"""
因子算子框架 - Polars 实现（最终精简版）
- 因子自行生成 ID
- parameters 仅含计算参数（不含因子引用）
- required_factor_ids 是因子ID字符串列表
- calc_factor 通过 self.parameters 和 self.required_factor_ids 获取所需信息
"""

from abc import ABC, abstractmethod
from typing import List, Literal, Dict, Any
from collections import defaultdict, deque
import json
import polars as pl


def _normalize_params(params: Dict[str, Any]) -> str:
    if not params:
        return ""
    return json.dumps(sorted(params.items()), separators=(",", ":"))

def _simple_factor_id(name: str, params: Dict[str, Any]) -> str:
    """
    生成简洁因子ID，如:
        ("sma", {"window": 5}) → "sma_5"
        ("return", {"days": 20}) → "return_20"
        ("rank", {"input": "sma_5"}) → "rank_sma_5"
    
    要求: params 的值必须是简单类型（str/int/float/bool）
    """
    if not params:
        return name

    # 提取所有参数值，按 key 排序保证一致性
    parts = []
    for k in sorted(params.keys()):
        v = params[k]
        if isinstance(v, (str, int, float, bool)):
            # 布尔转小写字符串
            if isinstance(v, bool):
                v = str(v).lower()
            parts.append(str(v))
        else:
            raise ValueError(f"Unsupported parameter type for '{k}': {type(v)}. "
                             f"Only str/int/float/bool allowed for simple ID.")

    return f"{name}_{'_'.join(parts)}"


class BaseFactor(ABC):
    def __init__(self, name: str, parameters: Dict[str, Any], required_factor_ids: List[str]):
        self.name = name
        self.parameters = parameters
        self.required_factor_ids = required_factor_ids
        self.factor_id = self._generate_factor_id()

    def _generate_factor_id(self) -> str:
        return _simple_factor_id(self.name, self.parameters)

    def get_factor_id(self) -> str:
        return self.factor_id

    @abstractmethod
    def calc_factor(self, group_df: pl.DataFrame) -> pl.Series:
        pass

    @property
    @abstractmethod
    def operator_type(self) -> Literal["stock", "date"]:
        pass


class StockWiseFactor(BaseFactor):
    @property
    def operator_type(self) -> Literal["stock"]:
        return "stock"

    def _sectional_roll(self, df: pl.DataFrame) -> pl.DataFrame:
        df_sorted = df.sort(["ts_code", "trade_date"])
        result = (
            df_sorted
            .group_by("ts_code", maintain_order=True)
            .map_groups(lambda g: g.with_columns(self.calc_factor(g)))
            .select(["ts_code", "trade_date", self.factor_id])
        )
        return result

    def apply(self, df: pl.DataFrame) -> pl.DataFrame:
        missing = [fid for fid in self.required_factor_ids if fid not in df.columns]
        if missing:
            raise ValueError(f"Missing dependencies for {self.factor_id}: {missing}")
        long_table = self._sectional_roll(df)
        return df.join(
            long_table.select(["ts_code", "trade_date", self.factor_id]),
            on=["ts_code", "trade_date"],
            how="left"
        )


class DateWiseFactor(BaseFactor):
    @property
    def operator_type(self) -> Literal["date"]:
        return "date"

    def _sectional_roll(self, df: pl.DataFrame) -> pl.DataFrame:
        df_sorted = df.sort(["trade_date", "ts_code"])
        result = (
            df_sorted
            .group_by("trade_date", maintain_order=True)
            .map_groups(lambda g: g.with_columns(self.calc_factor(g)))
            .select(["ts_code", "trade_date", self.factor_id])
        )
        return result

    def apply(self, df: pl.DataFrame) -> pl.DataFrame:
        missing = [fid for fid in self.required_factor_ids if fid not in df.columns]
        if missing:
            raise ValueError(f"Missing dependencies for {self.factor_id}: {missing}")
        long_table = self._sectional_roll(df)
        return df.join(
            long_table.select(["ts_code", "trade_date", self.factor_id]),
            on=["ts_code", "trade_date"],
            how="left"
        )


class FactorGraph:
    def __init__(self):
        self._factors = {}  # factor_id -> factor

    def add_factor(self, factor: BaseFactor):
        fid = factor.get_factor_id()
        if fid in self._factors:
            raise ValueError(f"Factor '{fid}' already registered.")
        self._factors[fid] = factor

    def _topological_sort(self, target_ids: List[str]) -> List[str]:
        all_factors = set()
        queue = deque(target_ids)
        while queue:
            f = queue.popleft()
            if f not in all_factors:
                all_factors.add(f)
                if f in self._factors:
                    for dep in self._factors[f].required_factor_ids:
                        if dep not in all_factors:
                            queue.append(dep)

        to_compute = {f for f in all_factors if f in self._factors}
        indegree = {f: 0 for f in to_compute}
        adj = defaultdict(list)

        for f in to_compute:
            for dep in self._factors[f].required_factor_ids:
                if dep in to_compute:
                    adj[dep].append(f)
                    indegree[f] += 1

        queue = deque([f for f in to_compute if indegree[f] == 0])
        order = []
        while queue:
            node = queue.popleft()
            order.append(node)
            for nb in adj[node]:
                indegree[nb] -= 1
                if indegree[nb] == 0:
                    queue.append(nb)

        print("\n=== Factor Dependency Graph ===")
        to_compute = {f for f in all_factors if f in self._factors}
        for fid in sorted(to_compute):
            deps = self._factors[fid].required_factor_ids
            compute_deps = [d for d in deps if d in to_compute]  # 只显示可计算的依赖
            print(f"{fid} -> {compute_deps}")
        print("================================\n")

        if len(order) != len(to_compute):
            print(len(order), len(to_compute))
            raise RuntimeError("Circular dependency!")
        return order

    def compute(self, df: pl.DataFrame, target_factor_ids: List[str]) -> pl.DataFrame:
        exec_order = self._topological_sort(target_factor_ids)
        current_df = df.clone()
        for fid in exec_order:
            print(fid)
            if fid in current_df.columns:
                continue
            factor = self._factors[fid]
            current_df = factor.apply(current_df)
        return current_df