text: from gym import spaces
  import numpy as np
  from pettingzoo import AECEnv
  from pettingzoo.utils.agent_selector import agent_selector
  from gym.utils import seeding
  from pettingzoo.utils import wrappers
  
  
  def make_env(raw_env):
      def env(**kwargs):
          env = raw_env(**kwargs)
          world = env.world
          env = wrappers.AssertOutOfBoundsWrapper(env)
          backup_policy = "taking zero action (no movement, communication 0)"
          env = wrappers.NanNoOpWrapper(env, 0, backup_policy)
          env = wrappers.OrderEnforcingWrapper(env)
          env.world = world
          return env
      return env
  
  
  class SimpleEnv(AECEnv):
  
      metadata = {'render.modes': ['human']}
  
      def __init__(self, scenario, world, max_frames, local_ratio=None):
          super(SimpleEnv, self).__init__()
  
          self.seed()
  
          self.max_frames = max_frames
          self.scenario = scenario
          self.world = world
          self.local_ratio = local_ratio
  
          self.scenario.reset_world(self.world, self.np_random)
  
          self.num_agents = len(self.world.agents)
          self.agents = [agent.name for agent in self.world.agents]
          self._index_map = {agent.name: idx for idx, agent in enumerate(self.world.agents)}
  
          self._agent_selector = agent_selector(self.agents)
  
          # set spaces
          self.action_spaces = dict()
          self.observation_spaces = dict()
          for agent in self.world.agents:
              space_dim = 1
              if agent.movable:
                  space_dim *= self.world.dim_p * 2 + 1
              if not agent.silent:
                  space_dim *= self.world.dim_c
  
              obs_dim = len(self.scenario.observation(agent, self.world))
              self.action_spaces[agent.name] = spaces.Discrete(space_dim)
              self.observation_spaces[agent.name] = spaces.Box(low=-np.float32(np.inf), high=+np.float32(np.inf), shape=(obs_dim,), dtype=np.float32)
  
          self.steps = 0
  
          self.current_actions = [None] * self.num_agents
  
          self.viewer = None
  
          self.input_structures = {agent: self.scenario.get_input_structure(self.world.agents[self._index_map[agent]],
                                                                            self.world) for agent in self.agents}
  
  
      def seed(self, seed=None):
          self.np_random, seed = seeding.np_random(seed)
  
      def observe(self, agent):
          return self.scenario.observation(self.world.agents[self._index_map[agent]], self.world).astype(np.float32)
  
      def update_info(self):
          for agent in self.agents:
              self.infos[agent] = self.scenario.get_info(self.world.agents[self._index_map[agent]], self.world)
  
      def reset(self, observe=True):
          self.scenario.reset_world(self.world, self.np_random)
  
          self.rewards = {name: 0. for name in self.agents}
          self.dones = {name: False for name in self.agents}
          self.infos = {name: {} for name in self.agents}
  
          self._reset_render()
  
          self.agent_selection = self._agent_selector.reset()
          self.steps = 0
  
          self.current_actions = [None] * self.num_agents
          self.update_info()
  
          if observe:
              return self.observe(self.agent_selection)
          else:
              return
  
      def _execute_world_step(self):
          # set action for each agent
          for i, agent in enumerate(self.world.agents):
              action = self.current_actions[i]
              scenario_action = []
              if agent.movable:
                  mdim = self.world.dim_p * 2 + 1
                  scenario_action.append(action % mdim)
                  action //= mdim
              if not agent.silent:
                  scenario_action.append(action)
  
              self._set_action(scenario_action, agent, self.action_spaces[agent.name])
  
          self.world.step()
  
          global_reward = 0.
          if self.local_ratio is not None:
              global_reward = float(self.scenario.global_reward(self.world))
  
          for agent in self.world.agents:
              agent_reward = float(self.scenario.reward(agent, self.world, self.np_random))
              if self.local_ratio is not None:
                  reward = global_reward * (1 - self.local_ratio) + agent_reward * self.local_ratio
              else:
                  reward = agent_reward
  
              self.rewards[agent.name] = reward
  
      # set env action for a particular agent
      def _set_action(self, action, agent, action_space, time=None):
          agent.action.u = np.zeros(self.world.dim_p)
          agent.action.c = np.zeros(self.world.dim_c)
          # process action
  
          if agent.movable:
              # physical action
              agent.action.u = np.zeros(self.world.dim_p)
              # process discrete action
              if action[0] == 1:
                  agent.action.u[0] = -1.0
              if action[0] == 2:
                  agent.action.u[0] = +1.0
              if action[0] == 3:
                  agent.action.u[1] = -1.0
              if action[0] == 4:
                  agent.action.u[1] = +1.
  
              sensitivity = 5.0
              if agent.accel is not None:
                  sensitivity = agent.accel
              agent.action.u *= sensitivity
              action = action[1:]
          if not agent.silent:
              # communication action
              agent.action.c = np.zeros(self.world.dim_c)
  
              agent.action.c[action[0]] = 1.0
              action = action[1:]
          # make sure we used all elements of action
          assert len(action) == 0
  
      def step(self, action, observe=True):
          current_idx = self._index_map[self.agent_selection]
          next_idx = (current_idx + 1) % self.num_agents
          self.agent_selection = self._agent_selector.next()
  
          self.current_actions[current_idx] = action
  
          if next_idx == 0:
              self._execute_world_step()
              self.steps += 1
              if self.steps >= self.max_frames:
                  for a in self.agents:
                      self.dones[a] = True
  
          if observe:
              next_observation = self.observe(self.agent_selection)
          else:
              next_observation = None
          self.update_info()
          return next_observation
  
      def render(self, mode='human'):
          from . import rendering
  
          if self.viewer is None:
              self.viewer = rendering.Viewer(700, 700)
  
          # create rendering geometry
          if self.render_geoms is None:
              # import rendering only if we need it (and don't import for headless machines)
              # from gym.envs.classic_control import rendering
              # from multiagent._mpe_utils import rendering
              self.render_geoms = []
              self.render_geoms_xform = []
              for entity in self.world.entities:
                  geom = rendering.make_circle(entity.size)
                  xform = rendering.Transform()
                  if 'agent' in entity.name:
                      geom.set_color(*entity.color[:3], alpha=0.5)
                  else:
                      geom.set_color(*entity.color[:3])
                  geom.add_attr(xform)
                  self.render_geoms.append(geom)
                  self.render_geoms_xform.append(xform)
  
              # add geoms to viewer
              self.viewer.geoms = []
              for geom in self.render_geoms:
                  self.viewer.add_geom(geom)
  
              self.viewer.text_lines = []
              idx = 0
              for agent in self.world.agents:
                  if not agent.silent:
                      tline = rendering.TextLine(self.viewer.window, idx)
                      self.viewer.text_lines.append(tline)
                      idx += 1
  
          alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
          # for agent in self.world.agents:
          idx = 0
          for idx, other in enumerate(self.world.agents):
              if other.silent:
                  continue
              if np.all(other.state.c == 0):
                  word = '_'
              else:
                  word = alphabet[np.argmax(other.state.c)]
  
              message = (other.name + ' sends ' + word + '   ')
  
              self.viewer.text_lines[idx].set_text(message)
              idx += 1
  
          # update bounds to center around agent
          all_poses = [entity.state.p_pos for entity in self.world.entities]
          cam_range = np.max(np.abs(np.array(all_poses))) + 1
          self.viewer.set_max_size(cam_range)
          # update geometry positions
          for e, entity in enumerate(self.world.entities):
              self.render_geoms_xform[e].set_translation(*entity.state.p_pos)
          # render to display or array
          return self.viewer.render(return_rgb_array=mode == 'rgb_array')
  
      # reset rendering assets
      def _reset_render(self):
          self.render_geoms = None
          self.render_geoms_xform = None
  
      def close(self):
          if self.viewer is not None:
              self.viewer.close()
              self.viewer = None
          self._reset_render()
  
  