from __future__ import annotations

import json
from dataclasses import dataclass, asdict, field
from typing import Any, Dict, List, Optional


def to_json(obj: Any) -> str:
    return json.dumps(obj, default=lambda o: o.__dict__, sort_keys=True)


@dataclass
class Signal:
    venue: str
    timestamp: int  # unix epoch ms
    metrics: Dict[str, Any]  # privacy-budget bounded features
    version: int = 1

    def to_json(self) -> str:
        return to_json(asdict(self))


@dataclass
class SignalDelta:
    venue: str
    timestamp: int
    delta: Dict[str, Any]
    version: int = 1

    def to_json(self) -> str:
        return to_json(asdict(self))


@dataclass
class ProvenanceProof:
    venue: str
    timestamp: int
    merkle_root: str
    proof_path: List[str] = field(default_factory=list)

    def to_json(self) -> str:
        return to_json(asdict(self))


@dataclass
class PrivacyBudget:
    venue: str
    limit: float  # e.g., max entropy to reveal
    used: float = 0.0

    def to_json(self) -> str:
        return to_json(asdict(self))


@dataclass
class AuditLog:
    event: str
    timestamp: int
    details: Dict[str, Any] = field(default_factory=dict)

    def to_json(self) -> str:
        return to_json(asdict(self))


@dataclass
class AggregatedSignal:
    """Cross-venue aggregated signal with privacy budgeting."""
    venue_set: List[str]
    feature_vector: Dict[str, float]
    privacy_budget_used: float
    nonce: int = 0
    merkle_proof: List[str] = field(default_factory=list)

    def to_json(self) -> str:
        return to_json(asdict(self))


@dataclass
class PlanDelta:
    """Incremental updates to analytics plans (e.g., reweighting, windows)."""
    delta_id: str
    venue: str
    timestamp: int
    delta: Dict[str, Any]
    version: int = 1

    def to_json(self) -> str:
        return to_json(asdict(self))