"""비교용 baseline 정책 (NegotiationPolicy 구현). 학습 정책과 같은 인터페이스로 하네스에 등록."""

import numpy as np

from negotiation.policies.base import ActionDecision, NegotiationPolicy, PolicyContext, Transition


class RandomPolicy(NegotiationPolicy):
    """가용 액션 중 무작위 선택. 학습하지 않음(update no-op). 학습 정책의 하한 비교군."""

    name = "random"

    def __init__(self, seed: int = 0):
        self.rng = np.random.default_rng(seed)

    def _available(self, ctx: PolicyContext):
        used = ctx.episode.used_action_ids if ctx.episode else set()
        avail = [a for a in range(ctx.action_space_size) if a not in used]
        return avail or list(range(ctx.action_space_size))

    def select(self, ctx: PolicyContext) -> ActionDecision:
        avail = self._available(ctx)
        a = int(self.rng.choice(avail))
        if ctx.episode:
            ctx.episode.mark_used(a)
        return ActionDecision(action_id=a, propensity=1.0 / len(avail), available_actions=avail)

    def update(self, transition: Transition) -> None:
        pass

    def predict_action_dist(self, ctx: PolicyContext) -> np.ndarray:
        avail = self._available(ctx)
        dist = np.zeros(ctx.action_space_size)
        for a in avail:
            dist[a] = 1.0 / len(avail)
        return dist


class StaticPolicy(NegotiationPolicy):
    """항상 고정 카드(기본 action 0). '정적 운영'(학습 없음) 비교군."""

    name = "static"

    def __init__(self, fixed_action: int = 0):
        self.fixed = fixed_action

    def select(self, ctx: PolicyContext) -> ActionDecision:
        used = ctx.episode.used_action_ids if ctx.episode else set()
        a = self.fixed if self.fixed not in used else next(
            (x for x in range(ctx.action_space_size) if x not in used), self.fixed)
        if ctx.episode:
            ctx.episode.mark_used(a)
        return ActionDecision(action_id=a, propensity=1.0, available_actions=[a])

    def update(self, transition: Transition) -> None:
        pass