from __future__ import annotations

from dataclasses import dataclass, field
import json
from pathlib import Path
from typing import Any


@dataclass
class Order2Result:
    """Measurements and selections produced by the order-2 interaction method."""

    depth: int
    baseline_nll: float
    single_nll: dict[int, float]
    pair_nll: dict[tuple[int, int], float]
    first_order: dict[int, float] = field(default_factory=dict)
    second_order: dict[tuple[int, int], float] = field(default_factory=dict)
    delete_order: list[int] = field(default_factory=list)
    greedy_path: list[dict[str, Any]] = field(default_factory=list)

    def build_interactions(self) -> "Order2Result":
        d0 = float(self.baseline_nll)
        self.first_order = {
            i: float(self.single_nll[i]) - d0 for i in range(self.depth)
        }
        self.second_order = {}
        for i in range(self.depth):
            for j in range(i + 1, self.depth):
                self.second_order[(i, j)] = (
                    float(self.pair_nll[(i, j)])
                    - float(self.single_nll[i])
                    - float(self.single_nll[j])
                    + d0
                )
        return self

    def build_greedy_path(self, max_delete: int | None = None) -> "Order2Result":
        if not self.first_order or not self.second_order:
            self.build_interactions()

        if max_delete is None:
            max_delete = self.depth - 1
        if not 0 <= max_delete < self.depth:
            raise ValueError(f"max_delete must be in [0, {self.depth - 1}]")

        deleted: list[int] = []
        deleted_set: set[int] = set()
        path: list[dict[str, Any]] = []
        cumulative = 0.0

        for step in range(max_delete):
            candidates: list[tuple[float, int]] = []
            for i in range(self.depth):
                if i in deleted_set:
                    continue
                interaction = sum(
                    self.second_order[tuple(sorted((i, j)))] for j in deleted
                )
                marginal = self.first_order[i] + interaction
                candidates.append((marginal, i))

            marginal, chosen = min(candidates, key=lambda z: (z[0], z[1]))
            deleted.append(chosen)
            deleted_set.add(chosen)
            cumulative += marginal
            path.append(
                {
                    "step": step + 1,
                    "deleted_layer": chosen,
                    "marginal_predicted_nll_change": float(marginal),
                    "cumulative_predicted_nll_change": float(cumulative),
                }
            )

        self.delete_order = deleted
        self.greedy_path = path
        return self

    def select(self, target_layers: int) -> dict[str, list[int]]:
        if not 1 <= target_layers <= self.depth:
            raise ValueError(f"target_layers must be in [1, {self.depth}]")
        n_delete = self.depth - target_layers
        if len(self.delete_order) < n_delete:
            self.build_greedy_path(max_delete=n_delete)
        deleted = list(self.delete_order[:n_delete])
        deleted_set = set(deleted)
        retained = [i for i in range(self.depth) if i not in deleted_set]
        return {"retained_layers": retained, "deleted_layers": deleted}

    def to_dict(self) -> dict[str, Any]:
        return {
            "method": "order-2 interaction greedy",
            "depth": self.depth,
            "baseline_nll": float(self.baseline_nll),
            "single_nll": {str(k): float(v) for k, v in self.single_nll.items()},
            "pair_nll": {f"{i},{j}": float(v) for (i, j), v in self.pair_nll.items()},
            "first_order_delta": {str(k): float(v) for k, v in self.first_order.items()},
            "second_order_interaction": {
                f"{i},{j}": float(v) for (i, j), v in self.second_order.items()
            },
            "delete_order": list(self.delete_order),
            "greedy_path": list(self.greedy_path),
            "complete": True,
        }

    def save_json(self, path: str | Path) -> None:
        path = Path(path)
        path.parent.mkdir(parents=True, exist_ok=True)
        tmp = path.with_suffix(path.suffix + ".tmp")
        tmp.write_text(json.dumps(self.to_dict(), indent=2))
        tmp.replace(path)

    @classmethod
    def from_dict(cls, obj: dict[str, Any]) -> "Order2Result":
        pair = {}
        for key, value in obj.get("pair_nll", {}).items():
            i, j = (int(x) for x in key.split(","))
            pair[(i, j)] = float(value)
        second = {}
        for key, value in obj.get("second_order_interaction", {}).items():
            i, j = (int(x) for x in key.split(","))
            second[(i, j)] = float(value)
        result = cls(
            depth=int(obj["depth"]),
            baseline_nll=float(obj["baseline_nll"]),
            single_nll={int(k): float(v) for k, v in obj.get("single_nll", {}).items()},
            pair_nll=pair,
            first_order={
                int(k): float(v) for k, v in obj.get("first_order_delta", {}).items()
            },
            second_order=second,
            delete_order=[int(x) for x in obj.get("delete_order", [])],
            greedy_path=list(obj.get("greedy_path", [])),
        )
        return result

    @classmethod
    def load_json(cls, path: str | Path) -> "Order2Result":
        return cls.from_dict(json.loads(Path(path).read_text()))