序权重")
    learning_enabled: bool = Field(description="是否开启在线学习")


class AIObservabilityResponse(BaseModel):
    metrics: dict[str, float | int] = Field(description="AI 调用观测指标")


class IngestAsyncResponse(BaseModel):
    task_id: str = Field(description="异步任务 ID")
    status: str = Field(description="任务状态")


class QueueStatusResponse(BaseModel):
    queued: int = Field(description="当前队列中任务数量")
    processed: int = Field(description="历史处理成功数量")
    failed: int = Field(description="历史处理失败数量")


class MatchAsyncWorkersRequest(BaseModel):
    job_id: str = Field(description="岗位 ID")
    top_n: int = Field(default=10, ge=1, le=50, description="返回条数，范围 1~50")


class MatchAsyncJobsRequest(BaseModel):
    worker_id: str = Field(description="工人 ID")
    top_n: int = Field(default=10, ge=1, le=50, description="返回条数，范围 1~50")


class MatchAsyncResponse(BaseModel):
    task_id: str = Field(description="异步任务 ID")
    status: str = Field(description="任务状态")
    items: list[MatchResult] | None = Field(default=None, description="任务完成后返回的匹配结果")


class SystemOpsResponse(BaseModel):
    traffic: dict[str, float | int] = Field(description="全局流量护栏与错误窗口指标")
    cache: dict[str, float | int | str] = Field(description="缓存命中与大小")
    ingest_queue: QueueStatusResponse = Field(description="异步入库队列状态")
    match_queue: QueueStatusResponse = Field(description="异步匹配队列状态")


class PromptOutput(BaseModel):
    content: dict
    raw_text: str


class QueryFilters(BaseModel):
    entity_type: str
    city: str | None = None
    region: str | None = None
    categories: list[str] = Field(default_factory=list)
    tags: list[str] = Field(default_factory=list)
    skills: list[str] = Field(default_factory=list)

    @field_validator("entity_type")
    @classmethod
    def validate_entity_type(cls, value: str) -> str:
        if value not in {"job", "worker"}:
            raise ValueError("entity_type must be job or worker")
        return value