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use bitmaps::Bitmap;
use std::cmp::max;
use std::collections::HashMap;
use std::hash::Hash;
use std::io;

fn parsei(s: &str) -> i32 {
    s.parse().unwrap()
}
const NUM_LETTERS: usize = 26;

benblack769

use bitmaps::Bitmap;
use std::cmp::max;
use std::collections::HashMap;
use std::hash::Hash;
use std::io;

fn parsei(s: &str) -> i32 {
    s.parse().unwrap()
}
const NUM_LETTERS: usize = 26;

benblack769

import os
import torch
import argparse
import numpy as np
from copy import deepcopy
from typing import Optional, Tuple
from torch.utils.tensorboard import SummaryWriter

from tianshou.utils import BasicLogger
from tianshou.env import DummyVectorEnv

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from all.environments import MultiagentAtariEnv
from all.experiments.multiagent_env_experiment import MultiagentEnvExperiment
from all.presets import atari
from all.agents import Agent
from all.logging import DummyWriter
from all.presets import IndependentMultiagentPreset, Preset
from all.core import State
import torch

benblack769

from copy import deepcopy
from numpy import float32
import os
from supersuit import dtype_v0

import ray
import argparse
from ray import tune
from ray.rllib.agents.registry import get_agent_class
from ray.rllib.env import PettingZooEnv

benblack769

from copy import deepcopy
from numpy import float32
import os
from supersuit import dtype_v0

import ray
from ray import tune
from ray.rllib.agents.registry import get_agent_class
from ray.rllib.env import PettingZooEnv
from pettingzoo.classic import leduc_holdem_v2

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import copy
from torch.optim import Adam
from torch.optim.lr_scheduler import CosineAnnealingLR
from all.approximation import QDist, FixedTarget
from all.agents import Rainbow, RainbowTestAgent
from all.bodies import DeepmindAtariBody
from all.logging import DummyWriter
from all.memory import PrioritizedReplayBuffer, NStepReplayBuffer
from all.optim import LinearScheduler
from all.presets.atari.models import nature_rainbow

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import numpy as np

class random_state_swap:
    '''
    usage:
    np_random, _ = gym.seeding.np_random(seed)
    with random_state_swap(np_random):
        # uses np_random's local random state
        r = np.random.normal(size=10)
        # can safely call external libraries which use np.random

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from pettingzoo.tests.all_modules import all_environments
from pettingzoo.tests.seed_test import calc_hash
import time
import random
import numpy as np
import supersuit

import gym
import random
import json

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from ray.rllib.env.multi_agent_env import MultiAgentEnv


class PettingZooEnv(MultiAgentEnv):
    """An interface to the PettingZoo MARL environment library.

    See: https://github.com/PettingZoo-Team/PettingZoo

    Inherits from MultiAgentEnv and exposes a given AEC
    (actor-environment-cycle) game from the PettingZoo project via the