from __future__ import annotations

import random
from collections import deque
from dataclasses import dataclass
from typing import Deque

import numpy as np


@dataclass
class Transition:
    state: np.ndarray
    action: int
    reward: float
    next_state: np.ndarray
    done: float


class ReplayBuffer:
    def __init__(self, capacity: int = 10000, seed: int | None = None) -> None:
        if capacity <= 0:
            raise ValueError("capacity must be > 0")
        self.capacity = capacity
        self.buffer: Deque[Transition] = deque(maxlen=capacity)
        self._random = random.Random(seed)

    def __len__(self) -> int:
        return len(self.buffer)

    def add(self, state: np.ndarray, action: int, reward: float, next_state: np.ndarray, done: bool) -> None:
        self.buffer.append(
            Transition(
                state=np.asarray(state, dtype=np.float32),
                action=int(action),
                reward=float(reward),
                next_state=np.asarray(next_state, dtype=np.float32),
                done=float(done),
            )
        )

    def sample(self, batch_size: int) -> list[Transition]:
        return self._random.sample(list(self.buffer), batch_size)